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. 2022 May;235(1):e13806.
doi: 10.1111/apha.13806. Epub 2022 Mar 7.

Ribosome accumulation during early phase resistance training in humans

Daniel Hammarström  1   2 , Sjur J Øfsteng  1 , Nicolai B Jacobsen  1 , Krister B Flobergseter  1 , Bent R Rønnestad  1 , Stian Ellefsen  1   3
Affiliations

Ribosome accumulation during early phase resistance training in humans

Daniel Hammarström et al. Acta Physiol (Oxf). 2022 May.

Abstract

Aim: To describe ribosome biogenesis during resistance training, its relation to training volume and muscle growth.

Methods: A training group (n = 11) performed 12 sessions (3-4 sessions per week) of unilateral knee extension with constant and variable volume (6 and 3-9 sets per session respectively) allocated to either leg. Ribosome abundance and biogenesis markers were assessed from vastus lateralis biopsies obtained at baseline, 48 hours after sessions 1, 4, 5, 8, 9 and 12, and after eight days of de-training, and from a control group (n = 8). Muscle thickness was measured before and after the intervention.

Results: Training led to muscle growth (3.9% over baseline values, 95% CrI: [0.2, 7.5] vs. control) with concomitant increases in total RNA, ribosomal RNA, upstream binding factor (UBF) and ribosomal protein S6 with no differences between volume conditions. Total RNA increased rapidly in response to the first four sessions (8.6% [5.6, 11.7] per session), followed by a plateau and peak values after session 8 (49.5% [34.5, 66.5] above baseline). Total RNA abundance was associated with UBF protein levels (5.0% [0.2, 10.2] per unit UBF), and the rate of increase in total RNA levels predicted hypertrophy (0.3 mm [0.1, 0.4] per %-point increase in total RNA per session). After de-training, total RNA decreased (-19.3% [-29.0, -8.1]) without muscle mass changes indicating halted biosynthesis of ribosomes.

Conclusion: Ribosomes accumulate in the initial phase of resistance training with abundances sensitive to training cessation and associated with UBF protein levels. The average accumulation rate predicts muscle training-induced hypertrophy.

Keywords: muscle hypertrophy; resistance training; ribosome biogenesis.

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Conflict of interest statement

The authors do not have any conflict of interest to declare.

Figures

FIGURE 1
FIGURE 1
(A) Study design showing muscle biopsy sampling, thickness and strength assessments time points together with number of sets per session (CONST blue bars, VAR red bars). Assessments time points in the negative control group is shown in the lower panel. (B) Observed training loads in response to CONST and VAR volume protocols. Training outcomes are shown as within condition changes and in comparison to the control group (muscle strength, C; muscle thickness D). Intervals in C and D indicate 95% CI.
FIGURE 2
FIGURE 2
Protein (A and B) and mRNA abundances (D and E) of rpS6 and UBF. Non‐transparent gray points and error bars represent statistically robust results (a 95% CI not containing 0). C shows western blots and total protein stains from a representative participant. mRNA data are normalized per total RNA. Intervals in A, B, D and E indicate 95% CI
FIGURE 3
FIGURE 3
Total RNA and ribosomal RNA subspecies in response to resistance training. (A) shows primer locations targeting different ribosomal RNA subspecies in qPCR analyses. Ribosomal RNA species measured by qPCR and compared to control was affected by training (B,C), but did not show clear differences between volume conditions (D). Total RNA increased compared to non‐training controls to post‐training (12 sessions) and tended to normalize after de‐training (E,F). Time‐course analysis revealed the greatest increase during the first four sessions (G). Error bars shows 95% CI. Asterisk in D indicates robust differences between volume conditions (a 95% CI of pairwise differences not containing 0). Points in D and G show abundances after de‐training for reference
FIGURE 4
FIGURE 4
Predictions of muscle thickness increase based on total RNA increases (A) and total RNA abundance (B; see Table 2). Model estimates shown as black lines with 95% CI are averaged over values from men and women. Individual plots of estimates total RNA increases over time are shown in C together with results from leave‐one‐out analysis (D). Leave‐one‐out analysis shows the effect of removing a single participant (grey point and error bars) and individual values from the total RNA per time estimates where red points represent bounds of the 95% CI and circles represent mean estimates
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