Prader-Willi

1
Prader-Willi Angelman Syndromes

• Both of these genetic disorders are caused by
  deletion of a region of chromosome 15.
• However, the syndromes differ
• Prader-Willi Syndrome - obesity, mental
  retardation, short stature. (abbreviated PWS)
• Angelman Syndrome - uncontrollable laughter,
  jerky movements, and other motor and mental
  symptoms. (abbreviated AS)
• Syndrome that develops depends upon the parent
  that provided the mutant chromosome.

2
PWS Mouse model
PWS
AS Mouse model
AS
From Annu Rev Genomics Hum Genet
3
Introduction
4
Goal Identify loci associated with variation in
expression levels
Nucleus
regulators
Genomic DNA
mRNA
mRNA
Target
5
Cis and Trans regulation
Target gene expression phenotype
6
Data

• Centre d'Etude du Polymorphisme Humain (CEPH)
  families are Utah residents with ancestry from
  northern and western Europe.
• 14 families with genotype and expression data
  available for all parents and a mean of eight
  offspring (range 7-9)

7
Method Linkage analysis

IBD1
IBD0
IBD identical-by-descent
8
For a particular target gene expression
t-statistics
SNP1 2 3 4 5 Genetic
Locus
9
Cis and trans- regulation

• Under criteria 1,
• 27/142 (19) expression phenotype have only a
  single cis-regulator.
• 110/142 (77.5) expression phenotype have only a
  single trans-regulator.
• 2 /142 have a cis and a trans-acting regulator
• 3 /142 gene expression have two trans-acting
  regulator
• Under criteria 2,
• 164 / 984 (16) has multiple regulators

10
Se requiere modelos de regulación de
expresión génica
11
GAL Genes Eukaryotic Transcriptional Regulation

• Unlike prokaryotes, eukaryotes do not have genes
  in operons (most mRNAs are not polycistronic).
• The GAL genes of S. cerevisiae are the paradigm
  for eukaryotic gene regulation
• Galactose is metabolized by GAL gene products

Gal1p
Galactose
Gal-1-P
UDP-Glu
Gal7p
Gal10p
UDP-Gal
Glu-1-P
Glu-6-P
Gal5p
Glycolysis
12
EukaryoticTranscription
Proximal
Distal

• Proteins bind to distal elements called
  ENHANCERS.
• DNA folding allows these elements to be far from
  the start site for transcription.
• Proteins bound to the distal sites promote the
  binding of RNA polymerase to the proximal
  elements.

13
GAL Genes A Transcriptional Program

• The response to galactose is very complex, with a
  number of genes being turned on or off.
• The central regulator is a protein called Gal4p.
• Gal4p binds to enhancer elements in DNA and
  activates transcription under some circumstances.

14
Gal4p A Transcriptional Regulator

• Gal4p binds to enhancer elements near genes that
  it regulates (e.g., GAL1).
• Gal4p also binds to Gal80p.
• Gal80p is necessary for activation of gene
  expression.
• When galactose binds to Gal80p, the Gal4p-Gal80p
  complex can activate transcription.
• This activation has now been studied at the level
  of the whole genome

• This figure shows data from a microarray
  experiment (Science 2902306 2000).

15
Examining Transcriptional Regulation

• MICROARRAYS have become very popular as tools to
  study gene regulation.
• A microarray is a small glass slide on which
  cDNAs of many (or all) genes in an organism have
  been dotted.
• cDNA is made using mRNAs present under certain
  conditions (or in a certain tissue) and labeled
  with fluorescent dyes.
• Then, the labeled cDNA are hybridized to the
  microarray and the fluorescence determined.
• There is a nice animation describing this at
• http//www.bio.davidson.edu/courses/genomics/chip/
  chip.html
• Does this examine transcriptional regulation?

16
Examining Transcriptional Regulation

• This basic method was extended for the Gal4p
  study that we have been discussing discussed.
• For this study, the researchers tagged the Gal4p
  protein so the could purify from the cell.
• Then, they chemically cross-linked it to DNA and
  purified it.
• This allowed them to purify the DNA that Gal4p
  was bound to in the cell.
• The DNA that Gal4p was bound to in the cell was
  labeled and used to probe the microarray.
• Does this examine transcriptional regulation?

17
Examining Transcriptional Regulation

• This study established several interesting facts
• The Gal4p binding sites in the DNA are sometimes
  bound by Gal4p in the absence of galactose,
  others are bound only in the presence of
  galactose.
• So the trigger is more complex than simply
  whether or not the Gal4p protein can bind.
• This more complex regulation involves Gal80p, an
  inhibitor.

Two possible models for regulation of
the Gal4p-Gal80p complex by galactose.
The models differ only in the exact binding sites
for Gal80p.
18
How do Eukaryotic Transcriptional Regulators Work?

• There are a few specific types of proteins that
  act to increase transcriptional activity
• Many proteins have an acidic domain.
• Surprisingly, these acid-blob proteins often
  require a hydrophobic residue embedded in an
  acidic region.
• Both Gal4p and the herpes simplex virus VP16
  protein (an transcriptional regulator for this
  virus) have acid blobs.
• Glutamine-rich and Proline-rich transcriptional
  activation domains have been characterized.
• These protein regions activate transcription when
  fused to other DNA-binding domains.
• Alternatively, they can be recruited by
  protein-protein interactions - e.g., a
  DNA-binding protein binds the enhancer, and it
  contains a region that recruits and acid-blob
  protein.

19
Using Eukaryotic Transcriptional Regulators

• The yeast 2-hybrid system exploits these features
  of eukaryotic transcription factors to examine
  protein-protein interactions.
• The DNA-binding and transcription activating
  regions of Gal4p can be separated.
• Interestingly, if you fuse one protein to the
  Gal4p DNA-binding domain (BD) and a second
  protein that it interacts (physically) with to
  the Gal4p transcriptional activating domain (AD),
  one can see transcriptional activation

20
How do Eukaryotic Transcriptional Regulators Work?

• Another interesting phenomenon that is sometimes
  seen with transcription factor is SQUELCHING.
• Overexpression of transcription activators like
  Gal4p can result in a general inhibition of
  transcriptional activity.
• How does this happen?
• Presumably, specific transcription factors like
  Gal4p act by recruiting basal transcription
  factors.
• In fact, some basal factors that physically
  interact with these transcription activating
  domains have been found.
• Basal factors are factors involved in recruiting
  RNA polymerase II to a large number of promoters.
• So overexpressing proteins with these
  transcription activating domains can actually
  turn gene expression off, by competing for these
  factors.

21
How do Eukaryotic Transcriptional Regulators Work?

• At least one way is by altering the packing of
  DNA into chromatin.
• The role of chromatin structure in the regulation
  of transcription is an area of very active
  investigation.
• However, two important factors that play clear
  roles in transcriptional regulation are known
• DNA METHYLATION - A subset of cytosine (C)
  residues are modified by methylation.
• HISTONE ACETYLATION - Histones can be modified by
  acetylation.

22
Chromatin

• Remember, DNA in eukaryotes packs into CHROMATIN.
• HISTONES form the NUCLEOSOME, which DNA loops
  around.
• EUCHROMATIN - less compact actively transcribed
• HETEROCHROMATIN - more compact transcriptionally
  inactive.
• Heterochromatin can be either constitutive or
  facultative.

23
DNA Methylation

• Genes that are transcriptionally inactive are
  often METHYLATED.
• In eukaryotes, cytosine residues are modified by
  methylation.
• Typically, the sites of methylation are CG
  dinucleotides (vertebrates).
• This allows maintenance through replication.

CYTOSINE
METHYL-C
24
Histone Acetylation

• HISTONES in transcriptionally active genes are
  often ACETYLATED.
• Acetylation is the modification of lysine
  residues in histones.
• Reduces positive charge, weakens the interaction
  with DNA.
• Makes DNA more accessible to RNA polymerase II
• Enzymes that ACETYLATE HISTONES are recruited to
  actively transcribed genes.
• Enzymes that remove acetyl groups from histones
  are recruited to methylated DNA.
• There are additional types of histone
  modification as well, such as methylation of the
  histones.

25
Genetic Imprinting

• Remember that DNA methylation can be maintained
  through replication.
• This allows the packing of chromatin to be passed
  on - just like a gene sequence.
• However, differences in chromatin packing are not
  as stable as gene sequences.
• Heritable but potentially reversible changes in
  gene expression are called EPIGENETIC phenomena
• Vertebrates use these differences in chromatin
  packing to IMPRINT certain patterns of gene
  regulation.
• Some genes show MATERNAL IMPRINTING while other
  show PATERNAL IMPRINTING.
• The alleles of some genes that are inherited from
  the relevant parent are methylated, and therefore
  are not expressed.

26
Prader-Willi Angelman Syndromes

• Both of these genetic disorders are caused by
  deletion of a region of chromosome 15.
• However, the syndromes differ
• Prader-Willi Syndrome - obesity, mental
  retardation, short stature. (abbreviated PWS)
• Angelman Syndrome - uncontrollable laughter,
  jerky movements, and other motor and mental
  symptoms. (abbreviated AS)
• Syndrome that develops depends upon the parent
  that provided the mutant chromosome.

27
PWS Mouse model
PWS
AS Mouse model
AS
From Annu Rev Genomics Hum Genet
28
Prader-Willi Angelman Syndromes

• Prader-Willi Syndrome - develops when the
  abnormal copy of chromosome 15 is inherited from
  the father.
• Angelman Syndrome - develops when the abnormal
  copy of chromosome 15 is inherited from the
  mother.
• The differences reflect the fact that some loci
  are IMPRINTED - so only the allele inherited from
  one parent is expressed.
• The region contains both maternally and
  paternally imprinted genes.

29
Methylation and Gene Regulation

• For imprinted genes, the pattern of gene
  regulation is dependent upon the parent that
  donated the chromosome.
• The methylation pattern is reprogrammed in the
  germ line.
• There are other examples of methylation changes
  the regulate gene expression.
• In mammals, one of the two X chromosomes in
  females is inactivated.
• The inactivated X is methylated.

30
POR LO TANTO EXPRESION DE GENES ES
IMPORTANTE PARA ENTENDER HERENCIA GENETICA
31
Genomics, Bioinformatics, and Gene
Regulation Marc S. Halfon, Ph.D. mshalfon_at_buffalo
.edu Department of Biochemistry Center of
Excellence in Bioinformatics and the Life
Sciences Based on presentation for UB/CCR Summer
Program in Bioinformatics 2004
32
Genome Sequencing
As of 6/25/04 (As of 7/25/05) 1128 (1496) genome
projects 199 (274) complete (includes 28 (36)
eukaryotes) 508 (728) prokaryotic genomes in
progress 421 (494) eukaryotic genomes in
progress smallest archaebacterium Nanoarchaeum
equitans 500 kb Bacillus anthracis
(anthrax) 5228 kb S. cerivisiae (yeast) 12,069
kb Arabidopsis thaliana 115,428 kb Drosophila
melanogaster (fruit fly) 137,000 kb Anopheles
gambiae (malaria mosquito) 278,000 kb Oryza
sativa (rice) 420,000 kb Mus musculus
(mouse) 2,493,000 kb Homo sapiens
(human) 2,900,000 kb http//www.genomesonline.or
g/
33

• Genome sequencing helps in
• identifying new genes (gene discovery)
• looking at chromosome organization and structure
• finding gene regulatory sequences
• comparative genomics
• These in turn lead to advances in
• medicine
• agriculture
• biotechnology
• understanding evolution and other basic science
  questions

34
Because of the vast amounts of data that are
generated, we need new approaches

• high throughput assays
• robotics
• high speed computing
• statistics
• bioinformatics

35
Whats in a genome?

• Genes (i.e., protein coding)
• But. . . only lt2 of the human genome encodes
  proteins
• Other than protein coding genes, what is there?
• genes for noncoding RNAs (rRNA, tRNA, miRNAs,
  etc.)
• structural sequences (scaffold attachment
  regions)
• regulatory sequences
• junk (including transposons, retroviral
  insertions, etc.)
• Its still uncertain/controversial how much of
  the genome is composed of any of these classes
• The answers will come from experimentation and
  bioinformatics. We will discuss further only gene
  regulation.

36
Gene expression must be regulated in
TIME
Wolpert, L. (2002) Principles of Development New
York Oxford University Press. p. 31
37
Gene expression must be regulated in
SPACE
Paddock S.W. (2001). BioTechniques 30 756 - 761.
38
Gene expression must be regulated in
Stern, D. (1998). Nature 396, 463 - 466
ABUNDANCE
39
What happens when gene regulation goes awry?
Developmental abnormalities (birth defects)
1
2
3
6
4
5
Disease - chronic myeloid leukemia -
rheumatoid arthritis
photo credits Wolpert, L. (2002) Principles of
Development New York Oxford University Press.
pp. 183, 340
40
Genes can be regulated at many levels
The Central Dogma
41
Looking at the transcriptome DNA microarrays
One way of looking at the transcriptome is with
DNA microarrays. With microarrays, the
expression of thousands of genes can be assessed
in a single experiment. cDNAs or
oligonucleotides representing all genes in the
genome are deposited on a glass slide using a
robotic arrayer
Benfey, P. and Protopapas, A. Genomics. 2005. New
Jersey Pearson Prentice Hall. pp. 131-2
42
Exploring the Metabolic and Genetic Control
ofGene Expression on a Genomic ScaleJoseph L.
DeRisi, Vishwanath R. Iyer, Patrick O. Brown
43
Microarray
44
MicroArray

• Allows measuring the mRNA level of thousands of
  genes in one experiment -- system level response
• The data generation can be fully automated by
  robots
• Common experimental themes

• Time Course (when)
• Tissue Type (where)
• Response (under what conditions)
• Perturbation Mutation/Knockout, Knock-in
• Over-expression

45
Looking at the transcriptome DNA microarrays
cell type A
make labeled cDNA
extract mRNA
hybridize to microarray
cell type B
more in A
more in B
equal in A B
46
Looking at the transcriptome microarrays
statistical processing and analysis
47
Which Genes to select?

• For each gene (row) compute a score defined by
• sample mean of X - sample mean of Y
• divided by
• standard deviation of X standard deviation
  of Y
• XALL, YAML
• Genes (rows) with highest scores are selected.

They have a method
That seems to work well.

• 34 new leukemia samples
• 29 are predicated with 100 accuracy 5 weak
  predication cases

Seems to work ! Improvement?
48
Study of cell-cycle regulated genes

• Rate of cell growth and division varies
• Yeast(120 min), insect egg(15-30 min) nerve
  cell(no)fibroblast(healing wounds)
• Regulation irregular growth causes cancer
• Goal find what genes are expressed at each
  state of cell cycle
• Yeast cells Spellman et al (2000)
• Fourier analysis cyclic pattern

49
Yeast Cell Cycle(adapted from Molecular Cell
Biology, Darnell et al)
Most visible event
50
Example of the time curve Histone Genes
(HTT2) ORF YNL031C Time course
Histone
51
(No Transcript)
52
Why clustering make sense biologically?
The rationale is
Genes with high degree of expression similarity
are likely to be functionally related and may
participate in common pathways. They may be
co-regulated by common upstream regulatory
factors.
Rationale behind massive gene expression analysis
Simply put,
Profile similarity implies functional association
53
Some protein complexes
Protein rarely works as a single unit
54
Gene profiles and correlation

• Pearson's correlation coefficient, a simple
  way of describing the strength of linear
  association between a pair of random variables,
  has become the most popular measure of gene
  expression similarity.
• 1.Cluster analysis average linkage,
  self-organizing map, K-mean, ...
• 2.Classification nearest neighbor,linear
  discriminant analysis, support vector machine,
• 3.Dimension reduction methods PCA ( SVD)

55
CC has been used by Gauss, Bravais, Edgeworth
Sweeping impact in data analysis is due to
Galton(1822-1911) Typical laws of heridity in
man Karl Pearson modifies and popularizes the
use. A building block in multivariate analysis,
of which clustering, classification, dim. reduct.
are recurrent themes
As a statistician, how can you ignore the time
order ? (Isnt it true that the use of sample
correlation relies on the assumption that data
are I.I.D. ???)

56
.acerca de probabilidades.
57
Microarrays can show us when and where genes are
expressed. But what regulates this expression?
58
Mechanisms of transcriptional regulation
regulation in trans transcription factors
regulation in cis promoters enhancers binding
sites
59
Identifying transcription factor binding sites
Usually, binding sites are first determined
empirically. Most transcription factors can
bind to a range of similar sequences. We can
represent these in either of two ways, as a
consensus sequence, or as a position weight
matrix (PWM). Once we know the binding site, we
can search the genome to find all of the
(predicted) binding sites.
60
Binding site (motif) representations
TCCGGAAGC TCCGGATGC TCCGGATCT CATGGATGC CCAGGAAGT
GGTGGATGC ACCGGATGC
7 characterized binding sites for a certain
transcription factor
TCCCTGGATAGCT
consensus sequence
A 111007200 T 302000502 G 110770060 C 254000015
PWM and logo
61
Finding binding sites in the genome
TCCCTGGATAGCCT
Consensus sequences make searching easy, e.g. by
using regular expressions in Perl while(ltSEQUEN
CEgt) if (_ /TCCTCGGATAGCCT/)
do something All positions in the
motif are treated the same.
62
Finding binding sites in the genome

A PWM allows us to assign more importance to more
invariant positions. We can calculate a score
based on the probability of a given nucleotide
being in a given position.
TCCGGAAGC scores higher than TCCGGATCT as GC is
preferred over CT in the last two positions
63
Finding binding sites in the genome
Binding site motifs can be predicted
computationally from the regulatory regions of
genes with similar expression patterns. For
instance, the promoter regions of genes that
cluster in a microarray experiment can be
used. (How can the promoter regions be
extracted? You should know enough Perl at this
point to be able to do this, given a
well-annotated sequence database.)
64
Finding binding sites in the genome
seq1TTTTTATTTTTCTGAATCACCACTTGATATTGCTTCACAGAACT
seq2CGGGCGGTGAGGCAGAGAAAGAGACCACTTGAAATGTAGTAATA
seq3CACTTGAATTTTTCTGCACGCAGTTTTTATTTTTACTTTTCTTG
seq4CGCGTTCGTTATTTGTTGTTGACCACTTGAATTGATTGCTTTAT
seq5ATCCCGGTCGAGGTGCACTTGATGTTTTCAATGGAAATGTTGCC
seq6TCTGCAGATTTATGGCCCAACGCTCATTTAACAATTAAAGTGGG
seq7GCATTAACTCTCACTTCAAAAAATCATATAAACACCTCTAATAT
seq8TATATTTTCTCGCCACTTAAATAGTTTTCAATGCCAATGGCAGG
seq9ATCCTTATCGAAGCACTTGGATTTTAAAGCAATCTTTTGAACAC
seq1TTTTTATTTTTCTGAATCACCACTTGATATTGCTTCACAGAACT
seq2CGGGCGGTGAGGCAGAGAAAGAGACCACTTGAAATGTAGTAATA
seq3CACTTGAATTTTTCTGCACGCAGTTTTTATTTTTACTTTTCTTG
seq4CGCGTTCGTTATTTGTTGTTGACCACTTGAATTGATTGCTTTAT
seq5ATCCCGGTCGAGGTGCACTTGATGTTTTCAATGGAAATGTTGCC
seq6TCTGCAGATTTATGGCCCAACGCTCATTTAACAATTAAAGTGGG
seq7GCATTAACTCTCACTTCAAAAAATCATATAAACACCTCTAATAT
seq8TATATTTTCTCGCCACTTAAATAGTTTTCAATGCCAATGGCAGG
seq9ATCCTTATCGAAGCACTTGGATTTTAAAGCAATCTTTTGAACAC
65
Finding binding sites in the genome
How meaningful are the sites we find? Only
experiments can tell us for sure However, we
can get some hints using statistical analysis
Example 1 We just found the motif CACTTGA
upstream of co-expressed genes. Is it
over-represented in this set compared to a random
selection of genes?
Search 100 random sets of genes. Find the mean
and standard deviation. z observed -
expected/standard deviation
66
Finding binding sites in the genome
Example 2 Many regulatory regions contain
multiple binding sites for the same transcription
factor. Is the motif found an unusually large
number of times in a short stretch of
sequence?
Crudely Probability of finding a 7 bp motif 4-7
1/16,384 i.e., expect only about 1 motif every
16 kb. Thus, finding several close together is
very unlikely.
67
Transcription factors, binding sites, and target
genes
identify transcription factors

• genetic screens
• one-hybrid assays
• sequence motifs/homology

find all motifs in genome
identify binding motif

• computational searching
• ChIP-chip

• bioinformatics (e.g., Gibbs sampling on
  microarray data)
• molecular biology using purified protein or
  protein extracts

identify target genes

• computational searching
• microarrays
• genetic screens

68
How well does it work?

• Although not always that difficult
  computationally, these approaches are complex
  biologically
• Predicted and in vitro binding data do not always
  accurately reflect what takes place in vivo
• Transcription factor binding can be affected by
  local concentration, by chromatin structure, and
  by interactions with other transcription factors
• Many predicted sites may therefore have no actual
  role
• Functional testing of predictions is very
  important

69
Putting things together cis-Regulatory Modules
(enhancers)
Gene regulation is combinatorial several
transcription factors bind simultaneously We
can search for co-occurrence of multiple
transcription factors to try to identify
regulatory modules Another way to try to find
regulatory modules is through comparative genomics
identity (seq1 vs seq2)
predicted regulatory element
sequence
70
Why bother?
Ultimately, wed like to be able to describe all
of development in terms of gene expression and
regulation. That is, in every cell, at every
time, which genes are on or off, and why?
71
Gene Regulatory Networks
Even knowing just a little of this gets
incredibly complicated
Regulatory gene network for sea urchin
endomesoderm specification
Davidson et al. (2002) Science 2951669
72
But imagine understanding how we go from
http//www.alphascientists.com/embryology_images/c
leavage_stage_embryos.html
here . . .
http//nobelprize.org/medicine
. . . to here . . .
. . . to here!
73
Further Reading Wasserman, W. W. and A.
Sandelin (2004). "Applied Bioinformatics For The
Identification Of Regulatory Elements." Nature
Reviews Genetics 5(4) 276-287. Halfon, M. S.
and A. M. Michelson (2002). "Exploring Genetic
Regulatory Networks in Metazoan Development
Methods and Models." Physiol Genomics 10(3)
131-43. Davidson, E. H. (2001). Genomic
Regulatory Systems. San Diego, Academic
Press. Carroll, S. B., J. K. Grenier, et al.
(2001). From DNA to Diversity. Molecular
Genetics and the Evolution of Animal Design.
Massachusetts, Blackwell Science.