Andrey Alexeyenko

andrej.alekseenko@scilifelab.se

Functional analysis with pathways and networks

FunCoup: on-line interactome resource

Andrey Alexeyenko and Erik L.L. Sonnhammer (2009) Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Research.

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Data integration in predicted networks

Species-specific networks:evolutionary conservation

Mouse vs. human

Cancer-specific networks:links inferred from

expression, methylation, mutations

Functional couplingtranscription transcription transcription methylation methylation methylation mutation methylation mutation transcriptionmutation mutation

+ mutated gene

State-of-the-art method to beat:

Reverse engeneering froma single

source (usually transcriptome)

Do gene networks tell the story?

• Yellow diamonds: somatic mutations in prostate cancer

• Pink crosses: also mutated in glioblastome (TCGA)

State-of-the-art method to beat:Frequency analysis of somatic mutations

Network analysis made statistically sound:compared to a reference and quantified

N links_real = 12

N links_expected = 4.65

Standard deviation = 1.84

Z = (N links_observed – N links_expected) / SD = 3.98P-value = 0.0000344FDR < 0.1

Actual network: observed pattern A random pattern

Question:Is ANXA2 related to TGFbeta signaling?

Network enrichment analysis: what is the reference?

We refer to a randomized network: preserves topology, lost biology

• Link swapping (Perl): (original algorithm by Maslov & Sneppen, 2003) – work with Simon Merid.

• De novo network generation (C++): work with T. McCormack, O. Frings, E. Sonnhammer.

• Matrix permutation (R): work with Woojoo Lee, Yudi Pawitan.

Biological analysis of differential expression

Functional set?

Our alternative:Network enrichment analysis

Altered genes

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No. of positives, random groups

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State-of-the-art method to beat:Gene set enrichment analysis

Network enrichment analysis: applications

N links_real = 6N links_expected = 1.00

Standard deviation = 1.25Z = 3.97P-value = 0.0000356

Question:Does gene expression in MAT230414 relate to “response to tumor cell”?

Pathway characterization Detection of driver mutations Coherence of genome alterations

N links_real = 55N links_expected = 37.05

Standard deviation = 3.59Z = 3.59P-value = 0.00016

Question:Could copy number alteration in EHR in HOU501106 lead to changes of its transcriptome and proteome?

N links_real = 0N links_expected = 1.05

Standard deviation = 0.80Z = -1.31P-value = 0.905

Question: Are CNA in HOU501106 coherent?

State-of-the-art method to beat: Observational science

Analysis of cancer-specific wiringPathway network of normal

vs. tumor tissues

Edges connect pathways given a higher (N>9; p0<0.01; pFDR<0.20) number of gene-gene links (pfc>0.5) between them (seen as edge labels). Known pathways (circles) are classified as: • signaling,• metabolic,• cancer,• other disease.

Blue lines: evidence from mRNA co-expression under normal conditions + ALL human & mouse data.

Red lines: evidence from mRNA co-expression in expO tumor samples + ALL human data + mouse PPI.

Node size: number of pathway members in the network.

Edge opacity: p0.

Edge thickness: number of gene-gene links.

Arrow of time: network prospective(work with J. Meyer, Duke Univ.)

Alexeyenko et al. Dynamic zebrafish interactome reveals transcriptional mechanisms of dioxin toxicity PLoS One. 2010

Question: Routes of embryonic intoxication in fish?Answer: through cytochrome P4501A, calcium and iron metabolism,neuronal and retinal development

Genetic association of sequence variants near AGER/NOTCH4 and dementia.Bennet AM, Reynolds CA, Eriksson UK, Hong MG, Blennow K, Gatz M, Alexeyenko A, Pedersen NL, Prince JA.J Alzheimers Dis. 2011;24(3):475-84.

Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease.Hong MG, Alexeyenko A, Lambert JC, Amouyel P, Prince JA.J Hum Genet. 2010 Oct;55(10):707-9. Epub 2010 Jul 29.

Analysis of lipid pathway genes indicates association of sequence variation near SREBF1/TOM1L2/ATPAF2 with dementia risk.Reynolds CA, Hong MG, Eriksson UK, Blennow K, Wiklund F, Johansson B, Malmberg B, Berg S, Alexeyenko A, Grönberg H, Gatz M, Pedersen NL, Prince JA.Hum Mol Genet. 2010 May 15;19(10):2068-78. Epub 2010 Feb 18.

Validation of candidate disease genes(work with Jonathan Prince, MEB, KI)

Question: Is there extra evidence for GWAS-candidates to be involved?Answer: Yes, for some…

Mutations accumulated in somatic genomes of cancer cell lines

(work with Pelin Akan, Joakim Lundeberg)

Question: Do mutations within a cancer genome behave like a quasi-pathway?Answer: Yes.

Question: How similar are mutation patterns in different cancers?Answer: A lot, but only for drivers and at the pathway level.

Pathway view on the set of toxicity-associated alleles

The analysis detected more significantly enriched pathways than for the negative control gene sets of the same size (215 vs. 139; p0 < 0.001; FDR<0.05). More specifically, many thus found pathways were associated with cancer, apoptosis, cell division etc.

Red node: list of top 50 genes with most contrast allele patternsGrey node: negative control listYellow: enriched/depleted pathwaysEdge width: no. of gene-gene links in the networkEdge opacity: confidenceGreen edges: enrichmentRed edges: depletion

Edges produced by less than 3 list genes are not shown

Experimental perturbations of syndecan-1 in cancer cells(work with T. Szatmari, K. Dobra, KI Huddinge)

Lines:Red: depletionBlue: enrichment

Question: Second-order downstream targets of syndecan modulation?Answer: Segments of cell cycle etc.

Network analysis:what’s in it for you?

• Prioritization among multiple “technical positives”

• Generalization “genes => pathways”, hence new, lower, dimension

• Analysis of individual genes “as if” pathways• Testing biological hypotheses, validation• Modular part of high-throughput analytic

pipelines

Network analysis:how to succeed?

• Analyze prioritized candidates (from genotyping, DE, GWAS…) rather than any genes.

• Do not lean on single “interesting” network links. Employ statistics!i.e.

“concrete questions” => “testable hypotheses” => “concrete answers”

The amount of information in known gene networks is enormous.

Let’s just use it!

Acknowledgements:

Erik Sonnhammer’s bioinformatics group

KICancer

http://FunCoup.sbc.su.se

Erik Sonnhammer,Thomas Schmitt,Oliver Frings,Andreas Tjärnberg, Dimitri Guala, Mun-Gwan Hong, Jonathan Prince,Rochellys Diaz Heijtz, Angelo de Milito, Meng Chen, Simin Merid, Yudi Pawitan,Woojoo Lee,Erik Aurell,Joakim Lundeberg,Pelin Akan,Joel Meyer,Katalin Dobra,Tunde Szatmari, Serhiy Souchelnytskyi,Ingemar Ernberg,

Gene network discovery:getting rid of spurious links

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Data processing inequality:“Direct links convey more information than indirect ones”PCIT algorithm:Partial Correlation & Information Theory