The genomic diversity across strains of a species forms the genetic basis for differences in their behavior. A large-scale assessment of sequence variation has been made possible by the growing availability of strain-specific whole-genome sequences (WGS) and with the advent of large-scale databases of laboratory-acquired mutations. We define the Escherichia coli "alleleome" through a genome-scale assessment of amino acid (AA) sequence diversity in open reading frames across 2,661 WGS from wild-type strains. We observe a highly conserved alleleome enriched in mutations unlikely to affect protein function. In contrast, 33,000 mutations acquired in laboratory evolution experiments result in more severe AA substitutions that are rarely achieved by natural selection. Large-scale assessment of the alleleome establishes a method for the quantification of bacterial allelic diversity, reveals opportunities for synthetic biology to explore novel sequence space, and offers insights into the constraints governing evolution.

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.

Salmonella strains are traditionally classified into serovars based on their surface antigens. While increasing availability of whole-genome sequences has allowed for more detailed subtyping of strains, links between genotype, serovar, and host remain elusive. Here we reconstruct genome-scale metabolic models for 410 Salmonella strains spanning 64 serovars. Model-predicted growth capabilities in over 530 different environments demonstrate that: (1) the Salmonella accessory metabolic network includes alternative carbon metabolism, and cell wall biosynthesis; (2) metabolic capabilities correspond to each strain's serovar and isolation host; (3) growth predictions agree with 83.1% of experimental outcomes for 12 strains (690 out of 858); (4) 27 strains are auxotrophic for at least one compound, including L-tryptophan, niacin, L-histidine, L-cysteine, and p-aminobenzoate; and (5) the catabolic pathways that are important for fitness in the gastrointestinal environment are lost amongst extraintestinal serovars. Our results reveal growth differences that may reflect adaptation to particular colonization sites.

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in iJL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.

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.

Availability and implementation

ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb.

Supplementary information

Supplementary data are available at Bioinformatics online.

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. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens.