Biology subjects

Meyerson, M.

Publications and source records attributed to Meyerson, M..

25 records · Page 2Linked to original sources

bioRxiv2021

Define protein variant functions with high-complexity mutagenesis libraries and enhanced mutation detection software

Pooled variant expression libraries can test the phenotypes of thousands of variants of a gene in a single multiplexed experiment. In a library encoding all single-amino-acid substitutions of a protein, each variant differs from its reference only at a single codon-position located anywhere along the coding sequence. Consequently, accurately identifying these variants by sequencing is a major technical challenge. A popular but expensive brute-force approach is to divide the pool of variants into multiple smaller sub-libraries that each contains variants of a small region and that must each be constructed and screened individually, but that can then be PCR-amplified and fully sequenced with a single read to allow direct readout of variant abundance. Here we present an approach to screen very large variant libraries with mutations spanning a wide region in a single pool, including library design criteria and mutant-detection algorithms that permit reliable calling and counting of variants from large-scale sequencing data.

systems biology↗

bioRxiv2021

Transcriptional and Functional Activity of Canine Hemangiosarcoma to Support Hematopoiesis Demonstrate Bone Marrow Nurse Cell Ontogeny

Hemangiosarcoma and angiosarcoma are soft-tissue sarcomas of blood vessel-forming cells in dogs and humans, respectively. These vasoformative sarcomas are aggressive and highly metastatic, with disorganized, irregular blood-filled vascular spaces. Our objective was to define molecular programs which support the niche that enables progression of canine hemangiosarcoma and human angiosarcoma. Dog-in-mouse hemangiosarcoma xenografts recapitulated the vasoformative and highly angiogenic morphology and molecular characteristics of primary tumors. Blood vessels in the tumors were complex and disorganized, and they were lined by both donor and host cells, a trait that was not observed in xenografts from canine osteosarcoma and lymphoma. In some cases, the xenografted hemangiosarcoma cells created exuberant myeloid hyperplasia and gave rise to lymphoproliferative tumors of mouse origin. We did not uncover a definitive transmissible etiology, but our functional analyses indicate that hemangiosarcoma cells generate a microenvironment that supports expansion and differentiation of hematopoietic progenitor populations. We conclude that canine hemangiosarcomas, and possibly human angiosarcomas, originate from stromal cells that are part of the bone marrow niche and that these cells may also support the growth of hematopoietic tumors. SignificanceWe demonstrate that molecular programs supporting expansion of immune and inflammatory cells in hemangiosarcoma resemble those of bone marrow niche cells, providing insights into the potential roles of these cells - whether physiological or pathological - in creating a permissive environment for the progression of hematopoietic malignancies.

cancer biology↗

bioRxiv2020

Noncanonical junctions in subgenomic RNAs of SARS-CoV-2 lead to variant open reading frames.

BackgroundSARS-CoV-2, a positive-sense RNA virus in the family Coronaviridae, has caused a worldwide pandemic of coronavirus disease 2019 or COVID-19 Coronaviruses generate a tiered series of subgenomic RNAs (sgRNAs) through a process involving homology between transcriptional regulatory sequences (TRS) located after the leader sequence in the 5 UTR (the TRS-L) and TRS located near the start of structural and accessory proteins (TRS-B) near the 3 end of the genome. In addition to the canonical sgRNAs generated by SARS-CoV-2, non-canonical sgRNAs (nc-sgRNAs) have been reported. However, the consistency of these nc-sgRNAs across viral isolates and infection conditions is unknown. The comprehensive definition of SARS-CoV-2 RNA products is a key step in understanding SARS-CoV-2 pathogenesis. MethodsHere, we report an integrative analysis of eight independent SARS-CoV-2 transcriptomes generated using three sequencing strategies, five host systems, and seven viral isolates. Read-mapping to the SARS-CoV-2 genome was used to determine the 5 and 3 coordinates of all identified junctions in viral RNAs identified in these samples. ResultsUsing junctional abundances, we show nc-sgRNAs make up as much as 33% of total sgRNAs in vitro, are largely consistent in abundance across independent transcriptomes, and increase in abundance over time during in vitro infection. By assessing the homology between sequences flanking the 5 and 3 junction points, we show that nc-sgRNAs are not associated with TRS-like homology. By incorporating read coverage information, we find strong evidence for subgenomic RNAs that contain only 5 regions of ORF1a. Finally, we show that non-canonical junctions change the landscape of viral open reading frames. ConclusionsWe identify canonical and non-canonical junctions in SARS-CoV-2 sgRNAs and show that these RNA products are consistently generated across many independent viral isolates and sequencing approaches. These analyses highlight the diverse transcriptional activity of SARS-CoV-2 and offer important insights into SARS-CoV-2 biology.

microbiology↗

bioRxiv2019

Non-oncology drugs are a source of previously unappreciated anti-cancer activity

Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects cell line viability. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at https://depmap.org.

cancer biology↗

bioRxiv2019

The Tangent copy-number inference pipeline for cancer genome analyses

MotivationSomatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas (TCGA), the Broad Institute Genome Characterization Center developed the Tangent copy-number inference pipeline to generate copy-number profiles using single-nucleotide polymorphism (SNP) array and whole-exome sequencing (WES) data from over 10,000 pairs of tumors and matched normal samples. Here, we describe the Tangent pipeline, which begins with DNA sequencing data in the form of .bam files or raw SNP array probe-level intensity data, and ends with segmented copy-number calls to facilitate the identification of novel genes potentially targeted by SCNAs. We also describe a modification of Tangent, Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available.\n\nResultsTangent Normalization offers substantial signal-to-noise ratio (SNR) improvements compared to conventional normalization methods in both SNP array and WES analyses. The improvement in SNRs is achieved primarily through noise reduction with minimal effect on signal. Pseudo-Tangent also reduces noise when few normal samples are available. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data.\n\nAvailability and ImplementationTangent is available at https://github.com/coyin/tangent and as a Docker image (https://hub.docker.com/r/coyin/tangent). Tangent is also the normalization method for the Copy Number pipeline in Genome Analysis Toolkit 4 (GATK4).\n\nContactmatthew_meyerson@dfci.harvard.edu, rameen@broadinstitute.org, gadgetz@broadinstitute.org

bioinformatics↗

bioRxiv2017

Pan-cancer study of heterogeneous RNA aberrations

We present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called \"bridged\" fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.

genomics↗

bioRxiv2017

Genome-wide detection of structural variants and indels by local assembly

Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at-scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABAs performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs, and substantially improved detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (< 1,000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types, and found that templated-sequence insertions occur in ~4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized SVs.

genomics↗