Quantitative Proteomics and Computational Analysis Elucidate Potential Paternal Biomarkers to Distinguish Idiopathic Recurrent Pregnancy Loss from Unexplained Infertility.

Quantitative Proteomics and Computational Analysis Elucidate Potential Paternal Biomarkers to Distinguish Idiopathic Recurrent Pregnancy Loss from Unexplained Infertility. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative Proteomics and Computational Analysis Elucidate Potential Paternal Biomarkers to Distinguish Idiopathic Recurrent Pregnancy Loss from Unexplained Infertility. Ankita Gupta, Ritu Khosla, Prachi Patel, Alia Siddiqui, Mrinmoy Chakraborty, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8447183/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Recurrent Pregnancy Loss (RPL) is a complex reproductive disorder that affects 0.5–3% of couples worldwide, with nearly 40–50% of cases remaining idiopathic despite thorough evaluation. While maternal factors have been extensively studied, the molecular mechanisms underlying paternal contributions to RPL remain poorly understood. This study aimed to elucidate the molecular alterations in spermatozoa from male partners of couples with idiopathic RPL and identify potential biomarkers distinguishing RPL from Unexplained Infertility (UI) and fertile controls through high-throughput proteomic and machine learning–based analyses. Methods: Semen samples from Control (n=3), RPL (n=3) and UI (n=3) were analyzed using LC MS/MS–based quantitative proteomics. A total of 6,354 proteins were identified. Differentially expressed proteins (DEPs) were determined via the limma statistical framework, with RPL specific signatures defined through an intersection strategy. Functional and pathway enrichment analyses were performed using KEGG, and GO, from MSigDB database, followed by biomarker discovery using MetaboAnalyst 6.0 with Partial Least Squares Discriminant Analysis (PLS-DA) and Receiver Operating Characteristic (ROC) validation. Results: Proteomic profiling revealed 63 RPL-specific DEPs, including 32 upregulated and 31 downregulated proteins. Upregulated proteins were primarily involved in energy metabolism, DNA repair and cytoskeletal regulation, while downregulated proteins impaired antioxidant defence and metabolic control. PLS-DA analysis established all 63 DEPs to be good classifiers for RPL (VIP score>1). Univariate biomarker analysis further confirmed 30 of these proteins to be capable of perfectly distinguishing RPL from UI and control (AUC>1) with some of the key determinants being XRCC4, USP1, BDKRB1, GPD1L, and IQGAP1. Conclusions: This integrated proteomic and computational analysis provides the first comprehensive molecular characterization of idiopathic RPL sperm proteome. The identified RPL-specific protein panel offers promising biomarkers for diagnosis and potential therapeutic targets, emphasizing the crucial role of paternal factors in recurrent pregnancy loss. Recurrent pregnancy loss proteomics spermatozoa biomarkers machine learning infertility Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 BACKGROUND Recurrent pregnancy loss (RPL) is a distressing, complex and deeply medical condition that affects a significant number of couples worldwide. Defined by the World Health Organization as two or more consecutive miscarriages before the 20th week of gestation, RPL impacts roughly 0.5–3% of couples of reproductive age planning for conception[ 1 ]. The main problem in case of RPL is not conception, but to sustain the pregnancy till birth of the foetus. RPL has many possible causes, including chromosomal abnormalities (with 2–5% of couples having a partner with a balanced translocation), structural issues in the reproductive tract, hormonal imbalances, immune disorders, infections (like chronic endometritis, found in 10–27% of cases), antiphospholipid syndrome and blood clotting problems [ 2 , 3 ]. Estimates suggest pure male factor infertility ranges from 2.5% to 12% overall. Rates vary by region for example, 4.5–6% in North America, 9% in Australia, and up to 12% in Eastern Europe. One study even reported male factors alone accounting for 25.6% of infertility cases[ 4 ]. A recent Indian study reported that among 2,348 infertile men, about 63% had normal semen parameters (normozoospermia), suggesting that standard semen analysis may not always explain the cause of infertility[ 5 ]. Studies also highlight male factors, such as changes in sperm DNA, chromatin structure, epigenetics, and increased reactive oxygen species (ROS), as potential contributors to RPL[ 6 ]. In spite of many known factors contributing to RPL, atleast 40% cases remain unexplained even after detailed clinical examination. In recent years, significant efforts have been made to improve the diagnosis of such idiopathic cases particularly in context of male infertility factors. Nevertheless, despite ongoing research, there is still limited understanding of consistent genetic contributors, reliable biomarkers, and their potential clinical applications in RPL. Idiopathic male infertility is believed to result from a complex interplay of genetic and environmental factors. As a result, many cases remain unresolved, prompting researchers to explore advanced approaches like omics technologies including genomics, epigenomics, transcriptomics, proteomics and metabolomics to uncover the underlying causes and improve diagnosis and treatment strategies [ 7 , 8 ]. A biomarker is a measurable biological indicator that can detect or monitor the presence and progression of a disease. Ideally, it should be accurate, affordable, minimally invasive, and capable of identifying disease at an early stage when intervention is most effective. Proteomics plays a crucial role in biomarker discovery by focusing on the actual proteins expressed in cells their structure, function, and interactions offering insights that gene expression alone cannot provide. Since protein expression varies across cell types and conditions, proteomic analysis offers a more precise and dynamic understanding of disease mechanisms. Since sperm provides half of an embryo’s genetic material, sperm proteomics can be a key in understanding male factors responsible in causing idiopathic recurrent pregnancy loss (iRPL). Specific changes in the sperm proteome have been linked to disruptions in sperm function, supported by various protein identification methods. High-throughput proteomics, especially mass spectrometry (MS), allows large-scale profiling of sperm proteins. While targeted studies focus on measuring a few key proteins using antibodies and assays, MS has enabled the identification of thousands of proteins related to the testis and mature sperm, greatly deepening our understanding of sperm biology[ 9 ]. In the context of male infertility, proteomics has revealed important changes in protein expression that may help explain underlying causes and identify potential diagnostic markers. However, when it comes to unexplained recurrent pregnancy loss (URPL), proteomic research remains limited. Earlier methods like two-dimensional gel electrophoresis (2-DE) and DIGE were complex and provided only partial protein profiles [ 10 , 11 ]. In lieu of the above, we used advanced mass spectrometry based high-throughput proteomic analysis to semen samples, aiming to uncover the lesser-known paternal contributions to URPL and gain new insights into its molecular basis. Differentially expressed proteins (DEPs) and potential biomarkers for iRPL were selected through statistical and bioinformatic analyses by comparing the iRPL protein expression profile with both healthy controls as well as males who had unexplained infertility. To the best of our knowledge this is the first study comparing iRPL protein expression together with both the aforementioned study groups. This approach offers a deeper, more detailed view of proteins exclusively expressed in iRPL, which can help us gain new molecular insights and can be further explored as potential therapeutic targets. MATERIALS AND METHODS Patient Recruitment and Grouping A total of nine semen samples were collected from male partners of couples attending the infertility clinic. Participants were divided into three groups: Control (n = 3, fertile men without known infertility issues), Recurrent Pregnancy Loss (RPL; n = 3, male partners of women with ≥ 2 consecutive miscarriages), and Unexplained Infertility (UI; n = 3, couples with infertility of unknown etiology despite standard evaluation). Demographic and Clinical Data Collection Demographic details, including age, body mass index (BMI), and duration of infertility (DOI), were recorded at the time of sample collection. Lifestyle information was obtained via structured questionnaire, covering smoking, alcohol consumption, and exercise habits. Clinical parameters such as serum testosterone, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) were measured using standard chemiluminescent immunoassay kits in the institutional clinical laboratory. Statistical Analysis Demographic, lifestyle, and clinical parameters (age, BMI, DOI, smoking, alcohol, exercise, testosterone, FSH, and LH) were summarized as Mean ± Standard Deviation (SD) for each group (Control, RPL, and UI). For continuous variables (age, BMI, DOI, testosterone, FSH, LH), comparisons between groups were performed using one-way analysis of variance (ANOVA) followed by post-hoc Tukey’s test where applicable. For categorical variables (smoking, alcohol, exercise), comparisons were made using the Chi-square test or Fisher’s exact test depending on sample size. A p -value < 0.05 was considered statistically significant. LC-MS Methodology Semen samples were collected and processed under standard laboratory conditions to assess membrane-associated protein composition. For LC–MS/MS analysis, 100 µL of membrane fraction was mixed with 200 µL of RIPA buffer, vortexed, and sonicated for 10 seconds before centrifugation at 13,500 rpm for 10 min to collect the supernatant. Protein concentration was determined using the Bradford assay (25 µL sample with 245 µL reagent, absorbance measured at 650 nm). For protein reduction, ammonium bicarbonate buffer was added, followed by 10 µL of DTT and incubation at 60°C for 1 hour. Alkylation was performed by adding 10 µL of IAA and incubating in the dark for 25–30 minutes. Enzymatic digestion was carried out by adding 10 µL of trypsin and incubating at 37°C for 20–24 hours, after which the reaction was stopped with 5 µL of 0.1% formic acid. Peptide cleanup was performed using C18 columns, which were activated with Buffer A (50% acetonitrile:50% water) and equilibrated with Buffer B (5% acetonitrile:95% water). The digested samples (200–300 µL) were loaded, washed with Buffer B, and eluted in three steps with Buffer C (70% acetonitrile:30% water) in volumes of 30 µL, 40 µL, and 30 µL, each followed by centrifugation. Eluted fractions were dried for at least 2 hours, reconstituted in 50 µL of 0.1% formic acid, and subjected to LC–MS/MS analysis. Peptide identification and scoring were performed using the Mascot server. Data Preprocessing The raw protein abundance matrix, comprising 6,354 quantified proteins across nine samples (n = 3 per group: Control, RPL, and UI), was used for all downstream analyses. Initial quality assessment was performed using density distribution and boxplot of samples for Log2 transformed data, along with Principal Component Analysis (PCA). Differential Expression Analysis To isolate effective and specific proteomic signature for Recurrent Pregnancy Loss (RPL), we employed a multi-step comparative analysis. Differential expression analysis was performed for three contrasts: RPL vs. Control, RPL vs. Unexplained Infertility (UI), and UI vs. Control using the ‘limma’ package, a well-regarded statistical method for proteomics. Linear models were fitted for each protein using the ‘lmFit’ function, followed by empirical Bayes moderation (eBayes) to improve statistical power. For each contrast, proteins were considered significantly differentially expressed if they met the thresholds of absolute log2 fold change greater than one (∣log2​FC∣ > 2) and p-value < 0.05. Pathway and Functional Enrichment Analysis To identify the biological functions of the RPL-specific protein signature, the commonly upregulated and downregulated protein lists were analysed separately for pathway enrichment. Over-representation analysis (ORA) was performed using the ‘enricher’ function within the ‘clusterProfiler’ R package. Pathway definitions for Gene Ontology: Biological Process (GO: BP) and KEGG were obtained from the human C5 and C2 collections, respectively, of the Molecular Signatures Database (MSigDB) using the ‘msigdbr’ R package. To explore functional connectivity, protein-protein interaction (PPI) networks were constructed for the signature proteins using the STRING database (v11.5). Machine Learning Based Biomarker Identification Using MetaboAnalyst Biomarker analysis was conducted in MetaboAnalyst 6.0. The peak intensities list of RPL-specific proteins was first filtered using the relative standard deviation (RSD) to remove features with low variance. To correct for sample-to-sample variation, the filtered data was then normalized by median. The normalized data was log2-transformed and auto-scaled (mean-centred and divided by the standard deviation) to ensure all features were on a comparable scale for subsequent statistical analysis. Potential biomarkers were identified using a Partial Least Squares Discriminant Analysis (PLSDA) model, ranking all features by their Variable Importance in Projection (VIP) score. The classification performance of these top-ranked features was then evaluated; individual feature performance was measured by the Area Under the Curve (AUC) in a univariate Receiver Operating Characteristic (ROC) analysis. RESULTS Data Quality and Global Proteomic Overview Semen samples from 9 subjects (3 RPL, 3 UI and 3 Controls) were collected and processed to extract proteins. As summarized in Table 1 , there were no statistically significant differences among the three groups Control, RPL, and UI in terms of age, body mass index (BMI), duration of infertility, or hormonal parameters (p > 0.05). The mean age ranged from 34.6 ± 2.5 years in controls to 37.0 ± 3.6 years in the UI group, while BMI values remained comparable across all groups (25.8–26.2 kg/m²). Semen quality parameters, including volume, concentration, motility, and morphology, were within the normal WHO (2021) reference limits in all participants, indicating the absence of overt male-factor infertility. Although sperm DNA fragmentation (SDF) was higher in RPL (33.2 ± 2.6%) and UI (37.4 ± 3.46%) groups compared to controls (24.5 ± 8.2%), the difference did not reach statistical significance (p = 0.06). Lifestyle variables such as smoking, alcohol consumption, and exercise frequency showed no notable intergroup bias. Collectively, these results suggest that the three cohorts were well matched demographically and clinically, ensuring that the observed proteomic differences primarily reflect underlying molecular alterations rather than confounding physiological or lifestyle factors. Table 1 Demographic, clinical, and semen characteristics of study participants Parameter Control (n = 3) RPL (n = 3) UI (n = 3) p value Age (years), mean ± SD 34.6 ± 2.5 36.3 ± 2.5 37.0 ± 3.6 0.62 BMI (kg/m²), mean ± SD 26.2 ± 1.1 25.8 ± 1.0 26.2 ± 1.1 0.83 DOI (months), mean ± SD 3.0 ± 1.0 2.3 ± 0.5 4.0 ± 1.0 0.14 Semen pH, mean ± SD 7.63 ± 0.15 7.7 ± 0.3 7.73 ± 0.17 0.93 Semen parameters (Acc. To WHO 2021) Volume (mL) 2.2 ± 0.15 2.23 ± 0.25 2.4 ± 0.15 0.44 Concentration (million/mL) 59 ± 3.6 57.6 ± 7.5 61 ± 6.24 0.79 Total motile count, TMC (million) 57.6 ± 2.51 57.6 ± 4.1 60 ± 3.60 0.65 Progressive motility, PR (%) 40.6 ± 1.52 40.6 ± 3.05 42 ± 3.05 0.68 Normal morphology (%) 4.3 ± 0.57 4.3 ± 1.5 4 ± 1 0.91 SDF (%), mean ± SD 24.5 ± 8.20 33.2 ± 2.6 37.4 ± 3.46 0.06 Lifestyle factors, n (%) Smoking (yes) 1 (33.3) 1 (33.3) 2 (66.7) -- Alcohol (yes) 2 (66.7) 1 (33.3) 2 (66.7) -- Exercise (yes) 2 (66.7) 2 (66.7) 1 (33.3) -- Clinical factors, mean ± SD Testosterone (nmol/L)* 15.0 ± 1.0 16.0 ± 1.0 16.3 ± 2.1 0.53 FSH (mIU/mL) 7.0 ± 1.0 8.0 ± 1.0 7.6 ± 1.5 0.42 LH (mIU/mL) 5.3 ± 0.6 6.0 ± 1.0 6.3 ± 0.6 0.31 Values are presented as mean ± standard deviation (SD) for continuous variables and as number (percentage) for categorical variables. Comparisons among Control (n = 3), Recurrent Pregnancy Loss (RPL; n = 3), and Unexplained Infertility (UI; n = 3) groups were performed using one-way ANOVA for continuous variables and Chi-square/Fisher’s exact test for categorical variables. Semen parameters were evaluated according to WHO 2021 guidelines. SDF: sperm DNA fragmentation; BMI: body mass index; DOI: duration of infertility; TMC: total motile count; PR: progressive motility; FSH: follicle-stimulating hormone; LH: luteinizing hormone. A p-value < 0.05 was considered statistically significant. Differential Expression of Sperm Proteome in Fertility Loss Conditions Proteins fractions extracted from semen samples were analyzed by LC–MS/MS, which identified 6,354 proteins in total, expressed across all the three groups (Supplementary Table 1). Following initial pre-processing, peak intensity data of the Control, RPL and UI proteins was log2 transformed and then used to determine differentially expressed proteins (DEPs) in different comparison groups. In order to maintain stringency in the data, only proteins with log2FC > 3.0, p < 0.05 were considered as upregulated and proteins with log2FC < 3.0, p < 0.05 were considered downregulated. UI vs Control comparison On comparing expression of proteins in UI group against control group, we identified 90 upregulated and 304 downregulated proteins (Fig. 1 a) with KIRREL3, MORF4L1, THAP10, GIPC1, ZNF727 and ZNF35 being the top upregulated proteins and RRP36, AFP, ERN2, ACOT11 and ERBIN amongst top downregulated proteins (Supplementary Table 2). Direct functional relevance of KIRREL3, THAP10 and ZNF727 in context of infertility has not yet been reported and would be interesting to explore in the future work. However, a recent transcriptomic study reported differential expression of GIPC1 in cases of spontaneous miscarriages [ 12 ]. GIPC1 is known to interact with myosin 6 (a motor protein involved in cell division and migration) and 5T4/TPBG (a trophoblastic glycoprotein associated with embryonic development) suggesting that its overexpression can lead to enhanced proliferation, cytoskeletal malformation and excessive endometrial invasion eventually causing foetal loss[ 13 ]. MORF4L1, also known as MRG15, is an epigenetic regulator which plays significant role in chromatin remodelling and DNA damage response[ 14 ]. Its upregulation can be a potential cause of genomic instability leading to the death of the developing foetus. On the contrary, RRP36 is a nuclear protein whose downregulation impairs ribosome production and can lead to rapid growth defects[ 15 ]. ACOT11 controls the levels of free fatty acids and coenzyme A, which are required for energy production and cell membrane synthesis[ 16 ]. Consequently. ACOT11 would be essential for formation of new cells and tissues during embryogenesis, and it’s suppressed expression could therefore considerably affect the embryonic development. KEGG Pathway analysis of the DEPs obtained in this group revealed overrepresentation of proteins related to primary immunodeficiency, calcium signalling, purine & pyrimidine metabolism and gap junctions (Supplementary Fig. 1a) GO analysis indicated enrichment of biological processes like Organelle transport, regulation of action potential, post transcriptional regulation of gene expression, response to cAMP proteoglycan biosynthetic process and different metabolic processes (lipid, steroid, and triglyceride) (Supplementary Fig. 1b). RPL vs Control comparison The RPL vs. Control comparison identified a substantial number of DEPs, with 159 proteins upregulated and 465 downregulated in the RPL group (Fig. 1 b). THAP10, ZNF727, B4GALNT2, ACADVL, SAV1, GIPC1, and XRCC4 were among the top upregulated significant proteins. AFP, CEACAMM4, FAM20B, RITA1, ARGHGAP11A, and LRRC14B are among the top downregulated proteins of this contrast group (Supplementary Table 3). The proteins unique to this indicate a profound reconfiguration of cellular metabolism, intercellular adhesion, and cytoskeletal regulation. B4GALNT2 is known to be involved in implantation of the developing embryo to the endometrial cells [ 17 ]. Hence, it would be reasonable to assume that its upregulation can lead to early or defective implantation leading to miscarriage. On the other hand, XRCC4 is a DNA repair enzyme involved in joining double strand breaks [ 18 ] and its overexpression can disrupt the balance of DNA repair during fetal development. THAP10 has been shown to inhibit proliferation but promote differentiation in myeloid cells [ 19 ]. It is thus plausible that its overexpression in sperm can eventually lead to early differentiation of the formed embryo, thereby leading to a defective fetus and eventual loss of pregnancy. It would be interesting to elucidate in future studies the exact mechanism by which they may be causing repeated pregnancy loss. In contrast, the downregulation of RITA1 and FAM20B signifies a significant compromise in the mechanisms governing cytoskeletal integrity. FAM20B is a glycan kinase that phosphorylates xylose residues and triggers peptidoglycan biosynthesis. Knockout studies in mice have proved that FAM20B knockout could cause embryonic lethality through organ hypoplasia[ 20 ]. Similarly, RITA1 is a microtubule associated protein whose deficiency leads to impaired motility and migration of trophoblastic cells leading to preeclampsia and fetal loss. KEGG and GO Pathway analysis of these DEPs further suggests upregulation of proteins associated with Fatty Acid Metabolism, translation initiation, Butanoate metabolism, Adherent junction and Gap junctions (Supplementary Fig. 2a), along with dysregulation of important processes such as ubiquitin dependent protein catabolism, and purine and pyrimidine metabolism (Supplementary Fig. 2b). Identifying Dysregulated Proteins unique for RPL Interestingly 210 proteins were observed to be commonly dysregulated in both UI and RPL when compared to control (Supplementary Fig. 3). Further, although many common proteins and pathways were upregulated in both RPL and UI, yet some unique signatures were evidently present only in RPL group. In order to decipher these unique signatures, an RPL vs. UI comparison was also conducted and only those proteins were selected which were either up or downregulated in RPL as compared to both Control and UI (Supplementary Table 4). 63 proteins were found to be exclusively dysregulated in RPL including 32 upregulated proteins (Fig. 2 a, Table 2 ) and 31 downregulated proteins (Fig. 2 b, Table 3 ). Hierarchical clustering of the mentioned proteins shows distinct difference in expression in the three sample groups: Control, RPL and UI (Fig. 2 c, 2 d). These proteins were clearly capable of differentiating RPL condition from both Control and UI, as apparent from the results of PCA analysis conducted using all the 6354 protein and only the 63 RPL associated proteins (Fig. 3 a, 3 b). Table 2 RPL associated Upregulated Proteins List of differentially expressed proteins (DEPs) significantly upregulated in the RPL group compared with both Control and Unexplained Infertility (UI) groups. Log2FC(R/C) represents the log₂ fold change between RPL and Control; Log2FC(R/U) represents the log₂ fold change between RPL and UI. p value(R/C) and p_value(R/U) denote statistical significance assessed using the limma linear modeling framework with empirical Bayes moderation. Only proteins with |log₂FC| > 3.0 and p < 0.05 in both comparisons were included. These 32 proteins form part of the RPL-specific proteomic signature. Gene Symbol Log2FC(R/C) p_value(R/C) p_value(R/U) Log2FC(R/U) B4GALNT2 9.334009 0.000284 0.01047 5.73523 ACADVL 8.809357 0.001686 0.008386 6.965326 USP38 6.880284 0.003809 0.009832 5.929472 PRAC1 6.038087 0.004533 0.030441 4.302766 XRCC4 5.934681 0.001213 0.003112 5.231141 CEP250 5.812092 0.00449 0.005851 5.58257 TMEM209 5.630711 0.00212 0.009084 4.529716 ANO8 5.56687 0.008003 0.002401 6.663003 ERLEC1 5.548783 0.008929 0.006278 5.874161 ADM 5.222143 0.016599 0.018435 5.11897 NCOR2 5.160744 0.013582 0.003554 6.400458 RNF31 5.132094 0.019291 0.044753 4.278232 HECW1 5.093796 0.025561 0.027714 5.008082 HMGCS1 5.034827 0.010065 0.011632 4.910709 TSC22D4 4.795233 0.015568 0.009775 5.207229 MARCHF3 4.707494 0.031078 0.042817 4.375276 UAP1 4.707141 0.040639 0.038103 4.778433 FARSA 4.703316 0.030515 0.04002 4.424195 ERCC3 4.702486 0.030693 0.011422 5.696663 USP1 4.676015 0.005433 0.001849 5.452616 ZNF606 4.636567 0.020161 0.040149 4.000827 PLEKHH2 4.586699 0.015027 0.031038 3.967222 THBS2 4.486846 0.035481 0.0394 4.380092 FDXACB1 4.338707 0.034624 0.030423 4.464511 KCTD8 4.26388 0.029012 0.002018 6.640042 PLIN4 4.229464 0.041644 0.039229 4.28928 RBM27 4.180165 0.016129 0.007067 4.820029 ADAMTS13 4.058306 0.016332 0.041025 3.346207 BDKRB1 3.88453 0.023879 0.003824 5.311085 ZBTB8A 3.584088 0.041346 0.031791 3.805361 NAP1L1 3.312913 0.041619 0.018668 3.930434 MPHOSPH8 3.066611 0.044402 0.036639 3.208268 Table 3 RPL associated Downregulated Proteins Gene Symbol Log2FC(R/C) p_value(R/C) p_value(R/U) Log2FC(R/U) NTRK1 -7.55697 0.005127 0.04061 -5.14005 RMDN1 -6.74627 0.001811 0.033109 -4.14842 SEMA4C -6.65035 0.010303 0.041516 -5.03548 SUPT20HL2 -6.54558 0.001636 0.029589 -4.07037 QRSL1 -6.47665 0.00149 0.034877 -3.83718 MED16 -6.43879 0.004175 0.028064 -4.61244 PRKACG -6.32843 0.006939 0.028467 -4.88618 IQGAP1 -5.79117 0.006324 0.012505 -5.1687 PRMT8 -5.73841 0.022503 0.046116 -4.89578 EPCAM -5.5141 0.016787 0.032654 -4.81476 CTU2 -5.49066 0.007365 0.049967 -3.75845 KIAA1328 -5.46222 0.011976 0.026122 -4.70551 CACNA1D -5.45832 0.010327 0.019677 -4.85254 ZNF460 -5.39764 0.00664 0.019435 -4.47292 SLC2A13 -5.31966 0.008686 0.035157 -4.06722 BRIP1 -5.21194 0.0107 0.03707 -4.07832 ZBTB37 -5.09634 0.004696 0.009571 -4.55078 ZNF681 -5.06544 0.022709 0.028227 -4.84213 SMIM39 -4.92898 0.027395 0.044204 -4.42463 ZNF429 -4.89775 0.011705 0.025285 -4.23089 NUDCD1 -4.84851 0.005482 0.0041 -5.0652 SAMD9 -4.66098 0.013112 0.046183 -3.58803 TP53RK -4.60226 0.021501 0.035229 -4.14498 GPD1L -4.5689 0.005492 0.000956 -5.80814 CRYBG2 -4.46302 0.033204 0.031949 -4.50121 TFF3 -4.30278 0.007537 0.012691 -3.93892 CCDC158 -4.10885 0.030189 0.038086 -3.90071 AKAP6 -3.79168 0.035976 0.036565 -3.77768 GPATCH2 -3.67213 0.042214 0.03268 -3.89434 DRC11L -3.33607 0.049595 0.035006 -3.62378 TCP10L -3.31352 0.037442 0.028804 -3.51197 List of differentially expressed proteins (DEPs) significantly downregulated in the RPL group compared with both Control and Unexplained Infertility (UI) groups. Log2FC(R/C) represents the log₂ fold change between RPL and Control; Log2FC(R/U) represents the log₂ fold change between RPL and UI. p_value(R/C) and p_value(R/U) indicate statistical significance determined using the limma linear modeling framework with empirical Bayes moderation. Only proteins with |log₂FC| > 3.0 and p < 0.05 in both comparisons were included. These 31 proteins form part of the RPL-specific downregulated proteomic signature. Our analysis identified several key proteins with changed expression, suggesting a significant dysregulation of transcriptional regulation, stress response, and cellular signalling pathways. Among the downregulated proteins were crucial regulators of cell survival and transcription, such as NTRK1, MED16, and SUPT20HL2, indicating a potential impairment of pro-survival signalling and gene expression programs essential for pregnancy. Furthermore, the downregulation of SEMA4C and IQGAP1 suggests altered cell-cell communication and cytoskeletal dynamics, processes critical for successful embryonic implantation. Conversely, we observed a notable upregulation in proteins associated with cellular maintenance and stress response. The increased levels of DNA repair factors XRCC4 and PRAC1 may point to a response to genomic instability, while the upregulation of metabolic enzymes like ACADVL and structural components such as CEP250 could indicate a broad cellular adaptation to metabolic and structural stress. Functional Analysis of the RPL Signature Points to Dysregulated Metabolism To understand the biological functions of the RPL-specific signature, pathway over-representation analysis was performed through ‘ClusterProfiler’ on commonly dysregulated proteins using the KEGG (C2) and Gene Ontology: Biological Processes (GO: BP) (C5) gene sets from MSigDB database. The pathways linked to metabolic adaptation and cellular stress, such as the metabolism of amino sugars, nucleotides, ketones, and steroids, as well as general reactions to abiotic stimuli, were significantly (p < 0.05) enriched in the upregulated proteins (Fig. 4 a). This points to a cellular environment that is experiencing severe metabolic stress, necessitating a change in energy and biosynthesis pathways. The downregulated proteome, in contrast, was enriched for pathways that regulate ion homeostasis and membrane electrophysiology, such as 'potassium ion transport', 'regulation of membrane potential' and 'membrane repolarization' (Fig. 4 b). According to the KEGG pathway analysis, dysregulation of cellular response towards metabolic stress and DNA damage in the RPL condition is implicated by the enrichment of metabolic pathways such as 'Fatty acid metabolism', 'Butanoate metabolism', 'Aminoacyl-tRNA biosynthesis' and DNA repair pathways such as 'Nucleotide excision repair', 'non-homologous end-joining', and signalling pathways such as 'Notch signalling' and 'GPCR signalling' (Fig. 5 a). On the contrary, the downregulated proteins were mostly associated with significant intracellular signalling cascades (Fig. 5 b). These included the 'MAPK signalling pathway', 'Apoptosis', 'GnRH signalling pathway', and 'Calcium signalling pathway', reflecting the widespread repression of key signals required for hormonal responsiveness and cell survival. Protein–protein interaction (PPI) networks: To further understand the functional relationships of the 63 RPL-associated proteins, a protein-protein interaction (PPI) network was constructed using the STRING database along with The Markov Cluster (MCL) Algorithm for significant upregulated (Fig. 6 a) and downregulated (Fig. 6 b) proteins. The networks revealed that majority of the proteins upregulated in RPL against UI and Control including XRCC4, USP38, USP1, RBM27, NAP1L1 and ERCC3 were primarily involved in DNA repair complex while some others acted as transcriptional regulators and endoplasmic reticulum quality control regulators. Downregulated proteins on the other hand were the ones commonly involved in regulation of insulin secretion and Protein kinase A regulatory subunit binding, followed by maintenance of chromosomal stability and transcriptional regulation. Our results are very much in line with the already published data which demonstrates that metabolic dysfunction, insulin disbalance and oxidative stress can lead to increased DNA fragmentation due to elevated levels of double stranded breaks consequently causing poor reproductive outcomes including pregnancy loss[ 21 ]. The analysis further identified UBEK2K, ERCC3, ERLEC1, and USP1 as nodes amongst the upregulated proteins and PRKACG, GPD1L, NTRK1, QRSL1 and RMDN1 amongst downregulated proteins, underscoring their roles as key regulatory points within the RPL-specific protein network. Identification of Potential Biomarkers for RPL Using Metabo Analyst Raw peak intensity data for the RPL associated proteins was first normalized for better comparison across samples (Supplementary Fig. 4) and features (Supplementary Fig. 5). PLSDA classification analysis was performed to obtain VIP scores of the features in different comparison groups: RPL vs Control and RPL vs UI. Out of these we generated a list of proteins with VIP score more than 1 in both comparisons. This was then mapped with above identified 63 dysregulated proteins unique to RPL. VIP plots showing top 20 features of importance differentiating RPL against Control (Fig. 7 a) and RPL against UI (Fig. 7 b) were obtained. Interestingly, all 63 proteins unique to RPL were found to be of high importance with VIP score greater than 1 (Supplementary Table 5). Therefore all 63 protein signatures for the RPL condition were then verified through a broad univariate biomarker analysis using MetaboAnalyst. By comparing the RPL group to all other samples, we found 30 features to be significantly dysregulated (p < 0.05) that demonstrated perfect individual classification ability (AUC = 1.0) which included 12 upregulated proteins and 18 downregulated proteins (Supplementary Table 5). XRCC4, USP1, ADM, AKAP6, BDKRB1, CEP250, ERLEC1, GPD1L, HMGCS1, IQGAP1, MED16, MPHOSPH8, NAP1L1 and NUDCD1 were some important candidates. The panel of these 30 proteins indicate a strong and distinct molecular signature associated exclusively with the RPL condition. ROC curve and box plot of some functionally relevant upregulated RPL specific proteins with AUC 1 (Figs. 8 a, 8 b, 8 c) and downregulated RPL specific proteins with AUC 1 (Figs. 8 d, 8 e, 8 f) clearly show their evident differentiating ability. DISCUSSION Recurrent pregnancy loss affects 0.5-3% of couples worldwide, with approximately 40–50% of cases remaining unexplained despite comprehensive clinical evaluation[ 2 ]. While female factors have been extensively studied, paternal contributions particularly at the molecular level remain poorly characterized [ 22 , 23 ]. Our study addresses this critical gap through high-throughput LC-MS/MS-based proteomic profiling, representing the first comprehensive two-way comparison (RPL vs. Control and UI) to isolate truly RPL-specific molecular alterations. Integrated proteomic analysis identified 63 differentially expressed proteins (DEPs) distinguishing RPL from both fertile controls and UI groups. These proteins represent molecular alterations in processes like energy metabolism, DNA repair, cytoskeletal organization, and cellular signalling all of which are essential for a healthy and successful pregnancy[ 2 , 3 ]. Proteomic findings aided with PLSDA based machine learning analysis additionally established the importance of these proteins to demarcate the RPL group from both Control and UI. Further, univariate analysis affirmed the capability of 30 out of these 63 proteins as best suited classification models to distinguish RPL protein signature from that of Control as well as UI. Some of the top upregulated proteins as per this classification were USP1, XRCC4, CEP250, ERLEC1, ADM, HMGCS1, TSC22D4 and ACADVL. In recent years it has come to light that activation of pyroptosis (a necrotizing and inflammatory programmed cell death) drives the release of inflammatory factors like IL-1β and IL-18 which can trigger an immune response interfering immune homeostasis during pregnancy leading to miscarriage[ 24 ]. Interestingly, a recent study by Zhao et al. provided substantial evidence that USP1 plays a crucial role in promoting inflammasome-mediated pyroptosis raising the possibility of its vital role in pregnancy loss[ 25 ]. XRCC4, a core component of the non-homologous end-joining (NHEJ) DNA-repair pathway, is indispensable for the repair of double-strand breaks[ 26 ]. Although its role in maintaining genomic integrity is well established, no direct reports exist linking XRCC4 expression in sperm to RPL. Given that sperm DNA fragmentation is significantly elevated in idiopathic RPL cases[ 27 ], the observed XRCC4 upregulation in our dataset likely reflects an exacerbated DNA-repair response to oxidative stress in the male gamete. ERLEC1, is a lectin protein which tags misfolded glycoproteins in the endoplasmic reticulum. Its overexpression can cause increase in the burden of misfolded proteins in the cell by their spurious tagging thereby elevating ER stress implicated in many cases of miscarriage. ACADVL (very-long-chain acyl-CoA dehydrogenase) plays a critical role in mitochondrial fatty acid β-oxidation and ATP generation. A recent study demonstrated that fatty-acid oxidation enzymes, including ACADVL, are essential for sperm motility and energy homeostasis in human spermatozoa [ 28 ]. This supports the interpretation that enhanced ACADVL expression in RPL sperm may reflect a compensatory mechanism against oxidative stress. However, excessive metabolic activation could elevate reactive oxygen species (ROS), leading to lipid peroxidation and DNA damage, both of which are frequently observed in idiopathic RPL [ 29 , 30 ]. Interestingly, several downregulated proteins were also identified, including those associated with antioxidant defence and hormonal regulation pathways. For instance, the reduced expression of regulatory proteins such as SIRT1 and NFKB1 may indicate compromised stress tolerance and reduced DNA integrity, in agreement with previous studies linking impaired antioxidant activity to sperm dysfunction in RPL[ 1 ]. Collectively, our results indicate that idiopathic RPL is associated with distinct sperm proteomic alterations involving energy metabolism, DNA repair, cytoskeletal dynamics and signal transduction. Many novel proteins have been identified whose direct link with pregnancy loss is not yet proven, but their existing data suggest a strong positive link providing avenues for further investigation into their roles in iRPL. The findings extend the growing body of evidence that paternal factors play a crucial role in pregnancy outcomes, complementing recent literature emphasizing sperm DNA integrity, oxidative stress, and proteomic alterations in RPL-associated infertility. CONCLUSIONS This study provides a comprehensive proteomic and computational insight into the molecular basis of iRPL from the paternal perspective. Despite comparable clinical and semen parameters across all groups, distinct proteomic alterations were identified in the RPL cohort, highlighting the role of subtle molecular dysfunctions over overt clinical abnormalities. The 63 differentially expressed proteins unique to RPL, enriched in pathways related to DNA repair, oxidative stress, and metabolic regulation, reveal a clear disruption of cellular homeostasis in spermatozoa. Machine learning based PLS-DA and ROC analyses further refined this dataset to a panel of 30 proteins, including XRCC4, USP1, BDKRB1, GPD1L, and IQGAP1, capable of accurately distinguishing RPL from both control and UI groups. Together, these findings underline the importance of sperm proteome integrity in sustaining successful pregnancies and open new possibilities for diagnostic and therapeutic interventions targeting paternal factors in recurrent pregnancy loss. Abbreviations 2 DE –Two–Dimensional Gel Electrophoresis AUC Area Under the Curve BMI Body Mass Index cAMP Cyclic Adenosine Monophosphate C18 Octadecyl Carbon Chain (chromatography resin) DIGE Differential Gel Electrophoresis DEP(s) Differentially Expressed Protein(s) DNA Deoxyribonucleic Acid DOI Duration of Infertility DSB Double–Strand Break DTT Dithiothreitol ER Endoplasmic Reticulum FSH Follicle–Stimulating Hormone GO Gene Ontology GPCR G–Protein–Coupled Receptor HMGCS1 3 –Hydroxy–3–Methylglutaryl–CoA Synthase 1 IAA Iodoacetamide IVF In Vitro Fertilization KEGG Kyoto Encyclopedia of Genes and Genomes LC MS/MS –Liquid Chromatography–Tandem Mass Spectrometry LH Luteinizing Hormone log2FC Log2 Fold Change MCL Markov Cluster Algorithm MS Mass Spectrometry MSigDB Molecular Signatures Database NHEJ Non–Homologous End Joining OR Odds Ratio ORA Over–Representation Analysis PCA Principal Component Analysis PPI Protein–Protein Interaction PLS DA –Partial Least Squares Discriminant Analysis PR Progressive Motility QC Quality Control RIPA Radioimmunoprecipitation Assay RPL Recurrent Pregnancy Loss ROC Receiver Operating Characteristic ROS Reactive Oxygen Species RPM Revolutions Per Minute SDA Standard Deviation SDF Sperm DNA Fragmentation STRING Search Tool for the Retrieval of Interacting Genes/Proteins TMC Total Motile Count UI Unexplained Infertility USP1 / USP38 Ubiquitin–Specific–Processing Proteases VIP Variable Importance in Projection WHO World Health Organization XRCC4 X–Ray Repair Cross Complementing Protein 4 Declarations Ethics approval and consent to participate The study protocol was approved by the institutional review board of Independent Ethics Committee, Indian Fertility Society, New Delhi (IRB No. ECR/222/indt/DL/2015/RR-21). Informed consent was confirmed (or waived) by the Independent Ethics Committee. Conflict of interest : The authors declare no conflict of interest. Funding: No funding Author Contribution Conceptualization: RS, NK, SS, RK. Data curation: PP, AG, MC. Formal analysis: RK, AS, MC, NK, PP, AG. Investigation: AG,NK, PP, RK, FR. Methodology: PP, RK,MC, AG, NK. Project administration: NK, SS, RK. Resources: AG, FR, NK, JS. Software: RK, AC, JS. Supervision: NK, RK. RS. Validation: NK, AG, RK, RS, Visualization: JS, NK, RK, RS . Writing – original draft: AG, PP, MC, AS. Writing – review & editing: NK, RS, RK, JS Acknowledgements: The authors are thankful to the Independent Ethics Committee, Indian Fertility Society, Drug Controller General of India for providing the ethical approval for conducting the study. Authors are also thankful to Origyn Fertility & IVF centre, New Delhi for providing infrastructure and Amity University, Uttar Pradesh for technical support. Data Availability The proteomic datasets generated and/or analysed during the current study are **not publicly available due to institutional ethical restrictions** on human sample data. However, the data are **available from the corresponding author on reasonable request** for research purposes and with appropriate ethics approval.All additional results supporting the conclusions of this study are provided within the article and its supplementary information files. References Naglot S, et al. Label-free proteomics of spermatozoa identifies candidate protein markers of idiopathic recurrent pregnancy loss. Reprod Biol. 2021;21(3):100539. Li J, et al. Multiomics studies investigating recurrent pregnancy loss: an effective tool for mechanism exploration. Front Immunol. 2022;13:826198. Priyadarshinee L et al. A cross sectional study of pregnancy outcome in women with recurrent pregnancy loss. Int J Reprod Contracept Obstet Gynecol. 12(8): p. 2501. Leslie S, Soon-Sutton T, Khan M. Male Infertility.[Updated 2023 Mar 3]. StatPearls Publishing; 2023. StatPearls [Internet]. Treasure Island (FL). Choudhary S et al. Male Infertility: Causes and management at a tertiary care center in India. Cureus, 2023. 15(9). Naglot S, et al. Male contributory factors in recurrent pregnancy loss. Reproductive Sci. 2023;30(7):2107–21. Podgrajsek R, et al. Insight into the complexity of male infertility: a multi-omics review. Syst biology reproductive Med. 2024;70(1):73–90. Omolaoye TS, et al. Omics and male infertility: highlighting the application of transcriptomic data. Life. 2022;12(2):280. Mohanty G, Samanta L. Redox regulation & sperm function: A proteomic insight. Indian J Med Res. 2018;148(Suppl 1):S84–91. Pacheco RI, et al. New insights on sperm function in male infertility of unknown origin: a multimodal approach. Biomolecules. 2023;13(10):1462. Liu K, et al. Proteomics profiling reveals lipid metabolism abnormalities during oogenesis in unexplained recurrent pregnancy loss. Front Immunol. 2024;15:1397633. Wang P, et al. Transcriptomics-determined chemokine‐cytokine pathway presents a common pathogenic mechanism in pregnancy loss and spontaneous preterm birth. Am J Reprod Immunol. 2021;86(1):e13398. Katoh M. Functional proteomics, human genetics and cancer biology of GIPC family members. Exp Mol Med. 2013;45(6):e26–26. Zhang L, et al. Identification of MORF4L1 as an endogenous substrate of CRBN and its potential role as a therapeutic target in cancer. Sci Rep. 2025;15(1):2384. Gérus M, et al. Evolutionarily conserved function of RRP36 in early cleavages of the pre-rRNA and production of the 40S ribosomal subunit. Mol Cell Biol. 2010;30(5):1130–44. Tillander V, Alexson SE, Cohen DE. Deactivating fatty acids: acyl-CoA thioesterase-mediated control of lipid metabolism. Trends Endocrinol Metabolism. 2017;28(7):473–84. Duca M, Malagolini N, Dall’Olio F. The Role of the Sda Carbohydrate Antigen and That of Its Cognate Glycosyltransferase B4GALNT2 in Health and Disease. SynBio. 2025;3(1):6. Chen Y, et al. Androgen signalling stabilizes genomes to counteract senescence by promoting XRCC4 transcription. EMBO Rep. 2023;24(12):e56984. Li Y, et al. A novel epigenetic AML1-ETO/THAP10/miR‐383 mini‐circuitry contributes to t (8; 21) leukaemogenesis. EMBO Mol Med. 2017;9(7):933–49. Brommage R, Powell DR, Vogel P. Predicting human disease mutations and identifying drug targets from mouse gene knockout phenotyping campaigns. Volume 12. Disease Models & Mechanisms; 2019. p. dmm038224. 5. Rong J, et al. Systemic impacts of diabetes on spermatogenesis and intervention strategies: multilayered mechanism analysis and cutting-edge therapeutic approaches. Reproductive Biology Endocrinol. 2025;23(1):122. Muncey W, et al. The paternal role in pregnancy loss. Andrology. 2025;13(1):146–50. Kaltsas A et al. Paternal Contributions to Recurrent Pregnancy Loss: Mechanisms, Biomarkers, and Therapeutic Approaches. Medicina, 2024. 60(12): p. 1920. Wang J, et al. Pyroptosis is involved in the immune microenvironment regulation of unexplained recurrent miscarriage. Mamm Genome. 2024;35(2):256–79. Zhao X, et al. Multi-regulatory potency of USP1 on inflammasome components promotes pyroptosis in thyroid follicular cells and contributes to the progression of Hashimoto's thyroiditis. Mol Med. 2024;30(1):121. Li N, et al. Perspective in the mechanisms for repairing sperm DNA damage. Reproductive Sci. 2025;32(1):41–51. Wan XJ, et al. Correlation of the sperm DNA fragmentation index with semen parameters and its impact on fresh embryo transfer outcomes—a retrospective study. PeerJ. 2025;13:e19451. Li Y, et al. IKBA phosphorylation governs human sperm motility through ACC-mediated fatty acid beta-oxidation. Commun Biology. 2023;6(1):323. Busnelli A, et al. Sperm DNA fragmentation and idiopathic recurrent pregnancy loss: results from a multicenter case–control study. Andrology. 2023;11(8):1673–81. Davies R, et al. The role of seminal oxidative stress in recurrent pregnancy loss. Antioxidants. 2023;12(3):723. Additional Declarations No competing interests reported. Supplementary Files FinalSupplementaryFile.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8447183","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572767578,"identity":"59bd8619-3a35-4625-9294-f6cfcd3af912","order_by":0,"name":"Ankita Gupta","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Ankita","middleName":"","lastName":"Gupta","suffix":""},{"id":572767582,"identity":"bc58a4cc-9b00-4230-a81e-a3dc73ae15b3","order_by":1,"name":"Ritu Khosla","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Ritu","middleName":"","lastName":"Khosla","suffix":""},{"id":572767583,"identity":"440595a5-772a-4f38-90fd-888762a5f06d","order_by":2,"name":"Prachi Patel","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Prachi","middleName":"","lastName":"Patel","suffix":""},{"id":572767584,"identity":"e150f6f3-4c60-4694-871b-039449b29ea0","order_by":3,"name":"Alia Siddiqui","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Alia","middleName":"","lastName":"Siddiqui","suffix":""},{"id":572767585,"identity":"ed04593a-9e4c-42b2-bb6f-91acc2b7f31b","order_by":4,"name":"Mrinmoy Chakraborty","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Mrinmoy","middleName":"","lastName":"Chakraborty","suffix":""},{"id":572767586,"identity":"69e33b3d-eac9-4706-a406-406a3cabfbc5","order_by":5,"name":"Jaswinder Singh Maras","email":"","orcid":"","institution":"Institute of Liver and Biliary Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jaswinder","middleName":"Singh","lastName":"Maras","suffix":""},{"id":572767592,"identity":"dbe5d755-7d04-4b20-845e-84c5d05c206b","order_by":6,"name":"Rashmi Sharma","email":"","orcid":"","institution":"Origyn Fertility and IVF centre New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Rashmi","middleName":"","lastName":"Sharma","suffix":""},{"id":572767604,"identity":"d681db02-504b-4297-aa35-cd7bcf7f1d5b","order_by":7,"name":"Fiyazur Rahman","email":"","orcid":"","institution":"Origyn Fertility and IVF centre New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Fiyazur","middleName":"","lastName":"Rahman","suffix":""},{"id":572767610,"identity":"0281f504-0d0f-440d-80a5-a17e9a0aa430","order_by":8,"name":"Shivani Sharda","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Shivani","middleName":"","lastName":"Sharda","suffix":""},{"id":572767612,"identity":"08055fc5-e4f7-4f5e-9bd3-cec3117ae795","order_by":9,"name":"Navkiran Kaur","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACZoYEICnB3sAAZsBIIrTwHAAqTiBWCwhISCTAVBPQYs5+xtjg4R67PMmZj59uePjjMAM/e44BXi2WPTnGCQnPkoulpdPMbiQkHGaQ7HmDX4vBgbTkAwkHmBPnSSdAtBjcIGCLwflnIC31ifMkj38Da7EnqOVG8uGEhAOHE2dL8EBtkSCo5fFhg4QDxxNn9uSU3UhIS+eROPOsgIDDEpslfxyoTpxx/Pi2mz9srOX425M34NWCDpp5SFIOAnUk6xgFo2AUjILhDwDspk5UX8lMAQAAAABJRU5ErkJggg==","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Navkiran","middleName":"","lastName":"Kaur","suffix":""}],"badges":[],"createdAt":"2025-12-25 07:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8447183/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8447183/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100397158,"identity":"50579214-4e01-417b-9171-734d33e9d695","added_by":"auto","created_at":"2026-01-16 11:41:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3667460,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPTJRE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/637d589339e35b79e7b546dc.docx"},{"id":100397242,"identity":"4b92daad-e423-46f2-a152-8ae7ed72d7f6","added_by":"auto","created_at":"2026-01-16 11:41:49","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11575,"visible":true,"origin":"","legend":"","description":"","filename":"5bbdebaa9f8740ba82653a26c0ca9b2b.json","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/980ce75cea693c61168a60c2.json"},{"id":100397191,"identity":"c5705f36-4c75-4770-8ffc-e93155963bf2","added_by":"auto","created_at":"2026-01-16 11:41:40","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1285794,"visible":true,"origin":"","legend":"","description":"","filename":"FinalSupplementaryFile.zip","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/a3f4b2bc7ea9ca87faf308ec.zip"},{"id":100396843,"identity":"f9f74fe5-4863-4f80-861c-ce9a07d41284","added_by":"auto","created_at":"2026-01-16 11:41:12","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132406,"visible":true,"origin":"","legend":"","description":"","filename":"5bbdebaa9f8740ba82653a26c0ca9b2b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/86dca55f71e26bbe5fca5059.xml"},{"id":100396716,"identity":"2b874a75-8eb1-43c3-b9c0-72c65b53dd83","added_by":"auto","created_at":"2026-01-16 11:41:00","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47543,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/ae0bdaa0808e7296cf893bf6.png"},{"id":100397140,"identity":"f3af7cc4-2ae0-4def-a689-29d6e5956cd7","added_by":"auto","created_at":"2026-01-16 11:41:35","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104462,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/392de225e23e6ca41abe092e.png"},{"id":100397246,"identity":"9a2d57c0-0ac8-4249-9153-92cfe4eeb19c","added_by":"auto","created_at":"2026-01-16 11:41:49","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36840,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/10571fb1a16167c7d4f231c7.png"},{"id":100396647,"identity":"a44a563e-043d-4429-9a26-e05c3f68551e","added_by":"auto","created_at":"2026-01-16 11:40:56","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92587,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/8a3a9ef643d5207539b16991.png"},{"id":100397156,"identity":"15c32f71-1880-46b8-8976-96255d37588b","added_by":"auto","created_at":"2026-01-16 11:41:39","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90035,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/d992cef03907ab29bb2bb128.png"},{"id":100397122,"identity":"67c9743c-a7d2-4cc5-b88c-e79e58d09e2f","added_by":"auto","created_at":"2026-01-16 11:41:33","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":248372,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/7eb1e2cbc46f5cc1d62a32af.png"},{"id":100396957,"identity":"29b2f6fb-ce7d-4535-9d0c-bfa96a16be62","added_by":"auto","created_at":"2026-01-16 11:41:25","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25887,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/b4dde1d2ba1cf92eb5ce21f8.png"},{"id":100396697,"identity":"f2219bc4-919f-4fb6-ba1a-c44795148aa7","added_by":"auto","created_at":"2026-01-16 11:40:59","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39512,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/57b85166a7998e73969907e6.png"},{"id":100396987,"identity":"7279a90b-d585-4a01-96bb-38ebdefb68f7","added_by":"auto","created_at":"2026-01-16 11:41:27","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132173,"visible":true,"origin":"","legend":"","description":"","filename":"5bbdebaa9f8740ba82653a26c0ca9b2b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/c0bfeef0339f4eef2df38b59.xml"},{"id":100396702,"identity":"3f38c0c6-7f10-4db8-ac45-a10d76f79289","added_by":"auto","created_at":"2026-01-16 11:41:00","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144578,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/4e78a9ca62632a50e2864d9c.html"},{"id":100396660,"identity":"6b3e9027-ff31-40e1-85ae-21d97572b28b","added_by":"auto","created_at":"2026-01-16 11:40:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":270422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSignificantly differentially expressed proteins (P-value \u0026lt; 0.05, |Log2FC| \u0026gt; 2.5) in: (a) UI vs Control, (b) RPL vs Control. Upregulated DEPs are shown in red whereas Downregulated DEPs are shown in blue. Top 20 up and down regulated proteins are labelled.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/3b12ba394e91473a118c9081.png"},{"id":100396822,"identity":"a472a6b3-ab54-41bf-b6d4-905d14edbcb7","added_by":"auto","created_at":"2026-01-16 11:41:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":678414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVenn diagram shows presence of commonly (a) Upregulated and (b) Downregulated proteins observed uniquely in RPL when compared with both Control and UI. Hierarchical clustering of significant differentially expressed proteins in RPL reveals distinct expression patterns across Control, RPL, and UI group in (c) 32 upregulated proteins and (d) 31 downregulated proteins. Group-specific clusters and consistent intra-group profiles highlight condition-driven proteomic variation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/e305b107b269e5d4f971680d.jpeg"},{"id":100397237,"identity":"966722d1-2096-431c-ac45-bacaa0f5343f","added_by":"auto","created_at":"2026-01-16 11:41:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247880,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePCA plots of 63 commonly dysregulated proteins for RPL compared to UI and Control. There was no separation between RPL, UI and Control groups when all the 6354 proteins were used (a), however, samples showed complete segregation of RPL from UI and control groups when only significant commonly dysregulated proteins (63 proteins with p-value 0.05) were considered (b). Each point represents an individual sample, coloured by its experimental group: Control (blue), RPL (orange), and UI (green). Shaded ellipses denote the 95% confidence interval for each group.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/089658e859ff18fbcd30e6f5.png"},{"id":100396871,"identity":"300f727a-b714-45cc-8253-1d3049ccbb6d","added_by":"auto","created_at":"2026-01-16 11:41:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":437129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGO: Biological Processes pathway enrichment of upregulated and downregulated DEPs for RPL against UI and Control. Gene count Distribution shows relative gene counts associated with top 10 upregulated pathways (a). Significantly enriched (p-value \u0026lt;0.05), 10 upregulated and 10 downregulated, pathways are shown in bar-plot (b). Dotted line represents -log10 p-value threshold i.e. 1.30.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/276e9946440ab3d66ecb4f77.png"},{"id":100397114,"identity":"f54f20a5-e6c9-4657-8d49-754c9dfb55e5","added_by":"auto","created_at":"2026-01-16 11:41:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":431730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKEGG Pathway analysis of upregulated and downregulated DEPs for RPL against UI and Control. Gene count Distribution shows relative gene counts associated with top 10 upregulated pathways (a). Significantly enriched (p-value \u0026lt;0.05), 10 downregulated pathways are shown in bar-plot (b).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/d9bb4ffecc3733506b0c625f.png"},{"id":100396888,"identity":"d980db04-d8b6-4635-ba06-6c1aa3b6b0e9","added_by":"auto","created_at":"2026-01-16 11:41:15","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1179544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProtein-Protein Interaction Networks and Functional Clustering of RPL-Associated upregulated (a) and downregulated (b) DEPs using the STRING database. Distinct colours indicate separate clusters identified by MCL. Accompanying tables detail the cluster number, corresponding colour, and the count of proteins within each cluster for the upregulated and downregulated networks, respectively, along with functional enrichment.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/21ed4396608318766ebc5f7b.jpeg"},{"id":100397241,"identity":"dd0f4de8-6354-4775-b8ae-a4d96abc2177","added_by":"auto","created_at":"2026-01-16 11:41:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":147157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTop 20 differentially expressed proteins (DEPs) identified by PLS-DA, ranked by Variable Importance in Projection (VIP) score. The analysis compares (a) idiopathic recurrent pregnancy loss (iRPL) vs. Control and (b) iRPL vs. UI. Higher VIP scores signify a greater contribution to group separation. The coloured boxes on the right illustrate the relative concentration of each protein within the respective sample groups\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/0785be7c4bcfe5a81b729699.png"},{"id":100396659,"identity":"5bf772a0-29ea-4ae8-b45f-6fd3a0268bc6","added_by":"auto","created_at":"2026-01-16 11:40:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":306699,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis and box plot representation of top RPL specific differentially expressed proteins with high discriminating power between RPL, UI and Control. Top panel shows upregulated proteins a) XRCC4 b) USP1 and c) ERLEC1 and bottom panel shows downregulated proteins d) SUPT20HL2 e) QRSL1 and f) MED16\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/ac1ff795110fb71b655c2632.png"},{"id":102749373,"identity":"cc22094f-2c00-4de4-9da8-55cc09ff474a","added_by":"auto","created_at":"2026-02-16 09:12:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4872640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/fb193cbc-92de-408b-8e78-8d7127a80cb2.pdf"},{"id":100397245,"identity":"950e44db-f628-4bff-96cc-6ffcfe79506c","added_by":"auto","created_at":"2026-01-16 11:41:49","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1285794,"visible":true,"origin":"","legend":"","description":"","filename":"FinalSupplementaryFile.zip","url":"https://assets-eu.researchsquare.com/files/rs-8447183/v1/b116a0d21933acc7361378b0.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQuantitative Proteomics and Computational Analysis Elucidate Potential Paternal Biomarkers to Distinguish Idiopathic Recurrent Pregnancy Loss from Unexplained Infertility.\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eRecurrent pregnancy loss (RPL) is a distressing, complex and deeply medical condition that affects a significant number of couples worldwide. Defined by the World Health Organization as two or more consecutive miscarriages before the 20th week of gestation, RPL impacts roughly 0.5\u0026ndash;3% of couples of reproductive age planning for conception[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The main problem in case of RPL is not conception, but to sustain the pregnancy till birth of the foetus. RPL has many possible causes, including chromosomal abnormalities (with 2\u0026ndash;5% of couples having a partner with a balanced translocation), structural issues in the reproductive tract, hormonal imbalances, immune disorders, infections (like chronic endometritis, found in 10\u0026ndash;27% of cases), antiphospholipid syndrome and blood clotting problems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Estimates suggest pure male factor infertility ranges from 2.5% to 12% overall. Rates vary by region for example, 4.5\u0026ndash;6% in North America, 9% in Australia, and up to 12% in Eastern Europe. One study even reported male factors alone accounting for 25.6% of infertility cases[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A recent Indian study reported that among 2,348 infertile men, about 63% had normal semen parameters (normozoospermia), suggesting that standard semen analysis may not always explain the cause of infertility[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies also highlight male factors, such as changes in sperm DNA, chromatin structure, epigenetics, and increased reactive oxygen species (ROS), as potential contributors to RPL[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn spite of many known factors contributing to RPL, atleast 40% cases remain unexplained even after detailed clinical examination. In recent years, significant efforts have been made to improve the diagnosis of such idiopathic cases particularly in context of male infertility factors. Nevertheless, despite ongoing research, there is still limited understanding of consistent genetic contributors, reliable biomarkers, and their potential clinical applications in RPL. Idiopathic male infertility is believed to result from a complex interplay of genetic and environmental factors. As a result, many cases remain unresolved, prompting researchers to explore advanced approaches like omics technologies including genomics, epigenomics, transcriptomics, proteomics and metabolomics to uncover the underlying causes and improve diagnosis and treatment strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA biomarker is a measurable biological indicator that can detect or monitor the presence and progression of a disease. Ideally, it should be accurate, affordable, minimally invasive, and capable of identifying disease at an early stage when intervention is most effective. Proteomics plays a crucial role in biomarker discovery by focusing on the actual proteins expressed in cells their structure, function, and interactions offering insights that gene expression alone cannot provide. Since protein expression varies across cell types and conditions, proteomic analysis offers a more precise and dynamic understanding of disease mechanisms. Since sperm provides half of an embryo\u0026rsquo;s genetic material, sperm proteomics can be a key in understanding male factors responsible in causing idiopathic recurrent pregnancy loss (iRPL).\u003c/p\u003e \u003cp\u003eSpecific changes in the sperm proteome have been linked to disruptions in sperm function, supported by various protein identification methods. High-throughput proteomics, especially mass spectrometry (MS), allows large-scale profiling of sperm proteins. While targeted studies focus on measuring a few key proteins using antibodies and assays, MS has enabled the identification of thousands of proteins related to the testis and mature sperm, greatly deepening our understanding of sperm biology[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the context of male infertility, proteomics has revealed important changes in protein expression that may help explain underlying causes and identify potential diagnostic markers. However, when it comes to unexplained recurrent pregnancy loss (URPL), proteomic research remains limited. Earlier methods like two-dimensional gel electrophoresis (2-DE) and DIGE were complex and provided only partial protein profiles [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn lieu of the above, we used advanced mass spectrometry based high-throughput proteomic analysis to semen samples, aiming to uncover the lesser-known paternal contributions to URPL and gain new insights into its molecular basis. Differentially expressed proteins (DEPs) and potential biomarkers for iRPL were selected through statistical and bioinformatic analyses by comparing the iRPL protein expression profile with both healthy controls as well as males who had unexplained infertility. To the best of our knowledge this is the first study comparing iRPL protein expression together with both the aforementioned study groups. This approach offers a deeper, more detailed view of proteins exclusively expressed in iRPL, which can help us gain new molecular insights and can be further explored as potential therapeutic targets.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Recruitment and Grouping\u003c/h2\u003e \u003cp\u003eA total of nine semen samples were collected from male partners of couples attending the infertility clinic. Participants were divided into three groups: Control (n\u0026thinsp;=\u0026thinsp;3, fertile men without known infertility issues), Recurrent Pregnancy Loss (RPL; n\u0026thinsp;=\u0026thinsp;3, male partners of women with \u0026ge;\u0026thinsp;2 consecutive miscarriages), and Unexplained Infertility (UI; n\u0026thinsp;=\u0026thinsp;3, couples with infertility of unknown etiology despite standard evaluation).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDemographic and Clinical Data Collection\u003c/h3\u003e\n\u003cp\u003eDemographic details, including age, body mass index (BMI), and duration of infertility (DOI), were recorded at the time of sample collection. Lifestyle information was obtained via structured questionnaire, covering smoking, alcohol consumption, and exercise habits. Clinical parameters such as serum testosterone, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) were measured using standard chemiluminescent immunoassay kits in the institutional clinical laboratory.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDemographic, lifestyle, and clinical parameters (age, BMI, DOI, smoking, alcohol, exercise, testosterone, FSH, and LH) were summarized as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation (SD) for each group (Control, RPL, and UI). For continuous variables (age, BMI, DOI, testosterone, FSH, LH), comparisons between groups were performed using one-way analysis of variance (ANOVA) followed by post-hoc Tukey\u0026rsquo;s test where applicable. For categorical variables (smoking, alcohol, exercise), comparisons were made using the Chi-square test or Fisher\u0026rsquo;s exact test depending on sample size. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLC-MS Methodology\u003c/h3\u003e\n\u003cp\u003eSemen samples were collected and processed under standard laboratory conditions to assess membrane-associated protein composition. For LC\u0026ndash;MS/MS analysis, 100 \u0026micro;L of membrane fraction was mixed with 200 \u0026micro;L of RIPA buffer, vortexed, and sonicated for 10 seconds before centrifugation at 13,500 rpm for 10 min to collect the supernatant. Protein concentration was determined using the Bradford assay (25 \u0026micro;L sample with 245 \u0026micro;L reagent, absorbance measured at 650 nm). For protein reduction, ammonium bicarbonate buffer was added, followed by 10 \u0026micro;L of DTT and incubation at 60\u0026deg;C for 1 hour. Alkylation was performed by adding 10 \u0026micro;L of IAA and incubating in the dark for 25\u0026ndash;30 minutes. Enzymatic digestion was carried out by adding 10 \u0026micro;L of trypsin and incubating at 37\u0026deg;C for 20\u0026ndash;24 hours, after which the reaction was stopped with 5 \u0026micro;L of 0.1% formic acid. Peptide cleanup was performed using C18 columns, which were activated with Buffer A (50% acetonitrile:50% water) and equilibrated with Buffer B (5% acetonitrile:95% water). The digested samples (200\u0026ndash;300 \u0026micro;L) were loaded, washed with Buffer B, and eluted in three steps with Buffer C (70% acetonitrile:30% water) in volumes of 30 \u0026micro;L, 40 \u0026micro;L, and 30 \u0026micro;L, each followed by centrifugation. Eluted fractions were dried for at least 2 hours, reconstituted in 50 \u0026micro;L of 0.1% formic acid, and subjected to LC\u0026ndash;MS/MS analysis. Peptide identification and scoring were performed using the Mascot server.\u003c/p\u003e\n\u003ch3\u003eData Preprocessing\u003c/h3\u003e\n\u003cp\u003eThe raw protein abundance matrix, comprising 6,354 quantified proteins across nine samples (n\u0026thinsp;=\u0026thinsp;3 per group: Control, RPL, and UI), was used for all downstream analyses. Initial quality assessment was performed using density distribution and boxplot of samples for Log2 transformed data, along with Principal Component Analysis (PCA).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Expression Analysis\u003c/h2\u003e \u003cp\u003eTo isolate effective and specific proteomic signature for Recurrent Pregnancy Loss (RPL), we employed a multi-step comparative analysis. Differential expression analysis was performed for three contrasts: RPL vs. Control, RPL vs. Unexplained Infertility (UI), and UI vs. Control using the \u0026lsquo;limma\u0026rsquo; package, a well-regarded statistical method for proteomics. Linear models were fitted for each protein using the \u0026lsquo;lmFit\u0026rsquo; function, followed by empirical Bayes moderation (eBayes) to improve statistical power. For each contrast, proteins were considered significantly differentially expressed if they met the thresholds of absolute log2 fold change greater than one (∣log2​FC∣ \u0026gt; 2) and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePathway and Functional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eTo identify the biological functions of the RPL-specific protein signature, the commonly upregulated and downregulated protein lists were analysed separately for pathway enrichment. Over-representation analysis (ORA) was performed using the \u0026lsquo;enricher\u0026rsquo; function within the \u0026lsquo;clusterProfiler\u0026rsquo; R package. Pathway definitions for Gene Ontology: Biological Process (GO: BP) and KEGG were obtained from the human C5 and C2 collections, respectively, of the Molecular Signatures Database (MSigDB) using the \u0026lsquo;msigdbr\u0026rsquo; R package. To explore functional connectivity, protein-protein interaction (PPI) networks were constructed for the signature proteins using the STRING database (v11.5).\u003c/p\u003e\n\u003ch3\u003eMachine Learning Based Biomarker Identification Using MetaboAnalyst\u003c/h3\u003e\n\u003cp\u003eBiomarker analysis was conducted in MetaboAnalyst 6.0. The peak intensities list of RPL-specific proteins was first filtered using the relative standard deviation (RSD) to remove features with low variance. To correct for sample-to-sample variation, the filtered data was then normalized by median. The normalized data was log2-transformed and auto-scaled (mean-centred and divided by the standard deviation) to ensure all features were on a comparable scale for subsequent statistical analysis.\u003c/p\u003e \u003cp\u003ePotential biomarkers were identified using a Partial Least Squares Discriminant Analysis (PLSDA) model, ranking all features by their Variable Importance in Projection (VIP) score. The classification performance of these top-ranked features was then evaluated; individual feature performance was measured by the Area Under the Curve (AUC) in a univariate Receiver Operating Characteristic (ROC) analysis.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Quality and Global Proteomic Overview\u003c/h2\u003e \u003cp\u003eSemen samples from 9 subjects (3 RPL, 3 UI and 3 Controls) were collected and processed to extract proteins. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no statistically significant differences among the three groups Control, RPL, and UI in terms of age, body mass index (BMI), duration of infertility, or hormonal parameters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The mean age ranged from 34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 years in controls to 37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 years in the UI group, while BMI values remained comparable across all groups (25.8\u0026ndash;26.2 kg/m\u0026sup2;). Semen quality parameters, including volume, concentration, motility, and morphology, were within the normal WHO (2021) reference limits in all participants, indicating the absence of overt male-factor infertility. Although sperm DNA fragmentation (SDF) was higher in RPL (33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6%) and UI (37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46%) groups compared to controls (24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2%), the difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.06). Lifestyle variables such as smoking, alcohol consumption, and exercise frequency showed no notable intergroup bias. Collectively, these results suggest that the three cohorts were well matched demographically and clinically, ensuring that the observed proteomic differences primarily reflect underlying molecular alterations rather than confounding physiological or lifestyle factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, clinical, and semen characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRPL (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUI (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOI (months), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemen pH, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSemen parameters (Acc. To WHO 2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration (million/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal motile count, TMC (million)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgressive motility, PR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal morphology (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDF (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLifestyle factors, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eClinical factors, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTestosterone (nmol/L)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cem\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables and as number (percentage) for categorical variables. Comparisons among Control (n\u0026thinsp;=\u0026thinsp;3), Recurrent Pregnancy Loss (RPL; n\u0026thinsp;=\u0026thinsp;3), and Unexplained Infertility (UI; n\u0026thinsp;=\u0026thinsp;3) groups were performed using one-way ANOVA for continuous variables and Chi-square/Fisher\u0026rsquo;s exact test for categorical variables. Semen parameters were evaluated according to WHO 2021 guidelines. SDF: sperm DNA fragmentation; BMI: body mass index; DOI: duration of infertility; TMC: total motile count; PR: progressive motility; FSH: follicle-stimulating hormone; LH: luteinizing hormone. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Expression of Sperm Proteome in Fertility Loss Conditions\u003c/h2\u003e \u003cp\u003eProteins fractions extracted from semen samples were analyzed by LC\u0026ndash;MS/MS, which identified 6,354 proteins in total, expressed across all the three groups (Supplementary Table\u0026nbsp;1). Following initial pre-processing, peak intensity data of the Control, RPL and UI proteins was log2 transformed and then used to determine differentially expressed proteins (DEPs) in different comparison groups. In order to maintain stringency in the data, only proteins with log2FC\u0026thinsp;\u0026gt;\u0026thinsp;3.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as upregulated and proteins with log2FC\u0026thinsp;\u0026lt;\u0026thinsp;3.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered downregulated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUI vs Control comparison\u003c/h2\u003e \u003cp\u003eOn comparing expression of proteins in UI group against control group, we identified 90 upregulated and 304 downregulated proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) with KIRREL3, MORF4L1, THAP10, GIPC1, ZNF727 and ZNF35 being the top upregulated proteins and RRP36, AFP, ERN2, ACOT11 and ERBIN amongst top downregulated proteins (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eDirect functional relevance of KIRREL3, THAP10 and ZNF727 in context of infertility has not yet been reported and would be interesting to explore in the future work. However, a recent transcriptomic study reported differential expression of GIPC1 in cases of spontaneous miscarriages [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. GIPC1 is known to interact with myosin 6 (a motor protein involved in cell division and migration) and 5T4/TPBG (a trophoblastic glycoprotein associated with embryonic development) suggesting that its overexpression can lead to enhanced proliferation, cytoskeletal malformation and excessive endometrial invasion eventually causing foetal loss[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. MORF4L1, also known as MRG15, is an epigenetic regulator which plays significant role in chromatin remodelling and DNA damage response[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Its upregulation can be a potential cause of genomic instability leading to the death of the developing foetus. On the contrary, RRP36 is a nuclear protein whose downregulation impairs ribosome production and can lead to rapid growth defects[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. ACOT11 controls the levels of free fatty acids and coenzyme A, which are required for energy production and cell membrane synthesis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently. ACOT11 would be essential for formation of new cells and tissues during embryogenesis, and it\u0026rsquo;s suppressed expression could therefore considerably affect the embryonic development.\u003c/p\u003e \u003cp\u003eKEGG Pathway analysis of the DEPs obtained in this group revealed overrepresentation of proteins related to primary immunodeficiency, calcium signalling, purine \u0026amp; pyrimidine metabolism and gap junctions (Supplementary Fig.\u0026nbsp;1a) GO analysis indicated enrichment of biological processes like Organelle transport, regulation of action potential, post transcriptional regulation of gene expression, response to cAMP proteoglycan biosynthetic process and different metabolic processes (lipid, steroid, and triglyceride) (Supplementary Fig.\u0026nbsp;1b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRPL vs Control comparison\u003c/h2\u003e \u003cp\u003eThe RPL vs. Control comparison identified a substantial number of DEPs, with 159 proteins upregulated and 465 downregulated in the RPL group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). THAP10, ZNF727, B4GALNT2, ACADVL, SAV1, GIPC1, and XRCC4 were among the top upregulated significant proteins. AFP, CEACAMM4, FAM20B, RITA1, ARGHGAP11A, and LRRC14B are among the top downregulated proteins of this contrast group (Supplementary Table\u0026nbsp;3). The proteins unique to this indicate a profound reconfiguration of cellular metabolism, intercellular adhesion, and cytoskeletal regulation.\u003c/p\u003e \u003cp\u003eB4GALNT2 is known to be involved in implantation of the developing embryo to the endometrial cells [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Hence, it would be reasonable to assume that its upregulation can lead to early or defective implantation leading to miscarriage. On the other hand, XRCC4 is a DNA repair enzyme involved in joining double strand breaks [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and its overexpression can disrupt the balance of DNA repair during fetal development. THAP10 has been shown to inhibit proliferation but promote differentiation in myeloid cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is thus plausible that its overexpression in sperm can eventually lead to early differentiation of the formed embryo, thereby leading to a defective fetus and eventual loss of pregnancy. It would be interesting to elucidate in future studies the exact mechanism by which they may be causing repeated pregnancy loss. In contrast, the downregulation of RITA1 and FAM20B signifies a significant compromise in the mechanisms governing cytoskeletal integrity. FAM20B is a glycan kinase that phosphorylates xylose residues and triggers peptidoglycan biosynthesis. Knockout studies in mice have proved that FAM20B knockout could cause embryonic lethality through organ hypoplasia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, RITA1 is a microtubule associated protein whose deficiency leads to impaired motility and migration of trophoblastic cells leading to preeclampsia and fetal loss.\u003c/p\u003e \u003cp\u003eKEGG and GO Pathway analysis of these DEPs further suggests upregulation of proteins associated with Fatty Acid Metabolism, translation initiation, Butanoate metabolism, Adherent junction and Gap junctions (Supplementary Fig.\u0026nbsp;2a), along with dysregulation of important processes such as ubiquitin dependent protein catabolism, and purine and pyrimidine metabolism (Supplementary Fig.\u0026nbsp;2b).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying Dysregulated Proteins unique for RPL\u003c/h2\u003e \u003cp\u003eInterestingly 210 proteins were observed to be commonly dysregulated in both UI and RPL when compared to control (Supplementary Fig.\u0026nbsp;3). Further, although many common proteins and pathways were upregulated in both RPL and UI, yet some unique signatures were evidently present only in RPL group. In order to decipher these unique signatures, an RPL vs. UI comparison was also conducted and only those proteins were selected which were either up or downregulated in RPL as compared to both Control and UI (Supplementary Table\u0026nbsp;4). 63 proteins were found to be exclusively dysregulated in RPL including 32 upregulated proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and 31 downregulated proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Hierarchical clustering of the mentioned proteins shows distinct difference in expression in the three sample groups: Control, RPL and UI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). These proteins were clearly capable of differentiating RPL condition from both Control and UI, as apparent from the results of PCA analysis conducted using all the 6354 protein and only the 63 RPL associated proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRPL associated Upregulated Proteins \u003cem\u003eList of differentially expressed proteins (DEPs) significantly upregulated in the RPL group compared with both Control and Unexplained Infertility (UI) groups. Log2FC(R/C) represents the log₂ fold change between RPL and Control; Log2FC(R/U) represents the log₂ fold change between RPL and UI. p value(R/C) and p_value(R/U) denote statistical significance assessed using the limma linear modeling framework with empirical Bayes moderation. Only proteins with |log₂FC| \u0026gt; 3.0 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in both comparisons were included. These 32 proteins form part of the RPL-specific proteomic signature.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog2FC(R/C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep_value(R/C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep_value(R/U)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLog2FC(R/U)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4GALNT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.334009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.73523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACADVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.809357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.965326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSP38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.880284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.929472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.038087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.302766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXRCC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.934681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.231141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEP250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.812092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.58257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMEM209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.630711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.529716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.56687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.663003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERLEC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.548783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.874161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.222143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.11897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCOR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.160744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.400458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNF31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.132094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.278232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHECW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.093796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.008082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHMGCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.034827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.910709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSC22D4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.795233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.207229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMARCHF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.707494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.375276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.707141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.778433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFARSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.703316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.424195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERCC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.702486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.696663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.676015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.452616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.636567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.000827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLEKHH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.586699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.967222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHBS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.486846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.380092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDXACB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.338707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.464511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCTD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.26388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.640042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLIN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.229464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.28928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBM27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.180165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.820029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADAMTS13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.058306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.346207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDKRB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.88453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.311085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZBTB8A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.584088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.805361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAP1L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.312913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.930434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPHOSPH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.066611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.208268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRPL associated Downregulated Proteins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog2FC(R/C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep_value(R/C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep_value(R/U)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLog2FC(R/U)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNTRK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.55697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.14005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMDN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.74627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.14842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEMA4C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.65035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.03548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUPT20HL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.54558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.07037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQRSL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.47665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.83718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMED16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.43879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.61244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRKACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.32843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.88618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQGAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.79117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.1687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRMT8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.73841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.89578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.5141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.81476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.49066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.75845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIAA1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.46222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.70551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCACNA1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.45832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.85254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.39764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.47292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC2A13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.31966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.06722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRIP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.21194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.07832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZBTB37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.09634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.55078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.06544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.84213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMIM39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.92898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.42463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.89775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.23089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUDCD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.84851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.0652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAMD9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.66098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.58803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53RK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.60226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.14498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPD1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.5689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.80814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRYBG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.46302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.50121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.30278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.93892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCDC158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.10885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.90071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKAP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.79168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.77768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPATCH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.67213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.89434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRC11L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.33607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.62378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCP10L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.31352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.51197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eList of differentially expressed proteins (DEPs) significantly downregulated in the RPL group compared with both Control and Unexplained Infertility (UI) groups. Log2FC(R/C) represents the log₂ fold change between RPL and Control; Log2FC(R/U) represents the log₂ fold change between RPL and UI. p_value(R/C) and p_value(R/U) indicate statistical significance determined using the limma linear modeling framework with empirical Bayes moderation. Only proteins with |log₂FC| \u0026gt; 3.0 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in both comparisons were included. These 31 proteins form part of the RPL-specific downregulated proteomic signature.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOur analysis identified several key proteins with changed expression, suggesting a significant dysregulation of transcriptional regulation, stress response, and cellular signalling pathways. Among the downregulated proteins were crucial regulators of cell survival and transcription, such as NTRK1, MED16, and SUPT20HL2, indicating a potential impairment of pro-survival signalling and gene expression programs essential for pregnancy. Furthermore, the downregulation of SEMA4C and IQGAP1 suggests altered cell-cell communication and cytoskeletal dynamics, processes critical for successful embryonic implantation. Conversely, we observed a notable upregulation in proteins associated with cellular maintenance and stress response. The increased levels of DNA repair factors XRCC4 and PRAC1 may point to a response to genomic instability, while the upregulation of metabolic enzymes like ACADVL and structural components such as CEP250 could indicate a broad cellular adaptation to metabolic and structural stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Analysis of the RPL Signature Points to Dysregulated Metabolism\u003c/h2\u003e \u003cp\u003eTo understand the biological functions of the RPL-specific signature, pathway over-representation analysis was performed through \u0026lsquo;ClusterProfiler\u0026rsquo; on commonly dysregulated proteins using the KEGG (C2) and Gene Ontology: Biological Processes (GO: BP) (C5) gene sets from MSigDB database.\u003c/p\u003e \u003cp\u003eThe pathways linked to metabolic adaptation and cellular stress, such as the metabolism of amino sugars, nucleotides, ketones, and steroids, as well as general reactions to abiotic stimuli, were significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) enriched in the upregulated proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This points to a cellular environment that is experiencing severe metabolic stress, necessitating a change in energy and biosynthesis pathways. The downregulated proteome, in contrast, was enriched for pathways that regulate ion homeostasis and membrane electrophysiology, such as 'potassium ion transport', 'regulation of membrane potential' and 'membrane repolarization' (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the KEGG pathway analysis, dysregulation of cellular response towards metabolic stress and DNA damage in the RPL condition is implicated by the enrichment of metabolic pathways such as 'Fatty acid metabolism', 'Butanoate metabolism', 'Aminoacyl-tRNA biosynthesis' and DNA repair pathways such as 'Nucleotide excision repair', 'non-homologous end-joining', and signalling pathways such as 'Notch signalling' and 'GPCR signalling' (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the contrary, the downregulated proteins were mostly associated with significant intracellular signalling cascades (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). These included the 'MAPK signalling pathway', 'Apoptosis', 'GnRH signalling pathway', and 'Calcium signalling pathway', reflecting the widespread repression of key signals required for hormonal responsiveness and cell survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProtein\u0026ndash;protein interaction (PPI) networks:\u003c/h2\u003e \u003cp\u003eTo further understand the functional relationships of the 63 RPL-associated proteins, a protein-protein interaction (PPI) network was constructed using the STRING database along with The Markov Cluster (MCL) Algorithm for significant upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) and downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) proteins. The networks revealed that majority of the proteins upregulated in RPL against UI and Control including XRCC4, USP38, USP1, RBM27, NAP1L1 and ERCC3 were primarily involved in DNA repair complex while some others acted as transcriptional regulators and endoplasmic reticulum quality control regulators. Downregulated proteins on the other hand were the ones commonly involved in regulation of insulin secretion and Protein kinase A regulatory subunit binding, followed by maintenance of chromosomal stability and transcriptional regulation. Our results are very much in line with the already published data which demonstrates that metabolic dysfunction, insulin disbalance and oxidative stress can lead to increased DNA fragmentation due to elevated levels of double stranded breaks consequently causing poor reproductive outcomes including pregnancy loss[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The analysis further identified UBEK2K, ERCC3, ERLEC1, and USP1 as nodes amongst the upregulated proteins and PRKACG, GPD1L, NTRK1, QRSL1 and RMDN1 amongst downregulated proteins, underscoring their roles as key regulatory points within the RPL-specific protein network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Potential Biomarkers for RPL Using Metabo Analyst\u003c/h2\u003e \u003cp\u003eRaw peak intensity data for the RPL associated proteins was first normalized for better comparison across samples (Supplementary Fig.\u0026nbsp;4) and features (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003ePLSDA classification analysis was performed to obtain VIP scores of the features in different comparison groups: RPL vs Control and RPL vs UI. Out of these we generated a list of proteins with VIP score more than 1 in both comparisons. This was then mapped with above identified 63 dysregulated proteins unique to RPL. VIP plots showing top 20 features of importance differentiating RPL against Control (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea) and RPL against UI (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) were obtained. Interestingly, all 63 proteins unique to RPL were found to be of high importance with VIP score greater than 1 (Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTherefore all 63 protein signatures for the RPL condition were then verified through a broad univariate biomarker analysis using MetaboAnalyst. By comparing the RPL group to all other samples, we found 30 features to be significantly dysregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) that demonstrated perfect individual classification ability (AUC\u0026thinsp;=\u0026thinsp;1.0) which included 12 upregulated proteins and 18 downregulated proteins (Supplementary Table\u0026nbsp;5). XRCC4, USP1, ADM, AKAP6, BDKRB1, CEP250, ERLEC1, GPD1L, HMGCS1, IQGAP1, MED16, MPHOSPH8, NAP1L1 and NUDCD1 were some important candidates. The panel of these 30 proteins indicate a strong and distinct molecular signature associated exclusively with the RPL condition. ROC curve and box plot of some functionally relevant upregulated RPL specific proteins with AUC 1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec) and downregulated RPL specific proteins with AUC 1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef) clearly show their evident differentiating ability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eRecurrent pregnancy loss affects 0.5-3% of couples worldwide, with approximately 40\u0026ndash;50% of cases remaining unexplained despite comprehensive clinical evaluation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While female factors have been extensively studied, paternal contributions particularly at the molecular level remain poorly characterized [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our study addresses this critical gap through high-throughput LC-MS/MS-based proteomic profiling, representing the first comprehensive two-way comparison (RPL vs. Control and UI) to isolate truly RPL-specific molecular alterations.\u003c/p\u003e \u003cp\u003eIntegrated proteomic analysis identified 63 differentially expressed proteins (DEPs) distinguishing RPL from both fertile controls and UI groups. These proteins represent molecular alterations in processes like energy metabolism, DNA repair, cytoskeletal organization, and cellular signalling all of which are essential for a healthy and successful pregnancy[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Proteomic findings aided with PLSDA based machine learning analysis additionally established the importance of these proteins to demarcate the RPL group from both Control and UI. Further, univariate analysis affirmed the capability of 30 out of these 63 proteins as best suited classification models to distinguish RPL protein signature from that of Control as well as UI.\u003c/p\u003e \u003cp\u003eSome of the top upregulated proteins as per this classification were USP1, XRCC4, CEP250, ERLEC1, ADM, HMGCS1, TSC22D4 and ACADVL. In recent years it has come to light that activation of pyroptosis (a necrotizing and inflammatory programmed cell death) drives the release of inflammatory factors like IL-1β and IL-18 which can trigger an immune response interfering immune homeostasis during pregnancy leading to miscarriage[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Interestingly, a recent study by Zhao \u003cem\u003eet al.\u003c/em\u003e provided substantial evidence that USP1 plays a crucial role in promoting inflammasome-mediated pyroptosis raising the possibility of its vital role in pregnancy loss[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eXRCC4, a core component of the non-homologous end-joining (NHEJ) DNA-repair pathway, is indispensable for the repair of double-strand breaks[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although its role in maintaining genomic integrity is well established, no direct reports exist linking XRCC4 expression in sperm to RPL. Given that sperm DNA fragmentation is significantly elevated in idiopathic RPL cases[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the observed XRCC4 upregulation in our dataset likely reflects an exacerbated DNA-repair response to oxidative stress in the male gamete.\u003c/p\u003e \u003cp\u003eERLEC1, is a lectin protein which tags misfolded glycoproteins in the endoplasmic reticulum. Its overexpression can cause increase in the burden of misfolded proteins in the cell by their spurious tagging thereby elevating ER stress implicated in many cases of miscarriage.\u003c/p\u003e \u003cp\u003eACADVL (very-long-chain acyl-CoA dehydrogenase) plays a critical role in mitochondrial fatty acid β-oxidation and ATP generation. A recent study demonstrated that fatty-acid oxidation enzymes, including ACADVL, are essential for sperm motility and energy homeostasis in human spermatozoa [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This supports the interpretation that enhanced ACADVL expression in RPL sperm may reflect a compensatory mechanism against oxidative stress. However, excessive metabolic activation could elevate reactive oxygen species (ROS), leading to lipid peroxidation and DNA damage, both of which are frequently observed in idiopathic RPL [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, several downregulated proteins were also identified, including those associated with antioxidant defence and hormonal regulation pathways. For instance, the reduced expression of regulatory proteins such as SIRT1 and NFKB1 may indicate compromised stress tolerance and reduced DNA integrity, in agreement with previous studies linking impaired antioxidant activity to sperm dysfunction in RPL[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollectively, our results indicate that idiopathic RPL is associated with distinct sperm proteomic alterations involving energy metabolism, DNA repair, cytoskeletal dynamics and signal transduction. Many novel proteins have been identified whose direct link with pregnancy loss is not yet proven, but their existing data suggest a strong positive link providing avenues for further investigation into their roles in iRPL. The findings extend the growing body of evidence that paternal factors play a crucial role in pregnancy outcomes, complementing recent literature emphasizing sperm DNA integrity, oxidative stress, and proteomic alterations in RPL-associated infertility.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study provides a comprehensive proteomic and computational insight into the molecular basis of iRPL from the paternal perspective. Despite comparable clinical and semen parameters across all groups, distinct proteomic alterations were identified in the RPL cohort, highlighting the role of subtle molecular dysfunctions over overt clinical abnormalities. The 63 differentially expressed proteins unique to RPL, enriched in pathways related to DNA repair, oxidative stress, and metabolic regulation, reveal a clear disruption of cellular homeostasis in spermatozoa.\u003c/p\u003e \u003cp\u003eMachine learning based PLS-DA and ROC analyses further refined this dataset to a panel of 30 proteins, including XRCC4, USP1, BDKRB1, GPD1L, and IQGAP1, capable of accurately distinguishing RPL from both control and UI groups. Together, these findings underline the importance of sperm proteome integrity in sustaining successful pregnancies and open new possibilities for diagnostic and therapeutic interventions targeting paternal factors in recurrent pregnancy loss.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003e2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eDE\u003c/b\u003e\u0026ndash;Two\u0026ndash;Dimensional Gel Electrophoresis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ecAMP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclic Adenosine Monophosphate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eC18\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOctadecyl Carbon Chain (chromatography resin)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDIGE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferential Gel Electrophoresis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDEP(s)\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Protein(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDNA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDOI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDuration of Infertility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDSB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDouble\u0026ndash;Strand Break\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDTT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDithiothreitol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eER\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEndoplasmic Reticulum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFSH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFollicle\u0026ndash;Stimulating Hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGPCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eG\u0026ndash;Protein\u0026ndash;Coupled Receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHMGCS1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e3\u003c/b\u003e\u0026ndash;Hydroxy\u0026ndash;3\u0026ndash;Methylglutaryl\u0026ndash;CoA Synthase 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIAA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIodoacetamide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIVF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIn Vitro Fertilization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKEGG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eMS/MS\u003c/b\u003e\u0026ndash;Liquid Chromatography\u0026ndash;Tandem Mass Spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLuteinizing Hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003elog2FC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLog2 Fold Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMCL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMarkov Cluster Algorithm\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMass Spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMSigDB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Signatures Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNHEJ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon\u0026ndash;Homologous End Joining\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eORA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOver\u0026ndash;Representation Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePPI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein\u0026ndash;Protein Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePLS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eDA\u003c/b\u003e\u0026ndash;Partial Least Squares Discriminant Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgressive Motility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRIPA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadioimmunoprecipitation Assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRPL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRecurrent Pregnancy Loss\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive Oxygen Species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRPM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRevolutions Per Minute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSDA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSDF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSperm DNA Fragmentation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTRING\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSearch Tool for the Retrieval of Interacting Genes/Proteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTMC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Motile Count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnexplained Infertility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUSP1 / USP38\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUbiquitin\u0026ndash;Specific\u0026ndash;Processing Proteases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVIP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariable Importance in Projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eXRCC4\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eX\u0026ndash;Ray Repair Cross Complementing Protein 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe study protocol was approved by the institutional review board of Independent Ethics Committee, Indian Fertility Society, New Delhi (IRB No. ECR/222/indt/DL/2015/RR-21). Informed consent was confirmed (or waived) by the Independent Ethics Committee.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflict of interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: RS, NK, SS, RK. Data curation: PP, AG, MC. Formal analysis: RK, AS, MC, NK, PP, AG. Investigation: AG,NK, PP, RK, FR. Methodology: PP, RK,MC, AG, NK. Project administration: NK, SS, RK. Resources: AG, FR, NK, JS. Software: RK, AC, JS. Supervision: NK, RK. RS. Validation: NK, AG, RK, RS, Visualization: JS, NK, RK, RS . Writing \u0026ndash; original draft: AG, PP, MC, AS. Writing \u0026ndash; review \u0026amp; editing: NK, RS, RK, JS\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003e The authors are thankful to the Independent Ethics Committee, Indian Fertility Society, Drug Controller General of India for providing the ethical approval for conducting the study. Authors are also thankful to Origyn Fertility \u0026amp; IVF centre, New Delhi for providing infrastructure and Amity University, Uttar Pradesh for technical support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe proteomic datasets generated and/or analysed during the current study are **not publicly available due to institutional ethical restrictions** on human sample data. However, the data are **available from the corresponding author on reasonable request** for research purposes and with appropriate ethics approval.All additional results supporting the conclusions of this study are provided within the article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaglot S, et al. Label-free proteomics of spermatozoa identifies candidate protein markers of idiopathic recurrent pregnancy loss. Reprod Biol. 2021;21(3):100539.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, et al. Multiomics studies investigating recurrent pregnancy loss: an effective tool for mechanism exploration. Front Immunol. 2022;13:826198.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePriyadarshinee L et al. A cross sectional study of pregnancy outcome in women with recurrent pregnancy loss. Int J Reprod Contracept Obstet Gynecol. 12(8): p. 2501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeslie S, Soon-Sutton T, Khan M. Male Infertility.[Updated 2023 Mar 3]. StatPearls Publishing; 2023. StatPearls [Internet]. Treasure Island (FL).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhary S et al. Male Infertility: Causes and management at a tertiary care center in India. Cureus, 2023. 15(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaglot S, et al. Male contributory factors in recurrent pregnancy loss. Reproductive Sci. 2023;30(7):2107\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePodgrajsek R, et al. Insight into the complexity of male infertility: a multi-omics review. Syst biology reproductive Med. 2024;70(1):73\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmolaoye TS, et al. Omics and male infertility: highlighting the application of transcriptomic data. Life. 2022;12(2):280.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty G, Samanta L. Redox regulation \u0026amp; sperm function: A proteomic insight. Indian J Med Res. 2018;148(Suppl 1):S84\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacheco RI, et al. New insights on sperm function in male infertility of unknown origin: a multimodal approach. Biomolecules. 2023;13(10):1462.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu K, et al. Proteomics profiling reveals lipid metabolism abnormalities during oogenesis in unexplained recurrent pregnancy loss. Front Immunol. 2024;15:1397633.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, et al. Transcriptomics-determined chemokine‐cytokine pathway presents a common pathogenic mechanism in pregnancy loss and spontaneous preterm birth. Am J Reprod Immunol. 2021;86(1):e13398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatoh M. Functional proteomics, human genetics and cancer biology of GIPC family members. Exp Mol Med. 2013;45(6):e26\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, et al. Identification of MORF4L1 as an endogenous substrate of CRBN and its potential role as a therapeutic target in cancer. Sci Rep. 2025;15(1):2384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026eacute;rus M, et al. Evolutionarily conserved function of RRP36 in early cleavages of the pre-rRNA and production of the 40S ribosomal subunit. Mol Cell Biol. 2010;30(5):1130\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTillander V, Alexson SE, Cohen DE. Deactivating fatty acids: acyl-CoA thioesterase-mediated control of lipid metabolism. Trends Endocrinol Metabolism. 2017;28(7):473\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuca M, Malagolini N, Dall\u0026rsquo;Olio F. The Role of the Sda Carbohydrate Antigen and That of Its Cognate Glycosyltransferase B4GALNT2 in Health and Disease. SynBio. 2025;3(1):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, et al. Androgen signalling stabilizes genomes to counteract senescence by promoting XRCC4 transcription. EMBO Rep. 2023;24(12):e56984.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, et al. A novel epigenetic AML1-ETO/THAP10/miR‐383 mini‐circuitry contributes to t (8; 21) leukaemogenesis. EMBO Mol Med. 2017;9(7):933\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrommage R, Powell DR, Vogel P. Predicting human disease mutations and identifying drug targets from mouse gene knockout phenotyping campaigns. Volume 12. Disease Models \u0026amp; Mechanisms; 2019. p. dmm038224. 5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRong J, et al. Systemic impacts of diabetes on spermatogenesis and intervention strategies: multilayered mechanism analysis and cutting-edge therapeutic approaches. Reproductive Biology Endocrinol. 2025;23(1):122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuncey W, et al. The paternal role in pregnancy loss. Andrology. 2025;13(1):146\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaltsas A et al. \u003cem\u003ePaternal Contributions to Recurrent Pregnancy Loss: Mechanisms, Biomarkers, and Therapeutic Approaches.\u003c/em\u003e Medicina, 2024. 60(12): p. 1920.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, et al. Pyroptosis is involved in the immune microenvironment regulation of unexplained recurrent miscarriage. Mamm Genome. 2024;35(2):256\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, et al. Multi-regulatory potency of USP1 on inflammasome components promotes pyroptosis in thyroid follicular cells and contributes to the progression of Hashimoto's thyroiditis. Mol Med. 2024;30(1):121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, et al. Perspective in the mechanisms for repairing sperm DNA damage. Reproductive Sci. 2025;32(1):41\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan XJ, et al. Correlation of the sperm DNA fragmentation index with semen parameters and its impact on fresh embryo transfer outcomes\u0026mdash;a retrospective study. PeerJ. 2025;13:e19451.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, et al. IKBA phosphorylation governs human sperm motility through ACC-mediated fatty acid beta-oxidation. Commun Biology. 2023;6(1):323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusnelli A, et al. Sperm DNA fragmentation and idiopathic recurrent pregnancy loss: results from a multicenter case\u0026ndash;control study. Andrology. 2023;11(8):1673\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies R, et al. The role of seminal oxidative stress in recurrent pregnancy loss. Antioxidants. 2023;12(3):723.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Recurrent pregnancy loss, proteomics, spermatozoa, biomarkers, machine learning, infertility","lastPublishedDoi":"10.21203/rs.3.rs-8447183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8447183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRecurrent Pregnancy Loss (RPL) is a complex reproductive disorder that affects 0.5–3% of couples worldwide, with nearly 40–50% of cases remaining idiopathic despite thorough evaluation. While maternal factors have been extensively studied, the molecular mechanisms underlying paternal contributions to RPL remain poorly understood. This study aimed to elucidate the molecular alterations in spermatozoa from male partners of couples with idiopathic RPL and identify potential biomarkers distinguishing RPL from Unexplained Infertility (UI) and fertile controls through high-throughput proteomic and machine learning–based analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Semen samples from Control (n=3), RPL (n=3) and UI (n=3) were analyzed using LC MS/MS–based quantitative proteomics. A total of 6,354 proteins were identified. Differentially expressed proteins (DEPs) were determined via the \u003cem\u003elimma\u003c/em\u003estatistical framework, with RPL specific signatures defined through an intersection strategy. Functional and pathway enrichment analyses were performed using KEGG, and GO, from \u003cem\u003eMSigDB\u003c/em\u003e database, followed by biomarker discovery using MetaboAnalyst 6.0 with Partial Least Squares Discriminant Analysis (PLS-DA) and Receiver Operating Characteristic (ROC) validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Proteomic profiling revealed 63 RPL-specific DEPs, including 32 upregulated and 31 downregulated proteins. Upregulated proteins were primarily involved in energy metabolism, DNA repair and cytoskeletal regulation, while downregulated proteins impaired antioxidant defence and metabolic control. PLS-DA analysis established all 63 DEPs to be good classifiers for RPL (VIP score\u0026gt;1). Univariate biomarker analysis further confirmed 30 of these proteins to be capable of perfectly distinguishing RPL from UI and control (AUC\u0026gt;1) with some of the key determinants being XRCC4, USP1, BDKRB1, GPD1L, and IQGAP1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This integrated proteomic and computational analysis provides the first comprehensive molecular characterization of idiopathic RPL sperm proteome. The identified RPL-specific protein panel offers promising biomarkers for diagnosis and potential therapeutic targets, emphasizing the crucial role of paternal factors in recurrent pregnancy loss.\u003c/p\u003e","manuscriptTitle":"Quantitative Proteomics and Computational Analysis Elucidate Potential Paternal Biomarkers to Distinguish Idiopathic Recurrent Pregnancy Loss from Unexplained Infertility.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 08:28:21","doi":"10.21203/rs.3.rs-8447183/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9792a921-0228-412e-8686-2bf50c48b2f5","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-15T12:55:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 08:28:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8447183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8447183","identity":"rs-8447183","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}