Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine

Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation).The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment

A Statistical Framework for the Analysis of ChIP-Seq Data

Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its increasing and well-deserved popularity, there is little work that investigates and accounts for sources of biases in the ChIP-Seq technology. These biases typically arise from both the standard pre-processing protocol and the underlying DNA sequence of the generated data

The Plasticizer Bisphenol A Perturbs the Hepatic Epigenome: A Systems Level Analysis of the miRNome

Ubiquitous exposure to bisphenol A (BPA), an endocrine disruptor (ED), has raised concerns for both human and ecosystem health. Epigenetic factors, including microRNAs (miRNAs), are key regulators of gene expression during cancer. The effect of BPA exposure on the zebrafish epigenome remains poorly characterized. Zebrafish represents an excellent model to study cancer as the organism develops a disease that resembles human cancer. Using zebrafish as a systems toxicology model, we hypothesized that chronic BPA-exposure impacts the miRNome in adult zebrafish and establishes an epigenome more susceptible to cancer development. After a 3 week exposure to 100 nM BPA, RNA from the liver was extracted to perform high throughput mRNA and miRNA sequencing. Differential expression (DE) analyses comparing BPA-exposed to control specimens were performed using established bioinformatics pipelines. In the BPA-exposed liver, 6188 mRNAs and 15 miRNAs were differently expressed (q ≤ 0.1). By analyzing human orthologs of the DE zebrafish genes, signatures associated with non-alcoholic fatty liver disease (NAFLD), oxidative phosphorylation, mitochondrial dysfunction and cell cycle were uncovered. Chronic exposure to BPA has a significant impact on the liver miRNome and transcriptome in adult zebrafish with the potential to cause adverse health outcomes including cancer

Investigating the effects of chronic low-dose radiation exposure in the liver of a hypothermic zebrafish model

Mankind's quest for a manned mission to Mars is placing increased emphasis on the development of innovative radio-protective countermeasures for long-term space travel. Hibernation confers radio-protective effects in hibernating animals, and this has led to the investigation of synthetic torpor to mitigate the deleterious effects of chronic low-dose-rate radiation exposure. Here we describe an induced torpor model we developed using the zebrafish. We explored the effects of radiation exposure on this model with a focus on the liver. Transcriptomic and behavioural analyses were performed. Radiation exposure resulted in transcriptomic perturbations in lipid metabolism and absorption, wound healing, immune response, and fibrogenic pathways. Induced torpor reduced metabolism and increased pro-survival, anti-apoptotic, and DNA repair pathways. Coupled with radiation exposure, induced torpor led to a stress response but also revealed maintenance of DNA repair mechanisms, pro-survival and anti-apoptotic signals. To further characterise our model of induced torpor, the zebrafish model was compared with hepatic transcriptomic data from hibernating grizzly bears (Ursus arctos horribilis) and active controls revealing conserved responses in gene expression associated with anti-apoptotic processes, DNA damage repair, cell survival, proliferation, and antioxidant response. Similarly, the radiation group was compared with space-flown mice revealing shared changes in lipid metabolism

Hypoxia-dependent mitochondrial fission regulates endothelial progenitor cell migration, invasion, and tube formation

Tumor undergo uncontrolled, excessive proliferation leads to hypoxic microenvironment. To fulfill their demand for nutrient, and oxygen, tumor angiogenesis is required. Endothelial progenitor cells (EPCs) have been known to the main source of angiogenesis because of their potential to differentiation into endothelial cells. Therefore, understanding the mechanism of EPC-mediated angiogenesis in hypoxia is critical for development of cancer therapy. Recently, mitochondrial dynamics has emerged as a critical mechanism for cellular function and differentiation under hypoxic conditions. However, the role of mitochondrial dynamics in hypoxia-induced angiogenesis remains to be elucidated. In this study, we demonstrated that hypoxia-induced mitochondrial fission accelerates EPCs bioactivities. We first investigated the effect of hypoxia on EPC-mediated angiogenesis. Cell migration, invasion, and tube formation was significantly increased under hypoxic conditions; expression of EPC surface markers was unchanged. And mitochondrial fission was induced by hypoxia time-dependent manner. We found that hypoxia-induced mitochondrial fission was triggered by dynamin-related protein Drp1, specifically, phosphorylated DRP1 at Ser637, a suppression marker for mitochondrial fission, was impaired in hypoxia time-dependent manner. To confirm the role of DRP1 in EPC-mediated angiogenesis, we analyzed cell bioactivities using Mdivi-1, a selective DRP1 inhibitor, and DRP1 siRNA. DRP1 silencing or Mdivi-1 treatment dramatically reduced cell migration, invasion, and tube formation in EPCs, but the expression of EPC surface markers was unchanged. In conclusion, we uncovered a novel role of mitochondrial fission in hypoxia-induced angiogenesis. Therefore, we suggest that specific modulation of DRP1-mediated mitochondrial dynamics may be a potential therapeutic strategy in EPC-mediated tumor angiogenesis

Discovering Transcription Factor Binding Sites in Highly Repetitive Regions of Genomes with Multi-Read Analysis of ChIP-Seq Data

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is rapidly replacing chromatin immunoprecipitation combined with genome-wide tiling array analysis (ChIP-chip) as the preferred approach for mapping transcription-factor binding sites and chromatin modifications. The state of the art for analyzing ChIP-seq data relies on using only reads that map uniquely to a relevant reference genome (uni-reads). This can lead to the omission of up to 30% of alignable reads. We describe a general approach for utilizing reads that map to multiple locations on the reference genome (multi-reads). Our approach is based on allocating multi-reads as fractional counts using a weighted alignment scheme. Using human STAT1 and mouse GATA1 ChIP-seq datasets, we illustrate that incorporation of multi-reads significantly increases sequencing depths, leads to detection of novel peaks that are not otherwise identifiable with uni-reads, and improves detection of peaks in mappable regions. We investigate various genome-wide characteristics of peaks detected only by utilization of multi-reads via computational experiments. Overall, peaks from multi-read analysis have similar characteristics to peaks that are identified by uni-reads except that the majority of them reside in segmental duplications. We further validate a number of GATA1 multi-read only peaks by independent quantitative real-time ChIP analysis and identify novel target genes of GATA1. These computational and experimental results establish that multi-reads can be of critical importance for studying transcription factor binding in highly repetitive regions of genomes with ChIP-seq experiments

Network-based Trajectory Analysis of Topic Interaction Map for Text Mining of COVID-19 Biomedical Literature

Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which makes it hard to follow the research in this area without dedicated efforts. It is practically impossible to implement this task manually due to the high volume of the relevant literature. Text mining has been considered to be a powerful approach to address this challenge, especially the topic modeling, a well-known unsupervised method that aims to reveal latent topics from the literature. However, in spite of its potential utility, the results generated from this approach are often investigated manually. Hence, its application to the COVID-19 literature is not straightforward and expert knowledge is needed to make meaningful interpretations. In order to address these challenges, we propose a novel analytical framework for estimating topic interactions and effective visualization for topic interpretation. Here we assumed that topics constituting a paper can be positioned on an interaction map, which belongs to a high-dimensional Euclidean space. Based on this assumption, after summarizing topics with their topic-word distributions using the biterm topic model, we mapped these latent topics on networks to visualize relationships among the topics. Moreover, in the proposed approach, we developed a score that is helpful to select meaningful words that characterize the topic. We interpret the relationships among topics by tracking the change of relationships among topics using a trajectory plot generated with different levels of word richness. These results together provide deeply mined and intuitive representation of relationships among topics related to a specific research area. The application of this proposed framework to the PubMed literature shows that our approach facilitates understanding of the topics constituting the COVID-19 knowledge

Sparse Partial Least Squares Classification for High Dimensional Data

Partial least squares (PLS) is a well known dimension reduction method which has been recently adapted for high dimensional classification problems in genome biology. We develop sparse versions of the recently proposed two PLS-based classification methods using sparse partial least squares (SPLS). These sparse versions aim to achieve variable selection and dimension reduction simultaneously. We consider both binary and multicategory classification. We provide analytical and simulation-based insights about the variable selection properties of these approaches and benchmark them on well known publicly available datasets that involve tumor classification with high dimensional gene expression data. We show that incorporation of SPLS into a generalized linear model (GLM) framework provides higher sensitivity in variable selection for multicategory classification with unbalanced sample sizes between classes. As the sample size increases, the two-stage approach provides comparable sensitivity with better specificity in variable selection. In binary classification and multicategory classification with balanced sample sizes, the two-stage approach provides comparable variable selection and prediction accuracy as the GLM version and is computationally more efficient.