Decoding gut microbiota by imaging analysis of fecal samples.

The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis.

High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli.

Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype-genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of ß-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to ß-lactams compared to strains directly selected by ß-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.

Prediction of Cross-resistance and Collateral Sensitivity by Gene Expression profiles and Genomic Mutations.

In adaptive evolution, an increase in fitness to an environment is frequently accompanied by changes in fitness to other environmental conditions, called cross-resistance and sensitivity. Although the networks between fitness changes affect the course of evolution substantially, the mechanisms underlying such fitness changes are yet to be fully elucidated. Herein, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system, and quantified how the acquisition of resistance to one stressor alters the resistance to other stressors. We demonstrated that resistance changes could be quantitatively predicted based on changes in the transcriptome of the resistant strains. We also identified several genes and gene functions, for which mutations were commonly fixed in the strains resistant to the same stress, which could partially explain the observed cross-resistance and collateral sensitivity. The integration of transcriptome and genome data enabled us to clarify the bacterial stress resistance mechanisms.

Improvement of isopropanol tolerance of Escherichia coli using adaptive laboratory evolution and omics technologies.

Isopropanol (IPA) is the secondary alcohol that can be dehydrated to yield propylene. To produce IPA using microorganisms, a significant issue is that the toxicity of IPA causes retardation or inhibition of cell growth, decreasing the yield. One possible strategy to overcome this problem is to improve IPA tolerance of production organisms. For the understanding of tolerance to IPA, we performed parallel adaptive laboratory evolution (ALE) of Escherichia coli under IPA stress. To identify the genotypic change during ALE, we performed genome re-sequencing analyses of obtained tolerant strains. To verify which mutations were contributed to IPA tolerance, we constructed the mutant strains and quantify the IPA tolerance of the constructed mutants. From these analyses, we found that five mutations (relA, marC, proQ, yfgO, and rraA) provided the increase of IPA tolerance. To understand the phenotypic change during ALE, we performed transcriptome analysis of tolerant strains. From transcriptome analysis, we found that expression levels of genes related to biosynthetic pathways of amino acids, iron ion homeostasis, and energy metabolisms were changed in the tolerant strains. Results from these experiments provide fundamental bases for designing IPA tolerant strains for industrial purposes.