Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay

The integrative analysis of highâ throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory element function and its contribution to human disease. Here, we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five diseaseâ associated human enhancers and nine diseaseâ associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cellâ types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of diseaseâ associated genetic variation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151884/1/humu23797_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151884/2/humu23797.pd

Quantitative Studies of Amyloidogenic Protein Residue Interaction Networks and Abnormal Ammonia Metabolism in Neurotoxicity and Disease

Investigating similarities among neurological diseases can provide insight into disease processes. Two prominent commonalities of neurological diseases are the formation of amyloid deposits and altered ammonia and glutamate metabolism. Computational techniques were used to explore these processes in several neurological diseases. Residue interaction networks (RINs) abstract protein structure into a series of nodes (representing residues) and edges (representing connections between residues likely to interact). Analyzing the RINs of monomeric forms of amyloidogenic proteins for common network features revealed similarities not previously known. First, amyloidogenic variants of lysozyme were used to demonstrate the usefulness of RINs to the study of amyloidogenic proteins. Next, I compared RINs of amyloidogenic proteins with randomized control networks and a group of real protein controls and found similarities in network structures unique to amyloidogenic proteins. The use of 3D structure data and network structure data of amyloid-beta (1-42) (Abeta42) in a hydrophobic, membrane-mimicking solvent led to the identification of an interaction between Val24 and Ile31 as potentially involved in preventing Abeta aggregation. Since Abeta causes oxidative damage, since the ammonia metabolism enzyme glutamine synthetase is particularly susceptible to oxidative damage, and since glutamate plays a central role in neuronal function, I expanded my research to include the study of ammonia and glutamate metabolism in neurological diseases. A computational model of the effects of the interactions between the amount of dietary protein and the activities of ammonia metabolism enzymes on blood and brain ammonia levels supports potentially important roles for these enzymes in the protection of neural function. Next, I reviewed the role of amino acid catabolism in Alzheimer’s disease (AD). Common tissue pathology and the ability of memantine, an NMDA receptor antagonist, to relieve symptoms in patients and animal models of AD, major depressive disorder (MDD), and type 2 diabetes (T2D) further support a role for ammonia and glutamate metabolism in disease. Lastly, I found that single nucleotide polymorphisms (SNPs) in select ammonia metabolism genes are associated with these three diseases. The results presented in this dissertation demonstrate that investigating neurological diseases using computational approaches can provide great insight into the common underlying pathologies

Quantitative Studies of Amyloidogenic Protein Residue Interaction Networks and Abnormal Ammonia Metabolism in Neurotoxicity and Disease

Investigating similarities among neurological diseases can provide insight into disease processes. Two prominent commonalities of neurological diseases are the formation of amyloid deposits and altered ammonia and glutamate metabolism. Computational techniques were used to explore these processes in several neurological diseases. Residue interaction networks (RINs) abstract protein structure into a series of nodes (representing residues) and edges (representing connections between residues likely to interact). Analyzing the RINs of monomeric forms of amyloidogenic proteins for common network features revealed similarities not previously known. First, amyloidogenic variants of lysozyme were used to demonstrate the usefulness of RINs to the study of amyloidogenic proteins. Next, I compared RINs of amyloidogenic proteins with randomized control networks and a group of real protein controls and found similarities in network structures unique to amyloidogenic proteins. The use of 3D structure data and network structure data of amyloid-beta (1-42) (Abeta42) in a hydrophobic, membrane-mimicking solvent led to the identification of an interaction between Val24 and Ile31 as potentially involved in preventing Abeta aggregation. Since Abeta causes oxidative damage, since the ammonia metabolism enzyme glutamine synthetase is particularly susceptible to oxidative damage, and since glutamate plays a central role in neuronal function, I expanded my research to include the study of ammonia and glutamate metabolism in neurological diseases. A computational model of the effects of the interactions between the amount of dietary protein and the activities of ammonia metabolism enzymes on blood and brain ammonia levels supports potentially important roles for these enzymes in the protection of neural function. Next, I reviewed the role of amino acid catabolism in Alzheimer’s disease (AD). Common tissue pathology and the ability of memantine, an NMDA receptor antagonist, to relieve symptoms in patients and animal models of AD, major depressive disorder (MDD), and type 2 diabetes (T2D) further support a role for ammonia and glutamate metabolism in disease. Lastly, I found that single nucleotide polymorphisms (SNPs) in select ammonia metabolism genes are associated with these three diseases. The results presented in this dissertation demonstrate that investigating neurological diseases using computational approaches can provide great insight into the common underlying pathologies

Novel organoid models for the functional validation of pancreatic cancer genomic variants

With a five-year survival rate of 9%, pancreatic ductal adenocarcinoma (PDAC) has the one of the worst prognoses of all cancers. Some limitations in the understanding of the disease are due to the lack of representative in vitro patient tumour models. In order to overcome this unmet preclinical need, this thesis outlines the establishment of a method for the development of organoid and isogenic matched primary cancer cell line models from patient derived xenograft (PDX) tumours. In order to create a patient-reflective yet versatile in vitro model, the matched primary cell line was developed further and subsequently generated organoids, termed cell line organoids (CLOs). These CLOs represent the phenotypic and transcriptomic profile of the original organoids and PDX tumour. Recent genome wide association studies (GWAS) and pathway analyses have implicated genes and single nucleotide polymorphisms (SNPs) from the maturity onset diabetes of the young (MODY) gene set and the Pujana ATM Pearson correlation coefficient (PCC) network in the development of PDAC. The biological functionality of the genomic variants identified from the GWAS-enriched pathways were assessed using in silico methods and experimental dual luciferase reporter assays. Genes in the MODY pathway such as hepatocyte nuclear factor-1 alpha/beta (HNF1A and HNF1B) act as transcription factors. Their role in cancer progression was assessed through single and double CRISPR knockouts in PDAC primary cell cultures. CUT&RUN (cleavage under targets and release using nuclease) was performed to identify genes regulated by HNF1A and HNF1B TFs. Additionally, targeting the DNA damage response (DDR) pathway in non-BRCA mutated PDAC was assessed using a novel drug which mimics the effect of a BRCA2 mutation. In conclusion, this thesis shows the development of novel, adaptable organoid models for functional validation of genomic variants in PDAC. Furthermore, biological investigation of GWAS pathway identified SNPs and genes from the MODY and Pujana ATM PCC pathways highlights the powerful nature of these tools in identifying genomic variants associated with PDAC. It also highlights the importance of functional experimental analysis to provide better understanding of their role in the development and progression of PDAC