Oral exposure to bisphenol S is associated with alterations in the oviduct proteome of an ovine model, with aggravated effects in overfed females.

BACKGROUND: Bisphenol S (BPS) is a substitute for bisphenol A in plastic manufacturing and, as a potential endocrine disruptor, may alter the physiology of the oviduct, in which fertilization and early embryo development take place in mammals. The objective of this study was to assess the effect of a daily dietary exposure to BPS combined with a contrasted diet on the oviduct fluid proteome using an ovine model. RESULTS: Eighty adult cyclic ewes were allotted to four groups (20/group): overfed (OF) consuming 50 µg/kg/day of BPS in their diet, underfed (UF) consuming 50 µg/kg/day of BPS, and non-exposed controls in each diet group. After three months, the mean body condition score, plasma levels of glucose and non-esterified fatty acids were significantly higher in OF than in UF females. The proteins in collected OF samples (50 µg) were analyzed by nanoliquid chromatography coupled with tandem mass spectrometry (nanoLC-MS/MS). Overall, 1563 proteins were identified, among which 848 were quantified. Principal component analysis of the data revealed a clear discrimination of samples according to the diet and a segregation between BPS-exposed and non-exposed females in overfed ewes. Hierarchical clustering of differentially abundant proteins (DAPs) identified two clusters of 101 and 78 DAPs according to the diet. Pairwise comparisons between groups revealed a stronger effect of BPS in OF than in UF females (70 vs. 24 DAPs) and a stronger effect of the diet in BPS-exposed than non-exposed females (56 vs. 36 DAPs). Functional analysis of DAPs showed an enrichment in metabolic processes, immune system, cell response to stress, and reproductive processes. CONCLUSIONS: This work highlights for the first time the important impact of BPS on the oviduct proteome, with larger effects seen in OF than UF females. These results, together with previous ones, raise health concerns for everyone and call for a greater regulation of BPS in the food industry.

Arabidopsis cell wall composition determines disease resistance specificity and fitness.

Plant cell walls are complex structures subject to dynamic remodeling in response to developmental and environmental cues and play essential functions in disease resistance responses. We tested the specific contribution of plant cell walls to immunity by determining the susceptibility of a set of Arabidopsis cell wall mutants (cwm) to pathogens with different parasitic styles: a vascular bacterium, a necrotrophic fungus, and a biotrophic oomycete. Remarkably, most cwm mutants tested (29/34; 85.3%) showed alterations in their resistance responses to at least one of these pathogens in comparison to wild-type plants, illustrating the relevance of wall composition in determining disease-resistance phenotypes. We found that the enhanced resistance of cwm plants to the necrotrophic and vascular pathogens negatively impacted cwm fitness traits, such as biomass and seed yield. Enhanced resistance of cwm plants is not only mediated by canonical immune pathways, like those modulated by phytohormones or microbe-associated molecular patterns, which are not deregulated in the cwm tested. Pectin-enriched wall fractions isolated from cwm plants triggered immune responses in wild-type plants, suggesting that wall-mediated defensive pathways might contribute to cwm resistance. Cell walls of cwm plants show a high diversity of composition alterations as revealed by glycome profiling that detect specific wall carbohydrate moieties. Mathematical analysis of glycome profiling data identified correlations between the amounts of specific wall carbohydrate moieties and disease resistance phenotypes of cwm plants. These data support the relevant and specific function of plant wall composition in plant immune response modulation and in balancing disease resistance/development trade-offs.

Effects of the active amyloid beta immunotherapy CAD106 on PET measurements of amyloid plaque deposition in cognitively unimpaired APOE ε4 homozygotes.

INTRODUCTION: Alzheimer's Prevention Initiative Generation Study 1 evaluated amyloid beta (Aß) active immunotherapy (vaccine) CAD106 and BACE-1 inhibitor umibecestat in cognitively unimpaired 60- to 75-year-old participants at genetic risk for Alzheimer's disease (AD). The study was reduced in size and terminated early. Results from the CAD106 cohort are presented. METHODS: Sixty-five apolipoprotein E ε4 homozygotes with/without amyloid deposition received intramuscular CAD106 450 µg (n = 42) or placebo (n = 23) at baseline; Weeks 1, 7, 13; and quarterly; 51 of them had follow-up Aß positron emission tomography (PET) scans at 18 to 24 months. RESULTS: CAD106 induced measurable serum Aß immunoglobulin G titers in 41/42 participants, slower rates of Aß plaque accumulation (mean [standard deviation] annualized change from baseline in amyloid PET Centiloid: -0.91[5.65] for CAD106 versus 8.36 [6.68] for placebo; P < 0.001), and three amyloid-related imaging abnormality cases (one symptomatic). DISCUSSION: Despite early termination, these findings support the potential value of conducting larger prevention trials of Aß active immunotherapies in individuals at risk for AD. HIGHLIGHTS: This was the first amyloid-lowering prevention trial in persons at genetic risk of late-onset Alzheimer's disease (AD). Active immunotherapy targeting amyloid (CAD106) was tested in this prevention trial. CAD106 significantly slowed down amyloid plaque deposition in apolipoprotein E homozygotes. CAD106 was generally safe and well tolerated, with only three amyloid-related imaging abnormality cases (one symptomatic). Such an approach deserves further evaluation in larger AD prevention trials.

Global Public Perceptions of Genomic Data Sharing: What Shapes the Willingness to Donate DNA and Health Data?

Analyzing genomic data across populations is central to understanding the role of genetic factors in health and disease. Successful data sharing relies on public support, which requires attention to whether people around the world are willing to donate their data that are then subsequently shared with others for research. However, studies of such public perceptions are geographically limited and do not enable comparison. This paper presents results from a very large public survey on attitudes toward genomic data sharing. Data from 36,268 individuals across 22 countries (gathered in 15 languages) are presented. In general, publics across the world do not appear to be aware of, nor familiar with, the concepts of DNA, genetics, and genomics. Willingness to donate one's DNA and health data for research is relatively low, and trust in the process of data's being shared with multiple users (e.g., doctors, researchers, governments) is also low. Participants were most willing to donate DNA or health information for research when the recipient was specified as a medical doctor and least willing to donate when the recipient was a for-profit researcher. Those who were familiar with genetics and who were trusting of the users asking for data were more likely to be willing to donate. However, less than half of participants trusted more than one potential user of data, although this varied across countries. Genetic information was not uniformly seen as different from other forms of health information, but there was an association between seeing genetic information as special in some way compared to other health data and increased willingness to donate. The global perspective provided by our "Your DNA, Your Say" study is valuable for informing the development of international policy and practice for sharing genomic data. It highlights that the research community not only needs to be worthy of trust by the public, but also urgent steps need to be taken to authentically communicate why genomic research is necessary and how data donation, and subsequent sharing, is integral to this.

Return of genomic results does not motivate intent to participate in research for all: Perspectives across 22 countries.

PURPOSE: The aim of this study was to determine how attitudes toward the return of genomic research results vary internationally. METHODS: We analyzed the "Your DNA, Your Say" online survey of public perspectives on genomic data sharing including responses from 36,268 individuals across 22 low-, middle-, and high-income countries, and these were gathered in 15 languages. We analyzed how participants responded when asked whether return of results (RoR) would motivate their decision to donate DNA or health data. We examined variation across the study countries and compared the responses of participants from other countries with those from the United States, which has been the subject of the majority of research on return of genomic results to date. RESULTS: There was substantial variation in the extent to which respondents reported being influenced by RoR. However, only respondents from Russia were more influenced than those from the United States, and respondents from 20 countries had lower odds of being partially or wholly influenced than those from the United States. CONCLUSION: There is substantial international variation in the extent to which the RoR may motivate people's intent to donate DNA or health data. The United States may not be a clear indicator of global attitudes. Participants' preferences for return of genomic results globally should be considered.

Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics.

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose of a drug under a specific schedule. Evaluating drug schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) to estimate the maximum tolerated dose regimen (MTD-regimen) at the end of the dose-escalation stage of a trial. We modeled the binary toxicity via a PD endpoint and estimated the dose regimen toxicity relationship through the integration of a dose regimen PD model and a PD toxicity model. For the first model, we considered nonlinear mixed-effects models, and for the second one, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. In an extensive simulation study, the DRtox outperformed traditional designs in terms of proportion of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information helped provide more precise estimates for the entire dose regimen toxicity curve; therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox was developed to be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected.

Development and evaluation of a core genome multilocus sequence typing scheme for Paenibacillus larvae, the deadly American foulbrood pathogen of honeybees.

Paenibacillus larvae is the causative agent of the fatal American foulbrood disease in honeybees (Apis mellifera). Strain identification is vital for preventing the spread of the disease. To date, the most accessible and robust scheme to identify strains is the multilocus sequence typing (MLST) method. However, this approach has limited resolution, especially for epidemiological studies. As the cost of whole-genome sequencing has decreased and as it becomes increasingly available to most laboratories, an extended MLST based on the core genome (cgMLST) presents a valuable tool for high-resolution investigations. In this study, we present a standardized, robust cgMLST scheme for P. larvae typing using whole-genome sequencing. A total of 333 genomes were used to identify, validate and evaluate 2419 core genes. The cgMLST allowed fine-scale differentiation between samples that had the same profile using traditional MLST and allowed for the characterization of strains impossible by MLST. The scheme was successfully used to trace a localized Swedish outbreak, where a cluster of 38 isolates was linked to a country-wide beekeeping operation. cgMLST greatly enhances the power of a traditional typing scheme, while preserving the same stability and standardization for sharing results and methods across different laboratories.

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens.

Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose-finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLTo ) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose-allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD-regimen compared to the recommendation of the dose-allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

Bayesian methods for the analysis of early-phase oncology basket trials with information borrowing across cancer types.

Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, which assesses the efficacy of treatment simultaneously on multiple cancer types that have a common aberration. Although the assumption of homogeneous treatment effects seems reasonable given the shared aberration, in reality, the treatment effect may vary by cancer type, and potentially only a subgroup of the cancer types respond to the treatment. Various approaches have been proposed to increase the trial power by borrowing information across cancer types, which, however, tend to inflate the type I error rate. In this article, we review some representative Bayesian information borrowing methods for the analysis of early-phase basket trials. We then propose a novel method called the Bayesian hierarchical model with a correlated prior (CBHM), which conducts more flexible borrowing across cancer types according to sample similarity. We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach, and Liu's two-stage approach. Simulation results show that all information borrowing approaches substantially improve the power of independent analysis if a large proportion of the cancer types truly respond to the treatment. Our proposed CBHM approach shows an advantage over the existing information borrowing approaches, with a power similar to that of EXNEX or Liu's approach, but the potential to provide substantially better control of type I error rate.

Robust designs in longitudinal studies accounting for parameter and model uncertainties - application to count data.

Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte-Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters, as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncertainties.

An MCMC method for the evaluation of the Fisher information matrix for non-linear mixed effect models.

Non-linear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal design based on the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. In recent years, estimation algorithms for NLMEMs have transitioned from linearization toward more exact higher-order methods. Optimal design, on the other hand, has mainly relied on first-order (FO) linearization to calculate the FIM. Although efficient in general, FO cannot be applied to complex non-linear models and with difficulty in studies with discrete data. We propose an approach to evaluate the expected FIM in NLMEMs for both discrete and continuous outcomes. We used Markov Chain Monte Carlo (MCMC) to integrate the derivatives of the log-likelihood over the random effects, and Monte Carlo to evaluate its expectation w.r.t. the observations. Our method was implemented in R using Stan, which efficiently draws MCMC samples and calculates partial derivatives of the log-likelihood. Evaluated on several examples, our approach showed good performance with relative standard errors (RSEs) close to those obtained by simulations. We studied the influence of the number of MC and MCMC samples and computed the uncertainty of the FIM evaluation. We also compared our approach to Adaptive Gaussian Quadrature, Laplace approximation, and FO. Our method is available in R-package MIXFIM and can be used to evaluate the FIM, its determinant with confidence intervals (CIs), and RSEs with CIs.

Arabidopsis wat1 (walls are thin1)-mediated resistance to the bacterial vascular pathogen, Ralstonia solanacearum, is accompanied by cross-regulation of salicylic acid and tryptophan metabolism.

Inactivation of Arabidopsis WAT1 (Walls Are Thin1), a gene required for secondary cell-wall deposition, conferred broad-spectrum resistance to vascular pathogens, including the bacteria Ralstonia solanacearum and Xanthomonas campestris pv. campestris, and the fungi Verticillium dahliae and Verticillium albo-atrum. Introduction of NahG, the bacterial salicylic acid (SA)-degrading salicylate hydroxylase gene, into the wat1 mutant restored full susceptibility to both R. solanacearum and X. campestris pv. campestris. Moreover, SA content was constitutively higher in wat1 roots, further supporting a role for SA in wat1-mediated resistance to vascular pathogens. By combining transcriptomic and metabolomic data, we demonstrated a general repression of indole metabolism in wat1-1 roots as shown by constitutive down-regulation of several genes encoding proteins of the indole glucosinolate biosynthetic pathway and reduced amounts of tryptophan (Trp), indole-3-acetic acid and neoglucobrassicin, the major form of indole glucosinolate in roots. Furthermore, the susceptibility of the wat1 mutant to R. solanacearum was partially restored when crossed with either the trp5 mutant, an over-accumulator of Trp, or Pro35S:AFB1-myc, in which indole-3-acetic acid signaling is constitutively activated. Our original hypothesis placed cell-wall modifications at the heart of the wat1 resistance phenotype. However, the results presented here suggest a mechanism involving root-localized metabolic channeling away from indole metabolites to SA as a central feature of wat1 resistance to R. solanacearum.

A Bayesian dose-finding design for drug combination clinical trials based on the logistic model.

In early phase dose-finding cancer studies, the objective is to determine the maximum tolerated dose, defined as the highest dose with an acceptable dose-limiting toxicity rate. Finding this dose for drug-combination trials is complicated because of drug-drug interactions, and many trial designs have been proposed to address this issue. These designs rely on complicated statistical models that typically are not familiar to clinicians, and are rarely used in practice. The aim of this paper is to propose a Bayesian dose-finding design for drug combination trials based on standard logistic regression. Under the proposed design, we continuously update the posterior estimates of the model parameters to make the decisions of dose assignment and early stopping. Simulation studies show that the proposed design is competitive and outperforms some existing designs. We also extend our design to handle delayed toxicities.

Influence of Temperature and Precipitation on the Effectiveness of Water, Sanitation, and Handwashing Interventions against Childhood Diarrheal Disease in Rural Bangladesh: A Reanalysis of the WASH Benefits Bangladesh Trial.

BACKGROUND: Diarrheal disease is a leading cause of childhood morbidity and mortality globally. Household water, sanitation, and handwashing (WASH) interventions can reduce exposure to diarrhea-causing pathogens, but meteorological factors may impact their effectiveness. Information about effect heterogeneity under different weather conditions is critical to refining these targeted interventions. OBJECTIVES: We aimed to determine whether temperature and precipitation modified the effect of low-cost, point-of-use WASH interventions on child diarrhea. METHODS: We analyzed data from a trial in rural Bangladesh that compared child diarrhea prevalence between clusters (N=720) that were randomized to different WASH interventions between 2012 and 2016 (NCT01590095). We matched temperature and precipitation measurements to diarrhea outcomes (N=12,440 measurements, 6,921 children) by geographic coordinates and date. We estimated prevalence ratios (PRs) using generative additive models and targeted maximum likelihood estimation to assess the effectiveness of each WASH intervention under different weather conditions. RESULTS: Generally, WASH interventions most effectively prevented diarrhea during monsoon season, particularly following weeks with heavy rain or high temperatures. The PR for diarrhea in the WASH interventions group compared with the control group was 0.49 (95% CI: 0.35, 0.68) after 1 d of heavy rainfall, with a less-protective effect [PR=0.87 (95% CI: 0.60, 1.25)] when there were no days with heavy rainfall. Similarly, the PR for diarrhea in the WASH intervention group compared with the control group was 0.60 (95% CI: 0.48, 0.75) following above-median temperatures vs. 0.91 (95% CI: 0.61, 1.35) following below-median temperatures. The influence of precipitation and temperature varied by intervention type; for precipitation, the largest differences in effectiveness were for the sanitation and combined WASH interventions. DISCUSSION: WASH intervention effectiveness was strongly influenced by precipitation and temperature, and nearly all protective effects were observed during the rainy season. Future implementation of these interventions should consider local environmental conditions to maximize effectiveness, including targeted efforts to maintain latrines and promote community adoption ahead of monsoon seasons. https://doi.org/10.1289/EHP13807.

Influence of hydrometeorological risk factors on child diarrhea and enteropathogens in rural Bangladesh.

BACKGROUND: A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change. OBJECTIVES: Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change. METHODS: We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters. RESULTS: Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null. DISCUSSION: Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.

Distribution of infectious and parasitic agents among three sentinel bee species across European agricultural landscapes.

Infectious and parasitic agents (IPAs) and their associated diseases are major environmental stressors that jeopardize bee health, both alone and in interaction with other stressors. Their impact on pollinator communities can be assessed by studying multiple sentinel bee species. Here, we analysed the field exposure of three sentinel managed bee species (Apis mellifera, Bombus terrestris and Osmia bicornis) to 11 IPAs (six RNA viruses, two bacteria, three microsporidia). The sentinel bees were deployed at 128 sites in eight European countries adjacent to either oilseed rape fields or apple orchards during crop bloom. Adult bees of each species were sampled before their placement and after crop bloom. The IPAs were detected and quantified using a harmonised, high-throughput and semi-automatized qPCR workflow. We describe differences among bee species in IPA profiles (richness, diversity, detection frequencies, loads and their change upon field exposure, and exposure risk), with no clear patterns related to the country or focal crop. Our results suggest that the most frequent IPAs in adult bees are more appropriate for assessing the bees' IPA exposure risk. We also report positive correlations of IPA loads supporting the potential IPA transmission among sentinels, suggesting careful consideration should be taken when introducing managed pollinators in ecologically sensitive environments.

Disruption of abscisic acid signaling constitutively activates Arabidopsis resistance to the necrotrophic fungus Plectosphaerella cucumerina.

Plant resistance to necrotrophic fungi is regulated by a complex set of signaling pathways that includes those mediated by the hormones salicylic acid (SA), ethylene (ET), jasmonic acid (JA), and abscisic acid (ABA). The role of ABA in plant resistance remains controversial, as positive and negative regulatory functions have been described depending on the plant-pathogen interaction analyzed. Here, we show that ABA signaling negatively regulates Arabidopsis (Arabidopsis thaliana) resistance to the necrotrophic fungus Plectosphaerella cucumerina. Arabidopsis plants impaired in ABA biosynthesis, such as the aba1-6 mutant, or in ABA signaling, like the quadruple pyr/pyl mutant (pyr1pyl1pyl2pyl4), were more resistant to P. cucumerina than wild-type plants. In contrast, the hab1-1abi1-2abi2-2 mutant impaired in three phosphatases that negatively regulate ABA signaling displayed an enhanced susceptibility phenotype to this fungus. Comparative transcriptomic analyses of aba1-6 and wild-type plants revealed that the ABA pathway negatively regulates defense genes, many of which are controlled by the SA, JA, or ET pathway. In line with these data, we found that aba1-6 resistance to P. cucumerina was partially compromised when the SA, JA, or ET pathway was disrupted in this mutant. Additionally, in the aba1-6 plants, some genes encoding cell wall-related proteins were misregulated. Fourier transform infrared spectroscopy and biochemical analyses of cell walls from aba1-6 and wild-type plants revealed significant differences in their Fourier transform infrared spectratypes and uronic acid and cellulose contents. All these data suggest that ABA signaling has a complex function in Arabidopsis basal resistance, negatively regulating SA/JA/ET-mediated resistance to necrotrophic fungi.

Effect of medical staff training on vaccination coverage in outpatients with cancer: An interventional multicenter before-and-after study.

Purpose: Despite widely disseminated guidelines, pneumococcal and influenza vaccination coverage (VC) remains insufficient in patients with cancer receiving cancer treatment. We performed an interventional study to evaluate VC in patients with cancer treated at the medical oncology departments of three North-of-France hospitals and to assess the effect of medical staff training on VC in these patients. Methods: A standardized questionnaire assessed VC in adult patients with cancer receiving anticancer treatment at three day hospitals during December 2-7, 2019. Subsequently (January 2020), we organized educational training sessions for medical staff from each hospital to discuss the current vaccination guidelines. To assess the impact of training on pneumococcal and influenza VC, we re-administered the same questionnaire in March 2020. Because there are no specific guidelines on Diphtheria-Tetanus-Pertussis (DTP) vaccination and no improvement was expected, DTP VC acted as an internal control. Results: In total, 272 patients from all three hospitals were enrolled in the "before study"; 156 patients from only two hospitals were enrolled in the "after study" as medical training and data collection at the third were impossible because of administrative reasons and COVID-19 pandemic. The predictors were age for DTP VC; treatment center for pneumococcal VC; and age, sex, and tumor histology (adenocarcinoma vs. others) for influenza VC. Neither influenza VC (42.6% vs. 55.1%, p = 0.08), nor pneumococcal VC were significantly improved post-intervention (11.8% vs. 15.4%, p = 1). There seems to be a small effect in the most fragile for influenza VC. Conclusion: As expected, VC was very low in patients with cancer, consistent with the literature. There was no impact of the intervention for pneumococcal and influenza VC.

Bisphenol A and bisphenol S both disrupt ovine granulosa cell steroidogenesis but through different molecular pathways.

BACKGROUND: Ovarian granulosa cells (GC) are essential for the development and maturation of a proper oocyte. GC are sensitive to endocrine disruptors, including bisphenol A (BPA) and its analogue bisphenol S (BPS), plasticisers present in everyday consumer products. BPA exhibits greater binding affinity for the membrane oestrogen receptor (GPER) than for the nuclear oestrogen receptors (ERα and ERß). Here, we analysed the effects of BPA and BPS on the steroidogenesis of ovine GC in vitro, as well as their early mechanisms of action, the ovine being a relevant model to study human reproductive impairment. Disruption of GC steroidogenesis might alter oocyte quality and consequently fertility rate. In addition, we compared the effects of a specific GPER agonist (G-1) and antagonist (G-15) to those of BPA and BPS. Ewe GC were cultured with BPA or BPS (10 or 50 µM) or G-1 (1 µM) and/or G-15 (10 µM) for 48 h to study steroidogenesis. RESULTS: Both BPA and BPS (10 µM) altered the secretion of progesterone, however, only BPS (10 µM) affected oestradiol secretion. RNA-seq was performed on GC after 1 h of culture with BPA or BPS (50 µM) or G-1 (10 µM), followed by real-time PCR analyses of differentially expressed genes after 12, 24 and 48 h of culture. The absence of induced GPER target genes showed that BPA and BPS did not activate GPER in GC after 1 h of treatment. These molecules exhibited mainly independent early mechanisms of action. Gene ontology analysis showed that after 1 h of treatment, BPA mainly disrupted the expression of the genes involved in metabolism and transcription, while BPS had a smaller effect and impaired cellular communications. BPA had a transient effect on the expression of CHAC1 (NOTCH signalling and oxidative balance), JUN (linked to MAPK pathway), NR4A1 (oestradiol secretion inhibition), ARRDC4 (endocytose of GPCR) and KLF10 (cell growth, differentiation and apoptosis), while expression changes were maintained over time for the genes LSMEM1 (linked to MAPK pathway), TXNIP (oxidative stress) and LIF (cell cycle regulation) after 12 and 48 h, respectively. CONCLUSION: In conclusion, although they exhibited similar effects, BPA and BPS impaired different molecular pathways in GC in vitro. New investigations will be necessary to follow the temporal changes of these genes over time, as well as the biological processes involved.