Objective:Posttraumatic stress disorder (PTSD) is a debilitating neuropsychiatric disease that is highly comorbid with major depressive disorder (MDD) and bipolar disorder. The overlap in symptoms is ...hypothesized to stem from partially shared genetics and underlying neurobiological mechanisms. To delineate conservation between transcriptional patterns across PTSD and MDD, the authors examined gene expression in the human cortex and amygdala in these disorders.Methods:RNA sequencing was performed in the postmortem brain of two prefrontal cortex regions and two amygdala regions from donors diagnosed with PTSD (N=107) or MDD (N=109) as well as from neurotypical donors (N=109).Results:The authors identified a limited number of differentially expressed genes (DEGs) specific to PTSD, with nearly all mapping to cortical versus amygdala regions. PTSD-specific DEGs were enriched in gene sets associated with downregulated immune-related pathways and microglia as well as with subpopulations of GABAergic inhibitory neurons. While a greater number of DEGs associated with MDD were identified, most overlapped with PTSD, and only a few were MDD specific. The authors used weighted gene coexpression network analysis as an orthogonal approach to confirm the observed cellular and molecular associations.Conclusions:These findings provide supporting evidence for involvement of decreased immune signaling and neuroinflammation in MDD and PTSD pathophysiology, and extend evidence that GABAergic neurons have functional significance in PTSD.
DNA methylation (DNAm) is a critical regulator of both development and cellular identity and shows unique patterns in neurons. To better characterize maturational changes in DNAm patterns in these ...cells, we profile the DNAm landscape at single-base resolution across the first two decades of human neocortical development in NeuN+ neurons using whole-genome bisulfite sequencing and compare them to non-neurons (primarily glia) and prenatal homogenate cortex.
We show that DNAm changes more dramatically during the first 5 years of postnatal life than during the entire remaining period. We further refine global patterns of increasingly divergent neuronal CpG and CpH methylation (mCpG and mCpH) into six developmental trajectories and find that in contrast to genome-wide patterns, neighboring mCpG and mCpH levels within these regions are highly correlated. We integrate paired RNA-seq data and identify putative regulation of hundreds of transcripts and their splicing events exclusively by mCpH levels, independently from mCpG levels, across this period. We finally explore the relationship between DNAm patterns and development of brain-related phenotypes and find enriched heritability for many phenotypes within identified DNAm features.
By profiling DNAm changes in NeuN-sorted neurons over the span of human cortical development, we identify novel, dynamic regions of DNAm that would be masked in homogenate DNAm data; expand on the relationship between CpG methylation, CpH methylation, and gene expression; and find enrichment particularly for neuropsychiatric diseases in genomic regions with cell type-specific, developmentally dynamic DNAm patterns.
While a definitive understanding of schizophrenia etiology is far from current reality, an increasing body of evidence implicates perturbations in early development that alter the trajectory of brain ...maturation in this disorder, leading to abnormal function in early childhood and adulthood. This atypical development likely arises from an interaction of many brain cell types that follow distinct developmental paths. Because both cellular identity and development are governed by the transcriptome and epigenome, two levels of gene regulation that have the potential to reflect both genetic and environmental influences, mapping "omic" changes over development in diverse cells is a fruitful avenue for schizophrenia research. In this review, we provide a survey of human brain cellular composition and development, levels of genomic regulation that determine cellular identity and developmental trajectories, and what is known about how genomic regulation is dysregulated in specific cell types in schizophrenia. We also outline technical challenges and solutions to conducting cell type-specific functional genomic studies in human postmortem brain.
Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample. ...Multiple fluorescent dyes, each measuring a unique molecule, are simultaneously measured and subsequently "unmixed" to provide a read-out for each molecular signal. This strategy allows for measuring highly multiplexed signals in a single data capture session, such as multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as post-mortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images.
This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using vertex component analysis, and then performs one of three unmixing algorithms derived from remote sensing. We evaluate these remote sensing algorithms' performances on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot, which is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis, and thereby provide an end-to-end solution for multispectral fluorescence image analysis and quantification.
In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.
Gene annotations, such as those in GENCODE, are derived primarily from alignments of spliced cDNA sequences and protein sequences. The impact of RNA-seq data on annotation has been confined to major ...projects like ENCODE and Illumina Body Map 2.0.
We aligned 21,504 Illumina-sequenced human RNA-seq samples from the Sequence Read Archive (SRA) to the human genome and compared detected exon-exon junctions with junctions in several recent gene annotations. We found 56,861 junctions (18.6%) in at least 1000 samples that were not annotated, and their expression associated with tissue type. Junctions well expressed in individual samples tended to be annotated. Newer samples contributed few novel well-supported junctions, with the vast majority of detected junctions present in samples before 2013. We compiled junction data into a resource called intropolis available at http://intropolis.rail.bio . We used this resource to search for a recently validated isoform of the ALK gene and characterized the potential functional implications of unannotated junctions with publicly available TRAP-seq data.
Considering only the variation contained in annotation may suffice if an investigator is interested only in well-expressed transcript isoforms. However, genes that are not generally well expressed and nonetheless present in a small but significant number of samples in the SRA are likelier to be incompletely annotated. The rate at which evidence for novel junctions has been added to the SRA has tapered dramatically, even to the point of an asymptote. Now is perhaps an appropriate time to update incomplete annotations to include splicing present in the now-stable snapshot provided by the SRA.
Abstract
DNA methylation (DNAm) is an epigenetic regulator of gene expression and a hallmark of gene-environment interaction. Using whole-genome bisulfite sequencing, we have surveyed DNAm in 344 ...samples of human postmortem brain tissue from neurotypical subjects and individuals with schizophrenia. We identify genetic influence on local methylation levels throughout the genome, both at CpG sites and CpH sites, with 86% of SNPs and 55% of CpGs being part of methylation quantitative trait loci (meQTLs). These associations can further be clustered into regions that are differentially methylated by a given SNP, highlighting the genes and regions with which these loci are epigenetically associated. These findings can be used to better characterize schizophrenia GWAS-identified variants as epigenetic risk variants. Regions differentially methylated by schizophrenia risk-SNPs explain much of the heritability associated with risk loci, despite covering only a fraction of the genomic space. We provide a comprehensive, single base resolution view of association between genetic variation and genomic methylation, and implicate schizophrenia GWAS-associated variants as influencing the epigenetic plasticity of the brain.
ObjectiveNeurodevelopmental disorders presumably involve events that occur during brain development. The authors hypothesized that neuropsychiatric disorders considered to be developmental in ...etiology are associated with susceptibility genes that are relatively upregulated during fetal life (i.e., differentially expressed).MethodThe authors investigated the presence of prenatal expression enrichment of susceptibility genes systematically, as composite gene sets associated with six neuropsychiatric disorders in the microarray-based “BrainCloud” dorsolateral prefrontal cortex transcriptome.ResultsUsing a fetal/postnatal log2-fold change threshold of 0.5, genes associated with syndromic neurodevelopmental disorders (N=31 genes, p=3.37×10–3), intellectual disability (N=88 genes, p=5.53×10–3), and autism spectrum disorder (N=242 genes, p=3.45×10–4) were relatively enriched in prenatal transcript abundance, compared with the overall transcriptome. Genes associated with schizophrenia by genome-wide association studies were not preferentially fetally expressed (N=106 genes, p=0.46), nor were genes associated with schizophrenia by exome sequencing (N=212 genes, p=0.21), but specific genes within copy-number variant regions associated with schizophrenia were relatively enriched in prenatal transcript abundance, and genes associated with schizophrenia by meta-analysis were functionally enriched for some neurodevelopmental processes. In contrast, genes associated with neurodegenerative disorders were significantly underexpressed during fetal life (N=46 genes, p=1.67×10–3).ConclusionsThe authors found evidence for relative prenatal enrichment of putative susceptibility genes for syndromic neurodevelopmental disorders, intellectual disability, and autism spectrum disorder. Future transcriptome-level association studies should evaluate regions other than the dorsolateral prefrontal cortex, at other time points, and incorporate further RNA sequencing analyses.
Epigenetic mechanisms such as altered DNA methylation have been suggested to play a role in autism, beginning with the classical association of Prader-Willi syndrome, an imprinting disorder, with ...autistic features.
Here we tested for the relationship of paternal sperm DNA methylation with autism risk in offspring, examining an enriched-risk cohort of fathers of autistic children.
We examined genome-wide DNA methylation (DNAm) in paternal semen biosamples obtained from an autism spectrum disorder (ASD) enriched-risk pregnancy cohort, the Early Autism Risk Longitudinal Investigation (EARLI) cohort, to estimate associations between sperm DNAm and prospective ASD development, using a 12-month ASD symptoms assessment, the Autism Observation Scale for Infants (AOSI). We analysed methylation data from 44 sperm samples run on the CHARM 3.0 array, which contains over 4 million probes (over 7 million CpG sites), including 30 samples also run on the Illumina Infinium HumanMethylation450 (450K) BeadChip platform (∼485 000 CpG sites). We also examined associated regions in an independent sample of post-mortem human brain ASD and control samples for which Illumina 450K DNA methylation data were available.
Using region-based statistical approaches, we identified 193 differentially methylated regions (DMRs) in paternal sperm with a family-wise empirical P-value family-wise error rate (FWER) <0.05 associated with performance on the Autism Observational Scale for Infants (AOSI) at 12 months of age in offspring. The DMRs clustered near genes involved in developmental processes, including many genes in the SNORD family, within the Prader-Willi syndrome gene cluster. These results were consistent among the 75 probes on the Illumina 450K array that cover AOSI-associated DMRs from CHARM. Further, 18 of 75 (24%) 450K array probes showed consistent differences in the cerebellums of autistic individuals compared with controls.
These data suggest that epigenetic differences in paternal sperm may contribute to autism risk in offspring, and provide evidence that directionally consistent, potentially related epigenetic mechanisms may be operating in the cerebellum of individuals with autism.
MicroRNAs (miRNAs) are small non-coding RNAs (sncRNAs) that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets which results in inhibition of ...translation and typically destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA-target interactions by crosslinking and ligation of sncRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high throughput sequencing of the chimeric sncRNA:target RNAs. Despite the strength of these direct profiling approaches, standardized pipelines for effectively analyzing the resulting chimeric sncRNA:target RNA sequencing data are not readily available. Here we present SCRAP, a robust Small Chimeric RNA Analysis Pipeline for the bioinformatic processing of chimeric sncRNA:target RNA sequencing data. SCRAP consists of two parts, each of which are specifically optimized for the distinctive characteristics of chimeric small RNA sequencing reads: first, read processing and alignment and second, peak calling and annotation. We apply SCRAP to benchmark chimeric sncRNA:target RNA sequencing datasets generated by distinct molecular approaches, and compare SCRAP to existing chimeric RNA analysis pipelines. SCRAP has minimal hardware requirements, is cross-platform, and contains extensive annotation to broaden accessibility for processing small chimeric RNA sequencing data and enable insights about the targets of small non-coding RNAs in regulating diverse biological systems.
Genome-wide association studies (GWAS) have identified many genomic loci associated with risk for schizophrenia, but unambiguous identification of the relationship between disease-associated variants ...and specific genes, and in particular their effect on risk conferring transcripts, has proven difficult. To better understand the specific molecular mechanism(s) at the schizophrenia locus in 11q25, we undertook cis expression quantitative trait loci (cis-eQTL) mapping for this 2 megabase genomic region using postmortem human brain samples. To comprehensively assess the effects of genetic risk upon local expression, we evaluated multiple transcript features: genes, exons, and exon-exon junctions in multiple brain regions-dorsolateral prefrontal cortex (DLPFC), hippocampus, and caudate. Genetic risk variants strongly associated with expression of SNX19 transcript features that tag multiple rare classes of SNX19 transcripts, whereas they only weakly affected expression of an exon-exon junction that tags the majority of abundant transcripts. The most prominent class of SNX19 risk-associated transcripts is predicted to be overexpressed, defined by an exon-exon splice junction between exons 8 and 10 (junc8.10) and that is predicted to encode proteins that lack the characteristic nexin C terminal domain. Risk alleles were also associated with either increased or decreased expression of multiple additional classes of transcripts. With RACE, molecular cloning, and long read sequencing, we found a number of novel SNX19 transcripts that further define the set of potential etiological transcripts. We explored epigenetic regulation of SNX19 expression and found that DNA methylation at CpG sites near the primary transcription start site and within exon 2 partially mediate the effects of risk variants on risk-associated expression. ATAC sequencing revealed that some of the most strongly risk-associated SNPs are located within a region of open chromatin, suggesting a nearby regulatory element is involved. These findings indicate a potentially complex molecular etiology, in which risk alleles for schizophrenia generate epigenetic alterations and dysregulation of multiple classes of SNX19 transcripts.
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