Biology subjects

Correia, K.

Publications and source records attributed to Correia, K..

6 recordsLinked to original sources

bioRxiv2018

Pan-genome-scale network reconstruction: a framework to increase the quantity and quality of metabolic network reconstructions throughout the tree of life

A genome-scale network reconstruction (GENRE) represents the knowledgebase of an organism and can be used in a variety of applications. The drop in genome sequencing costs has led to an increase in sequenced genomes, but the number of curated GENRE s has not kept pace. This gap hinders our ability to study physiology across the tree of life. Furthermore, our analysis of yeast GENRE s has found they contain significant commission and omission errors, especially in central metabolism. To address these quantity and quality issues for GENRE s, we propose open and transparent curation of the pan-genome, pan-reactome, pan-metabolome, and pan-phenome for taxons by research communities, rather than for a single species. We outline our approach with a Fungi pan-GENRE by integrating AYbRAH, our ortholog database, and AYbRAHAM, our new fungal reaction database. This pan-GENRE was used to compile 33 yeast/fungi GENRE s in the Dikarya subkingdom, spanning 600 million years. The fungal pan-GENRE contains 1547 orthologs, 2726 reactions, 2226 metabolites, and 10 compartments. The strain GENRE s have a wider genomic and metabolic than previous yeast and fungi GENRE s. Metabolic simulations show the amino acid yields from glucose differs between yeast lineages, indicating metabolic networks have evolved in yeasts. Curating ortholog and reaction databases for a taxon can be used to increase the quantity and quality of strain GENRE s. This pan-GENRE framework provides the ability to scale high-quality GENRE s to more branches in the tree of life.

bioinformatics↗

bioRxiv2018

Flux balance analysis predicts NADP phosphatase and NADH kinase are critical to balancing redox during xylose fermentation in Scheffersomyces stipitis

Xylose is the second most abundant sugar in lignocellulose and can be used as a feedstock for next-generation biofuels by industry. Saccharomyces cerevisiae, one of the main workhorses in biotechnology, is unable to metabolize xylose natively but has been engineered to ferment xylose to ethanol with the xylose reductase (XR) and xylitol dehydrogenase (XDH) genes from Scheffersoymces stipitis. In the scientific literature, the yield and volumetric productivity of xylose fermentation to ethanol in engineered S. cerevisiae still lags S. stipitis, despite expressing of the same XR-XDH genes. These contrasting phenotypes can be due to differences in S. cerevisiaes redox metabolism that hinders xylose fermentation, differences in S. stipitis redox metabolism that promotes xylose fermentation, or both. To help elucidate how S. stipitis ferments xylose, we used flux balance analysis to test various redox balancing mechanisms, reviewed published omics datasets, and studied the phylogeny of key genes in xylose fermentation. In vivo and in silico xylose fermentation cannot be reconciled without NADP phosphatase (NADPase) and NADH kinase. We identified eight candidate genes for NADPase. PHO3.2 was the sole candidate showing evidence of expression during xylose fermentation. Pho3.2p and Pho3p, a recent paralog, were purified and characterized for their substrate preferences. Only Pho3.2p was found to have NADPase activity. Both NADPase and NAD(P)H-dependent XR emerged from recent duplications in a common ancestor of Scheffersoymces and Spathaspora to enable efficient xylose fermentation to ethanol. This study demonstrates the advantages of using metabolic simulations, omics data, bioinformatics, and enzymology to reverse engineer metabolism.

microbiology↗

bioRxiv2018

Interspecies malate-pyruvate shuttle drives amino acid exchange in organohalide-respiring microbial communities

Most microorganisms in the biosphere live in communities and develop coordinated metabolisms via trading metabolites. In this study, we sought to deconstruct the metabolic interdependency in organohalide-respiring microbial communities enriched with Dehalobacter restrictus (Dhb), using a complementary approach of computational metabolic modeling and experimental validation. Dhb possesses a complete set of genes for amino acid biosynthesis yet requires amino acid supplementation. We reconciled this discrepancy using Flux Balance Analysis with consideration for cofactor availability, enzyme promiscuity, and shared protein expression patterns of several Dhb strains. Experimentally, 13C incorporation assays, growth assays, and metabolite analysis of strain PER-K23 cultures were performed to validate the model predictions. The model resolved that Dhbs amino acid dependency results from restricted NADPH regeneration and diagnosed that malate supplementation can replenish intracellular NADPH using malic enzyme. Interestingly, we observed unexpected export of glutamate and pyruvate in parallel to malate consumption in the strain PER-K23 cultures. Further experiments on Dhb-enriched consortium ACT-3 suggested an interspecies malate-pyruvate shuttle between Dhb and a glutamate-auxotrophic Bacteroides sp., reminiscent of the mitochondrial malate shunt pathway in eukaryotic cells. Altogether, this study reveals that redox constraints and metabolic complementarity are important driving forces for amino acid exchange in anaerobic microbial communities.

microbiology↗

bioRxiv2018

Genome-scale metabolic network reconstruction of the chloroform-respiring Dehalobacter restrictus strain CF

BackgroundOrganohalide-respiring bacteria (OHRB) play an important role in the global halogen cycle and bioremediation of industrial sites contaminated with chlorinated organics. One notable OHRB is Dehalobacter restrictus strain CF, which is capable of respiring chloroform to dichloromethane. Improved bioremediation strategies could be employed with a greater understanding of D. restrictus metabolism in isolate and community cultures. To this end, we reconstructed the genome-scale metabolic network of D. restrictus to study its metabolism in future studies using flux balance analysis.\n\nMethodThe RAST annotation server and Model SEED framework were used to obtain a draft metabolic network reconstruction. Additional curation was required for its acetyl-CoA sources, the Wood-Ljungdahl pathway, TCA cycle, electron transport chain, hydrogenase complexes, and formate dehydrogenase complexes.\n\nResultsiHH623 is the first curated genome-scale metabolic model in the Peptococcaceae family. It spans 1087 reactions and 983 metabolites, covering 623 genes (21% of all ORFs). Its potential sources of acetyl-CoA are pyruvate ferredoxin oxidoreductase, pyruvate formate lyase, acetyl-CoA synthetase, phosphate acetyltransferase, and CO-methylating acetyl-CoA synthase. NADPH may be regenerated by isocitrate dehydrogenase, malic enzyme, NADP-reducing hydrogenase, cytosolic formate dehydrogenase, ferredoxin-dependent bifurcating transhydrogenase, 5-methyltetrahydrofolate dehydrogenase, and 5-10-methylenetetrahydrofolate. Additional reactions that were added or removed to the D. restrictus reconstruction are discussed.\n\nConclusionsWe reconstructed the genome-scale metabolic network of D. restricus by obtaining an initial draft with the RAST server and Model SEED framework. Curation was required for D. restricus acetyl-CoA sources, TCA cycle, electron transport chain, hydrogenase complexes, and formate dehydrogenase complexes. This metabolic model can be used to decipher D. restrictus metabolism in isolate and community cultures in future studies, or as a template to reconstruct the metabolic network of other Peptococcaceae species. The extensive curation of the draft metabolic network reconstruction highlights the need to be cautious of automated metabolic network reconstruction.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2017

Reconstructing the evolution of metabolism in budding yeasts

Budding yeasts inhabit a range of environments by exploiting various metabolic traits. The genetic bases for these traits are mostly unknown, preventing their addition or removal in a chassis organism for metabolic engineering. To help understand the molecular evolution of these traits in yeasts, we created Analyzing Yeasts by Reconstructing Ancestry of Homologs (AYbRAH), an open-source database of predicted and manually curated ortholog groups for 33 diverse fungi and yeasts in Dikarya, spanning 600 million years of evolution. OrthoMCL and OrthoDB were used to cluster protein sequence into ortholog and homolog groups, respectively; MAFFT and PhyML were used to reconstruct the phylogeny of all homolog groups. Ortholog assignments for enzymes and small metabolite transporters were compared to their phylogenetic reconstruction, and curated to resolve any discrepancies. Information on homolog and ortholog groups can be viewed in the AYbRAH web portal (https://kcorreia.github.io/aybrah/) to review ortholog groups, predictions for mitochondrial localization and transmembrane domains, literature references, and phylogenetic reconstructions. Ortholog groups in AYbRAH were compared to HOGENOM, KEGG Orthology, OMA, eggNOG, and PANTHER. PANTHER and OMA had the most congruent ortholog groups with AYbRAH, while the other phylogenomic databases had greater amounts of under-clustering, over-clustering, or no ortholog annotations for proteins. Future plans are discussed for AYbRAH, and recommendations are made for other research communities seeking to create curated ortholog databases.

evolutionary biology↗