Profiles of Extracellular miRNA in Cerebrospinal Fluid and Serum from Patients with Alzheimer's and Parkinson's Diseases Correlate with Disease Status and Features of Pathology

<div><p>The discovery and reliable detection of markers for neurodegenerative diseases have been complicated by the inaccessibility of the diseased tissue- such as the inability to biopsy or test tissue from the central nervous system directly. RNAs originating from hard to access tissues, such as neurons within the brain and spinal cord, have the potential to get to the periphery where they can be detected non-invasively. The formation and extracellular release of microvesicles and RNA binding proteins have been found to carry RNA from cells of the central nervous system to the periphery and protect the RNA from degradation. Extracellular miRNAs detectable in peripheral circulation can provide information about cellular changes associated with human health and disease. In order to associate miRNA signals present in cell-free peripheral biofluids with neurodegenerative disease status of patients with Alzheimer's and Parkinson's diseases, we assessed the miRNA content in cerebrospinal fluid and serum from postmortem subjects with full neuropathology evaluations. We profiled the miRNA content from 69 patients with Alzheimer's disease, 67 with Parkinson's disease and 78 neurologically normal controls using next generation small RNA sequencing (NGS). We report the average abundance of each detected miRNA in cerebrospinal fluid and in serum and describe 13 novel miRNAs that were identified. We correlated changes in miRNA expression with aspects of disease severity such as Braak stage, dementia status, plaque and tangle densities, and the presence and severity of Lewy body pathology. Many of the differentially expressed miRNAs detected in peripheral cell-free cerebrospinal fluid and serum were previously reported in the literature to be deregulated in brain tissue from patients with neurodegenerative disease. These data indicate that extracellular miRNAs detectable in the cerebrospinal fluid and serum are reflective of cell-based changes in pathology and can be used to assess disease progression and therapeutic efficacy.</p></div

Lewy body progression-associated miRNAs.

<p>Ordinal regression analysis was implemented in order to detect miRNAs with monotonic expression patterns across Lewy body stages. Lewy body stages were defined with the Unified Staging System for Lewy Body Disorders as described by Beach et al <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094839#pone.0094839-Beach1" target="_blank">[46]</a>. Specific CSF Lewy body stage subgroups consisted of: no Lewy bodies (n = 126), Limbic type (n = 30) and Neocortical type (n = 21). Similarly, Lewy body subcategories in the SER were comprised of: no Lewy bodies (n = 113), Limbic type (n = 23) and Neocortical type (n = 20). We report predictor variables with the lowest Akaike Information Criterion (AIC) and that satisfy assumptions of the OLR. p-Value* is unadjusted.</p

Ordinal regression analysis reveals miRNAs with progressive expression trends across increasing amyloid plaque density.

<p>(<b>A</b>) We plotted two miRNAs (miR-195-5p, miR-101-3p) detected in CSF from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094839#pone-0094839-t006" target="_blank">Table? 6</a> that showed consistent expression changes with increased density of plaques. (<b>B</b>) miR-106-5p and miR-30b-5p, detected in SER and selected from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094839#pone-0094839-t006" target="_blank">Table? 6</a>, showed significant fit across increasing plaque density stages.</p

Ordinal regression analysis reveals miRNAs with trends in Lewy body progression.

<p>(<b>A</b>) We plotted two miRNAs (miR-34a-5p and miR-374-5p) detected in CSF from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094839#pone-0094839-t007" target="_blank">Table? 7</a> that showed consistent expression change with progression of Lewy bodies. (<b>B</b>) We plotted two miRNAs (miR-130b-3p and miR-181b-5p) detected in SER from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094839#pone-0094839-t007" target="_blank">Table? 7</a> that showed consistent expression changes with progression of Lewy bodies.</p

miRNAs associated with plaque density score.

<p>Neuropathological examination disclosed total plaque-density score ranging from 1–15 for each subject. Scores were divided into three groups corresponding to low plaque-density score (1–5), moderate plaque-density score (6–10) and high plaque-density score (11–15). Ultimately, plaque density subgroups consisted of stage 1 (n = 58), stage 2 (n = 41) and stage 3 (n = 85) subjects for CSF and stage 1 (n = 55), stage 2 (n = 35) and stage 3 (n = 74) for SER. The ordinal regression method was used to model the relationship between the ordinal outcome variable, plaque density score, and normalized miRNA counts as explanatory variable. Delta AIC quantifies the information loss associated with using each model relative to the best approximating model. We report miRNAs with the lowest AIC value and . p-Value* is unadjusted.</p

Additional file 2: of Evaluation of commercially available small RNASeq library preparation kits using low input RNA

Figure S1. Density plot of read lengths for all three kits and tissues respectively by site. Site2 sequenced to a length of 76 nts, whereas all of Site1 samples were sequenced to <=50 nts. Figure S2. Comparison of percentage of reads assigned to the various RNA biotypes for read length restricted to less than 50 nts versus read length = 76 nts. Site2 sequenced to a length of 76 nts. Figure S3. PCA plot showing that the BiooScientific NEXTFlex samples from Site2 cluster by themselves indicating a batch effect. Also, the figure on the right shows the number of miRNAs detected > 10 counts for the two input amounts 10 ng and 1 μg by Site for the BiooScientific NEXTFlex samples. (PDF 5418 kb

Novel miRNAs in CSF and SER predicted by miRDeep2.

<p>To be listed, the potential miRNA had to be present in at least 30% of either the SER or the CSF samples, and have more than 5 counts on average across all samples. Column one contains the precursor sequence predicted by miRDeep2 for the potential mature miRNA detected. Column two is the percentage of serum samples in which the miRNA was present (total number of serum samples examined: 196). Column three is the percentage of CSF samples in which the miRNA was detected (total number of CSF samples examined: 203). Column four represents the total percentage of samples in which the miRNA was detected.</p

miRNAs associated with neurofibrillary tangle score.

<p>Neuropathological examination disclosed total neurofibrillary tangele scores. We binned the data 0–15, in increasing increments, for each subject. Scores were divided into three groups corresponding to low neurofibrillary tangeles score (0–4), moderate neurofibrillary tangeles score (5–9) and high neurofibrillary tangeles score (10–15). Ultimately, neurofibrillary tangle subgroups consisted of stage 1 (n = 73), stage 2 (n = 58) and stage 3 (n = 53) subjects for CSF and stage 1 (n = 71), stage 2 (n = 49) and stage 3 (n = 44) for SER. Ordinal logistic regression analysis (OLR) was implemented in order to fit miRNA expression data across the three ordered groups. Delta AIC quantifies the information loss associated with using each model relative to the best approximating model. We report predictor variables with the lowest Akaike Information Criterion (AIC) and that satisfy assumptions of the OLR. p-Value* is unadjusted.</p

Additional file 1: of Evaluation of commercially available small RNASeq library preparation kits using low input RNA

Table S1. Percentage of input reads aligned to the human transcriptome, human rRNA, UniVec contaminant sequences and discarded because they are too short (< 15 nts) and unmapped to the human transcriptome. Table S2. Median (Inter-quartile range) of percentage of input reads aligned to the human transcriptome, human rRNA, UniVec contaminant sequences. Table S3. Percentage of reads aligned to the human transcriptome to each RNA biotype for all samples. Table S4. Median (Inter-quartile range) of percentage of input reads aligned to different RNA biotypes between the three sequencing kits. Table S5. Median (IQR) of percentage of input reads aligned and comparison of input amount of RNA. Table S6. Median (IQR) of percentage of input reads aligned and comparison between the two sites for the two input amounts of RNA. Table S7. Median (IQR) of number of miRNAs greater than 10 counts detected in at least 25% of the samples between the two sites for the two input amounts of RNA. Table S8. Pearson’s and Spearman’s correlation coefficient by tissue, kit and input amount. Table S9. Kit specific miRNAs found in each tissue for each kit. The top 5 miRNAs for each tissue that have expression greater than 10 RPM in one kit, but less than 5 RPM in the other two are presented for each tissue and kit. Table S10. miRNAs included on the custom made FirePlex Panel. The columns denote the number of samples that had above detection-limit expression in each tissue. Table S11. Database of RNA biotypes used. (XLSX 89 kb

miRNAs significantly different in SER samples from PD vs. PDD and Control vs. AD.

<p>Sample size for serum consisted of PD (n = 322), PDD (n = 188), AD (n = 53) and Control (n = 62) subjects. Results were filtered at corrected p-value <0.05. The logarithmic base 2 fold change (FC) is relative to the first listed group for each comparison. P-Values are adjusted for multiple corrections.</p