Dynamic Modularity of Host Protein Interaction Networks in Salmonella Typhi Infection

Background

Salmonella Typhi is a human-restricted pathogen, which causes typhoid fever and remains a global health problem in the developing countries. Although previously reported host expression datasets had identified putative biomarkers and therapeutic targets of typhoid fever, the underlying molecular mechanism of pathogenesis remains incompletely understood.

Methods

We used five gene expression datasets of human peripheral blood from patients suffering from S. Typhi or other bacteremic infections or non-infectious disease like leukemia. The expression datasets were merged into human protein interaction network (PIN) and the expression correlation between the hubs and their interacting proteins was measured by calculating Pearson Correlation Coefficient (PCC) values. The differences in the average PCC for each hub between the disease states and their respective controls were calculated for studied datasets. The individual hubs and their interactors with expression, PCC and average PCC values were treated as dynamic subnetworks. The hubs that showed unique trends of alterations specific to S. Typhi infection were identified.

Results

We identified S. Typhi infection-specific dynamic subnetworks of the host, which involve 81 hubs and 1343 interactions. The major enriched GO biological process terms in the identified subnetworks were regulation of apoptosis and biological adhesions, while the enriched pathways include cytokine signalling in the immune system and downstream TCR signalling. The dynamic nature of the hubs CCR1, IRS2 and PRKCA with their interactors was studied in detail. The difference in the dynamics of the subnetworks specific to S. Typhi infection suggests a potential molecular model of typhoid fever.

Conclusions

Hubs and their interactors of the S. Typhi infection-specific dynamic subnetworks carrying distinct PCC values compared with the non-typhoid and other disease conditions reveal new insight into the pathogenesis of S. Typhi.     

Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P _meta<1×10⁻⁴, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.     

Chapter 2: Data-Driven View of Disease Biology

Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.

What to Learn in This Chapter

• What a functional relationship network represents.
• The fundamentals of Bayesian inference for genomic data integration.
• How to build a network of functional relationships between genes using examples of functionally related genes and diverse experimental data.
• How computational scientists study disease using data driven approaches, such as integrated networks of protein-protein functional relationships.
• Strategies to assess predictions from a functional relationship network

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

ASpediaFI: Functional Interaction Analysis of Alternative Splicing Events

Alternative splicing(AS) regulates biological processes governing phenotypes and diseases. Differential AS(DAS) gene test methods have been developed to investigate important exonic expression from high-throughput datasets. However, the DAS events extracted using statistical tests are insufficient to delineate relevant biological processes. In this study, we developed a novel application, Alternative Splicing Encyclopedia: Functional Interaction(ASpediaFI), to systemically identify DAS events an...     

Multi-Tissue Computational Modeling Analyzes Pathophysiology of Type 2 Diabetes in MKR Mice

Computational models using metabolic reconstructions for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM) can provide a better understanding of disease pathophysiology and avoid high experimentation costs. There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids'' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective.     

Purification and biochemical characterization of Msi3, an essential Hsp110 molecular chaperone in Candida albicans

Hsp110s are unique and essential molecular chaperones in the eukaryotic cytosol. They play important roles in maintaining cellular protein homeostasis. Candida albicans is the most prevalent yeast opportunistic pathogen that causes fungal infections in humans. As the only Hsp110 in Candida albicans, Msi3 is essential for the growth and infection of Candida albicans. In this study, we have expressed and purified Msi3 in nucleotide-free state and carried out biochemical analyses. Sse1 is the major Hsp110 in budding yeast S. cerevisiae and the best characterized Hsp110. Msi3 can substitute Sse1 in complementing the temperature-sensitive phenotype of S. cerevisiae carrying a deletion of SSE1 gene although Msi3 shares only 63.4% sequence identity with Sse1. Consistent with this functional similarity, the purified Msi3 protein shares many similar biochemical activities with Sse1 including binding ATP with high affinity, changing conformation upon ATP binding, stimulating the nucleotide-exchange for Hsp70, preventing protein aggregation, and assisting Hsp70 in refolding denatured luciferase. These biochemical characterizations suggested that Msi3 can be used as a model for studying the molecular mechanisms of Hsp110s.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12192-021-01213-5.     

Identification of Attractive Drug Targets in Neglected-Disease Pathogens Using an In Silico Approach

Background

The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.

Methodology/Principal Findings

Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.

Conclusions/Significance

Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community''s current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.     

Hsp90 modulates PPARγ activity in a mouse model of nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is a highly prevalent complication of obesity, yet cellular mechanisms that lead to its development are not well defined. Previously, we have documented hepatic steatosis in mice carrying a mutation in the Sec61a1 gene. Here we examined the mechanism behind NAFLD in Sec61a1 mutant mice. Livers of mutant mice exhibited upregulation of Pparg and its target genes Cd36, Cidec, and Lpl, correlating with increased uptake of fatty acid. Interestingly, these mice also displayed activation of the heat shock response (HSR), with elevated levels of heat shock protein (Hsp) 70, Hsp90, and heat shock factor 1. In cell lines, inhibition of Hsp90 function reduced Pparγ signaling and protein levels. Conversely, overexpression of Hsp90 increased Pparγ signaling and protein levels by reducing degradation. This may occur via a physical interaction as Hsp90 and Pparγ coimmunoprecipitated in vivo. Furthermore, inhibition of Hsp90 in Sec61a1 mutant hepatocytes also reduced Pparγ protein levels and signaling. Finally, overexpression of Hsp90 in liver cell lines increased neutral lipid accumulation, and this accumulation was blocked by Hsp90 inhibition. Our results show that the HSR and Hsp90 play an important role in the development of NAFLD, opening new avenues for the prevention and treatment of this highly prevalent disease.     

Menin links the stress response to genome stability in Drosophila melanogaster

Background

The multiple endocrine neoplasia type I gene functions as a tumor suppressor gene in humans and mouse models. In Drosophila melanogaster, mutants of the menin gene (Mnn1) are hypersensitive to mutagens or gamma irradiation and have profound defects in the response to several stresses including heat shock, hypoxia, hyperosmolarity and oxidative stress. However, it is not known if the function of menin in the stress response contributes to genome stability. The objective of this study was to examine the role of menin in the control of the stress response and genome stability.

Methodology/Principal Findings

Using a test of loss-of-heterozygosity, we show that Drosophila strains lacking a functional Mnn1 gene or expressing a Mnn1 dsRNA display increased genome instability in response to non-lethal heat shock or hypoxia treatments. This is also true for strains lacking all Hsp70 genes, implying that a precise control of the stress response is required for genome stability. While menin is required for Hsp70 expression, the results of epistatic studies indicate that the increase in genome instability observed in Mnn1 lack-of-function mutants cannot be accounted for by mis-expression of Hsp70. Therefore, menin may promote genome stability by controlling the expression of other stress-responsive genes. In agreement with this notion, gene profiling reveals that Mnn1 is required for sustained expression of all heat shock protein genes but is dispensable for early induction of the heat shock response.

Conclusions/Significance

Mutants of the Mnn1 gene are hypersensitive to several stresses and display increased genome instability when subjected to conditions, such as heat shock, generally regarded as non-genotoxic. In this report, we describe a role for menin as a global regulator of heat shock gene expression and critical factor in the maintenance of genome integrity. Therefore, menin links the stress response to the control of genome stability in Drosophila melanogaster.