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Archive for the ‘Genomic Endocrinology’ 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

 

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

 

Scientists think excessive population growth is a cause of scarcity and environmental degradation. A male pill could reduce the number of unintended pregnancies, which accounts for 40 percent of all pregnancies worldwide.

 

But, big drug companies long ago dropped out of the search for a male contraceptive pill which is able to chemically intercept millions of sperm before they reach a woman’s egg. Right now the chemical burden for contraception relies solely on the female. There’s not much activity in the male contraception field because an effective solution is available on the female side.

 

Presently, male contraception means a condom or a vasectomy. But researchers from Center for Drug Discovery at Baylor College of Medicine, USA are renewing the search for a better option—an easy-to-take pill that’s safe, fast-acting, and reversible.

 

The scientists began with lists of genes active in the testes for sperm production and motility and then created knockout mice that lack those genes. Using the gene-editing technology called CRISPR, in collaboration with Japanese scientists, they have so far made more than 75 of these “knockout” mice.

 

They allowed these mice to mate with normal (wild type) female mice, and if their female partners don’t get pregnant after three to six months, it means the gene might be a target for a contraceptive. Out of 2300 genes that are particularly active in the testes of mice, the researchers have identified 30 genes whose deletion makes the male infertile. Next the scientists are planning a novel screening approach to test whether any of about two billion chemicals can disable these genes in a test tube. Promising chemicals could then be fed to male mice to see if they cause infertility.

 

Female birth control pills use hormones to inhibit a woman’s ovaries from releasing eggs. But hormones have side effects like weight gain, mood changes, and headaches. A trial of one male contraceptive hormone was stopped early in 2011 after one participant committed suicide and others reported depression. Moreover, some drug candidates have made animals permanently sterile which is not the goal of the research. The challenge is to prevent sperm being made without permanently sterilizing the individual.

 

As a better way to test drugs, Scientists at University of Georgia, USA are investigating yet another high-tech approach. They are turning human skin cells into stem cells that look and act like the spermatogonial cells in the testes. Testing drugs on such cells might provide more accurate leads than tests on mice.

 

The male pill would also have to start working quickly, a lot sooner than the female pill, which takes about a week to function. Scientists from University of Dundee, U.K. admitted that there are lots of challenges. Because, a women’s ovary usually release one mature egg each month, while a man makes millions of sperm every day. So, the male pill has to be made 100 percent effective and act instantaneously.

 

References:

 

https://www.technologyreview.com/s/603676/the-search-for-a-perfect-male-birth-control-pill/

 

https://futurism.com/videos/the-perfect-male-birth-control-pill-is-coming-soon/?utm_source=Digest&utm_campaign=c42fc7b9b6-EMAIL_CAMPAIGN_2017_03_20&utm_medium=email&utm_term=0_03cd0a26cd-c42fc7b9b6-246845533

 

http://www.telegraph.co.uk/women/sex/the-male-pill-is-coming—and-its-going-to-change-everything/

 

http://www.mensfitness.com/women/sex-tips/male-birth-control-pill-making

 

http://health.howstuffworks.com/sexual-health/contraception/male-bc-pill.htm

 

http://europe.newsweek.com/male-contraception-side-effects-study-pill-injection-518237?rm=eu

 

http://edition.cnn.com/2016/01/07/health/male-birth-control-pill/index.html

 

http://www.nhs.uk/Conditions/contraception-guide/Pages/male-pill.aspx

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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

 

 

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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

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Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants

 

Reporter: Kelly Perlman, Life Sciences Student and Research Assistant, McGill University

 

Researchers from Pfizer Global Research and Development, 23andMe, and the Massachusetts General Hospital have published a study in Nature Genetics, pinpointing 15 genetic loci associated with the risk of developing major depressive disorder (MDD) in individuals of European ancestry. Evidence from previous research suggests that MDD is heritable, but the details of the specific gene correlates are unclear. The identification of loci where single nucleotide polymorphisms (SNPs) related to MDD exist could provide better insight into the neurobiology of depression, and therefore better treatment options.

23andMe, a private biotechnology company situated in California, offers a DNA sequencing service in which consumers send in a saliva swab for testing, and later receive a report listing the findings of the analysis related to ancestry, physical and behavioral traits, along with risk of inheriting certain diseases. The participants of this study had agreed to provide the results of their genetic testing for scientific research.

The results of 75,607 participants with self-reported diagnoses of depression were compared to the results of 231,747 participants reporting having never experienced depression. This data was combined with the results of previously published MDD genome-wide association studies (GWAS). To test the whether these results could be replicated, another set of results from 23andMe was analyzed, in which there were 45,773 MDD subjects, and 106,354 controls.

After the joint analysis, 17 SNPs were identified at 15 different loci. Tissue and gene enrichment assays showed that the genes that were over-expressed in the CNS were related to functions including neurodevelopment, histone methylation, neurogenesis and synaptic modification.

The team then created a weighted genetic risk score (GRS) in which they compared the 17 SNPs with factors including medication use, comorbid diseases and behavioral phenotypes, all of which were correlated with the GRS. Of note, the GRS was very highly correlated with age of onset of MDD.

The crowdsourcing of genetic data proves to be an efficient and powerful tool for large-scale MDD studies. Pooling large subject databases together is essential in order to account for the heterogeneous nature of the disease. Despite not being able to precisely assess each subject’s disease phenotype, scientists can make more rapid headway by collaborating with biotechnology companies in the quest to better understand the biological mechanisms of depression. Ron Perlis, M.D., M.Sc., of the Massachusetts General Hospital and co-author of this paper explained that “finding genes associated with depression should help make clear that this is a brain disease, which we hope will decrease the stigma still associated with these kinds of illnesses”.

 

Details on specific significant genes:

http://www.genecards.org/cgi-bin/carddisp.pl?gene=OLFM4

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMEM161B

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEF2C

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEIS2

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMCO5A

http://www.genecards.org/cgi-bin/carddisp.pl?gene=NEGR1

 

SOURCES

Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., . . . Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics Nat Genet. doi:10.1038/ng.3623

Major Depressive Disorder Loci Discovered in Large GWAS Enabled by 23andMe Participants’ Data. (2016, August 01). Retrieved August 09, 2016, from https://www.genomeweb.com/microarrays-multiplexing/major-depressive-disorder-loci-discovered-large-gwas-enabled-23andme

 

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

 

In an African cichlid fish, Astatotilapia burtoni, fertile females select a mate and perform a stereotyped spawning / mating routine, offering quantifiable behavioral outputs of neural circuits. A male fish attracts a fertile female by rapidly quivering his brightly colored body. If she chooses him, he guides her back to his territory, where he quivers some more as she pecks at fish egg–colored spots on his anal fin. Next, she lays eggs and quickly scoops them up in her mouth. With a mouthful of eggs, she continues pecking at the male’s spots, “believing” them to be eggs to be collected. As she does, he releases sperm from near his anal fin, which she also gathers. This fertilizes the eggs, and she carries the embryos in her mouth for two weeks as they develop.

 

But, the question was how these females can time their reproduction to coincide with when they are fertile. The female fish will not approach or choose males until they are ready to reproduce, so there must be something in their brains that signals when sexual behavior will be required. The scientists began by considering signaling molecules previously associated with sexual behavior and reproduction, and showed that PGF2α injection activates a naturalistic pattern of sexual behavior in female Astatotilapia burtoni. They would engage in mating behavior even if they were non-fertile, doing the quiver dance with males, but wouldn’t actually lay eggs since they had none.

 

The scientists also identified cells in the brain that transduce the prostaglandin signal to mate and showed that the gonadal steroid 17α, 20β-dihydroxyprogesterone modulates mRNA levels of the putative receptor for PGF2α. The scientists keyed in on a receptor for PGF2α in the preoptic area (POA) within the hypothalamus of the brain, a region involved in sexual behavior across animals. They suspected that when PGF2α levels elevated in the fish, the molecule attaches to this receptor and triggers sexual behavior. Then they used CRISPR/Cas9 to generate PGF2α receptor knockout fish. This gene deletion or knockout uncoupled the sexual behavior from fertility status to prove that the receptor of PGF2α is necessary for the initiation of sexual behavior.

 

The finding has parallels across all vertebrates, and might influence the understanding of social behavior in humans. The next steps for this work will involve understanding other behaviors that are regulated by this receptor, and the finding provides insight into both the evolution of reproduction and sexual behaviors. In mammals and other vertebrates, PGF2α promotes the onset of labor and motherly behaviors, and this present research, coupled with other studies, suggests that PGF2α signaling has a common ancestral function associated with birth and its related behaviors.

 

References:

 

http://www.ncbi.nlm.nih.gov/pubmed/26996507

 

http://news.stanford.edu/news/2016/march/fish-mating-behavior-031716.html

 

 

http://www.academia.edu/676252/The_Genetics_of_Female_Sexual_Behaviour

 

https://scifeeds.com/news/scientists-identify-genetic-switch-for-female-sexual-behavior/

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Fat Cells Reprogrammed to Make Insulin

Curator: Larry H. Bernstein, MD, FCAP

 

A New Use for Love Handles, Insulin-Producing Beta Cells

http://www.genengnews.com/gen-news-highlights/a-new-use-for-love-handles-insulin-producing-beta-cells/81252612/

http://www.genengnews.com/Media/images/GENHighlight/112856_web9772135189.jpg

 

Scientists at the Swiss Federal Institute of Technology (ETH) in Zurich have found an exciting new use for the cells that reside in the undesirable flabby tissue—creating pancreatic beta cells. The ETH researchers extracted stem cells from a 50-year-old test subject’s fatty tissue and reprogrammed them into mature, insulin-producing beta cells.

The findings from this study were published recently in Nature Communications in an article entitled “A Programmable Synthetic Lineage-Control Network That Differentiates Human IPSCs into Glucose-Sensitive Insulin-Secreting Beta-Like Cells.”

The investigators added a highly complex synthetic network of genes to the stem cells to recreate precisely the key growth factors involved in this maturation process. Central to the process were the growth factors Ngn3, Pdx1, and MafA; the researchers found that concentrations of these factors change during the differentiation process.

For instance, MafA is not present at the start of maturation. Only on day 4, in the final maturation step, does it appear, its concentration rising steeply and then remaining at a high level. The changes in the concentrations of Ngn3 and Pdx1, however, are very complex: while the concentration of Ngn3 rises and then falls again, the level of Pdx1 rises at the beginning and toward the end of maturation.

Senior study author Martin Fussenegger, Ph.D., professor of biotechnology and bioengineering at ETH Zurich’s department of biosystems science and engineering stressed that it was essential to reproduce these natural processes as closely as possible to produce functioning beta cells, stating that “the timing and the quantities of these growth factors are extremely important.”

The ETH researchers believe that their work is a real breakthrough, in that a synthetic gene network has been used successfully to achieve genetic reprogramming that delivers beta cells. Until now, scientists have controlled such stem cell differentiation processes by adding various chemicals and proteins exogenously.

“It’s not only really hard to add just the right quantities of these components at just the right time, but it’s also inefficient and impossible to scale up,” Dr. Fussenegger noted.

While the beta cells not only looked very similar to their natural counterparts—containing dark spots known as granules that store insulin—the artificial beta cells also functioned in a very similar manner. However, the researchers admit that more work needs to be done to increase the insulin output.

“At the present time, the quantities of insulin they secrete are not as great as with natural beta cells,” Dr. Fussenegger stated. Yet, the key point is that the researchers have for the first time succeeded in reproducing the entire natural process chain, from stem cell to differentiated beta cell.

In future, the ETH scientists’ novel technique might make it possible to implant new functional beta cells in diabetes sufferers that are made from their adipose tissue. While beta cells have been transplanted in the past, this has always required subsequent suppression of the recipient’s immune system—as with any transplant of donor organs or tissue.

“With our beta cells, there would likely be no need for this action since we can make them using endogenous cell material taken from the patient’s own body,” Dr. Fussenegger said. “This is why our work is of such interest in the treatment of diabetes.”

A programmable synthetic lineage-control network that differentiates human IPSCs into glucose-sensitive insulin-secreting beta-like cells

Pratik SaxenaBoon Chin HengPeng BaiMarc FolcherHenryk Zulewski & Martin Fussenegger
Nature Communications7,Article number:11247
         doi:10.1038/ncomms11247

Synthetic biology has advanced the design of standardized transcription control devices that programme cellular behaviour. By coupling synthetic signalling cascade- and transcription factor-based gene switches with reverse and differential sensitivity to the licensed food additive vanillic acid, we designed a synthetic lineage-control network combining vanillic acid-triggered mutually exclusive expression switches for the transcription factors Ngn3 (neurogenin 3; OFF-ON-OFF) and Pdx1 (pancreatic and duodenal homeobox 1; ON-OFF-ON) with the concomitant induction of MafA (V-maf musculoaponeurotic fibrosarcoma oncogene homologue A; OFF-ON). This designer network consisting of different network topologies orchestrating the timely control of transgenic and genomic Ngn3, Pdx1 and MafA variants is able to programme human induced pluripotent stem cells (hIPSCs)-derived pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells, whose glucose-stimulated insulin-release dynamics are comparable to human pancreatic islets. Synthetic lineage-control networks may provide the missing link to genetically programme somatic cells into autologous cell phenotypes for regenerative medicine.

Cell-fate decisions during development are regulated by various mechanisms, including morphogen gradients, regulated activation and silencing of key transcription factors, microRNAs, epigenetic modification and lateral inhibition. The latter implies that the decision of one cell to adopt a specific phenotype is associated with the inhibition of neighbouring cells to enter the same developmental path. In mammals, insights into the role of key transcription factors that control development of highly specialized organs like the pancreas were derived from experiments in mice, especially various genetically modified animals1, 2, 3, 4. Normal development of the pancreas requires the activation of pancreatic duodenal homeobox protein (Pdx1) in pre-patterned cells of the endoderm. Inactivating mutations of Pdx1 are associated with pancreas agenesis in mouse and humans5, 6. A similar cell fate decision occurs later with the activation of Ngn3 that is required for the development of all endocrine cells in the pancreas7. Absence of Ngn3 is associated with the loss of pancreatic endocrine cells, whereas the activation of Ngn3 not only allows the differentiation of endocrine cells but also induces lateral inhibition of neighbouring cells—via Delta-Notch pathway—to enter the same pancreatic endocrine cell fate8. This Ngn3-mediated cell-switch occurs at a specific time point and for a short period of time in mice9. Thereafter, it is silenced and becomes almost undetectable in postnatal pancreatic islets. Conversely, Pdx1-positive Ngn3-positive cells reduce Pdx1 expression, as Ngn3-positive cells are Pdx1 negative10. They re-express Pdx1, however, as they go on their path towards glucose-sensitive insulin-secreting cells with parallel induction of MafA that is required for proper differentiation and maturation of pancreatic beta cells11. Data supporting these expression dynamics are derived from mice experiments1, 11, 12. A synthetic gene-switch governing cell fate decision in human induced pluripotent stem cells (hIPSCs) could facilitate the differentiation of glucose-sensitive insulin-secreting cells.

In recent years, synthetic biology has significantly advanced the rational design of synthetic gene networks that can interface with host metabolism, correct physiological disturbances13 and provide treatment strategies for a variety of metabolic disorders, including gouty arthritis14, obesity15 and type-2 diabetes16. Currently, synthetic biology principles may provide the componentry and gene network topologies for the assembly of synthetic lineage-control networks that can programme cell-fate decisions and provide targeted differentiation of stem cells into terminally differentiated somatic cells. Synthetic lineage-control networks may therefore provide the missing link between human pluripotent stem cells17 and their true impact on regenerative medicine18, 19, 20. The use of autologous stem cells in regenerative medicine holds great promise for curing many diseases, including type-1 diabetes mellitus (T1DM), which is characterized by the autoimmune destruction of insulin-producing pancreatic beta cells, thus making patients dependent on exogenous insulin to control their blood glucose21, 22. Although insulin therapy has changed the prospects and survival of T1DM patients, these patients still suffer from diabetic complications arising from the lack of physiological insulin secretion and excessive glucose levels23. The replacement of the pancreatic beta cells either by pancreas transplantation or by transplantation of pancreatic islets has been shown to normalize blood glucose and even improve existing complications of diabetes24. However, insulin independence 5 years after islet transplantation can only be achieved in up to 55% of the patients even when using the latest generation of immune suppression strategies25, 26. Transplantation of human islets or the entire pancreas has allowed T1DM patients to become somewhat insulin independent, which provides a proof-of-concept for beta-cell replacement therapies27, 28. However, because of the shortage of donor pancreases and islets, as well as the significant risk associated with transplantation and life-long immunosuppression, the rational differentiation of stem cells into functional beta-cells remains an attractive alternative29, 30. Nevertheless, a definitive cure for T1DM should address both the beta-cell deficit and the autoimmune response to cells that express insulin. Any beta-cell mimetic should be able to store large amounts of insulin and secrete it on demand, as in response to glucose stimulation29, 31. The most effective protocols for the in vitro generation of bonafide insulin-secreting beta-like cells that are suitable for transplantation have been the result of sophisticated trial-and-error studies elaborating timely addition of complex growth factor and small-molecule compound cocktails to human pancreatic progenitor cells32, 33, 34. The differentiation of pancreatic progenitor cells to beta-like cells is the most challenging part as current protocols provide inconsistent results and limited success in programming pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells35, 36, 37. One of the reasons for these observations could be the heterogeneity in endocrine differentiation and maturation towards a beta cell phenotype. Here we show that a synthetic lineage-control network programming the dynamic expression of the transcription factors Ngn3, Pdx1 and MafA enables the differentiation of hIPSC-derived pancreatic progenitor cells to glucose-sensitive insulin-secreting beta-like cells (Supplementary Fig. 1).

 

Vanillic acid-programmable positive band-pass filter

The differentiation pathway from pancreatic progenitor cells to glucose-sensitive insulin-secreting pancreatic beta-cells combines the transient mutually exclusive expression switches of Ngn3 (OFF-ON-OFF) and Pdx1 (ON-OFF-ON) with the concomitant induction of MafA (OFF-ON) expression10,11. Since independent control of the pancreatic transcription factors Ngn3, Pdx1 and MafA by different antibiotic transgene control systems responsive to tetracycline, erythromycin and pristinamycin did not result in the desired differential control dynamics (Supplementary Fig. 2), we have designed a vanillic acid-programmable synthetic lineage-control network that programmes hIPSC-derived pancreatic progenitor cells to specifically differentiate into glucose-sensitive insulin-secreting beta-like cells in a seamless and self-sufficient manner. The timely coordination of mutually exclusive Ngn3 and Pdx1 expression with MafA induction requires the trigger-controlled execution of a complex genetic programme that orchestrates two overlapping antagonistic band-pass filter expression profiles (OFF-ON-OFF and ON-OFF-ON), a positive band-pass filter for Ngn3 (OFF-ON-OFF) and a negative band-pass filter, also known as band-stop filter, for Pdx1 (ON-OFF-ON), the ramp-up expression phase of which is linked to a graded induction of MafA (OFF-ON).

The core of the synthetic lineage-control network consists of two transgene control devices that are sensitive to the food component and licensed food additive vanillic acid. These devices are a synthetic vanillic acid-inducible (ON-type) signalling cascade that is gradually induced by increasing the vanillic acid concentration and a vanillic acid-repressible (OFF-type) gene switch that is repressed in a vanillic acid dose-dependent manner (Fig. 1a,b). The designer cascade consists of the vanillic acid-sensitive mammalian olfactory receptor MOR9-1, which sequentially activates the G protein Sα (GSα) and adenylyl cyclase to produce a cyclic AMP (cAMP) second messenger surge38 that is rewired via the cAMP-responsive protein kinase A-mediated phospho-activation of the cAMP-response element-binding protein 1 (CREB1) to the induction of synthetic promoters (PCRE) containing CREB1-specific cAMP response elements (CRE; Fig. 1a). The co-transfection of pCI-MOR9-1 (PhCMV-MOR9-1-pASV40) and pCK53 (PCRE-SEAP-pASV40) into human mesenchymal stem cells (hMSC-TERT) confirmed the vanillic acid-adjustable secreted alkaline phosphatase (SEAP) induction of the designer cascade (>10nM vanillic acid; Fig. 1a). The vanillic acid-repressible gene switch consists of the vanillic acid-dependent transactivator (VanA1), which binds and activates vanillic acid-responsive promoters (for example, P1VanO2) at low and medium vanillic acid levels (<2μM). At high vanillic acid concentrations (>2μM), VanA1 dissociates from P1VanO2, which results in the dose-dependent repression of transgene expression39 (Fig. 1b). The co-transfection of pMG250 (PSV40-VanA1-pASV40) and pMG252 (P1VanO2-SEAP-pASV40) into hMSC-TERT corroborated the fine-tuning of the vanillic acid-repressible SEAP expression (Fig. 1b).

Figure 1: Design of a vanillic acid-responsive positive band-pass filter providing an OFF-ON-OFF expression profile.

Design of a vanillic acid-responsive positive band-pass filter providing an OFF-ON-OFF expression profile.

http://www.nature.com/ncomms/2016/160411/ncomms11247/images_article/ncomms11247-f1.jpg

a) Vanillic acid-inducible transgene expression. The constitutively expressed vanillic acid-sensitive olfactory G protein-coupled receptor MOR9-1 (pCI-MOR9-1; PhCMV-MOR9-1-pA) senses extracellular vanillic acid levels and triggers G protein (Gs)-mediated activation of the membrane-bound adenylyl cyclase (AC) that converts ATP into cyclic AMP (cAMP). The resulting intracellular cAMP surge activates PKA (protein kinase A), whose catalytic subunits translocate into the nucleus to phosphorylate cAMP response element-binding protein 1 (CREB1). Activated CREB1 binds to synthetic promoters (PCRE) containing cAMP-response elements (CRE) and induces PCRE-driven expression of human placental secreted alkaline phosphatase (SEAP; pCK53, PCRE-SEAP-pA). Co-transfection of pCI-MOR9-1 and pCK53 into human mesenchymal stem cells (hMSC-TERT) grown for 48h in the presence of increasing vanillic acid concentrations results in a dose-inducible SEAP expression profile. (b) Vanillic acid-repressible transgene expression. The constitutively expressed, vanillic acid-dependent transactivator VanA1(pMG250, PSV40-VanA1-pA, VanA1, VanR-VP16) binds and activates the chimeric promoter P1VanO2 (pMG252, P1VanO2-SEAP-pA) in the absence of vanillic acid. In the presence of increasing vanillic acid concentrations, VanA1 is released from P1VanO2, and transgene expression is shut down. Co-transfection of pMG250 and pMG252 into hMSC-TERT grown for 48h in the presence of increasing vanillic acid concentrations results in a dose-repressible SEAP expression profile. (c) Positive band-pass expression filter. Serial interconnection of the synthetic vanillic acid-inducible signalling cascade (a) with the vanillic acid-repressible transcription factor-based gene switch (b) by PCRE-mediated expression of VanA1 (pSP1, PCRE-VanA1-pA) results in a two-level feed-forward cascade. Owing to the opposing responsiveness and differential sensitivity to vanillic acid, this synthetic gene network programmes SEAP expression with a positive band-pass filter profile (OFF-ON-OFF) as vanillic acid levels are increased. Medium vanillic acid levels activate MOR9-1, which induces PCRE-driven VanA1 expression. VanA1remains active and triggers P1VanO2-mediated SEAP expression in feed-forward manner, which increases to maximum levels. At high vanillic acid concentrations, MOR9-1 maintains PCRE-driven VanA1 expression, but the transactivator dissociates from P1VanO2, which shuts SEAP expression down. Co-transfection of pCI-MOR9-1, pSP1 and pMG252 into hMSC-TERT grown for 48h in the presence of increasing vanillic acid concentrations programmes SEAP expression with a positive band-pass profile (OFF-ON-OFF). Data are the means±s.d. of triplicate experiments (n=9).

The opposing responsiveness and differential sensitivity of the control devices to vanillic acid are essential to programme band-pass filter expression profiles. Upon daisy-chaining the designer cascade (pCI-MOR9-1; PhCMV-MOR9-1-pASV40; pSP1, PCRE-VanA1-pASV40) and the gene switch (pSP1, PCRE-VanA1-pASV40; pMG252, P1VanO2-SEAP-pASV40) in the same cell, the network executes a band-pass filter SEAP expression profile when exposed to increasing concentrations of vanillic acid (Fig. 1c). Medium vanillic acid levels (10nM to 2μM) activate MOR9-1, which induces PCRE-driven VanA1 expression. VanA1 remains active within this concentration range and, in a feed-forward amplifier manner, triggers P1VanO2-mediated SEAP expression, which gradually increases to maximum levels (Fig. 1c). At high vanillic acid concentrations (2μM to 400μM), MOR9-1 maintains PCRE-driven VanA1 expression, but the transactivator is inactivated and dissociates from P1VanO2, which results in the gradual shutdown of SEAP expression (Fig. 1c).

Vanillic acid-programmable lineage-control network

For the design of the vanillic acid-programmable synthetic lineage-control network, constitutive MOR9-1 expression and PCRE-driven VanA1 expression were combined with pSP12 (pASV40-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pASV40) for endocrine specification and pSP17(PCREm-Pdx1cm-2A-MafAcm-pASV40) for maturation of developing beta-cells (Fig. 2a,b). ThepSP12-encoded expression unit enables the VanA1-controlled induction of the optimized bidirectional vanillic acid-responsive promoter (P3VanO2) that drives expression of a codon-modified Ngn3cm, the nucleic acid sequence of which is distinct from its genomic counterpart (Ngn3g) to allow for quantitative reverse transcription–PCR (qRT–PCR)-based discrimination. In the opposite direction, P3VanO2 transcribes miR30Pdx1g-shRNA, which exclusively targets genomicPdx1 (Pdx1g) transcripts for RNA interference-based destruction and is linked to the production of a blue-to-red medium fluorescent timer40 (mFT) for precise visualization of the unit’s expression dynamics in situ. pSP17 contains a dicistronic expression unit in which the modified high-tightness and lower-sensitivity PCREm promoter (see below) drives co-cistronic expression of Pdx1cm andMafAcm, which are codon-modified versions producing native transcription factors that specifically differ from their genomic counterparts (Pdx1g, MafAg) in their nucleic acid sequence. After individual validation of the vanillic acid-controlled expression and functionality of all network components (Supplementary Figs 2–9), the lineage-control network was ready to be transfected into hIPSC-derived pancreatic progenitor cells. These cells are characterized by high expression of Pdx1g and Nkx6.1 levels and the absence of Ngn3g and MafAg production32, 33, 34 (day 0:Supplementary Figs 10–16).

 

Figure 2: Synthetic lineage-control network programming differential expression dynamics of pancreatic transcription factors.

Synthetic lineage-control network programming differential expression dynamics of pancreatic transcription factors.

 

http://www.nature.com/ncomms/2016/160411/ncomms11247/images/ncomms11247-f2.jpg

(a) Schematic of the synthetic lineage-control network. The constitutively expressed, vanillic acid-sensitive olfactory G protein-coupled receptor MOR9-1 (pCI-MOR9-1; PhCMV-MOR9-1-pA) senses extracellular vanillic acid levels and triggers a synthetic signalling cascade, inducing PCRE-driven expression of the transcription factor VanA1 (pSP1, PCRE-VanA1-pA). At medium vanillic acid concentrations (purple arrows), VanA1 binds and activates the bidirectional vanillic acid-responsive promoter P3VanO2 (pSP12, pA-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pA), which drives the induction of codon-modified Neurogenin 3 (Ngn3cm) as well as the coexpression of both the blue-to-red medium fluorescent timer (mFT) for precise visualization of the unit’s expression dynamics and miR30pdx1g-shRNA (a small hairpin RNA programming the exclusive destruction of genomic pancreatic and duodenal homeobox 1 (Pdx1g) transcripts). Consequently, Ngn3cm levels switch from low to high (OFF-to-ON), and Pdx1g levels toggle from high to low (ON-to-OFF). In addition, Ngn3cm triggers the transcription of Ngn3g from its genomic promoter, which initiates a positive-feedback loop. At high vanillic acid levels (orange arrows), VanA1 is inactivated, and both Ngn3cm and miR30pdx1g-shRNA are shut down. At the same time, the MOR9-1-driven signalling cascade induces the modified high-tightness and lower-sensitivity PCREm promoter that drives the co-cistronic expression of the codon-modified variants of Pdx1 (Pdx1cm) and V-maf musculoaponeurotic fibrosarcoma oncogene homologue A (MafAcm; pSP17, PCREm-Pdx1cm-2A-MafAcm-pA). Consequently, Pdx1cm and MafAcm become fully induced. As Pdx1cm expression ramps up, it initiates a positive-feedback loop by inducing the genomic counterparts Pdx1g and MafAg. Importantly, Pdx1cm levels are not affected by miR30Pdx1g-shRNA because the latter is specific for genomic Pdx1g transcripts and because the positive feedback loop-mediated amplification of Pdx1gexpression becomes active only after the shutdown of miR30Pdx1g-shRNA. Overall, the synthetic lineage-control network provides vanillic acid-programmable, transient, mutually exclusive expression switches for Ngn3 (OFF-ON-OFF) and Pdx1 (ON-OFF-ON) as well as the concomitant induction of MafA (OFF-ON) expression, which can be followed in real time (Supplementary Movies 1 and 2). (b) Schematic illustrating the individual differentiation steps from human IPSCs towards beta-like cells. The colours match the cell phenotypes reached during the individual differentiation stages programmed by the lineage-control network shown in a.

Following the co-transfection of pCI-MOR9-1 (PhCMV-MOR9-1-pASV40), pSP1 (PCRE-VanA1-pASV40), pSP12 (pASV40-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pASV40) and pSP17(PCREm-Pdx1cm-2A-MafAcm-pASV40) into hIPSC-derived pancreatic progenitor cells, the synthetic lineage-control network should override random endogenous differentiation activities and execute the pancreatic beta-cell-specific differentiation programme in a vanillic acid remote-controlled manner. To confirm that the lineage-control network operates as programmed, we cultivated network-containing and pEGFP-N1-transfected (negative-control) cells for 4 days at medium (2μM) and then 7 days at high (400μM) vanillic acid concentrations and profiled the differential expression dynamics of all of the network components and their genomic counterparts as well as the interrelated transcription factors and hormones in both whole populations and individual cells at days 0, 4, 11 and 14 (Figs 2 and 3 and Supplementary Figs 11–17).

 

Figure 3: Dynamics of the lineage-control network.

Dynamics of the lineage-control network.

http://www.nature.com/ncomms/2016/160411/ncomms11247/images/ncomms11247-f3.jpg

(a,b) Quantitative RT–PCR-based expression profiling of the pancreatic transcription factors Ngn3cm/g, Pdx1cm/g and MafAcm/g in hIPSC-derived pancreatic progenitor cells containing the synthetic lineage-control network at days 4 and 11. Data are the means±s.d. of triplicate experiments (n=9). (cg) Immunocytochemistry of pancreatic transcription factors Ngn3cm/g, Pdx1cm/g and MafAcm/g in hIPSC-derived pancreatic progenitor cells containing the synthetic lineage-control network at days 4 and 11. hIPSC-derived pancreatic progenitor cells were co-transfected with the lineage-control vectors pCI-MOR9-1 (PhCMV-MOR9-1-pA), pSP1 (PCRE-VanA1-pA), pSP12 (pA-Ngn3cm←P3VanO2right arrowmFT-miR30Pdx1g-shRNA-pA) and pSP17 (PCREm-Pdx1cm-2A-MafAcm) and immunocytochemically stained for (c) VanA1 and Pdx1 (day 4), (d) VanA1 and Ngn3 (day 4), (e) VanA1 and Pdx1 (day 11), (f) MafA and Pdx1 (day 11) as well as (g) VanA1 and insulin (C-peptide) (day 11). The cells staining positive for VanA1 are containing the lineage-control network. DAPI, 4′,6-diamidino-2-phenylindole. Scale bar, 100μm.

…….

Multicellular organisms, including humans, consist of a highly structured assembly of a multitude of specialized cell phenotypes that originate from the same zygote and have traversed a preprogrammed multifactorial developmental plan that orchestrates sequential differentiation steps with high precision in space and time19, 51. Because of the complexity of terminally differentiated cells, the function of damaged tissues can for most medical indications only be restored via the transplantation of donor material, which is in chronically short supply52.

Despite significant progress in regenerative medicine and the availability of stem cells, the design of protocols that replicate natural differentiation programmes and provide fully functional cell mimetics remains challenging29, 53. For example, efforts to generate beta-cells from human embryonic stem cells (hESCs) have led to reliable protocols involving the sequential administration of growth factors (activin A, bone morphogenetic protein 4 (BMP-4), basic fibroblast growth factor (bFGF), FGF-10, Noggin, vascular endothelial growth factor (VEGF) and Wnt3A) and small-molecule compounds (cyclopamine, forskolin, indolactam V, IDE1, IDE2, nicotinamide, retinoic acid, SB−431542 and γ-secretase inhibitor) that modulate differentiation-specific signalling pathways31, 54, 55. In vitro differentiation of hESC-derived pancreatic progenitor cells into beta-like cells is more challenging and has been achieved recently by a complex media formulation with chemicals and growth factors32, 33, 34.

hIPSCs have become a promising alternative to hESCs; however, their use remains restricted in many countries56. Most hIPSCs used for directed differentiation studies were derived from a juvenescent cell source that is expected to show a higher degree of differentiation potential compared with older donors that typically have a higher need for medical interventions37, 57, 58. We previously succeeded in producing mRNA-reprogrammed hIPSCs from adipose tissue-derived mesenchymal stem cells of a 50-year-old donor, demonstrating that the reprogramming of cells from a donor of advanced age is possible in principle59.

Recent studies applying similar hESC-based differentiation protocols to hIPSCs have produced cells that release insulin in response to high glucose32, 33, 34. This observation suggests that functional beta-like cells can eventually be derived from hIPSCs32, 33. In our hands, the growth-factor/chemical-based technique for differentiating human IPSCs resulted in beta-like cells with poor glucose responsiveness. Recent studies have revealed significant variability in the lineage specification propensity of different hIPSC lines35, 60 and substantial differences in the expression profiles of key transcription factors in hIPSC-derived beta-like cells33. Therefore, the growth-factor/chemical-based protocols may require further optimization and need to be customized for specific hIPSC lines35. Synthetic lineage-control networks providing precise dynamic control of transcription factor expression may overcome the challenges associated with the programming of beta-like cells from different hIPSC lines.

Rather than exposing hIPSCs to a refined compound cocktail that triggers the desired differentiation in a fraction of the stem cell population, we chose to design a synthetic lineage-control network to enable single input-programmable differentiation of hIPSC-derived pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells. In contrast with the use of growth-factor/chemical-based cocktails, synthetic lineage-control networks are expected to (i) be more economical because of in situ production of the required transcription factors, (ii) enable simultaneous control of ectopic and chromosomally encoded transcription factor variants, (iii) tap into endogenous pathways and not be limited to cell-surface input, (iv) display improved reversibility that is not dependent on the removal of exogenous growth factors via culture media replacement, (v) provide lateral inhibition, thereby reducing the random differentiation of neighbouring cells and (vi) enable trigger-programmable and (vii) precise differential transcription factor expression switches.

The synthetic lineage-control network that precisely replicates the endogenous relative expression dynamics of the transcription factors Pdx-1, Ngn3 and MafA required the design of a new network topology that interconnects a synthetic signalling cascade and a gene switch with differential and opposing sensitivity to the food additive vanillic acid. This differentiation device provides different band-pass filter, time-delay and feed-forward amplifier topologies that interface with endogenous positive-feedback loops to orchestrate the timely expression and repression of heterologous and chromosomally encoded Ngn3, Pdx1 and MafA variants. The temporary nature of the engineering intervention, which consists of transient transfection of the genetic lineage-control components in the absence of any selection, is expected to avoid stable modification of host chromosomes and alleviate potential safety concerns. In addition, the resulting beta-cell mass could be encapsulated inside vascularized microcontainers28, a proven containment strategy in prototypic cell-based therapies currently being tested in animal models of prominent human diseases14, 15, 16, 61, 62 as well as in human clinical trials28.

The hIPSC-derived beta-like cells resulting from this trigger-induced synthetic lineage-control network exhibited glucose-stimulated insulin-release dynamics and capacity matching the human physiological range and transcriptional profiling, flow cytometric analysis and electron microscopy corroborated the lineage-controlled stem cells reached a mature beta-cell phenotype. In principle, the combination of hIPSCs derived from the adipose tissue of a 50-year-old donor59 with a synthetic lineage-control network programming glucose-sensitive insulin-secreting beta-like cells closes the design cycle of regenerative medicine63. However, hIPSCs that are derived from T1DM patients, differentiated into beta-like cells and transplanted back into the donor would still be targeted by the immune system, as demonstrated in the transplantation of segmental pancreatic grafts from identical twins64. Therefore, any beta-cell-replacement therapy will require complementary modulation of the immune system either via drugs30, 65, engineering or cell-based approaches66, 67 or packaging inside vascularizing, semi-permeable immunoprotective microcontainers28.

Capitalizing on the design principles of synthetic biology, we have successfully constructed and validated a synthetic lineage-control network that replicates the differential expression dynamics of critical transcription factors and mimicks the native differentiation pathway to programme hIPSC-derived pancreatic progenitor cells into glucose-sensitive insulin-secreting beta-like cells that compare with human pancreatic islets at a high level. The design of input-triggered synthetic lineage-control networks that execute a preprogrammed sequential differentiation agenda coordinating the timely induction and repression of multiple genes could provide a new impetus for the advancement of developmental biology and regenerative medicine.

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

Adipocyte Derived Stroma Cells: Their Usage in Regenerative Medicine and Reprogramming into Pancreatic Beta-Like Cells

Curator: Evelina Cohn, Ph.D.

https://pharmaceuticalintelligence.com/2016/03/03/adipocyte-derived-stroma-cells-their-usage-in-regenerative-medicine-and-reprogramming-into-pancreatic-beta-like-cells/

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