Broad Institute

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.

Subtype-specific genomic alterations define new targets for soft-tissue sarcoma therapy.

Abstract: Soft-tissue sarcomas, which result in approximately 10,700 diagnoses and 3,800 deaths per year in the United States, show remarkable histologic diversity, with more than 50 recognized subtypes. However, knowledge of their genomic alterations is limited. We describe an integrative analysis of DNA sequence, copy number and mRNA expression in 207 samples encompassing seven major subtypes. Frequently mutated genes included TP53 (17% of pleomorphic liposarcomas), NF1 (10.5% of myxofibrosarcomas and 8% of pleomorphic liposarcomas) and PIK3CA (18% of myxoid/round-cell liposarcomas, or MRCs). PIK3CA mutations in MRCs were associated with Akt activation and poor clinical outcomes. In myxofibrosarcomas and pleomorphic liposarcomas, we found both point mutations and genomic deletions affecting the tumor suppressor NF1. Finally, we found that short hairpin RNA (shRNA)-based knockdown of several genes amplified in dedifferentiated liposarcoma, including CDK4 and YEATS4, decreased cell proliferation. Our study yields a detailed map of molecular alterations across diverse sarcoma subtypes and suggests potential subtype-specific targets for therapy.

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

Abstract: INTRODUCTION During embryogenesis, pluripotent cells gradually become specialized and acquire distinct functions and morphologies. Because much of the specification process is controlled through changes in gene expression, the identification of the transcriptional trajectories underlying cell fate acquisition is paramount to understanding and manipulating development. RATIONALE Traditional approaches have studied specific fate decisions by analyzing the transcription of a few selected marker genes or by profiling isolated, predefined cell populations. The advent of large-scale single-cell RNA sequencing (scRNA-seq) provides the means to comprehensively define the gene expression states of all embryonic cells as they acquire their fates. This technology raises the possibility of identifying the molecular trajectories that describe cell fate specification by sampling densely during embryogenesis and connecting the transcriptomes of cells that have similar gene expression profiles. However, the numerous transcriptional states and branch points, as well as the asynchrony in developmental processes, pose major challenges to the computational reconstruction of developmental trajectories from scRNA-seq data. RESULTS We generated single-cell transcriptomes from 38,731 cells during early zebrafish embryogenesis at high temporal resolution, spanning 12 stages from the onset of zygotic transcription through early somitogenesis. We took two complementary approaches to identify the transcriptional trajectories in the data. First, we developed a simulated diffusion-based computational approach, URD, which identified the trajectories describing the specification of 25 cell types in the form of a branching tree. Second, we identified modules of coexpressed genes and connected them across developmental time. Combining the reconstructed developmental trajectories with differential gene expression analysis uncovered gene expression cascades leading to each cell type, including previously unidentified markers and candidate regulators. Combining these trajectories with Seurat, which infers the spatial positions of cells on the basis of their transcriptomes, connected the earlier spatial position of progenitors to the later fate of their descendants. Inspection of the developmental tree led to new insights about molecular specification in zebrafish. For example, the first branch point in the tree indicated that the first molecular specification event may not only separate the germ layers but also define the axial versus nonaxial mesendoderm. Additionally, some developmental branch points contained intermediate cells that expressed genes characteristic of multiple downstream cell fates. Gene expression analysis at one such branch point (the axial mesoderm) suggested that the intermediate cells switch their specification from one fate (notochord) to another (prechordal plate). Last, analysis of single-cell transcriptomes from a Nodal-signaling mutant revealed that even at the whole-transcriptome level, mutant cells were canalized into a subset of wild-type states and did not adopt any transcriptional states not observed in wild type, despite abnormal developmental signaling. CONCLUSION These findings reconstruct the gene expression trajectories during the embryogenesis of a vertebrate and highlight the concurrent canalization and plasticity of cell type specification. The scRNA-seq data and developmental tree provide a rich resource for future studies in zebrafish: The raw and processed data and the URD software are available for download, and the data can be browsed interactively online. Last, this approach provides a broadly applicable framework with which to reconstruct complex developmental trajectories from single-cell transcriptomes.

Toward an online repository of Standard Operating Procedures (SOPs) for (meta)genomic annotation.

Abstract: The methodologies used to generate genome and metagenome annotations are diverse and vary between groups and laboratories. Descriptions of the annotation process are helpful in interpreting genome annotation data. Some groups have produced Standard Operating Procedures (SOPs) that describe the annotation process, but standards are lacking for structure and content of these descriptions. In addition, there is no central repository to store and disseminate procedures and protocols for genome annotation. We highlight the importance of SOPs for genome annotation and endorse an online repository of SOPs.

De novo somatic mutations in components of the PI3K-AKT3-mTOR pathway cause hemimegalencephaly

Abstract: De novo somatic mutations in focal areas are well documented in diseases such as neoplasia but are rarely reported in malformation of the developing brain. Hemimegalencephaly (HME) is characterized by overgrowth of either one of the two cerebral hemispheres. The molecular etiology of HME remains a mystery. The intractable epilepsy that is associated with HME can be relieved by the surgical treatment hemispherectomy, allowing sampling of diseased tissue. Exome sequencing and mass spectrometry analysis in paired brain-blood samples from individuals with HME (n = 20 cases) identified de novo somatic mutations in 30% of affected individuals in the PIK3CA, AKT3 and MTOR genes. A recurrent PIK3CA c.1633G>A mutation was found in four separate cases. Identified mutations were present in 8-40% of sequenced alleles in various brain regions and were associated with increased neuronal S6 protein phosphorylation in the brains of affected individuals, indicating aberrant activation of mammalian target of rapamycin (mTOR) signaling. Thus HME is probably a genetically mosaic disease caused by gain of function in phosphatidylinositol 3-kinase (PI3K)-AKT3-mTOR signaling.

Chromatin marks identify critical cell types for fine mapping complex trait variants

Abstract: If trait-associated variants alter regulatory regions, then they should fall within chromatin marks in relevant cell types. However, it is unclear which of the many marks are most useful in defining cell types associated with disease and fine mapping variants. We hypothesized that informative marks are phenotypically cell type specific; that is, SNPs associated with the same trait likely overlap marks in the same cell type. We examined 15 chromatin marks and found that those highlighting active gene regulation were phenotypically cell type specific. Trimethylation of histone H3 at lysine 4 (H3K4me3) was the most phenotypically cell type specific (P < 1 × 10(-6)), driven by colocalization of variants and marks rather than gene proximity (P < 0.001). H3K4me3 peaks overlapped with 37 SNPs for plasma low-density lipoprotein concentration in the liver (P < 7 × 10(-5)), 31 SNPs for rheumatoid arthritis within CD4(+) regulatory T cells (P = 1 × 10(-4)), 67 SNPs for type 2 diabetes in pancreatic islet cells (P = 0.003) and the liver (P = 0.003), and 14 SNPs for neuropsychiatric disease in neuronal tissues (P = 0.007). We show how cell type-specific H3K4me3 peaks can inform the fine mapping of associated SNPs to identify causal variation.