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The etiopathogenesis of severe COVID-19 remains unknown. Indeed given major confounding factors (age and co-morbidities), true drivers of this condition have remained elusive. Here, we employ an unprecedented multi-omics analysis, combined with artificial intelligence, in a young patient cohort where major co-morbidities have been excluded at the onset. Here, we established a three-tier cohort of individuals younger than 50 years without major comorbidities. These included 47 "critical" (in the ICU under mechanical ventilation) and 25 "non-critical" (in a noncritical care ward) COVID-19 patients as well as 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cells proteomics, cytokine profiling and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing and structural causal modeling led to key findings. Critical patients were characterized by exacerbated inflammation, perturbed lymphoid/myeloid compartments, coagulation and viral cell biology. Within a unique gene signature that differentiated critical from noncritical patients, several driver genes promoted severe COVID-19 among which the upregulated metalloprotease ADAM9 was key. This gene signature was replicated in an independent cohort of 81 critical and 73 recovered COVID-19 patients, as were ADAM9 transcripts, soluble form and proteolytic activity. Ex vivo ADAM9 inhibition affected SARS-CoV-2 uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, COVID-19 cohort, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. The key driver, ADAM9, interfered with SARS-CoV-2 biology. A repositioning strategy for anti-ADAM9 therapeutic is feasible. One sentence summaryEtiopathogenesis of severe COVID19 in a young patient population devoid of comorbidities.
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BackgroundCancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical. ObjectiveTo identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET) MethodData was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission ([≥]24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool. ResultsTraining and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died. Conclusions and RelevanceCORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.
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The ability to visualize the cellular effects of a somatic mutation is relevant to studies of cell kinetics and carcinogenesis. In the colon, mutagen administration leads to scattered crypt-restricted loss of activity of the X-linked enzyme glucose-6-phosphate dehydrogenase (G6PD); it has been shown that this is due to somatic mutation in the G6PD gene. Mutagen-induced crypt-restricted immunopositivity for metallothionein (MT) has been reported in one study in the mouse colon; if this is also due to somatic mutation, it provides a simple method for studying the phenomenon which could be carried out on paraffin sections. This study shows that, as in the G6PD model, the frequency of crypt-restricted immunopositivity for MT is very low in untreated animals, but increases proportionately with the dose of mutagen administered. There is a good overall correlation of a range of MT-positive crypt frequencies with those derived from studies using G6PD. As with the G6PD model, the MT-positive crypt phenotype evolves over time after mutagen administration; initially individual crypts include both positive and negative phenotype cells, but later almost all involved crypts are composed entirely of MT-positive cells. The frequency of MT-positive crypts stabilizes after a few weeks and remains at the same level 6 months later. All these observations are qualitatively identical to those found using the G6PD model and provide strong evidence that stable, crypt-restricted immunopositivity for MT results from a mutation affecting expression of the metallothionein gene in a colonic stem cell. This model will provide a useful tool to study factors influencing stem cell mutation frequency and cell kinetics in the colon.
