The mammalian target of rapamycin (mTOR) pathway integrates signals from different nutrient sources, including amino acids and glucose. Compounds that inhibit mTOR kinase activity such as rapamycin and everolimus can suppress seizures in some chronic animal models and in patients with tuberous sclerosis. However, it is not known whether mTOR inhibitors exert acute anticonvulsant effects in addition to their longer term antiepileptogenic effects. To gain insights into how rapamycin suppresses seizures, we investigated the anticonvulsant activity of rapamycin using acute seizure tests in mice.Following intraperitoneal injection of rapamycin, normal four-week-old male NIH Swiss mice were evaluated for susceptibility to a battery of acute seizure tests similar to those currently used to screen potential therapeutics by the US NIH Anticonvulsant Screening Program. To assess the short term effects of rapamycin, mice were seizure tested in ≤6 hours of a single dose of rapamycin, and for longer term effects of rapamycin, mice were tested after 3 or more daily doses of rapamycin.The only seizure test where short-term rapamycin treatment protected mice was against tonic hindlimb extension in the MES threshold test, though this protection waned with longer rapamycin treatment. Longer term rapamycin treatment protected against kainic acid-induced seizure activity, but only at late times after seizure onset. Rapamycin was not protective in the 6 Hz or PTZ seizure tests after short or longer rapamycin treatment times. In contrast to other metabolism-based therapies that protect in acute seizure tests, rapamycin has limited acute anticonvulsant effects in normal mice.The efficacy of rapamycin as an acute anticonvulsant agent may be limited. Furthermore, the combined pattern of acute seizure test results places rapamycin in a third category distinct from both fasting and the ketogenic diet, and which is more similar to drugs acting on sodium channels. Epilepsy affects 0.5–1% of the US population but ∼20–30% of patients do not respond to the two initial medications prescribed in vitro also have not provided strong support for acute effects of rapamycin. Short-term exposure of neurons in vitro to rapamycin did not alter neuronal firing under baseline conditions, and it had limited benefits under conditions of provoked neuronal firing Inhibitors of mTOR may improve seizure control in other chronic epilepsy models where the underlying cause of epilepsy is not due to mutations in the TOR pathway. For example, rapamycin suppressed behavioral spasms in the doxorubicin/lipopolysaccharide/p-chlorophenylalanine model of infantile spasms Rapamycin has not been systematically tested in a battery of acute seizure tests like those used routinely to screen candidate therapeutics in preclinical trials Male NIH Swiss mice  aged 3–4 weeks were acclimatized to the animal care facility for 1–5 days and housed four per cage. All mice were fed unrestricted normal rodent chow . Animals were not fasted except where indicated, when food was withdrawn 24 h prior to the first dose of rapamycin.This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Johns Hopkins Animal Care and Use Committee (Protocol # MO10M254). All animal protocols are approved by the Animal Care and Use Committee at Johns Hopkins Medical Institutions.At 4 weeks of age, mice were injected intraperitoneally with rapamycin 4.5 mg/kg or 6 mg/kg body weight  prepared from a stock solution  diluted to a final concentration of 4% (v/v) ethanol in the vehicle immediately prior to use. Vehicle consisted of 5% polyethylene glycol 400  and 5% Tween 80  Relative levels of mTOR activity were assessed from immunoblots comparing phosphorylated and total levels of ribosomal S6 protein. Tissues were analyzed 0.5 h, 1 h, 3 h, and 6 h after a single rapamycin injection, and after 3 d (∼75 h) of daily rapamycin injections. Mice were sacrificed by cervical dislocation and rapidly dissected neocortex and hippocampi were immediately frozen. Lysates of frozen tissues were prepared with RIPA buffer ) and separated by SDS-PAGE (12%). Blots were probed overnight with antibodies against S6 and phospho-S6 (Ser240/244) , and for 2 h with horseradish peroxidase-conjugated anti-rabbit secondary antibody . Protein bands were visualized with ECL reagent . Band intensity was quantified using ImageJ software (NIH).Each mouse was tested for seizures only once. Personnel performing seizure testing and assessments were blinded to treatment group assignment. Except where indicated, seizure tests were performed at the indicated number of hours following a single dose of rapamycin (4.5 mg/kg), or mice were tested 3 h after receiving three doses of rapamycin (4.5 mg/kg) given at 24 h intervals (75 h).The maximal electroshock threshold (MES-T) test was performed as described previously The 6 Hz test was administered using the same apparatus, stimulus frequency (6 Hz), pulse width (0.2 msec), and shock duration (3 sec) as described previously Kainic acid was injected intraperitoneally  as described previously PTZ  was infused at a constant rate (0.05 ml/min) as reported previously One animal per cage selected with a random number generator was used to measure glucose and the ketone body β-hydroxybutyrate  as previously reported 50 and the current where half the mice experienced any seizure behavior in the 6 Hz test, or CC50) were performed using Minitab 16 . t-tests, Mann-Whitney U tests, and nonlinear curve fits were performed using GraphPad Prism 4 .Probit analyses  screens dozens of potential compounds annually for potential therapeutic use As a first approximation of timing for seizure testing, the effect of rapamycin on mTOR activity in mouse brains was evaluated by assessing the phosphorylation status of ribosomal protein S6, a known downstream target of mTOR Mice were protected against the traditional outcome in the MES-T test, tonic hindlimb extension (THLE), at 3 h and 6 h after rapamycin injection, but not at earlier time points . DespiteThe 6 Hz test reveals the protective effects of the ketogenic diet in mice Because of its utility in demonstrating the anticonvulsant effects of intermittent fasting, we also used the intraperitoneal kainic acid test, which potentially could mimic the effects of rapamycin Using the same 6 h and 3 d rapamycin treatment regimes as above, mice were not protected against PTZ-induced seizures for any of the seizure behaviors scored, including first twitch, initial clonus, terminal clonus, or tonic hindlimb extension . It is uThe mTOR pathway is sensitive to changes in glucose levels Although mTOR activity is suppressed by metabolism-based therapies that protect in acute seizure tests A, AMPA, and NMDA receptor blockers showed no difference in current-voltage (I-V) relationships (vs. vehicle) To provide potential insight into the mechanisms of rapamycin, we compared its acute seizure test profile with other anticonvulsant compounds. Protection by rapamycin against MES-T-induced seizures, albeit transient, combined with a lack of protective effects against PTZ- and 6 Hz-induced seizures is similar to the profile of three other anticonvulsants, specifically phenytoin, lamotrigine, and topiramate  [33], [3A receptor function Rapamycin exhibits a dichotomous effect in the kainic acid test, as it protects only at the latest times after the onset of seizure activity see . WhetherThe use of seizure-naïve, non-epileptic mice in this study allowed us to examine the short-term effects of rapamycin on nonpathological tissue. This is in contrast to long-term effects in disease models that may be dependent on a specific pathological context . The use of seizure-naïve, non-epileptic mice is a strategy that has successfully identified a wide variety of anticonvulsants used in the clinic 50 between rapamycin- and vehicle-treated mice  appears to be necessary to prevent seizures in preclinical models, as outlined previously. A requirement for prolonged rapamycin treatment is consistent with our finding that a 3-day treatment with rapamycin is more effective than a short 6 h treatment prior to kainic acid-induced seizures. Potentially more important than length of treatment, it appears that rapamycin is more effective in seizure models where mTOR activity is significantly increased at baseline, rather than situations where mTOR may be only transiently increased. This suggests the need for further study of rapamycin in situations where there is no known underlying mTOR-related pathology.


Autism is a highly heritable neurodevelopmental disorder, yet the genetic underpinnings of the disorder are largely unknown. Aberrant brain overgrowth is a well-replicated observation in the autism literature; but association, linkage, and expression studies have not identified genetic factors that explain this trajectory. Few studies have had sufficient statistical power to investigate whole-genome gene expression and genotypic variation in the autistic brain, especially in regions that display the greatest growth abnormality. Previous functional genomic studies have identified possible alterations in transcript levels of genes related to neurodevelopment and immune function. Thus, there is a need for genetic studies involving key brain regions to replicate these findings and solidify the role of particular functional pathways in autism pathogenesis. We therefore sought to identify abnormal brain gene expression patterns via whole-genome analysis of mRNA levels and copy number variations (CNVs) in autistic and control postmortem brain samples. We focused on prefrontal cortex tissue where excess neuron numbers and cortical overgrowth are pronounced in the majority of autism cases. We found evidence for dysregulation in pathways governing cell number, cortical patterning, and differentiation in young autistic prefrontal cortex. In contrast, adult autistic prefrontal cortex showed dysregulation of signaling and repair pathways. Genes regulating cell cycle also exhibited autism-specific CNVs in DNA derived from prefrontal cortex, and these genes were significantly associated with autism in genome-wide association study datasets. Our results suggest that CNVs and age-dependent gene expression changes in autism may reflect distinct pathological processes in the developing versus the mature autistic prefrontal cortex. Our results raise the hypothesis that genetic dysregulation in the developing brain leads to abnormal regional patterning, excess prefrontal neurons, cortical overgrowth, and neural dysfunction in autism. Autism is a disorder characterized by aberrant social, communication, and restricted and repetitive behaviors. It develops clinically in the first years of life. Toddlers and children with autism often exhibit early brain enlargement and excess neuron numbers in the prefrontal cortex. Adults with autism generally do not display enlargement but instead may have a smaller brain size. Thus, we investigated DNA and mRNA patterns in prefrontal cortex from young versus adult postmortem individuals with autism to identify age-related gene expression differences as well as possible genetic correlates of abnormal brain enlargement, excess neuron numbers, and abnormal functioning in this disorder. We found abnormalities in genetic pathways governing cell number, neurodevelopment, and cortical lateralization in autism. We also found that the key pathways associated with autism are different between younger and older autistic individuals. These findings suggest that dysregulated gene pathways in the early stages of neurodevelopment could lead to later behavioral and cognitive deficits associated with autism. Clinical and subclinical manifestations of autism begin during the first years of life The average age of individuals studied in more than fifty postmortem analyses of the autistic brain is 22 years Thus, there seems to be a gap between neuroanatomical and cellular abnormalities reported for autism at younger ages and molecular pathologies in autism at older ages. Molecular pathologies specifically present at younger ages in autism are largely unknown and unexplored. Also, whether some molecular abnormalities detected in the older autistic brain reflect conditions unique to the older brain and whether others are common to both younger and older autistic brains are unknown. Genome-wide analyses of the genes, pathways and processes that exhibit dysregulation specifically in the young autistic brain have not been pursued to a great degree. This is particularly true for the prefrontal and temporal cortex, where abnormalities of growth and function during early development are pronounced and likely to contribute significantly to social, communication, language and emotional deficits associated with autism. Additionally, although copy number variation (CNV) has been hypothesized to play an important role in the pathogenesis of autism We examined the linked hypotheses that underlying neuroanatomical differences between younger and adult individuals with autism are a result of differences in molecular pathology. We hypothesize that some molecular pathologies at younger ages in autism may provide insight into the very early neural developmental processes that lead to the disorder. To do so, we identified abnormal genetic pathways in the young autistic brain, determined expression patterns that distinguished the young from the adult autistic brain and identified evidence for gene expression dysregulation that is age-independent using genome-wide expression and genotyping techniques. We found age-dependent gene expression differences between young and adult autistic brains as well as rare and common genetic variants associated with autism that have important roles in neurodevelopment.First, we analyzed genome-wide expression of 57 frozen samples of dorsolateral prefrontal cortex  and post-hoc pair-wise ANOVA-based comparisons were performed to identify genes exhibiting expression differences in young autistic vs. young control DLPFC compared to adult autistic vs. adult control DLPFC. An overall ANOVA-based F-test p-value<0.05  was used to identify genes showing an effect of interaction between age and diagnosis for further analyses. From the two-way ANOVA results, we attempted to tease out expression differences between: 1) young autism, 2) young control, 3) adult autism and 4) adult control groups.Of the genes exhibiting an effect of interaction from ANOVA-based F-tests with p-values<0.05, 102 genes resulted in ANOVA-based post-hoc contrasts between young autistic and control groups with p-value<0.05 . Seven hGenes exhibiting a diagnosis main effect and a post-hoc pairwise contrast  were further subjected to enrichment analyses in the MetaCore software suite (MetaCore from GeneGo Inc.) as well as the online enrichment program DAVID Since early brain overgrowth and the clinical onset of autistic symptoms occur at young ages in autism, we focused on unique gene expression differences between autistic and control cases below the age of 14 years to identify genes that may be dysregulated early in autism pathogenesis. One hundred two genes were differentially expressed in young autistic cases compared with the young control group and exhibited a diagnosis x age group interaction effect  suggested that DNA damage-response, cell cycle and apoptosis-related MetaCore pathways (‘M-Pathways’) were significantly altered . Seven hundred thirty six genes were differentially expressed based on this analysis . These gThe Cell Differentiation Map Folder included significantly dysregulated genes RELN, BTRC, BMP4, MAPK10 and NTRK3 . This MaThese analyses suggest that in the adult autistic brain, cell differentiation, mitogenic, apoptotic and remodeling and repair functions could be components of recovery responses, in accordance with previous reports Finally, we examined genes showing a main effect of diagnosis to identify differentially expressed between autistic and control cases independent of age . More than 2000 such genes were detected based on a simple contrast between autism and control brains . Among the probes detected at p<0.05 were 6 genes important for Tissue Remodeling and Wound Repair  and 7 genes important for Immune System Response . Ultimately, we found some consistency between genes detected in our analyses with those of a previous functional genomic study, particularly within domains of repair and immune response.To further investigate the findings of dysregulated genetic functions in young autistic cases particularly, we determined whether CNVs in autistic cases were distinct from those in controls. We genotyped prefrontal cortex samples from 55 of the 57 total autistic and control cases in our study. After quality control, CNV enrichment was analyzed in 30 DLPFC cases from male and female autistic and control cases  using PeRestricting analysis to male cases, genes identified in the autistic group were enriched in cell cycle  and cytoskeleton  GeneGO Process Networks. No significant enrichment was identified in the gene content of controls . SimilarTo extend these genotypic findings and specifically test whether common variants of cell cycle genes are associated with autism, we determined whether common variants in processes involved in cell cycle and other hypothesized regulatory processes were associated with autism using two previously analyzed SNP datasets: one from the Autism Genetic Resource Exchange (AGRE) and National Institutes of Mental Health genotyped at the Broad Institute and Johns Hopkins Medical Institute (Broad/JHMI) and another from AGRE genotyped at the Children's Hospital of Philadelphia (CHOP) We found nominal associations of only cell cycle-related genes with ASD . BecauseUsing genome-wide expression and CNV detection techniques, we discovered specific developmental pathways and processes in the prefrontal cortex that are significantly abnormal in younger autistic cases, other non-developmental processes that are dysregulated in older autism cases and, finally, processes common across younger and older ages in autism. Age-dependent gene expression alterations may underlie the differences in cellular and neuroanatomical pathologies between young and adult ages in autism that have been previously reported in MRI and postmortem research It is widely held that maldevelopment of the prefrontal cortex, in conjunction with several other cortical and subcortical regions, plays a pivotal role in the social, communication, cognitive and emotion processing deficits in autism. In the present study, the young autistic prefrontal cortex displayed disturbances in critical developmental pathways and processes that govern cell number, cortical patterning and differentiation, including those regulating proliferation, cell cycle, DNA damage response and apoptosis and survival. We hypothesize that such dysregulation could be the basis of the 67% excess neuron numbers also reported for prefrontal cortex in children with autism We also found strong evidence of abnormalities in cortical patterning pathways that regulate normal anterior/posterior and dorsal/ventral patterning as well as right/left asymmetry. At young ages in autism, MRI evidence indicates disruption of normal cortical patterning with gray matter enlargement greatest in anterior dorsolateral prefrontal cortex and least in posterior occipital and ventral orbital prefrontal cortex In contrast to the younger autistic brain To complement analyses of age-specific expression pathology in autism, we also identified gene expression abnormalities in autism that were age independent. Among the most abnormally expressed genes were those that regulate DNA-damage response, apoptosis and immune system response functions. These genetic systems play important roles during development of the central nervous system More work remains to be done, however, to uncover the significance of these pathways in the postnatal brain. These results may point to mechanisms in the brain that are continuously pathological throughout the lifespan in autism and suggest possible candidates for genetic therapies.in vivo studies, such as Pinto et al. While large sample We speculate that the aberrances in these genes, which may be distinct from individual to individual, may collectively underlie the repair or response mechanisms in the mature autistic brain. Commonalities across autistic brains may converge at the level of disturbances in these genetic pathways, and monitoring most commonly affected pathways across functional genomic studies may help subgroup different pathogenic mechanisms.Though these genetic pathways with high impact on autism susceptibility may be similar between studies, there are also copious pathways that are disparate. The differences in the results between our study and Voineagu et al. Our analyses using mRNA and DNA from the same cortices suggest that a large and heterogeneous array of genes and gene expression abnormalities regulate multiple foundational prenatal processes critical to cortex formation. Although DNA defects vary from autistic case to case, the diverse genetic deletions seem to underlie a relatively common biological theme, hitting a shared set of gene pathways that impact cell cycle, DNA damage detection and repair, migration, neural patterning and cell differentiation. The set of functional gene pathways identified by our direct analyses of autistic brain tissue are consistent with those identified by CNV pathway enrichment analyses in living autistic patients In this study, we found evidence for distinct gene expression anomalies in the prefrontal cortex of young autistic individuals that may underlie structural and functional maldevelopment of this and other association cortices. Our evidence shows that some important gene expression abnormalities in the prefrontal cortex change with age in autism and such changes may be related to findings in other studies that report a shift in morphology and function in the adult autistic brain. Enrichment of CNVs and association analysis of cell cycle gene sets, in accordance with early expression defects of cell cycle mechanisms, suggest that genetic variation may contribute to this dysregulation. We also found that irrespective of age, the expression of atypical developmental processes distinguish the autistic brain from that of control individuals. The modulation of these expression mechanisms may underlie the abnormal brain growth trajectory in autism. Further knowledge of the specific developmental neurobiological mechanisms behind the age-dependent anomalies reported here could point to distinct early developmental processes that lead to autism, uncover mechanisms that respond to early pathologies in the mature brain and suggest novel molecular targets for prevention strategies and treatment over the course of the disorder.All cases are deceased and are deidentified by the brain banks where tissue was obtained. However, the same human protections procedures were employed as for live subjects. Research procedures employed in this study were approved by the institutional review board of the University of California, San Diego (protocol number 091205).A full description of the methods is provided in Four hundred fifty ng of total RNA from each of 57 cases was submitted to Illumina, Inc.  for DASL-based labeling and hybridization to the Illumina HumanRef8 v3 microarray. Using biotinylated random primers and oligo-dT, 200 ng RNA was converted to cDNA. The biotinylated cDNA was then immobilized to a streptavidin-coated solid support and annealed by 24526 pairs of oligonucleotides (18626 genes). Following extension and ligation, the ligated oligonucleotides were PCR-amplified with biotinylated or fluorophore-labeled universal primers and captured using streptavidin paramagnetic beads. Finally, the products were washed and denatured before hybridization to the BeadChips at 58°C for 16 hours. A BeadArray Reader extracted images and read fluorescence intensities, and all data was uploaded into BeadStudio software for quality control and processing.Raw data exported from Illumina's BeadStudio underwent outlier removal, log2 transformation and quantile normalization as implemented in the lumi package for R/Bioconductor http://linus.nci.nih.gov/BRBArrayTools.html). Filtering was performed based on variance and minimum intensity. Probes with <20% of expression values having at least a 1.5-fold up- or down-regulation from the gene's median value across cases, and probes with a minimum log intensity of less than 15 were excluded.Filtering and differential expression analyses were performed using BRBArrayTools  and 2 levels of categorical age . Interaction probes of p<0.05, corresponding to an empirical FDR of 0.27 www.genego.com/metacore.php) and DAVID (http://david.abcc.ncifcrf.gov/) to examine the biological and functional relevance of differentially expressed genes. These genes were uploaded into MetaCore and filtered for known expression specific to the brain or the fetal brain. The default background gene list was used for all enrichment analyses. All results met threshold of corrected p<0.05 and FDR<0.1 Enrichment analyses were performed using the MetaCore software suite  according to the manufacturer's instructions in a 96-well, 10 µL format using standard PCR conditions. One µL of cDNA template, 250 nM of forward and reverse primer and 5 µL of PCR Master Mix (Roche) were mixed for each reaction.We took the geometric mean of all reference genes and the difference between this mean and the average intensity of experimental genes to find the delta Ct for each experimental gene. Subsequently, log2 fold change was assessed using -(T-C) where T = delta Ct of gene of the autistic case and C = delta Ct of gene of the control case. Using Spearman's rank correlation, the log2 fold changes of these 19 genes across qPCR and microarray platforms were found to be correlated at R = 0.78  and SNP calls were made using the Illumina Genome Studio software.http://www.openbioinformatics.org/penncnv/) implementing the wave adjustment procedure via the “–gcmodel” argument CNV calls were first obtained using the PennCNV software (http://projects.tcag.ca/cgi-bin/variation/gbrowse/hg18) were filtered without upper size limits. No large-size CNVs (>1 Mb) were identified in single cases that could potentially drive the gene enrichment results. We next analyzed males and female cases together  that merges results of PennCNV, QuantiSNP and GNOSIS. The analysis of the male cases yielded about 350 CNVs (11 autism and 13 controls). Gene enrichment was performed considering both the gene content of gene-rich CNVs and the nearest gene at the 5- and 3-prime end of gene-desert CNVs. As performed with PennCNV alone, we next analyzed male and female cases together but were unable to compare the gene enrichment of the two categories due to the significant difference in the number of cases that passed QC (12 autism versus 19 controls).Due to possible high false positive rates, we re-analyzed all cases using CNVision, a recently described analysis pipeline GO enrichment of the gene content of these regions was also performed using the MetaCore software suite (FDR<0.01 and p<0.05). GeneGO Network Processes of each of these gene lists are reported in For gene association analysis we utilized the AGRE-NIMH Broad/Johns Hopkins Medical Institute (Broad/JHMI) sample as our experimental dataset using original quality control filters and the Children's Hospital of Philadelphia (CHOP) sample dataset as a replication sample, as previously described http://www.geneontology.org/), GeneGO (http://www.genego.com/metacore.php) and the Wnt homepage (http://www.stanford.edu/group/nusselab/cgi-bin/wnt/). We used PLINK http://pngu.mgh.harvard.edu/~purcell/plink/) to retrieve the SNPs within a window of 20 kb around each gene and to perform set-based association analysis.Gene sets were created using Gene Ontology  in the cell cycle gene set were then broken down into 21 subsets of genes denoting specific phases of the cell cycle using Ingenuity and set-based tests were again performed on the experimental dataset. Subsets that were found to be significant in the Broad/JHMI sample were then tested on the CHOP sample for replication using the same set-based analysis.Figure S1Venn diagram showing differentially expressed genes in young autism vs. young control, adult autism vs. adult control and diagnosis main effect ANOVA analyses.(TIF)Click here for additional data file.Figure S2Log2 fold change correlations of selected genes detected by microarray and RT-PCR, and primer sequences. (A) Log2 Fold change detected by RT-PCR is depicted on the x-axis, and change detected by microarray is on the y-axis. Spearman's rank correlation detected R = 0.78  correlation between microarray and qPCR detection of fold change. (B) Forward and reverse primer sequences for 19 experimental and 3 reference genes used for RT-PCR to validate gene expression microarray results.(TIF)Click here for additional data file.Figure S3Third most significant map folder of diagnosis main effect analysis. Graph on left shows fold change of genes in Immune Response Map Folder. This map folder was third most significant in the comparison between all autistic and all control cases. From each category of differentially expressed genes in the all autistic vs. all control comparison , network(TIF)Click here for additional data file.Figure S4Enrichment of CNVs detected in the DLPFC of male and female samples. Metacore analysis of GeneGO Processes significantly represented in genes contained within: (A) total CNVs and B) filtered  CNVs. Blue bars, controls. Red bars, autistic cases. C) Average number of CNVs in autistic cases and controls. D) Average number of genes per CNV in autistic and control cases. E) Average CNV size comparison between autistic cases and controls. C–E) No statistically significant differences were found between autistic cases and controls, suggesting non-biased CNV detection potentially due to the small sample size. F) Size distribution of the filtered CNVs in the autistic cases is shown.(TIF)Click here for additional data file.Table S1Demographics and clinical information of postmortem cases. (A) ID, diagnosis, age, gender, study, DLPFC RNA integrity number (RIN), basis of diagnosis, ADI measures, and intellectual disability for cases in present study are documented. RIN #1 was performed at the UCSD Biogem core, RIN #2 was performed at UCSF in the Wynshaw-Boris lab, and RIN #3 was performed at the Allen Institute. ADI-R = Autism Diagnostic Interview-Revised; ADOS = Autism Diagnostic Observation Schedule; CNV = copy number variation; ISH = in situ hybridization; Comm = Communication; R & R = Restricted and Repetitive behavior; DLPFC = Dorsolateral Prefrontal Cortex; RIN = RNA integrity number; M = male; F = female; v = verbal; nv = nonverbal; Y = yes; N = no; TARF = existing diagnostic status on donation from The Autism Research Foundation, no other records available; ATP = existing diagnostic status on case from the Autism Tissue Program, no documents available at this date. Pink X = case passed quality control measures for gene expression/CNV analysis; black X = case was processed but did not pass quality control measures for CNV analysis. (B). Detailed clinical information of autistic cases. Available clinical information, including information from the ADI-R assessment, noted seizure activity and intellectual disability, is provided for autistic cases in this study. Assessments were made by a clinical psychologist (C.C.B.). ADI = Autism Diagnostic Interview; RIN = RNA Integrity Score; HX = History; DO = Diagnosis; ADI Comm Score = ADI-R Communication Score; ADI R&R Score = ADI-R Restricted and Repetitive Behavior Score; IVF = In Vitro Fertilization. (C) Detailed clinical information of autistic and control cases. Available clinical information is provided for autistic and control cases in this study. Assessments were made by a clinical psychologist (C.C.B.). HX = History; DO = Diagnosis; IVF = In Vitro Fertilization; PMI = Postmortem Interval.(PDF)Click here for additional data file.Table S2Differentially expressed genes in young autistic cases. All 102 differentially expressed genes in the posthoc comparison passing a threshold of p<0.05 between young autism and young control cases of genes showing an effect of interaction between diagnosis and categorical age. Gene symbols, Illumina probe IDs, p-Value of difference, cytogenetic bands and fold changes are listed. Bold genes are shown in (PDF)Click here for additional data file.Table S3Dysregulated genes of young autistic cases and previously identified candidate genes and loci. Loci of 102 differentially expressed genes in the posthoc comparison and references of previous linkage/association/CNV studies implicating the candidate locus and references of other genes neighboring the candidate loci are listed. NEGATIVE = negative association/linkage.(PDF)Click here for additional data file.Table S4Top downregulated and upregulated genes in adult autistic cases. Genes showing greatest fold change difference in the posthoc comparison passing a threshold of p<0.05 between adult autism and adult control cases of genes showing an effect of interaction between diagnosis and categorical age. Gene symbols, Illumina probe IDs, p-Value of difference, cytogenetic bands and fold changes are listed.(PDF)Click here for additional data file.Table S5Top downregulated and upregulated genes of male autistic cases across all young and older ages (2 to 56 years). Genes showing greatest fold change differences and a main effect of diagnosis passing a threshold of p<0.05 between autism and control cases, Illumina probe IDs, p-value of difference, cytogenetic bands and fold changes are listed.(PDF)Click here for additional data file.Table S6Significant dysregulated pathways of autistic cases across all young and older ages (2 to 56 years). Dysregulated pathways enriched in genes showing a main effect of diagnosis  identified using the MetaCore software suite between autism and control cases, p-value and network objects are listed. Pathways are sorted by pathway category name.(PDF)Click here for additional data file.Table S7Chromosomal locations of CNV regions found in all autistic cases . Chromosomal location, number (N) of SNPs, size of the SNP in base pairs (bp) and annotated gene content of CNV regions found in all autistic cases  are listed. Overlapping regions with the same gene content but different breakpoints are listed as distinct CNVs. Enrichment results are presented in (PDF)Click here for additional data file.Table S8Chromosomal locations of CNV regions found in all control cases . Chromosomal location, number (N) of SNPs, size of the SNP in base pairs (bp) and annotated gene content of CNV regions found in all control cases  are listed. Overlapping regions with the same gene content but different breakpoints are listed as distinct CNVs. Enrichment results are presented in (PDF)Click here for additional data file.Table S9References for genes in noted pathways and networks. Important genes from the pathways and networks identified by gene expression analyses are listed. Gene category, references, and analysis corresponding to these genes are presented. Gex = gene expression.(PDF)Click here for additional data file.Text S1Supporting materials. Supplementary methods, acknowledgements and figures are provided.(DOCX)Click here for additional data file.


Atmospheric conditions fundamentally influence the timing, intensity, energetics, and geography of avian migration. While radar is typically used to infer the influence of weather on the magnitude and spatiotemporal patterns of nocturnal bird migration, monitoring the flight calls produced by many bird species during nocturnal migration represents an alternative methodology and provides information regarding the species composition of nocturnal migration. We used nocturnal flight call (NFC) recordings of at least 22 migratory songbirds (14 warbler and 8 sparrow species) during fall migration from eight sites along the mainland and island coasts of Rhode Island to evaluate five hypotheses regarding NFC detections. Patterns of warbler and sparrow NFC detections largely supported our expectations in that (1) NFC detections associated positively and strongly with wind conditions that influence the intensity of coastal bird migration and negatively with regional precipitation; (2) NFCs increased during conditions with reduced visibility ; (3) NFCs decreased with higher wind speeds, presumably due mostly to increased ambient noise; and (4) coastal mainland sites recorded five to nine times more NFCs, on average, than coastal nearshore or offshore island sites. However, we found little evidence that (5) nightly or intra-night patterns of NFCs reflected the well-documented latitudinal patterns of migrant abundance on an offshore island. Despite some potential complications in inferring migration intensity and species composition from NFC data, the acoustic monitoring of NFCs provides a viable and complementary methodology for exploring the spatiotemporal patterns of songbird migration as well as evaluating the atmospheric conditions that shape these patterns. Atmospheric dynamics fundamentally influence the timing, intensity, energetics, and geography of avian migration In the northeastern United States during southbound fall migration, many nocturnal passerine migrants concentrate along the Atlantic Coast and on offshore land masses under specific weather conditions The influence of weather on the magnitude and spatiotemporal patterns of nocturnal bird migration has been inferred primarily using radar e.g., , althougWe evaluated five hypotheses regarding the detection of migrant songbird NFCs along the Atlantic Coast of southern New England in relation to atmospheric and ambient conditions, as well as coastal context: (1) NFC detections vary strongly with the atmospheric conditions that influence the intensity of bird migration in general and coastal migration in particular  nearshore island at the southern end of Narragansett Bay (2), an offshore island located approximately 15 km south of mainland Rhode Island (o below the horizon), from 8 September to 8 November in 2010 and from 8 September to 10 November in 2011. However, we truncated the recordings 15 min prior to morning civil twilight (about 45 min prior to sunrise) due to frequent vocalizations from birds on the ground near the microphone; the resulting nightly recordings increased in length (10.3–13.0 h) over the course of the recording seasons. Occasional equipment malfunctions resulted in incomplete coverage during these periods, and we did not record at all sites in each year  from which we manually classified calls and discarded false detections . To assess the effects of varying ambient noise on the detection of high frequency flight calls, we used Raven Pro to calculate the average power (dB) in the 6–11 kHz frequency range during the first hour of each night at each microphone.We filtered potential flight calls from nightly recordings using a band-limited energy detector algorithm in Raven Pro 1.3 . We specified the algorithm to extract high frequency band flight calls , which included most migratory species of warblers (Parulidae) and sparrows (Emberizidae) in eastern North America Passerina cyanea; 1%); we excluded these calls from further analyses.We assigned NFCs to species when possible, but more commonly into a complex of similar species o) as the target direction to better capture those combinations of wind direction and speed that indicate recent cold front passage and are also more likely to induce a coastal flight path, and perhaps the coastal effect We derived atmospheric conditions based primarily on weather data from observations at five National Weather Service Automated Surface Observing System (ASOS) stations occurring within 50 km of the centroid of microphone locations . ASOS re0β is an intercept, and the remaining βs describe their associated explanatory variables; siteβ and yearβ are categorical variables and comprise seven and two parameters, respectively. We fit a single smooth term  to allow for potential nonlinear seasonal  effects using the default thin plate regression spline; we allowed this smooth to interact with the categorical site variable, resulting in the possible estimation of separate smooths for each site. We did not evaluate any additional interactions to avoid over-parameterizing the model relative to the data, nor did we expect interactions among atmospheric conditions to possess much ecological relevance. We estimated the θ parameter for the negative binomial distribution with a similarly-structured generalized linear model, although replaced the spline for seasonal effects with a third-order polynomial. A first-order autoregressive (AR-1) error structure reasonably accounted for serial correlation in residuals. We grouped the correlation structure within each site and year combination to expedite GAM estimation site or year variables. We omitted cloud ceiling from consideration prior to analysis due to its high collinearity with cloud cover . To avoid biased parameter estimates and standard errors when evaluating hypotheses, we did not eliminate any terms from the models We used generalized additive models GAMs;  to explodetectP) as a function of two explanatory variables, and weighted by the number of manually detected NFCs, using a generalized linear model (glm function in base R) with the following formulation:0β is the intercept and the remaining βs describe their associated explanatory variables. We sampled from all eight sites, so siteβ comprises seven parameters. The noise variable represents the average power (dB) in the 6–11 kHz frequency range in each 10-minute recording.We compared automated detections with manually quantified NFCs in a sample of 10-minute recordings to evaluate the effectiveness of our automated detector settings. We selected 12 recordings from each site, with two notable constraints: recordings contained ≥4 warbler or sparrow NFCs  and occurred on separate nights. We again ignored NFCs too weak to assign confidently to warblers or sparrows, or NFCs belonging to other bird families. Thus, we estimated detector efficiency under generally favorable recording conditions and compared efficiency among sites and across a restricted range of ambient noise conditions. We modeled the logit of the proportion of successfully detected NFCs in each recording  during the fall migrations of 2010–2011 . Most waWarblers and sparrows exhibited similar general patterns of NFC detections, but slightly different phenologies  and 3. GWarblers exhibited similar intra-night NFC detection patterns regardless of geographic context . SpecifiWarbler and sparrow NFC detections increased substantially with wind conditions indicative of recent cold front passage and favorable for coastal migration . Additio2 = 9.92, df = 7, P = 0.19) or with background noise in relatively low-noise conditions . The automated detector extracted 55–60%  of flight calls attributable to warblers and sparrows under reasonably good recording conditions.We found little evidence that the efficiency of our automated detector algorithm varied among sites (likelihood ratio test [LRT]: χWe used NFC recordings of at least 22 migratory songbirds (14 warbler and 8 sparrow species) during fall migration from multiple sites along mainland and island coasts of Rhode Island to evaluate hypotheses regarding NFC detections. Patterns of warbler and sparrow NFC detections largely supported our expectations that (1) NFC detections were associated positively and strongly with wind profit and negatively with regional precipitation; (2) NFCs increased with reduced visibility for migrants ; (3) NFCs decreased with higher wind speeds, presumably due mostly to increased ambient noise; and (4) coastal mainland sites recorded five to nine times more NFCs, on average, than coastal nearshore or offshore island sites. However, we found little evidence that (5) nightly or intra-night patterns of NFCs reflected the well-documented latitudinal patterns of migrant abundance on Block Island.Atmospheric and ambient conditions can influence the detection rates of NFCs directly by inducing a change in the rate at which migrants call, or indirectly by influencing the number of birds aloft or NFC detectability. Certain atmospheric conditions commonly are associated with increased numbers of birds aloft in north temperate areas  or decreasing visibility (reviewed in sensuWe further expected certain atmospheric and biological conditions to increase background noise and decrease our ability to detect NFCs, and thus associate negatively to NFC detections. Wind and insects comprised the primary sources of noise in this study. Wind speed associated strongly and negatively with NFC detections. The strong correlation between wind speed and background noise measurements at each site (r = 0.59, P<0.001, df = 622; ‘within-site’ correlation We recorded warbler and sparrow NFCs in multiple coastal contexts along the Atlantic Coast of the northeastern United States, an important migratory corridor during autumn migration. NFC detections occurred episodically over the fall migration season, similar to migration intensity in the region e.g., , presumaThere exists a well-documented pattern in ‘on-the-ground’ migrant densities on Block Island: migrants occur in higher densities on the northern half of the island, where they prepare for reoriented flights to the mainland or subsequent migratory flights Migration activity along the Atlantic Coast, as assessed by radar, generally peaks in the few hours following sunset and declines steadily thereafter Acoustic monitoring is relatively inexpensive compared to radar, the equipment can be automated, and it provides information not readily obtained from other methodologies, including species composition and phenology information for vocal species and the ability to detect secretive, rare, or species otherwise difficult to survey


To discover novel transcription factor spatial binding constraints in vivo, we developed a new integrative computational method, genome wide event finding and motif discovery (GEM). GEM resolves ChIP data into explanatory motifs and binding events at high spatial resolution by linking binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. GEM analysis of 63 transcription factors in 214 ENCODE human ChIP-Seq experiments recovers more known factor motifs than other contemporary methods, and discovers six new motifs for factors with unknown binding specificity. GEM's adaptive learning of binding-event read distributions allows it to further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of closely spaced binding events of the same factor. In a systematic analysis of in vivo sequence-specific transcription factor binding using GEM, we have found hundreds of spatial binding constraints between factors. GEM found 37 examples of factor binding constraints in mouse ES cells, including strong distance-specific constraints between Klf4 and other key regulatory factors. In human ENCODE data, GEM found 390 examples of spatially constrained pair-wise binding, including such novel pairs as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4A/FOXA1. The discovery of new factor-factor spatial constraints in ChIP data is significant because it proposes testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control.An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of  The letters in our genome spell words and phrases that control when each gene is activated. To understand how these words and phrases function in health and disease, we have developed a new computational method to determine what word positions in our genomic text are used by each genome regulatory protein, and how these active words are spaced relative to one another. Our method achieves exceptional spatial accuracy by integrating experimental data with the text of our genome to find the precise words that are regulated by each protein factor. Using this analysis we have discovered novel word spacings in the experimental data that suggest novel genome grammatical control constructs. The functional syntax of DNA motifs in regulatory elements is thus an essential component of cellular regulatory control. Appropriately spaced motifs can facilitate cooperative homo-dimeric or hetero-dimeric factor binding, while overlapping motifs can implement competitive binding by steric hindrance. Cooperative and competitive binding are an integral part of complex cellular regulatory logic functions in vivo, we have developed a new method called GEM that simultaneously resolves the location of protein-DNA interactions and discovers explanatory DNA sequence motifs with an integrated model of ChIP-Seq or ChIP-exo reads and proximal DNA sequences. We define a binding event location as the single base position at the center of a protein-DNA interaction. GEM reciprocally improves motif detection using binding event locations, and binding event predictions using discovered motifs. In doing so, GEM offers a more principled approach than simply snapping binding event predictions to the closest instance of the motif, and indeed, GEM does not require that all binding events are associated with strong motifs. GEM offers both improved spatial accuracy of binding event predictions and improved motif discovery in ChIP-Seq and ChIP-exo datasets.To discover novel pair-wise factor spatial binding constraints in vivo binding in the specific cellular conditions GEM's unbiased computational approach has enabled us to discover novel binding constraints between transcription factors from sequenced ChIP experiments. These spatial constraints directly suggest biological regulatory mechanisms that will be useful in future studies. Other methods to resolve binding events in sequenced ChIP data identify statistically enriched regions of ChIP-Seq read density and the peak points of enrichment within those regions Here we review our GEM derived results, discuss these results in the context of current data production projects, and detail our methods.We compared GEM's spatial resolution to six well known ChIP-Seq analysis methods, including GPS We found that GEM has the best spatial resolution among tested methods. Spatial resolution is the average absolute value difference between the computationally predicted locations of binding events and the nearest match to a proximal consensus motif. From all observations, spatial resolution is corrected for a fixed offset by subtracting the mean difference before averaging the absolute value differences. To ensure a fair comparison, we used 428 shared GABP binding sites that are predicted by all seven tested methods and which contain an instance of the GABP motif within 100 bp. GEM exactly locates the events at the motif position in 56.5% of these events . For a dGEM is also better at resolving closely spaced binding events We tested GEM's ability to discover biologically relevant DNA-binding motifs in data from the ENCODE project We found GEM outperforms all of the compared motif discovery approaches, even when allowing each method to make multiple motif predictions , S3. TheWe then tested GEM on ENCODE ChIP-Seq experiments for 9 distinct transcription factors with no publically described DNA binding motif. For 6 of these transcription factors, GEM discovers novel motifs that are consistent with expected binding sequences based on a small number of binding sites characterized in the literature, or similarity to the known binding preferences of related proteins . For exaChIP-exo aims to improve transcription factor binding spatial resolution by extensively digesting ChIP fragments down to the DNA that is protected by the bound protein complex GEM improves upon the spatial resolution of binding event detection over other methods for ChIP-exo data . To inveWe examined if GEM could detect pairs of transcription factors that bind to the genome with characteristic pair-wise spacing, beginning with the well-known hetero-dimeric pair Sox2-Oct4 We were able to discover Sox2-Oct4 transcription factor spatial binding constraints by combining GEM binding calls from Sox2 and Oct4 ChIP-Seq data. We applied GEM independently to mouse ES cell Sox2 and Oct4 ChIP-Seq data We next studied pair-wise binding relationships between 14 sequence-specific transcription factors  and two transcriptional regulators (p300 and Suz12) in mouse ES cells by applying GEM to a large compendium of ChIP-Seq binding data The discovered pair-wise spatial binding constraints reveal complex relationships among the factors. For example, Klf4 exhibits constrained binding with Sox2 but much less significantly with Oct4 . Howeverin vivo binding of a number of key transcription factors in ES cells.Of the 123 regions where Sox2, Klf4, and other sites display constrained spacing, 109 (89%) are annotated instances of the RLTR9 ERVK family of long terminal repeat elements. It is interesting to note that while Bourque, et al. found an association between Oct4/Sox2 co-binding sites and other members of the ERVK repeat class We computed statistically significant pair-wise spatially constrained binding events between 46 transcription factors characterized in 184 ENCODE ChIP-Seq data sets in five different cell lines. Each transcription factor ChIP was processed independently by GEM so that we could assess any differences in observed binding between cell lines and biological replicates.We found that 390 pairs of transcription factors have significant binding distance constraints within 100 bp of each other , S9. TheObserved novel genome wide spatial binding constraints include c-Fos:c-Jun/USF1, CTCF/Egr1, HNF4α/FOXA1. We find that USF1 often binds 4 bp from c-Fos:c-Jun . This biin vivo binding relationships between transcription factors at high spatial resolution. Our analysis has been made possible by sequenced ChIP data and a new computational method, GEM, which provides exceptional spatial resolution.Collectively, our results demonstrate that it is now possible to reveal aspects of functional genome syntax by surveying in vivo binding, SpaMo cannot confidently determine the spacing between binding events and the factors involved, especially for motifs that are shared by a family of TFs. Furthermore, SpaMo excludes repetitive sequences GEM makes binding predictions and observes spatial constraints by discovering significant events utilizing both motifs and observed read coverage information. Prior work has documented specific genomic regions extensively targeted by multiple transcription factors (TFs) We expect that the genome grammatical rules that are suggested here will be examined in further studies to elucidate mechanisms of transcriptional control, and potential protein-protein interactions that have regulatory consequences. Exploration of other genome grammatical constructs can be accomplished with the use of further ChIP experiments and GEM.The GEM algorithm consists of six phases:Predict protein-DNA binding event locations with a sparse priorDiscover the set of enriched k-mers at binding event locationsCluster the set of enriched k-mers into k-mer equivalence classesGenerate a positional prior for event discovery with the most enriched k-mer equivalence classPredict improved protein-DNA binding event locations with a k-mer based positional priorRepeat motif discovery (Steps 2–3) from the Phase 5 improved event locations.Initial protein-DNA binding event locations are predicted by GPS GEM discovers a set of enriched k-mers by comparing k-mer frequencies between positive sequences and negative control sequences. The positive set consists of 61 bp sequences centered on the predicted binding locations from Phase 1, and a negative set consists of 61 bp sequences that are 300 bp away from binding locations and that don't overlap positive sequences. We count the number of positive and negative sequences that contain instances of each possible k-mer (hit count), treating each k-mer and its reverse complement as the same sequence. A k-mer is considered enriched if the hypergeometric p-value GEM next clusters the enriched k-mers into equivalence classes that describe similar DNA binding preferences . Each eqA k-mer class is initialized with the most enriched k-mer and any other enriched k-mers that differ by a single base from the most enriched k-mer.Positive set sequences that match the k-mer class are selected, and any enriched k-mer that appears in a 2k+1 bp window around a class match are tested for addition to the class. An enriched k-mer must have the same alignment offset to window sequences in at least one third of its occurrences to be added to the class.A Position Weight Matrix (PWM) is constructed from positive set sequences that match the class. A PWM is constructed with weighted matched positive set sequences centered on the class match and a zero order Markov model learned from negative set sequences. For PWM construction a positive set sequence is weighted by its binding event read count and the distance in bases between the sequence's class match and the estimated binding event position. The distance weighting function we use was fit to characterized ChIP-Seq data, and is the logistic distribution with mean 0 and variance 13. PWMs are trimmed to find the PWM with the most significant hypergeometric p-value between the positive and negative sequences. PWM matching is defined as at least 60% of the maximum PWM score Positive set sequences that match the resulting PWM are extracted and aligned by the PWM instances and any enriched k-mer that appears in a 2k+1 bp window around a PWM match are tested for addition to the class. An enriched k-mer must have the same alignment offset to window sequences in at least 1/3 of its occurrences to be added to the class.Step 3 and 4 are iterated until the PWM hypergeometric p-value between the positive and negative sequences no longer improves.After finding the primary k-mer equivalence class, GEM searches for other classes. To accomplish this, the previous seed k-mer is removed from the enriched k-mer pool and PWM motif occurrences are masked in the sequences. The process of building new k-mer equivalence classes is repeated until no more significantly enriched PWMs can be constructed. Rarely, a secondary motif PWM can become more significantly enriched than the primary motif. If this happens, the motif finding process is restarted using the seed k-mer of this secondary motif.Phase 4 of GEM uses the primary k-mer equivalence class to compute a Dirichlet prior that will be used for binding event discovery in Phase 5. The genome is segmented into independent separable regions  by dividing at read gaps that are larger than 500 bp and further excluding regions that contain fewer than 6 reads GEM employs a generative mixture model that describes the likelihood of a set of ChIP-Seq reads being generated from a set of protein-DNA interaction events originating at specific DNA sequences. The model generates protein-DNA interaction events that are biased to occur at explanatory DNA sequences by a k-mer based positional prior. Each event then independently generates reads following an empirical read spatial distribution that describes the probability of reads given the distance from the event M, we consider N ChIP-Seq reads that have been mapped to genome locations R = {r1, …, Nr} and M all possible protein-DNA interaction events at single base locations B = {b1, …, Mb}. We represent the latent assignments of reads to events that caused them as Z = {z1, …, Nz}, where indicator function 1(nz = m) = 1 when read n is caused by the event m.Formally, in an evaluated region of length n is based on a mixture of possible binding events:The probability of a read M is the number of possible events; π denotes the parameter vector of mixing probabilities, and mπ is the probability of event m; p(nr | m) is the probability of read n being generated from event m and can be determined from the empirical spatial distribution of reads given the event where The overall likelihood of the observed set of reads is:p(π) on binding event probabilities π:We make two prior assumptions about the binding events: 1) binding events prefer to occur at the sequence specific DNA motif positions; 2) binding events are relatively sparse throughout the genome. To incorporate these assumptions, we place a negative Dirichlet prior sα is the uniform sparse prior parameter governing the degree of sparseness, sα>0; mα denotes the binding event specific prior parameter and its value is proportional to mC, the positional prior count underlying event m (as defined in Phase 4):where μ is a parameter to tune the effect of motif based prior, 0≤μ<1. In this study, we choose μ = 0.8.where m have more occurrences at binding events genome wide, it is more likely to cause a binding event at that genome position. The parameter mα is scaled such that all the values of possible mα will be less than sα. Therefore the k-mer based prior will not force the model to predict a binding event at a motif position when the observed reads do not provide sufficient evidence of a protein-DNA interaction event.The rationale is that if the k-mers mapped to position sα+mα remains constant when we estimate the parameters in the mixture model. Therefore, we can solve the mixture model using Expectation-Maximization (EM) algorithm Since the k-mers underlying the possible binding event positions and their counts are known, the value of term −The complete-data log penalized likelihood is:1(nz = m) is the indicator function.where In the E Step we have:γ(nz = m) can be interpreted as the fraction of read n that is assigned to event m.where i we find parameter In the M step, on iteration under the constraint mN is the effective number of reads assigned to event m, or the binding strength of event m. Intuitively, the effective read count of an event is decreased by a pseudo-count sα for the sparseness penalty, and is increased by a pseudo-count mα for the k-mer motif at position m. If for event m, the value of mπ becomes zero, the model is restructured to eliminate it where The EM algorithm is deemed to have converged when the change in likelihood falls below a small value, for example 1e−5.sα+mα is negative, a binding event supported by enriched k-mers may still be eliminated if it is not sufficiently supported by read data. In addition, a binding event not supported by enriched k-mers may still survive if it is sufficiently supported by the read data.Since the value of term −The predicted binding events are tested for significance as described previously The read spatial distribution of binding events is updated after each round of binding event prediction.Phase 6 repeats Phase 2 and 3 motif discovery using the binding events predicted from Phase 5. As described in the results section , the spahttp://cgs.csail.mit.edu/gem). For analysis with mammalian genomes, GEM requires about 5–15 G memory.GEM is a stand-alone Java software that takes alignment files of ChIP-Seq reads and a genome sequence as input and reports a list of predicted binding events and the explanatory binding motifs. It can be downloaded from our web site (http://mendel.stanford.edu/SidowLab/downloads/quest/) and was pre-aligned to hg18 genome.214 ENCODE ChIP-Seq datasets that have an embargo date before Oct 28, 2011 and have known motifs in public databases were downloaded from the ENCODE project website GEM was applied to 214 ENCODE ChIP-Seq data. The motif PWMs output by GEM were collected. An alternate pipeline used the GPS peak-finder th motifs.We collected known binding preference motifs from the TRANSFAC The genome-wide performance of spatial resolution in ChIP-Seq event calls is evaluated as following. We define effective spatial resolution as the average absolute value of the distance between genome coordinates of predicted binding events and the middle of the corresponding high-scoring binding motif hit. Because the center of the motif hit may not represent the true center of a binding event, the offsets to the motif were centered by subtracting the mean offsets. We compare spatial resolution on the “matched” set of predictions that are called by all the methods and correspond to the same high-scoring binding motif. Only those events within 100 bp of a motif match are included in the calculation. An alternative evaluation with all the events that have a motif at a distance less than 100 bp is also performed.The genome-wide performance of proximal event discovery in ChIP-Seq data is evaluated as follows. For GABP dataset, we compared GEM against other 6 methods  genome wide. We define a set of candidate sites that all have at least one event detected by all seven methods, and that contain two or more GABP motifs separated by less than 500 bp. We discovered 477 such sites. For each of these sites, we count the number of events discovered by different methods. GABP motif was retrieved from TRANSFAC database (M00341) In this study, to test GEM's ability to automatically adapt to ChIP-exo data, we initialized GEM with a ChIP-Seq empirical read distribution, and ran GEM with one extra run (phase 5 and 6) so that GEM could use more accurately positioned events to refine the read distribution and use it for final prediction. In practice, the user can directly initialize GEM with a ChIP-exo empirical read distribution (provided with GEM software) and apply GEM the same way as analyzing ChIP-Seq data.in vivo binding spatial relationship between a pair of transcription factors A and B in the certain cell type and condition, we apply GEM independently to ChIP-Seq data from A and B to predict the respective binding sites. To compute the distribution of spacing between A relative to B, we compute the offsets of A binding sites from B binding sites within a 201 bp window. The sequence strand of the binding predictions is oriented using the B motif when a match to the motif is present, and B is placed in the middle of the window. The occurrences of A at each offset position are summed over all the B sites to produce the empirical spatial distribution. In this study, we evaluate three different methods to call binding sites: GEM binding calls, GPS binding calls, and GPS binding calls that are snapped to a motif within 50 bp if one is present. Another motif distance for snapping binding calls, 100 bp, was also tested and the result was very similar to the 50 bp distance.To study the To determine if a specific spacing is significant, we compute the p-value of the number of occurrences of factor A at that offset position using a Poisson test. The parameter of Poisson distribution is set as the mean number of occurrences across all the positions in the [−400 bp −200 bp] and [200 bp 400 bp] windows, assuming there are no significant spatial binding constraints in these windows. The p-value is corrected for multiple hypotheses testing using Bonferroni correction by multiplying the p-value by the number of positions in the window and the total number of pair wise tests across all cell types. The significance threshold for corrected p-value is 1e−8. Because the strand orientation of bound sequences cannot be oriented consistently when comparing multiple factor pairs, we report the absolute distance between the most significant interacting factor pairs in Dataset S1Public database motif matrices used in this study.(TXT)Click here for additional data file.Dataset S2GEM discovered primary and secondary motif matrices from ENCODE data.(TXT)Click here for additional data file.Dataset S3GEM discovered primary and secondary motif matrices from mouse ES cell data.(TXT)Click here for additional data file.Figure S1Spatial accuracy evaluation using all the binding events.(PDF)Click here for additional data file.Figure S2GEM improves the spatial resolution of Reb1 ChIP-exo data event prediction.(PDF)Click here for additional data file.Figure S3Color chart representation of 100 bp sequences in the regions with 6 bp Sox2/Oct4 binding constraint.(PDF)Click here for additional data file.Figure S4Spatial binding constraints detected from mouse ES cells.(PDF)Click here for additional data file.Figure S5Spatial relationship between Klf4 and other 15 factors in mouse ES cells.(PDF)Click here for additional data file.Figure S6Sox2/Klf4/Esrrb/Nr5a2/Tcfcp2l1 bound regions are bound by p300 and marked by H3K27ac.(PDF)Click here for additional data file.Figure S7Spatial binding constraints detected from ENCODE GM12878 cells.(PDF)Click here for additional data file.Figure S8Spatial binding constraints detected from ENCODE HepG2 cells.(PDF)Click here for additional data file.Figure S9Spatial binding constraints detected from ENCODE HeLa-S3 cells.(PDF)Click here for additional data file.Figure S10Spatial binding constraints detected from ENCODE H1 cells.(PDF)Click here for additional data file.Figure S11Color chart representation of 100 bp sequences in 259 regions with 4 bp c-Fos:c-Jun/USF1 binding constraint.(PDF)Click here for additional data file.Figure S12Color chart representation of 100 bp sequences in 315 regions with 4 bp CTCF/Egr1 binding constraint.(PDF)Click here for additional data file.Figure S13Color chart representation of 100 bp sequences in 4215 regions with a wide range of HNF4A/FOXA1 binding constraints.(PDF)Click here for additional data file.Figure S14K-mer class motif clustering.(PDF)Click here for additional data file.Table S1Known motifs recovered by GEM in ENCODE data.(PDF)Click here for additional data file.Table S2Performance of motif discovery methods by individual ENCODE ChIP-Seq experiments.(PDF)Click here for additional data file.Table S3Overall performance of motif discovery methods for ENCODE data.(PDF)Click here for additional data file.Table S4Novel motifs discovered by GEM in ENCODE data.(PDF)Click here for additional data file.Table S5Motifs of mouse ES cell factors discovered by GEM.(PDF)Click here for additional data file.Table S6All significant pairwise spatial binding constraints detected from mouse ES cell ChIP-Seq data.(PDF)Click here for additional data file.Table S7Non-redundant significant pairwise spatial binding constraints detected from mouse ES cell ChIP-Seq data, consolidated to non-redundant factor pairs.(PDF)Click here for additional data file.Table S8All significant pairwise spatial binding constraints detected from ENCODE (5 cell types) ChIP-Seq data.(TXT)Click here for additional data file.Table S9Non-redundant significant pairwise spatial binding constraints detected from ENCODE (5 cell types) ChIP-Seq data.(TXT)Click here for additional data file.


Xenopus laevis causes loss of blood suggesting that in contrast to its role in mammals, FOG might normally function to repress erythropoiesis in this species. Using loss-of-function analysis, we demonstrate that FOG is essential to support primitive red blood cell (RBC) development in Xenopus. Moreover, we show that it is specifically required to prevent excess apoptosis of circulating primitive RBCs and that in the absence of FOG, the pro-apoptotic gene Bim-1 is strongly upregulated. To identify domains of FOG that are essential for blood development and, conversely, to begin to understand the mechanism by which overexpressed FOG represses primitive erythropoiesis, we asked whether FOG mutants that are unable to interact with known co-factors retain their ability to rescue blood formation in FOG morphants and whether they repress erythropoiesis when overexpressed in wild type embryos. We find that interaction of FOG with the Nucleosome Remodeling and Deacetylase complex (NuRD), but not with C-terminal Binding Protein, is essential for normal primitive RBC development. In contrast, overexpression of all mutant and wild type constructs causes a comparable repression of primitive erythropoiesis. Together, our data suggest that a requirement for FOG and its interaction with NuRD during primitive erythropoiesis are conserved in Xenopus and that loss of blood upon FOG overexpression is due to a dominant-interfering effect.Friend of GATA (FOG) plays many diverse roles in adult and embryonic hematopoiesis, however the mechanisms by which it functions and the roles of potential interaction partners are not completely understood. Previous work has shown that overexpression of FOG in  Xenopus embryos is called the ventral blood island (VBI). The VBI is an intraembryonic structure that forms when distinct populations of cells from the rostral and caudal mesoderm converge during gastrulation and subsequently elongate along the ventral midline globin, which initiates in the anterior aspect of the VBI and extends posteriorly as more hemoglobin is synthesized. During the late tailbud stage (roughly stage 35–36), the heart begins to contract and RBCs begin circulating within the embryo Vertebrate blood development takes place in two waves, referred to as primitive and definitive hematopoiesis. Primitive hematopoiesis is the initial wave of blood development in the embryo in which blood progenitor cells in the mesoderm give rise mainly to primitive red blood cells (RBCs), although some white blood cells are also produced at this time. By comparison, definitive hematopoiesis constitutes the second wave of blood development in which hematopoietic stem cells give rise to blood cells of all lineages  functions as a critical transcriptional cofactor for both GATA-1 and GATA-2 during hematopoiesis. FOG is a large multi-domain protein that includes nine conserved zinc fingers, four of which mediate GATA binding mFOG-1 and mFOG-2. Although they appear to be functionally redundant  in this process is less clear. Only one FOG homolog has been identified in Xenopus laevis (Xenopus transcript or mouse FOG-2 (mFOG-2) in Xenopus embryos, both of which led to defects in primitive erythropoiesis Xenopus is significantly different from its role in mice and fish.While an essential role for FOG-1 during hematopoiesis has been well documented in mice and fish, the role of s laevis . It is min vivo is thus still open.Mice that harbor a targeted knock-in allele of FOG that lacks CtBP binding capacity show no defects in hematopoiesis, suggesting that this domain may be dispensable for normal function Xenopus. We find that xFOG and its interaction with the NuRD complex are required for primitive erythropoiesis, but that direct interaction with CtBP is dispensable. While we find that overexpression of xFOG also disrupts normal blood development, we show that it does so independent of its ability to interact with other cofactors known to be required for blood. This phenomenon is thus more consistent with a dominant-negative effect of xFOG overexpression rather than a reflection of its endogenous function. Finally, we show that loss of xFOG leads to upregulation of the pro-apoptotic gene Bim-1 and excessive apoptosis of RBCs. Taken together, these studies show that FOG function is in fact conserved in Xenopus and is important for primitive RBC survival.In the current study, we have used a loss of function approach to address discrepancies in the literature regarding FOG function during primitive hematopoiesis in This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Institutional Animal Care and Use Committee of Oregon Health and Science University (Protocol number A719).Xenopus laevis females by injection of 50 IU of human chorionic gonadotropin (Sigma) into the dorsal lymph sac to induce spawning the night before egg collection. Embryos were staged according to Nieuwkoop and Faber in vitro transcription of linearized template cDNA using a MegaScript kit (Ambion) and injected into the two vegetal blastomeres on the ventral side of eight-cell embryos to target prospective blood forming cells, as described previously Ovulation was induced in adult Xenopus FOG was obtained by 5′ RACE using oligonucleotide primers complementary to sequence near the 5′ end of the published partial length xFOG cDNA (accession no. AF241228). RNA was isolated from Xenopus embryos at stage 34, reverse transcribed and used as a template for PCR-mediated amplification of an xFOG cDNA (GenBank accession number GU384581) that encodes the entire open reading frame, as determined by alignment with FOG from other species (xFOG gene was identified by searching The Gene Index Project database (Accession number TC411807). Morpholino antisense oligonucleotides (MOs) complimentary to both alleles of xFOG (xFOGa: 5′-ATTGCTCTGTTTCCTTCTGGACATG-3′ and xFOGb: GCTGGAGGACAAGGCAGGATCAAGC) were purchased from Gene Tools, LLC . Equal amounts of the two FOG MOs were mixed and the dose was titrated to 40 ng per embryo. Sequence encoding a MYC epitope tag was appended to the 5′ end of the xFOG open reading frame by PCR-mediated amplification. Silent mutations to prevent morpholino annealing were engineered using a QuikChange XL (Stratagene) mutagenesis kit to generate a FOG rescue construct (xFOGr) with the following sequence at the 5′ end: 5′ATGTGGAA CAA AAA CTT ATT TCT GAA GAA GAT CT AGA CGG AAG CAG AGT AAC TC CCC AGA CAG. MYC tag is in bold, morpholino 2 target sequence is underlined, translation start site is italicized and silent mutations are bold and italicized. xFOG or xFOGr was used as a template to generate cDNAs encoding xFOGΔCtBP and xFOGΔNuRD by PCR-mediated mutagenesis. xFOGΔCtBP has a two-residue substitution that converts the CtBP binding motif from PIDLSK to PIASSK. This mutation has been shown to disrupt binding of CtBP to mouse FOG-2 Sequence encoding the N-terminus of  species . A closein situ hybridization according to the protocol outlined in ODC: F 5′-TGC AGA GCC TGG GAG ATA CT-3′, R 5′-CAT TGG CAG CAT CTT CTT CA-3′; GATA-1: F 5′-GGA GAC GGA ATG CAA GTG GAG AC-3′, R 5′- CCT GCT GCT CAC CTT TCG GTT C-3′ Bim1: F 5′-CAG AAC TGT GGA TAG CAC AGG AAC-3′, R 5′- CAG ACA AGC TGA GTC ACT TCT CG-3′.Total RNA was isolated and Northern blots were hybridized with antisense riboprobes as described previously Xenopus FOG into pCS2+. To verify that FOG MO targets xFOG but not xFOGr, 0.5 µg of each construct was injected into Xenopus embryos at the 2–4 cell stage. Embryos were cultured to stage 15 and protein was harvested from by freon extraction as described previously xFOG and xFOG mutant expression vectors were generated by subcloning full-length The xGATA-2HA expression vector has been described previously Tails were severed from tadpoles and blood was collected into medium containing 0.7X PBS, 0.5% BSA and 10 IU/ml of heparin. Cells were concentrated onto slides using a cytospin and stained with the Hema 3 stain set (Fisher Diagnostics). A minimum of 20 embryos were bled per experimental group and for each embryo the number of cells present in four random fields was counted at 20X magnification. Each experiment was repeated a minimum of three times and results were pooled.Blood from stage 42 embryos was collected onto slides as described above and assessed for apoptosis using the DeadEnd Fluorometric TUNEL kit (Promega). Nuclei of cells were counterstained with propidium iodide to determine total numbers of cells. A minimum of 20 embryos were bled per experimental group and for each embryo the number of apoptotic and total cells present in four random fields was counted at 20X magnification. Each experiment was repeated a minimum of three times and results were pooled.Xenopus, we isolated a full-length Xenopus FOG cDNA  was injected alone, or together with FOG MO into Xenopus embryos at the two-cell stage, and expression of xFOG was examined by Western analysis of neurula stage (stage 15) embryonic extracts using antibodies specific for the MYC tag. Levels of xFOG-MYC protein translated from wild type RNA were reduced in the presence of the MO, whereas there was no effect on translation of a rescue RNA (xFOGr-MYC) containing silent mutations that prevent MO annealing  .To ask whether FOG is required for primitive erythropoiesis in nnealing , demonstnnealing  in ordernd (VBI) , which wnd (VBI) . Globin f FOG MO , and allglobin in FOG morphants by co-injection of xFOGr RNA, which does not anneal to the MO. Given prior studies demonstrating that overexpression of FOG can cause loss of blood globin expression when injected alone. We found that injection of 200 pg of xFOGr RNA alone did not significantly effect globin expression  relative to wild type controls would be necessary to definitively address this question in vivo.To better understand the relevance of FOG interaction with CtBP and NuRD in ud stage . FOG morpression . These sXenopus primitive hematopoiesis, xFOGΔNuRD was not able to rescue loss of globin in FOG morphants in multiple experiments  was unchanged as analyzed by in situ hybridization in stage 15 FOG morphants (not shown). We therefore looked to identify erythroid defects at later stages. To test whether xFOG affects primitive RBC differentiation or maintenance later in erythroid development, we collected total circulating blood cells from individual embryos at the tadpole stage (stage 42) and examined them for changes in RBC number and morphology. Compared to wild type sibling controls, the overall number of RBCs in FOG MO-injected embryos was drastically reduced  stages of development , we observed a more severe reduction in globin expression that paralleled the dramatic loss of circulating RBCs , whereas levels of GATA-1 are decreased (Zbtb7a/POKEMON/FBI-1), which in turn represses transcription of the pro-apoptotic gene, BimGATA-1 transcripts are reduced in FOG, morphants may reflect a requirement for FOG in maintenance or upregulation of GATA-1 gene expression as development proceeds. Alternatively, given that regulation of most GATA-1-dependent genes requires binding to FOG, the loss of primitive erythropoiesis in FOG morphants may be due to loss of GATA-1 function, rather than expression, and this leads to upregulation of Bim1 expression and excessive apoptosis of primitive RBCs. In this scenario, the reduction in levels of globin and GATA-1 transcripts would be due to loss of RBCs rather than loss of transcription. Taken together, our data provide evidence for a model in which FOG regulates GATA-1 function and/or expression, which in turn directly or indirectly represses expression of pro-apoptotic genes such as Bim in order to support RBC survival.We also examined expression of the hematopoietic specific gene, ecreased . GATA-1 Figure S1Alignment of Xenopus FOG with human, mouse and fish FOG homologs. Alignment of full-length X. laevis FOG with zebrafish, mouse and human FOG-1 (DOC)Click here for additional data file.Figure S2Mutations in xFOG do not affect steady state levels of FOG protein. Western analysis of cell lysates from HeLa cells transiently transfected with MYC epitope tagged wild type and mutant xFOG cDNA constructs. Steady state levels of xFOG protein are normalized to expression of Actin and reported as a percentage of the wild type control below each lane. Asterisk indicates a non-specific background band that is observed in the absence of xFOG. ΔN = xFOGΔNuRD, ΔC = xFOGΔCtBP, ΔNΔC = xFOGΔNuRD/ΔCtBP, 4ZM = xFOG4ZM.(TIF)Click here for additional data file.Figure S3GATA-2 binding is severely impaired in the xFOG4ZM mutant. Wild type and mutant MYC epitope tagged xFOG constructs were transfected into HeLa cells alone, or together with HA-tagged GATA-2. Western blots (WB) of cell lysates (input) or anti-MYC immunoprecipitates (IP) were probed with an anti-HA antibody to detect interaction with GATA-2-HA. Levels of GATA-2 protein detected by co-IP are normalized to the GATA-2 input and to the xFOG IP and reported as a percentage of the wild type control below each lane. ΔN = xFOGΔNuRD, ΔC = xFOGΔCtBP, ΔNΔC = xFOGΔNuRD/ΔCtBP, 4ZM = xFOG4ZM.(TIF)Click here for additional data file.


XK gene revealed a previously unreported hemizygous single base-pair frameshift deletion at exon 1 . In conclusion, we hereby describe a rare phenotype of a patient with McLeod syndrome which was discovered coincidentally during routine blood group testing and consecutively genetically confirmed.We present a 70-year-old male patient of Greek origin with choreatic movements of the tongue and face, lower limb muscle weakness, peripheral neuropathy, elevated creatinephosphokinase (CPK), acanthocytosis and haemolysis in the absence of Kell RBC antigens with an additional Factor IX-deficiency. Genetic testing for mutations in the three exons of the  •McLeod syndrome with chorea, muscle wasting, and peripheral neuropathy•Acanthocytosis and haemolysis in the absence of Kell RBC antigens•McLeod syndrome with an additional Factor IX deficiency•XK geneNovel hemizygous single base-pair frameshift deletion in the  Their common ground is the prediction of an absent or truncated, non-functional gene product XK frameshift mutation associated with a McLeod syndrome without signs of chronic granulomatous disease (CGD).McLeod syndrome is a rare X-linked multisystemic disease caused by mutations in the 2A 70-year-old Greek man was referred to the Neurology Department of the Papageorgiou Hospital in Thessaloniki. The main initial finding was choreatiform tongue movements reported by the haematology unit that detected a weak expression of K red blood cell antigens, acanthocytosis, haemolysis, and a form of Factor IX deficiency when screening his blood type prior to a blood transfusion needed for a scheduled surgical procedure. Those tongue movements were noted recently and were attributed by the family and the proband to his ill-fitting denture. The patient also had a slight generalised restlessness with frequent changes of posture but without any tic-like or choreiform movements of any particular part of his body apart from his tongue and the face. Neurological findings on examination included chorea of the face and tongue, and lower limb muscle weakness with muscle wasting.His past medical history includes severe progressive skeletal deformities after the age of 50, with osteoarthritis of the knees bilaterally that resulted in severe varus deformities of the knees.The patient had a brother who died at the age of 60, and presented with severe facial dyskinesias, chorea and schizophrenia like psychosis. His sister, who died at the age of 70 after an acute ischemic stroke, was referred as being healthy until that incident. His 38-year-old daughter and his 35-year-old son are both healthy A. There 3Laboratory investigation revealed blood type group A1, DAT negative (−), IAT positive (+). Antibody identification panel testing demonstrated panagglutination of the same intensity in all cellular lines in the LISS/Coombs phase. Enzyme phase, 4 °C incubation phase and auto control (A/C) were negative. His detailed blood group profile was: C (+), c (+), D (+), E (−), e (+), Cw (−), K (−), k (−), Kpa (−), Kpb (−), P1 (+), Lea (−), Leb (+), Lua (−), Lub (+), Jka (+), Jkb (+), M (+), N (+), S (+), s (+), Fya (−), and Fyb (+). Peripheral blood smear revealed the existence of acanthocytes  at a per4XK gene detected a hemizygous single base-pair frameshift deletion . We hereby were able to expand the mutational spectrum of XK mutations leading to McLeod syndrome.The diagnosis of McLeod syndrome is generally based on the haematological findings of weak Kell antigens. Therefore, carriers of this phenotype could be diagnosed mainly in blood banks The authors declare no conflict of interest.