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1.
Nucleic Acids Res ; 2021 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-34614189

RESUMO

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

3.
Epilepsia ; 62(9): 2123-2132, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34245010

RESUMO

OBJECTIVE: The first antiseizure medication (ASM) is ineffective or intolerable in 50% of epilepsy cases. Selection between more than 25 available ASMs is guided by epilepsy factors, but also age and comorbidities. Randomized evidence for particular patient subgroups is seldom available. We asked whether register data could be used for retention rate calculations based on demographics, comorbidities, and ASM history, and quantified the potential improvement in retention rates of the first ASM in several large epilepsy cohorts. We also describe retention rates in patients with epilepsy after traumatic brain injury and dementia, patient groups with little available evidence. METHODS: We used medical, demographic, and drug prescription data from epilepsy cohorts from comprehensive Swedish registers, containing 6380 observations. By analyzing 381 840 prescriptions, we studied retention rates of first- and second-line ASMs for patients with epilepsy in multiple sclerosis (MS), brain infection, dementia, traumatic brain injury, or stroke. The rank of retention rates of ASMs was validated by comparison to published randomized control trials. We identified the optimal stratification for each brain disease, and quantified the potential improvement if all patients had received the optimal ASM. RESULTS: Using optimal stratification for each brain disease, the potential improvement in retention rate (percentage points) was MS, 20%; brain infection, 21%; dementia, 14%; trauma, 21%; and stroke, 14%. In epilepsy after trauma, levetiracetam had the highest retention rate at 80% (95% confidence interval [CI] = 65-89), exceeding that of the most commonly prescribed ASM, carbamazepine (p = .04). In epilepsy after dementia, lamotrigine (77%, 95% CI = 68-84) and levetiracetam (74%, 95% CI = 68-79) had higher retention rates than carbamazepine (p = .006 and p = .01, respectively). SIGNIFICANCE: We conclude that personalized ASM selection could improve retention rates and that national registers have potential as big data sources for personalized medicine in epilepsy.

4.
Cell Syst ; 12(8): 780-794.e7, 2021 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-34139154

RESUMO

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.


Assuntos
Biomarcadores/análise , COVID-19/patologia , Progressão da Doença , Proteoma/fisiologia , Fatores Etários , Contagem de Células Sanguíneas , Gasometria , Ativação Enzimática , Humanos , Inflamação/patologia , Aprendizado de Máquina , Prognóstico , Proteômica , SARS-CoV-2/imunologia
5.
Nat Biotechnol ; 39(7): 846-854, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33767396

RESUMO

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.


Assuntos
Proteômica/métodos , Espectrometria de Massas em Tandem , Arabidopsis/metabolismo , Biomarcadores/metabolismo , COVID-19/sangue , COVID-19/diagnóstico , Linhagem Celular , Humanos , Peptídeos/análise , Proteoma/análise , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Índice de Gravidade de Doença
6.
Proteomics ; 21(6): e2000093, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33452728

RESUMO

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.


Assuntos
Benchmarking , Saccharomyces cerevisiae , Proteoma , Proteômica , Reprodutibilidade dos Testes
7.
Nat Commun ; 12(1): 190, 2021 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-33420025

RESUMO

The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.


Assuntos
Teorema de Bayes , Genoma Fúngico , Leveduras/genética , Leveduras/metabolismo , Aprendizado de Máquina , Modelos Biológicos , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/crescimento & desenvolvimento , Saccharomyces cerevisiae/metabolismo , Esqualeno Mono-Oxigenase/metabolismo , Temperatura , Leveduras/enzimologia , Leveduras/crescimento & desenvolvimento
8.
Nat Commun ; 11(1): 6141, 2020 12 01.
Artigo em Inglês | MEDLINE | ID: mdl-33262328

RESUMO

Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.


Assuntos
Aprendizado Profundo , Evolução Molecular , Regulação da Expressão Gênica , Sequências Reguladoras de Ácido Nucleico , Animais , Bactérias/genética , Sequência de Bases , Drosophila melanogaster/genética , Humanos , Camundongos , RNA Mensageiro/genética , Saccharomyces cerevisiae/genética
9.
Cell Syst ; 11(1): 11-24.e4, 2020 07 22.
Artigo em Inglês | MEDLINE | ID: mdl-32619549

RESUMO

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.


Assuntos
Proteínas Sanguíneas/metabolismo , Infecções por Coronavirus/sangue , Pneumonia Viral/sangue , Proteômica/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus/isolamento & purificação , Biomarcadores/sangue , Proteínas Sanguíneas/análise , COVID-19 , Infecções por Coronavirus/classificação , Infecções por Coronavirus/patologia , Infecções por Coronavirus/virologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias/classificação , Pneumonia Viral/classificação , Pneumonia Viral/patologia , Pneumonia Viral/virologia , SARS-CoV-2 , Adulto Jovem
10.
Curr Biol ; 29(10): 1712-1720.e7, 2019 05 20.
Artigo em Inglês | MEDLINE | ID: mdl-31080084

RESUMO

Some species responded successfully to prehistoric changes in climate [1, 2], while others failed to adapt and became extinct [3]. The factors that determine successful climate adaptation remain poorly understood. We constructed a reference genome and studied physiological adaptations in the Alpine marmot (Marmota marmota), a large ground-dwelling squirrel exquisitely adapted to the "ice-age" climate of the Pleistocene steppe [4, 5]. Since the disappearance of this habitat, the rodent persists in large numbers in the high-altitude Alpine meadow [6, 7]. Genome and metabolome showed evidence of adaptation consistent with cold climate, affecting white adipose tissue. Conversely, however, we found that the Alpine marmot has levels of genetic variation that are among the lowest for mammals, such that deleterious mutations are less effectively purged. Our data rule out typical explanations for low diversity, such as high levels of consanguineous mating, or a very recent bottleneck. Instead, ancient demographic reconstruction revealed that genetic diversity was lost during the climate shifts of the Pleistocene and has not recovered, despite the current high population size. We attribute this slow recovery to the marmot's adaptive life history. The case of the Alpine marmot reveals a complicated relationship between climatic changes, genetic diversity, and conservation status. It shows that species of extremely low genetic diversity can be very successful and persist over thousands of years, but also that climate-adapted life history can trap a species in a persistent state of low genetic diversity.


Assuntos
Adaptação Biológica , Clima , Variação Genética , Genoma , Marmota/genética , Animais , Filogenia , Densidade Demográfica
11.
Cell Syst ; 7(3): 269-283.e6, 2018 09 26.
Artigo em Inglês | MEDLINE | ID: mdl-30195436

RESUMO

A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.


Assuntos
Fosfotransferases/genética , Saccharomyces cerevisiae/fisiologia , Deleção de Sequência/genética , Regulação Fúngica da Expressão Gênica , Técnicas de Inativação de Genes , Estudos de Associação Genética , Aprendizado de Máquina , Metaboloma , Microrganismos Geneticamente Modificados , Proteoma
12.
Sci Rep ; 8(1): 4346, 2018 03 12.
Artigo em Inglês | MEDLINE | ID: mdl-29531254

RESUMO

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5-8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale.


Assuntos
Cromatografia Líquida/métodos , Peptídeos/análise , Proteoma/análise , Proteômica/métodos , Proteínas de Saccharomyces cerevisiae/análise , Saccharomyces cerevisiae/metabolismo , Análise Custo-Benefício , Humanos , Células K562 , Software , Espectrometria de Massas em Tandem/métodos
13.
Nat Microbiol ; 3(4): 514-522, 2018 04.
Artigo em Inglês | MEDLINE | ID: mdl-29556107

RESUMO

Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.


Assuntos
Bactérias/metabolismo , Meios de Cultura/química , Microbioma Gastrointestinal/fisiologia , Aminoácidos/metabolismo , Bactérias/classificação , Bactérias/crescimento & desenvolvimento , Ácidos Graxos Voláteis/metabolismo , Humanos , Mucinas/metabolismo
14.
Nat Commun ; 8: 16018, 2017 07 10.
Artigo em Inglês | MEDLINE | ID: mdl-28691704

RESUMO

Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite-enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism.


Assuntos
Evolução Biológica , Compartimento Celular , Enzimas/metabolismo , Redes e Vias Metabólicas , Regulação Alostérica , Retroalimentação Fisiológica , Humanos , Metaboloma , Modelos Biológicos
15.
Curr Opin Syst Biol ; 6: 37-45, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32923746

RESUMO

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.

16.
Cell ; 167(2): 553-565.e12, 2016 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-27693354

RESUMO

Genome-metabolism interactions enable cell growth. To probe the extent of these interactions and delineate their functional contributions, we quantified the Saccharomyces amino acid metabolome and its response to systematic gene deletion. Over one-third of coding genes, in particular those important for chromatin dynamics, translation, and transport, contribute to biosynthetic metabolism. Specific amino acid signatures characterize genes of similar function. This enabled us to exploit functional metabolomics to connect metabolic regulators to their effectors, as exemplified by TORC1, whose inhibition in exponentially growing cells is shown to match an interruption in endomembrane transport. Providing orthogonal information compared to physical and genetic interaction networks, metabolomic signatures cluster more than half of the so far uncharacterized yeast genes and provide functional annotation for them. A major part of coding genes is therefore participating in gene-metabolism interactions that expose the metabolism regulatory network and enable access to an underexplored space in gene function.


Assuntos
Aminoácidos/biossíntese , Metaboloma , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/metabolismo , Fatores de Transcrição/metabolismo , Aminoácidos/genética , Cromatina/metabolismo , Deleção de Genes , Regulação Fúngica da Expressão Gênica , Redes Reguladoras de Genes , Metaboloma/genética , Metabolômica/métodos , Família Multigênica , Fosfatidilinositol 3-Quinases/genética , Fosfatidilinositol 3-Quinases/metabolismo , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genética , Fatores de Transcrição/genética , Transcrição Genética
17.
Nat Microbiol ; 1: 15030, 2016 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-27572163

RESUMO

The regulation of gene expression in response to nutrient availability is fundamental to the genotype-phenotype relationship. The metabolic-genetic make-up of the cell, as reflected in auxotrophy, is hence likely to be a determinant of gene expression. Here, we address the importance of the metabolic-genetic background by monitoring transcriptome, proteome and metabolome in a repertoire of 16 Saccharomyces cerevisiae laboratory backgrounds, combinatorially perturbed in histidine, leucine, methionine and uracil biosynthesis. The metabolic background affected up to 85% of the coding genome. Suggesting widespread confounding, these transcriptional changes show, on average, 83% overlap between unrelated auxotrophs and 35% with previously published transcriptomes generated for non-metabolic gene knockouts. Background-dependent gene expression correlated with metabolic flux and acted, predominantly through masking or suppression, on 88% of transcriptional interactions epistatically. As a consequence, the deletion of the same metabolic gene in a different background could provoke an entirely different transcriptional response. Propagating to the proteome and scaling up at the metabolome, metabolic background dependencies reveal the prevalence of metabolism-dependent epistasis at all regulatory levels. Urging a fundamental change of the prevailing laboratory practice of using auxotrophs and nutrient supplemented media, these results reveal epistatic intertwining of metabolism with gene expression on the genomic scale.


Assuntos
Epistasia Genética , Regulação Fúngica da Expressão Gênica , Metabolismo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Redes Reguladoras de Genes
18.
Proc Natl Acad Sci U S A ; 112(20): 6449-54, 2015 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-25941371

RESUMO

Microbial communities populate most environments on earth and play a critical role in ecology and human health. Their composition is thought to be largely shaped by interspecies competition for the available resources, but cooperative interactions, such as metabolite exchanges, have also been implicated in community assembly. The prevalence of metabolic interactions in microbial communities, however, has remained largely unknown. Here, we systematically survey, by using a genome-scale metabolic modeling approach, the extent of resource competition and metabolic exchanges in over 800 communities. We find that, despite marked resource competition at the level of whole assemblies, microbial communities harbor metabolically interdependent groups that recur across diverse habitats. By enumerating flux-balanced metabolic exchanges in these co-occurring subcommunities we also predict the likely exchanged metabolites, such as amino acids and sugars, that can promote group survival under nutritionally challenging conditions. Our results highlight metabolic dependencies as a major driver of species co-occurrence and hint at cooperative groups as recurring modules of microbial community architecture.


Assuntos
Redes e Vias Metabólicas/fisiologia , Consórcios Microbianos/fisiologia , Interações Microbianas/fisiologia , Modelos Biológicos , Simbiose , Consórcios Microbianos/genética , Filogenia , Especificidade da Espécie , Estatísticas não Paramétricas
19.
PLoS Comput Biol ; 10(4): e1003572, 2014 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-24762675

RESUMO

One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the level of individual reactions, metabolite levels are non-linearly dependent on enzyme abundances as per the reaction kinetics mechanisms. Secondly, specific metabolite pools are tightly interlinked with the rest of the metabolic network through their production and consumption reactions. While the role of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different biological perturbations, namely gene knockout, nutrient shock and nutrient change. While the kinetic constraints applied at the level of individual reactions were found to be poor descriptors of the mRNA-metabolite relationship, their use in the context of the network enabled us to correlate changes in the expression of enzyme-coding genes to the alterations in metabolite levels. Our results highlight the key contribution of metabolic network connectivity in mediating cellular control over metabolite levels, and have implications towards bridging the gap between genotype and metabolic phenotype.


Assuntos
Expressão Gênica , Redes e Vias Metabólicas/genética , Cinética
20.
Mol Syst Biol ; 8: 581, 2012 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-22569339

RESUMO

Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16,345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome-scale metabolic models with these new sequence-function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction.


Assuntos
Enzimas/genética , Metagenoma/genética , Metagenômica/métodos , Mapeamento Cromossômico , Bases de Dados Genéticas , Enzimas/metabolismo , Humanos , Redes e Vias Metabólicas , Modelos Biológicos , Análise de Sequência de DNA , Biologia de Sistemas
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