A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19

Abstract

Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders.

Fig 1. Reconstructed network paths from SARS-CoV-2 proteins to HLH genes.

This network shows all the paths connecting the SARS-CoV-2 proteins to the HLH proteins (genes). The red nodes represent the SARS-CoV-2 proteins, the yellow nodes are the human host proteins that directly interact with SARS-CoV-2 proteins, the green nodes are the intermediate interacting host proteins, and the blue nodes denote the target HLH proteins (genes). The edge weights in the network represent the interaction strength (or probability).

Fig 2. HLH genes are significantly enriched within the SARS-CoV-2 host protein interactome.

A connectivity score was calculated for each of the genes of interest (e.g. HLH genes in this work). We further analyzed the network connectivity of all genes to the SARS-CoV2 proteins (or randomly picked genes). With these two analyses, we ended up with two lists of connectivity scores: SA (for HLH genes) and SB (for all background genes). Then, we calculated the statistical significance (p-value) using a one-sided Mann-Whitney rank test to determine whether SA is significantly smaller than SB (stronger connectivity). SA significant p-value implies that the list of proteins (genes) of interest is “significantly connected” to the SARS-CoV2 proteins. A) A list of 10 known COVID19 related genes (differential genes in severe COVID19 patients) have statistically stronger connections to the SARS-CoV-2 proteins compared with all background genes (p-value = 0.017). B) A list of 100 random genes does not “significantly” connect to the SARS-CoV-2 proteins. C) The 23 Male infertility genes do not “significantly” connect to the SARS-CoV-2 proteins. D) The 11 HLH genes have statistically (p-value = 0.00821) stronger connections to the SARS-CoV-2 proteins (compared with all background genes). E) A list of 45 vascular angiogenesis genes linked to both H1N1 and SARS-CoV-2 pulmonary infections significantly (p-value = 4.89e-5) connect to the SARS-CoV-2 proteins. The 11 HLH genes have the smallest mean/median connectivity score compared to all the gene lists analyzed. Please note that the p-values here only indicate whether the input gene lists have significantly smaller connectivity scores than all the background genes, and they could be affected by the size of the gene list. To compare the strength of the “connectivity” of input gene lists to SARS-CoV-2 proteins, we should also look at the mean (represented by a green triangle) and the median (represent by a vertical line) connectivity scores.

Fig 3. Differential expression of HLH genes in COVID19 associated health conditions.

Gene expression of lung cells under different health conditions that have been associated with COVID19[2, 28, 29] was analyzed. The health conditions include Sex (Male Vs. Female healthy individuals), Smoking status (current smokers vs never smokers), Cancer (Lung cancer vs. Normal), COPD (COPD vs. Normal), Diabetes (Type 1 Diabetes vs. Normal), Hypertension (Severe PH: mPAP>40mmHg vs. without PH: mPAP<20mmHg), and Aged (> = 60 years vs. <60 years healthy individuals). All the genes in these datasets were sorted under each condition, based on the log fold change (condition vs control). Genes whose fold change was among the top 25% were classified as high (H) and those whose fold change was among the bottom 25% were classified as low (L). Finally, the 11 HLH genes were assessed to determine whether they are among the H or L genes in each condition. The red blocks represent HLH genes that were highly expressed (H, top 25%) in condition (vs. control) while the blue blocks represent HLH genes that were lowly expressed (L, bottom 25%) in condition (vs. control). We compared the HLH genes and all background genes in terms of H/L expression under various conditions. We first counted the number of H/L (differentially expressed between the condition and control) for each of the HLH genes, and then for each of the background genes. Next, we used a one-sided Mann-Whitney rank test to determine whether the HLH genes have larger absolute fold changes, (i.e, are differentially expressed), in COVID19 associated conditions compared to all the background genes significantly (p-value<0.05). The average number of H/L conditions for HLH genes (red or blue blocks) is 3.82, which is significantly larger (one-sided rank-sum test p-value = 1.01e-4) than the average number of H/L conditions for all genes (1.70).

Fig 4. Expression of HLH genes in Control, inactive sJIA and active sJIA Neutrophils.

Gene expression of HLH-SARS-CoV-2 and positive COVID-19 genes (S1 Table) in sJIA was calculated from GEO series GSE122552[54]. Data was mapped to the hg38 genome and normalized by reads per kilobase per million (RPKM). Values for HLH genes were displayed for control and sJIA patients that were either in remission (inactive sJIA) or had active symptoms (active sJIA).

Fig 5. Model of NET-mediated endothelial damage contributing to pulmonary vascular thrombosis in severe COVID-19.

Infection by SARS-CoV-2 in vulnerable population will lead to hyperinflammation either from underlying genetic mutations, specific epigenetic landscapes or external factors, that will result in the increase circulation of acute phase reactants such as CRP and pro-inflammatory cytokines associated with neutrophilia like IL-6, IL-17A/F and CXCL8 (IL-8). IL-17A activates the endothelium to induce neutrophil adhesion [105], where the increase in CRP can trigger the release of NETs, resulting in damage to the endothelium as well as aggregation and activation of platelets. Additionally, the presence of SARS-CoV-2 E protein in type II pneumocytes could disturb the surfactant cargo via its interaction with AP3B1, leading to impaired secretion of SP-D and greater NET formation by septal and intra-alveolar neutrophils increasing the risk of thrombosis in the pulmonary microvasculature. In some predisposed patients the combinations of these mechanisms will lead to severe COVID-19 complications. The identification of mediators of this pro-coagulation cascade is essential in achieving the two-fold task of identifying vulnerable populations and developing a personalized medicine approach.