gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Abstract

Microbial metabolic processes greatly impact ecosystem functioning and the physiology of multi-cellular host organisms. The inference of metabolic capabilities and phenotypes from genome sequences with the help of prior biomolecular knowledge stored in online databases remains a major challenge in systems biology. Here, we present gapseq: a novel tool for automated pathway prediction and metabolic network reconstruction from microbial genome sequences. gapseq combines databases of reference protein sequences (UniProt, TCDB), in tandem with pathway and reaction databases (MetaCyc, KEGG, ModelSEED). This enables the statistical prediction of an organism's metabolic capabilities from sequence homology and pathway topology criteria. By incorporating a novel LP-based gap-filling algorithm, gapseq facilitates the construction of genomescale metabolic models that are suitable for metabolic phenotype predictions by using constraint-based flux analysis. We validated gapseq by comparing predictions to experimental data for more than 1, 000 bacterial organisms comprising over 10, 000 phenotypic traits that include enzyme activity, energy sources, fermentation products, and gene essentiality. This large-scale phenotypic trait prediction test showed, that gapseq yields an overall accuracy of 80% and thereby outperforming other commonly used reconstruction tools. Furthermore, we illustrate the application of gapseq-reconstructed models to simulate biochemical interactions between microorganisms in multi-species communities. Altogether, gapseq (https://github.com/jotech/gapseq) is a new method that improves the predictive potential of automated metabolic network reconstructions and further increases their applicability in biotechnological, ecological, and medical research.

CeMbio - TheCaenorhabditis elegansMicrobiome Resource

“…The bacterial cells used for this experiment were from the same The genome assemblies served as input for the reconstruction of metabolic networks, using gapseq 1.0 (Zimmermann et al 2020a). The reconstruction of metabolic networks consists of systematic annotation of genomic metabolic genes that are then linked to the corresponding enzymatic reactions.…”

PhenoMapping: A protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models

“… There exist multiple examples of gap-fillers, as summarized before ( Pan and Reed, 2018 ). Some recent examples are: gapseq ( Zimmermann et al., 2020 ) or OptFill ( Schroeder and Saha, 2020 ). If there exists an alternative biochemistry that rescues the knockout, integrate it into the GEM.…”

Gut Microbiota and the Quality of Oral Anticoagulation in Vitamin K Antagonists Users: A Review of Potential Implications

“…However, not all the microbial enzymes involved in these chemical transformations have been described. Indeed, not all metabolic activities or functional profiles in these microbial communities can be predicted in metabolomic studies [ 41 ]. Thus, metabolic functions are assigned to superfamilies that can catalyze several different chemical reactions.…”