News

Differential decay rates in MDA-LM2 vs. MDA cells

The presence of the structural RNA stability element (sSRE) family of mRNA elements distinguishes transcript stability in metastatic MDA-LM2 breast cancer cell lines from that seen in its parental MDA cell line. Each bin contains differential decay rate measurements for roughly 350 transcripts. From left (more stable in MDA) to right (more stable in MDA-LM2), sRSE-carrying transcripts were enriched among those destabilized in MDA-LM2 cells. The TEISER algorithm collectively depicts sSREs as a generic stem-loop with blue and red circles marking nucleotides with low and high GC content, respectively. Also included are mutual information (MI) values and their associated z-scores. 

Gene expression analysis has become a widely used method for identifying interactions between genes within regulatory networks. If fluctuations in the expression levels of two genes consistently shift in parallel over time, the logic goes, it is reasonable to hypothesize that they are regulated by the same factors. However, such analyses have typically focused on steady-state gene expression, and have not accounted for modifications that messenger RNAs (mRNAs) can undergo during the time between their transcription from DNA and their translation into proteins. Researchers now understand that certain stem loop structures in mRNAs make it possible for proteins to bind to them, often causing RNA degradation and subsequently modulating protein synthesis. From the perspective of systems biology, this can have implications for the activity of entire regulatory networks, and recent studies have even suggested that aberrations in mRNA stability can play a role in disease initiation and progression.

In a new paper published in the journal Nature, Department of Systems Biology Professor Saeed Tavazoie and collaborators at the Rockefeller University describe a new computational and experimental approach for identifying post-transcriptional modifications and investigating their effects in biological systems. In a study of metastatic breast cancer, they determined that when the protein TARBP2 binds to a specific structural element in mRNA transcripts, it increases the likelihood that cancer cells will become invasive and spread. Interestingly, they also found that TARBP2 causes metastasis by binding transcripts of two genes — amyloid precursor protein (APP) and zinc finger protein 395 (ZNF395) — that have previously been implicated in Alzheimer’s disease and Huntington’s disease, respectively. Although the nature of this intersection between the regulatory networks underlying cancer and neurodegenerative diseases is unclear, the finding raises a tantalizing question about whether these very different disorders might be linked at some basic biological level.

geWorkbench screenshot

A new version of geWorkbench lets researchers access a range of powerful, integrated bioinformatics tools using a standard web browser. Here, an ARACNe-generated gene regulatory network is displayed using the Cytoscape Web plugin.

Since its creation in 2005, investigators in Columbia University’s Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) have developed a large number of computational tools for studying biological systems from the perspectives of structural biology and systems biology. To consolidate and disseminate these tools to the wider research community, MAGNet developed geWorkbench (genomics Workbench), a free, open-source bioinformatics application that gathers all of the Center’s software and databases into one integrated software platform. These include applications for the analysis of cellular regulatory networks, protein structure, DNA and protein sequences, gene expression, and other kinds of biological data.

Initially, geWorkbench was made available as a software package that users could install and run on their local computers. Now, in a major upgrade, MAGNet has released a web-based version that makes these tools accessible through a browser interface.

Personalized Medicine

Illustration by Davide Bonazzi, courtesy of Columbia Medicine.

The cover article of the Spring 2014 issue of Columbia Medicine reports on a new, Columbia University-wide effort to harness the potential of new scientific approaches and technological developments to advance the personalized treatment of cancer and other diseases. Announced in February by Columbia President Lee Bollinger, an interdisciplinary task force has been formed to coordinate the scientific, policy, and clinical components necessary to achieve this goal. The Department of Systems Biology, including its Center for Computational Biology and Bioinformatics and JP Sulzberger Columbia Genome Center, has been identified as a key partner in this interdisciplinary effort. As the article reports:

Rapidly evolving technologies that make DNA sequencing dramatically faster and less expensive along with new technologies to monitor virtually all aspects of cell physiology have led to the generation of unprecedented amounts of information (big data) that is starting to yield to new computational approaches and high speed computers, all of which promise to make diagnosis and treatment as patient-specific and precise as possible.

To harness the potential created by these scientific advances for the benefit of patients, [College of Physicians & Surgeons] Dean Lee Goldman, MD, has made personalized medicine a key goal of the medical school’s strategic plan. “At Columbia, we have enthusiastic consensus in support of personalized medicine—the personalized application of scientific advances to modern diagnosis and treatment, easily accessible and attentive care for the people who entrust us with their health, and personalized education for each of our individual students,” says Dr. Goldman…

At the center of Columbia’s personalized medicine effort is the new Department of Systems Biology, which brings together researchers specializing in molecular biology, genetics, computational biology and bioinformatics, structural biology, mathematics, chemistry and chemical biology, physics, computer science, and other fields. According to founding director and chair Andrea Califano, PhD, the Clyde and Helen Wu Professor of Chemical Systems Biology, the new department seeks to provide an in-depth, systemwide characterization of all molecular interactions. It is this systems-level approach to disease biology that can systematically identify disease drivers and druggable targets for the 90 percent of cancer patients who lack a clearly actionable genetic mutation. This has become possible only in recent years through major advances in science and technology that require a fully interdisciplinary approach.

The article describes how research by Department of Systems Biology faculty members including Dr. Califano, Nicholas Tatonetti, Brent Stockwell, and Tuuli Lappalainen, along with that of investigators in departments across the university, is contributing to this ambitious initiative. Read the complete article here.

Sayantan Bose Receives Best Poster Award

Associate Professor Harmen Bussemaker (left) presents the best poster award to postdoc Sayantan Bose for his efforts to develop a high-throughput platform for performing single-cell RNA-Seq.

On May 30, 2014, the Columbia University Department of Systems Biology and its Center for Multiscale Analysis of Genomic and Cellular Networks (MAGNet) held its annual retreat at Tappan Hill Mansion in Tarrytown, NY. The event provided an overview of some recent work undertaken by Department investigators, and provided a comfortable setting for department members to socialize and exchange ideas about their current research interests.

Comparing human and mouse prostate cancer networks

Computational synergy analysis depicting FOXM1 and CENPF regulons from the human (left) and mouse (right) interactomes showing shared and nonshared targets. Red corresponds to overexpressed targets and blue to underexpressed targets.

Two genes work together to drive the most lethal forms of prostate cancer, according to new research by investigators in the Columbia University Department of Systems Biology.  These findings could lead to a diagnostic test for identifying those tumors likely to become aggressive and to the development of novel combination therapy for the disease.

The two genes—FOXM1 and CENPF—had been previously implicated in cancer, but none of the prior studies suggested that they might work synergistically to cause the most aggressive form of prostate cancer. The study was published today in the online issue of Cancer Cell.