Comprehensive analysis of transcriptomics and metabolomics to reveal the mechanism of moxibustion ameliorating aging kidney injury
Description
In the present study, we investigated the mechanism of moxibustion in improving renal injury in aging.Integrated analysis of untargeted metabolomics and transcriptomics revealed characteristic alterations in metabolites and genes in the aging kidney, and demonstrated that Cndp2, Pde7b, Pde1c, Adh7, and Adh1 were key targets of moxibustion in alleviating kidney injury of naturally aged mice, and the mechanism underlying this process could be achieved by regulating purine metabolism, histidine metabolism and glycolytic/gluconeogenic pathways. We presented four sets of data, all of which have been analyzed to identify differences among the three groups: young, old, and GS-Moxi. The first set includes kidney function indicators such as kidney organ coefficient, BUN and Scr data, as well as the evaluation of staining results Tubular injury score and Fibrotic area (%). The second set comprises heatmap, kegg pathway and ROC analysis of metabolomics. The third set consists of transcription omics data including heatmap and kegg pathway analysis. The fourth set contains validation data for gene expression and qRT-PCR in the transcriptomics of Cndp2, Pde7b, Pde1c, Adh7 and Adh1. All quantitative results were represented as the means±standard deviation (SD), and statistical analysis was performed by Graphpad Prism (Version 8.0). Differences between two groups were assessed using the unpaired two-tailed Student’s t-test. One-way analysis of variance was performed to compare more than two parametric groups. P <0.05 was considered statistically significant.
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LC-MS/MS analysis After sum normalization, the processed data were analyzed using the R package (rolls). Multivariate data analysis was performed using Partial-least squares discrimination analysis (PLS-DA) as a supervised method to identify important variables with discriminative power. PLS‐DA models were validated based on multiple correlation coefficient (R2) and cross-validation R2 (Q2) in cross‐validation and permutation test by applying 2000 iterations (P>0.001). Metabolites with VIP scores greater than 1 and P-values less than 0.05 were selected for further analysis. The heat plot was performed by Multi-Experiment Viewer (MeV) software 4.9.0 after unit variance scaling for each metabolite. The metabolites were blasted against the online Kyoto Encyclopedia of Genes and Genomes (KEGG: http://geneontology.org/) and were subsequently mapped to pathways in KEGG. RNA-seq analysis Total RNA was extracted from the kidney using TRIzol reagent (Thermo Fisher Scientific, 15596026). The A260/A280 absorbance ratio of the RNA samples was determined with a Nanodrop ND-2000 system (Thermo Scientific, USA), and the RIN of the RNA was measured with an Agilent Bioanalyzer 4150 system (Agilent Technologies, CA, USA). Only qualified samples will be used for library construction. Paired-end libraries were prepared using an ABclonal mRNA-seq Lib Prep Kit (ABclonal, China) following the manufacturer’s instructions. The mRNA was purified from 1 μg of total RNA using oligo (dT) magnetic beads, followed by fragmentation carried out using divalent cations at elevated temperatures in ABclonal First Strand Synthesis Reaction Buffer. Subsequently, first-strand cDNAs were synthesized with random hexamer primers and Reverse Transcriptase (RNase H) using mRNA fragments as templates, followed by second-strand cDNA synthesis using DNA polymerase I, RNAseH, buffer, and dNTPs. The synthesized double-stranded cDNA fragments were then ligated with adapters for the preparation of the paired-end library. The adapter-ligated cDNA was used for PCR amplification. PCR products were purified using the AMPure XP system, and the quality of the library was assessed on an Agilent Bioanalyzer 4150 system. Finally, the library preparation was sequenced on an Illumina Novaseq 6000 and 150 bp paired end reads were generated. Data generated from the Illumina platform are used for bioinformatics analysis. All analysis is performed using an in-house pipeline from Shanghai Applied Protein Technology.