INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species

Exposure of Toluene Diisocyanate Induces DUSP6 and p53 through Activation of TRPA1 Receptor

Toluene diisocyanate (TDI), a major intermediate agent used in the manufacturing industry, causes respiratory symptoms when exposed to the human body. In this study, we aimed to determine the molecular mechanism of TDI toxicity. To investigate the impact of TDI exposure on global gene expression, we performed transcriptomic analysis of human bronchial epithelial cells (BEAS-2B) after TDI treatment. Differentially expressed genes (DEGs) were sorted and used for clustering and network analysis. Among DEGs, dual-specificity phosphatase 6 (DUSP6) was one of the genes significantly changed by TDI exposure. To verify the expression level of DUSP6 and its effect on lung cells, the mRNA and protein levels of DUSP6 were analyzed. Our results showed that DUSP6 was dose-dependently upregulated by TDI treatment. Thereby, the phosphorylation of ERK1/2, one of the direct inhibitory targets of DUSP6, was decreased. TDI exposure also increased the mRNA level of p53 along with its protein and activity which trans-activates DUSP6. Since TRPA1 is known as a signal integrator activated by TDI, we analyzed the relevance of TRPA1 receptor in DUSP6 regulation. Our data revealed that up-regulation of DUSP6 mediated by TDI was blocked by a specific antagonist against TRPA1. TDI exposure attenuated the apoptotic response, which suggests that it promotes the survival of cancerous cells. In conclusion, our results suggest that TDI induces DUSP6 and p53, but attenuates ERK1/2 activity through TRPA1 receptor activation, leading to cytotoxicity.

Revealing the Angiopathy of Lacrimal Gland Lesion in Type 2 Diabetes

For a better understanding of diabetic angiopathy (DA), the potential biomarkers in lacrimal DA and its potential mechanism, we evaluated the morphological and hemodynamic alterations of lacrimal glands (LGs) in patients with type 2 diabetes and healthy counterparts by color Doppler flow imaging (CDFI). We further established a type 2 diabetic mice model and performed hematoxylin-eosin (HE) staining, immunofluorescence staining of CD31, RNA-sequencing analysis, and connectivity map (CMap) analysis. We found atrophy and ischemia in patients with type 2 diabetes and mice models. Furthermore, we identified 846 differentially expressed genes (DEGs) between type 2 diabetes mellitus (T2DM) and vehicle mice by RNA-seq. The gene ontology (GO) analysis indicated significant enrichment of immune system process, regulation of blood circulation, apoptotic, regulation of secretion, regulation of blood vessel diameter, and so on. The molecular complex detection (MCODE) showed 17 genes were involved in the most significant module, and 6/17 genes were involved in vascular disorders. CytoHubba revealed the top 10 hub genes of DEGs, and four hub genes (App, F5, Fgg, and Gas6) related to vascular regulation were identified repeatedly by MCODE and cytoHubba. GeneMANIA analysis demonstrated functions of the four hub genes above and their associated molecules were primarily related to the regulation of circulation and coagulation. CMap analysis found several small molecular compounds to reverse the altered DEGs, including disulfiram, bumetanide, genistein, and so on. Our outputs could empower the novel potential targets to treat lacrimal angiopathy, diabetes dry eye, and other diabetes-related diseases.

Strict network analysis of evolutionary conserved and brain-expressed genes reveals new putative candidates implicated in Intellectual Disability and in Global Development Delay

OBJECTIVES

Intellectual Disability (ID) and Global Development Delay (GDD) are frequent reasons for referral to genetic services and although they present overlapping phenotypes concerning cognitive, motor, language, or social skills, they are not exactly synonymous. Aiming to better understand independent or shared mechanisms related to these conditions and to identify new candidate genes, we performed a highly stringent protein-protein interaction network based on genes previously related to ID/GDD in the Human Phenotype Ontology portal.

METHODS

ID/GDD genes were searched for reliable interactions through STRING and clustering analysis was applied to detect biological complexes through the MCL algorithm. Six coding hub genes (TP53, CDC42, RAC1, GNB1, APP, and EP300) were recognised by the Cytoscape NetworkAnalyzer plugin, interacting with 1625 proteins not yet associated with ID or GDD. Genes encoding these proteins were explored by gene ontology, associated diseases, evolutionary conservation, and brain expression.

RESULTS

One hundred and seventy-two new putative genes playing a role in enriched processes/pathways previously related to ID and GDD were revealed, some of which were already postulated to be linked to ID/GDD in additional databases.

CONCLUSIONS

Our findings expanded the aetiological genetic landscape of ID/GDD and showed evidence that both conditions are closely related at the molecular and functional levels.

Evolutionary and Functional Analysis of Korean Native Pig Using Single Nucleotide Polymorphisms

Time and cost-effective production of next-generation sequencing data has enabled the performance of population-scale comparative and evolutionary studies for various species, which are essential for obtaining the comprehensive insight into molecular mechanisms underlying species- or breed-specific traits. In this study, the evolutionary and functional analysis of Korean native pig (KNP) was performed using single nucleotide polymorphism (SNP) data by comparative and population genomic approaches with six different mammalian species and five pig breeds. We examined the evolutionary history of KNP SNPs, and the specific genes of KNP based on the uniqueness of non-synonymous SNPs among the used species and pig breeds. We discovered the evolutionary trajectory of KNP SNPs within the used mammalian species as well as pig breeds. We also found olfaction-associated functions that have been characterized and diversified during evolution, and quantitative trait loci associated with the unique traits of KNP. Our study provides new insight into the evolution of KNP and serves as a good example for a better understanding of domestic animals in terms of evolution and domestication using the combined approaches of comparative and population genomics.

Using INTERSPIA to Explore the Dynamics of Protein-Protein Interactions Among Multiple Species

INTER-Species Protein Interaction Analysis (INTERSPIA) is a web application for identifying diverse patterns of protein-protein interactions (PPIs) in different species. Given a set of proteins of interest to the user, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins as well as different or common patterns of PPIs among the proteins in multiple species through a server-side pipeline. Second, it visualizes the dynamics of PPIs in multiple species via an easy-to-use web interface. This article contains a basic protocol describing how to visualize diverse patterns of PPIs of input proteins in multiple species, and how to use them for functional analysis in the web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/. © 2019 by John Wiley & Sons, Inc. Basic Protocol: Running INTERSPIA using a list of input proteins.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

New advances in extracting and learning from protein-protein interactions within unstructured biomedical text data

Protein-protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein-protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.