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Event: 1910

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

Mucus Viscosity, Increased

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
Mucus Viscosity, Increased

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
Level of Biological Organization

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
Organ term

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
glycosylation mucin-5AC increased

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


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
Homo sapiens Homo sapiens High NCBI
Mus musculus Mus musculus Moderate NCBI
Canis lupus familiaris Canis lupus familiaris Moderate NCBI
Equus caballus Equus caballus Moderate NCBI
Lithobates catesbeianus Rana catesbeiana Moderate 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
All life stages High

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help
Term Evidence
Mixed 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

Various mucosal surfaces, such as the luminal sides of the urogenital, gastrointestinal, reproductive and respiratory tracts, are lined by mucus, a complex biopolymer that forms a barrier to environmental insults and maintains lubrication (Lai Samuel K. et al., 2009). Chemically, mucus consists of large glycoproteins called mucins (MUCs). Both secreted, gel-forming mucins (e.g. MUC2, MUC5AC, MUC5B and MUC19) and membrane-bound mucins (e.g. MUC1, MUC4, MUC13, MUC16, MUC20, MUC21 and MUC22) are found in the lungs (Atanasova and Reznikov, 2019). Mucins are very heterogeneous, with protein backbones and carbohydrates making up approx. 20% and 80% of their molecular weight, respectively. Cysteine residues in the carboxy and amino terminals of mucin backbones facilitate end-to-end disulfide bonding, resulting in dimerization and multimerization (Ma et al., 2018; Rose and Voynow, 2006). This results in a complex hydrated porous molecular network or gel aggregates that, together with secreted host defense proteins, DNA, lipids, cellular debris and immune cells, make up airway mucus (Atanasova and Reznikov, 2019; Thornton and Sheehan, 2004). 

According to Girod et al., “Mucus is a highly non-Newtonian viscoelastic material. Under a discontinuous stress, induced by ciliary motion during active stroke or by cough, the mucus starts to instantaneously deform and, once the stress is removed (as during the recovery period of beating or after cessation of coughing), the mucus relaxes…” (Girod et al., 1992). Mucin content in mucus typically accounts for 2–5% of mucus, with MUC5AC and MUC5AB being the most abundant mucins in airway mucus, whereas water accounts for between 90–98% of mucus mass. Increased mucin production, differential mucin glycosylation or a change in the proportions of the various mucins as is seen in many pulmonary diseases (e.g. cystic fibrosis, asthma, COPD) can therefore increase mucus viscosity. Water availability in and ionic composition of the immediate environment also influence the physical properties of mucus (Hill et al., 2018; Thornton et al., 2008). For example, there is a 5- to 10-fold greater mucin-to-water ratio in patients with cystic fibrosis than in healthy subjects. This results from the CFTR defect-induced inadequate airway hydration and imbalances in ASL ion concentrations that lead to increased mucus viscosity, causes mucus impaction to the consistency of rubber and hence hampers effective mucociliary transport (Fahy and Dickey, 2010; Gheber et al., 1998; Lai et al., 2009).

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). ?

There is no standardized method to determine increased mucus viscosity. In their recent review “Strategies for measuring airway mucus and mucins”, Atanasova and Reznikov as well as Chen and colleagues describe the most widely applied methods for collection and analysis of mucus as well as the associated challenges (Atanasova and Reznikov, 2019; Chen et al., 2019).  Because both physical and chemical properties of mucus are dependent on its composition, many studies examine mucin content of or the contributions of the various mucin proteins to a given mucus sample, by using for example chromatography (historically) and, more recently, mass spectrometry (Atanasova and Reznikov, 2019). The latter not only permits the distinction between the different mucin proteins and their abundances but also facilitates the analysis of glycosylation patterns (Jensen et al., 2010; Mulagapati et al., 2017). This may provide useful insights into mucus viscoelastic properties, because mucin backbone O-glycosylation was linked to higher molecular rigidity, extended conformation and increased hydration (Gum, 1992; Verdugo, 2012). Simpler methods that do not require mucus or sputum collection include imaging after staining of histological specimens with special stains, such as Alcian Blue and Periodic Acid–Schiff, lectins and mucin antibodies and subsequent quantitative image analysis (Atanasova and Reznikov, 2019). This approach can be applied to both in vitro systems (e.g. 3D organotypic airway cultures) and ex vivo tissues.  In addition to focusing on mucins, direct rheometry–still considered the gold standard to determine viscosity and elasticity–is performed to characterize the physical properties of mucus (Atanasova and Reznikov, 2019). There are dynamic and non-dynamic techniques that can be used and, independent of the method chosen, two parameters are normally examined: (i) viscosity or loss modulus (G″), which is the extent to which the gel resists the tendency to flow, and (ii) elasticity or storage modulus (G′), which measures the tendency for the gel to recover its original shape following stress-induced deformation (Girod et al., 1992; Lai et al., 2009). The most common rheometers are the rotational rheometers, which measure the macrorheological properties of a sample. In a rotational rheometer, a continuous shear motion is applied to the material of interest by the relative rotative motion of two surfaces (; accessed 4 June 2021). However, other types of rheometers, such as a cone-and-plate rheometer or a capillary viscometer can also be used (Chen et al., 2019; Lai et al., 2014). All these techniques draw on the behavior of mucus when subjected to different shear rates, torques, strains or tractions (Atanasova and Reznikov, 2019). The choice will depend on both the availability of the equipment and the sample size. Because rheometry requires rather large sample volumes, more novel techniques utilizing fluorescence imaging have been developed to interrogate mucus viscoelasticity in, for example, in vitro studies. One of these techniques involves the tracking of particle movement, another fluorescence recovery after photobleaching (FRAP) (Lai et al., 2009).  Current knowledge indicates that airway mucus has intermediate viscoelasticity, with a viscosity in the range of 12–15 Pa ∙s, a relaxation time of ca. 40 s and an elastic modulus of 1 Pa (Chen et al., 2019; Lai et al., 2009). In comparison, mucus from cystic fibrosis patients, which exhibits altered mucin glycosylation patterns (Boat et al., 1976) as well as lower water and salt content (Baconnais et al., 1999; Kopito et al., 1973), has a much higher viscosity that can reach up to 110 Pa ∙s at shear rates of 0.1 s−1 (Carlson et al., 2018; Rose and Voynow, 2006; Rubin, 2007).  

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


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 ( (OECD, 2015). More help

Atanasova, K.R. and Reznikov, L.R. (2019). Strategies for measuring airway mucus and mucins. Respir. Res. 20, 261.

Baconnais, S., Tirouvanziam, R., Zahm, J.M., De Bentzmann, S., Péault, B., Balossier, G., et al. (1999). Ion composition and rheology of airway liquid from cystic fibrosis fetal tracheal xenografts. Am. J. Respir. Cell Mol. Biol. 20, 605-611.

Carlson, T.L., Lock, J.Y. and Carrier, R.L. (2018). Engineering the Mucus Barrier. Annu. Rev. Biomed. Eng. 20, 197-220.

Chen, Z., Zhong, M., Luo, Y., Deng, L., Hu, Z. and Song, Y. (2019). Determination of rheology and surface tension of airway surface liquid: a review of clinical relevance and measurement techniques. Respir. Res. 20, 1-14.

Fahy, J.V. and Dickey, B.F. (2010). Airway mucus function and dysfunction. New Engl. J. Med. 363, 2233-2247.

Gheber, L., Korngreen, A. and Priel, Z. (1998). Effect of viscosity on metachrony in mucus propelling cilia. Cell Motil. Cytoskeleton 39, 9-20.

Girod, S., Zahm, J., Plotkowski, C., Beck, G. and Puchelle, E. (1992). Role of the physiochemical properties of mucus in the protection of the respiratory epithelium. Eur. Respir. J. 5, 477-487.

Gum, J. (1992). Mucin genes and the proteins they encode: structure, diversity, and regulation. Am. J. Respir. Cell Mol. Biol. 7, 557-557.

Hill, D.B., Long, R.F., Kissner, W.J., Atieh, E., Garbarine, I.C., Markovetz, M.R., et al. (2018). Pathological mucus and impaired mucus clearance in cystic fibrosis patients result from increased concentration, not altered pH. Eur. Respir. J. 52, 1801297.

Jensen, P.H., Kolarich, D. and Packer, N.H. (2010). Mucin‐type O‐glycosylation–putting the pieces together. FEBS J. 277, 81-94.

Kopito, L.E., Kosasky, H.J. and Shwachman, H. (1973). Water and electrolytes in cervical mucus from patients with cystic fibrosis. Fertil. Steril. 24, 512-516.

Lai, S.K., Wang, Y.-Y., Wirtz, D. and Hanes, J. (2009). Micro- and macrorheology of mucus. Adv. Drug Deliv. Rev. 61, 86-100.

Lai, Y., Dilidaer, D., Chen, B., Xu, G., Shi, J., Lee, R.J., et al. (2014). In vitro studies of a distillate of rectified essential oils on sinonasal components of mucociliary clearance. Am. J. Rhinol. Allergy 28, 244-248.

Ma, J., Rubin, B.K. and Voynow, J.A. (2018). Mucins, mucus, and goblet cells. Chest 154, 169-176.

Mulagapati, S., Koppolu, V. and Raju, T.S. (2017). Decoding of O-linked glycosylation by mass spectrometry. Biochemistry 56, 1218-1226.

Rose, M.C. and Voynow, J.A. (2006). Respiratory Tract Mucin Genes and Mucin Glycoproteins in Health and Disease. Physiol. Rev. 86, 245-278.

Rubin, B.K. (2007). Mucus structure and properties in cystic fibrosis. Paediatr. Respir. Rev. 8, 4-7.

Thornton, D.J. and Sheehan, J.K. (2004). From mucins to mucus: toward a more coherent understanding of this essential barrier. Proc. Am. Thorac. Soc. 1, 54-61.

Thornton, D.J., Rousseau, K. and Mcguckin, M.A. (2008). Structure and function of the polymeric mucins in airways mucus. Ann. Rev. Physiol. 70, 459-486.

Verdugo, P. (2012). Supramolecular dynamics of mucus. Cold Spring Harb. Perspect. Med. 2, a009597.