Bridging Functional Genomics and Toxicogenomics Through DNA Microarrays in a Fish Model

In a case study of finding gene expression signatures for environmental stressors in Cyprinodon variegatus, this dissertation examines several important issues of applying DNA microarray technology to fish toxicogenomics. The most relevant disciplines, fish toxicogenomics and computational systems biology are reviewed in Chapter 1. Chapter 2 reviews major aspects of DNA microarray technology. On DNA microarrays, even for probes that target the same transcript, large variations are seen in the probe signals. These variations are partly dependent and partly independent on probe sequences. Chapter 3 estimates the sequence independent variation by combining experimental and computational approaches. Chapter 4 and 5 take on the central problem of sequence dependent variations, that is, modeling the physiochemistry of microarray hybridization. I propose a new competitive hybridization model, which demonstrates good success on publically available benchmark data. This new model leads the way to quantification of absolute target concentration, and brings critical insights into probe design and data interpretation on DNA microarrays. Our model relies on the accuracy of computing duplexing energy, which does yet not take into account secondary structures of probes and targets. I further explore the structural effects in Chapter 6. In order to see the complete Abstract, please download the dissertation

Blood Transcriptomics and Metabolomics for Mersonalized Medicine

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting N10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment

Trackable and scalable LC-MS metabolomics data processing using asari.

Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable

Constructing a fish metabolic network model

We report the construction of a genome-wide fish metabolic network model, MetaFishNet, and its application to analyzing high throughput gene expression data. This model is a stepping stone to broader applications of fish systems biology, for example by guiding study design through comparison with human metabolism and the integration of multiple data types. MetaFishNet resources, including a pathway enrichment analysis tool, are accessible at http://metafishnet.appspot.com

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination.

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels

Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing.

With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography-Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized quality control (QC) plasma samples and from real biological studies, the new method resulted in feature tables with better consistency, as well as better down-stream analysis results. The method can be a useful addition to the tools available for large studies involving multiple batches. The method is available as part of the apLCMS package. Download link and instructions are at https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca

Network Topology of Biological Aging and Geroscience-Guided Approaches to COVID-19.

Aging has emerged as the greatest and most prevalent risk factor for the development of severe COVID-19 infection and death following exposure to the SARS-CoV-2 virus. The presence of multiple co-existing chronic diseases and conditions of aging further enhances this risk. Biological aging not only enhances the risk of chronic diseases, but the presence of such conditions further accelerates varied biological processes or hallmarks implicated in aging. Given growing evidence that it is possible to slow the rate of many biological aging processes using pharmacological compounds has led to the proposal that such geroscience-guided interventions may help enhance immune resilience and improve outcomes in the face of SARS-CoV-2 infection. Our review of the literature indicates that most, if not all, hallmarks of aging may contribute to the enhanced COVID-19 vulnerability seen in frail older adults. Moreover, varied biological mechanisms implicated in aging do not function in isolation from each other, and exhibit intricate effects on each other. With all of these considerations in mind, we highlight limitations of current strategies mostly focused on individual single mechanisms, and we propose an approach which is far more multidisciplinary and systems-based emphasizing network topology of biological aging and geroscience-guided approaches to COVID-19