Protocol for mixed-method study by LOng COvid Multidisciplinary consortium: Optimising Treatments and servIces acrOss the NHS (LOCOMOTION) (preprint)

Introduction Long COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services. We seek to optimise long COVID care both within and outside specialist clinics, including improving access, reducing inequalities, helping patients manage their symptoms effectively at home, and providing guidance and decision support for primary care. We aim to establish a ‘gold standard’ of care by systematically analysing symptom clusters and current practices, iteratively improving pathways and systems of care, and working to disseminate better practices. Methods and analysis This mixed-method, multi-site study is informed by the principles of applied health services research, quality improvement, co-design, and learning health systems. It was developed in close partnership with patients (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment; and help with returning to work and other roles) and with front-line clinicians. Workstreams and tasks to optimise assessment, treatment and monitoring are based in three contrasting settings: [1] specialist management in 10 long COVID clinics across the UK, via a quality improvement collaborative, experience-based co-design and targeted efforts to reduce inequalities of access; [2] patient self-management at home, with technology-supported monitoring; and [3] generalist management in primary care, harnessing electronic record data to study population phenotypes and develop evidence-based decision support, referral pathways and prioritisation criteria across the primary-secondary care interface, along with analysis of costs. Study governance includes an active patient advisory group. Ethics and dissemination LOCOMOTION is sponsored by the University of Leeds and approved by Yorkshire & The Humber - Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers to influence service specifications and targeted funding streams. Study registration ClinicalTrials.gov NCT05057260 ; ISRCTN15022307 .

Altered Sub-Genomic RNA Expression in SARS-CoV-2 B.1.1.7 Infections (preprint)

SARS-CoV-2 lineage B.1.1.7 viruses are more transmissible, may lead to greater clinical severity, and result in modest reductions in antibody neutralization. subgenomic RNA (sgRNA) is produced by discontinuous transcription of the SARS-CoV-2 genome and is a crucial step in the SARS-CoV-2 life cycle. Applying our tool (periscope) to ARTIC Network Oxford Nanopore genomic sequencing data from 4400 SARS-CoV-2 positive clinical samples, we show that normalised sgRNA expression profiles are significantly increased in B.1.1.7 infections (n=879). This increase is seen over the previous dominant circulating lineage in the UK, B.1.177 (n=943), which is independent of genomic reads, E gene cycle threshold and day of illness when sampling occurred. A noncanonical subgenomic RNA which could represent ORF9b is significantly enriched in B.1.1.7 SARS-CoV-2 infections, potentially as a result of a triple nucleotide mutation leading to amino acid substitution D3L in nucleocapsid in this lineage which increases complementarity with the genomic leader sequence. These findings provide a unique insight into the biology of B.1.1.7 and support monitoring of sgRNA profiles in sequence data to evaluate emerging potential variants of concern.

periscope: sub-genomic RNA identification in SARS-CoV-2 ARTIC Network Nanopore Sequencing Data (preprint)

We have developed periscope, a tool for the detection and quantification of sub-genomic RNA in ARTIC network protocol generated Nanopore SARS-CoV-2 sequence data. We applied periscope to 1155 SARS-CoV-2 sequences from Sheffield, UK. Using a simple local alignment to detect reads which contain the leader sequence we were able to identify and quantify reads arising from canonical and non-canonical sub-genomic RNA. We were able to detect all canonical sub-genomic RNAs at expected abundances, with the exception of ORF10, suggesting that this is not a functional ORF. A number of recurrent non-canonical sub-genomic RNAs are detected. We show that the results are reproducible using technical replicates and determine the optimum number of reads for sub-genomic RNA analysis. Finally variants found in genomic RNA are transmitted to sub-genomic RNAs with high fidelity in most cases. This tool can be applied to tens of thousands of sequences worldwide to provide the most comprehensive analysis of SARS-CoV-2 sub-genomic RNA to date.