The epoxiconazole and tebuconazole fungicides impair granulosa cells functions partly through the aryl hydrocarbon receptor (AHR) signalling with contrasted effects in obese, normo-weight and polycystic ovarian syndrome (PCOS) patients.

Polycystic ovarian syndrome (PCOS), frequently associated to obesity, is the main reproductive disorder in women in age to procreate. Some evidence suggests that pesticides can result in alterations of the female reproductive system, including polycystic ovary syndrome (PCOS). Here, we detected two fungicides, Tebuconazole (Tb) and Epoxiconazole (Epox) in the soils and waters of French area. Our hypothesis is that these two triazoles could be associated to the etiology of PCOS. We used the human KGN cell line and primary human granulosa cells (hGCs) from different group of patients: normal weight non PCOS (NW), normal weight PCOS (PCOS NW), obese (obese) and obese PCOS (PCOS obese). We exposed in vitro these cells to Tb and Epox from 0 up to 10 mM for 24 and 48 h and analysed cell viability and steroidogenesis. In hGCs NW, cell viability was reduced from 12.5 µM for Tb and 75 µM for Epox. In hGCs NW, Epox decreased progesterone (Pg) and estradiol (E2) secretions and inhibited STAR, HSD3B and CYP19A1 mRNA expressions from 25 µM and increased AHR mRNA expression from 75 µM. Tb exposure also reduced steroid secretion and STAR and CYP19A1 mRNA expressions and increased AHR mRNA expression but at cytotoxic concentrations. Silencing of AHR in KGN cells reduced inhibitory effects of Tb and Epox on steroid secretion. Tb and Epox exposure decreased more steroid secretion in hGCs from obese, PCOS NW and PCOS obese groups than in NW group. Moreover, we found a higher gene expression of AHR within these three groups. Taken together, both Epox and Tb reduced steroidogenesis in hGCs through partly AHR and Tb was more cytotoxic than Epox. These triazoles alter more strongly PCOS and/or obese hGCs suggesting that human with reproductive disorders are more sensitive to triazoles exposure.

In vitro exposure to triazoles used as fungicides impairs human granulosa cells steroidogenesis.

Triazoles are the main components of fungicides used in conventional agriculture. Some data suggests that they may be endocrine disruptors. Here, we found five triazoles, prothioconazole, metconazole, difenoconazole, tetraconazole, and cyproconazole, in soil or water from the Centre-Val de Loire region of France. We then studied their effects from 0.001 µM to 1000 µM for 48 h on the steroidogenesis and cytotoxicity of ovarian cells from patients in this region and the human granulosa line KGN. In addition, the expression of the aryl hydrocarbon receptor (AHR) nuclear receptor in KGN cells was studied. Overall, all triazoles reduced the secretion of progesterone, estradiol, or both at doses that were non-cytotoxic but higher than those found in the environment. This was mainly associated, depending on the triazole, with a decrease in the expression of CYP51, STAR, CYP11A1, CYP19A1, or HSD3B proteins, or a combination thereof, in hGCs and KGN cells and an increase in AHR in KGN cells.

A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability.

Groundwater level (GWL) simulations allow the generation of reconstructions for exploring the past temporal variability of groundwater resources or provide the means for generating projections under climate change on decadal scales. In this context, analyzing GWLs affected by low-frequency variations is crucial. In this study, we assess the capabilities of three deep learning (DL) models (long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM)) in simulating three types of GWLs affected by varying low-frequency behavior: inertial (dominated by low-frequency), annual (dominated by annual cyclicity) and mixed (in which both annual and low-frequency variations have high amplitude). We also tested if maximal overlap discrete wavelet transform pre-processing (MODWT) of input variables helps to better identify the frequency content most relevant for the models (MODWT-DL models). Only external variables (i.e., precipitation, air temperature as raw data, and effective precipitation (EP)) were used as input. Results indicate that for inertial-type GWLs, MODWT-DL models with raw data were notably more accurate than standalone models. However, DL models performed well for annual-type GWLs, while using EP as input, with MODWT-DL models exhibiting only minor improvements. Using raw data as input improved MODWT-DL models compared to standalone models; nevertheless, all models using EP performed better for annual-type GWLs. For mixed-type GWLs, while using EP as input, MODWT-DL models performed well, with substantial improvements over standalone models. Using raw data as input, improvement of MODWT-DL models is marginal compared to that of standalone models; nevertheless, they perform better than standalone models with EP. The Shapley Additive exPlanations (SHAP) approach used to interpret models highlighted that they preferentially learned from low-frequency in precipitation data to achieve the best simulations for inertial and mixed GWLs. This study showed that MODWT-based input pre-processing is highly suitable to better simulate low-frequency varying GWLs.