Discovering the gene expression signature associated with a cellular state is one of the basic quests in majority of biological studies. For most of the clinical and cellular manifestations, these molecular differences may be exhibited across multiple layers of gene regulation like genomic variations, gene expression, protein translation and post-translational modifications. These system wide variations are dynamic in nature and their crosstalk is overwhelmingly complex, thus analyzing them separately may not be very informative. This necessitates the integrative analysis of such multiple layers of information to understand the interplay of the individual components of the biological system. Recent developments in high throughput RNA sequencing and mass spectrometric (MS) technologies to probe transcripts and proteins made these as preferred methods for understanding global gene regulation. Subsequently, improvements in "big-data" analysis techniques enable novel conclusions to be drawn from integrative transcriptomic-proteomic analysis. The unified analyses of both these data types have been rewarding for several biological objectives like improving genome annotation, predicting RNA-protein quantities, deciphering gene regulations, discovering disease markers and drug targets. There are different ways in which transcriptomics and proteomics data can be integrated; each aiming for different research objectives. Here, we review various studies, approaches and computational tools targeted for integrative analysis of these two high-throughput omics methods.
During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying variants of concern (VOC). Viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host single nucleotide variations (iSNVs). Analysing 1347 samples collected till June 2020, we recorded 16 410 iSNV sites throughout the SARS-CoV-2 genome. We found ∼42% of the iSNV sites to be reported as SNVs by 30 September 2020 in consensus sequences submitted to GISAID, which increased to ∼80% by 30th June 2021. Following this, analysis of another set of 1774 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) lineage-defining variations appeared as iSNVs before getting fixed in the population. Besides, mutations in RdRp as well as RNA-editing by APOBEC and ADAR deaminases seem to contribute to the differential prevalence of iSNVs in hosts. We also observe hyper-variability at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions.
Since its zoonotic transmission in the human host, the SARS-CoV-2 virus has infected millions and has diversified extensively. A hallmark feature of viral system survival is their continuous evolution and adaptation within the host. RNA editing via APOBEC and ADAR family of enzymes has been recently implicated as the major driver of intra-host variability of the SARS-CoV-2 genomes. Analysis of the intra-host single-nucleotide variations (iSNVs) in SARS-CoV-2 genomes at spatio-temporal scales can provide insights on the consequence of RNA editing on the establishment, spread and functional outcomes of the virus. In this study, using 1,347 transcriptomes of COVID-19 infected patients across various populations, we find variable prevalence of iSNVs with distinctly higher levels in Indian population. Our results also suggest that iSNVs can likely establish variants in a population. These iSNVs may also contribute to key structural and functional changes in the Spike protein that confer antibody resistance.
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