Joaquín Dopazo

Clinical Bioinformatics Area

Fundación Progreso y Salud,

Functional Genomics Node, (INB-ELIXIR-ES),

Bioinformatics in Rare Diseases (BiER-CIBERER),

Sevilla, Spain.

Functional profile of the pre- to

post-mortem transition in blood

http://bioinfo.cipf.es http://www.babelomics.org @xdopazo

GTEx Project community meeting,

CRG, Barcelona, April 20th, 2017

Complex phenotypes (e.g. genetic

diseases) have a modular nature

• With the development of systems biology, studies have shown that

phenotypically similar diseases are often caused by functionally related

genes, being referred to as the modular nature of human genetic

diseases (Oti and Brunner, 2007; Oti et al, 2008).

• This modularity suggests that causative genes for the same or

phenotypically similar diseases may generally reside in the same

biological module, either a protein complex (Lage et al, 2007), a sub-

network of protein interactions (Lim et al, 2006) , or a pathway (Wood et

al, 2007)

Disease genes are close in the interactome

Goh 2007 PNAS

Same disease

in different

populations is

caused by

different

genes

affecting the

same

functions Fernandez, 2013, Orphanet J Rare Dis.

Two problems: defining

functional modules and

modeling their behavior Gene ontology:

descriptive;

unstructured

functional labels

Interactome:

relationships among

components but

unknown function

Pathways:

relationships among

components and

their functional roles

Models

Enrichment methods. GO, etc. (simple statistical tests)

Connectivity models. Protein-protein, protein-DNA and protein-small molecule interactions (tests on network properties)

Computational models. Models of signalling pathways, metabolic pathways, regulatory pathways, etc.

Mathematical models. Kinetic models including stoichiometry, balancing reactions, etc.

How realistic are models of

functional modules?

Beyond static biomarkers—The activity

of signalling networks as an alternate

biomarker? Fey et al., Sci. Signal. 8, ra130 (2015).

Inability of JNK activation (that mediates

apoptosis) is associated to bad prognostic,

irrespective of MYCN amplification status

Problem:

ODE can

efficiently

solve only

small

systems

Construct, activity inferred

How functional activity is defined in

a pathway module? What “pathway activity” detected by enrichment methods really means in

terms of cell functionality? Does it make sense?

Pathways are multifunctional:

Different and often opposite

functions are triggered by the

same pathway. E.g.: death and

survival

The same gene can trigger different

(and often opposite) responses,

depending on the stimulus

Survival

Death

Defining functional activities

within pathways Transforming decontextualized gene expression measurements into highly-informative values that account for functions. Obvious example of functional module: signaling pathway.

Receptors Effectors

Important fact: when the

signal reaches the end of a

circuit triggers a function

Important assumption:

collective changes in gene

expression within the

context of a signaling

circuit are proxies of

changes in protein

activation

Decomposition of a pathway

into its elementary circuits

Different levels of abstraction

𝑆𝑛 = 𝜐𝑛 ∙ 1 − 1− 𝑠𝑎

𝑠𝑎∈𝐴

⋅ 1− 𝑠𝑖

𝑠𝑖∈𝐼

From individual gene

expression profiles

To profiles of circuit

activity (and

functional activity)

Two types of activities

Signal propagation models of

signaling pathways

Signaling activity trigger cell functions

directly related to cancer progression

Actually, signal activity triggers

all the cancer hallmarks

Hanahan, Weinberg, 2011

Hallmarks of cancer: the next

generation. Cell 144, 646

Negative regulation of release of cytochrome c

from mitochondria (inhibition of apoptosis)

The inferred function activity is more

correlated to the phenotype (survival) than

the activity of any gene in the circuit

p-val=5.9x10-8

Functional analysis of the pre- to

post-mortem process in blood The availability in GTEx of pre- and post-mortem whole blood samples

(albeit not paired), provides a unique opportunity to assess the

functional response to death triggered by blood cells.

Whole Blood Differential signaling

analysis was carried out using 393

GTEx samples annotated with “Whole

Blood” sub-tissue (169 pre-mortem and

224 post-mortem).

Points # Samples Time (minutes)

Pre 169 Pre-mortem

T1 56 >0 & < =406

T2 56 >406 & < =635

T3 56 >635 & < =867

T4 55 >867 & < =1401

Analysis

pipeline

Immune response deactivation

Necrosis and Cell division arrest

Carbohydrate and lipid metabolism deactivation

Blood coagulation Hemostasis DNA damage repair

DNA synthesis Fibrinolysis

Five main patterns of functional responses (according to signal transduction activity)

NF-kappa B signaling pathway

HIF-1 signaling pathway

Plasminogen

activation Hemostasis

cAMP signaling pathway

Fibrinolysis

Blood

coagulation

Molecular mechanism of the blood coagulation process

Molecular mechanism of metabolic switch to hypoxia

Platelet activation pathway

cGMP-PKG signaling pathway

Response to hypoxia

HIF-1 signaling pathway

mTOR signaling pathway

Tricarboxylic acid cycle

Glycolysis

RIG-I-like receptor signaling pathway

MAPK signaling pathway

response to interleukin-1

positive regulation of interleukin-8 production

Apoptosis

NOD-like receptor signaling pathway

Neutrophil activation

Fc epsilon RI signaling pathway

Natural killer cell mediated cytotoxicity negative regulation of

natural killer cell chemotaxis

defense response to

virus, bacterium, etc.

positive regulation of

interferon-alpha production

positive regulation of IL-8

production

Molecular mechanism of immune response deactivation

Conclusions

http://hipathia.babelomics.org http://pathact.babelomics.org

• Differential signaling activity uncover the molecular mechanisms involved in

the pre- to post-mortem transition in blood.

• Conventional: testing one-gene-at-a-time independently and then seeking for

a collective functional interpretation. New: directly quantifying and testing

changes in signal activity over different cell functions.

• Computational models are “actionable” and allow in silico predictions of

possible interventions. Rational targeted interventions are feasible for post-

mortem tissue preservation for transplantation purposes, etc.

• hipathia R script available at https://github.com/babelomics/hipathia

• Bioconductor package coming soon

Clinical Bioinformatics Area

Fundación Progreso y Salud, Sevilla, Spain, and…

...the INB-ELIXIR-ES, National Institute of Bioinformatics and the BiER (CIBERER Network of Centers for Research in Rare Diseases)

@xdopazo @ClinicalBioinfo Follow us on twitter

In collaboration with:

Pedro Ferreira and

Roderic Guigó

CRG, Barcelona

Alicia Amadoz

Marta Hidalgo

Jose Carbonell Cankut Çubuk

https://www.slideshare.net/xdopazo/functional-profile-of-the-pre-to-postmortem-transition-in-blood