Microbiology & Cell Biology
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/10
Browse
2 results
Search Results
Item TrxR1, Gsr, and oxidative stress determine hepatocellular carcinoma malignancy(2019-06) McLoughlin, Michael R.; Orlicky, David J.; Prigge, Justin R.; Krishna, Pushya; Talago, Emily A.; Cavigli, Ian R.; Eriksson, Sofi; Miller, Colin G.; Kundert, Jean A.; Sayin, Volkan I.; Sabol, Rachel A.; Heinemann, Joshua; Brandenberger, Luke O.; Iverson, Sonya V.; Bothner, Brian; Papagiannakopoulos, Thales; Shearn, Colin T.; Arner, Elias S. J.; Schmidt, Edward E.Thioredoxin reductase-1 (TrxR1)-, glutathione reductase (Gsr)-, and Nrf2 transcription factor-driven antioxidant systems form an integrated network that combats potentially carcinogenic oxidative damage yet also protects cancer cells from oxidative death. Here we show that although unchallenged wild-type (WT), TrxR1-null, or Gsr-null mouse livers exhibited similarly low DNA damage indices, these were 100-fold higher in unchallenged TrxR1/Gsr–double-null livers. Notwithstanding, spontaneous cancer rates remained surprisingly low in TrxR1/Gsr-null livers. All genotypes, including TrxR1/Gsr-null, were susceptible to N-diethylnitrosamine (DEN)-induced liver cancer, indicating that loss of these antioxidant systems did not prevent cancer cell survival. Interestingly, however, following DEN treatment, TrxR1-null livers developed threefold fewer tumors compared with WT livers. Disruption of TrxR1 in a marked subset of DEN-initiated cancer cells had no effect on their subsequent contributions to tumors, suggesting that TrxR1-disruption does not affect cancer progression under normal care, but does decrease the frequency of DEN-induced cancer initiation. Consistent with this idea, TrxR1-null livers showed altered basal and DEN-exposed metabolomic profiles compared with WT livers. To examine how oxidative stress influenced cancer progression, we compared DEN-induced cancer malignancy under chronically low oxidative stress (TrxR1-null, standard care) vs. elevated oxidative stress (TrxR1/Gsr-null livers, standard care or phenobarbital-exposed TrxR1-null livers). In both cases, elevated oxidative stress was correlated with significantly increased malignancy. Finally, although TrxR1-null and TrxR1/Gsr-null livers showed strong Nrf2 activity in noncancerous hepatocytes, there was no correlation between malignancy and Nrf2 expression within tumors across genotypes. We conclude that TrxR1, Gsr, Nrf2, and oxidative stress are major determinants of liver cancer but in a complex, context-dependent manner.Item Real-Time Digitization of Metabolomics Patterns from a Living System Using Mass Spectrometry(2014-10) Heinemann, Joshua; Noon, Brigit; Mohigmi, Mohammad J.; Mazurie, Aurélien J.; Dickensheets, David L.; Bothner, BrianThe real-time quantification of changes in intracellular metabolic activities has the potential to vastly improve upon traditional transcriptomics and metabolomics assays for the prediction of current and future cellular phenotypes. This is in part because intracellular processes reveal themselves as specific temporal patterns of variation in metabolite abundance that can be detected with existing signal processing algorithms. Although metabolite abundance levels can be quantified by mass spectrometry (MS), large-scale real-time monitoring of metabolite abundance has yet to be realized because of technological limitations for fast extraction of metabolites from cells and biological fluids. To address this issue, we have designed a microfluidic-based inline small molecule extraction system, which allows for continuous metabolomic analysis of living systems using MS. The system requires minimal supervision, and has been successful at real-time monitoring of bacteria and blood. Feature-based pattern analysis of Escherichia coli growth and stress revealed cyclic patterns and forecastable metabolic trajectories. Using these trajectories, future phenotypes could be inferred as they exhibit predictable transitions in both growth and stress related changes. Herein, we describe an interface for tracking metabolic changes directly from blood or cell suspension in real-time.