Profound effect of profiling platform and normalization strategy on detection of differentially expressed microRNAs

Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms. We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data. We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments

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RNA quality control in miRNA expression analysis Application Not

Postprandial transfer of colostral extracellular vesicles and their protein and miRNA cargo in neonatal calves

Extracellular vesicles (EVs) such as exosomes are key regulators of intercellular communication that can be found in almost all bio fluids. Although studies in the last decade have made great headway in discerning the role of EVs in many physiological and pathophysiological processes, the bioavailability and impact of dietary EVs and their cargo still remain to be elucidated. Due to its widespread consumption and high content of EV-associated microRNAs and proteins, a major focus in this field has been set on EVs in bovine milk and colostrum. Despite promising in vitro studies in recent years that show high resiliency of milk EVs to degradation and uptake of milk EV cargo in a variety of intestinal and blood cell types, in vivo experiments continue to be inconclusive and sometimes outright contradictive. To resolve this discrepancy, we assessed the potential postprandial transfer of colostral EVs to the circulation of newborn calves by analysing colostrum-specific protein and miRNAs, including specific isoforms (isomiRs) in cells, EV isolations and unfractionated samples from blood and colostrum. Our findings reveal distinct populations of EVs in colostrum and blood from cows that can be clearly separated by density, particle concentration and protein content (BTN1A1, MFGE8). Postprandial blood samples of calves show a time-dependent increase in EVs that share morphological and protein characteristics of colostral EVs. Analysis of miRNA expression profiles by Next-Generation Sequencing gave a different picture however. Although significant postprandial expression changes could only be detected for calf EV samples, expression profiles show very limited overlap with highly expressed miRNAs in colostral EVs or colostrum in general. Taken together our results indicate a selective uptake of membrane-associated protein cargo but not luminal miRNAs from colostral EVs into the circulation of neonatal calves

Quantification noise in single cell experiments

In quantitative single-cell studies, the critical part is the low amount of nucleic acids present and the resulting experimental variations. In addition biological data obtained from heterogeneous tissue are not reflecting the expression behaviour of every single-cell. These variations can be derived from natural biological variance or can be introduced externally. Both have negative effects on the quantification result. The aim of this study is to make quantitative single-cell studies more transparent and reliable in order to fulfil the MIQE guidelines at the single-cell level. The technical variability introduced by RT, pre-amplification, evaporation, biological material and qPCR itself was evaluated by using RNA or DNA standards. Secondly, the biological expression variances of GAPDH, TNFα, IL-1β, TLR4 were measured by mRNA profiling experiment in single lymphocytes. The used quantification setup was sensitive enough to detect single standard copies and transcripts out of one solitary cell. Most variability was introduced by RT, followed by evaporation, and pre-amplification. The qPCR analysis and the biological matrix introduced only minor variability. Both conducted studies impressively demonstrate the heterogeneity of expression patterns in individual cells and showed clearly today's limitation in quantitative single-cell expression analysis

Prostaglandins in Superovulation Induced Bovine Follicles During the Preovulatory Period and Early Corpus Luteum

The aim of this study was to characterize the regulation pattern of prostaglandin family members namely prostaglandin F2alpha (PTGF), prostaglandin E2 (PTGE), their receptors (PTGFR, PTGER2, PTGER4), cyclooxygenase 2 (COX-2), PTGF synthase (PTGFS), and PTGE synthase (PTGES) in the bovine follicles during preovulatory period and early corpus luteum (CL). Ovaries containing preovulatory follicles or CL were collected by transvaginal ovariectomy (n = 5 cows/group), and the follicles were classified: (I) before GnRH treatment; (II) 4 h after GnRH; (III) 10 h after GnRH; (IV) 20 h after GnRH; (V) 25 h after GnRH, and (VI) 60 h after GnRH (early CL). In these samples, the concentrations of progesterone (P4), estradiol (E2), PTGF and PTGE were investigated in the follicular fluid (FF) by validated EIA. Relative mRNA abundance of genes encoding for prostaglandin receptors (PTGFR, PTGER2, PTGER4), COX-2, PTGFS and PTGES were quantified by RT-qPCR. The localization of COX-2 and PTGES were investigated by established immunohistochemistry in fixed follicular and CL tissue samples. The high E2 concentration in the FF of the follicle group before GnRH treatment (495.8 ng/ml) and during luteinizing hormone (LH) surge (4 h after GnRH, 574.36 ng/ml), is followed by a significant (P<0.05) downregulation afterwards with the lowest level during ovulation (25 h after GnRH, 53.11 ng/ml). In contrast the concentration of P4 was very low before LH surge (50.64 mg/ml) followed by a significant upregulation (P < 0.05) during ovulation (537.18 ng/ml). The mRNA expression of COX-2 increased significantely (P < 0.05) 4 h after GnRH and again 20 h after GnRH, followed by a significant decrease (P < 0.05) after ovulation (early CL). The mRNA of PTGFS in follicles before GnRH was high followed by a continuous and significant downregulation (P < 0.05) afterwards. In contrast, PTGES mRNA abundance increased significantely (P < 0.05) in follicles 20 h after GnRH treatment and remained high afterwards. The mRNA abundance of PTGFR, PTGER2, and PTGER4 in follicles before GnRH was high, followed by a continuous and significant down regulation afterwards and significant increase (P < 0.05) only after ovulation (early CL). The low concentration of PTGF (0.04 ng/ml) and PTGE (0.15 ng/ml) in FF before GnRH, increased continuously in follicle groups before ovulation and displayed a further significant and dramatic increase (P < 0.05) around ovulation (101.01 ng/ml, respectively, 484.21 ng/ml). Immunohistochemically, the granulosa cells showed an intensive signal for COX-2 and PTGES in follicles during preovulation and in granulosa-luteal cells of the early CL. In conclusion, our results indicate that the examined bovine prostaglandin family members are involved in the local mechanisms regulating final follicle maturation and ovulation during the folliculo-luteal transition and CL formation

Tumor Necrosis Factor Alpha and Insulin-Like Growth Factor 1 Induced Modifications of the Gene Expression Kinetics of Differentiating Skeletal Muscle Cells

Introduction TNF-alpha levels are increased during muscle wasting and chronic muscle degeneration and regeneration processes, which are characteristic for primary muscle disorders. Pathologically increased TNF-alpha levels have a negative effect on muscle cell differentiation efficiency, while IGF1 can have a positive effect;therefore, we intended to elucidate the impact of TNF-alpha and IGF1 on gene expression during the early stages of skeletal muscle cell differentiation. Methodology/Principal Findings This study presents gene expression data of the murine skeletal muscle cells PMI28 during myogenic differentiation or differentiation with TNF-alpha or IGF1 exposure at 0 h, 4 h, 12 h, 24 h, and 72 h after induction. Our study detected significant coregulation of gene sets involved in myoblast differentiation or in the response to TNF-alpha. Gene expression data revealed a time-and treatment-dependent regulation of signaling pathways, which are prominent in myogenic differentiation. We identified enrichment of pathways, which have not been specifically linked to myoblast differentiation such as doublecortin-like kinase pathway associations as well as enrichment of specific semaphorin isoforms. Moreover to the best of our knowledge, this is the first description of a specific inverse regulation of the following genes in myoblast differentiation and response to TNF-alpha: Aknad1, Cmbl, Sepp1, Ndst4, Tecrl, Unc13c, Spats2l, Lix1, Csdc2, Cpa1, Parm1, Serpinb2, Aspn, Fibin, Slc40a1, Nrk, and Mybpc1. We identified a gene subset (Nfkbia, Nfkb2, Mmp9, Mef2c, Gpx, and Pgam2),which is robustly regulated by TNF-alpha across independent myogenic differentiation studies. Conclusions This is the largest dataset revealing the impact of TNF-alpha or IGF1 treatment on gene expression kinetics of early in vitro skeletal myoblast differentiation. We identified novel mRNAs, which have not yet been associated with skeletal muscle differentiation or response to TNF-alpha. Results of this study may facilitate the understanding of transcriptomic networks underlying inhibited muscle differentiation in inflammatory diseases

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data. Rigorous standardization and extensive validation are required to guarantee reliability, reproducibility and comparability of research findings. Hypotheses based on flawed experimental conditions can be inconsistent and even misleading. Comparable to the well-established MIQE guidelines for qPCR experiments, this work aims at establishing guidelines for experimental design and pre-analytical sample processing, standardization of library preparation and sequencing reactions, as well as facilitating data analysis. We highlight bottlenecks in small RNA-Seq experiments, point out the importance of stringent quality control and validation, and provide a primer for differential expression analysis and biomarker discovery. Following our recommendations will en-courage better sequencing practice, increase experimental transparency and lead to more reproducible small RNA-Seq results. This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions