Differential protein expression profiles in human sperm from teratozoospermic and normozoospermic men identify LTBP1 and TGF-βR1 as potential biomarkers within the TGF-β signalling pathway

Paweena Kaewman; Sutisa Nudmamud-Thanoi; Patcharada Amatyakul; Chuchard Punsawad; Sittiruk Roytrakul; Samur Thanoi

doi:10.1371/journal.pone.0342465

. 2026 Feb 13;21(2):e0342465. doi: 10.1371/journal.pone.0342465

Differential protein expression profiles in human sperm from teratozoospermic and normozoospermic men identify LTBP1 and TGF-βR1 as potential biomarkers within the TGF-β signalling pathway

Paweena Kaewman ^1,^2,^#, Sutisa Nudmamud-Thanoi ^1,^2,^*,^#, Patcharada Amatyakul ^3,⁴, Chuchard Punsawad ^5,⁶, Sittiruk Roytrakul ⁷, Samur Thanoi ^8,^*,^#

Editor: Suresh Yenugu⁹

PMCID: PMC12904376 PMID: 41686770

Abstract

Among cases of infertility, up to 50% are attributed to male infertility factors. Male infertility is associated with alterations in sperm proteins that are essential for normal sperm function. Alterations in proteins within the transforming growth factor-beta (TGF-β) signalling pathway are linked with several types of male infertility. However, the underlying mechanisms remain unclear. In this study, we proposed to use proteomic analysis to identify the proteomic profile in human sperm from normozoospermic (NOR) and teratozoospermic (TER) men. The results indicated 39 overlapping proteins associated with the TGF-β signalling pathway. Six proteins that were differentially expressed and had a log2 fold change of ≥ 1 or ≤ −1 were considered to be the differentially expressed proteins in human sperm between the groups. Among these proteins, the latent-transforming growth factor beta-binding protein 1 (LTBP1) was significantly increased in the TER group compared to the NOR group. Immunocytochemistry revealed that the protein expression of TGF-β receptor type 1 (TGF-βR1) was localised in the human sperm head. It was also significantly increased in the TER group. Validation analysis revealed that the mRNA expression levels of LTBP1 and TGFBR1 genes were significantly upregulated in the TER group relative to the NOR group. Interestingly, an increase in LTBP1 and TGF-βR1 protein expression was correlated with a decrease in the percentage of normal sperm morphology. Our findings, for the first time, demonstrate a significant association between the expression of LTBP1 and TGF-βR1 and abnormal sperm morphology, supporting their consideration as novel exploratory biomarkers for further investigation in infertility, especially in TER men.

Introduction

Until now, most of the infertile population (approximately 50–80 million persons worldwide) has been involved in the male infertility problem [1]. The World Health Organization (WHO) defines the failure to conceive after 12 months of regular, unprotected sexual intercourse as the characteristic of infertility [2]. The cause of most cases of male infertility is unknown, which is classified as idiopathic male infertility [3]. Abnormalities in semen parameters are associated with idiopathic male infertility. Because the major cause of male infertility is unknown, semen analysis is fundamentally used to investigate male infertility. Sperm concentration, motility, and morphology are the main sperm parameters that are used as key indicators in the assessment of male infertility. Men with normal values for these sperm parameters are defined as normozoospermic (NOR) men, whereas men with poor sperm quality are defined by several terms. Teratozoospermic (TER) men are defined as men revealing abnormal sperm morphology, which is one of several terms for men with poor sperm quality. Additionally, poor sperm morphology is a major cause of male infertility, contributing to over 90% of male infertility cases [4]. Infertility clinics frequently encounter men with TER conditions [5]. Considering the unknown causes of male infertility, numerous factors associated with sperm function and fertilisation have been extensively studied. Moreover, due to the lack of information regarding the therapeutic target proteins, there have been concerns about the study of sperm proteins from infertile men.

Proteomic analysis has been used to study the differences in the proteomic profile and identification of seminal plasma and sperm proteins from infertile men [6,7]. More evidence supports the idea that the changes in the protein profile of sperm can adversely affect fertility. Most studies focus on comparing the protein expression profiles of NOR men with those of infertile men exhibiting abnormal sperm parameters, such as asthenozoospermia (characterised by defective sperm motility) and oligoasthenozoospermia (characterised by defects in both sperm count and motility) [8–12]. Notably, a previous study has indicated the differentially expressed proteins (DEPs) in men with TER and oligoasthenoteratozoospermia (characterised by defects in sperm count, motility, and morphology) [13]. Although the sperm protein biomarkers have been widely studied, the entire scope of pathological changes is yet unknown. Several growth factors, such as insulin-like growth factor-1 (IGF1), epidermal growth factor (EGF), and transforming growth factor beta (TGF-β) in human sperm, have been associated with male infertility [14]. The TGF-β signalling pathway consists of many components associated with signal transduction, including TGF-β ligands (TGF-β1, TGF-β2, and TGF-β3), receptors (TGF-β receptor types 1 and 2), and mediators. The interaction of TGF-β ligands with their receptors results in the triggering of a complex intracellular transduction pathway. The TGF-β ligands have been identified in several organs and participate in cell homeostasis and development [15,16]. TGF-β1 and its receptor (TGF-βR1) are expressed in human Leydig cells, Sertoli cells, spermatogonia, spermatocytes, and sperm cells [17]. The components of the TGF-β signalling pathway play a role in the male reproductive system, especially in seminal plasma and sperm. TGF-β contained in the seminal plasma acts as an immune regulatory cytokine involved in embryonic development and implantation in the female reproductive tract [18]. Even though extensive research has been done on sperm protein biomarkers, the understanding of the proteomic alterations underlying male infertility remains limited, particularly in cases of TER. The potential involvement of the TGF-β signalling pathway in regulating sperm morphology has been hypothesised but not yet clearly elucidated.

Therefore, this study aimed to investigate the proteomic profile of human sperm from TER men, with a focus on identifying key proteins within the TGF-β signalling pathway that may contribute to abnormal sperm morphology.

Materials and methods

Semen samples and analysis

Forty-two volunteer patients who obtained semen analysis donated semen samples between December 2017 and September 2018 at the Naresuan Infertility Centre, Faculty of Medicine, Naresuan University, Thailand, for examination. The experimental protocols for this study were reviewed and approved by the Naresuan University Institutional Review Board (IRB No. P1-0012/2565; COE No. 015/2022) and the Human Research Ethics Committee of Walailak University (Approval Nos. WUEC-21-213-01 and WUEC-21-213-02). Written informed consent was provided by each volunteer. The researchers did not have access to identifiable information during or after data collection. Archived semen samples were accessed for proteomic and immunocytochemistry analyses on 15 March 2022 as part of the approved study protocol. The semen preparation and analysis, including sperm morphology, were studied, as previously reported by Kaewman et al. [19]. In this investigation, all semen samples showed normal in macroscopic examination, including appearance, liquefaction, and pH value [20]. They had a sperm concentration ≥ 15 x 10⁶ per ml. The NOR group, which consisted of 20 volunteer patients, had normal values for all parameters, including sperm concentration (≥ 15 x 10⁶ per ml), progressive motility (≥ 32%), and morphology (normal form ≥ 4%). The TER group included 22 volunteer patients who had normal sperm concentration and progressive motility but abnormal sperm morphology (normal form < 4%). For the proteomic, reverse transcription-quantitative polymerase chain reaction, and immunocytochemistry analyses, all semen samples were kept at −80°C.

Proteomic analysis

Proteomic analysis was conducted using liquid chromatography–tandem mass spectrometry (LC-MS/MS). The human sperm proteins were extracted from each individual semen sample (n = 20 NOR and n = 22 TER). A cold homogenising buffer was used to homogenise sperm samples. After centrifuging, the pellet was homogenised in lysis buffer. The Pierce BCA Protein Assay kit (Thermo Fisher Scientific) was used to quantify protein. Individuals extracted proteins from two groups of samples were examined by proteome analysis. Protein digestion was performed after reducing and alkylating, with incubation for an hour at 56°C and an hour at room temperature, respectively. Ammonium bicarbonate (10 mM) was used to dilute all solutions. In the in-solution digestion, proteins were then treated with 50 ng of trypsin overnight at 37°C. Before resuspending the eluted peptides in 0.1% formic acid, they were dried. The eluted peptides solution was centrifuged before being resuspended in the water used for liquid chromatography–mass spectrometry (LC-MS). Peptide evaluation was carried out using the Impact II UHR-TOF MS system (Bruker Daltonics Ltd.) in conjunction with the nanoLC system (Thermo Fisher Scientific). The protein quantification associated with the Homo sapiens database was carried out using the MaxQuant version 1.6.6.0 software and the Andromeda search engine [21]. Label-free quantitation with MaxQuant’s standard settings was applied. For the main search, we allowed for a maximum of two missed cleavages and a mass tolerance of 0.6 daltons. Trypsin and carbamidomethylation of cysteine were used as digestive enzymes and a fixed modification, respectively. Methionine oxidation and the acetylation of the protein N-terminus were used to achieve variable modifications. Protein identification was restricted to peptides with a minimum of 7 amino acids and at least one unique peptide. We considered only proteins with at least two peptides. The protein-level false discovery rate (FDR) was controlled at 1% based on reversed protein sequences. Five was the maximum number of modifications per peptide. Missing values were handled using Perseus software (version 1.6.6.0) by inputting a constant zero for proteins not detected in all samples, providing a consistent baseline for statistical analysis [22]. Log2 (Intensity) values were used to quantify the expression levels of protein in human sperm.

Bioinformatics analysis

The total number of proteins identified in NOR and TER groups was analysed using a Venn diagram [23]. The overlapping proteins in human sperm between the NOR and TER groups were also identified. An online tool for gene functional annotation, the Database for Annotation, Visualisation, and Integrated Discovery (DAVID version 6.8; https://david.ncifcrf.gov/), was used to assess pathway enrichment [24]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were also determined. We employed the UniProt Knowledgebase (UniProt KB; https://www.uniprot.org) to annotate the information of the proteins. Moreover, the interactions between proteins were visualised using the Search Tool for the Retrieval of Interacting Genes (STRING) online tool (https://string-db.org/).

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis

Validation analysis was performed via RT-qPCR on sperm samples from 14 NOR and 16 TER men who showed detectable activity in the TGF-β signalling pathway. The FavorPrep™ Tissue Total RNA Mini Kit (Favorgen Biotech Corp, Taiwan) was used to extract total RNA from the sperm samples following the manufacturer’s protocol. The mRNA was reverse transcribed into cDNA using the Maxime™ RT PreMix (Oligo dT Primer) (iNtRON Biotechnology, Korea). The primers for the target genes, including the LTBP1 gene (forward primer 5’-TGAATGCCAGCACCGTCATCTC-3’; reverse primer 5’-CTGGCAAACACTCTTGTCCTCC-3’) and the TGFBR1 gene (forward primer 5’-TCAGCTCTGGTTGGTGTCAG-3’; reverse primer 5’-ATGTGAAGATGGGCAAGACC-3’), were used. The glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene (forward primer 5’-CTCAACGACCACTTTGTCAAGCTCA-3’; reverse primer 5’-GGTCTTACTCCTTGGAGGCCATGTG-3’) was used as an internal control to normalise the mRNA expression of the target genes. Each PCR product was amplified from 4 ng of cDNA template using qPCRBIO SyGreen Mix (PCR Biosystems, UK). Quantitative PCR was conducted on a QuantStudio™ 5 Real-Time PCR System (Thermo Fisher Scientific, USA). We used the 2-∆∆Cq method to measure the relative mRNA expression levels of LTBP1 and TGFBR1 genes.

Immunocytochemistry analysis

To determine the TGF-βR1 localization and protein expression, immunocytochemistry was performed on fixed sperm smears. Briefly, sperm were placed on adhesive microscope slides before being air-dried at room temperature. A rabbit polyclonal anti-TGF-βR1 antibody (Abcam; ab31013) was used as a primary antibody for immunoreaction. The immunoreactive signals of TGF-βR1 were amplified by using avidin-biotinylated horseradish peroxidase complexes (Vector). DAB (3,3’-Diaminobenzidine) (Vector) was used to detect TGF-βR1 immunoreactive signals. Before the measurement of the immunoreactive signals of TGF-βR1, the sections were counterstained with hematoxylin and then mounted with histopathology mounting medium. The high-resolution digital images were taken from the immunocytochemistry-stained slides using a microscope camera. The quantitative protein expression of TGF-βR1 was determined from 200 sperm in each sample. ImageJ software (NIH, Bethesda, MD, freely available at https://imagej.nih.gov/ij/) was used for determining the intensity values of immunoreactions in each spermatozoon. The expression levels of TGF-βR1 were quantified as the integrated optical density per area, with values normalised to the control group and represented as relative optical density (ROD).

Statistical analysis

The normal distribution of the data was estimated using a Shapiro-Wilk test. The statistical difference between the two groups was analysed using a Student’s t-test (parametric data). The data were shown as mean ± SEM. Statistically significant was considered at p < 0.05. Multiple testing correction was performed using the Benjamini-Hochberg procedure, and results were reported as FDR-adjusted p-values (q-values), with a significance threshold set at an FDR of 0.01. Moreover, Pearson’s correlation coefficient was used to investigate the correlation between protein expressions in human sperm and the percentage of normal sperm morphology.

Results

Proteomic analysis of human sperm from TER and NOR men

A Venn diagram indicated that 1,161 proteins and 1,243 proteins were uniquely expressed in the NOR and TER groups, respectively. A total of 9,088 overlapping proteins were identified between these groups (see Fig 1). The KEGG pathway analysis was used to identify the most relevant biological pathways for the overlapping proteins. The top 5 categories, which were classified based on protein count, were metabolic pathways, herpes simplex virus 1 infection, human papillomavirus infection, PI3K-Akt signalling pathway, and MAPR signalling pathway. This study highlighted the TGF-β signalling pathway, which was identified as one of the top 40 categories within the KEGG pathway analysis. We identified 39 overlapping proteins in human sperm between the TER and NOR groups, associated with the TGF-β signalling pathway (see S1 Table). Their enrichment in the TGF-β signalling pathway is shown in Fig 2.

Six of the 39 overlapping proteins with a log2 fold change ≥ 1 or ≤ −1 were defined as DEPs. Table 1 provides a description of the log2 fold change in these 6 DEPs. E3 ubiquitin-protein ligase SMURF1 (SMURF1) and hemojuvelin (HJV) were downregulated, whereas activin receptor type-2B (ACVR2B), fibrillin-1 (FBN1), mothers against decapentaplegic homolog 6 (SMAD6), and latent-transforming growth factor beta-binding protein 1 (LTBP1) were upregulated. We found that among these proteins, the LTBP1 protein expression was significantly increased in the TER group compared with the NOR group, with a log2 fold change of 1.681 (p < 0.05) (see Table 1 and Fig 3). Despite not reaching statistical significance following FDR correction, the LTBP1 exhibited a high fold-change and clear biological relevance in the proteomic screen. To verify this candidate, we performed independent validation using RT-qPCR, which successfully confirmed its differential expression.

Table 1. The list of the 6 DEPs in the TGF-β signalling pathway in human sperm (TER versus NOR).

Accession number	Protein name	Gene symbol	Log 2 fold change^a (TER/NOR)	p-value	q-value
Q9HCE7	E3 ubiquitin-protein ligase SMURF1	SMURF1	−2.529	0.165	0.711
Q6ZVN8	Hemojuvelin	HJV	−1.075	0.059	0.570
Q13705	Activin receptor type-2B	ACVR2B	1.179	0.216	0.693
P35555	Fibrillin-1	FBN1	1.205	0.150	0.696
O43541	Mothers against decapentaplegic homolog 6	SMAD6	1.412	0.508	0.843
Q14766	Latent-transforming growth factor beta-binding protein 1	LTBP1	1.681	0.020	0.433

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^aValues greater than 0 indicate up-regulation, while values less than 0 indicate down-regulation.

Fig 3 — Values are shown as mean ± SEM, TER (n = 16) versus NOR (n = 14). *p < 0.05; unpaired Student’s t test.

Furthermore, the protein–protein interaction network analysis using the STRING database demonstrated the relationships among the 6 DEPs in the TGF-β signalling pathway and their interactions with TGF-βR1 (see Fig 4). Notably, TGF-βR1 and LTBP1 were implicated in TGF-β receptor activity, TGF-β binding, and cytokine binding, as indicated by Gene Ontology (GO) terms related to molecular function.

Fig 4 — These proteins are included in the TGF-β signalling pathway. The red, green, and blue colours represent molecular function terms, including TGF-β receptor activity, TGF-β binding, and cytokine binding, respectively.

Validation of LTBP1 and TGFBR1 mRNA expression in human sperm

To validate the results of the proteomic analysis, we quantified the mRNA expression of LTBP1 and TGFBR1 genes in the sperm samples. The RT-qPCR results confirmed a significant differential expression of both genes. The relative mRNA expression of these genes was shown in Fig 5. The expression of the LTBP1 gene in the TER group was significantly increased compared to the NOR group (1.23 ± 0.05 versus 1.03 ± 0.06, p < 0.01), which is consistent with the proteomic analysis results. Moreover, the expression of the TGFBR1 gene in the TER group was also significantly increased compared to the NOR group (1.16 ± 0.02 versus 1.01 ± 0.03, p < 0.001).

Fig 5 — Values are shown as mean ± SEM, TER (n = 16) versus NOR (n = 14). **p < 0.01 and ***p < 0.001; unpaired Student’s t test.

TGF-βR1 protein expression in human sperm

Based on the results of immunocytochemistry analysis, the TGF-βR1 protein expression was localised in the human sperm head. The TGF-βR1 immunostaining was detected predominantly at the postacrosomal region and equatorial segment (posterior acrosome). Moreover, the expression was found in some parts of the anterior acrosome (see Fig 6). The levels of TGF-βR1 protein expression, which were measured in the human sperm head, were significantly increased in the TER group (1.12 ± 0.02) compared with the NOR group (1.00 ± 0.03) (see Fig 7).

Fig 6 — A black arrow indicates an immunopositive spermatozoon, whereas a red arrow indicates an immunonegative spermatozoon.

Fig 7 — Values are shown as mean ± SEM, TER (n = 16) versus NOR (n = 14). **p < 0.01; unpaired Student’s t test.

Correlations of LTBP1 and TGF-βR1 protein expression in human sperm with sperm morphology

We analysed the correlation study of the protein expression of LTBP1 and TGF-βR1 with the result of sperm morphology, which has previously been reported in our previous study [19]. The negative correlations between the percentage of normal sperm morphology and the protein expression of LTBP1 (r = −0.4588, p = 0.0108) and TGF-βR1 (r = −0.4851, p = 0.0066) were reported (see Fig 8).

Fig 8 — **(A)** LTBP1 **(B)** TGF-βR1. A linear regression line (black line) was fitted to all data points.

Discussion

This study extends the proteome data on sperm from infertile men by specifically focusing on TER men. The findings were derived from individual samples, with appropriate sample sizes in each group, including NOR and TER men. According to our findings, metabolic pathways were the most relevant of the KEGG pathway discoveries in the overlapping proteins in human sperm between TER and NOR men. In the previous study, three KEGG pathways, including metabolic pathways, endocytosis, and spliceosome, were reported to be among the top 20 of pathway enrichment findings in sperm from infertile men with severe oligoasthenoteratozoospermia [13]. This result being in accordance with our result, we suggest that metabolism may be necessary in maintaining normal sperm function. There was evidence of the relationship between the metabolic syndrome and the TGF-β signalling pathway. Gene-gene interactions within the TGF-β family contribute to the risk of metabolic syndrome, which can impact male infertility [25,26].

The quantitative proteomic profiles obtained in this study enabled a comparison of protein expression changes in human sperm. Our results from the KEGG analysis revealed that the overlapping proteins in the sperm from the TER and NOR men were associated with the TGF-β signalling pathway. This pathway involves reproductive functions such as spermatogenesis, sperm maturation, and sperm functions. The members of the TGF-β signalling pathway are subdivided into two functional groups, including the TGF-β and bone morphogenetic protein (BMP) families. The TGF-β family includes TGF-βs, Activins, nodals, and some growth and differentiation Factors (GDFs). TGF-β ligands are cytokines that belong to the peptidic growth factor family. They are present in two forms, including latent or acid-activatable (inactive) and free (active) forms. There are several types of receptors for TGF-β signal transduction. Activin receptor types 1 and 2 mediate signals for activin. Moreover, TGF-βR1 and TGF-βR2 are activated by the ligands TGF-β isoforms 1, 2, and 3, which trigger signal transduction through TGF-β mediators [27]. The binding proteins, which act as mediators, consist of the latency-associated peptide (LAP) and LTBP1. These binding proteins are important for the activation of active TGF-β release. The TGF-β ligands are secreted into the extracellular medium through the formation of a latent complex. The signal is subsequently transmitted by attaching to their receptors, forming ligand-receptor complexes, and then signalling to the intracellular mediators, known as Smads [28]. Endogenous proteins that bind to and neutralise activins, such as follistatin, may inhibit TGF family ligand signalling. Interestingly, the present study identified 39 overlapping proteins belonging to the TGF-β signalling pathway, as shown in S1 Table. These proteins consist of the TGF-β ligands (TGF-β2 and TGF-β3), activins and their receptors, BMPs and their receptors, regulators (such as follistatin and inhibitory Smads (Smad6 and Smad7)), and mediators (such as ligand-binding proteins).

TGF-β ligands have been identified in several organs. All isoforms of TGF-β have been reported in the male reproductive system, including the testis, epididymis, vas deferens, seminal plasma, and sperm. TGF-β family protein dysfunction has been linked to a variety of human diseases [29,30]. Several studies revealed that the TGF-β signalling pathway is necessary for testicular formation; therefore, it can enhance testicular disease and infertility [31]. It also regulates luteinizing hormone and testosterone production [32]. Moreover, TGF-βs are required for testicular development, normal spermatogenesis, and normal testicular morphology [33]. They control the function of spermatogenic, Sertoli, and Leydig cells through their receptors [34–36]. Under abnormal testicular conditions, the level of TGF-β1 expression in testicular tumors was significantly higher than in normal testicular tissues [37]. Voisin et al. explain the role of TGF-β ligands, particularly TGF-β1 and TGF-β3, in the epididymis, where they have been discovered in different species. The results indicate that the TGF-β ligands act as an inhibitor of epididymal epithelial cell growth to limit uncontrolled proliferation. Their receptors are found in the principal cells of the same region [38]. Other studies also support the expression of TGF-β isoforms and receptors found in the epididymis and vas deferens. The homeostasis and function of the male reproductive tract are controlled by TGF-β signalling in the epididymis [39,40]. TGF-β1 acts as an immunosuppressive factor in the human seminal plasma [41]. However, the TGF-β1 concentration in seminal plasma was not significantly different between NOR and infertile men [42]. TGF-β1 has been reported to localise in the posterior acrosome, the neck, and the middle of the tail [43]. In the present study, although changes in TGF-βR1 protein expression in human sperm were not detected by the proteomic analysis, they were observed using immunocytochemistry. We revealed that its localisation was detected predominantly in the human sperm head, especially in the postacrosomal region and equatorial segment (posterior acrosome). Our results, together with previous findings, indicate that the TGF-β ligands and their receptors were also localised in the same region, which is in the sperm head. However, the localisation of TGF-β ligands was not detected at the acrosomal region of the head, which is consistent with the localisation of the TGF-βR1 that was found in the present study. It might be suggested in parallel with other studies that TGF-β ligands may act as an immunosuppressive factor to protect the integrity and morphology of human sperm, but they may not involve acrosome reaction. Moreover, a previous study reported that infertile men with reduced levels of TGF-β often exhibit increased sperm DNA fragmentation [44]. TER men exhibited higher levels of sperm DNA fragmentation and inflammatory markers compared to other infertile men [45]. In this study, the significant upregulation of TGF-βR1 at both the mRNA and protein levels in TER men points toward TGF-β-mediated immune activation, potentially reflecting a response to the higher prevalence of abnormal sperm morphology. This hypothesis can be supported by our finding of a negative correlation between its expression and the percentage of normal sperm morphology. In addition, the expression of TGF-βR1 also enhances sperm quality and serum sex hormone levels. The TGF-βR1 blocker can significantly increase sperm count and motility [17]. Because TGF-β ligands act as immunosuppressive factors, the activity of TGF-β through its receptors (such as TGF-βR1) was significantly higher in the sperm to compensate for the immune activation, resulting in the maintenance of sperm functions.

Other components of the TGF-β family have been reported in several studies. The latent TGF-β binding proteins (LTBPs) are large multidomain glycoproteins that are important for the activation of TGF-β secretion and extracellular TGF-β function. LTBP1 is the only isoform in the LTBP family that can bind to all TGF-β isoforms, including TGF-β1, TGF-β2, and TGF-β3. The complex of TGF-β and LAP is referred to as a small latent complex. Its interactions with TGF-β ligands enhance the complex’s folding and secretion into the extracellular matrix. LTBPs also assist in the conversion of latent TGF-β to free active TGF-β. The accumulation and inappropriate activation of latent TGF-β are controlled by LTBP-mediated matrix incorporation [46]. According to previous studies, LTBP participates in TGF-β production, release, and activation [46,47]. The levels of TGF-β and its ability to target correctly in the extracellular matrix are important in the signal regulation of TGF-β functions [48]. The changes in LTBP levels result in defects in several organ systems, such as cardiovascular and lung defects [46]. The transgenic study indicated that LTBP1 overexpression was related to the increase in TGF-β activity [49]. Moreover, the study in the absence of LTBP expression strongly supports a role for LTBP in TGF-β bioavailability and activation [50,51]. Increased latent and active TGF-β levels occurred along with overexpression of LTBP1. These results support the important role of LTBP1 in controlling TGF-β activity. We also observed a significant upregulation in LTBP1 expression at both mRNA and protein levels in the sperm of men with TER, which inversely correlated with the percentage of normal sperm morphology. While the association between upregulation of TGF-βR1 and LTBP1 and abnormal sperm morphology is compelling, it is important to explicitly acknowledge that our study did not account for certain unmeasured confounders, such as subclinical localised inflammation and individual lifestyle factors. These factors could potentially influence TGF-β signalling and sperm quality. However, the study of LTBP1 and TGF-βR1 as biomarkers in larger populations and mechanistic studies exploring their roles affecting sperm morphology will strengthen the translational potential of these findings. We conclude that sperm proteins, such as TGF-family proteins that are specifically relevant to reproductive function, play an important role in sperm function. The effect of TGF-β, one of the anti-inflammatory cytokines, in sperm might increase to regulate the inflammatory response during the fertilisation process, which occurs highly in infertile men. Currently, the proteomic profile of human sperm is very interesting to study for the evaluation of disease progression in various human diseases. Male infertility is one of the important clinical areas of interest. Understanding the proteomic profile of sperm cells in male infertility may be essential to distinguishing individuals based on their infertility conditions and for identifying biomarkers useful in male fertility diagnosis [52]. Current knowledge of proteomic profiles in human sperm is still limited, especially regarding sperm samples derived from infertile men. Alterations in the proteomic profile have been reported in asthenozoospermia and oligozoospermia but not in TER [10].

Conclusion

Overall, our findings compare the protein profiles of human sperm from TER and NOR men. The TGF-β signalling pathway was the focus of the KEGG pathways identified through analysis of overlapping proteins between the two groups. Thirty-nine overlapping proteins in human sperm were identified as members of the TGF-β signalling pathway. Among them, LTBP1 was the most predominantly altered protein in the TER men. Validation of LTBP1 and TGFBR1 genes, key components of the TGF-β signalling pathway, revealed a significant increase in their mRNA expression levels in the sperm of TER men. Furthermore, the results from immunocytochemistry analysis indicated a significant increase of TGF-βR1 protein in human sperm from TER men compared to NOR men. An increase in LTBP1 and TGF-βR1 expression in human sperm is associated with a decrease in the percentage of normal sperm morphology. This result indicates that these proteins may be involved in the mechanism of spermatogenesis. We suggest that LTBP1 and TGF-βR1 proteins have the most impact as sperm protein biomarkers defining infertility related to abnormal sperm morphology. Notably, our findings provide novel evidence supporting the investigation of proteomic profiles within the TGF-β signalling pathway, highlighting their potential relevance as sperm biomarkers for infertility, particularly in the case of TER. They are primarily used to estimate an individual’s fertility potential. The insights in the present study will further help in investigating the pathogenic mechanisms of male infertility.

Supporting information

S1 Table. The list of the 39 overlapping proteins in the TGF-β signalling pathway in human sperm (TER versus NOR).

(DOCX)

pone.0342465.s001.docx^{(35.7KB, docx)}

Acknowledgments

We appreciate the facilities support from Naresuan University, Walailak University, and the National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Thailand. The authors are grateful to the staff of the Naresuan Infertility Centre for providing help with sample collection.

Data Availability

The LC-MS/MS datasets generated during this study are available in the ProteomeXchange repository under the accession IDs JPST004285 and PXD072557 (via jPOST: https://repository.jpostdb.org/preview/7054576026955c0503b3d8, Access key: 9498). Raw MALDI-TOF MS and clinical data of this study are available from the corresponding author upon reasonable request.

Funding Statement

This research was supported by Naresuan University, including the University Income Fund (grant number R2567C018), the Global and Frontier Research University Fund (grant number R2567C003) and the Frontier Research and Innovation Clusters Grant (grant number R2569C004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0342465.r001

Decision Letter 0

Suresh Yenugu

12 Sep 2025

Dear Dr. Nudmamud-Thanoi,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: This manuscript presents a robust proteomic investigation comparing human sperm protein expression between normozoospermic (NOR) and teratozoospermic (TER) men, with a focused exploration of the TGF-β signaling pathway. The integration of LC–MS/MS-based proteomic profiling with immunocytochemistry and correlation with sperm morphology provides a compelling dataset. The emphasis on LTBP1 and TGF-βR1 as candidate biomarkers for sperm morphological abnormalities in TER is well-placed and novel. However, some methodological gaps, statistical considerations, and interpretive extrapolations must be addressed for the study to reach its full potential.

1. The title is specific, indicating both the biological system (human sperm), the condition studied (teratozoospermia), and the signaling pathway of interest (TGF-β). Consider specifying that LTBP1 and TGF-βR1 are identified as potential biomarkers to make the title more impactful.

2. The short title could be clearer: consider “TGF-β pathway proteins in teratozoospermic sperm” for better focus.

3. In Abstract: Highlight the novelty of the finding (i.e., LTBP1 and TGF-βR1’s correlation with sperm morphology) more explicitly.

4. The introduction could more directly state the hypothesis or research question. Consider summarizing the literature review more concisely to keep the narrative focused.

5. Provide the objective as a standalone sentence, possibly in a separate subheading to enhance clarity for readers and reviewers.

6. A methodology flowchart or summary table would improve readability. Expand slightly on how missing data were handled (e.g., why a constant zero was chosen for imputation).

7. Statistical analysis: Clarify the number of biological replicates used for all analyses. Apply or justify the absence of multiple comparison correction for the proteomics dataset.

8. Consider reordering figures to follow the sequence of presentation in the results. A summary table of key proteins with function annotations would enhance accessibility.

9. Strengthen the discussion of limitations: e.g., cross-sectional nature, lack of functional assays. Briefly propose next steps (e.g., validating biomarkers in larger cohorts or mechanistic studies).

10. In conclusion: Reinforce how this work fills the previously identified gap

Reviewer #2: Review Report

Title: Differential protein expression profiles in human sperm from teratozoospermic and normozoospermic men: A spotlight on proteins in the TGF-β signalling pathway

Strengths

1. Relevant topic: Male infertility and sperm proteomics are of significant clinical and scientific interest.

2. Novelty: Focus on TGF-β pathway proteins (LTBP1 and TGF-βR1) in teratozoospermia is interesting and could provide biomarker insights.

3. Methodology: Use of LC-MS/MS and immunocytochemistry adds robustness through complementary approaches.

4. Ethics & data: Proper IRB approval, consent, and data availability are reported.

Major Issues (Require Revision)

1. Sample size and statistical power:

o Only 42 men included (20 NOR, 22 TER). This is underpowered for proteomic studies reporting 9,000+ proteins. Authors should justify sample size and power.

o Number of samples used in proteomics vs immunocytochemistry is inconsistent (not all 42 analyzed). Needs clarification.

2. Data presentation and filtering:

o Reported 9,088 DEPs between groups seems biologically unrealistic given small n. Criteria for defining DEPs (fold change, p-value/FDR) are unclear.

o Cutoff of “1.0 fold change” is misleading (does this mean log2 fold = 1? or just ≥1.0 difference?). Needs proper explanation.

o No multiple testing correction (e.g., Benjamini–Hochberg) applied in proteomic analysis → high risk of false positives.

3. Validation of findings:

o Only two proteins (LTBP1, TGF-βR1) are discussed/validated, while 39 DEPs in TGF-β pathway were identified. Why were others ignored?

o Western blot or quantitative validation (qPCR, targeted proteomics) is missing. Immunocytochemistry alone is insufficient for robust validation.

4. Interpretation / causality:

o Discussion often overstates causality (e.g., suggesting therapeutic targets) based only on correlation data. Needs more cautious language.

o Biological plausibility of linking LTBP1/TGF-βR1 directly to teratozoospermia is not fully developed. Other confounders (age, lifestyle, comorbidities) not addressed.

5. Figures / tables:

o Figures are difficult to interpret (low resolution, not well labeled).

o S1 Table with 39 DEPs should include statistical significance (p/FDR values), fold change, accession IDs.

6. Language and clarity:

o Manuscript has frequent grammatical issues and redundant statements. Needs thorough English editing for clarity and conciseness.

Minor Issues

1. Abstract mentions “cut-off 1.0 fold change” → should be clarified (likely log2FC).

2. Methods: Proteomic workflow details are too technical for PLOS ONE (e.g., buffer recipes). Focus should be on reproducibility and data processing.

3. Data availability statement should specify raw proteomics files deposited in a public repository (e.g., PRIDE), not only “within manuscript.”

4. References: Some older references (1990s, early 2000s) dominate. Update with recent proteomics literature (esp. 2022–2024).

5. Ethical statement: Consent type (written/oral) not clearly stated, as required by PLOS ONE.

Recommendation

Decision: Major Revision (not reject).

• The study has potential but currently lacks sufficient statistical rigor, validation, and clarity for publication.

• Authors must:

o Clarify statistical thresholds and apply multiple-testing correction.

o Justify sample size.

o Provide stronger validation beyond immunocytochemistry.

o Improve figure/table presentation.

o Revise language for clarity and avoid overinterpretation.

If these issues are addressed, the manuscript could be suitable for publication in PLOS ONE, which accepts technically sound work even if incremental. In its current form, it is not acceptable without substantial revision.

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Reviewer #1: Yes: Dr. Chandramohan Ramasamy, Ph.D.

Reviewer #2: Yes: Krishna Chaitanya Mantravadi

**********

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Attachment

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pone.0342465.s002.docx^{(17.2KB, docx)}

PLoS One. 2026 Feb 13;21(2):e0342465. doi: 10.1371/journal.pone.0342465.r002

Author response to Decision Letter 1

27 Oct 2025

Respond to Reviewers' Comments:

Reviewer #1:

Author response: In accordance with this recommendation, we have revised the title to specify that LTBP1 and TGF-βR1 are identified as potential biomarkers, thereby making the title more specific and impactful. The revised title now reads as follows: “Differential protein expression profiles in human sperm from teratozoospermic and normozoospermic men identify LTBP1 and TGF-βR1 as potential biomarkers within the TGF-β signalling pathway” [Lines 2-3].

2. The short title could be clearer: consider “TGF-β pathway proteins in teratozoospermic sperm” for better focus.

Author response: We appreciate the reviewer’s helpful suggestion. We have revised the short title to “TGF-β signalling pathway proteins in teratozoospermic sperm” to improve clarity and focus.

3. In Abstract: Highlight the novelty of the finding (i.e., LTBP1 and TGF-βR1’s correlation with sperm morphology) more explicitly.

Author response: We have revised the Abstract section to more explicitly highlight the novelty of our findings, emphasizing the correlation of LTBP1 and TGF-βR1 with sperm morphology, as stated: “Our findings, for the first time, demonstrate a significant association between the expression of LTBP1 and TGF-βR1 and abnormal sperm morphology, supporting their consideration as novel exploratory biomarkers for further investigation in infertility, especially in TER men.” [Lines 37-40].

4. The introduction could more directly state the hypothesis or research question. Consider summarizing the literature review more concisely to keep the narrative focused.

Author response: We have revised the Introduction section to more clearly state the hypothesis and research question, as stated: “Even though extensive research has been done on sperm protein biomarkers, understanding of the proteomic alterations underlying male infertility remains limited, particularly in cases of TER. The potential involvement of the TGF-β signalling pathway in regulating sperm morphology has been hypothesized but not yet clearly elucidated.” [Lines 79-82]. Additionally, we have condensed the literature review to focus on the most relevant studies, improving the clarity and flow of the narrative.

5. Provide the objective as a standalone sentence, possibly in a separate subheading to enhance clarity for readers and reviewers.

Author response: We thank the reviewer for highlighting this important point. In response, we have revised the manuscript to present the study objective as a standalone sentence under a separate subheading, thereby improving clarity for readers and reviewers, as stated: “Therefore, this study aimed to investigate the proteomic profile of human sperm from TER men, with a focus on identifying key proteins within the TGF-β signalling pathway that may contribute to abnormal sperm morphology.” [Lines 83-85].

6. A methodology flowchart or summary table would improve readability. Expand slightly on how missing data were handled (e.g., why a constant zero was chosen for imputation).

Author response: In the Methods section, we have clarified that missing values in the proteomics analysis were handled using Perseus software (version 1.6.6.0) with a constant value of zero, as stated: “Missing values were handled using Perseus software (version 1.6.6.0) by imputing a constant zero for proteins not detected in all samples, providing a consistent baseline for statistical analysis [22].” [Lines 128-130].

7. Statistical analysis: Clarify the number of biological replicates used for all analyses. Apply or justify the absence of multiple comparison correction for the proteomics dataset.

Author response: In this study, sperm samples from 20 NOR and 22 TER men were analysed as biological replicates for proteomic analysis, as stated: “The human sperm protein was extracted from each semen sample (n = 20 NOR and n = 22 TER).” [Lines 108-109]. Results from proteomics analysis identified LTBP1 as a key protein involved in the TGF-β signalling pathway under TER conditions. Among the analysed samples, LTBP1 expression was detected in 14 samples from the NOR group and 16 samples from the TER group. Therefore, these samples were selected for subsequent protein validation using immunocytochemistry to investigate the relationship between LTBP1 and TGF-βR1. This information has been clearly stated in the Methods section as follows: “Sperm samples from 14 NOR and 16 TER men exhibiting detectable LTBP1 expression were selected for validation analysis using immunocytochemistry” [Lines 143-144].

Moreover, we used a relatively small sample size and an exploratory proteomic approach aimed at identifying potential candidate proteins for further validation. Therefore, we presented unadjusted p-values while clearly acknowledging the potential for false positives. This limitation has now been stated clearly in the revised manuscript (Discussion section). The Discussion section has been updated, as stated: “Statistical analyses were performed without multiple testing correction due to the exploratory purpose and limited sample size of the dataset.” [Lines 264-266].

8. Consider reordering figures to follow the sequence of presentation in the results. A summary table of key proteins with function annotations would enhance accessibility.

Author response: We have carefully checked the figures and confirmed that their order appropriately follows the sequence of presentation in the Results section. Regarding the summary table, since this study focuses on KEGG pathways as the primary functional annotations, we did not include additional annotations such as Biological Process, Molecular Function, or Cellular Component for each protein. However, we have provided the Accession numbers in the table, which allow readers to access these additional functional annotations if desired.

Author response: The limitations of the study have now been clearly stated in the revised manuscript (Discussion section), including the absence of multiple testing correction in the statistical analyses and the need for further validation. In addition, we have proposed future directions, including validating the identified biomarkers in larger independent cohorts and performing mechanistic studies to further elucidate their roles in sperm morphology regulation, as stated: “Validation of LTBP1 and TGF-βR1 as biomarkers in larger populations and mechanistic studies exploring their roles affecting sperm morphology will strengthen the translational potential of these findings.” [Lines 325-327].

10. In conclusion: Reinforce how this work fills the previously identified gap

Author response: We thank the reviewer for providing this valuable guidance. We have revised the Conclusion section to more explicitly highlight how our study addresses the previously identified gap, as stated: “We suggest that LTBP1 and TGF-βR1 proteins have the most impact as sperm protein biomarkers defining infertility related to abnormal sperm morphology. Notably, our findings provide novel evidence supporting the investigation of proteomic profiles within the TGF-β signalling pathway, highlighting their potential relevance as sperm biomarkers for infertility, particularly in the case of TER.” [Lines 348-352].

Reviewer #2

Major Issues (Require Revision)

1. Sample size and statistical power:

o Only 42 men included (20 NOR, 22 TER). This is underpowered for proteomic studies reporting 9,000+ proteins. Authors should justify sample size and power.

Author response: We acknowledge that the sample size in the present study is relatively limited for large-scale proteomic analysis. However, the sample size was determined based on prior power calculations primarily optimized for gene expression analysis using immunohistochemistry, which served as the main validation approach in this study. To clarify our sample-size rationale, we performed standard power calculations for an independent two-sample, two-tailed t-test (α = 0.05). The required sample size is highly dependent on the expected effect size (Cohen’s d). Based on standard benchmarks, detecting a large effect (d = 0.8) at a statistical power between 0.6 and 0.8 requires approximately 16-25 participants per group. Therefore, our sample size (20 NOR, 22 TER) provides adequate power to detect large effect-size changes. Notably, our study followed a discovery-to-validation workflow. After the proteomic discovery phase, we targeted molecular validation on the best candidates. We used strict quality control and filtering to focus on proteins that had significant changes that were biologically meaningful.

Our study design is consistent with several prior human proteomic investigations that successfully utilized modest sample sizes (typically 8–10 samples per group) during the discovery phase and still identified biologically relevant and statistically robust differential proteins, particularly when complemented by downstream validation. For example, Lee et al. (2024) demonstrated through simulation analyses that small group sizes (n = 8-13) are commonly applied in discovery or proof-of-concept studies, acknowledging that while statistical power for small effect sizes may be limited, such designs remain effective for identifying large and biologically relevant differences [1]. Similarly, Wang et al. (2019) analysed plasma proteomes from eight young and aged individuals and detected significantly differentially expressed proteins with adequate statistical control [2]. Furthermore, Liu et al. (2012) reported that a group size of 10 (male vs. female) achieved approximately 95% completeness of qualitatively identified urinary proteins and peptides [3]. Collectively, these studies support the appropriateness of our sample size for preliminary proteomic discovery.

References

1. Lee KH, Assassi S, Mohan C, Pedroza C. Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study. BMC Genomics. 2024;25(1):1086. doi: 10.1186/s12864-024-11018-2.

2. Wang H, Zhu X, Shen J, Zhao E-F, He D, Shen H, et al. Quantitative iTRAQ-based proteomic analysis of differentially expressed proteins in aging in human and monkey. BMC Genomics. 2019;20(1):725. doi: 10.1186/s12864-019-6089-z.

3. Liu X, Shao C, Wei L, Duan J, Wu S, Li X, et al. An individual urinary proteome analysis in normal human beings to define the minimal sample number to represent the normal urinary proteome. Proteome Science. 2012;10(1):70. doi: 10.1186/1477-5956-10-70.

o Number of samples used in proteomics vs immunocytochemistry is inconsistent (not all 42 analysed). Needs clarification.

Author response: Results from proteomics analysis identified LTBP1 as a key protein involved in the TGF-β signalling pathway under TER conditions. Among the samples analysed, LTBP1 expression was detected in 14 samples from the NOR group and 16 samples from the TER group. Therefore, only these samples were selected for subsequent protein validation using immunohistochemistry to investigate the relationship between LTBP1 and TGF-βR1.

2. Data presentation and filtering:

o Reported 9,088 DEPs between groups seems biologically unrealistic given small n. Criteria for defining DEPs (fold change, p-value/FDR) are unclear.

Author response: Originally, the 9,088 DEPs referred to proteins that were detected in both the NOR and TER groups, regardless of whether they were differentially expressed. However, we recognize that using DEPs for this explanation was inaccurate. Therefore, we have revised it to overlapping proteins for clarity and a more accurate description.

o Cutoff of “1.0 fold change” is misleading (does this mean log2 fold = 1? or just ≥1.0 difference?). Needs proper explanation.

Author response: We thank the reviewer for this helpful suggestion. We have revised the text to clarify that differences in protein expression levels between experimental conditions are quantified using the log2 fold change. This clarification has been incorporated into the revised manuscript including:

- The Abstract section has been updated as follows: “Six proteins that were differentially expressed and had a log2 fold change of ≥ 1 or ≤ -1 were considered to be the differentially expressed proteins in human sperm between the groups.” [Lines 30-32].

- The Results section has been updated as follows: “Six of the 39 overlapping proteins with a log2 fold change ≥ 1 or ≤ -1 were defined as DEPs.” [Lines 183].

- Both Table 1 and S1 Table have been revised [Lines 191-193].

o No multiple testing correction (e.g., Benjamini–Hochberg) applied in proteomic analysis → high risk of false positives.

Author response: In this study, we used a relatively small sample size and an exploratory proteomic approach aimed at identifying potential candidate proteins for further validation. Therefore, we presented unadjusted p-values while clearly acknowledging the potential for false positives. This limitation has now been stated clearly in the revised manuscript (Discussion section). The Discussion section has been updated, as stated: “Statistical analyses were performed without multiple testing correction due to the exploratory purpose and limited sample size of the dataset.” [Lines 264-266].

3. Validation of findings:

o Only two proteins (LTBP1, TGF-βR1) are discussed/validated, while 39 DEPs in TGF-β pathway were identified. Why were others ignored?

Author response: Although 39 overlapping proteins were identified as part of the TGF-β signalling pathway, we focused on LTBP1 because it showed the most significant differential expression (log2 fold change = 1.681 and p = 0.02) as shown in Table 1. In addition, TGF-βR1 was included for validation due to its close functional relationship with LTBP1 and its reported association with sperm morphology.

o Western blot or quantitative validation (qPCR, targeted proteomics) is missing. Immunocytochemistry alone is insufficient for robust validation.

Author response: We appreciate the reviewer’s valuable comment. We agree that adding quantitative validations such as Western blot or qPCR would further strengthen the findings. However, our study focused on exploratory proteomics combined with immunocytochemistry to validate both the expression level and localization of candidate proteins. Immunocytochemistry in this study was performed as a quantitative validation approach, comparable to Western blot and qPCR. While immunocytochemistry provides additional advantages in demonstrating specific localization and expression patterns of target proteins within sperm, which is information that cannot be obtained from Western blot or qPCR. Therefore, we consider immunocytochemistry to provide strong and sufficient quantitative validation for the scope of this study. Consistent with our approach, several previous studies have utilized immunohistochemistry to validate their proteomics results, confirming its reliability as a complementary validation method [1, 2].

References

1. Richter A, Fichtner A, Joost J, Brockmeyer P, Kauffmann P, Schliephake H, et al. Quantitative proteomics identifies biomarkers to distinguish pulmonary from head and neck squamous cell carcinomas by immunohistochemistry. J Pathol Clin Res. 2022;8(1):33-47. doi: 10.1002/cjp2.244. PubMed PMID: 34647699; PubMed Central PMCID: PMCPMC8682946.

2. Ichimata S, Hata Y, Yoshinaga T, Katoh N, Kametani F, Yazaki M, et al. Amyloid-Forming Corpora Amylacea and Spheroid-Type Amyloid Deposition: Comprehensive Analysis Using Immunohistochemistry, Proteomics, and a Literature Review. Int J Mol Sci. 2024;25(7). doi: 10.3390/ijms25074040. PubMed PMID: 38612850; PubMed Central PMCID: PMCPMC11012059.

4. Interpretation / causality:

o Discussion often overstates causality (e.g., suggesting ther

Attachment

Submitted filename: Response to Reviewers_.docx

pone.0342465.s004.docx^{(42.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342465.r003

Decision Letter 1

Suresh Yenugu

21 Nov 2025

Dear Dr. Nudmamud-Thanoi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

It is advised to deposit all the available data, conduct thorough statistical analyses and to substantiate the differential expression by standard techniques like Western blotting.

Please submit your revised manuscript by Jan 05 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: No

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4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The authors have appropriately addressed all of my comments, and I recommend that this manuscript be accepted for publication.

Reviewer #2: The authors have addressed several editorial and clarity issues; however, key scientific and compliance concerns remain. First, the raw proteomics data are not deposited in a public repository, which does not meet PLOS ONE’s mandatory data availability requirements; this must be corrected by depositing raw LC-MS/MS files (e.g., PRIDE accession) or by providing a journal-approved controlled-access justification. Second, the proteomics statistical analysis requires re-evaluation: multiple-testing correction (FDR) should be applied and reported, or an explicit and technically sound justification must be provided along with evidence that major findings (e.g., LTBP1) remain robust under alternative imputation and significance thresholds. Third, the validation strategy remains insufficient. Immunocytochemistry alone does not substantiate abundance differences; an orthogonal quantitative method (e.g., Western blot, PRM/SRM targeted proteomics) is required or a detailed technical explanation for its infeasibility must be provided along with an alternative quantitative approach. Additionally, the manuscript should further moderate causal interpretations and explicitly acknowledge unmeasured confounders. With these issues resolved and data availability brought into compliance, the manuscript may become suitable for publication.

**********

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Reviewer #1: Yes: Dr. CHANDRAMOHAN RAMASAMY, RESEARCH SCIENTIST, TULANE UNIVERSITY

Reviewer #2: Yes: Krishna Chaitanya Mantravadi

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PLoS One. 2026 Feb 13;21(2):e0342465. doi: 10.1371/journal.pone.0342465.r004

Author response to Decision Letter 2

18 Jan 2026

We have carefully addressed all comments and suggestions provided by the reviewers and the editor, as detailed below.

Response to Reviewers

Reviewer #1:

The authors have appropriately addressed all of my comments, and I recommend that this manuscript be accepted for publication.

Author response: We sincerely thank the reviewer for the time and effort in reviewing our manuscript. We appreciate the positive feedback and the helpful suggestions provided during the revision process, which have significantly improved the quality of our work.

Reviewer #2

The authors have addressed several editorial and clarity issues; however, key scientific and compliance concerns remain.

1. First, the raw proteomics data are not deposited in a public repository, which does not meet PLOS ONE’s mandatory data availability requirements; this must be corrected by depositing raw LC-MS/MS files (e.g., PRIDE accession) or by providing a journal approved controlled-access justification.

Author response: We thank the reviewer for highlighting this important point. We have added in section of Data availability, as stated: “The LC-MS/MS datasets generated during this study are available in the ProteomeXchange repository under the accession IDs JPST004285 and PXD072557 (via jPOST: https://repository.jpostdb.org/preview/7054576026955c0503b3d8, Access key: 9498). Raw MALDI-TOF MS and clinical data of this study are available from the corresponding author upon reasonable request.”.

2. Second, the proteomics statistical analysis requires re-evaluation: multiple-testing correction (FDR) should be applied and reported, or an explicit and technically sound justification must be provided along with evidence that major findings (e.g., LTBP1) remain robust under alternative imputation and significance thresholds.

Author response: We appreciate the reviewer’s focus on statistical rigor. In accordance with the reviewer's suggestion, the Benjamini-Hochberg procedure was applied to the proteomic dataset to control the false discovery rate (FDR) for multiple comparisons. The results were reported as FDR-adjusted p-values (q-values), with a significant threshold set at an FDR of 0.01. In the initial proteomic screen, LTBP1 was identified as a candidate of interest based on its high fold-change and nominal significance, though it did not maintain significance after FDR correction. To rigorously test this finding, we performed independent validation via the reverse transcription-quantitative polymerase chain reaction (RT-qPCR). This targeted analysis confirmed a significant increase in LTBP1 mRNA levels, validating the biological relevance of the protein.

The statistical analysis in the Methods section has been updated as follows: “Multiple testing correction was performed using the Benjamini-Hochberg procedure, and results were reported as FDR-adjusted p-values (q-values), with a significance threshold set at an FDR of 0.01.” [Lines 176-178].

We updated the Results section to include the following: “We found that among these proteins, the LTBP1 protein expression was significantly increased in the TER group compared with the NOR group, with a log2 fold change of 1.681 (p < 0.05) (see Table 1 and Fig 3). Despite not reaching statistical significance following FDR correction, the LTBP1 exhibited a high fold-change and clear biological relevance in the proteomic screen. To verify this candidate, we performed independent validation using RT-qPCR, which successfully confirmed its differential expression.” [Lines 205-210]. We have revised Table 1 and S1 Table to include both the p-values and q-values for all reported proteins.

3. Third, the validation strategy remains insufficient. Immunocytochemistry alone does not substantiate abundance differences; an orthogonal quantitative method (e.g., Western blot, PRM/SRM targeted proteomics) is required or a detailed technical explanation for its infeasibility must be provided along with an alternative quantitative approach. Additionally, the manuscript should further moderate causal interpretations and explicitly acknowledge unmeasured confounders. With these issues resolved and data availability brought into compliance, the manuscript may become suitable for publication.

Author response: We appreciate the reviewer’s constructive feedback regarding the need for robust quantification. To address this, we have now incorporated RT-qPCR as an independent, orthogonal quantitative approach to validate our proteomic findings. We updated the Methods section to include the RT-qPCR analysis as following: “Validation analysis was performed via RT-qPCR on sperm samples from 14 NOR and 16 TER men who showed detectable activity in the TGF-β signalling pathway. The FavorPrep™ Tissue Total RNA Mini Kit (Favorgen Biotech Corp, Taiwan) was used to extract total RNA from the sperm samples following the manufacturer's protocol. The mRNA was reverse transcribed into cDNA using the Maxime™ RT PreMix (Oligo dT Primer) (iNtRON Biotechnology, Korea). The primers for the target genes, including the LTBP1 gene (forward primer 5'-TGAATGCCAGCACCGTCATCTC-3'; reverse primer 5'-CTGGCAAACACTCTTGTCCTCC-3') and the TGFBR1 gene (forward primer 5'-TCAGCTCTGGTTGGTGTCAG-3'; reverse primer 5'-ATGTGAAGATGGGCAAGACC-3'), were used. The glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene (forward primer 5'-CTCAACGACCACTTTGTCAAGCTCA-3'; reverse primer 5'-GGTCTTACTCCTTGGAGGCCATGTG-3') was used as an internal control to normalise the mRNA expression of the target genes. Each PCR product was amplified from 4 ng of cDNA template using qPCRBIO SyGreen Mix (PCR Biosystems, UK). Quantitative PCR was conducted on a QuantStudio™ 5 Real-Time PCR System (Thermo Fisher Scientific, USA). We used the 2-∆∆Cq method to measure the relative mRNA expression levels of LTBP1 and TGFBR1 genes.” [Lines 142-157].

Our new results have been added to demonstrate a significant increase in mRNA levels for key targets, including LTBP1 and TGFBR1 genes, which directly corroborate the trends observed on our proteomic screen and immunocytochemistry. This dual-level validation (mRNA and protein) provides a more comprehensive and quantitative basis for our findings. The manuscript has been updated in the Results section as follows: “Validation of LTBP1 and TGFBR1 mRNA expression in human sperm

To validate the results of the proteomic analysis, we quantified the mRNA expression of LTBP1 and TGFBR1 genes in the sperm samples. The RT-qPCR results confirmed a significant differential expression of both genes. The relative mRNA expression of these genes was shown in Fig 5. The expression of the LTBP1 gene in the TER group was significantly increased compared to the NOR group (1.23 ± 0.05 vs. 1.03 ± 0.06, p < 0.01), which is consistent with the proteomic analysis results. Moreover, the expression of the TGFBR1 gene in the TER group was also significantly increased compared to the NOR group (1.16 ± 0.02 vs. 1.01 ± 0.03, p < 0.001).” [Lines 229-236]. Figure 5 and its caption have been added as follows: “Fig 5. The mRNA expression of LTBP1 and TGFBR1 genes in human sperm. Values are shown as mean ± SEM, TER (n = 16) vs. NOR (n = 14). **p < 0.01 and ***p < 0.001; unpaired Student’s t test.” [Lines 238-240].

The manuscript has been updated in the Discussion section as follows: “In this study, the significant upregulation of TGF-βR1 at both the mRNA and protein levels in TER men points toward TGF-β-mediated immune activation, potentially reflecting a response to the higher prevalence of abnormal sperm morphology.” [Lines 332-334] and “We also observed a significant upregulation in LTBP1 expression at both mRNA and protein levels in the sperm of men with TER, which inversely correlated with the percentage of normal sperm morphology.” [Lines 356-358]. The Conclusion section has been updated, as stated: “Validation of LTBP1 and TGFBR1 genes, key components of the TGF-β signalling pathway, revealed a significant increase in their mRNA expression levels in the sperm of TER men.” [Lines 381-383]. The Abstract section has been updated, as stated: “Validation analysis revealed that the mRNA expression levels of LTBP1 and TGFBR1 genes were significantly upregulated in the TER group relative to the NOR group.” [Lines 34-36].

In accordance with the Reviewer's suggestion, we have updated the Discussion section (Lines 358-362) to explicitly acknowledge potential unmeasured confounders as follow: “While the association between upregulation of TGF-βR1 and LTBP1 and abnormal sperm morphology is compelling; it is important to explicitly acknowledge that our study did not account for certain unmeasured confounders, such as subclinical localized inflammation and individual lifestyle factors. These factors could potentially influence TGF-β signalling and sperm quality.”.

Response to Editor:

1. It is advised to deposit all the available data, conduct thorough statistical analyses and to substantiate the differential expression by standard techniques like Western blotting.

Author response: We thank the Editor for these constructive suggestions, which have significantly strengthened the manuscript. We have addressed each point as follows:

• Data deposition: In the interest of transparency and reproducibility, we have deposited the LC-MS/MS datasets and search result files into the ProteomeXchange repository under the accession IDs JPST004285 and PXD072557 (via jPOST: https://repository.jpostdb.org/preview/7054576026955c0503b3d8, Access key: 9498).

• Thorough statistical analysis: The manuscript now explicitly acknowledges that LTBP1 did not survive global FDR correction. However, we provide evidence that its differential expression is robust under alternative statistical frameworks. We justified prioritizing LTBP1 based on its consistent fold-change and nominal p-value, which were independently confirmed through orthogonal RT-qPCR validation.

• Substantiation of differential expression: To validate the proteomic findings using an orthogonal quantitative method, we performed RT-qPCR to measure the mRNA expression levels of LTBP1 and TGFBR1 genes. While Western blotting was suggested, we decided on RT-qPCR as it provides high sensitivity and a broader dynamic range for quantifying gene expression changes. Our targeted analysis confirmed a significant increase in LTBP1 and TGFBR1 mRNA levels, successfully validating the biological relevance of this candidate at the transcriptional level. This cross-validation between the proteome and transcriptome provides strong evidence that the observed changes represent a true biological signal.

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PLoS One. doi: 10.1371/journal.pone.0342465.r005

Decision Letter 2

Suresh Yenugu

26 Jan 2026

<p>Differential protein expression profiles in human sperm from teratozoospermic and normozoospermic men identify LTBP1 and TGF-βR1 as potential biomarkers within the TGF-β signalling pathway

PONE-D-25-30555R2

Dear Dr. Nudmamud-Thanoi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

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Reviewer #2: Reviewer Evaluation of Author Rebuttal

Overall assessment

The authors have substantively and satisfactorily addressed the major concerns raised during peer review. The rebuttal demonstrates clear engagement with reviewer feedback, appropriate corrective actions, and improved methodological transparency. From a reviewer’s perspective, the manuscript is now acceptable for publication, with only minor optional refinements suggested.

1. Data availability and PLOS ONE compliance

The authors have fully addressed data availability requirements. Raw LC-MS/MS data have been deposited in ProteomeXchange with valid accession numbers (JPST004285, PXD072557 via jPOST). Access information is clearly provided, and availability of clinical and MALDI-TOF MS data is transparently stated. This satisfies PLOS ONE’s mandatory data-sharing policies.

2. Proteomics statistics and multiple-testing correction

The response appropriately applies Benjamini–Hochberg false discovery rate correction and transparently reports both nominal p-values and FDR-adjusted q-values. The authors correctly acknowledge that LTBP1 does not survive global FDR correction and have reframed it as a biologically relevant candidate rather than a definitive discovery. Interpretation is cautious and statistically sound.

3. Validation strategy and quantitative rigor

The addition of RT-qPCR as an orthogonal quantitative validation substantially strengthens the manuscript. Methods are clearly described, sample sizes are appropriate, and results demonstrate robust differential expression of both LTBP1 and TGFBR1. While RT-qPCR does not directly quantify protein abundance, the convergent evidence from proteomics, immunocytochemistry, and transcript-level validation is acceptable for a discovery-focused PLOS ONE study.

4. Causal interpretation and confounders

The authors have appropriately moderated causal language and explicitly acknowledged unmeasured confounders, including potential inflammatory and lifestyle factors. Interpretations are now framed as associative rather than causal, aligning with observational study standards.

5. Editorial quality

The rebuttal is clear, professional, and well-structured. Revisions are consistently reflected across the manuscript sections, with precise line references provided.

Minor optional suggestions

These are not required for acceptance:

- Consider explicitly referring to LTBP1 as a “prioritized candidate protein” in the Results.

- Include a brief statement noting that transcript abundance may not always directly reflect protein abundance.

Final recommendation

Accept for publication. All major scientific, statistical, and compliance issues have been satisfactorily addressed, and the manuscript now meets the standards for publication in PLOS ONE.

**********

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Reviewer #2: Yes: Krishna Chaitanya Mantravadi

**********

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PLoS One. doi: 10.1371/journal.pone.0342465.r006

Acceptance letter

Suresh Yenugu

PONE-D-25-30555R2

PLOS One

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. The list of the 39 overlapping proteins in the TGF-β signalling pathway in human sperm (TER versus NOR).

(DOCX)

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Data Availability Statement

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PERMALINK

Differential protein expression profiles in human sperm from teratozoospermic and normozoospermic men identify LTBP1 and TGF-βR1 as potential biomarkers within the TGF-β signalling pathway

Paweena Kaewman

Sutisa Nudmamud-Thanoi

Patcharada Amatyakul

Chuchard Punsawad

Sittiruk Roytrakul

Samur Thanoi

Roles

Abstract

Introduction

Materials and methods

Semen samples and analysis

Proteomic analysis

Bioinformatics analysis

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis

Immunocytochemistry analysis

Statistical analysis

Results

Proteomic analysis of human sperm from TER and NOR men

Fig 1. A Venn diagram showing overlaps between human sperm proteins among the NOR group (green circle) and TER group (blue circle).

Fig 2. Enrichment of the 39 overlapping proteins (red squares) in the TGF-β signalling pathway in human sperm (TER versus NOR).

Table 1. The list of the 6 DEPs in the TGF-β signalling pathway in human sperm (TER versus NOR).

Fig 3. The LTBP1 protein expression levels in human sperm.

Fig 4. The STRING protein-protein interaction network.

Validation of LTBP1 and TGFBR1 mRNA expression in human sperm

Fig 5. The mRNA expression of LTBP1 and TGFBR1 genes in human sperm.

TGF-βR1 protein expression in human sperm

Fig 6. Immunostaining for TGF-βR1 expression in human sperm at 1000x magnification.

Fig 7. The TGF-βR1 protein expression in human sperm.

Correlations of LTBP1 and TGF-βR1 protein expression in human sperm with sperm morphology

Fig 8. Correlations between the percentage of normal sperm morphology and the expression of proteins in human sperm.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Suresh Yenugu

Roles

Author response to Decision Letter 1

Decision Letter 1

Suresh Yenugu

Roles

Author response to Decision Letter 2

Decision Letter 2

Suresh Yenugu

Roles

Acceptance letter

Suresh Yenugu

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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