Researchers at the University of Tsukuba have conducted a high-throughput growth assay and used machine learning to address the genetic and environmental interplay of bacterial growth.

The paper is published in the journal Communications Biology.

The findings indicated that the environmental chemicals affecting growth have varying impacts depending on the amount of sugar present. Moreover, the research team demonstrated that the changes in growth caused by genetic and environmental changes offset each other.

Genetic and environmental factors influence cell growth. Although previous studies have evaluated the influence of these factors on growth, conducting studies that assess such factors in a cross-talk manner is necessary. The genetic and environmental interactions still need further investigation. In this study, high-throughput biological experiments and machine learning data analysis were conducted to investigate the impact of gene-chemical interactions on bacterial growth.

A total of 115 genetically distinct Escherichia coli strains were cultivated under 135 nutritional conditions, with combinations of 48 different chemicals. A substantial dataset including approximately 14,000 growth profiles was achieved by high-throughput growth assay. Machine learning was used to analyze the dataset and investigate the impact of chemical nutrients on growth.

The research team discovered that the impact of the 48 chemicals on the 115 distinct bacterial strains varies, depending on the presence of sugar. Moreover, the gene-chemical interaction was evaluated using a theoretical model, demonstrating that the growth alterations resulting from the genetic and environmental discrepancies offset one another. This countervailing effect likely represents a general strategy for bacterial survival in the natural environment.

This study provides valuable insights into the factors influencing cell growth, thereby enhancing our understanding of universal principles. These insights will improve the effectiveness of industrial applications such as cell culture optimization.