Percentage of scoDEGs in each group that correlate with viral load.

(XLSX)</p

DEGs, orthoDEGs, and scoDEGs from K18-hACE2 mice and humans in response to SARS-CoV-2 infection.

(XLSX)</p

Principal component, hierarchical cluster and viral load analyses.

(A) Scatter plots showing principal component 1 (PC1) vs. PC2 derived from 14,918 single-copy orthologues from all samples/accessions. Read counts were TMM-normalized and log2-transformed. Expression values were calculated by subtracting each TMM-log2 count from the row mean of all samples for each gene (i.e. deviation from row mean). (B) Hierarchical cluster analysis using the top 500 orthologues according to PC1 and PC2 loadings as in A. Clustering distance was Euclidean, and clustering method was Ward’s linkage. FFPE = Formalin-fixed, paraffin-embedded. (C) For all union-scoDEGs (a sco-DEG in at least 1 sample, n = 5880), across all samples/accessions (n = 69), Pearson correlations were undertaken comparing gene expression (log2 TMM normalized read count for each scoDEG) with percent viral reads (viral read count as a percentage of all read counts for host protein coding genes). Significance (p) and correlation (r) were generated for all scoDEGs. A histogram showing distribution of r values is shown, with colors indicating p and r cutoffs. The 110 genes that correlated well (red) were analyzed using the Molecular Signatures Data Base (MSigDB) available online via Enrichr, with the top 2 annotations shown. (D) The percent viral reads for the 69 samples/accessions are shown on the y axis, and were plotted against expression (log2 TMM counts) of the 110 genes in C. As expected, as correlating union-scoDEGs were selected from D (red), a significant correlation emerged when all 110 union-scoDEGs are taken together; linear regression (black line), p = 2.02 x 10E-149, r = 0.29.</p

Mouse and human DEGs; overlaps and concordances between species.

(A) DEGs from lungs/lung tissues infected with SARS-CoV-2 were identified in K18-hACE2 mouse and human studies (n = number of DEGs). DEGs were generated from original RNA-Seq data provided herein (New Data), re-analyzed from previously published RNA-Seq data (Fastq files re-analyzed), or were obtained from publications (published DEG lists). All datasets were derived from RNA-Seq, except Ackerman which was obtained from a Nanostring study. Coloring of bars: Green—non-orthologous between mouse and human; Orange—with one or both species having multiple orthologues; Purple—both species having a single copy orthologue. A total of 9 Groups (5 K18-hACE2 and 4 human) were considered in the subsequent analyses. (B) The union of all K18-hACE2 scoDEGs was used to compare mouse and human for up- and down-regulated scoDEGs. ‘n’ refers to the number of scoDEGs for each group. Percentages within the Venn diagram (gray boxes) show the percentage of scoDEGs exclusive to that group (i.e. a scoDEG in that group but no other group. E.g. 1752/2216 x 100 = 79%). The boxed overlap percentages represent the percentage of mouse scoDEGs that are also scoDEGs in one or more human studies (e.g. 2216-1752/2216 x 100 ≈ 21% for up-regulated scoDEGS and 1119-1397/1397 x100 ≈20% for down-regulated scoDEGs). (C) Pearson correlation of mean log2FC changes of single-copy orthologues that were DEGs in either any mouse group or any human group or both. (D) Pearson correlation of mean log2FCs of single-copy orthologues that were DEGs in both one or more mouse groups and one or more human groups. scoDEGs that had inconsistent mean expression between species (i.e. were upregulated in one species and down-regulated in another) are shown yellow. The percentage of scoDEGs with inconsistent expression (yellow boxes) is provided relative to the total number of scoDEGs.</p

Pair-wise comparisons between groups of differential gene expression.

Upper-left Euler diagrams show the amount of overlap between groups regarding DEGs (green and purple circles) and scoDEGs (red and blue circles) for all possible group-wise combinations. Green and red circles relate to row names, while purple and blue circles relate to column names. Size of circles indicates the number of DEGs/scoDEGs, as produced by EdgeR analysis or, in the case of Ackermann and Blanco-Melo, as obtained from the authors. Lower-right Each cell contains information pertaining to the group-wise comparison indicated by the row and column names. Overlap—for each pair-wise comparison between groups the number of scoDEGs that were common to both groups is shown as a percentage of the total number of scoDEGs in the comparison.–log p and r—for each pair-wise comparison, gene expression was compared using the union of scoDEGs for those groups (i.e. single-copy orthologues that were differentially expressed in one or both groups, and that were present in the gene lists for both groups). Pearson correlations were then performed using the log2 fold-changes (log2FC) of those single-copy orthologues to provide–log p and r values. Ackerman provides high r values as this analysis only evaluated expression of 249 inflammation genes (see Table 1). Cells are colored using scales on the right. For upper left and lower right, colored boarders indicate whether comparisons are mouse-human (green), human-human (blue), or mouse-mouse (orange). (PDF)</p

Upstream Regulator annotations enriched in DEG lists.

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Wu-exclusive up-regulated DEGs.

(A) Overlap between all human groups for human-exclusive up-regulated DEGs (up-regulated in any human group, but no mouse group). 1298 genes were upregulated only in Wu and no other human or mouse groups. (B) Although only up-regulated in Wu these 1298 DEGs, nevertheless, return very similar IPA Cytokine USR pathways as those shown in Fig 4A and 4B. (C) The 3%, 18% and 21% of DEGs in the IL6R, TNF and IFNg networks (Fig 4D IL6R, S5 Fig TNF, and S6 Fig IFNG) that were up-regulated only in human (green) comprised 194 genes. Of these DEGs, 73% were found up-regulated exclusively in the Wu dataset. (D) When these 143 DEGs were analysed by IPA Diseases or Functions the highest and lowest annotation by z-score suggest more cell survival and less cell death, consistent with Fig 3C. Thus IL6R, TNF, and IFNG networks contain genes that are also associated with cell survival. The presence of DEGs in these later networks that are only up-regulated in humans (Fig 4D IL6R, S5 Fig TNF, and S6 Fig IFNG, green) is largely due to the Wu dataset. The RNA-Seq data suggests that the tissues used to generate the Wu dataset had less virus (Fig 2A) and less cell death (as also seen in Fig 3C), with pathways somewhat distinct (Fig 3A), perhaps because these samples were collected at a later time point when recovery was well underway and/or because a series of medication were used by the patients. The 3%, 18% and 21% of network genes up-regulated in humans might suggest humans up-regulate these network genes in response to SARS-CoV2 infection, whereas mice do not. However, this may largely be due to the fact that no comparable mouse data set was available (e.g. medicated in the same way). (PDF)</p

mACE2-hACE2 mice.

(A) The transgenic construct used for generation of mACE2-hACE2 mice containing the mACE2 promoter and hACE2 followed by a poly A. (B) Genotyping transgenic mice, a 374 bp PCR fragment indicates the presence of hACE2. (C) mACE2-hACE2 mice (n = 16 on day 0) were weighed at the indicated times, with 4 mice euthanized on days 2, 4, 6 and 10. K18-hACE2 mice were infected with the same dose of SARS-CoV-2QLD02 (n = 8) and were all euthanized on day 5. (D) Nasal turbinate tissue titers on the indicated days post infection. Limit of detection ≈2 log10CCID50/g (ND–ND detected). (E) Lung H&E 6 dpi showing loss of alveolar spaces (a—remaining spaces) (left), cellular infiltrates (white dashed ovals), smooth muscle hypertrophy/hyperplasia (h), and bronchial sloughing (black dashed oval). (PDF)</p

Number of RNA-Seq reads aligned to protein-coding genes.

For each sample, reads were aligned to either the mouse GRCm39 M26 or human GRCh38 v37 reference genome using STAR. Reads aligning to protein-coding genes were counted using RSEM. The total number of reads aligned to protein coding genes are shown for each sample. Due to low coverage in Blanco-Melo infected samples, read data were not re-analysed for this dataset. Instead, differential expression results were obtained from the original publication [43]. (PDF)</p

Table_1_Rapid inactivation and sample preparation for SARS-CoV-2 PCR-based diagnostics using TNA-Cifer Reagent E.xlsx

RT-qPCR remains a key diagnostic methodology for COVID-19/SARS-CoV-2. Typically, nasal or saliva swabs from patients are placed in virus transport media (VTM), RNA is extracted at the pathology laboratory, and viral RNA is measured using RT-qPCR. In this study, we describe the use of TNA-Cifer Reagent E in a pre-clinical evaluation study to inactivate SARS-CoV-2 as well as prepare samples for RT-qPCR. Adding 1 part TNA-Cifer Reagent E to 5 parts medium containing SARS-CoV-2 for 10 min at room temperature inactivated the virus and permitted RT-qPCR detection. TNA-Cifer Reagent E was compared with established column-based RNA extraction and purification methodology using a panel of human clinical nasal swab samples (n = 61), with TNA-Cifer Reagent E showing high specificity (100%) and sensitivity (97.37%). Mixtures of SARS-CoV-2 virus and TNA-Cifer Reagent E could be stored for 3 days at room temperature or for 2 weeks at 4°C without the loss of RT-qPCR detection sensitivity. The detection sensitivity was preserved when TNA-Cifer Reagent E was used in conjunction with a range of VTM for saliva samples but only PBS (Gibco) and Amies Orange for nasal samples. Thus, TNA-Cifer Reagent E improves safety by rapidly inactivating the virus during sample processing, potentially providing a safe means for molecular SARS-CoV-2 testing outside traditional laboratory settings. The reagent also eliminates the need for column-based and/or automated viral RNA extraction/purification processes, thereby providing cost savings for equipment and reagents, as well as reducing processing and handling times.</p