Network analysis of Down syndrome and SARS-CoV-2 identifies risk and protective factors for COVID-19

doi:10.21203/rs.3.rs-34625/v1

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Research Article

Network analysis of Down syndrome and SARS-CoV-2 identifies risk and protective factors for COVID-19

https://doi.org/10.21203/rs.3.rs-34625/v1

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posted 12 Jun, 2020

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Background: SARS-CoV-2 has spread uncontrollably worldwide while we still ignore how particularly vulnerable populations, such as Down syndrome (DS) individuals are affected by the COVID-19 pandemic. Individuals with DS have more risk of infections with respiratory complications and present signs of auto-inflammation. They also suffer from multiple comorbidities that are associated with poorer COVID-19 prognosis in the general population. All this might place DS individuals at higher risk of SARS-CoV-2 infection or poorer clinical outcomes.

Methods: In order to get insight into the interplay between DS genes and SARS-cov2 infection and pathogenesis we retrieved the genes belonging to the molecular pathways involved in COVID-19 and the host proteins interacting with viral proteins from SARS-CoV-2. We therefore analyzed the overlaps of these genes with HSA21 genes, HSA21 interactors and other genes consistently differentially expressed in DS (using public transcriptomic datasets) creating a DS-SARS-CoV-2 network.

Results: We detected COVID-19 protective and risk factors that might affect the susceptibility of individuals with DS both at the infection stage and in the progression to acute respiratory distress syndrome.

Conclusion: Our analysis suggests that at the infection stage DS individuals might be more susceptible to infection due to triplication of TMPRSS2, that primes the viral S protein for entry in the host cells, even though the anti-viral interferon I signaling is upregulated in DS and this might increase the initial anti-viral response. In the second pro-inflammatory immunopathogenic phase of the infection, the prognosis for DS patients might worsen due to upregulation of inflammatory genes that might favor the typical cytokine storm of COVID-19. We also detected strong downregulation of the NLRP3 gene, critical for maintenance of homeostasis against pathogenic infections, possibly leading to bacterial infection complications.

Bioinformatics

Epigenetics & Genomics

Down syndrome

COVID-19

SARS-CoV-2

coronavirus

interferon

TMPRSS2

network analysis

transcriptomics

meta-analysis

trisomy 21

The recent outbreak of the novel severe acute respiratory syndrome SARS-coronavirus 2 (SARS-CoV–2) has caused more than 3,5 million cases of COVID–19 and is responsible for at least 252,444 deaths (05/05/20), with figures expected to rise in the coming months.

COVID–19 is particularly severe in elderly individuals (>60 years old), especially those with chronic medical conditions (i.e., hypertension, diabetes, cardiovascular disease, chronic respiratory disease or cancer). However, fast spread of SARS-Cov–2 has precluded the study of other possibly vulnerable populations, such as individuals with intellectual disability (ID). Down syndrome (DS) is the most common genetic form of ID. Individuals with DS suffer from multiple comorbidities, such as obesity, type I diabetes or congenital heart disease (CHD), that are associated with poorer COVID–19 prognosis in the general population. Hypotonia, developmental delay, obstructive sleep apnea, craniofacial anomalies, immune deficiency, and cardiac problems, as well as gastroesophageal reflux, are also frequent in DS and contribute to the increased risk of respiratory tract infections. They typically develop more severe complications during other viral respiratory infections such as influenza(1) and respiratory syncytial virus (RSV)(2), including pneumonia, hospitalization, intubation and greater mortality due to secondary bacterial infections. Another biological risk factor is immune response, which is substantially impaired in DS(3). Deficits in trisomic individuals include functional anomalies in a variety of immune cells (i.e., T- and B-cells, monocytes and neutrophils), and, of importance to immune response and vaccine-development, suboptimal antibody responses(4). A number of studies found reductions in T and B cells, and increases in serum inflammatory cytokine levels in peripheral blood of people with DS, suggesting suboptimal responses to immunization(5). In the general population, severe COVID–19 patients typically show increased cytokine levels (IL–6, IL–10, IL–2, IL–7 and TNF-α)(6), lymphopenia (in CD4+ and CD8+ T cells), and decreased IFN-γ expression in CD4+ T cells are associated with severe COVID–19 and the so-called “cytokine storm”(7), an excessive immune response to external stimuli that predicts worse COVID–19 prognosis(8) and correlates with the severity of pathogenic coronavirus infections(9). DS is characterized by an ‘inflammatory priming’ of immune cells, which is in line with the elevated levels of cytokines(10) in blood in the absence of viral infections(11). Furthermore, the interferon response (IFN), key for initiating and amplifying cytokine release, is chronically overactive in DS(12) and Interferon Alpha and Beta Receptor Subunits 1 and 2 (IFNAR1 and IFNAR2), that form a heterodimeric interferon receptor, are also triplicated in trisomic cells. Finally, recent evidence suggests that an increase in Transmembrane protease, serine 2 (TMPRSS2) expression results in increased infection/establishment of infection with lower viral titers. Cell entry of coronaviruses depends on binding of the viral spike (S) proteins to cellular receptors and on S protein priming by host cell proteases. The TMPRSS2 gene is located on Chr21q22, which is a region specifically rich in genes important for Down syndrome’s pathophysiology(13, 14). It encodes for a serine protease that proteolytically activates the S protein for its interaction with the angiotensin-converting enzyme 2 (ACE2) as the entry receptor, allowing the entrance of the SARS-CoV–2 virus in the host cell(15). In fact, a TMPRSS2 inhibitor approved for clinical use blocked entry has been proposed as a treatment option(14). All these factors could lead to a more vulnerable scenario in DS, in theory induced by the triplication of the genes encoded by human chromosome 21 (HSA21). However, gene expression studies showed that, even if HSA21 shows the highest percentage of differentially expressed genes, the transcriptome deregulation extends genome wide, with many non-HSA21 genes upregulated or downregulated(16).

To understand the potential differential impact of COVID19 in individuals with DS compared to the general population, we here mapped the transcriptomic changes induced by the trisomy onto the pathways and proteins known to be affected by SARS-CoV–2. We reasoned that besides TMPRSS2, other genes consistently deregulated in DS could define genetic risk factors of DS-SARS-CoV–2 comorbidity. We analyzed 69 DS transcriptomic and proteomic studies and we detected that genes mapping on chromosome 21 (HSA21 genes) were consistently deregulated across different tissues and states. We also reasoned that the levels of proteins interacting with or regulated by HSA21 genes (HSA21 neighbors) are more likely to be deregulated in cases of trisomy 21.

Sources

In order to get insight on the interplay between DS genes and SARS-cov2 infection and pathogenesis we looked at the pathways related with infection with corona-virus as reported in WikiPathways (https://www.wikipathways.org/index.php/Portal:Disease/COVIDPathways) and at the recently published SARS-CoV–2-Human Protein-Protein Interactome(17). Protein-protein interactions were retrieved from the STRING database version 10, using a score cut-off > = 215 for the network, and >900 for the HSA21 neighbors.

Transcriptomic data were extracted from Gene Omnibus Expression GEO, and Array Express. Supplementary Table 1 lists all datasets that were used in this analysis, with their references.

Analysis of micro-array data

For the analysis micro-array data–quality check, background correction, and normalization–we used two different R packages, because of the different platforms used.

“Affy”(18) and “oligo”(19) for the analysis of Affymetrix GeneChip data at the probe level

“Beadarray”(20) for illumina bead-based arrays

In most cases the differential expression analysis of microarray data was performed using moderated t-statistics with the package limma(21, 22). When subsetting the samples in different contrasts was not possible for sample size related problem, we used the function duplicateCorrelation function from the statmod package(23), for blocking for other variables such as sex and age.

Analysis of RNA-seq data

All RNA-sequencing experiments were from illumina. In this case reads were downloaded from the Sequence Read Archive using fastq-dump and mapped to the mm10 for mouse and GRCh38.p12 for human samples. For gene annotation of the mapped reads, we used gencode.vM17 for mouse and gencode.v28 for human samples. Differential expression analysis between trisomic and wild type samples was performed with DESeq2(24), blocking for external factors (sex, age, etc.) when possible.

Gene annotation

For the annotation of the ensembl gene identifiers (IDs) we used the biomaRt package, using the getLDS function to map mouse ensembl gene IDs to their human orthologous; the org.Mm.eg.db and org.Hs.eg.db packages(25, 26).

For the annotation of the microarray probes we used the following R packages:

Affymetrix Human Genome U133 Set: hgu133a.db

Affymetrix Human Genome U133 Plus 2.0 Array: hgu133plus2

Affymetrix Murine Genome U74v2: mgu74av2.db

Affymetrix huex10 annotation data: huex10sttranscriptcluster.db

Affymetrix hugene10 annotation data: hugene10sttranscriptcluster.db

Affymetrix hugene20 annotation data: hugene20sttranscriptcluster.db

Affymetrix mogene10 annotation data: mogene10sttranscriptcluster.db

Affymetrix Mouse Genome 430 2.0 Array annotation data: mouse4302.db

Illumina HumanWG6v2 annotation data: illuminaHumanv2.db

Illumina MouseWG6v2 annotation data: illuminaMousev2.db

Illumina HumanHT12v4 annotation data: illuminaHumanv4.db

Illumina HumanHT12v3 annotation data: illuminaHumanv3.db

Detection of consistently differentially expressed genes

We selected as differentially expressed (DE) genes the top 500 genes changing at least 1.5 times with an Benjamini adjusted p-value <0.05. In order to define a gene as “consistently DE” we analyzed the heavy tail distribution of differential expression occurrences across all genes and selected all genes occurring at least 4 times, which corresponded to less than 5% of the distribution. Graph-based analysis and visualization was performed with the igraph package.

To get insight into the interplay between genes consistently deregulated in DS and SARS- cov2 infection and pathogenesis we built a DS-SARS-CoV–2 network. We considered “DS genes”, HSA21 genes, HSA21 neighbors, and non-HSA21 genes found consistently deregulated in at least 4 DS transcriptomic studies (Supplementary Table 1). We then mapped these genes on COVID–19 related pathways (as reported in WikiPathways) and on the interactome(17) coming from a mass-spectrometry affinity study with proteins of SARS-CoV–2(17) ending up with 118 nodes that we connected based on their functional and physical annotated interactions (Figure 1, Supplementary Figure 1).

Approximately half of the nodes in the DS-SARS-CoV–2 network (55 nodes) contained proteins involved in host Covid–19 related pathways, while the other half (59 nodes) contained host proteins known to interact with viral proteins. Six of these nodes were HSA21 genes, and 92 HSA21 neighbor genes. Overall, 26 genes were differentially expressed in at least 4 DS transcriptomic studies, 16 of which were neither a HSA21 gene nor a HSA21 gene neighbor (Figure 1). These genes could determine a different susceptibility to Covid–19 infection.

In this network we detected several pathway connected with COVID–19 that are affected in DS that will be discussed in the following sections (Figure 2).

Mechanisms of viral entry

Trisomy of TMPRSS2 might enhance virus activation

TMPRSS2 was upregulated across the different DS studies, with a median fold change of 1.59, in cortical tissue(28, 29), white blood cells(30), lymphoblastoid cell lines(28), iPSCs(31), mouse fetal liver and placental tissue(32) (Figure 3A). This suggests that the proteolytic activation of the viral spike protein for its interaction with the receptor ACE2 (angiotensin I converting enzyme 2), would be favored in DS, allowing increased entrance of the SARS-CoV–2 virus in the host cell. Conversely, we found downregulation of the endosomal protease cathepsin B that, similarly to cathepsin L, could also prime S-protein, suggesting that the preferential virus entry in DS is through TMPRSS2. In fact, only TMPRSS2 activity is essential for viral spread and pathogenesis in the infected host whereas CatB/L activity is dispensable(33–35). However, ACE2 itself is not consistently differentially expressed, and it is not a HSA21 neighbor. We found ACE2 upregulated in DS induced pluripotent stem cells (iPSCs) (homo sapiens) (31), downregulated in peripheral blood from DS individuals(36); slightly upregulated in the hippocampus of the DS model Ts1Cje (6–7 months old)(37), covering most of the region orthologous to human chromosome 21q22, DS human postnatal inferior temporal cortex(29) and adult dorsolateral prefrontal cortex(29), and slightly downregulated in post-mortem dorsolateral DS prefrontal cortex(38) (Figure 3B).

Other ACE2-related mechanisms

Interestingly, in DS we detected an upregulation of the bradykinin receptor B1 (BDKRB1)(29, 39), one of the HSA21 neighbors revealed in our analysis, and of the metalloprotease CPA3, that is normally upregulated in asthma patients. BDKRB1 upregulation, as part of the kallikrein-kinin system could determine a higher susceptibility in DS individuals to ARDS. Following viral binding, ACE2 expression and activity is eventually downregulated by the virus through multiple mechanisms, preventing it from performing its usual function in states of health(40). The downregulation of ACE2 signaling induces the kallikrein-kinin system which is activated during inflammatory conditions with vascular-alveolar fluid extravasation, leukocyte extravasation and recruitment to the lung and acute respiratory distress syndrome (ARDS), lung injury and pneumonia(41).

The interferon signaling is activated in DS

We detected 21 proteins in our network belonging to the IFN-I signaling pathway, and several of them were found upregulated in DS transcriptomic datasets: IFNAR1(12, 29, 42, 43), IFNAR2(12, 29, 32, 42, 44–47), IFNA2(29), IFNB1(29), STAT1(42), STAT2(48),

OAS1(12, 49), OAS2(12, 50), NF- KBIA(36, 38, 49). Of those, two genes present in three copies in trisomic cells are the Interferon Alpha and Beta Receptor Subunit 1 and 2 (IFNAR1 and IFNAR2), that form a heterodimeric interferon receptor. IFNAR1 and IFNAR2 initiate the innate antiviral immune response, that leads to the phosphorylation of STAT1-STAT2, that dimerize and activate transcription of inflammatory genes in the nucleus. OAS1 and OAS2 are two HSA21 neighbor proteins, activated by the interferon signaling pathway, leading to activation of the RNAseL that, in turn, leads to the degradation of the viral genome(51). On the other hand, Nuclear Factor kappa B (NF- kB) inhibitor alpha, is activated by the inflammatory NF- kB signaling and inhibits the translocation of the same NF- kB into the nucleus. With the exception of IFNA1 expression, type I interferons (IFNA2 and IFNB1) were all upregulated, indicating that the axis IFNAR-STAT-OAS is upregulated in Down syndrome.

A recent paper shows that SARS-CoV–2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues(52). Therefore, we predict that virus entry might be significantly increased in DS patients that have both an increased interferon signaling and triplication of the protein S-priming through TMPRSS2.

Tightly connected to this pathway, the MAPK signaling acts as an integration point of several biological processes (53). In this pathway, we found 7 genes downregulated (BCL2(28, 30, 36, 54), FOS(29, 39, 46, 54–56), IFITM2(36, 54, 56), MAPK3(36, 39, 56), MAPK10(29, 54), MAPK13(30, 46, 57), MAPK14(29, 30)) and 5 upregulated (MAPK1(42, 58), MAPK11(12, 59), IFITM1(29, 38, 42, 49), IFI27(29, 36, 48, 49, 60) and BST2(12, 29, 38, 46, 48, 54)). Interestingly, some of these proteins, such as Interferon Induced Transmembrane Protein 1 (IFITM1), Interferon Induced Protein 27 (IFI27) and Bone marrow stromal antigen 2 precursor (BST2), are all interferon-induced proteins with antiviral properties. Specifically, IFITM1 is active against multiple viruses, including SARS-CoVs(61), preventing the viral fusion after endocytosis and the release of viral contents into the cytosol.

The viral Orf3a protein from SARS-CoV can bind TRAF3 and activate the NLRP3 inflammasome(62), leading to the cytokine storm. Given the higher basal inflammation in DS we would have expected the inflammasome to be upregulated. Instead, we detected a strong downregulation of the NLRP3 gene(36), critical for maintenance of homeostasis against pathogenic infections, as previously identified(63), along with lower levels of the gene for the NF- kB subunit RELA. Actually, even if the IFN-I signaling in the beginning induces an antiviral response, it eventually exerts an anti-inflammatory action inhibiting the NLRP3 inflammasome through STAT1. Although this could potentially be beneficial in later stage to shut inflammation down, it could also be one of the reasons why DS patients with influenza often manifest bacterial infection complications (64).

Moreover, DS individuals could be more susceptible to late onset complication such as lung fibrosis upon COVID19 infection, because they upregulate some of the cytokines responsible for the so-called “cytokine storm”. Specifically, we detected upregulation of the chemokine CXCL10(42) that induces chemotaxis and stimulation of monocytes, and of Interleukin 10(29). IL10 is an anti-inflammatory cytokine necessary for regulated resolution of inflammation. However, IL10 recruits fibrocytes and activates M2 macrophages in a CCL2/CCR2 axis and mice overexpressing IL10 show lung fibrosis(65).

Finally, we found upregulated ZAP70 (29), a tyrosine kinase that regulates motility, adhesion and cytokine expression of mature T-cells, as well as thymocyte development. A recent study found the SARS-CoV–2 nsp9 and nsp10 interact with NKRF(66), that inhibits IL–8 and IL–6 induction by competing with NF-КB for promoter binding. This interaction would lead to IL8/IL6 induction, and, among other, inhibition of ZAP70.

Apoptosis is inhibited in DS

As regards apoptosis, it is known that the apoptotic effect of SARS-CoV is mediated by its M protein through inhibition of the proto-oncogene AKT1(67). Interestingly, AKT1 is a HSA21 neighbor and is consistently upregulated(58). We therefore speculate that trisomic cells might be less sensitive to the apoptotic effect of coronaviruses.

Apoptosis can also be induced through endoplasmic reticulum (ER) stress. As a matter of fact, coronavirus replication is associated with the endoplasmic reticulum and this often induces ER stress, with subsequent activation of the unfolded protein response (UPR). UPR leads to the blockade of protein synthesis, and subsequent apoptosis through EIF2AK2(68). EIF2AK2 is an interferon-induced serine-threonine protein kinase (PKR), that, once activated by the viral RNA, phosphorylates and inhibits the initiation factor eIF2α, preventing viral replication. The coronavirus’ non-structural related protein 15 is an endoribonuclease that can inhibit EIF2AK2 preventing a premature block of protein synthesis upon viral infection(69). However, in DS, EIF2AK2 levels are elevated(29, 37- 39), and therefore, once again, trisomic cells might be more resistant to this inhibition. Supporting this, the serine/threonine-protein phosphatase PP1-alpha catalytic subunit PPP1CA, one of the three catalytic subunits of protein phosphatase 1 (PP1), is also downregulated in DS(29, 39), leading again to the translation shutoff mediated by phosphorylated eIF2α.

Host proteins interacting with viral proteins are enriched in viral life cycle and tight junction

When we analyzed the portion of DS-SARS-CoV–2 network of host proteins interacting with viral proteins (squared nodes) we found that they were enriched in two main categories: the biological process “viral life cycle” and the KEGG pathway “tight junction”. Among the first are worth mentioning the nucleoporin NUP62(30, 36) and NUP210(36, 39), that are downregulated in DS. Upon viral infection, nucleoporins are normally degraded to suppress innate immune responses, and improve viral replication and transmission(70). This may indicate altered virus-host interactions in DS leading to more efficient viral innate immune evasion(71). As regards the tight junction pathway, we detected 4 genes upregulated and 2 downregulated in DS. Even though the net effect of these deregulations cannot be predicted, interestingly, many viruses, including coronaviruses, disrupt epithelial tight junctions of the respiratory tract that serve as a barrier to invaders(72).

Another interesting protein is a disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1) that interacts with the viral helicase/triphosphatase nsp13. While the significance of this interaction is unknown, it is known that ADAMTS1 is triplicated and upregulated in DS, both in blood datasets and in DS lung(12, 27, 36, 39, 73), where it contributes to the global anti-angiogenic milieu leading to higher risk for developing pulmonary arterial hypertension (PAH) in infants with DS. Moreover, it induces fibrosis in myocardial viral infection(74, 75).

Antiviral properties of EGCG

Compared to SARS-CoV, SARS-CoV2 seems to be much more infectious. This might be due to the ability of the virus to be primed not only by TMPRSS2 but also by FURIN.

EGCG, the main polyphenol of green tea, showed some beneficial effects on cognition in a phase II clinical trial with DS individuals(76). Interestingly, EGCG perfectly fits in the FURIN pocket and is therefore predicted to be a FURIN inhibitor(77–79). Concordantly, lipophilic EGCG derivatives showed antioxidant and antiviral properties(80). Therefore, the use of EGCG in individuals with DS, might be considered in the context of this world pandemic.

COVID–19 is a new disease and there is limited information regarding risk factors for infection and worse prognosis. It has been suggested that individuals with DS should be considered a vulnerable at-risk population for severe COVID–19 as recognized by the Trisomy 21 Research Society recommendations (www.t21rs.org). Based on currently available information, DS individuals are in fact easily predicted to be at higher risk for severe illness from COVID–19, as they present increased prevalence of medical comorbidities associated with worse prognosis, including diabetes, cardiovascular disease, and respiratory problems. Besides, individuals with DS present clinical history of infections, increased rates of hospitalization upon respiratory viral infections, and higher mortality rates from pneumonia and sepsis and the immune dysregulation caused by trisomy 21 may result in an exacerbated cytokine release syndrome relative to euploid population. Indeed, previous work reported increased pro-inflammatory markers in plasma of a DS mouse model (Ts65Dn)(81), intrinsic lymphopenia(82) and elevated levels of pro-inflammatory cytokines, IL–6, MCP–1, IL–22 and TNF-α in individuals with DS(12).

Our analysis showed that a number of host-virus interactions are altered in DS, including viral entry and viral spreading that could be significantly different in DS individuals compared to the general population (Figure 4). Viral entry could be facilitated in DS thanks to TMPRSS2 triplication that leads to increased activation of the viral S protein and also thanks to tight junctions’ downregulation, since tight junctions hamper virus’ endocytosis. Another protease able to prime the S protein is FURIN, which can be inhibited by EGCG, a polyphenol that has been used in DS individuals. Other transcriptional disturbances detected in DS could influence inflammation and viral replication such as triplication of IFNAR1/2 results in activation of the interferon (IFN) I response and subsequently of the JAK/STAT/OAS pathway, that in turn, leads both to increased inflammation but also to activation of the RNAseL that could degrade the viral RNA. We detected upregulation in DS of the interferon-induced antiviral proteins IFI27, BST2, IFITM1, that inhibit viral genome release. ER stress, the unfolded protein response, and the IFN signaling lead to upregulation of EIF2AK2 that phosphorylate/inactivated EIF2α leading to the block of protein synthesis. The virus inhibits EIF2AK2 to allow viral replications, while in DS upregulation of EIF2AK2 together with downregulation of the phosphatase PPP1CA might block protein synthesis. However, the downregulation of nucleoporins in DS might, instead, favor viral replication. The viral protein M inhibits AST1 leading to apoptosis which could be counteracted in DS by AKT1 upregulation.

Interestingly, some molecular processes seem to be protective against Covid–19 in DS. Interferon signaling, for example, leads to downregulation of the NLRP3 inflammasome. However, this might make DS patients more susceptible to post-viral complications, such as bacterial infections.

Other potentially protective molecular signatures we detected in DS are:

the upregulation of antiviral proteins such as IFITM1, IFI27 and BST2, probably mediated by interferon I signaling
resistance to the apoptotic effect of coronaviruses (that might favor however they replication in a first moment)

Resistance to the prevention of the block of protein synthesis mediated by the virus

In fact, at first glance the triplication of IFNAR1 and IFNAR2 with consequent upregulation of the anti-viral Type I interferon signaling might suggest higher defenses in DS individuals in a first stage of the infection. However, this might be overcome by overexpression of TMPRSS2, with subsequent priming of the viral Spike protein for the binding with the ACE2 receptors. TMPRSS2 overexpression was detected in several tissues, which may also favor the appearance of a number of extra-pulmonary COVID–19 effects. Besides, the interferon signaling itself leads to upregulation of the ACE2 receptors in airway epithelial cells as shown in the general population. Both these processes would lead to a facilitated virus entry. Unfortunately, in our datasets we did not have the transcriptome of airway epithelial cells to check if in DS there are more ACE2 receptors.

Trisomy of HSA21 could also facilitate some mechanisms involved in the severity of COVID–19. Once the coronavirus spreads enough the cytokine storm in DS might have more severe consequence due to higher levels of chemokines (such as CXCL10), inducing chemotaxis and stimulation of monocytes, and Interleukin 10 (IL10), that even though it is classified as an anti-inflammatory interleukin, it recruits fibrocytes and activates M2 macrophages and may lead to later lung fibrosis. As such, the cytokine storm could be potentiated in DS as a result of increased levels of the chemokine CXCL10 and IL10 that leads to increased fibrocytes and M2 macrophages activation leading to lung fibrosis.

Moreover, the upregulation of the IFN signaling leads to increased bradykinin signaling (via ACE2 upregulation), that together with higher levels of CPA3 and ADAMTS1 can lead to lung fibrosis. The IFN signaling eventually downregulates the NLRP3 inflammasome (that is normally activated by the viral orf3a) which is downregulated in DS, leading to higher susceptibility to secondary bacterial infections.

On balance, we consider that DS individuals might be particularly at risk during this pandemic, both at the stage of infection and for the prognosis once the cytokine storms begin (Figure 4). Future epidemiological data would help to investigate this hypothesis.

ACE2, angiotensin I converting enzyme 2

ADAMTS1, a disintegrin and metalloproteinase with thrombospondin motifs 1 ARDS, acute respiratory distress syndrome

BST2, Bone marrow stromal antigen 2 precursor BDKRB1, bradykinin receptor B1

DS, Down syndrome

ER, endoplasmic reticulum

HSA21, human chromosome 21

iPSC, Induced pluripotent stem cells

IFNAR1 and IFNAR2, Interferon Alpha and Beta Receptor Subunit 1 and 2 IFI27, Interferon Induced protein 27

IFITM1, Interferon Induced Transmembrane protein 1 IL10, Interleukin 10

NFKBIA, Nuclear Factor kappa B inhibitor alpha PAH, Pulmonary Arterial Hypertension

SARS-CoV–2, Sever acute respiratory Syndrome Coronavirus 2 S, Spike

UPR, unfolded protein response

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

Available upon request

Competing interests

We declare no competing interest

Funding

The lab of MD is supported by the CRG Severo Ochoa excellence grant (SEV–2016–0571); the CIBER of Rare Diseases; and Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement de la Generalitat de Catalunya (Grups consolidats 2017 SGR 926). We also acknowledge the support of the Spanish Ministry of Science and Innovation and Universities (MSIU) to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya.

Authors’ contributions

ID performed the analysis, wrote the manuscript and performed the figures. MD proposed the original idea and wrote the manuscript.

Acknowledgements

We acknowledge Professor Andre Strydom for giving feedback on the manuscript and the Trisomy 21 Research Society for sharing the statement on Covid–19.

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Network analysis of Down syndrome and SARS-CoV-2 identifies risk and protective factors for COVID-19

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Version 1

Abstract

Figures

Background

Methods

Sources

Analysis of micro-array data

Analysis of RNA-seq data

Gene annotation

Detection of consistently differentially expressed genes

Results

Mechanisms of viral entry

Trisomy of TMPRSS2 might enhance virus activation

Other ACE2-related mechanisms

The interferon signaling is activated in DS

Apoptosis is inhibited in DS

Host proteins interacting with viral proteins are enriched in viral life cycle and tight junction

Antiviral properties of EGCG

Discussion

Conclusion

List Of Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Funding

Authors’ contributions

Acknowledgements

References

Supplementary Files

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