Reports using computed tomography (CT) to estimate thigh skeletal muscle cross-sectional area and mean muscle attenuation are often difficult to evaluate due to inconsistent methods of quantification ...and/or poorly described analysis methods. This CT tutorial provides step-by-step instructions in using free, NIH Image J software to quantify both muscle size and composition in the mid-thigh, which was validated against a robust commercially available software, SliceOmatic. CT scans of the mid-thigh were analyzed from 101 healthy individuals aged 65 and older. Mean cross-sectional area and mean attenuation values are presented across seven defined Hounsfield unit (HU) ranges along with the percent contribution of each region to the total mid-thigh area. Inter-software correlation coefficients ranged from R2 = 0.92-0.99 for all specific area comparisons measured using the Image J method compared to SliceOmatic. We recommend reporting individual HU ranges for all areas measured. Although HU range 0-100 includes the majority of skeletal muscle area, HU range -29 to 150 appears to be the most inclusive for quantifying total thigh muscle. Reporting all HU ranges is necessary to determine the relative contribution of each, as they may be differentially affected by age, obesity, disease, and exercise. This standardized operating procedure will facilitate consistency among investigators reporting computed tomography characteristics of the thigh on single slice images. Trial Registration: ClinicalTrials.gov NCT02308228.
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Progressive resistance exercise training (PRT) is the most effective known intervention for combating aging skeletal muscle atrophy. However, the hypertrophic response to PRT is variable, and this ...may be due to muscle inflammation susceptibility. Metformin reduces inflammation, so we hypothesized that metformin would augment the muscle response to PRT in healthy women and men aged 65 and older. In a randomized, double‐blind trial, participants received 1,700 mg/day metformin (N = 46) or placebo (N = 48) throughout the study, and all subjects performed 14 weeks of supervised PRT. Although responses to PRT varied, placebo gained more lean body mass (p = .003) and thigh muscle mass (p < .001) than metformin. CT scan showed that increases in thigh muscle area (p = .005) and density (p = .020) were greater in placebo versus metformin. There was a trend for blunted strength gains in metformin that did not reach statistical significance. Analyses of vastus lateralis muscle biopsies showed that metformin did not affect fiber hypertrophy, or increases in satellite cell or macrophage abundance with PRT. However, placebo had decreased type I fiber percentage while metformin did not (p = .007). Metformin led to an increase in AMPK signaling, and a trend for blunted increases in mTORC1 signaling in response to PRT. These results underscore the benefits of PRT in older adults, but metformin negatively impacts the hypertrophic response to resistance training in healthy older individuals. ClinicalTrials.gov Identifier: NCT02308228.
Because metformin reduces inflammation, we hypothesized that it would augment the muscle response to progressive resistance exercise training (PRT) in healthy older participants. Following 14 weeks of PRT, metformin blunted gains in lean mass, thigh muscle mass, and thigh muscle density compared to placebo. Metformin did not affect increases in muscle macrophage abundance. However, metformin increased AMPK signaling, leading to a reduced mean increase in mTOR signaling.
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Abstract
Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of ...failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson’s correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.
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Area of muscle, fat, and bone is often measured in thigh CT scans when tissue composition is a key outcome. SliceOmatic software is commonly referenced for such analysis but published methods may be ...insufficient for new users. Thus, a quick start guide to calculating thigh composition using SliceOmatic has been developed.
CT images of the thigh were collected from older (69 ± 4 yrs, N = 24) adults before and after 12-weeks of resistance training. SliceOmatic was used to segment images into seven density regions encompassing fat, muscle, and bone from -190 to +2000 Hounsfield Units HU. The relative contributions to thigh area and the effects of tissue density overlap for skin and marrow with muscle and fat were determined.
The largest contributors to the thigh were normal fat (-190 to -30 HU, 29.1 ± 7.4%) and muscle (35 to 100 HU, 48.9 ± 8.2%) while the smallest were high density (101 to 150 HU, 0.79 ± 0.50%) and very high density muscle (151 to 200 HU, 0.07 ± 0.02%). Training significantly (P<0.05) increased area for muscle in the very low (-29 to -1 HU, 5.5 ± 7.9%), low (0 to 34 HU, 9.6 ± 16.8%), normal (35 to 100 HU, 4.2 ± 7.9%), and high (100 to 150 HU, 70.9 ± 80.6%) density ranges for muscle. Normal fat, very high density muscle and bone did not change (P>0.05). Contributions to area were altered by ~1% or less and the results of training were not affected by accounting for skin and marrow.
When using SliceOmatic to calculate thigh composition, accounting for skin and marrow may not be necessary. We recommend defining muscle as -29 to +200 HU but that smaller ranges (e.g. low density muscle, 0 to 34 HU) can easily be examined for relationships with the health condition and intervention of interest.
Clinicaltrials.gov NCT02261961.
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The extracellular matrix (ECM) in skeletal muscle plays an integral role in tissue development, structural support, and force transmission. For successful adaptation to mechanical loading, remodeling ...processes must occur. In a large cohort of older adults, transcriptomics revealed that genes involved in ECM remodeling, including matrix metalloproteinase 14 (MMP14), were the most upregulated following 14 weeks of progressive resistance exercise training (PRT). Using single‐cell RNA‐seq, we identified macrophages as a source of Mmp14 in muscle following a hypertrophic exercise stimulus in mice. In vitro contractile activity in myotubes revealed that the gene encoding cytokine leukemia inhibitory factor (LIF) is robustly upregulated and can stimulate Mmp14 expression in macrophages. Functional experiments confirmed that modulation of this muscle cell‐macrophage axis facilitated Type I collagen turnover. Finally, changes in LIF expression were significantly correlated with MMP14 expression in humans following 14 weeks of PRT. Our experiments reveal a mechanism whereby muscle fibers influence macrophage behavior to promote ECM remodeling in response to mechanical loading.
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Targeting the programmed death 1/programmed death ligand 1 (PD-1/PD-L1) pathway with immunotherapy has revolutionized the treatment of many cancers. Somatic tumor mutational burden (TMB) and ...T-cell-inflamed gene expression profile (GEP) are clinically validated pan-tumor genomic biomarkers that can predict responsiveness to anti-PD-1/PD-L1 monotherapy in many tumor types. We analyzed the association between these biomarkers and the efficacy of PD-1 inhibitor in 11 commonly used preclinical syngeneic tumor mouse models using murinized rat anti-mouse PD-1 DX400 antibody muDX400, a surrogate for pembrolizumab. Response to muDX400 treatment was broadly classified into three categories: highly responsive, partially responsive, and intrinsically resistant to therapy. Molecular and cellular profiling validated differences in immune cell infiltration and activation in the tumor microenvironment of muDX400-responsive tumors. Baseline and on-treatment genomic analysis showed an association between TMB, murine T-cell-inflamed gene expression profile (murine-GEP), and response to muDX400 treatment. We extended our analysis to investigate a canonical set of cancer and immune biology-related gene signatures, including signatures of angiogenesis, myeloid-derived suppressor cells, and stromal/epithelial-to-mesenchymal transition/TGFβ biology previously shown to be inversely associated with the clinical efficacy of immune checkpoint blockade. Finally, we evaluated the association between murine-GEP and preclinical efficacy with standard-of-care chemotherapy or antiangiogenic agents that previously demonstrated promising clinical activity, in combination with muDX400. Our profiling studies begin to elucidate the underlying biological mechanisms of response and resistance to PD-1/PD-L1 blockade represented by these models, thereby providing insight into which models are most appropriate for the evaluation of orthogonal combination strategies.
Metformin and statins are currently the focus of large clinical trials testing their ability to counter age-associated declines in health, but recent reports suggest that both may negatively affect ...skeletal muscle response to exercise. However, it has also been suggested that metformin may act as a possible protectant of statin-related muscle symptoms. The potential impact of combined drug use on the hypertrophic response to resistance exercise in healthy older adults has not been described. We present secondary statin analyses of data from the MASTERS trial where metformin blunted the hypertrophy response in healthy participants (>65 years) following 14 weeks of progressive resistance training (PRT) when compared to identical placebo treatment (
= 94). Approximately one-third of MASTERS participants were taking prescribed statins. Combined metformin and statin resulted in rescue of the metformin-mediated impaired growth response to PRT but did not significantly affect strength. Improved muscle fiber growth may be associated with medication-induced increased abundance of CD11b+/CD206+ M2-like macrophages. Sarcopenia is a significant problem with aging and this study identifies a potential interaction between these commonly used drugs which may help prevent metformin-related blunting of the beneficial effects of PRT.
ClinicalTrials.gov, NCT02308228, Registered on 25 November 2014.
Changes in skeletal muscle are an important aspect of overall health. The collection of human muscle to study cellular and molecular processes for research requires a needle biopsy procedure which, ...in itself, can induce changes in the tissue. To investigate the effect of repeat tissue sampling, we collected skeletal muscle biopsy samples from vastus lateralis separated by 7 days. Cellular infiltrate, central nucleation, enlarged extracellular matrix, and rounding of muscle fibers were used as indices to define muscle damage, and we found that 16/26 samples (61.5%) revealed at least two of these symptoms in the secondary biopsy. The presence of damage influenced outcome measures usually obtained in human biopsies. Damaged muscle showed an increase in the number of small fibers even though average fiber and fiber type-specific cross-sectional area (CSA) were not different. This included higher numbers of embryonic myosin heavy chain-positive fibers (
= 0.001) as well as elevated satellite cell number (
= 0.02) in the damaged areas and higher variability in satellite cell count in the total area (
= 0.04). Collagen content was higher in damaged (
= 0.0003) as well as nondamaged areas (
= 0.05) of the muscle sections of the damaged compared with the nondamaged group. Myofibrillar protein and ribonucleic acid (RNA) fractional synthesis rates were not significantly different between the damaged compared with the nondamaged group. Results indicate that common outcomes as well as outcome variability in human muscle tissue are affected by previous biopsies. Therefore, the extent of potential damage should be assessed when performing repeated biopsies.
Indices of damage can be found in repeated biopsy samples of nonintervened control legs. Variables, directly and not directly related to muscle damage or regeneration, were compromised in second biopsy. There is a need to determine potential damage within muscle tissue when repeated muscle sampling is part of the study design. Muscle biopsy sampling may be a source of increased heterogeneity in human muscle data.
In older individuals, hypertrophy from progressive resistance training (PRT) is compromised in approximately one-third of participants in exercise trials. The objective of this study was to establish ...novel relationships between baseline muscle features and/or their PRT-induced change in vastus lateralis muscle biopsies with hypertrophy outcomes. Multiple linear regression analyses adjusted for sex were performed on phenotypic data from older adults (
= 48 participants, 70.8 ± 4.5 yr) completing 14 wk of PRT. Results show that baseline muscle size associates with growth regardless of hypertrophy outcome measure fiber cross-sectional area (fCSA), β = -0.76, Adj.
< 0.01; thigh muscle area by computed tomography (CT), β = -0.75, Adj.
< 0.01; dual-energy X-ray absorptiometry (DXA) thigh lean mass, β = -0.47, Adj.
< 0.05. Furthermore, loosely packed collagen organization (CO, β = -0.44, Adj.
< 0.05) and abundance of CD11b+/CD206- immune cells (β = -0.36, Adj.
= 0.10) were negatively associated with whole muscle hypertrophy, with a significant sex interaction on the latter. In addition, a composite hypertrophy score generated using all three measures reinforces significant fiber level findings that changes in myonuclei (MN) (β = 0.67, Adj.
< 0.01), changes in immune cells (β = 0.48, Adj.
< 0.05; both CD11b+/CD206+and CD11b+/CD206- cells), and capillary density (β = 0.56, Adj.
< 0.01) are significantly associated with growth. Exploratory single-cell RNA-sequencing of CD11b+ cells in muscle in response to resistance exercise showed that macrophages have a mixed phenotype. Collagen associations with macrophages may be an important aspect in muscle response heterogeneity. Detailed histological phenotyping of muscle combined with multiple measures of growth response to resistance training in older persons identify potential new mechanisms underlying response heterogeneity and possible sex differences.
Extensive analyses of muscle features associated with muscle size and resistance training response in older persons, including sex differences, and evaluation of multiple measures of hypertrophy are discussed. Collagen organization and CD11b-expressing immune cells offer potential new targets to augment growth response in older individuals. A hypertrophy composite score reveals that changes in immune cells, myonuclei, and capillary density are critically important for overall muscle growth while sc-RNAseq reveals evidence for macrophage heterogeneity.
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