Autism Linked to Increased Genetic Change in Regions of Genome Instability

Children with autism have increased levels of genetic change in regions of the genome prone to DNA rearrangements, so called “hotspots,” according to a research discovery to be published in the print edition of the journal Human Molecular Genetics. The research indicates that these genetic changes come in the form of an excess of duplicated DNA segments in hotspot regions and may affect the chances that a child will develop autism — a behavioral disorder that affects about 1 of every 88 children in the United States, according to the Centers for Disease Control.

Earlier work had identified, in children with autism, a greater frequency of rare DNA deletions or duplications, known as DNA copy number changes. These rare and harmful events are found in approximately 5 to 10 percent of cases, raising the question as to what other genetic changes might contribute to the disorders known as autism spectrum disorders.

The new research shows that children with autism have — in addition to these rare events — an excess of duplicated DNA including more common variants not exclusively found in children with autism, but are found at elevated levels compared to typically developing children. The research collaboration includes groups led at Penn State by Scott Selleck; at the University of California Davis/MIND Institute by Isaac Pessah, Irva Hertz-Picciotto, Flora Tassone, and Robin Hansen; and at the University of Washington by Evan Eichler.

The investigators also found that the balance of DNA duplications and deletions in children with autism was different from that found in more severe developmental disorders, such as intellectual disability or multiple congenital anomalies, where the levels of both deletions and duplications are increased compared to controls, and are even higher than in children with autism.

They also found that children who had more difficulty with daily living skills also had the greatest level of copy number change throughout their genome. “These measures of adaptive behavior provide an indication of the severity of the impairment in the children with autism. These behaviors were significantly correlated with the amount of DNA copy number change,” Selleck said, emphasizing that the research revealed “clear and graded effects of the genetic change.”

"These results beg the question as to the origin of this genetic change," Selleck said. "The increased levels of both rare and common variants suggests the possibility that these individuals are predisposed to genetic alteration."

A vigorous debate is ongoing in the research community about the degree of genetic versus environmental contributions to autism. Selleck said the finding of an overall increase in genetic change in children with autism heightens the need to search for the basis of this variation. “We know that environmental factors can affect the stability of the genome, but we don’t know if the DNA copy number change we detect in these children is a result of environmental exposures, nutrition, medical factors, lifestyle, genetic susceptibility, or combinations of many elements together,” Selleck said. “The elevated levels of common variants is telling us something. It suggests that pure selection of randomly generated variants may not be the whole story.”

The Penn State team includes Department of Biochemistry and Molecular Biology Associate Professor Marylyn Ritchie and Assistant Professor Santhosh Girirajan. “The relationship between the level of copy number change and the degree of neurodevelopmental disability is something we have noted previously for large, rare variants” says Girirajan, “but this work extends those observations to common copy number variants, suggesting the level of copy number change in children with autism is larger than we had appreciated.” Girirajan, the first author of the study, coordinated the effort between the Penn State and University of Washington researchers.

NSF-funded Superhero Supercomputer Helps Battle Autism

'Gordon,' a supercomputer with unique flash memory, helps identify gene-related paths to treating mental disorders

When it officially came online at the San Diego Supercomputer Center (SDSC) in early January 2012, Gordon was instantly impressive. In one demonstration, it sustained more than 35 million input/output operations per second—then, a world record.

Input/output operations are an important measure for data intensive computing, indicating the ability of a storage system to quickly communicate between an information processing system, such as a computer, and the outside world. Input/output operations specify how fast a system can retrieve randomly organized data common in large datasets and process it through data mining applications.

The supercomputer’s record-breaking feat wasn’t a surprise; after all, Gordon is named after a comic strip superhero, Flash Gordon.

Gordon’s new and unique architecture employs massive amounts of the type of flash memory common in cell phones and laptops—hence its name. The system is used by scientists whose research requires the mining, searching and/or creating of large databases for immediate or later use, including mapping genomes for applications in personalized medicine and examining computer automation of stock trading by investment firms on Wall Street.

Commissioned by the National Science Foundation (NSF) in 2009 for $20 million, Gordon is part of NSF’s Extreme Science and Engineering Discovery Environment, or XSEDE program, a nationwide partnership comprising 16 high-performance computers and high-end visualization and data analysis resources.

"Gordon is a unique machine in NSF’s Advanced Cyberinfrastructure/XSEDE portfolio," said Barry Schneider, NSF program director for advanced cyberinfrastructure. “It was designed to handle scientific problems involving the manipulation of very large data. It is differentiated from most other resources we support in having a large solid-state memory, 4 GB per core, and the capability of simulating a very large shared memory system with software.”

Last month, a team of researchers from SDSC, the United States and the Institute Pasteur in France reported in the journal Genes, Brain and Behavior that they used Gordon to devise a novel way to describe a time-dependent gene-expression process in the brain that can be used to guide the development of treatments for mental disorders such as autism-spectrum disorders and schizophrenia.

The researchers identified the hierarchical tree of coherent gene groups and transcription-factor networks that determine the patterns of genes expressed during brain development. They found that some “master transcription factors” at the top level of the hierarchy regulated the expression of a significant number of gene groups.

The scientists’ findings can be used for selection of transcription factors that could be targeted in the treatment of specific mental disorders.

"We live in the unique time when huge amounts of data related to genes, DNA, RNA, proteins, and other biological objects have been extracted and stored," said lead author Igor Tsigelny, a research scientist with SDSC as well as with UC San Diego’s Moores Cancer Center and its Department of Neurosciences.

"I can compare this time to a situation when the iron ore would be extracted from the soil and stored as piles on the ground. All we need is to transform the data to knowledge, as ore to steel. Only the supercomputers and people who know what to do with them will make such a transformation possible," he said.

Humanoid robot helps train children with autism

“Aiden, look!” piped NAO, a two-foot tall humanoid robot, as it pointed to a flat-panel display on a far wall. As the cartoon dog Scooby Doo flashed on the screen, Aiden, a young boy with an unruly thatch of straw-colored hair, looked in the direction the robot was pointing.

Aiden, who is three and a half years old, has been diagnosed with autism spectrum disorder (ASD). NAO (pronounced “now”) is the diminutive “front man” for an elaborate system of cameras, sensors and computers designed specifically to help children like Aiden learn how to coordinate their attention with other people and objects in their environment. This basic social skill is called joint attention. Typically developing children learn it naturally. Children with autism, however, have difficulty mastering it and that inability can compound into a variety of learning difficulties as they age.

An interdisciplinary team of mechanical engineers and autism experts at Vanderbilt University have developed the system and used it to demonstrate that robotic systems may be powerful tools for enhancing the basic social learning skills of children with ASD. Writing in the March issue of the IEEE Transactions on Neural Systems and Rehabilitation Engineering, the researchers report that children with ASD paid more attention to the robot and followed its instructions almost as well as they did those of a human therapist in standard exercises used to develop joint attention skill.

The finding indicates that robots could play a crucial role in responding to the “public health emergency” that has been created by the rapid growth in the number of children being diagnosed with ASD. Today, one in 88 children (one in 54 boys) are being diagnosed with ASD. That is a 78 percent increase in just four years. The trend has major implications for the nation’s healthcare budget because estimates of the lifetime cost of treating ASD patients ranges from four to six times greater than for patients without autism.

“This is the first real world test of whether intelligent adaptive systems can make an impact on autism,” said team member Zachary Warren, who directs the Treatment and Research Institute for Autism Spectrum Disorders (TRIAD) at Vanderbilt’s Kennedy Center.

Atypical brain circuits may cause slower gaze shifting in infants who later develop autism

Infants at 7 months of age who go on to develop autism are slower to reorient their gaze and attention from one object to another when compared to 7-month-olds who do not develop autism, and this behavioral pattern is in part explained by atypical brain circuits.

Those are the findings of a new study led by University of North Carolina School of Medicine researchers and published online March 20 by the American Journal of Psychiatry.

"These findings suggest that 7-month-olds who go on to develop autism show subtle, yet overt, behavioral differences prior to the emergence of the disorder. They also implicate a specific neural circuit, the splenium of the corpus callosum, which may not be functioning as it does in typically developing infants, who show more rapid orienting to visual stimuli," said Jed T. Elison, PhD, first author of the study.

Elison worked on the study, conducted as part of the Infant Brain Imaging Study (IBIS) Network, for his doctoral dissertation at UNC. He now is a postdoctoral fellow at the California Institute of Technology. The study’s senior author is Joseph Piven, MD, professor of psychiatry, director of the Carolina Institute for Developmental Disabilities at UNC, and the principle investigator of the IBIS Network.

The IBIS Network consists of research sites at UNC, Children’s Hospital of Philadelphia, Washington University in St. Louis, the University of Washington in Seattle, the University of Utah in Salt Lake City, and the Montreal Neurological Institute at McGill University, and the University of Alberta are currently recruiting younger siblings of children with autism and their families for ongoing research.

"Difficulty in shifting gaze and attention that we found in 7-month-olds may be a fundamental problem in autism," Piven said. "Our hope is that this finding may help lead us to early detection and interventions that could improve outcomes for individuals with autism and their families."

The study included 97 infants: 16 high-risk infants later classified with an autism spectrum disorder (ASD), 40 high-risk infants not meeting ASD criteria (i.e., high-risk-negative) and 41 low-risk infants. For this study, infants participated in an eye-tracking test and a brain scan at 7 months of age a clinical assessment at 25 months of age.

The results showed that the high-risk infants later found to have ASD were slower to orient or shift their gaze (by approximately 50 miliseconds) than both high-risk-negative and low-risk infants. In addition, visual orienting ability in low-risk infants was uniquely associated with a specific neural circuit in the brain: the splenium of the corpus callosum. This association was not found in infants later classified with ASD.

The study concluded that atypical visual orienting is an early feature of later emerging ASD and is associated with a deficit in a specific neural circuit in the brain.

Difficulty in Recognizing Faces in Autism Linked to Performance in a Group of Neurons

Neuroscientists at Georgetown University Medical Center (GUMC) have discovered a brain anomaly that explains why some people diagnosed with autism cannot easily recognize faces — a deficit linked to the impairments in social interactions considered to be the hallmark of the disorder.

They also say that the novel neuroimaging analysis technique they developed to arrive at this finding is likely to help link behavioral deficits to differences at the neural level in a range of neurological disorders.

The final manuscript published March 15 in the online journal NeuroImage: Clinical, the scientists say that in the brains of many individuals with autism, neurons in the brain area that processes faces (the fusiform face area, or FFA) are too broadly “tuned” to finely discriminate between facial features of different people. They made this discovery using a form of functional magnetic resonance imaging (fMRI) that scans output from the blueberry-sized FFA, located behind the right ear.

“When your brain is processing faces, you want neurons to respond selectively so that each is picking up a different aspect of individual faces. The neurons need to be finely tuned to understand what is dissimilar from one face to another,” says the study’s senior investigator, Maximilian Riesenhuber, PhD, an associate professor of neuroscience at GUMC.

“What we found in our 15 adult participants with autism is that in those with more severe behavioral deficits, the neurons are more broadly tuned, so that one face looks more like another, as compared with the fine tuning seen in the FFA of typical adults,” he says.

“And we found evidence that reduced selectivity in FFA neurons corresponded to greater behavioral deficits in everyday face recognition in our participants. This makes sense. If your neurons cannot tell different faces apart, it makes it more difficult to tell who is talking to you or understand the facial expressions that are conveyed, which limits social interaction.”

Riesenhuber adds that there is huge variation in the ability of individuals diagnosed with autism to discriminate faces, and that some autistic people have no problem with facial recognition.

“But for those that do have this challenge, it can have substantial ramifications — some researchers believe deficits in face processing are at the root of social dysfunction in autism,” he says.

The neural basis for face processing

Neuroscientists have used traditional fMRI studies in the past to probe the neural bases of behavioral differences in people with autism, but these studies have produced conflicting results, says Riesenhuber.  “The fundamental problem with traditional fMRI techniques is that they can tell which parts of the brain become active during face processing, but they are poor at directly measuring neuronal selectivity,” he says, “and it is this neuronal selectivity that predicts face processing performance, as shown in our previous studies.”

To test their hypothesis that differences in neuronal selectivity in the FFA are foundational to differences in face processing abilities in autism, Riesenhuber and the study’s lead author, neuroscientist Xiong Jiang, PhD, developed a novel brain imaging analysis technique, termed local regional heterogeneity, to estimate neuronal selectivity.

“Local regional heterogeneity, or Hcorr, as we called it, is based on the idea that neurons that have similar selectivities will on average show similar responses, whereas neurons that like different stimuli will respond differently,” says Jiang. “This means that individuals with face processing deficits should show more homogeneous activity in their FFA than individuals with more typical face recognition abilities.”

They tested the method in 15 adults with autism and 15 adults without the disorder. The autistic participants also underwent a standard assessment of social/behavioral functioning.

The researchers found that in each autistic participant, behavioral ability to tell faces apart was tightly linked to levels of tuning specificity in the right FFA as estimated with Hcorr. This finding was confirmed by another advanced imaging technique, fMRI rapid adaptation, shown by the group in previous work to be a good estimator of neuronal selectivity.

“Compared to the more well-established fMRI-rapid adaptation technique, Hcorr has several significant advantages,” says Jiang. “Hcorr is more sensitive and can estimate neuronal selectivity as well as fMRI rapid adaptation, but with much shorter scans, and Hcorr can even estimate neuronal selectivity using data from resting state scans, thus making the technique suitable even for individuals that cannot perform complicated tasks in the scanner, such as low-functioning autistic adults, or young children.”

“The study suggests that, just as in typical adults, the FFA remains the key region responsible for face processing and that changes in neuronal selectivity in this area are foundational to the variability in face processing abilities found in autism. Our study identifies a clear target for intervention,” says Riesenhuber. Indeed, after the study was completed, the researchers successfully attempted to improve facial recognition skills in an autistic participant. They showed the participant pairs of faces that were very dissimilar at first, but became increasingly similar, and found that FFA tuning improved along with behavioral ability to tell the faces apart. “This suggests high-level brain areas may still be somewhat plastic in adulthood,” says Riesenhuber.

'Network' analysis of the brain may explain features of autism

A look at how the brain processes information finds a distinct pattern in children with autism spectrum disorders. Using EEGs to track the brain’s electrical cross-talk, researchers from Boston Children’s Hospital have found a structural difference in brain connections. Compared with neurotypical children, those with autism have multiple redundant connections between neighboring brain areas at the expense of long-distance links.

The study, using a “network analysis” like that used to study airlines or electrical grids, may help in understanding some classic behaviors in autism. It was published February 27 in BioMed Central’s open access journal BMC Medicine, accompanied by a commentary.

"We examined brain networks as a whole in terms of their capacity to transfer and process information," says Jurriaan Peters, MD, of the Department of Neurology at Boston Children’s Hospital, who is co-first author of the paper with Maxime Taquet, a PhD student in Boston Children’s Computational Radiology Laboratory. "What we found may well change the way we look at the brains of autistic children."

Peters, Taquet and senior authors Simon Warfield, PhD, of the Computational Radiology Laboratory and Mustafa Sahin, MD, PhD, of Neurology, analyzed EEG recordings from two groups of autistic children: 16 children with classic autism, and 14 children whose autism is part of a genetic syndrome known as tuberous sclerosis complex (TSC). They compared these readings with EEGs from two control groups—46 healthy neurotypical children and 29 children with TSC but not autism.

In both groups with autism, there were more short-range connections within different brain region, but fewer connections linking far-flung areas.

A brain network that favors short-range over long-range connections seems to be consistent with autism’s classic cognitive profile—a child who excels at specific, focused tasks like memorizing streets, but who cannot integrate information across different brain areas into higher-order concepts.

"For example, a child with autism may not understand why a face looks really angry, because his visual brain centers and emotional brain centers have less cross-talk," Peters says. "The brain cannot integrate these areas. It’s doing a lot with the information locally, but it’s not sending it out to the rest of the brain."

Yale researchers spot attention deficits in babies who later develop autism

Researchers at Yale School of Medicine are able to detect deficits in social attention in infants as young as six months of age who later develop Autism Spectrum Disorders (ASD). Published in the current issue of Biological Psychiatry, the results showed that these infants paid less attention to people and their activities than typically developing babies.

Katarzyna Chawarska, associate professor at the Yale Child Study Center, and her colleagues investigated whether six-month-old infants later diagnosed with ASD showed prodromal symptoms — early signs of ASD such as an impaired ability to attend to social overtures and activities of others. Before this study, it had not been clear whether these prodromal symptoms were present in the first year of life.

“This study highlights the possibility of identifying certain features linked to visual attention that can be used for pinpointing infants at greatest risk for ASD in the first year of life,” said Chawarska. “This could make earlier interventions and treatments possible.”

Autism Blood Test Shows Promise

Diagnosing autism could soon be much simpler, with researchers saying this week that they’ve developed a blood test that appears to identify those with the disorder even before symptoms are apparent.

The early-stage test developed at Boston Children’s Hospital may be able to flag about two-thirds of those with autism, researchers reported in the journal PLOS ONE.

Currently, clinicians rely on observation to screen children for autism. Most kids are not diagnosed until after age 4, according to the U.S. Centers for Disease Control and Prevention.

But a blood test offers the promise of flagging kids and potentially enrolling them in early intervention programs even before symptoms appear.

In order to develop the test, researchers analyzed blood samples from 66 boys with autism and 33 without the developmental disorder in an effort to establish patterns. Ultimately, the scientists were able to focus on a group of 55 genes that they used to successfully identify autism with 68 percent accuracy in a second test group made up of 104 people with autism and 82 controls.

“It’s clear that no single mutation or even a single pathway is responsible for all cases,” said Isaac Kohane of Boston Children’s Hospital who worked on the research. “By looking at this 55-gene signature, which can capture disruptions in multiple pathways at once, we can say with about 70 percent accuracy, ‘this child does not have autism,’ or ‘this child could be at risk,’ putting him at the head of the queue for early intervention and evaluation. And we can do it relatively inexpensively and quickly.”

The blood test is not yet ready for prime time, researchers said, but it has been licensed to the company SynapDx for further exploration and potential commercialization.