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1.
Nature ; 600(7889): 500-505, 2021 12.
Artículo en Inglés | MEDLINE | ID: mdl-34880489

RESUMEN

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.


Asunto(s)
Aterosclerosis , Microbioma Gastrointestinal , Microbiota , Clostridiales , Humanos , Metaboloma
2.
Lancet ; 401(10378): 762-771, 2023 03 04.
Artículo en Inglés | MEDLINE | ID: mdl-36739882

RESUMEN

BACKGROUND: One in four pregnancies end in a pregnancy loss. Although the effect on couples is well documented, evidence-based treatments and prediction models are absent. Fetal aneuploidy is associated with a higher chance of a next successful pregnancy compared with euploid pregnancy loss in which underlying maternal conditions might be causal. Ploidy diagnostics are therefore advantageous but challenging as they require collection of the pregnancy tissue. Cell-free fetal DNA (cffDNA) from maternal blood has the potential for evaluation of fetal ploidy status, but no large-scale validation of the method has been done. METHODS: In this prospective cohort study, women with a pregnancy loss were recruited as a part of the Copenhagen Pregnancy Loss (COPL) study from three gynaecological clinics at public hospitals in Denmark. Women were eligible for inclusion if older than 18 years with a pregnancy loss before gestational age 22 weeks (ie, 154 days) and with an intrauterine pregnancy confirmed by ultrasound (including anembryonic sac), and women with pregnancies of unknown location or molar pregnancies were excluded. Maternal blood was collected while pregnancy tissue was still in situ or within 24 h after pregnancy tissue had passed and was analysed by genome-wide sequencing of cffDNA. Direct sequencing of the pregnancy tissue was done as reference. FINDINGS: We included 1000 consecutive women, at the time of a pregnancy loss diagnosis, between Nov 12, 2020, and May 1, 2022. Results from the first 333 women with a pregnancy loss (recruited between Nov 12, 2020, and Aug 14, 2021) were used to evaluate the validity of cffDNA-based testing. Results from the other 667 women were included to evaluate cffDNA performance and result distribution in a larger cohort of 1000 women in total. Gestational age of fetus ranged from 35-149 days (mean of 70·5 days [SD 16·5], or 10 weeks plus 1 day). The cffDNA-based test had a sensitivity for aneuploidy detection of 85% (95% CI 79-90) and a specificity of 93% (95% CI 88-96) compared with direct sequencing of the pregnancy tissue. Among 1000 cffDNA-based test results, 446 (45%) were euploid, 405 (41%) aneuploid, 37 (4%) had multiple aneuploidies, and 112 (11%) were inconclusive. 105 (32%) of 333 women either did not manage to collect the pregnancy tissue or collected a sample classified as unknown tissue giving a high risk of being maternal. INTERPRETATION: This validation of cffDNA-based testing in pregnancy loss shows the potential and feasibility of the method to distinguish euploid and aneuploid pregnancy loss for improved clinical management and benefit of future reproductive medicine and women's health research. FUNDING: Ole Kirks Foundation, BioInnovation Institute Foundation, and the Novo Nordisk Foundation.


Asunto(s)
Aborto Espontáneo , Ácidos Nucleicos Libres de Células , Embarazo , Humanos , Femenino , Lactante , Recién Nacido , Estudios Prospectivos , Feto , Aneuploidia , ADN , Diagnóstico Prenatal/métodos
3.
BMC Microbiol ; 24(1): 66, 2024 Feb 27.
Artículo en Inglés | MEDLINE | ID: mdl-38413885

RESUMEN

BACKGROUND: Candida albicans is a fungal pathogen causing human infections. Here we investigated differential gene expression patterns and functional enrichment in C. albicans strains grown under different conditions. METHODS: A systematic GEO database search identified 239 "Candida albicans" datasets, of which 14 were selected after rigorous criteria application. Retrieval of raw sequencing data from the ENA database was accompanied by essential metadata extraction from dataset descriptions and original articles. Pre-processing via the tailored nf-core pipeline for C. albicans involved alignment, gene/transcript quantification, and diverse quality control measures. Quality assessment via PCA and DESeq2 identified significant genes (FDR < = 0.05, log2-fold change > = 1 or <= -1), while topGO conducted GO term enrichment analysis. Exclusions were made based on data quality and strain relevance, resulting in the selection of seven datasets from the SC5314 strain background for in-depth investigation. RESULTS: The meta-analysis of seven selected studies unveiled a substantial number of genes exhibiting significant up-regulation (24,689) and down-regulation (18,074). These differentially expressed genes were further categorized into 2,497 significantly up-regulated and 2,573 significantly down-regulated Gene Ontology (GO) IDs. GO term enrichment analysis clustered these terms into distinct groups, providing insights into the functional implications. Three target gene lists were compiled based on previous studies, focusing on central metabolism, ion homeostasis, and pathogenicity. Frequency analysis revealed genes with higher occurrence within the identified GO clusters, suggesting their potential as antifungal targets. Notably, the genes TPS2, TPS1, RIM21, PRA1, SAP4, and SAP6 exhibited higher frequencies within the clusters. Through frequency analysis within the GO clusters, several key genes emerged as potential targets for antifungal therapies. These include RSP5, GLC7, SOD2, SOD5, SOD1, SOD6, SOD4, SOD3, and RIM101 which exhibited higher occurrence within the identified clusters. CONCLUSION: This comprehensive study significantly advances our understanding of the dynamic nature of gene expression in C. albicans. The identification of genes with enhanced potential as antifungal drug targets underpins their value for future interventions. The highlighted genes, including TPS2, TPS1, RIM21, PRA1, SAP4, SAP6, RSP5, GLC7, SOD2, SOD5, SOD1, SOD6, SOD4, SOD3, and RIM101, hold promise for the development of targeted antifungal therapies.


Asunto(s)
Antifúngicos , Candida albicans , Antifúngicos/farmacología , Proteínas Fúngicas/genética , Proteínas Fúngicas/metabolismo , Superóxido Dismutasa-1 , Virulencia
4.
Proc Natl Acad Sci U S A ; 118(13)2021 03 30.
Artículo en Inglés | MEDLINE | ID: mdl-33753486

RESUMEN

Microbial variations in the human gut are harbored in temporal and spatial heterogeneity, and quantitative prediction of spatiotemporal dynamic changes in the gut microbiota is imperative for development of tailored microbiome-directed therapeutics treatments, e.g. precision nutrition. Given the high-degree complexity of microbial variations, subject to the dynamic interactions among host, microbial, and environmental factors, identifying how microbiota colonize in the gut represents an important challenge. Here we present COmputing the DYnamics of microbiota (CODY), a multiscale framework that integrates species-level modeling of microbial dynamics and ecosystem-level interactions into a mathematical model that characterizes spatial-specific in vivo microbial residence in the colon as impacted by host physiology. The framework quantifies spatiotemporal resolution of microbial variations on species-level abundance profiles across site-specific colon regions and in feces, independent of a priori knowledge. We demonstrated the effectiveness of CODY using cross-sectional data from two longitudinal metagenomics studies-the microbiota development during early infancy and during short-term diet intervention of obese adults. For each cohort, CODY correctly predicts the microbial variations in response to diet intervention, as validated by available metagenomics and metabolomics data. Model simulations provide insight into the biogeographical heterogeneity among lumen, mucus, and feces, which provides insight into how host physical forces and spatial structure are shaping microbial structure and functionality.


Asunto(s)
Colon/fisiología , Conducta Alimentaria/fisiología , Microbioma Gastrointestinal/fisiología , Modelos Biológicos , Obesidad/dietoterapia , Adulto , Variación Biológica Individual , Colon/microbiología , Estudios Transversales , Conjuntos de Datos como Asunto , Heces/microbiología , Humanos , Hidrodinámica , Lactante , Estudios Longitudinales , Metabolómica , Metagenómica , Obesidad/sangre , Obesidad/metabolismo , Obesidad/microbiología , Análisis Espacio-Temporal
5.
Metab Eng ; 75: 119-130, 2023 01.
Artículo en Inglés | MEDLINE | ID: mdl-36503050

RESUMEN

The hybrid cybernetic model (HCM) approach is a dynamic modeling framework that integrates enzyme synthesis and activity regulation. It has been widely applied in bioreaction engineering, particularly in the simulation of microbial growth in different mixtures of carbon sources. In a HCM, the metabolic network is decomposed into elementary flux modes (EFMs), whereby the network can be reduced into a few pathways by yield analysis. However, applying the HCM approach on conventional genome-scale metabolic models (GEMs) is still a challenge due to the high computational demands. Here, we present a HCM strategy that introduced an optimized yield analysis algorithm (opt-yield-FBA) to simulate metabolic dynamics at the genome-scale without the need for EFMs calculation. The opt-yield-FBA is a flux-balance analysis (FBA) based method that can calculate optimal yield solutions and yield space for GEM. With the opt-yield-FBA algorithm, the HCM strategy can be applied to get the yield spaces and avoid the computational burden of EFMs, and it can therefore be applied for developing dynamic models for genome-scale metabolic networks. Here, we illustrate the strategy by applying the concept to simulate the dynamics of microbial communities.


Asunto(s)
Genoma , Redes y Vías Metabólicas , Redes y Vías Metabólicas/genética , Algoritmos , Simulación por Computador , Modelos Biológicos , Análisis de Flujos Metabólicos
6.
Metab Eng ; 79: 1-13, 2023 09.
Artículo en Inglés | MEDLINE | ID: mdl-37364774

RESUMEN

Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities.


Asunto(s)
Microbioma Gastrointestinal , Probióticos , Humanos , Microbioma Gastrointestinal/genética , Ingeniería Metabólica , Probióticos/uso terapéutico , Consorcios Microbianos
7.
Gut ; 71(12): 2463-2480, 2022 12.
Artículo en Inglés | MEDLINE | ID: mdl-35017197

RESUMEN

OBJECTIVES: Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome's functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. DESIGN: We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. RESULTS: Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. CONCLUSION: Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity. TRIAL REGISTRATION NUMBER: NCT02059538.


Asunto(s)
Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Obesidad Mórbida , Complejo Vitamínico B , Humanos , Ratones , Animales , Prebióticos , Obesidad Mórbida/cirugía , Biotina/farmacología , Complejo Vitamínico B/farmacología , Ratones Endogámicos C57BL , Obesidad/metabolismo , Inflamación
8.
Metab Eng ; 72: 311-324, 2022 07.
Artículo en Inglés | MEDLINE | ID: mdl-35508267

RESUMEN

High-level production of recombinant proteins in industrial microorganisms is often limited by the formation of misfolded proteins or protein aggregates, which consequently induce cellular stress responses. We hypothesized that in a yeast Alzheimer's disease (AD) model overexpression of amyloid-ß peptides (Aß42), one of the main peptides relevant for AD pathologies, induces similar phenotypes of cellular stress. Using this humanized AD model, we previously identified suppressors of Aß42 cytotoxicity. Here we hypothesize that these suppressors could be used as metabolic engineering targets to alleviate cellular stress and improve recombinant protein production in the yeast Saccharomyces cerevisiae. Forty-six candidate genes were individually deleted and twenty were individually overexpressed. The positive targets that increased recombinant α-amylase production were further combined leading to an 18.7-fold increased recombinant protein production. These target genes are involved in multiple cellular networks including RNA processing, transcription, ER-mitochondrial complex, and protein unfolding. By using transcriptomics and proteomics analyses, combined with reverse metabolic engineering, we showed that reduced oxidative stress, increased membrane lipid biosynthesis and repressed arginine and sulfur amino acid biosynthesis are significant pathways for increased recombinant protein production. Our findings provide new insights towards developing synthetic yeast cell factories for biosynthesis of valuable proteins.


Asunto(s)
Enfermedad de Alzheimer , Proteínas de Saccharomyces cerevisiae , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Péptidos beta-Amiloides/química , Péptidos beta-Amiloides/genética , Péptidos beta-Amiloides/metabolismo , Humanos , Estrés Oxidativo/genética , Proteínas Recombinantes/genética , Proteínas Recombinantes/metabolismo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismo
9.
Mol Syst Biol ; 17(10): e10427, 2021 10.
Artículo en Inglés | MEDLINE | ID: mdl-34676984

RESUMEN

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.


Asunto(s)
Redes y Vías Metabólicas , Saccharomyces cerevisiae , Evolución Molecular , Duplicación de Gen , Transferencia de Gen Horizontal , Genoma , Redes y Vías Metabólicas/genética , Saccharomyces cerevisiae/genética
10.
Microb Cell Fact ; 21(1): 241, 2022 Nov 23.
Artículo en Inglés | MEDLINE | ID: mdl-36419034

RESUMEN

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.


Asunto(s)
Microbioma Gastrointestinal , Microbiota , Humanos , Metagenómica , Disbiosis , Aprendizaje Automático
11.
Environ Microbiol ; 23(2): 713-727, 2021 02.
Artículo en Inglés | MEDLINE | ID: mdl-32627309

RESUMEN

Environmental stressors, especially low temperature, are very common on the earth's dryland systems. Terrestrial cyanobacteria have evolved with cold adaptability in addition to extreme dryness and high irradiation resistance. The dryland soil surface-dwelling species, Nostoc flagelliforme, serves as a potential model organism to gain insights into cyanobacterial cold adaptation. In this study, we performed transcriptomic analysis of N. flagelliforme samples in response to low temperature. The results revealed that the biological processes, such as terpenoid biosynthetic process, oxidoreductase activity, carbohydrate metabolism, biosynthesis of secondary metabolites, lipid and nitrogen metabolism, were significantly and dynamically changed during the cold stress. It was noteworthy that the transcription of the denitrification pathway for ammonia accumulation was enhanced, implying an importance for nitrogen utilization in stress resistance. In addition, characterization of a cold-responsive hypothetical gene csrnf1 found that it could greatly improve the cold-resistant performance of cells when it was heterologously expressed in transgenic Nostoc sp. PCC 7120. It was also found that csrnf1 transgenic strain exhibited resistance to nitrogen-deficient environmental stress. Considering that dryland cyanobacteria have to cope with low temperature on infertile soils, this study would enrich our understanding on the importance of multifunction of the genes for environmental cold adaptation in drylands.


Asunto(s)
Adaptación Fisiológica/fisiología , Respuesta al Choque por Frío/fisiología , Nostoc/metabolismo , Nostoc/fisiología , Adaptación Fisiológica/genética , Metabolismo de los Hidratos de Carbono/fisiología , Frío , Ecosistema , Perfilación de la Expresión Génica , Humedad , Metabolismo de los Lípidos/fisiología , Nitrógeno/metabolismo , Nostoc/genética , Oxidorreductasas/metabolismo , Metabolismo Secundario/fisiología , Suelo , Terpenos/metabolismo , Transcriptoma/genética
12.
BMC Biotechnol ; 21(1): 46, 2021 07 30.
Artículo en Inglés | MEDLINE | ID: mdl-34330235

RESUMEN

BACKGROUND: Limosilactobacillus reuteri (earlier known as Lactobacillus reuteri) is a well-studied lactic acid bacterium, with some specific strains used as probiotics, that exists in different hosts such as human, pig, goat, mouse and rat, with multiple body sites such as the gastrointestinal tract, breast milk and mouth. Numerous studies have confirmed the beneficial effects of orally administered specific L. reuteri strains, such as preventing bone loss and promoting regulatory immune system development. L. reuteri ATCC PTA 6475 is a widely used strain that has been applied in the market as a probiotic due to its positive effects on the human host. Its health benefits may be due, in part, to the production of beneficial metabolites. Considering the strain-specific effects and genetic diversity of L. reuteri strains, we were interested to study the metabolic versatility of these strains. RESULTS: In this study, we aimed to systematically investigate the metabolic features and diversities of L. reuteri strains by using genome-scale metabolic models (GEMs). The GEM of L. reuteri ATCC PTA 6475 was reconstructed with a template-based method and curated manually. The final GEM iHL622 of L. reuteri ATCC PTA 6475 contains 894 reactions and 726 metabolites linked to 622 metabolic genes, which can be used to simulate growth and amino acids utilization. Furthermore, we built GEMs for the other 35 L. reuteri strains from three types of hosts. The comparison of the L. reuteri GEMs identified potential metabolic products linked to the adaptation to the host. CONCLUSIONS: The GEM of L. reuteri ATCC PTA 6475 can be used to simulate metabolic capabilities and growth. The core and pan model of 35 L. reuteri strains shows metabolic capacity differences both between and within the host groups. The GEMs provide a reliable basis to investigate the metabolism of L. reuteri in detail and their potential benefits on the host.


Asunto(s)
Genoma Bacteriano , Limosilactobacillus reuteri/genética , Limosilactobacillus reuteri/metabolismo , Animales , Cabras , Especificidad del Huésped , Humanos , Limosilactobacillus reuteri/crecimiento & desarrollo , Ratones , Ratas , Porcinos
13.
BMC Bioinformatics ; 20(1): 551, 2019 Nov 06.
Artículo en Inglés | MEDLINE | ID: mdl-31694544

RESUMEN

BACKGROUND: Kluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45 °C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus. RESULTS: Here, we reported the first genome-scale metabolic model of Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include the missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures. CONCLUSIONS: K. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provides a new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement.


Asunto(s)
Genoma Bacteriano , Kluyveromyces/genética , Kluyveromyces/metabolismo , Modelos Biológicos , Biomasa , Vías Biosintéticas/genética , Fermentación , Reproducibilidad de los Resultados , Riboflavina/biosíntesis , Saccharomyces cerevisiae/genética , Estrés Fisiológico/genética , Temperatura
14.
BMC Genomics ; 20(1): 517, 2019 Jun 25.
Artículo en Inglés | MEDLINE | ID: mdl-31234773

RESUMEN

BACKGROUND: In the biochemical milieu of human colon, bile acids act as signaling mediators between the host and its gut microbiota. Biotransformation of primary to secondary bile acids have been known to be involved in the immune regulation of human physiology. Several 16S amplicon-based studies with inflammatory bowel disease (IBD) subjects were found to have an association with the level of fecal bile acids. However, a detailed investigation of all the bile salt biotransformation genes in the gut microbiome of healthy and IBD subjects has not been performed. RESULTS: Here, we report a comprehensive analysis of the bile salt biotransformation genes and their distribution at the phyla level. Based on the analysis of shotgun metagenomes, we found that the IBD subjects harbored a significantly lower abundance of these genes compared to the healthy controls. Majority of these genes originated from Firmicutes in comparison to other phyla. From metabolomics data, we found that the IBD subjects were measured with a significantly low level of secondary bile acids and high levels of primary bile acids compared to that of the healthy controls. CONCLUSIONS: Our bioinformatics-driven approach of identifying bile salt biotransformation genes predicts the bile salt biotransformation potential in the gut microbiota of IBD subjects. The functional level of dysbiosis likely contributes to the variation in the bile acid pool. This study sets the stage to envisage potential solutions to modulate the gut microbiome with the objective to restore the bile acid pool in the gut.


Asunto(s)
Bacterias/metabolismo , Ácidos y Sales Biliares/metabolismo , Microbioma Gastrointestinal/genética , Bacterias/clasificación , Bacterias/genética , Proteínas Bacterianas/genética , Biotransformación/genética , Disbiosis/metabolismo , Disbiosis/microbiología , Firmicutes/genética , Firmicutes/metabolismo , Humanos , Enfermedades Inflamatorias del Intestino/metabolismo , Enfermedades Inflamatorias del Intestino/microbiología , Metagenómica
15.
FEMS Yeast Res ; 19(7)2019 11 01.
Artículo en Inglés | MEDLINE | ID: mdl-31584649

RESUMEN

Understanding genotype-phenotype relationship is fundamental in biology. With the benefit from next-generation sequencing and high-throughput phenotyping methodologies, there have been generated much genome and phenome data for Saccharomyces cerevisiae. This makes it an excellent model system to understand the genotype-phenotype relationship. In this paper, we presented the reconstruction and application of the yeast pan-genome in resolving genotype-phenotype relationship by a machine learning-assisted approach.


Asunto(s)
Genoma Fúngico , Aprendizaje Automático , Saccharomyces cerevisiae/genética , Regulación Fúngica de la Expresión Génica , Genotipo , Secuenciación de Nucleótidos de Alto Rendimiento , Fenotipo
16.
Appl Microbiol Biotechnol ; 103(9): 3727-3736, 2019 May.
Artículo en Inglés | MEDLINE | ID: mdl-30915502

RESUMEN

Shea tree (Vitellaria paradoxa) is one economically important plant species that mainly distributes in West Africa. Shea butter extracted from shea fruit kernels can be used as valuable products in the food and cosmetic industries. The most valuable composition in shea butter was one kind of triacylglycerol (TAG), 1,3-distearoyl-2-oleoyl-glycerol (SOS, C18:0-C18:1-C18:0). However, shea butter production is limited and little is known about the genetic information of shea tree. In this study, we tried to reveal genetic information of shea tree and identified shea TAG biosynthetic genes for future shea butter production in yeast cell factories. First, we measured lipid content, lipid composition, and TAG composition of seven shea fruits at different ripe stages. Then, we performed transcriptome analysis on two shea fruits containing obviously different levels of SOS and revealed a list of TAG biosynthetic genes potentially involved in TAG biosynthesis. In total, 4 glycerol-3-phosphate acyltransferase (GPAT) genes, 8 lysophospholipid acyltransferase (LPAT) genes, and 11 diacylglycerol acyltransferase (DGAT) genes in TAG biosynthetic pathway were predicted from the assembled transcriptome and 14 of them were cloned from shea fruit cDNA. Furthermore, the heterologous expression of these 14 potential GPAT, LPAT, and DGAT genes in Saccharomyces cerevisiae changed yeast fatty acid and lipid profiles, suggesting that they functioned in S. cerevisiae. Moreover, two shea DGAT genes, VpDGAT1 and VpDGAT7, were identified as functional DGATs in shea tree, showing they might be useful for shea butter (SOS) production in yeast cell factories.


Asunto(s)
Proteínas de Plantas/genética , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Sapotaceae/genética , Triglicéridos/biosíntesis , Levaduras/genética , Levaduras/metabolismo , Vías Biosintéticas , Diacilglicerol O-Acetiltransferasa/genética , Diacilglicerol O-Acetiltransferasa/metabolismo , Frutas/genética , Frutas/metabolismo , Ingeniería Metabólica , Sapotaceae/enzimología , Sapotaceae/metabolismo , Transcriptoma
17.
Mar Drugs ; 17(5)2019 May 18.
Artículo en Inglés | MEDLINE | ID: mdl-31109094

RESUMEN

The mass spectrometry-based metabolomics approach has become a powerful tool for the quantitative analysis of small-molecule metabolites in biological samples. Nostoc flagelliforme, an edible cyanobacterium with herbal value, serves as an unexploited bioresource for small molecules. In natural environments, N. flagelliforme undergoes repeated cycles of rehydration and dehydration, which are interrupted by either long- or short-term dormancy. In this study, we performed an untargeted metabolite profiling of N. flagelliforme samples at three physiological states: Dormant (S1), physiologically fully recovered after rehydration (S2), and physiologically partially inhibited following dehydration (S3). Significant metabolome differences were identified based on the OPLS-DA (orthogonal projections to latent structures discriminant analysis) model. In total, 183 differential metabolites (95 up-regulated; 88 down-regulated) were found during the rehydration process (S2 vs. S1), and 130 (seven up-regulated; 123 down-regulated) during the dehydration process (S3 vs. S2). Thus, it seemed that the metabolites' biosynthesis mainly took place in the rehydration process while the degradation or possible conversion occurred in the dehydration process. In addition, lipid profile differences were particularly prominent, implying profound membrane phase changes during the rehydration-dehydration cycle. In general, this study expands our understanding of the metabolite dynamics in N. flagelliforme and provides biotechnological clues for achieving the efficient production of those metabolites with medical potential.


Asunto(s)
Desecación , Regulación Bacteriana de la Expresión Génica/fisiología , Metaboloma/fisiología , Nostoc/química , Membrana Celular/genética , Membrana Celular/metabolismo , Ambiente , Nostoc/metabolismo , Agua
18.
Metab Eng ; 49: 128-142, 2018 09.
Artículo en Inglés | MEDLINE | ID: mdl-30075203

RESUMEN

Malnutrition is a severe non-communicable disease, which is prevalent in children from low-income countries. Recently, a number of metagenomics studies have illustrated associations between the altered gut microbiota and child malnutrition. However, these studies did not examine metabolic functions and interactions between individual species in the gut microbiota during health and malnutrition. Here, we applied genome-scale metabolic modeling to model the gut microbial species, which were selected from healthy and malnourished children from three countries. Our analysis showed reduced metabolite production capabilities in children from two low-income countries compared with a high-income country. Additionally, the models were also used to predict the community-level metabolic potentials of gut microbes and the patterns of pairwise interactions among species. Hereby we found that due to bacterial interactions there may be reduced production of certain amino acids in malnourished children compared with healthy children from the same communities. To gain insight into alterations in the metabolism of malnourished (stunted) children, we also performed targeted plasma metabolic profiling in the first 2 years of life of 25 healthy and 25 stunted children. Plasma metabolic profiling further revealed that stunted children had reduced plasma levels of essential amino acids compared to healthy controls. Our analyses provide a framework for future efforts towards further characterization of gut microbial metabolic capabilities and their contribution to malnutrition.


Asunto(s)
Aminoácidos/sangre , Trastornos de la Nutrición del Niño , Disbiosis , Microbioma Gastrointestinal , Genoma Bacteriano , Niño , Trastornos de la Nutrición del Niño/sangre , Trastornos de la Nutrición del Niño/genética , Trastornos de la Nutrición del Niño/microbiología , Preescolar , Disbiosis/sangre , Disbiosis/genética , Disbiosis/microbiología , Femenino , Humanos , Masculino
19.
BMC Genomics ; 18(1): 33, 2017 01 05.
Artículo en Inglés | MEDLINE | ID: mdl-28056772

RESUMEN

BACKGROUND: Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of production of FFA to be commercially viable; thus new chassis strains that require less engineering are needed. Although more than 120 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. RESULTS: Here we present the FFA SC (FFASC), an in silico screening method that evaluates the potential for FFA production and excretion of cyanobacterial strains based on their proteomes. A literature search allowed for the compilation of 64 proteins, most of which influence FFA production and a few of which affect FFA excretion. The proteins are classified into 49 orthologous groups (OGs) that helped create rules used in the scoring/ranking of algorithms developed to estimate the potential for FFA production and excretion of an organism. Among 125 cyanobacterial strains, FFASC identified 20 candidate chassis strains that rank in their FFA producing and excreting potential above the specifically engineered reference strain, Synechococcus sp. PCC 7002. We further show that the top ranked cyanobacterial strains are unicellular and primarily include Prochlorococcus (order Prochlorales) and marine Synechococcus (order Chroococcales) that cluster phylogenetically. Moreover, two principal categories of enzymes were shown to influence FFA production the most: those ensuring precursor availability for the biosynthesis of lipids, and those involved in handling the oxidative stress associated to FFA synthesis. CONCLUSION: To our knowledge FFASC is the first in silico method to screen cyanobacteria proteomes for their potential to produce and excrete FFA, as well as the first attempt to parameterize the criteria derived from genetic characteristics that are favorable/non-favorable for this purpose. Thus, FFASC helps focus experimental evaluation only on the most promising cyanobacteria.


Asunto(s)
Biología Computacional/métodos , Cianobacterias/genética , Cianobacterias/metabolismo , Ácidos Grasos no Esterificados/biosíntesis , Algoritmos , Análisis por Conglomerados , Simulación por Computador , Cianobacterias/clasificación , Redes y Vías Metabólicas , Fotosíntesis , Filogenia , Proteoma , Proteómica/métodos
20.
Environ Microbiol ; 19(3): 1103-1119, 2017 03.
Artículo en Inglés | MEDLINE | ID: mdl-27902881

RESUMEN

Magnetotactic bacteria (MTB) are a group of phylogenetically and physiologically diverse Gram-negative bacteria that synthesize intracellular magnetic crystals named magnetosomes. MTB are affiliated with three classes of Proteobacteria phylum, Nitrospirae phylum, Omnitrophica phylum and probably with the candidate phylum Latescibacteria. The evolutionary origin and physiological diversity of MTB compared with other bacterial taxonomic groups remain to be illustrated. Here, we analysed the genome of the marine magneto-ovoid strain MO-1 and found that it is closely related to Magnetococcus marinus MC-1. Detailed analyses of the ribosomal proteins and whole proteomes of 390 genomes reveal that, among the Proteobacteria analysed, only MO-1 and MC-1 have coding sequences (CDSs) with a similarly high proportion of origins from Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria and Gammaproteobacteria. Interestingly, a comparative metabolic network analysis with anoxic network enzymes from sequenced MTB and non-MTB successfully allows the eventual prediction of an organism with a metabolic profile compatible for magnetosome production. Altogether, our genomic analysis reveals multiple origins of MO-1 and M. marinus MC-1 genomes and suggests a metabolism-restriction model for explaining whether a bacterium could become an MTB upon acquisition of magnetosome encoding genes.


Asunto(s)
Genoma Bacteriano , Magnetosomas , Proteobacteria/clasificación , Proteobacteria/genética , Secuencia de Bases , Deltaproteobacteria/genética , Evolución Molecular , Magnetosomas/genética , Filogenia , Proteobacteria/ultraestructura
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