Advertisements
Feeds:
Posts
Comments

Archive for the ‘Population genetics’ Category


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

 

A mutated gene called RAS gives rise to a signalling protein Ral which is involved in tumour growth in the bladder. Many researchers tried and failed to target and stop this wayward gene. Signalling proteins such as Ral usually shift between active and inactive states.

 

So, researchers next tried to stop Ral to get into active state. In inacvtive state Ral exposes a pocket which gets closed when active. After five years, the researchers found a small molecule dubbed BQU57 that can wedge itself into the pocket to prevent Ral from closing and becoming active. Now, BQU57 has been licensed for further development.

 

Researchers have a growing genetic data on bladder cancer, some of which threaten to overturn the supposed causes of bladder cancer. Genetics has also allowed bladder cancer to be reclassified from two categories into five distinct subtypes, each with different characteristics and weak spots. All these advances bode well for drug development and for improved diagnosis and prognosis.

 

Among the groups studying the genetics of bladder cancer are two large international teams: Uromol (named for urology and molecular biology), which is based at Aarhus University Hospital in Denmark, and The Cancer Genome Atlas (TCGA), based at institutions in Texas and Boston. Each team tackled a different type of cancer, based on the traditional classification of whether or not a tumour has grown into the muscle wall of the bladder. Uromol worked on the more common, earlier form, non-muscle-invasive bladder cancer, whereas TCGA is looking at muscle-invasive bladder cancer, which has a lower survival rate.

 

The Uromol team sought to identify people whose non-invasive tumours might return after treatment, becoming invasive or even metastatic. Bladder cancer has a high risk of recurrence, so people whose non-invasive cancer has been treated need to be monitored for many years, undergoing cystoscopy every few months. They looked for predictive genetic footprints in the transcriptome of the cancer, which contains all of a cell’s RNA and can tell researchers which genes are turned on or off.

 

They found three subgroups with distinct basal and luminal features, as proposed by other groups, each with different clinical outcomes in early-stage bladder cancer. These features sort bladder cancer into genetic categories that can help predict whether the cancer will return. The researchers also identified mutations that are linked to tumour progression. Mutations in the so-called APOBEC genes, which code for enzymes that modify RNA or DNA molecules. This effect could lead to cancer and cause it to be aggressive.

 

The second major research group, TCGA, led by the National Cancer Institute and the National Human Genome Research Institute, that involves thousands of researchers across USA. The project has already mapped genomic changes in 33 cancer types, including breast, skin and lung cancers. The TCGA researchers, who study muscle-invasive bladder cancer, have looked at tumours that were already identified as fast-growing and invasive.

 

The work by Uromol, TCGA and other labs has provided a clearer view of the genetic landscape of early- and late-stage bladder cancer. There are five subtypes for the muscle-invasive form: luminal, luminal–papillary, luminal–infiltrated, basal–squamous, and neuronal, each of which is genetically distinct and might require different therapeutic approaches.

 

Bladder cancer has the third-highest mutation rate of any cancer, behind only lung cancer and melanoma. The TCGA team has confirmed Uromol research showing that most bladder-cancer mutations occur in the APOBEC genes. It is not yet clear why APOBEC mutations are so common in bladder cancer, but studies of the mutations have yielded one startling implication. The APOBEC enzyme causes mutations early during the development of bladder cancer, and independent of cigarette smoke or other known exposures.

 

The TCGA researchers found a subset of bladder-cancer patients, those with the greatest number of APOBEC mutations, had an extremely high five-year survival rate of about 75%. Other patients with fewer APOBEC mutations fared less well which is pretty surprising.

 

This detailed knowledge of bladder-cancer genetics may help to pinpoint the specific vulnerabilities of cancer cells in different people. Over the past decade, Broad Institute researchers have identified more than 760 genes that cancer needs to grow and survive. Their genetic map might take another ten years to finish, but it will list every genetic vulnerability that can be exploited. The goal of cancer precision medicine is to take the patient’s tumour and decode the genetics, so the clinician can make a decision based on that information.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/29117162

 

https://www.ncbi.nlm.nih.gov/pubmed/27321955

 

https://www.ncbi.nlm.nih.gov/pubmed/28583312

 

https://www.ncbi.nlm.nih.gov/pubmed/24476821

 

https://www.ncbi.nlm.nih.gov/pubmed/28988769

 

https://www.ncbi.nlm.nih.gov/pubmed/28753430

 

Advertisements

Read Full Post »


Decline in Sperm Count – Epigenetics, Well-being and the Significance for Population Evolution and Demography

 

Dr. Marc Feldman, Expert Opinion on the significance of Sperm Count Decline on the Future of Population Evolution and Demography

Dr. Sudipta Saha, Effects of Sperm Quality and Quantity on Human Reproduction

Dr. Aviva Lev-Ari, Psycho-Social Effects of Poverty, Unemployment and Epigenetics on Male Well-being, Physiological Conditions affecting Sperm Quality and Quantity

 

Recent studies concluded via rigorous and comprehensive analysis found that Sperm Count (SC) declined 52.4% between 1973 and 2011 among unselected men from western countries, with no evidence of a ‘leveling off’ in recent years. Declining mean SC implies that an increasing proportion of men have sperm counts below any given threshold for sub-fertility or infertility. The high proportion of men from western countries with concentration below 40 million/ml is particularly concerning given the evidence that SC below this threshold is associated with a decreased monthly probability of conception.

1.Temporal trends in sperm count: a systematic review and meta-regression analysis 

Hagai Levine, Niels Jørgensen, Anderson Martino‐Andrade, Jaime Mendiola, Dan Weksler-Derri, Irina Mindlis, Rachel Pinotti, Shanna H SwanHuman Reproduction Update, July 25, 2017, doi:10.1093/humupd/dmx022.

Link: https://academic.oup.com/humupd/article-lookup/doi/10.1093/humupd/dmx022.

2. Sperm Counts Are Declining Among Western Men – Interview with Dr. Hagai Levine

https://news.afhu.org/news/sperm-counts-are-declining-among-western-men?utm_source=Master+List&utm_campaign=dca529d919-EMAIL_CAMPAIGN_2017_07_27&utm_medium=email&utm_term=0_343e19a421-dca529d919-92801633

3. Trends in Sperm Count – Biological Reproduction Observations

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

4. Long, mysterious strips of RNA contribute to low sperm count – Long non-coding RNAs can be added to the group of possible non-structural effects, possibly epigenetic, that might regulate sperm counts.

http://casemed.case.edu/cwrumed360/news-releases/release.cfm?news_id=689

https://scienmag.com/long-mysterious-strips-of-rna-contribute-to-low-sperm-count/

Dynamic expression of long non-coding RNAs reveals their potential roles in spermatogenesis and fertility

Published: 29 July 2017
Thus, we postulated that some lncRNAs may also impact mammalian spermatogenesis and fertility. In this study, we identified a dynamic expression pattern of lncRNAs during murine spermatogenesis. Importantly, we identified a subset of lncRNAs and very few mRNAs that appear to escape meiotic sex chromosome inactivation (MSCI), an epigenetic process that leads to the silencing of the X- and Y-chromosomes at the pachytene stage of meiosis. Further, some of these lncRNAs and mRNAs show strong testis expression pattern suggesting that they may play key roles in spermatogenesis. Lastly, we generated a mouse knock out of one X-linked lncRNA, Tslrn1 (testis-specific long non-coding RNA 1), and found that males carrying a Tslrn1 deletion displayed normal fertility but a significant reduction in spermatozoa. Our findings demonstrate that dysregulation of specific mammalian lncRNAs is a novel mechanism of low sperm count or infertility, thus potentially providing new biomarkers and therapeutic strategies.

This article presents two perspectives on the potential effects of Sperm Count decline.

One Perspective identifies Epigenetics and male well-being conditions

  1. as a potential explanation to the Sperm Count decline, and
  2. as evidence for decline in White male longevity in certain geographies in the US since the mid 80s.

The other Perspective, evaluates if Sperm Count Decline would have or would not have a significant long term effects on Population Evolution and Demography.

The Voice of Prof. Marc Feldman, Stanford University – Long term significance of Sperm Count Decline on Population Evolution and Demography

Poor sperm count appears to be associated with such demographic statistics as life expectancy (1), infertility (2), and morbidity (3,4). The meta-analysis by Levine et al. (5) focuses on the change in sperm count of men from North America, Europe, Australia, and New Zealand, and shows a more than 50% decline between 1973 and 2011. Although there is no analysis of potential environmental or lifestyle factors that could contribute to the estimated decline in sperm count, Levine et al. speculate that this decline could be a signal for other negative changes in men’s health.

Because this study focuses mainly on Western men, this remarkable decline in sperm count is difficult to associate with any change in actual fertility, that is, number of children born per woman. The total fertility rate in Europe, especially Italy, Spain, and Germany, has slowly declined, but age at first marriage has increased at the same time, and this increase may be more due to economic factors than physiological changes.

Included in Levine et al.’s analysis was a set of data from “Other” countries from South America, Asia, and Africa. Sperm count in men from these countries did not show significant trends, which is interesting because there have been strong fertility declines in Asia and Africa over the same period, with corresponding increases in life expectancy (once HIV is accounted for).

What can we say about the evolutionary consequences for humans of this decrease? The answer depends on the minimal number of sperm/ml/year that would be required to maintain fertility (per woman) at replacement level, say 2.1 children, over a woman’s lifetime. Given the smaller number of ova produced per woman, a change in the ovulation statistics of women would be likely to play a larger role in the total fertility rate than the number of sperm/ejaculate/man. In other words, sperm count alone, absent other effects on mortality during male reproductive years, is unlikely to tell us much about human evolution.

Further, the major declines in fertility over the 38-year period covered by Levine et al. occurred in China, India, and Japan. Chinese fertility has declined to less than 1.5 children per woman, and in Japan it has also been well below 1.5 for some time. These declines have been due to national policies and economic changes, and are therefore unlikely to signal genetic changes that would have evolutionary ramifications. It is more likely that cultural changes will continue to be the main drivers of fertility change.

The fastest growing human populations are in the Muslim world, where fertility control is not nearly as widely practiced as in the West or Asia. If this pattern were to continue for a few more generations, the cultural evolutionary impact would swamp any effects of potentially declining sperm count.

On the other hand, if the decline in sperm count were to be discovered to be associated with genetic and/or epigenetic phenotypic effects on fetuses, newborns, or pre-reproductive humans, for example, due to stress or obesity, then there would be cause to worry about long-term evolutionary problems. As Levine et al. remark, “decline in sperm count might be considered as a ‘canary in the coal mine’ for male health across the lifespan”. But to date, there is little evidence that the evolutionary trajectory of humans constitutes such a “coal mine”.

References

  1. Jensen TK, Jacobsen R, Christensen K, Nielsen NC, Bostofte E. 2009. Good semen quality and life expectancy: a cohort study of 43,277 men. Am J Epidemiol 170: 559-565.
  2. Eisenberg ML, Li S, Behr B, Cullen MR, Galusha D, Lamb DJ, Lipshultz LI. 2014. Semen quality, infertility and mortality in the USA. Hum Reprod 29: 1567-1574.
  3. Eisenberg ML, Li S, Cullen MR, Baker LC. 2016. Increased risk of incident chronic medical conditions in infertile men: analysis of United States claims data. Fertil Steril 105: 629-636.
  4. Latif T, Kold Jensen T, Mehlsen J, Holmboe SA, Brinth L, Pors K, Skouby SO, Jorgensen N, Lindahl-Jacobsen R. Semen quality is a predictor of subsequent morbidity. A Danish cohort study of 4,712 men with long-term follow-up. Am J Epidemiol. Doi: 10.1093/aje/kwx067. (Epub ahead of print]
  5. Levine H, Jorgensen N, Martino-Andrade A, Mendiola J, Weksler-Derri D, Mindlis I, Pinotti R, Swan SH. 2017. Temporal trends in sperm count: a systematic review and meta-regression analysis. Hum Reprod Update pp. 1-14. Doi: 10.1093/humupd/dmx022.

SOURCE

From: Marcus W Feldman <mfeldman@stanford.edu>

Date: Monday, July 31, 2017 at 8:10 PM

To: Aviva Lev-Ari <aviva.lev-ari@comcast.net>

Subject: Fwd: text of sperm count essay

Psycho-Social Effects of Poverty, Unemployment and Epigenetics on Male Well-being, Physiological Conditions as POTENTIAL effects on Sperm Quality and Quantity and Evidence of its effects on Male Longevity

The Voice of Carol GrahamSergio Pinto, and John Juneau II , Monday, July 24, 2017, Report from the Brookings Institute

  1. The IMPACT of Well-being, Stress induced by Worry, Pain, Perception of Hope related to Employment and Lack of employment on deterioration of Physiological Conditions as evidence by Decrease Longevity

  2. Epigenetics and Environmental Factors

The geography of desperation in America

Carol GrahamSergio Pinto, and John Juneau II Monday, July 24, 2017, Report from the Brookings Institute

In recent work based on our well-being metrics in the Gallup polls and on the mortality data from the Centers for Disease Control and Prevention, we find a robust association between lack of hope (and high levels of worry) among poor whites and the premature mortality rates, both at the individual and metropolitan statistical area (MSA) levels. Yet we also find important differences across places. Places come with different economic structures and identities, community traits, physical environments and much more. In the maps below, we provide a visual picture of the differences in in hope for the future, worry, and pain across race-income cohorts across U.S. states. We attempted to isolate the specific role of place, controlling for economic, socio-demographic, and other variables.

One surprise is the low level of optimism and high level of worry in the minority dense and generally “blue” state of California, and high levels of pain and worry in the equally minority dense and “blue” states of New York and Massachusetts. High levels of income inequality in these states may explain these patterns, as may the nature of jobs that poor minorities hold.

We cannot answer many questions at this point. What is it about the state of Washington, for example, that is so bad for minorities across the board? Why is Florida so much better for poor whites than it is for poor minorities? Why is Nevada “good” for poor white optimism but terrible for worry for the same group? One potential issue—which will enter into our future analysis—is racial segregation across places. We hope that the differences that we have found will provoke future exploration. Readers of this piece may have some contributions of their own as they click through the various maps, and we welcome their input. Better understanding the role of place in the “crisis” of despair facing our country is essential to finding viable solutions, as economic explanations, while important, alone are not enough.

https://www.brookings.edu/research/the-geography-of-desperation-in-america/?utm_medium=social&utm_source=facebook&utm_campaign=global

 

Read Full Post »


Finding the Actions That Alter Evolution

The biologist Marcus Feldman creates mathematical models that reveal how cultural traditions can affect the evolution of a species.

By Elizabeth Svoboda

January 5, 2017

In a commentary in Nature, you and your co-authors wrote, “We hold that organisms are constructed in development, not simply ‘programmed’ to develop by genes.” What does “constructed in development” mean?

It means there’s an interaction between the subject and the environment. The idea of a genetic blueprint is not tenable in light of all that is now known about how all sorts of environmental contingencies affect traits. For many animals it’s like that. Even plants — the same plant that is genetically identical, if you put it in this environment, it’s going to look totally different from if you put it in that environment.

We now have a better picture of the regulatory process on genes. Epigenetics changes the landscape in genetics because it’s not only the pure DNA sequence which influences what’s going on at the level of proteins and enzymes. There’s this whole other stuff, the other 95 percent of the genome, that acts like rheostats — you slide this thing up and down, you get more or less of this protein. It’s a critical thing in how much of this protein is going to be made. It’s interesting to think about the way in which cultural phenomena, which we used to think were things by themselves, can have this effect on how much messenger RNA is made, and therefore on many aspects of gene regulation.

Article to review and VIEW VIDEO

https://www.quantamagazine.org/20170105-marcus-feldman-interview-culture-and-evolution/

 

ABOUT QUANTA

Quanta Magazine’s mission is to enhance public understanding of research developments in mathematics and the physical and life sciences. Quanta articles do not necessarily represent the views of the Simons Foundation. Learn more

Read Full Post »


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

 

MicroRNAs (miRNAs) are a group of small non-coding RNA molecules that play a major role in posttranscriptional regulation of gene expression and are expressed in an organ-specific manner. One miRNA can potentially regulate the expression of several genes, depending on cell type and differentiation stage. They control every cellular process and their altered regulation is involved in human diseases. miRNAs are differentially expressed in the male and female gonads and have an organ-specific reproductive function. Exerting their affect through germ cells and gonadal somatic cells, miRNAs regulate key proteins necessary for gonad development. The role of miRNAs in the testes is only starting to emerge though they have been shown to be required for adequate spermatogenesis. In the ovary, miRNAs play a fundamental role in follicles’ assembly, growth, differentiation, and ovulation.

 

Deciphering the underlying causes of idiopathic male infertility is one of the main challenges in reproductive medicine. This is especially relevant in infertile patients displaying normal seminal parameters and no urogenital or genetic abnormalities. In these cases, the search for additional sperm biomarkers is of high interest. This study was aimed to determine the implications of the sperm miRNA expression profiles in the reproductive capacity of normozoospermic infertile individuals. The expression levels of 736 miRNAs were evaluated in spermatozoa from normozoospermic infertile males and normozoospermic fertile males analyzed under the same conditions. 57 miRNAs were differentially expressed between populations; 20 of them was regulated by a host gene promoter that in three cases comprised genes involved in fertility. The predicted targets of the differentially expressed miRNAs unveiled a significant enrichment of biological processes related to embryonic morphogenesis and chromatin modification. Normozoospermic infertile individuals exhibit a specific sperm miRNA expression profile clearly differentiated from normozoospermic fertile individuals. This miRNA cargo has potential implications in the individuals’ reproductive competence.

 

Circulating or “extracellular” miRNAs detected in biological fluids, could be used as potential diagnostic and prognostic biomarkers of several disease, such as cancer, gynecological and pregnancy disorders. However, their contributions in female infertility and in vitro fertilization (IVF) remain unknown. Polycystic ovary syndrome (PCOS) is a frequent endocrine disorder in women. PCOS is associated with altered features of androgen metabolism, increased insulin resistance and impaired fertility. Furthermore, PCOS, being a syndrome diagnosis, is heterogeneous and characterized by polycystic ovaries, chronic anovulation and evidence of hyperandrogenism, as well as being associated with chronic low-grade inflammation and an increased life time risk of type 2 diabetes. Altered miRNA levels have been associated with diabetes, insulin resistance, inflammation and various cancers. Studies have shown that circulating miRNAs are present in whole blood, serum, plasma and the follicular fluid of PCOS patients and that these might serve as potential biomarkers and a new approach for the diagnosis of PCOS. Presence of miRNA in mammalian follicular fluid has been demonstrated to be enclosed within microvesicles and exosomes or they can also be associated to protein complexes. The presence of microvesicles and exosomes carrying microRNAs in follicular fluid could represent an alternative mechanism of autocrine and paracrine communication inside the ovarian follicle. The investigation of the expression profiles of five circulating miRNAs (let-7b, miR-29a, miR-30a, miR-140 and miR-320a) in human follicular fluid from women with normal ovarian reserve and with polycystic ovary syndrome (PCOS) and their ability to predict IVF outcomes showed that these miRNAs could provide new helpful biomarkers to facilitate personalized medical care for oocyte quality in ART (Assisted Reproductive Treatment) and during IVF (In Vitro Fertilization).

 

References:

 

http://link.springer.com/chapter/10.1007%2F978-3-319-31973-5_12

 

http://onlinelibrary.wiley.com/doi/10.1111/andr.12276/abstract;jsessionid=F805A89DCC94BDBD42D6D60C40AD4AB0.f03t03

 

http://www.sciencedirect.com/science/article/pii/S0009279716302241

 

http://link.springer.com/article/10.1007%2Fs10815-016-0657-9

 

http://www.nature.com/articles/srep24976

 

 

Read Full Post »

Milestones in Physiology & Discoveries in Medicine and Genomics: Request for Book Review Writing on Amazon.com


physiology-cover-seriese-vol-3individualsaddlebrown-page2

Milestones in Physiology

Discoveries in Medicine, Genomics and Therapeutics

Patient-centric Perspective 

http://www.amazon.com/dp/B019VH97LU 

2015

 

 

Author, Curator and Editor

Larry H Bernstein, MD, FCAP

Chief Scientific Officer

Leaders in Pharmaceutical Business Intelligence

Larry.bernstein@gmail.com

Preface

Introduction 

Chapter 1: Evolution of the Foundation for Diagnostics and Pharmaceuticals Industries

1.1  Outline of Medical Discoveries between 1880 and 1980

1.2 The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century

1.3 The Classification of Microbiota

1.4 Selected Contributions to Chemistry from 1880 to 1980

1.5 The Evolution of Clinical Chemistry in the 20th Century

1.6 Milestones in the Evolution of Diagnostics in the US HealthCare System: 1920s to Pre-Genomics

 

Chapter 2. The search for the evolution of function of proteins, enzymes and metal catalysts in life processes

2.1 The life and work of Allan Wilson
2.2  The  evolution of myoglobin and hemoglobin
2.3  More complexity in proteins evolution
2.4  Life on earth is traced to oxygen binding
2.5  The colors of life function
2.6  The colors of respiration and electron transport
2.7  Highlights of a green evolution

 

Chapter 3. Evolution of New Relationships in Neuroendocrine States
3.1 Pituitary endocrine axis
3.2 Thyroid function
3.3 Sex hormones
3.4 Adrenal Cortex
3.5 Pancreatic Islets
3.6 Parathyroids
3.7 Gastointestinal hormones
3.8 Endocrine action on midbrain
3.9 Neural activity regulating endocrine response

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

 

Chapter 4.  Problems of the Circulation, Altitude, and Immunity

4.1 Innervation of Heart and Heart Rate
4.2 Action of hormones on the circulation
4.3 Allogeneic Transfusion Reactions
4.4 Graft-versus Host reaction
4.5 Unique problems of perinatal period
4.6. High altitude sickness
4.7 Deep water adaptation
4.8 Heart-Lung-and Kidney
4.9 Acute Lung Injury

4.10 Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics

 

Chapter 5. Problems of Diets and Lifestyle Changes

5.1 Anorexia nervosa
5.2 Voluntary and Involuntary S-insufficiency
5.3 Diarrheas – bacterial and nonbacterial
5.4 Gluten-free diets
5.5 Diet and cholesterol
5.6 Diet and Type 2 diabetes mellitus
5.7 Diet and exercise
5.8 Anxiety and quality of Life
5.9 Nutritional Supplements

 

Chapter 6. Advances in Genomics, Therapeutics and Pharmacogenomics

6.1 Natural Products Chemistry

6.2 The Challenge of Antimicrobial Resistance

6.3 Viruses, Vaccines and immunotherapy

6.4 Genomics and Metabolomics Advances in Cancer

6.5 Proteomics – Protein Interaction

6.6 Pharmacogenomics

6.7 Biomarker Guided Therapy

6.8 The Emergence of a Pharmaceutical Industry in the 20th Century: Diagnostics Industry and Drug Development in the Genomics Era: Mid 80s to Present

6.09 The Union of Biomarkers and Drug Development

6.10 Proteomics and Biomarker Discovery

6.11 Epigenomics and Companion Diagnostics

 

Chapter  7

Integration of Physiology, Genomics and Pharmacotherapy

7.1 Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension

7.2 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

7.3 Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

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

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

7.6 Imaging Biomarker for Arterial Stiffness: Pathways in Pharmacotherapy for Hypertension and Hypercholesterolemia Management

7.7 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic drugs and Cholinesterase Inhibitors

7.8 Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

7.9 Preserved vs Reduced Ejection Fraction: Available and Needed Therapies

7.10 Biosimilars: Intellectual Property Creation and Protection by Pioneer and by

7.11 Demonstrate Biosimilarity: New FDA Biosimilar Guidelines

 

Chapter 7.  Biopharma Today

8.1 A Great University engaged in Drug Discovery: University of Pittsburgh

8.2 Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

8.3 Predicting Tumor Response, Progression, and Time to Recurrence

8.4 Targeting Untargetable Proto-Oncogenes

8.5 Innovation: Drug Discovery, Medical Devices and Digital Health

8.6 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

8.7 Nanotechnology and Ocular Drug Delivery: Part I

8.8 Transdermal drug delivery (TDD) system and nanotechnology: Part II

8.9 The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

8.10 Natural Drug Target Discovery and Translational Medicine in Human Microbiome

8.11 From Genomics of Microorganisms to Translational Medicine

8.12 Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad

 

Chapter 9. BioPharma – Future Trends

9.1 Artificial Intelligence Versus the Scientist: Who Will Win?

9.2 The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

9.3 The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

9.4 Heroes in Medical Research: The Postdoctoral Fellow

9.5 NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

9.6 1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

9.7 Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

9.8 Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science

9.9 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

9.10 The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research

Epilogue

Read Full Post »


genomicsinpersonalizedmedicinecovervolumeone

Content Consultant: Larry H Bernstein, MD, FCAP

Genomics Orientations for Personalized Medicine

Volume One

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

electronic Table of Contents

Chapter 1

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

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

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

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

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

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

Chapter 2

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

2.2 DNA structure and Oligonucleotides

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

2.4 Genomics and Evolution

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

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

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

Chapter 3

3.1 Big Data in Genomic Medicine

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

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

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

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

3.6 Identification of Biomarkers that are Related to the Actin Cytoskeleton

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

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

Chapter 4

4.1 ENCODE Findings as Consortium

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

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

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

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

4.6 Quantum Biology And Computational Medicine

4.7 The Underappreciated EpiGenome

4.8 Unraveling Retrograde Signaling Pathways

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

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

Chapter 5

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

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

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

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

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

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

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

5.8  Modeling Targeted Therapy

5.9 Transphosphorylation of E-coli Proteins and Kinase Specificity

5.10 Genomics of Bacterial and Archaeal Viruses

Chapter 6

6.1  Directions for Genomics in Personalized Medicine

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

6.3 Mitochondrial Damage and Repair under Oxidative Stress

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

6.5 Mechanism of Variegation in Immutans

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

6.7 Cardiac Ca2+ Signaling: Transcriptional Control

6.8 Unraveling Retrograde Signaling Pathways

6.9 Reprogramming Cell Fate

6.10 How Genes Function

6.11 TALENs and ZFNs

6.12 Zebrafish—Susceptible to Cancer

6.13 RNA Virus Genome as Bacterial Chromosome

6.14 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome 

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

6.16  Transphosphorylation of E-coli proteins and kinase specificity

6.17 Genomics of Bacterial and Archaeal Viruses

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

Chapter 7

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

7.2 Consumer Market for Personal DNA Sequencing: Part 4

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

7.4 Drugging the Epigenome

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

7.6 Personalized Medicine: Clinical Aspiration of Microarrays

Chapter 8

8.1 Personalized Medicine as Key Area for Future Pharmaceutical Growth

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

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

8.4 Nanotechnology, Personalized Medicine and DNA Sequencing

8.5 Targeted Nucleases

8.6 Transcript Dynamics of Proinflammatory Genes

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

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

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

Chapter 9

9.1 Personal Tale of JL’s Whole Genome Sequencing

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

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

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

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

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

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

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

 Chapter 10

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

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

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

10.4 Treatment for Metastatic HER2 Breast Cancer

10.5 Personalized Medicine in NSCLC

10.6 Gene Sequencing – to the Bedside

10.7 DNA Sequencing Technology

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

Chapter 11

11.1 mRNA Interference with Cancer Expression

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

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

11.4 A microRNA Prognostic Marker Identified in Acute Leukemia 

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

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

11.7 When Clinical Application of miRNAs?

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

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

11.10  MicroRNA Molecule May Serve as Biomarker

11.11 What about Circular RNAs?

Chapter 12

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

12.2 Otto Warburg, A Giant of Modern Cellular Biology

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

12.4 Hypothesis – Following on James Watson

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

12.6 AKT signaling variable effects

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

12.8 Phosphatidyl-5-Inositol signaling by Pin1

Chapter 13

13.1 Nanotech Therapy for Breast Cancer

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

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

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

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

13.6 In focus: Melanoma Genetics

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

13.8 Breast Cancer and Mitochondrial Mutations

13.9  Long noncoding RNA network regulates PTEN transcription

Chapter 14

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

14.2 Nanotechnology and HIV/AIDS treatment

14.3 IRF-1 Deficiency Skews the Differentiation of Dendritic Cells

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

14.5  Five Malaria Genomes Sequenced

14.6 Rheumatoid Arthritis Risk

14.7 Approach to Controlling Pathogenic Inflammation in Arthritis

14.8 RNA Virus Genome as Bacterial Chromosome

14.9 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome

Chapter 15

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

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

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

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

15.5 BARI 2D Trial Outcomes

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

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

15.8 Ca2+ signaling: transcriptional control

15.9 Lp(a) Gene Variant Association

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

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

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

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

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

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

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

Chapter 16

16.1 Can Resolvins Suppress Acute Lung Injury?

16.2 Lipoxin A4 Regulates Natural Killer Cell in Asthma

16.3 Biological Therapeutics for Asthma

16.4 Genomics of Bronchial Epithelial Dysplasia

16.5 Progression in Bronchial Dysplasia

Chapter 17

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

17.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis

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

17.4  Usp9x: Promising Therapeutic Target for Pancreatic Cancer

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

Chapter 18

18.1 Ubiquitin Pathway Involved in Neurodegenerative Disease

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

18.3 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic Drugs and Cholinesterase Inhibitors

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

18.5 Cell Transplantation in Brain Repair

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

Chapter 19

19.1 Genetics and Male Endocrinology

19.2 Genomic Endocrinology and its Future

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

19.4 Therapeutic Targets for Diabetes and Related Metabolic Disorders

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

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

19.7 Gene Trap Mutagenesis in Reproductive Research

19.8 Pregnancy with a Leptin-Receptor Mutation

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

19.10 Reproductive Genetic Testing

Chapter 20

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

20.2 Understanding the Role of Personalized Medicine

20.3 Attitudes of Patients about Personalized Medicine

20.4  Genome Sequencing of the Healthy

20.5   Genomics in Medicine – Tomorrow’s Promise

20.6  The Promise of Personalized Medicine

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

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

Chapter 21

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

Introduction

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

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

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

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

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

21.2 CRISPR in Other Labs

21.2.1 CRISPR @MIT – Genome Surgery

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

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

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

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

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

21.3 Patents Awarded and Pending for CRISPR

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

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

2.4 CRISPR/Cas9 Applications

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

21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF

21.4.3 In vivo validated mRNAs

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

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

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

Summary

Read Full Post »


Exome Aggregation Consortium (ExAC), generated the largest catalogue so far of variation in human protein-coding regions: Sequence data of 60,000 people, NOW is a publicly accessible database

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 8/22/2016

“The ExAC resource gives us incredible insight when evaluating a patient’s genome sequence in the clinic,” said Heidi Rehm, HMS associate professor of pathology at Brigham and Women’s Hospital, medical clinical director of the Broad’s Clinical Research Sequencing Platform and chief laboratory director of the Laboratory for Molecular Medicine at Partners HealthCare Personalized Medicine.

“In our own research, using the ExAC resource has allowed us to apply novel statistical methods to identify several new severe developmental disorders,” said Matthew Hurles, a researcher at the Wellcome Trust Sanger Institute and frequent user of the ExAC database. “Resources such as ExAC exemplify the benefits that can be achieved for families coping with rare genetic diseases, as a result of the mass altruism of many research participants who allow their data to be aggregated and shared.”

SOURCE

http://hms.harvard.edu/news/going-wide-and-deep?utm_source=Silverpop&utm_medium=email&utm_content=s3&utm_campaign=08.22.16.HMS

 

These variant data already guide diagnoses and treatment

E. V. Minikel et al. Sci. Transl. Med. 8, 322ra9; 2016

Quantifying prion disease penetrance using large population control cohorts

Science Translational Medicine  20 Jan 2016:
Vol. 8, Issue 322, pp. 322ra9
DOI: 10.1126/scitranslmed.aad5169

and

R. Walsh et al. Genet. Med. http://dx.doi.org/10.1038/gim.2016.90; 2016).

Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples

Genetics in Medicine
(2016)
doi:10.1038/gim.2016.90
Published online
17 August 2016

The ExAC project plans to grow over the next year to include 120,000 exome and 20,000 whole-genome sequences. It relies on the willingness of large research consortia to cooperate, and highlights the huge value of sharing, aggregation and harmonization of genomic data. This is also true for patient variants — there is a need for databases that provide greater confidence in variant interpretation, such as the US National Center for Biotechnology Information’s ClinVar database.

SOURCE

Nature536,249(18 August 2016)doi:10.1038/536249a

Read Full Post »

Older Posts »