Bioinformática aplicada al estudio del control de la expresión de genes en el cerebro humano
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Transcript of Bioinformática aplicada al estudio del control de la expresión de genes en el cerebro humano
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Juan A. Bo*a Ins-tute of Neurology, University College London, UK Facultad de Informá-ca, Universidad de Murcia, Spain
Algorithmic Approaches for the construc3on of gene co-‐expression networks from control brain 3ssue samples mRNA
RNA-‐seq Substan-a nigra and Putamen brain co-‐expression networks on the UKBEC project to study Parkinson’s Disease
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The central dogma of biology
source Wikipedia
We use pre-‐mRNA
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Chapter I. The dataset
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Braineacv2, RNA-‐seq based, focused on Parkinson’s Disease
l Affects 1% to 2% of the population older than 65 years l Symptons: resting tremor, bradykinesia, rigidity and impairment in ability
to initiate and sustain movements l The hallmark of this disease is the progressive loss of dopaminergic
neurons, mainly in the substantia nigra
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Chapter II. The computa-onal model
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Network analysis: aprioris-c versus free approaches
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Are networks something more than a fancy graph and nice plots?
Yes they are!! • Can be used to iden-fy the ac-ve pathways in specific samples (cases vs. controls)
• Describe subsystems (i.e. cell types) • Iden-fy candidate genes (GBA)
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To create networks we need to es-mate links between genes
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From gene expression to gene co-‐expression networks
TREM2 forms a receptor signaling complex with TYROBP, which triggers the ac-va-on of immune responses in macrophages and dendri-c cells, and the func-onal polymorphism of TREM2 is r e p o r t e d t o b e a s s o c i a t e d w i t h neurodegenera-ve disorders such as Alzheimer’s disease (AD).
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From gene expression to gene co-‐expression networks
TREM2 forms a receptor signaling complex with TYROBP, which triggers the ac-va-on of immune responses in macrophages and dendri-c cells, and the func-onal polymorphism of TREM2 is r e p o r t e d t o b e a s s o c i a t e d w i t h neurodegenera-ve disorders such as Alzheimer’s disease (AD).
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From gene expression to gene co-‐expression networks
TREM2 forms a receptor signaling complex with TYROBP, which triggers the ac-va-on of immune responses in macrophages and dendri-c cells, and the func-onal polymorphism of TREM2 is r e p o r t e d t o b e a s s o c i a t e d w i t h neurodegenera-ve disorders such as Alzheimer’s disease (AD).
TYROBP TREM2 0.76
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From gene expression to gene co-‐expression networks
TREM2 forms a receptor signaling complex with TYROBP, which triggers the ac-va-on of immune responses in macrophages and dendri-c cells, and the func-onal polymorphism of TREM2 is r e p o r t e d t o b e a s s o c i a t e d w i t h neurodegenera-ve disorders such as Alzheimer’s disease (AD).
TYROBP TREM2 0.76
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But before reaching that
• Scale free topology assump-on – The degree distribu-on p(k) of a network follows a power law so p(k) ~ k-‐ϒ
– Evidence supports this for many organisms (ϒ is approx. 2.2)
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But before reaching that (& 2)
• Modularity assump-on – Varia-on coefficient of organisms, Ci = ︎ 2n/ki(ki – 1) with n number of direct links connec-ng the ki nearest neighbours of i-‐th node, suggests strong modular organiza-on
– Evidence suggests the coefficient of varia-on is higher than expected in SFT networks
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But before reaching that (& 3)
• Hierarchies solve this apparent dilemma
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Chapter III. The problem
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Our main focus: Parkison's Disease l Affects 1% to 2% of the population older than 65 years l Symptons: resting tremor, bradykinesia, rigidity and impairment in ability
to initiate and sustain movements l The hallmark of this disease is the progressive loss of dopaminergic
neurons, mainly in the substantia nigra excitatory
inhibitory
Substantia Nigra Pars Compacta
Brain regions most typically affected by adult-onset disease
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Step 1: RPKM exonic gene quantification and CQN normalization Step 2: RPKM-CQN > 0.2 & missingness < 70% Step 3: Data correcting for Sex, Age and 7/8 Peer axes Step 4: WGCNA “signed” network construction Step 5: k-Means optimization of module partitions Step 6: Network assessment Step 7: Within tissue and between tissues subsystem characterization
33670 Ensembl genes
Approx. 19K genes, two datasets
Two corrected datasets
SNIG and PUTM networks And gene modules assignment
Modified gene modules assignment for SNIG and
PUTM Quality metrics for networks and
Gene partitions
Functional characterization, correlation with traits, gene
function prediction
Steps on the pipeline Outcomes
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Co-expression analysis methodology
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A measure of similarity between genes, values in [0,1]
From similarity to adjacency, hard thresholding
From similarity to adjacency, sou thresholding
From adjacency to TOM values
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From TOM values to clusters by 1-‐TOM as a distance
complete linkage hierarchical approach for clustering summarisa-on based on eigenvalue
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l Hierarchical clustering's results are highly variable depending on linkage (max/complete, min/single, average linkages)
l Module membership (MM) of g is the correlation of g and
the 1st PC of gene expression (module eigengene)
l This doesn't necessarily mean all genes are in the best module according to MM
l Previous approaches based on reassigning some/all genes l k-means algorithm helps finding a better partition in which
genes are (hopefully) assigned to a module in a more natural way
Why do we need an optimization process for WGCNA
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A k-‐means heuris-c How does it work?
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Outline of the op-miza-on
Accepted in BCM Systems Biology 03/04/17
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Chapter IV. The results
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What we get from the optimization • More accurate par--on construc-on • Bever func-on annota-on for modules • Bever cell markers enrichment • More preserved modules across similar -ssues
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How to assess the accuracy of a co-expression network
cluster driven validation
data driven validation by replication
Are the gene groups good according to a
given index
same tissue similar tissue
same network model
diff. network model
Biology: Does my module
make sense?
functional characterization
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How to assess the accuracy of a co-expression network
cluster driven validation
data driven validation by replication
Are the gene groups good according to a
given index
same tissue similar tissue
same network model
diff. network model
Biology: Does my module
make sense?
functional characterization
03/04/17
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Replication in GTEx GNAT networks for Substantia Nigra
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Mantel fold SNIG GTEx coexpression within0.
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*** 340*** 412
*** 449*** 385
*** 574** 295
*** 250*** 427*** 457*** 783*** 505
*** 417*** 579
*** 521 244
* 410 260 88
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Mantel fold SNIG microarray binary between
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1.5
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*** 701*** 624
*** 760*** 837
*** 1070*** 837*** 365*** 624*** 653*** 460
*** 658 477 475
417 743 406 579 402
149
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Replication in GTEx GNAT networks for Putamen
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Mantel fold PUTM GTEx coexpression within
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0.5
1.0
1.5
2.0
2.5
3.0
*** 429
*** 275
* 72
*** 372
*** 268
*** 444
*** 486
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*** 541
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Mantel fold PUTM GTEx binary between0.
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*** 268
*** 372
*** 429
*** 611
*** 486
*** 444
*** 275
*** 759
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*** 617
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*** 574
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*** 386
72
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How to assess the accuracy of a co-expression network
cluster driven validation
data driven validation by replication
Are the gene groups good according to a
given index
same tissue similar tissue
same network model
diff. network model
Biology: Does my module
make sense?
functional characterization
03/04/17
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Asignment of biological func3on to modules with gProfiler
• Based on GO (BP, MF, CC) and gProfileR • Fisher's exact test and Bonferroni corrected p-‐values • What should we expect?
• Normal cell processes like respira-on, cell development, immune func-on
• But also brain related terms (hopefully movement disorders, signalling) in some of the modules
• What should we consider when looking for enrichment? • GO is not a closed world ontology • Something not found doesn't imply it doesn't exist
• Genes can play new roles • Groups of genes can have new func-ons • It is possible to find modules with no GO and s-ll be valid
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Significant similarities in practically all modules
This is a tabular View of significant agreements (Fisher's Exact test) on genes between modules from the two tissues
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Subsystems
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Subsystems cell type & function
Neuron cells, Synapse/NADH
Microglia cells, Immune system
Nucleus, transcription
Neuron, astrocytes & microglia cell types Response to stimulus
Endothelial cell type, Cell division
Oligodendrocytes cell type, synapse & ion transport
Mitochondrion
Cytosolic rybosome
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Lessons learned l The default WGCNA can be improved to get more
coherent gene groups l Network analysis reveals
l cell specific subsystems in putamen and substantia nigra
l Interesting differences between the two tissues at the subsystem level
Ongoing work l Models to explain the differences between subsystems l Function prediction for non coding species and intergenic
regions
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Acknowledgements
University College London
Jana Vandrovcova Sebastian Guelfi Karishma D'sha John Hardy Mar Matarin Daniah Trabzuni
King's College London
Mike Weale Mina Ryten Paola Forabosco Adai Ramasamy
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