Differential Gene Expression in Human Fibroblasts Simultaneously Exposed to Ionizing Radiation and Simulated Microgravity

9.44.8 Differential Gene Expression in Human Fibroblasts Simultaneously Exposed to Ionizing Radiation and Simulated Microgravity Polina Malatesta, Konstantinos Kyriakidis, Megumi Hada, Hiroko Ikeda, Akihisa Takahashi, Premkumar B. Saganti, Alexandros G. Georgakilas and Ioannis Michalopoulos Special Issue DNA Damage and DNA Repair in Cancer Edited by Prof. Dr. Alexandros Georgakilas and Prof. Dr. Lorenzo Manti Article https://doi.org/10.3390/biom14010088 Citation: Malatesta, P.; Kyriakidis, K.; Hada, M.; Ikeda, H.; Takahashi, A.; Saganti, P.B.; Georgakilas, A.G.; Michalopoulos, I. Differential Gene Expression in Human Fibroblasts Simultaneously Exposed to Ionizing Radiation and Simulated Microgravity. Biomolecules 2024,14, 88. https:// doi.org/10.3390/biom14010088 Academic Editor: Da-Tian Bau Received: 4 October 2023 Revised: 23 December 2023 Accepted: 4 January 2024 Published: 10 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). biomolecules Article Differential Gene Expression in Human Fibroblasts Simultaneously Exposed to Ionizing Radiation and Simulated Microgravity Polina Malatesta 1,2 , Konstantinos Kyriakidis 1,3,4 , Megumi Hada 5, Hiroko Ikeda 6, Akihisa Takahashi 7, Premkumar B. Saganti 5, Alexandros G. Georgakilas 2,* and Ioannis Michalopoulos 1,* 1 Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece; [email protected] (P.M.); [email protected] (K.K.) 2DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, 15780 Athens, Greece 3Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece 4UC Santa Cruz Genomics Institute, Santa Cruz, CA 95060, USA 5Radiation Institute for Science & Engineering, Prairie View A&M University, Prairie View, TX 77446, USA; [email protected] (M.H.); [email protected] (P.B.S.) 6Department of Life Science, Faculty of Science and Engineering, Kindai University, Higashiosaka 577-8502, Japan; [email protected] 7Gunma University Heavy Ion Medical Center, Maebashi 371-8511, Japan; [email protected] *Correspondence: [email protected] (A.G.G.); [email protected] (I.M.) Abstract: During future space missions, astronauts will be exposed to cosmic radiation and microgravity ( µ G), which are known to be health risk factors. To examine the differentially expressed genes (DEG) and their prevalent biological processes and pathways as a response to these two risk factors simultaneously, 1BR-hTERT human fibroblast cells were cultured under 1 gravity (1G) or simulated µ G for 48 h in total and collected at 0 (sham irradiated), 3 or 24 h after 1 Gy of X-ray or Carbon-ion (C-ion) irradiation. A three-dimensional clinostat was used for the simulation of µ G and the simultaneous radiation exposure of the samples. The RNA-seq method was used to produce lists of differentially expressed genes between different environmental conditions. Over-representation analyses were performed and the enriched biological pathways and targeting transcription factors were identified. Comparing sham-irradiated cells under simulated µ G and 1G conditions, terms related to response to oxygen levels and muscle contraction were identified. After irradiation with X-rays or C-ions under 1G, identified DEGs were found to be involved in DNA damage repair, signal transduction by p53 class mediator, cell cycle arrest and apoptosis pathways. The same enriched pathways emerged when cells were irradiated under simulated µ G condition. Nevertheless, the combined effect attenuated the transcriptional response to irradiation which may pose a subtle risk in space flights. Keywords: space flight; cosmic radiation; microgravity; differentially expressed genes; gene networks 1. Introduction Space flight conditions differ to those on Earth due to cosmic ionizing radiation (IR) and the absence of gravity, known as microgravity ( µ G), both of which pose health risk factors to humans, causing complex DNA damage and genome instability [ 1 – 5 ]. It is crucial to gain more insights on these factors, as astronauts will be continuously exposed to them in long-duration exploration missions, such as those to the Moon or Mars, which require humans to remain in space for days, months and even years. Space radiation risks, arising mainly from solar energetic particles (SEPs) and galactic cosmic rays (GCRs), involve a flux comprising 2% electrons and 98% nuclei, with the Biomolecules 2024,14, 88. https://doi.org/10.3390/biom14010088 https://www.mdpi.com/journal/biomolecules Biomolecules 2024,14, 88 2 of 21 nuclear component being ~87% hydrogen, ~12% helium and ~1% high atomic number and energy (HZE) particles [ 6 ]. Despite low GCR particle flux levels, their high linear energy transfer (LET) induces intense ionization in matter [ 7 , 8 ]. During Earth-to-Mars manned missions using Hohmann transfer [ 9 ] which will last 2–3 years [ 10 ], astronauts will face an estimated ~1.01 Sv [ 11 ] radiation exposure, increasing the risk of cancer, central nerve system decrements, degenerative tissue effects [ 12 ], and irreversible chromosomal instability risks due to HZE particle exposure. During lengthy planetary flights, astronauts face both potential radiation hazards and simultaneous exposure to microgravity ( µ G). Living organisms undergo physiological changes in varying gravitational conditions, including muscle atrophy, reduced bone density, immune function decline and endocrine disorders. The space environment, comprising GCRs and reduced gravity, necessitates testing for possible additive or synergistic effects. Altered DNA repair mechanisms due to gravity changes can impede cellular responses to space radiation, increasing the risk of DNA damage accumulation and tumorigenesis [ 13 ]. Recent studies highlight that spaceflight stressors like ionizing radiation (IR) and/or microgravity disrupt the wound healing process, affecting pathways like inflammation and proliferation [ 14 ]. Microgravity significantly impacts cell death, migration and gene expression in tumor cells, including cancer stem cells, and alters the effects of chemotherapeutic drugs [ 15 ]. While extensive research has focused on the impact of either radiation or microgravity alone, limited studies have addressed their combined effects. Previous attempts were found to be challenging, as older clinostats had to pause rotation which simulates microgravity to irradiate the cells [ 16 , 17 ], potentially introducing additional gravitational stimuli, and thus activating specific signaling cascades. In deep space beyond the Van Allen belts, galactic cosmic rays consist of both highenergy and low-energy radiation. To investigate the combined effects of space radiation and microgravity, considering lunar and Mars explorations and long-term stays in space in the near future, it was decided to use carbon ions as high-energy radiation and X-rays as lowenergy radiation. To maintain the consistent simulated µ G condition before, during and after exposure to radiation, a 3D clinostat that allows samples to be rotated and irradiated simultaneously was developed [ 18 ] and used in this study. This clinostat was previously used for the study of the differential expression of exclusively nine cell cycle-related genes in response to X-ray or Carbon-ion (C-ion) irradiation, with and without simulated µ G, in human fibroblasts [ 1 ]. The raw transcriptomic data produced from that previous study [ 1 ] were reanalyzed in the current work, applying a systems biology approach, to identify all differentially expressed genes between various conditions and their predominant processes they participate in and to identify a possible synergy between radiation and µG. 2. Materials and Methods 1BR-hTERT human fibroblast cells were cultured in CO 2 -independent medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% (v/v) fetal bovine serum (MP Biomedicals, Santa Ana, CA, USA), 200 mM L-glutamine (Thermo Fisher Scientific, Waltham, MA, USA), and penicillin–streptomycin mixed solution (Nacalai Tesque, Kyoto, Kyoto, Japan) at 37 ◦ C under 1G or simulated µ G for 48 h in total. The samples were collected 0 (sham irradiated), 3 or 24 h after X-ray or C-ion irradiation at 1 Gy. X-ray irradiation was performed using an X-ray generator (200 kV, 14.6 mA, aluminum filter (0.3 mm thick), MultiRad225, Faxitron Bioptics, LLC, Tucson, AZ, USA) equipped with a high-speed shutter. C-ion irradiation was performed using a synchrotron (Gunma University Heavy Ion Medical Center (GHMC), Maebashi, Gunma, Japan) and respiratory gating signals with a doseaveraged LET of 50 keV/ µ m at the center of the 6 cm spread-out Bragg peak of the beam with energy of 290 MeV/n. The dose rate was approximately 0.03 Gy/min for both X-ray and C-ion irradiation under the simulated µ G or 1G conditions. Simulated µ G was accomplished using a three-dimensional clinostat [ 18 , 19 ]. This device can manipulate the effect of gravity through the 3D rotation of two orthogonal axes and by continuously changing the direction of gravity. The X:Y ratios of clino-rotation were set at 11:13 rpm and 66 ◦ /s:78 ◦ /s using a Biomolecules 2024,14, 88 3 of 21 special controller to maintain suitable conditions, which means that it does not use random speed and random direction. The rotation angle between the Z-axis of the 3D clinostat (i.e., the axis of radiation exposure) and the normal line of the sample holder on the 3D clinostat, θ , was kept at less than 12 ◦ , assuming the X:Y ratio of the clino-rotation was 11:13 rpm. Because different research groups are performing simulated microgravity experiments under various conditions with different types of simulators and cell line, we think that it is important to carefully consider the experimental conditions and provide details such as simulator limitations in order to avoid misinterpretation of the results [ 20 ]. Among such limitations, we used adherent human fibroblasts in a thin culture vessel completely filled with medium (without bubbles) to minimize stress on the cells as much as possible. It is not necessary to change the medium under our simulated microgravity conditions until the sampling. From our previous data of cell growth, which did not differ significantly between rotating and standing conditions after 48 h of culture, we believe that it is unlikely that cells are subjected to shear stress under our experimental conditions using our system. The samples were irradiated when in horizontal position, without pausing the rotation, for 0.2 s every minute. In total, 12 conditions were studied (Table 1) in triplicate [1]. Table 1. Names and combinations of the type of radiation (C-ion or X-ray), collection time points (0 (sham irradiated), 3 or 24 h) and gravity (1G or simulated µG) for the 12 conditions studied. Name Irradiation Maintenance Time Gravity C-ion X-ray 0 h 3 h 24 h 1G Simulated µG C0G − − +− − +− C0µG− − +− − − + C3G + − − +−+− C3µG + − − +− − + C24G + − − − + + − C24µG + − − − +−+ X0G − − +− − +− X0µG− − +− − − + X3G −+−+−+− X3µG−+−+− − + X24G −+− − + + − X24µG−+− − +−+ 2.1. RNA-Sequencing RNA from each of the 36 samples, was extracted using TRIzol™ Reagent (Thermo Fisher Scientific, Waltham, MA, USA) and its quality was assessed using the RNA 6000 Pico Kit (Agilent Technologies, Santa Clara, CA, USA). rRNA was depleted using the NEBNext ® rRNA Depletion Kit (New England Biolabs, Ipswich, MA, USA). Then, RNA-Seq library was prepared using NEBNext ® Ultra™ Directional RNA Library Prep Kit for Illumina ® (New England Biolabs, Ipswich, MA, USA). Paired-end sequencing (2 × 36 bp) was performed with NextSeq 500 (Illumina, San Diego, CA, USA) at Tsukuba i-Laboratory LLP (Tsukuba, Ibaraki, Japan) [1]. Four FASTQ files were produced from each sample. 2.2. Differentially Expressed Gene Analysis FASTQ files of each sample were concatenated, and the integrity of the resulting files was checked, using in-house scripts. Quality control and alignment of their reads were carried out via the RNA-seq workflow from the bcbio-nextgen bioinformatics framework (version 1.2.9) [ 21 ] (Figure 1). To ensure that the library generation and sequencing quality were suitable for further analysis, FastQC [ 22 ] was used to examine the raw reads for quality issues. Then, raw reads were aligned to the GRCh38 (hg38) version of the human reference genome (FASTA and GTF files) with the splice-aware aligner STAR ( version 2.6.1d ) in two-pass mode (the first pass discovers new splice junctions and inserts them into the junction database, and the second pass calls junctions and calculates their counts) [ 23 ]. Biomolecules 2024,14, 88 4 of 21 Moreover, Salmon (version 1.7.0) was run in alignment-based mode, using the genome alignments from STAR (BAM files) and the reference transcriptome FASTA file, to generate abundance estimates for known splice isoforms [ 24 ]. Bcbio assessed the complexity and quality of the RNA-seq data by quantifying ribosomal RNA (rRNA) content and the genomic context of alignments in known transcripts and introns using a combination of custom tools and Qualimap [ 25 ]. MultiQC [ 26 ] was then used for quality control and assurance analysis of the resulting BAM files by comparing to metrics gathered from bcbionextgen, samtools [ 27 ], Salmon, STAR, Qualimap, and FastQC. Next, we quantitated reads by assigning them to genes (features) annotated in Ensembl (release 105) and counting them with the featureCounts tool [ 28 ] or preferably tximport [ 29 ]. Gene counts were processed for DGEA, using DESeq2 (version 1.38.2) [ 30 ] default settings through the bcbioRNASeq R package (version 0.5.1) [ 31 ] (Figure 1). Moreover, log fold change shrinkage for visualization and ranking was performed calling the lfcShrink function of the DESeq2 package, replacing p-values with s-values produced by the apeglm estimation method [ 32 ]. S-value was proposed as a statistic giving the aggregate false sign rate for tests with equal or lower s-value than the one considered [ 33 ]. Exported lists containing statistically significant differentially expressed genes (DEGs) include metrics such as Log 2 Fold Change (Log 2 FC) and s-values for each gene. The lists were further annotated by bcbioRNASeq to include HGNC [ 34 ] gene symbols and names. The threshold for statistical significance was set at svalue < 0.001, as suggested [ 35 ]. Using this method, the statistically significant differentially expressed genes between various pairs of biological conditions (Table 2) were identified. , x FOR PEER REVIEW Differentially Expressed Gene Analysis FASTQ files of each sample were concatenated, and the integrity of the resulting files seq workflow from the bcbio reference genome (FASTA and GTF files) with the splice pass mode (the first pass discovers new splice junctions and inserts them nome alignments from STAR (BAM files) and the reference transcriptome FASTA file, to assurance analysis of the resulting BAM files by comparing to metrics gathered from default settings through the p statistically significant differentially expressed genes (DEGs) include metrics such as Log cal significance was set at s tically significant differentially expressed genes between various pairs of biological conditions (Table 2) were identified. Figure 1. Gene counts were produced from FASTQ files, through the nextgen pipeline (in lavender background). Lists of differentially expressed genes were proFigure 1. RNA-seq analysis pipeline. Gene counts were produced from FASTQ files, through the bcbio-nextgen pipeline (in lavender background). Lists of differentially expressed genes were produced through the bcbioRNASeq pipeline (in light orange background). Table 2. Comparisons between biological conditions (Table 1) that were performed in DEG analyses. Comparisons Description X3G-X0G Early response to X-ray under 1G X3µG-X0µG Early response to X-ray under µG X0µG-X0G Response to µG in sham-X-ray-irradiated cells X3µG-X3G Response to µG in cells collected 3 h after X-ray irradiation (X3µG-X0µG)-(X3G-X0G) Interaction between early response to X-ray and response to µG X24G-X0G Late response to X-ray under 1G X24µG-X0µG Late response to X-ray under µG X24µG-X24G Response to µG in cells collected 24 h after X-ray irradiation (X24µG-X0µG)-(X24G-X0G) Interaction between late response to X-ray and response to µG Biomolecules 2024,14, 88 5 of 21 Table 2. Cont. Comparisons Description X24G-X3G Late vs. early response to X-ray under 1G X24µG-X3µG Late vs. early response to X-ray under µG (X24µG-X3µG)-(X24G-X3G) Interaction between late vs. early response to X-ray and response to µG X3µG-X0G Early response to X-ray irradiation and µG combined effect X24µG-X0G Late response to X-ray irradiation and µG combined effect C3G-C0G Early response to C-ion under 1G C3µG-C0µG Early response to C-ion under µG C0µG-C0G Response to µG in sham-C-ion-irradiated cells C3µG-C3G Response to µG in cells collected 3 h after C-ion irradiation (C3µG-C0µG)-(C3G-C0G) Interaction between early response to C-ion and response to µG C24G-C0G Late response to C-ion under 1G C24µG-C0µG Late response to C-ion under µG C24µG-C24G Response to µG in cells collected 24 h after C-ion irradiation (C24µG-C0µG)-(C24G-C0G) Interaction between late response to C-ion and response to µG C24G-C3G Late vs. early response to C-ion under 1G C24µG-C3µG Late vs. early response to C-ion under µG (C24µG-C3µG)-(C24G-C3G) Interaction between late vs. early response to C-ion and response to µG C3µG-C0G Early response to C-ion irradiation and µG combined effect C24µG-C0G Late response to C-ion irradiation and µG combined effect 2.3. Biological Term Enrichment Analysis Gene term enrichment analyses were performed to identify the prevalent biological processes and pathways overand under-expressed genes of each DEG analysis participate in using WebGestalt [ 36 ]. The over-representation analysis (ORA) method [ 37 ] was employed, applying BH [ 38 ] multiple test adjustment. The threshold for statistical significance was set at a false discovery rate (FDR) < 0.05. The functional databases that were used for biological term enrichment were Biological Process, Cellular Component and Molecular Function from Gene Ontology [ 39 ], KEGG [ 40 ] and Transcription Factor Targeting and miRNA Targeting networks from MSigDB [ 41 ]. In order to identify and depict overlapping genes or biological terms between comparisons of conditions, Venn diagrams were produced using a webtool developed by the Bioinformatics and Evolutionary Genomics group of Ghent University at https://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 7 January 2022). 2.4. Protein–Protein Interaction Network Analysis STRING [ 42 ]-based protein–protein interaction (PPI) network analyses were performed for the DEGs and PPI networks were constructed for each DEG analysis from all comparisons in order to discover their functional associations. The average local clustering coefficient [ 43 ] served as a measure of how connected the produced PPI networks were and PPI enrichment p-values provide the probability to obtain the observed number of edges by chance. To identify the hub genes of the PPI networks, i.e., the genes with the highest degree of connectivity, the interactions of each gene were counted and genes with the highest number of edges were pinpointed. 3. Results Lists of upand down-regulated genes were produced through the DEG analysis from all comparisons between two different biological conditions and subsequently, biological term over-representation analyses were performed. 3.1. Early-Response Genes to X-ray Irradiation under 1G From the comparison between X-ray (low-LET) irradiated cells collected 3 h postirradiation and sham-irradiated ones under 1G (X3G-X0G) (Table S1), 112 over-expressed genes were found in total, and among them, CDKN1A,MDM2,PURPL,PTCHD4,TP53INP1, PAPPA and BTG2 stood out. In particular, the expression of CDKN1A and MDM2 had approximately quadrupled. Likewise, 108 genes were found to be statistically significant Biomolecules 2024,14, 88 6 of 21 under-expressed ones. Down-regulated genes FAM111B,ZNF367 and MCM10 stood out. Concerning the over-expressed genes, enrichment analyses for Gene Ontology biological processes and KEGG pathways highlighted the p53 signaling pathway. Biological processes related to response to stimulus and apoptosis were also identified. Focusing on underexpressed genes, biological term over-representation analyses in all Gene Ontology aspects, as well as in KEGG pathways, highlighted cell cycle and carcinogenesis-related terms. E2F was identified as a transcription factor targeting down-regulated genes. 3.2. Late-Response Genes to X-ray Irradiation under 1G From the comparison between X-ray-irradiated cells collected 24 h post-irradiation (late response) and sham-irradiated ones under 1G (X24G-X0G) (Table S2), 571 upand 1026 down-regulated genes were found. The over-expressed genes PURPL,PTCHD4 and PAPPA were found to be predominant. PURPL, for example, suppresses basal p53 levels and promotes tumorigenicity in colorectal cancer [ 44 ]. Enrichment analyses in all Gene Ontology aspects highlighted terms related to cell proliferation and cardiovascular system development, such as angiogenesis. The main KEGG pathway identified was as rather expected, the p53 signaling pathway, indicating the response to varying types of stresses like radiation, hypoxia, oxidative attack [ 45 ] and even simulated µ G [ 46 ]. FOXO4 was identified as the transcription factor controlling the expression of up-regulated genes. Among the down-regulated genes, many stood out. A few were MKI67,ASPM,CENPF,ANLN,CDC20, DLGAP5,CCNB1,CEP55 and PLK1. Enrichment analyses in all Gene Ontology domains, as well as in KEGG pathways, highlighted cell cycle and DNA repair-related terms. E2F was found to control the expression of down-regulated genes. 3.3. Latevs. Early-Response Genes to X-ray Irradiation under 1G From the comparison between X-ray-irradiated cells collected 3 (early) and 24 (late) h post-irradiation under 1G (X24G-X3G) (Table S3), 255 upand 619 down-regulated genes were identified. For the up-regulated genes, enrichment analyses concerning Gene Ontology aspects highlighted anatomical morphogenesis and response to stimulus related terms. The predominant KEGG pathway identified was mitophagy. FOXO4 was identified as an up-regulated gene-targeting transcription factor. Among the down-regulated genes, MKI67, ASPM,CENPF,TOP2A and PRC1 stood out. Over-representation analyses in all Gene Ontology categories, as well as in KEGG pathways, highlighted cell cycle and DNA repair related terms. E2F was identified as an under-expressed gene-targeting transcription factor . 3.4. Early-Response Genes to C-ion Irradiation under 1G The high LET of C-ions is expected to elicit quantitatively and qualitatively different responses compared to low LET including not only the DNA damage response (DDR) pathways but also inflammatory and immune system activation and systemic effects [ 47 ]. From the comparison between C-ion-irradiated cells collected 3 h post-irradiation and shamirradiated ones under 1G (C3G-C0G) (Table S4), 159 over-expressed genes were identified in total. Among those ones, CDKN1A,MDM2,PAPPA,TNFRSF10B,BTG2,TP53INP1, PTCHD4 and PURPL stood out, in particular, the expression of CDKN1A (p21) and MDM2 had log 2 fold change of 2.5 and 2.2. CDKN1A is a downstream gene to TP53 and often showed to act as a negative regulator of the cellular levels of TP53 [ 48 ]. Likewise, 114 genes were found to be under-expressed in a statistically significant manner. The down-regulated genes PLK1,MKI67,BUB1B and DTL were most prominent. Concerning the over-expressed genes, enrichment analyses in all Gene Ontology aspects highlighted apoptosis and type I interferon signaling pathway related terms. KEGG pathways identified the p53 signaling pathway. LEF1 was identified as a gene-targeting transcription factor. Concentrating on under-expressed genes, biological term over-representation analyses in all Gene Ontology domains, as well as in KEGG pathways, highlighted cell cycle-related terms. E2F was identified as a targeting transcription factor for under-expressed genes. Biomolecules 2024,14, 88 7 of 21 3.5. Late-Response Genes to C-ion Irradiation under 1G From the comparison between C-ion irradiated collected 24 h post-irradiation and sham-irradiated cells under 1G (C24G-C0G) (Table S5), 620 upand 1022 down-regulated genes were found. Over-expressed genes PTCHD4,PURPL,BTG2 and CDKN1A were found to be predominant. Biological term enrichment analyses in all Gene Ontology categories highlighted terms related to cell proliferation and cardiovascular system development, such as angiogenesis. The predominant KEGG pathway identified was the p53 signaling pathway. The transcription factor Forkhead Box O4 was also identified. Among the downregulated genes, many stood out. A few were MKI67,ASPM,CENPF,ANLN,CDC20, DLGAP5,CCNB1,CEP55 and PLK1. Enrichment analyses in all Gene Ontology aspects, as well as in KEGG pathways, highlighted cell cycle and DNA repair related terms. E2F was identified as a gene-targeting transcription factor. 3.6. Latevs. Early-Response Genes to C-ion Irradiation under 1G From the comparison between C-ion-irradiated cells collected 24 and 3 h post-irradiation under 1G (C24G-C3G) (Table S6), 147 upand 563 down-regulated genes were identified. Statistically significant terms were not found after performing over-representation analyses for over-expressed genes. For under-expressed genes, enrichment analyses in all Gene Ontology domains, as well as in KEGG pathways, highlighted cell cycle-related terms. Biological processes related to response to stimulus and DNA repair were identified. E2F was found as a gene-targeting transcription factor. Down-regulated genes, whose expression was found to be expressed 3–5 times less, were MKI67,H2BC18,H1-3,H2AC18,TMPO,H4C4 and H3C3. 3.7. Effects of Simulated µG on Sham-Irradiated Cells From the comparison of sham-irradiated cells under simulated µ G and 1G (X0 µ G-X0G (Table S7) and C0 µ G-C0G (Table S8)), up-regulated genes were identified and among those, PCDHGC4 and PCLO were prominent. After performing over-representation analyses for over-expressed genes, statistically significant terms were not found. Down-regulated genes were also identified and TTN and MSTN were found to be predominant based on their log 2 fold changes. For under-expressed genes, enrichment analyses in all Gene Ontology aspects highlighted the response to oxygen levels, muscle contraction and regulation of blood circulation-related terms. The prevalent KEGG pathway identified was Pathogenic Escherichia coli infection. SRF was identified as a gene-targeting transcription factor for down-regulated genes. 3.8. Response to Simulated µG in Cells Collected 3 h after C-ion Irradiation In the comparison referring to the response to simulated µ G in cells collected 3 h after C-ion irradiation (C3 µ G-C3G) (Table S9), cell cycle-promoting terms, such as cell division, were over-represented in up-regulated genes. In comparisons involving the response to simulated µ G in cells collected 3 h after X-ray irradiation (X3 µ G-X3G) (Table S10), 24 h after X-ray irradiation (X24 µ G-X24G) (Table S11), or 24 h after C-ion irradiation (C24 µ G-C24G) (Table S12), no enriched biological terms were identified. 3.9. Early-Response Genes to X-ray Irradiation and Simulated µG Combined Effect From the comparison between X-ray-irradiated cells collected 3 h post-irradiation under simulated µ G and sham-irradiated under 1G (X3 µ G-X0G) (Table S13), 76 over-expressed genes were found and among those CDKN1A,MDM2,FDXR,PTCHD4,TP53INP1,BTG2 and GDF15 stood out. Likewise, 21 under-expressed genes were found to be statistically significant. Down-regulated genes FAM111B,ZNF367 and VIM-AS1 stood out. Concerning the over-expressed genes, enrichment analyses for Gene Ontology biological processes and KEGG pathways highlighted the p53 signaling pathway. Biological processes related to response to stimulus and apoptosis were also identified. Focusing on underexpressed genes, biological term over-representation analyses highlighted cell cycle-related biological processes. Biomolecules 2024,14, 88 8 of 21 3.10. Late-Response Genes to X-ray Irradiation and Simulated µG Combined Effect From the comparison between X-ray-irradiated cells collected 24 h post-irradiation under simulated µ G and sham-irradiated cells under 1G (X24 µ G-X0G) (Table S14), 877 upand 1429 down-regulated genes were found. Over-expressed genes PTCHD4,PURPL and PAPPA were found to be predominant. Enrichment analyses in all Gene Ontology aspects highlighted terms related to cell proliferation and cardiovascular system development. The main KEGG pathway identified was the p53 signaling pathway. The transcription factor FOXO4 was also identified as a trans-activator of up-regulated genes. Furthermore, from over-expressed microRNAs: MIR-17, MIR-20A and MIR-106A were discovered. Among the down-regulated genes, many stood out. A few were MKI67,ASPM,TPX2,IQGAP3, CENPF,KIF20B,CCNB1 and CEP55. Enrichment analyses in all Gene Ontology domains highlighted cell cycle and DNA repair related terms. The predominant KEGG pathway was found to be carcinogenesis. E2F was identified as a gene-targeting transcription factor for under-expressed genes. 3.11. Early-Response Genes to C-ion Irradiation and Simulated µG Combined Effect From the comparison between C-ion-irradiated cells collected 3 h post-irradiation under simulated µ G and sham-irradiated under 1G (C3 µ G-C0G) (Table S15), 184 overexpressed genes were found and among those CDKN1A,MDM2,PTCHD4,BTG2,PAPPA, TP53INP1, and PURPL stood out. Likewise, 136 under-expressed genes were found to be statistically significant. Down-regulated genes MKI67 and DTL were found to be prominent. Concerning the over-expressed genes, enrichment analyses in all Gene Ontology aspects, as well as KEGG pathways, highlighted the p53 signaling pathway. LEF1 was identified as a gene-targeting transcription factor for up-regulated genes. Concentrating on under-expressed genes, biological term over-representation analyses in all Gene Ontology domains, as well as KEGG pathway, highlighted cell cycle, circulatory system development and protein digestion and absorption related terms. E2F was identified as a gene-targeting transcription factor. 3.12. Late-Response Genes to C-ion Irradiation and Simulated µG Combined Effect From the comparison between C-ion-irradiated cells collected 24 h post-irradiation under simulated µ G and sham-irradiated under 1G (C24 µ G-C0G) (Table S16), 478 upand 803 down-regulated genes were found. Over-expressed genes PTCHD4,PURPL,BTG2 and CDKN1A were found to be predominant. Biological term enrichment analyses in all Gene Ontology categories highlighted terms related to apoptosis and response to stress. The predominant KEGG pathway identified was the p53 signaling pathway. The transcription factor Forkhead Box O4 was also identified. Among the down-regulated genes, many stood out. A few were MKI67,H2BC18,H1-3,H4C4,H3C10,H2AC13 and H3C3. Enrichment analyses in all Gene Ontology aspects highlighted cell cycle and DNA repair related terms. The predominant KEGG pathways identified are cell cycle and carcinogenesis related. E2F was identified as a gene-targeting transcription factor for under-expressed genes. 3.13. Detection of Apoptosis-, DNA Damage Repairor Cell Cycle-Related Genes From the comparisons between X-ray or C-ion-irradiated cells collected 3 or 24 h post-irradiation under simulated µ G and sham-irradiated under 1G (X3 µ G-X0G) (Table S13), (X24 µ G-X0G) (Table S14), (C3 µ G-C0G) (Table S15) and (C24 µ G-C0G) (Table S16), apoptosis-related genes BLOC1S2,EDA2R,TP53INP1,MDM2,CDKN1A,FAS and BCL2L1 were found to be up-regulated, DNA damage repair-related genes BRCA1,POLQ,BLM and H2AFX and cell cycle-related genes MKI67,CDT1,CDC6,MSH6 and TERT were found to be down-regulated. 3.14. Overlaps between Earlyand Late-Response Genes to X-ray and C-ion Radiation under 1G Venn diagrams were created for upand down-regulated genes between four comparisons under 1G: Early response to C-ion irradiation (C3G-C0G), late response to C-ion Biomolecules 2024,14, 88 15 of 21 , x FOR PEER REVIEW Figure 5. Figure 5. Log 2 fold changes of up-regulated genes 3 h (X3) or 24 h (X24) after X-ray exposure vs. sham irradiation and 3 h (C3) or 24 h (C24) after C ion exposure vs. sham irradiation under 1G (G) (in blue) or simulated microgravity (µG) (in orange). Comparing sham-irradiated cells under simulated µ G and 1G, enrichment analyses in statistically significant down-regulated genes, highlighted terms related to the response to oxygen levels, muscle contraction and regulation of blood circulation. Based on their log2 fold changes, TTN and MSTN were identified as the most prominent down-regulated genes. TTN encodes for the lengthiest human protein, Titin, which controls sarcomere elasticity and contraction and is linked to the development of muscle atrophy [ 58 ]. As µ G induces skeletal muscle atrophy [ 59 , 60 ], TTN under-expression under µ G could be responsible for muscle mass loss in space flights. It has been found that an effect of µ G is also the reduced human ventilatory response to hypoxia [61]. Biological processes related to the defense response (immune effector process and type I interferon signaling pathway) were highlighted in genes that were only differentially expressed under C-ion (high-LET) radiation but not in genes that were differentially expressed under X-ray (low-LET) radiation. Type I interferons are components of the early immune response. Strong associations between response to radiation and immune system and inflammation have been suggested in the past [ 62 , 63 ]. Although DNA repair was identified as an over-represented term in both aforementioned gene subsets, DDR terms such as double-strand break repair, double-strand break repair via the less-accurate non-homologous end joining (NHEJ) and non-recombinational repair were found exclusively in the genes that were differentially expressed under high-LET radiation [ 64 ]. The increased complexity of damage is often associated with a lethality increase compared to low-LET radiations [ 2 ]. Based also on our data for X-ray and C-ion-irradiated human G2-phase cells, it is suggested that classical NHEJ will make an initial attempt to repair the DSBs [ 65 , 66 ]. Examining up-regulated genes due to X-ray (low-LET) radiation, biological processes related to angiogenesis were discovered. Late down-regulated genes were found to be related to DNA damage repair, as it was previously shown [ 67 ]. Last but not least, relating to the biological effects of IR, the generation of oxidative stress is expected [ 68 ]. The lysyl oxidase (LOX) gene family contains five members: LOX;LOXL1;LOXL2;LOXL3; and Biomolecules 2024,14, 88 16 of 21 LOXL4 [ 69 ], all of which were found over-expressed as a response to radiation according to our results [ 70 , 71 ]. Hydrogen peroxide is a side product of this catalytic reaction. LOX proteins are expressed in fibroblasts [ 72 ]. Aberrant expression is involved in tumor invasion and metastasis [ 73 ]. Thus, lysyl oxidases provide targets for pharmacological and therapeutic intervention. Another cytochrome c oxidase subunit which is up-regulated in our experiments is cytochrome c oxidase subunit 7C (COX7C). Cytochrome c oxidase is related to the regulation of oxidative phosphorylation [ 74 ]. Other oxidases that we discovered to be over-expressed due to IR include Acyl-CoA oxidase 2 (ACOX2), Aldehyde oxidase 1 (AOX1) and Quiescin sulfhydryl oxidase 1 (QSOX1), all involved in the regulation of reactive oxygen species (ROS) homeostasis [ 75 – 83 ]. In addition, Glutathione peroxidase 1 (GPX1), a major antioxidant enzyme [ 84 ], was also found to be over-expressed as a response to IR in our analysis. , x FOR PEER REVIEW Figure 6. On a parallel direction of application of our accumulated data presented, a significant number of genes found to be affected by the combined effect of microgravity and radiaBLOC1S2 EDA2R TP53INP1 POLQ H2AFX CDT1 MSH6 TERT the fight against cancer even without any in statistically significant down Figure 6. Log 2 fold changes of down-regulated genes 3 h (X3) or 24 h (X24) after X-ray exposure vs. sham irradiation and 3 h (C3) or 24 h (C24) after C ion exposure vs. sham irradiation under 1G (G) (in blue) or simulated microgravity (µG) (in orange). Conclusively, as key gene signatures, we have identified that PLK1,BRCA1,CCNB1, AURKB,CDK1,CHEK1,RAD51,CCNA2 and TOP2A are hub genes in protein interaction networks for DEGs from the comparison between X-ray or C-ion-irradiated cells collected 24 h post-irradiation and sham-irradiated ones under 1G condition. Also, BRCA1 and RAD51 are associated with damage repair of DNA breaks. We note that PLK1,CCNB1, AURKB,CDK1,CHEK1,CCNA2 and TOP2A play a critical role in the process of mitosis. Biomolecules 2024,14, 88 17 of 21 Our current findings suggest that human cells exposed to microgravity may significantly change their response to a genuine stressor like radiation. Alterations in biological responses to space-related radiations are known and the challenge that we aimed to address in this study was to bring into surface key processes impacted by the specific types of radiations and irradiation methodologies probing the exposure of human cells to space radiations. Therefore, the field is open for the use of these results in the development of new tools and methodologies to overcome tumor resistance beyond the current use in space missions. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/biom14010088/s1, Table S1: X3G-X0G. Lists of DEGs and enriched biological terms in early response to X-ray under 1G; Table S2: X24G-X0G. Lists of DEGs and enriched biological terms in late response to X-ray under 1G; Table S3: X24G-X3G. Lists of DEGs and enriched biological terms in late vs. early response to X-ray under 1G; Table S4: C3GC0G. Lists of DEGs and enriched biological terms in early response to C-ion radiation under 1G; Table S5: C24G-C0G . Lists of DEGs and enriched biological terms in late response to C-ion radiation under 1G; Table S6: C24G-C3G . Lists of DEGs and enriched biological terms in late vs. early response to C-ion radiation under 1G; Table S7: X0 µ G-X0G. Lists of DEGs and enriched biological terms in response to simulated µ G in sham-X-ray-irradiated cells; Table S8: C0 µ G-C0G. Lists of DEGs and enriched biological terms in response to simulated µ G in sham-C-ion-irradiated cells; Table S9: C3µG-C3G . Lists of DEGs and enriched biological terms in response to simulated µ G in cells collected 3 h after C-ion irradiation; Table S10: X3 µ G-X3G. Lists of DEGs and enriched biological terms in response to simulated µ G in cells collected 3 h after X-ray irradiation; Table S11: X24µG-X24G . Lists of DEGs in response to simulated µ G in cells collected 24 h after X ray-irradiation; Table S12: C24 µ G-C24G. Lists of DEGs and enriched biological terms in response to simulated µ G in cells collected 24 h after C-ion irradiation; Table S13: X3µG-X0G . Lists of DEGs and enriched biological terms in early response to X-ray irradiation and simulated µ G combined effect; Table S14: X24 µ G-X0G. Lists of DEGs and enriched biological terms in late response to X-ray irradiation and simulated µ G combined effect; Table S15: C3µG-C0G . Lists of DEGs and enriched biological terms in early response to C-ion irradiation and simulated µ G combined effect; Table S16: C24 µ G-C0G. Lists of DEGs and enriched biological terms in late response to C-ion irradiation and simulated µ G combined effect; Table S17: (X3µG-X0µG)-(X3G-X0G) . Lists of DEGs in response to the interaction between early response to X-ray and response to simulated µG; Table S18: (X24µG-X0µG)-(X24G-X0G). Lists of DEGs in response to the interaction between late response to X-ray and response to simulated µ G; Table S19: (C3 µ G-C0 µ G)-(C3G-C0G). Lists of DEGs in response to the interaction between early response to C-ion and response to simulated µ G; Table S20: (C24 µ G-C0 µ G)-(C24G-C0G). Lists of DEGs in response to the interaction between late response to C-ion and response to simulated µ G; Table S21: (X24 µ G-X3 µ G)-(X24G-X3G). Lists of DEGs in response to the interaction between late vs. early response to X-ray and response to simulated µ G; Table S22: (C24 µ G-C3 µ G)-(C24GC3G). Lists of DEGs in response to the interaction between late vs. early response to C-ion and response to simulated µ G; Table S23: X3 µ G-X0 µ G. Lists of DEGs and enriched biological terms in early response to X-ray under simulated µ G; Table S24: X24µG-X0µG . Lists of DEGs and enriched biological terms in late response to X-ray under simulated µ G; Table S25: X24µG-X3µG . Lists of DEGs and enriched biological terms in late vs. early response to X-ray under simulated µ G; Table S26: C3µG-C0µG . Lists of DEGs and enriched biological terms in early response to C-ion under simulated µ G; Table S27: C24µG-C0µG . Lists of DEGs and enriched biological terms in late response to C-ion under simulated µ G; Table S28: C24µG-C3µG . Lists of DEGs and enriched biological terms in late vs. early response to C-ion under simulated µG. Author Contributions: Conceptualization, M.H., A.T. and P.B.S.; methodology, P.M., K.K., M.H., H.I., A.T. and I.M.; software, P.M., K.K. and I.M.; validation, M.H., H.I. and A.T.; formal analysis, P.M., K.K. and I.M.; investigation, M.H., A.T., P.B.S., A.G.G. and I.M.; resources, M.H., H.I. and A.T.; data curation, P.M. and K.K.; writing—original draft preparation, P.M., K.K., A.G.G. and I.M.; writing— review and editing, P.M., K.K., M.H., H.I., A.T., P.B.S., A.G.G. and I.M.; visualization, P.M. and I.M.; supervision, M.H., A.T., P.B.S., A.G.G. and I.M.; project administration, A.T.; funding acquisition, M.H., A.T. and P.B.S. All authors have read and agreed to the published version of the manuscript. Biomolecules 2024,14, 88 18 of 21 Funding: This research was funded by the MEXT Grant-in-Aid for Scientific Research on Innovative Areas, Japan “Living in Space” (grant no. JP15H05945), Gunma University’s Promotion of Scientific Research, and NASA Space Biology Program (grant no. 80NSSC19K0133). A.G.G. would like to acknowledge funding from project 21GRD02 BIOSPHERE that has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States, the contribution of the COST Action CA21169 ‘DYNALIFE’ supported by COST (European Cooperation in Science and Technology). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Ikeda, H.; Muratani, M.; Hidema, J.; Hada, M.; Fujiwara, K.; Souda, H.; Yoshida, Y.; Takahashi, A. Expression Profile of Cell Cycle-Related Genes in Human Fibroblasts Exposed Simultaneously to Radiation and Simulated Microgravity. Int. J. Mol. Sci. 2019,20, 4791. [CrossRef] [PubMed] 2. Hada, M.; Ikeda, H.; Rhone, J.R.; Beitman, A.J.; Plante, I.; Souda, H.; Yoshida, Y.; Held, K.D.; Fujiwara, K.; Saganti, P.B.; et al. Increased Chromosome Aberrations in Cells Exposed Simultaneously to Simulated Microgravity and Radiation. Int. J. Mol. Sci. 2018,20, 43. [CrossRef] 3. Yamanouchi, S.; Adachi, T.; Yoshida, Y.; Rhone, J.; Mao, J.-H.; Fujiwara, K.; Saganti, P.B.; Takahashi, A.; Hada, M. The combined effect of simulated microgravity and radiation on chromosome aberrations in human peripheral blood lymphocytes. Biol. Sci. Space 2021,35, 15–23. [CrossRef] 4. Michalettou, T.D.; Michalopoulos, I.; Costes, S.V.; Hellweg, C.E.; Hada, M.; Georgakilas, A.G. A Meta-Analysis of the Effects of High-LET Ionizing Radiations in Human Gene Expression. Life 2021,11, 115. [CrossRef] [PubMed] 5. Maxwell, C.A.; Fleisch, M.C.; Costes, S.V.; Erickson, A.C.; Boissiere, A.; Gupta, R.; Ravani, S.A.; Parvin, B.; Barcellos-Hoff, M.H. Targeted and nontargeted effects of ionizing radiation that impact genomic instability. Cancer Res. 2008,68, 8304–8311. [CrossRef] [PubMed] 6. Simpson, J.A. Elemental and Isotopic Composition of the Galactic Cosmic Rays. Ann. Rev. Nuclear Part. Sci. 1983,33, 323–382. [CrossRef] 7. Maalouf, M.; Durante, M.; Foray, N. Biological effects of space radiation on human cells: History, advances and outcomes. J. Radiat. Res. 2011,52, 126–146. [CrossRef] 8. Nelson, G.A. Space Radiation and Human Exposures, A Primer. Radiat. Res. 2016,185, 349–358. [CrossRef] 9. Hohmann, W. Die Erreichbarkeit der Himmelskörper; Oldenbourg: München, Germany, 1925. 10. Owen, S.T.; Watson, M.D.; Rodriguez, M.A. Exergy Efficiency of Interplanetary Transfer Vehicles. In Proceedings of the Conference on Systems Engineering Research, Charlottesville, GA, USA, 8–9 May 2018. 11. Hassler, D.M.; Zeitlin, C.; Wimmer-Schweingruber, R.F.; Ehresmann, B.; Rafkin, S.; Eigenbrode, J.L.; Brinza, D.E.; Weigle, G.; Bottcher, S.; Bohm, E.; et al. Mars’ surface radiation environment measured with the Mars Science Laboratory’s Curiosity rover. Science 2014,343, 1244797. [CrossRef] 12. Chancellor, J.C.; Scott, G.B.; Sutton, J.P. Space Radiation: The Number One Risk to Astronaut Health beyond Low Earth Orbit. Life 2014,4, 491–510. [CrossRef] 13. Moreno-Villanueva, M.; Wu, H. Radiation and microgravity—Associated stress factors and carcinogensis. Reach 2019,13, 100027. [CrossRef] 14. Radstake, W.E.; Gautam, K.; Miranda, S.; Vermeesen, R.; Tabury, K.; Rehnberg, E.; Buset, J.; Janssen, A.; Leysen, L.; Neefs, M.; et al. The Effects of Combined Exposure to Simulated Microgravity, Ionizing Radiation, and Cortisol on the In Vitro Wound Healing Process. Cells 2023,12, 246. [CrossRef] [PubMed] 15. Topal, U.; Zamur, C. Microgravity, Stem Cells, and Cancer: A New Hope for Cancer Treatment. Stem Cells Int. 2021,2021, 5566872. [CrossRef] [PubMed] 16. Indo, H.P.; Tomiyoshi, T.; Suenaga, S.; Tomita, K.; Suzuki, H.; Masuda, D.; Terada, M.; Ishioka, N.; Gusev, O.; Cornette, R.; et al. MnSOD downregulation induced by extremely low 0.1 mGy single and fractionated X-rays and microgravity treatment in human neuroblastoma cell line, NB-1. J. Clin. Biochem. Nutr. 2015,57, 98–104. [CrossRef] [PubMed] 17. Wang, T.; Sun, Q.; Xu, W.; Li, F.; Li, H.; Lu, J.; Wu, L.; Wu, Y.; Liu, M.; Bian, P. Modulation of modeled microgravity on radiation-induced bystander effects in Arabidopsis thaliana. Mutat. Res. 2015,773, 27–36. [CrossRef] [PubMed] 18. Ikeda, H.; Souda, H.; Puspitasari, A.; Held, K.D.; Hidema, J.; Nikawa, T.; Yoshida, Y.; Kanai, T.; Takahashi, A. Development and performance evaluation of a three-dimensional clinostat synchronized heavy-ion irradiation system. Life Sci. Space Res. 2017, 12, 51–60. [CrossRef] [PubMed] Biomolecules 2024,14, 88 19 of 21 19. Ikeda, H.; Souda, H.; Puspitasari, A.; Held, K.D.; Hidema, J.; Nikawa, T.; Yoshida, Y.; Kanai, T.; Takahashi, A. A New System for Three-dimensional Clinostat Synchronized X-irradiation with a High-speed Shutter for Space Radiation Research. Biol. Sci. Space 2016,30, 8–16. [CrossRef] 20. Hauslage, J.; Cevik, V.; Hemmersbach, R. Pyrocystis noctiluca represents an excellent bioassay for shear forces induced in ground-based microgravity simulators (clinostat and random positioning machine). NPJ Microgravity 2017,3, 12. [CrossRef] 21. Chapman, B.; Kirchner, R.; Pantano, L.; Naumenko, S.; De Smet, M.; Beltrame, L.; Khotiainsteva, T.; Sytchev, I.; Valls Guimera, R.; Kern, J.; et al. Bcbio/Bcbio-Nextgen: V1.2.9; Zenodo: Geneva, Switzerland, 2021. 22. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data, Babraham Bioinformatics; Babraham Institute: Cambridge, UK, 2010. 23. Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013,29, 15–21. [CrossRef] 24. Patro, R.; Duggal, G.; Love, M.I.; Irizarry, R.A.; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 2017,14, 417–419. [CrossRef] 25. García-Alcalde, F.; Okonechnikov, K.; Carbonell, J.; Cruz, L.M.; Götz, S.; Tarazona, S.; Dopazo, J.; Meyer, T.F.; Conesa, A. Qualimap: Evaluating next-generation sequencing alignment data. Bioinformatics 2012,28, 2678–2679. [CrossRef] [PubMed] 26. Ewels, P.; Magnusson, M.; Lundin, S.; Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016,32, 3047–3048. [CrossRef] [PubMed] 27. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009,25, 2078–2079. [CrossRef] [PubMed] 28. Liao, Y.; Smyth, G.K.; Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2013,30, 923–930. [CrossRef] [PubMed] 29. Soneson, C.; Love, M.I.; Robinson, M.D. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Res 2016,4. [CrossRef] [PubMed] 30. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014,15, 550. [CrossRef] [PubMed] 31. Steinbaugh, M.J.; Pantano, L.; Kirchner, R.D.; Barrera, V.; Chapman, B.A.; Piper, M.E.; Mistry, M.; Khetani, R.S.; Rutherford, K.D.; Hofmann, O.; et al. bcbioRNASeq: R package for bcbio RNA-seq analysis. F1000Res 2018,6, 1976. [CrossRef] 32. Zhu, A.; Ibrahim, J.G.; Love, M.I. Heavy-tailed prior distributions for sequence count data: Removing the noise and preserving large differences. Bioinformatics 2018,35, 2084–2092. [CrossRef] 33. Stephens, M. False discovery rates: A new deal. Biostatistics 2016,18, 275–294. [CrossRef] 34. Seal, R.L.; Braschi, B.; Gray, K.; Jones, T.E.M.; Tweedie, S.; Haim-Vilmovsky, L.; Bruford, E.A. Genenames.org: The HGNC resources in 2023. Nucleic Acids Res. 2023,51, D1003–D1009. [CrossRef] 35. Zhu, A.; Ibrahim, J.G.; Love, M.I. Effect Size Estimation with Apeglm. 2022. Available online: https://bioconductor.org/ packages/devel/bioc/vignettes/apeglm/inst/doc/apeglm.html (accessed on 7 January 2023). 36. Liao, Y.; Wang, J.; Jaehnig, E.J.; Shi, Z.; Zhang, B. WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019,47, W199–W205. [CrossRef] [PubMed] 37. Khatri, P.; Sirota, M.; Butte, A.J. Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Comput. Biol. 2012,8, e1002375. [CrossRef] 38. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B 1995,57, 289–300. [CrossRef] 39. Gene Ontology Consortium. The Gene Ontology Knowledgebase in 2023. Genetics 2023,224, iyad031. [CrossRef] [PubMed] 40. Kanehisa, M.; Furumichi, M.; Sato, Y.; Kawashima, M.; Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023,51, D587–D592. [CrossRef] [PubMed] 41. Liberzon, A.; Birger, C.; Thorvaldsdottir, H.; Ghandi, M.; Mesirov, J.P.; Tamayo, P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015,1, 417–425. [CrossRef] 42. Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S.; et al . The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2022,51, D638–D646. [CrossRef] 43. Watts, D.J.; Strogatz, S.H. Collective dynamics of ‘small-world’ networks. Nature 1998,393, 440–442. [CrossRef] 44. Li, X.L.; Subramanian, M.; Jones, M.F.; Chaudhary, R.; Singh, D.K.; Zong, X.; Gryder, B.; Sindri, S.; Mo, M.; Schetter, A.; et al. Long Noncoding RNA PURPL Suppresses Basal p53 Levels and Promotes Tumorigenicity in Colorectal Cancer. Cell Rep. 2017, 20, 2408–2423. [CrossRef] 45. Pflaum, J.; Schlosser, S.; Müller, M. p53 Family and Cellular Stress Responses in Cancer. Front. Oncol. 2014,4, 285. [CrossRef] 46. Shi, L.; Tian, H.; Wang, P.; Li, L.; Zhang, Z.; Zhang, J.; Zhao, Y. Spaceflight and simulated microgravity suppresses macrophage development via altered RAS/ERK/NF κ B and metabolic pathways. Cell. Mol. Immunol. 2021,18, 1489–1502. [CrossRef] [PubMed] 47. Nikitaki, Z.; Velalopoulou, A.; Zanni, V.; Tremi, I.; Havaki, S.; Kokkoris, M.; Gorgoulis, V.G.; Koumenis, C.; Georgakilas, A.G. Key biological mechanisms involved in high-LET radiation therapies with a focus on DNA damage and repair. Expert Rev. Mol. Med. 2022,24, e15. [CrossRef] Biomolecules 2024,14, 88 20 of 21 48. Broude, E.V.; Demidenko, Z.N.; Vivo, C.; Swift, M.E.; Davis, B.M.; Blagosklonny, M.V.; Roninson, I.B. p21 (CDKN1A) is a negative regulator of p53 stability. Cell Cycle 2007,6, 1468–1471. [CrossRef] [PubMed] 49. Nickoloff, J.A.; Boss, M.K.; Allen, C.P.; LaRue, S.M. Translational research in radiation-induced DNA damage signaling and repair. Transl. Cancer Res. 2017,6, S875–S891. [CrossRef] [PubMed] 50. Okazaki, R. Role of p53 in Regulating Radiation Responses. Life 2022,12, 1099. [CrossRef] [PubMed] 51. Fu, Z.; Tindall, D.J. FOXOs, cancer and regulation of apoptosis. Oncogene 2008,27, 2312–2319. [CrossRef] 52. Tachiiri, S.; Katagiri, T.; Tsunoda, T.; Oya, N.; Hiraoka, M.; Nakamura, Y. Analysis of gene-expression profiles after gamma irradiation of normal human fibroblasts. Int. J. Radiat. Oncol. Biol. Phys. 2006,64, 272–279. [CrossRef] [PubMed] 53. Kis, E.; Szatmari, T.; Keszei, M.; Farkas, R.; Esik, O.; Lumniczky, K.; Falus, A.; Safrany, G. Microarray analysis of radiation response genes in primary human fibroblasts. Int. J. Radiat. Oncol. Biol. Phys. 2006,66, 1506–1514. [CrossRef] 54. Ge, C.; Su, F.; Fu, H.; Wang, Y.; Tian, B.; Liu, B.; Zhu, J.; Ding, Y.; Zheng, X. RNA Profiling Reveals a Common Mechanism of Histone Gene Downregulation and Complementary Effects for Radioprotectants in Response to Ionizing Radiation. Dose-Response. 2020,18, 1559325820968433. [CrossRef] [PubMed] 55. Ishii, K.; Ishiai, M.; Morimoto, H.; Kanatsu-Shinohara, M.; Niwa, O.; Takata, M.; Shinohara, T. The Trp53-Trp53inp1-Tnfrsf10b pathway regulates the radiation response of mouse spermatogonial stem cells. Stem Cell Rep. 2014,3, 676–689. [CrossRef] 56. Fu, X.; Wang, Y.; Wu, G.; Zhang, W.; Xu, S.; Wang, W. Long noncoding RNA PURPL promotes cell proliferation in liver cancer by regulating p53. Mol. Med. Rep. 2019,19, 4998–5006. [CrossRef] [PubMed] 57. Grimm, D.; Schulz, H.; Kruger, M.; Cortes-Sanchez, J.L.; Egli, M.; Kraus, A.; Sahana, J.; Corydon, T.J.; Hemmersbach, R.; Wise, P.M.; et al. The Fight against Cancer by Microgravity: The Multicellular Spheroid as a Metastasis Model. Int. J. Mol. Sci. 2022, 23, 3073. [CrossRef] [PubMed] 58. Ottenheijm, C.A.; Knottnerus, A.M.; Buck, D.; Luo, X.; Greer, K.; Hoying, A.; Labeit, S.; Granzier, H. Tuning passive mechanics through differential splicing of titin during skeletal muscle development. Biophys. J. 2009,97, 2277–2286. [CrossRef] [PubMed] 59. Droppert, P.M. A review of muscle atrophy in microgravity and during prolonged bed rest. J. Br. Interplanet. Soc. 1993,46, 83–86. 60. Vandenburgh, H.; Chromiak, J.; Shansky, J.; Del Tatto, M.; Lemaire, J. Space travel directly induces skeletal muscle atrophy. FASEB J. 1999,13, 1031–1038. [CrossRef] [PubMed] 61. Prisk, G.K.; Elliott, A.R.; West, J.B. Sustained microgravity reduces the human ventilatory response to hypoxia but not to hypercapnia. J. Appl. Physiol. 2000,88, 1421–1430. [CrossRef] 62. Georgakilas, A.G.; Pavlopoulou, A.; Louka, M.; Nikitaki, Z.; Vorgias, C.E.; Bagos, P.G.; Michalopoulos, I. Emerging molecular networks common in ionizing radiation, immune and inflammatory responses by employing bioinformatics approaches. Cancer Lett. 2015,368, 164–172. [CrossRef] 63. Zhang, F.; Manna, S.; Pop, L.M.; Chen, Z.J.; Fu, Y.X.; Hannan, R. Type I Interferon Response in Radiation-Induced Anti-Tumor Immunity. Semin. Radiat. Oncol. 2020,30, 129–138. [CrossRef] 64. Takahashi, A.; Kubo, M.; Ma, H.; Nakagawa, A.; Yoshida, Y.; Isono, M.; Kanai, T.; Ohno, T.; Furusawa, Y.; Funayama, T.; et al. Nonhomologous end-joining repair plays a more important role than homologous recombination repair in defining radiosensitivity after exposure to high-LET radiation. Radiat. Res. 2014,182, 338–344. [CrossRef] [PubMed] 65. Grabarz, A.; Barascu, A.; Guirouilh-Barbat, J.; Lopez, B.S. Initiation of DNA double strand break repair: Signaling and singlestranded resection dictate the choice between homologous recombination, non-homologous end-joining and alternative endjoining. Am. J. Cancer Res. 2012,2, 249–268. [PubMed] 66. Averbeck, N.B.; Topsch, J.; Scholz, M.; Kraft-Weyrather, W.; Durante, M.; Taucher-Scholz, G. Efficient Rejoining of DNA DoubleStrand Breaks despite Increased Cell-Killing Effectiveness following Spread-Out Bragg Peak Carbon-Ion Irradiation. Front. Oncol. 2016,6, 28. [CrossRef] [PubMed] 67. Sutherland, B.M.; Bennett, P.V.; Sutherland, J.C.; Laval, J. Clustered DNA Damages Induced by X Rays in Human Cells. Radiat. Res. 2002,157, 611–616. [CrossRef] 68. Nikitaki, Z.; Hellweg, C.E.; Georgakilas, A.G.; Ravanat, J.L. Stress-induced DNA damage biomarkers: Applications and limitations. Front. Chem. 2015,3, 35. [CrossRef] [PubMed] 69. Molnar, J.; Fong, K.S.; He, Q.P.; Hayashi, K.; Kim, Y.; Fong, S.F.; Fogelgren, B.; Szauter, K.M.; Mink, M.; Csiszar, K. Structural and functional diversity of lysyl oxidase and the LOX-like proteins. Biochim. Biophys. Acta 2003,1647, 220–224. [CrossRef] [PubMed] 70. Pinnell, S.R.; Martin, G.R. The cross-linking of collagen and elastin: Enzymatic conversion of lysine in peptide linkage to alpha-aminoadipic-delta-semialdehyde (allysine) by an extract from bone. Proc. Natl. Acad. Sci. USA 1968,61, 708–716. [CrossRef] [PubMed] 71. Siegel, R.C.; Pinnell, S.R.; Martin, G.R. Cross-linking of collagen and elastin. Properties of lysyl oxidase. Biochemistry 1970, 9, 4486–4492. [CrossRef] [PubMed] 72. Hong, H.H.; Uzel, M.I.; Duan, C.; Sheff, M.C.; Trackman, P.C. Regulation of lysyl oxidase, collagen, and connective tissue growth factor by TGF-beta1 and detection in human gingiva. Lab. Investig. 1999,79, 1655–1667. [PubMed] 73. Wang, W.; Wang, X.; Yao, F.; Huang, C. Lysyl Oxidase Family Proteins: Prospective Therapeutic Targets in Cancer. Int. J. Mol. Sci. 2022,23, 12270. [CrossRef] 74. Ludwig, B.; Bender, E.; Arnold, S.; Hüttemann, M.; Lee, I.; Kadenbach, B. Cytochrome c Oxidase and the Regulation of Oxidative Phosphorylation. Chembiochem 2001,2, 392–403. [CrossRef] Biomolecules 2024,14, 88 21 of 21 75. Ferdinandusse, S.; Denis, S.; Van Roermund, C.W.T.; Preece, M.A.; Koster, J.; Ebberink, M.S.; Waterham, H.R.; Wanders, R.J.A. A novel case of ACOX2 deficiency leads to recognition of a third human peroxisomal acyl-CoA oxidase. Biochim. Biophys. Acta Mol. Basis Dis. 2018,1864, 952–958. [CrossRef] 76. Poll-The, B.T.; Roels, F.; Ogier, H.; Scotto, J.; Vamecq, J.; Schutgens, R.B.; Wanders, R.J.; Van Roermund, C.W.; Van Wijland, M.J.; Schram, A.W.; et al. A new peroxisomal disorder with enlarged peroxisomes and a specific deficiency of acyl-CoA oxidase (pseudo-neonatal adrenoleukodystrophy). Am. J. Hum. Genet. 1988,42, 422–434. [PubMed] 77. Terao, M.; Garattini, E.; Romao, M.J.; Leimkuhler, S. Evolution, expression, and substrate specificities of aldehyde oxidase enzymes in eukaryotes. J. Biol. Chem. 2020,295, 5377–5389. [CrossRef] [PubMed] 78. Garrido, C.; Leimkuhler, S. The Inactivation of Human Aldehyde Oxidase 1 by Hydrogen Peroxide and Superoxide. Drug Metab. Dispos. 2021,49, 729–735. [CrossRef] 79. Chakravarthi, S.; Jessop, C.E.; Willer, M.; Stirling, C.J.; Bulleid, N.J. Intracellular catalysis of disulfide bond formation by the human sulfhydryl oxidase, QSOX1. Biochem. J. 2007,404, 403–411. [CrossRef] [PubMed] 80. Heckler, E.J.; Alon, A.; Fass, D.; Thorpe, C. Human quiescin-sulfhydryl oxidase, QSOX1: Probing internal redox steps by mutagenesis. Biochemistry 2008,47, 4955–4963. [CrossRef] [PubMed] 81. Ilani, T.; Alon, A.; Grossman, I.; Horowitz, B.; Kartvelishvily, E.; Cohen, S.R.; Fass, D. A secreted disulfide catalyst controls extracellular matrix composition and function. Science 2013,341, 74–76. [CrossRef] 82. Javitt, G.; Grossman-Haham, I.; Alon, A.; Resnick, E.; Mutsafi, Y.; Ilani, T.; Fass, D. cis-Proline mutants of quiescin sulfhydryl oxidase 1 with altered redox properties undermine extracellular matrix integrity and cell adhesion in fibroblast cultures. Protein Sci. 2019,28, 228–238. [CrossRef] 83. Grossman, I.; Alon, A.; Ilani, T.; Fass, D. An inhibitory antibody blocks the first step in the dithiol/disulfide relay mechanism of the enzyme QSOX1. J. Mol. Biol. 2013,425, 4366–4378. [CrossRef] 84. De Haan, J.B.; Cooper, M.E. Targeted antioxidant therapies in hyperglycemia-mediated endothelial dysfunction. Front. 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