Archive for June 2010
Implications for Autism Or Just Interesting? “Epigenetic and immune function profiles associated with posttraumatic stress disorder”
Posted June 25, 2010on:
Hello friends –
One of my tangential pubmed alerts notified me of this study the other day: Epigenetic and immune function profiles associated with posttraumatic stress disorder
The biologic underpinnings of posttraumatic stress disorder (PTSD) have not been fully elucidated. Previous work suggests that alterations in the immune system are characteristic of the disorder. Identifying the biologic mechanisms by which such alterations occur could provide fundamental insights into the etiology and treatment of PTSD. Here we identify specific epigenetic profiles underlying immune system changes associated with PTSD. Using blood samples (n = 100) obtained from an ongoing, prospective epidemiologic study in Detroit, the Detroit Neighborhood Health Study, we applied methylation microarrays to assay CpG sites from more than 14,000 genes among 23 PTSD-affected and 77 PTSD-unaffected individuals. We show that immune system functions are significantly overrepresented among the annotations associated with genes uniquely unmethylated among those with PTSD. We further demonstrate that genes whose methylation levels are significantly and negatively correlated with traumatic burden show a similar strong signal of immune function among the PTSD affected. The observed epigenetic variability in immune function by PTSD is corroborated using an independent biologic marker of immune response to infection, CMV—a typically latent herpesvirus whose activity was significantly higher among those with PTSD. This report of peripheral epigenomic and CMV profiles associated with mental illness suggests a biologic model of PTSD etiology in which an externally experienced traumatic event induces downstream alterations in immune function by reducing methylation levels of immune-related genes.
Essentially the authors took a bunch of people that are more likely to experience stressful situations and PTSD, urban Detroit residents, who amazingly report PTSD symptoms at twice the level that previous studies have found in analysis of larger areas. [Apparently, getting physically attacked is more common there, which gives rise to PTSD even more than ‘other traumatic event types’, and was reported by 50% of the participants from a larger study which formed the population pool of this study. (!!)] With this population base, blood was drawn and methylation profiles were analyzed between participants who reported PTSD symptoms (n=23) and those who ‘only’ had ‘potentially traumatic events’ (PTE). PTSD and ‘controls’ where matched by race, age, sex, and blood profiles.
Once methylation levels were identified, a functional annotation clustering analysis was performed, which I believe is similar a pathway analysis; essentially a bioinformatic tool to gain insight into which biological functions were being manipulated as a result of differential methylation of the genome. This is a powerful new tool in discerning what is happening in autism and elsewhere, and I expect it will provide some surprising answers in the future. Here is their text on what they found:
Consistent with previous findings from gene expression (4, 5) and psychoeneuroimmunologic studies (3), each of the top three FACs determined from uniquely unmethylated genes among PTSD-affected individuals shows a strong signature of immune system involvement. This signature includes genes from the innate immune system (e.g.,TLR1 andTLR3), as well as from genes that regulate innate and adaptive immune system processes (e.g., IL8, LTA, and KLRG-1). In contrast, pathways and processes relevant to organismal development in general—and neurogenesis in particular—figure prominently among the genes uniquely unmethylated in the PTSD-unaffected group (e.g., CNTN2 and TUBB2B; Fig. S2). Notably, similar clusters were obtained using an alternative approach based on genes differentially methylated between the two groups at P < 0.01, with annotations in the top five FACs that include signal, cell proliferation, developmental process, neurologic system process, and inflammatory response
Keeping in mind that reduced methylation results in increased gene expression, if we take a look at Table 1, some of the parallels to autism jump out a little more robustly:
In the ‘Uniquely Unmethylated’ (i.e., higher expression), area, we find that participants affected by PTSD had showed greater enrichment in genes related to the immune response, and specifically the inflammatory response and innate immune response. Our evidence for similar immunological profiles in the autism realm is deep, and includes multiple observations of an active immune response in the CNS, highly significant over expression of genes related to immune function in the CNS, several observations of known upregulators of the innate immune response that are associated with inflammatory conditions, and multiple studies finding an exaggerated innate immune response in vitro when compared to controls. The correlations with developmental process and neuron creation are pretty straightforward.
In the ‘Uniquely Methylated’ area (i.e., lower expression), the sensory perception differences hit close to home, and xenobiotic metabolism has been implicated by several studies.
Going further, the researchers attempted to evaluate for correlations between the number of potentially traumatic experiences and the methylation profile, and somewhat unsurprisingly found that as the number of experiences increased, the methylation differentials showed wider variation.
Here again we see a distinct signature of immune-related methylation profiles among the PTSD-affected group only. More specifically, we see methylation profiles that are suggestive of immune activation among persons with more PTE exposure in the genes that are significantly negatively correlated with increasing number of PTEs—a pattern reflective of that observed for the uniquely unmethylated genes in this same group (Table 1).
From the discussion section:
Among the many analyses performed in this work, the immune related functions identified in the PTSD-affected group were consistently identified only among gene sets with relatively lower levels of methylation (Tables 1 and 2). Demethylation has previously been shown to correlate with increased expression in several immune system–related genes (reviewed in ref. 22), including some identified here [e.g., IL8 (23)]. In contrast, methylation profiles among the PTSD-unaffected are distinguished by neurogenesis-related functional annotations. Neural progenitor cells have previously been identified in the adult human hippocampus (24); however, stress can inhibit cell proliferation and neurogenesis in this brain region (reviewed in ref. 25), and recent work suggests that adult neurogenesis may be regulated by components of the immune system (reviewed in ref. 26). Thus, immune dysfunction among persons with PTSD may be influenced by epigenetic profiles that are suggestive of immune activation or enhancement and also by an absence of epigenetic profiles that would be consistent with the development of normal neural-immune interactions (27).
Among the genes uniquely methylated in the PTSD-affected group, it is striking that the second most enriched cluster—sensory perception of sound—directly reflects one of the three major symptom clusters that define the disorder (Fig. 3B). Genes in this FAC thatmay be particularly salient to this symptom domain include otospiralin (OTOS),which shows decreased expression in guinea pigs after acoustic stress (28) and otoferlin (OTOF), mutations in which have been linked to nonsyndromic hearing loss in humans (29). Exaggerated acoustic startle responses, often measured via heart rate or skin conductance after exposure to a sudden, loud tone, have been well documented among the PTSD affected (30) and are indicative of a hyperarousal state that characterizes this symptom domain. Notably, prospective studies have demonstrated that an elevated startle response is a consequence of having PTSD, because the response was not present immediately after exposure to trauma but developed with time among trauma survivors who developed the disorder (30, 31).
My son had some very severe auditory related problems earlier in his life, and still occasionally struggles with either sudden loud noises, or some very specific noises, such as some dog barks, or the sound of an infant crying. Previously the only physiology based attempt at an explanation I’d heard of for this type of response involved fine grained brain architecture and consequent filtering and/or overexcitation problems. The idea that sound sensitivities in particular can be obtained environmentally is of particular interest to the autism community.
From the common sense angle, I find this completely fascinating; we’ve known for a long time that living with consistent stress is bad for you with a variety of nasty endpoints, but this type of finding narrows down the means by which this happens. In the far off future, perhaps targeted methyl affecting drugs could be considered for people who experience extremely stressful events, as sort of a ‘PTSD vaccine’ [hehe] could be developed.
From an ASD perspective, increased feeling of anxiety, or just generally being ‘stressed out’ is a consistent finding both in research and from what I’ve read of readings from people with autism on the Internet. I’ve seen several explanations, with sensory based problems being mentioned several times. From a biological standpoint we seem to have a growing body of evidence of an abnormally regulated stress response in the autism cohort. An internet friend of mine, Loftmatt, has written extensively on his thoughts concerning the increase in stress in modern society and the mechanisms by which this could be contributing to our apparent observations of an increase in autism. This study would seem to provide insight towards a possible mechanism by which a frequent state of stress could lead to some of our immunological findings in the autism realm; a possibility I hadn’t considered previously when trying to detangle a means by which our observations of immune activation were not participating in autistic behavior. The thought of a feedback loop also strikes me looking at this, something causes a feeling of extreme stress, which leads to abnormal methylation levels and genetic expression, which leads to increased physiological (and behavioral?) alterations, and even more stress.
I may try to poke through the supplementary materials to see if any specific genes or pathways found to be differentially regulated have parallels in some of the other studies we’ve seen recently such as Garbett or Hu, although this may be somewhat of a crapshoot unless I could figure out how to actually submit gene lists to GSEA and read the responses.
And we may need to consider the possibility that these types of effects can be trans-generational. One of the most fascinating studies I’ve seen on epigentics involved exactly that, a multi-generational effect of famine in Holland, wherein the grandchildren of women who were pregnant during a time of famine bore striking differences in a variety of endpoints compared to children whose grandmothers were not pregnant during that time.
The more we learn, the more complicated the world becomes.
Posted June 11, 2010on:
Hello friends –
I ran across this one on accident the other day (why wasn’t it in one of my pubmed alerts?):
Previous studies have demonstrated an association between preterm delivery and increased risk of special educational need (SEN). The aim of our study was to examine the risk of SEN across the full range of gestation.
Methods and Findings
We conducted a population-based, retrospective study by linking school census data on the 407,503 eligible school-aged children resident in 19 Scottish Local Authority areas (total population 3.8 million) to their routine birth data. SEN was recorded in 17,784 (4.9%) children; 1,565 (8.4%) of those born preterm and 16,219 (4.7%) of those born at term. The risk of SEN increased across the whole range of gestation from 40 to 24 wk: 37–39 wk adjusted odds ratio (OR) 1.16, 95% confidence interval (CI) 1.12–1.20; 33–36 wk adjusted OR 1.53, 95% CI 1.43–1.63; 28–32 wk adjusted OR 2.66, 95% CI 2.38–2.97; 24–27 wk adjusted OR 6.92, 95% CI 5.58–8.58. There was no interaction between elective versus spontaneous delivery. Overall, gestation at delivery accounted for 10% of the adjusted population attributable fraction of SEN. Because of their high frequency, early term deliveries (37–39 wk) accounted for 5.5% of cases of SEN compared with preterm deliveries (<37 wk), which accounted for only 3.6% of cases.
Gestation at delivery had a strong, dose-dependent relationship with SEN that was apparent across the whole range of gestation. Because early term delivery is more common than preterm delivery, the former accounts for a higher percentage of SEN cases. Our findings have important implications for clinical practice in relation to the timing of elective delivery
[Full paper from link. Emphasis is mine]
Essentially the authors evaluated gestational lengths with a fine tooth comb to discern if ‘early’, though not technically ‘pre-term’ delivery was associated with a ‘special education need’ (SEN), which in this case embodies a range of developmental problems including dyslexia, autism, or even physical problems like deafness or vision problems.
What the authors found was that there were subtle, but real effects in the likelyhood of having a special education need for non full term births that was dose dependent, but even included children that would not necessarily be considered early by existing standards.
Our study demonstrated a strong trend of decreasing risk of SEN with advancing gestational age at birth. The key finding of the present analysis is that this trend continued across gestational ages classified as term. Although the risk of SEN was highest among infants who were delivered preterm (<37 wk gestation), these accounted for only 5.1% of deliveries. Therefore, only a relatively small proportion of SEN (3.5%) could be attributed to preterm delivery. By contrast, 39.6% of infants were delivered between 37 and 39 wk gestation. Therefore, whilst these early term infants had only a moderately increased risk, 5.3% of SEN cases could be attributed to early term delivery.
The authors claim that the finding of effects at early, but not pre-term gestational lengths is one that is largely missing from existing studies, which have not taken these date ranges into consideration, or the ones that did, were not studying for cognitive problems, and indeed, excluded children with these criteria. Curiously, they also report an increase in SEN in children who had extra gestational periods, i.e., > 41 weeks in some studies.
The authors make absolutely no speculation as to what might be driving increased special education needs as the result of premature or early birth.
Looking at their results, one of the most striking things is that the impact did not alter if elective (i.e. C-Section) versus non-elective births were used as a variable. But this has deep ramifications for the autism storyline, which holds that if there are environmental factors that can contribute to autism, they are prenatal, and indeed, are often thought to involve insults very early in the prenatal period. In this case, we know that a genetic or environmental force isn’t contributing to the early birth, because it didn’t matter if the birth was spontaneous or not. The only area for an effect is postnatal. That is a big, big difference in the narrative.
Is this a matter of some just in time epigenetic programming happening in the womb that doesn’t get a chance to finish up in early births? Alternatively it could be that early birth allows for environmental exposures that the infant is not quite prepared to deal with. Or it could be both, or neither, or an illusory finding, but if these findings can be replicated, it raises a lot of questions about the sacred line between prenatal and postnatal environmental influences.
Unfortunately, the raw data for this project doesn’t seem to be available online; it might be really nice to see if there were patterns to be observed had particular salience to our population of interest.
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.