Browsing by Subject "lactic acidosis"
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Item Open Access Cellular Responses to Lactic Acidosis in Human Cancers(2010) Chen, Julia Ling-YuThe physiology of the tumor microenvironment is characterized by lower oxygen (hypoxia), higher lactate, extracellular acidosis and glucose starvation. We examined the global, transcriptional cellular responses to each of these microenvironmental stresses in vitro, projected them onto clinical breast cancer patients' samples in vivo, and returned to perform further in vitro experiments to investigate the potential mechanisms involved in these stress responses. The reciprocal exchange of information was critical and advanced our understanding of the potential clinical relevance of cellular responses.
Our expression array result showed that lactic acidosis induces a strong response, distinct from that of hypoxia in human mammalian epithelial cells (HMECs), indicating lactic acidosis is not only a by-product of hypoxia but has a unique role as a stimulant to cells in the tumor microenvironment. Cellular responses to lactosis and acidosis further demonstrated that acidosis was the main driving force in the lactic acidosis response. These responding gene signatures were then statistically projected into clinical breast cancer patients' expression data sets. The hypoxia response, as reported previously, was associated with bad prognosis, where as the lactic acidosis and acidosis responses, were associated with good prognosis. Additionally, the acidosis response could be used to separate breast tumors with high versus low aggressiveness based on its inversed correlation with metastatic character. We further discovered that lactic acidosis, in contrast to hypoxia, abolished Akt signaling. Moreover, it downregulated glycolysis and shifted energy utilization towards aerobic respiration.
We continued to examine the cellular response to lactic acidosis temporally in MCF7 cells, a breast cancer cell line. The lactic acidosis response of MCF7 cells also showed the prognostic result of better clinical outcomes in datasets of breast cancer patients. The lactic acidosis responses of HMEC and MCF cells were highly correlated. Strikingly in MCF7 cells, lactic acidosis and glucose deprivation actually induced similar transcriptional profiles, with only a few genes being oppositely regulated. Furthermore, lactic acidosis, similar to glucose starvation, induced AMPK signaling and abolished mTOR. However, lactic acidosis and glucose deprivation induced opposite glucose uptake phenotypes. Lactic acidosis significantly repressed glucose uptake whereas glucose deprivation significantly induced it. Among the genes differentially regulated by these two stresses, thioredoxin-interacting protein (TXNIP) was among the most different. The negative regulatory role of TXNIP on glucose uptake has been demonstrated previously. In the cancer research field, TXNIP is recognized as a tumor suppressor gene. We observed that lactic acidosis induced TXNIP strongly and most importantly, TXNIP played a critical role in regulating glucose uptake in cells under lactic acidosis. Furthermore, MondoA, the transcription factor and glucose sensor previously reported to regulate TXNIP induction upon glucose exposure, was also responsible for regulating TXNIP under lactic acidosis. We demonstrated that TXNIP not only plays an important role in the lactic acidosis response but also has strong prognostic power to separate breast cancer patients based on survival.
Item Open Access Genetic Determinants of Cancer Cell Survival in Tumor Microenvironment Stresses(2015) Keenan, Melissa MarieIn order to propagate a solid tumor, cancer cells must adapt to and survive under various tumor microenvironment (TME) stresses, such as hypoxia or lactic acidosis. Additionally, cancer cells exposed to these stresses are more resistant to therapies, more likely to metastasize and often are worse for patient prognosis. While the presence of these stresses is generally negative for cancer patients, since these stresses are mostly unique to the TME, they also offer an opportunity to develop more selective therapeutics. If we achieve a better understanding of the adaptive mechanisms cancer cells employ to survive the TME stresses, then hopefully we, as a scientific community, can devise more effective cancer therapeutics specifically targeting cancer cells under stress. To systematically identify genes that modulate cancer cell survival under stresses, we performed shRNA screens under hypoxia or lactic acidosis. From these screens, we discovered that genetic depletion of acetyl-CoA carboxylase alpha (ACACA or ACC1) or ATP citrate lyase (ACLY) protected cancer cells from hypoxia-induced apoptosis. Furthermore, the loss of ACLY or ACC1 reduced the levels and activities of the oncogenic transcription factor ETV4. Silencing ETV4 also protected cells from hypoxia-induced apoptosis and led to remarkably similar transcriptional responses as with silenced ACLY or ACC1, including an anti-apoptotic program. Metabolomic analysis found that while α-ketoglutarate levels decrease under hypoxia in control cells, α-ketoglutarate was paradoxically increased under hypoxia when ACC1 or ACLY were depleted. Supplementation with α-ketoglutarate rescued the hypoxia-induced apoptosis and recapitulated the decreased expression and activity of ETV4, likely via an epigenetic mechanism. Therefore, ACC1 and ACLY regulated the levels of ETV4 under hypoxia via increased α-ketoglutarate. These results reveal that the ACC1/ACLY-α-ketoglutarate-ETV4 axis is a novel means by which metabolic states regulate transcriptional output for life vs. death decisions under hypoxia. Since many lipogenic inhibitors are under investigation as cancer therapeutics, our findings suggest that the use of these inhibitors will need to be carefully considered with respect to oncogenic drivers, tumor hypoxia, progression and dormancy. More broadly, our screen provides a framework for studying additional tumor cell stress-adaption mechanisms in the future.
Item Open Access Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.(Journal of personalized medicine, 2021-11) Pattharanitima, Pattharawin; Thongprayoon, Charat; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; Kaewput, Wisit; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; O'Corragain, Oisin A; Mao, Michael A; Garovic, Vesna D; Qureshi, Fawad; Dillon, John J; Cheungpasitporn, WisitLactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.Item Open Access Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.(Journal of clinical medicine, 2021-10) Pattharanitima, Pattharawin; Thongprayoon, Charat; Kaewput, Wisit; Qureshi, Fawad; Qureshi, Fahad; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; O'Corragain, Oisin A; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; Guru, Pramod K; Mao, Michael A; Garovic, Vesna D; Dillon, John J; Cheungpasitporn, WisitLactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.