Bioinformatics and Biology Insights最新文献

Objective: Babesiosis is a significant haemoparasitic infection caused by apicomplexan parasites of the genus Babesia. This infection has continuously threatened cattle farmers owing to its devastating effects on productivity and severe economic implications. Failure to curb the increase of the infection has been attributed to largely ineffective vaccines. This study was designed to develop a potential vaccine candidate.

Method: Rhoptry-associated protein-1 (RAP-1) was used to identify and design a potential multi-epitope vaccine candidate due to its immunogenic properties through an immunoinformatics approach.

Results and conclusions: A multi-epitope vaccine comprising 11 CD8+, 17 CD4+, and 3 B-cell epitopes was constructed using the AAY, GPGPG, and KK linkers. Beta-defensin-3 was added as an adjuvant to potentiate the immune response using the EAAK linker. The designed vaccine was computationally predicted to be antigenic (antigenicity scores: 0.6), soluble (solubility index: 0.730), and non-allergenic. The vaccine construct comprises 595 amino acids with a molecular weight of 64 152 kDa, an instability and aliphatic index of 13.89 and 65.82, which confers stability with a Grand average of hydropathicity (GRAVY) value of 0.122, indicating the hydrophobicity of the construct. Europe has the highest combined class population coverage, with a percentage of 96.07%, while Central America has the lowest population coverage, with a value of 22.94%. The DNA sequence of the vaccine construct was optimized and successfully cloned into a pET-28a (+) plasmid vector. Analysis of binding interactions indicated the stability of the complex when docked with Toll-like receptor-2 (TLR-2). The subunit vaccine construct was predicted to induce and boost sufficient host cellular and humoral responses in silico. However, further experimental research and analysis is required to validate the findings.

Limitation: This study is purely computational, and further experimental validation of these findings through in vivo and in vitro conditions is required.

Background: Human papillomavirus (HPV) causes disease through complex interactions between viral and host proteins, with the PI3K signaling pathway playing a key role. Proteins like AKT, IQGAP1, and MMP16 are involved in HPV-related cancer development. Traditional methods for studying protein-protein interactions (PPIs) are labor-intensive and time-consuming. Computational models are becoming more popular as they are less labor-intensive and often more efficient. This study aimed to develop a deep learning model to predict interactions between HPV and host proteins.

Method: To achieve this, available HPV and host protein interaction data was retrieved from the protocol of Eckhardt et al and used to train a Recurrent Neural Network algorithm. Training of the model was performed on the SPYDER (scientific python development environment) platform using python libraries; Scikit-learn, Pandas, NumPy, and TensorFlow. The data was split into training, validation, and testing sets in the ratio 7:1:2, respectively. After the training and validation, the model was then used to predict the possible interactions between HPV 31 and 18 E6 and E7, and host oncoproteins AKT, IQGAP1 and MMP16.

Results: The model showed good performance, with an MCC score of 0.7937 and all other metrics above 88%. The model predicted an interaction between E6 and E7 of both HPV types with AKT, while only HPV31 E7 was shown to interact with IQGAP1 and MMP16 with confidence scores of 0.9638 and 0.5793, respectively.

Conclusion: The current model strongly predicted HPVs E6 and E7 interactions with PI3K pathway, and the viral proteins may be involved in AKT activation, driving HPV-associated cancers. This model supports the robust prediction of interactomes for experimental validation.

De novo assembly of transcriptomes from species without reference genome remains a common problem in functional genomics. While methods and algorithms for transcriptome assembly are continually being developed and published, the quality of de novo assemblies using short reads depends on the complexity of the transcriptome and is limited by several types of errors. One problem to overcome is the research gap regarding the best method to use in each study to obtain high-quality de novo assembly. Currently, there are no established protocols for solving the assembly problem considering the transcriptome complexity. In addition, the accuracy of quality metrics used to evaluate assemblies remains unclear. In this study, we investigate and discuss how different variables accounting for the complexity of RNA-Seq data influence assembly results independently of the software used. For this purpose, we simulated transcriptomic short-read sequence datasets from high-quality full-length predicted transcript models with varying degrees of complexity. Subsequently, we conducted de novo assemblies using different assembly programs, and compared and classified the results using both reference-dependent and independent metrics. These metrics were assessed both individually and combined through multivariate analysis. The degree of alternative splicing and the fragment size of the paired-end reads were identified as the variables with the greatest influence on the assembly results. Moreover, read length and fragment size had different influences on the reconstruction of longer and shorter transcripts. These results underscore the importance of understanding the composition of the transcriptome under study, and making experimental design decisions related to the need to work with reads and fragments of different sizes. In addition, the choice of assembly software will positively impact the final assembly outcome. This selection will affect the completeness of represented genes and assembled isoforms, as well as contribute to error reduction.

Atherosclerotic cardiovascular diseases (CVDs) are closely linked to factors such as familial hypercholesterolemia (FH), often caused by mutations in low-density lipoprotein receptor (LDLR) and apolipoprotein B (APOB). Through a comprehensive bioinformatic analysis, we identified novel LDLR and APOB mutations and their cardiovascular disease (CVD) implications, focusing on unique variants in the Vietnamese population. We used homology modeling to predict protein structures; in addition, through protein-protein molecular docking, we assessed how these mutations affect binding affinities. We identified 10 novel binding residues exclusive to the wild-type and precursor LDLR isoforms, including ASP-47, GLY-48, and GLU-51. Analyses of 154 complexes revealed 5 isoforms with low binding affinities and notable hydrogen-bonding interactions-APOB (Arg3527Trp)-LDLR (Cys318Arg), APOB (His3583Leu)-LDLR (Cys104Tyr), APOB wild-LDLR (Glu228Lys), APOB (Phe2469Cys)-LDLR (Glu288Lys), and APOB wild-LDLR (Ser130Ter). These results suggest strong and potentially detrimental interactions among these proteins. Furthermore, they highlight the molecular mechanisms underlying CVD development, reveal potential therapeutic targets, enhance our understanding of genetic variations, and could guide FH research.

MicroRNAs play a significant role in the development of cancers, including lung cancer. A recent study revealed that smoking, a key risk factor for lung cancer, increased the levels of hsa-mir-301a in the tumor tissues of patients with lung squamous cell carcinoma (LUSC). The aim of the current study is to investigate the mechanism by which tobacco smoke increases hsa-mir-301a levels in LUSC tumor tissues using bioinformatics analysis. Bioinformatics tools and online databases, including The Cancer Genome Atlas (TCGA), LinkedOmics, and Encyclopedia of RNA Interactomes (ENCORI), were applied in this study. Our results showed a correlation between the upregulation of hsa-mir-301a in LUSC tissues and smoking exposure. However, no correlation was discovered between patients' smoking status and the expression level of the hsa-mir-301a host gene, SKA2, prompting us to investigate possible changes in microRNA processing under tobacco smoke exposure. In silico results using online platforms suggest that post-transcriptional processes, which involve the RNA-binding proteins DGCR8 and FUS, contribute to the elevation of mature hsa-mir-301a levels in smoking patients with LUSC. Our findings suggest that RNA-binding proteins play a key role in controlling the processing of hsa-mir-301a, indicating a complex regulation of hsa-mir-301a in the LUSC tissues of smokers.

Background: Coronary artery disease (CAD) is one of the leading causes of death worldwide. The buildup of atherosclerotic plaque, including lipids and cellular waste, characterizes this disease. Smooth muscle cells (SMCs) can migrate and proliferate to form a fibrous cap that stabilizes the atherosclerotic plaque in response to plaque buildup. However, in some severe cases, the fibrous cap is unable to prevent plaque rupture, which can lead to a thrombotic event causing a stroke or myocardial infarction. Studies have been conducted to identify genes associated with this disease. However, the influence of sex on CAD risk is poorly understood due to the complexity of the disease and the lack of women in clinical studies.

Methods: This study is investigated with a unique collection of human aortic smooth muscle cells (huASMCs) derived from 118 male and 33 female individuals who either underwent a heart transplant or were victims of motor vehicle accidents. In this investigation, we explore differentially expressed genes between males and females related to atherosclerosis using a unique RNAseq dataset of human aortic SMCs.

Results: Our study identified 8 genes (CHST1, DKK2, DLL4, EIF1AXP1, GALNT13, NOTCH4, SELL, SPARCL1) that exhibit sex-biased effects in SMCs. Of these, 6 genes were found in the Athero-Express dataset and 5 of them were associated with atherosclerosis-relevant phenotypes. We discovered a novel NOTCH4/DLL4 pathway that plays a role in the differential expression of these genes between males and females. This pathway is linked to coronary artery physiology and may play a role in the pathophysiology of coronary artery disease that differs between the sexes.

Conclusions: Overall, this investigation shows that differentially expressed genes between males and females in human aortic SMCs exist.

Environmental pollution has become a worldwide concern that requires rigorous efforts from all sectors of society to monitor, control, and remediate it. In environmental pollution control, Cupriavidus gilardii CR3 has become a model organism to study resistance to heavy metals as a means of bacterial bioremediation. This research aimed to single out regulatory element analysis and conduct a comparative genome study of the heavy metal resistance genes in the complete genome of C gilardii CR3 using bioinformatics and omics tools. Comparative genome analysis, promoter prediction, common motif identification, transcriptional start site identification, gene annotation, and transcription factor identification search are the major steps to understanding gene expression and regulation. MEME Suit, TOMTOM, Prokka, Rapid Annotation utilizing Subsystem Technology (RAST), Orthologous Average Nucleotide Identity Software Tool (OAT), and EziBio databases or programs were the major tools used in this study. Fourteen transcriptional factors were identified and predicted from the most credible and lowest candidate motifs with an e-value of 3.0e-009, which was statistically the utmost remarkable candidate motif. A detailed evaluation was further performed, and 14 transcriptional factors were identified as in activation, repression, and dual functions. The data revealed that most transcriptional factors identified were used for activation rather than repression. The C gilardii CR3 genome contains many genes responsible for resisting heavy metals such as mercury, cadmium, zinc, copper, and arsenate. As a result, regulatory elements will lay a solid basis for understanding genes responsible for heavy metal bioremediation. It was concluded that further studies with wet lab support could be conducted for confirmation. Moreover, other advanced bioinformatics and omics technologies are needed to strengthen the results.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a high transmissibility profile which favors the accumulation of mutations along its genome, providing the emergence of new variants. In this context, haplotype studies have allowed mapping specific regions and combining approaches and tracking phylogenetic changes. During the COVID-19 pandemic, it was notorious that home environments favored the circulation of SARS-CoV-2, in this study we evaluated 1,407 individuals positive for SARS-CoV-2, in which we located 53 families in the period from June 2021 to February 2023. The epidemiological data were collected in E-SUS notifica and SIVEP-gripe. Then, the genetic material was extracted using the commercial kit and the viral load was evaluated and the viral genomes were sequenced using the Illumina MiSeq methodology. In addition, the circulation of 3 variants and their respective subvariants was detected. The delta variant represented the highest number of cases with 45%, the Omicron variant 43% and the lowest number with 11% of cases the Gamma variants. There were cases of families infected by different subvariants, thus showing different sources of infection. The haplotype network showed a distribution divided into 6 large clusters that were established according to the genetic characteristics observed by the algorithm and 224 Parsimony informative sites were found. In addition, 92% of subjects were symptomatic and 8% asymptomatic. The secondary attack rate of this study was 8.32%. Therefore, we can infer that the home environment favors the spread of SARS-CoV-2, so it is of paramount importance to carry out genomic surveillance in specific groups such as intradomiciliary ones.

It is necessary to accurately capture the growth trajectory of fluorescence where the best fit, precision, and relative efficiency are essential. Having this in mind, a new family of growth functions called TWW (Tabatabai, Wilus, Wallace) was introduced. This model is capable of accurately analyzing quantitative polymerase chain reaction (qPCR). This new family provides a reproducible quantitation of gene copies and is less labor-intensive than current quantitative methods. A new cycle threshold based on TWW that does not need the assumption of equal reaction efficiency was introduced. The performance of TWW was compared with 3 classical models (Gompertz, logistic, and Richard) using qPCR data. TWW models the relationship between the cycle number and fluorescence intensity, outperforming some state-of-the-art models in performance measures. The 3-parameter TWW model had the best model fit in 68.57% of all cases, followed by the Richard model (28.57%) and the logistic (2.86%). Gompertz had the worst fit in 88.57% of all cases. It had the best precision in 85.71% of all cases followed by Richard (14.29%). For all cases, Gompertz had the worst precision. TWW had the best relative efficiency in 54.29% of all cases, while the logistic model was best in 17.14% of all cases. Richard and Gompertz tied for the best relative efficiency in 14.29% of all cases. The results indicate that TWW is a good competitor when considering model fit, precision, and efficiency. The 3-parameter TWW model has fewer parameters when compared to the Richard model in analyzing qPCR data, which makes it less challenging to reach convergence.

The interaction between programmed cell death protein 1 (PD-1) and its ligand PD-L1 plays a crucial role in tumor immune evasion, presenting a critical target for cancer immunotherapy. Despite being effective, current monoclonal antibodies present some drawbacks such as high costs, toxicity, and resistance development. Therefore, the development of small-molecule inhibitors is necessary, especially those derived from natural sources. In this study, benzosampangine is predicted as a promising PD-L1 inhibitor, with potential applications in cancer immunotherapy. Utilizing the high-resolution crystal structure of human PD-L1 (PDB ID: 5O45), we screened 511 natural compounds, identifying benzosampangine as a top candidate with exceptional inhibitory properties. Molecular docking predicted that benzosampangine exhibits a strong binding affinity for PD-L1 (-9.4 kcal/mol) compared with established controls such as CA-170 (-6.5 kcal/mol), BMS-202 (-8.6 kcal/mol), and pyrvinium (-8.9 kcal/mol). The compound's predicted binding efficacy is highlighted by robust interactions with key amino acids (ILE54, TYR56, GLN66, MET115, ILE116, SER117, ALA121, ASP122) within the active site, notably forming 3 Pi-sulfur interactions with MET115-an interaction absents in control inhibitors. In addition, ADMET profiling suggests that over the control molecules, benzosampangine has several key advantages, including favorable solubility, permeability, metabolic stability, and low toxicity, while adhering to Lipinski's rule of five. Molecular dynamic simulations predict the stability of the benzosampangine-PD-L1 complex, reinforcing its potential to sustain inhibition of the PD-1/PD-L1 pathway. MMGBSA analysis calculated a binding free energy (ΔGbind) of -39.39 kcal/mol for the benzosampangine-PD-L1 complex, with significant contributions from Coulombic, lipophilic, and Van der Waals interactions, validating the predicted docking results. This study investigates in silico benzosampangine, predicting its better molecular interactions and pharmacokinetic profile compared with several already known PD-L1 inhibitors.