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Third Annual TCGC: The Clinical Genome Conference, San Francisco, June 10-12, 2014 by Bio-IT World and Cambridge Healthtech Institute

Posted in Biological Networks, Gene Regulation and Evolution, CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, tagged , , , on December 4, 2013| Leave a Comment »

Third Annual TCGC: The Clinical Genome Conference, San Francisco, June 10-12, 2014 by Bio-IT World and Cambridge Healthtech Institute

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 5/1/2014

Register by May 2 for

Hotel Kabuki, San Francisco, CA

June 10 – 12, 2014

FINAL AGENDA

CLINICAL GENOME

conference

THE 3rd ANNUAL

Mining the Genome for Medicine Clinical Genome Conference.com

TCGC

The unstoppable march of genomics into clinical practice continues. In an ideal world, the expanding use of genomic tools will identify disease before the onset of clinical symptoms and determine individualized drug treatment leading to precision medicine. However, many challenges remain or the successful translation of genomic knowledge and technologies into health advances and actionable patient care. Join vital discussions of the applications, questions and solutions surrounding clinical genome analysis.

KEYNOTE SPEAKERS

Atul Butte, M.D., Ph.D.

Division Chief and Associate Professor, Stanford University School of Medicine; Director, Center for Pediatric Bioinformatics, Lucile Packard Children’s Hospital

David Galas, Ph.D.

Principal Scientist, Pacific Northwest Diabetes Research Institute

Gail P. Jarvik, M.D., Ph.D.

Head, Division of Medical Genetics, Arno G. Motulsky Endowed Chair in Medicine and Professor, Medicine and Genome Sciences, University of Washington Medical Center

John Pfeifer, M.D., Ph.D.

Vice Chair, Clinical Affairs, Pathology and Immunology; Professor, Pathology and Immunology, Washington University

John Quackenbush, Ph.D.

Professor, Dana-Farber Cancer Institute and Harvard School of Public Health; Co-Founder and CEO, GenoSpace

Topics Include:

• Working with the Payer Process

• Genome Variation and Clinical Utility

• NGS Is Guiding Therapies

• NGS Is Redefining Genomics

• Interpretation and Translation to the Client

• Integrating Genomic Data into the Clinic

ClinicalGenomeConference.com

Cambridge Healthtech Institute

250 First Avenue, Suite 300

Needham, MA 02494

www.healthtech.com

 

TUESDAY, JUNE 10

7:30 am Conference Registration and Morning Coffee

Working with the Payer Process

8:30 Chairperson’s Opening Remarks

»»KEYNOTE PRESENTATION

8:45 Case Study on Working through the Payer Process

John Pfeifer, M.D., Ph.D., Vice Chair, Clinical Affairs, Pathology; Professor,

Pathology and Immunology; Professor, Obstetrics and Gynecology, Washington

University School of Medicine

If next-generation sequencing (NGS) is to become a part of patient care in routine clinical practice (whether in the setting of oncology or in the setting of inherited genetic disorders), labs that perform clinical NGS must be reimbursed for the testing they provide. Genomics and Pathology Services at Washington University in St. Louis (GPS@WUSTL) will be used as a case study of a national reference lab that has been successful in achieving high levels of reimbursement for the clinical NGS testing it performs, including from private payers. The reasons for GPS’s success will be discussed, including NGS test design, clinical focus of testing, use of different models for reimbursement and payer education.

9:30 Implementation of Clinical Cancer Genomics within an Integrated

Healthcare System

Lincoln D. Nadauld, M.D., Ph.D., Director, Cancer Genomics, Intermountain Healthcare

Precision cancer medicine involves the detection of tumor-specific DNA alterations followed by treatment with therapeutics that specifically target the actionable mutations. Significant advances in genomic technologies have now rendered extended genomic analyses of human malignancies technologically and financially feasible for clinical adoption. Intermountain Healthcare, an integrated healthcare delivery system, is taking advantage of these advances to programmatically implement genomics into the regular treatment of cancer patients to improve clinical outcomes and reduce treatment costs.

10:00 PANEL DISCUSSION:

Payer’s Dilemma: Evolution vs. Revolution

As falling genome sequencing costs help clinicians refine patient diagnoses and therapeutic approaches, new complexities arise over insurance coverage of such tests, classification by CPT codes and other reimbursement issues. Experts on this panel will discuss payer challenges and changes—both rapid and gradual—occurring alongside these advances in clinical genomics.

Moderator: Katherine Tynan, Ph.D., Business Development & Strategic Consulting for Diagnostics

Companies, Tynan Consulting LLC

Panelists:

Tonya Dowd, MPH, Director, Reimbursement Policy and Market Access, Quorum Consulting

Mike M. Moradian, Ph.D., Director of Operations and Molecular Genetics Scientist, Kaiser

Permanente Southern California Regional Genetics Laboratory

Rina Wolf, Vice President of Commercialization Strategies, Consulting and Industry Affairs, XIFIN

Additional Panelists to be Announced

10:45 Networking Coffee Break

11:15 Beyond Genomics: Preparing for the Avalanche of Post-Genomic

Clinical Findings

Jimmy Lin, M.D., Ph.D., President, Rare Genomics Institute

Whole genomic and exomics sequencing applied clinically is revealing newly discovered genes and syndromes at an astonishing rate. While clinical databases and variant annotation continue to grow, much of the effort needed is functional analysis and clinical correlation. At RGI, we are building a comprehensive functional genomics platform that includes electronic health records, biobanking, data management, scientific idea crowdsourcing and contract research sourcing.

11:45 The MMRF CoMMpass Clinical Trial: A Longitudinal Observational

Trial to Identify Genomic Predictors of Outcome in Multiple Myeloma

Jonathan J. Keats, Ph.D., Assistant Professor, Integrated Cancer Genomics Division, Translational

Genomics Research Institute

12:15 pm Luncheon Presentation: Sponsored by

Big Data & Little Data – From Patient Stratification

to Precision Medicine

Colin Williams, Ph.D., Director, Product Strategy, Thomson Reuters

Molecular data has the power, when unlocked, to transform our understanding of disease to support drug discovery and patient care. The key to unlocking this potential is ‘humanising’ the data, through tools and techniques, to a level that supports interpretation by Life Science professionals. This talk will focus on strategies for extracting insight from ‘big data’ by shrinking it to ‘little data’, with a focus on applications to support patient stratification in drug discovery and for practising precision medicine in a clinical setting.

Genome Variation and Clinical Utility

1:45 Chairperson’s Remarks

»»KEYNOTE PRESENTATION

1:50 Lessons from the Clinical Sequencing Exploratory

Research (CSER) Consortium: Genomic Medicine

Implementation

Gail P. Jarvik, M.D., Ph.D., Head, Division of Medical Genetics, Arno G. Motulsky Endowed Chair in Medicine and Professor, Medicine and Genome

Sciences, University of Washington Medical Center

Recent technologies have led to affordable genomic testing. However, implementation of genomic medicine faces many hurdles. The Clinical Sequencing Exploratory Research (CSER) Consortium, which includes nine genomic medicine projects, was formed to explore these challenges and opportunities. Dr. Jarvik is the PI of a CSER genomic medicine project and of the CSER coordinating center. She will focus on the frequency of exomic incidental findings, including those of the 56 genes recommended for incidental finding return by the ACMG. The CSER group has annotated the putatively pathogenic and novel variants of the Exome Variant Server (EVS) to estimate the rate of these in individuals of European and African ancestry. Experience with consenting and returning incidental findings will also be reviewed.

2:35 Decoding the Patient’s Genome: Clinical Use of Genome-Wide

Sequencing Data

Elizabeth Worthey, Ph.D., Assistant Professor, Pediatrics & Bioinformatics Program, Human & Molecular Genetics Center, Medical College of Wisconsin

Despite significant advances in our understanding of the genetic basis of disease, genomewide identification and subsequent interpretation of the molecular changes that lead to human disease represent the most significant challenges in modern human genetics.

Starting in 2009 at MCW, we have performed clinical WGS and WES to diagnose patients coming from across all clinical specialties. I will discuss findings, pros and cons in approach, challenges remaining and where we go next.

3:05 Analyzing Variants with a DTC Genetics Database

Brian Naughton, Ph.D., Founding Scientist, 23andMe, Inc.

Sequencing a genome results in dozens of potentially disease-causing variants (VUS). I describe some examples of using the 23andMe database, including quick recontact of participants, to determine if a variant is disease-causing.

3:35 Refreshment Break in the Exhibit Hall with Poster Viewing

 

Genome Interpretation Software Solutions: Software Spotlights

(Sponsorship Opportunities Available)

Obtaining clinical genome data is rapidly becoming a reality, but analyzing and interpreting the data remains a bottleneck. While there are many commercial software solutions and pipelines for managing raw genome sequence data, providing the medical interpretation and delivering a clinical diagnosis will be the critical step in fulfilling the promise of genomic medicine. This session will showcase how genome data analysis companies are streamlining the genomic diagnostic pipeline through:

• Transferring raw sequencing data

• Interpreting genetic variations

• Building new software and cloud-based analysis pipelines

• Investigating the genetic basis of disease or drug response

• Integrating with other clinical data systems

• Creating new medical-grade databases

• Reporting relevant clinical information in a physician-friendly manner

• Continuous learning feedback

4:15 Software Spotlight #1

4:30 Copy Number Variant Detection Using Sponsored by

Next-Generation Sequencing: State of the Art

Alexander Kaplun, Ph.D., Field Applications Scientist, BIOBASE

This talk will provide a short review about the current state of the art in detection of larger variants that have an important role in many diseases such as haplotypes, indels, repeats, copy number variants (CNVs), structural variants (SVs) and fusion genes using NGS methods, and an outlook to their use for pharmacogenomic genotyping.

4:45 Software Spotlight #3

5:00 Software Spotlight #4

5:15 Software Spotlight #5

5:30 Pertinence Metric Enables Hypothesis-Independent Sponsored by

Genome-Phenome Analysis in Seconds

Michael M. Segal, M.D., Ph.D., Chief Scientist, SimulConsult

Genome-phenome analysis combines processing of a genomic variant table and comparison of the patient’s findings to those of known diseases (“phenome”). In a study of 20 trios, accuracy was 100% when using trios with family-aware calling, and close to that if only probands were used. The gene pertinence metric calculated in the analysis was 99.9% for the causal genes. The analysis took seconds and was hypothesis-independent as to form of inheritance or number of causal genes. Similar benefits were found in gene discovery situations.

6:00 Welcome Reception in the Exhibit Hall with Poster Viewing

7:00 Close of Day

WEDNESDAY, JUNE 11

7:30 am Breakfast Presentation (Sponsorship Opportunity Available) or Morning Coffee

NGS Is Guiding Therapies

8:30 Chairperson’s Opening Remarks

8:35 Next-Generation Sequencing Approaches for Identifying Patients

Who May Benefit from PARP Inhibitor Therapy

Mitch Raponi, Ph.D., Senior Director and Head, Molecular Diagnostics, Clovis Oncology

The following questions will be addressed: What biomarkers should we be focusing on to identify appropriate patients who will likely benefit from PARP inhibitors? How can we apply next-generation sequencing technologies to identify all patients who will respond to the PARP inhibitor rucaparib? What regulatory challenges are we faced with for approval of NGS companion diagnostics?

9:05 Whole-Genome and Whole-Transcriptome Sequencing to Guide

Therapy for Patients with Advanced Cancer

Glen J. Weiss, M.D., MBA, Director, Clinical Research, Cancer Treatment Centers of America

Treating advanced cancer with agents that target a single-cell surface receptor, up-regulated or amplified gene product or mutated gene has met with some success; however, eventually the cancer progresses. We used next-generation sequencing technologies (NGS) including whole-genome sequencing (WGS), and where feasible, whole-transcriptome sequencing (WTS) to identify genomic events and associated expression changes in advanced cancer patients. While the initial effort was a slower process than anticipated due to a variety of issues, we demonstrated the feasibility of using NGS in advanced cancer patients so that treatments for patients with progressing tumors may be improved. This lecture will highlight some of these challenges and where we are today in bringing NGS to patients.

9:35 The SmartChip TE™ Target Enrichment System for Sponsored by

Clinical Next-Gen Sequencing

Gianluca Roma, MS MBA, Director, Product Management, WaferGen Biosystems

10:05 Coffee Break in the Exhibit Hall with Poster Viewing

Data Mining

»»KEYNOTE PRESENTATION

10:45 Translating a Trillion Points of Data into

Therapies, Diagnostics and New Insights into Disease

Atul Butte, M.D., Ph.D., Division Chief and Associate Professor, Stanford University School of Medicine; Director, Center for Pediatric Bioinformatics,

Lucile Packard Children’s Hospital; Co-Founder, Personalis and Numedii

There is an urgent need to translate genome-era discoveries into clinical utility, but the difficulties in making bench-to-bedside translations have been well described. The nascent field of translational bioinformatics may help. Dr. Butte’s lab at Stanford builds and applies tools that convert more than a trillion points of molecular, clinical and epidemiological data— measured by researchers and clinicians over the past decade—into diagnostics, therapeutics and new insights into disease. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his lab’s work on using publicly available molecular measurements to find new uses for drugs, including drug repositioning for inflammatory bowel disease, discovering new treatable inflammatory mechanisms of disease in type 2 diabetes and the evaluation of patients presenting with whole genomes sequenced.

11:30 DGIdb – Mining the Druggable Genome

Malachi Griffith, Ph.D., Research Faculty, Genetics, The Genome Institute, Washington University School of Medicine

In the era of high-throughput genomics, investigators are frequently presented with lists of mutated or otherwise altered genes implicated in human disease. Numerous resources exist to generate hypotheses about how such genomic events might be targeted therapeutically or prioritized for drug development. The Drug-Gene Interaction database (DGIdb) mines these resources and provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially druggable genes. DGIdb can be accessed at dgidb.org.

12:00 pm Sponsored Presentation (Opportunity Available)

12:30 Luncheon Presentation (Sponsorship Opportunity Available)

 

The unstoppable march of genomics into clinical practice continues. In an ideal world, the expanding use of genomic tools will identify disease before the onset of clinical symptoms and determine individualized drug treatment leading to precision medicine. However, many challenges remain for the successful translation of genomic knowledge and technologies into health advances and clinical practice.

Bio-IT World and Cambridge Healthtech Institute are again proud to host the Third Annual TCGC: The Clinical Genome Conference, inviting stakeholders from all arenas impacting clinical genomics to share new findings and solutions for advancing the application of clinical genome medicine.

TCGC brings together many constituencies for frank and vital discussion of the applications, questions and solutions surrounding clinical genome analysis, including scientists, physicians, diagnosticians, genetic counselors, bioinformaticists, ethicists, regulators, insurers, lawyers and administrators.

Topics addressing successful translation of genomic knowledge and technologies into advancement of clinical utility (medicines and diagnostics) include but are not limited to:

Scientific Investigation and Interpretation

  • Technologies/Platforms
  • WGS/Exome/Single-Cell Sequencing
  • Drug and Diagnostic Targets
  • Interpretation and Analysis Pipelines
  • Case Studies

Clinical Integration and Implementation

  • Mechanisms to Monitor Genomic Medicine
  • Determining Clinical Utility
  • Standardization/Regulation/Certification
  • Reimbursement
  • Data Management
  • Diagnostic Lab Infrastructure
  • HIT/Data Integration
  • Reporting Results to Patients/Physicians

Call for Speakers
For a limited time, we are inviting researchers and clinicians applying genome analysis tools in clinical settings, as well as regulators and administrators implementing genomics into the clinic, to submit proposals for platform presentations. Please note that due to limited speaking slots, preference is given to abstracts from those within pharmaceutical and biopharmaceutical companies, regulators and those from academic centers. Additionally, as per CHI policy, a select number of vendors/consultants who provide products and services to these genomic researchers are offered opportunities for podium presentation slots based on a variety of Corporate Sponsorships.

All proposals are subject to review by the organizers and Scientific Advisory Committee.

Please click here to submit a proposal.

Submission deadline for priority consideration: November 15, 2013

For more details on the conference, please contact:
Mary Ann Brown
Executive Director, Conferences
Cambridge Healthtech Institute
250 First Avenue, Suite 300
Needham, MA 02494
T:  781-972-5497
E:  mabrown@healthtech.com

For exhibit and sponsorship opportunities, please contact:
Jay Mulhern
Manager, Business Development, Conferences & Media
Cambridge Healthtech Institute
250 First Avenue, Suite 300
Needham, MA 02494
T: 781-972-1359
E: jmulhern@healthtech.com

SOURCE

http://www.clinicalgenomeconference.com/

 

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Cardiology, Genomics and Individualized Heart Care: Framingham Heart Study (65 y-o study) & Jackson Heart Study (15 y-o study)

Posted in Atherogenic Processes & Pathology, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cardiovascular Pharmacogenomics, Cardiovascular Research, Cerebrovascular and Neurodegenerative Diseases, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Coagulation Therapy and Internal Bleeding, Computational Biology/Systems and Bioinformatics, Diabetes Mellitus, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Delivery Platform Technology, Electrophysiology, FDA Regulatory Affairs, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, HTN in Youth, Interviews with Scientific Leaders, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical R&D Investment, Pharmacogenomics, Pharmacotherapy of Cardiovascular Disease, Population Health Management, Genetics & Pharmaceutical, Proteomics, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, Translational Research, tagged , , , , , on December 1, 2013| Leave a Comment »

Cardiology, Genomics and Individualized Heart Care

Curator: Aviva Lev-Ari, PhD, RN

Article ID #90: Cardiology, Genomics and Individualized Heart Care: Framingham Heart Study (65 y-o study) & Jackson Heart Study (15 y-o study). Published on 12/1/2014

WordCloud Image Produced by Adam Tubman

 

The topic of Cardiology, Genomics and Individualized Heart Care is been developed in the following forthcoming e-Book on a related subject matter:

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

This e-Book has the following Parts:

PART 1
Genomics and Medicine

Introduction to Volume Three
1.1: Genomics and Medicine: The Physician’s View
1.2: Ribozymes and RNA Machines – Work of Jennifer A. Doudn
1.3: Genomics and Medicine: The Geneticist’s View
1.4: Genomics in Medicine – Establishing a Patient-Centric View of Genomic Data

PART 2
Epigenetics- Modifiable Factors Causing Cardiovascular Diseases

2.1 Diseases Etiology

2.1.1 Environmental Contributors Implicated as Causing Cardiovascular Diseases
2.1.2 Diet: Solids and Fluid Intake
2.1.3 Physical Activity and Prevention of Cardiovascular Diseases
2.1.4 Psychological Stress and Mental Health: Risk for Cardiovascular Diseases
2.1.5 Correlation between Cancer and Cardiovascular Diseases
2.1.6 Medical Etiologies for Cardiovascular Diseases: Evidence-based Medicine – Leading DIAGNOSES of Cardiovascular Diseases, Risk Biomarkers and Therapies
2.1.7 Signaling Pathways
2.1.8 Proteomics and Metabolomics

2.2 Assessing Cardiovascular Disease with Biomarkers

2.2.1 Issues in Genomics of Cardiovascular Diseases
2.2.2 Endothelium, Angiogenesis, and Disordered Coagulation
2.2.3 Hypertension BioMarkers
2.2.4 Inflammatory, Atherosclerotic and Heart Failure Markers
2.2.5 Myocardial Markers

2.3  Therapeutic Implications: Focus on Ca(2+) signaling, platelets, endothelium

2.3.1 The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors

2.3.2 Platelets in Translational Research ­ 2

2.3.3 The Final Considerations of the Role of Platelets and Platelet Endothelial Reactions in Atherosclerosis

2.3.4 Nitric Oxide Synthase Inhibitors (NOS-I)

2.3.5 Resistance to Receptor of Tyrosine Kinase

2.3.6 Oxidized Calcium Calmodulin Kinase and Atrial Fibrillation

2.3.7 Advanced Topics in Sepsis and the Cardiovascular System at its End Stage

2.4 Comorbidity of Diabetes and Aging

PART 3
Determinants of Cardiovascular Diseases
Genetics, Heredity and Genomics Discoveries

Introduction
3.1 Why cancer cells contain abnormal numbers of chromosomes (Aneuploidy)
3.2 Functional Characterization of Cardiovascular Genomics: Disease Case Studies @ 2013 ASHG
3.3 Leading DIAGNOSES of Cardiovascular Diseases covered in Circulation: Cardiovascular Genetics, 3/2010 – 3/2013
3.4  Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease

PART 4
Individualized Medicine Guided by Genetics and Genomics Discoveries

4.1 Preventive Medicine: Cardiovascular Diseases
4.2 Gene-Therapy for Cardiovascular Diseases
4.3 Congenital Heart Disease/Defects
4.4 Pharmacogenomics for Cardiovascular Diseases

SOURCE

http://pharmaceuticalintelligence.com/biomed-e-books/series-a-e-books-on-cardiovascular-diseases/volume-three-etiologies-of-cardiovascular-diseases-epigenetics-genetics-genomics/

The Next Frontier in Heart Care

Research Aims to Personalize Treatment With Genetics

Nov. 25, 2013 7:18 p.m. ET

VIEW VIDEO

http://online.wsj.com/news/articles/SB10001424052702304281004579220373600912930#!

Two influential heart studies are joining forces to bring the power of genetics and other 21st century tools to battle against heart disease and stroke. Ron Winslow and study co-director Dr. Vasan Ramachandran explain. Photo: Shubhangi Ganeshrao Kene/Corbis.

Scientists from two landmark heart-disease studies are joining forces to wield the power of genetics in battling the leading cause of death in the U.S.

Cardiologists have struggled in recent years to score major advances against heart disease and stroke. Although death rates have been dropping steadily since the 1960s, progress combating the twin diseases has plateaued by other measures.

Genetics has had a profound impact on cancer treatment in recent years. Now, heart-disease specialists hope genetics will reveal fresh insight into the interaction between a

  • person’s biology,
  • living habits and
  • medications

that can better predict who is at risk of a heart attack or stroke.

“There’s a promise of new treatments with this research,” said Daniel Jones, chancellor of the University of Mississippi and former principal investigator of the 15-year-old Jackson Heart Study, a co-collaborator in the new genetics initiative.

Scienc e Source /Photo Researchers Inc. (hearts); below, l-r: Boston University; Robert Jordan/Univ. of Miss.; Jay Ferchaud/Univ. of Miss Medical Center

Prevention efforts also could improve with the help of genetics research, Dr. Jones said. For example, an estimated 75 million Americans currently have high blood pressure, or hypertension, but only about half of those are able to control it with medication. It can take months of trial-and-error for a doctor to get the right dose or combination of pills for a patient. Researchers hope genetic and other information might enable doctors to identify subgroups of hypertension that respond to specific treatments and target patients with an appropriate therapy.

Also collaborating on the genetics project is the 65-year-old Framingham Heart Study. Its breakthrough findings decades ago linked heart disease to such factors as smoking, high blood pressure and high cholesterol. Framingham findings have been a foundation of cardiovascular disease prevention policy for a half-century.

More than 15,000 people have participated in the Framingham study. The Jackson study, with more than 5,000 participants, was launched in 1998 to better understand risk factors in African-Americans, who were underrepresented in Framingham and who bear a higher burden of cardiovascular disease than the rest of the population. Both studies are funded by the National Heart, Lung, and Blood Institute, part of the National Institutes of Health.

Exactly how the collaboration, announced last week, will proceed hasn’t been determined. One promising area is the “biobank,” the collection of more than one million blood and other biological samples gathered during biennial checkups of Framingham study participants going back more than a half century.

The samples are stored in freezers in an underground earthquake-proof facility in Massachusetts, said Vasan Ramachandran, a Boston University scientist who takes over at the beginning of next year as principal investigator of the Framingham Heart Study. Another 40,000 samples from the Jackson study are kept in freezers in Vermont. By subjecting samples to DNA sequencing and other tests, researchers say they may be able to identify variations linked to progression of cardiovascular disease—or protection from it.

Each study is likely to enroll new participants as part of the collaboration to allow tracking of risk factors and diet and exercise habits, for instance, in real time instead of only during infrequent checkups.

Heart disease is linked to about 800,000 deaths a year in the U.S. In 2010, some 200,000 of those deaths could have been avoided, including more than 112,300 deaths among people younger than 65, according to a recent analysis by the Centers for Disease Control and Prevention. But those avoidable deaths reflected a 3.8% per year decline in mortality rates during the previous 10 years.

Now, widespread prevalence of obesity and diabetes threatens to undermine such gains. And a large gap remains between how white patients and minorities—especially African-Americans—benefit from effective strategies.

There have been few new transformative cardiovascular treatments since the mid-1980s to early 1990s, when a stream of large-scale trials of new agents ranging from clot-busters to treat heart attacks to the mega class of statins electrified the cardiology field with evidence of significant improvements in survival from the disease. One reason: Some of those remedies have proven tough to beat with new treatments.

What’s more, use of the current menu of medicines for reducing heart risk remains an imprecise art. Besides

  • blood pressure drugs,
  • cholesterol-lowering statins

also are widely prescribed. Drug-trial statistics show that to prevent a single first heart attack in otherwise healthy patients can require prescribing a statin to scores of patients, but no one knows for sure who actually benefits and who doesn’t.

“It would be great if we could make some more paradigm-shifting discoveries,” said Michael Lauer, director of cardiovascular sciences at the NHLBI, which is a part of the National Institutes of Health.

Finding new treatments isn’t the only aim of the new project. “You could use existing therapies smarter,” said Joseph Loscalzo, chairman of medicine at Brigham and Women’s Hospital in Boston.

The American Heart Association launched the initiative and has committed $30 million to it over the next five years. The AHA sees the project as critical to its goal to achieve a 20% improvement in cardiovascular health in the U.S. while also reducing deaths from heart disease and stroke by 20% for the decade ending in 2020, said Nancy Brown, the nonprofit organization’s chief executive.

The Jackson study has already identified characteristics of cardiovascular risk among African-American patients “that may have promise for new insights” in a collaborative effort, said Adolfo Correa, professor of medicine and pediatrics at University of Mississippi Medical Center and interim director of the Jackson study.

For instance, there is a higher prevalence of obesity among Jackson participants than seen in the Framingham cohorts. Obesity is associated with high blood pressure, diabetes and cardiovascular risk. Diabetes is also more prevalent among blacks than whites.

But African-Americans of normal weight appear to have higher rates of hypertension and diabetes than whites of normal weight. “The question is, should [measures] for defining diabetes be different or the same for the [different] populations and are they associated with the same risk of cardiovascular disease?” said Dr. Correa. The collaboration, he said, may provide better comparisons.

Researchers, who plan to use tools other than genetics, think more might be learned about blood pressure and heart and stroke risk by monitoring patients in real time using mobile devices rather than taking readings only in periodic office visits. For example, high blood pressure during sleep or spikes during exercise could indicate risks that don’t show up in a routine measurement in the doctors’ office.

A big challenge is making sense of the huge amounts of data involved in sequencing DNA and linking it to

  • medical records,
  • diet and
  • exercise habits and other variables that influence risk.

“The analytical methods for sorting out these complex relationships are still in evolution,” said Dr. Loscalzo, of Brigham and Women’s Hospital. “The cost of sequencing is getting cheaper and cheaper. The hard part is analyzing the data.”

Write to Ron Winslow at ron.winslow@wsj.com

SOURCE

http://online.wsj.com/news/articles/SB10001424052702304281004579220373600912930#!

The e-Reader is advised to to review tightly related articles in

http://pharmaceuticalintelligence.com/biomed-e-books/series-a-e-books-on-cardiovascular-diseases/volume-three-etiologies-of-cardiovascular-diseases-epigenetics-genetics-genomics/

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Searchable Genome for Drug Development

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmacogenomics, Population Health Management, Genetics & Pharmaceutical, Proteomics, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , on December 1, 2013| Leave a Comment »

Searchable Genome for Drug Development

Reporter: Aviva Lev-Ari, PhD, RN

The Druggable Genome Is Now Googleable

By Aaron Krol

November 22, 2013 | Relationships between human genetic variation and drug responses are being documented at an accelerating rate, and have become some of the most promising avenues of research for understanding the molecular pathways of diseases and pharmaceuticals alike. Drug-gene interactions are a cornerstone of personalized medicine, and learning about the drugs that mediate gene expression can point the way toward new therapeutics with more targeted effects, or novel disease targets for existing drugs. So it may seem surprising that, until October of this year, a researcher interested in pharmacogenetics generally needed the help of a dedicated bioinformatician just to access the known background on a gene’s drug associations.

Obi and Malachi Griffith are particularly dedicated bioinformaticians, who specialize in applying data analytics to cancer research, a rich field for drug-gene information. Like many professionals in their budding field, the Griffiths pursued doctoral research in bioinformatics applications at a time when this was not quite recognized as a distinct discipline, and quickly found their data-mining talents in hot demand. “We found ourselves answering the same questions over and over again,” says Malachi. “A clinician or researcher, who perhaps wasn’t a bioinformatician, would have a list of genes, and would ask, ‘Well, which of these genes are kinases? Which of these genes has a known drug or is potentially druggable?’ And we would spend time writing custom scripts and doing ad hocanalyses, and eventually decided that you really shouldn’t need a bioinformatics expert to answer this question for you.”

The Griffiths – identical twin brothers, though Malachi helpfully sports a beard – had by this time joined each other at one of the world’s premiere genomic research centers, the Genome Institute at Washington University in St. Louis, and figured they had the resources to improve this state of affairs. The Genome Institute is generously funded by the NIH and was a major contributor to the Human Genome Project; the Griffiths had congregated there deliberately after completing post-doctoral fellowships at the Lawrence Berkeley National Laboratory in California (Obi) and the Michael Smith Genome Sciences Centre in Vancouver (Malachi). “When we finished our PhDs, we knew we would like to set up a lab together,” says Obi. At the Genome Institute, they pitched the idea of building a free, searchable online database of drug-gene associations, and soon the Drug Gene Interaction Database (DGIdb) was under development.

In Search of the Druggable Genome

Existing public databases, like DrugBank, the Therapeutic Target Database, and PharmGKB, were the first ports of call, where a wealth of information was waiting to be re-aggregated in a searchable format. “For their use cases [these databases] are quite powerful,” says Obi. “They were just missing that final component, which is user accessibility for the non-informatics expert.” Getting all this data into DGIdb was and remains the most labor-intensive part of the project. At least two steps removed from the original sources establishing each interaction, the Griffiths felt they had to reexamine each data point, tracing it back to publication and scrutinizing its reliability. “It’s sort of become a rite of passage in our group,” says Malachi. “When new people join the lab, they have to really dig into this resource, learn what it’s all about, and then contribute some of their time toward manual curation.”

The website’s main innovation, however, is its user interface, which presents itself like Google but returns results a little more like a good medical records system. The homepage lets you enter a gene or panel of genes into a search box, and if desired, add a few basic filters. Entering search terms brings up a chart that quickly summarizes any known drug interactions, which can then be further filtered or tracked back to the original sources. The emphasis is not on a detailed breakdown of publications or molecular behavior, but on immediately viewing which drugs affect a given gene’s expression and how. “We did try to place quite a bit of emphasis on creating something that was intuitive and easy to use,” says Malachi. Beta testing involved watching unfamiliar users navigate the website and taking notes on how they interacted with the platform.

DGIdb went live in February of this year, followed by a publication in Nature Methods this October, and the database is now readily accessible at http://dgidb.org/. The code is open source and can be modified for any specific use case, using the Perl, Ruby, Shell, or Python programming languages, and the Genome Institute has also made available their internal API for users who want to run documents through the database automatically, or perform more sophisticated search functions. User response will be key to sustaining and expanding the project, and the Griffiths are looking forward to an update that draws on outside researchers’ knowledge. “A lot of this information [on drug-gene interactions] really resides in the minds of experts,” says Malachi, “and isn’t in a form that we can easily aggregate it from… We’re really motivated to have a crowdsourcing element, so that we can start to harness all of that information.” In the meantime, the bright orange “Feedback” button on every page of the site is being bombarded with requests to add specific interactions to the database.

Not all these interactions are easy to validate. “Another area that we’re really actively trying to pursue,” adds Malachi, “is getting information out of sources where text mining is required, where information is really not in a form where the interaction between genes and drugs is laid out quickly.” He cites the example of clinicaltrials.gov, where the results of all registered clinical trials in the United States are made available online. This surely includes untapped material on drug-gene interactions, but nowhere are those results neatly summarized. “You either have a huge manual curation problem on your hands – there’s literally hundreds of thousands of clinical trial records – or you have to come up with some kind of machine learning, text-mining approach.” So far, the Genome Institute has been limited to manual curation for this kind of scenario, but with a resource as large as the clinical trials registry, the Griffiths hope to bring their programming savvy to bear on a more efficient attack.

In the meantime, new resources are continuously being brought into the database, rising from eleven data sources on launch to sixteen now, with more in the curation pipeline. DGIdb is already regularly incorporated in the Genome Institute’s research. Every cancer patient sequenced at Washington University has her genetic data run first through an analytics pipeline to find genes with unusual variants or levels of expression, and then through DGIdb to see whether any of these genes are known to be druggable. This is an ideal use case for the database, which is presently biased toward cancer-related interactions, the Griffiths’ own area of research.

The twins have a personal investment in advancing cancer therapeutics. Their mother died in her forties from an aggressive case of breast cancer, while Obi and Malachi were still in high school, and their family has continued to suffer disproportionately from cancer ever since. Says Obi, “We’ve had the opportunity to see [everything from] terrible, tragic outcomes… to the other end of the spectrum, where advances in the way cancer is treated were able to really make a huge difference to both our cousin and our brother,” both in remission after life-threatening cases of childhood leukemia and Ewing’s sarcoma, respectively. “Everyone can tell these stories,” Malachi adds, “but we’ve had a little more than our fair share.”

DGIdb can’t influence cancer care directly – most of the data available on drug-gene interactions is too tentative for clinical use – but it can spur research into more personalized treatments for genetically distinct cancers, and increasingly for other diseases as more information is brought inside. Meanwhile, companies like Foundation Medicine and MolecularHealth are drawing on similar drug-gene datasets, narrowed down to the most actionable information, to tailor clinical action to individual cancer patients. The Griffiths are cautiously optimistic that research like the Genome Institute’s is approaching the crucial tipping point where finely tuned clinical decisions could be made based on a patient’s genetic profile. “We’re still firmly on the academic research side,” says Malachi, but “we’re definitely at the stage where this idea needs to be pursued aggressively.”

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Liver Toxicity halts Clinical Trial of IAP Antagonist for Advanced Solid Tumors

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, BioSimilars, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Genetics & Innovations in Treatment, Genome Biology, Health Economics and Outcomes Research, Health Law & Patient Safety, Human Immune System in Health and in Disease, Liver & Digestive Diseases Research, Personalized and Precision Medicine & Genomic Research, tagged , , , , , , , , , , , , , , , , , , , , , on November 25, 2013| 1 Comment »

Liver Toxicity halts Clinical Trial of IAP Antagonist for Advanced Solid Tumors

Curator: Stephen J. Williams, Ph.D.

UPDATED 8/12/2022

Athough not related to IAP Antagonists this update does report 2 deaths from IDILI or idiosynchratic drug induced liver injury from a gene therapy trial using an AAV (adeno associated virus) targeting the disease spinal muscular atrophy.  Please see below after reading about IDILI.

 

A recent press release on FierceBiotech reported the FDA had put a halt on a phase 1 study for advanced refractory solid tumors and lymphomas of Curis Inc. oral inhibitor of apoptosis (IAP) antagonist CUDC-427.  The FDA placed the trial on partial clinical hold following reports of a death of a patient from severe liver failure.  The single-agent, dose escalation Phase 1 study was designed to determine the maximum tolerated dose and recommended doses for a Phase 2 trial. The press release can be found at:

http://www.fiercebiotech.com/press-releases/curis-reports-third-quarter-2013-financial-results-and-provides-cudc-427-de.

According to the report one patient with breast cancer that had metastasized to liver, lungs, bone, and ovaries developed severe hepatotoxicity as evidenced by elevated serum transaminase activities (AST and ALT) and hyper-billirubinemia.  Serum liver enzyme activities did not attenuate upon discontinuation of CUDC-427.  This was unlike prior experience to the CUDC-427 drug, in which decreased hepatic function was reversed upon drug discontinuation.  The patient died from liver failure one month after discontinuation of CUDC-427.

It was noted that no other patient had experienced such a serious, irreversible liver dysfunction.

Although any incidence of hepatotoxicity can be cause for concern, the incidence of IDIOSYNCRATIC IRREVERSIBLE HEPATOTOXICITY warrants a higher scrutiny.

Four general concepts can explain toxicity profiles and divergences between individuals:

  1. Toxicogenomics: Small differences in the genetic makeup between individuals (such as polymorphisms (SNP) could result in differences in toxicity profile for a drug.  This ais a serious possibility as only one patient presented with such irreversible liver damage
  2. Toxicodynamics:  The toxicologic effect is an extension of the pharmacologic mechanism of action (or  lack thereof: could there have been alternate signaling pathways activated in this patient or noncanonical mechanism)
  3. Toxicokinetic:  The differences in toxicological response due to differences in absorption, distribution, metabolism, excretion etc. (kinetic parameters)
  4. Idiosyncratic: etiology is unknown; usually a minority of adverse effects

 

Since there is not enough information to investigate toxicogenomic or toxicokinetic mechanisms for this compound, the rest of this post will investigate the possible mechanisms of hepatotoxicity due to IAP antagonists and clues from other clinical trials which might shed light on a mechanism of toxicity (toxicodynamic) or idiosyncratic events.

Therefore this post curates the current understanding of drug-induced liver injury (DILI), especially focusing on a type of liver injury referred to as idiosyncratic drug-induced liver injury (IDILI) in the context of:

  1. Targeted and newer chemotherapies such as IAP antagonists
  2. Current concepts of mechanisms of IDILI including:

i)        Inflammatory responses provoked by presence of disease

ii)      Cellular stresses, provoked by disease, uncovering NONCANONICAL toxicity pathways

iii)    Pharmacogenomics risk factors of IDILI

Eventually this post aims to stimulate the discussion: 

  • Given inflammation, genetic risk factors, and cellular stresses (seen in clinical setting) have been implicated in idiosyncratic drug-induced liver injury from targeted therapies, should preclinical hepatotoxicity studies also be conducted in the presence of the metastatic disease?
  • Does inflammation and cellular stress from clinical disease unmask NONCANONICAL pharmacologic and/or toxicological mechanisms of action?

Classification of types of Cellular Liver injury:  A listing of types of cellular injury is given for review

I.     Hepatic damage after Acute Exposure

A. Cytotoxic (Necrotic):  irreversible cell death characterized by loss of cell membrane integrity, intracellular swelling, nuclear shrinkage (pyknosis) and eventual cytoplasmic breakdown of nuclear DNA (either by a process known as karyolysis or karyorhexus) localized inflammation as a result of release of cellular constituents.  Intracellular ATP levels are commonly seen in necrotic death.  Necrosis, unlike apoptosis, does not require a source of ATP.  A nice review by Yoshihide Tsujimoto describing and showing (by microscopy) the  differences between apoptosis and necrosis can be found here.

B. Cholestatic:  hepatobiliary dysfunction with bile stasis and accumulation of bile salts.  Cholestatic injury can result in lipid (particularly cholesterol) accumulation in cannicular membranes resulting in decreased permeability of the membrane, hyperbillirubinemia and is generally thought to result in metabolic defects.

C. Lipid Peroxidation: free radical generation producing peroxide of cellular lipids, generally resulting in a cytotoxic cell death

II.     Hepatic damage after Chronic Exposure

A. Chirrotic: Chronic morphologic alteration of the liver characterized by the presence of septae of collagen distributed throughout the major portion of the liver; Forms fibrous sheaths altering hepatic blood flow, resulting in a necrotic process with scar tissue; Alteration of hepatic metabolic systems.

B. Carcinogenesis

III. Idiosyncratic Drug Induced Liver Injury

The aforementioned mechanisms of hepatotoxicity are commonly referred to as the “intrinsic” (or end target-organ) toxicity mechanisms.  Idiosyncratic drug-induced liver injury (IDILI) is not well understood but can be separated into allergic and nonallergic reactions.  Although the risk of acute liver failure associated with idiosyncratic hepatotoxins is low (about 1 in ten thousand patients) there are more than 1,000 drugs and herbal products associated with this type of toxic reaction. Idiosyncratic drug induced liver failure usually gets a black box warning from the FDA. Idiosyncratic drug-induced liver injury differs from “intrinsic” toxicity in that IDILI:

  • Happens in a minority of patients (susceptible patients)
  • Not reproducible in animal models
  • Not dose-dependent
  • Variable time of onset
  • Variable liver pathology (not distinctive lesions)
  • Not related to drug’s pharmacologic mechanism of action (trovafloxacin IDILI vs. levofloxacin)

A great review in Perspectives in Pharmacology written by Robert Roth and Patricia Ganey at Michigan State University explains these differences between intrinsic and idiosyncratic drug-induced hepatotoxicity[1] (however authors do note that there are many similarities between the two mechanisms).    It is felt that drug sensitivity (allergic) and inflammatory responses (nonallergic) may contribute to the occurrence of IDILI.  For instance lipopolysaccharide (LPS) form bacteria can potentiate acetaminophen toxicity.  In fact animal models of IDILI have been somewhat successful:

  • co-treatment of rats and mice with nontoxic doses of trovafloxacin (casues IDILI in humans) and LPS resulted in marked hepatotoxicity while no hepatotoxicity seen with levofloxacin plus LPS[2]
  • correlates well with incidence of human IDILI (adapted from a review Inflammatory Stress and Idiosyncratic Hepatotoxicity: Hints from Animal Models (in Pharmacology Reviews)[3].  Idiosyncratic injury damage has been reported for diclofenac, halothane, and sulinac.  These drugs also show hepatotoxicity in the LPS model for IDILI.
  • Roth and Ganey suggest the reason why idiosyncratic hepatotoxicity is not seen  in most acute animal toxicity studies is that, in absence of stress/inflammation  IDILI occurrence is masked by lethality but stress/inflammation shifts increases sensitivity to liver injury at a point before lethality is seen

IDILdosestressrossmantheory

Figure.  Idiosyncratic toxic responses of the liver.    In the absence of stress and/or genetic factors, drug exposure may result in an idiosyncratic liver injury (IDILI) at a point (or dose) beyond the therapeutic range and lethal exposure for that drug.  Preclinical studies, usually conducted at sublethal doses, would not detect DILI .  Stress and/or genetic factors sensitize the liver to toxic effects of the drug (synergism) and DILI is detected at exposure levels closer to therapeutic range.  Note IDILI is not necessarily dose-dependent but cellular stress (like ROS or inflammation) may expose NONCANONICAL mechanisms of drug action or toxicity which result in IDILI. Model adapted from Roth and Ganey.

What Stress factors contribute to IDILI?

Various stresses including inflammation from bacterial, viral infections ,inflammatory cytokines  and stress from reactive oxygen (ROS) have been suggested as mechanisms for IDILI.

  1. Inflammation/Cytokines (also discussed in other sections of this post):  Inflammation has long been associated with human cases of DILI.    Many cytokines and inflammatory mediators have been implicated including TNFα, IL7, TGFβ, and IFNϒ (viral infection) leading some to conclude that serum measurement of cytokines could be a potential biomarker for DILI[4].  In addition, ROS (see below) is generated from inflammation and also considered a risk factor for DILI[5].
  2. Reactive Oxygen (ROS)/Reactive Metabolites: Oxidative stress, either generated from reactive drug metabolites or from mitochondrial sources, has been shown to be involved in apoptotic and necrotic cell death.  Both alterations in the enzymes involved in the generation of and protection from ROS have been implicated in increased risk to DILI including (as discussed further) alterations in mitochondrial superoxide dismutase 2 (SOD2) and glutathione S-transferases.  Both ROS and inflammatory cytokines can promote JNK signaling, which has been implicated in DILI[6].

Dr. Neil Kaplowitz suggested that we:

“develop a unifying hypothesis that involves underlying genetic or acquired mitochondrial abnormalities as a major determinant of susceptibility for a number of drugs that target mitochondria and cause DILI. The mitochondrial hypothesis, implying gradually accumulating and initially silent mitochondrial injury in heteroplasmic cells which reaches a critical threshold and abruptly triggers liver injury, is consistent with the findings that typically idiosyncratic DILI is delayed (by weeks or months), that increasing age and female gender are risk factors and that these drugs are targeted to the liver and clearly exhibit a mitochondrial hazard in vitro and in vivo. New animal models (e.g., the Sod2(+/-) mouse) provide supporting evidence for this concept. However, genetic analyses of DILI patient samples are needed to ultimately provide the proof-of-concept”[7].

Clin Infect Dis. 2004 Mar 38(Supplement 2) S44-8, Figure 1

Clin Infect Dis. 2004 Mar 38(Supplement 2) S44-8, Figure 3

Figures. Mechanisms of Drug-Induced Liver Injury and Factors related to the occurrence of  DILI (used with permission from Oxford Press; reference [7])

To this end, Dr. Brett Howell and other colleagues at the Hamner-UNC Institute for Drug Safety Sciences (IDSS) developed an in-silico model of DILI ( the DILISym™ model)which is based on  depletion of cellular ATP and reactive metabolite formation as indices of DILI.

Have there been Genetic Risk Factors identified for DILI?

Candidate-gene-associated studies (CGAS) have been able to identify several genetic risk factors for DILI including:

  1. Uridine Diphosphate Glucuronosyltransferase 2B7 (UGT2B7): variant increased susceptibility to diclofenac-induced DILI
  2. Adenosine triphosphate-binding cassette C2 (ABCC2) variant ABCC-24CT increased susceptibility to diclofenac-induced DILI
  3. Glutathione S-transferase (GSTT1): patients with a double GSTT1-GSTM1 null genotype had a significant 2.7 fold increased risk of DILI from nonsteroidal anti-inlammatory agents, troglitazone and tacrine.  GSTs are involved in the detoxification of phase 1 metabolites and also protect against cellular ROS.

Although these CGAS confirmed these genetic risk factors,  Stefan Russman suggests a priori genome-wide association studies (GWAS) might provide a more complete picture of genetic risk factors for DILI as CGAS is limited due to

  1. Candidate genes are selected based on current mechanisms and knowledge of DILI so genetic variants with no known knowledge of or mechanistic information would not be detected
  2. Many CGAS rely on analysis of a limited number of SNP and did not consider intronic regions which may control gene expression

A priori GWAS have the advantage of being hypothesis-free, and although they may produce a high number of false-positives, new studies of genetic risk factors of ximelagatran, flucioxaciliin and diclofenac-induced liver injury are using a hybrid approach which combines the whole genome and unbiased benefits of GWAS with the confirmatory and rational design of CGAS[8-10].

Even though idiosyncratic DILI is rare, the severity, unpredictable onset, and unknown etiology and risk factors have prompted investigators such as Stefan Russmann from University Hospital Zurich and Ignazio Grattagliano from University of Bari to suggest:

Identification of risk factors for rare idiosyncratic hepatotoxicity requires special networks that contribute to data collection and subsequent identification of environmental as well as genetic risk factors for clinical cases of idiosyncratic DILI[11].

Therefore, a DILI network project (DILIN) had been developed to collect samples and detailed genetic and clinical data on IDILI cases from multiple medical centers.  The project aims to identify the upstream and downstream genetic risk factors for IDILI[12].  Please see a SlideShare presentation here of the goals of the DILI network project.

Drs Colin Spraggs and Christine Hunt had reviewed possible genetic risk factors of DILI seen with various tyrosine kinase inhibitors (TKIs) including Lapatinib (Tykerb/Tyverb©, a dual inhibitor of  HER2/EGFR heterodimer) and paopanib (Votrient©; a TKI that targets VEGFR1,2,3 and PDGFRs)[13].

From a compilation of studies:

  • Elevation in serum bilirubin during treatment with lapatinib and pazopanib are associated with UGT1A1 polymorphism related to Gilbert’s syndrome (a clinically benign syndrome)
  • Anecdotal evidence shows that polymorphisms of lapatinib and pazopanib metabolizing enzymes may contribute to differences seen in onset of DILI
  • Pazopanib-induced elevations of ALT correlate with HFE variants, suggesting alterations in iron transport may predispose to DILI
  • Strong correlations between lapatinib-induced DILI and class II HLA locus suggest inflammatory stress response important in DILI

Note that these clinical findings were not evident from the preclinical tox studies. According to the European Medicines Agency assessment report for Tykerb states: “the major findings in repeat dose toxicity studies were attributed to lapatinib pharmacology (epithelial effect in skin and GI system.  The toxic events occurred at exposures close to the human exposure at the recommended dose.  Repeat-dose toxicity studies did not reveal important safety concerns than what would be expected from the mode of action”.

However, it should be noted that in high dose repeat studies in mice and rats, severe lethality was seen with hematologic, gastrointestinal toxicities in combination with altered blood chemistry parameters and yellowing of internal organs.

IAP Antagonists, Mechanism of Action, and Clinical Trials:

A few IAP antagonists which are in early stage development include:

  • Norvatis IAP Inhibitor LCL161: at 2012 San Antonia Breast Cancer Symposium, a phase 1 trial in triple negative breast cancer showed promising results when given in combination with paclitaxel.
  • Ascenta Therapeutics IAP inhibitor AT-406 in phase 1 in collaboration with Debiopharm S.A. showed antitumor efficacy in xenograft models of breast, pancreatic, prostate and lung cancer. The development of this compound is described in a paper by Cai et. al.

National Cancer Institute sponsored trials using antagonists of IAPs include

  • Phase II Study of Birinapant for Advanced Ovarian, Fallopian Tube, and Peritoneal Cancer (NCI-12-C-0191). Principle Investigator: Dr. Christina Annunziata. See the protocol summary. More open trials for this drug are located here.  Closed trials including safety studies can be found here.
  • A Phase 1 non-randomized dose escalation study to determine maximum tolerated dose (MTD) and characterize the safety for the TetraLogic compound TL32711 had just been completed. Results have not been published yet.
  • Closed Clinical trials with the IAP antagonist HGS1029 in advanced solid tumors determined that weekly i.v. administration of HGS1029 reported a safety issue for primary outcome measures

A great review on IAP proteins and their role as regulators of apoptosis and potential targets for cancer therapy [14] can be found as a part of a Special Issue in Experimental Oncology “Apoptosis: Four Decades Later”.  Human IAPs (inhibitors of apoptosis) consist of eight proteins involved in cell death, immunity, inflammation, cell cycle, and migration including:

In general, IAP proteins are directly involved in inhibiting apoptosis by binding and directly inhibiting the effector cysteine protease caspases (caspase 3/7) ultimately responsible for the apoptotic process [15].  IAPs were actually first identified in baculoviral genomes because of their ability to suppress host-cell death responses during viral infection [16]. IAP proteins are often overexpressed in cancers [17].

Apoptosis is separated into two pathways, defined by the initial stress or death signal and the caspases involved:

  1. Extrinsic pathway: initiated by TNFα and death ligand FasLigand;  involves caspase-8; process inhibited by IAP1/2
  2. Intrinsic pathway: initiated by DNA damage, irradiation, chemotherapeutics; mitochondrial pathway involving caspase 9 and cytochrome c release from mitochondria; mitochondria also releases SMAC/DIABLO, which binds and inhibits XIAP (XIAP inhibits the Intrinsic apoptotic pathway.

 intrinsicextrinsicapoptosiswikidot

 

Intrinsic and Extrinsic pathways of apoptosis. Figure photocredit (wikidot.com)

The Curis IAP antagonist (and others) is a SMAC small molecule mimetic. It is interesting to note [18, 19] that IAP antagonists can result in death by

  • Apoptosis: an IAP antagonist in presence of competent TNFα signaling
  • Necrosis: seen with IAP inhibitors in cells with altered TNFα signaling or with presence of caspase inhibitors

IAPs are also involved in the regulation of signaling pathways such as:

NF-ΚB signaling pathway

NF-ΚB is a “rapid-acting” transcription factor which has been found to be overexpressed in various cancers.  Under most circumstances NF-ΚB translocation to the nucleus results in transcription of genes related to cell proliferation and survival.  NF-ΚB signaling is broken down in two pathways

  1. Canonical:  Canonical pathway can be initiated (for example in inflammation) when TNF-α binds its receptors activating  death domains (TRADD)
  2. Noncanonical: since requires new protein synthesis takes longer than canonical signaling.  Can be initiated by other TNF like ligands like CD40

IAP1/2 is a negative regulator of the noncanonical NF-ΚB signaling pathway by promoting proteosomal degradation of the TRAF signaling complex. A wonderfully annotated list of NF-ΚB target genes can be found on the Thomas Gilmore lab site at Boston University at http://www.bu.edu/nf-kb/gene-resources/target-genes/ .

NF-ΚB has been considered a possible target for chemotherapeutic development however Drs. Veronique Baud and Michael Karin have pondered the utility of IAP antagonists as a good target in their review: Is NF-ΚB a good target for cancer therapy?: Hopes and pitfalls [20].  The authors discuss issues such that IAP antagonism induced both the classical and noncanonical NF-ΚB pathway thru NIK stabilization, resulting in stabilization of NF-ΚB signaling and thereby undoing any chemotherapeutic effect which would be desired.

AKT signaling

IAPs have been shown to interact with other proteins including a report that SIAP regulates AKT activity and caspase-3-dependent cleavage during cisplatin-induced apoptosis in human ovarian cancer cells and could be another mechanism involved in cisplatin resistance[21].   In addition there have been reports that IAPs can regulate JNK and MAPK signaling.

Therefore, IAPs are involved in CANONICAL and NONCANONICAL pathways.

IAPs can Regulate Pro-Inflammatory Cytokines

A recent 2013 JBC paper [22]showed that IAPs and their antagonists can regulate spontaneous and TNF-induced proinflammatory cytokine and chemokine production and release

  • IAP required for production of multiple TNF-induced proinflammatory mediators
  • IAP antagonism decreased TNF-mediated production of chemokines and cytokines
  • But increased spontaneous release of chemokines

In addition Rume Damgaard and Mads Gynd-Hansen have suggested that IAP antagonists may be useful in treating inflammatory diseases like Crohn’s disease as IAPs regulate innate and acquired immune responses[23].

Toxicity profiles of IAP antagonists

NOTE: In a paper in Toxicological Science from 2012[24], Rebecca Ida Erickson form Genentech reported on the toxicity profile of the IAP antagonist GDC-0152 from a study performed in dogs and rats. A dose-dependent toxicity profile from i.v. administration was consistent with TNFα-mediated toxicity with

  • Elevated plasma cytokines and an inflammatory leukogram
  • Increased serum transaminases
  • Inflammatory infiltrate and apoptosis/necrosis in multiple tissues

In a related note, a similar type of fatal idiosyncratic hepatotoxicity was reported in a 62 year-old man treated with the Raf kinase inhibitor sorafenib for renal cell carcinoma[25]: Fatal case of sorafenib-associated idiosyncratic hepatotoxicity in the adjuvant treatment of a patient with renal cell carcinoma; Case Report  in BMC Cancer.

At week four after initiation of sorafenib treatment, the patient noticed increasing fatigue, malaise, gastrointestinal discomfort and abdominal rash.  Although treatment was discontinued, jaundice developed and blood test revealed an acute hepatitis with

  • Elevated serum ALT
  • Elevated serum alkaline phosphatase
  • Increased prothrombin time
  • Increased LDH

…elevated levels seen in the case with the aforementioned IAP antagonist.  Autopsy revealed

  • Lobular hepatitis
  • Mononuclear cell infiltrate
  • Hepatocyte necrosis

These findings are in line with a drug-induced inflammation and IDILI. In addition to hepatotoxicity, renal insufficiency developed in this patient. The authors had suggested the death was probably due to “an idiosyncratic allergic reaction to sorafenib manifesting as hepatotoxicity with associated renal impairment”.  The authors also noted that genome wide association studies of idiosyncratic drug-induced liver injury support involvement of major histocompatibility complex (MHC) polymorphisms[26].  MHC involvement has also been associated with lapatanib and pazopanib hepatotoxicity [27, 28].

Curis has been involved in another novel oncology therapeutic, a first in class.

Last year Roche and Genentech had won approval for a Hedgehog pathway inhibitor vismodegib for treatment of advanced basal cell carcinoma (reported at FierceBiotech©). Vismodegib was initially developed in collaboration with Curis, Inc.  The hedgehog signaling pathway, which controls the function of Gli factors (involved in stem cell differentiation), is overactive in advanced basal cell carcinoma as well as other cancer types.

As an additional reference, the FDA National Center for Toxicological Research has developed THE LIVER TOXICITY KNOWLEDGE BASE (LTKB).

“The LTKB is a project designed to study drug-induced liver injury (DILI). Liver toxicity is the most common cause for the discontinuation of clinical trials on a drug, as well as the most common reason for an approved drug’s withdrawal from the marketplace. Because of this, DILI has been identified by the FDA’s Critical Path Initiatives as a key area of focus in a concerted effort to broaden the agency’s knowledge for better evaluation tools and safety biomarkers.”

A nice SlideShow of Toxicity of Targeted Therapies can be found here: http://www.slideshare.net/RashaHaggag/toxicities-of-targeted-therapies

Also please note that ALL GENES in this article are linked to their GENECARD 

UPDATED 8/12/2022

 

Zolgensma Gene Therapy Linked to 2 Deaths in SMA Patients, Novartis Reports

The 2 deaths, due to acute liver failure, occurred in patients treated in Kazakhstan and Russia.

Two children with spinal muscular atrophy (SMA) have died after being treated with onasemnogene abeparvovec (Zolgensma; Novartis) from acute liver failure, a known safety risk of the therapy.1

Novartis has updated the FDA and other regulatory agencies in countries that Zolgensma is approved in, including Russia and Kazakhstan, where the deaths occurred. The company will also update the labeling of Zolgensma to include the deaths.

“While this is important safety information, it is not a new safety signal and we firmly believe in the overall favorable risk/benefit profile of Zolgensma, which to date has been used to treat more than 2300 patients worldwide across clinical trials, managed access programs, and in the commercial setting,” Novartis said in an emailed statement to BioPharma Dive.2

Zolgensma’s labeling includes the risk of liver injury and instructs clinicians to assess liver function before treatment and to manage liver enzyme counts with steroid treatment. The 2 deaths occurred 5 to 6 weeks after the one-time infusion and 1 to 10 days after corticosteroid treatment was tapered, according to an initial report from Stat News.1

READ MORE: Zolgensma Shows Efficacy in SMA With 3 SMN2 Copies

An FDA advisory committee meeting that took place last fall identified risks of adeno associated virus (AAV) gene therapies including, specifically, Zolgensma.2 The committee recommended caution, but nothing that would hinder gene therapy development.

Zolgensma, which was approved in the US in May 2019, just recently demonstrated further positive data from SPR1NT (NCT03505099), a phase 3 multicenter, single-arm trial on its effect in presymptomatic children with SMA in 2 articles published in Nature Medicine.3,4

All children in both the type 1 and type 2 cohorts achieved the ability to independently sit and most achieved other age-appropriate milestones including standing and walking. None of the children in the study required respiratory support or nutritional support, and there were no serious treatment-related adverse events observed.

“The robust data from both the 2- and 3-copy SPR1NT cohorts are being published together for the first time, further supporting the significant and clinically meaningful benefit of Zolgensma in presymptomatic babies with SMA,” Shephard Mpofu, MD, SVP, chief medical officer, Novartis Gene Therapies, said in a previous statement.5 “When treated with Zolgensma prior to the onset of symptoms, not only did all 29 patients enrolled in SPR1NT survive, but were thriving—breathing and eating on their own, with most even sitting, standing, and walking without assistance.”

REFERENCE

1. Silverman E. Novartis reports two children died from acute liver failure after treatment with Zolgensma gene therapy. STAT. August 11, 2022. https://www.statnews.com/pharmalot/2022/08/11/novartis-zolgensma-liver-failure-gene-therapy-death/

2. Pagliarulo N. Novartis reports deaths of two patients treated with Zolgensma gene therapy. BioPharma Dive. August 12, 2022. https://www.biopharmadive.com/news/novartis-zolgensma-patient-death-liver-injury/629542/

3. Strauss KA, Farrar MA, Muntoni F, et al. Onasemnogeneabeparvovec for presymptomatic infants with two copies of SMN2 at risk for spinal muscular atrophy type 1: the Phase III SPR1NT trial. Nat Med. Published online June 17, 2022. doi:10.1038/s41591-022-01866-42

4. Strauss KA, Farrar MA, Muntoni F, et al. Onasemnogeneabeparvovec for presymptomatic infants with three copies of SMN2 at risk for spinal muscular atrophy: the Phase III SPR1NT trial. Nat Med. Published online June 17, 2022.doi: 10.1038/s41591-022-01867-3

5. Novartis announces Nature Medicine publication of Zolgensma data demonstrating age-appropriate milestones when treating children with SMA presymptomatically. News release. Novartis. June 17, 2022. https://firstwordpharma.com/story/5597735

 

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4.            Laverty HG, Antoine DJ, Benson C, Chaponda M, Williams D, Kevin Park B: The potential of cytokines as safety biomarkers for drug-induced liver injury. European journal of clinical pharmacology 2010, 66(10):961-976.

5.            Schwabe RF, Brenner DA: Mechanisms of Liver Injury. I. TNF-alpha-induced liver injury: role of IKK, JNK, and ROS pathways. American journal of physiology Gastrointestinal and liver physiology 2006, 290(4):G583-589.

6.            Seki E, Brenner DA, Karin M: A liver full of JNK: signaling in regulation of cell function and disease pathogenesis, and clinical approaches. Gastroenterology 2012, 143(2):307-320.

7.            Kaplowitz N: Drug-induced liver injury. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2004, 38 Suppl 2:S44-48.

8.            Kindmark A, Jawaid A, Harbron CG, Barratt BJ, Bengtsson OF, Andersson TB, Carlsson S, Cederbrant KE, Gibson NJ, Armstrong M et al: Genome-wide pharmacogenetic investigation of a hepatic adverse event without clinical signs of immunopathology suggests an underlying immune pathogenesis. The pharmacogenomics journal 2008, 8(3):186-195.

9.            Aithal GP, Ramsay L, Daly AK, Sonchit N, Leathart JB, Alexander G, Kenna JG, Caldwell J, Day CP: Hepatic adducts, circulating antibodies, and cytokine polymorphisms in patients with diclofenac hepatotoxicity. Hepatology 2004, 39(5):1430-1440.

10.          Daly AK, Aithal GP, Leathart JB, Swainsbury RA, Dang TS, Day CP: Genetic susceptibility to diclofenac-induced hepatotoxicity: contribution of UGT2B7, CYP2C8, and ABCC2 genotypes. Gastroenterology 2007, 132(1):272-281.

11.          Russmann S, Kullak-Ublick GA, Grattagliano I: Current concepts of mechanisms in drug-induced hepatotoxicity. Current medicinal chemistry 2009, 16(23):3041-3053.

12.          Fontana RJ, Watkins PB, Bonkovsky HL, Chalasani N, Davern T, Serrano J, Rochon J: Drug-Induced Liver Injury Network (DILIN) prospective study: rationale, design and conduct. Drug safety : an international journal of medical toxicology and drug experience 2009, 32(1):55-68.

13.          Spraggs CF, Xu CF, Hunt CM: Genetic characterization to improve interpretation and clinical management of hepatotoxicity caused by tyrosine kinase inhibitors. Pharmacogenomics 2013, 14(5):541-554.

14.          de Almagro MC, Vucic D: The inhibitor of apoptosis (IAP) proteins are critical regulators of signaling pathways and targets for anti-cancer therapy. Experimental oncology 2012, 34(3):200-211.

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21.          Asselin E, Mills GB, Tsang BK: XIAP regulates Akt activity and caspase-3-dependent cleavage during cisplatin-induced apoptosis in human ovarian epithelial cancer cells. Cancer research 2001, 61(5):1862-1868.

22.          Kearney CJ, Sheridan C, Cullen SP, Tynan GA, Logue SE, Afonina IS, Vucic D, Lavelle EC, Martin SJ: Inhibitor of apoptosis proteins (IAPs) and their antagonists regulate spontaneous and tumor necrosis factor (TNF)-induced proinflammatory cytokine and chemokine production. The Journal of biological chemistry 2013, 288(7):4878-4890.

23.          Damgaard RB, Gyrd-Hansen M: Inhibitor of apoptosis (IAP) proteins in regulation of inflammation and innate immunity. Discovery medicine 2011, 11(58):221-231.

24.          Erickson RI, Tarrant J, Cain G, Lewin-Koh SC, Dybdal N, Wong H, Blackwood E, West K, Steigerwalt R, Mamounas M et al: Toxicity profile of small-molecule IAP antagonist GDC-0152 is linked to TNF-alpha pharmacology. Toxicological sciences : an official journal of the Society of Toxicology 2013, 131(1):247-258.

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28.          Xu CF, Reck BH, Goodman VL, Xue Z, Huang L, Barnes MR, Koshy B, Spraggs CF, Mooser VE, Cardon LR et al: Association of the hemochromatosis gene with pazopanib-induced transaminase elevation in renal cell carcinoma. Journal of hepatology 2011, 54(6):1237-1243.

Other articles on the site about Toxicology and Pharmacology of New Classes of Cancer Chemotherapy include:

FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules

Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma

DNA Methultransferases – Implications to Epigenetic Regulation and Cancer Therapy Targeting: James Shen, PhD

Molecular Profiling in Cancer Immunotherapy: Debraj GuhaThakurta, PhD

AT13148 – A Novel Oral Multi-AGC Kinase Inhibitor Has Potent Antitumor Activity

Targeting Mitochondrial-bound Hexokinase for Cancer Therapy

Breast Cancer, drug resistance, and biopharmaceutical targets

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

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

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Risk of Bias in Translational Science

Posted in Accountable Care Organizations, Affordable Care Act, Best evidence, Bias measurement tools, Biomedical Measurement Science, Computational Biology/Systems and Bioinformatics, Evidence-based decision-making, FDA Regulatory Affairs, Health Economics and Outcomes Research, Health Law & Patient Safety, HealthCare IT, Inferential analysis, Intra-rater error, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Quality Assurance, Random Error, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, Systematic Error (Bias), Technology Transfer: Biotech and Pharmaceutical, Total error, Translational Effectiveness, Translational Research, Translational Science, tagged , , , , , , , , on November 3, 2013| Leave a Comment »

Risk of Bias in Translational Science

Author: Larry H. Bernstein, MD, FCAP

and

Curator: Aviva Lev-Ari, PhD, RN

 

Assessment of risk of bias in translational science

Andre Barkhordarian1, Peter Pellionisz2, Mona Dousti1, Vivian Lam1,Lauren Gleason1, Mahsa Dousti1, Josemar Moura3 and Francesco Chiappelli14*  

1Oral Biology & Medicine, School of Dentistry, UCLA, Evidence-Based Decisions Practice-Based Research Network, Los Angeles, USA

2Pre-medical program, UCLA, Los Angeles, CA

3School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

4Evidence-Based Decisions Practice-Based Research Network, UCLA School of Dentistry, Los Angeles, CA

Journal of Translational Medicine 2013, 11:184   http://dx.doi.org/10.1186/1479-5876-11-184
http://www.translational-medicine.com/content/11/1/184

This is an Open Access article distributed under the terms of the Creative Commons Attribution License 
http://creativecommons.org/licenses/by/2.0

Abstract

Risk of bias in translational medicine may take one of three forms:

  1. a systematic error of methodology as it pertains to measurement or sampling (e.g., selection bias),
  2. a systematic defect of design that leads to estimates of experimental and control groups, and of effect sizes that substantially deviate from true values (e.g., information bias), and
  3. a systematic distortion of the analytical process, which results in a misrepresentation of the data with consequential errors of inference (e.g., inferential bias).

Risk of bias can seriously adulterate the internal and the external validity of a clinical study, and, unless it is identified and systematically evaluated, can seriously hamper the process of comparative effectiveness and efficacy research and analysis for practice. The Cochrane Group and the Agency for Healthcare Research and Quality have independently developed instruments for assessing the meta-construct of risk of bias. The present article begins to discuss this dialectic.

Background

As recently discussed in this journal [1], translational medicine is a rapidly evolving field. In its most recent conceptualization, it consists of two primary domains:

  • translational research proper and
  • translational effectiveness.

This distinction arises from a cogent articulation of the fundamental construct of translational medicine in particular, and of translational health care in general.

The Institute of Medicine’s Clinical Research Roundtable conceptualized the field as being composed by two fundamental “blocks”:

  • one translational “block” (T1) was defined as “…the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention and their first testing in humans…”, and
  • the second translational “block” (T2) was described as “…the translation of results from clinical studies into everyday clinical practice and health decision making…” [2].

These are clearly two distinct facets of one meta-construct, as outlined in Figure 1. As signaled by others, “…Referring to T1 and T2 by the same name—translational research—has become a source of some confusion. The 2 spheres are alike in name only. Their goals, settings, study designs, and investigators differ…” [3].

1479-5876-11-184-1 Fig 1. TM construct

Figure 1. Schematic representation of the meta-construct of translational health carein general, and translational medicine in particular, which consists of two fundamental constructs: the T1 “block” (as per Institute of Medicine’s Clinical Research Roundtable nomenclature), which represents the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention as well as their first testing in humans, and the T2 “block”, which pertains to translation of results from clinical studies into everyday clinical practice and health decision making [[3]]. The two “blocks” are inextricably intertwined because they jointly strive toward patient-centered research outcomes (PCOR) through the process of comparative effectiveness and efficacy research/review and analysis for clinical practice (CEERAP). The domain of each construct is distinct, since the “block” T1 is set in the context of a laboratory infrastructure within a nurturing academic institution, whereas the setting of “block” T2 is typically community-based (e.g., patient-centered medical/dental home/neighborhoods [4]; “communities of practice” [5]).

For the last five years at least, the Federal responsibilities for “block” T1 and T2 have been clearly delineated. The National Institutes of Health (NIH) predominantly concerns itself with translational research proper – the bench-to-bedside enterprise (T1); the Agency for Healthcare Research Quality (AHRQ) focuses on the result-translation enterprise (T2). Specifically: “…the ultimate goal [of AHRQ] is research translation—that is, making sure that findings from AHRQ research are widely disseminated and ready to be used in everyday health care decision-making…” [6]. The terminology of translational effectiveness has emerged as a means of distinguishing the T2 block from T1.

Therefore, the bench-to-bedside enterprise pertains to translational research, and the result-translation enterprise describes translational effectiveness. The meta-construct of translational health care (viz., translational medicine) thus consists of these two fundamental constructs:

  • translational research and
  • translational effectiveness,

which have distinct purposes, protocols and products, while both converging on the same goal of new and improved means of

  • individualized patient-centered diagnostic and prognostic care.

It is important to note that the U.S. Patient Protection and Affordable Care Act (PPACA, 23 March 2010) has created an environment that facilitates the pursuit of translational health care because it emphasizes patient-centered outcomes research (PCOR). That is to say, it fosters the transaction between translational research (i.e., “block” T1)(TR) and translational effectiveness (i.e., “block” T2)(TE), and favors the establishment of communities of practice-research interaction. The latter, now recognized as practice-based research networks, incorporate three or more clinical practices in the community into

  • a community of practices network coordinated by an academic center of research.

Practice-based research networks may be a third “block” (T3)(PBTN) in translational health care and they could be conceptualized as a stepping-stone, a go-between bench-to-bedside translational research and result-translation translational effectiveness [7]. Alternatively, practice-based research networks represent the practical entities where the transaction between

  • translational research and translational effectiveness can most optimally be undertaken.

It is within the context of the practice-based research network that the process of bench-to-bedside can best seamlessly proceed, and it is within the framework of the practice-based research network that

  • the best evidence of results can be most efficiently translated into practice and
  • be utilized in evidence-based clinical decision-making, viz. translational effectiveness.

Translational effectiveness

As noted, translational effectiveness represents the translation of the best available evidence in the clinical practice to ensure its utilization in clinical decisions. Translational effectiveness fosters evidence-based revisions of clinical practice guidelines. It also encourages

  • effectiveness-focused,
  • patient-centered and
  • evidence-based clinical decision-making.

Translational effectiveness rests not only on the expertise of the clinical staff and the empowerment of patients, caregivers and stakeholders, but also, and

  • most importantly on the best available evidence [8].

The pursuit of the best available evidence is the foundation of

  • translational effectiveness and more generally of
  • translational medicine in evidence-based health care.

The best available evidence is obtained through a systematic process driven by

  • a research question/hypothesis that is articulated about clearly stated criteria that pertain to the
  • patient (P), the interventions (I) under consideration (C), for the sought clinical outcome (O), within a given timeline (T) and clinical setting (S).

PICOTS is tested on the appropriate bibliometric sample, with tools of measurements designed to establish the level (e.g., CONSORT) and the quality of the evidence. Statistical and meta-analytical inferences, often enhanced by analyses of clinical relevance [9], converge into the formulation of the consensus of the best available evidence. Its dissemination to all stakeholders is key to increase their health literacy in order to ensure their full participation

  • in the utilization of the best available evidence in clinical decisions, viz., translational effectiveness.

To be clear, translational effectiveness – and, in the perspective discussed above, translational health care – is anchored on obtaining the best available evidence,

  • which emerges from highest quality research.
  • which is obtained when errors are minimized.

In an early conceptualization [10], errors in research were presented as

  • those situations that threaten the internal and the external validity of a research study –

that is, conditions that impede either the study’s reproducibility, or its generalization. In point of fact, threats to internal and external validity [10] represent specific aspects of systematic errors (i.e., bias) in the

  • research design,
  • methodology and
  • data analysis.

Thence emerged a branch of science that seeks to

  • understand,
  • control and
  • reduce risk of bias in research.

Risk of bias and the best available evidence

It follows that the best available evidence comes from research with the fewest threats to internal and to external validity – that is to say, the fewest systematic errors: the lowest risk of bias. Quality of research, as defined in the field of research synthesis [11], has become synonymous with

  • low bias and contained risk of bias [12–15].

Several years ago, the Cochrane group embarked on a new strategy for assessing the quality of research studies by examining potential sources of bias. Certain original areas of potential bias in research were identified, which pertain to

(a) the sampling and the sample allocation process, to measurement, and to other related sources of errors (reliability of testing),

(b) design issues, including blinding, selection and drop-out, and design-specific caveats, and

(c) analysis-related biases.

A Risk of Bias tool was created (Cochrane Risk of Bias), which covered six specific domains:

1. selection bias,

2. performance bias,

3. detection bias,

4. attrition bias,

5. reporting bias, and

6. other research protocol-related biases.

Assessments were made within each domain by one or more items specific for certain aspects of the domain. Each items was scored in two distinct steps:

1. the support for judgment was intended to provide a succinct free-text description of the domain being queried;

2. each item was scored high, low, or unclear risk of material bias (defined here as “…bias of sufficient magnitude to have a notable effect on the results or conclusions…” [16]).

It was advocated that assessments across items in the tool should be critically summarized for each outcome within each report. These critical summaries were to inform the investigator so that the primary meta-analysis could be performed either

  • only on studies at low risk of bias, or for
  • the studies stratified according to risk of bias [16].

This is a form of acceptable sampling analysis designed to yield increased homogeneity of meta-analytical outcomes [17]. Alternatively, the homogeneity of the meta-analysis can be further enhanced by means of the more direct quality-effects meta-analysis inferential model [18].

Clearly, one among the major drawbacks of the Cochrane Risk of Bias tool is

  • the subjective nature of its assessment protocol.

In an effort to correct for this inherent weakness of the instrument, the Cochrane group produced

  • detailed criteria for making judgments about the risk of bias from each individual item[16], and
  • that judgments be made independently by at least two people, with any discrepancies resolved by discussion [16].

This approach to increase the reliability of measurement in research synthesis protocols

  • is akin to that described by us [19,20] and by AHRQ [21].

In an effort to aid clinicians and patients in making effective health care related decisions, AHRQ developed an alternative Risk of Bias instrument for enabling systematical evaluation of evidence reporting [22]. The AHRQ Risk of Bias instrument was created to monitor four primary domains:

1. risk of bias: design, methodology, analysis scoring – low, medium, high

2. consistency: extent of similarity in effect sizes across studies within a bibliome scoring – consistent, inconsistent, unknown

3. directness: unidirectional link between the interventions of interest and the sought outcome, as opposed to multiple links in a casual chain scoring – direct, indirect

4. precision: extent of certainty for estimate of effect with respect to the outcome scoring – precise, imprecise In addition, four secondary domains were identified:

a. Dose response association: pattern of a larger effect with greater exposure (Present/Not Present/Not Applicable or Not Tested)

a. Confounders: consideration of confounding variables (Present/Absent)

a. Strength of association: likelihood that the observed effect is large enough that it cannot have occurred solely as a result of bias from potential confounding factors (Strong/Weak)

a. Publication bias

The AHRQ Risk of Bias instrument is also designed to yield an overall grade of the estimated risk of bias in quality reporting:

•Strength of Evidence Grades (scored as high – moderate – low – insufficient)

This global assessment, in addition to incorporating the assessments above, also rates:

–major benefit

–major harm

–jointly benefits and harms

–outcomes most relevant to patients, clinicians, and stakeholders

The AHRQ Risk of Bias instrument suffers from the same two major limitations as the Cochrane tool:

1. lack of formal psychometric validation as most other tools in the field [21], and

2. providing a subjective and not quantifiable assessment.

To begin the process of engaging in a systematic dialectic of the two instruments in terms of their respective construct and content validity, it is necessary

  • to validate each for reliability and validity either by means of the classic psychometric theory or generalizability (G) theory, which allows
  • the simultaneous estimation of multiple sources of measurement error variance (i.e., facets)
  • while generalizing the main findings across the different study facets.

G theory is particularly useful in clinical care analysis of this type, because it permits the assessment of the reliability of clinical assessment protocols.

  • the reliability and minimal detectable changes across varied combinations of these facets are then simply calculated [23], but
  • it is recommended that G theory determination follow classic theory psychometric assessment.

Therefore, we have commenced a process of revision the AHRQ Risk of Bias instrument by rendering questions in primary domains quantifiable (scaled 1–4),

  • which established the intra-rater reliability (r = 0.94, p < 0.05), and
  • the criterion validity (r = 0.96, p < 0.05) for this instrument (Figure 2).

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Figure 2. Proportion of shared variance in criterion validity (A) and inter-rater reliability (B) in the AHRQ Risk of Bias instrument revised as described.
Two raters were trained and standardized [20] with the revised AHRQ Risk of Bias and with the R-Wong instrument, which has been previously validated[24]. Each rater independently produced ratings on a sample of research reports with both instruments on two separate occasions, 1–2 months apart. Pearson correlation coefficient was used to compute the respective associations. The figure shows Venn diagrams to illustrate the intersection between each two sets data used in the correlations. The overlap between the sets in each panel represents the proportion of shared variance for that correlation. The percent of unexplained variance is given in the insert of each panel.

A similar revision of the Cochrane Risk of Bias tool may also yield promising validation data. G theory validation of both tools will follow. Together, these results will enable a critical and systematic dialectical comparison of the Cochrane and the AHRQ Risk of Bias measures.

Discussion

The critical evaluation of the best available evidence is critical to patient-centered care, because biased research findings are fundamentally invalid and potentially harmful to the patient. Depending upon the tool of measurement, the validity of an instrument in a study is obtained by means of criterion validity through correlation coefficients. Criterion validity refers to the extent to which one measures or predicts the value of another measure or quality based on a previously well-established criterion. There are other domains of validity such as: construct validity and content validity that are rather more descriptive than quantitative. Reliability however is used to describe the consistency of a measure, the extent to which a measurement is repeatable. It is commonly assessed quantitatively by correlation coefficients. Inter-rater reliability is rendered as a Pearson correlation coefficient between two independent readers, and establishes equivalence of ratings produced by independent observers or readers. Intra-rater reliability is determined by repeated measurement performed by the same subject (rater/reader) at two different points in time to assess the correlation or strength of association of the two sets of scores.

To establish the reliability of research quality assessment tools it is necessary, as we previously noted [20]:

•a) to train multiple readers in sharing a common view for the cognitive interpretation of each item. Readers must possess declarative knowledge a factual form of information known to be static in nature a certain depth of knowledge and understanding of the facts about which they are reviewing the literature. They must also have procedural knowledge known as imperative knowledge that can be directly applied to a task in this case a clear understanding of the fundamental concepts of research methodology, design, analysis and inference.

•b) to train the readers to read and evaluate the quality of a set of papers independently and blindly. They must also be trained to self-monitor and self-assess their skills for the purpose of insuring quality control.

•c) to refine the process until the inter-rater correlation coefficient and Cohen coefficient of agreement are about 0.9 (over 81% shared variance). This will establishes that the degree of attained agreement among well-trained readers is beyond chance.

•d) to obtain independent and blind reading assessments from readers on reports under study.

•e) to compute means and standard deviation of scores for each question across the reports, repeat process if the coefficient of variations are greater than 5% (i.e., less than 5% error among the readers across each questions).

The quantification provided by instruments validated in such a manner to assess the quality and the relative lack of bias in the research evidence allows for the analysis of the scores by means of the acceptable sampling protocol. Acceptance sampling is a statistical procedure that uses statistical sampling to determine whether a given lot, in this case evidence gathered from an identified set of published reports, should be accepted or rejected [12,25]. Acceptable sampling of the best available evidence can be obtained by:

•convention: accept the top 10 percentile of papers based on the score of the quality of the evidence (e.g., low Risk of Bias);

•confidence interval (CI95): accept the papers whose scores fall at of beyond the upper confidence limit at 95%, obtained with mean and variance of the scores of the entire bibliome;

•statistical analysis: accept the papers that sustain sequential repeated Friedman analysis.

To be clear, the Friedman test is a non-parametric equivalent of the analysis of variance for factorial designs. The process requires the 4-E process outlined below:

•establishing a significant Friedman outcome, which indicates significant differences in scores among the individual reports being tested for quality;

•examining marginal means and standard deviations to identify inconsistencies, and to identify the uniformly strong reports across all the domains tested by the quality instrument

•excluding those reports that show quality weakness or bias

•executing the Friedman analysis again, and repeating the 4-E process as many times as necessary, in a statistical process akin to hierarchical regression, to eliminate the evidence reports that exhibit egregious weakness, based on the analysis of the marginal values, and to retain only the group of report that harbor homogeneously strong evidence.

Taken together, and considering the domain and the structure of both tools, expectations are that these analyses will confirm that these instruments are two related entities, each measuring distinct aspects of bias. We anticipate that future research will establish that both tools assess complementary sub-constructs of one and the same archetype meta-construct of research quality.

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  12. Chiappelli F: The science of research synthesis: a manual of evidence-based research for the health sciences. Hauppauge NY: NovaScience Publisher, Inc; 2008. OpenURL

  13. Higgins JPT, Green S: Cochrane handbook for systematic reviews of interventions version 5.0.1. Chichester, West Sussex, UK: John Wiley & Sons. The Cochrane collaboration; 2008. OpenURL

  14. CRD: Systematic Reviews: CRD’s guidance for undertaking reviews in health care. National Institute for Health Research (NIHR). University of York, UK: Center for reviews and dissemination; 2009. PubMed Abstract| Publisher Full Text OpenURL

  15. McDonald KM, Chang C, Schultz E: Closing the quality Gap: revisiting the state of the science. Summary report. U.S. Department of Health & Human Services. AHRQ, Rockville, MD: Summary report. AHRQ publication No. 12(13)-E017; 2013. OpenURL


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Nitric Oxide Synthase Inhibitors (NOS-I)

Posted in Chemical Biology and its relations to Metabolic Disease, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Infectious Disease & New Antibiotic Targets, Nitric Oxide in Health and Disease, Population Health Management, Genetics & Pharmaceutical, Proteomics, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , , , , , , on November 2, 2013| Leave a Comment »

Nitric Oxide Synthase Inhibitors (NOS-I)

Author: Larry H Bernstein, MD, FCAP

Curator: Stephen J. Williams, PhD

and

Co-Curator: Aviva Lev-Ari, PhD, RN

 

This recent article sheds a new light on nitric oxide and the activity of NOS in reactive oxygen species generation and the effect of NOS inhibitors in bacteria.

Structural and Biological Studies on Bacterial Nitric Oxide Synthase Inhibitors

Jeffrey K. Holdena, Huiying Lia, Qing Jingb, Soosung Kangb, Jerry Richoa, Richard B. Silvermanb,1, and Thomas L. Poulosb,1
Agman@chem.northwestern.edu
Author contributions: J.K.H. designed research; J.K.H. and J.R. performed research; Q.J. and S.K. contributed new reagents/analytic tools; J.K.H., H.L., R.B.S., and T.L.P. analyzed data; and J.K.H., R.B.S., and T.L.P. wrote the paper.

PNAS Oct 21, 2013;       http://dx.doi.org/10.1073/pnas.1314080110
This article is a PNAS Direct Submission
Data deposition: The atomic coordinates and structure factors have been deposited in the Protein Data Bank
Edited by Douglas C. Rees, Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA, and approved September 23, 2013 (received for review July 29, 2013)
Keywords:  crystallography, antibiotics, nitric oxide, NOS inhibitors, Bacillus subtilis, gram positive bacteria

Significance

Nitric oxide (NO) produced by bacterial nitric oxide synthase has recently been shown to

Using Bacillus subtilis as a model system, we identified

  • two NOS inhibitors that work in conjunction with an antibiotic to kill B. subtilis.

Moreover, comparison of inhibitor-bound crystal structures between the bacterial NOS and mammalian NOS revealed an unprecedented

  • mode of binding to the bacterial NOS that can be further exploited for future structure-based drug design.

Overall, this work is an important advance in developing inhibitors against gram-positive pathogens.

Abstract

Nitric oxide (NO) produced by bacterial NOS functions as

  • a cytoprotective agent against oxidative stress in Staphylococcus aureus, Bacillus anthracis, and Bacillus subtilis.

The screening of several NOS-selective inhibitors uncovered two inhibitors with potential antimicrobial properties. These two compounds

  • impede the growth of B. subtilis under oxidative stress, and
  • crystal structures show that each compound exhibits a unique binding mode.

Both compounds serve as excellent leads for the future development of antimicrobials against bacterial NOS-containing bacteria.

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Cancer Mutations Across the Landscape

Posted in Biological Networks, Gene Regulation and Evolution, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, tagged , , , , , , , , , , , , , , , , on November 1, 2013| Leave a Comment »

Cancer Mutations Across the Landscape

Curator: Larry H. Bernstein, MD, FCAP

This is an up-to-date article about the significance of mutations found in 12 major types of cancer.

Cancer Mutations Across the Landscape

Word Cloud by Daniel Menzin

UPDATED 4/24/2020  The genomic landscape of pediatric cancers: Curation of WES/WGS studies shows need for more data

Mutational landscape and significance across 12 major cancer types

Cyriac Kandoth1*, Michael D. McLellan1*, Fabio Vandin2, Kai Ye1,3, Beifang Niu1, Charles Lu1, et al.

1The Genome Institute, Washington University in St Louis, Missouri 63108, USA. 2Department of Computer Science, Brown University, Providence, Rhode Island 02912, USA. 3Department of Genetics, Washington University in St Louis, Missouri 63108, USA. 4Department of Medicine, Washington University in St Louis, Missouri 63108, USA. 5Siteman Cancer Center, Washington University in St Louis, Missouri 63108, USA. 6Department of Mathematics, Washington University in St Louis, Missouri 63108, USA.

NATURE 17 Oct 2013;  5 0 2      http://dx.doi.org/10.1038/nature12634

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate

  1. the distributions of mutation frequencies,
  2. types and contexts across tumour types, and
  3. establish their links to tissues of origin,
  4. environmental/ carcinogen influences, and
  5. DNA repair defects.

Using the integrated data sets, we identified 127 significantly mutated genes from well-knownand emerging cellular processes in cancer.

  1. (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase,Wnt/b-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control)
  2. (for example, histone, histone modification, splicing, metabolism and proteolysis)

The average number of mutations in these significantly mutated genes varies across tumour types;

  1. most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small.
  2. Mutations in transcriptional factors/regulators show tissue specificity, whereas
  3. histone modifiers are often mutated across several cancer types.

Clinical association analysis identifies genes having a significant effect on survival, and

  • investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis.

Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment

Introduction

The advancement of DNA sequencing technologies now enables the processing of thousands of tumours of many types for systematic mutation discovery. This expansion of scope, coupled with appreciable progress in algorithms1–5, has led directly to characterization of signifi­cant functional mutations, genes and pathways6–18. Cancer encompasses more than 100 related diseases19, making it crucial to understand the commonalities and differences among various types and subtypes. TCGA was founded to address these needs, and its large data sets are providing unprecedented opportunities for systematic, integrated analysis.

We performed a systematic analysis of 3,281 tumours from 12 cancer types to investigate underlying mechanisms of cancer initiation and progression. We describe variable mutation frequencies and contexts and their associations with environmental factors and defects in DNA repair. We identify 127 significantlymutated genes (SMGs) from diverse signalling and enzymatic processes. The finding of a TP53-driven breast, head and neck, and ovarian cancer cluster with a dearth of other mutations in SMGs suggests common therapeutic strategies might be applied for these tumours. We determined interactions among muta­tions and correlated mutations in BAP1, FBXW7 and TP53 with det­rimental phenotypes across several cancer types. The subclonal structure and transcription status of underlying somatic mutations reveal the trajectory of tumour progression in patients with cancer.

Standardization of mutation data

Stringent filters (Methods) were applied to ensure high quality muta­tion calls for 12 cancer types: breast adenocarcinoma (BRCA), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), colon and rectal carcinoma (COAD, READ),bladder urothelial carcinoma (BLCA), kidney renal clear cell carcinoma (KIRC), ovarian serous carcinoma (OV) and acute myeloid leukaemia (LAML; conventionally called AML) (Supplementary Table 1). A total of 617,354 somatic mutations, consisting of

  • 398,750 missense,
  • 145,488 silent,
  • 36,443 nonsense,
  • 9,778 splice site,
  • 7,693 non-coding RNA,
  • 523 non-stop/readthrough,
  • 15,141 frameshift insertions/deletions (indels) and
  • 3,538 inframe indels,

were included for downstream analyses (Supplementary Table 2).

Distinct mutation frequencies and sequence context

Figure 1a shows that AML has the lowest median mutation frequency and LUSC the highest (0.28 and 8.15 mutations per megabase (Mb), respectively). Besides AML, all types average over 1 mutation per Mb, substantially higher than in pediatric tumours20. Clustering21 illus­trates that

  • mutation frequencies for KIRC, BRCA, OV and AML are normally distributed within a single cluster, whereas
  • other types have several clusters (for example, 5 and 6 clusters in UCEC and COAD/ READ, respectively) (Fig. 1a and Supplementary Table 3a, b).

In UCEC, the largest patient cluster has a frequency of approximately 1.5 muta­tions per Mb, and

  • the cluster with the highest frequency is more than 150 times greater.

Multiple clusters suggest that factors other than age contribute to development in these tumours14,16. Indeed,

  • there is a significant correlation between high mutation frequency and DNA repair pathway genes (for example, PRKDC, TP53 and MSH6) (Sup­plementary Table 3c). Notably,
  • PRKDC mutations are associated with high frequency in BLCA, COAD/READ, LUAD and UCEC, whereas
  • TP53 mutations are related with higher frequencies in AML, BLCA, BRCA, HNSC, LUAD, LUSC and UCEC (all P < 0.05).

Mutations in POLQ and POLE associate with high frequencies in multiple cancer types; POLE association in UCEC is consistent with previous observations14.

Comparison of spectra across the 12 types (Fig. 1b and Supplemen­tary Table 3d) reveals that LUSC and LUAD contain increased C>A transversions, a signature of cigarette smoke exposure10. Sequence context analysis across 12 types revealed

  • the largest difference being in C>T transitions and C>G transversions (Fig. 1c).

The frequency of thymine 1-bp (base pair) upstream of C>G transversions is mark­edly higher in BLCA, BRCA and HNSC than in other cancer types (Extended Data Fig. 1). GBM, AML, COAD/READ and UCEC have similar contexts in that

  • the proportions of guanine 1 base downstream of C>T transitions are between
    • 59% and 67%, substantially higher than the approximately 40% in other cancer types.

Higher frequencies of transition mutations at CpG in gastrointestinal tumours, including colorectal, were previously reported22. We found three additional cancer types (GBM, AML and UCEC) clustered in the C>T mutation at CpG, consistent with previous findings of

  • aberrant DNA methylation in endometrial cancer23 and glioblastoma24.

BLCA has a unique signature for C>T transitions compared to the other types (enriched for TC) (Extended Data Fig. 1).

Significantly mutated genes

Genes under positive selection, either in individual or multiple tumour types, tend to display higher mutation frequencies above background. Our statistical analysis3, guided by expression data and curation (Methods), identified 127 such genes (SMGs; Supplementary Table 4). These SMGs are involved in a wide range of cellular processes, broadly classified into 20 categories (Fig. 2), including

  • transcription factors/regulators, histone modifiers, genome integrity, receptor tyrosine kinase signal­ling, cell cycle, mitogen-activated protein kinases (MAPK) signalling, phosphatidylinositol-3-OH kinase (PI(3)K) signalling, Wnt/ -catenin signalling, histones, ubiquitin-mediatedproteolysis, and splicing (Fig. 2).

The identification of MAPK, PI(3)K and Wnt/ -catenin signaling path­ways is consistent with classical cancer studies. Notably, newer categories (for example, splicing, transcription regulators, metabolism, proteolysis and histones) emerge as exciting guides for the development of new therapeutic targets. Genes categorized as histone modifiers (Z = 0.57), PI(3)K signalling (Z = 1.03), and genome integrity (Z = 0.66) all relate to more than one cancer type, whereas

  • transcription factor/regulator (Z = 0.40), TGF- signalling (Z = 0.66), and Wnt/ -catenin signalling (Z = 0.55) genes tend to associate with single types (Methods).

Notably, 3,053 out of 3,281 total samples (93%) across the Pan-Cancer collection had at least one non-synonymous mutation in at least one SMG. The average number of point mutations and small indels in these genes varies across tumour types, with the highest (,6 mutations per tumour) in UCEC, LUAD and LUSC, and the lowest (,2 mutations per tumour) in AML, BRCA, KIRC and OV. This suggests that the numbers of both cancer-related genes (only 127 identified in this study) and cooperating driver mutations required during oncogenesis are small (most cases only had 2–6) (Fig. 3), although large-scale structural rearrangements were not included in this analysis.

Common mutations

The most frequently mutated gene in the Pan-Cancer cohort is TP53 (42% of samples). Its mutations predominate in serous ovarian (95%) and serous endometrial carcinomas (89%) (Fig. 2). TP53 mutations are also associated with basal subtype breast tumours. PIK3CA is the second most commonly mutated gene, occurring frequently (>10%) in most cancer types except OV, KIRC, LUAD and AML. PIK3CA mutations frequented UCEC (52%) and BRCA (33.6%), being speci­fically enriched in luminal subtype tumours. Tumours lacking PIK3CA mutations often had mutations in PIK3R1, with the highest occur­rences in UCEC (31%) and GBM (11%) (Fig. 2).

Many cancer types carried mutations in chromatin re-modelling genes. In particular, histone-lysine N-methyltransferase genes (MLL2 (also known as KMT2D), MLL3 (KMT2C) and MLL4 (KMT2B)) clus­ter in bladder, lung and endometrial cancers, whereas the lysine (K)-specific demethylase KDM5C is prevalently mutated in KIRC (7%). Mutations in ARID1A are frequent in BLCA, UCEC, LUAD and LUSC, whereas mutations in ARID5B predominate in UCEC (10%) (Fig. 2).

Fig. 1. Distribution of mutation frequencies across 12 cancer types.

Fig. 1.  | Distribution of mutation frequencies across 12 cancer types.

Dashed grey and solid white lines denote average across cancer types and median for each type, respectively. b, Mutation spectrum of six transition (Ti) and transversion (Tv) categories for each cancer type. c, Hierarchically clustered mutation context (defined by the proportion of A, T, C and G nucleotides within ±2bp of variant site) for six mutation categories. Cancer types correspond to colours in a. Colour denotes degree of correlation: yellow (r = 0.75) and red (r = 1).

Fig. 2.  The 127 SMGs from 20 cellular processes in cancer identified in and Pan-Cancer are shown, with the highest percentage in each gene among 12 (not shown)

Fig. 3. Distribution of mutations in 127 SMGs across Pan-Cancer cohort.

Fig. 3. | Distribution of mutations in 127 SMGs across Pan-Cancer cohort.

Box plot displays median numbers of non-synonymous mutations, with outliers shown as dots. In total, 3,210 tumours were used for this analysis (hypermutators excluded).

Figure 4 | Unsupervised clustering based on mutation status of SMGs. Tumours having no mutation or more than 500 mutations were excluded. A mutation status matrix was constructed for 2,611 tumours. Major clusters of mutations detected in UCEC, COAD, GBM, AML, KIRC, OV and BRCA were highlighted.
Complete gene list shown in Extended Data Fig. 3.  (not shown)

Fig. 5. Driver initiation and progression mutations and tumour clonal mutation is in the subclone

Figure 5 | Driver initiation and progression mutations and tumour clonal mutation is in the subclone

Survival Analysis

We examined which genes correlate with survival using the Cox proportional hazards model, first analysing individual cancer types using age and gender as covariates; an average of 2 genes (range: 0–4) with mutation frequency 2% were significant (P<_0.05) in each type (Supplementary Table 10a and Extended Data Fig. 6). KDM6A and ARID1A mutations correlate with better survival in BLCA (P = 0.03, hazard ratio (HR) = 0.36, 95% confidence interval (CI): 0.14–0.92) and UCEC (P = 0.03, HR = 0.11, 95% CI: 0.01–0.84), respectively, but mutations in SETBP1, recently identified with worse prognosis in atypical chronic myeloid leukaemia (aCML)31, have a significant detrimental effect in HNSC (P = 0.006, HR = 3.21, 95% CI: 1.39–7.44). BAP1 strongly correlates with poor survival (P = 0.00079, HR = 2.17, 95% CI: 1.38–3.41) in KIRC. Conversely, BRCA2 muta­tions (P = 0.02, HR = 0.31, 95% CI: 0.12–0.85) associate with better survival in ovarian cancer, consistent with previous reports32,33; BRCA1 mutations showed positive correlation with better survival, but did not reach significance here.

We extended our survival analysis across cancer types, restricting our attention to the subset of 97 SMGs whose mutations appeared in 2% of patients having survival data in 2 tumour types. Taking type, age and gender as covariates, we found 7 significant genes: BAP1, DNMT3A, HGF, KDM5C, FBXW7, BRCA2 and TP53 (Extended Data Table 1).  In particular, BAP1 was highly significant (P = 0.00013, HR = 2.20, 95% CI: 1.47–3.29, more than 53 mutated tumours out of 888 total), with mutations associating with detrimental outcome in four tumour types and notable associations in KIRC (P = 0.00079), consistent with a recent report28, and in UCEC(P = 0.066). Mutations in several other genes are detrimental, including DNMT3A (HR = 1.59), previously identified with poor prognosis in AML34, and KDM5C (HR = 1.63), FBXW7 (HR = 1.57) and TP53 (HR = 1.19). TP53 has significant associations with poor outcome in KIRC (P = 0.012), AML (P = 0.0007) and HNSC (P = 0.00007). Conversely, BRCA2 (P = 0.05, HR = 0.62, 95% CI: 0.38 to 0.99) correlates with survival benefit in six types, including OV and UCEC (Supplementary Table 10a, b). IDH1 mutations are associated with improved prognosis across the Pan-Cancer set (HR = 0.67, P = 0.16) and also in GBM (HR = 0.42, P = 0.09) (Supplementary Table 10a, b), consistent with previous work.35

 Driver mutations and tumour clonal architecture

To understand the temporal order of somatic events, we analysed the variant allele fraction (VAF) distribution of mutations in SMGs across AML, BRCA and UCEC (Fig. 5a and Supplementary Table 11a) and other tumour types (Extended Data Fig. 7). To minimize the effect of copy number alterations, we focused on mutations in copy neutral segments. Mutations in TP53 have higher VAFs on average in all three cancer types, suggesting early appearance during tumorigenesis.

It is worth noting that copy neutral loss of heterozygosity is commonly found in classical tumour suppressors such as TP53, BRCA1, BRCA2 and PTEN, leading to increased VAFs in these genes. In AML, DNMT3A (permutation test P = 0), RUNX1 (P = 0.0003) and SMC3 (P = 0.05) have significantly higher VAFs than average among SMGs (Fig. 5a and Supplementary Table 11b). In breast cancer, AKT1, CBFB, MAP2K4, ARID1A, FOXA1 and PIK3CA have relatively high average VAFs. For endometrial cancer, multiple SMGs (for example, PIK3CA, PIK3R1, PTEN, FOXA2 and ARID1A) have similar median VAFs. Conversely, KRAS and/or NRAS mutations tend to have lower VAFs in all three tumour types (Fig. 5a), suggesting NRAS (for example, P = 0 in AML) and KRAS (for example, P = 0.02 in BRCA) have a progression role in a subset of AML, BRCA and UCEC tumours. For all three cancer types, we clearly observed a shift towards higher expression VAFs in SMGs versus non-SMGs, most apparent in BRCA and UCEC (Extended Data Fig. 8a and Methods).

Previous analysis using whole-genome sequencing (WGS) detected subclones in approximately 50% of AML cases15,36,37; however, ana­lysis is difficult using AML exome owing to its relatively few coding mutations. Using 50 AML WGS cases, sciClone (http://github.com/ genome/sciclone) detected DNMT3A mutations in the founding clone for 100% (8 out of 8) of cases and NRAS mutations in the subclone for 75% (3 out of 4) of cases (Extended Data Fig. 8b). Among 304 and 160 of BRCA and UCEC tumours, respectively, with enough coding muta­tions for clustering, 35% BRCA and 44% UCEC tumours contained subclones. Our analysis provides the lower bound for tumour hetero­geneity, because only coding mutations were used for clustering. In BRCA, 95% (62 out of 65) of cases contained PIK3CA mutations in the founding clone, whereas 33% (3 out of 9) of cases had MLL3 muta­tions in the subclone. Similar patterns were found in UCEC tumours, with 96% (65 out of 68) and 95% (62 out of 65) of tumours containing PIK3CA and PTEN mutations, respectively, in the founding clone, and 9% (2 out of22) ofKRAS and 14% (1 out of 7) ofNRAS mutations in the subclone (Extended Data Fig. 8b and Supplementary Table 12).

Mutation con­text (-2 to +2 bp) was calculated for each somatic variant in each mutation category, and hierarchical clustering was then performed using the pairwise mutation context correlation across all cancer types. The mutational significance in cancer (MuSiC)3 package was used to identify significant genes for both indi­vidual tumour types and the Pan-Cancer collective. An R function ‘hclust’ was used for complete-linkage hierarchical clustering across mutations and samples, and Dendrix30 was used to identify sets of approximately mutual exclusive muta­tions. Cross-cancer survival analysis was based on the Cox proportional hazards model, as implemented in the R package ‘survival’ (http://cran.r-project.org/web/ packages/survival/), and the sciClone algorithm (http://github.com/genome/sci-clone) generated mutation clusters using point mutations from copy number neutral segments. A complete description of the materials and methods used to generate this data set and its results is provided in the Methods.

References (20 of 38)

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  3. Dees, N. D. et al. MuSiC: Identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012).
  4. Roth, A. et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics 28, 907–913 (2012).
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UPDATED 4/24/2020  The genomic landscape of pediatric cancers: Curation of WES/WGS studies shows need for more data

The genomic landscape of pediatric cancers: Implications for diagnosis and treatment

BY E. ALEJANDRO SWEET-CORDERO, JACLYN A. BIEGEL

SCIENCE15 MAR 2019 : 1170-1175

Source: https://science.sciencemag.org/content/363/6432/1170

Abstract

The past decade has witnessed a major increase in our understanding of the genetic underpinnings of childhood cancer.  Genomic sequencing studies have highlighted key differences between pediatric and adult cancers.  Whereas many adult cancers are characterized by a high number of somatic mutations, pediatric cancers typically have few somatic mutations but a higher prevalence of germline alterations in cancer predisposition genes.  Also noteworthy is the remarkable heterogeneity in the types of genetic alterations that likely drive the growth of pediatric cancers, including copy number alterations, gene fusions, enhancer hijacking events, and chromoplexy.  Because most studies have genetically profiled pediatric cancers only at diagnosis, the mechanisms underlying tumor progression, therapy resistance, and metastasis remain poorly understood.  We discuss evidence that points to a need for more integrative approaches aimed at identifying driver events in pediatric cancers at both diagnosis and relapse.  We also provide an overview of key aspects of germline predisposition for cancer in this age group.

Approximately 300,000 children from infancy to age 14 are diagnosed with cancer worldwide every year (1). Some of the cancer types affecting the pediatric population are also seen in adolescents and young adults (AYA), but it has become increasingly clear that cancers in the latter age group have unique biological characteristics that can affect prognosis and therapy (2). Pediatric and AYA cancer patients present with a heterogeneous set of diseases that can be broadly subclassified as leukemias, brain tumors, and non–central nervous system (CNS) solid tumors. These subgroups contain numerous distinct clinical entities, many of which are still poorly characterized from a molecular standpoint.

Recent large-scale genomic analyses have increased our understanding of the genetic drivers of pediatric cancer and have helped to identify new clinically relevant subtypes. These studies have also underscored the distinct nature of the genetic alterations in pediatric and AYA cancers versus adult cancers. Of particular note, the number of somatic mutations in most pediatric cancers is substantially lower than that in adult cancers (3, 4). Exceptions are tumors in children who carry germline mutations that compromise repair of DNA damage (5). For many pediatric cancers, driver events are conditioned on the developmental stage in which the tumor arises. For example, a mutation occurring in one developmental compartment (e.g., a muscle stem cell) may lead to cancer, whereas the same mutation in another compartment does not (6). Pediatric cancer genomes are also characterized by specific patterns of copy number alterations and structural alterations [chromoplexy (7), chromothripsis (8)] that are prognostic indicators in several cancer subtypes. Gene fusion events have long been recognized as oncogenic drivers in many pediatric cancers; however, advanced sequencing technologies have revealed that the number of fusion partners is greater than previously thought, and that previously undetected gene rearrangements may also function as drivers. Finally, germline mutations in a wide spectrum of genes that predispose to cancer appear to play a greater role in pediatric cancer than previously appreciated (9, 10).

Somatic alterations in pediatric cancers

Genome landscape studies

Early large-scale sequencing studies of pediatric cancers identified novel driver genes while also underscoring the overall low mutational burden (11–14).  Whole exome sequencing studies of Wilms tumor, T-cell acute lymphoblastic leukemia (TALL), and acute myeloid leukemia (CML) identified some recurring mutations such as

  • FLT3-IDT
  • WT1
  • NUP98-NST1 gene fusion

however many of the driver genes were subtype specific.  Other fusion events were seen (by RNASeq) such as

  • EWS-FL1
  • Bcr-Abl
  • MYB-QK1

as well as multiple epigenetic events such as methylations.

REFERENCES

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Gene Expression: Algorithms for Protein Dynamics

Posted in Biological Networks, Gene Regulation and Evolution, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Scientist: Career considerations, Statistical Methods for Research Evaluation, tagged , , , , , , on October 26, 2013| Leave a Comment »

Gene Expression: Algorithms for Protein Dynamics

Reporter:  Aviva Lev-Ari, PhD, RN

Stanford-developed algorithm reveals complex protein dynamics behind gene expression

BY KRISTA CONGER

Michael Snyder

In yet another coup for a research concept known as “big data,” researchers at the Stanford University School of Medicine have developed a computerized algorithm to understand the complex and rapid choreography of hundreds of proteins that interact in mindboggling combinations to govern how genes are flipped on and off within a cell.

To do so, they coupled findings from 238 DNA-protein-binding experiments performed by the ENCODE project — a massive, multiyear international effort to identify the functional elements of the human genome — with a laboratory-based technique to identify binding patterns among the proteins themselves.

The analysis is sensitive enough to have identified many previously unsuspected, multipartner trysts. It can also be performed quickly and repeatedly to track how a cell responds to environmental changes or crucial developmental signals.

“At a very basic level, we are learning who likes to work with whom to regulate around 20,000 human genes,” said Michael Snyder, PhD, professor and chair of genetics at Stanford. “If you had to look through all possible interactions pair-wise, it would be ridiculously impossible. Here we can look at thousands of combinations in an unbiased manner and pull out important and powerful information. It gives us an unprecedented level of understanding.”

Snyder is the senior author of a paper describing the research published Oct. 24 in Cell. The lead authors are postdoctoral scholars Dan Xie, PhD, Alan Boyle, PhD, and Linfeng Wu, PhD.

Proteins control gene expression by either binding to specific regions of DNA, or by interacting with other DNA-bound proteins to modulate their function. Previously, researchers could only analyze two to three proteins and DNA sequences at a time, and were unable to see the true complexities of the interactions among proteins and DNA that occur in living cells.

The challenge resembled trying to figure out interactions in a crowded mosh pit by studying a few waltzing couples in an otherwise empty ballroom, and it has severely limited what could be learned about the dynamics of gene expression.

The ENCODE, for the Encyclopedia of DNA Elements, project was a five-year collaboration of more than 440 scientists in 32 labs around the world to reveal the complex interplay among regulatory regions, proteins and RNA molecules that governs when and how genes are expressed. The project has been generating a treasure trove of data for researchers to analyze for the last eight years.

In this study, the researchers combined data from genomics (a field devoted to the study of genes) and proteomics (which focuses on proteins and their interactions). They studied 128 proteins, called trans-acting factors, which are known to regulate gene expression by binding to regulatory regions within the genome. Some of the regions control the expression of nearby genes; others affect the expression of genes great distances away.

The researchers used 238 data sets generated by the ENCODE project to study the specific DNA sequences bound by each of the 128 trans-acting factors. But these factors aren’t monogamous; they bind many different sequences in a variety of protein-DNA combinations. Xie, Boyle and Snyder designed a machine-learning algorithm to analyze all the data and identify which trans-acting factors tend to be seen together and which DNA sequences they prefer.

Wu then performed immunoprecipitation experiments, which use antibodies to identify protein interactions in the cell nucleus. In this way, they were able to tell which proteins interacted directly with one another, and which were seen together because their preferred DNA binding sites were adjoining.

“Before our work, only the combination of two or three regulatory proteins were studied, which oversimplified how gene regulators collaborate to find their targets,” Xie said. “With our method we are able to study the combination of more than 100 regulators and see a much more complex structure of collaboration. For example, it had been believed that a key regulator of cell proliferation called FOS typically only works with JUN protein family members. We show, in addition to JUN, FOS has different partners under different circumstances. In fact, we found almost all the canonical combinations of two or three trans-acting factors have many more partners than we previously thought.”

To broaden their analysis, the researchers included data from other sources that explored protein-binding patterns in five cell types. They found that patterns of co-localization among proteins, in which several proteins are found clustered closely on the DNA to govern gene expression, vary according to cell type and the conditions under which the cells are grown. They also found that many of these clusters can be explained through interactions among proteins, and that not every protein bound to DNA directly.

“We’d like to understand how these interactions work together to make different cell types and how they gain their unique identities in development,” Snyder said. “Furthermore, diseased cells will have a very different type of wiring diagram. We hope to understand how these cells go astray.”

Other Stanford co-authors include life science research assistant Jie Zhai and life science research associate Trupti Kawli, PhD.

The research was supported by the National Human Genome Research Institute (grants U54HG004558 and U54HG006996).

Information about Stanford’s Department of Genetics, which also supported the work, is available at http://genetics.stanford.edu.

PRINT MEDIA CONTACT
Krista Conger | Tel (650) 725-5371
kristac@stanford.edu
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M.A. Malone | Tel (650) 723-6912
mamalone@stanford.edu

Stanford Medicine integrates research, medical education and patient care at its three institutions – Stanford University School of Medicine, Stanford Hospital & Clinics and Lucile Packard Children’s Hospital. For more information, please visit the Office of Communication & Public Affairs site at

http://mednews.stanford.edu/.http://med.stanford.edu/ism/2013/october/snyder.html?goback=%2Egde_5180384_member_5799368448383397888#sthash%2EhU03LKIX%2Edpuf

 

Dynamic trans-Acting Factor Colocalization in Human Cells

Cell, Volume 155, Issue 3, 713-724, 24 October 2013
Copyright © 2013 Elsevier Inc. All rights reserved.
10.1016/j.cell.2013.09.043

Authors

    • Highlights
    • Colocalization patterns of 128 TFs in human cells
    • An application of SOMs to study high-dimensional TF colocalization patterns
    • Colocalization patterns are dynamic through stimulation and across cell types
    • Many TF colocalizations can be explained by protein-protein interaction

    Summary

    Different trans-acting factors (TFs) collaborate and act in concert at distinct loci to perform accurate regulation of their target genes. To date, the cobinding of TF pairs has been investigated in a limited context both in terms of the number of factors within a cell type and across cell types and the extent of combinatorial colocalizations. Here, we use an approach to analyze TF colocalization within a cell type and across multiple cell lines at an unprecedented level. We extend this approach with large-scale mass spectrometry analysis of immunoprecipitations of 50 TFs. Our combined approach reveals large numbers of interesting TF-TF associations. We observe extensive change in TF colocalizations both within a cell type exposed to different conditions and across multiple cell types. We show distinct functional annotations and properties of different TF cobinding patterns and provide insights into the complex regulatory landscape of the cell.

    http://www.cell.com/abstract/S0092-8674%2813%2901217-8#!

    Personalized medicine aims to assess medical risks, monitor, diagnose and treat patients according to their specific genetic composition and molecular phenotype. The advent of genome sequencing and the analysis of physiological states has proven to be powerful (Cancer Genome Atlas Research Network, 2011). However, its implementation for the analysis of otherwise healthy individuals for estimation of disease risk and medical interpretation is less clear. Much of the genome is difficult to interpret and many complex diseases, such as diabetes, neurological disorders and cancer, likely involve a large number of different genes and biological pathways (Ashley et al., 2010,Grayson et al., 2011,Li et al., 2011), as well as environmental contributors that can be difficult to assess. As such, the combination of genomic information along with a detailed molecular analysis of samples will be important for predicting, diagnosing and treating diseases as well as for understanding the onset, progression, and prevalence of disease states (Snyder et al., 2009).

    Presently, healthy and diseased states are typically followed using a limited number of assays that analyze a small number of markers of distinct types. With the advancement of many new technologies, it is now possible to analyze upward of 105 molecular constituents. For example, DNA microarrays have allowed the subcategorization of lymphomas and gliomas (Mischel et al., 2003), and RNA sequencing (RNA-Seq) has identified breast cancer transcript isoforms (Li et al., 2011,van der Werf et al., 2007,Wu et al., 2010,Lapuk et al., 2010). Although transcriptome and RNA splicing profiling are powerful and convenient, they provide a partial portrait of an organism’s physiological state. Transcriptomic data, when combined with genomic, proteomic, and metabolomic data are expected to provide a much deeper understanding of normal and diseased states (Snyder et al., 2010). To date, comprehensive integrative omics profiles have been limited and have not been applied to the analysis of generally healthy individuals.

    To obtain a better understanding of: (1) how to generate an integrative personal omics profile (iPOP) and examine as many biological components as possible, (2) how these components change during healthy and diseased states, and (3) how this information can be combined with genomic information to estimate disease risk and gain new insights into diseased states, we performed extensive omics profiling of blood components from a generally healthy individual over a 14 month period (24 months total when including time points with other molecular analyses). We determined the whole-genome sequence (WGS) of the subject, and together with transcriptomic, proteomic, metabolomic, and autoantibody profiles, used this information to generate an iPOP. We analyzed the iPOP of the individual over the course of healthy states and two viral infections (Figure 1A). Our results indicate that disease risk can be estimated by a whole-genome sequence and by regularly monitoring health states with iPOP disease onset may also be observed. The wealth of information provided by detailed longitudinal iPOP revealed unexpected molecular complexity, which exhibited dynamic changes during healthy and diseased states, and provided insight into multiple biological processes. Detailed omics profiling coupled with genome sequencing can provide molecular and physiological information of medical significance. This approach can be generalized for personalized health monitoring and medicine.

     

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    Nobel Prize in Physiology or Medicine 2013 for Cell Transport: James E. Rothman of Yale University; Randy W. Schekman of the University of California, Berkeley; and Dr. Thomas C. Südhof of Stanford University

    Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomedical Measurement Science, Calcium, Calcium Signaling, Calmodulin Kinase and Contraction, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Scientist: Career considerations, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , , , on October 7, 2013| Leave a Comment »

    Nobel Prize in Physiology or Medicine 2013 for Cell Transport: James E. Rothman of Yale University; Randy W. Schekman of the University of California, Berkeley; and Dr. Thomas C. Südhof of Stanford University

    Reporter: Aviva Lev-Ari, PhD, RN

    Comments by Graduate Students of the nobel Prize Recipients and other in NYT, 10/7/2013:

    I had the privilege of meeting Randy Schekman a few times when I was a postdoc at Berkeley. In addition to pioneering the understand of cellular trafficking, he was also a great colleague and educator (of undergrads, grad students, postdocs). Hats off to a wonderful scientist who also pays it forward to future generations as a mentor!

    Last couple years, including this year, the Nobel for Physiology or Medicine Award has been dominated by Cell Biologists. I think this highlights how understanding cells is really the key to most medicine.
    Paul Knoepfler
    http://www.ipscell.com

    I guess UC Berkeley will have to add a few more Nobel Laureate Parking Spots on their campus now!
    Yes, in parking-challenged Berkeley campus, some of the best parking spots are reserved for the Nobel Laureate Faculty. They have so many winners, and rather spotty on-campus parking, so they don’t want such brains to go hunt for parking. They reason that the Laureates should be doing better things, like more research, or assisting newer researchers and students. A most elegant solution!
    I don’t think there is any other institution anywhere in the world that has dedicated parking for their Nobel-winning employees. Or has so many Nobels on the payroll. But then, there is just one Cal.
    This prize is another testament to UC Berkeley’s standing.
    Congratulations to the scientists, and a big thank you to their institutions that allowed them the freedom and resources to pursue their ideas.

    Randy Schekman awarded 2013 Nobel Prize in Physiology or Medicine

    By Robert Sanders, Media Relations | October 7, 2013

    BERKELEY —

    ScheckmanRandy Schekman, who will share the 2013 Nobel Prize in Physiology or Medicine (Peg Skorpinski photo)

    Randy W. Schekman, professor of molecular and cell biology at the University of California, Berkeley, has won the 2013 Nobel Prize in Physiology or Medicine for his role in revealing the machinery that regulates the transport and secretion of proteins in our cells. He shares the prize with James E. Rothman of Yale University and Thomas C. Südhof of Stanford University.

    Discoveries by Schekman about how yeast secrete proteins led directly to the success of the biotechnology industry, which was able to coax yeast to release useful protein drugs, such as insulin and human growth hormone. The three scientists’ research on protein transport in cells, and how cells control this trafficking to secrete hormones and enzymes, illuminated the workings of a fundamental process in cell physiology.

    Schekman is UC Berkeley’s 22nd Nobel Laureate, and the first to receive the prize in the area of physiology or medicine.

    In a statement, the 50-member Nobel Assembly lauded Rothman, Schekman and Südhof for making known “the exquisitely precise control system for the transport and delivery of cellular cargo. Disturbances in this system have deleterious effects and contribute to conditions such as neurological diseases, diabetes, and immunological disorders.”

    “My first reaction was, ‘Oh, my god!’ said Schekman, 64, who was awakened at his El Cerrito home with the good news at 1:30 a.m. “That was also my second reaction.”

    Be part of our developing story on Storify and Twitter: Tweet your congratulations to Professor Schekman, using hashtag #BerkeleyNobel.

    Also see:

    Happy ending for Berkeley’s newest Nobel winner

    Schekman and Rothman separately mapped out one of the body’s critical networks, the system in all cells that shuttles hormones and enzymes out and adds to the cell surface so it can grow and divide. This system, which utilizes little membrane bubbles to ferry molecules around the cell interior, is so critical that errors in the machinery inevitably lead to death.

    “Ten percent of the proteins that cells make are secreted, including growth factors and hormones, neurotransmitters by nerve cells and insulin from pancreas cells,” said Schekman, a Howard Hughes Medical Institute Investigator and a faculty member in the Li Ka Shing Center for Biomedical and Health Sciences.

    Schekman on the phoneSchekman takes a call at home after getting the news. (Carol Ness photo)

    In what some thought was a foolish decision, Schekman decided in 1976, when he first joined the College of Letters and Science at UC Berkeley, to explore this system in yeast. In the ensuing years, he mapped out the machinery by which yeast cells sort, package and deliver proteins via membrane bubbles to the cell surface, secreting proteins important in yeast communication and mating. Yeast also use the process to deliver receptors to the surface, the cells’ main way of controlling activities such as the intake of nutrients like glucose.

    In the 1980s and ’90s, these findings enabled the biotechnology industry to exploit the secretion system in yeast to create and release pharmaceutical products and industrial enzymes. Today, diabetics worldwide use insulin produced and discharged by yeast, and most of the hepatitis B vaccine used around the world is secreted by yeast. Both systems were developed by Chiron Corp. of Emeryville, Calif., now part of Novartis International AG, during the 20 years Schekman consulted for the company.

    Various diseases, including some forms of diabetes and a form of hemophilia, involve a hitch in the secretion system of cells, and Schekman is now investigating a possible link to Alzheimer’s disease.

    “Our findings have aided people in understanding these diseases,” said Schekman.

    Based on the machinery discovered by Schekman and Rothman, Südhof subsequently discovered how nerve cells release signaling molecules, called neurotransmitters, which they use to communicate.

    For his scientific contributions, Schekman was elected to the National Academy of Sciences in 1992, received the Gairdner International Award in 1996 and the Lasker Award for basic and clinical research in 2002. He was elected president of the American Society for Cell Biology in 1999. On Oct. 3, Schekman received the Otto Warburg Medal of the German Society for Biochemistry and Molecular Biology, which is considered the highest German award in the fields of biochemistry and molecular biology.

    Schekman, formerly editor of the journal Proceedings of the National Academy of Sciences, currently is editor-in-chief of the new open access journal eLife.

    Schekman and his wife, Nancy Walls, have two adult children.

    MORE INFORMATION

    SOURCE

    tanford Report, October 7, 2013

    Thomas Südhof wins Nobel Prize in Physiology or Medicine

    Neuroscientist Thomas Südhof, MD, professor of molecular and cellular physiology at the Stanford School of Medicine, won the 2013 Nobel Prize in Physiology or Medicine.

    BY KRISTA CONGER

    Steve FischThomas SudhofThomas Sudhof won the 2013 Nobel Prize in Physiology or Medicine.

    Neuroscientist Thomas Südhof, MD, professor of molecular and cellular physiology at the Stanford University School of Medicine, won the 2013 Nobel Prize in Physiology or Medicine.

    He shared the prize with James Rothman, PhD, a former Stanford professor of biochemistry, andRandy Schekman, PhD, who earned his doctorate at Stanford under the late Arthur Kornberg, MD, another winner of the Nobel Prize in Physiology or Medicine.

    The three were awarded the prize “for their discoveries of machinery regulating vesicle traffic, a major transport system in our cells.” Rothman is now a professor at Yale University, and Schekman is a professor at UC-Berkeley.

    “I’m absolutely surprised,” said Südhof, who was in the remote town of Baeza in Spain to attend a conference and give a lecture. “Every scientist dreams of this. I didn’t realize there was chance I would be awarded the prize. I am stunned and really happy to share the prize with James Rothman and Randy Schekman.”

    The three winners will share a prize that totals roughly $1.2 million, with about $413,600 going to each.

    Robert Malenka, MD, Stanford’s Nancy Friend Pritzker Professor in Psychiatry and Behavioral Sciences, is at the conference with Südhof, a close collaborator. “He’s dazed, tired and happy,” Malenka said by phone. “The only time I’ve seen him happier was when his children were born.”

    Südhof, the Avram Goldstein Professor in the School of Medicine, received the award for his work in exploring how neurons in the brain communicate with one another across gaps called synapses. Although his work has focused on the minutiae of how molecules interact on the cell membranes, the fundamental questions he’s pursuing are large.

    “The brain works by neurons communicating via synapses,” Südhof said in a phone conversation this morning. “We’d like to understand how synapse communication leads to learning on a larger scale. How are the specific connections established? How do they form? And what happens in schizophrenia and autism when these connections are compromised?” In 2009, he published research describing how a gene implicated in autism and schizophrenia alters mice’s synapses and produces behavioral changes in the mice, such as excessive grooming and impaired nest building, that are reminiscent of these human neuropsychiatric disorders.

    Lloyd Minor, MD, dean of the School of Medicine, said, “Thomas Südhof is a consummate citizen of science. His unrelenting curiosity, his collaborative spirit, his drive to ascertain the minute details of cellular workings, and his skill to carefully uncover these truths — taken together it’s truly awe-inspiring.

    “He has patiently but relentlessly probed one of the fundamental questions of medical science — perhaps the fundamental question in neuroscience: How nerve cells communicate with each other. The answer is at the crux of human biology and of monumental importance to human health. Dr. Südhof’s receipt of this prize is inordinately well-deserved, and I offer him my heartfelt congratulations. His accomplishment represents what Stanford Medicine and the biomedical revolution are all about.”

    The Nobel committee called Südhof on his cell phone after trying his home in Menlo Park, Calif. His wife, Lu Chen, PhD, associate professor of neurosurgery and of psychiatry and behavioral sciences, then gave the committee his cell phone number to reach him in Spain.

    “The phone rang three times before I decided to go downstairs and pick it up,” Chen said. “I thought it was one of my Chinese relatives who couldn’t figure out the time zone.”

    Chen and Südhof have two young children, and Südhof has four adult children from a previous marriage. “I was very surprised,” Chen said, “but he’s more concerned about how I’ll get the kids up this morning in time for school.”

    “I was expecting a call from a colleague about the conference I’m here to attend, so I pulled off in a parking lot,” said Südhof, who was driving from Madrid to Baeza at the time he received the announcement. “I hadn’t slept at all the previous night, and I certainly wasn’t expecting a call from the Nobel committee.”

    On the day he got the call from the Nobel committee, he was scheduled to give a talk at a conference, Membrane Traffic at the Synapse: The Cell Biology of Synaptic Plasticity, held in a 17th-century building that now serves as a conference center.

    “Professor Sudhof’s contributions to the understanding of how cells operate have been of enormous importance to medicine, and to his own work in understanding how connections form within the human brain,” said Stanford President John Hennessy. “The recognition by the Nobel committee is a remarkable achievement.”

    Südhof, who is also a Howard Hughes Medical Institute investigator, has spent the past 30 years prying loose the secrets of the synapse, the all-important junction where information, in the form of chemical messengers called neurotransmitters, is passed from one neuron to another. The firing patterns of our synapses underwrite our consciousness, emotions and behavior. The simple act of taking a step forward, experiencing a fleeting twinge of regret, recalling an incident from the morning commute or tasting a doughnut requires millions of simultaneous and precise synaptic firing events throughout the brain and peripheral nervous system.

    Even a moment’s consideration of the total number of synapses in the typical human brain adds up to instant regard for that organ’s complexity. Coupling neuroscientists’ ballpark estimate of 200 billion neurons in a healthy adult brain with the fact that any single neuron may share synaptic contacts with as few as one or as many as 1 million other neurons (the median is somewhere in the vicinity of 10,000) suggests that your brain holds perhaps 2 quadrillion synapses — 10,000 times the number of stars in the Milky Way.

    “The computing power of a human or animal brain is much, much higher than that of any computer,” said Südhof. “A synapse is not just a relay station. It is not even like a computer chip, which is an immutable element. Every synapse is like a nanocomputer all by itself. The amount of neurotransmitter released, or even whether that release occurs at all, depends on that particular synapse’s previous experience.”

    Much of a neuron can be visualized as a long, hollow cord whose outer surface conducts electrical impulses in one direction. At various points along this cordlike extension are bulbous nozzles known as presynaptic terminals, each one housing myriad tiny, balloon-like vesicles containing neurotransmitters and each one abutting a downstream (or postsynaptic) neuron.

    When an electrical impulse traveling along a neuron reaches one of these presynaptic terminals, calcium from outside the neuron floods in through channels that open temporarily, and a portion of the neurotransmitter-containing vesicles fuse with the surrounding bulb’s outer membrane and spill their contents into the narrow gap separating the presynaptic terminal from the postsynaptic neuron’s receiving end.

    Südhof, along with other researchers worldwide, has identified integral protein components critical to the membrane fusion process. Südhof purified key protein constituents sticking out of the surfaces of neurotransmitter-containing vesicles, protruding from nearby presynaptic-terminal membranes, or bridging them. Then, using biochemical, genetic and physiological techniques, he elucidated the ways in which the interactions among these proteins contribute to carefully orchestrated membrane fusion: As a result, synaptic transmission is today one of the best-understood phenomena in neuroscience.

    Südhof, who was born in Germany in 1955, received an MD in 1982 from Georg-August-Universität in Göttingen. He came to Stanford in 2008 after 25 years at the University of Texas Southwestern Medical Center at Dallas, where he first worked as a postdoctoral fellow at the laboratories of Michael Brown, MD, and Joseph Goldstein, MD.. Brown and Goldstein were awarded the Nobel Prize in Physiology or Medicine in 1985 for their work in understanding the regulation of cholesterol metabolism. In 1986, Südhof established his own laboratory at the university.

    Südhof became an HHMI investigator in 1991, and moved to Stanford as a professor in molecular and cellular physiology in 2008.

    The proteins Südhof has focused on for close to three decades are disciplined specialists. They recruit vesicles, bring them into “docked” positions near the terminals, herd calcium channels to the terminal membrane, and, cued by calcium, interweave like two sides of a zipper and force the vesicles into such close contact with terminal membranes that they fuse with them and release neurotransmitters into the synaptic gap. Although these specialists perform defined roles at the synapses, similar proteins, discovered later by Südhof and others, play comparable roles in other biological processes ranging from hormone secretion to fertilization of an egg during conception to immune cells’ defense against foreign invaders.

    “We’ve made so many major advances during the past 50 years in this field, but there’s still much more to learn,” said Südhof, who in a 2010 interview with The Lancet credited his bassoon instructor as his most influential teacher for helping him to learn the discipline to practice for hours on end. “Understanding how the brain works is one of the most fundamental problems in neuroscience.”

    Südhof’s accomplishments also earned him the 2013 Lasker Basic Medical Research Award. He is a member of the National Academy of Sciences, the Institute of Medicine and the American Academy of Arts & Sciences. He also is a recipient of the 2010 Kavli Prize in neuroscience.

    In the Lancet interview, Südhof defined basic research as an approach often neglected in the pursuit of medicine. “This ‘solid descriptive science,’ like neuroanatomy or biochemistry, [are] disciplines that cannot claim to immediately understand functions or provide cures, but which form the basis for everything we do.”

    Südhof said this morning he is excited to speak with his family about the prize, although it may be too much for his youngest children, ages 3 and 4, to grasp. “I will try to explain it to them,” he said. “It will be a wonderful occasion.” He noted that he has already received congratulatory calls from two of his four adult children. For them, the news may have come as less of a surprise.

    “The Nobel prize became an inevitable topic of conversation when Tom won the Lasker award,” Chen said. “But the two of us share a feeling that one should never work for prizes.”

    “Everyone has pegged him as a potential Nobel prize winner for many years,” said Malenka, who described the scene at the conference during the lunch hour. “It was just a matter of time. The attendees were clapping and cheering for him.”

    Although he plans to return to the United States as soon as possible, Südhof has no plans to let the award slow his research — or even his plans for the day. He responded to an inquiry with a characteristically low-key reply. “Well, I think I’ll go ahead and give my talk.”

    SOURCE

    Rothman Lab

    Membrane fusion is a fundamental biological process for organelle formation, nutrient uptake, and the secretion of hormones and neurotransmitters.

    It is central to vesicular transport, storage, and release in many areas of endocrine and exocrine physiology, and imbalances in these processes give rise to important diseases, such as diabetes.

    We employ diverse biophysical, biochemical, and cell biological approaches to characterize the fundamental participants in intracellular transport processes.

    flippedcellfull
    Time lapse images of fusing flipped-SNARE cells.

    SNARE Overview

    Over 30 years ago, we observed what we interpreted to be vesicular transport in crude extracts of tissue culture cells. In subsequent years we found that we had reconstituted vesicle trafficking in the Golgi, including the process of membrane fusion. Using this assay as a guide, we purified as a required factor the NEM-Sensitive Fusion protein (NSF). This led to the purification of the Soluble NSF Attachment Factor (SNAP), which bound NSF to Golgi membranes, and then with these tools discovered that the receptors for SNAP in membranes were actually complexes of proteins (which we called SNAREs) which we envisioned could potentially partner as a bridge between membranes to contribute to the process of membrane fusion and provide specificity to it (as captured in the ‘SNARE hypothesis’ proposed at the time).

    We now know that organisms have a large family of SNARE proteins that indeed form cognate partnerships in just this way, and that NSF is an ATPase that (using SNAP as an adaptor protein) disrupts the SNARE complex after fusion is complete so its subunits can be recycled for repeated use. Recombinant cognate SNAREs introduced into artificial bilayers or expressed ectopically on the outside of cells ( “flipped SNAREs”) spontaneously and efficiently result in membrane (or cell) fusion, demonstrating that the SNARE complex is not only necessary but is sufficient for fusion. There are many proteins known and rapidly being discovered which closely regulate this vital process, but the muscle – if not always the brains – is in the SNAREs. Compartmental specificity is encoded to a remarkable degree in the functional partnering of SNARE proteins, a fact which is in no way inconsistent with the emerging contribution of upstream regulatory components (like rabGTPases and tethering complexes) to domain/compartment specificity.

    Current Research & Projects

    Our lab is working to elucidate the underlying mechanisms of vesicular transport within cells and the secretion of proteins and neurotransmitters.

    Projects include:

    1. The biochemical and biophysical mechanisms of vesicle budding and fusion;
    2. Cellular regulation of vesicle fusion in exocytosis and synaptic transmission;
    3. Structural and functional organization of the Golgi apparatus from a cellular systems view.

    We take an interdisciplinary approach which includes cell-free biochemistry, single molecule biophysics, high resolution optical imaging of single events/single molecules in the cell and in cell-free formats.

    The overall goal is to understand transport pathways form structural mechanism to cellular physiology. The latter is facilitated by high throughput functional genomics at the cellular level (see Yale Center for High Throughput Cell Biology).

    SNAREpins

    We have a strong interest in new lab members who bring backgrounds in chemistry, physics, and engineering.

    SOURCE

    http://medicine.yale.edu/cellbio/rothman/index.aspx

    3 Americans Win Joint Nobel Prize in Medicine

    Reuters

    From left: Randy W. Schekman, Thomas C. Südhof and James E. Rothman.

    <nyt_byline>

    By 
    Published: October 7, 2013 151 Comments

    Three Americans won the Nobel Prize in Physiology or Medicine Monday for discovering the machinery that regulates how cells transport major molecules in a cargo system that delivers them to the right place at the right time in cells.

    Science Twitter Logo.
     

    The Karolinska Institute in Stockholmannounced the winners: James E. Rothman of Yale University; Randy W. Schekman of the University of California, Berkeley; and Dr. Thomas C. Südhof of Stanford University.

    The molecules are moved around cells in small packages called vesicles, and each scientist discovered different facets that are needed to ensure that the right cargo is shipped to the correct destination at precisely the right time.

    Their research solved the mystery of how cells organize their transport system, the Karolinska committee said. Dr. Schekman discovered a set of genes that were required for vesicle traffic. Dr. Rothman unraveled protein machinery that allows vesicles to fuse with their targets to permit transfer of cargo. Dr. Südhof revealed how signals instruct vesicles to release their cargo with precision.

    The tiny vesicles, which have a covering known as membranes, shuttle the cargo between different compartments or fuse with the membrane. The transport system activates nerves. It also controls the release of hormones.

    Disturbances in this exquisitely precise control system cause serious damage that, in turn, can contribute to conditions like neurological diseases, diabetes and immunological disorders.

    Dr. Schekman, 64, who was born in St. Paul, used yeast cells as a model system when he began his research in the 1970s. He found that vesicles piled up in parts of the cell and that the cause was genetic. He went on to identify three classes of genes that control different facets of the cell’s transport system. Dr. Schekman studied at the University of California in Los Angeles and at Stanford University, where he obtained his Ph.D. in 1974.

    In 1976, he joined the faculty of the University of California, Berkeley, where he is currently professor in the Department of Molecular and Cell Biology. Dr. Schekman is also an investigator at the Howard Hughes Medical Institute.

    Dr. Rothman, 63, who was born in Haverhill, Mass., studied vesicle transport in mammalian cells in the 1980s and 1990s. He discovered that a protein complex allows vesicles to dock and fuse with their target membranes. In the fusion process, proteins on the vesicles and target membranes bind to each other like the two sides of a zipper. The fact that there are many such proteins and that they bind only in specific combinations ensures that cargo is delivered to a precise location.

    The same principle operates inside the cell and when a vesicle binds to the cell’s outer membrane to release its contents. Dr. Rothman received a Ph.D. from Harvard Medical School in 1976, was a postdoctoral fellow at Massachusetts Institute of Technology, and moved in 1978 to Stanford University, where he started his research on the vesicles of the cell. Dr. Rothman has also worked at Princeton University, Memorial Sloan-Kettering Cancer Institute and Columbia University.

    In 2008, he joined the faculty of Yale University where he is currently professor and chairman in the Department of Cell Biology. Some of the genes Dr. Schekman discovered in yeast coded for proteins correspond to those Dr. Rothman identified in mammals. Collectively, they mapped critical components of the cell´s transport machinery.

    Dr. Südhof, 57, who was born in Göttingen, Germany, studied neurotransmission, the process by which nerve cells communicate with other cells in the brain. At the time he set out to explore the field 25 years ago, much of it was virgin scientific territory. Researchers had not identified a single protein in the neurotransmission process.

    Dr. Südhof helped transform what had been a rough outline into a number of molecular activities to provide insights into the elaborate mechanisms at the crux of neurological activities, from the simplest to the most sophisticated. He did so by systematically identifying, purifying and analyzing proteins that can rapidly release chemicals that underlie the brain’s activities. The transmission process can take less than a thousandth of a second.

    Dr. Südhof studied at the Georg-August-Universität in Göttingen, where he received a medical degree in 1982 and a doctorate in neurochemistry the same year. In 1983, he moved to the University of Texas Southwestern Medical Center in Dallas. Dr. Südhof, who has American citizenship, became an investigator at the Howard Hughes Medical Institute in 1991 and was appointed professor of molecular and cellular physiology at Stanford University in 2008.

    All three scientists have won other awards, including the Lasker Prize, for their research.

    <nyt_correction_bottom>

    This article has been revised to reflect the following correction:

    Correction: October 7, 2013

    An earlier version of this article misstated Randy W. Schekman’s age. He is 64, not 65.

    SOURCE

    http://www.nytimes.com/2013/10/08/health/3-win-joint-nobel-prize-in-medicine.html?_r=0

    Nobel for Cell Transport

    October 07, 2013

    This year’s Nobel Prize in Physiology or Medicine is going jointly to three scientists for their work figuring out how cells transport their cargo, according to the Karolinska Institute. They will share the $1.25 million prize.

    “Imagine hundreds of thousands of people who are traveling around hundreds of miles of streets; how are they going to find the right way? Where will the bus stop and open its doors so that people can get out?” says Nobel committee secretary Goran Hansson, according to the Associated Press. “There are similar problems in the cell.”

    By studying yeast cells with defective vesicles, Randy Schekman from the University of California, Berkeley, uncovered three classes of genes that control transportation within the cell, the New York Times adds. Schekman was awakened in California by the call from Stockholm. “I wasn’t thinking too straight. I didn’t have anything elegant to say,” he tells the AP. “All I could say was ‘Oh my God,’ and that was that.” Schekman adds that he called his lab manager to arrange a celebration in the lab.

    Meanwhile, Yale University’s James Rothman discovered a protein complex that allows vesicles to bind to their intended membrane targets, getting the vesicle contents to a specific location. Rothman notes that he recently lost funding for work building on his discovery, and says that he hopes that having won the Nobel will help him when he reapplies.

    And Thomas Südhof at Stanford University systematically studied how nerve cells communicate, finding that vesicles full of neurotransmitters bind to cell membranes to release their contents through a molecular mechanism that responds to the presence of calcium ions. He was on his way to a give a talk when he got his call. “I got the call while I was driving and like a good citizen I pulled over and picked up the phone,” Südhof says to the AP. “To be honest, I thought at first it was a joke. I have a lot of friends who might play these kinds of tricks.”

    SOURCE

    Other related articles published on these Open Access Online Scientific Journal include the following:

    The Series on Cardiovascular Disease and the role of Calcium Signaling consists of the following articles:

    Part I: Identification of Biomarkers that are Related to the Actin Cytoskeleton

    Larry H Bernstein, MD, FCAP

    http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/

    Part II: Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility

    Larry H. Bernstein, MD, FCAP, Stephen Williams, PhD and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/

    Part III: Renal Distal Tubular Ca2+ Exchange Mechanism in Health and Disease

    Larry H. Bernstein, MD, FCAP, Stephen J. Williams, PhD
 and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/09/02/renal-distal-tubular-ca2-exchange-mechanism-in-health-and-disease/

    Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

    Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/09/08/the-centrality-of-ca2-signaling-and-cytoskeleton-involving-calmodulin-kinases-and-ryanodine-receptors-in-cardiac-failure-arterial-smooth-muscle-post-ischemic-arrhythmia-similarities-and-differen/

    Part V: Heart, Vascular Smooth Muscle, Excitation-Contraction Coupling (E-CC), Cytoskeleton, Cellular Dynamics and Ca2 Signaling

    Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/26/heart-smooth-muscle-excitation-contraction-coupling-cytoskeleton-cellular-dynamics-and-ca2-signaling/

    Part VI: 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

    Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/01/calcium-molecule-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/

    Part VII: Cardiac Contractility & Myocardium Performance: Ventricular Arrhythmiasand Non-ischemic Heart Failure – Therapeutic Implications for Cardiomyocyte Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses

    Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/

    Part VIII: Disruption of Calcium Homeostasis: Cardiomyocytes and Vascular Smooth Muscle Cells: The Cardiac and Cardiovascular Calcium Signaling Mechanism

    Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/09/12/disruption-of-calcium-homeostasis-cardiomyocytes-and-vascular-smooth-muscle-cells-the-cardiac-and-cardiovascular-calcium-signaling-mechanism/

    Part IX: Calcium-Channel Blockers, Calcium Release-related Contractile Dysfunction (Ryanopathy) and Calcium as Neurotransmitter Sensor

    Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

    Part X: Synaptotagmin functions as a Calcium Sensor: How Calcium Ions Regulate the fusion of vesicles with cell membranes during Neurotransmission

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

    http://pharmaceuticalintelligence.com/2013/09/10/synaptotagmin-functions-as-a-calcium-sensor-how-calcium-ions-regulate-the-fusion-of-vesicles-with-cell-membranes-during-neurotransmission/

    Read Full Post »

    Selective Ion Conduction

    Posted in Bio Instrumentation in Experimental Life Sciences Research, Biomedical Measurement Science, Chemical Biology and its relations to Metabolic Disease, Computational Biology/Systems and Bioinformatics, Cytoskeleton, Electrophysiology, K, Na-K transport, Proteomics, tagged , , , , , , , , , , , , on October 7, 2013| Leave a Comment »

    Selective Ion Conduction

    Reviewer and Curator: Larry H Bernstein, MD, FCAP

    Introduction

    This is Part III of a series of articles on Translational Medicine in water transport or paracellelar flow, and ion conductance.  The first article was solely on the aquaporins (Part I), and the second goes from paracellular flow (dealing with paracellin-1 and the familay of claudins.  These proteins factor into a number of diseases of kidney function and Mg(2+) homeostasis, as well as a relationship between congestive heart failure related to infarct remodeling and the sodium-calcium transporter, with a model for treatment (Part II).  The last explores the basis of selective ion conduction, based on the 2003 Nobel Prize presentation by Roderick MacKinnon (Part III).

    POTASSIUM CHANNELS AND THE ATOMIC BASIS OF SELECTIVE ION CONDUCTION

    Nobel Lecture, December 8, 2003
    Roderick MacKinnon
    Howard Hughes Medical Institute, Laboratory of Molecular Neurobiology and Biophysics, Rockefeller University ,NewYork, NY 

    Water is an electrically polarizable substance, which means that its molecules rearrange in an ion’s electric field, pointing negative oxygen atoms in the direction of cations and positive hydrogen atoms toward anions. These electrically stabilizing interac­tions are much weaker in a less polarizable substance such as oil. Thus, an ion will tend to stay in the water on either side of a cell membrane rather than en­ter and cross the membrane. And yet numerous cellular processes, ranging from electrolyte transport across epithelia to electrical signal production in neurons, depend on the flow of ions across the membrane. To mediate the flow, specific protein catalysts known as ion channels exist in the cell mem­brane. Ion channels exhibit the following three essential properties: (1) they conduct ions rapidly, (2) many ion channels are highly selective, meaning only certain ion species flow while others are excluded, (3) their function is regu­lated by processes known as gating, that is, ion conduction is turned on and off in response to specific environmental stimuli. Figure 1 summarizes these properties (figure 1).

    MacKinnon. Fig 1 Ion channels exhibit three basic properties

    Figure 1. Ion channels exhibit three basic properties depicted in the cartoon. They conduct specific ions (for example K ) at high rates, they are selective (a K  channel essentially excludes Na ), and conduction is turned on and off by opening and closing a gate, which can be regulated by an external stimulus such as ligand-binding or membrane voltage. The relative size of K and Naions is shown.

    The modern history of ion channels began in 1952 when Hodgkin and Huxley published their seminal papers on the theory of the action potential in the squid giant axon (Hodgkin and Huxley, 1952a; Hodgkin and Huxley, 1952b; Hodgkin and Huxley, 1952c; Hodgkin and Huxley, 1952d). A funda­mental element of their theory was that the axon membrane undergoes changes in its permeability to Na+ and K+ ions. The Hodgkin-Huxley theory did not address the mechanism by which the membrane permeability changes occur: ions could potentially cross the membrane through channels or by a carrier-mediated mechanism. In their words ‘Details of the mecha­nism will probably not be settled for some time’ (Hodgkin and Huxley, 1952a). It is fair to say that the pursuit of this statement has accounted for much ion channel research over the past fifty years.

    As early as 1955 experimental evidence for channel mediated ion flow was obtained when Hodgkin and Keynes measured the directional flow of K+ ions across axon membranes using the isotope 42K+ (Hodgkin and Keynes, 1955). They observed that K+ flow in one direction across the membrane depends on flow in the opposite direction, and suggested that ‘the ions should be con­strained to move in single file and that there should, on average, be several ions in a channel at any moment’. Over the following two decades Armstrong and Hille used electrophysiological methods to demonstrate that Na+ and K+ ions cross cell membranes through unique protein pores – Na+ channels and K+ channels – and developed the concepts of selectivity filter for ion discrim­ination and gate for regulating ion flow (Hille, 1970; Hille, 1971; Hille, 1973; Armstrong, 1971; Armstrong et al., 1973; Armstrong and Bezanilla, 1977; Armstrong, 1981). The patch recording technique invented by Neher and Sakmann then revealed the electrical signals from individual ion channels, as well as the extraordinary diversity of ion channels in living cells throughout nature (Neher and Sakmann, 1976).

    The past twenty years have been the era of molecular biology for ion chan­nels. The ability to manipulate amino acid sequences and express ion chan­nels at high levels opened up entirely new possibilities for analysis. The ad­vancement of techniques for protein structure determination and the devel­opment of synchrotron facilities also created new possibilities. For me, a sci­entist who became fascinated with understanding the atomic basis of life’s electrical system, there could not have been a more opportune time to enter the field.

    The past twenty years have been the era of molecular biology for ion channels. The ability to manipulate amino acid sequences and express ion channels at high levels opened up entirely new possibilities for analysis. The advancement of techniques for protein structure determination and the development of synchrotron facilities also created new possibilities. For me, a scientist who became fascinated with understanding the atomic basis of life’s electrical system, there could not have been a more opportune time to enter the field. 

    MY EARLY STUDIES: THE K+ CHANNEL SIGNATURE SEQUENCE

    The cloning of the Shaker K+ channel gene from Drosophila melanogaster by Jan, Tanouye, and Pongs revealed for the first time a K+ channel amino acid se­quence and stimulated efforts by many laboratories to discover which of these amino acids form the pore, selectivity filter, and gate (Tempel et al., 1987; Kamb et al., 1987; Pongs et al., 1988). At Brandeis University in Chris Miller’s laboratory I had an approach to find the pore amino acids. Chris and I had just completed a study showing that charybdotoxin, a small protein from scor­pion venom, inhibits a K+ channel isolated from skeletal muscle cells by plug­ging the pore and obstructing the flow of ions (MacKinnon and Miller, 1988). In one of those late night ‘let’s see what happens if’ experiments while taking a molecular biology course at Cold Spring Harbor I found that the toxin – or what turned out to be a variant of it present in the charybdotoxin prepara­tion – inhibited the Shaker K+ channel (MacKinnon et al., 1988; Garcia et al., 1994). This observation meant I could use the toxin to find the pore, and it did not take very long to identify the first site-directed mutants of the Shaker K+ channel with altered binding of toxin (MacKinnon and Miller, 1989). I continued these experiments at Harvard Medical School where I began as as­sistant professor in 1989. Working with my small group at Harvard, including Tatiana Abramson, Lise Heginbotham, and Zhe Lu, and sometimes with Gary Yellen at Johns Hopkins University, we reached several interesting conclusions concerning the architecture of K+ channels. They had to be tetramers in which four subunits encircle a central ion pathway (MacKinnon, 1991). This conclusion was not terribly surprising but the experiments and analysis to reach it gave me great pleasure since they required only simple measure­ments and clear reasoning with binomial statistics. We also deduced that each subunit presents a ‘pore loop’ to the central ion pathway (figure 2) (MacKinnon, 1995).

    MacKinnon. Fig 2. tetramer K channel

    Figure 2. Early picture of a tetramer K+ channel with a selectivity filter made of pore loops. A linear representation of a Shaker K+ channel subunit on top shows shaded hydrophobic segments S1 to S6 and a region designated the pore loop. A partial amino acid sequence from the Shaker K+ channel pore loop highlights amino acids shown to interact with ex-tracellular scorpion toxins (*), intracellular tetraethylammonium (↑) and K+ ions (+). The pore loop was proposed to reach into the membrane (middle) and form a selectivity filter at the center of four subunits (bottom).

    This ‘loop’ formed the binding sites for scorpion toxins (MacKinnon and Miller, 1989; Hidalgo and MacKinnon, 1995; Ranganathan et al., 1996) as well as the small-molecule inhibitor tetraethylammonium ion (MacKinnon and Yellen, 1990; Yellen et al., 1991), which had been used by Armstrong and Hille decades earlier in their pioneering analysis of K+ channels (Armstrong, 1971; Armstrong and Hille, 1972). Most important to my thinking, mutations of certain amino acids within the ‘loop’ affected the channel’s ability to discriminate between K+ and Na+, the selectivity hallmark of K+ channels (Heginbotham et al., 1992; Heginbotham et al., 1994). Meanwhile, new K+ channel genes were discovered and they all had one ob­vious feature in common: the very amino acids that we had found to be im­portant for K+ selectivity were conserved (figure 3). We called these amino acids the K+ channel signature sequence, and imagined four pore loops somehow forming a selectivity filter with the signature sequence amino acids inside the pore (Heginbotham et al., 1994; MacKinnon, 1995).

    Figure 3. The K+ channel signature sequence shown as single letter amino acid code (blue) is highly conserved in organisms throughout the tree of life. Some K+ channels contain six membrane-spanning segments per subunit (6TM) while others contain only two (2TM). 2TM K+ channels correspond to 6TM K+ channels without the first four membrane-span­ning segments (S1-S4 in figure 2).

    When you consider the single channel conductance of many K+ channels found in cells you realize just how incredible these molecular devices are. With typical cellular electrochemical gradients, K+ ions conduct at a rate of 107 to 108 ions per second. That rate approaches the expected collision fre­quency of K+ ions from solution with the entryway to the pore. This means that K+ ions flow through the pore almost as fast as they diffuse up to it. For this to occur the energetic barriers in the channel have to be very low, some­thing like those encountered by K+ ions diffusing through water. All the more remarkable, the high rates are achieved in the setting of exquisite selectivity: the K+ channel conducts K+, a monovalent cation of Pauling radius 1.33 Å, while essentially excluding Na+, a monovalent cation of Pauling radius 0.95 Å. And this ion selectivity is critical to the survival of a cell. How does nature ac­complish high conduction rates and high selectivity at the same time? The an­swer to this question would require knowing the atomic structure formed by the signature sequence amino acids, that much was clear. The conservation of the signature sequence amino acids in K+ channels throughout the tree of life, from bacteria (Milkman, 1994) to higher eukaryotic cells, implied that nature had settled upon a very special solution to achieve rapid, selective K+ conduction across the cell membrane. For me, this realization provided in­spiration to want to directly visualize a K+ channel and its selectivity filter.

    THE KCSA STRUCTURE AND SELECTIVE K+ CONDUCTION

    I did not know how we would ever reach the point of ob­taining enough K+ channel protein to attempt crystallization, but the K+ channel signature sequence continued to appear in a growing number of prokaryotic genes, making expression in Escherichia coli possible. We focused our effort on a bacterial K+ channel called KcsA from Streptomyces lividans, dis­covered by Schrempf (Schrempf et al., 1995). The KcsA channel has a simple topology with only two membrane spanning segments per subunit corre­sponding to the Shaker K+ channel without S1 through S4 (figure 2). Despite its prokaryotic origin KcsA closely resembled the Shaker K+ channel’s pore amino acid sequence, and even exhibited many of its pharmacological prop­erties, including inhibition by scorpion toxins (MacKinnon et al., 1998). This surprised us from an evolutionary standpoint, because why should a scorpion want to inhibit a bacterial K+ channel! But from the utilitarian point of view of protein biophysicists we knew exactly what the scorpion toxin sensitivity meant, that KcsA had to be very similar in structure to the Shaker K+ channel.

    The KcsA channel produced crystals but they were poorly ordered and not very useful in the X-ray beam. After we struggled for quite a while I began to wonder whether some part of the channel was intrinsically disordered and in­terfering with crystallization. Fortunately my neighbor Brian Chait and his postdoctoral colleague Steve Cohen were experts in the analysis of soluble proteins by limited proteolysis and mass spectrometry, and their techniques applied beautifully to a membrane protein. We found that KcsA was as solid as a rock, except for its C-terminus. After removing disordered amino acids from the c-terminus with chymotrypsin the crystals improved dramatically, and we were able to solve an initial structure at a resolution of 3.2 Å (Doyle et al., 1998). We could not clearly see K+ in the pore at this resolution, but my years of work on K+ channel function told me that Rb+ and Cs+ should be valuable electron dense substitutes for K+, and they were. Rubidium and Cs+ difference Fourier maps showed these ions lined up in the pore – as Hodgkin and Keynes might have imagined in 1955 (Hodgkin and Keynes, 1955).

    The KcsA structure was altogether illuminating, but before I describe it, I will depart from chronology to explain the next important technical step. A very accurate description of the ion coordination chemistry inside the selec­tivity filter would require a higher resolution structure. With 3.2 Å data we could infer the positions of the main-chain carbonyl oxygen atoms by apply­ing our knowledge of small molecule structures, that is our intu­ition, but we needed to see the selectivity filter atoms in detail. A high-resolu­tion structure was actually quite difficult to obtain. After more than three ad­ditional years of work by João and then Yufeng (Fenny) Zhou we finally man­aged to produce high-quality crystals by attaching monoclonal Fab fragments to KcsA. These crystals provided the information we needed, a structure at a resolution of 2.0 Å in which K+ ions could be visualized in the grasp of selectivity filter protein atoms (figure 4) (Zhou et al., 2001b). What did the K+ channel structure tell us and why did nature conserve the K+ channel signa­ture sequence amino acids?

    MacKinnon Fig 4. Electron density KcsA K channel

    Figure 4. Electron density (2Fo-Fc contoured at 2 ir) from a high-resolution structure of the KcsA K+ channel is shown as blue mesh. This region of the channel features the selectivity filter with K+ ions and water molecules along the ion pathway. The refined atomic model is shown in the electron density. Adapted from (Zhou et al., 2001b).

    Not all protein structures speak to you in an understandable language, but the KcsA K+ channel does. Four subunits surround a central ion pathway that crosses the membrane (figure 5A). Two of the four subunits are shown in fig­ure 5B with electron density from K+ ions and water along the pore. Near the center of the membrane the ion pathway is very wide, forming a cavity about 10 Å in diameter with a hydrated K+ ion at its center. Each subunit directs the C-terminal end of a ‘pore helix’, shown in red, toward the ion. The C-termi­nal end of an á-helix is associated with a negative ‘end charge’ due to car­bonyl oxygen atoms that do not participate in secondary structure hydrogen bonding, so the pore helices are directed as if to stabilize the K+ ion in the cavity. At the beginning of this lecture I raised the fundamental issue of the cell membrane being an energetic barrier to ion flow because of its oily inte­rior. KcsA allows us to intuit a simple logic encoded in its structure, and elec­trostatic calculations support the intuition (Roux and MacKinnon, 1999): the K+ channel lowers the membrane dielectric barrier by hydrating a K+ ion deep inside the membrane, and by stabilizing it with á-helix end charges.

    MacKinnon Fig 5. KcsA K+ channel pore-helices (red) and selectivity filter (yellow)

    Figure 5. (A) A ribbon representation of the KcsA K+ channel with its four subunits colored uniquely. The channel is oriented with the extracellular solution on top. (B) The KcsA K+ channel with front and back subunits removed, colored to highlight the selectivity filter (yellow). Electron density in blue mesh is shown along the ion pathway. Labels identify the pore, outer, and inner helices and the inner helix bundle. The outer and inner helices correspond to S5 and S6 in figure 2

    How does the K+ channel distinguish K+ from Na+? Our earlier mutagene-sis studies had indicated that the signature sequence amino acids would be re­sponsible for this most basic function of a K+ channel. Figure 6 shows the structure formed by the signature sequence – the selectivity filter – located in the extracellular third of the ion pathway. The glycine amino acids in the se­quence TVGYG have dihedral angles in or near the left-handed helical region of the Ramachandran plot, as does the threonine, allowing the main-chain carbonyl oxygen atoms to point in one direction, toward the ions along the pore. It is easy to understand why this sequence is so conserved among K+ channels: the alternating glycine amino acids permit the required dihedral angles, the threonine hydroxyl oxygen atom coordinates a K+ ion, and the side-chains of valine and tyrosine are directed into the protein core sur­rounding the filter to impose geometric constraint.

    MacKinnon Figure 6. Detailed structure of the K+ selectivity filter

    Figure 6. Detailed structure of the K+ selectivity filter (two subunits). Oxygen atoms coordi­nate K+ ions (green spheres) at positions 1 to 4 from the extracellular side. Single letter amino acid code identifies select signature sequence amino acids. Yellow, blue and red cor­respond to carbon, nitrogen and oxygen atoms, respectively. Green and gray dashed lines show oxygen-K+ and hydrogen bonding interactions.

    The end result when the subunits come together is a narrow tube consisting of four equal spaced K+ binding sites, labeled 1 to 4 from the extracellular side. Each binding site is a cage formed by eight oxygen atoms on the vertices of a cube, or a twisted cube called a square antiprism (figure 7). The binding sites are very similar to the single alkali metal site in nonactin, a K+ selective antibiotic with nearly identical K+-oxygen distances (Dobler et al., 1969; Dunitz and Dobler, 1977). The principle of K+ selectivity is implied in a subtle feature of the KcsA crystal structure. The oxygen atoms surrounding K+ ions in the selectivity filter are arranged quite like the water molecules surrounding the hydrated K+ ion in the cavity. This comparison conveys a visual impression of binding sites in the filter paying for the energetic cost of K+ dehydration. The Na+ ion is appar­ently too small for these K+-sized binding sites, so its dehydration energy is not compensated.

    MacKinnon Fig 7 K+ channel mimics the hydration shell surrounding a K+ ion

    Figure 7. A K+ channel mimics the hydration shell surrounding a K+ ion. Electron density (blue mesh) for K+ ions in the filter and for a K+ ion and water molecules in the central cav­ity are shown. White lines highlight the coordination geometry of K+ in the filter and in wa­ter. Adapted from (Zhou et al., 2001b).

    The question that compelled us most after seeing the structure was exactly how many ions are in the selectivity filter at a given time? To begin to under­stand how ions move through the filter we needed to know the stoichiometry of the ion conduction reaction, and that meant knowing how many ions can occupy the filter. Four binding sites were apparent, but are they all occupied at once? Four K+ ions in a row separated by an average center-to-center dis­tance of 3.3 Å seemed unlikely for electrostatic reasons. From an early stage we suspected that the correct number would be closer to two, because two ions more easily explained the electron density we observed for the larger al­kali metal cations Rb+ and Cs+ (Doyle et al., 1998; Morais-Cabral et al., 2001). Quantitative evidence for the precise number of ions came with the high-res­olution structure and with the analysis of Tl+ (Zhou and MacKinnon, 2003). Thallium is the most ideally suited ‘K+ analog’ because it flows through K+ channels, has a radius and dehydration energy very close to K+, and has the favorable crystallographic attributes of high electron density and an anom­alous signal. The one serious difficulty in working with Tl+ is its insolubility with Cl–. Fenny meticulously worked out the experimental conditions and de­termined that on average there are between two and two and a half conduct­ing ions in the filter at once, with an occupancy at each position around one half.

    We also observed that if the concentration of K+ (or Tl+) bathing the crys­tals is lowered sufficiently (below normal intracellular levels) then a reduc­tion in the number of ions from two to one occurs and is associated with a structural change to a ‘collapsed’ filter conformation, which is pinched closed in the middle (Zhou et al., 2001b; Zhou and MacKinnon, 2003). At concentrations above 20 mM the entry of a second K+ ion drives the filter to a ‘conductive’ conformation, as shown in figure 8. Sodium on the other hand does not drive the filter to a ‘conductive’ conformation even at concentra­tions up to 500 mM.

    MacKinnon Figure 8. The selectivity filter can adopt two conformations

    Figure 8. The selectivity filter can adopt two conformations. At low concentrations of K+ on average one K+ ion resides at either of two sites near the ends of the filter, which is col­lapsed in the middle. At high concentrations of K+ a second ion enters the filter as it changes to a conductive conformation. On average, two K+ ions in the conductive filter re­side at four sites, each with about half occupancy.

    The K+-induced conformational change has thermodynamic consequences for the affinity of two K+ ions in the ‘conductive’ filter. It implies that a frac­tion of the second ion’s binding energy must be expended as work to bring about the filter’s conformational change, and as a result the two ions will bind with reduced affinity. To understand this statement at an intuitive level, rec­ognize that for two ions to reside in the filter they must oppose its tendency to collapse and force one of them out, i.e. the two-ion ‘conductive’ conforma­tion is under some tension, which will tend to lower K+ affinity. This is a de­sirable property for an ion channel because weak binding favors high con­duction rates. The same principle, referred to as the ‘induced fit’ hypothesis, had been proposed decades earlier by enzymologists to explain high speci­ficity with low substrate affinity in enzyme catalysis (Jencks, 1987).

    In the ‘conductive’ filter if two K+ ions were randomly distributed then they would occupy four sites in six possible ways. But several lines of evidence hint­ed to us that the ion positions are not random. For example Rb+ and Cs+ ex-hibit preferred positions with obviously low occupancy at position 2 (Morais-Cabral et al., 2001; Zhou and MacKinnon, 2003). In K+ we observed an un­usual doublet peak of electron density at the extracellular entryway to the se­lectivity filter, shown in figure 9 (Zhou et al., 2001b). We could explain this density if K+ is attracted from solution by the negative protein surface charge near the entryway and at the same time repelled by K+ ions inside the filter. Two discrete peaks implied two distributions of ions in the filter.

    MacKinnon Fig 9 Figure 9. Two K+ ions in the selectivity filter are hypothesized to exist predominantly in two specific configurations 1,3 and 2,4 as shown.

    Figure 9. Two K+ ions in the selectivity filter are hypothesized to exist predominantly in two specific configurations 1,3 and 2,4 as shown. K+ ions and water molecules are shown as green and red spheres, respectively. Adapted from (Zhou et al., 2001b).

    Discrete configurations of an ion pair suggested a mechanism for ion con­duction (figure 10A) (Morais-Cabral et al., 2001). The K+ ion pair could dif­fuse back and forth between 1,3 and 2,4 configurations (bottom pathway), or alternatively an ion could enter the filter from one side of the membrane as the ion-water queue moves and a K+ exits at the opposite side (the top path­way). Movements would have to be concerted because the filter is no wider than a K+ ion or water molecule. The two paths complete a cycle: in one com­plete cycle each ion moves only a fraction of the total distance through the fil­ter, but the overall electrical effect is to move one charge all the way.

    MacKinnon Fig 10 Figure 10. The selectivity filter is represented as five square planes of oxygen atoms.

    Figure 10. (A) Through-put cycle for K+ conduction invoking 1,3 and 2,4 configurations. The selectivity filter is represented as five square planes of oxygen atoms. K+ and water are shown as green and red spheres, respectively. (B) Simulated K+ flux around the cycle is graphed as a function of the energy difference between the 1,3 and 2,4 configurations. Adapted from (Morais-Cabral et al., 2001).

    A simulation of ions diffusing around the cycle offers a possible explanation: maximum flux is achieved when the energy difference between the 1,3 and 2,4 configurations is zero because that is the condition under which the ‘energy landscape’ for the con­duction cycle is smoothest (figure 10B). The energetic balance between the configurations therefore might reflect the optimization of conduction rate by natural selection (Morais-Cabral et al., 2001). It is not so easy to demonstrate this point experimentally but it is certainly fascinating to ponder.

    COMMON STRUCTURAL PRINCIPLES UNDERLIE K+ AND Cl– SELECTIVITY

    The focus of this lecture is K+ channels, but for a brief interlude I would like to show you a Cl– selective transport protein. By comparing a K+ channel and a Cl– ‘channel’ we can begin to appreciate familiar themes in nature’s solu­tions to different problems: getting cations and anions across the cell mem­brane. ClC Cl– channels are found in many different cell types and are associ­ated with a number of physiological processes that require Cl– ion flow across lipid membranes (Jentsch et al., 1999; Maduke et al., 2000). As is the case for K+ channels, ClC family genes are abundant in prokaryotes, a fortunate cir­cumstance for protein expression and structural analysis. When Raimund Dutzler joined my laboratory he, Ernest Campbell and I set out to address the structural basis of Cl– ion selectivity. We determined crystal structures of two bacterial members of the ClC Cl– channel family, one from Escherichia coli (EcClC) and another from Salmonella typhimurium (StClC) (Dutzler et al., 2002). Recent studies by Miller on the function of EcClC have shown that it is actually a Cl– – proton exchanger (Accardi and Miller, 2004). We do not yet know why certain members of this family of Cl– transport proteins function as channels and others as exchangers, but the crystal structures are fascinating and give us a view of Cl– selectivity. Architecturally the ClC proteins are unre­lated to K+ channels, but if we focus on the ion pathway certain features are similar (figure 11).

    MacKinnon Fig 11 CIC Cl transport protein

    Figure 11. The overall architecture of K+ channels and ClC Cl– transport proteins is very dif­ferent but certain general features are similar. One similarity shown here is the use of á-he-lix end charges directed toward the ion pathway. The negative C-terminal end charge (red) points to K+. The positive N-terminal end charge (blue) points to Cl–.

    As we saw in K+ channels, the ClC proteins have a-helices pointed at the ion pathway, but the direction is reversed with the positive charge of the N-terminus close to Cl–. This makes perfect sense for lowering the dielectric barrier for a Cl– ion. In ClC we see that ions in its selectivity fil­ter tend to be coordinated by main chain protein atoms, with amide nitrogen atoms surrounding Cl– instead of carbonyl oxygen atoms surrounding K+ (figure 12). We also see that both the K+ and Cl– selectivity filters contain multiple close-spaced binding sites and appear to contain more than one ion, perhaps to exploit electrostatic repulsion between ions in the pore. I find these simi­larities fascinating. They tell us that certain basic physical principles are im­portant, such as the use of á-helix end charges to lower the dielectric barrier when ions cross the lipid membrane.

    TRYING TO SEE A K+ CHANNEL OPEN AND CLOSE

    channels conduct when called upon by a specific stimulus such as the binding of a ligand or a change in membrane voltage (Hille, 2001). The processes by which ion conduction is turned on are called gating. The con­duction of ions occurs on a time scale that is far too rapid to involve very large protein conformational changes.

    Figure 12. K+ and Cl- selectivity filters make use of main chain atoms to coordinate ions

    Figure 12. K+ and Cl– selectivity filters make use of main chain atoms to coordinate ions: car­bonyl oxygen atoms for K+ ions (green spheres) and amide nitrogen atoms for Cl– ions (red spheres). Both filters contain multiple close-spaced ion binding sites. The Cl– selectivity fil­ter is that of a mutant ClC in which a glutamate amino acid was changed to glutamine (Dutzler et al., 2003).

    In the KcsA K+ channel gating is controlled by intracellular pH and lipid membrane composition, but unfortunately the KcsA channel’s open proba­bility reaches a maximum value of only a few percent in functional assays (Cuello et al., 1998; Heginbotham et al., 1998). At first we had no definitive way to know whether a gate was open or closed in the crystal structures. In the 1970s Armstrong had proposed the existence of a gate near the intracel­lular side of the membrane in voltage dependent K+ channels because he could ‘trap’ large organic cations inside the pore between a selectivity filter near the extracellular side and a gate near the intracellular side (Armstrong, 1971; Armstrong, 1974). Following these ideas we crystallized KcsA with a heavy atom version of one of his organic cations, tetrabutyl antimony (TBA), and found that it binds inside the central cavity of KcsA (Zhou et al., 2001a). This was very interesting because the ~10 Å diameter of TBA far exceeds the pore diameter leading up to the cavity: in KcsA the intracellular pore entry­way is constricted to about 3.5 Å by the inner helix bundle (figure 5B). Seeing TBA ‘trapped’ in the cavity behind the inner helix bundle evoked Arm-strong’s classical view of K+ channel gating, and implied that the inner helix bundle serves as a gate and is closed in KcsA. Mutational and spectroscopic studies in other laboratories also pointed to the inner helix bundle as a pos­sible gate-forming structural element (Perozo et al., 1999; del Camino et al., 2000).

    We subsequently determined the crystal structure of MthK, complete K+ channel containing RCK domains, from Methanobacterium thermoautotrophicus (figure 13) (Jiang et al., 2002a). This structure was extremely informative. The RCK domains form a ‘gating ring’ on the intracellular side of the pore. In clefts between domains we could see what appeared to be divalent cation binding sites, and the crys­tals had been grown in the presence of Ca2+. In functional assays we discov­ered that the open probability of the MthK channel increased as Ca2+ or Mg2+ concentration was raised, giving us good reason to believe that the crystal structure should represent the open conformation of a K+ channel.

    In our MthK structure the inner helix bundle is opened like the aperture of a camera (figure 14) (Jiang et al., 2002b). As a result, the pathway leading up to the selectivity filter from the intracellular side is about 10 Å wide, explaining how Armstrong’s large organic cations can enter the cavity to block a K+ chan-nel, and how K+ ions gain free access to the selectivity filter through aqueous diffusion. By comparing the KcsA and MthK channel structures it seemed that we were looking at examples of closed and opened K+ channels, and could easily imagine the pore undergoing a conformational change from closed to open.

    In the crystal of KvAP the voltage sen­sors, held by monoclonal Fab fragments, adopted a non-native conformation. This observation in itself is meaningful as it underscores the intrinsic flexibil­ity of voltage sensors: in contrast Fab fragments had little effect on the more rigid KcsA K+ channel and ClC Cl– channel homolog, both of which we deter­mined in the presence and absence of Fab fragments (Doyle et al., 1998; Zhou et al., 2001b; Dutzler et al., 2002; Dutzler et al., 2003). KvAP’s voltage sensors contain a hydrophobic helix-turn-helix element with arginine residues beside the pore (Jiang et al., 2003a).

    The KvAP structure and associated functional studies have provided a conceptual model for voltage-dependent gating – one in which the voltage sensors move at the protein-lipid interface in response to a balance between hydrophobic and electrostatic forces. Rees and colleagues at the California Institute of Technology determined the structure of a voltage regulated mechanosensitive channel called MscS, and although it is unrelated to traditional voltage-dependent channels, it too con­tains hydrophobic helix-turn-helix elements with arginine residues apparent­ly against the lipid membrane (Bass et al., 2002). MscS and KvAP are fascinat­ing membrane protein structures. They do not fit into the standard category of membrane proteins with rigid hydrophobic walls against the lipid mem­brane core. I find such proteins intriguing.

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    Larry H Bernstein, MD, FCAP

    http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/

    Part II: Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility

    Larry H. Bernstein, MD, FCAP, Stephen Williams, PhD and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/

    Part III: Renal Distal Tubular Ca2+ Exchange Mechanism in Health and Disease

    Larry H. Bernstein, MD, FCAP, Stephen J. Williams, PhD
 and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/09/02/renal-distal-tubular-ca2-exchange-mechanism-in-health-and-disease/

    Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

    Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/09/08/the-centrality-of-ca2-signaling-and-cytoskeleton-involving-calmodulin-kinases-and-ryanodine-receptors-in-cardiac-failure-arterial-smooth-muscle-post-ischemic-arrhythmia-similarities-and-differen/

    Part V: Heart, Vascular Smooth Muscle, Excitation-Contraction Coupling (E-CC), Cytoskeleton, Cellular Dynamics and Ca2 Signaling

    Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/26/heart-smooth-muscle-excitation-contraction-coupling-cytoskeleton-cellular-dynamics-and-ca2-signaling/

    Part VI: 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

    Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/01/calcium-molecule-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/

    Part VII: Cardiac Contractility & Myocardium Performance: Ventricular Arrhythmiasand Non-ischemic Heart Failure – Therapeutic Implications for Cardiomyocyte Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses

    Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/

    Part VIII: Disruption of Calcium Homeostasis: Cardiomyocytes and Vascular Smooth Muscle Cells: The Cardiac and Cardiovascular Calcium Signaling Mechanism

    Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

    http://pharmaceuticalintelligence.com/2013/09/12/disruption-of-calcium-homeostasis-cardiomyocytes-and-vascular-smooth-muscle-cells-the-cardiac-and-cardiovascular-calcium-signaling-mechanism/

    Part IX: Calcium-Channel Blockers, Calcium Release-related Contractile Dysfunction (Ryanopathy) and Calcium as Neurotransmitter Sensor

    Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

    Part X: Synaptotagmin functions as a Calcium Sensor: How Calcium Ions Regulate the fusion of vesicles with cell membranes during Neurotransmission

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

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