2015 News

Yaniv Erlich
Yaniv Erlich. Photo: Jared Leeds.

A new article published online in Nature Genetics reports that short tandem repeats, a class of genetic alterations in which short motifs of nucleotide base pairs occur multiple times in a row, play a role in modulating gene expression. Leading the study was Yaniv Erlich, an assistant professor in the Columbia University Department of Computer Science and core member of the New York Genome Center who recently joined the Center for Computational Biology and Bioinformatics.

As an article in Columbia Engineering explains, the findings reveal a new class of genome regulation.

Papers

Each year, participants in the ISCB/RECOMB Conference on Regulatory and Systems Genomics select publications over the past year that they consider to have made the most significant contributions to the field. During the most recent conference, held in Philadelphia on November 15-18, 2015, the top 10 papers were announced. Among those selected were four involving Columbia University Department of Systems Biology investigators. 

Peter Sims, Sagi Shapira, and Harris Wang

Assistant Professors Peter Sims, Sagi Shapira, and Harris Wang recently moved into a new Department of Systems Biology laboratory space designed to facilitate the development of new technologies for biological and biomedical research. Photo: Lynn Saville.

The Columbia University Department of Systems Biology has opened a new experimental research hub focused on biotechnology development. Occupying one and a half floors in the Mary Woodard Lasker Biomedical Research Building at Columbia University Medical Center, the facility will promote the design and implementation of new experimental methods for the study and engineering of biological systems. It will also enable a substantial expansion of Columbia’s next-generation genome sequencing capabilities.

The first occupants of the new facility are the laboratories of Department of Systems Biology Assistant Professors Sagi Shapira, Peter Sims, and Harris Wang, along with the Genome Sequencing and Analysis Center of the JP Sulzberger Columbia Genome Center. The community is slated to grow, as currently unoccupied space will soon accommodate additional Columbia University faculty labs that are also developing new biotechnologies.

Andrea CalifanoAndrea Califano, the Clyde and Helen Wu Professor of Chemical Systems Biology and Chair of the Columbia University Department of Systems Biology, has been named a recipient of a National Cancer Institute Outstanding Investigator Award. The seven-year grant will support the development of systematic approaches for identifying the molecular factors that lead to cancer progression and to the emergence of drug resistance at the single-cell level. 

Columbia University iGEM Team 2015

The Columbia University 2015 iGEM Team (l-r): Hudson Lee, Suppawat Kongthong, Jacky Cheung, Kenya Velez, Samuel Magaziner, and faculty moderator Harris Wang.

A team of undergraduate students based at Columbia University for the first time participated in this year’s International Genetically Engineered Machine Foundation (iGEM) competition. Supervised by Department of Systems Biology Assistant Professor Harris Wang, the team spent this past summer developing a project that used synthetic biology methods to engineer an edible, probiotic consortium of bacteria that could regulate hunger and digestion. In September they presented their results at the iGEM Giant Jamboree in Boston, MA, where they received a silver medal for their efforts. (For more informtion about their project, see the Columbia iGEM Team website.)

“I think it’s fantastic that this ambitious group of undergraduates worked so hard to represent Columbia University on this international stage,” says Dr. Wang. “Columbia has one of the great undergraduate colleges, and now that we have a critical mass of interested students and faculty laboratories with expertise in synthetic biology, we think iGEM offers a valuable opportunity to compete with and learn from teams at other leading institutions.”

The Department of Systems Biology and Center for Computational Biology and Bioinformatics are pleased to announce that three Columbia University faculty members have recently joined our community. Kam Leong, the Samuel Y. Sheng Professor of Biomedical Engineering at Columbia University, is now an interdisciplinary faculty member in the Department of Systems Biology. In addition, Yaniv Erlich and Guy Sella are now members of the Center for Computational Biology and Bioinformatics (C2B2). Their addition to the Department and to C2B2 will bring new expertise that will benefit our research and education activities, incorporating perspectives from fields such as nanotechnology, bioinformatics, and evolutionary genomics.

Deep sequencing class

A new team-taught course covers both the experimental and analytical basics of next-generation sequencing. Assistant Professor Chaolin Zhang led the discussion in a recent class. Photo: Lynn Saville.

As the cost of next-generation sequencing has fallen, it has become a ubiquitous and indispensable tool for research across the biomedical sciences. DNA and RNA sequencing — along with other technologies for profiling phenomena such as de novo mutations, protein-nucleic acid interactions, chromatin accessibility, ribosome activity, and microRNA abundance — now make it possible to observe multiple layers of cellular function on a genome-wide scale.

Regardless of a biologist’s chosen area of investigation, such methods have made it possible to explore many exciting new kinds of problems. At the same time, however, it has also dramatically transformed the expertise that young scientists need to develop in order to participate in cutting-edge biological research. Bringing students up to speed with the pace of change in next-generation sequencing has posed a particular challenge for educators.

Now, a new multidisciplinary, graduate-level course organized by the Columbia University Department of Systems Biology is enabling young investigators to begin incorporating these powerful new tools into their studies and future research. Designed by assistant professors Yufeng Shen, Peter Sims, and Chaolin Zhang, the course covers both the experimental principles of next-generation sequencing and key statistical methods for analyzing the enormous datasets that such technologies produce. In this way, it gives students a strong grounding in principles that are critical for more advanced graduate courses as well as the ability to begin applying deep sequencing technologies to investigate the questions they are interested in pursuing.

As Dr. Sims explains, “Whether you are a graduate student in systems biology, biochemistry, or microbiology, the chance that you are going to be doing next-generation sequencing is pretty high. At the same time, it’s completely not taught at the undergraduate level. There is no text book nor is there any time in a typical undergraduate biology curriculum to get into this in any kind of detail. Even at top-tier universities students come into graduate school without having any experience with it, and often they’re expected to jump right into this kind of research. We decided that this was a problem we had to fix.”

Calendar

The Department of Systems Biology is pleased to announce the speakers in its 2015-2016 Seminar Series. The seminar series features leading investigators working in a diverse set of fields, including stem cell differentiation, regulatory genomics, virus-host interactions, evolutionary genomics, cancer genomics, RNA biology, and retrotransposon biology. Please save the dates!

All events will be held in the Department of Systems Biology Common Room (ICRC 816), unless indicated otherwise. Additional details about these events will be provided at the links below as they become available.

For a continually updated calendar of all Department of Systems Biology events, and to see an archive of past seminars, visit systemsbiology.columbia.edu/events.

Breast cancer cells

A histological slide of cancerous breast tissue. The pink "riverways" are normal connective tissue while areas stained blue are cancer cells. (Source: National Cancer Institute)

Investigators at Columbia University Medical Center and the Icahn School of Medicine at Mount Sinai have discovered a molecular signaling mechanism that drives a specific type of highly aggressive breast cancer. As reported in a paper in Genes & Development, a team led by Jose Silva and Andrea Califano determined that the gene STAT3 is a master regulator of breast tumors lacking hormone receptors but testing positive for human epidermal growth receptor 2 (HR-/HER2+). The researchers also characterized a pathway including IL-6, JAK2, STAT3, and S100A8/9 — genes already known to play important roles within the immune response — as being essential for the survival of HR-/HER2+ cancer cells. Additional tests showed that disrupting this pathway severely limits the ability of these cells to survive.

These findings are particularly exciting because the pathway the researchers identified contains multiple targets for which known FDA-approved drugs exist. The paper reports that when these drugs were tested in disease models, the cancer cells showed a dramatic response, suggesting promising strategies for the treatment of the HR-/HER2+ cancer subtype. A clinical trial is now underway to investigate the effects of these approaches in humans.

Saeed TavazoieSaeed Tavazoie, a professor in the Columbia University Department of Systems Biology, has been named a recipient of a 2015 National Institutes of Health Transformative Research Award. The grant will support research to develop state-of-the-art experimental and computational methods for comprehensively mapping and modeling all pairwise molecular interactions inside cells. 

The Transformative Research Award is a part of the NIH Common Fund’s High-Risk, High-Reward Research program, which provides critical funding to scientists it recognizes as being exceptionally creative and who propose particularly innovative approaches to solving key problems in biomedical research. The Transformative Research Award is designed to support projects that use methods and perspectives that are unconventional and untested, but show great potential to create or overturn fundamental paradigms.

Kyle AllisonKyle Allison, a Systems Biology Fellow in the Columbia University Department of Systems Biology and recent winner of a National Institutes of Health Early Independence Award, is featured this week in a blog post authored by NIH Director Francis Collins. The article highlights Dr. Allison’s ongoing efforts to use approaches based in systems biology to understand bacterial persistence, a phenomenon that in clinical settings can often lead to dangerous, difficult-to-manage infections.

Read the complete post here: Creative Minds: Searching for Solutions to Chronic Infection.

Electronic media offer valuable tools for learning, but what is the best way to integrate these technologies within the traditional university setting?  Brent Stockwell, a faculty member in the Columbia University Department of Systems Biology, recently asked himself this question about blended learning, an educational approach he had begun incorporating into his undergraduate biochemistry class. As Columbia News reports, the results of this investigation have been published in the journal Cell:

Oliver Hobert
Oliver Hobert

Oliver Hobert, an interdisciplinary faculty member of the Department of Systems Biology, has received a Javits Neuroscience Investigator Award from the National Institute of Neurological Disorders and Stroke (NINDS). This prestigious grant provides long-term support for investigators who have demonstrated exceptional achievement throughout their careers. The award will enable the Hobert Lab to pursue a new project investigating sex-based differences in the regulation of neuronal identity.

Also a Professor of Biochemistry and Molecular Biophysics and an Investigator of the Howard Hughes Medical Institute, Dr. Hobert is known for his research using C. elegans to understand the molecular programs that control cell-type differentiation within the nervous system. C. elegans has become an invaluable model organism for studying the nervous system because it contains just over 300 neurons whose development has been studied in great detail.

Alex Lachmann
Alex Lachmann during his presentation to the RNA-Seq "boot camp."

In June 2015, the Columbia University Department of Systems Biology held a five-part lecture series focusing on advanced applications of RNA-Seq in biological research. The talks covered topics such as the use of RNA-Seq for studying heterogeneity among single cells, RNA-Seq experimental design, statistical approaches for analyzing RNA-Seq data, and the utilization of RNA-Seq for the prediction of molecular interaction networks. The speakers and organizers have compiled a list of lecture notes and study materials for those wishing to learn more. Click on the links below for more information.

DeMAND graphical abstract
By analyzing drug-induced changes in disease-specific patterns of gene expression, a new algorithm called DeMAND identifies the genes involved in implementing a drug's effects. The method could help predict undesirable off-target interactions, suggest ways of regulating a drug's activity, and identify novel therapeutic uses for FDA-approved drugs, three critical challenges in drug development.

Researchers in the Columbia University Department of Systems Biology have developed an efficient and accurate method for determining a drug’s mechanism of action — the cellular machinery through which it produces its pharmacological effect. Considering that most drugs, including widely used ones, act in ways that are not completely understood at the molecular level, this accomplishment addresses a key challenge to drug development. The new approach also holds great potential for improving drugs’ effectiveness, identifying better combination therapies, and avoiding dangerous drug-induced side effects.

According to Andrea Califano, the Clyde and Helen Wu Professor of Chemical Systems Biology and co-senior author on the study, “This new methodology makes it possible for the first time to generate a genome-wide footprint of the proteins that are responsible for implementing or modulating the activity of a drug. The accuracy of the method has been the most surprising result, with up to 80% of the identified proteins confirmed by experimental assays.”

Nathan Johns and Antonio Gomes

Nathan Johns and Antonio Gomes in the Wang Lab received this year's Distinguished Poster Award.

On June 5, 2015, approximately 150 faculty members, postdocs, and graduate students from the Columbia University Department of Systems Biology gathered at the Tappan Hill Mansion in Tarrytown, New York, for a day filled with illuminating presentations and conversations. The meeting has become a high point of the year for the Department, providing a relaxed environment to socialize and learn about some of the newest methods and discoveries emerging from other laboratories.

PhenoGraph

PhenoGraph, a new algorithm developed in Dana Pe'er's laboratory, proved capable of accurately identifying AML stem cells, reducing high-dimensional single cell mass cytometry data to an interpretable two-dimensional graph. Image courtesy of Dana Pe'er.

A key problem that has emerged from recent cancer research has been how to deal with the enormous heterogeneity found among the millions of cells that make up an individual tumor. Scientists now know that not all tumor cells are the same, even within an individual, and that these cells diversify into subpopulations, each of which has unique properties, or phenotypes. Of particular interest are cancer stem cells, which are typically resistant to existing cancer therapies and lead to relapse and recurrence of cancer following treatment. Finding better ways to distinguish and characterize cancer stem cells from other subpopulations of cancer cells has therefore become an important goal, for once these cells are identified, their vulnerabilities could be studied with the aim of developing better, long lasting cancer therapies.

In a paper just published online in Cell, investigators in the laboratories of Columbia University’s Dana Pe’er and Stanford University’s Garry Nolan describe a new method that takes an important step toward addressing this challenge. As Dr. Pe’er explains, “Biology has come to a point where we suddenly realize there are many more cell types than we ever imagined possible. In this paper, we have created an algorithm that can very robustly identify such subpopulations in a completely automatic and unsupervised way, based purely on high-dimensional single-cell data. This new method makes it possible to discover many new cell subpopulations that we have never seen before.”

Monthly disease risk

Columbia scientists used electronic records of 1.7 million New York City patients to map the statistical relationship between birth month and disease incidence. Image courtesy of Nicholas Tatonetti.

Columbia University Medical Center reports on a new study in the Journal of American Medical Informatics Association led by Nicholas Tatonetti, also an assistant professor in the Department of Systems Biology.

Tracking clones

After identifying T cell clones that react against donated kidney tissue in vitro, new computational methods developed in Yufeng Shen's Lab are used to track their frequency following organ transplant. The findings can help to predict transplant rejection or tolerance.

When a patient receives a kidney transplant, a battle often ensues. In many cases, the recipient’s immune system identifies the transplanted kidney as a foreign invader and mounts an aggressive T cell response to eliminate it, leading to a variety of destructive side effects. To minimize complications, many transplant recipients receive drugs that suppress the immune response. These have their own consequences, however, as they can lead to increased risk of infections. For these reasons, scientists have been working to gain a better understanding of the biological mechanisms that determine transplant tolerance and rejection. This knowledge could potentially improve physicians’ ability to predict the viability of an organ transplant and to provide the best approach to immunosuppression therapy based on individual patients’ immune system profiles.

Yufeng Shen, an assistant professor in the Columbia University Department of Systems Biology and JP Sulzberger Columbia Genome Center, together with Megan Sykes, director of the Columbia Center for Translational Immunology at the Columbia University College of Physicians and Surgeons, recently took an encouraging step toward this goal. In a paper published in Science Translational Medicine, they report that the deletion of specific donor-resistant T cell clones in the transplant recipient can support tolerance of a new kidney. Critical to this discovery was the development of a new computational genomics approach by the Shen Lab, which makes it possible to track how frequently rare T cell clones develop and how their frequencies change following transplantation. The paper suggests both a general strategy for understanding the causes of transplant rejection and a means of identifying biomarkers for predicting how well a transplant recipient will tolerate a new kidney.

Topology of cancer

The Columbia University Center for Topology of Cancer Evolution and Heterogeneity will combine mathematical approaches from topological data analysis with new single-cell experimental technologies to study cellular diversity in solid tumors. Image courtesy of Raul Rabadan.

The National Cancer Institute’s Physical Sciences in Oncology program has announced the creation of a new center for research and education based at Columbia University. The Center for Topology of Cancer Evolution and Heterogeneity will develop and utilize innovative mathematical and experimental techniques to explore how genetic diversity emerges in the cells that make up solid tumors. In this way it will address a key challenge facing cancer research in the age of precision medicine — how to identify the clonal variants within a tumor that are responsible for its growth, spread, and resistance to therapy. Ultimately, the strategies the Center develops could be used to identify more effective biomarkers of disease and new therapeutic strategies.

Gut-Brain Microbiota
A grant from the Office of Naval Research will support the development of three foundational synthetic biology technologies for engineering the human gut microbiota.

Harris Wang, an assistant professor in the Columbia University Department of Systems Biology, has been selected for the Office of Naval Research 2015 Young Investigators Program. This highly selective program promotes the development of early-career academic scientists whose research shows exceptional promise and creativity. With the support of this award, Dr. Wang will extend his research in the field of synthetic biology to develop new technologies for engineering the gut microbiome, the ecosystem of bacteria that inhabit the human digestive system. These new methods, Wang anticipates, could provide new ways of designing communities of different microbial species and ultimately modulating interactions between the gut, the immune system, and the brain.

Rodney Rothstein
Rodney Rothstein

The Columbia University Department of Systems Biology congratulates Rodney Rothstein on his election to the National Academy of Sciences. The NAS is a private, non-profit society of distinguished scholars that provides independent, objective advice to the nation on matters related to science and technology. Scientists elected to the NAS are chosen by their peers in recognition of their distinguished and continuing achievements in original research.

Comorbidity between Mendelian disease and cancer
Researchers in the Rabadan Lab have found that comorbidity between Mendelian diseases and cancer may result from shared genetic factors.

Genetic diseases can arise in a variety of ways. Mendelian disorders, for example, occur when specific mutations in single genes — called germline mutations — are inherited from either of one’s two parents. Well-known examples of Mendelian diseases include cystic fibrosis, sickle cell disease, and Duchenne muscular dystrophy. Other genetic diseases, including cancer, result from somatic mutations, which occur in individual cells during a person’s lifetime. Because the genetic origins of Mendelian diseases and cancer are so different, they are typically understood to be distinct phenomena. However, scientists in the Columbia University Department of Systems Biology have found evidence that there might be interesting genetic connections between them. 

In a paper just published in Nature Communications, postdoctoral research scientist Rachel Melamed and colleagues in the laboratory of Associate Professor Raul Rabadan report on a new method that uses knowledge about Mendelian diseases to suggest mutations involved in cancer. The study takes advantage of an enormous collection of electronic health records representing over 110 million patients, a substantial percentage of US residents. The authors show that clinical co-occurrence of Mendelian diseases and cancer, known as comorbidity, can be tied to genetic changes that play roles in both diseases. The paper also identifies several specific relationships between Mendelian diseases and the cancers melanoma and glioblastoma.

Some factors in the expo some

The exposome incorporates factors such as the environment we inhabit, the food we eat, and the drugs we take.

Although genomics has dramatically improved our understanding of the molecular origins of certain human genetic diseases, our health is also influenced by exposures to our surrounding environment. Molecules found in food, air and water pollution, and prescription drugs, for example, interact with genetic, molecular, and physiologic features within our bodies in highly personalized ways. The nature of these relationships is important in determining who is immune to such exposures and who becomes sick because of them.

In the past, methods for studying this interface have been limited because of the complexity of the problem. After all, how could we possibly cross-reference a lifetime’s worth of exposures with individual genetic profiles in any kind of meaningful way? Recently, however, an explosion in the generation of quantitative data related to the environment, health, and genetics — along with new computational methods based in machine learning and bioinformatics — have made this landscape ripe for exploration.

At this year’s South by Southwest Interactive Festival in Austin, Texas, Department of Systems Biology Assistant Professor Nicholas Tatonetti and his collaborator Chirag Patel (Harvard Medical School) discussed the remarkable new opportunities that “big data” approaches offer for investigating this landscape. Driving Tatonetti and Patel’s approach is a concept called the exposome. First proposed by Christopher Wild (University of Leeds) in 2005, an exposome represents all of the environmental exposures a person has experienced during his or her life that could play a role in the onset of chronic diseases. Tatonetti and Chirag’s presentation highlighted how investigation of the exposome has become tractable, as well as the important roles that individuals can play in supporting this effort.

In the following interview, Dr. Tatonetti discusses some of the approaches his team is using to explore the exposome, and how the project has evolved out of his previous research.

ALK-negative ALCL mutation map
A map of mutations observed in ALK-negative anaplastic large cell lymphoma. (Credit: Dr. Rabadan)

The following article is reposted with permission from the Columbia University Medical Center Newsroom. Find the original here.

The first-ever systematic study of the genomes of patients with ALK-negative anaplastic large cell lymphoma (ALCL), a particularly aggressive form of non-Hodgkin’s lymphoma (NHL), shows that many cases of the disease are driven by alterations in the JAK/STAT3 cell signaling pathway. The study also demonstrates, in mice implanted with human-derived ALCL tumors, that the disease can be inhibited by compounds that target this pathway, raising hopes that more effective treatments might soon be developed. The study, led by researchers at Columbia University Medical Center (CUMC) and Weill Cornell Medical College, was published today in the online edition of Cancer Cell.

Gut bacteria

Photo by David Gregory and Debbie Marshall, Wellcome Images. 

Recent deep sequencing studies are providing an increasingly detailed picture of the genetic composition of the human microbiome, the diverse collection of bacterial species that inhabit the gut. At the same time, however, little is known about the dynamics of these colonies, particularly why certain microbial strains outcompete others in the same environment. In a new paper published in the journal Molecular Systems Biology, Department of Systems Biology Assistant Professor Harris Wang, in collaboration with Georg Gerber and researchers at Harvard University, report on their development of the first method for using functional metagenomics to identify genes within commensal bacterial genomes that give them an evolutionary fitness advantage.

Bacterial evolutionary relationships

Image courtesy of Germán Plata and Dennis Vitkup.

Columbia News has just published an article covering recent research by associate professor Dennis Vitkup and postdoctoral research scientist Germán Plata that uses simulations of bacterial metabolism as a lens for studying how phenotypes adapt and diversify across evolutionary time scales. The article reports:

Despite their omnipresence, microbial evolutionary adaptations are often challenging to study, partly due to the difficulty of growing diverse bacteria in the lab. “Probably less than a dozen bacteria are really well studied in the laboratory,” Vitkup says.

Writing in the journal Nature this past January, Vitkup and Plata applied computational tools to investigate bacterial evolutionary adaptations by simulating metabolism for more than 300 bacterial species, covering the entire microbial tree of life.

Andrea Califano and Aris Floratos
Andrea Califano and Aris Floratos will lead an effort to reclassify tumors catalogued in TCGA according to their master regulators.

Andrea Califano and Aris Floratos, faculty members in the Columbia University Department of Systems Biology, have received a two-year, $624,236 subcontract to develop a new classification system of cancer subtypes. The agreement was awarded through a subcontract from Leidos Biomedical Research, Inc., which operates the Frederick National Laboratory for Cancer Research for the federal government.  

By performing an integrative analysis of genomic data from the Cancer Genome Atlas (TCGA) and proteomic data from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), the researchers plan to recategorize tumors collected in TCGA based on the master regulator genes that determine their state. This is in contrast to other approaches based on expression of genes that reflect tissue lineage and proliferative processes. In addition, the team will link the genetics of each tumor sample to the specific master regulators that determine its state using a recently published novel algorithm (DIGGIT). Ultimately, the project aims to provide a more useful catalog of pan-cancer subtypes that could help to identify biomarkers and therapeutic targets for specific kinds of tumors, and ultimately provide a resource to guide the next generation of precision medicine.

“We have to reevaluate the way in which we organize tumors within subtypes, using both gene expression data and mutational data,” says Dr. Califano. “Right now the common approach is to classify tumor types based on rather generic genes that are differentially expressed between subtypes. But most of these genes play no role in actually driving the disease. We want to shift the emphasis and classify tumors based on the genes that truly regulate tumor state and survival.”

Harris Wang

Harris Wang, an assistant professor in the Columbia University Department of Systems Biology and Department of Pathology and Cell Biology, has been selected to receive a 2015 Alfred P. Sloan Foundation Research Fellowship in computational and evolutionary molecular biology. This two-year, $50,000 grant will support work that combines methods from synthetic biology and computational biology to study how horizontal gene transfer contributes to microbial evolution.

Since 1955, the Sloan Research Fellowship program has supported outstanding early-career scientists in recognition of their achievements and their potential to make important contribution to their fields. This year’s fellows included 126 investigators, with 12 awardees in the field of computational and evolutionary molecular biology. Other disciplines represented in the awards include chemistry, computer science, economics, mathematics, neuroscience, ocean sciences, and physics.

Hynek WichterleThe JP Sulzberger Columbia Genome Center is pleased to announce that Hynek Wichterle has been appointed as associate director. In this role he will advise on stem cell related projects and coordinate interactions between Columbia Stem Cell Facility and the Columbia Genome Center's High-Throughput Screening Facility.

In addition to his position at the Columbia Genome Center, Dr. Wichterle is also an associate professor holding a joint appointment in the Departments of Pathology & Cell Biology and Neuroscience (in Neurology) at Columbia University Medical Center. He received his MS degree from Charles University in Prague and his PhD degree from The Rockefeller University. He trained at Columbia University, where he became assistant professor in 2004 and associate professor in 2012. He serves as a co-director of the Columbia Stem Cell Initiative and as a Vice-Chief of the Division of Regenerative Medicine in the Department of Rehabilitation & Regenerative Medicine.

Autism Spectrum Disorders Genetic Network

Network of autism-associated genes. (Credit: Dennis Vitkup)

The following article is reposted with permission from the Columbia University Medical Center Newsroom. Find the original here.

People with autism have a wide range of symptoms, with no two people sharing the exact type and severity of behaviors. Now a large-scale analysis of hundreds of patients and nearly 1000 genes has started to uncover how diversity among traits can be traced to differences in patients’ genetic mutations. The study, conducted by researchers at Columbia University Medical Center, was published Dec. 22 in the journal Nature Neuroscience.

Autism researchers have identified hundreds of genes that, when mutated, likely increase the risk of developing autism spectrum disorder (ASD). Much of the variability among people with ASD is thought to stem from the diversity of underlying genetic changes, including the specific genes mutated and the severity of the mutation.

“If we can understand how different mutations lead to different features of ASD, we may be able to use patients’ genetic profiles to develop accurate diagnostic and prognostic tools and perhaps personalize treatment,” said senior author Dennis Vitkup, PhD, associate professor of systems biology and biomedical informatics at Columbia University’s College of Physicians & Surgeons.

Sequence of genomic alterations in CLLA graph representing the sequence of genomic alterations in chronic lymphocytic leukemia (CLL). Each node represents a mutation, with arrows indicating temporal relationships between them. The size of the nodes indicates the number of patients in the study who exhibited the alteration, while the thickness of the lines shows how often the temporal relationships between nodes were seen. The method the researchers use enabled them to identify multiple, distinct evolutionary patterns in CLL.

As biologists have gained a better understanding of cancer, it has become clear that tumors are often driven not by a single mutation, but by a series of genetic changes that correspond to particular stages of cancer progression. In this sense, a tumor is constantly evolving, with different groups of cells that harbor distinctive mutations multiplying at different rates, depending on their fitness for particular disease states. As the search for more effective cancer diagnostics and therapies continues, one key question is how to disentangle the order in which mutations occur in order to understand how tumors change over time. Being able to predict how a tumor will behave based on signs seen early in the course of disease could enable the development of new diagnostics that could better inform treatment planning.

In a paper just published in the journal eLife, a team of investigators led by Department of Systems Biology Associate Professor Raul Rabadan reports on a new computational strategy for addressing this challenge. Their framework, called tumor evolutionary directed graphs (TEDG), considers next-generation sequencing data from tumor samples from a large number of patients. Using TEDG to analyze cancer cells in patients with chronic lymphocytic leukemia (CLL), they were able to develop a model of how the disease’s mutational landscape changes from its initial onset to its late stages. Their findings suggest that CLL may not be just the result of a single evolutionary path, but can evolve in alternative ways.