Genes: Regulation and Structure Many slides from various sources, including S. Batzoglou,

Genes: Regulation and Genes: Regulation and StructureStructure

Many slides from various sources, including S. Batzoglou,

Cells respond to environment

Heat

FoodSupply

Responds toenvironmentalconditions

Various external messages

Genome is fixed – Cells are dynamic

• A genome is static

Every cell in our body has a copy of same genome

• A cell is dynamic Responds to external conditions Most cells follow a cell cycle of division

• Cells differentiate during development

Gene regulation

• Gene regulation is responsible for dynamic cell

• Gene expression varies according to:

Cell type Cell cycle External conditions Location

Where gene regulation takes place

• Opening of chromatin

• Transcription

• Translation

• Protein stability

• Protein modifications

Transcriptional Regulation

• Strongest regulation happens during transcription

• Best place to regulate: No energy wasted making intermediate products

• However, slowest response timeAfter a receptor notices a change:

1. Cascade message to nucleus

2. Open chromatin & bind transcription factors

3. Recruit RNA polymerase and transcribe

4. Splice mRNA and send to cytoplasm

5. Translate into protein

Transcription Factors Binding to DNA

Transcription regulation:

Certain transcription factors bind DNA

Binding recognizes DNA substrings:

Regulatory motifs

Promoter and Enhancers

• Promoter necessary to start transcription

• Enhancers can affect transcription from afar

Regulation of Genes

GeneRegulatory Element

RNA polymerase(Protein)

Transcription Factor(Protein)

DNA

Regulation of Genes

Gene

RNA polymerase

Transcription Factor(Protein)

Regulatory Element

DNA

Regulation of Genes

Gene

RNA polymerase

Transcription Factor

Regulatory Element

DNA

New protein

Example: A Human heat shock protein

• TATA box: positioning transcription start

• TATA, CCAAT: constitutive transcription

• GRE: glucocorticoid response

• MRE: metal response

• HSE: heat shock element

TATASP1CCAAT AP2HSEAP2CCAATSP1

promoter of heat shock hsp70

0--158

GENE

Gene expression

Protein

RNA

DNA

transcription

translation

CCTGAGCCAACTATTGATGAA

PEPTIDE

CCUGAGCCAACUAUUGAUGAA

The Genetic Code

Eukaryotes vs Prokaryotes

• Eukaryotic cells are characterized by membrane-bound compartments, which are absent in prokaryotes.

• “Typical” human & bacterial cells drawn to scale.

BIOS Scientific Publishers Ltd, 1999

Brown Fig 2.1

Prokaryotic genes – searching for ORFs.

- Small genomes have high gene density

Haemophilus influenza – 85% genic - No introns- Operons

One transcript, many genes

- Open reading frames (ORF) – contiguous set of codons, start with Met-codon, ends with

stop codon.

Example of ORFs.

There are six possible ORFs in each sequence for both directions of transcription.

Eukaryotes vs Prokaryotes

• Eukaryotic cells are characterized by membrane-bound compartments, which are absent in prokaryotes.

• “Typical” human & bacterial cells drawn to scale.

BIOS Scientific Publishers Ltd, 1999

Brown Fig 2.1

Gene structure

exon1 exon2 exon3intron1 intron2

transcription

translation

splicing

exon = protein-codingintron = non-coding

Codon:A triplet of nucleotides that is converted to one amino acid

Gene structure

exon1 exon2 exon3intron1 intron2

transcription

translation

splicing

exon = codingintron = non-coding

Finding genes

Start codonATG

5’ 3’

Exon 1 Exon 2 Exon 3Intron 1 Intron 2

Stop codonTAG/TGA/TAA

Splice sites

atg

tga

ggtgag

ggtgag

ggtgag

caggtg

cagatg

cagttg

caggccggtgag

0. We can sequence the mRNA

• Expressed Sequence Tag (EST) sequencing is expensive

• It has some false positive rates (aberrant splicing)

• The method sequences all RNAs and not just those that code for genes

• This is difficult for rare genes (those that are expressed rarely or in low quantities.

• Still this is an invaluable source of information (when available)

Biology of Splicing

(http://genes.mit.edu/chris/)

1. Consensus splice sites

(http://www-lmmb.ncifcrf.gov/~toms/sequencelogo.html)

Donor: 7.9 bitsAcceptor: 9.4 bits(Stephens & Schneider, 1996)

2. Recognize “coding bias”

• Each exon can be in one of three framesag—gattacagattacagattaca—gtaag Frame 0ag—gattacagattacagattaca—gtaag Frame 1ag—gattacagattacagattaca—gtaag Frame 2

Frame of next exon depends on how many nucleotides are left over from previous exon

• Codons “tag”, “tga”, and “taa” are STOP No STOP codon appears in-frame, until end of gene Absence of STOP is called open reading frame (ORF)

• Different codons appear with different frequencies—coding bias

2. Recognize “coding bias”

Amino Acid SLC DNA codonsIsoleucine I ATT, ATC, ATALeucine L CTT, CTC, CTA, CTG, TTA, TTGValine V GTT, GTC, GTA, GTGPhenylalanine F TTT, TTCMethionine M ATGCysteine C TGT, TGCAlanine A GCT, GCC, GCA, GCG Glycine G GGT, GGC, GGA, GGG Proline P CCT, CCC, CCA, CCGThreonine T ACT, ACC, ACA, ACGSerine S TCT, TCC, TCA, TCG, AGT, AGCTyrosine Y TAT, TACTryptophan W TGGGlutamine Q CAA, CAGAsparagine N AAT, AACHistidine H CAT, CACGlutamic acid E GAA, GAGAspartic acid D GAT, GACLysine K AAA, AAGArginine R CGT, CGC, CGA, CGG, AGA, AGGStop codons Stop TAA, TAG, TGA

Can map 61 non-stop codons to frequencies & take log-odds ratios

3. Genes are “conserved”

Approaches to gene finding

• Homology Procrustes

• Ab initio Genscan, Genie, GeneID

• Comparative TBLASTX, Rosetta

• Hybrids GenomeScan, GenieEST, Twinscan, SLAM…

HMMs for single species gene finding: Generalized HMMs

HMMs for gene finding

GTCAGAGTAGCAAAGTAGACACTCCAGTAACGC

exon exon exonintronintronintergene intergene

GHMM for gene finding

TAA A A A A A A A A A A AA AAT T T T T T T T T T T T T T TG GGG G G G GGGG G G G GCC C C C C C

Exon1 Exon2 Exon3

duration

Observed duration times

Better way to do it: negative binomial

• EasyGene:

Prokaryotic

gene-finder

Larsen TS, Krogh A

• Negative binomial with n = 3

Splice Site Models

• WMM: weight matrix model = PSSM (Staden 1984)

• WAM: weight array model = 1st order Markov (Zhang & Marr 1993)

• MDD: maximal dependence decomposition (Burge & Karlin 1997) decision-tree like algorithm to take significant pairwise dependencies into

account

Splice site detection

5’ 3’Donor site

Position

-8 … -2 -1 0 1 2 … 17

A 26 … 60 9 0 1 54 … 21C 26 … 15 5 0 1 2 … 27G 25 … 12 78 99 0 41 … 27T 23 … 13 8 1 98 3 … 25