Biology subjects

Brown, W.

Publications and source records attributed to Brown, W..

3 recordsLinked to original sources

bioRxiv2018

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study

Limited health literacy can be a barrier to healthcare delivery, but widespread classification of patient health literacy is challenging. We applied natural language processing and machine learning on a large sample of 283,216 secure messages sent from 6,941 patients to their clinicians for this study to develop and validate literacy profiles as indicators of patients health literacy. All patients were participants in Kaiser Permanente Northern Californias DISTANCE Study. We created three literacy profiles, comparing performance of each literacy profile against a gold standard of patient self-report. We also analyzed associations between the literacy profiles and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical data such as sex, race, continuous medication gaps and severe hypoglycemia. Literacy profiles varied in their test characteristics, with C-statistics ranging from 0.61-0.74. Relationships between literacy profiles and health outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy profiles as having limited health literacy were older and more likely minority; had poorer medication adherence and glycemic control; and higher rates of hypoglycemia, comorbidities and healthcare utilization. This research represents the first successful attempt to use natural language processing and machine learning to measure health literacy. Literacy profiles offer an automated and economical way to identify patients with limited health literacy and a greater vulnerability to poor health outcomes.

bioinformatics↗

bioRxiv2018

Biased signaling downstream of epidermal growth factor receptor regulates proliferative versus apoptotic response to ligand

Inhibition of EGFR signaling by small molecule kinase inhibitors and monocloncal antibodies has proven effective in the treatment of multiple cancers. In contrast, metastatic breast cancers (BC) derived from EGFR-expressing mammary tumors are inherently resistant to EGFR-targeted therapies. Mechanisms that contribute to this inherent resistance remain poorly defined. Here we show that in contrast to primary tumors, ligand-mediated activation of EGFR in metastatic BC is dominated by STAT1 signaling. This change in downstream signaling leads to apoptosis and growth inhibition in response to EGF in metastatic BC cells. Mechanistically, these changes in downstream signaling result from an increase in the internalized pool of EGFR in metastatic cells, increasing physical access to the nuclear pool of STAT1. Along these lines, an EGFR mutant that is defective in endocytosis is unable to elicit STAT1 phosphorylation and apoptosis. Additionally, inhibition of endosomal signaling using an EGFR inhibitor linked to a nuclear localization signal specifically prevents EGF-induced STAT1 phosphorylation and cell death, without affecting EGFR:ERK1/2 signaling. Pharmacologic blockade of ERK1/2 signaling through the use of the allosteric MEK1/2 inhibitor, trametinib, dramatically biases downstream EGFR signaling toward a STAT1 dominated event, resulting in enhanced EGF-induced apoptosis in metastatic BC cells. Importantly, combined administration of trametinib and EGF also facilitated an apoptotic switch in EGFR-transformed primary tumor cells, but not normal mammary epithelial cells. These studies reveal a fundamental distinction for EGFR function in metastatic BC. Furthermore, the data demonstrate that pharmacological biasing of EGFR signaling toward STAT1 activation is capable of revealing the apoptotic function of this critical pathway.

cancer biology↗

bioRxiv2017

The Antiviral and Cancer Genomic DNA Deaminase APOBEC3H Is Regulated by a RNA-Mediated Dimerization Mechanism

Human APOBEC3H and homologous single-stranded DNA cytosine deaminases are unique to mammals. These DNA editing enzymes function in innate immunity by restricting the replication of viruses and transposons. Misregulated APOBEC3H also contributes to cancer mutagenesis. Here we address the role of RNA in APOBEC3H regulation. APOBEC3H co-purifies with RNA as an inactive protein, and RNase A treatment yields enzyme preparations with stronger DNA deaminase activity. RNA binding-defective mutants are DNA hypermutators. Chromatography profiles and crystallographic data demonstrate a mechanism in which double-stranded RNA mediates enzyme dimerization. RNA binding is required for APOBEC3H cytoplasmic localization and for packaging into HIV-1 particles and antiviral activity. Related DNA deaminases including other APOBEC3 family members and the antibody gene diversification enzyme AID also bind RNA and are predicted to have a similar RNA binding motif suggesting mechanistic conservation and relevance to innate and adaptive immunity and to multiple diseases.\n\nHIGHLIGHTSRNA inhibits human APOBEC3H DNA cytosine deaminase activity\n\nRNA binding mutants are DNA hypermutators\n\nX-ray structure demonstrates an RNA duplex-mediated APOBEC3H dimerization mechanism\n\nRNA binding is required for packaging into HIV-1 particles and antiviral activity

microbiology↗