Bioinformatics and Biology Insights最新文献

Aims: Antibiotic resistance is currently a major challenge to scientists. Thus, attempts have been made to develop new compounds with antimicrobial activity. In this research, a new antimicrobial peptide with antibacterial activity was isolated from the plant Urtica dioica.

Methods: A new antimicrobial peptide, named cliotide U1, was purified through precipitation with ammonium sulfate and reverse-phase high-performance liquid chromatography. In silico methods analyzed the physicochemical properties of cliotide U1. The properties of the peptide, including antibacterial activity, pH stability, heat stability, cytotoxicity, and hemolytic activity, were also examined.

Findings: The purified peptide was composed of 35 amino acids with a hydrophobicity ratio of 63% and a net charge of + 5. The antibacterial activity of cliotide U1 was observed against gram-negative and gram-positive bacteria with a minimum inhibitory concentration (MIC) of 1 to 4 µM. Cliotide U1 had less than 2% cytotoxic activity at the MIC range against the human embryonic kidney cell line 293 with no clear hemolytic activity. The stability of cliotide U1 was preserved at various temperatures (10-60°C) and pH (6-9).

Conclusion: Our results demonstrated that cliotide U1 had potent antibacterial potential against gram-negative and gram-positive bacteria. Considering its properties, cliotide U1 can be introduced as a novel antibacterial candidate for expanding new therapeutic drugs.

Nucleotide-binding domain leucine-rich repeat (NLR) proteins are a key component of the plant innate immune system. In plant genomes, NLRs exhibit considerable presence/absence variation and sequence diversity. Recent advances in sequencing technologies have made the generation of high-quality novel plant genome assemblies considerably more straightforward. Accurately identifying NLRs from these genomes is a prerequisite for improving our understanding of NLRs and identifying novel sources of disease resistance. While several tools have been developed to predict NLRs, they are hampered by low accuracy, speed, and availability. Here, the NLR annotation tool Resistify is presented. Resistify is an easy-to-use, rapid, and accurate tool to identify and classify NLRs from protein sequences. Applying Resistify to the RefPlantNLR database demonstrates that it can correctly identify NLRs from a diverse range of species. Applying Resistify in combination with tools to identify transposable elements to a panel of Solanaceae genomes reveals a previously undescribed association between NLRs and Helitron transposable elements.

In recent years, several machine learning (ML) approaches have been proposed to predict gene expression signal and chromatin features from the DNA sequence alone. These models are often used to deduce and, to some extent, assess putative new biological insights about gene regulation, and they have led to very interesting advances in regulatory genomics. This article reviews a selection of these methods, ranging from linear models to random forests, kernel methods, and more advanced deep learning models. Specifically, we detail the different techniques and strategies that can be used to extract new gene-regulation hypotheses from these models. Furthermore, because these putative insights need to be validated with wet-lab experiments, we emphasize that it is important to have a measure of confidence associated with the extracted hypotheses. We review the procedures that have been proposed to measure this confidence for the different types of ML models, and we discuss the fact that they do not provide the same kind of information.

Transposable elements (TEs) or transposons are thought to play roles in animal physiological processes, such as germline, early embryonic, and brain development, as well as aging. However, their roles have not been systematically investigated through experimental studies. In this study, we created a catalog of genes directly involved in replication, excision, or integration of transposon-coding DNA, which we refer to as transposon DNA processing genes (TDPGs). Specifically, to bridge the gap to experimental studies, we sought potentially functional TDPGs which maintain intact open reading frames and the amino acids at their catalytic cores on the latest long-read genome assembly of Caenorhabditis elegans, VC2010. Among 52 519 TE loci, we identified 145 potentially functional TDPGs encoded in long terminal repeat elements, long interspersed nuclear elements, terminal inverted repeat elements, Helitrons, and Mavericks/Polintons. Our TDPG catalog, which contains a feasible number of genes, allows for the experimental manipulation of TE mobility in vivo, regardless of whether the TEs are autonomous or non-autonomous, thereby potentially promoting the study of the physiological functions of TE mobility.

Clinical and experimental studies have demonstrated that type 2 diabetes mellitus (T2DM) affects the brain structure and function, in particular the hippocampus, leading to cognitive impairments. However, the molecular mechanisms underlying cognitive deficits induced by T2DM are not fully understood. In this study, we aimed to investigate the effects of T2DM on behavior, the proteome profile in the hippocampus, and the potential molecular pathways involved in the development of cognitive dysfunction in T2DM mice. We found that the diabetic mice exhibited cognitive impairment in the novel object location recognition test and the novel object recognition test. The proteomic analysis revealed that various molecular pathways were involved in this context. These included the upregulation of proteins in the protein synthesis and folding pathway (EIF5A, RSP24, and PPIB), endocytosis and cellular trafficking (VPS24, SNX12, and ARP2/3), cannabinoid receptor interacting (CRIP1), ubiquitination (SKP1), and oxidative stress response (NUDT3). Downregulated proteins were related to mitochondria function (ANT1), neuronal development (ELP1), protein glycosylation (RPN2), and endocytosis (VPS4). Our study shows that T2DM mice exhibit neurocognitive impairment, which is linked to the dysregulation of hippocampal proteins involved in various molecular pathways. These findings contribute to a better understanding of the pathophysiology of T2DM-related cognitive impairment and may identify molecular targets for drug development to treat T2DM-associated cognitive impairment conditions.

Genomic repeats are functionally ubiquitous structural units found in all genomes. Studying these repeats of different origins is essential for understanding the evolution and adaptation of a given organism. These repeating patterns have manifold signatures and structures with varying degrees of homology, making their identification challenging. To address this challenge, we developed a new algorithm and software that can rapidly and accurately detect any repeated sequences de novo with varying degrees of homology in genomic sequences in interspersed or clustered repeats. Numerous forms of repeated sequences and complex patterns can be identified, even for complex sequence variants and implicit or mixed types of repeat blocks. Direct and inverted-repeat elements, perfect and imperfect microsatellite repeats, and any short or long tandem repeat belonging to a wide range of higher-order repeat structures of telomeres or large satellite sequences can be detected. By combining precision and versatility, our tool contributes significantly to elucidating the intricate landscape of genomic repeats.

COVID-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) had an adverse effect globally because it caused a global pandemic with several million deaths. This virus possesses spike protein that is cleaved or activated by Furin-like protease enzymes occurring by mammalian lung or respiratory cells to enter the mammalian body. The addition of the Furin cleavage site in SARS-CoV-2 makes it a more infectious and emerging virus than its ancestor's viruses. Phylogenetic relationships of coronavirus spike proteins have analyzed and mapped Furin recognition motif on the tree using bioinformatics tools such as GTEx, KEGG, GO, NCBI, PolyPhen-2, SNAP2, PANTHER, Hidden Markov Models (Fathmm), Phd-single-nucleotide polymorphism (SNP), I-TASSER, Modpred, Phobius, SIFT, iPTREE-STAB, and PROVEAN. During this study, it has been found that in certain regions, Furin SNPs have some relation with the susceptibility to SARS-CoV-2. Whereas in other regions, the effects are very negligible. Finally, our study demonstrates that Furin SNPs have a strong relationship with susceptibility to SARS-CoV-2. As it helps to cleave the spike protein of the virus, thus it can be targeted to inhibit at a particular site to prevent the SARS-CoV-2 from the entrance into the body.

Salmonella enterica is a gram-negative bacterium that demonstrates a remarkable ability to acquire antibiotic resistance genes (ARGs). The role of the CRISPR-Cas system in influencing antibiotic resistance in S. enterica is still under investigation. This study explores the distribution and impact of CRISPR-Cas systems on antibiotic resistance by analyzing 316 S. enterica genomes. We conducted sequence alignments, phylogenetic analyses, and conservation studies on Cas genes, direct repeats (DRs), and leader sequences. Promoter predictions and RNA secondary structure analyses were also performed. ARGs were identified, and their correlation with Cas gene clusters was evaluated. Our findings revealed that 82.33% of strains possess complete CRISPR-Cas systems, while 17.66% have orphan CRISPRs. We identified 290 distinct DRs, most of which formed stable stem-loop structures, although no promoter regions were detected within the leader sequences. Most spacers were chromosome-targeting, with a smaller proportion homologous to phages and plasmids. Importantly, strains with complete CRISPR-Cas systems showed a higher incidence of ARGs compared with those with orphan or no CRISPR systems. Specifically, the incidence of ARGs was 54.3% higher in strains with complete CRISPR-Cas systems than in strains without CRISPR-Cas systems, and 15.1% higher than in strains with orphan CRISPRs. Spearman's correlation analysis confirmed a statistically significant but weak correlation between the presence of Cas genes and the frequency of ARGs (P-value = 3.892e-06). These results suggest that CRISPR-Cas systems may play a role in the acquisition of ARGs, potentially through mutations under antibiotic pressure. Future studies should investigate mutations, particularly in Cas3-the signature protein of type I CRISPR-Cas systems. In addition, experimental validation, such as culturing S. enterica strains with complete CRISPR-Cas systems under different antibiotic conditions, followed by sequencing to assess the uptake or absence of newly acquired ARGs, would help clarify the potential role of CRISPR-Cas systems in bacterial adaptation to antimicrobial pressures.

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.