Penh shows increased sensitivity in dose-dependent responses following SARS-CoV infection.

<p>Penh is a classically used, and derived measure of respiratory distress. (A) Penh is derived by assessing several measures of the respiratory response curve (peak expiratory flow of breath (PEF), peak inspiratory flow of breath (PIF), time of expiratory portion of breath (Te) and time required to exhale 65% of breath volume (Tr) (B) Following SARS-CoV infection of C57BL/6J animals, we identified significant differences in Penh across a range of doses relative to mock animals (black = mock, blue = 10^3 SARS, green = 10^4, red = 10^5; four animals per group). Within a time point, letters indicate groups that are NOT significantly different from each other. Significant effects of treatment on Penh was determined via partial F-test. Following significance assessment, those treatment groups different from each-other were assessed by Tukey’s HSD post-hoc analysis. All such differences are denoted at a p<0.05 level, and are marked as follows: * = mock different from all infected, # = 10^5 dose different from all others, % = mock different from all doses, 10^3 different from 10^4 and 10^5 doses.</p

Mid-tidal expiratory flow (EF50) demonstrated dose-dependent increase following SARS-CoV infection.

<p>(A) Measuring the flow rate at which 50% of the tidal volume has been expelled, EF50 provides information about the early portion of the respiratory curve and has demonstrated notable differences between normal (black), asthmatic/allergic (Gray), and viral infection (red). (B) Following SARS-CoV infection of C57BL/6J animals, we identified significant differences in EF50 across a range of doses relative to mock animals (black = mock, blue = 10^3 SARS, green = 10^4, red = 10^5; four animals per group). Significant effects of treatment on EF50 was determined via partial F-test. Following significance assessment, those treatment groups different from each-other were assessed by Tukey’s HSD post-hoc analysis. All such differences are denoted at a p<0.05 level, and are marked as follows: * = mock different from all infected, # = mock different from all infected; 10^3 different from 10^4 and 10^5, % = mock different from 10^4 and 10^5 doses.</p

The shape of the exhalatory flow curve (Rpef) indicated changes following infection with SARS-CoV.

<p>(A) Rpef measures the ratio of time to peak expiratory follow (PEF) relative to the total expiratory time. For both hypoxia (gray) and SARS-CoV infection (red), the time to PEF decreases relative to normal (black). However, the length of breath expands following SARS-CoV infection, causing significant drop in Rpef values relative to baseline. (B) Following SARS-CoV infection of C57BL/6J animals, we identified significant differences in Rpef across a range of doses relative to mock animals (black = mock, blue = 10^3 SARS, green = 10^4, red = 10^5; four animals per group). Significant effects of treatment on Rpef was determined via partial F-test. Following significance assessment, those treatment groups different from each-other were assessed by Tukey’s HSD post-hoc analysis. All such differences are denoted at a p<0.05 level, and are marked as follows: * = mock different from all infected, # = 10^5 dose different from all others.</p

Dose-dependent respiratory stress following SARS-CoV infection.

<p>Four C57BL/6J animals per group were either mock-infected (black) or infected with increasing doses of SARS-CoV (10^3, Blue; 10^4, Green; 10^5, Red). Weight loss (A), Mortality (B), and Respiratory parameters (Sup. Data) were measured through 7 days post infection. Whether there was a significant effect of treatment on weight loss (A) was determined via partial F-test. Following significance assessment, those treatment groups different from each-other were assessed by Tukey’s HSD post-hoc analysis. All such differences are denoted at a p<0.05 level, and are marked as follows: * = mock different from all infected, # = mock different from all infected; 10^3 different from 10^4 and 10^5, % = mock different from 10^4 and 10^5 doses. C) UPGMA (<b>U</b>nweighted <b>P</b>air <b>G</b>roup <b>M</b>ethod with <b>A</b>rithmetic Mean)-Clustered correlation matrix describing the relationship between various plethysmographic outputs. For each pair of transformed phenotypes, the correlation between these phenotypes was calculated. The color of each cell relates to the strength of correlation (ranging from -1 at light blue, no correlation being black, and a +1 correlation being bright yellow). In this way strong positive and negative relationships, as well as clusters of tightly related phenotypes could be identified across the range of SARS-CoV dose responses.</p

New Metrics for Evaluating Viral Respiratory Pathogenesis

<div><p>Viral pathogenesis studies in mice have relied on markers of severe systemic disease, rather than clinically relevant measures, to evaluate respiratory virus infection; thus confounding connections to human disease. Here, whole-body plethysmography was used to directly measure changes in pulmonary function during two respiratory viral infections. This methodology closely tracked with traditional pathogenesis metrics, distinguished both virus- and dose-specific responses, and identified long-term respiratory changes following both SARS-CoV and Influenza A Virus infection. Together, the work highlights the utility of examining respiratory function following infection in order to fully understand viral pathogenesis.</p></div

Differential responses to two respiratory pathogens.

<p>C57BL/6J mice were mock-infected (Black, n = 3) or infected with 10^4 of SARS-CoV (Green, n = 4) or 10^4 IAV-H1N1-09 (Orange, n = 4), and Airflow resistance, penH (A) or the shape of the expiratory force curve, Rpef (B) were measured through 28 days post infection. Significant effects of treatment on respiratory responses were determined via partial F-test. Following significance assessment, those treatment groups different from each-other were assessed by Tukey’s HSD post-hoc analysis. All such differences are denoted at a p<0.05 level, and are marked as follows: * = mock different from all infected, # = SARS different from mock and flu; %Flu different from SARS and mock, $ = Flu different from mock.</p

Phenotypic relationships.

<p>Heat map of the relationships between lung pathology, titer and weight loss in the preCC. Yellow indicates positive correlation and blue indicates negative correlation.</p

Lung pathology in select preCC mice.

<p>(A). OR63f51—normal parenchyma. (B). OR181f61—airway debris and cuffing and edema surrounding the associated vasculature. (C) OR220f57—denuded airway blocked with debris. (D) OR380f64 –perivascular cuffing including eosinophilia. (E) OR941f69 –alveolitis including hyaline membrane formation, arrows point to hyaline membranes. (F) OR5030f128 –normal airway and associated vasculature.</p

Genotypes matrix (markers x samples)

Genotypes matrix (markers x samples) as comma-separated values. First six headers are chromosome, marker name, genetic position (cM), physical position (bp, in build mm10/GRCm38 of the mouse reference genome), reference allele ("A1"), alternate allele ("A2"). Remaining column headers are sample names

Genotypes as a compressed R data object (*.rds)

Genotypes matrix (markers x samples) as an R data object for use with the `argyle` package (https://github.com/andrewparkermorgan/argyle). The object itself is just a matrix, with marker and sample metadata as attributes. For details see the `argyle` manuscript: Morgan AP (2016) argyle: an R package for analysis of Illumina genotyping arrays. G3 6: 281-286. http://www.g3journal.org/content/6/2/28