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Record W2601751516 · doi:10.1515/cclm-2016-0898

miRNAs, single nucleotide polymorphisms (SNPs) and age-related macular degeneration (AMD)

2017· review· en· W2601751516 on OpenAlexaff
John Paul SanGiovanni, Peter M. SanGiovanni, Przemysław Sapieha, Vincent De Guire

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcGill UniversityUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsSingle-nucleotide polymorphismmicroRNABiologyMacular degenerationGeneticsSNPTranscriptomeGeneMedicineGenotypeGene expression

Abstract

fetched live from OpenAlex

Advanced age-related macular degeneration (AAMD) is a complex sight-threating disease of public health significance. Micro RNAs (miRNAs) have been proposed as biomarkers for AAMD. The presence of certain single nucleotide polymorphisms (SNPs) may influence the explanatory value of these biomarkers. Here we present findings from an integrated approach used to determine whether AAMD-associated SNPs have the capacity to influence miRNA-mRNA pairing and, if so, to what extent such pairing may be manifested in a discrete AAMD transcriptome. Using a panel of 8854 SNPs associated with AAMD at p-values ≤5.0E-7 from a cohort of >30,000 elderly people, we identified SNPs in miRNA target-encoding constituents of: (1) regulator of complement activation (RCA) genes (rs390679, CFHR1, p≤2.14E-214 | rs12140421, CFHR3, p≤4.63E-29); (2) genes of major histocompatibility complex (MHC) loci (rs4151672, CFB, p≤8.91E-41 | rs115404146, HLA-C, p≤6.32E-12 | rs1055821, HLA-B, p≤1.93E-9 | rs1063355, HLA-DQB1, p≤6.82E-14); and (3) genes of the 10q26 AAMD locus (rs1045216, PLEKHA1, p≤4.17E-142 | rs2672603, ARMS2, p≤7.14E-46). We used these findings with existing data on AAMD-related retinal miRNA and transcript profiles for the purpose of making inferences on SNP-mRNA-miRNA-AAMD relationships. Four of 12 miRNAs significantly elevated in AAMD retina (hsa-miR-155-5p, hsa-let-7a-5p, hsa-let-7b-5p hsa-let-7d-5p) also showed strong pairing capacity (TarBase 7.1 context++ score <-0.2, miRanda 3.3 pairing score >150) with miRNA target transcripts encoded by AAMD-associated SNPs resident in HLA-DQB1 (rs1063355, hsa-miR-155-5p) and TGFBR1 (rs868, hsa-let-7). Three of the 12 miRNAs overexpressed in AAMD retina are inducible by NFkB and have high affinity targets in the complement factor H (CFH) mRNA 3' UTR. We used ENSEMBL to identify polymorphic regions in the CFH mRNA 3' UTR with the capacity to disrupt miRNA-mRNA pairing. Two variants (rs766666504 and rs459598) existed in DNA sequence encoding the seed region of hsa-miR-146a-5p in the CFH mRNA 3' UTR - as this miRNA is also elevated in both vitreous and serum of people with AAMD, it shows great value as a biomarker. Our findings suggest that knowledge on the nature of DNA sequence variation may increase the explanatory power of miRNA biomarkers in genetically diverse populations, while yielding information with which to develop: (1) mechanistic tests on processes implicated in AMD pathogenesis; and, (2) site-specific small molecules (synthetic mimetics or anti-miRNAs) with preventive or therapeutic efficacy for AAMD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.371
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations38
Published2017
Admission routes1
Has abstractyes

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