Identifying Functionally Important Elements in the Human Reference Genome

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Do curso por Novosibirsk State University

From Disease to Genes and Back

34 classificações

Novosibirsk State University

From Disease to Genes and Back

34 classificações

Human genetics explores the genetically determined similarities and differences between human beings. This scientific discipline encompasses a variety of related fields such as molecular genetics, genomics, population genetics and medical genetics. Study of human genetics can help to find answers to questions regarding the inheritance and development of different human phenotypes. The field of medical genetics is quickly growing and dynamically developing thanks to the new technologies such as the next-generation sequencing. Most human diseases have a genetic component. This genetic component varies by disease. Some rare diseases appear to be completely determined by the genome, whereas more common diseases arise from a complex interplay of many genes, the environment and chance. The understanding of how our genomes contribute to disease susceptibility offers the prospect of large gains: it may guide disease diagnostics and prognostics and help in developing new therapies. The overall goal of this course is to describe how the researchers find genes responsible for different diseases and how this information is used to better understand and fight these diseases. You will learn about current approaches for finding single genetic variants underlying monogenic (Mendelian) diseases and sets of variants responsible for more complex, multifactorial ones. Furthermore, you will learn how the identification of these genetic variants makes it possible to understand how the affected biological pathways lead to disease development. During the final week of the course, we will talk more about clinical applications of the genetic findings. Upon completing the course, you will be able to: - give examples of monogenic and complex disorders - recognise patterns of Mendelian inheritance of monogenic diseases; - understand and describe principles and methods of gene mapping ; - describe the main steps and principles of genome-wide association studies (GWAS); - give examples of modern technologies that are currently used to find variants underlying human diseases; - discuss the approaches to finding causative variants underlying complex disorders; - discuss the possibilities and areas of application of genetic findings.

Na lição

Introduction. Human genome

This week you will learn about human genome organisation. This week is very important as all this knowledge will form a basis for all of the subsequent weeks of the course. Michel Georges will tell you about the structural organisation of the human genome, the mechanisms contributing to the genome variability, the main types of genetic variation (SNPs, CNVs, aneuploidy, etc.), and differences between alleles and genotypes. You will also learn the techniques used to detect different types of variations in human genome and you will find out how we can follow the inheritance of genetic material through generations.

Identifying Functionally Important Elements in the Human Reference Genome12:16

Conheça os instrutores

Marianna Bevova
PhD, Director of GIGA Doctoral School
University of Liège (GIGA)
Michel Georges
PhD, Professor, GIGA Research Director
University of Liège
Gert Matthijs
PhD, Professor
Center for Human Genetics, University of Leuven
Lennart Karssen
PhD, Owner and Chief Computational Scientist
PolyOmica
Natalia Aulchenko
M.Sc., Project manager
Theoretical and Applied Functional Genomics Laboratory
Yakov Tsepilov
PhD, Senior Researcher
Theoretical and Applied Functional Genomics Laboratory
Sodbo Sharapov
M.Sc, Junior Researcher
Theoretical and Applied Functional Genomics Laboratory
Alexander Tashkeev
Skolkovo Institute of Science and Technology (Skoltech), Junior Researcher
Laboratory of Computational and Structural Transcriptomics
Yurii Aulchenko
PhD, Professor, Head of Theoretical and Applied Functional Genomics Laboratory
Theoretical and Applied Functional Genomics Laboratory

So the human genome project

culminated in the first complete sequence of a human genome,

an enormous book with three billion characters, ACGTs in succession.

Of course, the main idea was to identify

especially the functionally important elements in this genome,

the genes and their genes switches.

When we annotate a new genome,

it's a good idea to start by simplifying your life by masking the repetitive sequences.

And there are excellent tools to do that

especially RepeatMasker and its associated database, Repbase.

Once we have done that, we can,

for instance, target the genes.

And what has probably yielded the most information when it

comes to gene annotation is the exploitation of

their key feature that is genes are transcribed

into RNA copies in at least one of the cells of our bodies.

So we are able to sequence the transcriptome,

that is the entire collection of RNA molecules in different cells,

and transcriptome characterization and sequencing has rapidly evolved over time.

It went from Sanger sequencing cDNA and EST libraries to

more recently massive parallel or next generation sequencing of stranded RNA libraries,

small RNA libraries, or tags such as SAGE and

CAGE tags which are targeting the three

prime and the five prime end respectively of genes.

All these sequences, all these reads,

corresponding to RNA molecules can be mapped back to the human reference genome, thereby,

by definition, identifying non-coding and coding genes,

and in fact, defining exon and intron structure.

One slight problem I would say with this approach

has been the observation of so-called pervasive transcription.

It is becoming increasingly apparent that not everything that is transcribed,

in fact, corresponds to a gene.

As an example, distant regulatory elements

seem to generate small transcripts when they are active.

But overall, the mapping back of sequences

corresponding to RNA is probably the most effective way to identify genes.

I should nevertheless briefly mention the fact that bioinformaticians

have developed so-called ab initio gene prediction software.

These programs exploit distinctive sequence features of genes and allow you to

directly identify quite effectively at least the protein coding genes in your genome.

But let's now focus on the gene switches.

They're obviously extremely important functional elements and

identifying them is a bit more complicated than the genes.

You may remember that the human genome project not

only meant sequencing the human genome,

but also the mouse genome and after that the dog genome and after that the bovine genome.

At the present time, there are maybe 30 mammalian genomes that have been obtained.

Why was that an integral part of the human genome project?

Well, the purpose from the onset was to use

these comparative genomics information to

identify so-called evolutionarily constrained elements.

If we compare the genomes of these different species,

there are parts of these genomes that are completely different between different species.

This typically corresponds to regions of the genome that

can evolve fairly rapidly without affecting

the fitness of the individuals within these species

because they don't carry functionally very important elements,

at least not conserved ones.

On the other hand, there are parts of the genome that seem not to be able to evolve.

They seem to be frozen, constrained.

This is because mutations in these parts of the genome are deleterious.

They affect the fitness and therefore,

they don't get fixed in the populations.

It's like if these pieces of the genome cannot evolve.

So if you align the genome of multiple species,

you look at the parts that are the same across all the species,

you virtually know by certainty having sequenced

29 species that this must be diagnostic of a functionally important element.

And these can, for instance, be used to identify gene switches.

More than 1 million evolutionary constraint elements

have been identified in the mammalian genome using this approach.

So this is a very powerful and efficient approach,

but, of course, it can only identify conserved switches.

We might be interested in identifying

the switches that are unique to primates if not humans.

Of course, they're not going to be shared with the bovine.

So do we have other methods that we could apply to more

directly identify gene switches? Well, there are.

And I'll mention four different biochemical approaches.

The first one is so-called ChIP-seq for

chromatin immunoprecipitation combined with

next generation sequencing targeting transcription factors.

So imagine that you know a transcription factor very well,

you even have an antibody that is specific for that transcription factor.

You may want to identify

all the genes switches to which this transcription factor binds in a given cell type.

How do you do that?

You take the cell type of interest, a sample of it,

you freeze the chromatin by exposing it to formaldehyde,

you break down this frozen chromatin in pieces,

and you incubate the resulting solution with

an antibody that will specifically recognize your transcription factor.

This would allow you to so-called immunoprecipitate these parts of the chromatin.

With the transcription, because it's frozen,

comes the DNA that is defining the distance regulatory elements

of interest and multiple ones because

the transcription factor will bind across the genome.

What you do then is you reverse the cross-linking,

and you release the DNA that was co-immunoprecipitated with the transcription factor.

You put that in next generation sequencers and you will obtain reads,

sequence reads, corresponding to the distant regulatory elements.

If you map these sequences back to the reference human genome,

by definition, you identify the distant regulatory elements that you were after.

So this is, of course,

a very nice and effective approach,

but it can only work for transcription factors that are

well-known and for which very good antibodies are available.

And this is certainly not the case for all transcription factors.

There are alternative methods that I will refer to as

generic methods because they don't require that information.

One is based also on ChIP-seq,

but the antibodies that you use do not target transcription factors,

but rather the post-translational modifications

of the amino terminus of histones that will

allow us by means of the histone code to

recognize the enhancers, silencers and promoters.

So we will take the same tissue as we did before.

But we will now perform ChIP-seq experiments

sequentially using antibodies that recognize specific histone modifications.

We then put all the information back together using, for instance,

a Hidden Markov Model and can identify active enhancers,

poised enhancers, silenced enhancers, et cetera.

We can identify it without knowing

the transcription factor these distant regulatory elements.

Another genetic methods is not based on immunoprecipitation,

but will rather exploit a feature that is typical of active switches.

That is the corresponding chromatin is relatively open.

It's less condensed than the rest of the genome.

And as a consequence,

it becomes more easily accessible to nucleases to which you might expose the chromatin or

to in vitro generated transposons that

will preferentially integrate where the chromatin is open.

DNase-Seq is the name of the first method and

ATAC-Seq based on transposons is the name of the second method.

Combined with next generation sequencing,

they allow you to identify

gene switches without knowing the transcription factors that bind to them either.

Well, this is an advantage and sometimes a disadvantage.

Once you have identified these switches in a generic way,

you may a posteriori like to answer the question which

transcription factor might bind to the things that I have identified.

In fact, if we look in detail at the result of DNase-Seq experiments,

we will see that the experiments generates so-called footprinting patterns.

So if we look at the piles of reads that are defining these gene switches,

we will see that not all the bases are equally covered.

This is because the protein factor binds specifically to parts of the DNA which are,

therefore, protected from DNase.

And if we look at these footprints,

we can actually identify transcription factor binding

sites that will allow us to go back to the transcription factor a posteriori.

The last method that I would like to refer to is

chromatin conformation capture which can either be done in a targeted way,

but is increasingly done at the genome-wide level.

It is then called Hi-C seq.

The idea there is that one will try to identify pieces

of DNA that although far away on the chromosome,

are brought close to each other in the nucleus by virtue of

this looping structure that is formed when a gene switch is active in a given cell type.

Hi-C allows you to identify two pieces of

DNA that are brought close to each other by this process in the nucleus.

Combined with so-called promoter capture Hi-C,

it is a very effective new way to identify

distant regulatory elements controlling specific proximal promoters.

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