Genetic analysis of Caenorhabditis elegans pry-1/Axin suppressors identifies genes involved in reproductive structure development, stress responses, and aging.

The Axin family of scaffolding proteins regulates a wide array of developmental and post-developmental processes in eukaryotes. Studies in the nematode Caenorhabditis elegans have shown that the Axin homolog PRY-1 plays essential roles in multiple tissues. To understand the genetic network of pry-1, we focused on a set of genes that are differentially expressed in the pry-1-mutant transcriptome and are linked to reproductive structure development. Knocking down eight of the genes (spp-1, clsp-1, ard-1, rpn-7, cpz-1, his-7, cdk-1, and rnr-1) via RNA interference efficiently suppressed the multivulva phenotype of pry-1 mutants. In all cases, the ectopic induction of P3.p vulval precursor cell was also inhibited. The suppressor genes are members of known gene families in eukaryotes and perform essential functions. Our genetic interaction experiments revealed that in addition to their role in vulval development, these genes participate in one or more pry-1-mediated biological events. Whereas four of them (cpz-1, his-7, cdk-1, and rnr-1) function in both stress response and aging, two (spp-1 and ard-1) are specific to stress response. Altogether, these findings demonstrate the important role of pry-1 suppressors in regulating developmental and post-developmental processes in C. elegans. Given that the genes described in this study are conserved, future investigations of their interactions with Axin and their functional specificity promises to uncover the genetic network of Axin in metazoans.

A Digital Case-Finding Algorithm for Diagnosed but Untreated Hepatitis C: A Tool for Increasing Linkage to Treatment and Cure.

BACKGROUND AND AIMS: Although chronic HCV infection increases mortality, thousands of patients remain diagnosed-but-untreated (DBU). We aimed to (1) develop a DBU phenotyping algorithm, (2) use it to facilitate case finding and linkage to care, and (3) identify barriers to successful treatment. APPROACH AND RESULTS: We developed a phenotyping algorithm using Java and SQL and applied it to ~2.5 million EPIC electronic medical records (EMRs; data entered January 2003 to December 2017). Approximately 72,000 EMRs contained an HCV International Classification of Diseases code and/or diagnostic test. The algorithm classified 10,614 cases as DBU (HCV-RNA positive and alive). Its positive and negative predictive values were 88% and 97%, respectively, as determined by manual review of 500 EMRs randomly selected from the ~72,000. Navigators reviewed the charts of 6,187 algorithm-defined DBUs and they attempted to contact potential treatment candidates by phone. By June 2020, 30% (n = 1,862) had completed an HCV-related appointment. Outcomes analysis revealed that DBU patients enrolled in our care coordination program were more likely to complete treatment (72% [n = 219] vs. 54% [n = 256]; P < 0.001) and to have a verified sustained virological response (67% vs. 46%; P < 0.001) than other patients. Forty-eight percent (n = 2,992) of DBU patients could not be reached by phone, which was a major barrier to engagement. Nearly half of these patients had Fibrosis-4 scores ≥ 2.67, indicating significant fibrosis. Multivariable logistic regression showed that DBUs who could not be contacted were less likely to have private insurance than those who could (18% vs. 50%; P < 0.001). CONCLUSIONS: The digital DBU case-finding algorithm efficiently identified potential HCV treatment candidates, freeing resources for navigation and coordination. The algorithm is portable and accelerated HCV elimination when incorporated in our comprehensive program.