• Energy Research

Alcoholic liver disease is a leading cause of mortality. Chronic alcohol consumption is accompanied by intestinal dysbiosis, and development of alcoholic liver disease requires gut-derived bacterial products. However, little is known about how alterations to the microbiome contribute to pathogenesis of alcoholic liver disease.We used the Tsukamoto-French mouse model, which involves continuous intragastric feeding of isocaloric diet or alcohol for 3 weeks. Bacterial DNA from the cecum was extracted for deep metagenomic sequencing. Targeted metabolomics assessed concentrations of saturated fatty acids in cecal contents. To maintain intestinal metabolic homeostasis, diets of ethanol-fed and control mice were supplemented with saturated long-chain fatty acids (LCFA). Bacterial genes involved in fatty acid biosynthesis, amounts of lactobacilli, and saturated LCFA were measured in fecal samples of nonalcoholic individuals and patients with active alcohol abuse.Analyses of intestinal contents from mice revealed alcohol-associated changes to the intestinal metagenome and metabolome, characterized by reduced synthesis of saturated LCFA. Maintaining intestinal levels of saturated fatty acids in mice resulted in eubiosis, stabilized the intestinal gut barrier, and reduced ethanol-induced liver injury. Saturated LCFA are metabolized by commensal Lactobacillus and promote their growth. Proportions of bacterial genes involved in fatty acid biosynthesis were lower in feces from patients with active alcohol abuse than controls. Total levels of LCFA correlated with those of lactobacilli in fecal samples from patients with active alcohol abuse but not in controls.In humans and mice, alcohol causes intestinal dysbiosis, reducing the capacity of the microbiome to synthesize saturated LCFA and the proportion of Lactobacillus species. Dietary approaches to restore levels of saturated fatty acids in the intestine might reduce ethanol-induced liver injury in patients with alcoholic liver disease.

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

AbstractLiver damage due to chronic alcohol use is among the most prevalent liver diseases. Alcohol consumption frequency is a strong factor of microbiota variance. Here we use isotope labeled [1-13C] ethanol, metagenomics, and metatranscriptomics in ethanol-feeding and intragastric mouse models to investigate the metabolic impacts of alcohol consumption on the gut microbiota. First, we show that although stable isotope labeled [1-13C] ethanol contributes to fatty acid pools in the liver, plasma, and cecum contents of mice, there is no evidence of ethanol metabolism by gut microbiota ex vivo under anaerobic conditions. Next, we observe through metatranscriptomics that the gut microbiota responds to ethanol-feeding by activating acetate dissimilation, not by metabolizing ethanol directly. We demonstrate that blood acetate concentrations are elevated during ethanol consumption. Finally, by increasing systemic acetate levels with glyceryl triacetate supplementation, we do not observe any impact on liver disease, but do induce similar gut microbiota alterations as chronic ethanol-feeding in mice. Our results show that ethanol is not directly metabolized by the gut microbiota, and changes in the gut microbiota linked to ethanol are a side effect of elevated acetate levels. De-trending for these acetate effects may be critical for understanding gut microbiota changes that cause alcohol-related liver disease.

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.

La production de biocarburants et de précurseurs de bioénergie par des micro-organismes phototrophes, tels que les microalgues et les cyanobactéries, est une alternative prometteuse aux carburants conventionnels obtenus à partir de ressources non renouvelables. Plusieurs espèces de microalgues ont été étudiées comme candidats potentiels pour la production de biocarburants, en grande partie en raison de leur capacité métabolique exceptionnelle à accumuler de grandes quantités de lipides. La modélisation basée sur les contraintes, une approche de biologie des systèmes qui prédit avec précision le phénotype métabolique des phototrophes, a été déployée pour identifier les conditions de culture appropriées ainsi que pour explorer des stratégies d'amélioration génétique pour la bioproduction. Des modèles métaboliques de base ont été utilisés pour mieux comprendre le métabolisme central du carbone chez les micro-organismes photosynthétiques. Plus récemment, des modèles complets à l'échelle du génome, y compris des informations spécifiques aux organites à haute résolution, ont été développés pour acquérir de nouvelles connaissances sur le métabolisme des usines de cellules phototrophes. Ici, nous passons en revue l'état actuel de l'art de la modélisation basée sur les contraintes et du développement de méthodes informatiques et discutons de la façon dont les modèles avancés ont conduit à une précision de prédiction accrue et donc à une production améliorée de lipides chez les microalgues. La producción de biocombustibles y precursores de bioenergía por microorganismos fototróficos, como microalgas y cianobacterias, es una alternativa prometedora a los combustibles convencionales obtenidos a partir de recursos no renovables. Se han investigado varias especies de microalgas como posibles candidatas para la producción de biocombustibles, en su mayor parte debido a su excepcional capacidad metabólica para acumular grandes cantidades de lípidos. Se ha implementado el modelado basado en restricciones, un enfoque de biología de sistemas que predice con precisión el fenotipo metabólico de los fotótrofos, para identificar las condiciones de cultivo adecuadas, así como para explorar estrategias de mejora genética para la bioproducción. Se emplearon modelos metabólicos básicos para obtener información sobre el metabolismo central del carbono en microorganismos fotosintéticos. Más recientemente, se han desarrollado modelos exhaustivos a escala genómica, que incluyen información específica de orgánulos en alta resolución, para obtener nuevos conocimientos sobre el metabolismo de las fábricas de células fototróficas. Aquí, revisamos el estado actual del arte del modelado basado en restricciones y el desarrollo de métodos computacionales y discutimos cómo los modelos avanzados condujeron a una mayor precisión de predicción y, por lo tanto, a una mejor producción de lípidos en las microalgas. Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae. يعد إنتاج الوقود الحيوي وسلائف الطاقة الحيوية بواسطة الكائنات الحية الدقيقة ذات التغذية الضوئية، مثل الطحالب الدقيقة والبكتيريا الزرقاء، بديلاً واعداً للوقود التقليدي الذي يتم الحصول عليه من الموارد غير المتجددة. تم التحقيق في العديد من أنواع الطحالب الدقيقة كمرشحين محتملين لإنتاج الوقود الحيوي، ويرجع ذلك في الغالب إلى قدرتها الأيضية الاستثنائية على تجميع كميات كبيرة من الدهون. تم نشر النمذجة القائمة على القيود، وهي نهج بيولوجي للأنظمة يتنبأ بدقة بالنمط الظاهري الأيضي للضوئيات، لتحديد ظروف الاستزراع المناسبة وكذلك لاستكشاف استراتيجيات التعزيز الوراثي للإنتاج الحيوي. تم استخدام نماذج التمثيل الغذائي الأساسية لاكتساب نظرة ثاقبة على استقلاب الكربون المركزي في الكائنات الحية الدقيقة الضوئية. في الآونة الأخيرة، تم تطوير نماذج شاملة على نطاق الجينوم، بما في ذلك المعلومات الخاصة بالعضيات بدقة عالية، لاكتساب رؤية جديدة في استقلاب مصانع الخلايا الضوئية. هنا، نستعرض الحالة الراهنة لفن النمذجة القائمة على القيود وتطوير الطريقة الحسابية ونناقش كيف أدت النماذج المتقدمة إلى زيادة دقة التنبؤ وبالتالي تحسين إنتاج الدهون في الطحالب الدقيقة.

Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

Microbial electrosynthesis (MES) is a process that uses electricity as an energy source for driving the production of chemicals and fuels using microorganisms and CO2 or organics as carbon sources. The development of this highly interdisciplinary technology on the interface between biotechnology and electrochemistry requires knowledge and expertise in a variety of scientific and technical areas. The rational development and commercialization of MES can be achieved at a faster pace if the research data and findings are reported in appropriate and uniformly accepted ways. Here we provide a framework for reporting on MES research and propose several pivotal performance indicators to describe these processes. Linked to this study is an online tool to perform necessary calculations and identify data gaps. A key consideration is the calculation of effective energy expenditure per unit product in a manner enabling cross comparison of studies irrespective of reactor design. We anticipate that the information provided here on different aspects of MES ranging from reactor and process parameters to chemical, electrochemical, and microbial functionality indicators will assist researchers in data presentation and ease data interpretation. Furthermore, a discussion on secondary MES aspects such as downstream processing, process economics and life cycle analysis is included.