Impact of chemical mixtures from wastewater treatment plant effluents on human immune cell activation: An effect-based analysis

BACKGROUND

Humans are exposed to many different chemicals on a daily basis, mostly as chemical mixtures, usually from food, consumer products and the environment. Wastewater treatment plant effluent contains mixtures of chemicals that have been discarded or excreted by humans and not removed by water treatment. These effluents contribute directly to water pollution, they are used in agriculture and may affect human health. The possible effect of such chemical mixtures on the immune system has not been characterized.

OBJECTIVE

The aim of this study was to investigate the effect of extracts obtained from four European wastewater treatment plant effluents on human primary immune cell activation.

METHODS

Immune cells were exposed to the effluent extracts and modulation of cell activation was performed by multi-parameter flow cytometry. Messenger-RNA (mRNA) expression of genes related to immune system and hormone receptors was measured by RT-PCR.

RESULTS

The exposure of immune cells to these extracts, containing 339 detected chemicals, significantly reduced the activation of human lymphocytes, mainly affecting T helper and mucosal-associated invariant T cells. In addition, basophil activation was also altered upon mixture exposure. Concerning mRNA expression, we observed that 12 transcripts were down-regulated by at least one extract while 11 were up-regulated. Correlation analyses between the analyzed immune parameters and the concentration of chemicals in the WWTP extracts, highlighted the most immunomodulatory chemicals.

DISCUSSION

Our results suggest that the mixture of chemicals present in the effluents of wastewater treatment plants could be considered as immunosuppressive, due to their ability to interfere with the activation of immune cells, a process of utmost importance for the functionality of the immune system. The combined approach of immune effect-based analysis and chemical content analysis used in our study provides a useful tool for investigating the effect of environmental mixtures on the human immune response.

Hypoxia induced deregulation of sphingolipids in colon cancer is a prognostic marker for patient outcome

Sphingolipids are important for the physicochemical properties of cellular membranes and deregulated in tumors. In human colon cancer tissue ceramide synthase (CerS) 4 and CerS5 are reduced which correlates with a reduced survival probability of late-stage colon cancer patients. Both enzymes are reduced after hypoxia in advanced colorectal cancer (CRC) cells (HCT-116, SW620) but not in non-metastatic CRC cells (SW480, Caco-2). Downregulation of CerS4 or CerS5 in advanced CRC cells enhanced tumor formation in nude mice and organoid growth in vitro. This was accompanied by an enhanced proliferation rate and metabolic changes leading to a shift towards the Warburg effect. In contrast, CerS4 or CerS5 depletion in Caco-2 cells reduced tumor growth in vivo. Lipidomic and proteomic analysis of membrane fractions revealed significant changes in tumor-promoting cellular pathways and cellular transporters. This study identifies CerS4 and CerS5 as prognostic markers for advanced colon cancer patients and provides a comprehensive overview about the associated cellular metabolic changes. We propose that the expression level of CerS4 and CerS5 in colon tumors could serve as a basis for decision-making for personalized treatment of advanced colon cancer patients. Trial registration: The study was accredited by the study board of the Deutsche Krebsgesellschaft (Registration No: St-D203, 2017/06/30, retrospectively registered).

OVOL2 sustains postnatal thymic epithelial cell identity

Distinct pathways and molecules may support embryonic versus postnatal thymic epithelial cell (TEC) development and maintenance. Here, we identify a mechanism by which TEC numbers and function are maintained postnatally. A viable missense allele (C120Y) of Ovol2, expressed ubiquitously or specifically in TECs, results in lymphopenia, in which T cell development is compromised by loss of medullary TECs and dysfunction of cortical TECs. We show that the epithelial identity of TECs is aberrantly subverted towards a mesenchymal state in OVOL2-deficient mice. We demonstrate that OVOL2 inhibits the epigenetic regulatory BRAF-HDAC complex, specifically disrupting RCOR1-LSD1 interaction. This causes inhibition of LSD1-mediated H3K4me2 demethylation, resulting in chromatin accessibility and transcriptional activation of epithelial genes. Thus, OVOL2 controls the epigenetic landscape of TECs to enforce TEC identity. The identification of a non-redundant postnatal mechanism for TEC maintenance offers an entry point to understanding thymic involution, which normally begins in early adulthood.

Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus

In an era of increasing need for precision medicine, machine learning has shown promise in making accurate acute myocardial infarction outcome predictions. The accurate assessment of high-risk patients is a crucial component of clinical practice. Type 2 diabetes mellitus (T2DM) complicates ST-segment elevation myocardial infarction (STEMI), and currently, there is no practical method for predicting or monitoring patient prognosis. The objective of the study was to compare the ability of machine learning models to predict in-hospital mortality among STEMI patients with T2DM. We compared six machine learning models, including random forest (RF), CatBoost classifier (CatBoost), naive Bayes (NB), extreme gradient boosting (XGBoost), gradient boosting classifier (GBC), and logistic regression (LR), with the Global Registry of Acute Coronary Events (GRACE) risk score. From January 2016 to January 2020, we enrolled patients aged > 18 years with STEMI and T2DM at the Affiliated Hospital of Zunyi Medical University. Overall, 438 patients were enrolled in the study [median age, 62 years; male, 312 (71%); death, 42 (9.5%]). All patients underwent emergency percutaneous coronary intervention (PCI), and 306 patients with STEMI who underwent PCI were enrolled as the training cohort. Six machine learning algorithms were used to establish the best-fit risk model. An additional 132 patients were recruited as a test cohort to validate the model. The ability of the GRACE score and six algorithm models to predict in-hospital mortality was evaluated. Seven models, including the GRACE risk model, showed an area under the curve (AUC) between 0.73 and 0.91. Among all models, with an accuracy of 0.93, AUC of 0.92, precision of 0.79, and F1 value of 0.57, the CatBoost model demonstrated the best predictive performance. A machine learning algorithm, such as the CatBoost model, may prove clinically beneficial and assist clinicians in tailoring precise management of STEMI patients and predicting in-hospital mortality complicated by T2DM.