News

Andrew Anzalone and Sakellarios ZairisMD/PhD students Andrew Anzalone and Sakellarios Zairis combined approaches based in chemical biology, synthetic biology, and computational biology to develop a new method for protein engineering.

The ribosome is a reliable machine in the cell, precisely translating the nucleotide code carried by messenger RNAs (mRNAs) into the polypeptide chains that form proteins. But although the ribosome typically reads this code with uncanny accuracy, translation has some unusual quirks. One is a phenomenon called -1 programmed ribosomal frameshifting (-1 PRF), in which the ribosome begins reading an mRNA one nucleotide before it should. This hiccup bumps translation “out of frame,” creating a different sequence of three-nucleotide-long codons. In essence, -1 PRF thus gives a single gene the unexpected ability to code for two completely different proteins.

Recently Andrew Anzalone, an MD/PhD student in the laboratory of Virginia Cornish, set out to explore whether he could take advantage of -1 PRF to engineer cells capable of producing alternate proteins. Together with Sakellarios Zairis, another MD/PhD student in the Columbia University Department of Systems Biology, the two developed a pipeline for identifying RNA motifs capable of producing this effect, as well as a method for rationally designing -1 PRF “switches.” These switches, made up of carefully tuned strands of RNA bound to ligand-sensing aptamers, can react to the presence of a specific small molecule and reliably modulate the ratio in the production of two distinct proteins from a single mRNA. The technology, they anticipate, could offer a variety of exciting new applications for synthetic biology. A paper describing their approach and findings has been published in Nature Methods.

cQTLs modify TF binding

Cofactors work with transcription factors (TFs) to enable efficient transcription of a TF's target gene. The Bussemaker Lab showed that genetic alterations in the cofactor gene (cQTLs) change the nature of this interaction, affecting the connectivity between the TF and its target gene. This, combined with other factors called aQTLs that affect the availability of the TF in the nucleus, can lead to downstream changes in gene expression.

When different people receive the same drug, they often respond to it in different ways — what is highly effective in one patient can often have no benefit or even cause dangerous side effects in another. From the perspective of systems biology, this is because variants in a person’s genetic code lead to differences in the networks of genes, RNA, transcription factors (TFs), and other proteins that implement the drug’s effects inside the cell. These multilayered networks are much too complex to observe directly, and so systems biologists have been developing computational methods to infer how subtle differences in the genome sequence produce these effects. Ultimately, the hope is that this knowledge could improve scientists’ ability to identify drugs that would be most effective in specific patients, an approach called precision medicine.

In a paper published in the Proceedings of the National Academy of Sciences, a team of Columbia University researchers led by Harmen Bussemaker proposes a novel approach for discovering some critical components of this molecular machinery. Using statistical methods to analyze biological data in a new way, the researchers identified genetic alterations they call connectivity quantitative trait loci (cQTLs), a class of variants in transcription cofactors that affect the connections between specific TFs and their gene targets.

Staphylococcus epidermis
Interactions between human cells and the bacteria that inhabit our bodies can affect health. Here, Staphylococcus epidermis binds to nasal epithelial cells. (Image courtesy of Sheetal Trivedi and Sean Sullivan.)

Launched in 2014 by investigators in the Mailman School of Public Health, the CUMC Microbiome Working Group brings together basic, clinical, and population scientists interested in understanding how the human microbiome—the ecosystems of bacteria that inhabit and interact with our tissues and organs—affects our health. Computational biologists in the Department of Systems Biology have become increasingly involved in this interdepartmental community, contributing expertise in analytical approaches that make it possible to make sense of the large data sets that microbiome studies generate.

February 24, 2016

Barry Honig Named ISCB Fellow

Barry Honig The International Society for Computational Biology has elected Professor Barry Honig to its 2016 ISCB Class of Fellows. The award recognizes distinguished ISCB members who shown excellence in research and/or service to the computational biology community. Dr. Honig’s award acknowledges his “seminal contributions to protein structure prediction and molecular electrostatics, and his more recent work on protein function prediction, protein-DNA recognition, and cell-cell adhesion.”

The International Society for Computational Biology is the largest professional society for scientists working in the fields of computational biology and bioinformatics. The 2016 Class of Fellows will be presented at its annual Intelligent Systems for Molecular Biology (ISMB) conference, to be held July 8-12, 2016 in Orlando, Florida.

Nicholas Tatonetti
Nicholas Tatonetti is an assistant professor in the Department of Biomedical Informatics and Department of Systems Biology.

A team of Columbia University Medical Center (CUMC) scientists led by Nicholas Tatonetti has identified several drug combinations that may lead to a potentially fatal type of heart arrhythmia known as torsades de pointes (TdP). The key to the discovery was a new bioinformatics pipeline called DIPULSE (Drug Interaction Prediction Using Latent Signals and EHRs), which builds on previous methods Tatonetti developed for identifying drug-drug interactions (DDIs) in observational data sets. The results are reported in a new paper in the journal Drug Safety and are covered in a detailed multimedia feature published by the Chicago Tribune.

The algorithm mined data contained in the US FDA Adverse Event Reporting System (FAERS) to identify latent signals of DDIs that cause QT interval prolongation, a disturbance in the electrical cycle that coordinates the heartbeat. It then validated these predictions by looking for their signatures in electrocardiogram results contained in a large collection of electronic health records at Columbia. Interestingly, the drugs the investigators identified do not cause the condition on their own, but only when taken in specific combinations.

Previously, no reliable methods existed for identifying these kinds of combinations. Although the findings are preliminary, the retrospective confirmation of many of DIPULSE’s predictions in actual patient data suggests its effectiveness, and the investigators plan to test them experimentally in the near future.