Identifying metabolic enzymes with multiple types of association evidence

BACKGROUND: Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes. RESULTS: We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases. CONCLUSION: We illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities

Functional Implications of DNA Methylation in Adipose Biology.

The twin epidemics of obesity and type 2 diabetes (T2D) are a serious health, social, and economic issue. The dysregulation of adipose tissue biology is central to the development of these two metabolic disorders, as adipose tissue plays a pivotal role in regulating whole-body metabolism and energy homeostasis (1). Accumulating evidence indicates that multiple aspects of adipose biology are regulated, in part, by epigenetic mechanisms. The precise and comprehensive understanding of the epigenetic control of adipose tissue biology is crucial to identifying novel therapeutic interventions that target epigenetic issues. Here, we review the recent findings on DNA methylation events and machinery in regulating the developmental processes and metabolic function of adipocytes. We highlight the following points: 1) DNA methylation is a key epigenetic regulator of adipose development and gene regulation, 2) emerging evidence suggests that DNA methylation is involved in the transgenerational passage of obesity and other metabolic disorders, 3) DNA methylation is involved in regulating the altered transcriptional landscape of dysfunctional adipose tissue, 4) genome-wide studies reveal specific DNA methylation events that associate with obesity and T2D, and 5) the enzymatic effectors of DNA methylation have physiological functions in adipose development and metabolic function

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis.

Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network-including SLC and ABC "drug" transporters, "drug" metabolizing enzymes (DMEs), and regulatory genes-is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60-70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney "remote sensing and signaling network" consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules

Viral hijacking of cellular metabolism.

This review discusses the current state of the viral metabolism field and gaps in knowledge that will be important for future studies to investigate. We discuss metabolic rewiring caused by viruses, the influence of oncogenic viruses on host cell metabolism, and the use of viruses as guides to identify critical metabolic nodes for cancer anabolism. We also discuss the need for more mechanistic studies identifying viral proteins responsible for metabolic hijacking and for in vivo studies of viral-induced metabolic rewiring. Improved technologies for detailed metabolic measurements and genetic manipulation will lead to important discoveries over the next decade

Prediction of Metabolic Pathways Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations

On the Road to Accurate Biomarkers for Cardiometabolic Diseases by Integrating Precision and Gender Medicine Approaches

The need to facilitate the complex management of cardiometabolic diseases (CMD) has led to the detection of many biomarkers, however, there are no clear explanations of their role in the prevention, diagnosis or prognosis of these diseases. Molecules associated with disease pathways represent valid disease surrogates and well-fitted CMD biomarkers. To address this challenge, data from multi-omics types (genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and nutrigenomics), from human and animal models, have become available. However, individual omics types only provide data on a small part of molecules involved in the complex CMD mechanisms, whereas, here, we propose that their integration leads to multidimensional data. Such data provide a better understanding of molecules related to CMD mechanisms and, consequently, increase the possibility of identifying well-fitted biomarkers. In addition, the application of gender medicine also helps to identify accurate biomarkers according to gender, facilitating a differential CMD management. Accordingly, the impact of gender differences in CMD pathophysiology has been widely demonstrated, where gender is referred to the complex interrelation and integration of sex (as a biological and functional marker of the human body) and psychological and cultural behavior (due to ethnical, social, and religious background). In this review, all these aspects are described and discussed, as well as potential limitations and future directions in this incipient field

Epigenomes in Cardiovascular Disease.

If unifying principles could be revealed for how the same genome encodes different eukaryotic cells and for how genetic variability and environmental input are integrated to impact cardiovascular health, grand challenges in basic cell biology and translational medicine may succumb to experimental dissection. A rich body of work in model systems has implicated chromatin-modifying enzymes, DNA methylation, noncoding RNAs, and other transcriptome-shaping factors in adult health and in the development, progression, and mitigation of cardiovascular disease. Meanwhile, deployment of epigenomic tools, powered by next-generation sequencing technologies in cardiovascular models and human populations, has enabled description of epigenomic landscapes underpinning cellular function in the cardiovascular system. This essay aims to unpack the conceptual framework in which epigenomes are studied and to stimulate discussion on how principles of chromatin function may inform investigations of cardiovascular disease and the development of new therapies

Pharmacogenetics : the science of predictive clinical pharmacology

The study of pharmacogenetics has expanded from what were initially casual family-based clinical drug response observations, to a fully-fledged science with direct therapeutic applications, all within a time-span of less than 60 years. A wide spectrum of polymorphisms, located within several genes, are now recognised to influence the pharmacokinetics and pharmacodynamics of the majority of drugs within our therapeutic armamentarium. This information forms the basis for the new development of pharmacogenetic genotyping tests, which can be used to predict the therapeutic and/or adverse effects of a specific drug in a particular patient. Pharmacogenetic-guided, patient targeted therapy has now become the developing fulcrum of personalized medicine, as it provides the best means to optimize benefit/risk ratio in pharmacological management.peer-reviewe

Circulating Pro- and Anti-Inflammatory Metabolites and Its Potential Role in Rheumatoid Arthritis Pathogenesis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that affects synovial joints, leading to inflammation, joint destruction, loss of function, and disability. Although recent pharmaceutical advances have improved the treatment of RA, patients often inquire about dietary interventions to improve RA symptoms, as they perceive pain and/or swelling after the consumption or avoidance of certain foods. There is evidence that some foods have pro- or anti-inflammatory effects mediated by diet-related metabolites. In addition, recent literature has shown a link between diet-related metabolites and microbiome changes, since the gut microbiome is involved in the metabolism of some dietary ingredients. But diet and the gut microbiome are not the only factors linked to circulating pro- and anti-inflammatory metabolites. Other factors including smoking, associated comorbidities, and therapeutic drugs might also modify the circulating metabolomic profile and play a role in RA pathogenesis. This article summarizes what is known about circulating pro- and anti-inflammatory metabolites in RA. It also emphasizes factors that might be involved in their circulating concentrations and diet-related metabolites with a beneficial effect in RA

EFICAz²: enzyme function inference by a combined approach enhanced by machine learning

©2009 Arakaki et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/107doi:10.1186/1471-2105-10-107Background: We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. Results: We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz², exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz² and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz² generates considerably more unique assignments than KEGG. Conclusion: Performance benchmarks and the comparison with KEGG demonstrate that EFICAz² is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz² web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.htm