scholarly journals Dynamic urine proteome changes in a rat model of simvastatin-induced skeletal muscle injury

2022 ◽  
pp. 104477
Author(s):  
Jing Wei ◽  
Yuhang Huan ◽  
Ziqi Heng ◽  
Chenyang Zhao ◽  
Lulu Jia ◽  
...  
2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Bruno Paun ◽  
Daniel García Leon ◽  
Alex Claveria Cabello ◽  
Roso Mares Pages ◽  
Elena de la Calle Vargas ◽  
...  

Abstract Background Skeletal muscle injury characterisation during healing supports trauma prognosis. Given the potential interest of computed tomography (CT) in muscle diseases and lack of in vivo CT methodology to image skeletal muscle wound healing, we tracked skeletal muscle injury recovery using in vivo micro-CT in a rat model to obtain a predictive model. Methods Skeletal muscle injury was performed in 23 rats. Twenty animals were sorted into five groups to image lesion recovery at 2, 4, 7, 10, or 14 days after injury using contrast-enhanced micro-CT. Injury volumes were quantified using a semiautomatic image processing, and these values were used to build a prediction model. The remaining 3 rats were imaged at all monitoring time points as validation. Predictions were compared with Bland-Altman analysis. Results Optimal contrast agent dose was found to be 20 mL/kg injected at 400 μL/min. Injury volumes showed a decreasing tendency from day 0 (32.3 ± 12.0mm3, mean ± standard deviation) to day 2, 4, 7, 10, and 14 after injury (19.6 ± 12.6, 11.0 ± 6.7, 8.2 ± 7.7, 5.7 ± 3.9, and 4.5 ± 4.8 mm3, respectively). Groups with single monitoring time point did not yield significant differences with the validation group lesions. Further exponential model training with single follow-up data (R2 = 0.968) to predict injury recovery in the validation cohort gave a predictions root mean squared error of 6.8 ± 5.4 mm3. Further prediction analysis yielded a bias of 2.327. Conclusion Contrast-enhanced CT allowed in vivo tracking of skeletal muscle injury recovery in rat.


2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Bruno Paun ◽  
Daniel García Leon ◽  
Alex Claveria Cabello ◽  
Roso Mares Pages ◽  
Elena de la Calle Vargas ◽  
...  

An amendment to this paper has been published and can be accessed via the original article.


Phytomedicine ◽  
2021 ◽  
pp. 153791
Author(s):  
Maria Sikorska ◽  
Małgorzata Dutkiewicz ◽  
Oliwia Zegrocka – Stendel ◽  
Magdalena Kowalewska ◽  
Iwona Grabowska ◽  
...  

2021 ◽  
Author(s):  
Jing Wei ◽  
Yuhang Huan ◽  
Ziqi Heng ◽  
Chenyang Zhao ◽  
Youhe Gao

Background: Statin-associated muscle symptoms (SAMS) are the main side effects of statins. Currently, there are no effective biomarkers for accurate clinical diagnosis. Urine is not subject to homeostatic control and therefore accumulates early changes, making it an ideal biomarker source. We therefore examined urine proteome changes associated with SAMS in an animal model. Methods: Here, we established a SAMS rat model by intragastric intubation with simvastatin (80 mg/kg). Biochemical analyses and hematoxylin and eosin (H&E) staining were used to evaluate the degree of muscle injury. The urine proteome on days 3, 6, 9 and 14 was profiled using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) with the data-independent acquisition (DIA) method. Results: Differential proteins on day 14 of SAMS were mainly associated with glycolysis/gluconeogenesis, pyruvate metabolism, metabolism of reactive oxygen species and apoptosis, all of which were reported to be associated with the pathological mechanism of SAMS. Among the 14 differentially expressed proteins on day 3, FIBG, OSTP and CRP were associated with muscle damage, while EHD1, CUBN and FINC were associated with the pathogenic mechanisms of SAMS. MYG and PRVA increased dramatically compared with CK elevation in serum on day 14 of SAMS. Conclusions: Our preliminary results indicated that the urine proteome can reflect early changes in the SAMS rat model, providing the potential for monitoring drug side effects in future clinical research. Keywords: Urine proteome, statin-associated muscle symptoms, animal model, biomarkers


2009 ◽  
Vol 28 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Andres J. Quintero ◽  
Vonda J. Wright ◽  
Freddie H. Fu ◽  
Johnny Huard

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