Drs. Daniel Rotroff and Alison Motsinger-Reif, in collaboration with the international Metformin Genetics (MetGen) Consortium, have uncovered new genetic evidence of how the benefits of the world’s most commonly used Type 2 diabetes drug, Metformin, may vary between individuals. Metformin is the first-line antidiabetic drug and has over 100 million users worldwide, yet its mechanism of action remains unclear. The study conducted a genome-wide association study (GWAS) that identified a genetic variant in the gene encoding the glucose transporter GLUT2, a protein that plays an important role in transporting glucose inside the body. They showed that those people who carried this variant had reduced levels of GLUT2 in the liver and other tissues resulting in a defect in how the body handles glucose. Metformin acted to specifically reverse this deficiency resulting in a better response to metformin in people carrying this gene variant. This is the largest precision medicine study on an anti-diabetic drug performed to date, and represents an exciting step towards personalized therapy for Type 2 diabetes.
The entire publication can be viewed here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007158/
Please help us in congratulating Jeremy Ash (Bioinformatics, Ph.D.)who recently won second place in the competitive Nvidia ACS COMP poster contest. His work is relevant for the development of extremely predictive QSAR models needed for lead optimization, which is of great interest to pharmaceutical companies for modeling technology. Congratulations, Jeremy!!
This one-day short course offers a brief introduction to command-line operations using the Linux operating system. The primary objective is to provide you with beginner-level familiarity accessing and using a Linux system for common research computing tasks.
This one-day short course is an introduction to the R language for Bioinformatics applications. The main learning objective is to introduce practical coding skills that will allow you to perform basic Bioinformatics tasks and interact with popular tools/applications. These tasks include the creation, extraction, and manipulation of large data sets; the basics of graphics and visualization; and the automation of analysis using scripts. You will develop skills to perform these tasks in the context of practical application vignettes. Previous coding experience is not necessary.
Expression Analysis (Drs. Steffen Heber, Alison Motsinger-Reif, Fred Wright & Yi-Hui Zhou)
– Mon., June 13th & Tues., June 14th
This two-day short course will cover basic concepts of gene expression, including analyses of microarrays and of transcriptomic sequencing. The basic principles of differential expression testing, multiple hypotheses, and false discoveries will be covered. Specific other topics include: the principles of pathway analysis and the utility of genomic annotation; alignment and calling of sequence-based methods, using tools such as tophat, cufflinks, and cuffdiff; and tools for RNA sequence analysis, including the negative binomial model, edgeR, DESeq. CAMERA-ROAST-Voom for pathway analysis.
The Bioinformatics Research Center will be hosting weekly Industry and Government partners in informal talks aimed at partnership, collaboration, and the sharing of ideas. Each Monday at 11:30am, we will welcome a different partner to speak to our Graduate students, faculty and research staff. Talks last roughly 30 minutes, and are followed by pizza. Schedule is as follows:
Drs. Pierce (Chemistry), Fourches (Chemistry, BRC), and Elfenbein (CVM) have received a grant from the Research and Innovation Funding (RISF) program. Their research project is entitled “Development of Novel Therapeutics to Modulate Bacterial Biofilms” and will be conducted in 2016.
The Environmental Protection Agency (EPA) has awarded a 3-year grant to fund a collaboration between Oregon State University (Robert Tanguay, Jane La Du, Mike Simonich, Chris Sullivan) and North Carolina State University (David Reif) entitled “System Toxicological Approaches to Define Flame Retardant Adverse Outcome Pathways”.
From the EPA webpage:
A team of researchers from Oregon State University and North Carolina State University proposes to conduct the first comprehensive in vivo,structure-activity based toxicity studies of flame retardant chemicals (FRCs), including FRCs that EPA has phased out, FRCs that companies manufacture now, and FRCs that companies have proposed as replacements. (They) will test the hypothesis that the toxicity of FRCs will be highly dependent on their chemical structure.
Growing experimental evidences suggest the existence of direct relationships between the surface chemistry of nanomaterials and their biological effects. Herein, we have employed computational approaches to design a set of biologically active carbon nanotubes (CNTs) with controlled protein binding and cytotoxicity. Quantitative structure–activity relationship (QSAR) models were built and validated using a dataset of 83 surface-modified CNTs. A subset of a combinatorial virtual library of 240 000 ligands potentially attachable to CNTs was selected to include molecules that were within the chemical similarity threshold with respect to the modeling set compounds. QSAR models were then employed to virtually screen this subset and prioritize CNTs for chemical synthesis and biological evaluation. Ten putatively active and 10 putatively inactive CNTs decorated with the ligands prioritized by virtual screening for either protein-binding or cytotoxicity assay were synthesized and tested. We found that all 10 putatively inactive and 7 of 10 putatively active CNTs were confirmed in the protein-binding assay, whereas all 10 putatively inactive and 6 of 10 putatively active CNTs were confirmed in the cytotoxicity assay. This proof-of-concept study shows that computational models can be employed to guide the design of surface-modified nanomaterials with the desired biological and safety profiles.
Authors: Denis Fourches, Dongqiuye Pu, Liwen Li, Hongyu Zhou, Qingxin Mu, Gaoxing Su, Bing Yan & Alexander Tropsha
The Bioinformatics Research Center was pleased to present the 2015 C. Clark Cockerham Guest Lecture, with guest speaker, Dr. Nancy Cox of Vanderbilt University. The title of her lecture was, ““New Kinds of Data Integration: Genome x Transcriptome x Electronic Medical Records”. The Bioinformatics Research Center would like to thank all those who attended and especially Dr. Nancy Cox for her time and efforts.
At the recent annual Symposium on Emerging Technologies in Computational Chemistry, BRC Faculty member, Dr. Denis Fourches won the ACS award for the best presentation. The COMP division (Computers in Chemistry) holds a Symposium on Emerging Technologies in Computational Chemistry at the American Chemical Society Fall National Meeting every year. The objective of the symposium is to stimulate, reward, and publicize methodological advances in computational chemistry. Schrödinger, Inc., sponsors a $1,000 prize for the best talk at the symposium. Pre-selected talks are evaluated at the meeting by a panel of experts based on the quality of the presentation and the impact that the research will have on the future of computational chemistry and allied sciences. More info at http://web2011.acscomp.org/awards/symposium-on-emerging-computational-technologies.
Recent research conducted by BRC Director Fred Wright and Ivan Rusyn of Texas A&M University (https://news.ncsu.edu/2015/05/wright-chem-sensitivity/) resulted in a treasure trove of data on variation of chemical sensitivities in human cell lines. In collaboration with the National Institute of Environmental Health Sciences and the National Center for Advancing Translational Sciences, the researchers had studied over 1000 cell lines and exposure to 179 chemicals, with the goal of mapping genetic variation in toxicity response. Says Wright “the data are so rich that we really are just scratching the surface.” Expanding the collaboration to include Sage Bionetworks and a crowd-sourcing initiative known as Dream Challenges, the bioinformatics community was enlisted to further mine the data, providing prediction algorithms that can aid future researchers in predicting toxicity based on DNA profiles and chemical structures. An article describing the results of this crowdsourcing effort recently appeared in Nature Biotechnology (http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html).
Image credit: Nature Biotechnology (2015) doi:10.1038/nbt.3299, under Creative Commons License