Fascinating Study – Immune transcriptome alterations in the temporal cortex of subjects with autism
Posted June 7, 2010on:
Hello friends –
I’ve been referencing this paper in some discussions online for a while; I’ve read it, and in fact, while working on another project, got the opportunity to speak with one of the authors of the paper. It’s a very cool paper with a lot of information in it, some of which, could be considered inconvenient findings. Here is the abstract:
Autism is a severe disorder that involves both genetic and environmental factors. Expression profiling of the superior temporal gyrus of six autistic subjects and matched controls revealed increased transcript levels of many immune system related genes. We also noticed changes in transcripts related to cell communication, differentiation, cell cycle regulation and chaperone systems. Critical expression changes were confirmed by qPCR (BCL6, CHI3L1, CYR61, IFI16, IFITM3, MAP2K3, PTDSR, RFX4, SPP1, RELN, NOTCH2, RIT1, SFN, GADD45B, HSPA6, HSPB8and SERPINH1). Overall, these expression patterns appear to be more associated with the late recovery phase of autoimmune brain disorders, than with the innate immune response characteristic of neurodegenerative diseases. Moreover, a variance-based analysis revealed much greater transcript variability in brains from autistic subjects compared to the control group, suggesting that these genes may represent autism susceptibility genes and should be assessed in follow-up genetic studies.
(emphasis is mine) [Full paper freely available from that link]
I am particularly intrigued by the second bolded sentence regarding the “these expression patterns appear to be more associated with the later recovery phase of autoimmune brain disorders, than the innate immune response characteristic of neurodegenerative diseases”. I’ve had it put to me previously that we should not necessarily implicate neuroinflammation in autism, the argument being that even though we had evidence of chronically activated microglia, what we do not seem to have evidence for is actual damage to the brain, and ergo, the neuroinflammation may actually be a byproduct of having autism, as opposed to playing a causative role, or that in fact, the neuroinflammation might even be beneficial. There have been some other places where the claim has been made that because our profile of neuroinflammation doesn’t match more classically recognized neurodegenerative disorders (i.e., MS/Alzheimer’s/Parkinson’s), that therefore, certain environmental agents need not be fully investigated as a potential contributor to autism. This is the first time that I am aware that someone has attempted to classify the neuroinflammatory pattern observed in autism not only as distinctly different from classical neurodegenerative diseases, but to also go so far as to provide a more refined example.
From the Introduction:
In order to better understand the molecular changes associated with ASD, we assessed the transcriptome of the temporal cortex of postmortem brains from autistic subjects and compared it to matched healthy controls. This assessment was performed using oligonucleotide DNA microarrays on six autistic-control pairs. While the sample size is limited by the availability of high-quality RNA from postmortem subjects with ASD, this sample size is sufficient to uncover robust and relatively uniform changes that may be characteristic of the majority of subjects. Our study revealed a dramatic increase in the expression of immune system-related genes. Furthermore, transcripts of genes involved in cell communication, differentiation, cell cycle regulation and cell death were also profoundly affected. Many of the genes altered in the temporal cortex of autistic subjects are part of the cytokine signaling/regulatory pathway, suggesting that a dysreactive immune process is a critical driver of the observed ASD-related transcriptome profile.
I was initially very skeptical about this, with a sample set so small, wasn’t it difficult to ascertain if their findings were by chance or not? It turns out, the answer depends on the type of datapoint you are evaluating against. A powerful tool in use by the researchers is a recent addition to the genetic analysis research suite, not only the ability to scan for thousands of gene activity levels simultaneously, but the use of known gene networks to identify if among those thousands of results, related genes are being expressed differentially. This is important for some amazingly robust findings presented later in the paper, so lets sidetrack a little bit. Here is a nice overview of the process being used:
Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. A common approach involves focusing on a handful of genes at the top and bottom of L (i.e., those showing the largest difference) to discern telltale biological clues. This approach has a few major limitations.
(i) After correcting for multiple hypotheses testing, no individual gene may meet the threshold for statistical significance, because the relevant biological differences are modest relative to the noise inherent to the microarray technology.
(ii) Alternatively, one may be left with a long list of statistically significant genes without any unifying biological theme. Interpretation can be daunting and ad hoc, being dependent on a biologist’s area of expertise.
(iii) Single-gene analysis may miss important effects on pathways. Cellular processes often affect sets of genes acting in concert. An increase of 20% in all genes encoding members of a metabolic pathway may dramatically alter the flux through the pathway and may be more important than a 20-fold increase in a single gene.
(iv) When different groups study the same biological system, the list of statistically significant genes from the two studies may show distressingly little overlap (3).
So, back to Garbett, not only did the authors find a great number of genes overexpressed in the autism group (and a smaller number, underexpressed), when they threw their thousands of results of individual genes into the GSEA, what came back was that several genetic pathways were very significantly altered, many of them immune mediated. This is a big step in understanding in my opinion. I believe we have likely come full circle on our understanding of very high penetrance genes that might be driving towards a developmental trajectory of autism; i.e., Rhett, Fragile-X. But using this technique we can determine if entire biological pathways are altered by measuring the output of genes. Specifically the point made in bullet (iii) stands out to me; having a twenty fold increase in a single gene might not be too big a deal if the other participants in the proteins function cannot be altered by twenty fold as a result due to other rate limiting constraints; but if we can see related sets of genes with similar expression profiles, we can get a much better picture of the biological results of different expression.
The methods get dense pretty quickly, but are worth a shot to show how thorough the researchers were to insure that their findings were likely to be signficant. Essentially they performed three different statistical tests against their results of differentially expressed genes and broke their results down into genes that passed all three tests, two of three test, or one of three tests. Furthermore, a selected twenty genes were targeted with qPCR validation, which in all cases showed the expected directionality; i.e., if the expression was increased in the transcriptome analysis, qPCR analysis confirmed the increased expression.To provide another benchmark, they tested for other genes known to be associated with autism, REELN, and GFAP and found results consistent with other papers.
Having determined a large number of differentially expressed genes, the authors then went to try to analyze the known function of these genes.
These classifications were performed on a selected gene set that is differentially expressed between AUT and CONT subjects; based on the success of our qPCR validation, we decided to perform this analysis using transcripts that both reported an |ALR>1| and that reached p<0.05 in at least 2/3 statistical significance comparisons. Of 221 such transcripts, 186 had increased expression in AUT compared to CONT, while only 35 genes showed reduced expression in the AUT samples. We subjected these transcripts to an extensive literature search and observed that 72 out of 193 (37.3%) annotated and differentially expressed transcripts were either immune system related or cytokine responsive transcripts (Supplemental Material 2). Following this first classification, we were able to more precisely sub-classify these 72 annotated genes into three major functional subcategories, which overlap to a different degree; 1) cell communication and motility, 2) cell fate and differentiation, and 3) chaperones (Figure 3). The deregulation of these gene pathways might indicate that the profound molecular differences observed in the temporal cortex of autistic subjects possibly originate from an inability to attenuate a cytokine activation signal.
That last sentence packs a lot of punch for a couple of reasons. It would seem to be consistent with their statements regarding a “late recovery phase” of an autoimmune disorder; i.e., an immune response was initiated at some point in the past, but has yet to be completely silenced. This also isn’t the first time that the idea of problems regulating an immune response (i.e., the inability to attenuate a cytokine activation signal) has been suggested from clinical findings, for example, in Decreased transforming Growth Factor Beta1 in Autism: A Potential Link Between Immune Dysregulation and Impairment in Clinical Behavioral Outcomes, the authors found an inverse correlation between TGF-Beta1 and autism behavioral severity:
Given that a major role of TGFβ1 is to control inflammation, the negative correlations observed for TGFβ1 and behaviors may suggest that there is increased inflammation and/or ongoing inflammatory processes in subjects that exhibit higher (worse) behavioral scores.
As such, TGFβ has often been considered as one of the crucial regulators within the immune system and a key mediator in the development of autoimmune and systemic inflammation.
In summary, this study demonstrates that there is a significant reduction in TGFβ1 levels in the plasma of young children who have ASD compared with typically developing children and with non-ASD developmentally delayed controls who were frequency-matched on age. Such immune dysregulation may predispose to the development of autoimmunity and/or adverse neuroimmune interactions that could occur during critical windows in development.
[full paper from the link]
The theme of a critical window of development and enduring consequences of insults during that window is one that is getting more and more attention recently; this is an area that is going to get more and more attention as time goes by, and eventually, as the clinical data continues to pile up, meaningless taglines aren’t going to be enough to keep us from dispassionately evaluating our actions.
The Discussion section is particularly nice, I’ll try not to just quote the entire thing. Here are the really juicy parts.
The results of our study suggest that 1) in autism, transcript induction events greatly outnumbers transcript repression processes; 2) the neocortical transcriptome of autistic individuals is characterized by a strong immune response; 3) the transcription of genes related to cell communication, differentiation and cell cycle regulation is altered, putatively in an immune system-dependent manner, and 4) transcriptome variability is increased among autistic subjects, as compared to matched controls. Furthermore, our study also provides additional support for previously reported involvement of MET, GAD1, GFAP, RELN and other genes in the pathophysiology of autism. While the findings were obtained on a limited sample size, the statistical power, together with the previously reported postmortem data by other investigators suggest that the observed gene expression changes are likely to be critically related to the pathophysiology seen in the brain of the majority of ASD patients.
There is some description of studies using gene expression testing in the autism realm where the authors ultimately conclude that technical and methodological differences between the studies make them difficult to tie together coherently. There is another small section re-iterating the findings that were similar to single gene studies; i.e., REELN, MET, and GAD genes.
The most prominent expression changes in our dataset are clearly related to neuroimmune disturbances in the cortical tissue of autistic subjects. The idea of brain inflammatory changes in autism is not novel; epidemiological, (DeLong et al., 1981; Yamashita et al., 2003; Libbey et al., 2005) serological studies (Vargas et al., 2005; Ashwood et al., 2006) and postmortem studies (Pardo et al., 2005; Vargas et al., 2005; Korkmaz et al., 2006) over the last 10 years have provided compelling evidence that immune system response is an essential contributor to the pathophysiology of this disorder (Ashwood et al. 2006). Finally, converging post-mortem assessments and measurements of cytokines in the CSF of autistic children (Vargas et al., 2005), may indicating an ongoing immunological process involving multiple brain regions
Nothing really new here to anyone that is paying attention, but good information for the extremely common, gross oversimplification that ‘immune abnormalities’ have been found in autism, but we don’t have any good reason to think they may be part of the problem. Of course, this is an argument you’ll see a lot of the time regarding everyone’s favorite environmental agent.
Altered immune system genes are often observed across various brain disorders, albeit there are notable differences between the observed transcriptome patterns. The majority of neuroimmune genes found activated in the autistic brains overlap with mouse genes that are activated during the late recovery or “repair” phase in experimental autoimmune encephalomyelitis (Baranzini et al., 2005). This suggests a presence of an innate immune response in autism. However, the altered IL2RB, TH1TH2, and FAS pathways suggest a simultaneously occurring, T cell-mediated acquired immune response. Based on these combined findings we propose that the expression pattern in the autistic brains resembles a late stage autoimmune event rather than an acute autoimmune response or a non-specific immune activation seen in neurodegenerative diseases. Furthermore, the presence of an acquired immune component could conceivably point toward a potential viral trigger for an early-onset chronic autoimmune process leading to altered neurodevelopment and to persistent immune activation in the brain. Interestingly, recently obtained gene expression signatures of subjects with schizophrenia (Arion et al., 2007) show a partial, but important overlap with the altered neuroimmune genes found here in autism. These commonly observed immune changes may represent a long-lasting consequence of a shared, early life immune challenge, perhaps occurring at different developmental stages and thus affecting different brain regions, or yielding distinct clinical phenotypes due to different underlying premorbid genetic backgrounds.
The last sentence, regarding ‘long-lasting’ consequences of early life immune challenges is one that has a large, and growing body of evidence in the literature that report physiological and behavioral similarities to autism. We also have recent evidence that hospitilization for viral or bacterial infection during childhood is associated with an autism diagnosis. There is, of course, a liberal sprinkling of ‘mays’, ‘propose’, and ‘conceivably’ caveats in place here.
Earlier I mentioned that the authors studied gene networks in addition to single gene expressions., and that some of those findings were very significant. The results of this are found in Table 2. In one discussion, I had it pointed out to me by ScienceMom that it appeared that some of the networks were not found to be statistically significant (and ergo we should not necessarily assume that immune dysfunction was a participant in autism). [If you look at Table 2, some networks like a p value of 0000]. I decided to use the data in this paper for another project that isn’t ready yet, but in that process I was able to speak directly with one of the authors of this paper. I asked him about this, and he told me that this was a function of space limitations; all of the gene networks described were found to be statistically signficant, but in some instances there wasn’t enough space to typeset the p value. In fact, some networks were found to be differentially expressed with a p-value of .000000000000001. (!!!!!!!!) That isn’t a value that you see very often.
I recently got a copy of Mitochondrial dysfunction in Autism Spectrum Disorders: cause or effect, which shares an author with this paper, Persico. In that paper, they reference Immune Transcriptome Alterations In the Temporal Cortex of Subjects With Autism, invoking a potential cascade effect of prenatal immune challenge, inherited calcium transport deficiencies, and resultant mitochondrial dysfunction that could lead to autism. I’ve generally stayed away from the mitochondria stuff in the discussion realm; even though I think it is probably somewhat important to some children, and critically important to a select few children, I’ve mostly found that the discussion of mitochondrial issues is comprised of two sets of people talking past one another so as to prove something, or disprove something about everyones favorite environmental agent; but this is a neat paper that I’d like to get to eventually.