This is the material and methods, results and discussion section from my manuscript.
“Title: TSGA10 Induces Autophagy and Apoptosis in HeLa Cells by Targeting BCL-2
1.	Material and methods 
1.1.	Chemicals and Reagents
The following primary antibodies were acquired from Santa Cruz Biotechnology: goat anti-TSGA10, mouse anti-BCL-2, mouse anti-BAX, goat anti-caspase-3, goat anti-LC3I, and goat anti-LC3II. Additional reagents utilized were cisplatin and NH4Cl from Sigma‒Aldrich, rapamycin from InvivoGen, G418 from Alexis Biochemicals, acridine orange and Annexin V/PI assay kits from Abcam and Sigma‒Aldrich, Lipofectamine 2000, pcDNA3.1 vector from Invitrogen, trypsin-EDTA from Invitrogen, and T4 DNA ligase from Takara Bio. All other chemical reagents were purchased from Sigma‒Aldrich.
1.2.	Plasmid Construction
To generate a TSGA10 expression vector, the following primers were used for PCR amplification: 5’-AGGATCCGCCACCATGATGCGAAGTAGGTC-3’ containing a BamHI restriction site (underlined) and 5’-AGGAATTCGTCAGAAATCTCTGTAGC-3’ containing an EcoRI site (underlined). These primers targeted the coding sequence of the TSGA10 gene (Accession No. AF254756.1). Approval for using cell lines was obtained from the research ethics committee at Kermanshah University of Medical Sciences. PCR was performed using the above primers and 10 μg of human testis cDNA under the following conditions: initial denaturation at 94°C for 1 min, followed by 35 cycles of denaturation at 94°C for 1 min, annealing at 60°C for 90 sec, extension at 72°C for 1 min, and a final extension at 72°C for 10 min [4]. This generated a 2115 bp PCR product flanked by BamHI and EcoRI sites. The amplified PCR product was double digested with BamHI and EcoRI, purified, and ligated into the BamHI/EcoRI-linearized pcDNA3.1 vector using T4 DNA ligase. The ligation mixture was transformed into DH5α competent cells. Recombinant clones were verified by restriction analysis, PCR screening, and DNA sequencing to validate the clone reading frame.
1.3.	Cell culture and transfection
HeLa cells obtained from the Pasteur Institute were utilized as a tumor cell model, while primary human skin fibroblasts were isolated as previously described and used as normal control cells. Cell line authentication was performed by STR profiling [24]. Cells were cultured in DMEM/F12 supplemented with 10% fetal bovine serum, 100 μg/ml streptomycin, and 100 U/ml penicillin at 37°C in a humidified 5% CO2 incubator. For transfection, cells were seeded into 24-well plates at a density of 1-2 × 105 cells/well. At ~85-90% confluence, cells were transfected using Lipofectamine 2000 with different amounts of plasmid DNA (0.5-2 μg). Transfected cells were selected in medium containing 1 mg/ml G418 for four weeks to generate stable polyclonal populations. Individual G418-resistant colonies were isolated and expanded to generate clonal cell strains. TSGA10-transfected cells (H.pc-TSGA10) and empty vector control cells (H.pc) were maintained separately in G418 (200 μg/ml) to ensure stable expression. TSGA10 mRNA and protein expression were analyzed in resistant clones by real-time PCR, immunoblotting, and immunocytochemistry.
1.4.	Cell viability assay
HeLa cells were seeded at 5×103 cells/well in 96-well plates in 10% FBS medium and cultured for 48 and 72 hrs. For hypoxia treatment, plates were incubated in a hypoxia chamber equilibrated to 1% O2 using a nitrogen gas purge to displace oxygen. Parallel normoxic control plates were maintained in a standard cell culture incubator. Cell viability was measured by adding 20 μl/well of 5 mg/ml MTT reagent and incubating for 4 hrs, followed by dissolution of formazan crystals in 200 μl DMSO. Absorbance was read at 570 nm using a microplate spectrophotometer, with background subtraction at 630 nm. Experiments were performed in sextuplicate.
2.5. Real-time PCR
H.pc and H.pc-TSGA10 cells were cultured under normoxic or hypoxic conditions, with or without 100 nM rapamycin treatment, for 18 hrs to induce autophagy. Total RNA was extracted using TRIzol reagent and reverse transcribed to cDNA. Target gene expression was analyzed by real-time PCR using Takara SYBR Green master mix and the Rotor-Gene 6000 system with the following cycling conditions: 95°C for 15 min, then 40 cycles of 95°C for 15 sec, 60°C for 15 sec, and 72°C for 20 sec, followed by melting curve analysis from 60-95°C. Relative expression analysis was conducted using the REST software tool.
2.6. Western blot
H.pc and H.pc-TSGA10 cells were cultured under normoxic or hypoxic conditions and lysed in Tris buffer (0.075 M, pH 7.6) containing 9 M urea. The Bradford assay estimated the protein concentration. SDS‒PAGE resolved proteins (40-50 μg per sample) were transferred to PVDF membranes under reducing conditions. Membranes were blocked in 5% nonfat milk in PBS for 2 hrs at 25°C and probed with primary antibodies against TSGA10, BAX, BCL-2, caspase-3, LC3I, and LC3II, followed by HRP-conjugated secondary antibodies. Protein bands were visualized using chemiluminescence detection. Band intensities were quantified by densitometry, normalized to β-actin expression, and analyzed using TotalLab software.
2.7. Acidic Vesicle Detection
To monitor acidic vesicle formation, H.pc and H.pc-TSGA10 cells were stained with acridine orange (1 μg/ml) for 30 min under normoxic or hypoxic conditions, with or without rapamycin treatment. Cells were fixed in 4% paraformaldehyde and examined by fluorescence microscopy. The intensity of red fluorescence reflects the abundance of acidic vesicles and the degree of autophagy.
2.8. Molecular modeling and docking
Due to a lack of crystallographic data, homology modeling (HM) was utilized to generate a 3D structural model of TSGA10 (Q9BZW7). The TSGA10 sequence was obtained from UniProt and used to query the Protein Data Bank on BLAST to identify suitable HM templates based on high sequence identity. The templates selected were 3S4R (vimentin), 3CBU (glutathione S-transferase), 4IKM (CARD8), 1OBA (phage CP-1), 4E61 (Bim1p), and 2IXU (Cpl-1). Using multiple templates enhances model accuracy for proteins with low identity (<40%) [25]. Models were constructed with MODELLER 9.8 [26] and evaluated using PROCHECK [27] to select the best model with the lowest probability density function (PDF) and least violations. Loop refinement was performed using Modeler's ab initio loop prediction.
The HADDOCK web server [27] was utilized to model TSGA10-BCL2 interactions using docking guided by experimental data. The BCL2 structure was obtained from the PDB (1G5M). Docking was performed in three stages: rigid body energy minimization, semiflexible simulated annealing, and flexible explicit solvent refinement. Residues designated as “active” (experimentally implicated in binding) and “passive” (neighboring residues) were used to guide docking. The lowest energy model was selected for analysis.
2.9. Statistical analysis
Data are presented as the mean ± SD. Statistical comparisons between groups were conducted by appropriate methods such as t test, one-way or two-way ANOVA followed by post hoc tests (Tukey's test) using GraphPad Prism 8 software. P < 0.05 was considered statistically significant.
2.	Results
2.1.	TSGA10 expression
Stably transfected HeLa clones overexpressing TSGA10 were generated and analyzed for TSGA10 mRNA levels by quantitative PCR. Relative to control HeLa-pcDNA3.1 cells, TSGA10 mRNA expression was increased by 1.5 ± 0.34-fold in clone 1, 3.4 ± 0.2-fold in clone 2, and 5.8 ± 0.38-fold in clone 3. Human fibroblasts exhibited 3.93 ± 0.45-fold higher TSGA10 mRNA than control HeLa cells (Figure 1A). Densitometry confirmed that clone 3 had the highest TSGA10 protein expression among the clones (Figure 1B). Immunocytochemical analysis further validated TSGA10 overexpression in the transfected HeLa clones (Figure 1C).
Figure 1. Analysis of autophagy- and apoptosis-related gene and protein expression in H.pc and H.pc-TSGA10 cells under normoxic and hypoxic conditions. (A) Quantitative PCR analysis demonstrated significantly increased TSGA10 mRNA levels in three H.pc-TSGA10 clones compared to wild-type H.pc cells and normal human fibroblasts. (B) Western blot analysis revealed markedly higher TSGA10 protein expression in H.pc-TSGA10 clones versus controls. (C) Immunocytochemical staining confirmed abundant TSGA10 protein in H.pc-TSGA10 but not wild-type H.pc cells. (D) Western blotting showed increased LC3A-II and LC3B-II levels, markers of autophagosome formation, in H.pc-TSGA10 cells and normal fibroblasts treated with the autolysosomal inhibitor NH4Cl under hypoxia compared to normoxia. (F) Quantitative PCR analysis revealed decreased anti-apoptotic BCL-2 and increased pro-apoptotic BAX mRNA levels in H.pc-TSGA10 versus H.pc cells under hypoxia. (G, H) Western blots demonstrated reduced BCL-2 and elevated BAX protein expression in H.pc-TSGA10 cells under hypoxia. (I) The BAX/BCL-2 protein ratio significantly increased in H.pc-TSGA10 cells under hypoxia compared to normoxia and wild-type H.pc cells. Data represent the mean ± SD of 5-6 independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001 compared to controls.
2.2.	TSGA10 Overexpression Induces Autophagy in HeLa Cells
To investigate the role of TSGA10 in regulating autophagy, we first overexpressed TSGA10 in HeLa cells. We selected clone 3, which exhibited a 5.8-fold increase in TSGA10 mRNA, for further analysis. Immunoblotting revealed enhanced LC3II expression and LC3II/LC3I ratio (indicative of autophagosome accumulation) in H.pc-TSGA10 versus H.pc cells under both normoxic and hypoxic conditions, with or without the autolysosomal inhibitor NH4Cl (10 mM) (Figure 1F). To determine if TSGA10 upregulates autophagy-related genes, we performed real-time PCR analysis of Beclin1, BNIP3, NIX, ATG5, LC3I, and LC3II mRNA levels in H.pc-TSGA10 and control H.pc cells under normoxic and hypoxic conditions. We found that H.pc-TSGA10 cells exhibited significantly higher expression of all measured autophagy-related genes than H.pc cells (Figure 2). Furthermore, acridine orange staining and fluorescence microscopy revealed that H.pc-TSGA10 cells developed more acidic cytoplasmic compartments faster than H.pc cells under basal and rapamycin-treated conditions, reflecting greater autophagic flux (Figure 3).
Figure 2. Expression of autophagy-related genes in H.pc-TSGA10 cells compared to H.pc cells under normoxic and hypoxic conditions with or without rapamycin treatment. Quantitative real-time PCR and acridine orange staining were utilized to analyze autophagy marker mRNA and protein levels. Data are presented as the mean ± standard deviation from 5-6 independent experiments. Statistical significance relative to the control is indicated as *P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 3. Acridine orange analysis in H.pc-TSGA10 and H.pc cells with or without rapamycin treatment.
2.3.	TSGA10 Overexpression Stimulates Apoptosis in HeLa Cells
We analyzed the expression of pro-apoptotic BAX, anti-apoptotic BCL-2, and executor caspase-3 in H.pc-TSGA10 versus control cells to investigate whether TSGA10 overexpression induces apoptosis. Immunoblotting and real-time PCR demonstrated reduced BCL-2 expression and elevated BAX levels in H.pc-TSGA10 cells under normoxic and hypoxic conditions compared to controls (Figure 1H-I). The BAX/Bcl2 ratio, an indicator of apoptotic drive, was significantly higher in H.pc-TSGA10 cells. Under normoxia and hypoxia, cleaved caspase-3 was increased in H.pc-TSGA10 cells relative to H.pc cells, while procaspase-3 levels were reciprocally reduced, confirming greater caspase-3 activation (Figure 4). These results demonstrate that TSGA10 overexpression enhances apoptosis in HeLa cells.
Figure 4. Analysis of caspase-3 activation in H.pc-TSGA10 cells compared to parental H.pc cells following 48 hours of normoxic or hypoxic incubation. (A) Immunoblot analysis of procaspase-3 and cleaved (active) caspase-3 expression. (B, C) Densitometric quantitation of procaspase-3 and cleaved caspase-3 levels normalized to the β-actin loading control. Data represent the mean ± standard deviation of 4-5 independent experiments. Statistical significance relative to the control is indicated as *P < 0.05, **P < 0.01, and ***P < 0.001.
2.4.	TSGA10 Overexpression Reduces Cell Viability
We performed MTT assays to determine the impact of TSGA10 overexpression on HeLa cell viability. Under both normoxic and hypoxic conditions, TSGA10-overexpressing cells exhibited significantly decreased viability compared to control cells, possibly due to upregulated autophagy and apoptosis. Moreover, treatment with the autophagy inducer rapamycin or the apoptosis inducer cisplatin reduced viability at lower concentrations in H.pc-TSGA10 versus H.pc cells (Figure 5A-D). HeLa cells transfected with higher amounts of the TSGA10 expression vector showed markedly lower viability than cells transfected with an empty vector, further confirming the cytotoxic effects of TSGA10 (Figure 6A-D).
Figure 5. (A, B) Viability comparison of H.pc-TSGA10 cells with H.pc cells treated with or without various concentrations of rapamycin after 48 hours of incubation under normoxia and hypoxia. (C, D) Viability comparison of H.pc-TSGA10 cells to H.pc cells treated with or without different concentrations of cisplatin after 48 hours of incubation. Data are presented as the mean ± SD from n=6 independent experiments. Statistical significance is denoted as ** P < 0.01 and *** P < 0.001 compared to the control.
Figure 6. (A, B) HeLa cells were transfected with 1.5 μg of pcDNA3.1-TSGA10 or pcDNA3.1 and then treated with or without various concentrations of rapamycin (100-800 nM) for 72 hours in normoxia and hypoxia. (C, D) HeLa cells were transfected with different concentrations of pcDNA3.1-TSGA10 or pcDNA3.1 (0.5-2 μg) and then treated with or without 200 nM rapamycin for 72 hours in normoxia and hypoxia (n = 6, mean ± SD, ** P < 0.01, *** P < 0.001 compared to the control).
2.5.	Molecular Modeling and Docking
Molecular modeling and docking approaches were utilized to generate three-dimensional structural models of TSGA10. The TSGA10 sequence was aligned with six template protein structures and modeled using the MODELLER program. Ramachandran plot analysis of the resulting models by PROCHECK revealed that 90.3% of amino acid residues occupied the most favored or additionally allowed regions (Figure 7A), indicating the overall high stereochemical quality of the predicted model. Molecular docking using the HADDOCK web server was subsequently performed to model the interaction between TSGA10 and BCL-2 domains and gain insights into their binding mode (Figure 7B). The total interaction energy, van der Waals energy, and electrostatic energy values were examined to evaluate the TSGA10-BCL2 binding capability. Docking analysis demonstrated that TSGA10 binds BCL-2 more strongly through electrostatic forces than via van der Waals interactions (Table 1).
Figure 7. Structural model evaluation of TSGA10 and its docking to Bcl-2. (A) Ramachandran plot analysis of TSGA10 shows the distribution of residues in the most favored (red), additionally allowed (yellow), generously allowed (pale yellow), and disallowed (white color) regions. (B) The complex structure of TSGA10 docked with Bcl-2 was obtained using computational methods.
  To identify key interacting residues at the TSGA10/BCL-2 interface, per-residue decomposition of the total binding energy was conducted. Intermolecular nonbonded contacts were detected using Discovery Studio, and residue pairs exhibiting favorable interaction energies were analyzed (Table 1). These residues' van der Waals, electrostatic, and total interaction energies were calculated using the "Calculate Interaction Energy" protocol in Discovery Studio, with the CHARMM force field and implicit distance-dependent dielectrics. For all residue pairs examined, electrostatic interactions contributed substantially more than hydrophobic forces to the total interaction energy. This suggests that the TSGA10/BCL-2 binding mode involves charged side chain interactions rather than van der Waals contacts. Among the identified pairs, LYS169-GLU200 and GLU691-ARG207 possessed the strongest electrostatic and overall interaction energies.
Interaction pair	VdW energy (kcal/mol)	Electrostatic energy (kcal/mol)	Interaction energy (kcal/mol)
TSGA10 – BCL-2	-43.0048	-253.236	-296.24
LYS169:HZ1 - ASP196:OD2	-0.86185	-29.5956	-30.4574
LYS169:HZ2 - GLU200:OE2	5.30767	-41.7245	-36.4168
ASP243:OD1 - ARG107:NH2	-1.01023	-17.9569	-18.9671
ARG512:HH11 - SER205:O	0.251528	-10.3206	-10.0691
ARG512:HH11 - MET206:O	-0.65217	-16.9378	-17.5899
GLU513:OE1 - ARG207:HH11	5.61914	-32.2724	-26.6533
LYS532:NZ - GLU200:OE1	-0.23984	-11.2684	-11.5083
ARG539:NH2 - ASP196:OD2	-0.81043	-21.3965	-22.2069
GLU691:OE1 - ARG207:NH2	0.663735	-36.8467	-36.1829














Table 1. Total interaction energy, van der Waals forces, and electrostatic components for the entire TSGA10/BCL-2 complex and the critical residue pairs involved.
3.	Discussion
This study demonstrated that the expression of autophagy-related genes was elevated in HPC-TSGA10 cells compared to HPC cells under both normoxic and hypoxic conditions. Acridine orange staining revealed increased acidic vesicles in HPC-TSGA10 cells, confirming the higher autophagy rate. Furthermore, HPC-TSGA10 cells exhibited increased BAX expression and BAX/BCL2 ratio, enhanced cleaved caspase-3, and decreased BCL-2 expression relative to HPC cells in normoxic and hypoxic conditions. This corroborated the higher apoptosis rate in HPC-TSGA10 cells. Additionally, stably and transiently transfected cells with pcDNA3.1-TSGA10 displayed considerable reductions in cell viability under normoxic and hypoxic conditions.
Autophagy plays a critical role in determining the fate of cells and can be utilized clinically to induce type II programmed cell death, effectively destroying cancer cells [11]. Several cancers have impaired function of Beclin1, an autophagy regulator and tumor suppressor gene [28]. Elevated expression of Beclin1 can stimulate autophagy, preventing tumor development [22]. BNIP3 and BNIP3L belong to the BCL-2 (BH3-Only) family and can prompt autophagy-mediated cell death by disrupting the Beclin1/BCL-2 or Beclin1/Bcl-xL complexes. Moreover, some apoptosis inhibitors can also trigger autophagy by increasing Beclin1 levels or disrupting its interaction with BCL-2 family proteins [28].
A study by Roghanian et al. found that TSGA10 interacts with vimentin [20], and Wang et al. reported that decreased vimentin levels are linked to enhanced autophagy through Beclin1 upregulation [22]. Additionally, HIF-1α regulates autophagy by inducing specific pro- and anti-apoptotic proteins, such as BNIP3, BNIP3L, and BCL-2 [29,30].
The induction of autophagy-dependent cell death by ATG5 leads to the conversion of LC3I to LC3II, a crucial step for autophagosome formation [31]. Our study revealed a significant increase in ATG5 and LC3II expression in HPC-TSGA10 cells versus HPC cells, indicating that TSGA10 overexpression stimulates autophagy. ATG5 also plays a vital role in apoptosis as a caspase downstream effector, and its induction triggers apoptosis via calpain activation [31,32].
We previously showed that TSGA10 overexpression disrupted the HIF-1α axis [4], which regulates autophagy and apoptosis through Beclin1 and its interaction with caspase-8 and members of the Bcl-2 family of proteins [33]. We hypothesized that TSGA10 overexpression might prompt autophagy and apoptosis by modulating angiogenic factor expression and downstream signaling pathways (Figure 8). For instance, TSGA10 overexpression decreased VEGF, CXCL12, and CXCR4 expression, which promotes cell survival, migration, and angiogenesis [4]. VEGF triggers transient induction of autophagy in endothelial cells via AMPKα1, which is required for VEGF-induced angiogenesis [34] and can also prevent endothelial cell apoptosis induced by high glucose by reducing ROS generation, calcium overload, and the Bax/Bcl-2 ratio [35]. Moreover, CXCR4 induces autophagy in EBV-associated gastric carcinoma cells, promoting cell survival and persistent latent infection [36], and increased progesterone levels hinder autophagy by upregulating CXCL12/CXCR4 expression [37].
According to a previous study, TSGA10 upregulation inhibited BCL-2 expression by elevating P21 [38]. As tumor cells overexpressing BCL-2 resist apoptosis [39], BCL-2 inhibition through TSGA10 overexpression can potentially sensitize tumor cells to programmed cell death. Furthermore, TSGA10 overexpression induces G2/M cell cycle arrest, ultimately prompting apoptosis [40]. The decrease in cell viability following TSGA10 overexpression was mainly due to apoptosis induced by G2/M cell cycle arrest and, to a lesser degree, BCL-2 inhibition. TSGA10 overexpression also decreases MMP9 expression by inhibiting HIF-1α [4], which mediates hyperglycemia-induced oxidative stress and cell death via apoptosis and pyroptosis in human cardiac stem cells [41] (Figure 8).
Figure 8. Schematic illustration of possible antitumor mechanisms by which TSGA10 overexpression induces autophagy and apoptosis mediated by inhibition of BCL-2 and HIF1-α.
  The TSGA10 gene on chromosome 2 (q11.2) contains 19 exons [5] and plays essential roles in various biological processes, including centriole head-tail connection assembly, mitochondrial sheath organization, and embryonic development [7,8,42,43]. In mice, TSGA10 expression is detected in diverse normal cells and tissues, such as the cerebellum, bone marrow, thyroid, brain, eye bulb, cecum, hematopoietic stem cells, and testis. However, TSGA10 overexpression has been associated with several pathological conditions, including liver carcinoma, ovarian tumors, prostate and bladder cancers, colon cancer, malignant melanoma, cutaneous T-cell lymphoma, and autoimmune diseases [2,3,5].
TSGA10 is widely recognized as a critical factor in spermatogenesis [43]. Nonetheless, the high cell proliferation and differentiation rates associated with this process generate a hypoxic environment that increases the risk of tumor formation in testis tissue [44]. Consequently, HIF-1α and downstream effector expression levels are higher in the testis than in other tissues [45]. HIF-1α overexpression has been linked to multiple cancer types, as it confers apoptosis resistance and promotes tumor progression [46]. Interestingly, TSGA10 was shown to interact with HIF-1α, preventing its nuclear localization and transcriptional activity. This suggests that TSGA10 may modulate the hypoxic response in testis tissue and cancer cells.
Understanding the mechanisms regulating cell proliferation, the cell cycle, and survival in testis cells under hypoxia is essential for maintaining homeostasis and preventing testicular cancer. TSGA10 may play a role in modulating these mechanisms by interacting with HIF-1α, a transcription factor that mediates cellular adaptation to hypoxia by activating various target genes involved in apoptosis, autophagy [33], angiogenesis [47], glucose metabolism [48], erythropoiesis [49], and tumor development [46]. HIF-1α is crucial in maintaining a balance between oxygen demand and supply in tumor development and is a critical molecule enabling cells to adapt to hypoxic stress by regulating the expression of many genes [46]. Accordingly, HIF-1α inhibition by TSGA10 may suppress excessive cell proliferation and growth, preventing cancer progression.
Our previous research showed that a 55-kDa TSGA10 protein fragment can inhibit HIF-1α:P300/CBP complex formation by binding its TAD-C and PAS-B domains, leading to HIF-1α inactivation. This interaction indirectly inhibits BCL-2 [4]. The BH2 domain of BCL-2, which is crucial for its anti-apoptotic activity and BAX interaction [50], contains eight of the nine residues participating in its TSGA10 interaction, as indicated by molecular docking studies. Therefore, TSGA10 may inhibit BCL-2 oncogenic activity by targeting this specific domain. Energy analysis revealed a vital negative free energy for the TSGA10/BCL-2 complex, indicating stable electrostatically driven binding. Interestingly, BCL-2 has been reported to inhibit inflammation by interacting with NLRP1, preventing inflammasome activation through a loop between BH4 and BH3 motifs [51], which is not impacted by TSGA10 interaction.
Although the precise function of TSGA10 remains unclear, it is involved in autophagy and apoptosis, in addition to its established spermatogenesis role. Previous [4] and present studies confirm that TSGA10 functions as a tumor suppressor gene. This study demonstrates that the TSGA10 expression level is critical for its antitumor effects on cancer cells. Therefore, we utilized an expression vector to achieve maximal TSGA10 expression, inducing autophagy and apoptosis while reducing cancer cell viability. Hence, TSGA10 may be an innovative autophagy and apoptosis regulator in cancer cells. Although some studies have classified TSGA10 as a cancer-testis antigen (CTA) due to its high testis and tumor expression [3], it is widely expressed in normal human tissues. Our current and previous studies indicate that TSGA10 has a tumor-suppressive function. Thus, TSGA10 may be better considered a tumor-associated marker than a CTA. Our findings suggest that TSGA10 is a promising cancer therapy and management candidate.”
I submitted it to PLOS ONE journal and they commented as follows:
“Reviewer #1: In the manuscript submitted by Rezazadeh, et al., the authors conducted experiments to evaluate the function of TSGA10 gene in the regulation of autophagy and apoptosis. It was reported that overexpression of TSGA10 induced autophagy and apoptosis in HeLa cells, probably through the regulation of various autophagy- or apoptosis-related genes.

There are some major concerns regarding the study, as listed below:
1.Usually for studying the function of a specific gene, overexpression AND knock-down are both required. Therefore, the authors need to conduct the same experiments with knock-down in either the same type of cell or another type of cell.
2.It is not clear why TSGA10 showed no difference under both normoxic and hypoxic conditions. The authors should provide a convincible explanation for this phenomenon.
3.The authors’ conclusion that TSGA10 acts through Bcl-2 is in doubt. In the Western blot, it was shown that the expression of Bcl-2 was decreased, and then molecular docking suggested a direct binding of the two proteins. These are two distinct mechanisms, how did they work together? Or independent? Furthermore, co-IP is a simple method to detect the interaction of the two proteins, a much stronger evidence for their claim.

Minor issues:
Most of the Western blot images are not in good quality.
Reviewer #2: In this manuscript, Rezazadeh et al investigated the effects of overexpression of Testis specific gene antigen 10 (TSGA10) on autophagy and cell viability of Hela cells.
Overexpression of TSGA10 significantly increases both autophagy- and apoptosis-related genes, and thus induces autophagy and reduces cell viability. Meanwhile, the authors found that Bcl-2 decreased. Considering Bcl-2 functions, using Molecular docking authors claimed that TSGA10 can inhibit the anti-apoptotic activity of Bcl-2. This manuscript only described multiple phenotypes of overexpression of TSGA10 with little mechanism presentation, and physiological role study. The data quality and writing of this manuscript is poor.
Major point:
In the manuscript, a few autophagy-related genes are surveyed, and the conclusion that the expression of autophagy-related genes is increased is made. As more autophagy genes are not been tested, and if some of them are downregulated the conclusion is not supported. Therefore, a transcriptomics study is necessary.
The relationship between autophagy induction and reduction of cell viability in TSGA10 overexpression cells needs to be examined.
The authors try to use molecular dynamics to model TSGA10-BCL2 interactions. However, there is no evidence for existing TSGA10-BCL2 interaction. Moreover, the physical interaction of TSGA10 and BCL2 does not help to explain the phenotypes of TSGA10 overexpression.
Authors hypothesized that anti-apoptotic activity of Bcl-2 is reduced in TSGA10 overexpression cells. The experiment evidence is required.

Minor points
For each Figure, the font and size should be consistent.
By reading Figure legends, I cannot understand what the authors try to present. For example, Figure 5. "(A, B) HeLa cells were transfected with 1.5 μg of pcDNA3.1-TSGA10 or pcDNA3.1
and then treated with or without various concentrations of rapamycin (100-800 nM) for 72 hours in normoxia and hypoxia. "
The labels in Figures should be easy to follow. For example, Figure 1A Y-axel "Relative mRNA expression", we do not know which mRNA is quantified.
The quality of WB such as 1G, 1H and 4 is unacceptable.”

Please rewrite the discussion section based on these comments, especially limitations in the discussion section. Write it in the tone, style, and structure of a perfect article published in a high-impact journal such as The Lancet or Nature. Mention that further experiments such as tests, cell lines and knock down were not conducted because of budget limitation. Write it in the best way you can. Make in-text citations.