Genome-edited people

In the near future, we may talk about a “genome-edited person,” a phrase that I picked up at the Stem Cell Meeting on the Mesa last week.  This idea was raised around experiments showing that it’s possible to engineer hematopoietic stem cells to resist HIV infection and essentially eradicate the virus from an infected individual (a mouse, in this case).  These experiments spring from a bone marrow transplant that was done in an HIV-positive patient with acute myelogenous leukemia.  The patient received bone marrow from a donor with a rare mutation that makes his T-cells resistant to HIV infection.  So far, the patient is HIV- and cancer-free.  Through genome editing, that same mutation could be introduced into a patient’s hematopoietic stem cells and reintroduced into their bone marrow.

Six years ago, Shinya Yamanaka discovered that just four transcription factors are needed to reprogram a cell whose fate was sealed into a stem cell.  This year, he shared the Nobel Prize in Physiology or Medicine for his contribution to regenerative medicine.  This technology is a like a science fiction dream come true.  We can take someone’s skin cells or fat cells and turn them into any other cell in the body- neurons, blood, heart cells that beat.  If people have a genetic disorder, this could be corrected through genome editing of stem cells we create from their body.  This technology will change medicine, but it’s still in its infancy.  This was a common theme of several talks: the science isn’t perfect.

More researchers are studying iPS cells at the same time that we are improving our ability to accurately sequence the entire genome and epigenome.  Turns out, reprogramming introduces small changes, some of which may be significant. Gene activity changes because DNA methylation states change.  Mutations are introduced by retrotransposons (sometimes called “jumping genes”), pieces of DNA that move around and can jump back into the genome at the wrong spot.  So the DNA is the same, but not.  A bump in the road that’s sure to be understood or overcome with time.

The California Institute for Regenerative Medicine (CIRM), which was founded by a state ballot measure to fund and direct stem cell research in the state, manages the larger picture of stem cell research and related translational medicine in the state.  Part of their plan includes stem cell banking and developing Genomic Centers of Excellence.  Together, these will help us better understand the genetics, genomics, and epigenetics of stem cells.  For now, the field is aware that reprogramming has unintended consequences. It’s important that the public is aware of this, too.  Patients and patient-advocates are very eager to see this technology available clinically, but scientists want to make sure that we’re not unintentionally hurting people by using it.  Once the technology is sorted out, both stem cell therapy and genome editing may become commonplace.

Too much of a good thing

Kids are getting 7-15% of their total energy from sweetened drinks.  The beverage industry claims that this is small potatoes.  “Focusing on a small source of calories rather than on the total diet is a misplaced allocation of resources,” according to the American Beverage Association, quoted in the LA times.  But is it really a small source of calories?

Three studies published last week in The New England Journal of Medicine indicate the answer is “no.”  In two of the papers, researchers subbed zero-calorie drinks for sweetened drinks.  The de Ruyter study was especially well controlled: they made the drinks to look and taste identical, the drinks were given out in school and most kids drank them over their morning break. Also, they collected urine from the participants to confirm compliance.  The take-home message?  Kids who drink sugary drinks are fatter and have a higher BMI.

The third paper looked at adults (gulp!) and found that if you are genetically predisposed to obesity, then drinking sweet beverages in an extra-risky proposition.  They screened people for common genetic variations (SNPs) that are associated with high BMI.  For those folks, one sugary drink a day is much more likely to tip the scales to overweight or obese than for people who have “skinny” genes.

A pound of weight gain comes from consuming 3500 more calories than you need.  Lots of kids drink 300 calories worth of sugary drinks every day.   Unless they are scaling back the food they eat by 300 calories, those drinks add up to an extra 2 pounds per month, or 24 pounds per year.  Follow the math, and it equals an obesity epidemic.

It’s unrealistic to think that kids will only drink thirst-quenching water and satiety-inducing milk.  My grandmother’s advice rings truer than ever today: “too much of a good thing is not a good thing.”

25 years later…

What is so compelling about a 25-year old article?  I found myself on the Nature Biotechnology website earlier this week. Wednesday, to be exact. And I was surprised to see, on the “most emailed” list, an article published in 1987.  Over the course of the week, it disappeared from the list. Moved down Thursday, and gone by the weekend. So you’ll just have to trust me.

The article is about optimizing the transfer genes into tomato plants. Published in 1987, puts it square in the infancy of GMOs. The first field trials on herbicide-resistant tobacco plants happened just one year earlier. In this article, researchers used bacteria, Agrobacterium tumefaciens, to insert a gene that makes the plants resistant to Round-up. The ability of Agrobacterium to transfer genes into plants was first described in 1977. Basically, the bacteria have a small loop of DNA, the T1 plasmid, which can be shuttled into plant cells. In nature, the bacteria use this to turn plants into their personal resource warehouses. Agrobacterium give plants genes to make a class of chemicals called opines. They grow in tumors on the plants, and the bacteria exploit the plant’s new opines for their own energy and nitrogen production.

We can denude the T1 plasmid of all its genes except for the ones responsible for DNA transfer, and then insert the genes we are interested in. In this case, herbicide tolerance. Voila, genetically modified organism.

The point of this paper was really to optimize gene transfer into tomatoes using Agrobacterium. It’s a finicky bacteria, so you have to optimize your procedure for each species. We’re still doing this.

Despite my best googling efforts, I’m not sure WHY this article popped up on the most emailed list. Do share if you have an idea. Its humility and openness was refreshing: they didn’t have all the answers. The discussion is full of speculation and unknowns: they didn’t know exactly why some things worked better than others. Papers, behemoths now, come across differently.

But my favorite part? The “Methods” section was about as long as the “Results” section. I wouldn’t feel uncomfortable attempting to repeat their experiments with those methods in hand, and the last time I saw a cotyledon was middle school. Now we shuttle Methods to the online supplemental information. Even with all that space, people aren’t wont to wax eloquently on laboratory techniques. Often we just say “as was previously described…(ref X),” which may, or may not, actually be the case.

Improving scientific accuracy and reproducibility has been a hot topic lately. If that’s the goal, more people should read this article and think about the way it used to be.

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Original paper:  Fillatti et al.  Efficient transfer of a glyphosate tolerance gene into tomato using a binary Agrobacterium tumefaciens vector. Biotechnology, July 1987.

The dream: just one shot

We are engaged in an arms race with the flu virus, but we have a secret weapon hidden in our DNA.  We can make antibodies that neutralize a bunch of different flu variants.

Viruses are little packets of DNA cloaked in proteins.  We make antibodies that target the outer protein shell, flagging the virus as an invader to be destroyed.  In the arms race, viruses evade detection by making constant changes to that outer coating.  But not the whole coating, just a piece of it that is referred to as the “head.”  We might gain a foothold in this race because the flu keeps the same “stem” region across many of its flavors, including H1N1 and the much-talked about H5N1 (avian flu).  In response to the pandemic 2009 H1N1 infection, some people made broadly neutralizing antibodies directed against that stem region. We all have the gene to do it; the trick seems to be finding the right immunogen to show to the immune system.  The seasonal vaccines we get at the doctor’s office haven’t been able to elicit such a response, yet, though people who have been exposed to many flu vaccines are more likely to have broadly neutralizing antibodies.  So all those shots you got weren’t for nothing.

Research is bringing us closer to that dream of a universal flu vaccine.  It’s been making headlines in the big journals this month. A paper published in Nature this week shows what makes for a good universal antibody and how the immune system needs to display it.  A paper in Science Express earlier this month found that people can make broadly universal antibodies that target both influenzas A and B.  If all the pieces come together, it could be the end of annual flu vaccines for future generations.

Watching the world go by

Stingless bees live in dense tropical forests with canopies that reach over 40 meters (130 feet) high.  Lush as tropical forests seem to us, competition for resources is tough if you’re a bee.  When they find new food, they keep going back.  And they’ll bring friends.  How do bees remember where they found that last tasty morsel in a vast three-dimensional world?

Honey bees use a “visual odometer”, which gauges how far they’ve traveled based on the flow of images across their visual fields.  New research by a team from UCSD shows that the distantly related stingless bee does, too.  By papering stripes on the inside of tubes, which contained feeders of unscented sugar water, the researchers were able toy with the stingless bees’ visual odometer.  As the bees fly through the tubes, they see alternating black and white stripes.  Seeing the stripes pass by, the bees learned exactly how far into the tube they had to fly to find sugar water.  Once they were all trained up, the researchers altered the tubes and looked to see how the bees navigated. By varying stripe width (and therefore the number of stripes bees pass by on their way to food), they confirmed that stingless bees don’t count stripes.  Rather, they rely on their visual odometer, which tracks how fast the world passes by to estimate how far they’ve flown.  That visual odometer breaks down when the stripes run the length of the tube and their world looks…exactly the same.  Making the tube narrower or wider messes the bees up in a whole new way: the bees perceive the stripes moving at a different rate, and they navigate accordingly- going either too far, or not far enough.  Lastly, the researchers found that stingless bees use their visual odometer to measure distances in all three dimensions, not just laterally from the nest.  A good thing for bees that live in the forest’s understory.

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My lemonade’s only half done.  Tell me more about these stingless bees!

Stingless bees don’t do the “waggle dance” like the honey bee.  They have their own form of communication: they jostle, vibrate, and make noises to share information with the colony.  Most of the information seems to be on the order of:  “I found food!  It’s super sweet!  And close!”  Not “go here for food.”  For the purposes of guiding hive-mates, they leave scent trails that can last about 10 minutes and they lead expeditions back to new food sources.  Stingless bees and honey bees are distantly related, though the stingless bee is much older, with the oldest known fossil dating back 80 million years.

References:

A stingless bee can use visual odometry to estimate both height and distance.  Eckles MA, Roubik DW, Nieh JC.  The Journal of Experimental Biology, Sept 15, 2012. doi:10.1242/jeb.070540

Signals and cues in the recruitment behavior of stingless bees.  Barth FG, Hrncir M, Jarau S. Journal of Comparative Physiology, 2008.  DOI 10.1007/s00359-008-0321-7

Image credit:  David Cappaert, available under CCL.  Source:  http://tolweb.org/Apinae

The social networking game

Our lives are dominated by social interactions that seem to be getting ever thicker.  We make new friends in real world, follow people on Twitter, circle people on G+, and add “friends” to our Facebook pages, to name a few.  What governs how we behave in these and other social situations?  And why do we have so many “friends”?

Turns out, we’re nicer when we get to pick our friends.  As for why we’ve got so many, it’s probably because there’s relatively little risk to expanding our network online.  When the stakes are higher, people tend to limit their interactions to people who are nice to work with.

These findings come from social experiments using the classic strategy game, the Prisoner’s Dilemma.  The game is played over multiple rounds, and in each round players choose between cooperating and defecting.  There’s fallout from either decision, based on the player’s action and the actions of his associates.  The game can be mapped to social, business, political, evolutionary, and environmental situations.

In this particular version of the game, dubbed “The Social Networking Game” by the researchers, players accumulated points based on their network of associates.  Points were assigned as follows, tallied throughout the game and players paid out based on their score:

Cooperator-cooperator:  4 pts each

Cooperator-defector:  -1 point to the cooperator, 7 pts to the defector

Defector-defector:  1 pt each.

Defectors in a sea of collaborators stand to be victors, but if everyone behaves that way, no one gets rich.  Each game went on for 12 rounds.  Between rounds, people could forge new bonds or sever existing ones.  In general, people chose to expand their networks with other cooperators and tolerated the few defectors, but those few bad apples eventually spoiled the barrel.

So the researchers modified the reward system to really penalize the cooperators for keeping those defectors around:

Cooperator-cooperator:  4 pts each

Cooperator-defector:  -5 point to the cooperator, 7 pts to the defector

Defector-defector:  -1 pt each.

Now people formed tight cooperating networks for almost the entire game (when players know how many rounds are in the game, they switch to defecting at the end in a last-ditch points effort).  Furthermore, early defectors were swiftly booted from the circle and essentially ostracized.

So what does it all mean?

In casual social situations, when we get to choose our colleagues and prune our social networks, we tend to wind up in groups that are highly collaborative. Especially when there are serious consequences for acting selfishly.  Of course, we can’t always choose who we work with, and these findings apply to decision-making in a wide variety of interactions.  But in a day-to-day context, putting ourselves in situations that allow for updating our networks probably has some social benefit.  Online social networks are a paradigm of picking who your friends are and pruning your network.  It’s sort of this experiment on a grand scale.  Perhaps, then, part of social media’s attraction?

Image credit:  http://tribune.com.pk/story/237013/the-social-networks/

Reference:  Cooperation and assortivity with dynamic partner updating.  J Wang, S Suri, DJ Watts.  PNAS, epub Aug 17th, 2012- open access publication.

Now you’re talkin’ my (neuronal) language!

Prosthetic retinas help blind people see light, but not much more.  We thought it was just a resolution problem, but new research shows that it’s also a data problem.

The retina, which is the light-sensing neuronal tissue in the back of the eye, processes images through several steps.  Cells called photoreceptors detect light.  They pass their information to other neurons for an intermediate processing step, and then that newly coded information is passed to retinal ganglion cells.  Retinal ganglion cells connect straight into the brain.  Voila, vision.

In diseases of blindness, photoreceptors die but those other cells are fine and well.  Prosthetics take advantage of that by stimulating retinal ganglion cells based on what an external camera sees.  In the lab is an alternative to the implantable devices currently used: a blue-light sensing protein is hooked onto retinal ganglion cells.  Flashes of blue light corresponding to the world around stimulate the retinal ganglion cells.  In either case, retinal ganglion cells replace dead photoreceptors.  The problem is that retinal ganglion cells (and the brain) aren’t equipped to handle raw information like that.  They are supposed to get the final message.  Firing retinal ganglion cells in response to light doesn’t send a very detailed message to the brain.

The solution is to create an “encoder.”  The new prosthetic works like this: a camera takes in the scene, as before.  Then, a computer algorithm turns images into electrical pulses. The computer does the intermediate processing normally done by other cells in the retina. The algorithm is based on how normal mice process visual information.  A blue LED sends out flashes of light to match the encoder’s electrical pulses.  This stimulates the blue-light detecting protein that’s been engineered onto retinal ganglion cells.  The end result?  Retinal ganglion cells in blind mice fire in about the same pattern as retinal ganglion cells in a mouse that’s not blind.  Voila, vision.

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Reference:  Retinal prosthetic strategy with the capacity to restore normal vision.  Sheila Nirenberg and Chentan Pandarinath.  PNAS epub Aug 13, 2012. doi: 10.1073/pnas.1207035109

Also, a Nature News article by Geoff Brumfiel.  “Prosthetic Retina helps restore sight in mice.”  Nature, Aug 13, 2012.

Image is a schematic of a traditional prosthetic retina.  Credit:  http://www.nsf.gov/od/lpa/news/03/pr03115_images.htm