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Comparative analysis of bioinformatics tools to characterize SARS-CoV-2 subgenomic RNAs.

ABSTRACT: During the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), positive-sense genomic RNA and subgenomic RNAs (sgRNAs) are synthesized by a discontinuous process of transcription characterized by a template switch, regulated by transcription-regulating sequences (TRS). Although poorly known about makeup and dynamics of sgRNAs population and function of its constituents, next-generation sequencing approaches with the help of bioinformatics tools have made a significant contribution to expand the knowledge of sgRNAs in SARS-CoV-2. For this scope to date, Periscope, LeTRS, sgDI-tector, and CORONATATOR have been developed. However, limited number of studies are available to compare the performance of such tools. To this purpose, we compared Periscope, LeTRS, and sgDI-tector in the identification of canonical (c-) and noncanonical (nc-) sgRNA species in the data obtained with the Illumina ARTIC sequencing protocol applied to SARS-CoV-2-infected Caco-2 cells, sampled at different time points. The three software showed a high concordance rate in the identification and in the quantification of c-sgRNA, whereas more differences were observed in nc-sgRNA. Overall, LeTRS and sgDI-tector result to be adequate alternatives to Periscope to analyze Fastq data from sequencing platforms other than Nanopore.

SUBMITTER: Lavezzari D

PROVIDER: S-EPMC10520259 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Comparative analysis of bioinformatics tools to characterize SARS-CoV-2 subgenomic RNAs.

Lavezzari Denise D Mori Antonio A Pomari Elena E Deiana Michela M Fadda Antonio A Bertoli Luca L Sinigaglia Alessandro A Riccetti Silvia S Barzon Luisa L Piubelli Chiara C Delledonne Massimo M Capobianchi Maria Rosaria MR Castilletti Concetta C

Life science alliance 20230925 12

During the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), positive-sense genomic RNA and subgenomic RNAs (sgRNAs) are synthesized by a discontinuous process of transcription characterized by a template switch, regulated by transcription-regulating sequences (TRS). Although poorly known about makeup and dynamics of sgRNAs population and function of its constituents, next-generation sequencing approaches with the help of bioinformatics tools have made a significant co ...[more]

PMID: 37748810

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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

Comparative analysis of bioinformatics tools to characterize SARS-CoV-2 subgenomic RNAs.

Publications

Comparative analysis of bioinformatics tools to characterize SARS-CoV-2 subgenomic RNAs.

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