The Effects of Ethanol on the Pancreatic Cell Line Transcriptomes

Pancreatic Ductal Adenocarcinoma (PDAC) is a highly aggressive cancer that develops from cells in the pancreas. Currently, PDAC has a 5-year survival rate of only 10% and it makes up about 7% of all cancer deaths (1). Certain risk factors are associated with PDAC development, including family history of cancer, obesity, diabetes, pancreatitis, alcohol consumption, and smoking. While several studies have assessed alcohol consumption and its contribution to PDAC development, there is conflicting evidence to whether or not alcohol actually promotes PDAC. Work from our lab indicates that specific subtypes of pancreatic cancer are associated with a patient’s drinking status, which may influence treatment strategies and patient outcomes (2). This raises the question; How does alcohol affect cancerous and pre-cancerous pancreatic cells? In this study, we performed RNA-Sequencing on ethanol treated pancreatic cells in different stages of cancer progression may provide insight to the effects of ethanol on the etiology of this disease. We analyzed the protein coding genes that were differentially expressed between non-treated and ethanol treated cells and performed functional analysis to better understand the impact of ethanol on the biological processes in pancreatic cells.https://digitalcommons.unmc.edu/surp2020/1013/thumbnail.jp

Delineating the role of FANCA in glucose-stimulated insulin secretion in β cells through its protein interactome

Hyperinsulinemia affects 72% of Fanconi anemia (FA) patients and an additional 25% experience lowered glucose tolerance or frank diabetes. The underlying molecular mechanisms contributing to the dysfunction of FA pancreas β cells is unknown. Therefore, we sought to evaluate the functional role of FANCA, the most commonly mutated gene in FA, in glucosestimulated insulin secretion (GSIS). This study reveals that FANCA or FANCB knockdown impairs GSIS in human pancreas β cell line EndoC-βH3. To identify potential pathways by which FANCA might regulate GSIS, we employed a proteomics approach to identify FANCA protein interactions in EndoC-βH3 differentially regulated in response to elevated glucose levels. Glucose-dependent changes in the FANCA interaction network were observed, including increased association with other FA family proteins, suggesting an activation of the DNA damage response in response to elevated glucose levels. Reactive oxygen species increase in response to glucose stimulation and are necessary for GSIS in EndoC-βH3 cells. Glucose-induced activation of the DNA damage response was also observed as an increase in the DNA damage foci marker γ-H2AX and dependent upon the presence of reactive oxygen species. These results illuminate the role of FANCA in GSIS and its protein interactions regulated by glucose stimulation that may explain the prevalence of β cell-specific endocrinopathies in FA patients

De novo Assembly of the Burying Beetle Nicrophorus orbicollis (Coleoptera: Silphidae) Transcriptome Across Developmental Stages with Identification of Key Immune Transcripts

Burying beetles (Nicrophorus spp.) are among the relatively few insects that provide parental care while not belonging to the eusocial insects such as ants or bees. This behavior incurs energy costs as evidenced by immune deficits and shorter life-spans in reproducing beetles. In the absence of an assembled transcriptome, relatively little is known concerning the molecular biology of these beetles. This work details the assembly and analysis of the Nicrophorus orbicollis transcriptome at multiple developmental stages. RNA-Seq reads were obtained by next-generation sequencing and the transcriptome was assembled using the Trinity assembler. Validation of the assembly was performed by functional characterization using Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Differential expression analysis highlights developmental stage-specific expression patterns, and immunity-related transcripts are discussed. The data presented provides a valuable molecular resource to aid further investigation into immunocompetence throughout this organism’s sexual development

De novo Assembly and Analysis of the Chilean Pencil Catfish Trichomycterus areolatus Transcriptome

Trichomycterus areolatus is an endemic species of pencil catfish that inhabits the riffles and rapids of many freshwater ecosystems of Chile. Despite its unique adaptation to Chile’s high gradient watersheds and therefore potential application in the investigation of ecosystem integrity and environmental contamination, relatively little is known regarding the molecular biology of this environmental sentinel. Here, we detail the assembly of the Trichomycterus areolatus transcriptome, a molecular resource for the study of this organism and its molecular response to the environment. RNA-Seq reads were obtained by next-generation sequencing with an Illumina® platform and processed using PRINSEQ. The transcriptome assembly was performed using TRINITY assembler. Transcriptome validation was performed by functional characterization with KOG, KEGG, and GO analyses. Additionally, differential expression analysis highlights sex-specific expression patterns, and a list of endocrine and oxidative stress related transcripts are included

A Novel Approach to Metabolic Pathway Modeling

Computational methods of metabolic pathway modeling allow a better understanding of health, disease, and basic biological principles of regulation and metabolism, yet, it is a laborious process. Using the queueing theory, an approach commonly employed to evaluate telecommunication networks, significantly reduces the computational power required to generate simulated results, while simultaneously reducing expansion of errors inherent to classical approaches. The novel approach to calculating biomolecular interactions, requires less computational effort and yields results at least as predictive of molecular outcomes as currently employed methods. A non-trivial amount of cells were simulated and the average concentrations per cell were graphed as a function of time. Variations of 10% of glucose levels are randomly computed for every simulated second. Our data shows that we are able to simulate the glycolysis pathway in human cancer cells in accord with experimentally derived data 30 minutes post-stimulation

Dynamic Modeling and Stochastic Simulation of Metabolic Networks

Throughout our current generation, scientific studies, with the help of increased technological methods, have enabled the investigation of biology at nanoscale levels. Nevertheless, such systems necessitate the use of computational methods to comprehend the complex interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential equations producing a deterministic result which negates the intrinsic heterogeneity of intracellular systems. More recently, stochastic modeling approaches have gained popularity with the capability of providing more realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, reduces the computational power required to generate simulated results, while simultaneously reducing expansion of errors inherent to classical deterministic approaches. Herein, we present the application of queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and pharmacological inhibition in glycolysis to further exemplify modeling capabilities

Combined Alcohol Exposure and KRAS Mutation in Human Pancreatic Ductal Epithelial Cells Induces Proliferation and Alters Subtype Signatures Determined by Multi-Omics Analysis

Pancreatic Ductal adenocarcinoma (PDAC) is an aggressive cancer commonly exhibiting KRAS-activating mutations. Alcohol contributes to the risk of developing PDAC in humans, and murine models have shown alcohol consumption in the context of KRAS mutation in the pancreas promotes the development of PDAC. The molecular signatures in pancreas cells altered by alcohol exposure in the context of mutant KRAS could identify pathways related to the etiology of PDAC. In this study, we evaluated the combined effects of alcohol exposure and KRAS mutation status on the transcriptome and proteome of pancreatic HPNE cell models. These analyses identified alterations in transcription and translational processes in mutant KRAS cells exposed to alcohol. In addition, multi-omics analysis suggests an increase in the correlation between mRNA transcript and protein abundance in cells exposed to alcohol with an underlying KRAS mutation. Through differential co-expression, SERPINE1 was found to be influential for PDAC development in the context of mutant KRAS and ethanol. In terms of PDAC subtypes, alcohol conditioning of HPNE cells expressing mutant KRAS decreases the Inflammatory subtype signature and increases the Proliferative and Metabolic signatures, as we previously observed in patient samples. The alterations in molecular subtypes were associated with an increased sensitivity to chemotherapeutic agents gemcitabine, irinotecan, and oxaliplatin. These results provide a framework for distinguishing the molecular dysregulation associated with combined alcohol and mutant KRAS in a pancreatic cell line model

Omics Analysis in Cancer and Development

Advancements in next-generation sequencing technologies have made omics technologies accessible to more researchers and large omics datasets are becoming more common in biological research. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have been pivotal in our current understanding of development, how developmental defects can lead to cancer, and importantly, our approach to chemotherapeutic treatment for cancer patients. This dissertation describes two projects using both experimental and omics technologies to investigate molecular mechanisms in Pancreatic Ductal Adenocarcinoma (PDAC) and mammary gland development. Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease that is difficult to treat with current therapies. Subtyping PDAC based on their underlying molecular features is hoped to stratify patients for individualize treatment and better therapeutic outcomes. Recent work suggests modifiable risk factors influence subtype development in patients, for example alcohol use. However, the underlying mechanisms of alcohol use on subtype development is unclear. The first project in this dissertation focuses on alcohol use in KRAS-mutated pancreatic cells, using both experimental and omics technologies to investigate alcohol’s influence in subtype development. CTD phosphatase 1 (Ctdp1) is a phosphatase that regulates phosphorylation of the C-terminal domain of the RNA polymerase II, a major polymerase essential for transcribing protein-coding mRNAs. Recent work from our lab suggests Ctdp1 involvement in functional mammary gland development, milk production and prevention of cancer. Moreover, Ctdp1 may possess stem-like functions. The second part of this dissertation describes the impact loss of Ctdp1 has on mammary epithelial cells. Using single-cell RNA sequencing, we show Ctdp1 loss prevents basal cell development through mTORC2 dephosphorylation

Stochastic Simulation of Cellular Metabolism

Increased technological methods have enabled the investigation of biology at nanoscale levels. Such systems require the use of computational methods to comprehend the complex interactions that occur. The dynamics of metabolic systems have been traditionally described utilizing differential equations without fully capturing the heterogeneity of biological systems. Stochastic modeling approaches have recently emerged with the capacity to incorporate the statistical properties of such systems. However, the processing of stochastic algorithms is a computationally intensive task with intrinsic limitations. Alternatively, the queueing theory approach, historically used in the evaluation of telecommunication networks, can significantly reduce the computational power required to generate simulated results while simultaneously reducing the expansion of errors. We present here the application of queueing theory to simulate stochastic metabolic networks with high efficiency. With the use of glycolysis as a well understood biological model, we demonstrate the power of the proposed modeling methods discussed herein. Furthermore, we describe the simulation and pharmacological inhibition of glycolysis to provide an example of modeling capabilities

Delineating the role of FANCA in glucose-stimulated insulin secretion in β cells through its protein interactome

Hyperinsulinemia affects 72% of Fanconi anemia (FA) patients and an additional 25% experience lowered glucose tolerance or frank diabetes. The underlying molecular mechanisms contributing to the dysfunction of FA pancreas β cells is unknown. Therefore, we sought to evaluate the functional role of FANCA, the most commonly mutated gene in FA, in glucosestimulated insulin secretion (GSIS). This study reveals that FANCA or FANCB knockdown impairs GSIS in human pancreas β cell line EndoC-βH3. To identify potential pathways by which FANCA might regulate GSIS, we employed a proteomics approach to identify FANCA protein interactions in EndoC-βH3 differentially regulated in response to elevated glucose levels. Glucose-dependent changes in the FANCA interaction network were observed, including increased association with other FA family proteins, suggesting an activation of the DNA damage response in response to elevated glucose levels. Reactive oxygen species increase in response to glucose stimulation and are necessary for GSIS in EndoC-βH3 cells. Glucose-induced activation of the DNA damage response was also observed as an increase in the DNA damage foci marker γ-H2AX and dependent upon the presence of reactive oxygen species. These results illuminate the role of FANCA in GSIS and its protein interactions regulated by glucose stimulation that may explain the prevalence of β cell-specific endocrinopathies in FA patients