Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g. mammals and microbes) using diverse types of data

Translating Metabolic Reprogramming into New Targets for Kidney Cancer.

In the age of bioinformatics and with the advent of high-powered computation over the past decade or so the landscape of biomedical research has become radically altered. Whereas a generation ago, investigators would study their "favorite" protein or gene and exhaustively catalog the role of this compound in their disease of interest, the appearance of omics has changed the face of medicine such that much of the cutting edge (and fundable!) medical research now evaluates the biology of the disease nearly in its entirety. Couple this with the realization that kidney cancer is a "metabolic disease" due to its multiple derangements in biochemical pathways [1, 2], and clear cell renal cell carcinoma (ccRCC) becomes ripe for data mining using multiple omics approaches

Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes

The advent of genomic-, transcriptomic- and proteomic-based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process-level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro-organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem-level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers

Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

Development of FuGO: An ontology for functional genomics investigations

The development of the Functional Genomics Investigation Ontology (FuGO) is a collaborative, international effort that will provide a resource for annotating functional genomics investigations, including the study design, protocols and instrumentation used, the data generated and the types of analysis performed on the data. FuGO will contain both terms that are universal to all functional genomics investigations and those that are domain specific. In this way, the ontology will serve as the “semantic glue” to provide a common understanding of data from across these disparate data sources. In addition, FuGO will reference out to existing mature ontologies to avoid the need to duplicate these resources, and will do so in such a way as to enable their ease of use in annotation. This project is in the early stages of development; the paper will describe efforts to initiate the project, the scope and organization of the project, the work accomplished to date, and the challenges encountered, as well as future plans

Mapping the Human Exposome to Uncover the Causes of Breast Cancer.

Breast cancer is an important cause of morbidity and mortality for women, yet a significant proportion of variation in individual risk is unexplained. It is reasonable to infer that unexplained breast cancer risks are caused by a myriad of exposures and their interactions with genetic factors. Most epidemiological studies investigating environmental contribution to breast cancer risk have focused on a limited set of exposures and outcomes based on a priori knowledge. We hypothesize that by measuring a rich set of molecular information with omics (e.g., metabolomics and adductomics) and comparing these profiles using a case-control design we can pinpoint novel environmental risk factors. Specifically, exposome-wide association study approaches can be used to compare molecular profiles between controls and either breast cancer cases or participants with phenotypic measures associated with breast cancer (e.g., high breast density, chronic inflammation). Current challenges in annotating compound peaks from biological samples can be addressed by creating libraries of environmental chemicals that are breast cancer relevant using publicly available high throughput exposure and toxicity data, and by mass spectra fragmentation. This line of discovery and innovation will extend understanding of how environmental exposures interact with genetics to affect health, and provide evidence to support new breast cancer prevention strategies

Dissecting apoptosis the omics way

A combined analysis of transcription, translation and protein degradation reveals the global effects of an anticancer drug on tumour cells