Unlocking The Mysteries of DNA Adducts with Artificial Intelligence.

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Intra-genomic heterogeneity in CpG dinucleotide composition in dengue virus.

PURPOSE: Dengue virus is a life-threatening virus and cases of dengue infection have been increasing steadily in the past decades causing millions of deaths every year. So far, there is no vaccine that works effectively on all serotypes. Recently, CpG-recoded vaccines have proved to be effective against few viruses. METHODS: In this study, evaluation and interpretation of more than 4547 Dengue virus genome sequences were included for analyzing novel CpG dinucleotides rich regions which are shared amid all serotypes. Genomic regions of DENV were synonymously CpG recoded using in silico methods and analyzed for adaptation in both human and Aedes spp. hosts based on CAI scores. RESULTS: The analysis mirrored that serotypes 1, 3, and 4 shared CpG islands present in common regions. DENV-2 CpG islands showed no similarity with any of the CpG islands present in other serotypes. While DENV-3 sequences were found to possess the maximum number of conserved CpG islands stretches; DENV-2 was found to possess the lowest number. We found that all serotypes (with an exception of serotype 2) have CpG island in their 3' UTR. In silico CpG recoding of DENV genomic regions resulted in ∼ 3 fold increase of CpG dinucleotide frequency and comparative analysis based on CAI scores showed decreased adaptive fitness of CpG recoded DENV inside human host. CONCLUSION: These CG-dinucleotide-enriched RNA sequences can be targeted by ZAP (zinc-finger antiviral protein) which can differentiate between host mRNA and viral mRNA. Our in silico findings can further be exploited for CpG-recoding of DENV genomes which can evoke cellular and humoral immune responses by recruiting ZAP-induced RNA degradation machinery and hence providing a promising approach for vaccine development.

Comparative Genomics and Integrated Network Approach Unveiled Undirected Phylogeny Patterns, Co-mutational Hot Spots, Functional Cross Talk, and Regulatory Interactions in SARS-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has resulted in 92 million cases in a span of 1 year. The study focuses on understanding population-specific variations attributing its high rate of infections in specific geographical regions particularly in the United States. Rigorous phylogenomic network analysis of complete SARS-CoV-2 genomes (245) inferred five central clades named a (ancestral), b, c, d, and e (subtypes e1 and e2). Clade d and subclade e2 were found exclusively comprised of U.S. strains. Clades were distinguished by 10 co-mutational combinations in Nsp3, ORF8, Nsp13, S, Nsp12, Nsp2, and Nsp6. Our analysis revealed that only 67.46% of single nucleotide polymorphism (SNP) mutations were at the amino acid level. T1103P mutation in Nsp3 was predicted to increase protein stability in 238 strains except for 6 strains which were marked as ancestral type, whereas co-mutation (P409L and Y446C) in Nsp13 were found in 64 genomes from the United States highlighting its 100% co-occurrence. Docking highlighted mutation (D614G) caused reduction in binding of spike proteins with angiotensin-converting enzyme 2 (ACE2), but it also showed better interaction with the TMPRSS2 receptor contributing to high transmissibility among U.S. strains. We also found host proteins, MYO5A, MYO5B, and MYO5C, that had maximum interaction with viral proteins (nucleocapsid [N], spike [S], and membrane [M] proteins). Thus, blocking the internalization pathway by inhibiting MYO5 proteins which could be an effective target for coronavirus disease 2019 (COVID-19) treatment. The functional annotations of the host-pathogen interaction (HPI) network were found to be closely associated with hypoxia and thrombotic conditions, confirming the vulnerability and severity of infection. We also screened CpG islands in Nsp1 and N conferring the ability of SARS-CoV-2 to enter and trigger zinc antiviral protein (ZAP) activity inside the host cell.IMPORTANCE In the current study, we presented a global view of mutational pattern observed in SARS-CoV-2 virus transmission. This provided a who-infect-whom geographical model since the early pandemic. This is hitherto the most comprehensive comparative genomics analysis of full-length genomes for co-mutations at different geographical regions especially in U.S. strains. Compositional structural biology results suggested that mutations have a balance of opposing forces affecting pathogenicity suggesting that only a few mutations are effective at the translation level. Novel HPI analysis and CpG predictions elucidate the proof of concept of hypoxia and thrombotic conditions in several patients. Thus, the current study focuses the understanding of population-specific variations attributing a high rate of SARS-CoV-2 infections in specific geographical regions which may eventually be vital for the most severely affected countries and regions for sharp development of custom-made vindication strategies.