Biology subjects

Weichert, W.

Publications and source records attributed to Weichert, W..

3 recordsLinked to original sources

bioRxiv2019

Expression of 9-O- and 7,9-O-acetyl modified sialic acids in cells and their effects on influenza viruses

Sialic acids (Sia) are widely displayed on the surfaces of cells and tissues. Sia come in a variety of chemically modified forms, including those with acetyl modifications at the C7, C8, and C9 positions. Here, we analyzed the distribution and amounts of these acetyl modifications in different human and canine cells. As Sia or their variant forms are receptors for influenza A and influenza C viruses, we examined the effects of these modifications on virus infections. We confirmed that 9-O-acetyl and 7,9-O-acetyl modified Sia are widely but variably expressed across cell lines from both humans and canines. While they were expressed on the cell surface of canine MDCK cell lines, they were located primarily within the Golgi compartment of human HEK-293 and A549 cells. The O-acetyl modified Sia were expressed at low levels of 1-2% of total Sia in these cell lines. We knocked out and over-expressed the sialate O-acetyltransferase gene (CasD1), and knocked out the sialate O-acetylesterase gene (SIAE) using CRISPR/Cas9 editing. Knocking out CasD1 removed 7,9-O- and 9-O-acetyl Sia expression, confirming previous reports. However, over-expression of CasD1 and knockout of SIAE gave only modest increases in 9-O-acetyl levels in cells and no change in 7,9-O-acetyl levels, indicating that there are complex regulations of these modifications. These modifications were essential for influenza C infection, but had no obvious effect on influenza A infection.\n\nIMPORTANCESialic acids are key glycans that are involved in many different normal cellular functions, as well as being receptors for many pathogens. However, Sia come in diverse chemically modified forms. Here we examined and manipulated the expression of 7,9-O- and 9-O-acetyl modified Sia on cells commonly used in influenza virus and other research by engineering the enzymes that produce or remove the acetyl groups.

cell biology↗

bioRxiv2019

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

PurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).\n\nMaterials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.\n\nResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fishers exact test P<0.001).\n\nConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

bioinformatics↗

bioRxiv2019

Distinct immune evasion in APOBEC-enriched, HPV-negative HNSCC

Immune checkpoint inhibition leads to response in some patients with head and neck squamous cell carcinoma (HNSCC). Robust biomarkers are lacking to date.\n\nWe analyzed viral status, gene expression signatures, mutational load and mutational signatures in whole exome and RNA-sequencing data of the HNSCC TCGA dataset (N = 496) and a validation set (DKTK MASTER cohort, N = 10). Public single-cell gene expression data from 17 HPV-negative HNSCC were separately re-analyzed.\n\nAmong HPV-negative HNSCC, APOBEC3-associated TCW motif mutations but not total single nucleotide variant burden were significantly associated with inflammation. APOBEC3-enriched HPV-negative HNSCC showed higher T-cell inflammation and immune checkpoint expression. Mutations in immune-evasion pathways were enriched in these tumors. APOBEC3B and 3C expression was identified in tumor cells and correlated with tumor inflammation.\n\nWe identified an APOBEC-enriched subgroup of HPV-negative HNSCC with a distinct immunogenic phenotype, potentially mediating response to immunotherapy.

cancer biology↗