Systems Biology and Pangenome of Salmonella 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. First, we developed a bottom-up systems biology approach to O-antigen metabolism by building a reconstruction of Salmonella O-antigen biosynthesis and used it to (i) update 410 existing Salmonella strain-specific metabolic models, (ii) predict a strain's serogroup and its O-antigen glycan synthesis capability (yielding 98% agreement with experimental data), and (iii) extend our workflow to more than 1,400 Gram-negative strains. Second, we used a top-down pangenome analysis to elucidate the genetic basis for intraserogroup O-antigen structural variations. We assembled a database of O-antigen gene islands from over 11,000 sequenced Salmonella strains, revealing (i) that gene duplication, pseudogene formation, gene deletion, and bacteriophage insertion elements occur ubiquitously across serogroups; (ii) novel serotypes in the group O:4 B2 variant, as well as an additional genotype variant for group O:4, and (iii) two novel O-antigen gene islands in understudied subspecies. We thus comprehensively defined the genetic basis for O-antigen diversity.IMPORTANCE Lipopolysaccharides are a major component of the outer membrane in Gram-negative bacteria. They are composed of a conserved lipid structure that is embedded in the outer leaflet of the outer membrane and a polysaccharide known as the O-antigen. O-antigens are highly variable in structure across strains of a species and are crucial to a bacterium's interactions with its environment. They constitute the first line of defense against both the immune system and bacteriophage infections and have been shown to mediate antimicrobial resistance. The significance of our research is in identifying the metabolic and genetic differences within and across O-antigen groups in Salmonella strains. Our effort constitutes a first step toward characterizing the O-antigen metabolic network across Gram-negative organisms and a comprehensive overview of genetic variations in Salmonella

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

Acinetobacter baumannii 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. Well-curated and accurate genome-scale models (GEMs) of A. baumannii would be useful for improving treatment options. We present an updated and improved genome-scale reconstruction of A. baumannii AYE, named iCN718, that improves and standardizes previous A. baumannii AYE reconstructions. iCN718 has 80% accuracy for predicting gene essentiality data and additionally can predict large-scale phenotypic data with as much as 89% accuracy, a new capability for an A. baumannii reconstruction. We further demonstrate that iCN718 can be used to analyze conserved metabolic functions in the A. baumannii core genome and to build strain-specific GEMs of 74 other A. baumannii strains from genome sequence alone. iCN718 will serve as a resource to integrate and synthesize new experimental data being generated for this urgent threat pathogen

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

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

High-quality genome-scale metabolic modelling of \u3ci\u3ePseudomonas putida\u3c/i\u3e highlights its broad metabolic capabilities

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas

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. 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