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It’s important to spend time outside the lab. And before you ask, that’s not why the blog has been dormant. I was teaching this last semester (a general biochemistry lab), plus working with mem...
It’s important to spend time outside the lab. And before you ask, that’s not why the blog has been dormant. I was teaching this last semester (a general biochemistry lab), plus working with members of my lab to get several papers out the door. Stay tuned for more on that front. Next time I'll go back to CRISPR blogging. But for now, let’s talk about staying out of ruts.
The IGI/Berkeley isn't my first group leader rodeo. I spent several years in biotech/pharma, and while there I learned an important lesson the hard way. It's very important to spend time outside of the lab thinking about something other than science. This is not the advice you’ll hear from some mentors, especially when you’re starting out as a group leader. But it’s oh-so-important for the marathon of a career in science.
When you’re just getting started, you'll spend a lot of time executing. Mentors often stress the importance of execution to the detriment of everything else. But there’s a line to draw, and it is entirely possible to spend too much time doing and thinking about the science that's right in front of your face. Why?
As scientists (and in many other professions), we aspire to do something important. This takes creative and surprising ideas - approaches or entire projects that creatively solve difficult problems. It’s very hard to have surprising ideas when you’re in execution mode. Personally, it’s even hard for me to have surprising ideas at scientific conferences. While scientific meetings are great places to hear about diverse science and even synthesize ideas, I rarely find myself coming up with anything radically new. I learn a ton at meetings, but mostly I'm putting facts in my brain. For me, creating a big idea from scratch takes backpacking.
When I go backpacking with my wife, we typically spend 10-15 hours per day hiking. She's also a scientist, but working in a totally different field than myself (infectious disease epidemiology). 10-15 hours is a lot of time for us to talk about far-ranging ideas in each other's fields, but also to chat about ideas other than science and to let our minds wander. For the first half day or so, I find that I’m still thinking about immediate problems in the back of my mind: how do I write the next section of this paper, or how do I help a postdoc design this next experiment. But after that, I stop trying to actively fix problems. Sometimes thinking about other things leaves my brain in a place I didn’t expect. Sometimes those are good ideas. I don’t always have good ideas while backpacking, but when I do they tend to be things I never would have thought of at the lab. By the end of my last backpacking trip, I had mentally outlined two R01 grants and realized out an interesting angle for a biotech NewCo I'm starting.
Maybe your version of backpacking is a pottery class, or binge-watching Netflix, or scuba diving. But having something like that and making enough time for it is super important. Having new ideas lets you see ways around problems rather than through them. And perhaps more importantly, it will save your sanity. Execution mode is hard and stressful. Creative mode is fun and relaxing. To avoid burn-out, mix execution with creativity. Reserve significant time for fun on a regular basis. You’re going to be doing something your entire life. Hopefully your work is globally fun, but it won’t always be locally fun. Try to make your life globally fun. But if you’re a workaholic, rest assured that fun will make your work better. And it will hopefully make you a happier person as well.
My lab recently published a paper, together with outstanding co-corresponding authors David Martin (CHOR...
My lab recently published a paper, together with outstanding co-corresponding authors David Martin (CHORI) and Dana Carroll (University of Utah), in which we used CRISPR to reverse the causative allele for sickle cell disease in bone marrow stem cells. This work got some press, so unlike other papers from the lab I won’t use the blog to explain what we did and found. You can go else where for that. But I do want to explain the motivation behind the work, as well as why we chose this approach.
Next-generation gene editing is already transforming the way scientists do research, but it also holds a great deal of promise for the cure of genetic diseases. One of the most most tractable genetic diseases for gene editing is sickle cell disease (SCD). The molecular basis has been known since 1949, so it’s relatively well understood. Its root cause is in bone marrow stem cells (aka hematopoietic stem cells, or “HSCs”), which are easy to get to with editing reagents. It’s monogenic and recessive, so you only need to reverse one disease allele for a cure. There’s no widely-used cure - though bone marrow transplantation from healthy donors can cure the disease, very few patients get the transplant for a variety of reasons (unlike severe combined immuno deficiency, another HSC disease in which most patients do get transplants). And we know from various sources that editing just 2-5% of alleles can provide benefit to patients (equates to 4-10% of cells, due to the Hardy-Weinberg principle).
But while several groups have tried to make a preclinical gene editing candidate to cure SCD by replacing the disease allele, most efforts have so far met with challenges. Things at first looked very promising in model cell lines, but when people moved to HSCs they found that the efficiency of allele conversion dropped substantially. Efficacious replacement of disease alleles is something of a holy grail for gene editing, but has so far lagged behind our ability to disrupt sequences.
In the field of SCD, problems with sequence replacement have prompted efforts to find a way to use sequence disruption to ameliorate the disease. The most promising of these approaches lead to the up-regulation of fetal hemoglobin through a variety of mechanisms, including tissue-specific disruption of Bcl11A (it needs to be tissue-specific because Bcl11A does a lot of things in a lot of cells). Upregulation of fetal hemoglobin has been observed in humans (during Hereditary Persistence of Fetal Hemoglobin, or HPFH), and is protective against the sickling of adult hemoglobin.
Fetal hemoglobin upregulation strategies are very exciting, make for some fascinating basic research, and could eventually lead to new treatments for SCD patients. There are a few people in my lab working along these lines, and several other groups are doing incredible work in this area. But while the editing is easier, there are still some major challenges to clinical translation, mostly due to the new, mostly unexplored biology surrounding tissue-specific disruption of Bcl11A.
We decided to go back to the basics, and find out if our work on flap-annealing donors and the Cas9 RNP could bring the “boring” approach of sequence replacement back on the table. In this case, we want to be as boring as possible. I’d rather not get surprised by new biological discoveries about gene regulation while we’re working in patients.
We also wanted our approach to be as general as possible - to develop something that works for SCD but is accessible to clinical researchers everywhere. That would fit the democratization theme of Cas9. Both the nuclease targeting reagent and the approach to allele replacement would be fast, cheap, and easy for everyone to use, so that everyone could ask questions about their own system in HSCs and maybe even develop gene editing cures for the particular disease in which they’re working.
This is in contrast to some viral-based editing strategies that seem reasonably effective but are very slow to iterate and have a high barrier to entry. If possible, I’d rather develop something that any hematopoietic researcher or clinical hematologist can pick up and use, with rapid turn around from idea to implementation. That’s the way you get to clinician-driven cures for rare genetic diseases, which is the long-term promise of easily reprogrammable gene editing.
All of the above is why we used Cas9 RNP and coupled it with flap-annealing single stranded DNA donors. We found that we didn’t even need to use chemical protection of the guide RNA or single stranded DNA, though we certainly tried that. In short-term editing we found very high levels of editing, and in long-term mouse experiments we found edits almost five-fold higher than previously reported. I think this is certainly good enough for researchers to start using this method to tackle their own interesting biologies. But for us, there’s still a lot of work to be done before this becomes a cure for SCD.
First, we need to try scaling up our editing reagents and method (Cas9, guide RNA, donors, stem cells, and electroporation) to clinical levels, and we also need to source them with clinical purity. This is a non-trivial step, but absolutely critical to eventually starting a clinical trial. Second, we need to establish the safety of our approach. We found one major off-target site while doing the editing, and it lies in a gene desert. But just because it’s not near anything that looks important doesn’t mean we don’t need to do functional safety studies! (see my previous blog post about safety for gene editing for more on this) We have a battery of safety studies planned in non-human models, and we want to take a close look to see whether we can take the next step into the clinic.
My collaborators and I are committed to the clinical application of gene editing to cure sickle cell disease, and we hope to start a clinical trial within the next five years. But the data will guide us - we want to be very careful, so that that we know that the cure is not worse than the disease. There are many moving parts involved in these translational steps and some of them are a little slow, so please stay tuned as we try to bring a breakthrough cure to patients with SCD.
Cas9 is usually pretty good at gene knockout. Except when it isn’t. Most people who have gotten their feet wet with gene editing have had an experience like that in the following gel, in which so...
Cas9 is usually pretty good at gene knockout. Except when it isn’t. Most people who have gotten their feet wet with gene editing have had an experience like that in the following gel, in which some guides work very well but others are absolute dogs.
This post is all about establishing safety for CRISPR gene editing cures for human disease. Note that I did not say this post is about gene editing off-targets. We’ll get there, but you...
This post is all about establishing safety for CRISPR gene editing cures for human disease. Note that I did not say this post is about gene editing off-targets. We’ll get there, but you might be surprised by what I have to say.
Contrary to what some might say or write, most of us gene editors do not have our heads in the sand when it comes to safety. From a pre-clinical, discovery research point of view, the safety of a given gene editing technology is relatively meaningless. There are many dirty small molecules out there that you’d never want to put in a person but are ridiculously useful to help unravel new biology. Given that pre-clinical researchers have the luxury of doing things like complementation/re-expression experiments and isolating multiple independent clones, let’s dissociate everyone's personal research experience with CRISPR (all pre-clinical at this point) from questions of safety. Those experiences are useful and informative, but so far anecdotal and not necessarily tightly linked to the clinic.
Despite what we gene editors like to think, CRISPR safety is not a completely brand new world full of unexplored territory. While there are some important unanswered questions, there’s a lot of precedent. Not only are other gene editing technologies already in the clinic, but non-specific DNA damaging agents are actually effective chemotherapies (e.g. cisplatin, temozolomide, etoposide). In the latter case, the messiness of the DNA damage is the whole point of the therapy.
Here are a few thoughts about the safety of a theoretical CRISPR gene editing therapy, in mostly random order. I’ll preface this by saying that, while I have experience in the drug industry, I’m by no means an expert on clinical safety and defer to the real wizards.
Let’s start with a big point: as with any disease, the safety of a gene editing therapy is all about the indication. The safety profile of a treatment for a glioblastoma (very few good treatment options for a fatal disease with fast progression) will be very different than a treatment for eczema. And the safety tolerance of a glioblastoma treatment that increases progression-free survival by only two days is going to look different than one that increases overall survival by five years. So there won’t be One True Rule for gene editing safety, since most of the equation will be written by the disease rather than the therapy.
While most of the safety equation is about the disease, the treatment itself of course needs to be taken into account. At heart, gene editing reagents are DNA damaging agents, and so genotoxicity is a big concern. Does the intervention itself disrupt a tumor suppressor and lead to cancer? Does it break a key metabolic enzyme and lead to cell death? As mentioned above, there are plenty of DNA damaging agents that wreak havoc in the genome, but are tolerated because due to risk/reward and lack of a better alternative (I’m especially looking at you, temozolomide). The key to this point is function of what gets disrupted.
With CRISPR, we have the marked advantage that guide RNAs tend to hit certain places within the genome. We know how to design the on-target and are still figuring out how to predict and measure the off-target. But even with perfect methods for off-targets, we’d still need to do the functional test. Consider a “traditional” therapeutic (small molecule or biologic) - while an in vitro off-target panel based on biochemistry is valuable, it’s no substitute at all for normal-vs-tumor kill curves (as an example). And even those kill curves are no substitute for animal models. The long term, functional safety profile of a gene editing reagent is the key question, and with CRISPR I’d argue that we’re still too early in the game to know what to expect. The good news is that ZFNs so far seem pretty good, giving me a lot of hope for gene editing as a class.
You’d think that determining an exhaustive list of off-target sequences would be a critical part of any CRISPR safety profile. But in the example above, contrasting in vitro biochemical assays with organismal models, I consider lists of off-targets to be equivalent to the biochemical assay. I’m going to be deliberately controversial for a moment and posit that, for a therapeutic candidate, you shouldn’t put much weight on its list of off-target sites. As stated above, you should instead care about what those off-targets are doing, and for that you might not even need to know where the off-targets are located.
When choosing candidate therapeutics in a pre-clinical mode, lists of off-target sequences can be very useful in order to prioritize reagents. If one guide RNA hits two off-target sites and another hits two hundred, you’d probably choose the former rather than the latter. But what if one the two off-targets is p53? What if the two-hundred are all intergenic? Given the fitness advantage to oncogenic mutations, the math involved in using sequencing (even capture-based technologies) to detect very rare off-target sites is daunting. Being able to detect a 1 in a million sequence-based event sounds incredible, but what if you need to edit as many as 20 million cells for a bone marrow transplant? That’s twenty cells you might be turning cancerous but never even know it. Now we come right back around to function - you should care much more about the functional effect of your gene edit rather than a list of sequences. That list of sequences is nice for orders-of-magnitude and useful to choose candidate reagents, but it’s no substitute for function.
There are two big questions around gene editing immunogenicity: the immunogenicity of the reagent itself, and on-target immunogenicity if the edit introduces a sequence that’s novel to the patient. What happens when the reagent itself induces a long-term immune response? For therapies that require repeat dosing, this can kill a program (hence a huge amount of work put into humanizing antibodies). A therapy that causes someone to get very sick on the second dose is not much good, nor is it useful if antibodies raised to the therapy end up blocking the treatment. But what about in situ gene editing?
Most of in situ gene editing reagents are synthetic or bacterial and so one might raise antibodies against them, but the therapy itself is (ideally), one-shot-to-cure. In that case, as long as there’s not a strong naive immune response, maybe it doesn’t matter if you develop antibodies to the editing reagent? There are few answers here for CRISPR, and most work with ZFNs has been with ex vivo edits, where the immune system isn’t exposed to the editing reagent. Time will tell if this is a problem, and animal models will be key. Even more subtle, what happens when a gene edit causes re-expression of a “normal” protein that a patient has never before expressed (e.g. editing the sickle codon to turn mutant hemoglobin into wild type hemoglobin)?
The potential for a new immune response against a new “self” protein is probably related to the extent of the change - a single amino acid change (e.g. sickle cell) is probably less likely to cause problems than introducing a transgene (e.g. Sangamo’s work inserting enzymes into the albumin locus for lysosomal storage disorders and hemophilia). But once again, I’ve heard a lot of questions and worry about this problem but very few answers. In vivo experiments are desperately needed, and the closer to a human immune system the better.
As you’ve probably gathered by now, I have a healthy respect for functional characterization when it comes to safety. That’s why it’s absolutely critical that we keep moving forward and not let theoretical worries about arbitrary numbers of off-targets stifle innovation without data. These are tools that could some day help patients in desperate need and with few other options, so let the truly predictive functional data rule the day.
This post is the first in a new, ongoing series: what are big challenges for CRISPR-based technologies, what progress have we made so far, and what might we look forward to in the near future? I’...
This post is the first in a new, ongoing series: what are big challenges for CRISPR-based technologies, what progress have we made so far, and what might we look forward to in the near future? I’ll keep posting in this series on an irregular basis, so stay tuned for your favorite topic. These posts aren't meant to belittle any of the amazing advances made so far in these various sub-fields, but to look ahead to all the good things on the horizon. I’m certain these issues are front and center in the minds of people working in these fields, and this series of posts is aimed to bring casual readers up to speed with what’s going to be hot.
First up is CRISPR imaging, in which Cas proteins are used to visualize some cellular component in either fixed or live cells. This is a hugely exciting area. 3C/4C/Hi-C/XYZ-C technologies give great insight into the proximity of two loci averaged over large numbers of cells at a given time point. But what happens in each individual cell? Or in real time? We already know that location matters, but we’re just scratching the surface on what, when, how, or why.
CRISPR imaging got started when Stanley Qi and Bo Huang fused GFP to catalytically inactivated dCas9 to look at telomeres in living cells. Since then, we’ve seen similar approaches (fluorescent proteins or dyes brought to a region through Cas9) and a lot of creativity used to multiplex up to three colors. There’s a lot more out there, but I want to focus on the future...
What's the major challenge for live cell CRISPR imaging in the near future?
Most CRISPR imaging techniques have trouble with signal to noise. It is so far not possible to see a fluorescent Cas9 binding a single copy locus when there are so many Cas9 molecules floating around the nucleus. So far imaging has side-stepped signal to noise by either targeting repeat sequences (putting multiple fluorescent Cas9s in one spot) or recruiting multiple fluorophores to one Cas9. Even then, most CRISPR imaging systems rely on leaky expression from uninduced inducible promoters to keep Cas9 copy number on par with even repetitive loci. Single molecule imaging of Halo-Cas9 has been done in live cells, but again only at repeats. Even fixed cell imaging has trouble with non-repetitive loci. Sensitivity is also a problem for RCas9 imaging - this innovation allowed researchers to use Cas9 directed to specific RNAs to follow transcripts in living cells. But it was mostly explored with highly expressed (e.g. GAPDH) or highly concentrated (e.g. stress granule) RNAs. How can we track a single copy locus, or ideally multiple loci simultaneously, to see how nuclear organization changes over time?
Someone’s going to crack the sensitivity problem, allowing people to watch genomic loci in living cells in real time. Will we learn how intergenic variants alter nuclear organization to induce disease? Will we see noncoding RNAs interacting with target mRNAs during development? With applications this big, I know many people are working on the problem and I’m sure there will be some big developments soon.
I'm going to take a step away from CRISPR for a moment and instead discuss preprints in biology. Physicists, mathematicians, and astronomers have been posting manuscripts online before peer-revie...
I'm going to take a step away from CRISPR for a moment and instead discuss preprints in biology. Physicists, mathematicians, and astronomers have been posting manuscripts online before peer-reviewed publication for quite a while on arxiv.org. Biologists have recently gotten in on the act with CSHL's biorxiv.org, but there are others such as PeerJ. At first the main posters were computational biologists, but a recent check shows manuscripts in evo-devo, gene editing, and stem cell biology. The preprint crowd has been quite active lately, with a meeting at HHMI and a l33t-speak hashtag #pr33ps on twitter.
I recently experimented with preprints by posting two of my lab's papers on biorxiv: non-homologous oligos subvert DNA repair to increase knockout events in challenging contexts, and using the Cas9 RNP for cheap and rapid sequence replacement in human hematopoietic stem cells. Why did I do this, and how did it go?
There's been some divisive opinions around whether or not preprints are a good thing. Do they establish fair precedence for a piece of work and get valuable information into the community faster than slow-as-molasses peer review? Or do they confuse the literature and encourage speed over solid science?
In thinking about this, I've tried to divorce the issue of preprints from that of for-profit scientific publication. I found that doing so clarified the issue a lot in my mind.
Why try posting a preprint? Because it represents the way I want science to look. While a group leader in industry, I was comfortable with relative secrecy. We published a lot, but there were also things that my group did not immediately share because our focus was on making therapies for patients. But in academia, sharing and advancing human knowledge are fundamental to the whole endeavor. Secrecy, precedence, and so on are just career-oriented externalities bolted on basic science. I posted to biorxiv because I hoped that lots of people would read the work, comment on it, and we could have an interesting discussion. In some ways, I was hoping that the experience would mirror what I enjoy most about scientific meetings - presenting unpublished data and then having long, stimulating conversations about it. Perhaps that's a good analogy - preprints could democratize unpublished data sharing at meetings, so that everyone in the world gets to participate and not just a few people in-the-know.
How well did it go? As of today the PDF of one paper has been downloaded about 230 times (I'm not counting abstract views), while the other was downloaded about 630 times. That's nice - hundreds of people read the manuscripts before they were even published! But only one preprint has garnered a comment, and that one was not particularly useful: "A++++, would read again." Even the twitter postings about each article were mostly 'bots or colleagues just pointing to the preprint. I appreciate the kind words and attention, but where is the stimulating discussion? I've presented the same unpublished work at several meetings, and each time it led to some great questions, after-talk conversations, and has sparked a few nice collaborations. All of this discussion at meetings has led to additional experiments that strengthened the work and improved the versions we submitted to journals. But so far biorxiv seems to mostly be a platform for consumption rather than a place for two-way information flow.
Where does that leave my thoughts on preprints? I still love the idea of preprints as a mechanism for open sharing of unpublished data. But how can we build a community that not only reads preprints but also talks about them? Will I post more preprints on biorxiv? Maybe I'll try again, but preprints are still an experiment rather than a resounding success.
PS - Most journals openly state that preprints do not conflict with eventual submission to a journal, but Cell Press has said that they consider preprints on a case-by-case basis. This has led to some avid preprinters declaring war against Cell Press' "draconian" policies, assuming that the journals are out to kill preprints for profit motives alone. By contrast, I spoke at some length with a senior Cell Press editor about preprints in biology and had an incredibly stimulating phone call - the editor had thought about the issues around preprinting in great depth, probably even more thoroughly than the avid preprinters. I eventually submitted one of the preprinted works to a Cell Press journal without issue. Though I eventually moved the manuscript to another journal, that decision had nothing to do with the work having been preprinted.