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Relationship: 1315

Title

The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Increase, Mucin production leads to Chronic, Mucus hypersecretion

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
EGFR Activation Leading to Decreased Lung Function adjacent High Moderate Karsta Luettich (send email) Under development: Not open for comment. Do not cite Under Development

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
mouse Mus musculus Moderate NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help
Sex Evidence
Mixed Moderate

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
Adult Low

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Chronic mucus hypersecretion, i.e., the sustained production of mucus, is a main feature of chronic lung diseases. The presence of goblet cell hyperplasia or goblet cell metaplasia in the lungs of chronic obstructive pulmonary disease, asthma and cystic fibrosis patients has been inferred as cause for sustained mucus production, because the increased number (or increased size) of goblet cells is associated with an increase in the volume of mucus produced (Jackson, 2001; Innes et a. 2006; Rose and Voynow, 2006; Munkholm and Mortensen, 2014). 

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help

Mucus hypersecretion is a feature of animal models of asthma (Shim et al., 2001; Singer et al., 2004; Song et al., 2016) and occurs in mice and rats following inhalation of e.g. acrolein and cigarette smoke (Deshmukh et al., 2008; Yang et al., 2012; Chen et al., 2013; Vlahos and Bozinovski, 2014; Liu et al., 2017). There appears to be no consensus as to the "chronicity" of mucus hypersecretion, and because there are no standardized measures of mucus hypersecretion, experimental evidence is limited. Clinically, (chronic) mucus hypersecretion is defined as coughing and sputum production for >3 months in at least two consecutive years and called "chronic bronchitis" (Vestbo, 2002). Long-term smokers with and without airflow obstruction present with chronic mucus hypersecretion and increased mucin production (O'Donnell et al., 2004; Caramori et al., 2004; Innes et al., 2006; Kim et al., 2008).

Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

Chronic mucus hypersecretion, i.e., the sustained production of mucus, is the key symptom of COPD and asthma, and is also observed in patients with bronchiectasis and cystic fibrosis. To a certain extent, it can also be modeled in animals as has been shown in mouse models of asthma. We therefore consider this KER to be biologically plausible with moderate confidence.

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

Caramori et al. (2009) found no correlation between MUC5AC immunostaining and the presence of chronic bronchitis. Kim et al. (2015) reported higher goblet cell numbers and mucin volume density in healthy smokers than in COPD patients and also no difference in mucin volume density between smokers with and without chronic bronchitis.

In some instances, sputum or phlegm production/output may have been considered quantitative evidence for chronic mucus hypersecretion. However, Danahay and Jackson (2005) noted that "[sputum] represents an indirect measure of the contribution that mucus makes to that part of the airway secretions that is amenable to clearance. It is possible that the bulk of the disease modifying potential of the mucus-hypersecretory phenotype does not directly relate to cleared mucus/sputum..."

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help

There was a marked increase in MUC5AC immunostaining in the bronchial epithelium of smokers compared to nonsmokers, and there was a significant correlation between % MUC5AC-stained epithelial area and the numbers of epithelial cells staining positively for both MUC5AC and PAS (O'Donnell et al., 2004).

In the bronchiolar epithelium, intraluminal AB/PAS staining was significantly more frequent among COPD subjects than smokers or never-smokers (1 ⁄ 6, 2 ⁄ 11 and 7 ⁄ 9 in never-smokers, smokers and COPD subjects). MUC5AC expression was also significantly higher in COPD subjects compared with smokers and never-smokers (score [0 indicating absence of staining, 1 indicating a staining limited to cilia, 2 indicating supranuclear cytoplasmic staining, 3 indicating supranuclear cytoplasmic staining and staining of goblet cells]: 2 (1–2.3) in COPD vs 0 (0–1) in never-smokers and 0.5 (0–1) in smokers) (Caramori et al., 2009).

In a small study of 24 cigarette smokers and 19 non-smoking control subjects, the goblet cell number per surface area of basal lamina in the large airways was 80% higher in smokers (56,232 + 5611 vs 41,996 + 4610), with a 30% higher mean volume of individual goblet cells ( 2,925 + 173 µm3 vs 2,259 + 192 µm3) than in non-smokers. MUC5AC immunostaining in the surface airway epithelium was also 80% higher in smokers than in control subjects (volume of epithelial MUC5AC per surface area of basal lamina: 6.82 + 0.98 µm3/µm2 vs 3.70 + 0.69 µm3/µm2) (Innes et al., 2006).

Time-scale
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help

Unknown

Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help

Unknown

Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Unknown

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

Mucus hypersecretion occurs in mice and rats (Shim et al., 2001; Singer et al., 2004; Song et al., 2016; Deshmukh et al., 2008; Yang et al., 2012; Chen et al., 2013; Vlahos and Bozinovski, 2014; Liu et al., 2017) and in humans (Vestbo, 2002; O'Donnell et al., 2004; Caramori et al., 2004; Innes et al., 2006; Kim et al., 2008).

References

List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Caramori, G., Casolari, P., Di Gregorio, C., Saetta, M., Baraldo, S., Boschetto, P., et al. (2009). MUC5AC expression is increased in bronchial submucosal glands of stable COPD patients. Histopathology 55, 321-331.

Chen, P., Deng, Z., Wang, T., Chen, L., Li, J., Feng, Y., et al. (2013). The potential interaction of MARCKS-related peptide and diltiazem on acrolin-induced airway mucus hypersecretion in rats. Int. Immunopharmacol. 17, 625-632.

Danahay, H., and Jackson, A.D. (2005). Epithelial mucus-hypersecretion and respiratory disease. Curr. Drug Targets Inflamm. Allergy 4, 651-664.

Deshmukh, H.S., Shaver, C., Case, L.M., Dietsch, M., Wesselkamper, S.C., Hardie, W.D., et al. (2008). Acrolein-activated matrix metalloproteinase 9 contributes to persistent mucin production. Am. J. Respir. Cell Mol. Biol. 38, 446-454.

Jackson, A.D. (2001). Airway goblet-cell mucus secretion. Trends Pharmacol. Sci. 22, 39-45.

Kim, V., Kelemen, S.E., Abuel-Haija, M., Gaughan, J.P., Sharafkaneh, A., Evans, C.M., et al. (2008). Small airway mucous metaplasia and inflammation in chronic obstructive pulmonary disease. COPD 5, 329-338.

Kim, V., Oros, M., Durra, H., Kelsen, S., Aksoy, M., Cornwell, W.D., et al. (2015). Chronic Bronchitis and Current Smoking Are Associated with More Goblet Cells in Moderate to Severe COPD and Smokers without Airflow Obstruction. PLoS ONE 10, e0116108. 

Liu, Z., Geng, W., Jiang, C., Zhao, S., Liu, Y., Zhang, Y., et al. (2017). Hydrogen-rich saline inhibits tobacco smoke-induced chronic obstructive pulmonary disease by alleviating airway inflammation and mucus hypersecretion in rats. Exp. Biol. Med. 242, 1534-1541. 

Munkholm, M., and Mortensen, J. (2014). Mucociliary clearance: pathophysiological aspects. Clin. Physiol. Funct. Imaging 34, 171-177.

O’Donnell, R., Richter, A., Ward, J., Angco, G., Mehta, A., Rousseau, K., et al. (2004). Expression of ErbB receptors and mucins in the airways of long term current smokers. Thorax 59, 1032-1040.

Rose, M.C., and Voynow, J.A. (2006). Respiratory tract mucin genes and mucin glycoproteins in health and disease. Physiol. Rev. 86, 245-278. 

Shim, J.J., Dabbagh, K., Ueki, I.F., Dao-Pick, T., Burgel, P.R., Takeyama, K., et al. (2001). IL-13 induces mucin production by stimulating epidermal growth factor receptors and by activating neutrophils. Am. J. Physiol. Lung Cell. Mol. Physiol. 280, L134-140.

Singer, M., Martin, L.D., Vargaftig, B.B., Park, J., Gruber, A.D., Li, Y., et al. (2004). A MARCKS-related peptide blocks mucus hypersecretion in a mouse model of asthma. Nat. Med. 10, 193-196.

Song, L., Tang, H., Liu, D., Song, J., Wu, Y., Qu, S., et al. (2016). The chronic and short-term effects of gefinitib on airway remodeling and inflammation in a mouse model of asthma. Cell. Physiol. Biochem. 38, 194-206.

Vestbo, J. (2002). Epidemiological studies in mucus hypersecretion. Novartis Found. Symp. 248, 3-12; discussion: 12-19, 277-282.

Vlahos, R., and Bozinovski, S. (2014). Recent advances in pre-clinical mouse models of COPD. Clin. Sci. 126, 253-265. 

Yang, T., Luo, F., Shen, Y., An, J., Li, X., Liu, X., et al. (2012). Quercetin attenuates airway inflammation and mucus production induced by cigarette smoke in rats. Int. Immunopharmacol. 13, 73-81.