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1.
Cell Metab ; 2021 Oct 19.
Artigo em Inglês | MEDLINE | ID: mdl-34687652

RESUMO

Autoimmunity is energetically costly, but the impact of a metabolically active state on immunity and immune-mediated diseases is unclear. Ly6Chi monocytes are key effectors in CNS autoimmunity with an elusive role in priming naive autoreactive T cells. Here, we provide unbiased analysis of the immune changes in various compartments during cold exposure and show that this energetically costly stimulus markedly ameliorates active experimental autoimmune encephalomyelitis (EAE). Cold exposure decreases MHCII on monocytes at steady state and in various inflammatory mouse models and suppresses T cell priming and pathogenicity through the modulation of monocytes. Genetic or antibody-mediated monocyte depletion or adoptive transfer of Th1- or Th17-polarized cells for EAE abolishes the cold-induced effects on T cells or EAE, respectively. These findings provide a mechanistic link between environmental temperature and neuroinflammation and suggest competition between cold-induced metabolic adaptations and autoimmunity as energetic trade-off beneficial for the immune-mediated diseases.

2.
Curr Opin Endocr Metab Res ; 20: 100285, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34676307

RESUMO

The deficiency of micronutrients, including vitamins and minerals, is estimated to affect two billion people worldwide and can have devastating immediate and long-term consequences. Major causes range from inadequate micronutrient consumption mostly owing to a lack of dietary diversity, to poor nutrient absorption in the gastrointestinal tract as a result of clinical or pathological conditions. Recent studies in model organisms and humans demonstrated that intestinal microbiota plays an important role in the de novo biosynthesis and bioavailability of several micronutrients and might be a major determinant of human micronutrient status. Here, we address the importance of the gut microbiome for maintaining the balance of host vitamins and minerals and explore its potential therapeutic benefits and implications on human health.

3.
Elife ; 102021 08 03.
Artigo em Inglês | MEDLINE | ID: mdl-34340747

RESUMO

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.


Assuntos
Desenho de Fármacos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos , Preparações Farmacêuticas/metabolismo , Animais , Antimetabólitos Antineoplásicos/química , Antimetabólitos Antineoplásicos/metabolismo , Antivirais/química , Antivirais/farmacologia , COVID-19/tratamento farmacológico , Bases de Dados de Produtos Farmacêuticos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/etiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/metabolismo , Fluoruracila/química , Fluoruracila/metabolismo , Humanos , Preparações Farmacêuticas/química , Fluxo de Trabalho
4.
Commun Biol ; 4(1): 492, 2021 04 22.
Artigo em Inglês | MEDLINE | ID: mdl-33888858

RESUMO

Interspecific interactions are thought to govern the stability and functioning of microbial communities, but the influence of the spatial environment and its structural connectivity on the potential of such interactions to unfold remain largely unknown. Here we studied the effects on community growth and microbial diversity as a function of environmental connectivity, where we define environmental connectivity as the degree of habitat fragmentation preventing microbial cells from living together. We quantitatively compared growth of a naturally-derived high microbial diversity community from soil in a completely mixed liquid suspension (high connectivity) to growth in a massively fragmented and poorly connected environment (low connectivity). The low connectivity environment consisted of homogenously-sized miniature agarose beads containing random single or paired founder cells. We found that overall community growth was the same in both environments, but the low connectivity environment dramatically reduced global community-level diversity compared to the high connectivity environment. Experimental observations were supported by community growth modeling. The model predicts a loss of diversity in the low connectivity environment as a result of negative interspecific interactions becoming more dominant at small founder species numbers. Counterintuitively for the low connectivity environment, growth of isolated single genotypes was less productive than that of random founder genotype cell pairs, suggesting that the community as a whole profited from emerging positive interspecific interactions. Our work demonstrates the importance of environmental connectivity for growth of natural soil microbial communities, which aids future efforts to intervene in or restore community composition to achieve engineering and biotechnological objectives.


Assuntos
Fenômenos Fisiológicos Bacterianos , Microbiota , Microbiologia do Solo , Bactérias/classificação
5.
Environ Microbiome ; 16(1): 8, 2021 Apr 29.
Artigo em Inglês | MEDLINE | ID: mdl-33926576

RESUMO

BACKGROUND: Bioaugmentation aims to use the capacities of specific bacterial strains inoculated into sites to enhance pollutant biodegradation. Bioaugmentation results have been mixed, which has been attributed to poor inoculant growth and survival in the field, and, consequently, moderate catalytic performance. However, our understanding of biodegradation activity mostly comes from experiments conducted under laboratory conditions, and the processes occurring during adaptation and invasion of inoculants into complex environmental microbiomes remain poorly known. The main aim of this work was thus to study the specific and different cellular reactions of an inoculant for bioaugmentation during adaptation, growth and survival in natural clean and contaminated non-sterile soils, in order to better understand factors limiting bioaugmentation. RESULTS: As inoculant we focused on the monoaromatic compound-degrading bacterium Pseudomonas veronii 1YdBTEX2. The strain proliferated in all but one soil types in presence and in absence of exogenously added toluene. RNAseq and differential genome-wide gene expression analysis illustrated both a range of common soil responses such as increased nutrient scavenging and recycling, expression of defense mechanisms, as well as environment-specific reactions, notably osmoprotection and metal homeostasis. The core metabolism of P. veronii remained remarkably constant during exponential growth irrespective of the environment, with slight changes in cofactor regeneration pathways, possibly needed for balancing defense reactions. CONCLUSIONS: P. veronii displayed a versatile global program, enabling it to adapt to a variety of soil environments in the presence and even in absence of its target pollutant toluene. Our results thus challenge the widely perceived dogma of poor survival and growth of exogenous inoculants in complex microbial ecosystems such as soil and provide a further basis to developing successful bioaugmentation strategies.

6.
Cell Metab ; 32(4): 575-590.e7, 2020 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-32916104

RESUMO

Osteoporosis is the most prevalent metabolic bone disease, characterized by low bone mass and microarchitectural deterioration. Here, we show that warmth exposure (34°C) protects against ovariectomy-induced bone loss by increasing trabecular bone volume, connectivity density, and thickness, leading to improved biomechanical bone strength in adult female, as well as in young male mice. Transplantation of the warm-adapted microbiota phenocopies the warmth-induced bone effects. Both warmth and warm microbiota transplantation revert the ovariectomy-induced transcriptomics changes of the tibia and increase periosteal bone formation. Combinatorial metagenomics/metabolomics analysis shows that warmth enhances bacterial polyamine biosynthesis, resulting in higher total polyamine levels in vivo. Spermine and spermidine supplementation increases bone strength, while inhibiting polyamine biosynthesis in vivo limits the beneficial warmth effects on the bone. Our data suggest warmth exposure as a potential treatment option for osteoporosis while providing a mechanistic framework for its benefits in bone disease.

7.
Commun Biol ; 3(1): 379, 2020 07 15.
Artigo em Inglês | MEDLINE | ID: mdl-32669688

RESUMO

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from 14C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.


Assuntos
Citometria de Fluxo/métodos , Microbiota , Aprendizado de Máquina Supervisionado , Biodiversidade , Biomassa , Escherichia coli/genética , Ensaios de Triagem em Larga Escala/métodos , Humanos , Microbiota/genética , Redes Neurais de Computação , RNA Ribossômico 16S/genética , Microbiologia da Água
8.
Int J Mol Sci ; 21(11)2020 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-32492936

RESUMO

Chronic exposure of pancreatic ß-cells to elevated nutrient levels impairs their function and potentially induces apoptosis. Like in other cell types, AMPK is activated in ß-cells under conditions of nutrient deprivation, while little is known on AMPK responses to metabolic stresses. Here, we first reviewed recent studies on the role of AMPK activation in ß-cells. Then, we investigated the expression profile of AMPK pathways in ß-cells following metabolic stresses. INS-1E ß-cells and human islets were exposed for 3 days to glucose (5.5-25 mM), palmitate or oleate (0.4 mM), and fructose (5.5 mM). Following these treatments, we analyzed transcript levels of INS-1E ß-cells by qRT-PCR and of human islets by RNA-Seq; with a special focus on AMPK-associated genes, such as the AMPK catalytic subunits α1 (Prkaa1) and α2 (Prkaa2). AMPKα and pAMPKα were also evaluated at the protein level by immunoblotting. Chronic exposure to the different metabolic stresses, known to alter glucose-stimulated insulin secretion, did not change AMPK expression, either in insulinoma cells or in human islets. Expression profile of the six AMPK subunits was marginally modified by the different diabetogenic conditions. However, the expression of some upstream kinases and downstream AMPK targets, including K-ATP channel subunits, exhibited stress-specific signatures. Interestingly, at the protein level, chronic fructose treatment favored fasting-like phenotype in human islets, as witnessed by AMPK activation. Collectively, previously published and present data indicate that, in the ß-cell, AMPK activation might be implicated in the pre-diabetic state, potentially as a protective mechanism.


Assuntos
Proteínas Quinases Ativadas por AMP/metabolismo , Apoptose , Regulação Enzimológica da Expressão Gênica , Ilhotas Pancreáticas/enzimologia , Adulto , Animais , Glicemia/análise , Feminino , Frutose/metabolismo , Perfilação da Expressão Gênica , Homeostase , Humanos , Insulina/metabolismo , Insulinoma/enzimologia , Masculino , Pessoa de Meia-Idade , Ácido Oleico/análise , Ácido Palmítico/análise , Fenótipo , RNA-Seq , Ratos , Estresse Fisiológico
10.
NPJ Syst Biol Appl ; 6(1): 1, 2020 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-32001719

RESUMO

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.


Assuntos
Adaptação Biológica/fisiologia , Biologia Computacional/métodos , Adaptação Biológica/genética , Bactérias/genética , Bactérias/metabolismo , Fenômenos Bioquímicos , Biodegradação Ambiental , Genoma , Redes e Vias Metabólicas/genética , Modelos Biológicos , Pseudomonas/genética , Pseudomonas/metabolismo , Análise de Sistemas
11.
PLoS Comput Biol ; 15(5): e1007036, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31083653

RESUMO

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.


Assuntos
Redes e Vias Metabólicas , Modelos Biológicos , Biologia Computacional , Bases de Dados Factuais , Bases de Dados Genéticas , Escherichia coli/genética , Escherichia coli/metabolismo , Expressão Gênica , Genoma Bacteriano , Cinética , Metabolômica , Biologia de Sistemas , Termodinâmica
12.
Mol Ecol ; 28(9): 2224-2237, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30864192

RESUMO

Bacteria that engage in long-standing associations with particular hosts are expected to evolve host-specific adaptations that limit their capacity to thrive in other environments. Consistent with this, many gut symbionts seem to have a limited host range, based on community profiling and phylogenomics. However, few studies have experimentally investigated host specialization of gut symbionts and the underlying mechanisms have largely remained elusive. Here, we studied host specialization of a dominant gut symbiont of social bees, Lactobacillus Firm5. We show that Firm5 strains isolated from honey bees and bumble bees separate into deep-branching host-specific phylogenetic lineages. Despite their divergent evolution, colonization experiments show that bumble bee strains are capable of colonizing the honey bee gut. However, they were less successful than honey bee strains, and competition with honey bee strains completely abolished their colonization. In contrast, honey bee strains of divergent phylogenetic lineages were able to coexist within individual bees. This suggests that both host selection and interbacterial competition play important roles in host specialization. Using comparative genomics of 27 Firm5 isolates, we found that the genomes of honey bee strains harbour more carbohydrate-related functions than bumble bee strains, possibly providing a competitive advantage in the honey bee gut. Remarkably, most of the genes encoding carbohydrate-related functions were not conserved among the honey bee strains, which suggests that honey bees can support a metabolically more diverse community of Firm5 strains than bumble bees. These findings advance our understanding of the genomic changes underlying host specialization.


Assuntos
Abelhas/microbiologia , Microbioma Gastrointestinal/fisiologia , Genoma Bacteriano , Lactobacillus/genética , Simbiose/genética , Animais , Bacteriocinas/genética , Genes Bacterianos , Glicosídeo Hidrolases/genética , Lactobacillus/isolamento & purificação , Filogenia , Suíça
13.
Proc Natl Acad Sci U S A ; 116(15): 7298-7307, 2019 04 09.
Artigo em Inglês | MEDLINE | ID: mdl-30910961

RESUMO

Thousands of biochemical reactions with characterized activities are "orphan," meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.


Assuntos
Bases de Dados de Proteínas , Enzimas , Anotação de Sequência Molecular , Análise de Sequência de Proteína , Sítios de Ligação , Enzimas/química , Enzimas/genética
14.
ACS Synth Biol ; 7(8): 1858-1873, 2018 08 17.
Artigo em Inglês | MEDLINE | ID: mdl-30021444

RESUMO

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia toward sustainable production of the second generation of biofuels, with methyl ethyl ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methylbutanenitrile for further study. We reconstructed 3 679 610 novel biosynthetic pathways toward these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18 622 were found to be the most biologically feasible ones on the basis of thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.


Assuntos
Butanonas/metabolismo , Vias Biossintéticas/genética , Vias Biossintéticas/fisiologia , Biologia Computacional/métodos , Escherichia coli/genética , Engenharia Metabólica/métodos , Biologia Sintética/métodos
15.
Appl Environ Microbiol ; 84(18)2018 09 15.
Artigo em Inglês | MEDLINE | ID: mdl-30006400

RESUMO

Escherichia coli, commonly used in chemotaxis studies, is attracted mostly by amino acids, sugars, and peptides. We envisioned modifying the chemotaxis specificity of E. coli by expressing heterologous chemoreceptors from Pseudomonas putida enabling attraction either to toluene or benzoate. The mcpT gene encoding the type 40-helical bundle (40H) methyl-accepting chemoreceptor for toluene from Pseudomonas putida MT53 and the pcaY gene for the type 40H receptor for benzoate and related molecules from P. putida F1 were expressed from the trg promoter on a plasmid in motile wild-type E. coli MG1655. E. coli cells expressing McpT accumulated in chemoattraction assays to sources with 60 to 200 µM toluene, although less strongly than the response to 100 µM serine, but statistically significantly stronger than that to sources without any added attractant. An McpT-mCherry fusion protein was detectably expressed in E. coli and yielded weak but distinguishable membranes and polar foci in 1% of cells. E. coli cells expressing PcaY showed weak attraction to 0.1 to 1 mM benzoate, but 50 to 70% of cells localized the PcaY-mCherry fusion to their membrane. We conclude that implementing heterologous receptors in the E. coli chemotaxis network is possible and, upon improvement of the compatibility of the type 40H chemoreceptors, may bear interest for biosensing.IMPORTANCE Bacterial chemotaxis might be harnessed for the development of rapid biosensors, in which chemical availability is deduced from cell accumulation to chemoattractants over time. Chemotaxis of Escherichia coli has been well studied, but the bacterium is not attracted to chemicals of environmental concern, such as aromatic solvents. We show here that heterologous chemoreceptors for aromatic compounds from Pseudomonas putida at least partly functionally complement the E. coli chemotaxis network, yielding cells attracted to toluene or benzoate. Complementation was still inferior to native chemoattractants, like serine, but our study demonstrates the potential for obtaining selective sensing for aromatic compounds in E. coli.


Assuntos
Proteínas de Bactérias/genética , Benzoatos/metabolismo , Quimiotaxia , Escherichia coli/fisiologia , Expressão Gênica , Proteínas Quimiotáticas Aceptoras de Metil/genética , Pseudomonas putida/genética , Tolueno/metabolismo , Proteínas de Bactérias/metabolismo , Fatores Quimiotáticos/metabolismo , Escherichia coli/genética , Proteínas Quimiotáticas Aceptoras de Metil/metabolismo
16.
Metab Eng ; 41: 173-181, 2017 05.
Artigo em Inglês | MEDLINE | ID: mdl-28433737

RESUMO

Mono-ethylene glycol (MEG) is an important petrochemical with widespread use in numerous consumer products. The current industrial MEG-production process relies on non-renewable fossil fuel-based feedstocks, such as petroleum, natural gas, and naphtha; hence, it is useful to explore alternative routes of MEG-synthesis from gases as they might provide a greener and more sustainable alternative to the current production methods. Technologies of synthetic biology and metabolic engineering of microorganisms can be deployed for the expression of new biochemical pathways for MEG-synthesis from gases, provided that such promising alternative routes are first identified. We used the BNICE.ch algorithm to develop novel and previously unknown biological pathways to MEG from synthesis gas by leveraging the Wood-Ljungdahl pathway of carbon fixation of acetogenic bacteria. We developed a set of useful pathway pruning and analysis criteria to systematically assess thousands of pathways generated by BNICE.ch. Published genome-scale models of Moorella thermoacetica and Clostridium ljungdahlii were used to perform the pathway yield calculations and in-depth analyses of seven (7) newly developed biological MEG-producing pathways from gases, including CO2, CO, and H2. These analyses helped identify not only better candidate pathways, but also superior chassis organisms that can be used for metabolic engineering of the candidate pathways. The pathway generation, pruning, and detailed analysis procedures described in this study can also be used to develop biochemical pathways for other commodity chemicals from gaseous substrates.


Assuntos
Dióxido de Carbono/metabolismo , Monóxido de Carbono/metabolismo , Clostridium , Etilenoglicol/metabolismo , Hidrogênio/metabolismo , Engenharia Metabólica/métodos , Moorella , Clostridium/genética , Clostridium/metabolismo , Moorella/genética , Moorella/metabolismo
17.
Biotechnol J ; 12(1)2017 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-27897385

RESUMO

Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli.


Assuntos
Algoritmos , Biologia Computacional/métodos , Escherichia coli/metabolismo , Redes e Vias Metabólicas , Carbono/metabolismo , Simulação por Computador , Bases de Dados Factuais , Enzimas/química , Enzimas/metabolismo , Glicólise , Fluxo de Trabalho
18.
ACS Synth Biol ; 5(10): 1155-1166, 2016 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-27404214

RESUMO

Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework BNICE.ch along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database ( http://lcsb-databases.epfl.ch/atlas/ ) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.


Assuntos
Fenômenos Bioquímicos , Bases de Dados Genéticas , Engenharia Metabólica , Biologia Sintética , Fenômenos Fisiológicos Celulares , Internet , Redes e Vias Metabólicas , Metaboloma , Reprodutibilidade dos Testes
19.
Curr Opin Chem Biol ; 28: 99-104, 2015 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-26177079

RESUMO

Designing putative metabolic pathways is of great interest in synthetic biology. Retrobiosynthesis is a discipline that involves the design, evaluation, and optimization of de novo biosynthetic pathways for the production of high-value compounds and drugs from renewable resources and natural or engineered enzymes. The best candidate pathways are then engineered within a metabolic network of microorganisms that serve as synthetic platforms for synthetic biology. The complexity of biological chemistry and metabolism requires computational approaches to explore the full possibilities of engineering synthetic pathways towards target compounds. Herein, we discuss recent developments in the design of computational tools for retrosynthetic biochemistry and outline the workflow and design elements for such tools.


Assuntos
Vias Biossintéticas , Biologia Sintética/métodos , Biologia de Sistemas/métodos , Animais , Simulação por Computador , Humanos , Modelos Biológicos
20.
Metab Eng ; 23: 1-8, 2014 May.
Artigo em Inglês | MEDLINE | ID: mdl-24395008

RESUMO

Lipids are important compounds for human physiology and as renewable resources for fuels and chemicals. In lipid research, there is a big gap between the currently available pathway-level representations of lipids and lipid structure databases in which the number of compounds is expanding rapidly with high-throughput mass spectrometry methods. In this work, we introduce a computational approach to bridge this gap by making associations between metabolic pathways and the lipid structures discovered increasingly thorough lipidomics studies. Our approach, called NICELips (Network Integrated Computational Explorer for Lipidomics), is based on the formulation of generalized enzymatic reaction rules for lipid metabolism, and it employs the generalized rules to postulate novel pathways of lipid metabolism. It further integrates all discovered lipids in biological networks of enzymatic reactions that consist their biosynthesis and biodegradation pathways. We illustrate the utility of our approach through a case study of bis(monoacylglycero)phosphate (BMP), a biologically important glycerophospholipid with immature synthesis and catabolic route(s). Using NICELips, we were able to propose various synthesis and degradation pathways for this compound and several other lipids with unknown metabolism like BMP, and in addition several alternative novel biosynthesis and biodegradation pathways for lipids with known metabolism. NICELips has potential applications in designing therapeutic interventions for lipid-associated disorders and in the metabolic engineering of model organisms for improving the biobased production of lipid-derived fuels and chemicals.


Assuntos
Simulação por Computador , Bases de Dados Genéticas , Metabolismo dos Lipídeos/fisiologia , Software , Humanos
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