Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis

Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH2PO4, K2HPO4, FeSO4, MnSO4, and MgSO4 and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett—Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors influenced the production of delta-endotoxins are FeSO4, K2HPO4, starch and soybean meal. Indeed, it was found that soybean meal, K2HPO4, KH2PO4 and starch also showed positive effect on the delta-endotoxins production. However, FeSO4 and MnSO4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett—Burman design) joined with Bayesian networks method could be used for identification of effect variables on delta-endotoxins variation

Modeling-based optimization approaches for the development of Anti- Agrobacterium tumefaciens activity using Streptomyces sp TN71

A new aerobic bacterium TN71 was isolated from Tunisian Saharan soil and has been selected for its antimicrobial activity against phytopathogenic bacteria. Based on cellular morphology, physiological characterization and phylogenetic analysis, this isolate has been assigned as Streptomyces sp. TN71 strain. In an attempt to increase its anti-Agrobacterium tumefaciens activity, GYM + S (glucose, yeast extract, malt extract and starch) medium was selected out of five different production media and the medium composition was optimized. Plackett-Burman design (PBD) was used to select starch, malt extract and glucose as parameters having significant effects on antibacterial activity and a Box-Behnken design was applied for further optimization. The analysis revealed that the optimum concentrations for anti-A. tumefaciens activity of the tested variables were 19.49 g/L for starch, 5.06 g/L for malt extract and 2.07 g/L for glucose. Several Artificial Neural Networks (ANN): the Multilayer perceptron (MLP) and the Radial basis function (RBF) were also constructed to predict anti-A. tumefaciens activity. The comparison between experimental with predicted outputs from ANN and Response Surface Methodology (RSM) were studied. ANN model presents an improvement of 12.36% in terms of determination coefficients of anti A. tumefaciens activity. To our knowledge, this is the first work reporting the statistical versus artificial intelligence based modeling for optimization of bioactive molecules against phytopathogen

Artificial Intelligence to Improve the Food and Agriculture Sector

The world population is expected to reach over 9 billion by 2050, which will require an increase in agricultural and food production by 70% to fit the need, a serious challenge for the agri-food industry. Such requirement, in a context of resources scarcity, climate change, COVID-19 pandemic, and very harsh socioeconomic conjecture, is difficult to fulfill without the intervention of computational tools and forecasting strategy. Hereby, we report the importance of artificial intelligence and machine learning as a predictive multidisciplinary approach integration to improve the food and agriculture sector, yet with some limitations that should be considered by stakeholders

Dempster-Shafer Theory for the Prediction of Auxin-Response Elements (AuxREs) in Plant Genomes

Auxin is a major regulator of plant growth and development; its action involves transcriptional activation. The identification of Auxin-response element (AuxRE) is one of the most important issues to understand the Auxin regulation of gene expression. Over the past few years, a large number of motif identification tools have been developed. Despite these considerable efforts provided by computational biologists, building reliable models to predict regulatory elements has still been a difficult challenge. In this context, we propose in this work a data fusion approach for the prediction of AuxRE. Our method is based on the combined use of Dempster-Shafer evidence theory and fuzzy theory. To evaluate our model, we have scanning the DORNRÖSCHEN promoter by our model. All proven AuxRE present in the promoter has been detected. At the 0.9 threshold we have no false positive. The comparison of the results of our model and some previous motifs finding tools shows that our model can predict AuxRE more successfully than the other tools and produce less false positive. The comparison of the results before and after combination shows the importance of Dempster-Shafer combination in the decrease of false positive and to improve the reliability of prediction. For an overall evaluation we have chosen to present the performance of our approach in comparison with other methods. In fact, the results indicated that the data fusion method has the highest degree of sensitivity (Sn) and Positive Predictive Value (PPV)

Contamination Assessment of Durum Wheat and Barley Irrigated with Treated Wastewater through Physiological and Biochemical Effects and Statistical Analyses

The present work focused on the impact of crop irrigation by treated wastewater (TWW) on soil fertility, in germination, and growth of two species of cereals (T. turgidum and H. vulgare). This investigation was conducted at the germination stage (controlled condition) and in pots containing a soil irrigated with wastewater in comparison with controlled soil. Germination rate, vigor index, seedling growth, total fresh mass, chlorophyll content, proline, ascorbate peroxidase (APX), guaiacol peroxidase (GPX), and catalase (CAT) activities were measured. Similar effects were shown on both species which emphasize the important role of antioxidant enzymes in the defense against oxidative stress induced by prolonged reuse of TWW. The disturbing effect of the reuse TWW on soil fertility, germination, and development of young plants (T. turgidum and H. vulgare) was linked to the presence of micropollutants in TWW. Data were analyzed by R language using a nonparametric statistical hypothesis test. These have caused the disorganization of many physiological mechanism targets, especially growth disorders observed under different abiotic stress conditions. In conclusion, high salt and heavy metal concentrations contained in the TWW are the major constraints related to the reuse of TWW. Hence, repetitive irrigation with this water can induce, at long term, soil contamination which can limit plant production and crop contamination

Computational Approach for Structural Feature Determination of Grapevine NHX Antiporters

Plant NHX antiporters are responsible for monovalent cation/H+ exchange across cellular membranes and play therefore a critical role for cellular pH regulation, Na+ and K+ homeostasis, and salt tolerance. Six members of grapevine NHX family (VvNHX1-6) have been structurally characterized. Phylogenetic analysis revealed their organization in two groups: VvNHX1-5 belonging to group I (vacuolar) and VvNHX6 belonging to group II (endosomal). Conserved domain analysis of these VvNHXs indicates the presence of different kinds of domains. Out of these, two domains function as monovalent cation-proton antiporters and one as the aspartate-alanine exchange; the remaining are not yet with defined function. Overall, VvNHXs proteins are typically made of 11-13 putative transmembrane regions at their N-terminus which contain the consensus amiloride-binding domain in the 3rd TM domain and a cation-binding site in between the 5th and 6th TM domain, followed by a hydrophilic C-terminus that is the target of several and diverse regulatory posttranslational modifications. Using a combination of primary structure analysis, secondary structure alignments, and the tertiary structural models, the VvNHXs revealed mainly 18 α helices although without β sheets. Homology modeling of the 3D structure showed that VvNHX antiporters are similar to the bacterial sodium proton antiporters MjNhaP1 (Methanocaldococcus jannaschii) and PaNhaP (Pyrococcus abyssi)

Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil

Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture