Chronic obstructive pulmonary disease (COPD) is a type of lung disease characterized by persistent bronchitis and emphysema. Current therapy is restricted to alleviate lung tissue inflammation, but is not able to stabilize or improve lung function of patients making necessary to understand the underlying molecular mechanisms of COPD. Genome-wide gene expression of lung tissue provides a powerful tool to elucidate molecular mechanism of COPD patients. In particular, Bayesian Networks (BNs) have been applied to infer genetic regulatory interactions from microarray gene expression data. In this study we aim obtain a clearer understanding of the genes interaction in COPD patients by learning a BN over microarray expression data. A subset of genes was selected for the study fulfilling that i) the genes were significantly expressed in COPD stage 4 and ii) there is reported gene-gene experimental association. The reported associations are introduced as prior biological knowledge in the reconstruction

Bernal Arzola, Victor

Bischoff, Rainer

Brandsma, C.

Ceoldo, Giacomo

Faiz, Alen

Grzegorczyk, Marco

Guryev, Victor

Horvatovich, Peter

Timens, Willem

van den Berge, Maarten

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Gene regulatory network reconstruction with prior knowledge over mRNA data for COPD patients and controls

ARTS repository - University of Groningen

   University of GroningenGene regulatory network reconstruction with prior knowledge over mRNA data for COPDpatients and controlsBernal Arzola, Victor; Brandsma, C.; Faiz, Alen; Guryev, Victor; Timens, Willem; van denBerge, Maarten; Bischoff, Rainer; Grzegorczyk, Marco; Horvatovich, PeterPublished in:Proceedings of the 32nd International Workshop on Statistical Modelling (IWSM), Johann Bernoulli Institute,Rijksuniversiteit Groningen, Netherlands, 3–7 July 2017.IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionFinal author's version (accepted by publisher, after peer review)Publication date:2017Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Bernal Arzola, V., Brandsma, C., Faiz, A., Guryev, V., Timens, W., van den Berge, M., Bischoff, R.,Grzegorczyk, M., & Horvatovich, P. (2017). Gene regulatory network reconstruction with prior knowledgeover mRNA data for COPD patients and controls. In M. Grzegorczyk, & G. Ceoldo (Eds.), Proceedings ofthe 32nd International Workshop on Statistical Modelling (IWSM), Johann Bernoulli Institute,Rijksuniversiteit Groningen, Netherlands, 3–7 July 2017. (Vol. 2, pp. 2015). University of Groningen.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Gene regulatory network reconstruction withprior knowledge over mRNA data for COPDpatients and controlsVictor Bernal 12, Corry A. Brandsma3, Alen Faiz5, VictorGuryev4, Wim Timens3, Maarten van den Berge5, RainerBischoff2, Marco Grzegorczyk 1, Peter Horvatovich21 Johann Bernoulli Institute (JBI), Groningen Rijksuniversiteit, Groningen, NL.2 Department of Pharmacy, Analytical Biochemistry, Rijksuniversiteit Gronin-gen, NL.3 Universitair Medisch Centrum Groningen (UMCG), Department of Pathologyand Medical Biology, Rijksuniversiteit Groningen, NL.4 Universitair Medisch Centrum Groningen (UMCG), ERIBA, RijksuniversiteitGroningen, NL.5 Universitair Medisch Centrum Groningen (UMCG), Department of PulmonaryDiseases, Rijksuniversiteit Groningen, NL.E-mail for correspondence: v.a.bernal.arzola@rug.nlAbstract: Chronic obstructive pulmonary disease (COPD) is a type of lungdisease characterized by persistent bronchitis and emphysema. Current therapyis restricted to alleviate lung tissue inflammation, but is not able to stabilize orimprove lung function of patients making necessary to understand the underlyingmolecular mechanisms of COPD. Genome-wide gene expression of lung tissueprovides a powerful tool to elucidate molecular mechanism of COPD patients. Inparticular, Bayesian Networks (BNs) have been applied to infer genetic regulatoryinteractions from microarray gene expression data. In this study we aim obtaina clearer understanding of the genes interaction in COPD patients by learning aBN over microarray expression data. A subset of genes was selected for the studyfulfilling that i) the genes were significantly expressed in COPD stage 4 and ii)there is reported gene-gene experimental association. The reported associationsare introduced as prior biological knowledge in the reconstruction.Keywords: COPD; Bayesian Networks; Microarray Gene Expression; Introduc-tion of prior knowledge.This paper was published as a part of the proceedings of the 32nd Interna-tional Workshop on Statistical Modelling (IWSM), Johann Bernoulli Institute,Rijksuniversiteit Groningen, Netherlands, 3–7 July 2017. The copyright remainswith the author(s). Permission to reproduce or extract any parts of this abstractshould be requested from the author(s).2 Gene regulatory network over COPD mRNA1 IntroductionChronic obstructive pulmonary disease (COPD) is characterized by dis-tinct phenotypes as, emphysema, chronic bronchitis and fibrosis. The ex-act nature of the abnormal lung tissue repair processes in COPD lungs isunknown, hence a better understanding the underlying molecular mecha-nisms is thus crucial. Bayesian Networks (BNs) have been applied to infergenetic regulatory interactions from microarray gene expression data. Thisinference problem is particularly hard because of the relatively small size ofthe data sets. Moreover the asessment of the inference for COPD results isunfeasible as there is a lack of known gold standards from the biological lit-erature. The aim of the present study is the reconstruction of a regulatorynetwork from a microarray gene expression data set for COPD (stage 4)patients using BNs with prior biological knowledge coming from STRING(database of known and predicted protein-protein interactions). Inferenceis done using with Markov Chain Monte Carlo (MCMC) sampling follow-ing the approach of Werhli et al. (2007). Two networks are obtained andcompared, one for the controls and one for the COPD data.2 Bayesian Networks with prior knowledgeBayesian Networks (BNs) represent probabilistic relationships between in-teracting agents (e.g. genes) in the form of conditional independence. ForBNs the model is a Directed Acyclic Graph (DAG), a set of nodes thatare connected by (directed) edges without cycles. We denote by G the net-work structure, which is the set of nodes and edges, and by q the interactionstrength. Our objective is to learn the network structure G directly from thescratch microarray data D. Nodes are associated with genes and the edgesindicate interactions between the genes. A Bayesian learning approach usesthe posterior P (G|D)P (G|D) = P (G|D)P (G)/P (D)where P (G) is called the prior over graphs, P (D|G) the marginal likelihood,P (D) is a scaling constant and P (G|D) is called the posterior probability.The term P (D|G) is the result of integrating the likelihood P (D, q|G) overall the possible values of the parameters q. Calculating P (G|D) for allpossible network structure G is computationally impossible due to P (D) isintractable. Gene expression data implies a large number of genes with onlya relative small number of measurements. As a consequence the posteriorP (G|D) will not have a clear maximum G∗ . A solution is to use a MCMCsampling based on Metropolis Hasting algorithm (MH). This produces aMarkov Chain that reaches P (G|D) as its stationary distribution (undersome conditions). Generally different DAGs can represent the same prob-abilistic relationships (i.e they can be equivalent). The set of equivalentDAGs is called the equivalence class and is represented by a CPDAG.Bernal et al. 3Introducing prior knowledge for learning BNs is done following Werhli etal. (2007). The prior knowledge is encoded in a matrix B whose entriesBij ∈ [0, 1] represent the belief about the presence/absence of an edge. Theelements Bij greater than/less than 0.5 reflects evidence in favor/againstthe presence of the edge Gij , and Bij = 0.5 as uninformative. The priorover graphs takes the form of a Gibbs distribution,P (G|β) = e−βE(G)/Z(β)The function E(G) measures the agreement between a sampled network Gand B (e.g. using the Hamming distance). The parameter β indicates theweight of the respective source of knowledge to the data, and Z(β) is anormalization constant.3 Evaluation of the COPD dataset andImplementationThe data consist of genome-wide gene expression profiling in lung tissuesamples of 72 subjects, where 48 exhibited COPD (stage 4). The others 24are non-COPD tissues (Controls). The 60 most significant genes based on p-value and log2(FC) > 2 were checked in STRING data base, obtaining that7 genes (FGG, RORC, FGA, OSMR, NR1D1, CSF3, THBS1) had reportedexperimental and/or data base associations. This set of genes is augmentedas follows: i) the Pearson’s correlation matrix ρ is built for Controls andCOPD, and ii) a pair of genes is selected if |ρcontrols| < 0.25 and |ρCOPD| >0.75 . In this way 8 additional genes are obtained, namely MT1M, IL1RL1,MT1P3, MT1A, SLCO4A, GPA33, TTN, ZBED2. In total 2 networks werereconstructed for the forementioned genes i) Controls with prior knowledge,and ii) COPD with prior knowledge. The coupling parameter β is defined in[0, 30]. The MCMC was implemented with the REV move from Grzegorczyk(2008) for number of 9x105 iterations, with thining every 1000 DAGs. Thefirst half of the sample was discarded as part of the burn in phase andModel Averaging was performed over the final set of 501 CPDAGs.4 Results and ConclusionsAfter the burn in phase the 75th percentile of β (in Controls and COPDcases) is less than 1. This suggest that COPD’s gene regulation has noapparent commonalities with previously reported interaction. Table 1 showsa summary of the interactions that considerably changed between Controlsand COPD. In particular interactions like FGG-FGA, and MT1P3-MT1Aare reasonable as they belong to the same family. On the other hand NR1D1has no a well known association with fibrogenes, this interaction (FGG-NR1D1) is present in Controls (mediated by FGA) and COPD (directly).Our next research plan is to extend the gene set. We will provide moreprecise biological interpretations in the presentation.4 Gene regulatory network over COPD mRNAFIGURE 1. Reconstructed regulatory network for Control and COPD.On the right: Network from STRING with experimental and database associa-tions. Doble arrows stand for reported associations. On the left: Reconstructednetwork. The blue edges stand for gene interactions in the Controls. The seg-mented red edges stand for gene interactions in the COPD. There are no commonedges. Only edges with a frequency above 0.75 are shown.TABLE 1. Marginal edge frequency changes This table presents a set of rel-evant gene-gene interaction whose edge frequency had a notable change betweenthe network for the Controls and the one for COPD.Controls COPDFGG-FGA 0.770 0.314FGA-NR1D1 0.906 0.078MT1P3-MT1A 0.890 0.614THBS1-MT1A 0.932 0.502RORC-NR1D1 0.760 0.114ReferencesGrzegorczyk, M., Husmeier, D. (2008). Improving the structure MCMC sam-pler for Bayesian networks by introducing a new edge reversal move.Machine Learning, 71, 265 – 305.Werhli A., Husmeier, D. (2007). Reconstructing Gene Regulatory Networkswith Bayesian Networks by Combining Expression Data with Mul-tiple Sources of Prior Knowledge.Stat Appl Genet Mol Biol, 6(1),1 – 45.

Gene regulatory network reconstruction with prior knowledge over mRNA data for COPD patients and controls

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