Abstract:We recently developed a binary (i.e., young vs. old) classifier using human muscle RNA profiles that accurately distinguished the age of multiple tissue types. Pathway analysis did not reveal regulators of these 150 genes, so we used reverse genetics and pharmacologic methods to explore regulation of gene expression. Using small interfering RNA, well-studied age-related factors (i.e., rapamycin, resveratrol, TNF-α, and staurosporine), quantitative real-time PCR and clustering analysis, we studied gene–gene int… Show more
“…The expression of LAMTOR5 was not regulated by exercise, yet Figure 6 reveals parts of the LAMTOR 5 “interactome” relate to muscle activity (e.g., PRDX1 interacts with TXNL1 , and MTRF1 interacts with LAMTOR5 ). These interactions can be explored further using reverse genetic strategies and pharmacological tools in human primary cells ( Crossland et al., 2017a ). Figure 5 Genes Regulated by Potentially Related Physiological Conditions Show Substantial Pathway Overlap (A and B) Circos plots showing the overlap in gene expression (A) and Gene Ontology (B) between the HypAt model and additional biological signatures of potentially related physiological conditions (e.g., insulin sensitivity; RT [resistance training]; age; ET [endurance training]).…”
Section: Discussionmentioning
confidence: 99%
“…Human primary skeletal muscle cells were isolated from muscle biopsies from healthy young adults and cultured as previously described ( Crossland et al., 2017a , 2017b ). Myogenic cell enrichment was carried out using magnetic-activated cell sorting (MACS), using anti-CD56 microbeads and myoblasts were used for experimentation at passage 5-6.…”
Summary
Loading of skeletal muscle changes the tissue phenotype reflecting altered metabolic and functional demands. In humans, heterogeneous adaptation to loading complicates the identification of the underpinning molecular regulators. A within-person differential loading and analysis strategy reduces heterogeneity for changes in muscle mass by ∼40% and uses a genome-wide transcriptome method that models each mRNA from coding exons and 3′ and 5′ untranslated regions (UTRs). Our strategy detects ∼3–4 times more regulated genes than similarly sized studies, including substantial UTR-selective regulation undetected by other methods. We discover a core of 141 genes correlated to muscle growth, which we validate from newly analyzed independent samples (n = 100). Further validating these identified genes via RNAi in primary muscle cells, we demonstrate that members of the core genes were regulators of protein synthesis. Using proteome-constrained networks and pathway analysis reveals notable relationships with the molecular characteristics of human muscle aging and insulin sensitivity, as well as potential drug therapies.
“…The expression of LAMTOR5 was not regulated by exercise, yet Figure 6 reveals parts of the LAMTOR 5 “interactome” relate to muscle activity (e.g., PRDX1 interacts with TXNL1 , and MTRF1 interacts with LAMTOR5 ). These interactions can be explored further using reverse genetic strategies and pharmacological tools in human primary cells ( Crossland et al., 2017a ). Figure 5 Genes Regulated by Potentially Related Physiological Conditions Show Substantial Pathway Overlap (A and B) Circos plots showing the overlap in gene expression (A) and Gene Ontology (B) between the HypAt model and additional biological signatures of potentially related physiological conditions (e.g., insulin sensitivity; RT [resistance training]; age; ET [endurance training]).…”
Section: Discussionmentioning
confidence: 99%
“…Human primary skeletal muscle cells were isolated from muscle biopsies from healthy young adults and cultured as previously described ( Crossland et al., 2017a , 2017b ). Myogenic cell enrichment was carried out using magnetic-activated cell sorting (MACS), using anti-CD56 microbeads and myoblasts were used for experimentation at passage 5-6.…”
Summary
Loading of skeletal muscle changes the tissue phenotype reflecting altered metabolic and functional demands. In humans, heterogeneous adaptation to loading complicates the identification of the underpinning molecular regulators. A within-person differential loading and analysis strategy reduces heterogeneity for changes in muscle mass by ∼40% and uses a genome-wide transcriptome method that models each mRNA from coding exons and 3′ and 5′ untranslated regions (UTRs). Our strategy detects ∼3–4 times more regulated genes than similarly sized studies, including substantial UTR-selective regulation undetected by other methods. We discover a core of 141 genes correlated to muscle growth, which we validate from newly analyzed independent samples (n = 100). Further validating these identified genes via RNAi in primary muscle cells, we demonstrate that members of the core genes were regulators of protein synthesis. Using proteome-constrained networks and pathway analysis reveals notable relationships with the molecular characteristics of human muscle aging and insulin sensitivity, as well as potential drug therapies.
“… Fang et al (2017) predicted possible other targets of interest for the anti-cancer effects of resveratrol based on statistical analysis of publicly available databases of deposited bioactivity (such as ChEMBL, Binding DB, and TCGA) combined with by machine learning. Crossland et al (2017) observed that while resveratrol did reverse transcription of genes that are the most perturbed due to aging in patient derived muscle cells and tissue, it did not reverse the disease (aging) gene signature when they observed the effect at a systems level. Interestingly, the authors state that based on their findings, SIRT1 and AMPK may not be a regulator of healthy aging, further explaining the lack of translational activity for resveratrol.…”
Resveratrol (3, 4′, 5-trihydroxy-trans-stilbene) is a natural phytoalexin found in grapes and has long been thought to be the answer to the “French Paradox.” There is no shortage of preclinical and clinical studies investigating the broad therapeutic activity of resveratrol. However, in spite of many comprehensive reviews published on the bioactivity of resveratrol, there has yet to be a report focused on the variety and complexity of its structural binding properties, and its multi-targeted role. An improved understanding of disease mechanisms at the systems level has enabled targeted polypharmacology to mature into a rational drug discovery approach. Unlike traditional hit-to-lead campaigns that typically optimize activity and selectivity for a single target, polypharmacological drugs aim to selectively target multiple proteins, while avoiding critical off target interactions. This strategy bears promise of improved efficacy and reduced clinical attrition. This review seeks to investigate whether the bioactivity of resveratrol is due to a polypharmacological effect or promiscuity of the phenolic small molecule by examining the modes of binding with its diverse collection of protein targets. We focused on annotated targets, identified via the ChEMBL database, and matched these targets to a representative structure deposited in the Protein Data Bank (PDB), as crystal structures are most informative in understanding modes of binding at the atomic level. We discuss the structural aspects of resveratrol itself that permits binding to multiple proteins in various signaling pathways. Furthermore, we suggest that resveratrol’s bioactivity is a result of scaffold promiscuity rather than polypharmacology, and the variety of binding modes across targets display little similarity in the pattern of target interaction.
“…The expression of LAMTOR5 was not regulated by exercise, yet Figure 6 reveals parts of the LAMTOR5 ''interactome'' relate to muscle activity (e.g., PRDX1 interacts with TXNL1, and MTRF1 interacts with LAMTOR5). These interactions can be explored further using reverse genetic strategies and pharmacological tools in human primary cells (Crossland et al, 2017a).…”
Section: Leveraging Transcriptional Network To Investigate Chronic Disease and Identify Potential Therapiesmentioning
confidence: 99%
“…Human primary skeletal muscle cells were isolated from muscle biopsies from healthy young adults and cultured as previously described (Crossland et al, 2017a(Crossland et al, , 2017b. Myogenic cell enrichment was carried out using magnetic-activated cell sorting (MACS), using anti-CD56 microbeads and myoblasts were used for experimentation at passage 5-6.…”
Section: Human Primary Muscle Cell Culturementioning
Loading of skeletal muscle changes the tissue phenotype reflecting altered metabolic and functional demands. In humans, heterogeneous adaptation to loading complicates the identification of the underpinning molecular regulators. A within-person differential loading and analysis strategy reduces heterogeneity for changes in muscle mass by $40% and uses a genome-wide transcriptome method that models each mRNA from coding exons and 3 0 and 5 0 untranslated regions (UTRs). Our strategy detects $3-4 times more regulated genes than similarly sized studies, including substantial UTR-selective regulation undetected by other methods. We discover a core of 141 genes correlated to muscle growth, which we validate from newly analyzed independent samples (n = 100). Further validating these identified genes via RNAi in primary muscle cells, we demonstrate that members of the core genes were regulators of protein synthesis. Using proteome-constrained networks and pathway analysis reveals notable relationships with the molecular characteristics of human muscle aging and insulin sensitivity, as well as potential drug therapies.
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