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As a member of Columbia University’s Program for Mathematical Genomics (PMG) , Tal Korem, PhD, is bringing his interests in systems biology, quantitative research, and the human microbiome to areas of clinical relevance. For Dr. Korem, that clinical focus is women’s reproductive health. 

“There is still a lot we don’t understand that relates to women’s health, to fertility, and to birth outcomes, and how microbes play a role in all of this,” says Dr. Korem, assistant professor of systems biology, with a joint appointment in obstetrics and gynecology at Columbia University Vagelos College of Physicians and Surgeons. A current focus of the Korem lab is preterm birth, i.e., birth that occurs prior to 37 weeks of gestation, though Dr. Korem intends to expand into other areas such as infertility and endometriosis. 

Tal Korem, PhD
Tal Korem, PhD

Dr. Korem’s interest in  women’s health research is personal, stemming from several impactful experiences that hit close to home. 

“My aunt passed away from ovarian cancer and I have seen friends and family members struggle with idiopathic infertility,” he says. “Also, witnessing the complications with the birth of my first child, which involved emergency procedures, motivated my interest in this area, and I am very excited about the potential to contribute to women’s health with my own research.” 

Dr. Korem, a native of Tel Aviv, Israel, is the first in his family to earn a PhD, and had entered academia as a medical student. After completing  his undergraduate degree, he enrolled in a MD/PhD graduate program. There, he realized that research was what he enjoyed the most. He is a trained computational biologist, and studied under Professor Eran Segal at the Weizmann Institute of Science, where his work focused on the  human microbiome, a complex system of microbial communities that inhabit every body part. 

A wide range of research topics, from studies related to pediatric cancer and glioblastoma to soil microbial communities and electronic health records analysis—were presented and discussed at this year’s Department of Systems Biology (DSB) retreat. 

DSB Retreat 2019
Eugene Douglass, postdoctoral research scientist in the Andrea Califano lab, was one of the featured presenters at the two-day retreat. For a photo gallery, view the DSB Retreat Photo Album.

Held over two days for the first time, October 3 to 4 in Ellenville, NY, the retreat gave DSB faculty, post-docs, and students a chance to get away from the bustle of New York City, learn about their peers’ research, both from Morningside labs and CUIMC labs, and network. The department this year expanded its annual program over two days, encouraging more peer-to-peer connecting and devoted the spotlight specifically to research by young investigators. 

DSB researchers and graduate students participated in a poster competition held the first evening, and reviewed by Systems Biology faculty judges. At the end of the second day’s program, three Best Poster winners were announced by Andrea Califano, Dr, chair of the department. Poster competition winners this year were: Dafni Glinos , PhD, postdoctoral researcher in the lab of Dr. Tuuli Lappalainen at New York Genome Center/Systems Biology; Alexander Kitaygorodsky , a graduate student in the lab of Dr. Yufeng Shen; and Jordan Metz , an MD/PhD graduate student in the lab of Dr. Peter Sims. The poster winners gave presentations on the final day of the retreat and received a cash prize and an award certificate. 

Winning Research:

Dafni Glinos

Long-Read Sequencing to Study Allelic Effects on Transcriptome Structure

Dr. Tuuli Lappalainen Science Study

The illustration above depicts with an example of four genes, how knowing how variable genes are in the normal population helps to find candidate disease genes in a patient. Above, top: Tuuli Lappalainen, PhD; bottom: Pejman Mohammadi, PhD.

For individuals with rare diseases, getting a diagnosis is often a long and complicated odyssey. Over the past few years, this has been greatly improved by genome sequencing that can pinpoint the mutation that breaks a gene and leads to a severe disease. However, this approach is still unsuccessful in the majority of patients, largely because of our inability to read the genome to identify all mutations that disrupt gene function.

In a new study published on October 10 in Science , researchers from New York Genome Center , Columbia University , and Scripps Research Institute propose a solution to this problem. Building a new computational method for analyzing genomes together with transcriptome data from RNA-sequencing, they can now identify genes where genetic variants disrupt gene expression in patients and improve the diagnosis of rare genetic disease.

The new method introduced in this study, Analysis of Expression Variation or ANEVA, first takes allele-specific expression data from a large reference sample of healthy individuals to understand how much genetic regulatory variation each gene harbors in the normal population. Then, using the ANEVA Dosage Outlier Test, researchers can analyze the transcriptome of any individual – such as a patient – to find a handful of genes where he or she carries a genetic variant with an unusually large effect compared to what healthy individuals have. By applying this test to a cohort of muscle dystrophy and myopathy patients, the researchers demonstrated  the performance of their method and diagnosed additional patients where previous methods of genome and RNA analysis had failed to find the broken genes.

MAGIC - Wang Lab

Illustrated here: (a) In contrast to traditional approaches to cultivate microbes first and then test for genetic accessibility, MAGIC harnesses horizontal gene transfer in the native environment to genetically modify bacteria in situ. (b) MAGIC implementation to transfer replicative or integrative pGT vectors from an engineered donor strain into amenable recipients in a complex microbiome. Replicative vectors feature a broad-host range origin of replication (oriR), while integrative vectors contain a transposable Himar cassette and transposase. The donor E. coli strain contains genomically integrated conjugative transfer genes (tra) and a mCherry gene. Transconjugant bacteria are detectable based on expression of an engineered payload that includes GFP and an antibiotic resistance gene (abr).

A team of researchers, led by Dr. Harris Wang of the Department of Systems Biology , has engineered bacteria to benefit and improve the overall health of our gut microbiome. In a proof-of-concept paper published in Nature Methods , Dr. Wang and his team demonstrate MAGIC, an innovative gene delivery system that ‘hacks’ the gut microbiome to perform any desired function, from harvesting energy from food and protecting against pathogen invasion to bolstering anti-inflammatory properties and regulating immune responses.

“The MAGIC system allows us to insert new gene functions directly into an existing microbiome without permanently altering the composition of the microbiome as a whole,” says Sway Chen , an MD/PhD student in the Wang lab and co-author of the study.

Time lapse of a developing drosophila embryo. (Credit: Carlos Sanchez-Higueras/Hombría lab/CABD)

Every animal, from an ant to a human, contains in their genome pieces of DNA called Hox genes. Architects of the body, these genes are keepers of the body’s blueprints; they dictate how embryos grow into adults, including where a developing animal puts its head, legs and other body parts. Scientists have long searched for ways to decipher how Hox genes create this body map; a key to decoding how we build our bodies.

Now an international group of researchers from Columbia University and the Spanish National Research Council (CSIC) based at the Universidad Pablo de Olavide in Seville, Spain have found one such key: a method that can systematically identify the role each Hox gene plays in a developing fruit fly. Their results, reported recently in Nature Communications , offer a new path forward for researchers hoping to make sense of a process that is equal parts chaotic and precise, and that is critical to understanding not only growth and development but also aging and disease.

“The genome, which contains thousands of genes and millions of letters of DNA, is the most complicated code ever written,” said study co-senior author Richard Mann , PhD, principal investigator at Columbia’s Mortimer B. Zuckerman Mind Brain Behavior Institute and a faculty member in the Department of Systems Biology . “Deciphering this code has proven so difficult because evolution wrote it in fits and starts over hundreds of millions of years. Today’s study offers a key to cracking that code, bringing us closer than ever to understanding how Hox genes build a healthy body, or how this process gets disrupted in disease.”

Read the full article at the Zuckerman Institute