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Archive for the ‘Genome Biology’ Category


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

Read Full Post »


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 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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The Genome Structure of CORONAVIRUS, SARS-CoV-2

“I awaited for this article for 60 days”

Aviva Lev-Ari, PhD, RN

Reporter: Aviva Lev-Ari, PhD, RN

 

Note:

  • The four letters of DNA are A, C, G and T.
  • In RNA molecules like the coronavirus genome, the T (thymine) is replaced with U (uracil).

Sources:

  • Fan Wu et al., Nature;
  • National Center for Biotechnology Information;
  • Dr. David Gordon, University of California, San Francisco;
  • Dr. Matthew B. Frieman and Dr. Stuart Weston, University of Maryland School of Medicine;
  • Dr. Pleuni Pennings, San Francisco State University;
  • David Haussler and Jason Fernandes, U.C. Santa Cruz Genomics Institute; Journal of Virology;
  • Annual Review of Virology.

Model sources:

  • Coronavirus by Maria Voigt, RCSB Protein Data Bank headquartered at Rutgers University–New Brunswick;
  • Ribosome from Heena Khatter et al., Nature;
  • Proteins from Yang Zhang’s Research Group, University of Michigan.

Bad News Wrapped in Protein: Inside the Coronavirus Genome

A virus is “simply a piece of bad news wrapped up in protein,” the biologists Jean and Peter Medawar wrote in 1977.

In January, scientists deciphered a piece of very bad news: the genome of SARS-CoV-2, the virus that causes Covid-19. The sample came from a 41-year-old man who worked at the seafood market in Wuhan where the first cluster of cases appeared.

Researchers are now racing to make sense of this viral recipe, which could inspire drugs, vaccines and other tools to fight the ongoing pandemic.

A String of RNA

Viruses must hijack living cells to replicate and spread. When the coronavirus finds a suitable cell, it injects a strand of RNA that contains the entire coronavirus genome.

The genome of the new coronavirus is less than 30,000 “letters” long. (The human genome is over 3 billion.) Scientists have identified genes for as many as 29 proteins, which carry out a range of jobs from making copies of the coronavirus to suppressing the body’s immune responses.

The first sequence of RNA letters reads:

auuaaagguuuauaccuucccagguaacaaaccaaccaacuuucgaucucuuguagaucuguucucuaaacgaacuuuaaaaucuguguggcugucacucggcugcaugcuuagugcacucacgcaguauaauuaauaacuaauuacugucguugacaggacacgaguaacucgucuaucuucugcaggcugcuuacgguuucguccguguugcagccgaucaucagcacaucuagguuucguccgggugugaccgaaagguaag

This sequence recruits machinery inside the infected cell to read the RNA letters — acg and u — and translate them into coronavirus proteins.

The full coronavirus genome and the proteins it encodes are shown below.

A Chain of Proteins · ORF1ab

The first viral protein created inside the infected cell is actually a chain of 16 proteins joined together. Two of these proteins act like scissors, snipping the links between the different proteins and freeing them to do their jobs.

See graph at https://www.nytimes.com/interactive/2020/04/03/science/coronavirus-genome-bad-news-wrapped-in-protein.html

Research on other coronaviruses has given scientists a good understanding of what some of the SARS-CoV-2 proteins do. But other proteins are far more mysterious, and some might do nothing at all.

Cellular Saboteur · NSP1

This protein slows down the infected cell’s production of its own proteins. This sabotage forces the cell to make more virus proteins and prevents it from assembling antiviral proteins that could stop the virus.

auggagagccuugucccugguuucaacgagaaaacacacguccaacucaguuugccuguuuuacagguucgcgacgugcucguacguggcuuuggagacuccguggaggaggucuuaucagaggcacgucaacaucuuaaagauggcacuuguggcuuaguagaaguugaaaaaggcguuuugccucaacuugaacagcccuauguguucaucaaacguucggaugcucgaacugcaccucauggucauguuaugguugagcugguagcagaacucgaaggcauucaguacggucguaguggugagacacuugguguccuugucccucaugugggcgaaauaccaguggcuuaccgcaagguucuucuucguaagaacgguaauaaaggagcugguggccauaguuacggcgccgaucuaaagucauuugacuuaggcgacgagcuuggcacugauccuuaugaagauuuucaagaaaacuggaacacuaaacauagcagugguguuacccgugaacucaugcgugagcuuaacggaggg

Mystery Protein · NSP2

Scientists aren’t sure what NSP2 does. The other proteins it attaches to may offer some clues. Two of them help move molecule-filled bubbles called endosomes around the cell.

gcauacacucgcuaugucgauaacaacuucuguggcccugauggcuacccucuugagugcauuaaagaccuucuagcacgugcugguaaagcuucaugcacuuuguccgaacaacuggacuuuauugacacuaagagggguguauacugcugccgugaacaugagcaugaaauugcuugguacacggaacguucugaaaagagcuaugaauugcagacaccuuuugaaauuaaauuggcaaagaaauuugacaccuucaauggggaauguccaaauuuuguauuucccuuaaauuccauaaucaagacuauucaaccaaggguugaaaagaaaaagcuugauggcuuuauggguagaauucgaucugucuauccaguugcgucaccaaaugaaugcaaccaaaugugccuuucaacucucaugaagugugaucauuguggugaaacuucauggcagacgggcgauuuuguuaaagccacuugcgaauuuuguggcacugagaauuugacuaaagaaggugccacuacuugugguuacuuaccccaaaaugcuguuguuaaaauuuauuguccagcaugucacaauucagaaguaggaccugagcauagucuugccgaauaccauaaugaaucuggcuugaaaaccauucuucguaaggguggucgcacuauugccuuuggaggcuguguguucucuuauguugguugccauaacaagugugccuauuggguuccacgugcuagcgcuaacauagguuguaaccauacagguguuguuggagaagguuccgaaggucuuaaugacaaccuucuugaaauacuccaaaaagagaaagucaacaucaauauuguuggugacuuuaaacuuaaugaagagaucgccauuauuuuggcaucuuuuucugcuuccacaagugcuuuuguggaaacugugaaagguuuggauuauaaagcauucaaacaaauuguugaauccugugguaauuuuaaaguuacaaaaggaaaagcuaaaaaaggugccuggaauauuggugaacagaaaucaauacugaguccucuuuaugcauuugcaucagaggcugcucguguuguacgaucaauuuucucccgcacucuugaaacugcucaaaauucugugcguguuuuacagaaggccgcuauaacaauacuagauggaauuucacaguauucacugagacucauugaugcuaugauguucacaucugauuuggcuacuaacaaucuaguuguaauggccuacauuacaggugguguuguucaguugacuucgcaguggcuaacuaacaucuuuggcacuguuuaugaaaaacucaaacccguccuugauuggcuugaagagaaguuuaaggaagguguagaguuucuuagagacgguugggaaauuguuaaauuuaucucaaccugugcuugugaaauugucgguggacaaauugucaccugugcaaaggaaauuaaggagaguguucagacauucuuuaagcuuguaaauaaauuuuuggcuuugugugcugacucuaucauuauugguggagcuaaacuuaaagccuugaauuuaggugaaacauuugucacgcacucaaagggauuguacagaaaguguguuaaauccagagaagaaacuggccuacucaugccucuaaaagccccaaaagaaauuaucuucuuagagggagaaacacuucccacagaaguguuaacagaggaaguugucuugaaaacuggugauuuacaaccauuagaacaaccuacuagugaagcuguugaagcuccauugguugguacaccaguuuguauuaacgggcuuauguugcucgaaaucaaagacacagaaaaguacugugcccuugcaccuaauaugaugguaacaaacaauaccuucacacucaaaggcggu

Untagging and Cutting · NSP3

NSP3 is a large protein that has two important jobs. One is cutting loose other viral proteins so they can do their own tasks. It also alters many of the infected cell’s proteins.

Normally, a healthy cell tags old proteins for destruction. But the coronavirus can remove those tags, changing the balance of proteins and possibly reducing the cell’s ability to fight the virus.

gcaccaacaaagguuacuuuuggugaugacacugugauagaagugcaagguuacaagagugugaauaucacuuuugaacuugaugaaaggauugauaaaguacuuaaugagaagugcucugccuauacaguugaacucgguacagaaguaaaugaguucgccuguguuguggcagaugcugucauaaaaacuuugcaaccaguaucugaauuacuuacaccacugggcauugauuuagaugaguggaguauggcuacauacuacuuauuugaugagucuggugaguuuaaauuggcuucacauauguauuguucuuucuacccuccagaugaggaugaagaagaaggugauugugaagaagaagaguuugagccaucaacucaauaugaguaugguacugaagaugauuaccaagguaaaccuuuggaauuuggugccacuucugcugcucuucaaccugaagaagagcaagaagaagauugguuagaugaugauagucaacaaacuguuggucaacaagacggcagugaggacaaucagacaacuacuauucaaacaauuguugagguucaaccucaauuagagauggaacuuacaccaguuguucagacuauugaagugaauaguuuuagugguuauuuaaaacuuacugacaauguauacauuaaaaaugcagacauuguggaagaagcuaaaaagguaaaaccaacagugguuguuaaugcagccaauguuuaccuuaaacauggaggagguguugcaggagccuuaaauaaggcuacuaacaaugccaugcaaguugaaucugaugauuacauagcuacuaauggaccacuuaaagugggugguaguuguguuuuaagcggacacaaucuugcuaaacacugucuucauguugucggcccaaauguuaacaaaggugaagacauucaacuucuuaagagugcuuaugaaaauuuuaaucagcacgaaguucuacuugcaccauuauuaucagcugguauuuuuggugcugacccuauacauucuuuaagaguuuguguagauacuguucgcacaaaugucuacuuagcugucuuugauaaaaaucucuaugacaaacuuguuucaagcuuuuuggaaaugaagagugaaaagcaaguugaacaaaagaucgcugagauuccuaaagaggaaguuaagccauuuauaacugaaaguaaaccuucaguugaacagagaaaacaagaugauaagaaaaucaaagcuuguguugaagaaguuacaacaacucuggaagaaacuaaguuccucacagaaaacuuguuacuuuauauugacauuaauggcaaucuucauccagauucugccacucuuguuagugacauugacaucacuuucuuaaagaaagaugcuccauauauagugggugauguuguucaagaggguguuuuaacugcugugguuauaccuacuaaaaaggcugguggcacuacugaaaugcuagcgaaagcuuugagaaaagugccaacagacaauuauauaaccacuuacccgggucaggguuuaaaugguuacacuguagaggaggcaaagacagugcuuaaaaaguguaaaagugccuuuuacauucuaccaucuauuaucucuaaugagaagcaagaaauucuuggaacuguuucuuggaauuugcgagaaaugcuugcacaugcagaagaaacacgcaaauuaaugccugucuguguggaaacuaaagccauaguuucaacuauacagcguaaauauaaggguauuaaaauacaagagggugugguugauuauggugcuagauuuuacuuuuacaccaguaaaacaacuguagcgucacuuaucaacacacuuaacgaucuaaaugaaacucuuguuacaaugccacuuggcuauguaacacauggcuuaaauuuggaagaagcugcucgguauaugagaucucucaaagugccagcuacaguuucuguuucuucaccugaugcuguuacagcguauaaugguuaucuuacuucuucuucuaaaacaccugaagaacauuuuauugaaaccaucucacuugcugguuccuauaaagauugguccuauucuggacaaucuacacaacuagguauagaauuucuuaagagaggugauaaaaguguauauuacacuaguaauccuaccacauuccaccuagauggugaaguuaucaccuuugacaaucuuaagacacuucuuucuuugagagaagugaggacuauuaagguguuuacaacaguagacaacauuaaccuccacacgcaaguuguggacaugucaaugacauauggacaacaguuugguccaacuuauuuggauggagcugauguuacuaaaauaaaaccucauaauucacaugaagguaaaacauuuuauguuuuaccuaaugaugacacucuacguguugaggcuuuugaguacuaccacacaacugauccuaguuuucuggguagguacaugucagcauuaaaucacacuaaaaaguggaaauacccacaaguuaaugguuuaacuucuauuaaaugggcagauaacaacuguuaucuugccacugcauuguuaacacuccaacaaauagaguugaaguuuaauccaccugcucuacaagaugcuuauuacagagcaagggcuggugaagcugcuaacuuuugugcacuuaucuuagccuacuguaauaagacaguaggugaguuaggugauguuagagaaacaaugaguuacuuguuucaacaugccaauuuagauucuugcaaaagagucuugaacgugguguguaaaacuuguggacaacagcagacaacccuuaaggguguagaagcuguuauguacaugggcacacuuucuuaugaacaauuuaagaaagguguucagauaccuuguacgugugguaaacaagcuacaaaauaucuaguacaacaggagucaccuuuuguuaugaugucagcaccaccugcucaguaugaacuuaagcaugguacauuuacuugugcuagugaguacacugguaauuaccaguguggucacuauaaacauauaacuucuaaagaaacuuuguauugcauagacggugcuuuacuuacaaaguccucagaauacaaagguccuauuacggauguuuucuacaaagaaaacaguuacacaacaaccauaaaaccaguuacuuauaaauuggaugguguuguuuguacagaaauugacccuaaguuggacaauuauuauaagaaagacaauucuuauuucacagagcaaccaauugaucuuguaccaaaccaaccauauccaaacgcaagcuucgauaauuuuaaguuuguaugugauaauaucaaauuugcugaugauuuaaaccaguuaacugguuauaagaaaccugcuucaagagagcuuaaaguuacauuuuucccugacuuaaauggugaugugguggcuauugauuauaaacacuacacacccucuuuuaagaaaggagcuaaauuguuacauaaaccuauuguuuggcauguuaacaaugcaacuaauaaagccacguauaaaccaaauaccugguguauacguugucuuuggagcacaaaaccaguugaaacaucaaauucguuugauguacugaagucagaggacgcgcagggaauggauaaucuugccugcgaagaucuaaaaccagucucugaagaaguaguggaaaauccuaccauacagaaagacguucuugaguguaaugugaaaacuaccgaaguuguaggagacauuauacuuaaaccagcaaauaauaguuuaaaaauuacagaagagguuggccacacagaucuaauggcugcuuauguagacaauucuagucuuacuauuaagaaaccuaaugaauuaucuagaguauuagguuugaaaacccuugcuacucaugguuuagcugcuguuaauagugucccuugggauacuauagcuaauuaugcuaagccuuuucuuaacaaaguuguuaguacaacuacuaacauaguuacacgguguuuaaaccguguuuguacuaauuauaugccuuauuucuuuacuuuauugcuacaauuguguacuuuuacuagaaguacaaauucuagaauuaaagcaucuaugccgacuacuauagcaaagaauacuguuaagagugucgguaaauuuugucuagaggcuucauuuaauuauuugaagucaccuaauuuuucuaaacugauaaauauuauaauuugguuuuuacuauuaaguguuugccuagguucuuuaaucuacucaaccgcugcuuuagguguuuuaaugucuaauuuaggcaugccuucuuacuguacugguuacagagaaggcuauuugaacucuacuaaugucacuauugcaaccuacuguacugguucuauaccuuguaguguuugucuuagugguuuagauucuuuagacaccuauccuucuuuagaaacuauacaaauuaccauuucaucuuuuaaaugggauuuaacugcuuuuggcuuaguugcagagugguuuuuggcauauauucuuuucacuagguuuuucuauguacuuggauuggcugcaaucaugcaauuguuuuucagcuauuuugcaguacauuuuauuaguaauucuuggcuuaugugguuaauaauuaaucuuguacaaauggccccgauuucagcuaugguuagaauguacaucuucuuugcaucauuuuauuauguauggaaaaguuaugugcauguuguagacgguuguaauucaucaacuuguaugauguguuacaaacguaauagagcaacaagagucgaauguacaacuauuguuaaugguguuagaagguccuuuuaugucuaugcuaauggagguaaaggcuuuugcaaacuacacaauuggaauuguguuaauugugauacauucugugcugguaguacauuuauuagugaugaaguugcgagagacuugucacuacaguuuaaaagaccaauaaauccuacugaccagucuucuuacaucguugauaguguuacagugaagaaugguuccauccaucuuuacuuugauaaagcuggucaaaagacuuaugaaagacauucucucucucauuuuguuaacuuagacaaccugagagcuaauaacacuaaagguucauugccuauuaauguuauaguuuuugaugguaaaucaaaaugugaagaaucaucugcaaaaucagcgucuguuuacuacagucagcuuaugugucaaccuauacuguuacuagaucaggcauuagugucugauguuggugauagugcggaaguugcaguuaaaauguuugaugcuuacguuaauacguuuucaucaacuuuuaacguaccaauggaaaaacucaaaacacuaguugcaacugcagaagcugaacuugcaaagaauguguccuuagacaaugucuuaucuacuuuuauuucagcagcucggcaaggguuuguugauucagauguagaaacuaaagauguuguugaaugucuuaaauugucacaucaaucugacauagaaguuacuggcgauaguuguaauaacuauaugcucaccuauaacaaaguugaaaacaugacaccccgugaccuuggugcuuguauugacuguagugcgcgucauauuaaugcgcagguagcaaaaagucacaacauugcuuugauauggaacguuaaagauuucaugucauugucugaacaacuacgaaaacaaauacguagugcugcuaaaaagaauaacuuaccuuuuaaguugacaugugcaacuacuagacaaguuguuaauguuguaacaacaaagauagcacuuaaggguggu

Bubble Maker · NSP4

Combining with other proteins, NSP4 helps build fluid-filled bubbles within infected cells. Inside these bubbles, parts for new copies of the virus are constructed.

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Protein Scissors · NSP5

This protein makes most of the cuts that free other NSP proteins to carry out their own jobs.

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Bubble Factory · NSP6

Works with NSP3 and NSP4 to make virus factory bubbles.

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Copy Assistants · NSP7 and NSP8

These two proteins help NSP12 make new copies of the RNA genome, which can ultimately end up inside new viruses.

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At the Heart of the Cell · NSP9

This protein infiltrates tiny channels in the infected cell’s nucleus, which holds our own genome. It may be able to influence the movement of molecules in and out of the nucleus — but for what purpose, no one knows.

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Genetic Camouflage · NSP10

Human cells have antiviral proteins that find viral RNA and shred it. This protein works with NSP16 to camouflage the virus’s genes so that they don’t get attacked.

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Copy Machine · NSP12

This protein assembles genetic letters into new virus genomes. Researchers have found that the antiviral remdesivir interferes with NSP12 in other coronaviruses, and trials are now underway to see if the drug can treat Covid-19.

The infected cell begins reading the RNA sequence for NSP12:

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Then it backtracks and reads c again, continuing as:

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Another sequence, NSP11, overlaps part of the same stretch of RNA. But it’s not clear if the tiny protein encoded by this gene has any function at all.

Unwinding RNA · NSP13

Normally, virus RNA is wound into intricate twists and turns. Scientists suspect that NSP13 unwinds it so that other proteins can read its sequence and make new copies.

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Viral Proofreader · NSP14

As NSP12 duplicates the coronavirus genome, it sometimes adds a wrong letter to the new copy. NSP14 cuts out these errors, so that the correct letter can be added instead.

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Cleaning Up · NSP15

Researchers suspect that this protein chops up leftover virus RNA as a way to hide from the infected cell’s antiviral defenses.

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More Camouflage · NSP16

NSP16 works with NSP10 to help the virus’s genes hide from proteins that chop up viral RNA.

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Spike Protein · S

The spike protein is one of four structural proteins — SEM and N — that form the outer layer of the coronavirus and protect the RNA inside. Structural proteins also help assemble and release new copies of the virus.

The S proteins form prominent spikes on the surface of the virus by arranging themselves in groups of three. These crownlike spikes give coronaviruses their name.

Part of the spike can extend and attach to a protein called ACE2 (in yellow below), which appears on particular cells in the human airway. The virus can then invade the cell.

The gene for the spike protein in SARS-CoV-2 has an insertion of 12 genetic letters: ccucggcgggca. This mutation may help the spikes bind tightly to human cells — a crucial step in its evolution from a virus that infected bats and other species.

A number of scientific teams are now designing vaccines that could prevent the spikes from attaching to human cells.

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Escape Artist · ORF3a

The SARS-CoV-2 genome also encodes a group of so-called “accessory proteins.” They help change the environment inside the infected cell to make it easier for the virus to replicate.

The ORF3a protein pokes a hole in the membrane of an infected cell, making it easier for new viruses to escape. It also triggers inflammation, one of the most dangerous symptoms of Covid-19.

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ORF3b overlaps the same RNA, but scientists aren’t sure if SARS-CoV-2 uses this gene to make proteins.

Envelope Protein · E

The envelope protein is a structural protein that helps form the oily bubble of the virus. It may also have jobs to do once the virus is inside the cell. Researchers have found that it latches onto proteins that help turn our own genes on and off. It’s possible that pattern changes when the E protein interferes.

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Membrane Protein · M

Another structural protein that forms part of the outer coat of the virus.

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Signal Blocker · ORF6

This accessory protein blocks signals that the infected cell would send out to the immune system. It also blocks some of the cell’s own virus-fighting proteins, the same ones targeted by other viruses such as polio and influenza.

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Virus Liberator · ORF7a

When new viruses try to escape a cell, the cell can snare them with proteins called tetherin. Some research suggests that ORF7a cuts down an infected cell’s supply of tetherin, allowing more of the viruses to escape. Researchers have also found that the protein can trigger infected cells to commit suicide — which contributes to the damage Covid-19 causes to the lungs.

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ORF7b overlaps this same stretch of RNA, but it’s not clear what, if anything, the gene does.

Mystery Protein · ORF8

The gene for this accessory protein is dramatically different in SARS-CoV-2 than in other coronaviruses. Researchers are debating what it does.

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Nucleocapsid Protein · N

The N protein protects the virus RNA, keeping it stable inside the virus. Many N proteins link together in a long spiral, wrapping and coiling the RNA:

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The accessory proteins ORF9b and ORF9c overlap this same stretch of RNA. ORF9b blocks interferon, a key molecule in the defense against viruses, but it’s not clear if ORF9c is used at all.

Mystery Protein · ORF10

Close relatives of the SARS-CoV-2 virus don’t have the gene for this tiny accessory protein, so it’s hard to know what it’s for yet — or even if the virus makes proteins from it.

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End of the Line

The coronavirus genome ends with a snippet of RNA that stops the cell’s protein-making machinery. It then trails away as a repeating sequence of aaaaaaaaaaaaa

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Other related articles in this Open Access Online Scientific Journal include the following:

 

  • Structure-guided Drug Discovery: (1) The Coronavirus 3CL hydrolase (Mpro) enzyme (main protease) essential for proteolytic maturation of the virus and (2) viral protease, the RNA polymerase, the viral spike protein, a viral RNA as promising two targets for discovery of cleavage inhibitors of the viral spike polyprotein preventing the Coronavirus Virion the spread of infection

Curators and Reporters: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/12/structure-guided-drug-discovery-1-the-coronavirus-3cl-hydrolase-mpro-enzyme-main-protease-essential-for-proteolytic-maturation-of-the-virus-and-2-viral-protease-the-rna-polymerase-the-viral/

  • Predicting the Protein Structure of Coronavirus: Inhibition of Nsp15 can slow viral replication and Cryo-EM – Spike protein structure (experimentally verified) vs AI-predicted protein structures (not experimentally verified) of DeepMind (Parent: Google) aka AlphaFold

Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/08/predicting-the-protein-structure-of-coronavirus-inhibition-of-nsp15-can-slow-viral-replication-and-cryo-em-spike-protein-structure-experimentally-verified-vs-ai-predicted-protein-structures-not/

  • Promise of Synthetic Biology for Covid-19 Vaccine

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2020/03/23/promise-of-synthetic-biology-for-covid-19-vaccine/

  • Glycobiology vs Proteomics: Glycobiologists Prespective in the effort to explain the origin, etiology and potential therapeutics for the Coronavirus Pandemic (COVID-19).

 Curator: Ofer Markman, PhD

https://pharmaceuticalintelligence.com/2020/03/26/glycobiology-vs-proteomics-glycobiologists-prespective-in-the-effort-to-explain-the-origin-etiology-and-potential-therapeutics-for-the-coronavirus-pandemic-covid-19/

  • Worldwide trial uses AI to quickly identify ideal Covid-19 treatments

Reporter : Irina Robu, PhD

https://pharmaceuticalintelligence.com/2020/04/09/worldwide-trial-uses-ai-to-quickly-identify-ideal-covid-19-treatments/

  • Updated listing of COVID-19 vaccine and therapeutic trials from NIH Clinical Trials.gov

Curator: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2020/04/16/updated-listing-of-covid-19-vaccine-and-therapeutic-trials-from-nih-clinical-trials-gov/

  • Actemra, immunosuppressive which was designed to treat rheumatoid arthritis but also approved in 2017 to treat cytokine storms in cancer patients SAVED the sickest of all COVID-19 patients

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/04/14/actemra-immunosuppressive-which-was-designed-to-treat-rheumatoid-arthritis-but-also-approved-in-2017-to-treat-cytokine-storms-in-cancer-patients-saved-the-sickest-of-all-covid-19-patients/

  • Innate Immune Genes and Two Nasal Epithelial Cell Types: Expression of SARS-CoV-2 Entry Factors – COVID19 Cell Atlas

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/04/23/innate-immune-genes-and-two-nasal-epithelial-cell-types-expression-of-sars-cov-2-entry-factors-covid19-cell-atlas/

 

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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

Reporter: Stephen J. Williams, PhD

 Minisymposium: Evaluating Cancer Genomics from Normal Tissues through Evolution to Metastatic Disease

Oncologic therapy shapes the fitness landscape of clonal hematopoiesis

April 28, 2020, 4:10 PM – 4:20 PM

Presenter/Authors
Kelly L. Bolton, Ryan N. Ptashkin, Teng Gao, Lior Braunstein, Sean M. Devlin, Minal Patel, Antonin Berthon, Aijazuddin Syed, Mariko Yabe, Catherine Coombs, Nicole M. Caltabellotta, Mike Walsh, Ken Offit, Zsofia Stadler, Choonsik Lee, Paul Pharoah, Konrad H. Stopsack, Barbara Spitzer, Simon Mantha, James Fagin, Laura Boucai, Christopher J. Gibson, Benjamin Ebert, Andrew L. Young, Todd Druley, Koichi Takahashi, Nancy Gillis, Markus Ball, Eric Padron, David Hyman, Jose Baselga, Larry Norton, Stuart Gardos, Virginia Klimek, Howard Scher, Dean Bajorin, Eder Paraiso, Ryma Benayed, Maria Arcilla, Marc Ladanyi, David Solit, Michael Berger, Martin Tallman, Montserrat Garcia-Closas, Nilanjan Chatterjee, Luis Diaz, Ross Levine, Lindsay Morton, Ahmet Zehir, Elli Papaemmanuil. Memorial Sloan Kettering Cancer Center, New York, NY, University of North Carolina at Chapel Hill, Chapel Hill, NC, University of Cambridge, Cambridge, United Kingdom, Dana-Farber Cancer Institute, Boston, MA, Washington University, St Louis, MO, The University of Texas MD Anderson Cancer Center, Houston, TX, Moffitt Cancer Center, Tampa, FL, National Cancer Institute, Bethesda, MD

Abstract
Recent studies among healthy individuals show evidence of somatic mutations in leukemia-associated genes, referred to as clonal hematopoiesis (CH). To determine the relationship between CH and oncologic therapy we collected sequential blood samples from 525 cancer patients (median sampling interval time = 23 months, range: 6-53 months) of whom 61% received cytotoxic therapy or external beam radiation therapy and 39% received either targeted/immunotherapy or were untreated. Samples were sequenced using deep targeted capture-based platforms. To determine whether CH mutational features were associated with tMN risk, we performed Cox proportional hazards regression on 9,549 cancer patients exposed to oncologic therapy of whom 75 cases developed tMN (median time to transformation=26 months). To further compare the genetic and clonal relationships between tMN and the proceeding CH, we analyzed 35 cases for which paired samples were available. We compared the growth rate of the variant allele fraction (VAF) of CH clones across treatment modalities and in untreated patients. A significant increase in the growth rate of CH mutations was seen in DDR genes among those receiving cytotoxic (p=0.03) or radiation therapy (p=0.02) during the follow-up period compared to patients who did not receive therapy. Similar growth rates among treated and untreated patients were seen for non-DDR CH genes such as DNMT3A. Increasing cumulative exposure to cytotoxic therapy (p=0.01) and external beam radiation therapy (2×10-8) resulted in higher growth rates for DDR CH mutations. Among 34 subjects with at least two CH mutations in which one mutation was in a DDR gene and one in a non-DDR gene, we studied competing clonal dynamics for multiple gene mutations within the same patient. The risk of tMN was positively associated with CH in a known myeloid neoplasm driver mutation (HR=6.9, p<10-6), and increased with the total number of mutations and clone size. The strongest associations were observed for mutations in TP53 and for CH with mutations in spliceosome genes (SRSF2, U2AF1 and SF3B1). Lower hemoglobin, lower platelet counts, lower neutrophil counts, higher red cell distribution width and higher mean corpuscular volume were all positively associated with increased tMN risk. Among 35 cases for which paired samples were available, in 19 patients (59%), we found evidence of at least one of these mutations at the time of pre-tMN sequencing and in 13 (41%), we identified two or more in the pre-tMN sample. In all cases the dominant clone at tMN transformation was defined by a mutation seen at CH Our serial sampling data provide clear evidence that oncologic therapy strongly selects for clones with mutations in the DDR genes and that these clones have limited competitive fitness, in the absence of cytotoxic or radiation therapy. We further validate the relevance of CH as a predictor and precursor of tMN in cancer patients. We show that CH mutations detected prior to tMN diagnosis were consistently part of the dominant clone at tMN diagnosis and demonstrate that oncologic therapy directly promotes clones with mutations in genes associated with chemo-resistant disease such as TP53.

  • therapy resulted also in clonal evolution and saw changes in splice variants and spliceosome
  • therapy promotes current DDR mutations
  • clonal hematopoeisis due to selective pressures
  • mutations, variants number all predictive of myeloid disease
  • deferring adjuvant therapy for breast cancer patients with patients in highest MDS risk group based on biomarkers, greatly reduced their risk for MDS

5704 – Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases

Presenter/AuthorsOlivia W. Lee, Akash Mitra, Won-Chul Lee, Kazutaka Fukumura, Hannah Beird, Miles Andrews, Grant Fischer, John N. Weinstein, Michael A. Davies, Jason Huse, P. Andrew Futreal. The University of Texas MD Anderson Cancer Center, TX, The University of Texas MD Anderson Cancer Center, TX, Olivia Newton-John Cancer Research Institute and School of Cancer Medicine, La Trobe University, AustraliaDisclosures O.W. Lee: None. A. Mitra: None. W. Lee: None. K. Fukumura: None. H. Beird: None. M. Andrews: ; Merck Sharp and Dohme. G. Fischer: None. J.N. Weinstein: None. M.A. Davies: ; Bristol-Myers Squibb. ; Novartis. ; Array BioPharma. ; Roche and Genentech. ; GlaxoSmithKline. ; Sanofi-Aventis. ; AstraZeneca. ; Myriad Genetics. ; Oncothyreon. J. Huse: None. P. Futreal: None.

Abstract: Brain metastases (BM) occur in 10-30% of patients with cancer. Approximately 200,000 new cases of brain metastases are diagnosed in the United States annually, with median survival after diagnosis ranging from 3 to 27 months. Recently, studies have identified significant genetic differences between BM and their corresponding primary tumors. It has been shown that BM harbor clinically actionable mutations that are distinct from those in the primary tumor samples. Additional genomic profiling of BM will provide deeper understanding of the pathogenesis of BM and suggest new therapeutic approaches.
We performed whole-exome sequencing of BM and matched tumors from 41 patients collected from renal cell carcinoma (RCC), breast cancer, lung cancer, and melanoma, which are known to be more likely to develop BM. We profiled total 126 fresh-frozen tumor samples and performed subsequent analyses of BM in comparison to paired primary tumor and extracranial metastases (ECM). We found that lung cancer shared the largest number of mutations between BM and matched tumors (83%), followed by melanoma (74%), RCC (51%), and Breast (26%), indicating that cancer type with high tumor mutational burden share more mutations with BM. Mutational signatures displayed limited differences, suggesting a lack of mutagenic processes specific to BM. However, point-mutation heterogeneity revealed that BM evolve separately into different subclones from their paired tumors regardless of cancer type, and some cancer driver genes were found in BM-specific subclones. These models and findings suggest that these driver genes may drive prometastatic subclones that lead to BM. 32 curated cancer gene mutations were detected and 71% of them were shared between BM and primary tumors or ECM. 29% of mutations were specific to BM, implying that BM often accumulate additional cancer gene mutations that are not present in primary tumors or ECM. Co-mutation analysis revealed a high frequency of TP53 nonsense mutation in BM, mostly in the DNA binding domain, suggesting TP53 nonsense mutation as a possible prerequisite for the development of BM. Copy number alteration analysis showed statistically significant differences between BM and their paired tumor samples in each cancer type (Wilcoxon test, p < 0.0385 for all). Both copy number gains and losses were consistently higher in BM for breast cancer (Wilcoxon test, p =1.307e-5) and lung cancer (Wilcoxon test, p =1.942e-5), implying greater genomic instability during the evolution of BM.
Our findings highlight that there are more unique mutations in BM, with significantly higher copy number alterations and tumor mutational burden. These genomic analyses could provide an opportunity for more reliable diagnostic decision-making, and these findings will be further tested with additional transcriptomic and epigenetic profiling for better characterization of BM-specific tumor microenvironments.

  • are there genomic signatures different in brain mets versus non metastatic or normal?
  • 32 genes from curated databases were different between brain mets and primary tumor
  • frequent nonsense mutations in TP53
  • divergent clonal evolution of drivers in BMets from primary
  • they were able to match BM with other mutational signatures like smokers and lung cancer signatures

5707 – A standard operating procedure for the interpretation of oncogenicity/pathogenicity of somatic mutations

Presenter/AuthorsPeter Horak, Malachi Griffith, Arpad Danos, Beth A. Pitel, Subha Madhavan, Xuelu Liu, Jennifer Lee, Gordana Raca, Shirley Li, Alex H. Wagner, Shashikant Kulkarni, Obi L. Griffith, Debyani Chakravarty, Dmitriy Sonkin. National Center for Tumor Diseases, Heidelberg, Germany, Washington University School of Medicine, St. Louis, MO, Mayo Clinic, Rochester, MN, Georgetown University Medical Center, Washington, DC, Dana-Farber Cancer Institute, Boston, MA, Frederick National Laboratory for Cancer Research, Rockville, MD, University of Southern California, Los Angeles, CA, Sunquest, Boston, MA, Baylor College of Medicine, Houston, TX, Memorial Sloan Kettering Cancer Center, New York, NY, National Cancer Institute, Rockville, MDDisclosures P. Horak: None. M. Griffith: None. A. Danos: None. B.A. Pitel: None. S. Madhavan: ; Perthera Inc. X. Liu: None. J. Lee: None. G. Raca: None. S. Li: ; Sunquest Information Systems, Inc. A.H. Wagner: None. S. Kulkarni: ; Baylor Genetics. O.L. Griffith: None. D. Chakravarty: None. D. Sonkin: None.AbstractSomatic variants in cancer-relevant genes are interpreted from multiple partially overlapping perspectives. When considered in discovery and translational research endeavors, it is important to determine if a particular variant observed in a gene of interest is oncogenic/pathogenic or not, as such knowledge provides the foundation on which targeted cancer treatment research is based. In contrast, clinical applications are dominated by diagnostic, prognostic, or therapeutic interpretations which in part also depends on underlying variant oncogenicity/pathogenicity. The Association for Molecular Pathology, the American Society of Clinical Oncology, and the College of American Pathologists (AMP/ASCO/CAP) have published structured somatic variant clinical interpretation guidelines which specifically address diagnostic, prognostic, and therapeutic implications. These guidelines have been well-received by the oncology community. Many variant knowledgebases, clinical laboratories/centers have adopted or are in the process of adopting these guidelines. The AMP/ASCO/CAP guidelines also describe different data types which are used to determine oncogenicity/pathogenicity of a variant, such as: population frequency, functional data, computational predictions, segregation, and somatic frequency. A second collaborative effort created the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets to provide a harmonized vocabulary that provides an evidence-based ranking system of molecular targets that supports their value as clinical targets. However, neither of these clinical guideline systems provide systematic and comprehensive procedures for aggregating population frequency, functional data, computational predictions, segregation, and somatic frequency to consistently interpret variant oncogenicity/pathogenicity, as has been published in the ACMG/AMP guidelines for interpretation of pathogenicity of germline variants. In order to address this unmet need for somatic variant oncogenicity/pathogenicity interpretation procedures, the Variant Interpretation for Cancer Consortium (VICC, a GA4GH driver project) Knowledge Curation and Interpretation Standards (KCIS) working group (WG) has developed a Standard Operating Procedure (SOP) with contributions from members of ClinGen Somatic Clinical Domain WG, and ClinGen Somatic/Germline variant curation WG using an approach similar to the ACMG/AMP germline pathogenicity guidelines to categorize evidence of oncogenicity/pathogenicity as very strong, strong, moderate or supporting. This SOP enables consistent and comprehensive assessment of oncogenicity/pathogenicity of somatic variants and latest version of an SOP can be found at https://cancervariants.org/wg/kcis/.

  • best to use this SOP for somatic mutations and not rearangements
  • variants based on oncogenicity as strong to weak
  • useful variant knowledge on pathogenicity curated from known databases
  • the recommendations would provide some guideline on curating unknown somatic variants versus known variants of hereditary diseases
  • they have not curated RB1 mutations or variants (or for other RBs like RB2? p130?)

 

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Innate Immune Genes and Two Nasal Epithelial Cell Types: Expression of SARS-CoV-2 Entry Factors – COVID19 Cell Atlas

Reporter: Aviva Lev-Ari, PhD, RN

 

SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes

Abstract

We investigated SARS-CoV-2 potential tropism by surveying expression of viral entry-associated genes in single-cell RNA-sequencing data from multiple tissues from healthy human donors. We co-detected these transcripts in specific respiratory, corneal and intestinal epithelial cells, potentially explaining the high efficiency of SARS-CoV-2 transmission. These genes are co-expressed in nasal epithelial cells with genes involved in innate immunity, highlighting the cells’ potential role in initial viral infection, spread and clearance. The study offers a useful resource for further lines of inquiry with valuable clinical samples from COVID-19 patients and we provide our data in a comprehensive, open and user-friendly fashion at www.covid19cellatlas.org.

To further characterize specific epithelial cell types expressing ACE2, we evaluated ACE2 expression within the lung and airway epithelium. We found that, despite a low level of expression overall, ACE2 was expressed in multiple epithelial cell types across the airway, as well as in alveolar epithelial type II cells in the parenchyma, consistently with previous studies9,10,11. Notably, nasal epithelial cells, including two previously described clusters of goblet cells and one cluster of ciliated cells, show the highest expression among all investigated cells in the respiratory tree (Fig. 1b). We confirmed enriched ACE2 expression in nasal epithelial cells in an independent scRNA-seq study that includes nasal brushings and biopsies. The results were consistent; we found the highest expression of ACE2 in nasal secretory cells (equivalent to the two goblet cell clusters in the previous dataset) and ciliated cells (Fig. 1b).

In addition, scRNA-seq data from an in vitro epithelial regeneration system from nasal epithelial cells corroborated the expression of ACE2 in goblet/secretory cells and ciliated cells in air–liquid interface cultures (Extended Data Fig. 1). Notably, the differentiating cells in the air–liquid interface acquire progressively more ACE2 (Extended Data Fig. 1). The results also suggest that this in vitro culture system may be biologically relevant for the study of SARS-CoV-2 pathogenesis.

Coronavirus Entry Genes Highly Expressed in Two Nasal Epithelial Cell Types

Apr 23, 2020

staff reporter

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TEM of SARS-CoV-2 particles; Credit: NIAID-RML

This story has been updated to include information on a related study appearing in Cell.

NEW YORK – Two types of cells inside the nose express high levels of the genes encoding proteins the SARS-CoV-2 uses to enter cells, suggesting they are the likely entry points for the virus.

SARS-CoV-2, the virus that causes COVID-19, uses its spike protein to bind to cellular receptors in the human body. The virus relies on the ACE2 receptor protein and the TMPRSS2 protease to enter cells, but which cells are initially infected has been unclear.

An international team of researchers used single-cell RNA sequencing datasets put together by the Human Cell Atlas consortium to search for cell types that express both the ACE2 and TMPRSS2 genes. As they reported in Nature Medicine Thursday, they found a number of cells in different organs express the genes encoding these proteins, but they homed in on cells of the respiratory system, especially goblet cells and ciliated cells in the nose.

“Mucus-producing goblet cells and ciliated cells in the nose had the highest levels of both these [genes], of all cells in the airways,” first author Waradon Sungnak from the Wellcome Sanger Institute said in a statement. “This makes these cells the most likely initial infection route for the virus.”

Using the Human Cell Atlas dataset, Sungnak and his colleagues analyzed ACE2 and TMPRSS2 expression in a range of tissues, including not only respiratory tissue — previous analyses using immunohistochemistry had detected both ACE2 and TMPRSS2 in the nasal and bronchial epithelium — but also tissue from the eyes, digestive tract, muscle, and more.

ACE2 gene expression was generally low across the datasets analyzed, while TMPRSS2 was more broadly expressed, the researchers found. This suggested that ACE2 expression might be the limiting factor for viral entry in initial infections.

However, ACE2 was expressed in a number of epithelial cell types of respiratory tissues, and its expression was particularly high among goblet cells and ciliated cells of the nose. The researchers confirmed this finding using data from two other scRNA-seq studies.

Other genes often co-expressed alongside ACE2 in the respiratory system included ones involved in carbohydrate metabolism — possibly due to their role in goblet cell mucin synthesis — and those involved in innate and antiviral immune functions.

The ACE2 and TMPRSS2 genes were also expressed outside of the respiratory system, including by cells of the cornea and the lining of the intestine, which the researchers noted is in line with some clinical reports suggesting fecal shedding of the virus.

Where these viral entry receptor genes are expressed in the respiratory system could influence how transmissible a virus is. The researchers compared the tissue expression patterns of these viral receptor genes to those of receptor genes used by other coronaviruses and influenza viruses. The receptors used by highly infectious viruses like influenza were expressed more in the upper airway, while receptors for less infectious viruses like MERSCoV were expressed in the lower airway. This indicated to the researchers that the spatial distribution of the viral receptors may influence how transmissible a virus is.

“This is the first time these particular cells in the nose have been associated with COVID-19,” study co-author Martijn Nawijn from the University Medical Center Groningen and the HCA Lung Biological Network said in a statement. “The location of these cells on the surface of the inside of the nose make them highly accessible to the virus, and also may assist with transmission to other people.”

Another study that appeared as a preprint at Cell also used single-cell RNA-sequencing datasets from humans, nonhuman primates, and mice to examine where cells expressing both the ACE2 and TMPRSS2 genes are located. Those researchers, led by the Broad Institute’s Jose Ordovas-Montanes, found both genes were expressed among type II pneumocytes and ileal absorptive enterocytes as well as among nasal goblet secretory cells.

SOURCE

https://www.genomeweb.com/infectious-disease/coronavirus-entry-genes-highly-expressed-two-nasal-epithelial-cell-types?utm_source=Sailthru&utm_medium=email&utm_campaign=GWDN%20Thurs%20PM%202020-04-23&utm_term=GW%20Daily%20News%20Bulletin#.XqIbG1NKgdU

SOURCE for Original Research Study 

Sungnak, W., Huang, N., Bécavin, C. et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat Med (2020). https://doi.org/10.1038/s41591-020-0868-6

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

In-vitro fertilisation (IVF) is now regarded as a huge clinical success which has benefitted an estimated 16 million parents, at the time the development not only sparked moral outrage but led to political and legislative constraints. Patients undergoing IVF may be presented with numerous assisted reproductive treatments purportedly increasing the chances of pregnancy. Such commercialised “IVF add-ons” often come at high costs without clinical evidence of validity. Additionally, long-term studies of children born through IVF have historically been scarce and inconsistent in their data collection. This has meant that potential genetic predispositions, such as increased body fat composition and blood pressure, as well as congenital abnormalities long associated with IVF births, lack proof of causality.

 

With Preimplantation genetic testing mutated embryos are automatically discarded, whereas CRISPR could correct mutations to increase the number of viable embryos for implantation. Moreover, in instances where all embryos in a given cycle are destined to develop with severe or lethal mutations, CRISPR could bring success for otherwise doomed IVF treatments. Genetic screening programs offered to couples in hot-spot areas of carrier frequency of monogenic disorders have had huge success in alleviating regional disease burdens. Carried out since the 1970s these programs have altered the course of natural evolution, but few would dispute their benefits in preventing heritable disease transmission.

 

Mutations are as inevitable as death and taxes. Whilst age is considered one of the largest factors in de-novo mutation generation, it appears that these are inherited primarily from the paternal line. Thus, the paternal age of conception predominantly determines the mutation frequency inherited by children. Whereas advanced maternal age is not associated with mutagenic allele frequency but chromosomal abnormalities. The risk of aneuploidy rises steadily in mothers over the age of 26. Although embryos are screened for aneuploidy prior to implantation, with so many other factors simultaneously being screened the probability of having enough embryos remaining to allow for 50% rate of blastocyte development in-vitro are often fairly low.

 

Despite IVF being used routinely for over 40 years now, it’s not abundantly clear if, or how often, IVF may introduce genomic alternations or off-target affects in embryos. Likewise, scientists and clinicians are often unable to scrutinise changes produced through natural cellular processes including recombination and aging. So, it may be OK to do controlled experiments on using CRISPR to try and prevent multi-generational suffering. But, there has to be a long term investigation on the side effects of germline genome editing. Science has advanced a lot but still there are lot of things that are yet to be described or discovered by science. Trying to reduce human suffering should not give rise to new bigger sufferings and care must be taken not to create a Frankenstein.

 

References:

 

http://www.frontlinegenomics.com/news/29321/opinion-piece-morally-is-germline-genome-editing-all-that-different-to-ivf/

 

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Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Curator: Stephen J. Williams, PhD

 

 

From the POLICY FORUM ETHICS AND DIVERSITY Section of Science

Ethics of inclusion: Cultivate trust in precision medicine

 See all authors and affiliations

Science  07 Jun 2019:
Vol. 364, Issue 6444, pp. 941-942
DOI: 10.1126/science.aaw8299

Precision medicine is at a crossroads. Progress toward its central goal, to address persistent health inequities, will depend on enrolling populations in research that have been historically underrepresented, thus eliminating longstanding exclusions from such research (1). Yet the history of ethical violations related to protocols for inclusion in biomedical research, as well as the continued misuse of research results (such as white nationalists looking to genetic ancestry to support claims of racial superiority), continue to engender mistrust among these populations (2). For precision medicine research (PMR) to achieve its goal, all people must believe that there is value in providing information about themselves and their families, and that their participation will translate into equitable distribution of benefits. This requires an ethics of inclusion that considers what constitutes inclusive practices in PMR, what goals and values are being furthered through efforts to enhance diversity, and who participates in adjudicating these questions. The early stages of PMR offer a critical window in which to intervene before research practices and their consequences become locked in (3).

Initiatives such as the All of Us program have set out to collect and analyze health information and biological samples from millions of people (1). At the same time, questions of trust in biomedical research persist. For example, although the recent assertions of white nationalists were eventually denounced by the American Society of Human Genetics (4), the misuse of ancestry testing may have already undermined public trust in genetic research.

There are also infamous failures in research that included historically underrepresented groups, including practices of deceit, as in the Tuskegee Syphilis Study, or the misuse of samples, as with the Havasupai tribe (5). Many people who are being asked to give their data and samples for PMR must not only reconcile such past research abuses, but also weigh future risks of potential misuse of their data.

To help assuage these concerns, ongoing PMR studies should open themselves up to research, conducted by social scientists and ethicists, that examines how their approaches enhance diversity and inclusion. Empirical studies are needed to account for how diversity is conceptualized and how goals of inclusion are operationalized throughout the life course of PMR studies. This is not limited to selection and recruitment of populations but extends to efforts to engage participants and communities, through data collection and measurement, and interpretations and applications of study findings. A commitment to transparency is an important step toward cultivating public trust in PMR’s mission and practices.

From Inclusion to Inclusive

The lack of diverse representation in precision medicine and other biomedical research is a well-known problem. For example, rare genetic variants may be overlooked—or their association with common, complex diseases can be misinterpreted—as a result of sampling bias in genetics research (6). Concentrating research efforts on samples with largely European ancestry has limited the ability of scientists to make generalizable inferences about the relationships among genes, lifestyle, environmental exposures, and disease risks, and thereby threatens the equitable translation of PMR for broad public health benefit (7).

However, recruiting for diverse research participation alone is not enough. As with any push for “diversity,” related questions arise about how to describe, define, measure, compare, and explain inferred similarities and differences among individuals and groups (8). In the face of ambivalence about how to represent population variation, there is ample evidence that researchers resort to using definitions of diversity that are heterogeneous, inconsistent, and sometimes competing (9). Varying approaches are not inherently problematic; depending on the scientific question, some measures may be more theoretically justified than others and, in many cases, a combination of measures can be leveraged to offer greater insight (10). For example, studies have shown that American adults who do not self-identify as white report better mental and physical health if they think others perceive them as white (1112).

The benefit of using multiple measures of race and ancestry also extends to genetic studies. In a study of hypertension in Puerto Rico, not only did classifications based on skin color and socioeconomic status better predict blood pressure than genetic ancestry, the inclusion of these sociocultural measures also revealed an association between a genetic polymorphism and hypertension that was otherwise hidden (13). Thus, practices that allow for a diversity of measurement approaches, when accompanied by a commitment to transparency about the rationales for chosen approaches, are likely to benefit PMR research more than striving for a single gold standard that would apply across all studies. These definitional and measurement issues are not merely semantic. They also are socially consequential to broader perceptions of PMR research and the potential to achieve its goals of inclusion.

Study Practices, Improve Outcomes

Given the uncertainty and complexities of the current, early phase of PMR, the time is ripe for empirical studies that enable assessment and modulation of research practices and scientific priorities in light of their social and ethical implications. Studying ongoing scientific practices in real time can help to anticipate unintended consequences that would limit researchers’ ability to meet diversity recruitment goals, address both social and biological causes of health disparities, and distribute the benefits of PMR equitably. We suggest at least two areas for empirical attention and potential intervention.

First, we need to understand how “upstream” decisions about how to characterize study populations and exposures influence “downstream” research findings of what are deemed causal factors. For example, when precision medicine researchers rely on self-identification with U.S. Census categories to characterize race and ethnicity, this tends to circumscribe their investigation of potential gene-environment interactions that may affect health. The convenience and routine nature of Census categories seemed to lead scientists to infer that the reasons for differences among groups were self-evident and required no additional exploration (9). The ripple effects of initial study design decisions go beyond issues of recruitment to shape other facets of research across the life course of a project, from community engagement and the return of results to the interpretation of study findings for human health.

Second, PMR studies are situated within an ecosystem of funding agencies, regulatory bodies, disciplines, and other scholars. This partly explains the use of varied terminology, different conceptual understandings and interpretations of research questions, and heterogeneous goals for inclusion. It also makes it important to explore how expectations related to funding and regulation influence research definitions of diversity and benchmarks for inclusion.

For example, who defines a diverse study population, and how might those definitions vary across different institutional actors? Who determines the metrics that constitute successful inclusion, and why? Within a research consortium, how are expectations for data sharing and harmonization reconciled with individual studies’ goals for recruitment and analysis? In complex research fields that include multiple investigators, organizations, and agendas, how are heterogeneous, perhaps even competing, priorities negotiated? To date, no studies have addressed these questions or investigated how decisions facilitate, or compromise, goals of diversity and inclusion.

The life course of individual studies and the ecosystems in which they reside cannot be easily separated and therefore must be studied in parallel to understand how meanings of diversity are shaped and how goals of inclusion are pursued. Empirically “studying the studies” will also be instrumental in creating mechanisms for transparency about how PMR is conducted and how trade-offs among competing goals are resolved. Establishing open lines of inquiry that study upstream practices may allow researchers to anticipate and address downstream decisions about how results can be interpreted and should be communicated, with a particular eye toward the consequences for communities recruited to augment diversity. Understanding how scientists negotiate the challenges and barriers to achieving diversity that go beyond fulfilling recruitment numbers is a critical step toward promoting meaningful inclusion in PMR.

Transparent Reflection, Cultivation of Trust

Emerging research on public perceptions of PMR suggests that although there is general support, questions of trust loom large. What we learn from studies that examine on-the-ground approaches aimed at enhancing diversity and inclusion, and how the research community reflects and responds with improvements in practices as needed, will play a key role in building a culture of openness that is critical for cultivating public trust.

Cultivating long-term, trusting relationships with participants underrepresented in biomedical research has been linked to a broad range of research practices. Some of these include the willingness of researchers to (i) address the effect of history and experience on marginalized groups’ trust in researchers and clinicians; (ii) engage concerns about potential group harms and risks of stigmatization and discrimination; (iii) develop relationships with participants and communities that are characterized by transparency, clear communication, and mutual commitment; and (iv) integrate participants’ values and expectations of responsible oversight beyond initial informed consent (14). These findings underscore the importance of multidisciplinary teams that include social scientists, ethicists, and policy-makers, who can identify and help to implement practices that respect the histories and concerns of diverse publics.

A commitment to an ethics of inclusion begins with a recognition that risks from the misuse of genetic and biomedical research are unevenly distributed. History makes plain that a multitude of research practices ranging from unnecessarily limited study populations and taken-for-granted data collection procedures to analytic and interpretive missteps can unintentionally bolster claims of racial superiority or inferiority and provoke group harm (15). Sustained commitment to transparency about the goals, limits, and potential uses of research is key to further cultivating trust and building long-term research relationships with populations underrepresented in biomedical studies.

As calls for increasing diversity and inclusion in PMR grow, funding and organizational pathways must be developed that integrate empirical studies of scientific practices and their rationales to determine how goals of inclusion and equity are being addressed and to identify where reform is required. In-depth, multidisciplinary empirical investigations of how diversity is defined, operationalized, and implemented can provide important insights and lessons learned for guiding emerging science, and in so doing, meet our ethical obligations to ensure transparency and meaningful inclusion.

References and Notes

  1. C. P. Jones et al Ethn. Dis. 18496 (2008).
  2. C. C. GravleeA. L. NonC. J. Mulligan
  3. S. A. Kraft et al Am. J. Bioeth. 183 (2018).
  4. A. E. Shields et al Am. Psychol. 6077 (2005).

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Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

 

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

 

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

  • millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
  • In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
  • we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
  • Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

  • fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
  • Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
  • 1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
  • refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

  • a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
  • developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

  • shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
  • building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

  • To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
  • Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

  • In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
  • enhanced capabilities to perform functions that are not feasible now.
  • AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
  • the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

  • Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

  • finding new ways to share clinical data that are as open as possible and as closed as necessary.
  • moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
  • working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
  • deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

  • genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
  • Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
  • Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
  • AI will help guide the development of individually matched
  • genetic patient screenings
  • the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

  • the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
  • But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

  • we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
  • This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
  • The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

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