New molecular mechanisms and novel biomarkers associated with Extracellular Vesicles in Polycystic Kidney Disease: from preclinical models to patients

ESCOLA DE DOUTORAMENTO INTERNACIONAL DA USC Marta Vizoso González Tese de doutoramento New molecular mechanisms and novel biomarkers associated with Extracellular Vesicles in Polycystic Kidney Disease: from preclinical models to patients Santiago de Compostela, 2022 Programa de Doutoramento en Investigación e Desenvolvemento de Medicamentos TESIS DE DOCTORADO NEW MOLECULAR MECHANISMS AND NOVEL BIOMARKERS ASSOCIATED WITH EXTRACELLULAR VESICLES IN POLYCYSTIC KIDNEY DISEASE: FROM PRECLINICAL MODELS TO PATIENTS Marta Vizoso González ESCUELA DE DOCTORADO INTERNACIONAL DE LA UNIVERSIDAD DE SANTIAGO DE COMPOSTELA PROGRAMA DE DOCTORADO EN INVESTIGACIÓN Y DESARROLLO DE MEDICAMENTOS SANTIAGO DE COMPOSTELA / LUGO 2022 AUTORIZACIÓN DEL AUTOR/A DE LA TESIS D/Dña. Marta Vizoso González Título de la tesis: New molecular mechanisms and novel biomarkers associated with Extracellular Vesicles in Polycystic Kidney Disease: from preclinical models to patients Presento mi tesis, siguiendo el procedimiento adecuado al Reglamento y declaro que: 1) La tesis abarca los resultados de la elaboración de mi trabajo. 2) De ser el caso, en la tesis se hace referencia a las colaboraciones que tuvo este trabajo. 3) Confirmo que la tesis no incurre en ningún tipo de plagio de otros autores ni de trabajos presentados por mí para la obtención de otros títulos. 4) La tesis es la versión definitiva presentada para su defensa y coincide la versión impresa con la presentada en formato electrónico. Y me comprometo a presentar el Compromiso Documental de Supervisión en el caso que el original no esté depositado en la Escuela. En Santiago de Compostela. 18 de agosto de 2022. Asdo. Marta Vizoso González DECLARACIÓN DEL A DIRECTOR/TUTOR DE LA TESIS D/Dña. Miguel Ángel García González En condición de: Director/a Título de la tesis: New molecular mechanisms and novel biomarkers associated with Extracellular Vesicles in Polycystic Kidney Disease: from preclinical models to patients INFORMA: Que la presente tesis, se corresponde con el trabajo realizado por D/Dña Marta Vizoso González, bajo mi dirección/tutorización, y autorizo su presentación, considerando que reúne los resquisitos exigidos en el Reglamento de Estudios de Doctorado de la USC, y que como director/tutor de esta no incurre en las causas de abstención establecidas en la Ley 40/2015. En Santiago de Compostela. 18 de agosto de 2022. Asdo. Miguel Ángel García González DECLARACIÓN DEL DIRECTOR/TUTOR DE LA TESIS D/Dña. Alejandro Sánchez Barreiro En condición de: Director y Tutor Título de la tesis: New molecular mechanisms and novel biomarkers associated with Extracellular Vesicles in Polycystic Kidney Disease: from preclinical models to patients INFORMA: Que la presente tesis, se corresponde con el trabajo realizado por D/Dña Marta Vizoso González, bajo mi dirección/tutorización, y autorizo su presentación, considerando que reúne los resquisitos exigidos en el Reglamento de Estudios de Doctorado de la USC, y que como director/tutor de esta no incurre en las causas de abstención establecidas en la Ley 40/2015. En Santiago de Compostela. 18 de agosto de 2022. Asdo. Alejandro Sánchez Barreiro AGRADECIMIENTOS Este viaje comenzó hace mucho tiempo, siendo tan solo una niña curiosa a la que le descubrieron el mundo de la ciencia y que quería hacer el doctorado en Carolina del Norte. Hasta allí no me fui, pero si que, si me encuentro escribiendo estas líneas, es porque he conseguido mi objetivo de realizar el doctorado. Pero este trayecto no he estado sola, como dice La Vecina Rubia, la vida está formada por momentos y los míos tienen nombre de persona, de forma que al pensar en esta etapa se me llena la cabeza y los recuerdos de nombres que me han ido acompañando y que, sin ellos, estar aquí hubiese sido imposible. Me gustaría empezar agradeciendo a mis directores. En primer lugar, gracias Miguel, por haber confiado en mi desde un primer momento y haber depositado la responsabilidad de avanzar con este proyecto. Me gustaría destacar que, precisamente gracias a ti, no soy la misma persona que entró en el laboratorio en el 2017, ambos sabemos que ha habido un tremendo de desarrollo tanto personal como profesional, de forma que considero que gracias a tus aportaciones ahora mismo siento que soy capaz de enfrentarme a las diferentes etapas y obstáculos que se me puedan ir presentando. Al igual que muchas gracias también a mi otro director, Alejandro, por enseñarme otra visión de este mundo y enseñarme el campo de la nanofarmacología, al igual que haber estado presente siempre que te he necesitado. No puedo continuar por otro lugar que no sea mi laboratorio, donde tantas horas he pasado estos últimos años y que, gracias a todos los que forman parte de él, han hecho que lo sintiese como mi segunda casa. Primero a aquellos con los que he empezado pero que ya no están al terminar, gracias Vanesa por tu bondad y consejos, y gracias Adrián, porque es enorme lo que me has enseñado y eres un referente científico en mi vida. Gracias a María y a Noa, por enseñarme no solo a nivel profesional, si no en diferentes aspectos de la vida que esperar y como actuar, son largas las conversaciones que hemos tenido y el gran apoyo que me habéis brindado estos años. Gracias Laura, tu alegría y simpatía son contagiosas, pero tu ayuda y apoyo han sido un factor clave para que ahora mismo pueda estar escribiendo estas líneas. Gracias Raquel, no puedo no tener palabras bonitas y de agradecimiento para ti, tu forma de ser es maravillosa y siempre estás cuando más se necesita para echar una mano o dar palabras de ánimo. Laura y Raquel ha sido un placer haber coincido y compartido esta etapa con vosotras, sé que os queda lo más duro pero no tengo ninguna duda de que acabaremos celebrándolo por todo lo alto y riéndonos de todo lo que hemos pasado, sois muy especiales. Gracias Fernando por todas las palabras de ánimo que me has ofrecido, también estoy segura de que acabarás consiguiendo todo lo que te propongas. Pero en especial, mis mayores agradecimientos son para Ana, mi gran apoyo desde el principio y sé que no solo hasta aquí, si no que vas a formas parte de mi vida. Eres una persona con una fuerza envidiable que consigues transmitírnosla para que nosotros podamos atravesar los baches que se nos presentan, además de ser una profesional admirable. Eres una de las personas más especiales que he conocido y puedo decir claramente que sin ti hubiese tirado la toalla en algún momento, por eso y por tantas cosas que ambas sabemos, no puedo decir más que eres una verdadera amiga con todas las letras. Al igual que gracias al servicio de Nefrología, por ofrecer su ayuda para que este proyecto haya podido salir adelante. Pero no solo he tenido el laboratorio de Nefro, si no que me he sentido parte de mi laboratorio adoptivo, el laboratorio 3. Tamara y Nerea, gracias por apoyarme en todos los momentos durante este tiempo, tener paciencia para enseñarme todo lo que se y he podido aplicar, e incluso, hasta por aprender de mi campo para ayudarme. No sólo me llevo dos compañeras más, me llevo dos buenas amigas de la que pocas hay. Tamara sé que ahora termina una etapa, pero créeme, vas a llegar hasta donde quieras porque eres una tremenda de profesional, con una calidad humana increíble, y me gustaría seguir viviéndolo contigo como hasta ahora que incluso hemos compartido el final de la nuestra etapa de predocs, compartiendo unos buenos agobios y animándonos mutuamente. Nerea eres maravillosa, confía en ti como hacemos los demás porque vales oro, sé que siempre que me pase algo vas a estar a mi lado escuchándome y apoyándome en todas mis decisiones como has hecho hasta ahora, te has convertido en alguien esencial en mi vida. Y por supuesto gracias a María, eres el ejemplo en el que todas deberíamos reflejarnos, gracias por todas las lecciones científicas que tanto me han aportado, tu tiempo y tu cariño, pero también gracias por las grandes conversaciones de feminismo y sororidad. Gracias al servicio de proteómica, me habéis salvado con vuestra ayuda. Gracias Carmen, por tu ayuda en momentos más críticos para mí. Gracias María, por tu dedicación y conversaciones eternas sanadoras. Gracias Pili, transmites una energía increíble, siempre tan dispuesta a ayudar y a escuchar. Y, sobre todo, gracias Susana, no podría escribir en unas líneas todo lo que has hecho por mí, estoy eternamente agradecida, tu paciencia y ayuda son infinitas, has confiado en mi cuando ni yo me veía capaz y de una manera desinteresada, eres tremendamente admirable en todos los aspectos profesionales y personales. También a otras personas del IDIS con las que he compartido estos años. En especial mención a Miriam, mi gran descubrimiento y compañera de batallas, eres una persona muy especial. Gracias también a Eloi, Ana y María (Lab 18), Alana, Sandra y Laura (Lab 7), Marinela (Lab 6), Carolina (Lab 17), Pablo, Roi, María y Víctor (Lab 16), Raquel (Lab 14), Lorena, Ángeles y Cris (Lab 10), Clara (Lab 9), María Jesús, porque en mayor o menor medida habéis formado parte de este camino tanto profesional como personalmente. Pero una parte fundamental es mi familia, sin vosotros no sería quien soy. Gracias papá, porque sin ti no hubiese empezado esta etapa, y por todo tu cariño y apoyo diario. Gracias mamá, porque sin ti no lo hubiese conseguido, siempre has confiado en mí más que yo misma. Y gracias a mis hermanos, por su apoyo y confianza incondicional. Y sobre todo, gracias a ti Chema, gracias por implicarte en lo que significa la investigación y la realización del doctorado, por tu vitalidad contagiosa, por creer en mi siempre, por apoyarme hasta en los momentos más duros, por comprenderme y animarme a partes iguales. Gracias por convertirte en un gran compañero de vida y demostrarme tanto cada día. Muchas gracias a todos. RESUMO A enfermidade renal poliquística (PKD) é un grupo heteroxéneo de trastornos monoxénicos caracterizados pola acumulación de quistes cheos de líquido no ril que causan insuficiencia renal crónica e enfermidade renal terminal, e tamén pode afectar a outros órganos como o fígado. As enfermidades quísticas son unha das principais causas da enfermidade renal crónica. A PKD engloba diferentes enfermidades, entre as que destaca a enfermidade renal poliquística autosómica dominante (ADPKD). A ADPKD ten unha incidencia de 1 de cada 500 a 1 de cada 1.000 nacidos vivos, e é causada o 85% das veces por mutacións no xene PKD1, que codifica a policistina-1 (PC1), mentres que o 15% restante é causada por mutacións no xene PKD2, que codifica a Policistina-2 (PC2). Tanto a PC1 como a PC2 son proteínas transmembrana, a PC1 actúa como mecanosensor na membrana plasmática das células epiteliais do túbulo renal e detecta alteracións e sinais do espazo extracelular, sendo un regulador crucial da conversión de sinais mecánicos en respostas intracelulares; mentres que a PC2 é unha canle iónica non selectiva permeable ao Ca2+. Describíronse que estas proteínas están dispostas como un complexo heterotetramétrico que comprende unha PC1 e tres subunidades PC2 e aparecen predominantemente na membrana plasmática e no cilio primario. Non obstante, a fisiopatoloxía das policistinas aínda non está clara. Os mecanismos moleculares na formación dos quistes de ADPKD é un tema moi debatido que aínda non está claramente definido, entre as teorías máis compartidas está a teoría dos "two-hit", na que é necesaria unha segunda mutación somática no alelo non mutado; ou o modelo de dose, onde o inicio do quiste prodúcese porque a redución do nivel de proteína funcional é inferior ao nivel límite necesario para a súa correcta funcionalidade. Desde que se describiron os xenes e proteínas responsables da PKD, establecéronse múltiples modelos animais in vitro e in vivo para o estudo da PKD. Identificáronse e estudáronse diversos mecanismos implicados na ADPKD, incluíndo a proliferación, a apoptose, o metabolismo, a fibrose e a inflamación, entre outros. Non obstante, aínda se descoñece o mecanismo que leva ao inicio da formación de quistes. Do mesmo xeito, actualmente non existe unha terapia completamente eficaz para a PKD, só fármacos que retarden a progresión da enfermidade ou o tratamento de síntomas como a hipertensión, e finalmente a única opción é a terapia de substitución renal mediante diálise ou transplante. O crecente avance no coñecemento da fisiopatoloxía da ADPKD permitiu o desenvolvemento de novas dianas terapéuticas potenciais, entre as que se atopa a única terapia aprobada, o Tolvaptan, un antagonista selectivo do receptor da vasopresina tipo 2 (V2R). O Tolvaptan reduce o aumento do volume renal nun período de 3 anos e tamén retarda o deterioro da función renal. En cambio, ten efectos secundarios como poliuria, nicturia, sede e polidipsia, así como hepatotoxicidade. De forma que o tratamento con Tolvaptan só se recomenda actualmente para pacientes adultos con ADPKD con progresión rápida. Polo tanto, proponse que unha comprensión máis profunda da fisiopatoloxía da PKRAD levará ao descubrimento de novos tratamentos para evitar a necesidade de terapia de substitución renal en pacientes con ADPKD. O seguimento da progresión da ADPKD baséase na medición da taxa de filtración glomerular (eGFR), normalmente mediante a medida da creatinina sérica. Non obstante, este parámetro ten unha especificidade e sensibilidade baixas e non predice a progresión da enfermidade. Actualmente existen tamén outros parámetros como as relacións xenotipofenotipo e a medición do volume renal total que axudan a definir mellor o deterioro da función renal. Polo tanto, a identificación de novos biomarcadores que permitan predecir se a enfermidade está a progresar en diferentes pacientes permitiría un prognóstico máis preciso e ofrecería un aumento significativo da calidade de vida dos pacientes. Unha opción podería ser a través do perfil proteómico das Vesículas Extracelulares urinarias, xa que a urina proporciona unha fonte facilmente dispoñible de biofluídos e partículas derivadas das células renais. Ademais, a recollida de mostras non é invasiva e forma parte da práctica clínica habitual. As vesículas extracelulares (VEs) constitúen un grupo heteroxéneo de estruturas limitadas por unha bicapa lipídica liberada polas células. Son secretadas pola maioría dos tipos celulares e, polo tanto, están presentes en todos os fluídos biolóxicos. As VEs presentan funcións fisiolóxicas polivalentes e están implicados na regulación das principais vías de sinalización e na comunicación intercelular. A carga da VE é específica do tipo celular e adoita estar afectada polo estado fisiolóxico ou patolóxico da célula. Esta carga da VE prodúcese durante a bioxénese da VE e pode incluír compoñentes celulares como ADN, ARN, lípidos, metabolitos e proteínas citosólicas e da superficie celular. Unha vez liberados no espazo extracelular, as VE poden chegar ás células receptoras e liberar o seu contido para producir respostas funcionais que afectan o seu estado fisiopatolóxico. O crecente interese pola composición dos VE tamén expuxo a posibilidade de utilizalos como biomarcadores da progresión de varios estados de enfermidade. O desenvolvemento de técnicas para illar e enriquecer EVs para definir a súa carga selectiva pode mellorar a sensibilidade á procura de tales biomarcadores. Existe unha gran variedade de técnicas de illamento de EV que se basean nas súas propiedades físicas e moleculares como a densidade, o tamaño, a carga e as proteínas da superficie EV para facilitar a súa purificación. Entre as técnicas de illamento máis utilizadas están: ultracentrifugación, ultrafiltración, cromatografía de exclusión por tamaño, inmunoafinidade e técnicas baseadas en axentes precipitantes. Non obstante, cada unha destas técnicas ten vantaxes e inconvenientes, polo que o gran reto que rodea ás VEs é atopar un método de illamento de referencia factible, preciso e fiable para analizar as VEs para investigación e aplicacións clínicas. Os principais fluídos biolóxicos nos que se identificaron e caracterizaron as VEs circulantes son o sangue (plasma ou soro), a urina, a saliva e o leite, entre outros. As VE urinarias (uVE) identificáronse tanto en persoas sans como en pacientes con enfermidade renal, e son producidas e segregadas na urina por todo tipo de células renais de cada segmento da nefrona. A evidencia suxire que os uVEs son capaces de ser interiorizados por outras células e poden modular a súa función, o que indica a comunicación intra-nefrona ao longo do tracto urinario. Precisamente por estas características, teñen un excelente potencial como fonte de biomarcadores que poderían mellorar e complementar outras medicións clínicas. Os uVEs son facilmente accesibles de forma non invasiva, están dispoñibles en gran cantidade e son susceptibles de mostraxe frecuente. Ademais, como noutras enfermidades, é moi probable que o contido das vesículas urinarias se altere para reflectir a lesión renal e a progresión da enfermidade mediante a modulación das células veciñas, proporcionando así unha fonte distinta e rica en biomarcadores da enfermidade. Por outra banda, descríbese amplamente que as VEs están fortemente glicosilados cunha variedade de glicosaminoglicanos (GAG) unidos a proteínas e lípidos da membrana das VEs, o que destaca a importancia dos patróns de glicosilación dos EV na súa captación e interacción coas células receptoras. Ademais diso, comprobouse que o patrón de glicación está alterado en relación a diferentes enfermidades, como as enfermidades renais. Tendo en conta isto, proponse o estudo do compoñente glicosilado utilizando a ferramenta de recente desenvolvemento, ExoGAG, que permite o illamento dos GAG e a fracción unida a eles, como unha ferramenta prometedora na busca de biomarcadores en relación coa progresión da ADPKD. Neste contexto, este traballo ten como obxectivos principais o estudo das vías de sinalización implicadas na cistoxénese para o desenvolvemento de novas estratexias terapéuticas mediante o estudo da proteómica diferencial de riles de Wild type e mutantes dun modelo murino ortólogo de ADPKD e o desenvolvemento dun nova ferramenta diagnóstica que tamén nos permite anticipar a evolución da enfermidade, mediante a caracterización da glicoproteína e da fracción vesicular unida aos GAGs. Ademais, compararase o potencial desta ferramenta como método de illamento de EVs con outros xa establecidos. En primeiro lugar, realizouse a proteómica diferencial cuantitativa de Wild type e mutantes do modelo animal condicionado para a deleción de Pkd1 (Pkd1cond/cond; Tam-Cre). No primeiro enfoque, fíxose unha comparación dos riles de ratos sacrificados o día posnatal 30, momento no que a enfermidade está totalmente desenvolvida, demostrado polo alto índice de quistes, así como o número de quistes, o gran tamaño dos riles e o grave dano renal. Neste estudo atopáronse 152 proteínas expresadas de forma diferencial (DEP) de forma significativa e cun fold change superior a 2; dos cales 96 están upregulados nos mutantes e 56 downregulados. Estes resultados descubriron un gran número de proteínas nos mutantes no día 30 e que están relacionadas con varios procesos biolóxicos así como con diferentes compoñentes celulares, o que fai que o enfoque do estudo do complexo ADPKD, así como revalide o feito de que na actualidade, as vías principais e secundarias da cistoxénese aínda son difíciles de identificar. Por iso, para tentar atopar aquelas vías e proteínas relacionadas coas fases iniciais da cistoxénese, procedeuse á comparación de riles de Wild type e mutantes sacrificados o día posnatal 18, onde a enfermidade aínda non está claramente desenvolvida coa función renal practicamente intacta, pero onde xa se atopa un certo número de quistes. Neste caso, só se atoparon 14 DEPs, todos os cales estaban downregulados nos mutantes. As vías nas que se atopan estas proteínas están relacionadas co sistema inmunitario, o metabolismo e os procesos relacionados coa membrana celular, o que indica que estas vías xa están alteradas ao comezo da enfermidade. Non obstante, para comparar os resultados obtidos nas diferentes condicións, decidiuse analizar de novo os resultados, pero, neste caso, combinando as bibliotecas proteómicas. Deste xeito, o número de proteínas identificadas aumentou, identificándose agora 2498 proteínas fronte ás 1283 proteínas identificadas na biblioteca do día 30 e as 971 identificadas nas do día 18. Con esta nova análise, observouse que os DEP identificados varían, con todo, aínda están implicados nas mesmas vías celulares. Nesta nova análise, no día 18 posnatal, atopáronse 15 DEP, 14 estaban upregulados e 1 downregulados nos mutantes. As proteínas upreguladas están relacionadas con procesos biolóxicos como a tradución e a regulación de quinasas e con compoñentes celulares como os ribosomas e a matriz extracelular. Pola contra, a proteína downregulada está relacionada co metabolismo. Por outra banda, atopáronse un total de 213 DEP no día posnatal 30, 156 foron regulados ao alza e 57 a baixa. Neste caso, as proteínas upreguladas estaban relacionadas coas vías citoesqueléticas, a adhesión celular, a activación de torrentes de sinalización e compoñentes como a membrana plasmática e a matriz extracelular. Por outra banda, e como se observou nas fases iniciais, as proteínas downreguladas están claramente relacionadas co metabolismo. Estes datos suxiren que en fases avanzadas de progresión da enfermidade, aumenta o número de proteínas desreguladas e as vías relacionadas. Do mesmo xeito, estes resultados reforzan a idea de que o crecemento do quiste pasa por dúas etapas principais: o inicio do quiste e a súa progresión. O que podemos observar é que hai vías que xa están alteradas nos estadios iniciais da enfermidade e que continúan alterando ao longo dela, como a vía translacional, a matriz extracelular ou o metabolismo. Ademais, só aparecen 3 proteínas (VIME, SPON1 e RBM3) desreguladas, neste caso upreguladas nos mutantes, desde os estadios iniciais e que permanecen alteradas co avance da enfermidade, o que as faría atractivas como posible diana terapéutica, xa que sería posible modular a súa expresión desde as primeiras fases. En segundo lugar, desenvolvemos unha nova ferramenta, ExoGAG, que nos permite buscar novos biomarcadores diagnósticos ou pronósticos da enfermidade nunha mostra de urina. Este método baséase no illamento dos GAGs e da fracción asociada na mostra. A primeira análise con este método realizouse mediante a análise de mostras de urinas do modelo animal ortólogo de ADPKD utilizado previamente en diferentes estadios da enfermidade, desde a fase renal inicial ata a terminal, pasando por estadios avanzados. Na análise proteómica entre mostras de urina de Wild type e Mutantes, a fracción obtida mostrou unha serie de proteínas alteradas na enfermidade, entre as que destaca UROM, sendo a única atopada en menor abundancia en Mutantes e que decrece co transcurso da enfermiedad, e outras como ALBU ou AMBP, que aumentan. coa progresión da enfermidade. Ademais, estes resultados validáronse mediante a comparación e secuenciación do perfil proteico total das mostras illadas, que varía coa presenza da enfermidade así como coa súa evolución. Esta análise foi replicada en mostras humanas de pacientes con ADPKD con mutacións tanto no xene PKD1 como no xene PKD2. Observouse un perfil consistente de proteínas asociadas a GAG nas mostras de control. Non obstante, os nosos resultados revelaron que o patrón constante está alterado nos pacientes con ADPKD en función da función renal correlacionada coa creatinina sérica, sendo máis extremo nos estadios máis graves da enfermidade, pero comezando a alterarse nos estadios iniciais da enfermidade con niveis de creatinina relativamente baixos. Ademais, descubrimos que a variación no perfil dos pacientes era específica do tipo de enfermidade, como se pode ver entre o perfil dos pacientes con mutacións en PKD1 e PKD2 onde este patrón ten un cambio máis pronunciado desde respecto a fases iniciais da enfermidade. Como anteriormente, a secuenciación proteómica destas diferenzas no perfil proteico levou á identificación dunha serie de marcadores. A validación e cuantificación destes potenciais marcadores levou á identificación de UROM, CERU, AMBP e VTNC como biomarcadores potenciais da progresión da enfermidade. É interesante o comportamento da UROM, que diminúe en abundancia co avance da enfermidade mesmo antes dunha variación considerable da creatinina sérica. Mentres que CERU, AMBP e VTNC aumentan en fases avanzadas da enfermidade. Por outra banda, a combinación do estudo destes marcadores definidos pola relación dos que aumentan fronte a UROM aumenta o seu valor predictivo ante a progresión da enfermidade, e mesmo aumenta o valor diagnóstico, como ocorre, por exemplo, coa ratio UROM./AMBP. En terceiro lugar, ExoGAG caracterizouse como un novo método de illamento para VE. O potencial como ferramenta de illamento de VEs foi descuberto despois de que a análise proteómica de illamento con ExoGAG revelase que a gran maioría das proteínas estaban relacionadas coas VEs. Ademais, a caracterización mediante microscopía electrónica e inmunofluorescencia confirmou a presenza destes VEs, que aparecían illados individualmente ou pertencentes a un complexo xunto coa Uromodulina. Xa se describiu o atrapamento de VEs por filamentos de Uromodulina, considerándose un contaminante para o estudo dos EV en urina, polo que se propón a súa depleción. Pero os resultados anteriormente comentados lévannos a pensar que o feito de que estas vesículas estean unidas á Uromodulina non é un feito aleatorio senón máis ben unha cuestión fisiolóxica, xa que co curso da enfermidade como a ADPKD, o compoñente de Uromodulina diminúe, mostrando que desempeña un papel importante aínda descoñecido. Posteriormente realizáronse diferentes comparacións co método máis utilizado para o illamento de EVs, a Ultracentrifugación, tendo en conta que o volume empregado en ExoGAG é dez veces menor que o da Ultracentrifugación. A análise de espectrometría de masas mostrou un alto rendemento e robustez no compoñente proteico illado por ExoGAG, sendo similar ao da Ultracentrifugación, ademais de mostrar que con ExoGAG, ademais do compoñente vesicular coñecido, illase un compoñente extracelular específico debido ao tipo de illamento producido pola fracción asociada.a GAGs. A caracterización do compoñente vesicular mediante Nanotracking Analysis (NTA) determinou unha alta eficiencia no que respecta ao número de partículas illadas con ExoGAG, obtendo VEs de tamaño similar aos illados por Ultracentrifugación. Ademais, a caracterización dos VEs illados con estes dous métodos coa tecnoloxía ExoView®, revelou que con ExoGAG se obtén un maior rendemento no illamento de VEs por mL de mostra de urina e que á súa vez o tipo de VE illado con este método determinada polo seu perfil de tetraspaninas (CD63, CD81 e CD9) é unha representación máis fiable dos VE na mostra de urina que a illada con Ultracentrifugación. A aplicación de ExoGAG como método de illamento de VEs para buscar novos biomarcadores asociados á progresión de ADPKD revelou que o perfil de tetraspaninas aparece alterado desde o inicio da enfermidade. O compoñente CD63 aparece diminuído en comparación cos controis en todas as fases da enfermidade, mesmo en fases moi iniciais, tanto en pacientes con mutacións en PKD1 como en PKD2. Por outra banda, o compoñente CD81 increméntase en pacientes con ADPKD en comparación cos controis, de xeito que nos pacientes con mutación en PKD1 este compoñente aparece aumentado en todos os estadios da enfermidade mentres que en pacientes con mutación en PKD2 diminúe ao longo da enfermidade, o que converteríao nun biomarcador con capacidade diagnóstica. Con estes resultados observamos non só que o perfil de tetraspanina cambia na ADPKD, senón tamén que pode ser específico do tipo de mutación causante, e o seu estudo pode ser proposto como candidato a novos biomarcadores para o diagnóstico e progresión da enfermidade. Por outra banda, a súa aplicación na validación no compoñente vesicular dos marcadores obtidos no compoñente proteico asociado a GAGs, UROM, CERU, AMBP e VTNC comprobou que só parte do compoñente de UROM e VTNC se correlaciona co observado previamente. Isto lévanos a pensar que non só o compoñente dos EV e a súa carga xoga un papel importante ao longo da enfermidade, senón que tamén existe un complexo formado por VEs e proteínas asociadas que é a parte que está realmente alterada e que xoga un papel fundamental no curso da enfermidade. En resumo, identificáronse novas dianas e vías implicadas na fisiopatoloxía do ADPKD mediante a tecnoloxía de proteómica cuantitativa SWATH-MS. Do mesmo xeito que se identificaron aquelas vías que foron alteradas desde o inicio e ao longo da enfermidade, destacando o papel de VIME, SPON1 e RBM3 como posibles dianas terapéuticas, pola súa implicación dende as fases iniciais. Por outra banda, ExoGAG é un novo método analítico que separa o compoñente específico asociado aos GAGs da mostra. Esta fracción permitiunos identificar un patrón urinario de proteínas asociadas aos GAGs que se ve alterado na progresión da enfermidade. Todos estes datos mostraron os nosos resultados prometedores na busca de novos biomarcadores para o diagnóstico e prognóstico de enfermidades renais, como a ADPKD, así como para axudarnos a comprender mellor a fisiopatoloxía subxacente á enfermidade. Ademais, ExoGAG é unha nova ferramenta que nos permite un illamento eficiente das Vesículas Extracelulares en comparación con outros métodos de uso habitual. O compoñente vesicular illado é específico para a asociación con GAG e non só illa os VEs, senón tamén un complexo formado por proteínas como a Uromodulina e estas Ves que parece estar alterados en relación coa ADPKD. Isto pode desenmascarar un novo mecanismo de sinalización dentro do ril que necesita máis estudos para comprender o mecanismo fisiopatolóxico subxacente. ABSTRACT Polycystic kidney disease (PKD) is a group of monogenic disorders that result in renal cyst development, which cause chronic renal disease (CKD) and end-stage renal disease (ERSD), and other organs, such as the liver, may also be affected. PKD includes a number of different diseases including autosomal dominant polycystic kidney disease (ADPKD). ADPKD has an incidence of 1 in 500 to 1 in 1000 live births and is caused 85% of the time by mutations in the PKD1 gene, which encodes Polycystin-1 (PC1), while the remaining 15% is caused by mutations in the PKD2 gene, which encodes Polycystin-2 (PC2). Since the genes and their encoded proteins were described, several in vitro and animal models were developed for the study of PKD and multiple molecular pathways were discovered and characterized. However, the central and key mechanism that trigger cyst formation remains unclear. In the same way, there is currently no fully efficient therapy for PKD, only drugs that slow the progression of the disease or treatment of symptoms such as hypertension, and finally the only option is renal replacement therapy by dialysis or transplantation. The increasing understanding of the pathophysiology of ADPKD has led to the development of new potential therapeutic targets, including the only approved therapy, Tolvaptan, a selective vasopressin type 2 receptor (V2R) antagonist. Tolvaptan reduces the increase in renal volume over 3 years, and also slows the deterioration of renal function. In contrast, Tolvaptan has side effects such as polyuria, nocturia, thirst and polydipsia, as well as hepatotoxicity. Monitoring the progression of ADPKD is based on the measurement of the glomerular filtration rate (eGFR), usually by quantifying serum creatinine. However, this parameter has a low specificity and sensitivity and is not predictive of disease progression. Currently, there are also other parameters such as genotype-phenotype relationships and measurement of total renal volume that help to better define renal function decline. Therefore, the identification of new biomarkers that can predict disease progression in different patients would allow a more accurate prognosis and offer a significant increase in the quality of life of patients and their families. One option could be through the characterization of the proteomic profile of urinary Extracellular Vesicles, as urine provides a readily available source of biofluids and cell-derived particles. Moreover, sample collection is non-invasive and part of routine clinical practice. Extracellular vesicles (EVs) are a heterogeneous group of structures bounded by a lipid bilayer released by most cell type and present in all biological fluids. EVs have exhibited versatile physiological functions and are involved in the regulation of major signaling and intercellular communication pathways. EV loading is cell type specific and is often affected by the physiological or pathological state of the cell. This EV cargo is produced during EV biogenesis and may include cellular components such as DNA, RNA, lipids, metabolites, and cytosolic and cell surface proteins. Once released into the extracellular space, EVs can reach the recipient cells and release their contents to produce functional responses that affect their pathophysiological state, as the intranephron communication carried out by urinary EVs. On the other hand, it is widely described that EVs are heavily glycosylated with a variety of glycosaminoglycans (GAGs) bound to proteins and lipids of the EV membrane. In this context, the main objectives of this work are to study the signaling pathways involved in cystogenesis for the development of new therapeutic strategies by studying the differential proteomics of Wild Type and Mutant kidneys of an orthologous murine model of ADPKD and the development of a new diagnostic tool that will also allow us to anticipate the evolution of the disease by characterizing the glycoprotein and vesicular fraction bound to GAGs. In addition, the potential of this tool as an EV isolation method will be compared with other established methods. First, we performed a quantitative differential proteomics of Wild Type and Mutants of the animal model conditioned for Pkd1 deletion (Pkd1cond/cond; Tam-Cre) at two different points of disease development, at early and advanced stages. We identified novel molecular pathways and targets in PKD field, consistent with the fact that in advanced stages of disease progression the number of deregulated proteins and pathways increases. We highlight the appearance of 3 proteins (VIME, SPON1 and RBM3) that are deregulated from the initial stages and remain altered as the disease progresses, which would make them attractive as possible therapeutic targets, as it would be possible to modulate their expression from the early stages. Secondly, we developed a new tool, ExoGAG, which allows us to search for new diagnostic or prognostic biomarkers of disease in a urine sample. This method is based on the isolation of GAGs and the associated fraction in the sample. A constant profile of GAGassociated proteins has been observed in control samples. However, our results revealed that the constant pattern is altered in ADPKD patients as a function of renal function correlated with serum creatinine, being more extreme in the more severe stages of the disease but starting to modify in early stages of the disease. Proteomic sequencing of these differences in protein profile led to the identification of a number of markers. Among them, the behavior of Uromodulin is interesting, which decreases in abundance with disease progression even before a considerable variation in serum creatinine. While Ceruloplasmin, protein AMBP and Vitronectin increase in advanced stages of the disease. On the other hand, the combination of the study of these markers defined by the ratio of those that increase against Uromodulin increases their predictive value in the face of disease progression. Thirdly, the characterization of ExoGAG as a new method of EV isolation showed that in this isolation there are EVs found individually and others belonging to a complex together with Uromodulin. The previously results lead us to believe that the fact that these vesicles are bound to Uromodulin is not a random event but a physiological issue, as with the course of the disease like ADPKD the Uromodulin component decreases, showing that it plays an important yet unknown role. On the other hand, comparison with one of the most widely used methods for EVs isolation, ultracentrifugation, showed that ExoGAG is a high performance, efficient and robust method. In addition to demonstrating that the representation of EVs according to their tetraspanin profile isolated with ExoGAG is more reliable than that of the urine sample than those isolated with Ultracentrifugation. The application of ExoGAG as a method of EV isolation to search for new biomarkers associated with ADPKD progression has revealed that from the onset of the disease, the profile of tetraspanins CD63, CD81 and CD9 appears altered and, in fact, it may be specific to the type of causative mutation, and its study could be proposed as a candidate for new biomarkers for the diagnosis and progression of the disease. Moreover, its application in the validation in the vesicular component of the markers obtained in the protein component associated with GAGs, UROM, CERU, AMBP and VTNC found that only part of the UROM and VTNC component correlates with what was previously observed. This leads us to think that not only the EV component and its load play an important role throughout the disease, but that there is also a complex formed by EVs and associated proteins that is the part that is really altered and plays a fundamental role in the course of the disease. 4.3.5.4 Characterization of the origin of Extracellular Vesicles isolated with ExoGAG. ........................................................................................................................................ 153 4.3.6 Characterisation of the vesicular profile of Extracellular Vesicles isolated with ExoGAG and comparison with those isolated by ultracentrifugation. ............................... 154 4.3.6.1 Quantification and size analysis using NanoTracking Analysis (NTA) ..... 154 4.3.6.2 Quantification, size analysis and Tetraspanin characterization using ExoView® ....................................................................................................................... 156 4.3.7 Characterization of the vesicular profile of EVs associated with the onset and progression of autosomal dominant polycystic disease. ..................................................... 159 4.3.7.1 Characterization of the Tetraspanin profile of Extracellular Vesicles in patients with mutation in PKD1 ................................................................................................... 159 4.3.7.2 Characterization of the Tetraspanin profile of Extracellular Vesicles in patients with mutation in PKD2 ................................................................................................... 160 4.3.7.3 Comparison of the Tetraspanin profile of Extracellular Vesicles in patients with mutation in PKD1 and PKD2 ................................................................................. 162 4.3.7.4 Characterization and validation of the possible biomarkers in the vesicular component of ExoGAG isolation in the progression of autosomal dominant polycystic disease. ............................................................................................................................ 163 Discussion........................................................................................................................ 167 5. Discussion.................................................................................................................... 169 5.1 identification of new signaling pathways involved in cystogenesis by quantitative differential proteomic study in Polycystic Kidney Disease mouse model ............................. 169 5.2 New biomarkers in urine for diagnosis and prognosis of ADPKD ........................... 173 5.3 Characterization of urine Extracellular Vesicles isolated by ExoGAG .................... 179 Conclusions ..................................................................................................................... 185 6. Conclusions ................................................................................................................. 187 Bibliography .................................................................................................................... 189 Bibliography .................................................................................................................... 191 Supplemental Material..................................................................................................... 227 1. Supplemental Figures .................................................................................................. 227 2. Supplemental Tables ................................................................................................... 230 Annexes ........................................................................................................................... 317 ANNEX 1: authorisation of the bioethics committee for human and experimental animal studies ..................................................................................................................................... 319 1.1 Bioethics Committee authorization for human studies ............................................. 319 1.2 Bioethics Committee authorization for studies on experimental animals ................. 321 33 ABREVIATIONS A1AT: alpha-1-antitrypsin ADPKD: Autosomal Dominant Polycystic Kidney Disease AFF4: Asymmetrical Flow Field-Flow Fractionation ARPKD: Autosomal Recessive Polycystic Kidney Disease AMBP: Protein Alpha-1-microglobulin ALBU: albumin APOA4: Apolipoprotein A-IV BUN: Blood Urea Nitrogen CAH1: Carbonic anhydrase 1 CERU: Ceruloplasmin CKD: chronic kidney disease CREAT: serum creatinine DAPI: 4′,6-diamidino-2-phenylindole DBA: Dolichos Biflorus Agglutinin DDA: Data Dependent Acquisition DIA: Data Independent Acquisition DEP: differentially expressed proteins DPP4: Dipeptidyl peptidase 4 DTT: dithiothreitol ECM: Extracellular Matrix ESRD: End Stage Renal Disease EVs: extracellular vesicles FDR: false discovery rate FIBB: Fibrinogen beta chain GAG: glycosaminoglycan 34 GAPDH: Glyceraldehyde-3-phosphate dehydrogenase GFB: glomerular filtration barrier GFR: glomerular filtration rate GPI: glycosylphosphatidylinositol IF: Immunofluorescence ILV: Intraluminal Vesicles IP: Immunoprecipitation ISEV: International Society for Extracellular Vesicles KDIGO: Kidney Disease: Improving Global Outcomes KO: knockout LG3BP: Galectin-3-binding protein LUM: Lumican MVB: Multivesicular Body Mut: Mutant NEP: Neprilysin NTA: NanoTracking Analysis SDS: sodium dodecyl sulfate PBS: Phosphate buffered saline PC1: Polycystin-1 PC2: Polycystin-2 PCA: Principal Component Analysis PCP: Planar Cell Polarity PCR: Polymerase chain reaction pI: isoelectric point PKD: polycystic kidney disease RBM3: RNA-binding protein 3 35 RET4: Retinol-binding protein 4 RT: room temperature RT-PCR: Reverse-transcription quantitative PCR SEC: Size Exclusion Chromatography SPON1: Spondin-1 TEM: Transmission electronic microscopy TTHY: Transthyretin TRFE: Serotransferrin UC: Ultracentrifugation uEV: urine Extracellular Vesicle UF: Ultrafiltration UMOD: Uromodulin UROM: Uromodulin VIME: Vimentin VTNC: Vitronectin WT: wild type ZA2G: Zinc-alpha-2-glycoprotein INTRODUCTION 38 Introduction 39 1. INTRODUCTION 1.1 FUNCTION AND PHYSIOLOGY OF THE KIDNEY The kidneys are paired organs that lie retroperitoneally on the posterior abdominal wall. The main function of the kidneys is the elimination of waste substances through the production of urine. The kidneys excrete about 1.5 litres of urine per day and they are capable of filtering 20% of the blood pumped by the heart (1,2). In addition to their excretory function, kidneys also perform endocrine (by secreting vitamin D, renin and erythropoietin), metabolic and blood pressure regulation (by activation of the renin-angiotensin-aldosterone axis), body water and thirst (by osmoregulation) functions. They also regulate oxygen supply to red blood cells, erythropoiesis, acid-base haemostasis, calcium and phosphate metabolism, potassium balance, and they are involved in the immune system and drug metabolism (2,3). Anatomically, each kidney presents a superior and inferior pole, a convex border placed laterally, and a concave medial border with a marked depression, the hilum, which contains the renal vessels and the renal pelvis (1). The kidneys consist of a 1 cm thick outer part called cortex and an inner part called medulla made up of renal pyramids, the apices of which point towards the renal hilum. These apices end in minor calyces and these in turn end in major calyces, which merge into the renal pelvis from which the ureter arises (Figure 1, left) (4). Each kidney is formed by functional units called nephrons; about one million nephrons in humans and 12-16,000 in mice (3). Structurally, each nephron consists of a filtering body, called the glomerulus, surrounded by Bowman's capsule, located in the cortex; and a long tubule divided into several segments. These segments are: the proximal tubule, the loop of Henle and the distal tubule which interconnect in the collecting tubules ending in the renal pelvis (Figure 1, right). (2). Figure 1. Anatomy of the kidney (left) and nephron (right). This figure was made using Biorender. MARTA VIZOSO GONZÁLEZ 40 The renal artery, originating from the aorta, branches into afferent arterioles that carry blood to the capillaries that form part of the glomerulus. The filtration process is mediated by the glomerular filtration barrier (GFB) is in the glomerulus and consists of fenestrated endothelial cells, a basement membrane and a layer of highly epithelial cells called podocytes. Blood is filtered through GFB which is permeable to water and low molecular weight solutes. The filtrate passes into the proximal tubule where two-thirds of it is reabsorbed (5). Unfiltered blood leaves the glomerulus via the efferent arteriole. In the proximal tubule, there are receptors able to reabsorb proteins and other components as vitamins or hormones. The loop of Henle oversees concentrating the urine and reabsorbing Ca2+ and Mg2+, while K+ excretion is carried out in the distal and collecting tubule. Regulation of Na+ and Cl-, essential in blood pressure control, takes place in almost the entire nephron (4,5). Once the blood has been filtered in the glomerulus, it flows into the efferent arteriole and then through the peritubular capillaries and the vasa recta, forming a microvasculature around the nephron. The vasa recta are parallel to the loop of Henle and return ions and to the circulation. Finally, blood exits the kidney via the renal vein to the vena cava (5). 1.2 KIDNEY DISEASE Kidney disease is defined as a heterogeneous group of pathological conditions affecting kidney structure and function. These alterations are associated with increased risk of kidney failure and complete loss of renal function or, even, development of complications in other organ systems, especially the cardiovascular system. The clinical presentation and progress of the renal disease are variable. Kidney disease can be classified as acute or chronic, dependent on duration, ≤3 months or >3 months, respectively. The association between acute kidney injury and chronic kidney disease is complex: acute kidney injury can lead to chronic kidney disease, and chronic kidney disease increases the risk of acute kidney injury (2,6). Chronic kidney disease (CKD) has become one of the major public health problems in recent decades, affecting approximately 10% of the global adult population. However, a wide range of CKD prevalence has been observed in different studies, which has been related to clinical, environmental, geographical and socio-economic variables as well as to methodological differences. (6,7). Recent studies assign the prevalence in Spain at around 15% (6). By 2040, CKD is expected to become the fifth cause of death globally (8,9). The epidemiological significance of CKD is based on two fundamental aspects: the replacement treatment of advanced CKD by dialysis or kidney transplantation, although affecting only 1% of CKD patients; and the very significant increase in the risk of cardiovascular morbidity and mortality and total mortality (6). There are many potential causes for chronic kidney diseases, including genetics, and nongenetic causes. There are causes that are common and well researched, such as diabetes, glomerulonephritis, and cystic kidney diseases, but relationship in CKD is not yet understood. Multiple genetic and environmental risk factors contribute to kidney diseases, making identification of the underlying pathophysiologic mechanisms difficult (8,9). For instance, despite a close association between CKD and hypertension, whether hypertension is a cause or a consequence of CKD is still controversial (8). Introduction 47 Figure 6. Altered pathways in ADPKD and potential therapeutic approaches. Agents with potential therapeutic approaches are represented in red; ant = antagonist; inh = inhibitor. Adenylate cyclase 6 (AC-VI); protein kinase B (AKT) ; AMP kinase (AMPK); v-raf murine sarcoma viral oncogene homolog B1 (B-raf); cyclin-dependent kinase (CDK); cystic fibrosis transmembrane conductance regulator (CFTR); epidermal growth factor (EGF); E-prostanoid receptor 2 (EP 2 R); extracellular signal-regulated protein kinase (ERK); glycogen synthase kinase 3 (GSK3); insulin-like growth factor 1 (IGF1); inositol 1,4,5trisphosphate receptor (IP 3 R); mitogenactivated protein kinase (MAPK); mammalian target for rapamycin (mTOR); polycystin-1 (PC1); polycystin-2 (PC2); phosphodiesterase (PDE); prostaglandin E2 (PGE 2); phosphatidylinositol 3-kinase (PI3K); protein kinase A (PKA); phospholipase A2 (PLA 2); phospholipase C-γ2 (PLCγ); somatostatin sst2 receptor (R); ras homolog enriched in brain (Rheb); ryanodine receptor (RyR); secreted Frizzled-related protein 4 (sFRP4); tyrosine kinase (TK); tumor necro-sis factor-α (TNF-α); TSC = tuberous sclerosis proteins tuberin (TSC2) and hamartin (TSC1); vasopressin V2 receptor (V2R); vascular endothelial growth factor (VEGF). Image taken from Reiterová, J., Int. J. Mol. Sci, 2022 allowed by MDPI Open Access. 1.3.1.3.1 Polycystins and Ca2+ channel Polycystins could be located in the endoplasmic reticulum. PC2, interacting with PC1, acts as a Ca2+ channel in the endoplasmic reticulum. C-terminus of PC2 also interacts with other calcium channels such as the inositol-3-phosphate receptor (IP3R) and can prolong IP3dependent calcium release (78,79). Ca2+ is responsible for activating phosphodiesterase (PDE) that regulates the cAMP to AMP pathway and for inhibiting adenylate cyclase 6 (AC6) that regulates the ATP to cAMP pathway, thus controlling cAMP levels. Besides, this also activate the PI3K/Akt pathway, activated Akt can influence apoptosis, growth, proliferation, metabolism, angiogenesis, survival, protein synthesis and transcription (80). Activation of PI3K/Akt pathway may decrease the level of intracellular calcium, then PC2 is recruited to the plasma membrane where it forms complex with PC1 serving as an influx calcium channel (38,80). MARTA VIZOSO GONZÁLEZ 48 1.3.1.3.2 Proliferation and apoptosis In ADPKD, there is an increment both of cell proliferation and apoptosis. (81). Inactivation of PKD1 and PKD2 genes, decrease intracellular Ca2+ levels, leading to increased cAMP level (82). This increase of cAMP leads to an increase in protein kinase A (PKA) activity activating the MAPK/ERK pathway via Src and Ras/Raf/ERK, and this in turn activates the mTOR pathway (83). Both pathways activate the synthesis of proteins involved in the cell cycle and thus cell proliferation, leading to enlargement of cystic cells (53,82). In addition, the vasopressin 2 receptor (V2R) has been shown to be upregulated in PKD, which activates the formation of more cAMP via AC6 (79,84). PC1 is responsible for inhibiting mTOR activity by modulating the TSC1/2 complex, so mutations in PC1 lead to an increase in mTOR (85). mTOR complex regulates cell growth and proliferation, together with actin cytoskeleton and apoptosis (38). On other hand, PC1 produces the resistance to apoptosis by activation PI3K by the interaction with the Carboxi-terminal tail (CTT) of PC1 to tuberin protecting the phosphorylation of Akt (38,80). Moreover, several PKD studies have shown increased activity of proteins with apoptotic activity as caspases and cytochrome c, as well as the c-Myc proto-oncogene and the BCL-2 gene (86). 1.3.1.3.3 Altered fluid secretion Increased fluid secretion is a factor in ADPKD cyst expansion and growth, which causes a dysregulation of the ion gradients that are present in normal renal nephrons. Unlike normal renal epithelium, where fluids are reabsorbed, in cystic epithelium they are secreted into the cyst lumen stimulated by cAMP. PKA, activated by cAMP, will phosphorylate the cystic fibrosis transmembrane conductance regulator (CFTR) channel, which facilitates Clsecretion (87). Also involved in secretion are the NKCC1 (Na+-K+-Clcotransporter) channels that require K+ efflux from KCa3.1 (activated by cAMP) to supply Clto CFTR (88). Reabsorption and passage of water across the epithelial plasma membrane in is mediated in part by aquaporins. PKA also phosphorylates Aquaporin-2 (AQP2) that goes to the apical membrane increasing water reabsorption and urine concentration (82). 1.3.1.3.4 Fibrosis and inflammation, role of extracellular matrix and epithelialmesenchymal transition Fibrosis is the accumulation of extracellular matrix (ECM) in the extracellular region of cells and it is a condition that tends to increase with cyst growth and progressive renal function decline (89). In advanced stages of the disease, large amounts of ECM (collagens, fibronectin, proteoglycans) are deposited between cysts throughout the enlarged kidney associated with interstitial inflammation (89). One of the characteristics of fibrosis is the infiltration of inflammatory cells. Inflammation is one of the protective systems against foreign molecules, characteristic of the innate immune system response (89,90). Different studies showed that inflammatory pathways such as the JAK/STAT and NF-κB pathways were found to be activated in the disease (91). Phosphorylation of NF-κB by inflammatory cytokines (TNF-α, IL-1α and β, IL-6, Ccl3 y Ccl4) leads to its translocation to the nucleus to activate gene transcription (92,93). In the same way, some cytokines binding to their receptors activate JAK proteins, which phosphorylate STAT proteins leading to their translocation to the nucleus to activate gene transcription. The Cterminal tail of PC1 can amplify the signal of STAT proteins. This tail is upregulated in the presence of kidney damage or PKD, leading to increased expression of STAT proteins (94). Introduction 49 On the other hand, it has been observed that there is an increase in ECM proteins such as collagen (95); alterations in extracellular matrix turnover, which is carried out by matrix metalloproteinases (MMPs) and their inhibitors (TIMPs) (90); and an increase in proteins that act in cell-matrix interaction such as integrins and endothelial growth factor receptor (VEGFR) (89,96). In PKD, there is an imbalance between cytokines and growth factors (90). Transforming growth factor beta (TGF-β) is considered the cytokine inducing the epithelial to mesenchymal transition (EMT) by phosphorylating SMAD2/3. SMAD2/3 inhibits GSK3β (glycogen synthase kinase 3 beta), which causes that SNAIL1 remains in the nucleus promoting the loss of epithelial markers, as E-caderin, and increasing mesenchymal markers, as collagen, fibronectin or vimentin (97). 1.3.1.3.5 Metabolism Growing evidence of alterations in cell metabolism have been described in the past decade. ADPKD cells shift their mode of energy production from oxidative phosphorylation and fatty acid metabolism to alternative pathways, such as glycolysis (98– 100). In addition, the polycystins seem to play regulatory roles in modulating mechanisms related to energy production and utilization, including AMP-activated protein kinase (AMPK) (101), PPARα (102), PPARγ co-activator 1 α (PGC1α), calcium signaling at mitochondria, mechanistic target of rapamycin complex 1 (mTORC1) (103), cAMP (104) and cystic fibrosis transmembrane conductance regulator (CFTR) as well as the expression of crucial components of the mitochondrial energy production apparatus (105). Moreover, new studies suggests that PC1 may link cell-matrix signaling to mitochondrial activity through the translocation of Cterminal cleavage product of PC1 (CTT) and play a direct role in regulating mitochondrial function and metabolism (106). 1.3.1.3.6 Role of cilia PKD are cilia-related disorders or ciliopathies (107). All PC1 and PC2 proteins are localized, plus other cellular localizations, in apical and basal zones of primary cilium (15). The cilium seems to have a central role in cyst formation, deletion of the cilium leads to cyst formation (108). Surprisingly, in conditioned, cilia-deleted mice with mutations in the Pkd1 or Pkd2 genes, cyst growth has been shown to be reduced compared to models with mutations in only the Pkd1 and Pkd2 genes (109). Thus, cyst formation depends on the intact cilium when polycystins disappear (109,110) but cyst formation due to loss of the cilium is independent of polycystins (61). 1.3.1.3.7 Wnt signaling The Wnt signaling pathway regulates essential biological functions. Wnt is implied in controlling proliferation, differentiation, motility, polarity, embryogenesis and organogenesis, among other (111). It is divided into two ways, the canonical Wnt/β-catenin pathway, and a β-catenin independent pathway that is responsible for planar cell polarity and tissue morphogenesis. In the canonical pathway, Wnt binds to the Frizzled (Fz) receptor in the presence of lowdensity lipoprotein (LDL) receptor 5 or 6 (LRP5/6) and activates Dishevelled (Dvl) thereby inhibiting β-catenin proteolysis. Thus, it accumulates in the cytosol together with T cell factor (TCF) and translocate to the nucleus to transcriptionally activate Wnt genes (112,113). Carboxy-terminal part of PC1 inhibits the interaction between β-catenin and TCF. The MARTA VIZOSO GONZÁLEZ 50 canonical Wnt pathway is activated in ADPKD patients. PC1 could affect the cystogenesis during embryogenesis by the influence on Wnt canonical pathway (38). Noncanonical Wnt pathway plays role in determination of planar cellular polarity (PCP) and motility. In the non-canonical pathway, Wnt also binds to the Fz receptor and activates Dvl. Dvl regulates cytoskeleton rearrangement by activating RhoA (activates ROCK), Rac1 (activates JNK) or profilin. Proteins of the main PCP pathway are important in signal transduction where loss of PCP is frequently observed in cystic cells. By other hand, activation of the noncanonical pathway leads to an increase of Ca2+. This pathway following with Gprotein-dependent activation of phospholipase C. Wnt can bind to the extracellular domain of PC1 which leads whole cell currents and Ca2+ influx dependent on PC2 (38,114). 1.3.1.4 Therapies for ADPKD The principal aim of the current PKD therapies is to reduce symptoms because there is no approved specific therapy. Although, some important advances have centered on reducing the growth of the cysts and slow the decline of kidney function. However, there is currently no effective treatment for ADPKD, only drugs that slow the progression of the disease, and finally renal replacement therapy (RRT) by dialysis or transplantation is the unique option. General recommendations in ADPKD are healthy diet and lifestyle, cardiovascular exercise and avoid smocking, with an strict control of hypertension (38) Increasing knowledge of the pathophysiology of the ADPKD has laid the development of new potential therapeutic targets (Figure 6). Unfortunately, many of these therapeutic targets are involved not only in cyst formation, but also in numerous canonical physiological processes through the body that are important for cellular proliferation, growth, and repair, so drugs to these targets often present severe adverse effects that limit their utility or even these therapies could be very effective in animal models, but clinical studies reject them because of systemic adverse effects and toxicity (115). Some of the potential treatments being studied focus on inhibiting the increase in cAMP, and thus the growth of cysts, or act on the different pathways with which cAMP interacts. The unique approved therapy is Tolvaptan (Jinarc®), a selective vasopressin type 2 receptor (V2R) antagonist. It is based on the role of arginine vasopressinmediated cAMP as a driver of fluid secretion. Tolvaptan reduces the increase in kidney volume in the 3-year study period, and also slows the decline in kidney function (38,116). In contrast, Tolvaptan presents secondary effects as polyuria, nycturia, thirst and polydipsia (115), as well as hepatotoxicity (117). Nowadays, Tolvaptan therapy is only recommended to ADPKD adult patients with rapid progression of the disease (38). Another of the most advanced possible treatments is the use of somatostatin analogues, that binds to G protein-coupled receptors and inhibits intracellular cAMP production in liver and kidney. Although, there is no currently evidence that somatostatin analogues should be used in patients with polycystic renal disease, because the decline of the eGFR is not deaccelerated. However, they should come into account in patients with high volume polycystic liver to put off liver transplantation (38,115). Other therapies that are under preclinical and clinical trials by targeting the following objectives (Figure 6) (38,115): • cAMP pathway as Lixivaptan, a selective V2R antagonist with lower secondary effects than Tolvaptan Introduction 51 • CFTR and potassium channels • Chloride channel TMEM16A by inhibitors • EGF receptor pathway as Tasevatinib, a multikinase inhibitor • AMPActivator Protein Kinase as Metformin, that activates AMPK and inhibits CFTR by phosphorylation, or Statins • MAPK Pathway-Raf Kinase by inhibitors as Sorafenib, another multi-kinase inhibitor. • Dietary interventions and caloric restriction • Metabolism with 2-Deoxyglucose, an inhibitor of the glycolytic pathway • mTOR inhibitors • Intracellular calcium and cell cycle regulation with calcimimetics or Cyclin Dependent Kinases inhibitors • microRNA blockers as inhibitors of miR-17, miR-192 and miR-194 Deeper knowledge of the pathophysiology of ADPKD will hopefully bring new treatments to prevent the need for kidney replacement therapy in ADPKD patients. 1.3.1.5 Diagnosis of ADPKD The diagnosis of ADPKD depends on the stage of the disease (35). First clinical manifestations are early-onset hypertension, abdominal pain, haematuria or urinary tract infections (15). When the disease is fully established, the diagnosis is based on the patient's history and physical examination, from a clinical point of view (16,118). However, providing a definitive diagnosis can be difficult as the disease can be mistaken with other diseases with similar symptoms. Therefore, other complementary tests are necessary, such as imaging or genetic tests (15). Presymptomatic diagnosis usually is done in risk family members by abdominal imaging and the presence of bilateral cyst means affection of the disease. Ultrasonography is the most used imaging method due to its price, but computerized tomography (CT) and Magnetic Resonance Imaging (MRI) are more sensible, accurate and provide essential information for prognosis (15,119). The problem with imaging techniques is that they are not sensitive enough for patients younger than 30 years where the cysts are smaller, or for patients with a family history of mild disease. Therefore, detection of mutations in these genes by genetic testing is the most appropriate diagnostic test (120). It has been described that the disease-causing mutation may be interacting with other PKD or ciliopathy genes modifying the phenotype and increasing the genetic complexity of the disease (35). For this reason, next-generation sequencing techniques are being used that allow the study of several genes simultaneously (121). 1.3.1.6 Prognosis and End-Stage Renal Disease in ADPKD In ADPKD patients monitoring the renal functional decline using eGFR in standard practice, however predicting the rate of progression to ESRD remains a major clinical problem due to high clinical variability of ADPKD (34,37,122). The current state-of-art of assessment of prognosis is based in two approaches: genetic testing and measurement of total kidney volume. MARTA VIZOSO GONZÁLEZ 52 On the other hand, genotype-phenotype correlation demonstrated that the gene and the type of mutation are key factors in the variability of ADPKD. Patients with mutations in PKD1 present more rapid progression than those patients with mutations in PKD2 (40,46,69). Moreover, the type of mutation is also critical where patients with PKD1 truncated mutations are associated with worse renal prognosis than those with PKD1 non-truncating mutations (46,69). Taking into account these predictive genetic factors with clinical information, a prognostic algorithm, the Predicting Renal Outcomes in Polycystic Kidney Disease (PROPKD) provides a stratification of risk in decline eGFR and progression to ESRD (34,45). However, this algorithm although is very specific has limited sensitivity (119) On the other hand, the total kidney volume increases throughout the disease whereas a stable or slowly declining eGFR is caused by the hyperfiltration of surviving nephrons (123). The measurement of height-adjusted total kidney volume (htTKV) by MRI could predict of eGFR decline (124). However, this measurement by segmentation is not yet part of clinical routine. These parameters, monitoring of serum creatinine, genotype-phenotype correlation and htTKV do not predict future changes in disease prognosis. The identification of new marker that could predict whether patients have a rapid or slower functional decline to ESRD, would allow for more accurate prognosis and offer a significant increase in quality of life for patients and family members (125). Besides, it would provide the time for treatment to slow disease progression and might measure efficacy of response to treatments. One opportunity may be through characterization of proteomic profiling of urinary Extracellular Vesicles, as urine provides a readily available source of biofluids and cell-derived particles. Additionally to preliminary results from our research group that have shown that Extracellular Vesicles play an important role in the course of the disease, which would make them an attractive resource for the search for biomarkers, even with the development by our laboratory of a new method of isolating them (patent registration WO 2017/042416 A1). Introduction 53 1.4 EXTRACELLULAR VESICLES Extracellular Vesicles (EVs) comprise a heterogeneous group of structures delimited by a lipidic bilayer released by cells and cannot replicate (126). They are secreted by most cell types, and therefore, they are present in all biological fluids. EVs have presented multipurpose physiological functions and are involved in the regulation of main routes of signaling and intercellular communication (127). EVs could be classified according to their size and biogenesis in 3 main groups (Figure 7): exosomes, microvesicles and apoptotic bodies. Exosomes are the smallest population of EVs (measuring around 50-150 nm) and are released after fusion of multivesicular bodies (MVBs) with the plasma membrane (128). Microvesicles are a group of vesicular structures between 501000 nm that are generated by outward budding and fission of the plasma membrane and released into the extracellular space (129). And apoptotic bodies are the largest EVs with a size between 100nm to 5000nm that are released when plasma membrane blebbing occurs during apoptosis (127,130) Figure 7. Biogenesis and release of Extracellular Vesicles. Exosomes are formed from the endosomal membrane invagination causing the formation of the early endosomes and then multivesicular bodies (MVB). The vesicles that reside in the MVBs fuse with the lysosome to degradation or fuse with the plasma membrane and release. Microvesicles are generated by evagination and direct fission of the plasma membrane and released into the extracellular space. Apoptotic bodies are released by blister generation during programmed cell death. This figure was made using Biorender. However, since consensus among experts on the study of EVs on specific markers of EV subtype of particular biogenesis pathway remains difficult, change of classification of EVs urged. The following classification of EV subtypes is proposed by the International Society of Extracellular Vesicles that refer to: a) physical characteristics, such as size being “small EVs” < 200nm and “medium/large EVs” > 200nm, b) biochemical composition (CD63+/CD81+- EVs, Annexin A5-stained EVs, etc.); or c) descriptions of conditions or cell of origin as podocyte EVs, large oncosomes or apoptotic bodies (126). 1.4.1 Biogenesis of EV Even though, exosomes and microvesicles have different processes of biogenesis at distinct sites in the cell, common intracellular mechanisms are involved in both entities’ biogenesis (Figure 7). MARTA VIZOSO GONZÁLEZ 54 1.4.1.1 Biogenesis of exosomes For the generation of exosomes, first of all an invagination of the plasma membrane is needed, forming a cup-shaped structure called the early endosome, with the contribution the trans-Golgi network. Early endosomes mature into late endosomes, and during this process accumulate intraluminal vesicles (IVs) in their lumen becoming into what is known as multivesicular endosome or body (MVBs) (128,131). The IVs that are formed by invagination into the early endosomal membrane sequester proteins and lipids that are specifically stored in the early endosomal membrane (132) (Figure 8). Finally, MVBs can fuse with lysosomes to be degraded or with the plasma membrane releasing the exosomes (128,131). It has been shown that the formation of MVBs and ILVs may be mediated by the endosomal sorting complex required for transport (ESCRT) (Figure 8). This complex is a machinery composed of four separate ESCRT proteins (0, I, II and III) and accessory proteins such as ALIX and TSG101 that assist to facilitate the formation of MVBs (133). The ESCRT mechanism is initiated by the recognition of ubiquitinated proteins on the endosomal membrane through the ubiquitin-binding subunits of ESCRT-0. ESCRT-I and -II complexes are responsible for membrane deformation and then, interact with ESCRT-III, a complex involved in vesicle scission. After scission, the ESCRT-III complex is cleaved from the membrane of the MVBs by the vacuolar protein sorting-associated protein 4 (Vps4) (134,135). Apart from this pathway dependent on the 4 ESCRT complexes, ESCRT-independent pathways of exosome biogenesis have been suggested (Figure 8). One of these pathways is ESCRT-III dependent through the interaction of the syndecan-syntenin complex with ALIX in the cytoplasm, an interaction that is involved in the recruitment of vesicular cargo and could directly recruit ESCRT-III (128,132,133,136,137). Moreover, there are other pathways of EVs biogenesis suggested completely ESCRT-independent through lipid and tetraspanin-dependent mechanisms. In this case, the main mediators of the biogenesis process are lipids such as ceramide, or tetraspanins such as CD63, CD81, CD82 or CD9. Thus, on the one hand, the key role of putative neutral sphingomyelinase (nSMase), which hydrolyze sphingomyelin into ceramide, and phospholipase D2, which allows hydrolysis of phosphatidylcholine into phosphatidic acid, has been demonstrated (137,138). Furthermore, tetraspanin CD63 was shown to be required for the formation of LVs and subsequent release of extracellular vesicles by ESCRT-independent invagination of the melanosome membrane (139). The transport of MVBs to the plasma membrane for fusion and release of exosomes is like other intracellular vesicle transport mechanisms involving their interaction with the cytoskeleton and with different GTPases such as Rab family (Rab27a, Rab27b, Rab 35 and Rab11) (140,141) (Figure 9). The final step of exosome secretion is the fusion of MVBs with the plasma membrane, directed by several proteins including the Rab, Alix, TSG101 or the soluble N-ethylmaleimide-sensitive fusion attachment protein receptor (SNARE) to make the protein complex. The SNARE complex leads membrane fusion and exosome secretion. The accumulated MVBs are coupled to the plasma membrane via the trans-SNARE complex, consisting of V-SNAREs and T-SNAREs on endosomes and plasma membranes, respectively, leading to the release of exosomes into the extracellular environment (134,142) (Figure 9). Introduction 55 Figure 8. Biogenesis of Extracellular Vesicles. Image of biogenesis of Microvesicles and Exosomes. ESCRTindependent and some ESCRT-dependent pathways for exosome biogenesis are represented. Several sorting machineries are involved in the different steps: 1, lipids and membrane‑associated proteins are clustered in discrete membrane microdomains; 2, these microdomains also participate in the recruitment of soluble components, such as cytosolic proteins and RNA species; 3 these clustered microdomains in cooperation with additional machineries encourages membrane budding and fission of the plasma membrane to the extracellular medium or the membrane of MVE towards the lumen of it. Transmembrane proteins sorted on exosomes and microvesicles keep the same topology as at the plasma membrane. ESCRT: endosomal sorting complex required for transport, ALIX: ALG‑2 interacting protein X; ARF6: ADP‑ribosylation factor 6, ILV: intraluminal Vesicle, MVE: multivesicular body. Image taken from Van Niel, G., Nature Reviews Molecular Cell Biology, 2018 allowed by Springer Nature. 1.4.1.2 Biogenesis of microvesicles Microvesicles or ectosomes are originated from direct plasma membrane budding and fission (Figure 8 and Figure 9). (143). Notably, the molecular machineries of biogenesis are partly common to exosomes and microvesicles, including ESCRT proteins and the generation of ceramide (128), except for specific lipid species flipping between the faces of the budding membrane, which has been uniquely reported for microvesicle budding. The biogenesis of microvesicles requires molecular rearrangements in the plasma membrane, as changes in lipid components and protein composition as well as in Ca2+ levels. The asymmetry of lipid component is tightly regulated by the Ca2+ aminophospholipid translocases, as flippases that translocate phospholipids from the outside of the membrane to the inside, while flopases transfer phospholipids from the inside to the outside (128,144,145). The formation of these vesicles from the membrane is induced by the translocation of phosphatidylserine to the outer membrane; and this process is completed by the contraction of cytoskeletal structures through interactions between actin and myosin (144,146) (Figure 8 and Figure 9). MARTA VIZOSO GONZÁLEZ 56 Figure 9. Intracellular trafficking routes in the biogenesis of Extracellular Vesicles. Cargoes are targeted to MVEs originate from endocytosis at the plasma membrane. Regressive transport towards the trans-Golgi network or recycling back to the plasma membrane will redirect cargoes from their targeting to the MVE. These processes are regulated by RAS‑related protein (RAB) GTPases. MVEs that do not go to lysosomes or autophagosomes for degradation are transported along microtubules to the plasma membrane. At this step, docking and fusion are the two final processes required for exosome release that are mediated by RABs, actin and SNARE. ARF6: ADP‑ribosylation factor 6; RAL‑1: RAL (Ras‑related GTPase) homolog; SNAP23: synaptosomal‑associated protein 23; SYX‑5: syntaxin 5; VAMP3: vesicle‑associated membrane protein 3. Image taken from Van Niel, G., Nature Reviews Molecular Cell Biology, 2018 allowed by Springer Nature. 1.4.2 Composition of EV The cargo of EVs is cell type specific and are often affected by the physiological or pathological state of the cell (147–149). This EV cargo is targeted during EV biogenesis, can include many components of a cell, such as DNA, RNA, lipids, metabolites, and cytosolic and cell surface proteins (Figure 10). Most studies on the biochemical composition of EVs involve the analysis of heterogeneous EVs populations due to the difficulties of different isolation methods to obtain pure vesicle subtypes. Therefore, the actual composition of each EV subtype is generally unknown (134,150). Studies on the composition of EVs have led to the creation of public databases that catalogue the components associated with EVs. These include Vesiclepedia (www.microvesicles.org/) (151), ExoCarta (www.exocarta.org) (152), EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas/#/) (153) and EV track (https://evtrack.org/index.php) (154). Introduction 63 Figure 12. Graphical summary of mainly used EV isolation methods. A) The starting containing EVs and proteins in suspension. B) Ultracentrifugation renders an EV pellet that also contains proteins (dUC pellet), which can be further purified by discontinuous ultracentrifugation (disc-UC), like in sucrose, or by density gradient (DG) ultracentrifugation to separate EVs by their density. Ultrafiltration (C), size-exclusion chromatography (SEC; D) and asymmetrical flow filed-flow fractionation (AF4; E) separate molecules by their size. C) Ultrafiltration allows the separation of molecules according to the molecular weight cutoff (size) of the filter pore used. D) In SEC, the first to elute are the molecules bigger than the matrix pores (EVs), while smaller particles within the fractionation range (proteins) get slowed down by entering the matrix bead pores and so elute later on. E) In AF4, a cross-flow (field) perpendicular to the longitudinal laminar flow forces particles towards the semipermeable membrane. F) Precipitation-based isolation relies on the addition of precipitants to concentrate all particles in one pellet. G) Immunoaffinity isolation is based on EV capture using a specific antibody that recognizes an EV-specific marker, coupled to beads that can be separated by centrifugation or magnetically. Image taken from Monguió-Tortajada, M., Cellular and Molecular Life Science, 2019 allowed by Springer Nature. 1.4.6 Characterization of EV Once the EVs have been isolated, it is important to evaluate and confirm the EVs isolated by the method used to isolate these particles. To do that, The International Society for Extracellular Vesicles (ISEV) recommends that the EV sample could be characterized by three criteria: quantitative measure of the source from which the EVs are obtained (number of secreting cells, volume of biofluid used, etc.); abundance of VEs in the sample (total number of particles and/or protein or lipid content) and presence of VE-associated components and coisolated non-vesicular components (126). Particle number can be determined using light scattering technologies, such as nanoparticle tracking analysis (NTA), which measures particle concentration and size based on their Brownian motion (198); by flow cytometry (199), by transmission electron microscopy (TEM) or cryo-ME (200) or by surface plasmon resonance (SPR) (201), among others. Something to have in mind is that particle quantification using light scattering systems, SPR, and similar techniques often leads to an overestimation of the number of VEs, as these techniques are not specific for VEs and also detect co-isolated particles, including lipoproteins and other protein aggregates (202). MARTA VIZOSO GONZÁLEZ 64 Regarding the characterization based on the detection of markers associated with EVs, it has been described that the protein composition differs in the different subtypes of EVs obtained depending on the isolation method used (203), which makes it difficult to propose specific and panmarkers of EVs. However, the ISEV proposes 3 essential categories that should be analyzed to demonstrate the presence of EVs in a sample (126): • Transmembrane or GPI-anchoring proteins, associated with the plasma membrane and/or endosomes, which demonstrate the presence of the lipid bilayer present in VEs. Among these proteins are the tetraspanins (CD9, CD81, CD63, CD82); complement binding proteins such as CD55 and CD59; integrins or proteoglycans including syndecans. • Cytosolic proteins that are actively incorporated into VEs and that can bind to transmembrane proteins, demonstrating the presence of lipid bilayer structures that enclose intracellular material. Among them would be proteins of the ESCRT-I/II/III complex such as TSG101 and its accessory proteins such as ALIX or Vps4A/B; flotillin-1 and 2 (FLOT1/2) or heat shock proteins such as HSC70, HSP90AB1 and HSP70. • Proteins that are not associated with vesicular components and that are normally co-isolated with VEs. Their presence would help assess the degree of purity of the isolate. In biological fluids such as plasma or serum, the presence of lipoprotein contaminants (204) and albumin, among others, stands out. Thus, apolipoproteins A1/2 and B (APOA1/2, APOB) and albumin could be used as negative controls for VEs. In urine samples, uromodulin usually coprecipitates with Ves (205), so it could also be used as a negative marker. The most common techniques used to detect the markers associated with EVs are: Western Blot, which consists of an analytical technique that allows the identification of specific proteins present in the sample under study (206); ELISA systems (enzyme-linked immunosorbent assay) that allow capturing, detecting, characterizing and quantifying VEs (207); flow cytometry designed to detect populations of VEs (208) or mass spectometry that would allow the analysis of several proteins at the same time (209). 1.4.7 EVs in Kidney Disease 1.4.7.1. Urinary EVs Analysis of urine is routinely used in clinical practice (210). In fact, urine is the second most used biofluid for clinical diagnostics after blood. One advantage is that it can be easily and frequently collected in a non-invasive manner and in large quantities (211). The presence of EVs in urine (uEVs) was first documented by Wiggins et al in 1986 in the electron microscopy images of a pellet of a 100000g ultracentrifugation in normal urine (212). But it was not until 2004 that sufficient attention began to be paid to them, following the publication of Pisitkun et al, where they characterized the uEV component by mass spectrometry (213). This initial characterization of the proteome of uEVs and the variation of the molecular composition in pathological situations revealed the potential as a new source of biomarker (214). uEVs have been identified in both healthy and patients with renal disease (215,216). They are produced and secreted into the urine by all types of renal cells from every segment of the nephron, including the podocytes (217,218), mesangial cells (219) and epithelial cells from Introduction 65 proximal tubule (220,221), distal tubule epithelial (222), and collecting duct (223). Evidence suggests that uEVs are able to be internalized by other cells and may modulate their function, indicating an intra-nephron communication along the urinary track (222). It has been described that EVs could be internalized by proximal tubular epithelial cells through cilia (224). Renal EVs have been shown to enable signaling in the kidney to support homeostasis, differentiation, innate immunity (Hiemstra et al., 2014) and nephron function (216,218,225,226) . In addition, it is suggested that a subset of uEVs enters the urine from the circulation (227) by smaller membrane-pores of ~6nm in healthy state or larger perturbed membrane-pores in pathological conditions (228). Alternatively, it is possible that uEVs include non-vesicular circulating proteins because they are endocytosed from the blood by renal tubular cells (229), and then released to the urinary space in the EVs. The study of uEVs rather than the bulk urine reduces the complexity of the urinary proteome as well as enabled the detection of low concentration molecules related to EVs. The protein composition of EVs has been extensively studied indicating that only the 0.6-3% of the protein in urine is associated with uEVs (230,231), and that the 66% of identified proteins in EVs are not identified in bulk urine (232,233). This means that characterization of isolated EVs enriches low abundance proteins that may be important for diagnostic or prognostic. In fact, the use of new generation mass spectrometry instruments has extended the uEV proteome to over 3000 proteins, enabling deeper analysis of EV biology and identification of additional biomarker candidates (234,235). 1.4.7.1.1. Removal of contaminating proteins A major challenge to effective EV separation is the highly abundant urinary glycoprotein uromodulin, which forms long polymers that can entrap small EVs (213,236). Uromodulin is a glycoprotein exclusively produced by renal epithelial cells of the thick ascending limb of Henle’s loop. After undergoing an intracellular maturation process, it is cleavage and release into the tubule fluid and, subsequently, the urine. Uromodulin has the capacity of polymerize and form a mesh-like structure (205,237,238). Trapped EVs will then co-pellet with uromodulin at low centrifugation speeds and reduce the yield of serial centrifugation recovery. Several approaches have been shown to release entrapped vesicles such as addition of the reducing agent dithiothreitol (DTT), the detergent 3-[(3Cholamidopropyl) dimethylammonio]- 1-propanesulfonate (CHAPS) or alkaline buffers. (205,236,239,240). 1.4.7.2 EVs as biomarkers of renal disease progression As EVs play a key role in intercommunication in both healthy and disease states, derive from all segments of the nephron and are reflective of the pathological state of the origin cell, (216,241–243) they hold excellent potential as a source of biomarkers that could refine and complement other clinical measurements. Current biomarkers for kidney disease, such as the estimated glomerular filtration rate (eGFR) based on serum creatinine, are insensitive and non-specific biomarkers and may not always accurately reflect the changes produced in early stages and throughout the disease (216,241). uEVs are easily accessible non-invasively, available in large quantities and susceptible to frequent longitudinal sampling. Moreover, similar to other disease conditions, it is highly likely that the urinary vesicle content is altered to reflect renal injury and disease progression through modulation of neighboring cells, and thus would provide a distinct and rich source of disease biomarkers (216,242,244–246). MARTA VIZOSO GONZÁLEZ 66 Standard analytical methods and high-throughput omics have been applied in urinary EV biomarker investigation, leading to the discovery of numerous potential EV-based biomarkers for a variety of diseases. Early studies focused mainly on urogenital diseases and they were able to identified protein, mRNA, miRNA, lipid and metabolite biomarkers for prostate, bladder, and renal cancers (247–251). In fact, two RNAs associated with prostate, PCA3, and TMPRSS2:ERG, were identified in uEVs (252) that enable the creation of a prostate cancer diagnostic test (253). The potential of urinary exosomes as biomarkers of disease has already been demonstrated in other kidney-related diseases such as cystinuria (254), diabetic nephropathy (255), glomerulonephritis (256), renal fibrosis/CKD (257), lupus nephritis (258), and in the diagnosis and prognosis the tubulopathies Gitelman’s and Bartter’s syndromes (259). For example, studies in CKD patients with kidney fibrosis have identified a down-regulation of miR-29 and miR-200, miRNAs implied in the fibrotic responses, in EVs from patients at later stages of fibrosis compared to patients with milder fibrosis (260,261). 1.4.7.2.1. EVs in ADPKD In ADPKD, there is a clinical need to identify patients with a risk of rapdigly progressing to End Stage Renal Disease (ESRD). To this end, studies have been carried out to analyze the content of EVs, especially proteins, in search of possible markers of progression. It is thanks to them that it has been discovered that proteins such as β-microglobulin (B2M) and monocyte chemotactic protein-1 (MCP1), plakins and some mediators of the complement system (262) can be altered in ADPKD patients and they can be useful in conjunction with conventional markers of disease progression (263,264). Urinary EVs have also been isolated from ADPKD patients in different stages of the disease comparing the protein content to control samples and with other causes of CKD (224,262,265). Alterations are not only seen at the protein level but also at the miRNA level, it has been described that several miRNAs of the miR-192/miR-194-2 and miR-30 families are deregulated in ADPKD uEVs (266). Therefore, it was the necessity of deeper research in the search for new biomarkers that would allow identification and stratification of ADPKD patients at risk of progressing to ESRD rapidly compared to those with a slower rate of progression might be identified from their urinary EV protein profiles. 1.4.8 Seeking for new biomarkers: Glycosylation and EVs It is widely described that EVs are heavily glycosylated with a variety of glycans linked to proteins and lipids of the EV membrane, enriched in specific glycoconjugates (267–269). Several studies have highlighted the importance of the glycosylation patterns of EVs in their uptake and interaction with recipient cells (270,271). Glycosaminoglycans (GAGs) are large non-ramified polysaccharides made from repeated sequences of disaccharides, formed by one amino sugar (D-galactosamine or D-glucosamine) and uronic acid (such as L-glucuronic or L-iduronic acids). GAGs appear linked by Nglycosidic or O-glycosidic bonds to proteins and lipids forming glycoproteins as proteoglycans and glycolipids (272). Proteoglycans work as structural and mechanical support, attachment surface to ECM. Proteoglycans are implied in signal transduction, cytokine, inflammatory regulation and as in intercellular communication (269,273). The glycation pattern has been found to be altered in relation to different diseases, such as infections and cancer; it has also been found to be altered in relation to kidney disease (273,274). Introduction 67 Taking this into account, we propose the study of the glycosylated component using the newly developed tool, ExoGAG, which allows the isolation of GAGs and the fraction bound to them, as a promising tool in the search for biomarkers in relation to the progression of Autosomal Dominant Polycystic Kidney Disease. OBJECTIVES Objectives 71 2. OBJECTIVES 1. Elucidate the molecular biology of ADPKD in order to define the pathways involved in the pathophysiology of the disease, because a variety of mechanism involved have been identified but the key mechanism involved in the cystogenesis is still unknown. Moreover, the increase in the knowledge of the pathophysiology of the ADPKD could develop new potential therapeutic targets. To achieve this goal, we propose: A. Compare the differential proteome of Wild type and polycystic kidneys of an orthologous ADPKD mouse model by the quantitative SWATH-MS analysis. B. Comparison of the molecular mechanism that are altered in the onset and progression of the cyst by the proteomic analysis carried out at two different stages of the disease, early and severe stage. C. Validation by other transcriptomics and proteomics techniques of targets with potential therapeutic interest. 2. Up to date, the parameters used in the monitorization of renal disease have limited power in differential diagnosis, low specificity and sensitivity and none is able to predict the outcome of the disease in an accurate and efficient way. So, we propose the identification of new biomarkers that could improve diagnosis and provide insights in the prognosis of the renal disease by: A. Development of ExoGAG, a new diagnostic and prognostic tool that is based in the specific isolation of the fraction bound to Glycosaminoglycans. B. Application of this new tool in the monitorization and characterization of the course of disease in urine samples of both murine model and patients with ADPKD. C. Analysis of the fraction isolated with this new tool by mass spectrometry to identify possible biomarkers D. Validation by other proteomic techniques of this potential biomarkers. 3. An emerging field in the source of biomarkers is the study of Extracellular Vesicles. However, although there are a wide variety of Extracellular Vesicles isolation techniques, the great challenge is to find a gold-standard accurate and reliable method for research and able to be applied in clinical routine. To this end we propose: A. Use of the newly developed tool ExoGAG in the isolation of the GAG-associated fraction in the isolation of Extracellular Vesicles. B. Characterization of the vesicular component isolated with this new isolation method and its comparison with other commonly used methods of extracellular vesicle isolation, such as ultracentrifugation. C. Application in the discovery of new biomarkers associated with Extracellular Vesicles in ADPKD Material and Methods 79 3.5 PROTEOMICS 3.5.1 Protein digestion for proteomics. We used different amount of protein depends of the assay: In Chapter 1, we used 100 µg of the total protein from p18 and p30 kidneys; In Chapter 2, we used 20 µg of the total protein from mice and human urine quantified by similar method to Lowry protein assay (Bio-Rad® No. 5000112), for protein analysis. In Chapter 3, for comparison between ExoGAG and ultracentrifugation isolation, we used the whole isolated sample from 3 mL of urine in the case of ExoGAG and 30 mL of urine for ultracentrifugation. Proteins were diluted in loading-buffer (25% Tris-Base at 0.5 M, 0.4% SDS pH 6.8, 20% glycerol, 0.2% 2-ß-mercaptoethanol and 0.001% bromophenol blue (Merck, Germany), after boiling for 5 minutes. SDS-PAGE gels were used to concentrate the samples into a single band about 3-4 mm thick, just after “stacking” part. By other hand, if we wanted to know the proteins belonged to a specific band, we let the gel run until they were properly separated. In this work, 10% acrylamide gels were used with 1.5M Tris-Base pH 8.8, 10% SDS (Sigma-Aldrich®), 10% APS (Sigma-Aldrich®) and 1% TEMED (Bio-Rad®). For visualizing the bands, the gels are stained with QC-Colloidal Coomassie reagent (Bio-Rad®) according to the manufacturer's maximum sensitivity protocol. The bands of the different samples are then cut with a scalpel for subsequent in-gel digestion following the standard Shevchenko protocol, with minor modifications (277–279). The bands were washed three times with 50 mM NH4HCO3 (Sigma-Aldrich®) and 50% methanol (Scharlau®) for 10 minutes and dehydrated in acetonitrile, previously to vacuum drying. Then, proteins were reduced in 10 mM dithiothreitol (Sigma-Aldrich®) in 10 mM NH4HCO3 (Sigma-Aldrich®) at 56°C for 30 min and alkylated with 55 mM iodoacetamide (Sigma-Aldrich®) in 50 mM NH4HCO3for 20 min in the dark. Lastly, the gel pieces were again washed and dehydrated. Porcine trypsin (Promega®) was added at a final concentration of 20 ng/μl in 20 mM NH4HCO3 overnight at 37ºC to make a protein digestion. The generated peptides were extracted by three 20 min incubations with 60% acetonitrile and 0.5% formic acid, pooled and dried under vacuum in a SpeedVac to be stored at -20ºC until use. 3.5.2 Mass spectrometry by LC-MSMS Mass spectrometry analyses were performed using a Triple TOF 6600 (Sciex®), which allows both qualitative and quantitative analysis using the recently developed SWATHTM acquisition method (Sciex®) in conjunction with a nanoHPLC, Eksigent Technologies nanoLC 400 (Sciex®). 3.5.2.1 Qualitative analysis using a DDA (Dependent Data Analysis) method In this technique the digested peptides were resuspended in 20 μl of mobile phase A (2% ACN (acetonitrile), 0.1%, formic acid), by sonication for 10 min. 4 μl of each sample were injected into a loop (5 μl/min) where the peptide mixture was properly resuspended in mobile phase A (95% H2O, 0.1% formic acid). The peptides were passed to a reverse phase precolumn based on YMC TRIART C18 silica, 5 x 0.5 mm, 3 mm particle size and 120Å pore size (YMC Technologies) where possible contaminants that may damage the column were removed. This pre-column is connected online to a Chrom XP C18, 150 x 0.30 mm, 3 mm particle size and 120Å pore size capture column (Eksigent, ABSciex®) where peptides are separated by polarity MARTA VIZOSO GONZÁLEZ 80 at a rate of 5 μL/min. The elution gradient of the peptides ranges from 2% to 90% mobile phase B. Peptides were separated using a 90-minute gradient. Data acquisition was performed on a quadrupole-TOF mass spectrometer, 6600 (SCIEX®) using a data dependent workflow (DDA) operating with the software Analyst TF 1.7.1 (SCIEX®). A 250 ms survey scan was performed from 400 to 1250 m/z followed by MS/MS experiments from 100 to 1500 m/z (25 ms of acquisition time) for a total cycle time of 2.8 s. The fragmented precursors were added to the dynamic exclusion list for 15 s, any ion with charge +1 was excluded from the MS/MS analysis. After MS/MS acquisition, the files were processed with ProteinPilotTM 5.0.1 software (SCIEX) using the ParagonTM algorithm for database searching and ProgroupTM for data clustering. Searches were conducted using a mouse or human-specific, database, the UniProt Swiss-Prot database for Mouse or Human. It is necessary to specify iodoacetamide cysteine-alkylation and trypsin digestion. The false discovery rate (FDR) was set to 1 for peptides and proteins with a confidence score above 99%. (280) 3.5.2.2 Quantitative analysis by SWATHTM (Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra) DIA (Data-independent acquisition) Method 3.5.2.2.1 Generation of the reference spectral libraries To perform a sequential window acquisition of all theoretical mass spectra (SWATH™), it is necessary to build a MS/MS spectral library; this process is usually done by starting from peptide mixtures (sample pools) for each experimental sample group. This information allows us to obtain the chromatographic elution traces of a group of ions and the specific fragments for each confidence peptide. It also contains additional information such as charge state and relative intensity, which can be used to assess the confidence with which each peptide is identified. The quality of the library is a very important factor for the quantification process. The library was generated using the previously DDA method described but using a shorter gradient, a 40 min gradient. 3.5.2.2.2 Quantitative analysis by SWATHTM The SWATH–MS acquisition was performed using a DIA (data-independent acquisition) method. In this case, we used 4 groups (p18 WT, p18 KO, p30 WT, p30 KO) with 3 biological replicates per group and 3 technical replicates per sample. Each sample (4 μg) was analyzed using an LC-MS system with an LC gradient as described for the spectral library construction but using a SWATH-MS acquisition method. For each set of samples, the width of the 100 variable windows was adjusted according to the ionic density found in the library DDA runs using a Sciex SWATH variable window spreadsheet. Thus, the SWATH method was based on a cycle of repetitions that consisted of the acquisition of 100 TOF MS/MS scans (limited to 400 to 1500 m/z, high sensitivity mode and 50 ms acquisition time) of isolation windows of sequential overlapping precursor variables with widths (1 m/z overlap) covering the 400 to 1250 m/z mass range with a previous TOF MS scan (400 to 1500 m/z, 50 ms acquisition time) for each cycle. Total cycle time was 6.3 s. For each set of samples, the width of the 100 variable windows was optimized according to the ion density found in the peptide/protein library previously created by DDA method. For processing the SWATH data from the micro-LC MS/MS equipment, PeakView software (version 2.2) with SWATH Acquisition MicroApp (version 2.0) was used. Material and Methods 81 Specifically, PeakView attributed a score and an FDR to each peptide in relation to the microLC MS/MS data and the SWATH library. The retention times from the peptides that were selected for each protein were realigned in each run according to the iRT peptides spiked in each sample and eluted along the whole-time axis. The extracted ion chromatograms were then generated for each selected fragment ion; the peak areas for the peptides were obtained by summing the peak areas from the corresponding fragment ions. Areas under curve are then extracted from all those proteins that fulfill the following conditions: at least 10 peptides with an FDR <1%; in addition, each of these peptides must have at least 7 transitions (fragments). When all these requirements are met, the area extracted is specific and the quantitative value is very accurate. It is the area for each protein that ultimately became the absolute quantifiable value (arbitrary units) and upon which relative abundance values were calculated for each protein in the proteome. The integrated peak areas (processed “.mrkvw” files from PeakView) were directly exported to MarkerView software (AB SCIEX®) for relative quantitative analysis. MarkerView data alignment compensates for minor variations in both mass and retention time values, ensuring that identical compounds in different samples are accurately compared to each other. The first step to perform with the areas is the normalization of areas of the data to eliminate possible variations between samples of the same group, done by total sum areas algorithm. Once the normalization is done, the program allows Principal Component Analysis (PCAs), in which it is possible to see how the samples are separated according to the values of the protein areas. This analysis shows the presence of differential proteins that allow the separation of the groups as well as the uniformity between replicates. Finally, the Student’s t-test statistical analysis (MarkerView) provides the p-values and fold change values for the proteins between the different groups. The results can be exported to Excel spreadsheets, where the p-value, mean, median, fold change and log fold change data are displayed. To analyze the differential proteins between groups, these data were processed manually and a p-value < 0.05 and a fold change > 2 were set as criteria for a statistically significant protein(278,279). 3.5.2.3 Semi-quantitative analysis by spectral count using Scaffold software The gel band protein Quantification was performed using a Spectral count method. Protein Pilot was searched with a fragment ion mass tolerance of 0,100 Da and a parent ion tolerance of 0,050 Da. MS/MS were normalized between samples using Scaffold 5.0 by the sum of the unweighted spectral counts for each sample method in order to define a sample specific scaling factor and then this was applied to all proteins in all the samples. Therefore Scaffold (version Scaffold 5.0.0, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95,0% probability by the Percolator posterior error probability calculation (281). Protein identifications were accepted if they could be established at greater than 99,0% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (282). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. MARTA VIZOSO GONZÁLEZ 82 3.5.3 Functional Analysis. Different functional analysis was performed using differentially expressed proteins (DEPs) with the aim to evaluate the most relevant interaction networks, related pathways or cellular components and other associated proteins or pathways. The functional analysis was performed using the open access software FunRich (http://funrich.org/index.html), which is a tool for functional enrichment analysis. Besides, to perform the protein-protein interaction analysis and, also, functional enrichment analysis between the identified proteins, we used the STRING database (https://string-db.org). These tools were based on the characterization of Gene Ontology Terms Enrichment. We also used the statistical analysis program GraphPad 9 to obtain the so-called volcano plots representing the significant proteins of each group analyzed and the tool Heatmapper (http://www.heatmapper.ca/expression/) for the creation of heatmaps. 3.6 GENE EXPRESSION ANALYSIS. To confirm the findings obtained in the quantitative mass spectrometry assays, we decided to analyze the expression levels of the genes coding for the identified proteins in the different groups of mice. For this experiment we used a total of 24 kidney samples, 12 coming from p18 mice and 12 from p30 mice. Within each group, 6 samples were from Wild Type phenotypes and 6 samples from mutants. One microgram of total kidney RNA was treated with DNase I (Invitrogen®, No. 18068015). Then, reverse transcription was performed with SuperScript® III First-Strand Synthesis System kit (Invitrogen®, No. 18080051), and gene expression analysis by quantitative real-time PCR using FastStart Universal SYBR GREEN Master (ROX) (Roche® No. 04913914001) in a Mx3005P system (Agilent Technologies®). The primers used for this study were designed with Primer3Plus (https://www.bioinformatics.nl/cgibin/primer3plus/primer3plus.cgi) and are based on cDNA reference sequences published in Ensembl genome browser (https://www.ensembl.org/index.html), considering that the amplified region consisted of the end of one exon and the beginning of the next and was between 80-130 bp in size. The primer pairs were checked to ensure that they were specific and did not amplify other regions of the DNA using the UCSC in-Silico PCR online tool (Mouse, Dec. 2011 version (GRGm38/mm10); www.genome.ucsc.edu/cgi-bin/hgPcr). The full sequences of all primers used in the study are the following: Protein Gene Primer sequence (5’-3’) HPRT Hprt Forward 5’-CAATGCAAACTTTGCTTTCCC-3’ Reverse 5’-TCCTTTTCACCAGCAAGCTTG-3’ VIME Vim Forward 5'-CGAGAGAAATTGCAGGAGGA-3' Reverse 5'-AGACGTGCCAGAGAAGCATT-3' SPON1 Spon1 Forward 5'-GGAAGCAGCTACCGAGTGAC-3' Reverse 5'-AGGTGCCTGCATGATCTTCT-3' RBM3 Rbm3 Forward 5'-CGAGTTGATCATGCAGGAAA-3' Reverse 5'-CCTGGTCTCCACCACCTCTA-3' The following program was used for all complete PCR reactions: a denaturation step with 30 s at 95 °C; 45 cycles of 30 s at 95 °C, 1 min at 60 °C and 30 s at 72 °C; followed by a cooling step. Each sample was run in triplicate in each experiment. The results were analyzed through Material and Methods 83 the absolute quantification method. Consequently, a standard curve with known concentrations was used in the assay and, in addition, each sample was normalized to a constantly expression gene. In this study, the Hprt gene was used as the housekeeping gene. 3.7 ELECTROPHORESIS AND WESTERN BLOT ANALYSIS Total protein content was determined using a similar method to Lowry protein assay (Bio-Rad® #5000112) and the same amount of protein (40µg in Chapter 1 and 20µg in Chapter 2) was incubated with Laemmli Loading Buffer (25% Tris-Base at 0.5 M, 0.4% SDS pH 6.8, 20% glycerol, 0.2% 2-ß-mercaptoethanol and 0.001% bromophenol blue (Merck, Germany) and boiled at 95°C for 5 minutes and separated in 10% or 12 % SDS-PAGE gel under reducing conditions. For total protein staining, the gel was incubated with Oriole (Bio-Rad® #1610496) for 90 minutes. The stained gels were scanned using a Typhoon fluorescence scanner (GE Healthcare). For continue to Western Blot analysis, proteins were transferred to nitrocellulose membranes (Bio-Rad® #1620115) in wet transfer at 300mA for an hour and a half. To avoid non-specific binding, membranes were blocked for 1 hour at room temperature using SuperBlock Blocking Buffer (Thermo Fisher® #37515). Thereafter, membranes were incubated overnight at 4°C with primary antibodies. Next day, they were washed in 0.3% Tween-20 in PBS and were incubated for 1 hour at room temperature with the HRP-linked secondary antibodies. Signals were developed with the SuperSignalTM West Pico PLUS Chemiluminescent Substrate kit (Thermo Fisher® #34580) or PierceTM ECL Western Blotting Substrate kit (Thermo Fisher® #32109) prior to visualization in the ChemiDocTM Imaging System (Bio-Rad®). Protein bands were quantified using ImageJ® Lab software (v 4.0.1). Levels of the proteins were corrected for GAPDH, used as housekeeping. The following primary antibodies were used for Western Blot: - In Chapter 1: anti-VIME (1:500-1:1000, Santa Cruz Biotechnology Inc® #sc-6260), anti-SPON1 (1:500, Santa Cruz Biotechnology Inc® #sc-390182), anti-RBM3 (1:500, Santa Cruz Biotechnology Inc® #sc-390139) and anti-GAPDH (1:10000, Millipore®, #mab374) - In Chapter 2: antiDPP4 (1:250, Santa Cruz Biotechnology Inc® #sc52642), antiLUM (1:250, Santa Cruz Biotechnology Inc® #sc166871), anti-NEP (1:250, Santa Cruz Biotechnology Inc® #sc46656), anti-CAH1 (1:250; Santa Cruz Biotechnology Inc® #sc 393490), anti-LGALS3BP (1:250, Santa Cruz Biotechnology Inc® #sc374541), anti-CERU (1:500, Santa Cruz Biotechnology Inc® #sc365205), antiFIBB (1:250, Santa Cruz Biotechnology Inc® #sc271035), anti-APOAIV (1:250, Santa Cruz Biotechnology Inc® #sc374543), anti-AMBP (1:2501:1000, Santa Cruz Biotechnology Inc® #sc81948), anti-A1AT (1:250; Santa Cruz Biotechnology Inc® #sc166018), anti-UROM (1:10000, Biomedical-BTI), anti-ALBU (1:500-1:2000, Santa Cruz Biotechnology Inc® #sc271605), anti-VTNC (1:250; Santa Cruz Biotechnology Inc® #sc74484) - In Chapter 3: anti-ALIX, (1:500, Santa Cruz Biotechnology Inc® #sc53538), antiCD81(1:500, Santa Cruz Biotechnology Inc® #sc166029), anti-SYNTENIN-1(1:250, Santa Cruz Biotechnology Inc® #sc100336), anti-UROM (1:10000, Biomedical-BTI). MARTA VIZOSO GONZÁLEZ 84 Secondary antibodies Goat anti-Rabbit IgG HRP (Thermo Fisher® #31460) or Goat antiMouse IgG HRP (Thermo Fisher® #31430) were used where appropiated. 3.7.1 Western blot 2D Isoelectric focusing (IEF) was performed in pH 3-10 immobilized pH gradient (IPG) strips of 7cm (Bio-Rad® #1632000). 20µg of protein were re-suspended in 250µL of DeStreakTM Buffer (Bio-Rad® #17-6003-19) with ampholytes (0.1% servalyte 3-10 and 2-4, 0.05% servalyte 9-11, SERVA), and subjected to solubilization during 1 hour a RT. After solubilization, the mixture was centrifuged at 18000g for 15 minutes and discarded the pellet. Samples were load to each trip and submitted to active rehydration at 50V during 12h before IEF. IEF was performed in a Protean IEF Cell focusing unit (BioRad) until 20000 V/total h reached. After IEF, the IPG strips were equilibrated for 15 min in 4M urea, 2M thiourea, 50mM Tris pH 6.8, 2% SDS, 12mM DTT and 30% glycerol with 1% DTT and 15 min in 4M urea, 2M thiourea, 50mM Tris pH 6.8, 2% sodium dodecyl sulphate (SDS), 12mM dithiothreitol (DTT) and 30% glycerol with 4% iodoacetamide (all reagents from Sigma-Aldrich). Second dimension was performed in 10-12% SDS-PAGE and continue with Western Blot as previously described. 3.8 TISSUE IMMUNOFLUORESCENCE For immunofluorescence, sodium citrate buffer pH=6 (Agilent Dako® #S2369) was used for antigen retrieval and tissue was blocked with antibody diluent (Agilent Dako® #K8006). Sections were then washed three times with PBS for 5 minutes and incubated with primary antibodies: anti-VIME (1:500, Santa Cruz Biotechnology Inc® #sc-6260) and DBA-rhodamine labelled (1:100, Vector Laboratories®#FL-1031). Alexa-Fluor 488 goat anti-mouse (Thermo Fisher® , #A28175) was the secondary antibody. Finally, tissues were incubated with DAPI (ThermoFisher Scientific® #D1306). 3.9 EXTRACELLULAR VESICLE CHARACTERIZATION 3.9.1 Transmission electron microscopy (TEM) Transmission Electron Microscopy (TEM) is one of the most used imaging methods for the study of EVs. Due to its high resolution, it allows individual morphology and size to be determined. In this thesis, the TEM was carried out in the Microscopy Service of the University of Santiago de Compostela (USC). First, the pellet obtained by the isolation with ExoGAG was resuspended in PBS. Then, 5 μL of pellet or supernatant of ExoGAG were spotted on carbon film-coated electron microscopy grids (Formvar, Gilder-Grids). Subsequently, they were contrasted with a phosphotungstic acid solution. Images were acquired at 200 kV on a JEOL 2010 microscope. 3.9.2 Immunofluorescence We started with the isolation of 500 µL of urine with ExoGAG, which we resuspend in 1 mL of PBS. For the labelling of Extracellular Vesicles, we added 5 µL of Vybrant™ DiD labelling solution (V22887, Invitrogen®), which is a lipophilic fluorophore that intercalates into the lipid bilayer, and incubated at room temperature for 15 minutes in dark. After that, we washed the sample by centrifugated it at 16000g for 15 minutes at 4ºC and resuspended in 1mL of PBS. Then, we fixed the sample with 1mL of 4% of Paraformaldehyde for 20 minutes and permeabilized with 1mL 0.25% Triton X100 for 20 minutes. The sample was blocked with Material and Methods 85 antibody diluent (Agilent Dako® #K8006) for 1 hour in a wheel. Then, the sample was incubated with anti-UROM (1:250, Biomedical-BTI) primary antibody overnight at 4ºC in a wheel. Alexa-Fluor 488 goat anti-rabbit (Thermo Fisher®, #A-11008) was the secondary antibody and was incubated for 1 hour at room temperature in a wheel. We washed the sample by centrifugated it at 16000g for 15 minutes at 4ºC and resuspended in 1mL of PBS. Finally, we resuspended in 20 µL of PBS and 20 µL of mounting medium and put in the slide. Images were taken on a Leica TCS SP8 confocal microscope. 3.9.3 Nanoparticle Tracking Analysis (NTA) Nanoparticle Tracking Analysis (NTA) allows rapid determination of the size and number of particles by light scattering and Brownian motion of the suspended particles (283). The pure urine and the pellet and supernatant of ExoGAG as well as Ultracentrifugation ones were diluted in miliQ water and injected using a 1mL syringe (BD, Plastipak) in the Malvern NanoSight NS300 (v3.3 Malvern Panaytical) equipment according to the manufacturer's instructions. As a negative control, milliQ water was used. The measurements were made with the SOP-Standard measurement as follows: each sample analysis was performed for 60 seconds making three measurements using the NanoSight automatic analysis setup, the syringe pump speed to 40, the chamber level was selected between 8 to 12 and the detection threshold in 11. 3.9.4 ExoView® assay ExoView® (NanoView Bioscience) is a new fully automated tool that provides multi-level and comprehensive measurements of the EV size, particle number, as well as phenotype characterization and colocalization of different biomarkers by single vesicle fluorescence. The core of this technology lies in the specific immunocapture of EVs, specifically via an antibodyfunctionalized microarray chip. Captured particles can be measured in two ways: count and multiplexed marker expression by immunofluorescence or count and size by interferometry (284,285). We performed the analysis in ExoView R100 and R200 and using ExoView® Tetraspanin kit assays (NANEV-TETRA-C, Human-tetraspanin). In this case, the chips were sculpted with capture antibodies against Tetraspanins such as CD81, CD63 and CD9. The protocol was followed according to the manufacturer's instructions. After tempering all reagents and chips used to room temperature, the chips were pre-scanned to measure the background signal. 50 µL of each the samples diluted in incubation buffer were added carefully to different chips and incubated overnight without shaking. Next day, we washed with solution A for 3 minutes under agitation. To observe the protein cargo inside the EVs, the captured EVs were fixed on the chip with solution C for 10 minutes, and then the EV membrane was permeabilized with solution D for 10 minutes under agitation, if we only wanted to analyze membrane proteins these steps were omitted. Then we washed again with solution A for 3 minutes under agitation. The fluorophore-labelled antibodies were then diluted in blocking solution. The fluorophore-labelled antibodies used were: provided Tetraspanin antibodies CF488-anti-CD9, CF647-anti-CD63 and CF555-anti-CD81 for Tetraspanin profile; and labelled antibodies DyLight755-anti-UROM, AF647-anti-AMBP, AF488-anti-CERU, AF594anti-VTNC for biomarker characterization. 250 µL/chip of the fluorescently labelled antibody mixture (0.6 µL/antibody chip in the case of detection of membrane proteins, and 1.5 µL/antibody chip to detect proteins inside EVs) were incubated in the dark for one hour with gentle agitation. After incubation with the different antibodies, we washed with solution A and B for 3 minutes under agitation. Finally, the chips were then washed with distilled water and dried at room temperature. Once dried, they were scanned for interferometric and fluorescence imaging. Results were analyzed in the software provided with the ExoView® Software Suite, MARTA VIZOSO GONZÁLEZ 86 adjusting the fluorescence cut-offs individually to restrict the number of detected particles on MIgG capture spots. 3.9.4.1 Fluorescence labelling of antibodies Prior to characterization by ExoView®, antibodies to the proteins of interest were labelled with different fluorophore conjugation kits depending on the excitation field. We used the following primary antibodies: anti-CERU (Santa Cruz Biotechnology Inc® #sc365205), anti-AMBP (Santa Cruz Biotechnology Inc® #sc81948), anti-UROM (Santa Cruz Biotechnology Inc® # sc-271022), and anti-VTNC (Santa Cruz Biotechnology Inc® #sc74484). Alexa Fluor® 488 - Lightning-Link® (AB236553, Abcam), Alexa Fluor® 594 - LightningLink® (AB269822, Abcam), Alexa Fluor® 647 - Lightning-Link® (AB269823, Abcam) and DyLight® 755-Lightning-Link® (AB201805, Abcam) fast conjugation kits were used according to manufacturer provided protocol. First of all, we needed an antibody concentration of 1 mg/mL, to achieve this we concentrated the antibodies using centrifugation filtration units (Amicon® Ultra-0.5 mL 10K, Merck Millipore) according to the manufacturer's recommendations. 1 µL of Modifier reagent was added for every 10 µL of antibody (10 µg) and gently shaken. Then, the mixture was transferred to the lyophilized conjugation mixture vial and carefully resuspended and incubated in the dark at room temperature for 15 minutes. After this incubation time, 1 µL of Quencher reagent was added for every 10 µL of antibody initially used. 3.10 STATISTICAL ANALYSIS Data are presented as mean ± SEM for all cases (bar or point plots). First, normal distribution was confirmed by the Kolmogorov–Smirnov test. For comparisons between two groups twotailed Student’s t test was used to determine significance of differences. For multiple comparisons, One-Way ANOVA analysis was performed. P-values less than 0.05 were considered statistically significant (*). Furthermore, ** corresponds to p-values < 0.01 and *** to p-values < 0.001. All data sets were analyzed with GraphPad Prism® Software (version 9.0). The proteomic statistical analysis was performed using Markerview software in the SWATH analysis or Scaffold in the semi quantitative analysis by spectral count. All the information about the analysis was found in the proteomic section. RESULTS Results 95 Figure 23. Functional enrichment analysis of Biological Process GO terms of downregulated proteins at postnatal day 30. String functional enrichment analysis according to the percentage of features or related to background dataset for downregulated proteins (A and B, respectively). (C) FunRich functional analysis. The highest FDR and p-value top 20 features are shown. On the other hand, we also performed an analysis of cellular component (Figure 24 and Figure 25). For upregulated proteins, most enriched components were associated with organelles, extracellular region and membrane (Figure 24A). If we considered most enriched features attending to the background dataset, we observed terms as fibrinogen complex and cytoskeleton related proteins, and blood microparticles (Figure 24B). Analysis of cellular component with FunRich tool obtained similar results with most enriched terms associated with extracellular space, plasma membrane and cytoskeleton (Figure 24C). On contrast, downregulated proteins were clearly related to mitochondrion, as well as membrane, according to String database both of percentage of features as well as related to background dataset (Figure 25A and B). Moreover, according to FunRich analysis, these terms appeared also enriched (Figure 25C). MARTA VIZOSO GONZÁLEZ 96 Figure 24. Functional enrichment analysis of Cellular Component GO terms of upregulated proteins at postnatal day 30. String functional enrichment analysis according to the percentage of features or related to background dataset for upregulated proteins (A and B, respectively). (C) FunRich functional analysis. The highest FDR and p-value top 20 features are shown. Results 97 Figure 25. Functional enrichment analysis of Cellular Component GO terms of downregulated proteins at postnatal day 30. String functional enrichment analysis according to the percentage of features or related to background dataset for downregulated proteins (A and B, respectively). (C) FunRich functional analysis. The highest FDR and p-value top 20 features are shown. 4.1.3 Quantitative proteomic analysis of significant deregulated proteins in ADPKD mice at postnatal day 18. Due to the high number of DEPs and involved pathways identified, we decided to perform the same differential comparison between the proteome Wild Types (WT) and mutants (KO or Mut, interchangeably) mice kidneys from ADPKD Pkd1cond/cond; Tam-Cre mouse but in an early stage of the disease with mild phenotype, day 18. The aim is to analyze targets and pathways that are more related to cyst initiation than to cyst progression, it means, those altered in early stages (Figure 26). MARTA VIZOSO GONZÁLEZ 98 Figure 26. Illustrated workflow scheme. (A) First, we used an ADPKD mouse model (Pkd1cond/cond; Tam-Cre mice, inactivating Pkd1 gene at day 10-11) to obtain cystic (Cre+) and no-cystic (Cre-) kidneys at postnatal day 18. (B) All kidneys were phenotypically studied to characterized the WT and KO kidneys(B). (C) Next, the total protein was extracted, quantified and trypsin digested. After digestion using LC-MS/MS we made a SWATH-MS analysis to obtain the differential kidney proteome. (D) Finally, we performed a functional analysis using bioinformatic tools to find novel therapeutic targets and pathways involved in the renal cystogenesis process. The cystic kidney characterization by hematoxylin-eosin stained shown a beginner cystic phenotype in mutants at day 18 (Figure 27A-C) with a not significantly increase in the kidney weight (Figure 27D). However, the KO samples present a significative renal damage measured by the BUN (Figure 27E). Results 99 Figure 27. Characterization of the cystic kidney phenotype at day 18. (A) Representative macroscopic and microscopic images of hematoxylin-eosin stained from kidneys of Wild Type and Mutant mice sacrificed at day 18. Scale bar, 2mm (upper panel) and 100µm (lower panel). A n of 3 samples per group were used. (B, C) Renal cystic index and number of cysts of the different phenotypes. (D) Kidney weight to body weight ratios and (E) Blood Urea Nitrogen (BUN) concentrations of distinct groups. Bars represent means ± SEM in all cases. p<0.05 by Student’s t-test (two-tail) was considered as a significant result: ns represent not significative; * p<0.05; ** p<0.01; *** p<0.001. For the proteomic approach, we used three kidneys per group of mice sacrificed at postnatal day 18. We used a pool of samples of each condition to create the spectral library, obtaining a number of 971 identified proteins with a critical FDR < 1%. Once the library and the SWATHTM method were created, each sample was analyzed individually by mass spectrometry. In this analysis, a total of 159 DEPs were significantly found. PCA analysis was performed to compare the obtained data across the samples and demonstrated that samples separated and grouped correctly (Figure 28). Proteins showing a significant difference in abundance with a two-fold increase or decrease and an adjusted p-value significant between Mutant and Wild Type samples are shown in Supplementary Table 2 with its respectively fold-changed and adjusted p-value observed. In total, 14 proteins accomplished these characteristics and, in this case, all of them were downregulated in KO samples. Volcano plot showed graphically these variations (Figure 29). Moreover, using heat map analysis, we could observe differences among protein abundance and how these values clustered (Figure 30) between groups. In this case, we observed that all proteins identified had a similar behavior among the same group samples. Again, the heatmap and PCA data showed reproducibility among the triplicates sample as tightly clustered samples from each group in both analyses. MARTA VIZOSO GONZÁLEZ 100 Figure 28. Unsupervised PCA where red and green circles represent Mutant and Wild Type samples, respectively. Figure 29. Volcano plot showing proteins with significance differences of protein abundance and fold change >2. X-axis shows log2 of fold change and y-axis shows -log10 of p-value. Green dots mark downregulated proteins in Mutants. Results 101 Figure 30. Heatmap figure of unsupervised cluster analysis with a fold change > 2. Color gradient is determined by the Row-Z-Score, where red represent increase and green decrease of protein abundance. 4.1.3.1 Protein-protein interaction and Pathway analysis Protein–protein or cluster analysis based on String software was also made to explore possible protein–protein interactions. In this case, our downregulated DEPs obtained in the quantitative analysis showed three small clusters of proteins (Figure 31). However, due to the little number of proteins, String software was not able to stablish the GO terms analysis. On the other hand, FunRich tool allowed us to identify several biological process and cellular component of these proteins (Figure 32). Identified biological process terms are related to immune system, metabolism and membrane processes (Figure 32A). If we observed the cellular component terms, we noted that are related to mitochondrion and other organelles as peroxisomes and ribosomes (Figure 32B). Figure 31. Protein clusters according to String of differential expressed proteins at postnatal day 18. MARTA VIZOSO GONZÁLEZ 102 Figure 32. Functional enrichment analysis using FunRich software at postnatal day 18. FunRich functional enrichment of biological process and cellular component GO terms for downregulated proteins (A and B, respectively). The highest FDR and p-value top 20 features are shown. 4.1.4 Quantitative proteomic analysis of significant deregulated proteins in ADPKD mice at postnatal days 18 and 30. Next, in order to improve the library and the data obtained in the proteomics on postnatal days 18 and 30, we decided to analyze all the samples together and thus generate a common library. In addition, as in this case all comparisons will be made between the different groups, both postnatal 18 and 30 days, in this approach that all samples will be analyzed in the same process and the results will be more comparative. So, the final workflow scheme is represented in the Figure 33. The aim is to analyze targets and pathways that are related to cyst initiation and are continued deregulated in cyst progression. Targets will be validated by another transcriptomic and proteomic techniques. Results 103 Figure 33. Illustrated workflow scheme. (A) First, we used an ADPKD mouse model (Pkd1cond/cond; Tam-Cre mice, inactivating Pkd1 gene at day 10-11) to obtain cystic and no-cystic kidneys at postnatal day 18 and day 30 to be phenotypically studied (B). (C) Next, the total protein was extracted, quantified and trypsin digested. After digestion using LC-MS/MS we made a SWATH-MS analysis to obtain the differential kidney proteome. (D) We performed a functional analysis using bioinformatic tools to find novel therapeutic targets and pathways involved in the cystogenesis process. (E) Finally, we validated the possible targets by other transcriptomic and proteomic techniques. Phenotype characterization of all groups is shown in Figure 34. The cystic kidney characterization by hematoxylin-eosin stained shown more severe cystic phenotype in mutants at day 30 than at day 18 (Figure 34A-C). While at postnatal day 18 the cystic index was not significantly elevated, at day 30 it was, as well as the number of cysts also increased (Figure 34C and D). Same happened with the kidney weight, with the significantly increase in the kidney weight at day 30 (Figure 34E). However, the renal damage measured by the BUN level appeared significative at day 18 but not significative at day 30, maybe due to the data dispersion (Figure 34F). MARTA VIZOSO GONZÁLEZ 104 Figure 34. Characterization of the cystic kidney phenotype at day 18 and day 30. Representative macroscopic and microscopic images of hematoxylin-eosin stained from kidneys of Wild Type and Mutant mice sacrificed at day 18 (A) and day 30 (B). Scale bar, 2mm (upper panel) and 100µm (lower panel). A n of 3 samples per group were used. (C, D) Renal cystic index and number of cysts of the different phenotypes. (E) Kidney weight to body weight ratios and (F) Blood Urea Nitrogen (BUN) concentrations of different groups. Bars represent means ± SEM in all cases. p<0.05 by Student’s t-test (two-tail) was considered as a significant result: ns represent not significative; * p<0.05; ** p<0.01; *** p<0.001. For the proteomic approach, we used kidneys of Wild Type and Mutant mice sacrificed at postnatal days 18 and 30 and a n of 3 per group. We used a pool of samples of each condition (postnatal 18 or 30 days) and group (WT or KO) to create the spectral library, obtaining a number of 2498 identified proteins with a critical FDR < 1%, a greater number of proteins compared with those obtained in separately libraries. Once the library and the SWATHTM method were created, each sample was analyzed individually by mass spectrometry. PCA was Results 111 Figure 44. Functional enrichment analysis of Biological Process GO terms of upregulated proteins at postnatal day 30. String functional enrichment analysis according to the percentage of features or related to background dataset for upregulated proteins (A and B, respectively). (C) FunRich functional analysis. The highest FDR and p-value top 20 features are shown. MARTA VIZOSO GONZÁLEZ 112 Figure 45. Functional enrichment analysis of Biological Process GO terms of downregulated proteins at postnatal day 30. String functional enrichment analysis according to the percentage of features or related to background dataset for downregulated proteins (A and B, respectively). (C) FunRich functional analysis. The highest FDR and p-value top 20 features are shown. On the other hand, we also performed an analysis of cellular component (Figure 46 and Figure 47). For upregulated proteins, most enriched components were associated with cytoplasm, organelles and membrane (Figure 46A). If we considered most enriched features attending to the background dataset, we observed terms mostly related to cytoskeleton (Figure 46B). Analysis of cellular component with FunRich tool obtained similar results with most enriched terms associated with cytoplasm, plasma membrane, extracellular space and cytoskeleton (Figure 46). On contrast, downregulated proteins were distinctly related to mitochondrion, as well as membrane, according to String database both of percentage of features as well as related to background dataset (Figure 47A and B). Moreover, according to FunRich analysis, these terms appeared also enriched as well as other terms as exosome related terms (Figure 47C). Results 113 Figure 46. Functional enrichment analysis of Cellular Component GO terms of upregulated proteins at postnatal day 30. String functional enrichment analysis according to the percentage of features or related to background dataset for upregulated proteins (A and B, respectively). (C) FunRich functional analysis. The highest FDR and p-value top 20 features are shown. MARTA VIZOSO GONZÁLEZ 114 Figure 47. Functional enrichment analysis of Cellular Component GO terms of downregulated proteins at postnatal day 30. String functional enrichment analysis according to the percentage of features or related to background dataset for downregulated proteins (A and B, respectively). (C) FunRich functional analysis. The highest FDR and p-value top 20 features are shown. 4.1.4.3 Comparative study between libraries After that, we decided to compare the result obtained by the different libraries to distinguish those primary pathways of cystogenesis from those secondary to it. We compared the DEPs with a fold change > 2 of the libraries done only with samples from postnatal 18 or 30 days and the library generated with the combination of both days (Figure 48). We observed that a postnatal day 18 no proteins were shared between libraries in both upregulated and downregulated proteins. In fact, surprisingly, while in the library only from postnatal day 18 there are only downregulated proteins in KO, in the joint library most of them are upregulated in KO samples. When we analyzed what happened at postnatal day 30, we noted that only 38 upregulated proteins were shared between libraries, what meant a 40% of only day 30 library and almost 25% of the joint library; and 26 downregulated proteins were shared between libraries, a 45% of each library. Results 115 Figure 48. Venn diagrams of results of different libraries. Comparison of DEPs with a fold change > 2 between p18 and p30 alone libraries and the p18+p30 joint library. However, due to the low overlap between the DEPs with a fold change > 2, we decided to compare the pathways in which they are involved in order to obtain more valuate information. This could only be done at postnatal day 30, because we found both upregulated and downregulated proteins. For this we decided to do FunRich analysis. As we could see in Figure 49 and Figure 50, virtually all of the most enriched pathways are shared both in terms of biological processes and in the cellular component. Figure 49. Comparison of pathways involved in upregulated proteins according to FunRich. GO enrichment of protein significantly upregulated according to FunRich showing 20 most enriched biological process and cellular component with p-value <0,05 by hypergeometric test. * Means equal GO term † means similar GO term. MARTA VIZOSO GONZÁLEZ 116 Figure 50. Comparison of pathways involved in downregulated proteins according to FunRich. GO enrichment of protein significantly downregulated according to FunRich showing 20 most enriched biological process and cellular component with p-value <0,05 by hypergeometric test. * Means equal GO term † means similar GO term. 4.1.4.4 Comparative study of differential proteome of postnatal days 18 and 30. Finally, we decided to see if there was any protein that started to be altered at postnatal day 18 and continue altered in more severe stages at postnatal day 30, to study those proteins that are constant throughout the process of cystogenesis. For this purpose, we compare differential proteome of day 18 and day 30 using the joint library. Only 3 proteins were shared among all DEPs with a fold change > 2 (Figure 51A and B). To revalidate this result, we compared the normalized summatory of areas of the SWATH analysis of these three proteins and we observed how all of them were upregulated in KO samples (Figure 51B). In summary, Vimentin, Spondin-1 and RNA-binding protein 3 are the only proteins that were altered in early and advanced stages of the disease. Results 117 Figure 51. Comparison of differential proteome of postnatal day 18 and day 30. A) Venn diagrams showed shared upregulated and downregulated proteins between each day. B) Box and whiskers plot of normalized summatory spectral areas of SWATH analysis of the three shared proteins between postnatal day 18 and day 30, minimum to maximum and all points are represented. Statistical analysis was performed using Student’s t-test comparing conditions inside each group. p<0.05 by Student’s t-test (two-tail) was considered as a significant result: ns represent not significative; * p<0.05; ** p<0.01. Table 1. Proteins significatively altered with a fold change > 2 in postnatal day 18 (p18) and day 30 (p30) in the differential comparison between Wild Type and Mutants. FC means Fold Change Entry Name UniProt Name Name FC p18 FC p30 P20152 VIME_MOUSE Vimentin ↑ 2,29 2,44 Q8VCC9 SPON1_MOUSE Spondin-1 ↑ 3,18 3,2 O89086 RBM3_MOUSE RNA-binding protein 3 ↑ 2,16 4,2 4.1.4.5 Validation of target candidates The three proteins altered in postnatal day 18 and day 30 were selected for validation using other methodology approaches: transcriptomic with RT-qPCR and proteomic using Western Blot. Validation was performed using others six independent samples of each group. Gene expression analysis by RT-PCR showed that only Vimentin is significatively overexpressed which correlated with the increased of protein abundance in KO samples (Figure 52A), although Spondin-1 showed a tendency to be overexpressed but not significantly (Figure 52B). On the other hand, RNA-binding protein 3 was no significatively altered at postnatal day 18, in contrast, it was downregulated at postnatal day 30, the opposite situation as the protein abundance by SWATH analysis (Figure 52C). MARTA VIZOSO GONZÁLEZ 118 Figure 52. Validation of possible targets by RT-qPCR. Gene expression of Vimentin (A), Spondin-1 (B) and RNA-binding protein (C). A number of 6 samples were used for each group. Hprt expression was used as housekeeping. Bars represent means ± SEM. Student`s t-test with two-tail was used and a value of P<0.05 was considered significant. ns: not significant (p≥0.05), * p<0.05, ** p<0.01. Moreover, we performed the validation analysis using Western blot as other proteomic approach. In this case, all three (Vimentin, Spondin-1 and RNA-binding protein 3) were validated as upregulated at postnatal day 30. However, none of them are validated at postnatal day 18, even without seeing any trend (Figure 53 and Supplementary Figure 1). Figure 53. Validation of possible targets by Western Blot. The protein abundance of the three possible targets of our quantitative proteomic SWATH MS data were validated by Western Blot (WB) analysis: (A) Vimentin, (B) Spondin-1 and (C) RNA-binding protein 3. GAPDH was used as housekeeping. Bars represent means ± SEM. Student`s t-test with two-tail was used and a value of P<0.05 was considered significant. * p<0.05, ** p<0.01. Full WBs are shown in Supplemental Figure 1. To further investigate why these proteins were not validated by Western Blot at day 18 of development, we performed the 2-diamensional Western Blot. By this approach, we were able Results 119 to distinguish the different isoforms and post-translational modifications, not possible in a 1DPAGE gel. For all 3 proteins (Figure 54A, B and C), we could see the majority spot corresponding to the protein without modification and different spots between WT and Mut that indicate that this proteins have a lot of different modifications that make more difficult to quantified it by Western Blot 1D. Figure 54. Validation of possible targets at postnatal day 18 by Western Blot 2D. Vimentin (A), Spondin1 (B) and RNA-binding protein 3 (C) analysis by 2 dimensions Western Blot, separated by a isoelectric point from 3 to 10. Furthermore, to obtain more information and observe what happen in vivo, we performed an immunofluorescence assay. We started studying Vimentin, because it was the only that was validated by RT-PCR and seemed to be altered by Western Blot 2D and we combined with stained the collecting duct cyst with DBA, which are the most prominent and big cyst in our mouse model as well as in ADPKD. As we could see in Figure 55, Vimentin behaved in two ways. On one side, as filaments with orientation that follow the orientation of the tubules of the nephron. This occurred from the papilla and along the entire medullary zone. These filaments were thin in Wild type samples, and wider and thickened in cystic individuals. On the other side, there was staining in the more cortical area of the nephron, where Vimentin surrounds the tubule, what appeared stained in both Wild Type and mutant samples. In both postnatal day 18 and day 30 mutant samples, we observed the cortical cysts stained green, these were smaller in diameter than the DBA cysts. Similarly, the tubular cells constituting these cysts were usually not as flattened as those belonging to DBA cysts. DBA cysts are predominantly found in the medulla, so they do not coincide with the cysts stained with Vimentin. However, we could observe some cysts that appear to be stained by both antibodies. Generally, Vimentin seemed to behave differentially between mutant and Wild type samples from early stages of disease. MARTA VIZOSO GONZÁLEZ 120 Figure 55.Validation of Vimentin at postnatal days 18 and 30 by Immunofluorescence. Immunofluorescence staining of Vimentin in green, collecting duct marker DBA in red and nuclei marker DAPI in blue. Scale bar 100 µm. Overall, SWATH-MS proteomic data provided us interesting new targets and molecular pathways for future works and possible new therapeutic approaches for ADPKD. We identified those pathways that are altered early in and throughout the course of the disease, as well as possible targets for their modulation among which we highlight Vimentin, Spondin-1 and RNAbinding protein 3 due to its alteration at all stages of the disease. Results 127 Figure 61. Protein analysis of ExoGAG pellet. Whole protein component of urine samples precipitated with ExoGAG from controls (A), patients with ADPKD type I (B) and patients with ADPKD type II (C) staining with Oriole (BioRad®). Patients were ordered according to the increase in serum creatinine (Creat). 4.2.2.2 Identification of biomarkers in ADPKD patients by mass spectrometry sequencing To identify new biomarkers and understand the changes produced by the disease, we cut the more differential bands for mass spectrometry sequencing and analysis by qualitative DDA method and semi-quantitative analysis by spectral count (Supplementary Figure 3 and Supplementary Table 9,10,11,12,13,14,15,16,17,18,19 and 20). This analysis were performed using ExoGAG pellets from patients with mutations in PKD2 because of its more noticeable variations. The principal differential proteins are shown in Table 3. Remarkably, as we observed with mice urine, only one protein, UROM, decreases in abundance with disease progression. Moreover, ALBU, APOA4, CERU, FIBA, FIBB, FIBG and AMBP that were upregulated in mice appeared also increased throughout the disease. Table 3. Summary list of principal proteins with differences in abundance throughout the disease in ExoGAG pellet in PKD2 patients. Protein name Uniprot name Disease progression Uromodulin UROM_HUMAN ↓ Albumin ALBU_HUMAN ↑ Alpha-1-antitrypsin A1AT_HUMAN ↑ Apolipoprotein A-IV APOA4_HUMAN ↑ Carbonic anhydrase 1 CAH1_HUMAN ↑ Ceruloplasmin CERU_HUMAN ↑ Dipeptidyl peptidase 4 DPP4_HUMAN ↑ Galectin-3-binding protein LG3BP_HUMAN ↑ MARTA VIZOSO GONZÁLEZ 128 Fibrinogen alpha chain FIBA_HUMAN ↑ Fibrinogen beta chain FIBB_HUMAN ↑ Fibrinogen gamma chain FIBG_HUMAN ↑ Pro-epidermal growth factor EGF_HUMAN ↑ Protein AMBP AMBP_HUMAN ↑ Serotransferrin TRFE_HUMAN ↑ Vitronectin VTNC_HUMAN ↑ 4.2.2.2.1 Validation of identified biomarkers by Western blot in ADPKD patients with mutation in PKD2 Then, we decided to validate these possible biomarkers in other patients with mutation in PKD2 by other techniques as Western Blot. First, we tested the protein fingerprint of the samples we used. Once again, the pattern was altered related to the disease progression (Figure 62A). Afterwards, Western Blot analysis revealed that 9 of these 16 proteins were validated (Figure 62). UROM decreased in abundance in the 100 kDa band with disease progression (Figure 62B) and ALBU, A1AT, APOA4, CERU, LG3BP, FIBB, AMBP and VTNC increased (Figure 62C, D, E, F, G, H, I and J). The increase was most reflected in samples from patients with high creatinine levels. Although ALBU, CERU, AMBP and VTNC (Figure 62C, F, I and J) began to show an increase in lower creatinine levels. A schematic representation of the semi-quantitative mass spectrometry results of these validated proteins is shown in Table 4, where we could observe that almost all proteins appear in all weights. Results 129 Figure 62. Validation of possible targets by Western Blot in patients with mutation in PKD2. On the top, the whole protein component of urine samples precipitated with ExoGAG of patients with mutation in PKD2 staining with Oriole (BioRad®). The protein abundance of the possible biomarkers of our semi-quantitative proteomic data were validated by Western Blot: UROM (B), ALBU (C), A1AT (D), APOA4 (E), CERU (F), LG3BP (G),FIBB (H), AMBP (I), VTNC (J). Low means creatinine levels <1.1, Medium means creatinine levels between 1.2-2 and High means creatinine levels >2. MARTA VIZOSO GONZÁLEZ 130 Table 4. Schematic representation of the maximum protein abundance per band by semi-quantitative analysis in ExoGAG pellets. Semi-quantitative analysis is based in Total Spectral Count. Maximal Total spectral count: >1000 √√√√√, 500-1000 √√√√, 100-500 √√√, 50-100 √√, 1-50 √, 0 -. kDa UROM ALBU A1AT APOA4 CERU LG3BP FIBB AMBP VTNC 130-95 √√√√√ √√√ √ - √ √√ - √ √ 95-72 √ √√√ √ - √ √ √ √ √√ 72-55 √√√ √√√√√ √√√ - √ √ √√ √√ √√√ 55 √√ √√√ √ - √ √ √ √√ √ 55-34 √√ √√√ √ √√ √ √ √√ √√√√√ √ 34-26 √ √√ √ √ √ - √ √√ √ 4.2.2.2.2 Validation of identified biomarkers by Western blot in ADPKD patients with mutation in PKD1 Figure 63. Validation of possible targets by Western Blot in patients with mutation in PKD1. On the top, the whole protein component of urine samples precipitated with ExoGAG of patients with mutation in PKD1 Results 131 staining with Oriole (BioRad®). The protein abundance of the possible biomarkers of our semi-quantitative proteomic data were validated by Western Blot: UROM (B), ALBU (C), CERU (D), FIBB (E), AMBP (F), VTNC (G). Low means creatinine levels <1.1, Medium means creatinine levels between 1.2-2 and High means creatinine levels >2. We studied too how these markers behaved in patients with mutations in PKD1 (Figure 63). As before, we tested the protein fingerprint of the samples we used, and we saw again how the fingerprint was altered related to the disease progression (Figure 63A). Over again, UROM decreased in the 100 kDa band with the disease (Figure 63B) and ALBU, CERU, FIBB, AMBP and VTCN increased (Figure 63C, D, E, F and G). Although there was a sample with low creatinine that acted similar as the high creatinine. Moreover, it seemed that there were more AMBP in PKD1 than in PKD2 because we need to dilute the antibody to not saturated the image. 4.2.2.2.3 Quantification of biomarkers for possible use as disease markers Considering the results of validations obtained for both PKD2 and PKD1 mutation patients, we could conclude that the markers that seemed to correlate best with the disease progression were UROM, CERU, AMBP and VTNC. For patients with mutation in PKD2, densitometry analysis revealed that decreased of UROM correlated significatively with the increased of creatinine, even in medium values of creatinine (Figure 64A). AMBP showed a small growth at medium creatinine, especially if we take into account the value corrected for total protein, with the trend of a large increase in high creatinine values (Figure 64C). CERU and VTNC seemed constant in low and medium creatinine or, even, with a tiny trend of decreased in medium, but with a trend to raise in high creatinine (Figure 64B and D). Figure 64. Densitometry analysis of Western Blot of possible targets in patients with mutation in PKD2. Densitometry of UROM (A), CERU (B), AMBP (C) and VTNC (D) bands and the ratio with the whole protein of the same sample obtained by Oriole staining (BioRad®) used as housekeeping. Bars represent means ± SEM. Ordinary MARTA VIZOSO GONZÁLEZ 132 One-way ANOVA with multiple comparations was used and a value of P<0.05 was considered significant. Only significative comparisons are shown: * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. In contrast, for patients with mutation in PKD1, densitometry analysis revealed that UROM showed a decreasing trend with increasing creatinine levels, being significant only between the early and late stages (Figure 65A). AMBP showed a significatively increased in high creatinine in relation to low and medium ones (Figure 65C). In this case, CERU presented the tendency to decrease at medium levels of creatinine, but it did not increase in high respecting to low values (Figure 65B). And VTNC, again, seemed a tiny trend of decrease in medium, but with a significatively raised in high creatinine (Figure 65D). Figure 65. Densitometry analysis of Western Blot of possible targets in patients with mutation in PKD1. Densitometry of UROM (A), CERU (B), AMBP (C) and VTNC (D) bands and the ratio with the whole protein of the same sample obtained by Oriole staining (BioRad®) used as housekeeping. Bars represent means ± SEM. Ordinary One-way ANOVA with multiple comparations was used and a value of P<0.05 was considered significant. Only significative comparisons are shown: * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001. Then, in order to try to improve the marker anticipation capacity, we studied the ratio between the markers that tend to appear (CERU, AMBP and VTNC) and disappear as UROM. In patients with mutation in PKD2, the UROM/CERU ratio showed a tendency, although not significant, to anticipate changes in serum creatinine increase (Figure 66A). In contrast, in PKD1 patients, the UROM/CERU ratio did not improve the detection (Figure 66D). The ratio UROM/AMBP displayed very high values in PKD2 patients with low creatinine with a drastic drop in medium and high creatinine levels (Figure 66B). Instead, the UROM/AMBP ratio in PKD1 mutation patients shows a progressive decrease trend from medium creatinine values (Figure 66E). The UROM/VTNC ratio shows in both types of patients the same opposite trend in mean creatinine values (Figure 66C and F). All these data show that the markers do not only depend on the functional state of the kidney but also behave in a specific way in the different forms of the disease. Results 133 Figure 66. Ratio of the densitometry analysis of CERU, AMBP and VTNC to UROM for patients with mutations in PKD2 and PKD1. Bars represent means ± SEM. Ordinary One-way ANOVA with multiple comparations was used and a value of P<0.05 was considered significant. Only significative comparisons are shown. 4.2.2.3 Characterization of the not isolated component with ExoGAG (ExoGAG supernatant) 4.2.2.3.1 Chracterization of the ExoGAG superanatant protein profile in controls and patients Once the component isolated in the ExoGAG pellet (P) was characterized, we proceeded to compare it with that which was not isolated in the supernatant (S) and with the total urine (U) (Figure 67). As we could observed, the ExoGAG isolation enrich in a component of its own, quite different from what remains in the supernatant. The sum of the profile observed in the pellet and in the supernatant results in the profile of the whole urine, which is logical, although the profile of the supernatant is more similar to the profile of the whole urine. Again, we observed a constant fingerprint in control samples when we looked at the components separately (Figure 67A). However, the constant pattern was altered in PKD patients (Figure 67B and C). In addition to the alteration in the ExoGAG pellet, we also observed an alteration in the supernatant and in the urine itself corresponding to the course of the disease. This alteration depend on renal function correlated with serum creatinine, being more extreme in the most severe stages of the disease, but beginning to be altered at initial stages with relatively low creatinine levels. All over again, the variation of the profile in patients was characteristic of the disease, as can be observed between ADPKD type I (Figure 67B) and ADPKD type II (Figure 67C), where again shown a more pronounced change. MARTA VIZOSO GONZÁLEZ 134 Figure 67. Protein analysis of ExoGAG pellet, ExoGAG supernatant and whole urine sample. Whole protein component of isolated fraction of ExoGAG (P), its supernatant (S) and the whole urine samples controls (A), patients with ADPKD type I (B) and patients with ADPKD type II (C). All were stained with Oriole (BioRad®). Patients were ordered according to the increase in serum creatinine (Creat) levels. Low means creatinine levels <1.1, Medium means creatinine levels between 1.2-2 and High means creatinine levels >2. 4.2.2.3.2 Identification of biomarkers in ADPKD patients by mass spectrometry sequencing Again, the variation in the protein profile in supernatants was more severe in patients with mutations in PKD2 (Figure 68). Subsequent, to identify new differential proteins and understand the changes produced by the disease, we cut the more differential bands for mass spectrometry sequencing and analysis by qualitative DDA method and semi-quantitative analysis by spectral count (Supplementary Figure 4 and Supplementary Table 21, 22, 23, 24, 25, 26, 27, 28 and 29). Results 135 Figure 68. Protein analysis of ExoGAG supernatants of patients with mutation in PKD2. Whole protein component of ExoGAG supernatant staining with Oriole (BioRad®). Patients were ordered according to the increase in serum creatinine (Creat) levels. The principal differential proteins are shown in Table 5. In this case, we only found proteins that increase in abundance throughout the disease. The proteins ALBU, and AMBP were also upregulated and tested as biomarkers in ExoGAG pellet. Table 5. Summary list of principal proteins with differences in abundance throughout the disease of ExoGAG supernatants in PKD2 patients. Protein Name Uniprot Name Disease progression Albumin ALBU_HUMAN ↑ Alpha-1-acid glycoprotein 1 A1AG1_HUMAN ↑ Keratin, type II cytoskeletal 1 K2C1_HUMAN ↑ Keratin, type I cytoskeletal 10 K1C10_HUMAN ↑ Retinol-binding protein 4 RET4_HUMAN ↑ Serotransferrin TRFE_HUMAN ↑ Transthyretin TTHY_HUMAN ↑ Protein AMBP AMBP_HUMAN ↑ Zinc-alpha-2-glycoprotein ZA2G_HUMAN ↑ 4.2.2.3.3 Validation of identified biomarkers by Western Blot in ADPKD patients As we made previously in ExoGAG pellet, we decided to validate the differential proteins in other patients with mutation in PKD2 by Western Blot. We used the supernatant of the same samples that we validated in ExoGAG pellet previously. First, we tested the protein fingerprint of the samples we used and once again, the pattern was altered related to the disease progression (Figure 69A), but not so accused in medium levels of creatinine. Then, Western Blot analysis revealed that 6 of these 9 proteins were validated (Figure 69). All of them increased with the disease progression with the increase most pronounced in samples from patients with high creatinine levels. Although RET4, TTHY, AMBP and ZA2G began to show an increase in lower creatinine levels (Figure 69C, D, F and G). A schematic representation of the semi-quantitative mass spectrometry results of these validated proteins is shown in Table 6, where we could observe that almost all proteins appear in all weights, except RET4. MARTA VIZOSO GONZÁLEZ 136 Figure 69. Validation of possible targets in ExoGAG supernatants by Western Blot in patients with mutation in PKD2. On the top, the whole protein component of ExoGAG supernatants of patients with mutation in PKD2 staining with Oriole (BioRad®). The protein abundance of the possible biomarkers of our semi-quantitative proteomic data were validated by Western Blot. Low means creatinine levels <1.1, Medium means creatinine levels between 1.2-2 and High means creatinine levels >2. Table 6. Schematic representation of the maximum protein abundance per band by semi-quantitative analysis in ExoGAG supernatants. Semi-quantitative analysis is based in Total Spectral Count. Maximal Total spectral count: >1000 √√√√√, 500-1000 √√√√, 100-500 √√√, 50-100 √√, 1-50 √, 0 -. kDa ALBU RET4 TTHY TRFE AMBP ZA2G 130-95 √√√ - √ √ - - 95 √√√ - √ √ √ - 95-72 √√√ - √ √√√ √ - 72-55 √√√√√ - √ √√√ √ √ 55 √√√√√ - √ √√√ √ √ 55-43 √√√√ - √ √√ √ √√√ 40-30 √√√√ - √ √√√ √√√ √ 30-26 √√√√ - √ √√√ √√ √ 25-20 √√√√ √√√ √ √√ √ √ As before, we decided to study how these markers behaved in patients with mutations in PKD1 (Figure 70). We used the supernatant of the same samples that we validated in ExoGAG Results 143 when we characterized ExoGAG supernatant we only found independent EV (Figure 75B). The size of the EVs was variable, from more than 200nm to less than 50nm. Figure 75. Representative transmission electronic images (TEM) of ExoGAG pellet (A) and supernatant (B). (A) EVs that form part of a complex or individually EVs are found in ExoGAG pellet. (B) Remaining EVs in the ExoGAG supernatant. After saw this result, we wanted to validate if these complexes were formed by EVs and Uromodulin. As it was previously described, Uromodulin formed long polymeric filaments that entrapped abundant EVs (205,239). The immunofluorescence of DiD, a lipophilic marker for membranes, and Uromodulin revealed that both colocalize and this means that Uromodulin form part of these complexes with extracellular vesicles (Figure 76). In addition to being part of these complexes, it was also observed that there are isolated Uromodulin-positive EVs. In fact, as we saw in TEM, we observed that with ExoGAG isolation we obtained these complexes and EVs that stood alone (Figure 75 and Figure 76). MARTA VIZOSO GONZÁLEZ 144 Figure 76. Immunofluorescence of ExoGAG pellet. Red marks DiD lipophilic fluorescent stain for labelling membranes, in this case, Extracellular Vesicle membranes. Green marks Uromodulin, one of the most abundant glycoprotein of the urine. Scale bar 25µm 4.3.3 Comparison of ExoGAG and other Extracellular Vesicle isolation techniques. All this data led us to believe that we were isolating Extracellular Vesicles, so we decided to compare this new isolation method with other commonly used methods. We compared our method with ultracentrifugation, one of the most used techniques that is an intermediate recovery and specificity method for EVs isolation, and immunocapture with CD9, that is a low recovery but high specificity method (126). In Figure 77, we shown a summary of the types of samples obtained after the corresponding isolations methods as well as the initial volumes from which they have been taken as a starting point. It is important to note the difference between the volume needed for Ultracentrifugation isolation and what we need for ExoGAG isolation. Results 145 Figure 77. Schematic representation of EVs isolation techniques used. Starting volume as well as type of sample that we could obtain are also represented. We first made a Uromodulin component characterization. We compared the Uromodulin obtained by the immunoprecipitation with CD9 isolation, the ExoGAG isolation and the immunoprecipitation with Uromodulin itself, therefore we were able to compared not only the isolation by immunoprecipitation of the total sample, but also the isolation of each of the components of the ExoGAG isolation (Figure 78A). As we used 2 ml of sample for the immunoprecipitations, we tried to replicate the same in the ExoGAG isolation but this was only possible in pellet samples, in the supernatant samples and total urine only 200 µL of sample were used, a ratio of 10 times less sample amount. The first thing that was observed is that in all cases the ExoGAG pellet component was the most enrichment in Uromodulin and that in the Supernatant it was not appreciated. The next most enriched one to the same level as above is UMOD immunoprecipitation in total urine. Also noteworthy was the presence of uromodulin in the CD9 immunoprecipitation of total urine, which demonstrates, together with the uromodulin obtained in the immunoprecipitation of the pellet, that part of the vesicles which were CD9 positive were also associated with or carry uromodulin (Figure 78A). When we analyzed other EV-specific markers, we observed that they behave differently depending on the type of isolation. In the case of internal vesicle markers such as ALIX, it is only found in the 2mL ExoGAG pellet sample, or SYNTENIN-1, that appeared in immunoprecipitation with CD9 from urine. However, another membrane marker, CD81, appeared in all samples immunoprecipitated with CD9 but in none of the ExoGAG samples (Figure 78B). The next comparison we made was with ultracentrifugation method. In this first approach we compared not only the isolations separately, but also the components not isolated by each technique, the isolation with another of them, both with immunoprecipitation with CD9 and ExoGAG (Figure 78C). Only ultracentrifugation and ExoGAG pellets were positive for both MARTA VIZOSO GONZÁLEZ 146 markers. Although SYNTENIN-1 was also positive for isolation with ExoGAG from the ultracentrifugation supernatant and for isolation by immunoprecipitation with CD9 per se and for ExoGAG supernatant (Figure 78C). This again showed that the behavior of the markers was dependent on the type of isolation. Figure 78. Comparative study among types of EV isolation. (A) Uromodulin levels comparation among the immunoprecipitation with the EV marker CD9 of the pellet (P) and supernatant (S) of ExoGAG and urine (U), pellet (P) and supernatant (S) of ExoGAG isolation, the total urine and the immunoprecipitation with UMOD of the pellet (P) and supernatant (S) of ExoGAG and urine (U). (B) EV markers (ALIX, CD81 and SYNTENIN-1) evaluation among the immunoprecipitation with the EV marker CD9 of the pellet (P) and supernatant (S) of ExoGAG and urine, pellet (P) and supernatant (S) of ExoGAG isolation and the whole urine (U). (C) EV markers (ALIX and SYNTENIN-1) among Ultracentrifugation, ExoGAG isolation and immunoprecipitation with CD9. Inside Ultracentrifugation, we performed ExoGAG isolation of supernatant as well as immunoprecipitation with CD9 of supernatant and previous ExoGAG supernatant of ultracentrifugation supernatant. Inside ExoGAG, we performed immunoprecipitation with CD9 of the supernatant of ExoGAG isolation. * marks the band of ALIX in the pellet of ExoGAG. 4.3.4 Optimization and characterization of the ExoGAG isolation. Once we had characterized the presence of extracellular vesicles, we decided to find out what is the optimal starting sample volume for the ExoGAG isolation. The presence of ALIX and SINTENIN-1 markers appeared from a volume of 1mL of urine. However, we have a clearest detection using 3 mL of urine (Figure 79A). Therefore, we characterized the presence of these markers from 3 ml of urine in different control samples, and we observed ALIX, CD81 Results 147 and SINTENIN-1 in all samples, but with a non-homogeneous level that seems to be sampledependent as well as isolation-dependent (Figure 79B). To see if this variation in detection is characteristic of ExoGAG isolation, we used different proportions of soluble protein to see if this ratio affects isolation performance. Therefore, using the pellet isolated with ultracentrifugation as a control, we resuspended it in different ratios of soluble protein and re-precipitated it with ExoGAG. No differences were observed in the level of marker obtained when the ratio of soluble protein in the sample increased. It was even possible to observe again that these markers are present both in the isolation by ultracentrifugation and in ExoGAG isolation (Figure 79C). Figure 79. Optimization and characterization of the ExoGAG isolation protocol. (A) EV markers (ALIX and SYNTENIN-1) in different volumes of urine precipitated with ExoGAG. (B) EV markers (ALIX, CD81 and SYNTENIN1) in the precipitation of 3 ml of urine of control samples. (C) Validation of the ExoGAG isolation with different proportion of soluble protein in the sample. 4.3.5 Protein characterization by mass spectrometry of the differences between the component isolated with ExoGAG and ultracentrifugation techniques Mass spectrometry is a technique that allow protein fingerprint identification at once. Moreover, this technique has a great advantage over the typically antibody-based methods, such as Western Blot, because it offers greater specificity, reproducibility and typical limits of quantification lower than fmol range. It also removes some of the antibody specificity issues as peptides unique to each protein are measured (298). Knowing the benefits that mass spectrometry can offer us, we decided to use it to try to complement the detection of vesicle-associated markers in ExoGAG isolation. 4.3.5.1 Optimization of the protocol for mass spectrometry sequencing of ExoGAG isolation We tested different ways of sample preparation for mass spectrometric sequencing because we know that we have majority proteins in our samples such as Uromodulin and that this can affect the sensitivity of the identification of the minoritary proteins present in the sample. MARTA VIZOSO GONZÁLEZ 148 The approaches we used were: - Sequencing directly (Direct). In this case, it was sequenced just after the isolation with ExoGAG. The sample was concentrated on a gel band and cut out for further trypsin digestion and mass spectrometry sequencing. - ExoGAG pellet precipitation with the methanol-chloroform protocol (Precipitated). Methanol-chloroform protein precipitation was performed to eliminate possible buffer contaminants prior to in gel concentration for trypsin digestion and mass spectrometry sequencing. - Sequencing of the sample by banding (Bands). In this case, the sample was loaded onto the gel and allowed to separate all the proteins by weight. The entire gel was then cut into four bands that cover the entire sample, trypsin digested and mass spectrometry sequenced. The best sequencing approach was the banding method as it identified 3 times more proteins than direct sequencing and almost twice as many as methanol-chloroform precipitation (Figure 80). Figure 80. Optimization of the protocol for mass spectrometry sequencing by Qualitative DDA method of ExoGAG isolation. (A) Number of identified proteins according to each type of sequencing method. (B) Venn diagram showing the common and different proteins of each method. 4.3.5.2 Characterization and comparison by mass spectrometry of the component isolated with ExoGAG and ultracentrifugation Using the same sample isolated by ExoGAG and UC, mass spectrometry sequencing was performed to analyze and compare protein component isolated by both methods. For ExoGAG isolation, 3mL of urine were used and for UC isolation, 30mL of the same urine sample was used (10 times more volume) (Figure 81A). A mean of 630 proteins were identified in ExoGAG isolation in contrast to 745 identified proteins in UC, despite the huge volume difference there was no significative difference in the number of identified proteins between both methods, what mean that ExoGAG has a good yield with 10 times less volume (Figure 81B). Next, we decided to compare the percentage of shared proteins among samples isolated with the same technique. If we compared samples two by two and taking all identified proteins by the two samples like a whole set, the percentage mean of shared proteins for ExoGAG is 55%, a bit more than the 52,8% found in UC samples (Figure 81C). However, if we compared the identified proteins of samples individually (left samples related to down samples), the Results 149 shared percentage increased to 72% of ExoGAG and 70% of UC isolation (Figure 81D), meaning robustness in techniques. Finally, 303 proteins were shared in all ExoGAG samples and 318 in UC samples (Figure 81E), some less than half of mean identified proteins. Figure 81. Characterization of identified proteins isolated by ExoGAG or UC by mass spectrometry. (A) Schematic representation of sequenced samples. (B) Comparison between the number of identified proteins in ExoGAG or Ultracentrifugation. (C) Percentage of shared proteins in samples isolated by same technique, left ExoGAG and right Ultracentrifugation, and compared identified proteins by two samples as a unique set. (D) Percentage of shared proteins in samples isolated by same technique, left ExoGAG and right Ultracentrifugation, and compared identified proteins by sample, left to down. (E) Venn diagram of identified proteins of each MARTA VIZOSO GONZÁLEZ 150 sample, left ExoGAG where 303 were shared by all samples and right Ultracentrifugation where 318 where shared by all samples. Bars represent means ± SEM. p<0.05 by T-student test (two tail) was considered significative result: ns represents not significative. More than 50% of identified proteins are common between ExoGAG and UC, 58% for ExoGAG dataset and 52% for UC (Figure 82A) (Supplementary Table 30). These decreased to 42% and 40%, respectively, if we considered only the shared proteins for all samples (Figure 82B) (Supplementary Table 30). Using the bioinformatic tool FunRich to analyze the cellular component of these identified protein sets (Figure 82C), we found that exosomes was the most enriched component for ExoGAG and UC shared proteins, followed by cytoplasm, plasma membrane and organelles as lysosomes, nucleus or mitochondrion. The set of identified proteins only by UC was more enriched in cytoplasm, exosomes and organelles than those identified only in ExoGAG, in fact it seemed that identified proteins only by UC had a similar pattern as ExoGAG and UC shared proteins, less in the extracellular component that was most enriched component in only identified proteins in ExoGAG. The same pattern was observed when we analyzed the cellular component of the shared proteins in all samples, even the differences were more pronounced (Figure 82D). In this case, almost all proteins of ExoGAG and UC shared proteins and only UC proteins were related to exosomes, followed, again, by proteins related to cytoplasm, plasma membrane and organelles as lysosomes, nucleus or mitochondrion. In contrast, those only identified proteins in ExoGAG were related mostly to extracellular components. This means that ExoGAG isolation allow us to obtain exosome component, but with an important component of extracellular fraction. Figure 82. Comparison of identified proteins isolated by ExoGAG or UC by mass spectrometry. Venn diagram of common identified proteins in ExoGAG and UC (A) and shared proteins by all samples (B). FunRich analysis for cellular component of only identified proteins by ExoGAG isolation (blue), shared ExoGAG and UC proteins (yellow) and only identified proteins by UC only proteins (red) (C) and shared proteins by all samples(D). Results 151 Then, we decided to compare mass spectrometry results with Vesiclepedia dataset (151)(Figure 83) (Supplementary Table 30). A huge percentage of proteins, 97%, of both ExoGAG and UC identified proteins were shared with Vesiclepedia dataset (Figure 83A). However, these percentage decreased if we considered the urine Vesiclepedia, the Vesiclepedia experiments regarded only to urine samples, 86% shared with ExoGAG and 90% with UC identified proteins. Moreover, when we studied the shared proteins by all samples, we observed how almost 100% of ExoGAG proteins and 100% of UC proteins were shared with Vesiclepedia and urine Vesiclepedia (Figure 83B). In fact, if we considered top 100 proteins described in Vesiclepedia or urine Vesiclepedia, more than 90% were shared with ExoGAG and UC identified proteins (Figure 83C). Nevertheless, these percentages decreased tremendously if we chose the shared proteins by all samples and compared to Vesiclepedia dataset, to 34% in ExoGAG proteins and to 56% of UC proteins (Figure 83D top). In contrast, these percentages raised up again with urine Vesiclepedia dataset, more than 70% in ExoGAG and more than 80% in UC shared proteins by all samples (Figure 83D bottom). To further investigate what happened to these proteins that were not shared with Vesiclepedia or urine Vesiclepedia, we compared those ExoGAG or UC proteins that were not shared with urine Vesiclepedia because they englobed not shared with Vesiclepedia too, using FunRich tool. Related to cellular component, ExoGAG proteins were more enriched in extracellular and plasma membrane components, and UC proteins were more related to mitochondrion and actin linked components (Figure 83E). The site of expression of these proteins were in a similar percentage in serum component, however, interesting twice ExoGAG proteins were expressed in urine more than UC proteins (Figure 83F). All these data demonstrate that ExoGAG is a good EV isolation method with consistent data compared to the current state of art of EV research. MARTA VIZOSO GONZÁLEZ 152 Figure 83. Analysis by comparison of identified proteins isolated by ExoGAG or UC by mass spectrometry with Vesiclepedia dataset. Comparison between all ExoGAG and UC identified proteins (A) and shared proteins by all samples (B) to Vesiclepedia and Urine Vesiclepedia datasets using FunRich software. C and D, respectively, analysis of 100 top proteins of Vesiclepedia and Urine Vesiclepedia contrasted to ExoGAG and UC identified proteins and shared proteins by all samples. (E) FunRich analysis for cellular component for ExoGAG (blue) and UC (yellow) not present proteins in Urine Vesiclepedia. (F) FunRich analysis for site of expression for ExoGAG (blue) and UC (yellow) not present proteins in Urine Vesiclepedia. 4.3.5.3 Characterization of the component isolated with ExoGAG according to the guideline of Extracellular Vesicle studies. An schematic illustration of identified proteins in ExoGAG isolation (Supplementary Table 30) according to MISEV18 guideline of proteomic characterization is represented in Figure 84 (126). For this purpose, mass spectrometry is an optimum method due to the capacity of allowing the characterization of many proteins at once. Three categories of markers that must be found to demonstrate the presence of EVs (Categories 1 and 2) and the purity from common contaminants (Category 3), unless there are no universal “negative controls”. Analysis of proteins of category 4 is required when declaring small EVs, and of category 5 to support functional assays. The categories are: Supplemental Material 255 Q6IFX2 K1C42_MOUSE Keratin, type I cytoskeletal 42 3 6,19 5 Q8VED5 K2C79_MOUSE Keratin, type II cytoskeletal 79 2 3,58 5 P12246 SAMP_MOUSE Serum amyloid P-component 4 16,96 4 P08228 SODC_MOUSE Superoxide dismutase [Cu-Zn] 3 23,38 4 Q9DBH5 LMAN2_MOUSE Vesicular integral-membrane protein VIP36 3 8,66 4 P08226 APOE_MOUSE Apolipoprotein E 3 12,22 4 Q02819 NUCB1_MOUSE Nucleobindin-1 3 7,41 4 P63101 1433Z_MOUSE 14-3-3 protein zeta/delta 2 9,80 4 O09051 GUC2B_MOUSE Guanylate cyclase activator 2B 2 25,47 4 P81117 NUCB2_MOUSE Nucleobindin-2 2 7,14 4 P35230 REG3B_MOUSE Regenerating islet-derived protein 3-beta 3 17,71 4 P14211 CALR_MOUSE Calreticulin 2 4,33 4 O70570 PIGR_MOUSE Polymeric immunoglobulin receptor 2 2,72 4 O08709 PRDX6_MOUSE Peroxiredoxin-6 2 8,04 4 P01887 B2MG_MOUSE Beta-2-microglobulin 1 7,56 4 O54990 PROM1_MOUSE Prominin-1 3 5,07 3 P18761 CAH6_MOUSE Carbonic anhydrase 6 2 11,99 3 P57780 ACTN4_MOUSE Alpha-actinin-4 2 2,41 3 P70412 CUZD1_MOUSE CUB and zona pellucida-like domaincontaining protein 1 2 6,27 3 Q9DAK9 PHP14_MOUSE 14 kDa phosphohistidine phosphatase 2 25,00 3 Q61592 GAS6_MOUSE Growth arrest-specific protein 6 2 3,56 3 P01635 KV5A3_MOUSE Ig kappa chain V-V region K2 (Fragment) 2 20,00 3 P28665 MUG1_MOUSE Murinoglobulin-1 2 1,29 3 P49183 DNAS1_MOUSE Deoxyribonuclease-1 2 6,69 3 Q9D3H2 OBP1A_MOUSE Odorant-binding protein 1a 2 14,11 3 P45700 MA1A1_MOUSE Mannosyl-oligosaccharide 1,2-alphamannosidase IA 2 3,66 3 Q8VCG4 CO8G_MOUSE Complement component C8 gamma chain 2 12,38 3 Q8BPB5 FBLN3_MOUSE EGF-containing fibulin-like extracellular matrix protein 1 2 4,06 3 Q80XI7 VOME_MOUSE Vomeromodulin 3 6,77 3 P97290 IC1_MOUSE Plasma protease C1 inhibitor 1 2,18 3 Q80YX1 TENA_MOUSE Tenascin 2 0,95 2 Q8BFR5 EFTU_MOUSE Elongation factor Tu, mitochondrial 1 2,43 2 Q9WVJ9 FBLN4_MOUSE EGF-containing fibulin-like extracellular matrix protein 2 2 8,13 2 Q8R121 ZPI_MOUSE Protein Z-dependent protease inhibitor 2 4,91 2 O88322 NID2_MOUSE Nidogen-2 2 2,21 2 Q07235 GDN_MOUSE Glia-derived nexin 2 6,04 2 P01843 LAC1_MOUSE Ig lambda-1 chain C region 2 27,62 2 Q923D2 BLVRB_MOUSE Flavin reductase (NADPH) 2 12,62 2 P56480 ATPB_MOUSE ATP synthase subunit beta, mitochondrial 2 4,73 2 O88968 TCO2_MOUSE Transcobalamin-2 2 5,58 2 P30933 SVS5_MOUSE Seminal vesicle secretory protein 5 2 22,95 2 P82198 BGH3_MOUSE Transforming growth factor-beta-induced protein ig-h3 1 1,90 2 Q99LJ1 FUCO_MOUSE Tissue alpha-L-fucosidase 1 2,43 2 MARTA VIZOSO GONZÁLEZ 256 Q91V98 CD248_MOUSE Endosialin 1 1,31 2 P07214 SPRC_MOUSE SPARC 1 4,64 2 P09803 CADH1_MOUSE Cadherin-1 1 1,58 2 P15947 KLK1_MOUSE Kallikrein-1 1 8,43 2 P30275 KCRU_MOUSE Creatine kinase U-type, mitochondrial 1 2,39 2 P28825 MEP1A_MOUSE Meprin A subunit alpha 1 1,21 2 Q9Z1T2 TSP4_MOUSE Thrombospondin-4 1 1,56 2 P35441 TSP1_MOUSE Thrombospondin-1 1 0,94 2 A2AEP0 OBP1B_MOUSE Odorant-binding protein 1b 2 17,54 1 P39061 COIA1_MOUSE Collagen alpha-1(XVIII) chain 1 0,96 1 P55288 CAD11_MOUSE Cadherin-11 1 1,26 1 Q3UV17 K22O_MOUSE Keratin, type II cytoskeletal 2 oral 2 2,69 1 Q80Z19 MUC2_MOUSE Mucin-2 (Fragments) 1 0,37 1 Q01721 GAS1_MOUSE Growth arrest-specific protein 1 1 3,21 1 P51910 APOD_MOUSE Apolipoprotein D 1 6,35 1 P16125 LDHB_MOUSE L-lactate dehydrogenase B chain 1 2,69 1 Q8K1H9 OBP2A_MOUSE Odorant-binding protein 2a 1 5,11 1 P04938 MUP11_MOUSE Major urinary protein 11 56 69,61 Q8BH35 CO8B_MOUSE Complement component C8 beta chain 4 7,64 P01668 KV3AG_MOUSE Ig kappa chain V-III region PC 7210 2 30,91 P62984 RL40_MOUSE Ubiquitin-60S ribosomal protein L40 2 17,19 P01869 IGH1M_MOUSE Ig gamma-1 chain C region, membranebound form 2 5,60 P01631 KV2A7_MOUSE Ig kappa chain V-II region 26-10 3 17,70 P84244 H33_MOUSE Histone H3.3 2 10,29 P01867 IGG2B_MOUSE Ig gamma-2B chain C region 2 6,44 P06684 CO5_MOUSE Complement C5 1 0,77 O09010 LFNG_MOUSE Beta-1,3-Nacetylglucosaminyltransferase lunatic fringe 1 2,91 Q9QWL7 K1C17_MOUSE Keratin, type I cytoskeletal 17 4 8,31 Q9CQI3 GMFB_MOUSE Glia maturation factor beta 1 16,20 Q07968 F13B_MOUSE Coagulation factor XIII B chain 2 1,49 O88593 PGRP1_MOUSE Peptidoglycan recognition protein 1 1 6,59 Q9R1Q9 VAS1_MOUSE V-type proton ATPase subunit S1 1 3,89 Q9D733 GP2_MOUSE Pancreatic secretory granule membrane major glycoprotein GP2 1 2,26 Q9CQM5 TXD17_MOUSE Thioredoxin domain-containing protein 17 1 7,32 Q62009 POSTN_MOUSE Periostin 1 1,67 Q3TA59 JMJD8_MOUSE JmjC domain-containing protein 8 1 4,80 P35459 LY6D_MOUSE Lymphocyte antigen 6D 1 11,02 P26339 CMGA_MOUSE Chromogranin-A 1 4,10 P01878 IGHA_MOUSE Ig alpha chain C region 1 4,36 P01636 KV5A4_MOUSE Ig kappa chain V-V region MOPC 149 1 12,04 P57096 PSCA_MOUSE Prostate stem cell antigen 1 8,13 Q62165 DAG1_MOUSE Dystroglycan 1 1,79 P52480 KPYM_MOUSE Pyruvate kinase PKM 1 2,07 Supplemental Material 257 Q9Z1R3 APOM_MOUSE Apolipoprotein M 1 3,68 Q60854 SPB6_MOUSE Serpin B6 1 2,65 Q9D0L8 MCES_MOUSE mRNA cap guanine-N7 methyltransferase 1 1,72 P68373 TBA1C_MOUSE Tubulin alpha-1C chain 1 3,34 P09036 ISK1_MOUSE Serine protease inhibitor Kazal-type 1 1 8,75 Q9WTS2 FUT8_MOUSE Alpha-(1,6)-fucosyltransferase 1 1,04 Q8K419 LEG4_MOUSE Galectin-4 1 2,15 P81183 IKZF2_MOUSE Zinc finger protein Helios 1 1,90 P16110 LEG3_MOUSE Galectin-3 1 5,68 Q9JMK0 B4GT5_MOUSE Beta-1,4-galactosyltransferase 5 1 2,06 P06330 HVM51_MOUSE Ig heavy chain V region AC38 205.12 1 16,10 O09159 MA2B1_MOUSE Lysosomal alpha-mannosidase 1 1,28 Q8VHC3 SELM_MOUSE Selenoprotein M 1 9,66 Q8BHL4 RAI3_MOUSE Retinoic acid-induced protein 3 1 3,65 P60843 IF4A1_MOUSE Eukaryotic initiation factor 4A-I 1 2,46 Q8BNJ6 NETO2_MOUSE Neuropilin and tolloid-like protein 2 1 1,33 O88312 AGR2_MOUSE Anterior gradient protein 2 homolog 1 6,29 Q9D2G2 ODO2_MOUSE Dihydrolipoyllysine-residue succinyltransferase component of 2oxoglutarate dehydrogenase complex, mitochondrial 1 1,76 Q91W90 TXND5_MOUSE Thioredoxin domain-containing protein 5 1 3,36 P16294 FA9_MOUSE Coagulation factor IX 1 1,91 Supplementary Table 9. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 1 (ExoGAG isolated fraction of control sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P07911 UROM_HUMAN Uromodulin 236 63,13 745 P02768 ALBU_HUMAN Albumin 94 69,79 665 P68871 HBB_HUMAN Hemoglobin subunit beta 25 95,24 67 P02042 HBD_HUMAN Hemoglobin subunit delta 18 48,98 41 Q08380 LG3BP_HUMAN Galectin-3-binding protein 18 29,91 30 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 15 21,43 24 P69905 HBA_HUMAN Hemoglobin subunit alpha 16 71,13 23 P01133 EGF_HUMAN Pro-epidermal growth factor 12 12,01 19 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 8 34,55 14 P05155 IC1_HUMAN Plasma protease C1 inhibitor 8 16,80 14 P02647 APOA1_HUMAN Apolipoprotein A-I 9 31,84 12 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 8 13,87 12 Q6EMK4 VASN_HUMAN Vasorin 6 11,89 10 P02760 AMBP_HUMAN Protein AMBP 7 19,32 8 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 4 11,66 7 P01834 IGKC_HUMAN Immunoglobulin kappa constant 4 79,44 7 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 4 7,86 7 P08473 NEP_HUMAN Neprilysin 3 4,00 7 P01009 A1AT_HUMAN Alpha-1-antitrypsin 5 16,99 6 P00738 HPT_HUMAN Haptoglobin 3 8,13 6 MARTA VIZOSO GONZÁLEZ 258 P15144 AMPN_HUMAN Aminopeptidase N 4 4,55 5 P08779 K1C16_HUMAN Keratin, type I cytoskeletal 16 4 7,82 5 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 4 19,26 4 P63261 ACTG_HUMAN Actin, cytoplasmic 2 2 9,07 3 P12109 CO6A1_HUMAN Collagen alpha-1(VI) chain 3 3,79 3 P02671 FIBA_HUMAN Fibrinogen alpha chain 2 2,54 3 P02787 TRFE_HUMAN Serotransferrin 2 7,16 3 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 2 4,73 2 P01023 A2MG_HUMAN Alpha-2-macroglobulin 2 2,92 2 P16070 CD44_HUMAN CD44 antigen 2 2,56 2 P02679 FIBG_HUMAN Fibrinogen gamma chain 2 7,73 2 P62805 H4_HUMAN Histone H4 1 7,77 2 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 2 23,58 2 P01833 PIGR_HUMAN Polymeric immunoglobulin receptor 1 2,49 2 P01008 ANT3_HUMAN Antithrombin-III 1 4,96 1 P01024 CO3_HUMAN Complement C3 1 0,78 1 P51884 LUM_HUMAN Lumican 1 2,37 1 P13473 LAMP2_HUMAN Lysosome-associated membrane glycoprotein 2 1 1,95 1 Q6UXB8 PI16_HUMAN Peptidase inhibitor 16 1 2,16 1 Q695T7 S6A19_HUMAN Sodium-dependent neutral amino acid transporter B(0)AT1 1 1,73 1 P02766 TTHY_HUMAN Transthyretin 1 19,05 1 P08670 VIME_HUMAN Vimentin 1 2,15 1 P36957 ODO2_HUMAN Dihydrolipoyllysine-residue succinyltransferase component of 2oxoglutarate dehydrogenase complex, mitochondrial 1 1,77 Q9BTM1 H2AJ_HUMAN Histone H2A.J 1 6,98 P01871 IGHM_HUMAN Immunoglobulin heavy constant mu 1 3,31 Supplementary Table 10. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 2 (ExoGAG isolated fraction of low creatinine PKD2 sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P07911 UROM_HUMAN Uromodulin 299 68,44 1331 P02768 ALBU_HUMAN Albumin 65 69,62 373 P68871 HBB_HUMAN Hemoglobin subunit beta 40 95,24 112 Q08380 LG3BP_HUMAN Galectin-3-binding protein 33 40,34 88 P02042 HBD_HUMAN Hemoglobin subunit delta 28 57,14 71 P01133 EGF_HUMAN Pro-epidermal growth factor 25 21,71 49 P08473 NEP_HUMAN Neprilysin 28 40,27 47 P69905 HBA_HUMAN Hemoglobin subunit alpha 22 71,83 40 P02760 AMBP_HUMAN Protein AMBP 13 22,73 32 P27487 DPP4_HUMAN Dipeptidyl peptidase 4 17 25,59 27 Q6EMK4 VASN_HUMAN Vasorin 15 16,34 27 P54802 ANAG_HUMAN Alpha-N-acetylglucosaminidase 17 29,34 26 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 15 47,58 26 Supplemental Material 259 Q8WUM4 PDC6I_HUMAN Programmed cell death 6-interacting protein 14 19,24 22 P00450 CERU_HUMAN Ceruloplasmin 13 16,34 21 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 11 28,22 21 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 15 22,83 21 P15144 AMPN_HUMAN Aminopeptidase N 19 20,06 14 P98164 LRP2_HUMAN Low-density lipoprotein receptor-related protein 2 8 1,74 12 P05155 IC1_HUMAN Plasma protease C1 inhibitor 6 13,60 12 P01834 IGKC_HUMAN Immunoglobulin kappa constant 5 66,36 11 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 7 10,27 11 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 7 18,78 11 P07359 GP1BA_HUMAN Platelet glycoprotein Ib alpha chain 3 4,76 10 P01024 CO3_HUMAN Complement C3 5 3,13 9 P63261 ACTG_HUMAN Actin, cytoplasmic 2 5 18,13 8 P01833 PIGR_HUMAN Polymeric immunoglobulin receptor 4 6,41 8 P01009 A1AT_HUMAN Alpha-1-antitrypsin 4 12,44 7 P16070 CD44_HUMAN CD44 antigen 5 5,26 7 P78385 KRT83_HUMAN Keratin, type II cuticular Hb3 4 5,68 7 P02751 FINC_HUMAN Fibronectin 3 1,51 6 P62805 H4_HUMAN Histone H4 3 29,13 6 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 5 55,66 6 P51884 LUM_HUMAN Lumican 5 19,53 6 P02787 TRFE_HUMAN Serotransferrin 2 3,01 6 P13727 PRG2_HUMAN Bone marrow proteoglycan 3 13,06 5 P13473 LAMP2_HUMAN Lysosome-associated membrane glycoprotein 2 1 1,95 5 P00738 HPT_HUMAN Haptoglobin 4 12,32 4 Q99880 H2B1L_HUMAN Histone H2B type 1-L 2 11,90 3 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 4 18,70 3 Q15323 K1H1_HUMAN Keratin, type I cuticular Ha1 3 8,41 3 P01042 KNG1_HUMAN Kininogen-1 1 1,24 3 P11279 LAMP1_HUMAN Lysosome-associated membrane glycoprotein 1 1 2,64 3 Q695T7 S6A19_HUMAN Sodium-dependent neutral amino acid transporter B(0)AT1 2 3,31 3 Q9BQE3 TBA1C_HUMAN Tubulin alpha-1C chain 1 3,34 3 P02647 APOA1_HUMAN Apolipoprotein A-I 10 38,95 2 Q53GD3 CTL4_HUMAN Choline transporter-like protein 4 1 1,97 2 Q9NSB4 KRT82_HUMAN Keratin, type II cuticular Hb2 1 1,95 2 P31431 SDC4_HUMAN Syndecan-4 2 11,11 2 P08670 VIME_HUMAN Vimentin 2 5,15 2 P01023 A2MG_HUMAN Alpha-2-macroglobulin 1 0,68 1 P31327 CPSM_HUMAN Carbamoyl-phosphate synthase [ammonia], mitochondrial 1 0,73 1 P10909 CLUS_HUMAN Clusterin 1 2,67 1 P02679 FIBG_HUMAN Fibrinogen gamma chain 1 4,41 1 Q07075 AMPE_HUMAN Glutamyl aminopeptidase 1 1,25 1 Q9BXP8 PAPP2_HUMAN Pappalysin-2 1 0,50 1 MARTA VIZOSO GONZÁLEZ 260 P04004 VTNC_HUMAN Vitronectin 1 3,14 1 P62269 RS18_HUMAN 40S ribosomal protein S18 1 5,26 P12814 ACTN1_HUMAN Alpha-actinin-1 1 1,35 P39060 COIA1_HUMAN Collagen alpha-1(XVIII) chain 1 0,51 Q9BTM1 H2AJ_HUMAN Histone H2A.J 2 12,40 O76009 KT33A_HUMAN Keratin, type I cuticular Ha3-I 2 4,95 O76013 KRT36_HUMAN Keratin, type I cuticular Ha6 2 3,21 P19012 K1C15_HUMAN Keratin, type I cytoskeletal 15 3 6,14 O43451 MGA_HUMAN Maltase-glucoamylase, intestinal 1 0,54 Q92542 NICA_HUMAN Nicastrin 1 1,55 Q5VTE0 EF1A3_HUMAN Putative elongation factor 1-alpha-like 3 1 2,38 Q9BVA1 TBB2B_HUMAN Tubulin beta-2B chain 1 3,15 P62987 RL40_HUMAN Ubiquitin-60S ribosomal protein L40 1 12,50 Supplementary Table 11. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 3 (ExoGAG isolated fraction of medium creatinine PKD2 sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P07911 UROM_HUMAN Uromodulin 205 64,69 708 P02768 ALBU_HUMAN Albumin 52 61,58 239 P68871 HBB_HUMAN Hemoglobin subunit beta 31 84,35 98 P02042 HBD_HUMAN Hemoglobin subunit delta 23 71,43 59 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 19 53,33 42 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 17 35,58 37 P69905 HBA_HUMAN Hemoglobin subunit alpha 17 71,83 35 Q08380 LG3BP_HUMAN Galectin-3-binding protein 16 29,74 30 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 18 27,95 27 P02760 AMBP_HUMAN Protein AMBP 14 30,40 27 P00450 CERU_HUMAN Ceruloplasmin 20 22,91 26 P01024 CO3_HUMAN Complement C3 20 14,13 24 Q6EMK4 VASN_HUMAN Vasorin 11 16,34 22 P01834 IGKC_HUMAN Immunoglobulin kappa constant 9 79,44 17 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 16 27,23 17 P01023 A2MG_HUMAN Alpha-2-macroglobulin 10 8,68 15 P02647 APOA1_HUMAN Apolipoprotein A-I 11 40,45 15 P51884 LUM_HUMAN Lumican 10 33,43 15 P08473 NEP_HUMAN Neprilysin 9 13,60 15 P0C0L5 CO4B_HUMAN Complement C4-B 11 9,58 14 Q6UXB8 PI16_HUMAN Peptidase inhibitor 16 9 23,54 14 P07359 GP1BA_HUMAN Platelet glycoprotein Ib alpha chain 1 1,69 14 P0C0L4 CO4A_HUMAN Complement C4-A 10 9,40 13 P15144 AMPN_HUMAN Aminopeptidase N 9 10,75 12 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 5 55,66 12 P05155 IC1_HUMAN Plasma protease C1 inhibitor 5 12,40 10 P01833 PIGR_HUMAN Polymeric immunoglobulin receptor 12 15,45 10 Supplemental Material 261 P02787 TRFE_HUMAN Serotransferrin 6 9,46 10 P13646 K1C13_HUMAN Keratin, type I cytoskeletal 13 6 12,88 8 P01009 A1AT_HUMAN Alpha-1-antitrypsin 4 11,00 7 P00738 HPT_HUMAN Haptoglobin 6 17,98 7 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 8 18,46 7 P01133 EGF_HUMAN Pro-epidermal growth factor 4 3,48 7 P63261 ACTG_HUMAN Actin, cytoplasmic 2 6 19,47 6 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 5 23,51 6 P04259 K2C6B_HUMAN Keratin, type II cytoskeletal 6B 5 9,40 6 P16070 CD44_HUMAN CD44 antigen 3 4,18 4 Q9NPY3 C1QR1_HUMAN Complement component C1q receptor 4 8,59 4 P10643 CO7_HUMAN Complement component C7 4 6,17 4 P19013 K2C4_HUMAN Keratin, type II cytoskeletal 4 3 6,18 4 P54802 ANAG_HUMAN Alpha-N-acetylglucosaminidase 3 5,79 3 P01031 CO5_HUMAN Complement C5 2 1,31 3 P02671 FIBA_HUMAN Fibrinogen alpha chain 1 0,92 3 P08779 K1C16_HUMAN Keratin, type I cytoskeletal 16 5 9,30 3 Q9BXP8 PAPP2_HUMAN Pappalysin-2 1 0,50 3 P00747 PLMN_HUMAN Plasminogen 1 1,98 3 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 1 2,13 2 P00915 CAH1_HUMAN Carbonic anhydrase 1 1 3,45 2 P27487 DPP4_HUMAN Dipeptidyl peptidase 4 2 2,22 2 P02679 FIBG_HUMAN Fibrinogen gamma chain 2 6,18 2 P23142 FBLN1_HUMAN Fibulin-1 1 1,71 2 Q07075 AMPE_HUMAN Glutamyl aminopeptidase 1 1,25 2 P19823 ITIH2_HUMAN Inter-alpha-trypsin inhibitor heavy chain H2 2 2,75 2 Q8WUM4 PDC6I_HUMAN Programmed cell death 6-interacting protein 1 1,15 2 P02766 TTHY_HUMAN Transthyretin 1 8,84 2 P08670 VIME_HUMAN Vimentin 1 2,15 2 P06576 ATPB_HUMAN ATP synthase subunit beta, mitochondrial 1 1,89 1 Q9HCU0 CD248_HUMAN Endosialin 1 1,06 1 P06396 GELS_HUMAN Gelsolin 1 1,66 1 P02790 HEMO_HUMAN Hemopexin 1 2,38 1 P06310 KV230_HUMAN Immunoglobulin kappa variable 2-30 1 10,83 1 P01700 LV147_HUMAN Immunoglobulin lambda variable 1-47 1 11,11 1 Q14624 ITIH4_HUMAN Inter-alpha-trypsin inhibitor heavy chain H4 1 0,97 1 P98164 LRP2_HUMAN Low-density lipoprotein receptor-related protein 2 1 0,21 1 P13473 LAMP2_HUMAN Lysosome-associated membrane glycoprotein 2 1 1,95 1 P04004 VTNC_HUMAN Vitronectin 1 2,51 1 P06681 CO2_HUMAN Complement C2 1 1,60 P06312 KV401_HUMAN Immunoglobulin kappa variable 4-1 1 7,44 P62987 RL40_HUMAN Ubiquitin-60S ribosomal protein L40 1 12,50 MARTA VIZOSO GONZÁLEZ 262 Supplementary Table 12.Protein list of all identified proteins by qualitative mass spectrometry analysis in band 4 (ExoGAG isolated fraction of medium creatinine PKD2 sample) Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02768 ALBU_HUMAN Albumin 416 82,59 1348 P01009 A1AT_HUMAN Alpha-1-antitrypsin 82 70,10 201 P07911 UROM_HUMAN Uromodulin 66 31,09 173 P68871 HBB_HUMAN Hemoglobin subunit beta 33 84,35 87 P19013 K2C4_HUMAN Keratin, type II cytoskeletal 4 31 47,94 73 P01860 IGHG3_HUMAN Immunoglobulin heavy constant gamma 3 30 55,44 64 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 24 34,28 51 P02042 HBD_HUMAN Hemoglobin subunit delta 20 54,42 51 P01042 KNG1_HUMAN Kininogen-1 28 31,37 49 P10451 OSTP_HUMAN Osteopontin 24 55,41 47 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 21 44,85 46 P02760 AMBP_HUMAN Protein AMBP 28 30,68 44 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 16 35,58 43 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 14 40,79 40 P69905 HBA_HUMAN Hemoglobin subunit alpha 16 71,83 35 P01019 ANGT_HUMAN Angiotensinogen 14 28,87 34 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 20 30,43 34 P02787 TRFE_HUMAN Serotransferrin 22 33,09 31 P16070 CD44_HUMAN CD44 antigen 12 5,66 26 P01024 CO3_HUMAN Complement C3 16 11,37 25 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 14 24,49 23 P51884 LUM_HUMAN Lumican 10 33,43 23 P02538 K2C6A_HUMAN Keratin, type II cytoskeletal 6A 12 19,68 19 P13647 K2C5_HUMAN Keratin, type II cytoskeletal 5 16 26,95 17 P16278 BGAL_HUMAN Beta-galactosidase 9 14,48 16 P02647 APOA1_HUMAN Apolipoprotein A-I 8 33,33 15 P01877 IGHA2_HUMAN Immunoglobulin heavy constant alpha 2 5 18,24 15 P05543 THBG_HUMAN Thyroxine-binding globulin 10 27,95 15 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 12 20,55 14 P04004 VTNC_HUMAN Vitronectin 9 14,44 11 P0C0L5 CO4B_HUMAN Complement C4-B 6 3,90 10 P00751 CFAB_HUMAN Complement factor B 8 12,43 10 P19440 GGT1_HUMAN Glutathione hydrolase 1 proenzyme 4 8,61 9 P02790 HEMO_HUMAN Hemopexin 7 16,45 9 P19823 ITIH2_HUMAN Inter-alpha-trypsin inhibitor heavy chain H2 5 8,77 9 P00450 CERU_HUMAN Ceruloplasmin 7 7,51 8 Q9Y5Y7 LYVE1_HUMAN Lymphatic vessel endothelial hyaluronic acid receptor 1 4 9,01 8 Q6EMK4 VASN_HUMAN Vasorin 5 9,96 8 P13727 PRG2_HUMAN Bone marrow proteoglycan 3 17,57 7 Supplemental Material 263 P02748 CO9_HUMAN Complement component C9 5 9,84 7 P08185 CBG_HUMAN Corticosteroid-binding globulin 4 10,37 7 Q08380 LG3BP_HUMAN Galectin-3-binding protein 6 12,82 6 P63261 ACTG_HUMAN Actin, cytoplasmic 2 3 11,20 5 P08174 DAF_HUMAN Complement decay-accelerating factor 2 5,51 5 P02671 FIBA_HUMAN Fibrinogen alpha chain 2 2,43 5 Q9UBX5 FBLN5_HUMAN Fibulin-5 4 9,15 5 P00738 HPT_HUMAN Haptoglobin 5 16,01 5 P01834 IGKC_HUMAN Immunoglobulin kappa constant 3 47,66 5 P15144 AMPN_HUMAN Aminopeptidase N 4 4,14 4 P01780 HV307_HUMAN Immunoglobulin heavy variable 3-7 3 23,93 4 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 3 37,74 4 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 3 10,08 3 P01008 ANT3_HUMAN Antithrombin-III 4 9,05 3 P18428 LBP_HUMAN Lipopolysaccharide-binding protein 3 8,32 3 Q96PD5 PGRP2_HUMAN N-acetylmuramoyl-L-alanine amidase 1 2,43 3 P51688 SPHM_HUMAN N-sulphoglucosamine sulphohydrolase 2 4,78 3 P14618 KPYM_HUMAN Pyruvate kinase PKM 2 4,14 3 P23470 PTPRG_HUMAN Receptor-type tyrosine-protein phosphatase gamma 3 2,56 3 P81605 DCD_HUMAN Dermcidin 2 12,73 2 P19827 ITIH1_HUMAN Inter-alpha-trypsin inhibitor heavy chain H1 1 1,10 2 Q66K66 TM198_HUMAN Transmembrane protein 198 1 2,50 2 P00915 CAH1_HUMAN Carbonic anhydrase 1 1 3,45 1 P02675 FIBB_HUMAN Fibrinogen beta chain 1 1,63 1 Q99880 H2B1L_HUMAN Histone H2B type 1-L 1 7,14 1 P01871 IGHM_HUMAN Immunoglobulin heavy constant mu 1 1,77 1 Q6UXB8 PI16_HUMAN Peptidase inhibitor 16 1 2,16 1 P05155 IC1_HUMAN Plasma protease C1 inhibitor 1 2,00 1 P19961 AMY2B_HUMAN Alpha-amylase 2B 1 2,93 P15291 B4GT1_HUMAN Beta-1,4-galactosyltransferase 1 1 2,01 P39060 COIA1_HUMAN Collagen alpha-1(XVIII) chain 1 1,71 P36957 ODO2_HUMAN Dihydrolipoyllysine-residue succinyltransferase component of 2oxoglutarate dehydrogenase complex, mitochondrial 1 1,77 A0A0B4J1 X5 HV374_HUMAN Immunoglobulin heavy variable 3-74 4 31,62 P0DP08 HVD82_HUMAN Immunoglobulin heavy variable 4-38-2 1 7,69 Q6UXS9 CASPC_HUMAN Inactive caspase-12 1 6,16 P02533 K1C14_HUMAN Keratin, type I cytoskeletal 14 3 5,30 P33908 MA1A1_HUMAN Mannosyl-oligosaccharide 1,2-alphamannosidase IA 1 1,69 Q13477 MADCA_HUMAN Mucosal addressin cell adhesion molecule 1 1 3,93 P04180 LCAT_HUMAN Phosphatidylcholine-sterol acyltransferase 1 3,18 Q96M19 CL067_HUMAN Putative transmembrane protein encoded by LINC00477 1 4,22 P53597 SUCA_HUMAN Succinate--CoA ligase [ADP/GDPforming] subunit alpha, mitochondrial 1 4,33 MARTA VIZOSO GONZÁLEZ 264 P62987 RL40_HUMAN Ubiquitin-60S ribosomal protein L40 1 12,50 Supplementary Table 13. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 5 (ExoGAG isolated fraction of medium creatinine PKD2 sample) Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 50 55,83 210 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 51 60,30 198 P02768 ALBU_HUMAN Albumin 48 66,67 190 P07911 UROM_HUMAN Uromodulin 32 23,75 85 P02760 AMBP_HUMAN Protein AMBP 35 25,28 81 P68871 HBB_HUMAN Hemoglobin subunit beta 22 83,67 67 P13646 K1C13_HUMAN Keratin, type I cytoskeletal 13 31 58,95 53 P01009 A1AT_HUMAN Alpha-1-antitrypsin 19 39,71 48 P02042 HBD_HUMAN Hemoglobin subunit delta 16 53,74 43 P69905 HBA_HUMAN Hemoglobin subunit alpha 11 71,13 30 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 16 25,47 30 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 17 24,83 29 Q9Y5Y7 LYVE1_HUMAN Lymphatic vessel endothelial hyaluronic acid receptor 1 9 18,01 27 P01780 HV307_HUMAN Immunoglobulin heavy variable 3-7 11 48,72 21 Q5VTE0 EF1A3_HUMAN Putative elongation factor 1-alpha-like 3 1 2,38 19 P02790 HEMO_HUMAN Hemopexin 8 18,83 17 P01019 ANGT_HUMAN Angiotensinogen 10 21,24 16 P10451 OSTP_HUMAN Osteopontin 6 19,11 16 P05154 IPSP_HUMAN Plasma serine protease inhibitor 9 22,17 16 P02787 TRFE_HUMAN Serotransferrin 9 14,90 16 P02750 A2GL_HUMAN Leucine-rich alpha-2-glycoprotein 6 18,73 15 P08571 CD14_HUMAN Monocyte differentiation antigen CD14 10 33,87 15 P02679 FIBG_HUMAN Fibrinogen gamma chain 8 24,94 14 P08670 VIME_HUMAN Vimentin 8 19,31 14 P02647 APOA1_HUMAN Apolipoprotein A-I 6 27,34 12 P0C0L5 CO4B_HUMAN Complement C4-B 4 2,64 11 P01024 CO3_HUMAN Complement C3 7 5,29 10 P0DP03 HV335_HUMAN Immunoglobulin heavy variable 3-30-5 5 32,48 9 P48668 K2C6C_HUMAN Keratin, type II cytoskeletal 6C 7 12,77 9 P51884 LUM_HUMAN Lumican 4 11,83 9 P02774 VTDB_HUMAN Vitamin D-binding protein 6 14,14 9 Q9UBX5 FBLN5_HUMAN Fibulin-5 5 12,72 8 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 5 9,47 8 P01042 KNG1_HUMAN Kininogen-1 5 9,01 8 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 7 20,40 7 P0DP01 HV108_HUMAN Immunoglobulin heavy variable 1-8 5 19,66 7 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 5 15,80 6 P16070 CD44_HUMAN CD44 antigen 4 5,26 6 P02748 CO9_HUMAN Complement component C9 3 5,55 6 Supplemental Material 271 P19827 ITIH1_HUMAN Inter-alpha-trypsin inhibitor heavy chain H1 22 19,32 51 P07911 UROM_HUMAN Uromodulin 21 20,94 41 P02042 HBD_HUMAN Hemoglobin subunit delta 10 41,50 40 P02760 AMBP_HUMAN Protein AMBP 16 19,60 38 P04217 A1BG_HUMAN Alpha-1B-glycoprotein 15 25,05 33 P19823 ITIH2_HUMAN Inter-alpha-trypsin inhibitor heavy chain H2 14 16,60 32 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 14 20,96 29 P69905 HBA_HUMAN Hemoglobin subunit alpha 9 38,03 24 P05155 IC1_HUMAN Plasma protease C1 inhibitor 8 19,00 21 P01871 IGHM_HUMAN Immunoglobulin heavy constant mu 9 23,40 20 Q14520 HABP2_HUMAN Hyaluronan-binding protein 2 8 10,54 16 Q06033 ITIH3_HUMAN Inter-alpha-trypsin inhibitor heavy chain H3 7 8,65 14 P05154 IPSP_HUMAN Plasma serine protease inhibitor 6 17,98 14 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 8 27,27 12 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 10 15,58 12 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 7 11,56 11 P43652 AFAM_HUMAN Afamin 9 15,86 9 P00450 CERU_HUMAN Ceruloplasmin 5 7,14 8 P15311 EZRI_HUMAN Ezrin 6 9,56 8 P02671 FIBA_HUMAN Fibrinogen alpha chain 7 9,24 8 Q08380 LG3BP_HUMAN Galectin-3-binding protein 4 6,84 8 P07225 PROS_HUMAN Vitamin K-dependent protein S 6 6,95 8 P01009 A1AT_HUMAN Alpha-1-antitrypsin 4 9,09 7 P00740 FA9_HUMAN Coagulation factor IX 5 9,76 7 P02679 FIBG_HUMAN Fibrinogen gamma chain 4 9,27 7 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 5 16,56 7 P02647 APOA1_HUMAN Apolipoprotein A-I 5 22,47 6 P02790 HEMO_HUMAN Hemopexin 2 4,54 6 P04196 HRG_HUMAN Histidine-rich glycoprotein 6 14,67 6 Q6UXB8 PI16_HUMAN Peptidase inhibitor 16 3 7,13 6 P22792 CPN2_HUMAN Carboxypeptidase N subunit 2 2 5,14 5 P01031 CO5_HUMAN Complement C5 4 2,57 5 P05546 HEP2_HUMAN Heparin cofactor 2 4 6,81 5 Q6UX06 OLFM4_HUMAN Olfactomedin-4 2 5,10 5 P00747 PLMN_HUMAN Plasminogen 5 6,30 5 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 2 4,49 4 P10909 CLUS_HUMAN Clusterin 4 13,14 4 P01834 IGKC_HUMAN Immunoglobulin kappa constant 2 32,71 4 Q9Y5Y7 LYVE1_HUMAN Lymphatic vessel endothelial hyaluronic acid receptor 1 2 5,59 4 P01833 PIGR_HUMAN Polymeric immunoglobulin receptor 1 1,18 4 P00748 FA12_HUMAN Coagulation factor XII 2 2,60 3 P02748 CO9_HUMAN Complement component C9 3 5,55 3 P02751 FINC_HUMAN Fibronectin 3 1,89 3 P0DOY2 IGLC2_HUMAN Immunoglobulin lambda constant 2 3 37,74 3 Q14624 ITIH4_HUMAN Inter-alpha-trypsin inhibitor heavy chain H4 3 3,12 3 MARTA VIZOSO GONZÁLEZ 272 Q96PD5 PGRP2_HUMAN N-acetylmuramoyl-L-alanine amidase 2 2,95 3 P10451 OSTP_HUMAN Osteopontin 1 2,87 3 P16070 CD44_HUMAN CD44 antigen 1 1,08 2 P10643 CO7_HUMAN Complement component C7 1 1,07 2 P81605 DCD_HUMAN Dermcidin 2 20,00 2 P02675 FIBB_HUMAN Fibrinogen beta chain 2 3,46 2 P00738 HPT_HUMAN Haptoglobin 2 5,42 2 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 2 7,08 2 Q6EMK4 VASN_HUMAN Vasorin 1 2,23 2 P63261 ACTG_HUMAN Actin, cytoplasmic 2 1 1,87 1 P08697 A2AP_HUMAN Alpha-2-antiplasmin 1 2,24 1 P13727 PRG2_HUMAN Bone marrow proteoglycan 1 4,96 1 Q9NPY3 C1QR1_HUMAN Complement component C1q receptor 1 1,69 1 Q12805 FBLN3_HUMAN EGF-containing fibulin-like extracellular matrix protein 1 1 1,83 1 P06396 GELS_HUMAN Gelsolin 1 1,66 1 P0DJD9 PEPA5_HUMAN Pepsin A-5 1 2,83 1 Q9BTM1 H2AJ_HUMAN Histone H2A.J 1 6,98 P02533 K1C14_HUMAN Keratin, type I cytoskeletal 14 2 3,81 P07333 CSF1R_HUMAN Macrophage colony-stimulating factor 1 receptor 1 1,75 O60279 SUSD5_HUMAN Sushi domain-containing protein 5 1 1,43 P62987 RL40_HUMAN Ubiquitin-60S ribosomal protein L40 1 12,50 Supplementary Table 17. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 9 (ExoGAG isolated fraction of high creatinine PKD2 sample) Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02768 ALBU_HUMAN Albumin 959 88,83 4293 P01009 A1AT_HUMAN Alpha-1-antitrypsin 102 68,18 303 P04004 VTNC_HUMAN Vitronectin 62 47,91 213 P02787 TRFE_HUMAN Serotransferrin 55 59,74 112 P68871 HBB_HUMAN Hemoglobin subunit beta 35 95,24 107 P19823 ITIH2_HUMAN Inter-alpha-trypsin inhibitor heavy chain H2 30 32,66 97 P02671 FIBA_HUMAN Fibrinogen alpha chain 37 36,26 86 P02675 FIBB_HUMAN Fibrinogen beta chain 42 71,49 85 P02760 AMBP_HUMAN Protein AMBP 31 25,57 76 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 24 45,86 71 P51884 LUM_HUMAN Lumican 19 33,43 69 P02042 HBD_HUMAN Hemoglobin subunit delta 22 63,27 66 P01024 CO3_HUMAN Complement C3 42 33,13 64 P07911 UROM_HUMAN Uromodulin 29 29,22 61 P02790 HEMO_HUMAN Hemopexin 27 47,40 57 P01042 KNG1_HUMAN Kininogen-1 25 25,31 50 P02748 CO9_HUMAN Complement component C9 24 34,53 48 P01008 ANT3_HUMAN Antithrombin-III 21 45,26 44 Supplemental Material 273 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 19 52,41 43 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 16 25,62 41 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 18 43,64 40 P01860 IGHG3_HUMAN Immunoglobulin heavy constant gamma 3 18 38,99 37 P69905 HBA_HUMAN Hemoglobin subunit alpha 15 71,83 36 P01019 ANGT_HUMAN Angiotensinogen 8 21,44 27 P0C0L5 CO4B_HUMAN Complement C4-B 17 14,68 26 P19827 ITIH1_HUMAN Inter-alpha-trypsin inhibitor heavy chain H1 19 24,48 26 Q14520 HABP2_HUMA N Hyaluronan-binding protein 2 8 17,68 23 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 11 23,62 22 P07478 TRY2_HUMAN Trypsin-2 7 8,10 22 P01861 IGHG4_HUMAN Immunoglobulin heavy constant gamma 4 15 33,03 21 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 15 37,08 21 P00734 THRB_HUMAN Prothrombin 12 19,61 20 P00740 FA9_HUMAN Coagulation factor IX 12 29,07 19 Q08380 LG3BP_HUMA N Galectin-3-binding protein 9 19,15 19 Q9Y5Y7 LYVE1_HUMAN Lymphatic vessel endothelial hyaluronic acid receptor 1 5 11,49 19 P16070 CD44_HUMAN CD44 antigen 7 5,66 17 P16278 BGAL_HUMAN Beta-galactosidase 7 11,67 15 P04196 HRG_HUMAN Histidine-rich glycoprotein 7 14,48 15 P01877 IGHA2_HUMAN Immunoglobulin heavy constant alpha 2 9 27,65 15 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 9 16,78 15 P02765 FETUA_HUMA N Alpha-2-HS-glycoprotein 5 20,44 14 P02647 APOA1_HUMA N Apolipoprotein A-I 7 29,59 14 P07358 CO8B_HUMAN Complement component C8 beta chain 9 19,97 14 P02679 FIBG_HUMAN Fibrinogen gamma chain 7 22,08 14 P00751 CFAB_HUMAN Complement factor B 7 11,52 12 P10451 OSTP_HUMAN Osteopontin 5 23,25 12 P05155 IC1_HUMAN Plasma protease C1 inhibitor 4 10,00 12 P00450 CERU_HUMAN Ceruloplasmin 6 8,45 10 P08185 CBG_HUMAN Corticosteroid-binding globulin 7 25,43 10 P13727 PRG2_HUMAN Bone marrow proteoglycan 6 33,78 9 P27797 CALR_HUMAN Calreticulin 3 9,83 9 Q9UBX5 FBLN5_HUMAN Fibulin-5 4 9,15 8 P08697 A2AP_HUMAN Alpha-2-antiplasmin 4 12,63 7 P05154 IPSP_HUMAN Plasma serine protease inhibitor 5 13,55 7 Q06033 ITIH3_HUMAN Inter-alpha-trypsin inhibitor heavy chain H3 1 1,69 6 P18428 LBP_HUMAN Lipopolysaccharide-binding protein 4 12,27 6 Q66K66 TM198_HUMA N Transmembrane protein 198 1 2,50 6 P04217 A1BG_HUMAN Alpha-1B-glycoprotein 3 5,86 5 P02749 APOH_HUMAN Beta-2-glycoprotein 1 4 19,13 5 P01834 IGKC_HUMAN Immunoglobulin kappa constant 4 44,86 5 MARTA VIZOSO GONZÁLEZ 274 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 1 14,15 5 P05543 THBG_HUMAN Thyroxine-binding globulin 3 11,57 5 P05546 HEP2_HUMAN Heparin cofactor 2 1 1,40 4 P02774 VTDB_HUMAN Vitamin D-binding protein 2 6,96 4 P10909 CLUS_HUMAN Clusterin 2 5,12 3 P08174 DAF_HUMAN Complement decay-accelerating factor 1 3,15 3 P00738 HPT_HUMAN Haptoglobin 2 5,91 3 P29622 KAIN_HUMAN Kallistatin 2 4,68 3 P0DJD9 PEPA5_HUMAN Pepsin A-5 2 4,90 3 P22891 PROZ_HUMAN Vitamin K-dependent protein Z 3 7,75 3 P01031 CO5_HUMAN Complement C5 1 1,13 2 P02751 FINC_HUMAN Fibronectin 2 1,30 2 P06396 GELS_HUMAN Gelsolin 2 3,84 2 Q6EMK4 VASN_HUMAN Vasorin 1 2,38 2 P00915 CAH1_HUMAN Carbonic anhydrase 1 1 3,45 1 Q99880 H2B1L_HUMAN Histone H2B type 1-L 1 7,14 1 P62805 H4_HUMAN Histone H4 1 7,77 1 Q6UXB8 PI16_HUMAN Peptidase inhibitor 16 1 2,16 1 P62269 RS18_HUMAN 40S ribosomal protein S18 1 5,26 A0A0C4DH4 3 HV70D_HUMA N Immunoglobulin heavy variable 2-70D 1 5,88 A0A0B4J1X5 HV374_HUMAN Immunoglobulin heavy variable 3-74 1 5,98 P36268 GGT2_HUMAN Inactive glutathione hydrolase 2 2 3,69 Q13477 MADCA_HUMA N Mucosal addressin cell adhesion molecule 1 2 9,42 O96000 NDUBA_HUMA N NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 10 1 8,72 P35241 RADI_HUMAN Radixin 1 1,54 O60518 RNBP6_HUMA N Ran-binding protein 6 1 0,81 P53597 SUCA_HUMAN Succinate--CoA ligase [ADP/GDP-forming] subunit alpha, mitochondrial 1 4,33 P62987 RL40_HUMAN Ubiquitin-60S ribosomal protein L40 1 12,50 Supplementary Table 18. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 10 (ExoGAG isolated fraction of high creatinine PKD2 sample) Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02760 AMBP_HUMAN Protein AMBP 289 67,61 1151 P02768 ALBU_HUMAN Albumin 144 75,53 436 P68871 HBB_HUMAN Hemoglobin subunit beta 42 94,56 141 P02042 HBD_HUMAN Hemoglobin subunit delta 30 61,90 93 P02679 FIBG_HUMAN Fibrinogen gamma chain 34 51,21 86 P69905 HBA_HUMAN Hemoglobin subunit alpha 29 75,35 82 P01024 CO3_HUMAN Complement C3 48 38,42 73 P02671 FIBA_HUMAN Fibrinogen alpha chain 31 33,95 70 P02675 FIBB_HUMAN Fibrinogen beta chain 36 59,67 67 P06727 APOA4_HUMAN Apolipoprotein A-IV 26 59,34 66 Supplemental Material 275 P19823 ITIH2_HUMAN Inter-alpha-trypsin inhibitor heavy chain H2 21 20,08 53 P02787 TRFE_HUMAN Serotransferrin 30 46,56 51 P01009 A1AT_HUMAN Alpha-1-antitrypsin 18 50,00 46 P10909 CLUS_HUMAN Clusterin 17 26,28 44 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 3 9,63 42 P07911 UROM_HUMAN Uromodulin 20 21,25 40 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 22 30,28 39 P00738 HPT_HUMAN Haptoglobin 20 42,61 35 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 12 28,83 35 P00751 CFAB_HUMAN Complement factor B 18 15,71 33 P00734 THRB_HUMAN Prothrombin 16 33,44 32 P07478 TRY2_HUMAN Trypsin-2 11 8,10 32 P25311 ZA2G_HUMAN Zinc-alpha-2-glycoprotein 18 57,05 31 P0C0L4 CO4A_HUMAN Complement C4-A 20 15,42 28 P0C0L5 CO4B_HUMAN Complement C4-B 20 15,14 28 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 17 50,61 28 P04004 VTNC_HUMAN Vitronectin 15 21,97 26 P02790 HEMO_HUMAN Hemopexin 14 32,68 25 P19827 ITIH1_HUMAN Inter-alpha-trypsin inhibitor heavy chain H1 13 14,27 25 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 16 29,11 25 Q5VTE0 EF1A3_HUMAN Putative elongation factor 1-alphalike 3 2 4,98 23 P02647 APOA1_HUMAN Apolipoprotein A-I 10 41,20 18 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 10 21,83 15 P05090 APOD_HUMAN Apolipoprotein D 10 31,22 13 P02748 CO9_HUMAN Complement component C9 6 12,88 13 P06396 GELS_HUMAN Gelsolin 8 12,53 13 P27797 CALR_HUMAN Calreticulin 4 15,11 12 P04196 HRG_HUMAN Histidine-rich glycoprotein 6 14,10 12 P14543 NID1_HUMAN Nidogen-1 8 8,26 12 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 5 55,66 11 P02763 A1AG1_HUMAN Alpha-1-acid glycoprotein 1 7 27,86 10 P04406 G3P_HUMAN Glyceraldehyde-3-phosphate dehydrogenase 6 18,21 10 P01871 IGHM_HUMAN Immunoglobulin heavy constant mu 3 7,06 10 B9A064 IGLL5_HUMAN Immunoglobulin lambda-like polypeptide 5 4 17,76 10 P01042 KNG1_HUMAN Kininogen-1 7 9,47 10 P02774 VTDB_HUMAN Vitamin D-binding protein 6 15,19 10 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 8 21,99 9 P36955 PEDF_HUMAN Pigment epithelium-derived factor 8 22,25 9 P00747 PLMN_HUMAN Plasminogen 5 8,15 9 P07355 ANXA2_HUMAN Annexin A2 7 25,66 8 Q95604 1C17_HUMAN HLA class I histocompatibility antigen, Cw-17 alpha chain 1 3,76 7 P05155 IC1_HUMAN Plasma protease C1 inhibitor 4 10,00 7 P63261 ACTG_HUMAN Actin, cytoplasmic 2 5 18,13 6 MARTA VIZOSO GONZÁLEZ 276 Q08380 LG3BP_HUMAN Galectin-3-binding protein 6 12,31 6 P01860 IGHG3_HUMAN Immunoglobulin heavy constant gamma 3 15 37,40 6 P51884 LUM_HUMAN Lumican 4 13,02 6 P01019 ANGT_HUMAN Angiotensinogen 4 12,16 5 P00450 CERU_HUMAN Ceruloplasmin 4 5,45 5 P00742 FA10_HUMAN Coagulation factor X 2 6,15 5 Q12805 FBLN3_HUMAN EGF-containing fibulin-like extracellular matrix protein 1 3 10,14 5 Q9Y5Y7 LYVE1_HUMAN Lymphatic vessel endothelial hyaluronic acid receptor 1 5 11,80 5 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 3 8,45 4 P08758 ANXA5_HUMAN Annexin A5 3 9,38 4 P31327 CPSM_HUMAN Carbamoyl-phosphate synthase [ammonia], mitochondrial 1 0,73 4 Q9UBR2 CATZ_HUMAN Cathepsin Z 4 16,17 4 P05546 HEP2_HUMAN Heparin cofactor 2 5 7,82 4 Q14520 HABP2_HUMAN Hyaluronan-binding protein 2 4 7,50 4 P02750 A2GL_HUMAN Leucine-rich alpha-2-glycoprotein 3 7,78 4 Q12907 LMAN2_HUMAN Vesicular integral-membrane protein VIP36 3 12,08 4 P19652 A1AG2_HUMAN Alpha-1-acid glycoprotein 2 3 16,92 3 P04217 A1BG_HUMAN Alpha-1B-glycoprotein 3 6,67 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin 2 1,83 3 P02652 APOA2_HUMAN Apolipoprotein A-II 1 9,00 3 P02649 APOE_HUMAN Apolipoprotein E 2 7,57 3 P00915 CAH1_HUMAN Carbonic anhydrase 1 2 8,05 3 P16070 CD44_HUMAN CD44 antigen 1 1,62 3 P00740 FA9_HUMAN Coagulation factor IX 2 4,34 3 P39059 COFA1_HUMAN Collagen alpha-1(XV) chain 3 2,67 3 P05156 CFAI_HUMAN Complement factor I 2 3,09 3 P81605 DCD_HUMAN Dermcidin 1 12,73 3 P49411 EFTU_HUMAN Elongation factor Tu, mitochondrial 2 5,31 3 P62879 GBB2_HUMAN Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-2 2 6,18 3 Q14624 ITIH4_HUMAN Inter-alpha-trypsin inhibitor heavy chain H4 3 4,19 3 P13647 K2C5_HUMAN Keratin, type II cytoskeletal 5 4 6,61 3 P07195 LDHB_HUMAN L-lactate dehydrogenase B chain 2 5,09 3 P10451 OSTP_HUMAN Osteopontin 2 8,28 3 P41222 PTGDS_HUMAN Prostaglandin-H2 D-isomerase 3 25,79 3 Q13510 ASAH1_HUMAN Acid ceramidase 1 3,04 2 P01008 ANT3_HUMAN Antithrombin-III 2 5,39 2 P02749 APOH_HUMAN Beta-2-glycoprotein 1 2 8,99 2 P13727 PRG2_HUMAN Bone marrow proteoglycan 1 4,96 2 Q9UBX5 FBLN5_HUMAN Fibulin-5 1 2,01 2 P62805 H4_HUMAN Histone H4 1 7,77 2 P06312 KV401_HUMAN Immunoglobulin kappa variable 4-1 1 7,44 2 Q8WWA0 ITLN1_HUMAN Intelectin-1 1 3,20 2 P32119 PRDX2_HUMAN Peroxiredoxin-2 2 13,13 2 P19320 VCAM1_HUMAN Vascular cell adhesion protein 1 1 2,03 2 Supplemental Material 277 P04083 ANXA1_HUMAN Annexin A1 1 3,76 1 P41181 AQP2_HUMAN Aquaporin-2 0 0,00 1 P98160 PGBM_HUMAN Basement membrane-specific heparan sulfate proteoglycan core protein 1 0,32 1 P24855 DNAS1_HUMAN Deoxyribonuclease-1 1 4,61 1 Q9HCU0 CD248_HUMAN Endosialin 1 1,06 1 Q9UNN8 EPCR_HUMAN Endothelial protein C receptor 1 3,78 1 P02751 FINC_HUMAN Fibronectin 1 0,63 1 P01834 IGKC_HUMAN Immunoglobulin kappa constant 5 60,75 1 Q9BQE3 TBA1C_HUMAN Tubulin alpha-1C chain 1 2,00 1 Q6EMK4 VASN_HUMAN Vasorin 1 2,38 1 P08670 VIME_HUMAN Vimentin 1 2,15 1 P62258 1433E_HUMAN 14-3-3 protein epsilon 1 4,71 P09525 ANXA4_HUMAN Annexin A4 1 5,02 P00966 ASSY_HUMAN Argininosuccinate synthase 0 0,00 P06576 ATPB_HUMAN ATP synthase subunit beta, mitochondrial 1 1,89 P20160 CAP7_HUMAN Azurocidin 1 5,18 P21810 PGS1_HUMAN Biglycan 1 2,99 O43633 CHM2A_HUMAN Charged multivesicular body protein 2a 1 4,05 P10645 CMGA_HUMAN Chromogranin-A 1 4,16 Q03591 FHR1_HUMAN Complement factor H-related protein 1 1 3,33 O76062 ERG24_HUMAN Delta(14)-sterol reductase 1 2,63 P36957 ODO2_HUMAN Dihydrolipoyllysine-residue succinyltransferase component of 2oxoglutarate dehydrogenase complex, mitochondrial 1 1,77 Q15485 FCN2_HUMAN Ficolin-2 1 4,47 P09467 F16P1_HUMAN Fructose-1,6-bisphosphatase 1 1 4,73 O75487 GPC4_HUMAN Glypican-4 1 2,34 P01624 KV315_HUMAN Immunoglobulin kappa variable 3-15 1 7,83 P24592 IBP6_HUMAN Insulin-like growth factor-binding protein 6 1 6,25 P19012 K1C15_HUMAN Keratin, type I cytoskeletal 15 4 8,11 P61626 LYSC_HUMAN Lysozyme C 1 8,11 P40925 MDHC_HUMAN Malate dehydrogenase, cytoplasmic 1 3,59 Q8IWI9 MGAP_HUMAN MAX gene-associated protein 1 0,40 Q8IUH5 ZDH17_HUMAN Palmitoyltransferase ZDHHC17 1 1,74 O43653 PSCA_HUMAN Prostate stem cell antigen 2 13,82 P35241 RADI_HUMAN Radixin 1 1,54 Q9Y490 TLN1_HUMAN Talin-1 1 0,59 Q5TCY1 TTBK1_HUMAN Tau-tubulin kinase 1 1 0,61 O60635 TSN1_HUMAN Tetraspanin-1 1 5,39 Q66K66 TM198_HUMAN Transmembrane protein 198 1 2,50 P62987 RL40_HUMAN Ubiquitin-60S ribosomal protein L40 1 12,50 Q9Y279 VSIG4_HUMAN V-set and immunoglobulin domaincontaining protein 4 1 2,51 MARTA VIZOSO GONZÁLEZ 278 Supplementary Table 19. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 11 (ExoGAG isolated fraction of high creatinine PKD2 sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P07911 UROM_HUMAN Uromodulin 176 54,37 412 P02768 ALBU_HUMAN Albumin 85 70,44 200 P68871 HBB_HUMAN Hemoglobin subunit beta 37 84,35 79 P02042 HBD_HUMAN Hemoglobin subunit delta 23 54,42 47 P02760 AMBP_HUMAN Protein AMBP 22 30,68 45 P01857 IGHG1_HUMAN Immunoglobulin heavy constant gamma 1 17 38,79 34 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 15 30,98 33 P01860 IGHG3_HUMAN Immunoglobulin heavy constant gamma 3 15 36,34 31 P01024 CO3_HUMAN Complement C3 26 19,72 28 P02787 TRFE_HUMAN Serotransferrin 22 31,23 27 P69905 HBA_HUMAN Hemoglobin subunit alpha 15 71,13 25 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 20 32,14 22 P01023 A2MG_HUMAN Alpha-2-macroglobulin 16 12,28 18 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 14 24,83 14 P00450 CERU_HUMAN Ceruloplasmin 9 10,42 12 P01834 IGKC_HUMAN Immunoglobulin kappa constant 7 67,29 12 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 10 19,42 11 P01833 PIGR_HUMAN Polymeric immunoglobulin receptor 9 11,91 10 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 5 55,66 8 P02647 APOA1_HUMAN Apolipoprotein A-I 6 25,84 7 P98164 LRP2_HUMAN Low-density lipoprotein receptor-related protein 2 4 0,92 6 P02679 FIBG_HUMAN Fibrinogen gamma chain 5 10,60 5 P00738 HPT_HUMAN Haptoglobin 6 15,52 5 Q6UXB8 PI16_HUMAN Peptidase inhibitor 16 4 9,29 4 P08670 VIME_HUMAN Vimentin 3 5,15 4 P01009 A1AT_HUMAN Alpha-1-antitrypsin 3 9,09 3 P10643 CO7_HUMAN Complement component C7 3 3,08 3 Q08380 LG3BP_HUMAN Galectin-3-binding protein 2 4,27 3 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 3 11,90 3 P13647 K2C5_HUMAN Keratin, type II cytoskeletal 5 3 5,59 3 Q9BXP8 PAPP2_HUMAN Pappalysin-2 3 1,51 3 P05155 IC1_HUMAN Plasma protease C1 inhibitor 3 7,60 3 P01031 CO5_HUMAN Complement C5 0 0,00 2 P27487 DPP4_HUMAN Dipeptidyl peptidase 4 2 2,35 2 P02671 FIBA_HUMAN Fibrinogen alpha chain 2 2,43 2 P63261 ACTG_HUMAN Actin, cytoplasmic 2 1 4,80 1 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 1 2,13 1 P15144 AMPN_HUMAN Aminopeptidase N 1 1,03 1 P00915 CAH1_HUMAN Carbonic anhydrase 1 1 3,45 1 P0C0L5 CO4B_HUMAN Complement C4-B 1 0,86 1 Q9NPY3 C1QR1_HUMAN Complement component C1q receptor 1 1,69 1 Supplemental Material 279 P81605 DCD_HUMAN Dermcidin 1 12,73 1 P02675 FIBB_HUMAN Fibrinogen beta chain 1 1,63 1 P02790 HEMO_HUMAN Hemopexin 1 2,38 1 P06310 KV230_HUMAN Immunoglobulin kappa variable 2-30 1 10,83 1 P04433 KV311_HUMAN Immunoglobulin kappa variable 3-11 1 7,83 1 P01624 KV315_HUMAN Immunoglobulin kappa variable 3-15 1 7,83 1 Q9Y5Y7 LYVE1_HUMAN Lymphatic vessel endothelial hyaluronic acid receptor 1 1 2,80 1 P11279 LAMP1_HUMAN Lysosome-associated membrane glycoprotein 1 1 2,16 1 P13473 LAMP2_HUMAN Lysosome-associated membrane glycoprotein 2 1 1,95 1 P00747 PLMN_HUMAN Plasminogen 2 2,10 1 P04004 VTNC_HUMAN Vitronectin 1 2,51 1 P08519 APOA_HUMAN Apolipoprotein(a) 1 8,29 Q92496 FHR4_HUMAN Complement factor H-related protein 4 2 1,21 Q04695 K1C17_HUMAN Keratin, type I cytoskeletal 17 2 3,70 P62987 RL40_HUMAN Ubiquitin-60S ribosomal protein L40 1 12,50 Supplementary Table 20. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 12 (ExoGAG isolated fraction of high creatinine PKD2 sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02768 ALBU_HUMAN Albumin 90 64,53 205 P01834 IGKC_HUMAN Immunoglobulin kappa constant 65 92,52 159 P02760 AMBP_HUMAN Protein AMBP 43 51,99 84 P01024 CO3_HUMAN Complement C3 58 36,14 72 P68871 HBB_HUMAN Hemoglobin subunit beta 27 82,31 58 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 30 33,74 55 P01860 IGHG3_HUMAN Immunoglobulin heavy constant gamma 3 28 31,83 55 P01861 IGHG4_HUMAN Immunoglobulin heavy constant gamma 4 26 32,11 47 P00915 CAH1_HUMAN Carbonic anhydrase 1 18 43,30 32 P02042 HBD_HUMAN Hemoglobin subunit delta 16 52,38 32 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 11 55,66 31 B9A064 IGLL5_HUMAN Immunoglobulin lambda-like polypeptide 5 9 26,64 25 P41222 PTGDS_HUMAN Prostaglandin-H2 D-isomerase 20 36,84 24 P00450 CERU_HUMAN Ceruloplasmin 18 18,31 20 P69905 HBA_HUMAN Hemoglobin subunit alpha 11 51,41 19 P02790 HEMO_HUMAN Hemopexin 13 25,54 18 P01009 A1AT_HUMAN Alpha-1-antitrypsin 10 22,97 17 P01615 KVD28_HUMAN Immunoglobulin kappa variable 2D-28 10 50,00 17 P02671 FIBA_HUMAN Fibrinogen alpha chain 11 14,09 16 P02787 TRFE_HUMAN Serotransferrin 14 18,91 16 P07911 UROM_HUMAN Uromodulin 16 21,09 16 P06310 KV230_HUMAN Immunoglobulin kappa variable 2-30 8 31,67 15 MARTA VIZOSO GONZÁLEZ 280 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 13 20,50 15 P00738 HPT_HUMAN Haptoglobin 14 39,41 14 P01614 KVD40_HUMAN Immunoglobulin kappa variable 2D-40 8 36,36 14 P04433 KV311_HUMAN Immunoglobulin kappa variable 3-11 13 41,74 14 P06312 KV401_HUMAN Immunoglobulin kappa variable 4-1 9 51,24 12 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 15 22,77 12 P02679 FIBG_HUMAN Fibrinogen gamma chain 10 20,53 11 P02647 APOA1_HUMAN Apolipoprotein A-I 9 38,20 10 P06727 APOA4_HUMAN Apolipoprotein A-IV 8 21,97 10 P01599 KV117_HUMAN Immunoglobulin kappa variable 1-17 7 29,06 9 P36955 PEDF_HUMAN Pigment epithelium-derived factor 9 18,42 9 P01780 HV307_HUMAN Immunoglobulin heavy variable 3-7 6 40,17 7 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 7 12,68 7 P02743 SAMP_HUMAN Serum amyloid P-component 7 29,15 6 P25311 ZA2G_HUMAN Zinc-alpha-2-glycoprotein 5 22,48 6 P0C0L5 CO4B_HUMAN Complement C4-B 6 3,50 5 P01602 KV105_HUMAN Immunoglobulin kappa variable 1-5 4 24,79 5 Q9Y5Y7 LYVE1_HUMAN Lymphatic vessel endothelial hyaluronic acid receptor 1 5 11,80 5 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 4 9,46 4 P06396 GELS_HUMAN Gelsolin 4 6,90 4 Q14624 ITIH4_HUMAN Inter-alpha-trypsin inhibitor heavy chain H4 4 6,67 4 Q96DA0 ZG16B_HUMAN Zymogen granule protein 16 homolog B 5 28,85 4 P63261 ACTG_HUMAN Actin, cytoplasmic 2 4 13,07 3 P02763 A1AG1_HUMAN Alpha-1-acid glycoprotein 1 2 8,46 3 P05090 APOD_HUMAN Apolipoprotein D 2 12,70 3 P0DP03 HV335_HUMAN Immunoglobulin heavy variable 3-30-5 2 23,08 3 P01700 LV147_HUMAN Immunoglobulin lambda variable 1-47 2 25,64 3 P80748 LV321_HUMAN Immunoglobulin lambda variable 3-21 3 29,06 3 P19827 ITIH1_HUMAN Inter-alpha-trypsin inhibitor heavy chain H1 5 6,92 3 P15941 MUC1_HUMAN Mucin-1 3 3,59 3 P01023 A2MG_HUMAN Alpha-2-macroglobulin 2 1,36 2 P01019 ANGT_HUMAN Angiotensinogen 4 9,69 2 P07451 CAH3_HUMAN Carbonic anhydrase 3 3 13,85 2 P02748 CO9_HUMAN Complement component C9 2 4,11 2 P00746 CFAD_HUMAN Complement factor D 2 15,42 2 P81605 DCD_HUMAN Dermcidin 2 12,73 2 P02675 FIBB_HUMAN Fibrinogen beta chain 3 7,13 2 A0A0C4DH69 KV109_HUMAN Immunoglobulin kappa variable 1-9 3 26,50 2 P19823 ITIH2_HUMAN Inter-alpha-trypsin inhibitor heavy chain H2 2 2,75 2 P02750 A2GL_HUMAN Leucine-rich alpha-2-glycoprotein 2 5,76 2 P30041 PRDX6_HUMAN Peroxiredoxin-6 2 8,93 2 Q96FE7 P3IP1_HUMAN Phosphoinositide-3-kinase-interacting protein 1 2 9,89 2 P04004 VTNC_HUMAN Vitronectin 2 5,65 2 P00918 CAH2_HUMAN Carbonic anhydrase 2 1 3,46 1 Supplemental Material 287 P02787 TRFE_HUMAN Serotransferrin 5 6,73 6 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 3 6,62 4 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 4 11,17 4 P02652 APOA2_HUMAN Apolipoprotein A-II 3 37,00 4 Q9GZZ8 LACRT_HUMAN Extracellular glycoprotein lacritin 3 16,67 4 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 4 44,34 4 Q8N1N4 K2C78_HUMAN Keratin, type II cytoskeletal 78 4 5,77 4 P16070 CD44_HUMAN CD44 antigen 2 2,97 3 P12273 PIP_HUMAN Prolactin-inducible protein 3 15,07 3 P02760 AMBP_HUMAN Protein AMBP 3 8,81 3 P07911 UROM_HUMAN Uromodulin 5 7,50 3 P10909 CLUS_HUMAN Clusterin 2 7,57 2 P01619 KV320_HUMAN Immunoglobulin kappa variable 3-20 2 21,55 2 O95968 SG1D1_HUMAN Secretoglobin family 1D member 1 2 36,67 2 P02675 FIBB_HUMAN Fibrinogen beta chain 1 1,63 1 P10153 RNAS2_HUMAN Non-secretory ribonuclease 1 8,70 1 P01833 PIGR_HUMAN Polymeric immunoglobulin receptor 1 2,09 1 P05109 S10A8_HUMAN Protein S100-A8 1 11,83 1 Q13867 BLMH_HUMAN Bleomycin hydrolase 1 2,42 P01040 CYTA_HUMAN Cystatin-A 1 12,24 Q5D862 FILA2_HUMAN Filaggrin-2 1 0,46 Q7Z794 K2C1B_HUMAN Keratin, type II cytoskeletal 1b 16 16,44 Q16378 PROL4_HUMAN Proline-rich protein 4 1 11,19 P06702 S10A9_HUMAN Protein S100-A9 1 13,16 Q29RF7 PDS5A_HUMAN Sister chromatid cohesion protein PDS5 homolog A 1 0,90 Q5T750 XP32_HUMAN Skin-specific protein 32 1 3,20 Supplementary Table 26. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 6C (ExoGAG supernatant fraction of medium creatinine PKD2 sample sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02768 ALBU_HUMAN Albumin 450 79,80 742 P25311 ZA2G_HUMAN Zinc-alpha-2-glycoprotein 82 67,11 145 P07478 TRY2_HUMAN Trypsin-2 50 9,72 90 P02787 TRFE_HUMAN Serotransferrin 58 62,46 89 P02763 A1AG1_HUMAN Alpha-1-acid glycoprotein 1 52 50,75 84 P00738 HPT_HUMAN Haptoglobin 49 62,56 77 P19652 A1AG2_HUMAN Alpha-1-acid glycoprotein 2 32 49,75 56 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 24 35,56 37 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 23 39,04 31 P01009 A1AT_HUMAN Alpha-1-antitrypsin 16 39,71 25 P02647 APOA1_HUMAN Apolipoprotein A-I 13 47,19 24 P68871 HBB_HUMAN Hemoglobin subunit beta 11 85,03 23 P04217 A1BG_HUMAN Alpha-1B-glycoprotein 13 32,53 22 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 13 31,29 20 MARTA VIZOSO GONZÁLEZ 288 P02766 TTHY_HUMAN Transthyretin 8 65,31 20 P02750 A2GL_HUMAN Leucine-rich alpha-2-glycoprotein 11 33,14 17 P69905 HBA_HUMAN Hemoglobin subunit alpha 7 62,68 13 P0DOX5 IGG1_HUMAN Immunoglobulin gamma-1 heavy chain 10 26,73 13 P06396 GELS_HUMAN Gelsolin 9 11,25 11 P05154 IPSP_HUMAN Plasma serine protease inhibitor 8 22,66 11 P07911 UROM_HUMAN Uromodulin 8 13,12 10 P0DOX7 IGK_HUMAN Immunoglobulin kappa light chain 6 31,78 9 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 7 15,41 8 P31025 LCN1_HUMAN Lipocalin-1 6 29,55 8 P02654 APOC1_HUMAN Apolipoprotein C-I 4 36,14 7 P11117 PPAL_HUMAN Lysosomal acid phosphatase 5 10,64 6 P61626 LYSC_HUMAN Lysozyme C 4 41,22 6 P0DJD8 PEPA3_HUMAN Pepsin A-3 6 10,57 6 P0DJI8 SAA1_HUMAN Serum amyloid A-1 protein 4 35,25 6 P02652 APOA2_HUMAN Apolipoprotein A-II 3 39,00 5 P0DJI9 SAA2_HUMAN Serum amyloid A-2 protein 4 18,85 5 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 3 10,87 4 P16070 CD44_HUMAN CD44 antigen 2 2,97 4 P10909 CLUS_HUMAN Clusterin 3 8,91 4 Q9GZZ8 LACRT_HUMAN Extracellular glycoprotein lacritin 3 22,46 4 P02790 HEMO_HUMAN Hemopexin 4 11,26 4 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 5 17,28 4 P0DOY2 IGLC2_HUMAN Immunoglobulin lambda constant 2 3 37,74 4 Q7Z794 K2C1B_HUMAN Keratin, type II cytoskeletal 1b 4 5,36 4 P13647 K2C5_HUMAN Keratin, type II cytoskeletal 5 4 6,78 4 P15586 GNS_HUMAN N-acetylglucosamine-6-sulfatase 3 5,25 4 Q8IYS5 OSCAR_HUMAN Osteoclast-associated immunoglobulin-like receptor 3 14,18 4 Q07075 AMPE_HUMAN Glutamyl aminopeptidase 2 2,19 3 P10153 RNAS2_HUMAN Non-secretory ribonuclease 2 9,32 3 P12273 PIP_HUMAN Prolactin-inducible protein 3 15,07 3 P02760 AMBP_HUMAN Protein AMBP 2 5,40 3 P17050 NAGAB_HUMAN Alpha-N-acetylgalactosaminidase 2 5,11 2 P02749 APOH_HUMAN Beta-2-glycoprotein 1 2 10,72 2 P08185 CBG_HUMAN Corticosteroid-binding globulin 2 6,42 2 Q01459 DIAC_HUMAN Di-N-acetylchitobiase 2 7,79 2 P01042 KNG1_HUMAN Kininogen-1 2 4,66 2 Q16378 PROL4_HUMAN Proline-rich protein 4 2 17,16 2 O95968 SG1D1_HUMAN Secretoglobin family 1D member 1 2 36,67 2 P35542 SAA4_HUMAN Serum amyloid A-4 protein 3 21,54 2 Q66K66 TM198_HUMAN Transmembrane protein 198 1 2,50 2 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 3 7,36 1 P06280 AGAL_HUMAN Alpha-galactosidase A 1 4,20 P02655 APOC2_HUMAN Apolipoprotein C-II 1 10,89 P02656 APOC3_HUMAN Apolipoprotein C-III 1 16,16 Supplemental Material 289 P05090 APOD_HUMAN Apolipoprotein D 1 5,82 Q9UNN8 EPCR_HUMAN Endothelial protein C receptor 1 3,78 P02671 FIBA_HUMAN Fibrinogen alpha chain 1 1,50 Q5D862 FILA2_HUMAN Filaggrin-2 1 0,46 Q16769 QPCT_HUMAN Glutaminyl-peptide cyclotransferase 1 3,88 P06870 KLK1_HUMAN Kallikrein-1 1 4,96 P19012 K1C15_HUMAN Keratin, type I cytoskeletal 15 3 5,04 P02788 TRFL_HUMAN Lactotransferrin 2 2,68 P13473 LAMP2_HUMAN Lysosome-associated membrane glycoprotein 2 1 1,95 O75556 SG2A1_HUMAN Mammaglobin-B 1 12,63 Q13421 MSLN_HUMAN Mesothelin 1 2,22 P05109 S10A8_HUMAN Protein S100-A8 1 11,83 P06702 S10A9_HUMAN Protein S100-A9 1 13,16 Supplementary Table 27. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 7D (ExoGAG supernatant fraction of high creatinine PKD2 sample sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02768 ALBU_HUMAN Albumin 417 83,25 1090 P02760 AMBP_HUMAN Protein AMBP 186 53,41 508 P02787 TRFE_HUMAN Serotransferrin 62 57,74 126 P0DOX5 IGG1_HUMAN Immunoglobulin gamma-1 heavy chain 35 37,42 93 P07478 TRY2_HUMAN Trypsin-2 32 15,79 90 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 40 38,20 77 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 27 41,41 64 P01860 IGHG3_HUMAN Immunoglobulin heavy constant gamma 3 25 35,54 57 P00738 HPT_HUMAN Haptoglobin 26 60,34 52 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 26 43,02 47 P02763 A1AG1_HUMAN Alpha-1-acid glycoprotein 1 20 75,62 43 P00751 CFAB_HUMAN Complement factor B 22 15,45 37 P0DOX8 IGL1_HUMAN Immunoglobulin lambda-1 light chain 15 25,93 37 P25311 ZA2G_HUMAN Zinc-alpha-2-glycoprotein 25 49,33 37 P05090 APOD_HUMAN Apolipoprotein D 11 31,22 36 P02774 VTDB_HUMAN Vitamin D-binding protein 17 35,86 34 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 19 30,82 33 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 17 62,26 31 P19652 A1AG2_HUMAN Alpha-1-acid glycoprotein 2 12 44,28 29 P02647 APOA1_HUMAN Apolipoprotein A-I 15 50,19 29 P01834 IGKC_HUMAN Immunoglobulin kappa constant 11 87,85 25 P01009 A1AT_HUMAN Alpha-1-antitrypsin 12 30,62 24 P68871 HBB_HUMAN Hemoglobin subunit beta 14 93,20 21 P01042 KNG1_HUMAN Kininogen-1 14 17,24 21 P02790 HEMO_HUMAN Hemopexin 9 28,79 17 P69905 HBA_HUMAN Hemoglobin subunit alpha 6 57,04 14 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 7 18,68 12 MARTA VIZOSO GONZÁLEZ 290 P41222 PTGDS_HUMAN Prostaglandin-H2 D-isomerase 9 38,95 12 P01871 IGHM_HUMAN Immunoglobulin heavy constant mu 4 10,15 11 P0C0L5 CO4B_HUMAN Complement C4-B 4 1,89 10 P02766 TTHY_HUMAN Transthyretin 6 64,63 9 P00747 PLMN_HUMAN Plasminogen 4 6,79 8 P24592 IBP6_HUMAN Insulin-like growth factor-binding protein 6 1 6,25 7 P13647 K2C5_HUMAN Keratin, type II cytoskeletal 5 7 10,68 7 P61626 LYSC_HUMAN Lysozyme C 3 33,11 7 P02652 APOA2_HUMAN Apolipoprotein A-II 3 21,00 6 P06396 GELS_HUMAN Gelsolin 5 7,29 6 P31025 LCN1_HUMAN Lipocalin-1 4 22,16 6 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 4 11,33 5 P01780 HV307_HUMAN Immunoglobulin heavy variable 3-7 4 33,33 5 Q14624 ITIH4_HUMAN Inter-alpha-trypsin inhibitor heavy chain H4 4 5,16 5 P02533 K1C14_HUMAN Keratin, type I cytoskeletal 14 4 6,78 5 P08571 CD14_HUMAN Monocyte differentiation antigen CD14 2 9,87 5 P01833 PIGR_HUMAN Polymeric immunoglobulin receptor 2 3,27 5 P04217 A1BG_HUMAN Alpha-1B-glycoprotein 2 7,27 4 Q9GZZ8 LACRT_HUMAN Extracellular glycoprotein lacritin 3 16,67 4 P02675 FIBB_HUMAN Fibrinogen beta chain 2 5,29 4 P01764 HV323_HUMAN Immunoglobulin heavy variable 3-23 4 35,04 4 P0DJI8 SAA1_HUMAN Serum amyloid A-1 protein 4 42,62 4 P02679 FIBG_HUMAN Fibrinogen gamma chain 1 3,31 3 P04216 THY1_HUMAN Thy-1 membrane glycoprotein 2 14,91 3 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 2 5,72 2 P01024 CO3_HUMAN Complement C3 2 1,20 2 P01619 KV320_HUMAN Immunoglobulin kappa variable 3-20 3 35,34 2 P06312 KV401_HUMAN Immunoglobulin kappa variable 4-1 3 28,93 2 P15586 GNS_HUMAN N-acetylglucosamine-6-sulfatase 1 2,17 2 P06310 KV230_HUMAN Immunoglobulin kappa variable 2-30 1 10,83 1 O95968 SG1D1_HUMAN Secretoglobin family 1D member 1 1 12,22 1 P02656 APOC3_HUMAN Apolipoprotein C-III 1 16,16 P10909 CLUS_HUMAN Clusterin 6 17,37 Supplementary Table 28. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 9B (ExoGAG supernatant fraction of low creatinine PKD2 sample sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02768 ALBU_HUMAN Albumin 45 56,81 83 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 23 34,16 34 P00738 HPT_HUMAN Haptoglobin 20 42,36 31 P68871 HBB_HUMAN Hemoglobin subunit beta 12 93,20 30 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 17 33,39 28 P02766 TTHY_HUMAN Transthyretin 11 69,39 27 Supplemental Material 291 P02647 APOA1_HUMAN Apolipoprotein A-I 16 53,18 26 P02760 AMBP_HUMAN Protein AMBP 8 17,33 20 P01009 A1AT_HUMAN Alpha-1-antitrypsin 12 35,65 19 P0DOX5 IGG1_HUMAN Immunoglobulin gamma-1 heavy chain 11 24,28 17 P01834 IGKC_HUMAN Immunoglobulin kappa constant 7 79,44 17 P69905 HBA_HUMAN Hemoglobin subunit alpha 7 64,08 15 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 12 27,13 14 P80188 NGAL_HUMAN Neutrophil gelatinase-associated lipocalin 10 60,10 13 P31025 LCN1_HUMAN Lipocalin-1 6 26,70 12 P0DJI8 SAA1_HUMAN Serum amyloid A-1 protein 6 52,46 11 P02763 A1AG1_HUMAN Alpha-1-acid glycoprotein 1 5 27,36 10 P0DJI9 SAA2_HUMAN Serum amyloid A-2 protein 6 36,07 10 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 7 22,39 8 P01042 KNG1_HUMAN Kininogen-1 5 7,45 8 P25311 ZA2G_HUMAN Zinc-alpha-2-glycoprotein 5 17,11 8 P02652 APOA2_HUMAN Apolipoprotein A-II 3 39,00 7 P02654 APOC1_HUMAN Apolipoprotein C-I 4 36,14 7 P98160 PGBM_HUMAN Basement membrane-specific heparan sulfate proteoglycan core protein 4 0,98 7 P08779 K1C16_HUMAN Keratin, type I cytoskeletal 16 5 12,26 6 P48668 K2C6C_HUMAN Keratin, type II cytoskeletal 6C 6 12,06 6 P12273 PIP_HUMAN Prolactin-inducible protein 4 36,30 6 P01871 IGHM_HUMAN Immunoglobulin heavy constant mu 2 3,53 5 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 5 44,34 5 P61626 LYSC_HUMAN Lysozyme C 4 41,22 5 P10153 RNAS2_HUMAN Non-secretory ribonuclease 2 9,32 5 Q9GZZ8 LACRT_HUMAN Extracellular glycoprotein lacritin 3 16,67 4 P13647 K2C5_HUMAN Keratin, type II cytoskeletal 5 4 6,95 4 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 1 2,13 3 P13646 K1C13_HUMAN Keratin, type I cytoskeletal 13 2 4,80 3 O95968 SG1D1_HUMAN Secretoglobin family 1D member 1 2 36,67 3 P02787 TRFE_HUMAN Serotransferrin 2 3,73 3 P02655 APOC2_HUMAN Apolipoprotein C-II 2 19,80 2 P13987 CD59_HUMAN CD59 glycoprotein 10 25,00 2 P17900 SAP3_HUMAN Ganglioside GM2 activator 2 9,33 2 P41222 PTGDS_HUMAN Prostaglandin-H2 D-isomerase 2 8,42 2 P05109 S10A8_HUMAN Protein S100-A8 2 23,66 2 P35030 TRY3_HUMAN Trypsin-3 2 6,91 2 P10909 CLUS_HUMAN Clusterin 1 5,12 1 P02790 HEMO_HUMAN Hemopexin 1 2,38 1 P07911 UROM_HUMAN Uromodulin 4 6,72 1 P02656 APOC3_HUMAN Apolipoprotein C-III 1 16,16 Q86WR0 CCD25_HUMAN Coiled-coil domain-containing protein 25 1 4,33 P02042 HBD_HUMAN Hemoglobin subunit delta 6 46,26 P06310 KV230_HUMAN Immunoglobulin kappa variable 2-30 1 10,83 MARTA VIZOSO GONZÁLEZ 292 P04433 KV311_HUMAN Immunoglobulin kappa variable 3-11 1 7,83 P01619 KV320_HUMAN Immunoglobulin kappa variable 3-20 1 13,79 O75556 SG2A1_HUMAN Mammaglobin-B 1 12,63 P0DJD9 PEPA5_HUMAN Pepsin A-5 1 2,83 P02775 CXCL7_HUMAN Platelet basic protein 1 7,81 Q16378 PROL4_HUMAN Proline-rich protein 4 1 11,19 P06702 S10A9_HUMAN Protein S100-A9 1 13,16 P02753 RET4_HUMAN Retinol-binding protein 4 1 4,98 Q8WVN6 SCTM1_HUMAN Secreted and transmembrane protein 1 1 7,66 P35542 SAA4_HUMAN Serum amyloid A-4 protein 1 6,92 Supplementary Table 29. Protein list of all identified proteins by qualitative mass spectrometry analysis in band 9D (ExoGAG supernatant fraction of high creatinine PKD2 sample sample). Entry Name UniProt Name Protein Name Peptides(95%) %Cov(95) Total spectrum count P02768 ALBU_HUMAN Albumin 287 81,77 766 P02787 TRFE_HUMAN Serotransferrin 50 44,41 101 P07477 TRY1_HUMAN Trypsin-1 25 11,74 99 P00738 HPT_HUMAN Haptoglobin 38 64,78 92 P02753 RET4_HUMAN Retinol-binding protein 4 29 41,29 87 P0DOX5 IGG1_HUMAN Immunoglobulin gamma-1 heavy chain 26 41,43 75 P01834 IGKC_HUMAN Immunoglobulin kappa constant 23 88,79 68 P0DOX7 IGK_HUMAN Immunoglobulin kappa light chain 24 47,20 68 P98160 PGBM_HUMAN Basement membrane-specific heparan sulfate proteoglycan core protein 33 3,69 60 P01859 IGHG2_HUMAN Immunoglobulin heavy constant gamma 2 18 28,83 60 P02647 APOA1_HUMAN Apolipoprotein A-I 30 67,79 51 P01009 A1AT_HUMAN Alpha-1-antitrypsin 20 47,85 49 P02760 AMBP_HUMAN Protein AMBP 25 47,16 45 P04264 K2C1_HUMAN Keratin, type II cytoskeletal 1 25 33,39 43 P25311 ZA2G_HUMAN Zinc-alpha-2-glycoprotein 18 41,95 31 P68871 HBB_HUMAN Hemoglobin subunit beta 12 82,31 30 P0DOX8 IGL1_HUMAN Immunoglobulin lambda-1 light chain 8 34,26 29 P35527 K1C9_HUMAN Keratin, type I cytoskeletal 9 19 34,51 28 P13645 K1C10_HUMAN Keratin, type I cytoskeletal 10 17 26,88 27 P69905 HBA_HUMAN Hemoglobin subunit alpha 10 68,31 22 P0DOY3 IGLC3_HUMAN Immunoglobulin lambda constant 3 9 69,81 22 P80188 NGAL_HUMAN Neutrophil gelatinase-associated lipocalin 9 45,96 21 P41222 PTGDS_HUMAN Prostaglandin-H2 D-isomerase 14 42,63 21 P02763 A1AG1_HUMAN Alpha-1-acid glycoprotein 1 10 45,27 20 P01011 AACT_HUMAN Alpha-1-antichymotrypsin 10 22,93 19 P17900 SAP3_HUMAN Ganglioside GM2 activator 7 34,72 18 P02042 HBD_HUMAN Hemoglobin subunit delta 8 52,38 17 P01042 KNG1_HUMAN Kininogen-1 6 11,02 14 P02766 TTHY_HUMAN Transthyretin 7 53,06 14 Supplemental Material 293 P02790 HEMO_HUMAN Hemopexin 7 21,21 13 P31025 LCN1_HUMAN Lipocalin-1 10 34,66 13 P19652 A1AG2_HUMAN Alpha-1-acid glycoprotein 2 4 17,41 11 P55290 CAD13_HUMAN Cadherin-13 4 6,59 9 P02679 FIBG_HUMAN Fibrinogen gamma chain 6 17,66 9 P05451 REG1A_HUMAN Lithostathine-1-alpha 4 27,71 9 P02652 APOA2_HUMAN Apolipoprotein A-II 6 21,00 8 P02654 APOC1_HUMAN Apolipoprotein C-I 4 33,73 8 P02675 FIBB_HUMAN Fibrinogen beta chain 5 15,27 8 P01876 IGHA1_HUMAN Immunoglobulin heavy constant alpha 1 7 22,38 8 P61626 LYSC_HUMAN Lysozyme C 5 46,62 8 P0C0L5 CO4B_HUMAN Complement C4-B 2 1,20 7 P07360 CO8G_HUMAN Complement component C8 gamma chain 4 25,74 7 Q9GZZ8 LACRT_HUMAN Extracellular glycoprotein lacritin 4 19,57 7 P02671 FIBA_HUMAN Fibrinogen alpha chain 5 6,47 7 P48668 K2C6C_HUMAN Keratin, type II cytoskeletal 6C 6 10,46 7 P0DJI8 SAA1_HUMAN Serum amyloid A-1 protein 6 51,64 7 P0DJI9 SAA2_HUMAN Serum amyloid A-2 protein 7 35,25 7 P01024 CO3_HUMAN Complement C3 1 0,96 6 P22352 GPX3_HUMAN Glutathione peroxidase 3 5 28,32 6 P01780 HV307_HUMAN Immunoglobulin heavy variable 3-7 5 42,74 6 P04217 A1BG_HUMAN Alpha-1B-glycoprotein 2 4,65 5 P13647 K2C5_HUMAN Keratin, type II cytoskeletal 5 4 6,61 5 P02788 TRFL_HUMAN Lactotransferrin 2 3,94 5 P00450 CERU_HUMAN Ceruloplasmin 2 2,07 4 A0A0C4DH69 KV109_HUMAN Immunoglobulin kappa variable 1-9 4 34,19 4 P01619 KV320_HUMAN Immunoglobulin kappa variable 3-20 5 48,28 4 Q14624 ITIH4_HUMAN Inter-alpha-trypsin inhibitor heavy chain H4 2 2,26 4 P02533 K1C14_HUMAN Keratin, type I cytoskeletal 14 4 7,20 4 Q16378 PROL4_HUMAN Proline-rich protein 4 1 5,97 4 P00734 THRB_HUMAN Prothrombin 2 4,66 4 P02774 VTDB_HUMAN Vitamin D-binding protein 2 3,80 4 P01008 ANT3_HUMAN Antithrombin-III 1 3,02 3 P06727 APOA4_HUMAN Apolipoprotein A-IV 2 5,56 3 P02655 APOC2_HUMAN Apolipoprotein C-II 2 19,80 3 P07339 CATD_HUMAN Cathepsin D 1 2,43 3 P10909 CLUS_HUMAN Clusterin 2 8,91 3 P53634 CATC_HUMAN Dipeptidyl peptidase 1 3 4,32 3 P06312 KV401_HUMAN Immunoglobulin kappa variable 4-1 2 23,14 3 P01700 LV147_HUMAN Immunoglobulin lambda variable 1-47 2 25,64 3 P61916 NPC2_HUMAN NPC intracellular cholesterol transporter 2 1 5,96 3 P07998 RNAS1_HUMAN Ribonuclease pancreatic 3 24,36 3 P35542 SAA4_HUMAN Serum amyloid A-4 protein 2 14,62 3 P02765 FETUA_HUMAN Alpha-2-HS-glycoprotein 3 8,45 2 P06396 GELS_HUMAN Gelsolin 2 2,69 2 MARTA VIZOSO GONZÁLEZ 294 P01591 IGJ_HUMAN Immunoglobulin J chain 2 12,58 2 P06310 KV230_HUMAN Immunoglobulin kappa variable 2-30 2 10,83 2 A0A075B6I0 LV861_HUMAN Immunoglobulin lambda variable 8-61 2 13,11 2 P05155 IC1_HUMAN Plasma protease C1 inhibitor 2 5,60 2 P05109 S10A8_HUMAN Protein S100-A8 2 23,66 2 O95968 SG1D1_HUMAN Secretoglobin family 1D member 1 2 12,22 2 P05090 APOD_HUMAN Apolipoprotein D 1 5,82 1 P00751 CFAB_HUMAN Complement factor B 1 1,18 1 A0A0C4DH68 KV224_HUMAN Immunoglobulin kappa variable 2-24 1 10,83 1 P80748 LV321_HUMAN Immunoglobulin lambda variable 3-21 1 13,68 1 P07911 UROM_HUMAN Uromodulin 2 2,50 1 P02656 APOC3_HUMAN Apolipoprotein C-III 1 16,16 P54253 ATX1_HUMAN Ataxin-1 1 0,98 Q9UIF8 BAZ2B_HUMAN Bromodomain adjacent to zinc finger domain protein 2B 1 0,28 P13987 CD59_HUMAN CD59 glycoprotein 7 25,00 Q9NS71 GKN1_HUMAN Gastrokine-1 1 8,04 P01594 KV133_HUMAN Immunoglobulin kappa variable 1-33 2 29,06 P04432 KVD39_HUMAN Immunoglobulin kappa variable 1D-39 3 34,19 P0DOX6 IGM_HUMAN Immunoglobulin mu heavy chain 4 7,99 O00468-6 AGRIN_HUMAN Isoform 6 of Agrin 6 3,52 O75556 SG2A1_HUMAN Mammaglobin-B 1 12,63 Q9Y4C0 NRX3A_HUMAN Neurexin-3 1 0,43 Q9WC28 POLN_HEVHY Non-structural polyprotein pORF1 1 0,41 O75594 PGRP1_HUMAN Peptidoglycan recognition protein 1 1 7,65 Q06830 PRDX1_HUMAN Peroxiredoxin-1 1 5,53 P12273 PIP_HUMAN Prolactin-inducible protein 3 24,66 Q8TC56 FA71B_HUMAN Protein FAM71B 1 1,16 Q8WVN6 SCTM1_HUMAN Secreted and transmembrane protein 1 1 7,66 Supplementary Table 30. Summary of results of ExoGAG and Ultracentrifugation isolation mass spectrometry sequencing. Total proteins identified in ExoGAG and Ultracentrifugation samples as well as common proteins for all ExoGAG or Ultracentrifugation samples are described. Uniprot Name is used as identifier. Proteins identified in ExoGAG samples Proteins identified in UC samples ExoGAG Proteins shared by all samples UC Proteins shared by all samples 1433B_HUMAN 1433B_HUMAN 1433E_HUMAN 1433B_HUMAN 1433E_HUMAN 1433E_HUMAN 1433Z_HUMAN 1433E_HUMAN 1433G_HUMAN 1433F_HUMAN 4F2_HUMAN 1433Z_HUMAN 1433S_HUMAN 1433G_HUMAN 6PGL_HUMAN 6PGD_HUMAN 1433T_HUMAN 1433S_HUMAN A1AG1_HUMAN 6PGL_HUMAN 1433Z_HUMAN 1433T_HUMAN A1AT_HUMAN A1AT_HUMAN 2AAA_HUMAN 1433Z_HUMAN A1BG_HUMAN A2MG_HUMAN 4F2_HUMAN 2AAA_HUMAN A2AP_HUMAN AATC_HUMAN 6PGD_HUMAN 4F2_HUMAN A2GL_HUMAN ABHEB_HUMAN 6PGL_HUMAN 6PGD_HUMAN A2MG_HUMAN ABI1_HUMAN A1AG1_HUMAN 6PGL_HUMAN A4_PANTR ACE_HUMAN A1AG2_HUMAN A1AG1_HUMAN AACT_HUMAN ACOC_HUMAN Supplemental Material 295 A1AT_HUMAN A1AG2_HUMAN ACE_HUMAN ACTG_HUMAN A1BG_HUMAN A1AT_HUMAN ACTG_HUMAN ACTN4_HUMAN A2AP_HUMAN A1BG_HUMAN ACY1_HUMAN ACY1_HUMAN A2GL_HUMAN A2GL_HUMAN AGAL_HUMAN ADSV_HUMAN A2MG_HUMAN A2MG_HUMAN AGRIN_HUMAN AK1A1_HUMAN A2ML1_HUMAN AAAT_HUMAN AK1A1_HUMAN AL1A1_HUMAN A4_PANTR AACT_HUMAN AL1A1_HUMAN AL3B1_HUMAN AACT_HUMAN AATC_HUMAN ALBU_HUMAN AL9A1_HUMAN AATC_HUMAN AATM_HUMAN ALS_HUMAN ALBU_HUMAN AATM_HUMAN ABHDA_HUMAN AMBP_HUMAN ALDOA_HUMAN ABHDA_HUMAN ABHEB_HUMAN AMPE_HUMAN ALDOB_HUMAN ABHEB_HUMAN ABI1_HUMAN AMPN_HUMAN ALDR_HUMAN ABLM2_HUMAN ABR_HUMAN AMYP_HUMAN AMBP_HUMAN ACE_HUMAN ACBP_HUMAN ANAG_HUMAN AMPB_HUMAN ACE2_HUMAN ACE_HUMAN ANGL2_HUMAN AMPE_HUMAN ACOC_HUMAN ACE2_HUMAN ANGT_HUMAN AMPN_HUMAN ACPH_HUMAN ACOC_HUMAN ANT3_HUMAN ANX11_HUMAN ACTG_HUMAN ACOT1_HUMAN ANX11_HUMAN ANXA2_HUMAN ACTN1_HUMAN ACPH_HUMAN ANXA4_HUMAN ANXA3_HUMAN ACTN3_HUMAN ACSL4_HUMAN ANXA5_HUMAN ANXA4_HUMAN ACTN4_HUMAN ACTB_HUMAN ANXA7_HUMAN ANXA5_HUMAN ACTS_HUMAN ACTBL_HUMAN APLP2_HUMAN ANXA6_HUMAN ACY1_HUMAN ACTG_HUMAN APOA_HUMAN ANXA7_HUMAN ADAS_HUMAN ACTN1_HUMAN APOA1_HUMAN APOA1_HUMAN ADH6_HUMAN ACTN2_HUMAN APOA2_HUMAN APOA2_HUMAN ADHX_HUMAN ACTN3_HUMAN APOA4_HUMAN APOC1_HUMAN ADT2_HUMAN ACTN4_HUMAN APOC1_HUMAN APOD_HUMAN ADT3_HUMAN ACTS_HUMAN APOD_HUMAN APT_HUMAN AFAM_HUMAN ACTZ_HUMAN APOE_HUMAN AQP1_HUMAN AGAL_HUMAN ACY1_HUMAN AQP2_HUMAN AQP2_HUMAN AGRF5_HUMAN ADAS_HUMAN ASAH1_HUMAN ARL3_HUMAN AGRG1_HUMAN ADH1G_HUMAN AT1B1_HUMAN ARRD1_HUMAN AGRIN_HUMAN ADHX_HUMAN B4GA1_HUMAN ASSY_HUMAN AGRL1_HUMAN ADRO_HUMAN B4GT1_HUMAN AT1A1_HUMAN AHNK_HUMAN ADSV_HUMAN BGAL_HUMAN AT1B1_HUMAN AIFM1_HUMAN ADT1_HUMAN BHMT1_HUMAN ATPA_HUMAN AK1A1_HUMAN ADT2_HUMAN BROX_HUMAN ATPB_HUMAN AK1C1_HUMAN ADT3_HUMAN BTD_HUMAN BAIP2_HUMAN AK1C3_HUMAN AHNK_HUMAN C1RL_HUMAN BASP1_HUMAN AL1A1_HUMAN AIDA_HUMAN CAB39_HUMAN BDH2_HUMAN AL1L1_HUMAN AIFM1_HUMAN CAD13_HUMAN BHMT1_HUMAN AL7A1_HUMAN AK1A1_HUMAN CADM1_HUMAN BI2L1_HUMAN AL9A1_HUMAN AK1C1_HUMAN CADM4_HUMAN BLVRB_HUMAN ALAT1_HUMAN AK1C3_HUMAN CAH2_HUMAN BROX_HUMAN ALBU_HUMAN AKA12_HUMAN CALB1_HUMAN BST1_HUMAN ALDOA_HUMAN AL1A1_HUMAN CAN7_HUMAN CAB39_HUMAN ALDOB_HUMAN AL1L1_HUMAN CATD_HUMAN CAH2_HUMAN ALDR_HUMAN AL3B1_HUMAN CATZ_HUMAN CALB1_HUMAN ALS_HUMAN AL7A1_HUMAN CBG_HUMAN CAN1_HUMAN AMBP_HUMAN AL9A1_HUMAN CBPE_HUMAN CAN5_HUMAN AMD_HUMAN ALBU_HUMAN CBPM_HUMAN CAN7_HUMAN AMPE_HUMAN ALDH2_HUMAN CBPQ_HUMAN CAPG_HUMAN AMPL_HUMAN ALDOA_HUMAN CBR1_HUMAN CAPZB_HUMAN AMPN_HUMAN ALDOB_HUMAN CD14_HUMAN CAYP1_HUMAN AMY1A_HUMAN ALDR_HUMAN CD248_HUMAN CAZA2_HUMAN MARTA VIZOSO GONZÁLEZ 296 AMY2B_HUMAN AMBP_HUMAN CD44_HUMAN CB39L_HUMAN AMYP_HUMAN AMPB_HUMAN CD59_HUMAN CBPM_HUMAN ANAG_HUMAN AMPD1_HUMAN CD63_HUMAN CBR1_HUMAN ANGL1_HUMAN AMPE_HUMAN CD9_HUMAN CD2AP_HUMAN ANGL2_HUMAN AMPL_HUMAN CEL_HUMAN CD59_HUMAN ANGT_HUMAN AMPN_HUMAN CERU_HUMAN CD9_HUMAN ANO6_HUMAN AN13B_HUMAN CETP_HUMAN CDC42_HUMAN ANPRC_HUMAN ANCHR_HUMAN CFAB_HUMAN CEAM1_HUMAN ANT3_HUMAN ANO6_HUMAN CFAI_HUMAN CHM1A_HUMAN ANTR1_HUMAN ANT3_HUMAN CHM1B_HUMAN CHM1B_HUMAN ANX10_HUMAN ANX11_HUMAN CHM2A_HUMAN CHM2A_HUMAN ANX11_HUMAN ANX13_HUMAN CHM4B_HUMAN CHM2B_HUMAN ANXA1_HUMAN ANXA1_HUMAN CHMP5_HUMAN CHM4A_HUMAN ANXA2_HUMAN ANXA2_HUMAN CILP2_HUMAN CHM4B_HUMAN ANXA4_HUMAN ANXA3_HUMAN CLC14_HUMAN CHMP3_HUMAN ANXA5_HUMAN ANXA4_HUMAN CLM9_HUMAN CHMP5_HUMAN ANXA6_HUMAN ANXA5_HUMAN CLUS_HUMAN CHMP6_HUMAN ANXA7_HUMAN ANXA6_HUMAN CN37_HUMAN CLH1_HUMAN AOC1_HUMAN ANXA7_HUMAN CNDP2_HUMAN CLIC1_HUMAN APLP2_HUMAN ANXA9_HUMAN CNTN1_HUMAN CN37_HUMAN APOA_HUMAN AOXA_HUMAN CO3_HUMAN CNDP2_HUMAN APOA1_HUMAN AP1B1_HUMAN CO4B_HUMAN CPNE3_HUMAN APOA2_HUMAN AP1G1_HUMAN CO6A1_HUMAN CPNE8_HUMAN APOA4_HUMAN AP2A1_HUMAN CO6A3_HUMAN CR1_HUMAN APOB_HUMAN AP2A2_HUMAN CO9_HUMAN CRYL1_HUMAN APOC1_HUMAN AP2B1_HUMAN COFA1_HUMAN CTDS1_HUMAN APOC2_HUMAN AP2M1_HUMAN CPN2_HUMAN CTL2_HUMAN APOC3_HUMAN APOA1_HUMAN CPNE3_HUMAN CTL4_HUMAN APOD_HUMAN APOA2_HUMAN CPNE8_HUMAN CYBR1_HUMAN APOE_HUMAN APOA4_HUMAN CPVL_HUMAN CYFP1_HUMAN APOH_HUMAN APOB_HUMAN CSF1_HUMAN CYRIB_HUMAN APOM_HUMAN APOC1_HUMAN CSPG4_HUMAN DAF_HUMAN APT_HUMAN APOC2_HUMAN CTL2_HUMAN DPEP1_HUMAN AQP1_HUMAN APOC3_HUMAN CTL4_HUMAN DPP4_HUMAN AQP2_HUMAN APOD_HUMAN CUBN_HUMAN EF1A3_HUMAN AQP7_HUMAN APOE_HUMAN DDAH2_HUMAN EHD1_HUMAN ARF5_HUMAN APOH_HUMAN DERM_HUMAN EHD4_HUMAN ARGAL_HUMAN APOM_HUMAN DNAS1_HUMAN ENOA_HUMAN ARGI1_HUMAN APT_HUMAN DPEP1_HUMAN EPS8_HUMAN ARK72_HUMAN AQP1_HUMAN DPP2_HUMAN ES8L1_HUMAN ARK73_HUMAN AQP2_HUMAN DPP4_HUMAN ES8L2_HUMAN ARK74_HUMAN AQP7_HUMAN DSC2_HUMAN EZRI_HUMAN ARL15_HUMAN AR13B_HUMAN EF1A3_HUMAN FLOT1_HUMAN ARL3_HUMAN ARC1A_HUMAN EGF_HUMAN FLOT2_HUMAN ARLY_HUMAN ARC1B_HUMAN ENDD1_HUMAN FRK_HUMAN ARMT1_HUMAN ARF1_HUMAN ENOA_HUMAN G3P_HUMAN ARP2_HUMAN ARF4_HUMAN EPCR_HUMAN G6PI_HUMAN ARP3_HUMAN ARF5_HUMAN EPS8_HUMAN GBB1_HUMAN ARPC2_HUMAN ARF6_HUMAN EZRI_HUMAN GBB2_HUMAN ARRD1_HUMAN ARGAL_HUMAN FA11_HUMAN GDIB_HUMAN ARSA_HUMAN ARK72_HUMAN FA12_HUMAN GDIR1_HUMAN ARSB_HUMAN ARK73_HUMAN FAM3C_HUMAN GELS_HUMAN ARSF_HUMAN ARL15_HUMAN FAT4_HUMAN GGT1_HUMAN ASAH1_HUMAN ARL3_HUMAN FBLN3_HUMAN GLTP_HUMAN ASM_HUMAN ARL6_HUMAN FBLN4_HUMAN GNA11_HUMAN Supplemental Material 303 GMPR2_HUMAN GDIA_PANTR GNA11_HUMAN GDIB_HUMAN GNA12_HUMAN GDIR1_HUMAN GNA13_HUMAN GDIR2_HUMAN GNAI1_HUMAN GELS_HUMAN GNAI2_HUMAN GET3_HUMAN GNAI3_HUMAN GFPT1_HUMAN GNAQ_HUMAN GGACT_HUMAN GNAS1_HUMAN GGCT_HUMAN GNAS2_HUMAN GGH_HUMAN GNPI1_HUMAN GGT1_HUMAN GNS_HUMAN GGT2_HUMAN GOLM1_HUMAN GGTL2_HUMAN GP180_HUMAN GIPC1_HUMAN GP1BA_HUMAN GLGB_HUMAN GPC1_HUMAN GLO2_HUMAN GPC3_HUMAN GLOD4_HUMAN GPC4_HUMAN GLRX1_HUMAN GPC5B_HUMAN GLTP_HUMAN GPC5C_HUMAN GLYC_HUMAN GPC6_HUMAN GLYM_HUMAN GPDA_HUMAN GMDS_HUMAN GPNMB_HUMAN GNA1_HUMAN GPV_HUMAN GNA11_HUMAN GPVI_HUMAN GNA13_HUMAN GPX3_HUMAN GNA14_HUMAN GRAN_HUMAN GNAI1_HUMAN GRHPR_HUMAN GNAI2_HUMAN GRP75_HUMAN GNAI3_HUMAN GSHB_HUMAN GNAO_HUMAN GSLG1_HUMAN GNAQ_HUMAN GSTA1_HUMAN GNAS1_HUMAN GSTA2_HUMAN GNAS2_HUMAN GSTA5_HUMAN GNAZ_HUMAN GSTM1_HUMAN GNPI1_HUMAN GSTM2_HUMAN GPC5B_HUMAN GSTM3_HUMAN GPC5C_HUMAN GSTM5_HUMAN GPD1L_HUMAN GSTO1_HUMAN GPDA_HUMAN GSTP1_HUMAN GPX4_HUMAN GSTT1_HUMAN GRAN_HUMAN GTR5_HUMAN GRB2_HUMAN GUAD_HUMAN GRB7_HUMAN H13_HUMAN GRHPR_HUMAN H2AJ_HUMAN GRP75_HUMAN H2B1M_HUMAN GSH1_HUMAN H32_HUMAN GSHB_HUMAN H4_HUMAN GSHR_HUMAN HAVR2_HUMAN GSTA1_HUMAN HBA_HUMAN GSTA2_HUMAN HBB_HUMAN GSTM1_HUMAN HBD_HUMAN GSTM2_HUMAN HBG2_HUMAN GSTM3_HUMAN HCD2_HUMAN GSTO1_HUMAN HCDH_HUMAN GSTP1_HUMAN MARTA VIZOSO GONZÁLEZ 304 HECW2_HUMAN GSTT1_HUMAN HEM2_HUMAN GTR2_HUMAN HEMO_HUMAN GTR5_HUMAN HEP2_HUMAN GXLT2_HUMAN HEXA_HUMAN H12_HUMAN HEXB_HUMAN H2AJ_HUMAN HGD_HUMAN H2B1M_HUMAN HGFA_HUMAN H32_HUMAN HLAA_HUMAN H4_HUMAN HLAE_HUMAN H90B2_HUMAN HMCN1_HUMAN HBA_HUMAN HNRPK_HUMAN HBB_HUMAN HPPD_HUMAN HBD_HUMAN HPT_HUMAN HBE_HUMAN HRG_HUMAN HCD2_HUMAN HS71B_HUMAN HCDH_HUMAN HS71L_HUMAN HDHD2_HUMAN HS90A_HUMAN HEBP1_HUMAN HS90B_HUMAN HEBP2_HUMAN HSP72_HUMAN HEM2_HUMAN HSP7C_HUMAN HEMO_HUMAN HSPB1_HUMAN HGD_HUMAN HTRA1_HUMAN HGS_HUMAN HV102_HUMAN HINT3_HUMAN HV307_HUMAN HLAA_HUMAN HV311_HUMAN HLAH_HUMAN HV315_HUMAN HNMT_HUMAN HV323_HUMAN HNRPK_HUMAN HV335_HUMAN HOME1_HUMAN HV349_HUMAN HORN_HUMAN HV372_HUMAN HPCA_HUMAN HV374_HUMAN HPPD_HUMAN HV428_HUMAN HPRT_HUMAN HV551_HUMAN HPT_HUMAN HV601_HUMAN HS105_HUMAN HVC05_HUMAN HS12A_HUMAN HVD82_HUMAN HS71B_HUMAN HYAL1_HUMAN HS71L_HUMAN HYES_HUMAN HS90A_HUMAN HYOU1_HUMAN HS90B_HUMAN I18BP_HUMAN HSP72_HUMAN IBP2_HUMAN HSP74_HUMAN IBP3_HUMAN HSP7C_HUMAN IBP7_HUMAN HSPB1_HUMAN IC1_HUMAN HTAI2_HUMAN ICAM2_HUMAN HVC05_HUMAN ICOSL_HUMAN HYES_HUMAN IDHC_HUMAN HYOU1_HUMAN IDUA_HUMAN I5P1_HUMAN IF4A2_HUMAN IC1_HUMAN IF6_HUMAN IDHC_HUMAN IGA2_HUMAN IDHP_HUMAN IGE_HUMAN IF172_HUMAN IGG1_HUMAN IF4A2_HUMAN IGHA1_HUMAN IF4G1_HUMAN Supplemental Material 305 IGHA2_HUMAN IF4H_HUMAN IGHG2_HUMAN IF5AL_HUMAN IGHG3_HUMAN IFT27_HUMAN IGHG4_HUMAN IGA2_HUMAN IGHM_HUMAN IGG1_HUMAN IGJ_HUMAN IGHA1_HUMAN IGK_HUMAN IGHA2_HUMAN IGKC_HUMAN IGHG2_HUMAN IGL1_HUMAN IGHM_HUMAN IGLC2_HUMAN IGJ_HUMAN IGLC3_HUMAN IGK_HUMAN IGLL1_HUMAN IGKC_HUMAN IGM_HUMAN IGL1_HUMAN ILEU_HUMAN IGLC2_HUMAN IPSP_HUMAN IGLC3_HUMAN IQEC2_HUMAN IGSF8_HUMAN IQGA1_HUMAN IL18_HUMAN IQGA3_HUMAN ILEU_HUMAN ISLR_HUMAN IMB1_HUMAN IST1_HUMAN IMPA1_HUMAN ITA3_HUMAN IMPA2_HUMAN ITAV_HUMAN INADL_HUMAN ITB1_HUMAN INP5E_HUMAN ITIH1_HUMAN IPSP_HUMAN ITIH2_HUMAN IPYR_HUMAN ITIH3_HUMAN IQGA1_HUMAN ITIH4_HUMAN IQGA2_HUMAN ITIH5_HUMAN ISOC1_HUMAN ITLN1_HUMAN IST1_HUMAN ITM2B_HUMAN ITA1_HUMAN JMJD8_HUMAN ITA3_HUMAN JUNB_HUMAN ITAV_HUMAN K1549_HUMAN ITB1_HUMAN K1C10_HUMAN ITB3_HUMAN K1C13_HUMAN ITB5_HUMAN K1C14_HUMAN ITB8_HUMAN K1C15_HUMAN ITBP1_HUMAN K1C16_HUMAN ITCH_HUMAN K1C17_HUMAN ITIH1_HUMAN K1C19_HUMAN ITM2B_HUMAN K1C9_HUMAN JIP4_HUMAN K1H1_HUMAN K1C10_HUMAN K1H2_HUMAN K1C13_HUMAN K22E_HUMAN K1C14_HUMAN K2C1_HUMAN K1C15_HUMAN K2C4_HUMAN K1C16_HUMAN K2C5_HUMAN K1C17_HUMAN K2C6A_HUMAN K1C19_HUMAN K2C6B_HUMAN K1C9_HUMAN K2C6C_HUMAN K1H1_HUMAN K2C78_HUMAN K2C1_HUMAN K2C8_HUMAN K2C5_HUMAN KAIN_HUMAN K2C6A_HUMAN KCRB_HUMAN K2C6B_HUMAN KHK_HUMAN K2C6C_HUMAN MARTA VIZOSO GONZÁLEZ 306 KIF12_HUMAN K2C78_HUMAN KLK1_HUMAN K2C8_HUMAN KLK3_HUMAN KAD1_HUMAN KLKB1_HUMAN KALRN_HUMAN KLOT_HUMAN KAP0_HUMAN KNG1_HUMAN KAP2_HUMAN KPYM_HUMAN KAPCA_HUMAN KPYR_HUMAN KAPCB_HUMAN KR111_HUMAN KC1G3_HUMAN KR87P_HUMAN KCD12_HUMAN KRA21_HUMAN KCRB_HUMAN KRT35_HUMAN KCRM_HUMAN KRT36_HUMAN KCRS_HUMAN KRT81_HUMAN KCTD3_HUMAN KRT82_HUMAN KCY_HUMAN KRT83_HUMAN KDM7A_HUMAN KRT85_HUMAN KHK_HUMAN KT33B_HUMAN KIF12_HUMAN KV105_HUMAN KIF27_HUMAN KV109_HUMAN KIF3A_HUMAN KV116_HUMAN KIF3B_HUMAN KV117_HUMAN KIF5A_HUMAN KV133_HUMAN KIFA3_HUMAN KV224_HUMAN KINH_HUMAN KV229_HUMAN KLH41_HUMAN KV230_HUMAN KLK3_HUMAN KV311_HUMAN KMT5B_HUMAN KV315_HUMAN KNG1_HUMAN KV320_HUMAN KPCA_HUMAN KV37_HUMAN KPCD_HUMAN KV401_HUMAN KPCE_HUMAN KVD15_HUMAN KPCI_HUMAN KVD16_HUMAN KPCZ_HUMAN KVD20_HUMAN KPYM_HUMAN KVD28_HUMAN KRIT1_HUMAN KVD39_HUMAN KRT81_HUMAN L1CAM_HUMAN KRT82_HUMAN LACRT_HUMAN KRT85_HUMAN LAIR1_HUMAN KSYK_HUMAN LAIR2_HUMAN KV127_HUMAN LAMP1_HUMAN KV133_HUMAN LAMP2_HUMAN KV230_HUMAN LAP2A_HUMAN KV311_HUMAN LAR4B_HUMAN KV315_HUMAN LBP_HUMAN KV320_HUMAN LCAT_HUMAN KV401_HUMAN LCN1_HUMAN KVD28_HUMAN LDHA_HUMAN KVD40_HUMAN LDHB_HUMAN LACRT_HUMAN LEG3_HUMAN LAMP1_HUMAN LFG3_HUMAN LAMP2_HUMAN LG3BP_HUMAN LANC1_HUMAN LHPP_HUMAN LANC2_HUMAN LICH_HUMAN LAP2A_HUMAN LIPL_HUMAN LASP1_HUMAN Supplemental Material 307 LITAF_HUMAN LCK_HUMAN LMAN2_HUMAN LCN1_HUMAN LMNA_HUMAN LDHA_HUMAN LPPRC_HUMAN LDHB_HUMAN LRC15_HUMAN LEG3_HUMAN LRC59_HUMAN LEG4_HUMAN LRP2_HUMAN LEG8_HUMAN LRP4_HUMAN LFG3_HUMAN LRRN4_HUMAN LG3BP_HUMAN LTBP2_HUMAN LGUL_HUMAN LTP_HHV11 LHPP_HUMAN LUM_HUMAN LIN7C_HUMAN LV147_HUMAN LITAF_HUMAN LV151_HUMAN LKHA4_HUMAN LV208_HUMAN LMAN2_HUMAN LV211_HUMAN LMBD1_HUMAN LV214_HUMAN LMTK2_HUMAN LV310_HUMAN LPPRC_HUMAN LV319_HUMAN LRC57_HUMAN LV321_HUMAN LRC59_HUMAN LV460_HUMAN LRC8A_HUMAN LV469_HUMAN LRP2_HUMAN LV657_HUMAN LTOR1_HUMAN LV743_HUMAN LV147_HUMAN LV861_HUMAN LV321_HUMAN LY75_HUMAN LXN_HUMAN LYAG_HUMAN LYN_HUMAN LYAM1_HUMAN LYNX1_HUMAN LYNX1_HUMAN LYPA1_HUMAN LYPA1_HUMAN LYPL1_HUMAN LYSC_HUMAN LYSC_HUMAN LYST_HUMAN MA1A1_HUMAN LYVE1_HUMAN MAL2_HUMAN MA1A1_HUMAN MARCS_HUMAN MA1C1_HUMAN MASP2_HUMAN MA2A1_HUMAN MATK_HUMAN MA2A2_HUMAN MB12A_HUMAN MA2B1_HUMAN MB12B_HUMAN MA2B2_HUMAN MDHC_HUMAN MABP1_HUMAN MDHM_HUMAN MADCA_HUMAN MDR1_HUMAN MAL2_HUMAN MEAK7_HUMAN MALR1_HUMAN MED23_HUMAN MAMC2_HUMAN MEMO1_HUMAN MASP2_HUMAN MERL_HUMAN MB12A_HUMAN MET_HUMAN MBTP1_HUMAN METK1_HUMAN MDHC_HUMAN MFGM_HUMAN MDHM_HUMAN MGA_HUMAN MDR1_HUMAN MIF_HUMAN MEGF8_HUMAN MINY1_HUMAN METK2_HUMAN MIPO1_HUMAN MFAP4_HUMAN MITD1_HUMAN MFGM_HUMAN MK01_HUMAN MGA_HUMAN MKRN2_HUMAN MARTA VIZOSO GONZÁLEZ 308 MGAL_HUMAN ML12A_HUMAN MGAT1_HUMAN MLRS_HUMAN MGT5A_HUMAN MOB1A_HUMAN MINP1_HUMAN MOES_HUMAN MITD1_HUMAN MOT5_HUMAN MLEC_HUMAN MP2K1_HUMAN MMP7_HUMAN MPP5_HUMAN MMP9_HUMAN MPPB_HUMAN MMRN2_HUMAN MRCKB_HUMAN MOB1A_HUMAN MRP_HUMAN MOES_HUMAN MRP8_HUMAN MOT12_HUMAN MSRA_HUMAN MPPB_HUMAN MSTRO_HUMAN MPRI_HUMAN MTPN_HUMAN MRO2A_HUMAN MUC1_HUMAN MSLN_HUMAN MUC4_HUMAN MUC1_HUMAN MVP_HUMAN MUC18_HUMAN MXRA8_HUMAN MUC20_HUMAN MYADM_HUMAN MUC5B_HUMAN MYH1_HUMAN MVP_HUMAN MYH10_HUMAN MXRA5_HUMAN MYH2_HUMAN MXRA8_HUMAN MYH4_HUMAN MY18B_HUMAN MYH6_HUMAN MYH1_HUMAN MYH7_HUMAN MYH2_HUMAN MYH8_HUMAN MYH4_HUMAN MYH9_HUMAN MYH7_HUMAN MYL1_HUMAN MYH8_HUMAN MYL6_HUMAN MYH9_HUMAN MYO1B_HUMAN MYO1C_HUMAN MYO1C_HUMAN MYOC_HUMAN MYO1D_HUMAN MYOM2_HUMAN MYO1E_HUMAN MYPC2_HUMAN MYO5B_HUMAN NAAA_HUMAN MYO6_HUMAN NADC_HUMAN MYOF_HUMAN NAGAB_HUMAN MYOM1_HUMAN NAPSA_HUMAN MYOM2_HUMAN NCAM1_HUMAN MYPC1_HUMAN NCHL1_HUMAN MYPC2_HUMAN NDUS1_HUMAN NAGK_HUMAN NECT2_HUMAN NAPSA_HUMAN NECT4_HUMAN NBEL2_HUMAN NEGR1_HUMAN NCKP1_HUMAN NEO1_HUMAN NDKB_HUMAN NEP_HUMAN NDRG1_HUMAN NEUR1_HUMAN NDUS1_HUMAN NEUS_HUMAN NEBL_HUMAN NGAL_HUMAN NEBU_HUMAN NHLC3_HUMAN NECT2_HUMAN NHRF1_HUMAN NED4L_HUMAN NHRF3_HUMAN NEP_HUMAN NICA_HUMAN NFASC_HUMAN NID1_HUMAN NHRF1_HUMAN NIT2_HUMAN NHRF2_HUMAN Supplemental Material 309 NOL6_HUMAN NHRF3_HUMAN NPC2_HUMAN NIBA2_HUMAN NQO2_HUMAN NICA_HUMAN NRP1_HUMAN NIT2_HUMAN NSDHL_HUMAN NNRE_HUMAN NSF_HUMAN NPHN_HUMAN NT5C_HUMAN NPS3A_HUMAN NUCB1_HUMAN NPT2C_HUMAN NUCB2_HUMAN NRAM2_HUMAN NUCL_HUMAN NSDHL_HUMAN NXF2_HUMAN NSF_HUMAN OAF_HUMAN NUCL_HUMAN OAT_HUMAN OAT_HUMAN ODP2_HUMAN OBSCN_HUMAN ODPB_HUMAN ODO2_HUMAN OGFD3_HUMAN ODP2_HUMAN OLFM4_HUMAN ODPB_HUMAN OMD_HUMAN OLA1_HUMAN OSCAR_HUMAN OLFM4_HUMAN OSTP_HUMAN OSTF1_HUMAN OTUB1_HUMAN OTUB1_HUMAN P210L_HUMAN OXSR1_HUMAN P3IP1_HUMAN P2RX4_HUMAN P5CR2_HUMAN P5CR2_HUMAN P5CS_HUMAN P5CS_HUMAN PA1B2_HUMAN PA1B2_HUMAN PADC1_HUMAN PA1B3_HUMAN PAG15_HUMAN PACN2_HUMAN PAPP2_HUMAN PACN3_HUMAN PARK7_HUMAN PACS1_HUMAN PBLD_HUMAN PADI3_HUMAN PCBP2_HUMAN PAK4_HUMAN PCD16_HUMAN PAR6B_HUMAN PCDGK_HUMAN PARK7_HUMAN PCDH1_HUMAN PARVA_HUMAN PCKGC_HUMAN PBLD_HUMAN PCP_HUMAN PCBP1_HUMAN PCX3_HUMAN PCBP2_HUMAN PCYOX_HUMAN PCDGC_HUMAN PD1L2_HUMAN PCKGC_HUMAN PDC10_HUMAN PCX3_HUMAN PDC6I_HUMAN PCYOX_HUMAN PDCD6_HUMAN PDC10_HUMAN PDE4D_HUMAN PDC6I_HUMAN PDGFD_HUMAN PDCD6_HUMAN PDIA1_HUMAN PDE4D_HUMAN PDIA2_HUMAN PDE8A_HUMAN PDIA3_HUMAN PDIA1_HUMAN PDIA4_HUMAN PDIA3_HUMAN PDIA6_HUMAN PDIA4_HUMAN PEBP1_HUMAN PDIA6_HUMAN PEDF_HUMAN PDLI5_HUMAN PEF1_HUMAN PDXK_HUMAN PEPA3_HUMAN PDZ1I_HUMAN PEPA5_HUMAN PEBP1_HUMAN MARTA VIZOSO GONZÁLEZ 310 PEPD_HUMAN PEF1_HUMAN PEPL_HUMAN PEPA5_HUMAN PF4V_HUMAN PEPD_HUMAN PFKAL_HUMAN PEPL_HUMAN PGAM1_HUMAN PERM_HUMAN PGAM4_HUMAN PF4V_HUMAN PGBM_HUMAN PFKAL_HUMAN PGCA_HUMAN PFKAM_HUMAN PGDH_HUMAN PGAM1_HUMAN PGFRB_HUMAN PGAM2_HUMAN PGK1_HUMAN PGDH_HUMAN PGM1_HUMAN PGFRB_HUMAN PGRP1_HUMAN PGK1_HUMAN PGRP2_HUMAN PGM1_HUMAN PGS1_HUMAN PGM2_HUMAN PGS2_HUMAN PGRP1_HUMAN PH4H_HUMAN PHAR4_HUMAN PHB_HUMAN PHB_HUMAN PIGR_HUMAN PHS_HUMAN PIP_HUMAN PI42C_HUMAN PKHD1_HUMAN PI4KA_HUMAN PLAK_HUMAN PICAL_HUMAN PLBL2_HUMAN PIGR_HUMAN PLCA_HUMAN PIP_HUMAN PLD3_HUMAN PIPNA_HUMAN PLDX1_HUMAN PIWL3_HUMAN PLF4_HUMAN PKD2_HUMAN PLMN_HUMAN PKHA1_HUMAN PLOD3_HUMAN PKHB2_HUMAN PLS1_HUMAN PKN2_HUMAN PLSI_HUMAN PLA2R_HUMAN PLST_HUMAN PLCA_HUMAN PMGT1_HUMAN PLCB3_HUMAN PNCB_HUMAN PLCB4_HUMAN PNPH_HUMAN PLCD1_HUMAN PNPO_HUMAN PLCG2_HUMAN PODO_HUMAN PLD3_HUMAN PODXL_HUMAN PLLP_HUMAN PON1_HUMAN PLPP1_HUMAN PPAL_HUMAN PLS1_HUMAN PPAP_HUMAN PLS4_HUMAN PPGB_HUMAN PLSI_HUMAN PPIA_HUMAN PLST_HUMAN PPIB_HUMAN PLXB2_HUMAN PPIC_HUMAN PMVK_HUMAN PPT2_HUMAN PNCB_HUMAN PRDX1_HUMAN PNPH_HUMAN PRDX2_HUMAN PNPO_HUMAN PRDX3_HUMAN PODO_HUMAN PRDX4_HUMAN PODXL_HUMAN PRDX5_HUMAN POTEE_HUMAN PRDX6_HUMAN PP1B_HUMAN PRG2_HUMAN PP1R7_HUMAN PROC_HUMAN PP2AA_HUMAN PROL4_HUMAN PPAC_HUMAN Supplemental Material 311 PROM1_HUMAN PPAL_HUMAN PROM2_HUMAN PPAP_HUMAN PROS_HUMAN PPBI_HUMAN PROZ_HUMAN PPBT_HUMAN PRSS8_HUMAN PPCE_HUMAN PRTN3_HUMAN PPIA_HUMAN PSA_HUMAN PPIB_HUMAN PSA1_HUMAN PRDX1_HUMAN PSA3_HUMAN PRDX2_HUMAN PSA4_HUMAN PRDX3_HUMAN PSA6_HUMAN PRDX4_HUMAN PSA7_HUMAN PRDX5_HUMAN PSB2_HUMAN PRDX6_HUMAN PSB5_HUMAN PRG2_HUMAN PSB8_HUMAN PROF1_HUMAN PSCA_HUMAN PROF2_HUMAN PSD1_HUMAN PROL4_HUMAN PTCD3_HUMAN PROM1_HUMAN PTER_HUMAN PROM2_HUMAN PTGDS_HUMAN PRSS8_HUMAN PTGR1_HUMAN PRVA_HUMAN PTN13_HUMAN PSA_HUMAN PTPRG_HUMAN PSA3_HUMAN PTPRJ_HUMAN PSA4_HUMAN PTPRK_HUMAN PSA6_HUMAN PTPRO_HUMAN PSA7_HUMAN PTPRS_HUMAN PSB2_HUMAN PTPRZ_HUMAN PSB6_HUMAN PVR_HUMAN PSCA_HUMAN PYGM_HUMAN PSMD2_HUMAN PZP_HUMAN PSMD9_HUMAN QCR1_HUMAN PSME1_HUMAN QOR_HUMAN PTCD3_HUMAN QPCT_HUMAN PTER_HUMAN QSOX1_HUMAN PTGDS_HUMAN R4RL2_HUMAN PTGR1_HUMAN RAB10_HUMAN PTGR2_HUMAN RAB14_HUMAN PTH1R_HUMAN RAB1A_HUMAN PTN11_HUMAN RAB1B_HUMAN PTN13_HUMAN RAB21_HUMAN PTN23_HUMAN RAB25_HUMAN PTN6_HUMAN RAB4A_HUMAN PTPA_HUMAN RAB4B_HUMAN PTPRB_HUMAN RAB5A_HUMAN PTPRJ_HUMAN RAB5B_HUMAN PTPRO_HUMAN RAB5C_HUMAN PTTG_HUMAN RAB7A_HUMAN PUR9_HUMAN RAB8A_HUMAN PXL2B_HUMAN RAC1_HUMAN PYGB_HUMAN RADI_HUMAN PYGM_HUMAN RAI3_HUMAN PZP_HUMAN RALA_HUMAN QCR1_HUMAN RALB_HUMAN QOR_HUMAN RAN_HUMAN RAB10_HUMAN MARTA VIZOSO GONZÁLEZ 312 RAP1A_HUMAN RAB12_HUMAN RAP1B_HUMAN RAB13_HUMAN RAP2B_HUMAN RAB14_HUMAN RASH_HUMAN RAB17_HUMAN RASK_HUMAN RAB18_HUMAN RB11B_HUMAN RAB19_HUMAN RBM22_HUMAN RAB1A_HUMAN RENBP_HUMAN RAB1B_HUMAN RENR_HUMAN RAB21_HUMAN RET4_HUMAN RAB23_HUMAN RHCG_HUMAN RAB25_HUMAN RHOA_HUMAN RAB2A_HUMAN RHOV_HUMAN RAB2B_HUMAN RINI_HUMAN RAB35_HUMAN RISC_HUMAN RAB3A_HUMAN RL17_HUMAN RAB3C_HUMAN RL40_HUMAN RAB4B_HUMAN RLA0_HUMAN RAB5A_HUMAN RLA2_HUMAN RAB5B_HUMAN RNAS1_HUMAN RAB5C_HUMAN RNAS2_HUMAN RAB6A_HUMAN RNT2_HUMAN RAB6B_HUMAN ROBO4_HUMAN RAB7A_HUMAN ROR1_HUMAN RAB7L_HUMAN RPN1_HUMAN RAB8A_HUMAN RRAS_HUMAN RAB8B_HUMAN RRAS2_HUMAN RABP2_HUMAN RS16_HUMAN RAC1_HUMAN RSSA_HUMAN RADI_HUMAN S10A6_HUMAN RAGP1_HUMAN S10A8_HUMAN RAI3_HUMAN S10A9_HUMAN RALA_HUMAN S12A1_HUMAN RALB_HUMAN S12A3_HUMAN RAN_HUMAN S13A2_HUMAN RAP1A_HUMAN S17A5_HUMAN RAP1B_HUMAN S22A2_HUMAN RAP2B_HUMAN S22A6_HUMAN RASH_HUMAN S22A8_HUMAN RASK_HUMAN S22AC_HUMAN RASL1_HUMAN S23A1_HUMAN RASM_HUMAN S26A4_HUMAN RASN_HUMAN S36A2_HUMAN RB11B_HUMAN S4A4_HUMAN RB22A_HUMAN S4A8_HUMAN RB27A_HUMAN S6A19_HUMAN RB27B_HUMAN SAA1_HUMAN RD23B_HUMAN SAA2_HUMAN RET4_HUMAN SAA4_HUMAN RFIP1_HUMAN SAHH_HUMAN RHBT3_HUMAN SAMP_HUMAN RHCG_HUMAN SAP_HUMAN RHG01_HUMAN SAP3_HUMAN RHG05_HUMAN SBP1_HUMAN RHG12_HUMAN SC5A2_HUMAN RHG18_HUMAN MARTA VIZOSO GONZÁLEZ 319 ANNEX 1: AUTHORISATION OF THE BIOETHICS COMMITTEE FOR HUMAN AND EXPERIMENTAL ANIMAL STUDIES 1.1 BIOETHICS COMMITTEE AUTHORIZATION FOR HUMAN STUDIES ANNEXES 320 MARTA VIZOSO GONZÁLEZ 321 1.2 BIOETHICS COMMITTEE AUTHORIZATION FOR STUDIES ON EXPERIMENTAL ANIMALS ANNEXES 322 Polycystic kidney disease (PKD) is a group of monogenic disorders resulting in renal cyst development, which cause chronic renal disease (CKD), and other organs, as liver, may be affected. Although several molecular pathways were discovered, the key mechanism remains unclear with no efficient therapy. PKD monitoring progression is based on the measurement of low specificity and sensitivity parameters which are unable to predict disease evolution. Hence, the main objectives of this work are studying cystogenesis-related signaling pathways for new therapeutic strategies and the development of a new diagnostic tool that allow to anticipate disease progression by characterizing the glycoprotein and vesicular fraction. In conclusion, the present thesis expands the knowledge about pathophysiology and biomarkers in PKD.