High resolution mapping and positional cloning of ENU-induced mutations in the Rw region of mouse chromosome 5

<p>Abstract</p> <p>Background</p> <p>Forward genetic screens in mice provide an unbiased means to identify genes and other functional genetic elements in the genome. Previously, a large scale ENU mutagenesis screen was conducted to query the functional content of a ~50 Mb region of the mouse genome on proximal Chr 5. The majority of phenotypic mutants recovered were embryonic lethals.</p> <p>Results</p> <p>We report the high resolution genetic mapping, complementation analyses, and positional cloning of mutations in the target region. The collection of identified alleles include several with known or presumed functions for which no mutant models have been reported (<it>Tbc1d14</it>, <it>Nol14</it>, <it>Tyms</it>, <it>Cad</it>, <it>Fbxl5</it>, <it>Haus3</it>), and mutations in genes we or others previously reported (<it>Tapt1</it>, <it>Rest</it>, <it>Ugdh</it>, <it>Paxip1</it>, <it>Hmx1, Otoe, Nsun7</it>). We also confirmed the causative nature of a homeotic mutation with a targeted allele, mapped a lethal mutation to a large gene desert, and localized a spermiogenesis mutation to a region in which no annotated genes have coding mutations. The mutation in <it>Tbc1d14 </it>provides the first implication of a critical developmental role for RAB-GAP-mediated protein transport in early embryogenesis.</p> <p>Conclusion</p> <p>This collection of alleles contributes to the goal of assigning biological functions to all known genes, as well as identifying novel functional elements that would be missed by reverse genetic approaches.</p

The Patient-Provider Relationship Is Associated with Hepatitis C Treatment Eligibility: A Prospective Mixed-Methods Cohort Study.

Hepatitis C virus (HCV) treatment has the potential to cure the leading cause of cirrhosis and hepatocellular carcinoma. However, only those deemed eligible for treatment have the possibility of this cure. Therefore, understanding the determinants of HCV treatment eligibility is critical. Given that effective communication with and trust in healthcare providers significantly influences treatment eligibility decisions in other diseases, we aimed to understand patient-provider interactions in the HCV treatment eligibility process. This prospective cohort study was conducted in the VA Pittsburgh Healthcare System. Patients were recruited after referral for gastroenterology consultation for HCV treatment with interferon and ribavirin. Consented patients completed semi-structured interviews and validated measures of depression, substance and alcohol use, and HCV knowledge. Two coders analyzed the semi-structured interviews. Factors associated with patient eligibility for interferon-based therapy were assessed using multivariate logistic regression. Of 339 subjects included in this analysis, only 56 (16.5%) were deemed eligible for HCV therapy by gastroenterology (GI) providers. In the multivariate logistic regression, patients who were older (OR = 0.96, 95%CI = 0.92-0.99, p = .049), reported concerns about the GI provider (OR = 0.40, 95%CI = 0.10-0.87, p = 0.02) and had depression symptoms (OR = 0.32, 95%CI = 0.17-0.63, p = 0.001) were less likely to be eligible. Patients described barriers that included feeling stigmatized and poor provider interpersonal or communication skills. In conclusion, we found that patients' perceptions of the relationship with their GI providers were associated with treatment eligibility. Establishing trust and effective communication channels between patients and providers may lower barriers to potential HCV cure

Demographic and Clinical Characteristics.

<p>Demographic and Clinical Characteristics.</p

Variables Meeting Selection Criteria from the Univariate Analyses of Treatment Eligibility.

<p>Variables Meeting Selection Criteria from the Univariate Analyses of Treatment Eligibility.</p

Clinical and Study Flow.

<p>Clinical and Study Flow.</p

Multivariable Logistic Regression Model for Treatment Eligibility.

<p>Multivariable Logistic Regression Model for Treatment Eligibility.</p