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1 Οκτ 2013 (πριν από 4 χρόνια και 10 μέρες)

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Work package
number

5

Start date or starting event:

Month 0

Work package
title

Systems biology data analysis

Activity Type
1

RTD

Participant
number

1

2

3

4

5

6

7

8

Participant
short name

EMBL

UniFi

UNIMAN

UAVR

CNRS

AG

UniPg

CEA
-
CNG

Person
-
months
per

participant:

36

18

0

6

0

6

0

12


Objectives


This work package is central to the formulation of predictive models and biomarkers of
antifungal drug resistance through the integration and meta
-
analysis of publicly available and
newly produced multi
-
omics

datasets on fungal
-
host interaction in immunocompromised and
immunocompetent, fungal infection patients and disease
-
free individuals. We will start by
integrating relevant public datasets identified in WP4 with
a priori

knowledge on antifungal
drugs and d
eveloping a pipeline to produce initial hypotheses of genes relevant to the
development of resistance/sensitivity.


We will apply statistical meta
-
analysis methods to gene and protein expression data from
WP2 to identify genes and pathways whose regulation

is affected by antifungal drug
-
induced
stress. The identified gene networks will be used to build predictive systems biology models
of antifungal drug resistance. These results will be used to inform validation study experiment
design and feed back into W
P1 and WP3. Several wet
-
lab/
in silico

iterations are likely to
happen as the project progresses and more data is accumulated.


CEA
-
CNG, UniFi, AG and EMBL will collaborate on a broad comparative analysis of fungal
genomes sequenced and deposited into WP4 s
equence management and presentation system.
This analysis will help to identify putative druggable elements conserved and differentiated
between the genomes. In parallel, a genome
-
wide association study of fungal disease
susceptibility will be performed by

EMBL and CEA
-
CNG, identifying sequence variations in
the human genome predisposing to specific disease variants or with significant drug
resistance profiles.


The comprehensive findings on drug resistant genes in fungal species, predictive model
incorpora
ting disease susceptibility traits, and experimental validation results (both positive
and negative) will be deposited into the knowledge base developed in WP4, and disseminated
widely for public use.


Description of work

(possibly broken down into tasks)
, and role of participants


5.1. Identification of public datasets & bioinformatics data analysis and integration
pipeline development




1

Please indicate
one

activity per work package:


RTD = Research and technological development; DEM = Demonstration; MGT = Management of the
consortium; OTHER = Other specific activi
ties, if applicable
(
including any
activities to prepare for the
dissemination and/or exploitation of project results
,
and coordination activities).


We will identify publicly available datasets that are relevant to the goals specifiied in WP1
and WP3, i.e., transcriptom
ic and proteomic studies of human and mouse immune system
components, unstimulated and exposed to various pathogens, as well as microarray and other
datasets from previous studies on filamentous fungi specifically. These datasets will be
integrated and loa
ded for pre
-
processing and statistical meta
-
analysis (following task) into the
data management system developed in WP4.


5.2. Statistical meta
-
analysis method development and pathway analysis

We will apply statistical meta
-
analysis and pathway analysis me
thods developed at the
EMBL

and UniFi initially to the public datasets curated in the first task.
W
e will elucidate genes and
pathways involved in non
-
specific immune response; the curated fungi multi
-
omics datasets
will be analyzed to produce initial drug

resistance gene targets. We will develop a pathway
-
based environment for concurrent analysis and comparison of transcriptomics, metabolomics
and proteomics data. The reference dataset generated in the cohort experiment (WP1 and
WP2) will be used to compar
e the performance of the different metrics integrating the
probability of alteration of a sector of the cellular network (pathway) and the relative
importance of that pathway in the context of the biological problem.
The developed methods
will use selected

statistical metrics to
generate dynamically a Pathway Signature (PS) for

each
experiment
. Statistical clustering of the experiments based on the PS will then enable us to
assess similarity between experiments in the database, and mine the database for gen
es and
pathways that contribute to the enhancement of DC functions. We will feed these results to
the WP4 knowledge base and to partners in WP1 and WP3 for iterative experimental
validation and assessment from a biological perspective. In later stages of t
he project the
developed methodology will be applied to newly produced datasets on antifungal
-
resistant
strains


results will connect and be used in tasks 1.4 and 3.5 in WP1 and WP3, respectively.
The resulting validated biomarkers of drug resistance and
sensitivity and genomic signatures
of fungi
-
specific immune response will be used to populate the KB in WP4.


5.3. Genome
-
wide association study of fungal disease susceptibility

Together with the
EMBL
, CNG will perform a genome
-
wide association study to id
entify risk
factors for fungal disease and to identify common variants with associated with susceptibility
to invasive and allergic fungal infections. The results will be entered into the KB in WP4.


5.4. Systems biology modelling of antifungals

sensitivity and host
-
pathogen interactions

We will use low
-
throughput data assembled in WP1, WP3 and the multi
-
omics

datasets from
WP2 to construct models that can predict antigen
-
specific immune response, as well as
models predictive of the sensitivity of a particular fungal strain to antifungal drug regimens.
We will develop a systematic algorithm for discovering netw
orks of regulatory modules,
which identifies regulatory modules and their regulation program by integrating genome
-
wide
expression data with prior knowledge that provides direct evidence of regulatory interactions.
We predict that this approach will be abl
e to identify functionally coherent modules and their
proper regulators. The models will be used to refine follow
-
up experiment design and will be
deposited into the KB in WP4.


5.5. Comparative analysis of sequenced fungal genomes

The
EMBL
, CNG and AG wi
ll annotate and analyze the genomes of sequenced fungal strains
in order to identify conserved drug
-
resistant elements as possible targets of drug design, and
to discover localized sequence variants that associate with increased pathogenicity, virulence
or

allergenicity. We will deposit the results in the KB in WP4.


5.6. Post
-
validation study analysis, model refinement, data integration and knowledge
base population

Among the wrap
-
up tasks of this project will be the issue of post
-
validation study model
an
alysis and refinement, in order to capture and integrate the most complete dataset and
publish models that can be reused in future antifungal drug research. The complete models of
pathogen recognition by the immune system, predictors and biomarkers of drug

resistance
and disease susceptibility will be curated, annotated and preserved in the Knowledge Base,
accessible freely through the project portal. We intend finally to integrate the produced in
silico resource into permanent larger databases at the
EMBL

and into fungal disease resources
such as the Aspergillus Website of the Fungal Research Trust
(http://www.aspergillus.org.uk).



Progress towards objectives

and details for each tasks


5.1. Identification of public datasets & bioinformatics data analysis

and integration
pipeline development



5.2. Statistical meta
-
analysis method development and pathway analysis



5.3. Genome
-
wide association study of fungal disease susceptibility



5.4. Systems biology modelling of antifungals sensitivity and host
-
pathog
en interactions



5.5. Comparative analysis of sequenced fungal genomes



5.6. Post
-
validation study analysis, model refinement, data integration and knowledge
base population




If
applicable,
explain the reasons for deviations from Annex I and their
impact on
other tasks as well as on available resources and planning




If applicable, explain the reasons for failing to achieve critical objectives and/or
not being on schedule and explain the impact on other tasks as well as on
available resources and
planning

(the explanations should be coherent with the
declaration by the project coordinator)




Use of resources

(
highlighting and explaining deviations between actual and planned
person
-
months per work package and per beneficiary in Annex 1
)




If applicable, propose corrective actions