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Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging
Curator: Aviva Lev-Ari, PhD, RN
Article ID #52: Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging. Published on 5/17/2013
WordCloud Image Produced by Adam Tubman
UPDATED on 7/12/2021
Abstract. Synthetic biology is a field of scientific research that applies engineering principles to living organisms and living systems.
Introduction. This article is intended as a perspective on the field of synthetic biology. …
To solve real-world problems using emerging abilities in synthetic biology, research must focus on a few ambitious goals, argues Dan Fletcher, Professor of bioengineering and biophysics, and chair of the Department of Bioengineering at the University of California, Berkeley, USA. He is also a Chan Zuckerberg Biohub Investigator.
Start Quote
Artificial blood cells. Blood transfusions are crucial in treatments for everything from transplant surgery and cardiovascular procedures to car accidents, pregnancy-related complications and childhood malaria (see go.nature.com/2ozbfwt). In the United States alone, 36,000 units of red blood cells and 7,000 units of platelets are needed every day (see go.nature.com/2ycr2wo).
But maintaining an adequate supply of blood from voluntary donors can be challenging, especially in low- and middle-income countries. To complicate matters, blood from donors must be checked extensively to prevent the spread of infectious diseases, and can be kept for only a limited time — 42 days or 5 days for platelets alone. What if blood cells could be assembled from purified or synthesized components on demand?
In principle, cell-like compartments could be made that have the oxygen-carrying capacity of red blood cells or the clotting ability of platelets. The compartments would need to be built with molecules on their surfaces to protect the compartments from the immune system, resembling those on a normal blood cell. Other surface molecules would be needed to detect signals and trigger a response.
In the case of artificial platelets, that signal might be the protein collagen, to which circulating platelets are exposed when a blood vessel ruptures5. Such compartments would also need to be able to release certain molecules, such as factor V or the von Willebrand clotting factor. This could happen by building in a rudimentary form of exocytosis, for example, whereby a membrane-bound sac containing the molecule would be released by fusing with the compartment’s outer membrane.
It is already possible to encapsulate cytoplasmic components from living cells in membrane compartments6,7. Now a major challenge is developing ways to insert desired protein receptors into the lipid membrane8, along with reconstituting receptor signalling.
Red blood cells and platelets are good candidates for the first functionally useful synthetic cellular system because they lack nuclei. Complex functions such as nuclear transport, protein synthesis and protein trafficking wouldn’t have to be replicated. If successful, we might look back with horror on the current practice of bleeding one person to treat another.
Human blood as viewed under a scanning electron microscope.Credit: Dennis Kunkel Microscopy/SPL
Designer immune cells. Immunotherapy is currently offering new hope for people with cancer by shaping how the immune system responds to tumours. Cancer cells often turn off the immune response that would otherwise destroy them. The use of therapeutic antibodies to stop this process has drastically increased survival rates for people with multiple cancers, including those of the skin, blood and lung9. Similarly successful is the technique of adoptive T-cell transfer. In this, a patient’s T cells or those of a donor are engineered to express a receptor that targets a protein (antigen) on the surface of tumour cells, resulting in the T cells killing the cancerous cells (called CAR-T therapies)10. All of this has opened the door to cleverly rewiring the downstream signalling that results in the destruction of tumour cells by white blood cells11.
What if researchers went a step further and tried to create synthetic cells capable of moving towards, binding to and eliminating tumour cells?
In principle, untethered from evolutionary pressures, such cells could be designed to accomplish all sorts of tasks — from killing specific tumour cells and pathogens to removing brain amyloid plaques or cholesterol deposits. If mass production of artificial immune cells were possible, it might even lessen the need to tailor treatments to individuals — cutting costs and increasing accessibility.
To ensure that healthy cells are not targeted for destruction, engineers would also need to design complex signal-processing systems and safeguards. The designer immune cells would need to be capable of detecting and moving towards a chemical signal or tumour. (Reconstituting the complex process of cell motility is itself a major challenge, from the delivery of energy-generating ATP molecules to the assembly of actin and myosin motors that enable movement.)
Researchers have already made cell-like compartments that can change shape12, and have installed signalling circuits within them13. These could eventually be used to control movement and mediate responses to external signals.
Smart delivery vehicles. The relative ease of exposing cells in the lab to drugs, as well as introducing new proteins and engineering genomes, belies how hard it is to deliver molecules to specific locations inside living organisms. One of the biggest challenges in most therapies is getting molecules to the right place in the right cell at the right time.
Harnessing the natural proclivity of viruses to deliver DNA and RNA molecules into cells has been successful14. But virus size limits cargo size, and viruses don’t necessarily infect the cell types researchers and clinicians are aiming at. Antibody-targeted synthetic vesicles have improved the delivery of drugs to some tumours. But getting the drug close to the tumour generally depends on the vesicles leaking from the patient’s circulatory system, so results have been mixed.
Could ‘smart’ delivery vehicles containing therapeutic cargo be designed to sense where they are in the body and move the cargo to where it needs to go, such as across the blood–brain barrier?
This has long been a dream of those in drug delivery. The challenges are similar to those of constructing artificial blood and immune cells: encapsulating defined components in a membrane, incorporating receptors into that membrane, and designing signal-processing systems to control movement and trigger release of the vehicle’s contents.
The development of immune-cell ‘backpacks’ is an exciting step in the right direction. In this, particles containing therapeutic molecules are tethered to immune cells, exploiting the motility and targeting ability of the cells to carry the molecules to particular locations15.
A minimal chassis for expression. In each of the previous examples, the engineered cell-like system could conceivably be built to function over hours or days, without the need for additional protein production and regulation through gene expression. For many other tasks, however, such as the continuous production of insulin in the body, it will be crucial to have the ability to express proteins, upregulate or downregulate certain genes, and carry out functions for longer periods.
Engineering a ‘minimal chassis’ that is capable of sustained gene expression and functional homeostasis would be an invaluable starting point for building synthetic cells that produce proteins, form tissues and remain viable for months to years. This would require detailed understanding and incorporation of metabolic pathways, trafficking systems and nuclear import and export — an admittedly tall order.
It is already possible to synthesize DNA in the lab, whether through chemically reacting bases or using biological enzymes or large-scale assembly in a cell16. But we do not yet know how to ‘boot up’ DNA and turn a synthetic genome into a functional system in the absence of a live cell.
Since the early 2000s, biologists have achieved gene expression in synthetic compartments loaded with cytoplasmic extract17. And genetic circuits of increasing complexity (in which the expression of one protein results in the production or degradation of another) are now the subject of extensive research. Still to be accomplished are: long-lived gene expression, basic protein trafficking and energy production reminiscent of live cells.
372/3:15 Genetic influence on LpPLA2 activity at baseline as evaluated in the exome chip-enriched GWAS study among ~13600 patients with chronic coronary artery disease in the STABILITY (STabilisation of Atherosclerotic plaque By Initiation of darapLadIb TherapY) trial. L. Warren, L. Li, D. Fraser, J. Aponte, A. Yeo, R. Davies, C. Macphee, L. Hegg, L. Tarka, C. Held, R. Stewart, L. Wallentin, H. White, M. Nelson, D. Waterworth.
Genetic influence on LpPLA2 activity at baseline as evaluated in the exome chip-enrichedGWASstudy among ~13600 patients with chronic coronary artery disease in the STABILITY (STabilisation of Atherosclerotic plaque By Initiation of darapLadIb TherapY) trial.
L. Warren1, L. Li1, D. Fraser1, J. Aponte1, A. Yeo2, R. Davies3, C. Macphee3, L. Hegg3,
L. Tarka3, C. Held4, R. Stewart5, L. Wallentin4, H. White5, M. Nelson1, D.
4) Uppsala Clinical Research Center, Department of Medical Sciences, Uppsala University, Uppsala, Sweden;
5) 5Green Lane Cardiovascular Service, Auckland Cty Hospital, Auckland, New Zealand.
STABILITY is an ongoing phase III cardiovascular outcomes study that compares the effects of darapladib enteric coated (EC) tablets, 160 mg versus placebo, when added to the standard of care, on the incidence of major adverse cardiovascular events (MACE) in subjects with chronic coronary heart disease (CHD). Blood samples for determination of the LpPLA2 activity level in plasma and for extraction of DNA was obtained at randomization. To identify genetic variants that may predict response to darapladib, we genotyped ~900K common and low frequency coding variations using Illumina OmniExpress GWAS plus exome chip in advance of study completion. Among the 15828 Intent-to-Treat recruited subjects, 13674 (86%) provided informed consent for genetic analysis. Our pharmacogenetic (PGx) analysis group is comprised of subjects from 39 countries on five continents, including 10139 Whites of European heritage, 1682 Asians of East Asian or Japanese heritage, 414 Asians of Central/South Asian heritage, 268 Blacks, 1027 Hispanics and 144 others. Here we report association analysis of baseline levels of LpPLA2 to support future PGx analysis of drug response post trial completion. Among the 911375 variants genotyped, 213540 (23%) were rare (MAF < 0.5%).
Our analyses were focused on the drug target, LpPLA2 enzyme activity measured at baseline. GWAS analysis of LpPLA2 activity adjusting for age, gender and top 20 principle component scores identified 58 variants surpassing GWAS-significant threshold (5e-08).
Genome-wide stepwise regression analyses identified multiple independent associations from PLA2G7, CELSR2, APOB, KIF6, and APOE, reflecting the dependency of LpPLA2 on LDL-cholesterol levels. Most notably, several low frequency and rare coding variants in PLA2G7 were identified to be strongly associated with LpPLA2 activity. They are V279F (MAF=1.0%, P= 1.7e-108), a previously known association, and four novel associations due to I1317N (MAF=0.05%, P=4.9e-8), Q287X (MAF=0.05%, P=1.6e-7), T278M (MAF=0.02%, P=7.6e-5) and L389S (MAF=0.04%, P=4.3e-4).
All these variants had enzyme activity lowering effects and each appeared to be specific to certain ethnicity. Our comprehensive PGx analyses of baseline data has already provided great insight into common and rare coding genetic variants associated with drug target and related traits and this knowledge will be invaluable in facilitating future PGx investigation of darapladib response.
Entry selected based on indirect or inconclusive evidence linking the gene product to ageing in humans or in one or more model systems
Description
APOC3 is involved in fat metabolism and may delay the catabolism of triglyceride-rich particles. Changes in APOC3 expression levels have been reported in aged mice [1754]. Results from mice suggest that FOXO1 may regulate the expression of APOC3 [1743]. Polymorphisms in the human APOC3 gene and promoter have been associated with lipoprotein profile, cardiovascular health, insulin (INS) sensitivity, and longevity [1756]. Therefore, APOC3 may impact on some age-related diseases, though its exact role in human ageing remains to be determined.
[2125] Pollin et al. (2008) A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection., PubMed
[1756] Atzmon et al. (2006) Lipoprotein genotype and conserved pathway for exceptional longevity in humans, PubMed
[1755] Araki and Goto (2004) Dietary restriction in aged mice can partially restore impaired metabolism of apolipoprotein A-IV and C-III, PubMed
[1743] Altomonte et al. (2004) Foxo1 mediates insulin action on apoC-III and triglyceride metabolism, PubMed
[1754] Araki et al. (2004) Impaired lipid metabolism in aged mice as revealed by fasting-induced expression of apolipoprotein mRNAs in the liver and changes in serum lipids, PubMed
[1753] Panza et al. (2004) Vascular genetic factors and human longevity, PubMed
[1752] Anisimov et al. (2001) Age-associated accumulation of the apolipoprotein C-III gene T-455C polymorphism C
Apolipoprotein C-III is a protein component of very low density lipoprotein (VLDL). APOC3 inhibitslipoprotein lipase and hepatic lipase; it is thought to inhibit hepatic uptake[1] of triglyceride-rich particles. The APOA1, APOC3 and APOA4 genes are closely linked in both rat and human genomes. The A-I and A-IV genes are transcribed from the same strand, while the A-1 and C-III genes are convergently transcribed. An increase in apoC-III levels induces the development of hypertriglyceridemia.
Clinical significance
Two novel susceptibility haplotypes (specifically, P2-S2-X1 and P1-S2-X1) have been discovered in ApoAI-CIII-AIV gene cluster on chromosome 11q23; these confer approximately threefold higher risk ofcoronary heart disease in normal[2] as well as non-insulin diabetes mellitus.[3]Apo-CIII delays the catabolism of triglyceride rich particles. Elevations of Apo-CIII found in genetic variation studies may predispose patients to non-alcoholic fatty liver disease.
^ Singh PP, Singh M, Kaur TP, Grewal SS (2007). “A novel haplotype in ApoAI-CIII-AIV gene region is detrimental to Northwest Indians with coronary heart disease”. Int J Cardiol130 (3): e93–5. doi:10.1016/j.ijcard.2007.07.029. PMID17825930.
^ Singh PP, Singh M, Gaur S, Grewal SS (2007). “The ApoAI-CIII-AIV gene cluster and its relation to lipid levels in type 2 diabetes mellitus and coronary heart disease: determination of a novel susceptible haplotype”. Diab Vasc Dis Res4 (2): 124–29. doi:10.3132/dvdr.2007.030. PMID17654446.
In 2013 we reported on the discovery that there is a
We determined genomewide associations with the presence of aortic-valve calcification (among 6942 participants) and mitral annular calcification (among 3795 participants), as detected by computed tomographic (CT) scanning; the study population for this analysis included persons of white European ancestry from three cohorts participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (discovery population). Findings were replicated in independent cohorts of persons with either CT-detected valvular calcification or clinical aortic stenosis.
CONCLUSIONS
Genetic variation in the LPA locus, mediated by Lp(a) levels, is associated with aortic-valve calcification across multiple ethnic groups and with incident clinical aortic stenosis. (Funded by the National Heart, Lung, and Blood Institute and others.)
We present below the MARKET LEADER in Interpretation of the Genomics Computations Results in the emerging new ERA of Medicine: Genomic Medicine, Knome.com and its home grown software power house.
A second Case study in the Advanced Genome Interpretation and Individualized Medicine presented following the Market Leader, is the Genome-Phenome Analyzer by SimulConsult,A Simultaneous Consult On Your Patient’s Diagnosis, Chestnut Hill, MA
2012: The Year When Genomic Medicine Started Paying Off
Luke Timmerman
An excerpt of an interesting article mentioning Knome [emphasis ours]…
Remember a couple of years ago when people commemorated the 10-year anniversary of the first draft human genome sequencing? The storyline then, in 200, was that we all went off to genome camp and only came home with a lousy T-shirt. Society, we were told, invested huge scientific resources in deciphering the code of life, and there wasn’t much of a payoff in the form of customized, personalized medicine.
That was an easy conclusion to reach then, when personalized medicine advocates could only point to a couple of effective targeted cancer drugs—Genentech’s Herceptin and Novartis’ Gleevec—and a couple of diagnostics. But that’s changing. My inbox the past week has been full of analyst reports from medical meetings, which mostly alerted readers to mere “incremental” advances with a number of genomic-based medicines and diagnostics. But that’s a matter of focusing on the trees, not the forest. This past year, we witnessed some really impressive progress from the early days of “clinical genomics” or “medical genomics.” The investment in deep understanding of genomics and biology is starting to look visionary.
The movement toward clinical genomics gathered steam back in June at the American Society of Clinical Oncology annual meeting. One of the hidden gem stories from ASCO was about little companies like Cambridge, MA-based Foundation Medicine and Cambridge, MA-based Knome that started seeing a surprising surge in demand from physicians for their services to help turn genomic data into medical information. The New York Times wrote a great story a month later about a young genomics researcher at Washington University in St. Louis who got cancer, had access to incredibly rich information about his tumors, and—after some wrestling with his insurance company—ended up getting a targeted drug nobody would have thought to prescribe without that information. And last month, I checked back on Stanford University researcher Mike Snyder, who made headlines this year using a smorgasbord of “omics” tools to correctly diagnose himself early with Type 2 diabetes, and then monitor his progress back into a healthy state–read the entire article
Knome and Real Time Genomics Ink Deal to Integrate and Sell the RTG Variant Platform on knoSYS™100 System
Partnership to bring accurate and fast genome analysis to translational researchers
CAMBRIDGE, MA – May 6, 2013 – Knome Inc., the genome interpretation company, and Real Time Genomics, Inc., the genome analytics company, today announced that the Real Time Genomics (RTG) Variant platform will be integrated into every shipment of the knoSYS™100 interpretation system. The agreement enables customers to easily purchase the RTG analytics engine as an upgrade to the system. The product will combine two world-class commercial platforms to deliver end-to-end genome analytics and interpretation with superior accuracy and speed. Financial terms of the agreement were not disclosed.
“In the past year demand for genome interpretation has surged as translational researchers and clinicians adopt sequencing for human disease discovery and diagnosis,” said Wolfgang Daum, CEO of Knome. “Concomitant with that demand is the need for accurate and easy-to-use industrial grade analysis that meets expectations of clinical accuracy. The RTG platform is both incredibly fast and truly differentiating to customers doing family studies, and we are excited to add such a powerful platform to the knoSYS ecosystem.”
The partnership simplifies the purchasing process by allowing knoSYS customers to purchase the RTG platform directly from Knome sales representatives.
“The Knome system is a perfect complementary channel to further expand our commercial effort to bring the RTG platform to market,” said Steve Lombardi, CEO of Real Time Genomics. “Knome has built a recognizable brand around human clinical genome interpretation, and by delivering the RTG platform within their system, both companies are simplifying genomics to help customers understand human disease and guide clinical actions.”
About Knome
Knome Inc. (www.knome.com) is a leading provider of human genome interpretation systems and services. We help clients in two dozen countries identify the genetic basis of disease, tumor growth, and drug response. Designed to accelerate and industrialize the process of interpreting whole genomes, Knome’s big data technologies are helping to pave the healthcare industry’s transition to molecular-based, precision medicine.
About Real Time Genomics
Real Time Genomics (www.realtimegenomics.com) has a passion for genomics. The company offers software tools and applications for the extraction of unique value from genomes. Its competency lies in applying the combination of its patented core technology and deep computational expertise in algorithms to solve problems in next generation genomic analysis. Real Time Genomics is a private San Francisco based company backed by investment from Catamount Ventures, Lightspeed Venture Partners, and GeneValue Ltd.
An excerpt of an interesting article mentioning Knome [emphasis ours]:
…Cambridge, Massachusetts–based Knome made one of the splashiest entries into the field, but has now turned entirely to contract research. The company began providing DTC whole-genome sequencing to independently wealthy individuals at a time when the price was still sky high. The company’s first client, Dan Stoicescu, was a former biotech entrepreneur who paid $350,000 to have his genome sequenced in 2008 so he could review it “like a stock portfolio” as new genetic discoveries unfolded4. About a year later, the company was auctioning off a genome, with such frills as a dinner with renowned Harvard genomics researcher George Church, at a starting price of $68,000; at the time, a full-genome sequence came at the price of $99,000, indicating that the cost of genome sequencing has been plummeting steadily.
Now, the company’s model is very different. “We stopped working with the ‘wealthy healthy’ in 2010,” says Jonas Lee, Knome’s chief marketing officer. “The model changed as sequencing changed.” The new emphasis, he says, is now on using Knome’s technology and technical expertise for genome interpretation. Knome’s customers are researchers, pharmaceutical companies and medical institutions, such as Johns Hopkins University School of Medicine in Baltimore, which in January signed the company up to interpret 1,000 genomes for a study of genetic variants underlying asthma in African American and African Caribbean populations.
Knome is trying to advance the clinical use of genomics, working with groups that “want to be prepared for what’s ahead,” Lee says. “We work with at least 50 academic institutions and 20 pharmaceutical companies looking at variants and drug response.” Cancer and idiopathic genetic diseases are the first sweet spots for genomic sequencing, he says. Although cancer genomics has been hot for a while, a recent string of discoveries of Mendelian diseases5 made by whole-genome sequencing has lit up that field, too. Lee is also confident, however, that “chronic diseases like heart disease are right behind those.” The company also provides software tools. The price for its KnomeDiscovery sequencing and analysis service starts at about $12,000 per sample–read the entire article here.
DNA decoder: Knome’s software can tease out medically relevant changes in DNA that could disrupt individual gene function or even a whole molecular pathway, as is highlighted here—certain mutations in the BRCA2 gene, which affects the function of many other genes, can be associated with an increased risk of breast cancer.
A genome analysis company called Knome is introducing software that could help doctors and other medical professionals identify genetic variations within a patient’s genome that are linked to diseases or drug response. This new product, available for now only to select medical institutions, is a patient-focused spin on Knome’s existing products aimed at researchers and pharmaceutical companies. The Knome software turns a patient’s raw genome sequence into a medically relevant report on disease risks and drug metabolism. The software can be run within a clinic’s own network—rather than in the cloud, as is the case with some genome-interpretation services—which keeps the information private.
Advances in DNA sequencing technology have sharply reduced the amount of time and money required to identify all three billion base pairs of DNA in a person’s genome. But the use of genomic information for medical decisions is still limited because the process creates such large volumes of data. Less than five years ago, Knome, based in Cambridge, Massachusetts, made headlines by offering what seemed then like a low price—$350,000—for a genome sequencing and profiling package. The same service now costs just a few thousand dollars.
Today, genome profiling has two main uses in the clinic. It’s part of the search for the cause of rare genetic diseases, and it generates tumor-specific profiles to help doctors discover the weaknesses of a patient’s particular cancer. But within a few years, the technique could move beyond rare diseases and cancer. The information gleaned from a patient’s genome could explain the origin of specific disease, could help save costs by allowing doctors to pretreat future diseases, or could improve the effectiveness and safety of medications by allowing doctors to prescribe drugs that are tuned to a person’s ability to metabolize drugs.
But teasing out the relevant genetic information from a patient’s genome is not trivial. To find the particular genetic variant that causes a specific disease or drug response can require expertise from many disciplines—from genetics to statistics to software engineering—and a lot of time. In any given patient’s genome, millions of places in that genome will differ from the standard of reference. The vast majority of these differences, or variants, will be unrelated to a patient’s medical condition, but determining that can take between 20 minutes and two hours for each variant, says Heidi Rehm, a clinical geneticist who directs the Laboratory for Molecular Medicine at Partners Healthcare Center for Personalized Genetic Medicine in Boston, and who will soon serve on the clinical advisory board of Knome. “If you scale that to … millions of variants, it becomes impossible.”
A software package like Knome’s can help whittle down the list based on factors such as disease type, the pattern of inheritance in a family, and the effects of given mutations on genes. Other companies have introduced Web- or cloud-based services to perform such an analysis, but Knome’s software suite can operate within a hospital’s network, which is critically important for privacy-concerned hospitals.
The greatest benefit of the widespread adoption of genomics in the clinic will come from the “clinical intelligence” doctors gain from networks of patient data, says Martin Tolar, CEO of Knome. Information about the association between certain genetic variants and disease or drug response could be anonymized—that is, no specific patient could be tied to the data—and shared among large hospital networks. Knome’s software will make it easy to share that kind of information, says Tolar.
“In the future, you could be in the situation where your physician will be able to pull the most appropriate information for your specific case that actually leads to recommendations about drugs and so forth,” he says.
The knoSYS™100 seamlessly integrates an interpretation application (knoSOFT) and informatics engine (kGAP) with a high-performance grid computer. Designed for whole genome, exome, and targeted NGS data, the knoSYS™100 helps labs quickly go “from reads to reports.”
Advanced Interpretation and Reporting Software
The knoSYS™100 ships with knoSOFT, an advanced application for managing sequence data through the informatics pipeline, filtering variants, running gene panels, classifying/interpreting variants, and reporting results.
knoSOFT has powerful and scalable multi-sample comparison features–capable of performing family studies, tumor/normal studies, and large case-control comparisons of hundreds of whole genomes.
Multiple simultaneous users (10) are supported, including technicians running sequence data through informatics pipeline, developers creating next-generation gene panels, geneticists researching causal variants, and production staff processing gene panels.
Co-authored by Dr. David Goldstein, Clinical and Scientific board member for Knome
Author summary: Genetic association studies analyze both phenotypes (such as disease status) and genotypes (at sites of DNA variation) of a given set of individuals. … more
Co-authored by Dr. George Church and Dr. Heidi Rehm, Clinical and Scientific Board Members for Knome
Author summary: An individual’s genetic profile plays an important role in determining risk for disease and response to medical therapy. The development of technologies that facilitate rapid whole-genome sequencing will provide unprecedented power in the estimation of disease risk. Here we develop methods to characterize genetic determinants of disease risk and … more
Co-authored by Dr. David Goldstein, Clinical and Scientific board member for Knome
Author summary: Schizophrenia is a highly heritable disease. While the drugs commonly used to treat schizophrenia offer important relief from some symptoms, other symptoms are not well treated, and the drugs cause serious adverse effects in many individuals. This has fueled intense interest over the years in identifying genetic contributors to … more
Co-authored by Dr. David Goldstein, Clinical and Scientific board member for Knome
Author summary: Metachondromatosis (MC) is an autosomal dominant condition characterized by exostoses (osteochondromas), commonly of the hands and feet, and enchondromas of long bone metaphyses and iliac crests. MC exostoses may regress or even resolve over time, and short stature … more
Introduction: There is an increasing consensus that whole-exome sequencing (WES) and whole-genome sequencing (WGS) will continue to improve in accuracy and decline in price and that the use of these technologies will eventually become an integral part of clinical medicine.1–7 … more
Publications by industry experts and thought-leaders
Augustine Kong, Michael L. Frigge, Gisli Masson, Soren Besenbacher, Patrick Sulem, Gisli Magnusson, Sigurjon A. Gudjonsson, Asgeir Sigurdsson, Aslaug Jonasdottir, Adalbjorg Jonasdottir, Wendy S. W. Wong, Gunnar Sigurdsson, G. Bragi Walters, Stacy Steinberg, Hannes Helgason, Gudmar Thorleifsson, Daniel F. Gudbjartsson, Agnar Helgason, Olafur Th. Magnusson, Unnur Thorsteinsdottir, & Kari Stefansson
Abstract: Mutations generate sequence diversity and provide a substrate for selection. The rate of de novo mutations is therefore of major importance to evolution. Here we conduct a study of genome-wide mutation rates by sequencing the entire genomes of 78 … more
Florian L. Muller, Simona Colla, Elisa Aquilanti, Veronica E. Manzo, Giannicola Genovese, Jaclyn Lee, Daniel Eisenson, Rujuta Narurkar, Pingna Deng, Luigi Nezi, Michelle A. Lee, Baoli Hu, Jian Hu, Ergun Sahin, Derrick Ong, Eliot Fletcher-Sananikone, Dennis Ho, Lawrence Kwong, Cameron Brennan, Y. Alan Wang, Lynda Chin, & Ronald A. DePinho
Abstract: Inactivation of tumour-suppressor genes by homozygous deletion is a prototypic event in the cancer genome, yet such deletions often encompass neighbouring genes. We propose that homozygous deletions in such passenger genes can expose cancer-specific therapeutic vulnerabilities when the collaterally … more
Karyn Meltz Steinberg, Francesca Antonacci, Peter H Sudmant, Jeffrey M Kidd, Catarina D Campbell, Laura Vives, Maika Malig, Laura Scheinfeldt, William Beggs, Muntaser Ibrahim, Godfrey Lema, Thomas B Nyambo, Sabah A Omar, Jean-Marie Bodo, Alain Froment, Michael P Donnelly, Kenneth K Kidd, Sarah A Tishkoff, & Evan E Eichler
Abstract: The 17q21.31 inversion polymorphism exists either as direct (H1) or inverted (H2) haplotypes with differential predispositions to disease and selection. We investigated its genetic diversity in 2,700 individuals, with an emphasis on African populations. We characterize eight structural haplotypes … more
Two networks: 40-Gigabit Infiniband QDR via a Mellanox Switch for storage traffic and HP ProCurve switch for network traffic
High performance computing cluster
Four nodes, each node with two 8-core/16 thread, 2.4Ghz, 64 bit Intel® Xeon® E5-2660 processor with 20MB cache, 128GB of DDR3 ECC 1600 memory; 2x2TB SATA drives (7,200RPM)
Metadata server
2x2TB 3.5″ drives with 6GB/sec SATA, RAID 1 and 2x300GB SSD (RAID 1)
Object storage server
Lustre array: Two 12x4TB arrays of 12 3.5″ drives with 6GB/sec serial SATA channels, each OSS powered by a 6-core Intel Xeon 64-bit processor running at 20GHz with 32GB RAM.
Our research services group uses a set of advanced software tools designed for whole genome and exome interpretation. These tools are also available to our clients through our knomeBASE informatics service. In addition to various scripts, libraries, and conversion utilities, these tools include knomeVARIANTS and knomePATHWAYS.
knomeVARIANTS
knome VARIANTS is a query kit that lets users search for candidate causal variants in studied genomes. It includes a query interface (see above), scripting libraries, and data conversion utilities.
Users select cases and controls, input a putative inheritance mode, and add sensible filter criteria (variant functional class, rarity/novelty, location in prior candidate regions, etc.) to automatically generate a sorted short-list of leading candidates. The application includes a SQL query interface to let users query the database as they wish, including by complex or novel sets of criteria.
In addition to querying, the application lets users export subsets of the database for viewing in MS Excel. Subsets can be output that target common research foci, including the following:
Sites implicated in phenotypes, regardless of subject genotypes
Sites where at least one studied genome mismatches the reference
Sites where a particular set of one or more genomes, but no other genomes, show a novel variant
Sites in phenotype-implicated genes
Sites with nonsense, frameshift, splice-site, or read-through variants, relative to reference
Sites where some but not all subject genome were called
knomePATHWAYS
knomePATHWAYS is a visualization tool that overlays variants found in each sample genome onto known gene interaction networks in order to help spot functional interactions between variants in distinct genes, and pathways enriched for variants in cases versus controls, differential drug responder groups, etc.
knomePATHWAYS integrates reference data from many sources, including GO, HPRD, and MsigDB (which includes KEGG and Reactome data). The application is particularly helpful in addressing higher-order questions, such as finding candidate genes and protein pathways, that are not readily addressed from tabular annotation data alone.
A Simultaneous Consult On Your Patient’s Diagnosis
Clinicians can get a “simultaneous consult” about their patient’s diagnosis using SimulConsult’s diagnostic decision support software.
Using the free “phenome” version, medical professionals can enter patient findings into the software and get an initial differential diagnosis and suggestions about other useful findings, including tests. The database used by the software has > 4,000 diagnoses, most complete for genetics and neurology. It includes all genes in GeneTests and all diseases in GeneReviews. The information about diseases is entered by clinicians, referenced to the literature and peer-reviewed by experts. The software takes into account pertinent negatives, temporal information, and cost of tests, information ignored in other diagnostic approaches. It transforms medical diagnosis by lowering costs, reducing errors and eliminating the medical diagnostic odysseys experienced by far too many patients and their families.
Using the “genome-phenome analyzer” version, a lab can combine a genome variant table with the phenotypic data entered by the referring clinician, thereby using the full power of genome + phenome to arrive at a diagnosis in seconds. An innovative measure of pertinence of genes focuses attention on the genes accounting for the clinical picture, even if more than one gene is involved. The referring clinician can use the results in the free phenome version of the software, for example adding information from confirmatory tests or adding new findings that develop over time. For details, click here.
Michael M. Segal MD, PhD, Founder,Chairman and Chief Scientist. Dr. Segal did his undergraduate work at Harvard and his MD and PhD at Columbia, where his thesis project outlined rules for the types of chemical synapses that will form in a nervous system. After his residency in pediatric neurology at Columbia, he moved to Harvard Medical School, where he joined the faculty and developed the microisland system for studying small numbers of brain neurons in culture. Using this system, he developed a simplified model of epilepsy, work that won him national and international young investigator awards, and set the stage for later work on the molecular mechanism of attention deficit disorder. Dr. Segal has a long history of interest in computers, and patterned the SimulConsult software after the way that experienced clinicians actually think about diagnosis. He is on the Electronic Communication Committee of the Child Neurology Society and the Scientific Program Committee of the American Medical Informatics Association.
DNA decoder: Knome’s software can tease out medically relevant changes in DNA that could disrupt individual gene function or even a whole molecular pathway, as is highlighted here—certain mutations in the BRCA2 gene, which affects the function of many other genes, can be associated with an increased risk of breast cancer.
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