scispace - formally typeset
Search or ask a question
Institution

Broad Institute

NonprofitCambridge, Massachusetts, United States
About: Broad Institute is a nonprofit organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 6584 authors who have published 11618 publications receiving 1522743 citations. The organization is also known as: Eli and Edythe L. Broad Institute of MIT and Harvard.


Papers
More filters
Journal ArticleDOI
17 Jan 2018-Nature
TL;DR: It is shown that chromosomally unstable tumour cells co-opt chronic activation of innate immune pathways to spread to distant organs by sustaining a tumour cell-autonomous response to cytosolic DNA.
Abstract: Chromosomal instability is a hallmark of cancer that results from ongoing errors in chromosome segregation during mitosis. Although chromosomal instability is a major driver of tumour evolution, its role in metastasis has not been established. Here we show that chromosomal instability promotes metastasis by sustaining a tumour cell-autonomous response to cytosolic DNA. Errors in chromosome segregation create a preponderance of micronuclei whose rupture spills genomic DNA into the cytosol. This leads to the activation of the cGAS-STING (cyclic GMP-AMP synthase-stimulator of interferon genes) cytosolic DNA-sensing pathway and downstream noncanonical NF-κB signalling. Genetic suppression of chromosomal instability markedly delays metastasis even in highly aneuploid tumour models, whereas continuous chromosome segregation errors promote cellular invasion and metastasis in a STING-dependent manner. By subverting lethal epithelial responses to cytosolic DNA, chromosomally unstable tumour cells co-opt chronic activation of innate immune pathways to spread to distant organs.

878 citations

Journal ArticleDOI
Jacy R Crosby1, Gina M. Peloso2, Gina M. Peloso3, Paul L. Auer4, David R. Crosslin5, Nathan O. Stitziel6, Leslie A. Lange7, Yingchang Lu8, Zheng-Zheng Tang7, He Zhang9, George Hindy10, Nicholas G. D. Masca11, Kathleen Stirrups12, Stavroula Kanoni12, Ron Do2, Ron Do3, Goo Jun9, Youna Hu9, Hyun Min Kang9, Chenyi Xue9, Anuj Goel13, Martin Farrall13, Stefano Duga14, Pier Angelica Merlini, Rosanna Asselta14, Domenico Girelli15, Oliviero Olivieri15, Nicola Martinelli15, Wu Yin16, Dermot F. Reilly16, Elizabeth K. Speliotes9, Caroline S. Fox17, Kristian Hveem18, Oddgeir L. Holmen19, Majid Nikpay20, Deborah N. Farlow3, Themistocles L. Assimes21, Nora Franceschini7, Jennifer G. Robinson22, Kari E. North7, Lisa W. Martin23, Mark A. DePristo3, Namrata Gupta3, Stefan A. Escher10, Jan-Håkan Jansson24, Natalie R. van Zuydam25, Colin N. A. Palmer25, Nicholas J. Wareham26, Werner Koch27, Thomas Meitinger27, Annette Peters, Wolfgang Lieb28, Raimund Erbel, Inke R. König29, Jochen Kruppa29, Franziska Degenhardt30, Omri Gottesman8, Erwin P. Bottinger8, Christopher J. O'Donnell17, Bruce M. Psaty5, Bruce M. Psaty31, Christie M. Ballantyne32, Christie M. Ballantyne33, Gonçalo R. Abecasis9, Jose M. Ordovas34, Jose M. Ordovas35, Olle Melander10, Hugh Watkins13, Marju Orho-Melander10, Diego Ardissino, Ruth J. F. Loos8, Ruth McPherson20, Cristen J. Willer9, Jeanette Erdmann29, Alistair S. Hall36, Nilesh J. Samani11, Panos Deloukas37, Panos Deloukas38, Panos Deloukas12, Heribert Schunkert27, James G. Wilson39, Charles Kooperberg40, Stephen S. Rich41, Russell P. Tracy42, Danyu Lin7, David Altshuler2, David Altshuler3, Stacey Gabriel3, Deborah A. Nickerson5, Gail P. Jarvik5, L. Adrienne Cupples43, L. Adrienne Cupples26, Alexander P. Reiner40, Alexander P. Reiner5, Eric Boerwinkle33, Sekar Kathiresan2, Sekar Kathiresan3 
TL;DR: Rare mutations that disrupt AP OC3 function were associated with lower levels of plasma triglycerides and APOC3, and carriers of these mutations were found to have a reduced risk of coronary heart disease.
Abstract: Background Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. Methods We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. Results An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10 − 20 ), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P = 8×10 − 10 ). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P = 4×10 − 6 ). Conclusions Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.)

877 citations

Journal ArticleDOI
TL;DR: Improvements in economics, resolution, and ease of use make CEL-Sequ2 uniquely suited to single-cell RNA-Seq analysis in terms of economics,resolution, and easing of use.
Abstract: Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm’s C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use.

875 citations

Journal ArticleDOI
16 Aug 2013-Science
TL;DR: In this article, the authors investigated the localization mechanisms of the Xist lncRNA during X-chromosome inactivation (XCI), a paradigm of lncRN-mediated chromatin regulation.
Abstract: Many large noncoding RNAs (lncRNAs) regulate chromatin, but the mechanisms by which they localize to genomic targets remain unexplored. We investigated the localization mechanisms of the Xist lncRNA during X-chromosome inactivation (XCI), a paradigm of lncRNA-mediated chromatin regulation. During the maintenance of XCI, Xist binds broadly across the X chromosome. During initiation of XCI, Xist initially transfers to distal regions across the X chromosome that are not defined by specific sequences. Instead, Xist identifies these regions by exploiting the three-dimensional conformation of the X chromosome. Xist requires its silencing domain to spread across actively transcribed regions and thereby access the entire chromosome. These findings suggest a model in which Xist coats the X chromosome by searching in three dimensions, modifying chromosome structure, and spreading to newly accessible locations.

875 citations

Posted ContentDOI
Evan Bolyen1, Jai Ram Rideout1, Matthew R. Dillon1, Nicholas A. Bokulich1, Christian C. Abnet, Gabriel A. Al-Ghalith2, Harriet Alexander3, Harriet Alexander4, Eric J. Alm5, Manimozhiyan Arumugam6, Francesco Asnicar7, Yang Bai8, Jordan E. Bisanz9, Kyle Bittinger10, Asker Daniel Brejnrod6, Colin J. Brislawn11, C. Titus Brown3, Benjamin J. Callahan12, Andrés Mauricio Caraballo-Rodríguez13, John Chase1, Emily K. Cope1, Ricardo Silva13, Pieter C. Dorrestein13, Gavin M. Douglas14, Daniel M. Durall15, Claire Duvallet5, Christian F. Edwardson16, Madeleine Ernst13, Mehrbod Estaki15, Jennifer Fouquier17, Julia M. Gauglitz13, Deanna L. Gibson15, Antonio Gonzalez18, Kestrel Gorlick1, Jiarong Guo19, Benjamin Hillmann2, Susan Holmes20, Hannes Holste18, Curtis Huttenhower21, Curtis Huttenhower22, Gavin A. Huttley23, Stefan Janssen24, Alan K. Jarmusch13, Lingjing Jiang18, Benjamin D. Kaehler23, Kyo Bin Kang13, Kyo Bin Kang25, Christopher R. Keefe1, Paul Keim1, Scott T. Kelley26, Dan Knights2, Irina Koester13, Irina Koester18, Tomasz Kosciolek18, Jorden Kreps1, Morgan G. I. Langille14, Joslynn S. Lee27, Ruth E. Ley28, Ruth E. Ley29, Yong-Xin Liu8, Erikka Loftfield, Catherine A. Lozupone17, Massoud Maher18, Clarisse Marotz18, Bryan D Martin30, Daniel McDonald18, Lauren J. McIver22, Lauren J. McIver21, Alexey V. Melnik13, Jessica L. Metcalf31, Sydney C. Morgan15, Jamie Morton18, Ahmad Turan Naimey1, Jose A. Navas-Molina18, Jose A. Navas-Molina32, Louis-Félix Nothias13, Stephanie B. Orchanian18, Talima Pearson1, Samuel L. Peoples30, Samuel L. Peoples33, Daniel Petras13, Mary L. Preuss34, Elmar Pruesse17, Lasse Buur Rasmussen6, Adam R. Rivers35, Ii Michael S Robeson36, Patrick Rosenthal34, Nicola Segata7, Michael Shaffer17, Arron Shiffer1, Rashmi Sinha, Se Jin Song18, John R. Spear37, Austin D. Swafford18, Luke R. Thompson38, Luke R. Thompson39, Pedro J. Torres26, Pauline Trinh30, Anupriya Tripathi13, Anupriya Tripathi18, Peter J. Turnbaugh9, Sabah Ul-Hasan40, Justin J. J. van der Hooft41, Fernando Vargas18, Yoshiki Vázquez-Baeza18, Emily Vogtmann, Max von Hippel42, William A. Walters28, Yunhu Wan, Mingxun Wang13, Jonathan Warren43, Kyle C. Weber44, Kyle C. Weber35, Chase Hd Williamson1, Amy D. Willis30, Zhenjiang Zech Xu18, Jesse R. Zaneveld30, Yilong Zhang45, Rob Knight18, J. Gregory Caporaso1 
24 Oct 2018-PeerJ
TL;DR: QIIME 2 provides new features that will drive the next generation of microbiome research, including interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
Abstract: We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

875 citations


Authors

Showing all 7146 results

NameH-indexPapersCitations
Eric S. Lander301826525976
Albert Hofman2672530321405
Frank B. Hu2501675253464
David J. Hunter2131836207050
Kari Stefansson206794174819
Mark J. Daly204763304452
Lewis C. Cantley196748169037
Matthew Meyerson194553243726
Gad Getz189520247560
Stacey Gabriel187383294284
Stuart H. Orkin186715112182
Ralph Weissleder1841160142508
Chris Sander178713233287
Michael I. Jordan1761016216204
Richard A. Young173520126642
Network Information
Related Institutions (5)
Howard Hughes Medical Institute
34.6K papers, 5.2M citations

96% related

Salk Institute for Biological Studies
13.1K papers, 1.6M citations

94% related

Fred Hutchinson Cancer Research Center
30.9K papers, 2.2M citations

93% related

Scripps Research Institute
32.8K papers, 2.9M citations

93% related

Genentech
17.1K papers, 1.4M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202337
2022628
20211,727
20201,534
20191,364
20181,107