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Event: 1954

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

Gut microbiota, alteration

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
Gut dysbiosis

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

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

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
ACE2 dysregulation leads to gut dysbiosis KeyEvent Cataia Ives (send email) Under development: Not open for comment. Do not cite Under Development


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
rhesus macaques Macaca mulatta High NCBI
cynomolgus monkeys Macaca fascicularis Moderate NCBI
Mus musculus Mus musculus 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
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
Male High
Female 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

Gut dysbiosis features one or more of the following non-mutually exclusive characteristics 1,2:

  • Bloom of pathobionts (members of the commensal microbiota that have the potential to cause pathology). Such bacteria are typically present at low relative abundances but proliferate when aberrations occur in the intestinal ecosystem

  • Loss of commensals, the reduction or complete loss of normally residing members of the microbiota, which can be the consequence of microbial killing or diminished bacterial proliferation

  • Loss of diversity, characteristic of disease-associated dysbiosis

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). ?

Among the culture independent methods, nucleic acids (amplicon sequencing, WGS, metatranscriptomics), proteins (metaproteomics) and metabolites (metabolomics) based methodologies are used to obtain the taxonomical and/or the functional profiles of the gut microbiome 12.

The comparative analysis between meta-data obtained from the profiling of different groups (e.g. healthy vs diseased subjects) is used to detect dysbiosis. 

Comparative analysis of TAXONOMIC SIGNATURES - Gut microbiome diversity measurements include alpha diversity (among the commonly used estimators there are: Shannon index, Chao1 index, Operational Taxonomic Unit (OTU) richness, Simpson index, Faith’s Phylogenetic diversity, and Abundance-based Coverage Estimators (ACE) index); beta diversity (e.g. UniFrac, Bray-Curtis dissimilarity).

To qualify the term dysbiosis, several indexes have been defined and applied (Table 1, references in the table are detailed in 13) 13. Such indexes, interpreted in the context of the clinical findings, may help to characterize diseases and adverse conditions, predict treatment outcomes, and provide information other than the commonly used alpha and beta diversity assessments.

The Firmicutes/Bacteroidetes (F/B) ratio is widely accepted to have an important influence in maintaining normal intestinal homeostasis. Increased or decreased F/B ratio is used as an indicator of dysbiosis 14. However, other compositional changes at family, genus, or species level, might be more relevant than the Firmicutes/Bacteroidetes ratio.

Comparative analysis of FUNCTIONAL SIGNATURES - It has been noted by several groups that function seems more highly conserved across samples than across taxa, suggesting that function is more resilient across communities than the individual strains that come and go 15. In contrast to the indexes described above, evidence of functional dysbiosis are obtained by comparing e.g. metagenomic functional compositions predicted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database 16, proportions of Clusters of Orthologous Groups (COG) encoding proteins, functional states based on peptides and/or proteins identified by MS 17, metabolomic profiles 18.

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

Homo sapiens

nonhuman primate model (Rhesus macaques and cynomolgus macaques)9


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


  1. Hooks and O’Malley. Dysbiosis and Its Discontents. mBio 8 (2017).

  1. Levy, M., Kolodziejczyk, A., Thaiss, C. et al. Dysbiosis and the immune system. Nat Rev Immunol 17, 219–232 (2017).

  1. Zuo T, Zhang F, Lui GCY, Yeoh YK, Li AYL, Zhan H, et al. Alterations in Gut Microbiota of Patients With COVID-19 During Time of Hospitalization. Gastroenterology. 159:944–55 e8. (2020). 

  2. Gu S, Chen Y, Wu Z, Chen Y, Gao H, Lv L, et al. Alterations of the Gut Microbiota in Patients With Coronavirus Disease 2019 or H1N1 Influenza. Clin Infect Dis. 71:2669–78 (2020). 

  3. Petrillo M, Brogna C, Cristoni S et al. Increase of SARS-CoV-2 RNA load in faecal samples prompts for rethinking of SARS-CoV-2 biology and COVID-19 epidemiology. F1000Research, 10:370 (2021).

  4. Gubernatorova EO, Gorshkova EA, Polinova AI, Drutskaya MS. IL-6: Relevance for immunopathology of SARS-CoV-2. Cytokine Growth Factor Rev. 53:13–24 (2020).

  5. Yeoh YK, Zuo T, Lui GC, Zhang F, Liu Q, Li AY, et al. Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. Gut. 70:698–706 (2021). 

  6. Forslund, S.K., Chakaroun, R., Zimmermann-Kogadeeva, M. et al. Combinatorial, additive and dose-dependent drug–microbiome associations. Nature (2021).

  7. Sokol H, Contreras V, Maisonnasse P,  et al. SARS-CoV-2 infection in nonhuman primates alters the composition and functional activity of the gut microbiota. Gut Microbes.  13(1):1-19 (2021).

  8. Saraswati S and Sitaraman R. Aging and the human gut microbiota—from correlation to causality. Front. Microbiol. 5:764 (2015). 

  9.  O’Toole P W  and Jeffery I B. Gut microbiota and aging. Science 350, 1214-1215 (2015)

  10. Ursell LK, Metcalf JL, Parfrey LW, Knight R. Defining the human microbiome. Nutr Rev. 70 Suppl 1(Suppl 1):S38-S44. (2012).

  11. Wei S, Bahl MI, Baunwall SMD, Hvas CL, Licht TR. Determining Gut Microbial Dysbiosis: a Review of Applied Indexes for Assessment of Intestinal Microbiota Imbalances. Appl Environ Microbiol.  87(11):e00395-21. (2021).

  12. Stojanov, S.; Berlec, A.; Štrukelj, B. The Influence of Probiotics on the Firmicutes/Bacteroidetes Ratio in the Treatment of Obesity and Inflammatory Bowel disease. Microorganisms 8, 1715. (2020). 

  13. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486:207–214. (2012). 

  14. Wang WL, Xu SY, Ren ZG, Tao L, Jiang JW, Zheng SS. Application of metagenomics in the human gut microbiome. World J Gastroenterol 21(3):803-814 (2015).

  15. Sajulga R, Easterly C, Riffle M, Mesuere B, Muth T, Mehta S, et al. Survey of metaproteomics software tools for functional microbiome analysis. PLoS ONE 15(11): e0241503. (2020).

  16. Peter E. Larsen, Yang Dai, Metabolome of human gut microbiome is predictive of host dysbiosis, GigaScience, 4, s13742–015–0084–3. (2015).