Medtronic’s CoreValve System Sustains Positive Outcomes Through Two Years in Extreme Risk Patients

Reporter: Aviva Lev-Ari, PhD, RN



Cytolinguistics— Exact Communication Mechanism in Genomics

Author: Bill Zheng, MD, PhD


Genomics studies have uncovered many sequences related hereditary diseases, however, there are fewer studies delves into the investigation on how the genomics language are being stored, processed, programmed, formulated and being operated systematically. Recently, ML Squadrito et al[1] using the exosomes to study the inter-cellular communications, however, it could not explain the cellular function at context leve. In fact, giving the complexity of the genomics structure, it may seems very difficult to dissect directly the grammatical structure of the genomics language. Therefore, although many organisms genomics are being sequenced, still there is no clue about the fundamental mechanisms of life.


It is logical to assume that any sophisticated machines needs to be run by an operating system, as PC is often by Microsoft windows, Mac by iOS, etc., the biological cell genomics must also been administered by a special biological OS. To elucidate these special biological operating system, Wenling ZHENG and Wenli Ma[2] having published a series of biological hypothesis, detailing the language characteristics of the genomics, the functional genomics as well as the proteomics.


The hypothesis proposed that the protein and polypeptide in extracelluar space are very similar to the properties of the human language. This similarity coincide so so well that structures capable of conveying information inside and outside the cell was being called as the Cytologue.


The researches on the semantics of such cytologues are being called as the Cytolinguistics. At least at the protein level, the linguistics characteristics of the proteins are very prominent. For example, native English is composed of 26 English letters, while natural proteins is composed of 20 amino acids. The 26 English letters produced tremendous amount literature, from Shakespeare to contemporary novelist. The 20 amino acids composed of all the forms of biological structures as well as hormones, cytokines and functional pepetides.


Further studies revealed that the genomics are very similar with that of the computer database, as being the data structure of the computer hard-disk, while the functional genomics are similar to that of machine languages that are functioning in the computer random access memory (RAM). Such striking similarity pointed out a possibility that natural evolution and the technology evolution might be meeting one another, and that the working mechanisms of the biological cells may be similar as that of a computer. The possibility though needs to be studied and verified, should be beneficial for the biological studies. With this integrative comparison, biological cell’s working mechanisms could be studied more systematically.


The hypothesis though difficult to be further validated directly, extrapolations from the hypothesis shed light on several basic biological questions. The hypothesis extrapolated that with informatics processes inside the biological cells should be modified from the central dogma into a more integrated triangle dogma, so as to explain how the first DNA code being created, or how the DNA code evolves from simpler to more complicated. Currently, the central dogma pointed out the cellular hereditary information is from DNA to RNA to Protein in a single direction. Several Nobel Laureates discovered that RNA itself exhibited versatility of being ribozymes, and RNA can be retro-transcribed into cDNA. The protein sequence information could not be communicated to DNA / RNA directly, except through the so-called signal transduction pathway, which are time consuming and imprecisely. The hypothesis predicted that there are direct conductive pathway to exactly transmit information from the protein sequences in the extra-cellular space to the nuclear DNA. Such type of accuracy and exactness have been in existence in computer keyboard, which input alphabetical letters to the computer hard-disk. Similar mechanisms had been proposed by this hypothesis, so as to address more rationally the enigmas of cancer, virus infection, aging, cellular timing, biological memory, as well as traditional Chinese Medicine (TCM) and acupuncture.


The following table demonstrated the similarity between biological language (the cytologue) and that of the natural human languages.


Structure of Forms Processing Way Machine Code Language Type
English 26 Letters Computer Binary System Spelling Type
Chinese Hanzi: Strokesstructures Computer Binary System Structure Type
Japanese 52 Kanas and Hanzi Computer Binary System Mixed Type
Cytologue 20 Amino Acids and 3D Structure Cell Triplet Code of Nucleic Acid Mixed Type



  1. Mario Leonardo Squadrito, Caroline Baer, Frédéric Burdet, Claudio Maderna, Gregor D. Gilfillan, Robert Lyle, Mark Ibberson,Michele De Palma. Endogenous RNAs Modulate MicroRNA Sorting to Exosomes and Transfer to Acceptor Cells. Cell, August 21, 2014; DOI:10.1016/j.celrep.2014.07.035


  1. Wenling Zheng, Wenli Ma, Cytolingustics, Science & Technology Review, 1994, 12 (9408):3-6. http://www.kjdb.org/CN/volumn/volumn_1475.shtml

Combining Genetic has Identified Schizophrenia Subtypes

Reporter: Aviva Lev-Ari, PhD, RN

Schizophrenia Subtypes Identified By Combining Genetic, Clinical Data

NEW YORK (GenomeWeb) – Schizophrenia encompasses several distinct subtypes, each characterized by different symptoms and genetic variant clusters, according to a new study.

As they reported in the American Journal of Psychiatry, members of a Washington University-led team that included representatives from the Molecular Genetics of Schizophrenia Consortium brought together genome-wide SNP profiles for thousands of individuals with or without schizophrenia to start defining sets of risk variants that co-occur.

After verifying dozens of these SNP clusters in still other study cohorts, the team folded in clinical information to find eight sets of risk variants that consistently coincided with the presence or lack of certain schizophrenia symptoms and seemed to represent new schizophrenia classes or subtypes.

“Schizophrenia is a group of heritable disorders caused by a moderate number of separate genotypic networks associated with several distinct clinical syndromes,” the study’s authors wrote, noting that these findings hint at the possibility of refining the way schizophrenia is diagnosed and treated in the future.

Past studies suggest that many risk variants contribute to schizophrenia, though it has been difficult to define the complete suite of genetic interactions that can produce the highly heritable condition. For their part, the study’s authors speculated that “schizophrenia heritability is not missing but is distributed into different networks of interacting genes that influence different people.”

“[Genes] function in concert much like an orchestra, and to understand how they’re working, you have to know not just who the members of the orchestra are but how they interact,” Washington University psychiatry and genetics researcher Robert Cloninger, a senior author on the study, said in a statement.

In an effort to define risk gene networks for schizophrenia — and potentially link them to phenotypic features found in those with the disease — he and his colleagues started by scrutinizing genotype profiles for 4,196 schizophrenic individuals with known phenotypic features enrolled through the Molecular Genetics of Schizophrenia project.

When they compared those SNP patterns to the genotypes present in 3,827 unaffected controls, the researchers narrowed in on hundreds of variants that appeared to be over-represented in individuals with schizophrenia.

A closer look at those variants revealed 42 SNPs clusters that were present in one or more individuals. Generally speaking, the team found that larger groups of variants tended to turn up in fewer and fewer schizophrenia cases and vice versa.

One trio of shared risk SNPs turned up in more than 250 individuals with schizophrenia, for instance, while a larger set of two-dozen schizophrenia-associated variants fell in 70 cases.

For their follow-up analyses, the researchers not only considered the level of schizophrenia risk associated with each SNP cluster but also the shared clinical features found in individuals carrying similar variant sets.

In the process, the team defined eight schizophrenia subtypes that were subsequently replicated in more than 1,000 additional cases enrolled through the Clinical Antipsychotic Trial of Intervention Effectiveness and the Psychiatric Genomics Consortium projects.

These sub-groups included individuals with varying types of symptoms and severity — from auditory or other hallucinations to disorganized speech and behavior — which were linked to SNP sets associated with schizophrenia risk to varying degrees.

“[W]e were able to find that different sets of genetic variations were leading to distinct clinical syndromes,” Cloninger said. “So I think this really could change the way people approach understanding the causes of complex diseases.”



Personalized Medicine Coalition Recognizes Mark Levin with Award for Leadership

Reporter: Aviva Lev-Ari, PhD, RN

Tiffany Harrington
Personalized Medicine Coalition

Personalized Medicine Coalition Recognizes Mark Levin with Award for Leadership

Award to be presented November 12, 2014 highlights lifelong personalized medicine vision

WASHINGTON (Sept. 15, 2014) — The Personalized Medicine Coalition (PMC) today announced that Mr. Mark Levin, co-founder of and partner at Third Rock Ventures, will receive the 2014 Leadership in Personalized Medicine Award for his long commitment to personalized medicine.

“I have always felt that the successful implementation of the principles of personalized medicine into medical practice will depend on the embrace of these principles in the business community,” stated Raju Kucherlapati, Ph.D., Paul C. Cabot professor in the Harvard Medical School Department of Genetics and member of the Presidential Commission for the Study of Bioethical Issues, who nominated Levin for this year’s recognition. His letter stated that no one better exemplifies that commitment than Levin. “Mark relentlessly pursues perfection, and is daring to bring forth novel ideas that can change the world… This is what leadership is all about.”

Mark Levin is a co-founder of Third Rock Ventures and an industry leader with 40 years of experience, most of which were spent launching and building biotechnology companies, including many focused on personalized medicine. At Third Rock Ventures, Levin runs the discovery process to conceive and launch companies around disruptive technologies and innovative science that promise to dramatically improve patients’ lives.

Brian Munroe, PMC board member and senior vice president of government affairs at Endo Health Solutions, seconded the nomination, noting that Levin founded PMC “in the belief that personalized medicine has the potential to revolutionize pharma and biotech,” but that personalized medicine “faces challenges in funding, regulation and implementation, and that the only way the field can reach its potential is if all the stakeholders band together.”

Prior to Third Rock, Mark was co-founder of Mayfield Fund’s life sciences effort, where he was also the founding CEO of Tularik, Cell Genesys/Abgenix, Focal, Stem Cells and Millennium Pharmaceuticals. He served as CEO of Millennium Pharmaceuticals for 12 years. Earlier in his career, he was an engineer and project leader at Lilly and Genentech. He holds an M.S. in chemical and biochemical engineering from Washington University.

“Thank you for this wonderful honor,” stated Mr. Levin. “The best part of the last 40 years has been working with incredible people… to make a difference for patients. It cannot get any better than that!”

Previous recipients of the award include Dr. Janet Woodcock, director of the Food and Drug Administration’s Center for Drug Evaluation and Research, Dr. Elizabeth G. Nabel, director of the National Heart, Lung and Blood Institute at the National Institutes of Health, Dr. Ralph Snyderman, chancellor emeritus of Duke University, Health and Human Services Secretary Michael Levitt, Brook Byers of Kleiner Perkins Caufield & Byers, Dr. William Dalton, president and CEO of the Moffitt Cancer Center, Dr. Leroy Hood, president and co-founder of the Institute for Systems Biology, Randal W. Scott, Ph.D., founder, Genomic Health Inc. and current chairman and CEO, InVitae Corporation, and Kathy Giusti, founder and CEO of the Multiple Myeloma Research Foundation.

Mr. Levin will be honored at the PMC Boston Cocktail Reception the evening of November 11 at the Hotel Commonwealth in Boston, Mass., and the award will be presented at the Personalized Medicine Conference, which will be held at Harvard Medical School on November 12, 2014, and at which Mr. Levin will deliver an address on his vision of personalized medicine. The Personalized Medicine Conference is co-hosted and presented by Partners HealthCare Center for Personalized Genetic Medicine (PCPGM), Harvard Business School and Harvard Medical School. The Conference is organized by PCPGM in association with the American Association for Cancer Research and PMC. The distinctive collaboration of this group of organizations reflects the diversity, depth and breadth of the Conference’s program.


From: <tharrington@personalizedmedicinecoalition.org>
Date: 15 Sep 2014 12:09:21 -0400
To: <avivalev-ari@alum.berkeley.edu>
Subject: Mark Levin to Receive PMC Leadership Award

gCell’s Cell Line Experiments go to Genentech: Tox Screens vs Cancer Cells


Reporter: Aviva Lev-Ari, PhD, RN

gCell Gives Genentech Corporate Memory of Cell Line Experiments

By Bio-IT World Staff

September 15, 2014 | Like a lot of large pharma and biotech companies adapting to the era of big data, Genentech has lately found that its preclinical models, the animals and cell lines in its inventory, don’t scale up as neatly as the information they generate. It’s one kind of challenge to share tox screen results across a large organization, but another issue entirely to keep tabs on thousands of rodents and cancer cells traveling across the facilities.

Genentech, however, has stood apart in the industry for making a concentrated effort to upgrade its legacy systems where they no longer meet the expanding needs of its preclinical studies. “The organization over the last seven or eight years has made leaps and bounds in unifying and streamlining processes that are important for the research pipeline,” says Richard Neve, a scientist in the company’s Discovery Oncology program.

BPLast year, Bio-IT World awarded Genentech a Best Practices Award for work on a platform that tracks the history and whereabouts of the tens of thousands of animals kept in the company’s Mouse Genetics Department. That project demonstrated that attention to these kinds of mundane processes can yield significant efficiencies and savings, and the company has continued to apply that philosophy in other areas. At this year’s Bio-IT World Conference & Expo in April, Genentech was again recognized, this time for its gCell system, which has revamped the ordering and use of cell lines in basic research. The project, on which Neve took a lead role along with Senior Software Engineer Jean Yuan, won Bio-IT World’s top prize in the category of Knowledge Management at the Best Practices Awards ceremony.

A Sprawling Network of Cell Lines

Genentech is a multi-billion-dollar titan of biotech, but before gCell, its procedures for working with cell lines were not much different from the smallest startups. If a project lead needed a new cell line for a particular experiment, she would contact a vendor, have it delivered to her lab, and take responsibility for analysis and storage. Cells might be distributed to other labs, or siloed in a freezer; data might be shared with collaborators, or locked away in project notes.

“There was no formalized process for organizing cell lines in Genentech,” says Neve. “You often found that multiple labs had ordered the same cell line, and were keeping separate stocks.” Worse than the redundancy was the risk of errors creeping into the preclinical pipeline. Left unattended, cell lines have a tendency to quickly rack up mutations as they adapt to the unnatural environment of the lab. They can also pick up some tenacious contaminants; one recent study suggested as many as one in ten projects using cell lines may be affected by contamination with mycoplasma, tiny bacteria that are resistant to most antibiotics. While genetic assays that can identify cell lines by the short tandem repeats (STRs) in their genomes are widely available, Genentech previously had no oversight of whether and how labs made use of them.

Meanwhile, the company’s stock of cell lines continued to grow. Genentech now stores an estimated 90,000 vials of cells, in which over 1,800 lines are represented from different tissues, disease states, and species. In 2009, the company began a project to track cell lines that enter and travel through Genentech facilities in support of a central biobank, and over the past five years, that system has evolved into gCell, which now features not only the bank and tracking measures, but also a robust bioinformatics platform for identifying each cell line and keeping its internal history.

Even identifying the cell lines is more difficult than it sounds. “No one in the field has a standardized nomenclature,” says Neve, so vendors and academic groups use a variety of naming systems when a new cell line is created. Genentech implemented three unique identifiers for each cell line in its stocks: the vendor’s ID, rendered in a uniform syntax; an STR profile; and a single-nucleotide polymorphism (SNP) profile, for faster and less expensive cell line profiling.

The gCell platform also introduced a controlled vocabulary for describing the cell lines in its library, including their tissues of origin and diagnoses. “The most important part comes down to the associated metadata, like the pathologic terms that are used,” says Neve. “For example, we found in different vendor databases that there are almost 70 different ways to define adenocarcinoma. We worked with pathologists to define standardized terms, and then we went through three or four thousand cell lines that we have manually curated.”

By describing all cell lines with the same standard terms, gCell makes the Genentech cell bank rapidly searchable. A PI can immediately pull up every cell line in the library representing a particular subset of cancer, or every cell line from the liver, and order vials. The gCell vocabulary also helps with unified data analysis downstream, as large projects pull together datasets from different labs, experiments, and cell sources.

Unique Cell Line Histories

Although core fields in gCelluse a rigid set of terms, the profiles are also flexible enough to capture other types of information researchers would want to know. A comments section allows gCell administrators to record things like assay results, drug sensitivities, and phenotypic observations.

“That gives us corporate memory on what has happened to a given cell line,” says Neve. “We can also follow that back based upon the batches and the log profile in the database, to really know what was done with a cell line, what happened to it, and why certain actions were taken.”

Applying that memory requires Genentech to keep tabs on its cell lines at every stage of use. The company uses unique barcodes assigned to each vial of cells and location on the corporate campus. By scanning barcodes on the vials of cells and the racks and tanks where they’re stored, researchers keep a real-time record of where cell lines travel. “We have an ongoing project to track a sample from the original inventory down,” says Jean Yuan, a bioinformatics lead on the project. “Because gCell has become a centralized resource, it has become easier to track who ordered a cell line, what kind of assays have been done, and in which labs.” Genentech also uses its cell line IDs to note parental and daughter lines, so researchers can trace back when related cell lines diverged.

The volume of preclinical testing at Genentech means that these processes have to be as user friendly as possible. “We’re supplying around fifty orders a day to research,” says Neve, adding up to around 40,000 deliveries over the life of the program. While gCell adds some quality control routines to the use of cell lines, it also dramatically speeds up distribution: Genentech now promises next-day delivery to PIs who order frozen vials from the biobank.

In the course of implementing gCell, Genentech has built up a database of STR and SNP profiles that could potentially offer benefits to science well beyond the company’s walls. When the tracking program was first unrolled, Genentech created profiles for every cell line in its possession — in some cases discovering that its stocks were mislabeled or contaminated in the process. It also expanded this reference database to popular cell lines in vendor archives, often improving on the canonical STR profiles, according to Neve. He and his colleagues have now submitted a paper to Nature that details this genetic fingerprinting reference of over 3,000 cell lines, as well as offering recommendations for a standard nomenclature.

“We’re hoping that people will start to use this unified vocabulary and annotation,” he says. “That will make it much easier, not only to search for samples and data externally, but also to integrate that data for large analyses.”

New Efficiencies

It isn’t easy to put a dollar value on gCell’s benefit to Genentech, but the company estimates it is now saving $1.7 million a year simply by eliminating redundancies in ordering cell lines, performing all the freezing and storage in a single location, and reducing the frequency of genetic profiling of cell lines. The gCell platform can also tamp down on redundant assays and experiments, by tying results permanently to a cell line’s profile and making them accessible to all researchers in the company.

Still, Neve and Yuan say the biggest value of gCell is greater confidence in preclinical studies. By controlling for contamination, and making it easier for labs to check one another’s data, gCell can go a long way toward holding down experimental error. This added security in preclinical testing can make all the difference when it comes time to choose which projects advance to clinical trials and which are left on the cutting board.

“In my own lab, we’ve been making some drug-resistant lines,” adds Neve, “and because we’re very alert to this, we’ve been doing the SNP profiling on them. We found a couple of mistakes, and luckily we caught them before we did any important experiments.”

As with other improvements to Genentech’s preclinical pipeline, gCell may seem like little more than an accounting exercise, but an enormous amount of thought goes into making it smoothly functional and delivering a benefit that will be felt by every researcher in the company. As Genentech begins to publicize its system and fingerprinting database over the coming months, one can hope to see other drug companies follow the example of gCell, and ensure that the management of preclinical models keeps pace with discovery pipelines that depend more and more on enormous stores of data.





The GOBLET Training Portal: A Global Repository of Bioinformatics Training Materials, Courses and Trainers

Reporter: Aviva Lev-Ari, PhD, RN


The GOBLET Training Portal: A Global Repository of Bioinformatics Training Materials, Courses and Trainers

  1. Manuel Corpas1,*,,
  2. Rafael C. Jimenez2,,
  3. Erik Bongcam-Rudloff3,
  4. Aidan Budd4,
  5. Michelle D. Brazas5,
  6. Pedro L. Fernandes6,
  7. Bruno Gaeta7,
  8. Celia van Gelder8,
  9. Eija Korpelainen9,
  10. Fran Lewitter10,
  11. Annette McGrath11,
  12. Daniel MacLean12,
  13. Patricia M. Palagi13,
  14. Kristian Rother14,
  15. Jan Taylor15,
  16. Allegra Via16,
  17. Mick Watson17,
  18. Maria Victoria Schneider1 and
  19. Teresa K. Attwood18

+Author Affiliations

  1. 1The Genome Analysis Centre, Norwich, UK, 2ELIXIR, Wellcome Trust Genome Campus, Hinxton, UK, 3The Swedish University for Agricultural Sciences, Uppsala, Sweden, 4European Molecular Biology Laboratory, Heidelberg, Germany, 5Ontario Institute for Cancer Research, Toronto, Canada, 6Instituto Gulbenkian de Ciência, Oeiras, Portugal, 7The University of New South Wales, Sydney, Australia, 8Netherlands Bioinformatics Centre and Department of Bioinformatics, Radboud Medical Center, Nijmegen, The Netherlands, 9CSC – IT Center for Science Ltd., Espoo, Finland, 10Whitehead Institute for Biomedical Research, MIT, Cambridge, Mass. US, 11CSIRO, Bioinformatics Core, Canberra, 12The Sainsbury Laboratory, Norwich Research Park, Norwich, UK, 13SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, Genève, Switzerland,14Academis, Illstrasse 12, 12161 Berlin, Germany, 15The Nowgen Centre, 29 Grafton Street, Manchester, UK, 16Department of Physics, Sapienza University, Rome, Italy, 17The Roslin Institute, Edinburgh, UK, 18The University of Manchester, Manchester, UK
  1. *To whom correspondence should be addressed. Manuel Corpas, E-mail:manuel.corpas@tgac.ac.uk
  • Received July 8, 2014.
  • Revision received August 29, 2014.
  • Accepted August 31, 2014.


Summary: Rapid technological advances have led to an explosion of biomedical data in recent years. The pace of change has inspired new, collaborative approaches for sharing materials and resources to help train life scientists both in the use of cutting-edge bioinformatics tools and databases, and in how to analyse and interpret large datasets. A prototype platform for sharing such training resources was recently created by the Bioinformatics Training Network (BTN). Building on this work, we have created a centralised portal for sharing training materials and courses, including a catalogue of trainers and course organisers, and an announcement service for training events. For course organisers, the portal provides opportunities to promote their training events; for trainers, the portal offers an environment for sharing materials, for gaining visibility for their work and promoting their skills; for trainees, it offers a convenient one-stop shop for finding suitable training resources and identifying relevant training events and activities locally and world-wide.

Availability: http://mygoblet.org/training-portal

Contact: manuel.corpas@tgac.ac.uk

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.




Originally posted on ORGANIC CHEMISTRY SELECT:

Reaction repeatedly inserts organolithium compound into carbon-boron bond, creating chains up to 10 carbons long.

Reaction repeatedly inserts organolithium compound into carbon-boron bond, creating chains up to 10 carbons long

Copying Nature’s Assembly Line

Organic Synthesis: Successive homologation reactions let chemists tailor carbon chain’s conformation
Organic chemists have long admired nature for its ability to perform chemistry in an assembly-line style, wherein the same reaction or sets of reactions are carried out repeatedly to create a target molecule. Polyketide natural products, for example, are biosynthesized via such an assembly-line process.
Now, chemists at England’s University of Bristol report an assembly-line reaction that can be done in a flask (Nature 2014, DOI: 10.1038/nature13711).

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