Exosome characterization and exosomal RNA preparation
The supernatants from the culture media from the two groups were collected and used for the exosome isolation. The exosome morphology was evaluated by TEM. NanoFCM was used for the detection of exosome transmembrane proteins (CD9 and CD81) and to determine the particle concentration and size distribution.
As shown in Fig. 1, the typical morphology of exosomes can be observed by TEM; the exosome transmembrane proteins CD9 and CD91 showed positive expression in 14.1% and 8.7% of the exosomes, respectively; the average concentration was 7.45×109 particles/ml, and the particle size was mainly distributed in the 60–80-nm range (Fig. 2). The concentration of exosomal RNA extracted from the exosomes in each sample varied from 0.0021 to 0.0039 μg/μl. Quality control and an RIN evaluation were performed, and the obtained values indicated that the samples met the standards for the subsequent sequencing analysis.
Analysis of the differential expression of miRNAs
In total, 1853 miRNAs were detected, and 506 miRNAs overlapped between the infection group and non-infection group (Fig. 3). A differential miRNA cluster analysis intuitively displayed the specific expression profiles of the miRNAs in the different experimental treatments (Fig. 4), and the advanced volcano map displays the significant differential expression of multiple miRNAs (Fig. 5). The data showed that, compared with the non-infection group, there were 20 miRNAs upregulated and 7 miRNAs downregulated significantly in the infection group. The expression levels and the up- or downregulation relationships are presented in Table 2. Among the detected miRNAs, mmu-miR-27b-3p, -93-5p, -25-3p, -1198-5p, -let-7c-5p and -let-7a-5p were expressed at relatively high levels. Target gene prediction and GO and KEGG enrichment analyses of these six significantly differentially expressed miRNAs were subsequently performed.
Finally, a number of 5643 target genes were predicted by using TargetScan (v5.0) and miRanda (v3.3a) software. GO and KEGG enrichment analysis of the screened target genes was then performed using the OmicStudio tools (https://www.omicstudio.cn/tool), and in total, 5576 target genes were effectively enriched.
GO enrichment analysis
The biological process (BP) enrichment analysis revealed that 92.88%, 71.98%, 45.57%, 43.33%, 43.33%, 34.36%, 30.28%, 27.73%, 25.39% and 22.84% of the target genes mainly participated in signal transduction, the G protein-coupled receptor signalling pathway, response to stimulation, the detection of chemical stimulation related to olfactory perception, olfactory perception, the positive regulation of transcription through RNA polymerase II, transcription regulation of DNA template, multicellular biological development, cell differentiation and phosphorylation, respectively. In addition, 18.05% of the target genes also participated in the oxidation–reduction process, protein transport, phosphorylation and ubiquitination, ion transport, the cell cycle and the regulation of apoptosis.
As demonstrated by the cellular component (CC) enrichment analysis, 90.91%, 63.42% and 58.69% of the target genes were involved in cell membrane composition, cytoplasmic composition, and plasma membrane composition, respectively. In addition, 52.03%, 35.28%, and 28.85% of the target genes participated in the composition of the nucleus, cytoplasmic cells, and nucleoplasm, respectively.
The molecular function (MF) enrichment analysis demonstrated that 65.59%, 33.03%, 22.53% and 20.25% of the target genes participated in protein binding, metal ion binding, G protein-coupled receptor activity and transferase activity, respectively. In addition, some genes were found to be involved in the combination of nucleotides and ATP and DNA molecules. The results of the GO enrichment analysis are presented in Fig. 6.
KEGG enrichment analysis
The results of the KEGG enrichment analysis revealed that the target genes primarily participated in the modulatory control of human diseases, metabolism and biological systems. In addition, a large proportion of genes were classified into the categories of cellular process, environmental information processing and genetic information processing. In this section, the percentage of genes refers to the percentage of significantly enriched genes in the corresponding secondary categories.
As shown in Fig. 7A, most significantly enriched target genes (almost three-quarters of all enriched genes) were related to infectious diseases (associated with viral, parasitic and bacterial infections) and cancer in the category of human diseases. In addition, the percentage of genes in each secondary category exceeded 50%. Within the metabolism category, 51 (54.26%), 122 (47.84%), 84 (47.73%), 111 (49.33%) and 92 (53.18%) target genes were significantly enriched in nucleotide metabolism, lipid metabolism, amino acid metabolism, carbohydrate metabolism, and glycan biosynthesis and metabolism, respectively. The percentage of enriched genes in each secondary category related to organismal systems exceeded 40% as follows: ageing (56.06%), sensory system (52.81%), development and regeneration system (50.52%), nervous system (50.3%), endocrine system (49.82%), environmental adaptation (46.11%), circulatory system (48.08%), immune system (49.28%), digestive system (45.66%), and excretory system (40.22%). Within the environmental information processing category, 249 genes were predominantly enriched in pathways related to signalling molecule interactions, 585 genes were related to signal transduction, and 16 genes were related to membrane transduction. Moreover, within the genetic information processing category, 47 genes were enriched in nucleotide replication and repair, and 132 genes were enriched in protein folding, sorting and degradation. Strikingly, the data related to the cell process category revealed that there were 183 target genes predominantly gathered in modulating the control of cell growth and death. Moreover, the rich factor analysis (Fig. 7B) showed that these target genes were predominantly enriched in apoptosis, focal adhesion, neuroactive ligand–receptor interaction, p53 signalling pathway and hepatitis B. This finding is crucial for our subsequent screening of the target genes involved in modulating apoptosis in macrophages infected with Mtb.
Visualization analysis of the KEGG enrichment results
Metascape (https://metascape.org) was selected to visualize and analyse the biological functions of the target genes enriched in cell growth and death regulation. A total of 183 genes were transformed into their corresponding M. musculus Entrez gene IDs, and related enrichment analyses were then performed. The top twenty clusters and their representative enriched terms were converted into a network layout.
As indicated in the bar graph shown in Fig. 8, apoptosis (ko04210), oocyte meiosis (ko04114), cell cycle (ko04110), hepatitis B (mmu05161) and p53 signalling (WP2902) were the top five significantly enriched terms across the input genes, and the specific gene annotation information of each cluster is summarized in Table 3. The result demonstrated that these genes were mainly involved in signalling, cellular processes and the regulation of metabolic and biological processes. The same result is displayed in Fig. 9, where each node stands for an enriched term, coloured first based on its cluster ID and then according to its p value.
An extended protein–protein interaction network was framed, and 9 clusters were extracted using the MCODE algorithm. The pathways and biological processes enrichment analysis was utilized to each MCODE component independently, as shown in Fig. 10. Three optimal scoring terms were preserved as the functional description of the relevant components and highlighted in Table 4. The results demonstrated that the enriched genes were predominantly involved in regulating the cellular apoptosis process and oocyte meiosis. MCODE_1 and MCODE_3 were associated with the regulation of the cell cycle; MCODE_2 and MCODE_4 mostly participated in apoptosis pathways; MCODE_5 was associated with the longevity regulating pathway; and MCODE_6 was associated with an interleukin-6 family signalling pathway and CD4+ or CD8+ α-β T cell lineage commitment. As shown by the data presented in Table 5, apoptosis was the term with the best score obtained from the MCODE analysis.
The relative expression of miRNA in exosomes was detected by qRT–PCR to validate the accuracy of the microRNA-Seq results. Six biological samples (three per group) were prepared, and exosomes were isolated by ultracentrifugation. Eight miRNAs were randomly selected from the list of differentially expressed miRNAs, and their relative expression levels in each sample were detected and are presented in Fig. 11.