Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Correction: StanDep: Capturing transcriptomic variability improves context-specific metabolic models.

[This corrects the article DOI: 10.1371/journal.pcbi.1007764.].

What are housekeeping genes?

The concept of "housekeeping gene" has been used for four decades but remains loosely defined. Housekeeping genes are commonly described as "essential for cellular existence regardless of their specific function in the tissue or organism", and "stably expressed irrespective of tissue type, developmental stage, cell cycle state, or external signal". However, experimental support for the tenet that gene essentiality is linked to stable expression across cell types, conditions, and organisms has been limited. Here we use genome-scale functional genomic screens together with bulk and single-cell sequencing technologies to test this link and optimize a quantitative and experimentally validated definition of housekeeping gene. Using the optimized definition, we identify, characterize, and provide as resources, housekeeping gene lists extracted from several human datasets, and 10 other animal species that include primates, chicken, and C. elegans. We find that stably expressed genes are not necessarily essential, and that the individual genes that are essential and stably expressed can considerably differ across organisms; yet the pathways enriched among these genes are conserved. Further, the level of conservation of housekeeping genes across the analyzed organisms captures their taxonomic groups, showing evolutionary relevance for our definition. Therefore, we present a quantitative and experimentally supported definition of housekeeping genes that can contribute to better understanding of their unique biological and evolutionary characteristics.

Inferring a spatial code of cell-cell interactions across a whole animal body.

Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.

A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes.

Media and feed optimization have fueled many-fold improvements in mammalian biopharmaceutical production, but genome editing offers an emerging avenue for further enhancing cell metabolism and bioproduction. However, the complexity of metabolism, involving thousands of genes, makes it unclear which engineering strategies will result in desired traits. Here we present a comprehensive pooled CRISPR screen for CHO cell metabolism, including ~16,000 gRNAs against ~2500 metabolic enzymes and regulators. Using this screen, we identified a glutamine response network in CHO cells. Glutamine is particularly important since it is often over-fed to drive increased TCA cycle flux, but toxic ammonia may accumulate. With the screen we found one orphan glutamine-responsive gene with no clear connection to our network. Knockout of this novel and poorly characterized lipase, Abhd11, substantially increased growth in glutamine-free media by altering the regulation of the TCA cycle. Thus, the screen provides an invaluable targeted platform to comprehensively study genes involved in any metabolic trait, and elucidate novel regulators of metabolism.

StanDep: Capturing transcriptomic variability improves context-specific metabolic models.

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep.

Assessing key decisions for transcriptomic data integration in biochemical networks.

To gain insights into complex biological processes, genome-scale data (e.g., RNA-Seq) are often overlaid on biochemical networks. However, many networks do not have a one-to-one relationship between genes and network edges, due to the existence of isozymes and protein complexes. Therefore, decisions must be made on how to overlay data onto networks. For example, for metabolic networks, these decisions include (1) how to integrate gene expression levels using gene-protein-reaction rules, (2) the approach used for selection of thresholds on expression data to consider the associated gene as "active", and (3) the order in which these steps are imposed. However, the influence of these decisions has not been systematically tested. We compared 20 decision combinations using a transcriptomic dataset across 32 tissues and showed that definition of which reaction may be considered as active (i.e., reactions of the genome-scale metabolic network with a non-zero expression level after overlaying the data) is mainly influenced by thresholding approach used. To determine the most appropriate decisions, we evaluated how these decisions impact the acquisition of tissue-specific active reaction lists that recapitulate organ-system tissue groups. These results will provide guidelines to improve data analyses with biochemical networks and facilitate the construction of context-specific metabolic models.

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

Dietary serine-microbiota interaction enhances chemotherapeutic toxicity without altering drug conversion.

The gut microbiota metabolizes drugs and alters their efficacy and toxicity. Diet alters drugs, the metabolism of the microbiota, and the host. However, whether diet-triggered metabolic changes in the microbiota can alter drug responses in the host has been largely unexplored. Here we show that dietary thymidine and serine enhance 5-fluoro 2'deoxyuridine (FUdR) toxicity in C. elegans through different microbial mechanisms. Thymidine promotes microbial conversion of the prodrug FUdR into toxic 5-fluorouridine-5'-monophosphate (FUMP), leading to enhanced host death associated with mitochondrial RNA and DNA depletion, and lethal activation of autophagy. By contrast, serine does not alter FUdR metabolism. Instead, serine alters E. coli's 1C-metabolism, reduces the provision of nucleotides to the host, and exacerbates DNA toxicity and host death without mitochondrial RNA or DNA depletion; moreover, autophagy promotes survival in this condition. This work implies that diet-microbe interactions can alter the host response to drugs without altering the drug or the host.

Comparative Evaluation of the Remineralizing Potential of Commercially Available Agents on Artificially Demineralized Human Enamel: An In vitro Study.

BACKGROUND: Caries is highly prevalent multifactorial disease, but its progression can be prevented in the initial stage of demineralization through remineralization (RML). Various materials have been proposed for the same, successful outcome can prove to be a boon in the prevention of caries. AIM: The aim of the study is to assess the RML potential of four commercially available agents so as to restore the enamel closest to its previous microhardness levels. MATERIALS AND METHODS: Sixty permanent intact premolars were randomly divided into six groups: Four test groups - (1) bioactive glass (BAG) Novamin (SHY-NM), (2) nano-hydroxyapatite (nHAp) (Acclaim), (3) functionalized tricalcium phosphate (f-TCP) (Clinpro Tooth Crème), and (4) grape seed extract (GSE); one positive control - (5) fluoride (1000 ppm) containing dentifrice (Colgate Calci-Lock); and one negative control - (6) distilled water. The samples were initially evaluated for baseline surface microhardness (SMH); later on, these samples were placed in the demineralizing solution for 48 h in an incubator at 37°C, and postdemineralization again SMH was measured. Thereafter, the samples were subjected to the pH cycling for consecutive 21 days, and SMH was recorded. The SMH was evaluated using a Vickers microhardness tester. Statistical analysis was done using a post hoc Tukey test for each group based on the stage of treatment and one-way ANOVA for comparison among different groups. RESULTS: BAG Novamin showed SMH recovery at 96.75% followed by f-TCP at 95.83%, nHAp at 90.88%, and GSE at 48.71%. Statistically significant differences were observed between the first three groups and the rest of the groups after RML stage. CONCLUSION: BAG Novamin, f-TCP, and nHAp showed considerable RML followed to a lesser extent by GSE.

Accuracy of the integrated electronic apex locator in locating simulated perforation under various irrigating solutions in an in vitro study

Aim: This study's objective was to assess the accuracy of the integrated apex locator in identifying artificial root canal perforations in the presence of saline, chlorhexidine, sodium hypochlorite, QMix, and MTAD. Methods: The root canals of 60 single-rooted extracted human teeth were perforated artificially at a point 10 mm away from the root apex. After measuring the actual lengths up to the perforation point, the teeth were then put within an alginate mould for measurements using an integrated apex locator. Using a #20 K-file in the presence of NaCl, CHX, MTAD, NaOCl, and QMix, an electronic apex locator was used to measure the perforations electronically in accordance with the manufacturer's instructions. Between the measurements, each canal was dried with paper points after being irrigated with distilled water. The accuracy of all the readings was calculated at ± 0.5 mm. Statistical analyses were performed using the Z-test. Results: In comparison to the MTAD, NaOCl, and Qmix, saline and chlorhexidine scored more readings in the ± 0.5 mm range of the perforation site, and the difference was statistically significant. Conclusion: The most precise electronic measurements of artificial perforation were obtained in the presence of chlorhexidine or saline

Comparative Evaluation of Treatment of Localized Gingival Recessions with Coronally Advanced Flap Using Microsurgical and Conventional Techniques.

BACKGROUND: Coverage of gingival recession is a very precision-oriented procedure. Employment of operating microscope has proved to be a boon in various surgical procedures and therefore can have positive benefits on the outcome of a procedure. AIM: The aim of this study is to find out whether the use of an operating microscope in the surgical treatment of Millers Class I and Class II gingival recession defects could improve the outcome in terms of root coverage and final tissue appearance compared to those done by the conventional technique. MATERIALS AND METHODS: This clinical study was carried out on ten patients with the presence of bilateral isolated gingival recession classified as Miller's Class I or Class II recession defect. The split-mouth design was used where coronally advanced flap with the placement of platelet-rich fibrin was done in defects in test (microsurgical) and control (conventional) groups. Various clinical parameters were recorded at baseline and then postoperatively at 3-months and 6-month intervals. RESULTS: The visual analog scale scores showed a statistically significant difference between scores while all other parameters had no statistically significant difference in intergroup comparison after 3 and 6 months. CONCLUSION: While microscope permitted less traumatic and minimally invasive procedure, both groups showed convincing improvement in clinical parameters.

Modeling Meets Metabolomics-The WormJam Consensus Model as Basis for Metabolic Studies in the Model Organism Caenorhabditis elegans.

Metabolism is one of the attributes of life and supplies energy and building blocks to organisms. Therefore, understanding metabolism is crucial for the understanding of complex biological phenomena. Despite having been in the focus of research for centuries, our picture of metabolism is still incomplete. Metabolomics, the systematic analysis of all small molecules in a biological system, aims to close this gap. In order to facilitate such investigations a blueprint of the metabolic network is required. Recently, several metabolic network reconstructions for the model organism Caenorhabditis elegans have been published, each having unique features. We have established the WormJam Community to merge and reconcile these (and other unpublished models) into a single consensus metabolic reconstruction. In a series of workshops and annotation seminars this model was refined with manual correction of incorrect assignments, metabolite structure and identifier curation as well as addition of new pathways. The WormJam consensus metabolic reconstruction represents a rich data source not only for in silico network-based approaches like flux balance analysis, but also for metabolomics, as it includes a database of metabolites present in C. elegans, which can be used for annotation. Here we present the process of model merging, correction and curation and give a detailed overview of the model. In the future it is intended to expand the model toward different tissues and put special emphasizes on lipid metabolism and secondary metabolism including ascaroside metabolism in accordance to their central role in C. elegans physiology.

C-shaped canal in maxillary first molars: a case report.

C-shaped configuration in the upper maillary first molar is an extremely rare appearance (0.12%). This case reports management of the tooth with such a configuration as well as depiction of its internal morpholgy and external morphology through spiral computed tomography and dentascan in the contralateral tooth with similar morphology. After careful clinical observation and confirmation through spiral computed tomography, it was concluded that the teeth had Melton category I configuration with fused roots.

Epistatic interactions among metabolic genes depend upon environmental conditions.

When the effect of the state of one gene is dependent on the state of another gene in more than an additive or a neutral way, the phenomenon is termed epistasis. In particular, positive epistasis signifies that the impact of the double deletion is less severe than the neutral combination, while negative epistasis signifies that the double deletion is more severe. Epistatic interactions between genes affect the fitness landscape of an organism in its environment and are believed to be important for the evolution of sex and the evolution of recombination. Here we use large-scale computational metabolic models of microorganisms to study epistasis computationally using Flux Balance Analysis (FBA). We study what the effects of the environment are on epistatic interactions between metabolic genes in three different microorganisms: the model bacterium E. coli, the cyanobacteria Synechocystis PCC6803 and the model green algae, C. reinhardtii. Prior studies have shown that under standard laboratory conditions epistatic interactions between metabolic genes are dominated by positive epistasis. We show here that epistatic interactions depend strongly upon environmental conditions, i.e. the source of carbon, the carbon/oxygen ratio, and for photosynthetic organisms, the intensity of light. By a comparative analysis of flux distributions under different conditions, we show that whether epistatic interactions are positive or negative depends upon the topology of the carbon flow between the reactions affected by the pair of genes being considered. Thus complex metabolic networks can show epistasis even without explicit interactions between genes, and the direction and the scale of epistasis are dependent on network flows. Our results suggest that the path of evolutionary adaptation in fluctuating environments is likely to be very history dependent because of the strong effect of the environment on epistasis.

Comparative evaluation of NovaMin desensitizer and Gluma desensitizer on dentinal tubule occlusion: a scanning electron microscopic study.

PURPOSE: In this study, the effect of calcium sodium phosphosilicate (NovaMin) desensitizing agent, which is a powder-based system, and hydroxyethyl methacrylate and glutaraldehyde (Gluma desensitizer), which is liquid-based system, on dentinal tubule occlusion was analyzed by scanning electron microscope. The effects of the above two along with one control group were compared to determine the more effective method of sealing the dentinal tubules after initial application. METHODS: Twenty specimens were allocated to each of 3 groups: Control, Gluma desensitizer, and NovaMin. Two additional samples were also prepared and treated with Gluma and NovaMin; these samples were longitudinally fractured. The specimens were prepared from extracted sound human premolars and were stored in 10% formalin at room temperature. The teeth were cleaned of gross debris and then sectioned to provide one to two dentin specimens. The dentin specimens were etched with 6% citric acid for 2 minutes and rinsed in distilled water. Control discs were dried, and the test discs were treated with the desensitizing agents as per the manufacturer's instructions. The discs as well as longitudinal sections were later analyzed under the scanning electron microscope. The proportions of completely occluded, partially occluded, and open tubules within each group were calculated. The ratios of completely and partially occluded tubules to the total tubules for all the groups was determined, and the data was statistically analyzed using nonparametric tests and statistical significance was calculated. RESULTS: NovaMin showed more completely occluded tubules (0.545±0.051) while Gluma desensitizer showed more partially occluded tubules (0.532±0.075). The differences among all the groups were statistically significant (P≤ 0.05). CONCLUSION: Both materials were effective in occluding dentinal tubules but NovaMin appeared more promising in occluding tubules completely after initial application.