MicroRNAs in CSF as prodromal biomarkers for Huntington disease in the PREDICT-HD study.

OBJECTIVE: To investigate the feasibility of microRNA (miRNA) levels in CSF as biomarkers for prodromal Huntington disease (HD). METHODS: miRNA levels were measured in CSF from 60 PREDICT-HD study participants using the HTG protocol. Using a CAG-Age Product score, 30 prodromal HD participants were selected based on estimated probability of imminent clinical diagnosis of HD (i.e., low, medium, high; n = 10/group). For comparison, participants already diagnosed (n = 15) and healthy controls (n = 15) were also selected. RESULTS: A total of 2,081 miRNAs were detected and 6 were significantly increased in the prodromal HD gene expansion carriers vs controls at false discovery rate q < 0.05 (miR-520f-3p, miR-135b-3p, miR-4317, miR-3928-5p, miR-8082, miR-140-5p). Evaluating the miRNA levels in each of the HD risk categories, all 6 revealed a pattern of increasing abundance from control to low risk, and from low risk to medium risk, which then leveled off from the medium to high risk and HD diagnosed groups. CONCLUSIONS: This study reports miRNAs as CSF biomarkers of prodromal and diagnosed HD. Importantly, miRNAs were detected in the prodromal HD groups furthest from diagnosis where treatments are likely to be most consequential and meaningful. The identification of potential biomarkers in the disease prodrome may prove useful in evaluating treatments that may postpone disease onset. CLINICALTRIALSGOV IDENTIFIER: NCT00051324.

Novel microRNA discovery using small RNA sequencing in post-mortem human brain.

BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression mainly through translational repression of target mRNA molecules. More than 2700 human miRNAs have been identified and some are known to be associated with disease phenotypes and to display tissue-specific patterns of expression. METHODS: We used high-throughput small RNA sequencing to discover novel miRNAs in 93 human post-mortem prefrontal cortex samples from individuals with Huntington's disease (n = 28) or Parkinson's disease (n = 29) and controls without neurological impairment (n = 36). A custom miRNA identification analysis pipeline was built, which utilizes miRDeep* miRNA identification and result filtering based on false positive rate estimates. RESULTS: Ninety-nine novel miRNA candidates with a false positive rate of less than 5 % were identified. Thirty-four of the candidate miRNAs show sequence similarity with known mature miRNA sequences and may be novel members of known miRNA families, while the remaining 65 may constitute previously undiscovered families of miRNAs. Nineteen of the 99 candidate miRNAs were replicated using independent, publicly-available human brain RNA-sequencing samples, and seven were experimentally validated using qPCR. CONCLUSIONS: We have used small RNA sequencing to identify 99 putative novel miRNAs that are present in human brain samples.

Correction: RNA Sequence Analysis of Human Huntington Disease Brain Reveals an Extensive Increase in Inflammatory and Developmental Gene Expression.

[This corrects the article DOI: 10.1371/journal.pone.0143563.].

RNA Sequence Analysis of Human Huntington Disease Brain Reveals an Extensive Increase in Inflammatory and Developmental Gene Expression.

Huntington's Disease (HD) is a devastating neurodegenerative disorder that is caused by an expanded CAG trinucleotide repeat in the Huntingtin (HTT) gene. Transcriptional dysregulation in the human HD brain has been documented but is incompletely understood. Here we present a genome-wide analysis of mRNA expression in human prefrontal cortex from 20 HD and 49 neuropathologically normal controls using next generation high-throughput sequencing. Surprisingly, 19% (5,480) of the 28,087 confidently detected genes are differentially expressed (FDR<0.05) and are predominantly up-regulated. A novel hypothesis-free geneset enrichment method that dissects large gene lists into functionally and transcriptionally related groups discovers that the differentially expressed genes are enriched for immune response, neuroinflammation, and developmental genes. Markers for all major brain cell types are observed, suggesting that HD invokes a systemic response in the brain area studied. Unexpectedly, the most strongly differentially expressed genes are a homeotic gene set (represented by Hox and other homeobox genes), that are almost exclusively expressed in HD, a profile not widely implicated in HD pathogenesis. The significance of transcriptional changes of developmental processes in the HD brain is poorly understood and warrants further investigation. The role of inflammation and the significance of non-neuronal involvement in HD pathogenesis suggest anti-inflammatory therapeutics may offer important opportunities in treating HD.

Analytical thresholds and sensitivity: establishing RFU thresholds for forensic DNA analysis.

Determining appropriate analytical thresholds (ATs) for forensic DNA analysis is critical to maximize allele detection. In this study, six methods to determine ATs for forensic DNA purposes were examined and compared. Four of the methods rely on analysis of the baseline noise of a number of negatives, while two utilize the relationship between relative fluorescence unit signal and DNA input in the polymerase chain reaction (PCR) derived from a dilution series ranging from 1 to 0.06 ng. Results showed that when a substantial mass of DNA (i.e., >1 ng) was amplified, the baseline noise increased, suggesting the application of an AT derived from negatives should only be applied to samples with low levels of DNA. Further, the number and intensity of these noise peaks increased with increasing injection times, indicating that to maximize the ability to detect alleles, ATs should be validated for each post-PCR procedure employed.

Maximizing allele detection: Effects of analytical threshold and DNA levels on rates of allele and locus drop-out.

Interpretation of DNA evidence depends upon the ability of the analyst to accurately compare the DNA profile obtained from an item of evidence and the DNA profile of a standard. This interpretation becomes progressively more difficult as the number of 'drop-out' and 'drop-in' events increase. Analytical thresholds (AT) are typically selected to ensure the false detection of noise is minimized. However, there exists a tradeoff between the erroneous labeling of noise as alleles and the false non-detection of alleles (i.e. drop-out). In this study, the effect ATs had on both types of error was characterized. Various ATs were tested, where three relied upon the analysis of baseline signals obtained from 31 negative samples. The fourth AT was determined by utilizing the relationship between RFU signal and DNA input. The other ATs were the commonly employed 50, 150 and 200 RFU thresholds. Receiver Operating Characteristic (ROC) plots showed that although high ATs completely negated the false labeling of noise, DNA analyzed with ATs derived using analysis of the baseline signal exhibited the lowest rates of drop-out and the lowest total error rates. In another experiment, the effect small changes in ATs had on drop-out was examined. This study showed that as the AT increased from ∼10 to 60 RFU, the number of heterozygous loci exhibiting the loss of one allele increased. Between ATs of 60 and 150 RFU, the frequency of allelic drop-out remained constant at 0.27 (±0.02) and began to decrease when ATs of 150 RFU or greater were utilized. In contrast, the frequency of heterozygous loci exhibiting the loss of both alleles consistently increased with AT. In summary, for samples amplified with less than 0.5ng of DNA, ATs derived from baseline analysis of negatives were shown to decrease the frequency of drop-out by a factor of 100 without significantly increasing rates of erroneous noise detection.