Quote from Даниил on November 20, 2021, 4:55 pm

https://alexberenson.substack.com/p/vaccinated-english-adults-under-60

Quote from Johannes on November 26, 2021, 5:10 pm

I spent a long time going through the data and I feel like I’m getting closer to understanding what actually happens after vaccination, especially the order in which everything happens. The main problem with the data is that so many genes are upregulated it becomes difficult to sort them into functional networks.

Vaccine-induced retinoic acid-related genes

There is one group of co-regulated genes exhibiting a distinct expression pattern that I’ve named vaccine-induced retinoic acid-related genes (VIRRG), since the expression of many VIRRGs can be induced by retinoic acid (RA). Genes from this group were significantly downregulated after the first dose, significantly upregulated after the second dose and slightly less on D28, and remained elevated nonsignificantly on D42. The VIRRGs include, in no particular order

• Prostaglandin G/H synthase 2 (PTGS2), which is directly induced by all-trans-retinoic acid (Alique, Herrero et al. 2007) and metabolizes RA to the pro-inflammatory (4S)-OH-RA; furthermore PTGS2 is induced by the original SARS-CoV (Liu, Yang et al. 2007)
• Class E basic helix-loop-helix protein 40 (BHLHE40), which represses transcriptional activity of retinoic acid-activated retinoic acid receptors RXR (Cho, Noshiro et al. 2009)
• DNA damage-inducible transcript 3 protein (DDIT3), which is activated in response to a variety of cell stresses including endoplasmic reticulum (ER) stress, and additionally by retinoic acid (Nakanishi, Tomaru et al. 2008)
• Cyclin-dependent kinase inhibitor 1 (CDKN1A), which is activated in response to DNA damage and inhibits DNA synthesis
• 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3), which plays a role in cancer (Shi, Pan et al. 2017)
• cAMP-specific 3',5'-cyclic phosphodiesterase 4B (PDE4B), which hydrolyzes cAMP and plays a role in inflammation
• Mitogen-activated protein kinase kinase kinase 8 (MAP3K8), which negatively regulates type I interferons but positively regulates interferon gamma (IFNG)
• Interleukin-6 (IL6), a cytokine with a number of different functions

I also programmed a new algorithm which has proven to be more reliable than Pearson’s correlation coefficient in identifying correlations between genes, which I’ll be describing in more detail soon. With the new algorithm I discovered that all eight VIRRG members are correlated with cyclic AMP-dependent transcription factor ATF-3 (ATF3):

Note that oncostatin-M (OSM) is from the same protein family as IL6, highly correlated with IL6 (data not shown) and regulates the expression of IL6 (Van Wagoner, Choi et al. 2000). Furthermore, DDIT3 was shown to form transcriptionally inactive heterodimers with ATF3 (Chen, Wolfgang et al. 1996), specifically nucleus-localized DDIT3, whereas cytoplasmic DDIT3 induces ATF3 (Jauhiainen, Thomsen et al. 2012). Since both DDIT3 and ATF3 are downregulated this would suggest that after the first vaccination dose DDIT3 preferentially localizes to the nucleus where it represses ATF3.

Cross-referencing the genes with known ATF3 transcription factor targets from the ENCODE dataset (Rouillard, Gundersen et al. 2016) revealed that only 4 of the 8 VIRRGs are directly regulated by ATF3, however 7 of 8 VIRRG members are target genes of BHLHE40 (including IL6 but not OSM). Therefore, it is likely that BHLHE40 is directly responsible for the downregulation of the other 7 VIRRGs. In fact, the dysregulation of BHLHE40, which is formerly known as stimulated by retinoic acid 14 (STRA14), directly links vitamin A not only to vaccine adverse events (VAEs) but also to SARS-CoV-2 infection and even to the original SARS-CoV. BHLHE40 was found to be overactivated in moderate COVID-19 patients but underactivated in severe COVID-19 patients (Vázquez-Jiménez, Avila-Ponce De León et al. 2021), and PTGS2, which is a target gene of BHLHE40 was shown to be induced by the original SARS-CoV spike protein (Liu, Yang et al. 2007). The pathogenesis of both COVID-19 and VAEs is now becoming increasingly clear:

1. The vaccine is administered, nanoparticles enter cells via endocytosis, S mRNA is released from nanoparticles and translated, assembled S translocates to the cell membranes where it is cleaved, and the cleavage products are released into systemic circulation (Palmer and Bhakdi 2021)
2. S cleavage products bind to STRA6, preventing transport of retinol into cells
3. Decreased retinol in cells is initially very beneficial: significantly less damaged DNA (DDIT3 -32%, p < 0.05; CDKN1A -55%, p < 0.05), less inflammation (PTGS2 -51%, p <0.05; HIF1A -27% n.s., PDE4B -56%, p < 0.01) and increased respiratory capacity since protein kinase C delta type (PRKCD) is up 73% (p < 0.05)
4. These effects don’t last very long as more and more retinol is released into the bloodstream that cannot be taken up anywhere since STRA6 is blocked by S; additionally since RBP4 is a target gene of STAT5, and since STAT5 is phosphorylated by STRA6 there is potentially an undersupply of serum RBP4; as a consequence more and more unbound retinol diffuses into cells and BHLHE40 gets expressed again within 1–2 days
5. In otherwise healthy people gene expression normalizes within a week
6. On administration of the second vaccine dose, however, the body rapidly begins expressing retinoic acid-inducible genes; it is unclear whether this is due to actual increases in cellular retinoic acid concentrations or whether this is a preemptive immune response induced by recognition of the spike protein by pattern recognition receptors
7. Gene expression does not fully normalize within 20 days of reinfection/second vaccine dose, with VIRRGs still elevated roughly 40% by D42, some significantly

Quote from Даниил on November 20, 2021, 4:55 pm

https://alexberenson.substack.com/p/vaccinated-english-adults-under-60

While I agree with the premise that the vaccine should cause a measurable increase in mortality, this comment raises some valid concerns with the statistic.

Bibliography

Alique, M., J. F. Herrero and F. J. Lucio-Cazana (2007). "All-trans retinoic acid induces COX-2 and prostaglandin E2 synthesis in SH-SY5Y human neuroblastoma cells: involvement of retinoic acid receptors and extracellular-regulated kinase 1/2." J Neuroinflammation 4: 1.

Chen, B. P., C. D. Wolfgang and T. Hai (1996). "Analysis of ATF3, a transcription factor induced by physiological stresses and modulated by gadd153/Chop10." Molecular and cellular biology 16(3): 1157-1168.

Cho, Y., M. Noshiro, M. Choi, K. Morita, T. Kawamoto, K. Fujimoto, Y. Kato and M. Makishima (2009). "The basic helix-loop-helix proteins differentiated embryo chondrocyte (DEC) 1 and DEC2 function as corepressors of retinoid X receptors." Mol Pharmacol 76(6): 1360-1369.

Jauhiainen, A., C. Thomsen, L. Strömbom, P. Grundevik, C. Andersson, A. Danielsson, M. K. Andersson, O. Nerman, L. Rörkvist, A. Ståhlberg and P. Åman (2012). "Distinct cytoplasmic and nuclear functions of the stress induced protein DDIT3/CHOP/GADD153." PloS one 7(4): e33208-e33208.

Liu, M., Y. Yang, C. Gu, Y. Yue, K. K. Wu, J. Wu and Y. Zhu (2007). "Spike protein of SARS-CoV stimulates cyclooxygenase-2 expression via both calcium-dependent and calcium-independent protein kinase C pathways." Faseb j 21(7): 1586-1596.

Nakanishi, M., Y. Tomaru, H. Miura, Y. Hayashizaki and M. Suzuki (2008). "Identification of transcriptional regulatory cascades in retinoic acid-induced growth arrest of HepG2 cells." Nucleic Acids Research 36(10): 3443-3454.

Palmer, M. and S. Bhakdi (2021). "The Pfizer mRNA vaccine: pharmacokinetics and toxicity." Doctors for COVID Ethics.

Rouillard, A. D., G. W. Gundersen, N. F. Fernandez, Z. Wang, C. D. Monteiro, M. G. McDermott and A. Ma’ayan (2016). "The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins." Database 2016.

Shi, L., H. Pan, Z. Liu, J. Xie and W. Han (2017). "Roles of PFKFB3 in cancer." Signal Transduction and Targeted Therapy 2(1): 17044.

Van Wagoner, N. J., C. Choi, P. Repovic and E. N. Benveniste (2000). "Oncostatin M regulation of interleukin-6 expression in astrocytes: biphasic regulation involving the mitogen-activated protein kinases ERK1/2 and p38." J Neurochem 75(2): 563-575.

Vázquez-Jiménez, A., U. E. Avila-Ponce De León, M. Matadamas-Guzman, E. A. Muciño-Olmos, Y. E. Martínez-López, T. Escobedo-Tapia and O. Resendis-Antonio (2021). "On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers." Frontiers in Immunology 12(3676).

Quote from Даниил on November 26, 2021, 5:26 pm

Quote from Johannes on November 26, 2021, 5:10 pm

I spent a long time going through the data and I feel like I’m getting closer to understanding what actually happens after vaccination, especially the order in which everything happens. The main problem with the data is that so many genes are upregulated it becomes difficult to sort them into functional networks.

Vaccine-induced retinoic acid-related genes

There is one group of co-regulated genes exhibiting a distinct expression pattern that I’ve named vaccine-induced retinoic acid-related genes (VIRRG), since the expression of many VIRRGs can be induced by retinoic acid (RA). Genes from this group were significantly downregulated after the first dose, significantly upregulated after the second dose and slightly less on D28, and remained elevated nonsignificantly on D42. The VIRRGs include, in no particular order

• Prostaglandin G/H synthase 2 (PTGS2), which is directly induced by all-trans-retinoic acid (Alique, Herrero et al. 2007) and metabolizes RA to the pro-inflammatory (4S)-OH-RA; furthermore PTGS2 is induced by the original SARS-CoV (Liu, Yang et al. 2007)
• Class E basic helix-loop-helix protein 40 (BHLHE40), which represses transcriptional activity of retinoic acid-activated retinoic acid receptors RXR (Cho, Noshiro et al. 2009)
• DNA damage-inducible transcript 3 protein (DDIT3), which is activated in response to a variety of cell stresses including endoplasmic reticulum (ER) stress, and additionally by retinoic acid (Nakanishi, Tomaru et al. 2008)
• Cyclin-dependent kinase inhibitor 1 (CDKN1A), which is activated in response to DNA damage and inhibits DNA synthesis
• 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3), which plays a role in cancer (Shi, Pan et al. 2017)
• cAMP-specific 3',5'-cyclic phosphodiesterase 4B (PDE4B), which hydrolyzes cAMP and plays a role in inflammation
• Mitogen-activated protein kinase kinase kinase 8 (MAP3K8), which negatively regulates type I interferons but positively regulates interferon gamma (IFNG)
• Interleukin-6 (IL6), a cytokine with a number of different functions

I also programmed a new algorithm which has proven to be more reliable than Pearson’s correlation coefficient in identifying correlations between genes, which I’ll be describing in more detail soon. With the new algorithm I discovered that all eight VIRRG members are correlated with cyclic AMP-dependent transcription factor ATF-3 (ATF3):

Note that oncostatin-M (OSM) is from the same protein family as IL6, highly correlated with IL6 (data not shown) and regulates the expression of IL6 (Van Wagoner, Choi et al. 2000). Furthermore, DDIT3 was shown to form transcriptionally inactive heterodimers with ATF3 (Chen, Wolfgang et al. 1996), specifically nucleus-localized DDIT3, whereas cytoplasmic DDIT3 induces ATF3 (Jauhiainen, Thomsen et al. 2012). Since both DDIT3 and ATF3 are downregulated this would suggest that after the first vaccination dose DDIT3 preferentially localizes to the nucleus where it represses ATF3.

Cross-referencing the genes with known ATF3 transcription factor targets from the ENCODE dataset (Rouillard, Gundersen et al. 2016) revealed that only 4 of the 8 VIRRGs are directly regulated by ATF3, however 7 of 8 VIRRG members are target genes of BHLHE40 (including IL6 but not OSM). Therefore, it is likely that BHLHE40 is directly responsible for the downregulation of the other 7 VIRRGs. In fact, the dysregulation of BHLHE40, which is formerly known as stimulated by retinoic acid 14 (STRA14), directly links vitamin A not only to vaccine adverse events (VAEs) but also to SARS-CoV-2 infection and even to the original SARS-CoV. BHLHE40 was found to be overactivated in moderate COVID-19 patients but underactivated in severe COVID-19 patients (Vázquez-Jiménez, Avila-Ponce De León et al. 2021), and PTGS2, which is a target gene of BHLHE40 was shown to be induced by the original SARS-CoV spike protein (Liu, Yang et al. 2007). The pathogenesis of both COVID-19 and VAEs is now becoming increasingly clear:

1. The vaccine is administered, nanoparticles enter cells via endocytosis, S mRNA is released from nanoparticles and translated, assembled S translocates to the cell membranes where it is cleaved, and the cleavage products are released into systemic circulation (Palmer and Bhakdi 2021)
2. S cleavage products bind to STRA6, preventing transport of retinol into cells
3. Decreased retinol in cells is initially very beneficial: significantly less damaged DNA (DDIT3 -32%, p < 0.05; CDKN1A -55%, p < 0.05), less inflammation (PTGS2 -51%, p <0.05; HIF1A -27% n.s., PDE4B -56%, p < 0.01) and increased respiratory capacity since protein kinase C delta type (PRKCD) is up 73% (p < 0.05)
4. These effects don’t last very long as more and more retinol is released into the bloodstream that cannot be taken up anywhere since STRA6 is blocked by S; additionally since RBP4 is a target gene of STAT5, and since STAT5 is phosphorylated by STRA6 there is potentially an undersupply of serum RBP4; as a consequence more and more unbound retinol diffuses into cells and BHLHE40 gets expressed again within 1–2 days
5. In otherwise healthy people gene expression normalizes within a week
6. On administration of the second vaccine dose, however, the body rapidly begins expressing retinoic acid-inducible genes; it is unclear whether this is due to actual increases in cellular retinoic acid concentrations or whether this is a preemptive immune response induced by recognition of the spike protein by pattern recognition receptors
7. Gene expression does not fully normalize within 20 days of reinfection/second vaccine dose, with VIRRGs still elevated roughly 40% by D42, some significantly

Quote from Даниил on November 20, 2021, 4:55 pm

https://alexberenson.substack.com/p/vaccinated-english-adults-under-60

While I agree with the premise that the vaccine should cause a measurable increase in mortality, this comment raises some valid concerns with the statistic.

Bibliography

Alique, M., J. F. Herrero and F. J. Lucio-Cazana (2007). "All-trans retinoic acid induces COX-2 and prostaglandin E2 synthesis in SH-SY5Y human neuroblastoma cells: involvement of retinoic acid receptors and extracellular-regulated kinase 1/2." J Neuroinflammation 4: 1.

Chen, B. P., C. D. Wolfgang and T. Hai (1996). "Analysis of ATF3, a transcription factor induced by physiological stresses and modulated by gadd153/Chop10." Molecular and cellular biology 16(3): 1157-1168.

Cho, Y., M. Noshiro, M. Choi, K. Morita, T. Kawamoto, K. Fujimoto, Y. Kato and M. Makishima (2009). "The basic helix-loop-helix proteins differentiated embryo chondrocyte (DEC) 1 and DEC2 function as corepressors of retinoid X receptors." Mol Pharmacol 76(6): 1360-1369.

Jauhiainen, A., C. Thomsen, L. Strömbom, P. Grundevik, C. Andersson, A. Danielsson, M. K. Andersson, O. Nerman, L. Rörkvist, A. Ståhlberg and P. Åman (2012). "Distinct cytoplasmic and nuclear functions of the stress induced protein DDIT3/CHOP/GADD153." PloS one 7(4): e33208-e33208.

Liu, M., Y. Yang, C. Gu, Y. Yue, K. K. Wu, J. Wu and Y. Zhu (2007). "Spike protein of SARS-CoV stimulates cyclooxygenase-2 expression via both calcium-dependent and calcium-independent protein kinase C pathways." Faseb j 21(7): 1586-1596.

Nakanishi, M., Y. Tomaru, H. Miura, Y. Hayashizaki and M. Suzuki (2008). "Identification of transcriptional regulatory cascades in retinoic acid-induced growth arrest of HepG2 cells." Nucleic Acids Research 36(10): 3443-3454.

Palmer, M. and S. Bhakdi (2021). "The Pfizer mRNA vaccine: pharmacokinetics and toxicity." Doctors for COVID Ethics.

Rouillard, A. D., G. W. Gundersen, N. F. Fernandez, Z. Wang, C. D. Monteiro, M. G. McDermott and A. Ma’ayan (2016). "The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins." Database 2016.

Shi, L., H. Pan, Z. Liu, J. Xie and W. Han (2017). "Roles of PFKFB3 in cancer." Signal Transduction and Targeted Therapy 2(1): 17044.

Van Wagoner, N. J., C. Choi, P. Repovic and E. N. Benveniste (2000). "Oncostatin M regulation of interleukin-6 expression in astrocytes: biphasic regulation involving the mitogen-activated protein kinases ERK1/2 and p38." J Neurochem 75(2): 563-575.

Vázquez-Jiménez, A., U. E. Avila-Ponce De León, M. Matadamas-Guzman, E. A. Muciño-Olmos, Y. E. Martínez-López, T. Escobedo-Tapia and O. Resendis-Antonio (2021). "On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers." Frontiers in Immunology 12(3676).

 

While I agree with the premise that the vaccine should cause a measurable increase in mortality, this comment raises some valid concerns with the statistic

Yes, but this, to put it mildly, raises concerns about vaccines until proven otherwise.

Quote from Johannes on November 26, 2021, 5:59 pm

Quote from Даниил on November 26, 2021, 5:26 pm

Yes, but this, to put it mildly, raises concerns until proven otherwise.

It’s not really that concerning at all because the chart simply shows the statistical effect of vaccinating old people first. For example, if the average age of the population is 30, and you initially only vaccinate people of age >50, the average age of the vaccinated group will be higher and therefore the death rate will be higher, like in the chart. As more young people get vaccinated both death rates will converge, which can also be seen in the chart, since the death rate decreases from 3.3 to 2.2 in the vaccinated group. The downtrend is consistent and does not diverge much, so we would probably have to populate this chart with data until the average age of each group remains constant. In fact, the death rate is likely declining in the unvaccinated group for the same reason, since more people of age >18 are excluded as they get vaccinated while parents are less inclined to vaccine children of ages 10–17, further causing the death rate to decrease.

Edit: I do think that the vaccines are very concerning, and I've provided ample evidence in my other posts; I just don't think this particular statistic is very meaningful.

Quote from Даниил on November 26, 2021, 6:19 pm

get vaccinated both death rates will converge, which can also be seen in the chart, since the death rate decreases from 3.3 to 2.2 in the vaccinated group

The mortality rate among the unvaccinated also decreases from 1.2 to 0.9 for the same time period. For some reason, mortality in the end is decreasing in both groups and it may seem that the schedules are converging, because the mortality rate among those vaccinated is 2 times higher.

Quote from Johannes on November 29, 2021, 1:22 pm

I spent the last two weeks of my life carefully going through the data trying to make sense of it, only to discover that the data was falsified by the original authors and therefore meaningless.

To recap, in the original study, samples were collected from 6 participants on 8 different days, but data was conspicuously missing for 3 of 6 participants only on day 1 (the day after the first vaccine dose). Even though the authors mention removing some samples from the RNA-seq data, no such mention is given for the CITE-seq data.

I’ve been tweaking my correlation/causality algorithm, still trying to determine which gene is ultimately responsible for the strange expression of genes on day 1 post-vaccination. After hours of calculations my algorithm implicated ATP-dependent RNA helicase DDX3Y (DDX3Y) and the related DDX3X as the root cause. DDX3X is X-linked and therefore present in men and women whereas DDX3Y is Y-linked and therefore only present in men. Now, take a look at the raw data for DDX3X and DDX3Y:

Participant 2051, who is listed as female in the metadata, appears to express the male-specific DDX3Y gene only on day 1 post-vaccination, while participant 2052, who is listed as male, appears to not express DDX3Y only on day 1. These results are biologically impossible, and I also can’t think of any honest mistake that would have produced results similar to these.

However, an explanation that makes perfect sense is that the authors wanted to cover something up that only occurred in female participants and only on day 1, so they went and removed all the female samples, relabeled participant 2052 as male and participant 2051 as female so nobody would notice. Then, to further cover it up they manually swapped all the values for sex-specific genes, but forgot to swap the values for DDX3Y on day 1.

The silver lining is that—incredibly—my algorithm was able to identify the root cause of the problem, but the fact remains that basically everything I’ve posted in this thread is meaningless because the conclusions are based on falsified data.

Quote from Hermes on November 29, 2021, 3:04 pm

What you're writing about is so far advanced, I really can't say anything other than I'm impressed by your meticulousness. You're discovering how rigged the Covid science is. Kudos to that. I feel your frustration. You're going totally Sisyphus on this, just to learn it's all f*****g bogus science.

Quote from Даниил on December 4, 2021, 1:42 am

@johannes2 have you seen this study?

https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC8013358/#!po=39.8876

Quote from Johannes on January 7, 2022, 9:21 am

In early December, I emailed the Nature editors about the issues with the data. Surprisingly, I received a reply directly from the authors yesterday, in which they assert that the 50% reduced number of samples on day 1 was intentional and that it was described as such in the study. Why they would want to conduct a study to determine the effects of vaccination, only to not sample all of the available participants remains unclear to me.

Furthermore, the authors produced a figure that corroborates all of the issues I’ve raised. Bizarrely, they use the figure to argue that everything is in order and that there was no manipulation. Therefore, I also analyzed the RNA-Seq dataset and found even more inconsistencies, indicating that it wasn’t just the CITE-Seq data that was manipulated, but also the RNA-Seq data. I’ve attached all the correspondence to this post, so you can decide for yourself which party is more credible.

Quote from Даниил on December 4, 2021, 1:42 am

@johannes2 have you seen this study?

https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC8013358/#!po=39.8876

It's a very interesting study, and it raises more questions than it provides answers. I would love to find out whether the interaction between holo-S and STRA6 induces dissociation of retinol from S to RBP1, whether it also induces phosphorylation of JAK2 and whether retinol is actually the physiological ligand for the S fatty acid-binding pocket. It could even be that retinol is a required co-factor for viral replication, since some have proposed retinol to be a co-factor for respiration (Hammerling 2016). I personally don’t agree with that theory, and good arguments against it have been raised by (de Oliveira 2015).

Bibliography

de Oliveira, M. R. (2015). "Vitamin A and Retinoids as Mitochondrial Toxicants." Oxidative Medicine and Cellular Longevity 2015.

Hammerling, U. (2016). "Retinol as electron carrier in redox signaling, a new frontier in vitamin A research." Hepatobiliary Surgery and Nutrition 5(1): 15-28.