Bioinformatics and Biology Insights最新文献

RhizoBindingSites is a de novo depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the Rhizobium, Sinorhizobium, Bradyrhizobium, Azorhizobium, and Mesorhizobium genera covering 9 representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome, and there was a more significant number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. O-matrices of 3187 TFs and S-matrices of 2754 TFs from 9 species were deposited in RhizoBindingSites and RhizoBindingSites v2.0, respectively. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of Rhizobium etli CFN42, Rhizobium leguminosarum bv. viciae 3841, and Sinorhizobium meliloti 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).

Objectives: This article focuses on the detection of cells in low-contrast brightfield microscopy images; in our case, it is chronic lymphocytic leukaemia cells. The automatic detection of cells from brightfield time-lapse microscopic images brings new opportunities in cell morphology and migration studies; to achieve the desired results, it is advisable to use state-of-the-art image segmentation methods that not only detect the cell but also detect its boundaries with the highest possible accuracy, thus defining its shape and dimensions.

Methods: We compared eight state-of-the-art neural network architectures with different backbone encoders for image data segmentation, namely U-net, U-net++, the Pyramid Attention Network, the Multi-Attention Network, LinkNet, the Feature Pyramid Network, DeepLabV3, and DeepLabV3+. The training process involved training each of these networks for 1000 epochs using the PyTorch and PyTorch Lightning libraries. For instance segmentation, the watershed algorithm and three-class image semantic segmentation were used. We also used StarDist, a deep learning-based tool for object detection with star-convex shapes.

Results: The optimal combination for semantic segmentation was the U-net++ architecture with a ResNeSt-269 background with a data set intersection over a union score of 0.8902. For the cell characteristics examined (area, circularity, solidity, perimeter, radius, and shape index), the difference in mean value using different chronic lymphocytic leukaemia cell segmentation approaches appeared to be statistically significant (Mann-Whitney U test, P < .0001).

Conclusion: We found that overall, the algorithms demonstrate equal agreement with ground truth, but with the comparison, it can be seen that the different approaches prefer different morphological features of the cells. Consequently, choosing the most suitable method for instance-based cell segmentation depends on the particular application, namely, the specific cellular traits being investigated.

The quorum-sensing (QS) machinery in disease-causing microorganisms is critical in developing antibiotic resistance. In Pseudomonas aeruginosa, QS is involved in biofilm formation, virulence factors production, and general tolerance to antimicrobials. Owing to the major role QS plays, interference in the process is probably a facile route to overcome antimicrobial resistance. Some furanone-derived compounds from marine sources have shown promising anti-QS activity. However, their protein targets and potential mechanisms of action have not been explored. To elucidate their potential protein targets in this study, marine metabolites with furanone backbones similar to their cognitive autoinducers (AIs) were screened against various QS receptors (LasR, RhlR, and PqsR) using molecular docking and molecular dynamics (MD) simulation techniques. The order by which the compounds bind to the receptors follows LasR > RhlR > PqsR. Compounds exhibited remarkable stability against LasR and RhlR, likely because the AIs of these receptors are structural analogs of furanones. Furanones with shorter alkyl side chains bound strongly against RhlR. The presence of halogens improved binding against various receptors. PqsR, with its hydrophobic-binding site and structurally different AIs, showed weaker binding. This study provides a molecular basis for the design of potent antagonists against QS receptors using marine-derived furanones.

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.

Brucellosis is a chronic and debilitating disease in humans, causing great economic losses in the livestock industry. Making an effective vaccine is one of the most important concerns for this disease. The new mRNA vaccine technology due to its accuracy and high efficiency has given promising results in various diseases. The objective of this research was to create a novel mRNA vaccine with multiple epitopes targeting Brucella melitensis. Seventeen antigenic proteins and their appropriate epitopes were selected with immunoinformatic tools and surveyed in terms of toxicity, allergenicity, and homology. Then, their presentation and identification by MHC cells and other immune cells were checked with valid tools such as molecular docking, and a multi-epitope protein was modeled, and after optimization, mRNA was analyzed in terms of structure and stability. Ultimately, the immune system's reaction to this novel vaccine was evaluated and the results disclosed that the designed mRNA construct can be an effective and promising vaccine that requires laboratory and clinical trials.

Objectives: Oxidative stress is implicated in several metabolic cascades involved in glucose control. Hence, investigating antioxidant and antidiabetic activities is crucial for discovering an effective diabetes mellitus (DM) agent. This study was aimed at probing the therapeutic efficacy of hydro-ethanolic extract of Combretum paniculatum (HECP) and gas chromatography-flame ionization detector (GC-FID)-identified phytochemicals as novel agents for DM.

Methods: We determined the total phenols, flavonoids, and antioxidant vitamins in HECP using standard methods. A GC-FID was used to decipher phytochemicals of HECP. The antioxidant and antidiabetic activities of HECP were assessed using in vitro and in silico approaches.

Results: The results revealed that HECP is affluent in phenols, flavonoids, and vitamin E and demonstrated engaging antioxidant activities in 1,1-diphenyl-2-picryl-hydroxyl (DPPH; IC₅₀ = 0.83 µg/mL), thiobarbituric acid-reactive substances TBARS; IC₅₀ = 2.28 µg/mL), and ferric-reducing antioxidant power assay (FRAP; IC₅₀ = 2.89 µg/mL). Compared with the reference drug, acarbose, HECP exhibited good α-amylase and α-glucosidase inhibitory capacity, having IC₅₀ values of 14.21 and 13.23 µg/mL, respectively, against 13.06 and 11.71 µg/mL recorded for acarbose. More so, the extract's top 6 scoring phytochemicals (rutin, kaempferol, epicatechin, ephedrine, naringenin, and resveratrol) had strong interactions with amino acid residues within and around α-amylase and α-glucosidase active site domains. All the compounds but rutin had favourable drug-like characteristics, pharmacokinetics, and safety profiles when compared with acarbose.

Conclusion: Altogether, our results vindicate the use of this herb in treating DM locally and reveal that it has pharmaceutically active components that could be used as novel leads in the development of DM drugs.

Pre-eclampsia (PE) is a severe pregnancy complication that is more common in patients with systemic lupus erythematosus (SLE). Although the exact causes of these conditions are not fully understood, the immune system plays a key role. To investigate the connection between SLE and PE, we analyzed genes associated with SLE that may contribute to the development of PE. We collected 9 microarray data sets from the NCBI GEO database and used Limma to identify the differentially expressed genes (DEGs). In addition, we employed weighted gene co-expression network analysis (WGCNA) to pinpoint the hub genes of SLE and examined immune infiltration using Cibersort. By constructing a protein-protein interaction (PPI) network and using CytoHubba, we identified the top 20 PE hub genes. Subsequently, we created a nomogram and conducted a receiver operating characteristic (ROC) analysis to predict the risk of PE. Our analysis, including gene set enrichment analysis (GSEA) and PE DEGs enrichment analysis, revealed significant involvement in placenta development and immune response. Two pivotal genes, BCL6 and MME, were identified, and their validity was confirmed using 5 data sets. The nomogram demonstrated good diagnostic performance (AUC: 0.82-0.96). Furthermore, we found elevated expression levels of both genes in SLE peripheral blood mononuclear cells (PBMCs) and PE placental specimens within the case group. Analysis of immune infiltration in the SLE data set showed a strong positive correlation between the expression of both genes and neutrophil infiltration. BCL6 and MME emerged as crucial genes in lupus-related pregnancies associated with the development of PE, for which we devised a nomogram. These findings provide potential candidate genes for further research in the diagnosis and understanding of the pathophysiology of PE.

Phosphoinositide-3-kinases (PI3 K) are pivotal regulators of cell signaling implicated in various cancers. Particularly, mutations in the PIK3CA gene encoding the p110α catalytic subunit drive oncogenic signaling, making it an attractive therapeutic target. Our study conducted in silico exploration of 31 PIK3CA mutations across breast, endometrial, colon, and ovarian cancers, assessing their impacts on response to PI3Kα inhibitors and identifying potential non-toxic inhibitors and also elucidating their effects on protein stability and flexibility. Specifically, we observed significant alterations in the stability and flexibility of the PI3 K protein induced by these mutations. Through molecular docking analysis, we evaluated the binding interactions between the selected inhibitors and the PI3 K protein. The filtration of ligands involved calculating chemical descriptors, incorporating Veber and Lipinski rules, as well as IC50 values and toxicity predictions. This process reduced the initial dataset of 1394 ligands to 12 potential non-toxic inhibitors, and four reference inhibitors with significant biological activity in clinical trials were then chosen based on their physico-chemical properties. This analysis revealed Lig5's exceptional performance, exhibiting superior affinity and specificity compared to established reference inhibitors such as pictilisib. Lig5 formed robust binding interactions with the PI3 K protein, suggesting its potential as a highly effective therapeutic agent against PI3 K-driven cancers. Furthermore, molecular dynamics simulations provided valuable insights into Lig5's stability and its interactions with PI3 K over 100 ns. These simulations supported Lig5's potential as a versatile inhibitor capable of effectively targeting various mutational profiles of PI3 K, thereby mitigating issues related to resistance and toxicity commonly associated with current inhibitors.

COVID 19 pandemic is still ongoing, having taken more than 6 million human lives with it, and it seems that the world will have to learn how to live with the virus around. In consequence, there is a need to develop different treatments against it, not only with vaccines, but also new medicines. To do this, human-virus protein-protein interactions (PPIs) play a key part in drug-target discovery, but finding them experimentally can be either costly or sometimes unreliable. Therefore, computational methods arose as a powerful alternative to predict these interactions, reducing costs and helping researchers confirm only certain interactions instead of trying all possible combinations in the laboratory. Malivhu is a tool that predicts human-virus PPIs through a 4-phase process using machine learning models, where phase 1 filters ssRNA(+) class virus proteins, phase 2 filters Coronaviridae family proteins and phase 3 filters severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) species proteins, and phase 4 predicts human-SARS-CoV/SARS-CoV-2/MERS protein-protein interactions. The performance of the models was measured with Matthews correlation coefficient, F1-score, specificity, sensitivity, and accuracy scores, getting accuracies of 99.07%, 99.83%, and 100% for the first 3 phases, respectively, and 94.24% for human-SARS-CoV PPI, 94.50% for human-SARS-CoV-2 PPI, and 95.45% for human-MERS PPI on independent testing. All the prediction models developed for each of the 4 phases were implemented as web server which is freely available at https://kaabil.net/malivhu/.

The mitigation of cadmium (Cd) pollution, a significant ecological threat, is of paramount importance. Pseudomonas aeruginosa harbors 2 Cd resistance genes, namely, cadR and cadA. Presently, our focus is on the identification and characterization of the cation-transporting P-type ATPase (cadA) in Pseudomonas aeruginosa BC15 through in silico methods. The CadA protein and its binding capacities remain poorly understood, with no available structural elucidation. The presence of the cadA gene in P aeruginosa was confirmed, showing a striking 99% sequence similarity with both P aeruginosa and P putida. Phylogenetic analysis unveiled the evolutionary relationship between CadA protein sequences from various Pseudomonas species. Physicochemical analysis demonstrated the stability of CadA, revealing a composition of 690 amino acids, a molecular weight of 73 352.85, and a predicted isoelectric point (PI) of 5.39. Swiss-Model homology modelling unveiled a 33.73% sequence homology with CopA (3J09), and the projected structure indicated that 89.3% of amino acid residues were situated favourably within the Ramachandran plot, signifying energetic stability. Notably, the study identified metal-binding sites in CadA, namely, H3, C30, C32, C35, H48, C89, and C106. Docking studies revealed a higher efficiency of Cd binding with CadA compared to other heavy metals. This underscores the crucial role of N-terminal cysteine residues in Cd removal. It is evident that CadA of P aeruginosa BC15 plays a crucial role in Cd tolerance, rendering it a potential microorganism for Cd toxicity bioremediation. The structural and functional elucidation of CadA, facilitated by this study, holds promise for advancing cost-effective strategies in the remediation of cadmium-contaminated environments.