Shannon Ellis

Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism.

Recent studies of genomic variation associated with autism have suggested the existence of extreme heterogeneity. Large-scale transcriptomics should complement these results to identify core molecular pathways underlying autism. Here we report results from a large-scale RNA sequencing effort, utilizing region-matched autism and control brains to identify neuronal and microglial genes robustly dysregulated in autism cortical brain. Remarkably, we note that a gene expression module corresponding to M2-activation states in microglia is negatively correlated with a differentially expressed neuronal module, implicating dysregulated microglial responses in concert with altered neuronal activity-dependent genes in autism brains. These observations provide pathways and candidate genes that highlight the interplay between innate immunity and neuronal activity in the aetiology of autism.

Reproducible RNA-seq analysis using recount2

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Transcriptome analysis of cortical tissue reveals shared sets of downregulated genes in autism and schizophrenia

Autism (AUT), schizophrenia (SCZ) and bipolar disorder (BPD) are three highly heritable neuropsychiatric conditions. Clinical similarities and genetic overlap between the three disorders have been reported; however, the causes and the downstream effects of this overlap remain elusive. By analyzing transcriptomic RNA-sequencing data generated from post-mortem cortical brain tissues from AUT, SCZ, BPD and control subjects, we have begun to characterize the extent of gene expression overlap between these disorders. We report that the AUT and SCZ transcriptomes are significantly correlated (P<0.001), whereas the other two cross-disorder comparisons (AUT-BPD and SCZ-BPD) are not. Among AUT and SCZ, we find that the genes differentially expressed across disorders are involved in neurotransmission and synapse regulation. Despite the lack of global transcriptomic overlap across all three disorders, we highlight two genes, IQSEC3 and COPS7A, which are significantly downregulated compared with controls across all three disorders, suggesting either shared etiology or compensatory changes across these neuropsychiatric conditions. Finally, we tested for enrichment of genes differentially expressed across disorders in genetic association signals in AUT, SCZ or BPD, reporting lack of signal in any of the previously published genome-wide association study (GWAS). Together, these studies highlight the importance of examining gene expression from the primary tissue involved in neuropsychiatric conditions-the cortical brain. We identify a shared role for altered neurotransmission and synapse regulation in AUT and SCZ, in addition to two genes that may more generally contribute to neurodevelopmental and neuropsychiatric conditions.

RNA-Seq optimization with eQTL gold standards

BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.ResultsTo address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.ConclusionAs each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one’s data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments.

Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder

Integration of emerging epigenetic information with autism spectrum disorder (ASD) genetic results may elucidate functional insights not possible via either type of information in isolation. Here we use the genotype and DNA methylation (DNAm) data from cord blood and peripheral blood to identify SNPs associated with DNA methylation (meQTL lists). Additionally, we use publicly available fetal brain and lung meQTL lists to assess enrichment of ASD GWAS results for tissue-specific meQTLs. ASD-associated SNPs are enriched for fetal brain (OR = 3.55; P < 0.001) and peripheral blood meQTLs (OR = 1.58; P < 0.001). The CpG targets of ASD meQTLs across cord, blood, and brain tissues are enriched for immune-related pathways, consistent with other expression and DNAm results in ASD, and reveal pathways not implicated by genetic findings. This joint analysis of genotype and DNAm demonstrates the potential of both brain and blood-based DNAm for insights into ASD and psychiatric phenotypes more broadly.“There have been a number of recent epigenetic studies on autism spectrum disorder. Here, the authors integrate genetic and epigenetic data from cord and peripheral blood and also from brain tissues to show the potential of blood-based epigenetic data to provide insights into psychiatric disorders.”

Exaggerated CpH methylation in the autism-affected brain

BackgroundThe etiology of autism, a complex, heritable, neurodevelopmental disorder, remains largely unexplained. Given the unexplained risk and recent evidence supporting a role for epigenetic mechanisms in the development of autism, we explored the role of CpG and CpH (H = A, C, or T) methylation within the autism-affected cortical brain tissue.MethodsReduced representation bisulfite sequencing (RRBS) was completed, and analysis was carried out in 63 post-mortem cortical brain samples (Brodmann area 19) from 29 autism-affected and 34 control individuals. Analyses to identify single sites that were differentially methylated and to identify any global methylation alterations at either CpG or CpH sites throughout the genome were carried out.ResultsWe report that while no individual site or region of methylation was significantly associated with autism after multi-test correction, methylated CpH dinucleotides were markedly enriched in autism-affected brains (~2-fold enrichment at p < 0.05 cutoff, p = 0.002).ConclusionsThese results further implicate epigenetic alterations in pathobiological mechanisms that underlie autism.

Improving the value of public RNA-seq expression data by phenotype prediction

Abstract Publicly available genomic data are a valuable resource for studying normal human variation and disease, but these data are often not well labeled or annotated. The lack of phenotype information for public genomic data severely limits their utility for addressing targeted biological questions. We develop an in silico phenotyping approach for predicting critical missing annotation directly from genomic measurements using well-annotated genomic and phenotypic data produced by consortia like TCGA and GTEx as training data. We apply in silico phenotyping to a set of 70 000 RNA-seq samples we recently processed on a common pipeline as part of the recount2 project. We use gene expression data to build and evaluate predictors for both biological phenotypes (sex, tissue, sample source) and experimental conditions (sequencing strategy). We demonstrate how these predictions can be used to study cross-sample properties of public genomic data, select genomic projects with specific characteristics, and perform downstream analyses using predicted phenotypes. The methods to perform phenotype prediction are available in the phenopredict R package and the predictions for recount2 are available from the recount R package. With data and phenotype information available for 70,000 human samples, expression data is available for use on a scale that was not previously feasible.

How to Share Data for Collaboration

ABSTRACT Within the statistics community, a number of guiding principles for sharing data have emerged; however, these principles are not always made clear to collaborators generating the data. To bridge this divide, we have established a set of guidelines for sharing data. In these, we highlight the need to provide raw data to the statistician, the importance of consistent formatting, and the necessity of including all essential experimental information and pre-processing steps carried out to the statistician. With these guidelines we hope to avoid errors and delays in data analysis.

recount: A large-scale resource of analysis-ready RNA-seq expression data

recount is a resource of processed and summarized expression data spanning nearly 60,000 human RNA-seq samples from the Sequence Read Archive (SRA). The associated recount Bio-conductor package provides a convenient API for querying, downloading, and analyzing the data. Each processed study consists of meta/phenotype data, the expression levels of genes and their underlying exons and splice junctions, and corresponding genomic annotation. We also provide data summarization types for quantifying novel transcribed sequence including base-resolution coverage and potentially unannotated splice junctions. We present workflows illustrating how to use recount to perform differential expression analysis including meta-analysis, annotation-free base-level analysis, and replication of smaller studies using data from larger studies. recount provides a valuable and user-friendly resource of processed RNA-seq datasets to draw additional biological insights from existing public data. The resource is available at https://jhubiostatistics.shinyapps.io/recount/.

Hong Kong stock exchange opens to biotechs

VOLUME 36 NUMBER 6 JUNE 2018 NATURE BIOTECHNOLOGY tal health tools have been published—a quarter of those in 2017 alone, according to IQVIA. Novartis’ interest in digital therapeutics was piqued when its leaders noticed efficacy papers being published “left and right,” says Joris Van Dam, executive director of digital therapeutics at Novartis Institutes for BioMedical Research. Although, the Basel-based company already had programs such as Elevate MS, which collects sensor-based data on people with MS, and a collaboration with Propeller Health to develop a sensor to track usage of Novartis’ Breezhaler inhaler for chronic obstructive pulmonary disease. In the partnership, Novartis and Pear will further develop THRIVE for schizophrenia, and also design and develop the software to treat mental health symptoms related to MS, such as depression, anxiety and cognitive impairment. In Pear’s collaboration with Sandoz, a division of Novartis in Holzkirchen, Germany, the companies aim to commercialize reSET and, if approved, reSET-O. Elsewhere, Roche’s French pharma unit in March announced the expansion of a partnership with Voluntis, both in Paris, to develop a digital therapeutic that makes personalized recommendations to people with cancer to help them manage symptoms. Voluntis’ long-running collaboration with Paris-based Sanofi has boosted its development of software for diabetes management. Such partnerships will help software startups break into the therapeutics market, says St. Claire. “Something digital health companies are really struggling with is getting market traction,” she says. “The physician market is super-fragmented and difficult to tackle. If you want to [market a prescription-only app], I think the most successful route is going to be through a pharma company.” But Akili’s Martucci says he’s not ruling out the go-it-alone approach. “We don’t know what the full commercialization models for digital therapeutics will be,” he says. “I think it would be a mistake to hinge that entirely on pharma.” Akili partnered early, in 2014, with Pfizer, to test its video game platform as a potential biomarker in early Alzheimer’s disease. Something pharma companies want to see in a medical software developer is a clear plan for regulatory approval. Novartis chose to partner with Pear partly because “they had FDA approval squarely in their path,” says van Dam at Novartis. “It’s much more of a natural fit to our current business” to work on a product that will be vetted by regulatory agencies, rather than wellness apps that don’t require such reviews, he says. Right now, that pathway in the US looks promising. The FDA has created what it calls the “pre-cert” program, in which digital health companies can get pre-certified by the agency. Once they have, their software products go through a streamlined review, or no review, depending on the risk of the product. The FDA selected nine companies, including Pear, to participate in a pre-cert pilot program. Companies will be examined using several metrics, including cyber security, user safety, customer service, product quality and clinical responsibility, says Caccomo. The agency plans to announce the expansion of the pre-cert program by the end of 2018, she says. “What we’re trying to do is something similar to a TSA precheck,” says Stephanie Caccomo, a spokesperson for the agency, alluding to the US government’s expedited security screening system for low-risk travelers. “The FDA recognizes that our traditional regulatory paradigm for traditional hardware medical devices doesn’t necessarily correlate with the design, development, and execution phases that software developers use,” she says. Medical app developers say the program will allow them to be nimble, continually improving their software, without having to stop and wait for the FDA. “In the software world, changes are critical to staying relevant, because data is so much faster than traditional modalities,” says Jo Masterson, COO at 2Morrow, a smoking cessation and behavioral app developer. “For a software company, one of the things that is initially really intimidating about the FDA is not so much the clinical trials and data but the fact that changes are hard to make,” she says. Of course, if digital therapeutics makers successfully ingratiate themselves into the pharma world, complete with FDA approvals and prescription-only marketing, they could, in some respects, lose one of the hallmarks of the digital world: accessibility. “I’d hate to see everything go prescription,” says Masterson. “The real promise of digital health is to remove some of the barriers to health care,” she says. If app developers all make their products prescription-only, “you have to have health care before you can get health care.” Emily Waltz Nashville, Tennessee Hong Kong stock exchange opens to biotechs