Engineering a Better Way to Deliver Therapeutic Genes to Muscles
Posted on by Dr. Francis Collins
Amid all the progress toward ending the COVID-19 pandemic, it’s worth remembering that researchers here and around the world continue to make important advances in tackling many other serious health conditions. As an inspiring NIH-supported example, I’d like to share an advance on the use of gene therapy for treating genetic diseases that progressively degenerate muscle, such as Duchenne muscular dystrophy (DMD).
As published recently in the journal Cell, researchers have developed a promising approach to deliver therapeutic genes and gene editing tools to muscle more efficiently, thus requiring lower doses . In animal studies, the new approach has targeted muscle far more effectively than existing strategies. It offers an exciting way forward to reduce unwanted side effects from off-target delivery, which has hampered the development of gene therapy for many conditions.
In boys born with DMD (it’s an X-linked disease and therefore affects males), skeletal and heart muscles progressively weaken due to mutations in a gene encoding a critical muscle protein called dystrophin. By age 10, most boys require a wheelchair. Sadly, their life expectancy remains less than 30 years.
The hope is gene therapies will one day treat or even cure DMD and allow people with the disease to live longer, high-quality lives. Unfortunately, the benign adeno-associated viruses (AAVs) traditionally used to deliver the healthy intact dystrophin gene into cells mostly end up in the liver—not in muscles. It’s also the case for gene therapy of many other muscle-wasting genetic diseases.
The heavy dose of viral vector to the liver is not without concern. Recently and tragically, there have been deaths in a high-dose AAV gene therapy trial for X-linked myotubular myopathy (XLMTM), a different disorder of skeletal muscle in which there may already be underlying liver disease, potentially increasing susceptibility to toxicity.
To correct this concerning routing error, researchers led by Mohammadsharif Tabebordbar in the lab of Pardis Sabeti, Broad Institute of MIT and Harvard and Harvard University, Cambridge, MA, have now assembled an optimized collection of AAVs. They have been refined to be about 10 times better at reaching muscle fibers than those now used in laboratory studies and clinical trials. In fact, researchers call them myotube AAVs, or MyoAAVs.
MyoAAVs can deliver therapeutic genes to muscle at much lower doses—up to 250 times lower than what’s needed with traditional AAVs. While this approach hasn’t yet been tried in people, animal studies show that MyoAAVs also largely avoid the liver, raising the prospect for more effective gene therapies without the risk of liver damage and other serious side effects.
In the Cell paper, the researchers demonstrate how they generated MyoAAVs, starting out with the commonly used AAV9. Their goal was to modify the outer protein shell, or capsid, to create an AAV that would be much better at specifically targeting muscle. To do so, they turned to their capsid engineering platform known as, appropriately enough, DELIVER. It’s short for Directed Evolution of AAV capsids Leveraging In Vivo Expression of transgene RNA.
Here’s how DELIVER works. The researchers generate millions of different AAV capsids by adding random strings of amino acids to the portion of the AAV9 capsid that binds to cells. They inject those modified AAVs into mice and then sequence the RNA from cells in muscle tissue throughout the body. The researchers want to identify AAVs that not only enter muscle cells but that also successfully deliver therapeutic genes into the nucleus to compensate for the damaged version of the gene.
This search delivered not just one AAV—it produced several related ones, all bearing a unique surface structure that enabled them specifically to target muscle cells. Then, in collaboration with Amy Wagers, Harvard University, Cambridge, MA, the team tested their MyoAAV toolset in animal studies.
The first cargo, however, wasn’t a gene. It was the gene-editing system CRISPR-Cas9. The team found the MyoAAVs correctly delivered the gene-editing system to muscle cells and also repaired dysfunctional copies of the dystrophin gene better than the CRISPR cargo carried by conventional AAVs. Importantly, the muscles of MyoAAV-treated animals also showed greater strength and function.
Next, the researchers teamed up with Alan Beggs, Boston Children’s Hospital, and found that MyoAAV was effective in treating mouse models of XLMTM. This is the very condition mentioned above, in which very high dose gene therapy with a current AAV vector has led to tragic outcomes. XLMTM mice normally die in 10 weeks. But, after receiving MyoAAV carrying a corrective gene, all six mice had a normal lifespan. By comparison, mice treated in the same way with traditional AAV lived only up to 21 weeks of age. What’s more, the researchers used MyoAAV at a dose 100 times lower than that currently used in clinical trials.
While further study is needed before this approach can be tested in people, MyoAAV was also used to successfully introduce therapeutic genes into human cells in the lab. This suggests that the early success in animals might hold up in people. The approach also has promise for developing AAVs with potential for targeting other organs, thereby possibly providing treatment for a wide range of genetic conditions.
The new findings are the result of a decade of work from Tabebordbar, the study’s first author. His tireless work is also personal. His father has a rare genetic muscle disease that has put him in a wheelchair. With this latest advance, the hope is that the next generation of promising gene therapies might soon make its way to the clinic to help Tabebordbar’s father and so many other people.
 Directed evolution of a family of AAV capsid variants enabling potent muscle-directed gene delivery across species. Tabebordbar M, Lagerborg KA, Stanton A, King EM, Ye S, Tellez L, Krunnfusz A, Tavakoli S, Widrick JJ, Messemer KA, Troiano EC, Moghadaszadeh B, Peacker BL, Leacock KA, Horwitz N, Beggs AH, Wagers AJ, Sabeti PC. Cell. 2021 Sep 4:S0092-8674(21)01002-3.
Muscular Dystrophy Information Page (National Institute of Neurological Disorders and Stroke/NIH)
X-linked myotubular myopathy (Genetic and Rare Diseases Information Center/National Center for Advancing Translational Sciences/NIH)
Somatic Cell Genome Editing (Common Fund/NIH)
Mohammadsharif Tabebordbar (Broad Institute of MIT and Harvard and Harvard University, Cambridge, MA)
Sabeti Lab (Broad Institute of MIT and Harvard and Harvard University)
NIH Support: Eunice Kennedy Shriver National Institute of Child Health and Human Development; Common Fund
Seeing the Cytoskeleton in a Whole New Light
Posted on by Dr. Francis Collins
It’s been 25 years since researchers coaxed a bacterium to synthesize an unusual jellyfish protein that fluoresced bright green when irradiated with blue light. Within months, another group had also fused this small green fluorescent protein (GFP) to larger proteins to make their whereabouts inside the cell come to light—like never before.
To mark the anniversary of this Nobel Prize-winning work and show off the rainbow of color that is now being used to illuminate the inner workings of the cell, the American Society for Cell Biology (ASCB) recently held its Green Fluorescent Protein Image and Video Contest. Over the next few months, my blog will feature some of the most eye-catching entries—starting with this video that will remind those who grew up in the 1980s of those plasma balls that, when touched, light up with a simulated bolt of colorful lightning.
This video, which took third place in the ASCB contest, shows the cytoskeleton of a frequently studied human breast cancer cell line. The cytoskeleton is made from protein structures called microtubules, made visible by fluorescently tagging a protein called doublecortin (orange). Filaments of another protein called actin (purple) are seen here as the fine meshwork in the cell periphery.
The cytoskeleton plays an important role in giving cells shape and structure. But it also allows a cell to move and divide. Indeed, the motion in this video shows that the complex network of cytoskeletal components is constantly being organized and reorganized in ways that researchers are still working hard to understand.
Jeffrey van Haren, Erasmus University Medical Center, Rotterdam, the Netherlands, shot this video using the tools of fluorescence microscopy when he was a postdoctoral researcher in the NIH-funded lab of Torsten Wittman, University of California, San Francisco.
All good movies have unusual plot twists, and that’s truly the case here. Though the researchers are using a breast cancer cell line, their primary interest is in the doublecortin protein, which is normally found in association with microtubules in the developing brain. In fact, in people with mutations in the gene that encodes this protein, neurons fail to migrate properly during development. The resulting condition, called lissencephaly, leads to epilepsy, cognitive disability, and other neurological problems.
Cancer cells don’t usually express doublecortin. But, in some of their initial studies, the Wittman team thought it would be much easier to visualize and study doublecortin in the cancer cells. And so, the researchers tagged doublecortin with an orange fluorescent protein, engineered its expression in the breast cancer cells, and van Haren started taking pictures.
This movie and others helped lead to the intriguing discovery that doublecortin binds to microtubules in some places and not others . It appears to do so based on the ability to recognize and bind to certain microtubule geometries. The researchers have since moved on to studies in cultured neurons.
This video is certainly a good example of the illuminating power of fluorescent proteins: enabling us to see cells and their cytoskeletons as incredibly dynamic, constantly moving entities. And, if you’d like to see much more where this came from, consider visiting van Haren’s Twitter gallery of microtubule videos here:
 Doublecortin is excluded from growing microtubule ends and recognizes the GDP-microtubule lattice. Ettinger A, van Haren J, Ribeiro SA, Wittmann T. Curr Biol. 2016 Jun 20;26(12):1549-1555.
Lissencephaly Information Page (National Institute of Neurological Disorders and Stroke/NIH)
Wittman Lab (University of California, San Francisco)
Green Fluorescent Protein Image and Video Contest (American Society for Cell Biology, Bethesda, MD)
NIH Support: National Institute of General Medical Sciences
Whole-Genome Sequencing Plus AI Yields Same-Day Genetic Diagnoses
Posted on by Dr. Francis Collins
Back in April 2003, when the international Human Genome Project successfully completed the first reference sequence of the human DNA blueprint, we were thrilled to have achieved that feat in just 13 years. Sure, the U.S. contribution to that first human reference sequence cost an estimated $400 million, but we knew (or at least we hoped) that the costs would come down quickly, and the speed would accelerate. How far we’ve come since then! A new study shows that whole genome sequencing—combined with artificial intelligence (AI)—can now be used to diagnose genetic diseases in seriously ill babies in less than 24 hours.
Take a moment to absorb this. I would submit that there is no other technology in the history of planet Earth that has experienced this degree of progress in speed and affordability. And, at the same time, DNA sequence technology has achieved spectacularly high levels of accuracy. The time-honored adage that you can only get two out of three for “faster, better, and cheaper” has been broken—all three have been dramatically enhanced by the advances of the last 16 years.
Rapid diagnosis is critical for infants born with mysterious conditions because it enables them to receive potentially life-saving interventions as soon as possible after birth. In a study in Science Translational Medicine, NIH-funded researchers describe development of a highly automated, genome-sequencing pipeline that’s capable of routinely delivering a diagnosis to anxious parents and health-care professionals dramatically earlier than typically has been possible .
While the cost of rapid DNA sequencing continues to fall, challenges remain in utilizing this valuable tool to make quick diagnostic decisions. In most clinical settings, the wait for whole-genome sequencing results still runs more than two weeks. Attempts to obtain faster results also have been labor intensive, requiring dedicated teams of experts to sift through the data, one sample at a time.
In the new study, a research team led by Stephen Kingsmore, Rady Children’s Institute for Genomic Medicine, San Diego, CA, describes a streamlined approach that accelerates every step in the process, making it possible to obtain whole-genome test results in a median time of about 20 hours and with much less manual labor. They propose that the system could deliver answers for 30 patients per week using a single genome sequencing instrument.
Here’s how it works: Instead of manually preparing blood samples, his team used special microbeads to isolate DNA much more rapidly with very little labor. The approach reduced the time for sample preparation from 10 hours to less than three. Then, using a state-of-the-art DNA sequencer, they sequence those samples to obtain good quality whole genome data in just 15.5 hours.
The next potentially time-consuming challenge is making sense of all that data. To speed up the analysis, Kingsmore’s team took advantage of a machine-learning system called MOON. The automated platform sifts through all the data using artificial intelligence to search for potentially disease-causing variants.
The researchers paired MOON with a clinical language processing system, which allowed them to extract relevant information from the child’s electronic health records within seconds. Teaming that patient-specific information with data on more than 13,000 known genetic diseases in the scientific literature, the machine-learning system could pick out a likely disease-causing mutation out of 4.5 million potential variants in an impressive 5 minutes or less!
To put the system to the test, the researchers first evaluated its ability to reach a correct diagnosis in a sample of 101 children with 105 previously diagnosed genetic diseases. In nearly every case, the automated diagnosis matched the opinions reached previously via the more lengthy and laborious manual interpretation of experts.
Next, the researchers tested the automated system in assisting diagnosis of seven seriously ill infants in the intensive care unit, and three previously diagnosed infants. They showed that their automated system could reach a diagnosis in less than 20 hours. That’s compared to the fastest manual approach, which typically took about 48 hours. The automated system also required about 90 percent less manpower.
The system nailed a rapid diagnosis for 3 of 7 infants without returning any false-positive results. Those diagnoses were made with an average time savings of more than 22 hours. In each case, the early diagnosis immediately influenced the treatment those children received. That’s key given that, for young children suffering from serious and unexplained symptoms such as seizures, metabolic abnormalities, or immunodeficiencies, time is of the essence.
Of course, artificial intelligence may never replace doctors and other healthcare providers. Kingsmore notes that 106 years after the invention of the autopilot, two pilots are still required to fly a commercial aircraft. Likewise, health care decisions based on genome interpretation also will continue to require the expertise of skilled physicians.
Still, such a rapid automated system will prove incredibly useful. For instance, this system can provide immediate provisional diagnosis, allowing the experts to focus their attention on more difficult unsolved cases or other needs. It may also prove useful in re-evaluating the evidence in the many cases in which manual interpretation by experts fails to provide an answer.
The automated system may also be useful for periodically reanalyzing data in the many cases that remain unsolved. Keeping up with such reanalysis is a particular challenge considering that researchers continue to discover hundreds of disease-associated genes and thousands of variants each and every year. The hope is that in the years ahead, the combination of whole genome sequencing, artificial intelligence, and expert care will make all the difference in the lives of many more seriously ill babies and their families.
 Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, Ellsworth K, Camp B, Kint CI, Yacoubian C, Farnaes L, Bainbridge MN, Beebe C, Braun JJA, Bray M, Carroll J, Cakici JA, Caylor SA, Clarke C, Creed MP, Friedman J, Frith A, Gain R, Gaughran M, George S, Gilmer S, Gleeson J, Gore J, Grunenwald H, Hovey RL, Janes ML, Lin K, McDonagh PD, McBride K, Mulrooney P, Nahas S, Oh D, Oriol A, Puckett L, Rady Z, Reese MG, Ryu J, Salz L, Sanford E, Stewart L, Sweeney N, Tokita M, Van Der Kraan L, White S, Wigby K, Williams B, Wong T, Wright MS, Yamada C, Schols P, Reynders J, Hall K, Dimmock D, Veeraraghavan N, Defay T, Kingsmore SF. Sci Transl Med. 2019 Apr 24;11(489).
DNA Sequencing Fact Sheet (National Human Genome Research Institute/NIH)
Genomics and Medicine (NHGRI/NIH)
Genetic and Rare Disease Information Center (National Center for Advancing Translational Sciences/NIH)
Stephen Kingsmore (Rady Children’s Institute for Genomic Medicine, San Diego, CA)
NIH Support: National Institute of Child Health and Human Development; National Human Genome Research Institute; National Center for Advancing Translational Sciences