Seminal plasma cfDNA fragmentomics landscape delineates male infertility subtypes

Seminal plasma cfDNA fragmentomics landscape delineates male infertility subtypes | 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 Seminal plasma cfDNA fragmentomics landscape delineates male infertility subtypes Xiaoyu Wu, Fan Meng, Huiping Zhang, Zeqing Li, Kai Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6200607/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 While cell-free DNA (cfDNA) fragmentomics has transformed liquid biopsy applications in prenatal screening and oncology, its potential in male reproductive health remains uncharted. Methods Through integrated whole-genome sequencing and jagged end sequencing (Jag-Seq) coupled with non-CpG methylation analysis, we established the first fragmentomic atlas of seminal plasma (SP) cfDNA from 18 healthy donors, with 20 plasma cfDNA samples. And we applied this method to 33 infertility cases (14 varicocele / 19 non-obstructive azoospermia), to obtain disease-specific characteristics. ROC curve analysis was employed to study the potential diagnostic ability for these two diseases. Results Size distribution profiling showed SP cfDNA enrichment in short fragments (< 150bp) with bimodal distribution (151bp main peak/110bp subpeak), contrasting with plasma's sharp 166-bp peak pattern ( P < 0.001). Motif analysis identified SP-specific patterns: elevated AAAA-end motif frequency and A-base preference at positions − 2 to -4. And SP showed higher jagged end index based on Jag-Seq ( P < 0.0001). For disease, varicocele exhibited 7 different frequency motifs and longer jagged end length while non-obstructive azoospermia demonstrated higher methylation level at CH sites. Translating these findings to clinical contexts, we developed a ROC curve analysis integrating all fragmentomic signatures, achieving 83% accuracy in distinguishing varicocele and 87% accuracy in distinguishing non-obstructive azoospermia. Conclusions This research highlights the distinct cfDNA profiles in SP and demonstrates the potential of cfDNA metrics as biomarkers for diagnosing male infertility subtypes, and the disease-specific cfDNA dynamics offering new avenues for non-invasive diagnostic tools in reproductive medicine. cell-free DNA Male infertility Fragmentomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cell-free DNA (cfDNA) fragmentomics - the systematic analysis of DNA fragment characteristics including size distribution, end motifs, and jagged end profiles - has emerged as a transformative approach in liquid biopsy research[ 1 , 2 ]. The characteristic of fragment size, 166bp peak dominant in plasma cfDNA, are associated with nucleosomes and liker DNA[ 3 ]. Four-mer end motifs profile is association with pathophysiologic conditions such as pregnancy, transplantation, and cancer. For instance, the abundance of plasma DNA motif CCCA was lower in HCC patients than in healthy controls[ 4 ]. Crucially, jagged end formation (single-stranded protruding ends) is modulated by both nuclease concentration gradients across biofluids and the mode of cellular demise: apoptosis typically produces cleaner double-strand breaks via caspase-activated DNase, whereas necrosis releases DNA with complex terminal modifications due to uncontrolled protease activity[ 3 , 5 , 6 ]. These multilayered features - encompassing cleavage positioning relative to nucleosomes, fragment length distributions, and strand end complexity - collectively serve as molecular fingerprints, encoded by both the tissue origin of cfDNA, the enzyme activity in body fluids and underlying pathophysiological states. These multidimensional features-spanning nucleosome-phased cleavage sites, size distribution periodicity, and jagged end microarchitecture-function as dynamic molecular fingerprints that influenced by the cellular origin of cfDNA, the enzyme activity in body fluids, and disease pathophysiology, ultimately enabling non-invasive biomarker[ 7 ]. While blood-derived cfDNA fragmentomics has been extensively characterized in prenatal screening and oncology[ 8 – 10 ], emerging evidence reveals fluid-type specific fragmentation signatures. Notably, urine cfDNA demonstrates unique fragmentomics patterns[ 11 ]: (1) pronounced fragmentation with a dominant sub-100 bp population versus the predominant 166 bp peak in plasma; (2) the jagged end length distribution displayed 10-nt periodicities, and the jagged end index profile showed weakly oscillating major peaks but with the strongly oscillating minor peaks; (3) bladder cancer-associated urinary cfDNA had lower jagged end indexed than controls without bladder cancer. These fluid-specific signatures underscore the urine cfDNA fragmentomics as biomarkers for bladder cancer detection. Despite these advancements, seminal plasma (SP) cfDNA remains poorly understudied. As a complex biofluid comprising secretions from testicular (5%), epididymal (10%), prostatic (30%), and seminal vesicle-derived (60%)[ 12 ], SP provides a unique window into male reproductive pathophysiology. The dynamic spermatogenic process – encompassing germ cell proliferation, meiotic recombination, and apoptotic selection – drives extensive chromatin reorganization, potentially imparting unique fragmentation patterns of SP cfDNA. Pierre's study evaluated the association between levels of SP cfDNA and sperm fertility criteria[ 13 ]. They compared SP cfDNA concentration in non-obstructive azoospermia (NOA) samples and samples with various sperm pathologies to detect a potential link between free DNA levels and male infertility. Previous studies had not specifically focused on or utilized SP cfDNA fragmentation information, nor have they explored its alterations in different diseases. The fragmentomic landscape and its diagnostic potential in male infertility remain uncharted. This knowledge gap is particularly critical for two prevalent infertility etiologies: NOA, characterized by complete spermatogenic failure, and varicocele (VC), where venous reflux induces testicular microenvironmental alterations. Current diagnostic paradigms rely on invasive testicular biopsies for NOA[ 14 ]and subjective physical examinations for VC[ 15 ], highlighting the urgent need for non-invasive and precise biomarkers reflecting underlying molecular pathologies. In this article, we used an integrative multi-omics framework combining whole-genome sequencing, jagged end sequencing (Jag-Seq) with non-CpG cytosine methylation analysis, and receiver operating characteristic (ROC) curve analysis. This platform enables two critical discoveries: fluid-specific fragmentation signatures distinguishing SP cfDNA from plasma cfDNA, disease-specific fragmentation patterns differentiating NOA and VC from healthy controls. Methods Human sample collection and processing Male patients diagnosed with VC (n = 14), NOA (n = 19) and age-matched healthy controls (n = 18) were enrolled. All participants were Asian. The study protocol was approved by the Institutional Review Board of Wuhan Huake Reproductive Hospital, with written informed consent obtained from all participants prior to enrollment. Comprehensive demographic characteristics and clinical parameters are detailed in Supplemental Table 1. The study design was shown in Fig. 1 . Firstly, we performed fragmentomic comparisons of seminal plasma cfDNA (n = 18) and blood plasma cfDNA (n = 20) using WGS and Jag-Seq, analyzing fragment size distribution, end motifs heat map, preferred end motif, jagged end length, methylation level, and the relationship of cfDNA size and jagged end length. Then we performed a translational application varicocele (n = 14) and non-obstructive azoospermia (n = 19) to develop a diagnostic model. Sample Collection and Processing Following 3–5 days of sexual abstinence, participants collected seminal samples via masturbation into sterile wide-mouthed containers. Semen samples were immediately transported to the laboratory for centrifugation at 4000 × g (10 min, room temperature). The obtained SP was aliquoted and stored at − 80℃ until analysis. Plasma from the blood samples was collected through centrifugation at 1600 × g (10 min, 4℃). The obtained plasma supernatant was aliquoted and stored at − 80℃ until analysis. DNA Extraction and Quantification DNA was extracted from approximately 0.4 mL of SP or plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen). All DNA samples underwent dual quality assessment: purity analysis via NanoDrop spectrophotometer (Thermo Fisher Scientific) and precise quantification using Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). The specific sequencing protocol were shown in Methods of Additional file. Motif diversity score calculation To examine the distribution of motif frequencies out of a total of 256 motifs, we utilized normalized Shannon entropy as a mathematical method for computing the motif diversity score (MDS), which is defined by the following equation: represents the frequency of a specific motif[ 4 ]. This metric ranges theoretically from 0 to 1, with maximal value (MDS = 1) indicating perfectly uniform motif distribution and minimal value (MDS→0) reflecting extreme skewness. If the frequencies of the 256 4-mer motifs were evenly distributed, the MDS would reach its maximum value, 1. For example, if one motif accounted for 99% of the total frequency while the others shared the remaining 1%, the MDS would approach 0. Therefore, a declining MDS value indicates greater skewness in the frequency distribution of motifs, while increasing values denote enhanced distributional equilibrium. Jagged jaggedness index based on the filling of methylated cytosines This method assesses DNA end jaggedness by leveraging methylated cytosine incorporation during end repair. For DNA molecules with 5′ or 3′ overhangs, DNA polymerase extends jagged end using methylated dCTP alongside standard nucleotides. Bisulfite treatment selectively converts unmethylated cytosines to uracils, preserving methylated cytosines in both strands. Methylation densities are calculated for Read1 (original strand, M1) and Read2 (synthesized strand, M2 ≈ 100%), with the jaggedness index based on the filling of methylated cytosines (JI-M) defined as: $$\:JI-M=\frac{M2-M1}{M2}\times\:100\%$$ , where M1 represents the methylation density of read1, and M2 represents the methylation density of read2. Statistical Analysis Clinical characteristics of the study were described as median and range for continuous variables. Differences between groups were analyzed by Mann–Whitney U -tests for continuous (not normally distributed) variables. Statistical analyses and figure plot were performed using Python; all statistical tests were two-sided, and P values smaller than 0.05 were defined as significant. There was no adjustment of the significance level for multiple testing. Results Fluid difference in cfDNA concentration and size distribution SP showed similar cfDNA concentration with plasma (Supplement Table). Fragment length distribution analysis revealed marked fluid-specific patterns: while plasma cfDNA displayed a characteristic sharp peak at 166bp, SP cfDNA showed a broadened 151bp peak and 110bp sub-peak (Fig. 2A-B). The logarithmic scale plot showed a more pronounced oscillation in plasma cfDNA with the strong 160 bp periodicity and attenuated periodicity in SP cfDNA (Fig. 2C). The individual size distribution of each sample was shown in Supplement Figs. 1 and 2. And 50-bp bin analysis identified significant distribution frequency differences across 51-200bp ranges, with SP demonstrating higher abundance of shorter fragments (< 150 bp) and plasma showed more longer fragments (151–200 bp) ( P < 0.0001) (Fig. 2D-G). These fragmentation signatures collectively suggest tissue-specific DNA processing mechanisms, with SP cfDNA enriched in both apoptotic remnants from spermiogenesis and protected high-molecular-weight DNA. End motif profiling of cfDNA in difference plasma The result of end motif profiling was shown in Fig. 3 , which revealed most of 256 distinct end motifs differing between cfDNA in SP and plasma. Hierarchical clustering of 256 4-mer motifs demonstrated fluid-specific grouping, suggesting that the distinct enzyme activities in each body fluid led to similar cleavage preferences and, consequently, similar 4-mer motif frequencies (Fig. 3 A ). Notably, the 10 motifs with the most significant differences ( P < 0.0001) showed striking fluid specificity: six motifs (including AAXX) were enriched in SP, while five (notably CCTT and ACCT) predominated in plasma (Fig. 3 B), emphasizing the most biologically relevant variations in cfDNA end motifs between SP and blood plasma. SeqLogo analysis illustrates that the first position of the 5’ end predominantly showed a predominance for C, while subsequent positions exhibit clear differences. In plasma, the second position preferred C, whereas in SP, it is A; the third position in plasma preferred T, while A is preferred in SP; and the fourth position in plasma shows a preference for T, compared to A in SP (Fig. 3 C-D). MDS quantified this disparity, with SP (0.9508(0.9351–0.9604)) exhibiting higher than blood (0.9455(0.9352–0.9546)) ( P = 0.0005 ), indicative of more heterogeneous cleavage patterns in the male reproductive system (Fig. 3 E). The coordinated motif differences - from global clustering to nucleobase-level positional biases - strongly suggest that SP cfDNA originates through unique chromatin fragmentation processes, potentially involving fluid-specific nucleases and distinct apoptotic regulation during spermiogenesis. Overall description and comparison of cfDNA jagged end features Jag-seq investigates the characteristics of jagged end in cfDNA, focusing on fragment length distribution, methylation levels, and the relationship of jagged end length and overall cfDNA length. Figure 4 A revealed distinct fragmentation patterns between plasma cfDNA and SP cfDNA, with plasma cfDNA demonstrating significantly shorter peak fragments and greater size heterogeneity than SP cfDNA. The methylation-cfDNA size relationship displayed striking compartmentalization ( Fig. 4 B ) . While plasma cfDNA exhibited a fluctuating methylation pattern, with methylation levels oscillating between 70% and 10% as DNA length increased, SP cfDNA showed a relatively stable increase in methylation levels across size fractions. The relationship between average jag length and cfDNA exhibited a similar pattern: plasma cfDNA showed fluctuating jag lengths, ranging from 10 to 35, as DNA length increased, whereas SP cfDNA demonstrated a more stable increase, reaching a plateau around 200 bp (Fig. 4 C). SP cfDNA fragments carried longer average jagged ends compared to plasma cfDNA, when cfDNA longer than 100bp. Notably, JI-M showed SP (78.37(61.26–87.90)) was significantly higher than plasma cfDNA (12.72(1.17–47.92)) ( P = 2.03*10 − 12 ) (Fig. 4 D). Disease-Specific cfDNA fragmentation signatures Comparative analysis of SP cfDNA architecture revealed distinct pathological profiles across cohorts (NOA: n = 19, VC: n = 14, Controls: n = 18). Size distribution analysis demonstrated similar patterns (Fig. 5 A-B) and the logarithmic scale plot showed a more pronounced oscillation in NOA SP cfDNA (Fig. 5 C). The individual size distribution of each sample was shown in Supplement Figs. 3 and 4 . Hierarchical clustering of 256 4-mer motifs demonstrated disease-specific grouping (Fig. 5 D). End motif profiling uncovered the distinct predominance in position − 1, cytosine predominance in VC and Thymine in NOA, suggesting differences in degradation or origin among the groups (Fig. 5 E-F). End motif analysis identified 7 VC-associated motifs ( P < 0.05), and 66 NOA-specific motifs (top 10 in Fig. 5 H). MDS showed the tendency of increase in both VC (0.9505(0.9293–0.9637) and NOA (0.9535(0.9356–0.9644) SP compared to controls (0.9455(0.9352–0.9546)), indicating more diverse in disease (Fig. 5 I). NOA showed longer jagged ends, and jagged end length-cfDNA size analysis demonstrated 10-bp periodicity was enhanced in NOA (Fig. 5 K). Strikingly, global hypomethylation at CH sites distinguished NOA from controls, suggesting defective epigenetic reprogramming during spermatogenesis (Fig. 5 L). And NOA (48.54(4.78–80.96), P = 0.0003) displayed significantly lower JI-M vs controls (78.37(61.26–87.90)), while VC (72.09(49.44–83.43), P = 0.592) showed the downward trend (Fig. 5 J). NOA SP cfDNA showed longer jagged ends when cfDNA size 130 bp (Fig. 5 M-N). These multi-modal fragmentation signatures - from nucleosomal positioning to methylome architecture - collectively demonstrate that cfDNA analysis could be used to non-invasively diagnose different testicular pathophysiological states. Diagnostic potential of cfDNA features in male infertility subtype To evaluate the clinical utility of cfDNA fragmentation features, we performed multiclass ROC analysis comparing three diagnostic dimensions: (1) NOA vs controls, (2) VC vs controls. Five key metrics were assessed: cfDNA size distribution, MDS, JI-M, CH-site methylation levels, and jagged end length. The composite model achieved superior diagnostic performance with AUC = 0.83 for VC detection and AUC = 0.87 for NOA identification (Fig. 6 ). Discussion This study establishes the first multi-dimensional atlas of SP cfDNA architecture, revealing four cardinal distinctions through integrated WGS and Jag-seq fragmentomic analysis. SP cfDNA exhibits bimodal size distribution with broader peak at 151 bp and 110 bp sub-peak, contrasting with plasma cfDNA 166 bp sharp peak pattern. The frequency of sub-nucleosome length (< 150 bp) is higher in SP, suggesting testicular-specific fragmentation processes. Hierarchical clustering of 256 4-mer motifs demonstrated fluid-specific grouping, and the second base position of cfDNA preferred C in plasma, whereas SP cfDNA retains A-preferred bases at positions − 2 to -4. SP cfDNA displays enhanced 10-bp periodicity, longer jagged ends, and higher JI-M than plasma cfDNA, correlating with testis-specific nucleases. In this study, we investigated that difference of two biofluid cfDNA fragmentomics. On the other hand, the disease-associated SP cfDNA fragmentomics were present in patients with VC and NOA. This finding may have physiological relevance, reflecting tissue-specific cfDNA release patterns, and pathophysiological importance, providing insights into reproductive health and the potential for novel biomarkers. Previous studies on plasma cfDNA have primarily reported unimodal distribution, typically with fragments around 166 bp, corresponding to nucleosome-protected DNA during apoptosis in somatic cells [ 2 ]. This study shows abundance of short fragments and bimodal distribution (110 bp and 151bp peaks) in SP cfDNA may be linked to the unique biological processes occurring in the male reproductive system, such as spermatogenesis and apoptosis, DNA Histone-to-protamine replacement during sperm nuclear condensation, and activity of Deoxyribonuclease (DNase). The activity of DNase Ⅰ family nucleases in body fluids were different, which are known to be active during cell death and tissue turnover, could be involved in generating these shorter fragments[ 5 ] [ 16 , 17 ]. The activity of DNase Ⅰ in semen(25 ± 4.8 *10 6 units/g protein) was higher than serum (4.6 ± 1.9 *10 6 units/g protein), which may cause different DNA cleavage and cfDNA fragmentation [ 18 ]. But the activity of DNase Ⅰ subtype in SP need to be measured precisely. The cfDNA end motif analysis revealed notable differences between SP and blood plasma, suggesting tissue-specific nuclease activity and distinct degradation mechanisms[ 1 ]. The positional nucleotide bases at motif sites - A predominance at positions − 2~-4 in SP versus C/T in plasma cfDNA. The unique preferred end motifs possibly reflected the activity of reproductive system-associated nucleases, as higher concentration of DNase Ⅰ in SP[ 18 ]. These motifs resulted from apoptosis or other programmed cell death processes during spermatogenesis, where endonucleases cleave DNA at precise sites[ 19 ]. While in contrast, blood plasma cfDNA typically displays end motifs indicative of general cellular turnover, reflecting more ubiquitous degradation processes across various tissues. MDS elevated in SP, which constitutes one of the features of the subsequent disease diagnosis, and correlates with the concentration of SP DNase Ⅰ, reflecting “systemic” nuclease perturbation. Compared to traditional diagnostic methods, such as semen analysis, imaging, or biopsy, cfDNA offers a minimally invasive, reproducible, and molecular-level approach to evaluating male reproductive health[ 20 ]. When investigating whether diseases were related to the structural genomics of cfDNA, we chose these two diseases for our study. VC is the most common disease of male infertility, which is caused by localized hypoxia and oxidative stress due to obstruction of spermatic reflux[ 15 ]. And we wanted to focus on whether localized lesions such as hypoxia could alter the fragmentomics characteristics of SP cfDNA. NOA is the most severe spermatogenic dysfunction, and the spermatogenic blockage leads to abnormal germ cell apoptosis[ 14 ]. We wanted to focus on how the alterations originating from the genomics of germ cells affect the fragmentation of cfDNA. In this study, distinct cfDNA structural characteristics, including fragment length distribution, end motifs, and jagged end patterns, were difference among patients with VC, NOA, and healthy controls. VC cfDNA showed C-end motifs and NOA showed T-preferred and position − 1 motifs. NOA cfDNA showed enhanced 10-bp periodicity, which mirrored urinary cfDNA profiles, suggesting shared nuclease regulation[ 11 ]. The global hypomethylation at CH sites was significantly higher in NOA, and jagged end length were difference in disease, which were related in reduced nucleosome protection permits nuclease access and aberrant methylation disrupts cfDNA-protein interactions in NOA. These fragmentomics features achieved 87% diagnostic accuracy in distinguishing NOA, and 83% diagnostic accuracy in distinguishing VC. The unique ability to capture dynamic changes in tissue-specific cell death, turnover, and transcriptional activity makes it an attractive, non-invasive tool for early diagnosis, prognosis, and treatment monitoring in patients with VC, azoospermia, or other reproductive disorders. This study, while providing valuable insights into the structural characteristics of cfDNA in seminal plasma, does have several limitations. Firstly, the sample size was relatively small, which may limit the generalizability of the findings. A larger cohort is needed to validate these results and ensure that the observed cfDNA patterns are consistent across diverse populations. Additionally, technical limitations such as variations in cfDNA extraction and quantification methods could affect the reproducibility of the results. Additionally, exploring cfDNA characteristics beyond VC and azoospermia could provide further insights into its diagnostic and prognostic value. In conclusion, we investigated the differences of fluid-specific cfDNA fragmentomics, their application in VC and NOA. Furthermore, the disease-specific fragmentomics opens new diagnostic avenues, where cfDNA could be used to monitor reproductive disorders or assess the efficacy of treatments such as vasectomy, infertility interventions, or cancer therapies targeting the reproductive system. Abbreviations cfDNA cell-free DNA SP seminal plasma Jag-Seq jagged end sequencing VC varicocele NOA non-obstructive azoospermia MDS motif diversity score JI-M jaggedness index based on the filling of methylated cytosines nt nucleotides bp base pair,ROC,receiver operating characteristic AUC area under the ROC curve DNase Deoxyribonuclease Declarations Ethics approval and consent to participate The trial was conducted under Institutional Review Board of Wuhan Huake Reproductive Hospital. Written informed consent was obtained from all patients as well as to use and share data and specimens collected for the study. The study was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. Acknowledgements The authors gratefully acknowledge the contributions of the patients who participated in the study. Authors’ contributions XW, ZL and KZ performed data analysis and interpretation. XW, FM, HZ, and KZ provided supports for interpretations and discussion about results. HZ and KZ corresponded specimen storage. HZ provided clinical supports during the clinical trial. XW, FM, HZ, ZQ and KZ contributed to manuscript design and interpretation of data. XW and FM wrote the manuscript with comments and contributions from all authors. All authors read and approved the final manuscript. Funding KZ was supported by National Science Foundation of Hubei Province (2023AFB735, JC2RYB202500538). Consent for publication Informed consent for publication was obtained from the patients who participated in the study. Competing interests All authors declare that they have no competing interests. References Serpas L, Chan RWY, Jiang P, Ni M, Sun K, Rashidfarrokhi A, et al. Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA. Proceedings of the National Academy of Sciences of the United States of America. 2019;116(2):641-9. Lo YMD, Han DSC, Jiang P, Chiu RWK. Epigenetics, fragmentomics, and topology of cell-free DNA in liquid biopsies. Science (New York, NY). 2021;372(6538). Jiang P, Xie T, Ding SC, Zhou Z, Cheng SH, Chan RWY, et al. Detection and characterization of jagged ends of double-stranded DNA in plasma. Genome research. 2020;30(8):1144-53. Jiang P, Sun K, Peng W, Cheng SH, Ni M, Yeung PC, et al. Plasma DNA End-Motif Profiling as a Fragmentomic Marker in Cancer, Pregnancy, and Transplantation. Cancer discovery. 2020;10(5):664-73. 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Chan RWY, Serpas L, Ni M, Volpi S, Hiraki LT, Tam LS, et al. Plasma DNA Profile Associated with DNASE1L3 Gene Mutations: Clinical Observations, Relationships to Nuclease Substrate Preference, and In Vivo Correction. American journal of human genetics. 2020;107(5):882-94. Han DSC, Ni M, Chan RWY, Chan VWH, Lui KO, Chiu RWK, et al. The Biology of Cell-free DNA Fragmentation and the Roles of DNASE1, DNASE1L3, and DFFB. American journal of human genetics. 2020;106(2):202-14. Nadano D, Yasuda T, Kishi K. Measurement of deoxyribonuclease I activity in human tissues and body fluids by a single radial enzyme-diffusion method. Clinical chemistry. 1993;39(3):448-52. Heitzer E, Auinger L, Speicher MR. Cell-Free DNA and Apoptosis: How Dead Cells Inform About the Living. Trends in molecular medicine. 2020;26(5):519-28. Ranucci R. Cell-Free DNA: Applications in Different Diseases. Methods in molecular biology (Clifton, NJ). 2019;1909:3-12. Additional Declarations No competing interests reported. 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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-6200607","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427142200,"identity":"d875fc2a-6ef2-434a-bf44-b4ce9c6e9715","order_by":0,"name":"Xiaoyu Wu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wu","suffix":""},{"id":427142201,"identity":"7bd702bf-b722-4536-9ebe-a2a534b8acb0","order_by":1,"name":"Fan Meng","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Meng","suffix":""},{"id":427142202,"identity":"37c7b7b1-b11b-43d1-b377-097ce94f61b9","order_by":2,"name":"Huiping Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Huiping","middleName":"","lastName":"Zhang","suffix":""},{"id":427142203,"identity":"72589798-06d0-48f4-b0b8-73c7372833a0","order_by":3,"name":"Zeqing Li","email":"","orcid":"","institution":"Hubei University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zeqing","middleName":"","lastName":"Li","suffix":""},{"id":427142204,"identity":"99faaf95-95d8-4f0e-b950-766d5faa6f09","order_by":4,"name":"Kai Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACCQbGBhBi4JeACIDYRGqRnAHkHSBOC9RkgxvEapFsP9zA+HOHnZzx7R7jzx8YbGQ3HGB+9gCfFmmexAYGyTPJxmZ3zphJHGBIM95wgM3cAJ8WOQagFsM25sRtN3LMgA47nLjhAA+bBF4t/A8bGBLb6hM3z8gx/nCA4T9hLdISQFsOtgENl8gxADrsAGEtkjMeNjA2th03lriRViZxxiDZeOZhNjO8WiTOpz9g/NlWLcc/I3nzh4oKO9m+483P8GoBAvYfCDYoqJgJqB8Fo2AUjIJRQBgAAPmqSnEkXqM7AAAAAElFTkSuQmCC","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-03-11 06:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6200607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6200607/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78752253,"identity":"135be46d-6302-4615-b08f-f04c0e7e5937","added_by":"auto","created_at":"2025-03-18 12:04:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and analytical workflow.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, we performed fragmentomic comparisons of seminal plasma cfDNA (n=18) and blood plasma cfDNA (n=20) using WGS and Jag-Seq, analyzing fragment size distribution, end motifs heat map, preferred end motif, jagged end length, methylation level, and the relationship of cfDNA size and jagged end length. Then we performed a translational application varicocele (n=14) and non-obstructive azoospermia (n=19) to develop a diagnostic model.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/22b6819c2c85a3c183e24826.png"},{"id":78752254,"identity":"10cb1c33-3343-40b0-89f1-72b442f9e941","added_by":"auto","created_at":"2025-03-18 12:04:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFluid-specific cfDNA fragmentation signatures across size domains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Global fragment length distribution (0-1000 bp). \u003cstrong\u003eB\u003c/strong\u003e Short-fragment magnification (0-300 bp), SP cfDNA (blue) exhibits bimodal distribution with dominant 151 bp and 110 bp peaks, contrasting with plasma cfDNA's (red) singular 166 bp peak. \u003cstrong\u003eC\u003c/strong\u003e A logarithmic scale plot highlights the more pronounced differences in fragment size distribution. \u003cstrong\u003eD-G\u003c/strong\u003eDifferential frequency analysis fragment density in a 50 bp window (51-100, 101-150, 151-200, 201-250 bp).\u003c/p\u003e\n\u003cp\u003e* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05，** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001，**** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001；Mann–Whitney \u003cem\u003eU\u003c/em\u003e-tests. Data represent the median (range).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/409ae8b71ccda93eaa5de5d5.png"},{"id":78752264,"identity":"737170da-fb9b-4ce7-8f0e-b86e9a1d8be7","added_by":"auto","created_at":"2025-03-18 12:04:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":381226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnd Motif Composition of cfDNA Fluid-specific cfDNA end motif architectures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Hierarchical clustering of 256 4-mer end motifs. \u003cstrong\u003eB\u003c/strong\u003e The ten motifs with the most significant differences are shown the most pronounced variations between cfDNA in seminal plasma and blood plasma. \u003cstrong\u003eC-D\u003c/strong\u003e Positional nucleotide preferred analysis in plasma and SP, at 5' ends. \u003cstrong\u003eE\u003c/strong\u003e MDS differences between SP and plasma.\u003c/p\u003e\n\u003cp\u003e* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05，** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001，**** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001；Mann–Whitney \u003cem\u003eU\u003c/em\u003e-tests. Data represent the median (range).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/cb9c2fb64901449cbe88bc76.png"},{"id":78752262,"identity":"a40d1144-a259-454c-9fb5-53dae20524dc","added_by":"auto","created_at":"2025-03-18 12:04:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":270451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJagged-end architecture reveals fluid-specific cfDNA fragmentation patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Length frequency of cfDNA jagged ends. \u003cstrong\u003eB\u003c/strong\u003eRelationship between methylation levels at CH sites and cfDNA size. \u003cstrong\u003eC\u003c/strong\u003e Average jagged end length compared to DNA fragment length, indicating a notable difference in jagged end lengths between samples. \u003cstrong\u003eD\u003c/strong\u003e JI-M of SP cfDNA was higher than plasma cfDNA.\u003c/p\u003e\n\u003cp\u003e* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05，** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001，**** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001；Mann–Whitney \u003cem\u003eU\u003c/em\u003e-tests. Data represent the median (range).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/c362428ffd13b3ff60aea1c8.png"},{"id":78753172,"identity":"367cddf0-14a3-419f-a8a3-38424ef2d33a","added_by":"auto","created_at":"2025-03-18 12:12:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":949946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisease-specific cfDNA signatures in SP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Distribution of cfDNA fragment lengths under 1000 bp. \u003cstrong\u003eB\u003c/strong\u003e Distribution of cfDNA fragment lengths under 300 bp. \u003cstrong\u003eC\u003c/strong\u003eLogarithmic scale of cfDNA fragment length distribution, showing the frequency distribution in greater detail. \u003cstrong\u003eD\u003c/strong\u003e Hierarchical clustering of 256 4-mer end motifs across groups, revealing differences in motif composition. \u003cstrong\u003eE-F\u003c/strong\u003ePositional nucleotide preferred analysis in VC and NOA SP , at 5' ends. \u003cstrong\u003eG\u003c/strong\u003eSeven end motifs with differential expression in VC relative to the control, indicating motif changes associated with VC. \u003cstrong\u003eH\u003c/strong\u003e Top ten end motifs with differential expression in NOA compared to the control group, providing insights into specific motif alterations in NOA. \u003cstrong\u003eI\u003c/strong\u003e Comparison of MDS across groups. \u003cstrong\u003eJ\u003c/strong\u003e JI-M comparing jagged end irregularity, with NOA showing lower values than the control group. \u003cstrong\u003eK\u003c/strong\u003e Distribution of jagged end lengths across groups. \u003cstrong\u003eL\u003c/strong\u003e Relationship between methylation levels at CH sites and DNA fragment length. \u003cstrong\u003eM\u003c/strong\u003e Comparing average jagged end length relative to DNA fragment length in each group. \u003cstrong\u003eN\u003c/strong\u003e Comparing median jagged end length relative to DNA fragment length in each group. Comparisons were made with the Control SP group.\u003c/p\u003e\n\u003cp\u003e* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05，** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001，**** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001；Mann–Whitney \u003cem\u003eU\u003c/em\u003e-tests. Data represent the median (range).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/75b1700681581c8d40d2e671.png"},{"id":78754296,"identity":"6207faeb-0317-4fa8-bebe-8955b1017d97","added_by":"auto","created_at":"2025-03-18 12:28:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":144064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC of cfDNA in NOA and VC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Diagnostic model using ROC curves in VC. \u003cstrong\u003eB\u003c/strong\u003eDiagnostic model using ROC curves in NOA, and the cfDNA size distribution, JI-M, methylation level at CH sites, jagged end length and MDS were applied.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/5631deead7109016d812b98a.png"},{"id":78754590,"identity":"6906814c-89e7-4531-8602-64a7ee714ac9","added_by":"auto","created_at":"2025-03-18 12:36:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2626987,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/8ac664e6-3faf-436e-bd9b-2e1e550e1bb8.pdf"},{"id":78752261,"identity":"b82c8334-87c2-49be-8be8-3bed073f4004","added_by":"auto","created_at":"2025-03-18 12:04:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3568409,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6200607/v1/4c887a5c35cdc0df93c21b7a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seminal plasma cfDNA fragmentomics landscape delineates male infertility subtypes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCell-free DNA (cfDNA) fragmentomics - the systematic analysis of DNA fragment characteristics including size distribution, end motifs, and jagged end profiles - has emerged as a transformative approach in liquid biopsy research[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The characteristic of fragment size, 166bp peak dominant in plasma cfDNA, are associated with nucleosomes and liker DNA[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Four-mer end motifs profile is association with pathophysiologic conditions such as pregnancy, transplantation, and cancer. For instance, the abundance of plasma DNA motif CCCA was lower in HCC patients than in healthy controls[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Crucially, jagged end formation (single-stranded protruding ends) is modulated by both nuclease concentration gradients across biofluids and the mode of cellular demise: apoptosis typically produces cleaner double-strand breaks via caspase-activated DNase, whereas necrosis releases DNA with complex terminal modifications due to uncontrolled protease activity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These multilayered features - encompassing cleavage positioning relative to nucleosomes, fragment length distributions, and strand end complexity - collectively serve as molecular fingerprints, encoded by both the tissue origin of cfDNA, the enzyme activity in body fluids and underlying pathophysiological states. These multidimensional features-spanning nucleosome-phased cleavage sites, size distribution periodicity, and jagged end microarchitecture-function as dynamic molecular fingerprints that influenced by the cellular origin of cfDNA, the enzyme activity in body fluids, and disease pathophysiology, ultimately enabling non-invasive biomarker[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile blood-derived cfDNA fragmentomics has been extensively characterized in prenatal screening and oncology[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], emerging evidence reveals fluid-type specific fragmentation signatures. Notably, urine cfDNA demonstrates unique fragmentomics patterns[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]: (1) pronounced fragmentation with a dominant sub-100 bp population versus the predominant 166 bp peak in plasma; (2) the jagged end length distribution displayed 10-nt periodicities, and the jagged end index profile showed weakly oscillating major peaks but with the strongly oscillating minor peaks; (3) bladder cancer-associated urinary cfDNA had lower jagged end indexed than controls without bladder cancer. These fluid-specific signatures underscore the urine cfDNA fragmentomics as biomarkers for bladder cancer detection.\u003c/p\u003e \u003cp\u003eDespite these advancements, seminal plasma (SP) cfDNA remains poorly understudied. As a complex biofluid comprising secretions from testicular (5%), epididymal (10%), prostatic (30%), and seminal vesicle-derived (60%)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], SP provides a unique window into male reproductive pathophysiology. The dynamic spermatogenic process \u0026ndash; encompassing germ cell proliferation, meiotic recombination, and apoptotic selection \u0026ndash; drives extensive chromatin reorganization, potentially imparting unique fragmentation patterns of SP cfDNA. Pierre's study evaluated the association between levels of SP cfDNA and sperm fertility criteria[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. They compared SP cfDNA concentration in non-obstructive azoospermia (NOA) samples and samples with various sperm pathologies to detect a potential link between free DNA levels and male infertility. Previous studies had not specifically focused on or utilized SP cfDNA fragmentation information, nor have they explored its alterations in different diseases. The fragmentomic landscape and its diagnostic potential in male infertility remain uncharted. This knowledge gap is particularly critical for two prevalent infertility etiologies: NOA, characterized by complete spermatogenic failure, and varicocele (VC), where venous reflux induces testicular microenvironmental alterations. Current diagnostic paradigms rely on invasive testicular biopsies for NOA[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]and subjective physical examinations for VC[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], highlighting the urgent need for non-invasive and precise biomarkers reflecting underlying molecular pathologies.\u003c/p\u003e \u003cp\u003eIn this article, we used an integrative multi-omics framework combining whole-genome sequencing, jagged end sequencing (Jag-Seq) with non-CpG cytosine methylation analysis, and receiver operating characteristic (ROC) curve analysis. This platform enables two critical discoveries: fluid-specific fragmentation signatures distinguishing SP cfDNA from plasma cfDNA, disease-specific fragmentation patterns differentiating NOA and VC from healthy controls.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman sample collection and processing\u003c/h2\u003e \u003cp\u003eMale patients diagnosed with VC (n\u0026thinsp;=\u0026thinsp;14), NOA (n\u0026thinsp;=\u0026thinsp;19) and age-matched healthy controls (n\u0026thinsp;=\u0026thinsp;18) were enrolled. All participants were Asian. The study protocol was approved by the Institutional Review Board of Wuhan Huake Reproductive Hospital, with written informed consent obtained from all participants prior to enrollment. Comprehensive demographic characteristics and clinical parameters are detailed in Supplemental Table\u0026nbsp;1. The study design was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFirstly, we performed fragmentomic comparisons of seminal plasma cfDNA (n\u0026thinsp;=\u0026thinsp;18) and blood plasma cfDNA (n\u0026thinsp;=\u0026thinsp;20) using WGS and Jag-Seq, analyzing fragment size distribution, end motifs heat map, preferred end motif, jagged end length, methylation level, and the relationship of cfDNA size and jagged end length. Then we performed a translational application varicocele (n\u0026thinsp;=\u0026thinsp;14) and non-obstructive azoospermia (n\u0026thinsp;=\u0026thinsp;19) to develop a diagnostic model.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Collection and Processing\u003c/h3\u003e\n\u003cp\u003eFollowing 3\u0026ndash;5 days of sexual abstinence, participants collected seminal samples via masturbation into sterile wide-mouthed containers. Semen samples were immediately transported to the laboratory for centrifugation at 4000 \u003cem\u003e\u0026times; g\u003c/em\u003e (10 min, room temperature). The obtained SP was aliquoted and stored at \u0026minus;\u0026thinsp;80℃ until analysis.\u003c/p\u003e \u003cp\u003ePlasma from the blood samples was collected through centrifugation at 1600 \u003cem\u003e\u0026times; g\u003c/em\u003e (10 min, 4℃). The obtained plasma supernatant was aliquoted and stored at \u0026minus;\u0026thinsp;80℃ until analysis.\u003c/p\u003e\n\u003ch3\u003eDNA Extraction and Quantification\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from approximately 0.4 mL of SP or plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen). All DNA samples underwent dual quality assessment: purity analysis via NanoDrop spectrophotometer (Thermo Fisher Scientific) and precise quantification using Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). The specific sequencing protocol were shown in Methods of Additional file.\u003c/p\u003e\n\u003ch3\u003eMotif diversity score calculation\u003c/h3\u003e\n\u003cp\u003eTo examine the distribution of motif frequencies out of a total of 256 motifs, we utilized normalized Shannon entropy as a mathematical method for computing the motif diversity score (MDS), which is defined by the following equation: represents the frequency of a specific motif[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This metric ranges theoretically from 0 to 1, with maximal value (MDS\u0026thinsp;=\u0026thinsp;1) indicating perfectly uniform motif distribution and minimal value (MDS\u0026rarr;0) reflecting extreme skewness. If the frequencies of the 256 4-mer motifs were evenly distributed, the MDS would reach its maximum value, 1. For example, if one motif accounted for 99% of the total frequency while the others shared the remaining 1%, the MDS would approach 0. Therefore, a declining MDS value indicates greater skewness in the frequency distribution of motifs, while increasing values denote enhanced distributional equilibrium.\u003c/p\u003e\n\u003ch3\u003eJagged jaggedness index based on the filling of methylated cytosines\u003c/h3\u003e\n\u003cp\u003eThis method assesses DNA end jaggedness by leveraging methylated cytosine incorporation during end repair. For DNA molecules with 5\u0026prime; or 3\u0026prime; overhangs, DNA polymerase extends jagged end using methylated dCTP alongside standard nucleotides. Bisulfite treatment selectively converts unmethylated cytosines to uracils, preserving methylated cytosines in both strands. Methylation densities are calculated for Read1 (original strand, M1) and Read2 (synthesized strand, M2\u0026thinsp;\u0026asymp;\u0026thinsp;100%), with the jaggedness index based on the filling of methylated cytosines (JI-M) defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:JI-M=\\frac{M2-M1}{M2}\\times\\:100\\%$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere M1 represents the methylation density of read1, and M2 represents the methylation density of read2.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eClinical characteristics of the study were described as median and range for continuous variables. Differences between groups were analyzed by Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e-tests for continuous (not normally distributed) variables. Statistical analyses and figure plot were performed using Python; all statistical tests were two-sided, and \u003cem\u003eP\u003c/em\u003e values smaller than 0.05 were defined as significant. There was no adjustment of the significance level for multiple testing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFluid difference in cfDNA concentration and size distribution\u003c/h2\u003e \u003cp\u003eSP showed similar cfDNA concentration with plasma (Supplement Table). Fragment length distribution analysis revealed marked fluid-specific patterns: while plasma cfDNA displayed a characteristic sharp peak at 166bp, SP cfDNA showed a broadened 151bp peak and 110bp sub-peak (Fig.\u0026nbsp;2A-B). The logarithmic scale plot showed a more pronounced oscillation in plasma cfDNA with the strong 160 bp periodicity and attenuated periodicity in SP cfDNA (Fig.\u0026nbsp;2C). The individual size distribution of each sample was shown in Supplement Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2. And 50-bp bin analysis identified significant distribution frequency differences across 51-200bp ranges, with SP demonstrating higher abundance of shorter fragments (\u0026lt;\u0026thinsp;150 bp) and plasma showed more longer fragments (151\u0026ndash;200 bp) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;2D-G). These fragmentation signatures collectively suggest tissue-specific DNA processing mechanisms, with SP cfDNA enriched in both apoptotic remnants from spermiogenesis and protected high-molecular-weight DNA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnd motif profiling of cfDNA in difference plasma\u003c/h2\u003e \u003cp\u003eThe result of end motif profiling was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which revealed most of 256 distinct end motifs differing between cfDNA in SP and plasma. Hierarchical clustering of 256 4-mer motifs demonstrated fluid-specific grouping, suggesting that the distinct enzyme activities in each body fluid led to similar cleavage preferences and, consequently, similar 4-mer motif frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e).\u003c/b\u003e Notably, the 10 motifs with the most significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) showed striking fluid specificity: six motifs (including AAXX) were enriched in SP, while five (notably CCTT and ACCT) predominated in plasma (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), emphasizing the most biologically relevant variations in cfDNA end motifs between SP and blood plasma. SeqLogo analysis illustrates that the first position of the 5\u0026rsquo; end predominantly showed a predominance for C, while subsequent positions exhibit clear differences. In plasma, the second position preferred C, whereas in SP, it is A; the third position in plasma preferred T, while A is preferred in SP; and the fourth position in plasma shows a preference for T, compared to A in SP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). MDS quantified this disparity, with SP (0.9508(0.9351\u0026ndash;0.9604)) exhibiting higher than blood (0.9455(0.9352\u0026ndash;0.9546)) (\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.0005\u003c/em\u003e), indicative of more heterogeneous cleavage patterns in the male reproductive system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The coordinated motif differences - from global clustering to nucleobase-level positional biases - strongly suggest that SP cfDNA originates through unique chromatin fragmentation processes, potentially involving fluid-specific nucleases and distinct apoptotic regulation during spermiogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOverall description and comparison of cfDNA jagged end features\u003c/h2\u003e \u003cp\u003eJag-seq investigates the characteristics of jagged end in cfDNA, focusing on fragment length distribution, methylation levels, and the relationship of jagged end length and overall cfDNA length. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA revealed distinct fragmentation patterns between plasma cfDNA and SP cfDNA, with plasma cfDNA demonstrating significantly shorter peak fragments and greater size heterogeneity than SP cfDNA. The methylation-cfDNA size relationship displayed striking compartmentalization \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. While plasma cfDNA exhibited a fluctuating methylation pattern, with methylation levels oscillating between 70% and 10% as DNA length increased, SP cfDNA showed a relatively stable increase in methylation levels across size fractions. The relationship between average jag length and cfDNA exhibited a similar pattern: plasma cfDNA showed fluctuating jag lengths, ranging from 10 to 35, as DNA length increased, whereas SP cfDNA demonstrated a more stable increase, reaching a plateau around 200 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). SP cfDNA fragments carried longer average jagged ends compared to plasma cfDNA, when cfDNA longer than 100bp. Notably, JI-M showed SP (78.37(61.26\u0026ndash;87.90)) was significantly higher than plasma cfDNA (12.72(1.17\u0026ndash;47.92)) (\u003cem\u003eP\u0026thinsp;=\u0026thinsp;2.03*10\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;12\u003c/em\u003e\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDisease-Specific cfDNA fragmentation signatures\u003c/h2\u003e \u003cp\u003eComparative analysis of SP cfDNA architecture revealed distinct pathological profiles across cohorts (NOA: n\u0026thinsp;=\u0026thinsp;19, VC: n\u0026thinsp;=\u0026thinsp;14, Controls: n\u0026thinsp;=\u0026thinsp;18). Size distribution analysis demonstrated similar patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B) and the logarithmic scale plot showed a more pronounced oscillation in NOA SP cfDNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The individual size distribution of each sample was shown in Supplement Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Hierarchical clustering of 256 4-mer motifs demonstrated disease-specific grouping (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). End motif profiling uncovered the distinct predominance in position \u0026minus;\u0026thinsp;1, cytosine predominance in VC and Thymine in NOA, suggesting differences in degradation or origin among the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F). End motif analysis identified 7 VC-associated motifs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and 66 NOA-specific motifs (top 10 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). MDS showed the tendency of increase in both VC (0.9505(0.9293\u0026ndash;0.9637) and NOA (0.9535(0.9356\u0026ndash;0.9644) SP compared to controls (0.9455(0.9352\u0026ndash;0.9546)), indicating more diverse in disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). NOA showed longer jagged ends, and jagged end length-cfDNA size analysis demonstrated 10-bp periodicity was enhanced in NOA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). Strikingly, global hypomethylation at CH sites distinguished NOA from controls, suggesting defective epigenetic reprogramming during spermatogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eL). And NOA (48.54(4.78\u0026ndash;80.96), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003) displayed significantly lower JI-M vs controls (78.37(61.26\u0026ndash;87.90)), while VC (72.09(49.44\u0026ndash;83.43), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.592) showed the downward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). NOA SP cfDNA showed longer jagged ends when cfDNA size\u0026thinsp;\u0026lt;\u0026thinsp;130 bp fragments, while VC exhibited longer jagged ends when cfDNA size\u0026thinsp;\u0026gt;\u0026thinsp;130 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eM-N). These multi-modal fragmentation signatures - from nucleosomal positioning to methylome architecture - collectively demonstrate that cfDNA analysis could be used to non-invasively diagnose different testicular pathophysiological states.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic potential of cfDNA features in male infertility subtype\u003c/h2\u003e \u003cp\u003eTo evaluate the clinical utility of cfDNA fragmentation features, we performed multiclass ROC analysis comparing three diagnostic dimensions: (1) NOA vs controls, (2) VC vs controls. Five key metrics were assessed: cfDNA size distribution, MDS, JI-M, CH-site methylation levels, and jagged end length. The composite model achieved superior diagnostic performance with AUC\u0026thinsp;=\u0026thinsp;0.83 for VC detection and AUC\u0026thinsp;=\u0026thinsp;0.87 for NOA identification (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study establishes the first multi-dimensional atlas of SP cfDNA architecture, revealing four cardinal distinctions through integrated WGS and Jag-seq fragmentomic analysis. SP cfDNA exhibits bimodal size distribution with broader peak at 151 bp and 110 bp sub-peak, contrasting with plasma cfDNA 166 bp sharp peak pattern. The frequency of sub-nucleosome length (\u0026lt;\u0026thinsp;150 bp) is higher in SP, suggesting testicular-specific fragmentation processes. Hierarchical clustering of 256 4-mer motifs demonstrated fluid-specific grouping, and the second base position of cfDNA preferred C in plasma, whereas SP cfDNA retains A-preferred bases at positions \u0026minus;\u0026thinsp;2 to -4. SP cfDNA displays enhanced 10-bp periodicity, longer jagged ends, and higher JI-M than plasma cfDNA, correlating with testis-specific nucleases. In this study, we investigated that difference of two biofluid cfDNA fragmentomics. On the other hand, the disease-associated SP cfDNA fragmentomics were present in patients with VC and NOA. This finding may have physiological relevance, reflecting tissue-specific cfDNA release patterns, and pathophysiological importance, providing insights into reproductive health and the potential for novel biomarkers.\u003c/p\u003e \u003cp\u003ePrevious studies on plasma cfDNA have primarily reported unimodal distribution, typically with fragments around 166 bp, corresponding to nucleosome-protected DNA during apoptosis in somatic cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This study shows abundance of short fragments and bimodal distribution (110 bp and 151bp peaks) in SP cfDNA may be linked to the unique biological processes occurring in the male reproductive system, such as spermatogenesis and apoptosis, DNA Histone-to-protamine replacement during sperm nuclear condensation, and activity of Deoxyribonuclease (DNase). The activity of DNase Ⅰ family nucleases in body fluids were different, which are known to be active during cell death and tissue turnover, could be involved in generating these shorter fragments[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The activity of DNase Ⅰ in semen(25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 *10\u003csup\u003e6\u003c/sup\u003e units/g protein) was higher than serum (4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 *10\u003csup\u003e6\u003c/sup\u003e units/g protein), which may cause different DNA cleavage and cfDNA fragmentation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. But the activity of DNase Ⅰ subtype in SP need to be measured precisely.\u003c/p\u003e \u003cp\u003eThe cfDNA end motif analysis revealed notable differences between SP and blood plasma, suggesting tissue-specific nuclease activity and distinct degradation mechanisms[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The positional nucleotide bases at motif sites - A predominance at positions \u0026minus;\u0026thinsp;2~-4 in SP versus C/T in plasma cfDNA. The unique preferred end motifs possibly reflected the activity of reproductive system-associated nucleases, as higher concentration of DNase Ⅰ in SP[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These motifs resulted from apoptosis or other programmed cell death processes during spermatogenesis, where endonucleases cleave DNA at precise sites[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While in contrast, blood plasma cfDNA typically displays end motifs indicative of general cellular turnover, reflecting more ubiquitous degradation processes across various tissues. MDS elevated in SP, which constitutes one of the features of the subsequent disease diagnosis, and correlates with the concentration of SP DNase Ⅰ, reflecting \u0026ldquo;systemic\u0026rdquo; nuclease perturbation.\u003c/p\u003e \u003cp\u003eCompared to traditional diagnostic methods, such as semen analysis, imaging, or biopsy, cfDNA offers a minimally invasive, reproducible, and molecular-level approach to evaluating male reproductive health[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. When investigating whether diseases were related to the structural genomics of cfDNA, we chose these two diseases for our study. VC is the most common disease of male infertility, which is caused by localized hypoxia and oxidative stress due to obstruction of spermatic reflux[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. And we wanted to focus on whether localized lesions such as hypoxia could alter the fragmentomics characteristics of SP cfDNA. NOA is the most severe spermatogenic dysfunction, and the spermatogenic blockage leads to abnormal germ cell apoptosis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We wanted to focus on how the alterations originating from the genomics of germ cells affect the fragmentation of cfDNA. In this study, distinct cfDNA structural characteristics, including fragment length distribution, end motifs, and jagged end patterns, were difference among patients with VC, NOA, and healthy controls. VC cfDNA showed C-end motifs and NOA showed T-preferred and position \u0026minus;\u0026thinsp;1 motifs. NOA cfDNA showed enhanced 10-bp periodicity, which mirrored urinary cfDNA profiles, suggesting shared nuclease regulation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The global hypomethylation at CH sites was significantly higher in NOA, and jagged end length were difference in disease, which were related in reduced nucleosome protection permits nuclease access and aberrant methylation disrupts cfDNA-protein interactions in NOA. These fragmentomics features achieved 87% diagnostic accuracy in distinguishing NOA, and 83% diagnostic accuracy in distinguishing VC. The unique ability to capture dynamic changes in tissue-specific cell death, turnover, and transcriptional activity makes it an attractive, non-invasive tool for early diagnosis, prognosis, and treatment monitoring in patients with VC, azoospermia, or other reproductive disorders.\u003c/p\u003e \u003cp\u003eThis study, while providing valuable insights into the structural characteristics of cfDNA in seminal plasma, does have several limitations. Firstly, the sample size was relatively small, which may limit the generalizability of the findings. A larger cohort is needed to validate these results and ensure that the observed cfDNA patterns are consistent across diverse populations. Additionally, technical limitations such as variations in cfDNA extraction and quantification methods could affect the reproducibility of the results. Additionally, exploring cfDNA characteristics beyond VC and azoospermia could provide further insights into its diagnostic and prognostic value.\u003c/p\u003e \u003cp\u003eIn conclusion, we investigated the differences of fluid-specific cfDNA fragmentomics, their application in VC and NOA. Furthermore, the disease-specific fragmentomics opens new diagnostic avenues, where cfDNA could be used to monitor reproductive disorders or assess the efficacy of treatments such as vasectomy, infertility interventions, or cancer therapies targeting the reproductive system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecfDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecell-free DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eseminal plasma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJag-Seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ejagged end sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evaricocele\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNOA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-obstructive azoospermia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emotif diversity score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJI-M\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ejaggedness index based on the filling of methylated cytosines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ent\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enucleotides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ebp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebase pair,ROC,receiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the ROC curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNase\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonuclease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trial was conducted under Institutional Review Board of Wuhan Huake Reproductive Hospital. Written informed consent was obtained from all patients as well as to use and share data and specimens collected for the study. The study was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the contributions of the patients who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXW, ZL and KZ performed data analysis and interpretation. XW, FM, HZ, and KZ provided supports for interpretations and discussion about results. HZ and KZ corresponded specimen storage. HZ provided clinical supports during the clinical trial. XW, FM, HZ, ZQ and KZ contributed to manuscript design and interpretation of data. XW and FM wrote the manuscript with comments and contributions from all authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKZ was supported by National Science Foundation of Hubei Province (2023AFB735, JC2RYB202500538).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for publication was obtained from the patients who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSerpas L, Chan RWY, Jiang P, Ni M, Sun K, Rashidfarrokhi A, et al. Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA. 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Dysregulation of apoptotic pathway candidate genes and proteins in infertile azoospermia patients. Fertility and sterility. 2015;104(3):736-43.e6.\u003c/li\u003e\n\u003cli\u003eTempleton A. Varicocele and infertility. Lancet (London, England). 2003;361(9372):1838-9.\u003c/li\u003e\n\u003cli\u003eChan RWY, Serpas L, Ni M, Volpi S, Hiraki LT, Tam LS, et al. Plasma DNA Profile Associated with DNASE1L3 Gene Mutations: Clinical Observations, Relationships to Nuclease Substrate Preference, and In Vivo Correction. American journal of human genetics. 2020;107(5):882-94.\u003c/li\u003e\n\u003cli\u003eHan DSC, Ni M, Chan RWY, Chan VWH, Lui KO, Chiu RWK, et al. The Biology of Cell-free DNA Fragmentation and the Roles of DNASE1, DNASE1L3, and DFFB. American journal of human genetics. 2020;106(2):202-14.\u003c/li\u003e\n\u003cli\u003eNadano D, Yasuda T, Kishi K. Measurement of deoxyribonuclease I activity in human tissues and body fluids by a single radial enzyme-diffusion method. Clinical chemistry. 1993;39(3):448-52.\u003c/li\u003e\n\u003cli\u003eHeitzer E, Auinger L, Speicher MR. Cell-Free DNA and Apoptosis: How Dead Cells Inform About the Living. Trends in molecular medicine. 2020;26(5):519-28.\u003c/li\u003e\n\u003cli\u003eRanucci R. Cell-Free DNA: Applications in Different Diseases. Methods in molecular biology (Clifton, NJ). 2019;1909:3-12.\u003c/li\u003e\n\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":"cell-free DNA, Male infertility, Fragmentomics","lastPublishedDoi":"10.21203/rs.3.rs-6200607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6200607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile cell-free DNA (cfDNA) fragmentomics has transformed liquid biopsy applications in prenatal screening and oncology, its potential in male reproductive health remains uncharted.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThrough integrated whole-genome sequencing and jagged end sequencing (Jag-Seq) coupled with non-CpG methylation analysis, we established the first fragmentomic atlas of seminal plasma (SP) cfDNA from 18 healthy donors, with 20 plasma cfDNA samples. And we applied this method to 33 infertility cases (14 varicocele / 19 non-obstructive azoospermia), to obtain disease-specific characteristics. ROC curve analysis was employed to study the potential diagnostic ability for these two diseases.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSize distribution profiling showed SP cfDNA enrichment in short fragments (\u0026lt;\u0026thinsp;150bp) with bimodal distribution (151bp main peak/110bp subpeak), contrasting with plasma's sharp 166-bp peak pattern (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Motif analysis identified SP-specific patterns: elevated AAAA-end motif frequency and A-base preference at positions \u0026minus;\u0026thinsp;2 to -4. And SP showed higher jagged end index based on Jag-Seq (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). For disease, varicocele exhibited 7 different frequency motifs and longer jagged end length while non-obstructive azoospermia demonstrated higher methylation level at CH sites. Translating these findings to clinical contexts, we developed a ROC curve analysis integrating all fragmentomic signatures, achieving 83% accuracy in distinguishing varicocele and 87% accuracy in distinguishing non-obstructive azoospermia.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis research highlights the distinct cfDNA profiles in SP and demonstrates the potential of cfDNA metrics as biomarkers for diagnosing male infertility subtypes, and the disease-specific cfDNA dynamics offering new avenues for non-invasive diagnostic tools in reproductive medicine.\u003c/p\u003e","manuscriptTitle":"Seminal plasma cfDNA fragmentomics landscape delineates male infertility subtypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 12:03:57","doi":"10.21203/rs.3.rs-6200607/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":"82ad157f-f563-4c98-83b0-9375fbad7c0c","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-09T09:08:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 12:03:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6200607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6200607","identity":"rs-6200607","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}