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Food-grade Lactic Acid Bacteria (LAB) have been safely consumed for centuries by humans in fermented foods. Thus, they are good candidates to develop novel oral vectors, constituting attractive alternatives to attenuated pathogens, for mucosal delivery strategies. Herein, this review summarizes our research, up until now, on the use of LAB as mucosal delivery vectors for therapeutic proteins and DNA vaccines. Most of our work has been based on the model LAB Lactococcus lactis , for which we have developed efficient genetic tools, including expression signals and host strains, for the heterologous expression of therapeutic proteins such as antigens, cytokines and enzymes. Resulting recombinant lactococci strains have been tested successfully for their prophylactic and therapeutic effects in different animal models: i) against human papillomavirus type 16 (HPV-16)-induced tumors in mice, ii) to partially prevent a bovine β-lactoglobulin (BLG)-allergic reaction in mice and iii) to regulate body weight and food consumption in obese mice. Strikingly, all of these tools have been successfully transposed to the Lactobacillus genus, in recent years, within our laboratory. Notably, anti-oxidative Lactobacillus casei strains were constructed and tested in two chemically-induced colitis models. In parallel, we also developed a strategy based on the use of L. lactis to deliver DNA at the mucosal level, and were able to show that L. lactis is able to modulate the host response through DNA delivery. Today, we consider that all of our consistent data, together with those obtained by other groups, demonstrate and reinforce the interest of using LAB, particularly lactococci and lactobacilli strains, to develop novel therapeutic protein mucosal delivery vectors which should be tested now in human clinical trials.

Background The expression of vaccine antigens in lactic acid bacteria (LAB) is a safe and cost-effective alternative to traditional expression systems. In this study, we investigated i) the expression of Human papillomavirus type 16 (HPV-16) L1 major capsid protein in the model LAB Lactococcus lactis and ii) the ability of the resulting recombinant strain to produce either capsomer-or virus-like particles (VLPs). Results and conclusion HPV-16 L1 gene was cloned into two vectors, pCYT and pSEC, designed for controlled intra- or extracellular heterologous expression in L. lactis , respectively. The capacity of L. lactis harboring either pCYT:L1 or pSEC:L1 plasmid to accumulate L1 in the cytoplasm and supernatant samples was confirmed by Western blot assays. Electron microscopy analysis suggests that, L1 protein produced by recombinant lactococci can self-assemble into structures morphologically similar to VLPs intracellularly. The presence of conformational epitopes on the L. lactis -derived VLPs was confirmed by ELISA using an anti-HPV16 L1 capsid antigen antibody. Our results support the feasibility of using recombinant food-grade LAB, such as L. lactis , for the production of L1-based VLPs and open the possibility for the development of a new safe mucosal vector for HPV-16 prophylactic vaccination.

Different studies have described the successful use of recombinant lactic acid bacteria (recLAB) to deliver anti-inflammatory molecules at the mucosal level to treat Inflammatory Bowel Disease (IBD). In order to identify the best strategy to treat IBD using recLAB, we compared the efficacy of different recombinant strains of Lactococcus lactis (the model LAB) secreting two types of anti-inflammatory molecules: cytokines (IL-10 and TGF-[beta]1) and serine protease inhibitors (Elafin and Secretory Leukocyte Protease Inhibitor: SLPI), using a dextran sulfate sodium (DSS)-induced mouse model of colitis. Our results show that oral administration of recombinant L. lactis strains expressing either IL-10 or TGF-[beta]1 display moderate anti-inflammatory effects in inflamed mice and only for some clinical parameters. In contrast, delivery of either serine protease inhibitors Elafin or SLPI by recLAB led to a significant reduction of intestinal inflammation for all clinical parameters tested. Since the best results were obtained with Elafin-producing L. lactis strain, we then tried to enhance Elafin expression and hence its delivery rate by producing it in a L. lactis mutant strain inactivated in its major housekeeping protease, HtrA. Strikingly, a higher reduction of intestinal inflammation in DSS-treated mice was observed with the Elafin-overproducing htrA strain suggesting a dose-dependent Elafin effect. Altogether, these results strongly suggest that serine protease inhibitors are the most efficient anti-inflammatory molecules to be delivered by recLAB at the mucosal level for IBD treatment.

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models’ output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.

IntroductionSuicide has a complex aetiology and is a result of the interaction among the risk and protective factors at the individual, healthcare system and population levels. Therefore, policy and decision makers and mental health service planners can play an important role in suicide prevention. Although a number of suicide risk predictive tools have been developed, these tools were designed to be used by clinicians for assessing individual risk of suicide. There have been no risk predictive models to be used by policy and decision makers for predicting population risk of suicide at the national, provincial and regional levels. This paper aimed to describe the rationale and methodology for developing risk predictive models for population risk of suicide.Methods and analysisA case–control study design will be used to develop sex-specific risk predictive models for population risk of suicide, using statistical regression and machine learning techniques. Routinely collected health administrative data in Quebec, Canada, and community-level social deprivation and marginalisation data will be used. The developed models will be transformed into the models that can be readily used by policy and decision makers. Two rounds of qualitative interviews with end-users and other stakeholders were proposed to understand their views about the developed models and potential systematic, social and ethical issues for implementation; the first round of qualitative interviews has been completed. We included 9440 suicide cases (7234 males and 2206 females) and 661 780 controls for model development. Three hundred and forty-seven variables at individual, healthcare system and community levels have been identified and will be included in least absolute shrinkage and selection operator regression for feature selection.Ethics and disseminationThis study is approved by the Health Research Ethnics Committee of Dalhousie University, Canada. This study takes an integrated knowledge translation approach, involving knowledge users from the beginning of the process.

Suicide is a significant public health issue. Many risk prediction tools have been developed to estimate an individual's risk of suicide. Risk prediction models can go beyond individual risk assessment; one important application of risk prediction models is population health planning. Suicide is a result of the interaction among the risk and protective factors at the individual, health care system, and community levels. Thus, policy and decision makers can play an important role in suicide prevention. However, few prediction models for the population risk of suicide have been developed. This study aims to develop and validate prediction models for the population risk of suicide using health administrative data, considering individual-, health system-, and community-level predictors. We used a case-control study design to develop sex-specific risk prediction models for suicide, using the health administrative data in Quebec, Canada. The training data included all suicide cases (n=8899) that occurred from January 1, 2002, to December 31, 2010. The control group was a 1% random sample of living individuals in each year between January 1, 2002, and December 31, 2010 (n=645,590). Logistic regression was used to develop the prediction models based on individual-, health care system-, and community-level predictors. The developed model was converted into synthetic estimation models, which concerted the individual-level predictors into community-level predictors. The synthetic estimation models were directly applied to the validation data from January 1, 2011, to December 31, 2019. We assessed the performance of the synthetic estimation models with four indicators: the agreement between predicted and observed proportions of suicide, mean average error, root mean square error, and the proportion of correctly identified high-risk regions. The sex-specific models based on individual data had good discrimination (male model: C=0.79; female model: C=0.85) and calibration (Brier score for male model 0.01; Brier score for female model 0.005). With the regression-based synthetic models applied in the validation data, the absolute differences between the synthetic risk estimates and observed suicide risk ranged from 0% to 0.001%. The root mean square errors were under 0.2. The synthetic estimation model for males correctly predicted 4 of 5 high-risk regions in 8 years, and the model for females correctly predicted 4 of 5 high-risk regions in 5 years. Using linked health administrative databases, this study demonstrated the feasibility and the validity of developing prediction models for the population risk of suicide, incorporating individual-, health system-, and community-level variables. Synthetic estimation models built on routinely collected health administrative data can accurately predict the population risk of suicide. This effort can be enhanced by timely access to other critical information at the population level.

Alnus glutinosa response to Frankia alni is driven by several sequential physiological modifications that include calcium spiking, root hair deformation, penetration, induction of primordium, formation and growth of nodule. Here, we have conducted a transcriptomic study to analyse plant responses to Frankia alni at early stages of symbiosis establishment. Forty-two genes were significantly activated by either with a Frankia culture supernatant or with living cells separated from the roots by a dialysis membrane permitted to identify plant genes which expression changes upon early contact with Frankia. Most of these genes encode biological processes, including oxidative stress and response to stimuli. The most upregulated gene is the non-specific lipid transfer protein (nsLTP) encoding gene with a fold change of 141. Physiological experiments showed that nsLTP increases Frankia nitrogen fixation at sub-lethal concentration. Immunohistochemistry experiments conducted at an early infection stage indicated that nsLTP protein is localized at the deformed root hair region after Frankia inoculation and later in nodules, precisely around bacterial vesicles. Taken together, these results suggest that nsLTP acts at early and late stages of symbiosis, probably by increasing nitrogen uptake by Frankia. Competing Interest Statement The authors have declared no competing interest.