Novel CRISPR-derived ‘base editors’ surgically alter DNA or RNA, offering new ways to fix mutations

Since the start of the CRISPR craze 5 years ago, scientists have raced to invent ever-more-versatile or efficient variations of this powerful tool, which vastly simplifies the editing of DNA. Two studies published in Science and Nature this week broaden CRISPR’s reach further still, honing a subtler approach to modifying genetic material that’s called base editing. One study extends a strategy for editing DNA, whereas the other breaks new ground by base editing its molecular cousin, RNA.

Both open new avenues for genetic research and even curing diseases. “One shouldn’t view base editors as better than CRISPR—they’re just different,” says David Liu, a chemist at Harvard University who pioneered DNA base editing in a paper in Nature last year and co-authored the latest Nature paper. “It’s like, what’s better, a boat or a car?”

CRISPR, adapted from a primitive bacterial immune system, does its handiwork by first cutting the double-stranded DNA at a target site in a genome. Base editing, in contrast, does not cut the double helix, but instead uses enzymes to precisely rearrange some of the atoms in one of the four bases that make up DNA or RNA, converting the base into a different one without altering the bases around it. That ability greatly increases the options for altering genetic material. “It’s a very worthwhile addition and it’s here to stay,” says CRISPR researcher Erik Sontheimer of the University of Massachusetts Medical School in Worcester.

Many human diseases are caused by the mutation of a single base. CRISPR has difficulty correcting these so-called point mutations efficiently and cleanly, so base editing could provide a more effective approach. After Liu’s initial report, a group in China used DNA base editing to correct a disease-causing mutation in human embryos cloned from a patient with a genetic blood disorder.

Conventional CRISPR uses a guide RNA (gRNA) coupled with an enzyme known as a nuclease, most commonly Cas9, that together attach to a specific stretch of DNA bases; the nuclease then snips the double helix. A cellular repair mechanism attempts to rejoin the cut DNA ends, but occasionally inserts or deletes bases, which turns the DNA code into gibberish and can knock out a targeted gene. “Gene editing based on nucleases is very good at inactivating genes,” says CRISPR researcher Feng Zhang of the Broad Institute in Cambridge, Massachusetts.

Yet CRISPR, he notes, “is less efficient at making precise changes.” To fix a point mutation, a CRISPR-Cas9 system must also introduce a strand of “donor” DNA that has the correct base and then rely on a second cellular mechanism called homology-directed repair (HDR). But HDR works poorly unless cells are dividing, which means this strategy doesn’t function in, say, brain and muscle cells that no longer copy themselves. Even in dividing cells, the donor DNA rarely slots into the cut spot.


Getting to the point of mutations

Base editors borrow from CRISPR’s components—guide RNAs (gRNAs) and Cas9 or other nucleases—but don’t cut the double helix and instead chemically alter single bases with deaminase enzymes such as TadA and ADAR.

Base-editing systems, which borrow heavily from CRISPR’s tool kit, readily work in nondividing cells. DNA has four nucleotide bases—A, C, T, and G—and base editing changes one to another. In Liu’s 2016 study, his team fused gRNA with a “dead” Cas9 (dCas9) that cannot cut the whole double helix but still unzips it at the correct spot. To this complex the researchers tethered an enzyme, APOBEC1, which triggers a series of chemical reactions that ultimately change C to T. DNA’s base-pairing rules, which specify that a T on one DNA strand pairs with an A on the opposite strand, govern a subsequent change. The dCas9 was further modified to nick the unedited strand, which gooses the cell’s DNA repair mechanism into converting the G that originally paired with C into an A that pairs with the new T.

That first DNA base editor could not address the most common point mutations associated with human diseases—accounting for about half—which have A•T where there should be G•C. The new editor from Liu’s group can now make this fix. The team again fused gRNA with a dCas9, but there is no known enzyme that can convert A to G in DNA. So the lab developed one from TadA, an enzyme in the bacterium Escherichia coli. The new enzyme converts A to a base called inosine, or I. Either a cellular repair mechanism or the process of the DNA copying itself then changes the I to a G. “The big deal here is engineering the TadA enzyme to do something fairly unnatural,” says George Church of Harvard, who studies CRISPR. “My hat is off to them.”

Zhang’s team created its RNA base-editor system by fusing gRNA with a different dead nuclease, dCas13, and a natural enzyme that converts A to I in RNA. Unlike in DNA, that’s where the changes stop. The I-containing RNA simply performs as if it had a G in that spot.

Because RNA carries the genetic message from DNA to the cell’s proteinmaking factories, or can directly perform acts such as gene regulation, it, too, is an appealing target for therapies. But an RNA only sticks around in a cell for a short time. That means RNA base editors likely would have to be repeatedly administered to work as a therapeutic, which Zhang and his co-authors suggest may make sense for transient conditions, such as localized inflammation.

Although the short-lived nature of RNA makes base editing less attractive for many therapies, Sontheimer sees an upside, too. “In some ways, it’s safer to work on RNA,” he says. Researchers worry that genome editing could accidentally affect the wrong part of the genome—a change that would be permanent with a DNA base editor. “If there’s some degree of off targeting, you’re not permanently etching those mistakes into the underlying genome” with an RNA base editor, Sontheimer says.

Church says base editing should be evaluated “case-by-case” for whether it offers advantages over CRISPR and other technologies that alter nucleic acids. “People make it sound like [changing bases] was not possible before. In fact it was hard or just inefficient,” he notes.

Zhang and Liu stress that it could be several years before base-editing therapies enter clinical trials—and longer until it’s clear whether the strategy offers advantages over existing gene therapies. “It’s both scientifically short-sighted and long-term incorrect to conclude that base editing is going to be a better way to do human genetic therapy,” Liu says. What’s already clear, however, is that powerful alternatives to standard CRISPR are now in the game.

(Source: Science and AAAS,  by Jon Cohen)

These gene-edited pigs are hardy and lean—but how will they taste?

“Lean” may not be the term you associate with a good bacon strip or pork chop. But these leaner, cold-hardier piglets, created through CRISPR gene editing, could be a hit with the pork industry. The threat of hypothermia forces cold-climate farms to invest in heat lamps and other accommodations for their shivering piglets. And fatter pig breeds—though tasty—tend to grow more slowly and consume more feed than leaner ones to produce the same amount of meat. As an alternative to conventional breeding, researchers used the gene-editing technology CRISPR to introduce a gene called UCP1. Thought to have disappeared from the ancestors of modern pigs about 20 million years ago, the gene helps cells dissipate more heat and burn fat. Twelve transgenic piglets endowed with a mouse UCP1 gene were better able to maintain their body temperature than their unmodified counterparts when they were exposed to cold for a 4-hour period, the authors report today in the Proceedings of the National Academy of Sciences. And when the pigs were killed, fat made up less of their carcass weight—about 15% versus 20% in unmodified controls—while their average percentage of lean meat increased from about 50% to 53%. Will less fat make them less tasty? The authors don’t expect UCP1 to reduce the fat that accumulates in muscle fibers and contributes to flavor, but they’re now producing more pigs to make sure.

(Source: Biology, Plants & Animals, by Kelly Servick)

Gene Editing: Promises and Challenges

Source: Harvard T. H. Chan School of Public Health

GENE EDITING: Promises and Challenges

Presented jointly with NBC News Digital
May 19, 2017

In labs and in clinical trials, scientists are seeking ways to rewrite DNA, a building block of life. Tools such as zinc-finger nucleases (ZFNs), TAL effector nucleases (TALENs) and, more recently, CRISPR/Cas9 have the power to seek out and replace faulty DNA. The possibilities seem almost limitless: with the ability to edit DNA at will, researchers theoretically could wipe out malaria-causing mosquitos, make disease- and pest-proof crops without the need for pesticides, and cure genetic diseases, such as sickle cell anemia and cystic fibrosis. Cancer is another target, with human clinical trials using CRISPR already underway, while, in separate efforts, HIV has been reportedly eliminated in mice thanks to the tool.

But scientists and ethicists alike are worried about the speed at which the gene editing field is moving — and the implications of the results. In this panel, we discussed the promises and challenges presented by gene editing for individual and public health. What scientific and ethical hurdles must be overcome before tools like CRISPR and others can move safely and more widely out of the lab and into fields, farms, and hospitals

Global Biochips Market size and forecast By 2020

Bio-microsystem is a group of miniaturized and integrated devices for biological or biochemical reactions in diagnostics, monitoring, therapy, and research and development. Some of the advantages of bio-microsystems are parallelism, integrated intelligence, low cost, speed, complexity and redundancy. Biochip is one of the examples of technical development of bio-microsystem. Biochip is a collection of microarrays arranged on a solid substrate which allows hundreds or thousands of complex biochemical reactions such as decoding genes in few seconds. Biochips are used in variety of applications such as research application in biotechnology such as genomics and proteomics, drug screening and development and molecular diagnostics. It also offers other diagnostic applications such as microfluidic technologies, microarray and biosensors. Biochip is also used to analyze organic molecules associated with living organisms. Biochip helps in identifying gene sequences, airborne toxins, environmental pollutants and other biochemical constituents. There are various types of biochips such as DNA chips, lab-on-a-chip and protein chips. Chip based analysis is mainly used in on-site diagnostics.

North America dominates the global market for biochips due to large number of aging population and broad technical applications of biochips. Asia followed by the Europe are expected to show high growth rates in the next five years in global biochips market. China and India are expected to be the fastest growing biochips markets in Asia-Pacific region. Some of the key driving forces for biochip market in emerging countries are increasing R&D investment, large pool of patients and rising government funding.

In recent times there is increased use of biochips due to increasing cancer treatment and diagnostics. Rise in personalized medicine, drug discovery and life science research, need for high speed diagnostics and increased government funding are some of the key factors driving the growth for global biochips market. In addition, increasing healthcare awareness is also fuelling the growth of global biochips market. However, limited technical knowledge related to biochips, low acceptance due to high cost and availability of alternative technologies are some of the major factors restraining the growth for global biochip market.

Increasing R&D investment and outsourcing of pharmaceutical companies would lead to growth in biochips market in Asia. In addition, broaden application of biochips products would develop opportunity for global biochip market. However, high cost involved in manufacturing of biochips could lead a challenge for global biochips market. Some of the trends for global biochips market are outsourcing of biochips technology, which would help in reducing labor cost and capital requirement. Some of the major companies operating in the global biochips market are Affymetric Inc, Illumina Inc, GE Healthcare Ltd, Agilent Technologies Inc. Roche NimbleGen, Life Technologies Corporation, EMD Millipore., Bio-Rad Laboratories Inc, Abbott Laboratories and Fluidigm Corporation.

( Source: Persistence Market Research)

How Will Genomics Enter Day-to-day Medicine?

Summary:  A quiet transformation has been brewing in medicine, as large-scale DNA results become increasingly available to patients and healthcare providers. Amid a cascade of data, physicians, counselors and families are sorting out how to better understand and use this information in making health care decisions.

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National experts who have gathered in Clinical Genetics Think Tank meetings at two large pediatric hospitals recently issued their first recommendations for integrating genomics into clinical practice.

The recommendations appeared online May 12, 2016 in Genetics in Medicine, co-led by Ian D. Krantz, M.D., director of the Individualized Medical Genetics Center at The Children’s Hospital of Philadelphia (CHOP), and Ronald D. Cohn, M.D., co-director of the Centre for Genetic Medicine at the Hospital for Sick Kids in Toronto.

“As genetic testing has become more complex, it’s being applied across many more medical specialties and into primary care,” said Krantz, a clinical geneticist. “These tests will move toward broad use in screening healthy populations, and our recommendations aim to help people better integrate testing results into clinical practice.”

Krantz and co-author Sarah Bowdin, M.D., of the Centre for Genetic Medicine at the Hospital for Sick Kids in Toronto, spearheaded the two Clinical Genetics Think Tanks, hosted at their respective hospitals in 2014 and 2015.

Co-authors of the recommendations are other Think Tank participants: clinical geneticists, genetic counselors, and laboratory professionals and bioinformatics experts. “Our co-authors represent the main stakeholders in this field,” said Krantz. “We also included patients and parents in the Think Tanks, to incorporate their experiences in dealing with these concerns on an everyday basis.”

Krantz added that he and Bowdin launched the Think Tanks after hearing from colleagues struggling with many similar issues as other institutions established clinical genomic and exome sequencing programs. Among those challenges were how to best interpret DNA findings, how to report to patients and clinicians about gene variants of uncertain significance, how to report secondary findings unrelated to the primary reason for the testing, and how to share findings with other centers. “As each institution independently developed its own procedures, we thought that exchanging experiences across our field could improve overall practice.”

The recommendations address the pretesting process (including selecting patients and obtaining insurance coverage), patient and clinician education, interpreting sequence data, and posttest patient care (including how to return test findings and offer reevaluation of data). Another broad area, added Krantz, is phenotyping–establishing consistent terminology for patients’ clinical characteristics, so that clinicians can better interpret the significance of DNA results, share data across centers, and ultimately standardize care for patients.

Krantz compared these new challenges to a more straightforward clinical situation–obtaining a targeted genetic test for fragile X syndrome–in which a test reveals whether a patient has a specific DNA change that causes fragile X symptoms. In contrast, current clinical genome and exome sequencing produces many unknowns: for each individual, test results yield many variants of uncertain significance, as well as secondary findings, which are genetic variants unrelated to the primary condition for which a patient is tested.

Facing a flood of DNA data, families told other Think Tank participants that they often preferred two posttest sessions to discuss test findings–one to learn the principal diagnostic results, and a second session to discuss secondary findings that are medically actionable.

Crucially, Krantz added, the data from genomic testing are dynamic–as new scientific knowledge accumulates, the significance of data changes: some findings of uncertain significance will become clearer, and will become medically actionable in the future, so that healthcare providers will need to devise ways to systematically offer future reevaluation of a patient’s genome. “We need to make these data longitudinal, not static,” he said.

One emerging issue raised in the Think Tanks is how to best integrate genomic results into each patient’s electronic health record. This becomes all the more important, said Krantz, as clinical sequencing moves toward general screening of healthy patient populations, including newborns, as part of the progression toward precision medicine.

One conversation with a family, added Krantz, helped to drive home that issue. He was explaining results of genomic testing in a child with multiple medical issues. After learning the unexpected secondary finding that their child carried a cancer predisposition gene, the parents asked about performing the test for their healthy child too.

“We have framed this document not as a set of overt guidelines, but as recommendations, which we expect to change as our field evolves,” said Krantz. He added that future Think Tanks may meet to address new challenges.

(Source: The above post is reprinted from materials provided by Children’s Hospital of Philadelphia. CHOP News)

Global Genomics Market Outlook: 2015-2020

Genomics is a discipline which analyzes the function and structure of genomes. It uses various sampling, sequencing, and data analysis and interpretation techniques to decode, assemble, and analyze genomes. The knowledge of complete set of DNA helps to identify certain genetic diseases, develop best course of treatment, and contribute to precision medicine.

With the significant decrease in the sequencing costs and rising investments in the pharmaceutical industry, the global genomics market is forecast to grow at a CAGR of 15.1% to be worth $19,938.6 million by 2020.

This growth is further driven by the technological innovations in bioinformatics, increasing clinical capabilities, and more clinically relevant sequencing timescales. However, need of significant clinical investment, lack of funding in the emerging markets, rising consolidation mainly in the instruments market, and ethical and legal challenges will act as a constraint to industry growth during the forecast period.

The global genomics market is segmented by methods, technology, instruments, consumables, services, and geography. The genomics industry is still at a nascent stage with many untapped markets present across the globe. However, the sequencing method is relatively at a mature stage, especially, in the developed markets. As, the scale of genomes data grows, the data analysis and interpretation market is expected to grow at a significant rate in the near future. Next-generation DNA sequencing (NGS) technology has transformed biomedical research, making genome and RNA sequencing an affordable and commonly used tool for a wide variety of research applications. As a result, the market has been stressed to manage the enormous data output from this process. Therefore, the complexity and sheer amount of data generated by NGS has led to a need for genomic centers to form bioinformatics teams in order to analyze the output data.

North America is the major market in the global genomics market and is expected to dominate this market during the forecast period, with the U.S. contributing a major share, followed by Europe, and Asia-Pacific. On the other hand, the Asian market, especially India and China, is expected to witness a boost in demand for genomics market during the forecast period, as a result of their economic development, increasing genetic research and development activities, drastically reduced mass scale genetic testing costs, and the growing focus of the major players in this region.

The key players in the global genomics market are Affymetrix, Inc., Agilent Technologies, BGI (Beijing Genomics Institute), Illumina, Inc., Thermo Fisher Scientific, Inc., Bio-Rad Laboratories, Inc., Cepheid, GE Healthcare, Qiagen N.V, Roche Holding AG, Pacific Biosciences of California, Inc., Oxford Nanopore Technologies Ltd., Beckman Coulter Genomics, Inc., Perkin Elmer, Inc., DNASTAR, Inc, Genomatix Software Gmbh, and GenoLogics Life Sciences Software, Inc.,

The global genomics market is segmented by methods, technology, instruments, consumables, services, and geography:

Genomics Methods/Stages

– Sampling,

– Sequencing,

– Analysis,

– Interpretation

– Application

Genomics Technology

– PCR

– Sequencing

– Microarray

– Nucleic acid Extraction & Purification

Genomics Instruments

– PCR

– NGS Platforms

– DNA Microarrays

– Nucleic acid Extraction and Purification Systems

– DNA Sequencers

– Others

NGS Platforms

– Illumina

– Thermo

– Roche

– Pacific Biosciences

Genomics Consumables

– PCR

– DNA Sequencing

– Nucleic acid extraction and purification systems

– Genechips

– Microarrays

– Others

Genomics Services

– Laboratory Services

– Software

Genomics Market, By Geography

North America

o U.S.

o Canada

Europe

o U.K.

o Germany

o France

o Italy

o Spain

o Rest of Europe

Asia-Pacific

o Japan

o China

o India

o Rest of Asia-Pacific

– Rest of the World

o Latin America

o Middle East and Africa

(Source: PRNewswire)

Using deep learning to analyze genetic mutations

Full article written by David Beyer can be found here: Deep learning meets genome biology

  • The application of deep learning to genomic medicine is off to a promising start; it could impact diagnostics, intensive care, pharmaceuticals and insurance.
  • The “genotype-phenotype divide”—our inability to connect genetics to disease phenotypes—is preventing genomics from advancing medicine to its potential.
  • Deep learning can bridge the genotype-phenotype divide, by incorporating an exponentially growing amount of data, and accounting for the multiple layers of complex biological processes that relate the genotype to the phenotype.
  • Deep learning has been successful in applications where humans are naturally adept, such as image, text, and speech understanding. The human mind, however, isn’t intrinsically designed to understand the genome. This gap necessitates the application of “super-human intelligence” to the problem.
  • Efforts in this space must account for underlying biological mechanisms; overly simplistic, “black box” approaches will drive only limited value.

(Source: Deep Genomics)