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.

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.

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.

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.

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.

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).

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).

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.

DECIPHER, a search-based approach to chimera identification for 16S rRNA sequences.

DECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchime's performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (<0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (http://DECIPHER.cee.wisc.edu).

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.

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.

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.

Systematic evaluation of single mismatch stability predictors for fluorescence in situ hybridization.

The mismatch discrimination potential of probes in fluorescence in situ hybridization can be defined as the difference between the melting formamide points of perfect complementary and mismatched duplexes (Delta[FA](m)). Using a combined experimental and theoretical approach, Delta[FA](m) was determined for a set of 35 mismatched probes targeting seven locations in the 16S rRNA of Escherichia coli. The mismatches were created by changing single nucleotides on the probes, while maintaining the target unmodified. Estimated Delta[FA](m) values were used to systematically evaluate four predictors of mismatch stability: weighted mismatch (WM) scores from the software arb, published statistical summary of microarray hybridizations, free energy of mismatch stability (DeltaDeltaG degrees (1)) and theoretical Delta[FA](m) estimations obtained with a thermodynamic model. Based on the predictors' ability to explain variability in Delta[FA](m) and to discriminate weak mismatches from strong ones, DeltaDeltaG degrees (1) and WM scores from arb (with an updated set of relative strength parameters) were demonstrated to be adequate estimators of mismatch stability, with DeltaDeltaG degrees (1) offering the benefit of capturing the variability associated with nearest-neighbour effects and being compatible with thermodynamic models of in situ hybridization. The use of DeltaDeltaG degrees (1) and WM in probe design was illustrated as a tool that complements experimental design approaches.

Quantification of target molecules needed to detect microorganisms by fluorescence in situ hybridization (FISH) and catalyzed reporter deposition-FISH.

Fluorescence in situ hybridization (FISH) with rRNA-targeted oligonucleotide probes is a method that is widely used to detect and quantify microorganisms in environmental samples and medical specimens by fluorescence microscopy. Difficulties with FISH arise if the rRNA content of the probe target organisms is low, causing dim fluorescence signals that are not detectable against the background fluorescence. This limitation is ameliorated by technical modifications such as catalyzed reporter deposition (CARD)-FISH, but the minimal numbers of rRNA copies needed to obtain a visible signal of a microbial cell after FISH or CARD-FISH have not been determined previously. In this study, a novel competitive FISH approach was developed and used to determine, based on a thermodynamic model of probe competition, the numbers of 16S rRNA copies per cell required to detect bacteria by FISH and CARD-FISH with oligonucleotide probes in mixed pure cultures and in activated sludge. The detection limits of conventional FISH with Cy3-labeled probe EUB338-I were found to be 370 +/- 45 16S rRNA molecules per cell for Escherichia coli hybridized on glass microscope slides and 1,400 +/- 170 16S rRNA copies per E. coli cell in activated sludge. For CARD-FISH the values ranged from 8.9 +/- 1.5 to 14 +/- 2 and from 36 +/- 6 to 54 +/- 7 16S rRNA molecules per cell, respectively, indicating that the sensitivity of CARD-FISH was 26- to 41-fold higher than that of conventional FISH. These results suggest that optimized FISH protocols using oligonucleotide probes could be suitable for more recent applications of FISH (for example, to detect mRNA in situ in microbial cells).

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.