IL-13 is a driver of COVID-19 severity

Donlan, Alexandra N; Sutherland, Tara E.; Marie, Chelsea; Preissner, Saskia; Bradley, Ben T; Carpenter, Rebecca; Sturek, Jeffrey M.; Jennie, Z.; Moreau, Brett; Donowitz, Jeffrey R.; Buck, Gregory A.; Serrano, Myrna G.; Burgess, Stacey L.; Abhyankar, Mayuresh M.; Mura, Cameron; Bourne, Philip E.; Preißner, Robert; Young, M.P.H. Shiu M.; Lyons, Genevieve R; Loomba, Johanna; Ratcliffe, Sarah J.; Poulter, Melinda D.; Mathers, Amy J; Day, Anthony J.; Mann, Barbara J.; Allen, Judith E.; Petri, William A.

doi:10.1101/2020.06.18.20134353

Cited by 20 publications

(19 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, our analysis shows that TMPRSS2 expression is not altered by interferon Accordingly, it has recently been reported that interferon inflammation does not modulate TMPRSS2 expression but upregulates ACE2 expression [12]. Instead, TMPRSS2 expression in airway epithelia is highly upregulated by IL-13 [12,14], a highly expressed cytokine in plasma of COVID-19 patients that require ventilation [13]. Furthermore, IL-13 is also involved in allergic airway type 2 inflammation, asthma and chronic obstructive pulmonary disease (COPD) [12,14], thus suggesting a further mechanism for COVID-19 susceptibility and severity.…”

Section: Analysis Of Tmprss2 Expression Datamentioning

confidence: 49%

Expression and co-expression analyses of TMPRSS2, a key element in COVID-19

Piva¹,

Sabanovic²,

Cecati³

et al. 2020

Eur J Clin Microbiol Infect Dis

View full text Add to dashboard Cite

The ACE2 receptor is, so far, the best-known host factor for SARS-CoV-2 entry, but another essential element, the TMPRSS2 protease, has recently been identified. Here, we have analysed TMPRSS2 expression data in the lung correlating them with age, sex, diabetes, smoking habits, exposure to pollutant and other stimuli, in order to highlight which factors might alter TMPRSS2 expression, and thus impact the susceptibility to infection and COVID-19 prognosis. Moreover, we reported TMPRSS2 polymorphisms affecting its expression and suggested the ethnic groups more prone to COVID-19. Finally, we also highlighted a gender-specific co-expression between TMPRSS2 and other genes related to SARS-CoV-2 entry, maybe explaining the higher observed susceptibility of infection in men. Our results could be useful in designing potential prevention and treatment strategies regarding the COVID-19.

show abstract

Section: Analysis Of Tmprss2 Expression Datamentioning

confidence: 49%

Expression and co-expression analyses of TMPRSS2, a key element in COVID-19

Piva¹,

Sabanovic²,

Cecati³

et al. 2020

Eur J Clin Microbiol Infect Dis

View full text Add to dashboard Cite

show abstract

“…Considering the novelty of COVID-19 pathophysiology, we aimed to include all possible and detectable abnormalities and therefore used the CheXNeXT system that can accurately identify 14 different pathologies. In this context, it is noteworthy that using cytokine/chemokine data on hospitalised patients with COVID-19, Donlan et al 11 have shown that circulating concentration of interleukin-13 (IL-13) can predict the need for mechanical ventilation. Since IL-13 can contribute to pulmonary eosinophilia and tissue remodelling, it is thus possible that radiographically detectable texture alterations that are neither captured by the RALE score nor the CheX-NeXT system may accompany these cytokine profiles.…”

Section: Discussionmentioning

confidence: 99%

“…The established and validated CheXNeXt deep learning algorithm 10 as well as the PXR network 11 are based on the DenseNet121 12 architecture. While the CheXNeXt predicts one or more of 14 lung pathologies from an X-ray image of the chest, the PXR network scores an X-ray image for severity of acute respiratory distress syndrome (ARDS).…”

Section: Network Architecturementioning

confidence: 99%

Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19

et al. 2021

View full text Add to dashboard Cite

ObjectivesThere exists a wide gap in the availability of mechanical ventilator devices and their acute need in the context of the COVID-19 pandemic. An initial triaging method that accurately identifies the need for mechanical ventilation in hospitalised patients with COVID-19 is needed. We aimed to investigate if a potentially deteriorating clinical course in hospitalised patients with COVID-19 can be detected using all X-ray images taken during hospitalisation.MethodsWe exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images acquired from 528 hospitalised patients with COVID-19. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for the purpose of validation.ResultsWe found that our deep learning model predicted the need for mechanical ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately 3 days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24%–13.25%.ConclusionsOur deep learning model accurately predicted the need for mechanical ventilation early during hospitalisation of patients with COVID-19. Until effective preventive or treatment measures become widely available for patients with COVID-19, prognostic stratification as provided by our model is likely to be highly valuable.

show abstract

“…An alternative hypothesis suggests a central role for bradykinin in the inflammation and increased vascular permeability observed in COVID-19 [ 104 ]. Respiratory failure appears to be associated in particular with a type 2 immune response, typified by IL-13 production, which is higher in patients requiring mechanical ventilatory support [ 105 ]. Although type 2 immune responses are protective in the gut [ 106 , 107 ], they are deleterious in the lung [ 108 ].…”

Section: Clinicalmentioning

confidence: 99%