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Background Comparative and comprehensive omics studies have recently been conducted to provide a com‑ prehensive understanding of the biological mechanisms underlying infertility. However, because these huge omics datasets often contain irrelevant information, editing strategies for summarizing and filtering the data are neces‑ sary prerequisite steps for identifying biomarkers of male fertility. Here, we attempted to integrate omics data from spermatozoa with normal and below‑normal fertility from boars and bulls, including transcriptomic, proteomic, and metabolomic data. Pathway enrichment analysis was conducted and visualized using g:Profiler, Cytoscape, Enrich‑ mentMap, and AutoAnnotation to determine fertility‑related biological functions according to species. Results In particular, gamete production and protein biogenesis‑associated pathways were enriched in bull sper ‑ matozoa with below‑normal fertility, whereas mitochondrial‑associated metabolic pathways were enriched in boar spermatozoa with normal fertility. These results indicate that below‑normal fertility may be determined by aberrant regulation of protein synthesis during spermatogenesis, and the modulation of reactive oxygen species generation to maintain capacitation and the acrosome reaction governs boar sperm fertility. Conclusion Overall, this approach demonstrated that distinct molecular pathways drive sperm fertility in mammals in a species‑ dependent manner. Moreover, we anticipate that searching for species‑specific signaling pathways may aid in the discovery of fertility‑related biomarkers within large omics datasets. Keywords Integrated signaling pathways, Male fertility, Metabolomics, Proteomics, Spermatozoa transcriptomics male infertility may contribute to male fertility monitor- Background ing, diagnosis, and therapy. Male infertility is linked to complicated physiological Comprehensive and comparative omics studies have and biochemical mechanisms, and basic semen analysis recently been conducted in animals and humans to pro- is insufficient for fully deciphering the underlying causes vide a better understanding of the complex multifactorial . Elucidating the molecular mechanisms involved in processes linked to male fertility at the gene [2–5], tran- script [6–8], protein [9–14], and metabolite levels [6, 15– 18]. The development of new molecular methodologies to determine male fertility potential or sperm dysfunc- Yoo‑ Jin Park and Won‑Ki Pang contributed equally to this work. tion has been facilitated by the accumulation of massive *Correspondence: Myung‑Geol Pang fertility-related omics datasets that serve as promising firstname.lastname@example.org resources for identifying biomarkers of male fertility [10, Department of Animal Science & Technology and BET Research Institute, 19]. However, the large scale and unbiased nature of these Chung‑Ang University, Anseong, Gyeonggi‑do, 17546, Republic of Korea studies means that they inevitably include data irrelevant © The Author(s) 2023. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Park et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 2 of 14 to fertility status; for example, most identified genes are Differentially expressed metabolites in spermatozoa with not correlated with fertility. Hence, filtering of redundant normal or below-normal fertility were integrated accord- or irrelevant data through rigorous testing and summa- ing to species, and genes with inconsistent results among rization of relevant data are required for field use . the studies were removed. To this end, we aimed to consolidate comparative omics data including transcriptomic, proteomic, and metabo- Pathway enrichment analysis and visualization lomic from boar and bull spermatozoa with normal and Pathway enrichment analysis was performed by follow- below-normal fertility. We also built and visualized new ing a step-by-step procedure to facilitate the interpreta- pathway results as enrichment maps based on the sum- tion of transcriptome and proteome data as described by marized data to provide additional information on func- Reimand et al.  with some modifications. All tran - tionally associated genes, rather than focusing only on script and protein lists of interest were investigated using identification of valuable markers. Because bovine and the pathway enrichment analysis with g:Profiler, which porcine spermatozoa have digital fertility data following searches a collection of gene sets representing Gene artificial insemination, which reflect a wide spectrum of Ontology (GO) terms, pathways, and networks. The field fertility level, we attempted to collect the omics data AutoAnnotate Cytoscape tool was used to automatically from these species for this work [10, 20]. annotate pathways based on the GO, Kyoto Encyclope- dia of Genes and Genomes (KEGG), and Reactome data- Materials and methods bases to obtain similar groupings representing significant Data collection from transcriptomic studies biological themes (Version 3.8.2). The PubMed database was searched for comprehensive A list of differentially expressed metabolites between and comparative research articles using the terms “tran- normal and below-normal fertility bull spermatozoa was scriptomic”, “boar spermatozoa”, “bovine spermatozoa”, compiled, and enriched pathways were examined using and “fertility”. Further relevant studies were identified by Metabolite Set Enrichment Analysis from MetaboAna- searching the reference lists of the cited articles. Differ - lyst 4.0 (www. metab oanal yst. ca). entially expressed transcripts in spermatozoa with nor- mal or below-normal fertility were integrated according Sample preparation to species, and genes with inconsistent results among the Frozen semen samples from ten bulls (Hanwoo, Korean studies were removed (Table 1). native cattle) that represented a wide range of filed fertil - ity levels were obtained from the Hanwoo Improvement Data collection from proteomic studies Program of the National Agriculture Cooperative Federa- The PubMed database was searched for comprehensive tion (NACF) of Korea. According to our previous study and comparative research articles using the terms “prot- , the 60 d non-return rate (NRR) was used as the indi- eomic”, “porcine spermatozoa”, “bovine spermatozoa”, and cator of bull fertility, and the ten bulls were divided into “fertility”. According to species, differentially expressed two groups; a normal fertility group (80.49 ± 2.92, n = 5) proteins in spermatozoa with normal or below-normal and a below-normal fertility (57.00 ± 2.73, n = 5). NRR fertility were integrated, and genes having contradictory was calculated from recent 3-year of historical data had results across studies were eliminated. at least 100 breeding with at least ten herds. To evaluate the fertility-related proteomic enrichment Liquid semen samples from six boars (Yorkshire) with pathways, four comparative and comprehensive prot- known litter size were obtained from Grand-Grand Par- eomic studies in spermatozoa between normal (high or ents farm (Sunjin Co., Danyang Korea). Based on the fertile) and below-normal (low or infertile) fertility bulls average litter size, ten boar semen samples were divided were considered for evaluation [10, 28–30]. In these into normal (15.26 ± 0.08, n = 3) and below-normal fertil- studies, bull spermatozoa represented a wide range of ity (13.34 ± 0.23, n = 3). Average litter size was calculated field fertility levels (60% ~ 90% of non-return rate) and at based on the records of at least 100 sows by artificial least 16%  to 34% of differences between normal and insemination. below-normal fertility were detected . Western blot analysis Data collection from metabolomic studies Western blot analysis was conducted with the pooled The PubMed database was searched for comprehen - semen samples from normal and below-normal fertility sive and comparative research articles using the terms spermatozoa to minimize individual differences. Moreo - “metabolomic”, “porcine spermatozoa”, “bovine sperma- ver, the analysis was repeated at least three times with tozoa”, and “fertility”. Further relevant studies were identi- sperm samples from another set of semen batches from fied by searching the reference lists of the cited articles. each group. Based on the signaling pathway enrichment P ark et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 3 of 14 Table 1 Criteria for the inclusion of studies in this study and summary of the reviewed publications including samples, fertility score, the applied omics approaches, detailed methods, and statistic method Screening (Terms) Eligibility Included Fertility Detailed methods Statistic Reference Bovine spermatozoa Included (n = 4) The percentage devia‑ DNA microarray 2‑fold Feugang et al.  Fertility Excluded (n = 28) tion of its conception Transcriptomic ‑ Review paper (n = 3) rate from the average n = 32 ‑ Not sperm cells conception of all bulls ‑ Testicular cells (n = 3) Normal > 5.1% ‑ Epididymis (n = 2) Below‑normal < ‑ Embryo (n = 3) − 10.8% ‑ Not related to fertility Field conception rates Agilent microarray 1.5‑fold Saraf et al.  ‑ Motility (n = 3) Normal > 43% ‑ Cryostress (n = 1) Below‑normal < 25% ‑ Entire sperm (n = 6) Field conception rates Quantitative RT‑PCR Correlation Parthipan et al.  ‑ No transcriptomic Normal > 40% studies (n = 6) Below‑normal < 40% ‑ Different species (n = 1) Field conception rates RNA Seq 2‑fold Card et al.  Normal > 1.8 Below‑normal < − 0.4 Porcine spermatozoa Included (n = 6) The deviation of both DNA microarray Significant Alvarez‑Rodriguez et al. Fertility Excluded (n = 15) the farrowing rate (FR) (P < 0.05)  Transcriptomic ‑ Not sperm cells and litter size (LS) n = 21 ‑ Testicular cells (n = 2) Normal: FR > 0.45, ‑ Epididymis (n = 1) LS > 0.15 ‑ Seminal plasma (n = 1) Below‑normal: ‑ Microbiome (n = 1) FR > − 0.12, LS > − 0.18 ‑ Not related to fertility Litter size Quantitative RT‑PCR Correlation Pang et al. ‑ Motility (n = 2) Normal > 17  ‑ Entire sperm (n = 1) Below‑normal < 17 ‑ Quality (n = 2) Litter size Quantitative RT‑PCR Correlation Kim et al.  ‑ No transcriptomic Normal > 14.0 studies (n = 2) Below‑normal < 10.8 Different species (n = 3) The deviation of both RNA Seq Significant Alvarez‑Rodriguez et al. the farrowing rate (FR) (P < 0.05)  and litter size (LS) Normal: FR > 0.94, LS > 0.11 Below‑normal: FR > − 0.42, LS > − 0.14 The deviation of both DNA microarray Significant Alvarez‑Rodriguez et al. the farrowing rate (FR) (P < 0.05)  and litter size (LS) Normal: FR > 0.45, LS > 0.15 Below‑normal: FR > − 0.12, LS > − 0.18 Litter size Quantitative RT‑PCR Correlation Kang et al.  Normal > 13.6 Below‑normal < 11.2 Park et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 4 of 14 Table 1 (continued) Screening (Terms) Eligibility Included Fertility Detailed methods Statistic Reference Bovine spermatozoa Included (n = 4) 60‑ d non‑return rate 2‑Dimensional electro ‑ 2‑fold Park et al.  Fertility Excluded (n = 44) (NRR) phoresis (2‑DE) Proteomic ‑ Review paper (n = 6) Normal ≥ 70% n = 48 ‑ Not sperm cells Below‑normal < 70% ‑ Seminal plasma The percent deviation Multidimensional 2‑fold Peddinti et al.  (n = 10) of its conception from protein identification ‑ Testicular cells (n = 3) the average conception technology ‑ Etc (n = 2) of all bulls ‑ Not related to fertility Normal > 5.1% ‑ Functionality (n = 4) Below‑normal < ‑ Cryostress (n = 4) − 10.8% ‑ Entire sperm (n = 4) ‑ Heatstress (n = 2) Fertility solution iTRAQ and mass spec‑ > 1.2‑fold or < 0.8‑fold D’Amours et al.  ‑ Etc (n = 2) (SOL, Zero is average trometry ‑ No proteomic studies fertility) (n = 5) Normal > 2 ‑ Full text available/ Below‑normal < − 3.0 Limitation to access the protein information SOL 2‑DE Significant (P < 0.05) D’Amours et al.  (n = 2) Normal > 3.0 Below‑normal < − 5.0 Porcine spermatozoa Included (n = 4) Litter size 2‑DE 3‑fold Kwon et al.  Fertility Excluded (n = 31) Normal > 12 Proteomic ‑ Review paper (n = 8) Below‑normal < 10 n = 35 ‑ Not sperm cells Litter size 2‑DE 3‑fold Kwon et al.  ‑ Seminal plasma (n = 6) Normal > 12 ‑ Not related to fertility Below‑normal < 10 ‑ Maturation (n = 3) Litter size 2‑DE 4‑fold Kwon et al.  ‑ Functionality (n = 1) Normal > 11 ‑ Preservation (n = 5) Below‑normal < 6 ‑ Entire spermatozoa (n = 2) Litter size iTRAQ and strong 1.2‑fold Chen et al.  ‑ No proteomic studies Normal > 10.8 cation‑ exchange (n = 4) Below‑normal < 9.6 chromatography (SCX) ‑ ETC (n = 2) fractionation Bovine spermatozoa Included (n = 3) Conception rate LC‑MS/MS analysis Significant (P < 0.05) Saraf et al.  Fertility Excluded (n = 6) Normal > 43% Metabolomic ‑ Review paper (n = 1) Below‑normal < 25% n = 9 ‑ Not sperm cells In vitro fertilization rate LC‑MS/MS analysis 2‑fold Longobardi et al.  ‑ Seminal plasma (n = 2) The percent deviation GC‑MS Significant (P < 0.05) Menezes et al.  ‑ Crossbreed (n = 1) of its conception from ‑ Functionality (n = 1) the average conception ‑ Cryostress (n = 1) of all bulls Normal > 3.6% Below‑normal < − 3.8% analysis, we tried to determine whether the species- and transferred to polyvinylidene difluoride membranes specific or fertility-related proteins are differentially (Amersham Bioscience Corp., Amersham, UK). And then expressed between normal and below-normal fertility membranes were blocked with 5% skim milk, and incu- using Western blot analysis. Both bovine and porcine bated with primary antibodies against enolase 1 (ENO1), semen samples were subjected to discontinuous Percoll glucose transporter 3 (GLUT3), glutathione peroxidase 4 separation in accordance with our previous studies [10, (GPX4), cytochrome b-c1 complex subunit 1, mitochon- 13]. The sperm cells (1 × 10 cells) were lysates with Lae- drial (UQCRC1), UQCRC2, NADH:Ubiquinone oxidore- mmli sample buffer consisting 63 mmol/L Tris-Cl, 10% ductase core subunit S8 (NDUFS8), ATP synthase, H glycerol, 10% sodium dodecyl sulphate (SDS), 5% bromo- transporting, mitochondrial F0 complex (ATP5F), volt- phenol blue, and 5% mercaptoethanol. Total sperm cell age-dependent anion channel 2 (VDAC2), actin-related lysates were loaded into 12% SDS-polyacrylamide gel protein T2 (ACTRT2), cytochrome c oxidase polypeptide P ark et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 5 of 14 5, mitochondrial (COX5) A, COX5B, COX6B, parkin- below-normal fertility group (Fig. 1A , FDR < 0.05). More- sonism-associated deglycase (PARK7), cytochrome C over, ClueGO analysis revealed that the ‘spermatogenesis’ (CYC1), and α-tubulin. All antibodies were purchased signaling pathway was uniquely upregulated in bovine from Abcam (Abcam, Cambridge, MA, USA). Protein spermatozoa from normal fertility group, while two Bio- expression was visualized with Clarity Western ECL sub- logical Process GO terms, including aerobic respiration strate (Bio-Rad, CA, USA) and quantified using ImageJ and cytoplasmic translation was upregulated in bovine software (National Institutes of Health, Bethesda, MD, spermatozoa from below-normal fertility group (Fig. 1B). USA). Otherwise, the ‘synaptonemal structure complex’ asso- ciated Biological Process GO term was upregulated in Results boar spermatozoa from normal fertility group, while the Functional annotation and enrichment maps ‘intrinsic component membrane’ related enrichment of fertility‑related transcriptomes pathway was significantly enriched in the porcine sper - We summarized the significant differentially expressed matozoa from below-normal fertility group (Fig. 1A, fertility-related transcriptomic markers to construct FDR < 0.05). In ClueGO analysis of porcine spermato- comprehensive and novel functional enrichment maps. zoa, we did not detect any upregulated Biological Process Based on the previous transcriptomic studies, a total 470 GO terms in normal fertility group. However, only one transcripts were identified as a subset of transcripts that enrichment pathway, C-C chemokine receptor activity, are possibly relevant for bovine sperm fertility [8, 21–23, was identified in the below-normal fertility porcine sper - 33]. One hundred and forty-three transcripts were highly matozoa (Fig. 1C, FDR < 0.05). expressed in spermatozoa from normal fertility bulls, while 327 transcripts were abundant in spermatozoa Functional annotation and enrichment maps from below-normal fertility bulls (Table 1 and Additional of fertility‑related proteomes file 1: Table S1). A total 74 transcripts including 68 of Following the filter out of hypothetical or predicted pro - abundant in normal-fertility and 5 transcripts of highly teins, a total of 56 proteins, including 33 proteins highly expressed in below-normal fertility porcine spermato- expressed in normal fertility bull spermatozoa and 23 zoa was identified from 6 transcriptomic studies [24–27, proteins highly expressed in below-normal fertility bull 34] and used to gain a deep functional fertility-related spermatozoa, were taken into consideration for the path- insights (Table 1 and Additional file 1: Table S1). way enrichment analysis. Four comparative proteomic Pathway enrichment analyses of markers that were studies were considered for evaluation of fertility-related significantly differentially expressed between spermato - signaling pathways in boar spermatozoa [9, 13, 31, 35] zoa with normal and below-normal fertility from bulls (Table 1 and Additional file 1: Table S3). Litter size was (Additional file 1: Table S1) or boars (Additional file 1: used as an indicator of boar sperm fertility and the lit- Table S2) were conducted using g:Profiler and visual - ter size for the normal and below-normal fertility boar ized by EnrichmentMap and ClueGO application of spermatozoa were more than 10.8 and less than 10.19, Cytoscape. Pathways with similar biological functions respectively (Table 1 and Additional file 1: Table S4). were automatically defined and clustered on the enrich - For pathway enrichment analysis, 147 proteins were ment maps using the AutoAnnotate Cytoscape appli- accounted, including 98 proteins that are abundant in cation, and the significance of the enrichment of each boar spermatozoa with normal fertility and 49 proteins pathway was determined using the false discovery rate that are abundant in spermatozoa with below-normal (FDR; Fig. 1). Gene Ontology terms was visualized with fertility. EnrichmentMap of Cytoscape to identify the species- To analyze the GO enrichment pathways associated specific Biological Process according to their fertil - with fertility-related proteins, differentially expressed ity. In bovine, the ‘motile cilium sperm’ was clustered proteins between spermatozoa with normal and below- together in the bovine spermatozoa from normal fertility normal fertility from bulls (Additional file 1: Table S3) or group, while one enriched pathway, ‘electron transport boars (Additional file 1: Table S4) were used to explore complex’, was clustered only in the spermatozoa from the major Biological Process GO enrichment pathways (See figure on next page.) Fig. 1 Signaling pathways from transcripts differentially expressed between bull and boar spermatozoa with normal and below‑normal fertility. A Enrichment map created based on the differentially expressed transcripts in bull and boar spermatozoa with normal and below‑normal fertility. Blue and yellow, enrichment in bovine and porcine spermatozoa with normal fertility, respectively; green and pink, enrichment in bovine spermatozoa with below‑normal fertility. Biological Process GO terms based on the differentially expressed transcripts in (B) bovine and (C) porcine spermatozoa with normal and below‑normal fertility. Blue, enrichment in spermatozoa with normal fertility; pink, enrichment in spermatozoa with below‑normal fertility. Enrichment maps were created using g:Profiler, Cytoscape, EnrichmentMap, and ClueGO with FDR Q < 0.05 Park et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 6 of 14 Electron transport complex Synaptonemal structure complex Motile cilium sperm Intrinsic component membrane Highly expressed transcriptomes Highly expressed proteins in normal fertile bovine in normal fertile boar Highly expressed transcriptomes Highly expressed proteins in below-normal fertile bovine in below-normal fertile boar B Motile cillium assembly % Genes cluster Overlapped 50% Normal Fertility 100% 50% Below-normal Fertility 100% Aerobic respiration P-value Spermatogenesis 0.10.001 Cytoplasmic translation 50% Below-normal Fertility 100% C-C chemokine receptor activity Fig. 1 (See legend on previous page.) P ark et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 7 of 14 using g:Profiler. The pathways were visualized and anno - proteins were used as the spermatogenesis markers [36– tated using Enrichment Map and ClueGO based on the 38] which was uniquely clustered in fertility related tran- GO, KEGG, and Reactome databases. scriptomes from bovine spermatozoa (Fig. 1B). Although As shown in Fig. 2A, the ‘motile cilium’ associated ENO1 protein level was upregulated in bovine spermato- enriched pathway was upregulated in normal fertility zoa from normal fertility group, there was no significant bovine spermatozoa, while ‘chaperonin complex chap- difference in GPX4 level of bovine spermatozoa between erone’ was upregulated in below-normal fertility group. normal and below-normal group (Fig. 3A). The ‘mitochondrial proton transport’ enrichment path - In porcine spermatozoa from normal fertility group, way was clustered together in bovine and porcine sper- four Biological Process GO terms, including ‘phenol- matozoa regardless fertility. However, we did not detect containing compound metabolic process’, ‘TCA cycle’, any specific enrichment pathway in boar spermatozoa ‘generation of precursor metabolites and energy’, and (Fig. 2A, FDR < 0.05). Among the varied ‘mitochondrial ‘regulation of mitochondrial outer membrane permea- proton transport’ related enrichment pathways, four bilization involved in apoptotic signaling pathway’ were enriched clusters, including ‘acid oxoacid metabolic’, identified by ClueGO analysis (Fig. 2C). However, no ‘carbohydrate derivative’, ‘electron transport chain’, and enrichment pathway was detected in below-normal fer- ‘lactate metabolic process’ were upregulated in por- tility group. Based on the enrichment pathways, the cine spermatozoa from normal fertility group (Fig. 2B, level of nine mitochondrial related proteins, including FDR < 0.05). Otherwise, only one enrichment pathway NDUFS8, UQCRC1, COX5B, COX5A, ATP5F, PARK7, ‘phospholipase A2 activity’ was clustered in porcine GPX4, COX6B, and CYC1, were compared between spermatozoa from below-normal fertility group (Fig. 2B, normal and below-normal fertility group. Only three FDR < 0.05). proteins showed that different level according to fertil - ity; one protein, NDUFS8, was upregulated in normal Differential fertility‑related protein level in bovine fertility group, while two proteins, including UQCRC1 and porcine spermatozoa and COX5B, were upregulated in below-normal fertility As shown in Fig. 2, the ‘mitochondrial proton transport’ group (Fig. 3B, P < 0.05). was annotated together in the bovine and porcine sper- matozoa regardless their fertility. Therefore, we tried Functional annotation and enrichment maps to analyze the different mitochondrial protein level in of fertility‑related metabolomes spermatozoa between normal and below-normal fertil- A total of 32 metabolites including 17 metabolomes ity group. The ‘inner mitochondrial membrane protein in highly expressed in normal fertility and 15 metabo- complex’ was highly represented in proteins, including lomes in highly expressed in below-normal fertility was NDUFs, ATP synthase subunits, and UQCRC2, differen - identified from 3 comparative metabolomic studies [6, tially expressed in bovine spermatozoa according to fer- 17, 18] and used for better understanding of fertility- tility (Fig. 2B, FDR < 0.05). Similarly, the NDUFS8 protein related functional insights (Table 1 and Additional file 1: level was upregulated in bovine spermatozoa from nor- Table S5). mal fertility group, while UQCRC2 and ATP5F protein We compiled a list of the metabolomes that were dif- level were upregulated in spermatozoa from below-nor- ferentially expressed between bull spermatozoa with mal fertility group (Fig. 3A, P < 0.05). Moreover, sperm normal and below-normal fertility (Additional file 1: flagellum associated with enrichment pathways, includ - Table S5) and used MetaboAnalyst 4.0 to examine the ing sperm flagellum and myelin sheath, were most highly enriched pathways (Fig. 3). Pyruvate metabolism and gly- enriched in fertility-related proteins in bovine sperma- colysis-associated metabolites were highly upregulated tozoa (Fig. 2B, FDR < 0.05). VDAC2, which is a sperm in bull spermatozoa with normal fertility, whereas fatty flagellum and myelin sheath marker, was upregulated in acid metabolism and oxidation-related metabolites were bovine spermatozoa from below-normal fertility group upregulated in bull spermatozoa with below-normal fer- (Fig. 3A, P < 0.05). Also, VDAC2, ENO1, and GPX4 tility (Fig. 3). (See figure on next page.) Fig. 2 Important signaling pathway‑related to proteins differentially expressed between bull or boar spermatozoa with normal and below‑normal fertility. A Integration of an enrichment map of differentially expressed proteins in spermatozoa from bulls and boars with normal and below‑normal fertility. Blue, enrichment in bull spermatozoa with normal fertility; green, enrichment in bull spermatozoa with below‑normal fertility; yellow, enrichment in boar spermatozoa with normal fertility; pink, enrichment in boar spermatozoa with below‑normal fertility. B Biological Process GO terms based on the differentially expressed proteins in (B) bovine and (C) porcine spermatozoa with normal and below‑normal fertility. Blue, enrichment in spermatozoa with normal fertility; pink, enrichment in spermatozoa with below‑normal fertility. The enrichment map was created using g:Profiler, Cytoscape, and EnrichmentMap with FDR Q < 0.01 Park et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 8 of 14 Mitochondrial proton transport Motile cilium Chaperonin complex Chaperone Highly expressed in normal fertile bovine Highly expressed in below-normal fertile bovine P-value Highly expressed in normal fertile boar Highly expressed in below-normal fertile boar 0.1 0.001 ROPN1 ALB VDAC2 ATP5F1 Protein folding chaperon HSP90AA1 Sperm flagellum and motility NDUFA8 PHB ANXA2 NDUFB10 Inner mitochondrial TEKT1 ATP5O membrane protein AK1 % Genes cluster complex Overlapped 50% Normal fertility ATP5FJ 100% ATP5FI 50% Below-normal 100% fertility P-value 0.1 0.001 CTSB HPRT1 Phenol-containing compound TCA cycle metabolic process MICOS13 PARK7 SLC25A6 PGK1 LOC100524873 PGAM2 FDX1 MDH2 FH ATP5IF1 HADH COX5B CYCS Regulation of mitochondrial ACAA2 outer membrane ATP5ME ETFB DLST permeabilization invovled in HADHA apoptotic signaling pathway ATP5MF Generation of precursor PKM TPI1 metabolites and energy ATP5PO % Genes cluster Overlapped P-value 50% Normal fertility 100% 50% Below-normal 0.1 0.001 fertility 100% Fig. 2 (See legend on previous page.) P ark et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 9 of 14 Fig. 3 Enrichment signaling pathway related protein expression in the spermatozoa. Different expression patterns of proteins annotated with enrichment signaling pathways in (A) bovine and (B) porcine spermatozoa between normal and below‑normal fertility group were analyzed by western blot. Data represent the mean of three experiments ± standard error of the mean (SEM) Discussion 11, 15–18]. Although these huge fertility-related omics In depth omics studies have recently been carried out datasets can offer prospective sources for discover - in human and animal spermatozoa to provide a better ing male fertility biomarkers which contribute to assess understanding of male fertility at the genes [2–5], tran- male fertility potential or sperm dysfunctions, these script [6–8], protein [9–14], and metabolite levels [6, studies invariably contain information that is unrelated Park et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 10 of 14 to male fertility status. Therefore, it is necessary to filter to a comprehensive understanding of fertility-related out redundant or irrelevant data through rigorous test- mechanisms at the gene, transcript, protein, and metabo- ing and to summarize pertinent data . Therefore, lite levels (Fig. 4). this study was to accumulate the fertility-related tran- Although spermatozoa are transcriptionally and trans- scriptomic, proteomic, and metabolomic data from boar lationally dormant cells after leaving the testis, residual and bull spermatozoa. Moreover, rather than focusing RNA produced in the early stages of spermatogenesis only on the identification of valuable markers associated stays in spermatozoa and can be transmitted to oocytes with male fertility, enrichment maps based on the con- to deliver the paternal epigenetic information [40, 41]. solidated omics data were analyzed to provide additional Various types of RNAs, such as messenger RNA, riboso- information on functionally linked genes and contribute mal RNA, mitochondrial RNA, long non-coding RNA, Fig. 4 Metabolite set enrichment analysis of metabolites differentially expressed between bull spermatozoa with normal and below‑normal fertility. Functional enrichment analysis of pathways from bull spermatozoa with normal (A) and below‑normal (B) fertility. MetaboAnalyst 4.0 was used to examine and interpret the enrichment pathways. The color depth and column length indicate the degree of disturbance P ark et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 11 of 14 small non-coding RNA, and transfer RNA, contribute hypothesize that the regulation of ATP and ROS levels to sperm morphological and functional parameters, through the well-orchestrated respiratory pathways in embryonic development, and pregnancy outcomes, as spermatozoa may be important for maintaining sperm determined using recently developed transcriptomic fertility in mammalian spermatozoa. analyses [7, 24, 42, 43]. Moreover, comparative whole- Moreover, our integrated omics studies represented transcriptome sequencing analysis of spermatozoa with that sperm flagellum and motility associated enrichment normal and below-normal fertility has provided novel pathways were positively related to bovine fertility. Fol- molecular markers for evaluating the fertilizing poten- lowing glycolysis and the TCA cycle, OXPHOS occurs in tial of spermatozoa [7, 8, 21, 23, 24, 33]. Indeed, their the mitochondrial-rich midpiece of spermatozoa, gen- lack of transcription and translation coupled with their erating large amounts of ATP and reactive oxygen spe- abundant, highly specialized, compartmentalized nature cies (ROS). ATP and ROS are both required for boosting makes mature spermatozoa a useful model for prot- the hyperactivation of spermatozoa during capture and eomic analysis of fertility [1, 44]. On the basis of these fertilization; ROS stimulate adenylyl cyclase to catalyze studies, we investigated the significant fertility-related the synthesis of cyclic AMP from ATP, leading to pro- transcriptomic and proteomic markers to establish com- tein kinase A activation, which stimulates the phospho- prehensive and novel functional enrichment maps. Our rylation of tyrosine residues in the fibrous sheath of the integrated transcriptomic and proteomic data showed sperm flagellum, resulting in hyperactivation of sperma - that mitochondrial-associated biological processes are tozoa [48, 49]. Moreover, ROS regulate calcium efflux closely related to sperm fertility, regardless of species. and membrane fluidity to facilitate the sperm–oocyte Among the various mitochondrial-associated signaling fusion and fertilization [50–52]. Therefore, we postulate pathways, the inner mitochondrial membrane protein that fine-tuning ROS and ATP synthesis during sperm complex related to proton transport-associated enrich- capacitation and fertilization may control the fertility of ment pathways was positively related to bovine fertility. bull spermatozoa. Moreover, the TCA cycle, generation of energy metabo- It is noteworthy that spermatogenesis and structure- lites, and mitochondrial function related to apoptotic associated signaling pathways, such as chaperonin com- signaling pathways were positively associated with boar plex associated- and protein folding-related signaling sperm fertility. Respiratory pathways, including glycoly- pathways, were specifically identified in bovine sperma - sis, the TCA cycle, and mitochondrial electron transport tozoa, and negatively related to bovine fertility. The endo - chain, are essential for energy provision in spermatozoa plasmic reticulum is an essential cellular component that to maintain their viability, motility, and fertilizing ability plays a vital role in the folding and assembly of newly syn- . Although glycolysis in the principal piece of sper- thesized proteins in mammalian cells. However, because matozoa is one of the main sources of ATP for support- the endoplasmic reticulum is eliminated during sper- ing motility, the massive ATP production required for matogenesis, a highly active protein synthesis and folding regulating sperm motility, capacitation, and chromatin event occurs before the completion of spermatogenesis integrity is produced via OXPHOS in the mitochondria . Thus, we suggest that the upregulation of protein . Activation of OXPHOS through sequential electron folding pathways in bull spermatozoa with below-normal transfer in the inner mitochondrial membrane facilitates fertility may reflect the aberrant protein folding during ATP production, and ROS are necessary for capacitation spermatogenesis, which ultimately determines low fer- and fertilization . TCA- and OXPHOS-connected tility in bull spermatozoa. This hypothesis is in line with signaling pathways were enriched in both bull and boar our previous research, which showed that male fertility spermatozoa with normal fertility, corroborating with factors, such as sperm motility, are determined in the tes- the results of our previous study, which showed that tes or epididymis before spermatozoa have fully matured TCA cycle signaling pathway-related proteins were sub- . stantially expressed in spermatozoa from fertile males The female reproductive tract contains many compared to those from infertile males . However, chemokines that trigger chemotaxis to control sperm excessive ROS production can cause lipid peroxidation motility and capacitation to optimize contact with oocytes and DNA damage in spermatozoa, leading to infertility. [55, 56]. Several chemokine receptors, including CCR5 and ROS also reduce the intracellular ATP content, which CCR6 were identified in the spermatozoa, which induce leads to a decrease in flagellar beat frequency, result - chemoattraction before fertilization [57, 58]. Especially, ing in sperm motility loss [46, 47]., Additionally, sper- sperm chemotaxis is regulated by calcium efflux through matozoa with downregulated respiratory pathways may the plasma membrane calcium ATPase, which works exhibit a reduced capacity for preventing damage caused as the sperm-activating and attracting factor . An by ROS, resulting in a decrease in fertility. Therefore, we increase chemotaxis in spermatozoa through the increase Park et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 12 of 14 of the intracellular calcium level accelerated the hyperac- structure, function, and permeability . However, tivation, capacitation, and the acrosome reaction [59, 60]. because moderate ROS levels are required for sperm Although the role of chemotaxis reaction in spermato- capacitation, the acrosome reaction, and fertilization, zoa is proposed as the essential process for capacitation we hypothesize that upregulation of fatty acid levels and fertilization, the chemokine receptor activity-related in the sperm membrane may impair these processes, enrichment pathway is negatively related to sperm fertil- resulting in a loss of fertility. ity. Collectively, these findings led us to hypothesize that upregulation of chemokine receptor activity in boar sper- Conclusions matozoa might elevate the chemotaxis behavior, resulting This study was to accumulate fertility-related omics in premature capacitation and infertility. data from boar and bull spermatozoa to provide com- The dynamic relationships between genes, tran - prehensive fertility-related mechanisms from gene scripts, proteins, and metabolites allow biological sys- to protein levels. Mitochondrial-associated signaling tems to function as a cohesive unit. Metabolites play an pathways were commonly identified as the significant essential role in the biochemical environment because fertility-related signaling pathways in both species; we they serve as the primary components of all other bio- also found species-specific pathways. Our analyses indi - chemical structures, such as proteins, genes, and tran- cate the proper synthesis and folding of proteins during scripts . Metabolomics is a key scientific field in the spermatogenesis may determine the bull sperm fertility. post-genomics era that investigates small molecules to In contrast, the modulation of chemotaxis activity may complement genomic, proteomic, and transcriptom- determine boar sperm fertility to maintain the optimal ics and aids in the identification of novel disease bio - capacitation state and the acrosome reaction. Although markers and therapeutic strategies . Metabolomic the further study is required for confirmation of different analysis has also been used in the field of male infer - gene expression by western blot and RT-PCR between tility research to identify fertility-related metabolomic normal and below-normal spermatozoa to identify markers in spermatozoa or seminal plasma [63, 64]. specific species-dependent mechanisms, this study Although most metabolomic investigations have been provide preliminary information about different molec - conducted in humans, we were able to find three com - ular pathways that govern sperm fertility in mammals prehensive and comparative metabolomic studies on depending on the species. Therefore, we propose that bull spermatozoa [6, 17, 18]. By reanalyzing these data- screening for species-specific signaling pathways may sets, we found that pyruvate metabolism and glycoly- help identify fertility-related biomarkers within massive sis-associated metabolites were highly upregulated in omics data. Also, this study may be useful for address- bull spermatozoa with normal fertility, whereas fatty ing species-specific fertility control systems to improve acid metabolism and oxidation-related metabolites animal productivity. Furthermore, employing such eval- were upregulated in bull spermatozoa with below- uation processes may contribute to the elucidation of normal fertility. During spermatogenesis, energy pro- yet unknown characteristics of spermatozoa, which may duction systems shift from glycolysis to OXPHOS contribute to enhancing reproduction. depending on the ATP requirement of the cell . Although OXPHOS is a more efficient pathway for ATP Abbreviations production than glycolysis, glycolysis is also required ACTRT2 Actin‑related protein T2 for maintaining sperm motility in the principal piece. ATP5F ATP synthase, H transporting, mitochondrial F0 complex COX5 Cytochrome c oxidase polypeptide 5, mitochondrial Thus, we anticipated that spermatozoa with downregu - CYC1 Cytochrome C lated glycolysis would be unable to generate sufficient ENO1 Enolase 1 ATP to maintain sperm motility, resulting in below- FDR False discovery rate GLUT3 Glucose transporter 3 normal fertility. Following epididymal maturation, the GO Gene Ontology proportion of polyunsaturated fatty acids (PUFAs) in GPX4 Glutathione peroxidase 4 the sperm plasma membrane is increased to enhance KEGG Kyoto Encyclopedia of Genes and Genomes mGlu receptor Metabotropic glutamate receptor membrane integrity and fusion ability for fertilization; NDUFS8 NADH:Ubiquinone oxidoreductase core subunit S8 however, this also enhances the sensitivity of spermato- OXPHOS Oxidative phosphorylation zoa to oxidative stress [66, 67]. When ROS production PARK7 Parkinsonism‑associated deglycase PUFAs Polyunsaturated fatty acids exceeds the capacity of the antioxidant defense system, ROS Reactive oxygen species PUFA-containing spermatozoa undergo lipid peroxida- TCA Tricarboxylic acid tion. Thus, PUFAs protect cells from damage caused by UQCRC1 Cytochrome b‑ c1 complex subunit 1 VDAC2 Voltage‑ dependent anion channel 2 high levels of ROS, which cause changes in membrane P ark et al. Journal of Animal Science and Biotechnology (2023) 14:28 Page 13 of 14 8. Card CJ, Krieger KE, Kaproth M, Sartini BL. Oligo‑ dt selected spermatozoal Supplementary Information transcript profiles differ among higher and lower fertility dairy sires. Anim The online version contains supplementary material available at https:// doi. Reprod Sci. 2017;177:105–23. org/ 10. 1186/ s40104‑ 023‑ 00836‑1. 9. Kwon WS, Oh SA, Kim YJ, Rahman MS, Park YJ, Pang MG. Proteomic approaches for profiling negative fertility markers in inferior boar sperma‑ Additional file 1: Table S1. Summary of differentially expressed tozoa. Sci Rep. 2015;5:13821. transcripts in bull spermatozoa with normal and below‑normal fertil‑ 10. Park YJ, Kwon WS, Oh SA, Pang MG. Fertility‑related proteomic ity. Table S2. Summary of differentially expressed transcripts in boar profiling bull spermatozoa separated by percoll. J Proteome Res. spermatozoa with normal and below‑normal fertility. Table S3. Summary 2012;11(8):4162–8. of differentially expressed proteins in bull spermatozoa with normal and 11. Martinez‑Heredia J, de Mateo S, Vidal‑ Taboada JM, Ballesca JL, Oliva R. below‑normal fertility. Table S4. Summary of differentially expressed Identification of proteomic differences in asthenozoospermic sperm proteins in boar spermatozoa with normal and below‑normal fertility. samples. Hum Reprod. 2008;23(4):783–91. Table S5. Summary of differentially expressed metabolites in bovine 12. Moscatelli N, Lunetti P, Braccia C, Armirotti A, Pisanello F, De Vittorio M, spermatozoa with normal and below‑normal fertility. et al. Comparative proteomic analysis of proteins involved in bioener‑ getics pathways associated with human sperm motility. Int J Mol Sci. 2019;20(12):3000. Authors’ contributions 13. Kwon WS, Rahman MS, Lee JS, Yoon SJ, Park YJ, Pang MG. Discovery of YJP and MGP conceptualized; Conceptualization: YJP and MGP; Writing: predictive biomarkers for litter size in boar spermatozoa. Mol Cell Prot‑ YJP and WKP; Data collection: WKP; Review and Revision: YJP and MGP. The eomics. 2015;14(5):1230–40. author(s) read and approved the final manuscript. 14. Kwon WS, Rahman MS, Lee JS, You YA, Pang MG. Improving litter size by boar spermatozoa: application of combined h33258/ctc staining in field Funding trial with artificial insemination. Andrology. 2015;3(3):552–7. 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Journal of Animal Science and Biotechnology – Springer Journals
Published: Mar 2, 2023
Keywords: Integrated signaling pathways; Male fertility; Metabolomics; Proteomics; Spermatozoa transcriptomics
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