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Archive for the ‘Micronutrients’ Category


Live 12:00 – 1:00 P.M  Mediterranean Diet and Lifestyle: A Symposium on Diet and Human Health : October 19, 2018

Reporter: Stephen J. Williams, Ph.D.

12.00 The Italian Mediterranean Diet as a Model of Identity of a People with a Universal Good to Safeguard Health?

Prof. Antonino De Lorenzo, MD, PhD.

Director of the School of Specialization in Clinical Nutrition, University of Rome “Tor Vergata”

It is important to determine how our bodies interacts with the environment, such as absorption of nutrients.

Studies shown here show decrease in life expectancy of a high sugar diet, but the quality of the diet, not just the type of diet is important, especially the role of natural probiotics and phenolic compounds found in the Mediterranean diet.

The WHO report in 2005 discusses the unsustainability of nutrition deficiencies and suggest a proactive personalized and preventative/predictive approach of diet and health.

Most of the noncommunicable diseases like CV (46%) cancer 21% and 11% respiratory and 4% diabetes could be prevented and or cured with proper dietary approaches

Italy vs. the US diseases: in Italy most disease due to environmental contamination while US diet plays a major role

The issue we are facing in less than 10% of the Italian population (fruit, fibers, oils) are not getting the proper foods, diet and contributing to as we suggest 46% of the disease

The Food Paradox: 1.5 billion are obese; we notice we are eating less products of quality and most quality produce is going to waste;

  •  growing BMI and junk food: our studies are correlating the junk food (pre-prepared) and global BMI
  • modern diet and impact of human health (junk food high in additives, salt) has impact on microflora
  • Western Diet and Addiction: We show a link (using brain scans) showing correlation of junk food, sugar cravings, and other addictive behaviors by affecting the dopamine signaling in the substantia nigra
  • developed a junk food calculator and a Mediterranean diet calculator
  • the intersection of culture, food is embedded in the Mediterranean diet; this is supported by dietary studies of two distinct rural Italian populations (one of these in the US) show decrease in diet
  • Impact of diet: have model in Germany how this diet can increase health and life expectancy
  • from 1950 to present day 2.7 unit increase in the diet index can increase life expectancy by 26%
  • so there is an inverse relationship with our index and breast cancer

Environment and metal contamination and glyphosate: contribution to disease and impact of maintaining the healthy diet

  • huge problem with use of pesticides and increase in celiac disease

12:30 Environment and Health

Dr. Iris Maria Forte, PhD.

National Cancer Institute “Pascale” Foundation | IRCCS · Department of Research, Naples, Italy

Cancer as a disease of the environment.  Weinberg’s hallmarks of Cancer reveal how environment and epigenetics can impact any of these hallmarks.

Epigenetic effects

  • gene gatekeepers (Rb and P53)
  • DNA repair and damage stabilization

Heavy Metals and Dioxins:( alterations of the immune system as well as epigenetic regulations)

Asbestos and Mesothelioma:  they have demonstrated that p53 can be involved in development of mesothelioma as reactivating p53 may be a suitable strategy for therapy

Diet, Tomato and Cancer

  • looked at tomato extract on p53 function in gastric cancer: tomato extract had a growth reduction effect and altered cell cycle regulation and results in apoptosis
  • RBL2 levels are increased in extract amount dependent manner so data shows effect of certain tomato extracts of the southern italian tomato (     )

Antonio Giordano: we tested whole extracts of almost 30 different varieties of tomato.  The tomato variety  with highest activity was near Ravela however black tomatoes have shown high antitumor activity.  We have done a followup studies showing that these varieties, if grow elsewhere lose their antitumor activity after two or three generations of breeding, even though there genetics are similar.  We are also studying the effects of different styles of cooking of these tomatoes and if it reduces antitumor effect

please see post https://news.temple.edu/news/2017-08-28/muse-cancer-fighting-tomatoes-study-italian-food

 

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Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

Real Time Conference Coverage for Scientific and Business Media: Unique Twitter Hashtags and Handles per Conference Presentation/Session

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

LIVE 2018 The 21st Gabay Award to LORENZ STUDER, Memorial Sloan Kettering Cancer Center, contributions in stem cell biology and patient-specific, cell-based therapy

HUBweek 2018, October 8-14, 2018, Greater Boston – “We The Future” – coming together, of breaking down barriers, of convening across disciplinary lines to shape our future

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Benefits of Fiber in Diet

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

 

UPDATED on 1/15/2019

This is How Much Daily Fiber to Eat for Better Health – More appears better in meta-analysis — as in more than 30 g/day

by Ashley Lyles, Staff Writer, MedPage Today

In the systematic review, observational data showed a 15% to 30% decline in cardiovascular-related death, all-cause mortality, and incidence of stroke, coronary heart disease, type 2 diabetes, and colorectal cancer among people who consumed the most dietary fiber compared to those consuming the lowest amounts.

Whole grain intake yielded similar findings.

Risk reduction associated with a range of critical outcomes was greatest when daily intake of dietary fibre was between 25 g and 29 g. Dose-response curves suggested that higher intakes of dietary fibre could confer even greater benefit to protect against cardiovascular diseases, type 2 diabetes, and colorectal and breast cancer.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(18)31809-9.pdf

Eating more dietary fiber was linked with lower risk of disease and death, a meta-analysis showed.

According to observational studies, risk was reduced most for a range of critical outcomes from all-cause mortality to stroke when daily fiber consumption was between 25 grams and 29 grams, reported Jim Mann, PhD, of University of Otago in Dunedin, New Zealand, and colleagues in The Lancet.

By upping daily intake to 30 grams or more, people had even greater prevention of certain conditions: colorectal and breast cancer, type 2 diabetes, and cardiovascular diseases, according to dose-response curves the authors created.

Quantitative guidelines relating to dietary fiber have not been available, the researchers said. With the GRADE method, they determined that there was moderate and low-to-moderate certainty of evidence for the benefits of dietary fiber consumption and whole grain consumption, respectively.

Included in the systematic review were 58 clinical trials and 185 prospective studies for a total of 4,635 adult participants with 135 million person-years of information (one trial in children was included, but analyzed separately from adults). Trials and prospective studies assessing weight loss, supplement use, and participants with a chronic disease were excluded.

 

Food is digested by bathing in enzymes that break down its molecules. Those molecular fragments then pass through the gut wall and are absorbed in our intestines. But our bodies make a limited range of enzymes, so that we cannot break down many of the tough compounds in plants. The term “dietary fiber” refers to those indigestible molecules. These dietary fibers are indigestible only to us. The gut is coated with a layer of mucus, on which sits a carpet of hundreds of species of bacteria, part of the human microbiome. Some of these microbes carry the enzymes needed to break down various kinds of dietary fibers.

 

Scientists at the University of Gothenburg in Sweden are running experiments that are yielding some important new clues about fiber’s role in human health. Their research indicates that fiber doesn’t deliver many of its benefits directly to our bodies. Instead, the fiber we eat feeds billions of bacteria in our guts. Keeping them happy means our intestines and immune systems remain in good working order. The scientists have recently reported that the microbes are involved in the benefits obtained from the fruits-and-vegetables diet. Research proved that low fiber diet decreases the gut bacteria population by tenfold.

 

Along with changes to the microbiome there were also rapid changes observed in the experimental mice. Their intestines got smaller, and its mucus layer thinner. As a result, bacteria wound up much closer to the intestinal wall, and that encroachment triggered an immune reaction. After a few days on the low-fiber diet, mouse intestines developed chronic inflammation. After a few weeks, they started putting on fat and developing higher blood sugar levels. Inflammation can help fight infections, but if it becomes chronic, it can harm our bodies. Among other things, chronic inflammation may interfere with how the body uses the calories in food, storing more of it as fat rather than burning it for energy.

 

In a way fiber benefits human health is by giving, indirectly, another source of food. When bacteria finished harvesting the energy in the dietary fiber, they cast off the fragments as waste. That waste — in the form of short-chain fatty acids — is absorbed by intestinal cells, which use it as fuel. But the gut’s microbes do more than just make energy. They also send messages. Intestinal cells rely on chemical signals from the bacteria to work properly. The cells respond to the signals by multiplying and making a healthy supply of mucus. They also release bacteria-killing molecules. By generating these responses, gut bacteria help to maintain a peaceful coexistence with the immune system. They rest on the gut’s mucus layer at a safe distance from the intestinal wall. Any bacteria that wind up too close get wiped out by antimicrobial poisons.

 

A diet of fiber-rich foods, such as fruits and vegetables, reduces the risk of developing diabetes, heart disease and arthritis. Eating more fiber seems to lower people’s mortality rate, whatever be the cause. Researchers hope that they will learn more about how fiber influences the microbiome to use it as a way to treat disorders. Lowering inflammation with fiber may also help in the treatment of immune disorders such as inflammatory bowel disease. Fiber may also help reverse obesity. They found that fiber supplements helped obese people to lose weight. It’s possible that each type of fiber feeds a particular set of bacteria, which send their own important signals to our bodies.

 

References:

 

https://www.nytimes.com/2018/01/01/science/food-fiber-microbiome-inflammation.html

 

 

https://www.ncbi.nlm.nih.gov/pubmed/29276171

 

https://www.ncbi.nlm.nih.gov/pubmed/29276170

 

https://www.ncbi.nlm.nih.gov/pubmed/29486139

 

https://www.mayoclinic.org/healthy-lifestyle/nutrition-and-healthy-eating/in-depth/fiber/art-20043983

 

https://nutritiouslife.com/eat-empowered/high-fiber-diet/

 

http://www.eatingwell.com/article/287742/10-amazing-health-benefits-of-eating-more-fiber/

 

http://www.cookinglight.com/eating-smart/nutrition-101/what-is-a-high-fiber-diet

 

https://www.helpguide.org/articles/healthy-eating/high-fiber-foods.htm

 

https://www.gicare.com/diets/high-fiber-diet/

 

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

 

The trillions of microbes in the human gut are known to aid the body in synthesizing key vitamins and other nutrients. But this new study suggests that things can sometimes be more adversarial.

 

Choline is a key nutrient in a range of metabolic processes, as well as the production of cell membranes. Researchers identified a strain of choline-metabolizing E. coli that, when transplanted into the guts of germ-free mice, consumed enough of the nutrient to create a choline deficiency in them, even when the animals consumed a choline-rich diet.

 

This new study indicate that choline-utilizing bacteria compete with the host for this nutrient, significantly impacting plasma and hepatic levels of methyl-donor metabolites and recapitulating biochemical signatures of choline deficiency. Mice harboring high levels of choline-consuming bacteria showed increased susceptibility to metabolic disease in the context of a high-fat diet.

 

DNA methylation is essential for normal development and has been linked to everything from aging to carcinogenesis. This study showed changes in DNA methylation across multiple tissues, not just in adult mice with a choline-consuming gut microbiota, but also in the pups of those animals while they developed in utero.

 

Bacterially induced reduction of methyl-donor availability influenced global DNA methylation patterns in both adult mice and their offspring and engendered behavioral alterations. This study reveal an underappreciated effect of bacterial choline metabolism on host metabolism, epigenetics, and behavior.

 

The choline-deficient mice with choline-consuming gut microbes also showed much higher rates of infanticide, and exhibited signs of anxiety, with some mice over-grooming themselves and their cage-mates, sometimes to the point of baldness.

 

Tests have also shown as many as 65 percent of healthy individuals carry genes that encode for the enzyme that metabolizes choline in their gut microbiomes. This work suggests that interpersonal differences in microbial metabolism should be considered when determining optimal nutrient intake requirements.

 

References:

 

https://news.harvard.edu/gazette/story/2017/11/harvard-research-suggests-microbial-menace/

 

http://www.cell.com/cell-host-microbe/fulltext/S1931-3128(17)30304-9

 

https://www.ncbi.nlm.nih.gov/pubmed/23151509

 

https://www.ncbi.nlm.nih.gov/pubmed/25677519

 

http://mbio.asm.org/content/6/2/e02481-14

 

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Using “Cerebral Organoids” to Trace the Elemental Composition of a Developing Brain

Curator: Marzan Khan, B.Sc

A research focused on the detection of micronutrient accumulation in the developing brain has been conducted recently by a team of scientific researchers in Brazil(1). Their study was comprised of a cutting-edge technology human cerebral organoids, which are a close equivalent of the embryonic brain, in in-vitro models to identify some of the minerals essential during brain development using synchroton radiation(1). Since the majority of studies done on this matter have relied on samples from animal models, the adult brain or post-mortem tissue, this technique has been dubbed the “closest and most complete study system to date for understanding human neural development and its pathological manifestations”(2).

Cerebral organoids are three-dimensional miniature structures derived from human pluripotent stem cells that further differentiate into structures closely resembling the developing brain(2). Concentrating on two different time points during the developmental progression, the researchers illustrated the micronutrient content during an interval of high cell division marked on day 30 as well as day 40 when the organoids were starting to become mature neurons that secrete neurotransmitters, arranging into layers and forming synapses(2).

Synchrotron radiation X-ray fluorescence (SR-XRF) spectroscopy was used to discern each type of element present(2). After an incident beam of X-ray was directed at the sample, each atom emitted a distinct photon signature(2). Phosphorus (P), Potassium (P), Sulphur (S), Calcium (Ca), Iron (Fe), and Zinc (Zn) were found to be present in the samples in significant concentrations(2). Manganese (Mn), Nickel (Ni) and Copper (Cu) were also detected, but in negligible amounts, and therefore tagged as “ultratrace” elements(2). The distribution of these minerals, their concentration as well as their occurrence in pairs were examined at each interval(2).

Phosphorus was discovered to be the most abundant element in the cerebral organoid samples(3). Between the two time points at 30 days (cell proliferation) and 45 days (neuronal maturation) there was a marked decrease in P content(2). Since phosphorus is a major component of nucleotides and phospholipids, this reduction was clarified as a shift from a stage of cell division that requires the production of DNA and phospholipids, to a migratory and differentiation phase(2). Potassium levels were maintained during both phases, substantiating its role in mitotic cell division as well as cell migration over long distances(2). Sulfur levels were reportedly high at 30 days and 45 days(2). It was hypothesized that this element was responsible for the patterning of the organoids(2). Calcium, known to control transcription factors involved in neuronal differentiation and survival were detected in the micromolar range, along with zinc and iron(2). Zinc commits the differentiation of pluripotent stem cells into neuronal cells and iron is necessary for neuronal tissue expansion(2).

The cells in an embryo start to differentiate very early on- the neural plate is formed on the 16th day of contraception, which further folds and bulges out to become the nervous system (containing the brain and spinal cord regions)(3). Nutrients obtained from the mother are the primary sources of diet and energy for a developing embryo to fully differentiate and specialize into different organs(2). Lack of proper nutrition in pregnant mothers has been linked to many neurodegenerative diseases occurring in their progeny(2). Spina bifida which is characterized by the incomplete development of the brain and spinal cord, is a classic example of maternal malnutrition(2,4). Paucity of minerals in the diet of pregnant women are known to hamper learning and memory in children(2). Even Schizophrenia, Parkinson’s and Huntington’s disease have been associated to malnourishment(2). By showing the different types of elements present in statistically significant concentrations in cerebral organoids, the results of this study underscore the necessity of a healthy nourishment available to mothers during pregnancy for optimal development of the fetal brain(2).

References:

1.Kenny Walter. 02/10/2017. Study focuses on Microcutrients in Human Minibrains. RandDMagazine.http://www.rdmag.com/article/2017/02/study-focuses-micronutrients-human-minibrains?et_cid=5825577&et_rid=461755519&type=cta&et_cid=5825577&et_rid=461755519&linkid=conten

2.Sartore RC, Cardoso SC, Lages YVM, Paraguassu JM, Stelling MP, Madeiro da Costa RF, Guimaraes MZ, Pérez CA, Rehen SK.(2017)Trace elements during primordial plexiform network formation in human cerebral organoids. PeerJ 5:e2927https://doi.org/10.7717/peerj.292

3.Fetal Development: Baby’s Nervous System and Brain; What to expect; 20/07/201. http://www.whattoexpect.com/pregnancy/fetal-brain-nervous-system/

4. Spina Bifida Fact Sheet; National Institute of Neurological Disorders and Stroke National Institutes of Health, Bethesda, MD 20892

https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Spina-Bifida-Fact-Sheet

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

 

Zinc-Finger Nucleases (ZFNs) and Transcription Activator–Like Effector Nucleases (TALENs)

Reporter: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2013/03/04/talens-and-zfns/

 

Calcium Regulation Key Mechanism Discovered: New Potential for Neuro-degenerative Diseases Drug Development

Reporter: Aviva Lev-Ari, PhD., RN

https://pharmaceuticalintelligence.com/2013/01/17/calcium-regulation-key-mechanism-discovered-new-potential-for-neuro-degenerative-diseases-drug-development/

 

How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia

Curator: Larry H Bernstein, MD, FACP

https://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/

 

Erythropoietin (EPO) and Intravenous Iron (Fe) as Therapeutics for Anemia in Severe and Resistant CHF: The Elevated N-terminal proBNP Biomarker

Co-Author of the FIRST Article: Larry H. Bernstein, MD, FCAP

Reviewer and Curator of the SECOND and of the THIRD Articles: Larry H. Bernstein, MD, FCAP

Article Architecture Curator: Aviva Lev-Ari, PhD., RN

https://pharmaceuticalintelligence.com/2013/12/10/epo-as-therapeutics-for-anemia-in-chf/

 

The relationship of S amino acids to marasmic and kwashiorkor PEM

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/10/24/the-relationship-of-s-amino-acids-to-marasmic-and-kwashiorkor-pem/

 

Mutations in a Sodium-gated Potassium Channel Subunit Gene related to a subset of severe Nocturnal Frontal Lobe Epilepsy

Reporter: Aviva Lev-Ari, PhD., RN

https://pharmaceuticalintelligence.com/2012/10/22/mutations-in-a-sodium-gated-potassium-channel-subunit-gene-to-a-subset-of-severe-nocturnal-frontal-lobe-epilepsy/

 

Copper and its role on “progressive neurodegeneration” and death

Reported by: Dr. Venkat S. Karra, Ph.D.

https://pharmaceuticalintelligence.com/2012/08/14/copper-and-its-role-on-progressive-neurodegeneration-and-death/

 

Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

Reviewer and Curator: Larry H. Bernstein, MD, FCAP 

https://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/

 

Nutrition and Aging

Curator: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/10/25/nutrition-and-aging/

 

The Three Parent Technique to Avoid Mitochondrial Disease in Embryo

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

https://pharmaceuticalintelligence.com/2016/10/07/the-three-parent-technique-to-avoid-mitochondrial-disease-in-embryo/

 

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“Minerals in Medicine” –  40 Minerals that are crucial to Human Health and Biomedicine: Exhibit by NIH Clinical Center and The Smithsonian Institution National Museum of Natural History

Reporter: Aviva Lev-Ari, PhD, RN

 

Friday, September 9, 2016

NIH Clinical Center and The Smithsonian Institution partner to launch Minerals in Medicine Exhibition

What

The National Institutes of Health Clinical Center, in partnership with The Smithsonian Institution National Museum of Natural History, will open a special exhibition of more than 40 minerals that are crucial to human health and biomedicine. “Minerals in Medicine” is designed to enthrall and enlighten NIH Clinical Center’s patients, their loved ones, and the NIH community. Media are invited into America’s Research Hospital, the NIH Clinical Center, to experience this unique exhibition during a ribbon cutting ceremony on Monday September 12 at 4pm.

Beyond taking in the minerals’ arresting beauty, spectators can learn about their important role in keeping the human body healthy, and in enabling the creation of life-saving medicines and cutting edge medical equipment that is used in the NIH Clinical Center and healthcare facilities worldwide. The exhibition, which is on an eighteen-month loan from the National Museum of Natural History, includes specimens that were handpicked from the museum’s vast collection by NIH physicians in partnership with Smithsonian Institution geologists. Some of the minerals on display were obtained regionally as they are part of the Maryland and Virginia landscape.

Who

  • John I. Gallin, M.D., Director of the NIH Clinical Center
  • Jeffrey E. Post, Ph.D., Smithsonian Institution National Museum of Natural History, Chair of the Department of Mineral Sciences and Curator of the National Gem and Mineral Collection

When

Monday, September 12, 2016, 4:00 – 5:00 p.m.

Where

NIH Clinical Center (Building 10), 10 Center Drive, Bethesda, MD, 20892; 1st Floor near Admissions

How

RSVP encouraged, but not required, to attend in person. NIH Visitors Map: http://www.ors.od.nih.gov/maps/Pages/NIH-Visitor-Map.aspx

About the NIH Clinical Center: The NIH Clinical Center is the clinical research hospital for the National Institutes of Health. Through clinical research, clinician-investigators translate laboratory discoveries into better treatments, therapies and interventions to improve the nation’s health. More information: http://clinicalcenter.nih.gov.

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.

SOURCE

https://www.nih.gov/news-events/news-releases/nih-clinical-center-smithsonian-institution-partner-launch-minerals-medicine-exhibition

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Selye’s Riddle solved

Larry H. Bernstein, mD, FCAP, Curator

LPBI

 

Mathematicians Solve 78-year-old Mystery

Mathematicians developed a solution to Selye's riddle which has puzzled scientists for almost 80 years.
Mathematicians developed a solution to Selye’s riddle which has puzzled scientists for almost 80 years.

In previous research, it was suggested that adaptation of an animal to different factors looks like spending of one resource, and that the animal dies when this resource is exhausted. In 1938, Hans Selye introduced “adaptation energy” and found strong experimental arguments in favor of this hypothesis. However, this term has caused much debate because, as it cannot be measured as a physical quantity, adaptation energy is not strictly energy.

 

Evolution of adaptation mechanisms: Adaptation energy, stress, and oscillating death

Alexander N. Gorbana, , Tatiana A. Tyukinaa, Elena V. Smirnovab, Lyudmila I. Pokidyshevab,

Highlights

•   We formalize Selye׳s ideas about adaptation energy and dynamics of adaptation.
•   A hierarchy of dynamic models of adaptation is developed.
•   Adaptation energy is considered as an internal coordinate on the ‘dominant path’ in the model of adaptation.
•   The optimal distribution of resources for neutralization of harmful factors is studied.
•   The phenomena of ‘oscillating death’ and ‘oscillating remission’ are predicted.       

In previous research, it was suggested that adaptation of an animal to different factors looks like spending of one resource, and that the animal dies when this resource is exhausted.

In 1938, Selye proposed the notion of adaptation energy and published ‘Experimental evidence supporting the conception of adaptation energy.’ Adaptation of an animal to different factors appears as the spending of one resource. Adaptation energy is a hypothetical extensive quantity spent for adaptation. This term causes much debate when one takes it literally, as a physical quantity, i.e. a sort of energy. The controversial points of view impede the systematic use of the notion of adaptation energy despite experimental evidence. Nevertheless, the response to many harmful factors often has general non-specific form and we suggest that the mechanisms of physiological adaptation admit a very general and nonspecific description.

We aim to demonstrate that Selye׳s adaptation energy is the cornerstone of the top-down approach to modelling of non-specific adaptation processes. We analyze Selye׳s axioms of adaptation energy together with Goldstone׳s modifications and propose a series of models for interpretation of these axioms. Adaptation energy is considered as an internal coordinate on the ‘dominant path’ in the model of adaptation. The phenomena of ‘oscillating death’ and ‘oscillating remission’ are predicted on the base of the dynamical models of adaptation. Natural selection plays a key role in the evolution of mechanisms of physiological adaptation. We use the fitness optimization approach to study of the distribution of resources for neutralization of harmful factors, during adaptation to a multifactor environment, and analyze the optimal strategies for different systems of factors.

In this work, an international team of researchers, led by Professor Alexander N. Gorban from the University of Leicester, have developed a solution to Selye’s riddle, which has puzzled scientists for almost 80 years.

Alexander N. Gorban, Professor of Applied Mathematics in the Department of Mathematics at the University of Leicester, said: “Nobody can measure adaptation energy directly, indeed, but it can be understood by its place already in simple models. In this work, we develop a hierarchy of top-down models following Selye’s findings and further developments. We trust Selye’s intuition and experiments and use the notion of adaptation energy as a cornerstone in a system of models. We provide a ‘thermodynamic-like’ theory of organism resilience that, just like classical thermodynamics, allows for economics metaphors, such as cost and bankruptcy and, more importantly, is largely independent of a detailed mechanistic explanation of what is ‘going on underneath’.”

Adaptation energy is considered as an internal coordinate on the “dominant path” in the model of adaptation. The phenomena of “oscillating death” and “oscillating remission,” which have been observed in clinic for a long time, are predicted on the basis of the dynamical models of adaptation. The models, based on Selye’s idea of adaptation energy, demonstrate that the oscillating remission and oscillating death do not need exogenous reasons. The developed theory of adaptation to various factors gives the instrument for the early anticipation of crises.

Professor Alessandro Giuliani from Istituto Superiore di Sanità in Rome commented on the work, saying: “Gorban and his colleagues dare to make science adopting the thermodynamics style: they look for powerful principles endowed with predictive ability in the real world before knowing the microscopic details. This is, in my opinion, the only possible way out from the actual repeatability crisis of mainstream biology, where a fantastic knowledge of the details totally fails to predict anything outside the test tube.1

Citation: Alexander N. Gorban, Tatiana A. Tyukina, Elena V. Smirnova, Lyudmila I. Pokidysheva. Evolution of adaptation mechanisms: Adaptation energy, stress, and oscillating death. Journal of Theoretical Biology, 2016; DOI:10.1016/j.jtbi.2015.12.017. Voosen P. (2015) Amid a Sea of False Findings NIH tries Reform, The Chronicle of Higher Education.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Inflammatory Disorders: Articles published @ pharmaceuticalintelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

This is a compilation of articles on Inflammatory Disorders that were published 

@ pharmaceuticalintelligence.com, since 4/2012 to date

There are published works that have not been included.  However, there is a substantial amount of material in the following categories:

  1. The systemic inflammatory response
    https://pharmaceuticalintelligence.com/2014/11/08/introduction-to-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/
    https://pharmaceuticalintelligence.com/2014/11/09/summary-and-perspectives-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/
    https://pharmaceuticalintelligence.com/2015/12/19/neutrophil-serine-proteases-in-disease-and-therapeutic-considerations/
    https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/
    https://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
    https://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
    https://pharmaceuticalintelligence.com/2012/07/08/zebrafish-provide-insights-into-causes-and-treatment-of-human-diseases/
    https://pharmaceuticalintelligence.com/2016/01/25/ibd-immunomodulatory-effect-of-retinoic-acid-il-23il-17a-axis-correlates-with-the-nitric-oxide-pathway/
    https://pharmaceuticalintelligence.com/2015/11/29/role-of-inflammation-in-disease/
    https://pharmaceuticalintelligence.com/2013/03/06/can-resolvins-suppress-acute-lung-injury/
    https://pharmaceuticalintelligence.com/2015/02/26/acute-lung-injury/
  2. sepsis
    https://pharmaceuticalintelligence.com/2012/10/20/nitric-oxide-and-sepsis-hemodynamic-collapse-and-the-search-for-therapeutic-options/
  3. vasculitis
    https://pharmaceuticalintelligence.com/2015/02/26/acute-lung-injury/
    https://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
    https://pharmaceuticalintelligence.com/2012/11/20/the-potential-for-nitric-oxide-donors-in-renal-function-disorders/
  4. neurodegenerative disease
    https://pharmaceuticalintelligence.com/2013/02/27/ustekinumab-new-drug-therapy-for-cognitive-decline-resulting-from-neuroinflammatory-cytokine-signaling-and-alzheimers-disease/
    https://pharmaceuticalintelligence.com/2016/01/26/amyloid-and-alzheimers-disease/
    https://pharmaceuticalintelligence.com/2016/02/15/alzheimers-disease-tau-art-thou-or-amyloid/
    https://pharmaceuticalintelligence.com/2016/01/26/beyond-tau-and-amyloid/
    https://pharmaceuticalintelligence.com/2015/12/10/remyelination-of-axon-requires-gli1-inhibition/
    https://pharmaceuticalintelligence.com/2015/11/28/neurovascular-pathways-to-neurodegeneration/
    https://pharmaceuticalintelligence.com/2015/11/13/new-alzheimers-protein-aicd-2/
    https://pharmaceuticalintelligence.com/2015/10/31/impairment-of-cognitive-function-and-neurogenesis/
    https://pharmaceuticalintelligence.com/2014/05/06/bwh-researchers-genetic-variations-can-influence-immune-cell-function-risk-factors-for-alzheimers-diseasedm-and-ms-later-in-life/
  5. cancer immunology
    https://pharmaceuticalintelligence.com/2013/04/12/innovations-in-tumor-immunology/
    https://pharmaceuticalintelligence.com/2016/01/09/signaling-of-immune-response-in-colon-cancer/
    https://pharmaceuticalintelligence.com/2015/05/12/vaccines-small-peptides-aptamers-and-immunotherapy-9/
    https://pharmaceuticalintelligence.com/2015/01/30/viruses-vaccines-and-immunotherapy/
    https://pharmaceuticalintelligence.com/2015/10/20/gene-expression-and-adaptive-immune-resistance-mechanisms-in-lymphoma/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  6. autoimmune diseases: rheumatoid arthritis, colitis, ileitis, …
    https://pharmaceuticalintelligence.com/2016/02/11/intestinal-inflammatory-pharmaceutics/
    https://pharmaceuticalintelligence.com/2016/01/07/two-new-drugs-for-inflammatory-bowel-syndrome-are-giving-patients-hope/
    https://pharmaceuticalintelligence.com/2015/12/16/contribution-to-inflammatory-bowel-disease-ibd-of-bacterial-overgrowth-in-gut-on-a-chip/
    https://pharmaceuticalintelligence.com/2016/02/13/cytokines-in-ibd/
    https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/
    https://pharmaceuticalintelligence.com/2014/10/14/autoimmune-disease-single-gene-eliminates-the-immune-protein-isg15-resulting-in-inability-to-resolve-inflammation-and-fight-infections-discovery-rockefeller-university/
    https://pharmaceuticalintelligence.com/2015/03/01/diarrheas-bacterial-and-nonbacterial/
    https://pharmaceuticalintelligence.com/2016/02/11/intestinal-inflammatory-pharmaceutics/
    https://pharmaceuticalintelligence.com/2014/01/28/biologics-for-autoimmune-diseases-cambridge-healthtech-institutes-inaugural-may-5-6-2014-seaport-world-trade-center-boston-ma/
    https://pharmaceuticalintelligence.com/2015/11/19/rheumatoid-arthritis-update/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
    https://pharmaceuticalintelligence.com/2012/09/13/tofacitinib-an-oral-janus-kinase-inhibitor-in-active-ulcerative-colitis/
    https://pharmaceuticalintelligence.com/2013/03/05/approach-to-controlling-pathogenic-inflammation-in-arthritis/
    https://pharmaceuticalintelligence.com/2013/03/05/rheumatoid-arthritis-risk/
    https://pharmaceuticalintelligence.com/2012/07/08/the-mechanism-of-action-of-the-drug-acthar-for-systemic-lupus-erythematosus-sle/
  7. T cells in immunity
    https://pharmaceuticalintelligence.com/2015/09/07/t-cell-mediated-immune-responses-signaling-pathways-activated-by-tlrs/
    https://pharmaceuticalintelligence.com/2015/05/14/allogeneic-stem-cell-transplantation-9-2/
    https://pharmaceuticalintelligence.com/2015/02/19/graft-versus-host-disease/
    https://pharmaceuticalintelligence.com/2014/10/14/autoimmune-disease-single-gene-eliminates-the-immune-protein-isg15-resulting-in-inability-to-resolve-inflammation-and-fight-infections-discovery-rockefeller-university/
    https://pharmaceuticalintelligence.com/2014/05/27/immunity-and-host-defense-a-bibliography-of-research-technion/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
    https://pharmaceuticalintelligence.com/2013/04/14/immune-regulation-news/

Proteomics, metabolomics and diabetes

https://pharmaceuticalintelligence.com/2015/11/16/reducing-obesity-related-inflammation/

https://pharmaceuticalintelligence.com/2015/10/25/the-relationship-of-stress-hypermetabolism-to-essential-protein-needs/

https://pharmaceuticalintelligence.com/2015/10/24/the-relationship-of-s-amino-acids-to-marasmic-and-kwashiorkor-pem/

https://pharmaceuticalintelligence.com/2015/10/24/the-significant-burden-of-childhood-malnutrition-and-stunting/

https://pharmaceuticalintelligence.com/2015/04/14/protein-binding-protein-protein-interactions-therapeutic-implications-7-3/

https://pharmaceuticalintelligence.com/2015/03/07/transthyretin-and-the-stressful-condition/

https://pharmaceuticalintelligence.com/2015/02/13/neural-activity-regulating-endocrine-response/

https://pharmaceuticalintelligence.com/2015/01/31/proteomics/

https://pharmaceuticalintelligence.com/2015/01/17/proteins-an-evolutionary-record-of-diversity-and-adaptation/

https://pharmaceuticalintelligence.com/2014/11/01/summary-of-signaling-and-signaling-pathways/

https://pharmaceuticalintelligence.com/2014/10/31/complex-models-of-signaling-therapeutic-implications/

https://pharmaceuticalintelligence.com/2014/10/24/diabetes-mellitus/

https://pharmaceuticalintelligence.com/2014/10/16/metabolomics-summary-and-perspective/

https://pharmaceuticalintelligence.com/2014/10/14/metabolic-reactions-need-just-enough/

https://pharmaceuticalintelligence.com/2014/11/03/introduction-to-protein-synthesis-and-degradation/

https://pharmaceuticalintelligence.com/2015/09/25/proceedings-of-the-nyas/

https://pharmaceuticalintelligence.com/2014/10/31/complex-models-of-signaling-therapeutic-implications/

https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/

https://pharmaceuticalintelligence.com/2013/03/05/irf-1-deficiency-skews-the-differentiation-of-dendritic-cells/

https://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/

https://pharmaceuticalintelligence.com/2012/11/20/the-potential-for-nitric-oxide-donors-in-renal-function-disorders/

 

 

 

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3-D Printed Liver

Curator: Larry H. Bernstein, MD, FCAP

 

 

3D-printing a new lifelike liver tissue for drug screening

Could let pharmaceutical companies quickly do pilot studies on new drugs
February 15, 2016    http://www.kurzweilai.net/3d-printing-a-new-lifelike-liver-tissue-for-drug-screening

Images of the 3D-printed parts of the biomimetic liver tissue: liver cells derived from human induced pluripotent stem cells (left), endothelial and mesenchymal supporing cells (center), and the resulting organized combination of multiple cell types (right). (credit: Chen Laboratory, UC San Diego)

 

University of California, San Diego researchers have 3D-printed a tissue that closely mimics the human liver’s sophisticated structure and function. The new model could be used for patient-specific drug screening and disease modeling and could help pharmaceutical companies save time and money when developing new drugs, according to the researchers.

The liver plays a critical role in how the body metabolizes drugs and produces key proteins, so liver models are increasingly being developed in the lab as platforms for drug screening. However, so far, the models lack both the complex micro-architecture and diverse cell makeup of a real liver. For example, the liver receives a dual blood supply with different pressures and chemical constituents.

So the team employed a novel bioprinting technology that can rapidly produce complex 3D microstructures that mimic the sophisticated features found in biological tissues.

The liver tissue was printed in two steps.

  • The team printed a honeycomb pattern of 900-micrometer-sized hexagons, each containing liver cells derived from human induced pluripotent stem cells. An advantage of human induced pluripotent stem cells is that they are patient-specific, which makes them ideal materials for building patient-specific drug screening platforms. And since these cells are derived from a patient’s own skin cells, researchers don’t need to extract any cells from the liver to build liver tissue.
  • Then, endothelial and mesenchymal supporting cells were printed in the spaces between the stem-cell-containing hexagons.

The entire structure — a 3 × 3 millimeter square, 200 micrometers thick — takes just seconds to print. The researchers say this is a vast improvement over other methods to print liver models, which typically take hours. Their printed model was able to maintain essential functions over a longer time period than other liver models. It also expressed a relatively higher level of a key enzyme that’s considered to be involved in metabolizing many of the drugs administered to patients.

“It typically takes about 12 years and $1.8 billion to produce one FDA-approved drug,” said Shaochen Chen, NanoEngineering professor at the UC San Diego Jacobs School of Engineering. “That’s because over 90 percent of drugs don’t pass animal tests or human clinical trials. We’ve made a tool that pharmaceutical companies could use to do pilot studies on their new drugs, and they won’t have to wait until animal or human trials to test a drug’s safety and efficacy on patients. This would let them focus on the most promising drug candidates earlier on in the process.”

The work was published the week of Feb. 8 in the online early edition of Proceedings of the National Academy of Sciences.


Abstract of Deterministically patterned biomimetic human iPSC-derived hepatic model via rapid 3D bioprinting

The functional maturation and preservation of hepatic cells derived from human induced pluripotent stem cells (hiPSCs) are essential to personalized in vitro drug screening and disease study. Major liver functions are tightly linked to the 3D assembly of hepatocytes, with the supporting cell types from both endodermal and mesodermal origins in a hexagonal lobule unit. Although there are many reports on functional 2D cell differentiation, few studies have demonstrated the in vitro maturation of hiPSC-derived hepatic progenitor cells (hiPSC-HPCs) in a 3D environment that depicts the physiologically relevant cell combination and microarchitecture. The application of rapid, digital 3D bioprinting to tissue engineering has allowed 3D patterning of multiple cell types in a predefined biomimetic manner. Here we present a 3D hydrogel-based triculture model that embeds hiPSC-HPCs with human umbilical vein endothelial cells and adipose-derived stem cells in a microscale hexagonal architecture. In comparison with 2D monolayer culture and a 3D HPC-only model, our 3D triculture model shows both phenotypic and functional enhancements in the hiPSC-HPCs over weeks of in vitro culture. Specifically, we find improved morphological organization, higher liver-specific gene expression levels, increased metabolic product secretion, and enhanced cytochrome P450 induction. The application of bioprinting technology in tissue engineering enables the development of a 3D biomimetic liver model that recapitulates the native liver module architecture and could be used for various applications such as early drug screening and disease modeling.

Fernando

I wonder how equivalent are these hepatic cells derived from human induced pluripotent stem cells (hiPSCs) compared with the real hepatic cell populations.
All cells in our organism share the same DNA info, but every tissue is special for what genes are expressed and also because of the specific localization in our body (which would mean different surrounding environment for each tissue). I am not sure about how much of a step forward this is. Induced hepatic cells are known, but this 3-D print does not have liver shape or the different cell sub-types you would find in the liver.

I agree with your observation that having the same DNA information doesn’t account for variability of cell function within an organ. The regulation of expression is in RNA translation, and that is subject to regulatory factors related to noncoding RNAs and to structural factors in protein folding. The result is that chronic diseases that are affected by the synthetic capabilities of the liver are still problematic – toxicology, diabetes, and the inflammatory response, and amino acid metabolism as well. Nevertheless, this is a very significant step for the testing of pharmaceuticals. When we look at the double circulation of the liver, hypoxia is less of an issue than for heart or skeletal muscle, or mesothelial tissues. I call your attention to the outstanding work by Nathan O. Kaplan on the transhydrogenases, and his stipulation that there are significant differences between organs that are anabolic and those that are catabolic in TPNH/DPNH, that has been ignored for over 40 years. Nothing is quite as simple as we would like.

Fernando commented on 3-D printed liver

3-D printed liver Larry H. Bernstein, MD, FCAP, Curator LPBI 3D-printing a new lifelike liver tissue for drug …

I wonder how equivalent are these hepatic cells derived from human induced pluripotent stem cells (hiPSCs) compared with the real hepatic cell populations.
All cells in our organism share the same DNA info, but every tissue is special for what genes are expressed and also because of the specific localization in our body (which would mean different surrounding environment for each tissue). I am not sure about how much of a step forward this is. Induced hepatic cells are known, but this 3-D print does not have liver shape or the different cell sub-types you would find in the liver.

 

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A Reconstructed View of Personalized Medicine

Author: Larry H. Bernstein, MD, FCAP

 

There has always been Personalized Medicine if you consider the time a physician spends with a patient, which has dwindled. But the current recognition of personalized medicine refers to breakthrough advances in technological innovation in diagnostics and treatment that differentiates subclasses within diagnoses that are amenable to relapse eluding therapies.  There are just a few highlights to consider:

  1. We live in a world with other living beings that are adapting to a changing environmental stresses.
  2. Nutritional resources that have been available and made plentiful over generations are not abundant in some climates.
  3. Despite the huge impact that genomics has had on biological progress over the last century, there is a huge contribution not to be overlooked in epigenetics, metabolomics, and pathways analysis.

A Reconstructed View of Personalized Medicine

There has been much interest in ‘junk DNA’, non-coding areas of our DNA are far from being without function. DNA has two basic categories of nitrogenous bases: the purines (adenine [A] and guanine [G]), and the pyrimidines (cytosine [C], thymine [T], and  no uracil [U]),  while RNA contains only A, G, C, and U (no T).  The Watson-Crick proposal set the path of molecular biology for decades into the 21st century, culminating in the Human Genome Project.

There is no uncertainty about the importance of “Junk DNA”.  It is both an evolutionary remnant, and it has a role in cell regulation.  Further, the role of histones in their relationship the oligonucleotide sequences is not understood.  We now have a large output of research on noncoding RNA, including siRNA, miRNA, and others with roles other than transcription. This requires major revision of our model of cell regulatory processes.  The classic model is solely transcriptional.

  • DNA-> RNA-> Amino Acid in a protein.

Redrawn we have

  • DNA-> RNA-> DNA and
  • DNA->RNA-> protein-> DNA.

Neverthess, there were unrelated discoveries that took on huge importance.  For example, since the 1920s, the work of Warburg and Meyerhoff, followed by that of Krebs, Kaplan, Chance, and others built a solid foundation in the knowledge of enzymes, coenzymes, adenine and pyridine nucleotides, and metabolic pathways, not to mention the importance of Fe3+, Cu2+, Zn2+, and other metal cofactors.  Of huge importance was the work of Jacob, Monod and Changeux, and the effects of cooperativity in allosteric systems and of repulsion in tertiary structure of proteins related to hydrophobic and hydrophilic interactions, which involves the effect of one ligand on the binding or catalysis of another,  demonstrated by the end-product inhibition of the enzyme, L-threonine deaminase (Changeux 1961), L-isoleucine, which differs sterically from the reactant, L-threonine whereby the former could inhibit the enzyme without competing with the latter. The current view based on a variety of measurements (e.g., NMR, FRET, and single molecule studies) is a ‘‘dynamic’’ proposal by Cooper and Dryden (1984) that the distribution around the average structure changes in allostery affects the subsequent (binding) affinity at a distant site.

What else do we have to consider?  The measurement of free radicals has increased awareness of radical-induced impairment of the oxidative/antioxidative balance, essential for an understanding of disease progression.  Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Various studies have confirmed that metals activate signaling pathways and the carcinogenic effect of metals has been related to activation of mainly redox sensitive transcription factors, involving NF-kappaB, AP-1 and p53.

I have provided mechanisms explanatory for regulation of the cell that go beyond the classic model of metabolic pathways associated with the cytoplasm, mitochondria, endoplasmic reticulum, and lysosome, such as, the cell death pathways, expressed in apoptosis and repair.  Nevertheless, there is still a missing part of this discussion that considers the time and space interactions of the cell, cellular cytoskeleton and extracellular and intracellular substrate interactions in the immediate environment.

There is heterogeneity among cancer cells of expected identical type, which would be consistent with differences in phenotypic expression, aligned with epigenetics.  There is also heterogeneity in the immediate interstices between cancer cells.  Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. In the case of breast cancer, there is interaction with estrogen , and we refer to an androgen-unresponsive prostate cancer.

Finally,  the interaction between enzyme and substrates may be conditionally unidirectional in defining the activity within the cell.  The activity of the cell is dynamically interacting and at high rates of activity.  In a study of the pyruvate kinase (PK) reaction the catalytic activity of the PK reaction was reversed to the thermodynamically unfavorable direction in a muscle preparation by a specific inhibitor. Experiments found that in there were differences in the active form of pyruvate kinase that were clearly related to the environmental condition of the assay – glycolitic or glyconeogenic. The conformational changes indicated by differential regulatory response were used to present a dynamic conformational model functioning at the active site of the enzyme. In the model, the interaction of the enzyme active site with its substrates is described concluding that induced increase in the vibrational energy levels of the active site decreases the energetic barrier for substrate induced changes at the site. Another example is the inhibition of H4 lactate dehydrogenase, but not the M4, by high concentrations of pyruvate. An investigation of the inhibition revealed that a covalent bond was formed between the nicotinamide ring of the NAD+ and the enol form of pyruvate.  The isoenzymes of isocitrate dehydrogenase, IDH1 and IDH2 mutations occur in gliomas and in acute myeloid leukemias with normal karyotype. IDH1 and IDH2 mutations are remarkably specific to codons that encode conserved functionally important arginines in the active site of each enzyme. In this case, there is steric hindrance by Asp279 where the isocitrate substrate normally forms hydrogen bonds with Ser94.

Personalized medicine has been largely viewed from a lens of genomics.  But genomics is only the reading frame.  The living activities of cell processes are dynamic and occur at rapid rates.  We have to keep in mind that personalized in reference to genotype is not complete without reconciliation of phenotype, which is the reference to expressed differences in outcomes.

 

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Breast Cancer Extratumor Microenvironment has Effect on Progression

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Tumor Microenvironment Diversity Predicts Breast Cancer Outcomes

GEN News Highlights   Feb 17, 2016   http://www.genengnews.com/gen-news-highlights/tumor-microenvironment-diversity-predicts-breast-cancer-outcomes/81252378/

 

Intratumor heterogeneity, it is known, can complicate cancer treatments. Now it appears the same may be true of tumor microenvironment heterogeneity. According to a new study from the Institute of Cancer Research (ICR), London, breast cancers that develop within an “ecologically diverse” breast cancer microenvironment are particularly likely to progress and lead to death.

The study took an unusual approach: It combined ecological scoring methods with genome-wide profiling data. This approach, besides showing clinical utility in the evaluation of breast cancer outcomes, demonstrated that even so contextual a discipline as genomics can benefit from being placed within a larger context. In this case, the context is essentially Darwinian, albeit at a small scale.

Natural selection is typically studied at the level of ecosystems consisting of animals and plants. In the current study, however, it was assessed at the level of the tumor microenvironment, which consists of cancer cells, immune system lymphocytes, and stromal cells.

The ICR scientists, led by Yinyin Yuan, Ph.D., presented their work February 16 in the journal PLoS Medicine, in an article entitled “Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis.” The article describes how the scientists developed a tumor ecosystem diversity index (EDI), a scoring system that indicates the degree of microenvironmental heterogeneity along three spatial dimensions in solid tumors. EDI scores take account of “fully automated histology image analysis coupled with statistical measures commonly used in ecology.”

“[EDI] was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set,” wrote the authors. “In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types.”

By using the EDI, the ICR team was able to identify several particularly aggressive subgroups of breast cancer. In fact, the EDI was a stronger predictor of survival than many established markers for the disease.

The ICR researchers also looked at the EDI in addition to genetic factors. For example, the researchers found that the prognostic value of EDI was enhanced with the addition of TP53 mutation status. By integrating EDI data and genome-wide profiling data, the researchers identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors. These tumors, which showed high microenvironmental diversity, substratified patients into poor prognostic groups.

“Our findings show that mathematical models of ecological diversity can spot more aggressive cancers,” said Dr. Yuan. “By analyzing images of the environment around a tumor based on Darwinian natural selection principles, we can predict survival in some breast cancer types even more effectively than many of the measures used now in the clinic.

“In the future, we hope that by combining cell diversity scores with other factors that influence cancer survival, such as genetics and tumor size, we will be able to tell apart patients with more or less aggressive disease so we can identify those who might need different types of treatment.”

“This ingenious study…teaches us a valuable lesson,” added Paul Workman, Ph.D., chief executive of the ICR. “[We] should always remember that cancer cells are not developing and growing in isolation, but are part of a complex ecosystem that involves normal human cells, too. By better understanding these ecosystems, we aim to create new ways to diagnose, monitor and treat cancer.”

 

Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis

 

Background

The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.

Methods and Findings

We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10−4, hazard ratio = 1.47, 95% CI 1.17–1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26–2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.

Conclusions

To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.

Background

The human body contains millions of cells, all of which grow, divide, and die in an orderly fashion to build tissues during early life and to replace worn-out or dying cells and repair injuries during adult life. Sometimes, however, normal cells acquire genetic changes (mutations) that allow them to divide uncontrollably and to move around the body (metastasize), resulting in cancer. Because any cell in the body can acquire the mutations needed for cancer development, there are many types of cancer. For example, breast cancer, the most common cancer in women, begins when the cells in the breast that normally make milk become altered. Moreover, different types of cancer progress and evolve differently—some cancers grow quickly and kill their “host” soon after diagnosis, whereas others can be successfully treated with drugs, surgery, or radiotherapy. The behavior of individual cancers depends both on the characteristics of the cancer cells within the tumor and on the interactions between the cancer cells and the normal stromal cells (the connective tissue cells of organs) and other cells (for example, immune cells) that surround and feed cancer cells (the tumor microenvironment).

Why Was This Study Done?

Although recent studies have highlighted the importance of the tumor microenvironment for disease-related outcomes, little is known about how the heterogeneity of the tumor microenvironment—the diversity of non-cancer cells within the tumor—affects outcomes. Mathematical modeling suggests that tumors with heterogeneous and homogeneous microenvironments have different growth patterns and that heterogeneous microenvironments are more likely to be associated with aggressive cancers than homogenous microenvironments. However, the lack of methods to quantify the spatial variability and cellular composition across solid tumors has prevented confirmation of these predictions. Here, the researchers develop a computational system for quantifying microenvironmental heterogeneity in breast cancer based on tumor morphology (shape and form) in histological sections (tissue samples taken from tumors that are examined microscopically). They then use this system to analyze the associations between clinical outcomes, molecular changes, and microenvironmental heterogeneity in breast cancer.

What Did the Researchers Do and Find?

The researchers used automated image analysis and statistical analysis to develop the ecosystem diversity index (EDI), a numerical measure of microenvironmental heterogeneity in solid tumors. They compared the EDI with prognosis (likely outcome), key mutations, genome-wide copy number (tumor cells often contain abnormal numbers of copies of specific genes), and expression profiling data (the expression of several key proteins is altered in tumors) in a test set of 510 samples from patients with breast cancer and in a validation set of 516 additional samples. Among high-grade breast cancers (grade 3 cancers; the grade of a cancer indicates what the cells look like; high-grade breast cancers have a poor prognosis), but not among low-grade breast cancers (grades 1 and 2), a high EDI (high microenvironmental heterogeneity) was associated with a poor prognosis. Specifically, patients with grade 3 tumors and a high EDI had a ten-year disease-specific survival rate of 51%, whereas the remaining patients with grade 3 tumors had a ten-year survival rate of 70%. Notably, the combination of a high EDI with specific DNA alterations—mutations in a gene called TP53 and loss of genes on Chromosomes 4p14 and 5q13—improved the accuracy of prognosis among patients with grade 3 breast cancer and stratified them into subgroups with disease-specific five-year survival rates of 35%, 9%, and 32%, respectively.

What Do These Findings Mean?

These findings establish a method for measuring the spatial heterogeneity of the microenvironment of solid tumors and suggest that the measurement of tumor microenvironmental heterogeneity can be coupled with information about genomic alterations to provide an accurate way to predict outcomes among patients with high-grade breast cancer. The association between EDI, specific genomic alterations, and outcomes needs to be confirmed in additional patients. However, these findings suggest that microenvironmental heterogeneity might provide an additional biomarker to help clinicians identify those patients with advanced breast cancer who have a particularly bad prognosis. The ability to identify these patients is important because it will help clinicians target aggressive treatments to individuals with a poor prognosis and avoid the overtreatment of patients whose prognosis is more favorable. Finally, and more generally, these findings describe a new way to investigate the interactions between the tumor microenvironment and genomic alterations in cancer cells.

Additional Information

This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001961.

Citation: Natrajan R, Sailem H, Mardakheh FK, Arias Garcia M, Tape CJ, Dowsett M, et al. (2016) Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis.
PLoS Med 13(2): e1001961.     http://dx.doi.org:/10.1371/journal.pmed.1001961
Fig 1. In silico tumor dissection pipeline for quantifying spatial diversity in the tumor ecosystem.
Fig 1. In silico tumor dissection pipeline for quantifying spatial diversity in the tumor ecosystem. (A) Flow diagram depicting the overall study design. (B) Schematic of our pipeline for quantifying spatial diversity in pathological samples. H&E sections are morphologically classified and divided into regions to be spatially scored. The number of clusters k in the regional scores is indicative of the number of sub-populations of cell types in the tumor regions. (C) Examples of tumor regions with low and high diversity scores using the Shannon diversity index, accounting for cancer cells (outlined in green), lymphocytes (blue), and stromal cells (red). Cell classification is automated by image analysis. (D) The 3-D landscape of cell diversity scores on an example H&E section; the x- and y-axes are the geometric axes of the image, and the z-axis is cell diversity computed on a region-by-region basis. (E) The distribution of regional scores in a tumor from the METABRIC study with two regional clusters identified using Gaussian mixture clustering (grey shading: histogram; dashed black line: density; solid black lines: mixture components/clusters).
Fig 2. Application of EDI to 1,026 breast tumors from the METABRIC study.
Fig 2. Application of EDI to 1,026 breast tumors from the METABRIC study. (A) The frequencies of EDI scores in breast tumors. (B) H&E staining, distribution of classified cells (green: cancer; blue: lymphocyte; red: stromal cells), and the heatmap of regional diversity scores for a tumor with the highest EDI score (EDI = 5). (C) Representative regions from each of the clusters k1–k5 are shown in a tumor with EDI = 5, with cluster k1 having the lowest diversity score and k5 the highest. By mapping regional clusters to the H&E image, we can begin to interpret these clusters with different cell diversity. We observed predominantly cancer cells in k1, increasingly more stromal cells and ductal in situ carcinoma cells (DCIS) in k2, and a vessel in k3. Cluster k4 features extensive stromal lymphocytes between ductal in situ carcinoma components, while k5 shows tumor-infiltrating lymphocytes (TIL) associated with invasive carcinoma cells.
Fig 3. Reproducibility, stability, and independence of the EDI-high group in 507 grade 3 breast tumors.
Fig 3. Reproducibility, stability, and independence of the EDI-high group in 507 grade 3 breast tumors. (A) Kaplan–Meier curves of disease-specific survival to illustrate the prognosis of EDI-high samples compared to other grade 3 samples in two independent patient cohorts. Shown below the graph are the number of patients (the number of disease-specific events) per group for EDI-low (grey) and EDI-high (red). (B) Agreement of the EDI subtyping between 100% data and resampling with progressively fewer tumor regions in 200 repeats. (C) Distribution of known subtypes in grade 3 tumors stratified by EDI; asterisks mark subtypes enriched in the EDI-high group. (D) Kaplan–Meier curves illustrating the duration of disease-specific survival according to tumor size (left) and improvement of stratification with the addition of EDI information (right).
Accumulating evidence suggests that the interactions of cancer cells and stromal cells within their microenvironment govern disease progression, metastasis, and, ultimately, the evolution of therapeutic resistance [1–3]. Recent reports have highlighted the significance of the contribution of stromal gene expression and morphological structure as powerful prognostic determinants for a number of tumor types, emphasizing the importance of the tumor microenvironment in disease-related outcomes [4–7]. In breast cancer, a number of studies have demonstrated the prognostic correlation of individual cell types, including the immune cell infiltrate that predicts response to therapy [8–10], and the high percentage of tumor stroma that predicts poor prognosis in triple-negative disease but good prognosis in estrogen receptor (ER)–positive disease [11,12]. Nevertheless, different types of cells coexist with varying degrees of heterogeneity within a tumor. This fundamental feature of human tumors and the combinatorial effects of cell types have been largely ignored, and the collective implications for clinical outcome remain elusive. Consistent observations from mathematical models have highlighted that tumors with diverse microenvironments show growth patterns dramatically different from those of tumors with homogeneous environments [13] and are more likely to be associated with aggressive cancer phenotypes [2] that select for cell migration and eventual metastasis by allowing cancer cells to evolve more rapidly [14]. These observations highlight the need to understand the collective physiological characteristics and heterogeneity of tumor microenvironments. However, there is a lack of methods to quantify the high spatial variability and diverse cellular composition across different solid tumors. Moreover, the interplay of genomic alterations in cancer cells and microenvironmental heterogeneity and its subsequent role in treatment response have not been explored. Our aims were (i) to develop a computational system for quantifying microenvironmental heterogeneity based on tumor morphology in routine histological sections, (ii) to define the clinical implications of microenvironmental heterogeneity, and (iii) to integrate this histologybased index with RNA gene expression and DNA copy number profiling data to identify molecular changes associated with microenvironmental heterogeneity.
The Ecosystem Diversity Index To characterize the tumor ecosystem based on cell compositions, we developed a new index to be used in conjunction with our image analysis tool [16]. First, we used our automated morphological classification method [16] to identify and classify cells into cancer, lymphocyte, or stromal cell classes in H&E sections (Fig 1B). We next divided sections into smaller spatial regions and quantified the diversity of the tumor ecosystem in a tumor region j using the Shannon diversity index: dj ¼ Sm i pi logpi ; ð1Þ where m is the number of cell types and pi is the proportion of the ith cell type (Fig 1B and 1C). A high value of the Shannon diversity index dj reports a heterogeneous environment populated by many cell types, whilst a low value indicates a homogeneous environment (Fig 1C). Compared to other methods such as the Simpson index, the Shannon diversity index accounts for rare species and, hence, is less dominated by main species [17]. Subsequently, we derived the ecosystem diversity index (EDI) by applying unsupervised clustering that identifies the optimum number of clusters in the dataset in an unbiased manner, in order to group tumor regions and quantify the degree of spatial heterogeneity. Let D = d1,d2,…,dn be the Shannon index for n regions in a tumor. We used Gaussian mixture models to fit data D: D SK k¼1okNðmk; s2 kÞ: ð2Þ where μk, ,s2 k, and ωk are the mean, variance, and weight of a Gaussian distribution k, and K is the number of clusters. The Bayesian information criterion was then used to select the best number of clusters K [18]. We used K = 1–5 as the range of K to avoid small EDI groups (S1 Text). The final value of K thus is a measurement of heterogeneity and the score of EDI for a tumor.
Fig 5. The relationship between ecological heterogeneity and cancer genomic aberrations in 507 grade 3 tumors. (A) Genome-wide copy number aberrations in grade 3 breast tumors and genomic coordinates of genes with copy number aberrations enriched in the EDI-high group. Lengths of black lines denote level of enrichment significance with copy number gains (above the horizontal line) or losses (below the horizontal line). (B) Kaplan–Meier curves illustrating the duration of disease-specific survival in grade 3 breast cancer patients according to copy number loss of the 4p14 region (left) and the EDI-high group with additional information of 4p14 copy number loss (right). (C) Kaplan–Meier curves illustrating the duration of disease-specific survival according to copy number loss of the 5q13 region (left) and the EDI-high group with additional information of 5q13 copy number loss (right).
This study has a number of limitations. The motivation for our computational development was to use a data-driven model and measure the degree of spatial heterogeneity in tumor pathological specimens. In this model, only three major cell types in breast tumors were considered. Further sub-classification of the different types of stromal and immune cells by immunohistochemistry may add additional discriminatory value to our model. For dissecting spatial heterogeneity, we chose to use square regions with equal sizes. We found that EDI was correlated with the size of the region chosen for calculation of the Shannon diversity index, and as such the spatial heterogeneity is scale dependent. This phenomenon has been well described in a number of studies in ecology that show that a scale needs to be chosen that is appropriate for the ecological process under study [38,39], further highlighting the analogy between tumor studies and ecology. Similar to the recent observation that breast cancer subclonal heterogeneity is correlated with tumor size [35], we also found an association between microenvironmental heterogeneity and tumor size; hence, EDI may have more limited value in smaller tumors. However, small tumors were present in the EDI-high group, and addition of EDI within tumors grouped by size further stratified their prognosis. We found that EDI was prognostic only in grade 3 tumors in our study, which could be a limitation, given the possible discordance in grading between pathologists.
The identification of additional biomarkers in subgroups of patients that identify them as high risk is important for patient management and to avoid overtreatment for low-risk patients. We envision that the use of our measure of microenvironment heterogeneity, together with key genomic alterations, will enable the diagnosis of patients at very high risk of relapse and facilitate the enrollment of these patients into additional clinical trials for novel therapies or treatment intensification. Our novel computational approach provides a fully automated tool that is relatively easy to implement. Integration of this measure with genomic profiling provides additional prognostic information independent of known clinical parameters. The results of this study highlight the possibility of a grade-3-specific prognostic tool that may aid in further classification of high-grade breast cancer patients beyond standard assays such as ER and HER2 status.

 

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