Biology subjects

Hebbring, S.

Publications and source records attributed to Hebbring, S..

2 recordsLinked to original sources

bioRxiv2024

Identifying Family Structures from Obituaries and Matching them to Patients in an Electronic Heath Record.

MotivationFamily data is a valuable data source in bioinformatic research. This is because family members often share common genetic and environmental exposures. Collecting this family data is traditionally very labor intensive but advances in electronic health record (EHR) data mining has proven useful when identifying pedigrees linked to longitudinal health histories. These are called e-pedigrees. Unfortunately, e-pedigrees tend to miss the oldest generations who inherently have the longest and richest health histories. A good source of family data from older generations includes obituaries, as they have a formulaic nature making them a good candidate for natural language processing that can extract relationships to the decedent. While there have been several studies on obtaining such data from obituaries, we demonstrate for the first-time approaches that tie that information to an EHR. ResultsNLP extraction resulted in 8,166,534 family members being abstracted from 567,279 obituaries published in the state of Wisconsin. After matching decedent and family members to patients in the EHR, we identified 109,365 unique patients that were put in 34,158 pedigrees. The largest pedigree consisted of 21 individuals. Heritability of adult height was quantified (H2= 0.51 +- .04, P=< 1.00e-07) demonstrating this datas use in genetic research. The heritability data, coupled with overlapping data in a biobank, suggested 80% - 90% of familial relationships were accurately defined. The totality of these findings demonstrate obituaries with the oldest generations can be highly informative for bioinformatic research. Availability and ImplementationCode is available on GitHub at https://github.com/jgmayer672/ObituaryNLP.

bioinformatics↗

bioRxiv2023

Orphan nuclear receptor NR2E3 and its small-molecule agonist induce cancer cell apoptosis through regulating p53, IFNα and MYC pathways

Orphan nuclear receptor NR2E3 activates p53 and induces cancer cell apoptosis. Further studies on p53-dependent and -independent functions of wild-type and mutated NR2E3 are needed. Herein, we showed that NR2E3 enhanced p53-DNA interactions in diverse cancer cells and up-regulated p53 and IFN pathways while down-regulating MYC pathway in cervical cancer cells. Studies of "All of Us" and TCGA databases showed NR2E3 nonsynonymous mutations associating with four cancers. We stratified NR2E3 SNVs for their cancer implications with the p53 reporter. A cancer-associated NR2E3R97Hmutation not only lost the wild-types tumor-suppressing functions but also prohibited the wild-type from enhancing p53 acetylation. These observations implicated the potential for pharmaceutically activating NR2E3 to suppress cancer. Indeed, NR2E3s small-molecule agonist 11a repressed 2-D and 3-D cultures of primary cells and cell lines of cervical cancer, in which screening FDA-approved anti-cancer drugs identified HDAC-1/2 inhibitor Romidepsin operating synergistically with 11a. The underlying molecular mechanisms included 11as down-regulating the transcription of Multidrug Resistance Protein ABCB1 that Romidepsin up-regulated. Transcriptomics studies revealed three synergy modes: (1) "sum-up" mode that the p53 pathway activated individually by 11a and Romidepsin got stronger by the combo; (2) "antagonism" mode that Romidepsin counteracted the activation of the Kras pathway by 11a; and (3) "de novo" mode that the combo instead of each individual drug repressed the MYC pathway. Conclusively, our experiments provide new data supporting tumor-suppressor like functions for wild-type NR2E3, reveal roles of mutated NR2E3 in cancer, and address values of NR2E3s agonist 11a in cancer therapy alone and combined.

cancer biology↗