Abstract
Mast et al. analyzed transcriptome data derived from RNA-sequencing (RNA-seq) of COVID-19 patient bronchoalveolar lavage fluid (BALF) samples, as compared to BALF RNA-seq samples from a study investigating microbiome and inflammatory interactions in obese and asthmatic adults (Mast et al., 2021). Based on their analysis of these data, Mast et al. concluded that mRNA expression of key regulators of the extrinsic coagulation cascade and fibrinolysis were significantly reduced in COVID-19 patients. Notably, they reported that the expression of the extrinsic coagulation cascade master regulator Tissue Factor (F3) remained unchanged, while there was an 8-fold upregulation of its cognate inhibitor Tissue Factor Pathway Inhibitor (TFPI). From this they conclude that “pulmonary fibrin deposition does not stem from enhanced local [tissue factor] production and that counterintuitively, COVID-19 may dampen [tissue factor]-dependent mechanisms in the lungs”. They also reported decreased Activated Protein C (aPC) mediated anticoagulant activity and major increases in fibrinogen expression and other key regulators of clot formation. Many of these results are contradictory to findings in most of the field, particularly the findings regarding extrinsic coagulation cascade mediated coagulopathies. Here, we present a complete re-analysis of the data sets analyzed by Mast et al. This re-analysis demonstrates that the two data sets utilized were not comparable between one another, and that the COVID-19 sample set was not suitable for the transcriptomic analysis Mast et al. performed. We also identified other significant flaws in the design of their retrospective analysis, such as poor-quality control and filtering standards. Given the issues with the datasets and analysis, their conclusions are not supported.
Highlights
Since the emergence of SARS-CoV-2 in December of 2019, there have been over 230 million reported cases and more than 4.7 million deaths (Dong et al, 2020)
Our reanalysis of the data-sets and the experimental design utilized by Mast et al revealed the following serious issues that bring into question these conclusions; (1) the control group from Michalovich et al contains mostly samples that are not from healthy lungs and many samples are from people with multiple comorbidities, (2) the two groups that are compared use fundamentally dissimilar library preparation methods that cannot be validly compared, and (3) Zhou et al has insufficient read depth for it to be used for differential expression analysis
The first issue we identified was related to the designation of the bronchoalveolar lavage fluid (BALF) bulk RNA-sequencing samples from Michalovich et al (GEO data set - PRJNA434133) as “Healthy Controls” by Mast et al Analysis of the meta-data associated with the described “Healthy Control” subjects published in Michalovich et al demonstrates that their samples were overall not healthy and not representative of the average American population in terms of obesity (CDC, 2021b) (42.4%), smoking rates (CDC, 2020) (14.0%), and asthma (CDC, 2021a) (8%)
Summary
Since the emergence of SARS-CoV-2 in December of 2019, there have been over 230 million reported cases and more than 4.7 million deaths (Dong et al, 2020). Our reanalysis of the data-sets and the experimental design utilized by Mast et al revealed the following serious issues that bring into question these conclusions; (1) the control group from Michalovich et al contains mostly samples that are not from healthy lungs and many samples are from people with multiple comorbidities, (2) the two groups that are compared use fundamentally dissimilar library preparation methods that cannot be validly compared, and (3) Zhou et al has insufficient read depth for it to be used for differential expression analysis These issues are not readily observable in the published text of Mast et al, due to the use of Log-2 fold change and fold change in the text and figures, as well as the inclusion of only counts per million normalized counts in the supplemental files. These issues and clear contradictory evidence in the field, seriously compromise the accuracy of the differential expression analysis in Mast et al, and the validity of the conclusions reached by the authors
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