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Genomics and Bioinformatics

What is Functional Genomics?

• Functional genomics refers to the development and application of global (genome-wide or system-wide) experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics. (Hieter and Boguski 1997)

• Functional genomics as a means of assessing phenotype differs from more classical approaches primarily with respect to the scale and automation of biological investigations. (UCDavis Genome Center)

Functional GenomicsHunt & Livesey (eds.)

• cDNA Libraries• Differential Display• Representational Difference Analysis• Suppression Subtractive Hybridization• cDNA Microarrays• Serial Analysis of Gene Expression• 2-D Gel Electrophoresis

Functional Genomics

• Differential Gene expression– SAGE/MPSS– *Open systems*

• Identifying the Function of Genes– Functional Complementation– RNA interference/RNA silencing

Why We Need Functional Genomics

Organism # genes% of genes with inferred function

Completion date of genome

E. coli 4288 60 1997

yeast 6,600 40 1996

C. elegans 19,000 40 1998

Drosophila 12-14K 25 1999

Arabidopsis 25,000 40 2000

mouse ~30,000? 10-20 2002

human ~30,000? 10-20 2000

What is the limitation of functional genomics? 5P

Questions

• Functional genomics will not replace the time-honored use of genetics, biochemistry, cell biology and structural studies in gaining a detailed understanding of biological mechanisms.

SAGE & MPSS

• Serial Analysis of Gene Expression• Massively Parallel Signature Sequencing• Start from mRNA (euks)• Generate a short sequence tag (9-21 nt) for

each mRNA ‘species’ in a cell

SAGE

• Described by Velculescu et al. (1995)• Originally 9 bp tags, now LongSAGE 21 bp• 10-50 tags in a clone• Only requires a sequencer (and some time)

MPSS

• Proprietary technology; published 2000• Generates 17 nt “signature sequence”• Collects >1,000,000 signatures per sample• Requires 2 µg of mRNA and $$

Kamath et al. 2003

16,757 strains = 86% of predicted ORFsLooked for sterility or lethality(Nonv), slow growth (Gro) or defects (Vpep)1,722 strains (10.3% had such phenotypes)

Genes involved in basic metabolism & cell maintenance are enriched for Nonv phenotype

Genes involved in more complex ‘metazoan’ processes (signal transduction, transcriptional regulation) are enriched for Vpep phenotype

Nonv phenotypes highly underrepresented on the X chromosome

X chromosome is enriched for Vpep phenotypes

Basal functions of eukaryotes are shared:- lethal (Nonv) genes tended to be of ancient origin- ‘animal-specific’ genes tended to be non-lethal (Vpep)- almost no ‘worm-specific’ genes were lethal

Interactome: proteome scale data sets of protein-protein interaction. Protein network.

Methods: Yeast two hybrid, protein microarray, gene disruption phenotype, protein subcellular localization, mRNA expression profile, immunoprecipitation/mass spectrometry

Problem: false positive, false negative

Protein-Protein Interaction

Network Topology

Traveling Salesman Network (or Conference Site Map)

Columbia, SC

Boston, MA

New York, NYSan Francisco, LA

Fort Lauderdale, FL

Lincoln, NE

Dallas, TX

Washington DC, MDWichita, KS

Lake of the Ozarks, MS

Sioux Falls, SD

Orlando, FL

Iowa city, Iowa

Honolulu, HI

Anchorage, AL

Moab, UT

Steamboat, CO

Seattle, WA

1954 n=49

1962 n=33

1977 n=120

1987 n=532

1987 n=666

1987 n=2392

1994 n=7397

1998 n=13509

2001 n=15112

2004 n=24978

Chicago, IL

Denver, CO

Atlanta, GA

Protein-Protein Interaction:Extended Neighborhood

Protein-Protein Interaction:Assigning Weight on the Hub

S (PA, PB) = l [ 1+dj

1)]

l = 1

np nl

i=1 j PiTotal score:

np; maximum path lengthal; the weighting coefficient for paths of different lengthnl; number of paths with length lPli; nodes along the ith path of length l including the start and end nodesdj; degree of the nodes

Database

• Molecular Interaction (MINT) database (Zanzoni et al., 2002)

http://mint.bio.uniroma2.it/mint/Welcome.do• Datasets by Gavin et al (2002) and Ho et al

(2002)• Database of Interacting Proteins (DIP) by

Salwinski et al., 2004.

http://dip.doe-mbi.ucla.edu/

1. RNA polymerase II transcription process

2. To bridge between gene-specific transcription factors and the core RNAP II machinery

3. 25 subunits

4. Computational and 3D structural analysis

Mediator Complex

1. Carbon and energy source2. Adaptation of their metabolism based on

the available nutrients3. Regulate gene expression4. Glucose homeostasis regulates its lifespan

and aging in all eukayotes5. Snf1 protein kinase complex: key

components of the glucose repression and derepression pathway

Glucose Metabolism

1. The ultimate causes of aging are unknown

2. Multifactorial process

3. Mutation accumulation and oxidation

Aging

SRB1

SRB9SRB10

SRB8

MED3

MED2

NUT1

MED10

MED1

MED4

MED7

SRB7

SRB2

MED3

GAL11

SIN4RGR1

ROX3

SRB6

MED11

SRB4

SRB5

MED6

Mediator Closed Conformation

SRB6

ROX3

SRB4

MED6

MED11SRB2

SRB9

SRB10

SRB11

SRB8

MED3

GAL1

SIN4

MED2

RGR1

MED4

MED10

NUT1

MED7

MED8

SRB5

SRB7MED1

Mediator Open Conformation

ELM1

GCN5

TUP1

DMC1

CYCB

GLC7

SDS22

PAK1

TOS3

REG1

SNF4 SIP1

SNF1 SIP2

GALB3

SIP4

CAT8

MSN5

MIG1

SIT4

ACC1

SRB10

MED11

MED3

RB4

RGR1

MED1 SRB8

SRB7

MED4

SET1

SGS1

SLT2

SWI4

CRC1

SIP1

RAP1

ZDS2

ZDS1

SIR2

NET1 RAD50

SCD1

HOG1

TRK2

CYR1

HDA1

CDC25

RAS2 GPA2

GPR1 RAS1

ADA1 ESCB

CDC14

HAP4

SIR3

SIR4

GAL4

SRB2

MED10

GAL11

NUT1

ROX3

MED6

MED2 SRB5

MED8

MED7

SRB9

SRB4

SIN4

SRB6

SRB11

Mediator, Glucose, and Aging Network

Oxidative stress

Unknown KinaseActivity

Tup1RepressorActivity

PAU gene Expression

Plasma membraneStress

Aging Stress(cell senescence)

Tor Activity

Slt2Activity

Slt4Activity

Sir3

Regulated Longevity

Calorie restriction

Respiration

Fermentation

Sir2 Activity

Longevity

Hog1 Activity

Gre2 GeneExpression

Cell Wall RemodelingStress Response

pSir3

Slt2

Sit4

Osmotic stress