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Messer, J.

Publications and source records attributed to Messer, J..

2 recordsLinked to original sources

Development of a small molecule that corrects misfolding and increases secretion of Z α1-antitrypsin

Severe 1-antitrypsin deficiency results from the Z allele (Glu342Lys) that causes the accumulation of homopolymers of mutant 1-antitrypsin within the endoplasmic reticulum of hepatocytes in association with liver disease. We have used a DNA-encoded chemical library to undertake a high throughput screen to identify small molecules that bind to, and stabilise Z 1-antitrypsin. The lead compound blocks Z 1-antitrypsin polymerisation in vitro, reduces intracellular polymerisation and increases the secretion of Z 1-antitrypsin three-fold in mammalian cells including an iPSC model of disease. Crystallographic and biophysical analyses demonstrate that GSK716 and related molecules bind to a cryptic binding pocket, negate the local effects of the Z mutation and stabilise the bound state against progression along the polymerization pathway. Oral dosing of transgenic mice at 100 mg/kg three times a day for 20 days increased the secretion of Z 1-antitrypsin into the plasma by 7-fold. There was no observable clearance of hepatic inclusions with respect to controls. This study provides proof-of-principle that mutation ameliorating small molecules are a viable approach to treat protein conformational diseases.

pharmacology and toxicology↗

Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA

PurposeRecent data suggest that imaging radiomics features for a tumor could predict important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. For Head and Neck Squamous Cell Carcinoma (HNSCC), we perform a comprehensive study to discover the imaging-genomics associations and explore the potential of predicting tumor genomic alternations using radiomic features.\n\nMethodsOur retrospective study integrates whole-genome multi-omics data from The Cancer Genome Atlas (TCGA) with matched computed tomography imaging data from The Cancer Imaging Archive (TCIA) for the same set of 126 HNSCC patients. Linear regression analysis and gene set enrichment analysis are used to identify statistically significant associations between radiomic imaging features and genomic features. Random forest classifier is used to predict two key HNSCC molecular biomarkers, the status of human papilloma virus (HPV) and disruptive TP53 mutation, based on radiomic features.\n\nResultsWide-spread and statistically significant associations are discovered between genomic features (including miRNA expressions, protein expressions, somatic mutations, and transcriptional activities, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of HPV and TP53 mutation status using radiomic features achieves an area under the receiver operating characteristics curve (AUC) of 0.71 and 0.641, respectively.\n\nConclusionOur analysis suggests that radiomic features are associated with genomic characteristics in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.

cancer biology↗