Human Metabolic Network Research Articles

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

Understanding the metabolic consequences of human arylsulfatase A deficiency through a computational systems biology study

Background: The nervous system is responsible for the communication between the organism and its environment. This task is possible by the presence of the myelin sheath, which is a double membrane formed by about 75% lipids and 25% proteins. The sulfatide represents one of the main lipids of the myelin band; its degradation is catabolized by the enzyme Arylsulfatase A (ARSA), to generated galactosylceramide. Mutations affecting ARSA function lead to the neurodegenerative disease Metachromatic Leukodystrophy. This disease is characterized by accumulation of sulfatide within the band of myelin affecting its functionality. The biochemical consequences of ARSA deficiency are not well understood yet. Objective and Method: In this paper, we used an in-silico systems-biology approach to model the biochemical consequences of ARSA deficiency within a general human metabolic network (Recon2) and a glia cellular model. Results: We expected that ARSA deficiency mainly affected the glycosphingolipid pathways. However, the results suggest that mitochondrial metabolism and amino acid transport were the main reactions affected within both cellular models. In the glia cell model, it was highlighted the high number of affected reactions of neurotransmitters metabolism, while only a reduced effect was observed in reactions involved in glycosphingolipids metabolism. Conclusion: We hypothesize that ARSA deficiency might lead to metabolic consequences that not only compromise the myelin band or the glycosphingolipids metabolism but also the overall metabolic function of the nervous system. Furthermore, these results offer the bases for the design of in-vitro and in-vivo experiments that allow generating new knowledge of MLD pathophysiology and other neurodegenerative diseases.

Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.

Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients’ survival in an independent cohort than using the canonical human metabolic pathways (C-index = 0.69 vs. 0.52, respectively). Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism.

Understanding the Metabolic Consequences of Human Arylsulfatase A Deficiency through a Computational Systems Biology Study

The nervous system is responsible for the communication between the organism and its environment. This task is possible by the presence of the myelin sheath, which is a double membrane formed by about 75% lipids and 25% proteins. The sulfatide represents one of the main lipids of the myelin band; its degradation is catabolized by the enzyme Arylsulfatase A (ARSA), to generated galactosylceramide. Mutations affecting ARSA function lead to the neurodegenerative disease Metachromatic Leukodystrophy. This disease is characterized by accumulation of sulfatide within the band of myelin affecting its functionality. The biochemical consequences of ARSA deficiency are not well understood yet. In this paper, we used an in-silico systems-biology approach to model the biochemical consequences of ARSA deficiency within a general human metabolic network (Recon2) and a glia cellular model. We expected that ARSA deficiency mainly affected the glycosphingolipid pathways. However, the results suggest that mitochondrial metabolism and amino acid transport were the main reactions affected within both cellular models. In the glia cell model, it was highlighted the high number of affected reactions of neurotransmitters metabolism, while only a reduced effect was observed in reactions involved in glycosphingolipids metabolism. We hypothesize that ARSA deficiency might lead to metabolic consequences that not only compromise the myelin band or the glycosphingolipids metabolism but also the overall metabolic function of the nervous system. Furthermore, these results offer the bases for the design of in-vitro and in-vivo experiments that allow generating new knowledge of MLD pathophysiology and other neurodegenerative diseases.

Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models

The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model.A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa.

Abstract A14: 13C metabolomic and fluxomic study of human B-cell lymphoma metabolic network: Quantifying glutaminolysis and reductive carboxylation

Abstract Intracellular 13C LC-MS and MRS isotopomer data coupled with metabolic modeling allows quantitative determination of metabolic fluxes and other metabolic parameters in vivo/in situ (1). Utilizing these methods, we have determined how an mTOR inhibitor, rapamycin, alters glycolysis, the TCA cycle, the pentose phosphate pathway (PPP) and glutamine metabolism in B-cell lymphoma cell lines. This analysis has revealed previously unknown pharmacological activities for rapamycin. DLCL2 (germinal-center type of diffuse large B-cell lymphoma) and Ramos (Burkitt’s lymphoma resembling activated ABC type of B-cells) cells were incubated in glucose and glutamine free DMEM containing 10% fetal bovine serum, 5 mM unlabeled or 13C-labelled glucose, 2 mM unlabeled or [U-13C5, 15N2] glutamine and rapamycin for 6 h. The steady-state labeling patterns of TCA cycle metabolites (LC-MS) and temporal profiles for labeled lactate and glutamate (NMR) were monitored. Data were analyzed with compartmentalized metabolic network models that included glucose, lactate and glutamine transport, glycolysis, TCA cycle, PPP, α-ketoglutarate-glutamate and oxaloacetate-aspartate exchange, pyruvate carboxylase activity, anaplerosis at the succinyl-CoA level, pyruvate recycling through malic enzyme, lactate dehydrogenase activity, glutaminolysis, reductive carboxylation and de novo fatty acids synthesis. Isotope balance equations were derived for every network metabolite. The resulted sets of differential equations were solved to determine flux through various metabolic pathways by isotopomer/cumomer analysis (2). Statistics for the obtained parameters/fluxes was calculated by Monte Carlo simulations including experimental noise levels. We analyzed changes in expression of ~10,000 metabolic genes in DLCL2 and Ramos cells induced by rapamycin (by RNA-seq). Anticipated related alterations indicate that mTOR inhibition diminishes glycolysis, TCA cycle activity, de novo fatty acid biosynthesis, pyruvate cycling and PPP. Glutaminolisys was also affected due to gene PPAT expression (responsible for enzymatic glutamate production from glutamine). Metabolic Flux Analysis revealed that in DLCL2 rapamycin produced dramatic metabolic changes in reductive carboxylation (41% decrease), glutaminolysis (95% decrease), mitochondrial pyruvate carrier (50% increase) and pyruvate carboxylase (520% increase) activities. Further study of these effects is required, yet these data strongly suggest that multiple pathways are affected and, potentially, these metabolic signatures may serve as key biomarkers of effective mTOR inhibition. Our genomic and proteomic analysis indicates that the expression level of a large number of enzymes in human lymphoma cells has been modified by rapamycin-mediated mTOR inhibition. These affected enzymes are involved in eight metabolic pathways: glycolysis, pyruvate cycling/metabolism, TCA cycle, glutaminolysis, fatty acid metabolism, cholesterol metabolism, phospholipid metabolism and the pentose shunt. Information available from 13C multiplets in 13C spectra and MID of numerous metabolites significantly increases precision of calculated metabolic fluxes and allows estimation of contributions of different pathways to particular metabolite labeling patterns. The new modeling approaches developed here provide more effecient methods for investigation of detailed mechanisms of cancer metabolism and therefore enable effective identification of targets for combinatorial therapy. Dynamic high-resolution MR spectra and LC-MS mass-isotopomers are very sensitive to changing/adding biochemical pathways and flux values, and metabolic modeling allows one to check precisely the feasibility of assumed general bionetworks and alterations of particular metabolic pathways.

Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production

A major roadblock in the effective treatment of cancers is their heterogeneity, whereby multiple molecular landscapes are classified as a single disease. To explore the contribution of cellular metabolism to cancer heterogeneity, we analyse the Metabric dataset, a landmark genomic and transcriptomic study of 2,000 individual breast tumours, in the context of the human genome-scale metabolic network. We create personalized metabolic landscapes for each tumour by exploring sets of active reactions that satisfy constraints derived from human biochemistry and maximize congruency with the Metabric transcriptome data. Classification of the personalized landscapes derived from 997 tumour samples within the Metabric discovery dataset reveals a novel poor prognosis cluster, reproducible in the 995-sample validation dataset. We experimentally follow mechanistic hypotheses resulting from the computational study and establish that active serotonin production is a major metabolic feature of the poor prognosis group. These data support the reconsideration of concomitant serotonin-specific uptake inhibitors treatment during breast cancer chemotherapy.

Abstract PR11: Tissue-of-origin dictates the metabolic fate of branched chain amino acids in mutant Kras-driven cancers

Abstract Introduction: Growth signaling is associated with changes in metabolism in cell culture, but the relatively homogenous nature of culture systems leaves open the question of how specific mutations might behave differently in different tissue contexts. Indeed, previous work has demonstrated that tumor metabolic gene expression most closely resembles the tissue of origin for that tumor1, and that the same oncogenic driver can cause different metabolic phenotypes in different tissues2. We recently found that pancreatic cancers (PDAC) are associated with increased plasma branched-chain amino acid (BCAA) levels resulting from increased whole body protein turnover, while a model of non-small cell lung cancer (NSCLC) driven by the same genetic lesions displays the opposite changes in plasma BCAA levels3. Based on these data, we hypothesized that changes in plasma BCAAs might reflect a difference in the utilization of BCAA by tumors arising in each tissue. Methods: C57B6/J male mice heterozygous for the lox-stop-lox (LSL)-KrasG12D allele and homozygous for the Trp53flox/flox allele were exposed to an inhaled viral Cre-recombinase or crossed to a pancreas-specific Pdx-1-cre to generate models of NSCLC and PDAC respectively. Cell lines previously derived from these mouse models were used for in vitro and allograft studies. To identify the metabolic fates of BCAAs in vitro, we performed tracing studies utilizing stable-isotope 13C-Leu or 15N-Leu in place of the abundant 12C or 14N isotopomer. Similarly, we used amino acid defined diets in which stable isotope BCAAs were included to trace amino acid fate in vivo. Data: We first confirmed that plasma BCAA levels change in opposite directions early in disease in the isogenic mouse models of PDAC and NSCLC. Consistent with different metabolic fates of BCAAs, 13C stable isotope tracing in vivo revealed increased incorporation of BCAAs into tissue protein in NSCLC tumors, with decreased incorporation observed in PDAC tumors. NSCLC tumors also displayed increased generation ofα-ketoisocaproate, the transamination product of leucine, suggesting an additional role for BCAAs a nitrogen source in NSCLC. Further supporting this possibility, we found increased expression of branched-chain amino acid transferase 2 (Bcat2), but not other BCAA catabolic genes, in NSCLC tumors. To directly examine the fate of BCAA-derived nitrogen, we used 15N-BCAA tracing studies and CRISPR/Cas9-mediated knockout of BCAA catabolic genes. Tracing in vitro revealed increased transamination of 15N-Leu in NSCLC cells compared with PDAC cells. We also generated NSCLC and PDAC cells deficient in Bcat isoforms to assess the effects of Bcat knockout in both allograft and autochthonous models. Conclusions: Despite sharing an identical combination of mutations, mouse models of NSCLC and PDAC exhibit distinct phenotypes with regards to BCAA metabolism in tumors. The different fates of BCAAs in each of these tumor types might explain previously observed alterations in plasma BCAA levels and provides further insight into how tumors can influence whole body metabolic phenotypes in early cancer. 1. Hu, J., et al. Heterogeneity of tumor-induced gene expression changes in the human metabolic network. Nature biotechnology 31, 522-529 (2013). 2. Yuneva, M.O., et al. The Metabolic Profile of Tumors Depends on Both the Responsible Genetic Lesion and Tissue Type. Cell Metabolism 15, 157-170 (2012). 3. Mayers, J.R., et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nature Medicine (2014). Citation Format: Jared R. Mayers, Margaret E. Torrence, Shawn M. Davidson, Thales Papagiannakopoulos, Allison N. Lau, Tyler Jacks, Matthew G. Vander Heiden. Tissue-of-origin dictates the metabolic fate of branched chain amino acids in mutant Kras-driven cancers. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstractnr PR11.

A Multi-Scale Model of the Whole Human Body based on Dynamic Parsimonious Flux Balance Analysis

The multi-scale modelling approach is a powerful mathematical technique for simulating and analyzing complex biological systems such as the human body. This tool can help study the interactions of the various networks in a living organism, from the cellular level up to the population scale, in one framework. In this paper, a generic mathematical model is developed that describes human metabolism with 237 serum metabolites integrated with a chosen set of human metabolic networks. A new computational approach is presented for solving the resulting dynamic problem using parsimonious flux balance analysis (pFBA). To illustrate the performance of the proposed approach, the human hepatocyte genome scale model is selected for the metabolic network to be included. The simulation results show that the proposed approach has promise with respect to both computational efficiency and convergence. To demonstrate the potential application of the developed model, prediction of amino acid biomarkers for a set of inborn errors of metabolism (IEM) is considered as an example. All the simulations are performed using MATLAB and the COBRA toolbox. This framework has the potential to simulate various human metabolic disorders to help with the diagnosis of associated human diseases and to suggest novel treatment strategies. In addition, it opens the door to new opportunities for personalized medicine.

Diagnostic Metabolomic Blood Tests for Endoluminal Gastrointestinal Cancer--A Systematic Review and Assessment of Quality.

Advances in analytics have resulted in metabolomic blood tests being developed for the detection of cancer. This systematic review aims to assess the diagnostic accuracy of blood-based metabolomic biomarkers for endoluminal gastrointestinal (GI) cancer. Using endoscopic diagnosis as a reference standard, methodologic and reporting quality was assessed using validated tools, in addition to pathway-based informatics to biologically contextualize discriminant features. Twenty-nine studies (15 colorectal, 9 esophageal, 3 gastric, and 2 mixed) with data from 10,835 participants were included. All reported significant differences in hematologic metabolites. In pooled analysis, 246 metabolites were found to be significantly different after multiplicity correction. Incremental metabolic flux with disease progression was frequently reported. Two promising candidates have been validated in independent populations (both colorectal biomarkers), and one has been approved for clinical use. Networks analysis suggested modulation of elements of up to half of Edinburgh Human Metabolic Network subdivisions, and that the poor clinical applicability of commonly modulated metabolites could be due to extensive molecular interconnectivity. Methodologic and reporting quality was assessed as moderate-to-poor. Serum metabolomics holds promise for GI cancer diagnostics; however, future efforts must adhere to consensus standardization initiatives, utilize high-resolution discovery analytics, and compare candidate biomarkers with peer nonendoscopic alternatives.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival

Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

Reconstruction and validation of a constraint‐based metabolic network model for bone marrow‐derived mesenchymal stem cells

Over recent years, constraint-based modelling of metabolic networks has become increasingly popular; the models are suitable for system-level modelling of cell physiology. The goal of the present work was to reconstruct a constraint-based metabolic network model of bone marrow-derived mesenchymal stem cells (BMMSCs). To reconstruct a BMMSC-specific metabolic model, transcriptomic data of BMMSCs, and additionally, the human generic metabolic network model (Recon1) were used. Then, using the mCADRE algorithm, a draft metabolic network was reconstructed. Literature and proteomic data were subsequently used to refine and improve the draft. From this, iMSC1255 was derived to be the metabolic network model of BMMSCs. iMSC1255 has 1255 genes, 1850 metabolites and 2288 reactions. After including additional constraints based on previously reported experimental results, our model successfully predicted BMMSC growth rate and metabolic phenotypes. Here, iMSC1255 is introduced to be the metabolic network model of bone marrow-derived mesenchymal stem cells. Based on current knowledge, this is the first report on genome-scale reconstruction and validation of a stem cell metabolic network model.

Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis.

Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies.

Evidence for the relationship between the regulatory effects of microRNAs and attack robustness of biological networks

It has been previously suggested that microRNAs (miRNAs) have a tendency to regulate the important components of biological networks. The goal of the present study was to systematically test if one can establish a relationship between miRNA targets and the important components of biological networks (including human protein-protein interaction network, signaling network and metabolic network). For this analysis, we have studied the attack robustness of these networks. It has been previously shown that deletion of network vertices in descending order of their importance (e.g., in decreasing order of vertex degrees) can affect the network structure much more considerably. In the current study, we introduced three miRNA-based measures of importance: “miRNA count” (i.e., the number of miRNAs that regulate a given network component); average adjacent miRNA count, “AAmiC” (i.e., the average number of miRNAs regulating the targeted components adjacent to a given component); and total adjacent miRNA count, “TAmiC” (i.e., the total number of miRNAs regulating the targeted components adjacent to a given component). Our results suggest that “miRNA count” is only marginally capable of locating the important components of the networks, while TAmiC was the most relevant measure. By comparing TAmiC with the classical centrality measures (which are solely based on the network structure) when simultaneously removing vertices, we show that this measure is correlated to degree and betweenness centrality measures, while its performance is generally better than that of closeness and eigenvector centrality measures. The results of this study suggest that TAmiC which represents a measure based on both network structure and biological knowledge, can successfully determine the important network components indicating that miRNA regulation and network robustness are related.

Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites.

Background: A recent comparison showed the extensive similarities between the structural properties of metabolites in the reconstructed human metabolic network (“endogenites”) and those of successful, marketed drugs (“drugs”).Results: Clustering indicated the related but differential population of chemical space by endogenites and drugs. Differences between the drug-endogenite similarities resulting from various encodings and judged by Tanimoto similarity could be related simply to the fraction of the bitstrings set to 1. By extracting drug/endogenite substructures, we develop a novel family of fingerprints, the Drug Endogenite Substructure (DES) encodings, based on the ranked frequency of the various substructures. These provide a natural assessment of drug-endogenite likeness, and may be used as descriptors with which to derive quantitative structure-activity relationships (QSARs).Conclusions: “Drug-endogenite likeness” seems to have utility, and leads to a simple, novel and interpretable substructure-based molecular encoding for cheminformatics.

Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes.

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D.

Pathway reporter genes define molecular phenotypes of human cells.

BackgroundThe phenotype of a living cell is determined by its pattern of active signaling networks, giving rise to a “molecular phenotype” associated with differential gene expression. Digital amplicon based RNA quantification by sequencing is a useful technology for molecular phenotyping as a novel tool to characterize the state of biological systems.ResultsWe show here that the activity of signaling networks can be assessed based on a set of established key regulators and expression targets rather than the entire transcriptome. We compiled a panel of 917 human pathway reporter genes, representing 154 human signaling and metabolic networks for integrated knowledge- and data-driven understanding of biological processes. The reporter genes are significantly enriched for regulators and effectors covering a wide range of biological processes, and faithfully capture gene-level and pathway-level changes. We apply the approach to iPSC derived cardiomyocytes and primary human hepatocytes to describe changes in molecular phenotype during development or drug response. The reporter genes deliver an accurate pathway-centric view of the biological system under study, and identify known and novel modulation of signaling networks consistent with literature or experimental data.ConclusionsA panel of 917 pathway reporter genes is sufficient to describe changes in the molecular phenotype defined by 154 signaling cascades in various human cell types. AmpliSeq-RNA based digital transcript imaging enables simultaneous monitoring of the entire pathway reporter gene panel in up to 150 samples. We propose molecular phenotyping as a useful approach to understand diseases and drug action at the network level.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1532-2) contains supplementary material, which is available to authorized users.

NETWORK APPROACHES FOR ANALYSIS AND MODELING OF THE HUMAN METABOLISM

We describe a novel approach for metabolic network reconstruction in order to switch from the full reaction-metabolite scheme to a more synthetic description at a pathway level. The network thus obtained retains much information of the original model, allowing easier graphical visualizations and multiscale modeling. We apply our approach to the state-of-the-art database of human metabolic network (Recon2): our approach allows different ranking of the network elements based on its topology and on Markov dynamics induced by network structure.

Small-worldness and gender differences of large scale brain metabolic covariance networks in young adults: a FDG PET study of 400 subjects.

Many studies have demonstrated the small-worldness of the human brain, and have revealed a sexual dimorphism in brain network properties. However, little is known about the gender effects on the topological organization of the brain metabolic covariance networks. To investigate the small-worldness and the gender differences in the topological architectures of human brain metabolic networks. FDG-PET data of 400 healthy right-handed subjects (200 women and 200 age-matched men) were involved in the present study. Metabolic networks of each gender were constructed by calculating the covariance of regional cerebral glucose metabolism (rCMglc) across subjects on the basis of AAL parcellation. Gender differences of network and nodal properties were investigated by using the graph theoretical approaches. Moreover, the gender-related difference of rCMglc in each brain region was tested for investigating the relationships between the hub regions and the brain regions showing significant gender-related differences in rCMglc. We found prominent small-world properties in the domain of metabolic networks in each gender. No significant gender difference in the global characteristics was found. Gender differences of nodal characteristic were observed in a few brain regions. We also found bilateral and lateralized distributions of network hubs in the females and males. Furthermore, we first reported that some hubs of a gender located in the brain regions showing weaker rCMglc in this gender than the other gender. The present study demonstrated that small-worldness was existed in metabolic networks, and revealed gender differences of organizational patterns in metabolic network. These results maybe provided insights into the understanding of the metabolic substrates underlying individual differences in cognition and behaviors.