microRNA annotation
Since January 2015 we have been using the Gene Ontology (GO) to describe the biological roles of microRNAs (miRNAs). To date we have provided all manual human RNA GO annotations.

The RNA annotations are available in QuickGO, miRBase, NCBIGene, RNAcentral and AmiGO2, or in the regular GOA release files. In addition, our miRNA:target mRNA binding annotations are accessible for inclusion in network analyses from the PSICQUIC web service, in the EBI-GOA-miRNA file. Information about the data included in the PSICQUIC web service is also available.
Example: View the annotations associated with hsa-miR-133a-3p
miRNA curation guidelines
As part of this project we have drawn up miRNA curation guidelines (Huntley et al., 2016) in consultation with the Gene Ontology (GO) Consortium and experts in miRNA research, which cover how a curator should translate into GO annotations the common experimental assays used to investigate miRNA function, including how to annotate experimentally verified targets of miRNAs as well as the physiological effects that silencing has on the cell or organism. Note that GO Consortium guidelines have changed since the publication of the miRNA curation guidelines (Huntley et al., 2016). For more specific details see the Molecular interaction data set for miRNAs and their targets section below
Any queries about the miRNA project should be directed to r.lovering@ucl.ac.uk.
- 'physical association', meaning the miRNA has been shown experimentally to bind and regulate the mRNA (usually a luciferase assay)
- 'association', meaning the miRNA has been shown experimentally to regulate expression of the mRNA (e.g. by western blot or RT-PCR), but there was no experiment to demonstrate binding
Huntley, et al (2018) provides a description of how the PSI-MITAB format interactions are derived from the GO annotations:
A molecular interaction dataset (“EBI-GOA-miRNA”) was created in PSI-MI format and made available on the PSICQUIC web service, to enable computational access to miRNA interactions with their experimentally validated targets. The source of the information in this data set is GO annotations that we have created containing experimentally verified miRNA:target interaction data. GO annotations used for this purpose conform to the following criteria; the “Database Object ID” field of the GO annotation file must be a miRNA, specified by an RNAcentral ID, AND the “Annotation Extension” field must contain an mRNA target, specified by a UniProt ID (del-Toro et al. 2013; Huntley et al. 2014). For the PSICQUIC specification, those interactions described with the Molecular Function GO term mRNA binding involved in post-transcriptional gene silencing (GO:1903231) are assigned interaction type “physical association,” indicating direct binding of the miRNA to the mRNA target. Interactions described only with one of the following GO Biological Process terms: gene silencing by miRNA (GO:0035195); miRNA mediated inhibition of translation (GO:0035278); mRNA cleavage involved in gene silencing by miRNA (GO:0035279); deadenylation involved in gene silencing by miRNA (GO:0098806), but without the Molecular Function term above are assigned the interaction type “association,” indicating that the evidence demonstrated miRNA regulation of the target only (see “Data set availability” section below for the file format information and access to this data set).
miRNA annotation projects undertaken
Alzheimer's Research UK grant ARUK-NSG2018A-003
The ARUK funded the creation of over 1,000 GO annotations which have facilitated the detailed and high-quality annotation of 160 Alzheimer's-relevant microRNAs. A variety of focused projects were undertaken including the curation of:
- microRNAs that regulate microglial proteins
- The goal of this project was to annotate the microRNAs (miRNAs) that regulated the expression of the proteins with a role in microglial processes. Over 100 human miRNAs were annotated by Rachael Huntley and Barbara Kramarz, including hsa-miR-520b-3p, which we have annotated as regulating the expression of C3 and as positive regulation of complement activation, alternative pathway. In addition, 19 papers describing the experimentally verified mRNA targets of IL6 were curated, creating 90 annotations, and annotations describing 'neuron projection development' and 'gliogenesis' were contributed, yielding 41 annotations from 19 papers and 57 annotations from 13 papers, respectively.
- Alzheimer's Disease microRNA biomarkers
- Swarbrick S, et al., 2019, decribed a panel of 10 microRNAs suggested to be disregulated early in Alzheimer's disease. Barbara Kramarz curated 14 papers describing the microRNA target interactions for these microRNAs, when available, creating 54 annotations for 10 human microRNAs, including the potential biomarkers hsa-miR-146a-5p, hsa-miR-107, hsa-miR-26b-5p and hsa-miR-210-3p, and 9 proteins.
British Heart Foundation (BHF) grant RG/13/5/30112
The BHF funded the creation of over 3,600 GO annotations which have facilitated the detailed and high-quality annotation of almost 400 cardiovascular-relevant microRNAs. A variety of focused projects were undertaken including the curation of:
- BHF-funded research papers
- From 2014 to 2018, Rachael Huntley curated all BHF funded research papers describing the role of microRNAs in cardiovascular-relevant processes.
- Regulation of cardiac physiology
- Rachael Huntley curated 7 microRNAs, as these had been identified as modulating cardiac excitability; literature available for these miRNAs was curated up to March 2016.
- Angiogenesis
- Over a period of 3 months Vanessa Acquaah curated the role of microRNAs in angiogenesis. The two major cell types are involved in this process: endothelial and smooth muscle cells (SMC). During this project Vanessa annotated 114 papers that demonstrate the roles that miRNAs play in regulating endothelial and SMC cell proliferation, migration and endothelial tube formation. These papers were identified by searches in pubmed using the key terms “mirnas in angiogenesis”. GO annotations associated with a selection of these curated papers are available in QuickGO.
- Pulmonary artery remodelling
- Pulmonary hypertension is caused by impaired pulmonary arterial growth and remodelling. Vanessa Acquaah annotated 24 papers that describe the role of miRNAs in vascular smooth muscle cell pulmonary artery remodelling, creating 167 annotations. These papers were identified by searches in pubmed using the key words “miRNAs and pulmonary smooth muscle cells”.
Familial hypercholesterolemia, MSc project
Hao Chen, MSc project 2017. Familial hypercholesterolemia (FH) is a common inherited lipid disorder that can cause cardiovascular diseases (CVD) if untreated. Treatment on FH depend on management of plasma low-density-lipoprotein (LDL) cholesterol. High level of plasma LDL cholesterol can induce atherosclerosis and plaque buildup in the walls of the arteries. There is evidence that suggest miRNAs’ regulation of LDL-C is due to their regulation of FH-associated genes, such as LDLR, PCSK9, APOB, LDLRAP1, ABCA1 and ABCG1. This project created 290 GO annotations, of which 22 were transferred from mouse miRNAs to human miRNAs using the ISS evidence code. The prioritised mRNA targets are listed below:
# | HGNC Symbol | HGNC Name | UniProt ID |
1 | ABCA1 | ATP binding cassette subfamily A member 1 | |
2 | ABCG1 | ATP binding cassette subfamily G member 1 | |
3 | APOB | apolipoprotein B | |
4 | LDLR | low density lipoprotein receptor | |
5 | LDLRAP1 | low density lipoprotein receptor adaptor protein 1 | |
6 | PCSK9 | proprotein convertase subtilisin/kexin type 9 |
Angiogenesis, IBSc project
Marios Makris, IBSc project 2019. Angiogenesis is the formation of blood vessels from pre-existing vasculature, involving endothelial cell migration, proliferation, survival, vascular tube formation and regulation of angiogenic growth factors. A total of 237 GO annotations were created to associate 40 distinct microRNAs to 11 pro-angiogenic growth factors. The downstream angiogenesis-relevant effects of regulating these factors were also captured. The prioritised mRNA targets are listed below:
No. | Approved gene name | Approved Gene Symbol |
1 | Vascular endothelial growth factor A | VEGFA |
2 | Kinase insert domain receptor* | KDR* |
3 | Fibroblast growth factor 2 | FGF2 |
4 | Fibroblast growth factor receptor 1 | FGFR1 |
5 | Fibroblast growth factor receptor 2 | FGFR2 |
Aortic aneurysm, MSc project
Zara Umrao, MSc project 2015. Aortic aneurysm (AA) is a cardiovascular disease characterized by swelling of the aorta, the main vessel that runs from the heart to the abdomen. Recently, research has revealed the role of microRNAs (miRNA) in some diseases, including AA, which may enable a better understanding of the disease. The role of one such family of miRNAs, miRNA29, was investigated by creating annotations from existing literature. Forty papers were curated, from which 497 GO annotations were made. The annotations spanned a range of species, including human, mouse, rat, zebrafish and dog. The role of miRNA29 in regulating essential extracellular matrix proteins, such as elastin, collagen, fibrillin and matrix metalloproteinases was captured. In addition, the GO annotations also described the role of miRNA29s in activities, such as aorta morphogenesis, cell migration, cell proliferation, DNA methylation and epithelium regeneration.
Early heart development, MSc project
Vanessa Acquaah, MSc project 2016. Recent evidence has shown that miRNAs, in particular microRNA-1 (miR-1) are found in abundance within cardiac tissues and may be particularly important in early heart development. A total of 313 annotations were made through the use of experimental evidence from 49 papers using GOC curation guidelines. Annotations covered the role of miR-1 as well as other miRNAs in cardiomyocyte proliferation and cardiomyocyte differentiation. Additionally, development of cardiac neural crest cells through processes such as endothelial to mesenchymal transition (EMT) and differentiation of epithelial cells are processes regulated by miRNAs; these roles have been captured as GO annotations.
Regulation of amyloid-beta 'good' receptors, MSc project
Shirin Saverimuttu, MSc project 2018. Alzheimer’s disease (AD), the most common form of dementia, is characterised by amyloid beta (Aβ) accumulation in extracellular plaques. Moreover, Aβ accumulation has been hypothesised as a primary cause of the disease, with a dysregulation to the proteins involved in Aβ clearance thought to be critical. Interestingly, hundreds of microRNAs have been found to be altered in brain tissue affected by AD and several have been experimentally proven to regulate the expression of Aβ clearance proteins. This MSc project led to a total of 258 GO annotations being created. This allowed for knowledge describing how microRNAs regulate Aβ clearance proteins to be captured, as well as the capturing the downstream effects of these microRNAs. The prioritised mRNA targets are listed below:
# | HGNC Symbol | HGNC Name | UniProt ID |
1 | APOE | Apolipoprotein E | |
2 | CHRNA7* | Cholinergic receptor nicotinic alpha 7 subunit | |
3 | CLU | Clusterin | |
4 | FPR2 | Formyl peptide receptor 2 | |
5 | HSPG2 | Heparan sulfate proteoglycan 2 | |
6 | ITGA2* | Integrin subunit alpha 2 | |
7 | ITGAM* | Integrin subunit alpha M | |
8 | LRP1 | LDL receptor related protein 1 | |
9 | LRP2 | LDL receptor related protein 2 | |
10 | LDLR | Low density lipoprotein receptor | |
11 | MARCO* | Macrophage receptor with collagenous structure | |
12 | MSR1* | Macrophage scavenger receptor 1 | |
13 | PICALM* | Phosphatidylinositol binding clatherin assembly protein | |
14 | PRNP | Prion protein | |
15 | TLR2 | Toll like receptor 2 | |
16 | TLR4 | Toll like receptor 4 |
* there was no experimental evidence to support annotation of microRNAs regulating these proteins in July 2019
Regulation of interleukins
Shirin Saverimuttu is responsible for this project, the goal of which is to annotate microRNAs (miRNAs) that regulate the expression of interleukins with a role in Alzheimer’s-relevant processes. In order to achieve this goal, it was necessary to generating a priority list of interleukins (below) and then to identify their regulatory miRNAs. The miRNAs are identified on a target-by-target basis, firstly using mirTarBase and then with specific searches in PubMed. This project is funded from Functional Gene Annotation group’s discretionary funds. The prioritised mRNA targets are listed below:
# | HGNC Symbol | HGNC Name | UniProt ID |
1 | IL1A | Interleukin 1 alpha | P01583 |
2 | IL1B | Interleukin 1 beta | P01584 |
3 | IL1F10 | Interleukin 1 family member 10 | Q8WWZ1 |
4 | IL2 | Interleukin 2 | P60568 |
5 | IL3 | Interleukin 3 | P08700 |
6 | IL4 | Interleukin 4 | P05112 |
7 | IL5 | Interleukin 5 | P05113 |
8 | IL6 | Interleukin 6 | P05231 |
9 | IL7 | Interleukin 7 | P13232 |
CXCL8 | C-X-C motif chemokine ligand 8 (alias: interleukin 8) | P10145 | |
IL9 | Interleukin 9 | P15248 | |
IL10 | Interleukin 10 | P22301 | |
IL11 | Interleukin 11 | P20809 | |
IL12A | Interleukin 12A | P29459 | |
IL12B | Interleukin 12B | P29460 | |
IL13 | Interleukin 13 | P35225 | |
IL15 | Interleukin 15 | P40933 | |
IL16 | Interleukin 16 | Q14005 | |
IL17A | Interleukin 17A | Q16552 | |
IL17B | Interleukin 17B | Q9UHF5 | |
IL17C | Interleukin 17C | Q9P0M4 | |
IL17D | Interleukin 17D | Q8TAD2 | |
IL17F | Interleukin 17F | Q96PD4 | |
IL18 | Interleukin 18 | Q14116 | |
IL19 | Interleukin 19 | Q9UHD0 | |
IL20 | Interleukin 20 | Q9NYY1 | |
IL21 | Interleukin 21 | Q9HBE4 | |
IL22 | Interleukin 22 | Q9GZX6 | |
IL23A | Interleukin 23 alpha | Q9NPF7 | |
IL24 | Interleukin 24 | Q13007 | |
IL25 | Interleukin 25 | P01589 | |
IL26 | Interleukin 26 | Q9NPH9 | |
IL27 | Interleukin 27 | Q8NEV9 | |
IL31 | Interleukin 31 | Q6EBC2 | |
IL32 | Interleukin 32 | P24001 | |
IL33 | Interleukin 33 | O95760 | |
IL34 | Interleukin 34 | Q6ZMJ4 | |
IL36A | Interleukin 36 alpha | Q9UHA7 | |
IL36B | Interleukin 36 beta | Q9NZH7 | |
IL36G | Interleukin 36 gamma | Q9NZH8 | |
IL37 | Interleukin 37 | Q9NZH6 |
Endothelial connections at the Blood Brain Barrier
Katherine Thurlow, MSc project 2019. Evidence for cerebrovascular mechanisms, particularly increased breakdown and dysfunction of the blood-brain barrier (BBB), as key contributors to Alzheimer’s Disease (AD), the most common form of dementia, has grown in recent years,. The BBB is key in regulating influx and efflux of substances to the brain and as such maintains normal neuronal and cognitive function. Alterations in BBB constituent proteins could alter its ability to selectively transport molecules and result in a change in permeability to potentially toxic substances. The list of priority mRNA targets for this project was identified from Figure 2 of Sweeney et al., 2019. This MSc project led to a total of 253 GO annotations being created. This allowed for knowledge describing the role of microRNAs at the BBB to be captured. The prioritised mRNA targets are listed below:
# | UniProt ID | HGNC Symbol | HGNC Name |
1 | P60709 | ACTB | actin beta |
2 | P63261 | ACTG1 | actin gamma 1 |
3 | P33151 | CDH5 | cadherin 5 |
4 | O95832 | CLDN1 | claudin 1 |
5 | P56749 | CLDN12 | claudin 12 |
6 | O15551 | CLDN3 | claudin 3 |
7 | O00501 | CLDN5 | claudin 5 |
8 | P11532 | DMD | dystrophin |
9 | Q96AP7 | ESAM | endothelial cell adhesion molecule |
10 | Q9Y624 | F11R | F11 receptor |
11 | P17302 | GJA1 | gap junction protein alpha 1 |
12 | O95452 | GJB6 | gap junction protein beta 6 |
13 | P05556 | ITGB1 | Integrin subunit beta 1 |
14 | P57087 | JAM2 | junctional adhesion molecule 2 |
15 | Q9BX67 | JAM3 | junctional adhesion molecule 3 |
16 | P24043 | LAMA2 | Laminin subunit alpha-2 |
17 | P11047 | LAMC1 | Laminin subunit gamma-1 |
18 | Q86X29 | LSR | lipolysis stimulated lipoprotein receptor |
19 | Q16625 | OCLN | occludin |
20 | P16284 | PECAM1 | platelet and endothelial cell adhesion molecule 1 |
21 | Q07157 | TJP1 | tight junction protein 1 |
22 | Q9UDY2 | TJP2 | tight junction protein 2 |
23 | O95049 | TJP3 | tight junction protein 3 |
24 | P18206 | VCL | vinculin |
Regulation of key proteins involved in APP processing and Aβ formation
Sandra De Miranda Pinheiro, MSc project 2019. Accumulation of amyloid-beta (Aβ) and tau proteins in the brain is the hallmark of Alzheimer’s disease and lead to neurodegeneration. An imbalance between the production and the clearance of Aβ from the brain is thought to drive the development of the disease. Aβ derives from a larger protein, the amyloid precursor protein (APP), which is processed by different secretases in a sequential way. In the amyloidogenic pathway, β-secretase cleaves APP at the β-site and cleavage of the remaining APP fragment by γ-secretase results in the production of Aβ peptides. Increased APP expression has been associated with an increased risk of developing Alzheimer’s disease. This MSc project led to a total of 254 GO annotations being created. This allowed for knowledge describing how microRNAs regulate the processing of APP and the formation of Aβ. The prioritised mRNA targets are listed below:
# | HGNC Gene Symbol | HGNC Approved Name | Protein Group |
1 | APP | amyloid beta precursor protein | |
2 | ADAM9 | ADAM metallopeptidase domain 9 | Alpha-secretase |
3 | ADAM10 | ADAM metallopeptidase domain 10 | Alpha-secretase |
4 | ADAM17 | ADAM metallopeptidase domain 17 | Alpha-secretase |
5 | ADAM19 | ADAM metallopeptidase domain 19 | Alpha-secretase |
6 | APH1A | aph-1 homolog A | Gamma-secretase subunit |
7 | APH1B | aph-1 homolog B | Gamma-secretase subunit |
8 | BACE1 | beta-secretase 1 | Beta-Secretase 1 |
9 | NCSTN | nicastrin | Gamma-secretase |
10 | PSEN1 | presenilin 1 | Gamma-secretase |
11 | PSEN2 | presenilin 2 | Gamma-secretase |
12 | PSENEN | presenilin enhancer | Gamma-secretase subunit |
Regulation of Aβ-binding 'bad' receptors
Diana Luna Buitrago, MSc project 2020. Amyloid-beta (Aβ) accumulation within the brain and abnormal processing of Aβ by various Aβ-binding receptors are known to be characteristics of Alzheimer's disease pathology. Through previous studies, Aβ-binding receptors have been identified to be “good” or “bad”. Here, “bad” receptors are those that bind to Aβ to promote downstream neurotoxic processes associated with AD. MicroRNAs may contribute to AD by acting as regulators of Aβ-binding receptor expression, as microRNAs that alter “bad” Aβ-binding receptor levels and may influence the neurotoxicity of Aβ. The table below lists the microRNAs curated to regulate the expression of known “bad” Aβ-binding receptors during this project:
MicroRNAs identified to regulate key proteins of interest | ||||||
AMPA receptor subunits | Ephrin receptors | INSR | ||||
GRIA1* | GRIA2* | GRIA3* | GRIA4* | EPHA4 | EPHB2 | |
hsa-miR-124-3p | hsa-miR-124-3p hsa-miR-181b-5p hsa-miR-181a-5p | hsa-miR-124-3p | hsa-miR-28-5p | hsa-miR-10a-5p hsa-miR-519d-3p hsa-miR-335-5p hsa-miR-145-5p | hsa-miR-185-5p hsa-miR-520d-3p hsa-miR-204-5p | hsa-miR-195-5p hsa-miR-7-5p hsa-miR-1271-5p hsa-miR-15b-5p |
Frizzled receptors | AGER | NGFR | ||||
FZD5 | FZD4 | |||||
hsa-miR-29c-3p hsa-miR-515-5p hsa-miR-224-5p | hsa-miR-493-3p hsa-miR-199a-5p hsa-miR-29c-3p | hsa-miR-185-5p | hsa-miR-127-5p | hsa-miR-98-5p | hsa-miR-296-5p |
Regulation of tau-associated processes
Miao Long, MSc project 2020. Alzheimer's disease (AD) is characterised neuropathologically as the presence of intraneuronal neurofibrillary lesions expressing the Tau protein. One of the characteristic pathologies of sporadic AD patients, is hyperphosphorylation of Tau and neurofibrillary tangles. Remarkably, hundreds of microRNAs have been shown to be changed in AD-affected brain regions, with some of them being experimentally demonstrated to regulate tau-related activities. This project focused on capturing the role of microRNAs in regulation of proteins involved in Tau hyperphosphorylation. The mRNA targets identified in this project are listed below:
# | UniProt ID | HGNC Symbol | HGNC Name |
1 | P35222 | CTNNB1 | catenin beta 1 |
2 | O94907 | DKK1 | dickkopf WNT signaling pathway inhibitor 1 |
3 | P49841 | GSK3B | glycogen synthase kinase 3 beta |
4 | P10636 | MAPT | microtubule associated protein tau |
5 | Q9P0L2 | MARK1 | microtubule affinity regulating kinase 1 |
6 | P27448 | MARK3 | microtubule affinity regulating kinase 3 |
7 | Q96L34 | MARK4 | microtubule affinity regulating kinase 4 |
8 | Q969G9 | NKD1 | NKD inhibitor of WNT signaling pathway 1 |
9 | P67775 | PPP2CA | protein phosphatase 2 catalytic subunit alpha |
10 | P62714 | PPP2CB | protein phosphatase 2 catalytic subunit beta |
11 | Q13362 | PPP2R5C | protein phosphatase 2 regulatory subunit B'gamma |
12 | P10644 | PRKAR1A | protein kinase cAMP-dependent type I regulatory subunit alpha |
13 | Q04206 | RELA | RELA proto-oncogene, NF-kB subunit |
14 | Q13464 | ROCK1 | Rho associated coiled-coil containing protein kinase 1 |
15 | P01375 | TNF | tumor necrosis factor |
16 | Q5TCY1 | TTBK1 | tau tubulin kinase 1 |

The Functional Gene Annotation team is supported by Alzheimer's Research UK grant ARUK-NAS2017A-1 and the National Institute for Health Research University College London Hospitals Biomedical Research Centre.