Biology subjects

Kellis, M.

Publications and source records attributed to Kellis, M..

At least 91 records · Page 5Linked to original sources

bioRxiv2021

Single-cell dissection of the human cerebrovasculature in health and disease

Despite the importance of the blood-brain barrier in maintaining normal brain physiology and in understanding neurodegeneration and CNS drug delivery, human cerebrovascular cells remain poorly characterized due to their sparsity and dispersion. Here, we perform the first single-cell characterization of the human cerebrovasculature using both ex vivo fresh-tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples. We capture 31,812 cerebrovascular cells across 17 subtypes, including three distinct subtypes of perivascular fibroblasts as well as vasculature-coupled neurons and glia. We uncover human-specific expression patterns along the arteriovenous axis and determine previously uncharacterized cell type-specific markers. We use our newly discovered human-specific signatures to study changes in 3,945 cerebrovascular cells of Huntingtons disease patients, which reveal an activation of innate immune signaling in vascular and vasculature-coupled cell types and the concomitant reduction to proteins critical for maintenance of BBB integrity. Finally, our study provides a comprehensive resource molecular atlas of the human cerebrovasculature to guide future biological and therapeutic studies.

neuroscience↗

bioRxiv2021

Structural variant selection for high-altitude adaptation using single-molecule long-read sequencing

Structural variants (SVs) can be important drivers of human adaptation with strong effects, but previous studies have focused primarily on common variants with weak effects. Here, we used large-scale single-molecule long-read sequencing of 320 Tibetan and Han samples, to show that SVs are key drivers of selection under high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict driver enhancers, target genes, upstream regulators, and biological functions, which we validate using enhancer reporter and DNA pull-down assays. We reveal diverse Tibetan-specific SVs affecting the cis- and trans-regulatory circuitry of diverse biological functions, including hypoxia response, energy metabolism, lung function, etc. Our study greatly expands the global SV landscape, reveals the central role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles that SVs can play in human biology.

genomics↗

bioRxiv2021

Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders

Thousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.

genomics↗

bioRxiv2020

Sarbecovirus comparative genomics elucidates gene content of SARS-CoV-2 and functional impact of COVID-19 pandemic mutations

Despite its overwhelming clinical importance, the SARS-CoV-2 gene set remains unresolved, hindering dissection of COVID-19 biology. Here, we use comparative genomics to provide a high-confidence protein-coding gene set, characterize protein-level and nucleotide-level evolutionary constraint, and prioritize functional mutations from the ongoing COVID-19 pandemic. We select 44 complete Sarbecovirus genomes at evolutionary distances ideally-suited for protein-coding and non-coding element identification, create whole-genome alignments, and quantify protein-coding evolutionary signatures and overlapping constraint. We find strong protein-coding signatures for all named genes and for 3a, 6, 7a, 7b, 8, 9b, and also ORF3c, a novel alternate-frame gene. By contrast, ORF10, and overlapping-ORFs 9c, 3b, and 3d lack protein-coding signatures or convincing experimental evidence and are not protein-coding. Furthermore, we show no other protein-coding genes remain to be discovered. Cross-strain and within-strain evolutionary pressures largely agree at the gene, amino-acid, and nucleotide levels, with some notable exceptions, including fewer-than-expected mutations in nsp3 and Spike subunit S1, and more-than-expected mutations in Nucleocapsid. The latter also shows a cluster of amino-acid-changing variants in otherwise-conserved residues in a predicted B-cell epitope, which may indicate positive selection for immune avoidance. Several Spike-protein mutations, including D614G, which has been associated with increased transmission, disrupt otherwise-perfectly-conserved amino acids, and could be novel adaptations to human hosts. The resulting high-confidence gene set and evolutionary-history annotations provide valuable resources and insights on COVID-19 biology, mutations, and evolution.

genomics↗

bioRxiv2020

Genus-wide characterization of bumblebee genomes reveals variation associated with key ecological and behavioral traits of pollinators

Bumblebees are a diverse group of globally important pollinators in natural ecosystems and for agricultural food production. With both eusocial and solitary lifecycle phases, and some social parasite species, they are especially interesting models to understand social evolution, behavior, and ecology. Reports of many species in decline point to pathogen transmission, habitat loss, pesticide usage, and global climate change, as interconnected causes. These threats to bumblebee diversity make our reliance on a handful of well-studied species for agricultural pollination particularly precarious. To broadly sample bumblebee genomic and phenotypic diversity, we de novo sequenced and assembled the genomes of 17 species, representing all 15 subgenera, producing the first genus-wide quantification of genetic and genomic variation potentially underlying key ecological and behavioral traits. The species phylogeny resolves subgenera relationships while incomplete lineage sorting likely drives high levels of gene tree discordance. Five chromosome-level assemblies show a stable 18-chromosome karyotype, with major rearrangements creating 25 chromosomes in social parasites. Differential transposable element activity drives changes in genome sizes, with putative domestications of repetitive sequences influencing gene coding and regulatory potential. Dynamically evolving gene families and signatures of positive selection point to genus-wide variation in processes linked to foraging, diet and metabolism, immunity and detoxification, as well as adaptations for life at high altitudes. These high-quality genomic resources capture natural genetic and phenotypic variation across bumblebees, offering new opportunities to advance our understanding of their remarkable ecological success and to identify and manage current and future threats.

genomics↗

bioRxiv2020

High-Throughput 5' UTR Engineering for Enhanced Protein Production in Non-Viral Gene Therapies

Despite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major challenge. One approach to increase protein expression is by optimizing translation through the engineering of 5 untranslated regions (5 UTRs). Here, we developed a high-throughput strategy to design, screen, and optimize novel 5UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identified naturally occurring 5 UTRs with high translation efficiencies and used this information with in silico genetic algorithms to generate synthetic 5 UTRs. A total of [~]12,000 5 UTRs were then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identified three synthetic 5 UTRs that outperformed commonly used non-viral gene therapy plasmids in expressing protein payloads. Furthermore, combinatorial assembly of these 5 UTRs enabled even higher protein expression than obtained with each individual 5 UTR. In summary, we demonstrate that high-throughput screening of 5 UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies.

synthetic biology↗

bioRxiv2020

Correlating predicted epigenetic marks with expression data to find interactions between SNPs and genes

Despite all the work done, mapping GWAS SNPs in non-coding regions to their target genes remains a challenge. The SNP can be associated with target genes by eQTL analysis. Here we introduce a method to make these eQTLs more robust. Instead of correlating the gene expression with the SNP value like in eQTLs, we correlate it with epigenomic data. This epigenomic data is very expensive and noisy. We therefore predict the epigenomic data from the DNA sequence using the deep learning framework DeepSEA (Zhou and Troyanskaya, 2015).

bioinformatics↗

bioRxiv2020

Exome-wide age-of-onset analysis reveals exonic variants in ERN1, TACR3 and SPPL2C associated with Alzheimer's disease

Despite recent discovery in GWAS of genomic variants associated with Alzheimers disease (AD), its underlying biological mechanisms are still elusive. Discovery of novel AD-associated genetic variants, particularly in coding regions and from APOE {varepsilon}4 non-carriers, is critical for understanding the pathology of AD. In this study, we carried out an exome-wide association analysis of age-of-onset of AD with ~20,000 subjects and placed more emphasis on APOE {varepsilon}4 non-carriers. Using Cox mixed-effects models, we find that age-of-onset shows a stronger genetic signal than AD case-control status, capturing many known variants with stronger significance, and also revealing new variants. We identified two novel rare variants, rs56201815, a synonymous variant in ERN1, from the analysis of APOE {varepsilon}4 non-carriers, and a missense variant rs144292455 in TACR3. In addition, we detected rs12373123, a common missense variant in SPPL2C in the MAPT region in APOE {varepsilon}4 non-carriers. In an attempt to unravel their regulatory and biological functions, we found that the minor allele of rs56201815 was associated with lower average FDG uptake across five brain regions in ADNI. Our eQTL analyses based on 6198 gene expression samples from ROSMAP and GTEx revealed that the minor allele of rs56201815 was associated with elevated expression of ERN1, a key gene triggering unfolded protein response (UPR), in multiple brain regions, including posterior cingulate cortex and nucleus accumbens. Our cell-type-specific eQTL analysis of based on ~80,000 single nuclei in the prefrontal cortex revealed that the protective minor allele of rs12373123 significantly increased expression of GRN in microglia, and was associated with MAPT expression in astrocytes. These findings provide novel evidence supporting the hypothesis of the potential involvement of the UPR to ER stress in the pathological pathway of AD, and also give more insights into underlying regulatory mechanisms behind the pleiotropic effects of rs12373123 in multiple degenerative diseases including AD and Parkinsons disease.

neuroscience↗

bioRxiv2019

Plasma-derived exosomal analysis and deconvolution enables prediction and tracking of melanoma checkpoint blockade response

PurposeImmune checkpoint inhibitors (ICI) have demonstrated promising therapeutic benefit although a majority will not respond. Here we identify and validate predictive biomarkers from plasma-derived exosomes that allow non-invasive monitoring of tumor intrinsic and host immune status and prediction of ICI success.\n\nExperimental DesignTranscriptomic profiling of peripheral blood bulk exosomes and tumors from a discovery cohort of 50 patients with metastatic melanoma treated with ICI was undertaken; a further validation cohort of 30 patients was utilized to validate findings from the discovery cohort. We designed a Bayesian probabilistic model to partition bulk exosomes into tumor-specific and non-tumor-specific proportions.\n\nResultsExosomal RNA signatures exhibit significant correlations with tumor transcriptomes. Exosomal profiles reflect several key biological drivers of ICI resistance or melanoma progression, exhibit significantly differentially expressed genes and pathways, and correlate with and are predictive of clinical response to therapy. Our deconvolution model estimates contributions from tumor and non-tumor sources, enabling more precise interpretation of differentially-expressed genes and pathways. Exosomal RNA-seq mutational information can be used to segregate responders and non-responders.\n\nConclusionsPeripheral blood-derived exosomes can serve as a non-invasive biomarker to jointly probe tumor-intrinsic and immune changes to ICI, and can potentially function as predictive markers of ICI responsiveness and a monitoring tool for tumor persistence and immune activation.\n\nStatement of SignificanceWe use transcriptomic analysis of bulk, non-selected, peripheral blood derived exosomes to reveal both tumor-intrinsic and immune-derived signatures predictive of early response to immune checkpoint inhibitor therapy. We develop a novel computational model to classify exosomal transcripts into tumor and non-tumor components and establish relevance in immune checkpoint blockade therapy. We show that tumor driver load from RNA-seq mutational calls are significantly different between responders and non-responders.

cancer biology↗

bioRxiv2019

Integrative analysis of 10,000 epigenomic maps across 800 samples for regulatory genomics and disease dissection

To help elucidate genetic variants underlying complex traits, we develop EpiMap, a compendium of 833 reference epigenomes across 18 uniformly-processed and computationally-completed assays. We define chromatin states, high-resolution enhancers, activity patterns, enhancer modules, upstream regulators, and downstream target gene functions. We annotate 30,247 genetic variants associated with 534 traits, recognize principal and partner tissues underlying each trait, infer trait-tissue, tissue-tissue and trait-trait relationships, and partition multifactorial traits into their tissue-specific contributing factors. Our results demonstrate the importance of dense, rich, and high-resolution epigenomic annotations for complex trait dissection, and yield numerous new insights for understanding the molecular basis of human disease.

genomics↗

bioRxiv2019

Multi-resolution single-cell state characterization via joint archetypal/network analysis

Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown sources of variability, the existence of state continuum at the time-scale of observation, and the inevitable snapshot nature of experiments. As a way to address some of these challenges, here we introduce ACTIONet, a comprehensive framework that combines archetypal analysis and network theory to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet uses multilevel matrix decomposition and network reconstruction to simultaneously learn cell state patterns, quantify single-cell states, and reconstruct a reproducible structural representation of the transcriptional state space that is geometrically mapped to a color space. A color-enhanced quantitative view of cell states enables novel visualization, prediction, and annotation approaches. Using data from multiple tissues, organisms, and developmental conditions, we illustrate how ACTIONet facilitates the reconstruction and exploration of single-cell state landscapes.

genomics↗

bioRxiv2019

Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders

The reference human interactome has been instrumental in the systems-level study of the molecular inner workings of the cell, providing a framework to analyze the network context of disease associated gene perturbations. However, reference organismal interactomes do not capture the tissue- and cell type-specific context in which proteins and modules preferentially act. Emerging single-cell profiling technologies, which survey the transcriptional cell-state distribution of complex tissues, could be used to infer the single-cell context of gene interactions. Here we introduce SCINET (Single-Cell Imputation and NETwork construction), a computational framework that reconstructs an ensemble of cell type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression data. Subsequently, cell-level gene interaction probabilities and group-level gene interaction strengths are computed, resulting in cell type specific interactomes. We use SCINET to analyze the human cortex, reconstructing interactomes for the major cell types of the adult human brain. We identify network neighborhoods composed of topologically-specific genes that are central for cell-type influence but not for global interactome connectivity. We use the reconstructed interactomes to analyze the specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and neoplastic brain disorders; finding high variability across diseases, yet overall consistency in patterns of cell-type convergence for diseases of the same group. We infer for each disorder group disease gene networks with preferential cell-type specific activity that can aid the design and interpretation of cell-type resolution experiments. Finally, focusing on the pleiotropy of schizophrenia and bipolar disorder, we show how cell type specific interactomes enable the identification of disease genes with preferential influence on neuronal, glial, or glial-neuronal cells. The SCINET framework is applicable to any organism, cell-type/tissue, and reference network; it is freely available at https://github.com/shmohammadi86/SCINET.

systems biology↗

bioRxiv2019

Analysis of Genetically Regulated Gene Expression identifies a trauma type specific PTSD gene, SNRNP35

PTSD has significant genetic heritability; however, it is unclear how genetic risk influences tissue-specific gene expression. We used brain and non-brain transcriptomic imputation models to impute genetically regulated gene expression (GReX) in 9,087 PTSD-cases and 23,811 controls and identified thirteen significant GReX-PTSD associations. The results suggest substantial genetic heterogeneity between civilian and military PTSD cohorts. The top study-wide significant PTSD-association was with predicted downregulation of the Small Nuclear Ribonucleoprotein U11/U12 Subunit 35 (SNRNP35) in the BA9 region of the prefrontal cortex (PFC) in military cohorts. In peripheral leukocytes from 175 U.S. Marines, the observed PTSD differential gene expression correlated with the predicted blood GReX differences for these individuals, and deployment stress downregulated SNRNP35 expression, primarily in Marines with post-deployment PTSD. SNRNP35 is a subunit of the minor spliceosome complex and SNRNP35 knockdown in cells validated its functional importance in U12-intron splicing. Finally, mimicking acute activation of the endogenous stress axis in mice downregulated PFC Snrnp35 expression.

genetics↗

bioRxiv2019

A causal inference framework for estimating genetic variance and pleiotropy from GWAS summary data

MotivationMuch of research in genome-wide association studies has only searched for significantly associated signals without explicitly removing unwanted source of variation. Confounder correction is a necessary step to reveal causal effects, but often skipped in a summary-based analysis. ResultsWe present a novel causal inference algorithm that controls unwanted sources in genetic variance and covariance estimation tasks. We demonstrate substantially improved statistical power and accuracy in extensive simulations. In real-world applications on the UK biobank summary statistics data, our method recapitulates well-known pleiotropic modules, suggesting new insights into biobank-scale GWAS analysis. ContactYP (ypp@mit.edu) and MK (manoli@mit.edu)

bioinformatics↗

bioRxiv2018

A gene expression atlas of embryonic neurogenesis in Drosophila reveals complex spatiotemporal regulation of lncRNAs.

Cell type specification during early nervous system development in Drosophila melanogaster requires precise regulation of gene expression in time and space. Resolving the programs driving neurogenesis has been a major challenge owing to the complexity and rapidity with which distinct cell populations arise. To resolve the cell type-specific gene expression dynamics in early nervous system development, we have sequenced the transcriptomes of purified neurogenic cell types across consecutive time points covering critical events in neurogenesis. The resulting gene expression atlas comprises a detailed resource of global transcriptome dynamics that permits systematic analysis of how cells in the nervous system acquire distinct fates. We resolve known gene expression dynamics and uncover novel expression signatures for hundreds of genes among diverse neurogenic cell types, most of which remain unstudied. We also identified a set of conserved and processed long-noncoding RNAs (lncRNAs) that exhibit spatiotemporal expression during neurogenesis with exquisite specificity. LncRNA expression is highly dynamic and demarcates specific subpopulations within neurogenic cell types. Our spatiotemporal transcriptome atlas provides a comprehensive resource to investigate the function of coding genes and noncoding RNAs during critical stages of early neurogenesis.\n\nSummary statementWe present a spatiotemporal transcriptome during early Drosophila embryonic nervous system development, revealing a complex cell type-specific network of mRNAs and IncRNAs.

developmental biology↗

bioRxiv2018

Simultaneous profiling of DNA methylation and chromatin architecture in mixed populations and in single cells

Dynamic DNA methylation and three-dimensional chromatin architecture compose a major portion of a cells epigenome and play an essential role in tissue specific gene expression programs. Currently, DNA methylation and chromatin organization are generally profiled in separate assays. Here, we report Methyl-HiC, a method combining in situ Hi-C and whole genome bisulfite sequencing (WGBS) to simultaneously capture chromosome conformation and DNA methylome in a single assay. Methyl-HiC analysis of mouse embryonic stem cells reveals coordinated DNA methylation between distant yet spatially proximal genomic regions. Extension of Methyl-HiC to single cells further enables delineation of the heterogeneity of both chromosomal conformation and DNA methylation in a mixed cell population, and uncovers increased dynamics of chromatin contacts and decreased stochasticity in DNA methylation in genomic regions that replicate early during cell cycle.

genomics↗

bioRxiv2018

Predicting trait regulators by identifying co-localization of DNA binding and GWAS variants in regulatory regions

Genomic regions associated with complex traits and diseases are primarily located in non-coding regions of the genome and have unknown mechanism of action. A critical step to understanding the genetics of complex traits is to fine-map each associated locus; that is, to find the causal variant(s) that underlie genetic associations with a trait. Fine-mapping approaches are currently focused on identifying genomic annotations, such as transcription factor binding sites, which are enriched in direct overlap with candidate causal variants. We introduce CONVERGE, the first computational tool to search for co-localization of GWAS causal variants with transcription factor binding sites in the same regulatory regions, without requiring direct overlap. As a proof of principle, we demonstrate that CONVERGE is able to identify five novel regulators of type 2 diabetes which subsequently validated in knockdown experiments in pancreatic beta cells, while existing fine-mapping methods were unable to find any statistically significant regulators. CONVERGE also recovers more established regulators for total cholesterol compared to other fine-mapping methods. CONVERGE is therefore unique and complementary to existing fine-mapping methods and is useful for exploring the regulatory architecture of complex traits.

bioinformatics↗

bioRxiv2018

Elucidation of Codon Usage Signatures across the Domains of Life

Due to the degeneracy of the genetic code, multiple codons are translated into the same amino acid. Despite being synonymous, these codons are not equally used. Selective pressures are thought to drive the choice among synonymous codons within a genome, while GC content, which is generally attributed to mutational drift, is the major determinant of interspecies codon usage bias. Here we find that in addition to the bias caused by GC content, inter-species codon usage signatures can also be detected. More specifically, we show that a single amino acid, arginine, is the major contributor to codon usage bias differences across domains of life. We then exploit this finding, and show that the identified domain-specific codon bias signatures can be used to classify a given sequence into its corresponding domain with high accuracy. Considering that species belonging to the same domain share similar tRNA decoding strategies, we then wondered whether the inclusion of codon autocorrelation patterns might improve the classification performance of our algorithm. However, we find that autocorrelation patterns are not domain-specific, and surprisingly, are unrelated to tRNA reusage, in contrast to the common belief. Instead, our results reveal that codon autocorrelation patterns are a consequence of codon optimality throughout a sequence, where highly expressed genes display autocorrelated optimal codons, whereas lowly expressed genes display autocorrelated non-optimal codons.

evolutionary biology↗