Dietary probiotics should reach the intestine viable and in high numbers; therefore, they should tolerate the stress associated to the gastro-intestinal (GI) environment. Indeed, all along the different GI sections, probiotics are challenged by several sources of stress, including low pH, bile and digestive enzymes. Bacterial cells are equipped with various defense mechanisms to allow survival in hostile environments. The food matrix used to deliver beneficial bacteria may contribute to their probiotic action, e.g. by enhancing survival to stress and gut colonization. In this study, the survival of the lactic acid bacterium Lactobacillus plantarum WCFS1, a model probiotic strain, was examined in a human oro-gastric-intestinal (OGI) in vitro system, using different carrier matrices to compare protective and buffering properties. Higher survival was observed in complex and/or nutrient-rich matrices, and when potential prebiotics were added. The molecular response of L. plantarum to the OGI transit was analyzed by studying the transcriptional levels of genes involved in stress response and probiosis. The OGI steps of higher mortality corresponded to greater induction of stress genes, thus implying their involvement in adaptation to the gut environment. Plantaricins were significantly upregulated all along the different OGI sections; adhesion genes were mainly induced by gastric environment.

In this study, the probiotic potential of Lactobacillus plantarum wild-type and derivative mutant strains was investigated. Bacterial survival was evaluated in an in vitro system, simulating the transit along the human oro-gastro-intestinal tract. Interaction with human gut epithelial cells was studied by assessing bacterial adhesive ability to Caco-2 cells and induction of genes involved in innate immunity. L. plantarum strains were resistant to the combined stress at the various steps of the simulated gastrointestinal tract. Major decreases in the viability of L. plantarum cells were observed mainly under drastic acidic conditions (pH ≤ 2.0) of the gastric compartment. Abiotic stresses associated to small intestine poorly affected bacterial viability. All the bacterial strains significantly adhered to Caco-2 cells, with the ΔctsR mutant strain exhibiting the highest adhesion. Induction of immune-related genes resulted higher upon incubation with heat-inactivated bacteria rather than with live ones. For specific genes, a differential transcriptional pattern was observed upon stimulation with different L. plantarum strains, evidencing a possible role of the knocked out bacterial genes in the modulation of host cell response. In particular, cells from Δhsp18.55 and ΔftsH mutants strongly triggered immune defence genes. Our study highlights the relevance of microbial genetic background in host-probiotic interaction and might contribute to identify candidate bacterial genes and molecules involved in probiosis.

FtsH proteins are ubiquitous membrane-bound, ATP-dependent metalloproteases of the AAA family. In eubacteria, FtsH is involved in protein quality control under stress conditions. Lactobacillus plantarum is a widespread lactic acid bacterium that is encountered in several fermented food, including dairy products, vegetables and meat. In the present work the expression of the ftsH gene of L. plantarum was studied by quantitative real time RT-PCR in bacterial cultures subjected to various abiotic stresses. Both oxidative stress and addition of a membrane-fluidizing agent induced ftsH transcription, while a depletion of carbon-source repressed its mRNA level. Mutants deprived of the FtsH protease exhibited remarkable sensitivity to elevated temperature and increased salt concentration; conversely, overexpression of ftsH resulted in increased thermotolerance and resistance to salt. FtsH mutant had a reduced capacity to form biofilms on abiotic surfaces and exhibited different cell surface physico-chemical properties with respect to the wild type strain.

In this paper we propose MPP systems as modelling notation for ecological systems, and we show how they can be used together with simulation and statistical model checking tools to study properties of such kind of systems. As a case study we consider the ecological problem of stability of European water frog populations. The paper shows that MPP systems allow easy and concise modelling of real ecological problems. Moreover, MPP systems models can be easily simulated and translated into the PRISM input language to enable statistical model checking of propertie

International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset.