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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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