Chrome Extension
WeChat Mini Program
Use on ChatGLM

Skeletal Muscle Proteome Modifications Following Antibiotic-Induced Microbial Disturbances in Cancer Cachexia

JOURNAL OF PROTEOME RESEARCH(2024)

Université Clermont Auvergne | Catholic Univ Louvain | INRAE

Cited 0|Views2
Abstract
Cancer cachexia is an involuntary loss of body weight, mostly of skeletal muscle. Previous research favors the existence of a microbiota-muscle crosstalk, so the aim of the study was to evaluate the impact of microbiota alterations induced by antibiotics on skeletal muscle proteins expression. Skeletal muscle proteome changes were investigated in control (CT) or C26 cachectic mice (C26) with or without antibiotic treatment (CT-ATB or C26-ATB, n = 8 per group). Muscle protein extracts were divided into a sarcoplasmic and myofibrillar fraction and then underwent label-free liquid chromatography separation, mass spectrometry analysis, Mascot protein identification, and METASCAPE platform data analysis. In C26 mice, the atrogen mafbx expression was 353% higher than CT mice and 42.3% higher than C26-ATB mice. No effect on the muscle protein synthesis was observed. Proteomic analyses revealed a strong effect of antibiotics on skeletal muscle proteome outside of cachexia, with adaptative processes involved in protein folding, growth, energy metabolism, and muscle contraction. In C26-ATB mice, proteome adaptations observed in CT-ATB mice were blunted. Differentially expressed proteins were involved in other processes like glucose metabolism, oxidative stress response, and proteolysis. This study confirms the existence of a microbiota-muscle axis, with a muscle response after antibiotics that varies depending on whether cachexia is present.
More
Translated text
Key words
skeletal muscle,proteomics,antibiotics,cancer cachexia,microbial disturbances
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest