Biology subjects

Goodale, A.

Publications and source records attributed to Goodale, A..

13 recordsLinked to original sources

bioRxiv2026

MX2 Mediates Collapse of the HIV-1 Capsid

The HIV-1 capsid core encapsulates the viral genome and mediates its delivery into the host cells nucleus. It is composed of multiple copies of the Capsid (CA, p24Gag) protein, assembled into hexamers and pentamers to create a lattice that forms a fullerene-like cone. Myxovirus resistance 2 (MX2) is an HIV-1 restriction factor that binds to the capsid core and blocks nuclear import of the viral genome. Here, we define a minimal region of MX2 required for HIV-1 restriction and produce a corresponding functional recombinant protein. We have used cryo-electron microscopy to determine the structure of this MX2 fragment bound to the tri-hexamer interface of the capsid lattice, revealing a large, buried interface combining electrostatic and hydrophobic interactions. This structure, together with assays that measure capsid core destabilisation, shows that MX2 binding induces conformational rearrangements in the capsid lattice that culminate in a loss of integrity. These results support a model whereby MX2 exerts its antiviral activity by disrupting the capsid lattice, inducing premature fragmentation and preventing HIV-1 nuclear import. By revealing the structural basis for MX2-mediated restriction, this work also provides the framework for the development of anti-HIV molecules that mimic MX2 restriction.

microbiology↗

bioRxiv2025

O-mannosylation and protein maturation check-points represent therapeutic opportunities in BRAF fusion protein oncogenesis

Fusions between protein-coding genes are common oncogenic drivers across cancers, typically pairing a proto-oncogene with partner that does not independently drive cancer. In all therapeutically actionable fusions, the proto-oncogene is the drug target, the contributions to oncogenicity of the fusion partner have largely been ignored. We studied the role of BRAF fusion partners and found that they are necessary for transformation. In the setting of KIAA1549::BRAF, the most common fusion protein across brain tumors, we found that KIAA1549 is necessary for the oncogenicity of KIAA1549::BRAF and engenders a striking and specific dependency on the protein O-mannosyltransferase complex (POMT1/2). Specifically, we show that genetic silencing or pharmacologic inhibition of the protein O-mannosyltransferase complex (POMT1/2) reverses fusion-induced transformation, thereby representing a novel and MAPK independent therapeutic target. Furthermore, POMT1/2 is required to glycosylate and enable maturation of the K::B fusion protein. These findings represent a proof-of-concept for targeting the partners in oncogenic fusions as a potential cancer therapeutic strategy.

cancer biology↗

bioRxiv2024

Morphological map of under- and over-expression of genes in human cells

Cell Painting images offer valuable insights into a cells state and enable many biological applications, but publicly available arrayed datasets only include hundreds of genes perturbed. The JUMP (Joint Undertaking in Morphological Profiling) Cell Painting Consortium perturbed roughly 75% of the protein-coding genome in human U-2 OS cells, generating a rich resource of single-cell images and extracted features. These profiles capture the phenotypic impacts of perturbing 15,243 human genes, including overexpressing 12,609 genes (using open reading frames, ORFs) and knocking out 7,975 genes (using CRISPR-Cas9). We mitigated technical artifacts by rigorously evaluating data processing options and validated the datasets robustness and biological relevance. Analysis of phenotypic profiles revealed novel gene clusters and functional relationships, including those associated with mitochondrial function, cancer, and neural processes. The JUMP Cell Painting genetic dataset is a valuable resource for exploring gene relationships and uncovering novel functions.

bioinformatics↗

bioRxiv2023

Modular vector assembly enables rapid assessment of emerging CRISPR technologies

The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly-described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.

genomics↗

bioRxiv2023

Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma

A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames. To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a step-wise approach to employ multiple CRISPR-Cas9 screens to elucidate functional non-canonical ORFs implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream open reading frames (uORFs) exhibited selective functionality independent of the main coding sequence. One of these, ASNSD1-uORF or ASDURF, was upregulated, associated with the MYC family oncogenes, and was required for medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future cancer genomics studies seeking to define new cancer targets. HighlightsO_LIRibo-seq reveals widespread translation of non-canonical ORFs in medulloblastoma C_LIO_LIHigh-resolution CRISPR tiling reveals uORF functions in medulloblastoma C_LIO_LIASNSD1-uORF controls downstream pathways with the prefoldin-like complex C_LIO_LIASNSD1-uORF is necessary for medulloblastoma cell survival C_LI

cancer biology↗

bioRxiv2023

Optimization of Cas12a for multiplexed genome-scale transcriptional activation

Cas12a CRISPR technology, unlike Cas9, allows for facile multiplexing of guide RNAs from a single transcript, simplifying combinatorial perturbations. While Cas12a has been implemented for multiplexed knockout genetic screens, it has yet to be optimized for CRISPR activation (CRISPRa) screens in human cells. Here we develop a new Cas12a-based transactivation domain (TAD) recruitment system using the ALFA nanobody and demonstrate simultaneous activation of up to four genes. We screen a genome-wide library to identify modulators of growth and MEK inhibition and we compare these results to those obtained with open reading frame (ORF) overexpression and Cas9-based CRISPRa. We find that the activity of multiplexed arrays is largely predictable from the best-performing guide and provide criteria for selecting active guides. We anticipate that these results will greatly accelerate the exploration of gene function and combinatorial phenotypes at scale.

genomics↗

bioRxiv2023

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.

bioinformatics↗

bioRxiv2022

Mapping the landscape of genetic dependencies in chordoma

Identifying the spectrum of genes required for cancer cell survival can reveal essential cancer circuitry and therapeutic targets, but such a map remains incomplete for many cancer types. We applied genome-scale CRISPR-Cas9 loss-of-function screens to map the landscape of selectively essential genes in chordoma, a bone cancer with few validated targets. This approach confirmed a known chordoma dependency, TBXT (T; brachyury), and identified a range of additional dependencies, including PTPN11, ADAR, PRKRA, LUC7L2, SRRM2, SLC2A1, SLC7A5, FANCM, and THAP1. CDK6, SOX9, and EGFR, genes previously implicated in chordoma biology, were also recovered. We found genomic and transcriptomic features that predict specific dependencies, including interferon-stimulated gene expression, which correlates with ADAR dependence and is elevated in chordoma. Validating the therapeutic relevance of dependencies, small-molecule inhibitors of SHP2, encoded by PTPN11, had potent preclinical efficacy against chordoma. Our results generate an emerging map of chordoma dependencies to enable biological and therapeutic hypotheses.

cancer biology↗

bioRxiv2022

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks. Key references using this protocolVirtual screening for small-molecule pathway regulators by image-profile matching (https://doi.org/10.1016/j.cels.2022.08.003) - recent work examining the ability to use collected Cell Painting profiles to screen for regulators of a number of diverse biological pathways. JUMP Cell Painting dataset: images and profiles from two billion cells perturbed by 140,000 chemical and genetic perturbations (DOI) - the description of the main JUMP master public data set, using this protocol in the production of >200 TB of image data and >200 TB of measured profiles. Key data used in this protocolCell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (https://doi.org/10.1038/nprot.2016.105) - this paper provides the first step-by-step Cell Painting protocol ever released.

bioinformatics↗

bioRxiv2022

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

Identifying genetic and chemical perturbations with similar impacts on cell morphology can reveal compounds mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here, we create such a Resource dataset, CPJUMP1, where each perturbed gene is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same gene, and we find that identifying matches between chemical perturbations and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling, such as functional genomics and drug discovery.

bioinformatics↗

bioRxiv2020

Non-canonical open reading frames encode functional proteins essential for cancer cell survival

A key question in genome research is whether biologically active proteins are restricted to the [~]20,000 canonical, well-annotated genes, or rather extend to the many non-canonical open reading frames (ORFs) predicted by genomic analyses. To address this, we experimentally interrogated 553 ORFs nominated in ribosome profiling datasets. Of these 553 ORFs, 57 (10%) induced a viability defect when the endogenous ORF was knocked out using CRISPR/Cas9 in 8 human cancer cell lines, 257 (46%) showed evidence of protein translation when ectopically expressed in HEK293T cells, and 401 (73%) induced gene expression changes measured by transcriptional profiling following ectopic expression across 4 cell types. CRISPR tiling and start codon mutagenesis indicated that the biological effects of these non-canonical ORFs required their translation as opposed to RNA-mediated effects. We selected one of these ORFs, G029442--renamed GREP1 (Glycine-Rich Extracellular Protein-1)--for further characterization. We found that GREP1 encodes a secreted protein highly expressed in breast cancer, and its knock-out in 263 cancer cell lines showed preferential essentiality in breast cancer derived lines. Analysis of the secretome of GREP1-expressing cells showed increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth inhibitory effect of GREP1 knock-out. Taken together, these experiments suggest that the non-canonical ORFeome is surprisingly rich in biologically active proteins and potential cancer therapeutic targets deserving of further study.

cancer biology↗

bioRxiv2018

Up, down, and out: next generation libraries for genome-wide CRISPRa, CRISPRi, and CRISPR-Cas9 knockout genetic screens

Advances in CRISPR-Cas9 technology have enabled the flexible modulation of gene expression at large scale. In particular, the creation of genome-wide libraries for CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi), and CRISPR activation (CRISPRa) has allowed gene function to be systematically interrogated. Here, we evaluate numerous CRISPRko libraries and show that our recently-described CRISPRko library (Brunello) is more effective than previously published libraries at distinguishing essential and non-essential genes, providing approximately the same perturbation-level performance improvement over GeCKO libraries as GeCKO provided over RNAi. Additionally, we developed genome-wide libraries for CRISPRi (Dolcetto) and CRISPRa (Calabrese). Negative selection screens showed that Dolcetto substantially outperforms existing CRISPRi libraries with fewer sgRNAs per gene and achieves comparable performance to CRISPRko in the detection of gold-standard essential genes. We also conducted positive selection CRISPRa screens and show that Calabrese outperforms the SAM library approach at detecting vemurafenib resistance genes. We further compare CRISPRa to genome-scale libraries of open reading frames (ORFs). Together, these libraries represent a suite of genome-wide tools to efficiently interrogate gene function with multiple modalities.tracr

genomics↗

bioRxiv2017

Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

The CRISPR-Cas9 system has revolutionized gene editing both on single genes and in multiplexed loss-of-function screens, enabling precise genome-scale identification of genes essential to proliferation and survival of cancer cells. However, previous studies reported that an anti-proliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, particularly in the setting of copy number gain1-4. We performed genome-scale CRISPR-Cas9 essentiality screens on 342 cancer cell lines and found that this effect is common to all lines, leading to false positive results when targeting genes in copy number amplified regions. We developed CERES, a computational method to estimate gene dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy-number-specific effect, as well as variable sgRNA activity. We applied CERES to sets of screens performed with different sgRNA libraries and found that it reduces false positive results and provides meaningful estimates of sgRNA activity. As a result, the application of CERES improves confidence in the interpretation of genetic dependency data from CRISPR-Cas9 essentiality screens of cancer cell lines.

genomics↗