Background: Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: their magnitude is determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when heterogeneous samples are compared. Results: Methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. Herein, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. Conclusions: In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, “Relative to some important activity of the cell, what is changing?”

Cysteine is susceptible to a variety of modifications by reactive oxygen and nitrogen oxide species, including glutathionylation; and when two cysteines are involved, disulfide formation. Glutathione-cysteine adducts may be removed from proteins by glutaredoxin, whereas disulfides may be reduced by thioredoxin. Glutaredoxin is homologous to the disulfide-reducing thioredoxin and shares similar binding modes of the protein substrate. The evolution of these systems is not well characterized. A redox buffer, glutathione is conjugated to reactive cysteine of endogenous proteins, inducing conformational changes in the substrates, and affecting a signaling cascade. If a second cysteine is introduced into the sequence, the potential for disulfide formation exists. In favourable protein contexts, a bistable redox switch may be formed. Because of glutaredoxins similarities to thioredoxin, the mutated protein may be immediately exapted into the thioredoxin-dependent redox cycle upon the addition of the second cysteine. Here we searched for examples of protein substrates where the number of redox-active cysteine residues has changed throughout evolution. We focused on cross-strand disulfides (CSDs), the most common type of forbidden disulfide. We searched for proteins where the CSD is present, absent and also found as a single cysteine in protein orthologs. Three different proteins were selected for detailed study - CD4, ERO1 and AKT. We created phylogenetic trees, examining when the CSD residues were mutated during the protein evolution. We posit that the primordial cysteine is likely to be the active cysteine of the CSD attacked by thioredoxin. Thus a redox-active disulfide may be introduced into a protein structure by stepwise mutation of two residues in the native sequence to Cys. By extension, evolutionary acquisition of structural disulfides in proteins can potentially occur via transition through a redox-active disulfide state.

Over the course of ageing there is a natural and progressive loss of skeletal muscle mass. The onset and progression of age-related muscle wasting is associated with an attenuated activation of Akt-mTOR signalling and muscle protein synthesis in response to anabolic stimuli such as resistance exercise. MicroRNAs (miRNAs) are novel and important post-transcriptional regulators of numerous cellular processes. The role of miRNAs in the regulation of muscle protein synthesis following resistance exercise is poorly understood. This study investigated the changes in skeletal muscle miRNA expression following an acute bout of resistance exercise in young and old subjects with a focus on the miRNA species predicted to target Akt-mTOR signalling. Ten young (24.2±0.9 years) and 10 old (66.6±1.1 years) males completed an acute resistance exercise bout known to maximise muscle protein synthesis, with muscle biopsies collected before and 2 hours after exercise. We screened the expression of 754 miRNAs in the muscle biopsies and found 26 miRNAs to be regulated with age, exercise or a combination of both factors. Nine of these miRNAs are highly predicted to regulate targets within the Akt-mTOR signalling pathway and 5 miRNAs have validated binding sites within the 3′ UTRs of several members of the Akt-mTOR signalling pathway. The miR-99/100 family of miRNAs notably emerged as potentially important regulators of skeletal muscle mass in young and old subjects. This study has identified several miRNAs that were regulated with age or with a single bout of resistance exercise. Some of these miRNAs were predicted to influence Akt-mTOR signalling, and therefore potentially skeletal muscle mass. These miRNAs should be considered as candidate targets for in vivo modulation.

Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary “library size” by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2. To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations. Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities.