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Transcriptome Analysis Reveals Perturbed Molecular Pathways in Autism

1 June 2011. The brain displays consistent but abnormal patterns of gene expression in autism, according to a study appearing in Nature on May 25. By analyzing the expression profiles of thousands of genes, Daniel Geschwind of the University of California, Los Angeles, and colleagues found that typical differences seen between frontal and temporal cortex in controls were less pronounced in autism. Also, the researchers identified two sets of genes ("modules") that were misregulated in autism: one comprises neuronal genes that include several known autism susceptibility factors, and another contains immune system and glia-related genes.

The study reveals common molecular pathways perturbed in autism—a heterogeneous disorder that, like schizophrenia, seems to stem from a variety of risk factors. "At least two-thirds, maybe up to three-quarters, of the autism cases shared a common molecular pathology," Geschwind told SRF. "That amount of convergence was very surprising."

Detecting these distinctive molecular abnormalities required expansive thinking and a stomach for big matrices. While standard expression profiling looks for individual genes that differ in expression between cases and controls, Geschwind's team wanted to know what the relationships were among the genes themselves—was up- or downregulation of one gene tracked by others? Making pairwise comparisons across thousands of genes in a systematic, unbiased way revealed groups of genes whose expression is highly correlated (the so-called modules), a hallmark of genes whose function is intimately linked.

Geschwind's group has used this kind of "network transcriptome analysis" to identify basic functional units in healthy human brain, including modules related to cell types, synaptic function, and specific organelles (Oldham et al., 2008). The method has also narrowed in on gene expression differences in the brain that distinguish humans from chimpanzees (Oldham et al., 2006). The new study is the first time the method has been used to identify molecular pathology in a brain disorder.

Clusters and modules
First author Irina Voineagu and colleagues started by extracting RNA from postmortem brain samples from 19 people with autism and 17 healthy controls. The samples came from frontal cortex, temporal cortex, and cerebellum—regions associated with autism. Using a microarray to measure the transcript abundance of thousands of genes, they identified 444 genes in the cortical samples whose expression level differed significantly by a factor of at least 1.3 from that observed in control samples. Things looked fairly normal in the cerebellum, however, with only two genes differing between autism and control subjects. When the researchers grouped the expression profiles according to their similarity, they found that the autism cortex samples formed a cluster distinct from that of the controls—a finding validated in a second group of samples. This indicates that autism cortical samples can be consistently distinguished from controls based on their gene expression profiles.

To try to reduce the complexity of the transcriptome to its functional parts, Voineagu and colleagues delineated the sets of genes, or modules, whose expression levels were highly correlated with each other. Overall, 87 percent of the modules extracted from the autism samples resembled those in controls—indicating that the general organization of the brain transcriptome was fairly normal. But the autism modules deviated in other ways. For example, the distinct differences in expression normally observed between frontal and temporal cortex was not apparent for the corresponding module in the autism samples. This suggests that the two regions are less differentiated in autism, a difference that may reflect aberrant development.

Hints from hubs
Two other autism modules stood out from controls: one enriched for genes involved in neurons, synapses, and neurotransmitters was underexpressed in autism, whereas another consisting of genes related to the immune system, inflammatory responses, and glial markers was overexpressed. While these patterns distinguished autism samples from control samples, they could reflect either causal pathology or reaction to it. To get at this question, the researchers turned to association signals uncovered by genomewide association studies (GWAS) of autism, and found that their neuronal module—but not the immune one—was enriched for these signals more than expected by chance, consistent with it containing causal genetic factors.

The neuronal module was also enriched for known autism susceptibility genes, providing independent support for their role in the disorder, according to the authors. Some of these also constitute "hubs" within the module—genes whose expression co-varied the most with other genes in the module, displaying greater connectedness. These hubs identify functionally important genes, and, indeed, one was A2BP1, an alternative splicing regulator previously implicated in autism. Because A2BP1 expression was downregulated in autism, the researchers then used RNA sequencing to look for abnormalities in alternative splicing between three autism samples and three controls. This analysis turned up differential splicing in a number of neuronal genes, including A2BP1 targets, and adds splicing abnormalities to the possible mechanisms contributing to autism.

Standard gene-by-gene expression profiling was first applied to schizophrenia brain samples over 10 years ago (Mirnics et al., 2000), but the kind of network analysis used in the new study may help distill the complexity of expression patterns of thousands of genes into something more tractable. Though findings in schizophrenia postmortem tissue are complicated by confounds like medication history, the strategy of using GWAS signals to parse cause from consequence may resolve this. Ultimately, a systems-level understanding of what distinguishes a brain with schizophrenia from other brains could hasten the discovery of new, more widely effective treatments.—Michele Solis.

Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011 May 25. Abstract

Comments on Related News

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Karoly Mirnics, SRF Advisor
Submitted 31 October 2011
Posted 31 October 2011

Well done! Finally, some systematic transcriptome profiling of the human brain on a large scale. If we are ever going to crack neurodevelopmental disorders, such datasets will be absolutely critical. Exon-level transcriptome and associated genotyping data, brain regions, gender differences, developmental trajectories—this manuscript has it all. However, this is only a start, a catalogue of molecular events that begs to be explored. We see the complexity contained within the dataset, and it is simply mind-boggling. How do we make sense out of all this? Which changes are characteristic of interneurons, and which trajectories are projection neuron derived? How are the changes related to maturation of layers or various diseases? The mining of this dataset is far from over. It will be interesting to see what a WGCNA type of analysis will uncover in this proverbial gold mine. We need new ideas, we need new bioinformatic tools to look at this.

In addition, based on the presented data, we need to form precise, testable hypotheses. And then will come the hardest part—we need to test these hypotheses, and this will be incredibly time consuming and very low throughput. From in-vitro systems, transgenic models, electrophysiology, neurochemistry to imaging, we should use everything at our disposal.

While the generation of this dataset is clearly long overdue, I also must note the enormous price tag that these experiments carry. Very few laboratories/groups in the world have resources to perform such studies, and such fishing expeditions/dataset-generation projects are poorly suited to regular NIH-funded mechanisms.

View all comments by Karoly Mirnics

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Paul Harrison
Submitted 2 November 2011
Posted 3 November 2011
  I recommend the Primary Papers

The Nature papers by Colantuoni et al. (2011) and Kang et al. (2011) are landmark studies, not only because of the wealth of data about the human brain transcriptome across the lifespan that they contain, but as a resource for other researchers to dip into or mine as they wish. Both papers represent the culmination of extensive research programs, and are based ultimately on the crucial, sensitive, and often unappreciated task of collecting a sufficient number of well-characterized brains (Deep-Soboslay et al., 2011). In turn (as noted by Karoly Mirnics in his comment), they also attest to the importance of having funding schemes which permit this kind of ambitious, long-term, large-scale—and expensive—research. The papers set a new gold standard for human brain studies in terms of size and scope. They also illustrate the renaissance of postmortem brain research, and provide confirmation (if any was needed) that human brain diseases need direct study of human brains—including normative analyses across the lifespan—if their genetic, neurodevelopmental, and molecular aspects are to be understood (Kleinman et al., 2011).

The papers will take time to digest fully. Early impressions reveal several findings of particular interest and relevance to schizophrenia.

1. It's striking just how dramatic are the transcriptional changes, even across a restricted fetal time period. Simple notions of a "second trimester" origin of a disorder need to become more nuanced.

2. The flow of alterations between fetal and infant life, and the infant-aging similarities and differences also speak to the dynamic temporal nature of the transcriptome, its regulation, refinement, and recapitulation.

3. The extent of regional (and sex) differences in gene expression and exon usage—and the interactions of these with development—found by Kang et al. are noteworthy, too, again attesting to the sheer complexity of the transcriptomic landscape.

4. The eQTL data in both studies emphasize the importance of cis variation in regulation of gene expression, especially for SNPs around transcriptional start sites; the P value of 10-78 (Fig. 3b in Colantuoni et al.) must be a record for a human brain study!

The data provide a much more detailed (albeit more complex) context within which to interpret deviations from the normal transcriptional profile in those with, or at risk of, schizophrenia. Notwithstanding the huge number of data in these papers, many questions remain unanswered. There is a relative gap across mid-childhood—for obvious reasons—which later studies can fill in (c.f. the accompanying Nature editorial on the need to collect more brains from children). Future studies will also hopefully move to sequencing methods, extend to other brain regions, and address the daunting task of protein-based equivalent studies. Finally, as the authors of both papers note, the current data are from tissue homogenates, and so cannot reveal differential changes in one cell type from another. We can expect these last differences to be as complicated and fascinating as the temporal and regional profiles reported here.

A key issue for researchers interested in the neurobiology of genes involved in schizophrenia is how deep to dig when investigating the expression of a gene (as one aspect of its function or pathology) before deciding enough is enough. The data in these papers indicate that the answer is probably "very deep." Stretching the metaphor, the data also highlight that there may need to be several digs, across time and space, in looking for different kinds of molecular treasure.


Deep-Soboslay A, Benes FM, Haroutunian V, Ellis JK, Kleinman JE, Hyde TM. Psychiatric brain banking: three perspectives on current trends and future directions. Biol Psychiatry . 2011 Jan 15 ; 69(2):104-12. Abstract

Kleinman JE, Law AJ, Lipska BK, Hyde TM, Ellis JK, Harrison PJ, Weinberger DR. Genetic neuropathology of schizophrenia: new approaches to an old question and new uses for postmortem human brains. Biol Psychiatry . 2011 Jan 15 ; 69(2):140-5. Abstract

View all comments by Paul Harrison

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Marquis Vawter
Submitted 9 November 2011
Posted 10 November 2011
  I recommend the Primary Papers

Just a passing comment. I believe the study by Kang et al. shows an interesting change in gene expression of the MIR137, which was strongly implicated by GWAS.

Both of these papers are extremely useful, and welcomed for the study of eQTLs in human brain.

View all comments by Marquis Vawter

Related News: The Life and Times of the Human Brain Transcriptome

Comment by:  Yasue Horiuchi, Shin-ichi Kano, Akira Sawa (SRF Advisor)Ashley Wilson
Submitted 1 December 2011
Posted 1 December 2011

These two new papers show the spatial and temporal regulation of gene expression in the human brain across various ages. Although it is not novel to observe various patterns of gene expression during human brain development, systematic bioinformatics approaches using such enormous sample sizes will lead us to a new level of understanding the complexity of the transcriptome during development.

Both groups showed that age is a very strong contributor to global differences in gene expression compared to other variables such as sex, ethnicity, and inter-individual variation. Thus, transcriptional differences and changes are most pronounced during early development, gradually slowing through infancy, adolescence, and into adulthood—each stage having a clear transcriptional profile. Kang et al. further showed that gene expression is also spatially regulated. Furthermore, they found many co-expressed gene groups that were spatially and temporally regulated. They also reported sex-biased gene expression.

Our group, like many other laboratories, is trying to approach molecular mechanism(s) underlying schizophrenia by using patient-derived cells, especially induced pluripotent stem cells (Dolmetsch and Geschwind, 2011) and immature neurons obtained from nasal biopsy (Sawa and Cascella, 2009). The challenge in this approach has been the shortage of information on gene expression patterns during the neurodevelopmental trajectory. In this sense, these two outstanding papers provide all of us with useful information. If any future studies can address the spatial and temporal regulation of gene expression in each “specific” type of brain cell, this will be of further help to the field. Laser-captured microdissection could be a useful tool to obtain enriched populations of different cell types from tissue (Goswami et al., 2010; Tajinda et al., 2010). Such encyclopedia-type efforts may also be applied to reveal the epigenetic landscape of the brain in the future (Cheung et al., 2010).


Dolmetsch R, Geschwind DH. The human brain in a dish: the promise of iPSC-derived neurons. Cell . 2011 Jun 10 ; 145(6):831-4. Abstract

Sawa A, Cascella NG. Peripheral olfactory system for clinical and basic psychiatry: a promising entry point to the mystery of brain mechanism and biomarker identification in schizophrenia. Am J Psychiatry . 2009 Feb 1 ; 166(2):137-9. Abstract

Goswami DB, May WL, Stockmeier CA, Austin MC. Transcriptional expression of serotonergic regulators in laser-captured microdissected dorsal raphe neurons of subjects with major depressive disorder: sex-specific differences. J Neurochem . 2010 Jan 1 ; 112(2):397-409. Abstract

Tajinda K, Ishizuka K, Colantuoni C, Morita M, Winicki J, Le C, Lin S, Schretlen D, Sawa A, Cascella NG. Neuronal biomarkers from patients with mental illnesses: a novel method through nasal biopsy combined with laser-captured microdissection. Mol Psychiatry . 2010 Mar 1 ; 15(3):231-2. Abstract

Cheung I, Shulha HP, Jiang Y, Matevossian A, Wang J, Weng Z, Akbarian S. Developmental regulation and individual differences of neuronal H3K4me3 epigenomes in the prefrontal cortex. Proc Natl Acad Sci U S A . 2010 May 11 ; 107(19):8824-9. Abstract

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Comment by:  Karoly Mirnics, SRF Advisor
Submitted 28 August 2012
Posted 28 August 2012

This is perhaps the best and most comprehensive transcriptome dataset of schizophrenia generated to date. It has multiple strengths, including the use of four different cortical regions, the correlation of genetics with transcriptomics, and the use of strong bioinformatics including WGCNA analysis. The findings are quite revealing. The results suggest that there is a strong, common signature across brain areas BA21, BA32, and BA38 that encompasses genes related to transcription/translation, signal transduction, the cell cycle, cell adhesion, the immune response, apoptosis, and the cytoskeleton.

Perhaps surprisingly, the expression signature was far less prominent in prefrontal cortical area BA 46, which is one of the most affected regions in schizophrenia. However, it was also clear that each brain region had a unique, region-specific schizophrenia signature. In addition, this study reproduced and validated a number of previously reported findings related to oligodendrocyte and mitochondrial transcript deficits. Nevertheless, the results of the current study disagree with the previously reported and replicated outcomes of similar assessments of other cohorts: in this study, GABA system genes were upregulated, and gene ontology categories related to immune response were downregulated in subjects with schizophrenia.

This is certainly noteworthy, and this apparent discrepancy in findings will have to be addressed by future experiments. I wish that the authors had addressed this issue in their discussion. The combined transcriptomics-genetics results suggest that the oligodendrocyte, GABA, and glutamate modules are (at least partially) driven by genetic vulnerability, while other gene expression changes might be secondary/adaptational in nature.

Finally, the study suggests that interregional coexpression is attenuated in schizophrenia. A very similar hypothesis, using WGCNA analysis of samples with autism, has been proposed in autism by the Geschwind laboratory (see Voineagu et al., 2011, and the commentary of Korade and Mirnics, 2011). Voineagu et al. reported that the differential patterns of gene expression that normally distinguish the frontal and temporal cortices are significantly attenuated in the autistic brain, potentially leading to loss of functional specifications across the affected cortical areas. This is certainly worth further exploration, and the schematic hypothesis presented in Figure 5 of the current paper is a logical blueprint that nicely maps out a possible sequence of pathophysiological events in schizophrenia. Still, the actual sequence of the proposed events can be debated at the current time: it is very likely that the cascades proposed by Figure 1 in the commentary by Korade et al. and Figure 5 in the current manuscript will have to be revised as our knowledge accumulates.


Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature . 2011 May 25. Abstract

Korade Z, Mirnics K. Gene expression: the autism disconnect. Nature . 2011 June 15. Abstract

View all comments by Karoly Mirnics

Related News: Ambitious Genetic Integration Analysis of Schizophrenia Points to Early Brain Development

Comment by:  Roger Boshes
Submitted 10 August 2013
Posted 20 August 2013

These data suggest a "stem" circuit that may be common to many patients with schizophrenia, but subsequent de novo mutations may explain the protean manifestations of the disorder. Alternatively, this prefrontal perturbation may be related to a heritable, i.e., not a somatic, mutation that explains 80 percent heritability but not the protean phenotypic expression of the condition. Finally, it may be the link between schizophrenia and some flavors of autism.


Boshes RA, Manschreck TC, Konigsberg W. Genetics of the schizophrenias: a model accounting for their persistence and myriad phenotypes. Harv Rev Psychiatry. 2012 May-Jun; 20(3):119-29. Abstract

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