Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention.

The physiological activities within cells are mainly regulated through protein-protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated.

Glutamine-derived peptides: Current progress and future directions.

Glutamine, the most abundant amino acid in the body, plays a critical role in preserving immune function, nitrogen balance, intestinal integrity, and resistance to infection. However, its limited solubility and instability present challenges for its use a functional nutrient. Consequently, there is a preference for utilizing glutamine-derived peptides as an alternative to achieve enhanced functionality. This article aims to review the applications of glutamine monomers in clinical, sports, and enteral nutrition. It compares the functional effectiveness of monomers and glutamine-derived peptides and provides a comprehensive assessment of glutamine-derived peptides in terms of their classification, preparation, mechanism of absorption, and biological activity. Furthermore, this study explores the potential integration of artificial intelligence (AI)-based peptidomics and synthetic biology in the de novo design and large-scale production of these peptides. The findings reveal that glutamine-derived peptides possess significant structure-related bioactivities, with the smaller molecular weight fraction serving as the primary active ingredient. These peptides possess the ability to promote intestinal homeostasis, exert hypotensive and hypoglycemic effects, and display antioxidant properties. However, our understanding of the structure-function relationships of glutamine-derived peptides remains largely exploratory at current stage. The combination of AI based peptidomics and synthetic biology presents an opportunity to explore the untapped resources of glutamine-derived peptides as functional food ingredients. Additionally, the utilization and bioavailability of these peptides can be enhanced through the use of delivery systems in vivo. This review serves as a valuable reference for future investigations of and developments in the discovery, functional validation, and biomanufacturing of glutamine-derived peptides in food science.

Potential of FGF21 in type 2 diabetes mellitus treatment based on untargeted metabolomics.

Fibroblast growth factor 21 (FGF21) has promise for treating diabetes and its associated comorbidities. It has been found to reduce blood glucose in mice and humans; however, its underlying mechanism is not known. Here, the metabolic function of FGF21 in diabetes was investigated. Diabetic db/db mice received intraperitoneal injections of FGF21 for 28 days, the serum of each mouse was collected, and their metabolites were analyzed by untargeted metabolomics using UHPLC-MS/MS. It was found that FGF21 reduced blood glucose and oral glucose tolerance without causing hypoglycemia. Moreover, administration of FGF21 reduced the levels of TG and LDL levels while increasing those of HDL and adiponectin. Importantly, the levels of 45 metabolites, including amino acids and lipids, were significantly altered, suggesting their potential as biomarkers. We speculated that FGF21 may treat T2DM through the regulation of fatty acid biosynthesis, the TCA cycle, and vitamin digestion and absorption. These findings provide insight into the mechanism of FGF21 in diabetes and suggest its potential for treating diabetes.

Exopolysaccharide production by Lactobacillus plantarum T10 is responsible for the probiotic activity in enhancing intestinal barrier function in vitro and in vivo.

Lactobacillus probiotics exert their effects in a strain-specific and metabolite-specific manner. This study aims to identify lactobacilli that can effectively enhance the intestinal barrier function both in vitro and in vivo and to investigate the underlying metabolite and molecular mechanisms involved. Nine Lactobacillus isolates were evaluated for their ability to enhance the IPEC-J2 cellular barrier function and for their anti-inflammatory and anti-apoptotic effects in IPEC-J2 cells after an enterotoxigenic Escherichia coli challenge. Of the nine isolates, L. plantarum T10 demonstrated significant advantages in enhancing the cellular barrier function and displayed anti-inflammatory and anti-apoptotic activities in vitro. The bioactivities of L. plantarum T10 were primarily attributed to the production of exopolysaccharides, which exerted their effects through the TLR-mediated p38 MAPK pathway in ETEC-challenged IPEC-J2 cells. Furthermore, the production of EPS by L. plantarum T10 led to the alleviation of dextran sulfate sodium-induced colitis by reducing intestinal damage and enhancing the intestinal barrier function in mice. The EPS is classified as a heteropolysaccharide with an average molecular weight of 23.0 kDa. It is primarily composed of mannose, glucose, and ribose. These findings have practical implications for the targeted screening of lactobacilli used in the production of probiotics and postbiotics with strain-specific features of exopolysaccharides.

Supplementation of citrus pectin with whole-cell pectinase PG5 on Pichia pastoris promotes recovery of colitis and enhances intestinal barrier function in DSS-treated mice.

A whole-cell biocatalyst was developed by genetically engineering pectinase PG5 onto the cell surface of Pichia pastoris using Gcw12 as the anchoring protein. Whole-cell PG5 eliminated the need for enzyme extraction and purification, while also exhibiting enhanced thermal stability, pH stability, and resistance to proteases in vitro compared to free PG5. Magnetic resonance mass spectrometry analysis revealed that whole-cell PG5 efficiently degraded citrus pectin, resulting in the production of a mixture of pectin oligosaccharides. The primary components of the mixture were trigalacturonic acid, followed by digalacturonic acid and tetragalacturonic acid. Supplementation of citrus pectin with whole-cell PG5 resulted in a more pronounced protective effect compared to free PG5 in alleviating colitis symptoms and promoting the integrity of the colonic epithelial barrier in a mouse model of dextran sulfate sodium-induced colitis. Hence, this study demonstrates the potential of utilizing whole-cell pectinase as an effective biocatalyst to promote intestinal homeostasis in vivo.

Evaluating inter-rater reliability in the context of "Sysmex UN2000 detection of protein/creatinine ratio and of renal tubular epithelial cells can be used for screening lupus nephritis": a statistical examination.

BACKGROUND: The evaluation of inter-rater reliability (IRR) is integral to research designs involving the assessment of observational ratings by two raters. However, existing literature is often heterogeneous in reporting statistical procedures and the evaluation of IRR, although such information can impact subsequent hypothesis testing analyses. METHODS: This paper evaluates a recent publication by Chen et al., featured in BMC Nephrology, aiming to introduce an alternative statistical approach to assessing IRR and discuss its statistical properties. The study underscores the crucial need for selecting appropriate Kappa statistics, emphasizing the accurate computation, interpretation, and reporting of commonly used IRR statistics between two raters. RESULTS: The Cohen's Kappa statistic is typically used for two raters dealing with two categories or for unordered categorical variables having three or more categories. On the other hand, when assessing the concordance between two raters for ordered categorical variables with three or more categories, the commonly employed measure is the weighted Kappa. CONCLUSION: Chen and colleagues might have underestimated the agreement between AU5800 and UN2000. Although the statistical approach adopted in Chen et al.'s research did not alter their findings, it is important to underscore the importance of researchers being discerning in their choice of statistical techniques to address their specific research inquiries.

Kaistella yananensis sp. nov., a Novel Indoleacetic Acid-Producing Bacterium Isolated from the Root Nodules of Sophora davidii (Franch.) Skeels.

A novel endophytic bacterium, designated strain BT6-1-3T, was isolated from the root nodules of a leguminous shrub named Sophora davidii (Franch.) Skeels, found growing wild in Yan'an, Shaanxi Province, China. Cells were Gram-staining-negative, non-motile, catalase-positive, oxidase-positive, and did not produce H2S. Strain BT6-1-3T grew at 15-40 °C (optimum 30 °C), at pH 6.0-10.0 (optimum pH 9.0), and with 0-1% (w/v) NaCl (optimum 0.5%). The quinone system was menaquinone 6. The major fatty acids present in BT6-1-3T were iso-C11:0, iso-C15:0, and C16:0. The G+C content of genomic DNA was 39.4 mol% by whole genome sequencing. According to the analysis of 16S rRNA gene sequence, the closest relative was Kaistella montana WG4 (nucleotide identity was 97.6%). The genome of strain BT6-1-3T was sequenced, and the genome similarity was calculated using average nucleotide identity and genome-to-genome distance analysis with the genomes of other strains of Kaistella. Both strongly supported that the strain BT6-1-3T belonged to the genus Kaistella as a representative of a new species. Based on phylogenetic analysis, chemotaxonomic data, and physiological and biochemical characteristics, strain BT6-1-3T represents a new species of the genus Kaistella and is named as Kaistella yananensis sp. nov. Type strain is BT6-1-3T (= NBRC 115452T = CGMCC 1.60032T).

Implementing link prediction in protein networks via feature fusion models based on graph neural networks.

MOTIVATION: Protein-protein interactions serve as the cornerstone for various biochemical processes within biological organisms. Existing research methodologies predominantly employ link prediction techniques to analyze these interaction networks. However, traditional approaches often fall short in delivering satisfactory predictive performance when applied to multi-species datasets. Current computational methods largely focus on analyzing the network topology, resulting in a somewhat monolithic feature set. The integration of diverse features in the model could potentially yield superior performance and broader applicability. To this end, we propose an autoencoder model built on graph neural networks, designed to enhance both predictive performance and generalizability by leveraging the integration of gene ontology. RESULTS: In this research, we developed AGraphSAGE, a model specifically designed for analyzing protein-protein interaction network data. By seamlessly integrating gene ontology into the graph structure, we employed a dual-channel graph sampling and aggregation network that capitalizes on topological information to process high-dimensional features. Feature fusion is achieved through the implementation of graph attention mechanisms, and we adopted a link prediction framework as the experimental training model. Performance was evaluated on real-world datasets using key metrics, such as Area Under the Curve (AUC). A hyperparameter search space was established, and a Bayesian optimization strategy was applied to iteratively fine-tune the model, assessing the impact of various parameters on predictive efficacy. The experimental results validate that our proposed model is capable of effectively predicting protein-protein interactions across diverse biological species.

Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters.

BACKGROUND: In research designs that rely on observational ratings provided by two raters, assessing inter-rater reliability (IRR) is a frequently required task. However, some studies fall short in properly utilizing statistical procedures, omitting essential information necessary for interpreting their findings, or inadequately addressing the impact of IRR on subsequent analyses' statistical power for hypothesis testing. METHODS: This article delves into the recent publication by Liu et al. in BMC Cancer, analyzing the controversy surrounding the Kappa statistic and methodological issues concerning the assessment of IRR. The primary focus is on the appropriate selection of Kappa statistics, as well as the computation, interpretation, and reporting of two frequently used IRR statistics when there are two raters involved. RESULTS: The Cohen's Kappa statistic is typically utilized to assess the level of agreement between two raters when there are two categories or for unordered categorical variables with three or more categories. On the other hand, when it comes to evaluating the degree of agreement between two raters for ordered categorical variables comprising three or more categories, the weighted Kappa is a widely used measure. CONCLUSION: Despite not substantially affecting the findings of Liu et al.?s study, the statistical dispute underscores the significance of employing suitable statistical methods. Rigorous and accurate statistical results are crucial for producing trustworthy research.

Cannabidiol ameliorates inflammatory response partly by AGE-RAGE pathway in diabetic mice.

Cannabidiol (CBD), the most abundant nonpsychoactive constituent of Cannabis sativa plant, is a promising potential pharmacotherapy for the treatment of diabetes and associated comorbidities. Previous studies have shown the potential of CBD to prevent diabetes in mice, the precise mechanisms of action remain unclear. The purpose of this study was to explore the mechanism of CBD alleviating hyperglycemia. The results demonstrated that CBD reduced blood glucose of STZ-induced diabetic mice without causing hypoglycemia. To elucidate the possible mechanisms of CBD effect, RNA-seq analysis was performed on high glucose-induced human mesangial cells (HMCs). By cluster analysis of differential genes, the results showed that advanced glycation end products-receptor of advanced glycation endproducts (AGE-RAGE) pathway-related genes CCL2 and interleukin-1ß (IL-1ß) play an important role in the biological of CBD. The expression of CCL2 and IL-1ß were significantly increased in HMCs. Whereas, treatment with CBD decreased the expression of CCL2 and IL-1ß. In addition, CBD significantly reduced AGE-RAGE levels in high glucose-induced HMCs. Similar results were confirmed in diabetic mice. In conclusion, we discovered for the first time that CBD ameliorates hyperglycemia partly through AGE-RAGE mediated CCL2/IL-1ß pathway.

Using appropriate Kappa statistic in evaluating inter-rater reliability. Short communication on "Groundwater vulnerability and contamination risk mapping of semi-arid Totko river basin, India using GIS-based DRASTIC model and AHP techniques".

In this article, some misuses of Kappa statistic in the original paper [Chemosphere, 307, 135831] are discussed. By using DRASTIC and Analytic Hierarchy Process (AHP) models, the authors have assessed the groundwater vulnerability of Totko, India. High nitrate concentrations in groundwater have been found in highly vulnerable areas, and the accuracy of the models has been assessed through Pearson's correlation coefficient and Kappa coefficient. However, using Cohen's Kappa to estimate the intra-rater reliabilities (IRRs) of the two models is not appropriate on the condition of ordinal categorical variables in five categories in the original paper. We briefly introduce the Kappa statistic and propose to use weighted Kappa to compute IRRs under such conditions. To conclude, we recognize that this does not significantly alter the conclusions of the original paper, but it is necessary to ensure that the appropriate statistical tools are used.