Searching for human splice regulatory motifs ii network analysis of synthetic lethal interactions
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Searching for human splice-regulatory motifs & II. Network analysis of synthetic-lethal interactions. Fritz Roth Harvard Medical School Dept. of Biological Chemistry & Molecular Pharmacology. IPAM WorkshopJan 2006. Outline. Human alternative-splicing motif search

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Searching for human splice regulatory motifs ii network analysis of synthetic lethal interactions

Searching for human splice-regulatory motifs &II. Network analysis of synthetic-lethal interactions

Fritz Roth

Harvard Medical School

Dept. of Biological Chemistry & Molecular Pharmacology

IPAM WorkshopJan 2006


Outline

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis


Outline1

Outline

  • Human alternative-splicing motif search

    • Review mRNA splicing

    • Splice-junction expression data

    • Sequence neighborhoods

    • Clustering splice-junctions by usage

    • Results

  • Yeast synthetic-lethal network analysis


Brief review of canonical mrna splicing

Brief review of canonical mRNA splicing

Adapted from Molecular Cell Biology, Lodish et al.


Importance of alternative splicing

Importance of alternative splicing

~100,000 genes

~70% of human

multi-exon genes

alternatively spliced

35,000

25,000

human genes

5% alt. spliced

Antiquity

2004

2001


From correlation to regulatory mechanism

From correlation to regulatory mechanism

Roth et al , 1998,

Hughes et al, 2000

Tavazoie et al 1999,

Slides adapted from S. Tavazoie


Sequence neighborhood of a splice junction

Sequence neighborhood of a splice junction


Conservation constitutive vs alternative

Conservation: constitutive vs. alternative

5’ donor

neighborhood

3’ acceptor

neighborhood

(Sorek & Ast, Gen. Research, 2003)


Previous splicing motif searches

Previous splicing motif searches

  • Canonical splicing enhancers/repressors not associated with specific tissues (e.g. Brudno et al., Burge et al, Chasin et al)

  • Based on small (curated literature) set of alternatively spliced genes (e.g. , Brudno et al, Stamm et al., Fedorov et al.)

  • Based on expressed sequence tag (EST) datasets (Xu et al), biased towards 3’ ends of genes, can contain artificial splice variants due to Unigene clustering (Modrek & Lee, 2002)


Exon exon splice junction expression data

18 nt

+

18 nt

Exon-exon splice junction expression data

Pre-mRNA

Cassette exon

Alternative mature mRNAs

  • Every splice junction for ~11K reference mRNAs for ~10K genes

  • ~100K probe sequences on five arrays

  • 49 tissues & cell lines run in fluor-reversed pairs

  • full-length mRNA amplification

  • Johnson et al. Science. 2003 Dec 19;302(5653):2141-4.

  • Castle et al. Genome Biology. 2003;4(10):R66.


Tissue specific probes data

Tissue-specific probes & data


Flowchart

Flowchart

splice junction usage data


Tissue specific splice junction expression

probes

3’

5’

tissues

Tissue-specific splice junction expression

original

intensities

NPTB


Tissue specific splice junction expression1

probes

3’

5’

tissues

Tissue-specific splice junction expression

original

intensities

rescaled

intensities

NPTB


Tissue specific splice junction expression2

probes

3’

5’

tissues

Tissue-specific splice junction expression

original

intensities

rescaled

intensities

relative

splicing

NPTB


Tissue specific splice junction expression3

Tissue-specific splice junction expression

Synexin (ANXA7)


Flowchart1

cassette exons

alternative

donors / acceptors

cassette exons

Probe set choice

all

skipped cassette exons

alternative

donors / acceptors

Flowchart

splice junction usage data

Clustered splice junctions


Results heart muscle

Results: heart & muscle

Cassette exons skipped

in heart & skeletal muscle


Flowchart2

alternative

donors / acceptors

cassette exons

Probe set choice

all

Sequence neighborhood choice

intronic

all

exonic

proximal to acceptor

proximal

to donor

sequence neighborhoods

Flowchart

splice junction usage data

clustered splice junctions


Sequence neighborhood of a splice junction1

Sequence neighborhood of a splice junction


Alternative splice junction neighborhoods

Alternative splice junction neighborhoods

1

3

3

2

Immediate neighborhood

Neighborhood of junctions with shared splice site

Neighborhood of junctions of other competing junctions


Flowchart3

alternative

donors / acceptors

cassette exons

Probe set choice

all

Sequence neighborhood choice

intronic

all

exonic

proximal to acceptor

proximal

to donor

sequence neighborhoods

word-

based

motif-

based

Pattern finding

enriched sequence patterns

Flowchart

splice junction usage data

clustered splice junctions


Results heart muscle1

Results: heart & muscle

Cassette exons skipped

in heart & skeletal muscle

  • ACTAAC @ end of intron

  • - 8 out of 39 probes

  • - 14-fold enrichment

  • strong position bias

    • (often nearby)


Results brain

Results: brain

Cassette exons skipped in brain

26% of probes (9-fold enrichment)


Results ileum

Results: ileum

65% (15-fold)

76% (3-fold)


Results jejunum liver pancreas

Cluster

Results: jejunum, liver, pancreas


Results jejunum liver pancreas1

HNRPL

Cluster

Results: jejunum, liver, pancreas


Ipam workshop jan 2006

Results: jejunum, liver, pancreas

HNRPL

Cluster

HNRPL

RRM1 RRM2 RRM3 RRM4


Results jejunum liver pancreas2

HNRPL

Cluster

Results: jejunum, liver, pancreas

PTB

RRM1 RRM2 RRM3 RRM4

Protein interaction

HNRPL

RRM1 RRM2 RRM3 RRM4


Results jejunum liver pancreas3

HNRPL

Cluster

Results: jejunum, liver, pancreas

PTB

RRM1 RRM2 RRM3 RRM4

Protein interaction

HNRPL

RRM1 RRM2 RRM3 RRM4


Summary part i

validated

cis-regulatory

motifs

candidate

cis-regulatory

secondary strcture

Map to trans-acting

splicing factors

Summary, Part I

tissue-specific

alternative

splicing

candidate

cis-regulatory

motifs


Acknowledgments part i

Adnan Derti

George Church & Lab

Roth Lab

Rosetta/Merck

Jason Johnson

John Castle

Lee Lim

Adrian Krainer

Acknowledgments, Part I


Outline2

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis


Outline3

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis

    • Background

    • Overlap with other biological relationships

    • Network motifs

    • Predicting synthetic lethality

    • Role of transcription compensation in mutational robustness

    • SSL vs. protein interaction in predicting function


What is synthetic lethality

What is Synthetic Lethality?

Gene X

Gene Y

Cells live

Cells live

Cells die

Gene X

Gene Y

Gene X

Gene Y


What is synthetic sickness lethality ssl

What is Synthetic Sickness/Lethality (SSL)?

Gene X

Gene Y

Cells live

Cells live

Cells die

or grow slowly

Gene X

Gene Y

Gene X

Gene Y


An engine can run without one cylinder

An engine can run without one cylinder

(from http://www.cs.unc.edu/~geom/collide/videos.shtml)


Scenarios resulting in synthetic genetic interaction

3 compensatory pathways, 2 required

Partially redundant genes

2 partially redundant pathways

Protein complex tolerating 1 but not 2 mutations

A

B

H

H

B

K

A

F

A

J

F

G

E

C

G

E

C

L

B

B

C1

A

C2

C

D

D

M

D

E

F

D

E

I

I

SSL

Scenarios resulting in synthetic genetic interaction


A known sub network of ssl interactions

A known sub-network of SSL interactions

  • A Canadian consortium (Boone et al.) has made many double mutants

  • As of 2001:

    8 query genes

    x

    4500 nonessential “array” genes

    ≈ 36,000 tested pairs

    (Tong et al., Science, 2001)


The known sub network circa 2001

The known sub-network circa 2001

(Tong et al., Science, 2001)


The known ssl sub network circa 2004

The known SSL sub-network circa 2004

  • 160 query x 4500 nonessential ≈ 700,000 tested pairs (≈4% pairs)

~3800 interactions

(Tong et al., Science, 2004)


Outline4

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis

    • Background

    • Overlap with other biological relationships

    • Network motifs

    • Predicting synthetic lethality

    • Role of transcription compensation in mutational robustness

    • SSL vs. protein interaction in predicting function


Overlap between synthetic lethality other interactions

Overlap between synthetic lethality & other “interactions”


Overlap between synthetic lethality other interactions1

Overlap between synthetic lethality & other “interactions”


Overlap between synthetic lethality other interactions2

Overlap between synthetic lethality & other “interactions”


Overlap between synthetic lethality other interactions3

Overlap between synthetic lethality & other “interactions”


Overlap between synthetic lethality other interactions4

Overlap between synthetic lethality & other “interactions”


Overlap between synthetic lethality other interactions5

Overlap between synthetic lethality & other “interactions”


Overlap between synthetic lethality other interactions6

Overlap between synthetic lethality & other “interactions”


Scenarios resulting in synthetic interaction

3 partially redundant pathways, 2 required

Partially redundant genes

2 partially redundant pathways

Protein complex tolerating 1 but not 2 destabilizing mutations

A

B

H

H

B

K

A

F

A

J

F

G

E

C

G

E

C

L

B

B

C1

A

C2

C

D

D

M

D

E

F

D

E

I

I

SSL

Scenarios resulting in synthetic interaction

< 2%

< 4% *


Outline5

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis

    • Background

    • Overlap with other biological relationships

    • Network motifs

    • Predicting synthetic lethality

    • Role of transcription compensation in mutational robustness

    • SSL vs. protein interaction in predicting function


Network motifs simple building blocks of complex networks

“Network Motifs: Simple Building Blocks of Complex Networks”

R. Milo, S. Shen-Orr, , , U. Alon, Science (2003).


The synthetic lethal network has many triangles

The synthetic lethal network has many triangles

Xiaofeng Xin, Boone Lab


Motifs in an integrated s cerevisiae network

Motifs in an integrated S. cerevisiae network


Motifs in an integrated s cerevisiae network1

Motifs in an integrated S. cerevisiae network


Motifs themes one gene synthetic lethal with a complex

Motifs  Themesone gene synthetic lethal with a complex?


Motifs themes pair of synthetic lethal complexes

Motifs  Themespair of synthetic lethal complexes?


Thematic map of synthetic lethal complexes

Thematic map ofsynthetic-lethal complexes


Mapping pairs of synthetic lethal complexes

Mapping pairs ofsynthetic-lethal complexes


Outline6

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis

    • Background

    • Overlap with other biological relationships

    • Network motifs

    • Predicting synthetic lethality

    • Role of transcription compensation in mutational robustness

    • SSL vs. protein interaction in predicting function


Predicting synthetic lethality why

Predicting Synthetic Lethality: Why?

  • Especially if the consortia led by Boone and Boeke

  • are testing all yeast gene pairs

  • Simple Answer:

  • Even finished, this project is one strain, one organism, one phenotype (growth), and one growth condition.


Predicting synthetic lethality many weak predictors

Predicting Synthetic Lethality: Many weak predictors


Predicting synthetic lethality probabilistic decision trees

Predicting Synthetic Lethality: Probabilistic decision trees


Predicting synthetic lethality cross validation success

Predicting Synthetic Lethality: Cross-validation success

~80% sensitivity by testing ~20% of gene pairs (80/20 Rule!)


Outline7

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis

    • Background

    • Overlap with other biological relationships

    • Network motifs

    • Predicting synthetic lethality

    • Role of transcription compensation in mutational robustness

    • SSL vs. protein interaction in predicting function


Upregulation of compensatory genes

Upregulation of compensatory genes?

SSL

Gene M

Gene G

Anecdotally, this is rare (Lesage et al, 2004)


Upregulation of compensatory genes data sets examined

Upregulation of compensatory genes:Data sets examined

  • mRNA expression, mutant vs. wild type

    (Rosetta compendium, Hughes et al, 2000)

  • SSL genetic interactions (Tong et al.)


Upregulation of compensatory genes distribution of log g mutant m g wt

Upregulation of compensatory genes:Distribution of log (Gmutant_M / Gwt)]

116,863

935

Fraction of genes

Log ratios


Upregulation of compensatory genes how common is it

Upregulation of compensatory genes:How common is it?

Of 935 SSL M:G pairs examined, only

thirteen went up significantly

That is only four more than expected given the

fraction that went up in 120,000 non-SSL pairs


Thirteen examples of compensatory upregulation

Thirteen examples of compensatory upregulation

Six previously observed examples

FKS1 RLM1 Zhao, 1998

FKS1 SLT2 Lesage, 2004

GAS1 CHS3 Lesage, 2004

GAS1 KRE11 Lesage, 2004

GAS1 SLT2 Lesage, 2004

GAS1 YALO53W Lesage, 2004

Seven new examples

BNI1 SLT2

CDC42 GIC2

(translational compensation; Jacquenoud, 1998)

FKS1 KAI1

FKS1 PAL2

GAS1 YMR316C-A

SHE4 ARC40

SHE4 CHS7


Transcriptional compensation for gene loss

Transcriptional compensation for gene loss.

  • exists for a few SSL pairs

  • is extremely rare


Rationalization

Rationalization

  • Regulatory apparatus to detect gene loss…

    • Provides only a weak benefit if gene loss is rare

    • Large mutational target


Outline8

Outline

  • Human alternative-splicing motif search

  • Yeast synthetic-lethal network analysis

    • Background

    • Overlap with other biological relationships

    • Network motifs

    • Predicting synthetic lethality

    • Role of transcription compensation in mutational robustness

    • SSL vs. protein interaction in predicting function


Ssl vs protein interaction the arena

SSL vs protein interaction: The arena

Tested for

Protein intxn

Tested for genetic intxn

Tested

for both

data sets: 1 genetic, 5 protein


Ssl vs protein interaction

SSL vs protein interaction

Protein intxns are more accurate.

SSL intxns are more sensitive.


Ssl vs protein interaction combining with other relationships

SSL vs protein interaction: combining with other relationships

Common regulator

Gene co-occurence

Gene fusion

Gene neighborhood

Homology

mRNA coexpression

Chromosomal distance

Same localization

Same phenotype

Many ‘2-hop’ relationships


Ssl vs protein interaction combining with other relationships1

SSL vs protein interaction:combining with other relationships

True Positive Rate (Sensitivity)

False Positive Rate (1 – Specificity)


Summary part ii

Summary, Part II

  • SSL  other biological relationships

  • Motifs in an integrated S. cerevisiae network

  • Map of compensatory complexes

  • Predicting synthetic lethality

  • Transcriptional compensation plays a minor role in robustness to gene loss

  • SSL vs. protein interaction in predicting function: SSL wins but is complementary


Acknowledgments part ii

Roth Lab

Sharyl Wong

Lan Zhang

Oliver King

Debra Goldberg

Gabriel Berriz

Frank Gibbons

Others

Data Sources

SGD

MIPS

YPD

Canadian Synthetic Lethal Team

Charlie Boone

Amy Tong

Guillaume Lesage

Howard Bussey

Brenda Andrews

Xiaofeng Xin

Gary Bader

Zhijian Li

Others

Marc Vidal

Acknowledgments, Part II


Probabilistic decision trees

Probabilistic Decision Trees

  • Models conditional probability of one variable given a combination of others.

  • Capable of integrating many variables (built-in feature selection).

  • Provides intuition about why predictions are made.


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