A Novel IRAK4 Inhibitor DW18134 Ameliorates Peritonitis and Inflammatory Bowel Disease.

IRAK4 is a critical mediator in NF-κB-regulated inflammatory signaling and has emerged as a promising therapeutic target for the treatment of autoimmune diseases; however, none of its inhibitors have received FDA approval. In this study, we identified a novel small-molecule IRAK4 kinase inhibitor, DW18134, with an IC50 value of 11.2 nM. DW18134 dose-dependently inhibited the phosphorylation of IRAK4 and IKK in primary peritoneal macrophages and RAW264.7 cells, inhibiting the secretion of TNF-α and IL-6 in both cell lines. The in vivo study demonstrated the efficacy of DW18134, significantly attenuating behavioral scores in an LPS-induced peritonitis model. Mechanistically, DW18134 reduced serum TNF-α and IL-6 levels and attenuated inflammatory tissue injury. By directly blocking IRAK4 activation, DW18134 diminished liver macrophage infiltration and the expression of related inflammatory cytokines in peritonitis mice. Additionally, in the DSS-induced colitis model, DW18134 significantly reduced the disease activity index (DAI) and normalized food and water intake and body weight. Furthermore, DW18134 restored intestinal damage and reduced inflammatory cytokine expression in mice by blocking the IRAK4 signaling pathway. Notably, DW18134 protected DSS-threatened intestinal barrier function by upregulating tight junction gene expression. In conclusion, our findings reported a novel IRAK4 inhibitor, DW18134, as a promising candidate for treating inflammatory diseases, including peritonitis and IBD.

A novel germline BRCA1 mutation identified in a family with hereditary breast and ovarian cancer syndrome.

Pathogenic germline mutations occurring in the BRCA1 (MIM:113705) and BRCA2 (MIM: 600185), which always result in truncated protein or nonsense-mediated mRNA decay, have been identified to increase the risk of hereditary breast, ovarian, pancreatic, prostate, and melanoma cancers. Recent studies show that BRCA1/2 germline mutations also contribute to half of all hereditary breast and ovarian cancer (HBOC). In this case series, we reported a novel frameshift mutation of the BRCA1 gene. This novel frameshift mutation occurs in exon10 of BRCA1 and may result in a lack of the serine cluster domain and BRCA1 C-terminus domain, which mediates the function of BRCA1 in DNA repair and are responsible for activation function of BRCA1. The mutation was present in a Chinese hereditary male/female breast and ovarian cancer family characterized by a high incidence of breast cancer and/or ovarian cancer among the relatives and by a high incidence of triple negative breast cancer (TNBC). Our findings speculate that BRCA1 E1148Rfs*7 mutation may be related to the occurrence of HBOC and even TNBC. Interestingly, three cases of TNBC with this novel BRCA1 mutation in this case series showed a good disease-free survival, one of them has a disease-free survival up to 7 years. Therefore, further study is required to confirm that whether this mutation is associated with good prognosis of HBOC.

Peel learning for pathway-related outcome prediction.

MOTIVATION: Traditional regression models are limited in outcome prediction due to their parametric nature. Current deep learning methods allow for various effects and interactions and have shown improved performance, but they typically need to be trained on a large amount of data to obtain reliable results. Gene expression studies often have small sample sizes but high dimensional correlated predictors so that traditional deep learning methods are not readily applicable. RESULTS: In this article, we proposed peel learning, a novel neural network that incorporates the prior relationship among genes. In each layer of learning, overall structure is peeled into multiple local substructures. Within the substructure, dependency among variables is reduced through linear projections. The overall structure is gradually simplified over layers and weight parameters are optimized through a revised backpropagation. We applied PL to a small lung transplantation study to predict recipients' post-surgery primary graft dysfunction using donors' gene expressions within several immunology pathways, where PL showed improved prediction accuracy compared to conventional penalized regression, classification trees, feed-forward neural network and a neural network assuming prior network structure. Through simulation studies, we also demonstrated the advantage of adding specific structure among predictor variables in neural network, over no or uniform group structure, which is more favorable in smaller studies. The empirical evidence is consistent with our theoretical proof of improved upper bound of PL's complexity over ordinary neural networks. AVAILABILITY AND IMPLEMENTATION: PL algorithm was implemented in Python and the open-source code and instruction will be available at https://github.com/Likelyt/Peel-Learning. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.