ABSTRACT

   DNA fragmentation index (DFI), a new biomarker to diagnose male
   infertility, is closely associated with poor reproductive outcomes.
   Previous research reported that seminal microbiome correlated with
   sperm DNA integrity, suggesting that the microbiome may be one of the
   causes of DNA damage in sperm. However, it has not been elucidated how
   the microbiota exerts their effects. Here, we used a combination of 16S
   rRNA sequencing and untargeted metabolomics techniques to investigate
   the role of microbiota in high sperm DNA fragmentation index (HDFI). We
   report that increased specific microbial profiles contribute to high
   sperm DNA fragmentation, thus implicating the seminal microbiome as a
   new therapeutic target for HDFI patients. Additionally, we found that
   the amount of Lactobacillus species was altered: Lactobacillus iners
   was enriched in HDFI patients, shedding light on the potential
   influence of L. iners on male reproductive health. Finally, we also
   identified enrichment of the acetyl-CoA fermentation to butanoate II
   and purine nucleobase degradation I in the high sperm DNA fragmentation
   samples, suggesting that butanoate may be the target metabolite of
   sperm DNA damage. These findings provide valuable insights into the
   complex interplay between microbiota and sperm quality in HDFI
   patients, laying the foundation for further research and potential
   clinical interventions.

IMPORTANCE

   The DNA fragmentation index (DFI) is a measure of sperm DNA
   fragmentation. Because high sperm DNA fragmentation index (HDFI) has
   been strongly associated with adverse reproductive outcomes, this has
   been linked to the seminal microbiome. Because the number of current
   treatments for HDFI is limited and most of them have no clear efficacy,
   it is critical to understand how semen microbiome exerts their effects
   on sperm DNA. Here, we evaluated the semen microbiome and its
   metabolites in patients with high and low sperm DNA fragmentation. We
   found that increased specific microbial profiles contribute to high
   sperm DNA fragmentation. In particular, Lactobacillus iners was
   uniquely correlated with high sperm DNA fragmentation. Additionally,
   butanoate may be the target metabolite produced by the microbiome to
   damage sperm DNA. Our findings support the interaction between semen
   microbiome and sperm DNA damage and suggest that seminal microbiome
   should be a new therapeutic target for HDFI patients.

   KEYWORDS: microbiota, RNA, ribosomal, 16S, metabolomics

INTRODUCTION

   Recent meta-analyses of semen parameters from 1973 to 2019 indicate a
   significant global decline in sperm concentration (SC) and total sperm
   count (TSC), with the rate of reduction accelerating ([38]1). Inferior
   sperm quality has been strongly associated with adverse reproductive
   outcomes ([39]2). Nonetheless, the predictive capacity of routine semen
   analysis for assessing male fecundity is constrained ([40]3). The DNA
   fragmentation index (DFI), a measure of sperm DNA fragmentation, has
   gained clinical relevance ([41]4). Factors, such as reactive oxygen
   species (ROS) ([42]5) and inflammatory agents ([43]6), have been
   identified as contributors to sperm DNA damage. High levels of sperm
   DNA fragmentation are associated with reduced fertilization rates, poor
   embryo quality, lower pregnancy success, and increased miscarriage
   rates ([44]7).

   Current treatments for high DNA fragmentation index (HDFI) include
   antioxidant therapy, infection management, and lifestyle changes
   ([45]8), but these approaches often prove inadequate, especially in
   cases of idiopathic HDFI ([46]9). This has led to an increased reliance
   on assisted reproductive technologies (ARTs) ([47]5), yet the success
   rates vary, underscoring the need for alternative treatment strategies.

   The human microbiome, encompassing the collective genetic material of
   microbiota in various body niches, plays a vital role in physiological
   homeostasis ([48]10). Imbalances in the microbiome can lead to various
   pathologies ([49]11). Recent research has begun to explore the
   connection between the semen microbiome and semen quality ([50]12).
   Notably, a study by Sergio et al. using 16S rRNA gene sequencing
   revealed a negative correlation between certain bacteria and sperm DNA
   fragmentation in a Western Mediterranean cohort ([51]13). Thus, our
   study aims to further investigate the role of the semen microbiome in
   HDFI using both 16S rRNA and metabolome sequencing. We hypothesize that
   the seminal microbiota could significantly influence sperm DNA
   integrity, potentially offering new avenues for HDFI treatment.

MATERIALS AND METHODS

Study population and tissue sample collection

   All semen samples were taken during the participants’ abstinence period
   of 3–7 days and were left to liquefy for 30 min. Before collection, the
   subjects were instructed on the proper procedures to minimize the risk
   of contamination. The subjects washed their hands with soap two or
   three times, and the penis, including the glans and coronal sulcus, was
   cleaned with warm soapy water and then swabbed with 75% alcohol two or
   three times. Semen was ejaculated directly into a sterile glass
   receptacle, ensuring no contact with the inner walls to maintain
   sterility. Freshly collected semen was used for routine clinical tests,
   Gram staining, and microscopy. The reference for semen analysis is the
   sixth edition of the WHO laboratory manual for the examination and
   processing of human semen. Any remaining semen samples were transferred
   to sterile freezing tubes and placed in liquid nitrogen for at least 15
   min, before being transferred to −80°C freezer for storage until the
   next sample-processing session.

   Our inclusion criteria were the following: (i) DFI >30% or <15%; (ii)
   no family history of genetic disease; (iii) no history of genetic
   disease, systemic disease, known diseases such as varicocele that can
   affect DFI, or long-term exposure to radiation and toxic substances;
   (iv) no history of sexually transmitted diseases in the past 3 months;
   (v) no systemic corticosteroid, prescription antibiotics,
   immunosuppressive drugs, systemic corticosteroid use, or cancer
   chemotherapy in the past 3 months; (vi) no evidence of genitourinary
   infection.

Sperm chromatin structure assay

   In accordance with the protocols delineated by the manufacturer, the
   DNA fragmentation index (DFI) was assessed using acridine orange dye
   supplied by Zhuhai Anda Biological Engineering Co., LTD. The
   fluorescence patterns were captured utilizing a flow cytometer,
   specifically the DxPAthena B4-R2 model from Cytek Biosciences. The
   calculation of the DFI was then achieved by determining the ratio of
   red to total fluorescence intensity (the sum of red and green
   fluorescence intensities) ([52]14, [53]15).

Cross-incubation experiment

   Semen specimens were procured from 15 individuals, stratified into
   three distinct groups: (i) sperm donor group (n = 5); (ii) high DNA
   fragmentation index (HDFI) group (n = 5); and (iii) low DNA
   fragmentation index (LDFI) group (n = 5). A segment of each specimen
   was immediately used to ascertain the DNA fragmentation index
   (DFI-before).

   For HDFI and LDFI groups, the specimens were centrifuged twice at 1800
   rpm for 10 min at 37°C, and seminal plasma was segregated and stored in
   discrete tubes. For the sperm group, a refined density gradient
   centrifugation technique was utilized to segregate spermatozoa from
   seminal plasma to augment sperm quality. Specifically, gradient liquid
   was gently introduced to the upper layer of semen, followed by
   centrifugation at 300 × g (1,800 rpm) for 10 min at 37°C. The
   supernatant was subsequently discarded, and the sedimented material was
   transferred into phosphate-buffered saline (PBS; Servicebio, China),
   followed by another centrifugation at 300×g (1,800 rpm) for 5 min. This
   procedure was repeated twice to ensure the complete removal of residual
   gradient liquid, culminating in the resuspension of the sperm pellet in
   PBS. The treated semen specimens from the sperm group were then divided
   into three equivalent parts and added to either to the seminal plasma
   of the HDFI or LDFI group or to an identical volume of PBS.

   Preliminary evaluations of the DNA fragmentation index (DFI-T0) were
   executed on each semen specimen. Thereafter, the specimens were
   subjected to a 24-h incubation at 37°C. The specimens added in PBS were
   designated as DFI-T24, whereas those in the seminal plasma of the HDFI
   and LDFI groups were labeled as HDFI-T24 and LDFI-T24, respectively.

Microbial total DNA extraction and sequencing

   In this study, seven negative controls were meticulously implemented to
   minimize the risk of contamination, including potential sources: glass
   receptacle, both unopened and opened freezing tubes, ambient air,
   tabletop surfaces, and reagents, aligning with recommendations from a
   previous study ([54]16). Then, total genomic DNA was extracted using
   MagPure Soil DNA LQ Kit (Magan) following the manufacturer’s
   instructions. NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose
   gel electrophoresis were used to detect the concentration and purity of
   microbial DNA, and the extracted DNA was stored at −20°C.

   For the amplification of bacterial 16S rRNA genes, PCR was conducted
   using barcoded primers specific to the sequence, in conjunction with
   Takara Ex Taq (Takara), utilizing the previously extracted DNA as a
   template. The bacterial diversity analysis emphasized the amplification
   of the V3–V4 variable regions of the 16S rRNA genes, employing
   universal primers 343F (5′-TACGGRAGGCAGCAG-3′) and 798R
   (5′-AGGGTATCTAATCCT-3′) ([55]17). After secondary purification, the
   final amplicon was quantified using Qubit dsDNA Assay Kit (Thermo
   Fisher Scientific, USA). Sequencing of the constructed libraries was
   performed on an Illumina NovaSeq 6000 with 250-bp paired-end reads
   (Illumina Inc., San Diego, CA; OE Biotech Company; Shanghai, China).

16S rRNA data analysis

   The raw reads underwent a series of pre-processing steps. First,
   Cutadapt ([56]18) software was used to detect and cut off the adapter.
   Following trimming, the paired-end reads underwent a series of
   operations, including filtering low-quality sequences, denoising,
   merging, and identifying and removing chimera reads. This was achieved
   through the application of DADA2 ([57]19) with the default parameters
   of QIIME 2 (2020.11) ([58]20). Subsequently, the software generated
   representative reads and an abundance table for amplicon sequence
   variants (ASVs). The representative read for each ASV was selected
   using the QIIME2 package. To enhance taxonomic classification, all
   representative reads were annotated and subjected to a BLAST search
   against the Silva database (Version 138) ([59]21) using
   q2-feature-classifier with default parameters. To enhance the
   differentiation of genuine low-biomass signals from contamination and
   noise, efforts should be directed toward enhancing the alignment of
   experimental and computational pipelines. We utilized the SCRuB
   ([60]22) software to eliminate contaminating reads identified through
   negative control samples.

Community diversity analysis

   The α and β diversity analyses were analyzed using the QIIME2 software.
   The α-diversity is assessed using the Shannon index. The unweighted
   Unifrac distance matrix performed by R package was used for non-metric
   multidimensional scaling (NMDS) to estimate the β diversity. Then, the
   R package was used to analyze the significant differences between
   different groups using the ANOSIM statistical test.

Microbial composition difference analysis

   The taxonomy abundance spectrum was compared using the linear
   discriminant analysis effect size (LEfSe) method. To investigate the
   potential utility of microbiota in disease diagnosis and prediction, we
   utilized a machine learning approach, specifically random forest, to
   construct classification models based on the level of DFI. First, we
   calculated feature importance scores using the random forest algorithm,
   specifically the mean decrease accuracy, which indicates the
   contribution of each feature to the accuracy of the model.
   Subsequently, we conducted 10 trials of 10-fold cross-validation using
   random forest to identify optimal biomarkers. The cutoff point for
   selecting optimal biomarkers was determined based on the mean of the
   minimum cross-validation error. Random forest analysis was conducted
   using R software. The optimal biomarker sets were selected based on the
   cutoff point in the cross-validation error curve, defined as the
   minimum cross-validation error. Redundancy analysis (RDA) was performed
   in R ([61]http://cran.r-project.org/) using the vegan package with
   normalized OTU abundance and environmental chemical data. Redundancy
   analysis (RDA) bi-plot shows the correlation between the sequence
   abundance of microbiota communities and the environmental variables.

Non-targeted metabolic profiling

   Semen samples were subjected to metabolomics analysis. Non-targeted
   metabolic profiling procedure was followed as described previously
   ([62]23). Semen samples were thawed at room temperature and mixed with
   L-2-chlorophenylalanine as an internal standard. After vortexing, an
   ice-cold mixture of methanol and acetonitrile was added, followed by
   ultrasonication and storage at −20°C. The supernatant was collected
   after centrifugation, dried, and reconstituted with a mixture of
   methanol and water. After further vortexing and ultrasonication, the
   samples were stored at −20°C and centrifuged, and the supernatants were
   filtered and transferred to LC vials for storage at −80°C until LC–MS
   analysis. Quality control (QC) samples were prepared by pooling
   aliquots of all samples. Metabolomic analysis was conducted using an
   ACQUITY UPLC I-Class plus (Waters Corporation, Milford, USA) coupled
   with a Q-Exactive mass spectrometer. A gradient elution system of water
   and acetonitrile was used for separation on an ACQUITY UPLC HSS T3
   column. The mass range was from m/z 100 to 1,000, with a resolution of
   70,000 for full MS scans and 17,500 for HCD MS/MS scans. The mass
   spectrometer settings included spray voltages, gas flow rates, and
   temperature control for optimal performance.

Non-targeted metabolic data analysis

   The matrix was imported into R software (v.4.2.2) for conducting
   principal component analysis (PCA) to observe the overall distribution
   among the samples and assess the stability of the entire analysis
   process. Orthogonal partial least-squares discriminant analysis
   (OPLS-DA) and partial least-squares discriminant analysis (PLS-DA) were
   utilized to identify metabolites that exhibit differences between
   groups. To mitigate overfitting, sevenfold cross-validation and 200
   response permutation testing (RPT) were utilized to assess the model’s
   quality. Variable importance of projection (VIP) values obtained from
   the OPLS-DA model were used to rank the overall contribution of each
   variable to group discrimination. Subsequently, a two-tailed Student’s
   t-test was applied to validate the significance of differences in
   metabolites between groups. Differential metabolites were selected
   based on VIP values >1.0 and P < 0.05.

   Finally, the distinct metabolites were annotated using the metabolic
   pathways available in the KEGG database
   ([63]https://www.kegg.jp/kegg/pathway.html) to determine the pathways
   associated with these metabolites. Pathway enrichment analysis was
   conducted using the Python software package scipy.stats
   ([64]https://docs.scipy.org/doc/scipy/), with the most relevant
   biological pathway related to the level of DFI identified through
   Fisher’s exact test.

RESULTS

Subject recruitment and semen collection

   In this study, 102 participants were enrolled, including 54 men with
   HDFI (DFI > 30%) and 48 men with LDFI (DFI < 15%). The enrolled
   participants were divided into two cohorts ([65]Fig. 1): for cohort
   one, cross-incubation experiments were performed on 10 participants,
   with another five participants who served as sperm donors; for cohort
   two, 16S rRNA sequencing alongside non-targeted metabolic profiling was
   conducted on semen samples from 87 participants, comprising 40 with
   LDFI and 47 with HDFI. Clinical characteristics across the HDFI and
   LDFI groups were carefully matched ([66]Tables 1 and 2).

Fig 1.

   [67]Fig 1
   [68]Open in a new tab

   Participants enrolled in the study.

TABLE 1.

   Clinical parameters of cohort one participants[69]^a
   Parameters HDFI group (n = 5) LDFI group (n = 5) P
   Age (years) 35.00 ± 4.000 29.80 ± 3.633 0.0636
   DFI (%) 44.60 ± 12.09 6.690 ± 2.264 0.0001
   pH 7.440 ± 0.0894 7.440 ± 0.0894 >0.9999
   Sperm concentration 46.62 ± 20.71 65.93 ± 49.75 0.4462
   Total number of spermatozoa (×10^6) 104.1 ± 42.48 278.8 ± 259.8 0.176
   Progressive motility sperm (10^6/mL) 28.60 ± 2.302 68.00 ± 14.58 0.0003
   Non-progressive sperm (10^6/mL) 9.800 ± 2.168 12.40 ± 3.912 0.2298
   Immotile sperm (10^6/mL) 61.60 ± 3.209 19.60 ± 15.82 0.004
   [70]Open in a new tab
   ^^a

   DFI, DNA fragmentation index; HDFI, high sperm DNA fragmentation index;
   LDFI, low sperm DNA fragmentation index.

TABLE 2.

   Clinical parameters of cohort two participants[71]^a
   Parameters HDFI group (n = 47) LDFI group (n = 40) P
   Age (years) 39.72 ± 6.156 38.88 ± 4.195 0.4628
   DFI (%) 39.89 ± 9.993 9.649 ± 3.535 <0.0001
   pH 7.406 ± 0.136 7.433 ± 0.135 0.3721
   Sperm concentration 66.8 ± 57.21 80.53 ± 58.69 0.2739
   Total number of spermatozoa (×10^6) 249.2 ± 202.3 239.4 ± 128.7 0.7928
   Progressive motility sperm (10^6/mL) 17.27 ± 21.17 41.15 ± 33.07 0.0001
   Non-progressive sperm (10^6 /mL) 8.979 ± 13.59 16.51 ± 19.34 0.0365
   Immotile sperm (10^6/mL) 42.70 ± 35.11 22.87 ± 14.83 0.0013
   [72]Open in a new tab
   ^^a

   DFI, DNA fragmentation index; HDFI, high sperm DNA fragmentation index;
   LDFI, low sperm DNA fragmentation index.

Effect of seminal plasma composition on DFI

   To investigate the impact of seminal plasma composition on DFI,
   cross-incubation experiments were conducted ([73]Fig. 2A). Notably, an
   increase in DFI was observed following incubation with plasma from high
   DFI patients, suggesting that microbiota-derived compositions within
   seminal plasma could modify the semen environment ([74]Fig. 2B;
   [75]Table S1).

Fig 2.

   [76]Fig 2
   [77]Open in a new tab

   Overview of cross-incubation and composition of the seminal microbiota.
   (A) The experimental workflow for cross-incubation. (B) Boxplots
   illustrating the level of DFI after cross-incubation with seminal
   plasma from HDFI or LDFI groups. (C) Boxplots comparing the Shannon
   index α-diversity of microbial communities in HDFI and LDFI groups. (D)
   Principal-component analysis plots of β-diversity in the two groups.
   Stacked plot of genus composition of HDFI and LDFI groups at the genus
   level (E) and species level (F).

Seminal microbiota composition

   Initial analyses compared overall microbiome community compositions
   between the HDFI and LDFI groups. However, no significant differences
   were found in either α-diversity (Shannon index) or β-diversity
   (ANOSIM: P＞0.05) ([78]Fig. 2C and D). The subsequent analysis focused
   on identifying specific taxa with significant differences in abundance
   between the two groups, revealing notable changes in genera, including
   Escherichia–Shigella and Lactobacillus spp., with a significant
   increase in L. iners within the HDFI group ([79]Fig. 2E and F).

Relationship between seminal microbiota and level of DFI

   The linear discriminant analysis effect size (LEfSe) method identified
   differential bacteria at the genus level, highlighting enrichments of
   Peptostreptococcales–Tissierellales, Finegoldia spp., and
   Corynebacterium spp. in the LDFI group and Acinetobacter spp. in the
   HDFI group ([80]Fig. 3A). Random forest analysis further pinpointed
   Finegoldia spp., Acinetobacter spp., and Lactobacillus spp. as
   significant contributors to the HDFI and LDFI group classifications
   ([81]Fig. 3B). Through redundancy analysis, we identified key
   Lactobacillus spp. that correlated with the level of DFI ([82]Fig. 3C).
   Subsequently, we compared the differences in various Lactobacillus
   species between HDFI and LDFI groups and found significant alterations
   in the distribution proportions of different Lactobacillus species,
   particularly L. iners ([83]Fig. 3D). Additionally, L. iners was the
   most prevalent species in the HDFI group. Together, our results showed
   that microbial community structures were different between HDFI and
   LDFI groups.

Fig 3.

   [84]Fig 3
   [85]Open in a new tab

   Bioinformatic analysis of 16S rRNA gene sequencing data. (A) Linear
   discrimination analysis (LDA) effect size (LEfSe) analysis identified
   the microbes whose abundances significantly differed between the HDFI
   and LDFI groups. (B) Model candidates for disease discrimination were
   established using random forest model analysis. (C) Biplot of
   redundancy analysis (RDA) of the microbiota composition responding to
   the level of DFI. (D) Pie chart showing distribution proportions of
   different Lactobacillus species.

Differential microbiota and KEGG pathways

   Functional analysis of the differential seminal microbiota revealed
   enrichment in 34 metabolic pathways ([86]Fig. 4A), with the HDFI group
   showing significantly higher activity in pathways related to acetyl-CoA
   fermentation to butanoate II, cob(II)yrinate a,c-diamide biosynthesis
   I, ppGpp biosynthesis, and purine nucleobase degradation I ([87]Fig. 4B
   through E). Differential microbiota in the LDFI group exhibited a
   pronounced enrichment in amino acid metabolism ([88]Fig. 4F).

Fig 4.

   [89]Fig 4
   [90]Open in a new tab

   Functional alterations of the microbiota. Difference analyses of KEGG
   orthology (A–E) and metabolic pathways from all domains of life
   (metacyc) (F).

Semen microbiota alters sperm DFI by influencing metabolite composition

   Untargeted metabolomics analysis of seminal metabolomes from LDFI (n =
   40) and HDFI (n = 47) patients identified significant alterations in
   metabolite profiles ([91]Fig. 5A). A notable enrichment of 5,766
   metabolites was found in HDFI samples ([92]Fig. 5B), with a significant
   proportion being fatty acyls and carboxylic acids and derivatives
   ([93]Fig. 5C). Further pathway analysis revealed that upregulated
   metabolites were significantly enriched in three KEGG pathways
   ([94]Fig. 5D), whereas downregulated metabolites were associated with
   glutamatergic synapse, GABAergic synapse, and sphingolipid signaling
   pathways ([95]Fig. 5E).

Fig 5.

   [96]Fig 5
   [97]Open in a new tab

   Metabolite composition and KEGG analysis. (A) Compositional patterns of
   metabolites in HDFI and HDFI groups. (B) Volcano plot showing
   differential metabolites between HDFI and HDFI groups. (C) Pie chart
   showing the percentage of the types of differential metabolites. KEGG
   analysis of upregulated (D) and downregulated differential metabolites
   (E).

   Integrated analysis of metabolites and microbiota demonstrated
   significant correlations, particularly between Lactobacillus spp. and
   specific unsaturated fatty acids, and negative correlations between
   Corynebacterium spp. and metabolites, including testolic acid,
   succinyladenosine, and 16-phenoxy tetranor prostaglandin E2, indicating
   a complex interaction between seminal microbiota and metabolite
   composition affecting sperm DFI ([98]Fig. 6).

Fig 6.

   [99]Fig 6
   [100]Open in a new tab

   The co-occurrence network of differential metabolites and microbiota.

DISCUSSION

   Dysbiosis of the microbiome is closely related to semen quality.
   However, there was a paucity of research on its relationship with sperm
   DFI. Previous studies, such as the one conducted by Sergio et al., have
   leveraged full-length 16S rRNA gene sequencing to delineate the
   microbial characteristics of semen from infertile idiopathic patients.
   However, these investigations predominantly focused on idiopathic
   infertility, leaving the specific impact of seminal microbiota on DFI
   largely unexplored ([101]13). Addressing this gap, our study expanded
   the patient cohort and integrated non-targeted metabolomics with 16S
   rRNA gene sequencing to elucidate the influence of seminal microbiota
   on sperm DFI, thereby revealing the detrimental impact of
   microbiota-derived metabolites on sperm DNA integrity.

   Our comparative analysis identified distinct microbial compositions
   between HDFI and LDFI groups, with the HDFI group showing a prevalence
   of Actinomycetaceae and Acinetobacter spp., aligning with prior
   findings ([102]13). Notably, our redundancy analysis pinpointed a
   correlation between Lactobacillus species levels and DFI, particularly
   highlighting variations in Lactobacillus species distribution between
   groups. The predominance of L. iners in the HDFI group, a species
   traditionally associated with the vaginal microbiome and indicative of
   dysbiosis due to its link with pro-inflammatory factors ([103]24) and
   its ability to directly induce DNA damage in cancer cells ([104]25),
   suggests a novel microbial marker for semen quality and DFI. DNA damage
   leads to an increase in intracellular phosphorylation of serine
   ([105]26). Lactobacillus iners was negatively correlated with
   1-(2-methoxy-6Z-heptadecenyl)-sn-glycero-3-phosphoserine, a compound
   that may be a product of DNA damage. Additionally, Lactobacillus spp.
   were negatively associated with Corynebacterium spp., and L. iners, a
   species of Lactobacillus that lacks genes for the metabolism of amino
   acids and carbohydrates ([106]27), may stimulate the growth of
   Corynebacterium spp. by producing unsaturated fatty acids
   ([107]28[108]–[109]30), leading to a reduction in metabolites conducive
   to spermatogenesis.

   With further clustering of the differentially distributed microbiota
   into KEGG Orthology prediction results in the HDFI group, we found a
   significantly higher activation level of the acetyl-CoA fermentation to
   butanoate II pathway compared with the LDFI group. The acetyl-CoA
   fermentation to butanoate II pathway involves a series of
   enzyme-catalyzed reactions that convert acetyl-CoA to butanoate
   ([110]31). Butanoate salts are a class of compounds known as histone
   deacetylase inhibitors (HDIs). Histone deacetylases (HDACs) remove
   acetyl groups from lysine residues, resulting in condensed and
   transcriptionally silenced chromatin. HDIs block this action,
   potentially leading to hyperacetylation of histones ([111]32), which
   may affect the conversion of sperm DNA histones to protamines,
   resulting in increased sperm DNA fragmentation. Furthermore, we
   observed an elevated level of purine nucleobase degradation I pathway
   in the HDFI group. The activity of the purine metabolic pathway
   correlates with DNA damage. Disruption in purine metabolism can lead to
   erroneous encoding of DNA ([112]33), which may also be a potential
   cause of increased sperm DFI.

   Our correlation analyses underscored Acinetobacter spp.’s close
   association with DFI levels, and we identified its correlation with
   antioxidant metabolites through correlation analysis. Acinetobacter sp.
   is an opportunistic pathogen, and it can produce DNA-damaging
   metabolites, such as angucyclines, anthracyclines, bleomycins,
   enediynes, mitomycins, and quinoxalines. These molecules are combined
   with DNA and cause either DNA distortion, alkylation, crosslinking,
   and/or oxidative damage ([113]34). Acinetobacter spp. and other
   bacteria, including Serratia spp., Pseudomonas spp., and
   Stenotrophomonas spp., were identified in the genitourinary system and
   were related to reduction in sperm motility, lack in DNA integrity,
   destruction of mitochondrial functions and shape abnormalities of the
   sperms ([114]35). El-Gendy and his colleagues performed subcutaneous
   injection of extracts obtained from Acinetobacter spp. to albino rats
   and found that sperm deformities percentage was statistically
   significantly increased ([115]36). Therefore, we speculate that, in
   human semen, Acinetobacter spp. may deplete antioxidant metabolites and
   restrain the antioxidant capacity of sperm.

   We recognize certain limitations in our study. For instance, our
   conclusions were based on 16S rRNA sequencing, which can only detect
   microbial communities statistically significant and not other
   organisms, such as fungi and viruses, and we did not conduct further
   experimental validation. Traditional microbial culture methods serve as
   the gold standard for clinical detection of microbiota and can also
   provide further analysis of drug resistance. However, some microbiota
   may not grow in common culture media or may have low abundance, making
   it difficult to validate the microbiota identified by next-generation
   sequencing using traditional microbial culture methods ([116]37).
   Overall, our findings uncover a microbial perspective underlying sperm
   DNA integrity.

Conclusions

   This study not only provides a comprehensive overview of the metabolic
   interplay between microbiota and host in HDFI patient semen but also
   prompts further investigation into the microbiome’s role in semen
   quality. By combining non-targeted metabolomics and 16S rRNA gene
   sequencing, we offer insights into seminal microbiota’s potential
   adverse effects on sperm DNA, whether through antioxidant depletion or
   DNA-damage metabolite production. Future research is warranted to
   validate these findings and explore therapeutic interventions aimed at
   modulating the seminal microbiome to improve sperm quality and
   fertility outcomes.

ACKNOWLEDGMENTS