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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Curators:

 

THE VOICE of Aviva Lev-Ari, PhD, RN

In this curation we wish to present two breaking through goals:

Goal 1:

Exposition of a new direction of research leading to a more comprehensive understanding of Metabolic Dysfunctional Diseases that are implicated in effecting the emergence of the two leading causes of human mortality in the World in 2023: (a) Cardiovascular Diseases, and (b) Cancer

Goal 2:

Development of Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics for these eight subcellular causes of chronic metabolic diseases. It is anticipated that it will have a potential impact on the future of Pharmaceuticals to be used, a change from the present time current treatment protocols for Metabolic Dysfunctional Diseases.

According to Dr. Robert Lustig, M.D, an American pediatric endocrinologist. He is Professor emeritus of Pediatrics in the Division of Endocrinology at the University of California, San Francisco, where he specialized in neuroendocrinology and childhood obesity, there are eight subcellular pathologies that drive chronic metabolic diseases.

These eight subcellular pathologies can’t be measured at present time.

In this curation we will attempt to explore methods of measurement for each of these eight pathologies by harnessing the promise of the emerging field known as Bioelectronics.

Unmeasurable eight subcellular pathologies that drive chronic metabolic diseases

  1. Glycation
  2. Oxidative Stress
  3. Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
  4. Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
  5. Membrane instability
  6. Inflammation in the gut [mucin layer and tight junctions]
  7. Epigenetics/Methylation
  8. Autophagy [AMPKbeta1 improvement in health span]

Diseases that are not Diseases: no drugs for them, only diet modification will help

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

Exercise will not undo Unhealthy Diet

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

These eight Subcellular Pathologies driving Chronic Metabolic Diseases are becoming our focus for exploration of the promise of Bioelectronics for two pursuits:

  1. Will Bioelectronics be deemed helpful in measurement of each of the eight pathological processes that underlie and that drive the chronic metabolic syndrome(s) and disease(s)?
  2. IF we will be able to suggest new measurements to currently unmeasurable health harming processes THEN we will attempt to conceptualize new therapeutic targets and new modalities for therapeutics delivery – WE ARE HOPEFUL

In the Bioelecronics domain we are inspired by the work of the following three research sources:

  1. Biological and Biomedical Electrical Engineering (B2E2) at Cornell University, School of Engineering https://www.engineering.cornell.edu/bio-electrical-engineering-0
  2. Bioelectronics Group at MIT https://bioelectronics.mit.edu/
  3. The work of Michael Levin @Tufts, The Levin Lab
Michael Levin is an American developmental and synthetic biologist at Tufts University, where he is the Vannevar Bush Distinguished Professor. Levin is a director of the Allen Discovery Center at Tufts University and Tufts Center for Regenerative and Developmental Biology. Wikipedia
Born: 1969 (age 54 years), Moscow, Russia
Education: Harvard University (1992–1996), Tufts University (1988–1992)
Affiliation: University of Cape Town
Research interests: Allergy, Immunology, Cross Cultural Communication
Awards: Cozzarelli prize (2020)
Doctoral advisor: Clifford Tabin
Most recent 20 Publications by Michael Levin, PhD
SOURCE
SCHOLARLY ARTICLE
The nonlinearity of regulation in biological networks
1 Dec 2023npj Systems Biology and Applications9(1)
Co-authorsManicka S, Johnson K, Levin M
SCHOLARLY ARTICLE
Toward an ethics of autopoietic technology: Stress, care, and intelligence
1 Sep 2023BioSystems231
Co-authorsWitkowski O, Doctor T, Solomonova E
SCHOLARLY ARTICLE
Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion
14 Aug 2023Frontiers in Cell and Developmental Biology11:1087650
Co-authorsGrodstein J, McMillen P, Levin M
SCHOLARLY ARTICLE
30 Jul 2023Biochim Biophys Acta Gen Subj1867(10):130440
Co-authorsCervera J, Levin M, Mafe S
SCHOLARLY ARTICLE
Regulative development as a model for origin of life and artificial life studies
1 Jul 2023BioSystems229
Co-authorsFields C, Levin M
SCHOLARLY ARTICLE
The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left–Right Functional Differences
1 Jul 2023International Journal of Molecular Sciences24(13)
Co-authorsMasuelli S, Real S, McMillen P
SCHOLARLY ARTICLE
Bioelectricidad en agregados multicelulares de células no excitables- modelos biofísicos
Jun 2023Revista Española de Física32(2)
Co-authorsCervera J, Levin M, Mafé S
SCHOLARLY ARTICLE
Bioelectricity: A Multifaceted Discipline, and a Multifaceted Issue!
1 Jun 2023Bioelectricity5(2):75
Co-authorsDjamgoz MBA, Levin M
SCHOLARLY ARTICLE
Control Flow in Active Inference Systems – Part I: Classical and Quantum Formulations of Active Inference
1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):235-245
Co-authorsFields C, Fabrocini F, Friston K
SCHOLARLY ARTICLE
Control Flow in Active Inference Systems – Part II: Tensor Networks as General Models of Control Flow
1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):246-256
Co-authorsFields C, Fabrocini F, Friston K
SCHOLARLY ARTICLE
Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology
1 Jun 2023Cellular and Molecular Life Sciences80(6)
Co-authorsLevin M
SCHOLARLY ARTICLE
Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging
1 Jun 2023Drug Discovery Today28(6)
Co-authorsPio-Lopez L, Levin M
SCHOLARLY ARTICLE
Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine
12 May 2023Patterns4(5)
Co-authorsMathews J, Chang A, Devlin L
SCHOLARLY ARTICLE
Making and breaking symmetries in mind and life
14 Apr 2023Interface Focus13(3)
Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z
SCHOLARLY ARTICLE
The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis
14 Apr 2023Interface Focus13(3)
Co-authorsPio-Lopez L, Bischof J, LaPalme JV
SCHOLARLY ARTICLE
The collective intelligence of evolution and development
Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications
Co-authorsWatson R, Levin M
SCHOLARLY ARTICLE
Bioelectricity of non-excitable cells and multicellular pattern memories: Biophysical modeling
13 Mar 2023Physics Reports1004:1-31
Co-authorsCervera J, Levin M, Mafe S
SCHOLARLY ARTICLE
There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines
1 Mar 2023Biomimetics8(1)
Co-authorsBongard J, Levin M
SCHOLARLY ARTICLE
Transplantation of fragments from different planaria: A bioelectrical model for head regeneration
7 Feb 2023Journal of Theoretical Biology558
Co-authorsCervera J, Manzanares JA, Levin M
SCHOLARLY ARTICLE
Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind
1 Jan 2023Animal Cognition
Co-authorsLevin M
SCHOLARLY ARTICLE
Biological Robots: Perspectives on an Emerging Interdisciplinary Field
1 Jan 2023Soft Robotics
Co-authorsBlackiston D, Kriegman S, Bongard J
SCHOLARLY ARTICLE
Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model
1 Jan 2023Entropy25(1)
Co-authorsShreesha L, Levin M
5

5 total citations on Dimensions.

Article has an altmetric score of 16
SCHOLARLY ARTICLE
1 Jan 2023BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY138(1):141
Co-authorsClawson WP, Levin M
SCHOLARLY ARTICLE
Future medicine: from molecular pathways to the collective intelligence of the body
1 Jan 2023Trends in Molecular Medicine
Co-authorsLagasse E, Levin M

THE VOICE of Dr. Justin D. Pearlman, MD, PhD, FACC

PENDING

THE VOICE of  Stephen J. Williams, PhD

Ten TakeAway Points of Dr. Lustig’s talk on role of diet on the incidence of Type II Diabetes

 

  1. 25% of US children have fatty liver
  2. Type II diabetes can be manifested from fatty live with 151 million  people worldwide affected moving up to 568 million in 7 years
  3. A common myth is diabetes due to overweight condition driving the metabolic disease
  4. There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
  5. Thirty percent of ‘obese’ people just have high subcutaneous fat.  the visceral fat is more problematic
  6. there are people who are ‘fat’ but insulin sensitive while have growth hormone receptor defects.  Points to other issues related to metabolic state other than insulin and potentially the insulin like growth factors
  7. At any BMI some patients are insulin sensitive while some resistant
  8. Visceral fat accumulation may be more due to chronic stress condition
  9. Fructose can decrease liver mitochondrial function
  10. A methionine and choline deficient diet can lead to rapid NASH development

 

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Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Reporter: Stephen J. Williams, Ph.D.

The World Health Organization has identified 3 superbugs, or infective micororganisms displaying resistance to common antibiotics and multidrug resistance, as threats to humanity:

Three bacteria were listed as critical:

  • Acinetobacter baumannii bacteria that are resistant to important antibiotics called carbapenems. Acinetobacter baumannii are highly-drug resistant bacteria that can cause a range of infections for hospitalized patients, including pneumonia, wound, or blood infections.
  • Pseudomonas aeruginosa, which are resistant to carbapenems. Pseudomonas aeruginosa can cause skin rashes and ear infectious in healthy people but also severe blood infections and pneumonia when contracted by sick people in the hospital.
  • Enterobacteriaceae — a family of bacteria that live in the human gut — that are resistant to both carbepenems and another class of antibiotics, cephalosporins.

 

It has been designated critical need for development of  antibiotics to these pathogens.  Now researchers at Mcmaster University and others in the US had used artificial intelligence (AI) to screen libraries of over 7,000 chemicals to find a drug that could be repurposed to kill off the pathogen.

Liu et. Al. (1) published their results of an AI screen to narrow down potential chemicals that could work against Acinetobacter baumanii in Nature Chemical Biology recently.

Abstract

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Schematic workflow for incorporation of AI for antibiotic drug discovery for A. baumannii from 1. Liu, G., Catacutan, D.B., Rathod, K. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

Figure source: https://www.nature.com/articles/s41589-023-01349-8

Article Source: https://www.nature.com/articles/s41589-023-01349-8

  1. Liu, G., Catacutan, D.B., Rathod, K. et al.Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumanniiNat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

 

 

For reference to WHO and lists of most pathogenic superbugs see https://www.scientificamerican.com/article/who-releases-list-of-worlds-most-dangerous-superbugs/

The finding was first reported by the BBC.

Source: https://www.bbc.com/news/health-65709834

By James Gallagher

Health and science correspondent

Scientists have used artificial intelligence (AI) to discover a new antibiotic that can kill a deadly species of superbug.

The AI helped narrow down thousands of potential chemicals to a handful that could be tested in the laboratory.

The result was a potent, experimental antibiotic called abaucin, which will need further tests before being used.

The researchers in Canada and the US say AI has the power to massively accelerate the discovery of new drugs.

It is the latest example of how the tools of artificial intelligence can be a revolutionary force in science and medicine.

Stopping the superbugs

Antibiotics kill bacteria. However, there has been a lack of new drugs for decades and bacteria are becoming harder to treat, as they evolve resistance to the ones we have.

More than a million people a year are estimated to die from infections that resist treatment with antibiotics.The researchers focused on one of the most problematic species of bacteria – Acinetobacter baumannii, which can infect wounds and cause pneumonia.

You may not have heard of it, but it is one of the three superbugs the World Health Organization has identified as a “critical” threat.

It is often able to shrug off multiple antibiotics and is a problem in hospitals and care homes, where it can survive on surfaces and medical equipment.

Dr Jonathan Stokes, from McMaster University, describes the bug as “public enemy number one” as it’s “really common” to find cases where it is “resistant to nearly every antibiotic”.

 

Artificial intelligence

To find a new antibiotic, the researchers first had to train the AI. They took thousands of drugs where the precise chemical structure was known, and manually tested them on Acinetobacter baumannii to see which could slow it down or kill it.

This information was fed into the AI so it could learn the chemical features of drugs that could attack the problematic bacterium.

The AI was then unleashed on a list of 6,680 compounds whose effectiveness was unknown. The results – published in Nature Chemical Biology – showed it took the AI an hour and a half to produce a shortlist.

The researchers tested 240 in the laboratory, and found nine potential antibiotics. One of them was the incredibly potent antibiotic abaucin.

Laboratory experiments showed it could treat infected wounds in mice and was able to kill A. baumannii samples from patients.

However, Dr Stokes told me: “This is when the work starts.”

The next step is to perfect the drug in the laboratory and then perform clinical trials. He expects the first AI antibiotics could take until 2030 until they are available to be prescribed.

Curiously, this experimental antibiotic had no effect on other species of bacteria, and works only on A. baumannii.

Many antibiotics kill bacteria indiscriminately. The researchers believe the precision of abaucin will make it harder for drug-resistance to emerge, and could lead to fewer side-effects.

 

In principle, the AI could screen tens of millions of potential compounds – something that would be impractical to do manually.

“AI enhances the rate, and in a perfect world decreases the cost, with which we can discover these new classes of antibiotic that we desperately need,” Dr Stokes told me.

The researchers tested the principles of AI-aided antibiotic discovery in E. coli in 2020, but have now used that knowledge to focus on the big nasties. They plan to look at Staphylococcus aureus and Pseudomonas aeruginosa next.

“This finding further supports the premise that AI can significantly accelerate and expand our search for novel antibiotics,” said Prof James Collins, from the Massachusetts Institute of Technology.

He added: “I’m excited that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii.”

Prof Dame Sally Davies, the former chief medical officer for England and government envoy on anti-microbial resistance, told Radio 4’s The World Tonight: “We’re onto a winner.”

She said the idea of using AI was “a big game-changer, I’m thrilled to see the work he (Dr Stokes) is doing, it will save lives”.

Other related articles and books published in this Online Scientific Journal include the following:

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

(3 book series: Volume 1, 2&3, 4)

https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks

 

 

 

 

 

 

 

 

 

 

  • The Immune System, Stress Signaling, Infectious Diseases and Therapeutic Implications:

 

  • Series D, VOLUME 2

Infectious Diseases and Therapeutics

and

  • Series D, VOLUME 3

The Immune System and Therapeutics

(Series D: BioMedicine & Immunology) Kindle Edition.

On Amazon.com since September 4, 2017

(English Edition) Kindle Edition – as one Book

https://www.amazon.com/dp/B075CXHY1B $115

 

Bacterial multidrug resistance problem solved by a broad-spectrum synthetic antibiotic

The Journey of Antibiotic Discovery

FDA cleared Clever Culture Systems’ artificial intelligence tech for automated imaging, analysis and interpretation of microbiology culture plates speeding up Diagnostics

Artificial Intelligence: Genomics & Cancer

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Genetic variation causes human lupus, systemic lupus erythematosus (SLE)

Reporter: Aviva Lev-Ari, PhD, RN

 

 

TLR7 gain-of-function genetic variation causes human lupus

Abstract

Although circumstantial evidence supports enhanced Toll-like receptor 7 (TLR7) signalling as a mechanism of human systemic autoimmune disease1,2,3,4,5,6,7, evidence of lupus-causing TLR7 gene variants is lacking. Here we describe human systemic lupus erythematosus caused by a TLR7 gain-of-function variant. TLR7 is a sensor of viral RNA8,9 and binds to guanosine10,11,12. We identified a de novo, previously undescribed missense TLR7Y264H variant in a child with severe lupus and additional variants in other patients with lupus. The TLR7Y264H variant selectively increased sensing of guanosine and 2′,3′-cGMP10,11,12, and was sufficient to cause lupus when introduced into mice. We show that enhanced TLR7 signalling drives aberrant survival of B cell receptor (BCR)-activated B cells, and in a cell-intrinsic manner, accumulation of CD11c+ age-associated B cells and germinal centre B cells. Follicular and extrafollicular helper T cells were also increased but these phenotypes were cell-extrinsic. Deficiency of MyD88 (an adaptor protein downstream of TLR7) rescued autoimmunity, aberrant B cell survival, and all cellular and serological phenotypes. Despite prominent spontaneous germinal-centre formation in Tlr7Y264H mice, autoimmunity was not ameliorated by germinal-centre deficiency, suggesting an extrafollicular origin of pathogenic B cells. We establish the importance of TLR7 and guanosine-containing self-ligands for human lupus pathogenesis, which paves the way for therapeutic TLR7 or MyD88 inhibition.

SOURCE

Brown, G.J., Cañete, P.F., Wang, H. et al. TLR7 gain-of-function genetic variation causes human lupus. Nature 605, 349–356 (2022). https://doi.org/10.1038/s41586-022-04642-z

Scientists finally discover the cause of lupus

[Feb. 20, 2023: Alice Deeley, The Francis Crick Institute]

An international team of researchers has identified DNA mutations in a gene that senses viral RNA, as a cause of the autoimmune disease lupus, with the finding paving the way for the development of new treatments.

Lupus is a chronic autoimmune disease which causes inflammation in organs and joints, affects movement and the skin, and causes fatigue. In severe cases, symptoms can be debilitating and complications can be fatal.

In their genetic analysis, carried out at the Centre for Personalised Immunology at the Australian National University, the researchers found a single point mutation in the TLR7 gene. Via referrals from the US and the China Australia Centre of Personalised Immunology (CACPI) at Shanghai Renji Hospital, they identified other cases of severe lupus where this gene was also mutated.

To confirm that the mutation causes lupus, the team used CRISPR gene-editing to introduce it into mice. These mice went on to develop the disease and showed similar symptoms, providing evidence that the TLR7 mutation was the cause. The mouse model and the mutation were both named ‘kika’ by Gabriela, the young girl central to this discovery.

“While it may only be a small number of people with lupus who have variants in TLR7 itself, we do know that many patients have signs of overactivity in the TLR7 pathway. By confirming a causal link between the gene mutation and the disease, we can start to search for more effective treatments.”

The work may also help explain why lupus is about 10 times more frequent in females than in males. As TLR7 sits on the X chromosome, females have two copies of the gene while males have one. Usually, in females one of the X chromosomes is inactive, but in this section of the chromosome, silencing of the second copy is often incomplete. This means females with a mutation in this gene can have two functioning copies.

“There are other systemic autoimmune diseases, like rheumatoid arthritis and dermatomyositis, which fit within the same broad family as lupus. TLR7 may also play a role in these conditions.”

SOURCE

https://www-thebrighterside-news.cdn.ampproject.org/c/s/www.thebrighterside.news/amp/2202023-scientists-finally-discover-the-cause-of-lupus

Other related articles published in this Open Access Online Scientific Journal

Defective viral RNA sensing gene OAS1 linked to severe COVID-19

Reporter: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2021/11/22/defective-viral-rna-sensing-gene-oas1-linked-to-severe-covid-19/

T cell-mediated immune responses & signaling pathways activated by Toll-like Receptors

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/09/07/t-cell-mediated-immune-responses-signaling-pathways-activated-by-tlrs/

Issues Need to be Resolved With Immuno-Modulatory Therapies: NK cells, mAbs, and adoptive T cells

Curator: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2016/05/01/issues-need-to-be-resolved-with-immunomodulatory-therapies-nk-cells-mabs-and-adoptive-t-cells/

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/

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National Public Radio interview with Dr. Anthony Fauci on his optimism on a COVID-19 vaccine by early 2021

Reporter: Stephen J. Williams, PhD

Below I am giving a link to an important interview by NPR’s Judy Woodruff with Dr. Anthony Fauci on his thoughts regarding the recent spikes in cases, the potential for a COVID-19 vaccine by next year, and promising therapeutics in the pipeline.  The interview link is given below however I will summarize a few of the highlights of the interview.

 

Some notes on the interview

Judy Woodruff began her report with some up to date news regarding the recent spike and that Miami Florida has just ordered the additional use of facemasks.  She asked Dr. Anthony Fauci, head of the National Institute of Allergy and Infectious Diseases (NIAD), about if the measures currently in use are enough to bring this spike down.  Dr. Fauci said that we need to reboot our efforts, mainly because people are not doing three things which could have prevented this spike mainly

  1. universal wearing of masks
  2. distancing properly from each other
  3. close the bars and pubs (see Wisconsin bars packed after ruling)

It hasn’t been a uniform personal effort

Dr. Fauci on testing

We have to use the tests we have out there efficiently and effectively And we have to get them out to the right people who can do the proper identification, isolation, and do proper contract tracing and need to test more widely in a surveillance way to get a feel of the extent and penetrance of this community spread.  there needs to be support and money for these testing labs

We have a problem and we need to admit and own it but we need to do the things we know are effective to turn this thing around.

On Vaccines

“May be later this year”

His response to Merck’s CEO Ken Frazer who said officials are giving false hop if they say ‘end of year’ but Dr. Fauci disagrees.  He says a year end goal is not outlandish.

What we have been doing is putting certain things in line with each other in an unprecedented way.

Dr. Fauci went on to say that, in the past yes, it took a long time, even years to develop a vaccine but now they have been able to go from sequence of virus to a vaccine development program in days, which is unheard of.  Sixty two days later we have gone into phase 1 trials. the speed at which this is occurring is so much faster.  He says that generally it would take a couple of years to get a neutralizing antibody but we are already there.  Another candidate will be undergoing phase 3 trials by end of this month (July 2020).

He is “cautiously optimistic” that we will have one or more vaccines to give to patients by end of year because given the amount of cases it will be able to get a handle on safety and efficacy by late fall.

Now he says the game changer is that the government is working with companies to ramp up the production of doses of the candidate vaccines so when we find which one works we will have ample doses on hand.  He is worried about the anti vaccine movement derailing vaccine testing and vaccinations but says if we keep on informing the public we can combat this.

Going back to school

Dr. Fauci is concerned for the safety of the vulnerable in schools, including students and staff.  He wants the US to get down to a reasonable baseline of cases but in the US that baseline after the first wave was still significantly higher than in most countries, where the baseline was more like tens of cases not hundreds of cases.

For more information on COVID-19 Please go to our Coronavirus Portal at

https://pharmaceuticalintelligence.com/coronavirus-portal/

 

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

 

Obesity is a global concern that is associated with many chronic complications such as type 2 diabetes, insulin resistance (IR), cardiovascular diseases, and cancer. Growing evidence has implicated the digestive system, including its microbiota, gut-derived incretin hormones, and gut-associated lymphoid tissue in obesity and IR. During high fat diet (HFD) feeding and obesity, a significant shift occurs in the microbial populations within the gut, known as dysbiosis, which interacts with the intestinal immune system. Similar to other metabolic organs, including visceral adipose tissue (VAT) and liver, altered immune homeostasis has also been observed in the small and large intestines during obesity.

 

A link between the gut microbiota and the intestinal immune system is the immune-derived molecule immunoglobulin A (IgA). IgA is a B cell antibody primarily produced in dimeric form by plasma cells residing in the gut lamina propria (LP). Given the importance of IgA on intestinal–gut microbe immunoregulation, which is directly influenced by dietary changes, scientists hypothesized that IgA may be a key player in the pathogenesis of obesity and IR. Here, in this study it was demonstrate that IgA levels are reduced during obesity and the loss of IgA in mice worsens IR and increases intestinal permeability, microbiota encroachment, and downstream inflammation in metabolic tissues, including inside the VAT.

 

IgA deficiency alters the obese gut microbiota and its metabolic phenotype can be recapitulated into microbiota-depleted mice upon fecal matter transplantation. In addition, the researchers also demonstrated that commonly used therapies for diabetes such as metformin and bariatric surgery can alter cellular and stool IgA levels, respectively. These findings suggested a critical function for IgA in regulating metabolic disease and support the emerging role for intestinal immunity as an important modulator of systemic glucose metabolism.

 

Overall, the researchers demonstrated a critical role for IgA in regulating intestinal homeostasis, metabolic inflammation, and obesity-related IR. These findings identify intestinal IgA+ immune cells as mucosal mediators of whole-body glucose regulation in diet-induced metabolic disease. This research further emphasized the importance of the intestinal adaptive immune system and its interactions with the gut microbiota and innate immune system within the larger network of organs involved in the manifestation of metabolic disease.

 

Future investigation is required to determine the impact of IgA deficiency during obesity in humans and the role of metabolic disease in human populations with selective IgA deficiency, especially since human IgA deficiency is associated with an altered gut microbiota that cannot be fully compensated with IgM. However, the research identified IgA as a critical immunological molecule in the intestine that impacts systemic glucose homeostasis, and treatments targeting IgA-producing immune populations and SIgA may have therapeutic potential for metabolic disease.

 

References:

 

https://www.nature.com/articles/s41467-019-11370-y?elqTrackId=dc86e0c60f574542b033227afd0fdc8e

 

https://www.jci.org/articles/view/88879

 

https://www.nature.com/articles/nm.2353

 

https://diabetes.diabetesjournals.org/content/57/6/1470

 

https://www.sciencedirect.com/science/article/pii/S1550413115001047?via%3Dihub

 

https://www.sciencedirect.com/science/article/pii/S1550413115002326?via%3Dihub

 

https://www.sciencedirect.com/science/article/pii/S1931312814004636?via%3Dihub

 

https://www.nature.com/articles/nature15766

 

https://www.sciencedirect.com/science/article/pii/S1550413116000371?via%3Dihub

 

https://www.nature.com/articles/nm.2001

 

https://www.sciencedirect.com/science/article/abs/pii/S1550413118305047?via%3Dihub

 

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Highlighted Progress in Science – 2017

Reporter: Sudipta Saha, PhD

 

  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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LIVE – OCTOBER 16 – DAY 1- Koch Institute Immune Engineering Symposium 2017, MIT, Kresge Auditorium

Reporter: Aviva Lev-Ari, PhD, RN

 

 

Image Source:Koch Institute

Koch Institute

Immune Engineering Symposium 2017

http://kochinstituteevents.cvent.com/events/koch-institute-immune-engineering-symposium-2017/agenda-64e5d3f55b964ff2a0643bd320b8e60d.aspx

 

#IESYMPOSIUM

 

Image Source: Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Aviva Lev-Ari, PhD, RN will be in attendance covering the event in REAL TIME

@pharma_BI

@AVIVA1950

#IESYMPOSIUM

@KOCHINSTITUTE

  • The Immune System, Stress Signaling, Infectious Diseases and Therapeutic Implications: VOLUME 2: Infectious Diseases and Therapeutics and VOLUME 3: The Immune System and Therapeutics (Series D: BioMedicine & Immunology) Kindle Edition – on Amazon.com since September 4, 2017

https://www.amazon.com/dp/B075CXHY1B

SYMPOSIUM SCHEDULE

OCTOBER 16 – DAY 1

7:00 – 8:15 Registration

8:15 – 8:30Introductory Remarks
Darrell Irvine | MIT, Koch Institute; HHMI

  • Stimulating the Immune system not only sustaining it for therapies

K. Dane Wittrup | MIT, Koch Institute

8:30 – 9:45Session I
Moderator: Douglas Lauffenburger | MIT, Biological Engineering and Koch Institute

Garry P. Nolan – Stanford University School of Medicine
Pathology from the Molecular Scale on Up

  • Intracellular molecules,
  • how molecules are organized to create tissue
  • Meaning from data Heterogeneity is an illusion: Order in Data ?? Cancer is heterogeneous, Cells in suspension – number of molecules
  • System-wide changes during Immune Response (IR)
  • Untreated, Ineffective therapy, effective therapy
  • Days 3-8 Tumor, Lymph node…
  • Variation is a Feature – not a bug: Effective therapy vs Ineffective – intercellular modules – virtual neighborhoods
  • ordered by connectivity: very high – CD4 T-cells, CD8 T-cels, moderate, not connected
  • Landmark nodes, Increase in responders
  • CODEX: Multiples epitome detection
  • Adaptable to proteins & mRNA
  • Rendering antibody staining via removal to neighborhood mapping
  • Human tonsil – 42 parameters: CD7, CD45, CD86,
  • Automated Annotations of tissues: F, P, V,
  • Normal BALBs
  • Marker expression defined by the niche: B220 vs CD79
  • Marker expression defines the niche
  • Learn neighborhoods and Trees
  • Improving Tissue Classification and staining – Ce3D – Tissue and Immune Cells in 3D
  • Molecular level cancer imaging
  • Proteomic Profiles: multi slice combine
  • Theory is formed to explain 3D nuclear images of cells – Composite Ion Image, DNA replication
  • Replication loci visualization on DNA backbone – nascent transcriptome – bar code of isotopes – 3D  600 slices
  • use CRISPR Cas9 for Epigenetics

Susan Napier Thomas – Georgia Institute of Technology
Transport Barriers in the Tumor Microenvironment: Drug Carrier Design for Therapeutic Delivery to Sentinel Lymph Nodes

  • Lymph Nodes important therapeutics target tissue
  • Lymphatic flow support passive and active antigen transport to lymph nodes
  • clearance of biomolecules and drug formulations: Interstitial transport barriers influence clearance: Arteriole to Venule –
  • Molecular tracers to analyze in vivo clearance mechanisms and vascular transport function
  • quantifying molecular clearance and biodistribution
  • Lymphatic transport increases tracer concentrations within dLN by orders of magnitude
  • Melanoma growth results in remodeled tumor vasculature
  • passive transport via lymphatic to dLN sustained in advanced tumors despite abrogated cell trafficking
  • Engineered biomaterial drug carriers to enhance sentinel lymph node-drug delivery: facilitated by exploiting lymphatic transport
  • TLR9 ligand therapeutic tumor in situ vaccination – Lymphatic-draining CpG-NP enhanced
  • Sturcutral and Cellular barriers: transport of particles is restriced by
  • Current drug delivery technology: lymph-node are undrugable
  • Multistage delivery platform to overcome barriers to lymphatic uptake and LN targeting
  • nano particles – OND – Oxanorbornade OND Time sensitive Linker synthesized large cargo – NP improve payload
  • OND release rate from nanoparticles changes retention in lymph nodes – Axilliary-Brachial delivery
  • Two-stage OND-NP delivery and release system dramatically – OND acumulate in lymphocyte
  •  delivers payload to previously undraggable lymphe tissue
  • improved drug bioactivity  – OND-NP eliminate LN LYMPHOMAS
  • Engineered Biomaterials

Douglas Lauffenburger – MIT, Biological Engineering and Koch Institute
Integrative Multi-Omic Analysis of Tissue Microenvironment in Inflammatory Pathophysiology

  • How to intervene, in predictive manner, in immunesystem-associated complex diseases
  • Understand cell communication beteen immune cells and other cells, i.e., tumor cells
  • Multi-Variate in Vivo – System Approach: Integrative Experiment & COmputational Analysis
  • Cell COmmunication & Signaling in CHronic inflammation – T-cell transfer model for colitis
  • COmparison of diffrential Regulation (Tcell transfer-elicited vs control) anong data types – relying solely on mRNA can be misleading
  • Diparities in differential responses to T cell transfer across data types yield insights concerning broader multi-organ interactions
  • T cell transfer can be ascertained and validated by successful experimental test
  • Cell COmmunication in Tumor MIcro-Environment — integration of single-cell transcriptomic data and protein interaction
  • Standard Cluster Elucidation – Classification of cell population on Full gene expression Profiles using Training sets: Decision Tree for Cell Classification
  • Wuantification of Pairwise Cell-Cell Receptor/Ligand Interactions: Cell type Pairs vs Receptor/Ligand Interaction
  • Pairwise Cell-Cell Receptor/Ligand Interactions
  • Calculate strength of interaction and its statistical significance
  • How the interaction is related to Phenotypic Behaviors – tumor growth rate, MDSC levels,
  • Correlated the Interactions translated to Phynotypic behavior for Therapeutic interventions (AXL via macrophage and fibroblasts)
  • Mouth model translation to Humans – New machine learning approach
  • Pathways, false negative, tumor negative expression
  • Molecular vs Phynotypical expression
  • Categories of inter-species translation
  • Semi-supervised Learning ALgorithms on Transcriptomic Data can ascertain Key Pathways/Processes in Human IBD from mapping mouse IBD

9:45 – 10:15 Break

10:15 – 11:30Session II
Moderator: Tyler Jacks | MIT, Koch Institute; HHMI

Tyler Jacks – MIT, Koch Institute; HHMI
Using Genetically Engineered Mouse Models to Probe Cancer-Immune Interactions

  • Utility of genetically-engineered mouse models of Cancer:
  1. Immune Response (IR),
  2. Tumor0immune microenvironment
  • Lung adenocarcinoma – KRAS mutation: Genetically-engineered model, applications: CRISPR, genetic interactions
  • Minimal Immune response to KP lung tumors: H&E, T cells (CD3), Bcells (B220) for Lenti-x 8 weeks
  • Exosome sequencing : Modeling loss-and gain-of-function mutations in Lung Cancer by CRISPR-Cas9 – germline – tolerance in mice, In vivo CRISPR-induced knockout of Msh2
  • Signatures of MMR deficient
  • Mutation burden and response to Immunotherapy (IT)
  • Programmed neoantigen expression – robust infiltration of T cells (evidence of IR)
  • Immunosuppression – T cell rendered ineffective
  • Lymphoid infiltration: Acute Treg depletion results in T cell infiltration — this depletion causes autoimmune response
  • Lung Treg from KP tumor-bearing mice have a distinct transcriptional heterogeneity through single cell mRNA sequencing
  • KP, FOXP3+, CD4
  • Treg from no existent to existance, Treg cells increase 20 fold =>>>  Treg activation and effectiveness
  • Single cells cluster by tissue and cell type: Treg, CD4+, CD8+, Tetramer-CD4+
  • ILrl1/II-33r unregulated in Treg at late time point
  • Treg-specific deletion of IL-33r results in fewer effector Tregs in Tumor-bearing lungs
  • CD8+ T cell infiltration
  • Tetramer-positive T cells cluster according to time point: All Lung CD8+ T cells
  • IR is not uniform functional differences – Clones show distinct transcriptional profiles
  • Different phynotypes Exhaustive signature
  • CRISPR-mediated modulation of CD8 T cell regulatory genes
  • Genetic dissection of the tumor-immune microenvironment
  • Single cell analysis, CRISPR – CRISPRa,i, – Drug development

Wendell Lim – University of California, San Francisco

Synthetic Immunology: Hacking Immune Cells

  • Precision Cell therapies – engineered by synthetic biology
  • Anti CD19 – drug approved
  • CAR-T cells still face major problems
  1. success limited to B cells cancers = blood vs solid tumors
  2. adverse effects
  3. OFF-TUMOR effects
  • Cell engineering for Cancer Therapy: User remote control (drug) – user control safety
  • Cell Engineering for TX
  1. new sensors – decision making for
  2. tumor recognition – safety,
  3. Cancer is a recognition issue
  • How do we avoid cross-reaction with bystader tissue (OFF TISSUE effect)
  • Tumor recognition: More receptors & integration
  • User Control
  • synthetic NOTCH receptors (different flavors of synNotch) – New Universal platform for cell-to -cell recognition: Target molecule: Extracellular antigen –>> transciptional instruction to cell
  • nextgen T cell: Engineer T cell recognition circuit that integrates multiple inputs: Two receptors – two antigen priming circuit
  • UNARMED: If antigen A THEN receptor A activates CAR
  • “Bystander” cell single antigen vs “tumor” drug antigen
  • Selective clearance of combinatorial tumor – Boulian formulation, canonical response
  • Cell response: Priming –>> Killing: Spatial & Temporal choreographed cell
  • CAR expression while removed from primed cells deminished
  • Solid Tumor: suppress cell microenvironment: Selected response vs non-natural response
  • Immune stimulator IR IL2, IL12, flagellin in the payload — Ourcome: Immune enhancement “vaccination”
  • Immune suppression –  block
  • Envision ideal situation: Unarmed cells
  • FUTURE: identify disease signatures and vulnerabilities – Precision Medicine using Synthetic Biology

Darrell Irvine – MIT, Koch Institute; HHMI
Engineering Enhanced Cancer Vaccines to Drive Combination Immunotherapies

  • Vaccine to drive IT
  • Intervening in the cancer-immunity cycle – Peptide Vaccines
  • poor physiology  of solute transport to tissue
  • endogenous albumin affinity – Lymphe Node dying
  • Designing Albumin-hitchhiking vaccines
  • Amphiphile-vaccine enhance uptake in lymph nodes in small and large animal models
  • soluble vaccine vs Amphiphile-vaccine
  • DIRECTING Vaccines to the Lymph nodes
  • amph-peptide antigen: Prime, booster, tetramer
  • albimin-mediated LN-targeting of both antigen and adjuvant maximizes IR
  • Immuno-supressed microenvironment will not be overcome by vaccines
  • Replacing adoptive T cell transfer with potent vaccine
  • exploiting albumin biology for mucosal vaccine delivery by amph-vaccines
  • Amph-peptides and -adjuvants show enhanced uptake/retention in lung tissue
  •  Enhancing adoptive T cell therapy: loss of T cell functionality, expand in vivo
  • boost in vivo enhanced adoptive T cell therapy
  • CAR-T cells: Enable T cells to target any cell surface protein
  • “Adaptor”-targeting CAR-T cells to deal with tumor cell heterogeneity
  • Lymph node-targeting Amph as CAR T booster vaccine: prining, production of cytokines
  • Boosting CAR T with amph-caccines: anti FITC CAR-T by DSPE=PEG-FITC coated
  • Targeting FITC to lymph node antigen presenting cells
  • Modulatory Macrophages
  • Amph-FITC expands FITC-CAR T cells in vivo – Adjuvant is needed
  • Hijacking albumin’s natural trafficking pathway

11:30 – 1:00  Lunch Break

1:00 – 2:15Session III
Moderator: Darrell Irvine | MIT, Koch Institute; HHMI

Nicholas P. Restifo – National Cancer Institute
Extracellular Potassium Regulates Epigenetics and Efficacy of Anti-Tumor T Cells

Why T cell do not kill Cancer cells?

  • co-inhibition
  • hostile tumor microenvironment

CAR T – does not treat solid tumors

Somatic mutation

  1. resistence of T cell based IT due to loss of function mutations
  2. Can other genes be lost?

CRISPR Cas9 – used to identify agents – GeCKOv2 Human library

Two cell-type (2CT) CRISPR assay system for genome-wide mutagenesis

  • work flow for genome-scale SRISPR mutagenesis profiling of genes essential for T cell mediate cytosis
  • sgRNA enrichment at the individual gene level by multiple methods:
  1. subunits of the MHC Class I complex
  2. CRISPR mutagenesis cut germline
  • Measutring the generalizability of resistance mechanism and mice in vivo validation
  • Validation of top gene candidates using libraries: MART-1
  • Checkpoint blockade: cells LOF causes tumor growth and immune escape
  • Weird genesL Large Ribisomal Subunit Proteins are nor all essential for cell survival
  • Bias in enrichment of 60S vs 40S
  • Novel elements of MHC class I antigen processing and presentation
  • Association of top CRISPR hits with response rates to IT – antiCTLA-4
  • CRISPR help identify novel regulators of T cells
  • Analyzed sgRNA – second rarest sgRNA for gene BIRC2 – encoded the Baculoviral Inhibitor
  • Drugs that inhibit BIRC2
  • How T cells can kill tumor cells more efficiently
  • p38kiaseas target for adoptive immunotherapy
  • FACS-based – Mapk14
  • Potent targets p38 – Blockade PD-1 or p38 ??
  • p38 signaling: Inhibition augments expansion and memory-marked human PBMC and TIL cells, N. P. Restifo
  • Tumor killing capacity of human CD19-specific, gene engineered T cells

Jennifer Elisseeff – Johns Hopkins University
The Adaptive Immune Response to Biomaterials and Tissue Repair

  • design scafolds, tissue-specific microenvironment
  • clinical translation of biosynthetic implants for soft tissue reconstruction
  • Local environment affects biomaterials: Epidermis, dermis
  • CD4+ T cells
  • Immune system – first reponders to materials: Natural or Synthetic
  • Biological (ECM) scaffolds to repair muscle injury
  • Which immune cells enter the WOUND?
  • ECM alters Macrophages: CD86, CD206
  • Adaptive system impact on Macrophages: CD86
  • mTOR signaling pathway M2 depend on Th2 Cells in regeneration of cell healing of surgical wounds
  • Systemic Immunological changes
  • Is the response antigen specific? – IL-4 expression in ILN,
  • Tissue reconstruction Clinical Trial: FDA ask to look at what cells infiltrate the scaffold
  • Trauma/biomaterial response – Injury induction of Senescence, anti apoptosis
  • Injury to skin or muscle
  • Is pro-regenerative environment (Th2/M2) pro-tumorigenic?
  • SYNTHETIC Materials for scafolds
  • Biomaterials and Immunology
  1. Immune response to bioscafolds
  2. environment modulate the immune system
  • Regenerative Immunetherapy

Marcela Maus – Massachusetts General Hospital

Engineering Better T Cells

  • Comparing CD19 CARs for Leukemia – anti-CD19- directed CAR T cells with r/r B-cell ALL – age 3-25 – FDA approved Novartis tisagenlecleucel – for pediatric r/r/ ALL
  • Phase II in diffuse large B cell lymphoma. Using T cells – increases prospects for cure
  • Vector retroviral – 30 day expression
  • measuring cytokines release syndrome: Common toxicity with CAR 19
  • neurological toxicity, B-cell aplagia
  • CART issues with heme malignancies
  1. decrease cytokine release
  2. avoid neurological toxicity – homing
  3. new targets address antigene escape variants – Resistance, CD19 is shaded, another target needed
  4. B Cell Maturation Antigen (BCMA) Target
  5. Bluebird Bio: Response duratio up to 54 weeks – Active dose cohort
  6. natural ligand CAR based on April
  7. activated in response to TACI+ target cells – APRIL-based CARs but not BCMA-CAR is able to kill TACI+ target cells
  • Hurdles for Solid Tumors
  1. Specific antigen targets
  2. tumor heterogeneity
  3. inhibitory microenvironment
  • CART in Glioblastoma
  1. rationale for EGFRvIII as therapeutic target
  2. Preclinical Studies & Phase 1: CAR t engraft, not as highly as CD19
  3. Upregulation of immunosuppression and Treg infiltrate in CART EGFRvIII as therapeutic target, Marcela Maus
  • What to do differently?

 

2:15 – 2:45 Break

2:45 – 4:00 Session IV
Moderator: Arup K. Chakraborty | MIT, IMES

Laura Walker – Adimab, LLC
Molecular Dissection of the Human Antibody Response to Respiratory Syncytial Virus

  • prophylactic antibody is available
  • Barriers for development of Vaccine
  • Prefusion and Postfusion RSV structures
  • Six major antigenic sites on RSV F
  • Blood samples Infants less 6 month of age and over 6 month: High abundance RSV F -specific memory B Cells are group  less 6 month

Arup K. Chakraborty – MIT, Institute for Medical Engineering & Science
How to Hit HIV Where it Hurts

  • antibody  – Model IN SILICO
  • Check affinity of each Ab for the Seaman panel of strain
  • Breadth of coverage
  • immmunize with cocktail of variant antigens
  • Mutations on Affinity Maturation: Molecular dynamics
  • bnAb eveolution: Hypothesis – mutations evolution make the antigen binding region more flexible,
  • Tested hypothesisi: carrying out affinity maturation – LOW GERMLINE AFFINITY TO CONSERVE RESIDUES IN 10,000 trials, acquire the mutation (generation 300)

William Schief – The Scripps Research Institute
HIV Vaccine Design Targeting the Human Naive B Cell Repertoire

  • HIV Envelope Trimer Glycan): the Target of neutralizing Antibodies (bnAbs)
  • Proof of principle for germline-targeting: VRC)!-class bnAbs
  • design of a nanoparticle
  • can germline -targeting innumogens prime low frequency precursors?
  • Day 14 day 42 vaccinate
  • Precursor frequency and affinity are limiting for germline center (GC) entry at day 8
  • Germline-targeting immunogens can elicit robust, high quality SHM under physiological conditions of precursor frequency and affinity at day 8, 16, 36
  • Germline-targeting immunogens can lead to production of memory B cells

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G-protein-coupled receptor kinases in inflammation and disease – Nature.com

Reporter: Aviva Lev-Ari, PhD, RN

 

G-protein-coupled receptor kinases in inflammation and disease
Nature.com
Correspondence: N Parameswaran, Professor N Parameswaran, Department of Physiology, Michigan State University, East Lansing, MI 48824, USA.

Sourced through Scoop.it from: news.google.com

See on Scoop.itCardiovascular Disease: PHARMACO-THERAPY

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