The seemingly chaotic bacterial soup of the gut microbiome is more organized than it first appears and follows some of the same ecological laws that apply to birds, fish, tropical rainforests, and even complex economic and financial markets, according to a new paper in Nature Microbiology by researchers led by Dennis Vitkup , PhD, associate professor of systems biology , at Columbia Univesrity Irving Medical Center .

One of the main challenges facing researchers who study the gut microbiome is its sheer size and amazing organizational complexity. Many trillions of bacteria, representing thousands of different species, live in the human intestinal tract, interacting with each other and the environment in countless and constantly changing ways.

"Up to now, it has been an open question whether there are any natural laws describing dynamics of these complex bacterial communities.”-Dr. Vitkup

The study’s discovery of multiple principles of gut bacterial dynamics should help researchers to understand what makes a gut microbiome healthy, how it may become perturbed in disease and unhealthy diets, and also suggest ways we could alter microbiomes to improve health. Read the full article in the CUIMC Newsroom. 

The study is titled “Macroecological dynamics of gut microbiota.” The other contributors are Brian W. Ji (Columbia), Ravi U. Sheth (Columbia), Purushottam D. Dixit (Columbia and University of Florida, Gainesville, FL), and Konstantine Tchourine (Columbia).

Dr. Raul Rabadan coauthors new book that introduces techniques of topological data analysis, a rapidly growing subfield of mathematics. (Cambridge University Press)

The deluge of data in the diverse field of biology comes with it the challenge of extracting meaningful information from large biological data sets. A new book, Topological Data Analysis for Genomics and Evolution, introduces central ideas and techniques of topological data analysis and aims to explain in detail a number of specific applications to biology.

“High-throughput genomics has profoundly transformed the field of modern biology and has made it possible for scientists to make rapid scientific advances,” says the book’s co-author Dr. Raul Rabadan, professor of systems biology and founding director of Columbia University’s Program for Mathematical Genomics. “The explosion of data has hit biology, and as a result, we need new, more innovative analytical and computational tools to make sense of it all.”

Co-authored with Andrew J. Blumberg, PhD, professor of mathematics at University of Texas at Austin, the new book discusses techniques of topological data analysis, a rapidly developing subfield of mathematics that provides a methodology for analyzing the shape of data sets. The book offers several examples of these techniques and their use in multiple areas of biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes.

Raul Rabadan, PhD, (standing) with Francesco Brundu, postdoctoral research scientist in the Rabadan lab (Credit: Jeffrey Schifman)

Congratulations to Drs. Raul Rabadan and Xuebing Wu who were recently named a Highly Cited Researcher, according to the 2019 list from the Web of Science Group . Overall, Columbia University ranked 15th on the list of global institutions, with a total of 47 Highly Cited Researchers.

The Highly Cited Researchers list, which was released Nov. 19,  identifies scientists and social scientists who have produced multiple papers ranking in the top 1% by citations for their field and year of publication, demonstrating significant research influence among their peers.

Xuebing Wu, PhD

Dr. Rabadan is professor of systems biology , with a joint appointment in biomedical informatics, at Columbia’s Vagelos College of Physicians and Surgeons . At Columbia, the Rabadan lab consists of an interdisciplinary team developing mathematical and computational tools to extract useful biological information from large data sets. In 2017, Dr. Rabadan established the Program for Mathematical Genomics , a multidisciplinary research hub that brings together researchers from the fields of mathematics, physics, computer science, engineering, and medicine, with the common goal of solving pressing biomedical problems through quantitative methods and analyses. He also serves as program lead for the Cancer Genomics and Epigenomics Program at the Herbert Irving Comprehensive Cancer Center at NYP/Columbia. 

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

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. 

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. 

Protein engineering is a relatively young field that creates new proteins never seen before in nature. Today’s protein engineers usually create synthetic proteins by making small changes to the gene that encodes a naturally occurring protein. The variety of synthetic proteins range from stain-removing enzymes that have improved detergents to a long-acting insulin that’s used by millions of people with diabetes.

But two big unsolved challenges for protein engineers remain: The gene encoding the synthetic protein needs to be contained to prevent escape into other organisms and the gene needs to resist mutating over time so the protein doesn’t lose its function.

By merging two genes into a single DNA sequence, Columbia University synthetic biologists have created a method that could prevent human-engineered proteins from spreading into the wild, as well as stabilize synthetic proteins so they don’t change over time. The work, recently published in Science, was developed by Harris Wang, PhD, assistant professor of systems biology, with graduate student, Tomasz Blazejewski and postdoctoral scientist, Hsing-I Ho, PhD. 

In devising the method, the researchers were inspired by overlapping genes in viruses. When two different genes overlap, they occupy the same sequence of DNA. But the genes are read in different frames so that two different proteins are produced.

In overlapping genes, a random mutation in the sequence may not affect one gene, but it’s likely that it will harm the second gene.

“Overlapping genes essentially lock in a specific DNA sequence, and we thought we could exploit this idea for synthetic biology ...Ten years ago, we didn’t have the technology that would make this possible,” says Dr. Wang. “We didn’t have enough sequences in the database to make informed predictions and we didn’t have a way to synthesize long DNA sequences for testing our predictions.”

Columbia investigators win Chan Zuckerberg Initiative grants to accelerate development of cellular roadmap of the human body.

In two groundbreaking research projects contributing to the Human Cell Atlas, Columbia University scientists are tasked with mapping complete cells in the immune system and the human spine. The global effort is aiming to identify and define every cell type of the human body and create a collection of maps for navigating the cellular basis of human health and disease.

Peter Sims, PhD, assistant professor of systems biology

The Columbia teams, which include co-principal investigators from the Department of Systems Biology Drs. Peter Sims and Raul Rabadan , are among the 38 collaborative science teams launching the Chan Zuckerberg Initiative’s (CZI) Seed Networks for the Human Cell Atlas project announced today. The three-year projects, receiving a total of $68 million in award funding by Seed Networks, are collaborative groups that are bringing together expertise in science, computational biology, software engineering, and medicine to support the ongoing progress of the Human Cell Atlas .

Investigating the Immune System + Aging

Dr. Sims, part of an international team including close collaborator Dr. Donna Farber of the Department of Surgery , is combining single-cell sequencing technologies, data analysis, and immunology expertise to better understand how the immune system ages and gain new insights into how human diseases occur. 

Dr. Andrea Califano sits down with BioTechniques at AACR. Video: Courtesy of BioTechniques.

At the 2019 annual meeting of the American Association for Cancer Research (AACR), Dr. Andrea Califano sat down with BioTechniques News for an overview on the field of systems biology and its impact in cancer research and in precision medicine. Dr. Califano is a pioneering researcher in the fast-growing field of systems biology whose expertise is in developing innovative, systematic approaches to identify the molecular factors that lead to cancer progression and to the emergence of drug resistance at the single-cell level. A physicist by training, Dr. Califano is the Clyde and Helen Wu Professor of Chemical and Systems Biology, founding chair of the Department of Systems Biology at Columbia University Irving Medical Center, director of the Columbia Genome Center and a program leader at the Herbert Irving Comprehensive Cancer Center.

The video interview is part of the series, Behind the Technqiue by BioTechniques News. 

Phyllis Thangaraj, MD/PhD student (Tatonetti lab)

Aspiring physician-scientists from Columbia's Vagelos College of Physicians and Surgeons presented their research posters at the 14th annual MD-PhD Student Research Symposium on April 25. Their research delved into a range of topics, including Alzheimer’s disease, stroke, and stem cells. The event included a guest lecture by an alumna about her own career path as a physician-scientist, and culminated in the poster session judged by MD-PhD alumni who currently work at the University. Department of Systems Biology’s Phyllis Thangaraj, an MD/PhD student in the Nicholas Tatonetti lab , was named one of five poster winners at the event. 

She presented work on applying machine learning methods to phenotype acute ischemic stroke patients in the electronic health records. In cohort research studies, it is essential to identify a large number of subjects in an accurate and efficient manner, but often this requires time-consuming manual review of patient charts. 

“We applied machine learning methods to data within a patient’s electronic health records to develop a high-throughput way to define research cohorts,” explains Thangaraj. “Our test case is in acute ischemic stroke. We extracted clues within a person’s medical record that required minimal data processing to classify those who have had a stroke. In a separate cohort, the UK Biobank, we were able to use our model to identify patients with self-reported stroke but no mention in their medical data with 65-fold better precision than random selection of patients.” Although stroke was the test case in this particular work, she explained that their workflow could be applied to identify patients for cohorts of other diseases, particularly when the dataset has missing data.