Functional annotation of Candida albicans hypothetical proteins: a bioinformatics approach.

Candida albicans is a member of the ascomycetes class of fungi and it is an opportunistic pathogen species responsible for a wide range of fungal infections in humans. Bioinformatics and sequencing analysis of Candida proteomics has disclosed that around 69% proteome is still uncharacterized which needs to be annotated with functions. The NCBI-Genome has termed them as hypothetical proteins (HPs) in the whole proteome of Candida. Interpretation of this substantial portion of the proteome can reveal novel pharmacological targets for markers, drug development, and other therapeutics and so on. In this article, we have assigned functional annotation to these hypothetical proteins using bioinformatics methodologies. The advanced and robust computational models have been used to assign the preliminary functions to these putative HPs with high level of confidence. The findings of this study unveil some novel pharmacological targets for drug therapy and vaccines and it would help to identify novel molecular mechanisms underlying the fungal pathogenesis.

Understanding Longevity: SIN-3 and DAF-16 Revealed as Independent Players in Lifespan Regulation.

Aging is the process of gradual physio-biochemical deterioration. Although aging is inevitable, healthy aging is the key to individual and communal well-being. Therefore, it is essential to understand the regulation of aging. SIN-3/Sin-3 is a unique regulatory protein that regulates aging without DNA-binding activity. It functions by establishing multiple protein interactions. To understand the functional mechanism of this transcriptional regulator, the Caenorhabditis elegans protein interactome was assessed for SIN-3 interactions. DAF-16/FOXO emerged as one of the leading contenders for SIN-3-mediated regulation of aging. This study looks at the concerted role of SIN-3 and DAF-16 proteins in lifespan regulation. Phenotypic profiling for the mutants of these genes shows the functional accord between these 2 proteins with similar functions in stress response and vital biological processes. However, there were no significant physical interactions when checked for protein-protein interaction between SIN-3 and DAF-16 proteins. C. elegans genomics and transcriptomics data also indicated the possibilities of concerted gene regulation. This genetic regulation is more likely related to SIN-3 dominance on DAF-16 function. Overall, SIN-3 and DAF-16 proteins have strong functional interactions that ensure healthy aging. The influence of SIN-3 on DAF-16-mediated stress response is one of their convergence points in longevity regulation.

Diagnostic potential of SORT1 gene in coronary artery disease.

BACKGROUND: Genome-wide association studies identify SORT1 gene associated with risk of coronary artery disease (CAD). Sortilin protein enhances LDL absorption, form cell development, and atherosclerosis in macrophages. AIM: We therefore explored SORT1 expression in CAD patients and its gene expression's predictive usefulness for the severity of the disease. METHODOLOGY: This is a case control study and Quantitative real-time PCR; Sandwich ELISA and western blotting were used to determine the expression of SORT1 gene at the mRNA and protein level in two hundred healthy controls and two hundred patients with various CAD syndromes. RESULTS: CAD patients exhibit higher SORT1 gene expression in CAD patients, a higher concentration of sortilin in their plasma, and distinct expression patterns in various CAD syndromes. The study reveals a positive correlation between gene expression and the severity of coronary artery stenosis, the number of diseased vessels, and the presence of diabetes. ROC curve analysis of SORT1 gene expression both at mRNA and protein level showed strong discrimination between significant CAD and control subjects. CONCLUSION: Therefore, elevated SORT1 gene expression in various CAD syndromes may be a potential biomarker for the disease.

Structure-based drug discovery to identify SARS-CoV2 spike protein-ACE2 interaction inhibitors.

After the emergence of the COVID-19 pandemic in late 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has undergone a dynamic evolution driven by the acquisition of genetic modifications, resulting in several variants that are further classified as variants of interest (VOIs), variants under monitoring (VUM) and variants of concern (VOC) by World Health Organization (WHO). Currently, there are five SARS-CoV-2 VOCs (Alpha, Beta, Delta, Gamma and Omicron), two VOIs (Lambda and Mu) and several other VOIs that have been reported globally. In this study, we report a natural compound, Curcumin, as the potential inhibitor to the interactions between receptor binding domain (RBD(S1)) and human angiotensin-converting enzyme 2 (hACE2) domains and showcased its inhibitory potential for the Delta and Omicron variants through a computational approach by implementing state of the art methods. The study for the first time revealed a higher efficiency of Curcumin, especially for hindering the interaction between RBD(S1) and hACE-2 domains of Delta and Omicron variants as compared to other lead compounds. We investigated that the mutations in the RBD(S1) of VOC especially Delta and Omicron variants affect its structure compared to that of the wild type and other variants and therefore altered its binding to the hACE2 receptor. Molecular docking and molecular dynamics (MD) simulation analyses substantially supported the findings in terms of the stability of the docked complexes. This study offers compelling evidence, warranting a more in-depth exploration into the impact of these alterations on the binding of identified drug molecules with the Spike protein. Further investigation into their potential therapeutic effects in vivo is highly recommended.Communicated by Ramaswamy H. Sarma.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 - / - p73 + / + and p53 - / - p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Unveiling parental perspectives: COVID-19 vaccination for children in India.

Background: Irrespective of the availability of a safe and effective COVID-19 vaccine and its success rate in adults, administering vaccines to children remains a challenge for healthcare workers. Children's vaccine hesitancy among parents remains substantial and is exacerbated due to misleading information. In the present study, we aimed to investigate the hesitancy of parents and their concern about the vaccination and clinical characteristics of COVID-19 in their children. Methods: A cross-sectional web-based and offline survey comprised of questions about the demographic of children, the status of COVID-19 infection, its severity, vaccination status, sources of information, willingness, concerns and attitude of parents to vaccinate their children against the COVID-19 virus, was conducted. Overall, 846 responses from parents fulfilling the inclusion criteria were analysed by GraphPad Prism 5. Results: Out of the 846 responses, 51.2% (n = 433) of children were vaccinated against COVID-19. Out of vaccinated children (51.2%), 60.3% (n = 261) had experienced adverse events. Around 21% (n = 98) of children had a history of exposure to the SARS-CoV-2 virus. Among the infected children, 14.3% were asymptomatic and 85.7% had symptoms. Approximately 8% of children had comorbidities, with chronic lung diseases and asthma being the most common. Among the 846 participating parents, 59.5% were mothers and the remaining 40.5% were fathers. A total of 2.7% and 22.2% of parents were found hesitant to administer the COVID-19 vaccine to their children aged 15-18 years and below 15 years, respectively. Among hesitant parents, mothers were found slightly more hesitant as compared to fathers. Also, 35.5% of parents were found hesitant about their own COVID-19 vaccination. Furthermore, the concern for COVID-19 vaccine unwillingness among parents is that a child has already achieved natural immunity after COVID-19 infections (76.8%) followed by vaccine safety and its side effects. The motivating factors to convince parents for their children's COVID-19 vaccination were if their doctors recommend it, followed by detailed information on vaccine side effects and efficacy in children. The most trusted source of information for the parents was found to be the healthcare workers. Conclusion: These results suggest that data and reviews regarding the safety and efficacy of the COVID-19 vaccine readily available in the public domain could serve as a highly effective strategy for promoting and implementing widespread vaccination among children. By providing easily accessible and comprehensive information, public health authorities can address parental concerns, dispel misconceptions and foster a greater sense of trust in the vaccination process.

Critical Insights from Recent Outbreaks of Mycoplasma pneumoniae: Decoding the Challenges and Effective Interventions Strategies.

Mycoplasma pneumoniae (M. pneumoniae) continues to pose a significant disease burden on global public health as a respiratory pathogen. The antimicrobial resistance among M. pneumoniae strains has complicated the outbreak control efforts, emphasizing the need for robust surveillance systems and effective antimicrobial stewardship programs. This review comprehensively investigates studies stemming from previous outbreaks to emphasize the multifaceted nature of M. pneumoniae infections, encompassing epidemiological dynamics, diagnostic innovations, antibiotic resistance, and therapeutic challenges. We explored the spectrum of clinical manifestations associated with M. pneumoniae infections, emphasizing the continuum of disease severity and the challenges in gradating it accurately. Artificial Intelligence and Machine Learning have emerged as promising tools in M. pneumoniae diagnostics, offering enhanced accuracy and efficiency in identifying infections. However, their integration into clinical practice presents hurdles that need to be addressed. Further, we elucidate the pivotal role of pharmacological interventions in controlling and treating M. pneumoniae infections as the efficacy of existing therapies is jeopardized by evolving resistance mechanisms. Lessons learned from previous outbreaks underscore the importance of adaptive treatment strategies and proactive management approaches. Addressing these complexities demands a holistic approach integrating advanced technologies, genomic surveillance, and adaptive clinical strategies to effectively combat this pathogen.

Corrigendum: Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study.

[This corrects the article DOI: 10.3389/fphar.2023.1265573.].