In a tour de force of precision medicine, an international team led by two Columbia University Herbert Irving Comprehensive Cancer Center (HICCC) researchers has demonstrated the potential of a new approach to prostate cancer treatment. The iterative strategy, named OncoLoop, uses a sophisticated computer algorithm to match each patient to an “avatar” -- a carefully engineered laboratory model of a cancer that matches the molecular profile of the patient's tumor. By selecting drugs that target the master regulator proteins responsible for the genetic program driving the avatar’s tumor, the team can then validate the treatment regimen most likely to work in that patient.
“We take a patient, we match them to the set of mouse tumors we already have, and to the set of drug data that we already have, and then actually identify drugs that are most likely to work in both the patient and the model … test those drugs in those models, and then use that information to inform clinical treatment,” says Cory Abate-Shen, PhD, a senior author on the new work and member of the HICCC and department chair of Molecular Pharmacology and Therapeutics at Columbia University Vagelos College of Physicians and Surgeons (VP&S).
New Machine Learning Method Predicts Damaging Missense Variants
The premise of genomic medicine is that a person’s genomic characterization can be used to improve medical diagnosis, prognosis, and treatment. Each person, however, has millions of genetic variants, the vast majority of which have negligible impact on their health. How to determine which variants are relevant to a particular condition is a central issue in genomic medicine.
The issue is most pressing in the case of missense variants, which alter a single amino acid in proteins. Only about 20–30 percent of these mutations have a functional impact. Thus the question of how likely a variant is to change protein function—contributing to a health condition—is extremely uncertain for missense variants. As a result, most missense variants in clinical genetic testing are classified as VUS (variant of uncertain significance).
Yufeng Shen, PhD, an associate professor in the Department of Systems Biology and the Department of Biomedical Informatics, and his group have developed a new method for predicting which missense variants are potentially damaging. The method, called gMVP (graphical model for predicting Missense Variant Pathogenicity), uses one of the latest machine learning techniques, a graph attention model, to capture information relevant to predicting which variants are potentially damaging. Their paper, “Predicting Functional Effect of Missense Variants Using Graph Attention Neural Networks,” was published in Nature Machine Intelligence on November 15th, 2022.
One of the most recognizable characteristics of autism is an amazing diversity of associated behavioral symptoms. Clinicians view autism as a broad spectrum of related disorders, and the origin of the disease's heterogeneity has puzzled scientists, doctors, and affected families for decades.
In a recent study, led by Dennis Vitkup, PhD, associate professor of systems biology, researchers have made an important step towards understanding the biological mechanisms underlying the cognitive and behavioral diversity of autism cases triggered by de novo truncating mutations. These mutations occur in parents’ germline cells and usually strongly disrupt the functions of target genes. De novo truncating mutations are responsible for close to 5% of autism cases and up to 20% of cases seen clinically.
One of the immune system’s oldest branches, called complement, may be influencing the severity of COVID disease, according to a new study from Drs. Sagi Shapira and Nicholas Tatonetti at the Department of Systems Biology.
Drs. Nicholas Tatonetti (left) and Sagi Shapira
Among other findings linking complement to COVID, the researchers found that people with age-related macular degeneration—a disorder caused by overactive complement—are at greater risk of developing severe complications and dying from COVID.
The connection with complement suggests that existing drugs that inhibit the complement system could help treat patients with severe COVID-19.
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).
Videos of flies (5x speed) using FlyWalker, a program that enables scientists to label and track the position of each of the fly’s footfalls, thereby building a high-resolution picture of it’s walking gait. Top: normal fly walking at around 25 mm/s. Bottom: fly with its VNC serotonin neurons stimulated, which slows its speed to 15 mm/s (Credit: Clare Howard/Mann lab/Columbia's Zuckerman Institute)
A Columbia University study in fruit flies has identified serotonin as a chemical that triggers the body’s startle response, the automatic deer-in-the-headlights reflex that freezes the body momentarily in response to a potential threat. Today’s study reveals that when a fly experiences an unexpected change to its surroundings, such as a sudden vibration, release of serotonin helps to literally — and temporarily — stop the fly in its tracks.
These findings, recently published in Current Biology , offer broad insight into the biology of the startle response, a ubiquitous, yet mysterious, phenomenon that has been observed in virtually every animal studied to date, from flies to fish to people.
“Imagine sitting in your living room with your family and — all of a sudden — the lights go out, or the ground begins to shake,” said Richard Mann , PhD, a principal investigator at Columbia’s Mortimer B. Zuckerman Mind Brain Behavior Institute and the paper’s senior author. “Your response, and that of your family, will be the same: You will stop, freeze and then move to safety. With this study, we show in flies that a rapid release of the chemical serotonin in their nervous system drives that initial freeze. And because serotonin also exists in people, these findings shed light on what may be going on when we get startled as well.”
In the brain, serotonin is most closely associated with regulating mood and emotion. But previous research on flies and vertebrates has shown it can also affect the speed of an animal’s movement. The Columbia researchers’ initial goal was to more fully understand how the chemical accomplished this.
Congenital diaphragmatic hernia (CDH) is a severe birth defect. For babies born with CDH, their diaphragms are not developed properly, with some or all parts of the abdominal organs pushed into the chest. The displacement of these critical organs can have a significant impact on how the lungs develop and grow.
Yufeng Shen, PhD, associate professor of systems biology
“Many babies with this birth defect also have lung hypoplasia or pulmonary hypertension and babies have difficulty breathing. Even with advanced care available, the mortality rate is still about 20 percent,” says Dr. Shen, associate professor of systems biology at Columbia, with a joint appointment in the Department of Biomedical Informatics .
“One hypothesis is that the lung condition is not necessarily caused by the physical compression on the developing lungs in the chest,” explains Dr. Shen, “it can be caused by the same genetic defect that causes CDH. Finding those genes is absolutely necessary to improve care and develop effective treatment in the long run.”
Scientists have been aiming to identify new risk genes in CDH—and other developmental disorders—with the hope that with improved genetic diagnosis more tailored or long-term care for patients born with this defect could be provided, as well as potential targets for intervention down the road.
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.
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.
Long-Read Sequencing to Study Allelic Effects on Transcriptome Structure
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.
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.”
Researchers implement P-HIPSTer, an in silico computational framework that leverages protein structure information to identify approximately 282,000 protein-protein interactions across all fully-sequenced human-infecting viruses (1001 in all). This image highlights that in addition to rediscovering known biology, P-HIPSTer has yielded a series of new findings and enables discovery of a previously unappreciated universe of cellular circuits and biological principles that act on human-infecting viruses. (Image Courtesy of Dr. Sagi Shapira)
Researchers at Columbia University Irving Medical Center have leveraged a computational method to map protein-protein interactions between all known human-infecting viruses and the cells they infect. The method, along with the data that it generated, has spawned a wealth of information toward improving our understanding of how viruses manipulate the cells that they infect and cause disease. Among its findings, the work uncovered a role for estrogen receptor in regulating Zika Virus (ZIKV) infection, as well as links between cancer and the human papillomavirus (HPV).
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.”
The gut microbiome–composed of hundreds of different species of bacteria–is a complex community and a challenge for scientists to unravel. One specific challenge is the spatial distribution of different microbes, which are not evenly distributed throughout the gut. A new method developed by the lab of Dr. Harris Wang should help scientists locate and characterize these neighborhoods, which could shed light on how microbes influence the health of their hosts.
Techniques that can identify all species in the gut microbiome only work with homogenized samples (like stool), but methods that preserve spatial information can only cope with a handful of species.
Dr. Wang, assistant professor of systems biology and of pathology & cell biology, and graduate student Ravi Sheth in the Department of Systems Biology, tested the new technique with mice who switched from a low-fat to a high-fat diet. Diet is known to change the abundance of specific bacteria in the gut within days, but the new technique also revealed that the switch caused wholesale changes of microbial neighborhoods.
“Specific regions of bacteria were entirely lost with a switch in diet,” Sheth says. “This was exciting to us as it will give us clues to understanding how that change happens and how the change may impact health.”
Chaolin Zhang, PhD, associate professor of systems biology
A new study by researchers in Dr. Chaolin Zhang’s lab at Columbia’s Department of Systems Biology details a novel computational method that models how RNA-binding proteins (RBPs) recognize specific sites in the target RNA transcripts, precisely and accurately. The researchers’ findings include identification of entirely new motifs (RNA sequence patterns), and their research in complex RNA regulation contributes to our understanding of the molecular basis of disease and conditions, and down the road, could aid in the development of targeted therapies.
RNA has traditionally been considered mere “messengers” that transfer genetic information from DNA to proteins that ultimately carry out cellular functions. However, it is now increasingly appreciated that RNA can be tightly regulated to control gene expression and diversity protein products. RNA-binding proteins (RBPs) are at the center of such regulation, with important roles in many cellular processes, including cell function, transport, and location. Gaining mechanistic insights of the binding specificity of RBPs in a genome-wide scale helps advance our knowledge of gene regulation.
“RNA-binding proteins are crucial for gene expression,” says Dr. Feng, coauthor of the study and post-doctoral research scientist in the Zhang lab. “RNA is heavily regulated, and when this regulation goes wrong, instabilities or disease could occur.”
New research by Laura Landweber, PhD, who has joint appointments in the Department of Biochemistry and Molecular Biophysics, the Department of Systems Biology and the Department of Biological Sciences at Columbia University, is being featured by Columbia Univeristy Iriving Medical Center Newsroom.
As reported, a new study of a single-celled eukaryote with 16,000 tiny chromosomes may shed light on a recently discovered feature of the human genome.
Methyladenine, or 6mA—a modification of DNA common in Oxytricha trifallax—has only recently been found in multicellular organisms, with some studies suggesting a role in human disease and development.
Finding the enzymes that lay down the methyl marks will be critical to understanding what 6mA is doing in Oxytricha and other organisms, but the enzymes have been difficult to identify.
The new research—to be published in the June 13 issue of Cell—reveals how 6mA marks are made to the Oxytricha genome and suggests why the enzymes have been hard to find.
Read more about the Oxytricha genome and the Landweber lab’s new insights into 6mA and its potential role in human diseases.
Dr. Landweber has been studying Oxytricha for two decades and previously uncovered its 16,000 chromosomes. (See related Faculty Q+A and video.)
One of the structural variations detected in Anaerostipes hadrus, which is deleted in ~40% of the population (top), and associated with higher disease risk. Genes in this region (bottom) code a composite inositol catabolism - butyrate production pathway, potentially supplying the microbe with additional energy while supplying the host with butyrate, previously shown to have positive metabolic and anti-inflammatory effects. (Credit: Korem lab)
Our gut microbiome has been linked to everything from obesity and diabetes to heart disease and even neurological disorders and cancer. In recent years, researchers have been sorting through the multiple bacterial species that populate the microbiome, asking which of them can be implicated in specific disorders. But a paper recently published in Nature addressed a new question: "What if the same microbe is different in different people?" The study was co-led by Dr. Tal Korem , assistant professor of systems biology and core faculty member in the Program for Mathematical Genomics at Columbia University Irving Medical Center .
It has been long known that the genomes of microbes are not fixed from birth, as ours are. They are able to lose some of their genes, exchange genes with other microorganisms, or gain new ones from their environment. Thus, a detailed comparison of the genomes of seemingly identical bacteria will reveal sequences of DNA that occur in one genome and not others, or possibly sequences that appear just once in one and several times over in others. These differences are called structural variants. Structural variants - even tiny ones - can translate into huge differences in the ways that microbes interact with their human hosts. A variant might be the difference between a benign presence and a pathogenic one, or it could give bacteria resistance to antibiotics.
Columbia researchers have learned why some glioblastomas—the most common type of brain cancer—respond to immunotherapy. The findings, reported by the CUIMC Newsroom, could help identify patients who are most likely to benefit from treatment with immunotherapy drugs and lead to the development of more broadly effective treatments.
Fewer than 1 in 10 patients with glioblastoma respond to immunotherapy, which has shown remarkable success in the past few years in treating a variety of aggressive cancers. But there has been no way to know in advance which glioblastoma patients will respond. Like many other cancers, glioblastomas are able to prevent the immune system from attacking cancer cells. Cancers sometimes put the brakes on the immune system by acting on a protein called PD-1. Immunotherapy drugs called PD-1 inhibitors are designed to release those brakes, unleashing the immune system. Given the success of PD-1 inhibitors in other cancers, doctors were hopeful that the immunotherapy drugs would help patients with glioblastoma.
To understand why only a few of these tumors respond to the immunotherapy drugs, Dr. Rabadan’s team took a comprehensive look at the tumor microenvironment—which includes the tumor itself and all of the cells that support it—in 66 glioblastoma patients before and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Of these, 17 had a response to the drugs of six months or longer.
Nonresponsive tumors had more mutations in a gene called PTEN, which led to higher levels of macrophages, immune cells that usually help the body fight bacteria and other invaders. But in glioblastoma, the macrophages release a number of growth factors that promote the survival and spread of cancer cells.
Principal investigators on the PCF Challenge Award grant, from left to right: Andrea Califano, Michael Shen and Charles Drake.
Columbia University Irving Medical Center experts in prostate cancer will lead a new team research project that tests a novel approach for personalized cancer treatment.
The two-year project, funded by a $1 million Challenge Award from the Prostate Cancer Foundation (PCF) , combines cutting-edge techniques that include computational methods for targeted drug therapy, single-cell RNA sequencing and novel cancer immunotherapy. The combined approaches are behind a proof-of-concept clinical trial for patients with lethal metastatic prostate cancer.
PCF is recognized as the leading philanthropic organization for prostate cancer research. For the team at Columbia’s Herbert Irving Comprehensive Cancer Center (HICCC ), receiving a Challenge Award from the foundation was more than just an exciting achievement. It underscores CUIMC’s continued commitment to strengthen and expand its expertise in prostate cancer research and care through investments in faculty recruitment, enhanced emphases on bolstering basic science research and clinical trials centered on the disease and direct engagement with PCF.