Proteasome gene expression is controlled by coordinated functions of multiple transcription factors.

Proteasome activity is crucial for cellular integrity, but how tissues adjust proteasome content in response to catabolic stimuli is uncertain. Here, we demonstrate that transcriptional coordination by multiple transcription factors is required to increase proteasome content and activate proteolysis in catabolic states. Using denervated mouse muscle as a model system for accelerated proteolysis in vivo, we reveal that a two-phase transcriptional program activates genes encoding proteasome subunits and assembly chaperones to boost an increase in proteasome content. Initially, gene induction is necessary to maintain basal proteasome levels, and in a more delayed phase (7-10 days after denervation), it stimulates proteasome assembly to meet cellular demand for excessive proteolysis. Intriguingly, the transcription factors PAX4 and α-PALNRF-1 control the expression of proteasome among other genes in a combinatorial manner, driving cellular adaptation to muscle denervation. Consequently, PAX4 and α-PALNRF-1 represent new therapeutic targets to inhibit proteolysis in catabolic diseases (e.g., type-2 diabetes, cancer).

Proteasome gene expression is controlled by the coordinated functions of multiple transcription factors.

Proteasome activity is crucial for cellular integrity, but how tissues adjust proteasome content in response to catabolic stimuli is uncertain. Here, we demonstrate that transcriptional coordination by multiple transcription factors is required to increase proteasome content and activate proteolysis in catabolic states. Using denervated mouse muscle as a model system for accelerated proteolysis in vivo , we reveal that a two-phase transcriptional program activates genes encoding proteasome subunits and assembly chaperones to boost an increase in proteasome content. Initially, gene induction is necessary to maintain basal proteasome levels, and in a more delayed phase (7-10 d after denervation) it stimulates proteasome assembly to meet cellular demand for excessive proteolysis. Intriguingly, the transcription factors PAX4 and α-PAL NRF-1 control the expression of proteasome among other genes in a combinatorial manner, driving cellular adaptation to muscle denervation. Consequently, PAX4 and α-PAL NRF-1 represent new therapeutic targets to inhibit proteolysis in catabolic diseases (e.g. type-2 diabetes, cancer).

A semiautomated measurement of muscle fiber size using the Imaris software.

The size and shape of skeletal muscle fibers are affected by various physiological and pathological conditions, such as muscle atrophy, hypertrophy, regeneration, and dystrophies. Hence, muscle fiber cross-sectional area (CSA) is an important determinant of muscle health and plasticity. We adapted the Imaris software to automatically segment muscle fibers based on fluorescent labeling of the plasma membrane and measure muscle fiber CSA. Analysis of muscle cross sections by the Imaris semiautomated and manual approaches demonstrated a similar decrease in CSA of atrophying muscles from fasted mice compared with fed controls. In addition, we previously demonstrated that downregulation of the Ca2+-specific protease calpain-1 attenuates muscle atrophy. Accordingly, both the Imaris semiautomated and manual approaches showed a similar increase in CSA of fibers expressing calpain-1 shRNA compared with adjacent nontransfected fibers in the same muscle cross section. Although both approaches seem valid for measurements of muscle fiber size, the manual marking method is less preferable because it is highly time-consuming, subjective, and limits the number of cells that can be analyzed. The Imaris semiautomated approach is user-friendly, requires little training or optimization, and can be used to efficiently and accurately mark thousands of fibers in a short period. As a novel addition to the commonly used statistics, we also describe statistical tests that quantify the strength of an effect on fiber size, enabling detection of significant differences between skewed distributions that would otherwise not be detected using typical methods.