Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Myeloid differentiation factor 2 in the heart: Bench to bedside evidence for potential clinical benefits?

Cardiac inflammation has been involved in many pathological processes in the heart including cardiac hypertrophy, fibrosis, adverse remodeling, and dysfunction. Myeloid differentiation factor 2 (MD2) is a key mediating protein that has been shown to contribute to the inflammatory process. MD2 is required for the activation of TLR4 in the form of dimerization complex. Upon activation of TLR4, the signal can be sent through either myeloid differentiation primary response protein 88 (Myd88) or toll/interleukin-1 receptor (TIR) domain-containing adaptor inducing IFN-β (TRIF) proteins to activate the inflammatory response in cardiac tissue, after which the inflammatory cytokines and genes are produced. In patients with dilated cardiomyopathy, a positive correlation was demonstrated between the serum MD2 levels and mortality rate. Therefore, MD2 inhibition should provide beneficial effects in inflammation related to cardiac diseases such as obesity and heart failure. Multiple inhibitors of TLR4/MD2 interaction reportedly attenuated cardiac dysfunction and remodeling in animals with obesity and heart failure. In this review, we comprehensively summarized the reports from in vitro, in vivo, and clinical studies regarding the role of MD2 and the effects of MD2 inhibitors on cardiac inflammation, dysfunction, fibrosis, and remodeling. The information regarding the beneficial effects of MD2 inhibitors will be used to encourage future clinical use as a novel anti-inflammatory agent.

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.

iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6

A high-quality GSM model for Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed. The construction of this model involved performing a genome-wide reannotation to identify the metabolic capacity of the bacterium. A reaction representing the abstraction of the biomass composition was reconciled from several studies reported in the literature and previous models, and included in the model. The final model comprises two compartments and manifold automatically generated gene rules. The validation was performed with experimental data from recent studies, regarding the usability of carbon sources, the effect of the presence of oxygen, and the requirement of amino acids for growth. This model can be used to better understand the metabolism of this major pathogen, provide clues regarding new drug targets, and eventually design strategies for fighting infections by these bacteria.

Evolution of hierarchy in bacterial metabolic networks

Flow hierarchy is a useful way to characterize the movement of information and matter throughout a network. Hierarchical network organizations are shown to arise when there is a cost of maintaining links in the network. A similar constraint exists in metabolic networks, where costs come from reduced efficiency of nonspecific enzymes or from producing unnecessary enzymes. Previous analyses of bacterial metabolic networks have been used to predict the minimal nutrients that a bacterium needs to grow, its mutualistic relationships with other bacteria, and its major ecological niche. We use metabolic network inference to obtain metabolite flow graphs of 2935 bacterial metabolic networks and find that flow hierarchy evolves independently of modularity and other network properties. By inferring the ancestral metabolic networks and estimating the hierarchical character of the inferred network, we show that hierarchical structure first increased and later decreased over evolutionary history. Furthermore, hierarchical structure in the network is associated with slower growth rates; bacteria with hierarchy scores above the median grow on average 2.25 times faster than those with hierarchy scores below the median.

Reconstructing High-Quality Large-Scale Metabolic Models with merlin

Here, the basic principles of reconstructing genome-scale metabolic models with merlin are described. This tool covers the basic stages of this process, providing several tools that allow assembling models, using the sequenced genome as a starting point. merlin has two main modules, separating the process of annotating (enzymes, transporters, and compartments) on the genome from the process of model assembly, though information from the former is integrated in the latter after curation. Moreover, merlin provides several tools to curate the model, including tools for generating reactions' gene rules and placeholder entities for biomass precursors, such as proteins (e-protein) or nucleotides (e-DNA and e-RNA) among others.This tutorial covers each feature of merlin in detail, including the assessment of experimental data for the validation of the model.

Phylogeny of metabolic networks: a spectral graph theoretical approach

Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.

Habitat variability does not generally promote metabolic network modularity in flies and mammals

The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered.

Elucidation of primary metabolic pathways in Aspergillus species: orphaned research in characterizing orphan genes

Primary metabolism affects all phenotypical traits of filamentous fungi. Particular examples include reacting to extracellular stimuli, producing precursor molecules required for cell division and morphological changes as well as providing monomer building blocks for production of secondary metabolites and extracellular enzymes. In this review, all annotated genes from four Aspergillus species have been examined. In this process, it becomes evident that 80–96% of the genes (depending on the species) are still without verified function. A significant proportion of the genes with verified metabolic functions are assigned to secondary or extracellular metabolism, leaving only 2–4% of the annotated genes within primary metabolism. It is clear that primary metabolism has not received the same attention in the post-genomic area as many other research areas—despite its role at the very centre of cellular function. However, several methods can be employed to use the metabolic networks in tandem with comparative genomics to accelerate functional assignment of genes in primary metabolism. In particular, gaps in metabolic pathways can be used to assign functions to orphan genes. In this review, applications of this from the Aspergillus genes will be examined, and it is proposed that, where feasible, this should be a standard part of functional annotation of fungal genomes.

Metabolic networks are almost nonfractal: a comprehensive evaluation

Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.

Use of a global metabolic network to curate organismal metabolic networks

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.

Whole brain and brain regional coexpression network interactions associated with predisposition to alcohol consumption

To identify brain transcriptional networks that may predispose an animal to consume alcohol, we used weighted gene coexpression network analysis (WGCNA). Candidate coexpression modules are those with an eigengene expression level that correlates significantly with the level of alcohol consumption across a panel of BXD recombinant inbred mouse strains, and that share a genomic region that regulates the module transcript expression levels (mQTL) with a genomic region that regulates alcohol consumption (bQTL). To address a controversy regarding utility of gene expression profiles from whole brain, vs specific brain regions, as indicators of the relationship of gene expression to phenotype, we compared candidate coexpression modules from whole brain gene expression data (gathered with Affymetrix 430 v2 arrays in the Colorado laboratories) and from gene expression data from 6 brain regions (nucleus accumbens (NA); prefrontal cortex (PFC); ventral tegmental area (VTA); striatum (ST); hippocampus (HP); cerebellum (CB)) available from GeneNetwork. The candidate modules were used to construct candidate eigengene networks across brain regions, resulting in three "meta-modules", composed of candidate modules from two or more brain regions (NA, PFC, ST, VTA) and whole brain. To mitigate the potential influence of chromosomal location of transcripts and cis-eQTLs in linkage disequilibrium, we calculated a semi-partial correlation of the transcripts in the meta-modules with alcohol consumption conditional on the transcripts' cis-eQTLs. The function of transcripts that retained the correlation with the phenotype after correction for the strong genetic influence, implicates processes of protein metabolism in the ER and Golgi as influencing susceptibility to variation in alcohol consumption. Integration of these data with human GWAS provides further information on the function of polymorphisms associated with alcohol-related traits.

Genetic variability of mutans streptococci revealed by wide whole-genome sequencing

Background

Mutans streptococci are a group of bacteria significantly contributing to tooth decay. Their genetic variability is however still not well understood.

Results

Genomes of 6 clinical S. mutans isolates of different origins, one isolate of S. sobrinus (DSM 20742) and one isolate of S. ratti (DSM 20564) were sequenced and comparatively analyzed. Genome alignment revealed a mosaic-like structure of genome arrangement. Genes related to pathogenicity are found to have high variations among the strains, whereas genes for oxidative stress resistance are well conserved, indicating the importance of this trait in the dental biofilm community. Analysis of genome-scale metabolic networks revealed significant differences in 42 pathways. A striking dissimilarity is the unique presence of two lactate oxidases in S. sobrinus DSM 20742, probably indicating an unusual capability of this strain in producing H₂O₂ and expanding its ecological niche. In addition, lactate oxidases may form with other enzymes a novel energetic pathway in S. sobrinus DSM 20742 that can remedy its deficiency in citrate utilization pathway.

Using 67 S. mutans genomes currently available including the strains sequenced in this study, we estimates the theoretical core genome size of S. mutans, and performed modeling of S. mutans pan-genome by applying different fitting models. An “open” pan-genome was inferred.

Conclusions

The comparative genome analyses revealed diversities in the mutans streptococci group, especially with respect to the virulence related genes and metabolic pathways. The results are helpful for better understanding the evolution and adaptive mechanisms of these oral pathogen microorganisms and for combating them.

Does habitat variability really promote metabolic network modularity?

The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.

Inference of responsive metabolic pathways from time-series transcriptomic data with consideration of the metabolic network structure

The inference of responsive metabolic pathways from transcriptomic data remains a problem which needs to be solved. In this work, we make use of time-series transcriptomic data and the inherent structure of a metabolic network to examine the possibility of metabolic pathway inference. We present a method that calculates the state of each network metabolite for the different time points of a transcriptomic dataset. This forms the basis for metabolic pathway enrichment analysis based on time-series gene expression data. Application of the method to yeast transcriptomic datasets revealed metabolic pathways that showed the highest respective response during nitrogen starvation, amino acid starvation and under the influence of heat. Furthermore, key metabolic pathways related to the yeast cell cycle, like the lipid metabolism in the G1 phase, were identified. Therefore, a method for systematic determination of metabolic pathways that showed the highest change under given conditions was introduced. The proposed method allows for the analysis of transcriptomic data closely related to the metabolism of the cell by using the structure of the metabolic network as a framework for analysis, especially by using time-series transcriptomic data.