Computational modeling of the pancreas: lifelong simulations of pancreatitis

The aim of this study is to build a mathematical model of the pancreas and run this model in lifelong simulations in order to understand the mechanisms that predict an increased risk of pancreatitis. The dilemma is that pancreatitis is a complex process with multiple variables, which currently make the onset, severity and outcomes unpredictable in individual patients. Genetic, environmental and metabolic factors are likely to be important for disease severity and progression, with somewhat stochastic events initiating theprocess when stress leads to injury signals. Modeling should begin in the acinar cell, with ability to incorporate duct cells, inflammatory cells, other cells and their interactions into large model. In this work we attempted to build a foundational model that incorporates the main features and interactors. We built a framework that focuses on trypsinogen activation as a major cause of auto-digestion and injury. Additional variables such as production ofpancreatic secretory trypsin inhibitor(PSTI) molecules as a defense line against active trypsin as well as bicarbonate secretion were included in the model. The effects of mutation were modeled as modified rates of trypsinogen production/activation, trypsin inactivation, PSTI production, and bicarbonate secretion. Our framework contains three compartments that represent domains where trypsin could be activated. The domains are acinar cell, lumen of the acinus, and main duct of the pancreas. We used a stochastic approach to test the general flow of the simulation. The public health and translational significance of this model is thatit would help us to understand the physiology of the pancreas more deeply. It would also allow us to consider many factors that lead to pancreatitis and predict their behavior under different conditions (e.g., therapy)

A new workflow of fetal DNA prediction from cell-free DNA in maternal plasma

Prediction of fetal DNA allows diagnosing known/passed mutations before child’s birth. Public health significance of such early testing is that it can reassure parents who have negative results and offers timely information for those with abnormal results. My dissertation work presents a new approach of reconstructing fetal DNA from maternal plasma. The method works because plasma from pregnant women, which contains “cell-free DNA”, has been noted to contain fetal DNA as well as maternal DNA. I developed and tested a workflow that implements my suggested approach. The workflow was broken into several parts, each fully documented in this dissertation. Each step we have taken was supported with explanation of the logic driving the step. The approach works through the examination of sequencing data sets generated by short-read sequencing (also known as next-generation sequencing), by calling variation (single nucleotide polymorphisms, or SNPs) within those samples vis-à-vis a reference sequence. I developed and introduced a series of quality control criteria applied to SNPs to improve overall prediction. A novel single individual haplotyping method was developed and applied to haplotype the parental samples. The obtained parental haplotypes were incorporated into the workflow and along with parental genotypes were used to find transmitted haplotypes in the maternal plasma. The predicted haplotypes were then aligned to each other to obtain phased SNPs. For evaluation, I compared fetal SNPs predicted by my method against control fetal SNPs (from sequencing of fetal DNA). Overall prediction power is discussed. Possible ways of improvements that should affect the overall prediction are also described

Genetic risk factors for restenosis after percutaneous coronary intervention in Kazakh population

Background: After coronary stenting, the risk of developing restenosis is from 20 to 35 %. The aim of the present study is to investigate the association of genetic variation in candidate genes in patients diagnosed with restenosis in the Kazakh population. Methods: Four hundred fifty-nine patients were recruited to the study; 91 patients were also diagnosed with diabetes and were excluded from the sampling. DNA was extracted with the salting-out method. The patients were genotyped for 53 single-nucleotide polymorphisms. Genotyping was performed on the QuantStudio 12K Flex (Life Technologies). Differences in distribution of BMI score among different genotype groups were compared by analysis of variance (ANOVA). Also, statistical analysis was performed using R and PLINK v.1.07. Haplotype frequencies and LD measures were estimated by using the software Haploview 4.2. Results: A logistic regression analysis found a significant difference in restenosis rates for different genotypes. FGB (rs1800790) is significantly associated with restenosis after stenting (OR = 2.924, P = 2.3E−06, additive model) in the Kazakh population. CD14 (rs2569190) showed a significant association in the additive (OR = 0.08033, P = 2.11E−09) and dominant models (OR = 0.05359, P = 4.15E−11). NOS3 (rs1799983) was also highly associated with development of restenosis after stenting in additive (OR = 20.05, P = 2.74 E−12) and recessive models (OR = 22.24, P = 6.811E−10). Conclusions: Our results indicate that FGB (rs1800790), CD14 (rs2569190), and NOS3 (rs1799983) SNPs could be genetic markers for development of restenosis in Kazakh population. Adjustment for potential confounder factor BMI gave almost the same results