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Archive for the ‘Genome Biology’ Category


Precision Cardiology to Benefit from New Atlas of Cells of the Adult Human Heart

Reporters: Justin D. Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN

 

The Voice of Dr. Pearlman on potential clinical implications of the New Atlas:

 

Published on 9/24/2020 in Nature

Litviňuková, M., Talavera-López, C., Maatz, H. et al. Cells of the adult human heart. Nature (2020). https://doi.org/10.1038/s41586-020-2797-4

 

Abstract

Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and therapeutic strategies require deeper understanding of the healthy heart’s molecular processes. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavor. Here, using state-of-the-art analyses of large-scale single-cell and nuclei transcriptomes, we characterise six anatomical adult heart regions. Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes, and fibroblasts, revealing distinct atrial and ventricular subsets with diverse developmental origins and specialized properties. We define the complexity of the cardiac vasculature and its changes along the arterio-venous axis. In the immune compartment we identify cardiac resident macrophages with inflammatory and protective transcriptional signatures. Further, inference of cell-cell interactions highlight different macrophage-fibroblast-cardiomyocyte networks between atria and ventricles that are distinct from skeletal muscle. Our human cardiac cell atlas improves our understanding of the human heart and provides a healthy reference for future studies.

Author information

Affiliations

Corresponding authors

Correspondence to J. G. Seidman or Christine E. Seidman or Michela Noseda or Norbert Hubner or Sarah A. Teichmann.

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Double Mutant PI3KA Found to Lead to Higher Oncogenic Signaling in Cancer Cells

Curator: Stephen J. Williams, PhD

PIK3CA (Phosphatidylinsitol 4,5-bisphosphate (PIP2) 3-kinase catalytic subunit α) is one of the most frequently mutated oncogenes in various tumor types ([1] and http://www.sanger.ac.uk/genetics/CGP/cosmic). Oncogenic mutations leading to the overactivation of PIK3CA, especially in context in of inactivating PTEN mutations, result in overtly high signaling activity and associated with the malignant phenotype.

In a Perspective article (Double trouble for cancer gene: Double mutations in an oncogene enhance tumor growth) in the journal Science[2], Dr. Alex Toker discusses the recent results of Vasan et al. in the same issue of Science[3] on the finding that double mutations in the same allele of PIK3CA are more frequent in cancer genomes than previously identified and these double mutations lead to increased PI3K pathway activation, increased tumor growth, and increased sensitivity to PI3K inhibitors in human breast cancer.

 

 

From Dr. Melvin Crasto blog NewDrugApprovals.org

Alpelisib: PIK3CA inhibitor:

Alpelisib: New PIK3CA inhibitor approved for HER2 negative metastatic breast cancer

 

FDA approves first PI3K inhibitor for breast cancer

syn https://newdrugapprovals.org/2018/06/25/alpelisib-byl-719/

Today, the U.S. Food and Drug Administration approved Piqray (alpelisib) tablets, to be used in combination with the FDA-approved endocrine therapy fulvestrant, to treat postmenopausal women, and men, with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, PIK3CA-mutated, advanced or metastatic breast cancer (as detected by an FDA-approved test) following progression on or after an endocrine-based regimen.

The FDA also approved the companion diagnostic test, therascreen PIK3CA RGQ PCR Kit, to detect the PIK3CA mutation in a tissue and/or a liquid biopsy. Patients who are negative by

May 24, 2019

Today, the U.S. Food and Drug Administration approved Piqray (alpelisib) tablets, to be used in combination with the FDA-approved endocrine therapy fulvestrant, to treat postmenopausal women, and men, with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, PIK3CA-mutated, advanced or metastatic breast cancer (as detected by an FDA-approved test) following progression on or after an endocrine-based regimen.

The FDA also approved the companion diagnostic test, therascreen PIK3CA RGQ PCR Kit, to detect the PIK3CA mutation in a tissue and/or a liquid biopsy. Patients who are negative by the therascreen test using the liquid biopsy should undergo tumor biopsy for PIK3CA mutation testing.

“Piqray is the first PI3K inhibitor to demonstrate a clinically meaningful benefit in treating patients with this type of breast cancer. The ability to target treatment to a patient’s specific genetic mutation or biomarker is becoming increasingly common in cancer treatment, and companion diagnostic tests assist oncologists in selecting patients who may benefit from these targeted treatments,” said Richard Pazdur, M.D., director of the FDA’s Oncology Center of Excellence and acting director of the Office of Hematology and Oncology Products in the FDA’s Center for Drug Evaluation and Research. “For this approval, we employed some of our newer regulatory tools to streamline reviews without compromising the quality of our assessment. This drug is the first novel drug approved under the Real-Time Oncology Review pilot program. We also used the updated Assessment Aid, a multidisciplinary review template that helps focus our written review on critical thinking and consistency and reduces time spent on administrative tasks.”

Metastatic breast cancer is breast cancer that has spread beyond the breast to other organs in the body (most often the bones, lungs, liver or brain). When breast cancer is hormone-receptor positive, patients may be treated with anti-hormonal treatment (also called endocrine therapy), alone or in combination with other medicines, or chemotherapy.

The efficacy of Piqray was studied in the SOLAR-1 trial, a randomized trial of 572 postmenopausal women and men with HR-positive, HER2-negative, advanced or metastatic breast cancer whose cancer had progressed while on or after receiving an aromatase inhibitor. Results from the trial showed the addition of Piqray to fulvestrant significantly prolonged progression- free survival (median of 11 months vs. 5.7 months) in patients whose tumors had a PIK3CA mutation.

Common side effects of Piqray are high blood sugar levels, increase in creatinine, diarrhea, rash, decrease in lymphocyte count in the blood, elevated liver enzymes, nausea, fatigue, low red blood cell count, increase in lipase (enzymes released by the pancreas), decreased appetite, stomatitis, vomiting, weight loss, low calcium levels, aPTT prolonged (blood clotting taking longer to occur than it should), and hair loss.

Health care professionals are advised to monitor patients taking Piqray for severe hypersensitivity reactions (intolerance). Patients are warned of potentially severe skin reactions (rashes that may result in peeling and blistering of skin or mucous membranes like the lips and gums). Health care professionals are advised not to initiate treatment in patients with a history of severe skin reactions such as Stevens-Johnson Syndrome, erythema multiforme, or toxic epidermal necrolysis. Patients on Piqray have reported severe hyperglycemia (high blood sugar), and the safety of Piqray in patients with Type 1 or uncontrolled Type 2 diabetes has not been established. Before initiating treatment with Piqray, health care professionals are advised to check fasting glucose and HbA1c, and to optimize glycemic control. Patients should be monitored for pneumonitis/interstitial lung disease (inflammation of lung tissue) and diarrhea during treatment. Piqray must be dispensed with a patient Medication Guide that describes important information about the drug’s uses and risks.

Piqray is the first new drug application (NDA) for a new molecular entity approved under the Real-Time Oncology Review (RTOR) pilot program, which permits the FDA to begin analyzing key efficacy and safety datasets prior to the official submission of an application, allowing the review team to begin their review and communicate with the applicant earlier. Piqray also used the updated Assessment Aid (AAid), a multidisciplinary review template intended to focus the FDA’s written review on critical thinking and consistency and reduce time spent on administrative tasks. With these two pilot programs, today’s approval of Piqray comes approximately three months ahead of the Prescription Drug User Fee Act (PDUFA) VI deadline of August 18, 2019.

The FDA granted this application Priority Review designation. The FDA granted approval of Piqray to Novartis. The FDA granted approval of the therascreen PIK3CA RGQ PCR Kit to QIAGEN Manchester, Ltd.

https://www.fda.gov/news-events/press-announcements/fda-approves-first-pi3k-inhibitor-breast-cancer?utm_campaign=052419_PR_FDA%20approves%20first%20PI3K%20inhibitor%20for%20breast%20cancer&utm_medium=email&utm_source=Eloqua

 

Alpelisib

(2S)-1-N-[4-methyl-5-[2-(1,1,1-trifluoro-2-methylpropan-2-yl)pyridin-4-yl]-1,3-thiazol-2-yl]pyrrolidine-1,2-dicarboxamide

PDT PAT WO 2010/029082

CHEMICAL NAMES: Alpelisib; CAS 1217486-61-7; BYL-719; BYL719; UNII-08W5N2C97Q; BYL 719
MOLECULAR FORMULA: C19H22F3N5O2S
MOLECULAR WEIGHT: 441.473 g/mol
  1. alpelisib
  2. 1217486-61-7
  3. BYL-719
  4. BYL719
  5. UNII-08W5N2C97Q
  6. BYL 719
  7. Alpelisib (BYL719)
  8. (S)-N1-(4-Methyl-5-(2-(1,1,1-trifluoro-2-methylpropan-2-yl)pyridin-4-yl)thiazol-2-yl)pyrrolidine-1,2-dicarboxamide
  9. NVP-BYL719

Alpelisib is an orally bioavailable phosphatidylinositol 3-kinase (PI3K) inhibitor with potential antineoplastic activity. Alpelisib specifically inhibits PI3K in the PI3K/AKT kinase (or protein kinase B) signaling pathway, thereby inhibiting the activation of the PI3K signaling pathway. This may result in inhibition of tumor cell growth and survival in susceptible tumor cell populations. Activation of the PI3K signaling pathway is frequently associated with tumorigenesis. Dysregulated PI3K signaling may contribute to tumor resistance to a variety of antineoplastic agents.

Alpelisib has been used in trials studying the treatment and basic science of Neoplasms, Solid Tumors, BREAST CANCER, 3rd Line GIST, and Rectal Cancer, among others.

 

SYN 2

POLYMORPHS

https://patents.google.com/patent/WO2012175522A1/en

(S)-pyrrolidine-l,2-dicarboxylic acid 2-amide l-(4-methyl-5-[2-(2,2,2-trifluoro-l,l- dimethyl-ethyl)-pyridin-4-yl]-thiazol-2-yl)-amidei hereafter referred to as compound I,

is an alpha-selective phosphatidylinositol 3 -kinase (PI3K) inhibitor. Compound I was originally described in WO 2010/029082, wherein the synthesis of its free base form was described. There is a need for additional solid forms of compound I, for use in drug substance and drug product development. It has been found that new solid forms of compound I can be prepared as one or more polymorph forms, including solvate forms. These polymorph forms exhibit new physical properties that may be exploited in order to obtain new pharmacological properties, and that may be utilized in drug substance and drug product development. Summary of the Invention

In one aspect, provided herein is a crystalline form of the compound of formula I, or a solvate of the crystalline form of the compound of formula I, or a salt of the crystalline form of the compound of formula I, or a solvate of a salt of the crystalline form of the compound of formula I. In one embodiment, the crystalline form of the compound of formula I has the polymorph form SA, SB, Sc, or SD.

In another aspect, provided herein is a pharmaceutical composition comprising a crystalline compound of formula I. In one embodiment of the pharmaceutical composition, the crystalline compound of formula I has the polymorph form SA, SB,Sc, or So.

In another aspect, provided herein is a method for the treatment of disorders mediated by PI3K, comprising administering to a patient in need of such treatment an effective amount of a crystalline compound of formula I, particularly SA, SB, SC,or SD .

In yet another aspect, provided herein is the use of a crystalline compound of formula I, particularly SA, SB, SC, or SD, for the preparation of a medicament for the treatment of disorders mediated by PI3K.

 

Source: https://newdrugapprovals.org/?s=alpelisib&submit=

 

Pharmacology and Toxicology from drugbank.ca

Indication

Alpelisib is indicated in combination with fulvestrant to treat postmenopausal women, and men, with advanced or metastatic breast cancer.Label This cancer must be hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and PIK3CA­ mutated.Label The cancer must be detected by an FDA-approved test following progression on or after an endocrine-based regimen.Label

Associated Conditions

Contraindications & Blackbox Warnings

Learn about our commercial Contraindications & Blackbox Warnings data.

LEARN MORE

 

Pharmacodynamics

Alpelisib does not prolong the QTcF interval.Label Patients taking alpelisib experience a dose dependent benefit from treatment with a 51% advantage of a 200mg daily dose over a 100mg dose and a 22% advantage of 300mg once daily over 150mg twice daily.6 This suggests patients requiring a lower dose may benefit from twice daily dosing.6

Mechanism of action

Phosphatidylinositol-3-kinase-α (PI3Kα) is responsible for cell proliferation in response to growth factor-tyrosine kinase pathway activation.3 In some cancers PI3Kα’s p110α catalytic subunit is mutated making it hyperactive.3 Alpelisib inhibits (PI3K), with the highest specificity for PI3Kα.Label

TARGET ACTIONS ORGANISM
APhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform inhibitor Humans

Absorption

Alpelisib reached a peak concentration in plasma of 1320±912ng/mL after 2 hours.4 Alpelisib has an AUClast of 11,100±3760h ng/mL and an AUCINF of 11,100±3770h ng/mL.4 A large, high fat meal increases the AUC by 73% and Cmax by 84% while a small, low fat meal increases the AUC by 77% and Cmax by 145%.Label

Volume of distribution

The apparent volume of distribution at steady state is 114L.Label

Protein binding

Alpelisib is 89% protein bound.Label

Metabolism

Alpelisib is metabolized by hydrolysis reactions to form the primary metabolite.Label It is also metabolized by CYP3A4.Label The full metabolism of Alpelisib has yet to be determined but a series of reactions have been proposed.4,5 The main metabolic reaction is the substitution of an amine group on alpelisib for a hydroxyl group to form a metabolite known as M44,5 or BZG791.Label Alpelisib can also be glucuronidated to form the M1 and M12 metabolites.4,5

Hover over products below to view reaction partners

Route of elimination

36% of an oral dose is eliminated as unchanged drug in the feces and 32% as the primary metabolite BZG791 in the feces.Label 2% of an oral dose is eliminated in the urine as unchanged drug and 7.1% as the primary metabolite BZG791.Label In total 81% of an oral dose is eliminated in the feces and 14% is eliminated in the urine.Label

Half-life

The mean half life of alprelisib is 8 to 9 hours.Label

Clearance

The mean apparent oral clearance was 39.0L/h.4 The predicted clearance is 9.2L/hr under fed conditions.Label

Adverse Effects

Learn about our commercial Adverse Effects data.

LEARN MORE

 

Toxicity

LD50 and Overdose

Patients experiencing an overdose may present with hyperglycemia, nausea, asthenia, and rash.Label There is no antidote for an overdose of alpelisib so patients should be treated symptomatically.Label Data regarding an LD50 is not readily available.MSDS In clinical trials, patients were given doses of up to 450mg once daily.Label

Pregnancy, Lactation, and Fertility

Following administration in rats and rabbits during organogenesis, adverse effects on the reproductive system, such as embryo-fetal mortality, reduced fetal weights, and increased incidences of fetal malformations, were observed.Label Based on these findings of animals studies and its mechanism of action, it is proposed that alpelisib may cause embryo-fetal toxicity when administered to pregnant patients.Label There is no data available regarding the presence of alpelisib in breast milk so breast feeding mothers are advised not to breastfeed while taking this medication and for 1 week after their last dose.Label Based on animal studies, alpelisib may impair fertility of humans.Label

Carcinogenicity and Mutagenicity

Studies of carcinogenicity have yet to be performed.Label Alpelisib has not been found to be mutagenic in the Ames test.Label It is not aneugenic, clastogenic, or genotoxic in further assays.Label

Affected organisms

Not Available

Pathways

Not Available

Pharmacogenomic Effects/ADRs 

 

Not Available

 

Source: https://www.drugbank.ca/drugs/DB12015

References

  1. Yuan TL, Cantley LC: PI3K pathway alterations in cancer: variations on a theme. Oncogene 2008, 27(41):5497-5510.
  2. Toker A: Double trouble for cancer gene. Science 2019, 366(6466):685-686.
  3. Vasan N, Razavi P, Johnson JL, Shao H, Shah H, Antoine A, Ladewig E, Gorelick A, Lin TY, Toska E et al: Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kalpha inhibitors. Science 2019, 366(6466):714-723.

 

 

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Prime Editing as a New CRISPR Tool to Enhance Precision and Versatility

 

Reporter: Stephen J. Williams, PhD

 

CRISPR has become a powerful molecular for the editing of genomes tool in research, drug discovery, and the clinic

(see posts and ebook on this site below)

 

however, as discussed on this site

(see posts below)

there have been many instances of off-target effects where genes, other than the selected target, are edited out.  This ‘off-target’ issue has hampered much of the utility of CRISPR in gene-therapy and CART therapy

see posts

 

However, an article in Science by Jon Cohen explains a Nature paper’s finding of a new tool in the CRISPR arsenal called prime editing, meant to increase CRISPR specificity and precision editing capabilities.

PRIME EDITING PROMISES TO BE A CUT ABOVE CRISPR

By Jon Cohen | Oct 25th, 2019

Prime editing promises to be a cut above CRISPR Jon Cohen CRISPR, an extraordinarily powerful genome-editing tool invented in 2012, can still be clumsy. … Prime editing steers around shortcomings of both techniques by heavily modifying the Cas9 protein and the guide RNA. … ” Prime editing “well may become the way that disease-causing mutations are repaired,” he says.

Science Vol. 366, No. 6464; DOI: 10.1126/science.366.6464.406

The effort, led by Drs. David Liu and Andrew Anzalone at the Broad Institute (Cambridge, MA), relies on the modification of the Cas9 protein and guide RNA, so that there is only a nick in a single strand of the double helix.  The canonical Cas9 cuts both strands of DNA, and so relies on an efficient gap repair activity of the cell.  The second part, a new type of guide RNA called a pegRNA, contains an RNA template for a new DNA sequence to be added at the target location.  This pegRNA-directed synthesis of the new template requires the attachment of a reverse transcriptase enzymes to the Cas9.  So far Liu and his colleagues have tested the technology on over 175 human and rodent cell lines with great success.  In addition, they had also corrected mutations which cause Tay Sachs disease, which previous CRISPR systems could not do.  Liu claims that this technology could correct over 89% of pathogenic variants in human diseases.

A company Prime Medicine has been formed out of this effort.

Source: https://science.sciencemag.org/content/366/6464/406.abstract

 

Read an article on Dr. Liu, prime editing, and the companies that Dr. Liu has initiated including Editas Medicine, Beam Therapeutics, and Prime Medicine at https://www.statnews.com/2019/11/06/questions-david-liu-crispr-prime-editing-answers/

(interview by StatNews  SHARON BEGLEY @sxbegle)

As was announced, prime editing for human therapeutics will be jointly developed by both Prime Medicine and Beam Therapeutics, each focusing on different types of edits and distinct disease targets, which will help avoid redundancy and allow us to cover more disease territory overall. The companies will also share knowledge in prime editing as well as in accompanying technologies, such as delivery and manufacturing.

Reader of StatNews.: Can you please compare the pros and cons of prime editing versus base editing?

The first difference between base editing and prime editing is that base editing has been widely used for the past 3 1/2 years in organisms ranging from bacteria to plants to mice to primates. Addgene tells me that the DNA blueprints for base editors from our laboratory have been distributed more than 7,500 times to more than 1,000 researchers around the world, and more than 100 research papers from many different laboratories have been published using base editors to achieve desired gene edits for a wide variety of applications. While we are very excited about prime editing, it’s brand-new and there has only been one paper published thus far. So there’s much to do before we can know if prime editing will prove to be as general and robust as base editing has proven to be.

We directly compared prime editors and base editors in our study, and found that current base editors can offer higher editing efficiency and fewer indel byproducts than prime editors, while prime editors offer more targeting flexibility and greater editing precision. So when the desired edit is a transition point mutation (C to T, T to C, A to G, or G to A), and the target base is well-positioned for base editing (that is, a PAM sequence exists approximately 15 bases from the target site), then base editing can result in higher editing efficiencies and fewer byproducts. When the target base is not well-positioned for base editing, or when other “bystander” C or A bases are nearby that must not be edited, then prime editing offers major advantages since it does not require a precisely positioned PAM sequence and is a true “search-and-replace” editing capability, with no possibility of unwanted bystander editing at neighboring bases.

Of course, for classes of mutations other than the four types of point mutations that base editors can make, such as insertions, deletions, and the eight other kinds of point mutations, to our knowledge prime editing is currently the only approach that can make these mutations in human cells without requiring double-stranded DNA cuts or separate DNA templates.

Nucleases (such as the zinc-finger nucleases, TALE nucleases, and the original CRISPR-Cas9), base editors, and prime editors each have complementary strengths and weaknesses, just as scissors, pencils, and word processors each have unique and useful roles. All three classes of editing agents already have or will have roles in basic research and in applications such as human therapeutics and agriculture.

Nature Paper on Prime Editing CRISPR

Search-and-replace genome editing without double-strand breaks or donor DNA (6)

 

Andrew V. Anzalone,  Peyton B. Randolph, Jessie R. Davis, Alexander A. Sousa,

Luke W. Koblan, Jonathan M. Levy, Peter J. Chen, Christopher Wilson,

Gregory A. Newby, Aditya Raguram & David R. Liu

 

Nature volume 576, pages149–157(2019)

 

Abstract

Most genetic variants that contribute to disease1 are challenging to correct efficiently and without excess byproducts2,3,4,5. Here we describe prime editing, a versatile and precise genome editing method that directly writes new genetic information into a specified DNA site using a catalytically impaired Cas9 endonuclease fused to an engineered reverse transcriptase, programmed with a prime editing guide RNA (pegRNA) that both specifies the target site and encodes the desired edit. We performed more than 175 edits in human cells, including targeted insertions, deletions, and all 12 types of point mutation, without requiring double-strand breaks or donor DNA templates. We used prime editing in human cells to correct, efficiently and with few byproducts, the primary genetic causes of sickle cell disease (requiring a transversion in HBB) and Tay–Sachs disease (requiring a deletion in HEXA); to install a protective transversion in PRNP; and to insert various tags and epitopes precisely into target loci. Four human cell lines and primary post-mitotic mouse cortical neurons support prime editing with varying efficiencies. Prime editing shows higher or similar efficiency and fewer byproducts than homology-directed repair, has complementary strengths and weaknesses compared to base editing, and induces much lower off-target editing than Cas9 nuclease at known Cas9 off-target sites. Prime editing substantially expands the scope and capabilities of genome editing, and in principle could correct up to 89% of known genetic variants associated with human diseases.

 

 

From Anzolone et al. Nature 2019 Figure 1.

Prime editing strategy

Cas9 targets DNA using a guide RNA containing a spacer sequence that hybridizes to the target DNA site. We envisioned the generation of guide RNAs that both specify the DNA target and contain new genetic information that replaces target DNA nucleotides. To transfer information from these engineered guide RNAs to target DNA, we proposed that genomic DNA, nicked at the target site to expose a 3′-hydroxyl group, could be used to prime the reverse transcription of an edit-encoding extension on the engineered guide RNA (the pegRNA) directly into the target site (Fig. 1b, cSupplementary Discussion).

These initial steps result in a branched intermediate with two redundant single-stranded DNA flaps: a 5′ flap that contains the unedited DNA sequence and a 3′ flap that contains the edited sequence copied from the pegRNA (Fig. 1c). Although hybridization of the perfectly complementary 5′ flap to the unedited strand is likely to be thermodynamically favoured, 5′ flaps are the preferred substrate for structure-specific endonucleases such as FEN122, which excises 5′ flaps generated during lagging-strand DNA synthesis and long-patch base excision repair. The redundant unedited DNA may also be removed by 5′ exonucleases such as EXO123.

  • The authors reasoned that preferential 5′ flap excision and 3′ flap ligation could drive the incorporation of the edited DNA strand, creating heteroduplex DNA containing one edited strand and one unedited strand (Fig. 1c).
  • DNA repair to resolve the heteroduplex by copying the information in the edited strand to the complementary strand would permanently install the edit (Fig. 1c).
  • They had hypothesized that nicking the non-edited DNA strand might bias DNA repair to preferentially replace the non-edited strand.

Results

  • The authors evaluated the eukaryotic cell DNA repair outcomes of 3′ flaps produced by pegRNA-programmed reverse transcription in vitro, and performed in vitro prime editing on reporter plasmids, then transformed the reaction products into yeast cells (Extended Data Fig. 2).
  • Reporter plasmids encoding EGFP and mCherry separated by a linker containing an in-frame stop codon, +1 frameshift, or −1 frameshift were constructed and when plasmids were edited in vitro with Cas9 nickase, RT, and 3′-extended pegRNAs encoding a transversion that corrects the premature stop codon, 37% of yeast transformants expressed both GFP and mCherry (Fig. 1f, Extended Data Fig. 2).
  • They fused a variant of M—MLV-RT (reverse transcriptase) to Cas9 with an extended linker and this M-MLV RT fused to the C terminus of Cas9(H840A) nickase was designated as PE1. This strategy allowed the authors to generate a cell line containing all the required components of the primer editing system. They constructed 19 variants of PE1 containing a variety of RT mutations to evaluate their editing efficiency in human cells
  • Generated a pentamutant RT incorporated into PE1 (Cas9(H840A)–M-MLV RT(D200N/L603W/T330P/T306K/W313F)) is hereafter referred to as prime editor 2 (PE2).  These were more thermostable versions of RT with higher efficiency.
  • Optimized the guide (pegRNA) using a series of permutations and  recommend starting with about 10–16 nt and testing shorter and longer RT templates during pegRNA optimization.
  • In the previous attempts (PE1 and PE2 systems), mismatch repair resolves the heteroduplex to give either edited or non-edited products. So they next developed an optimal editing system (PE3) to produce optimal nickase activity and found nicks positioned 3′ of the edit about 40–90 bp from the pegRNA-induced nick generally increased editing efficiency (averaging 41%) without excess indel formation (6.8% average indels for the sgRNA with the highest editing efficiency) (Fig. 3b).
  • The cell line used to finalize and validate the system was predominantly HEK293T immortalized cell line
  • Together, their findings establish that PE3 systems improve editing efficiencies about threefold compared with PE2, albeit with a higher range of indels than PE2. When it is possible to nick the non-edited strand with an sgRNA that requires editing before nicking, the PE3b system offers PE3-like editing levels while greatly reducing indel formation.
  • Off Target Effects: Strikingly, PE3 or PE2 with the same 16 pegRNAs containing these four target spacers resulted in detectable off-target editing at only 3 out of 16 off-target sites, with only 1 of 16 showing an off-target editing efficiency of 1% or more (Extended Data Fig. 6h). Average off-target prime editing for pegRNAs targeting HEK3HEK4EMX1, and FANCFat the top four known Cas9 off-target sites for each protospacer was <0.1%, <2.2 ± 5.2%, <0.1%, and <0.13 ± 0.11%, respectively (Extended Data Fig. 6h).
  • The PE3 system was very efficient at editing the most common mutation that causes Tay-Sachs disease, a 4-bp insertion in HEXA(HEXA1278+TATC).

References

  1. Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res44, D862–D868 (2016).
  2. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science337, 816–821 (2012).
  3. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science339, 819–823 (2013).

 

  1. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science339, 823–826 (2013).
  2. Kosicki, M., Tomberg, K. & Bradley, A. Repair of double-strand breaks induced by CRISPR–Cas9 leads to large deletions and complex rearrangements.  Biotechnol. 36, 765–771 (2018).
  3. Anzalone, A.V., Randolph, P.B., Davis, J.R. et al.Search-and-replace genome editing without double-strand breaks or donor DNA. Nature576, 149–157 (2019). https://doi.org/10.1038/s41586-019-1711-4

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Most significant article published in the Society of Evolution, Medicine and Public Health won Prize: polygenic scores, polygenic adaptation, and human phenotypic differences

Reporter: Aviva Lev-Ari, PhD, RN 

 

UPDATED on 8/30/2020

Analysis of polygenic risk score usage and performance in diverse human populations

Abstract

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10−6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.

SOURCE

https://www.nature.com/articles/s41467-019-11112-0

The Voice of Prof. Marcus W. Feldman

You might be interested in the paper “interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” by N. Rosenberg, M. Edge, J. Pritchard, and M. Feldman, published in Evolution, Medicine and Public Health  (2019).    Rosenberg and Pritchard are my former PhD students, both full professors at Stanford, and M.Edge is a student of Rosenberg.

 

On Aug 28, 2020, at 4:36 PM, Horowitz, Barbara Natterson <natterson-horowitz@fas.harvard.edu> wrote:

Dear Dr. Rosenberg,

It is my pleasure in my role as President of the International Society for Evolution, Medicine and Public Health to inform you that your 2019 EMPH article, “Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” has won The George C. Williams Prize which is awarded each year to the first author of the most significant article published in the Society’s flagship journal, Evolution, Medicine and Public Health.  

The Prize recognizes the contributions of George C. Williams to evolutionary medicine and aims to encourage and highlight important research in this growing field. It includes $5,000 and an invitation to present at the online lecture series, Club EvMed. The Prize is made possible by donations from Doris Williams, Randolph Nesse, and other supporters of EMPH.

The winning article:

 

Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Evolution, Medicine, and Public Health, Volume 2019, Issue 1, 2019, Pages 26–34, https://doi.org/10.1093/emph/eoy036
Published:
27 December 2018

Article history

SOURCE

Abstract

Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.

INTRODUCTION

We are currently witnessing a surge in public interest in the intersection of evolutionary genetics with such topics as cognitive phenotypes, disease, race and heritability of human traits [1–7]. This attention emerges partly from recent advances in genomics, including the introduction of polygenic scores—the aggregation of estimated effects of genome-wide variants to predict the contribution of a person’s genome to a phenotypic trait [8–10]—and a new focus on polygenic adaptations, namely adaptations that have occurred by natural selection on traits influenced by many genes [11–13].

Theories involving natural selection have long been applied in the scientific literature to explain mean phenotypic differences among human populations [14–16]. Although new tools for statistical analysis of polygenic variation and polygenic adaptation provide opportunities for studying human evolution and the genetic basis of traits, they also generate potential for misinterpretation. In the past, public attention to research on human variation and its possible evolutionary basis has often been accompanied by claims that are not justified by the research findings [17]. Recognizing pitfalls in the interpretation of new research on human variation is therefore important for advancing discussions on associated sensitive and controversial topics.

The contribution of polygenic score distributions to phenotype distributions. Two populations are considered, populations 1 (red) and 2 (blue). Each population has a distribution of genetic propensities, which are treated as accurately estimated in the form of polygenic scores (left). The genetic propensity distribution and an environment distribution sum to produce a phenotype distribution (right). All plots have the same numerical scale. (A) Environmental differences amplify an underlying difference in genetic propensities. (B) Populations differ in their phenotypes despite having no differences in genetic propensity distributions. (C) Environmental differences obscure a difference in genetic propensities opposite in direction to the difference in phenotype means. (D) Similarity in phenotype distributions is achieved despite a difference in genetic propensity distributions by an intervention that reduces the environmental contribution for individuals with polygenic scores above a threshold. (E) Within populations, heritability is high, so that genetic variation explains the majority of phenotypic variation; however, the difference between populations is explained by an environmental difference. Panels (A–C and E) present independent normal distributions for genotype and environment that sum to produce normal distributions for phenotype. In (D), (genotype, environment) pairs are simulated from independent normal distributions and a negative constant—reflecting the effect of a medication or other intervention—is added to environmental contributions associated with simulated genotypic values that exceed a threshold

Summary

These limitations illustrate that much of the complexity embedded in use of polygenic scores—the effects of the environment on phenotype and its relationship to genotype, the proportion of variance explained, and the peculiarities of the underlying GWAS data that have been used to estimate effect sizes—is obscured by the apparent simplicity of the single values computed for each individual for each phenotype. Consequently, in using polygenic scores to describe genomic contributions to traits, particularly traits for which the total contribution of genetic variation to trait variation, as measured by heritability, is low—but even if it is high (Fig. 1E)—a difference in polygenic scores between populations provides little information about potential genetic bases for trait differences between those populations.

Unlike heritability, which ranges from 0 to 1 and therefore makes it obvious that the remaining contribution to phenotypic variation is summarized by its difference from 1, the limited explanatory role of genetics is not embedded in the nature of the polygenic scores themselves. Although polygenic scores encode knowledge about specific genetic correlates of trait variation, they do not change the conceptual framework for genetic and environmental contribution to population differences. Attributions of phenotypic differences among populations to genetic differences should therefore be treated with as much caution as similar genetic attributions from heritability in the pre-genomic era.

 

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CRISPR-Cas9 and the Power of Butterfly Gene Editing

Reporter: Madison Davis

Genome editing is a relatively new branch of genetic engineering that utilizes modern technologies in altering, inserting, or deleting selective DNA sequences within cells.  CRISPR-Cas9, otherwise known as “Clustered Regularly Interspaced Short Palindromic Repeat”, is a groundbreaking genome editing technique for scientists, as it is more efficient and allows for more precise genome changes at less of a cost in comparison to other editing methods.  The CRISPR-Cas9 procedure chiefly involves two biological molecules: an enzyme known as “Cas9” whose role is to cut the DNA during transcription, and a guide RNA molecule located within the Cas9 enzyme.  

The process of extracting and editing certain segments of DNA begins with identifying the respective segment of DNA to edit, typically around twenty nucleotides in length but can vary depending on the goal of the scientists.  This selection process can be based on prior knowledge of gene mapping sequences or random experimentation.  Upon identifying the segment, scientists will manually formulate a guide RNA molecule that matches the sequence of nucleotides found in the DNA sequence.  This gRNA molecule will then be placed in empty Cas9 enzymes.  Through the process of transcription, Cas9 enzymes will find and cut out the designated DNA sequence, where scientists are then able to insert, delete, or modify certain sequences by hand under high-definition microscopes.  

The usage of CRISPR can range from identifying tumor suppressor genes to gene mapping for species.  In recent years, it has been used more specifically to understand the evolutionary genetics behind butterfly wing patterns.  Butterfly wings are constructed from two separate layers that contain thousands of individual scales made of a hard protein called chitin.  Each individual scale contains embedded structures and pigments that reflect or absorb certain colors of light depending on their wavelengths.  Their unique structures allows certain butterfly species to exhibit wide ranges of color variation.  All together, these scales can act as identification, insulation, and camouflage. 

Through selective processing, scientists were able to identify how a loss in a certain genetic sequence labeled WntA results in a reduction in CSS (Central Symmetry Systems) and pattern boundaries, resulting in more abstract wing patterns.  A research expedition led by Anyi Mazo-Vargas experimented on two species, Heliconius erato demophoon and Heliconius sara sara.  Each butterfly wing pair composed of mainly black pigment with two main stripe patterns consisting of red and yellow and blue and white for each species, respectively.  When the WntA gene was removed in offspring, there was an increase in color pigment in areas that were previously black scales.   For instance, in Heliconius erato demophoon, there appeared to be more blurred red and yellow pigment rather than distinct colored stripe patterns.  The WntA gene was also experimented in monarch butterflies, where an absence in WnTA genes caused the initially black tipped-scales of the monarch wings to become a whiter, “bleached” pigment.

While efficient in scale, CRISPR-Cas9 editing system is often riddled with mosaic mutations, which can be a challenge in making valid conclusions in gene editing.  Mosaicism is a process of gene editing that results in an individual having multiple cells with different DNA sequences.  Not all cells of a singular individual contain the same genetic code.  When editing genetic sequences during the larva stage, not all subsequent cells are affected by such a change, and thus changes in butterfly wings can only be partially identified.  As CRISPR and other gene editing technologies continue to evolve, scientists should try to increase the accuracy of their experiments, such as editing genes in earlier germline cells or varying their experiments on more subspecies for more data analysis. 

 

SOURCES

“What Are Genome Editing and CRISPR-Cas9? – Genetics Home Reference – NIH.” U.S. National Library of Medicine, National Institutes of Health, 17 Aug. 2020, ghr.nlm.nih.gov/primer/genomicresearch/genomeediting.

Pak, Ekaterina. “CRISPR: A Game-Changing Genetic Engineering Technique.” Science in the News, 31 July 2014, sitn.hms.harvard.edu/flash/2014/crispr-a-game-changing-genetic-engineering-technique/.

Mazo-Vargas, A., Concha, C., Livraghi, L., Massardo, D., Wallbank, R., Zhang, L., Papador, J., Martinez-Najera, D., Jiggins, C., Kronforst, M., Breuker, C., Reed, R., Patel, N., McMillan, W. and Martin, A., 2020. Macroevolutionary Shifts Of Wnta Function Potentiate Butterfly Wing-Pattern Diversity. [online] PNAS. Available at: https://www.pnas.org/content/114/40/10701 [Accessed 20 August 2020].

Mehravar, Maryam, et al. “Mosaicism in CRISPR/Cas9-Mediated Genome Editing.” Developmental Biology, Academic Press, 22 Oct. 2018, www.sciencedirect.com/science/article/pii/S0012160618302513.

https://pharmaceuticalintelligence.com/2020/08/29/prime-editing-as-a-new-crispr-tool-to-enhance-precision-and-versatility/

 

 

CAST – Alternative to CRISPR/Cas9 3
Select CRISPR alternative for editing genes without cuttingCRISPR alternative for editing genes without cutting3
Select CRISPR applied to Human Germ LineCRISPR applied to Human Germ Line66
Select CRISPR/Cas9 & Gene EditingCRISPR/Cas9 & Gene Editing5
Select Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integrationTransposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration
3

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WordCloud Visualization of LPBI’s Top Sixteen Articles on GENOMICS by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

Curators: Stephen J. Williams, Aviva Lev-Ari, PhD, RN and WordCloud Producers: Daniel Menzin, Noam Steiner-Tomer, Zach Day, Ofer Markman, PhD

Now we will do the same for Genomics Volume I so for your pre class homework first read the following electronic Table of Contents

and then determine some themes around the table of contents into a mind map of what you think are the scientists/clinicians main themes on the field of Genomics

Genomics eTOC at

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-one-genomics-orientations-for-personalized-medicine/

Content Consultant: Larry H Bernstein, MD, FCAP

Genomics Orientations for Personalized Medicine

Volume One

http://www.amazon.com/dp/B018DHBUO6

genomicsebook3[1]

Image Collage by SJ Williams, PhD, Google Images in Assembly

Larry H Bernstein, MD, FCAP, Senior Editor

Triplex Medical Science, Trumbull, CT

Larry.bernstein@gmail.com

 and

Stephen J. Williams, PhD, Editor

Leaders in Pharmaceutical Business Intelligence, Philadelphia

sjwilliamspa@comcast.net

and

Aviva Lev-Ari, PhD, RN, Editor

Editor-in-Chief BioMed E-Book Series

Leaders in Pharmaceutical Business Intelligence, Boston

avivalev-ari@alum.berkeley.edu

Article Title (Live Link)All Time ViewsCategories of Research
#1


Big Data in Genomic Medicine
696Bio Instrumentation in Experimental Life Sciences ResearchChemical Biology and its relations to Metabolic DiseaseChemical GeneticsComputational Biology/Systems and BioinformaticsGenome BiologyGenomic Testing: Methodology for DiagnosisMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & PharmaceuticalStatistical Methods for Research EvaluationTechnology Transfer: Biotech and Pharmaceutical

#1

Big Data in Genomic Medicine

Article #1: Word Cloud by NT

   
Article Title (Live Link)All Time ViewsCategories of Research
#2


Unraveling Retrograde Signaling Pathways
~57Biological Networks, Gene Regulation and EvolutionCell Biology, Signaling & Cell CircuitsComputational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyInternational Global Work in PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPharmaceutical Industry Competitive Intelligence

#2

Unraveling Retrograde Signaling Pathways

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Article Title (Live Link)All Time ViewsCategories of Research
#3


Genomics of Bacterial and Archaeal Viruses
~69
Biological Networks, Gene Regulation and Evolution
Computational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyInfectious Disease & New Antibiotic TargetsInternational Global Work in PharmaceuticalMolecular Genetics & PharmaceuticalPharmaceutical Industry Competitive IntelligencePopulation Health Management, Genetics & PharmaceuticalScientist: Career considerationsTechnology Transfer: Biotech and Pharmaceutical

#3

Genomics of Bacterial and Archaeal Viruses

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Article Title (Live Link)All Time ViewsCategories of Research
#4


Genetics and Male Endocrinology
~103Biological Networks, Gene Regulation and EvolutionGenome BiologyGenomic Testing: Methodology for DiagnosisMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & PharmaceuticalReproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics

#4

Genetics and Male Endocrinology

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Article Title (Live Link)All Time ViewsCategories of Research
#5


Genomics and Evolution
~127Biological Networks, Gene Regulation and EvolutionChemical GeneticsComputational Biology/Systems and BioinformaticsGenome BiologyGenomic Testing: Methodology for DiagnosisMedical and Population GeneticsPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & Pharmaceutical

#5

Genomics and Evolution

Article #5: Word Cloud by DM

   
Article Title (Live Link)All Time ViewsCategories of Research
#6


Directions for Genomics in Personalized Medicine
668Biological Networks, Gene Regulation and EvolutionCANCER BIOLOGY & Innovations in Cancer TherapyCell Biology, Signaling & Cell CircuitsChemical Biology and its relations to Metabolic DiseaseGenome BiologyInternational Global Work in PharmaceuticalMetabolomicsMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPharmacogenomics

#6

Directions for Genomics in Personalized Medicine

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Article Title (Live Link)All Time ViewsCategories of Research
#7


FDA Warning for the Leader of Consumer Market for Personal DNA Sequencing: Part 4
~353Biomarkers & Medical DiagnosticsCardiovascular PharmacogenomicsChemical GeneticsComputational Biology/Systems and BioinformaticsGenome BiologyGenomic Testing: Methodology for DiagnosisMedical and Population GeneticsMetabolomicsMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPharmaceutical R&D InvestmentPharmacogenomicsPopulation Health Management, Genetics & PharmaceuticalReproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive GenomicsStem Cells for Regenerative MedicineTechnology Transfer: Biotech and Pharmaceutical

#7

FDA Warning for the Leader of Consumer Market for Personal DNA Sequencing: Part 4

Article #7: Word Cloud by DM

   
Article Title (Live Link)All Time ViewsCategories of Research
#8


Sunitinib brings Adult Acute Lymphoblastic Leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade
983
Bio Instrumentation in Experimental Life Sciences Research
Biological Networks, Gene Regulation and EvolutionBone Disease and Musculoskeletal DiseaseCANCER BIOLOGY & Innovations in Cancer TherapyCell Biology, Signaling & Cell CircuitsChemical Biology and its relations to Metabolic DiseaseChemical GeneticsComputational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyHealth Economics and Outcomes ResearchInterviews with Scientific LeadersMedical and Population GeneticsNephrologyNephrology & Regenerative medicinePersonalized and Precision Medicine & Genomic ResearchPharmaceutical AnalyticsPharmaceutical Industry Competitive IntelligencePharmaceutical R&D InvestmentPopulation Health Management, Genetics & Pharmaceutical

#8

Sunitinib brings Adult Acute Lymphoblastic Leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

Article #8: Word Cloud by DM

   
Article Title (Live Link)All Time ViewsCategories of Research
#9


Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
~267Alzheimer’s DiseaseBio Instrumentation in Experimental Life Sciences ResearchBiological Networks, Gene Regulation and EvolutionBiomarkers & Medical DiagnosticsCANCER BIOLOGY & Innovations in Cancer TherapyCell Biology, Signaling & Cell CircuitsChemical Biology and its relations to Metabolic DiseaseComputational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyImaging-based Cancer Patient ManagementInternational Global Work in PharmaceuticalMetabolomicsMolecular Genetics & PharmaceuticalNanotechnology for Drug DeliveryNutrigenomicsNutritionPersonalized and Precision Medicine & Genomic ResearchPharmaceutical Industry Competitive IntelligencePharmaceutical R&D InvestmentPharmacotherapy and Cell ActivityPopulation Health Management, Genetics & PharmaceuticalPopulation Health Management, Nutrition and PhytochemistryProteomicsTechnology Transfer: Biotech and Pharmaceutical

#9

Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

Article #9: Word Cloud by ZD

  
Article Title (Live Link)All Time ViewsCategories of Research
#10


Computational Genomics Center: New Unification of Computational Technologies at Stanford
~141Biological Networks, Gene Regulation and EvolutionBiomarkers & Medical DiagnosticsCardiovascular PharmacogenomicsChemical GeneticsComputational Biology/Systems and BioinformaticsGenome BiologyGenomic Testing: Methodology for DiagnosisMedical and Population GeneticsMetabolomicsMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & PharmaceuticalProteomicsReproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive GenomicsScientist: Career considerationsStatistical Methods for Research EvaluationTechnology Transfer: Biotech and Pharmaceutical

#10

Computational Genomics Center: New Unification of Computational Technologies at Stanford

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Article Title (Live Link)All Time ViewsCategories of Research
#11


DNA structure and Oligonucleotides
2,290Chemical Biology and its relations to Metabolic DiseaseChemical GeneticsComputational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyGenomic Testing: Methodology for DiagnosisInternational Global Work in PharmaceuticalMetabolomicsMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPharmaceutical Industry Competitive IntelligencePharmacogenomicsPopulation Health Management, Genetics & PharmaceuticalTechnology Transfer: Biotech and Pharmaceutical

#11

DNA structure and Oligonucleotides

Article #11: Word Cloud by ZD

   
Article Title (Live Link)All Time ViewsCategories of Research
#12


Drugging the Epigenome
~143Genome Biology 

#12

Drugging the Epigenome

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#13


Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell 
~73Bio Instrumentation in Experimental Life Sciences ResearchBiological Networks, Gene Regulation and EvolutionCANCER BIOLOGY & Innovations in Cancer TherapyCell Biology, Signaling & Cell CircuitsComputational Biology/Systems and BioinformaticsGenome BiologyGenomic Testing: Methodology for DiagnosisHealth Economics and Outcomes ResearchMedical and Population GeneticsPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & Pharmaceutical

#13

Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell 

Article #13: Word Cloud by ZD

   
Article Title (Live Link)All Time ViewsCategories of Research
#14


Genetic Basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes
~113Biological Networks, Gene Regulation and EvolutionChemical GeneticsComputational Biology/Systems and BioinformaticsGenome BiologyGenomic Testing: Methodology for DiagnosisMedical and Population GeneticsPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & PharmaceuticalProteomicsStatistical Methods for Research EvaluationTechnology Transfer: Biotech and Pharmaceutical

#14

Genetic Basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

Article #14: Word Cloud by DM

  
Article Title (Live Link)All Time ViewsCategories of Research
#15


Personal Tale of JL’s Whole Genome Sequencing
~143Bio Instrumentation in Experimental Life Sciences ResearchChemical GeneticsComputational Biology/Systems and BioinformaticsGenome BiologyGenomic Testing: Methodology for DiagnosisMedical and Population GeneticsMolecular Genetics & PharmaceuticalPatient Experience: Personal Memories of Invasive Medical IntervantionPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & PharmaceuticalTechnology Transfer: Biotech and Pharmaceutical 

#15

Personal Tale of JL’s Whole Genome Sequencing

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#16


Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry
~222Advanced Drug Manufacturing TechnologyBiological Networks, Gene Regulation and EvolutionBiomarkers & Medical DiagnosticsBioSimilarsChemical Biology and its relations to Metabolic DiseaseChemical GeneticsComputational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsDrug Delivery Platform TechnologyGenome BiologyGenomic Testing: Methodology for DiagnosisInterviews with Scientific LeadersMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchPharmaceutical Industry Competitive IntelligencePharmaceutical R&D InvestmentProteomicsScientist: Career considerationsSystemic Inflammatory Response Related Disorders

#16

Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry

Article #16: Word Cloud by DM

List of Contributors to Volume One

Larry Bernstein, MD, FCAP,  Senior Editor 

Introduction 1.1, 1.2, 1.4, 1.5, 2.2, 2.6, 3.1, 3.2, 3.3, 3.6, 4.6, 4.8, 5.8, 5.9, 5.10, 6.1, 6.2, 6.3, 6.5, 6.7, 6.8, 6.9, 6.10, 6.11, 6.12, 6.13, 6.14, 6.16, 6.17, 8.5, 8.6, 9.6, 10.4, 10.5, 10.6, 10.7, 11.1, 11.7, 11.10, 11.11, 12.2, 12.3, 12.4, 12.6, 12.8, 13.8, 13.9, 14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15.5, 15.8, 15.9, 15.9.4, 15.11, 16.1, 16.2, 16.3, 16.4, 16.5, 17.2, 18.1, 18.2, 18.5, 18.6, 20.2, 20.3, 20.4, 20.5, 20.6, Introduction-21, Summary-21, Volume Summary, Epilogue

Stephen J. Williams, PhD, Editor

2.3, 2.7, 6.15, 7.6, 8.8, 11.8, 12.5, 12.7, 15.3, 20.7, Introduction-21

Aviva Lev-Ari, PhD, RN, Editor-in-Chief, BioMed e-Books Series

1.6, 2.1, 2.5, 3.4, 3.5, 3.7, 3.8, 4.1, 4.4, 4.5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 6.18, 7.1, 7.2, 7.3, 7.4, 7.5, 8.1, 8.2, 8.3, 8.7, 8.9, 9.1, 9.2, 9.3, 9.4, 9.5, 9.8, 10.1, 10.2, 10.3, 10.8, 11.2, 11.3, 11.4, 11.5, 11.9, 12.1, 13.5 13.7, 15.1, 15.2, 15.4, 15.6, 15.7, 15.9.1, 15.9.2, 15.9.3, 15.9.5, 15.10, 17.1, 18.3, 18.4, 19.4, 19.5, 20.1, 20.8, 21.1.1, 21.1.2, 21.1.3, 21.1.4, 21.2.1, 21.2.2, 21.2.3, 21.2.4, 21.3.1, 21.3.2, 21.4.2

Sudipta Saha, PhD
1.3, 6.6, 11.6, 13.2, 13.3, 13.4, 19.1, 19.2, 19.6, 19.7, 19.8, 19.9, 19.10

Ritu Saxena, PhD
4.2, 6.4, 9.7, 13.6, 14.1, 17.3, 17.4, 17.5, 19.3

Tilda Barlyia, PhD
8.4, 13.1, 14.2

Anamika Sarkar, PhD
4.3

Marcus W Feldman, PhD, Professor of Computational BiologyStanford University, Department of Biology

2.4

Demet Sag, PhD

4.7, 4.9, 4.10

electronic Table of Contents

Chapter 1

1.1 Advances in the Understanding of the Human Genome The Initiation and Growth of Molecular Biology and Genomics – Part I

1.2 CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

1.3 DNA – The Next-Generation Storage Media for Digital Information

1.4 CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

1.5 Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

1.6 Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology

Chapter 2

2.1 2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

2.2 DNA structure and Oligonucleotides

2.3 Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell 

2.4 Genomics and Evolution

2.5 Protein-folding Simulation: Stanford’s Framework for Testing and Predicting Evolutionary Outcomes in Living Organisms – Work by Marcus Feldman

2.6 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures

2.7 Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

Chapter 3

3.1 Big Data in Genomic Medicine

3.2 CRACKING THE CODE OF HUMAN LIFE: The Birth of Bioinformatics & Computational Genomics – Part IIB 

3.3 Expanding the Genetic Alphabet and linking the Genome to the Metabolome

3.4 Metabolite Identification Combining Genetic and Metabolic Information: Genetic Association Links Unknown Metabolites to Functionally Related Genes

3.5 MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

3.6 Identification of Biomarkers that are Related to the Actin Cytoskeleton

3.7 Genetic basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

3.8 MIT Team Researches Regulatory Motifs and Gene Expression of Erythroleukemia (K562) and Liver Carcinoma (HepG2) Cell Lines

Chapter 4

4.1 ENCODE Findings as Consortium

4.2 ENCODE: The Key to Unlocking the Secrets of Complex Genetic Diseases

4.3 Reveals from ENCODE Project will Invite High Synergistic Collaborations to Discover Specific Targets  

4.4 Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence

4.5 Human Genome Project – 10th Anniversary: Interview with Kevin Davies, PhD – The $1000 Genome

4.6 Quantum Biology And Computational Medicine

4.7 The Underappreciated EpiGenome

4.8 Unraveling Retrograde Signaling Pathways

4.9  “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

4.10  DNA: One man’s trash is another man’s treasure, but there is no JUNK after all

Chapter 5

5.1 Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 

5.2 Computational Genomics Center: New Unification of Computational Technologies at Stanford

5.3 Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3

5.4 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

5.5 Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

5.6 NGS Market: Trends and Development for Genotype-Phenotype Associations Research

5.7 Speeding Up Genome Analysis: MIT Algorithms for Direct Computation on Compressed Genomic Datasets

5.8  Modeling Targeted Therapy

5.9 Transphosphorylation of E-coli Proteins and Kinase Specificity

5.10 Genomics of Bacterial and Archaeal Viruses

Chapter 6

6.1  Directions for Genomics in Personalized Medicine

6.2 Ubiquinin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

6.3 Mitochondrial Damage and Repair under Oxidative Stress

6.4 Mitochondria: More than just the “Powerhouse of the Cell”

6.5 Mechanism of Variegation in Immutans

6.6 Impact of Evolutionary Selection on Functional Regions: The imprint of Evolutionary Selection on ENCODE Regulatory Elements is Manifested between Species and within Human Populations

6.7 Cardiac Ca2+ Signaling: Transcriptional Control

6.8 Unraveling Retrograde Signaling Pathways

6.9 Reprogramming Cell Fate

6.10 How Genes Function

6.11 TALENs and ZFNs

6.12 Zebrafish—Susceptible to Cancer

6.13 RNA Virus Genome as Bacterial Chromosome

6.14 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome 

6.15 Telling NO to Cardiac Risk- DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

6.16  Transphosphorylation of E-coli proteins and kinase specificity

6.17 Genomics of Bacterial and Archaeal Viruses

6.18  Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 7

7.1 Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

7.2 Consumer Market for Personal DNA Sequencing: Part 4

7.3 GSK for Personalized Medicine using Cancer Drugs Needs Alacris Systems Biology Model to Determine the In Silico Effect of the Inhibitor in its “Virtual Clinical Trial”

7.4 Drugging the Epigenome

7.5 Nation’s Biobanks: Academic institutions, Research institutes and Hospitals – vary by Collections Size, Types of Specimens and Applications: Regulations are Needed

7.6 Personalized Medicine: Clinical Aspiration of Microarrays

Chapter 8

8.1 Personalized Medicine as Key Area for Future Pharmaceutical Growth

8.2 Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA

8.3 The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference, 11/28-29, 2012, Harvard Medical School, Boston, MA

8.4 Nanotechnology, Personalized Medicine and DNA Sequencing

8.5 Targeted Nucleases

8.6 Transcript Dynamics of Proinflammatory Genes

8.7 Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

8.8 Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

8.9 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 9

9.1 Personal Tale of JL’s Whole Genome Sequencing

9.2 Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics

9.3 Inform Genomics Developing SNP Test to Predict Side Effects, Help MDs Choose among Chemo Regimens

9.4 SNAP: Predict Effect of Non-synonymous Polymorphisms: How Well Genome Interpretation Tools could Translate to the Clinic

9.5  LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

9.6 The Initiation and Growth of Molecular Biology and Genomics – Part I

9.7 Personalized Medicine-based Cure for Cancer Might Not Be Far Away

9.8 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

 Chapter 10

10.1 Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)

10.2 Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)

10.3 Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

10.4 Treatment for Metastatic HER2 Breast Cancer

10.5 Personalized Medicine in NSCLC

10.6 Gene Sequencing – to the Bedside

10.7 DNA Sequencing Technology

10.8 Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry

Chapter 11

11.1 mRNA Interference with Cancer Expression

11.2 Angiogenic Disease Research Utilizing microRNA Technology: UCSD and Regulus Therapeutics

11.3 Sunitinib brings Adult acute lymphoblastic leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

11.4 A microRNA Prognostic Marker Identified in Acute Leukemia 

11.5 MIT Team: Microfluidic-based approach – A Vectorless delivery of Functional siRNAs into Cells.

11.6 Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4

11.7 When Clinical Application of miRNAs?

11.8 How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,

11.9 Potential Drug Target: Glycolysis Regulation – Oxidative Stress-responsive microRNA-320

11.10  MicroRNA Molecule May Serve as Biomarker

11.11 What about Circular RNAs?

Chapter 12

12.1 The “Cancer Establishments” Examined by James Watson, Co-discoverer of DNA w/Crick, 4/1953

12.2 Otto Warburg, A Giant of Modern Cellular Biology

12.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

12.4 Hypothesis – Following on James Watson

12.5 AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

12.6 AKT signaling variable effects

12.7 Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

12.8 Phosphatidyl-5-Inositol signaling by Pin1

Chapter 13

13.1 Nanotech Therapy for Breast Cancer

13.2 BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

13.3 Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes

13.4 Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors

13.5 Prostate Cancer: Androgen-driven “Pathomechanism” in Early onset Forms of the Disease

13.6 In focus: Melanoma Genetics

13.7 Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

13.8 Breast Cancer and Mitochondrial Mutations

13.9  Long noncoding RNA network regulates PTEN transcription

Chapter 14

14.1 HBV and HCV-associated Liver Cancer: Important Insights from the Genome

14.2 Nanotechnology and HIV/AIDS treatment

14.3 IRF-1 Deficiency Skews the Differentiation of Dendritic Cells

14.4 Sepsis, Multi-organ Dysfunction Syndrome, and Septic Shock: A Conundrum of Signaling Pathways Cascading Out of Control

14.5  Five Malaria Genomes Sequenced

14.6 Rheumatoid Arthritis Risk

14.7 Approach to Controlling Pathogenic Inflammation in Arthritis

14.8 RNA Virus Genome as Bacterial Chromosome

14.9 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome

Chapter 15

15.1 Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

15.2 Congestive Heart Failure & Personalized Medicine: Two-gene Test predicts response to Beta Blocker Bucindolol

15.3 DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

15.4 Peroxisome Proliferator-Activated Receptor (PPAR-gamma) Receptors Activation: PPARγ Transrepression for Angiogenesis in Cardiovascular Disease and PPARγ Transactivation for Treatment of Diabetes

15.5 BARI 2D Trial Outcomes

15.6 Gene Therapy Into Healthy Heart Muscle: Reprogramming Scar Tissue In Damaged Hearts

15.7 Obstructive coronary artery disease diagnosed by RNA levels of 23 genes – CardioDx, a Pioneer in the Field of Cardiovascular Genomic  Diagnostics

15.8 Ca2+ signaling: transcriptional control

15.9 Lp(a) Gene Variant Association

15.9.1 Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

15.9.2. Genomics & Genetics of Cardiovascular Disease Diagnoses: A Literature Survey of AHA’s Circulation Cardiovascular Genetics, 3/2010 – 3/2013

15.9.3 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

15.9.4 The Implications of a Newly Discovered CYP2J2 Gene Polymorphism Associated with Coronary Vascular Disease in the Uygur Chinese Population

15.9.5  Gene, Meis1, Regulates the Heart’s Ability to Regenerate after Injuries.

15.10 Genetics of Conduction Disease: Atrioventricular (AV) Conduction Disease (block): Gene Mutations – Transcription, Excitability, and Energy Homeostasis

15.11 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancers?

Chapter 16

16.1 Can Resolvins Suppress Acute Lung Injury?

16.2 Lipoxin A4 Regulates Natural Killer Cell in Asthma

16.3 Biological Therapeutics for Asthma

16.4 Genomics of Bronchial Epithelial Dysplasia

16.5 Progression in Bronchial Dysplasia

Chapter 17

17.1 Breakthrough Digestive Disorders Research: Conditions Affecting the Gastrointestinal Tract.

17.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis

17.3 Biomarkers-identified-for-recurrence-in-hbv-related-hcc-patients-post-surgery

17.4  Usp9x: Promising Therapeutic Target for Pancreatic Cancer

17.5 Battle of Steve Jobs and Ralph Steinman with Pancreatic cancer: How We Lost

Chapter 18

18.1 Ubiquitin Pathway Involved in Neurodegenerative Disease

18.2 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

18.3 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic Drugs and Cholinesterase Inhibitors

18.4 Ustekinumab New Drug Therapy for Cognitive Decline Resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

18.5 Cell Transplantation in Brain Repair

18.6 Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Chapter 19

19.1 Genetics and Male Endocrinology

19.2 Genomic Endocrinology and its Future

19.3 Commentary on Dr. Baker’s post “Junk DNA Codes for Valuable miRNAs: Non-coding DNA Controls Diabetes”

19.4 Therapeutic Targets for Diabetes and Related Metabolic Disorders

19.5 Secondary Hypertension caused by Aldosterone-producing Adenomas caused by Somatic Mutations in ATP1A1 and ATP2B3 (adrenal cortical; medullary or Organ of Zuckerkandl is pheochromocytoma)

19.6 Personal Recombination Map from Individual’s Sperm Cell and its Importance

19.7 Gene Trap Mutagenesis in Reproductive Research

19.8 Pregnancy with a Leptin-Receptor Mutation

19.9 Whole-genome Sequencing in Probing the Meiotic Recombination and Aneuploidy of Single Sperm Cells

19.10 Reproductive Genetic Testing

Chapter 20

20.1 Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

20.2 Understanding the Role of Personalized Medicine

20.3 Attitudes of Patients about Personalized Medicine

20.4  Genome Sequencing of the Healthy

20.5   Genomics in Medicine – Tomorrow’s Promise

20.6  The Promise of Personalized Medicine

20.7 Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

 20.8 Genomic Liberty of Ownership, Genome Medicine and Patenting the Human Genome

Chapter 21

Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era:  Medical Interpretation of the Genomics Frontier – CRISPR – Cas9

Introduction

21.1 Introducing CRISPR/Cas9 Gene Editing Technology – Works by Jennifer A. Doudna

21.1.1 Ribozymes and RNA Machines – Work of Jennifer A. Doudna

21.1.2 Evaluate your Cas9 gene editing vectors: CRISPR/Cas Mediated Genome Engineering – Is your CRISPR gRNA optimized for your cell lines?

21.1.3 2:15 – 2:45, 6/13/2014, Jennifer Doudna “The biology of CRISPRs: from genome defense to genetic engineering”

21.1.4  Prediction of the Winner RNA Technology, the FRONTIER of SCIENCE on RNA Biology, Cancer and Therapeutics  & The Start Up Landscape in BostonGene Editing – New Technology The Missing link for Gene Therapy?

21.2 CRISPR in Other Labs

21.2.1 CRISPR @MIT – Genome Surgery

21.2.2 The CRISPR-Cas9 System: A Powerful Tool for Genome Engineering and Regulation

Yongmin Yan and Department of Gastroenterology, Hepatology & Nutrition, University of Texas M.D. Anderson Cancer, Houston, USADaoyan Wei*

21.2.3 New Frontiers in Gene Editing: Transitioning From the Lab to the Clinic, February 19-20, 2015 | The InterContinental San Francisco | San Francisco, CA

21.2.4 Gene Therapy and the Genetic Study of Disease: @Berkeley and @UCSF – New DNA-editing technology spawns bold UC initiative as Crispr Goes Global

21.2.5 CRISPR & MAGE @ George Church’s Lab @ Harvard

21.3 Patents Awarded and Pending for CRISPR

21.3.1 Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method

21.3.2 The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century

2.4 CRISPR/Cas9 Applications

21.4.1  Inactivation of the human papillomavirus E6 or E7 gene in cervical carcinoma cells using a bacterial CRISPR/Cas 

21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF

21.4.3 In vivo validated mRNAs

 

21.4.6 Level of Comfort with Making Changes to the DNA of an Organism

21.4.7 Who will be the the First to IPO: Novartis bought in to Intellia (UC, Berkeley) as well as Caribou (UC, Berkeley) vs Editas (MIT)??

21.4.8 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development

Summary

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Articles on the Use of single cell analysis in COVID-19 research and A machine learning model that can Predict Base-editing Outcomes

 

Reporter: Aviva Lev-Ari, PhD, RN

 

From: Richard Lumb <contact@frontlinegenomics.com>

Date: July 1, 2020 at 6:05:55 AM EDT

To: avivalev-ari@alum.berkeley.edu

Subject: FLG Newsletter: Single cell analysis in COVID-19 research, a machine learning model that can predict base-editing outcomes & much more

Reply-To: contact@frontlinegenomics.com

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Front Line Genomics Newsletter

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Dear Aviva,

First of all, a big thank you to everyone who attended yesterday’s webinar on a new approach for exploring the dark genome. If you missed it, you can still watch it ‘on demand’ here.

In the last week, we’ve also launched two more webinar series. Both are free to attend and available live or on-demand:

Single Cell Online: A series of 4 webinars in July, starting on the 9th, focusing on unleashing the full power of single cell technologies. The series includes contributors from Novartis, Merck, Sanofi, Roche, the University of Gothenburg, MGI and Partek. Find out more and register here.

Driving FAIR in BioPharma: A series of 3 webinars in July and August, starting on the 21st July, exploring various use cases of FAIR data implementation to enable the potential of AI and ML in R&D. The series features contributors from AstraZeneca, Roche, Novartis, University of Oxford, ONTOFORCE, FDA, Eurofins and CDD. Click here to find out more and register.

Finally, this week on the website we have some fantastic content for you, including articles on the use of single cell analysis in COVID-19 research and a machine learning model that can predict base-editing outcomes. There’s also the latest DNA Today Podcasts focusing on infertility, featuring insights from genetic counsellors and the writer and producer of an explosive genetics mystery sci-fi movie called ANYA (check it out, it’s very thought provoking).

Stay safe everyone. 

Kind Regards,

Rich

Richard Lumb PhD

Founder & CEO

Front Line Genomics

J202, The Biscuit Factory, 100 Drummond Rd, London, SE16 4DG.

T:  +44 (0)208 191 8810

M: +44 (0)7739 251 898

E:  richard@frontlinegenomics.com

W: www.frontlinegenomics.com

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Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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SARS-CoV-2 is pre-adapted to Human Transmission, branches of evolution stemming from a less well-adapted human SARS-CoV-2-like virus have been found: The Role of SARS-CoV-2 Virus Progenitors for Future Virus Disease Transmission and Pandemic Re-Emergence

Reporter and Curator: Aviva Lev-Ari, PhD, RN – all bold face and colors are my additions

 

UPDATED on 6/4/2020

Former MI6 head claims COVID-19 was made in a Chinese lab

Sir Richard Dearlove said there was good evidence that the virus was engineered, but that it’s escape from the laboratory was accidental.

A former head of the British intelligence agency MI6 has said that he believes the COVID-19 virus was created in a lab and spread accidentally. Speaking to The Telegraph‘s Planet Normal podcast, Sir Richard Dearlove cited recent research which claimed to have found key evidence that the virus had been manipulated to bind to humans.

If accurate, the research would have far-reaching political effects as governments around the world re-examined their dealings with the Communist state, including raising the question of reparation payments from China to the rest of the world for the damage caused by the virus.

A former head of the British intelligence agency MI6 has said that he believes the COVID-19 virus was created in a lab and spread accidentally. Speaking to The Telegraph‘s Planet Normal podcast, Sir Richard Dearlove cited recent research which claimed to have found key evidence that the virus had been manipulated to bind to humans.

If accurate, the research would have far-reaching political effects as governments around the world re-examined their dealings with the Communist state, including raising the question of reparation payments from China to the rest of the world for the damage caused by the virus.

“I do think that this started as an accident,” Sir Richard told the Telegraph, citing a peer-reviewed paper by Professor Angus Dalgleish of St George’s Hospital at the University of London, and the Norwegian virologist Birger Sorensen.

According to Sir Richard, the pair claimed to have identified “inserted sections placed on the SARS-CoV-2 Spike surface,” which allow the virus to bind to human cells, in contrast to alternate theories that the virus originated in animals, likely bats and pangolins, and mutated naturally to make the jump to human hosts. And they warn that current efforts to develop a vaccine are likely to be unsuccessful, as the true causation of the virus’s effects are being misunderstood by other scientists. The researchers are therefore working on their own vaccine, produced by Immunor AS, a Norwegian pharmaceutical company led by Mr Sorensen according to the Telegraph.

The research paper was “a very important contribution to a debate which is now starting about how the virus evolved and how it got out and broke out as a pandemic”, Sir Richard said, adding: “I think this particular article is very important, and I think it will shift the debate.”

Dalgleish and Sorensen’s article was re-written a number of times after early versions failed to achieve publication. An early version seen by the Telegraph suggested COVID-19 be known as the “Wuhan virus,” and said that it was “beyond reasonable doubt that the Covid-19 virus is engineered.” The authors originally noted: “We are aware that these findings could have political significance and raise troubling questions.”

However, the paper was not accepted for publication until the authors had re-drafted to remove explicit claims against China. Following the edits, the science presented within the paper was deemed of sufficient worth for publication in the Quarterly Review of Biophysics Discovery, chaired by leading Stanford University and University of Dundee scientists.

SOURCE

https://www.jpost.com/health-science/former-mi6-head-covid-19-was-made-in-a-chinese-lab-630346?from=groupmessage

LPBI Position Statement

A.  SARS-CoV-2 is pre-adapted to Human Transmission

B.  Branches of evolution stemming from a less well-adapted human SARS-CoV-2-like virus have been found

C.  The Role of SARS-CoV-2 Virus Progenitors – ARE CLEAR

D. Virus Progenitors will potentiate Future Virus Disease Transmission

E.  Pandemic Re-Emergence – Is  InEVITABLE and Virus Progenitors will be the subject for 2nd generation of virus genetic engineering technologies for human infectivity

 

  • Top vaccine scientist says coronavirus is ‘almost perfectly human adapted’

The coronavirus that causes COVID-19 is “almost perfectly human adapted” — lending credence to the possibility it was man-made in a Chinese lab, a top Australian vaccine researcher says.

Nikolai Petrovsky was shocked when research found that the virus was more virulent in humans than any other animal, the Daily Mail reported.

He said it was like the new strain of coronavirus, called SARS-CoV-2, was “completely optimized from day one without the need to evolve” like other viruses.

“This is a new virus that has never been in humans before, but it has an extraordinarily high binding to human receptors, which is very surprising,” Petrovsky told the Mail. “It is almost perfectly human adapted, it couldn’t do any better.”

He said it is possible the virus was created in a lab in China — deepening suspicions that the global pandemic originated in Wuhan.

“We have to ask how that happened. Was it a complete fluke? It can be as nature has many shots at goal and you only see the ones that land,” Petrovsky said.

SOURCE

https://nypost.com/2020/05/27/top-vaccine-scientist-covid-19-is-almost-perfectly-human-adapted/

  • No known animal host and ‘almost perfect’ human adaption: Top Australian vaccine scientist reveals how COVID-19’s unique structure means it’s either man-made – or a ‘complete fluke’ of nature
  • Professor Nikolai Petrovsky said virus was better at attaching itself to human cells than to any other animal
  • It is so ‘perfectly adapted’ to infect humans that the possibility it was made in a Chinese lab can’t be ignored
  • Wuhan Institute of Virology studied bat coronaviruses and is theorised to have accidentally leaked COVID-19
  • Virus could have been formed naturally by mixing bat and pangolin versions, but this is statistically unlikely
  • Professor Petrovsky said the inquiry into virus origins needed urgently and should have started months ago

“This, plus the fact that no corresponding virus has been found to exist in nature, leads to the possibility that COVID-19 is a human-created virus. It is therefore entirely plausible that the virus was created in the biosecurity facility in Wuhan [WIV] by selection on cells expressing human ACE2 [receptor], a laboratory that was known to be cultivating exotic bat coronaviruses at the time.” https://www.washingtontimes.com/news/2020/may/21/australian-researchers-see-virus-design-manipulati/

Scientist in protective overall

“We can’t exclude the possibility that this came from a laboratory experiment rather than from an animal” – Prof Nikolai Petrovsky

Genetic engineering the quicker way to human infectivity

Commenting on Prof Petrovsky’s conclusion that SARS-CoV-2 could have originated from culture of a wild virus and selection in human cells, the London-based molecular geneticist Dr Michael Antoniou agreed that this scenario was plausible: “You can certainly develop a human-infective virus like SARS-CoV-2 by repeatedly passing a wild bat virus through human cells, in the way that Prof Petrovsky describes. You culture human cells with the virus, allowing the virus to replicate, and harvest the resulting viruses. This selects for the most human-infective viruses, which you use to re-infect more cells. By going through successive rounds of this process, you are gradually selecting for viruses that have acquired mutations leading to enhancement of human infectivity. Eventually you end up with a virus that is optimized for human infectivity.”

However, Dr Antoniou added that there are far quicker and more efficient ways to achieve this aim.

For example, if you start with little information about what your human-infective virus looks like, you can genetically engineer a large number of SARS-CoV spike protein variants within phages. Phages are viruses that can infect bacteria. Phages can be genetically engineered to express on their exterior coat the CoV spike protein with a different variant of the receptor binding domain (RBD) – the part of the spike protein that allows the virus to bind to the ACE2 protein on human cell surfaces and thus enables infection to take place. This collection of phage variants with different RBDs is called a “phage display library”. The “library” of variants is then cultured with human cells in order to select for those phages with spike protein variants that bind to the ACE2 receptor.

Then the DNA is extracted from the phage with the best-binding spike protein and sequenced. Based on the sequence, a whole virus optimized for human infectivity can be synthesized.

Alternatively, Dr Antoniou explained, if you start with some information, as is likely with a group of researchers experienced in coronavirus gain-of-function research, there is an even quicker way to create a human-infective virus. Given that past research indicates that the nature of the spike protein alone doesn’t determine infectivity, it seems sensible to generate a library of spike mutant proteins directly within a whole coronavirus, which would also contain any other components necessary for infectivity.

In this case, you would take a DNA clone of a coronavirus that you know to be close to human infectivity, based on the sequence of its RBD. (Manipulation of DNA clones of coronaviruses is the standard procedure used to generate mutant viruses, including chimeras, in gain-of-function experiments, such as those carried out by scientists at the University of North Carolina and the Wuhan Institute of Virology.) You would then use the genetic engineering technique of DNA synthesis to generate a large number of randomly mutated versions of the spike protein RBD. The RBD mutations that you engineer could be more narrowly targeted by focusing on those regions encoding the amino acids whose nature and positions you know to be most critical for docking onto the human ACE2 receptor. The mutant versions of the RBD would then be selected for strong binding to the ACE2 receptor and consequently high infectivity of human cells.

Both methods described above would not leave any “signature” of genetic engineering. That’s an important consideration, given that Prof Petrovsky believes that genetic engineering was not involved in the development of SARS-CoV-2 due to the absence of such a signature.

Genetic engineering likely

In GMWatch’s view, to bypass the efficient genetic engineering-based methods described by Dr Antoniou in favour of the more laborious culture and selection-only method suggested by Prof Petrovsky would seem a curious decision for any laboratory committed to investigating coronavirus gain-of-function, such as the WIV.

The conclusions that we draw from these two new papers and Dr Antoniou’s input are that the “zoonosis” theory of SARS-CoV-2’s origin looks increasingly open to question, that the lab escape theory appears to be a solidly based scenario and, if that is what happened, genetic engineering is highly likely to have played a part in the development of the virus.

Report by Claire Robinson

SOURCE

https://www.gmwatch.org/en/news/latest-news/19412-lab-escape-theory-of-sars-cov-2-origin-gaining-scientific-support

  • Why Was Wuhan Lab Locked Down When Outbreak Began?
Analysis by Dr. Joseph Mercola Fact Checked

GAs reported in “Bioweapon Labs Must Be Shut Down and Scientists Prosecuted,” there’s mounting evidence suggesting SARS-CoV-2 may have been leaked (whether inadvertently or not) from the biosafety level (BSL) 4 laboratory in Wuhan, China.1,2I’ve also interviewed bioweapons expert Francis Boyle and molecular biologist Judy Mikovits, both of whom have cited evidence that strongly points toward SARS-CoV-2 being an escaped laboratory creation.

Why Was Wuhan Lab Shut Down?

Fueling suspicions that SARS-CoV-2 escaped from the lab in Wuhan — and that it began far earlier than admitted — is an analysis3 of commercial telemetry (i.e., cellphone) data showing a significant and unusual reduction in device activity in and around the Wuhan Institute of Virology’s (WIV) National Biosafety Laboratory during October 2019.4,5,6According to the open source telemetry report,7 “Beginning on October 11, there was a substantial decrease in activity,” and “the last time a device is active prior to October 11 is October 6.”Between October 14 and October 19, there was no device activity in the area around the laboratory at all. “During this time, it is believed that roadblocks were put in place to prevent traffic from coming near the facility,” the report states. What’s more, between October 7 and October 24, there was no activity within the facility itself.While not concrete proof of a biohazard leak, the absence of cellphone traffic in and around the laboratory in October 2019 suggests the lab may have been shut down for a period, and the roads around it blocked off. The question is why?Amid accusations that the World Health Organization helped suppress information about the pandemic on behalf of China, a review of its handling of the COVID-19 pandemic will be conducted,8 although it is still unclear which body will conduct the review and when. Many are also asking just how independent such a review will or can be.According to Martenson, the fact that SARS-CoV-2’s spike protein has a furin cleavage site is “the smoking gun” that proves it was created in a lab. I invite you to review his easy-to-follow analysis in “The Smoking Gun Proving SARS-CoV-2 Is an Engineered Virus.”If the Nerd Has Power blogger is correct, and the bat virus RaTG13 was in fact fabricated in order to give the natural evolution theory of SARS-CoV-2 some credence, then the evidence for a man-made pandemic becomes all the more compelling. There’s also other evidence that raise serious questions about the origin of this pandemic virus. Other Evidence of ManipulationIn an earlier blog post, dated March 15, 2020, Nerd Has Power explains the importance of the S1 and S2 spikes of a given virus.38 In that post, the blogger also details significant changes found in the S1 portion of the SARS-CoV-2 spike protein, “which dictates which host a coronavirus targets,” whereas much of the rest of the spike is very similar to the bat coronaviruses ZC45 and ZXC21. According to the blogger:39

“… the details of these differences and the way the human and the bat viruses differ from each other here in S1, in my and many other people’s eyes, practically spell out the origin of the Wuhan coronavirus — it is created by people, not by nature.”

In my opinion, the strongest pieces of evidence so far all point toward SARS-CoV-2 being a laboratory creation. How it got released, however, and why, remains to be determined.The fact that the people responsible would want to cover it up is obvious, however, when you consider that the punishment in such an event could include life in prison for violating the Biological Weapons Anti-Terrorism Act of 1989.40

Sources & References

wuhan bio lab shut down

STORY AT-A-GLANCE

  • Fueling suspicions that SARS-CoV-2 escaped from the Wuhan lab is an analysis of commercial telemetry (i.e., cellphone) data showing a significant and unusual reduction in device activity in and around the Wuhan Institute of Virology’s National Biosafety Laboratory during October 2019
  • Between October 14 and October 19, there was no device activity in the area around the laboratory at all, and between October 7 and October 24, there was no activity within the facility itself
  • While not concrete proof of a biohazard leak, the absence of cellphone traffic in and around the laboratory in October 2019 suggests the lab may have been shut down for a period, and the roads around it blocked off
  • A crucial piece of the lab release hypothesis that is missing from media reports and scientific opinion is a clear description of the experiments being conducted at the Wuhan Institute of Virology
  • Researchers have engineered chimeric viruses where the gene for the cell entry protein (S protein receptor-binding domain) from one virus is replaced by that of another virus
  • Bioweapon Labs Must Be Shut Down and Scientists Prosecuted
Analysis by Dr. Joseph Mercola Fact Checked
  • Gain of Function Research at NIH

https://osp.od.nih.gov/biotechnology/gain-of-function-research/

 

  • A pneumonia outbreak associated with a new coronavirus of probable bat origin

Nature volume 579, pages270–273(2020)Cite this article

https://www.nature.com/articles/s41586-020-2012-7

 

  • A pneumonia outbreak associated with a new coronavirus of probable bat origin.

Zhou, P., Yang, X., Wang, X. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020). https://doi.org/10.1038/s41586-020-2012-7

Abstract

Since the outbreak of severe acute respiratory syndrome (SARS) 18 years ago, a large number of SARS-related coronaviruses (SARSr-CoVs) have been discovered in their natural reservoir host, bats1,2,3,4. Previous studies have shown that some bat SARSr-CoVs have the potential to infect humans5,6,7. Here we report the identification and characterization of a new coronavirus (2019-nCoV), which caused an epidemic of acute respiratory syndrome in humans in Wuhan, China. The epidemic, which started on 12 December 2019, had caused 2,794 laboratory-confirmed infections including 80 deaths by 26 January 2020. Full-length genome sequences were obtained from five patients at an early stage of the outbreak. The sequences are almost identical and share 79.6% sequence identity to SARS-CoV. Furthermore, we show that 2019-nCoV is 96% identical at the whole-genome level to a bat coronavirus. Pairwise protein sequence analysis of seven conserved non-structural proteins domains show that this virus belongs to the species of SARSr-CoV. In addition, 2019-nCoV virus isolated from the bronchoalveolar lavage fluid of a critically ill patient could be neutralized by sera from several patients. Notably, we confirmed that 2019-nCoV uses the same cell entry receptor—angiotensin converting enzyme II (ACE2)—as SARS-CoV.

References

  1. Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science310, 676–679 (2005).
  2. Ge, X.-Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature503, 535–538 (2013).
  3. Yang, L. et al. Novel SARS-like betacoronaviruses in bats, China, 2011. Emerg. Infect. Dis19, 989–991 (2013).
  4. Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog13, e1006698 (2017).

 

  • A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence

Menachery, V., Yount, B., Debbink, K. et al. A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence. Nat Med 21, 1508–1513 (2015). https://doi.org/10.1038/nm.3985

Abstract

The emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome (MERS)-CoV underscores the threat of cross-species transmission events leading to outbreaks in humans. Here we examine the disease potential of a SARS-like virus, SHC014-CoV, which is currently circulating in Chinese horseshoe bat populations1. Using the SARS-CoV reverse genetics system2, we generated and characterized a chimeric virus expressing the spike of bat coronavirus SHC014 in a mouse-adapted SARS-CoV backbone. The results indicate that group 2b viruses encoding the SHC014 spike in a wild-type backbone can efficiently use multiple orthologs of the SARS receptor human angiotensin converting enzyme II (ACE2), replicate efficiently in primary human airway cells and achieve in vitro titers equivalent to epidemic strains of SARS-CoV. Additionally, in vivo experiments demonstrate replication of the chimeric virus in mouse lung with notable pathogenesis. Evaluation of available SARS-based immune-therapeutic and prophylactic modalities revealed poor efficacy; both monoclonal antibody and vaccine approaches failed to neutralize and protect from infection with CoVs using the novel spike protein. On the basis of these findings, we synthetically re-derived an infectious full-length SHC014 recombinant virus and demonstrate robust viral replication both in vitro and in vivo. Our work suggests a potential risk of SARS-CoV re-emergence from viruses currently circulating in bat populations.

https://www.nature.com/articles/nm.3985/#citeas

 

Donato Gemmati, Barbara Bramanti, […] & Veronica Tisato

International Journal of Molecular Sciences (2020)

 

  • Evolutionary arms race between virus and host drives genetic diversity in bat SARS related coronavirus spike genes

Hua Guo, Bing-Jie Hu, Xing-Lou Yang, Lei-Ping Zeng, Bei Li, Song-Ying Ouyang, Zheng-Li Shi

doi: https://doi.org/10.1101/2020.05.13.093658

https://www.biorxiv.org/content/10.1101/2020.05.13.093658v1

 

LPBI Position 

A.  SARS-CoV-2 is pre-adapted to Human Transmission

B.  Branches of evolution stemming from a less well-adapted human SARS-CoV-2-like virus have been found

C.  The Role of SARS-CoV-2 Virus Progenitors – ARE CLEAR

D. Virus Progenitors will potentiate Future Virus Disease Transmission

E.  Pandemic Re-Emergence – Is  InEVITABLE and Virus Progenitors will be the subject for 2nd generation of genetic engineering technologies

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RNA from the SARS-CoV-2 virus taking over the cells it infects: Virulence – Pathogen’s ability to infect a Resistant Host: The Imbalance between Controlling Virus Replication versus Activation of the Adaptive Immune Response

Curator: Aviva Lev-Ari, PhD, RN – I added colors and bold face

 

UPDATED on 9/8/2020

What bats can teach us about developing immunity to Covid-19 | Free to read

Clive Cookson, Anna Gross and Ian Bott, London

https://www.ft.com/content/743ce7a0-60eb-482d-b1f4-d4de11182fa9?utm_source=Nature+Briefing&utm_campaign=af64422080-briefing-dy-20200908&utm_medium=email&utm_term=0_c9dfd39373-af64422080-43323101

 

UPDATED on 6/29/2020

Another duality and paradox in the Treatment of COVID-19 Patients in ICUs was expressed by Mike Yoffe, MD, PhD, David H. Koch Professor of Biology and Biological Engineering, Massachusetts Institute of Technology. Dr. Yaffe has a joint appointment in Acute Care Surgery, Trauma, and Surgical Critical Care, and in Surgical Oncology @BIDMC

on 6/29 at SOLUTIONS with/in/sight at Koch Institute @MIT

How Are Cancer Researchers Fighting COVID-19? (Part II)” Jun 29, 2020 11:30 AM EST

Mike Yoffe, MD, PhD 

In COVID-19 patients: two life threatening conditions are seen in ICUs:

  • Blood Clotting – Hypercoagulability or Thrombophilia
  • Cytokine Storm – immuno-inflammatory response
  • The coexistence of 1 and 2 – HINDERS the ability to use effectively tPA as an anti-clotting agent while the cytokine storm is present.

Mike Yoffe’s related domain of expertise:

Signaling pathways and networks that control cytokine responses and inflammation

Misregulation of cytokine feedback loops, along with inappropriate activation of the blood clotting cascade causes dysregulation of cell signaling pathways in innate immune cells (neutrophils and macrophages), resulting in tissue damage and multiple organ failure following trauma or sepsis. Our research is focused on understanding the role of the p38-MK2 pathway in cytokine control and innate immune function, and on cross-talk between cytokines, clotting factors, and neutrophil NADPH oxidase-derived ROS in tissue damage, coagulopathy, and inflammation, using biochemistry, cell biology, and mouse knock-out/knock-in models.  We recently discovered a particularly important link between abnormal blood clotting and the complement pathway cytokine C5a which causes excessive production of extracellular ROS and organ damage by neutrophils after traumatic injury.

SOURCE

https://www.bidmc.org/research/research-by-department/surgery/acute-care-surgery-trauma-and-surgical-critical-care/michael-b-yaffe

 

See

The Genome Structure of CORONAVIRUS, SARS-CoV-2

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/05/04/the-genome-structure-of-coronavirus-sars-cov-2-i-awaited-for-this-article-for-60-days/

 

Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19

Open Access Published:May 15, 2020DOI:https://doi.org/10.1016/j.cell.2020.04.026

Highlights

  • SARS-CoV-2 infection induces low IFN-I and -III levels with a moderate ISG response
  • Strong chemokine expression is consistent across in vitroex vivo, and in vivo models
  • Low innate antiviral defenses and high pro-inflammatory cues contribute to COVID-19

Summary

Viral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity. Because of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection. Here we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses. Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response. This response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6. We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.

Graphical Abstract

Keywords

Results

Defining the Transcriptional Response to SARS-CoV-2 Relative to Other Respiratory Viruses

To compare the transcriptional response of SARS-CoV-2 with other respiratory viruses, including MERS-CoV, SARS-CoV-1, human parainfluenza virus 3 (HPIV3), respiratory syncytial virus (RSV), and IAV, we first chose to focus on infection in a variety of respiratory cell lines (Figure 1). To this end, we collected poly(A) RNA from infected cells and performed RNA sequencing (RNA-seq) to estimate viral load. These data show that virus infection levels ranged from 0.1% to more than 50% of total RNA reads (Figure 1A).

Discussion

In the present study, we focus on defining the host response to SARS-CoV-2 and other human respiratory viruses in cell lines, primary cell cultures, ferrets, and COVID-19 patients. In general, our data show that the overall transcriptional footprint of SARS-CoV-2 infection was distinct in comparison with other highly pathogenic coronaviruses and common respiratory viruses such as IAV, HPIV3, and RSV. It is noteworthy that, despite a reduced IFN-I and -III response to SARS-CoV-2, we observed a consistent chemokine signature. One exception to this observation is the response to high-MOI infection in A549-ACE2 and Calu-3 cells, where replication was robust and an IFN-I and -III signature could be observed. In both of these examples, cells were infected at a rate to theoretically deliver two functional virions per cell in addition to any defective interfering particles within the virus stock that were not accounted for by plaque assays. Under these conditions, the threshold for PAMP may be achieved prior to the ability of the virus to evade detection through production of a viral antagonist. Alternatively, addition of multiple genomes to a single cell may disrupt the stoichiometry of viral components, which, in turn, may itself generate PAMPs that would not form otherwise. These ideas are supported by the fact that, at a low-MOI infection in A549-ACE2 cells, high levels of replication could also be achieved, but in the absence of IFN-I and -III induction. Taken together, these data suggest that, at low MOIs, the virus is not a strong inducer of the IFN-I and -III system, as opposed to conditions where the MOI is high.
Taken together, the data presented here suggest that the response to SARS-CoV-2 is imbalanced with regard to controlling virus replication versus activation of the adaptive immune response. Given this dynamic, treatments for COVID-19 have less to do with the IFN response and more to do with controlling inflammation. Because our data suggest that numerous chemokines and ILs are elevated in COVID-19 patients, future efforts should focus on U.S. Food and Drug Administration (FDA)-approved drugs that can be rapidly deployed and have immunomodulating properties.

SOURCE

https://www.cell.com/cell/fulltext/S0092-8674(20)30489-X

SARS-CoV-2 ORF3b is a potent interferon antagonist whose activity is further increased by a naturally occurring elongation variant

Yoriyuki KonnoIzumi KimuraKeiya UriuMasaya FukushiTakashi IrieYoshio KoyanagiSo NakagawaKei Sato

Abstract

One of the features distinguishing SARS-CoV-2 from its more pathogenic counterpart SARS-CoV is the presence of premature stop codons in its ORF3b gene. Here, we show that SARS-CoV-2 ORF3b is a potent interferon antagonist, suppressing the induction of type I interferon more efficiently than its SARS-CoV ortholog. Phylogenetic analyses and functional assays revealed that SARS-CoV-2-related viruses from bats and pangolins also encode truncated ORF3b gene products with strong anti-interferon activity. Furthermore, analyses of more than 15,000 SARS-CoV-2 sequences identified a natural variant, in which a longer ORF3b reading frame was reconstituted. This variant was isolated from two patients with severe disease and further increased the ability of ORF3b to suppress interferon induction. Thus, our findings not only help to explain the poor interferon response in COVID-19 patients, but also describe a possibility of the emergence of natural SARS-CoV-2 quasi-species with extended ORF3b that may exacerbate COVID-19 symptoms.

Highlights

  • ORF3b of SARS-CoV-2 and related bat and pangolin viruses is a potent IFN antagonist

  • SARS-CoV-2 ORF3b suppresses IFN induction more efficiently than SARS-CoV ortholog

  • The anti-IFN activity of ORF3b depends on the length of its C-terminus

  • An ORF3b with increased IFN antagonism was isolated from two severe COVID-19 cases

Competing Interest Statement

The authors have declared no competing interest.

Paper in collection COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv

 

SOURCE

https://www.biorxiv.org/content/10.1101/2020.05.11.088179v1

 

 

A deep dive into how the new coronavirus infects cells has found that it orchestrates a hostile takeover of their genes unlike any other known viruses do, producing what one leading scientist calls “unique” and “aberrant” changes.Recent studies show that in seizing control of genes in the human cells it invades, the virus changes how segments of DNA are read, doing so in a way that might explain why the elderly are more likely to die of Covid-19 and why antiviral drugs might not only save sick patients’ lives but also prevent severe disease if taken before infection.“It’s something I have never seen in my 20 years of” studying viruses, said virologist Benjamin tenOever of the Icahn School of Medicine at Mount Sinai, referring to how SARS-CoV-2, the virus that causes Covid-19, hijacks cells’ genomes.The “something” he and his colleagues saw is how SARS-CoV-2 blocks one virus-fighting set of genes but allows another set to launch, a pattern never seen with other viruses. Influenza and the original SARS virus (in the early 2000s), for instance, interfere with both arms of the body’s immune response — what tenOever dubs “call to arms” genes and “call for reinforcement” genes.The first group of genes produces interferons. These proteins, which infected cells release, are biological semaphores, signaling to neighboring cells to activate some 500 of their own genes that will slow down the virus’ ability to make millions of copies of itself if it invades them. This lasts seven to 10 days, tenOever said, controlling virus replication and thereby buying time for the second group of genes to act.This second set of genes produce their own secreted proteins, called chemokines, that emit a biochemical “come here!” alarm. When far-flung antibody-making B cells and virus-killing T cells sense the alarm, they race to its source. If all goes well, the first set of genes holds the virus at bay long enough for the lethal professional killers to arrive and start eradicating viruses.

“Most other viruses interfere with some aspect of both the call to arms and the call for reinforcements,” tenOever said. “If they didn’t, no one would ever get a viral illness”: The one-two punch would pummel any incipient infection into submission.

SARS-CoV-2, however, uniquely blocks one cellular defense but activates the other, he and his colleagues reported in a study published last week in Cell. They studied healthy human lung cells growing in lab dishes, ferrets (which the virus infects easily), and lung cells from Covid-19 patients. In all three, they found that within three days of infection, the virus induces cells’ call-for-reinforcement genes to produce cytokines. But it blocks their call-to-arms genes — the interferons that dampen the virus’ replication.

The result is essentially no brakes on the virus’s replication, but a storm of inflammatory molecules in the lungs, which is what tenOever calls an “unique” and “aberrant” consequence of how SARS-CoV-2 manipulates the genome of its target.

In another new study, scientists in Japan last week identified how SARS-CoV-2 accomplishes that genetic manipulation. Its ORF3b gene produces a protein called a transcription factor that has “strong anti-interferon activity,” Kei Sato of the University of Tokyo and colleagues found — stronger than the original SARS virus or influenza viruses. The protein basically blocks the cell from recognizing that a virus is present, in a way that prevents interferon genes from being expressed.

In fact, the Icahn School team found no interferons in the lung cells of Covid-19 patients. Without interferons, tenOever said, “there is nothing to stop the virus from replicating and festering in the lungs forever.”

That causes lung cells to emit even more “call-for-reinforcement” genes, summoning more and more immune cells. Now the lungs have macrophages and neutrophils and other immune cells “everywhere,” tenOever said, causing such runaway inflammation “that you start having inflammation that induces more inflammation.”

At the same time, unchecked viral replication kills lung cells involved in oxygen exchange. “And suddenly you’re in the hospital in severe respiratory distress,” he said.

In elderly people, as well as those with diabetes, heart disease, and other underlying conditions, the call-to-arms part of the immune system is weaker than in younger, healthier people, even before the coronavirus arrives. That reduces even further the cells’ ability to knock down virus replication with interferons, and imbalances the immune system toward the dangerous inflammatory response.

The discovery that SARS-CoV-2 strongly suppresses infected cells’ production of interferons has raised an intriguing possibility: that taking interferons might prevent severe Covid-19 or even prevent it in the first place, said Vineet Menachery of the University of Texas Medical Branch.

In a study of human cells growing in lab dishes, described in a preprint (not peer-reviewed or published in a journal yet), he and his colleagues also found that SARS-CoV-2 “prevents the vast amount” of interferon genes from turning on. But when cells growing in lab dishes received the interferon IFN-1 before exposure to the coronavirus, “the virus has a difficult time replicating.”

After a few days, the amount of virus in infected but interferon-treated cells was 1,000- to 10,000-fold lower than in infected cells not pre-treated with interferon. (The original SARS virus, in contrast, is insensitive to interferon.)

Ending the pandemic and preventing its return is assumed to require an effective vaccine to prevent infectionand antiviral drugs such as remdesivir to treat the very sick, but the genetic studies suggest a third strategy: preventive drugs.

It’s possible that treatment with so-called type-1 interferon “could stop the virus before it could get established,” Menachery said.

Giving drugs to healthy people is always a dicey proposition, since all drugs have side effects — something considered less acceptable than when a drug is used to treat an illness. “Interferon treatment is rife with complications,” Menachery warned. The various interferons, which are prescribed for hepatitis, cancers, and many other diseases, can cause flu-like symptoms.

But the risk-benefit equation might shift, both for individuals and for society, if interferons or antivirals or other medications are shown to reduce the risk of developing serious Covid-19 or even make any infection nearly asymptomatic.

Interferon “would be warning the cells the virus is coming,” Menachery said, so such pretreatment might “allow treated cells to fend off the virus better and limit its spread.” Determining that will of course require clinical trials, which are underway.

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