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1.
Nat Commun ; 12(1): 4728, 2021 08 05.
Artigo em Inglês | MEDLINE | ID: mdl-34354065

RESUMO

Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota.


Assuntos
Dieta , Microbioma Gastrointestinal/fisiologia , Redes e Vias Metabólicas , Modelos Biológicos , Algoritmos , Criança , Fenômenos Fisiológicos da Nutrição Infantil , Dieta Mediterrânea , Fermentação , Microbioma Gastrointestinal/genética , Humanos , Técnicas In Vitro , Lens (Planta)/química , Valor Nutritivo , RNA Ribossômico 16S/genética , Espanha
2.
Nutrients ; 13(7)2021 Jun 27.
Artigo em Inglês | MEDLINE | ID: mdl-34199047

RESUMO

The gut microbiota has a profound effect on human health and is modulated by food and bioactive compounds. To study such interaction, in vitro batch fermentations are performed with fecal material, and some experimental designs may require that such fermentations be performed with previously frozen stools. Although it is known that freezing fecal material does not alter the composition of the microbial community in 16S rRNA gene amplicon and metagenomic sequencing studies, it is not known whether the microbial community in frozen samples could still be used for in vitro fermentations. To explore this, we undertook a pilot study in which in vitro fermentations were performed with fecal material from celiac, cow's milk allergic, obese, or lean children that was frozen (or not) with 20% glycerol. Before fermentation, the fecal material was incubated in a nutritious medium for 6 days, with the aim of giving the microbial community time to recover from the effects of freezing. An aliquot was taken daily from the stabilization vessel and used for the in vitro batch fermentation of lentils. The microbial community structure was significantly different between fresh and frozen samples, but the variation introduced by freezing a sample was always smaller than the variation among individuals, both before and after fermentation. Moreover, the potential functionality (as determined in silico by a genome-scaled metabolic reconstruction) did not differ significantly, possibly due to functional redundancy. The most affected genus was Bacteroides, a fiber degrader. In conclusion, if frozen fecal material is to be used for in vitro fermentation purposes, our preliminary analyses indicate that the functionality of microbial communities can be preserved after stabilization.


Assuntos
Fermentação , Congelamento , Microbioma Gastrointestinal , Animais , Bovinos , Criança , Fezes/microbiologia , Armazenamento de Alimentos , Microbioma Gastrointestinal/genética , Humanos , Masculino , Microbiota , Leite , Projetos Piloto , RNA Ribossômico 16S/genética
3.
Leukemia ; 35(10): 3012-3016, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33972667

RESUMO

Clinical and genetic risk factors are currently used in multiple myeloma (MM) to stratify patients and to design specific therapies. However, these systems do not capture the heterogeneity of the disease supporting the development of new prognostic factors. In this study, we identified active promoters and alternative active promoters in 6 different B cell subpopulations, including bone-marrow plasma cells, and 32 MM patient samples, using RNA-seq data. We find that expression initiated at both regular and alternative promoters was specific of each B cell subpopulation or MM plasma cells, showing a remarkable level of consistency with chromatin-based promoter definition. Interestingly, using 595 MM patient samples from the CoMMpass dataset, we observed that the expression derived from some alternative promoters was associated with lower progression-free and overall survival in MM patients independently of genetic alterations. Altogether, our results define cancer-specific alternative active promoters as new transcriptomic features that can provide a new avenue for prognostic stratification possibilities in patients with MM.

4.
Leukemia ; 35(5): 1438-1450, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33597729

RESUMO

Multiple myeloma (MM) is an incurable disease, whose clinical heterogeneity makes its management challenging, highlighting the need for biological features to guide improved therapies. Deregulation of specific long non-coding RNAs (lncRNAs) has been shown in MM, nevertheless, the complete lncRNA transcriptome has not yet been elucidated. In this work, we identified 40,511 novel lncRNAs in MM samples. lncRNAs accounted for 82% of the MM transcriptome and were more heterogeneously expressed than coding genes. A total of 10,351 overexpressed and 9,535 downregulated lncRNAs were identified in MM patients when compared with normal bone-marrow plasma cells. Transcriptional dynamics study of lncRNAs in the context of normal B-cell maturation revealed 989 lncRNAs with exclusive expression in MM, among which 89 showed de novo epigenomic activation. Knockdown studies on one of these lncRNAs, SMILO (specific myeloma intergenic long non-coding RNA), resulted in reduced proliferation and induction of apoptosis of MM cells, and activation of the interferon pathway. We also showed that the expression of lncRNAs, together with clinical and genetic risk alterations, stratified MM patients into several progression-free survival and overall survival groups. In summary, our global analysis of the lncRNAs transcriptome reveals the presence of specific lncRNAs associated with the biological and clinical behavior of the disease.


Assuntos
Mieloma Múltiplo/genética , RNA Longo não Codificante/genética , Transcriptoma/genética , Apoptose/genética , Proliferação de Células/genética , Perfilação da Expressão Gênica/métodos , Regulação Neoplásica da Expressão Gênica/genética , Humanos , Intervalo Livre de Progressão
5.
Cancers (Basel) ; 12(7)2020 Jul 07.
Artigo em Inglês | MEDLINE | ID: mdl-32645997

RESUMO

The development of predictive biomarkers of response to targeted therapies is an unmet clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision oncology. In this work, we have developed and validated a large-scale bioinformatics tool named DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which is a valuable starting point for further drug development and drug repositioning projects.

6.
Reprod Toxicol ; 94: 55-64, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32344110

RESUMO

Developmental toxicity is defined as the occurrence of adverse effects on the developing organism as a result from exposure to a toxic agent. These alterations can have long-term acute effects. Current in vitro models present important limitations and the evaluation of toxicity is not entirely objective. In silico methods have also shown limited success, in part due to complex and varied mechanisms of action that mediate developmental toxicity, which are sometimes poorly understood. In this article, we compiled a dataset of compounds with developmental toxicity categories and annotated mechanisms of action for both toxic and non-toxic compounds (DVTOX). With it, we selected a panel of protein targets that might be part of putative Molecular Initiating Events (MIEs) of Adverse Outcome Pathways of developmental toxicity. The validity of this list of candidate MIEs was studied through the evaluation of new drug-target relationships that include such proteins, but were not part of the original database. Finally, an orthology analysis of this protein panel was conducted to select an appropriate animal model to assess developmental toxicity. We tested our approach using the zebrafish embryo toxicity test, finding positive results.


Assuntos
Bases de Dados Factuais , Embrião não Mamífero/efeitos dos fármacos , Teratógenos/toxicidade , Testes de Toxicidade , Peixe-Zebra , Animais , Biologia Computacional , Desenvolvimento Embrionário/efeitos dos fármacos , Humanos , Modelos Animais
7.
Bioinformatics ; 36(6): 1986-1988, 2020 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-31722383

RESUMO

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Proteínas , Software , Genes , Modelos Moleculares
8.
Front Microbiol ; 10: 848, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31105659

RESUMO

Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants. We used metagenomics data of fecal samples and nutritional data of 13 infants at five time points. The resulting networks for each time point were analyzed, finding significant alterations once solid food is introduced in the diet. Our work shows that solid food leads to a different pattern of output metabolites that can be potentially released from the gut microbiota to the host. Experimental validation is presented for ferulate, a neuroprotective metabolite involved in the gut-brain axis.

9.
Gigascience ; 8(4)2019 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-30942869

RESUMO

BACKGROUND: Aberrant alternative splicing plays a key role in cancer development. In recent years, alternative splicing has been used as a prognosis biomarker, a therapy response biomarker, and even as a therapeutic target. Next-generation RNA sequencing has an unprecedented potential to measure the transcriptome. However, due to the complexity of dealing with isoforms, the scientific community has not sufficiently exploited this valuable resource in precision medicine. FINDINGS: We present TranscriptAchilles, the first large-scale tool to predict transcript biomarkers associated with gene essentiality in cancer. This application integrates 412 loss-of-function RNA interference screens of >17,000 genes, together with their corresponding whole-transcriptome expression profiling. Using this tool, we have studied which are the cancer subtypes for which alternative splicing plays a significant role to state gene essentiality. In addition, we include a case study of renal cell carcinoma that shows the biological soundness of the results. The databases, the source code, and a guide to build the platform within a Docker container are available at GitLab. The application is also available online. CONCLUSIONS: TranscriptAchilles provides a user-friendly web interface to identify transcript or gene biomarkers of gene essentiality, which could be used as a starting point for a drug development project. This approach opens a wide range of translational applications in cancer.


Assuntos
Processamento Alternativo , Biomarcadores Tumorais , Biologia Computacional/métodos , Estudo de Associação Genômica Ampla/métodos , Neoplasias/genética , Oncogenes , Software , Algoritmos , Perfilação da Expressão Gênica , Humanos , Modelos Estatísticos , Isoformas de RNA , Transcriptoma , Interface Usuário-Computador , Fluxo de Trabalho
10.
Bioinformatics ; 35(21): 4350-4355, 2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-30923806

RESUMO

MOTIVATION: The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed. RESULTS: Here, we present a robust extension to MTA (rMTA), which additionally incorporates a worst-case scenario analysis and minimization of metabolic adjustment to evaluate the beneficial effect of gene knockouts. We show that rMTA complements MTA in the different datasets analyzed (gene knockout perturbations in different organisms, Alzheimer's disease and prostate cancer), bringing a more accurate tool for predicting therapeutic targets. AVAILABILITY AND IMPLEMENTATION: rMTA is freely available on The Cobra Toolbox: https://opencobra.github.io/cobratoolbox/latest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Redes e Vias Metabólicas , Software , Algoritmos , Genoma , Análise de Sistemas
11.
Nat Protoc ; 14(3): 639-702, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30787451

RESUMO

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.


Assuntos
Modelos Biológicos , Software , Genoma , Redes e Vias Metabólicas , Biologia de Sistemas
12.
Bioinformatics ; 35(3): 535-537, 2019 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-30052768

RESUMO

Motivation: The identification of minimal gene knockout strategies to engineer metabolic systems constitutes one of the most relevant applications of the COnstraint-Based Reconstruction and Analysis (COBRA) framework. In the last years, the minimal cut sets (MCSs) approach has emerged as a promising tool to carry out this task. However, MCSs define reaction knockout strategies, which are not necessarily transformed into feasible strategies at the gene level. Results: We present a more general, easy-to-use and efficient computational implementation of a previously published algorithm to calculate MCSs to the gene level (gMCSs). Our tool was compared with existing methods in order to calculate essential genes and synthetic lethals in metabolic networks of different complexity, showing a significant reduction in model size and computation time. Availability and implementation: gMCS is publicly and freely available under GNU license in the COBRA toolbox (https://github.com/opencobra/cobratoolbox/tree/master/src/analysis/gMCS). Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Algoritmos , Biologia Computacional , Redes e Vias Metabólicas , Genes Essenciais , Mutações Sintéticas Letais
13.
Cancer Res ; 78(21): 6320-6328, 2018 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-30232219

RESUMO

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.


Assuntos
Biologia Computacional/métodos , Neoplasias/genética , Algoritmos , Gráficos por Computador , Bases de Dados Factuais , Bases de Dados Genéticas , Genômica , Humanos , Internet , Oncologia , Proteômica , Software , Transcriptoma , Interface Usuário-Computador , Fluxo de Trabalho
14.
Mol Cell Oncol ; 5(1): e1389672, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29404389

RESUMO

The identification of therapeutic strategies exploiting the metabolic alterations of malignant cells is a relevant area in cancer research. Here, we discuss a novel computational method, based on the COBRA (COnstraint-Based Reconstruction and Analysis) framework for metabolic networks, to perform this task. Current and future steps are presented.

15.
Sci Rep ; 7(1): 14358, 2017 10 30.
Artigo em Inglês | MEDLINE | ID: mdl-29084986

RESUMO

Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve in-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine Phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on different leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used for in-silico gene essentiality analyses in order to maximize the potential for drug target identification in cancer.


Assuntos
Adenina Fosforribosiltransferase/metabolismo , Adenina Fosforribosiltransferase/fisiologia , Poliaminas/metabolismo , Adenosilmetionina Descarboxilase/metabolismo , Fenômenos Bioquímicos , Linhagem Celular Tumoral , Proliferação de Células , Simulação por Computador , Genes Essenciais/genética , Homeostase , Humanos , Leucemia/genética , Redes e Vias Metabólicas , Neoplasias/genética , Polieletrólitos
16.
Nat Commun ; 8(1): 459, 2017 09 06.
Artigo em Inglês | MEDLINE | ID: mdl-28878380

RESUMO

Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly available genome-scale loss-of-function screens, a possible mechanism by which the inhibition of RRM1 is effective in cancer is established. Overall, our approach shows promising results and lays the foundation to build a novel family of algorithms to target metabolism in cancer.Exploiting synthetic lethality is a promising approach for cancer therapy. Here, the authors present an approach to identifying such interactions by finding genetic minimal cut sets (gMCSs) that block cancer proliferation, and apply it to study the lethality of RRM1 inhibition in multiple myeloma.


Assuntos
Simulação por Computador , Neoplasias/genética , Neoplasias/metabolismo , Mutações Sintéticas Letais/genética , Linhagem Celular Tumoral , Regulação Neoplásica da Expressão Gênica , Inativação Gênica , Genes Neoplásicos , Humanos
17.
PLoS One ; 11(5): e0154583, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27145226

RESUMO

MOTIVATION: Gene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA) is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date. RESULTS: In this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE), evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.


Assuntos
Genes Essenciais , Análise do Fluxo Metabólico/métodos , Neoplasias/genética , Neoplasias/metabolismo , Algoritmos , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/metabolismo , Linhagem Celular Tumoral , Inativação Gênica , Glioblastoma/genética , Glioblastoma/metabolismo , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/metabolismo , Análise do Fluxo Metabólico/estatística & dados numéricos , Modelos Biológicos
18.
Bioinformatics ; 32(13): 2001-7, 2016 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-27153694

RESUMO

MOTIVATION: The concept of Minimal Cut Sets (MCSs) is used in metabolic network modeling to describe minimal groups of reactions or genes whose simultaneous deletion eliminates the capability of the network to perform a specific task. Previous work showed that MCSs where closely related to Elementary Flux Modes (EFMs) in a particular dual problem, opening up the possibility to use the tools developed for computing EFMs to compute MCSs. Until recently, however, there existed no method to compute an EFM with some specific characteristic, meaning that, in the case of MCSs, the only strategy to obtain them was to enumerate them using, for example, the standard K-shortest EFMs algorithm. RESULTS: In this work, we adapt the recently developed theory to compute EFMs satisfying several constraints to the calculation of MCSs involving a specific reaction knock-out. Importantly, we emphasize that not all the EFMs in the dual problem correspond to real MCSs, and propose a new formulation capable of correctly identifying the MCS wanted. Furthermore, this formulation brings interesting insights about the relationship between the primal and the dual problem of the MCS computation. AVAILABILITY AND IMPLEMENTATION: A Matlab-Cplex implementation of the proposed algorithm is available as a supplementary material CONTACT: fplanes@ceit.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Biologia Computacional/métodos , Análise do Fluxo Metabólico/métodos , Redes e Vias Metabólicas , Algoritmos , Escherichia coli/metabolismo , Humanos
19.
Bioinformatics ; 31(11): 1771-9, 2015 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-25618865

RESUMO

MOTIVATION: With the advent of meta-'omics' data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited. RESULTS: Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data.


Assuntos
Bactérias/metabolismo , Naftalenos/metabolismo , Poluentes do Solo/metabolismo , Bactérias/classificação , Bactérias/genética , Redes e Vias Metabólicas , Proteômica
20.
Brief Bioinform ; 16(2): 265-79, 2015 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-24626528

RESUMO

With the emergence of metabolic networks, novel mathematical pathway concepts were introduced in the past decade, aiming to go beyond canonical maps. However, the use of network-based pathways to interpret 'omics' data has been limited owing to the fact that their computation has, until very recently, been infeasible in large (genome-scale) metabolic networks. In this review article, we describe the progress made in the past few years in the field of network-based metabolic pathway analysis. In particular, we review in detail novel optimization techniques to compute elementary flux modes, an important pathway concept in this field. In addition, we summarize approaches for the integration of metabolic pathways with gene expression data, discussing recent advances using network-based pathway concepts.


Assuntos
Expressão Gênica , Redes e Vias Metabólicas , Algoritmos , Biologia Computacional , Escherichia coli/genética , Escherichia coli/metabolismo , Perfilação da Expressão Gênica/estatística & dados numéricos , Modelos Biológicos , Software
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