Co-community Structure in Time-varying Networks

In this report, we introduce the concept of co-community structure in time-varying networks. We propose a novel optimization algorithm to rapidly detect co-community structure in these networks. Both theoretical and numerical results show that the proposed method not only can resolve detailed co-communities, but also can effectively identify the dynamical phenomena in these networks.Comment: 5 pages, 6 figure

Functional Analysis of Single Nucleotide Polymorphisms Associated with Type 2 Diabetes

Type 2 diabetes (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a life-long, common, complex disease of major public health importance. To date, there have been 86 published studies that have reported 639 associations between single nucleotide polymorphisms (SNPs) and T2D in the GWAS Catalog database, and others studies in literature. However, the majority (~93%) of the SNPs emerging from these studies are located within noncoding sequence, complicating their functional evaluation. Recently, several lines of evidence have suggested the involvement of a proportion of such variants in transcriptional regulatory mechanisms, including modulation of promoter and enhancer elements and enrichment within expression quantitative trait loci (eQTL). In this study, we downloaded T2D-associated SNPs from GWASdb, a derived database that included the data from GWAS Catalog. We then annotated them with transcription factor (TF) motif, promoter/enhancer, and eQTL information followed by the construction of a TF-target network module, in order to better detect the underlying mechanism of genetic variants involving in T2D. We found that T2D associated SNPs were significantly enriched with functional information. In addition, we found that functional annotations could significantly improve the power of detecting causal variants and understanding their pathogenesis. Using the data collected from the Gene-Tissue Expression Project (GTEx), we could further find the target genes for those eQTL SNPs. When cross-referencing with the Drug Bank database, we were able to discover certain drugs that might regulate the expression of these genes and fight against T2D

An integrative functional genomics framework for effective identification of novel regulatory variants in genome–phenome studies

Background: Genome–phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases. Methods: In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx. Results: We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer’s disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1). Conclusions: This study offers powerful tools for exploring the functional consequences of variants generated from genome–phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits. Electronic supplementary material The online version of this article (10.1186/s13073-018-0513-x) contains supplementary material, which is available to authorized users

Mechanotransduction regulates inflammation responses of epicardial adipocytes in cardiovascular diseases

Adipose tissue is a crucial regulator in maintaining cardiovascular homeostasis by secreting various bioactive products to mediate the physiological function of the cardiovascular system. Accumulating evidence shows that adipose tissue disorders contribute to several kinds of cardiovascular disease (CVD). Furthermore, the adipose tissue would present various biological effects depending on its tissue localization and metabolic statuses, deciding the individual cardiometabolic risk. Crosstalk between adipose and myocardial tissue is involved in the pathophysiological process of arrhythmogenic right ventricular cardiomyopathy (ARVC), cardiac fibrosis, heart failure, and myocardial infarction/atherosclerosis. The abnormal distribution of adipose tissue in the heart might yield direct and/or indirect effects on cardiac function. Moreover, mechanical transduction is critical for adipocytes in differentiation, proliferation, functional maturity, and homeostasis maintenance. Therefore, understanding the features of mechanotransduction pathways in the cellular ontogeny of adipose tissue is vital for underlining the development of adipocytes involved in cardiovascular disorders, which would preliminarily contribute positive implications on a novel therapeutic invention for cardiovascular diseases. In this review, we aim to clarify the role of mechanical stress in cardiac adipocyte homeostasis and its interplay with maintaining cardiac function

Accuracy of triage strategies for human papillomavirus DNA-positive women in low-resource settings: A cross-sectional study in China

CareHPV is a human papillomavirus (HPV) DNA test for low-resource settings (LRS). This study assesses optimum triage strategies for careHPV-positive women in LRS

ARP2/3- and resection-coupled genome reorganization facilitates translocations [preprint]

DNA end-resection and nuclear actin-based movements orchestrate clustering of double-strand breaks (DSBs) into homology-directed repair (HDR) domains. Here, we analyze how actin nucleation by ARP2/3 affects damage-dependent and -independent 3D genome reorganization and facilitates pathologic repair. We observe that DNA damage, followed by ARP2/3-dependent establishment of repair domains enhances local chromatin insulation at a set of damage-proximal boundaries and affects compartment organization genome-wide. Nuclear actin polymerization also promotes interactions between DSBs, which in turn facilitates aberrant intra- and inter-chromosomal rearrangements. Notably, BRCA1 deficiency, which decreases end-resection, DSB mobility, and subsequent HDR, nearly abrogates recurrent translocations between AsiSI DSBs. In contrast, loss of functional BRCA1 yields unique translocations genome-wide, reflecting a critical role in preventing spontaneous genome instability and subsequent rearrangements. Our work establishes that the assembly of DSB repair domains is coordinated with multiscale alterations in genome architecture that enable HDR despite increased risk of translocations with pathologic potential

My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype

Massive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces (“edgetic”) and 311,022 functional sites (“nodetic”), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org

MGMT genomic rearrangements contribute to chemotherapy resistance in gliomas.

Temozolomide (TMZ) is an oral alkylating agent used for the treatment of glioblastoma and is now becoming a chemotherapeutic option in patients diagnosed with high-risk low-grade gliomas. The O-6-methylguanine-DNA methyltransferase (MGMT) is responsible for the direct repair of the main TMZ-induced toxic DNA adduct, the O6-Methylguanine lesion. MGMT promoter hypermethylation is currently the only known biomarker for TMZ response in glioblastoma patients. Here we show that a subset of recurrent gliomas carries MGMT genomic rearrangements that lead to MGMT overexpression, independently from changes in its promoter methylation. By leveraging the CRISPR/Cas9 technology we generated some of these MGMT rearrangements in glioma cells and demonstrated that the MGMT genomic rearrangements contribute to TMZ resistance both in vitro and in vivo. Lastly, we showed that such fusions can be detected in tumor-derived exosomes and could potentially represent an early detection marker of tumor recurrence in a subset of patients treated with TMZ

Research on an online self-organizing radial basis function neural network

A new growing and pruning algorithm is proposed for radial basis function (RBF) neural network structure design in this paper, which is named as self-organizing RBF (SORBF). The structure of the RBF neural network is introduced in this paper first, and then the growing and pruning algorithm is used to design the structure of the RBF neural network automatically. The growing and pruning approach is based on the radius of the receptive field of the RBF nodes. Meanwhile, the parameters adjusting algorithms are proposed for the whole RBF neural network. The performance of the proposed method is evaluated through functions approximation and dynamic system identification. Then, the method is used to capture the biochemical oxygen demand (BOD) concentration in a wastewater treatment system. Experimental results show that the proposed method is efficient for network structure optimization, and it achieves better performance than some of the existing algorithms