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Archive for the ‘Proteomics’ Category


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

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus virus was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019.

Image and Caption Credit: Alissa Eckert, MS; Dan Higgins, MAM available at https://phil.cdc.gov/Details.aspx?pid=23311

 

UPDATED on 3/11/2020

Coronaviruses

According to the World Health Organization, coronaviruses make up a large family of viruses named for the crown-like spikes found on their surface (Figure 1). They carry their genetic material in single strands of RNA and cause respiratory problems and fever. Like HIV, coronaviruses can be transmitted between animals and humans.  Coronaviruses have been responsible for the Severe Acute Respiratory Syndrome (SARS) pandemic in the early 2000s and the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015. While the most recent coronavirus, COVID-19, has caused international concern, accessible and inexpensive sequencing is helping us understand COVID-19 and respond to the outbreak quickly.

Figure 1. Coronaviruses with the characteristic spikes as seen under a microscope.

First studies that explore genetic susceptibility to COVID-19 are now being published. The first results indicate that COVID-19 infects cells using the ACE2 cell-surface receptor. Genetic variants in the ACE2 receptor gene are thus likely to influence how effectively COVID-19 can enter the cells in our bodies. Researchers hope to discover genetic variants that confer resistance to a COVID-19 infection, similar to how some variants in the CCR5 receptor gene make people immune to HIV. At Nebula Genomics, we are monitoring the latest COVID-19 research and will add any relevant discoveries to the Nebula Research Library in a timely manner.

The Role of Genomics in Responding to COVID-19

Scientists in China sequenced COVID-19’s genome just a few weeks after the first case was reported in Wuhan. This stands in contrast to SARS, which was discovered in late 2002 but was not sequenced until April of 2003. It is through inexpensive genome-sequencing that many scientists across the globe are learning and sharing information about COVID-19, allowing us to track the evolution of COVID-19 in real-time. Ultimately, sequencing can help remove the fear of the unknown and allow scientists and health professionals to prepare to combat the spread of COVID-19.

Next-generation DNA sequencing technology has enabled us to understand COVID-19 is ~30,000 bases long. Moreover, researchers in China determined that COVID-19 is also almost identical to a coronavirus found in bats and is very similar to SARS. These insights have been critical in aiding in the development of diagnostics and vaccines. For example, the Centers for Disease Control and Prevention developed a diagnostic test to detect COVID-19 RNA from nose or mouth swabs.

Moreover, a number of different government agencies and pharmaceutical companies are in the process of developing COVID-19 vaccines to stop the COVID-19 from infecting more people. To protect humans from infection inactivated virus particles or parts of the virus (e.g. viral proteins) can be injected into humans. The immune system will recognize the inactivated virus as foreign, priming the body to build immunity against possible future infection. Of note, Moderna Inc., the National Institute of Allergy and Infectious Diseases, and Coalition for Epidemic Preparedness Innovations identified a COVID-19 vaccine candidate in a record 42 days. This vaccine will be tested in human clinical trials starting in April.

For more information about COVID-19, please refer to the World Health Organization website.

SOURCE

https://blog.nebula.org/role-of-genomics-coronavirus-covid-19/?utm_source=Nebula%20Genomics&utm_medium=email&utm_campaign=COVID-19

Aviva Lev-Ari
13.3K Tweets

Aviva Lev-Ari
@AVIVA1950

My BIO lnkd.in/eEyn69r MediaPharma ex-SRI ex-MITRE ex-McGraw-Hill Followed by

Aviva Lev-Ari
@AVIVA1950

Predicting the #ProteinStructure of #Coronavirus: #Inhibition of #Nsp15 #Cryo-EM – #spike #protein structure (#experimentally verified) vs #AI-predicted protein structures (not verified) of

(

) #AlphaFold

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Eric Topol
@EricTopol
·
The problem w/ visionaries is that we don’t recognize them in a timely manner (too late) Ralph Baric @UNCpublichealth and Vineet Menachery deserve recognition for being 5 yrs ahead of #COVID19 nature.com/articles/nm.39 @NatureMedicine pnas.org/content/113/11 @PNASNews via @hoondy

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Senior, A.W., Evans, R., Jumper, J. et al. Improved protein structure prediction using potentials from deep learningNature 577, 706–710 (2020)https://doi.org/10.1038/s41586-019-1923-7

Abstract

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7. https://doi.org/10.1038/s41586-019-1923-7

[ALA added bold face]

COVID-19 outbreak

The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this novel pathogen. Other labs have shared experimentally-determined and computationally-predicted structures of some of the viral proteins, and still others have shared epidemiological data. We hope to contribute to the scientific effort using the latest version of our AlphaFold system by releasing structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19. We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community’s interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics. We’re indebted to the work of many other labs: this work wouldn’t be possible without the efforts of researchers across the globe who have responded to the COVID-19 outbreak with incredible agility.

Knowing a protein’s structure provides an important resource for understanding how it functions, but experiments to determine the structure can take months or longer, and some prove to be intractable. For this reason, researchers have been developing computational methods to predict protein structure from the amino acid sequence.  In cases where the structure of a similar protein has already been experimentally determined, algorithms based on “template modelling” are able to provide accurate predictions of the protein structure. AlphaFold, our recently published deep learning system, focuses on predicting protein structure accurately when no structures of similar proteins are available, called “free modelling”.  We’ve continued to improve these methods since that publication and want to provide the most useful predictions, so we’re sharing predicted structures for some of the proteins in SARS-CoV-2 generated using our newly-developed methods.

It’s important to note that our structure prediction system is still in development and we can’t be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful. We recently shared our results with several colleagues at the Francis Crick Institute in the UK, including structural biologists and virologists, who encouraged us to release our structures to the general scientific community now. Our models include per-residue confidence scores to help indicate which parts of the structure are more likely to be correct. We have only provided predictions for proteins which lack suitable templates or are otherwise difficult for template modeling.  While these understudied proteins are not the main focus of current therapeutic efforts, they may add to researchers’ understanding of SARS-CoV-2.

Normally we’d wait to publish this work until it had been peer-reviewed for an academic journal. However, given the potential seriousness and time-sensitivity of the situation, we’re releasing the predicted structures as we have them now, under an open license so that anyone can make use of them.

Interested researchers can download the structures here, and can read more technical details about these predictions in a document included with the data. The protein structure predictions we’re releasing are for SARS-CoV-2 membrane protein, protein 3a, Nsp2, Nsp4, Nsp6, and Papain-like proteinase (C terminal domain). To emphasise, these are predicted structures which have not been experimentally verified. Work on the system continues for us, and we hope to share more about it in due course.

Citation:  John Jumper, Kathryn Tunyasuvunakool, Pushmeet Kohli, Demis Hassabis, and the AlphaFold Team, “Computational predictions of protein structures associated with COVID-19”, DeepMind website, 5 March 2020, https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19

SARS-COV-2 MEMBRANE PROTEIN: A RENDERING OF ONE OF OUR PROTEIN STRUCTURE PREDICTIONS

SOURCES

Computational predictions of protein structures associated with COVID-19

https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19

AlphaFold: Using AI for scientific discovery 

https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery

 

DeepMind has shared its results with researchers at the Francis Crick Institute, a biomedical research lab in the UK, as well as offering it for download from its website.

“Normally we’d wait to publish this work until it had been peer-reviewed for an academic journal. However, given the potential seriousness and time-sensitivity of the situation, we’re releasing the predicted structures as we have them now, under an open license so that anyone can make use of them,” it said. [ALA added bold face]

There are 93,090 cases of COVID-19, and 3,198 deaths, spread across 76 countries, according to the latest report from the World Health Organization at time of writing. ®

SOURCE

https://www.theregister.co.uk/2020/03/06/deepmind_covid19_outbreak/

 

  • MHC content – The spike protein is thought to be the key to binding to cells via the angiotensin II receptor, the major mechanism the immune system uses to distinguish self from non-self

Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies

Syed Faraz Ahmed 1,† , Ahmed A. Quadeer 1, *,† and Matthew R. McKay 1,2, *

1 Department of Electronic and Computer Engineering, The Hong Kong University of Science and

Technology, Hong Kong, China; sfahmed@connect.ust.hk

2 Department of Chemical and Biological Engineering, The Hong Kong University of Science and

Technology, Hong Kong, China

* Correspondence: eeaaquadeer@ust.hk.com (A.A.Q.); m.mckay@ust.hk (M.R.M.)

These authors contributed equally to this work.

Received: 9 February 2020; Accepted: 24 February 2020; Published: 25 February 2020

Abstract:

The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

Keywords: Coronavirus; 2019-nCoV; 2019 novel coronavirus; SARS-CoV-2; COVID-19; SARS-CoV; MERS-CoV; T cell epitopes; B cell epitopes; vaccine [ALA added bold face]

SOURCE

https://www.mdpi.com/1999-4915/12/3/254/pdf

 

Selected Online COMMENTS to

https://forums.theregister.co.uk/forum/all/2020/03/06/deepmind_covid19_outbreak/

MuscleguySilver badge

Re: Protein structure prediction has been done for ages…

Not quite, Natural Selection does not measure methods, it measures outputs, usually at the organism level.

Sure correct folding is necessary for much protein function and we have prions and chaperone proteins to get it wrong and right.

The only way NS measures methods and mechanisms is if they are very energetically wasteful. But there are some very wasteful ones out there. Beta-Catenin at the end of point of Wnt signalling comes particularly to mind.

Chemist

Re: Does not matter at all

“Determining the structure of the virus proteins might also help in developing a molecule that disrupts the operation of just those proteins, and not anything else in the human body.”

Well it might, but predicting whether a ‘drug’ will NOT interact with any other of the 20000+ protein in complex organisms is well beyond current science. If we could do that we could predict/avoid toxicity and other non-mechanism related side-effects & mostly we can’t.

rob miller

Title

There are 480 structures on PDBe resulting from a search on ‘coronavirus,’ the top hits from MERS and SARS. PR stunt or not, they did win the most recent CASP ‘competition’, so arguably it’s probably our best shot right now – and I am certainly not satisfied that they have been sufficiently open in explaining their algorithms though I have not checked in the last few months. No one is betting anyone’s health on this, and it is not like making one wrong turn in a series of car directions. Latest prediction algorithms incorporate contact map predictions, so it’s not like a wrong dihedral angle sends the chain off in the wrong direction. A decent model would give something to run docking algorithms against with a series of already approved drugs, then we take that shortlist into the lab. A confirmed hit could be an instantly available treatment, no two year wait as currently estimated. [ALA added bold face]

jelabarre59Silver badge

Re: these structure predictions have not been experimentally verified

Naaaah. Can’t possibly be a stupid marketing stunt.

Well yes, a good possibility. But it can also be trying to build on the open-source model of putting it out there for others to build and improve upon. Essentially opening that “peer review” to a larger audience quicker. [ALA added bold face]

We shall see.

Anonymous Coward

Anonymous CowardWhat bothers me, besides the obvious PR stunt, is that they say this prediction is licensed. How can a prediction from software be protected by, I presume, patents? And if this can be protected without even verifying which predictions actually work, what’s to stop someone spitting out millions of random, untested predictions just in case they can claim ownership later when one of them is proven to work? [ALA added bold face]

 

 

SOURCES

 

  • AI-predicted protein structures could unlock vaccine for Wuhan coronavirus… if correct… after clinical trials It’s not quite DeepMind’s ‘Come with me if you want to live’ moment, but it’s close, maybe

Experimentally derived by a group of scientists at the University of Texas at Austin and the National Institute of Allergy and Infectious Diseases, an agency under the US National Institute of Health. They both feature a “Spike protein structure.”

  • Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation

See all authors and affiliations

Science  19 Feb 2020:
eabb2507
DOI: 10.1126/science.abb2507

 

  • Israeli scientists: We have developed a coronavirus vaccine

https://www.fromthegrapevine.com/health/coronavirus-vaccine-israel-migal-research-institute-david-zigdon

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

 

  • Group of Researchers @ University of California, Riverside, the University of Chicago, the U.S. Department of Energy’s Argonne National Laboratory, and Northwestern University solve COVID-19 Structure and Map Potential Therapeutics

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

https://pharmaceuticalintelligence.com/2020/03/06/group-of-researchers-solve-covid-19-structure-and-map-potential-therapeutic/

 

  • Is It Time for the Virtual Scientific Conference?: Coronavirus, Travel Restrictions, Conferences Cancelled Curator:

Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2020/03/06/is-it-time-for-the-virtual-scientific-conference-coronavirus-travel-restrictions-conferences-cancelled/

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Healing Powers of Fibrinogen

Reporter: Irina Robu, PhD

Recent research from University of Alberta is looking at the role of fibrinogen, the substrate of thrombin in regulating a natural defense mechanism in the body. Fibrinogen is a well-known protein that is essential for wound healing and blood clotting in the body. Levels of fibrinogen increase in inflammatory states as part of the acute-phase response.

However, daily variation in plasma fibrinogen levels weakens its potential as a biomarker of cardiovascular risk. The discovery is expected to contribute to enhanced diagnosis and treatments for patients in a variety of diseases ranging from inflammation, to heart failure, to cancer.

Yet, a study published in Scientific Reports in March 2019 show that fibrinogen can also be a natural inhibitor of an enzyme named MMP2, which is important for normal organ development and repair. The researchers believe an essential function of fibrinogen is to allow or disallow the enzyme to carry out its normal functions. Nevertheless, high levels of fibrinogen may disproportionately inhibit MMP2, that could result in arthritic and cardiac disorders.

The researcher highlights the inner workings of the MMP family of enzymes by having a greater understanding of how MMPs are regulated. They hypothesize that abnormal MMP2 activity could be an unwelcome side effect of common medications such as the cholesterol-lowering drugs and the antibiotic doxycycline, both of which are known to inhibit MMPs. They also emphasize that future therapeutic developments must strike a balance between the levels of MMPs and their inhibitors, such as fibrinogen, so that net MMP activity in the body remains at nearly normal levels.

SOURCE

https://www.technologynetworks.com/biopharma/news/healing-protein-also-hinders-320533?utm_campaign=NEWSLETTER_TN_Breaking%20Science%20News

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The Journey of Antibiotic Discovery

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

 

The term ‘antibiotic’ was introduced by Selman Waksman as any small molecule, produced by a microbe, with antagonistic properties on the growth of other microbes. An antibiotic interferes with bacterial survival via a specific mode of action but more importantly, at therapeutic concentrations, it is sufficiently potent to be effective against infection and simultaneously presents minimal toxicity. Infectious diseases have been a challenge throughout the ages. From 1347 to 1350, approximately one-third of Europe’s population perished to Bubonic plague. Advances in sanitary and hygienic conditions sufficed to control further plague outbreaks. However, these persisted as a recurrent public health issue. Likewise, infectious diseases in general remained the leading cause of death up to the early 1900s. The mortality rate shrunk after the commercialization of antibiotics, which given their impact on the fate of mankind, were regarded as a ‘medical miracle’. Moreover, the non-therapeutic application of antibiotics has also greatly affected humanity, for instance those used as livestock growth promoters to increase food production after World War II.

 

Currently, more than 2 million North Americans acquire infections associated with antibiotic resistance every year, resulting in 23,000 deaths. In Europe, nearly 700 thousand cases of antibiotic-resistant infections directly develop into over 33,000 deaths yearly, with an estimated cost over €1.5 billion. Despite a 36% increase in human use of antibiotics from 2000 to 2010, approximately 20% of deaths worldwide are related to infectious diseases today. Future perspectives are no brighter, for instance, a government commissioned study in the United Kingdom estimated 10 million deaths per year from antibiotic resistant infections by 2050.

 

The increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures – platforms – that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines.

 

The increase in drug-resistant pathogens is a consequence of multiple factors, including but not limited to high rates of antimicrobial prescriptions, antibiotic mismanagement in the form of self-medication or interruption of therapy, and large-scale antibiotic use as growth promotors in livestock farming. For example, 60% of the antibiotics sold to the USA food industry are also used as therapeutics in humans. To further complicate matters, it is estimated that $200 million is required for a molecule to reach commercialization, with the risk of antimicrobial resistance rapidly developing, crippling its clinical application, or on the opposing end, a new antibiotic might be so effective it is only used as a last resort therapeutic, thus not widely commercialized.

 

Besides a more efficient management of antibiotic use, there is a pressing need for new platforms capable of consistently and efficiently delivering new lead substances, which should attend their precursors impressively low rates of success, in today’s increasing drug resistance scenario. Antibiotic Discovery Platforms are aiming to screen large libraries, for instance the reservoir of untapped natural products, which is likely the next antibiotic ‘gold mine’. There is a void between phenotanypic screening (high-throughput) and omics-centered assays (high-information), where some mechanistic and molecular information complements antimicrobial activity, without the laborious and extensive application of various omics assays. The increasing need for antibiotics drives the relentless and continuous research on the foreground of antibiotic discovery. This is likely to expand our knowledge on the biological events underlying infectious diseases and, hopefully, result in better therapeutics that can swing the war on infectious diseases back in our favor.

 

During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

 

References:

 

https://www.mdpi.com/2079-6382/8/2/45/htm

 

https://www.ncbi.nlm.nih.gov/pubmed/19515346

 

https://www.ajicjournal.org/article/S0196-6553(11)00184-2/fulltext

 

https://www.ncbi.nlm.nih.gov/pubmed/21700626

 

http://www.med.or.jp/english/journal/pdf/2009_02/103_108.pdf

 

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

 

Protein kinase C (PKC) isozymes function as tumor suppressors in increasing contexts. These enzymes are crucial for a number of cellular activities, including cell survival, proliferation and migration — functions that must be carefully controlled if cells get out of control and form a tumor. In contrast to oncogenic kinases, whose function is acutely regulated by transient phosphorylation, PKC is constitutively phosphorylated following biosynthesis to yield a stable, autoinhibited enzyme that is reversibly activated by second messengers. Researchers at University of California San Diego School of Medicine found that another enzyme, called PHLPP1, acts as a “proofreader” to keep careful tabs on PKC.

 

The researchers discovered that in pancreatic cancer high PHLPP1 levels lead to low PKC levels, which is associated with poor patient survival. They reported that the phosphatase PHLPP1 opposes PKC phosphorylation during maturation, leading to the degradation of aberrantly active species that do not become autoinhibited. They discovered that any time an over-active PKC is inadvertently produced, the PHLPP1 “proofreader” tags it for destruction. That means the amount of PHLPP1 in patient’s cells determines his amount of PKC and it turns out those enzyme levels are especially important in pancreatic cancer.

 

This team of researchers reversed a 30-year paradigm when they reported evidence that PKC actually suppresses, rather than promotes, tumors. For decades before this revelation, many researchers had attempted to develop drugs that inhibit PKC as a means to treat cancer. Their study implied that anti-cancer drugs would actually need to do the opposite — boost PKC activity. This study sets the stage for clinicians to one day use a pancreatic cancer patient’s PHLPP1/PKC levels as a predictor for prognosis, and for researchers to develop new therapeutic drugs that inhibit PHLPP1 and boost PKC as a means to treat the disease.

 

The ratio — high PHLPP1/low PKC — correlated with poor prognoses: no pancreatic patient with low PKC in the database survived longer than five-and-a-half years. On the flip side, 50 percent of the patients with low PHLPP1/high PKC survived longer than that. While still in the earliest stages, the researchers hope that this information might one day aid pancreatic diagnostics and treatment. The researchers are next planning to screen chemical compounds to find those that inhibit PHLPP1 and restore PKC levels in low-PKC-pancreatic cancer cells in the lab. These might form the basis of a new therapeutic drug for pancreatic cancer.

 

References:

 

https://health.ucsd.edu/news/releases/Pages/2019-03-20-two-enzymes-linked-to-pancreatic-cancer-survival.aspx?elqTrackId=b6864b278958402787f61dd7b7624666

 

https://www.ncbi.nlm.nih.gov/pubmed/30904392

 

https://www.ncbi.nlm.nih.gov/pubmed/29513138

 

https://www.ncbi.nlm.nih.gov/pubmed/18511290

 

https://www.ncbi.nlm.nih.gov/pubmed/28476658

 

https://www.ncbi.nlm.nih.gov/pubmed/28283201

 

https://www.ncbi.nlm.nih.gov/pubmed/24231509

 

https://www.ncbi.nlm.nih.gov/pubmed/28112438

 

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Changes in Levels of Sex Hormones and N-Terminal Pro–B-Type Natriuretic Peptide as Biomarker for Cardiovascular Diseases

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

 

Considerable differences exist in the prevalence and manifestation of atherosclerotic cardiovascular disease (CVD) and heart failure (HF) between men and women. Premenopausal women have a lower risk of CVD and HF compared with men; however, this risk increases after menopause. Sex hormones, particularly androgens, are associated with CVD risk factors and events and have been postulated to mediate the observed sex differences in CVD.

 

B-type natriuretic peptides (BNPs) are secreted from cardiomyocytes in response to myocardial wall stress. BNP plays an important role in cardiovascular remodelling and volume homeostasis. It exerts numerous cardioprotective effects by promoting vasodilation, natriuresis, and ventricular relaxation and by antagonizing fibrosis and the effects of the renin-angiotensin-aldosterone system. Although the physiological role of BNP is cardioprotective, pathologically elevated N-terminal pro–BNP (NT-proBNP) levels are used clinically to indicate left ventricular hypertrophy, dysfunction, and myocardial ischemia. Higher NT-proBNP levels among individuals free of clinical CVD are associated with an increased risk of incident CVD, HF, and cardiovascular mortality.

 

BNP and NT-proBNP levels are higher in women than men in the general population. Several studies have proposed the use of sex- and age-specific reference ranges for BNP and NT-proBNP levels, in which reference limits are higher for women and older individuals. The etiology behind this sex difference has not been fully elucidated, but prior studies have demonstrated an association between sex hormones and NT-proBNP levels. Recent studies measuring endogenous sex hormones have suggested that androgens may play a larger role in BNP regulation by inhibiting its production.

 

Data were collected from a large, multiethnic community-based cohort of individuals free of CVD and HF at baseline to analyze both the cross-sectional and longitudinal associations between sex hormones [total testosterone (T), bioavailable T, freeT, dehydroepiandrosterone (DHEA), SHBG, and estradiol] and NT-proBNP, separately for women and men. It was found that a more androgenic pattern of sex hormones was independently associated with lower NT-proBNP levels in cross-sectional analyses in men and postmenopausal women.

 

This association may help explain sex differences in the distribution of NT-proBNP and may contribute to the NP deficiency in men relative to women. In longitudinal analyses, a more androgenic pattern of sex hormones was associated with a greater increase in NT-proBNP levels in both sexes, with a more robust association among women. This relationship may reflect a mechanism for the increased risk of CVD and HF seen in women after menopause.

 

Additional research is needed to further explore whether longitudinal changes in NT-proBNP levels seen in our study are correlated with longitudinal changes in sex hormones. The impact of menopause on changes in NT-proBNP levels over time should also be explored. Furthermore, future studies should aim to determine whether sex hormones directly play a role in biological pathways of BNP synthesis and clearance in a causal fashion. Lastly, the dual role of NTproBNP as both

  • a cardioprotective hormone and
  • a biomarker of CVD and HF, as well as
  • the role of sex hormones in delineating these processes,

should be further explored. This would provide a step toward improved clinical CVD risk stratification and prognostication based on

  • sex hormone and
  • NT-proBNP levels.

 

References:

 

https://www.medpagetoday.com/clinical-connection/cardio-endo/76480?xid=NL_CardioEndoConnection_2018-12-27

 

https://www.ncbi.nlm.nih.gov/pubmed/30137406

 

https://www.ncbi.nlm.nih.gov/pubmed/22064958

 

https://www.ncbi.nlm.nih.gov/pubmed/24036936

 

https://www.ncbi.nlm.nih.gov/pubmed/19854731

 

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

 

Once herpes simplex infects a person, the virus goes into hiding inside nerve cells, hibernating there for life, periodically waking up from its sleep to reignite infection, causing cold sores or genital lesions to recur. Research from Harvard Medical School showed that the virus uses a host protein called CTCF, or cellular CCCTC-binding factor, to display this type of behavior. Researchers revealed with experiments on mice that CTCF helps herpes simplex regulate its own sleep-wake cycle, enabling the virus to establish latent infections in the body’s sensory neurons where it remains dormant until reactivated. Preventing that latency-regulating protein from binding to the virus’s DNA, weakened the virus’s ability to come out of hiding.

 

Herpes simplex virus’s ability to go in and out of hiding is a key survival strategy that ensures its propagation from one host to the next. Such symptom-free latency allows the virus to remain out of the reach of the immune system most of the time, while its periodic reactivation ensures that it can continue to spread from one person to the next. On one hand, so-called latency-associated transcript genes, or LAT genes, turn off the transcription of viral RNA, inducing the virus to go into hibernation, or latency. On the other hand, a protein made by a gene called ICP0 promotes the activity of genes that stimulate viral replication and causes active infection.

 

Based on these earlier findings, the new study revealed that this balancing act is enabled by the CTCF protein when it binds to the viral DNA. Present during latent or dormant infections, CTCF is lost during active, symptomatic infections. The researchers created an altered version of the virus that lacked two of the CTCF binding sites. The absence of the binding sites made no difference in early-stage or acute infections. Similar results were found in infected cultured human nerve cells (trigeminal ganglia) and infected mice model. The researchers concluded that the mutant virus was found to have significantly weakened reactivation capacity.

 

Taken together, the experiments showed that deleting the CTCF binding sites weakened the virus’s ability to wake up from its dormant state thereby establishing the evidence that the CTCF protein is a key regulator of sleep-wake cycle in herpes simplex infections.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/29437926

 

https://hms.harvard.edu/news/viral-hideout?utm_source=Silverpop

 

https://www.ncbi.nlm.nih.gov/pubmed/30110885

 

https://www.ncbi.nlm.nih.gov/pubmed/30014861

 

https://www.ncbi.nlm.nih.gov/pubmed/18264117

 

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  1. Lungs can supply blood stem cells and also produce platelets: Lungs, known primarily for breathing, play a previously unrecognized role in blood production, with more than half of the platelets in a mouse’s circulation produced there. Furthermore, a previously unknown pool of blood stem cells has been identified that is capable of restoring blood production when bone marrow stem cells are depleted.

 

  1. A new drug for multiple sclerosis: A new multiple sclerosis (MS) drug, which grew out of the work of UCSF (University of California, San Francisco) neurologist was approved by the FDA. Ocrelizumab, the first drug to reflect current scientific understanding of MS, was approved to treat both relapsing-remitting MS and primary progressive MS.

 

  1. Marijuana legalized – research needed on therapeutic possibilities and negative effects: Recreational marijuana will be legal in California starting in January, and that has brought a renewed urgency to seek out more information on the drug’s health effects, both positive and negative. UCSF scientists recognize marijuana’s contradictory status: the drug has proven therapeutic uses, but it can also lead to tremendous public health problems.

 

  1. Source of autism discovered: In a finding that could help unlock the fundamental mysteries about how events early in brain development lead to autism, researchers traced how distinct sets of genetic defects in a single neuronal protein can lead to either epilepsy in infancy or to autism spectrum disorders in predictable ways.

 

  1. Protein found in diet responsible for inflammation in brain: Ketogenic diets, characterized by extreme low-carbohydrate, high-fat regimens are known to benefit people with epilepsy and other neurological illnesses by lowering inflammation in the brain. UCSF researchers discovered the previously undiscovered mechanism by which a low-carbohydrate diet reduces inflammation in the brain. Importantly, the team identified a pivotal protein that links the diet to inflammatory genes, which, if blocked, could mirror the anti-inflammatory effects of ketogenic diets.

 

  1. Learning and memory failure due to brain injury is now restorable by drug: In a finding that holds promise for treating people with traumatic brain injury, an experimental drug, ISRIB (integrated stress response inhibitor), completely reversed severe learning and memory impairments caused by traumatic brain injury in mice. The groundbreaking finding revealed that the drug fully restored the ability to learn and remember in the brain-injured mice even when the animals were initially treated as long as a month after injury.

 

  1. Regulatory T cells induce stem cells for promoting hair growth: In a finding that could impact baldness, researchers found that regulatory T cells, a type of immune cell generally associated with controlling inflammation, directly trigger stem cells in the skin to promote healthy hair growth. An experiment with mice revealed that without these immune cells as partners, stem cells cannot regenerate hair follicles, leading to baldness.

 

  1. More intake of good fat is also bad: Liberal consumption of good fat (monounsaturated fat) – found in olive oil and avocados – may lead to fatty liver disease, a risk factor for metabolic disorders like type 2 diabetes and hypertension. Eating the fat in combination with high starch content was found to cause the most severe fatty liver disease in mice.

 

  1. Chemical toxicity in almost every daily use products: Unregulated chemicals are increasingly prevalent in products people use every day, and that rise matches a concurrent rise in health conditions like cancers and childhood diseases, Thus, researcher in UCSF is working to understand the environment’s role – including exposure to chemicals – in health conditions.

 

  1. Cytomegalovirus found as common factor for diabetes and heart disease in young women: Cytomegalovirus is associated with risk factors for type 2 diabetes and heart disease in women younger than 50. Women of normal weight who were infected with the typically asymptomatic cytomegalovirus, or CMV, were more likely to have metabolic syndrome. Surprisingly, the reverse was found in those with extreme obesity.

 

References:

 

https://www.ucsf.edu/news/2017/12/409241/most-popular-science-stories-2017

 

https://www.ucsf.edu/news/2017/03/406111/surprising-new-role-lungs-making-blood

 

https://www.ucsf.edu/news/2017/03/406296/new-multiple-sclerosis-drug-ocrelizumab-could-halt-disease

 

https://www.ucsf.edu/news/2017/06/407351/dazed-and-confused-marijuana-legalization-raises-need-more-research

 

https://www.ucsf.edu/news/2017/01/405631/autism-researchers-discover-genetic-rosetta-stone

 

https://www.ucsf.edu/news/2017/09/408366/how-ketogenic-diets-curb-inflammation-brain

 

https://www.ucsf.edu/news/2017/07/407656/drug-reverses-memory-failure-caused-traumatic-brain-injury

 

https://www.ucsf.edu/news/2017/05/407121/new-hair-growth-mechanism-discovered

 

https://www.ucsf.edu/news/2017/06/407536/go-easy-avocado-toast-good-fat-can-still-be-bad-you-research-shows

 

https://www.ucsf.edu/news/2017/06/407416/toxic-exposure-chemicals-are-our-water-food-air-and-furniture

 

https://www.ucsf.edu/news/2017/02/405871/common-virus-tied-diabetes-heart-disease-women-under-50

 

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