Relatives of Crohn's disease patients and breast cancer: An overlooked condition

AbstractRecent data suggest that patients suffering from Crohn’s disease (CD) may be at higher risk of developing extra-intestinal malignancies. This is attributed to inflammation and immunodepression due to medications. However, a genetic predisposition cannot ruled out. In the present study we investigated the prevalence of breast cancer in first-degree female relatives of CD patients compared with relatives of patients without evidence of gastrointestinal diseases. A total of 1302 female first-degree relatives of CD patients and 1294 relatives of controls were included. We found that CD was an independent risk factor for breast cancer development (OR = 2.76, 95% CI = 1.2–6.2; p = 0.017), and this is particularly evident in mothers (3.6% vs 1%, p = 0.009 − OR = 3.7, 95% CI 1.4–10). Among CD group, smoking habit of CD patients was associated with increased risk of cancer compared with relatives of non-smokers (7.7% vs 2.9%, p = 0.01 – OR = 2.8 95% CI 1.2–6.6). Intriguingly, stage at diagnosis was significantly higher in CD relatives (p = 0.04). Our findings suggest that first-degree female relatives of CD patients are at higher risk of developing breast cancer but receive diagnosis at more advanced stages, therefore advocating the need of more active screening protocol in this population

Chestnut shell tannins: effects on intestinal inflammation and dysbiosis in zebrafish

The aim of the present study was to test the possible ameliorative efficacy of phytochemicals such as tannins on intestinal inflammation and dysbiosis. The effect of a chestnut shell (Castanea sativa) extract (CSE) rich in polyphenols, mainly represented by tannins, on k-carrageenan-induced intestinal inflammation in adult zebrafish (Danio rerio) was tested in a feeding trial. Intestinal inflammation was induced by 0.1% k-carrageenan added to the diet for 10 days. CSE was administered for10 days after k-carrageenan induced inflammation. The intestinal morphology and histopathology, cytokine expression, and microbiota were analyzed. The k-carrageenan treatment led to gut lumen expansion, reduction of intestinal folds, and increase of the goblet cells number, accompanied by the upregulation of pro-inflammatory factors (TNFα, COX2) and alteration in the number and ratio of taxonomic groups of bacteria. CSE counteracted the inflammatory status enhancing the growth of health helpful bacteria (Enterobacteriaceae and Pseudomonas), decreasing the pro-inflammatory factors, and activating the anti-inflammatory cytokine IL-10. In conclusion, CSE acted as a prebiotic on zebrafish gut microbiota, sustaining the use of tannins as food additives to ameliorate the intestinal inflammation. Our results may be relevant for both aquaculture and medical clinic field

A review on drug repurposing applicable to COVID-19

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application

Statistical analysis of protein structural features: relationships and PCA grouping

Subtle structural differences among homologous proteins may be responsible of the modulation of their functional properties. Therefore, we are exploring novel and strengthened methods to investigate in deep protein structure, and to analyze conformational features, in order to highlight relationships to functional properties. We selected some protein families based on their different structural class from CATH database, and studied in detail many structural parameters for these proteins. Some valuable results from Pearson’s correlation matrix have been validated with a Student’s t‐distribution test at a significance level of 5% (p‐value). We investigated in detail the best relationships among parameters, by using partial correlation. Moreover, PCA technique has been used for both single family and all families, in order to demonstrate how to find outliers for a family and extract new combined features. The correctness of this approach was borne out by the agreement of our results with geometric and structural properties, known or expected. In addition, we found unknown relationships, which will be object of further studies, in order to consider them as putative markers related to the peculiar structure‐function relationships for each family

Feature selection on a dataset of protein families: from exploratory data analysis to statistical variable importance

Proteins are characterized by several typologies of features (structural, geometrical, energy). Most of these features are expected to be similar within a protein family. We are interested to detect which features can identify proteins that belong to a family, as well as to define the boundaries among families. Some features are redundant: they could generate noise in identifying which variables are essential as a fingerprint and, consequently, if they are related or not to a function of a protein family. We defined an original approach to analyze protein features for defining their relationships and peculiarities within protein families. A multistep approach has been mainly performed in R environment: getting-cleaning data, exploratory data analysis and predictive modeling for classification. Ten protein families have been chosen by their CATH classification (different architectures), with rules over the number of structures, the length of the sequence and the choice of the chain. Properties investigated are secondary structures, hydrogen bonds, accessible surface areas, torsion angles, packing defects, number of charged residues, free energy of folding, volume and salt bridges. Kernel density estimation helps in discovering unusual multimodal profiles. Pearson correlation highlights statistical links between pairwise variables and Pearson distance provides a dendrogram with a clusterization of the features. PCA clusterizes the protein families and it detects outliers, sparse PCA performs a feature selection. Many classification algorithms have been used: decision trees (classical, boosting and bagging), SVMs (flexible discriminant analysis), centroid (nearest shrunken). The interest is on variable importance estimation. A 10-fold x 10 cross validation has been applied over the training set. Accuracy, K coefficient, sensitivity and specificity have been calculated for each methods. From the density plots, the percentage of mostly buried residues is significantly different for each family. Dissimilarity dendrogram shows separated clusters for secondary structures, torsion angles, defects and geometrical features. From the features network, torsion angles and surface variables result as peripheral (i.e. redundant) from the core of the graph. PCA biplot gives a good clustering for the protein families and sparse PCA confirm dendrogram results. Unifying all the results, these features are typical for our dataset: helix, strand, coil, turn, hydrogen bond, polar and charged accessible surface area, volume and residue buried for the most part. Random forest algorithm has the best performance values. Graphical multivariate procedures are good tools for the characterization of possible fingerprints about the protein families. Predictive models for classification and variable importance estimation help in performing feature selection. The work can be improved by the use of multivariate regression models and the increase of the protein families number.</jats:p

Searching for Chymase Inhibitors among Chamomile Compounds Using a Computational-Based Approach

Inhibitors of chymase have good potential to provide a novel therapeutic approach for the treatment of cardiovascular diseases. We used a computational approach based on pharmacophore modeling, docking, and molecular dynamics simulations to evaluate the potential ability of 13 natural compounds from chamomile extracts to bind chymase enzyme. The results indicated that some chamomile compounds can bind to the active site of human chymase. In particular, chlorogenic acid had a predicted binding energy comparable or even better than that of some known chymase inhibitors, interacted stably with key amino acids in the chymase active site, and appeared to be more selective for chymase than other serine proteases. Therefore, chlorogenic acid is a promising starting point for developing new chymase inhibitors

Virtual Screening of Natural Compounds as Potential PI₃K-AKT1 Signaling Pathway Inhibitors and Experimental Validation

A computational screening for natural compounds suitable to bind the AKT protein has been performed after the generation of a pharmacophore model based on the experimental structure of AKT1 complexed with IQO, a well-known inhibitor. The compounds resulted as being most suitable from the screening have been further investigated by molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis and toxicity profiles. Two compounds selected at the end of the computational analysis, i.e., ZINC2429155 (also named STL1) and ZINC1447881 (also named AC1), have been tested in an experimental assay, together with IQO as a positive control and quercetin as a negative control. Only STL1 clearly inhibited AKT activation negatively modulating the PI3K/AKT pathway

Nutraceutical search through the pipeline of pharmacophore-based virtual screening

Nutraceuticals are food or their parts, present in conventional or non-conventional form, with verified safety and health benefits, beyond their nutritional value. In this work, we describe a novel pipeline for nutraceutical compounds research in the field of pharmacophore screening, providing a new idea for drug discovery. In the first step, to identify novel nutraceuticals potentially active as inhibitors of a given enzyme, a pharmacophore model is generated, with its key chemical features, starting from the experimental structure of the complex with known protein inhibitors, with pharmacophores ranking based on statistical values of sensitivity and specificity. After the validation step, this pharmacophore model is used for 3D structural screening and mapping against a subset of known nutraceutical compounds, generated through DrugBank or against special subsets from ZINC (ZINC Drug Database - Zdd and ZINC In Man - Zim). Moreover, molecular docking is performed to verify binding affinity of compounds. The hits with a good binding energy are then investigated in more details, compared with their pharmacophore features and analysed for their interacting residues. Then, to have an in silico interpretation of the potential activity of the compounds, an integrated investigation is performed, by mining literature reports about the effects of the specific compound (or food containing it) against human diseases, extracting expression data from omics repositories, in the view of integrating these information with molecular pathways and networks. Output of our pipeline are candidates for in vitro and in vivo experiments, to test the hypothesis and verify if they could become novel potential drugs.</jats:p