MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription-quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.
Exosomes and other extracellular vesicles (commonly referred to as EVs) have generated a lot of attention for their potential applications in both diagnostics and therapeutics. The contents of these vesicles are the subject of intense research, and the relatively recent discovery of RNA inside EVs has raised interest in the biological function of these RNAs as well as their potential as biomarkers for cancer and other diseases. Traditional ultracentrifugation-based protocols to isolate EVs are labor-intensive and subject to significant variability. Various attempts to develop methods with robust, reproducible performance have not yet been completely successful. Here, we report the development and characterization of a spin column-based method for the isolation of total RNA from EVs in serum and plasma. This method isolates highly pure RNA of equal or higher quantity compared to ultracentrifugation, with high specificity for vesicular over non-vesicular RNA. The spin columns have a capacity to handle up to 4 mL sample volume, enabling detection of low-abundance transcripts in serum and plasma. We conclude that the method is an improvement over traditional methods in providing a faster, more standardized way to achieve reliable high quality RNA preparations from EVs in biofluids such as serum and plasma. The first kit utilizing this new method has recently been made available by Qiagen as “exoRNeasy Serum/Plasma Maxi Kit”.
There is an urgent need for new tools to combat the ongoing tuberculosis (TB) pandemic. Gene expression profiles based on blood signatures have proved useful in identifying genes that enable classification of TB patients, but have thus far been complex. Using real‐time PCR analysis, we evaluated the expression profiles from a large panel of genes in TB patients and healthy individuals in an Indian cohort. Classification models were built and validated for their capacity to discriminate samples from TB patients and controls within this cohort and on external independent gene expression datasets. A combination of only four genes distinguished TB patients from healthy individuals in both cross‐validations and on separate validation datasets with very high accuracy. An external validation on two distinct cohorts using a real‐time PCR setting confirmed the predictive power of this 4‐gene tool reaching sensitivity scores of 88% with a specificity of around 75%. Moreover, this gene signature demonstrated good classification power in HIV + populations and also between TB and several other pulmonary diseases. Here we present proof of concept that our 4‐gene signature and the top classifier genes from our models provide excellent candidates for the development of molecular point‐of‐care TB diagnosis in endemic areas.
Our objective was to identify microRNA (miRNA) biomarkers of drug-induced liver and kidney injury by profiling the circulating miRNome in patients with acetaminophen overdose. Plasma miRNAs were quantified in age- and sex-matched overdose patients with (N = 27) and without (N = 27) organ injury (APAP-TOX and APAP-no TOX, respectively). Classifier miRNAs were tested in a separate cohort (N = 81). miRNA specificity was determined in non-acetaminophen liver injury and murine models. Sensitivity was tested by stratification of patients at hospital presentation (N = 67). From 1809 miRNAs, 75 were 3-fold or more increased and 46 were 3-fold or more decreased with APAP-TOX. A 16 miRNA classifier model accurately diagnosed APAP-TOX in the test cohort. In humans, the miRNAs with the largest increase (miR-122-5p, miR-885-5p, miR-151a-3p) and the highest rank in the classifier model (miR-382-5p) accurately reported non-acetaminophen liver injury and were unaffected by kidney injury. miR-122-5p was more sensitive than ALT for reporting liver injury at hospital presentation, especially combined with miR-483-3p. A miRNA panel was associated with human kidney dysfunction. In mice, miR-122-5p, miR-151a-3p and miR-382-5p specifically reported APAP toxicity - being unaffected by drug-induced kidney injury. Profiling of acetaminophen toxicity identified multiple miRNAs that report acute liver injury and potential biomarkers of drug-induced kidney injury.
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