Biology subjects

Yoav Gilad

Publications and source records attributed to Yoav Gilad.

14 recordsLinked to original sources

bioRxiv2016

Batch effects and the effective design of single-cell gene expression studies

Single cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.

Genomics↗

bioRxiv2016

Host genetic variation in mucosal immunity pathways influences the upper airway microbiome

The degree to which host genetic variation can modulate microbial communities in humans remains an open question. Here we performed a genetic mapping study of the microbiome in two accessible upper airway sites, the nasopharynx and the nasal vestibule, during two seasons in 144 adult members of a founder population of European decent. We estimated the relative abundances (RAs) of genus level bacteria from 16S rRNA gene sequences and examined associations with 148,653 genetic variants (linkage disequilibrium [LD] r2 < 0.5) selected from among all common variants discovered in genome sequences in this population. We identified 37 microbiome quantitative trait loci (mbQTLs) that showed evidence of association with the RAs of 22 genera (q < 0.05), and were enriched for genes in mucosal immunity pathways. The most significant association was between the RA of Dermacoccus (phylum Actinobacteria) and a variant 8kb upstream of TINCR (rs117042385; p = 1.61[x]10-8; q = 0.002), a long non-coding RNA that binds to peptidoglycan recognition protein 3 (PGLYRP3) mRNA, a gene encoding a known antimicrobial protein. A second association was between a missense variant in PGLYRP4 (rs3006458) and the RA of an unclassified genus of family Micrococcaceae (phylum Actinobacteria) (p = 5.10[x]10-7; q = 0.032). Our findings provide evidence of host genetic influences on upper airway microbial composition in humans, and implicate mucosal immunity genes in this relationship.

Genetics↗

bioRxiv2015

Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling

Accurate annotation of protein coding regions is essential for understanding how genetic information is translated into biological functions. Here we describe riboHMM, a new method that uses ribosome footprint data along with gene expression and sequence information to accurately infer translated sequences. We applied our method to human lymphoblastoid cell lines and identified 7,273 previously unannotated coding sequences, including 2,442 translated upstream open reading frames. We observed an enrichment of harringtonine-treated ribosome footprints at the inferred initiation sites, validating many of the novel coding sequences. The novel sequences exhibit significant signatures of selective constraint in the reading frames of the inferred proteins, suggesting that many of these are functional. Nearly 40% of bicistronic transcripts showed significant negative correlation in the levels of translation of their two coding sequences, suggesting a key regulatory role for these novel translated sequences. Our work significantly expands the set of known coding regions in humans.

Genomics↗

bioRxiv2015

Mycobacterial infection induces a specific human innate immune response

The innate immune system provides the first response to pathogen infection and orchestrates the activation of the adaptive immune system. Though a large component of the innate immune response is common to all infections, pathogen-specific responses have been documented as well. The innate immune response is thought to be especially critical for fighting infection with Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis (TB). While TB can be deadly, only 5-10% of individuals infected with MTB develop active disease. The risk for disease susceptibility is, at least partly, heritable. Studies of inter-individual variation in the innate immune response to MTB infection may therefore shed light on the genetic basis for variation in susceptibility to TB. Yet, to date, we still do not know which properties of the innate immune response are specific to MTB infection and which represent a general response to pathogen infection. To begin addressing this gap, we infected macrophages with eight different bacteria, including different MTB strains and related mycobacteria, and studied the transcriptional response to infection. Although the ensued gene regulatory responses were largely consistent across the bacterial infection treatments, we were able to identify a novel subset of genes whose regulation was affected specifically by infection with mycobacteria. Genetic variants that are associated with regulatory differences in these genes should be considered candidate loci for explaining inter-individual susceptibility TB.\n\nAuthor SummaryTuberculosis (TB) is a deadly disease responsible for millions of deaths annually. It is caused by infection with Mycobacterium tuberculosis (MTB), an ancient human pathogen. Approximately a third of the worlds population is infected with MTB, yet only an estimated 5-10% of individuals will develop an active form of the disease. While this variation in TB susceptibility has been demonstrated to be heritable, we still know little about its underlying genetic basis. The genetic variation that affects TB susceptibility likely involves the innate immune system, which is our first line of defense against invading pathogens, because infection with MTB does not prevent future infections. However, we do not fully understand how the innate immune system differs in its response to MTB versus other bacteria. To investigate this further, we infected macrophages with MTB, related mycobacteria, and other bacteria, and measured how their gene expression levels changed in response. We identified a subset of genes that respond preferentially to infection with mycobacterial species. These genes provide insight into the interactions between MTB and the innate immune system and are candidate loci for explaining inter-individual susceptibility to TB.

Genomics↗

bioRxiv2015

Bacterial Infection Remodels the DNA Methylation Landscape of Human Dendritic Cells

DNA methylation is thought to be robust to environmental perturbations on a short time scale. Here, we challenge that view by demonstrating that the infection of human dendritic cells (DCs) with a pathogenic bacteria is associated with rapid changes in methylation at thousands of loci. Infection-induced changes in methylation occur primarily at distal enhancer elements, including those associated with the activation of key immune-transcription factors and genes involved in the crosstalk between DCs and adaptive immunity. Active demethylation is associated with extensive epigenetic remodeling and is strongly predictive of changes in the expression levels of nearby genes. Collectively, our observations show that rapid changes in methylation play a previously unappreciated role in regulating the transcriptional response of DCs to infection.

Genomics↗

bioRxiv2015

Genetic Variation, Not Cell Type of Origin, Underlies Regulatory Differences in iPSCs

The advent of induced pluripotent stem cells (iPSCs)1 revolutionized Human Genetics by allowing us to generate pluripotent cells from easily accessible somatic tissues. This technology can have immense implications for regenerative medicine, but iPSCs also represent a paradigm shift in the study of complex human phenotypes, including gene regulation and disease2-5. Yet, an unresolved caveat of the iPSC model system is the extent to which reprogrammed iPSCs retain residual phenotypes from their precursor somatic cells. To directly address this issue, we used an effective study design to compare regulatory phenotypes between iPSCs derived from two types of commonly used somatic precursor cells. We show that the cell type of origin only minimally affects gene expression levels and DNA methylation in iPSCs. Instead, genetic variation is the main driver of regulatory differences between iPSCs of different donors.

Genetics↗

bioRxiv2015

Reprogramming LCLs to iPSCs Results in Recovery of Donor-Specific Gene Expression Signature

Renewable in vitro cell cultures, such as lymphoblastoid cell lines (LCLs), have facilitated studies that contributed to our understanding of genetic influence on human traits. However, the degree to which cell lines faithfully maintain differences in donor-specific phenotypes is still debated. We have previously reported that standard cell line maintenance practice results in a loss of donor-specific gene expression signatures in LCLs. An alternative to the LCL model is the induced pluripotent stem cell (iPSC) system, which carries the potential to model tissue-specific physiology through the use of differentiation protocols. Still, existing LCL banks represent an important source of starting material for iPSC generation, and it is possible that the disruptions in gene regulation associated with long-term LCL maintenance could persist through the reprogramming process. To address this concern, we studied the effect of reprogramming mature LCLs to iPSCs on the ensuing gene expression patterns within and between six unrelated donor individuals. We show that the reprogramming process results in a recovery of donor-specific gene regulatory signatures. Since environmental contributions are unlikely to be a source of individual variation in our system of highly passaged cultured cell lines, our observations suggest that the effect of genotype on gene regulation is more pronounced in the iPSCs than in the LCL precursors. Our findings indicate that iPSCs can be a powerful model system for studies of phenotypic variation across individuals in general, and the genetic association with variation in gene regulation in particular. We further conclude that LCLs are an appropriate starting material for iPSC generation.

Genomics↗

bioRxiv2014

msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding

MotivationUnderstanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework underestimates the substantial variation in the DNase I cleavage profiles across factor-bound genomic locations and across replicate measurements of chromatin accessibility.\n\nResultsIn this work, we adapt a multi-scale modeling framework for inhomogeneous Poisson processes to better model the underlying variation in DNase I cleavage patterns across genomic locations bound by a transcription factor. In addition to modeling variation, we also model spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-Seq peaks for those factors. Finally, we propose an extension to this framework that allows for a more flexible background model and evaluate the additional gain in accuracy achieved when the background model parameters are estimated using DNase-seq data from naked DNA. The proposed model can also be applied to paired-end ATAC-seq and DNase-seq data in a straightforward manner.\n\nAvailabilitymsCentipede, a Python implementation of an algorithm to infer transcription factor binding using this model, is made available at https://github.com/rajanil/msCentipede

Genomics↗

bioRxiv2014

WASP: allele-specific software for robust discovery of molecular quantitative trait loci

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that our approach has a low error rate and is far more powerful than existing QTL mapping approaches.

Genomics↗

bioRxiv2014

Generation of a Panel of Induced Pluripotent Stem Cells From Chimpanzees: a Resource for Comparative Functional Genomics

Comparative genomics studies in primates are extremely restricted because we only have access to a few types of cell lines from non-human apes and to a limited collection of frozen tissues. In order to gain better insight into regulatory processes that underlie variation in complex phenotypes, we must have access to faithful model systems for a wide range of tissues and cell types. To facilitate this, we have generated a panel of 7 fully characterized chimpanzee (Pan troglodytes) induced pluripotent stem cell (iPSC) lines derived from fibroblasts of healthy donors. All lines appear to be free of integration from exogenous reprogramming vectors, can be maintained using standard iPSC culture techniques, and have proliferative and differentiation potential similar to human and mouse lines. To begin demonstrating the utility of comparative iPSC panels, we collected RNA sequencing data and methylation profiles from the chimpanzee iPSCs and their corresponding fibroblast precursors, as well as from 7 human iPSCs and their precursors, which were of multiple cell type and population origins. Overall, we observed much less regulatory variation within species in the iPSCs than in the somatic precursors, indicating that the reprogramming process has erased many of the differences observed between somatic cells of different origins. We identified 4,918 differentially expressed genes and 3,598 differentially methylated regions between iPSCs of the two species, many of which are novel inter-species differences that were not observed between the somatic cells of the two species. Our panel will help realise the potential of iPSCs in primate studies, and in combination with genomic technologies, transform studies of comparative evolution.

Evolutionary Biology↗

bioRxiv2014

The Genetic Architecture of Gene Expression Levels in Wild Baboons

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.

Genomics↗

bioRxiv2014

Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels.

DNA methylation is an important epigenetic regulator of gene expression. Recent studies have revealed widespread associations between genetic variation and methylation levels. However, the mechanistic links between genetic variation and methylation remain unclear. To begin addressing this gap, we collected methylation data at [~]300,000 loci in lymphoblastoid cell lines (LCLs) from 64 HapMap Yoruba individuals, and genome-wide bisulfite sequence data in ten of these individuals. We identified (at an FDR of 10%) 11,752 methylation QTLs (meQTLs)--i.e., loci in which genetic variation is associated with changes in DNA methylation. We found that meQTLs are frequently associated with changes in methylation at multiple CpGs across regions of up to 3 kb. Interestingly, meQTLs are also frequently associated with variation in other properties of gene regulation, including histone modifications, DNase I accessibility, chromatin accessibility, and expression levels of nearby genes. These observations suggest that genetic variants may lead to coordinated molecular changes in all of these regulatory phenotypes. One plausible driver of coordinated changes in different regulatory mechanisms is variation in transcription factor (TF) binding. Indeed, we found that SNPs that change predicted TF binding affinities are significantly enriched for associations with DNA methylation at nearby CpGs. Taken together, our observations are consistent with a model whereby changes in TF binding may frequently drive coordinated changes in DNA methylation, histone modification, and gene expression levels.\n\nAuthor SummaryDNA methylation is an important epigenetic mark that contributes to many biological processes including the regulation of gene expression. Genetic variation has been associated with quantitative changes in DNA methylation (meQTLs). We identified thousands of meQTLs using an assay that allowed us to measure methylation levels at around 300 thousand cytosines. We found that meQTLs are enriched with loci that is also associated with quantitative changes in gene expression, DNase I hypersensitivity, PolII occupancy, and a number of histone marks. This suggests that many molecular events are likely regulated in concert. Finally, we found that changes in transcription factor binding as well as transcription factor abundance are associated with changes in DNA methylation near transcription factor binding sites. Our results indicate that a subset of transcription factors actively regulate DNA methylation levels near their binding sites. This work contributes to our understanding of the regulation of DNA methylation in the larger context of gene regulatory landscape.

Genomics↗

bioRxiv2014

Epigenetic Modifications are Associated with Inter-species Gene Expression Variation in Primates

Changes in gene regulation level have long been thought to play an important role in evolution and speciation, especially in primates. Over the past decade, comparative genomic studies have revealed extensive inter-species differences in gene expression levels yet we know much less about the extent to which regulatory mechanisms differ between species. To begin addressing this gap, we performed a comparative epigenetic study in primate lymphoblastoid cell lines (LCLs), to query the contribution of RNA polymerase II (Pol II) and four histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) to inter-species variation in gene expression levels. We found that inter-species differences in mark enrichment near transcription start sites are significantly more often associated with inter-species differences in the corresponding gene expression level than expected by chance alone. Interestingly, we also found that first-order interactions among the histone marks and Pol II do not markedly contribute to the degree of association between the marks and inter-species variation in gene expression levels, suggesting that the marginal effects of the five marks dominate this contribution.

Genomics↗

bioRxiv2014

Impact of RNA degradation on measurements of gene expression

The use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be normalized, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. This concern has rendered the use of low quality RNA samples in whole-genome expression profiling problematic. Yet, low quality samples are at times the sole means of addressing specific questions - e.g., samples collected in the course of fieldwork. We sought to quantify the impact of variation in RNA quality on estimates of gene expression levels based on RNA-seq data. To do so, we collected expression data from tissue samples that were allowed to decay for varying amounts of time prior to RNA extraction. The RNA samples we collected spanned the entire range of RNA Integrity Number (RIN) values (a quality metric commonly used to assess RNA quality). We observed widespread effects of RNA quality on measurements of gene expression levels, as well as a slight but significant loss of library complexity in more degraded samples. While standard normalizations failed to account for the effects of degradation, we found that a simple linear model that controls for the effects of RIN can correct for the majority of these effects. We conclude that in instances where RIN and the effect of interest are not associated, this approach can help recover biologically meaningful signals in data from degraded RNA samples.

Genomics↗