Biological and Pharmaceutical Bulletin
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Sex-Mediated Differences in TNF Signaling- and ECM-Related Gene Expression in Aged Rat Kidney
Sang Gyun NohHee Jin JungSeungwoo KimRadha ArulkumarKi Wung ChungDaeui ParkYeon Ja ChoiHae Young Chung
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2023 Volume 46 Issue 4 Pages 552-562

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Abstract

Aging leads to the functional decline of an organism, which is associated with age and sex. To understand the functional change of kidneys depending on age and sex, we carried out a transcriptome analysis using RNA sequencing (RNA-Seq) data from rat kidneys. Four differentially expressed gene (DEG) sets were generated according to age and sex, and Gene Ontology analysis and overlapping analysis of Kyoto Encyclopedia of Genes and Genomes pathways were performed for the DEG sets. Through the analysis, we revealed that inflammation- and extracellular matrix (ECM)-related genes and pathways were upregulated in both males and females during aging, which was more prominent in old males than in old females. Furthermore, quantitative real-time PCR analysis confirmed that the expression of tumor necrosis factor (TNF) signaling-related genes, Birc3, Socs3, and Tnfrsf1b, and ECM-related genes, Cd44, Col3a1, and Col5a2, which showed that the genes were markedly upregulated in males and not females during aging. Also, hematoxylin–eosin (H&E) staining for histological analysis showed that renal damage was highly shown in old males rather than old females. In conclusion, in the rat kidney, the genes involved in TNF signaling and ECM accumulation are upregulated in males more than in females during aging. These results suggest that the upregulation of the genes may have a higher contribution to age-related kidney inflammation and fibrosis in males than in females.

INTRODUCTION

Aging is a biological process accompanied by continuous structural changes and the functional decline of an organism, which is associated with age and sex. Various studies have investigated the structural and functional changes determined by increasing chronological age in various organs. For example, an age-related decline in the glomerular filtration rate, a clinical indicator of renal function and health, is observed in the kidneys of aged individuals.1,2) Also, sex-related differences regarding aging mechanisms have been reported. Several sex-related factors including sexual dimorphism and sex hormones affect immunity and genomic instability, which contributes to the age-related changes.35) Furthermore, sex differences in change in structure, function, and gene expression have been reported during renal aging.6,7) These studies have demonstrated the importance of understanding how sex differences affect aging.

Aging is also associated with an increase in the pro-inflammatory status. This increase affects various tissues and organs and is an underlying cause of age-related diseases.5) Inflammatory signaling is upregulated, and pro-inflammatory transcription factors and cytokines are increased in older patients.8,9) In addition, bioinformatics analysis has been used to study the relationship between aging and inflammation.10) Inflammation is reported to be involved in renal aging and is an underlying cause of age-related kidney diseases.11,12) Furthermore, age-associated inflammatory responses differ between males and females, who show a differential expression of tumor necrosis factor (TNF) signaling.13,14)

Biological aging is strongly associated with various diseases, fibrosis being one of the most important. It is characterized by a rapid increase in fibroblasts and excessive accumulation of extracellular matrix (ECM) components, such as cell adhesion molecules (CAMs) and selectins in various tissues.15,16) Age related-fibrosis frequently affects multiple organs: the heart, lungs, liver, and kidneys.1720) Several genes and pathways involved in this pathology have been identified including several ECM-related signaling pathways.21,22) Sex as well as age is one of the factors that affect fibrosis. It has been reported that sexual dimorphism and sex-related hormones are associated with different development of fibrosis in both sexes.23,24) However, even though the effects of age and sex on fibrosis have been studied, there is a lack of integrative analysis of both factors on fibrosis.

Our previous studies revealed age-related inflammation in various organs is associated with the upregulation of inflammatory genes, transcription factors, and pathways,25,26) and several substances that decrease age-related inflammation could be pharmacological strategies to delay aging.27,28) Our bioinformatics studies suggest that the deterioration of metabolism is also associated with age-related inflammation.29,30) Furthermore, calorie restriction, a well-known anti-aging strategy, is effective for the prevention of age-related inflammation and fibrosis.3133) These findings indicate that the regulation of inflammatory and ECM-related signaling pathways may be a solution to treat age-related diseases. However, our studies had some limitations because they did not provide an in-depth analysis of sex-induced differences of genes and biological pathways regulating inflammation, metabolism, and fibrosis in the aging process.

Therefore, in the present study, we analyzed the transcriptomic data of rat kidneys to examine the differential expression of the genes and pathways involved in the aging process. We further identified genes involved in TNF signaling and ECM accumulation that were differently expressed depending on age and sex. In addition, quantitative real-time PCR (qRT-PCR) analysis confirmed that renal pro-inflammatory- and ECM-related genes were more upregulated in males than in females during aging.

MATERIALS AND METHODS

Animals

Young (5-month-old) and aged (20-month-old) male and female Sprague–Dawley (SD) rats were purchased from Samtako (Osan, KyungKiDo, Korea). All SD rats were maintained at 23 ± 2 °C with a relative humidity of 60 ± 5% and a 12 h light and dark cycle. The rats were fed with water vehicle and normal chow diet purchased from Samtako (20% protein, 4.5% fat, 6% fibre, 7% ash, 0.5% calcium, 1% phosphorus). Body weights (BW) of the rats used in this study were shown in Fig. 1. Tissues were immediately frozen in liquid nitrogen for isolation and analysis.

Fig. 1. Graph of Result of Body Weight in YM, OM, YF, and OF Groups

Data are expressed as mean ± standard error of the mean (S.E.M.). YM, young male; OM, old male; YF, young female, OF, old female. * p < 0.05 between the two groups, *** p < 0.001 between the two groups.

RNA Sequencing (RNA-Seq)

Total RNA was isolated from the cortex of the kidneys using the RiboEx reagent (GeneAll Biotechnology, Seoul, Korea), and samples from each group were pooled in equal quantities for RNA-Seq (n = 3 in each group). RNA integrity was assessed using an Agilent 2100 BioAnalyzer and TECAN Infinite F200 microplate reader (concentration value greater than 65 ng/µL, quantity value greater than 1 µg, and RNA integrity number (RIN) value greater than 6). RNA sequencing libraries were generated using the TruSeq stranded mRNA Sample Preparation Kit (Illumina, CA, U.S.A.). mRNA was separated from 1 µg of total RNA using oligo (dT) magnetic beads and fragmented. Single-stranded cDNAs were synthesized by random hexamer priming and converted to double-stranded cDNA. After end repair, A-tailing, and adapter ligation, cDNA libraries were amplified with PCR. The quality of the cDNA libraries was evaluated using an Agilent 2100 BioAnalyzer (Agilent, CA, U.S.A.). They were quantified using a KAPA library quantification kit (Kapa Biosystems, MA, U.S.A.) according to the manufacturer’s library quantification protocol. Following cluster amplification of denatured templates, Illumina NovaSeq 6000 platforms was used to generate 150-bp paired-end sequencing reads.

Differential Expression Analysis

The adapter sequences and ends of the reads with less than Phred quality score 20 were trimmed and simultaneously the reads shorter than 50 bp were removed by using Cutadapt (v.2.8).34) All remaining reads were mapped to the reference genome related to the species using the aligner STAR v.2.7.1a35) applying “-quant Mode Transcriptome SAM” option for the estimation of transcriptome expression level. ENCODE standard options (refer to “Alignment” of “Help” section in the html report). Transcript abundances were quantified using RSEM (v.1.3.1)36) considering the direction of the reads corresponding to the library protocol using option—strandedness. To improve the accuracy of the measurement, “—estimate-rspd” option was applied. All other options were set to default values. Fragments per kilobase million (FPKM) and transcripts per kilobase million (TPM) values were calculated to normalize the sequencing depth among the samples. Based on the estimated read counts in the previous step, differentially expressed genes (DEGs) were identified using R package TCC (v.1.26.0).37) The TCC package applies robust normalization strategies to compare tag count data. The normalization factors were calculated using the iterative edgeR38) method. After calculation, we selected DEGs whose p-values were less than 0.05. DEGs are presented in Supplementary Table S1. A volcano plot of the DEGs for each dataset was constructed using the criteria, which are based on the VolcaNoseR website.39)

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis of the DEGs

The GO enrichment and KEGG pathway analysis were performed to associate the common DEGs with the corresponding biological functions and to characterize the signaling pathways related to them. DAVID was used to perform GO and KEGG enrichment analyses (filtering options: p < 0.05). The results of GO and KEGG analyses are listed in Supplementary Tables S2 and S3. We overlapped the enriched KEGG pathways resulting from the analysis of the datasets and identified common, consistently altered KEGG pathways.

qRT-PCR

Primers for qRT-PCR were synthesized by Bioneer, Inc. (Daejeon, Korea). Total RNA was isolated from 20 mg rat kidney tissue using the RNeasy Mini Kit (Qiagen, Hilden, Germany) (n = 7 per group) and was converted to cDNA using reverse transcriptase and a synthesis kit from GenDEPOT. For quantifying mRNA levels by qRT-PCR analysis, the samples were amplified using SYBR Green (Bioneer, Daejeon, Korea) and run on the CFX Connect System (Bio-Rad Laboratories Inc., Hercules, CA, U.S.A.). The primer sequences used are listed in Supplementary Table S4.

Hematoxylin–Eosin (H&E) Staining

Kidneys were fixed in 10% neutral formalin and the paraffin-embedded sections were stained with H&E to determine renal damage.

Quantification and Statistical Analysis

The Student’s t-test was used to analyze the differences between two groups, and one-way ANOVA was conducted with Bonferroni post hoc tests to analyze the intergroup differences. Statistical significance was set at p < 0.05. The statistical analyses were performed using GraphPad Prism 5 (La Jolla, CA, U.S.A.).

RESULTS

Age- and Sex-Related Differences of Gene Expression in Rat Kidneys

We investigated the influence that age and sex might have on gene expression by extracting RNAs from rat kidney tissue and completing gene expression profiling for the different experimental groups. RNA sequencing (RNA-Seq) was employed. Groups were made according to age and sex: young males (YM), young females (YF), old males (OM), and old females (OF). Four sets containing the DEGs were obtained: DEGs between old males vs. young males (DEG set 1: OM vs. YM), old females vs. young females (DEG set 2: OF vs. YF), old males vs. old females (DEG set 3: OM vs. OF), and young males vs. young females (DEG set 4: YM vs. YF) (Fig. 2). DEG set 1 contained 780 genes. Out of these, 538 genes were upregulated and 242 were downregulated. DEG set 2 had a total of 346 genes, out of which 225 were upregulated, and 121 downregulated. A total of 730 genes were included in DEG set 3; out of them, 398 were upregulated, and 332 genes were downregulated. Finally, 508 genes were identified as belonging to DEG set 4. Out of these, 250 genes were upregulated and 258 downregulated (Fig. 3). The list of the DEGs from each set is presented in Supplementary Table S1. These results support the hypothesis that age influences gene expression in individuals of the same sex, and sex influences gene expression in individuals of similar ages, thereby inviting interest in the gene expression changes induced by these factors.

Fig. 2. Experimental Design for Comparative Transcriptome Analysis

Four sets of differentially expressed genes (DEGs) were generated. DEG set 1 includes the DEG between old male (OM) and young male (YM) groups. DEG set 2 includes the DEG between old female (OF) and young female (YF) groups. DEG set 3 includes the DEG between OM and OF groups. DEG set 4 includes the DEG between YM and YF groups.

Fig. 3. Age- and Sex-Induced Variations in Gene Expression (A) DEG Set 1, (B) DEG Set 2, (C) DEG Set 3, and (D) DEG Set 4

The criteria for a DEG are |FC| > 1.5 and p-value < 0.05. Red dots denote upregulated genes, while green dots denote downregulated genes in the set. Grey dots denote genes with no significant change. YM, young male; OM, old male; YF, young female; OF, old female.

GO Enrichment Analysis of Significant DEGs

To associate DEGs with their biological functions, we performed a GO enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, Accessed on 30th April, 2022). We obtained 335 significant (p-value < 0.05) GO terms amongst DEGs, when comparing OM to YM, of which 284 were over-represented and 51 were under-represented. When comparing OF to YF, 167 significant GOs were obtained, of which 116 were over-represented and 51 were under-represented. A total of 256 significant GOs were obtained when comparing OM to OF. Out of these, 205 were over-represented and 51 were under-represented. Finally, when comparing YM to YF, 102 significant GOs were obtained, out of which 31 were upregulated and 71 were downregulated. The top 15 upregulated and downregulated GOs were selected from each group and are represented in Fig. 4. GO analysis showed that compared to the YM group, in the OM group, the expression of genes related to the inflammatory and innate immune response was increased while the expression of genes related to the oxidation-reduction reactions and various metabolic processes was reduced (Fig. 4A). Compared to YF, in OF, the expression of the same genes was upregulated, whereas the expression of the genes involved in brown fat cell differentiation, circadian rhythm, and glutamate metabolism was downregulated (Fig. 4B). Compared to OF, in OM, the expression of the genes regulating the innate immune and inflammatory response was higher, and the expression of genes regulating oxidation-reduction reactions and various metabolic processes was decreased (Fig. 4C). In the case of YM and YF, both upregulated and downregulated genes were associated with various metabolic processes such as oxidation-reduction (Fig. 4D). Furthermore, in the OM group, collagen fibrils and ECM organization were over-represented GOs when compared with those in YM and OF. These results suggest ECM accumulates more in males during aging (Supplementary Table S2). This analysis revealed that inflammation- and ECM accumulation-related pathways and gene expression were upregulated, while most energy metabolism-related gene expression decreased significantly during aging in both genders; the changes were more pronounced in OM than in OF, whereas in YM and YF no significant differences between these genes and pathways were found.

Fig. 4. Top 15 Enriched GO Functions of the DEGs in (A) DEG Set 1 (OM vs. YM), (B) DEG Set 2 (OF vs. YF), (C) DEG Set 3 (OM vs. OF), and (D) DEG Set 4 (YM vs. YF) from SD Rats (each n = 3)

Red bars indicate upregulated gene sets and green bars represent downregulated gene sets in each set. X bar indicates the p-value. YM, young male; OM, old male; YF, young female; OF, old female.

Overlapping Analysis of KEGG Pathways

To examine genes and pathways whose expression were changed by both age and sex, we performed an overlapping analysis to identify common DEGs and pathways for DEG set 1, 2, and 3, which selected genes and pathways that were not only upregulated or downregulated in both sexes during aging but also represented sex-different expression. The results are displayed in Venn diagrams (Fig. 5). Representative terms for biological pathways were used in the context of the KEGG. In KEGG terminologies, 64 upregulated genes and nine downregulated genes were common. Nineteen pathways involved common upregulated genes, while three pathways involved common downregulated genes. Furthermore, 28 of the commonly upregulated genes in DEG set 1, 2, and 3 were associated with 19 commonly upregulated pathways in the same DEG sets that regulate immunity/inflammation and ECM accumulation, such as TNF signaling pathway, focal adhesion, and ECM-receptor interaction (Table 1). Thus, among the 28 commonly upregulated DEGs, C1qc, Serpine1, Cd4, Cd44, Itgam, Itgal, Itgb2, and Rac2 are involved in immunity/inflammation pathways, such as complement and coagulation cascades, hematopoietic cell lineage, leukocyte transendothelial migration, and natural killer cell mediated cytotoxicity. Birc3, Card11, Lbp, Itgal, Itgb2, Socs3, and Tnfrsf1b are involved in inflammatory pathways, such as the NF-kappa B signaling pathway, rheumatoid arthritis, and TNF signaling pathway. Cd4, Itgal, Itgam, Itgb2, Ptprc, Selplg, Cd44, Col1a1, Col3a1, Col5a2, Col6a1, Itgb4, Lamc2, Spp1, and TNC are involved in ECM-related pathways, such as cell adhesion molecules signaling and ECM-receptor interactions (Table 2). In contrast, among the three common downregulated KEGG pathways, two are metabolic pathways (e.g., tryptophan metabolism), however, Mep1b was the only common downregulated DEG that was not associated with metabolic pathways (Supplementary Table S5). Interestingly, fold change of the upregulated genes was higher in OM vs. YM set than in OF vs. YF set (Fig. 6A). Also, fold change of the genes involved in TNF signaling pathway and ECM-receptor interaction was higher in OM vs. YM set than in OF vs. YF set, suggesting that aging would increase the expression of the genes more in males than in females (Figs. 6B, C). In conclusion, aging is associated with an increase in immune/inflammatory response and ECM accumulation, which were more pronounced in males than in females.

Fig. 5. Venn Diagram of the Overlapping Parts of the DEGs and Enriched KEGG Pathways in DEG Set 1 (OM vs. YM, Green), DEG Set 2 (OF vs. YF, Red), and DEG Set 3 (OM vs. OF, Blue)

Overlapping parts of (A) upregulated and (B) down-regulated genes and overlapping parts of (C) upregulated and (D) downregulated KEGG pathways. YM, young male; OM, old male; YF, young female; OF, old female.

Fig. 6. Fold Change of Age- and Sex-Related Expression of Genes Involved in (a) the Common Upregulated Pathways, (b) TNF Signaling Pathway, and (c) ECM-Receptor Interaction

The genes involved in TNF signaling pathway and ECM-receptor interaction were more upregulated in males rather than females during the aging process. Data are expressed as mean ± S.E.M. YM, young male; OM, old male; YF, young female; OF, old female. * p < 0.05, ** p < 0.01, and *** p < 0.001 between the two groups.

Table 1. Common Upregulated Genes and KEGG Pathways in DEG Set 1 (OM vs. YM), DEG Set 2 (OF vs. YF), and DEG Set 3 (OM vs. OF)
TermGenes
AmoebiasisCol1a1, Col3a1, Col5a2, Itgam, Itgb2, Lamc2
Cell adhesion molecules (CAMs)Cd4, Itgal, Itgam, Itgb2, Ptprc, Selplg
Complement and coagulation cascadesC1qc, Serpine1
ECM-receptor interactionCd44, Col1a1, Col3a1, Col5a2, Col6a1, Itgb4, Lamc2, Spp1, Tnc
Focal adhesionBirc3, Col1a1, Col3a1, Col5a2, Col6a1, Itgb4, Lamc2, Parvg, Rac2, Spp1, Tnc
Hematopoietic cell lineageCd4, Cd44, Itgam
Leukocyte transendothelial migrationItgal, Itgam, Itgb2, Rac2
MalariaItgal, Itgb2
NF-kappa B signaling pathwayBirc3, Card11, Lbp
Natural killer cell mediated cytotoxicityItgal, Itgb2, Rac2
PI3K-Akt signaling pathwayCol1a1, Col3a1, Col5a2, Col6a1, Itgb4, Lamc2, Spp1, Tnc
PertussisC1qc, Itgam, Itgb2
PhagosomeCoro1a, Itgam, Itgb2, Olr1
Platelet activationCol1a1, Col3a1, Col5a2, Tbxas1,
Protein digestion and absorptionCol1a1, Col3a1, Col5a2, Col6a1
Rheumatoid arthritisItgal, Itgb2
Staphylococcus aureus infectionC1qc, Itgal, Itgam, Itgb2, Selplg
TNF signaling pathwayBirc3, Socs3, Tnfrsf1b
TuberculosisCoro1a, Itgam, Itgb2, Lbp, Lsp1
Table 2. Differential Gene Expression of Common Upregulated Genes Involved in the KEGG Pathways
Gene nameDescriptionFC (DEG set 1)FC (DEG set 2)FC (DEG set 3)
Birc3baculoviral IAP repeat-containing 37.212.173.66
C1qccomplement component 1, q subcomponent, C chain3.941.552.64
Card11caspase recruitment domain family, member 113.512.252.25
Cd4Cd4 molecule3.461.662.06
Cd44Cd44 molecule8.821.885.28
Col1a1collagen, type I, alpha 16.192.103.86
Col3a1collagen, type III, alpha 15.581.753.76
Col5a2collagen, type V, alpha 22.971.642.08
Col6a1collagen, type VI, alpha 15.031.663.03
Coro1acoronin, actin binding protein 1A4.821.762.60
Itgalintegrin, alpha L3.841.992.11
Itgamintegrin, alpha M8.632.873.23
Itgb2integrin, beta 22.712.032.14
Itgb4integrin, beta 43.411.742.11
Lamc2laminin, gamma 25.702.252.93
Lbplipopolysaccharide binding protein5.582.502.36
Lsp1lymphocyte-specific protein 13.781.602.33
Olr1oxidized low density lipoprotein (lectin-like) receptor 17.572.085.98
Parvgparvin, gamma4.082.232.41
Ptprcprotein tyrosine phosphatase, receptor type, C4.351.592.36
Rac2ras-related C3 botulinum toxin substrate 2 (rho family, small GTP binding protein Rac2)4.471.652.53
Selplgselectin P ligand5.132.232.73
Serpine1serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 19.781.955.82
Socs3suppressor of cytokine signaling 38.283.325.39
Spp1secreted phosphoprotein 116.802.604.47
Tbxas1thromboxane A synthase 1, platelet4.202.352.89
Tnctenascin C11.792.413.58
Tnfrsf1btumor necrosis factor receptor superfamily, member 1b4.001.862.06

* FC, fold change.

TNF Signaling- and ECM-Related Genes Expression Levels in Old Male Rat Kidneys

Transcriptomic data was verified using qRT-PCR (Fig. 7). Six DEGs were chosen based on the results of the previous analyses: TNF signaling-related genes Birc3, Socs3, and Tnfrsf1b and ECM-related genes Cd44, Col3a1, and Col5a2. The expression of TNF signaling-related genes was significantly upregulated in the OM group than in the YM group. No significant change in their expression was found in the OF group compared to the YF group, although a tendency for age-induced increase was observed. Similar results were obtained for ECM-related genes Cd44, Col3a1, and Col5a2. Our results revealed that TNF signaling and ECM accumulation in kidneys may be higher in males than in females during aging.

Fig. 7. Relative mRNA Expression of TNF Signaling-Related Genes (Birc3, Socs3, and Tnfrsf1b) and ECM-Related Genes (Cd44, Col3a1, and Col5a2) Detected by qRT-PCR

Data are expressed as mean ± S.E.M. Birc3, baculoviral IAP repeat-containing 3; Socs3, suppressor of cytokine signaling 3; Tnfrsf1b, tumor necrosis factor receptor superfamily, member 1b; Cd44, Cd44 molecule; Col3a1, collagen, type III, alpha 1; Col5a2, collagen, type V, alpha 2; YM, young male; OM, old male; YF, young female; OF, old female. * p < 0.05, ** p < 0.01, and *** p < 0.001 between the two groups.

Measurement of Age- and Sex-Related Renal Damage via Histological Analysis

Serum blood urea nitrogen (BUN) was measured to assess kidney function according to age and sex. As a result, the BUN concentration in the old male was much higher than that in the young male, whereas the BUN concentration in the old female did not increase compared to young female (Fig. 8A). Furthermore, H&E staining was performed to confirm the histological differences according to age and sex. According to the result, morphological changes such as tubular atrophy and accumulation of immune cells were markedly observed in old males compared to young males. Also, morphological changes and accumulation of immune cells were also observed in old females compared to young females. However, compared with the old males, the morphological changes and accumulation of immune cells in the old females were less remarkably detected (Fig. 8B). These results suggest that renal damage occurred more remarkably in old males, which is related to inflammation- and ECM-related genes that were significantly increased in old males.

Fig. 8. Renal Damage Determined by Measuring Blood Urea Nitrogen (BUN) Level and Hematoxylin–Eosin (H&E) Staining in SD Rats (n = 7 per Each Group)

(A) BUN levels which were measured by biochemical method in serum to measure kidney function. Data are expressed as mean ± S.E.M. (B) Representative images of the H&E staining of each group. YM, young male; OM, old male; YF, young female; OF, old female. ** p < 0.01 between the two groups, *** p < 0.001 between the two groups.

DISCUSSION

In this study, we conducted a transcriptomic analysis using RNA-Seq data from rat kidneys according to age and sex. As a result, DEGs, GOs, and KEGG pathways associated with immune response/inflammation and ECM accumulation were upregulated, whereas those associated with metabolism were downregulated in both sexes in aged individuals. The overlapping analysis showed that TNF signaling and ECM-receptor interaction are enhanced in both sexes during aging, but the phenomenon is more pronounced in the OM group than in OF group. These qRT-PCR data confirmed that the expression of TNF signaling-related genes Birc3, Socs3, and Tnfrsf1b and ECM-related genes Cd44, Col3a1, and Col5a2 was significantly higher in the OM group, but not in the OF group; thus, these genes are involved in age-related inflammation and ECM accumulation in old male rat kidneys.

We found that aging and sex differences are involved in the regulation of several inflammatory pathways, such as TNF signaling pathway. It has been well known that the genes related to TNF signaling pathway were upregulated during aging, leading to chronic inflammation.40,41) Our previous study also revealed that the TNF signaling pathway was upregulated during aging.42) Furthermore, TNF-α plays a well-known key role in senescence-associated secretory phenotype (SASP), and its upregulation promotes age-related inflammation and diseases.43,44) We highlighted that the TNF-signaling-related genes Birc3, Socs3, and Tnfrsf1b are highly upregulated in kidneys of old individuals, especially in males. Therefore, we suggest that the kidneys of male individuals are more vulnerable to chronic inflammation than those of females during aging.

In older patients, excessive accumulation of ECM, such as collagen, contributes to fibrosis.45) In addition, ECM-related genes are strongly associated with age-related renal fibrosis.46) Our previous studies found that matrix metalloproteinases 2 substrate proteins, known as components of ECM, accumulate in the kidneys of aged individuals.32) Furthermore, estrogen receptor beta is associated with protection against the deposition of ECM, which indicates our findings on more pronounced ECM accumulation in males than in females during aging.47) Collectively, these studies support the hypothesis that ECM-related genes are upregulated by increasing age, possibly leading to fibrosis, and the increase is higher in males than in females.

On the other hand, Mep1b was the only common downregulated genes involved in common downregulated pathways in our study. Mep1b protein is one of the metalloproteases, which is highly expressed in kidney.48) It has been well reported that Mep1b cleaves ECM components and cell adhesion molecules, and plays a role in procollagen processing and enhanced collagen fibril assembly which leads to fibrosis.49,50) Also, Mep1b is involved in activation of pro-inflammatory cytokines promoting inflammation, however, its role in metabolism has not been studied.51,52) These studies are not in accordance with our study, however, the relevance between Mep1b expression and aging needs to be studied in further studies because Mep1b downregulation might be associated with maintaining inflammation- and ECM-related homeostasis during the aging process.

The qRT-PCR data confirmed that TNF signaling-related genes and ECM-related genes are highly upregulated in the kidneys of aged males. Socs3 is a gene associated with pro-inflammatory signaling, and its expression is significantly higher in elderly individuals compared to younger individuals.53,54) The pro-inflammatory gene Tnfrsf1b is persistently upregulated in aged rats.55) In addition, TNF-α induces Tnfrsf1b expression and activates the nuclear factor-kappaB (NF-κB) signaling pathway, indicating that the expression of Tnfrsf1b is regulated by pro-inflammatory signaling pathways.56) Although there are few studies on Birc3, this gene is a known target for drugs used to treat bacterial infection and hepatic inflammation.57) These studies indicate that sex-specific upregulation of the TNF signaling-related genes may aggravate inflammation in male individuals during aging.

Cd44 activation is associated with ECM, which regulates various epidermal functions.58) In addition, Cd44 turns senescent endothelial cells into chronically adhesive, which suggests that the gene is related to aging.59) Cd44 is associated with pathological changes in renal fibrosis.60) Our study revealed that collagen-related genes associated with ECM accumulation and fibrosis are upregulated during aging. Similar results are reported by other studies; for example, the expression of Col3a1 is elevated in the kidneys of aged individuals.61,62) In addition, Col5a2 is related to ECM deposition in several diseases, while its downregulation reduces hepatic fibrosis.63,64) These studies suggest that the ECM-related genes Cd44, Col3a1, and Col5a2 may play a key role in age-related ECM accumulation in males.

Our histological analysis showed that renal damage was highly shown in males rather than females during aging. It is well reported that the kidney had aging-related damage because of inflammatory stress.41,65) Also, relationships between renal damage and sex difference have been widely studied, which indicated that males were more susceptible to renal damage than females.66,67) Furthermore, the studies on renal damage considering both age and sex revealed that sex-different renal changes during aging affected the damage in kidneys.68,69) These studies supported our idea that old males are vulnerable to renal damage rather than old females because inflammation- and ECM-related genes were more upregulated in males than in females during the aging process.

Our analysis revealed that the expressions of inflammation- and ECM-related genes were changed in sex-different manner during the aging process, which indicated that sex is one of the important factors that modulate inflammation and ECM accumulation. According to previous study, sex hormone estrogen and progesterone regulate the expression of pro-inflammatory cytokines, inflammation-related pathways including TNF-α, and ECM accumulation during aging, which indicated that females have sufficient sex hormone-related mechanisms modulating inflammation and ECM accumulation.7072) However, it is also reported that male hormone androgens, such as testosterone, decrease immune and inflammatory activity and modulate TNFs.73) Furthermore, sex chromosome is related to modulation of inflammation. It is reported that X chromosomes can modulate expression and activity of inflammatory cytokines and pathways.74) Also, females have X chromosome-linked protective mechanisms against inflammation.75) However, other studies demonstrated that X-linked genes contributed to enhanced cytokine production in females.76) Collectively, it is clear that both sex hormones and chromosomes can modulate inflammation and ECM accumulation, however, it still remains uncertain that they are involved in the sex-biased upregulation of inflammation- and ECM-related genes in males during aging. Therefore, it needs to be studied in further studies that both sex hormones and chromosomes affected age-related increase of inflammation and ECM accumulation in males.

In conclusion, this study revealed that the genes involved in TNF signaling and ECM accumulation are upregulated during aging in males more than in females. This suggests that TNF signaling-related genes Birc3, Socs3, and Tnfrsf1b and ECM-related genes Cd44, Col3a1, and Col5a2 play a more important role in males than in females during aging. Our analysis highlights the importance of sex differences in gene expression during the aging process.

Acknowledgments

This work was supported by a National Research Foundation Grant funded by the Korean government (Grant No. 2018R1A2A3075425). All animal experiments complied with the animal testing guidelines issued by the Pusan National University (PNU) and were approved by the PNU Institutional Animal Care and Use Committee (Approval No. PNU-2019-2282). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflict of Interest

The authors declare no conflict of interest.

Supplementary Materials

This article contains supplementary materials.

REFERENCES
 
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