Antibiotic growth promoters have been utilized for long time at subtherapeutic levels as feed supplements in monogastric animal rations. Because of their side-effects such as antibiotic resistance, reduction of beneficial bacteria in the gut, and dysbiosis, it is necessary to look for non-therapeutic alternatives. Probiotics play an important role as the key substitutes to antibacterial agents due to their many beneficial effects on the monogastric animal host. For instance, enhancement of the gut microbiota balance can contribute to improvement of feed utilization efficiency, nutrients absorption, growth rate, and economic profitability of livestock. Probiotics are defined as “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host.” They are available in diverse forms for use as feed supplements. Their utilization as feed additives assists in good digestion of feed ingredients and hence, making the nutrients available for promoting growth. Immunity can also be enhanced by supplementing probiotics to monogastrics diets. Moreover, probiotics can help in improving major meat quality traits and countering a variety of monogastric animals infectious diseases. A proper selection of the probiotic strains is required in order to confer optimal beneficial effects. The present review focuses on the general functional, safety, and technological screening criteria for selection of ideal Bacillus probiotics as feed supplements as well as their mechanism of action and beneficial effects on monogastric animals for improving production performance and health status. Supplementary Information The online version contains supplementary material available at 10.1007/s12602-022-09909-5.
The increasing challenge of antibiotic resistance requires not only the discovery of new antibiotics, but also the development of new alternative approaches. Therefore, in the present study, we investigated for the first time the antibacterial potential of phytic acid (myo-inositol hexakisphosphate, IP6), a natural molecule that is 'generally recognized as safe' (FDA classification), against the proliferation of common foodborne bacterial pathogens such as Listeria monocytogenes, Staphylococcus aureus and Salmonella Typhimurium. Interestingly, compared to citric acid, IP6 was found to exhibit significantly greater inhibitory activity (P<0.05) against these pathogenic bacteria. The minimum inhibitory concentration of IP6 varied from 0.488 to 0.97 mg/ml for the Gram-positive bacteria that were tested, and was 0.244 mg/ml for the Gram-negative bacteria. Linear and general models were used to further explore the antibacterial effects of IP6. The developed models were validated using experimental growth data for L. monocytogenes, S. aureus and S. Typhimurium. Overall, the models were able to accurately predict the growth of L. monocytogenes, S. aureus, and S. Typhimuriumin Polymyxin acriflavine lithium chloride ceftazidime aesculin mannitol (PAL-CAM), Chapman broth, and xylose lysine xeoxycholate (XLD) broth, respectively. Remarkably, the early logarithmic growth phase of S. Typhimurium showed a rapid and severe decrease in a period of less than one hour, illustrating the bactericidal effect of IP6. These results suggest that IP6 is an efficient antibacterial agent and can be used to control the proliferation of foodborne pathogens. It has promising potential for environmentally friendly applications in the food industry, such as for food preservation, food safety, and for prolonging shelf life.
Reliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.
Integral membrane proteins PEPT1 and PEPT2 are essential for reabsorbing almost all hydrolysed or filtered di- and tripeptides alongside a wide range of peptidomimetic drugs in the kidney. The aim of this study was to investigate the potential use of the fluorophore-conjugated dipeptide β-Ala-Lys (AMCA) as a biosensor for measuring peptide transport activity in brush border membrane vesicles isolated from the outer cortex (BBMV-OC) and outer medulla (BBMV-OM) (representing PEPT1 and PEPT2 respectively). The vesicles were isolated using a dual magnesium precipitation and centrifugation technique. Intravesicular fluorescence accumulation was measured after incubating extra-vesicular media at pH6.6 and different concentrations of β-Ala-Lys (AMCA) with vesicles pre-equilibrated at pH7.4. Both BBMV-OC and BMMV-OM showed accumulation of an intravesicular fluorescence signal after 20min incubation. Changing the extra-vesicular pH to 7.4 caused a significant reduction in the β-Ala-Lys (AMCA) uptake into BBMV-OC at concentrations >100μM. When different concentrations of dipeptide, Gly-Gln was added, there was a significant inhibition of 100μM β-Ala-Lys (AMCA) uptake into BBMV-OC and BMMV-OM, reaching 69% and 80%, respectively. Kinetic analysis of β-Ala-Lys (AMCA) at 20min showed that the K and V were 783.7±115.7μM and 2191.2±133.9ΔF/min/mg for BBMV-OC, while BMMV-OM showed significantly higher affinity, but lower capacity at K=93.6±21.9μM and V=935.8±50.2ΔF/min/mg. These findings demonstrate the applicability of β-Ala-Lys (AMCA) as a biosensor to measure the transport activity of the renal-type PEPT1 and PEPT2 in BBMV-OC and BMMV-OM respectively.
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