The Life and Times of the Human Brain Transcriptome
28 October 2011. A dynamic view of transcription in the human brain has been revealed in two papers published in Nature on 26 October 2011. Both studies tracked the expression patterns of tens of thousands of genes across the lifespan, and found that transcription is highest prenatally, slowing after birth—a pattern that presumably reflects the intricate work of building a brain. One study, led by Nenad Šestan of Yale University in New Haven, Connecticut, surveyed patterns of gene expression across 16 different brain regions at different ages. The second study, led by Joel Kleinman at the National Institute of Mental Health in Bethesda, Maryland, focused on the prefrontal cortex and found tight associations between individual gene expression and individual genetic variants but not between total genetic diversity and the entire transcription profile.
“There's a lot of genetic variation that makes us all unique, but the bottom line is that we have much more in common than not,” Kleinman told SRF.
These ambitious projects—involving thousands of genes, extensive postmortem brain collections, and plenty of analysis to make sense of it all—differ from previous surveys which were limited to a particular brain region or time point (e.g., Abrahams et al., 2007). The result is a comprehensive brain map of the ups and downs of gene expression during prenatal development, infancy, childhood, adolescence, and adulthood. This will serve as a reference for understanding the pathological events involved in brain disorders like schizophrenia. In particular, it can reveal the normal trajectories of expression for genes suspected in a disorder; it may finger new suspects by finding genes with tightly correlated patterns of expression with known risk factors; and when coupled with genetic information, it can start to outline the function of risk variants by probing their influence on gene expression.
“Ultimately, we want to find targets for treatment; this lets you take the first step in understanding how genetic variants increase risk for any brain disease,” Kleinman said.
Kleinman, whose group also collaborated with Šestan's on the first paper, was moved to look comprehensively at transcription patterns in the brain by earlier studies which linked schizophrenia-related variants to alternative transcripts that were preferentially expressed in fetal brain (e.g., Nakata et al., 2009). This made clear to him that the precise identity of a transcript, as well as its level of expression, across the lifespan would be essential for piecing together how genetic variants increase risk for a disease, he said.
Both studies have made their massive datasets freely available online. In addition, Tianzhang Ye of Kleinman’s group has created an application called BrainCloud that allows researchers to browse the expression patterns and related variants of any gene they are interested in. “There are a trillion pieces of information in the database, and we've mined it for some things that we're interested in, but we feel there is an infinite number of things it can be used for,” Kleinman said.
The first study took not one or two, but six first authors: Hyo Jung Kang, Yuka Imamura Kawasawa, Feng Cheng, Ying Zhu, Xuming Xu, and Mingfeng Li, all of Šestan's lab. The researchers assembled 57 postmortem brains from healthy donors, ranging in age from six weeks post-conception to 82 years old. Sixteen different brain regions were dissected out: 11 in the neocortex, plus the hippocampus, amygdala, striatum, thalamus, and the cerebellar cortex. RNA from these regions was extracted and quantified using a gene chip that probes 1.4 million whole transcripts or individual exons of 17,565 genes.
Apparently, change is the rule rather than the exception in the brain, with 90 percent of genes surveyed showing different expression patterns either in time, across different regions of the brain, or both. Most of this differential expression occurred prenatally, then settled down after birth. For example, while 57.7 percent of genes expressed in the neocortex changed their expression patterns temporally during fetal development, only 9 percent of these changed in postnatal development, and 0.7 percent did in adulthood. Spatially, the pattern of expression across regions became more similar after birth, with the holdout cerebellar cortex maintaining a transcription profile distinct from the rest of the brain. The researchers also monitored specific exon usage to get a handle on the dynamics of transcript diversity. They found that 90.2 percent of expressed genes showed signs of differential exon usage, with exon transcripts detected in some, but not all regions, or, conversely, at some, but not all time points. This indicates a precise orchestration of transcript type, location, and timing.
Sex, modules, and trajectories
A comparison of these transcription patterns between male and female brains highlighted 159 genes with sex-biased gene expression and 155 with sex-biased exon usage. The male-female differences were especially pronounced during fetal development, then faded after birth and into adulthood. Exons with sex-biased expression sometimes came from genes linked to brain diseases, including schizophrenia-related KCNH2 and autism-related NLGN4X. This suggests that the sex differences in the risk for certain disorders might stem from these sorts of transcriptional mechanisms.
Another way to distill meaning from the giant dataset is to look for groups of genes that co-vary their expression (see SRF related news story), which represent functional units in the brain (Oldham et al., 2008). The researchers identified 29 of these groups, called modules, with distinct patterns of spatial-temporal expression consistent with known developmental events. For example, one module with high expression early in fetal development in the neocortex, tapering off after birth, contained many genes involved in neural differentiation; in contrast, another module that ramped up its expression around birth was enriched for ion channels and neural ligand-receptor pairs. Interestingly, the “hub” genes in this module—those whose expression correlated the most with others in the module—included genes linked to depression (GDA) and schizophrenia (NRGN and RGS4). This suggests that these genes might be key drivers of the module’s transcriptional program.
Stepping away from this systems view, the researchers also examined the expression trajectories of individual genes implicated in autism and schizophrenia, including CNTNAP2, MET, NLGN4X, and NRGN. These revealed distinct spatial and temporal patterns of expression, which were then used to identify new suspects by looking for genes with similar patterns. For example, NRGN became highly expressed in the neocortex after birth, increased into late childhood, and remained high into adulthood; 50 transcripts with highly similar spatiotemporal expression patterns were identified (correlation coefficients greater than 0.80), and may be worth following up for a link to the disorder.
Ups and downs in prefrontal cortex
In the second study, first authors Carlo Colantuoni and Barbara Lipska of the NIMH assembled postmortem samples of prefrontal cortex from an impressive 269 healthy individuals, aged between 14 weeks post-conception and 78 years old. RNA was extracted from each sample and analyzed with a microarray containing 30,176 gene expression probes. Looking globally at the rate of expression change of all genes—a measure of how much they increased or decreased expression in a year—the researchers found the highest rates in prenatal tissue, which then dropped fivefold in infancy, and dropped again 90-fold in childhood from the prenatal peak. Expression held steady in adulthood, but started to climb again with aging, starting in the fifties and eventually surpassing the rate in adolescent brains. These fluctuations seem driven by individual genes with expression trajectories having distinct turning points at the same time.
To get a sense of the kinds of genes involved in these transcriptional shifts, the researchers compared the expression patterns of the 1,502 genes experiencing significant expression change before and after the particularly abrupt fetal-infant transition. Most of these genes had high expression during fetal development which decreased in infancy, and included many genes involved in axonal function—possibly reflecting the pruning of unused axon terminals. Genes with the inverse pattern, with low fetal expression that rose in infancy, were enriched for ATP synthesis—something that might reflect the rising energy demands of a growing and maturing brain. Genes with decreases in both fetal and infant periods consisted of many related to cell division, consistent with the known tapering off of cell proliferation at this time. Genes with increases during both stages were dominated by those related to the synapse.
The second half of the study was devoted to looking for potential genetic control knobs for expression. The researchers compared 625,439 SNP genotypes from DNA samples of each donor to expression of 30,176 different probes (a possible 19 billion associations). This uncovered 1,628 significant associations between individual SNPs and individual gene expression, with the strongest between a gene called ZSWIM7 and a SNP located within the gene itself, giving a stratospheric p value of 5.4 x 10-78. The expression levels of this gene for each of the three different genotypes were nearly non-overlapping across life, suggesting a strict genetic control.
Despite this evidence of individual SNPs associated with individual gene expression, things looked different on a global scale. When the researchers calculated a genetic distance that described the genetic similarity of their donors, and a transcriptional distance that reflected the similarity in their expression patterns, this did not turn up any associations. This suggests that even diverse genomes produce a remarkably similar transcription pattern in the brain.
Both studies set the stage for more thorough examinations of the complex transcription patterns in health and disease. These descriptions of the full scale of the transcriptome's diversity in the brain are an essential first step for comprehending how dynamic transcript changes translate into protein levels, or even phenotypes.—Michele Solis.
Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, Sousa AM, Pletikos M, Meyer KA, Sedmak G, Guennel T, Shin Y, Johnson MB, Krsnik Z, Mayer S, Fertuzinhos S, Umlauf S, Lisgo SN, Vortmeyer A, Weinberger DR, Mane S, Hyde TM, Huttner A, Reimers M, Kleinman JE, Sestan N. Spatio-temporal transcriptome of the human brain. Nature. 2011 Oct 26;478(7370):483-9. Abstract
Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, Colantuoni EA, Elkahloun AG, Herman MM, Weinberger DR, Kleinman JE. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature. 2011 Oct 26;478(7370):519-23. Abstract
Comments on News and Primary Papers
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 MirnicsComment 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 HarrisonComment 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.
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Primary Papers: Temporal dynamics and genetic control of transcription in the human prefrontal cortex.
Comment by: Takanori Hashimoto
Submitted 29 November 2011
Posted 29 November 2011
I am interested in the database on significant associations between SNPs and gene expression levels in the prefrontal cortex (PFC) because we might be able to understand how genetic variance associated with schizophrenia contributes to PFC dysfunction in schizophrenia. The database will allow us to identify functional genes whose expression levels in the PFC are controlled by schizophrenia-associated SNPs. If they are expressed and have specific roles in the mature PFC, we can assess their significance in the pathophysiology by evaluating expression changes in schizophrenia using postmortem brains. It is also possible that some of these genes have already been reported to exhibit altered expression levels in the PFC of schizophrenia subjects, contributing to specific aspects of PFC dysfunctions. Therefore, this database appears to be important for further understanding of the pathogenetic and pathophysiological mechanisms of schizophrenia and the development of efficient treatments for PFC dysfunction in schizophrenia.
However, to my disappointment, the access is restricted to NIH-funded principal investigators and not available to researchers across the world. I hope this part of the study also becomes open to researchers in nonprofit academic institutes worldwide.
View all comments by Takanori HashimotoComment 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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Comments on Related News
Related News: Deciphering Themes for Schizophrenia’s Genetic VariationComment by: Patrick Sullivan, SRF Advisor
, Danielle Posthuma
Submitted 16 November 2012
Posted 16 November 2012
Gilman et al. pose exceptionally important and salient questions: given that increasingly detailed genomic data have established that many genes are now strongly implicated in the etiology of schizophrenia, how do we understand this? How can these different components of the “parts list” for schizophrenia be pieced together to derive a cogent etiological hypothesis for further testing?
The authors use a new computational approach to address these questions, and derive lists related to axon guidance, neuronal cell mobility, synaptic function, and chromosomal remodeling. Additional analyses suggest the coherence of their lists. These are good clues that deserve further evaluation.
It was intriguing that the authors included multiple types of genetic variation—rare but potent copy number variants (e.g., Kirov et al., 2012), rare exonic mutations (Xu et al., 2012), and common variations from genomewide association studies (Ripke et al., 2011)—as most authors have tended to conduct these analyses separately.
In sum, a nice contribution to the literature and initial steps towards tackling a tough problem in human genetics. But, there are four issues for readers to bear in mind in evaluating the results.
First, we hope that the authors make their program freely available. This is the standard in the field. Many of us are interested in evaluating the capacities of their program. To our knowledge, it is not now available, although it has been used in multiple published papers. We could find no link in the paper or on the senior author’s lab page.
Second, readers need to remember that this was an in-silico analysis. It produces hypotheses but does not (and cannot) provide proof. The methods are subject to multiple biases, and it was not clear how well these were controlled (see point 4 as well). We wondered whether known biases like gene size and LD patterns were well controlled.
Third, we would have liked to see greater scholarship. There is an unfortunate trend for computational biologists to produce tools without benchmarking them against existing tools or rigorously determining power and error rates. The lack of finding significant clusters in control sets is insufficient in showing the validity of their program. Are the authors’ claims that their new tool represents superiority truly justified?
Moreover, there are a lot of tools for performing analyses of these sorts (e.g., INRICH, FORGE, MAGENTA, Ingenuity, ALIGATOR, among many others). Indeed, these sorts of analyses are in the toolkits of most psychiatric genetics groups and are routinely applied. Given that there are many papers reporting results, a scholarly treatment of how their results compare to those of others and what the added value of their program is would have been useful.
Fourth, and most importantly, pathway analysis is completely dependent on the input—the genetic findings and the pathways. The findings that the authors used had issues. The CNV list is likely to change soon as the PGC CNV group completes its integrated analyses of tens of thousands of subjects. The exome list was based on a small and atypical sample, and much larger studies are in preparation (see SRF comment). The authors did not seem to confront the issue that all humans contain a lot of deleterious exonic variation. And (spoiler alert), the GWAS list is soon to increase markedly. More and more precise findings are sure to alter the results.
The pathways used were pretty standard—GO, KEGG, protein-protein interaction databases. Unfortunately, although widely used, these pathways have multiple issues. The content of many GO annotations and KEGG pathways have not been constructed by experts in the area. As one salient example, synaptic gene lists in standard pathway databases were quite imperfectly related to lists created by experts (Ruano et al., 2010). The authors also relied somewhat uncritically on the PPI databases. These have multiple issues, and some (unpublished) data suggest substantial error (i.e., large fractions of the predicted interactions are not, in fact, real or biologically meaningful). The fraction of the proteome screened adequately by these methods is small. Some interactions in these databases are non-specific, or occur between molecules that are never in the same place at the same time.
Indeed, the genes overrepresented in PPI databases were selected due to disease relevance or biological importance (e.g., there is a lot of work on P53). In general, the more a gene is investigated, the more interactions are found.
Still, this is a key paper, albeit a snapshot based on imperfect input data, and we look forward to seeing whether additional analyses confirm a role in schizophrenia of the networks identified currently with their program.
Kirov G, Pocklington AJ, Holmans P, Ivanov D, Ikeda M, Ruderfer D, Moran J, Chambert K, Toncheva D, Georgieva L, Grozeva D, Fjodorova M, Wollerton R, Rees E, Nikolov I, van de Lagemaat LN, Bayés A, Fernandez E, Olason PI, Böttcher Y, Komiyama NH, Collins MO, Choudhary J, Stefansson K, Stefansson H, Grant SG, Purcell S, Sklar P, O'Donovan MC, Owen MJ. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry. 2012 Feb; 17(2):142-53. Abstract
Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, Sun Y, Levy S, Gogos JA, Karayiorgou M. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet. 2012 Oct 3. Abstract
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