functional genomics with an emphasis on yeast genomics, jef boeke november 2006 “beer is living...

Functional Genomicswith an emphasis on YEAST

Genomics, Jef Boeke

November 2006

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“Beer is living proof that God loves us and wants us to be happy” - Benjamin Franklin

Fermentation of mashed grains was probably considered a magical property of a properly cared-for vessel. We have been carrying around these vessels ever since and thus the cultivation of yeast has always been closely linked with human culture. It was not until Louis Pasteur's time that yeast was colony-purified. Saccharomyces cerevisiae was purified from European beers. Schizosaccharomyces pombe was purified from African millet beer and palm wine. Source: Charles Brenner web site, Dartmouth



What’s functional genomics?• Inferring gene function from genome-wide screens,

analyses, comparisons, etc.• Today: focus on experimental (not purely

bioinformatic) screens and profiling• Mutational analyses - how to make/analyze mutants• Transcript/Protein profiling• Protein interactions • Genetic interactions• Compound interactions• Complementation by metazoan orthologs

A two-part view of functional genomics

• Perturbing gene function– Knocking down/out gene function: mutants and RNAi– Overexpressing genes– Adding compounds

• Analyzing phenotypes and interactions– Growth and color; phenotypic reporters

Transcript/protein profiling– Protein interactions– Genetic interactions– Phenotype profiling



Saccharomyces cerevisiae life cycle

99% of yeast lab work is done with MATa and MAT haploids

Perturbing gene function genome wide

• Nonessential genes:Systematic deletion/gene replacement strategies

• Overexpression• Strategies for essential genes

– haploinsufficency– promoter shutoffs – Ts mutants

• siRNA/shRNA• Compound libraries (subject for another day)

The systematic yeast knockout project Disrupted all ~6000 ORFs in the yeast genome – the

YKO (yeast knock out) collectionNon-essential genes provided as:

• a mating type• mating type • diploid heterozygous for each YKO

• diploid homozygous for each YKOEssential genes (~15%):

• diploid heterozygous for each YKO Distributes YKO mutants to the academic community

for phenotypic analyses at a low cost

YAL068cYAL069w

KanR

YAL068cKanR

Overall strategy: Gene Replacement by Homologous Recombination

YKO strain for YAL069w yal069::kanMX

One tag One Deletion Strain

.

List of 20mer tags Deletion strains

However, each deletion strain is assigned two tags to increase robustness of data

1. GATTCGATAGCCGGCAAGG

2. CGATTTAGGAATGTCATAG

3. AGCTCATACCTAGTAACTA

6,200. AGCTCATACCTAGTAACTA

. .

YFG+YFGL YFGR

UPTAG DOWNTAG

wild-type YFG+ strain

mutant yfgD::kanMX strain

kanMXYFGL YFGR

Detail of UPTAG and DOWNTAG structure

The yeast knockouts (= “YKOs”) are tagged for array detection

Application of the YKO mutant set: manipulating populations of

mutantsUnselected population Selected population

X

Selection imposedGenetic or environmental

How this applies to humansBefore stress

Survivors

Stressed people

X

What genetic/environmental interaction did this guy in?

winner! winner! winner!

winner! winner! winner!

Scanned image of a TAG array or “function chip”: Control cell TAGs red; experimental cell TAGs green

Essential genes

• Knockouts - limited to haploinsufficiency analysis

• Promoter shutoff approach• dAMP alleles Schuldiner et al. 2005 Cell 123:507-19

• Temperature sensitive mutants; now done for about 300 genes

• Yeast ~1000 essential genes - genome wide reagents needed

Tet promoter alleles

• Tet-off system• Promoter is

substituted for native promoter

• About half of these “behave”

• Cell 118, 31-44; S. Mnaimneh, A. et al. 2004



Ts alleles• The classic genetic approach• Systematic PCR-based methods worked out;

these incorporate barcodes compatible with KO mutants

• Ts mutant alleles collected for ~500 of 1000 genes (C. Boone et al. unpublished)

• Technology developed for systematic Ts mutant generation with barcodes (Hieter/Boeke labs).

• Status: about 300 genes finished, remainder underway

Knockdown of gene function in metazoans - focus on mammals

• Knockdown ≠ Knockout!

• Gene traps

• Transposons and retrotransposons

• RNAi etc.

• Zinc finger nuclease

Genetraps 101• In mammals, biggest target is

introns• Typical gene-traps are

designed to prematurely truncate mRNAs; SA splice acceptor; pA polyadenylation signal; 1/2 chance it will truncate gene in theory

• They can incorporate a reporter

• Can be delivered by random transfection or by a retrovirus

• Retroviruses tend to show hotspots; limits usefulness

Gene-trap libraries• There are now multiple gene

trap libraries in ES cells• You can order the ES cell line

and differentiate it or …• Better yet… turn it into a

knockout mouse• http://www.genetrap.org/• Exercise: pick 3 genes and see if

you can find an existing ES cell line(s) for the major collections: Sanger Gene trap consortium; German Gene trap consortium. Then figure out where in your gene it inserted and the structure of the “mutant” transcript



Experimental Hematology, Volume 33, Pages 845-856; A. Forrai, L. Robb

Transposon/retrotransposon gene traps

• DNA transposons “Sleeping Beauty” and “PiggyBac” engineered to knock out genes, find promoters and more in mammalian systems

• 2 component system “Tranposase” expression cassette and mini-Transposon

• Retrotransposons L1 and ORFeus can be used similarly; 1-component system

• Can incorporate gene traps; greater randomness may allow better genomic coverage; also, can do mutagenesis in whole animal, bypassing time-consuming derivation of ES Cell process

RFP labeled gene-trap in DNA transposon in vivo



Red mice carry piggybac::RFP genetraps;Simple screen for hops into genes. Ding et al. Cell 2005; 122:473-83

Genome-wide RNAi methods•Basic strategy – apply double-stranded RNA of YFG to cells or organism of choice – you (usually) eliminate or reduce transcripts of YFG

•Very easy in C. elegans, which EATS E. coli – engineer coli to make dsRNA (plasmid vector with two convergent T7 promoters). Fraser et al. Nature 408:325

•Also works with mammalian cells – provided the dsRNA is provided as ~21-22 bp fragments so as to avoid provoking a non-specific interferon response. Effect is transient - a few days = “siRNA”

•Newer version is shRNA (synthetic hairpin RNA) which can be delivered and expressed via a DNA (plasmid or integrated lentivirus) vector and thus provoke a longer-term effect

RNA interference (RNAi)

Dicer

Gene Silencing

Argonaute (Slicer)

shRNAs provide a means for genome-wide “mutagenesis”

snRNA promoter

shRNA coding region

Plasmid or lentiviral vector

shRNA

AAAAA

Dicer action

siRNA

AAAAARISC/slicer action

RNAi libraries as “forward-genetic” tools

Resource # of clones (Species)

TRC: The RNAi ConsortiumshRNA clones

>100,000 clones planned(human, mouse & rat)

Vector

LentivirusPlasmid

Hannon/Elledge ConsortiumshRNA clones

LentivirusPlasmid

>100,000 clones planned(human, mouse & rat)

Notes

Nearly complete; U6 promoter; we have itat JHU (human and mouse)http://hitcores.bs.jhmi.edu/

Molecular barcodes;has GFP; miRNA promoter/5’ and 3’ UTRs

Many companiessiRNAs

Nonrenewable resource!More like a compound library

Works very well in TC cellsCan buy and use today (no viral packaging)Doesn’t work well in primary cells, in vivo (yet…)

Zn finger nuclease (ZFN) technology

Seminar this Friday at 4 PM from Sigma AldrichDarner Conference Room BRB G07; technology invented at

JHUSOPH! (Chandrasegaran)



Great site: http://bindr.gdcb.iastate.edu/ZiFiT/Homework/lab assignment: design a Zinc finger for YFG



Figure above shows how six different fingers collaborate to recognize two 9 bp sequences; nucleases (green) create double strand break (DSB). Figure to right A) shows how homologous recombination is used to repair DSBs in normal cells. B) shows how an engineered, cloned mutant version of the sequence (red) is recombined in at site of DSB created by ZFN

Analyzing phenotypes and interactions

• Growth and color; phenotypic reporters

• Transcript/Protein profiling

• Protein interactions

• Genetic interactions

• Phenotype microarrays

Yeast as tool - the awesome power of growth and color

• Yeast can grow• Yeast can make

colored colonies• Yeast can make

colored (or fluorescent) cells



Counterselectable markers

• URA3 - select for the gene on minimal medium lacking Ura

• Select against it on 5-Foa

• Wide use in reporter assays, etc.

• LYS2 - select for the gene on minimal medium lacking Lys

• Select against it on -aminoadipate

• Used a bit less, as LYS2 is ~3.5 kb, URA3 is <1 kb

Most important colony color markers

• ADE2 (also ADE1)• Mutant (ade2) colonies

are red on low Adenine media like YPD

• Mutants accumulate an intermediate “AIR”, which produces a red derivative

• SUP11/ade2-101 system and others

• MET15 (also MET2)• Mutant (met15) colonies

are black on media containing lead ions

• Mutant produces H2S which generates insoluble black lead sulfide

How to make/use reporters

• Choose your reporter: color, fluorescent, selectable, and/or counterselectable

• Fuse it to a “control element” - could be promoter, UTR, protein segment conferring instability, anything you think might be important

• Go wild screening for things that turn reporter on or off - it will help dissect the biology

GFP

YFGpromoter+

Generic 3’ UTRGFP

Copyright ©1999 by the National Academy of Sciences

Edskes, Herman K. et al. (1999) Proc. Natl. Acad. Sci. USA 96, 1498-1503

URE2/GFP

“prion domain”

A reporter for prion state, assayed microscopically

The “macro-array”approach of

phenotyping large mutant collections

Ross-MacDonald et al Nature 402;413

See also TRIPLES database for data on gene expression, localization, and

insertion mutant phenotypeshttp://ygac.med.yale.edu

Phenotypic microarrays (PMs)www.biolog.com

~2000 different “conditions”(96 shown here)NB- Instrument is available for use in our microarray facility

Transcript and protein profiling•The idea: measure the abundance of mRNAs or proteins in cells

•Compare the abundance of gene products under different conditions

•Examine the pattern or profile of abundances

•Premise: Genes that are in similar pathways may be coregulated and thus may have related functions

•There are many profiling methods available and more under development

•These profiles have many other purposes; e.g. diagnostics

•Most commonly used method is microarray analysis in which transcript levels are assayed by hybridization; essentially a “reverse Northern” of the entire genome

Pharmacogenomics:Comparing the effects of drugs and mutants;500 microarray experiments on different YKO mutants or drug treatments of wild-type cells

clus

tere

d pr

ofile

inde

x

ergosterol

-10 -5 -2 1 2 5 10fold repression fold induction

histone deacetylase

mating

MAPK signaling

ribosome/translation

tup1, ssn6HU/MMS/rnr1

isw1, isw2

sir2, sir3

cell wallergosterol

cell wall

cup5, vma8

mitochondria

PAU RNR2,3,4

amino acidbiosynthesis (AA)

mitochondrialfunction

calcineurin/PKC

mating

clustered transcript response index

S/C

Drug treatments ORmutants in genes

Transcripts of genes involved in indicated process/pathway

T. Hughes et al Cell 102:109

Even more profiling approaches

Yeast promoter strength (transcriptional frequency)and transcript stability dissectedhttp://web.wi.mit.edu/young/expression/

SAGE (serial analysis of gene expression)http://genome-www.stanford.edu/Saccharomyces/SAGE/AdvancedQuery.html

Protein profiling via 2-D gel or other separation/Mass spectrometry methodsLink et al. Nature Biotechnol 17:676

6000 promoter fusions to GFP; measure promoter strength rather than steady state RNA levelDimster-Denk et al. J. Lipid Research 40:850

Genome wide search for enzyme functions:GST fusions to every ORF (Science 286:1153)

GST YFG

X 60 YFGs

Make 96 GST fusion protein pools

Run 96 enzymatic assaysFind positives,Deconvolute positive pools

Or put all 6000 GST-His6 fusion proteins down as spots on chips…Zhu et al. Science 293:2101

Search for phospholipid or calmodulin binding proteins

Calmodulin binding motif

Two hybrid and mass spec analysis across the genome; cataloging protein-protein interactionshttp://portal.curagen.com and Uetz et al. Nature 403:623; Ito et al. PNAS 98:4569; Ho et al. Nature 415:180; Gavin et al., Nature 415:141 and 440:631; Krogan et al, Nature 440:637

Concept: identify all possible pairwise protein/protein interaction through yeast two-hybrid assay

Implementation: cross 6000 DB domain fusions (representing all 6000 full-length ORFs) by 6000 AD domain fusions = 36 * 10e6 analyses

Problems: many false positives and negatives

A complementary approach, affinity pulldown of protein complexes, identify complex members by Mass spectrometry

The biggest yeast MS dataset



d, Graphical representation of the complexes. This Cytoscape/GenePro screenshot displays patterns of evolutionary conservation of complex subunits. Each pie chart represents an individual complex, its relative size indicating the number of proteins in the complex. The thicknesses of the 429 edges connecting complexes are proportional to the number of protein–protein interactions between connected nodes. Complexes lacking connections shown at the bottom of this figure have <2 interactions with any other complex. Sector colors (see panel f) indicate the proportion of subunits sharing significant sequence similarity to various taxonomic groups (see Methods). Insets provide views of two selected complexes—the kinetochore machinery and a previously uncharacterized, highly conserved fructose-1,6-bisphosphatase-degrading complex (see text for details)—detailing specific interactions between proteins identified within the complex (purple borders) and with other proteins that interact with at least one member of the complex (blue borders). Colors indicate taxonomic similarity.Krogan et al 2006 Nature 440, 637-643

Interaction databases

• BioGRID http://www.thebiogrid.org/• DIP Database of Interacting Proteins dip.doe-

mbi.ucla.edu• BIND Biomolecular interaction database

www.bind.ca (recently privatized)

• These databases warehouse interaction data, and represent small pieces of interaction webs to make them more digestible

The yeast protein and genetic interaction maps:Dissecting the hairball of interactions

Protein-protein interactions tend to define linear pathways or “series” circuits

A complementary approach is needed to identify parallel pathways, and branchpoints or “parallel” circuits in the network of life

Synthetic lethality: what is it and what does it tell us?

yfg1 mutant – viableyfg2 mutant – viableyfg1 yfg2 double mutant - inviable

If the nature of the yfg mutants is unknown, many possible interpretations…BUT, if they are both null alleles, simplest interpretation is they are in redundant, parallel, or branched pathways

input

output

YFG1

YFG2

YFG3

YFG4

YFG5

YFG6

YFG7

YFG8

Thus, the patterns of lethality helps deduce pathway architecture

Genetic interactions; two methods SGA, SLAM

• SGA: Synthetic Genetic Array– Uses robots to make diploids, sporulate them, and

select for double mutant haploids (approx 384 at one time)

– Some double mutants don’t grow or grow slowly

• SLAM: Synthetic lethality analyzed by microarray– Uses pools or mixtures of mutants, “query gene”

knocked out by transformation– Readout is microarray/molecular barcodes or

TAGs

Genetic analysis of YKOs as a population:SLAM

Tag-array hybridization

Uptag Downtag

kanMX4uptag

kanMX4PCRuptagDowntag Downtag

Cy5 Cy3

Genetic perturbation

physiological stress

control pool experimental pool

Genomic DNA

A role of the CTF18m module in DNA replication checkpoint signaling

ctf8 ctf8 rad9dcc1 dcc1 rad9

cont

rol

wt rad9tof1 tof1 rad9

HUMM

S

HUMM

S

cont

rol

DNA damagingassaults

Mec1/Ddc2Drc1, Dbp11Pol, Rfc2,5

Mrc1Tof1/Csm3

Mec1/Ddc2Rad24Ddc1Mec3Rad17Rad9

Rad53 Chk1

Cell cycle arrest& DNA repair

Dun1

SFL interaction Physical interaction

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RAD9

SOD1

TSA1

CDC9RAD27

POL32

ELG1

RNR4

TOF1

CSM3

CTF18CTF8

DCC1

MRC1

DUN1

DDC1

MEC3RAD17

RAD24

CHK1

DRCsignaling

Ctf18/Ctf8/Dcc1

Oxidative stress

response

DNA replication

DDCDRC

Pan et al Cell 2006 124:1069-81

So much info, so little QC• Every method produces false negatives and false

positives• All of the methods seem to work well with “knowns”

but work much less well with unknown genes• Reasons may include functional redundancy,

complex, multiple functions or functions not evident under lab conditions

• Combinatorial informatic approaches need weighting to help evaluate strength of “links” between genes. Also, any single set of gene “links” is incomplete

• What is needed to have a better success rate at functional prediction is less links of low quality and more links of high quality

functional genomics with an emphasis on yeast genomics, jef boeke november 2006 “beer is living...

Documents

kanmx slide

yko yeast

dartmouth slide

day slide

environmental slide

yko mutants

yeast genomics

yko essential genes