Response to the letter to the editor by Kristensen MM, Helge JW and Dela F

To the Editor: We thank Kristensen MM et al. for pointing out missing and unclear information in the Materials and Methods and the Results section in our recent publication (Re zen et al. 2014), which we have now provided as part of a corrigendum, together with a Table S5 containing raw Nanostring data and Figure S1 with full Western blot images. In addition, we would like to report here that our own recalculations of FDR made in the process of correcting the paper were higher than reported in the original paper. The recalculated FDR is now 0.288, suggesting a higher number of false positives in our list of significantly changed miRNAs, which in turn also affects the interactive graph representing significant GO molecular function terms shared among predicted target genes of miR-206, miR-23a and let-7b represented in Figure 2 in the original paper. Nevertheless, we would like to point out that we still consider the list of 15 miRNA as good candidates potentially involved in bed rest-induced muscle atrophy. As shown in Table 2 in the original paper, many of the suggested miRNAs have in silico-predicted targets that belong to different metabolic pathways, which in turn were separately shown to be affected by physical inactivity of muscle. Because of the redundancy of the system, where the different predicted targets of each of our suggested miRNA share the same metabolic pathways (e.g. miR23a, 30b, 98, 101, 148b, 206, 145, 199a and let-7 have in silico-predicted targets that are involved in the insulin signalling pathway), it is difficult to determine which of the suggested miRNAs are false positive and which are the true positive. Data from animal studies, muscular disease models, exercise experiments and ageing research by other groups also support, to various extent, the involvement of miR-206, miR-23a, miR23b, miR-30b and miR-98 in muscle atrophy (Drummond et al. 2008, 2009, Granjon et al. 2009, Jeng et al. 2009, McCarthy et al. 2009, Williams et al. 2009, Nakasa et al. 2010, Nielsen et al. 2010, Wada et al. 2011, Liu et al. 2012, Dmitriev et al. 2013). Recently, a study analysing the expression of miRNA in different mouse models of muscle atrophy showed that each model has a different signature of miRNA changes and, more importantly, that there is a temporal change in miRNA expression (Soares et al. 2014). In denervation-induced muscle atrophy, initial global repression of miRNA expression was followed by individual adaptation of miRNA levels to changes in muscle. In regard to the Western blot comment, we recognize that in the original blot of GAPDH, the bands were not clearly visible. A shorter exposure time of this blot had more clearly visible bands, but was not included in the publication due to a film artefact. Therefore, we have selected another blot with more clearly visible bands and recalculated the values using GAPDH as a normalization control. We have prepared a new Figure 3 and also added a Figure S1 showing full Western blots (both figures are part of a corrigendum to Re zen et al. 2014). The results of the Western blot analysis remain significant. In regard to the validation comment, we agree that supplementary analysis using a different method should be performed whenever possible. In our case, as is often the case in studies using human samples, the biopsy amount was small, limiting the amount of total RNA, which excluded the possibility of performing a northern blot analysis, where much greater amounts of starting material are required. Following the letter by Kristensen et al., we have performed qPCR on the remaining five of the six pairs of samples included in the study. Briefly, 37 ng of total RNA per sample was amplified using Qiagen miScript PCR System for cDNA synthesis and qPCR analyses as recommended by the manufacturer. We analysed the expression of miR-206, miR-23a and let-7g. All tested miRNAs had the same sequence as in the Nanostring analyses. In order to be able to compare our results with those of Ringholm et al., as suggested by Kristensen et al., we used endogenous controls RNU6 and RNU48, as well as miR-126, which was expressed at a comparable level to the examined miRNAs and suggested by Normfinder as the most stable miRNA from the Nanostring data (Andersen et al. 2004). Comparison of Nanostring and qPCR data shows that the correlation between the two is highly dependent on the qPCR normalizer (RNU58, RNU6 or miR-126) used (Table 1). Furthermore, the choice of normalizer in case of RNU6 and RNU48 affected the direction of miRNA expression change in individual samples (Table 1). An example is let-7g, where in subject S3, we observe the opposite directions in measured post-/prebed rest ratio between Nanostring data and qPCR. Indeed, there is a negative correlation between Nanostring and qPCR data normalized to RNU48 or RNU6