[Slide 1 L3.1] Welcome back.
I'm Professor Rickus.
[Slide 2] In this lecture,
we're going to take a look at an Introduction to Transcription Networks.
So in this lecture,
we're going to talk about the idea of the cognitive problem of the cell.
We'll introduce you to transcription networks which are key
processing networks of the cell.
We'll talk about what is a transcription factor.
We'll review some of the basic mechanisms of gene expression,
particularly focusing on the differences between prokaryotes and
eukaryotes, and we'll look at a couple representations,
network views of real transcription factor networks.
[Slide 3] So let's first talk about this idea of the cognitive problem of the cell.
This concept is nicely articulated by Uri Alon in his textbook,
Introduction to Systems Biology, and what this concept really means
is that if you think about what a cell needs to do to stay alive,
one of the important things it must do is sample and
understand what's happening in the extracellular environment around the cell.
It needs to know what nutrients are available, it needs to know are there
stressors present feeling and interpreting mechanical stimulation.
Any chemotactic signals that are going to tell it to move in one direction or
another.
Are there other cells present recognizing other self cells?
Similar cells of the same type.
Any competition or cooperation with other nearby cells as well?
So the cell has to sense this extracellular environment and then,
it needs to create an internal representation of those external signals.
And this is important, it needs to adapt and adjust in order to survive
optimally in its whatever changing extracellular environment.
So that internal representation of extracellular signals has to,
among many things, but one of the most important,
change its gene expression in order to alter its cell state.
And then that cell state also gets represented back and
influences that gene expression change as well.
So sensing primarily happens through membrane transporters and
receptors on the surface of the cell, and in order to direct protein regulation.
And that representation and processing is primarily
one of the main ways this happens is through transcription networks.
[Slide 4] So, looking at a network representation of what we just said conceptually,
you think about the environment, that there's some set number, N number of
signals, we'll say, in the extracellular environment that must be interpreted.
So those get sensed and represented by a network of transcription factors.
Usually a smaller subset of the entire extracellular space.
Those transcription factors,
then, regulate the gene expression of the set of genes in the genome of the cell.
[Slide 5] So if we look at the scale of these networks, let's go to our friend E. coli
that we often start with, and think about the size of these networks.
And in E. coli, E. coli have several thousand types of interacting protein.
E. coli has about 4,500 genes, therefore protein types.
And there are about a couple million protein molecules per cell.
Now the transcription factor size of, at any given time in E. coli is
on the order of a few hundred, 300 transcription factors or so.
And this information processing, as we said, functions largely at this
level of the transcription network, but not exclusively of course.
So what is is the job of this network?
As we said, the job of the network is to represent and monitor the environment,
essentially calculate the amount at which each protein is needed and
determine the rate of gene expression, the protein production, of those cells.
[Slide 6] So, transcription factors, what are they?
Transcription factors are usually proteins,
therefore themselves encoded in genes.
They bind DNA to regulate the rate at which specific target genes are read or
transcribed.
They often are designed to transit rapidly between an active and inactive state.
So while their activity can be regulated by the level of the transcription factor,
they often have a faster mechanism of regulation where they themselves
activity can be turned on and off by a faster means, such as phosphorylation or
binding of a signal ligand or binding to other proteins.
[Slide 7] So, transcription factors can be either an activator or a repressor.
An activator increases the rate of transcription,
therefore is a form of positive control.
A repressor decreases rate of transcription and
therefore is a means of negative control.
Activation is more common then repression,
about 60 to 80 percent of the interactions are positive in E. coli and yeast.
Transcription factors usually act as an activator or a repressor but
sometimes a transcription factor can act as both to different genes.
And one transcription factor can act on multiple genes.
In fact, you may have heard the term, sometimes you will hear about
a transcription factor that is a master regulator, and what they're
referring to is a transcription factor that influences many genes,
it may be an important critical switch in, for example,
mammalian cell differentiation, or a large change in state of the cell.
[Slide 8] So, if we take a sort of a simplest diagram view of transcription,
again from Alon's Systems Biology textbook here, and we see if we look at our DNA and
just simply represent our regulatory region, we have promoter region,
and our coding region down here for a gene we'll call y.
Down in our promoter region, our polymerase binds.
Our RNA polymerase for transcription, where we get production of mRNA and
mRNA ribosomes bind, and we get translation and production of protein,
and some give a great number of molecules, proteins per unit time that are produced.
[Slide 9] So as we said, transcription factors can be activators.
In this case, our transcription factor we'll call X here.
We'll designate X without an asterisk as its inactive form but
there is some signal X that can activate it.
We'll represent its active form as X*.
So when this active transcription factor binds upstream here
from our promoter region, then we activate and increase the rate of transcription
and increase the rate of our protein production.
[Slide 10] So as we said, transcription factors can also act as repressors.
So in this case, when our transcription factor binds,
we actually block or reduce the rate of transcription.
And when our transcription factor is present,
then we block the production expression of that gene y.
In our network view then, we can represent this as x with our line and
flat line representing inhibition blocking expression of y.
[Slide 11] So let's take a second to remind ourselves about some differences in gene
expression between prokaryotes and eukaryotes.
So if we think about a prokaryotic cell such as bacteria, there's no nucleus,
so transcription and translation occur in the same location.
And in fact, translation can begin, and can occur,
before transcription's even done.
So here you can see translation happening on this still-growing mRNA strand.
Genes are often organized as operons.
In other words, having multiple genes on one mRNA strand.
There's no introns and therefore no alternative splicing, and we have simpler
genome structures, fewer genes and smaller promoters in prokaryotic cells.
[Slide 12] Now in eukaryotic cells, our genes contain introns and exons,
and therefore alternative splicing of mRNA can result in different gene products.
So the model of one gene, one protein doesn't hold true one-to-one, right?
We can have one gene coding region and
multiple proteins resulting.
The mRNA must be translocated outside the nucleus.
So the transcription occurs inside the nucleus, the mRNA is transported
outside the nucleus and translation occurs outside the nucleus.
And so transcription factors as a protein when they get produced
must then re-enter the nucleus in order to regulate gene expression.
And so that transport in and out of the nucleus is another possible means
of regulation on gene expression, the alternative splicing,
there are a lot more levels of regulation of gene expression making
regulation in eukaryotic cells much more complex than in prokaryotic cells.
[Slide 13] So back to our networks, let's take a look at a real E. coli transcription network.
So this is part of the transcription factor network in E. coli.
This network you're seeing here represents about 20% of the known interactions.
And one thing I wanted to point out,
you can see instances of some very highly connected nodes, for example.
So this protein here is connected to and
is regulating a very large number of other genes.
So this may be something that might be considered, for
example, a master regulator.
Each node in this network represents a gene or gene operon and
each arrow represents, it's a directed edge representing a direction
of that gene product having an effect on the expression of the other gene product.
And so that node I, for
example, would regulate node J where the arrow is pointing to.
In this particular network that we're looking at has about 420 nodes and
about 520 edges or connections between those.
The other thing to point out is the structure.
If you were to form a randomly connected network, the structure,
the connectivity in this network is not random.
And there are functional consequences for stability and
dynamics of the network by how it's organized.
And we'll begin to look at some of the motifs or
smaller organizations in this network that are enriched and occur.
And we'll look at some of the impact of that
on dynamics in the next coming lectures.
[Slide 14] So another view of a Transcription Network, this is again E. coli, this is
from the Curated Regulatory Transcription Network that comes from RegulonDB.
So here you're looking at a transcription factor network.
So again, these only contain transcription factors and how they regulate each other.
And you can see up at the top, these seven genes and
operans up here that are highly connected to all of these other transcription factors
down in the bottom part of our network.
So here it's a little bit more difficult to see the connections but it's easier
to see, sort of, the number and in it sort of a different view of the structure.
[Slide 15] So now, if we add in all the genes, all these white dots down here,
all the genes and operans these yellow transcription factors regulate,
now we can see even now a much larger network and how the transcription
factor network is interacting, regulating not only each other but
all of these other genes that have been curated so far in E. coli.
And one of the reasons why I wanted to point this out is so, if we look at this,
it's somewhat tractable, it's countable, right?
It's definable, but there's a lot of complexity in this network.
And when we engineer and re-engineer cells at the level of DNA, or
when we chemically perturb gene expression,
then we are operating inside such networks.
And so even to just simply have a conceptual view of thinking of a cell
in this way is important for when we think about even simple cell experiments,
and this really highlights a need for a systems level view of cell biology.
And the ability to work at many levels of abstraction becomes very important.
To be able to zoom in and work at a very detailed level,
at a particular gene within the network, but to also be able to zoom out and
think about how that gene or those protein products are connected with
the rest of the genes and protein products inside that cell.
[Slide 16] So, for further knowledge, I will point you to some excellent resources.
I've mentioned a couple of times Alon's textbook Intro to Systems of Biology,
going to the RegulonDB, the database that you can access free online and
as well as some videos that sort of further remind yourself and
exemplify some of the points we were talking about and
making about gene expression in prokaryotic cells and eukaryotic cells.
[Slide 17] So coming up, we're going to start with a simple mathematical representation of
gene expression.
And then we'll start to build some simple transcription network motifs
that exist in those larger networks and
we'll start with negative autoregulation and Feed Forward Loops.