Role of microRNA-223 in the regulation of poly(ADP-ribose) polymerase in pediatric patients with Crohn's disease.

OBJECTIVES: Crohn's disease (CD) is a multifactorial disease, characterized by oxidant-induced tissue injury with a possible activation of poly(ADP-ribose) polymerase (PARP)-1. MicroRNAs (miRs) can offer a potential link between the genetic susceptibility, environmental and immunologic factors in the pathogenesis of CD. Previously, PARP-1 was identified as a direct target gene of miR-223 in an epithelial cell line. Our aim was to examine PARP activation and miR-223 expression in colonic biopsies of pediatric CD. To support our in vivo findings, the effect of lipopolysaccharide (LPS) on same parameters was examined in HT-29 colonic epithelial cell line. METHODS: Colonic biopsies were taken from patients with macroscopically inflamed and intact mucosa with CD and controls. LPS treated HT-29 cells served as our in vitro model. To analyze the PARP-1 expression real-time PCR, Western blot and immunohistochemical analyses were used. PARP-1 enzymatic activity was assessed on the basis of poly(ADP-ribosyl)ated proteins. Expression of miR-223 was examined by real-time PCR. RESULTS: PARP-1 mRNA and miR-223 expression was significantly elevated, however, the amount of PARP-1 protein and poly(ADP-ribose) was reduced in pediatric CD compared to controls. LPS incubation did not affect the expression of PARP-1 mRNA, however, decreased miR-223 expression, and enhanced PARP-1 activity. CONCLUSIONS: In our study, we showed that the expression of miR-223 is up-regulated and poly(ADP-ribosyl)ation is reduced in pediatric patients with CD. Moreover, we confirmed their opposite change in LPS treated epithelial cells, too. These data suggest that the hypofunctionality of PARP-1 may play a potential role in the pathomechanism of CD.

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors.

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.