Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions

Abstract

The genomic, environmental, and evolutionary interruptions caused the changes in bacterial growth, which were stringently associated with changes in gene expression. The growth and gene expression changes remained unclear in response to these interruptions that occurred combinative. As a pilot study, whether and how bacterial growth was affected by the individual and dual interruptions of genome reduction, environmental stress, and adaptive evolution were investigated. Growth assay showed that the presence of the environmental stressors, i.e., threonine and chloramphenicol, significantly decreased the growth rate of the wild-type Escherichia coli, whereas not that of the reduced genome. It indicated a canceling effect in bacterial growth due to the dual interruption of the genomic and environmental changes. Experimental evolution of the reduced genome released the canceling effect by improving growth fitness. Intriguingly, the transcriptome architecture maintained a homeostatic chromosomal periodicity regardless of the genomic, environmental, and evolutionary interruptions. Negative epistasis in transcriptome reorganization was commonly observed in response to the dual interruptions, which might contribute to the canceling effect. It was supported by the changes in the numbers of differentially expressed genes (DEGs) and the enriched regulons and functions. Gene network analysis newly constructed 11 gene modules, one out of which was correlated to the growth rate. Enrichment of DEGs in these modules successfully categorized them into three types, i.e., conserved, responsive, and epistatic. Taken together, homeostasis in transcriptome architecture was essential to being alive, and it might be attributed to the negative epistasis in transcriptome reorganization and the functional differentiation in gene modules. The present study directly connected bacterial growth fitness with transcriptome reorganization and provided a global view of how microorganisms responded to genomic, environmental, and evolutionary interruptions for survival from wild nature.

Experimental evolution for the recovery of growth loss due to genome reduction

“…It demonstrated that the chromosomal architecture of gene expression patterns remained highly conserved, regardless of the considerably varied mutations. The homeostatic periodicity was consistent with our previous findings that the chromosomal periodicity of the transcriptome was independent of genomic or environmental variation 27,28 .…”

“…Genome mutation analysis and transcriptome analysis failed to identify the common gene categories or pathways that correlated to the evolution of the reduced genome; thus, the gene modules correlated to evolution were newly evaluated. The weighted gene co-expression network analysis (WGCNA) 41 was performed toward the evolved transcriptomes as tested previously 27 . Reconstruction of the 3,290 genes in the reduced genome led to 21 gene modules, comprising 8 to 320 genes per module (Fig.…”

“…The E. coli cells were collected at the exponential growth phase (i.e., 5×10 7 ∼ 2 ×10 8 cells/mL) and subjected to RNA sequencing, as described previously 27,44 . In brief, the bacterial growth was stopped by mixing with the iced 10% phenol ethanol solution.…”

“…The experimental evolution compensated for the genome reduction-mediated growth changes were considered stringently related to transcriptome reorganization. Previous studies have identified conserved features in transcriptome reorganization, despite significant disruption to gene expression patterns resulting from either genome reduction or experimental evolution 27–29 . The findings indicated that experimental evolution might reinstate growth rates that have been disrupted by genome reduction to maintain homeostasis in growing cells.…”

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

“…The transcriptome datasets were deposited in the DNA Data Bank of Japan (DDBJ) under the accession number DRA013628. The differential expressed genes (DEGs) were determined using DESeq2 package [53] , with a criterion of FDR< 0.05 [54] , where the read counts were directly used, as previously described [55] . The enrichment analysis was performed using the globally normalized datasets, as previously described [49] , [56] .…”