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Author

Manolis Kellis

Other affiliations: Broad Institute, Epigenomics AG, Harvard University  ...read more
Bio: Manolis Kellis is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 128, co-authored 405 publications receiving 112181 citations. Previous affiliations of Manolis Kellis include Broad Institute & Epigenomics AG.


Papers
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Posted ContentDOI
21 Oct 2022-bioRxiv
TL;DR: How VOCs have evolved to fine-tune viral protein expression and protein-protein interactions to evade both innate and adaptive immune responses is described, offering a likely explanation for increased transmission in humans.
Abstract: A series of SARS-CoV-2 variants of concern (VOCs) have evolved in humans during the COVID-19 pandemic—Alpha, Beta, Gamma, Delta, and Omicron. Here, we used global proteomic and genomic analyses during infection to understand the molecular responses driving VOC evolution. We discovered VOC-specific differences in viral RNA and protein expression levels, including for N, Orf6, and Orf9b, and pinpointed several viral mutations responsible. An analysis of the host response to VOC infection and comprehensive interrogation of altered virus-host protein-protein interactions revealed conserved and divergent regulation of biological pathways. For example, regulation of host translation was highly conserved, consistent with suppression of VOC replication in mice using the translation inhibitor plitidepsin. Conversely, modulation of the host inflammatory response was most divergent, where we found Alpha and Beta, but not Omicron BA.1, antagonized interferon stimulated genes (ISGs), a phenotype that correlated with differing levels of Orf6. Additionally, Delta more strongly upregulated proinflammatory genes compared to other VOCs. Systematic comparison of Omicron subvariants revealed BA.5 to have evolved enhanced ISG and proinflammatory gene suppression that similarly correlated with Orf6 expression, effects not seen in BA.4 due to a mutation that disrupts the Orf6-nuclear pore interaction. Our findings describe how VOCs have evolved to fine-tune viral protein expression and protein-protein interactions to evade both innate and adaptive immune responses, offering a likely explanation for increased transmission in humans. One sentence summary Systematic proteomic and genomic analyses of SARS-CoV-2 variants of concern reveal how variant-specific mutations alter viral gene expression, virus-host protein complexes, and the host response to infection with applications to therapy and future pandemic preparedness.

4 citations

Journal ArticleDOI
TL;DR: The 4th edition of the Joint RECOMB Conference on Systems Biology, Regulatory Genomics, and DREAM Challenges was held in Barcelona, Spain on October 14–19, 2011 and brought together computational and experimental scientists to discuss current research directions and latest findings, and to establish new collaborations towards a systems-level understanding of gene regulation and modeling of biological systems.
Abstract: Over the past 10 years, the study of cell regulatory processes and their integration within complex ‘‘systems-level’’ models of cell physiology and cell pathology has flourished, with geometric increase in scientific publications and impact on biology. Within the broad spectrum of molecular biology disciplines, systems biology and regulatory genomics are perhaps the ones that have been most characterized by the seamless and unique integration of computational and experimental sciences, allowing the rapid transformation of high-throughput data into complex computational models, of models into testable hypotheses, and finally of hypotheses into knowledge via experimental validation. Today, these disciplines are achieving maturity, as also demonstrated by the creation of several university departments, centers, and institutes dedicated to their study and by the popularity and growth of meeting such as the RECOMB Conference on Systems Biology, Regulatory Genomics, and DREAM Challenges. This event, which is currently in its fourth edition as a joint meeting, is particularly relevant as it combines unique computational and experimental perspectives, while also establishing a unique frame of reference, via the DREAM challenges, to objectively gauge the progress of our ability to dissect regulatory networks and to model biological processes. The 4th edition of the Joint RECOMB Conference on Systems Biology, Regulatory Genomics, and DREAM Challenges was held in Barcelona, Spain on October 14–19, 2011. The conference brought together computational and experimental scientists to discuss current research directions and latest findings, and to establish new collaborations towards a systems-level understanding of gene regulation and modeling of biological systems. The conference included oral presentations from accepted full-length manuscripts and from a few high-quality abstracts, as well as invited presentations from thought leaders in the field. Accepted full-length manuscripts that constitute significant theoretical advances to the fields of systems biology and regulatory genomics have been combined in a collection that is presented in the current issue of the Journal of Computational Biology.

3 citations

Posted ContentDOI
03 Nov 2015-bioRxiv
TL;DR: This work used computational machine learning algorithms on five histone modifications to predict gene expression in a variety of samples, revealing a high predictive accuracy, especially in cell cultures, with predictive ability dependent on sample type and anatomy.
Abstract: Here, we predict gene expression from epigenetic features based on public data available through the Epigenome Roadmap Project. This rich new dataset includes samples from primary tissues, which to our knowledge have not previously been studied in this context. Specifically, we used computational machine learning algorithms on five histone modifications to predict gene expression in a variety of samples. Our models reveal a high predictive accuracy, especially in cell cultures, with predictive ability dependent on sample type and anatomy. The relative importance of each histone mark feature varied across samples. We localized each histone mark signal to its relevant region, revealing that chromatin state enrichment varies greatly between histone marks. Our results provide several novel insights into epigenetic regulation of transcription in new contexts.

3 citations

Posted ContentDOI
24 Jun 2021-bioRxiv
TL;DR: In this article, a detailed single-cell dissection of the cell types and disease-associated gene expression changes in the living human heart, using cardiac biopsies collected during open-heart surgery from control, ischemic and non-ischemic heart failure patients, was provided.
Abstract: Ischemic heart disease is the single most common cause of death worldwide with an annual death rate of over 9 million people. Genome-wide association studies have uncovered over 200 genetic loci underlying the disease, providing a deeper understanding of the causal mechanisms leading to it. However, in order to understand ischemic heart disease at the cellular and molecular level, it is necessary to identify the cell-type-specific circuits enabling dissection of driver variants, genes, and signaling pathways in normal and diseased tissues. Here, we provide the first detailed single-cell dissection of the cell types and disease-associated gene expression changes in the living human heart, using cardiac biopsies collected during open-heart surgery from control, ischemic heart disease, and ischemic and non-ischemic heart failure patients. We identify 84 cell types/states, grouped in 12 major cell types. We define markers for each cell type, providing the first extensive reference set for the live human heart. These major cell types include cardiovascular cells (cardiomyocytes, endothelial cells, fibroblasts), rarer cell types (B lymphocytes, neurons, Schwann cells), and rich populations of previously understudied layer-specific epicardial and endocardial cells. In addition, we reveal substantial differences in disease-associated gene expression at the cell subtype level, revealing t arterial pericytes as having a central role in the pathogenesis of ischemic heart disease and heart failure. Our results demonstrate the importance of high-resolution cellular subtype mapping in gaining mechanistic insight into human cardiovascular disease.

3 citations

Posted ContentDOI
11 Nov 2018-bioRxiv
TL;DR: ConVERGE is 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, and is useful for exploring the regulatory architecture of complex traits.
Abstract: 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.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

34,830 citations

Journal ArticleDOI
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

30,684 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
23 Jan 2009-Cell
TL;DR: The current understanding of miRNA target recognition in animals is outlined and the widespread impact of miRNAs on both the expression and evolution of protein-coding genes is discussed.

18,036 citations

Journal ArticleDOI
TL;DR: The Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available, providing a unified solution for transcriptome reconstruction in any sample.
Abstract: Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.

15,665 citations