ABSTRACT
Previous studies have described RT-LAMP methodology for the rapid detection of SARS-CoV-2 in nasopharyngeal/oropharyngeal swab and saliva samples. Here we describe the validation of an improved simple sample preparation method for Direct SARS-CoV-2 RT-LAMP, removing the need for RNA extraction, using 559 swabs and 86,760 saliva samples from asymptomatic and symptomatic individuals across multiple healthcare settings. Using this improved method we report a diagnostic sensitivity (DSe) of 70.35% (95% CI 63.48-76.60%) on swabs and 84.62% (79.50-88.88%) on saliva, with diagnostic specificity (DSp) 100% (98.98-100.00%) on swabs and 100% (99.72-100.00%) on saliva when compared to RT-qPCR. Analysing samples with RT-qPCR ORF1ab CT values of <25 and <33 (high and medium-high viral loads, respectively), we found DSe of 100% (96.34-100%) and 77.78% (70.99-83.62%) for swabs, and 99.01% (94.61-99.97%) and 87.32% (80.71-92.31%) for saliva. We also describe RNA RT-LAMP (on extracted RNA) performed on 12,619 swabs and 12,521 saliva samples to provide updated performance data with DSe and DSp of 95.98% (92.74-98.06%) and 99.99% (99.95-100%) for swabs, and 80.65% (73.54-86.54%) and 99.99% (99.95-100%) for saliva, respectively. We also report on daily samples collected from one individual from symptom onset where both Direct and RNA RT-LAMP detected SARS-CoV-2 in saliva collected on all six days where symptoms were recorded, with RNA RT-LAMP detecting SARS-CoV-2 for an additional further day. The findings from these studies demonstrate that RT-LAMP testing of swabs and saliva is potentially applicable to a variety of use-cases, including frequent, interval-based testing of saliva from asymptomatic individuals via Direct RT-LAMP that may be missed using symptomatic testing alone.
ABSTRACT
Background The worldwide pandemic caused by SARS-CoV-2 has claimed millions of lives and has had a profound effect on global life. Understanding the pathogenicity of the virus and the body's response to infection is crucial in improving patient management, prognosis, and therapeutic strategies. To address this, we performed functional transcriptomic profiling to better understand the generic and specific effects of SARS-CoV-2 infection. Methods Whole blood RNA sequencing was used to profile a well characterised cohort of patients hospitalised with COVID-19, during the first wave of the pandemic prior to the availability of approved COVID-19 treatments and who went on to survive or die of COVID-19, and patients hospitalised with influenza virus infection between 2017 and 2019. Clinical parameters between patient groups were compared, and several bioinformatic tools were used to assess differences in transcript abundances and cellular composition. Results The analyses revealed contrasting innate and adaptive immune programmes, with transcripts and cell subsets associated with the innate immune response elevated in patients with influenza, and those involved in the adaptive immune response elevated in patients with COVID-19. Topological analysis identified additional gene signatures that differentiated patients with COVID-19 from patients with influenza, including insulin resistance, mitochondrial oxidative stress and interferon signalling. An efficient adaptive immune response was furthermore associated with patient survival, while an inflammatory response predicted death in patients with COVID-19. A potential prognostic signature was found based on a selection of transcript abundances, associated with circulating immunoglobulins, nucleosome assembly, cytokine production and T cell activation, in the blood transcriptome of COVID-19 patients, upon admission to hospital, which can be used to stratify patients likely to survive or die. Conclusions The results identified distinct immunological signatures between SARS-CoV-2 and influenza, prognostic of disease progression and indicative of different targeted therapies. The altered transcript abundances associated with COVID-19 survivors can be used to predict more severe outcomes in patients with COVID-19.