Poster #78 - Rima Zinjuwadia

Harnessing Computational and Statistical Techniques for Insights into Liver MPS RNA-Seq Data

Rima Zinjuwadia (Northeastern University, Novo Nordisk), Dan Sazer (Novo Nordisk), Dennis McDuffie (Novo Nordisk), Nate Sorensen (Novo Nordisk), Sara Toftegaard Hjuler (Novo Nordisk), Rachelle Prantil Baun (Novo Nordisk), Aditya Misra (Novo Nordisk), and Alex Drong (Novo Nordisk)

Metabolic disorders, particularly insulin resistance, affect millions worldwide, significantly elevating the risk of cardiovascular diseases and type 2 diabetes. This study employs a microphysiological system (MPS) liver model to investigate the role of specific targets in metabolic control, with a particular focus on the underlying mechanisms of insulin resistance. We examine the effects of gene knockdowns on gene expression profiles through bulk RNA sequencing data obtained from various experimental treatments. Functional assays, including insulin-sensitive inhibition of glucose output, were conducted alongside RNA-seq analyses to elucidate the physiological implications of these interventions on hepatocyte function. Our assessments reveal the intricate interplay between metabolic regulation and insulin signaling within liver physiology, supporting previous findings that highlight the liver's multifaceted roles in metabolic homeostasis. To analyze the data, a tailored downstream analytical pipeline was developed using Nextflow, incorporating differential expression analysis via the DESeq2 framework, pathway enrichment analysis, and batch effect correction through Combat-Seq. This innovative methodology allows users to select dynamic conditions aligned with predefined parameters, significantly enhancing reproducibility across diverse experimental contexts. Initial findings indicate considerable alterations in the expression of differentially expressed genes (DEGs) related to metabolic regulation pathways. Notably, power analysis results demonstrate the statistical power to detect significant effects across experimental conditions, confirming the robustness of our findings. Advanced visualization techniques further illustrate the complexity of gene expression changes resulting from the interventions. Specifically, the power statistics derived from the analysis highlight the experimental design's capability to uncover meaningful biological insights under stringent probabilities. This study provides a rigorous framework for untangling the complexities of metabolic disorders and establishes a reproducible model for investigating targets involved in metabolic regulation, supporting future research endeavors in the field.