1. Phenotype and the Interaction of Genetic Perturbations
Informatics for System Genetics

2. Phenotype and the Interaction of Genetic Perturbations
Introduction
Generalized derivation of genetic-interaction networks
Generation of a yeast invasiveness network
Local and global interaction patterns

3. Phenotype and the Interaction of Genetic Perturbations
Introduction

4. Network element activities and phenotype
microarray/proteomics: expression and physical interactions of each constituent
phenotype: a system variable
biomedicine

5. Directed Perturbations
Many systems have deletion projects/consortia/databases
yeast, worm, mouse, fly
Molecular biology methods can target large numbers of genes
antisense oligos, including morpholinos
RNA interference
inducible promoters

6. RNAi
Fraser et al. (Nature 408, p. 35, 2000) targeted 90% of genes on C. elegans chromosome I using RNA interference experiments, and classified resulting phenotypes.

7. Synthetic Genetic Array analysis
Systematic construction of double deletion mutants
A mutant is crossed to an array of ~5000 deletion mutants.
Observing synthetic lethal genetic interactions, generated a network of 291 interactions between 204 genes.
Tong et al. (Science 294, p. 2364, 2001)
Synthetic Genetic Array (SGA) Analysis In Saccharomyces cerevisiae over 80% of the ~6200 predicted genes are non-essential, implying that the genome is buffered from the phenotypic consequences of genetic perturbation. To evaluate function, we developed a method for systematic construction of double mutants, termed Synthetic Genetic Array (SGA) analysis, in which a query mutation is crossed to an array of ~5000 deletion mutants. The SGA system automates yeast genetic analysis allowing genetic manipulations on an unprecedented scale. In particular we are mapping synthetic lethal genetic interactions, inviable double mutant meiotic progeny, because they identify functional relationships between genes and pathways. In our initial proof of principle study, SGA analysis was applied to genes with roles in cytoskeletal organization (BNI1, ARP2, ARC40, BIM1), DNA synthesis/repair (SGS1, RAD27), or uncharacterized functions (BBC1, NBP2), and generated a network of 291 interactions between 204 genes. Systematic application of this approach should produce a global map of gene function and provide us with the first glimpse of the topology of genetic networks for eukaryotic cells. We are developing statistical models for automated scoring of double mutant growth rates and expanding the application of SGA analysis to high resolution genetic mapping, largescale backcrossing of the 5000 viable deletion alleles into specialized genetic backgrounds, and specialized arrays for synthetic gene dosage analysis.

8. SGA

9. Genetic-Interaction Databases

10. Phenotype Ontologies

11. Open Microscopy Environment (Sorger Lab)

12. What’s Needed
Parallel advances in in concepts and computational methods
Generalized derivation of genetic-interaction networks
Quantitative (at least ordered) phenotype data
Analysis of local and global interaction patterns

13. Phenotype and the Interaction of Genetic Perturbations
Generalized derivation of genetic-interaction networks

14. Genetic Interaction
- Interaction of two genetic perturbations in the determination of a phenotype
- Observed in the phenotypes of four genotypes:
a reference genotype, the “wild type”
a perturbed genotype, A
a perturbed genotype, B, with a perturbation of a different gene
a doubly perturbed genotype, AB.
- Perturbations may be of any form (null, loss-of-function, gain-of-function, dominant-negative, etc.).
- Two perturbations can interact in different ways for different phenotypes or under different environmental conditions.

15. Example

16. Hereford-Hartwell 1974

17. Hereford-Hartwell double mutant epistasis analysis

18. Hartwell: Synthetic Defects and Phenotype Buffering

19. Example

20. 75 Phenotype Inequalities in 9 (A)Symmetric Interaction Modes

21. Phenotype and the Interaction of Genetic Perturbations
Generation of a yeast invasiveness network

22. Dimorphic Fungal Pathogens
S. cerevisiae
Magnaporthe grisea
Cell elongation
Distal budding
Altered metabolism
Incomplete cell separation
Cell-cell and cell-substrate adhesion
Substrate invasion

23. Filamentous-Form System Properties
altered cell-cycle progression
cell elongation
unipolar distal budding
adhesion
host (substrate) invasion
altered metabolism

24. Key Pathways

25. Transformation of Knockout Strains with Multicopy Plasmids
Large-scale Genetic Perturbation
Transformation of Knockout Strains with Multicopy Plasmids
Rsr1
Bem1
Cdc42
Ras2
We transformed 118 homozygous diploid knockout strains plus a wildtype control strain with plasmids for constitutive overexpression of genes involved in regulation of filamentous growth.
Phd1
Ste11
Kss1
Ste12
Tec1
Flo8
Gln3
Msn1

26. Phenotype Analysis Strains: Phenotype: Conditions:
Wash Assay for agar invasion:
Strains:
Diploid S1278b mutants transformed with multicopy plasmids
Phenotype:
Agar invasion
Conditions:
High glucose, low nitrogen
prewash colony
postwash colony

27. Strain Construction Mata xxx::HygMX x Mata yyy::KanMX Mata xxx::NatMX
Mating
MATa
a/a
Sporulation
Haploid Selection
P-MFA1::HIS3
Homozygous Double Mutant
xxx yyy
Mata
xxx::HygMX
yyy::KanMX
Mata
xxx::NatMX
yyy::KanMX
Mate and select for HygR NatR to get diploid xxxD yyyD

28. Phenotype Analysis
Strains are pinned onto solid media in a 384-spot format.
Each strain is represented by 4 independent constructions.
4 replicates of each plate are pinned.
Each plate contains 48 spots of a wildtype vector control strain.

29. Analysis of agar invasion phenotypes of diploid mutant strains on low-nitrogen media
Incubate 4 days at 30o C
Wash plate
Pin strains onto low-nitrogen media
Scan plate
Scan washed plate
Prewash image
Postwash image

30. Quantitation of Invasiveness

31. Agar invasion can be visualized in the composite image.
Agar invasion phenotypes of diploid mutant strains on low-nitrogen media
Prewash image
Postwash image
flo1D
flo11D
bud6D
tpk2D
Agar invasion can be visualized in the composite image.
hmi1D
bud8D
rim9D
dia4D
dfg16D
isw1D
Invasive
Non-Invasive
Hyper

32. Phenotype Data Analysis
Calculate ratios of postwash signal/prewash signal
Raw data file from dapple processed to ID spots and subtract background.
Output contains X = prewash signal and Y = postwash signal for each spot.
Calculate the ratio Y/X for each spot.
Normalize data to allow comparison of strains on different plates
Each plate contains 48 wildtype controls.
Calculate the median Y/X ratio for the wildtype vector controls on each plate = Mn for plate n.
The correction factor for plate n is [median (all M values)/Mn].
Phenotype Error = MAX(MAD, 10%MAD)

33. Quantitative Phenotypes
Invasiveness

34. Example: Image Data

35. Example: Data Analysis

36. Phenotype Error

37. Data Subset

38. Entire Network

39. Interaction-Mode Distribution

40. Error Parameter Insensitivity

41. Phenotype and the Interaction of Genetic Perturbations
Local and global interaction patterns

42. Local Interaction, with Biological Processes
- Is there “monochromatic” interaction with modules?

43. Gene
Form
Interaction
Biological Process
‑log10P
PBS2
null
additive
signal transduction
2.99
small GTPase mediated signal transduction
2.96
STE12 gf
single-nonmonotonic to
protein targeting
2.87
STE11 da
noninteractive
cell cycle
2.73
PHD1
hypostatic to
invasive growth
2.68
PDE2
protein amino acid phosphorylation
2.56
HSL1
suppressed by
cell wall organization and biogenesis
2.52
STE20
2.31
EGT2
conditioned by
2.30
ISW1
suppresses
CLB1
protein metabolism
cell surface receptor linked signal transduction
2.28
BEM1
nucleobase, nucleoside, nucleotide and nucleic acid metabolism
2.25
RAS protein signal transduction
2.24
sporulation
TEC1
synthetic
intracellular signaling cascade
2.19
IPK1
M phase
1.95
epistatic to
metabolism
1.94
carbohydrate metabolism
BUD4
establishment of cell polarity
HMS1
1.83
YGR045C

44. Local Interaction, with Biological Processes
As noted for epistasis and synthesis…the results suggest there are characteristic network mechanisms to be found underlying the various modes of genetic interaction.

45. Global Interaction Patterns
genetic-interaction complexity
map similarities among perturbations in interaction patterns

46. Global Interaction Patterns

47. Mutual Information
and
and

48. Global Interaction Patterns

49. Gene1a
Gene2a
Commonb
Mutual Info.c
-log10P
STE20(gf)
STE12(gf) 99
1.8
16.3
PBS2(lf)
HOG1(lf)
101
1.2
14.1
CDC42(gf)
BEM1(gf)
1.0
9.5
100
1.5
9.2
HSL1(lf) 95
8.9
8.0
FLO8(gf)
1.3
6.7
TEC1(gf)
0.9
6.6
GLN3(gf)
1.4
6.3
0.7
5.0
SFL1(lf) 75
0.8
4.8 97
4.4
4.3 86
3.5
FKH2(lf)
YAP1(lf) 18
2.2
3.3
ISW1(lf) 17
2.4
RGS2(lf)
MID2(lf) 15
2.3 98
YJL142C(lf)
2.1
3.2
EGT2(lf) 16
3.1
3.0

50. A Mutual-Information Network

51. A Mutual-Information Network
…suggests mutual information reflects similarities in the global effects of perturbations on molecular information flows.

52. PhenotypeGenetics

53. Priorities
continuing advances in quantitative phenotype measurement and ontologies
reinforcement or revision of genetic-interaction mode definitions based on relevance to network mechanisms
extension of all genetic-interaction modes beyond phenotype ordering to incorporate parameters derived from phenotype magnitudes
comparative genetic-interaction analyses of multiple alleles (with different effects on function) of individual genes to learn how different levels of gene activity impact the network

54. Network modeling by iterative refinement
Data Acquisition
Phenotypes
Microarrays
Proteomics,…
Pathway/
Interaction
Databases
Network
Visualization and
Modeling
Analysis Modules
Network
Refinement

55. Phenotype and the Interaction of Genetic Perturbations
Informatics for System Genetics
Becky Drees Marisa Raymond
Vesteinn Thorsson Iliana Avila-Campillo
Greg Carter Paul Shannon
Alex Rives Tim Galitski