Biology subjects

Lahr, D. L.

Publications and source records attributed to Lahr, D. L..

6 recordsLinked to original sources

bioRxiv2025

Structural and functional basis of PU.1-BAF interaction enables targeting of lineage-specific transcription

Chromatin remodeling complexes like BAF finely regulate transcriptional programs by working in concert with transcription factors. However, evidence is lacking as to whether TFs interact directly with BAF and if so, what the mechanistic and structural principles governing these critical interactions are. Here, we establish direct engagement between a crucial and therapeutically relevant full-length human TF, PU.1 (SPI1), and BAF. Within this 1MDa+ complex, we precisely map the binding site of PU.1 to a YEATS-like domain on BAF60A and elucidate the structure of the PU.1-BAF60A complex. This work reveals that upon binding to BAF, a disordered region within the TF adopts a helical conformation, and that disruption of this functionally critical interface via knockdown abrogates the ability of PU.1 to rescue cell viability. To explore the druggability of TF-BAF protein-protein interactions (PPIs), we conducted a high-throughput screen that identified small molecules capable of disrupting the PU.1-BAF60A PPI by binding to BAF60A. Co-crystal structures reveal distinct compound binding modes that converge on a critical PU.1-BAF60A interaction hotspot. These findings define, for the first time, the structural interface between a human TF and a chromatin remodeling complex and establish a platform that enables the targeting of these interactions, a novel mechanism in cancer therapeutics.

biochemistry↗

bioRxiv2023

Hijacking the transcriptional activation potential of the BAF complex via Induced Proximity

The BAF (Brg/Brahma-associated factors) complex, also referred to as the mammalian Switch/Sucrose-Nonfermentable (mSWI/SNF) chromatin remodeling complex, plays a pivotal role in epigenetically regulating diverse transcriptional programs. BAFs chromatin remodeling activity, which enhances accessibility to transcriptional machinery, is critical for gene regulation. Recent studies have demonstrated that redirecting BAF complexes to bivalent promoters can alter the local epigenetic landscape, creating a permissive environment for transcription. As such, we hypothesize that redirecting BAF to "turn on" therapeutically relevant genes offers a potential approach for disease treatment. Using rapamycin as a chemical inducer of proximity (CIP) via CRISPR/Cas9 and FKBP/FRB dimerization, we redirected BAF complexes to the promoter of fetal hemoglobin (HBG), a therapeutic target for beta-globinopathies like sickle cell anemia and beta-thalassemia. This resulted in changes to the local chromatin and epigenetic landscapes, and increased HBG1 expression. Having confirmed BAFs ability to activate gene expression, we then performed a genome-wide CRISPR activation drop-out screen to identify genes that when activated by BAF, suppress cellular proliferation. In addition to known tumor suppressors, our screen identified a number of genes with the ability to inhibit cell proliferation when activated by BAF. Collectively, our findings highlight the potential for harnessing the BAFs intrinsic transcriptional activation capabilities for therapeutic purposes and lays the foundation for the potential development of therapeutics that function via induced proximity.

cell biology↗

bioRxiv2023

Pharmacologic inhibition of BAF chromatin remodeling complexes as a therapeutic approach to transcription factor-dependent cancers

The BRG/Brahma-associated factors (BAF or mSWI/SNF) family of chromatin remodeling complexes are critical regulators of gene expression and are major determinants of cancer and other diseases. Two paralog ATPases, SMARCA4 and SMARCA2 (BRG1 and BRM, respectively), provide the enzymatic activity required for chromatin remodeling. Here, we discover and characterize a novel series of compounds that potently and selectively inhibit SMARCA4/SMARCA2. Mutational and biochemical studies demonstrate that these inhibitors act through a unique mode of inhibition, distinct from reported SMARCA4/SMARCA2 inhibitors. Across a range of cancer cell lines, SMARCA4/SMARCA2 inhibition resulted in lineage-specific changes in chromatin accessibility at binding sites for key transcription factors (TFs). In uveal melanoma (UM), BAF inhibition resulted in loss of enhancer occupancy of SOX10 and MITF, two essential TFs, leading to down-regulation of the melanocytic gene expression program. In a mouse xenograft model of UM, SMARCA4/SMARCA2 inhibition was well tolerated and resulted in dose-dependent tumor regression correlating with pharmacodynamic modulation of BAF-target gene expression. These data provide the foundation for first-in-human studies of BAF ATPase inhibition as a novel therapeutic to treat TF-dependent cancers.

cancer biology↗

bioRxiv2017

The GCTx format and cmap{Py, R, M} packages: resources for the optimized storage and integrated traversal of dense matrices of data and annotations

MotivationComputational analysis of datasets generated by treating cells with pharmacological and genetic perturbagens has proven useful for the discovery of functional relationships. Facilitated by technological improvements, perturbational datasets have grown in recent years to include millions of experiments. While initial studies, such as our work on Connectivity Map, used gene expression readouts, recent studies from the NIH LINCS consortium have expanded to a more diverse set of molecular readouts, including proteomic and cell morphological signatures. Sharing these diverse data creates many opportunities for research and discovery, but the unprecedented size of data generated and the complex metadata associated with experiments have also created fundamental technical challenges regarding data storage and cross-assay integration.\n\nResultsWe present the GCTx file format and a suite of open-source packages for the efficient storage, serialization, and analysis of dense two-dimensional matrices. The utility of this format is not just theoretical; we have extensively used the format in the Connectivity Map to assemble and share massive data sets comprising 1.7 million experiments. We anticipate that the generalizability of the GCTx format, paired with code libraries that we provide, will stimulate wider adoption and lower barriers for integrated cross-assay analysis and algorithm development.\n\nAvailabilitySoftware packages (available in Matlab, Python, and R) are freely available at https://github.com/cmap\n\nSupplementary informationSupplementary information is available at clue.io/code.\n\nContactoana@broadinstitute.org

bioinformatics↗

bioRxiv2017

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology↗

bioRxiv2017

A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.\n\nHIGHLIGHTSO_LIA new gene expression profiling method, L1000, dramatically lowers cost\nC_LIO_LIThe Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profiles\nC_LIO_LIThis expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variants\nC_LIO_LIThe work establishes feasibility and utility of a truly comprehensive Connectivity Map\nC_LI

genomics↗