N = 1453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. We identify 236 CpGs in the prefrontal cortex, 95 CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex at Bonferroni significance, with none in the cerebellum. Our cross-cortex meta-analysis (N = 1408 donors) identifies 220 CpGs associated with neuropathology, annotated to 121 genes, of which 84 genes have not been previously reported at this significance threshold. We have replicated our findings using two further DNA methylomic datasets consisting of a further >600 unique donors. The meta-analysis summary statistics are available in our online data resource .Epigenome-wide association studies of Alzheimer’s disease have highlighted neuropathology-associated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six DNA methylomic studies of Alzheimer’s disease  to identify differentially methylated loci across cortex.Although epigenome-wide association studies of Alzheimer’s disease have highlighted neuropathology-associated DNA methylation differences, previous studies have been limited in sample size and brain region used. Here, the authors combine data from six DNA methylomic studies of Alzheimer’s disease ( The disease is defined by two key pathological hallmarks in the brain: extracellular plaques comprised of amyloid-beta protein and intracellular neurofibrillary tangles of hyperphosphorylated tau protein4. These neuropathological changes are thought to occur perhaps decades before clinical symptoms manifest and the disease is diagnosed4. AD is a multi-factorial and complex disease, with the risk of developing disease still largely unknown despite numerous genetic and epidemiological studies over recent years.Alzheimer’s disease (AD) is a chronic neurodegenerative disease that is accompanied by memory problems, confusion and changes in mood, behavior and personality. AD accounts for ~60% of dementia cases, which affected 43.8 million people worldwide in 201612. Independently these studies have identified a number of loci that show robust differential DNA methylation in disease, and many of these overlap between studies, for example, loci annotated to ANK1, RHBDF2, HOXA3, CDH23 and RPL13 have been consistently reported.Several studies have suggested that epigenetic mechanisms may play a role in disease etiology. In recent years a number of epigenome-wide association studies (EWAS) have been performed in AD brain samples, which have predominantly utilized the Illumina Infinium HumanMethylation450K BeadChip (450K array) in conjunction with bisulfite-treated DNA to assess levels of DNA methylation in cortical brain tissue from donors with varying degrees of AD pathology12, totalling 1453 independent donors, to identify robust and consistent differentially methylated loci associated with Braak stage, used as a measure of neurofibrillary tangle spread through the brain, in different brain regions and across the cortex. In our intra-tissue meta-analysis we identify 236 CpGs in the prefrontal cortex (N = 961 samples), 95 in the temporal gyrus (N = 608 samples) and ten in the entorhinal cortex (N = 189 samples) at Bonferroni significance, with none in the cerebellum (N = 533 samples). Our cross-cortex meta-analysis  identified 220 Bonferroni significant CpGs, which were replicated in two further independent DNA methylation datasets. Our meta-analysis approach provides additional power to detect DNA methylomic variation associated with AD pathology at novel loci, in addition to providing further replication of loci that have been previously identified in the smaller independent EWAS.Here we have performed a meta-analysis of six independent existing EWAS of AD brainN = 1453 unique donors) for a fixed-effect inverse variance weighted meta-analysis separately for each tissue   and we identified 236 Bonferroni significant differentially methylated positions (DMPs) , of which 193 were annotated to 137 genes, with 43 unannotated loci based on Illumina UCSC annotation , with a further 16 of the Bonferroni significant DMPs also annotated to this gene. This locus appeared to be particularly hypermethylated with higher Braak stage in the prefrontal cortex, and to a slightly lesser extent in the temporal gyrus , which is interesting given that the entorhinal cortex may succumb to pathology early in the disease process (Braak stage III). Of the 236 prefrontal cortex DMPs, 92% (217 probes) were nominally significant (P < 0.05) in the temporal gyrus, of which 12% (28 probes) were Bonferroni significant, whilst 9% (22 probes) were nominally significant in the entorhinal cortex, with 1% (3 probes) reaching Bonferroni significance  = 0.94, P = 6.17 × 10−112) and entorhinal cortex  and were enriched for probes with the same direction of effect  . This included several additional probes residing in genes already identified (from another probe) in earlier studies, for example, a further 16 probes in HOXA3 and two probes in PPT2/PRRT1. Interestingly, we also identified a number of novel genes, including some which featured multiple Bonferroni significant DMPs including for example, seven probes in AGAP2 and five probes in SLC44A2, amongst others. One other noteworthy novel Bonferroni significant DMP in the prefrontal cortex was cg08898775 , annotated to ADAM10, which encodes for α-secretase which cleaves APP in the non-amyloidogenic pathway. A differentially methylated region (DMR) analysis, which allowed us to identify areas of the genome consisting of ≥2 DMPs, revealed 262 significant DMRs in the prefrontal cortex  identified 95 Bonferroni significant probes, of which 75 were annotated to 53 genes, with 20 unannotated probes using Illumina UCSC annotation  and 82nd  most significant probes in the temporal gyrus also being annotated to nearby CpGs in that gene. This locus has been widely reported to be hypermethylated in AD from prior EWAS12, as well as in other neurodegenerative diseases such as Huntington’s disease and Parkinson’s disease13. Another noteworthy gene is RHBDF2, where five Bonferroni significant DMPs in the temporal gyrus were annotated to ; and which has been highlighted in previous EWAS in AD in the individual cohorts12. Of the 95 Bonferroni significant DMPs in the temporal gyrus, 88% (84 probes) were nominally significant in the prefrontal cortex, of which 29% (28 probes) were Bonferroni significant, whilst 54% (51 probes) were nominally significant in the entorhinal cortex, of which 6% (6 probes) were Bonferroni significant  and entorhinal cortex  and were enriched for the same direction of effect  , including some novel genes with multiple DMPs, for example, four probes in RGMA and two probes in CCND1, amongst others. Finally, our regional analysis highlighted 104 DMRs  and contained two probes (Sidak-corrected P = 1.72 × 10−21) , with three other genomic regions containing large, significant DMRs consisting of ≥10 probes, such as MCF2L , PRRT1/PPT2  and HOXA5 .A meta-analysis of temporal gyrus EWAS datasets , where we identified ten Bonferroni significant probes in our meta-analysis, all of which were hypermethylated with higher Braak stage . In total, eight of the ten DMPs in the entorhinal cortex had been reported previously at Bonferroni significance, including the two probes in ANK1. Two of the Bonferroni significant DMPs we identified in the entorhinal cortex were novel CpGs , having not been reported as Bonferroni significant in previous EWAS. Of the ten entorhinal cortex probes, 90% (9 probes) were nominally significant in the temporal gyrus, of which 60% (6 probes) were Bonferroni significant, whilst 70% (7 probes) were nominally significant in the prefrontal cortex, of which 30% (3 probes) were Bonferroni significant . The effect sizes of the ten Bonferroni significant DMPs in the entorhinal cortex were significantly correlated with the effect size of the same probes in the prefrontal cortex  and temporal gyrus  and were enriched for the same direction of effect   were significant in at least one of the other cortical regions we meta-analysed.The final cortical region we had available was the entorhinal cortex  in ≥ two cohorts, with ten of these probes being nominally significant in all six cohorts . This pattern of methylation was evident across all cortical tissues but was not seen in the cerebellum , MCF2L , PCNT  and RHBDF2 . The most significant probe we identified in our cross-cortex analysis was cg12307200 (Table P = 4.48 × 10−16), which is intergenic and found at chr3:188664632, located between the TPRG1 and LPP genes and had been previously reported at Bonferroni significance by De Jager and colleagues with respect to neuritic plaque burden6 and by Brokaw and colleagues with respect to post-mortem diagnosis12. Our cross-cortex meta-analysis approach has identified 174 novel DMPs (at Bonferroni significance), annotated to 102 genes. Although 11 of these genes had previously been reported at Bonferroni significance (another probe within that gene), the remaining 96 genes represent robust novel loci in AD. Many of these novel differentially methylated genes had multiple Bonferroni significant probes, for example, five probes in AGAP2, three probes in HOXB3 and SLC44A2, and two probes in CDH9, CPEB4, DUSP27, GCNT2, MAMSTR, PTK6, RGMA, RHOB, SMURF1, THBS1, ZNF238 and ZNF385A  signals  , 33 probes (chr7:27183133–27184667: P = 2.22 × 10−20) and ten probes (chr7:27143235–27143806: P = 1.75 × 10−18).We were interested to investigate whether specific functional pathways were differentially methylated in AD cortex and so performed a gene ontology pathway analysis of the 121 genes annotated to the 220 Bonferroni significant cross-cortex DMPs. We highlighted epigenetic dysfunction in numerous pathways , interestingly including a number of developmental pathways, mainly featuring the N = 45) and sorted neuronal and non-neuronal nuclei from the occipital cortex (N = 26) , neuronal  and non-neuronal datasets  with a similar enrichment for the same direction of effect  , which can generate a forest plot showing the ES and SE across any of the discovery cohorts and the Munich sample types for any of the 403,763 probes that passed our quality control. This allows researchers to determine the consistency of effects across cohorts for a given CpG site as well as the likely cellular origin of the signature. In addition, our tool can generate mini-Manhattan plots to show DMRs utilizing the summary statistics from the cross-cortex meta-analysis.To replicate our findings and to determine the cellular origin of DNA methylomic differences we used the estimated coefficients and SEs for these 220 probes generated in a seventh independent (Munich) cohort, which consisted of 450K data generated in the prefrontal cortex (6) Table . This co42) Fig. . The mos42) Fig. . We have15. As this is the successor to the 450K array (which had been used for the other seven cohorts), there are some differences in genome coverage, and for the 220 Bonferroni significant cross-cortex DMPs we had identified in the discovery cohorts, only 208 probes are also present on the EPIC array. For these overlapping 208 probes, we observed a significantly correlated effect size between the discovery dataset and the BDR dataset  .Finally, we had access to DNA methylation data generated in an eighth independent (Brains for Dementia Research [BDR]) cohort. This consisted of Illumina Infinium HumanMethylation EPIC BeadChip (EPIC array) data in the prefrontal cortex in 590 individuals16) Fig. , with al16 (Supplementary Data P = 4.76 × 10−10), and in CpG island shelves and non-CpG island areas of proximal promoters . However, DMPs located in CpG islands in the proximal promoter were under-represented . There was a significant over-representation of the 220 cross-cortex DMPs in the first exon , with an under enrichment within 1500 bp of the transcription start site  , or high pathology (Braak V–VI [AD]: N = 589) and used these as a training dataset. We then used elastic net regression to identify 110 probes in the 220 cross-cortex Bonferroni significant loci (Supplementary Data N = 996) (Supplementary Data N = 38) and the BDR replication samples (N = 454), where it achieved an AUC of 73.95%  and 70.36% , respectively  we used the xQTL resource to identify cis-mQTLs associated with the 220 Bonferroni significant cross-cortex DMPs17. We identified 200 Bonferroni corrected mQTLs, which were associated with DNA methylation at 18 of the 220 cross-cortex DMPs (Supplementary Data cis. None of these mQTLs overlapped with lead SNPs (or SNPs in LD) identified in the most recent genome-wide association study (GWAS) of diagnosed late-onset AD from Kunkle et al.18. Next, we were interested in exploring whether DNA methylation is enriched in genes known to harbor AD-associated genomic risk variants. Using the AD variants from Kunkle et al.18 we examined the enrichment of Braak-associated DNA methylation in 24 LD blocks harbouring risk variants. Twenty of these LD blocks contained >1 CpG site on the 450K array and using Brown’s method we combined P values within each of these 20 genomic regions. We observed Bonferroni-adjusted significant enrichment in the cross-cortex data in the HLADRB1 (Chr6: 32395036–32636434: adjusted P = 1.20 × 10−3), SPI1 , SORL1 , ABCA7  and ADAM10  LD regions (Supplementary Data DNA methylomic variation can be driven by genetic variation via methylation quantitative trait loci (mQTLs). To explore whether SNPs may be driving the methylation differences we observed . By performing a meta-analysis within each tissue, we have been able to identify 236, 95 and ten Bonferroni significant DMPs in the prefrontal cortex, temporal gyrus and entorhinal cortex, respectively. Although far fewer loci were identified in the entorhinal cortex compared to the other cortical regions, this is likely due to the reduced sample size in this tissue. In the cerebellum despite meta-analyzing >500 unique samples, we identified no Braak-associated DNA methylation changes. Furthermore, there was no correlation of the effect size of Bonferroni significant DMPs identified in any of the cortical regions with the effect size of the same probes in the cerebellum. Taken together, this suggests that DNA methylomic changes in AD are cortex cell type specific. This observation is interesting as the cerebellum is said to be spared from AD pathology, with an absence of neurofibrillary tangles, although some diffuse amyloid-beta plaques are reported19. Interestingly, a recent spatial proteomics study of AD reported a large number of protein changes in the cerebellum in AD; however, the proteins identified were distinct from other regions examined and thus the authors suggested a potential protective role20.This meta-analysis of AD EWAS utilizes six published independent sample cohorts with a range of cortical brain regions and cerebellum available as a discovery dataset. Two further independent cortical datasets were then used for replication, including data from sorted nuclei populations. Our data can be explored as part of an online searchable database, which can be found on our website  with three Bonferroni significant DMPs, which had not been identified at this significance threshold in previous EWAS DMP or DMR analyses in AD brain. The nomination of loci in the HOXB gene cluster is interesting; a recent study of human Huntington’s disease brain samples also highlighted significantly increased HOXB3 gene expression in the prefrontal cortex21, an interesting observation given that both AD and Huntington’s disease are disorders that feature dementia. Furthermore, we have recently reported AD-associated hypermethylation of the HOXB6 gene in AD blood samples22. Our pathway analysis highlighted differential methylation in a number of developmental pathways, mainly featuring the HOXA and HOXB gene clusters. Although it is unclear why developmental genes may be changed in a disease that primarily affects the elderly, it has been implied that genes such as these may be involved in neuroprotection after development23. A number of the other novel genes with multiple DMPs are also biologically relevant in the context of AD, for example, GCNT2 was recently shown to be differentially expressed in the Putamen between males and females with AD24. Interestingly, some of the protein products of genes we identified have also been previously linked with AD; PTK6 is a protein kinase whose activity has been shown to be altered in post-mortem AD brain25. Similarly, RGMA has been shown to be increased in AD brain, where it accumulated in amyloid-beta plaques26.Although many loci showed similar patterns of Braak-associated DNA methylation across the different cortical regions, some loci did show some regional specificity. In order to identify CpG sites that showed common DNA methylation changes in disease we performed a cross-cortex meta-analysis. Using this approach we identified 220 Bonferroni significant probes associated with Braak stage of which 46 probes had been previously reported at Bonferroni significance in the individual cohort studies that we had used for our meta-analysis, for example, two probes in N = 202) were not driven by genetic variation as only 18 of the 220 CpG sites have reported mQTLs. However, we did observe a significant enrichment of cross-cortex loci in the LD regions surrounding the AD-associated genetic variants HLADRB1, SPI1, SORL1, ABCA7 and ADAM10 after controlling for multiple testing. Finally, we have developed a classifier that could accurately predict control samples with low pathology, from those with a post-mortem AD diagnosis due to high pathology using methylation values for 110 of the 220 Bonferroni significant probes, further highlighting that distinct genomic loci reproducibly show epigenetic dysfunction in AD cortex. Although the clinical utility of such a classifier is limited as it is developed in post-mortem cortical brain tissue, it does illustrate that specific robust patterns of DNA methylation differences occur as the disease progresses. These signatures require further investigation as they could represent novel therapeutic targets, particularly given the classifier had an AUC > 70% in all the training and replication datasets. However, it is worth noting that the variance explained by the 110 CpG signature was lower in the replication datasets than the discovery samples, which could be due to a low sample number (Munich) or the different Illumina array platform (BDR).Our genomic enrichment analyses identified an over-representation of hypermethylated loci in AD and methylation in specific genomic features, for example, CpG islands in gene bodies, and shelves and non-CpG island regions in proximal promoters. We demonstrated that the majority of DMPs we identified  and given the falling cost of sequencing, future studies could exploit this by performing reduced representation bisulfite sequencing to substantially increase the coverage. In our study we have primarily used the UCSC annotation provided by Illumina to identify the gene relating to each DMP. However, this can lead to the annotation of overlapping genes, or no gene annotation, which can make it difficult to establish the gene of interest in the absence of functional studies. Our study has primarily focused on the results of a fixed-effects meta-analysis, as the majority of Bonferroni significant DMPs do not display a high degree of heterogeneity. However, ~15% of the cross-cortex DMPs did have a significant heterogeneity 30, with none of these studies utilizing >15 individuals for EWAS. Further studies exploring common and unique DNA methylation signatures and our classifier in other diseases characterized by dementia will be vital for identifying disease-specific epigenetic signatures that could represent novel therapeutic targets. Finally, one key issue for epigenetic studies in post-mortem tissue is the issue of causality, where it is not possible to determine whether disease-associated epigenetic loci are driving disease pathogenesis, or are a consequence of the disease, or even the medication used for treatment. One method that can be used to address this is Mendelian Randomization31 however, this does require the CpG site to have a strong association with a SNP. Given that we only identified mQTLs at 18 of the 220 Bonferroni significant cross-cortex DMPs, this approach is not suitable for most of the loci we identified. At an experimental level establishing causality is difficult to address in post-mortem human studies, and therefore longitudinal studies in animal models, or modelling methylomic dysfunction through epigenetic editing in vitro will be useful approaches to address these issues. In addition, examining DNA methylation signatures in brain samples in pre-clinical individuals (i.e. during midlife) will be important for establishing the temporal pattern of epigenetic changes relative to the pathology.Another limitation of our study is that we have focused our analyses on Braak (neurofibrillary tangle)-associated methylation changes, as this measure was available in all cohorts. Given that amyloid-beta is another neuropathological hallmark of AD, it would also be of interest to identify neuritic plaque-associated DMPs. Unfortunately, this was not feasible in the current study as this measure was not available in all samples. In a similar vein, we did not exclude individuals with mixed pathology, or protein hallmarks of other neurodegenerative diseases, such as the presence of lewy bodies, or TDP-43 pathology. In the future, larger meta-analyses should stratify by the presence of these protein aggregates, particularly given that very few EWAS have been undertaken in other dementias. Indeed, only three DNA methylomic studies have been undertaken in cortical samples of individuals with other dementias to dateIn summary, we present intra-tissue and cross-cortex meta-analyses of AD EWAS, highlighting numerous Bonferroni significant DMPs in the individual cortical regions and across the cortex, but not in the cerebellum, which were replicated in two independent cohorts. A number of these loci are novel and warrant further study to explore their role in disease etiology. We highlight that the nominated epigenetic changes are largely independent of genetic effects, with only 18 of the 220 Bonferroni significant DMPs showing a mQTL. We provide evidence that robust epigenomic changes in the cortex can predict the level of pathology in a sample. Looking to the future it will be important to explore the relationship between DNA methylation and gene expression in AD brain.5. The London 2 cohort comprised entorhinal cortex and cerebellum samples obtained from an additional 95 individuals from the MRC London Neurodegenerative Disease Brain Bank published by Smith and colleagues8. The Mount Sinai cohort comprised of prefrontal cortex and superior temporal gyrus tissue obtained from 146 individuals archived in the Mount Sinai Alzheimer’s Disease and Schizophrenia Brain Bank published by Smith and colleagues7. The Arizona 1 cohort consisted of 302 middle temporal gyrus and cerebellum samples from The Sun Health Research Institute Brain Donation Program32 published by Brokaw et al.12. The Arizona 2 cohort consisted of an additional 88 temporal gyrus and cerebellum samples from Lardonije et al.10. The ROSMAP cohort consisted of 711 samples from the Rush University Medical Center: Religious Order Study (ROS) and the Memory and Aging Project (MAP), which were previously published by De Jager and colleagues6. For replication purposes we used two further replication datasets. The Munich cohort from Neurobiobank Munich (NBM), which had bulk prefrontal cortex 450K array data from 45 donors, and 450K array data from fluorescence-activated cell sorted neuronal and non-neuronal  populations from the occipital cortex from 26 donors as described by Gasparoni et al.11. The Brains for Dementia Research (BDR) cohort consisted of bulk prefrontal cortex Illumina Infinium EPIC array data from 590 donors, as described by Shireby et al.15. Demographic information for all eight cohorts is available in Table Six sample cohorts were used for discovery in this study as they all had DNA methylation data generated on the 450K array for >50 donors, enabling us to take a powerful meta-analysis approach to identify DNA methylation differences in AD. As our analyses focused specifically on neuropathology (tau)-associated differential methylation, inclusion criteria for all samples used in the discovery or replication cohorts was having post-mortem neurofibrillary tangle Braak stage available. For each discovery sample cohort DNA methylation was quantified using the 450K array. The London 1 cohort comprised of prefrontal cortex, superior temporal gyrus, entorhinal cortex, and cerebellum tissue obtained from 113 individuals archived in the MRC London Neurodegenerative Disease Brain Bank and published by Lunnon et al.33 and Bioconductor 3.834. A MethylumiSet object was created from iDATs using the readEPIC function in the wateRmelon package35 and RGChannelSet object was created using the minfi package35. Samples were excluded from further steps if (a) the mean background intensity of negative probes <1000, (b) the mean detection P values >0.005, (c) the mean intensity of methylated or unmethylated signals were three standard deviations above or below the mean, (d) the bisulfite conversion efficiency <80%, (e) there was a mismatch between reported and predicted sex, or (f) the 65 SNP probes on the array show a modest level of correlation (using a cut-off of 0.65) between two samples (whereby the sample with the higher Braak score was retained). Sample and probe exclusion was performed using the pfilter function within the wateRmelon package36, with the following criteria used for exclusion: samples with a detection P > 0.05 in more than 5% of probes, probes with <three beadcount in 5% of samples and probes having 1% of samples with a detection P value >0.05. Finally, probes with common  SNPs in the single base extension position or probes that are nonspecific or mis-mapped were excluded38, leaving 403,763 probes for analysis. Samples numbers after quality control are those shown in Table All computations and statistical analyses were performed using R 3.5.2dasen function in the wateRmelon package36. For the discovery cohorts, DNA methylation data were corrected by regressing out the effects of age and sex in all samples in each cohort and tissue separately, with neuron/glia proportions included as an additional covariate in cortical regions. The neuron/glia proportions were calculated using the CETS package39, and were not included as a co-variate for the cerebellum as the neuronal nuclear protein (NeuN) that was used to generate the neuron/glia algorithm is not expressed by some cerebellar neurons40. These three variables  were regressed out of the data as we found that they strongly correlated with either of the first two principal components of the DNA methylation data in most of the datasets. Other potential sources of technical and biological variation  did not correlate as strongly with methylation in most datasets. We opted to use surrogate variables as a consistent method to control for variation derived from these measured and other unknown variables across all datasets. Surrogate variables were calculated using the sva function in the SVA package41. Linear regression analyses were then performed with respect to Braak stage (modelled as a continuous variable) using residuals and a variable number of surrogate variables for each study until the inflation index (lambda) fell below 1.2  lambda for all meta-analyses <1.2 and (b) P value inflation is commonly observed in many DNA methylation studies and standard methods to control for this in GWAS are not suitable for EWAS data42.Quantile normalization was applied using the metagen function within the Meta package43, which applies inverse variance weighting. The estimates and SEs from individual cohort Braak linear regression analyses were added to the model for each tissue. The prefrontal cortex analyses included three cohorts , the temporal gyrus analyses included four cohorts  and the entorhinal cortex analyses included two cohorts . The cerebellum analyses included data from four cohorts  although the cerebellum data for the Arizona 1 and 2 cohorts was generated in the same experiment, and so these were combined together as a single dataset. The ESs and corresponding SEs reported in this study correspond to the corrected DNA methylation (beta) difference between Braak 0 and Braak VI individuals. Bonferroni significance was defined as P < 1.238 × 10−7 to account for 403,763 tests. A fixed effects meta-analysis are the results primarily reported as it is the most appropriate model for our study as it can more reliably estimate the pooled effect and therefore the standard error and P value. However, in the Supplementary Data we do also report the results of the random-effects meta-analysis as ~10% of Bonferroni significant DMPs in the intra-tissue meta-analysis had high heterogeneity and in which case the results from the random-effects model should also be considered.We used the estimated coefficients and SEs from the six discovery cohorts to undertake an inverse variance intra-tissue meta-analysis independently in each available tissue using the lme function within the nlme package44. Estimate coefficients and SEs from each EWAS were extracted and were subjected to bacon42 to control for bias and inflation, after which a fixed-effect inverse variance meta-analysis was performed across all discovery cohorts using the metagen function. A fixed effects model was selected in this instance for consistency with the intra-tissue meta-analysis, although the random-effects meta-analysis results also shown in Supplementary Data As multiple cortical brain regions were available for the London 1 and Mount Sinai cohorts, a mixed model was performed using the 15.For the Munich replication cohort, we extracted the beta values for the 220 cross-cortex Bonferroni significant DMPs. This DNA methylation data were then corrected for age, sex and neuron/glia proportions (bulk tissue only) prior to performing a linear regression analysis with respect to Braak stage. For the BDR replication cohort, we were provided with beta values for the 208 cross-cortex Bonferroni significant DMPs that were present on the EPIC array. This data had been corrected for age, sex, neuron/glia proportions, batch and principal component 1, before the linear regression analysis was performed with respect to Braak stage, with Bacon used to control for inflation. Additional information on the BDR dataset can be found in Shireby et al.45 annotation version 4.0.4 . Pathway analyses were performed on the Illumina (UCSC) annotated genes corresponding to the 220 Bonferroni significant cross-cortex DMPs (N = 121 genes). We used the ‘gometh’ function within the missMethyl package (version 1.20.0)46, which performs one-sided hypergeometric tests and adjusts the test for the uneven number of probes per gene and pathway redundancy. The identified GO terms were subjected to the online tool REViGO (available at http://revigo.irb.hr/)47, to reduce the number of redundant functional terms based on semantic similarity between ontology terms. Resnik’s measure was used to compute the similarity of terms and a medium between terms similarity of 0.7 was allowed. As methylation at neighboring CpG sites can be highly correlated we used a method developed to identify SNPs in LD to identify independent signals14. For the 220 Bonferroni significant cross-cortex DMPs we used a threshold of r < 0.6 over 1 mb to identify 165 independent (non-highly correlated) methylation signals. To identify DMRs consisting of multiple DMPs we used the Python package comb-p48 with a distance of 500 bp and a seeded P value of 1.0 × 10−4. Comb-p was selected for DMR identification over alternative methods as it uses P values as input and so was the most suitable method for calling DMRs in the cross-cortex meta-analysis where multiple brain regions were available for some of the individuals. We have used comb-p to call DMRs in a number of our previous EWAS in AD, including studies where we have validated the top DMRs using an alternative technology such as pyrosequencing22.Probes were annotated for tables using both the Illumina (UCSC) gene annotation (which is derived from the genomic overlap of probes with RefSeq genes or up to 1500 bp from the transcription start site of a gene) and Genomic Regions Enrichment of Annotations Tool (GREAT)16 and performed a two-sided Fisher’s exact test comparing to all probes analysed . We also used a Fisher’s exact test to test for an enrichment of DMPs in genomic regions related to transcription based on the Illumina annotation . To investigate whether any of the 220 Bonferroni significant cross-cortex DMPs were driven by genetic variation we used the xQTL resource to identify which of these DMPs are established cis-mQTLs17. To explore whether Braak-associated methylation was enriched in known AD GWAS variants we used Brown’s method to combine together P values from our meta-analyses for probes residing in the LD blocks around the genome-wide significant (P < 5.0 × 10−8) GWAS variants identified by the stage one meta-analysis of Kunkle et al.18. Of the 24 LD blocks reported by Kunkle and colleagues, 20 contained >1 CpG site on the 450K array and the P values for each CpG in a given block were combined using Brown’s method, which accounts for the correlation structure between probes, with the regional P values adjusted to correct for multiple testing.To test for an enrichment of DMPs in specific genomic features  in certain genomic regions  we annotated all DMPs with Slieker annotationN = 407) and AD cases (Braak high [V–VI]: N = 589) from the cross cortex discovery dataset were used for training a classifier. A penalized regression model was used to select the optimum (N = 110) CpG probes from the 220 cross-cortex Bonferroni significant DMPs that determined case-control status in the training dataset using the R package GLMnet49. Elastic net uses a combination of ridge and lasso regression, in which alpha (α) = 0 corresponds to ridge, whilst α = 1 corresponds to lasso, the elastic net α parameter used was 0.5. The lambda value was derived when using 10-fold cross validation on the training dataset. The model was then tested for AUC ROC value, confidence intervals (CI) and variance explained in the testing dataset as well as the independent replication Munich  and BDR  prefrontal cortex datasets.For this analysis control samples (Braak low [0–II]: Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-17Reporting Summary


Conclusion: These results demonstrate that optical imaging can accurately estimate the redox state changes in newborn rat organs. It is also apparent that the FAO mutant’s heart tissue with a low redox ratio is probably more vulnerable to cumulative damages than kidneys and fails prematurely, contributing to sudden death.Background: Mitochondrial  I.Mitochondrial 2, and CO2. Electrons from NADH and FADH2 travel through complex I and complex II to complex IV of the electron transport chain (ETC), where they accept oxygen. Eventually, protons flow through complex V to trigger the conversion of ADP to ATP Heart and kidneys are highly dependent on mitochondrial oxidative phosphorylation due to their high oxygen consumption 2, could provide information about the electron transport chain’s (ETC) substrate supply rate 2) signals in mitochondrial This manuscript presents the extent of mitochondrial damage in the trifunctional protein deficiency rat model using a quantitative optical cryoimaging technique. Optical fluorescence imaging as a diagnostic tool with high sensitivity and specificity enables us to discriminate between diseased and non-diseased tissue Furthermore, cryo-temperature (−40°C) fluorescence imaging conserves the tissue’s metabolic state during imaging and provides a higher quantum yield of NADH and FAD than the room temperature measurements II.A.e4−/− mutant animals were generated using the CRISPR/CAS9 genome editing method (exon 4 excision – e4-/-) e4+/+ (wild-type control) and severe HADHAe4−/− were confirmed by Polymerase Chain Reaction assay and DNA sequencing [manuscript under review]. The heart sample size of N = 6 for wild-type control, and N = 11 for severe HADHAe4−/− were collected. For kidney samples, the number of the wild-type control and severe HADHAe4−/− samples is N = 5 and N = 12, respectively.Wild-type Sprague Dawley rats were purchased from Charles River, Wilmington, MA, housed, maintained, and cared for according to the federal, state, and local guidelines of Animal Welfare ACT and Health research Extension ACT. All our experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of Medical College of Wisconsin (AUA5533). Transgenic HADHAB.1)Newborn pups (P0) between 3–6hrs were decapitated by guillotine method, and the heart and kidneys were surgically removed, placed in a cryotube, and quickly immersed into an isopentane bath cooled in liquid nitrogen. All pups were euthanized to collect organs within 30min time period, and the tissue samples were stored at −80°C freezer until processed further. Tissue samples were deidentified by the tissue preparer until all the analysis was completed. The tissue samples for this study came from littermates of 6 different breeding pairs, and all originated eight generations before from a single founder animal. For histological evaluations of heart and kidney tissue, FFPE  blocks were prepared at the Children’s Research Institute Histology core.2)Tissues from newborn pups were collected within 2hrs of birth. The newborn pups were decapitated, slit open the thoracic /abdominal cavity using sharp scissors. Heart and kidney were harvested rapidly and immersed in 4% neutral buffered formalin for 24hrs. Following dehydration, paraffin embedding, a 4-3)Estimation of mitochondrial density was performed by standard immunohistochemistry method using an anti-Tomm20 antibody  on 4-4)The 3D cryo-imager is a custom-designed instrument designed and developed at the Biophotonics Lab, University of Wisconsin Milwaukee. A schematic of the Cryo-imaging system is shown in 5)NADH and FAD autofluorescence images (on average 150 slices per sample) from each group of kidneys and hearts were processed using MATLAB . A segmentation algorithm based on thresholding was applied to each slice to segment the region of interest on the tissue C.Statistical analyses were performed on the averaged redox ratio values as described previously III.e4−/−). Hearts with severe conditions (HADHAe4−/−) had a decreased RR compared to the control group reflecting a maximally oxidized metabolic status. e4−/−) conditions group than the wild-type control. The severe group had a 31.90% decrease in RR compared to the control, a significant difference between the two groups .e4−/−) rat pups are shown in e4−/−) mitochondrial dysfunctionality to control, showed a lower NADH, higher FAD in the former group. Kidney tissue showed smaller RR  than the heart tissue, suggesting a lower level of mitochondrial dysfunction in severe (HADHAe4−/−) conditions pups.Representative cryoimages of kidney the NADH, FAD, and RR obtained from the control and severe (HADHAe4−/−) conditions; however, the overall tissue architecture reveals no significant difference between wild-type control and severe (HADHAe4−/−) heart. Moreover, in e4−/−) condition shows no fibrosis, indicating a healthy organ structurally. We used translocase of outer mitochondrial membrane (TOMM40), a standard mitochondrial detection marker, to evaluate mitochondrial density in the heart tissue by immunohistochemistry (IHC). The quantification of antibody-mediated positive staining also shows insignificant differences in the organelle density in both wild-type and severe (HADHAe4−/−) conditions with a p-value of 0.19 (Hematoxylin-Eosin of the newborn heart is illustrated in  of 0.19 .FIGURE IV.e4−/−) conditions cause significant oxidation in heart and kidney tissue but at different levels. Since the heart depends heavily on mitochondrial integrity and function for its energy supply, extensive mitochondrial dysfunction could adversely affect heart function. We suggest that this extreme mitochondrial dysfunction could be a significant contributing factor to systemic heart failure and sudden death in severe (HADHAe4−/−) condition pups. Since we studied only the postnatal heart, which shows extensive oxidative status, but we do not know if the prenatal hearts are normal (future studies). However, it is worth noting that a reduction in the RR to 90–70% is sufficient to cause sudden death. Our wild-type control RR ranges between 0.943–1.427 while the severe (HADHAe4−/−) group is 0.75–0.92 suggesting anything below 0.92 could be detrimental to newborn survival.This study provides the influencing role of a mutant trifunctional protein-encoding gene in cardiac and kidney mitochondrial redox state. Moreover, the result of the volumetric mitochondrial redox state, a bioenergetic parameter to address the mitochondrial health and function, is presented in three-dimension. We show that severe  is a superior method to evaluate mitochondria. Snap freezing the tissue, imaging at ultra-low temperature, and measuring the autofluorescence ratio of endogenous components of the tissue ensures minimal sample manipulations gives us a reliable and accurate measure of the redox status of the tissue at the time of harvest. Along with the ability to minimize processing-induced variations, this method is also inclusive , thus giving the tissue’s exact overall redox status. Therefore, perturbations in NADH and FAD intensity reflect the level of mitochondrial dysfunction directly due to HADHA mutation. Large quantities of NADH and FADH2 supply, such as glomerular cells e4−/−) condition group’s cortical region reflects impaired ATP production because of decreased oxidation of the NADH and FADH2 and defective OxPhos. In contrast, the high RR in the medullary region could be attributed to normal ATP production because of anaerobic respiration.Our 3D optical cryo-imaging results revealed significant oxidation in cardiac redox state due to more oxidized NADH and less reduced FAD. In other words, cardiac cells’ ability to maintain balanced NADH/FAD for normal bioenergetics is diminished after extreme mitochondrial dysfunction due to trifunctional protein mutation. The decrease in the NADH level can impair the TCA cycle, which supplies the reduced equivalents to produce ATP via OxPhos. Some of the byproducts of OxPhos activity are reactive oxygen species, and the elevation in ROS production may further affect the function of ETC, leading to accumulated damages to mitochondria e4−/−) mutants having the reduced mitochondrial function (representing both ETC and FAO activities) could be a contributing factor for neonatal mortality while the healthy surviving littermate heterozygotes and wild-type controls having fully functional mitochondria. Interestingly since the estimation of mitochondrial content by Tomm40 staining was statistically identical, which means that most of these mitochondria in the homozygous mutants are either defective or nonfunctional. Further, redox ratio estimations in the mitochondrially dense (second only to the heart) kidney seem to show less significant difference among the various genotypes; Wild-type controls and mild (HADHAe4+/−) condition group compared to severe (HADHAe4−/−) group (e4−/−) group (lower RR levels), it strongly suggests that heart failure due to dysfunctional heart mitochondria is the reason for HADHA mutants neonatal death. Our redox-ratio evaluation does not pinpoint a specific pathway  that is severely affected as it measures the overall NADH/FAD levels but certainly differentiates which organ is most affected among the mitochondrial rich heart and kidney. Previous reports have suggested that during cardiac arrest and resuscitation, cardiac mitochondria functions differently from kidney, liver, and brain mitochondria Our data suggest that the severe (HADHA−) group  and 8, sV.e4−/−) transgenic mutant and wild-type rats with heart tissue displaying the most significant difference. Our 3D redox measurement data gives an accurate, unaltered view of the organ’s mitochondrial function.The current study investigated the contribution of HADHA encoded trifunctional protein mutation in cardiac and renal mitochondrial metabolism. We observed a significant difference in heart and kidney redox state of mitochondrial between severe (HADHA


SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype–phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed.Pathogenic variants in SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms. With higher-level phenotype terms inferred by the HPO structure, we assessed the frequencies of clinical features and investigated the association of these features with variant classes and locations within the NaV1.2 protein.We extracted phenotypic information from primary descriptions of SCN2A variants. Three independent principal components accounted for 33% of the phenotypic variance, allowing for separation of gain-of-function versus loss-of-function variants with good performance.We identified 413 unrelated individuals and derived a total of 10,860 HPO terms with 562 unique terms. Protein-truncating variants were associated with autism and behavioral abnormalities. Missense variants were associated with neonatal onset, epileptic spasms, and seizures, regardless of type. Phenotypic similarity was identified in 8/62 recurrent SCN2A-related disorders in unprecedented detail and revealing genotype–phenotype correlations along a multidimensional spectrum.Our work shows that translating clinical features into a computable format using a standardized language allows for quantitative phenotype analysis, mapping the phenotypic landscape of  Pathogenic variants in SCN2A have emerged as one of the most frequently diagnosed genetic etiologies of DEE5. The SCN2A gene encodes the α-subunit of the neuronally expressed type II voltage-gated sodium channel, also known as NaV1.27. Both gain-of-function (GoF) and loss-of-function (LoF) mechanisms have been implicated as underlying disease mechanisms, in addition to several complex functional alterations that cannot as easily be categorized9.Over the last decade, more than 100 genetic etiologies have been identified for neurodevelopmental disorders, which include the developmental and epileptic encephalopathies (DEE). The DEE are a group of childhood epilepsies associated with multiple neurological and non-neurological comorbidities that frequently start in the first years of life and are associated with drug-resistant epilepsySCN2A-related disorders is perplexing. Historically, the SCN2A gene was identified independently in three distinct phenotypes: benign familial infantile seizures11, autism spectrum disorders (ASD)12, and DEE15. While these conditions still represent the most well-recognized SCN2A-related phenotypes, many clinical presentations overlap, and others have been suggested16. It has been hypothesized that early-onset epilepsy phenotypes are mainly associated with GoF variants, while later-onset epilepsy and nonepilepsy phenotypes including autism and intellectual disability are associated with LoF variants17.The range of clinical presentation among the SCN2A-related disorders. Clinical descriptions are often limited and may be assessed through different data collection formats. Standardized terminologies, such as the terminology for seizures and epilepsies by the International League Against Epilepsy (ILAE), have changed since the initial description of SCN2A as a disease gene in 200118. Accordingly, assessing SCN2A-related phenotypes using a common framework is paramount to understand the phenotypic spectrum.Several challenges limit our current understanding of the full phenotypic spectrum and genotype–phenotype correlations in 19. We have recently demonstrated that the HPO framework can determine clinical similarities between individuals for gene discovery20, identify clinical constellations associated with de novo variants21, and delineate longitudinal disease phenotypes22.Several frameworks have been developed that allow for heterogeneous phenotypic data to be mapped to a common framework. The Human Phenotype Ontology (HPO) is the most frequently used dictionary for harmonization of clinical features  data to HPO concepts through a novel extraction tool allowed for prioritization of the correct genetic etiology in individuals with monogenic disordersSCN2A-related disorders and mapped phenotypic features to HPO terminology. We harmonized phenotypes across individuals, described the phenotypic landscape, explored phenotypic associations with specific variant types and locations, and examined phenotypic subgroups.Here, we assessed the clinical phenotypes in 413 unrelated individuals with SCN2A-related disorders between May 2001 and October 2019. We searched PubMed for studies using the search term “SCN2A” and “Nav1.2” and also identified variants using the Human Gene Mutation Database (HGMD) Professional 2020.225. We also included 21 individuals with SCN2A-related disorders recruited through the Epilepsy Genetics Research Project at Children’s Hospital of Philadelphia who have not been reported on previously. Only individuals with pathogenic SCN2A variants according to the criteria of the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) were included26. For familial cases, only the proband was included as performed in previous studies8.A review of the literature was performed to identify all reported cases of SCN2A-related disorders  Citation and Attribution Policy, as performed previously22. As a shorthand for negative phenotypes, we coded the absence of “Neurodevelopmental delay” (HP:0012758) as “No neurodevelopmental delay” (NP:0012758).In addition to “positive” phenotypes , we also assigned “negative” phenotypes . Negative phenotypes were only coded if absence of the specific phenotype was clearly documented in the literature . We used9 to identify SCN2A variants that had been previously analyzed functionally. We grouped these variants into GoF or LoF variants. Protein-truncating variants (PTV) in SCN2A were added to the LoF group, resulting in a total of 17 GoF missense variants and 57 LoF variants, including PTV and missense variants with demonstrated LoF effect  of each HPO term was defined as -log2(f), where f is the frequency of each HPO term in either the base or propagated data set.To overcome the heterogeneity in the depth of the HPO terminology, we performed automatic reasoning that added all applicable higher-level HPO terms up to the root of the HPO ontological tree for each individual. This propagation of HPO terms is well established for data harmonizationFor harmonization of negative HPO terms, we applied a reasoning method similar to propagation of positive HPO terms. However, in contrast to positive HPO terms, the inherent logic within negative HPO terms required an extension from general terms to more specific terms for absent phenotypes. Due to the size of the HPO tree, applying downward propagation resulted in a large number of negative term annotations. Therefore, we applied a “pruning” technique to remove redundant negative terms that cut the HPO tree at branches where no further information was obtained by propagation to more specific phenotypic terms .V1.2 channel, including all domains and segments. For the association of domains and segments, collectively referred to as “locations,” only missense variants were included. Association analyses for variant class and variant location were performed for all HPO terms in the propagated data set.We performed three separate analyses on the propagated HPO data set, including an association between PTV and missense variants and associations with topological domains within the Na21, a method based on that initially introduced by Resnik28. In brief, the similarity of a pair of terms is defined as the IC of their most informative common ancestor (MICA), i.e., the term encompassing them both that is least frequently encountered in the propagated HPO data set. The overall similarity of two individuals is derived from summation of the maximum similarities of all pairwise combinations of their respective sets of terms  to determine optimal cutoffs to distinguish between broader phenotypic groups and GoF versus LoF variants. ROC performance was measured and interpreted using the area under the curve (AUC) diagnostic. An AUC of 0.7 to 0.8 was considered acceptable performance, 0.8 to 0.9 was considered good performance, and >0.9 was considered great performance.Given the complexity of the phenotypic data set, we applied dimensionality reduction techniques to compress the highly dimensional HPO data set onto a lower-dimensional feature subspace with the goal of maintaining most of the relevant information. Given that HPO terms were coded either as “assigned” or “not assigned,” we used a logistic principal component analysis (PCA)30. We performed association analyses using Fisher’s exact test. We corrected for multiple testing using a false discovery rate (FDR) of 10%.All statistical analyses were performed using the R computational frameworkSCN2A-related phenotypes in the final analysis: 392 from published literature and 21 from the Epilepsy Genetic Research Project at Children’s Hospital of Philadelphia. The information in our study was derived from 122 independent publications between May 2001 and October 2019. Individuals were grouped into the following broad phenotypic categories (Supplementary Table n = 255), ASD (n = 60), benign familial neonatal–infantile seizures , other epilepsies (n = 27), and atypical phenotypes (n = 18). For individuals reported in multiple studies, the most recent phenotypic assessment was used.We included 413 unrelated individuals with n = 341), nonsense (n = 27), frameshift (n = 27), splice site (n = 13), in-frame deletion (n = 2), complex insertion and deletion (n = 1), duplication (n = 1), and deletion (n = 1). Frameshift, nonsense, splice site, and deletion variants were broadly categorized as PTV (n = 68). The in-frame deletion, duplication, and complex insertion variants were classified as neither missense nor PTV, and these individuals were excluded from variant analyses. We identified 62 variants in at least two unrelated individuals and 21 variants in at least three individuals  with a median of 23 HPO terms per individual. IC ranged from 0 to 8.69 with a mean IC of 6.63. We found 359 observed in at least two individuals. Phenotypic frequencies assessed within each phenotypic subgroup are shown in Fig. SCN2A-related disorders are documented, we analyzed the effect of propagation on HPO term frequencies. We compared the frequencies and the IC of each HPO term before and after propagation , “No autism” , and “No seizures” , using the shorthand for negative HPO terms referred to in “Materials and Methods.” The most common absent or negative HPO terms after propagation included “No hemiballismus” , “No microcephaly” , and “No hydrocephalus” .We separately analyzed phenotypes that were explicitly indicated as being absent in the original literature, which we encoded as negative HPO terms. A total of 475 negative HPO terms were initially assigned in 260/413 individuals , comprising a total of 18 unique negative base terms. The most frequently assigned negative HPO terms included “No abnormality of brain morphology”  and also among individuals with missense variants localized to either the S1 segment  or domain DIV . Phenotypic similarity within the group of individuals with missense variants increased when individuals with S5–S6 pore loop missense variants were excluded. The phenotypic similarity in individuals with missense variants in S1 was due to positive associations with “Status epilepticus” (HP:0002133) and “EEG with abnormally slow frequencies”  using “phenograms”  did not show overall phenotypic similarity between individuals, even though some individual phenotypic features were associated with this variant.Eight of the 62 recurrent variants and 6 of the 21 recurrent variants identified in three or more individuals were found to have significant phenotypic similarity . We assessed how these components distinguished existing phenotypic groups using ROC curves (Fig. SCN2A-related disorders onto a common phenotyping framework using HPO terms with methods to harmonize and analyze phenotype data across 562 distinct phenotypic terms. Our study reports the entire clinical literature on a complex neurodevelopmental disease encoded in standardized, computable phenotyping terminology including more than 10,800 phenotypic annotations.We report our effort to map existing data on SCN2A-related disorders and 296 HPO terms associated with specific variant classes. This analysis provides a significantly more nuanced view of genotype–phenotype correlation than previous studies that relied on qualitative observation of phenotype patterns33. We found that despite a large degree of heterogeneity, individuals with PTV are significantly less likely than those with missense variants to have seizures34. Overall, individuals with missense variants were more likely to present with early-onset epilepsy with multiple seizure types and abnormalities on electroencephalography.We described the overall frequency of 562 common and rare HPO terms associated with SCN2A-related disorders and missense variant location may be important in predicting specific phenotypic features38. Expanding on prior findings, our systematic phenotypic analysis identified a strong association of variants in the S5–S6 pore loop domain that is strongly associated with autism17. This was the only association between topological domains and phenotypes in our analysis.Missense variants make up the majority of variants reported in individuals with 22. The advantage of these techniques lies in the formal analysis of a large number of sparse phenotypic annotations that are difficult to compare manually; these techniques allow for phenotypic similarities in subgroups to emerge that are greater than expected by chance. For SCN2A-related disorders, this approach revealed several genotype–phenotype correlations that have not previously been recognized. We found that individuals with missense variants, especially when excluding S5–S6 pore loop missense variants; individuals with missense variants in domain DIV; and individuals with missense variants in any of the S1 segments are more phenotypically similar than expected. In addition, several recurrent SCN2A variants, most notably p.L1342P, p.A263V, and p.R853Q, showed significant phenotypic similarity. The p.R853Q variant with a homogeneous phenotype consisting of infantile spasms, hypsarrhythmia, and chorea is a prime example of this category. In summary, the computational approach in our study allowed us to demonstrate that individual recurrent variants within the SCN2A gene reliably produce distinct phenotypes. While this was previously known for some recurrent variants, our study highlights a much larger degree of genotype–phenotype association for recurrent variants than was previously known.The structured phenotyping language allowed us to identify subgroups and similarities between individuals using established data analysis techniques22, a wide range of other methods to quantify phenotypic similarity have been suggested39. We compared the performance of eight additional similarity algorithms from the literature (Supplementary 40. Integration of heterogeneous knowledge resources through graph embeddings holds promise to assist phenotypic similarity measurement and patient-level subgrouping. For example, the node similarity measurements in the HPO2Vec+ framework integrating data from resources such as DECIPHER, OMIM, and Orphanet have been shown to outperform traditional similarity algorithms40. Accordingly, in addition to optimizing traditional similarity algorithms, vectorized graph representations may be critical in the analysis of large phenotypic data in precision medicine approaches.While we used the Resnik algorithm in our analysis that we applied in previous studiesSCN2A-related disorders that separated these phenotypic groups. We found that PC2 primarily separates between existing GoF and LoF variants with good performance, which can be used to predict variant function. Given that the separation of GoF and LoF may predict treatment response to sodium channel blockers8, logistic PCA allows for a formal framework to prioritize individuals where treatment with sodium channel blockers may be indicated. Surprisingly, we found that PC1, separating the large DEE group from all other phenotypes, does not separate GoF from LoF variants, possibly due to the high frequency of missense variants in other non-DEE groups, such as BFNIS, indicating that the binary classification of variants into GoF and LoF classes is insufficient to explain the form of epilepsy an individual develops. In summary, our framework to predict variant function based on phenotypes has the potential to refine and complement emerging prediction frameworks based on sequence-based features41.Using logistic PCA, we found that the three major principal components align with three major phenotypic groups: DEE, autism, and self-limited familial neonatal–infantile seizures, formerly referred to as BFNIS. However, the overall stratification of phenotypes is more complex as these components are independent of each other and dimensionality reduction of phenotypic data enabled by harmonized phenotype data revealed a complex pattern within the SCN2A-related disorders in which the absence of early-onset seizures or neurodevelopmental abnormalities is thought to be important in distinguishing between disorders with dramatically different prognoses. We found a robust association of the absence of seizures with PTV, mirroring the inverse relationship with the presence of seizures with missense variants. Likewise, we found the absence of autism and developmental delay to be associated with missense variants, reflecting the contribution of the BFNIS group. Taken together, associations with negative phenotypes are important for highlighting critical phenotypic features that are relevant for treatment and counseling. For example, in relation to the overall phenotypic landscape of the SCN2A-related disorders, individuals with a novel missense variant are three times more likely not to have autism and almost 20 times more likely to not have any form of intellectual disability.An analysis of negative phenotypes is particularly pertinent to 22. While manual phenotyping ensures a qualitatively high mapping of phenotypic features, this approach is time-consuming and not scalable. Mapping HPO terms from standardized nomenclatures commonly used in routine clinical care or natural language processing from full-text medical literature or patient notes through tools such as Doc2HPO42 may allow for computational phenotyping to become scalable in the future.Our study exclusively relied on manually annotated information in parallel to our prior studiesSCN2A-related disorders in 413 individuals using a standardized phenotyping language after systematic data harmonization. This allowed us to analyze the clinical features of SCN2A-related disorders in unprecedented detail and identify phenotypic features associated with distinct variant classes and locations. Our results help define subclasses for future precision medicine approaches in SCN2A-related disorders and provide a general framework on how HPO terminology can be used to map and understand heterogeneous clinical data in rare disorders.We described the full phenotypic landscape of Supplementary materialSupplementary Tables


Graham Little – Piccardi – Lassueur (GLPL) syndrome is a rare dermatosis characterized by scarring alopecia, loss of pubic and axillary hair, and progressive development of variously located follicular papules. We report a first case ever of an autoimmune response in a patient suffering from GLPL syndrome.Immunofluorescence and immunoblot analysis were used in a variety of cell cultures including human, monkey, hamster, mouse and bovine cells to analyze the presence of autoantibodies in a GLPL patient.The autoimmune serum showed a pattern of centromere and spindle microtubule staining resembling that of the chromosomal passenger protein complex. By using a complex of proteins expressed in baculovirus, immunoblot analysis demonstrated that the INCENP protein is a major autoantigen in this patient with GLPL syndrome.An autoimmune response in GLPL syndrome is reported against the INCENP centromere protein. The occasional development of autoimmunity in GLPL patients could serve as a test in continuing efforts to detect this disease and for a more directed therapy based on the autoantigen response. The presence of cicatricial alopecia on the scalp, keratosis pilaris on the trunk and extremities, and non-cicatricial hair loss in the pubis and axillae was first described by Piccardi in 1914 and a year later by Graham Little in a patient sent by Lassueur. The GLPL syndrome is considered a form of follicular lichen planus (LP), characterized by lichenoid dermatosis, keratosis pilaris and progressive cicatricial alopecia of the scalp [A familial case of occurrence in a mother and her daughter was recently described with a HLA-DR1 in both patients . TopicalImmunofluorescence (IF) analysis is a common clinical procedure used in studying the development and behavior of several chronic diseases. Based on clinical features and histological findings, we decided to perform an initial IF test in a woman patient suffering GLPL syndrome. Autoantibodies were found to recognize a mitotic-associated antigen which behaves like the previously-described chromosomal passenger complex protein . Our finA 45 year old woman with alopecia in frontal area with follicular hyperkeratosis and other clinical features was diagnosed as suffering GLPL syndrome. Initial clinical test by IF on standard human HEP2-fixed cells indicated positive antinuclear autoantibodies (ANAs) and specific midbody localization (data not shown). Anti SM, SSB, SSA, CENP-B, Jo-1, ribosomal P, and histones were also initially determined negative by immunoblots.Culture cells from hamster (CHO), monkey (CV1), mouse (3T3) and bovine (MDBK) were grown on coverslips at 37°C in Dulbecco's modified Eagle's medium  containing 7% fetal calf serum. Cells were fixed in methanol for 10 min. at – 20°C for autoantibody staining. The human GLPL serum was used at 1:400 dilution in PBS for 45 min at 37°C followed by 4 × 5 min time washes in PBS. FITC-conjugated goat antihuman-Ig  was used as a second antibody at 1:100 dilutions in PBS for 45 min at 37°C. This was followed by 3 × 10 min washes in PBS. Cells were counterstained for Hoechst and mounted in PBS:glycerol 9:1. Staining was observed in a Zeiss epifluorescent microscope and immunofluorescence images were recorded by a CCD Spot Camera (Diagnostic Instruments Inc. USA). For double indirect immunofluorescence microscopy, antibodies against Aurora B (Abcam), INCENP (rabbit 1186), alfa-tubulin (Aldrich) and secondary antibodies coupled to FITC, Texas red or Cy5 (Jackson ImmunoResearch Laboratories) were used in human U2OS cells following methods previously described [2O2 until the color bands appeared. Rabbit specific serum to INCENP protein [Whole cell extracts were prepared from HeLa culture cells blocked with colcemid by lysis in SDS Laemmli buffer. Three samples of 10/20/40 micrograms of protein were loading on a gel. The cDNAs and baculovirus constructs for Aurora B, Surviving, and INCENP have been previously described . Insect  protein  was assaIn a routine clinical test by IF on standard HEP2 cells, the Graham Little-Piccardi-Lassueur serum showed a midbody staining reminiscent of chromosome passenger proteins (data not shown). To characterize in detail the human autoimmune response, U2OS culture cells were assayed by IF with GLPL serum showing a typical pattern of staining in mitosis and interphase cells. During prophase the autoantigen was found on the condensing chromosomes, gradually becoming concentrated at the centromeres Figure , GL. ThiThe next step in our study was to identify the GLPL autoantigen by performing immunoblot analysis. First, we used human whole cell extracts to test the reactivity of the GLPL serum by western blots. As shown in Figure It is known that most of the anti-nuclear autoimmune sera (ANAs) in humans are directed against macromolecular complexes of proteins or protein-nucleic acid structures . The cenINCENP is a major component of the centromere during several phases of the mitotic cell cycle where it plays an essential role in chromosome segregation and participates in the mechanisms regulating mitosis ,10. DeplThe GLPL syndrome represents a rare disease and documentation is scarce ,3. Our r


Western blotting analysis of the p21 ras oncoprotein was performed in seven normal laryngeal mucosa specimens and 43 primary laryngeal cancers. Varying p21 levels, expressed as optical density (OD), were found in normal mucosa  and in primary laryngeal tumours . When p21 expression in laryngeal cancer was compared with the normal counterpart, higher levels were found in neoplastic than in normal laryngeal tissue  (P = 0.023). Immunohistochemical analysis demonstrated that most of the tumour cells (more than 70%) were immunostained while the stromal component was unreactive. No correlation between p21 expression and tumour location, stage and histopathological grade was observed. The correlation between ras p21 protein expression and epidermal growth factor receptor (EGFR) levels was also investigated. EGFR-positive cases did not show any difference in p21 expression with respect to EGFR-negative cases . Our findings suggest that overexpression of p21 protein is associated with a malignant phenotype in laryngeal cancer. Further studies should be undertaken to evaluate whether the assessment of p21 protein expression may have clinical significance in laryngeal cancer.


The mesoderm of the amphibian embryo is formed through an inductive interaction in which vegetal cells of the blastula-staged embryo act on overlying equatorial cells. Candidate mesoderm-inducing factors include members of the transforming growth factor type β family such as Vg1, activin B, the nodal-related proteins and derrière.Xenopus development. Inhibition of nodal-related protein function causes the down-regulation of regionally expressed genes such as chordin, dickkopf and XSox17α/β, while genes that are mis-regulated in the absence of activin B tend to be more widely expressed and, interestingly, include several that are involved in cell cycle regulation. Consistent with the latter observation, cells of the involuting dorsal axial mesoderm, which normally undergo cell cycle arrest, continue to proliferate when the function of activin B is inhibited.Microarray analysis reveals different functions for activin B and the nodal-related proteins during early Xenopus development, and in doing so identify a new role for activin B during gastrulation.These observations reveal distinct functions for these two classes of the TGF-β family during early  The mesoderm of the amphibian embryo arises through an inductive interaction in which cells of the vegetal hemisphere act on overlying equatorial cells chordin, cerberus and nogginChordin and PintallavisSome attempts along these lines have been made, and it proves that although each of the factors is essential for normal development, their roles differ. For example, ablation of the maternal transcripts encoding Vg1 causes a reduction in anterior and dorsal development and the down-regulation of genes such as Here we perform microarray analyses of gene expression in embryos in which activin or nodal-related signalling has been inhibited. We find that activin and the nodal-related proteins regulate distinct and almost completely non-overlapping sets of genes, with those regulated by the nodal-related genes tending to be expressed in a more restricted pattern than those regulated by activin. It further proved that the nodal-related proteins often regulate the expression of genes involved in regional specification, while activin particularly regulates genes involved in the control of the cell cycle. Consistent with this observation, we find that inhibition of activin B in the early embryo causes dorsal axial mesodermal cells to fail to exit the cell cycle: the results of others Xenopus development, we have carried out microarray analyses of gene expression in embryos in which signalling by the two classes of factor has been disrupted. Activin signalling was blocked using an antisense morpholino oligonucleotide Xenopus tropicalis EST project X. laevis transcripts In an effort to understand the different requirements for activin B and the nodal-related genes during Xenopus laevis embryos from three different spawnings were injected with RNA encoding Cerberus-short (150 pg into each blastomere at the four-cell stage) or with antisense morpholino oligonucleotide MO3 (50 ng into the one-cell stage) (samples), or with water or antisense morpholino oligonucleotide mMO1 (50 ng) (controls). These doses of Cerberus-short RNA and MO3 were based on previous work For each series of experiments versus control) in expression levels in at least four out of the six microarrays and (ii) were significantly different . In embryos in which activin B signalling was inhibited, 40 oligonucleotides fulfilled these rigorous criteria, of which 8 were down regulated, and in those in which nodal signalling was inhibited, 20 oligonucleotides (representing 18 genes) were differentially expressed, of which 17 were down regulated  they showed at least a two fold difference  were identified by BLAST searches  for the activin B experiment and of 0.975 (p = 0.000) for the Cerberus-short experiment . RT-PCR Xenopus tropicalis embryos with probes generated by the polymerase chain reaction (PCR). Consistent with the different expression patterns of activin B and of the nodal-related genes The expression pattern of each differentially expressed gene was determined from the literature, where possible, or by carrying out in situ hybridisations using http://www.ncbi.nih.gov/). Interestingly, this analysis also revealed differences between embryos lacking activin B and those in which nodal related signalling is inhibited , indicates that in the absence of activin the expression of genes such as goosecoid, chordin and Xbra is reduced by 20–80%, depending on stage and dose of antisense morpholino oligonucleotide Xenopus laevis were obtained by artificial fertilisation, maintained in 10% normal amphibian medium Embryos of These were performed as described http://www.ncbi.nlm.nih.gov/projects/geo/index.cgi) (accession numbers GSE4771 and GSE4777).Microarray results were imported into Acuity (Axon) and normalised using Lowess normalisation. Data files were created for points which satisfied the following filter: (Sum of Medians) ≥500 AND (Flags) ≥0 AND (%>B532+1SD)≥55 OR (%>B635+1SD)≥55. This filter eliminates data points flagged as bad by GenePix, or that had the sum of media less than 500, or which had fewer than 55% of pixels above background. Points passing these criteria for at least four out of the six microarrays were used for further analysis. Oligonucleotides were considered to be differentially expressed when they showed at least a two fold difference in expression levels in four out of the six microarrays and had a q value of 0 as assessed by the Significance Analysis of Microarrays software ornithine decarboxylase (ODC) were as described Differential expression was validated by real-time RT-PCR using the Roche LightCycler 480. Primers specific for Xenopus tropicalis, essentially as described Xenopus tropicalis EST project This was carried out on embryos of Embryos to be subjected to frozen sectioning were fixed in 3.7% formaldehyde, 10% DMSO, 100 mM MOPS pH7.4, 2 mM EGTA, 1mM EDTA for 2 hr at room temperature and embedded in 25% sucrose, 15% cold water fish gelatin (Sigma) at room temperature for 24 hr. Sections (14 µm) were cut at −17°C and stored at −80°C. They were incubated overnight at 4°C with anti-phosphohistone H3 antibody  and then with anti rabbit IgG antibody coupled to Alexa 568 . Nuclei were counterstained with DAPI.Whole-mount immunostaining using anti-phosphohistone H3 antibody was performed as described


They negatively regulate target genes and have recently been implicated in tumourigenesis. Furthermore, miRNA expression profiling correlates with various cancers, with these genes thought to act as both tumour suppressors and oncogenes. Recently, a point mutation in the BRAF gene leading to a V600E substitution has been identified as the most common genetic change in papillary thyroid carcinoma (PTC) occurring in 29–69% of cases. This mutation leads to aberrant MAPK activation that is implicated in tumourigenesis.The aim of this study was to identify the effect that BRAF oncogene has on post-transcriptional regulation in PTC by using microRNA analysis.A unique miRNA expression signature differentiated between PTC cell lines with BRAF mutations and a normal thyroid cell line. 15 miRNAs were found to be upregulated and 23 miRNAs were downregulated. Several of these up/down regulated miRNAs may be involved in PTC pathogenesis. miRNA profiling will assist in the elucidation of disease pathogenesis and identification biomarkers and targets. Papillary thyroid carcinoma (PTC) is the most commonly occurring thyroid malignancy accounting for approximately 80% of cases. Genetically PTC is defined by several alterations which cause abnormal activation of the mitogen-activated protein kinase (MAPK) pathway, the most prevalent being point mutations in the intracellular signalling kinase BRAF ,2. An acDespite this range of knowledge thyroid carcinoma still poses a significant diagnostic challenge and understanding of the complete range of genes and pathways involved in its pathogenesis is incomplete. Until recently biomarker discovery has concentrated largely on protein coding RNAs in a quest to identify cancer associated genes involved in thyroid carcinogenesis -7. The r® RT-PCR method was used to measure the expression levels of a panel of 160 mature miRNAs in thyroid cell lines containing BRAF mutation.Recently several groups have utilised a range of techniques most notably northern blot analysis, cloning and oligonucleotide microarrays, in order to analyse miRNA expression profiles in different cancer types -26. In t® RT-PCR was used to measure the expression levels of 160 miRNAs in a collection of cell lines including a normal thyroid cell lines N-thy-ori 3-1 and two cell lines containing V600E BRAF point mutation [Nthy-oriBRAF and KAT-10]. A comparison was then made between Nthy-ori and BRAF mutated cell lines to determine miRNAs whose expression was differentially expressed between the two groups. ΔΔCT method was used to calculate fold change of miRNA expression between groups. 15 miRNAs were found to be upregulated with a fold change of > 2 in BRAF mutated cell lines when compared to normal.Stem-looped TaqManThree of these upregulated miRNAs showed significantly higher fold change than the other upregulated miRNAs, these are mir-200a, mir-200b and mir-141.23 miRNAs showed underexpression of > 2 fold when compared to the normal thyroid cell line. Moreover, three miRNAs showed significantly more underexpression compared to the other downregulated miRNAs. These miRNAs are as follows mir-127, mir-130a and mir-144.  are now recognized as an additional layer of post-transcriptional control that must be considered in order to understand the complexity of gene expression in humans. miRNAs are now known as a novel class of gene regulators that are involved in cancer related processes and deregulation or aberrant expression of miRNAs may contribute to human diseases, including cancer. Calin et al showed down-regulation of miRNA-15 and miRNA-16 in a majority of chronic lymphatic leukaemia (CLL). Altered miRNA expression has also been reported in breast cancer, glioblastoma, lung cancer and colorectal cancer -35.This study utilized a novel stem-looped TaqMan RT-PCR method to measure the expression levels of 160 mature miRNAs in a collection of thyroid cell lines. This method offers several distinct advantages over conventional miRNA detection methods including a higher sensitivity and specificity and a fast and simple methodology. The aim of this study was to correlate BRAF mutant status with an associated miRNA profile, with a view to elaborating the downstream regulatory effect of BRAF mutation.The data showed a set of differentially expressed miRNAs in cell lines with BRAF V600E mutation compared to a normal thyroid cell line Table . There wA number of miRNAs exhibited a difference in expression level between KAT10 cell line and normal but did not show a significant change in the BRAF transfected cell lines and normal. miRNAs specific to the PTC cell line with native mutant BRAF included, mir-10a [overexpressed with a fold change of 95 in KAT10] mir-23b [downregulated with a fold change of 2949] and mir-154 [downregulated with a fold change of 142]. It is possible these miRNAs may play a role in a later stage in tumour formation and progression; given the transfected cell line had only been exposed to mutant BRAF for a short period (3 passages) unlike KAT10 which represents a genuine PTC which has evolved with BRAT mutant as an intrinsic component. Alternatively, the difference may reflect the accumulation of several genetic insults in the process of tumourigenesis.RET and BRAF status of each PTC case was not disclosed.Recent studies in miRNA deregulation PTC found an aberrant miRNA expression profile in PTCs compared to normal thyroid tissues ,37. In pin vitro and not all the prediction algorithms yield overlapping lists therefore the lists may contain several false positives that may fit the criteria but do not interact with the miRNA in vitro. To overcome this, results were intersected from these databases to identify the genes commonly predicted by at least two of the algorithms. The results of the potential targets of several differentially expressed miRNAs are displayed in table Prediction of miRNA targets is an immediate challenge in miRNA research. Several computational approaches have been implemented for miRNA gene prediction using methods based on sequence conservation and/or structural similarity. To overcome the limitations of target prediction three of these databases MiRBase, TARGETSCAN and PICTAR were used in this study to predict possible targets of the differentially expressed miRNAs associated with BRAF mutated Table . ExampleIt is reasonable to expect that targets of downregulated miRNAs include oncogenes or genes encoding proteins with potential oncogenic function. Indeed among putative targets, several genes with potential oncogenic functions were found include RAB35, ERBB4, MAP3K3, VASP6, KIT, FOSB, NOTCH1. Upregulated miRNAs are purported to interact with tumour suppressor genes. Those identified from this study include MAD 4 and ST6. Several extracellular membrane molecules were also identified including; myotubulin, MAD1A, Cadherins. In general overexpression of an miRNA would be associated with downregulation of its target miRNA.Interestingly among these potential gene targets are a number of genes that have implications in thyroid carcinoma progression such as genes involved in thyroid function, cell cycle control, cell signalling, MAPKKK cascade, retinoic acid receptors, cell adhesion and cell structure associated genes. Therefore, it seems plausible that this subset of miRNAs may be important in the progression of thyroid carcinoma by working together or independently to regulate genes involved in thyroid tumourigenesisOur group has also previously examined gene expression levels in these same cell lines. This analysis was done by evaluating genome-wide gene expression levels in BRAF mutated cell lines and matched normal thyroid cell lines using the Applied Biosystems Gene Expression Arrays. Treatment comparisons were preformed resulting in lists of differentially expressed genes Figure . These gUp and down regulation of cell adhesion and chromosome remodelling molecules were also shown. BRAF mutation has previously been shown to induce chromosome instability and decrease cell- cell interactions  and our in vitro.The list of putative miRNA targets , Penicillin (100 U/ml) and Streptomycin (100 μg/ml). A BRAF V600E expressing plasmid, pMCEF-V600EB-RAF, was transfected into Nthy-ori 3-1. Transfections were carried out using Genejuice™ transfection agent  using the recommended protocol and transfected cells were grown in the presence of Gentamicin (Sigma Aldrich) antibiotic to yield pure cultures.Nthy-ori 3-1  is a thyroid follicular epithelial cell line derived from normal thyroid tissue of an adult that has been transfected with a plasmid encoding for the SV40 large T gene. KAT10 is derived from a papillary thyroid carcinoma cell line with heterozygous BRAF V600E mutation. Both cell lines were grown to confluence in a humidified atmosphere containing 5% CO6 cells) were collected and total RNA was extracted using RNeasy® mini kit . RNA quantity and quality are assessed using a NanoDrop® ND-1000 Spectrophotometer  and RNA 6000 Nano LabChip® Kit in conjunction with the Agilent 2100 Bioanalyser  as illustrated in figure ® SNP detection was used for BRAF V600E mutation detection as previously described by Smyth et al [WT], and FAM-5' CCA TCG AGA TTT CTC TGT AG 3' [BRAF-PMUT]. Amplification and analysis was performed using an ABI Prism 7000 Sequence Detection System  for 40 cycles .Following trypsinisation of cultured cells, cell pellets  and hybridized to each pre-hybed microarray in a 1.5 ml volume at 55°C for 16 hr. After hybridization, the arrays were washed with hybridization wash buffer and chemiluminscence rinse buffer. Enhanced chemiluminescenct signals were generated by incubating arrays with anti-digoxigenin alkaline phosphatase, enhanced with Chemiluminescence Enhancing Solution and finally by adding Chemiluminescence Substrate. Images were collected for each microarray using the 1700 analyser. Images were autogridded and the chemiluminescent signals were quantified, corrected for background and spot and spatially normalized. Replicates were performed. Microarrays were analysed using Spotfire Decision Site™ for functional genomics  and R version 1.9.1 [a free language and environment for statistical analysis and graphics] .® microRNA (miRNA) assays are designed to detect and quantify mature miRNAs using a looped-primer real time PCR. The human early access panel used in this study contained 160 individual assays for identified human miRNAs. The assay involved 2 steps: Step one; a Stem-looped RT, and Step two; a Real Time PCR. Briefly, single stranded cDNA was generated from total RNA sample by reverse transcription using the Applied Biosystems High-Capacity cDNA Archive Kit  following manufacturer's protocol. RT reactions contained 10 ng of total RNA, 50 nM stem-looped RT primer, 1 × RT buffer, 0.25 mM each of dNTPs, 3.33 U/μl Multiscribe reverse transcriptase and 0.25 U/μl RNase Inhibitor. PCR amplification was carried out using sequence specific primers on the Applied Biosystems 7900 HT Fast Real-Time PCR system. The reactions were incubated in a 96-well optical plate at 95°C for 10 min, following by 40 cycles of 95°C for 15 s and 60°C for 10 min. Analysis of relative miRNA expression data was performed using ΔΔCT method with hsa-let-7a as an endogenous control [Applied Biosystems TaqMan control . Two neg