Archive for the ‘Personalized and Precision Medicine & Genomic Research’ Category

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Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

Posted in Atherogenic Processes & Pathology, Biological Networks, Biomarkers & Medical Diagnostics, Cardiomyopathy, Cardiovascular Pharmacogenomics, Cardiovascular Research, Cell Biology, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Congenital Heart Disease, Electrophysiology, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Myocardial Metabolism, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Pharmacotherapy of Cardiovascular Disease, Population Health Management, Proteomics, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Technology Transfer: Biotech and Pharmaceutical, Translational Research, Translational Science, tagged Aviva Lev-Ari, Children's Hospital Boston, DNA, DNA Sequencing, genetics, Harvard Medical School, Human Genome Project, Illumina, Partners HealthCare, Personalized medicine on February 25, 2013| 3 Comments »

Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

Curator: Aviva Lev-Ari, PhD, RN

UPDATED on 5/4/2015

Goes to Clinic @MGH: Clinically validated versions of Exome Sequencing and Analysis using Broad-developed methods like Hybrid Capture, the Genome Analysis Toolkit (GATK), and MuTect

http://pharmaceuticalintelligence.com/2015/05/04/goes-to-clinic-mgh-clinically-validated-versions-of-exome-sequencing-and-analysis-using-broad-developed-methods-like-hybrid-capture-the-genome-analysis-toolkit-gatk-and-mutect/

Center for Personalized Genetic Medicine, Partners HealthCare and Harvard Medical School

The Partners HealthCare Center for Personalized Genetic Medicine offers technologies and technical support for the research activities of Partners investigators. Our objective is to help investigators advance their research programs and to provide the highest quality service, technical expertise, and leading technologies for genomics research. Our goal is to broaden the access to these technologies while offering the best customer service in the most cost conscious and time efficient manner possible.

We are organized into four principal service areas:

• sequence analysis,
• genotyping,
• expression analysis, and
• bioprocessing/sample management

Our platforms include next generation sequencing with Illumina HiSeq2000 and GA ii analyzers as well as Sanger sequencing using ABI 3730 XL sequence analyzers. Targeted custom genotyping is offered using Sequenom and Illumina GoldenGate panels as well as GWAS scale projects using Illumina Infinium and DNA methylation analysis using Illumina bead arrays. Expression analysis is available with capabilities for processing total RNA on either Affymetrix or Illumina arrays.

Through services from our BioSample Services Facility (BSF) and Partners Biorepository for Medical Discovery (PBMD) teams we provide a research platform for handling samples in a standardized manner to provide consistency from sample to sample. The BSF is able to assist investigators to configure projects utilizing your own samples or coupled with the PCPGM-PBMD we are able to support the integration of cohorts of samples selected from the PBMD into analysis on our genomics platforms.

 http://pcpgm.partners.org/research-services

DNA Sequencing

The DNA Sequencing Group at the Partners HealthCare Center for Personalized Genetic Medicine has a strong history of producing high quality, dependable, and informative results for collaborators and clients. The DNA Sequencing Group participated in the Human Genome Project, building the STS-Based BAC map for Human Chromosome 12, and providing Chromosome 12 tiling path clones to the Baylor Human Genome Sequencing Center for sequencing.

The group sequenced 113 BACs for the Mouse Genome Project, contributing 24 megabases of finished mouse sequences to the published Mouse Genome, as well as providing draft sequences for unique strains of several bacterial genomes, including Pseudomonas aeruginosa, and Vibrio cholerae. More recently, the group participated in identifying mutations linked to numerous diseases, either in collaborations or by providing client laboratories with full service resequencing and analysis.

Services by Project Goals

Mutation Identification via Resequencing

This facility provides full-service resequencing of regions of interest in one or more genomic DNAs, including the following:

• Discussion of the scope of the project and a cost quote
• Identification of genes in the region of interest as needed, with the Investigator
• Primer design using our automated system, to amplify desired regions
• Primer ordering
• QC of the primers on DNA standards, if required
• PCR amplification of DNA provided by Investigator
• PCR clean-up
• Sequencing reactions
• Sequencing reaction clean-up
• Sequence application to the ABI 3730 XL Analyzer
• Chromatograms are made available to Investigator over web (GIGPAD)
• Data assembly and analysis using Phred Phrap and PolyPhred
• One round of repeats and redesign if necessary
• Report of variations found throughout sequence
• Trouble shooting for 100% coverage if desired

Research Services

• Fee-for-service sequencing
• Fragment analysis / genotyping (Microsatellite Instability)

Technology Development

• New technology testing and development
• Collaborative Protocol development
• Beta-test site for instrumentation and software

Clinical Diagnosis

• Diagnostic test development
• Sequencing for clinical diagnostics group

Genomic Sequencing Projects

• Human
• Rodent
• Bacteria

http://pcpgm.partners.org/research-services/sequencing

Advancing Translational Genomics through Personalized Medicine Projects

The mission of the Partners HealthCare Center for Personalized Genetic Medicine (PCPGM) is to utilize genetics and genomics to promote and implement personalized medicine in caring for patients throughout the Partners HealthCare system and in health care nationally and globally.

The Personalized Medicine Project program was developed to support the clinical research efforts of junior Partners HealthCare investigators for translational genetics and genomic projects to advance personalized medicine.  The goal of this program is to identify biological markers that can be used as potential predictive tests.  This will be accomplished by:

• Leveraging the Partners HealthCare Research Patient Data Registry (RPDR) and the Partners Biorepository for Medical Discovery (PBMD), centralized locations where Partners HealthCare patient data and/or samples are stored.
• Identifying novel biological markers or new uses for existing markers.
• Focusing on tests that could:
  • improve diagnostic sensitivity or specificity;
  • further stratify patient groups with a given diagnosis;
  • predict improved clinical outcomes; or
  • assist with selection of therapies or methods to manage disease.

http://pcpgm.partners.org/biorepository/pmprojectsrfp

Harvard Medical School Genetics Training Program

The Harvard Medical School (HMS) Genetics Training Program is one of the oldest and largest programs in the country. It was founded by Drs. John Littlefield at the Massachusetts General Hospital and Park Gerald at Children’s Hospital Boston in the early 1970’s. The program has trained scientists and clinicians who have become leaders in academic genetics, and has supported investigators who have made major contributions to the clinical practice of genetics and genetics research.

The HMS Genetics Training Program is accredited by the ABMG in all areas of training – Clinical Genetics, Biochemical Genetics, Cytogenetics, and Molecular Genetics. This provides an opportunity for our trainees to become active candidates for board certification in a discipline(s) of medical genetics in addition to receiving laboratory training. The training laboratories and clinics of the program are centered at HMS and its affiliated institutions including Brigham and Women’s Hospital (BWH), the HMS Department of Genetics, Beth Israel Deaconess Medical Center (BIDMC), Children’s Hospital Boston (CHB), Dana Farber Cancer Institute (DFCI), and Massachusetts General Hospital (MGH). The HMS Genetics Training Program provides trainees the opportunity to take advantage of the extraordinarily rich academic environment offered at HMS and its affiliated institutions as well as the greater Boston scientific community.

 http://pcpgm.partners.org/educational-programs/harvard-medical-school-genetics-training-program

Cardiovascular Research Center @MGH

The Cardiovascular Research Center was founded in 1990, and occupies over 30,000 sq. ft. of laboratory space in both the Charlestown Navy Yard and the Richard B. Simches Research Building. Dr. Mark Fishman, now president of the Novartis Institutes for Biomedical Research, directed the Center from 1990 until 2002. From 2002-2005, Dr. Kenneth Bloch served as Interim Director and then in June 2005, the Massachusetts General Hospital welcomed Dr. Kenneth Chien as the new scientific director of the Cardiovascular Research Center. Prior to his MGH appointments, Dr. Chien directed the Institute for Molecular Medicine at the University of California at San Diego. An internationally recognized biologist specializing in cardiovascular science, he is a true pioneer in developing new therapeutic strategies to prevent the onset and progression of heart failure. Dr. Chien served as director until 2012.

Cardiovascular Research Center investigators have made many groundbreaking discoveries. Among these include:

• first identification of progenitor cells in the heart
• cloning of the first vertebrate cell death genes
• knocking out the genes that produce nitric oxide (NO), showing the importance of this molecule to atherosclerosis and stroke
• clinical use of NO to treat patients with pulmonary hypertension
• development of gene and cell transfer approaches to treat heart failure
• performance of the first large-scale genetic screen in a vertebrate (the zebrafish)
• identification of genes critical to cardiac pacemaking, rhythm, contractile function, and normal heart patterning
• discovery of a new methylase gene responsible for altering DNA structure during an individual’s lifetime

The Cardiovascular Research Center has taken great pride in the training of scientists with MDs and/or PhDs, as well as graduate students from a variety of Boston area institutions.

The Cardiovascular Research Center has two locations, one in the Charlestown Navy Yard and the other on the main campus’s Charles River Plaza complex in the Richard Simches Research Center.

Both the Simches and Navy Yard sites offer state of the art facilities, including tissue culture rooms, warm and cold rooms, histology rooms, autoclave facilities, hot labs, scope rooms and conference rooms. The Navy Yard lab has a topnotch zebrafish facility that is utilized by many scientists both inside and outside the Center, and a transgenic mouse core for both knock-ins and knock-outs. The Navy Yard facilities also contain echocardiogram equipment, specialized microscopes equipped with video capability for making movies, as well as a confocal microscope available to the Center researchers. The Simches lab houses the CVRC Stem Cell Biology + Therapy program, including a dedicated facility for human ES cell based technology, run by Dr. Chad Cowan, and future plans for high throughput screening facility to allow chemical screening in ESX cell based systems. Other cores available to researchers include a Cell Sorting and Flow Cytometry lab and a DNA sequencing core.

The Cardiology Laboratory for Integrative Physiology & Imaging lab is dedicated to large animal studies. An in house interventional cardiologist specializing in large animals performs the surgeries. In addition there are technicians that assist in the daily operations of the lab and can assist in experiment design and project implementation. This lab specializes in large animal imaging, CAT scans and catheter base manipulations. There is also an MRI imaging facility housed in the lab.

http://www2.massgeneral.org/cvrc/about.html

Genomics and Cardiovascular Medicine @MGH

Translational Medicine: Genomics and Proteomics @MGH The goal of the Translational Medicine Program is to harness the rich clinical cardiovascular population at the Massachusetts General Hospital to identify and validate novel genomic determinants of cardiovascular disease. Our goal is not to capture the entire cohort of cardiovascular patients presenting to Massachusetts General Hospital, but rather to focus our efforts on extremely well-phenotyped human models that are unique to cardiovascular disease. Of particular interest are “perturbational” studies in humans (e.g., cardiac exercise testing) that elicit robust phenotypes in affected individuals to serve as the springboard for analyses that span from genomics to proteomics and biochemical profiling. The Translational Medicine Program will involve a multidisciplinary group of investigators who contribute expertise in cardiovascular basic science, clinical cardiology, genetic/genomic epidemiology, bioinformatics, imaging, pathology, as well as clinical chemistry and mass spectrometry. While the Program in Translational Medicine will be physically located at the Massachusetts General Hospital Main Campus, the effort will leverage ongoing interdisciplinary collaborations with investigators at the Framingham Heart Study, the Broad Institute of M.I.T., Harvard University, and Harvard Medical School. Our goals are to:• Identify specific unmet needs in cardiovascular biomarker and pathway discovery (e.g., genomic markers of subclinical premature coronary artery disease, serum biomarkers of myocardial ischemia)• Match cutting-edge technologies with our unique patient cohorts for “first in man” studies• Establish the infrastructure necessary to phenotype patients with the targeted condition (from plasma samples, RNA, DNA, imaging, etc.) and enroll sufficiently sized cohort(s) with the requisite power to validate novel biomarkers.• Establish scientifically high priority research projects to target for independent funding.• Ultimately, develop novel therapeutic interventions.While efforts in translational investigation are already underway, this program will identify synergies between ongoing studies and catalyze new opportunities. Several of the ongoing projects that are anticipated to serve as cornerstones of this effort include:Proteomics and Metabolomics Studies (PI: Gerszten , Wang) Recent advances in proteomic and metabolic profiling technologies have enhanced the feasibility of high throughput patient screening for the diagnosis of disease states. Small biochemicals and proteins are the end result of the entire chain of regulatory changes that occur in response to physiological stressors, disease processes, or drug therapy. In addition to serving as biomarkers, both circulating metabolites and proteins participate as regulatory signals, such as in the control of blood pressure. Our ongoing studies have helped pioneer the application of novel mass spectrometry and liquid chromatography techniques to plasma analysis. In parallel with the profiling efforts, we have developed statistical software for functional pathway trend analysis and used it to demonstrate significant coordinate changes in specific pathways. Such analysis allows us to gain insight into the functionally relevant cellular mechanisms contributing to disease pathways and increases the likelihood that prospective biomarkers will be validated in other patient cohorts. Support for this effort would be synergistic with ongoing funding, including the recent appointment and support for Dr. Gerszten to lead a metabolomics initiative at the Broad Institute.Cardiovascular Genetics and Genomics Studies (PIs: Kathiresan, Newton-Cheh,Wang, and O’Donnell) Through the Human Genome Project and the International Haplotype Map project, researchers now have available the complete human genome sequence, a nearly complete set of common single nucleotide polymorphisms (SNPs), and a map of the patterns of correlation (“linkage disequilibrium”) among SNPs. Research on a large-scale is now possible to define associations of common, complex human cardiovascular diseases —such as myocardial infarction and sudden cardiac death—with genetic variants using candidate gene and genome-wide association studies, gene sequencing, and family-based linkage studies. Specific diseases and traits being studied by CVRC researchers include early-onset myocardial infarction, sudden cardiac death, blood lipids, blood pressure, electrocardiographic QT interval and blood hemostatic factor levels. These studies draw clinical material from the Massachusetts General Hospital and from collaborations with population-based epidemiologic cohorts such as the Framingham Heart Study. Like the metabolomics/proteomics work, these efforts build on the technologic and scientific expertise at the Broad Institute. Specifically, CVRC researchers leverage the Broad Institute’s expertise in large-scale genotyping, genomics, and statistical genetics. The collaboration between the Massachusetts General Hospital, the Framingham Heart Study, and the Broad Institute brings together resources that are unique to each institution to identify genes related to complex cardiovascular traits and to ultimately impact human health.Chemical Biology Program (PIs: Peterson and Shaw) Dr. Peterson’s group has championed the zebrafish as a tool for drug discovery. The zebrafish has become a widely used model organism because of its fecundity, its morphological and physiological similarity to mammals, the existence of many genomic tools and the ease with which large, phenotype-based screens can be performed. Because of these attributes, the zebrafish also provides opportunities to accelerate the process of drug discovery. By combining the scale and throughput of in vitro screens with the physiological complexity of animal studies, the zebrafish promises to contribute to several aspects of the drug development process, including target identification, disease modeling, lead discovery and toxicology. The Program in Translational Medicine will specifically support efforts to test novel pro-angiogenic factors (discovered as suppressors of the “gridlock” phenotype in zebrafish) on human cells such as circulating endothelial precursors.Dr. Shaw’s group is studying the cellular effects of human disease mutations in patient samples, by perturbing cells with a panel of thousands of drugs, and asking whether mutant versus wild-type cells react differently to a given biochemical (reminiscent of a genetic interaction screen). Dr. Shaw has demonstrated the feasibility of this approach using lymphoblast cell lines from a family affected by a monogenic form of diabetes (MODY1), and shown that glucocorticoid signaling differs between affected vs. unaffected patients. Because his studies incorporate the use of FDA-approved drugs, he can quickly identify both potentially “druggable” disease pathways as well as novel therapeutic agents. Further validation of these efforts in other monogenic disorders, such as LDL-receptor deficient patients is planned next. Ultimately this work will be extended to studies in complex genetic diseases.Director: Rob Gerszten, MDPrincipal Investigators: • Farouc Jaffer, MD, PhD • Sekar Kathiresan, MD • Chris Newton-Cheh, MD, MPH • Randall Peterson, PhD • Stanley Shaw, MD, PhD • Thomas Wang, MD

Genetic Basis of Cardiomyopathy

Original gene identification for Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy, Autosomal Dominant

McNally E, MacLeod H, Dellefave L. Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy, Autosomal Dominant. 2005 Apr 18 [Updated 2009 Oct 13]. In: Pagon RA, Bird TD, Dolan CR, et al., editors. GeneReviews™ [Internet]. Seattle (WA): University of Washington, Seattle; 1993-.

Summary

Disease characteristics. Autosomal dominant arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is characterized by progressive fibrofatty replacement of the myocardium that predisposes to ventricular tachycardia and sudden death in young individuals and athletes. It primarily affects the right ventricle; with time, it may also involve the left ventricle. The presentation of disease is highly variable even within families, and affected individuals may not meet established clinical criteria. The mean age at diagnosis is 31 years (±13; range: 4-64 years).

Available from:

http://www.ncbi.nlm.nih.gov/books/NBK1131/

Pan Cardiomyopathy Panel

@the Center for Personalized Genetic Medicine of Partners HealthCare and Harvard Medical School

The Pan Cardiomyopathy (PCM) Panel contains 51 cardiomyopathy genes including Titin (TTN), which encodes the largest human protein. This panel covers genes associated with HCM, DCM, RCM, LVNC, ARVC and CPVT and uses a combination of Next Generation Sequencing technology and conventional Sanger sequencing.

For illustrative reference, click to see one of our images or diagrams. Genes on Pan Cardiomyopathy Panels, Disease-Gene Associations, Gene Cellular Location.

Please select on the disease to read more: HCM,DCM, ARVC/CPVT, or LVNC.

Current Tests:

Pan Cardiomyopathy Panel – 51 genes

• HCM Panel – 18 genes^§
• DCM Panel – 27 genes^§
• ARVC/CPVT Panel – 8 genes^§
• LVNC Panel – 10 genes^§

^§Optional reflex to remaining genes

Storage Cardiomyopathy – please select a disease to learn more

• Unexplained Hypertrophy Panel (LAMP2 & PRKAG2)
• Fabry Disease – GLA Gene Sequencing
• Transthyretin Amyloidosis – TTR Gene Sequencing

For any other single gene tests, please call the LMM at 617-768-8499 or lmm@partners.org.

For Variant Classification Rules – Lab for Molecular Medicine (LMM)

http://pcpgm.partners.org/sites/default/files/LMM/Resources/LMM_VariantClassification_05.26.11.pdf 

For LMM Reference Sequences

http://pcpgm.partners.org/sites/default/files/LMM/Resources/LMMRefSeq-2.20.13.pdf

When to order which panel?

The Pan Cardiomyopathy panel may shorten the “testing odyssey” when a clear diagnosis has not been established. However, because many genes have not yet been associated with more than one cardiomyopathy, interpretation of novel variants may be more difficult when they are found in a gene that is not (yet) known to cause the patient’s cardiomyopathy. Please note: We are expecting an increase in “variants of unknown significance” and recommend careful consideration of the following factors when deciding whether to order the full panel or the disease specific sub-panels. The Pan Cardiomyopathy Panel may be best suited for patients who have already exhausted current testing options or whose clinical diagnosis is not yet clear. It may also be a good first line test for patients who have a family history where the number of living affected relatives would allow segregation analysis to establish or rule out pathogenicity for “variants of unknown significance (VUSs)”. Finally, the patient’s personal preferences should be considered as VUSs can cause anxiety.

Disease Backgrounds

Hypertrophic cardiomyopathy (HCM) is characterized by unexplained left ventricular hypertrophy (LVH) in a non-dilated ventricle. With a prevalence estimated to be ~1/500 in the general population, HCM is the most common monogenic cardiac disorder. To date, over 1000 variants have been identified in genes causative of HCM, most of which affect the sarcomere, the contractile unit of the cardiac muscle. In addition, defects in genes involved in storage diseases, such as LAMP2, PRKAG2 and GLA, typically cause systemic disease but may also result in predominant cardiac manifestations, which can mimic hypertrophic cardiomyopathy (HCM). For additional information about HCM, please visit GeneReviews. 

Dilated cardiomyopathy (DCM) is characterized by ventricular chamber enlargement and systolic dysfunction with normal left ventricular wall thickness. The estimated prevalence of DCM is 1/2,500 and about 20-35% of cases have a family history showing a predominantly autosomal mode of inheritance. To date, over 40 genes have been demonstrated to cause DCM, encoding proteins involved in the sarcomere, Z-disk, nuclear lamina, intermediate filaments and the dystrophin-associated glycoprotein complex. Variants in some genes cause additional abnormalities: LMNA variants are frequently found in DCM that occurs with progressive conduction system disease. Variants in the TAZ gene cause Barth syndrome, an X-linked cardioskeletal myopathy in infants. In addition, variants in several genes (including LMNA, DES, SGCD, TCAP and EMD) can cause DCM in conjunction with skeletal myopathy.  For additional information about DCM, please visit GeneReviews.

Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) is estimated to affect approximately 1/5,000 individuals in the general population, about half of which have a family history. The disease is characterized by replacement of myocytes by fatty or fibrofatty tissue, mainly in the right ventricle. The resulting manifestations are broad and include ventricular tachyarrhythmias and sudden death in young individuals and athletes. ARVC is typically inherited in an autosomal dominant fashion with incomplete penetrance and variable expressivity and to date, 5 ARVC genes (DSP, DSC2, DSG2, PKP2, TMEM43) have been identified, all but one (TMEM43) encode components of the desmosome. For more information about ARVC, please visit GeneReviews.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is typically characterized by exercise induced syncope due to ventricular tachycardia in individuals without structural heart disease. Two CPVT genes are known to date (RYR2 – autosomal dominant; CASQ2 – autosomal recessive). For more information about CPVT, please visit GeneReviews.

Left ventricular noncompaction (LVNC) has recently been established as a specific type of cardiomyopathy and is characterized by a spongy appearance of the left ventricular myocardium, resulting from an arrest in normal cardiac development. LVNC can be found in isolation or in association with other cardiomyopathies (HCM, DCM) as well as congenital cardiac abnormalities. The population prevalence is not known but LVNC is reported in ~0.014% of echocardiograms. LVNC is often familial and the genetic spectrum is beginning to emerge although it is not yet well defined. LVNC genes reported to date include ACTC, DTNA, LDB3, MYBPC3, MYH7, TAZ, and TNNT2 (Montserrat 2007, Klaassen 2008; Kaneda 2007, Zaragoza 2007; reviewed in: Maron 2006, Finsterer 2009). For more information about LVNC, please visit OMIM.org.

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For any additional information, please contact us at 617-768-8500 or lmm@partners.org.

SOURCE:

http://pcpgm.partners.org/lmm/tests/cardiomyopathy

Genes: 51 genesMethodology: A combination of next generation sequencing technology and Sanger sequencingAnalytical Sensitivity:Substitutions: 100% (95%CI=98.5-100)Small InDels: 95% (95%CI=83-99)Clinical Sensitivity: See below.Additional Links: Genetic Basis of Cardiomyopathy Booklet http://pcpgm.partners.org/sites/default/files/LMM/Resources/LMM_Cardiomyopathy_Booklet_2011.pdf

Cardiomyopathy

	Price	TAT	CPT Codes
Pan Cardiomyopathy Panel (51 Genes)  –  lmPCM-pnlAv2_L
	$3,950	8-12 wks	81479
HCM Panel (18 Genes)  –  lmPCM-pnlB_L
	$3,200	8-12 wks	81479
DCM Panel (27 Genes)  –  lmPCM-pnlCv2_L
	$3,850	8-12 wks	81479
ARVC/CPVT Panel (8 Genes)  –  lmPCM-pnlD_L
	$3,000	8-12 wks	81479
LVNC Panel (10 Genes)  –  lmPCM-pnlE_L
	$3,200	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (HCM Reflex)  –  lmPCM-pnlFv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (DCM Reflex)  –  lmPCM-pnlGv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (ARVC/CPVT Reflex)  –  lmPCM-pnlHv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (LVNC Reflex)  –  lmPCM-pnlIv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (Version 1 Reflex) – lmPCM-pnlL_L
	$750	8-12 wks	81479
Unexplained Cardiac Hypertrophy Panel (2 genes)  –  lmUCH-pnlA_L
	$1,500	3 wks	81479
ABCC9 Gene Sequencing  –  lmABCC9-a_L
	$1,800	3 wks	81479
ACTC Gene Sequencing  –  lmACTC-a_L
	$700	3 wks	81405
ACTN2 Gene Sequencing  –  lmACTN2-a_L
	$1,500	3 wks	81479
CSRP3 Gene Sequencing  –  lmCSRP3-a_L
	$900	3 wks	81479
CTF1 Gene Sequencing  –  lmCTF1-a_L
	$800	3 wks	81479
DES Gene Sequencing  –  lmDES-a_L
	$750	3 wks	81479
DSC2 Gene Sequencing  –  lmDSC2-a_L
	$1,150	3 wks	81479
DSG2 Gene Sequencing  –  lmDSG2-a_L
	$1,075	3 wks	81479
DSP Gene Sequencing  –  lmDSP-a_L
	$1,700	3 wks	81479
DTNA Gene Sequencing – lmDTNA-a_L
	$1,500	5-6 wks	81479
EMD Gene Sequencing  –  lmEMD-a_L
	$450	3 wks	81479
GLA Gene Sequencing  –  lmGLA-a_L
	$700	3 wks	81405
LAMP2 Gene Sequencing  –  lmLAMP2-a_L
	$700	3 wks	81405
LDB3 Gene Sequencing  –  lmLDB3-a_L
	$950	3 wks	81406
LMNA Gene Sequencing  –  lmLMNA-a_L
	$700	3 wks	81406
MYBPC3 Gene Sequencing  –  lmMYBPC3-a_L
	$1,500	3 wks	81407
MYH7 Gene Sequencing  –  lmMYH7-a_L
	$1,700	3 wks	81407
MYL2 Gene Sequencing  –  lmMYL2-a_L
	$700	3 wks	81405
MYL3 Gene Sequencing  –  lmMYL3-a_L
	$700	3 wks	81405
PKP2 Gene Sequencing  –  lmPKP2-a_L
	$1,500	3 wks	81479
PLN Gene Sequencing  –  lmPLN-a_L
	$400	3 wks	81479
PRKAG2 Gene Sequencing  –  lmPRKAG2-a_L
	$1,000	3 wks	81406
SCN5A Gene Sequencing – lmSCN5A-a_L
	$1,700	5-6 wks	81407
SGCD Gene Sequencing  –  lmSGCD-a_L
	$1,100	3 wks	81405
TAZ Gene Sequencing  –  lmTAZ-a_L
	$700	3 wks	81406
TCAP Gene Sequencing  –  lmTCAP-a_L
	$700	3 wks	81479
TMEM43 Gene Sequencing  –  lmTMEM43-a_L
	$700	3 wks	81479
TNNI3 Gene Sequencing  –  lmTNNI3-a_L
	$700	3 wks	81405
TNNT2 Gene Sequencing  –  lmTNNT2-a_L
	$1,000	3 wks	81406
TPM1 Gene Sequencing  –  lmTPM1-a_L
	$700	3 wks	81405
TTN Gene Sequencing  –  lmTTN-a_L
	$3,000	8-12 wks	81479
TTR Gene Sequencing – lmTTR-a_L
	$485	3 wks	81404
VCL Gene Sequencing  –  lmVCL-a_L
	$1,500	3 wks	81479

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Congenital Heart Disease/Defects

	Price	TAT	CPT Codes
Congenital Heart Disease Panel A (GATA4, NKX2-5, JAG1)  –  lmCHD-pnlA_L
	$1,300	4 wks	81479
ELN (Elastin) Gene Sequencing  –  lmELN-a_L
	$1,300	4 wks	81479
GATA4 Gene Sequencing  –  lmGATA4-a_L
	$750	3 wks	81479
JAG1 Gene Sequencing  –  lmJAG1-a_L
	$1,100	3 wks	81407
NKX2-5 Gene Sequencing  –  lmNKX2-5-a_L
	$600	3 wks	81479

SOURCE:

http://pcpgm.partners.org/lmm/ordering/prices-CPTcodes_revised#Cardio_Header

Lakdawala NK, Funke BH, Baxter S, Cirino A, Roberts AE, Judge DP, Johnson N, Mendelsohn NJ, Morel C, Care M, Chung WK, Jones C, Psychogios A, Duffy E, Rehm HL, White E, Seidman JG, Seidman CE, Ho CY.  Genetic Testing for Dilated Cardiomyopathy in Clinical Practice. J Card Fail 2012, In press.

Neri PM, Pollard SE, Volk LA, Newmark L, Varugheese M, Baxter S, Aronson SJ, Rehm HL, Bates DW. Usability of a Novel Clinician Interface for Genetic Results. J Biomed Informatics. 2012. In press.

Genomics @Brigham and Women’s Hospital and Harvard Medical School  

The goal of The Cardiovascular Genome Unit (TCGU) is to foster interdisciplinary interaction between clinical investigators and scientists to comprehensively explore the era of human genomic research. In particular, our aim would be to identify, categorize and characterize the genes and genetic pathways of the vascular and cardiac tissues of the cardiovascular system during oncogenesis, normal function and the pathogenesis of cardiovascular diseases.

    The Cardiovascular Genome Unit is responsible for indexing gene expression, profiling gene expression, identifying SNPs and generation of protein profiles from a wide variety of tissues representative of various anatomical regions as well as developmental and pathological stages in the cardiovascular system. This information resource emphasizes on cardiovascular disease and should aid in the discovery of disease causing genes, diagnostic and prognostic markers, drug targets, protein therapeutics and improved therapeutic strategies for cardiovascular disease.

    Our laboratory is the curator of a genome-based resource for molecular cardiovascular medicine consisting of over 52,000 ESTs generated from nine heart and artery libraries, representing different developmental stages and disease states (Liew et al 1994, Hwang et al 1997, Dempsey et al 2000). 

    This comprehensive catalogue of cardiac and hematopoietic genes is an unmined molecular resource for microarray analysis and a genetic gold mine for the discovery of genes that may play a role in cardiovascular disorders. In order to exploit this raw data, we propose to develop cDNA microarrays consisting of known and novel sequence-tagged genes. The arrayed clones provide an excellent substrate for expression profiling of cardiovascular disease, for example heart failure or ischemic heart disease, leading the potential discovery of diagnostic as well as prognostic markers.

    In order to accomplish the goals of the center, several cutting edge technologies are being employed.

The human cardiovascular research component of our labs.

One of the most efficient and effective strategies for the identification genes is the Expressed Sequence Tag (EST) approach.  In this approach, randomly selected cDNA clones are subjected to automated sequencing (PCR or plasmid templates) to generate a partial sequence from either the 5’- or 3’-end termed an EST.  This method allows for large-scale gene tagging and indexing from any tissue- or cell-type of interest.  A comprehensive cardiovascular gene index could be developed using a variety of cardiovascular tissues representing different anatomical, developmental and pathological states.

Comparing transcript profiles between different development or disease states is a powerful way to gain insight into the genetic changes underlying these events.  This is especially important when looking at complex systems, such as in development or disease (e.g. hypertension or atherosclerosis).  There are several unique approaches to this problem, several of which are:

a)      EST profile Comparison– After the production of a significant number of ESTs from 2 or more libraries, the frequencies of ESTs can be compared to identify those genes which are differentially expressed.     However, normalized or subtracted cDNA libraries cannot be used for this and this method is most effective for finding large differences in expression.

 

b)      cDNA Microarray Hybridization– The recent introduction of the cDNA microarray, a technology capable of analyzing the expression of thousands of genes simultaneously in a single experiment, may  provide one of the best ways to delineate gene expression patterns.  In the cDNA microarray, cDNA clones are spotted onto a glass slide matrix and hybridized with fluorescently labeled cDNA probes derived from total RNA pools of test and reference cells or tissues.  The signal intensity for each probe is quantified and any differences between the two samples becomes readily apparent.  Thus, the genetic changes underlying the phenotype of study can be identified at the level of a single gene. 

 

c)      Identification of Single Nucleotide Polymorphisms– SNPs are single-base heritable variations in the genome which occur once in approximately 1000 bases in the human genome and occur at a frequency of >1% in the human population.  SNPs provide an important genetic resource useful for disease gene discovery. including the identification of disease susceptible genes.  SNPs can be identified through comparison of EST sequences, DNA hybridization strategies and direct sequencing of genomic DNA.  The generation of a SNP database for genes expressed in the cardiovascular system will provide a valuable resource to aid in disease gene discovery. 

 

d)      Quantitative determination of expressed genes– the up- and down- regulated genes are crucial to the phenotypic expression of any given cell.  The frequency of gene expressed in development or disease state can be obtained from an EST approach using cDNA libraries as well as its intensity detected using microarrays.  Such results can be verified through RT-PCR analysis from the tissue samples.  A high through-put analysis of 96 samples can be performed by real-time PCR analyses.

Using our 10,000 element “CardioChip”, we elucidated over 100 differentially expressed genes in end-stage heart failure resulting from dilated cardiomyopathy. The results were published in

Am J Pathol. 2002 June; 160(6): 2035–2043.

Global Gene Expression Profiling of End-Stage Dilated Cardiomyopathy Using a Human Cardiovascular-Based cDNA Microarray

J. David Barrans,^*† Paul D. Allen,^‡ Dimitrios Stamatiou,^* Victor J. Dzau,^* and Choong-Chin Liew^*†

From Cardiovascular Genome Unit^*, the Department of Medicine, and the Department of Anesthesiology,^‡Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts; and the Department of Laboratory Medicine and Pathobiology,^†University of Toronto, Toronto, Ontario, Canada

Abstract

To obtain a genomic portrait of heart failure derived from end-stage dilated cardiomyopathy (DCM), we explored expression analysis using the CardioChip, a nonredundant 10,848-element human cardiovascular-based expressed sequence tag glass slide cDNA microarray constructed in-house. RNA was extracted from the left ventricular free wall of seven patients undergoing transplantation, and five nonfailing heart samples. Cy3- and Cy5-labeled (and reverse dye-labeled) cDNA probes were synthesized from individual diseased or nonfailing adult heart RNA, and hybridized to the array. More than 100 transcripts were consistently differentially expressed in DCM >1.5-fold (versus pooled nonfailing heart,P < 0.05). Atrial natriuretic peptide was found to be up-regulated in DCM (19-fold compared to nonfailing, P < 0.05), as well as numerous sarcomeric and cytoskeletal proteins (eg, cardiac troponin, tropomyosin), stress response proteins (eg, HSP 40, HSP 70), and transcription/translation regulators (eg, CCAAT box binding factor, eIF-1AY). Down-regulation was most prominently observed with cell-signaling channels and mediators, particularly those involved in Ca²⁺ pathways (Ca²⁺/calmodulin-dependent kinase, inositol 1,4,5-trisphosphate receptor, SERCA). Most intriguing was the co-expression of several novel, cardiac-enriched expressed sequence tags. Quantitative real-time reverse transcriptase-polymerase chain reaction of a selection of these clones verified expression. Our study provides a preliminary molecular profile of DCM using the largest human heart-specific cDNA microarray to date.

Dilated cardiomyopathy (DCM) is characterized clinically by left ventricular dilatation, wall thinning, and homogeneous dysfunction of the myocardium leading to congestive heart failure. Genetically, DCM seems to evolve through primary mutations in the genes of the sarcomeric proteins. ¹ However, recent evidence suggests that, despite distinct pathways leading to divergent endpoint phenotypes of each disease, there may exist some overlapping genetic modifiers leading to a conversion of one to the other. ² How this occurs is under question; to understand this, a better knowledge of the molecular pathways and intermediary regulators is required.

Global analysis of gene expression has proven to be a fruitful means of examining the overall molecular portrait of a particular event as well as seeking out novel candidate transcripts that may play a role in formulating the phenotype or genotype of interest. By using this strategy, multiple genes and pathways in complex disorders can be visualized simultaneously, allowing for a feasible platform from which to investigate new and interesting genes. Using expressed sequence tag technology, our laboratory has generated a compendium of genes expressed in the human cardiovascular system, with the ultimate goal of assembling the intricacies of development and of disease, particularly the pathways leading to heart failure. ³ Through a computer-based in silico strategy, we have been able to identify—in a large scale—both known and previously unsuspected genetic modulators contributing to the growth of the myocardium from fetal through adult, and from normal to a perturbed hypertrophic phenotype. In contrast a gene-by-gene approach in elucidating the genes and mechanisms involved is time-consuming and cumbersome.

Recently, microarray technology has been used as a means of large-scale screening of vast numbers of genes—if not whole genomes—that possess differential expression in two distinct conditions. Although new and exciting developments have arisen in such fields as cancer ⁴ and yeast, ⁵ advances in understanding the complexity of cardiovascular disease, ⁶ specifically DCM, have been limited. One recent study examined gene expression in two failing hearts using oligo-based arrays. ⁷ Although the GeneChip^® (Affymetrix, Santa Clara, CA) offers a carefully controlled systematic method of analysis, its current lack of user flexibility in its design hinders novel gene discovery currently available in tissue-specific arrays. Our laboratory has taken advantage of our vast previously acquired resources and has constructed what we believe to be the first ever custom-made cardiovascular-based cDNA microarray, which we term the “CardioChip.” ⁸ Its practicality and flexibility has allowed us to conceptualize the molecular events surrounding end-stage heart failure.

This report describes the most informative cDNA microarray-based analysis of end-stage heart failure derived from DCM currently available. Although we believe we have effectively demonstrated reproducibility and reliability of our technology (both for the entire array and for a selection of genes located on it), a larger n from our population would enhance the validity of our conclusions. Certainly, there exists no homogeneous heart failure genotype, especially among only seven DCM patients. Nonetheless, we have demonstrated a common expression pattern among our set of samples, from both microarray and QRT-PCR analysis. We are also limited by the genes (both in number and identity) present on this array. Although we are currently unable to spot every gene and gene cluster on our CardioChip, we have tried to draw from a diverse assortment of genes and gene pathways, both known and unknown. It must be emphasized that this investigation is not exhaustive; by no means does it attempt to fully characterize the molecular basis of heart failure. Its intention is to provide a preliminary portrait of global gene expression in complex cardiovascular disease using cDNA microarray and QRT-PCR technology, and to highlight the effectiveness of our ever-evolving platform for gene discovery. With even more patient samples and a CardioChip toward completeness, we will be in a better position to reap the important benefits from this initial work and expand our body of knowledge.

Am J Pathol. 2002 June; 160(6): 2035–2043.

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 http://issuu.com/pcpgm_lmm/docs/cmbig_lmmcardio/16

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New development in measuring mechanical properties of tissue

Posted in Health Economics and Outcomes Research, Imaging-based Cancer Patient Management, Medical Devices R&D and Inventions, Medical Devices R&D Investment, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Technology Transfer: Biotech and Pharmaceutical, tagged Cancer - General, Clinical trial, elastography, health, Magnetic resonance imaging, magnetic resonance imaging mri, OCT, research, science, tissue, tissue differentiation on February 22, 2013| Leave a Comment »

New development in measuring mechanical properties of tissue

Author – Writer: Dror Nir, PhD

• 

Measuring the effects induced onto imaging by the mechanical properties of tissue is a common approach to differentiate tissue abnormalities. In previous posts I discussed the applicability of imaging applications that visualize variations in tissue stiffness; e.g. ultrasound-elastography and MRI-elastography as aid in the diagnosis workflow of cancer. Today, I would like to report on a recent publication made in SPIE Newsroom describing an optical-imaging system to measure tissue stiffness at high resolution. I think that such emerging technologies should be followed up as they bear promise to bridge deficiencies of the traditional modalities currently in use.

Reporting on: Optical elastography probes mechanical properties of tissue at high resolution

By: David Sampson, Kelsey Kennedy, Robert McLaughlin and Brendan Kennedy

Information published at: SPIE Newsroom – Biomedical Optics & Medical Imaging

Probing the micro-mechanical properties of tissue using optical imaging might offer new surgical tools that enable improved differentiation of tissue pathologies, such as cancer or atherosclerosis.

11 January 2013, SPIE Newsroom. DOI: 10.1117/2.1201212.004605

Elastography is an emerging branch of medical imaging that uses mechanical contrast to better characterize tissue pathology than can be achieved with structural imaging alone. It achieves this by imaging a tissue’s response to mechanical loading. Although commercial products based on ultrasonography and magnetic resonance imaging (MRI) have been available for several years, these new modalities offer superior tissue differentiation deep in the human body. However, elastography is limited by its low resolution compared with the length scales relevant to many diseases. Increasing the resolution with optical techniques might offer new opportunities for elastography in medical imaging and surgical guidance.

An elastography system requires a means of loading the tissue to cause deformation and an imaging system with sufficient sensitivity and range to capture this deformation. Implicit in these requirements is access to the tissue of interest. Optical elastography has previously been largely based on schemes that suit small tissue samples rather than intact tissue in living humans. Additionally, such schemes have not had the sensitivity or range to produce high-fidelity images of mechanical properties. We have addressed both these issues in our recent work, developing the means to access tissues in vivo and improve the sensitivity and range of optical elastography using phase-sensitive optical coherence tomography as the underlying modality. The use of optical coherence tomography to perform elastography has come to be referred to as optical coherence elastography.¹

To make optical coherence elastography on human subjects feasible, we designed an annular piezoelectric loading transducer (see Figure 1), through which we could simultaneously image, enabling the first in vivo dynamic optical coherence elastography on human subjects.² We were subsequently able to extend this to three dimensions (see Figure 2), in collaboration with Stephen Boppart’s group at the University of Illinois at Urbana-Champaign.³ This extension took advantage of the high speed of spectral-domain optical coherence tomography, and the maturity of phase-sensitive detection techniques originally developed for Doppler flowmetry and microangiography.

 

Figure 1. Schematic (left) and photograph (right) of the annular load transducer and imaging optics for in vivo optical coherence elastography.

 

Figure 2. 2D images of in vivo human skin selected from 3D stacks. (a) Optical coherence tomography image and (b) the same image overlaid by the 2D dynamic elastogram recorded at 125Hz load frequency, highlighting the greater strain in the epidermis. Reprinted in modified form with permission.³

For general access to tissues in the body, optical coherence elastography faces two basic limitations. The free-space probe requires miniaturization for versatile access to tissue in confined or convoluted geometries. We addressed this in studies of the elastic properties of human airways using catheter-based anatomical optical coherence tomography.⁴

 

Figure 3. (a) Schematic diagram of needle optical coherence elastography. The phase difference Δφ=φ₁– φ₂ determines the displacement, d, when scaled by the wavelength, λ, and refractive index, n. (b) Needle and pig trachea. (c) Local displacement versus distance, with tissue boundaries indicated by red stars. (d) Representative histology. Reprinted in modified form with permission.⁶

More fundamentally, optical coherence tomography can only penetrate, at best, 1–2mm into most tissues, limiting it to superficial applications. To address this issue, we combined optical coherence elastography with needle probes, an active research area in our group (see Figure 3).⁵ We conveniently use the needle probe itself to deform the tissue during insertion.⁶ The deformation ahead of the needle tip depends on the mechanical properties of the tissue encountered, as well as on the nearby tissue environment, particularly on any interfaces ahead of it. We measure the local sub-micrometer displacement of the tissue between two positions of the moving needle probe. We plot this displacement versus distance ahead of the probe: see Figure 3(c). The slope of the displacement at location z is a measure of the local strain. A change in slope signifies a change in tissue stiffness; the steeper the slope, the softer the tissue (other things being equal). Figure 3 highlights this effect in a layered sample of pig trachea. The positions of the changes in slope correlate well with the tissue interfaces shown in the accompanying histology: see Figure 3(d).

The other key area of improvement we have focused on is lowering the optical coherence elastography noise floor by increasing the detection sensitivity, which is vital to make clinical imaging practical. We firstly showed that Gaussian-smoothed, weighted-least squares strain estimation improved the sensitivity of estimates by up to 12dB over conventional finite-difference methods.⁷ Next, we showed that performance could be further improved at low optical coherence tomo- graphy signal-to-noise ratios (and, therefore, at greater depths in tissue) by employing a 2D Fourier transform technique.⁸Combined with other system refinements, these improvements have enabled us to reach a displacement sensitivity of 300pm for typical optical coherence tomography signal-to-noise ratios in tissue, with room for improvement.

The Young’s modulus of soft tissue varies from kPa to tens of MPa, whereas the scattering coefficient of such tissues—which is largely responsible for determining the contrast of optical coherence tomography—is typically in the range 2–20mm⁻¹. This apparent native advantage in mechanical over optical contrast (see the example in Figure 4), combined with the maturation of optical coherence elastography methods, bodes well for the future. In our group, we are pursuing tumor-margin identification using elastography; others have begun to consider corneal elastography,^{9, 10} and still others are examining shear wave schemes with the aim of probing Young’s modulus much deeper in tissues.^11,12

 

Figure 4. Optical coherence tomography (a) and optical coherence elastography (b) images of the same phantom with two inclusions visible, showing enhanced mechanical over scattering contrast.

Optical elastography currently sits at a similar stage of development as ultrasound elastography did in 1999. Based on a similar trajectory, this field will rapidly expand over the next decade. Our recent results point to the first convincing applications of optical elastography being just around the corner.

We acknowledge funding for this work from Perpetual Trustees, the Raine Medical Research Foundation, the Cancer Council of Western Australia, the Australian Research Council, the National Health and Medical Research Council (Australia), and the National Breast Cancer Foundation (Australia).

---

David Sampson

Optical+Biomedical Engineering Laboratory
School of Electrical, Electronic and Computer Engineering

and
Centre for Microscopy, Characterisation and Analysis
The University of Western Australia

 

Perth, Australia

Kelsey Kennedy, Robert McLaughlin, Brendan Kennedy

Optical+Biomedical Engineering Laboratory
School of Electrical, Electronic and Computer Engineering
The University of Western Australia

Perth, Australia

---

References:

1. J. Schmitt, OCT elastography: imaging microscopic deformation and strain of tissue, Opt. Express 3(6), p. 199-211, 1998.doi:10.1364/OE.3.000199

2. B. F. Kennedy, T. R. Hillman, R. A. McLaughlin, B. C. Quirk, D. D. Sampson, In vivo dynamic optical coherence elastography using a ring actuator, Opt. Express 17(24), p. 21762-21772, 2009.doi:10.1364/OE.17.021762

3. B. F. Kennedy, X. Liang, S. G. Adie, D. K. Gerstmann, B. C. Quirk, S. A. Boppart, D. D. Sampson, In vivo three-dimensional optical coherence elastography, Opt. Express 19(7), p. 6623-6634, 2011.doi:10.1364/OE.19.006623

4. J. P. Williamson, R. A. McLaughlin, W. J. Noffsingerl, A. L. James, V. A. Baker, A. Curatolo, J. J. Armstrong, Elastic properties of the central airways in obstructive lung diseases measured using anatomical optical coherence tomography, Am. J. Resp. Crit. Care 183(5), p. 612-619, 2011.doi:10.1164/rccm.201002-0178OC

5. R. A. McLaughlin, B. C. Quirk, A. Curatolo, R. W. Kirk, L. Scolaro, D. Lorenser, P. D. Robbins, B. A. Wood, C. M. Saunders, D. D. Sampson, Imaging of breast cancer with optical coherence tomography needle probes: Feasibility and initial results, IEEE J. Sel. Topics Quantum Electron. 18(3), p. 1184-1191, 2012. doi:10.1109/JSTQE.2011.2166757

6. K. M. Kennedy, B. F. Kennedy, R. A. McLaughlin, D. D. Sampson, Needle optical coherence elastography for tissue boundary detection, Opt. Lett. 37(12), p. 2310-2312, 2012. doi:10.1364/OL.37.002310

7. B. F. Kennedy, S. H. Koh, R. A. McLaughlin, K. M. Kennedy, P. R. T. Munro, D. D. Sampson, Strain estimation in phase-sensitive optical coherence elastography, Biomed. Opt. Express 3(8), p. 1865-1879, 2012.doi:10.1364/BOE.3.001865

8. B. F. Kennedy, M. Wojtkowski, M. Szkulmowski, K. M. Kennedy, K. Karnowski, D. D. Sampson, Improved measurement of vibration amplitude in dynamic optical coherence elastography, Biomed. Opt. Express 3(12), p. 3138-3152, 2012. doi:10.1364/BOE.3.003138

9. R. K. Manapuram, S. R. Aglyamov, F. M. Monediado, M. Mashiatulla, J. Li, S. Y. Emelianov, K. V. Larin, In vivo estimation of elastic wave parameters using phase-stabilized swept source optical coherence elastography, J. Biomed. Opt. 17(10), p. 100501, 2012.doi:10.1117/1.JBO.17.10.100501

10. W. Qi, R. Chen, L. Chou, G. Liu, J. Zhang, Q. Zhou, Z. Chen, Phase-resolved acoustic radiation force optical coherence elastography, J. Biomed. Opt. 17(11), p. 110505, 2012. doi:10.1117/1.JBO.17.11.110505

11. C. Li, G. Guan, S. Li, Z. Huang, R. K. Wang, Evaluating elastic properties of heterogeneous soft tissue by surface acoustic waves detected by phase-sensitive optical coherence tomography, J. Biomed. Opt. 17(5), p. 057002, 2012. doi:10.1117/1.JBO.17.5.057002

12. M. Razani, A. Mariampillai, C. Sun, T. W. H. Luk, V. X. D. Yang, M. C. Kolios, Feasibility of optical coherence elastography measurements of shear wave propagation in homogeneous tissue equivalent phantoms,Biomed. Opt. Express 3(5), p. 972-980, 2012. doi:10.1364/BOE.3.00097

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

Posted in Biological Networks, Gene Regulation and Evolution, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Proteomics, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Biology, DNA Sequencing, Genome Institute, Illumina, Washington University in St. Louis, Washington University School of Medicine on February 20, 2013| 1 Comment »

Reporter: Aviva Lev-Ari, PhD, RN

Word Cloud by Daniel Menzin

UPDATED 3/27/2013

The Exome is Not Enough

March 27, 2013

Dan Koboldt at MassGenomics explains why exome sequencing often fails to identify causal variants, even in Mendelian disorders — “the very plausible possibility that a noncoding functional variant is responsible.”

Koboldt, the analysis manager in the human genetics group at the Genome Institute at Washington University, says that researchers shouldn’t overlook the importance of noncoding functional variants, which require a suite of technologies to detect, including RNA-seq, ChiP-seq, DNAse sequencing and footprinting, bisulfite sequencing, and chromosome conformation capture.

“These types of experiments generate a wealth of data about regulatory activity in genomes,” he says. “While studying each of these independently is certainly informative, integrative analysis will be required to elucidate how all of these different regulatory mechanisms work together.”

While this effort will require “robust statistical models, substantial computing resources, and productive collaboration among research groups, the end result “will be a far more complete understanding of how the genome works,” he says.

 

SOURCE:

http://www.genomeweb.com//node/1209591?hq_e=el&hq_m=1544811&hq_l=3&hq_v=e1df6f3681

Dan Koboldt works as a staff scientist in the Human Genetics group of the Genome Institute at Washington University in St. Louis. There, he works with scientists, physicians, programmers, and data analysts to understand the genetic basis of complex human diseases such as cancer, vision disorders, and metabolic syndromes through next-gen sequencing analysis. He received bachelor’s degrees in Computer Science and French from the University of Missouri-Columbia, and a master’s degree in Biology fromWashington University.

Dan has worked in the field of human genetics since 2003, when he joined the lab of Raymond E. Miller, which played a role in the International HapMap Project and later the genetic map of C. briggsae, a model organism related to C. elegans.

Disclaimer: The views expressed on this site, including blog posts and static pages, do not necessarily reflect the opinions of the Genome Institute at Washington University, the Washington University School of Medicine, or Washington University in St. Louis.

Before diving in with both feet, next-generation sequencing neophytes might want to take a gander at a post by Dan Koboldt at MassGenomics where he describes his 10 commandments for good next-gen sequencing.

In his post, Koboldt breaks up his instructions into four categories: analysis, publications, data sharing and submissions, and research ethics and cost.

His list includes some oft repeated warnings. For example, he cautions against reinventing the wheel when it comes to developing analysis software, and, for pity’s sake, don’t invent any more words that end in “ome” or “omics.”

Some other no-no’s, according to Koboldt, include publishing results before they’ve been vetted properly, testing new methods on simulated data only, and taking “unfair advantage of submitted data.”

He also admonishes newcomers to think a little bit about the cost of analysis without which “your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot,” and to have a care for the privacy of their study participants’ samples and data.

 http://www.genomeweb.com//node/1193731?hq_e=el&hq_m=1504568&hq_l=3&hq_v=df86f8764d

Ten Commandments for Next-Gen Sequencing

Just as the reach of next-generation sequencing has continued to grow — in both research and clinical realms — so too has the community of NGS users.  Some have been around since the early days. The days of 454 and Solexa sequencing. Since then, the field has matured at an astonishing pace. Many standards were established to help everyone make sense of this flood of data. The recent democratization of sequencing has made next-gen sequencing available to just about anyone.

And yet, there have been growing pains. With great power comes great responsibility. To help some of the newcomers into the field, I’ve drafted these ten commandments for next-gen sequencing.

NGS Analysis

1. Thou shalt not reinvent the wheel. In spite of rapid technological advances, NGS is not a new field. Most of the current “workhorse” technologies have been on the market for a couple of years or more. As such, we have a plethora of short read aligners, de novo assemblers, variant callers, and other tools already. Even so, there is a great temptation for bioinformaticians to write their own “custom scripts” to perform these tasks. There’s a new “Applications Note” every day with some tool that claims to do something new or better.

Can you really write an aligner that’s better than BWA? More importantly, do we need one? Unless you have some compelling reason to develop something new (as we did when we developed SomaticSniper and VarScan), take advantage of what’s already out there.

2. Thou shalt not coin any new term ending with “ome” or “omics”. We have enough of these already, to the point where it’s getting ridiculous. Genome, transcriptome, and proteome are obvious applications of this nomenclature. Epigenome, sure. But the metabolome, interactome, and various other “ome” words are starting to detract from the naming system. The ones we need have already been coined. Don’t give in to the temptation.

3. Thou shall follow thy field’s conventions for jargon. Technical terms, acronyms, and abbreviations are inherent to research. We need them both for precision and brevity. When we get into trouble is when people feel the need to create their own acronyms when a suitable one already exists. Is there a significant difference between next-generation sequencing (NGS), high-throughput sequencing (HTS), and massively parallel sequencing (MPS)?

Widely accepted terms provide something of a standard, and they should be used whenever possible. Insertion/deletion variants are indels, not InDels or INDELs DIPs. Structural variants are SVs, not SVars or GVs. We don’t need any more acronyms!

NGS Publications

These commandments address behaviors that get on my nerves, both as a blogger and a peer reviewer.

4. Thou shalt not publish by press release. This is a disturbing trend that seems to happen more and more frequently in our field: the announcement of “discoveries” before they have been accepted for publication. Peer review is the required vetting process for scientific research. Yes, it takes time and yes, your competitors are probably on the verge of the same discovery. That doesn’t mean you get to skip ahead and claim credit by putting out a press release.

There are already examples of how this can come back to bite you. When the reviewers trash your manuscript, or (gasp) you learn that a mistake was made, it looks bad. It reflects poorly on the researchers and the institution, both in the field and in the eyes of the public.

5. Thou shalt not rely only on simulated data. Often when I read a paper on a new method or algorithm, they showcase it using simulated data. This often serves a noble purpose, such as knowing the “correct” answer and demonstrating that your approach can find it. Even so, you’d better apply it to some real data too. Simulations simply can’t replicate the true randomness of nature and the crap-that-can-go-wrong reality of next-gen sequencing. There’s plenty of freely available data out there; go get some of it.

6. Thou shalt obtain enough samples. One consequence of the rapid growth of our field (and accompanying drop in sequencing costs) is that small sample numbers no longer impress anyone. They don’t impress me, and they certainly don’t impress the statisticians upstairs. The novelty of exome or even whole-genome sequencing has long worn off. Now, high-profile studies must back their findings with statistically significant results, and that usually means finding a cohort of hundreds (or thousands) of patients with which to extend your findings.

This new reality may not be entirely bad news, because it surely will foster collaboration between groups that might otherwise not be able to publish individually.

Data Sharing and Submissions

7. Thou shalt withhold no data. With some exceptions, sequencing datasets are meant to be shared. Certain institutions, such as large-scale sequencing centers in the U.S., are mandated by their funding agencies to deposit data generated using public funds on a timely basis following its generation. Since the usual deposition site is dbGaP, this means that IRB approvals and dbGaP certification letters must be in hand before sequencing can begin.

Any researchers who plan to publish their findings based on sequencing datasets will have to submit them to public datasets before publication.This is not optional. It is not “something we should do when we get around to it after the paper goes out.” It is required to reproduce the work, so it should really be done before a manuscript is submitted. Consider this excerpt from Nature‘s publication guidelines:

Data sets must be made freely available to readers from the date of publication, and must be provided to editors and peer-reviewers at submission, for the purposes of evaluating the manuscript.

For the following types of data set, submission to a community-endorsed, public repository is mandatory. Accession numbers must be provided in the paper.

The policies go on to list various types of sequencing data:

• DNA and RNA sequences
• DNA sequencing data (traces for capillary electrophoresis and short reads for next-generation sequencing)
• Deep sequencing data
• Epitopes, functional domains, genetic markers, or haplotypes.

Every journal should have a similar policy; most top-tier journals already do. Editors and referees need to enforce this submission requirement by rejecting any manuscripts that do not include the submission accession numbers.

8. Thou shalt not take unfair advantage of submitted data. Many investigators are concerned about data sharing (especially when mandated upon generation, not publication) from fear of being scooped. This is a valid concern. When you submit your data to a public repository, others can find it and (if they meet the requirements) use it. Personally, I think most of these fears are not justified — I mean, have you ever tried to get data out of dbGaP? The time it takes for someone to find, request, obtain, and use submitted data should allow the producers of the data to write it up.

Large-scale efforts to which substantial resources have been devoted — such as the Cancer Genome Atlas — have additional safeguards in place. Their data use policy states that, for a given cancer type, submitted data can’t be used until the “marker paper” has been published. This is a good rule of thumb for the NGS community, and something that journal editors (and referees) haven’t always enforced.

Just because you can scoop someone doesn’t mean that you should. It’s not only bad karma, but bad for your reputation. Scientists have long memories. They will likely review your manuscript or grant proposal sometime in the future. When that happens, you want to be the person who took the high road.

Research Ethics and Cost

9. Thou shalt not discount the cost of analysis. It’s true that since the advent of NGS technology, the cost of sequencing has plummeted. The cost of analysis, however, has not. And making sense of genomic data — alignment, quality control, variant calling, annotation, interpretation — is a daunting task indeed. It takes computational resources as well as expertise. This infrastructure is not free; in fact, it can be more expensive than the sequencing itself. 

Without analysis, your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot.

10. Thou shalt honor thy patients and their samples. Earlier this month, I wrote about how supposedly anonymous individuals from the CEPH collection were identified using a combination of genetic markers and online databases. It is a simple fact that we can no longer guarantee a sequenced sample’s anonymity. That simple fact, combined with our growing ability to interpret the possible consequences of an individual genome, means a great deal of risk for study volunteers.

We must safeguard the privacy of study participants — and find ways to protect them from privacy violations and/or discrimination — if we want their continued cooperation.

This means obtaining good consent documents and ensuring that they’re all correct before sequencing begins. It also means adhering to the data use policies those consents specify. As I’ve written before, samples are the new commodity in our field. Anyone can rent time on a sequencer. If you don’t make an effort to treat your samples right, someone else will.

Related Posts:

• Science and Science Fiction It occurred to me recently, while refereeing a manuscript, that publication in research is a lot more forgiving than publication in fiction. I’d just read …
• Identifying Samples from Genomic Data In last week’s issue of Science, Melissa Gymrek and colleagues from the lab of Yaniv Erlich (Whitehead) report a method for the triangulation the identity …
• Human Genetics Challenges in an Era of Cheap Sequencing Next-generation sequencing promises to reach unprecedented levels of throughput this year, driving down the cost of sequencing dramatically. Somewhere between the GridION, the Ion Proton, …
• Ethical Challenges for Clinical Genome Sequencing The past decade has been a turbulent one for the ream of genetic and genomic research. Since the completion of the human genome and beginning …

SOURCE:

http://massgenomics.org/2013/02/ngs-commandments.html

Dan Koboldt’s Publications

Bose R, Kavuri SM, Searleman AC, Shen W, Shen D, Koboldt DC, Monsey J, Goel N, Aronson AB, Li S, Ma CX, Ding L, Mardis ER, & Ellis MJ (2013).Activating HER2 mtations in HER2 gene amplification negative breast cancer. Cancer discovery PMID: 23220880

The 1000 Genomes Project Consortium (2012). An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65. DOI: 10.1038/nature11632

Cancer Genome Atlas Network (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490 (7418), 61-70 PMID:23000897

Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, Van Tine BA, Hoog J, Goiffon RJ, Goldstein TC, Ng S, Lin L, Crowder R, Snider J, Ballman K, Weber J, Chen K, Koboldt DC, Kandoth C, Schierding WS, McMichael JF, Miller CA, Lu C, Harris CC, McLellan MD, Wendl MC, DeSchryver K, Allred DC, Esserman L, Unzeitig G, Margenthaler J, Babiera GV, Marcom PK, Guenther JM, Leitch M, Hunt K, Olson J, Tao Y, Maher CA, Fulton LL, Fulton RS, Harrison M, Oberkfell B, Du F, Demeter R, Vickery TL, Elhammali A, Piwnica-Worms H, McDonald S, Watson M, Dooling DJ, Ota D, Chang LW, Bose R, Ley TJ, Piwnica-Worms D, Stuart JM, Wilson RK, & Mardis ER (2012). Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature, 486 (7403), 353-60 PMID: 22722193

Welch JS, Ley TJ, Link DC, Miller CA, Larson DE, Koboldt DC, Wartman LD, Lamprecht TL, Liu F, Xia J, Kandoth C, Fulton RS, McLellan MD, Dooling DJ, Wallis JW, Chen K, Harris CC, Schmidt HK, Kalicki-Veizer JM, Lu C, Zhang Q, Lin L, O’Laughlin MD, McMichael JF, Delehaunty KD, Fulton LA, Magrini VJ, McGrath SD, Demeter RT, Vickery TL, Hundal J, Cook LL, Swift GW, Reed JP, Alldredge PA, Wylie TN, Walker JR, Watson MA, Heath SE, Shannon WD, Varghese N, Nagarajan R, Payton JE, Baty JD, Kulkarni S, Klco JM, Tomasson MH, Westervelt P, Walter MJ, Graubert TA, DiPersio JF, Ding L, Mardis ER, & Wilson RK (2012). The origin and evolution of mutations in acute myeloid leukemia. Cell, 150 (2), 264-78 PMID: 22817890

Cancer Genome Atlas Network (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330-7 PMID: 22810696

Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC, Mooney TB, Callaway MB, Dooling D, Mardis ER, Wilson RK, & Ding L (2012). MuSiC: identifying mutational significance in cancer genomes.Genome research, 22 (8), 1589-98 PMID: 22759861

Walter MJ, Shen D, Ding L, Shao J, Koboldt DC, Chen K, Larson DE, McLellan MD, Dooling D, Abbott R, Fulton R, Magrini V, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Fan X, Grillot M, Witowski S, Heath S, Frater JL, Eades W, Tomasson M, Westervelt P, DiPersio JF, Link DC, Mardis ER, Ley TJ, Wilson RK, & Graubert TA (2012). Clonal architecture of secondary acute myeloid leukemia. The New England journal of medicine, 366(12), 1090-8 PMID: 22417201

Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, Arthur CD, White JM, Chen YS, Shea LK, Hundal J, Wendl MC, Demeter R, Wylie T, Allison JP, Smyth MJ, Old LJ, Mardis ER, & Schreiber RD (2012).Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature, 482 (7385), 400-4 PMID: 22318521

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, & Wilson RK (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research PMID: 22300766

Koboldt DC, Larson DE, Chen K, Ding L, & Wilson RK (2012). Massively parallel sequencing approaches for characterization of structural variation. Methods in molecular biology (Clifton, N.J.), 838, 369-84 PMID:22228022

Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, Krysiak K, Harris CC, Koboldt DC, Larson DE, McLellan MD, Dooling DJ, Abbott RM, Fulton RS, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Grillot M, Baty J, Heath S, Frater JL, Nasim T, Link DC, Tomasson MH, Westervelt P, DiPersio JF, Mardis ER, Ley TJ, Wilson RK, & Walter MJ (2011). Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature genetics, 44 (1), 53-7 PMID: 22158538

Larson DE, Harris CC, Chen K, Koboldt DC, Abbott TE, Dooling DJ, Ley TJ, Mardis ER, Wilson RK, & Ding L. (2011). SomaticSniper: Identification of Somatic Point Mutations in Whole Genome Sequencing Data.Bioinformatics, Online : doi: 10.1093/bioinformatics/btr665

Cancer Genome Atlas Research Network (2011). Integrated genomic analyses of ovarian carcinoma. Nature, 474 (7353), 609-15 PMID:21720365

Marth GT, Yu F, Indap AR, Garimella K, et al & the 1000 Genomes Project (2011). The functional spectrum of low-frequency coding variation.Genome biology, 12 (9) PMID: 21917140

Ross JA, Koboldt DC, Staisch JE, Chamberlin HM, Gupta BP, Miller RD, Baird SE, & Haag ES (2011). Caenorhabditis briggsae recombinant inbred line genotypes reveal inter-strain incompatibility and the evolution of recombination. PLoS genetics, 7 (7) PMID: 21779179

Bowne SJ, Humphries MM, Sullivan LS, Kenna PF, Tam LC, Kiang AS, Campbell M, Weinstock GM, Koboldt DC, Ding L, Fulton RS, Sodergren EJ, et al (2011). A dominant mutation in RPE65 identified by whole-exome sequencing causes retinitis pigmentosa with choroidal involvement. European journal of human genetics : EJHG, 19 (10) PMID:21938004

Link DC, Schuettpelz LG, Shen D, Wang J, Walter MJ, Kulkarni S, Payton JE, Ivanovich J, Goodfellow PJ, Le Beau M, Koboldt DC, Dooling DJ, Fulton RS, et al (2011). Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA : the journal of the American Medical Association, 305 (15), 1568-76 PMID: 21505135

Ley T, Ding L, Walter M, McLellan M, Lamprecht T, Larson D, Kandoth C, Payton J, Baty J, Welch J, Harris C, Lichti C, Townsend R, Fulton R, Dooling D, Koboldt D, et al. (2010). DNMT3A Mutations in Acute Myeloid Leukemia
New England Journal of Medicine DOI: 10.1056/NEJMoa1005143

Ding L, Wendl MC, Koboldt DC, & Mardis ER (2010). Analysis of next-generation genomic data in cancer: accomplishments and challenges. Human Molecular Genetics, 19 (R2):R188-96. PMID:20843826

Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, Tsalenko A, Sampas N, Bruhn L, Shendure J, 1000 Genomes Project, & Eichler EE (2010). Diversity of human copy number variation and multicopy genes. Science (New York, N.Y.), 330 (6004), 641-6 PMID: 21030649

The 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing. Nature, 467(7319), 1061-1073 DOI: 10.1038/nature09534

Bowne SJ, Sullivan LS, Koboldt DC, Ding L, Fulton R, Abbott RM, Sodergren EJ, Birch DG, Wheaton DH, Heckenlively JR, Liu Q, Pierce EA, Weinstock GM, & Daiger SP (2010). Identification of Disease-Causing Mutations in Autosomal Dominant Retinitis Pigmentosa (adRP) Using Next-Generation DNA Sequencing. Investigative ophthalmology & visual science PMID: 20861475

Fehniger, T., Wylie, T., Germino, E., Leong, J., Magrini, V., Koul, S., Keppel, C., Schneider, S., Koboldt, D., Sullivan, R., Heinz, M., Crosby, S., Nagarajan, R., Ramsingh, G., Link, D., Ley, T., & Mardis, E. (2010). Next-generation sequencing identifies the natural killer cell microRNA transcriptome Genome Research DOI: 10.1101/gr.107995.110

Ramsingh G, Koboldt DC, Trissal M, Chiappinelli KB, Wylie T, Koul S, Chang LW, Nagarajan R, Fehniger TA, Goodfellow P, Magrini V, Wilson RK, Ding L, Ley TJ, Mardis ER, & Link DC (2010). Complete characterization of the microRNAome in a patient with acute myeloid leukemia. BloodPMID: 20876853

Koboldt DC, Ding L, Mardis ER & Wilson RK. (2010). Challenges of sequencing human genomes. Briefings in Bioinformatics DOI:10.1093/bib/bbq016

Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, Harris CC, McLellan MD, Fulton RS, Fulton LL, Abbott RM, Hoog J, Dooling DJ, Koboldt DC, et al. (2010). Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature, 464 (7291), 999-1005 PMID:20393555

Koboldt DC and Miller RD (2010). Identification of polymorphic markers for genetic mapping. Genomics: Essential Methods, In Press.

Koboldt DC, Staisch J, Thillainathan B, Haines K, Baird SE, Chamberlin HM, Haag ES, Miller RD, & Gupta BP (2010). A toolkit for rapid gene mapping in the nematode Caenorhabditis briggsae. BMC genomics, 11 (1) PMID: 20385026

Voora D, Koboldt DC, King CR, Lenzini PA, Eby CS, Porche-Sorbet R, Deych E, Crankshaw M, Milligan PE, McLeod HL, Patel SR, Cavallari LH, Ridker PM, Grice GR, Miller RD, & Gage BF (2010). A polymorphism in the VKORC1 regulator calumenin predicts higher warfarin dose requirements in African Americans. Clinical pharmacology and therapeutics, 87 (4), 445-51 PMID: 20200517

Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, et al (2009). CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics (Oxford, England) PMID: 20031968

Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD, Chen K, Koboldt DC, et al (2009). Recurring mutations found by sequencing an acute myeloid leukemia genome. The New England journal of medicine, 361(11), 1058-66 PMID: 19657110

Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, Mardis ER, Weinstock GM, Wilson RK, & Ding L (2009). VarScan: variant detection in massively parallel sequencing of individual and pooled samples.Bioinformatics (Oxford, England), 25 (17), 2283-5 PMID: 19542151

Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M, Cook L, Abbott R, Larson DE, Koboldt DC, et al (2008). DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature, 456 (7218), 66-72 PMID: 18987736

Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, et al (2008). Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 455 (7216), 1069-75 PMID: 18948947

Cancer Genome Atlas Research Network (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455 (7216), 1061-8 PMID: 18772890

International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449 (7164), 851-61 PMID: 17943122

Sabeti PC, Varilly P, Fry B, et al (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449 (7164), 913-8 PMID: 17943131

Hillier LW, Miller RD, Baird SE, Chinwalla A, Fulton LA, Koboldt DC, & Waterston RH (2007). Comparison of C. elegans and C. briggsaegenome sequences reveals extensive conservation of chromosome organization and synteny. PLoS biology, 5 (7) PMID: 17608563

Stanley SL Jr, Frey SE, Taillon-Miller P, Guo J, Miller RD, Koboldt DC, Elashoff M, Christensen R, Saccone NL, & Belshe RB (2007). The immunogenetics of smallpox vaccination. The Journal of infectious diseases, 196 (2), 212-9 PMID: 17570108

Koboldt DC, Miller RD, & Kwok PY (2006). Distribution of human SNPs and its effect on high-throughput genotyping. Human mutation, 27(3), 249-54 PMID: 16425292

The International HapMap Consortium (2005). A haplotype map of the human genome. Nature, 437 (7063), 1299-1320 PMID: 16255080

Miller RD, Phillips MS, et al (2005). High-density single-nucleotide polymorphism maps of the human genome. Genomics, 86 (2), 117-26 PMID: 15961272

Other Writing by Dan Koboldt

Dan Koboldt is also the author of Get Your Baby to Sleep, a resource to help new parents whose baby won’t sleep with advice on establishing healthy baby sleep habits and handling baby sleep problems. He contributes to The Best of Twins and In Search of Whitetails blogs as well.

How would you like to start your own blog? See this guide to building a blog or website in 20 minutes. It walks you through setting up a site with open-source WordPress software, which happens to be what runs Massgenomics.

SOURCE:

 http://massgenomics.org/about

Other related articles on this Open Access Online Scientific Journal:

“Genome in a Bottle”: NIST’s new metrics for Clinical Human Genome Sequencing “Genome in a Bottle”: NIST’s new metrics for Clinical Human Genome Sequencing

http://pharmaceuticalintelligence.com/2012/09/06/genome-in-a-bottle-nists-new-metrics-for-clinical-human-genome-sequencing/

DNA – The Next-Generation Storage Media for Digital Information

http://pharmaceuticalintelligence.com/2012/08/27/dna-the-next-generation-storage-media-for-digital-information/

How Genome Sequencing is Revolutionizing Clinical Diagnostics

http://pharmaceuticalintelligence.com/2012/08/20/how-genome-sequencing-is-revolutionizing-clinical-diagnostics/

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

http://pharmaceuticalintelligence.com/2013/02/19/ngs-market-trends-and-development-for-genotype-phenotype-associations/

What is the Future for Genomics in Clinical Medicine?

http://pharmaceuticalintelligence.com/2013/02/17/what-is-the-future-for-genomics-in-clinical-medicine/

Genomically Guided Treatment after CLIA Approval: to be offered by Weill Cornell Precision Medicine Institute

http://pharmaceuticalintelligence.com/2013/02/06/genomically-guided-treatment-after-clia-approval-to-be-offered-by-weill-cornell-precision-medicine-institute/

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

http://pharmaceuticalintelligence.com/2013/02/04/inaugural-genomics-in-medicine-the-conference-program-211-122013-san-francisco-ca/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”

http://pharmaceuticalintelligence.com/2012/11/14/gsk-for-personalized-medicine-using-cancer-drugs-needs-alacris-systems-biology-model-to-determine-the-in-silico-effect-of-the-inhibitor-in-its-virtual-clinical-trial/

arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data

http://pharmaceuticalintelligence.com/2012/11/01/arraymap-genomic-feature-mining-of-cancer-entities-of-copy-number-abnormalities-cnas-data/

NGS Cardiovascular Diagnostics: Long-QT Genes Sequenced – A Potential Replacement for Molecular Pathology

http://pharmaceuticalintelligence.com/2012/10/01/ngs-cardiovascular-diagnostics-long-qt-genes-sequenced-a-potential-replacement-for-molecular-pathology/

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

http://pharmaceuticalintelligence.com/2012/09/18/speeding-up-genome-analysis-mit-algorithms-for-direct-computation-on-compressed-genomic-datasets/

Clinical Genetics, Personalized Medicine, Molecular Diagnostics, Consumer-targeted DNA – Consumer Genetics Conference (CGC) – October 3-5, 2012, Seaport Hotel, Boston, MA

http://pharmaceuticalintelligence.com/2012/09/06/clinical-genetics-personalized-medicine-molecular-diagnostics-consumer-targeted-dna-consumer-genetics-conference-cgc-october-3-5-2012-seaport-hotel-boston-ma/

“CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics” lays the manifold multivariate systems analytical tools that has moved the science forward to a groung that ensures clinical application.

http://pharmaceuticalintelligence.com/2013/02/13/cracking-the-code-of-human-life-the-birth-of-bioinformatics-and-computational-genomics/

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

Posted in Biological Networks, Gene Regulation and Evolution, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, International Global Work in Pharmaceutical, Interviews with Scientific Leaders, Medical and Population Genetics, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Pharmacogenomics, Proteomics, Scientist: Career considerations, Statistical Methods for Research Evaluation, tagged CLC, CLC Bio, DNA Sequencing, HUGO Gene Nomenclature Committee, Illumina, UCSC Genome Browser on February 19, 2013| Leave a Comment »

Curator: Aviva Lev-Ari, PhD, RN

WordCloud Image Produced by Adam Tubman

Dr. M. Michael Barmada, Associate Professor at Center for Computational Genetics, University of Pittsburgh, tells about how the hot topic of the times now – genetics – has challenged the computational resources across the University:

• Everybody has got a different focus – some are doing
• DNA-sequencing, some
• RNA-sequencing, some
• ChiP-seq, some
• methylation studies, and everybody wants to use the latest hottest technologies.

Associate Professor at Center for Computational Genetics at University of Pittsburgh, Dr. M. Michael Barmada

http://www.clcbio.com/blog/computational-resources-for-ngs-research-at-university-of-pittsburgh/

CLC bio Annual Survey Results

http://www.clcbio.com/wp-content/uploads/2013/02/annual_survey_clcbio.pdf?utm_source=Survey2012&utm_medium=CLC

CLC Bio has published the results of a survey of researchers in the next-generation sequencing market to find out which sequencers and software are used the most.

The company says it received responses from 708 individuals in 73 countries.

Not surprisingly, they found that Sequencers

• Illumina’s HiSeq and MiSeq are the most used instruments with about 34.6 percent and 21.3 percent of respondents, respectively, stating that they use the systems. Meanwhile,
• Roche’s 454 sequencers got 21.2 percent of the votes and
• Life Technologies’ Ion Torrent Personal Genome Machine got 11.5 percent of the responses.

In terms of Bioinformatics tools, the

• UCSC Genome Browser has the most use, according to the survey, with 28.9 percent of respondents reporting that they use the program. Next in line is
• Ensembl tools and then – 26.9
• Bowtie with  23.4 percent of the votes, respectively.

Also worth noting is that NGS is being used primarily for

• whole-genome sequencing — 40.8 percent of the votes — followed by
• RNA-seq and — 40.1 percent
• de novo sequencing with  39.8 percent of the votes, respectively.

Of the 708 respondents, about 24.6 percent work in the US, according to CLC. Also,

• 73 percent of respondents work in academic research while
• 9 percent work in industry, another
• 9 percent in government, and
• 6 percent work in not-for-profit organizations, according to the survey.

http://www.genomeweb.com//node/1192936?hq_e=el&hq_m=1502990&hq_l=2&hq_v=bea4dc08f7

We believe MedQL has the potential to be an effective time saver for researchers working with variant prioritization, making it a promising new plugin for CLC Genomics Workbench. We’re excited to add BioQL’s technology for evidence-based downstream analysis of Next Generation Sequencing data to our products.

Director of Global Partner Relations at CLC bio, Mikael Flensborg

Using CLC Genomics Workbench, a common workflow to detect causative mutations in medical genomics involves read mapping and variant detection. The result is a list of candidate gene variants that differ from the reference genome. The MedQL plugin uses an evidence-based approach to prioritize these genes for functional studies and, thereby, allowing researchers to focus their efforts on the most promising candidates.

CLC BIO AND BIOQL RELEASE MEDICAL GENOMICS PLUGIN FOR GENOTYPE–PHENOTYPE ASSOCIATIONS

Aarhus, Denmark — November 7, 2012 — Today, CLC bio and the independent software vendor, BioQL, announced the release of the MedQL Variant Prioritizer plugin for CLC Genomics Workbench. The plugin connects with MedQL’s online database to prioritize a list of variants in gene regions based on their degree of association with a given phenotype.

The MedQL database contains more than 20 million articles from Medline, indexed using a dictionary of nearly 300,000 terms from authoritative ontologies such as the HUGO Gene Nomenclature Committee (HGNC), the Human Disease Ontology, and the Online Mendelian Inheritance in Man (OMIM).

CLC BIO

We’re the world’s leading bioinformatics software developers and the only ones providing an analysis platform where both desktop and server software are seamlessly integrated and optimized for best performance.

Our wide range of analyses are available both through a user-friendly graphical user-interface as well as through command-line, allowing scientists to choose their preferred interface.

By developing our own proprietary algorithms, based on published methods, we have successfully accelerated the data calculations to achieve remarkable improvements in speed over comparable solutions.

Our enterprise platform serves as the backbone of sequence analysis pipelines for a large number of the world’s most prominent research institutions. With around 2000 different organizations as our customers around the globe, including the ten biggest pharmaceutical companies in the world, we have established ourselves as the market-leader in sequence analysis software.

One of our key strategies is to be ‘cross-platform’, which means we support all the major next generation sequencing platforms as well as traditional Sanger-based sequencing, effectively giving our customers a one-stop-shop for their analysis needs across all sequencing platforms.

http://www.clcbio.com/corporate/about-clc-bio/

 Desktop software for Sequence Analysis based on an overall level of subjects.

FEATURES
Next Generation Sequencing analysis
Genomics
Transcriptomics (Gene expression features also available in CLC Main Workbench)
Epigenomics
RNA secondary structure
BLAST searches
Protein analyses
Primer design
Assembly of Sanger sequencing data
Molecular cloning
Pattern discovery and motif search
Nucleotide analyses
GenBank Entrez searches
Sequence alignment
Phylogenetic trees
Detailed history log
Batch processing
Customization of your workbenches

 http://www.clcbio.com/sequencing-analysis/overview-of-applications/

CLC Genomics Machine

Our turnkey solution, for small research labs. It includes CLC Genomics Server and CLC Genomics Workbench. Everything is preinstalled on a powerful desktop computer or server blade – ready to plug-in and run from the day it is delivered.

CLC Genomics Factory

Our turnkey solution for medium and large research labs that needs a complete IT infrastructure for their NGS data analysis.

USER-FRIENDLY BIOINFORMATICS

Our software is made for biologists by biologists, so it’s easy to analyze, visualize, and compare DNA, RNA, and Protein data, as well as run advanced workflows with large and complicated datasets.

J. CRAIG VENTER INSTITUTE EXTENDS CLC BIO SITE LICENSE THROUGH 2017

Aarhus, Denmark — January 8, 2013 — Today CLC bio, the global leader in commercial sequence analysis software, announced that the J. Craig Venter Institute (JCVI) has extended their site license agreement with CLC bio through 2017.

JCVI has been utilizing CLC bio’s enterprise platform since 2009 and currently uses it on more than 30 research grants, including their work as part of the Human Microbiome Project (HMP). The HMP is a National Institutes of Health-funded project to catalogue and characterize the microbes living in and on the human body. Recently, the HMP Consortium published a series of papers with results from this work in Nature and PLOSone. CLC’s bio software was used in the analysis of this work.

The complexity and diversity of our research projects necessitates unique tools to analyze these increasingly large data sets. In our pursuit of excellence we always test and employ the best available tools for our research projects. As such we’re happy to announce the extension of our site license with CLC bio through 2017.

Karen Nelson, Ph.D., President, JCVI

http://www.clcbio.com/news/jcvi-extends-site-license/

For us, it’s always very exciting to see the results of all the intriguing research that our customers are doing, and no less so, when JCVI published their papers on the HMP project this summer. JCVI was one of our first site license deals with a premier institution in the genomics research field, and we’re proud to announce it has been extended for another five years.

Thomas Knudsen, CEO, CLC bio

The original 4-year site license agreement between JCVI and CLC bio was signed in the summer of 2009, and has now been extended by another 5 years, through 2017. JCVI deploys CLC bio’s platform in an integrated environment across multiple geographical locations and together with international collaborators.

http://www.clcbio.com/news/jcvi-extends-site-license/ 

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What is the Future for Genomics in Clinical Medicine?

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, 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, Genome Biology, Genomic Testing: Methodology for Diagnosis, Infectious Disease & New Antibiotic Targets, International Global Work in Pharmaceutical, Interviews with Scientific Leaders, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Industry Competitive Intelligence, Population Health Management, Genetics & Pharmaceutical, Scientist: Career considerations, Stem Cells for Regenerative Medicine, Systemic Inflammatory Response Related Disorders, Technology Transfer: Biotech and Pharmaceutical, tagged biopharmaceutical research, Cell potency, Cellular differentiation, Code of Human Life, designer drugs, DNA, drug targets, Embryonic stem cell, gene expression, Genome Research, genomics to bedside, Human Genome Project, Human subject research, Max Planck Institute for Molecular Biomedicine, Medical research, microbiome, National Human Genome Research Institute, oligomers, oligonucleotide, Personalized medicine, pioneer transcription factros, polynucleotides, post translational modifications, reproductive research, Research Methodology, Scripps Research Institute, Scripps Translational Science Institute, SNPs, Stem cell, Stress, synthetic nucleotides, Technology Transfer, telomerase, telomere, transcription factor identification, transcription factors, Transcriptomics, Translational Medicine, University of Cambridge on February 17, 2013| 5 Comments »

What is the Future for Genomics in Clinical Medicine?

Author and Curator: Larry H Bernstein, MD, FCAP

WordCloud Image Produced by Adam Tubman

Introduction

This is the last in a series of articles looking at the past and future of the genome revolution.  It is a revolution indeed that has had a beginning with the first phase discovery leading to the Watson-Crick model, the second phase leading to the completion of the Human Genome Project, a third phase in elaboration of ENCODE.  But we are entering a fourth phase, not so designated, except that it leads to designing a path to the patient clinical experience.
What is most remarkable on this journey, which has little to show in treatment results at this time, is that the boundary between metabolism and genomics is breaking down.  The reality is that we are a magnificent “magical” experience in evolutionary time, functioning in a bioenvironment, put rogether like a truly complex machine, and with interacting parts.  What are those parts – organelles, a genetic message that may be constrained and it may be modified based on chemical structure, feedback, crosstalk, and signaling pathways.  This brings in diet as a source of essential nutrients, exercise as a method for delay of structural loss (not in excess), stress oxidation, repair mechanisms, and an entirely unexpected impact of this knowledge on pharmacotherapy.  I illustrate this with some very new observations.

Gutenberg Redone

The first is a recent talk on how genomic medicine has constructed a novel version of the “printing press”, that led us out of the dark ages.

In our series The Creative Destruction of Medicine, I’m trying to get into critical aspects of how we can Schumpeter or reboot the future of healthcare by leveraging the big innovations that are occurring in the digital world, including digital medicine.

We have this big thing about evidence-based medicine and, of course, the sanctimonious randomized, placebo-controlled clinical trial. Well, that’s great if one can do that, but often we’re talking about needing thousands, if not tens of thousands, of patients for these types of clinical trials. And things are changing so fast with respect to medicine and, for example, genomically guided interventions that it’s going to become increasingly difficult to justify these very large clinical trials.

For example, there was a drug trial for melanoma and the mutation of BRAF, which is the gene that is found in about 60% of people with malignant melanoma. When that trial was done, there was a placebo control, and there was a big ethical charge asking whether it is justifiable to have a body count. This was a matched drug for the biology underpinning metastatic melanoma, which is essentially a fatal condition within 1 year, and researchers were giving some individuals a placebo.

The next observation is a progression of what he have already learned. The genome has a role is cellular regulation that we could not have dreamed of 25 years ago, or less. The role is far more than just the translation of a message from DNA to RNA to construction of proteins, lipoproteins, cellular and organelle structures, and more than a regulation of glycosidic and glycolytic pathways, and under the influence of endocrine and apocrine interactions. Despite what we have learned, the strength of inter-molecular interactions, strong and weak chemical bonds, essential for 3-D folding, we know little about the importance of trace metals that have key roles in catalysis and because of their orbital structures, are essential for organic-inorganic interplay. This will not be coming soon because we know almost nothing about the intracellular, interstitial, and intrvesicular distributions and how they affect the metabolic – truly metabolic events.

I shall however, use some new information that gives real cause for joy.

Reprogramming Alters Cells’ Fate

Kathy Liszewski

Gordon Conference  Report: June 21, 2012;32(11)

New and emerging strategies were showcased at Gordon Conference’s recent “Reprogramming Cell Fate” meeting. For example, cutting-edge studies described how only a handful of key transcription factors were needed to entirely reprogram cells.

M. Azim Surani, Ph.D., Marshall-Walton professor at the Gurdon Institute, University of Cambridge, U.K., is examining cellular reprogramming in a mouse model. Epiblast stem cells are derived from the early-stage embryonic stage after implantation of blastocysts, about six days into development, and retain the potential to undergo reversion to embryonic stem cells (ESCs) or to PGCs.”  They report two critical steps both of which are needed for exploring epigenetic reprogramming.  “Although there are two X chromosomes in females, the inactivation of one is necessary for cell differentiation. Only after epigenetic reprogramming of the X chromosome can pluripotency be acquired. Pluripotent stem cells can generate any fetal or adult cell type but are not capable of developing into a complete organism.”

The second read-out is the activation of Oct4, a key transcription factor involved in ESC development. The expression of Oct4 in epiSCs requires its proximal enhancer.  Dr. Surani said that their cell-based system demonstrates how a systematic analysis can be performed to analyze how other key genes contribute to the many-faceted events involved in reprogramming the germline.

Reprogramming Expressway

A number of other recent studies have shown the importance of Oct4 for self-renewal of undifferentiated ESCs. It is sufficient to induce pluripotency in neural tissues and somatic cells, among others. The expression of Oct4 must be tightly regulated to control cellular differentiation. But, Oct4 is much more than a simple regulator of pluripotency, according to Hans R. Schöler, Ph.D., professor in the department of cell and developmental biology at the Max Planck Institute for Molecular Biomedicine.

Oct4 has a critical role in committing pluripotent cells into the somatic cellular pathway. When embryonic stem cells overexpress Oct4, they undergo rapid differentiation and then lose their ability for pluripotency. Other studies have shown that Oct4 expression in somatic cells reprograms them for transformation into a particular germ cell layer and also gives rise to induced pluripotent stem cells (iPSCs) under specific culture conditions.

Oct4 is the gatekeeper into and out of the reprogramming expressway. By modifying experimental conditions, Oct4 plus additional factors can induce formation of iPSCs, epiblast stem cells, neural cells, or cardiac cells. Dr. Schöler suggests that Oct4 a potentially key factor not only for inducing iPSCs but also for transdifferention.  “Therapeutic applications might eventually focus less on pluripotency and more on multipotency, especially if one can dedifferentiate cells within the same lineage. Although fibroblasts are from a different germ layer, we recently showed that adding a cocktail of transcription factors induces mouse fibroblasts to directly acquire a neural stem cell identity.

Stem cell diagram illustrates a human fetus stem cell and possible uses on the circulatory, nervous, and immune systems. (Photo credit: Wikipedia)

English: Embryonic Stem Cells. (A) shows hESCs. (B) shows neurons derived from hESCs. (Photo credit: Wikipedia)

Transforming growth factor beta (TGF-β) is a secreted protein that controls proliferation, cellular differentiation, and other functions in most cells. http://en.wikipedia.org/wiki/TGFbeta (Photo credit: Wikipedia)

Pioneer Transcription Factors

Pioneer transcription factors take the lead in facilitating cellular reprogramming and responses to environmental cues. Multicellular organisms consist of functionally distinct cellular types produced by differential activation of gene expression. They seek out and bind specific regulatory sequences in DNA. Even though DNA is coated with and condensed into a thick fiber of chromatin. The pioneer factor, discovered by Prof. KS Zaret at UPenn SOM in 1996, he says, endows the competence for gene activity, being among the first transcription factors to engage and pry open the target sites in chromatin.

FoxA factors, expressed in the foregut endoderm of the mouse,are necessary for induction of the liver program. They found that nearly one-third of the DNA sites bound by FoxA in the adult liver occur near silent genes

A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication

ME Hubbi, K Shitiz, DM Gilkes, S Rey,….GL Semenza. Johns Hopkins University SOM
Sci. Signal 2013; 6(262) 10pgs. [DOI: 10.1126/scisignal.2003417]   http:dx.doi.org/10.1126/scisignal.2003417

http://SciSignal.com/A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication/

Many of the cellular responses to reduced O2 availability are mediated through the transcriptional activity of hypoxia-inducible factor 1 (HIF-1). We report a role for the isolated HIF-1α subunit as an inhibitor of DNA replication, and this role was independent of HIF-1β and transcriptional regulation. In response to hypoxia, HIF-1α bound to Cdc6, a protein that is essential for loading of the mini-chromosome maintenance (MCM) complex (which has DNA helicase activity) onto DNA, and promoted the interaction between Cdc6 and the MCM complex. The binding of HIF-1α to the complex decreased phosphorylation and activation of the MCM complex by the kinase Cdc7. As a result, HIF-1α inhibited firing of replication origins, decreased DNA replication, and induced cell cycle arrest in various cell types. To whom correspondence should be addressed. E-mail: gsemenza@jhmi.edu
Citation: M. E. Hubbi, Kshitiz, D. M. Gilkes, S. Rey, C. C. Wong, W. Luo, D.-H. Kim, C. V. Dang, A. Levchenko, G. L. Semenza, A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication. Sci. Signal. 6, ra10 (2013).

Identification of a Candidate Therapeutic Autophagy-inducing Peptide

Nature 2013;494(7436).    http://nature.com/Identification_of_a_candidate_therapeutic_autophagy-inducing_peptide/   http://www.ncbi.nlm.nih.gov/pubmed/23364696
http://www.readcube.com/articles/10.1038/nature11866

Beth Levine and colleagues have constructed a cell-permeable peptide derived from part of an autophagy protein called beclin 1. This peptide is a potent inducer of autophagy in mammalian cells and in vivo in mice and was effective in the clearance of several viruses including chikungunya virus, West Nile virus and HIV-1.

Could this small autophagy-inducing peptide may be effective in the prevention and treatment of human diseases?

PR-Set7 Is a Nucleosome-Specific Methyltransferase that Modifies Lysine 20 of

Histone H4 and Is Associated with Silent Chromatin

K Nishioka, JC Rice, K Sarma, H Erdjument-Bromage, …, D Reinberg.   Molecular Cell, Vol. 9, 1201–1213, June, 2002, Copyright 2002 by Cell Press   http://www.cell.com/molecular-cell/abstract/S1097-2765(02)00548-8

http://www.sciencedirect.com/science/article/pii/S1097276502005488           http://www.ncbi.nlm.nih.gov/pubmed/12086618
http://www.cienciavida.cl/publications/b46e8d324fa4aefa771c4d6ece4d2e27_PR-Set7_Is_a_Nucleosome-Specific.pdf

We have purified a human histone H4 lysine 20methyl-transferase and cloned the encoding gene, PR/SET07. A mutation in Drosophila pr-set7 is lethal: second in-star larval death coincides with the loss of H4 lysine 20 methylation, indicating a fundamental role for PR-Set7 in development. Transcriptionally competent regions lack H4 lysine 20 methylation, but the modification coincided with condensed chromosomal regions polytene chromosomes, including chromocenter euchromatic arms. The Drosophila male X chromosome, which is hyperacetylated at H4 lysine 16, has significantly decreased levels of lysine 20 methylation compared to that of females. In vitro, methylation of lysine 20 and acetylation of lysine 16 on the H4 tail are competitive. Taken together, these results support the hypothesis that methylation of H4 lysine 20 maintains silent chromatin, in part, by precluding neighboring acetylation on the H4 tail.

Next-Generation Sequencing vs. Microarrays

Shawn C. Baker, Ph.D., CSO of BlueSEQ
GEN Feb 2013
With recent advancements and a radical decline in sequencing costs, the popularity of next generation sequencing (NGS) has skyrocketed. As costs become less prohibitive and methods become simpler and more widespread, researchers are choosing NGS over microarrays for more of their genomic applications. The immense number of journal articles citing NGS technologies it looks like NGS is no longer just for the early adopters. Once thought of as cost prohibitive and technically out of reach, NGS has become a mainstream option for many laboratories, allowing researchers to generate more complete and scientifically accurate data than previously possible with microarrays.

Gene Expression

Researchers have been eager to use NGS for gene expression experiments for a detailed look at the transcriptome. Arrays suffer from fundamental ‘design bias’ —they only return results from those regions for which probes have been designed. The various RNA-Seq methods cover all aspects of the transcriptome without any a priori knowledge of it, allowing for the analysis of such things as novel transcripts, splice junctions and noncoding RNAs. Despite NGS advancements, expression arrays are still cheaper and easier when processing large numbers of samples (e.g., hundreds to thousands).
Methylation
While NGS unquestionably provides a more complete picture of the methylome, whole genome methods are still quite expensive. To reduce costs and increase throughput, some researchers are using targeted methods, which only look at a portion of the methylome. Because details of exactly how methylation impacts the genome and transcriptome are still being investigated, many researchers find a combination of NGS for discovery and microarrays for rapid profiling.

Diagnostics

They are interested in ease of use, consistent results, and regulatory approval, which microarrays offer. With NGS, there’s always the possibility of revealing something new and unexpected. Clinicians aren’t prepared for the extra information NGS offers. But the power and potential cost savings of NGS-based diagnostics is alluring, leading to their cautious adoption for certain tests such as non-invasive prenatal testing.
Cytogenetics
Perhaps the application that has made the least progress in transitioning to NGS is cytogenetics. Researchers and clinicians, who are used to using older technologies such as karyotyping, are just now starting to embrace microarrays. NGS has the potential to offer even higher resolution and a more comprehensive view of the genome, but it currently comes at a substantially higher price due to the greater sequencing depth. While dropping prices and maturing technology are causing NGS to make headway in becoming the technology of choice for a wide range of applications, the transition away from microarrays is a long and varied one. Different applications have different requirements, so researchers need to carefully weigh their options when making the choice to switch to a new technology or platform. Regardless of which technology they choose, genomic researchers have never had more options.

Sequencing Hones In on Targets

Greg Crowther, Ph.D.

GEN Feb 2013

Cliff Han, PhD, team leader at the Joint Genome Institute in the Los Alamo National Lab, was one of a number of scientists who made presentations regarding target enrichment at the “Sequencing, Finishing, and Analysis in the Future” (SFAF) conference in Santa Fe, which was co-sponsored by the Los Alamos National Laboratory and DOE Joint Genome Institute. One of the main challenges is that of target enrichment: the selective sequencing of genomic or transcriptomic regions. The polymerase chain reaction (PCR) can be considered the original target-enrichment technique and continues to be useful in contexts such as genome finishing. “One target set is the unique gaps—the gaps in the unique sequence regions. Another is to enrich the repetitive sequences…ribosomal RNA regions, which together are about 5 kb or 6 kb.” The unique-sequence gaps targeted for PCR with 40-nucleotide primers complementary to sequences adjacent to the gaps, did not yield the several-hundred-fold enrichment expected based on previously published work. “We got a maximum of 70-fold enrichment and generally in the dozens of fold of enrichment,” noted Dr. Han.

“We enrich the genome, put the enriched fragments onto the Pacific Biosciences sequencer, and sequence the repeats,” continued Dr. Han. “In many parts of the sequence there will be a unique sequence anchored at one or both ends of it, and that will help us to link these scaffolds together.” This work, while promising, will remain unpublished for now, as the Joint Genome Institute has shifted its resources to other projects.
At the SFAF conference Dr. Jones focused on going beyond basic target enrichment and described new tools for more efficient NGS research. “Hybridization methods are flexible and have multiple stop-start sites, and you can capture very large sizes, but they require library prep,” said Jennifer Carter Jones, Ph.D., a genomics field applications scientist at Agilent. “With PCR-based methods, you have to design PCR primers and you’re doing multiplexed PCR, so it’s limited in the size that you can target. But the workflow is quick because there’s no library preparation; you’re just doing PCR.” She discussed Agilent’s recently acquired HaloPlex technology, a hybrid system that includes both a hybridization step and a PCR step. Because no library preparation is required, sequencing results can be obtained in about six hours, making it suitable for clinical uses. However, the hybridization step allows capture of targets of up to 5 megabases—longer than purely PCR-based methods can deliver. The Agilent talk also provided details on the applications of SureSelect, the company’s hybridization technology, to Methyl-Seq and RNA-Seq research. With this technology, 120-mer baits hybridize to targets, then are pulled down with streptavidin-coated magnetic beads.
These are selections from the SFAF conference, which is expected to be a boost to work on the microbiome, and lead to infectious disease therapeutic approaches.

Summary

We have finished a breathtaking ride through the genomic universe in several sessions.  This has been a thorough review of genomic structure and function in cellular regulation.  The items that have been discussed and can be studied in detail include:

1.  the classical model of the DNA structure
2. the role of ubiquitinylation in managing cellular function and in autophagy, mitophagy, macrophagy, and protein degradation
3. the nature of the tight folding of the chromatin in the nucleus
4. intramolecular bonds and short distance hydrophobic and hydrophilic interactions
5. trace metals in molecular structure
6. nuclear to membrane interactions
7. the importance of the Human Genome Project followed by Encode
8. the Fractal nature of chromosome structure
9. the oligomeric formation of short sequences and single nucletide polymorphisms (SNPs)and the potential to identify drug targets
10. Enzymatic components of gene regulation (ligase, kinases, phosphatases)
11. Methods of computational analysis in genomics
12. Methods of sequencing that have become more accurate and are dropping in cost
13. Chromatin remodeling
14. Triplex and quadruplex models not possible to construct at the time of Watson-Crick
15. sequencing errors
16. propagation of errors
17. oxidative stress and its expected and unintended effects
18. origins of cardiovascular disease
19. starvation and effect on protein loss
20. ribosomal damage and repair
21. mitochondrial damage and repair
22. miscoding and mutational changes
23. personalized medicine
24. Genomics to the clinics
25. Pharmacotherapy horizons
26. driver mutations
27. induced pluripotential embryonic stem cell (iPSCs)
28. The association of key targets with disease
29. The real possibility of moving genomic information to the bedside
30. Requirements for the next generation of electronic health record to enable item 29

Related articles

• Retrovirus in the human genome is active in pluripotent stem cells (sciencedaily.com)
• Learning from the linker: New study sheds light on cellular reprogramming (phys.org)
• A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells (plosone.org)
• Stem Cell Therapies May Improve Following Finding That Retrovirus In The Human Genome Is Active In Pluripotent Stem Cells (medicalnewstoday.com)
• Leprosy spreads by reprogramming nerve cells into migratory stem cells (guardian.co.uk)
• Harvard Researchers: Embryonic Cells Can Cause Cancer (salem-news.com)

Other Related articles on this Open Access Online Scientific Journal, include the following:

http://pharmaceuticalintelligence.com/2013/01/14/oogonial-stem-cells-purified-a-view-towards-the-future-of-reproductive-biology/   SSaha

http://pharmaceuticalintelligence.com/2012/10/22/blood-vessel-generating-stem-cells-discovered/ RSaxena

http://pharmaceuticalintelligence.com/2012/08/22/a-possible-light-by-stem-cell-therapy-in-painful-dark-of-osteoarthritis-kartogenin-a-small-molecule-differentiates-stem-cells-to-chondrocyte-healthy-cartilage-cells/   ASarkar and RSaxena

http://pharmaceuticalintelligence.com/2012/08/07/human-embryonic-pluripotent-stem-cells-and-healing-post-myocardial-infarction/    LHB

http://pharmaceuticalintelligence.com/2013/02/03/genome-wide-detection-of-single-nucleotide-and-copy-number-variation-of-a-single-human-cell/  SJWilliams

http://pharmaceuticalintelligence.com/2013/01/09/gene-therapy-into-healthy-heart-muscle-reprogramming-scar-tissue-in-damaged-hearts/ ALev-Ari

http://pharmaceuticalintelligence.com/2013/01/03/differentiation-therapy-epigenetics-tackles-solid-tumors/  SJWilliams

http://pharmaceuticalintelligence.com/2012/12/09/naotech-therapy-for-breast-cancer/  TBarliya

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