Articles, Blog

2019 Killian Lecture: Gerald Fink, “What is a Gene?”

August 12, 2019

Good afternoon, and welcome
to the 47th annual James R. Killian, Jr. Faculty
Achievement Award lecture. I’m Susan Silbey, chair
of the MIT faculty. Before we begin, I will
ask that you please silence your electronic devices. And also, I invite, though
I don’t see too many, those at the edge of the room– there aren’t many seats
left to move to the middle, just in case few
stragglers come in, and we can accommodate them. The James R. Killian, Jr.
Faculty Achievement Award was established in
the spring of 1971 as a permanent tribute to
Dr. James R. Killian, Jr., president of MIT
from 1948 to 1959 and chairman of the
corporation from ’59 to ’71. The purpose of the Killian Award
is to recognize and celebrate extraordinary achievement
by MIT faculty members and to communicate their
accomplishments to members of the institute community. The title of Killian
lecturer is the highest honor that the MIT faculty may
bestow on a colleague. It is our privilege today to
be addressed by Gerald Fink, Margaret and Herman
Sokol professor in biomedical research, American
Cancer Society professor of genetics, and a member
of the Whitehead Institute for Biomedical Research. As noted by our colleagues on
the Killian Award Committee, which was chaired by
Professor Michael Strano, Professor Fink is among
the very few scientists who can be singularly credited
with fundamentally changing the way we approach
biomedical problems. He has made numerous
seminal contributions to understanding
the fundamentals of all nucleated
life on the planet, significantly
advancing our knowledge of many cellular
processes critical to life systems and human diseases. His research, which
includes understanding infectious disease,
how cells divide, and how genes are
controlled, has proved critical to modern biology. Professor Fink’s overarching
scientific achievement has been the development of
baker’s or brewer’s yeast into the premier model
for understanding the biology of
eukaryotes, that is, organisms whose cells have
a nuclei, which includes all known animals and plants. Yeast transformation
advanced the field of biology by making it possible
to do gene editing and targeting in yeast similar
to contemporary CRISPR, but– an importantly but–
approximately 40 years earlier. The ability to gene edit
yeast spurred many scientists to take a yeast
molecular biology course that Professor
Fink taught for 17 years at Cold Spring
Harbor Laboratory. Former students in that course
include three Nobel laureates and 28 members of the
National Academy of Sciences. Professor Fink’s discovery of
transposable elements in yeast laid the groundwork
for future studies of transposable elements. A later discovery of
filamentation in yeast uncovered a genetic mechanism
by which disease-causing fungi switch from a benign
to an infectious form and invade human tissues. This has led to a
better understanding of how Candida albicans, a
persistent human pathogen, can overpower the immune
system, offering clues that may lead to the development of
life-saving antifungal drugs. Professor Fink joined the
MIT faculty and the Whitehead Institute in 1982. He served as director of
the Whitehead Institute from 1990 to 2001,
establishing during this time the Whitehead MIT Genome
Center, which in 2003 became the cornerstone facility of the
newly launched Broad Institute. MIT’s premier place in the
world of biological research is due in no small part to
Professor Fink’s selfless, tireless, and generally
unheralded work in creating and nurturing
these institutions. Professor Fink’s
scientific accomplishments have been heralded and
recognized by many awards, including the National Academy
of Sciences Award in Molecular Biology, the George W. Beadle
Award from the Genetics Society of America, and
the Gruber International Prize in Genetics. He has served as president of
both the American Association for the Advancement of
Science and the Genetics Society of America, and he is
an elected member, or fellow, of the National
Academy of Sciences, the American Academy
of Arts and Sciences, the Institute of Medicine,
and the American Philosophical Society. He chaired a National
Research Council committee that produced a highly
influential report entitled Biotechnology Research
in an Age of Terrorism: Confronting the
Dual Use Dilemma. This report recommended
policies and practices that would allow
the continuation of legitimate research while
preventing the destructive use of biotechnology. In addition,
throughout this time, Professor Fink mentored more
than 100 graduate students and postdoctoral fellows and
is approaching the publication of his 300th paper. This is an extraordinary career. Jerry, will you join
me up here, please? [APPLAUSE] Gerald Fink, it is my privilege
to represent the MIT faculty in honoring you with James
R. Killian, Jr. Faculty Achievement Award. We recognize your
pioneering contributions to the genetics and engineering
of eukaryote systems and your scientific and
educational leadership in the biological sciences. Congratulations. [APPLAUSE] Thank you, Professor Silbey,
for your generous introduction and the committee for choosing
me as the Killian lecturer. I consider it a
tremendous honor, especially because it’s an award
coming from my peers, family, rather than from outsiders, who
often know your achievements, but don’t know your
contribution to your institution and colleagues. As I look out on
this audience, I realize what an amazing
group of talented scientists we have here at MIT
and how fortunate I am to be a member
of this community. I’d also like to thank
the institute affairs office for saturating the
campus with posters of me. [LAUGHTER] As I walk along Main
Street, strangers now stare at me as if I were someone
they must have seen before, but can’t quite recognize. [LAUGHTER] Now, I’m a real American. On my father’s side, my
grandfather came from Scotland. And on my mother’s
side, my grandfather came to America from
Lithuania through Brazil. I grew up in Freeport, New
York, a complacent suburb on the south shore
of Long Island, notable only for its easy
access to New York City and for its access to
Jones Beach State Park. The high school was
sleepy and uninspiring. Its heroes were the athletes
and their admiring cheerleaders. The successful launching
of the Russian satellite Sputnik in October 1957
awakened this sleepy town, electrified the
nation, and changed the course of American
science and my future. And I can still remember
the thrill one dark fall evening in 1957 when I
went out in our backyard and was hypnotized by
the new star in the sky as Sputnik slowly raced
towards the horizon. Overnight, science became
a national priority energized by the dread
of Soviet technology and technological superiority. Local engineers from Grumman
and Republic Aviation came to our high school and
taught math courses pro bono. Not only did this improve
my science education, but perhaps, equally
important for a teenager, the cheerleaders suddenly took
an interest in the smart kids. [LAUGHTER] I came to MIT in 1982 after 15
years as a professor at Cornell as the first appointed professor
at the Whitehead Institute. Now, probably, looking out here,
most of you probably cannot recall, but the idea of an
independent Whitehead Institute associated with MIT was
considered a radical experiment at the time. MIT was not sure it
wanted to take the leap. Concern among the
faculty spawned articles in The New York Times. The third and perhaps last time
the faculty in Massachusetts will convene to try to
resolve one of the most divisive conflicts– I think there’s a bit
of hyperbole here. [LAUGHTER] But it was a difficult time. Now, while still at Cornell, I
was on the visiting committee for the biology department
when the faculty was debating the wisdom of having
this separate institute. Now, as you know, the visiting
committees here at MIT are taken extremely seriously. At the committee meeting,
we sat around in a circle with the faculty, and there was
heated discussion, pro and con, about the possibility
of the Whitehead. I was fortuitously sitting next
to the president of MIT, Howard Johnson, who whispered
to me, do you think this would
be a good thing? I whispered back, absolutely
wonderful opportunity. Two weeks later, after I
had this whispering exchange with President Johnson, MIT
agreed to the Whitehead, and I was immediately offered
a position at the new Whitehead Institute. [LAUGHTER] Let me assure you
there was no collusion. [LAUGHTER] As we now know, MIT
made the right decision. Whitehead turned out to be a
successful pioneer, a pioneer experiment that
led, in my opinion, to the blossoming of
the Kendall Square area. And we should not
forget that Whitehead was the home of the
Human Genome Project and the model for the
many other institutes that have grown up
here now at MIT. Moreover, three previous
Killian awardees were Whitehead faculty. I came to MIT with
some trepidation. I was told by the chairman
of the biology department not to go across Main
Street after dark. It was too dangerous. Well, there was really nothing
on this side of Main Street, either, except MIT. So this is what it looked
like when the Whitehead was being constructed. Here’s Main Street. No Novartis. Back here, I think,
was a building that store popcorn and
beer for Fenway Park. OK. [LAUGHTER] There was only one
restaurant, the F&T Diner. I didn’t know– oh, yes. This was construction
of the Whitehead. This was the first
director, David Baltimore. This is Jack Whitehead, who
engendered this New York Times article. And you may not believe
it, but that’s me. [LAUGHTER] I didn’t know what it
would be like to leave the rustic, pastoral
countryside of Ithaca to come to the big
city, but my confidence was buoyed, as it
has always been, by my family, my wife, Rosalie,
and my two daughters, Julia and Jennifer. But MIT turned out to be much
better than I had imagined. It’s a real community
of scholars, and I was welcomed
into this community. Perhaps no better
indication of what I mean by a
community of scholars is to note the wonderful
people with whom I have collaborated in
research and published papers over the years. They were not only from
biology, this group on the left, but also from a number
of other departments. Alex van Oudenaarden when he was
in physics, Greg Stephanopoulos and Bob Langer in chemical
engineering, and David Gifford from EECS, all co-authors
of papers with me since I’ve been here. This is unique to
many institutions. Furthermore, it wasn’t
just in research. Another sense of
community is the number of compelling colleagues
who taught courses together with me. This group on the left taught
undergraduate courses with me. And the group on the right
taught graduate seminars together with me and
some graduate courses. Bob Horvitz probably
doesn’t remember that we taught a course
a number of years ago. So in this lecture, I would like
to address the question that has intrigued me and geneticists
throughout my career– what is a gene? In the lecture,
I hope to live up to a charge given to the
speaker for the Killian lecture. According to the terms
of the award, I quote, “these lectures
are intended to be at a level that will make them
understandable to a majority of the faculty and
students at MIT.” So you may ask, why
waste time on this question when the gene, what
it is, is common knowledge? You hear it all the time. It should be a simple task. It’s integrated into
our daily language. Everybody seems to
know what it is. A book has been
written about it. And PBS is planning a
special on the gene. The author of this book won a
Pulitzer Prize for this book, which many you may
have read, The Emperor of All Maladies, where
they get most things right. They also wrote a
book called The Gene, where much is incorrect. [LAUGHTER] And after reading it–
it’s 600 pages long. Not a real page turner. [LAUGHTER] I was completely confused
about what the gene was. [LAUGHTER] But you can’t avoid it. Gene talk is everywhere. I hear workers behind
the counter in Starbucks talking about their genes,
no doubt because companies like 23andMe or AncestryDNA– I’m not partisan here– look at your DNA and
promise to provide you not only with your ancestry,
but your potential health or why don’t you feel
chipper every morning. A little saliva is all it takes. You spit into a tube, and
you send it off to them. Well, I was teaching 703, and I
kind of poopooed this 23andMe. And several undergraduates
walked up to me and said, did I do it? They said, you’re a geneticist. Aren’t you interested? So I did it. And I got back this email. Here, Gerald Fink. [LAUGHTER] Our laboratory attempted
analysis of your saliva. I don’t have any DNA in my– I have no DNA. But I don’t need to worry. [LAUGHTER] I spit again. It worked. It worked the second time. Now, geneticists have struggled
with the definition of a gene since genetics
began as a science at the dawn of the 20th century. It’s a young science. And it may seem strange
that in his Nobel talk, one of the great geneticists,
T.H. Morgan, said, what are genes? Now that we locate them
in the chromosomes, are we justified in regarding
them as material units, as chemical bodies of a
higher order than molecules? And here’s the kicker. Frankly, these are questions
with which the working geneticist has not much
to concern himself, except now and then to
speculate as to the nature of the postulated elements. The discovery that DNA
is the genetic chemical body of heredity,
coupled with the ability to sequence the DNA
of whole genomes, requires the definition
of a gene for reasons that T.H. Morgan could
not have anticipated. The DNA sequence of a
genome yields a string of billions of nuclear bases
that are incomprehensible without the aid of a computer. But the computer, shown here,
is helpless in its search for genes within this
vast sea of nucleobases without an explicit
definition of a gene. You can’t tell the
computer, I don’t have a definition of the gene. If you try, the
computer says, hey, you just fed me three
billion base pairs and want to know how many
genes are in the genome. How about a definition? So 20th century
scientists must have a definition of the gene
that is computer compatible. And arriving at a computer
compatible definition of the gene took an
enormous amount of work and resulted in
four Nobel prizes. A current definition of the gene
used to program the computer is the sequence of bases
in DNA sufficient to encode a single polypeptide. This comes from
the central dogma that I think they teach
now in grade school. DNA makes RNA makes protein. And so what does a gene
look like to the computer? There’s a string of bases. And what the computer sees
is, oh, there’s an ATG. That’s like a capital
letter in a sentence. And then, it reads
along in frames of three these bases until it sees
the period in the sentence. In the decoding process,
each successive triplet code specifies the next
amino acid to be added to the polypeptide chain. So what about the code? Well, you can read it
right off this table. So if you look at
AUG, methionine, that would be the beginning of the
protein that it specified. And you go along
this whole chain by threes until you get
to one of these stop words, the period. So for the computer, a gene is
defined as this segment of DNA that’s translated into proteins
starting with a triplet, ATG, and ending with a period. So what does the genome
of protein coding genes look like to the computer? You can see the
smile on its face. It’s happy. Here’s gene one. Makes an RNA. Then, RNA translated
into a protein. Gene two. Gene three. Gene four. So when you read
in the newspaper that humans have
about 20,000 genes and the yeast genome
has about 5,500, that’s what they’re
talking about, these protein coding genes. Now, the question
of what is a gene became more pressing
for me in my own lab when we discovered how to
transform yeast with DNA because the ability
to transform yeast meant we could insert
any gene into yeast and express the protein. So what protein? Well, it could be whatever
your favorite gene was, but you had to know what
the code was for that gene. Let’s say you wanted to
express insulin, for example. You had to know what the
code for insulin was. You could get that
gene from human tissue, put it into yeast,
and now, yeast would decode it and
make human insulin. Indeed, half of all human
insulin is now made in yeast. Knowing what a gene is in terms
of the protein coding gene is really important. In fact, all of these proteins
are made now commercially in yeast. Hepatitis B, which
is eliminating hepatitis B in mainland China– the vaccine is made. Gardasil against
human cervical cancer. I mentioned insulin and
all the rest of these. So it spawned an industry
that continues today. Yeast is easy to grow not
only to make beer and wine, but to make all of these
medicinal products. But I think that what may
have the most profound effect on economics and
society in general is what I read in New
York Times on Tuesday. And that is “Behold the
Beefless Impossible Whopper.” Burger King is
introducing a Whopper made with a vegetarian
patty from the start up Impossible Foods. I have no stock in
Impossible Foods. [LAUGHTER] So this is a– you should know the
background to this, which is that a highly
respected scientist at Stanford University, Pat Brown– me and my colleagues know. Very imaginative guy. Got tired of academia. He’s a vegan. But he was forced
to taste a hamburger and realized as much as
he liked veggie burgers, they don’t taste the same. I’ve tried. If you put enough ketchup
on, they taste like ketchup. [LAUGHTER] And so he started– he left Stanford and started
a company, Impossible Foods. And he then did taste tests. What is it that
makes a hamburger taste like a hamburger? And it turns out that
it is the hemoglobin in the blood of the meat that
you’re eating from cattle. It’s this iron
containing protein. This is a true story. So what gave hamburgers
their distinctive taste was the cow’s blood,
specifically hemoglobin protein. Now, it turns out
that soybeans have a heme protein that mimics the
taste of hemoglobin in the cow. Legumes make like hemoglobin. So what did he do? He cloned the like
hemoglobin gene into yeast and produced tons of it
and made hamburger patties without any cows. The head of tasting
at Burger King said, quote, “employees
were not able to tell the difference between the old
meaty Whopper and the new one. People on my team who know
the Whopper inside and out– and I’m sure they do– they try it, and they
struggle to differentiate which one is which.” I think this is
fundamentally going to change agriculture and
farming and many other things. I will point out a
farm of 3,000 cattle produces the amount of
manure of a city of 500,000. That’s Sacramento or Atlanta. This is game-changing. Now, also, Professor
Silbey mentioned that I like yeast, which I do. I want to point out that when
you’re pumping gas in your car, 40% of the corn that’s
grown in the United States goes to feed yeast
to make ethanol. Right, Greg? And so this is not to feed
livestock, but to feed yeast. So, together with
Greg Stephanopoulos, we’ve used gene editing, whose
modern incarnation is CRISPR, to modify the yeast genome. And we tried to make any
little improvement you make when you’re dealing with– the US is aiming for 15
billion gallons of ethanol per year made by this process. Any little bit that you change
is, of course, transformative. So having a
definition of the gene has clearly been a benefit
for both basic research and human health. And you might want to
know not about yeast. How does a human genome
look to the computer? If you look at four
people’s genomes– these are past presidents. This is the current
president, in case you didn’t recognize it. The important thing to note is
how conservative evolution is. They all have the same
genes in the same order. Evolution is very conservative
with the protein coding genes. Now, this little
gene over here shows that there might
be spelling changes between Abraham Lincoln’s
genes and [INAUDIBLE] genes. Lincoln was thought to
have Marfan syndrome, so he might have a
coding difference because that gene encodes
a protein, fibrillin-1. And if he did,
that might account for his description of
him as tall and thin with long arms and legs. But the genes are all
in the same order. there’s no change in that. In fact, the remarkable
thing to me as a geneticist is how conservative
evolution is. If you look at these
protein coding genes in humans and the mouse,
much of the gene order is the same, even though
they had a common ancestor 75 million years ago. Why didn’t it all
get scrambled up? Something is keeping
these things in the order. So these are two
chromosomes, and this is the alignment of genes
on those two chromosomes that have been saved
over the millennia. It’s quite remarkable. It accounts for the
fact that if you don’t– why is a mouse such a
popular organism to work on? Well, if you don’t know
what a gene does in humans and you figure it
out in the mouse and you know where it is
on the human chromosome, there’s a pretty good chance
it has a similar function in the mouse. So the definition of a
gene as a DNA sequence that codes for a protein
coupled with the sequencing of the human genome
I think has– everybody does– revolutionized
molecular medicine. It’s been a
remarkable milestone. Genome sequencing, along
with computational power to compare and analyze genomes,
has led to important insights into basic science and disease. As an example, we
know the genetic basis for literally thousands of
inherited diseases caused by those spelling changes
I showed for Abe Lincoln in protein coding genes. Sickle cell disease,
which afflicts about 100,000
Americans roughly, it’s one of the earliest
molecular diseases identified where a component of hemoglobin
normally has the codeword CAG. Gluten codes for
this– amino acid glutamate, you have a
normal red blood cell. In sickle cells, there’s
been a change in the code to make valine instead
of glutamic acid, and so red cells sickle. These sickling cells
cause periodic episodes of pain called crises. And pain develops when
the sickle red blood cells block blood flow through
the tiny vessels to the chest, abdomen, and joints. Now, when I used this example
of a molecular disease to teach 703, an
MIT undergraduate came up to me at
the end and said, oh, just reverse the arrow. Then, you can cure the disease. It’s easy in PowerPoint. Very easy in PowerPoint. Unfortunately, the student asked
the question about reversal more than 10 years
ago at a time we didn’t know enough about how
red blood cells differentiate from bone marrow stem cells,
and we didn’t know enough about stem cells. And there was no CRISPR to edit
the gene and reverse the arrow. But now, armed with CRISPR
and really deep knowledge about stem cells, it
seems feasible to make the correction in
patient’s own stem cells use a gene editing by CRISPR,
coupled with knowledge about the bone marrow. Normally, in a bone marrow
transplant recipient, you have to get rid
of their stem cells, get stem cells from someone
who’s a compatible donor, and then, hopefully, they’ll
take home in the patient and regenerate good
red blood cells. But often, there are
immunological problems because the two people
are not the same person. But with this new
technology, you could reverse the sickle
cell trait in the bone marrow stem cells of this
person and populate them with their own cells,
not donor cells. This is not the germline
editing of embryos to make genetically modified
children, for which scientists have called a moratorium. This is similar to bone marrow
transplants used widely today to treat hematopoietic
diseases, and not the germline. You’d be your own donor,
populating your bone marrow with your own genetically
modified cells, but your eggs and sperm
would be unchanged. The protein coding gene makes
cures just around the corner is my title. And there are articles popping
up in The New York Times frequently because
I think it is true. We are close. I predict this type
of gene therapy will be one of the great
successes of modern medicine in the 21st century and is a
wonderful benefit of science achieved in our time, not only
the sequencing of the genomes, but the definition of a gene
as encoding for a protein. Now, with this
evangelical prediction, if this were a TED Talk, which
[INAUDIBLE] calls God talks, I could end the lecture here. As a recent TED
speaker ended her talk, my talk should get
your testosterone up and get your cortisol down. However, the sobering fact
is that these protein coding genes constitute only 2% of
all the DNA in your cells. What does your DNA really
look like to the computer? Well, here are the genes
we’ve been talking about. But 98% of it is
outside of these genes. And the computer is
frowning because they are invisible to the computer. The computer doesn’t like this. Now, what do you
do when you have a good definition that the
computer likes and gives you results, but can’t
explain 98% of the data? Do you know what happens? I don’t care what
field you’re in. It happens in all fields. You could ignore it, or
you could given a name that defies a definition. Geneticists could
call it dark matter. [LAUGHTER] But that name is already taken. [LAUGHTER] You could call it
garbage or junk DNA. Well, it ended up
being called junk DNA. According to Sydney
Brenner, you need to distinguish between
garbage and junk. Garbage, you throw out. Junk, you keep. [LAUGHTER] Right after I arrived
at MIT, my lab obtained results
suggesting the junk DNA was not just an uninteresting,
inert wasteland. We found that yeast
had jumping genes, genes that leapt from one
place on the chromosome to another by a process
of retrotransposition. And where did they end up? In the supposed junk DNA. So here, gene one, gene two. Here is the supposed junk. But these jumping genes
would jump around. And if you look at the DNA now,
not of the conserved protein coding genes, but
in this interstellar space in between them– Rafael has one here. Susan has one here. President Obama has one here. Abe had one there. They’re all in different places. And furthermore, instead
of DNA, RNA, protein, they just went DNA, RNA. They didn’t make protein. And furthermore, if
they tended to land near one of the protein coding
genes, as shown here for Abe, they affected the
expression of that gene. So here, we have
a real conundrum, which is they didn’t have the
classical DNA, RNA, protein, just DNA, RNA, but
they had a function. And they landed all in this
wild world of junk DNA. This alerted me and
many others to the fact that lots of junk DNA might be
making RNA, but not proteins. What was needed was a technique,
a very sensitive technique, to assess the total RNA
output of the genome from protein and
non-protein coding DNA. And a very sensitive method
was devised, not by me, to sequence all the RNAs
in a cell, called RNA-seq. And when you looked at the
total RNA output of the genome, you saw an amazing
collection of RNAs, not only those that made
proteins, but those that had no protein coding capacity. That is, they want DNA
to RNA, but no protein. No ATG and no open
reading frame. No capital letter and
no open reading frame. When I discussed this initially
with a computer biologist– a computational biologist. Excuse me. He’s a well-known
one here on campus. He told me, don’t worry. These RNAs from the
junk DNA are noise. But they turned
out not to be noise because these RNAs
have functions. And you know they have some
functions because they now have names. [LAUGHTER] MicroRNAs. LncRNAs. Long non-coding
RNAs is the acronym. RAN RNAs. And the list can go on. So these are, in
other words, what’s coming from
interstellar space here, is lots of non-protein
coding RNAs. And are they noise? No. OK, they’re not noise because
over the last 15 years or so, it turns out
these microRNAs are critical for controlling
gene expression of those protein coding genes. And the long non-coding
RNAs are critical, as well. These are so wild, I
won’t discuss them. And they’re antisense RNAs. So what is the
function of these RNAs? The image is just emerging. But, of course, they’re
invisible to the computer because we don’t have an
algorithm to identify them in the DNA or what they do. They’re in this vast
genome interstate. So while many scientists
sleep at night and sleep well at night without
a definition of the gene that includes DNA that codes
for these non-protein coding RNAs, which constitute
a huge percentage [INAUDIBLE],, more than the protein
coding, that we call genes, geneticists sleep like a baby. They wake up every
two hours crying. [LAUGHTER] But these RNAs are not
just recreational or noise. They’re regulatory, like
these microRNAs and lncRNAs depicted here, turning on or
off the protein coding genes. And only now are we
beginning to visualize the importance of this formerly
invisible part of the genome. As an example, I’ll focus on the
best known of one of these lnc, or non-coding, RNAs, which has
an important role in silencing genes, but only in females. As you know, females
have two X chromosomes, one they got from mom,
one they got from dad. And this lncRNA actually codes
one of the two X chromosomes, either the one
from mom or the one from dad randomly,
and shuts it off. That’s weird. Men are uninteresting. They get only their X
chromosome from their mother. And so that’s quite different
from females, who have two. Now, why would you
have this mechanism? Well, there’s a dosage problem. If women get two X chromosomes,
they should have twice as much of all those protein coding
gene products as the men. But they don’t. They have exactly
the same amount. It turns out that
this lncRNA shuts down most of the genes on one
of the two X chromosomes. Remember, she got one
of her X chromosomes– all you ladies got one
of your X chromosomes from your mother and the
other from your father, here shown as pink and green. This RNA shuts off
one or the other, and it does it early
in development. So men, for any cell that you
look at, all of their genes come from one X chromosome,
and they’re all one color. You can imagine color
blindness, for example. They’d be all one color. They would see or not see,
depending what gene they got from their mother. But women are much
more interesting. They have this polka dot
pattern because one of their X chromosomes, the one they
got from their father, from their dad, is
shut off and is now expressing only the
genes from the mother, or the other is only expressing
the one in the father. Now, if you look at this in a
mouse in a real experiment– and what we’re
looking at here, this was an experiment done at
Johns Hopkins in the laboratory of Jeremy Nathans. We’re looking at
individual neurons in the brain of the mouse. And you see for
individual neurons, some are expressing mom’s,
and some are expressing– I don’t know whether you
call mice mom and dad. One is expressing mom,
and one is expressing dad. And even to me, more remarkably,
this left-right asymmetry in some of the neurons– some neurons on the
left side of the brain are expressing one X
chromosome, and some, the other. And so this left-right
asymmetry is not seen in males. They’re either red or green. This is all due to this
long non-coding RNA, again, which would be
invisible to the computer. This is, to me, remarkable
results and, I think, to people in the field, suggests
that X chromosome mosaicism in the human central
nervous system caused by one of these strange
lncRNAs could generate female-specific
functional diversity that does not exist in males. And the similarities in
early brain development across mammals suggests that the
observations made in the mouse may apply to humans. In fact, there’s
already data suggesting inhomogeneities in human
female X chromosome mosaicism. And a question that
might arise in your mind is, does this variability
caused by this non-coding lncRNA lead to behavioral
differences, even among genetically
identical individuals? So these lncRNAs have
an important function, so important they now
have their own blog, and journals are now full of
what used to be called junk. I like the other side here– extremely volatile. Because they’re much
more evolutionarily mutable than the
protein coding genes. They’ve even got their
own blog, lncRNA blog. They do everything. Long intergenic
non-coding RNA promotes colorectal cancer, HIV
infection, [INAUDIBLE] kidney cancer. The literature is flooded
with that used to be noise. So I’ve been referring
to non-protein coding RNAs that don’t make proteins. There’s even junk in
protein coding genes that confuses the computer. Many protein coding genes
are discontinuous genes. That is, you go along. There’s an ATG. You see some coding. And then, there’s
some junk, which is called an intron, in
the middle of the gene. And then, the gene again. And then, this is
spliced out, as shown by Phil Sharpe in his
Nobel Prize-winning work. These genes are
mosaics, small pieces of coding sequences interrupted
by non-coding sequences here. The computer doesn’t recognize. It can’t tell when they come. These junk genes
here, which would be very nice to recognize
because then, you’d say, oh, there’s an
interrupted gene– but the computer can’t– they’re not consistent
enough for the computer to recognize them. People here have worked
trying to find those signals, but can’t find them. What’s even worse–
some of these genes– the intervening
sequences, the junk, is bigger than the gene
itself, the protein coding, or there are many
small coding sequences camouflaged by introns. So the computer goes along and
then suddenly has to start. It can’t find one
of these genes. So I point out, what
happened to this junk? It’s supposed to be
discarded, chewed up. Well, it turns out the
intron is not junk. Oh, my god. So together with Dave Bartell,
we just published a paper showing that excised linear
introns regulate growth in yeast. Namely, this piece of non-coding
RNA now has a function. These have no obvious
sequence conservation. They do have some
size constraints. I just want to emphasize
again that your genome is full of these protein coding
genes that were thought to be junk, but this paper shows
they have valuable information. Now, this paper
reincarnating the intron as genetic information
with function generally received a
rather complimentary, though I have to say,
surprised response from the readers of the paper. There are those that cannot
deal with this continued attack on the definition of the gene. It made them very anxious. Soon after publication,
I received this email from a highly regarded
biochemistry professor at Berkeley. “Just wild. Now, I have to
pay more attention to all that splicing crap.” OK. [LAUGHTER] So, as [INAUDIBLE] explained,
the current definition of a gene used to program the
computer, the sequence of bases and DNA sufficient to
code a single polypeptide, is inadequate to embrace all the
different informational units of the genome. The existence of these diverse
classes of RNAs, microRNAs, lncRNAs, antisense RNAs,
enhancer RNAs, intron RNAs, and many others
that are cropping up that don’t encode proteins
but are transcribed from distinct
sequences of DNA are evidence that there is no single
physical and functional unit heredity that we
can call the gene. Rather, the genome contains
many different categories of informational units, each of
which may be considered a gene. And the definition
I think that is compatible with
current knowledge is a DNA sequence that’s
transcribed into an RNA molecule with a function. This RNA centric
definition would permit a number of
categories of gene, each one denoted by an
adjective that describes the transcription unit. So there would not only
be protein coding genes, but lncRNA, intron, et cetera. Really, this is not radical. The terminology is
already becoming part of the vernacular. It’s part of the literature. The majority of
these RNA only units would currently be invisible
to a computer program to uncover only genes
that encode proteins. But progress is being made. For some RNA only genes, like
transfer RNA and ribosomal RNA, they’re so evolutionarily
conserved, the computer can easily spot those
signature sequences in the DNA. For micro RNAs,
progress is being made in identifying
secondary RNA folding structures that conserve
signatures for microRNAs. But so far, the lncRNAs and
other non-protein coding RNAs have not identified unified
identifying signatures that enable the computer
to see all of them in the vast sea of nucleotides. But I believe that the
importance of these genes for non-coding RNAs and the
flurry of activity around them– definitions that will work
for the computer will emerge. And I think as this attempt
to identify definition for these genes, the
computer will work together and be an indispensable ally of
the lab bench scientists trying to figure out function. So in genetics, we’ve
lost a simple definition of a gene, a definition
that lasted over 50 years. But loss of the definition
has spawned whole new fields trying to understand
the unknown, information in non-protein coding
junk DNA, which is, after all, 98%
of the information. As scientists
witnessing the unknown creates that intense moment
of exhilaration as a new vista unfolds, whether it’s, in your
field, discovering a new planet or unraveling the role
of unanticipated RNAs, it evokes an emotion that
I have personally found difficult to express in words. Perhaps, it’s best
expressed in music. [MUSIC PLAYING] To go where no one
has gone before and to see what no
one has seen before, this lure of these
unexpected voyages is what drew us all to
science in the first place. I look forward to enjoying
these voyages with you. Thank you. [APPLAUSE] Will you take questions? If you want me to. [APPLAUSE] Thank you, Jerry. At this time,
Professor Fink will answer one or two, maybe
three, questions about his work or the lecture. And so I will ask those who
want to pose the questions, as you’re forming the queue and
thinking about your questions, to let you also
know that afterward, I invite you all to a
reception downstairs in the lobby of building 13. Do we have any questions? I was about to ask one, but the
microphone is quite far away. OK. [INAUDIBLE] speak loudly. Go ahead. Jerry. Hi. Truly, truly congratulations. This is such a
well-deserved award. I’ve known you since
I was very small, and you’ve been always such a
wonderful mentor to all of us, all of your students,
and extended groups. So thank you very much,
and congratulations. I want to go to that
last point that you made that maybe our computers
will be our friends one day and sort of be happy
about the signal. And I want to challenge
that a little bit because I work on the computer. And we’ve looked a
lot for these signals, and they’re simply not there. I mean, basically,
it’s not that we haven’t looked for a code
for these non-coding RNAs. Coding genes have a code. [INAUDIBLE] they have
codons, et cetera. If you look evolutionarily,
you look transcriptionally, if you look at [INAUDIBLE],, if
you look in all kinds of ways, there doesn’t seem
to be a single code. So my question to you is, what
do you mean by that statement? Do you mean that we simply
haven’t found the code yet, or is it that we have to
think about it differently in some way? Is Dave Bartel here? [LAUGHTER] He should really answer. Because– [LAUGHTER] He should answer it
because for the microRNAs, there are signatures of the
microRNAs, which he has– And we’ve worked together
with Dave [INAUDIBLE] [LAUGHTER] So why are you asking me? [LAUGHTER] My question’s about
the lncRNAs and– The lncRNA. I don’t have an answer to that,
although there are some lncRNAs that are conserved. [INAUDIBLE] You’re younger than I am. You should solve this problem. [LAUGHTER] I was hoping you were
going to guide me. You’ve been doing
that my whole career. I have a simple question. Why do the microbiologists
go to sleep at night, and they sleep right
through the night because the DNA going to
RNA going to protein holds? It’s only in the eukaryotic
cells with nuclei that you have the
problem or the– how should I say it– the joy of searching
for these RNAs. Actually, bacteria have them. They don’t. They don’t have them. They have some small RNAs. Yeah. If you take a bacterium
like E. coli, 99% of it is either coding or
ribosomes or tRNA. And then, there’s a small
smattering of small RNAs. Now, there’s no such
thing as lncs or microRNAs that our equivalent. So there is something going
on in the eukaryotic cell, the cell that you study, that
is totally different than what’s going on in bacteria. And the question is, why? [LAUGHTER] I don’t know. [LAUGHTER] End it now? That’s it. Again. Would you all please
thank Professor Fink? [APPLAUSE]

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