Integrated Analysis of Transcriptome and sRNA Sequencing Reveals Mmu-miR-503-5p Regulates the Aluminum Chloride Stress Response of GC-1spg Cells by Targeting Islr

Abstract

Aluminum chloride (AlCl₃), a widely used inorganic polymeric coagulant in everyday products and industrial materials, has been associated with male reproductive toxicity, though its molecular mechanisms remain poorly understood. To investigate the complex molecular mechanisms underlying GC-1spg cells’ responses to AlCl₃ exposure, transcriptome and small RNA (sRNA) sequencing analyses were performed. Transcriptome sequencing identified 1168 differentially expressed genes (DEGs), while sRNA sequencing detected 65 differentially expressed microRNAs (DEMs). An mRNA–miRNA regulatory network was established, and functional enrichment analysis showed that its target genes were significantly associated with multiple signaling pathways, particularly the p53 pathway. Further validation via Western blot and Hoechst 33342 staining assays confirmed that GC-1spg cells underwent apoptosis upon AlCl₃ exposure via the p53 signaling pathway. Among the identified DEMs, mmu-miR-503-5p was found to enhance GC-1spg cells’ tolerance to AlCl₃-induced stress. Moreover, dual-luciferase reporter assays and RT-qPCR confirmed that mmu-miR-503-5p directly binds to the Islr gene, which plays a role in modulating GC-1spg cell tolerance to AlCl₃-induced stress. These findings provide critical insights into the molecular mechanisms governing GC-1spg cells’ responses to AlCl₃ exposure.

1. Introduction

Aluminum (Al) is the most abundant metal in the Earth’s crust, with global ore reserves estimated at 40–50 billion tons [1,2]. Furthermore, the utilization of Al-containing products is increasing in daily life, due to its low production cost, light weight, and malleability. Al can enter the land ecosystem, causing soil acidification and yield reduction [3]. It has been reported that the Al concentration has increased 2000 times compared to what it was six years prior in rivers in Xian, China. This situation is undeniably hazardous to the health of individuals who use the water from these rivers [4]. Consequently, humans can come into contact with Al. Humans are exposed to Al through both dietary and non-dietary sources. Aluminum salts are commonly added to a variety of commercially available foods, used as flocculants in drinking water treatment, and employed in food packaging and storage materials [5]. Additionally, significant non-dietary exposure occurs through the use of aluminum-containing adjuvants in vaccines, as well as in pharmaceuticals, cosmetics, sunscreens, deodorants, and makeup products [6]. Al can impair spermatogenesis and adversely affect sperm quality in humans through various mechanisms, such as inducing oxidative stress, disrupting cell signaling, and interfering with endocrine system function [7,8]. Al poses a significant threat to male reproductive health, and the biotoxicity associated with Al exposure has garnered widespread global attention.

Al exposure is toxic to male reproductive systems [9]. The administration of aluminum chloride (AlCl₃) in a rodent model led to decreases in sperm motility, concentration, and abnormal morphology, as well as reductions in testosterone levels and offspring numbers [10,11]. Oral administration of AlCl₃ to rats at environmentally relevant doses significantly reduced sperm count, daily sperm production, sperm motility, and the percentage of morphologically normal sperm [12]. AlCl₃ impacts the male reproductive tract detrimentally via multiple mechanisms. These involve heightened oxidative stress, modifications to membrane function, disruption of cell signaling pathways, inactivation or depletion of enzymes, and disturbance of the blood–testis barrier. Exposure to AlCl₃ increases ROS levels, resulting in oxidative damage to DNA and lipids, alterations in protein structure/function, and additional adverse outcomes [13]. Moreover, oxidative stress triggers apoptosis in germ cells, leading to impaired spermatogenesis and decreased ATP production, which subsequently compromises sperm motility [9]. Furthermore, AlCl₃ may interfere with calcium channels in Sertoli and Leydig cells, thereby impairing androgen production [14]. While it is well documented that AlCl₃ exerts male reproductive toxicity through oxidative stress, perturbation of calcium channels, and compromising the blood–testis barrier, the molecular regulators and associated gene networks underlying this toxicity in germ cells are still largely unclear.

As short, non-coding, single-stranded RNA molecules, microRNAs (miRNAs) mediate post-transcriptional gene silencing via the RNA interference (RNAi) mechanism, leading to reduced mRNA stability and inhibition of protein translation [15]. Accumulating evidence indicates that miRNAs mediate biological responses to heavy metal exposure. Co-exposure to fluoride and aluminum upregulates miR-29b-3p to inhibit Dusp2 expression, exacerbating apoptosis in NG108–15 cells [16]. The microRNAs miR-122, novel-miR6, miR-193a-3p, and miR-27a-5p represent potential molecular indicators of Cd-induced injury to the head kidney in common carp [17]. Additionally, methotrexate (METH) elevates miR-29a expression in testicular tissues, which correlates strongly with METH-induced testicular toxicity [18]. GC-1 spg cells are an immortalized cell line derived from mouse spermatogonia, widely used as an in vitro model for studying spermatogenesis and male germ cell biology. The role of miRNAs in GC-1spg cells exposed to AlCl₃ remains uncharacterized. Thus, identifying key miRNAs involved in AlCl₃-induced toxicity in GC-1spg cells is of paramount significance.

In this study, GC-1spg cells were divided into a control group and an AlCl₃-treated group, followed by transcriptome and small RNA (sRNA) sequencing. Bioinformatics approaches were applied to identify differentially expressed genes (DEGs) and microRNAs (DEMs). An integrative analysis of these datasets enabled the reconstruction of a miRNA–mRNA regulatory network. Apoptosis induced by AlCl₃ exposure was assessed using Western blotting and Hoechst 33342 staining. Additionally, the functional role of mmu-miR-503-5p in conferring resistance to AlCl₃ stress was further investigated. Collectively, these findings provide critical insights into the molecular mechanisms underlying GC-1spg cell responses to AlCl₃ toxicity.

2. Materials and Methods

2.1. Cell Culture and Transfection

The mouse-spermatogonia-derived GC-1spg cell line and the human embryonic kidney HEK-293T cell line were obtained from Baidi Biotech Ltd. (Zhejiang, China, C5066). Both lines were routinely cultured in Dulbecco’s Modified Eagle Medium (DMEM; Wuhan, China, Servicebio, G4511) supplemented with 10% (v/v) fetal bovine serum (FBS; Servicebio, G8002) and 1% (v/v) penicillin–streptomycin solution (Shanghai, China, Beyotime, C0222) under standard conditions of 37 °C and 5% CO₂ in a humidified incubator. The culture medium was replaced every 48 h to maintain optimal growth conditions. When cells reached approximately 80–90% confluence, they were passaged using the following procedure: Culture medium was aspirated, and cells were washed once with sterile phosphate-buffered saline (PBS) (Servicebio, G4202-500ML). Cells were then dissociated with 0.25% trypsin–EDTA (Servicebio, G4001-100ML) for 3 min at 37 °C. Trypsinization was neutralized by adding complete growth medium-containing serum. The cell suspension was centrifuged at 1000 rpm for 5 min, and the pellet was resuspended in fresh complete medium. For routine subculture, cells were typically split at a ratio of 1:4. All procedures were performed under sterile conditions in a biosafety cabinet.

2.2. Establishment of Gene Overexpression and Knockdown GC-1spg Cells

GC-1spg cells were seeded into 6-well plates at a density of 5 × 10⁵ cells per well. After attachment, they were transfected with either 500 ng of mmu-miR-503-5p-overexpression plasmid or Islr-knockdown plasmid using Lipo8000™ transfection reagent (Beyotime, C0533), following the manufacturer’s instructions. Cells transfected with an empty vector were used as negative controls. All plasmids were sourced from Sangon Biotech (Shanghai) Co., Ltd (Shanghai, China). Transfection efficiency and target gene modulation were subsequently confirmed at the mRNA level by reverse transcription–quantitative PCR (RT-qPCR).

2.3. Cell Counting Kit-8 Assay

Exponentially growing GC-1spg cells were seeded into 96-well plates at a density of 2000 cells per well. After adherence, cells were treated with culture media containing increasing concentrations of AlCl₃ (0, 2, 3, 4, and 5 mM), with the 0 mM group serving as the control. Each condition was performed in triplicate. Following 24 h of exposure, 10 µL of Cell Counting Kit-8 (CCK-8) reagent (Suzhou, China, UElandy, C6005M) was added to each well, and plates were incubated at 37 °C for 2 h. Cell viability was assessed by measuring absorbance at 450 nm using a microplate reader (Maennedorf, Switzerland, SPARK, TECAN). For functional assays, the precursor sequence of mmu-miR-503-5p was cloned into the PiggyBac Dual promoter-U6 (pBD-U6) vector to enable miRNA overexpression. GC-1spg cells were seeded in 96-well plates at 5 × 10³ cells/well, allowed to adhere for 24 h, and then transfected with 150 ng of pBD-U6 plasmid per well. After 48 h of transfection, cell proliferation was evaluated using the CCK-8 assay, with five independent biological replicates conducted for statistical robustness.

2.4. Transcriptome Sequencing and Analysis

Exponentially growing cells were seeded in 6-well plates and cultured until 75% confluence, after which they were treated with medium containing either 0 mM (control) or 3 mM AlCl₃. Three biological replicates were performed. After a 48 h treatment period, subsequent experiments were conducted. Total RNA was extracted using Trizol Reagent (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol. The integrity and quality of the extracted total RNA were evaluated using the NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA) and Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA).

For transcriptome library preparation, 2 µg of total RNA was processed using the VAHTS Universal V6 RNA-seq Library Prep Kit for Illumina^® (Nanjing, China, Vazyme, NR604-02). DNA fragments in the size range of 250–350 bp were subsequently selected using VAHTS DNA Clean Beads (Vazyme, N411-01). Paired-end sequencing (2 × 150 bp) was performed on the NovaSeq X Plus platform (Illumina, San Diego, CA, USA).

Raw sequencing reads were subjected to quality control and adapter trimming with fastp v0.20.0 (https://github.com/OpenGene/fastp, accessed on 15 January 2026), yielding high-quality clean reads. These reads were then aligned to the reference mouse genome (GCF_000001635.27; https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001635.27/, accessed on 15 January 2026) using HISAT2 v2.2.1 (https://daehwankimlab.github.io/hisat2/, accessed on 15 January 2026). Transcript abundance was quantified via StringTie v2.0.4 (https://github.com/gpertea/stringtie, accessed on 15 January 2026), with expression levels normalized as FPKM (Fragments Per Kilobase of transcript per Million mapped reads). Differential gene expression analysis was conducted using DESeq2 v1.10.1. Genes were defined as differentially expressed (DEGs) if they met the criteria of |log₂ (fold change)| ≥ 1 and a false discovery rate (FDR) < 0.05. Functional annotation of DEGs was carried out through Gene Ontology (GO) enrichment analysis using the GOseq R package v1.54.0, while pathway enrichment was assessed via KOBAS v3.0 with a significance threshold of p < 0.05.

2.5. Small RNA Sequencing and Analysis

Small RNA (sRNA) libraries were prepared from three biological replicates using the VAHTS™ Small RNA Library Prep Kit for Illumina (Vazyme, NR801-02), strictly following the manufacturer’s protocol. Sequencing was carried out on an Illumina NovaSeq 6000 platform to generate 50 bp single-end reads. Raw reads were processed using in-house Perl scripts provided by Tsingke Biotechnology Co., Ltd. (Beijing, China) to eliminate adapter dimers, poly-N-containing reads, and low-quality sequences. Clean reads were further filtered to remove ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), other non-coding RNAs (ncRNAs), and repetitive elements via alignment with the Bowtie aligner. The remaining reads were used for miRNA identification: known miRNAs were annotated by mapping to the mouse reference genome and cross-referencing with miRBase, while novel miRNAs were predicted based on sequence and structural features. The secondary structures of candidate novel miRNAs were assessed using the Randfold tool. Differentially expressed miRNAs (DEMs) were determined with DESeq2 v1.10.1, applying a significance threshold of p < 0.01 and |log₂ (fold change)| > 1.

2.6. Construction of the miRNA–Target Interaction Network

Target genes of DEMs were predicted using the TargetScan database according to the Total Context++ Score. The Total Context++ Score is a quantitative metric generated by TargetScan to predict the efficacy of miRNA–mRNA interactions. It integrates multiple features, including: seed match type, local AU content around the site, position of the site within the 3′UTR, presence of 3′-compensatory pairing, and target site abundance. A more negative Total Context++ Score indicates stronger predicted repression of the target gene by the miRNA. Since miRNAs typically suppress the expression of their target genes, we integrated the prediction results with gene expression profiles to identify putative functional interactions. Based on this integration, a miRNA–target regulatory network was constructed using Cytoscape v3.10.3.

2.7. Reverse-Transcription Quantitative PCR (RT-qPCR)

The same RNA samples used for RNA-seq were subjected to reverse transcription–quantitative PCR (RT-qPCR) for validation. For mRNA analysis, first-strand cDNA synthesis and qPCR were performed using ToloScript All-in-one RT EasyMix for qPCR (Tolobio, Shanghai, China; Cat. No. 22107) and 2× Universal Blue SYBR Green qPCR Master Mix (Servicebio, Wuhan, China; Cat. No. G3326-01), respectively, in accordance with the manufacturers’ protocols. GAPDH was used as the reference (housekeeping) gene. For miRNA quantification, reverse transcription was carried out with the miRNA First Strand cDNA Synthesis Kit (Polyadenylation Method; Servicebio, Wuhan, China; Cat. No. G3334-25), followed by qPCR using the same 2× Universal Blue SYBR Green qPCR Master Mix. U6 small nuclear RNA served as the internal control. All reactions were run in triplicate for each sample, and three independent biological replicates were included. Relative expression levels were calculated using the 2^−ΔΔCt method. The primer sequences used in this study are listed in Table S1.

2.8. Western Blot

Total cellular proteins were extracted using RIPA lysis buffer (Beyotime, P0013B) supplemented with 1 mM phenylmethylsulfonyl fluoride (PMSF; Beyotime, ST506). Protein concentrations were determined with the Detergent-Compatible Bradford Protein Assay Kit (Beyotime, P0006C). Equal amounts of protein were resolved by 12% SDS-PAGE (Epizyme, PG213) at a constant voltage of 80 V for 1 h. Proteins were then electrotransferred onto polyvinylidene fluoride (PVDF) membranes at 400 mA for 30 min. Membranes were blocked with QuickBlock™ Blocking Buffer (Beyotime, P0252) for 15 min at room temperature, followed by overnight incubation at 4 °C with primary antibodies diluted in blocking buffer. The following primary antibodies from Proteintech were used: GAPDH (1:10,000; 60004-1-Ig), BAX (1:50,000; 50599-2-Ig), p21 (1:1500; 82669-2-RR), p53 (1:25,000; 80077-1-RR), and phospho-p53 (Ser15) (1:5000; 80195-1-RR). After washing, membranes were incubated for 1 h at room temperature with HRP-conjugated secondary antibodies: goat anti-mouse IgG (Beyotime, A0216) or goat anti-rabbit IgG (Beyotime, A0208). Immunoreactive signals were visualized using BeyoECL Plus reagent (Beyotime, P0018S). Band intensities were quantified by densitometry using ImageJ v1.54 software, and all target protein levels were normalized to GAPDH. Experiments were independently repeated three times (biological replicates).

2.9. Dual-Luciferase Reporter Assay

The wild-type (WT) and mutant (MUT) fragments of the Islr 3′-UTR containing the predicted miR-503-5p binding sites were cloned into the pmirGLO dual-luciferase reporter vector using the Seamless Cloning Kit (Beyotime, D7010S), according to the manufacturer’s protocol. HEK-293T cells were seeded in 96-well plates at a density of 5 × 10³ cells per well and allowed to adhere overnight. Cells were then co-transfected with 75 ng of either WT or MUT pmirGLO reporter plasmid together with 75 ng of pU6-mmu-miR-503-5p expression plasmid. After 48 h of incubation, firefly luciferase activity was measured and normalized to Renilla luciferase activity to account for transfection efficiency. All experiments were independently repeated in three biological replicates.

2.10. Cell Apoptosis Assay

The Hoechst Staining Kit (C0003, Beyotime) was used to evaluate cell apoptosis according to the manufacturer’s instructions. Fluorescent signals were recorded using a fluorescence microscope (Floid, Invitrogen, Carlsbad, CA, USA). The nuclei of normal cells exhibited a typical blue color, while those of apoptotic cells appeared densely stained, either as intact dense structures or fragmented dense clumps with a slightly pale tint. Apoptotic cells were counted per 100 cells, and the assay was conducted with three biological replicates.

2.11. Superoxide Anion Assay

The concentration of superoxide anion was quantified with a dihydroethidium (DHE)-based detection kit (Reactive Oxygen Species Assay Kit for Superoxide Anion, Beyotime, S0064S), following the supplier’s instructions. Fluorescence intensity was measured with a microplate reader (SPARK, TECAN, Switzerland), and fluorescence images were captured using a fluorescence microscope (Floid, Invitrogen, USA). The experiment was performed with three biological replicates.

2.12. Statistical Analysis

All statistical analyses were conducted using SPSS version 20.0. Data are expressed as mean ± standard deviation (SD). Prior to applying parametric tests, the normality of each dataset was verified using the Shapiro–Wilk test, with p > 0.05 indicating a normal distribution. All datasets subjected to Student’s t-test or one-way ANOVA satisfied the assumptions of both normality and homogeneity of variances. Comparisons between two independent groups were performed using a two-tailed unpaired Student t-test. For experiments involving more than two groups, one-way analysis of variance (ANOVA) was employed, followed by Tukey’s post hoc test for pairwise comparisons. Statistical significance was defined as p < 0.05.

3. Results

3.1. Transcriptome Sequencing

To identify the appropriate AlCl₃ concentration for our experimental model, we conducted CCK-8 assays on GC-1spg cells. Among the concentrations tested, cell viability exhibited a significant decrease starting from 3 mM AlCl₃ (Figure S1). Based on this finding, 3 mM AlCl₃ was chosen for all subsequent analyses. In this study, six samples representing GC-1spg cells under control and 3 mM AlCl₃ exposure conditions at 48 h were sequenced. Paired-end sequencing of the six samples generated 302.66 million raw reads. After quality filtering, 277.36 million clean reads (91.64%) were obtained. Approximately 44.3 million clean reads were generated, of which 95.78% successfully aligned to the mouse reference genome, reflecting high-quality transcriptome sequencing (Table S2).

Differential expression analysis was carried out to detect differentially expressed genes (DEGs). A total of 742 upregulated and 426 downregulated genes were identified upon AlCl₃ exposure (Figure 1A and Table S3). KEGG analysis demonstrated that 52 pathways, including the p53 signaling pathway, MAPK signaling pathway, glioma, complement and coagulation cascades, microRNAs in cancer, and so on, were significantly enriched (p < 0.05) (Figure 1B and Table S4). Notably, 14 and 12 transcription factors exhibited significant upregulation and downregulation, respectively (Figure 1C and Table S3). To validate the RNA-seq results, five genes were selected based on their false discovery rate (FDR) values. A strong positive correlation was observed between the RNA-seq and RT-qPCR expression levels for all five genes, with Pearson correlation coefficients (R) of 0.994, 0.942, 0.775, 0.911, and 0.989 (all p < 0.05; Figure 2), confirming the reliability of the transcriptome data.

Figure 1.

Overview of differentially expressed genes (DEGs) in GC-1spg cells following AlCl₃ exposure. (A) Summary of DEG counts between control and AlCl₃-treated groups. Genes were defined as differentially expressed if they met the criteria of |log₂ (fold change)| ≥ 1 and a false discovery rate (FDR) < 0.05. (B) KEGG pathway enrichment analysis of DEGs, performed using KOBAS v3.0; pathways with p < 0.05 were considered significantly enriched. (C) Differentially expressed transcription factors identified within the DEG set.

Figure 2.

Validation of RNA-seq results by RT-qPCR. Five genes—Islr, Hamg2, Cnot6, Ccnd1, and Sgk1—were randomly selected for qPCR confirmation. Each experimental condition included three biological replicates, each analyzed in triplicate (technical replicates). Expression levels from RNA-seq (right axis) and RT-qPCR (left axis) showed a significant positive correlation (p < 0.05).

3.2. Small RNA Sequencing

To identify miRNAs responsive to AlCl₃ exposure in mouse GC-1spg cells, six small RNA (sRNA) libraries were prepared. Sequencing generated a total of 107.45 million raw reads, averaging 17.9 million per library. Following quality control, an average of 17.22 million high-quality clean reads per sample were retained for downstream analysis (Table S5). Length profiling of unique sRNA species showed that 22-nucleotide (nt) reads were the most prevalent, representing 37.72% of the total. The next most abundant lengths were 21 nt (15.30%), 23 nt (12.34%), and 18 nt (7.99%) (Figure S2).

To uncover miRNAs responsive to AlCl₃ exposure, the expression profiles of all annotated miRNAs were comprehensively analyzed. A total of 65 miRNAs showed significant differential expression following AlCl₃ treatment, including 17 upregulated and 48 downregulated miRNAs (Figure 3 and Table S6). For validation of the small RNA-seq results, four miRNAs were selected based on their statistical significance (p < 0.05). RT-qPCR measurements of these miRNAs exhibited strong concordance with sequencing data, with Pearson correlation coefficients (R) of 0.859, 0.976, 0.819, and 0.804 (all p < 0.05; Figure 4), confirming the reliability of the sRNA sequencing dataset.

Figure 3.

Differential expression patterns of miRNAs in GC-1spg cells following aluminum exposure. Expression values were row-wise standardized. Differentially expressed miRNAs (DEMs) were defined as those with |log₂ (fold change)| > 1 and a statistical significance of p < 0.01.

Figure 4.

RT-qPCR validation of small RNA sequencing results. Four miRNAs, miR-322-5p, miR-146b-5p, miR-146a-5p, and miR-503-5p, were randomly chosen for qPCR confirmation. Each experimental group included three biological replicates, each analyzed in triplicate (technical replicates). Expression levels from sRNA-seq (right axis) and RT-qPCR (left axis) showed a significant positive correlation (p < 0.05).

3.3. Expression Correlation of miRNAs with Their Predicted Target Genes

By integrating small RNA-seq data with TargetScan-predicted targets, the expression profiles of miRNAs and their corresponding target genes were jointly analyzed to elucidate the regulatory role of miRNAs in GC-1spg cells exposed to AlCl₃. A total of 2053 negatively interacting pairs (Total Context++ Score < −0.15) were identified upon AlCl₃ exposure, including 65 miRNAs and 723 target genes. Among all differentially expressed miRNAs (DEMs), mmu-miR-128-1-5p targeted the largest number of genes, with 112 predicted targets. The most significantly upregulated and downregulated DEMs were mmu-miR-146a-5p (targeting 2 genes) and mmu-miR-503-5p (targeting 11 genes), respectively (Figure 5; Table S7). KEGG pathway enrichment analysis revealed 36 significantly enriched pathways (p < 0.05), including the p53 signaling pathway, PI3K–Akt signaling pathway, and cytokine–cytokine receptor interaction (Figure S3; Table S8). Notably, 31 miRNAs—collectively targeting 19 genes—were found to be associated with the p53 signaling pathway (Table S9). Intriguingly, 28 of these pathways overlapped with those identified in the KEGG enrichment analysis of differentially expressed genes (DEGs), suggesting that DEMs may exert regulatory effects on DEGs through these shared signaling cascades.

Figure 5.

Regulatory network of the top five differentially expressed miRNAs and their target genes in GC-1spg cells exposed to aluminum. The ‘V’ shapes and triangles indicate upregulated and downregulated miRNAs, respectively. For miRNAs, the progression of color from yellow to black reflects an increase in degree values, with darker colors representing higher degrees. For the connecting lines, the progression of color from yellow to blue reflects an increase in Total Context++ Score, with deeper colors representing higher scores. All miRNA–target gene interactions are listed in Table S7.

3.4. AlCl₃ Stress Induces Significant Enrichment of the p53 Signaling Pathway in GC-1spg Cells

KEGG enrichment analysis revealed significant activation of the p53 signaling pathway in GC-1spg cells exposed to AlCl₃. This finding was further corroborated by gene set enrichment analysis (GSEA), which demonstrated a positive association between AlCl₃-induced stress and p53 pathway activity (Figure 6A). To explore the molecular mechanism, we examined the expression of P53 pathway components (P21, P53, phospho-P53, and BAX). Western blot analysis revealed that AlCl₃ exposure induced robust upregulation of these pro-apoptotic proteins (Figure 6B), suggesting transcriptional activation of the P53 pathway under aluminum stress. Notably, the upregulated BAX expression suggested that GC-1spg cells had undergone apoptosis. To further validate the apoptotic level under AlCl₃ stress, Hoechst 33342 staining was performed. Quantitative assessment revealed a significantly elevated apoptotic rate in AlCl₃-treated GC-1spg cells compared to untreated controls (Figure 6C). Additionally, superoxide anion production was markedly increased in response to AlCl₃-induced stress. (Figure 6D). Taken together, the results indicated that AlCl₃ stress triggers activation of the P53 signaling pathway and promotes apoptosis in GC-1spg cells.

Figure 6.

Significant enrichment of the p53 signaling pathway in GC-1spg cells under AlCl₃ stress. (A) Gene set enrichment analysis (GSEA) using DEGs. (B) Western blotting analysis of P21, P53, Phospho-P53, and BAX in the GC-1spg cells upon AlCl₃ stress. The ratio of target to internal standard is indicated below each band. (C) Apoptotic rate of GC-1spg cells upon AlCl₃ stress determined by Hoechst 33342 staining. Bars = 100 μm, ** p < 0.01. (D) Superoxide anion production in GC-1spg cells under AlCl₃ stress, as determined by DHE staining. Bars = 100 μm, * p < 0.05, ** p < 0.01.

3.5. Mmu-miR-503-5p Mediates AlCl₃ Stress Response Through Targeting Islr

Based on miRNA expression profiling, mmu-miR-503-5p displayed significant downregulation with the lowest p-value (4.22 × 10⁻²⁷), thus being selected for further investigation. GC-1spg cells with overexpression of mmu-miR-503-5p were successfully established (Figure 7A). CCK-8 assays demonstrated that overexpression of mmu-miR-503-5p markedly improved cellular resistance to AlCl₃-induced cytotoxicity (Figure 7B). Integrative miRNA–target correlation analysis (Figure 5; Table S7) identified Islr as a direct target of mmu-miR-503-5p. Notably, Islr expression was significantly upregulated in GC-1spg cells under AlCl₃ stress (Table S3), but downregulated upon mmu-miR-503-5p overexpression (Figure 7C). Reporter plasmids harboring either the wild-type or mutant miR-503-5p binding sequence in the Islr 3′-UTR were successfully generated (Figure 7D). Dual-luciferase assays confirmed that co-expression of mmu-miR-503-5p significantly suppressed luciferase activity from the wild-type reporter construct, whereas no significant repression was observed with the mutant version, thereby validating specific targeting of Islr by mmu-miR-503-5p. (Figure 7E). Furthermore, knockdown of Islr resulted in increased tolerance to AlCl₃-induced stress (Figure 7F). Overexpression of mmu-miR-503-5p and knockdown of Islr led to a significant decrease in superoxide anion production under AlCl₃-induced stress (Figure 7G). Together, these results indicate that mmu-miR-503-5p modulates the cellular response to AlCl₃ stress by directly targeting Islr.

Figure 7.

Mmu-miR-503-5p mediates AlCl₃ stress response through targeting Islr in GC-1spg cells. (A) RT-qPCR analysis of mmu-miR-503-5p expression in GC-1spg cells with mmu-miR-503-5p overexpression. ** p < 0.01. (B) GC-1spg cells with mmu-miR-503-5p overexpression exhibit enhanced tolerance to AlCl₃-induced stress. The experiment was conducted with five biological replicates. ** p < 0.01. (C) RT-qPCR analysis of Islr expression in GC-1spg cells with mmu-miR-503-5p overexpression. The experiment was conducted with three biological replicates. * p < 0.05. (D) Predicted binding sites between mmu-miR-503-5p and the 3′UTR of Islr. (E) Luciferase activation was detected in cells co-transfected with pU6-mmu-miR-503-5p and wild-type/mutation 3′UTR of Islr. * p < 0.05. (F) GC-1spg cells with Islr knockdown exhibit enhanced tolerance to AlCl₃-induced stress. * p < 0.05. (G) Superoxide anion production in GC-1spg cells with mmu-miR-503-5p overexpression and Islr knockdown under AlCl₃ stress, as determined by DHE staining, bars = 100 μm. * p < 0.05, ** p < 0.01, ns indicates non−significance.

4. Discussion

The exposure of GC-1spg cells to AlCl₃ involves the complex interplay of multiple genes and regulatory factors [19,20]. This study employed transcriptome and small RNA sequencing to characterize the genetic and molecular responses underlying AlCl₃ exposure in GC-1spg cells. In this study, we selected 3 mM AlCl₃ based on preliminary in vitro dose–response experiments using the CCK-8 assay (Figure S1). This concentration reduced GC-1spg cell viability, whereas no obvious effect was observed at 2 mM AlCl₃, providing a clear phenotypic effect for investigating molecular responses. We note that this concentration may not reflect physiological exposure levels. Notably, a recent study employed an even higher in vitro concentration (4 mM AlCl₃) in mouse GC-2spd cells to successfully elucidate ROS-mediated mitophagy and apoptosis mechanisms—highlighting that supraphysiological doses are often used in mechanistic toxicology to ensure robust signal detection [9]. We agree that such in vivo validation is important and plan to include it in future work.

A comprehensive transcriptome analysis of GC-1spg cells upon AlCl₃ exposure was performed. Several signaling pathways were significantly enriched, suggesting extensive effects on cellular processes. Notably, both the p53 signaling pathway and apoptosis-related pathways were enriched. Under aluminum stress, activated MAPK in spermatogonia phosphorylates p53, triggering p53 pathway activation. Activated p53 then upregulates pro-apoptotic genes, exacerbating spermatogonia apoptosis induced by aluminum. Balanced crosstalk among MAPK-p53–apoptosis pathways maintains spermatogonia viability; aluminum disrupts this balance. Impaired pathway crosstalk reduces spermatogonia resistance to aluminum, harming spermatogenesis [13].

MicroRNAs are important modulators of gene expression in spermatogenetic process [21]. Exposure of spermatogonia to toxicants can induce changes in miRNA expression levels. Specifically, the spermatocyte-derived GC-2spd cell line exhibited 40 differentially expressed miRNAs following exposure to polystyrene microplastics [22]. This study identified 2053 negative miRNA–target pairs, including 65 DEMs and 723 target genes. This extensive regulatory network indicates that miRNA-mediated post-transcriptional regulation in AlCl₃-stressed GC-1spg cells may feature “multiple miRNA–multiple target” synergy, rather than single-molecule independent action. In particular, mmu-miR-128-1-5p had the highest number of targets, with 112 genes confirmed as its potential binding partners. miR-128-1-5p plays a potential regulatory role in AlCl₃ exposure response by targeting key genes and pathways. It inhibits TGF-β1/Smad signaling to alleviate fibrosis [23], suppresses Gadd45g-mediated apoptosis to counteract oxidative stress [24], and regulates PRKCQ-related pathways to modulate cell proliferation and inflammation [25]. These pathways are closely linked to Al-induced toxicity. Future studies should verify its expression in Al-exposed models and confirm regulatory interactions with target genes.

Transcription factors act as central molecular “switches” for targeted regulation, orchestrating the cellular response to reproductive toxicity by alleviating oxidative stress, suppressing inflammatory responses, and fostering testicular homeostasis [26,27]. For instance, Etv4 was significantly upregulated in GC-1spg cells under AlCl₃ exposure. ETV4 regulates mitochondrial ROS in ovarian cancer [28], implying that Etv4 regulated ROS in GC-1spg cells under AlCl₃ exposure. The differentially expressed transcription factors identified in this study could serve as potential targets associated with spermatogonial toxicity, warranting further investigation.

Further experimental assays confirmed that apoptosis was induced through activation of the p53-BAX signaling axis, indicating that GC-1spg cells undergo apoptosis under aluminum stress. Similarly, exposure to AlCl₃ triggered the generation of reactive oxygen species (ROS), reduced cell viability and ATP production, and induced a decline in mitochondrial membrane potential (MMP) [9]. These changes subsequently led to mitophagy and apoptosis in GC-2spd cells. The mmu-miR-146a-5p was significantly upregulated in GC-1spg cells following AlCl₃ exposure. Notably, previous studies have reported that elevated miR-146a-5p expression is associated with β cell apoptosis and compromised insulin secretion [29], implying that mmu-miR-146a-5p may potentially participate in GC-1spg cell apoptosis following AlCl₃ exposure. Furthermore, elevated P21 protein levels implied that cell cycle arrest occurred upon AlCl₃ exposure. It has been reported that aluminum chloride exposure blocks the onset of spermatogenesis [10,30,31], and cell cycle arrest may play a critical role in this spermatogenetic arrest. Exposure to AlCl₃ resulted in marked downregulation of mmu-miR-16-1-3p and mmu-miR-16-2-3p in GC-1spg cells. Notably, miR-16-5p has been demonstrated to impact cell cycle regulatory genes in colorectal cancer models [32]. Collectively, these findings imply that the two miR-16 family members may be involved in mediating AlCl₃-induced cell cycle alterations in GC-1spg cells. We acknowledge that the enhanced aluminum tolerance in GC-1spg cells may not stem from a stress response, but rather from the indirect effect of apoptosis induced by 3 mM AlCl₃. This high aluminum dose could eliminate sensitive cells via apoptosis, leaving inherently resistant cells whose survival might be misinterpreted as “enhanced tolerance” rather than stress-mediated adaptation.

The functional involvement of miRNAs in aluminum-induced stress in GC-1spg cells has not been previously elucidated. Among the differentially expressed miRNAs identified, mmu-miR-503-5p was prioritized for functional validation. Ectopic overexpression of mmu-miR-503-5p enhanced cellular tolerance to AlCl₃-induced cytotoxicity, underscoring its pivotal role in the adaptive response to aluminum exposure. Notably, prior work has demonstrated that mmu-miR-503-5p is implicated in cellular responses to both genotoxic and non-genotoxic carcinogens in primary mouse hepatocytes. Mechanistically, this miRNA exerts its regulatory effects by targeting Ccnd2, a critical downstream effector within the p53 signaling pathway [33]. In addition, mmu-miR-503-5p was found to be significantly downregulated in a murine model of invasive pulmonary aspergillosis [34]. miR-503-5p can promote neuronal endoplasmic reticulum stress-mediated apoptosis in ischemic stroke [35]. These findings highlight the essential role of miR-503-5p in the regulation of apoptosis. Consistent with this, our study further reveals that mmu-miR-503-5p contributes to aluminum-induced apoptosis in GC-1spg cells, reinforcing its evolutionarily conserved function in apoptotic control.

ISLR has been reported to activate the PI3K–AKT signaling pathway in osteosarcoma cells, leading to increased phosphorylation of PI3K and AKT. This activation suppresses apoptosis by upregulating the anti-apoptotic protein Bcl-2 and downregulating the pro-apoptotic factors Bax and Caspase-3 [36]. The PI3K-AKT pathway is a well-characterized negative regulator of p53. In conclusion, we hypothesize that the mmu-miR-503-5p/Islr axis converges on the p53 pathway to regulate Al-induced apoptosis, and this regulatory effect is mediated by targeting the PI3K-AKT pathway. To validate this hypothesis, additional experiments will be performed in future studies. Furthermore, the target gene Islr of mmu-miR-503-5p also exhibited tolerance to AlCl₃-induced stress; however, its effect was less significant than that of mmu-miR-503-5p. These results suggest that mmu-miR-503-5p contributes to AlCl₃ tolerance by targeting additional genes, which remain to be further investigated. Notably, these conclusions about mmu-miR-503-5p and its target Islr are drawn from the GC-1spg cell line, and their applicability in more physiological contexts requires further validation. Additionally, direct experimental evidence verifying the functional causality between miR-503-5p/Islr and the p53 pathway remains lacking, and this gap warrants further investigation in subsequent studies.

Although this study was conducted in the immortalized mouse spermatogonial cell line GC-1spg, the findings may have important implications for human male reproductive health. The p53-mediated apoptotic pathway and miRNA regulatory networks identified here are highly conserved between mice and humans. Therefore, our results suggest that similar molecular mechanisms may operate in human spermatogonia under AlCl₃ stress. While direct validation in human cells is warranted, this work provides a mechanistic foundation for understanding Al-induced male reproductive toxicity and highlights potential biomarkers or protective targets for future translational research.

A notable limitation of this study is that all experiments were conducted using a single concentration of AlCl₃. As a result, we cannot definitively determine whether the observed apoptosis in GC-1spg cells represents a specific, early molecular event directly triggered by AlCl₃ exposure or a secondary consequence of general cellular stress resulting from cytotoxic overload. Future dose–response and time-course studies are warranted to dissect the temporal sequence of molecular events and clarify whether p53-mediated apoptosis acts as a primary driver or a downstream outcome of AlCl₃-induced toxicity.

5. Conclusions

In conclusion, we established a miRNA–mRNA regulatory network underlying the effects of aluminum exposure on GC-1spg cells. AlCl₃-induced stress leads to significant enrichment of the p53 signaling pathway in GC-1spg cells. Furthermore, mmu-miR-503-5p mediates the AlCl₃ stress response by targeting Islr. These findings are critical for elucidating the molecular mechanisms that govern GC-1spg cells’ responses to AlCl₃ exposure.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/toxics14020173/s1, Figure S1: Cell viability of GC-1spd cells at different AlCl₃ concentrations; Figure S2: Length distribution of the unique miRNAs; Figure S3 KEGG enrichment analysis of target genes; Table S1: Primers used in this study; Table S2: The RNA sequencing data statistics; Table S3: Differentially expressed genes between the control and treatment; Table S4: Enriched KEGG terms for differentially expressed genes; Table S5: The sRNA sequencing data statistics; Table S6: Differentially expressed miRNAs between the control and treatment; Table S7: Integrated correlation of the identified miRNAs and their target genes; Table S8: Enriched KEGG terms for target genes; Table S9: The miRNAs and their target genes involved in the p53 signaling pathway; Supplementary File 3: Uncropped images of the Western blots.

Author Contributions

Z.L. designed the experiments. J.H., Z.W., S.Y., D.X., Y.G. and J.Z. analyzed the data. J.H., Y.G. and Z.W. performed the experiments and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw sequencing data generated in this study have been deposited in the Genome Sequence Archive (GSA) at the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number CRA028966, and are freely available at https://ngdc.cncb.ac.cn/gsub/, accessed on 15 January 2026.

Conflicts of Interest

The authors report no potential conflicts of interest.

Funding Statement

This work was funded by the Youjiang Medical University for Nationalities (RZ2200002546), Guangxi Young Elite Scientist Sponsorship Program (GXYESS2025011), Inclusive Support Policy for Young Talents in Guangxi Zhuang Autonomous Region (First Batch) (to Zongling Liu), Research Capacity Building Project for Young and Middle-aged Teachers in Guangxi Higher Education Institutions (2023KY0552 and 2025KY0548), and the Joint Special Research Project on Regional Prevalent Diseases in Baise City (BK20224150).