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# Omic approaches to decipher the molecular mechanisms of fibrosis, and design new anti-fibrotic strategies

Sylvie Ricard-Blum, Adriana E Miele

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Sylvie Ricard-Blum, Adriana E Miele. Omic approaches to decipher the molecular mechanisms of fibrosis, and design new anti-fibrotic strategies. Seminars in Cell and Developmental Biology, 2020, 101, pp.161-169. 10.1016/j.semcdb.2019.12.009. hal-04998726

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# Omic approaches to decipher the molecular mechanisms of fibrosis, and design new anti-fibrotic strategies

Sylvie Ricard-Blum<fnref n="1" />* and Adriana E. Miele<fnref n="1" />

¹Univ Lyon, University Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry, UMR 5246, F-69622 Villeurbanne cedex, France.

**Corresponding author:**

*Professor Sylvie Ricard-Blum

ICBMS, UMR 5246, University Lyon 1

Lederer Building

43 Boulevard du 11 novembre 1918

F-69622 Villeurbanne cedex, France

Phone: +33 4 72 44 82 32

E-mail: sylvie.ricard-blum@univ-lyon1.fr

**Keywords:** Databases, Fibrosis, Extracellular matrix, Networks, Omics, Systems biology

**Abbreviations**<fnref n="1" />

**Abstract**

We review here omics approaches including transcriptomics, proteomics, glycomics, metabolomics and interactomics, databases and computational tools for omic and multi-omic investigations of fibrosis to understand the molecular mechanisms underlying fibrogenesis and fibrosis, to identify biomarkers of diagnosis, prognosis or disease progression, and new therapeutic targets and to design new anti-fibrotic drugs. We also provide perspectives for future studies

<footnote n="1">¹ Abbreviations: CFTR: cystic fibrosis transmembrane conductance regulator, DAVID: database for annotation, visualization and integrated discovery, DB: database, ECM: extracellular matrix, GAG: glycosaminoglycan, GEO: gene expression omnibus, KEGG: GC: gas chromatography, GO: Gene Ontology, GSEA: gene set enrichment analysis, KEGG: Kyoto encyclopedia of genes and genomes, LC: liquid chromatography, MALDI-TOF: matrix-assisted laser desorption ionization time-of-flight, MS: mass spectrometry, PDZ: post synaptic density protein (PSD95), Drosophila disc large tumor suppressor (Dlg1), and zonula occludens-1 protein (zo-1), SILAC: stable isotope labeling with amino acids in cell culture, SWATH: sequential window acquisition of all theoretical mass spectra, TAP: transcriptional coactivator with PDZ-binding motif, TGF-β: transforming growth factor-β, Wnt: wingless/integrated, YAP: yes-associated protein</footnote>
including lipid and glycosaminoglycan profiling, and the design of virtual patient models as a basis for personalised medicine and virtualisation of drug development.

## Introduction

Numerous aspects of fibrosis have been extensively characterized at the molecular level, and reviewed, namely alterations of the extracellular matrix (ECM) and signaling pathways, cells, tissues and organs, the discovery of new diagnosis or prognosis biomarkers, therapeutic targets, and bioactive compounds as potential drugs. A collection entitled Fibrosis: mechanisms and targets is available on Nature website (02 February 2016, https://www.nature.com/collections/nycrsfhlqm), and a special issue of Matrix Biology on fibrosis (volumes 68–69, pages 1-636, 2018, https://www.journals.elsevier.com/matrix-biology) has been recently published. It addresses the molecular mechanisms and translational aspects of fibrosis, including methods used to characterize fibrosis-induced changes, organ fibrosis affecting heart, kidney, liver, lung, skin, and central nervous system or associated with muscular dystrophy, and changes in the ECM. Omic approaches, associated with large-scale and high-throughput experiments, have been extensively used in the last decade to decipher the molecular mechanisms associated with diseases [1]. We focus in this review on various omics (i.e., genomics, transcriptomics, proteomics, glycomics and metabolomics), and multi-omics studies performed to investigate fibrosis in different organs (kidney, liver, and lung), and their use to provide information on the mechanisms of fibrosis, the discovery of diagnosis, prognosis and follow-up markers, and the design of new therapeutic strategies. Besides a global overview of several omic approaches, we highlight here several studies centered on key players in fibrotic diseases, namely the extracellular matrix [2–6], tissue remodeling (degradomics) [7–9] and transforming growth factor-β (TGF-β) signaling pathways [10–12]).

ECM is the structural scaffold of tissues and a dynamic structure, which controls cell behavior, survival and signaling, and plays a role in diseases [13]. Indeed, ECM accumulation and alterations occur in fibrotic diseases, whatever their origin [14–18]. Changes in ECM composition, assembly and mechanical and biological properties affect its major components including collagens [19], non-fibrillar components [20] such as tenascin-C [21], glycosaminoglycans (GAGs) and proteoglycans [22,23]. The proteoglycan syndecan-1 promotes lung fibrosis by regulating epithelial reprogramming through extracellular vesicles [12]. The localization, role, and remodeling of ECM in liver [14] and renal [17] fibrosis has been recently reviewed. The ECM accumulates in fibrotic tissues, and is more extensively cross-linked, and by other cross-links, than normal tissues [19,24–26]. The major mechanism of cross-linking is mediated by the members of the lysyl oxidase family, which are potential therapeutic targets [19,27,28]. The type of cross-links dictates the reversibility of fibrosis, which is associated with ECM cross-linking [26,29] and remodeling. ECM is also a source of potential biomarkers of fibrosis diagnosis and progression [17]. ECM is altered as a consequence of fibrosis but it is also a driver of fibrosis [15]. It provides a pro-fibrotic microenvironment for fibroblasts and recruited cells, and contributes to the formation of a fibrogenic niche [15,16]. The matricellular protein, tenascin-C, is a major component of the fibrogenic niche in kidney fibrosis [21]. Fibrosis perpetuates itself by activating normal cells into fibrogenic cells which create a pro-fibrotic environment [16]. The deciphering of fibrosis mechanisms is a prerequisite for the design of ECM-
targeted anti-fibrotic strategies, and drug discovery [14] [18], which are challenging. Existing therapeutic approaches have been reviewed by Walraven and Hinz [16], who emphasized the need to “(re)turn fibrotic scar into functional ECM” to restore organ functionality [16].

Signaling pathways are dysregulated in fibrosis. Three pathways converge to activate and maintain the myofibroblast phenotype, namely the TGF-β1/Smad pathway, which is of major importance in renal, hepatic, pulmonary and cardiac fibrosis and can be targeted for therapeutic purpose [1], the Wnt (Wingless/Integrated) pathway, and the yes-associated protein 1 (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) [10]. Notch, Wnt and Hedgehog contribute to the fibrotic response in kidney [11]. A global, integrative, approach to investigate these signal transduction pathways is required to decipher their molecular cross-talks in fibrosis [26] and their regulation by extracellular vesicles [12]. This systems biology approach implies the use of omic technologies, which aim at characterizing all the components of a given system (ECM, cellular compartment, tissue or organ) using high-throughput methods.

We review here various individual or combined omic approaches [30] (e.g. transcriptomics, epigenomics, proteomics, metabolomics, and interactomics) and tools used to analyze omic-generated data in order to better understand the molecular mechanisms of fibrosis, to find new therapeutics targets, and to design new anti-fibrotic drugs. The interest of omic studies in understanding tissue fibrosis is highlighted by selected examples as this review does not aim at fully covering omic studies related to fibrosis. In addition, we briefly describe the databases and computational tools useful to investigate fibrosis, and we present perspectives to address further omic profiling, and to design network-based drug development strategies.

# 1. Omic approaches and tools for omic studies of fibrosis

Omic technologies are associated with high-throughput profiling aiming at characterizing genomes, transcriptomes, proteomes, metabolomes, and glycomes to name a few in specific cellular compartments, cells, tissues and organisms. One major advantage of omic approaches is that they are not focused on a particular biomolecule (gene, mRNA, protein, glycan or lipid) or metabolite because they are performed at the genome, transcriptome, proteome, glycome, lipidome or metabolome scale. Furthermore, when omic data are deposited in publicly available databases they may be reprocessed and reused to address other biological questions that those answered in the initial study, and/or to perform meta-analyses. The limitations and the experimental design of omic studies depend on the biological samples (cells, tissues, fluids), the biomolecule, metabolite or modification analyzed, and on the techniques used. It is for example difficult to predict biological outcomes from genomic data only. There are guidelines for the design, analysis and interpretation of omic data [31,32] and for sample preparation, which is a critical point, for transcriptomic [33], proteomic [34,35] and glycomic [36] studies. The strengths and disadvantages of individual omics (genomics, transcriptomics, proteomics, metabolomics and epigenomics) have been reviewed together with their associated challenges [37–39].

## 1.1 Databases

Databases have been built to store large-scale datasets generated by omic analyses, and computational models of fibrosis or fibrotic signaling pathways. Indeed, omic data can be collected experimentally and analyzed in the same study. Alternatively, omic datasets can be extracted from
freely available from databases and repositories such as Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) [40] to perform computational analyses and answer biological questions [41–43]. For example, Wang et al. [42] analyzed three publicly available microarray datasets including 54 fibrotic and 34 normal samples to identify differentially expressed genes in idiopathic fibrosis. Proteomic databases of interest for the study of fibrosis are focused either on a compartment (e.g. the matrisome, the ensemble of ECM and ECM-associated proteins [2]) or on a tissue or an organ. MatrisomeDB, the ECM-protein knowledge database, is a dataset of curated proteomic data on the ECM of 15 different normal tissue types, and diseases including lung and liver fibroses (http://www.pepchem.org/matrisomedb) [44]. Publicly available general databases storing information on liver diseases, liver physiology, liver disease specific databases, toxic liver diseases, viral hepatitis and hepatocellular carcinoma are listed in a recent review [45]. A database for a model of liver fibrosis progression has been developed as a tissue atlas including static and dynamic mechanical measurements, ECM composition and data on cell identity, differentiation, proliferation and signaling [15]. The Kidney and Urinary Pathways Knowledge Base (KUPKB) comprises multi-omic data (miRNA, mRNA, protein and metabolite datasets) on kidney disease extracted from publications or databases [46], which can be queried *via* the iKUP web interface [47] and the KUPKB visualizer [48] (https://renalfibrosis.fr/laboratory/k-u-p, www.kupkb.org). A multi-omic expression database, CKDdb, storing microRNA, genomic, peptidomic, proteomic and metabolomic data has been designed for chronic kidney disease [49] (http://www.padb.org/ckddb/). Another multi-omic dataset (proteomics, mRNA and small RNA transcriptomics) of two murine models of kidney fibrosis is available at http://hbcreports.med.harvard.edu/fmm/ together with an interrogation tool (Mouse Kidney Fibromics browser) [50]. Omic databases on kidney disease listed in [51] with a proposed workflow for a data-driven approach in multi-omic data integration. The need for an open-access biorepository for idiopathic pulmonary fibrosis has been emphasized [52] but information related to cystic fibrosis are still split in different databases such as the cystic fibrosis mutation database (http://www.genet.sickkids.on.ca/), and the clinical and functional translation of CFTR (cystic fibrosis transmembrane conductance regulator https://www.cftr2.org/).

Although some studies only focus on protein-protein interaction networks (interactomics), most studies identifying differentially expressed genes (transcriptomics) or different protein (proteomics) profiles map the corresponding genes/proteins onto existing interaction networks [41] generated from interaction databases [6, 41, 53, 54]. This allows the identification of genes, proteins, biological pathways and interaction networks associated with fibrosis. Protein-protein interaction networks are generated using data from interaction databases. The STRING database (https://string-db.org/) [55] for functional protein association networks in fibrosis [6, 56] or host-parasite interactions in parasite-induced fibrosis [57], IntAct molecular interaction database (https://www.ebi.ac.uk/intact/) [58] and databases focused on a particular biological process such as innate immunity interactions & pathways (Innate DB [59], https://www.innatedb.com/), or a particular location such as the extracellular matrix (MatrixDB, which has been created by our laboratory [60–62], http://matrixdb.univ-lyon1.fr/) are used to map interactions of differentially expressed genes or proteins. MatrixDB database also integrates not only interaction data but also transcriptomic and proteomic data, and the advanced query interface we have developed allows users to build tissue- and disease-specific interactomes [61, 62]. Gene-gene interactions of
importance to investigate fibrosis can be retrieved from the UCSC Genome Browser [63] (https://genome.ucsc.edu/), which collects evidences from curated databases and from text-mining using the Literome machine-reading program, an automatic curation system to extract genomic knowledge from PubMed articles [64], as it has been done for liver fibrogenesis and fibrosis [65].

Several databases have implemented guidelines for reporting omic experiments in order to standardize the minimum information on biological samples, experimental conditions and data processing protocols. The GEO database is compliant with the Minimum Information About a Microarray Experiment (MIAME) [66], and the Minimum Information about a high-throughput Nucleotide SeQuencing Experiment (MINSEQUE, http://fged.org/projects/minseqe/). Interaction databases of the IMEX consortium (e.g. IntAct and MatrixDB) are compliant with the Minimum Information required for reporting a Molecular Interaction experiment (MIMIx) [67]. Guidelines for the Minimal Information about a Proteomics Experiment (MIAPE) [68] and the Minimum Information Required for A Glycomics Experiment (MIRAGE) [69–71] have also been developed. Omic data and databases should follow the four FAIR principles (Findability, Accessibility, Interoperability, and Reusability) [72].

## 1.2 Computational tools

Differentially expressed genes or proteins are analyzed with various computational tools to integrate functions with genomics and proteomics. [73] The Ingenuity for pathway enrichment [41,53], Gene Set Enrichment Analysis (GSEA) [74] and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) [41,53] have been used to analyze fibrosis datasets. The Ingenuity Pathway Analysis is a web-based software application for the analysis, integration, and interpretation of data derived from omic experiments including RNA sequencing, proteomics and metabolomics (https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis). GSEA is a knowledge-based approach for interpreting genome-wide expression data, which focuses on gene sets sharing biological roles, chromosomal location, or regulation [75] (http://software.broadinstitute.org/gsea). DAVID is used for functional interpretation of large lists of genes or proteins (http://david.ncifcrf.gov) [76,77], and can be accessed programmatically [78]. A practical step-by-step protocol for pathway enrichment analysis and visualization of omic data using g:Profiler, GSEA, Cytoscape and EnrichmentMap has been recently developed [79]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database used to understand high-level functions and biological systems from large-scale molecular datasets generated by high-throughput assays [80] (https://www.genome.jp/kegg/), including those collected on fibrotic samples [6,53,81–83]. The Gene Ontology (GO) knowledgebase created to annotate genes and gene products with three categories of terms, biological process, molecular function, and cellular components [84] (http://geneontology.org/) is used for enrichment analysis [42,57,81,83]. Reactome, a manually curated and peer-reviewed pathway database [85] (https://reactome.org/) is used to characterize differentially expressed genes and proteins, and to identify enriched functional pathways in datasets [82,86]. Cytoscape, an open source software platform for visualizing complex networks and integrating any type of attribute data [87] (https://cytoscape.org/), is frequently used to visualize and analyze interaction networks associated with thanks to its numerous Apps [41,43,56,81,83].
# 2 Gene, mRNA, proteins, glycan and metabolite profiling

## 2.1 Gene expression profiling (transcriptomics)

The measure of RNA expression (RNA transcripts) has been performed in numerous studies aiming to identify differentially expressed genes in fibrotic tissues compared to normal ones, at different fibrotic stages or during fibrosis progression [74], or members of the TGF-β signaling network in human kidney from gene expression patterns across hundreds of microarray datasets by data mining [88].

The analysis of gene expression by DNA microarray has been useful to identify individual proteases playing a role in fibrosis such as ADAMTS1 (A Disintegrin And Metalloproteinase with thrombospondin type 1 motif), which is a central activator of TGF-β in liver fibrosis [89]. Transcriptomics has been associated with proteomics and protein-protein interaction networks to decipher the mechanisms of CCl₄-induced liver fibrosis [81]. The combination of transcriptomics (mRNAs and micro-RNAs) and proteomics (matrisome proteins) has led to the identification of novel regulation of fibrotic programs by miRNAs in pulmonary fibrosis fibroblasts [90]. Candidate loci affecting the variability in alpha-smooth muscle actin levels, which reflects fibrogenesis, and total collagen content, reflecting fibrosis have been identified by a genome-wide association study to analyze the genetic susceptibility to liver fibrosis. According to a pathway enrichment analysis, the macrophage migration inhibitory factor signaling pathway might be involved in liver fibrosis [65].

The impact of transcriptomics on pulmonary fibrosis has been reviewed, and the interest of single-cell RNA sequencing and microenvironment analysis to address cellular and spatial heterogeneity of diseased tissue has been highlighted [91]. Indeed, single-cell transcriptome profiling provides the possibility to define the cell types and molecular pathways that are involved in fibrotic tissues, and is able to identify rare cells [92].

## 2.2. Protein profiling (proteomics)

Protein profiling in normal and fibrotic tissue samples by MS is performed to determine the changes associated with fibrosis. Several MS approaches are used for protein profiling, namely data-dependent acquisition and data-independent acquisition such as sequential window acquisition of all theoretical mass spectra (SWATH-MS) [3], label-free LC-MS (liquid chromatography-mass spectrometry) [54] approaches or those with labels such as isobaric tags for relative and absolute quantitation combined with LC-MS/MS [6] and the SILAC (stable isotope labeling with amino acids in cell culture) technique [5]. Sample preparation and MS-based proteomics used for profiling ECM in normal and fibrotic lesions have been reviewed by Taha and Naba [4]. The matrisome associated with fibrosis has been characterized [2–6], and ECM proteins differentially expressed in lung of patients with idiopathic pulmonary fibrosis have been used to build a regulatory network of focal adhesion including interaction data from the STRING database [6]. Other studies focus on molecular functions such as the remodeling process occurring in fibrotic tissues that involves various protease families including matrix metalloproteinases [7–9]. The degradome, defined as the complete set of proteases present in an organism [93], has been characterized by DNA microarrays developed by Chris Overall’s laboratory to analyze human and
murine proteases, protease homologues and inhibitors, which form the protease web [94–96] that highlights the complex interplay between these molecules [96].

A proteomic variant approach (ProVarA), has been designed to compare the protein profiles of specific disease-variants, and determine how disease-causing mutations rewire the interaction network of a protein, and affects its biological functions. This approach has been applied to cystic fibrosis associated with 2,025 mutations in CFTR to identify proteins interacting with each variant using an immunoprecipitation-based proteomic approach [97]. ProVarA is a step towards personalized medicine of genetic and acquired diseases.

Protein profiling by proteomic analysis of serum from control subjects and patients with different stages of fibrosis allows the identification of potential biomarkers as done in patients with chronic hepatitis B in order to find markers of the intermediate liver fibrosis stage, where the start of antiviral therapy will be the most efficient [98]. Five candidates were selected out of 139 proteins differentially expressed in the different fibrotic stages. Proteomics has also been combined with metabolomics to identify up- and down-regulated pathways and metabolites upon anti-fibrotic treatment by gypenoside in an experimental model of liver fibrosis [99].

## 2.3 Glycan profiling (glycomics)

Protein glycosylation plays a role in the pathogenesis and progression of various liver diseases. The measurements of changes in glycosylation of serum proteins is non-invasive, and is an alternative to liver biopsy. Methods have been developed to search for glycomic biomarkers in the serum of patients with fibrosis [100–102] and in experimental models of fibrosis [103,104] for diagnosis and for evaluating the anti-fibrotic activity of potential drugs. N-glycan profiles are analyzed using DNA sequencer-assisted-fluorophore-assisted carbohydrate electrophoresis [101–103], or by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry after enzymatic digestion and hydrophilic chromatography [100]. Glycan profiles have also been determined by lectin microarrays comprised of 37 lectins in a murine model of liver fibrosis to study the relationship between glycan profiles in fibrotic liver and glycan-related genes expression assesses by oligonucleotide microarrays [104]. The major N-glycomic changes in serum of cirrhotic patients are carried by immunoglobulins A and G [105,106].

## 2.4. Metabolite profiling (metabolomics)

Metabolomic profiling is used to identify metabolic pathways in fibrotic samples, to better understand the molecular mechanisms of fibrogenesis and fibrosis [107–109], and to identify non-invasive biomarkers of early detection of fibrosis [107,110], disease progression, for example to identify liver-transplanted patients at risk of graft loss from rapid fibrosis progression [111], prognosis or selection of a patient-specific therapeutic treatment [112]. Metabolomics can be combined with network pharmacology to refine our understanding of the mechanism of action of an anti-fibrotic drug [113]. Metabolite profiling is also useful to detect oncoming adverse events in patients such as acute pulmonary exacerbations in patients with cystic fibrosis [114], and to identify new therapeutic targets.

Metabolites can be profiled in tissues (e.g. pulmonary tissues [108]), serum [107,111], urine [107] [109], bronchoalveolar fluid [115], exhaled breath [116] and exhaled breath condensate [114,116] defined as breathomics [116] using several techniques, namely NMR [110,116], gas
chromatography coupled to MS (GC-MS) and GC-tandem MS [108,116], ultraperformance liquid chromatography coupled to MS [108,109,111,113,114]. Targeted MS detects relevant metabolites [115]. Breath volatile compounds can be detected using sensor systems such electronic noses [116]. Profiles of serum and urinary metabolites established by LC-MS has been associated with histological progression of liver fibrosis to identify liver potential biomarkers, fibrotic pathways and their changes in fibrogenesis [107].

**3. Molecular networks: mechanisms of diseases and drug discovery**

Molecular networks mostly include functional genomic networks comprised of gene interactions [82] and protein-protein interactions built either experimentally or with interaction data extracted from the above-mentioned databases. They usually comprise proteins encoded by differentially expressed genes identified in fibrotic samples by transcriptomics [41,53] or proteins exhibiting different proteomic profiles in fibrosis [6,97]. The networks can be centered on a single protein (i.e., haptoglobin associated with liver fibrosis, [56] or CFTR [117,118], a tissue (e.g. lung in idiopathic pulmonary fibrosis [6]) or several tissues as described to identify the modulators of fibrosis in systemic sclerosis, which targets skin, lung, esophagus, and peripheral blood to build tissue-specific functional genomic networks [82]. In genetic diseases such as cystic fibrosis, protein interaction networks are used to determine the rewiring of the network induced by mutations associated with the disease. Mutations can abolish or promote interactions and/or change their kinetics or affinity. Differences between the protein-protein interaction networks of wild type and mutant CFTR networks have been used to investigate the mechanisms of the disease and to identify therapeutic targets [118]. Gene interaction networks for expression quantitative trait loci-controlling genes have been built to investigate the molecular mechanisms through which they are involved in fibrogenesis [65].

Interaction networks are used to study host-pathogen interactions in fibrotic parasitic diseases to decipher the mechanisms of infection and of the host-pathogen interplay and its role in fibrosis. A large-scale investigation of *Leishmania* interactions with host ECM proteins and GAGs has been done by surface plasmon resonance imaging with 24 *Leishmania* strains, showing that most *Leishmania* strains target host regulators of angiogenesis [119]. The dataset of differentially expressed genes at acute and chronic stages of *Fasciola hepatica* infection has been investigated in the context of the immune response using InnateDB [59] to build the interaction networks of both stages of infection, and characterize their topology [86], namely the degree (the number of connections of a protein in the network) and the betweenness centrality (the number of shortest paths that passes through a protein in the network [120]). The characterization of parasite proteomes combined with their functional interaction network provide clues on the proteins associated with ECM and adhesion as shown for the hydatid fluid proteome of *Echinococcus granulosus* and *Echinococcus multilocularis* [57], which induces a severe liver fibrosis [24,25]. Network analysis of the respiratory disease interactome including 200 diseases and thousands of gene-chemical-disease relationships gave new clues on the mechanisms shared by these diseases and might identify new therapeutic targets or repurposed drug strategies [121]. Molecular networks are also used to design new therapeutic strategies. Indeed, a number of anti-fibrotic drugs are effective in various fibrotic diseases and organs [122]. Molecular networks are thus useful to characterize these
multiple component antifibrotic drugs, and help “to overcome the “one-effect/one-cause/one-target” magic bullet-type drug development paradigm” [123].

Protein-protein interaction networks are indeed of interest because protein-protein interactions can be targeted for drug discovery [124], either to develop inhibitors [125] or to promote their stabilization [126]. Csermely *et al.* [123] have defined two strategies in network-based drug discovery, the central hit strategy and the network influence strategy [123]. Controllability analysis of the human protein interaction network has identified indispensable. proteins as drug targets [127]. The side-effect resource SIDER documenting the relationship between drugs and their side effects (http://sideeffects.embl.de, [128]) is useful for drug network studies. It has been used to study the relationship between biological processes and side effects of drugs by merging drug-biological process and drug-side effect networks [129]. The incidence of drug side effect is significantly related to the network distance of drug targets and diseases genes [130].

The contextualization of biomolecular interaction networks with affinity (equilibrium dissociation constant, K_D) and kinetics (the association and dissociation rates) as in the interaction network established by the anti-angiogenic and anti-fibrotic protein endostatin at the surface of endothelial cells [131] and of structural data of biomolecular complexes [132,133] will be helpful to design tailored drugs targeting protein-protein interactions. They will be also required to build models of fibrosis-associated networks, and predict the impact of potential dugs and single nucleotide polymorphisms.

Other networks of interest to identify potential diagnostic and therapeutic targets for idiopathic pulmonary fibrosis are networks of dysregulated-micro RNAs and their direct gene targets [134], micro RNA-mRNA [43], and of circular RNAs and their micro RNAs and genes targets [83]. Circular RNAs are long noncoding RNAs with single-stranded covalently closed loops regulating gene expression involved in heart, lung and liver fibrosis. They may be markers of the disease and potential therapeutic targets for anti-fibrotic treatments [135].

## Conclusions and perspectives

Multi-omic approaches improve the understanding of the mechanisms leading to diseases, and are a step toward personalized or precision medicine [38]. This multi-omic approach has been applied to fibrotic diseases such as cystic fibrosis, for which a “CFTR Functional Landscape” [136] including the contribution of CFTR variants to CFTR proteostasis network and to a protein social network has been created. The balance of proteostasis and social networks, and their impact on the CFTR Functional Landscape are crucial for the onset and clinical progression of the disease [136]. Singh *et al.* have designed an integrative method called DIABLO (Data Integration Analysis for Biomarker discovery using Latent cOmponents) for identifying key molecular drivers from multi-omic assays [137]. DIABLO is implemented in the mixOmics R Bioconductor package dedicated to the multivariate analysis of biological datasets (http://mixomics.org [138]). The added value of combined omics (genomics, transcriptomics proteomics, epigenomics and metabolomics) has been recently reviewed for idiopathic pulmonary fibrosis [139,140], and multi-omics data have been translated into clinically relevant information to monitor microbial dynamics during acute exacerbations in patients with cystic fibrosis. The cystic fibrosis rapid response strategy is based on viromics, metagenomics, metatranscriptomics, and metabolomics [141]. The integration of
omic data is thus of major interest for the study of fibrotic diseases. However, the integration of multi-omic studies is challenging with respect to hosting, completeness, quality control, heterogenity and resources for integrative data analyses [142]. Omic data integration requires statistical and machine-learning tools for a multi-omic view [39]. Guidelines for the minimum information required to report individual omic experiments have been developed as described above, but they still have to be developed for multi-omic data. A simple common omics metadata checklist, based on existing ontologies and standards, has been proposed for data harmonization across omic studies in order to maximize their reproducibility and usability [143].

Most omic studies have analyzed genes, mRNAs, proteins and the N-glycome but few studies have been performed for lipid profiling. Lipidomic studies have been carried out in plasma [144] and broncho-alveolar lavage [145] of patients with cystic fibrosis [145], and in lung of mice with bleomycin-induced lung fibrosis [146]. Further lipidomics studies are clearly needed to determine the roles of lipids in fibrosis, and to identify potential lipid biomarkers.

Only part of the glycome, essentially the N-glycome, has been characterized in fibrotic diseases. However, the five sulfated mammalian GAGs and the unsulfated hyaluronan [147] play a role in fibrosis [12,22,23]. GAGs (heparan sulfate, chondroitin sulfate/dermatan sulfate and hyaluronan) are deposited in lungs of patients with idiopathic pulmonary fibrosis together with an increase in total sulfation of heparan sulfate and a higher proportion of sulfation in chondroitin sulfate/dermatan sulfate [148]. It would thus be interesting to profile GAGs in fibrotic tissues, in body fluids of fibrotic patients and in experimental models of fibrosis, and to add glycosaminoglycanomics [149] to the multi-omic approaches developed to investigate fibrosis. This should be eased by the recent development of analytical methods to quantitate and sequence GAGs including size exclusion chromatography - mass spectrometry from frozen tissue sections after on-slide enzymatic digestion [150], and LC-MS/MS sequencing of heparan sulfate oligosaccharides using hydrophilic interaction liquid chromatography and negative electron transfer dissociation [151]. Besides their profiling in fibrotic tissues, GAGs may be further investigated for therapeutic purpose. Chondroitin sulfate, which accumulates in cardiac fibrosis, can be targeted to treat this disease [152], whereas hyaluronan-based treatments can be effective in liver fibrosis [153].

Models will be helpful to refine our understanding of the molecular mechanisms of fibrosis, and to predict the effects of single nucleotide polymorphisms and drugs, and the outcome of the disease. A discrete model of TGF-β signaling networks has been built by integrating the 137 human signaling maps from the Pathway Interaction Database into a single unified dynamic model [154]. A transcriptional regulatory model of fibrosis progression in idiopathic fibrosis has also been generated [155] (http: www.sb.cs.cmu.edu/IPFReg). Last, the development of “virtual patient” models as a basis for personalised medicine and virtualisation of drug development (e.g. virtual clinical trials) based on patient-specific omic profiles is certainly a major trend for the future. It will allow the prediction of drug effects, including potential side effects, of single or several combined drugs on the molecular disease phenotype specific to the patient before in vivo clinical trials [156].

## Acknowledgements
The work performed by the authors was supported by the "Fondation de la Recherche Médicale, France [grant numbers DBI20141231336 to SRB], the Institut Français de Bioinformatique [ANR-11-INBS-0013, Glycomatrix project, call 2015] to SRB, and the Groupement de Recherche (GDR) GagoSciences [CNRS, GDR 3739, Structure, Fonction et Régulation des Glycosaminoglycanes to SRB. We thank Dr. Sylvain D. Vallet (ICBMS, UMR 5246, Villeurbanne, France) for his critical reading the manuscript.

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