A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. In our present study, we used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (i) they were both mapped, (ii) they predicted protective antigens, and (iii) they were completely identical to experimentally validated epitopes of SARS-CoV. Further, structural and molecular docking analyses were performed in order to understand the binding interactions of the identified immunodominant epitopes with human major histocompatibility complexes (MHC). Our study provides a set of potential immunodominant epitopes that could enable the generation of both antibody- and cell-mediated immunity. This could contribute to developing peptide vaccine-based adaptive immunotherapy against SARS-CoV-2 infections and prevent future pandemic outbreaks.

Several recent studies have demonstrated that low plasma 25(OH) vitamin D levels are associated with the risk of COVID-19 infection. The primary source of vitamin D production in humans is environmental UV radiation. In many viral respiratory diseases, peak infection rates are observed during winter due to reduced UV exposure and low temperatures. In Europe, the second wave of COVID-19 began early in the winter of 2020. Investigating the impact of seasonal temperature and UV exposure on COVID-19 transmission could thus aid in prevention and intervention. As such, we first performed a comprehensive meta-analysis of all related published literature based on the association of vitamin D and COVID-19, which supported the hypothesis that the low vitamin D level is a critical risk factor for COVID-19 infection. Next, to understand the potential impact of seasonal UV and temperature levels on COVID-19 cases, we analyzed meteorological data and daily COVID-19 cases per million in the populations of 26 European countries. We observed that low temperature, UV index, and cloud-free vitamin D UV dose (UVDVF) levels are negatively correlated with COVID-19 prevalence in Europe. Furthermore, a distributed lag non-linear model was used to assess the non-linear delayed effects of individual seasonal factors on COVID-19 cases. Such analysis highlighted the significantly delayed impact of UVDVF on the cumulative relative risk of COVID-19 infection. The findings of this study suggest that low UV exposure can affect the required production of vitamin D in the body, which substantially influences the dynamics of COVID-19 transmission and severity.

Viral infections can modulate the widespread alternations of cellular splicing, favouring viral replication within the host cells by overcoming host immune responses. However, how SARS-CoV-2 induces host cell differential splicing and affects the landscape of transcript alternation in severe COVID-19 infection remains elusive. Understanding the differential splicing and transcript alternations in severe COVID-19 infection may improve our molecular insights into the SARS-CoV-2 pathogenesis. In this study, we analysed the publicly available blood and lung transcriptome data of severe COVID-19 patients, blood transcriptome data of recovered COVID-19 patients at 12-, 16- and 24-week postinfection and healthy controls. We identified a significant transcript isoform switching in the individual blood and lung RNA-seq data of severe COVID-19-infected patients and 25 common genes that alter their transcript isoform in both blood and lung samples. Altered transcripts show significant loss of the open reading frame, functional domains and switch from coding to noncoding transcript, impacting normal cellular functions. Furthermore, we identified the expression of several novel recurrent chimeric transcripts in the blood samples from severe COVID-19 patients. Moreover, the analysis of the isoform switching into blood samples from recovered COVID-19 patients highlights that there is no significant isoform switching in 16- and 24-week postinfection, and the levels of expressed chimeric transcripts are reduced. This finding emphasizes that SARS-CoV-2 severe infection induces widespread splicing in the host cells, which could help the virus alter the host immune responses and facilitate the viral replication within the host and the efficient translation of viral proteins.

Salmonella enterica serovar typhi is the causative agent of typhoid fever in human. The available vaccine lacks the effectiveness and further overuse of the antibiotics throughout the past decades lead to the emergence of antibiotic-resistant strains. To reduce the spread of antibiotic resistance, it is essential to develop new vaccines. In this study using an extensive bioinformatic analysis based on reverse vaccinology and subtractive genomics-based approach, we searched the genome of S typhi . We found that two outermost membrane proteins LptD and LptE, which are responsible for the final transport of the LPS across the membrane having most of the essential signature for being vaccine target. Both of these proteins are found highly conserved and possess shared surface epitopes among several species of Salmonella pathogens. This indicates that these two proteins could be the potential target for vaccine development, which could be effective for a broad range of Salmonella pathogens.

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a varied group of tumors that originate from neuroendocrine cells found throughout the gastrointestinal tract. These tumors encompass a broad spectrum of biological behaviors, ranging from slow-growing, well-differentiated neuroendocrine tumors (GEP-NETs) to aggressive and poorly differentiated neuroendocrine carcinomas (GEP-NECs), complicating their accurate classification and effective treatment. While advances in molecular research have refined our understanding of these tumors, their complexity, unpredictable progression, and differential response to therapies remain major clinical hurdles. A significant clinical challenge is the accurate grading and diagnosis of GEP-NENs, which is traditionally reliant on subjective methods. However, innovative technologies, such as artificial intelligence-based diagnostics, multi-omics approaches, and precision oncology, are now offering solutions for more precise and reliable classification. Meanwhile, emerging therapies aiming to activate the immune response or modify the tumor environment present promising avenues for improved outcomes. Realizing the full potential of these advances will require a thoughtful integration of molecular insights with standardized diagnostic practices and evolving therapeutic strategies, ensuring that progress in research meaningfully informs and enhances patient care across diverse clinical settings. This review discusses new advancements and explores future directions toward personalized and effective treatments for GEP-NENs.