Biology subjects

Collado-Torres, L.

Publications and source records attributed to Collado-Torres, L..

47 records · Page 3Linked to original sources

bioRxiv2019

recount-brain: a curated repository of human brain RNA-seq datasets metadata

The usability of publicly-available gene expression data is often limited by the availability of high-quality, standardized biological phenotype and experimental condition information (\"metadata\"). We released the recount2 project, which involved re-processing [~]70,000 samples in the Sequencing Read Archive (SRA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) projects. While samples from the latter two projects are well-characterized with extensive metadata, the [~]50,000 RNA-seq samples from SRA in recount2 are inconsistently annotated with metadata. Tissue type, sex, and library type can be estimated from the RNA sequencing (RNA-seq) data itself. However, more detailed and harder to predict metadata, like age and diagnosis, must ideally be provided by labs that deposit the data.\n\nTo facilitate more analyses within human brain tissue data, we have complemented phenotype predictions by manually constructing a uniformly-curated database of public RNA-seq samples present in SRA and recount2. We describe the reproducible curation process for constructing recount-brain that involves systematic review of the primary manuscript, which can serve as a guide to annotate other studies and tissues. We further expanded recount-brain by merging it with GTEx and TCGA brain samples as well as linking to controlled vocabulary terms for tissue, Brodmann area and disease. Furthermore, we illustrate how to integrate the sample metadata in recount-brain with the gene expression data in recount2 to perform differential expression analysis. We then provide three analysis examples involving modeling postmortem interval, glioblastoma, and meta-analyses across GTEx and TCGA. Overall, recount-brain facilitates expression analyses and improves their reproducibility as individual researchers do not have to manually curate the sample metadata. recount-brain is available via the add_metadata() function from the recount Bioconductor package at bioconductor.org/packages/recount.

genomics↗

bioRxiv2019

Regulatory sites for known and novel splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants that affect neurological and psychiatric disease risk. Given that many causal variants are likely to operate by regulating gene expression, an improved understanding of the genetic control of gene expression in human brain is vital. However, the difficulties of sampling human brain, and its complexity, has meant that brain-related expression quantitative trait loci (eQTL) and allele specific expression (ASE) signals have been more limited in their explanatory power than might otherwise be expected. To address this, we use paired genomic and transcriptomic data from putamen and substantia nigra dissected from 117 brains, combined with a comprehensive set of analyses, to interrogate regulation at different stages of RNA processing and uncover novel transcripts. We identify disease-relevant regulatory loci and reveal the types of analyses and regulatory positions yielding the most disease-specific information. We find that splicing eQTLs are enriched for neuron-specific regulatory information; that ASE analyses provide highly cell-specific regulatory information; and that incomplete annotation of the brain transcriptome limits the interpretation of risk loci for neuropsychiatric disease. We release this rich resource of regulatory data through a searchable webserver, http://braineacv2.inf.um.es/.

bioinformatics↗

bioRxiv2018

Incomplete annotation of OMIM genes is likely to be limiting the diagnostic yield of genetic testing, particularly for neurogenetic disorders

Although the increasing use of whole-exome and whole-genome sequencing have improved the yield of genetic testing for Mendelian disorders, an estimated 50% of patients still leave the clinic without a genetic diagnosis. This can be attributed in part to our lack of ability to accurately interpret the genetic variation detected through next-generation sequencing. Variant interpretation is fundamentally reliant on accurate and complete gene annotation, however numerous reports and discrepancies between gene annotation databases reveals that the knowledge of gene annotation remains far from comprehensive. Here, we detect and validate transcription in an annotation-agnostic manner across all 41 different GTEx tissues, then connect novel transcription to known genes, ultimately improving the annotation of 63% of the known OMIM-morbid genes. We find the majority of novel transcription to be tissue-specific in origin, with brain tissues being most susceptible to misannotation. Furthermore, we find that novel transcribed regions tend to be poorly conserved, but are significantly depleted for genetic variation within humans, suggesting they are functionally significant and potentially have human-specific functions. We present our findings through an online platform vizER, which enables individual genes to be visualised and queried for evidence of misannotation. We also release all tissue-specific transcriptomes in a BED format for ease of integration with whole-genome sequencing data. We anticipate that these resources will improve the diagnostic yield for a wide range of Mendelian disorders.

bioinformatics↗

bioRxiv2018

Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

High-throughput sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of the biases associated with small RNA sequencing have focused on adapter ligation bias with limited evaluation of reverse transcription or amplification biases. Furthermore, evaluations of the accuracy of quantifications of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited and no study has yet evaluated differences in the quantification of isomiRs of altered length. In addition, no studies have yet compared the consistency of results derived from multiple moderate starting inputs. We therefore evaluated quantifications of miRNA and isomiRs using four library preparation kits, with various starting amounts, as well as quantifications following removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases. All methods resulted in false isomiR detection; however, the adapter-free method tested was especially prone to false isomiR detection. We demonstrate that using UMIs improves accuracy and we provide a guide for input amounts to improve consistency. Our data show differences and limitations of current methods, thus raising concerns about the validity of quantification of miRNA and isomiRs across studies. We advocate for the use of UMIs to improve accuracy and reliability of miRNA quantifications.

genomics↗

bioRxiv2018

Divergent neuronal DNA methylation patterns across human cortical development: Critical periods and a unique role of CpH methylation

We have characterized the landscape of DNA methylation (DNAm) across the first two decades of human neocortical development in NeuN+ neurons using whole-genome bisulfite sequencing and compared them to non-neurons (primarily glia) and prenatal homogenate cortex. We show that DNAm changes more dramatically during the first five years of postnatal life than during the entire remaining period. We further refined global patterns of increasingly divergent neuronal CpG and CpH methylation (mCpG and mCpH) into six developmental trajectories and found that in contrast to genome-wide patterns, neighboring mCpG and mCpH levels within these regions were highly correlated. We then integrated paired RNA-seq data and identified direct regulation of hundreds of transcripts and their splicing events exclusively by mCpH levels, independently from mCpG levels, across this period. We finally explored the relationship between DNAm patterns and development of brain-related phenotypes and found enriched heritability for many phenotypes within DNAm features we identify.

neuroscience↗

bioRxiv2018

Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease

BackgroundLate-onset Alzheimers disease (AD) is a complex age-related neurodegenerative disorder that likely involves epigenetic factors. To better understand the epigenetic state associated with AD represented as variation in DNA methylation (DNAm), we surveyed 420,852 DNAm sites from neurotypical controls (N=49) and late-onset AD patients (N=24) across four brain regions (hippocampus, entorhinal cortex, dorsolateral prefrontal cortex and cerebellum).\n\nResultsWe identified 858 sites with robust differential methylation, collectively annotated to 772 possible genes (FDR<5%, within 10kb). These sites were overrepresented in AD genetic risk loci (p=0.00655), and nearby genes were enriched for processes related to cell-adhesion, immunity, and calcium homeostasis (FDR<5%). We analyzed corresponding RNA-seq data to prioritize 130 genes within 10kb of the differentially methylated sites, which were differentially expressed and had expression levels associated with nearby DNAm levels (p<0.05). This validated gene set includes previously reported (e.g. ANK1, DUSP22) and novel genes involved in Alzheimers disease, such as ANKRD30B.\n\nConclusionsThese results highlight DNAm changes in Alzheimers disease that have gene expression correlates, implicating DNAm as an epigenetic mechanism underlying pathological molecular changes associated with AD. Furthermore, our framework illustrates the value of integrating epigenetic and transcriptomic data for understanding complex disease.

genomics↗

bioRxiv2018

Regional heterogeneity in gene expression, regulation and coherence in hippocampus and dorsolateral prefrontal cortex across development and in schizophrenia

Recent large-scale genomics efforts have better characterized the molecular correlates of schizophrenia in postmortem human neocortex, but not hippocampus which is a brain region prominently implicated in its pathogenesis. Here in the second phase of the BrainSeq Consortium (Phase II), we have generated RiboZero RNA-seq data for 900 samples across both the dorsolateral prefrontal cortex (DLPFC) and the hippocampus (HIPPO) for 551 individuals (286 affected by schizophrenia disorder: SCZD). We identify substantial regional differences in gene expression, in both pre- and post-natal life, and find widespread differences in how genes are regulated across development. By extending quality surrogate variable analysis (qSVA) to multiple brain regions, we identified 48 and 245 differentially expressed genes (DEG) by SCZD diagnosis (FDR<5%) in HIPPO and DLPFC, respectively, with surprisingly minimal overlap in DEG between the two brain regions. We further identified 205,618 brain region-dependent eQTLs (FDR<1%) and found that 124 GWAS risk loci contain eQTLs in at least one of the regions. We also identify potential molecular correlates of in vivo evidence of altered prefrontal-hippocampal functional coherence in schizophrenia. These results underscore the complexity and regional heterogeneity of the transcriptional correlates of schizophrenia, and suggest future schizophrenia therapeutics may need to target molecular pathologies localized to specific brain regions.

neuroscience↗

bioRxiv2018

Non-coding Class Switch Recombination-related transcription in human normal and pathological immune responses

BackgroundAntibody class switch recombination (CSR) to IgG, IgA or IgE is a hallmark of adaptive immunity, allowing antibody function diversification beyond IgM. CSR involves a deletion of the IgM/IgD constant region genes placing a new acceptor Constant (CH) gene, downstream of the VDJH exon. CSR depends on non-coding (CSRnc) transcription of donor I and acceptor IH exons, located 5 upstream of each CH coding gene. Although our knowledge of the role of CSRnc transcription has advanced greatly, its extension and importance in healthy and diseased humans is scarce.\n\nMethodsWe analyzed CSRnc transcription in 70,603 publicly available RNA-seq samples, including GTEx, TCGA and the Sequence Read Archive (SRA) using recount2, an online resource consisting of normalized RNA-seq gene and exon counts, as well as coverage BigWig files that can be programmatically accessed through R. CSRnc transcription was validated with a qRT-PCR assay for I, I{gamma}3 and I{gamma}1 in humans in response to vaccination.\n\nResultsWe mapped IH transcription for the human IgH locus, including the less understood IGHD gene. CSRnc transcription was restricted to B cells and is widely distributed in normal adult tissues, but predominant in blood, spleen, MALT-containing tissues, visceral adipose tissue and some so-called \"immune privileged\" tissues. However, significant I{gamma}4 expression was found even in non-lymphoid fetal tissues. CSRnc expression in cancer tissues mimicked the expression of their normal counterparts, with notable pattern changes in some common cancer subsets. CSRnc transcription in tumors appears to result from tumor infiltration by B cells, since CSRnc transcription was not detected in corresponding tumor-derived immortal cell lines. Additionally, significantly increased I5 transcription in ileal mucosa in Crohns disease with ulceration was found.\n\nConclusionsCSRnc transcription occurs in multiple anatomical locations beyond classical secondary lymphoid organs, representing a potentially useful marker of effector B cell responses in normal and pathological immune responses. The pattern of IH exon expression may reveal clues of the local immune response (i.e. cytokine milieu) in health and disease. This is a great example of how the public recount2 data can be used to further our understanding of transcription, including regions outside the known transcriptome.

immunology↗

bioRxiv2018

Dissecting transcriptomic signatures of neuronal differentiation and maturation using iPSCs

Human induced pluripotent stem cells (hiPSCs) are a powerful model of neural differentiation and maturation. We present a hiPSC transcriptomics resource on corticogenesis from 5 iPSC donor and 13 subclonal lines across nine time points over 5 broad conditions: self-renewal, early neuronal differentiation, neural precursor cells (NPCs), assembled rosettes, and differentiated neuronal cells that were validated using electrophysiology. We identified widespread changes in the expression of individual transcript features and their splice variants, gene networks, and global patterns of transcription. We next demonstrated that co-culturing human NPCs with rodent astrocytes resulted in mutually synergistic maturation, and that cell type-specific expression data can be extracted using only sequencing read alignments without potentially disruptive cell sorting. We lastly developed and validated a computational tool to estimate the relative neuronal maturity of iPSC-derived neuronal cultures and human brain tissue, which were maturationally heterogeneous but contained subsets of cells most akin to adult human neurons.

genomics↗

bioRxiv2018

RNA-seq transcript quantification from reduced-representation data in recount2

More than 70,000 short-read RNA-sequencing samples are publicly available through the recount2 project, a curated database of summary coverage data. However, no current methods can be directly applied to the reduced-representation information stored in this database to estimate transcript-level abundances. Here we present a linear model taking as input summary coverage of junctions and subdivided exons to output estimated abundances and associated uncertainty. We evaluate the performance of our model on simulated and real data, and provide a procedure to construct confidence intervals for estimates.

genomics↗

bioRxiv2017

Developmental effects of maternal smoking during pregnancy on the human frontal cortex transcriptome

Cigarette smoking during pregnancy is a major public health concern. While there are well-described consequences in early child development, there is very little known about the effects of maternal smoking on human cortical biology during prenatal life. We therefore performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on prenatal (N=33; 16 smoking-exposed) as well as adult (N=207; 57 active smokers) human post-mortem prefrontal cortices. Smoking exposure during the prenatal period was directly associated with differential expression of 14 genes; in contrast, during adulthood, despite a much larger sample size, only 2 genes showed significant differential expression (FDR<10%). Moreover, 1,315 genes showed significantly different exposure effects between maternal smoking during pregnancy and direct exposure in adulthood (FDR<10%) - these differences were largely driven by prenatal differences that were enriched for pathways previously implicated in addiction and synaptic function. Furthermore, prenatal and age-dependent differentially expressed genes were enriched for genes implicated in non-syndromic autism spectrum disorder (ASD) and were differentially expressed as a set between patients with ASD and controls in post-mortem cortical regions. These results underscore the enhanced sensitivity to the biological effect of smoking exposure in the developing brain and offer novel insight into the effects of maternal smoking during pregnancy on the prenatal human brain. They also begin to address the relationship between in utero exposure to smoking and the heightened risks for the subsequent development of neuropsychiatric disorders.\n\nOne Sentence SummaryMaternal smoking during pregnancy alters the expression of genes within the developing human cortex and these changes are enriched for genes implicated in neuropsychiatric disorders.

neuroscience↗