Profiling microRNA expression in the developing hippocampus of high responder and low responder rats
Bibliographic record
Abstract
Sprague-Dawlcy rats display variability in their locomotor response lo a novel environment, leading lo classification as High or Low Responders (HR or LR).HRs also display enhanced neuroendocrine stress reactivity compared lo LRs.Previous work demonstrates that Ihe hippocampus, which plays an important role in the hypoihalamicpituitary-adrcnal (HPA) axis stress response, exhibits differential mRNA expression early in postnatal development between selectively bred HRs and LRs.This thesis extends this work by examining expression patterns of microRNA, endogenous ~ 22-nucleotide single-stranded RNA molecules that regulate gene expression by targeting specificmRNAs for translational repression and/or degradation.This thesis develops a custom miRNA microarray platform to test the hypothesis that differences in hippoeampal miRNA expression will be present in a pattern similar lo mRNA expression profiles.These data demonslrale lhai the custom miRNA microarrays detect differences in miRNA expression between postnatal days (PND) 7. 14, and 21.Furthermore, a subset of microRNAs are differentially expressed between HRs and LRs at PND 14. and 21.Reducing Spot "Donuting" 37 2.5b NCode MicroRNA Labeling System Test Chips 39 Evaluation of Hybridization Chambers 39 Evaluation of NCode Control Spike-In Concentration 40 2.6 Printing the Experimental Microarrays 42 2.6a Description and Positioning of miRNA l*robes and Control Spots 42 2.6b Creating the Source Plates 44 2,6c Printing the Microarrays 45 2.7 MicroRNA and total RNA Isolation 45 2.7a MicroRNA Isolation 46 2.7b Total RNA Isolation 47 2.8 Assessment of RNA Quality and Quantity 47 2.9 Pre-soaking and Pre-hybridizingthe Microarrays 48 2.10 Overview of the Labelling Procedure 49 2.10a Polyadenylation of miRNA 49 2.10b Ligation of Alexa Fluor Dye Molecules 49 2.10c Hybridization Procedure 50 2.10*1 Array Wash Procedure 51 2.10c Microarray Scanning 52 2.11 Image and Statistical Analyses 54 2.11 a Overview of Analyses 54 2.1 lb ImaGene Image Analysis 54 2.11c Evaluating Grid-to-Grid Variation 55 2.1 Id Determining Presence/Absence of Signal 55 2.1 le Microarray Data Normalization 56 Within-Array Normalization 56 Between-Array Normalization 2.1 If Principal Components Analysis Identifying Outliers Evaluation Batch Effects v List of Abbreviations and Acronyms 5-IIIAA -5-hydroxyindoleacetic acid 5-IITl A -5-hydroxytryptamine 1A ACTH -adrenocorticotropic hormone AGO -Argonautc ATP -adenosine triphosphate BDNF -brain-derived neurotrophic factor BSA -bovine serum albumin CCR4 -chemokine (C-C motif) receptor 4 CDC42 -cell division cycle 42 CNS -central nervous system COXIV -cytochrome c oxidase CREB -cAMP response element-binding protein CRH -corticoirophin releasing hormone CTDSPI ("I'D (carboxy-terminal domain) small phosphatase 1 CTGF -connective tissue growth factor DCP1 decapping protein 1 1 >CP2 decapping protein 2 DDT dithiothreitol DKCP -dicihylpyrocarbonate DOPAC -3.4-dihyroxy-phenylaeetic acid EDTA elhylcncdiarninclelraacctic acid clE4E -cukaryolic Iranslalion initiation factor 4E cTF6 -cukaryolic translation initiation factor 6 EMRP -fragilc-X mental retardation protein GAL gene array list GIL/, -glucocorticoid-induced leucine zipper GR glucocorticoid receptor HPA -hypothalamic-pituitary-adrenal HR high responder HVA -homovanillic acid LC-NF.locus coeruleus-norepineplirine Limkl -LIM-domain kinase 1 I .R -low responder McCP2 methyl CpG-binding protein 2 MID -Middle (domain) miRNA -microRNA MR -mincralocorticoid receptor mRNA -messenger RNA NE -norepinephrine NOTl -negative regulator of transcription 1 P bodies -processing bodies PAZ -Piwi-Argonaute-Zwille (domain) PCA -principal components analysis PCR -polymerase chain reaction XI V .-•PFC -prefrontal cortex Pilx3 -paired-like homeodomain transcription factor 3 Pmp22 -peripheral myelin protein 22 PMT -photomultiplier tube PND -postnatal day Pre-miRNA -precursor microRNA Pri-miRNA -primary microRNA PVN paraventricular nucleus REST -REl-silencing transcription factor RISC -RNA-induced silencing complex RNAi -RNA interference RQ1 -RNA quality indicator SAM -significance analysis of microarrays SCP1 -sarcoplasmic calcium-binding protein 1 SDS -sodium dodecyl sulphate siRNA -short interfering RNA SSC -saline sodium citrate SSS -sensation seeking scale TBE -tris-borate-EDTA L'TR -untranslated region XRNI -exoribonuc lease enzyme 1 xii X -1
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".