Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism’s biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.

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Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

mSystems ◽

10.1128/msystems.00599-21 ◽

2021 ◽

Author(s):

Nana Y. D. Ankrah ◽

David B. Bernstein ◽

Matthew Biggs ◽

Maureen Carey ◽

Melinda Engevik ◽

...

Keyword(s):

Systems Biology ◽

Metabolic Modeling ◽

Ecological Processes ◽

Mechanistic Insight ◽

Microbiome Research ◽

Metabolic Models ◽

Genome Scale ◽

Insight Into

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research.

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Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets

10.1101/2021.01.27.428543 ◽

2021 ◽

Author(s):

Kuoyuan Cheng ◽

Laura Riva ◽

Sanju Sinha ◽

Lipika Ray Pal ◽

Nishanth Ulhas Nair ◽

...

Keyword(s):

Metabolic Modeling ◽

Metabolic Reprogramming ◽

Integrated Analysis ◽

Sequencing Data ◽

Human Patient ◽

Medical Needs ◽

Wide Range ◽

Genome Scale ◽

Host Metabolism

AbstractTremendous progress has been made to control the COVID-19 pandemic, including the development and approval of vaccines as well as the drug remdesivir, which inhibits the SARS-CoV-2 virus that causes COVID-19. However, remdesivir confers only mild benefits to a subset of patients, and additional effective therapeutic options are needed. Drug repurposing and drug combinations may represent practical strategies to address these urgent unmet medical needs. Viruses, including coronaviruses, are known to hijack the host metabolism to facilitate their own proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM). We find that SARS-CoV-2 infection can induce recurrent and complicated metabolic reprogramming spanning a wide range of metabolic pathways. We next applied the GEM-based metabolic transformation algorithm (MTA) to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. These predictions are enriched for validated targets from various published experimental drug and genetic screens. Further analyzing the RNA-sequencing data of remdesivir-treated Vero E6 cell samples that we generated, we predicted metabolic targets that act in combination with remdesivir. These predictions are enriched for previously reported synergistic drugs with remdesivir. Since our predictions are based in part on human patient data, they are likely to be clinically relevant. We provide our top high-confidence candidate targets for their evaluation in further studies, demonstrating host metabolism-targeting as a promising antiviral strategy.

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Faculty Opinions recommendation of Phylogenetic analysis based on genome-scale metabolic pathway reaction content.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1019702.222533 ◽

2004 ◽

Author(s):

William Martin

Keyword(s):

Phylogenetic Analysis ◽

Metabolic Pathway ◽

Genome Scale

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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Nature Communications ◽

10.1038/s41467-021-22989-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Joshua E. Lewis ◽

Melissa L. Kemp

Keyword(s):

Machine Learning ◽

Metabolic Flux ◽

Metabolic Modeling ◽

The Cancer Genome Atlas ◽

Radiation Response ◽

Metabolomics Data ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

Metabolic Biomarkers ◽

Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

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Nucleus-specific expression in the multinuclear mushroom-forming fungusAgaricus bisporusreveals different nuclear regulatory programs

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1721381115 ◽

2018 ◽

Vol 115 (17) ◽

pp. 4429-4434 ◽

Cited By ~ 15

Author(s):

Thies Gehrmann ◽

Jordi F. Pelkmans ◽

Robin A. Ohm ◽

Aurin M. Vos ◽

Anton S. M. Sonnenberg ◽

...

Keyword(s):

Agaricus Bisporus ◽

Sequencing Data ◽

Carbohydrate Active Enzymes ◽

Nuclear Level ◽

Specific Expression ◽

Phenotypic Differences ◽

Gene Level ◽

Nuclear Separation ◽

Insight Into ◽

Specific Mrna

Many fungi are polykaryotic, containing multiple nuclei per cell. In the case of heterokaryons, there are different nuclear types within a single cell. It is unknown what the different nuclear types contribute in terms of mRNA expression levels in fungal heterokaryons. Each cell of the mushroomAgaricus bisporuscontains two to 25 nuclei of two nuclear types originating from two parental strains. Using RNA-sequencing data, we assess the differential mRNA contribution of individual nuclear types and its functional impact. We studied differential expression between genes of the two nuclear types, P1 and P2, throughout mushroom development in various tissue types. P1 and P2 produced specific mRNA profiles that changed through mushroom development. Differential regulation occurred at the gene level, rather than at the locus, chromosomal, or nuclear level. P1 dominated mRNA production throughout development, and P2 showed more differentially up-regulated genes in important functional groups. In the vegetative mycelium, P2 up-regulated almost threefold more metabolism genes and carbohydrate active enzymes (cazymes) than P1, suggesting phenotypic differences in growth. We identified widespread transcriptomic variation between the nuclear types ofA. bisporus. Our method enables studying nucleus-specific expression, which likely influences the phenotype of a fungus in a polykaryotic stage. Our findings have a wider impact to better understand gene regulation in fungi in a heterokaryotic state. This work provides insight into the transcriptomic variation introduced by genomic nuclear separation.

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Archaeal biofilms: widespread and complex

Biochemical Society Transactions ◽

10.1042/bst20120304 ◽

2013 ◽

Vol 41 (1) ◽

pp. 393-398 ◽

Cited By ~ 52

Author(s):

Sabrina Fröls

Keyword(s):

Biofilm Formation ◽

Extracellular Polymeric Substances ◽

Bacterial Species ◽

Surface Adhesion ◽

Form Complex ◽

Abiotic Surfaces ◽

Archaeal Species ◽

Physical And Chemical ◽

Insight Into ◽

Mixed Communities

Biofilms or multicellular structures become accepted as the dominant microbial lifestyle in Nature, but in the past they were only studied extensively in bacteria. Investigations on archaeal monospecies cultures have shown that many archaeal species are able to adhere on biotic and abiotic surfaces and form complex biofilm structures. Biofilm-forming archaea were identified in a broad range of extreme and moderate environments. Natural biofilms observed are mostly mixed communities composed of archaeal and bacterial species of various abundances. The physiological functions of the archaea identified in such mixed communities suggest a significant impact on the biochemical cycles maintaining the flow and recycling of the nutrients on earth. Therefore it is of high interest to investigate the characteristics and mechanisms underlying the archaeal biofilm formation. In the present review, I summarize and discuss the present investigations of biofilm-forming archaeal species, i.e. their diverse biofilm architectures in monospecies or mixed communities, the identified EPSs (extracellular polymeric substances), archaeal structures mediating surface adhesion or cell–cell connections, and the response to physical and chemical stressors implying that archaeal biofilm formation is an adaptive reaction to changing environmental conditions. A first insight into the molecular differentiation of cells within archaeal biofilms is given.

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Genome-scale metabolic modeling identifies novel targets of redox metabolism in radiation-resistant tumors

Free Radical Biology and Medicine ◽

10.1016/j.freeradbiomed.2018.10.203 ◽

2018 ◽

Vol 128 ◽

pp. S88

Author(s):

Joshua Lewis ◽

Cristina Furdui ◽

David Boothman ◽

Melissa Kemp

Keyword(s):

Metabolic Modeling ◽

Redox Metabolism ◽

Radiation Resistant ◽

Novel Targets ◽

Genome Scale

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Genome-scale metabolic network reconstruction of model animals as a platform for translational research

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2102344118 ◽

2021 ◽

Vol 118 (30) ◽

pp. e2102344118

Author(s):

Hao Wang ◽

Jonathan L. Robinson ◽

Pinar Kocabas ◽

Johan Gustafsson ◽

Mihail Anton ◽

...

Keyword(s):

Transgenic Mice ◽

Metabolic Network ◽

Model Organisms ◽

Protein Overexpression ◽

Sequencing Data ◽

Proteomics Data ◽

Gm2 Ganglioside ◽

Species Specific ◽

Specific Reactions ◽

Genome Scale

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

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