Persistent Controllers as the key Model to identify permanent HIV Remission

DOCTORAL THESIS PERSISTENT CONTROLLERS AS THE KEY MODEL TO IDENTIFY PERMANENT HIV REMISSION THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN MOLECULAR BIOLOGY, BIOMEDICINE, AND CLINICAL RESEARCH Presented by Carmen Gasca Capote Supervisors Dr. Ezequiel Ruiz-Mateos Carmona Dra. Karin Neukam Academic advisor Dr. Luis Fernando López Cortés 2 Table of content Fundings ............................................................................................................................... 4 Abstract................................................................................................................................. 6 Resumen .............................................................................................................................. 9 Abbreviations ...................................................................................................................... 12 1. Introduction .................................................................................................................. 17 1.1. ECs: a heterogeneous and dynamic group of PWHIV............................................... 19 1.2. Factors associated to a spontaneous persistent HIV control ..................................... 21 1.2.1. Immunological factors associated to a persistent spontaneous HIV control ........ 21 1.2.2. Biomarkers associated with the maintenance and loss of virological control ...... 23 1.2.3. Impact of the HIV reservoir on the spontaneous persistent HIV control .............. 25 2. Hypothesis and objectives ........................................................................................... 30 3. Methods ....................................................................................................................... 34 3.1. Study design and participants ................................................................................... 35 3.2. PBMCs isolation ....................................................................................................... 36 3.3. DNA and RNA extraction .......................................................................................... 36 3.4. Cell-associated HIV-1 DNA quantitation .................................................................... 37 3.5. Cell-associated HIV-1 RNA quantitation .................................................................... 38 3.6. CCR5Δ32 deletion assay .......................................................................................... 38 3.7. FLIP-seq ................................................................................................................... 39 3.8. Integration site analysis ............................................................................................ 41 3.9. Sequence analysis ................................................................................................... 42 3.10. CD8+ T cell proliferation assay ............................................................................... 42 3.11. HIV-specific T-cell response .................................................................................... 43 3.12. Thymic function assay ............................................................................................ 45 3.13. Statistics ................................................................................................................. 47 3.14. Study approval ........................................................................................................ 47 4. Results ......................................................................................................................... 49 4.1. Characteristics and clinical parameters of study participants .................................... 50 4.2. Distinct HIV-1 reservoir landscape in PCs compared to TCs before losing virological control............................................................................................................................. 53 4.3. Distinct integration sites of intact genome proviruses in PCs compared with TCs before losing the virological control ................................................................................. 59 4.4. Cell-associated HIV-1 RNA levels in PCs compared with TCs before losing virological control............................................................................................................................. 61 4.5. Longitudinal analysis of viral reservoir landscape in PCs and TCs ............................ 63 4.6. Distinct signature of immune selection in intact and defective proviral sequences of PCs and TCs .................................................................................................................. 64 4.7. Distinct Gag-specific T cell responses in PCs and TCs ............................................. 69 4.8. Distinct HIV-specific CD8+ T cell proliferation in PCs and TCs.................................. 71 4.9. Thymic function in PCs compared with TCs before losing virological control ............ 72 4.10. Sex-based differences in HIV-1 reservoir landscape in PCs and TCs before losing virological control ............................................................................................................ 73 5. Discussion ................................................................................................................... 76 6. Conclusions ................................................................................................................. 88 7. References .................................................................................................................. 91 8. Annexes ..................................................................................................................... 107 Annex I. Scientific contributions associated with this doctoral thesis .............................. 108 Annex Ia. Publications................................................................................................ 108 Annex Ib. Conferences and meetings ........................................................................ 131 Annex Ic. Press release and other public dissemination............................................. 133 Annex II. Other scientific contributions conducted during the doctoral thesis ................. 135 Annex IIa. Publications............................................................................................... 135 Annex IIb. Conferences and meetings ....................................................................... 139 Annex III. Ethics committee reports. .............................................................................. 146 4 Fundings This doctoral thesis was supported by the following contracts and projects: Research contract funded by Health Research Fund, Carlos III Health Institute (ISCIII), associated to the project: “Role of plasmacytoid dendritic cells in the spontaneous control of HIV replication and its pathogenesis: Clinical relevance of the involved mechanisms”. PI16/0684. Predoctoral contract (PFIS) funded by Health Research Fund, Carlos III Health Institute (ISCIII), FI19/00083, associated to the project: “Clinical trial to evaluate the safety and efficacy of Vedolizumab combined with ART to achieve functional cure in HIV-1 infected patients without prior ART”. PI18/01532. Predoctoral contract (M-AES) funded by Health Research Fund, Carlos III Health Institute (ISCIII). MV20/00057. Graduate Research Assistant, Ragon Institute of Mass General, MIT and Harvard. Research contract funded by AstraZeneca Foundation (AstraZeneca Young Researchers Awards), associated to the project: “Post-vaccination response 5 markers in immunocompromised populations (MARVIC): Opportunities for the development of new immunotherapies for the prevention of COVID-19”. Research project funded by the Health Research Fund, Carlos III Health Institute (ISCIII). “Analysis of the quality of HIV-1 reservoir in persistent elite controllers and people on long-term cART, association with immune parameters”. PI22/01796. Research project funded by Gilead Sciences, S.L. “Analysis of the quality of HIV-1 reservoir in persistent elite controllers and people on long-term cART, association with immune parameters”. GLD22/00147. 6 Abstract Elite controllers (ECs) are a heterogeneous and dynamic group regarding virological, immunological and clinical factors. Approximately 25% of ECs eventually loss virological control. This has enabled to classify ECs in two different phenotypes: persistent elite controllers (PCs), people with HIV (PWHIV) that permanently maintain virological control in the absence of antiretroviral treatment (ART); and transient elite controllers (TCs), PWHIV who eventually lost the virological control after being able to control viral replication without ART. Several studies have shown that PCs and TCs present different immunological, virological, proteomic, metabolomic and microRNA (miRNA) profiles. However, in terms of virological factors, it is essential to deeply characterize the quality of the human immunodeficiency virus (HIV) reservoirs to distinguish both phenotypes. This differentiation is key to identifying the factors that lead to HIV progression and could pave the way for new strategies in the pursuit of an HIV cure. Seventeen PCs, that have maintained virological control for a median of 25.5 [22.3-31.3] years; ten TCs with sustained viral load above the detection limit, >40 HIV RNA copies/mL, during more than one year of follow-up before losing the virological control for a median of 1.2 [0.5-1.7] years; and 41 PWHIV on ART, were included in the study. 7 The characterization of the HIV-1 reservoir, from peripheral blood mononuclear cells (PBMCs), was performed using next-generation sequencing (NGS) techniques, such as full-length individual proviral sequencing (FLIP-seq), matched integration site and proviral sequencing (MIP-seq) and integration site loop amplification (ISLA). The immune footprints to broadly neutralizing antibodies (bnAbs) and to cytotoxic Tlymphocyte (CTL) escape mutations were determined by intact and defective proviral sequences. The cell-associated HIV-1 RNA levels and the thymic function were quantified by droplet digital PCR (ddPCR), from previously extracted RNA and DNA, respectively. The CD8+ T cell proliferation and HIV-specific T cell response assays were performed by multiparametric flow cytometry after stimulation with gag peptides. PCs and TCs before losing the virological control, presented significantly lower total, intact and defective proviruses compared to participants on ART. Although no significant difference was found in total and defective proviruses between PCs and TCs, the proportion of intact proviruses were significantly lower in PCs compared to TCs; indeed, the intact/defective HIV-DNA ratio was significantly higher in TCs. Moreover, non-clonally expanded intact proviruses were found in TCs, indicating a greater viral diversity in comparison to PCs. Interestingly, no genome-intact HIV-1 was detected in most of the PCs, predominantly among females. Furthermore, intact proviruses from TCs were located into permissive genic euchromatic positions and presented higher resistance to bnAbs recognition, before losing virological control, in contrast to PCs whose intact proviruses were located in centromeric satellite DNA or zinc-finger genes, both associated with heterochromatin features. Cell-associated HIV-1 RNA levels were significantly higher in TCs, and not detected in five PCs that corresponded to the participants with no intact provirus detected. Lower levels of 8 Gag-specific T cell response polyfunctionality and higher CD8+ T cell proliferation and thymic function were found in TCs before losing virological control compared to PCs. Our findings revealed a markedly distinct intact and defective proviral reservoir, and immunological landscapes associated with the loss and maintenance of persistent spontaneous HIV-control. These results suggest the need for, and can give guidance to, the design of future research to identify a distinct proviral landscape that may be associated with the persistent control of HIV-1 without ART. 9 Resumen Los controladores élite (CEs) son un grupo heterogéneo y dinámico en cuanto a factores virológicos, inmunológicos y clínicos. Aproximadamente el 25% de los CEs eventualmente pierde el control virológico. Esto ha permitido clasificar a los CEs en dos fenotipos diferentes: los controladores de élite persistentes (CPs), personas con virus de la inmunodeficiencia humana (VIH) (PVVIH) que mantienen permanentemente el control virológico en ausencia de tratamiento antirretroviral (TAR); y los controladores de élite transitorios (CTs), PVVIH que eventualmente perdieron el control virológico después de haber sido capaces de controlar la replicación viral sin TAR. Varios estudios han demostrado que los CPs y los CTs presentan perfiles inmunológicos, virológicos, proteómicos, metabolómicos y de microARN (miARN) diferentes. Sin embargo, en términos de factores virológicos, es esencial caracterizar en profundidad la calidad del reservorio del VIH para distinguir ambos fenotipos. Esta distinción resulta fundamental para identificar los factores que favorecen la progresión de la enfermedad por VIH y podría abrir nuevas vías hacia el desarrollo de estrategias innovadoras en la búsqueda de una cura. En el estudio se incluyeron 17 CPs, que mantuvieron el control virológico durante una mediana de 25,5 [22,3-31,3] años; diez CTs con carga viral sostenida por encima del límite de detección, >40 copias de ARN del VIH/mL, durante más de un 16 17 1. Introduction 18 1. Introduction Despite major scientific advances during more than 40 years, no functional or sterilizing cure is available for HIV-1 infection. Since the first case of acquired immunodeficiency syndrome (AIDS) was reported in the United States in 1981, 88.4 million of people have been infected with the human immunodeficiency virus (HIV) and about 42.3 million people have died due to HIV. By the end of 2023, approximately, 39.9 million of people were living with HIV-1 (PWHIV), with an estimated 1.3 million new infections reported worldwide, according to the most recent report from Joint United Nations Programme on HIV/AIDS (UNAIDS) [1]. The main barrier for eradicating HIV-1 is the latent reservoir, infected cells, mainly CD4+ T cells, harboring full length replication-competent proviruses that persist despite antiretroviral treatment (ART) [2]. ART has improved life expectancy [3], but its interruption, in most cases, leads to a viral rebound within a few weeks due to long-lived reservoirs. However, there are groups of PWHIV that can naturally control viral replication off ART to low HIV-1 levels, i.e., the viremic controllers (VC), or to undetectable levels in plasma by commercially available assay, i.e., elite controllers (ECs) or in the case of HIV-1 post-treatment controllers (PTC) after analytical treatment interruption (ATI). 19 For this reason, reaching an approach that involves permanent virological control without ART, even if the reservoir persists, is a crucial milestone that turns ECs in the closest opportunity to achieve an HIV cure. 1.1. ECs: a heterogeneous and dynamic group of PWHIV ECs are a unique group of PWHIV, less than 1%, that can naturally control viral replication to undetectable levels in plasma by current commercial assays in the absence of previous ART. However, some ECs can experience virological and/or immunological progression and AIDS and/or non-AIDS events [4-6]. This thesis is focused on virological progression; however, the long-term control of viral load to undetectable levels is, in most cases, accompanied by preserved long-term CD4+ T cell levels [7-9]. ECs are a heterogeneous and dynamic group regarding immunological and virological factors. Consequently, over the years, different ECs definitions have been described in literature, being important to bring consensus to identify the right study model for a permanent HIV remission. These differences have allowed to differentiate ECs in two different phenotypes: those who persistently maintain virological control indefinitely overtime [10, 11], the persistent elite controllers (PCs); and those who eventually lose the virological control after being able to control viral replication without ART, the transient elite controllers (TCs) (Figure 1). 20 Figure 1. Time course of HIV-1 infection in PCs and TCs. Following an initial acute phase, PCs successfully maintain virological control, with undetectable viral load and stable CD4+ T cell counts in the absence of ART (A). In contrast, TCs experience a virological progression, losing the virological control after being able to control viral replication in the absence of ART (B). The red line represents plasma HIV-1 RNA levels (right Y-axis, logarithmic scale, copies/mL), while the black line represents CD4+ T cell count (left Y-axis, cells/mm³). The X-axis indicates the years of follow-up. Most studies in ECs are cross-sectional and consequently both groups, PCs and TCs, have been analyzed in the same group over time. For that reason, without a 21 comprehensive and longitudinal immunological and virological characterization, it is difficult to determine if an ECs, that suppress viral replication, will lose the virological control at some point. However, after the analysis of several studies focused on PCs, also known as long-term non-progressor elite controllers (LTNP-EC) [12, 13], longterm elite controllers (LTECs) [14], exceptional elite controllers (EEC) [7], human immunodeficiency virus controllers (HICs) [15] and supercontrollers if they are also able to clear hepatitis C virus (HCV) [16], ECs can be classified as PCs if they maintain permanently undetectable viral load without ART for more than 20 years and with stable, non-declining CD4+ T cell counts with more than 500 cells/mm3 [7, 11, 15]. Although, previous studies have focused on distinguishing both phenotypes, PCs and TCs (Figure 2, please see page 28), the specific mechanisms responsible for the maintenance and loss of the spontaneous virological control in PCs and TCs, respectively, remain not clearly defined. 1.2. Factors associated to a spontaneous persistent HIV control 1.2.1. Immunological factors associated to a persistent spontaneous HIV control Several studies have associated the spontaneous HIV control, in the majority of cases, with host immune factors such as human leukocyte antigen class I alleles (HLA-I) [17-21], and the associated immune responses mediated by T cells [11, 2224]. Several HLA-B alleles have proved effective to restrict virus replication [20, 21] whereas others have been associated with risk of HIV progression [25, 26]. Some of these protective alleles, especially HLA-B*57 [13, 27, 28] and HLA-B*27 [29], are 22 overrepresented in PCs [7], suggesting that the associated HIV-1-specific cytotoxic T-lymphocyte (CTL) responses [18, 19, 27, 30] likewise the HIV-1-specific natural killer (NK) cell activation by certain killer cell immunoglobulin receptors (KIR) molecules, [31, 32] restricted by these alleles, may be crucial for the persistent spontaneous control of HIV-infection. Furthermore, more recently, the heterozygosity of HLA-I has also been related to HIV control but its effects on the HIV persistent control are still undetermined [33]. The existence of PCs, that maintain virological control without any of these alleles [34] suggests the presence of other immune mechanisms responsible for the persistent spontaneous HIV control [17]. These immune mechanisms may be also involved in a better ability of PCs, compared to TCs, to spontaneously clear or control other chronic infection such as HCV [16, 35, 36]. HCV spontaneous clearance has been linked to a strong virus-specific T cell response and polyfunctionality [37, 38], and a privileged innate immunity activity. A differentiating NK phenotype, increased CD57 and HLA-C-KIR levels [39, 40], as well as a preserved myeloid dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs) frequency, phenotype and antigen-presenting capacity have been associated to HCV spontaneous clearers [41, 42]. Furthermore, PCs present a stable CD4+ T cell counts and CD4 and CD8 T cell homeostasis alterations characterized by increased naïve T cells, non-senescent effector CD8+ T cells and central memory CD4+ T cells [14]. These attributes might be related to the greater and stable HIV-specific CD4+ [11] and CD8+ T cell responses over the years in PCs [7, 11, 13]. In addition, a highly polyfunctional HIV1-specific T cell response, especially in CD8+ T cells, has been associated with the control of viral replication in PCs [7, 11, 13]. By contrast, TCs, before losing 23 virological control, present lower CD4/CD8 ratio, with decreased CD4+ and increased CD8+ T cell levels [10, 15] and a lower and drastic decrease of HIVspecific CD4+ and CD8+ T cell response, compared to PCs, at one year before losing the virological control [11]. Importantly, the time before losing virological control might be key to distinguish both phenotypes since no differences were found in the HIV-specific T cell response at two years before the loss [11]. Other immunological factors associated with spontaneous control have been NK cells [35, 43] and dendritic cells (DC) [7, 35]. However, limited data are available regarding the role of NK and DC in the persistent virological control. It is known that PCs display a specific NK profile with an increase of CD16dimCD56dim subset [35, 43] and low NK CD56dim cells expressing HLA-DR, exhaustion markers such as TIGIT and LAG3, and CXCR6 [35]. Regarding DC, PCs have increased number of mDCs [7] and pDCs expressing the lymph node homing marker CCR7 [35]. 1.2.2. Biomarkers associated with the maintenance and loss of virological control Several studies have focused on identifying reliable biomarkers associated with the maintenance and loss of the virological control [11, 44-46] with the aim of: i) distinguishing ECs that are going to lose the virological control to treat them before the viral rebound; ii) identifying PCs as the premier model to identify permanent HIV remission. Specific metabolomic, proteomic and microRNAs (miRNA) profiles have been associated with the loss of virological control in TCs [44-46], suggesting that they might be important factors to restrict viral replication. 24 Concerning metabolism, PCs and TCs present different metabolomic signature with significant differences in glycolysis, the Krebs cycle, and amino acid catabolism pathways [46]. TCs metabolism is mainly characterized by an aerobic glycolytic metabolism, a deregulated mitochondrial function, oxidative stress and increased immunological activation [46]. TCs, compared to PCs, display a similar metabolic profile to what is generally describe for HIV infection, lower glucose concentration due to a significant increase in energy demand, and consequently, an increase in glycolytic intermediates as 3P-glycerate, phosphoenolpyruvate and pyruvate, and branched-chain amino acids as valine, that proved to be the main differentiating metabolomic factor between PCs and TCs [46]. The increased levels of Krebs cycle intermediates in TCs, as α-ketoglutarate, are possibly supplied by anaplerotic reactions with the aim of trying to compensate the lack of oxidative Krebs cycle activity [46]. The proteomic signature associated with the loss of the virological control is characterized by an increased expression of proteins related to transendothelial migration, coagulation, and inflammation mechanisms, some of them related to HIV1 replication and pathogenesis, and interactions with structural viral proteins [11, 45]. Coagulation factor XI, α-1-antichymotrypsin, ficolin-2, 14-3-3 protein, platelet-derived growth factor AA (PDGF-AA), RANTES and galectin-3-binding protein (LG3BP) are considered potential biomarkers to predict the loss of the virological control in TCs [11, 45]. Importantly, LG3BP previously associated with an increase in HIV-1 replication through a direct interaction with the viral envelope gp120 and host CD4+ T cells [47, 48], and the proinflammatory cytokine RANTES previously associated with HIV progression, proved to be the main biomarkers to identify TCs that are going to lose the virological control [49]. Other potential biomarkers are related to 25 extracellular vesicles (EV) [50]. PCs present greater levels of EV-associated cytokines with higher levels of EV IL-3 and EV tumor necrosis factor-related apoptosis inducing ligand (TRAIL), compared to TCs, that could be good identifiers to discriminate both phenotypes [50]. Furthermore, elevated levels of IL-8, IP-10 and activated CD8 T cells (CD38+HLA+DR+) have been associated with disease progression in ECs [51]. miRNAs expression is also involved in HIV infection, regulating genes that facilitate and/or restrict viral replication [52]. The expression of hsa-miR-27a-3p, hsamiR-376a-3p, and hsa-miR-199a-3p, related to lipid metabolism, have been associated with the loss of the virological control, suggesting a lipid dysregulation in TCs [44]. Among them, hsa-miR-199a-3p demonstrates to be the best miRNA to identify TCs that are going to lose the virological control [44]. The increased lipid levels in TCs, before the loss, might be a compensatory mechanism against the viral replication fueled by the aerobic glycolytic pathways that consequently may increase anabolic metabolism, as previously reported in TCs [46]. 1.2.3. Impact of the HIV reservoir on the spontaneous persistent HIV control First studies suggested that ECs were infected with attenuated quasi species of defective HIV-1 variants, specifically deletions in the nef/long terminal repeat (LTR) [53]. However, a later study in the same cohort demonstrated clinical progression in some of these ECs [54]. Subsequently, it was demonstrated that ECs can present replication-competent virus and yet control the viral replication [55, 56]. Originally, it was known that PCs presented low levels of total HIV-1 DNA with low viral diversity in env and gag genes compared to TCs [7, 11]. Currently, cutting-edge 32 Specific objectives • To characterize the HIV-1 reservoir, from PBMCs, using: • Next-generation sequencing (NGS) techniques: o Full-length individual proviral sequencing (FLIP-seq) to differentiate between intact and defective proviruses and assess possible clonal expansion. o Matched integration site and proviral sequencing (MIP-seq) and integration site loop amplification (ISLA) to identify the provirus integration site. • Droplet digital PCR (ddPCR) for HIV-1 RNA quantification. • To identify specific HIV-1 reservoir landscapes associated with direct and indirect immune parameters. • Direct immune parameters: o Multiparametric flow cytometry for HIV-specific T cell responses and CD8+ T cell proliferation analysis. o ddPCR for thymic function assessment. • Indirect immune parameters: o Analysis of immune pressure based on proviral sequences. Immune footprints associated with broadly neutralizing antibodies (bnAbs) and CTL escape mutations identified through analysis of intact and defective proviral sequences. 33 34 3. Methods 35 3. Methods 3.1. Study design and participants Participants were defined as ECs when viral load measurements were under the detection limit in the absence of ART for at least one year of follow-up [11]. PBMCs were collected from 27 ECs and 41 participants on ART. Cisgender women and men were included in the study. Ten ECs were classified as TCs and 17 as PCs. Participants were classified as TCs after experiencing a loss of virological control; sustained viral load above the detection limit during more than one year of follow-up (at least two consecutive detectable viral loads), as previously reported [11]. Using this classification, we have previously observed differences in immunological [11], proteomic [45], metabolomic [46], miRNA [44] and virological profiles [11] in PCs compared with TCs before losing virological control. Participants were classified as PCs after maintaining persistent virological control during the follow-up period. TCs were selected based on sample availability less than two years before losing virological control, according to previous findings [11]. Frozen PBMCs of these participants were obtained from Spanish HIV Hospital Gregorio Marañon BioBank belonging to the AIDS Research Network [63] and collecting participants’ clinical data from Red de Investigacion en Sida (RIS) Controllers Study Group Cohort (ECRIS) [5]. PCs samples were collected from Virgen del Rocio and Virgen Macarena 36 University Hospitals, Seville, Spain; Lausanne University Hospital, Lausanne, Switzerland; Virgen de las Nieves University Hospital, Granada, Spain; Reina Sofía University Hospital, Córdoba, Spain; Costa del Sol and Virgen de la Victoria Hospital, Málaga, Spain; Torrecardenas University Hospital, Almería, Spain, and Joan XXIII University Hospital, Tarragona, Spain. Samples of participants on ART were collected from Massachusetts General Hospital (MGH). 3.2. PBMCs isolation PBMCs were isolated using BD Vacutainer CPT Mononuclear Cell Preparation Tubes (BD Biosciences), with sodium heparin as anticoagulant, by density gradient centrifugation at the same day of blood collection. CPTs were centrifuged at 1,811g for 20 minutes at room temperature (RT). Afterward, PBMCs were cryopreserved in freezing medium (90% Fetal bovine serum (FBS) [Thermo Fisher Scientific] and 10% DMSO [PanReac AppliChem]) in liquid nitrogen until further use. 3.3. DNA and RNA extraction Genomic DNA and RNA were extracted from PBMCs using a blood DNA minikit (Omega Bio-Tek) and NucleoSpin RNA purification kit (Macherey-Nagel), respectively. DNA and RNA were quantified using the Qubit assay (Thermo Fisher Scientific) according to the manufacturer’s instructions. 37 3.4. Cell-associated HIV-1 DNA quantitation HIV-1 DNA was quantified from previously extracted DNA by ddPCR using the BIO-RAD QX200 Droplet Reader, as previously reported [64]. The PCR program was run according to the manufacturer’s protocol using an annealing temperature of 58°C. Primers and probes targeting gag regions and the viral 5′ LTR are shown in Table 1. Ribonuclease P protein subunit p30 (RPP30) was used as a housekeeping gene to normalize HIV-1 DNA copies. The primers and probe that were used to quantify RPP30 are shown in Table 1. Data were analyzed using Bio-Rad QuantaSoft software version 1.7.4. Table 1. Primers and probes targeting the gag regions, the viral 5′ long terminal repeat (LTR) and the housekeeping gene ribonuclease P protein subunit p30 (RPP30). Gag Forward 5′-CATGTTTTCAGCATTATCAGAAGGA-3′ Reverse 5′-TGCTTGATGTCCCCCCACT-3′ Probe 5′-VIC-CCACCCCACAAGATTTAAACACCATGCTAA-BHQ1-3′ LTR Forward 5′-TGTGTGCCCGTCTGTTGTGT-3′ Reverse 5′-GCCGAGRCCTGCGTCGAGAG-3′ Probe 5′-FAM (6-carboxyfluorescein)-CAGTGGCGCCCGAACAGGGA-BHQ1-3′ RPP30 Forward 5′-GATTTGGACCTGCGAGCG-3′ Reverse 5′-GCGGCTGTCTCCACAAGT-3′ Probe 5′-VIC-CTGACCTGAAGGCTCT-BHQ1-3′ The sequence direction is from the 5' to the 3' end. 38 3.5. Cell-associated HIV-1 RNA quantitation Cell-associated HIV-1 RNA was quantified from previously extracted RNA by ddPCR with the One-Step RT-ddPCR kit (Bio-Rad) using the BIO-RAD QX200 Droplet Reader, as previously reported [65]. The PCR program was run according to the manufacturer’s protocol using an annealing temperature of 58°C and the same primers and probes as previously described (see section 3.4, cell-associated HIV-1 DNA quantitation). TATA-box binding protein (TBP) was used as a housekeeping gene to normalize HIV-1 RNA copies. The primers and probe that were used to quantify TBP are shown in Table 2. Data were analyzed using the Bio-Rad QuantaSoft software version 1.7.4. Table 2. Primers and probe targeting the housekeeping gene TATA-box binding protein (TBP). TBP Forward 5’-CACGAACCACGGCACTGATT-3’ Reverse 5’-TTTTCTTGCTGCCAGTCTGGAC-3’ Probe 5’-HEX-TGTGCACAGGAGCCAAGAGTGAAGA/3-IABkFQ-3 The sequence direction is from the 5' to the 3' end. 3.6. CCR5Δ32 deletion assay CCR5Δ32 deletion was detected from genomic DNA, previously extracted from PBMCs, as previously reported [66]. Primers franking the 32-bp deletion in the CCR5 gene are shown in Table 3. The PCR program was run according to the 39 manufacturer’s protocol using an annealing temperature of 56°C in a reaction volume of 50 μl containing a final concentration of 1xPCR Buffer, 1.5 mM MgCl2, 10 mM dNTP mix, 0.2 uM primers and 2 Units/reaction PlatinumTM Taq DNA Polymerase (Invitrogen). PCR products were visualized by 2 % agarose gel electrophoresis (Quantify One and ChemiDoc MP Image Lab; BioRad). A 185-bp fragment accounting for the wild-type (WT) allele and a 153-bp fragment accounting for the deleted allele. Table 3. Primers franking the 32-bp deletion in the CCR5 gene. CCR5-Δ32 Forward 5’-GGAATCATCTTTACCAGATCTCAAAAA-3’ Reverse 5’-CATGATGGTGAAGATAAGCCTCACA-3’ The sequence direction is from the 5' to the 3' end. 3.7. FLIP-seq Genomic DNA, previously extracted from PBMCs, was diluted to single proviral genomes based on ddPCR results (see section 3.4, cell-associated HIV-1 DNA quantitation) and Poisson distribution statistics (one provirus was present in approximately 20-30% of wells). Afterwards, DNA was subjected to HIV-1 near-fullgenome amplification using a single-amplicon nested PCR approach, as previously reported [67]. The primers that were used for the first and second-round nested-PCR are shown in Table 4. 40 Table 4. First and second-round primers for HIV-1 near-full-genome amplification. FLIP-seq 1st Forward 5′-AAATCTCTAGCAGTGGCGCCCGAACAG-3′ 1st Reverse 5′-TGAGGGATCTCTAGTTACCAGAGTC-3′ 2nd Forward 5′-GCGCCCGAACAGGGACYTGAAARCGAAAG-3′ 2nd Reverse 5′-GCACTCAAGGCAAGCTTTATTGAGGCTTA-3′ The sequence direction is from the 5' to the 3' end. PCR products were visualized by 0.7% agarose gel electrophoresis (Quantify One and ChemiDoc MP Image Lab; BioRad) and all near full-length HIV-1 (≈8000 bp) were subjected to Illumina MiSeq sequencing at the MGH DNA Core facility. Short reads were de novo assembled using Ultracycler version 1.0 and aligned to HXB2 to identify large deleterious deletions (<8000 bp of the amplicon), out-offrame indels, premature/lethal stop codons, internal inversions, or packaging signal deletions (≥15 bp insertions and/or deletions relative to HXB2) through an automated pipeline written in Python programming language [68]. The presence/absence of APOBEC-3G/3F-associated hypermutations was determined using Los Alamos National Laboratory (LANL) HIV-1 Sequence Database Hypermut 2.0 program [69]. Viral sequences without any of the mutations previously mentioned were classified as intact genome sequences. An alternative analysis was used to classify a sequence as intact, as previously reported [70]. Phylogenetic distances between sequences were determined through maximum-likelihood trees in MEGA and visualized with Highlighter plots (https://www.hiv.lanl.gov/content/sequence/HIGHLIGHT/highlighter_top.html). 41 Clonality was determined by identical sequences, when any or less than or equal to three mismatches was found between proviral sequences. Nucleotide variations due to primer binding sites were not considered for clonality analysis. 3.8. Integration site analysis MIP-seq technique was used to profile the chromosomal locations of intact proviruses. Firstly, a whole genome amplification (WGA) was performed by a multiple displacement amplification (MDA) with Φ29 polymerase (REPLI-g Single Cell Kit) (Qiagen) [71], according to the manufacturer’s protocol. Subsequently, DNA from each well was split and separately subjected to viral sequencing and integration site analysis [67]. Integration sites of each intact provirus, obtained from the WGA, were identified by integration site loop amplification (ISLA) technique, as previously described [58]. A second WGA was performed when it was necessary to increase the amount of DNA. PCR products were subjected to NGS using Illumina MiSeq. MiSeq paired-end FASTQ files were demultiplexed; small reads (142 bp) were then aligned simultaneously to human reference genome GRCh38 and HIV-1 reference genome HXB2 using bwa-mem [72]. Biocomputational identification of integration sites was performed according to previously described procedures [58, 73]. Briefly, chimeric reads containing both human and HIV-1 sequences were evaluated for mapping quality based on: (i) HIV-1 coordinates mapping to the terminal nucleotides of the viral genome, (ii) absolute counts of chimeric reads, (iii) depth of sequencing coverage in the host genome adjacent to the viral integration site. The final list of integration sites and its corresponding chromosomal annotations 48 49 4. Results 50 4. Results 4.1. Characteristics and clinical parameters of study participants Twenty-seven ECs, with undetectable viral load in the absence of ART for at least one year of follow-up, were included in the study [11]. Ten ECs were classified as TCs after experiencing a loss of virological control during more than one year of follow-up, and 17 as PCs after maintaining persistent virological control during the follow-up period [11]. Characteristics and clinical parameters of PCs and TCs are detailed in Table 6. The PCs group was older and presented a longer time since HIV diagnosis than the TCs group (Table 6). There were no differences in the remaining variables. Fifty-three percent of PCs presented protective HLA-alleles, HLAB27/B57, versus 20% of TCs. Viral loads (HIV RNA copies/mL), CD4+ T cells (cells/mm3), and the CD4/CD8 ratio from the 10 TCs, before and after losing the virological control, are shown in Figure 4. The studied time points of the TCs preceded the loss of the virological control from 0.3 to 2 years. PCs have maintained undetectable viral load with no ART for a median of 25.5 [IQR, 22.3–31.3] years with a median of 791 [IQR, 647–1013] CD4+ T cells/mm3. 51 Table 6. Characteristics and clinical parameters of the study participants. Categorical variables are expressed as number and percentages (%), and continuous variables are expressed as median (interquartile ranges [IQR]). Chi-Square and MannWhitney U test was used to compare categorical and continuous variables, respectively. Pvalue <0.05 was considered statistically significant. ACD8+T cell count not available in TC1 and TC3. BHeterozygous for CCR5-Δ32 allele mutation, CCR5-Δ32 not available in P15. 1 Variables PC (n=17) TC (n=10) P-value Female, n (%) 6 (35.3) 4 (40.0) 0.810 Time before losing the control (years) [range] n/a 1.2 [0.5-1.7] n/a Age (years) [range] 55 [47 – 57] 41 [37-51] 0.040 Time since HIV diagnosis (years) [range] 25.5 [22.3-31.3] 16.8 [13.1-19.5] 0.005 CD4+ T cell counts (cell/mm3) [range] 791 [647-1013] 805 [644-902] 0.874 CD4/CD8 ratio [range]A 1 [0.5-1.3] 1 [0.6-1.6] 0.905 CCR5-Δ32, n (%) B 3 (18.8) 2 (20) 0.937 HLA-B57, n (%) 6 (35.3) 2 (20) 0.410 HLA-B27, n (%) 3 (17.7) 0 0.167 HLA-B27/B57, n (%) 9 (53.0) 2 (20) 0.099 HLA-B27/B57/CCR5-Δ32, n (%) 10 (62.5) 4 (40) 0.263 52 Figure 4. CD4+ T cell counts, viral load levels, and the CD4/CD8 ratio in TCs. CD4+ T cell counts and the CD4/CD8 ratio were represented in the left axes (CD4+ T cell count in orange and CD4/CD8 ratio in green) and viral load levels in the right axes (red). CD8+ T cell counts were unavailable in TC1 and TC3. The black dot represents the studied time point that preceded the loss of the virological control. 53 4.2. Distinct HIV-1 reservoir landscape in PCs compared to TCs before losing virological control The analysis of proviral sequences was performed using FLIP-Seq. The number of cells assayed, clades, and the total, intact and defective proviral sequences of the PCs and TCs are detailed in Table 7 and 8, respectively. Table 7. Characterization of HIV-1 reservoir, from PBMCs, by FLIP-seq and MIP-seq in PCs. Clades of intact HIV-1 proviral sequences, number of cells assayed and total, intact and 54 defectives sequences accounted for by each PCs. n/a: Not available, not possible to determine through the sequencing data. Table 8. Characterization of HIV-1 reservoir, from PBMCs, by FLIP-seq and MIP-seq in TCs. Clades of intact HIV-1 proviral sequences, number of cells assayed and total, intact and defectives sequences accounted for by each TCs. n/a: Not available, not possible to determine through the sequencing data. The individual HIV-1 proviral genome analysis was not different in the total (intact plus defective) (P = 0.167) (Figure 5A) nor the defective provirus levels (P = 0.141) (Figure 5B) between PCs (n = 17) and TCs (n = 10). Interestingly, the near full length intact provirus levels were significantly increased in TCs in comparison with PCs (P = 0.006) (Figure 5C) and importantly, no genome-intact HIV-1 was detected in 70.59% of PCs (indicated as grey dots in Figure 5C). Consequently, the 55 intact/defective HIV-DNA ratio was significantly higher in TCs compared with PCs, counting the clones (P = 0.013) or not (P = 0.005) (Figure 6, A and B, respectively). Significant differences were found in the total (Figure 5A), defective (Figure 5B), and intact provirus levels (Figure 5C) of PCs and TCs compared with participants on ART. PCs with higher levels of intact proviruses (n = 3) (Figure 5C) were derived from clonally expanded HIV-1 infected cells that accounted for 100% of all intact proviruses (Figure 7; left panel). However, clonally expanded intact proviruses were not observed in TCs (Figure 7; right panel). Figure 5. Analysis of HIV-1 proviral sequences in PCs, TCs and participants on ART. Total (A), defective (B) and intact proviruses (C) levels in PCs, TCs and participants on ART. 56 Grey dots represent values below the limit of detection (expressed as 0.05 copy/total number of analyzed cells without target identification). PCs and TCs are represented by unique identifiers (Table 7 and 8). Each dot represents a participant. Mann-Whitney U test was used to compare PCs, TCs and participants on ART. P value <0.05 was considered statistically significant. Figure 6. Intact/Defective HIV DNA ratio. Intact/Defective HIV DNA ratio, including clonal sequences (A). Intact/Defective HIV DNA ratio, excluding clonal sequences (B). Grey dots represent values below the limit of detection and for this calculation has been considered as zero. Each dot represents a participant. PCs and TCs are represented by unique identifiers (Table 7 and 8). Mann-Whitney U test was used to compare PCs and TCs participants. P value <0.05 was considered statistically significant. 57 Figure 7. Genome-proviral sequences in PCs and TCs. Circular maximum-likelihood phylogenetic trees for all genome-intact proviral sequences from PCs and TCs. HXB2, reference HIV-1 sequence. Dots with the same colors represent genome-intact proviral sequences from the same participant. PCs and TCs are represented by unique identifiers (Table 7 and 8). Clonal sequences are indicated by black arches. Further, significant differences were found in the proportion of nonclonal intact and defective sequences from PCs and TCs (Figure 8A). The proportion of intact proviruses (P = 0.006) and hypermutations (P = 0.013) were higher in TCs compared with PCs. However, the large deletion (LD) genome-defective provirus levels were lower in TCs compared with PCs (P = 0.004). No differences were found in the proportion of packaging signal defect (PSI) (P = 0.518), premature stop codon (PMSC) (P > 0.999) and internal inversion (P = 0.226) (Figure 8A) between PCs and TCs. Nevertheless, the proportion of sequences with internal inversion, counting clonal sequences, was significantly higher in PCs compared with TCs (P = 0.005), since internal inversion accounted for 57.9% of the HIV-1 total proviruses (81.8% were clonal) in the PC10 (Figure 8B). 64 three different time points, 13, three, and two years before losing virological control, in a total of 8.2 million cells. Interestingly, intact HIV-1 proviruses were found two years before losing control, not being detected 13 and three years preceding aborted virological control (Figure 11; TC10). High defective provirus levels, mainly with hypermutations, were found even 13 years before losing virological control. Figure 11. Longitudinal evolution of genome proviral reservoir landscape in PCs and TCs. Total, intact and defective proviruses levels in PC3, 4, 5, 6 and TC10 over time. 4.6. Distinct signature of immune selection in intact and defective proviral sequences of PCs and TCs We analyzed the frequencies of amino acid variations associated with sensitivity or resistance to bnAbs recognizing the CD4 binding site, the V2/V3 envelope regions, or the membrane proximal external region (MPER), as previously reported (23), per intact and defective proviral sequence in PCs and TCs. Overall, 65 TCs presented higher number of amino acid changes associated with resistance to bnAbs per intact (P = 0.021) (Figure 12A) and defective provirus (P = 0.007) (Figure 12B) than PCs. These differences were more remarkable for antibodies targeting the CD4 binding site (P = 0.011) and for antibodies recognizing the V2 envelope region (P < 0.0001) in intact provirus (Figure 13A) and MPER regions (P =0.080) in defective proviruses (Figure 13B). Interestingly, intact-genome sequences with higher number of bnAb-resistance sites in TCs (Figure 12A) belonged to the participant who was closer to losing virological control, 0.3 years before the loss (Figure 9; TC1). On the contrary, PCs presented higher frequencies of amino acid variations associated with sensitivity to bnAbs per intact (P= 0.065) (Figure 12C) and defective proviruses (P = 0.020) (Figure 12D). These differences were more pronounced for antibodies recognizing the V2 envelope region (P = 0.023) and MPER region (P =0.046) (Figure 14A) in intact provirus and the CD4 binding sites (P = 0.047) and V3 envelope region (P = 0.021) in defective proviruses (Figure 14B). Unexpectedly, the frequencies of amino acid variations associated with sensitivity to bnAbs recognizing the V3 envelope region in intact proviruses were significantly higher in TCs (P = 0.020) (Figure 14A) whereas the frequencies of amino acid variations associated with resistance to bnAbs recognizing the same region were lower (P =0.022) compared with PCs (Figure 13A). 66 Figure 12. Analysis of bnAb resistance and sensitivity signature sites in intact and defective proviral sequences of PCs and TCs. Number of bnAb resistance sites per intact (A) and defective provirus (B) in PCs and TCs. Number of bnAb sensitivity sites per intact (C) and defective provirus (D) in PCs and TCs. Each dot represents an intact or defective proviral sequence. PCs and TCs are represented by unique identifiers (Table 7 and 8). Mann-Whitney U test was used to compare PCs and TCs. P value <0.05 was considered statistically significant. 67 Figure 13. Analysis of bnAb associated with resistance to four classes of bnAbs in intact and defective proviral sequences of PCs and TCs. Number of bnAb resistance sites per intact (A) and defective (B) provirus in PCs and TCs. Four classes of bnAbs, specific for the CD4 binding site, V3 and V2 domain and MPER region, are represented. Each dot represents an intact or defective proviral sequence. Mann-Whitney U test was used to compare PCs and TCs. P value <0.05 was considered statistically significant. 68 Figure 14. Analysis of bnAb associated with sensitivity to four classes of bnAbs in intact and defective proviral sequences of PCs and TCs. Number of bnAb sensitivity sites per intact (A) and defective (B) provirus in PCs and TCs. Each dot represents an intact or defective proviral sequence. Mann-Whitney U test was used to compare PCs and TCs. P value <0.05 was considered statistically significant. We next analyzed signs of CTL-driven immune pressure in the proviral sequences of PCs and TCs. No differences were found in the proportion of WT CTL or escape variant per intact (Figure 15, A and B) and defective proviruses (Figure 15, C and D) of PCs and TCs. 69 Figure 15. Proportions of optimal CTL epitopes (restricted by autologous HLA class I alleles) with WT clade B consensus sequences or with previously described CTL escape mutations in PCs and TCs. Proportion of WT CTL epitopes per intact (A) and defective (C) provirus. Proportion of CTL escape variant per intact (B) and defective (D) provirus. Each dot represents an intact or defective proviral sequence. Mann-Whitney U test was used to compare PCs and TCs. P value <0.05 was considered statistically significant. 4.7. Distinct Gag-specific T cell responses in PCs and TCs Immunological differences between PCs and TCs have previously been associated with the loss of the virological control [11]. We compared and associated 70 immune parameters of 14 PCs and five TCs with the quality of the HIV-1 reservoir. No differences in the magnitude of the response, assayed by cytokine production (IL2, TNF-α, and IFN-Ƴ) after stimulation with Gag peptides in PCs and TCs, were observed neither in CD4+ nor in CD8+T cells (for gating strategy, see Figure 3). However, a lower frequency of polyfunctionality, defined as simultaneous production of IFN-Ƴ, TNF-α, IL-2, CD107a, and perforin (PRF) per T cell in response to Gag stimulation, was observed in central memory (CM) CD4+ T cells in TCs compared with PCs (P = 0.049) (Figure 16A). Significant positive correlations were found between total proviruses and the frequency of HIV-specific total memory (P = 0.037; r=0.900) and CM CD4+ T cell response (P = 0.037; r=0.900) (Figure 16B) in TCs but not in PCs (P = 0.311; r = 0.292 and P = 0.383; r = 0.253, respectively) (Figure 16C). 71 Figure 16. HIV-1-specific T cell response in PCs and TCs. HIV-1-specific CM CD4+ T cell polyfunctionality with up to five functional responses to Gag stimulation per T cell in PCs and TCs (A). The five functional responses to Gag stimulation represent the simultaneous production of IFN-γ, TNF-α, IL-2, CD107a and PRF per T cell. IFN-γ, TNF-α, IL-2, CD107a and PRF are shown in arcs in the polyfunctional distribution. Pestle and Spice were used for analysis. Correlations between Gag-specific CM T cell response with total HIV DNA levels (106 PBMCs) in TCs (B) and PCs (C). Each dot represents a participant. PCs and TCs are represented by unique identifiers (Table 7 and 8). Spearman test was used for nonparametric correlations. 4.8. Distinct HIV-specific CD8+ T cell proliferation in PCs and TCs Given the importance of the CD8+ T cell role in the spontaneous virological control [78], we analyzed CD8+ T cell proliferation in PCs and TCs after stimulation with an HIV (Gag)-specific peptide pool. Comparing the experimental condition (Figure 17, A–D; right panel) with the negative control (Figure 17, A–D; middle panel) we found that TC2 (Figure 9; TC2) and TC4 (Figure 17, A and B, respectively) presented a higher CD8+ T cell proliferation before losing the virological control than PC1 (Figure 9; PC1) and PC7 (Figure 17, C and D, respectively). After that, to prove the lack of proliferation in PCs, we analyzed the CD8+ T cell proliferation in two longitudinal samples, T0 (Figure 17C) and T1 (Figure 17E), one year after T0, of PC1 (Figure 9; PC1). The CD8+ T cell proliferation was not changed over time, since no differences were found between experimental (Figure 17, C and E; right panel) and negative control (Figure 17, C and E; middle panel) in any of the time points. The positive control stimulated with SEB validated the efficacy of the assay (Figure 17, A–E; left panel). 72 Figure 17. HIV-specific CD8+ T cell proliferation assay. TC2 (A), TC4 (B), PC1 T0 (C), PC7 (D) and PC1 T1 (one year after T0) (E). C+: stimulated PBMCs with Staphylococcal enterotoxin B (SEB) (left panel). C-: unstimulated PBMCs (middle panel). Experimental: stimulated PBMCs with HIV (Gag)-specific peptide (right panel) after five days in culture. 4.9. Thymic function in PCs compared with TCs before losing virological control Thymic function has been associated with HIV disease progression [79]. We assayed the sj/β-TREC ratio in 11 PCs and ten TCs by ddPCR. We observed that sj/β-TREC ratio was significantly increased in TCs in comparison with PCs (P = 0.024) (Figure 18A). Nevertheless, these differences were colineal with age, as we 73 observed an inverse correlation between age and sj/β-TREC ratio (r = –0.410; P = 0.065) (Figure 18B) and TCs were younger than PCs (Table 6). However, a significant positive correlation was found between sj/β-TREC ratio and the levels of intact proviruses in TCs (r = 0.709; P = 0.022) (Figure 18C) but not in PCs (data not shown). Figure 18. Thymic function in PCs and TCs. Dot graphs represent sj/β-TREC ratio (A). Correlations between sj/β-TREC ratio and age in PCs and TCs (B). Correlations between sj/β-TREC ratio and the frequency of intact HIV DNA (106 PBMCs) in TCs (C). Each dot represents a participant. PCs and TCs are represented by unique identifiers (Table 7 and 8). Mann-Whitney U test was used to compare PCs and TCs. Spearman test was used for nonparametric correlations. P value <0.05 was considered statistically significant. 4.10. Sex-based differences in HIV-1 reservoir landscape in PCs and TCs before losing virological control Women have been associated with a smaller HIV reservoir compared to men [80]. In fact, a greater number of women are found among ECs [4, 81], including the 80 ZNF genes [99] located in defined regions that are occupied by heterochromatin proteins of chromosome 19 [97, 98], as previously reported in a subset of ECs [59]. Unlike PCs, we did not detect intact clonally expanded HIV-1 infected cells in TCs [11]. These data suggest that the intact proviral reservoir of PCs, in contrast with TCs, seems mostly fueled by clonal proliferation of latently infected cells harboring early seeded intact proviruses. This finding is compatible with previous results in the overall EC population [59], probably because the more comprehensive analysis was performed in those ECs with intact proviral clones, consistent with the PC phenotype described in the present thesis. In addition, cell-associated HIV-1 RNA levels were significantly higher in TCs and positively correlated with total and intact proviruses. Notably, higher cell-associated HIV-1 RNA levels in PCs corresponded to PC1 and PC2, the participants that presented clonal intact provirus sequences located in centromeric satellite DNA or ZNF genes, reassuring the importance of the quality, rather than the quantity, of viral reservoir. This fact may indicate a production of viral proteins by defective proviruses in PCs [61], mostly driven by the higher proportion of LD proviruses observed in this phenotype [100] that could act as a therapeutic vaccine and could magnify the antiviral host immune activity in PCs. However, it is currently unknown whether the type of proteins produced by these defective proviruses is the same in PCs and TCs. If a distinct protein profile were found, it might have important therapeutic implications. Regarding the immune pressures that preceded the viral rebound, we found a lower number of bnAb sensitivity sites and, conversely, a higher number of bnAb resistance sites per intact and defective proviruses in TCs compared with PCs. These finding may be consistent with more selection among TCs with virus 81 resistance to humoral immune responses, but our data are not yet definitive as to whether this could result from the observed higher viral diversity in TCs or if selection for resistance was also occurring. Complementarily neutralization assays in plasma of TCs would have been performed, however this was not possible in this thesis by sample restriction and the difficulty of obtaining viral amplification in this type of sample. Curiously, we found the opposite phenomenon for bnAbs that recognize the V3 envelope region in intact genome proviruses, lower sensitivity, and higher resistance in PCs, compared with TCs, suggesting that the V3 envelope region may not be as important to viral control as the CD4 binding site, V2, and MPER regions. Interestingly, PC1, the participant that presented higher bnAb resistance sites per defective provirus in PCs, was the same participant that presented clonal intact provirus sequences located in centromeric satellite DNA and ZNF genes and higher cell-associated HIV-1 RNA levels. This finding confirms the persistence of defective provirus and its role in the production of viral proteins [61], and, consequently, in the antiviral immune response in PCs, as mentioned previously. However, the absence of differences in WT and CTL escape mutations may be biased by the fact that only clade B consensus sequences were analyzed, since clade A1 and F1 HIV-infected participants, including PC1 and TC1, were not included in the analysis due to a lack of information related to escape mutations for these clades. All these immunological proviral footprint data are in accordance with the lower T cell polyfunctionality found in TCs compared with PCs, as previously reported [11]. Interestingly the reduced quality of the Gag-specific T cell response in TCs was intimately associated with viral reservoir measurements. In effect, HIV-specific CD4+ T cells are known to be infected by the virus at higher frequencies than other 82 memory CD4+ T cells [101]. Moreover, active reservoirs have previously been reported to be enriched in CM T cells [102].These facts may explain the positive correlation found between HIV-specific CM response in CD4+ T cells with total proviruses in TCs, but not in PCs, again pointing out the ongoing HIV infection of these HIV-specific CD4+ memory T cells in TCs. These data were also associated with thymic function levels, which have previously been associated with HIV disease progression [79], in fact, the higher levels of thymic function in TCs compared with PCs positively correlated with intactgenome proviruses. Interestingly, the highest sj/β-TREC ratio belonged to TC1, the participant closer to lose the virological control. These data may indicate a compensatory mechanism of the adaptive immune system to maintain T cell number and suppress ongoing viral replication, probably coupled to a higher T cell turnover unable to maintain a fully functional CM virus specific T cell pool and eventually resulting in the loss of the virological control due to the decreased T cell polyfunctionality. Females are still underrepresented in HIV-1 research studies despite immunological and virological differences have been reported based on sexdifferences [103]. Although no significant differences were found in total, defective and intact proviruses between males and females in both groups, a greater proportion of female PCs did not present detectable intact proviruses compared to PCs males. In addition, four out of five PCs that corresponded with the one with no intact proviruses and with no cell-associated HIV-1 RNA detected were females. Females on ART have been associated with a smaller HIV-1 reservoir [80, 104] but with higher intact proviruses, probably derived from the higher frequency of clonallyexpanded proviruses, preferentially located in heterochromatin location compared to 83 men [105]. Estrogen, particularly 17-β-estradiol, is the key hormone responsible for regulating female physiology and metabolism [106]. Estrogen levels have been related to promote HIV-1 latency, inhibiting viral transcription [107] and therefore suggesting a role of female sex hormones in the progression of HIV. The transition into menopause, apparently earlier in women with HIV [108], characterized by decreasing estrogen levels, may have an impact on HIV reservoir dynamics [109]. However, sex-based differences in HIV-1 reservoir in ECs remains unknown, likewise the effects of menopause on maintaining virological control. Our results, with the limitation of the low numbers of female participants, might indicate a privilege phenotype in female PCs, associated with a permanent HIV control compared to males. Our results provide insight for further researches focused on HIV reservoir in PCs’ females, highlighting the need for a more comprehensive analysis, involving a larger cohort of women, stratified by age ,and including other studies focused on immunological and hormonal factors [110]. The main limitation of our study was the sample availability and consequently the need for more immunological data, especially in TCs, and above all, the number of sequences available per participant, particularly in PCs with no intact proviral sequences detected. We partially counteracted this limitation with the longitudinal analysis performed in participants with this profile. Additionally, it is notable that these participants are exceptional, and analyzing a larger number of cells does not guarantee finding more sequences, as it has been shown in participants with unique reservoir profiles such as the Esperanza patient, HIV pediatric patients, and a subgroup of ECs [60, 91, 111]. Another limitation of our study was the absence of tissue samples, which would have provided a more comprehensive analysis of the HIV-1 reservoir. While some studies in PWHIV have found evidences of proviral 84 compartmentalization [112-115], others suggest an exchange between viral reservoir cells in blood and tissue [116-119]. However, these studies did not focus on the EC phenotype, highlighting the need for a comprehensive analysis of different tissue reservoirs. Although identifying sex-based differences in the PCs HIV-1 reservoir was not the main objective of our study, we observed a greater proportion of female PCs without neither detectable intact proviruses nor cell-associated HIV-1 RNA compared to male PCs. Given the importance of developing potential HIV-1 curative strategies, it is crucial to determine whether females have a distinct, potentially advantageous reservoir profile. For this purpose, future studies will be needed in a larger cohort of female PCs, also incorporating hormonal analysis, and importantly considering age groups, particularly in relation to menopause, to fully understand the role of sex on the maintenance of the virological control. Together, we observed absence of detectable intact provirus sequences, mainly in females, and a deep viral latency in PCs, which seems to follow a “block and lock” mechanism [120], by silencing of intact proviral gene expression through chromosomal integration into repressive chromatin locations. By contrast, higher intact-genome proviral levels, transcriptionally active and with higher resistance to immune recognition, were observed in TCs before losing the virological control (Figure 20). Despite the higher thymic function and CD8+ T cell proliferation found in TCs, their lower Gag-specific T cell polyfunctionality may contribute to the loss of the virological control. In summary, our results showed a markedly distinct intact proviral reservoir and immunological landscapes associated with the loss and maintenance of persistent spontaneous HIV control. Currently, no persistent sterilizing cure is available for HIV-1 due to long-lived viral reservoirs. Interestingly, the eradication of HIV-1 reservoir cells has apparently 85 been possible by hematopoietic stem-cell transplantation (HSCT) in the Berlin [121], London [122, 123], Duesseldorf [124], New York [125]. and Geneva patients [126]. However, HSCT is an impractical approach as HIV cure strategy, due to the difficulty to scale this procedure to the general PWHIV population for safety reasons [127]. Taken this into consideration and all the above-mentioned, PCs turn into the closest model for achieving an HIV cure. Interestingly, this privileged immunological and virological profile has also been reported in other unique PWHIV as PTC [128]; and the pediatric cohorts [111, 129] and people on long-term ART [130], suggesting that they might maintain the virological control upon ATI. Additionally, this HIV cure profile found in PCs phenotype, re-open the question whether ART is necessary in some ECs. We believe that ART may be beneficial in TCs before losing virological control. This is compatible with some studies where ART has proved to decrease circulating CD4+ T cells, that contain replication competent HIV, and immune activation in VCs and some ECs [131, 132]. Consequently, we need to define biomarkers that may predict the loss of virological control and act accordingly. However, the profile compatible with an HIV cure found in PCs would advise against ART in these people. This is in favor of some findings that have not found any benefit of ART in ECs compared to PWHIV on ART [133]. This is why we believe that the recommendations to treat all ECs with ART should be taken with caution since these are based on studies that have not considered the natural heterogeneity of EC and their differentiation into TCs and PCs phenotypes. These findings are important, albeit not definitive, as they go one step further to identify the PC phenotype as the premier model of a permanent HIV remission, 86 determine the causes of the loss of spontaneous viral control, and identify PCs and other PWHIV with this distinct reservoir signature as potentially spontaneously cured. The identification of PWHIV on ART with this advantageous deep latency characteristics found in PCs could enable the discontinuation of ART, leading to substantial cost savings for the National Health System. Our results emphasize that confirmation of these hypotheses, in order to predict a higher likelihood of persistent spontaneous control, will require a larger sample of longitudinally sampled controllers in ongoing cohorts. Figure 20. Persistent controllers as the key model to identify permanent HIV remission. Schematic representation of the distinct HIV-1 reservoir landscape observed in PCs compared to TCs before the loss of virological control. 87 88 6. Conclusions 89 6. Conclusions • PCs and TCs before losing viral control presented a distinct proviral reservoir landscape in PBMCs. • Most of PCs (70.59%) exhibited undetectable intact provirus levels. In contrast, TCs, before the loss of virological control, showed higher levels of intact provirus with greater viral diversity. • PCs with detectable intact proviruses, derived completely from clonally expanded HIV-1 infected T cells, presented a deep viral latency, that seems to follow a “block and lock” mechanism, by silencing intact proviral gene expression through chromosomal integration into repressive chromatin locations, such as centromeric satellite DNA or zinc finger genes, both associated with heterochromatin features. 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Cell Host Microbe, 2023. 31(1): p. 83-96 e5. 131. Chun, T.W., Shawn Justement, J., Murray, D., et al., Effect of antiretroviral therapy on HIV reservoirs in elite controllers. J Infect Dis, 2013. 208(9): p. 1443-7. 132. Li, J.Z., Segal, F.P., Bosch, R.J., et al., Antiretroviral Therapy Reduces T-cell Activation and Immune Exhaustion Markers in Human Immunodeficiency Virus Controllers. Clin Infect Dis, 2020. 70(8): p. 1636-1642. 133. Crowell, T.A., Ganesan, A., Berry, S.A., et al., Hospitalizations among HIV controllers and persons with medically controlled HIV in the U.S. Military HIV Natural History Study. J Int AIDS Soc, 2016. 19(1): p. 20524. 112 113 114 115 116 117 118 119 120 121 128 129 130 131 Annex Ib. Conferences and meetings 1. Gasca-Capote, C.; Lian, X.; Gao, C.; Roseto, I.; Jiménez-León, M.R.; Gladkov, G.; Camacho-Sojo, M.I.; Pérez-Gómez, A.; Gallego, I.; Lopez-Cortes, L.E.; Bachiller. S.; Vitalle. J.; Mohamed Rafii-El-Idrissi Benhnia, Ostos, F.J.; Collado-Romacho, A.R.; Santos, J.; Palacios, R.; Gomez-Ayerbe, C.; MuñozMedina, L.; Ruiz-Sancho, A.; Frias, M.; Rivero-Juarez, A.; Roca-Oporto, C.; Hidalgo-Tenorio, C.; Rull, A.; Olalla, J.; Lopez-Ruz, M.A.; Vidal, F.; Vilades, C.; Mastrangelo, A.; Cavassini, M.; Espinosa, N.; Perreau, M.; Peraire, J.; Rivero, A.; López-Cortes, L.F.; Lichterfeld, M.; Yu, Xu.; Ruiz-Mateos, E."Impact of The HIV-1 Reservoir on the Loss Of Natural Viral Control: Implications for an HIV-1 Spontaneous Cure". Scientific Session 7: Immune control and loss of control in HIV-1 and HIV-2. Oral Communication. The HIV Reservoirs and Immune Control Conference. 2023. Ireland. 2. Gasca-Capote, C.; Lian, X.; Gao,C.; Roseto, I.; Jiménez-León, M.R.; Gladkov, G.; Pérez-Gómez, A.; Vidal, F.; Rivero, A.; López-Cortés, L.F.; Yu, X.; Lichterfeld, M.; Ruiz-Mateos, E. "Impact of HIV reservoir in the loss of natural elite control of HIV-1 infection". Themed discussion: Dynamics of proviral integrations. Code 452. Conference on Retroviruses and Opportunistic Infections (CROI). 2023. United States of America. 3. Gasca-Capote, C.; Lian, X.; Gao,C.; Roseto, I.; Jiménez-León, M.R.; Gladkov, G.; Pérez-Gómez, A.; Camacho-Sojo, M.I.; Vidal, F.; Rivero, A.; López-Cortés, L.F.; Yu, X.; Lichterfeld, M.; Ruiz-Mateos, E. "Distinct proviral reservoir 132 landscape in transient elite controllers before losing the viremia control compared to persistent controllers". Oral Poster (P-179). La Sociedad Española de Inmunología (SEI). 2023. Spain. 4. Gasca-Capote, C.; Lian, X.; Gao, C.; Roseto, I.; Jiménez-León, M.R.; Gladkov, G.; Pérez-Gómez, A.; Camacho-Sojo, M.I.; Vidal, F.; Rivero, A.; López-Cortés, L.F.; Yu, X.; Lichterfeld, M.; Ruiz-Mateos, E. "Impact of HIV reservoir in the loss of natural elite control of HIV-1 infection". Poster (EISI-011). XIX Foro de Investigadores. Institute of Biomedicine of Seville (IBiS). 2023. Spain 5. Gasca-Capote, C.; Lian, X.; Gao, C.; Roseto, I.; Jiménez-León, M.R.; Gladkov, G.; Pérez-Gómez, A.; Vidal, F.; Rivero, A.; López-Cortés, L.F.; Yu, X.; Lichterfeld, M.; Ruiz-Mateos, E."Impact of HIV reservoir in the loss of natural elite control of HIV-1 infection". Oral Poster (CodePO-19). XIII Congreso Nacional de GeSIDA. 2022. Spain. 133 Annex Ic. Press release and other public dissemination In addition to the publication (Gasca-Capote C, et al. JCI. 2024.) and conferences and meetings, radio and television interviews have been conducted to disseminate the findings of this project, such as CanalSur Jerez, CanalSurTV Despierta Andalucía, and OndaCero: Programa De Cero al Infinito. Results have also been shared on social media platforms like X, on the accounts of IBiS (>4k followers), Journal of Clinical Investigation (>50K followers), CSIC (>900K followers), and on the account of the minister of Science, Innovation, and Universities, Ms. Diana Morant (>38K followers), among others. • https://x.com/jclinicalinvest/status/1785662983110103213?s=46&t=fhMzsYvF nTqpopBN-8nWzg • https://x.com/csic/status/1780147244827541655?s=46&t=fhMzsYvFnTqpopB N-8nWzg • https://x.com/dianamorantr/status/1780161350846623854?s=46&t=fhMzsYvF nTqpopBN-8nWzg The results have also been disseminated across various internet platforms of general and scientific dissemination. • https://www.elespanol.com/sevilla/20240416/relevante-hallazgo-estudiosevillano-vih-puede-personas-curadas-sin-saberlo/848165454_0.html • https://www.us.es/actualidad-de-la-us/investigadores-del-ibis-descubrencaracteristicas-del-virus-compatibles-con-la 134 • https://www.abc.es/sevilla/ciudad/investigadores-sevillanos-descubrencaracteristicas-virus-compatibles-curacion-20240416125848-nts.html • https://rotaaldia.com/art/41745/dos-rotenos-entre-los-investigadores-quedescubren-caracteristicas-del-vih-compatibles-con-su-curacion • https://www.agenciasinc.es/Noticias/Descubren-caracteristicas-del-VIHcompatibles-con-su-curacion • https://isanidad.com/280329/cientificos-del-instituto-de-biomedicina-desevilla-descubren-caracteristicas-del-vih-compatibles-con-su-curacion/ • https://www.infobae.com/espana/2024/04/16/avance-contra-el-vih-cientificosespanoles-abren-nuevas-vias-para-desarrollar-una-cura-de-la-infeccion/ • https://www.elespanol.com/sevilla/20240418/universidad-sevilla-abre-nuevavia-curar-vih-mano-harvard-mit/848665201_0.html 135 Annex II. Other scientific contributions conducted during the doctoral thesis Annex IIa. Publications 1. Sun W, Gao C, Gladkov GT, Roseto I, Carrere L, Parsons EM, Gasca-Capote C, Frater J, Fidler S, Yu XG, Lichterfeld M; RIVER Trial Study Group. “Footprints of innate immune activity during HIV-1 reservoir cell evolution in earlytreated infection”. J Exp Med. 2024;221(11):e20241091. doi: 10.1084/jem.20241091. PMID: 39466203. 2. Muñoz-Muela E, Trujillo-Rodríguez M, Serna-Gallego A, Saborido-Alconchel A, Gasca-Capote C, Álvarez-Ríos A, Ruiz-Mateos E, Sviridov D, Murphy AJ, Lee MKS, López-Cortés LF, Gutiérrez-Valencia A. “HIV-1-DNA/RNA and immunometabolism in monocytes: contribution to the chronic immune activation and inflammation in people with HIV-1”. EBioMedicine. 2024; 108:105338. doi: 10.1016/j.ebiom.2024.105338. PMID: 39265504. 3. Jimenez-Leon MR*, Gasca-Capote C*, Roca-Oporto C, Espinosa N, Sobrino S, Fontillon-Alberdi M, Gao C, Roseto I, Gladkov G, Rivas-Jeremias I, Neukam K, Sanchez-Hernandez JG, Rigo-Bonnin R, Cervera-Barajas AJ, Mesones R, García F, Alvarez-Rios AI, Bachiller S, Vitalle J, Perez-Gomez A, Camacho-Sojo MI, Gallego I, Brander C, McGowan I, Mothe B, Viciana P, Yu X, Lichterfeld M, Lopez-Cortes LF, Ruiz-Mateos E. “Vedolizumab and ART in recent HIV-1 infection unveil the role of α4β7 in reservoir size”. JCI Insight. 136 2024;9(16):e182312. doi: 10.1172/jci.insight.182312. PMID: 38980725. *These authors contributed equally to this work. 4. Jimenez-Leon MR, Gasca-Capote C, Tarancon-Diez L, Dominguez-Molina B, Lopez-Verdugo M, Ritraj R, Gallego I, Alvarez-Rios AI, Vitalle J, Bachiller S, Camacho-Sojo MI, Perez-Gomez A, Espinosa N, Roca-Oporto C, Rafii-El-Idrissi Benhnia M, Gutierrez-Valencia A, Lopez-Cortes LF, Ruiz-Mateos E. “Toll-like receptor agonists enhance HIV-specific T cell response mediated by plasmacytoid dendritic cells in diverse HIV-1 disease progression phenotypes”. EBioMedicine. 2023;91:104549. doi: 10.1016/j.ebiom.2023. 104549. PMID: 37018973. 5. Real LM, Sáez ME, Corma-Gómez A, Gonzalez-Pérez A, Thorball C, Ruiz R, Jimenez-Leon MR, Gonzalez-Serna A, Gasca-Capote C, Bravo MJ, Royo JL, Perez-Gomez A, Camacho-Sojo MI, Gallego I, Vitalle J, Bachiller S, GutierrezValencia A, Vidal F, Fellay J, Lichterfeld M, Ruiz-Mateos E; Swiss HIV Cohort Study. “A metagenome-wide association study of HIV disease progression in HIV controllers”. iScience. 2023;26(7):107214. doi: 10.1016/j.isci.2023.107214. PMID: 37456859. 6. Vitallé J, Pérez-Gómez A, Ostos FJ, Gasca-Capote C, Jiménez-León MR, Bachiller S, Rivas-Jeremías I, Silva-Sánchez MDM, Ruiz-Mateos AM, MartínSánchez MÁ, López-Cortes LF, Rafii-El-Idrissi Benhnia M, Ruiz-Mateos E. “Immune defects associated with lower SARS-CoV-2 BNT162b2 mRNA 137 vaccine response in aged people”. JCI Insight. 2022;7(17):e161045. doi: 10.1172/ jci.insight.161045. PMID: 35943812. 7. Trujillo-Rodriguez M, Muñoz-Muela E, Serna-Gallego A, Praena-Fernández JM, Pérez-Gómez A, Gasca-Capote C, Vitallé J, Peraire J, Palacios-Baena ZR, Cabrera JJ, Ruiz-Mateos E, Poveda E, López-Cortés LE, Rull A, GutierrezValencia A, López-Cortés LF. “Clinical, laboratory data and inflammatory biomarkers at baseline as early discharge predictors in hospitalized SARSCoV-2 infected patients”. PLoS One. 2022;17(7):e0269875. doi: 10.1371/journal. pone.0269875. PMID: 35834501. 8. Pérez-Gómez A, Gasca-Capote C, Vitallé J, Ostos FJ, Serna-Gallego A, TrujilloRodríguez M, Muñoz-Muela E, Giráldez-Pérez T, Praena-Segovia J, NavarroAmuedo MD, Paniagua-García M, García-Gutiérrez M, Aguilar-Guisado M, Rivas-Jeremías I, Jiménez-León MR, Bachiller S, Fernández-Villar A, PérezGonzález A, Gutiérrez-Valencia A, Rafii-El-Idrissi Benhnia M, Weiskopf D, Sette A, López-Cortés LF, Poveda E, Ruiz-Mateos E; Virgen del Rocío Hospital COVID-19 and COHVID-GS Working Teams. “Deciphering the quality of SARS-CoV-2 specific T-cell response associated with disease severity, immune memory and heterologous response”. Clin Transl Med. 2022;12(4):e802. doi: 10.1002/ctm2. 802. PMID: 35415890. 9. Masip J*, Gasca-Capote C*, Jimenez-Leon MR, Peraire J, Perez-Gomez A, Alba V, Malo AI, Leal L, Martín CR, Rallón N, Viladés C, Olona M, Vidal F, RuizMateos E, Rull A; ECRIS integrated in the Spanish AIDS Research Network (Annex S1). “Differential miRNA plasma profiles associated with the 144 B5. Code: 224. Conference on Retroviruses and Opportunistic Infections (CROI). 2021. 19. Jimenez-Leon, M.R.; Gasca-Capote, C.; López-Verdugo, M.; Tarancon-Diez, L.; Trujillo Rodríguez, M.; Roca, C.; Espinosa, N.; Gutiérrez-Valencia, A.; Viciana, P.; López-Cortés, L.; Ruiz-Mateos, E. “Reversion of CD4+ T-cell exhaustion mediated by plasmacytoid dendritic cell”. P-D08. Code: 301. Conference on Retroviruses and Opportunistic Infections (CROI). 2020. United States of America. 20. pDC metabolism and HIV-disease progression. Meet the expert Ciencia Básica HIBIC. Oral Communication. Gilead Sciences, S.L. 2020. Spain. 21. Jimenez-Leon, M.R.; Gasca Capote, C.; Lopez Verdugo, M.; Tarancon-Diez, L.; Trujillo-Rodriguez, M.; Gutierrez-Valencia, A.; Cristina Roca, C.; Espinosa, N.; Viciana, P.; Lopez-Cortes, L.; Ruiz-Mateos, E. “Reversion of CD4+ T-cell exhaustion mediated by plasmacytoid dendritic cells after Toll like receptors agonist stimulation”. Oral poster (P0-20). XI Congreso Nacional GeSIDA.2019. Spain. 22. Jimenez-Leon, M.R., Gasca Capote, C.; Espinosa, N.; Roca, C.; Sobrino, S.; Fontillon, M.; Rivas Jeremías, I.; Trujillo-Rodriguez, M.; Gutierrez-Valencia, A.; Cervera, A.; Mesones, R.; Viciana, P.; Lopez-Cortes, L.; Ruiz-Mateos, E. “Safety and Efficacy of Vedolizumab Combined With Antiretroviral Therapy to Achieve Permanent Virological Remission in HIV-infected Subjects Without 145 Previous Antiretroviral Therapy”. Oral poster (PO-48). XI Congreso Nacional GeSIDA. 2019. Spain. 23. Jimenez-Leon, M.R.; Gasca Capote, C.; Lopez Verdugo, M.; Tarancon-Diez, L.; Trujillo-Rodriguez, M.; Gutierrez-Valencia, A.; Roca, C.; Espinosa, N.; Viciana, P.; Lopez-Cortes, L.; Ruiz-Mateos, E. “Reversion of CD4+ T-cell exhaustion mediated by plasmacytoid dendritic cells after Toll like receptors agonist stimulation”. Oral communication (OR-27). XXI Congreso SAEI (Sociedad Andaluza de Enfermedades Infecciosas). 2019. Spain. 24. Jimenez-Leon, M.R.; Gasca Capote, C.; Espinosa, N.; Roca, C.; Sobrino, S.; Fontillon, M.; Rivas Jeremías, I.; Trujillo-Rodriguez, M.; Gutierrez-Valencia, A.; Cervera, A.; Mesones, R.; Viciana, P.; Lopez-Cortes, L.; Ruiz-Mateos, E. “Safety and Efficacy of Vedolizumab Combined With Antiretroviral Therapy to Achieve Permanent Virological Remission in HIV-infected Subjects Without Previous Antiretroviral Therapy”. XXI Congreso SAEI (Sociedad Andaluza de Enfermedades Infecciosas). Oral communication (OR-26). XXI Congreso SAEI (Sociedad Andaluza de Enfermedades Infecciosas). 2019. Spain. 146 Annex III. Ethics committee reports 147 148