passionless Droning about autism

Posts Tagged ‘Genetic Expression

Hello friends –

I ran into a few abstracts,  read a few papers, and tried to get my way through one really dense paper in the past few weeks that got me thinking about this post.  It’s  all shook up, like pasta primavera in my head, but hopefully something cogent will come out the other end.  (?)

Of the metabolic conditions known to be associated with having a child with autism, hypothyroidism is one that I keep on running into by way of the pubmed alert grapevine.  By way of example, we have two studies that looked for autoimmune conditions in family members which found hypothyroidism to be one of many autoimmune diseases as a risk factor for autism, including,  Familial clustering of autoimmune disorders and evaluation of medical risk factors in autism, and Increased prevalence of familial autoimmunity in probands with pervasive developmental disorders.   This shouldn’t be too surprising, we know that, for example, perinatal hypothyroidism is a leading cause of mental retardation, with similar findings for the condition during pregnancy.  It turns out, it appears that rates of hypothyroidism are slightly increasing, though at this time, the increases are of relatively small proportions, and as such, may be artifacts unrelated to an actual increase in classically recognized hypothyroidism.  In any case, I think it is safe to say that interference with thyroid metabolism is something to be avoided at all costs when possible.

So after having read about that, this paper showed up in my inbox a while ago:

Effects of perinatal hypothyroidism on regulation of reelin and brain-derived neurotrophic factor gene expression in rat hippocampus: Role of DNA methylation and histone acetylation

Thyroid hormones have long been known to play important roles in the development and functions of the central nervous system, however, the precise molecular mechanisms that regulate thyroid hormone-responsive gene expression are not well understood. The present study investigated the role of DNA methylaion and histone acetylation in the effects of perinatal hypothyroidism on regulation of reelin and brain-derived neurotrophic factor (BDNF) gene expression in rat hippocampus. The findings indicated that the activities of DNA methyltransferase (DNMT), methylated reelin and BDNF genes were up-regulated, whereas, the activities of histone acetylases (HAT), the levels of global acetylated histone 3 (H3) and global acetylated histone 4 (H4), and acetylated H3, acetylated H4 at reelin promoter and at BDNF gene promoter for exon II were down-regulated in the hippocampus at the developmental stage of the hypothyroid animals. These results suggest that epigenetic modification of chromatin might underlie the mechanisms of hypothyroidism-induced down-regulation of reelin and BDNF gene expression in developmental rat hippocampus

This gets interesting for autism because reelin, and bdnf levels have been found to be decreased in several studies in the autism population, with direct measurements, genetic expression, mouse knockout based models of autism , and genomic alterations all being implicated.  There have been some negative genetic studies, but considering that it isn’t always the genes you have, but the genes you use, our other available evidence certainly points to BDNF and reelin involvement with some percentage of children with autism, and the association is such that a reduction in reelin or BDNF is a risk factor for developing autism.  It would seem that the paper above might give some insight into the lower level details of the effects of hypothyroidism and subsequent developmental trajectories; modifications of reelin expression; through epigentic mechanisms, no less!.  That’s pretty cool!

Then, I got my hands on a review paper that tries to go into detail as to the functional mechanism by which reelin deficiency could contribute to ASD, Neuroendocrine pathways altered in autism. Special role of reelin.  It is a review that touches on a variety of ways that reelin contributes to neurodevelopment that have findings in the autism realm, including neuronal targeting and migration during brain formation, interactions with the serotonin and GABA systems, testosterone, and oxytocin.   In short, there are plenty of ways that decreased reelin expression can impact development in ways that mirror our some of our observations in autism.

Of the many things that convince me that we are doomed, the proliferation of chemical compounds whose interactions within our bodies we scarcely understand is among them.   In my readings on endocrine disruptors, one thing I found that seemed to be worrying lots of researchers was that some classes of these chemicals are capable of interfering with thyroid metabolism, and in some cases interfering with development of cells known to be associated with autism.    Terrifyingly enough, since I read those papers, several others have come out, including Polybrominated Diphenylether (PBDE) Flame Retardants and Thyroid Hormone during Pregnancy and Mini-review: polybrominated diphenyl ether (PBDE) flame retardants as potential autism risk factors.     At this point, it is important to point out that, as far as I know, there have not been any studies showing that non occupational exposure to PDBEs or other environmental pollutants can lead to classically defined hypothyroidism, at least none that I know of. (?)    Be that as it may, I think it is realistic to assume any interference in thyroid metabolism is a bad thing, and while finding people in the outlier regions of hypo (or hyper) thyroidism gives us information on extreme environments, it would take someone with a lot of misplaced faith to assume that we can safely disturb thyroid metabolism just a little bit, and everything will come out in the wash.

I’ve had the argument made to me in the past that environmental pollutant driven increases in autism lacked biological plausible mechanisms; this argument is almost always made within a context of trying to defend the concept of a static rate of autism.  While the papers I’ve linked to above do not provide conclusive proof that our changing environment is causing more children to be born with autism, they do provide increasing evidence of a pathway from pollutants to ASD, and indeed,  the lack of biological plausibility becomes an increasingly flacid foundation on which to assume that our observations of an increased rate of autism are illusory.   Unfortunately, in my opinion, the focus on vaccines has contributed to the mindset that a static rate of autism (or nowadays, maybe a tiny increase), must be protected at all costs, including some ideas on the application of a precautionary principle that seem outright insane to me (or at least, the exact opposite of what I would consider to be a precautionary path).

One thing is for certain, the number of child bearing women in developing countries with measurable concentrations of chemicals known to interferre with thyroid metabolism nears 100% in the industrialized nation as we eat , drink, breathe and bathe in the microscopic remnants of packaging materials, deteriorating carpet fibers, and baby clothes that are made to be fire resistant.  This is an environment unambiguously different than that encountered by any other generation of infants in the history of mankind.  To believe that we can modify our environment so drastically without having an impact seems incredibly naive to me, or on some days, just plain old stupid.

– pD

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:

Immune transcriptome alterations in the temporal cortex of subjects with autism

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:

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

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.

– pD

Hello friends –

So this is a really cool paper by some folks that have a series of interesting stuff:  Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain.  Here is the abstract:

Autism is currently considered a multigene disorder with epigenetic influences. To investigate the contribution of DNA methylation to autism spectrum disorders, we have recently completed large-scale methylation profiling by CpG island microarray analysis of lymphoblastoid cell lines derived from monozygotic twins discordant for diagnosis of autism and their nonautistic siblings. Methylation profiling revealed many candidate genes differentially methylated between discordant MZ twins as well as between both twins and nonautistic siblings. Bioinformatics analysis of the differentially methylated genes demonstrated enrichment for high-level functions including gene transcription, nervous system development, cell death/survival, and other biological processes implicated in autism. The methylation status of 2 of these candidate genes, BCL-2 and retinoic acid-related orphan receptor alpha (RORA), was further confirmed by bisulfite sequencing and methylation-specific PCR, respectively. Immunohistochemical analyses of tissue arrays containing slices of the cerebellum and frontal cortex of autistic and age- and sex-matched control subjects revealed decreased expression of RORA and BCL-2 proteins in the autistic brain. Our data thus confirm the role of epigenetic regulation of gene expression via differential DNA methylation in idiopathic autism, and furthermore link molecular changes in a peripheral cell model with brain pathobiology in autism.

 [As always, any emphasis is my own.]

This group has published a couple of papers that utilized similar study groups, methodologies, and means to display their findings, all of which I would recommend to anyone interested in learning; specifically, Gene expression profiling of lymphoblastoid cell lines from monozygotic twins discordant in severity of autism reveals differential regulation of neurologically relevant genes [full paper available!], Gene expression profiling differentiates autism case-controls and phenotypic variants of autism spectrum disorders: evidence for circadian rhythm dysfunction in severe autism [full version available!], and Gene expression profiling of lymphoblasts from autistic and nonaffected sib pairs: altered pathways in neuronal development and steroid biosynthesis [full paper available!]. 
There are a couple of things I really like about their methodology and presentation style. 

1) Several studies, including the most recent, included twins with discordant autism severity as study participants as a way to gain insight into the impact of genetic expression, as opposed to genetic structure on autistic behaviors.  The highly cited heritability of autism in twins is used as evidence that the condition is predominantly mediated through genetics, and while no doubt genetic structure is important, by using genetic clones with different manifestations of autism severity, the authors are able to ascertain information about which genes are being affected in twins. 

2) The two stage nature of the study design allows for both large scale analysis of a great number of genes being expressed differentially by genome wide scan, the results of which can be used for highly targeted confirmation by tissue analysis.  Further, the use of cells available in the periphery, lymphobastoid cell lines (LLCs) as measurement points for genetic expression, allows for well thought out investigations of a very rare resource, post morten brain tissue from autistics.  In this instance, different methylation profiles identified from LLCs from blood samples gave the researchers a starting point for what to look for in the brain tissue. 

3) This paper ties together both genetic expression and epigenetics; i.e., not only that genes are being used differently, but it forwards our understandings of the means by which this is happening.  Earlier studies by this group have found differences in genetic expression previously, but hadn’t elucidated on the specific mechanisms of action, in this case, over methylation, and consequent silencing of genetic protein production. 

4) This is the first group of papers I’ve seen that have been using a bioinformatics approach to understanding the pathways affected by their findings; there may be other papers out there in the autism realm, (and almost certainly in others), that have been performing this type of analysis, but I haven’t run into them.  Several of their papers, including the circadian rhythm paper, provide illustrations of associations to biological conditions and pathologies associated with affected networks.  Here is an example from the latest paper.  (Sarcastic apologies for those running at 800 / 600)
 This type of illustration is the death knell for the argument that autism is a condition to be handled by psychologists; there are a couple of similar ones in the paper. 

Considering those points, here are some juicy parts from the paper itself.  From the introduction:

In this study, we use global methylation profiling of discordantly diagnosed monozygotic twins and their nonautistic siblings on CpG island arrays to test the hypothesis that differential gene expression in idiopathic autism is, at least in part, the result of aberrant methylation. Our study reveals distinct methylation differences in multiple genes between the discordant MZ twins as well as common epigenetic differences distinguishing the twins (the undiagnosed twin exhibiting milder autistic traits that are below the threshold for diagnosis) from nonautistic sibling controls.

There are essentially three groups, twins with different autism severity, and non autistic siblings.  One thing that I’m not cerrtain of here is whether or not there were methylation differences found between the twins and their non autistic siblings or not; the text above is a little unclear; i.e., as there are different mechanisms by which genetic expression can be modified besides methylation, this may mean that while there were expression differences found between autism and controls, those differences were not found to be attributed to differential methylation levels.  (?)

From the results:

Network analysis was then performed to examine the relationship between this set of genes and biological processes. As shown in Fig. 1B, many of the associated processes within the network, including synaptic regulation, fetal development, morphogenesis, apoptosis, inflammation, digestion, steroid biosynthesis, and mental deficiency, have been associated with autism. Two genes from this network, BCL-2 and RORA, were selected for further study because of their respective roles in apoptosis and morphogenesis/inflammation. Interestingly, BCL-2 protein has been previously demonstrated to be reduced in the cerebellum and frontal cortex of autistic subjects relative to control subjects (31, 32), but RORA, a nuclear steroid hormone receptor and transcriptional activator that is involved in Purkinje cell differentiation (33) and cerebellar development (34), has never before been implicated in autism. In addition, RORA, a regulator of circadian rhythm (35), is also neuroprotective against inflammation and oxidative stress (36), both of which are increased in autism (37, 38).

Several of the tables are pretty cumbersome to paste in, but do provide more detailed functional level impacts of some of the functions of the differentially methylated genes identified.  Even with the text above, however, we can see a lot of sweet spots being touched on, including several that were identified in previous studies by this group of researchers.  It also illustrates some of the very powerful techniques in use; a broad array of genes were scanned for differential expression, some with different expression and significant roles in processes known to be abnormal in the autism population are identified, and used for further, more pinpointed analysis. 

As noted, Fatemi found reduced BCL-2 in post mortem brain samples in two studies; one of the roles played by BCL-2 is apoptosis, or programmed cell death.  By way of example, here is a study that shows that knockout (or in this case, knockup) mice that overexpress BCL-2 have more Purkinje cells than their non modified counterparts, which states, in part:

Because bcl-2 overexpression has been shown to rescue other neurons from programmed cell death, the increase in Purkinje cell numbers in overexpressing bcl-2 transgenics suggests that Purkinje cells undergo a period of cell death during normal development.

Considering that reductions in Purkinje cells is among the most commonly found brain difference in autism, a reduction in BCL-2 seems appropriate.  The fact that it in this case it was methylation levels leading to a reduction in BCL-2 might also be of interest in regards to the Fairytale Of The Static Rate of Autism; here we have evidence that mechanisms other than genetic structure are leading to decreases in a protein known to protect Purkinje cells from apoptosis.  

I don’t know anything about RORA, but its list of functions make a lot of sense when we consider other findings; a relative dearth of a protein known to protect against neuroinflammation and oxidative stress and a regulatory role in the sleep cycle.

The authors also noticed a dose dependent relationship between expression levels, which in this case represented a silencing of genes and autism severity. 

Quantitative RT-PCR was used to confirm decreased expression of BCL-2 and RORA in autistic samples and to evaluate the effect of a global methylation inhibitor, 5-Aza-2-deoxycytidine, on gene expression. For both BCL-2 and RORA, gene expression was significantly higher (P_0.05) in the unaffected control than autistic co-twins (Fig. 4A). Generally, the diagnosed autistic co-twin (_A) had the lowest level of expression of BCL-2 and RORA, while the milder undiagnosed co-twin (_M) exhibited transcript levels between that observed for unaffected sibling controls and autistic co-twins. This suggests a quantitative relationship between phenotype and gene expression of these 2 genes, although additional studies are required to confirm this observation

Again, this makes plenty of sense if we believe that things like a neuroinflammation, oxidative stress have parts to play in the behavioral manifestation of autism; in this case, get more methylation, and hence, less RORA and BCL-2, which, in turns, makes you more susceptible to neuroinflammation, oxidative stress, and Purkinje cell development abnormalities. 

If we take the predisposition towards problems with inflammation for a closer look, we can find that several other papers, including Grigorenko, Enzo, and Ashwood have all found that a propensity for inflammation, or a propensity towards abnormal regulation of inflammation have correlations with autism severity.  Though potentially inconvenient, this would seem to lend additional evidence for a causal role of immune based pathology in autism, as opposed to autism causing immune abnormalities. 

The discussions section has a lot of good text that is largely a touch up on what we already have here.  Here are some good quotes:

In particular, functional and pathway analyses of the differentially  methylated/expressed genes showed enrichment of genes involved in inflammation and apoptosis, cellulardifferentiation, brain morphogenesis, growth rate, cytokine production, myelination, synaptic regulation, learning, and steroid biosynthesis, all of which have been shown to be altered in ASDs. The candidate genes were prioritized for further analyses by identifying the overlap between the differentially methylated genes and those that had been shown to be differentially expressed in the same set of samples in previous gene expression analyses (18). Pathway analyses of this filtered set of genes thus focused our attention on 2 genes, BCL-2 and RORA, as potential candidate genes for ASDs whose expression may be dysregulated byaberrant methylation.  As shown in Figs. 3 and 4, respectively, RORA was confirmed to be inversely differentially methylated and expressed in LCLs from autistic vs. nonautistic siblings,with expression dependent on methylation, as demonstrated by the absence of methylation in the presence of 5-Aza-2-deoxycytidine. Notably, we also show by immunohistochemical staining of cerebellar and frontal cortex regions of autistic vs. normal brain (Figs. 5, 8), that RORA protein is noticeably reduced in the majority ofthe autistic samples relative to age- and sex-matched controls. This reduction is also specifically demonstrated in Purkinje cells, which are dependent on RORA for both survival and differentiation (Fig. 7). These findings thus link molecular changes identified in a peripheral cell model of ASDs to actual pathological changes in the autistic brain, suggesting that LCLs is an appropriate surrogate for studies on autism.

Finally, this paper generated a lot of press, in part (I think), because somewhere, someone (the authors?), apparently made note of the fact that this type of feature, hypermethylation, is potentially treatable, raising the possibility of palliative avenues.  (Or was this just a function of the fact that it was a finding that wasn’t truly genetic, and thus, ‘fixable’?)  While technically true, I am of the opinion that this is a long ways off; the authors found large numbers of differentially methylated genes; some were also hypomethylated.  The drugs that we know are capable of epigenomic modifications right now, some are used in advanced cancer patients, for example, are not discriminatory in their actions.  What we really would need would be targeted unmethylators that we could use to attach to RORA and BCL-2 genes and specifically free them up to produce more protein.  The same week that this paper came out, another paper was published, entitled Epigenetic approaches to psychiatric disorders which speaks towards this complexity. 

–      pD

Hello friends –

A while ago I saw a completely fascinating Nova  called Ghost In Your Genes concerning the nascent field of epigenetics, the study of the relative expression of genes, which is a bit different than the presence or absence of genetic differences.   I’d recommend this program to anyone interested in learning.

In a general sense, our genes are simply blueprints for the production of proteins; the traditional model of genetic research involves structural changes in the genetic blueprints, so that we might understand that a person with a particular mutation might produce more, or less, of a particular protein than someone without that mutation.  As protein gradients are altered, physiological effects accumulate, and we can begin to associate genetic differences with identifiable classifications.

But.  It turns out, structural differences in the DNA aren’t the only way to affect the production of genes.  Genes can also be regulated by a variety of factors, and these changes in regulation, in turn, are measured as expression of genes, essentially a measure of which genes are active, or inactive, and to what extent.

From Wikipedia:

In biology, epigenetics is the study of inherited changes in phenotype (appearance) or gene expression caused by mechanisms other than changes in the underlying DNA sequence, hence the name epi- (Greek: επί– over, above) genetics. These changes may remain through cell divisions for the remainder of the cell’s life and may also last for multiple generations. However, there is no change in the underlying DNA sequence of the organism;[1] instead, non-genetic factors cause the organism’s genes to behave (or “express themselves”) differently.[2]

For another interesting write up, see this one by PZ Meyers; it gets technical quickly, but is a nice read.  Specific mechanisms aside, it is sufficient for our purposes that epigenetics is the study of how a variety of non structural changes can affect how our genes operate.

An analogy might of a series of car engines, sitting at idle.   All of them power a car, but at a structural level there are differences, most models are roughly generating the same amount of force at idle, but, for example, the Prius engine is generating much lower force than Porsche engine.   At a very general level, we might consider physical mutations of our genome and protein generation capacities to be equivalent to the difference between the Porshce and the Prius at idle.  But there are other means to affect the energy being put out by the engine, the accelerator, tweaking the cylinders, or a variety of other means. This is a big shift and weights heavily on the ‘genetic and environment interaction’ theme that gets a lot play in the autism realm.  Despite a lot of studies, and spectrums of dollars there have been very few findings involving autism genes that do anything but confer a very limited risk of a diagnosis.  Furthermore, a lot of the studies are finding that seemingly very common mutations are implicated, but with very delicate effects.  An example of this might be the MET genes, that have several neat papers (here, here, here), but the specific MET-C allele associated with autism is still very common, found in nearly fifty percent of everyone.  None the less, it is just a little more prevalent in the autism cohort, but the impact is very subtle, and likely dependent on the presence of a variety of other genes (or expression patterns), or other factors.  Excepting known genetic  conditions that confer great risk, but can be responsible for only a fraction of our autism, mutations such as Fragile-X or Rhett Syndrome, the vast majority of genetic findings impart small increases in risk.

But once we start looking at the wide array of different genetic expression in autism, it becomes clear that which genes you use, and to what extent, might be as important as which genes you are born with.  By way of example, a very cool paper out recently, “Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain ” that is available in full online and I would recommend to anyone interested in how analyzing epigenetic changes and the accompanying differential gene expression can teach us more about autism.  A lot of the press around this study involves the hope that eventually this kind of finding might lead a treatment opportunities, something I personally consider to be a long term goal that still faces significant technical hurdles; but it does gives us insight into the nature of autism, and the usefulness of the half truth ‘differently wired’ argument concerning autism treatments.

– pD

Note: Updated link to PZ.


Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 36 other followers


    • advice to caregivers: I was looking through some of your articles on this internet site and I think this internet site is real instructive! Keep posting.
    • regulated binary options brokers uk: manificent рut ᥙp, vеry informative. Ι ponder ѡhy thе other specialists of this sector don't notice tɦis. You shouⅼd proceed ʏour writin
    • SergioMib: http://loveawake.ru - Знакомства в Лопатино. Сайт знакомств в Лопатино бесплатно, без регис