MP75-13 IN-VIVO INHIBITION OF MIR-202-5P RESULTS IN SPERMATOGENIC KNOCKDOWN THROUGH TARGETING EPIDERMAL GROWTH FACTOR PATHWAYS & CELL CYCLE REGULATION
Bibliographic record
Abstract
You have accessJournal of UrologyInfertility: Basic Research & Pathophysiology (MP75)1 Apr 2019MP75-13 IN-VIVO INHIBITION OF MIR-202-5P RESULTS IN SPERMATOGENIC KNOCKDOWN THROUGH TARGETING EPIDERMAL GROWTH FACTOR PATHWAYS & CELL CYCLE REGULATION Ryan Flannigan*, Russell Hayden, Anna Mielnik, Alex Bolyakov, Peter Schlegel, and Darius Paduch Ryan Flannigan*Ryan Flannigan* More articles by this author , Russell HaydenRussell Hayden More articles by this author , Anna MielnikAnna Mielnik More articles by this author , Alex BolyakovAlex Bolyakov More articles by this author , Peter SchlegelPeter Schlegel More articles by this author , and Darius PaduchDarius Paduch More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557252.54021.e0AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Our group has previously demonstrated that men with Sertoli Cell Only Syndrome (SCO) demonstrate a 17x downregulation of miR-202-5p compared to testis biopsies from normal controls. We subsequently demonstrated that using an inducible model of SCO in mice does not deplete miR-202-5p levels. In this study, we sought to computationally evaluate mRNA targets for miR-202-5p, test for miR-202-5p to 3UTR interactions, and evaluate the impact of in vivo delivery of a miR-202-5p inhibitor on spermatogenesis. METHODS: Computationally predicted mRNA targets of miR-202-5p were created using RNAseq and miRNAseq biological data from 44 testis biopsies. A custom multi-parametric, multi-dimensional association of vectors was used to capture both linear and non-linear algorithms via neural network machine learning. To validate miR-202-5p interactions with the 3UTR of CHEK1 gene, a luciferase reporter assay was used. These reporter plasmids were transfected and later transduced in Human Embryonal Kidney (HEK293) cells prior to measuring luciferase activity in the presence of miR-202-5p. To test the in-vivo impact, 50nM FITC-labelled miR-202-5p inhibitor was injected into murine efferent ducts along with a scrambled control miRNA on the contralateral side. Testes were subsequently evaluated via histology and immunofluorescence. RESULTS: MiR-202-5p was found to be heavily implicated in epidermal growth factor (EGF) pathway regulation and cell cycle control. The computationally derived results were validated in vitro with using miRNA-3UTR luciferase reporters where miR-202-5p reduced the luciferase activity of cells transduced with CHEK1 3UTRs but not when the 3UTR was designed with a mutated seed region. We further confirmed the critical role miR-202-5p exerts during spermatogenesis by observing a knockdown of spermatogenesis in murine testis tubules in the presence of a miR-202-5p inhibitor, and maintenance of spermatogenesis in the presence of a scrambled control miRNA. CONCLUSIONS: In follow-up to our previous studies investigating the role of miR-202-5p, we demonstrated that miR-202-5p is largely implicated in EGF pathway regulation in Sertoli cells maintaining an end-differentiated state. Furthermore, we demonstrated that miR-202-5p is critical to spermatogenesis as in vivo inhibition results in spermatogenic knockdown and SCO histopathology. Further research is prudent to determine the role of miR-202-5p therapy to potentially restore spermatogenesis in men with non-obstructive azoospermia. Source of Funding: This work was supported by: P50 HD076210, U1 1U01HD074542-01, Frederick J. and Theresa Dow Wallace Fund of the New York Community Trust, the Mr. Robert S. Dow Foundation, Irena and Howard Laks Foundation; Urology Care Foundation Research Scholar Award Program and AUA New York Section E. Darracott Vaughan MD, Research Scholar Award. Vancouver, Canada; New York, NY© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1087-e1087 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ryan Flannigan* More articles by this author Russell Hayden More articles by this author Anna Mielnik More articles by this author Alex Bolyakov More articles by this author Peter Schlegel More articles by this author Darius Paduch More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".