Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

<i>Cyp6g1</i> Expression in the Digestive Tissues.

<p><i>Cyp6g1</i> overexpression was achieved in the midgut, Malpighian tubule and the fat body using the GAL4-UAS system. At 0ppm (A, A’), no significant differences were observed between genotypes. At 48ppm imidacloprid the overexpression line responded less as measured by Response Time, End Point RMR values and GLM analysis. All plots display mean RMR or β values with 95% confidence intervals.</p

Spinosad Resistance in nAChR Mutants.

<p>Armenia¹⁴, <i>Dα6</i>^<i>W337*</i> and <i>Dα6</i>^<i>nx</i> were tested using the Wiggle Index at 0ppm (A, A’), no significant differences were observed between genotypes. At 12 ppm (B, B’) both <i>Dα6</i> mutants displayed significantly delayed response times and responded less in the GLM analysis. However, while <i>Dα6</i>^<i>337*</i> differed from Armenia¹⁴ in End Point RMR value, <i>Dα6</i>^<i>nx</i> did not. At 48ppm (C, C’), while Response Times were still delayed in Dα6 mutants, End Point RMR values were not significantly different and GLM analysis indicated that only <i>Dα6</i>^<i>337*</i> responded more than Armenia¹⁴. All plots display mean RMR or β values with 95% confidence intervals.</p

Armenia¹⁴ Dose Response Curves.

<p>Armenia¹⁴ was tested using the Wiggle Index at 5 doses of 4 insecticides. Chlorantraniliprole (A) and Imidacloprid (B) each show rapid response times and dose dependent long term RMR values. Ivermectin (C) and Spinosad (D) responded differently, displaying delayed response times and similar long term RMR values. All points correspond to the mean RMR values with 95% confidence intervals. Boxes in each plot indicate values between 0 and 30 minutes magnified in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145051#pone.0145051.g004" target="_blank">Fig 4</a>.</p

Imidacloprid Resistance in nAChR Mutants.

<p>Armenia¹⁴, <i>Dα1</i>^<i>M4</i> and <i>Dβ2</i>^<i>L351R</i> were tested using the Wiggle Index. At 0ppm (A, A’), no significant differences were observed between genotypes. As measured by Response Time, End Point RMR values and GLM analysis at 12 and 48ppm imidacloprid (B, B’, C, C’) <i>Dα1</i>^<i>M4</i> responded the least, <i>Dβ2</i>^<i>L351R</i> displayed an intermediate phenotype and Armenia¹⁴ responded the most. All plots display mean RMR or β values with 95% confidence intervals.</p

Wiggle Index Output and RMR Calculation.

<p>The Wiggle Index produced heat maps and numeric estimates of total larval motility in each well at a given time. RMR values were calculated by dividing the WI value at time = x from the WI value from the same well at time = 0.</p

The Wiggle Index.

<p>The Wiggle Index measures total motility in a given well A) Individual wells are cropped out of a sequence of 250.jpeg images B) σ_S values are calculated for each pixel by calculating the standard deviation of all σ_FW values over the entire sequence. C) σ_S values from each pixel are filtered based on a threshold value and then averaged to yield the Wiggle Index (WI) value.</p

Evolution of genes and genomes on the Drosophila phylogeny

Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species