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

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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Managing Uncertainty in Metabolic Network Structure and Improving Predictions Using EnsembleFBA

10.1101/077636 ◽

2016 ◽

Author(s):

Matthew B. Biggs ◽

Jason A. Papin

Keyword(s):

Pseudomonas Aeruginosa ◽

Metabolic Network ◽

Small Molecule ◽

Essential Genes ◽

Model Organisms ◽

Network Structures ◽

Major Barrier ◽

Metabolic Systems ◽

Species Specific ◽

Genome Scale

AbstractGenome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel ensemble approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs and is compatible with many automated reconstruction approaches. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contribute disproportionately to the set of predicted essential reactions in a way that is unique to each Streptococcus species. These species-specific network structures lead to species-specific outcomes from small molecule interactions. Through these analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.Author SummaryMetabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a biochemical system will behave as well as to design biochemical systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which overcomes uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, we open the door to studying systems which would otherwise be infeasible.

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Molecular and phenotypic analysis of rodent models reveals conserved and species-specific modulators of human sarcopenia

Communications Biology ◽

10.1038/s42003-021-01723-z ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Anastasiya Börsch ◽

Daniel J. Ham ◽

Nitish Mittal ◽

Lionel A. Tintignac ◽

Eugenia Migliavacca ◽

...

Keyword(s):

Muscle Mass ◽

Inflammatory Responses ◽

Molecular Data ◽

Model Organisms ◽

Sequencing Data ◽

Phenotypic Analysis ◽

Age Related ◽

Analogous Data ◽

Species Specific ◽

And Function

AbstractSarcopenia, the age-related loss of skeletal muscle mass and function, affects 5–13% of individuals aged over 60 years. While rodents are widely-used model organisms, which aspects of sarcopenia are recapitulated in different animal models is unknown. Here we generated a time series of phenotypic measurements and RNA sequencing data in mouse gastrocnemius muscle and analyzed them alongside analogous data from rats and humans. We found that rodents recapitulate mitochondrial changes observed in human sarcopenia, while inflammatory responses are conserved at pathway but not gene level. Perturbations in the extracellular matrix are shared by rats, while mice recapitulate changes in RNA processing and autophagy. We inferred transcription regulators of early and late transcriptome changes, which could be targeted therapeutically. Our study demonstrates that phenotypic measurements, such as muscle mass, are better indicators of muscle health than chronological age and should be considered when analyzing aging-related molecular data.

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Plant Metabolic Network: A multi-species resource of plant metabolic information

10.1101/2021.03.30.437738 ◽

2021 ◽

Author(s):

Charles Hawkins ◽

Daniel Ginzburg ◽

Kangmei Zhao ◽

William Dwyer ◽

Bo Xue ◽

...

Keyword(s):

Metabolic Network ◽

Multiple Correspondence Analysis ◽

Model Organisms ◽

Plant Metabolism ◽

Online Resource ◽

Transcriptomics Data ◽

Cell Type Specific ◽

Genome Scale ◽

Metabolic Information

AbstractPlant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.One-sentence SummaryThe Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.

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Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

10.1101/772467 ◽

2019 ◽

Author(s):

Maureen A. Carey ◽

Gregory L. Medlock ◽

Michał Stolarczyk ◽

William A. Petri ◽

Jennifer L. Guler ◽

...

Keyword(s):

Metabolic Network ◽

Protozoan Parasite ◽

Global Impacts ◽

Metabolic Models ◽

Species Specific ◽

Experimental Findings ◽

Eukaryotic Organisms ◽

Genome Scale

AbstractProtozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

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PAREameters: computational inference of plant microRNA-mRNA targeting rules using RNA sequencing data

10.1101/710814 ◽

2019 ◽

Author(s):

Joshua Thody ◽

Vincent Moulton ◽

Irina Mohorianu

Keyword(s):

Rna Sequencing ◽

Target Prediction ◽

Model Organisms ◽

Sequencing Data ◽

Translation Rate ◽

Input Size ◽

Specific Subset ◽

Mirna Pathway ◽

The Stability ◽

Species Specific

ABSTRACTMicroRNAs (miRNAs) are short, non-coding RNAs that influence the translation-rate of mRNAs by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. This can be captured on a high-throughput scale using degradome sequencing, which supports miRNA target prediction by aligning degradation fragments to reference mRNAs enabling the identification of causal miRNA(s). The current criteria used for target prediction were inferred on experimentally validated A. thaliana interactions, which were adapted to fit that specific subset of miRNA interactions. In addition, the miRNA pathway in other organisms may have acquired specific changes, e.g. lineage-specific miRNAs or new miRNA-mRNA interactions, thus previous criteria may not be optimal. We present a new tool, PAREameters, for inferring targeting criteria from RNA sequencing datasets; the stability of inferred criteria under subsampling and the effect of input-size are discussed. We first evaluate its performance using experimentally validated miRNA-mRNA interactions in multiple A. thaliana datasets, including conserved and species-specific miRNAs. We then perform comprehensive analyses on the differences in flower miRNA-mRNA interactions in several non-model organisms and quantify the observed variations. PAREameters highlights an increase in sensitivity on most tested datasets when data-inferred criteria are used.

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In Search of Species-Specific SNPs in a Non-Model Animal (European Bison (Bison bonasus))—Comparison of De Novo and Reference-Based Integrated Pipeline of STACKS Using Genotyping-by-Sequencing (GBS) Data

Animals ◽

10.3390/ani11082226 ◽

2021 ◽

Vol 11 (8) ◽

pp. 2226

Author(s):

Sazia Kunvar ◽

Sylwia Czarnomska ◽

Cino Pertoldi ◽

Małgorzata Tokarska

Keyword(s):

Reference Genome ◽

De Novo ◽

Bos Taurus ◽

Model Organism ◽

Genotyping By Sequencing ◽

Model Organisms ◽

European Bison ◽

Model Animal ◽

Pcr Duplicates ◽

Species Specific

The European bison is a non-model organism; thus, most of its genetic and genomic analyses have been performed using cattle-specific resources, such as BovineSNP50 BeadChip or Illumina Bovine 800 K HD Bead Chip. The problem with non-specific tools is the potential loss of evolutionary diversified information (ascertainment bias) and species-specific markers. Here, we have used a genotyping-by-sequencing (GBS) approach for genotyping 256 samples from the European bison population in Bialowieza Forest (Poland) and performed an analysis using two integrated pipelines of the STACKS software: one is de novo (without reference genome) and the other is a reference pipeline (with reference genome). Moreover, we used a reference pipeline with two different genomes, i.e., Bos taurus and European bison. Genotyping by sequencing (GBS) is a useful tool for SNP genotyping in non-model organisms due to its cost effectiveness. Our results support GBS with a reference pipeline without PCR duplicates as a powerful approach for studying the population structure and genotyping data of non-model organisms. We found more polymorphic markers in the reference pipeline in comparison to the de novo pipeline. The decreased number of SNPs from the de novo pipeline could be due to the extremely low level of heterozygosity in European bison. It has been confirmed that all the de novo/Bos taurus and Bos taurus reference pipeline obtained SNPs were unique and not included in 800 K BovineHD BeadChip.

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Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

Journal of Bacteriology ◽

10.1128/jb.01583-07 ◽

2008 ◽

Vol 190 (8) ◽

pp. 2790-2803 ◽

Cited By ~ 175

Author(s):

Matthew A. Oberhardt ◽

Jacek Puchałka ◽

Kimberly E. Fryer ◽

Vítor A. P. Martins dos Santos ◽

Jason A. Papin

Keyword(s):

Pseudomonas Aeruginosa ◽

Metabolic Network ◽

Opportunistic Pathogen ◽

Scale Model ◽

Modeling Framework ◽

Major Life ◽

Metabolic Versatility ◽

A Genome ◽

Scale Properties ◽

Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

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Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

Molecular Omics ◽

10.1039/d0mo00135j ◽

2021 ◽

Author(s):

Ecehan Abdik ◽

Tunahan Cakir

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Metabolic Network ◽

Mouse Models ◽

Mus Musculus ◽

Metabolic Networks ◽

Systematic Investigation ◽

Omics Data ◽

Metabolic Alterations ◽

Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

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Humans and other commonly used model organisms are resistant to cycloheximide-mediated biases in ribosome profiling experiments

Nature Communications ◽

10.1038/s41467-021-25411-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Puneet Sharma ◽

Jie Wu ◽

Benedikt S. Nilges ◽

Sebastian A. Leidel

Keyword(s):

Human Cells ◽

Ribosome Profiling ◽

Model Organisms ◽

Treatment Conditions ◽

Genome Wide ◽

Optimized Protocol ◽

Gene Level ◽

Translation Inhibitor ◽

Species Specific ◽

Conformational Restrictions

AbstractRibosome profiling measures genome-wide translation dynamics at sub-codon resolution. Cycloheximide (CHX), a widely used translation inhibitor to arrest ribosomes in these experiments, has been shown to induce biases in yeast, questioning its use. However, whether such biases are present in datasets of other organisms including humans is unknown. Here we compare different CHX-treatment conditions in human cells and yeast in parallel experiments using an optimized protocol. We find that human ribosomes are not susceptible to conformational restrictions by CHX, nor does it distort gene-level measurements of ribosome occupancy, measured decoding speed or the translational ramp. Furthermore, CHX-induced codon-specific biases on ribosome occupancy are not detectable in human cells or other model organisms. This shows that reported biases of CHX are species-specific and that CHX does not affect the outcome of ribosome profiling experiments in most settings. Our findings provide a solid framework to conduct and analyze ribosome profiling experiments.

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Transcriptomic and Metabolic Network Analysis of Metabolic Reprogramming and IGF-1 Modulation in SCA3 Transgenic Mice

International Journal of Molecular Sciences ◽

10.3390/ijms22157974 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7974

Author(s):

Yu-Te Lin ◽

Yong-Shiou Lin ◽

Wen-Ling Cheng ◽

Jui-Chih Chang ◽

Yi-Chun Chao ◽

...

Keyword(s):

Network Analysis ◽

Transgenic Mice ◽

Signaling Pathways ◽

Metabolic Network ◽

Metabolic Reprogramming ◽

Spinocerebellar Ataxia Type ◽

Positive Effects ◽

Metabolic Network Analysis ◽

Potential Biomarker ◽

Metabolic Remodeling

Spinocerebellar ataxia type 3 (SCA3) is a genetic neurodegenerative disease for which a cure is still needed. Growth hormone (GH) therapy has shown positive effects on the exercise behavior of mice with cerebellar atrophy, retains more Purkinje cells, and exhibits less DNA damage after GH intervention. Insulin-like growth factor 1 (IGF-1) is the downstream mediator of GH that participates in signaling and metabolic regulation for cell growth and modulation pathways, including SCA3-affected pathways. However, the underlying therapeutic mechanisms of GH or IGF-1 in SCA3 are not fully understood. In the present study, tissue-specific genome-scale metabolic network models for SCA3 transgenic mice were proposed based on RNA-seq. An integrative transcriptomic and metabolic network analysis of a SCA3 transgenic mouse model revealed that metabolic signaling pathways were activated to compensate for the metabolic remodeling caused by SCA3 genetic modifications. The effect of IGF-1 intervention on the pathology and balance of SCA3 disease was also explored. IGF-1 has been shown to invoke signaling pathways and improve mitochondrial function and glycolysis pathways to restore cellular functions. As one of the downregulated factors in SCA3 transgenic mice, IGF-1 could be a potential biomarker and therapeutic target.

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