CRISPR Digest #10

Goodness gracious. It’s been almost two months since the last CRISPR update. Shame on me. I wonder how we all coped. So here is a concise Christmas CRISPR compendium.

In November, Michael Specter wrote a lengthy article on CRISPR in The New Yorker, which is well written and informative for a wide audience. There was one quote by Eric Lander, the director of the Broad Institute at MIT in Massachusetts, that I found particularly good:

There will be an enormous chart. Well, it will be electronic, and it will contain the therapeutic road map of every trick that cancer cells have—how they form, all the ways you can defeat them, and all the ways they can escape and defeat a treatment. And when we have that we win. Because every cancer cell starts naïve. It doesn’t know what we have waiting in the freezer for it. Infectious diseases are a different story; they share their knowledge as they spread. They learn from us as they move from person to person. But every person’s cancer starts naïve. And this is why we will beat it.

Although I hesitate to use martial words and phrases like “defeat” or “beat” cancer and find it difficult to believe that we will ever completely understand and be able to chart out “every trick that cancer cells have”, I do like the comparison with infectious diseases. Until now I have often tried to explain why I don’t think we can ever cure cancer by likening it to infectious disease agents such as viruses and bacteria: they mutate and evolve in response to changing environmental conditions and drug treatments. And so do cancer cells, but they are not passed between different cancer patients. It’s an obvious difference but I shall take care not to make cancer, a non-communicable disease, seem too similar to communicable diseases in the future.

On a more technical note, earlier this month a paper from Feng Zhang’s lab demonstrated an engineered Cas9 enzyme with reduced off-target effects (Slaymaker et al, 2015). One of the main impediments of using CRISPR/Cas9 for therapeutic applications is that the endonuclease, Cas9, creates DNA double-strand breaks not only at the intended site but also at distant sites, so called off-target sites. In this paper, with a comment here, they study the structure of the enzyme and make changes to the active site that “make sense” to increase the enzyme’s stringency. In everyday laboratory use, however, this more specific Cas9 probably isn’t necessary.

Lastly, Science has picked its scientific breakthrough of the year. Guess what it was:

Interestingly, the People’s Choice for Breakthrough of the Year was the pictures sent back from Pluto by the New Horizons probe. I find it much more fascinating to look deep down into our cells than far out into space, but to each his or her own, I suppose.

[EDIT] Not only did Science choose its Breakthrough, but Nature chose its top ten science influencers of the year – read the article here. Among them is Junjiu Huang who was the first to edit supernumerary human embryos using CRISPR earlier this year (Liang et al, 2015).

References:

Liang P, Xu Y, Zhang X, Ding C, Huang R, Zhang Z, Lv J, Xie X, Chen Y, Li Y, Sun Y, Bai Y, Songyang Z, Ma W, Zhou C, Huang J (2015) CRISPR/Cas9-mediated gene editing in human tripronuclear zygotes. Protein Cell: 1-10

Slaymaker IM, Gao L, Zetsche B, Scott DA, Yan WX, Zhang F (2015) Rationally engineered Cas9 nucleases with improved specificity. Science

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A Second Word on Evolution

Life as a PhD student is busy and doesn’t leave much time for other activities, including this blog. So last time, about a month ago, I left you with the question of how the genetic code may have evolved over time.

For decades some scientists have hypothesised that the genetic code evolved by a so-called direct templating mechanism (also known as the stereochemical hypothesis). That is, the strings of ribonucleotides that make up an RNA molecule could physically interact with amino acids, the building blocks of proteins. This interaction would promote the reaction of adjacent amino acids to start forming a longer polypeptide chain. For a review on the different hypotheses see Koonin & Novozhilov (2009).

One of the proponents of the stereochemical hypothesis is Bojan Zagrovic and his research group at the Max F. Perutz Laboratory in Vienna. They have published several papers on this topic and almost a year and a half ago I went to a symposium where Bojan Zagrovic gave a talk on exactly this topic. I wrote about the various presentations I heard there and then several months later a friend I had met during the Cold Spring Harbor Laboratory (CSHL) undergraduate research programme sent me a message saying he had been inspired, by the blog post, to do some research of his own.

In particular, John wanted to investigate whether there was a pattern behind the observed interactions between the amino acids in proteins and the ribonucleotides in RNA. To do this he and Rachel (another student from CSHL) used computational biology approaches to study a large published dataset of protein-RNA complexes. They found that there is a correlation between these physical interactions and the way the genetic code is laid out.

Once these findings had been made they wrote up a draft manuscript, including some figures, which were produced by Grace, a colleague of John’s at Carleton College in the USA. John asked whether I would mind reading the manuscript to give feedback and of course I was happy to do that. We started e-mailing back and forth and decided to extend the computational experiments, and I edited and expanded the text.

The most interesting result was that we could use the knowledge derived solely from the interaction data (blue and red bars) to predict, significantly more accurately than expected by chance (yellow bars), the amino acid sequence of a protein from its mRNA precursor:

Screen Shot 2015-12-07 at 23.00.28

Combining amino acid-nucleobase affinities with mRNA nucleobase content to predict amino acid sequences without universal genetic code. Copied directly from our paper.

In particular, the proteins that form the ribosome – the molecular machine that translates mRNA into protein in modern-day cells – were more accurately predicted than a random protein from our dataset, possibly suggesting that direct interactions between RNA and amino acids led to the formation of the first primitive ribosomes. However, as you can see, the prediction accuracies do not exceed 15% so all results from this paper need to be taken with a pinch of salt; I think the best we can do is say that our results strengthen the stereochemical hypothesis but by no means prove it. [In any case, the scientific method is only good at disproving theories.] Since the journal, Scientific Reports, is an open access journal anyone can read the paper here.

Overall, I am just proud that we managed to publish our work after a long and iterative process, including one revision. All of this was done long-distance via Skype and e-mail. We were all working or studying full-time at the same time and moreover, we did this without the help of a professor/group leader. In fact, none of us even has a PhD (yet).

Lastly, I have noticed a mini-surge in views of my blog posts pertaining to PhD interviews. Clearly the invitations for the next year have been sent out and I hope whoever is reading this is finding it helpful and: good luck!

References:

Cannon JGD, Sherman RM, Wang VMY, Newman GA (2015) Cross-species conservation of complementary amino acid-ribonucleobase interactions and their potential for ribosome-free encoding. Scientific Reports 5: 18054

Hlevnjak M, Zagrovic B (2015) Malleable nature of mRNA-protein compositional complementarity and its functional significance. Nucleic Acids Research 43: 3012-3021

Koonin EV, Novozhilov AS (2009) Origin and evolution of the genetic code: the universal enigma. IUBMB Life 61: 99-111

Polyansky AA, Zagrovic B (2013) Evidence of direct complementary interactions between messenger RNAs and their cognate proteins. Nucleic Acids Research 41: 8434-8443

de Ruiter A, Zagrovic B (2015) Absolute binding-free energies between standard RNA/DNA nucleobases and amino-acid sidechain analogs in different environments. Nucleic Acids Res 43: 708-718