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).

SUMMARY 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 if 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.

SUMMARY 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.

Summary 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 (www.wormflux.umassmed.edu), and will enable the unraveling of the mechanisms by which different macro- and micronutrients contribute to the animal's physiology.

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 (⌬G°o verall ) 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 ⌬G°o verall 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 ⌬G°o verall of ؊13.0 kcal/mol was proposed as a goal in the design of FISH probes to maximize hybridization efficiency without compromising specificity.Fluorescent in situ hybridization (FISH) has proven to be a powerful molecular method for identification, visualization, and quantification of organisms of interest in microbial communities (reviewed in reference 2). Originally introduced by DeLong et al. (11), the use of FISH expanded to the study of bacterial populations in environmental applications (for reviews, see references 1, 2, and 34) and medical applications (22). Despite the wide use of FISH for more than a decade, the design and use of FISH probes remain a highly empirical procedure. Since the rRNA primary chains contain regions of variable conservation, it is straightforward to determine a target site on the rRNA with the desired level of specificity based on sequence comparisons (1, 34). However, a limitation that is difficult to overcome is the inability to predict if the signal intensity of cells hybridized with the fluorophore-labeled probe is high enough to be detectable (2,13,34).Low fluorescent responses in hybridized samples can be related to a variety of factors, such as low ribosome content of cells, difficulty in permeating cell walls, and the inaccessibility of target sites due to either the secondary and tertiary structures of the rRNA (i.e., RNA-RNA interactions) or the effect of ribosomal proteins (i.e., RNA-protein interactions) (2). The former two depend solely on the studied organism and can be potentially circumvented by changes in the experimental protocol, such as using a different fixative to better permeate the cell wall (e.g., the methods of Rolle...

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.Fluorescence in situ hybridization (FISH) has been used for more than 2 decades in microbial ecology (since the publication of the article by DeLong et al. [5] in 1989) for the detection of whole cells of interest in environmental samples. Microbial ecological applications of FISH generally target rRNA as the phylogenetic marker and rely on the specific hybridization of labeled synthetic DNA probes with this complex molecule inside fixed cells. FISH offers unparalleled capabilities such as the quantification of target groups of organisms and visualization of their spatial distribution.The main challenge in FISH applications is the optimization of sensitivity and specificity during probe design (23). Throughout the years, several experimental techniques have been developed for the optimization of probe performance, such as the use of formamide for mismatch discrimination (13,19), the addition of unlabeled competitor oligonucleotides for improving mismatch discrimination (13), the empirical assessment of target accessibility (2, 7), the use of unlabeled helper oligonucleotides for improving accessibility (6), the amplification of signal using enzyme-labeled probes (catalyzed reporter deposition [CARD]-FISH) (16), and the use of cloned target for the determination of the formamide denaturation profiles (Clone-FISH) (18). Although highly useful, these tools have increased the complexity of probe design. In addition to the need for trial and error, experimental approaches without theoretical guidelines are limited by the physical availability of either organisms or clones belonging to target and nontarget microbial groups, making optimization cumbersome in some cases (24).There is a plethora of literature on the universal physicochemical properties of nucleic acid hybridizations (e.g., see the review by Turner [22]), which can potentially help the prediction of probe performance in FISH. To facilitate the use of this knowledge in FISH applications, we have recently developed thermodynamics-based mathematical models of FISH (24-26). The models simulate FISH in silico using thermodynamically established parameters that define DNA/RNA interactions between the probe and targeted rRNA, DNA/DNA interactions within the probe, and RNA/RNA interactions within the rRNA.Having been tested with model organisms (24-27), the mathematical models are expected to facilitate probe design. The models could potentially reduce the number of cycles of trial and error to reach the right probe and optimal hybridization conditions, provide a basis for evaluating the confidence level in experimental results (24), and serve as a subst...

Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue‐relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single‐cell RNA‐sequencing (scRNA‐seq) data from the nematode Caenorhabditis elegans. Our analysis recapitulated known tissue functions in C. elegans, captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA‐seq continues to provide higher‐resolution gene expression data.

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