Predict The Role Of LncRNA in Kidney Aging Based On RNA Sequencing Ⅱ

Correlation of lncRNA Gm43360 and its potential target mRNAs 

LncRNA Gm43360 was selected for study because it connected two lncRNA-mRNA coexpression networks. The expression level of Adra1a was positively correlated (rho=0.8650, p=0.026) with that of Gm43360, Csnk1a1 (casein kinase 1A1) was negatively correlated (rho=- 0.8084, p=0.0084) (Fig.  5a). To further verify the correlation of lncRNA Gm43360 and its potential target mRNAs Adra1a and Csnk1a1, an overexpression plasmid of lncRNA Gm43360 was designed in this study. The expression of Csnk1a1 decreased as expected, and Adra1a expression also decreased, but not significantly. Next, we knocked down lncRNA Gm43360 by siRNA to detect whether the expression of its target mRNAs was affected. As expected, Csnk1a1 was increased in lncRNA Gm43360 knockdown cells relative to the siRNA negative control group (Fig.  5b). Ten, we detected the role of lncRNA Gm43360 in the cell cycle. The cell counting kit-8 (CCK-8) results showed that lncRNA Gm43360 promoted cell viability (Fig. 5c). The results showed that lncRNA Gm43360 increased the percentage of S phase cells and decreased the percentage of G1 phase cells compared with the negative control (Fig.  5d). Te p53 and p21 proteins were involved in the regulation of the cell cycle and thus were the marks of cell aging. The expression levels of p53 and p21 were significantly decreased in the overexpression group relative to the control group. The expression levels of p53 and p21 were significantly increased in the siRNA group relative to the siRNA control group (Fig. 5e). Overexpression of lncRNA Gm43360 decreased the number of SA-β-gal-positive cells, and β-gal-positive cells increased in the group of transfect Gm43360 (Fig.  5f). Taken together, these results indicated that lncRNA Gm43360 inhibited the senescence of renal tubular epithelial cells by inhibiting the expression of Csnk1a1.

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Discussion 

In this study, we evaluated the mRNA expression data from young and old mouse kidneys and conducted bio-information analyses that disclosed the functions of the aberrantly expressed mRNAs. We observed 347 upregulated mRNAs and 355 downregulated mRNAs as well as 130 upregulated lncRNAs and 91 downregulated lncRNAs in old mouse kidneys compared with young mouse kidneys. In addition, these mRNAs were shown to be involved in aging-related pathways, such as oxidative phosphorylation, the AMPK signaling pathway, the Wnt signaling pathway, the Rap1 signaling pathway, and age-related disease, which demonstrated the functions of differentially expressed mRNAs in the pathogenesis of renal aging.

The long noncoding RNA NEAT1 is a protective factor in the progression of kidney fibrosis in renal tubular epithelial cells [21]. Coincidentally, in our sequencing results, lncRNA NEAT1 was expressed at lower levels in the kidneys of aged mice than in young mice, which is consistent with previous findings. LincRNA-Gm4419 accelerates infammation and fibrosis by NF-kB/NLRP3 inflammasome-mediated mechanisms in diabetic nephropathy [22]. In our sequencing results, we also found that the expression of Gm4419 was higher in the kidneys of aged mice than in young mice, but there was no signifcant difference. LncRNA Gm43360 is located on chromosome 5 (Chr5:122494022–122,494,908, 2887 bp). According to the UCSC Genome Browser, Gm43360 is located in the intron of the protein-coding gene Atp2a2. However, there have been no reports about the involvement of lncRNA Gm43360 in diseases to date. In this study, the knockdown of lncRNA Gm43360 promoted renal tubular epithelial cell senescence. In addition, we identified many differentially expressed lncRNAs in the sequencing results and investigated them.

LncRNAs were classified into cis-regulation or trans-regulation based on lncRNA regulatory mechanisms. Cis-acting lncRNAs could influence the expression of neighboring genes by depending on cis-acting elements, such as promoters, enhancers, and regulatory sequences, which are distances between lncRNAs and mRNAs of less than 100  kb. Trans-acting lncRNAs refer to lncRNAs leaving the site of transfection and operating at distant sites (i.e., the distance between lncRNAs and mRNAs is more than 100  kb) [23]. The lncRNA MAAT increases the expression of the neighboring gene Mbnl1 through a cis-regulatory module [24]. The lncRNA Pnky plays a role as a trans-acting regulator in cortical development [25]. In this study, there was a transregulatory relationship between lncRNA Gm43360 and Csnk1a1. Csnk1a1 is a tumor suppressor gene [26] that encodes a protein that participates in the cell cycle and the cell division process [27]. Csnk1a1 downregulation induces senescence-associated inflammatory response with growth arrest in colorectal tumors [26]. Csnk1a1 was downregulated when Gm43360 was upregulated; thus, it is likely that lncRNA Gm43360 participates in kidney aging by regulating Csnk1a1 expression.

Conclusions

Our investigation of the lncRNA-mRNA coexpression network in kidney aging revealed a lncRNA, lncRNA Gm43360, may play a protective role in kidney aging and expanded our understanding of the mechanisms involved in kidney aging

Materials and methods

Samples Young (3-month-old) and old (24-month-old) male C57BL/6J mice were purchased from the Experimental Animal Center of Xiamen University and were raised in a standard environment. All mice had free access to food and water. The mice were acclimated to the new facility for a month before they were sacrificed. The mice were anesthetized with urethane by intraperitoneal injection at a dose of 750  mg/kg before specimen collection. Young and old mice were sacrificed on the same day. Residual mouse kidney tissue was used for sequencing in each assay. RNA-seq and histopathology were performed on separate kidneys from the same mouse. Kidney tissue was collected from mice. One kidney was immediately immersed in 10% neutral buffered formalin for subsequent section embedding, and the other kidney was divided into several tissues and immediately placed in liquid nitrogen and then stored at -80 °C. The study was supported by the Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University (Shaanxi, China) (No. 2018-G-164). All methods were carried out by the animal ethics guidelines and regulations. This study was carried out in compliance with the ARRIVE guidelines.

Kidney histopathology 

Renal tissue samples of young and old mice were fixed in 10% paraformaldehyde solution overnight. Ten, of the sections were dehydrated, paraffin-embedded, and sectioned at 4-µm thickness. PAS and Masson's trichromatic staining were performed using standard protocols. Images were captured by the camera, and the staining-positive area was measured. The area for the histopathology camera images was calculated with Image-Pro Plus 6.0.

SA‑β‑gal staining 

The SA-β-gal activity was analyzed using an SA-β-gal staining kit (Cell Signaling Technology #9860) according to the manufacturer's protocol. The area of positive staining was measured, and SA-β-gal-positive cells were calculated using Image-Pro Plus 6.0.

RNA extraction library construction and sequencing 

Samples (each 5 mice in young and old mice) were used for lncRNA and mRNA expression analyses. Young mice were numbered 3M1, 3M2, 3M3, 3M4, and 3M5, and old mice were numbered 24M1, 24M2, 24M3, 24M4, and 24M5. Total RNA was extracted using Trizol reagent (thermofsher, 15,596,018) following the manufacturer's instruction. The total RNA quantity and purity were analyzed of Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent, CA, USA, 5067−1511), and high-quality RNA samples with RIN number>7.0 were used to construct a sequencing library. After extraction of total RNA, mRNA was purified from total RNA (5ug) using Dynabeads Oligo (dT) (Thermo Fisher, CA, USA) with two rounds of purification. Following purification, the mRNA was split into short fragments using divalent cations at high temperatures (Magnesium RNA Fragmentation Module (NEB, cat. e6150, USA) under 94℃ 5-7 min). Then the cleaved RNA fragments were reverse-transcribed to obtain the cDNA by SuperScript™ II Reverse Transcriptase (Invitrogen, cat. 1,896,649, USA), which were next used to synthesize U-labeled second-stranded DNAs with E. coli DNA polymerase I (NEB, cat.m0209, USA), RNase H (NEB, cat.m0297, USA) and dUTP Solution (Termo Fisher, cat.R0133, USA). An A-base was then added to the blunt ends of each strand, preparing them for ligation to the indexed adapters. Each adapter contained a T-base overhang for ligating the adapter to the A-tailed fragmented DNA. Dual-index adapters were ligated to the fragments, and size selection was performed with AMPureXP beads. After the heat-labile UDG enzyme (NEB, cat.m0280, USA) treatment of the U-labeled second-stranded DNAs, the ligated products were amplified with PCR by the following conditions: initial denaturation at 95℃ for 3 min; 8 cycles of denaturation at 98℃ for 15 s, annealing at 60℃ for 15  s, and extension at 72℃ for 30  s; and then final extension at 72℃ for 5  min. The average insert length for the final cDNA libraries was 300±50 bp. Finally, we performed the 2×150 bp paired-end sequencing (PE150) on an Illumina Novaseq™ 6000 (LC-Bio Technology CO., Ltd., Hangzhou, China) according to the manufacturer's protocol [28].

Bioinformatics analysis Sequence and filtering of Clean Reads 

A cDNA library constructed by technology from the pooled RNA from kidney samples of mice was sequenced and run with the Illumina NovaseqTM 6000 sequence platform. Using the Illumina paired-end RNA-seq approach, we sequenced the transcriptome, generating a total of millon 2×150  bp paired-end reads. Reads obtained from the sequencing machines include raw reads containing adapters or low-quality bases which will affect the following assembly and analysis. Thus, to get high-quality clean reads, reads were further filtered by Cutadapt [29] (https://cutadapt.readthedocs.io/en/stable/, version:cutadapt-1.9). The parameters were as follows: 

1) removing reads containing adapters; 

2) removing reads containing polyA and poly; 

3) removing reads containing more than 5% of unknown nucleotides (N);

4) removing low-quality reads containing more than 20% of low-quality (Q-value≤20) bases.

Ten sequence quality was verified using FastQC [30] (http://www.bioinformatics.babraham.ac.uk/projects/ fastqc/, 0.11.9). including the Q20, Q30, and GC content of the clean data. After that, a total of G bp of cleaned, paired-end reads were produced. The raw sequence data have been submitted to the NCBI Gene Expression Omnibus (GEO) datasets with accession number GSE154223. 

The reference genome/annotation was Mus_musculus.GRCm38. The source and version of the gene annotation used for analyses was Ensembl_v88.

Transcripts assembly

Firstly, Cutadapt [29] was used to remove the reads that contained adaptor contamination, low-quality bases, and undetermined bases. Ten sequence quality was very-fed using FastQC (http://www.bioinformatics.Babra ham.ac.uk/projects/fastqc/). We used Bowtie2 [31] and Hisat2 [32] to map reads to the genome of mouse. The mapped reads of each sample were assembled using StringTie [33]. Ten, all transcripts from kidney samples were merged to reconstruct a comprehensive transcriptome using Perl scripts. After the final transcriptome was generated, StringTie [33] and edgeR [34] were used to estimate the expression levels of all transcripts.

LncRNA identification 

First of all, transcripts that overlapped with known mRNAs and transcripts shorter than 200  bp were discarded. Then we utilized CPC [35] and CNCI [36] to predict transcripts with coding potential. All transcripts with CPC score <-1 and CNCI score<0 were removed. The remaining transcripts were considered as lncRNAs.

Differentially expressed analysis genes (DGEs) analysis 

StringTie [33] was used to perform expression levels for mRNAs and lncRNAs by calculating FPKM [37]. Genes differential expression analysis was performed by DESeq2 software between two different groups (and by edgeR between two samples) [34, 38]. The genes with the parameter of q value below 0.6 and absolute fold change>2 were considered differentially expressed genes.

Target gene prediction and functional analysis of lncRNAs 

To explore the function of function of lncRNAs, we predicted the cis-target genes of lncRNAs. LncRNAs may play a cis role in acting on neighboring target genes. In this study, coding genes in 100,000 upstream and downstream were selected by Perl script. Then, we showed a functional analysis of the target genes for lncRNAs by using the scripts the BLAST2GO [39]. The full commands link to the public domain was Jie-Li/README.md at main · dandan-li/Jie-Li (github.com).

Gene ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis 

The bioinformatics analysis for RNA sequencing was performed using OmicStudio tools (http://www.omicsudio. cn/tool). Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) [37–39] enrichment analysis were used to analyze the biological functions of the predicted target genes. GO analysis clarifies the main biological processes through three aspects: cell composition, molecular functions, and biological processes (http://www.geneontology. org/). The analysis frst puts all differentially expressed genes and background genes in the GO database. Each item is mapped, the number of genes in each item is calculated, and the hypergeometric distribution is used to perform hypothesis testing to obtain the P value of the enrichment result. The lower the P value, the more significant the enrichment result. KEGG is a database resource that analyzes the differentially expressed mRNAs by genetic biology to explore important pathways related to target genes (https://www.genome.jp/kegg/). The results are expressed by p-values; the lower the p-value, the more signifcant the enrichment result.

qRT–PCR

GAPDH was used as the endogenous control. Then, the relative expression levels of unknown genes were calculated. All primers were designed and synthesized specifically for this experiment. Primers for GAPDH have been commercialized. The specificity of Adra1a and Csnk1a1 primers was identified by the single peak of the melt curve.

Build mRNA‑lncRNA coexpression network 

Transregulation was based on research, and then the trans energy was calculated. The smaller the trans energy was, the higher the possibility of binding. Then, the correlation coefficient of mRNA-lncRNA was also calculated. The mRNA-lncRNA network was constructed by choosing a Spearman correlation coefficient exceeding 0.9. The mRNA-lncRNA coexpression network was constructed using Cytoscape software (v3.7.1).

Cell culture 

Mouse renal proximal tubular epithelial cells (MRPT-EpiCs) were cultured in epithelial cell medium-animal (EpiCM-a, Cat. #4131) containing 2% fetal bovine serum (FBS, Cat. #0010) and Epithelial Cell Growth Supplement-animal (EpiCGS-a, Cat. #4182) without antibiotics in a humidified incubator at 37  °C and 5% CO2. The culture medium was changed every 2–3 days. The cells in the logarithmic growth phase were sub-cultured when the growth reached 90%. The cell suspension digested by trypsin was seeded into 6-well plates with 2*104 -5*104 cells in each well. The cells used for transfection were all between generations 2 and 5.

Plasmid construction and cell transfection 

The lncRNA Gm43360 overexpression plasmid was constructed by GeneChem (Shanghai, China). The plasmid backbone was GV658, and the CMV promoter drove lncRNA expression. The cloned nucleotide sequence is provided in the supplementary material. For lncRNA Gm43360 knockdown, three lncRNA ENSMUST00000197656-target siRNAs (lncRNA Gm43360- siRNA1, 5'-CCUUCACUCCAGCUGGUAATT-3'; lncRNA Gm43360-siRNA2, 5'-CCCUGUCACUCA UGAAGUUTT-3'; and lncRNA Gm43360-siRNA3, 5'-GGUCAAAUAACUCAAUGGGTT-3') were designed and synthesized at GenePharma (Shanghai, China). According to the manufacturer's protocol, cells were transfected with 2500 ng of plasmid or 100 pmol of siRNA with 6  µl of Lipofectamine™ 2000 Transfection Reagent (Invitrogen, USA) per well. RNA and protein expression levels were detected at 48  h after transfection, and each experiment was repeated at least three times. Real-time quantitative PCR was used to validate the efficiency of lncRNA Gm43360 overexpression and knockdown.

Western blot analysis

Cultured cells were washed with ice-cold PBS, 120  µl of RIPA buffer (Heart, China) was added, and protease inhibitor and PMSF (Heart, China) were added for 30 min on ice. After collecting cells in different microcentrifuge tubes, the protein concentration was quantified. Loading buffer was added to each sample and then boiled for 7  min. Thirty micrograms of the sample were separated by 12% SDS–PAGE (Beyotime, China) and electrotransferred to PVDF membranes (Thermo Fisher, USA). Then, the membranes were blocked with 5% milk. The membranes were incubated with specific primary antibodies: anti-p21 (1:1000, Abcam, ab109199), anti-p53 (1:1000, Proteintech, 10442-1-AP), and anti-GAPDH (1:3000, Profintech, 60004-1-Ig) overnight at 4  °C. After washing with TBST, membranes were incubated with a secondary antibody for 1 h at room temperature. Protein bands were observed using a chemiluminescence kit (Millipore, USA). The expression of GAPDH was used to normalize protein levels.

Cell proliferation assay 

The capacity of cell proliferation was determined by the cell counting kit-8 (CCK-8) assay. Forty-eight hours after transfection, 90  µl of new medium and 10  µl of CCK-8 solution were added to each well (Beyotime, C0042). The cells were incubated for 1–4 h at 37 °C in 5% CO2 and measured at 450 nm by a universal microplate reader (Bio-Tek, USA).

Flow cytometric analysis

Cells were plated in 6-well plates the day before transfection. Forty-eight hours after transfection, the cells were trypsinized and centrifuged at 1000  rpm for 5 min. Then, the cells were fixed in 70% ethanol at 4 °C for at least 4 h. After centrifugation, RNase A and propidium iodide (PI) staining solution was added to the cells, and the cells were then incubated for 30  min at room temperature in the dark. Te-stained cells were analyzed using an ACEA NovoCyte (Biosciences, USA).

Statistical analysis

The measurement data by the normal distribution are presented as the mean±SD. The difference between the two different groups was determined using two-tailed unpaired Student's t-tests, and a p-value < 0.05 was considered to be statistically significant. All calculations were carried out using GraphPad Prism 8 (GraphPad Software, Inc., USA).

References 

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7. Wu YY, Kuo HC. Functional roles and networks of non-coding RNAs in the pathogenesis of neurodegenerative diseases. J Biomed Sci. 2020;27(1):49. 

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