Correlated chromosomal periodicities according to the growth rate and gene expression

Abstract

Linking genetic information to population fitness is crucial to understanding living organisms. Despite the abundant knowledge of the genetic contribution to growth, the overall patterns/features connecting genes, their expression, and growth remain unclear. To reveal the quantitative and direct connections, systematic growth assays of single-gene knockout Escherichia coli strains under both rich and poor nutritional conditions were performed; subsequently, the resultant growth rates were associated with the original expression levels of the knockout genes in the parental genome. Comparative analysis of growth and the transcriptome identified not only the nutritionally differentiated fitness cost genes but also a significant correlation between the growth rates of the single-gene knockout strains and the original expression levels of these knockout genes in the parental strain, regardless of the nutritional variation. In addition, the coordinated chromosomal periodicities of the wild-type transcriptome and the growth rates of the strains lacking the corresponding genes were observed. The common six-period periodicity was somehow attributed to the essential genes, although the underlying mechanism remains to be addressed. The correlated chromosomal periodicities associated with the gene expression-growth dataset were highly valuable for bacterial growth prediction and discovering the working principles governing minimal genetic information.

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

“…The temporal OD 600 reads were exported from the plate reader and processed with Python. The growth parameters r and K were evaluated according to previous reports [30, 46] using a previously developed Python program [30]. In brief, r was defined as the mean of three continuous logarithmic slopes of every two neighboring OD 600 values within the exponential growth phase using “gradient” in the “NumPy” library.…”

Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity

“…As the differentiation in decision-making components allowed the independent decision for varied growth phases, the previously reported correlated changes in the growth parameters 27, 28, 29, 30, 31 were supposed to be weakened. However, the three parameters remained significantly correlated (Fig.…”

“…8B). The relative expression level of each gene (protein) was calculated as the mean of biologically repeated transcripts, according to previous reports 30, 53 . The results showed that the relative abundances of intracellular Ile and BCAAs were significantly higher than their theoretical ratios, i.e., one or three out of 20 amino acids, 5 or 15%, respectively (Fig.…”

“…The three growth parameters are likely coordinated with each other. Previous studies have observed trade-offs between the growth rate and either the population size 27,28 or the lag time 29 within identical species, as well as correlated changes in the growth rate and saturated population size among genetically diversified strains 30,31 . Whether and how environmental diversity affects the three growth parameters remain unknown.…”

Global coordination of the mutation and growth rates across the genetic and nutritional variety in Escherichia coli

“…Variation in growth media was representative of environmental diversity. Media variation could intuitively disturb the growth rates, as experimentally demonstrated with the wild-type ( Aida et al, 2022 ), single-gene knockout ( Liu et al, 2020 ), genome-reduced ( Kurokawa et al, 2016 ), and laboratory-evolved strains ( Kurokawa et al, 2022 ). The changes in growth media also adjusted the mutation rates of the wild-type and genome-reduced strains ( Nishimura et al, 2017 ) and those of MMR deficient strains ( Ishizawa et al, 2015 ).…”