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The immunological features that distinguish COVID-19-associated acute respiratory distress syndrome (ARDS) from other causes of ARDS are incompletely understood. Here, we report the results of comparative lower respiratory tract transcriptional profiling of tracheal aspirate from 52 critically ill patients with ARDS from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a “cytokine storm,” we observe reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS is characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity. In silico analysis of gene expression identifies several candidate drugs that may modulate gene expression in COVID-19 ARDS, including dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 is characterized by impaired interferon-stimulated gene (ISG) expression. The relationship between SARS-CoV-2 viral load and expression of ISGs is decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients reveals distinct immunological features of COVID-19 ARDS.

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We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis.

RationaleUsing latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA.MethodsLCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard.ResultsA 2-class model best fit the population in VALID (p=0.0010) and in EARLI (p<0.0001). Class 2 comprised 27% and 37% of the populations in VALID and EARLI, respectively. Consistent with the previously described ‘hyperinflammatory’ subphenotype, Class 2 was characterised by higher proinflammatory biomarkers, acidosis and increased shock and worse clinical outcomes. The similarities between these and prior RCT-derived subphenotypes were further substantiated by the performance of the parsimonious classifier models in both cohorts (area under the curves 0.92–0.94). The hyperinflammatory subphenotype was associated with increased prevalence of chronic liver disease and neutropenia and reduced incidence of chronic obstructive pulmonary disease. Measurement of novel biomarkers showed significantly higher levels of matrix metalloproteinase-8 and markers of endothelial injury in the hyperinflammatory subphenotype, whereas, matrix metalloproteinase-9 was significantly lower.ConclusionPreviously described subphenotypes are generalisable to unselected populations of non-trauma ARDS.

We performed comparative lower respiratory tract transcriptional profiling of 52 critically ill patients with the acute respiratory distress syndrome (ARDS) from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a cytokine storm, we observed reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS was characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 was characterized by impaired interferon-stimulated gene expression (ISG). We found that the relationship between SARS-CoV-2 viral load and expression of ISGs was decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients with COVID-19 ARDS did not demonstrate cytokine storm but instead revealed a unique and dysregulated host response predicted to be modified by dexamethasone.

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BackgroundIn 2011, World Health Organization revised its recommendation for microbiological monitoring during treatment for multidrug-resistant tuberculosis (MDR-TB) by increasing the frequency of culture examination from quarterly to monthly after culture conversion. Implementing the recommendation requires substantial additional investment in laboratory infrastructure. The objective of this review is to provide cost evidence that is needed for national TB programs to budget for optimal monitoring strategies.Methods and FindingsWe conducted the first systematic literature review on unit cost estimates of three monitoring strategies: 1) smear only; 2) culture only; 3) combined smear and culture. 26 peer-reviewed studies were selected by searching 10 databases in English and Chinese for literature published between 1995 and 2012. Cost estimates were converted into 2010 constant USD and international dollars. We assessed the quality of the estimates using a matrix with five essential elements and provided a cost projection for the combined smear and culture tests where the data were available. The 26 studies reported the cost estimates in 16 predominantly high- or middle-income countries from 1993 to 2009. The estimated unit cost for smear, culture, and combined tests ranges from $0.26 to $10.50, $1.63 to $62.01, and $26.73 to $39.57, respectively. The ratio of culture to smear costs varies from 1.35 to 11.98. The wide range of estimates is likely attributable to using different laboratory methods in different regions and years and differing practices in collecting and reporting cost data. Most studies did not report information critical for generalizing their conclusions.ConclusionThe paucity and low quality of unit cost estimates for TB monitoring in resource-poor settings impose technical challenges in predicting the resources needed for strengthening microbiological monitoring. To improve the validity and comparability of the cost data, we strongly advocate the data collection, estimation, and reporting follow protocols proposed by WHO.