Hello.
I'm Vivian Cheung.
I'm an investigator of the Howard Hughes Medical Institute and a professor of pediatric neurology
at the Life Sciences Institute at University of Michigan.
This is the second part of my three-part series, where we're going to talk about, It's in our
RNA.
So, when I say that, It's in our RNA, I'm referring to the fact that there are RNA sequences
that are not identical to the DNA templates.
Perhaps this is not so surprising, if we think about what's already known.
So, in the late 1980s, Robert Benne's group identified to uridine insertions and deletions
in the mitochondrial RNA of trypanosomes.
And these are not encoded in the DNA, so these Us are only added into the RNA but not templated
by DNA.
Since then, there have been other examples of RNA sequences being different from their
DNA templates.
And in human we know of two families of deaminases -- APOBEC and ADAR -- where they deaminate
the cytosine and adenosine into uridine and inosine.
And, in recent years, my group identified additional types where RNA sequences differ
from the DNA template.
Since these are not mediated by any known mechanisms, we refer to them descriptively
as RNA-DNA sequence differences or, briefly, as RDDs.
So, let's start with the examples that are known.
So, APOBEC takes.. deaminates cytidine into uridine, and in the... one very well-characterized
example is in apolipoprotein B. So, in apolipoprotein B there is a cytidine that, in the intestinal
cells that have the APOBEC, this CAA is converted to an UAA.
And, in this case, it gives a 48 kiloDalton apolipoprotein B, because UAA encodes a stop
codon, so you ended up with a shorter protein that is 48 kiloDaltons and this is essential
for chylomicrons to carry cholesterol from the intestine to blood.
In other cells, that do not have this process, in liver, we have a full-length apolipoprotein
B ending up with a 100 kiloDalton form of apolipoprotein B protein, and this protein
is essential for binding of LDL to LDL receptor in the liver, for processing and release of
cholesterol.
So, as we can see that... both of these forms are essential and therefore this editing is
also essential.
Other examples are mediated by this ADAR family of deaminases...
ADAR family of deaminases have more targets than APOBEC.
And what ADAR does is to take adenosine and deaminate the cyt... the adenosine into inosine.
And, if you look at the structure of inosine, it's actually very similar to guanosine.
And, in fact, the... the inosine is base paired with cytosine, and the inosine is read as...
by the translation machinery as a guanosine.
So, even though I will refer to these as A-to-I editing, essentially, functionally, they are
A-to-G editing, so I'll refer to those interchangeably, either as A-to-I or A-to-G editing.
ADAR-mediated editing is found in all metazoans.
Knockout of ADAR in mice causes either embryonic lethality or that they have severe neurologic
diseases.
An example is in the AMPA family of receptors, includ... including the glutamate receptor,
where a CAG is edited to a CGG.
And this converts a glutamine to an arginine.
And this allows the AMPA receptor to prevent calcium influx and therefore allows the neurons,
which usually have these AMPA receptors, to prevent against excitotoxicity.
Besides the glutamate family of receptors, there are other channels and receptors with
RNA editing, and this includes the calcium channels or the potassium channels, and editing's
are also found in receptors such as the serotonin receptors.
And all these are essential for normal functions of neurons.
Several years ago, as high-throughput sequencing technology became available, since our lab
is interested in identifying DNA sequences that influence gene expression, we were very
eager to learn ways that we can sequence the DNA and RNA using the same technology platform.
So, we thought we would do a simple experiment, where we take cells from... the same cells...
extract DNA, extract RNA, and carry out both DNA sequencing and RNA sequencing, and, as
a way to check our sequencing technique, we thought we would just compare the same ones.
Those results... of course, since the DNA and RNA are from the same cells, we expect
them essentially to be identical, except perhaps for a few A-to-G editing or C-to-U editing
sites.
But, to our surprise, what we found was thousands of sites that differ from... where the RNA
sequences differ from the corresponding DNA.
And even for the A-to-G known sites, there are many more than what we would expect.
So, in some way, you can say we have a pretty simple question, of whether RNA sequences
are identical to their corresponding DNA.
But even though the question is simple, in order to find the solution, it's actually
not so simple.
A genome is very large; it's about 3 billion nucleotides in length.
And the... each sequence read is only, at most, a few hundred nucleotides in length.
So, in order for us to compare our RNA to our corresponding DNA, we have to be able
to assemble all the sequence reads, millions of them, into a cohesive picture before we
can compare them.
This is, in some way, very similar to if you're interested in comparing two puzzles.
You have to assemble all the pieces of the puzzle before you can compare them.
Similarly, we have to assemble all the sequence reads from RNAseq as well as from DNAseq before
we can compare them.
And the way that we do these sequence assemblies is by following a pretty complicated set of
analysis steps that we have published previously, and I won't step them through here with you.
Instead, what I would do is to point out some of the key steps.
In order for us to be sure of the DNA sequence and RNA sequence at a particular site, in
order for us to compare them, we only compare the ones that at least have ten high-quality
reads covering those sites, so we're certain of the sequence of that site.
And, for the DNA side, we want to be sure that they're only homozygous sites, so we
do not look at any SNPs or any sites that have sequence variants.
And, in order for us to call an RDD, at least 10% of those RNAseq reads have to be different
from the corresponding DNA, and those 10% have to be represented by at least two different
reads.
In addition, we require that two individuals have the same RDD.
So, let me give you one example.
Here is a ribosomal protein, where 56 reads from the DNAseq shows us that it's a G, so
we're pretty sure that this individual really only has a G at this site.
Then, in the corresponding RNA, we found there are 153 reads.
We found the G that is corresponded by... encoded by the underlying DNA, so there are
137 of those G reads.
But, in addition, we see 16 reads with a T sequence.
That T certainly is not encoded by the DNA, so here we will call this to be a G-to-T RDD.
These are certainly surprising findings, so we carried
out a set of experimental analyses to make sure that what we're seeing are indeed RNA
sequences that differ from the corresponding DNA.
The one thing we want to be sure of is that we're mapping both the RNAseq reads and the
DNAseq reads correctly, that they are really only at unique sites, that we don't want to
be comparing an RNAseq from one region of the genome to a highly similar but different
DNAseq in another part of the genome, so these have to be absolutely only mapping to one
site and therefore unique in that... in the genome.
To do that... so, let's take this example where we...
I have the RDD site marked with an X.
We put a primer in front of that RDD site and do primer extension.
We take the resulting sequence, and clone and sequence them by Sanger sequencing.
We then take the resulting clones and remap them back to the genome, to be sure that they
only map to one region.
And so... for example, in the first line, here, we have a G-to-A RDD.
We sequenced 31 clones in the forward direction and 9 clones in the reverse direction, and
all of them are mapping back to that chromosome 1 region, so we know for sure that it's mapping
to a unique and only one part of the genome.
And we did this for all the sites, here, thus showing that, indeed, our RDDs are in unique
parts of the genome.
The second step we used for validation is to check the reverse transcription.
So, we take a DNA, control DNA of known sequences, we do... then, transcribe them into RNA by
in vitro transcription, take those resulting RNAs and put them through reverse transcription
and sequencing.
It's the same way that we would do with an RNA sample.
From those results, we know that the reverse transcription error rate is only... it's less
than 0.2%, much lower than our RDD frequency, so, suggesting that reverse transcription
error does not explain our RDD findings.
Of course, sequencing error is very context-dependent, so we want to be sure that RDDs are not found
in sites with particularly high sequencing error rates, that they are particularly difficult
to sequence, for example.
So, what we did was to take regions that are flanking the RDD site and look at those sequences
carefully.
And what we did was... found was that there are very few errors, sequencing errors, around
those RDD sites, so, again suggesting that sequencing error cannot explain our RDD findings.
But, just to be sure, we also went back to the same cells, re-extracted DNA, re-extracted
RNA, and sequenced them using different methods, both by Sanger sequencing, by pyrosequencing,
and by droplet digital PCR.
And, for each of these results, we concluded that, at most, the false discovery rate in
our RDD identification is somewhe... is less than 10%, somewhere between 1 to 7%.
So, the majority of our RDD can be validated using different methods.
So, now let me tell you a bit about these RDDs.
What we found was that there's a great deal of consistency across individuals.
So, for example, if we see a C-to-A RDD in one person, we'll see a C-to-A RDD in the
other person as well; it won't be a C-to-G or C-to-U RDD.
So, as a result, when we look at the RDD types across individuals, they are highly similar,
as shown by the four examples here.
So, if there are really so many RDDs, we should be able to see them in the RNA sequence databases.
And so these expressed sequence tags essentially are cDNA clones that have been sequenced.
So, we looked into databases with these EST clones and indeed found these RDDs.
So, for example, here, in the first example is a heat shock protein where it's an A-to-C
RDD.
We see the C form that is not encoded by the DNA in EST from head and neck samples, from
B cell samples, and I'm showing you on the rest of the slide, here, some other RDDs that
we found just lying around in the EST database.
Let me return to variation, that I talked about in the first part of my talk.
There, I talked about... there's a lot of individual differences in gene expression
levels.
Here, I want to talk more about how RDD level also varies across individuals.
So, in this example is an A-to-G RNA editing site that was first identified in B cells.
Then, we looked at brain cortex from 3 individuals, 4 skin samples from 3 individuals, liver and
muscles from another 3 individuals.
We found that A-to-G editing was found in all samples, so this is found across all different
cell types, as well as different individuals.
But there's quite a bit of difference in editing levels between B cells, brain cortex, and
foreskins -- they are lower -- whereas we see higher editing in liver and muscles.
And each of the samples, here, there... it's done in 3 individuals and we see some individual
differences as well.
We see this not only in the RNA editing shown here, but also in other types of RNA editing
and other sites, and in other RDD examples as well.
As I mentioned briefly in the first part of my talk, that there are also individual differences
in RNA editing and RDD levels.
And each individual editing level, RDD level, is shown as a black dot, so we see that, in
some individuals, at a site there might be 40% editing or RDD.
In other individuals, there might be 100% percent.
And when we look at individuals who are genetically identical, such as in twins, we see that the
twins are very... have actually a pretty similar... in this case, RDD level of T-to-C than people
who are unrelated.
So, what I've shown here is that one...
although we have the same DNA sequence, we can end up having a different RNA transcript
by just changing the sequences at the RNA level.
But, can it go to the protein level?
It's kind of... one of the... kind of an important question.
So, let me come back to this ribosomal protein, where we see G in the DNA, and in some of
the RNA transcripts the G is converted to a T. And this is in the coding regions of
this ribosomal gene, so it changes an alanine to a serine.
The important question is, do we see both the alanine and serine form of this ribosomal
protein?
So, for the same cells that we sequenced the DNA and, say, sequenced the RNA, what we did
was also do a deep proteomic analysis by mass spectrometry.
So, from the mass spectrometry we indeed found both the alanine as well as the serine form
of this ribosomal protein.
So, showing that these RDDs are indeed propagated into the protein levels.
I show you this one example, but for many of these examples where the RDDs are in the
coding regions, we indeed find two protein isoforms that are being translated.
So, besides... after we have shown that these RDDs are pretty widespread throughout our
cells, other groups have also found and given examples of these RDDs that they identified.
So, in summary, what I've shown you is that there are... all 12 types of RNA-DNA sequence
differences are found in human cells, and that RDDs encoding exons are translated into
proteins, and there are individual differences in RNA editing as well as RDD levels.
And I'd like to acknowledge all my... individuals in my lab who conducted the work, as well
as our collaborators at University of Pennsylvania, at Cornell University, at NIH, as well as
at John Hopkins University.