Mechanotransduction and inflammation: an updated comprehensive representation

Mechanotransduction and inflammation: an updated comprehensive representation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Mechanotransduction and inflammation: an updated comprehensive representation Vennila Suryiagandhi , Ying Ma , Veronica Paparozzi , Tiziana Guarnieri , Biagio di Pietro , Giovanna Maria Dimitri , Paolo Tieri , Claudia Sala , Darong Lai , View ORCID Profile Christine Nardini doi: https://doi.org/10.1101/2024.06.12.598454 Vennila Suryiagandhi 1 Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC) , 00185 Roma, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ying Ma 2 Department of Computer Science and Engineering, Southeast University , 211189 Nanjing, P.R. China 1 Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC) , 00185 Roma, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Veronica Paparozzi 1 Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC) , 00185 Roma, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tiziana Guarnieri 1 Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC) , 00185 Roma, Italy 3 Department of Biological, Geological, and Environmental Sciences, Alma Mater Studiorum Università di Bologna , via Francesco Selmi 3, 40126 Bologna, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Biagio di Pietro 1 Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC) , 00185 Roma, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giovanna Maria Dimitri 4 Department of Information Engineering and Mathematics, University of Siena , Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paolo Tieri 1 Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC) , 00185 Roma, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claudia Sala 5 Department of Medical and Surgical Sciences, Alma Mater Studiorum Università di Bologna , Bologna, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Darong Lai 2 Department of Computer Science and Engineering, Southeast University , 211189 Nanjing, P.R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine Nardini 1 Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC) , 00185 Roma, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christine Nardini For correspondence: christine.nardini.rsrc{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Mechanotransduction is the process that enables the conversion of mechanical cues into biochemical signaling. While all our cells are well known to be sensitive to such stimuli, the details of the systemic interaction between mechanical input and inflammation are not always well integrated. Often, they are considered and studied in relatively compartmentalized areas, and we therefore argue here that to understand the relationship of mechanical stimuli with inflammation – with high translational potential - it is crucial to offer and analyze a unified view of mechanotransduction. We therefore offer here a pathway representation, recollected with the standard systems biology markup language (SBML) and explored with network biology approaches. We present RAC1 as an exemplar and emerging molecule with potential for medical translation. Introduction Mechanotransduction is the event that consists of the conversion of a mechanical signal into a biochemical one (sometimes via further transductions). All our cells are sensitive to mechanical stimuli, implying that all are able to perform this conversion. This competence is crucial in a number of situations, ranging from the very basic mechanisms of early embryonic development 1 , to the medically relevant spread of metastases 2 . These are two highly divergent variants of the epithelial mesenchymal transition (EMT), type I and III, respectively, which are relevant in experimental regenerative medicine and mechano-pharmacology 3 , 4 . Nevertheless, the exploitation of this sensitivity is not part of clinical routine, and, in medical practice only a selected set of specialties make explicit usage of the ability of mechanical stimuli to impinge on yet another EMT (type II) i.e. wound healing : dermatology for scarring and burning 5 , orthopedics for bone repair 6 . Our interest here is in the connection between mechanical stimuli and inflammation, namely, in the mechanical events whose transduction impacts on the inflammatory function, which plays a crucial role in wound healing. Although numerous pieces of information are available in the literature, their integration is relatively limited 7 . This hampers the development of a systemic overview that is crucial to fully capture the potential and limitations of using mechanical stimuli to modulate inflammation. One very effective frame to overcome this limitation is the representation of information in a standardized pathway, which exploits the network formalism. This allows to capture, rather than simplify, the complexity, enabling the representation of multiple connections (edges of the network, biological reactions) among entities (nodes of the network, biological molecules). This concept underpins the design of in silico pathways, enabling not only a faithful representation of the complexity of biological interactions, but also the exploitation of computational tools proper to the network theory (systems biology). This approach enables the identification of mathematically relevant nodes, whose biological importance has already been assessed 8 . Therefore, building on our earlier work 9 , we have here curated a state-of-the art standard representation of the pathway of mechanotransduction in relation to inflammation. This map is represented in a standardized format to ensure exportability, verifiability and expansion using the Systems Biology Markup Language (SBML 10 ). To the best of our knowledge this offers a unique representation of mechano-transduction in silico that evolves f rom our own previous effort and offers an invaluable tool for any further automated and systemic exploration. After curating this map, we performed two sets of experiments: the first to assess the differential impact of various types mechanical stimuli; the second to highlight how incorporating mechanotransduction within the representation of the inflammatory pathway (innate immune response) let emerge new molecules whose relevance as targets or biomarkers should be explored. Our experiments suggest that different types of mechanical stimuli elicit a bulk of shared functions, but also show emphasis on distinct molecular functions (as they can be seen in the enrichment and diffusion analysis), and that molecules whose inf lammatory relevance is generally disregarded, may become important shall mechanical stimuli be impacting on the system (see Topological analysis and in particular RAC1 and CALM1). Material and Methods 1 Construction of the Mechanotransduction SBML Pathway The mechano core map 9 has been updated with new entities from 12 recent (2015-2023) comprehensive review articles 11 – 22 and f ive research articles specifically related to the review of the role of the aryl hydrocarbon receptor (AhR 23 – 27 ), which, in addition to its recognized role as a sensor for endo - and xenobiotics, has recently been recognized as having role in the transduction of mechanical signals, particularly those associated with endothelial fluid shear stress and directional migration. Publications have been manually selected using the keywords “mechanotransduction”, “mechanical stimuli”, and “mechanical force”. The map construction was done by manual curation using CellDesigner.4.3 28 , it contains 8 different stimuli including -beyond the generic ( inflammation ) and experimental inflammatory stimuli (lipopolysaccharide LPS stress) - mechanical force, shear stress, stretch, stiffness, tension , and hydrostatic pressure . All components were updated with the standard MIRIAM (Minimal Information Requested in the Annotation of Models following-up on previously defined and accepted standards 29 ) using NCBI gene IDs for genes, UniProt ID for proteins and ChEBI ID (Chemical Entities of Biological Interest) for small molecules, within the SBML bqbiol:is label. Entities that have more than one ID were added to the bqbiol:hasPart element according to SBML rules 30 . Multiple proteins or genes with physical interaction are known as complexes , while multiple entities without physical interaction are known as sets . Sets are further divided in: DefinedSet (very general way of grouping molecules based on the fact that they share some common property, they often represent real complexes), CandidateSet (way of grouping molecules when information is incomplete and one of several candidate physical entities is responsible for a particular task , indicating potential alternatives), this latter attributes were added to the note , to be fully compliant with Reactome 31 . Finally, the literature pertains to experiments conducted on multiple organisms. This information is stored in the CellDesigner layer attribute. This constitutes the complete update of the mechanotransduction pathway in SBML and can be found at Biomodels ID MODEL2406110001 and in the Supplementary Material). 2. Construction of three mechano-inflammatory networks The SBML format enables systems biology approaches to the study of mechanotransduction. In particular, pathways represented as complex networks can be investigated in search of relevant nodes or structures, whose role as biomarkers or target is now well assessed. To perform this analysis we used Cytoscape (Version: 3.10.1) with the cy3sbml app 32 to import the SBML file. We first expanded the core mechanotrasduction network described above to include well known sub - pathways. For this, we manually identified 17 nodes whose relevance in inflammation is well known, to extend their neighborhood via Reactome ( Table 1 ). We name this network Mechano-Union . View this table: View inline View popup Table 1: Subpathways of the Mechanotransduction pathway We also defined a second network, by integrating the Mechano-Union to the Reactome Innate immune system (R-HSA-168249), this represents the expansion of the innate immune response to include also mechanical stimuli, in line with the idea of a greater inflammatory pathway able to account for physical transduction 7 . We name this network Mechano-Innate . Finally, our third network was obtained by simply importing the Reactome Innate immune system pathway to use it as a baseline for comparison of network analyses ( section 2.2 ). We name this network Innate . All three Cytoscape networks are available as Supplementary material (MechanoUnion.cyjs used in Section Diffusion and MechanoInnate.cyjs, Innate.cyjs used in Section Topological Analysis). 3 Diffusion Analysis Network propagation or diffusion analysis was done using the Diffusion App of Cytoscape 33 and applied to the Mechano-Union . Briefly, this analysis enables us to follow information f luxes (i.e. thermal energy accumulated on some nodes) over time. In a translational perspective, this allows us to explore how signaling f lows in the network, i.e. what are the final actuators of initial stimuli. For this reason the analysis was run, setting an amount of energy solely on the node representing a stimulus of interest (one by one), and collecting the amount of energy that reaches the nodes of the network at the end of the simulation, after heat has been transferred to the nodes encountered 34 . Results consist of a ranked list of the network nodes (i.e. with nodes having collected the highest amount of information/energy ranking higher). This type of output is well suited to perform an enrichment analysis which enables the identification of biological functions that are statistically significantly associated to the nodes that preserve energy: in other words, this allows the identification of the biological functions elicited by the stimulus 35 . This was done with R software version 4.0.4 and RStudio version 1.4.1106 by function gsea 35 in the Bioconductor Package clusterProfiler , and using the Hallmark gene sets 36 as functional references. Results significance is corrected also by the number of tests (i.e. eight stimuli). Given that Hallmark gene sets and Reactome pathways do not use the same standard notation, we performed an automatic names conversion on all SBML pathways before importing them in Cytoscape. The MIRIAM identifier (see Section 1 Construction of the Mechanotransduction SBML Pathway ) enclosed within the SBML’s sub-element “annotation” was extracted and used to query the BioMart R package (version 2.46.3) 37 . In particular we used the “hsapiens_gene_ensembl” and within this set the “uniprot_gn_id”, “entrezgene_id”, “ensembl_gene_id” and “ensembl_transcript_id” columns for the corresponding MIRIAM identifiers (Uniprot, NCBI and Ensembl for genes and transcripts, respectively). These values were used to extract the corresponding species’ name from the “external_gene_name” column, which is then saved in the attribute “name” of each selected species inside the SBML-converted list. In addition to this value, the “name” of each species also contains in brackets the compartment name. This was applied automatically to simple molecules (i.e. not to sets) to 19 SBML files (Mechanotransduction pathway, 17 pathways in Table 2 , and Reactome Innate immune system (R-HSA-168249)). View this table: View inline View popup Table 2. Immuno-mechano complexes 4 Network Topological Analysis Topological network analysis was run in Cytoscape on the Mechano-Innate and the Innate networks, the rationale being to assess what differences emerge shall we consider “classical” immune response ( Innate network) or also the mechanotransduction phenomenon ( Mechano-Innate network) as an entry point to the activation of immunity. Preprocessing involved the removal of small molecules given their ubiquity, which include: H 2 O, ATP, ADP, GTP, GDP, H + , Na + and Ca 2+ , as their high connectivity biases the topological analysis, ranking them very high. Further the corresponding cytoscape.JSON files were exported and the topological analysis was run with Python 3.8 and the NetworkX package to create directed graphs, a manipulation necessary to convert the JSON files that have species’ nodes connected to reaction nodes, into a network where species are represented in nodes and reactions in edges. This was achieved as follows: species nodes with identical names were merged and new edges between species nodes connected via the same reaction node were created, to preserve the biological pathway's structure. Finally, on these structures , topological analysis was applied computing four centrality measures: closeness indicate how close a node is to all other nodes; betweenness indicates the highest number of shortest paths (number of edges connecting two nodes); degree indicates the number of connected nodes and eigenvalue centrality gives a measure of the connections to nodes that have high connections. Finally, we compared, for the molecules shared between the two networks, the centrality measures. We retained the nodes whose differences were the highest, since this represents molecules whose impact on immunity changes most, shall we consider mechanical stimuli as relevant in triggering an immune response. We finally retained for the final analysis only molecules ranking high across all four measures, as a proxy for robustness. Therefore, this analysis lets emerge the molecules whose centrality changes most when considering mechanotransduction as an integral part of innate immunity, thus identifying molecules whose relevance might be overlooked as inflammatory targets, while they may become relevant when the system is perturbed by mechanical stimuli. Results and discussion Mechanotransduction Map Figure 1 shows the complexity of the mechanotransduction map. Major cellular compartments and eight different types of stimuli have been included with their associated transductions. Download figure Open in new tab Fig. 1. SBML representation of the Mechanotransduction map in SBGN format exported from CellDesigner. This figure is only illustrative of the complexity of the pathway, details can be found in the original SBML in the Supplementary Material or in the Biomodels repository with ID MODEL2406110001 Diffusion Analysis The diffusion analysis allows us to gain insight into the steady state (i.e. late) effect of a network perturbation, in our case the different types of mechanical stimuli. In particular, a given amount of energy (heat) is let to flow over time (simulation steps) across the network. According to the specific wiring of the network that will transport a reduced amount of the original energy (consumption) towards connected nodes, while avoiding nodes that are not reached before the exhaustion of this energy. Results come in the form of a list of all network molecules and their accompanying heat (Supplementary Material, Table S1 Diffusion). This type of information is appropriate for an enrichment analysis, and in particular, hot molecules represent high ranking genes (or associated proteins) that can be processed by gene set enrichment analysis 35 (see Methods) to identify which functions are majorly (and possibly differentially) affected by the stimuli. Fig 2 shows the results of the enrichment analysis (See also Table S2 Supplementary Material), indicating that in general all mechanical stimuli elicit inflammatory and proliferative functions. These functions are broadly in line with the wound healing process (or EMT type II 38 ) which proceeds across an early and transient inflammatory phase and eolves towards regeneration (requiring proliferation) and further remodeling. Download figure Open in new tab Fig. 2. Enrichment analysis (by GSEA) of the network genes ranked by heat, upon diffusion analysis. The functions on the vertical axis correspond to enriched Hallmark functions, while the horizontal axis reports the elicited stimulus. The size of the dots is inversely proportional to the statistical significance of the enrichment. It is nevertheless interesting to observe that, beyond these common functions, some specific patterns can be observed. Not surprisingly, inflammation and LPS , at this granularity of modeling, indicate a relatively uniform activation of all the functions identified. Conversely, mechanical force , which represents the indistinct mechanical stimulus that was modeled in our previous work 9 shows a visible enrichment on the tumor associated mTOR pathway 39 , this is in line with the literature that was adopted for that reconstruction, which turned out to have a particular emphasis on EMT type III. This bias was among the motivations to update our network with the current work. Finally, regarding the specific types of mechanical forces included in our model, we can observe few peculiar patterns. The first is concerned with hydrostatic pressure which elicits more specifically apical junctions (formation), a results that, if experimentally validated, could expand the information reported from the original literature 27 . The second regards shear stress , where the activity concerned with the GM2 checkpoint appears to be the most enriched. Interestingly we can also observe that mechanical stretch appears to elicit the same overall functions but with lower significance, suggesting, in this sense, a minor efficacy of this type of stimulus. Topological Analysis Topological analysis allows the identification of important nodes, where importance is defined based on the ability of such nodes to connect the network. The most assessed metrics to compute this importance include the degree , the betweenness, eigenvalue and stress centrality and the eigenvalue . In our analysis we computed all statistics to obtain a list of molecules whose importance is dramatically different when computing it in the Innate only or in the Mechano-Innate, i.e. including also mechanotransduction. We here discuss the role of these molecules and do so after selecting the more robust, i.e. the ones which change their importance across all four measures (full list and metrics in Table S3, Supplementary Material). The full list of such molecules and their complexes (34 nodes in total, including mostly complexes) that we name here immuno-mechano complexes , is available in Table 2 and Table S4 of the Supplementary Material. We retrieved from Reactome information on the main role of such complexes, and grouped them by six main areas of activity, color coded in Figure 3 (and Table S5 in Supplementary Material): the first pertains directly (and generically) to the response to pathogens. The second has to do with membrane lipids, this is not surprising, as it is well known that mechanical transduction consists of the activation of receptors by physical stress of the membrane and/or the extracellular matrix. Similarly we observe that the third area has to do with well-known key molecules including Nuclear Factor kappa B , NFKB 40 , Transmembrane Immune Signaling Adaptor (TYROBP) known as DAP12 41 and interferons (IFN alpha, beta). Finally, we highlight two specific molecules RAC1 and CALM1, whose biological role is known to be crucial in the regulation of ion signaling. While CALM1 mechano-mediated effects are less explored and so its role as drug target 42 , RAC1 (with RhoA) is a well-known key player in mechanotransduction 43,44 . Its relevance in inflammation is also known, and the modulation of RAC1 in numerous diseases from allergies 45 to cancer 46 is recognized. From a topological standpoint, the relevance of these molecules appears to be coherently increased across all measures of centrality (in the plasma membrane and in the cytosol, respectively, see Supplementary Material Table S3). This indicates that these molecules, when considering the effect of a mechanical stimulus on the inflammatory state of the system, have a potential to connect to other molecules (eigenvalue, closeness and degree) and more efficiently (betweenness), that is higher than what has been recognized so far, when inflammation was reduced to its more classical innate immune response definition. What we offer with our investigation is therefore the recommendation to explore the potential of constraining RAC1 (and CALM1) activity with mechanical stimuli, this and similar approaches (see for instance the modulation of Ca 2+ ion channels by magnetic stimuli 47 ), can efficiently complement pharmacological research and drug design. Web site address List of legends Table 1 Reactome pathways completing the Mechanotransduction pathway Table 2 List of the 34 Immuno-mechano complexes identified by topological network analysis Footnotes https://www.ebi.ac.uk/biomodels/MODEL2406110001 Reference list [1]. ↵ E. Farge , ‘ Mechanotransduction in development’ , Curr Top Dev Biol , vol. 95 , pp. 243 – 265 , 2011 , doi: 10.1016/B978-0-12-385065-2.00008-6 . OpenUrl CrossRef PubMed [2]. ↵ M. E. Fernandez-Sanchez et al. , ‘ Mechanical induction of the tumorigenic beta-catenin pathway by tumour growth pressure’ , Nature , vol. 523, no. 7558 , pp. 92 – 5 , Jul . 2015 , doi: 10.1038/nature14329 . OpenUrl CrossRef [3]. ↵ R. Geesala , Y.-M. Lin , K. Zhang , and X.-Z. Shi , ‘ Targeting Mechano-Transcription Process as Therapeutic Intervention in Gastrointestinal Disorders’ , Front. Pharmacol ., vol. 12 , Dec . 2021 , doi: 10.3389/fphar.2021.809350 . OpenUrl CrossRef [4]. ↵ M. Goßmann et al. , ‘ Mechano-Pharmacological Characterization of Cardiomyocytes Derived f rom Human Induced Pluripotent Stem Cells’ , Cell Physiol Biochem , vol. 38 , no. 3 , pp. 1182 – 1198 , 2016 , doi: 10.1159/000443124 . OpenUrl CrossRef [5]. ↵ C.-K. Hsu and R. Ogawa , ‘ Mechanobiology and Mechanotherapy for Skin Disorders’ , in Mechanobiology , John Wiley & Sons, Ltd , 2017 , pp. 221 – 237 . doi: 10.1002/9781118966174.ch14 . OpenUrl CrossRef [6]. ↵ M. J, N. M, and H. K , ‘ Mechanobiology-based physical therapy and rehabilitation after orthobiologic interventions: a narrative review’ , International orthopaedics , vol. 46 , no. 2 , Feb . 2022 , doi: 10.1007/s00264-021-05253-5 . OpenUrl CrossRef [7]. ↵ A.-L. Barabási , ‘ Network science’ , Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , vol. 371 , no . 1987 , p. 20120375 , Mar . 2013 , doi: 10.1098/rsta.2012.0375 . OpenUrl CrossRef PubMed [8]. ↵ J. E. Dent et al. , ‘ Mechanotransduction map: simulation model, molecular pathway, gene set’ , Bioinformatics (Oxford, England) , Nov . 2014 , doi: 10.1093/bioinformatics/btu776 . OpenUrl CrossRef PubMed [9]. ↵ M. Hucka et al. , ‘ The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core’ , J Integr Bioinform , vol. 15 , no. 1 , p./j/jib.2018.15.issue-1/jib-2017-0081/jib-2017-0081.xml, Mar . 2018 , doi: 10.1515/jib-2017-0081 . OpenUrl CrossRef [10]. ↵ P. Romani , L. Valcarcel-Jimenez , C. Frezza , and S. Dupont , ‘ Crosstalk between mechanotransduction and metabolism’ , Nat Rev Mol Cell Biol , vol. 22 , no. 1 , pp. 22 – 38 , Jan . 2021 , doi: 10.1038/s41580-020-00306-w . OpenUrl CrossRef [11]. ↵ J. M. Kefauver , A. B. Ward , and A. Patapoutian , ‘ Discoveries in structure and physiology of mechanically activated ion channels’ , Nature , vol. 587 , no . 7835 , pp. 567 – 576 , Nov . 2020 , doi: 10.1038/s41586-020-2933-1 . OpenUrl CrossRef PubMed [12]. Q. Gao and E. M. McNally , ‘ The Dystrophin Complex: structure, function and implications for therapy’ , Compr Physiol , vol. 5 , no. 3 , pp. 1223 – 1239 , Jul . 2015 , doi: 10.1002/cphy.c140048 . OpenUrl CrossRef PubMed [13]. M. Huse , ‘ Mechanical forces in the immune system’ , Nat Rev Immunol , vol. 17 , no. 11 , pp. 679 – 690 , Nov . 2017 , doi: 10.1038/nri.2017.74 . OpenUrl CrossRef [14]. S. Dupont and S. A. Wickström , ‘ Mechanical regulation of chromatin and transcription’ , Nat Rev Genet , vol. 23 , no. 10 , pp. 624 – 643 , Oct . 2022 , doi: 10.1038/s41576-022-00493-6 . OpenUrl CrossRef [15]. J. J. Saucerman , P. M. Tan , K. S. Buchholz , A. D. McCulloch , and J. H. Omens , ‘ Mechanical regulation of gene expression in cardiac myocytes and f ibroblasts’ , Nat Rev Cardiol , vol. 16 , no. 6 , pp. 361 – 378 , Jun . 2019 , doi: 10.1038/s41569-019-0155-8 . OpenUrl CrossRef PubMed [16]. T. Hodgkinson , D. C. Kelly , C. M. Curtin , and F. J. O’Brien , ‘ Mechanosignalling in cartilage: an emerging target for the treatment of osteoarthritis’ , Nat Rev Rheumatol , vol. 18 , no. 2 , pp. 67 – 84 , Feb . 2022 , doi: 10.1038/s41584-021-00724-w . OpenUrl CrossRef [17]. N. Wang et al. , ‘ Mechanotransduction pathways in articular chondrocytes and the emerging role of estrogen receptor-α’ , Bone Res , vol. 11 , no. 1 , pp. 1 – 19 , Mar . 2023 , doi: 10.1038/s41413-023-00248-x . OpenUrl CrossRef [18]. H. Du et al., ‘ Tuning immunity through tissue mechanotransduction’ , Nat Rev Immunol , vol. 23 , no. 3 , pp. 174 – 188 , Mar . 2023 , doi: 10.1038/s41577-022-00761-w . OpenUrl CrossRef [19]. D. G. S. Wilson , A. Tinker , and T. Iskratsch , ‘ The role of the dystrophin glycoprotein complex in muscle cell mechanotransduction’ , Commun Biol , vol. 5 , no. 1 , pp. 1 – 18 , Sep . 2022 , doi: 10.1038/s42003-022-03980-y . OpenUrl CrossRef [20]. E. Gracey et al. , ‘ Tendon and ligament mechanical loading in the pathogenesis of inf lammatory arthritis’ , Nat Rev Rheumatol , vol. 16 , no. 4 , pp. 193 – 207 , Apr . 2020 , doi: 10.1038/s41584-019-0364-x . OpenUrl CrossRef [21]. L. Qin , W. Liu , H. Cao , and G. Xiao , ‘ Molecular mechanosensors in osteocytes’ , Bone Res , vol. 8 , no. 1 , pp. 1 – 24 , Jun . 2020 , doi: 10.1038/s41413-020-0099-y . OpenUrl CrossRef [22]. ↵ C. Tomkiewicz et al. , ‘ The aryl hydrocarbon receptor regulates focal adhesion sites through a non-genomic FAK/Src pathway’ , Oncogene , vol. 32 , no. 14 , pp. 1811 – 1820 , Apr . 2013 , doi: 10.1038/onc.2012.197 . OpenUrl CrossRef PubMed Web of Science [23]. ↵ L. Larigot , L. Juricek , J. Dairou , and X. Coumoul , ‘ AhR signaling pathways and regulatory functions’ , Biochim Open , vol. 7 , pp. 1 – 9 , Dec . 2018 , doi: 10.1016/j.biopen.2018.05.001 . OpenUrl CrossRef PubMed [24]. J. Rey-Barroso et al. , ‘ The Dioxin receptor modulates Caveolin-1 mobilization during directional migration: role of cholesterol’ , Cell Communication and Signaling , vol. 12 , no. 1 , p. 57 , Sep . 2014 , doi: 10.1186/s12964-014-0057-7 . OpenUrl CrossRef PubMed [25]. J. Vondráček and M. Machala , ‘ Environmental Ligands of the Aryl Hydrocarbon Receptor and Their Effects in Models of Adult Liver Progenitor Cells’ , Stem Cells Int , vol. 2016 , p. 4326194 , 2016 , doi: 10.1155/2016/4326194 . OpenUrl CrossRef [26]. L. Burton et al. , ‘ Hydrostatic pressure regulates CYP1A2 expression in human hepatocytes via a mechanosensitive aryl hydrocarbon receptor-dependent pathway’ , American Journal of PhysiologyCell Physiology , vol. 318 , no. 5 , pp. C889 – C902 , May 2020 , doi: 10.1152/ajpcell.00472.2019 . OpenUrl CrossRef [27]. ↵ ‘Reactome: a knowledge base of biologic pathways and processes - PMC’ . Accessed: May 27 , 2024 . [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1868929/ [28]. ↵ M. G. Maturo , M. Soligo , G. Gibson , L. Manni , and C. Nardini , ‘ The greater inf lammatory pathway - high clinical potential by innovative predictive, preventive, and personalized medical approach’ , EPMA J , vol. 11 , no. 1 , pp. 1 – 16 , Mar . 2020 , doi: 10.1007/s13167-019-00195-w . OpenUrl CrossRef [29]. ↵ A. Subramanian et al. , ‘ Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles’ , in Proc Natl Acad Sci U S A , vol. 102, United States , 2005 , pp. 15545 – 50 . OpenUrl [30]. ↵ G. Korotkevich , V. Sukhov , and A. Sergushichev , ‘ Fast gene set enrichment analysis’ . bioRxiv , p. 060012 , Oct . 22, 2019 . doi: 10.1101/060012 . OpenUrl Abstract / FREE Full Text [31]. ↵ A. Liberzon , C. Birger , H. Thorvaldsdóttir , M. Ghandi , J. P. Mesirov , and P. Tamayo , ‘ The Molecular Signatures Database (MSigDB) hallmark gene set collection’ , Cell Syst , vol. 1 , no. 6 , pp. 417 – 425 , Dec . 2015 , doi: 10.1016/j.cels.2015.12.004 . OpenUrl CrossRef PubMed [32]. ↵ R. Kalluri and R. A. Weinberg , ‘ The basics of epithelial-mesenchymal transition’ , The Journal of clinical investigation , vol. 119 , no. 6 , pp. 1420 – 8 , Jun . 2009 , doi: 10.1172/jci39104 . OpenUrl CrossRef PubMed Web of Science [33]. ↵ P. Tieri , A. Termanini , E. Bellavista , S. Salvioli , M. Capri , and C. Franceschi , ‘ Charting the NF - kappaB pathway interactome map’ , in PLoS One , vol. 7, United States , 2012 , p. e32678 . OpenUrl [34]. ↵ K. S. Campbell and M. Colonna , ‘ DAP12: a key accessory protein for relaying signals by natural killer cell receptors’ , Int J Biochem Cell Biol , vol. 31 , no. 6 , pp. 631 – 636 , Jun . 1999 , doi: 10.1016/s1357-2725(99)00022-9 . OpenUrl CrossRef PubMed Web of Science [35]. ↵ L. Sotodosos-Alonso , M. Pulgarín-Alfaro , and M. A. del Pozo , ‘ Caveolae Mechanotransduction at the Interface between Cytoskeleton and Extracellular Matrix’ , Cells , vol. 12 , no. 6 , Art. no. 6, Jan . 2023 , doi: 10.3390/cells12060942 . OpenUrl CrossRef [36]. ↵ K. Ohashi , S. Fujiwara , and K. Mizuno , ‘ Roles of the cytoskeleton, cell adhesion and rho signalling in mechanosensing and mechanotransduction’ , The Journal of Biochemistry , vol. 161 , no. 3 , pp. 245 – 254 , Mar . 2017 , doi: 10.1093/jb/mvw082 . OpenUrl CrossRef PubMed [37]. ↵ P. M. Henson and D. L. Bratton , ‘ Allergy: Airway Epithelial Rac1 Suppresses Allergic Inf lammation’ , Current Biology , vol. 23 , no. 3 , pp. R104 – R106 , Feb . 2013 , doi: 10.1016/j.cub.2012.12.008 . OpenUrl CrossRef [38]. ↵ H. Marei and A. Malliri , ‘ Rac1 in human diseases: The therapeutic potential of targeting Rac1 signaling regulatory mechanisms’ , Small GTPases , vol. 8 , no. 3 , p. 139 , 2017 , doi: 10.1080/21541248.2016.1211398 . OpenUrl CrossRef PubMed [39]. ↵ O. Gorobets , S. Gorobets , T. Polyakova , and V. Zablotskii , ‘ Modulation of calcium signaling and metabolic pathways in endothelial cells with magnetic f ields’ , Nanoscale Adv ., vol. 6 , no. 4 , pp. 1163 – 1182 , Feb . 2024 , doi: 10.1039/D3NA01065A . OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted June 14, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Mechanotransduction and inflammation: an updated comprehensive representation Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Mechanotransduction and inflammation: an updated comprehensive representation Vennila Suryiagandhi , Ying Ma , Veronica Paparozzi , Tiziana Guarnieri , Biagio di Pietro , Giovanna Maria Dimitri , Paolo Tieri , Claudia Sala , Darong Lai , Christine Nardini bioRxiv 2024.06.12.598454; doi: https://doi.org/10.1101/2024.06.12.598454 Share This Article: Copy Citation Tools Mechanotransduction and inflammation: an updated comprehensive representation Vennila Suryiagandhi , Ying Ma , Veronica Paparozzi , Tiziana Guarnieri , Biagio di Pietro , Giovanna Maria Dimitri , Paolo Tieri , Claudia Sala , Darong Lai , Christine Nardini bioRxiv 2024.06.12.598454; doi: https://doi.org/10.1101/2024.06.12.598454 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (7644) Biochemistry (17725) Bioengineering (13915) Bioinformatics (42031) Biophysics (21486) Cancer Biology (18635) Cell Biology (25548) Clinical Trials (138) Developmental Biology (13397) Ecology (19937) Epidemiology (2067) Evolutionary Biology (24361) Genetics (15619) Genomics (22538) Immunology (17763) Microbiology (40468) Molecular Biology (17206) Neuroscience (88733) Paleontology (667) Pathology (2842) Pharmacology and Toxicology (4834) Physiology (7657) Plant Biology (15172) Scientific Communication and Education (2046) Synthetic Biology (4304) Systems Biology (9831) Zoology (2272)