Biology subjects

Wilcox, A.

Publications and source records attributed to Wilcox, A..

5 recordsLinked to original sources

bioRxiv2026

Genome-wide CRISPR knockout cell screening platform for the disease vector tick species Ixodes scapularis

The black legged tick, Ixodes scapularis, is a vector of the bacterium that causes Lyme disease and several other illnesses, including anaplasmosis, babesiosis, and tick-borne encephalitis. Although high-quality genome annotations are available for I. scapularis, functional understanding of I. scapularis genes is limited. To address this, we developed a platform for genome-wide CRISPR-Cas9 knockout screening in I. scapularis cells. To evaluate the platform, we performed a screen to identify genes associated with cellular fitness, and screens for resistance to treatment with copper chloride, Antimycin A, or Destruxin A (DA), a cyclic hexadepsipeptide produced by the pathogenic fungus Metarhizium anisopliae. In each case, the screens implicate specific sets of conserved and non-conserved I. scapularis genes in relevant cellular functions, providing the first experimental evidence of function for a large set of I. scapularis genes. Altogether, in this first-of-its-kind effort for the arthropod subclass Acari, we present an unbiased genome-wide CRISPR-Cas9 knockout cell screening platform, related resources, and datasets that will be broadly useful to efficiently uncover cellular functions of I. scapularis genes.

genetics↗

bioRxiv2024

Model-based prediction and ascription of deforestation risk within commodity sourcing domains: Improving traceability in the palm oil supply chain

Palm oil accounts for approximately 50% of global vegetable oil production, and trends in consumption have driven large-scale expansion of oil palm (Elaeis guineensis) plantations in Southeast Asia. This expansion has led to deforestation and other socio-environmental concerns that challenge consumer goods companies to meet no deforestation and sustainability commitments. In support of these commitments and supply chain traceability, we seek to improve on the current industry standard sourcing model for ascribing social and environmental risks to particular actors. Using passive geolocational traceability data (n = 3,355,437 cellular pings) and machine learning models, we evaluate the industry standard sourcing model, and we predict with high accuracy the undisclosed sourcing domains for 1,570 Indonesian and Malaysian palm oil mills on the Universal Mill List (as of November 2021). In combination with the World Wide Fund for Nature - Netherlands Forest Fore-sight, we use our predicted sourcing domains to provide an illustrative example of the proportional allocation of future deforestation, carbon loss, and biodiversity risk to relevant actors, permitting targeted outreach, contract negotiation, and mitigation of large-scale resource degradation. This example depends on a subset of model predictions in the absence of disclosed traceability data. The utilization of additional predictions or disclosed traceability data would influence and improve the results.

ecology↗

bioRxiv2019

Leaf -- An open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research

ObjectiveAcademic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of data that originate from multiple sources. Enterprise Data Warehouses (EDWs) have emerged as central resources for these data, but they often require an informatician to extract meaningful information, thereby limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying and extracting clinical data from heterogeneous data sources.\n\nMaterials and MethodsLeaf utilizes a flexible biomedical concept system to define hierarchical items and ontologies. Each Leaf concept contains both textual representations and associated SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.\n\nResultsLeaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an EDW with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org) and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf.\n\nDiscussionLeaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication from existing databases.\n\nConclusionLeaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly integrate with existing enterprise user authentication systems and clinical databases in situ. We demonstrate its unique technical strengths and success alongside its friendly user interface. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks and clinical studies involving large patient cohorts.

bioinformatics↗

bioRxiv2018

Comparative validation of breast cancer risk prediction models and projections for future risk stratification

BackgroundWell-validated risk models are critical for risk stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) tool for comparative model validation of five-year risk of invasive breast cancer in a prospective cohort, and to make projections for population risk stratification.\n\nMethodsPerformance of two recently developed models, iCARE-BPC3 and iCARE-Lit, were compared with two established models (BCRAT, IBIS) based on classical risk factors in a UK-based cohort of 64,874 women (863 cases) aged 35-74 years. Risk projections in US White non-Hispanic women aged 50-70 years were made to assess potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).\n\nResultsThe best calibrated models were iCARE-Lit (expected to observed number of cases (E/O)=0.98 (95% confidence interval [CI]=0.87 to 1.11)) for women younger than 50 years; and iCARE-BPC3 (E/O=1.00 (0.93 to 1.09)) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify ~500,000 women at moderate to high risk (>3% five-year risk). Additional information on MD and a PRS based on 172 variants is expected to increase this to ~3.6 million, and among them, ~155,000 invasive breast cancer cases are expected within five years.\n\nConclusionsiCARE models based on classical risk factors perform similarly or better than BCRAT or IBIS. Addition of MD and PRS can lead to substantial improvements in risk stratification. Independent prospective validation of integrated models is needed prior to clinical evaluation risk stratified breast cancer screening and prevention.

epidemiology↗

bioRxiv2018

Sulforaphane Alters the Acidification of the Vacuole to Trigger Cell Death

Sulforaphane (SFN) is a compound [1-isothiocyanato-4-(methylsulfinyl)- butane] found in broccoli and other cruciferous vegetables that is currently of interest because of its potential as a chemopreventive and a chemotherapeutic drug. Recent studies in a diverse range of cellular and animal models have shown that SFN is involved in multiple intracellular signaling pathways that regulate cell death, cell cycle progression, and cell invasion. In order to better understand the mechanisms of action behind SFN-induced cell death, we undertook an unbiased genome wide screen with the yeast knockout (YKO) library to identify SFN sensitive (SFNS) mutants. Our mutants were enriched with knockouts in genes linked to vacuolar function suggesting a link between this organelle and SFNs mechanism of action in yeast. Our subsequent work revealed that SFN increases the vacuolar pH of yeast cells and that varying the vacuolar pH can alter the sensitivity of yeast cells to the drug. In fact, several mutations that lower the vacuolar pH in yeast actually made the cells resistant to SFN (SFNR). Finally, we show that human lung cancer cells with more acidic compartments are also SFNR suggesting that SFNs mechanism of action identified in yeast may carry over to higher eukaryotic cells.

cell biology↗