Combinative workflow for mRNA vaccine development.

Recently, mRNA has gained a lot of attention in the field of vaccines, gene therapy, and protein replacement therapies. Herein, we are demonstrating a comprehensive approach to designing, cloning, and characterizing an antigenic cassette for the development of mRNA vaccine for COVID-19. The gene encoding the antigenic spike protein of the SARS-CoV-2 Omicron variant (B.1.1.529) was designed using the databases, characterized by in-silico tools, and assembled using overlapping oligonucleotide-based assembly by PCR. Next, the gene was cloned, mRNA was synthesized, and characterized using orthogonal approaches (Capillary electrophoresis, Sanger DNA sequencing, Next-generation sequencing, HPLC, qPCR, etc.). Furthermore, the antigen expression was monitored in-vitro using an animal cell model by western blot, flow cytometer, and surface plasmon resonance. The demonstrated approach has also been followed for developing the mRNA vaccines for various other indications such as Malaria, Herpes, Dengue, HPV, etc.

Leveraging the immunoinformatics approach for designing the SARS-CoV-2 omicron-specific antigenic cassette of mRNA vaccine.

Emergence of SARS-CoV-2 Omicron variant has presented a significant challenge to global health, demanding rapid development of mRNA-based vaccines. The mRNA-guided vaccine platforms offer various advantages over traditional vaccine platforms. The mRNA by nature is a short-lived molecule that guides the cells to manufacture antigenic proteins. In the present work, we have created an omicron spike antigenic protein sequence characterized by base composition analysis, modeling, and docking with the ACE-2 receptor. Further, we predicted the B-cell and T-cell epitopes followed by antigenicity, toxicity, and allergenicity. Finally, the protein was reverse translated, codon-optimized, and encoding mRNA sequence was checked for its stability by predicting the secondary structures. A comprehensive examination of in-silico data revealed 628.2 as a potent antigenic candidate that was finally used in Gemcovac®-OM, a heterologous booster mRNA vaccine for COVID-19.

Computational investigations into structure and function impact of novel mutations identified in targeted exons from ovarian cancer cell lines.

The lack of sensitive and specific biomarkers for ovarian cancer leads to late stage diagnosis of the disease in a majority of the cases. Mutation accumulation is the basis for cancer progression, thus identifying mutations is an important step in the disease diagnosis. In the present study, a comprehensive analysis of fifteen Next Generation Sequencing samples from thirteen ovarian cancer cell lines was carried out for the identification of new mutations. The study revealed eight clinically significant novel mutations in six ovarian cancer oncogenes, viz. SMARCA4, ARID1A, PPP2R1A, CTNNB1, DICER1 and PIK3CA. In-depth computational analysis revealed that the mutations affected the structure of the proteins in terms of stability, solvent accessible surface area and molecular dynamics. Moreover, the mutations were present in functionally significant domains of the proteins, thereby adversely affecting the protein functionality. PPI network for SMARCA4, CTNNB1, DICER1, PIK3CA, PPP2R1A and ARID1A showed that these genes were involved in certain significant pathways affecting various hallmarks of cancer. For further validation, in vitro studies were performed that revealed hypermutability of the CTNNB1 gene. Through this study we have identified some key mutations and have analysed their structural and functional impact. The study establishes some key mutations, which can be potentially explored as biomarker and drug target.Communicated by Ramaswamy H. Sarma.

Evaluation of self-amplifying mRNA platform for protein expression and genetic stability: Implication for mRNA therapies.

The consecutive launch of mRNA vaccines like mRNA-1273, BNT 162b2, and GEMCOVAC®-19 against COVID-19 has triggered the debate of long-term expression, safety, and genomic integration of the mRNA vaccine platforms. In the present study, we examined the longevity of antigenic protein expression of mRNA-614 and mRNA-S1LC based on self-amplifying mRNA (SAM) in Expi-293F™, HEK-293 T, and ARPE-19 cells. The protein expression was checked by sandwich-ELISA, FACS, luciferase activity assay, and Western blot. The transcribed antigenic mRNA was sequenced and found to be un-mutated. Additionally, no genomic integration of the reverse transcribed mRNA was observed even up to 7 days post-transfection as verified by PCR. Furthermore, we have generated high-quality 3D structures of non-structural proteins (nsPs) in silico and the genes encoding for the nsPs were cloned and expressed using the T7 system. Findings from the current study have strengthened the fact that the alphavirus-based SAM platform has the potential to become a modality in the upcoming years.

RNA-Seq Analysis of Clinical Samples from TCGA Reveal Molecular Signatures for Ovarian Cancer.

Identifying differentially expressed genes and co-expression modules lead to novel biomarkers. GO, pathway enrichment, network, and tumor stage analysis of 318 ovarian cancer samples from TCGA, categorised into primary and recurrent, pre-menopause and post-menopause, and early and late stage tumors was performed. Upregulated and downregulated genes in primary vs recurrent, early stage vs late-stage and pre-menopause vs post-menopause tumors were 84 and 62, 84 and 35, and 88 and 14, respectively. IRAK2 and CXCL8 had higher expression in recurrent tumors while REG1A had higher expression in post-menopause samples. In late stage tumors constant expression of IRAK2 and REG1A was observed, while that of CXCL8 and EGF decreased. These genes may be potential biomarkers for the diagnosis of the disease.

Computational studies reveal co-occurrence of two mutations in IL7R gene of high-grade serous carcinoma patients.

Major cause of mortality in ovarian cancer can be attributed to a lack of specific and sensitive biomarkers for diagnosis and prognosis of the disease. Uncovering the mutations in genes involved in crucial oncogenic pathways is a key step in discovery and development of novel biomarkers. Whole exome sequencing (WES) is a powerful method for the detection of cancer driver mutations. The present work focuses on identifying functionally damaging mutations in patients with high-grade serous ovarian carcinoma (HGSC) through computational analysis of WES. In this study, WES data of HGSC patients was retrieved from the genomic literature available in sequence read archive, the variants were identified and comprehensive structural and functional analysis was performed. Interestingly, I66T and V138I mutations were found to be co-occurring in the IL7R gene in four out of five HGSC patient samples investigated in this study. The V138I mutation was located in the fibronectin type-3 domain and computationally assessed to be causing disruptive effects on the structure and dynamics of IL7R protein. This mutation was found to be co-occurring with the neutral I66T mutation in the same domain which compensated the disruptive effects of V138I variant. These comprehensive studies point to a hitherto unexplored significant role of the IL7R gene in ovarian carcinoma. It is envisaged that the work will lay the foundation for the development of a novel biomarker with potential application in molecular profiling and in estimation of the disease prognosis.Communicated by Ramaswamy H. Sarma.