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Using a network based approach to interpret molecular profiling
data
A Yssel
UNIVERSITY OF PRETORIA
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Using a network based approach to interpret molecular profiling data
• PheNetic – Kathleen Marchal, UGent (BE) – Dries De Maeyer – User-friendly web server for sub-network
inference • How it works • Case study
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Network based approaches
• Statistical over representation vs network based approaches
• Network based approach – Combine interatomics knowledge (public
data, regulonDB, STRING, Biocyc etc) and represent as a network
– Results from molecular profiling experiment (micro array/ RNAseq etc…)
– Search for mechanistic insights
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Network based approaches
• Advantages: – Filters noise from gene list – Compensates for missing information – Provides better insight by incorporating
multiple molecular levels (protein, DNA, metabolic etc…)
Sub network inference by PheNetic
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PMC full text: Nucleic Acids Res. 2015 Jul 1; 43(Web Server issue): W244–W250. Published online 2015 Apr 15. doi: 10.1093/nar/gkv347
Input formats:
Network protein1, protein2, pp, undirected
gene1, gene2, metabolic, undirected protein3, gene3, sigma factor, directed …… ……
Molecular profiling data gene, log fold change, p-value (optional) Gene list N most differentially expressed genes or specific genes of interest
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Networks for organisms of interest
• Manually curated on PheNetic website (E. coli, Salmonella, Yeast)
• From STRING (P-P interactions) • AGRIS (Arabidopsis) • Regulon DB (E. coli) • TRRUST (human transcription factor) • etc
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• Use network + expression data • Generates probabilistic network (F = N with probabilities
on edges): – Edges connecting diff ex genes have higher probability
than those connecting genes that are not diff ex • Infers subnetwork (upstream or downstream mode):
– Trade off: Selecting least nr of edges and linking as many as possible genes from gene list
• Upstream : Terminal nodes must be regulatory interactions
• Downstream: Only paths following direction of network is valid
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Inner workings of Phenetic
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• PMC full text: • Nucleic Acids Res. 2015 Jul 1; 43(Web Server issue): W244–W250. • Published online 2015 Apr 15. doi: 10.1093/nar/gkv347
PheNetic run modes
Case study: Inhibiting bacterial biofilm formation by disrupting nucleotide biosynthesis
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Pyrimidine synthesis and biofilm formation: Salmonella
• Biofilm formation requires more pyrimidine resources than planktonic growth alone does
• Intact de novo synthesis, or sufficient pyrimidine salvage is needed to have biofilm formation
• Targeting pyrimidine biosynthesis would be a useful strategy to inhibit biofilms
WT control Knockout -> starvation Drug treated -> disruption
Knockout + uracil
Low drug concentration, planktonic growth not affected
Planktonic growth not affected
What are the effects of pyrimidine starvation on nucleotide derived molecules which are known to play a role in biofilms?
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• UDP-glucose a substrate for cellulose biosynthesis • c-di-GMP a signaling molecule that regulates biofilm formation
UDP-glucose (early “switch” phase)
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Pyrimidine deficient strain
Pyrimidine deficient strain + added uracil
c-di-GMP (early switch phase)
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Interesting result: Normally high c-di-GMP = increased biofilms ! Segregated pools of c-di-GMP diverse functions
Control Pyrimidine deficient strain Pyrimidine deficient strain + uracil
Unusual result: High c-di-GMP low biofilm!
Pyrimidine starvation
UDP-glucose ≈
Biofilm ↓
DGCs
global c-di-GMP↑ GTP ↑
?
?
• The mechanism of biofilm inhibition, despite increased c-di-GMP levels. • The link between pyrimidine starvation and increased c-di-GMP
production. • The global effects of pyrimidine starvation on cellular processes.
Transcriptomics
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Nutrient poor broth
Quantify biofilm as a control
Incubation (10h) WT control condition
Disrupted pyrimidine biosynthesis
Measure OD, extract RNA
Total genes: 5554 Mutant vs wild type: 849 genes diff regulated 450↑, 399↓
PheNetic output
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Down Up
Effect on nucleotide biosynthesis and c-di-GMP synthesis genes?
• Pyrimidine de novo ↑ • Pyrimidine salvage ↑ • Purine de novo ↑ • Purine salvage ↓ • c-di-GMP =
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Pyrimidines Down in starved strain Purines Up in starved strain
Pyrimidine starved
Control, WT
Why are purine levels (and c-di-GMP) increased?
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• Phenetic “upstream” network -> cluster of nucleotide biosynthesis genes
• Genes that are supposed to be repressed by PurR are not repressed
• Includes prsA – PRPP -> substrate nucleotide biosynthesis
• Continued increase in substrate availability + continued increase in enzyme levels
• Further experiments: What is interfering with PurR regulation (small RNAs)?
Down Up
Remaining question
• Mechanism of biofilm down-regulation despite high intracellular c-di-GMP levels.
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Effect on matric production
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• PheNetic “upstream” network • Extract regulatory network
controlling csgD (master regulator of biofilm formation)
• Curli is down-regulated • Cellulose not affected (not
shown) • Fis and RpoS differentially
regulated
Down Up
SUMMARY
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Pyrimidine nucleotides ↓
Pyrimidine starvation
Purine nucleotides↑
purF ↑
prsA↑
PRPP↑
PurF De novo purine biosynthesis
PrsA
PRA
Pyrimidine starvation causes increase in purine nucleotide pools a An unknown factor is preventing repression by PurR
Pyrimidine starvation causes increase in PrsA levels b
UDP-glucose ≈
Biofilm ↓
adrA ↓
csgB ↓
CsgD
DGCs
global c-di-GMP↑
GTP ↑
RpoS↓
?
?
PurR + guanine
PurR represses purine genes and some pyrimidine genesc
CsgB
AdrA
fis↑ Fis RpoS csgD ↓
Rsd, RpoD and other TFs
Conclusion
• PheNetic instrumental in giving us some clues
• Good network important: Garbage in = garbage out
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New version of PheNetic: eQTL and QTL
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Aknowledgement • Creators/ maintenanc of PheNetic
– Prof. Kathleen Marchal – Dr. Dries De Maeyer – Dr. Camilo Romero – Dr. Bram Weytjens
• Supervisors during my PhD – Prof Hans Steenackers – Prof Jos Vanderleyden
PheNetic address http://bioinformatics.intec.ugent.be/phenetic2/#/home PheNetic publications https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489255/ Thesis Dries De Maeyer https://lirias.kuleuven.be/bitstream/123456789/512601/1/20151218_thesis_DDM_final_acco_2.pdf
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