Purpose: To determine the relationship between germline pathogenic variants (PV) in cancer predisposition genes and the risk of ductal carcinoma in situ (DCIS).
Experimental Design: Germline PV frequencies in breast cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, PALB2, RAD51C, and RAD51D) were compared between DCIS cases and unaffected controls and between DCIS and invasive ductal breast cancer (IDC) cases from a clinical testing cohort (n = 9,887), a population-based cohort (n = 3,876), and the UK Biobank (n = 2,421). The risk of contralateral breast cancer (CBC) for DCIS cases with PV was estimated in the population-based cohort.
Reducing fibrous aggregates of protein tau is a possible strategy for halting progression of Alzheimer's dis-ease (AD). Previously we found that in vitro the D-peptide D-TLKIVWC disassembles tau fibrils from AD brains (AD-tau) into benign segments with no energy source present beyond ambient thermal agitation. This disassembly by a short peptide was unexpected, given that AD-tau is sufficiently stable to withstand disas-sembly in boiling SDS detergent.
View Article and Find Full Text PDFAmyloid fibrils of tau are increasingly accepted as a cause of neuronal death and brain atrophy in Alzheimer's disease (AD). Diminishing tau aggregation is a promising strategy in the search for efficacious AD therapeutics. Previously, our laboratory designed a six-residue, nonnatural amino acid inhibitor D-TLKIVW peptide (6-DP), which can prevent tau aggregation in vitro.
View Article and Find Full Text PDFReducing fibrous aggregates of protein tau is a possible strategy for halting progression of Alzheimer's disease (AD). Previously we found that the D-peptide D-TLKIVWC disassembles tau fibrils from AD brains (AD-tau) into benign segments with no energy source present beyond ambient thermal agitation. This disassembly by a short peptide was unexpected, given that AD-tau is sufficiently stable to withstand disassembly in boiling SDS detergent.
View Article and Find Full Text PDFMacromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information.
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