Spermidine is not an Independent Factor Regulating Limb Muscle Mass in Mice following Androgen Deprivation

Author(s):  
Bradley S Gordon ◽  
Michael L Rossetti ◽  
Robert A Casero, Jr.

Maintaining a critical amount of skeletal muscle mass is linked to reduced morbidity and mortality. In males, testicular androgens regulate muscle mass with a loss of androgens being critical as it is associated with muscle atrophy. Atrophy of the limb muscles is particularly important, but the pathways by which androgens regulate limb muscle mass remain equivocal. We used microarray analysis to identify changes to genes involved with polyamine metabolism in the tibialis anterior (TA) muscle of castrated mice. Of the polyamines, the concentration of spermidine (SPD) was significantly reduced in the TA of castrated mice. To assess whether SPD was an independent factor by which androgens regulate limb muscle mass, we treated castrated mice with SPD for 8 weeks and compared them to sham operated mice. Though this treatment paradigm effectively restored SPD concentrations in the TA muscles of castrated mice, mass of the limb muscles (i.e. TA, gastrocnemius, plantaris, and soleus) were not increased to the levels observed in sham animals. Consistent with those findings, muscle force production was also not increased by SPD treatment. Overall, these data demonstrate for the first time that SPD is not an independent factor by which androgens regulate limb skeletal muscle mass. NOVELTY BULLETS -Polyamines regulate growth in various cells/tissues -Spermidine concentrations are reduced in the limb skeletal muscle following androgen depletion -Restoring Spermidine concentrations in the limb skeletal muscle does not increase limb muscle mass or force production

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Deborah L Reichart ◽  
Richard T Hinkle ◽  
Frank R Lefever ◽  
Elizabeth T Dolan ◽  
Jeffrey A Dietrich ◽  
...  

2017 ◽  
Vol 20 (5) ◽  
pp. 660-669 ◽  
Author(s):  
Carine Fernandes de Souza ◽  
Mariana Carmem Apolinário Vieira ◽  
Rafaela Andrade do Nascimento ◽  
Mayle Andrade Moreira ◽  
Saionara Maria Aires da Câmara ◽  
...  

Abstract Objective: to analyze the relationship between handgrip strength and lower limb strength and the amount of segmental skeletal muscle mass in middle-aged and elderly women. Methods: an observational, cross-sectional, observational study of 540 women aged between 40 and 80 years in the cities of Parnamirim and Santa Cruz, Rio Grande do Norte, was performed. Sociodemographic data, anthropometric measurements, handgrip dynamometry, knee flexors and extensors of the dominant limbs, as well as the segmental muscle mass of the limbs were evaluated. Data were analyzed using Student's t-Test, Chi-square test, Effect Size and Pearson's Correlation (CI 95%). Results: there were statistically significant weak and moderate correlations between handgrip strength and upper limb muscle mass, knee flexion strength and lower limb muscle mass, and between knee extension strength and lower limb muscle mass for the age groups 40-59 years and 60 years or more (p<0.05). Conclusions: muscle strength correlates with skeletal muscle mass. It could therefore be an indicator of the decrease in strength. It is not the only such indicator, however, as correlations were weak and moderate, which suggests the need for more studies on this theme to elucidate which components may also influence the loss of strength with aging.


2020 ◽  
Vol 128 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Brent van der Heyden ◽  
Wouter R. P. H. van de Worp ◽  
Ardy van Helvoort ◽  
Jan Theys ◽  
Annemie M. W. J. Schols ◽  
...  

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noninvasively over time are instrumental to investigate and develop new therapies. In this work, we developed a fully automatic deep learning algorithm for segmentation of micro cone beam computed tomography images of the lower limb muscle complex in mice and subsequent muscle mass calculation. A deep learning algorithm was trained on manually segmented data from 32 mice. Muscle wet mass measurements were obtained from 47 mice and served as a data set for model validation and reverse model validation. The automatic algorithm performance was ~150 times faster than manual segmentation. Reverse validation of the algorithm showed high quantitative metrics (i.e., a Dice similarity coefficient of 0.93, a Hausdorff distance of 0.4 mm, and a center of mass displacement of 0.1 mm), substantiating the robustness and accuracy of the model. A high correlation ( R2 = 0.92) was obtained between the computed tomography-derived muscle mass measurements and the muscle wet masses. Longitudinal follow-up revealed time-dependent changes in muscle mass that separated control from lung tumor-bearing mice, which was confirmed as cachexia. In conclusion, this deep learning model for automated assessment of the lower limb muscle complex provides highly accurate noninvasive longitudinal evaluation of skeletal muscle mass. Furthermore, it facilitates the workflow and increases the amount of data derived from mouse studies while reducing the animal numbers. NEW & NOTEWORTHY This deep learning application enables highly accurate noninvasive longitudinal evaluation of skeletal muscle mass changes in mice with minimal requirement for operator involvement in the data analysis. It provides a unique opportunity to increase and analyze the amount of data derived from animal studies automatically while reducing animal numbers and analytical workload.


2013 ◽  
Author(s):  
Naeyer Helene De ◽  
Inge Everaert ◽  
Spaey Annelies De ◽  
Jean-Marc Kaufman ◽  
Youri Taes ◽  
...  

2018 ◽  
Author(s):  
Se-Hwa Kim ◽  
Soo-Kyung Kim ◽  
Young-Ju Choi ◽  
Seok-Won Park ◽  
Eun-Jig Lee ◽  
...  

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 771-P
Author(s):  
SODAI KUBOTA ◽  
HITOSHI KUWATA ◽  
SAKI OKAMOTO ◽  
DAISUKE YABE ◽  
KENTA MUROTANI ◽  
...  

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