Spatial metabolomics reveals skeletal myofiber subtypes

Integrated proteomics and metabolomics analysis of sclerosis-related proteins and femoral head necrosis following internal fixation of femoral neck fractures

Femoral head necrosis (FHN) is a serious complication after femoral neck fractures (FNF), often linked to sclerosis around screw paths. Our study aimed to uncover the proteomic and metabolomic underpinnings of FHN and sclerosis using integrated proteomics and metabolomics analyses. We identified differentially expressed proteins (DEPs) and metabolites (DEMs) among three groups: patients with FNF (Group A), sclerosis (Group B), and FHN (Group C). Using the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses, we examined the roles of these proteins and metabolites. Our findings highlight the significant differences across the groups, with 218 DEPs and 44 DEMs identified between the sclerosis and FNF groups, 247 DEPs and 31 DEMs between the FHN and sclerosis groups, and a stark 682 DEPs and 94 DEMs between the FHN and FNF groups. Activities related to carbonate dehydratase and hydrolase were similar in the FHN and sclerosis groups, whereas extracellular region and lysosome were prevalent in the FHN and FNF groups. Our study also emphasized the involvement of the PI3K-Akt pathway in sclerosis and FHN. Moreover, the key metabolic pathways were implicated in glycerophospholipid metabolism and retrograde endocannabinoid signaling. Using western blotting, we confirmed the pivotal role of specific genes/proteins such as ITGB5, TNXB, CA II, and CA III in sclerosis and acid phosphatase 5 and cathepsin K in FHN. This comprehensive analyses elucidates the molecular mechanisms behind sclerosis and FHN and suggests potential biomarkers and therapeutic targets, paving the way for improved treatment strategies. Further validation of the findings is necessary to strengthen the robustness and reliability of the results.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

MYH13, a superfast myosin expressed in extraocular, laryngeal and syringeal muscles

MYH13 is a unique type of sarcomeric myosin heavy chain (MYH) first detected in mammalian extraocular (EO) muscles and later also in vocal muscles, including laryngeal muscles of some mammals and syringeal muscles of songbirds. All these muscles are specialized in generating very fast contractions while producing relatively low force, a design appropriate for muscles acting against a much lower load than most skeletal muscles inserting into the skeleton. The definition of the physiological properties of muscle fibres containing MYH13 has been complicated by the mixed fibre type composition of EO muscles and the coexistence of different MYH types within the same fibre. A major advance in this area came from studies on isolated recombinant myosin motors and the demonstration that the affinity of actin-bound human MYH13 for ADP is much weaker than those of fast-type MYH1 (type 2X) and MYH2 (type 2A). This property is consistent with a very fast detachment of myosin from actin, a major determinant of shortening velocity. The MYH13 gene arose early during vertebrate evolution but was characterized only in mammals and birds and appears to have been lost in some teleost fish. The MYH13 gene is located at the 3' end of the mammalian fast/developmental gene cluster and in a similar position to the orthologous cluster in syntenic regions of the songbird genome. MYH13 gene regulation is controlled by a super-enhancer in the mammalian locus and deletion of the neighbouring fast MYH1 and MYH4 genes leads to abnormal MYH13 expression in mouse leg muscles.

Improved endurance capacity of diabetic mice during SGLT2 inhibition: Role of AICARP, an AMPK activator in the soleus

BACKGROUND

Diabetes is associated with an increased risk of deleterious changes in muscle mass and function or sarcopenia, leading to physical inactivity and worsening glycaemic control. Given the negative energy balance during sodium-glucose cotransporter-2 (SGLT2) inhibition, whether SGLT2 inhibitors affect skeletal muscle mass and function is a matter of concern. However, how SGLT2 inhibition affects the skeletal muscle function in patients with diabetes remains insufficiently explored. We aimed to explore the effects of canagliflozin (CANA), an SGLT2 inhibitor, on skeletal muscles in genetically diabetic db/db mice focusing on the differential responses of oxidative and glycolytic muscles.

METHODS

Db/db mice were treated with CANA for 4 weeks. We measured running distance and handgrip strength to assess skeletal muscle function during CANA treatment. At the end of the experiment, we performed a targeted metabolome analysis of the skeletal muscles.

RESULTS

CANA treatment improved the reduced endurance capacity, as revealed by running distance in db/db mice (414.9 ± 52.8 vs. 88.7 ± 22.7 m, P < 0.05). Targeted metabolome analysis revealed that 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranosyl 5'-monophosphate (AICARP), a naturally occurring AMP-activated protein kinase (AMPK) activator, increased in the oxidative soleus muscle (P < 0.05), but not in the glycolytic extensor digitorum longus muscle (P = 0.4376), with increased levels of AMPK phosphorylation (P < 0.01).

CONCLUSIONS

This study highlights the potential role of the AICARP/AMPK pathway in oxidative rather than glycolytic skeletal muscles during SGLT2 inhibition, providing novel insights into the mechanism by which SGLT2 inhibitors improve endurance capacity in patients with type 2 diabetes.

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

Exercise metabolism and adaptation in skeletal muscle

Viewing metabolism through the lens of exercise biology has proven an accessible and practical strategy to gain new insights into local and systemic metabolic regulation. Recent methodological developments have advanced understanding of the central role of skeletal muscle in many exercise-associated health benefits and have uncovered the molecular underpinnings driving adaptive responses to training regimens. In this Review, we provide a contemporary view of the metabolic flexibility and functional plasticity of skeletal muscle in response to exercise. First, we provide background on the macrostructure and ultrastructure of skeletal muscle fibres, highlighting the current understanding of sarcomeric networks and mitochondrial subpopulations. Next, we discuss acute exercise skeletal muscle metabolism and the signalling, transcriptional and epigenetic regulation of adaptations to exercise training. We address knowledge gaps throughout and propose future directions for the field. This Review contextualizes recent research of skeletal muscle exercise metabolism, framing further advances and translation into practice.

Extensive profiling of histidine-containing dipeptides reveals species- and tissue-specific distribution and metabolism in mice, rats, and humans

AIM

Histidine-containing dipeptides (HCDs) are pleiotropic homeostatic molecules with potent antioxidative and carbonyl quenching properties linked to various inflammatory, metabolic, and neurological diseases, as well as exercise performance. However, the distribution and metabolism of HCDs across tissues and species are still unclear.

METHODS

Using a sensitive UHPLC-MS/MS approach and an optimized quantification method, we performed a systematic and extensive profiling of HCDs in the mouse, rat, and human body (in n = 26, n = 25, and n = 19 tissues, respectively).

RESULTS

Our data show that tissue HCD levels are uniquely produced by carnosine synthase (CARNS1), an enzyme that was preferentially expressed by fast-twitch skeletal muscle fibres and brain oligodendrocytes. Cardiac HCD levels are remarkably low compared to other excitable tissues. Carnosine is unstable in human plasma, but is preferentially transported within red blood cells in humans but not rodents. The low abundant carnosine analogue N-acetylcarnosine is the most stable plasma HCD, and is enriched in human skeletal muscles. Here, N-acetylcarnosine is continuously secreted into the circulation, which is further induced by acute exercise in a myokine-like fashion.

CONCLUSION

Collectively, we provide a novel basis to unravel tissue-specific, paracrine, and endocrine roles of HCDs in human health and disease.

Muscle Injuries Induce a Prostacyclin-PPARγ/PGC1a-FAO Spike That Boosts Regeneration

It is well-known that muscle regeneration declines with aging, and aged muscles undergo degenerative atrophy or sarcopenia. While exercise and acute injury are both known to induce muscle regeneration, the molecular signals that help trigger muscle regeneration have remained unclear. Here, mass spectrometry imaging (MSI) is used to show that injured muscles induce a specific subset of prostanoids during regeneration, including PGG1, PGD2, and the prostacyclin PGI2. The spike in prostacyclin promotes skeletal muscle regeneration via myoblasts, and declines with aging. Mechanistically, the prostacyclin spike promotes a spike in PPARγ/PGC1a signaling, which induces a spike in fatty acid oxidation (FAO) to control myogenesis. LC-MS/MS and MSI further confirm that an early FAO spike is associated with normal regeneration, but muscle FAO became dysregulated during aging. Functional experiments demonstrate that the prostacyclin-PPARγ/PGC1a-FAO spike is necessary and sufficient to promote both young and aged muscle regeneration, and that prostacyclin can synergize with PPARγ/PGC1a-FAO signaling to restore aged muscles' regeneration and physical function. Given that the post-injury prostacyclin-PPARγ-FAO spike can be modulated pharmacologically and via post-exercise nutrition, this work has implications for how prostacyclin-PPARγ-FAO might be fine-tuned to promote regeneration and treat muscle diseases of aging.

XAI-enabled neural network analysis of metabolite spatial distributions

We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly identify biomarkers that can distinguish different classes of tissues, from several thousands of metabolite features. We also modified classic neural network architectures to construct a deep convolutional neural network that is more suitable for processing high-dimensional MSI data directly, instead of using dimension reduction techniques, and compared it to seven other machine learning analysis methods' performance in classification accuracy. After ascertaining the superiority of Channel-ResNet10, we used a novel channel selection-based XAI method to identify the key metabolite features that were responsible for its learning accuracy. These key metabolite biomarkers were then processed using MetaboAnalyst for pathway enrichment mapping. We found that Channel-ResNet10 was superior to seven other machine learning methods for MSI analysis, reaching > 98% accuracy in muscle aging and colorectal cancer datasets. We also used a novel channel selection-based XAI method to find that in young and aged muscle tissues, the differentially distributed metabolite biomarkers were especially enriched in the propanoate metabolism pathway, suggesting it as a novel target pathway for anti-aging therapy.