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Evidence of Differentially Expressed microRNAs in SSNHL

2020· article· en· W3004341932 on OpenAlexaff
Desmond A. Nunez

Bibliographic record

VenueThe Hearing Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsGeneBiologyDNAHearing lossRNAmicroRNAGeneticsGene expressionComputational biologyMedicineAudiology

Abstract

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Sensorineural hearing loss (SNHL) is a widely and increasingly prevalent sensory deficit that has been recently suggested to contribute to the onset of dementia in older adults.1 Age-related hearing loss (ARHL), the most common type of SNHL, affects over 90 percent of individuals aged 70 years and older.2 The slow development of ARHL and the presence of inter-current disease in most sufferers make determining the cause difficult.Shutterstock, hearing loss, DNA, RNATable 1: Differentially Expressed miRNAs in SSNHL Patients Compared with Normal-Hearing Controls7. Hearing loss, DNA, RNA.Table 2: Validated Target Genes Enriched Pathways of Differentially Expressed miRNAs7. Hearing loss, DNA, RNA.DNA that conveys a person's genetic code is in the central nucleus of the cell, yet it controls much of what takes places in the surrounding cell body and tissue. The DNA chain is made up of coding segments or genes—the blueprint for building the cell's proteins—separated from one another by non-coding segments. The DNA message is transmitted out of the nucleus by messenger ribonucleic acid (mRNA). The non-coding DNA segments were thought to have no function during translation beyond acting as breaks in the DNA code to prevent important gene messages from getting mixed up when the mRNA is built from the DNA strand's code. It is now clear that these non-coding segments of DNA are the home of microRNA (miRNA) “genes.” miRNAs are short chains of non-coding ribonucleic acids that regulate gene expression by binding to complementary sites on mRNAs. miRNA regulation of inner ear sensory cell aging and death may have an etiological role in ARHL, but there is little evidence of a similar role in sudden sensorineural hearing loss (SSNHL).3 SSNHL, an acute-onset, acquired hearing loss of undetermined etiology,4 provides an opportunity to better investigate the role of miRNA's in SNHL. The annual incidence of SSNHL in recent studies varies between 27 and 160 new cases per 100,000 individuals.5,6 The average age of patients presenting to the author's institution is 55 years old (Lee, et al., unpublished data), and the majority of those affected are otherwise in good health and free of the confounding inter-current diseases commonly present in the ARHL population. The sudden onset and significant nature of the hearing loss in SSNHL increase the likelihood of finding biological disease markers. Hence, our research group investigated serum miRNAs in SSNHL patients and age-matched normal-hearing control subjects in a recently published study.7 STUDY HIGHLIGHTS In our study, adult patients who presented within 28 days of onset with an average of 30 dB or greater SSNHL across three contiguous pure-tone audiometric (PTA) frequencies to a tertiary urban academic hospital in 2017 and 2018 were prospectively recruited with the approval of the University of British Columbia's Clinical Research Ethics Board. A contemporaneous age-matched control sample of subjects with average PTA hearing thresholds of 25 dB or better was recruited from the hospital staff and their contacts. Patients whose SSNHL cause was identified and controls with a personal or family history of hearing loss or features of current or past ear disease were excluded. About 10 milliliters of blood were collected from study participants for RNA isolation. TaqMan Low Density Array (TLDA) real-time PCR array cards were used for miRNA profiling of 754 human miRNAs. Relative miRNA content was calculated using the comparative cycle threshold method (Ct).7 The mean miRNA Ct values in the control and SSNHL groups were analyzed using students’ t-test to identify differentially expressed miRNAs that demonstrated a statistically significant ≥2.0 or ≤0.5 inter-group fold difference. The corresponding target genes of the significantly differentially expressed miRNAs were predicted using miRWalk 2.08at a cutoff p-value of <0.01. The DAVID bioinformatics database version 6.89 at a significant level of p <0.05 and miRTarBase10 were utilized for functional annotation and gene pathway enrichment analysis. A total of 36 SSNHL patients (mean age of 53.0 years) and 12 control subjects (50.9 years) were studied. Eight miRNAs were significantly differentially expressed in SSNHL patients’ sera compared to controls. Furthermore, 21 miRNAs were significantly differentially expressed between untreated SSNHL (n=04, 57.7 years) and treated SSNHL patients (n=32, 52.6 years; see Table 1). Most of these miRNAs are abundantly identified in the nervous system and the putative target mRNAs were primarily enriched in signaling pathways phosphatidyl inositol 3 kinase/protein Kinase B (PI3K/Akt), Ras, and mitogen-activated protein kinase (MAPK) (Table 2). Li, et al., in a smaller study11 of nine SSNHL patients and three controls, identified 24 differentially expressed miRNAs (DEMs) that were different from the ones we identified, but their KEGG pathway enrichment analysis also found the MAPK signaling pathway to be one enriched by the target genes of the identified DEMs. Brain-derived neurotrophic factor (BDNF) binds to tyrosine kinase B TrKB receptors expressed by inner ear spiral ganglion neurons (SGNs). This binding triggers the recruitment of multiple effector and adaptor proteins, including PI3K, which in turn activates protein kinase B Akt and MAPK. The kinases act on cytoplasmic proteins and nuclear transcription to stimulate SGN survival and neurite outgrowth.12 BDNF is a strong target of one of the DEMs our group identified miR-132-3p (Table 1) in keeping with previous evidence that miRNAs regulate BDNF activity in adults.13 SGN survival is strongly dependent upon the neutrophins BDNF, neurotrophin-3 (NT-3), and glial cell line-derived neurotrophic factor (GDNF), but their effects on SGNs differ.12 Increasing evidence shows that SSNHL patients illustrate differential expression of miRNAs compared with normal hearing controls. mRNAs of genes in the PI3K/Akt, Ras, and MAPK signalling pathways are targets of these DEMs, and miRNAs may be involved in the pathogenesis of SSNHL. In summary, SSNHL patients have different amounts of some miRNAs compared with normal-hearing individuals. These miRNAs control some of the most important aspects of cells such as their growth and death.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.277
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
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