Infection Strategies of Bacterial and Viral Pathogens through Pathogen–Human Protein–Protein Interactions

Since ancient times, even in today’s modern world, infectious diseases cause lots of people to die. Infectious organisms, pathogens, cause diseases by physical interactions with human proteins. A thorough analysis of these interspecies interactions is required to provide insights about infection strategies of pathogens. Here we analyzed the most comprehensive available pathogen–human protein interaction data including 23,435 interactions, targeting 5,210 human proteins. The data were obtained from the newly developed pathogen–host interaction search tool, PHISTO. This is the first comprehensive attempt to get a comparison between bacterial and viral infections. We investigated human proteins that are targeted by bacteria and viruses to provide an overview of common and special infection strategies used by these pathogen types. We observed that in the human protein interaction network the proteins targeted by pathogens have higher connectivity and betweenness centrality values than those proteins not interacting with pathogens. The preference of interacting with hub and bottleneck proteins is found to be a common infection strategy of all types of pathogens to manipulate essential mechanisms in human. Compared to bacteria, viruses tend to interact with human proteins of much higher connectivity and centrality values in the human network. Gene Ontology enrichment analysis of the human proteins targeted by pathogens indicates crucial clues about the infection mechanisms of bacteria and viruses. As the main infection strategy, bacteria interact with human proteins that function in immune response to disrupt human defense mechanisms. Indispensable viral strategy, on the other hand, is the manipulation of human cellular processes in order to use that transcriptional machinery for their own genetic material transcription. A novel observation about pathogen–human systems is that the human proteins targeted by both pathogens are enriched in the regulation of metabolic processes

Reconstruction of Protein-Protein Interaction Network of Insulin Signaling in Homo Sapiens

Diabetes is one of the most prevalent diseases in the world. Type 1 diabetes is characterized by the failure of synthesizing and secreting of insulin because of destroyed pancreatic β-cells. Type 2 diabetes, on the other hand, is described by the decreased synthesis and secretion of insulin because of the defect in pancreatic β-cells as well as by the failure of responding to insulin because of malfunctioning of insulin signaling. In order to understand the signaling mechanisms of responding to insulin, it is necessary to identify all components in the insulin signaling network. Here, an interaction network consisting of proteins that have statistically high probability of being biologically related to insulin signaling in Homo sapiens was reconstructed by integrating Gene Ontology (GO) annotations and interactome data. Furthermore, within this reconstructed network, interacting proteins which mediate the signal from insulin hormone to glucose transportation were identified using linear paths. The identification of key components functioning in insulin action on glucose metabolism is crucial for the efforts of preventing and treating type 2 diabetes mellitus

Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae

BACKGROUND: Control effective flux (CEF) of a reaction is the weighted sum of all fluxes through that reaction, derived from elementary flux modes (EFM) of a metabolic network. Change in CEFs under different environmental conditions has earlier been proven to be correlated with the corresponding changes in the transcriptome. Here we use this to investigate the degree of transcriptional regulation of fluxes in the metabolism of Saccharomyces cerevisiae. We do this by quantifying correlations between changes in CEFs and changes in transcript levels for shifts in carbon source, i.e. between the fermentative carbon source glucose and nonfermentative carbon sources like ethanol, acetate, and lactate. The CEF analysis is based on a simple stoichiometric model that includes reactions of the central carbon metabolism and the amino acid metabolism. RESULTS: The effect of the carbon shift on the metabolic fluxes was investigated for both batch and chemostat cultures. For growth on glucose in batch (respiro-fermentative) cultures, EFMs with no by-product formation were removed from the analysis of the CEFs, whereas those including any by-products (ethanol, glycerol, acetate, succinate) were omitted in the analysis of growth on glucose in chemostat (respiratory) cultures. This resulted in improved correlations between CEF changes and transcript levels. A regression correlation coefficient of 0.60 was obtained between CEF changes and gene expression changes in the central carbon metabolism for the analysis of 5 different perturbations. Out of 45 data points there were no more than 6 data points deviating from the correlation. Additionally, up- or down-regulation of at least 75% of the genes were in qualitative agreement with the CEF changes for all perturbations studied. CONCLUSION: The analysis indicates that changes in carbon source are associated with a high degree of hierarchical regulation of metabolic fluxes in the central carbon metabolism as the change in fluxes are correlating directly with the change in transcript levels of genes encoding their corresponding enzymes. For amino acid biosynthesis there was, however, not found to exist a similar correlation, and this may point to either post-transcriptional and/or metabolic regulation, or be due to the absence of a direct perturbation on the amino acid pathways in these experiments

Integration of metabolome data with metabolic networks reveals reporter reactions

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data

Integrative investigation of metabolic and transcriptomic data

BACKGROUND: New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods. RESULTS: Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures – PLS) using PLS toolbox in Matlab. CONCLUSION: The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model

Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network

Network-oriented analysis is essential to identify those parts of a cell affected by a given perturbation. The effect of neurodegenerative perturbations in the form of diseases of brain metabolism was investigated by using a newly reconstructed brain-specific metabolic network. The developed stoichiometric model correctly represents healthy brain metabolism, and includes 630 metabolic reactions in and between astrocytes and neurons, which are controlled by 570 genes. The integration of transcriptome data of six neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, multiple sclerosis, schizophrenia) with the model was performed to identify reporter features specific and common for these diseases, which revealed metabolites and pathways around which the most significant changes occur. The identified metabolites are potential biomarkers for the pathology of the related diseases. Our model indicated perturbations in oxidative stress, energy metabolism including TCA cycle and lipid metabolism as well as several amino acid related pathways, in agreement with the role of these pathways in the studied diseases. The computational prediction of transcription factors that commonly regulate the reporter metabolites was achieved through binding-site analysis. Literature support for the identified transcription factors such as USF1, SP1 and those from FOX families are known from the literature to have regulatory roles in the identified reporter metabolic pathways as well as in the neurodegenerative diseases. In essence, the reconstructed brain model enables the elucidation of effects of a perturbation on brain metabolism and the illumination of possible machineries in which a specific metabolite or pathway acts as a regulatory spot for cellular reorganization

Drug target identification in sphingolipid metabolism by computational systems biology tools: Metabolic control analysis and metabolic pathway analysis

AbstractSphingolipids regulate cellular processes that are critically important in cell’s fate and function in cancer development and progression. This fact underlies the basics of the novel cancer therapy approach. The pharmacological manipulation of the sphingolipid metabolism in cancer therapeutics necessitates the detailed understanding of the pathway. Two computational systems biology tools are used to identify potential drug target enzymes among sphingolipid pathway that can be further utilized in drug design studies for cancer therapy. The enzymes in sphingolipid pathway were ranked according to their roles in controlling the metabolic network by metabolic control analysis. The physiologically connected reactions, i.e. biologically significant and functional modules of network, were identified by metabolic pathway analysis. The final set of candidate drug target enzymes are selected such that their manipulation leads to ceramide accumulation and long chain base phosphates depletion. The mathematical tools’ efficiency for drug target identification performed in this study is validated by clinically available drugs