Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes.

Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness.

Adaptive laboratory evolution for improved tolerance of isobutyl acetate in Escherichia coli.

Previously, Escherichia coli was engineered to produce isobutyl acetate (IBA). Titers greater than the toxicity threshold (3 g/L) were achieved by using layer-assisted production. To avoid this costly and complex method, adaptive laboratory evolution (ALE) was applied to E.

Independent component analysis recovers consistent regulatory signals from disparate datasets.

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E.

A biochemically-interpretable machine learning classifier for microbial GWAS.

Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.

Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens.

The evolution of antimicrobial resistance (AMR) poses a persistent threat to global public health. Sequencing efforts have already yielded genome sequences for thousands of resistant microbial isolates and require robust computational tools to systematically elucidate the genetic basis for AMR. Here, we present a generalizable machine learning workflow for identifying genetic features driving AMR based on constructing reference strain-agnostic pan-genomes and training random subspace ensembles (RSEs).

Systems Biology and Pangenome of O-Antigens.

O-antigens are glycopolymers in lipopolysaccharides expressed on the cell surface of Gram-negative bacteria. Variability in the O-antigen structure constitutes the basis for the establishment of the serotyping schema. We pursued a two-pronged approach to define the basis for O-antigen structural diversity.

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics.

Escherichia coli B2 strains prevalent in inflammatory bowel disease patients have distinct metabolic capabilities that enable colonization of intestinal mucosa.

Background: Escherichia coli is considered a leading bacterial trigger of inflammatory bowel disease (IBD). E. coli isolates from IBD patients primarily belong to phylogroup B2.

The Two-Component System AgrAC Displays Four Distinct Genomic Arrangements That Delineate Genomic Virulence Factor Signatures.

Two-component systems (TCSs) consist of a histidine kinase and a response regulator. Here, we evaluated the conservation of the AgrAC TCS among 149 completely sequenced strains. It is composed of four genes: .

iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of AYE.

has become an urgent clinical threat due to the recent emergence of multi-drug resistant strains. There is thus a significant need to discover new therapeutic targets in this organism. One means for doing so is through the use of high-quality genome-scale reconstructions.

Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions.

Background: The efficacy of antibiotics against M. tuberculosis has been shown to be influenced by experimental media conditions. Investigations of M.

ssbio: a Python framework for structural systems biology.

Summary: Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

Background: The mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.