LaRocca, Thomas J.

Maximal aerobic exercise capacity [maximal oxygen consumption (V̇o 2max )] is one of the strongest predictors of morbidity and mortality. Aerobic exercise training can increase V̇o 2max , but inter-individual variability is marked and unexplained physiologically. The mechanisms underlying this variability have major clinical implications for extending human healthspan. Here, we report a novel transcriptome signature related to ΔV̇o 2max with exercise training detected in whole blood RNA. We used RNA-Seq to characterize transcriptomic signatures of ΔV̇o 2max in healthy women who completed a 16-wk randomized controlled trial comparing supervised, higher versus lower aerobic exercise training volume and intensity (4 training groups, fully crossed). We found significant baseline gene expression differences in subjects who responded to aerobic exercise training with robust versus little/no ΔV̇o 2max , and differentially expressed genes/transcripts were mostly related to inflammatory signaling and mitochondrial function/protein translation. Baseline gene expression signatures associated with robust versus little/no ΔV̇o 2max were also modulated by exercise training in a dose-dependent manner, and they predicted ΔV̇o 2max in this and a separate dataset. Collectively, our data demonstrate the potential utility of using whole blood transcriptomics to study the biology of inter-individual variability in responsiveness to the same exercise training stimulus.

Abstract Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.