Study: Haun et al., (2019)

Purpose: To determine if training affected actin and myosin protein concentrations, sarcoplasmic protein concentrations, glycogen concentrations, and mitochondrial volume.

Hypothesis: Individuals experiencing notable fCSA increases would experience a decrease in myosin and actin concentrations, a decrease in citrate synthase activity, and either no change or an increase in sarcoplasmic protein and glycogen concentrations.

Study Summary: These data challenge current dogma suggesting fCSA increases during high-volume resistance training are primarily driven through increases contractile protein content. The authors interpret the data to suggest sarcoplasmic hypertrophy is largely responsible for short-term fCSA increases.

Let’s dive deeper.

All of the subjects were from a previous study, which is discussed briefly at the very end of this article. In the current study, there were 15 responders measured by change in mean fiber cross-sectional area (fCSA). The participants used in the study were a subset of responders who increased fCSA as indicated in the white box.

Here’s how the fCSA, fat mass (FM) and fat-free mass (FFM) compared with those subjects in a response heterogeneity plot. You can see that those who gained the most FFM tended to either lose FM or not change much. You can also see that those who had the largest fCSA change tended to have smaller change in FFM. We covered measurement issues in the aforementioned manuscript where it was concluded that— “..different assessment techniques seem to disagree with one another, we posit that this conundrum provides tremendous opportunity for future researchers to build upon current methods or generate newer and more valid methods to better assess skeletal muscle hypertrophy.” Importantly, the data below isn’t correct for total body water which is an important limitation.

Let’s go through a few key results from the study:

1. There were no changes in glycogen content.

Glycogen is distributed within the muscle fibers in the subsarcolemma (5–15 %) intermyofibrillar (~75 %) and intramyofibrillar (5–15 %). Practically, this means it is stored within and between the myofibers. When we resistance train we use muscle glycogen as a fuel source, but rarely more than ~30–40% (Cholewa et al., 2019). The lack of a change in glycogen isn’t surprising given these were well-trained subjects, but then again, there is very little data on glycogen changes with chronic RT. I actually couldn’t find any direct data. I will admit that I thought some of the sarcoplasmic changes could be due to increases in glycogen — apparently not.

2. There were no changes in myosin protein content.

Myosin is one of the main proteins involved in muscle contractions. In the original analysis there were no statistically significant changes in myosin and actin protein concentrations (p = 0.052 Fig 1G–1I). However, given that both p-values approached statistical significance, “forced” post hoc tests were performed to see where the differences would be. These forced post hoc tests indicated myosin and actin protein concentrations significantly decreased from PRE to W6 (p = 0.035 for each target). Another method they could have used, only looking at pre and post is a paired t-test, which would have been significant (p= 0.035). Alternatively, planned contrasts for Week 0 and Week 6 could have been done pre-analysis, especially after finding the odd fCSA data in the other study. Independent of what analysis we use; this data supports the idea that myosin is decreasing in these participants.

Myosin protein content changes from Week 0 to Week 6. Each line represents a participant.

I’ve graphed the data without the three week timepoint. I love that they included the midpoint (week 3) in the original analysis. However, I wanted to look at the data a few different ways to visualize it because of the low sample size, especially since we know there is something odd going on at that week 3 timepoint. We also wouldn’t expect much hypertrophy in 3 weeks in trained participants. This data left me a little skeptical because there are a few people who seem to increase myosin protein content, so I regraphed the data a different way.

Myosin protein content changes from Week 0 to Week 6. Each bar represents a participant.

Here I used percent change from Week 0. We can now easily see that only three participants increased MHC. Therefore, I think we can comfortably say that myosin protein was decreased in most of the participants.

3. There were no changes in actin protein content.

The actin data was very similar to the myosin data, so I didn’t graph all of it, but the take-home is the same. There appears to be a decrease in actin.

The authors also found no changes in actin via phalloidin staining, but I’m not super confident in that method due to the flourescent images. I generally see a ton of background when using green with muscle histology.

3. There were increases in sarcoplasmic protein content.

This finding solidified that something was occuring in the sarcoplasm. In lab, we can use methods to separate the muscle into different fractions using buffers and centrifugation. Centrifugation separates proteins by weight, so that heavier proteins move towards the bottom. Afterward, we can identify the protein content within each fraction. This data indicate that protein content increased in the sarcoplasm. Therefore, proteins in the sarcoplasm were increasing in half the participants.

4. There were decreases in mitochondria.

There was a ~24% decrease in citrate synthase activity, which is a well accepted marker for mitochondria. Citrate synthase activity increases with endurance, high-intensity interval training, or resistance training (Porter et al., 2016) although few studies have been completed in subjects this highly trained. However, another study found a slightly lower level of citrate synthase activity in resistance trained athletes compared to controls (Salvadego et al., 2013) so the idea has some support in the literature. I also thought mitochondria would increase with this type of training—wrong again.

5. The proteomics data show an increase in sarcoplasmic proteins.

Proteomics is the large-scale study of proteins. It’s an easy way to look at a lot of proteins at once and is increasingly used to analyze complex protein populations in biological samples such as skeletal muscle.

Once you get data we can then use bioinformatics techniques to help interpret it.

Here, the authors used a pathway analysis (DAVID v6.8) to show increases glycolysis, acetylation, gluconeogenesis, and cytoplasmic proteins. They also used a KEGG pathway analysis which indicated that glycolysis/gluconeogenesis (8 up-regulated proteins) was significantly up-regulated from Week 0 to Week 6.

This is where things get really fun since I have a good bit of experience analyzing -omics type data. I pulled down their dataset and ran it through a few different analysis.

Reactome

First I ran an overrepresentation analysis: A statistical (hypergeometric distribution) test that determines whether certain Reactome pathways are over-represented (enriched) in the data. It answers the question ‘Does my list contain more proteins for pathway X than would be expected by chance?’ This test produces a probability score, which is corrected for false discovery rate using the Benjamani-Hochberg method. When we do a lot of comparisons we need to correct for the chance they could be false-positives or false-negatives. Here’s the results from timepoint 1 (Week 0) and timepoint 3 (Week 6). One of the major takeaways is how much metabolism pops out at week 6, which confirms what the authors found.