Biomaterials in Valvular Heart Diseases

Valvular heart disease (VHD) occurs as the result of valvular malfunction, which can greatly reduce patient\u27s quality of life and if left untreated may lead to death. Different treatment regiments are available for management of this defect, which can be helpful in reducing the symptoms. The global commitment to reduce VHD-related mortality rates has enhanced the need for new therapeutic approaches. During the past decade, development of innovative pharmacological and surgical approaches have dramatically improved the quality of life for VHD patients, yet the search for low cost, more effective, and less invasive approaches is ongoing. The gold standard approach for VHD management is to replace or repair the injured valvular tissue with natural or synthetic biomaterials. Application of these biomaterials for cardiac valve regeneration and repair holds a great promise for treatment of this type of heart disease. The focus of the present review is the current use of different types of biomaterials in treatment of valvular heart diseases

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens.

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes

SL-scan identifies synthetic lethal interactions in cancer using metabolic networks

Abstract Exploiting synthetic lethality is a promising strategy for developing targeted cancer therapies. However, identifying clinically significant synthetic lethal (SL) interactions among a large number of gene combinations is a challenging computational task. In this study, we developed the SL-scan pipeline based on metabolic network modeling to discover SL interaction. The SL-scan pipeline identifies the association between simulated Flux Balance Analysis knockout scores and mutation data across cancer cell lines and predicts putative SL interactions. We assessed the concordance of the SL pairs predicted by SL-scan with those of obtained from analysis of the CRISPR, shRNA, and PRISM datasets. Our results demonstrate that the SL-scan pipeline outperformed existing SL prediction approaches based on metabolic networks in identifying SL pairs in various cancers. This study emphasizes the importance of integrating multiple data sources, particularly mutation data, when identifying SL pairs for targeted cancer therapies. The findings of this study may lead to the development of novel targeted cancer therapies