Bai Zhang

Bio: Bai Zhang is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Dependency network & Biological network. The author has an hindex of 18, co-authored 45 publications receiving 1637 citations. Previous affiliations of Bai Zhang include Google & Tsinghua University.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

Abstract: To provide a detailed analysis of the molecular com-ponents and underlying mechanisms associatedwith ovarian cancer, we performed a comprehensivemass-spectrometry-based proteomic characteriza-tion of 174 ovarian tumors previously analyzed byThe Cancer Genome Atlas (TCGA), of which 169were high-grade serous carcinomas (HGSCs). Inte-grating our proteomic measurements with thegenomic data yielded a number of insights into dis-ease, such as how different copy-number alterna-tionsinfluencetheproteome,theproteinsassociatedwith chromosomal instability, the sets of signalingpathways that diverse genome rearrangementsconverge on, and the ones most associated withshort overall survival. Specific protein acetylationsassociated with homologous recombination defi-ciency suggest a potential means for stratifying pa-tients for therapy. In addition to providing a valuableresource,thesefindingsprovideaviewofhowtheso-maticgenomedrivesthecancerproteomeandasso-ciations between protein and post-translationalmodification levels and clinical outcomes in HGSC.

Differential dependency network analysis to identify condition-specific topological changes in biological networks

Abstract: Motivation: Significant efforts have been made to acquire data under different conditions and to construct static networks that can explain various gene regulation mechanisms. However, gene regulatory networks are dynamic and condition-specific; under different conditions, networks exhibit different regulation patterns accompanied by different transcriptional network topologies. Thus, an investigation on the topological changes in transcriptional networks can facilitate the understanding of cell development or provide novel insights into the pathophysiology of certain diseases, and help identify the key genetic players that could serve as biomarkers or drug targets. Results: Here, we report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities. We develop an efficient learning algorithm to learn the local dependency model using the Lasso technique. A permutation test is subsequently performed to estimate the statistical significance of each learned local structure. In testing on a simulation dataset, the proposed algorithm accurately detected all the genes with network topological changes. The method was then applied to the estrogen-dependent T-47D estrogen receptor-positive (ER+) breast cancer cell line datasets and human and mouse embryonic stem cell datasets. In both experiments using real microarray datasets, the proposed method produced biologically meaningful results. We expect DDN to emerge as an important bioinformatics tool in transcriptional network analyses. While we focus specifically on transcriptional networks, the DDN method we introduce here is generally applicable to other biological networks with similar characteristics. Availability: The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm. Contact: yuewang@vt.edu Supplementary information:Supplementary data are available at Bioinformatics online.

High-fat or ethinyl-oestradiol intake during pregnancy increases mammary cancer risk in several generations of offspring.

Abstract: Maternal exposures to environmental factors during pregnancy influence the risk of many chronic adult-onset diseases in the offspring. Here we investigate whether feeding pregnant rats a high-fat (HF)- or ethinyl-oestradiol (EE2)-supplemented diet affects carcinogen-induced mammary cancer risk in daughters, granddaughters and great-granddaughters. We show that mammary tumourigenesis is higher in daughters and granddaughters of HF rat dams and in daughters and great-granddaughters of EE2 rat dams. Outcross experiments suggest that the increase in mammary cancer risk is transmitted to HF granddaughters equally through the female or male germ lines, but it is only transmitted to EE2 granddaughters through the female germ line. The effects of maternal EE2 exposure on offspring's mammary cancer risk are associated with changes in the DNA methylation machinery and methylation patterns in mammary tissue of all three EE2 generations. We conclude that dietary and oestrogenic exposures in pregnancy increase breast cancer risk in multiple generations of offspring, possibly through epigenetic means. Environmental factors can influence one's susceptibility to cancer, but it is not clear whether such an influence extends beyond the directly exposed generations. Here, feeding pregnant rats with a high-fat diet or a hormone derivative, the authors observe increased breast cancer risk in up to three subsequent generations.

Overexpression of α (1,6) fucosyltransferase associated with aggressive prostate cancer

Abstract: Aberrant protein glycosylation is known to be associated with the development of cancers. The aberrant glycans are produced by the combined actions of changed glycosylation enzymes, substrates and transporters in glycosylation synthesis pathways in cancer cells. To identify glycosylation enzymes associated with aggressive prostate cancer (PCa), we analyzed the difference in the expression of glycosyltransferase genes between aggressive and non-aggressive PCa. Three candidate genes encoding glycosyltransferases that were elevated in aggressive PCa were subsequently selected. The expression of the three candidates was then further evaluated in androgen-dependent (LNCaP) and androgen-independent (PC3) PCa cell lines. We found that the protein expression of one of the glycosyltransferases, α (1,6) fucosyltransferase (FUT8), was only detected in PC3 cells, but not in LNCaP cells. We further showed that FUT8 protein expression was elevated in metastatic PCa tissues compared to normal prostate tissues. In addition, using tissue microarrays, we found that FUT8 overexpression was statistically associated with PCa with a high Gleason score. Using PC3 and LNCaP cells as models, we found that FUT8 overexpression in LNCaP cells increased PCa cell migration, while loss of FUT8 in PC3 cells decreased cell motility. Our results suggest that FUT8 may be associated with aggressive PCa and thus is potentially useful for its prognosis.

Neutrophil extracellular traps in immunity and disease

Abstract: Neutrophils are innate immune phagocytes that have a central role in immune defence. Our understanding of the role of neutrophils in pathogen clearance, immune regulation and disease pathology has advanced dramatically in recent years. Web-like chromatin structures known as neutrophil extracellular traps (NETs) have been at the forefront of this renewed interest in neutrophil biology. The identification of molecules that modulate the release of NETs has helped to refine our view of the role of NETs in immune protection, inflammatory and autoimmune diseases and cancer. Here, I discuss the key findings and concepts that have thus far shaped the field of NET biology.

LinkedOmics: analyzing multi-omics data within and across 32 cancer types.

Abstract: The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org.