SYSTEM FOR SIMULATING MOLECULAR INTERACTIONS INVOLVED IN INFLAMMATION

Abstract

The present invention relates to the filed of simulation of molecular interactions for assessing diseases and disease therapies. In particular, it relates to a system for simulating molecular interactions involved in inflammation in a subject, said system comprising a processing unit comprising a database comprising a plurality of datasets each comprising at least an identifier for a molecule suspected to be involved in the pathological process, data on molecular interactions of the said molecule with one or more other molecule, and at least one data characteristic that is allocated to the dataset, wherein each dataset has at least one relation with another dataset in the database based on molecular interactions, wherein the datasets are grouped into data compartments comprising datasets having identical data characteristics, and wherein the data characteristics are indicative for the biological function of a molecule in inflammation, and an computer program-based algorithm implemented in the processing unit which generates a network map based on the plurality of datasets in the database and which allows for identifying nodes within network map based on predefined parameters, and a visualization unit which allows for determination of the molecular interactions in the identified nodes. Moreover, the present invention contemplates a method for simulating molecular interactions involved in inflammation in a subject as well as the use of the system of the invention for simulating molecular interactions involved in inflammation.

Claims

1. A system for simulating molecular interactions involved in inflammation in a subject, said system comprising (I) a processing unit comprising (a) a database comprising a plurality of datasets each comprising (i) at least an identifier for a molecule suspected to be involved in the pathological process, (ii) data on molecular interactions of the said molecule with one or more other molecule, and (iii) at least one data characteristic that is allocated to the dataset, wherein each dataset has at least one relation with another dataset in the database based on molecular interactions; wherein the datasets are grouped into data compartments comprising datasets having identical data characteristics; and wherein the data characteristics are indicative for the biological function of a molecule in inflammation; and (b) an computer program-based algorithm implemented in the processing unit which generates a network map based on the plurality of datasets in the database and which allows for identifying nodes within network map based on predefined parameters; and (II) a visualization unit which allows for determination of the molecular interactions involved in inflammation in the identified nodes.

2. The system of claim 1, wherein said inflammation comprises inflammation resolution.

3. The system of claim 1, wherein said molecule is identified from a damage associated molecular pattern (DAMP) or from a pathogen associated molecular pattern (PAMP).

4. The system of claim 3, wherein said DAMP and/or said PAMP are derived from evaluation of publications in public scientific databases using an automated evaluation algorithm which identifies molecules suspected to be involved in inflammation.

5. The system of claim 1, wherein said biological function of a molecule in inflammation is selected from the group consisting of: Mast Cell degranulation, Macrophage Differentiation, Myeloid Cell Differentiation, Lymphocyte Differentiation, Immune Cell Differentiation, Regulation of Hemopoiesis, Initiation of Innate Immune Response, Pattern recognition Receptor Signaling Pathways, Cytokine Production, Regulation of Adaptive Immune Responses, T-cell Mediated Immune Response, T cell Selection, Regulation of B cell Proliferation, Regulation of Immunoglobulin Secretion, Regulation of T cell Proliferation, Regulation of Lymphocyte Proliferation, Inflammation Resolution, Gene Expression Regulation, Protein Modification, Blood Vessel Development, Immune Response, Hemopoiesis, Neuronal Development, and Protein Transport.

6. The system of claim 1, wherein said computer program-based algorithm implemented in the processing unit which generates a network map based on the plurality of datasets in the database and which allows for identifying nodes within network map comprises rules for gene prioritization, determining node degree, determining betweennecess centrality, motif identification, in particular, identification of feedback or feedforward loops, and/or determining association with inflammation.

7. The system of claim 1, wherein transcriptomics data obtained from the subject after administration of a drug are also provided in the database.

8. The system of claim 7, wherein the transcriptomics data are used to model the datasets in the database.

9. The system of claim 8, wherein datasets from the database are eliminated if no match is found between the identifier for a molecule in the dataset and the molecule identifier in the transcriptomics data.

10. The system of claim 7, wherein said drug is a multicomponent drug.

11. The system of claim 10, wherein said multicomponent drug is Traumeel.

12. The system of claim 1, wherein the visualization unit comprises a computer program-based algorithm which graphically arranges highly connected nodes close to each other and/or which identifies only nodes for graphical display that are intramodularly connected.

13. A method for simulating molecular interactions involved in inflammation in a subject, said method comprising (I) providing a processing unit comprising (a) a database comprising a plurality of datasets each comprising (i) at least an identifier for a molecule suspected to be involved in the pathological process, (ii) data on molecular interactions of the said molecule with one or more other molecule, and (iii) at least one data characteristic that is allocated to the dataset, wherein each dataset has at least one relation with another dataset in the database based on molecular interactions; wherein the datasets are grouped into data compartments comprising datasets having identical data characteristics; and wherein the data characteristics are indicative for the biological function of a molecule in inflammation; and (b) an computer program-based algorithm implemented in the processing unit which generates a network map based on the plurality of datasets in the database and which allows for identifying nodes within network map based on predefined parameters; (II) generating a network map based on the plurality of datasets in the database and identifying nodes within network map based on predefined parameters by executing the computer program-based algorithm implemented in the processing unit; and (III) determining molecular interactions involved in inflammation in the identified nodes by using a visualization unit whereby the molecular interactions involved in inflammation in a subject are simulated.

14. The method of claim 13, wherein said inflammation comprises inflammation resolution.

15. The method of claim 13, wherein said molecule is identified from a damage associated molecular pattern (DAMP) or from a pathogen associated molecular pattern (PAMP).

16. The method of claim 15, wherein said DAMP and/or said PAMP are derived from evaluation of publications in public scientific databases using an automated evaluation algorithm which identifies molecules suspected to be involved in inflammation.

17. The method of claim 13, wherein said biological function of a molecule in inflammation is selected from the group consisting of: Mast Cell degranulation, Macrophage Differentiation, Myeloid Cell Differentiation, Lymphocyte Differentiation, Immune Cell Differentiation, Regulation of Hemopoiesis, Initiation of Innate Immune Response, Pattern recognition Receptor Signaling Pathways, Cytokine Production, Regulation of Adaptive Immune Responses, T-cell Mediated Immune Response, T cell Selection, Regulation of B cell Proliferation, Regulation of Immunoglobulin Secretion, Regulation of T cell Proliferation, Regulation of Lymphocyte Proliferation, Inflammation Resolution, Gene Expression Regulation, Protein Modification, Blood Vessel Development, Immune Response, Hemopoiesis, Neuronal Development, and Protein Transport.

18. The method of claim 13, wherein said generating a network map based on the plurality of datasets in the database and identifying nodes within network map comprises gene prioritization, determining node degree, determining betweennecess centrality, motif identification, in particular, identification of feedback or feedforward loops, and/or determining association with inflammation.

19. The method of claim 13, wherein transcriptomics data obtained from the subject after administration of a drug are also provided in the database.

20. The method of claim 19, wherein the transcriptomics data are used to model the datasets in the database.

21. The method of claim 20, wherein datasets from the database are eliminated if no match is found between the identifier for a molecule in the dataset and the molecule identifier in the transcriptomics data.

22. The method of claim 19, wherein said drug is a multicomponent drug.

23. The method of claim 22, wherein said multicomponent drug is Traumeel.

24. The method of claim 13, wherein the visualization unit comprises a computer program-based algorithm which graphically arranges highly connected nodes close to each other and/or which identifies only nodes for graphical display that are intramodularly connected.

25. A method of using the system of claim 1, for simulating molecular interactions involved in inflammation in a subject.

26. The method of claim 25, wherein simulating molecular interactions involved in inflammation in a subject is applied for determining drug actions.

Description

FIGURES

[0082] FIG. 1: User interface of the tool which we designed for data storage and the integration of functions which allow for an automated handling, e.g. for the export of the data into the molecular interaction map (MIM) system.

[0083] FIG. 2: Workflow for the construction of a comprehensive MIM around acute inflammation. It was started with the identification of seed molecules (left panel of the figure). Subsequently, experimentally validated interacting molecular partners from several databases and literature mining (middle) were extracted. Finally, various regulatory layers (miRNA, TFs, lncRNAs) from state-of-art databases were added. Also included were metabolites associated with inflammation resolution. Further regulatory layers such as Drug molecules may be integrated (shown on the right).

[0084] FIG. 3: Interconnectivity of the network displayed as the amount of interactions between two modules.

[0085] FIG. 4: Various steps for layouting network components and the interactions among them.

[0086] FIG. 5: Common regulatory core among all the primary clinical indications of acute inflammation MIM. TFs are shown as grey nodes while the miRNAs as orang node.

[0087] FIG. 6: Common molecules among the 5 primary clinical indications of acute inflammation. Bursitis shares 86% of the nodes with tendinitis while 40% of the total nodes are common with tenosynovitis.

[0088] FIG. 7: Mapping of top 1000 up-regulated genes to gene/phenotype ontologies. Top 1000 genes of mouse transcriptome were mapped to inflammation related gene ontologies in a time dependent manner. Graphs show process specific number of genes within the top 1000 as the percentage of the whole GO-set.

EXAMPLES

[0089] The following Examples are merely meant to illustrate the invention. They shall, whatsoever, not be construed as limitations for the scope.

Example 1

Generating of a Network Map of Molecular Interactions in Inflammation (molecular Interaction Map, MIM)

[0090] Identification of Seed Molecules for the Construction of MIM

[0091] Construction of MIM is a highly structured workflow which starts with few seed molecules that are further extended with experimentally validated molecular components and several layers of regulatory information. Seed molecules for the construction of MIM were identified in three steps: [0092] 1) Screening of damage associated molecular patterns (DAMPs) [0093] 2) Screening of pathogen associated molecular patters (PAMPs) [0094] 3) Analysis of selected acute inflammatory clinical indication networks.

[0095] DAMPs and PAMPs were identified by manual literature search of more than 50 original research papers and review articles. We mainly focused on the DAMPs that are frequently appeared in the literature on acute inflammation due to damage of muscles/connecting tissues and physical injuries (Table 1).

TABLE-US-00001 TABLE 1 List of DAMPs frequently identified in acute inflammation DAMP Full name HMGB1 high-mobility group box 1 HA Glycosaminoglycan hyaluronan HS Heparan sulphate UA Uric Acid IL-1α interleukin (IL)-1α IL-1β interleukin (IL)-1β IL-16 interleukin 16 IL-18 interleukin 18 FGF-2 fibrobast growth factor Gal-3 Galectin-3 Gal-1 Galectin-1 EMAP-II endothelial monocyte-activating polypeptide-II NLRP3 NLR Family Pyrin Domain Containing 3 PYCARD PYD And CARD Domain Containing MIF Macrophage migration inhibitory factor CASP1 cysteine-aspartic acid protease 1 RP S19 Cross-linked dimer of ribosomal protein S19 LPC Lysophosphatidylcholine iPLA2 Intracellular Membrane-Associated Calcium- Independent Phospholipase A2 Beta TyrRS Tyrosyl-TRNA Synthetase S100A8/ Protein S100-A8 and S100 A-9 S100A9 CRT Calreticulin

[0096] For the PAMPs, the literature related to the acute inflammation due to infection with various pathogens was screened. Foreign particles interact with the receptors to trigger immune responses and thus associated receptors were used as the seed molecules for the construction of MIM. List of PAMP and recognizing receptor is shown in Table 2 below:

TABLE-US-00002 TABLE 2 List of PAMPs and their recognizing receptors in acute inflammation PAMP Recognizing receptor Cytoplasmic DNA AIM2 Bacterial Flagelli NLRC4 Bacterial Type III Secretion Systems NLRC4 Bacillus Anthracis Lethal Toxin NLRP1 ATP NLRP3 Marine toxin maitotoxin NLRP3 Crystals (Urate, Calcium NLRP3 Pyrophosphate Dihydrate, Silica, Asbestos) Diaminopimelic Acid NOD1 Muramyl Dipeptide NOD2 Triacyl Lipopeptides TLR1, TLR2 Peptidoglycan TLR2 Lipoarabinomannan TLR2 Envelope Glycoproteins TLR2 Phospholipomannan TLR2 Porins TLR2 Diacyl Lipopeptides TLR2, TLR6 Lipoteichoic acid TLR2, TLR6 dsRNA TLR3, RIG1, DDX58, IFIH1, EIF2AK2 Lipopolysaccharide TLR4 Mannan TLR4 Flagellin TLR5 ssRNA TLR7, TLR8

[0097] To keep the MIM close to clinical relevance, the literature mining (>50 publications and >15 review articles) on molecular events associated with various acute inflammatory clinical indications was also performed. From a list of 29 primary clinical indications of acute inflammations provided by HEEL GmbH, publically available literature (PubMed, OMIM) and disease-gene association database (DisGeNET, DISEASE, KEGG disease) was screened to find the genes associated with the clinical indication. Most of these databases are based on text mining algorithms to predict the relationship between biological entities, which in many cases results in false positive information. The association of key genes with the clinical indications was cross checked manually by screening the associated publication. The key genes associated with each of the primary clinical indications of acute inflammation are summarized in Table 3.

TABLE-US-00003 TABLE 3 List of primary clinical indications of acute inflammation with associated key genes, number of nodes and interaction in molecular interaction network Name of Clinical indication UMLS Id Key genes Bursitis C0006444 IL1B, IL6, COX-2, CXCL12 Subacromial Bursitis C0546953 IL1B, IL6, CXCL12, VEGFA Olecranon Bursitis C0263962 KDR Tendinitis C0039503 TP53, SIRT1 Tenosynovitis C0039520 HLA-B, HLA-C, TNF Epicondylitis C0039516 PRG4, COL5A1 De Quervain disease C0149870 ESR2 Acute Arthritis C0263678 GPI, RPS19

[0098] A unique set of genes from Table 1-3 was finally created as the seed molecules around which a comprehensive MIM was constructed. In total, 53 seed molecules around which the MIM was created were used.

[0099] Construction of MIM from Seed Molecules (Undirected Graph)

[0100] For all the seed molecules, information about the molecular interactions in the scope of acute inflammation was collected manually from the literature and also from several automated tools such as BisoGenet app available on Cytoscape4.0 which connects large number of databases.

[0101] In particular, collected was information about: [0102] source and target molecules [0103] possible modifiers (e.g. enzymes) [0104] type of modification (e.g. catalysis, phosphorylation) [0105] state of the molecules (e.g. phosphorylated) [0106] type of the molecules (proteins, miRNA, complexes, DNA, simple molecules) [0107] type of the interaction (positive, negative) [0108] references as PubMed-IDs

[0109] To structure the information in one place and bring together the data generated from other sources (e.g. STRNG-Database), a tool was created, which allows easily storage, handling and alteration of the Data and gives an opportunity to implement new function to manipulate the Data in respect of the upcoming tasks. The tool was created in the C# Microsoft .NET Framework using Microsoft Visual Studio Community 2017 Version 15.9.0. Fehler! Verweisquelle konnte nicht gefunden werden. shows the user interface in the current state of development (December 2018).

[0110] For this, all the seed molecules and extracted information about the first experimentally validated molecular targets along with direct interactions among themselves if any were provided. Mainly considered were experimentally validated molecular targets from DIP, BioGrid, HPRD, IntAct, MINT, BIND and String databases. Furthermore, several experimentally validated regulatory layers were connected, which include miRNAs from mirBase, miRTarBase, TriplexRNA; transcription factors from TRNSFAC, TRRUST and HTRIdb; lnc RNAs from lncRInter, EVLncRNAs, lncRNADisease databases. It is already evident that fatty acid metabolism play an important role in the biosynthesis of specialized pro-resolving molecules (including resolvins, protectins and maresins). Also collected was information about the biosynthesis of these inflammation resolution mediators from Reactome database and published literature and included them in the MIM along with all the associated enzymes. The overall process is summarized in FIG. 2. Currently the cytoscape version of the network has 1464 nodes and 3300 interactions.

[0111] Annotation and Enrichment of the MIM

[0112] Every gene in the MIM was enriched by its specific UNIPROT-, HGNC-, RefSeq-, Ensemble-, PubMed- and NCBI-ID as well as common aliases and the full name of the encoding Protein. This information was collected as a single file downloaded from the HGNC database on Nov. 14, 2018. In case of simple molecules, the CHEBI-ID was fetched for each molecule separately from the CHEBI database while gathering the interaction data from the literature.

[0113] Modularization of the MIM Based on Acute Inflammation Related Processes

[0114] For easy navigation and visualization of the MIM, the MIM was divided into various functional modules by assigning the molecules to gene ontology terms focused on acute inflammation. For this, the CytoScape plugin ClueGO was used, which utilizes the GeneOntology (GO) database to structure a list of genes into modules of GO-Terms. The results can be adjusted manually by defining parameters, e.g. the range of tree levels of the term in the GO-Hierarchy, the p-value for the association or the amount of genes per modules. To focus on immunology related processes, the GO database “immunological_process” was used in the first run of modularization and 230 of all 1464 (FIG. 2) genes to 116 GO terms were assigned, which were merged manually into 17 modules. In the second run, the more general database “biological_process” was used and 778 of the remaining 1234 genes to 79 GO terms were assigned, which again were merged into seven modules.

[0115] Inflammation Resolution Module

[0116] The major goal to design and construct the MIM was to understand the acute inflammation and inflammation resolution processes. As inflammation resolution process is not included in the GeneOntology database, genes were assigned manually to this module based on information available in the literature. A large number of specialized pro-resolving mediators (SPMs) were found for which the information on the biosynthesis and signaling pathways was extracted from the literature (PubMed) and pathway database (Reactome). Non-enzymatic mediators are named systematically with a nomenclature that unifies synonyms used in different publications. The nomenclature is based mainly on the ChEBI name (https://www.ebi.ac.uk/chebi/). Enzymes and proteins are named with their respective UniProt gene names (https://www.uniprot.org/). The value/direction of an interaction is characterized as positive, negative or neutral (with further specification, if necessary; e.g. “neutral (translocation from neutrophils to platelets)”), and the type of interaction is given in appropriate terminology (e.g. “catalysis”, “inactivation”, “phosphorylation”, etc.). If the interaction is a biosynthesis or inactivation step, the product of the reaction is further characterized as either a metabolite or a mediator. Additionally, information about cell types associated with the interaction is added if available. Lastly, every interaction is referenced by the PMID of the publications used in the acquisition of information about it. So far, this module contains 256 interactions, including (not limited to) the biosynthesis pathways of protectins, maresins, resolvins, lipoxins, respective precursors, respective inactivation pathways, and associated receptors. Fehler! Verweisquelle konnte nicht gefunden werden. shows all 25 modules with their total number of genes in which a gene can occur in several modules.

TABLE-US-00004 TABLE 4 List of the generated modules with their respective number of associated genes Module Term #Genes Mast Cell degranulation 17 Macrophage Differentiation 16 Myeloid Cell Differentiation 73 Lymphocyte Differentiation 65 Immune Cell Differentiation 61 Regulation of Hemopoiesis 51 Initiation of Innate Immune Response 111 Pattern recognition Receptor Signaling Pathways 56 Cytokine Production 27 Regulation of Adaptive Immune Responses 35 T-cell Mediated Immune Response 23 T cell Selection 18 Regulation of B cell Proliferation 20 Regulation of Immunoglobulin Secretion 10 Regulation of T cell Proliferation 75 Regulation of Lymphocyte Proliferation 42 Inflammation Resolution 230 Gene Expression Regulation 246 Protein Modification 193 Blood Vessel Development 123 Immune Response 146 Hemopoiesis 118 Neuronal Development 164 Protein Transport 168

[0117] Furthermore, the biosynthetic pathways of various inflammation mediators (leukotrienes, eoxins, prostaglandins, thromboxanes, hepoxilins and trioxilins) along with their precursors and respective inactivation pathways were also included in the MIM as an inflammation mediator module.

[0118] Network Visualization

[0119] To visualize the molecular interactions in a molecular interaction map (MIM), they need to be designed in applications capable to create and display this information in the systems biology graphical notation (SBGN). Most of these applications furthermore allow the visualization and mapping of experimental data in the map. Because of the big size of the MIM, a manual creation is either not possible, or require considerable amount of time and manpower. To overcome this, an automated export function was developed from the dataset into the Systems Biology Markup Language (SBML) file format which describes the MIM in the SBGN style and is readable by the application CellDesigner™ that was used as a tool to display and layout the map as well as to integrate time series transcriptomic data. The function automatically places all molecules in their respective modules in the map and applies shape and color to the node depending on the molecule type and also draws the interactions. The interconnectivity of the modules is visualized in FIG. 3 below indicating the number of interactions between two of the modules.

[0120] Layout of MIM for Easy Navigation

[0121] Molecular Interaction Maps (MIMs) MIMs are important source of molecular mechanisms of diseases, which can be used to formulate new hypotheses that can subsequently be tested by experiments. Such maps are enormous in size (i.e. contain large number of components) and complexity (i.e. containing intricate recurring structures), which make them difficult to visualize and explore. Better visualization will facilitate understanding the underlying mechanisms and enable efficient sharing and usage of knowledge in biomedical research. Although, the maps construction and visualization tools like CellDesigner and Cytoscape provide built-in layouting options but for large and modularized MIMs they cannot produce efficient visualization. The automatic layouting algorithms work well only for small maps. Visualization of large MIMs requires enormous manual efforts. Towards this, step by step procedures were developed to arrange network components and interactions for better visualization.

[0122] Step 1: Components were organized in a module based on their interactions with other modules (i.e. inter modular connections) in a way that most of the interacting arrows shouldn't cross each other. For example, the FIG. 4a on the left shows the connection of genes (G1, . . . , 4) and proteins (P1, . . . , 4) with other modules of the map.

[0123] In this case: [0124] G1, G3 and P3 are interacting with the left part of the network. [0125] G1, G2, P2 are interacting with the upper part of the network. [0126] G4 and P4 are interacting with the lower part of the network.

[0127] Here, based on the position of inter modular interactions, the components were placed either to the left, upper or bottom part of the module.

[0128] Step 2: The network components are organized based on their interactions within a module as well as their interactions with other modules of the map as shown in the FIG. 4b.

[0129] Step 3: In this step, other readability issues were resolve for components such as proteins and complexes that have many inter- and intra-modular connections (FIG. 4c).

[0130] Zoomable Image of the MIM in Browser

[0131] An aim was to bring the MIM in browser so that the community around the acute inflammation can easily be connected. First the literature was surveyed for algorithms and technique for displaying the MIM as an interactive image. OpenLayer and Google Maps based techniques were selected to bring the MIM in browser for easy visualization. More specifically, the MIM was divided into three layers. Basic layers are modules in the top level, sub-modules and species in the middle layer and species and interactions in the bottom layer. To reduce the computational effort and the requirements to the local internet tiling of the image was carried out. Tiling is a technique to cut images into a matrix of smaller images of the same size and store it in a specific folder structure. With this, only the currently viewed parts of the MIM can be loaded and displayed. Python scripts for tiling and layering were applied to test if the CellDesigner export of our map fits to the postproduction process of the MIM. MINERVA [https://minerva.pages.uni.lu] platform was used to visualize the map. A local MINERVA instance was installed and tested with MIM for various security and reliability issues.

[0132] MINERVA provides the possibility to use OpenLayer as the basic visualization library to enhance the data security in comparison to GoogleMaps. The tiling and layering process is fully automated and basic mouse operations for zooming and panning the MIM are also supported. In addition, the user can select nodes to see the underlying annotations and at the same time connect with various drug and chemical databases directly from MINERVA interface for further network analyses.

[0133] Making the MIM as a Directed Graph

[0134] Providing direction (e.g. activation, inhibition) to the edges connecting various nodes in the network is a crucial step for analyses and prediction of biomarkers and therapeutic candidates. This is one of the most important steps in the construction of a reliable disease map and requires lots of manual efforts. All the associated publications that highlight the interactions among biological partners were cross checked in order to find the regulation type and annotate the reactions accordingly. Systems biology resources such as BioModel databases were also used to assign regulatory directions to the connected components using in house scripts. Many of the databases are using text mining approaches to even highlight experimentally validated interactions with false positive information. This was a time taking exercise to define the regulation type (e.g. activation, suppression, activated complex etc.) in the construction of a disease MIM. However, this exercise is worth doing to properly curate the MIM and also for the downstream analysis related to the identification of regulatory motifs and prioritization of network components. So far, around 70% of the total interactions in the network are directed.

[0135] Identification of Common Regulators from Selected Primary Clinical Indications of Acute Inflammation

[0136] Some of the primary clinical indications of acute inflammation were identified, where the information about key genes was available in the literature. For those clinical indications, the common molecular regulators should be found. To achieve this task, first the clinical indication specific MIM was constructed using the workflow described in FIG. 2. For the miRNA regulatory layer, we used the miRTarBase release 6.1 and only considered those miRNAs which are experimentally shown to have strong repression efficiency on the target genes. List of key genes, number of nodes, interactions, number of known transcription factors and number of miRNAs strongly regulating target genes in the networks are shown in Table 5.

TABLE-US-00005 TABLE 5 Summary of MIM constructed around the selected primary clinical indication of acute inflammation Name of Clinical No. of No. of No. of No. of indication nodes interactions TFs miRNAs Bursitis 407 1013 158 217 Subacromial 407 1071 145 218 Bursitis Olecranon 341 891 103 192 Bursitis Tendinitis 994 4617 404 308 Tenosynovitis 232 423 61 132 Epicondylitis 127 188 18 89 De Quervain 300 677 95 176 disease Acute Arthritis 130 171 26 94

[0137] To identify common genes, TFs and miRNAs shared by all the primary clinical indications studied here, Cytoscape merge network tool was used to find the interactions among the clinical indication-specific networks. Transcription factors STAT3 and SP1 were found along with 48 experimentally validated miRNAs as the common regulators in all the 8 primary clinical indications of acute inflammation for which MIM was constructed (FIG. 4).

[0138] In case of bursitis, tendinitis, tenosynovitis, epicondylitis and acute arthritis, there were 54 common candidates (FIG. 5) including 4 transcription factors (EGR1, STAT3, SP1 and JUN) and 50 miRNAs. Role of STAT3 through IL-6 driven signaling is previously reported in the termination of inflammatory recruitment of neutrophils which is a crucial checkpoint in inflammation resolution (PMID: 18641358). Among the common miRNAs, we found miR-155 which has been identified as central regulator of the immune system (PMID: 19596814). Among others, miR-21 and miR-203a are already known for their role in acute inflammation resolution (PMID: 20956612).

Example 2

Integration of Traumeel Transcriptomics Data from Mouse Wound Healing Model onto the MIM and Investigations of the Traumeel Induced Phenotype

[0139] Integration of Traumeel Transcriptomics Data from Mouse Wound Healing Model onto the MIM

[0140] The RNA-seq data provided by HEEL encompassed 55419 transcripts also partly encoding for gene isomers. Gene expression levels were quantified by measurement of RPKM (reads per kilo million) values for each transcript. RPKM values were measured for 6-7 samples (n=6/7), at 8 time points (0 h-192 h), and for 4 conditions (untreated, saline-treated, Traumeel injection alone, Traumeel injection plus ointment). It was realized that in the RNA-seq data, RPKM values were provided for each of the transcripts associated with a particular gene. Further in the process of identification and analysis of differentially expressed genes and their association with higher level processes were identified by mapping GO terms. So far the association of GO terms at the gene transcript level is largely missing and generally the same GO terms are mapped to all the gene transcripts. In order to quickly estimate the differentially expressed genes from the RNA-seq data in different conditions and to map them on the MIM, the RPKM values were added of all the isomers of a gene for each replicate separately followed by calculating their average in different conditions. Then calculated log(2)-fold change of each of the gene was calculated by comparing various conditions (untreated vs traumeel injection alone; saline vs traumeel injection alone; untreated vs traumeel gel; and saline vs traumeel gel). Further, the log(2)-fold change data was integrated onto the MIM for each of the time points.

[0141] From the cytoscape version of the MIM, the differentially expressed network components (>1.5 log(2)-fold change and p-value<0.05) were extracted in untreated vs traumeel injection experimental condition at 12 hrs. The change in the expression pattern of this differentially expressed network was investigated at higher time point. It was observed that after 120 hrs, many of the nodes has same expression profile between the above mentioned two experimental conditions. At 192 hrs, none of the node differentially expressed indicating that inflammation is already resolved at this time point.

[0142] Evaluating the Influence of Traumeel Treatment on Biological Processes and Phenotypes Based on log(2)-Fold Change in Gene Expression

[0143] In a first attempt to identify how Traumeel affects core regulatory processes in the mouse wound healing model, gene sets were identified for selected biological processes and phenotypes using the Gene Ontology Consortium (GO, http://www.geneontology.org) and the Mouse Phenome Database (MP, https://phenome.jax.org). The pre-processed time course data (averaging gene isoforms and samples) for each GO/MP gene set was then extracted from the transcriptomic data and visualized in a line plot.

[0144] To get an impression on how the selected biological processes and phenotypes behave as a whole (shifting the focus from single molecules to complex functions) mean expression values for each time point were calculated and visualized as log(2)-fold change. The blue line shows the differential expression of the whole gene set whereas the orange respectively the grey line represent the gene sets for saline (S) and Traumeel treatment (T) alone. Saline was used as a reference in this context due to the assumption that the injection procedure itself as well as carrier solutions, e.g. saline, might influence transcription already.

[0145] Evaluating the Influence of Traumeel Treatment on Biological Processes and Phenotypes Based on Top 1000 Up-Regulated Genes

[0146] In order to identify the biological processes mostly affected by Traumeel treatment we extracted the top 1000 up-regulated genes for each time point (12 h-192 h) in untreated (U) and Traumeel treated (T) animals and mapped them to gene ontologies with the EnrichR gene set enrichment analysis web server (http://amp.pharm.mssm.edu/Enrichr/). In summary, a list of 3624 GO-terms was generated and submitted for further evaluation. As a first selection we extracted all T cell-, neutrophil-, macrophage- & migration-related gene ontologies. Secondly, we chose two biological processes related to inflammation in general for exemplification, namely “Neutrophil migration” and “Neutrophil degranulation”.

[0147] FIG. 7 shows the number of genes present in the top 1000 at each time point as percentage of the whole GO-related gene set, with the x-axis showing the time points and the y-axis the number of GO-related genes in the top 1000 in percent of the whole GO-set. As an example: If a GO-term contains 480 genes and 48 of those are present in the Top 1000 at t=24 h the y-value for this time point will be 10%. The comparison of Traumeel treated animals with untreated animals (FIG. 7) allows two major conclusions: First, the overall trend in genes activated in percent of GO-set remains similar (i.e. no disruption of physiology). Second, Traumeel seems to stimulate the expression of genes involved in inflammation related biological functions in the beginning (stronger initiation) whereas it inhibits the GO-set involvement towards the end (stronger resolution). This directed process intensification might be accountable for the inflammation resolving properties of Traumeel.

CITED LITERATURE

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