IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

BACKGROUND: Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. RESULTS: By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. CONCLUSION: IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer

Regulatory network characterization in development: challenges and opportunities [version 1; referees: 2 approved]

Embryonic development and stem cell differentiation, during which coordinated cell fate specification takes place in a spatial and temporal context, serve as a paradigm for studying the orderly assembly of gene regulatory networks (GRNs) and the fundamental mechanism of GRNs in driving lineage determination. However, knowledge of reliable GRN annotation for dynamic development regulation, particularly for unveiling the complex temporal and spatial architecture of tissue stem cells, remains inadequate. With the advent of single-cell RNA sequencing technology, elucidating GRNs in development and stem cell processes poses both new challenges and unprecedented opportunities. This review takes a snapshot of some of this work and its implication in the regulative nature of early mammalian development and specification of the distinct cell types during embryogenesis

Ab initio identification of transcription start sites in the Rhesus macaque genome by histone modification and RNA-Seq

Rhesus macaque is a widely used primate model organism. Its genome annotations are however still largely comparative computational predictions derived mainly from human genes, which precludes studies on the macaque-specific genes, gene isoforms or their regulations. Here we took advantage of histone H3 lysine 4 trimethylation (H3K4me3)’s ability to mark transcription start sites (TSSs) and the recently developed ChIP-Seq and RNA-Seq technology to survey the transcript structures. We generated 14 013 757 sequence tags by H3K4me3 ChIP-Seq and obtained 17 322 358 paired end reads for mRNA, and 10 698 419 short reads for sRNA from the macaque brain. By integrating these data with genomic sequence features and extending and improving a state-of-the-art TSS prediction algorithm, we ab initio predicted and verified 17 933 of previously electronically annotated TSSs at 500-bp resolution. We also predicted approximately 10 000 novel TSSs. These provide an important rich resource for close examination of the species-specific transcript structures and transcription regulations in the Rhesus macaque genome. Our approach exemplifies a relatively inexpensive way to generate a reasonably reliable TSS map for a large genome. It may serve as a guiding example for similar genome annotation efforts targeted at other model organisms

Evaluating diabetes and hypertension disease causality using mouse phenotypes

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used.</p> <p>Results</p> <p>Here we tapped the rich resource of mouse phenotype data and developed a method to quantify the probability that a gene perturbation causes the phenotypes of a disease. Using type II diabetes (T2D) and hypertension (HT) as study cases, we found that the genes, when perturbed, having high probability to cause T2D and HT phenotypes tend to be hubs in the interactome networks and are enriched for signaling pathways regulating metabolism but not metabolic pathways, even though the genes in these metabolic pathways are often the most significantly changed in expression levels in these diseases.</p> <p>Conclusions</p> <p>Compared to human genetic disease-based predictions, our mouse phenotype based predictors greatly increased the coverage while keeping a similarly high specificity. The disease phenotype probabilities given by our approach can be used to evaluate the likelihood of disease causality of disease-associated genes and genes surrounding disease-associated SNPs.</p

A modular network model of aging

Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein–protein interaction (PPI) networks during fruitfly and human brain aging. In both networks, there are two modules associated with the cellular proliferation to differentiation temporal switch that display opposite aging-related changes in expression. During fly aging, another couple of modules are associated with the oxidative–reductive metabolic temporal switch. These network modules and their relationships demonstrate (1) that aging is largely associated with a small number, instead of many network modules, (2) that some modular changes might be reversible and (3) that genes connecting different modules through PPIs are more likely to affect aging/longevity, a conclusion that is experimentally validated by Caenorhabditis elegans lifespan analysis. Network simulations further suggest that aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging

The effects of graded levels of calorie restriction : III. Impact of short term calorie and protein restriction on mean daily body temperature and torpor use in the C57BL/6 mouse

GRANT SUPPORT This work was supported by BBSRC BB009953/1 awarded to JRS and SEM. PK and CD were funded by the Erasmus exchange programme. JRS, SEM, DD, CG, LC, JJDH, YW, DELP, DL and AD are members of the BBSRC China Partnership Award, BB/J020028/1.Peer reviewedPublisher PD

The effects of graded levels of calorie restriction : XI. Evaluation of the main hypotheses underpinning the life extension effects of CR using the hepatic transcriptome

We would like to acknowledge the BSU staff for their invaluable help with caring for the animals.Peer reviewedPublisher PD

The effects of graded levels of calorie restriction : V. Impact of short term calorie and protein restriction on physical activity in the C57BL/6 mouse

Peer reviewedPublisher PD

Grp94 Protein Delivers γ-Aminobutyric Acid Type A (GABAA) Receptors to Hrd1 Protein-mediated Endoplasmic Reticulum-associated Degradation

This research was originally published in the Journal of Biological Chemistry. Xiao-Jing Di, Ya-Juan Wang, Dong-Yun Han, Yan-Lin Fu, Adam S. Duerfeldt, Brian S. J. Blagg and Ting-Wei Mu.Grp94 Protein Delivers γ-Aminobutyric Acid Type A (GABAA) Receptors to Hrd1 Protein-mediated Endoplasmic Reticulum-associated Degradation. Journal of Biological Chemistry. 2016; 291, 9526-9539.Proteostasis maintenance of γ-aminobutyric acid type A (GABAA) receptors dictates their function in controlling neuronal inhibition in mammalian central nervous systems. However, as a multisubunit, multispan, integral membrane protein, even wild type subunits of GABAA receptors fold and assemble inefficiently in the endoplasmic reticulum (ER). Unassembled and misfolded subunits undergo ER-associated degradation (ERAD), but this degradation process remains poorly understood for GABAA receptors. Here, using the α1 subunits of GABAA receptors as a model substrate, we demonstrated that Grp94, a metazoan-specific Hsp90 in the ER lumen, uses its middle domain to interact with the α1 subunits and positively regulates their ERAD. OS-9, an ER-resident lectin, acts downstream of Grp94 to further recognize misfolded α1 subunits in a glycan-dependent manner. This delivers misfolded α1 subunits to the Hrd1-mediated ubiquitination and the valosin-containing protein-mediated extraction pathway. Repressing the initial ERAD recognition step by inhibiting Grp94 enhances the functional surface expression of misfolding-prone α1(A322D) subunits, which causes autosomal dominant juvenile myoclonic epilepsy. This study clarifies a Grp94-mediated ERAD pathway for GABAA receptors, which provides a novel way to finely tune their function in physiological and pathophysiological conditions

Ab initio identification of transcription start sites in the Rhesus macaque genome by histone modification and RNA-Seq

Rhesus macaque is a widely used primate model organism. Its genome annotations are however still largely comparative computational predictions derived mainly from human genes, which precludes studies on the macaque-specific genes, gene isoforms or their regulations. Here we took advantage of histone H3 lysine 4 trimethylation (H3K4me3)’s ability to mark transcription start sites (TSSs) and the recently developed ChIP-Seq and RNA-Seq technology to survey the transcript structures. We generated 14 013 757 sequence tags by H3K4me3 ChIP-Seq and obtained 17 322 358 paired end reads for mRNA, and 10 698 419 short reads for sRNA from the macaque brain. By integrating these data with genomic sequence features and extending and improving a state-of-the-art TSS prediction algorithm, we ab initio predicted and verified 17 933 of previously electronically annotated TSSs at 500-bp resolution. We also predicted approximately 10 000 novel TSSs. These provide an important rich resource for close examination of the species-specific transcript structures and transcription regulations in the Rhesus macaque genome. Our approach exemplifies a relatively inexpensive way to generate a reasonably reliable TSS map for a large genome. It may serve as a guiding example for similar genome annotation efforts targeted at other model organisms