Originally Posted By FlashMan-7k:
Ok, now THIS is attention getting:

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1023849/Vaccine_surveillance_report_-_week_40.pdf

From page 13 (numbered 12 on the pdf) ... Column labelled "Rates among persons vaccinated with 2 doses (per 100,000)"

Negative efficacy vs catching the CCP crud, depending on your age group.  Yep... think I'm gonna be watching next month's report to see if this isn't a blip...
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Ok, I give up, the stupid forum upload a jpg thing isn't working and won't tell me why. Managed a reverse search and found a copy on the net:

https://westernrifleshooters.us/wp-content/uploads/2021/10/oops-uk-1024x555.png

Originally Posted By exponentialpi:
Welcome to 2021 gents.  It keeps giving 2020 a run for it's money.

https://www.ar15.com/media/mediaFiles/200878/RoundandRound_JPG-2142317.JPG

Originally Posted By FlashMan-7k:
Something to keep an eye out for:

My Johns Hopkins research team is leading a long-term study of natural immunity because the NIH and CDC are not doing it. They have $50 billion and 30,000 employees and yet can't seen to conduct one of the most important studies we need done to inform the public.

— Marty Makary MD, MPH (@MartyMakary) October 27, 2021

As people continue to ask if natural immunity is durable, public health officials continue to say "we don't know".  Well study it! Our health agencies continue to fail us time and time again.

In the next several weeks, we'll find out the results of our Hopkins study.

— Marty Makary MD, MPH (@MartyMakary) October 27, 2021

Originally Posted By Obelix45:

The CDC is beyond worthless.  They are intentionally undermining public health and scientific knowledge.  The fact that the public doesn't notice or care is disheartening to say the least.

Originally Posted By BlackTuono:
We have a new paper to digest:

https://www.mdpi.com/1999-4915/13/10/2056/htm

From the journal Viruses.

Synopsis - in vitro experiments have confirmed that spike proteins 1) concentrate around the nucleus in a cell 2) interfere with BRCA1 and other natural DNA repair mechanisms including 3) V(D)J recombination which is is an essential part of B and T cell development and building adaptive immunity (https://en.wikipedia.org/wiki/V)

This gives a putative mechanism for two anecdotal claims that have been circulating, mainly that T cells (CD4+ and CD8 in particular) get incredibly low for some people after the second and subsequent doses, and that cancer cell activity is heightened because of the lack of immune response. In this case it isn't just the response of immune cells that is being moderated but the intracellular action to repair DNA.

In case you didn't know, you all have cancer inside of you, pretty much guaranteed at any given time. Cells proliferate and encounter DNA replication errors when they split, leading to cancer eventually. Our bodies employ mechanisms to repair nicked or miscopied DNA at an intracellular level constantly. Showing that T cells can be affected is one thing, but the disruption of such a fundamental biochemical repair process is alarming when you consider we are instructing cells to churn out the spike protein itself. The authors make this point in their conclusions and propose it as a putative mechanism for some of the shot-related side effects being observed.
e
Are we going to see a lot more cancer and AIDS-like symptoms? Hopefully not in most cases, but some people are going to get unlucky and have this effect them chronically, especially if we enter a regime where regular boosters become the norm.

Originally Posted By BlackTuono:
We have a new paper to digest:

https://www.mdpi.com/1999-4915/13/10/2056/htm

From the journal Viruses.

Synopsis - in vitro experiments have confirmed that spike proteins 1) concentrate around the nucleus in a cell 2) interfere with BRCA1 and other natural DNA repair mechanisms including 3) V(D)J recombination which is is an essential part of B and T cell development and building adaptive immunity (https://en.wikipedia.org/wiki/V)

This gives a putative mechanism for two anecdotal claims that have been circulating, mainly that T cells (CD4+ and CD8 in particular) get incredibly low for some people after the second and subsequent doses, and that cancer cell activity is heightened because of the lack of immune response. In this case it isn't just the response of immune cells that is being moderated but the intracellular action to repair DNA.

In case you didn't know, you all have cancer inside of you, pretty much guaranteed at any given time. Cells proliferate and encounter DNA replication errors when they split, leading to cancer eventually. Our bodies employ mechanisms to repair nicked or miscopied DNA at an intracellular level constantly. Showing that T cells can be affected is one thing, but the disruption of such a fundamental biochemical repair process is alarming when you consider we are instructing cells to churn out the spike protein itself. The authors make this point in their conclusions and propose it as a putative mechanism for some of the shot-related side effects being observed.

Are we going to see a lot more cancer and AIDS-like symptoms? Hopefully not in most cases, but some people are going to get unlucky and have this effect them chronically, especially if we enter a regime where regular boosters become the norm.

Effect of SARS-CoV-2 proteins on vascular permeability

   Rossana Rauti, Meishar Shahoha, Yael Leichtmann-Bardoogo, Rami Nasser, Eyal Paz, Rina Tamir, Victoria Miller, Tal Babich, Kfir Shaked

   Department of Biomedical Engineering, Tel Aviv University, Israel; School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel; Blavatnik School of Computer Science, Tel Aviv University, Israel; Grass Center for Bioengineering, The Hebrew University of Jerusalem, Israel; The Center for Nanoscience and Nanotechnology, Tel Aviv University, Israel

Research Article Oct 25, 2021

   Cited 0 Views 1,400

Cite
as: eLife 2021;10:e69314 doi: 10.7554/eLife.69314
Abstract

Severe acute respiratory syndrome (SARS)-CoV-2 infection leads to severe disease associated with cytokine storm, vascular dysfunction, coagulation, and progressive lung damage. It affects several vital organs, seemingly through a pathological effect on endothelial cells. The SARS-CoV-2 genome encodes 29 proteins, whose contribution to the disease manifestations, and especially endothelial complications, is unknown. We cloned and expressed 26 of these proteins in human cells and characterized the endothelial response to overexpression of each, individually. Whereas most proteins induced significant changes in endothelial permeability, nsp2, nsp5_c145a (catalytic dead mutant of nsp5), and nsp7 also reduced CD31, and increased von Willebrand factor expression and IL-6, suggesting endothelial dysfunction. Using propagation-based analysis of a protein–protein interaction (PPI) network, we predicted the endothelial proteins affected by the viral proteins that potentially mediate these effects. We further applied our PPI model to identify the role of each SARS-CoV-2 protein in other tissues affected by coronavirus disease (COVID-19). While validating the PPI network model, we found that the tight junction (TJ) proteins cadherin-5, ZO-1, and β-catenin are affected by nsp2, nsp5_c145a, and nsp7 consistent with the model prediction. Overall, this work identifies the SARS-CoV-2 proteins that might be most detrimental in terms of endothelial dysfunction, thereby shedding light on vascular aspects of COVID-19.
Introduction

Coronavirus disease (COVID-19) caused by the 2019 novel coronavirus (2019-nCoV/SARS-CoV-2) led to a global pandemic in 2020. By late September 2021, coronavirus had infected more than 220 million people worldwide, causing over 4.5 million deaths. After the initial phase of the viral infection, ~ 30% of patients hospitalized with COVID-19 develop severe disease with progressive lung damage, known as severe acute respiratory syndrome (SARS), and a severe immune response. Interestingly, additional pathologies have been observed, such as hypoxemia and cytokine storm which, in some cases, lead to heart and kidney failure, and neurological symptoms. Recent observations suggest that these pathologies are mainly due to increased coagulation and vascular dysfunction (Lee et al., 2021; Libby and Lüscher, 2020; Siddiqi et al., 2020). It is currently believed that in addition to being a respiratory disease, COVID-19 might also be a ‘vascular disease’ (Lee et al., 2021), as it may result in a leaky vascular barrier and increased expression of von Willebrand factor (VWF) (Siddiqi et al., 2020), responsible for increased coagulation, cytokine release, and inflammation (Siddiqi et al., 2020; Teuwen et al., 2020; Aid et al., 2020; Potus et al., 2020; Wazny et al., 2020; Pum et al., 2021; Barbosa et al., 2021; Lin et al., 2020; Matarese et al., 2020; Xiao et al., 2020). Recent studies suggest that the main mechanism disrupting the endothelial barrier occurs in several stages: First, a direct effect on the endothelial cells that causes an immune response of the vascular endothelium (endotheliitis) and endothelial dysfunction. Second, lysis and death of the endothelial cells Teuwen et al., 2020; Xiao et al., 2020 followed by sequestering of human angiotensin I-converting enzyme 2 (hACE2) by viral spike proteins that activate the kallikrein–bradykinin and renin–angiotensin pathways, increasing vascular permeability (Teuwen et al., 2020; Varga et al., 2020). Last, overreaction of the immune system, during which a combination of neutrophils and immune cells producing reactive oxygen species, inflammatory cytokines (e.g., interleukin [IL]-1β, IL-6, and tumor necrosis factor), and vasoactive molecules (e.g., thrombin, histamine, thromboxane A2, and vascular endothelial growth factor), and the deposition of hyaluronic acid lead to disruption of endothelial junctions, increased vascular permeability, and leakage and coagulation (Libby and Lüscher, 2020; Teuwen et al., 2020; Varga et al., 2020). Of great interest is the effect on the brain’s vascular system. Cerebrovascular effects have been suggested to be among the long-lasting effects of COVID-19. Indeed, the susceptibility of brain endothelial cells to direct SARS-CoV-2 infection was found to increase due to increased expression of hACE2 in a blood flow-dependent manner, leading to a unique gene expression process that might contribute to the cerebrovascular effects of the virus (Pober and Sessa, 2007).

While many studies point out the importance of the vascular system in COVID-19 (Kaneko et al., 2021; Jung et al., 2020b; Nägele et al., 2020), only a few Pons et al., 2020; Chioh et al., 2020; Nascimento Conde et al., 2020; Buzhdygan et al., 2020 have looked at the direct vascular response to the virus. Most of those reports stem from either clinical observations, or in vitro studies or in vivo studies in which animals/cells were transfected with the SARS-CoV-2 virus and their systemic cellular response assessed, without pinpointing the specific viral protein(s) causing the observed changes. SARS-CoV-2 is an enveloped virus with a positive-sense, single-stranded RNA genome of ∼30 kb, encoding 29 proteins (Figure 1). These proteins can be classified as: structural proteins: S (spike proteins), E (envelope proteins), M (membrane proteins), N (nucleocapsid protein and viral RNA); nonstructural proteins: nsp1–16; open reading frame accessory proteins: orf3–10 (Kim et al., 2020; Hu et al., 2021). Table 1 summarizes the known effects of specific SARS-CoV-2 proteins (Gordon et al., 2020; Peng et al., 2020b; Procko, 2020; Cornillez-Ty et al., 2009; Romano et al., 2020; Hillen et al., 2020; Chi et al., 2003). The functionality of some of these is still unknown. Moreover, a considerable knowledge gap still exists regarding molecular mechanisms, especially the protein–protein interaction (PPI) pathways (Cowen et al., 2017), leading to tissue dysfunction.
Figure 1
Effect of severe acute respiratory syndrome (SARS)-CoV-2 proteins on endothelial cells.

(a) Sketch representing the main organs affected by SARS-CoV-2; (b) structure and gene composition of SARS-CoV-2.
Table 1
Severe acute respiratory syndrome (SARS)-CoV-2 proteins.
SARS-CoV-2 proteinsGeneral impact
 Structural proteins
S (spike)Spike protein, mediates binding to ACE2, fusion with host membrane
Surface glycoprotein, needs to be processed by cellular protease TMPRSS2 (Gordon et al., 2020)
M (membrane)Membrane glycoprotein, the predominant component of the envelope
A major driver for virus assembly and budding (Gordon et al., 2020)
E (envelope)Envelope protein, involved in virus morphogenesis and assembly
Coexpression of M and E is sufficient for virus-like particle formation and release (Gordon et al., 2020)
N (nucleocapsid)Nucleocapsid phosphoprotein binds to RNA genome (Gordon et al., 2020)
Nonstructural proteins
nsp1Leader sequence, suppresses host antiviral response
Antagonizes interferon induction to suppress host antiviral response (Gordon et al., 2020)
nsp2Interferes with host cell signaling, including cell cycle, cell-death pathways, and cell differentiation
May serve as an adaptor for nsp3
Not essential for virus replication, but deletion of nsp2 diminishes viral growth and RNA synthesis (Gordon et al., 2020; Procko, 2020)
nsp3nsp3–nsp4–nsp6 complex involved in viral replication
Functions as papain-like protease (Gordon et al., 2020)
nsp4nsp3–nsp4–nsp6 complex involved in viral replication (Gordon et al., 2020)
The complex is predicted to nucleate and anchor viral replication complexes on double-membrane vesicles in the cytoplasm (mitochondria)
nsp5Inhibits interferon I signaling processes by intervening in the NF-κB process and breaking down STAT one transcription factor
Functions as 3-chymotrypsin-like protease, cleaves the viral polyprotein (Gordon et al., 2020)
nsp5_c145aCatalytic dead mutant of nsp5 (Gordon et al., 2020)
nsp6nsp3–nsp4–nsp6 complex involved in viral replication
Limits autophagosome expansion
Components of the mitochondrial complex V (the complex regenerates ATP from ADP) copurify with nsp6 (Gordon et al., 2020)
nsp7Cofactor of nsp12
nsp7–nsp8 complex in part of RNA polymerase (nsp7, 8, 12 – replication complex)Affects electron transport, GPCR signaling, and membrane trafficking (Gordon et al., 2020; Peng et al., 2020b; Romano et al., 2020; Hillen et al., 2020)
nsp8Cofactor of nsp12
nsp7–nsp8 complex in part of RNA polymerase. Affects the signal recognition particle and mitochondrial ribosome (Gordon et al., 2020; Peng et al., 2020b; Romano et al., 2020; Chi et al., 2003)
nsp9ssRNA binding protein (can bind both DNA and RNA, but prefers ssRNA)
Interacts with the replication complex (nsp7, 8, 12) (Cornillez-Ty et al., 2009)
nsp10Cofactor of nsp16 and nsp14 (Romano et al., 2020)
Essential for nsp16 methyltransferase activity (stimulator of nsp16)
Zinc finger protein essential for replication (Gordon et al., 2020; Peng et al., 2020b)
nsp11Unknown function
nsp12Functions as an RNA-direct RNA polymerase, the catalytic subunitAffects the spliceosome (Gordon et al., 2020; Peng et al., 2020b; Romano et al., 2020; Hillen et al., 2020)
nsp13Has helicase and 5’ triphosphatase activity
Initiates the first step in viral mRNA capping nsp13,14,16 installs the cap structure onto viral mRNA in the cytoplasm instead of in the nucleus, where the host mRNA is capped (Gordon et al., 2020; Peng et al., 2020b; Romano et al., 2020; Ivanov et al., 2004)
nsp14In addition to the capping function of the methyltransferase, nsp14 is also an endonuclease (3’–5’ exoribonuclease) that corrects mutations during genome replication (Gordon et al., 2020; Peng et al., 2020b; Romano et al., 2020)
nsp15Endoribonuclease has uridine-specific endonuclease activity, essential for viral RNA synthesis (Gordon et al., 2020; Romano et al., 2020)
nsp16May involve complexation with nsp10 and nsp14, for stabilization of homoenzyme, for capping the mRNA (Gordon et al., 2020; Peng et al., 2020b; Romano et al., 2020)
Open reading frame (accessory factors)
orf3aPackaging into virions
Mediates trafficking of spike protein by providing ER/golgi retention signals
Induces IL-6b, activates NF-κB, activates the NLRP3 inflammasome (Gordon et al., 2020)
orf3bInterferon antagonist and involved in pathogenesis (Gordon et al., 2020)
orf6Type I interferon antagonist, suppresses the induction of interferon, and interferon signaling pathways (Gordon et al., 2020)
orf7aMay be related to viral-induced apoptosis (Gordon et al., 2020)
orf7bUnknown function
orf8Recombination hotspot
Induces ER stress and activates NLRP3 inflammasomes
Low similarity to SAR-CoV (Gordon et al., 2020)
orf9bSuppresses host antiviral response
Targets the mitochondrion-associated adaptor molecules MAVS and limits host cell interferon responses (Gordon et al., 2020)
orf9cNo evidence that this protein is expressed during SARS-CoV-2 infection (Gordon et al., 2020)
orf10No evidence that this protein is expressed during SARS-CoV-2 infection (Gordon et al., 2020)

To tackle these challenges, we cultured human umbilical vein endothelial cells (HUVECs) and systematically transduced them with lentiviral particles encoding 26 out of the 29 viral proteins, separately. The three remaining genes were not included in this study purely for technical reasons. We then examined their effects on HUVEC monolayer permeability and the expression of factors involved in vascular permeability and coagulation. The results were analyzed in the context of virus–host and host–host PPI networks. By combining the insights from the experimental and computational results, we generated a model that explains how each of the 26 proteins of SARS-CoV-2, including a mutated form of nsp5, the catalytic dead mutant termed nsp5_c145a, affects the protein network regulating vascular functionality. Moreover, once the PPI model was validated with our experimental data, we applied it to more than 250 proteins that have been identified in the literature as affected by the SARS-CoV-2 proteins. This enabled us to pinpoint the more dominant SARS-CoV-2 proteins and chart their effects. Overall, this work shows how each of the SARS-CoV-2 proteins differentially affects vascular functionality; moreover, once the model was validated, we applied it to identify how SARS-CoV-2 proteins interact with proteins that have been significantly correlated with changes in cell functionality.
Results
SARS-CoV-2 proteins impair barrier properties affecting cell-junction proteins

Increasing numbers of studies indicate a significant role for the vasculature in the physiological response to SARS-CoV-2. However, neither the exact molecular mechanism that leads to these effects nor the individual contribution of any of the SARS-CoV-2 proteins is known. Plasmids encoding SARS-CoV-2 proteins were cloned into lentivirus vectors, with eGFP-encoding vector used as a negative control. To shed light on the vascular response to the virus, HUVECs were cultured on different platforms, transduced with these lentiviral particles, and assessed for the effects of the virus proteins on different functionalities. Culturing HUVEC on Transwells or glass coverslips (Figure 2a) allowed us to identify how the specific proteins affect endothelial functionality. To ensure proper infection, the control vector included a GFP label, which enabled us to estimate infection efficiency at around 70 % (Figure 2a). Since the most basic function of the endothelium is to serve as a barrier, we sought to identify the changes in endothelium permeability in response to the SARS-CoV-2 proteins, and to pinpoint which of these proteins have the most significant effect. Barrier functions and properties were measured via trans-epithelial-endothelial electrical resistance (TEER), a standard method that identifies changes in impedance values, reflecting the integrity and permeability of the cell monolayer (Srinivasan et al., 2015). The GFP control and nine SARS-CoV-2 proteins did not show any significant change in TEER values (compared to the untreated condition), whereas 18 of the SARS-CoV-2 proteins caused significant changes in value (see plot in Figure 2b). The most dominant permeability changes were observed with nsp5_c145a, nsp13, nsp7, orf7a, and nsp2, with a 20–28% decrease in TEER values (Figure 2—figure supplement 1, and Figure 2c), in which the different SARS-CoV-2 proteins are listed and the gradual color change from red to violet represents the progressive reduction in TEER values. Figure 2—figure supplement 1 shows the comparison in TEER values before the infection and 3 and 4 days after the infection, showing that the permeability changes in the cells exposed to the viral proteins are maintained.
Figure 2 with 1 supplement
Effect of severe acute respiratory syndrome (SARS)-CoV-2 proteins on human umbilical vein endothelial cell (HUVEC) at day 3.

(a) Bright-field and fluorescent image of infected eGFP HUVEC, scale bar: 50 µm; (b) changes in barrier functions as a result of SARS-CoV-2 proteins were assessed by trans-epithelial-endothelial …

Next, we analyzed some of the proteins that exhibited the most significant (nsp2, nsp5_c145a, and nsp7) or least significant (S) changes in TEER value for changes in expression of the cell-junction proteins such as CD31 (Figure 2d and e), cadherin 1–5, occludin, and ZO 1–3 (presented later), indicating altered barrier functions. Analysis of the immunocytochemistry (ICC) (Figure 2d and e) showed, as expected, that nsp2, nsp5_c145a, and nsp7 significantly reduce the expression levels of CD31 compared to the untreated, eGFP, and S conditions, suggesting a deterioration in barrier function. Hence, these data show a differential effect of SARS-CoV-2 proteins on endothelial functionality and provide a mechanistic explanation for the reduction in endothelial integrity.
Increased endothelial inflammatory response caused by SARS-CoV-2 proteins

It is known that SARS-CoV-2 can cause a severe cytokine storm (Pum et al., 2021; Wang et al., 2020a) and a significant increase in coagulation-related pathologies. As we were interested in identifying the role of the vasculature in these observations, we stained and analyzed the expression level of VWF (Figure 3a and b), which is highly correlated with coagulation (Rietveld et al., 2019). Similar to the CD31 staining, we characterized only those proteins that resulted in a significant decrease in TEER values (nsp2, nsp5_c145a, and nsp7). As shown in Figure 3a and b, the control samples did not exhibit marked expression of VWF, whereas the cells transfected with nsp2, nsp5_c145a, and nsp7 showed a significant change in VWF expression. Moreover, as VWF is also associated with increased inflammation (Kawecki et al., 2017), we monitored changes in cytokine expression due to the different SARS-CoV-2 proteins (Figure 3c). We were particularly interested in IL-6, which has been identified as one of the most dominant cytokines expressed due to SARS-CoV-2 infection (Wang et al., 2020a; Akbari and Rezaie, 2020; Peruzzi et al., 2020; Liao et al., 2020b; Liao et al., 2020a). We observed that 13 out of the 26 proteins caused an increase in IL-6 secretion, 3 of which had resulted in a decrease in barrier function and increased VWF expression.
Figure 3
Human umbilical vein endothelial cell (HUVEC) response to specific proteins.

(a) Confocal reconstructions of HUVEC stained for von Willebrand factor (VWF) (green) and Hoechst (blue) for three conditions: control (untreated), eGFP, and nsp5_c145a, scale bar: 20 µm; (b) …
Correlation between vascular permeability impairment and viral proteins

We then investigated how SARS-CoV-2 causes the observed changes in HUVECs permeability. We collected sets of proteins responsible for specific functionalities of endothelial cells. We also constructed an integrated viral–host and host–host PPI network. For each viral protein and each prior functional set, we measured the network proximity between the viral protein and the human functional set using a network propagation algorithm. We scored the significance of these propagation calculations by comparing them to those obtained on random PPI networks with the same node degrees. Proteins receiving high and significant scores were most likely to interact with the specific SARS-CoV-2 protein and thus might cause the observed functional changes. When comparing the overall effects of the 26 SARS-CoV-2 proteins on endothelial TJ proteins (e.g., cadherin 1–5, occludin, and ZO 1–3), we found a correlation between the effects of the SARS-CoV-2 proteins and TEER values (Figure 4a). Moreover, some of the proteins that significantly affected the TEER parameters (Figure 2c) were also observed to be significantly proximal to the permeability-related set. These included nsp2, nsp7, and nsp13 (Figure 4a). Our algorithm identified cadherin-2, α-catenin, β-catenin, δ-catenin, and ZO 1 and 2 as the most susceptible proteins to SARS-CoV-2 infection (Figure 4b).
Figure 4
Correlation between viral protein effect on permeability and proximity to permeability-related proteins in a protein–protein interaction (PPI) network.

(a) Correlation of adjusted p-value versus permeability (Pearson = 0.295); (b) proximity between vascular proteins and the viral proteins.

To validate our PPI network model, we performed immunostaining of some TJ proteins (β-catenin, cadherin-5, ZO-1, and occludin) of HUVEC transfected with viral proteins and to compare it to the model prediction. Similar to the CD31 staining, we characterized only those proteins that significantly decreased TEER values (nsp2, nsp5_c145a, and nsp7) compared to the eGFP and untreated condition (Figure 5). As shown in Figure 5a–c, the cells transfected with nsp2, nsp5_c145a, and nsp7 showed a significant reduction in the β-catenin, cadherin-5, and ZO-1 intensity, confirming the ability of the SARS-CoV-2 proteins to impair endothelial permeability.
Figure 5
Tight-junctions impairment by severe acute respiratory syndrome (SARS)-CoV-2 proteins.

Immunocytochemistry (ICC) for (a) β-catenin (green), (b) cadherin-5 (green), (c) ZO-1 (green), (d) occludin (green), and Hoechst (blue) for five specified conditions: untreated, eGFP, nsp2, …

Once the model was validated, we used it to identify how the individual SARS-Cov-2 proteins affect nine other different vascular endothelial cells. As a starting point, we created a table (Table 2) (based on the literature) where we compared the expression of 12 different TJ proteins across nine different types of endothelial cells. We then applied the network-based model to identify which endothelial cells are more susceptible to the different SARS-Cov-2 proteins. As can be seen in Figure 6, there are significant differences in the response of various viral proteins on different types of vascular endothelial cells. For example,, the viral proteins nsp13, nsp11, orf6, and S seem to have a significant effect on the different types of vascular endothelial cells, according to the network score detected. However, the proteins m, E, n, nsp12, and nsp8 are the less interactive with the vascular cells.
Table 2
Comparison of tight junction (TJ) proteins expression among different types of vascular endothelial cells.
Endothelial cells typeTJ proteins
 Cadherin-2Cadherin-3Cadherin-4Cadherin-5δ-1-Cateninβ-Cateninα-CateninOccludinClaudin-5ZO-1ZO-2ZO-3
Human pulmonary artery endothelial cells (HPAECs) (Nakato et al., 2019; Chi et al., 2003; Ivanov et al., 2004; Ferreri et al., 2008; DiStefano et al., 2014; Zebda et al., 2013; Yuan et al., 2012; Wang et al., 2011)+−−++++++++−
Human umbilical vein endothelial cells (HUVECs) (Nakato et al., 2019; Chi et al., 2003; Ferreri et al., 2008; Wu et al., 2008; Dean et al., 2009; Polus et al., 2006; DeBusk et al., 2010; Wessells et al., 2009)++++++++++++
Human umbilical artery endothelial cells (HUAECs) (Nakato et al., 2019; Chi et al., 2003; Davis et al., 2003; Ikuno et al., 2017; Kevil et al., 1998; Kluger et al., 2013)−+−+++−+++++
Human great saphenous vein endothelial cells (HGSVECs) (Nakato et al., 2019; Chi et al., 2003; Latif et al., 2006; Murakami et al., 2008)−−−++−++++++
Human common carotid artery endothelial cells (HCCaECs) (Nakato et al., 2019; Chi et al., 2003)+−+−−+−+++++
Human aortic endothelial cells (HAoECs) (Nakato et al., 2019; Chi et al., 2003; Wu et al., 2017; Sandig et al., 1999; DeMaio et al., 2006)−−−−−+++++++
Human coronary artery endothelial cells (HCAECs) (Nakato et al., 2019; Chi et al., 2003; Wessells et al., 2009; Wu et al., 2004; Pinto et al., 2018)−−++++++++++
Human endocardial cells (HENDCs) (Nakato et al., 2019; Chi et al., 2003; Vestweber et al., 2009; Bao et al., 2017)+−−+−+++++−−
Human renal artery endothelial cells (HRAECs) (Nakato et al., 2019; Chi et al., 2003; Maciel et al., 2018)−−++−+−+++−−
Figure 6
Correlation between viral proteins and different types of vascular endothelial cells.

Correlation of adjusted p-value between vascular proteins identified in vascular endothelial cells and the viral proteins.

As our network propagation model is highly correlated with our experimental results, we applied it to other physiological systems that are known to be affected by SARS-CoV-2. We created a list of all proteins that are known to be affected by the SARS-CoV-2 proteins according to the literature (Supplementary file 1A, white columns). The table was composed of both proteins identified experimentally via western blot, proteomics, and immunohistochemistry (marked in blue) and those identified clinically as being highly correlated with loss of specific functionality in specific tissues (marked in red). We then applied the network-based model to identify which proteins in Supplementary file 1A are most susceptible to the different SARS-CoV-2 proteins. As can be seen in Figure 7—figure supplements 2–7, Supplementary file 1A and B, specific SARS-CoV-2 proteins were identified as affecting specific proteins in specific tissues. As expected, most of the SARS-CoV-2 proteins affected more than one protein, the most salient being nsp11, nsp4, and nsp7 (Figure 7b), each of which was predicted to affect more than 40 different proteins. An additional parameter that should be considered is the protein’s ‘distance’ from the viral proteins. This value represents the number of hops in the PPI network from a given protein to the viral proteins, where a value of 1 represents a direct viral–host connection. We hypothesized that the closer the distance between the viral proteins and the given protein, the more significant the viral effect. Supplementary file 1A (gray columns) and Figure 7c present the calculated distances. Most of the identified proteins in Supplementary file 1A were classified with a distance of 1 or 2 from the virus, suggesting more severe putative effects. A very clear example, are the T cells, macrophages, lung epithelial and cardiomyocytes which show that the most significant effect was by the viral proteins which present a close connection with the relative cell proteins reported. This suggest a potential effect on the related functional or metabolic pathway (Supplementary file 1A).
Figure 7 with 7 supplements
Protein identification using protein–protein interaction (PPI).

(a) PPI results for the severe acute respiratory syndrome (SARS)-CoV-2 proteins that have a significant effect on the proteins presented in SI Table 1 for each system; (b) number of proteins …
Discussion

Due to the impact of SARS-CoV-2, many studies have looked at the physiological responses to the virus (Lee et al., 2021; Libby and Lüscher, 2020; Siddiqi et al., 2020; Teuwen et al., 2020; Chioh et al., 2020). In this work, we sought to identify how specific SARS-CoV-2 proteins affect the vasculature by assessing the effect of individual SARS-CoV-2 proteins on endothelial cells (HUVEC). This approach has significant advantages: it enables pinpointing and isolating how each of the SARS-CoV-2 proteins independently affects the endothelial response, and directly measuring endothelial functionality. The HUVEC model, derived from the umbilical cord, is physiologically representative of the human vascular endothelium, allowing the study of the physiological and pathological conditions as well as the effects of novel drugs on human endothelium (Bouïs et al., 2001; Medina-Leyte et al., 2020). Among technical advantages, cultured HUVECs are a simple in vitro vascular endothelial model, particularly suitable for studying endothelial properties and dynamics as well as the putative role of adhesion molecules, the synthesis of extracellular proteins and blood vessel maturation (Vailhé et al., 2001).

The current study showed that almost 70 % (18 out of 26) of the SARS-CoV-2 proteins affect endothelial barrier integrity; however, the most significant proteins were nsp2, nsp5_c145a, and nsp7, which also induced upregulated expression of the coagulation factor VWF and cytokine release. These critical facts can shed light on the multiple pathologies observed in SARS-CoV-2 infection, including cytokine storm, increased coagulation and related diseases (e.g., heart attack and stroke) (Lee et al., 2021; Aid et al., 2020), increased cardiovascular disease, and increased neurological symptoms. The results presented here showed an effect of in vitro cultured endothelial cells, which may lead to vasculature leakiness, consequently causing exotoxicity (i.e., the penetration of toxic reagents from the blood into the brain). While there are many parameters associated with functional changes, the use of advanced tools, including network-based analysis, enabled us to elucidate the specific proteins and the specific interactions that are predicted to cause these changes. The PPI network enabled us to predict that the changes observed in barrier function are possibly due to interactions between host proteins such as cadherin 2, α-catenin, β-catenin, δ-catenin, and ZO 1 and 2, and at least with the viral proteins nsp2, nsp5_c145a, and nsp7. Moreover, we validated our PPI model performing further immunostaining analysis demonstrating not only the ability of the viral proteins to strongly impair TJ expression, but also to confirm the data predicted by our model in which some TJ proteins can be more affected than others.

PPI analysis revealed a highly correlated effect of nsp7 and nsp13 on β-catenin in endothelial cells (Figure 4b; Jung et al., 2020a; Lengfeld et al., 2017). Interestingly, neither nsp2 nor nsp5_c145a affected a high number of proteins (Figure 7b), whereas nsp7 did, as identified by the network. Analyzing the repertoire of SARS-CoV-2 proteins, we see almost no effect of the structural proteins; rather, mostly nonstructural and open reading frame proteins affected HUVEC functionality, manifested as decreased barrier function and increased cytokine secretion (Figures 2 and 3). While the nonstructural proteins are mainly responsible for replicating viral RNA, the open reading frame proteins are related to counteraction with the host immune system; some of these are localized to the mitochondria and have been shown to alter the mitochondrial antiviral signaling pathway (Miller et al., 2021). We found that the proteins most affecting barrier function (decreased TEER and decreased CD31, β-catenin, cadherin-5, and ZO-1 expression) and cytokine response (IL-6 secretion and VWF expression) were nsp2, nsp5_c145a, and nsp7 (Figure 2; Figure 3; Figure 6); nsp7 forms a replication complex with nsp8 and nsp12 that is essential for viral replication and transcription (Cowen et al., 2017; Peng et al., 2020a). Peng et al., 2020b suggested that in the core polymerase complex nsp7–nsp8–nsp12, nsp12 is the catalytic subunit, and nsp7 and nsp8 function as cofactors. They further suggested that the mechanism of activation mainly involves the cofactors rather than the catalytic subunit (Peng et al., 2020b). This might explain why we saw mainly an effect of the cofactor proteins on endothelial cells and almost no effect of the catalytic subunit. Network interactions Díaz, 2020 have shown that nsp7 has the most interactions with the host, suggesting a potential target for the treatment of COVID-19. Moreover, no mutations were found in nsp7 compared to nsp2 or nsp5_c145a (Kaushal et al., 2020), suggesting a conserved protein with a vital function in virus survival. The nsp13 protein has both helicase activity and 5’ triphosphatase activity, which play an important role in mRNA capping. We saw a significant effect of nsp13 on barrier function, but hardly any effect on cytokine secretion. Chen et al., 2020, suggested functional complexation between nsp8 and nsp12, the RdRp (RNA-dependent RNA polymerase) replication complex, and nsp13. Given the fact that we observed a substantial effect of nsp7 – one of the proteins of the replication complex – and an effect of nsp13 on HUVEC barrier function, complexation of nsp13 with the replication complex might indicate an important role for this complex in the impaired functionality of the HUVECs, and therefore in the propagation of the disease, and the known vascular damage seen in COVID-19 patients. As suggested by our model, nsp13 seems to have a strong effect also on other types of vascular endothelial cells (Figure 6) as well as on all cell types (Figure 7a), positioning nsp13 as one of the main targets for disease treatment.

It is important to note that the comparison between the different endothelial cell types revealed exciting differences in the TJ protein expression, which correlate to the different properties of the different cell types (Nakato et al., 2019). One of the major differences was that some endothelial cell lines do not have cadherin at all (e.g., HAoEC), or very limited amount of cadherin (e.g., HPAEC, HUAEC, HGSVEC). Our model suggests that some endothelial types (e.g., HUVEC, HUAEC, HGSVEC, HCAEC) are more susceptible to the SARS-Cov-2 virus. It, therefore, suggests that the treatment of one type of endothelial cell might be different from another type but offers the PPI model as a tool for initial prediction. Overall, the combination of identifying the differences in the TJ protein expression between the different endothelial cells and the use of the PPI model enabled us to pinpoint the differences in susceptibility to the disease and to identify which specific proteins have the most significant effect.

Many studies have looked at the SARS-CoV-2 interaction with nonpulmonary/nonvascular tissues (e.g., neurons, hepatocytes, immune components such as lymphocytes, macrophages, etc.) (Lee et al., 2021), as pathological studies identified a viral effect on these tissues, despite their very limited amount, or lack of ACE2 receptors. To better understand how SARS-CoV-2 interacts with and affects other tissues, we consolidated all of the proteins currently known to be affected by the virus into Supplementary file 1A. It is interesting to note that the most dominant SARS-CoV-2 proteins are nsp4, nsp11, and nsp7. Davies et al., 2020, identified the interaction of nsp2 with nsp4, both involved in endoplasmic reticulum (ER) calcium signaling and mitochondrial biogenesis. This suggests a new functional role in the host ER and mitochondrial organelle contact process and calcium homeostasis.

By now it is clear that vasculature plays a significant role in the physiological response to the virus. However, it is still unclear how the virus affects the vasculature, and if it can be found in the blood. This is a critical question, as it has significant consequences on the extent of the virus’s ability to affect the vasculature. Current studies demonstrate that the pulmonary vasculature is significantly affected and is one of the dominant triggers for the pathologies mentioned above. However, involvement with the rest of the vasculature is still unclear, as is whether the virus can be found in an active form in the blood circulation (Peng et al., 2020a; Chang et al., 2020; Orologas-Stavrou et al., 2020; Andersson et al., 2020; Escribano et al., 2020; Wang et al., 2020b). Some studies suggest that even if there are traces of SARS-CoV-2 in the blood, it is not in an active form and cannot cause disease or a systemic response (Andersson et al., 2020). On the other hand, some studies suggest that SARS-CoV-2 can be found in the blood, and can induce the disease and cause both cellular and systemic dysfunction (Peng et al., 2020a; Chang et al., 2020; Escribano et al., 2020). While this question is beyond the scope of this work, it is important to note that if future studies do identify the active form of SARS-CoV-2 in human blood, then the implications of our findings will apply to this systemic response as well (Ahmed et al., 2020; Park et al., 2020).

As already noted, the pathology is probably a combination of multiple conditions and pathways activated by the different proteins. However, our findings might open new avenues for future therapeutics. Moreover, most of the proteins that were identified as affected by SARS-CoV-2 had a distance factor of at most three to the human and viral proteins. This coincides with the current dogma, whereby proteins that have a shorter distance between them are more likely to be affected.

While beneficial, our approach has two major limitations: (a) our inability to identify the effect of multiple proteins and (b) our neglect of the effect of the coronavirus structure and binding on the cellular response. The former point can be overcome by expressing combinations of different SARS-CoV-2 proteins. However, since the SARS-CoV-2 expresses 29 proteins, there are about ~9 × 1030 possible protein combinations. Therefore, we decided to focus on individual proteins and allow further studies to pursue any combinations of interest. Regarding the latter limitation, we did not include the coronavirus structure (including the ACE2 receptors) in this study, because many studies have already demonstrated the cellular response to this structure (Chioh et al., 2020; Yang et al., 2020; Procko, 2020), and how tissues that do not have significant ACE2 expression (neurons, immune components such as B and T lymphocytes, and macrophages) are affected by the virus remains an open question.
Conclusions

Accumulating clinical evidence suggests that COVID-19 is a disease with vascular aspects. However, only a few studies have identified the specific role of each of the SARS-CoV-2 proteins in the cellular response leading to vascular dysfunctions. In this work, we characterized the endothelial response to each of 26 SARS-CoV-2 proteins and identified those that have the most significant effect on the barrier function. In addition, we used PPI network-based analysis to predict which of the endothelial proteins is most affected by the virus and to identify the specific role of each of the SARS-CoV-2 proteins in the observed changes in systemic protein expression. Overall, this work identified which of the SARS-CoV-2 proteins are most dominant in their effect on the physiological response to the virus. We believe that the data presented in this work will give us better insight into the mechanism by which the vasculature and the system respond to the virus, and will enable us to expedite drug development for the virus by targeting the identified dominant proteins.
Materials and methods
Generation of lentiviral SARS-CoV-2 plasmids
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Plasmids encoding the SARS-CoV-2 open reading frames proteins and eGFP control were a kind gift of Nevan Krogan (Addgene plasmid #141367–141395). Plasmids were acquired as bacterial LB–agar stabs and used per the provider’s instructions. Briefly, each stab was first seeded in LB agar (Bacto Agar; BD Biosciences, San Jose, CA) in 10 cm plates. Then, single colonies were inoculated into flasks containing LB (BD Difco LB Broth, Lennox) and 100 µg/ml penicillin (Biological Industries, Beit HaEmek, Israel). Transfection-grade plasmid DNA was isolated from each flask using the ZymoPURE II Plasmid Maxiprep Kit (Zymo Research, Irvine, CA) according to the manufacturer’s instructions.
Lentivirus preparation
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HEK293T cells (ATCC, Manassas, VA) were seeded in 10 cm cell culture plates at a density of 4 × 106 cells/plate. The cells were maintained in 293T medium composed of DMEM high glucose (4.5 g/l; Merck, Rahway, NJ) supplemented with 10 % fetal bovine serum (FBS; Biological Industries), 1× NEAA (Biological Industries), and 2 mM L-alanine–L-glutamine (Biological Industries, Israel). Lentiviral stocks, pseudo-typed with VSV-G, were produced in HEK293T cells as previously described (Kutner et al., 2009). Briefly, each of the pLVX plasmids containing the SARS-CoV-2 genes or EGFP for control were cotransfected with third-generation lentivirus helper plasmids at equimolar ratio; 48 hr later, the lentivirus-containing medium was collected and supernatant was clarified by centrifugation (500 g, 5 min) and filtration (0.45 µm, Millex-HV, Merck Millipore, Burlington, MA). All virus stocks were aliquoted and stored at –80 °C until thawed for subsequent use.
Endothelial cell culture
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HUVECs (C-12200, PromoCell GmbH, Heidelberg, Germany, tested negative for mycoplasma contamination) were used to test each viral protein’s impact on vascular properties. After thawing, the HUVECs were expanded in low-serum endothelial cell growth medium (PromoCell) at 37°C with 5% CO2 in a humidifying incubator, and used at passage p4–p6. Cells were grown to 80–90% confluence before being transferred to transparent polyethylene terephthalate Transwell supports (0.4 µm pore size, Greiner Bio-One, Austria) or a glass-bottom well plate (Cellvis, Mountain View, CA). Before seeding, the uncoated substrates were treated with Entactin-Collagen IV-Laminin (ECL) Cell Attachment Matrix (Merck) diluted in DMEM (10 µg/cm2) for 1 hr in the incubator. Then, the HUVECs were harvested using a DetachKit (PromoCell), were seeded inside the culture platforms at a density of 250,000 cells/cm2, and grown for 3 days. Then viral infection with the different plasmids was performed and its impact on cell behavior was tested 3 days later.
TEER measurement
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The barrier properties of the endothelial monolayer were evaluated by TEER measurements, 3 and 4 days after viral infection. TEER was measured with the Millicell ERS-2 Voltohmmeter (Merck Millipore). TEER values (Ω cm2) were calculated and compared to those obtained in a Transwell insert without cells, considered as a blank, in three different individual experiments, with two inserts used for each viral protein.
Immunofluorescence
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HUVEC plated on glass-bottom plates were rinsed in phosphate buffered saline (PBS) and fixed in 4 % paraformaldehyde (Sigma-Aldrich, Rehovot, Israel) for 20 min at RT, 5 days after viral infection. ICC was carried out after permeabilization with 0.1 % Triton X-100 (Sigma-Aldrich, Rehovot, Israel) in PBS for 10 min at RT and blocking for 30 min with 5 % FBS in PBS. The following primary antibodies were applied overnight in PBS at 4°C: rabbit anti-VWF (Abcam, Cambridge, UK), rabbit anti-CD31 (Abcam) against platelet endothelial cell adhesion molecule 1 (PECAM1), rabbit anti-β-catenin (Cell Signaling Technology, Danvers, MA), rabbit anti-cadherin-5 (Cell Signaling Technology, Danvers, MA), rabbit anti ZO-1 (Cell Signaling Technology, Danvers, MA), rabbit anti-occludin (Cell Signaling Technology, Danvers, MA). Cells were then washed three times in PBS and stained with the secondary antibody, anti-rabbit Alexa Fluor 488 (Invitrogen, Carlsbad, CA), for 1 hr at RT. After four washes with PBS, cells were incubated with Hoechst in PBS for 10 min at RT to stain the nuclei. After two washes with PBS, imaging was carried out using an inverted confocal microscope (Olympus FV3000-IX83) with suitable filter cubes and equipped with 20× (0.8 NA), 40× (0.95 NA), and 60× (1.42 NA) objectives. Image reconstruction and analysis were done using open-source ImageJ software (Schindelin et al., 2012).
Network analysis
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We scored the effect of each viral protein on selected human proteins using network propagation (Cowen et al., 2017). Specifically, a viral protein was represented by the set of its human interactors (Hu et al., 2021); each of these received a prior score, equal to 1 /n, where n is the size of the interactor set; these scores were propagated in a network of PPI (Almozlino et al., 2017). To assess the statistical significance of the obtained scores, we compared them to those computed on 1000 randomized networks that preserve node degrees. The PPI score was then compared versus the other random networks (this is empirical p-value). p-Values were adjusted for multiple testing using Benjamini–Hochberg FDR approach. For display purposes, the plotted p-value is the negative log of the p-value, which means numbers are non-negative and the higher is the more significant.
Quantitative ELISA for IL-6
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ELISA was performed on conditioned medium of infected HUVEC 3 days postinfection, according to the manufacturer’s recommendations (PeproTech Rehovot, Israel).
Statistical analysis
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The results are presented as mean ± SD, unless otherwise indicated. Statistically significant differences among multiple groups were evaluated by F-statistic with two-way ANOVA, followed by the Holm–Sidak test for multiple comparisons (GraphPad Prism 8.4.3). The difference between the two data sets was assessed and p < 0.05 was considered statistically significant.
Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. The custom scripts available in GitHub: https://github.com/raminass/covid_networks, (copy archived at https://archive.softwareheritage.org/swh:1:rev:b239ae7e0e72b722beb6d694436068541ea28dbb).
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Decision letter

   Arduino A Mangoni
   Reviewing Editor; Flinders Medical Centre, Australia
   Matthias Barton
   Senior Editor; University of Zurich, Switzerland
   Francesco Pasqualini
   Reviewer
   Gad Vatine
   Reviewer; Ben Gurion University

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

In their manuscript, Rauti, Shahoha et al., sought to identify the effect of SARS-CoV2 proteins on endothelial functions. They systematically transduced HUVECs cultured on a transwell with various SARS-CoV2 proteins from three classes including non-structural and accessory proteins. They have assessed the effect of separate overexpression of each of 26 proteins, and tested their effect on 26 of the 29 proteins in the SARS-CoV2 genome. The authors found that overexpression of some proteins had stronger effect on the barrier functions on HUVECs compared to other proteins. Using a PPI network analysis, they predicted endothelial proteins that may be affected by the viral protein, which may potentially mediate this effect. The findings add to the understanding how SARS-CoV2 may negatively affect endothelial cell homeostasis, which may contribute to vascular and thrombotic complications associated with a severe course of the disease in patients diagnosed with COVID-19.

Decision letter after peer review:

Thank you for submitting your article "Effect of SARS-CoV-2 proteins on vascular permeability" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Matthias Barton as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Francesco Pasqualini (Reviewer #1); Gad Vatine (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) The authors provide putative virus-host protein-protein interaction networks that could be very valuable for future translational efforts. However, it would be best to validate at least one of those on the HUVECS/TEER assay. Would it be possible to rescue endothelial dysfunction acting (pharmacologically or genetically) on any of the identified host proteins downstream of the viral protein?

2) The authors provide virus-host protein interaction networks for endothelial cells of various organs. This, again, could be a very valuable target identification exercise for future pharmacological work but it would need to be validated. Can the rescue experiment discussed above be replicated using the TEER assay, organ-specific endothelial cells, and pharmacological or genetic means to normalize the expression of host proteins downstream of the viral protein in the organ-specific PPI network?

3) More validation data will help but it might be worth revising the discussion as well, especially as the new validation data may make parts of the current Discussion redundant.

4) Page 4, line 98 – "Permeability was measured via trans-epithelial-endothelial 99 electrical resistance (TEER), a standard method that identifies changes in impedance values". TEER is a method for assessing barrier functions, and it is related to permeability, but permeability should be measured by fluorescent paracellular assays. These should be separated.

5) While significant, the changes in TEER are very small. The authors should specify in the figure legend, which statistical tests have been applied, and should specify that they have accounted for multiple comparison tests. Also, changes in TEER over time should be showed.

6) In order to avoid conclusions that may be linked to direct regulation on CD31, changes in protein expression should be made on additional proteins that are related to tight junctions such as ZO1.

7) Overall, all experiments were performed on a single source of in vitro human endothelial cells. The authors should discuss possibilities for additional endothelial cell models that could potentially be used for future studies, and discuss the advantages and disadvantages of HUVECs.

8) The authors develop a mathematic model to identify interactions of the SARS-CoV2 proteins with tissue targets. While this represents a novel an important approach, authors should show a validation of the model to prove its validity. If this cannot be applied in the HUVECs system, the authors should explain its limitations and at least discuss what possible models could be used to validate it in the future.

9) Since the authors identify WNT signaling as a key pathway, the authors cold consider manipulating the WNT pathway in HUVECs cells as a possible treatment for rescuing this effect.Reviewer #1:

The hypothesis of the authors is that non-spike SARS-CoV-2 proteins are responsible for the increased vascular permeability observed in COVID-19 patients during the primary infection and, possibly, long after it is resolved. To identify which protein(s) might be responsible, they used lentiviruses to express 26/29 SARS-CoV-2 proteins in human endothelial cells (HUVECS) using trans-epithelial-endothelial electrical resistance (TEER), a standard assessment of vascular permeability. Among the viral proteins that directly increased vascular permeability, the authors identified a subset (nsp2,29 nsp5_c145a (catalytic dead mutant of nsp5) and nsp7) capable of inducing broader endothelial dysfunction (downregulated CD31 and upregulated VWF and IL-6). Finally, they used protein-protein interaction analysis to semi-quantitatively speculate how these viral proteins may lead to endothelial dysfunction and increased vascular permeability in various human organs.

Strengths

The use of a library of LV to test each individual SARS-CoV-2 protein is interesting. While hard to use combinatorially, being able to isolate the influence of each protein will be important in understanding the secondary effect of COVID-19 infections (e.g., long COVID).

The combination of imaging and TEER to screen how each viral protein changes endothelial structure, function, or both.

The use of bio-informatics to relate the raw findings from the cellular assay with broader translational implications, namely through which pathways may viral protein end up affecting vascular permeability and endothelial dysfunction.

Weaknesses

I feel like the paper is missing two key additional validations.

First, the authors provide putative virus-host protein-protein interaction networks that could be very valuable for future translational efforts. However, it would be best to validate at least one of those on the HUVECS/TEER assay. Would it be possible to rescue endothelial dysfunction acting (pharmacologically or genetically) on any of the identified host proteins downstream of the viral protein?

Second, the authors provide virus-host protein interaction networks for endothelial cells of various organs. This, again, could be a very valuable target identification exercise for future pharmacological work but it would need to be validated. Can the rescue experiment discussed above be replicated using the TEER assay, organ-specific endothelial cells, and pharmacological or genetic means to normalize the expression of host proteins downstream of the viral protein in the organ-specific PPI network?Reviewer #2:

In their manuscript, Rauti, Shahoha et al., sought to identify the effect of SARS-CoV2 proteins on endothelial functions. They systematically transduced HUVECs cultured on a transwell with various SARS-CoV2 proteins from three classes including non-structural and accessory proteins. They have assessed the effect of separate overexpression of each of 26 proteins, and tested their effect on 26 of the 29 proteins in the SARS-CoV2 genome. The authors found that overexpression of some proteins had stronger effect on the barrier functions on HUVECs compared to other proteins. Using a PPI network analysis, they predicted endothelial proteins that may be affected by the viral protein, which may potentially mediate this effect.

The novelty of the study is in directly testing the response of endothelial cells to the virus. Previous reports focused on the phenotype in patients, or using animal models without specifying the exact proteins involved in the dysfunction. Gaining knowledge on the molecular mechanisms, especially the protein-protein interaction pathways that lead to tissue dysfunction, may help understanding the pandemic a potentially help develop treatments.
https://doi.org/10.7554/eLife.69314.sa1
Author response

   Essential revisions:

   1) The authors provide putative virus-host protein-protein interaction networks that could be very valuable for future translational efforts. However, it would be best to validate at least one of those on the HUVECS/TEER assay. Would it be possible to rescue endothelial dysfunction acting (pharmacologically or genetically) on any of the identified host proteins downstream of the viral protein?

We would like to thank the referee for his suggestion. We agree that it is important to show the validity of the model before using it on other proteins. Following the referee’s suggestion, we have performed a series of experiments to validate the model (new Figure 5). Our new data present high correlation between the PPI prediction and the experimental data.

   2) The authors provide virus-host protein interaction networks for endothelial cells of various organs. This, again, could be a very valuable target identification exercise for future pharmacological work but it would need to be validated. Can the rescue experiment discussed above be replicated using the TEER assay, organ-specific endothelial cells, and pharmacological or genetic means to normalize the expression of host proteins downstream of the viral protein in the organ-specific PPI network?

Following the referee’s suggestion to examine different organ-specific endothelial cells, we have made a unique comparison of the expression level of the different endothelial proteins in 9 different vascular endothelial cell types (see new Table 2).

Although we assumed that most proteins will be detected in these lines, we were surprised to learn that in some cell lines, like the HAoEC, all cadherin proteins (Cadherin 2-5) do not express while in others Catenin d is not expressed while in other most ZO proteins are missing. We were surprised to learn that such a comparison was never published, making this publication also unique in that way.

After we validated the PPI network, we used it to examine how the specific SARS-Cov-2 proteins affect the organ-specific endothelial cells (for 9 organ-specific vascular endothelial cells). Our results presented in new Figure 6 predict that not only the HUVEC permeability properties are impaired by the SARSCov-2 proteins, but other vascular endothelial cells are strongly affected as well. Interestingly, we see that different endothelial cells response differently to SARS-Cov-2, mainly due to the different expression of the tight junctions proteins.

   3) More validation data will help but it might be worth revising the discussion as well, especially as the new validation data may make parts of the current Discussion redundant.

The discussion was revised according to the new validation.

   4) Page 4, line 98 – "Permeability was measured via trans-epithelial-endothelial 99 electrical resistance (TEER), a standard method that identifies changes in impedance values". TEER is a method for assessing barrier functions, and it is related to permeability, but permeability should be measured by fluorescent paracellular assays. These should be separated.

We thank the author for this comment. The text was revised to better clarify this point.

   5) While significant, the changes in TEER are very small. The authors should specify in the figure legend, which statistical tests have been applied, and should specify that they have accounted for multiple comparison tests. Also, changes in TEER over time should be showed.

In this work we used ANOVA-multiple comparison test to account for the multiple comparison among different groups. This was described in the method section and per the reviewer request, it was also added to the figure legend.

The TEER dynamic is highly dependent on the cell density, age, and condition. Per the reviewer request, we added the TEER values of the cells in multiple time points (before the infection, 3 and 4 days after the infection (new Figure S1)).

   6) In order to avoid conclusions that may be linked to direct regulation on CD31, changes in protein expression should be made on additional proteins that are related to tight junctions such as ZO1.

We would like to thank the referee for this comment. We have performed additional set of experiments examining also the effect on ZO1 (and other 3 proteins, B-Catenin, Cadherine 5 and Occludin). The new data, now in new Figure 5, show that the viral proteins that showed a significant decrease in the TEER values as well as in the CD31 intensity, significant affect the expression of these other tight-junctions proteins.

   7) Overall, all experiments were performed on a single source of in vitro human endothelial cells. The authors should discuss possibilities for additional endothelial cell models that could potentially be used for future studies, and discuss the advantages and disadvantages of HUVECs.

As we discussed in “point 2”, we created a table which compares 9 organ-specific endothelial cells. We added the results as Table 2 and new Figure 6.

We also revised the text accordingly.

   8) The authors develop a mathematic model to identify interactions of the SARS-CoV2 proteins with tissue targets. While this represents a novel an important approach, authors should show a validation of the model to prove its validity. If this cannot be applied in the HUVECs system, the authors should explain its limitations and at least discuss what possible models could be used to validate it in the future.

As we discussed in “point 2”, we created a table which compares 9 organ-specific endothelial cells. We added the results as Table 2 and new Figure 6.

We also revised the text accordingly.

   9) Since the authors identify WNT signaling as a key pathway, the authors cold consider manipulating the WNT pathway in HUVECs cells as a possible treatment for rescuing this effect.

WNT was suggested as a relevant pathway due to its known contribution to BBB impairment in multiple sclerosis and other pathologies (Jung, Y., et al. ACS Infect. Dis. (2020); Lengfeld, J. E.,et al. Proc Natl Acad Sci (2017)). In addition, the ßcatenin is part of the WNT signaling, and we showed the validation of its effect by the SARS-Cov-2, i.e. decrease in expression caused by the virus infection. But since it is not the main claim of the paper, (the contribution of the WNT to the BBB impairment due to the SARS-Cov-2) we removed this point from the discussion. Manipulation of the WNT signaling pathway is beyond the scope of this paper.
https://doi.org/10.7554/eLife.69314.sa2
Article and author information
Author details

   Rossana Rauti
   Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
   Contribution Conceptualization, Formal analysis, Methodology, Project administration, Writing – original draft
   Contributed equally with Meishar Shahoha and Yael Leichtmann-Bardoogo
   Competing interests No competing interests declared
   ORCID icon

0000-0001-8569-0810
Meishar Shahoha

   School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
   Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

Contribution Conceptualization, Data curation, Investigation, Methodology, Visualization
Contributed equally with Rossana Rauti and Yael Leichtmann-Bardoogo
Competing interests No competing interests declared
ORCID icon
0000-0001-5947-484X
Yael Leichtmann-Bardoogo
Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
Contribution Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – original draft
Contributed equally with Rossana Rauti and Meishar Shahoha
Competing interests No competing interests declared
Rami Nasser
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
Contribution Formal analysis, Software
Competing interests No competing interests declared
Eyal Paz

   School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
   Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

Contribution Methodology, Writing - review and editing
Competing interests No competing interests declared
Rina Tamir
Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
Contribution Formal analysis, Investigation
Competing interests No competing interests declared
Victoria Miller
Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
Contribution Formal analysis, Investigation
Competing interests No competing interests declared
Tal Babich

   Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
   School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

Contribution Formal analysis, Investigation
Competing interests No competing interests declared
Kfir Shaked

   Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
   School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

Contribution Formal analysis, Investigation
Competing interests No competing interests declared
Avner Ehrlich
Grass Center for Bioengineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Contribution Methodology, Resources
Competing interests No competing interests declared
Konstantinos Ioannidis
Grass Center for Bioengineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Contribution Methodology, Resources
Competing interests No competing interests declared
Yaakov Nahmias
Grass Center for Bioengineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Contribution Methodology, Resources
Competing interests No competing interests declared
Roded Sharan
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
Contribution Formal analysis, Software
Competing interests No competing interests declared
Uri Ashery

   School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
   Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
   The Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv, Israel

Contribution Funding acquisition, Investigation, Supervision, Writing – original draft
Competing interests No competing interests declared
ORCID icon
0000-0001-6338-7888
Ben Meir Maoz

   Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
   Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
   The Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv, Israel

Contribution Conceptualization, Funding acquisition, Project administration, Supervision, Visualization, Writing – original draft
For correspondence [email protected]
Competing interests No competing interests declared
ORCID icon

   0000-0002-3823-7682

Funding
Israel Science Foundation (2248/19)

   Rossana Rauti
   Yael Leichtmann-Bardoogo
   Rina Tamir
   Victoria Miller
   Tal Babich
   Kfir Shaked
   Ben Meir Maoz

Azrieli Foundation

   Rossana Rauti
   Yael Leichtmann-Bardoogo
   Ben Meir Maoz

Horizon 2020 (SweetBrain 851765)

   Rossana Rauti
   Yael Leichtmann-Bardoogo
   Ben Meir Maoz

Aufzien Family Center for the Prevention and Treatment of Parkinson's Disease

   Rossana Rauti
   Meishar Shahoha
   Yael Leichtmann-Bardoogo
   Eyal Paz
   Rina Tamir
   Victoria Miller
   Tal Babich
   Kfir Shaked
   Uri Ashery
   Ben Meir Maoz

Deutsche Forschungsgemeinschaft (207/10-1)

   Rami Nasser
   Avner Ehrlich
   Konstantinos Ioannidis
   Yaakov Nahmias
   Roded Sharan

Teva Pharmaceutical Industries

   Yael Leichtmann-Bardoogo
   Ben Meir Maoz

Zimin

   Yael Leichtmann-Bardoogo
   Ben Meir Maoz

Ministry of Science and Technology, Israel (3-17351)

   Rossana Rauti
   Yael Leichtmann-Bardoogo
   Ben Meir Maoz

TCCP

   Uri Ashery
   Ben Meir Maoz

Israel Science Foundation (953/16)

   Rina Tamir
   Victoria Miller
   Tal Babich
   Kfir Shaked
   Ben Meir Maoz

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements

BMM was supported by the Azrieli Foundation, Israel Science Foundation (ISF grant: 2248/19), ERC SweetBrain 851765, TEVA, The Aufzien Family Center for the Prevention and Treatment of Parkinson’s Disease at Tel Aviv University, Zimin, Israel Ministry of Science and Technology (Grant No. 3–17351), and TCCP. UA was supported by the Israel Science Foundation (ISF grant 953/16), TEVA, The Aufzien Family Center for the Prevention and Treatment of Parkinson’s Disease at Tel Aviv University, the German Research Foundation (DFG) (NA: 207/10–1) and the Taube/Koret Global Collaboration in Neurodegenerative Diseases. RS was supported by the Israel Science Foundation (ISF grant 2417/20), within the Israel Precision Medicine Partership program. The work of YN, AE, and KI was supported by European Research Council Consolidator Grant OCLD (project no. 681870).
Senior Editor

   Matthias Barton, University of Zurich, Switzerland

Reviewing Editor

   Arduino A Mangoni, Flinders Medical Centre, Australia

Reviewers

   Francesco Pasqualini
   Gad Vatine, Ben Gurion University

Publication history

   Preprint posted: March 1, 2021 (view preprint)
   Received: April 12, 2021
   Accepted: October 9, 2021
   Version of Record published: October 25, 2021 (version 1)

Copyright

© 2021, Rauti et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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Categories and tags

   Research Article Microbiology and Infectious Disease SARS-COV-2 vasculature endothelium protein interactions

Research organism

   Human

Originally Posted By FlashMan-7k:
Why haven't certain people been tried in court and hung?

Originally Posted By HighDesert6920:
This sounds promising

https://www.pfizer.com/news/press-release/press-release-detail/pfizers-novel-covid-19-oral-antiviral-treatment-candidate

Originally Posted By HighDesert6920:
This sounds promising

https://www.pfizer.com/news/press-release/press-release-detail/pfizers-novel-covid-19-oral-antiviral-treatment-candidate

Originally Posted By Obelix45:
I took a look at the VAERS data today after reading the following article about "hot" lots of vaccines: Uh, That's Not A Conspiracy Theory - The Market Ticker

A few of these VAERS graphs I shared in another thread, but I made some more graphs this evening and I figured I would just share them all here in case anyone here isn't following other threads.  There are a lot of graphs, so I'll put them in spoilers.

First, here are some histograms of the age distribution of deaths for the vaccines, including manufacturer-specific histograms.  I find it interesting that the age distributions look very similar across manufacturers.  Another odd thing is that the Moderna distribution looks almost too perfect -- it peaks in the 81-85 age group and declines steadily toward younger and older age brackets, with no small peaks and valleys like the other two manufacturers.  Not necessarily a sign of manipulation because it could happen by chance, but it stuck out to me.

Click To View Spoiler



Next, here are some different ways of looking at the numbers.  These graphs below are for all manufacturers combined.  The drop in deaths in the summer (June and July) corresponds to fewer doses of vaccine being distributed.  However, the increase in deaths in August and September is qualitatively higher than I would have expected.  Although vaccinations went up in those months, they didn't go up very much compared to summer.  

The last histogram in this group shows the time lag between vaccination and death.  Notice that most deaths occur within 2 to 3 weeks of vaccination date.  But there are some that have a much longer lag.  Remember that VAERS data is unverified and there are probably some wacky entries in here.  I didn't examine them enough to give a decisive opinion on the entire group.  If I had a $6 billion budget like the FDA I would probably pay somebody to do that.  Or maybe not.  That yacht isn't going to pay off itself, amirite?  

Click To View Spoiler



Now some scatter plots showing date of vaccination (triangles) and date of death (circles) for each manufacturer.  Notice that the spread between vaccination and death starts to increase for all manufacturers after May, in an odd nonintuitive way.  It's not a gradually widening spread, it just kind of diverges after May.  Many deaths after May still happen shortly after vaccination, but there is a group of deaths after May for which vaccination date is April or earlier, without much in between.  Again, these scatter plots look similar regardless of manufacturer.  I find that very interesting.  It suggests a factor that might overshadow manufacturer-specific technology or quality control.  What is that factor? I don't know.

Click To View Spoiler



Finally, below are a couple graphs of the "hottest" Pfizer lot (EN6201) mentioned in this article:  Uh, That's Not A Conspiracy Theory - The Market Ticker

I also found this lot to be the "hottest" Pfizer lot, but I only counted around 40 deaths from it instead of 117 like the article says.  Maybe I made a mistake.  

Click To View Spoiler

If anyone else looks at this data, let me know what numbers you get.  I did this all in Excel rather quickly, so I can't guarantee the results are error free.

Originally Posted By planemaker:


The problem as I see it is you don't have the lot sizes. Without lot sizes, you can't look at actual *rates*. In theory, if you had death *rates* by lot, then you could say they are flat across lots (no QA/QC problems), normal distribution (some QA/QC problems), or disjoint (big QA/QC problems). Without having the lot sizes, you can't really compare the number of deaths directly. If one lot is 10x as large as the next, if the difference in deaths is 10x then they have the same *rate* and there really is nothing to look at relative to lots being hot or not.

One other thing that is also difficult to quantify is that early on, only the elderly and those with significant co-morbidities were getting the shots. That would likely tend to skew the adverse events (particularly deaths) because of the frailty factor. So, even though Karl looked at de-coupling the age difference, there was no way to de-couple the fact that early lot numbers were given more to folks more likely to have a harsher reaction.

Originally Posted By Obelix45:
I took a look at the VAERS data today after reading the following article about "hot" lots of vaccines: Uh, That's Not A Conspiracy Theory - The Market Ticker

A few of these VAERS graphs I shared in another thread, but I made some more graphs this evening and I figured I would just share them all here in case anyone here isn't following other threads.  There are a lot of graphs, so I'll put them in spoilers.

First, here are some histograms of the age distribution of deaths for the vaccines, including manufacturer-specific histograms.  I find it interesting that the age distributions look very similar across manufacturers.  Another odd thing is that the Moderna distribution looks almost too perfect -- it peaks in the 81-85 age group and declines steadily toward younger and older age brackets, with no small peaks and valleys like the other two manufacturers.  Not necessarily a sign of manipulation because it could happen by chance, but it stuck out to me.

Click To View Spoiler



Next, here are some different ways of looking at the numbers.  These graphs below are for all manufacturers combined.  The drop in deaths in the summer (June and July) corresponds to fewer doses of vaccine being distributed.  However, the increase in deaths in August and September is qualitatively higher than I would have expected.  Although vaccinations went up in those months, they didn't go up very much compared to summer.  

The last histogram in this group shows the time lag between vaccination and death.  Notice that most deaths occur within 2 to 3 weeks of vaccination date.  But there are some that have a much longer lag.  Remember that VAERS data is unverified and there are probably some wacky entries in here.  I didn't examine them enough to give a decisive opinion on the entire group.  If I had a $6 billion budget like the FDA I would probably pay somebody to do that.  Or maybe not.  That yacht isn't going to pay off itself, amirite?  

Click To View Spoiler



Now some scatter plots showing date of vaccination (triangles) and date of death (circles) for each manufacturer.  Notice that the spread between vaccination and death starts to increase for all manufacturers after May, in an odd nonintuitive way.  It's not a gradually widening spread, it just kind of diverges after May.  Many deaths after May still happen shortly after vaccination, but there is a group of deaths after May for which vaccination date is April or earlier, without much in between.  Again, these scatter plots look similar regardless of manufacturer.  I find that very interesting.  It suggests a factor that might overshadow manufacturer-specific technology or quality control.  What is that factor? I don't know.

Click To View Spoiler



Finally, below are a couple graphs of the "hottest" Pfizer lot (EN6201) mentioned in this article:  Uh, That's Not A Conspiracy Theory - The Market Ticker

I also found this lot to be the "hottest" Pfizer lot, but I only counted around 40 deaths from it instead of 117 like the article says.  Maybe I made a mistake.  

Click To View Spoiler


If anyone else looks at this data, let me know what numbers you get.  I did this all in Excel rather quickly, so I can't guarantee the results are error free.

Originally Posted By HighDesert6920:
lol - ok, well no doubt they are milking it for all its worth financially, while the politicians milk the pandemic for all its worth in control and power grabbing....but personally, I'm glad to see treatments emerging as alternatives to the so-called vaccines...

Originally Posted By HighDesert6920:
Yes, and groceries.  Not necessarily for the virus so much anymore - the fomite transmission has pretty much been shown to be minimal - but for the virus and everything else.

A study from several years ago (long before the chinese virus) found feces on nearly 3/4 shopping carts.  Look at the people in wally world next to you....they are wiping, picking, and licking all kinds of things....the fat hairy weirdo in line ahead of you is picking his nose, swiping his greasy hair back, farts and then reaches down to scratch his ass....then goes tap, tap, tap on the payment terminal....maybe sneezes in your direction...he was rummaging through all those bags of chips just before you picked out a bag.

So his nasty crotch snot is smeared across the top of your chip bag....now you've got the munchies, and open the bag of chips, rubbing your hands through that guys snot, and then you grab a handful of chips and snarf them down....infected snot and all!

What about those delicious apples you enjoy?  Ever drive by an apple orchard during picking season?  Of course, they're all illegals - but anyway, notice where the porta-potty units are - at the edge of the field...no hand washing stations to be seen....last night was chalupa night at Dos Gringos....makes Taco Bell look like quality...the guys in the field just went there last night for cheap beer and chalupas, now they have feel a power growler coming on right before picking shift.   It's a mess in there!  Unfortunately no hand washing stations...so just wipe hands on pants, and start picking fresh apples for the rich gringos.  Now at the store, the apples are all coated with....something...something you're going to eat - because cleaning foods before eating is for pussy doomers!  You're so  brave!

Originally Posted By Obelix45:
I took a look at the VAERS data today after reading the following article about "hot" lots of vaccines: Uh, That's Not A Conspiracy Theory - The Market Ticker

A few of these VAERS graphs I shared in another thread, but I made some more graphs this evening and I figured I would just share them all here in case anyone here isn't following other threads.  There are a lot of graphs, so I'll put them in spoilers.

First, here are some histograms of the age distribution of deaths for the vaccines, including manufacturer-specific histograms.  I find it interesting that the age distributions look very similar across manufacturers.  Another odd thing is that the Moderna distribution looks almost too perfect -- it peaks in the 81-85 age group and declines steadily toward younger and older age brackets, with no small peaks and valleys like the other two manufacturers.  Not necessarily a sign of manipulation because it could happen by chance, but it stuck out to me.

Click To View Spoiler



Next, here are some different ways of looking at the numbers.  These graphs below are for all manufacturers combined.  The drop in deaths in the summer (June and July) corresponds to fewer doses of vaccine being distributed.  However, the increase in deaths in August and September is qualitatively higher than I would have expected.  Although vaccinations went up in those months, they didn't go up very much compared to summer.  

The last histogram in this group shows the time lag between vaccination and death.  Notice that most deaths occur within 2 to 3 weeks of vaccination date.  But there are some that have a much longer lag.  Remember that VAERS data is unverified and there are probably some wacky entries in here.  I didn't examine them enough to give a decisive opinion on the entire group.  If I had a $6 billion budget like the FDA I would probably pay somebody to do that.  Or maybe not.  That yacht isn't going to pay off itself, amirite?  

Click To View Spoiler



Now some scatter plots showing date of vaccination (triangles) and date of death (circles) for each manufacturer.  Notice that the spread between vaccination and death starts to increase for all manufacturers after May, in an odd nonintuitive way.  It's not a gradually widening spread, it just kind of diverges after May.  Many deaths after May still happen shortly after vaccination, but there is a group of deaths after May for which vaccination date is April or earlier, without much in between.  Again, these scatter plots look similar regardless of manufacturer.  I find that very interesting.  It suggests a factor that might overshadow manufacturer-specific technology or quality control.  What is that factor? I don't know.

Click To View Spoiler



Finally, below are a couple graphs of the "hottest" Pfizer lot (EN6201) mentioned in this article:  Uh, That's Not A Conspiracy Theory - The Market Ticker

I also found this lot to be the "hottest" Pfizer lot, but I only counted around 40 deaths from it instead of 117 like the article says.  Maybe I made a mistake.  

Click To View Spoiler


If anyone else looks at this data, let me know what numbers you get.  I did this all in Excel rather quickly, so I can't guarantee the results are error free.

Originally Posted By Mach:
the point about needing percentages not just raw numbers made by planemaker is very valid. The distribution of age would not be consistent from lot number to lot number because of the way the vax was rolled  out and to who it was offered in the time variant.