Small RNA Profiling in Mycobacterium Provides Insights Into Stress Adaptability

Abstract

Mycobacteria encounter a number of environmental changes during infection and respond using different mechanisms. Small RNA (sRNA) is a post-transcriptionally regulatory system for gene functions and has been investigated in many other bacteria. This study used Mycobacterium tuberculosis and Mycobacterium bovis Bacillus Calmette-Guérin (BCG) infection models and sequenced whole bacterial RNAs before and after host cell infection. A comparison of differentially expressed sRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and target prediction was carried out. Six pathogenically relevant stress conditions, growth rate, and morphology were used to screen and identify sRNAs. From these data, a subset of sRNAs was differentially expressed in multiple infection groups and stress conditions. Many were found associated with lipid metabolism. Among them, ncBCG427 was significantly downregulated when BCG entered into macrophages and was associated with increased biofilm formation. The reduction of virulence possibility depends on regulating lipid metabolism.

Keywords: Mycobacterium bovis; Mycobacterium bovis BCG; mycobacteria; sRNA; stress.

FIGURE 1

Distribution of ncRNA length: (A) total, (B) antisense, and (C) intergenic ncRNAs.

FIGURE 2

RPKM distribution of ncRNAs: (A) Mycobacterium tuberculosis 1458/BCG RPKM distribution of ncRNAs between intracellular and extracellular bacteria (| FC| > 1.5). (B) RPKM distribution of ncRNAs between BCG and M. tb 1458 (| FC| > 1.5).

FIGURE 3

Mycobacterium tuberculosis 1458/BCG count distribution of differentially expressed ncRNAs in different types between intracellular and extracellular bacteria (| FC| > 1.5).

FIGURE 4

Venn diagram of the overlap after BCG and Mycobacterium tuberculosis 1458 ncRNA sequences are compared. In, intracellular; Ex, extracellular.

FIGURE 5

Annotation results of BCG and Mycobacterium tuberculosis 1458 ncRNAs by Rfam and BRSD sRNA databases.

FIGURE 6

Enrichment analysis of differentially expressed ncRNAs (| FC| > 1.5). (A) GO pathways (p < 0.1) of the targets most enriched in biological process, cell component, and molecular function. (B) Top 20 KEGG enrichment results (p < 0.1) of the target genes.

FIGURE 7

Detection of BCG and M. tb 1458 DEG ncRNAs under different stress culture model. (A) Iron starvation model; (B) carbon starvation model; (C) acid stress model; (D) oxidative stress model; (E) membrane stress model; (F) simulate granuloma stress model. The data used the mean ± SD of three replicate samples, t-test was used for statistical analysis, *p < 0.05, **p < 0.01, and ***p < 0.001.

FIGURE 8

Expression of candidate sRNAs in each overexpressing MS strain. Data are the mean ± SD of three replicate samples. ***p < 0.001 (t-test).

FIGURE 9

Growth curve of candidate sRNAs overexpressing Mycobacterium smegmatis MC²155. (A) ncBCG sRNA overexpressing M. smegmatis MC²155. (B) ncMTB sRNA overexpressing M. smegmatis MC²155.

FIGURE 10

Single-colony morphology of candidate sRNAs overexpressing Mycobacterium smegmatis MC²155.

FIGURE 11

Single-colony morphology and square measure of overexpressing Mycobacterium smegmatis MC² 155: MS_ncBCG427 and MS_ncMTB215. (A) Single-colony morphology of different strains. (B) Square of MS_ncBCG427. (C) Square of MS_ncMTB215. *p < 0.05; **p < 0.01 (t-test).

FIGURE 12

Biofilm-forming ability of candidate sRNA overexpressing strains. (A) Biofilm morphology. (B) Crystal violet staining results of MS_ncBCGs and (C) MS_ncMTBs compared to MSJ_Vector. *p < 0.05; **p < 0.01; ***p < 0.001 (t-test).

FIGURE 13

Differential expression of ncBCG427 predicted target genes in BCG. ^∗p < 0.05; ^∗∗p < 0.01; ^*⁣*⁣**p < 0.001 (t-test).