Ashkenazi Population Bottleneck Model
The consortium’s model of Ashkenazi Jewish ancestry suggests that the population’s history was shaped by three critical bottleneck events. The ancestors of both populations underwent a bottleneck sometime between 85,000 and 91,000 years ago, which was likely coincident with an Out-of-Africa event. The founding European population underwent a bottleneck at approximately 21,000 years ago, beginning a period of interbreeding between individuals of European and Middle Eastern ancestry. A severe bottleneck occurred in the Middle Ages, reducing the population to under 350 individuals. The modern-day Ashkenazi community emerged from this group.

An international research consortium led by Associate Professor Itsik Pe’er has produced a new panel of reference genomes that will significantly improve the study of genetic variation in Ashkenazi Jews. Using deep sequencing to analyze the genomes of 128 healthy individuals of Ashkenazi Jewish origin, The Ashkenazi Genome Consortium (TAGC) has just published a resource that will be much more effective than previously available European reference genomes for identifying disease-causing mutations within this historically isolated population. Their study also provides novel insights into the historical origins and ancestry of the Ashkenazi community. A paper describing their study has just been published online in Nature Communications.

The dataset produced by the consortium provides a high-resolution baseline genomic profile of the Ashkenazi Jewish population, which they revealed to be significantly different from that found in non-Jewish Europeans. In the past, clinicians’ only option for identifying disease-causing mutations in Ashkenazi individuals was to compare their genomes to more heterogeneous European reference sets. This new resource accounts for the historical isolation of this population, and so will make genetic screening much more accurate in identifying disease-causing mutations.

In an article that appears on the website of Columbia University’s Fu Foundation School of Engineering and Computer Science, Dr. Pe’er explains:

“Our study is the first full DNA sequence dataset available for Ashkenazi Jewish genomes... With this comprehensive catalog of mutations present in the Ashkenazi Jewish population, we will be able to more effectively map disease genes onto the genome and thus gain a better understanding of common disorders. We see this study serving as a vehicle for personalized medicine and a model for researchers working with other populations.”

In addition to offering an important resource for such future translational and clinical research, the paper’s findings also provide new insights that have implications for the much debated question of how European and Ashkenazi Jewish populations emerged historically.

Illumina NextSeq 500 at Columbia University

As genome sequencing technologies evolve, the JP Sulzberger Columbia Genome Center continues to provide the Columbia University biological and biomedical research community access to state-of-the-art tools. In its most recent acquisition, the Genome Center has just installed two Illumina NextSeq 500 sequencers. The NextSeq 500 is a flexible and efficient desktop sequencer that offers powerful high-throughput sequencing capabilities.

Columbia investigators who are experienced with the Illumina next-generation sequencing platform can now schedule time to use the NextSeq 500 for their own research. This new platform complements the Genome Center’s Illumina MiSeq, which is also available for self-service usage.

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.