One of the first things friends told me when I got the Prize was that I should be sure to come to Lindau for the meetings.
And I’ve been doing it now for 30 years.
It’s always a wonderful experience.
I meet old colleagues and new ones and of course the new crop of students.
So today I’m talking about synthetic biology.
And this means different things to different people.
To me if you’re using chemically synthesised genes or DNA in your work, you’re essentially a synthetic biologist.
It’s the same as in the past if you work with DNA and RNA you were a molecular biologist.
So that’s a very simple way of putting it.
A more elaborate definition of synthetic biology is that it’s the field of research
that engages in the design and assembly of genes and genetic pathways from chemically synthesised DNA
to produce organisms with new and improved functions that do not already exist in nature.
The key thing in synthetic biology is that you can do things...
that you’re not simply shuffling pieces that exist in nature but you can create new pieces.
A branch of synthetic biology is synthetic genomics which focuses on rewriting
and activating entire genomes or chromosomes to produce new ‘synthetic cells’.
Synthetic biology postulates an analogy to computers that the genome of a cell is the operating system
and the cytoplasm is the hardware.
So the DNA genome is simply a piece of software written in a 4 letter code, AGCT,
whereas in a computer it’s zeros and ones.
The cytoplasm is the hardware and that runs the operating system or the genome.
So the cytoplasm contains all of the parts and for example ribosomes, enzymes and proteins
that are necessary to express the information in the genome.
On the other hand the genome contains the information necessary to produce the cytoplasm and the cell envelop
and to replicate itself.
So each is worthless without the other.
We wouldn’t have any synthetic biology without dramatic advances in sequencing and DNA synthesis
that have occurred in the past couple of decades.
And this is why it’s uniquely a 21st century field of research and technology.
We see on the graph here that the ability to read DNA sequence really began in earnest around 1990
with the introduction of the first semi-automated DNA sequencers.
It was still expensive at that stage and you could only produce a limited amount of data.
But as these machines became fully automated and had much larger capacity,
the cost of reading dropped rapidly and continued to drop.
And then all of a sudden in the early 2000s there was a precipitous drop
as highly parallel DNA sequencing machines were introduced to the point that now the 1,000 dollar human genome is within reach.
On the other hand, writing DNA rather than just reading it,
writing for example the synthesis of oligonucleotides has not dropped nearly as dramatically.
But I think at some point we’ll experience a much more dramatic drop as this gets to the level of chips.
Writing DNA sequences, for example gene synthesis, has dropped about 2 orders of magnitude but it’s still quite expensive.
And that’s what’s limiting the development of the field.
What we need is cheap DNA synthesis and fast accurate DNA synthesis.
Here is another way of looking at things.
The blue curve shows the rate at which the number of transistors on electronic chips have increased.
This is Moore’s law which states that the number of transistors doubles every 18 months.
And that’s been a consistent picture for many years now.
On the other hand reading DNA, because of the highly parallel new sequencers, has taken a much more rapid course.
And even writing DNA is exceeding the rate of the classic Moore’s law.
In addition, the sizes of DNA that can be made is steadily increasing.
The largest currently is about a megabase of synthetic DNA.
Now, the power of synthetic biology is that you can synthesise any DNA sequence.
The older methods of genetic engineering that use restriction enzymes, PCR and oligonucleotide mutagenesis for example
always operate on sequences that already occur in nature.
With the synthetic biology approach you can take a gene,
you can change the codon sequence to that gene to fit it...
to adapt it to the codon frequencies for example in coli or whatever.
You could chemically synthesise that and you now have a sequence, a DNA sequence that does not occur anywhere else in nature.
You can’t do that with any of the older methods of engineering.
So it’s very powerful.
However we still largely work with what nature has given us, genes, promoters, ribosome binding sites.
And I’m talking particularly about single cell organisms which is the major focus currently.
Transcriptional terminators, operators and so on.
The genes themselves have evolved over millions of years by natural selection.
We don’t know yet how to sit at the computer and invent a new gene or amino acid sequence
that will fold into a predetermined structure.
Let alone have a specific enzymatic activity.
That’s in the future.
And I don’t know how far in the future.
As far as proteins go we can at best modify existing protein designs.
And that’s certainly facilitated by the ability to change the DNA sequence that codes for a particular protein.
So we’re still limited with adding, deleting, altering or rearranging DNA sequences using parts that generally already exist.
Now, I always have trouble understanding what people are telling me unless they give me an example.
So I’m going to give a few examples here.
One is the introduction of BioBricks which is really an idea developed about 10 years ago by Tom Knight at MIT.
And there’s a registry where you can go online and pick out any of a number of promoters with known strength.
Or you have a large registry of genes that you can use or other regulatory parts.
And these are provided.
They’ll actually send you the DNA or the sequences involved.
And you can put together various logical circuits with these pieces.
So there has developed kind of an international competition among students every year.
It’s called iGEM where student teams are given kits of parts
and they have to come up with novel new circuitry
and put it into a biological system for example coli and demonstrate that it does some particular operation.
That’s become quite popular.
Another example is what we call refactoring of existing pathways and genomes.
It’s a term that comes from software developers where you take for example a code that was written perhaps by some amateurs,
a spaghetti code that is very hard for somebody else to come and make sense out of it.
But if you refactor it and put it into a more logical group of functions without changing the output of the system,
that’s called refactoring.
Refactoring in the biological system can improve on evolution by arranging genes in a more logical and modular fashion.
Here’s an example. This is from Chris Voigt’s lab at MIT published a couple of years ago.
This is the classic example of refactoring.
He took the nitrogen fixation gene cluster from Klebsiella oxytoca which has about 20 genes in it. It’s a cluster.
Not all the genes in the cluster have to do with nitrogen fixation.
So he threw those away, he took the genes that were relevant to nitrogen fixation,
resynthesized them with codon frequencies appropriate for coli, he eliminated non-coding DNA,
removed transcription factors and basically randomized the codons but in an appropriate fashion.
He then organised into 4 operons, whereas the original cluster had 7 operons.
He added synthetic regulation and then some controlling and synthetic circuits
and comes up with a nice logical looking group of genes that people can understand.
And it’s something you can plug into an organism if you want to achieve nitrogen fixation.
You can also refactor genomes of various sorts, particularly viral genomes.
One of the first examples was refactoring T7 phage.
Here’s an example of refactoring of PhiX174.
And there are 6 genes that are overlapping.
And you simply refactor it by putting them all into a linear array without overlaps.
Building of synthetic bacterial and yeast genomes has come into the limelight recently.
Our group synthesised a complete bacterial genome and assembled it in yeast
and propagated it as DNA in a yeast cell and then transplanted it into a bacterial cytoplasm to produce a ‘synthetic cell’.
Just a couple of months ago an article was published in which chromosome 3 of yeast was completely replaced with synthetic DNA.
And they made some rather dramatic changes in the sequence and reduced the size of it by about 14%.
This opens up the ability now to completely remake the yeast chromosomes.
So our work in the past 10 years has been focused on what we call the minimal cell concept.
We’re interested in using synthetic genomics to build a minimal cell that has only the core machinery of life.
If you look at bacteria as they exist in nature, they often have fairly large genomes, several million base pairs.
The core machinery to run the cell is a very small fraction of that, maybe a few hundred genes.
Most of the DNA is occupied in various adaptive functions that enable the cells to adapt to many different issues in nature.
What we want to do is throw away all the adaptive functions and take only the core functions.
And we want to create a minimal cell with those core functions with the idea
that we’ll hopefully understand life better when we achieve that.
Richard Feynman has a famous quote: “What I cannot create I do not understand.”
We hope that if we create we will understand.
Our target for minimisation is mycoplasma mycoides which already in nature...
It’s a goat pathogen by the way. But it’s a small cell.
You can culture it readily in the laboratory.
And it makes colonies on an agar plate.
It’s capable of totally independent growth.
It has all of the requirements for free life.
It has a genome that’s a little over a million base pairs.
There are 865 protein coding genes and 43 RNA genes.
And it grows at a respectable rate of 60 to 70 minutes doubling time.
So if we’re going to take this cell and try to strip away unnecessary genes
and build a minimal cell, we need to be able to do 3 things.
The first thing, the thing that we were most worried about was
whether we would be able to chemically synthesise the genome
and have it as free DNA in a test tube and then bring it back to life.
We had to develop a method to transform it into a receptive compatible cytoplasm of a recipient cell
and have it take over that cell, a process we call transplantation.
We also had to develop methods to build large DNA molecules.
And thirdly we had to have methods to identify what genes to keep and which to throw away.
Genome transplantation in our original conception...
I think this model is probably fairly accurate.
We would take naked donor DNA genomes -for example extracted from a mycoplasma donor cell or perhaps extract it from yeast-
and introduce it by methods that are similar to regular DNA transformation into the recipient cell.
And then using selection for the donor genome after a number of generations every part of the cell
that came from the original genome is replaced and you have a cell now that has only the donor genome.
And all the parts in the cytoplasm are specified by that donor genome.
Just very briefly, the key here was to be able to extract the large DNA molecules intact
and then be able to somehow get them across the recipient cell membrane and to establish themselves.
So we took recipient cells, we took very gently extracted donor DNA,
combined them together in the presence of polyethylene glycol
which facilitates the uptake and then grow for a period on the plate.
And we were successful after a year or 2’s work in getting a procedure
that’s quite robust and yields a couple of hundred transplants typically.
We also learned how to put our mycoides genome into a yeast cell using yeast vector sequences that we added to the genome.
And then we learned how to extract the DNA intact out of a yeast cell and transplant it.
It took us a while to realise that in the yeast the methylation would be lost.
And so the genome was subject to restriction then by the recipient.
But we overcame that in either of 2 ways.
We removed the restriction systems from the recipient cell or we methylated the DNA.
And both of those worked.
So we had a means of reasonably efficient ability to take DNA in the test tube, a whole genome, and bring it to life.
We also at the same time were working on chemical synthesis and assembly of the whole genome.
And this was done in steps starting with oligonucleotides,
building one kilobase pieces which overlapped each other by 80 base pairs so they could be stitched together.
And then we went to 10 Kb pieces, 100 Kb pieces and finally the complete genome which was assembled in yeast.
In fact all of the last 3 assembly steps occurred in yeast which has a very powerful recombination system
that can operate on the overlaps to assemble the molecules together.
Now I turn to how we determined what genes we want to throw away and which ones we want to keep.
So we’ve developed to a high degree a process called global transposon mutagenesis.
If you introduce a transposon carrying an antibiotic resistance marker into a cell, it will jump somewhere into the genome.
If it goes into a gene, it will disrupt that gene and knock it out.
If the gene is essential, then you won’t get any colonies from that particular event,
whereas if it goes into a non-essential gene, you will get colonies which you can recover.
And then you can determine where the insertion site is by sequencing.
So using all of that methodology that we developed we’re ready to now start designing a minimal genome
and then building it and assembling it in yeast and then testing to see whether what we design works.
If it doesn’t, we make additional changes to the circuit again.
If we get something that does work but not very well, we can do modifications
and just keep doing this design-build-test cycle.
So, first let me elaborate more on our results of our transposon mutagenesis.
We used Tn5 because you have... you can purchase the transposase.
And so we constructed a small transposon
that has just a puromycin resistance gene flanked by terminators
and the 19 basepair sequences that are required for transposition.
We take that DNA which is about a kilobase in size and add stoichiometric amounts of the transposase
which binds to the ends and now makes it a very active transposon.
You prevent it from jumping into itself or into other DNA in the test tube by excluding, by having no magnesium present.
But once you introduce it into a cell,
it’s now a very active transposon and will jump into the cell, into some place in the genome.
So we prepared a library.
We introduced the transposon into our mycoplasma mycoides, so-called wild-type cell, and allowed it to insert.
And then we plated and obtained about 80,000 colonies typically on one of several plates.
First of all each colony arises from a cell that has a single insertion of a transposon somewhere in the genome.
The fact that the colony grows up means that you didn’t go into an essential part of the genome.
So we pool the colonies here and make what we call Library 0.
We isolate from a sample of that pool, we isolate DNA.
And then another sample of that pool we passage 4 times for about 50 doublings with the idea
that at the end of those 50 doublings only the fast growing cells will predominate.
In other words if our transposon jumped into a gene that’s absolutely non-essential,
that will appear in this final library which we call Library 4.
We then take those libraries and use inverse PCR
to determine the junction between the ends of the transposon and the genomic sequence.
Here’s a 19 base pair sequence.
And this is genomic DNA here.
So we were able to find the insertion points of the transposons.
And if you then map those on to a gene map...
Here for example is a cluster of genes that are obviously non-essential.
And we confirm that by deleting those and showing the cell was viable.
Here’s genes that are essential.
These have to do with initiating of DNA replication for example.
And you look through.
There are various clusters of genes that are non-essential with a few genes that are essential and so on.
So we can catalogue all of that in an excel sheet.
So, we saw 3 types of patterns.
Here is a gene CTP synthase which is not hit at all.
There are no transposons either in the 0 Library or the Library 4.
That’s an essential gene.
Here’s a gene that is hit by both libraries so it’s represented well.
It’s a fast growing knock out so that’s a non-essential gene.
And here’s one that’s hit heavily in the first library but it’s totally lost in the L4 library.
So it’s growing slowly, its impaired.
Whatever that gene does it’s not essential for life but it affects the viability of the cell.
And so we could classify all of our genes.
And we are able to put them into this large group of non-essential genes.
Here’s the core set of essential genes.
And here are the ones that are impaired.
So our design will focus on this set of genes.
And so we simply took all of the genes that we wanted to keep and put these purple arrows over them.
The ones we’re going to throw away are the yellow areas.
And we then just reduce the genome by putting together all of the genes that we want to keep.
So we have a genome that we call reduced genome design.
It’s about half the size of the original genome.
And here are sizes of the... So an important thing is that we... the designs are made on 1/8th sections of the genome.
So that we can troubleshoot each section if it doesn’t work very well.
And here’s a relative size of the wild-type genome in our reduced design.
So we went back to our next stage which is synthesis.
And Dan Gibson at our company, Synthetic Genomics Inc.
has developed automated DNA synthesising machines
which can fairly quickly assembly each of the 8 reduced genome design pieces which he then ships to Rockville.
You see in San Diego where I am we do the design and the building, Rockville does the testing.
So we just send the pieces of DNA there.
So, this is just briefly the 8 piece strategy.
We wanted to test each of the individual pieces.
So we combined each reduced piece with 7 wild-type pieces and then tested whether that genome is viable.
And it turns out all of the 8 pieces we designed work.
Some of them give a cell that’s a little bit slow in growth but they’re all quite viable.
So the idea now is can we combine all those together and be finished basically, have our reduced genome.
And the answer is no.
We couldn’t put them all together but we could put different groups of them together.
For example, here we have 2, 4, 6, 7 and 8 together and produce those colonies that are a little bit slow growing.
Why doesn’t it work?
We think it’s because there are undiscovered functional redundancies.
In other words there are essential functions that are...
So maybe fragment 6 has an essential function that if you knock it out by itself,
the cell is ok because the essential function is also carried by a gene on segment one.
But if you knock out both of those...
In other words if we combine segment 1 with segment 6, that vital function is knocked out.
And so we’re in the process now of discovering those few redundancies and correcting them.
So, in summary then we’ve designed 8 reduced segments that are viable.
And they work in several different combinations.
And eventually we hope to get a cell that has no genes that you can remove without severely effecting the growth.
Best case we think perhaps 50% reduction with a doubling time that’s not too far off from the wild-type.
And it will have a genome that’s smaller than any free living cell that’s found in nature.
That’s our goal. And just finally what can we do with a minimal cell?
There are a number of different things.
One that we have a lot of interest in is reorganising or defragging the cell so to speak.
Bringing all the genes that have common...
that are involved in common processes would be brought together so that you have a genome that’s very understandable.
Here are some of the people that have worked on this.
Thank you.
Applause.