A D-2-hydroxyglutarate dehydrogenase mutant reveals a critical role for ketone body metabolism in Caenorhabditis elegans development.

In humans, mutations in D-2-hydroxyglutarate (D-2HG) dehydrogenase (D2HGDH) result in D-2HG accumulation, delayed development, seizures, and ataxia. While the mechanisms of 2HG-associated diseases have been studied extensively, the endogenous metabolism of D-2HG remains unclear in any organism. Here, we find that, in Caenorhabditis elegans, D-2HG is produced in the propionate shunt, which is transcriptionally activated when flux through the canonical, vitamin B12-dependent propionate breakdown pathway is perturbed. Loss of the D2HGDH ortholog, dhgd-1, results in embryonic lethality, mitochondrial defects, and the up-regulation of ketone body metabolism genes. Viability can be rescued by RNAi of hphd-1, which encodes the enzyme that produces D-2HG or by supplementing either vitamin B12 or the ketone bodies 3-hydroxybutyrate (3HB) and acetoacetate (AA). Altogether, our findings support a model in which C. elegans relies on ketone bodies for energy when vitamin B12 levels are low and in which a loss of dhgd-1 causes lethality by limiting ketone body production.

Systems-level transcriptional regulation of Caenorhabditis elegans metabolism.

Metabolism is controlled to ensure organismal development and homeostasis. Several mechanisms regulate metabolism, including allosteric control and transcriptional regulation of metabolic enzymes and transporters. So far, metabolism regulation has mostly been described for individual genes and pathways, and the extent of transcriptional regulation of the entire metabolic network remains largely unknown. Here, we find that three-quarters of all metabolic genes are transcriptionally regulated in the nematode Caenorhabditis elegans. We find that many annotated metabolic pathways are coexpressed, and we use gene expression data and the iCEL1314 metabolic network model to define coregulated subpathways in an unbiased manner. Using a large gene expression compendium, we determine the conditions where subpathways exhibit strong coexpression. Finally, we develop "WormClust," a web application that enables a gene-by-gene query of genes to view their association with metabolic (sub)-pathways. Overall, this study sheds light on the ubiquity of transcriptional regulation of metabolism and provides a blueprint for similar studies in other organisms, including humans.

C. elegans as a model for inter-individual variation in metabolism.

Individuals can exhibit differences in metabolism that are caused by the interplay of genetic background, nutritional input, microbiota and other environmental factors1-4. It is difficult to connect differences in metabolism to genomic variation and derive underlying molecular mechanisms in humans, owing to differences in diet and lifestyle, among others. Here we use the nematode Caenorhabditis elegans as a model to study inter-individual variation in metabolism. By comparing three wild strains and the commonly used N2 laboratory strain, we find differences in the abundances of both known metabolites and those that have not to our knowledge been previously described. The latter metabolites include conjugates between 3-hydroxypropionate (3HP) and several amino acids (3HP-AAs), which are much higher in abundance in one of the wild strains. 3HP is an intermediate in the propionate shunt pathway, which is activated when flux through the canonical, vitamin-B12-dependent propionate breakdown pathway is perturbed5. We show that increased accumulation of 3HP-AAs is caused by genetic variation in HPHD-1, for which 3HP is a substrate. Our results suggest that the production of 3HP-AAs represents a 'shunt-within-a-shunt' pathway to accommodate a reduction-of-function allele in hphd-1. This study provides a step towards the development of metabolic network models that capture individual-specific differences of metabolism and more closely represent the diversity that is found in entire species.

Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective.

In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules the organism needs to carrying out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.

WormPaths: Caenorhabditis elegans metabolic pathway annotation and visualization.

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. Visualization of the metabolic pathways that comprise the metabolic network is extremely useful for interpreting a wide variety of experiments. Detailed annotated metabolic pathway maps for C. elegans are mostly limited to pan-organismal maps, many with incomplete or inaccurate pathway and enzyme annotations. Here, we present WormPaths, which is composed of two parts: (1) the careful manual annotation of metabolic genes into pathways, categories, and levels, and (2) 62 pathway maps that include metabolites, metabolite structures, genes, reactions, and pathway connections between maps. These maps are available on the 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 future, we envision further developing these maps to be more interactive, analogous to road maps that are available on mobile devices.

WormPaths: Caenorhabditis elegans metabolic pathway annotation and visualization.

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.

A Delicate Balance between Bacterial Iron and Reactive Oxygen Species Supports Optimal C. elegans Development.

Iron is an essential micronutrient for all forms of life; low levels of iron cause human disease, while too much iron is toxic. Low iron levels induce reactive oxygen species (ROS) by disruption of the heme and iron-sulfur cluster-dependent electron transport chain (ETC). To identify bacterial metabolites that affect development, we screened the Keio Escherichia coli collection and uncovered 244 gene deletion mutants that slow Caenorhabditis elegans development. Several of these genes encode members of the ETC cytochrome bo oxidase complex, as well as iron importers. Surprisingly, either iron or anti-oxidant supplementation reversed the developmental delay. This suggests that low bacterial iron results in high bacterial ROS and vice versa, which causes oxidative stress in C. elegans that subsequently impairs mitochondrial function and delays development. Our data indicate that the bacterial diets of C. elegans provide precisely tailored amounts of iron to support proper development.

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.

Bacterial Metabolism Affects the C. elegans Response to Cancer Chemotherapeutics.

The human microbiota greatly affects physiology and disease; however, the contribution of bacteria to the response to chemotherapeutic drugs remains poorly understood. Caenorhabditis elegans and its bacterial diet provide a powerful system to study host-bacteria interactions. Here, we use this system to study how bacteria affect the C. elegans response to chemotherapeutics. We find that different bacterial species can increase the response to one drug yet decrease the effect of another. We perform genetic screens in two bacterial species using three chemotherapeutic drugs: 5-fluorouracil (5-FU), 5-fluoro-2'-deoxyuridine (FUDR), and camptothecin (CPT). We find numerous bacterial nucleotide metabolism genes that affect drug efficacy in C. elegans. Surprisingly, we find that 5-FU and FUDR act through bacterial ribonucleotide metabolism to elicit their cytotoxic effects in C. elegans rather than by thymineless death or DNA damage. Our study provides a blueprint for characterizing the role of bacteria in the host response to chemotherapeutics.

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.

Molecular Basis for Differential Patterns of Drug Resistance in Influenza N1 and N2 Neuraminidase.

Neuraminidase (NA) inhibitors are used for the prevention and treatment of influenza A virus infections. Two subtypes of NA, N1 and N2, predominate in viruses that infect humans, but differential patterns of drug resistance have emerged in each subtype despite highly homologous active sites. To understand the molecular basis for the selection of these drug resistance mutations, structural and dynamic analyses on complexes of N1 and N2 NA with substrates and inhibitors were performed. Comparison of dynamic substrate and inhibitor envelopes and interactions at the active site revealed how differential patterns of drug resistance have emerged for specific drug resistance mutations, at residues I222, S246, and H274 in N1 and E119 in N2. Our results show that the differences in intermolecular interactions, especially van der Waals contacts, of the inhibitors versus substrates at the NA active site effectively explain the selection of resistance mutations in the two subtypes. Avoiding such contacts that render inhibitors vulnerable to resistance by better mimicking the dynamics and intermolecular interactions of substrates can lead to the development of novel inhibitors that avoid drug resistance in both subtypes.

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's physiology.

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.

Mathematical tools to optimize the design of oligonucleotide probes and primers.

The identification and quantification of specific organisms in mixed microbial communities often relies on the ability to design oligonucleotide probes and primers with high specificity and sensitivity. The design of these oligonucleotides (or "oligos" for short) shares many of the same principles in spite of their widely divergent applications. Three common molecular biology technologies that require oligonucleotide design are polymerase chain reaction (PCR), fluorescence in situ hybridization (FISH), and DNA microarrays. This article reviews techniques and software available for the design and optimization of oligos with the goal of targeting a specific group of organisms within mixed microbial communities. Strategies for enhancing specificity without compromising sensitivity are described, as well as design tools well suited for this purpose.

Automated design of probes for rRNA-targeted fluorescence in situ hybridization reveals the advantages of using dual probes for accurate identification.

Fluorescence in situ hybridization (FISH) is a common technique for identifying cells in their natural environment and is often used to complement next-generation sequencing approaches as an integral part of the full-cycle rRNA approach. A major challenge in FISH is the design of oligonucleotide probes with high sensitivity and specificity to their target group. The rapidly expanding number of rRNA sequences has increased awareness of the number of potential nontargets for every FISH probe, making the design of new FISH probes challenging using traditional methods. In this study, we conducted a systematic analysis of published probes that revealed that many have insufficient coverage or specificity for their intended target group. Therefore, we developed an improved thermodynamic model of FISH that can be applied at any taxonomic level, used the model to systematically design probes for all recognized genera of bacteria and archaea, and identified potential cross-hybridizations for the selected probes. This analysis resulted in high-specificity probes for 35.6% of the genera when a single probe was used in the absence of competitor probes and for 60.9% when up to two competitor probes were used. Requiring the hybridization of two independent probes for positive identification further increased specificity. In this case, we could design highly specific probe sets for up to 68.5% of the genera without the use of competitor probes and 87.7% when up to two competitor probes were used. The probes designed in this study, as well as tools for designing new probes, are available online (http://DECIPHER.cee.wisc.edu).

Exploiting extension bias in polymerase chain reaction to improve primer specificity in ensembles of nearly identical DNA templates.

We describe a semi-empirical framework that combines thermodynamic models of primer hybridization with experimentally determined elongation biases introduced by 3'-end mismatches for improving polymerase chain reaction (PCR)-based sequence discrimination. The framework enables rational and automatic design of primers for optimal targeting of one or more sequences in ensembles of nearly identical DNA templates. In situations where optimal targeting is not feasible, the framework accurately predicts non-target sequences that are difficult to distinguish with PCR alone. Based on the synergistic effects of disparate sources of PCR bias, we used our framework to robustly distinguish between two alleles that differ by a single base pair. To demonstrate the applicability to environmental microbiology, we designed primers specific to all recognized archaeal and bacterial genera in the Ribosomal Database Project, and have made these primers available online. We applied these primers experimentally to obtain genus-specific amplification of 16S rRNA genes representing minor constituents of an environmental DNA sample. Our results demonstrate that inherent PCR biases can be reliably employed in an automatic fashion to maximize sequence discrimination and accurately identify potential cross-amplifications. We have made our framework accessible online as a programme for designing primers targeting one group of sequences in a set with many other sequences (http://DECIPHER.cee.wisc.edu).

Interspecies systems biology uncovers metabolites affecting C. elegans gene expression and life history traits.

Diet greatly influences gene expression and physiology. In mammals, elucidating the effects and mechanisms of individual nutrients is challenging due to the complexity of both the animal and its diet. Here, we used an interspecies systems biology approach with Caenorhabditis elegans and two of its bacterial diets, Escherichia coli and Comamonas aquatica, to identify metabolites that affect the animal's gene expression and physiology. We identify vitamin B12 as the major dilutable metabolite provided by Comamonas aq. that regulates gene expression, accelerates development, and reduces fertility but does not affect lifespan. We find that vitamin B12 has a dual role in the animal: it affects development and fertility via the methionine/S-Adenosylmethionine (SAM) cycle and breaks down the short-chain fatty acid propionic acid, preventing its toxic buildup. Our interspecies systems biology approach provides a paradigm for understanding complex interactions between diet and physiology.

Modeling formamide denaturation of probe-target hybrids for improved microarray probe design in microbial diagnostics.

Application of high-density microarrays to the diagnostic analysis of microbial communities is challenged by the optimization of oligonucleotide probe sensitivity and specificity, as it is generally unfeasible to experimentally test thousands of probes. This study investigated the adjustment of hybridization stringency using formamide with the idea that sensitivity and specificity can be optimized during probe design if the hybridization efficiency of oligonucleotides with target and non-target molecules can be predicted as a function of formamide concentration. Sigmoidal denaturation profiles were obtained using fluorescently labeled and fragmented 16S rRNA gene amplicon of Escherichia coli as the target with increasing concentrations of formamide in the hybridization buffer. A linear free energy model (LFEM) was developed and microarray-specific nearest neighbor rules were derived. The model simulated formamide melting with a denaturant m-value that increased hybridization free energy (ΔG°) by 0.173 kcal/mol per percent of formamide added (v/v). Using the LFEM and specific probe sets, free energy rules were systematically established to predict the stability of single and double mismatches, including bulged and tandem mismatches. The absolute error in predicting the position of experimental denaturation profiles was less than 5% formamide for more than 90 percent of probes, enabling a practical level of accuracy in probe design. The potential of the modeling approach for probe design and optimization is demonstrated using a dataset including the 16S rRNA gene of Rhodobacter sphaeroides as an additional target molecule. The LFEM and thermodynamic databases were incorporated into a computational tool (ProbeMelt) that is freely available at http://DECIPHER.cee.wisc.edu.

Exploring the in situ accessibility of small subunit ribosomal RNA of members of the domains Bacteria and Eukarya to oligonucleotide probes.

The principle that the small subunit ribosomal RNA (ssu rRNA) is generally accessible to oligonucleotide probes designed to have high thermodynamic affinity was tested with Stenotrophomonas maltophilia, Rhodobacter sphaeroides, Bacillus subtilis, and Saccharomyces cerevisiae. Fluorescein-labeled probes, designed to have ΔG(overall)°=-14±1 and to avoid the potential of nucleobase-specific quenching, were used to target 20 randomly selected sites in each organism. A site was considered accessible if probe brightness was at least 10 times the background signal. With 30-h hybridizations, 71 out of 80 target sites passed the accessibility criterion. Three additional sites were demonstrated to be accessible with either longer hybridizations, which seemed to have a negative effect on some probes, or the addition of formamide to the hybridization buffer. The remaining 6 sites were demonstrated to be accessible by changing the fluorophore to Cy5, slightly modifying probe lengths, using dual-labeled fluorescein probes, or a combination of these approaches. Probe elongations were only needed in 4 probes, indicating a 95% success in correctly predicting ΔG(overall)°, the key parameter for the design of high affinity probes. In addition, 94% of the fluorescein labeled probes yielded bright signals, demonstrating that nucleobase-specific quenching of fluorescein is an important factor affecting probe brightness that can be predicted during probe design. Overall, the results support the principle that with a rational design of probes, it is possible to make most target sites in the ssu rRNA accessible.