Evolutionary Bioinformatics最新文献

Background: During leptospiral infection, the host innate immune response is initiated through recognition of pathogen-associated molecular patterns (PAMPs) by Toll-like receptor 2 (TLR2). Among these PAMPs, LipL32, Loa22, Lsa21, and lipopolysaccharide biosynthesis genes, are of particular interest.

Objective: This study aimed to investigate these molecules' genetic variability and evolutionary conservation in recently isolated clinical Leptospira strains from Sri Lanka.

Results: We analyzed the whole-genome sequences of 25 clinical Leptospira isolates obtained from patients across Sri Lanka, sequenced using long-read technology and annotated using a standardized pipeline. Genes encoding LipL32, Loa22, Lsa21, and enzymes within the lipopolysaccharide biosynthesis locus were extracted and analyzed for phylogenetic relationships and sequence variation. LipL32 and Loa22 were highly conserved across all isolates, with only a single amino acid substitution observed in each. In contrast, genes associated with lipopolysaccharide biosynthesis, specifically those encoding glycosyl transferase and a sodium-dependent anion transporter, exhibited notable genetic variation, including multiple single nucleotide polymorphisms leading to amino acid changes. The Lsa21 gene was present only in Leptospira interrogans strains and showed no protein-level variation. Leptospira borgpetersenii isolates demonstrated strong conservation across all gene targets at both nucleotide and protein levels.

Conclusion: Our findings highlight the high conservation of LipL32 and Loa22, reinforcing their potential as stable targets for molecular diagnostics and serological assays. In contrast, the variability observed in lipopolysaccharide biosynthesis genes suggests a possible role in immune evasion or adaptation, warranting further functional investigation. The restricted presence of Lsa21 in specific species also raises questions about its contribution to pathogenicity.

Background: Cronobacter sakazakii, a foodborne pathogen with a fatality rate of 33%, is a rod-shaped, Gram-negative, non-spore-forming bacterium responsible for causing meningitis, bacteremia, and necrotizing enterocolitis. Despite many unknown functions of hypothetical proteins in bacterial genomes, bioinformatic techniques have successfully annotated their roles in various pathogens.

Objectives: The aim of this investigation is to identify and annotate the structural and functional properties of a hypothetical protein (HP) from Cronobacter sakazakii 7G strain (accession no. WP_004386962.1, 277 residues) using computational tools.

Methods: Multiple bioinformatic tools were used to identify the homologous protein and to construct and validate its 3D structure. A 3D model was generated using SWISS-MODEL and validated using tools, developing a reliable 3D structure. The STRING and CASTp servers provided information on protein-protein interactions and active sites, identifying functional partners.

Results: The putative protein was soluble, stable, and localized in the cytoplasmic membranes, indicating its biological activity. Functional annotation identified TagJ (HsiE1) within the protein, a member of the ImpE superfamily involved in the transport of toxins and a part of the bacterial type VI secretion system (T6SS). The 3-dimensional structure of this protein was validated through molecular docking involving 6 different compounds. Among these, ceforanide demonstrated the strongest binding scores, -7.5 kcal/mol for the hypothetical protein and -7.2 kcal/mol for its main template protein (PDB ID: 4UQX.1).

Conclusion: Comparative genomics study suggests that the protein found in C. sakazakii may be a viable therapeutic target because it seems distinctive and different from human proteins. The results of multiple sequence alignment (MSA) and molecular docking supported HP's potential involvement as a T6SS. These in silico results represent that the examined HP could be valuable for studying C. sakazakii infections and creating medicines to treat C. sakazakii-mediated disorders.

Background: The WRKY gene family is identified as one of the most prominent transcription factor families in plants and is involved in various biological processes such as metabolism, growth and development, and response to biotic and abiotic stresses. In many plant species, the WRKY gene family was widely studied and analyzed but little to no information for Fortunella hindsii. However, the completion of the whole genome sequencing of Fortunella hindsii allowed us to investigate the genome-wide analysis of WRKY proteins.

Objective: The main objective of this study was to analyze and identify the WRKY gene family in Fortunella hindsii genome.

Methodology: Various bioinformatics approaches have been used to conduct this study.

Results: We constituted 46 members of the Fortunella hindsii WRKY gene family, which were unevenly distributed on all nine chromosomes. The phylogenetic relationship of predicted WRKY proteins of Fortunella hindsii with the WRKY proteins of Arabidopsis showed that 46 FhWRKY genes were divided into three main groups (G1, G2, G3) with five subgroups (2A, 2B, 2C, 2D, and 2E) of G2 group. Domain, conserved motif identification, and gene structure were conducted and the results found that these FhWRKY proteins have conserved identical characteristics within groups and maintain differences between groups. In silico subcellular localization, results showed that FhWRKY genes are located in the nucleus. The cis-regulatory element analysis identified several key CREs that are significantly associated with light, hormone responses, and stress. The gene ontology analysis of these predicted FhWRKY genes showed that these genes are significantly enriched in sequence-specific DNA binding, transcriptional activity, cellular biosynthesis, and metabolic processes.

Conclusion: Therefore, overall, our results provided an excellent foundation for further functional characterization of WRKY genes with an aim of Fortunella hindsii citrus crop improvement.

Background: Molecular epidemiology has shown the presence of four genotypes circulating across Africa, a paucity of data exists regarding phylogeography of the African Yellow fever (YF) genotypes. The need to fill this gap with spatiotemporal data from continuous YF outbreaks in Africa conceptualized this study; which aims to investigate the most recent transmission events and directional spread of yellow fever virus (YFV) using updated genomic sequence data.

Methods: Yellow fever sequence data was utilized along with epidemiologic data from outbreaks in Africa, to analyze the case/fatality distribution and genetic diversity. Phylodynamic and phylogeographic were utilized to investigate ancestral history, virus population dynamics, and geographic dispersal of yellow fever across Africa.

Results: There was a sharp increase in laboratory confirmed cases after year 2015, with Nigeria and the Democratic Republic of Congo having the highest numbers of cases. Phylogeny of the YF genotypes followed a previously reported pattern with distinct geographic clustering. Historical dispersal of YFV was discovered to have occurred from West into Central/East Africa, with recent introductions occurring in West Africa.

Conclusions: We have shown the continuous circulation of YF in Africa, with distinct genotype distributions within the west and central African sub-regions. We have also shown the potential contribution of African genotypes, in the historical dispersal of yellow fever. We advocate for expanded and integrated molecular surveillance of YFV and other Arboviruses in Africa.

Objective: Neisseria meningitidis is an encapsulated, diplococcus, kidney bean-shaped bacteria that causes bacterial meningitis. Our study hopes to advance our understanding of disease progression, the spread frequency of the bacteria in people, and the interactions between the bacteria and human body by identifying a functional protein, potentially serving as a target for meningococcal medicine in the future.

Methods: A hypothetical protein HP (PBJ89160.1) from N. meningitidis was employed in this study for extensive structural and functional characterization. In the predictive functional role of HP, several constitutive bioinformatics approaches are applied, such as prediction of physiological properties, domain and motif family function, secondary and tertiary structure prediction, energy minimization, quality validation, docking, and ADMET analysis. To create the protein's three-dimensional (3D) structure, a template protein (PDB_ID: 3GXA) is used with 99% sequence identity by homology modeling technique with the HHpred server. To mitigate the pathogenicity associated with the HP function, it was docked with the natural ligand methionine and five other drug compounds like Verapamil, Loperamide, Thioridazine, Chlorpromazine, and Auranofine.

Results: The protein is predicted to be acidic, soluble and hydrophilic by physicochemical properties analysis. Subcellular localization analysis demonstrated the protein to be periplasmic. The HP has an ATP-binding cassette transporter (also known as ABC transporter) involved in uptake of methionine (MetQ) that creates nutritional virulence in host. Energy minimization, multiple quality assessments, and validation value determination led to the conclusion that the HP model had a workable and acceptable quality. Following ADMET analysis and binding affinity assessments from the docking studies, Loperamide emerged as the most promising therapeutic compound, effectively inhibiting the ATP transporter activity of the HP.

Conclusion: Comparative genomic analysis revealed that this protein is specific to N. meningitidis and has no homologs in human proteins, thereby identifying it as a potential target for therapeutic intervention.

Background: Clostridium botulinum and Clostridium perfringens, 2 major foodborne pathogenic fusobacteria, have a variety of virulent protein types with nervous and enterotoxic pathogenic potential, respectively.

Objective: The relationship between the molecular evolution of the 2 Clostridium genomes and virulence proteins was studied via a bioinformatics prediction method. The genetic stability, main features of gene coding and structural characteristics of virulence proteins were compared and analyzed to reveal the phylogenetic characteristics, diversity, and distribution of virulence factors of foodborne Clostridium strains.

Methods: The phylogenetic analysis was performed via composition vector and average nucleotide identity based methods. Evolutionary distances of virulence genes relative to those of housekeeping genes were calculated via multilocus sequence analysis. Bioinformatics software and tools were used to predict and compare the main functional features of genes encoding virulence proteins, and the structures of virulence proteins were predicted and analyzed through homology modeling and a deep learning algorithm.

Results: According to the diversity of toxins, genome evolution tended to cluster based on the protein-coding virulence genes. The evolutionary transfer distances of virulence genes relative to those of housekeeping genes in C. botulinum strains were greater than those in C. perfringens strains, and BoNTs and alpha toxin proteins were located extracellularly. The BoNTs have highly similar structures, but BoNT/A/B and BoNT/E/F have significantly different conformations. The beta2 toxin monomer structure is similar to but simpler than the alpha toxin monomer structure, which has 2 mobile loops in the N-terminal domain. The C-terminal domain of the CPE trimer forms a "claudin-binding pocket" shape, which suggests biological relevance, such as in pore formation.

Conclusions: According to the genotype of protein-coding virulence genes, the evolution of Clostridium showed a clustering trend. The genetic stability, functional and structural characteristics of foodborne Clostridium virulence proteins reveal the taxonomy and diverse distribution of virulence factors.

Introduction: Predicting Self-interacting proteins (SIPs) is a crucial area of research in predicting protein functions, as well as in understanding gene-disease and disease-drug associations. These interactions are integral to numerous cellular processes and play pivotal roles within cells. However, traditional methods for identifying SIPs through biological experiments are often expensive, time-consuming, and have long cycles. Therefore, the development of effective computational methods for accurately predicting SIPs is not only necessary but also presents a significant challenge.

Results: In this research, we introduce a novel computational prediction technique, VGGNGLCM, which leverages protein sequence data. This method integrates the VGGNet deep convolutional neural network (VGGN) with the Gray-Level Co-occurrence Matrix (GLCM) to detect Self-interacting proteins associations. Specifically, we initially utilized Position Specific Scoring Matrix (PSSM) to capture protein evolutionary information and integrated key features from PSSM using GLCM. We then employed VGGNet as a predictive classifier, leveraging its capabilities for powerful learning and classification prediction. Subsequently, the extracted features were input into the VGGNet deep convolutional neural network to identify Self-interacting proteins. To evaluate the performance of the VGGNGLCM model, we conducted experiments using yeast and human datasets, achieving average accuracies of 95.68% and 97.72% respectively. Additionally, we compared the prediction performance of the VGGNet classifier with that of the Convolutional Neural Network (CNN) and the state-of-the-art Support Vector Machine (SVM) using the same feature extraction method. We also compared the prediction ability of VGGNGLCM with other existing approaches. The comparison results further demonstrate the superior performance of VGGNGLCM over other prediction models in this domain.

Conclusion: The experimental verification further strengthens the evidence that VGGNGLCM is effective and robust compared to existing methods. It also highlights the high accuracy and robustness of the VGGNGLCM model in predicting Self-interacting proteins (SIPs). Consequently, we believe that the VGGNGLCM method serves as a valuable computational tool and can catalyze extensive bioinformatics research related to SIPs prediction.

Objective: To explore different mRNA transcriptome patterns and RNA N6-methyladenosine (m6A) alteration in yunaconitine (YA)-treated HT22 mouse hippocampal neuron, and uncover the role of abnormal mRNA expression and RNA m6A modification in YA-induced neurotoxicity.

Methods: HT22 cells were treated with 0, 5, 10, and 50 μM of YA for 72 h to evaluate their viability and GSH content. Subsequently, mRNA-seq and MeRIP-seq analyses were performed on HT22 cells treated with 0 and 10 μM YA for 72 h, and molecular docking was used to simulate interactions between YA and differentially expressed m6A regulators. The mitochondrial membrane potential was examined using the JC-10 probe, and RT-qPCR was conducted to verify the expression levels of differentially expressed m6A regulatory factors, as well as to assess alterations in the mRNA expression levels of antioxidant genes.

Results: YA treatment significantly reduced the viability of HT22 cells and decreased GSH content. The mRNA-seq analysis obtained 1018 differentially expressed genes, KEGG and GO enrichment results of differentially expressed genes mainly comprise the nervous system development, cholinergic synapse, response to oxidative stress, and mitochondrial inner membrane. A total of 7 differentially expressed m6A regulators were identified by MeRIP-seq. Notably, molecular docking results suggested a stable interaction between YA and most of the differentially expressed m6A regulators.

Conclusion: This study showed that YA-induced HT22 cell damage was associated with the increased methylation modification level of target gene m6A and abnormal expression of m6A regulators.

The recombination plays a key role in promoting evolution of RNA viruses and emergence of potentially epidemic variants. Some studies investigated the recombination occurrence among SARS-CoV-2, without exploring its impact on virus-host interaction. In the aim to investigate the burden of recombination in terms of frequency and distribution, the occurrence of recombination was first explored in 44 230 Omicron sequences among BQ subvariants and the under investigation "ML" (Multiple Lineages) denoted sequences, using 3seq software. Second, the recombination impact on interaction between the Spike protein and ACE2 receptor as well as neutralizing antibodies (nAbs), was analyzed using docking tools. Recombination was detected in 56.91% and 82.20% of BQ and ML strains, respectively. It took place mainly in spike and ORF1a genes. For BQ recombinant strains, the docking analysis showed that the spike interacted strongly with ACE2 and weakly with nAbs. The mutations S373P, S375F and T376A constitute a residue network that enhances the RBD interaction with ACE2. Thirteen mutations in RBD (S373P, S375F, T376A, D405N, R408S, K417N, N440K, S477N, P494S, Q498R, N501Y, and Y505H) and NTD (Y240H) seem to be implicated in immune evasion of recombinants by altering spike interaction with nAbs. In conclusion, this "in silico" study demonstrated that the recombination mechanism is frequent among Omicron BQ and ML variants. It highlights new key mutations, that potentially implicated in enhancement of spike binding to ACE2 (F376A) and escape from nAbs (RBD: F376A, D405N, R408S, N440K, S477N, P494S, and Y505H; NTD: Y240H). Our findings present considerable insights for the elaboration of effective prophylaxis and therapeutic strategies against future SARS-CoV-2 waves.