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Event: 1848

Key Event Title

The KE title should describe a discrete biological change that can be measured. It should generally define the biological object or process being measured and whether it is increased, decreased, or otherwise definably altered relative to a control state. For example “enzyme activity, decreased”, “hormone concentration, increased”, or “growth rate, decreased”, where the specific enzyme or hormone being measured is defined. More help

Induced Toll Like Receptor (TLR) Dysregulation

Short name
The KE short name should be a reasonable abbreviation of the KE title and is used in labelling this object throughout the AOP-Wiki. The short name should be less than 80 characters in length. More help
TLR Activation/Dysregulation

Biological Context

Structured terms, selected from a drop-down menu, are used to identify the level of biological organization for each KE. Note, KEs should be defined within a particular level of biological organization. Only KERs should be used to transition from one level of organization to another. Selection of the level of biological organization defines which structured terms will be available to select when defining the Event Components (below). More help

Cell term

Further information on Event Components and Biological Context may be viewed on the attached pdf.The biological context describes the location/biological environment in which the event takes place.  For molecular/cellular events this would include the cellular context (if known), organ context, and species/life stage/sex for which the event is relevant. For tissue/organ events cellular context is not applicable.  For individual/population events, the organ context is not applicable. More help

Organ term

Further information on Event Components and Biological Context may be viewed on the attached pdf.The biological context describes the location/biological environment in which the event takes place.  For molecular/cellular events this would include the cellular context (if known), organ context, and species/life stage/sex for which the event is relevant. For tissue/organ events cellular context is not applicable.  For individual/population events, the organ context is not applicable. More help

Key Event Components

Further information on Event Components and Biological Context may be viewed on the attached pdf.Because one of the aims of the AOP-KB is to facilitate de facto construction of AOP networks through the use of shared KE and KER elements, authors are also asked to define their KEs using a set of structured ontology terms (Event Components). In the absence of structured terms, the same KE can readily be defined using a number of synonymous titles (read by a computer as character strings). In order to make these synonymous KEs more machine-readable, KEs should also be defined by one or more “event components” consisting of a biological process, object, and action with each term originating from one of 22 biological ontologies (Ives, et al., 2017; See List). Biological process describes dynamics of the underlying biological system (e.g., receptor signalling). The biological object is the subject of the perturbation (e.g., a specific biological receptor that is activated or inhibited). Action represents the direction of perturbation of this system (generally increased or decreased; e.g., ‘decreased’ in the case of a receptor that is inhibited to indicate a decrease in the signalling by that receptor).Note that when editing Event Components, clicking an existing Event Component from the Suggestions menu will autopopulate these fields, along with their source ID and description. To clear any fields before submitting the event component, use the 'Clear process,' 'Clear object,' or 'Clear action' buttons. If a desired term does not exist, a new term request may be made via Term Requests. Event components may not be edited; to edit an event component, remove the existing event component and create a new one using the terms that you wish to add. More help
Process Object Action

Key Event Overview

AOPs Including This Key Event

All of the AOPs that are linked to this KE will automatically be listed in this subsection. This table can be particularly useful for derivation of AOP networks including the KE. Clicking on the name of the AOP will bring you to the individual page for that AOP. More help
AOP Name Role of event in AOP Point of Contact Author Status OECD Status
TLR9 activation leading to ARDS and MOD KeyEvent Gillina Bezemer (send email) Under development: Not open for comment. Do not cite
Poor TLR function leading to high pathogen load MolecularInitiatingEvent Gillina Bezemer (send email) Under development: Not open for comment. Do not cite
ACE2 binding to viral S protein, Acute respiratory distress KeyEvent Young Jun Kim (send email) Open for comment. Do not cite

Stressors

This is a structured field used to identify specific agents (generally chemicals) that can trigger the KE. Stressors identified in this field will be linked to the KE in a machine-readable manner, such that, for example, a stressor search would identify this as an event the stressor can trigger. NOTE: intermediate or downstream KEs in one AOP may function as MIEs in other AOPs, meaning that stressor information may be added to the KE description, even if it is a downstream KE in the pathway currently under development.Information concerning the stressors that may trigger an MIE can be defined using a combination of structured and unstructured (free-text) fields. For example, structured fields may be used to indicate specific chemicals for which there is evidence of an interaction relevant to this MIE. By linking the KE description to a structured chemical name, it will be increasingly possible to link the MIE to other sources of chemical data and information, enhancing searchability and inter-operability among different data-sources and knowledgebases. The free-text section “Evidence for perturbation of this MIE by stressor” can be used both to identify the supporting evidence for specific stressors triggering the MIE as well as to define broad chemical categories or other properties that classify the stressors able to trigger the MIE for which specific structured terms may not exist. More help

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected from an ontology. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
humans Homo sapiens High NCBI
mice Mus sp. High NCBI
all species all species High NCBI

Life Stages

The structured ontology terms for life-stage are more comprehensive than those for taxa, but may still require further description/development and explanation in the free text section. More help
Life stage Evidence
Birth to < 1 month High
Old Age High
All life stages High

Sex Applicability

No help message More help
Term Evidence
Mixed Moderate
Male High

Key Event Description

A description of the biological state being observed or measured, the biological compartment in which it is measured, and its general role in the biology should be provided. For example, the biological state being measured could be the activity of an enzyme, the expression of a gene or abundance of an mRNA transcript, the concentration of a hormone or protein, neuronal activity, heart rate, etc. The biological compartment may be a particular cell type, tissue, organ, fluid (e.g., plasma, cerebrospinal fluid), etc. The role in the biology could describe the reaction that an enzyme catalyses and the role of that reaction within a given metabolic pathway; the protein that a gene or mRNA transcript codes for and the function of that protein; the function of a hormone in a given target tissue, physiological function of an organ, etc. Careful attention should be taken to avoid reference to other KEs, KERs or AOPs. Only describe this KE as a single isolated measurable event/state. This will ensure that the KE is modular and can be used by other AOPs, thereby facilitating construction of AOP networks. More help

Background

Toll-like receptors (TLRs) are a family of 13 conserved transmembrane receptors that are at the forefront of directing innate and adaptive immune responses against invading bacteria, fungi, viruses and parasites (Akira 2003, Takeda, Akira 2004, Pasare, Medzhitov 2005, Tal, Adini et al. 2020, van der Made, Simons et al. 2020). Upon activation TLRs initiate overlapping and distinct signaling pathways in various cell types such as macrophages (MP), conventinal DC (cDC), plasmacytoid DC (pDC), lamina propria DC (LPDC), and inflammatory monocytes (iMO). Engagement of TLR with specific stressors (e.g. PAMPs and DAMPs) induces conformational changes of TLRs that allow homo- or heterophilic interactions of TLRs and recruitment of adaptor proteins such as MyD88, TIRAP, TRIF, and TRAM to control intracellular signalling pathways leading to the synthesis and secretion of appropriate cytokines and chemokines by cells of the immune system. TLRs have various biological roles both in pathogen combat and tissue homeostasis.

This KE is first developed in context of COVID-19 CIAO project.

The key gatekeepers in detecting and combating viral infections are TLR3, TLR7, TLR8 and TLR9 and these are predominantly localized in intracellular compartments. In the setting of COVID-19, multiple TLRs are likely relevant in viral combat. Literature covering TLR triggering via SARS-CoV-2 derived PAMPS (Pathogen Associated Molecular Patterns) include:

  • TLR7 and TLR8 (+TLR3, TLR4, TLR6)  (Khanmohammadi and Rezaei, 2021)
  • TLR1, TLR4 and TLR6 activated by SARS-CoV-2 spike proteins (Choudhury A et al, 2020)
  • TLR9: Less CpG suppression in coronavirus compared to other viruses, for SARS-CoV-2 in the Envelope (E) open reading frame (E-ORF) and ORF10 (Ng et al., 2004; Digard et al. 2020) and multidisciplinary links described in Bezemer and Garssen, 2021

TLR dysregulation can be multi-fold:

  1. Underperformance of TLR function leading to poor pathogen combat. This is covered in AOP 378
  • COVID-19 patients having poor TLR function (due to polymorphisms) could potentially have less viral clearance capability and more adverse events leading to more severe disease and mortality. This has been shown for TLR7 loss of function polymorphisms (van der Made et al 2020). Knowledge Gap: it is not known if loss of function of other TLRs has a worse outcome in COVID-19 patients.
  1. Overperformance of TLR function contributing to exaggerated immune response/cytokine storm/thrombosis/progression into ARDS and MOD. This is covered in AOP377
  • TLR7 and TLR9 expression, measured by RNAseq gene analysis, is more elevated in black Americans than white Americans, which is proposed to explain in part the racial disparity in Covid-19 mortality rates via TLR mediated DC activation (Tal et al. 2020)
  • genetic mutations leading to TLR9 gain of function in human is associated with immune-mediated disease and with a higher incidence of ICU acquired infection (Chatzietal.,2018;Ng et al.,2010).
  • Higher presence of host derived TLR stressors in vulnerable patients can contribute to TLR overstimulation/dysregulation. (Bezemer and Garssen, 2021)

Different classes of "stressors" act on TLR activation/dysregulation

1.  Pathogen associated molecular patterns (PAMPs). TLRs can sense PAMPS during infection or upon exposure to stressors containing micro-organisms or fragments thereof (e.g. cigarette smoke, bioaerosols, house dust mite)

  • TLR1 is activated by bacterial Lipopeptides
  • TLR2 is activated by bacterial lipoproteins and glycolipids, TLR2 can form conformations with TLR1 and TLR6 to distinguish between diacyl and triacyl lipopeptides.
  • TLR3 is activated by viral double stranded RNA(dsRNA)
  • TLR4 is activated by Bacterial LPS
  • TLR5 is activated by Bacterial flagellig
  • TLR6 is activated by Bacterial lipopeptides and Fungal zymosan
  • TLR7 and 8 recognize viral single stranded RNA(ssRNA) and bacterial RNA.
  • TLR9 recognizes RNA and DNAmotifs that are rich in unmethylated Cytosine-phosphate-Guanine (CpG) sequences. CpG-motifs are higher expressed in the bacterial and viral genome compared to the vertebrate genome (Hemmi et al., 2000).

2. host derived Damage-Associated Molecular Patterns (DAMPS). Note that in the context and nomenclature of AOP these DAMPS cannot be labeld as "stressors" since they are derived from inside and not from outside, however these "pseudostressors" do act on the TLR receptors in similar way as PAMPs

  • TLR2 and TLR4 are activated by heat shock proteins 60 and 70  (HSP60 and HSP70); extracellular matrix components (ECM); oligosaccharides of hyaluronic acid (HA) and heparan sulfate (HS) (Piccinini AM and Midwood KS, 2010).

  • high-mobility group protein B1 (HMGB1) triggers TLR2, TLR4 and TLR9

  • Oxidative injury/Oxidized phospholipids  triggers TLR4 mediated NET formation
  • Human mitochondrial DNA (mtDNA), evolutionary derived from endosymbiont bacteria, contains unmethylated CpG-motifs and triggers inflammatory responses directly via TLR9 during injury and/or infection (Zhang et al., 2010).
  • Altered self-ligands, called carboxy-alkyl-pyrroleprotein adducts (CAPs), that are generated during oxidative stress, are known to aggravate TLR9/MyD88 pathway activation (Zhanget al., 2010;Panigrahi et al., 2013). CAPs have been shown to promote platelet activation, granule secretion, and aggregation in vitro and thrombosis in vivo (Panigrahi et al., 2013).

3. synthetic TLR triggers/blockers (agonists/antagonists) for therapeutic purposes. Examples include CpG-ODNs triggering TLR9 for vaccin adjuvants/cancer treatment/immuno-modulation

Several Modulating factors can contribute to TLR activation/dysregulation

  • Co-infection and Trauma (for instance ventilator induced damage) can induce increased levels of TLR9 stressor, mtDNA, which is known to drive worse outcome at ICU in setting of other disorders.  
  • High levels of Visceral Fat, can increase TLR9 expression levels ánd circulating mtDNA
  • Aging triggers both immunosenescence and inflammaging in part via impaired TLR function versus inappropriate triggering via increases of circulating DAMPS (Shaw et al 2011)
  • Genetic polymorphisms can lead to TLR dysregulation (TLR9 gain of function and TLR7 loss of function with worse outcome at ICU Chatzi et al 2018, van der Made et al 2020, Chen et al 2011, )
  • Circulating DAMPS such as mtDNA levels increase with age which is a familiar trait contributing to chronic inflammation, so called“inflamm-aging”in elderly people (Pinti et al., 2014).
  • Vitamin D inhibits expression levels of TLR9
  • Men, higher testosterone, higher TLR4

How It Is Measured or Detected

One of the primary considerations in evaluating AOPs is the relevance and reliability of the methods with which the KEs can be measured. The aim of this section of the KE description is not to provide detailed protocols, but rather to capture, in a sentence or two, per method, the type(s) of measurements that can be employed to evaluate the KE and the relative level of scientific confidence in those measurements. Methods that can be used to detect or measure the biological state represented in the KE should be briefly described and/or cited. These can range from citation of specific validated test guidelines, citation of specific methods published in the peer reviewed literature, or outlines of a general protocol or approach (e.g., a protein may be measured by ELISA).Key considerations regarding scientific confidence in the measurement approach include whether the assay is fit for purpose, whether it provides a direct or indirect measure of the biological state in question, whether it is repeatable and reproducible, and the extent to which it is accepted in the scientific and/or regulatory community. Information can be obtained from the OECD Test Guidelines website and the EURL ECVAM Database Service on Alternative Methods to Animal Experimentation (DB-ALM). ?

Patient specific Ex vivo analysis

  • Levels of TLR specific stressors (for instance for TLR9, cell free DNA/RNA, mtDNA) are measurable in biological samples (serum, plasma)
  • TLR gain of function and loss of function polymorphisms are measurable
  • TLR expression levels on different cell types and different tissues are measurable by mRNA analysis and by protein analysis
  • TLR function in response to stressors is measurable by analysing components of downstream cascades and read outs of inflammatory mediators (IL6, IL8, IL10, Il17, INF, TNFalpha, etc). This can be done by ex vivo stimulations of cells isolated from patients for instance PBMCs.

In vitro/ in vivo models

  • TLR Reporter assays
  • TLR knock-out mice

Domain of Applicability

This free text section should be used to elaborate on the scientific basis for the indicated domains of applicability and the WoE calls (if provided). While structured terms may be selected to define the taxonomic, life stage and sex applicability (see structured applicability terms, above) of the KE, the structured terms may not adequately reflect or capture the overall biological applicability domain (particularly with regard to taxa). Likewise, the structured terms do not provide an explanation or rationale for the selection. The free-text section on evidence for taxonomic, life stage, and sex applicability can be used to elaborate on why the specific structured terms were selected, and provide supporting references and background information.  More help

Cell applicability: TLRs are broadly expressed on various cell types. Examples include: epithelial cells, macrophages, neutrophils, platelets, dendritic cells, NK cells, Tcells, Bcells, neurons, Adipocytes.

Tissue/organ level : TLRs are broadly expressed in all vital tissues/organs: lung, heart, liver, spleen, kidney, brain, muscle, gut, skin

Taxonomic Applicability: TLRs are well conserved across species but between species variations are reported in terms of sensitivity towards stressors.  For instance certain CpG-ODNs have a stronger TLR9 activating potential in mice than in human and vice versa.

Life Stages: TLRs are expressed in all life stages but age variation of level of TLR activation/dysregulation are reported. In elderly immunoscenescence and inflammation are both linked to TLR dysregulation

Sex Applicability: Male and female subjects both express functionally active TLRs but sex differences have been reported. For instance certain TLR gain and/or loss of function polymorphisms have higher prevalence in men. Example of TLR7 loss of function (van der Made et al 2020) and TLR9 gain of function (Gao et al 2018, Traub et al 2012, Elsherif et al 2019). Higher testosterone in men has also been linked to higher TLR4 expression.

TLR7 is located in a region on the X-chromosome which have a high chance of escaping inactivation leading to higher expression levels in women. Estrogens trigger TLR7, which is higher in women. Exposure of Peripheral blood mononuclear cells (PBMC) to TLR7 ligands will cause a higher production of type I IFN (IFN-a) in female cells than male cells.  (Kovats, 2015;  Takahashi and Iwasaki, 2021; Libert et al.,  2010; Scully et al., 2020)

Evidence for Perturbation by Stressor

Overview for Molecular Initiating Event

When a specific MIE can be defined (i.e., the molecular target and nature of interaction is known), in addition to describing the biological state associated with the MIE, how it can be measured, and its taxonomic, life stage, and sex applicability, it is useful to list stressors known to trigger the MIE and provide evidence supporting that initiation. This will often be a list of prototypical compounds demonstrated to interact with the target molecule in the manner detailed in the MIE description to initiate a given pathway (e.g., 2,3,7,8-TCDD as a prototypical AhR agonist; 17α-ethynyl estradiol as a prototypical ER agonist). Depending on the information available, this could also refer to chemical categories (i.e., groups of chemicals with defined structural features known to trigger the MIE). Known stressors should be included in the MIE description, but it is not expected to include a comprehensive list. Rather initially, stressors identified will be exemplary and the stressor list will be expanded over time. For more information on MIE, please see pages 32-33 in the User Handbook.

References

List of the literature that was cited for this KE description. Ideally, the list of references, should conform, to the extent possible, with the OECD Style Guide (https://www.oecd.org/about/publishing/OECD-Style-Guide-Third-Edition.pdf) (OECD, 2015). More help

AKIRA, S., 2003. Toll-like receptor signaling. Journal of Biological Chemistry, 278(40), pp. 38105-38108.

Gillina F. G. Bezemer, Seil Sagar, Jeroen van Bergenhenegouwen, Niki A. Georgiou, Johan Garssen, Aletta D. Kraneveld and Gert Folkerts
Dual role of TLRs in asthma and COPD. Pharmacological Reviews April 1, 2012, 64 (2) 337-358; DOI: https://doi.org/10.1124/pr.111.004622
 

BEZEMER, G.F.G. and GARSSEN, J., 2021. TLR9 and COVID-19: A Multidisciplinary Theory of a Multifaceted Therapeutic Target. Frontiers in pharmacology, 11, pp. 601685.

KAWAI, T. and AKIRA, S., 2011. Toll-like Receptors and Their Crosstalk with Other Innate Receptors in Infection and Immunity. Immunity, 34(5), pp. 637-650.

PASARE, C. and MEDZHITOV, R., 2005. Toll-like receptors: Linking innate and adaptive immunity. Mechanisms of Lymphocyte Activation and Immune Regulation X: Innate Immunity, 560, pp. 11-18.

Piccinini AM, Midwood KS. DAMPening inflammation by modulating TLR signalling. Mediators Inflamm. 2010;2010:672395. doi:10.1155/2010/672395

Shaw AC, Panda A, Joshi SR, Qian F, Allore HG, Montgomery RR. Dysregulation of human Toll-like receptor function in aging. Ageing Res Rev. 2011;10(3):346-353. doi:10.1016/j.arr.2010.10.007

TAKEDA, K. and AKIRA, S., 2004. TLR signaling pathways. Seminars in immunology, 16(1), pp. 3-9.

TAL, Y., ADINI, A., ERAN, A. and ADINI, I., 2020. Racial disparity in Covid-19 mortality rates - A plausible explanation. Clinical immunology (Orlando, Fla.), 217, pp. 108481.

VAN DER MADE, C.I., SIMONS, A., SCHUURS-HOEIJMAKERS, J., VAN DEN HEUVEL, G., MANTERE, T., KERSTEN, S., VAN DEUREN, R.C., STEEHOUWER, M., VAN REIJMERSDAL, S.V., JAEGER, M., HOFSTE, T., ASTUTI, G., COROMINAS GALBANY, J., VAN DER SCHOOT, V., VAN DER HOEVEN, H., HAGMOLEN OF TEN HAVE, W., KLIJN, E., VAN DEN MEER, C., FIDDELAERS, J., DE MAST, Q., BLEEKER-ROVERS, C.P., JOOSTEN, L.A.B., YNTEMA, H.G., GILISSEN, C., NELEN, M., VAN DER MEER, J.W.M., BRUNNER, H.G., NETEA, M.G., VAN DE VEERDONK, F.L. and HOISCHEN, A., 2020. Presence of Genetic Variants Among Young Men With Severe COVID-19. Jama, .

Kovats, Cell Immunol. 2015 April; 294(2): 63–69;

Takahashi and Iwasaki, Science. 2021 Jan 22;371(6527):347-348

Libert et al., Nat Rev Immunol. 2010 Aug;10(8):594-604

Scully EP, et al. Nat Rev Immunol. 2020. PMID: 32528136