iRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network

<p>Abstract</p> <p>Background</p> <p><it>Rhodobacter sphaeroides </it>is one of the best studied purple non-sulfur photosynthetic bacteria and serves as an excellent model for the study of photosynthesis and the metabolic capabilities of this and related facultative organisms. The ability of <it>R. sphaeroides </it>to produce hydrogen (H₂), polyhydroxybutyrate (PHB) or other hydrocarbons, as well as its ability to utilize atmospheric carbon dioxide (CO₂) as a carbon source under defined conditions, make it an excellent candidate for use in a wide variety of biotechnological applications. A genome-level understanding of its metabolic capabilities should help realize this biotechnological potential.</p> <p>Results</p> <p>Here we present a genome-scale metabolic network model for <it>R. sphaeroides </it>strain 2.4.1, designated iRsp1095, consisting of 1,095 genes, 796 metabolites and 1158 reactions, including <it>R. sphaeroides</it>-specific biomass reactions developed in this study. Constraint-based analysis showed that iRsp1095 agreed well with experimental observations when modeling growth under respiratory and phototrophic conditions. Genes essential for phototrophic growth were predicted by single gene deletion analysis. During pathway-level analyses of <it>R. sphaeroides </it>metabolism, an alternative route for CO₂assimilation was identified. Evaluation of photoheterotrophic H₂production using iRsp1095 indicated that maximal yield would be obtained from growing cells, with this predicted maximum ~50% higher than that observed experimentally from wild type cells. Competing pathways that might prevent the achievement of this theoretical maximum were identified to guide future genetic studies.</p> <p>Conclusions</p> <p>iRsp1095 provides a robust framework for future metabolic engineering efforts to optimize the solar- and nutrient-powered production of biofuels and other valuable products by <it>R. sphaeroides </it>and closely related organisms.</p

A Persistence Detector for Metabolic Network Rewiring in an Animal

Biological systems must possess mechanisms that prevent inappropriate responses to spurious environmental inputs. Caenorhabditis elegans has two breakdown pathways for the short-chain fatty acid propionate: a canonical, vitamin B12-dependent pathway and a propionate shunt that is used when vitamin B12 levels are low. The shunt pathway is kept off when there is sufficient flux through the canonical pathway, likely to avoid generating shunt-specific toxic intermediates. Here, we discovered a transcriptional regulatory circuit that activates shunt gene expression upon propionate buildup. Nuclear hormone receptor 10 (NHR-10) and NHR-68 function together as a persistence detector in a type 1, coherent feed-forward loop with an AND-logic gate to delay shunt activation upon propionate accumulation and to avoid spurious shunt activation in response to a non-sustained pulse of propionate. Together, our findings identify a persistence detector in an animal, which transcriptionally rewires propionate metabolism to maintain homeostasis

WormPaths: Caenorhabditis elegans metabolic pathway annotation and visualization [preprint]

In our group, we aim to understand metabolism in the nematode Caenorhabditis elegans and its relationships with gene expression, physiology and the response to therapeutic drugs. On March 15, 2020, a stay-at-home order was put into effect in the state of Massachusetts, USA, to flatten the curve of the spread of the novel SARS-CoV2 virus that causes COVID-19. For biomedical researchers in our state, this meant putting a hold on experiments for nine weeks until May 18, 2020. To keep the lab engaged and productive, and to enhance communication and collaboration, we embarked on an in-lab project that we all found important but that we never had the time for: the detailed annotation and drawing of C. elegans metabolic pathways. As a result, we present WormPaths, which is composed of two parts: 1) the careful manual annotation of metabolic genes into pathways, categories and levels, and 2) 66 pathway maps that include metabolites, metabolite structures, genes, reactions, and pathway connections between maps. These maps are available on our WormFlux website. We show that WormPaths provides easy-to-navigate maps and that the different levels in WormPaths can be used for metabolic pathway enrichment analysis of transcriptomic data. In the unfortunate event of additional lockdowns, we envision further developing these maps to be more interactive, with an analogy of road maps that are available on mobile devices

Mechanistic Approach to the Problem of Hybridization Efficiency in Fluorescent In Situ Hybridization

In fluorescent in situ hybridization (FISH), the efficiency of hybridization between the DNA probe and the rRNA has been related to the accessibility of the rRNA when ribosome content and cell permeability are not limiting. Published rRNA accessibility maps show that probe brightness is sensitive to the organism being hybridized and the exact location of the target site and, hence, it is highly unpredictable based on accessibility only. In this study, a model of FISH based on the thermodynamics of nucleic acid hybridization was developed. The model provides a mechanistic approach to calculate the affinity of the probe to the target site, which is defined as the overall Gibbs free energy change (ΔG°(overall)) for a reaction scheme involving the DNA-rRNA and intramolecular DNA and rRNA interactions that take place during FISH. Probe data sets for the published accessibility maps and experiments targeting localized regions in the 16S rRNA of Escherichia coli were used to demonstrate that ΔG°(overall) is a strong predictor of hybridization efficiency and superior to conventional estimates based on the dissociation temperature of the DNA/rRNA duplex. The use of the proposed model also allowed the development of mechanistic approaches to increase probe brightness, even in seemingly inaccessible regions of the 16S rRNA. Finally, a threshold ΔG°(overall) of −13.0 kcal/mol was proposed as a goal in the design of FISH probes to maximize hybridization efficiency without compromising specificity

A Caenorhabditis elegans Genome-Scale Metabolic Network Model

Caenorhabditis elegans is a powerful model to study metabolism and how it relates to nutrition, gene expression, and life history traits. However, while numerous experimental techniques that enable perturbation of its diet and gene function are available, a high-quality metabolic network model has been lacking. Here, we reconstruct an initial version of the C. elegans metabolic network. This network model contains 1,273 genes, 623 enzymes, and 1,985 metabolic reactions and is referred to as iCEL1273. Using flux balance analysis, we show that iCEL1273 is capable of representing the conversion of bacterial biomass into C. elegans biomass during growth and enables the predictions of gene essentiality and other phenotypes. In addition, we demonstrate that gene expression data can be integrated with the model by comparing metabolic rewiring in dauer animals versus growing larvae. iCEL1273 is available at a dedicated website (wormflux.umassmed.edu) and will enable the unraveling of the mechanisms by which different macro- and micronutrients contribute to the animal\u27s physiology

Worms, bacteria, and micronutrients: an elegant model of our diet

Micronutrients are required in small proportions in a diet to carry out key metabolic roles for biomass and energy production. Humans receive micronutrients either directly from their diet or from gut microbiota that metabolize other nutrients. The nematode Caenorhabditis elegans and its bacterial diet provide a relatively simple and genetically tractable model to study both direct and microbe-mediated effects of micronutrients. Recently, this model has been used to gain insight into the relationship between micronutrients, physiology, and metabolism. In particular, two B-type vitamins, vitamin B12 and folate, have been studied in detail. Here we review how C. elegans and its bacterial diet provide a powerful interspecies systems biology model that facilitates the precise delineation of micronutrient effects and the mechanisms involved

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions

Understanding Metabolic Regulation at a Systems Level: Metabolite Sensing, Mathematical Predictions, and Model Organisms

Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth of transcriptomic, proteomic, and metabolomic data available for different cell types across various conditions, we are challenged with understanding global metabolic network regulation and the resulting metabolic outputs. Stoichiometric metabolic network modeling integrated with omics data has addressed this challenge by generating nonintuitive, testable hypotheses about metabolic flux rewiring. Model organism studies have also yielded novel insight into metabolic networks. This review covers three topics: the feedback loops inherent in metabolic regulatory networks, metabolic network modeling, and interspecies studies utilizing Caenorhabditis elegans and various bacterial diets that have revealed novel metabolic paradigms

mathFISH, a Web Tool That Uses Thermodynamics-Based Mathematical Models for In Silico Evaluation of Oligonucleotide Probes for Fluorescence In Situ Hybridization▿ †

Mathematical models of RNA-targeted fluorescence in situ hybridization (FISH) for perfectly matched and mismatched probe/target pairs are organized and automated in web-based mathFISH (http://mathfish.cee.wisc.edu). Offering the users up-to-date knowledge of hybridization thermodynamics within a theoretical framework, mathFISH is expected to maximize the probability of success during oligonucleotide probe design