Fritzemeier, C. J. | Search bioRxiv

bioRxiv2023

Metabolic complexity increases adaptability

Bacteria show a striking ability to adapt to new environments through horizontal gene transfer. Anecdotal evidence suggests that some bacteria are more adaptable than others: e.g., intestinal E. coli frequently spin off pathogenic strains adapted to other human tissues, while gastric Helicobacter pylori do not. However, it is unclear what determines the ability of individual strains or species to adapt to new environments. Here, we use pan-genome scale modeling to explore the ability of 102 different unicellular organisms to adapt to each of 5000+ diverse nutritional environments. While the small metabolic systems of specialized endosymbionts typically require 50+ additional metabolic reactions to adapt to new environments, different strains of the generalist E. coli require on average less than 5 new reactions. Thus, there is a positive feedback between metabolic complexity and adaptability, contrary to speculations that complex systems are generally less adaptable.

evolutionary biology↗

bioRxiv2018

Automated high-quality reconstruction of metabolic networks from high-throughput data

While new genomes are sequenced at ever increasing rates, their phenotypic analysis remains a major bottleneck of biomedical research. The generation of genome-scale metabolic models capable of accurate phenotypic predictions is a labor-intensive endeavor; accordingly, such models are available for only a small percentage of sequenced species. The standard metabolic reconstruction process starts from a (semi-)automatically generated draft model, which is then refined through extensive manual curation. Here, we present a novel strategy suitable for full automation, which exploits high-throughput gene knockout or nutritional growth data. We test this strategy by reconstructing accurate genome-scale metabolic models for three strains of Streptococcus, a major human pathogen. The resulting models contain a lower proportion of reactions unsupported by genomic evidence than the most widely used E. coli model, but reach the same accuracy in terms of knockout prediction. We confirm the models predictive power by analyzing experimental data for auxotrophy, additional nutritional environments, and double gene knockouts, and we generate a list of potential drug targets. Our results demonstrate the feasibility of reconstructing high-quality genome-scale metabolic models from high-throughput data, a strategy that promises to massively accelerate the exploration of metabolic phenotypes.\n\nSignificance statementReading bacterial genomes has become a cheap, standard laboratory procedure. A genome by itself, however, is of little information value - we need a way to translate its abstract letter sequence into a model that describes the capabilities of its carrier. Until now, this endeavor required months of manual work by experts. Here, we show how this process can be automated by utilizing high-throughput experimental data. We use our novel strategy to generate highly accurate metabolic models for three strains of Streptococcus, a major threat to human health.

systems biology↗