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Supplementary Methods for miR Extraction, qPCR, and statistical analysis

Supplementary Figure S2A and S2B: miR qPCR Assay Comparison

Background: Blood miRNAs are a new promising area of disease research, but variability in miRNA measurements may limit detection of true-positive findings. Here, we measured sources of miRNA variability and determine whether repeated measures can improve power to detect fold-change differences between comparison groups.Methods: Blood from healthy volunteers (N = 12) was collected at three time points. The miRNAs were extracted by a method predetermined to give the highest miRNA yield. Nine different miRNAs were quantified using different qPCR assays and analyzed using mixed models to identify sources of variability. A larger number of miRNAs from a publicly available blood miRNA microarray dataset with repeated measures were used for a bootstrapping procedure to investigate effects of repeated measures on power to detect fold changes in miRNA expression for a theoretical case–control study.Results: Technical variability in qPCR replicates was identified as a significant source of variability (P < 0.05) for all nine miRNAs tested. Variability was larger in the TaqMan qPCR assays (SD = 0.15–0.61) versus the qScript qPCR assays (SD = 0.08–0.14). Inter- and intraindividual and extraction variability also contributed significantly for two miRNAs. The bootstrapping procedure demonstrated that repeated measures (20%–50% of N) increased detection of a 2-fold change for approximately 10% to 45% more miRNAs.Conclusion: Statistical power to detect small fold changes in blood miRNAs can be improved by accounting for sources of variability using repeated measures and choosing appropriate methods to minimize variability in miRNA quantification.Impact: This study demonstrates the importance of including repeated measures in experimental designs for blood miRNA research.See all the articles in this CEBP Focus section, “Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology.”Cancer Epidemiol Biomarkers Prev; 23(12); 2658–66. ©2014 AACR.

Supplementary Figure S3A-S3C: Cumulative Distributions including 95% Confidence Intervals

Supplementary Results for Figures S1-S4

Supplementary Figure S1A and S1B: miR Extraction Methods Comparison

Supplementary Figure S4A and S4B: Comparison of Designs 0/1A/1B and Designs 0/3A/3B

Supplementary Legends for Figures S1-S4

Background: Blood miRNAs are a new promising area of disease research, but variability in miRNA measurements may limit detection of true-positive findings. Here, we measured sources of miRNA variability and determine whether repeated measures can improve power to detect fold-change differences between comparison groups. Methods: Blood from healthy volunteers (N = 12) was collected at three time points. The miRNAs were extracted by a method predetermined to give the highest miRNA yield. Nine different miRNAs were quantified using different qPCR assays and analyzed using mixed models to identify sources of variability. A larger number of miRNAs from a publicly available blood miRNA microarray dataset with repeated measures were used for a bootstrapping procedure to investigate effects of repeated measures on power to detect fold changes in miRNA expression for a theoretical case–control study. Results: Technical variability in qPCR replicates was identified as a significant source of variability (P < 0.05) for all nine miRNAs tested. Variability was larger in the TaqMan qPCR assays (SD = 0.15–0.61) versus the qScript qPCR assays (SD = 0.08–0.14). Inter- and intraindividual and extraction variability also contributed significantly for two miRNAs. The bootstrapping procedure demonstrated that repeated measures (20%–50% of N) increased detection of a 2-fold change for approximately 10% to 45% more miRNAs. Conclusion: Statistical power to detect small fold changes in blood miRNAs can be improved by accounting for sources of variability using repeated measures and choosing appropriate methods to minimize variability in miRNA quantification. Impact: This study demonstrates the importance of including repeated measures in experimental designs for blood miRNA research. See all the articles in this CEBP Focus section, “Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology.” Cancer Epidemiol Biomarkers Prev; 23(12); 2658–66. ©2014 AACR.

Glufosinate (GLF) at high levels in mammals causes convulsions and amnesia through a mechanism that is not completely understood. The structural similarity of GLF to glutamate (GLU) implicates the glutamatergic system as a target for GLF neurotoxicity. The current work examined in vitro GLF interaction with N-methyl-D-aspartate subtype GLU receptors (NMDARs) and GLT-1 transporters via [(3)H]CGP 39653 binding experiments and [(3)H]GLU uptake assays, respectively. GLF effects on neuronal network activity were assessed using microelectrode array (MEA) recordings in primary cultures of cortical neurons. GLF and its primary metabolite N-acetylglufosinate (NAcGLF) bind to the NMDAR; the IC50 value for GLF was 668 μM and for NAcGLF was about 100 μM. Concentrations of GLF greater than 1000 μM were needed to decrease GLU uptake through GLT-1. In MEA recordings from networks of rat primary cortical neurons, the concentration-responses for NMDA, GLF and NAcGLF on network mean firing rates (MFR) were biphasic, increasing at lower concentrations and decreasing below control levels at higher concentrations. Increases in MFR occurred between 3-10 μM NMDA (290% control, maximum), 100-300 μM NAcGLF (190% control, maximum) and 10-1000 μM GLF (340% control, maximum). The NMDAR antagonist MK801 attenuated both NMDA and GLF increases in MFR. The GLF concentration required to alter GLU transport through GLT-1 is not likely to be attained in vivo, and therefore not relevant to the neurotoxic mode of action. However, toxicokinetic data from reports of intentional human poisonings indicate that GLF concentrations in the CNS after acute exposure could reach levels high enough to lead to effects mediated via NMDARs. Furthermore, the newly characterized action of NAcGLF at the NMDAR suggests that both the parent compound and metabolite could contribute to neurotoxicity via this pathway.