Effect of LDHA Inhibition on TNF-α-Induced Cell Migration in Esophageal Cancers.

Cell migration is an essential part of the complex and multistep process that is the development of cancer, a disease that is the second most common cause of death in humans. An important factor promoting the migration of cancer cells is TNF-α, a pro-inflammatory cytokine that, among its many biological functions, also plays a major role in mediating the expression of MMP9, one of the key regulators of cancer cell migration. It is also known that TNF-α is able to induce the Warburg effect in some cells by increasing glucose uptake and enhancing the expression and activity of lactate dehydrogenase subunit A (LDHA). Therefore, the aim of the present study was to investigate the interrelationship between the TNF-α-induced promigratory activity of cancer cells and their glucose metabolism status, using esophageal cancer cells as an example. By inhibiting LDHA activity with sodium oxamate (SO, also known as aminooxoacetic acid sodium salt or oxamic acid sodium salt) or siRNA-mediated gene silencing, we found using wound healing assay and gelatin zymography that LDHA downregulation impairs TNF-α-dependent tumor cell migration and significantly reduces TNF-α-induced MMP9 expression. These effects were associated with disturbances in the activation of the ERK1/2 signaling pathway, as we observed by Western blotting. We also reveal that in esophageal cancer cells, SO effectively reduces the production of lactic acid, which, as we have shown, synergizes the stimulating effect of TNF-α on MMP9 expression. In conclusion, our findings identified LDHA as a regulator of TNF-α-induced cell migration in esophageal cancer cells by the ERK1/2 signaling pathway, suggesting that LDHA inhibitors that limit the migration of cancer cells caused by the inflammatory process may be considered as an adjunct to standard therapy in esophageal cancer patients.

Vitamin B6 in Health and Disease.

Vitamin B6 is a fascinating molecule involved in the vast majority of changes in the human body because it is a coenzyme involved in over 150 biochemical reactions. It is active in the metabolism of carbohydrates, lipids, amino acids, and nucleic acids, and participates in cellular signaling. It is an antioxidant and a compound with the ability to lower the advanced glycation end products (AGE) level. In this review, we briefly summarize its involvement in biochemical pathways and consider whether its deficiency may be associated with various diseases such as diabetes, heart disease, cancer, or the prognosis of COVID-19.

Learning of fuzzy cognitive maps using density estimate.

Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods.

Critical assessment of high-throughput standalone methods for secondary structure prediction.

Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the ß- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.

iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content.

Several descriptors of protein structure at the sequence and residue levels have been recently proposed. They are widely adopted in the analysis and prediction of structural and functional characteristics of proteins. Numerous in silico methods have been developed for sequence-based prediction of these descriptors. However, many of them do not have a public web-server and only a few integrate multiple descriptors to improve the predictions. We introduce iFC² (integrated prediction of fold, class, and content) server that is the first to integrate three modern predictors of sequence-level descriptors. They concern fold type (PFRES), structural class (SCEC), and secondary structure content (PSSC-core). The server exploits relations between the three descriptors to implement a cross-evaluation procedure that improves over the predictions of the individual methods. The iFC² annotates fold and class predictions as potentially correct/incorrect. When tested on datasets with low-similarity chains, for the fold prediction iFC² labels 82% of the PFRES predictions as correct and the accuracy of these predictions equals 72%. The accuracy of the remaining 28% of the PFRES predictions equals 38%. Similarly, our server assigns correct labels for over 79% of SCEC predictions, which are shown to be 98% accurate, while the remaining SCEC predictions are only 15% accurate. These results are shown to be competitive when contrasted against recent relevant web-servers. Predictions on CASP8 targets show that the content predicted by iFC² is competitive when compared with the content computed from the tertiary structures predicted by three best-performing methods in CASP8. The iFC² server is available at http://biomine.ece.ualberta.ca/1D/1D.html .

Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources.

MOTIVATION: Intrinsically disordered proteins play a crucial role in numerous regulatory processes. Their abundance and ubiquity combined with a relatively low quantity of their annotations motivate research toward the development of computational models that predict disordered regions from protein sequences. Although the prediction quality of these methods continues to rise, novel and improved predictors are urgently needed. RESULTS: We propose a novel method, named MFDp (Multilayered Fusion-based Disorder predictor), that aims to improve over the current disorder predictors. MFDp is as an ensemble of 3 Support Vector Machines specialized for the prediction of short, long and generic disordered regions. It combines three complementary disorder predictors, sequence, sequence profiles, predicted secondary structure, solvent accessibility, backbone dihedral torsion angles, residue flexibility and B-factors. Our method utilizes a custom-designed set of features that are based on raw predictions and aggregated raw values and recognizes various types of disorder. The MFDp is compared at the residue level on two datasets against eight recent disorder predictors and top-performing methods from the most recent CASP8 experiment. In spite of using training chains with

Novel scales based on hydrophobicity indices for secondary protein structure.

This paper is concerned with a branch of computational biology related to protein prediction and analysis of secondary structure of proteins. Although traditional methods use a simple amino acid composition to predict the secondary structure content, hydrophobicity has been recently found to improve the results in this and several related prediction tasks. To this end, we propose and analyze advantages of two new hydrophobicity index-based scales that incorporate information about long-range interactions along the protein sequence and contrast them with currently used raw hydrophobic index values. We also compare three leading hydrophobicity indices, i.e., Eisenberg's, Fauchere-Pliska's, and Cid's, using the proposed scales. The analysis is performed using fuzzy cognitive maps that quantify the strength of relation between the hydrophobicity scales/indices and the protein content values. A set of empirical tests that involve generation of fuzzy cognitive map models for a set of 200 low homology proteins have been performed. The results show that the secondary structure content along the protein sequence is characterized by about 2.5 times stronger relation with the two proposed hydrophobicity scales when compared with the currently used raw index values. The new scales exhibit stronger relation irrespective of the applied hydrobhobicity indices. Analysis of different scales shows superiority of the Eisenberg's hydrophobicity index, when used with the new scales. In contrast, the Fauchere-Pliska's index is found to perform better when compared with the two other indices when using raw hydrophobic index values that disregard the long-range interactions.