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Cell Types in Autism

By inventing a new computational pipeline called DAMAGES, Chaolin Zhang and Yufeng Shen showed that brain cell types on the left of the plot are more prone to have rare autism risk mutations than cell types at the right. Narrowing the focus to these types of cells also helped to identify a molecular signature of the disorder that involves haploinsufficiency. Figure: Human Mutation.

Autism, a spectrum of neurodevelopmental disorders typically identified during early childhood, is widely thought to be the result of genetic alterations that change how the growing brain is wired. Nevertheless, despite a substantial effort in the field of autism genetics, the specific alterations that place one child at greater risk than another remain elusive. Although the list of alterations associated with autism is growing, it has been difficult to conclusively distinguish those that truly increase disease risk from those that are merely coincident with it. One troubling reason for this is that research so far seems to indicate that specific genetic abnormalities associated with autism risk are extremely rare, with many being found only in single patients. This has made it hard to reproduce findings conclusively.

In a paper recently published in the journal Human Mutation, Department of Systems Biology faculty members Chaolin Zhang and Yufeng Shen describe a method and some new findings that could help to more precisely identify rare autism-driving alterations. A new analytical pipeline they call DAMAGES (Disease Associated Mutation Analysis using Gene Expression Signatures) uses a unique approach to identifying autism risk genes, looking at differences in gene expression among different cell types in the brain in order to focus more specifically on mechanisms that are likely to be relevant for autism. Using this approach, they identified a pronounced molecular signature that is shared by disease risk genes due to haploinsufficiency, a type of genetic alteration that causes a dramatic drop in the expression of a particular protein.

Yufeng Shen
Yufeng Shen's lab is interested in developing better computational methods for identifying rare genetic variants that increase disease risk.

On the surface, birth defects and cancer might not seem to have much in common. For some time, however, scientists have observed increased cancer risk among patients with certain developmental syndromes. One well-known example is seen in children with Noonan syndrome, who have an eightfold increased risk of developing leukemia. Recently, researchers studying the genetics of autism also observed mutations in PTEN, an important tumor suppressor gene. Although such findings have been largely isolated and anecdotal, they raise the tantalizing question of whether cancer and developmental disorders might be fundamentally linked.

According to a paper recently published in the journal Human Mutation, many of these similarities might not be just coincidental, but the result of shared genetic mutations. The study, led by Yufeng Shen, an Assistant Professor in the Columbia University Departments of Systems Biology and Biomedical Informatics, together with Wendy Chung, Kennedy Family Associate Professor of Pediatrics at Columbia University Medical Center, found that cancer-driving genes also make up more than a third of the risk genes for developmental disorders. Moreover, many of these genes appear to function through similar modes of action. The scientists suggest that this could make tumors “natural laboratories” for pinpointing and predicting the damaging effects of rare genetic alterations that cause developmental disorders.

“In comparison with cancer, there are relatively few patients with developmental disorders,” Shen explains, “For geneticists, this makes it hard to identify the risk genes solely based on statistical evidence of mutations from these patients. This study indicates that we should be able to use what we learn from cancer genetics — where much more data are available — to help in the interpretation of genetic data in developmental disorders.”

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.

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.

DIGGIT identifies mutations upstream of master regulators.

A new algorithm called DIGGIT identifies mutations that lie upstream of crucial bottlenecks within regulatory networks. These bottlenecks, called master regulators, integrate these mutations and become essential functional drivers of diseases such as cancer.

Although genome-wide association studies have made it possible to identify mutations that are linked to diseases such as cancer, determining which mutations actually drive disease and the mechanics of how they do so has been an ongoing challenge. In a paper just published in Cell, researchers in the lab of Andrea Califano describe a new computational approach that may help address this problem.

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.

Genes forming cluster I in the context of cellular signaling pathways

Genes forming cluster I in the context of cellular signaling pathways. Proteins encoded by cluster genes are shown in yellow, and those corresponding to other relevant genes that were present in the input data but not selected by the NETBAG+ algorithm are shown in cyan. 

In a new paper published in the journal Nature Neuroscience, Columbia University researchers report that many of the genes that are mutated in schizophrenia are organized into two main networks. Surprisingly, the study also found that a genetic network that leads to schizophrenia is very similar to a network that has been linked to autism. 

Using a computational approach called NETBAG+, Dennis Vitkup and colleagues performed network-based analyses of rare de novo mutations to map the gene networks that lead to schizophrenia. When they compared one schizophrenia network to an autism network described in a study he published last year, they discovered that different copy number variants in the same genes can lead to either schizophrenia or autism. The overlapping genes are important for processes such as axon guidance, synapse function, and cell migration — processes within the brain that have been shown to play a role in the development of these two diseases. These gene networks are particularly active during prenatal development, suggesting that the foundations for schizophrenia and autism are laid very early in life.

Gene clusters found using NETBAG analysis of de novo CNV regions observed in autistic individuals.

Gene clusters found using NETBAG analysis of de novo CNV regions observed in autistic individuals. A) The highest scoring cluster obtained using the search procedure with up to one gene per each CNV region. B) The cluster obtained using the search with up to two genes per region.

Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. A new network-based method developed at the lab of Dennis Vitkup was used to identify a large biological network of genes affected by rare de novo copy number variations (CNVs) in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes.