Dataset Information

Identification of consensus hairpin loop structure among the negative sense subgenomic RNAs of SARS-CoV-2.

ABSTRACT:

Background

SARS-CoV-2 is the causative agent of worldwide pandemic disease coronavirus disease 19. SARS-CoV-2 bears positive sense RNA genome that has organized and complex pattern of replication/transcription process including the generation of subgenomic RNAs. Transcription regulatory sequences have important role in the pausing of replication/transcription and generation of subgenomic RNAs.

Results

In the present bioinformatics analysis, a consensus secondary structure was identified among negative sense subgenomic RNAs of SARS-CoV-2. This consensus region is present at the adjacent of initiation codon.

Conclusions

This study proposed that consensus structured domain could involve in mediating the long pausing of replication/transcription complex and responsible for subgenomic RNA production.

Supplementary information

The online version contains supplementary material available at 10.1186/s42269-023-01002-3.

SUBMITTER: Bokolia NP

PROVIDER: S-EPMC9947893 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Identification of consensus hairpin loop structure among the negative sense subgenomic RNAs of SARS-CoV-2.

Bokolia Naveen Prakash NP Gadepalli Ravisekhar R

Bulletin of the National Research Centre 20230223 1

<h4>Background</h4>SARS-CoV-2 is the causative agent of worldwide pandemic disease coronavirus disease 19. SARS-CoV-2 bears positive sense RNA genome that has organized and complex pattern of replication/transcription process including the generation of subgenomic RNAs. Transcription regulatory sequences have important role in the pausing of replication/transcription and generation of subgenomic RNAs.<h4>Results</h4>In the present bioinformatics analysis, a consensus secondary structure was iden ...[more]

PMID: 36852284

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Project description:SARS-CoV-2 transcribes a set of subgenomic RNAs (sgRNAs) essential for the translation of structural and accessory proteins to sustain its life cycle. We applied RNA-seq on 375 respiratory samples from individual COVID-19 patients and revealed that the majority of the sgRNAs were canonical transcripts with N being the most abundant (36.2%), followed by S (11.6%), open reading frame 7a (ORF7a; 10.3%), M (8.4%), ORF3a (7.9%), ORF8 (6.0%), E (4.6%), ORF6 (2.5%), and ORF7b (0.3%); but ORF10 was not detected. The profile of most sgRNAs, except N, showed an independent association with viral load, time of specimen collection after onset, age of the patient, and S-614D/G variant with ORF7b and then ORF6 being the most sensitive to changes in these characteristics. Monitoring of 124 serial samples from 10 patients using sgRNA-specific real-time RT-PCR revealed a potential of adopting sgRNA as a marker of viral activity. Respiratory samples harboring a full set of canonical sgRNAs were mainly collected early within 1 to 2 weeks from onset, and most of the stool samples (90%) were negative for sgRNAs despite testing positive by diagnostic PCR targeting genomic RNA. ORF7b was the first to become undetectable and again being the most sensitive surrogate marker for a full set of canonical sgRNAs in clinical samples. The potential of using sgRNA to monitor viral activity and progression of SARS-CoV-2 infection, and hence as one of the objective indicators to triage patients for isolation and treatment should be considered. IMPORTANCE Attempts to use subgenomic RNAs (sgRNAs) of SARS-CoV-2 to identify active infection of COVID-19 have produced diverse results. In this work, we applied next-generation sequencing and RT-PCR to profile the full spectrum of SARS-CoV-2 sgRNAs in a large cohort of respiratory and stool samples collected throughout infection. Numerous known and novel discontinuous transcription events potentially encoding full-length, deleted and frameshift proteins were observed. In particular, the expression profile of canonical sgRNAs was associated with genomic RNA level and clinical characteristics. Our study found sgRNAs as potential biomarkers for monitoring infectivity and progression of SARS-CoV-2 infection, which provides an alternative target for the management and treatment of COVID-19 patients.

Project description:BackgroundSARS-CoV-2, a positive-sense RNA virus in the family Coronaviridae, has caused a worldwide pandemic of coronavirus disease 2019 or COVID-19. Coronaviruses generate a tiered series of subgenomic RNAs (sgRNAs) through a process involving homology between transcriptional regulatory sequences (TRS) located after the leader sequence in the 5' UTR (the TRS-L) and TRS located near the start of ORFs encoding structural and accessory proteins (TRS-B) near the 3' end of the genome. In addition to the canonical sgRNAs generated by SARS-CoV-2, non-canonical sgRNAs (nc-sgRNAs) have been reported. However, the consistency of these nc-sgRNAs across viral isolates and infection conditions is unknown. The comprehensive definition of SARS-CoV-2 RNA products is a key step in understanding SARS-CoV-2 pathogenesis.MethodsHere, we report an integrative analysis of eight independent SARS-CoV-2 transcriptomes generated using three sequencing strategies, five host systems, and seven viral isolates. Read-mapping to the SARS-CoV-2 genome was used to determine the 5' and 3' coordinates of all junctions in viral RNAs identified in these samples.ResultsUsing junctional abundances, we show nc-sgRNAs make up as much as 33% of total sgRNAs in cell culture models of infection, are largely consistent in abundance across independent transcriptomes, and increase in abundance over time during infection. By assessing the homology between sequences flanking the 5' and 3' junction points, we show that nc-sgRNAs are not associated with TRS-like homology. By incorporating read coverage information, we find strong evidence for subgenomic RNAs that contain only 5' regions of ORF1a. Finally, we show that non-canonical junctions change the landscape of viral open reading frames.ConclusionsWe identify canonical and non-canonical junctions in SARS-CoV-2 sgRNAs and show that these RNA products are consistently generated by many independent viral isolates and sequencing approaches. These analyses highlight the diverse transcriptional activity of SARS-CoV-2 and offer important insights into SARS-CoV-2 biology.

Dataset Information

Identification of consensus hairpin loop structure among the negative sense subgenomic RNAs of SARS-CoV-2.

Background

Results

Conclusions

Supplementary information

Publications

Identification of consensus hairpin loop structure among the negative sense subgenomic RNAs of SARS-CoV-2.

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