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Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).
Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: "What can causal networks tell us about metabolic pathways?". Using data from an Arabidopsis Bay[Formula: see text]Sha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies.
A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.
In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger "universal" network formed by the union of all species-specific networks. It remained to be understood, however, how the topological properties of this universal network influence the scaling law of functional content of genomes in the toolbox model. Here we answer this question by first analyzing the scaling properties of the toolbox model on arbitrary tree-like universal networks. We prove that critical branching topology, in which the average number of upstream neighbors of a node is equal to one, is both necessary and sufficient for quadratic scaling. We further generalize the rules of the model to incorporate reactions with multiple substrates/products as well as branched and cyclic metabolic pathways. To achieve its metabolic tasks, the new model employs evolutionary optimized pathways with minimal number of reactions. Numerical simulations of this realistic model on the universal network of all reactions in the KEGG database produced approximately quadratic scaling between the number of regulated pathways and the size of the metabolic network. To quantify the geometrical structure of individual pathways, we investigated the relationship between their number of reactions, byproducts, intermediate, and feedback metabolites. Our results validate and explain the ubiquitous appearance of the quadratic scaling for a broad spectrum of topologies of underlying universal metabolic networks. They also demonstrate why, in spite of "small-world" topology, real-life metabolic networks are characterized by a broad distribution of pathway lengths and sizes of metabolic regulons in regulatory networks.
This paper presents the first proteome network map for the sarcoplasmic fish proteome. A total of 183 non-redundant annotated proteins were identified in a shotgun proteome-wide analysis from 15 different fish species. The final protein compilation was investigated by integrated in-silico studies, including functional GO term enrichment, pathways studies and networks analysis. An in-silico interactomics map was built up merging all the identified proteins. The whole confidence network contains 84 nodes and 279 interactions. Most of the sarcoplasmic fish proteins were grouped under pathways and networks referring to energy, catabolism and lipid metabolism. As a new potential nutritional ingredient valuable bioactive peptides were also predicted after an in-silico human gastrointestinal digestion. As is presented in this study, the integrated global proteomics results and the bioinformatics analysis of the sarcoplasmic fish proteome show the feasibility of this approach to provide a comprehensive knowledge of this fraction since a functional and nutritional point of view.
Maize (Zea mays mays) oil is a rich source of polyunsaturated fatty acids (FAs) and energy, making it a valuable resource for human food, animal feed, and bio-energy. Although this trait has been studied via conventional genome-wide association study (GWAS), the single nucleotide polymorphism (SNP)-trait associations generated by GWAS may miss the underlying associations when traits are based on many genes, each with small effects that can be overshadowed by genetic background and environmental variation. Detecting these SNPs statistically is also limited by the levels set for false discovery rate. A complementary pathways analysis that emphasizes the cumulative aspects of SNP-trait associations, rather than just the significance of single SNPs, was performed to understand the balance of lipid metabolism, conversion, and catabolism in this study. This pathway analysis indicated that acyl-lipid pathways, including biosynthesis of wax esters, sphingolipids, phospholipids and flavonoids, along with FA and triacylglycerol (TAG) biosynthesis, were important for increasing oil and FA content. The allelic variation found among the genes involved in many degradation pathways, and many biosynthesis pathways leading from FAs and carbon partitioning pathways, was critical for determining final FA content, changing FA ratios and, ultimately, to final oil content. The pathways and pathway networks identified in this study, and especially the acyl-lipid associated pathways identified beyond what had been found with GWAS alone, provide a real opportunity to precisely and efficiently manipulate high-oil maize genetic improvement.
Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge.
Chlorophytum borivilianum, an endangered medicinal plant species is highly recognized for its aphrodisiac properties provided by saponins present in the plant. The transcriptome information of this species is limited and only few hundred expressed sequence tags (ESTs) are available in the public databases. To gain molecular insight of this plant, high throughput transcriptome sequencing of leaf RNA was carried out using Illumina's HiSeq 2000 sequencing platform. A total of 22,161,444 single end reads were retrieved after quality filtering. Available (e.g., De-Bruijn/Eulerian graph) and in-house developed bioinformatics tools were used for assembly and annotation of transcriptome. A total of 101,141 assembled transcripts were obtained, with coverage size of 22.42 Mb and average length of 221 bp. Guanine-cytosine (GC) content was found to be 44%. Bioinformatics analysis, using non-redundant proteins, gene ontology (GO), enzyme commission (EC) and kyoto encyclopedia of genes and genomes (KEGG) databases, extracted all the known enzymes involved in saponin and flavonoid biosynthesis. Few genes of the alkaloid biosynthesis, along with anticancer and plant defense genes, were also discovered. Additionally, several cytochrome P450 (CYP450) and glycosyltransferase unique sequences were also found. We identified simple sequence repeat motifs in transcripts with an abundance of di-nucleotide simple sequence repeat (SSR; 43.1%) markers. Large scale expression profiling through Reads per Kilobase per Million mapped reads (RPKM) showed major genes involved in different metabolic pathways of the plant. Genes, expressed sequence tags (ESTs) and unique sequences from this study provide an important resource for the scientific community, interested in the molecular genetics and functional genomics of C. borivilianum.
The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.
Fungal secondary metabolites are widely used as therapeutics and are vital components of drug discovery programs. A major challenge hindering discovery of novel secondary metabolites is that the underlying pathways involved in their biosynthesis are transcriptionally silent under typical laboratory growth conditions, making it difficult to identify the transcriptional networks that they are embedded in. Furthermore, while the genes participating in secondary metabolic pathways are typically found in contiguous clusters on the genome, known as biosynthetic gene clusters (BGCs), this is not always the case, especially for global and pathway-specific regulators of pathways' activities. To address these challenges, we used 283 genome-wide gene expression data sets of the ascomycete cell factory Aspergillus niger generated during growth under 155 different conditions to construct two gene coexpression networks based on Spearman's correlation coefficients (SCCs) and on mutual rank-transformed Pearson's correlation coefficients (MR-PCCs). By mining these networks, we predicted six transcription factors, named MjkA to MjkF, to regulate secondary metabolism in A. niger. Overexpression of each transcription factor using the Tet-On cassette modulated the production of multiple secondary metabolites. We found that the SCC and MR-PCC approaches complemented each other, enabling the delineation of putative global (SCC) and pathway-specific (MR-PCC) transcription factors. These results highlight the potential of coexpression network approaches to identify and activate fungal secondary metabolic pathways and their products. More broadly, we argue that drug discovery programs in fungi should move beyond the BGC paradigm and focus on understanding the global regulatory networks in which secondary metabolic pathways are embedded. IMPORTANCE There is an urgent need for novel bioactive molecules in both agriculture and medicine. The genomes of fungi are thought to contain vast numbers of metabolic pathways involved in the biosynthesis of secondary metabolites with diverse bioactivities. Because these metabolites are biosynthesized only under specific conditions, the vast majority of the fungal pharmacopeia awaits discovery. To discover the genetic networks that regulate the activity of secondary metabolites, we examined the genome-wide profiles of gene activity of the cell factory Aspergillus niger across hundreds of conditions. By constructing global networks that link genes with similar activities across conditions, we identified six putative global and pathway-specific regulators of secondary metabolite biosynthesis. Our study shows that elucidating the behavior of the genetic networks of fungi under diverse conditions harbors enormous promise for understanding fungal secondary metabolism, which ultimately may lead to novel drug candidates.
There is growing evidence that Alzheimer disease (AD) is a pervasive metabolic disorder with dysregulation in multiple biochemical pathways underlying its pathogenesis. Understanding how perturbations in metabolism are related to AD is critical to identifying novel targets for disease-modifying therapies. In this study, we test whether AD pathogenesis is associated with dysregulation in brain transmethylation and polyamine pathways.
Metabolic networks show great evolutionary plasticity, because they can differ substantially even among closely related prokaryotes. Any one metabolic network can also effectively compensate for the blockage of individual reactions by rerouting metabolic flux through other pathways. These observations, together with the continual discovery of new microbial metabolic pathways and enzymes, raise the possibility that metabolic networks are only weakly constrained in changing their complement of enzymatic reactions.
Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity.
An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme-encoding gene results in a non-viable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic network-based identification of compensatory rescue effects may open new avenues for genetic interventions.
The difficulty in annotating the vast amounts of biological information poses one of the greatest current challenges in biological research. The number of genomic, proteomic, and metabolomic datasets has increased dramatically over the last two decades, far outstripping the pace of curation efforts. Here, we tackle the challenge of curating metabolic network reconstructions. We predict organismal metabolic networks using sequence homology and a global metabolic network constructed from all available organismal networks. While sequence homology has been a standard to annotate metabolic networks it has been faulted for its lack of predictive power. We show, however, that when homology is used with a global metabolic network one is able to predict organismal metabolic networks that have enhanced network connectivity. Additionally, we compare the annotation behavior of current database curation efforts with our predictions and find that curation efforts are biased towards adding (rather than removing) reactions to organismal networks.
Reversible posttranslational modifications are emerging as critical regulators of mitochondrial proteins and metabolism. Here, we use a label-free quantitative proteomic approach to characterize the lysine succinylome in liver mitochondria and its regulation by the desuccinylase SIRT5. A total of 1,190 unique sites were identified as succinylated, and 386 sites across 140 proteins representing several metabolic pathways including β-oxidation and ketogenesis were significantly hypersuccinylated in Sirt5(-/-) animals. Loss of SIRT5 leads to accumulation of medium- and long-chain acylcarnitines and decreased β-hydroxybutyrate production in vivo. In addition, we demonstrate that SIRT5 regulates succinylation of the rate-limiting ketogenic enzyme 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) both in vivo and in vitro. Finally, mutation of hypersuccinylated residues K83 and K310 on HMGCS2 to glutamic acid strongly inhibits enzymatic activity. Taken together, these findings establish SIRT5 as a global regulator of lysine succinylation in mitochondria and present a mechanism for inhibition of ketogenesis through HMGCS2.
Clear cell renal cell carcinoma (ccRCC) is the predominant subtype of renal cell carcinoma (RCC). It is one of the most therapy-resistant carcinomas, responding very poorly or not at all to radiotherapy, hormonal therapy and chemotherapy. A more comprehensive understanding of the deregulated pathways in ccRCC can lead to the development of new therapies and prognostic markers. We performed a meta- analysis of 5 publicly available gene expression datasets and identified a list of co- deregulated genes, for which we performed extensive bioinformatic analysis coupled with experimental validation on the mRNA level. Gene ontology enrichment showed that many proteins are involved in response to hypoxia/oxygen levels and positive regulation of the VEGFR signaling pathway. KEGG analysis revealed that metabolic pathways are mostly altered in ccRCC. Similarly, Ingenuity Pathway Analysis showed that the antigen presentation, inositol metabolism, pentose phosphate, glycolysis/gluconeogenesis and fructose/mannose metabolism pathways are altered in the disease. Cellular growth, proliferation and carbohydrate metabolism, were among the top molecular and cellular functions of the co-deregulated genes. qRT-PCR validated the deregulated expression of several genes in Caki-2 and ACHN cell lines and in a cohort of ccRCC tissues. NNMT and NR3C1 increased expression was evident in ccRCC biopsies from patients using immunohistochemistry. ROC curves evaluated the diagnostic performance of the top deregulated genes in each dataset. We show that metabolic pathways are mostly deregulated in ccRCC and we highlight those being most responsible in its formation. We suggest that these genes are candidate predictive markers of the disease.
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