GLP-1-directed NMDA receptor antagonism for obesity treatment.

The N-methyl-D-aspartate (NMDA) receptor is a glutamate-activated cation channel that is critical to many processes in the brain. Genome-wide association studies suggest that glutamatergic neurotransmission and NMDA receptor-mediated synaptic plasticity are important for body weight homeostasis1. Here we report the engineering and preclinical development of a bimodal molecule that integrates NMDA receptor antagonism with glucagon-like peptide-1 (GLP-1) receptor agonism to effectively reverse obesity, hyperglycaemia and dyslipidaemia in rodent models of metabolic disease. GLP-1-directed delivery of the NMDA receptor antagonist MK-801 affects neuroplasticity in the hypothalamus and brainstem. Importantly, targeting of MK-801 to GLP-1 receptor-expressing brain regions circumvents adverse physiological and behavioural effects associated with MK-801 monotherapy. In summary, our approach demonstrates the feasibility of using peptide-mediated targeting to achieve cell-specific ionotropic receptor modulation and highlights the therapeutic potential of unimolecular mixed GLP-1 receptor agonism and NMDA receptor antagonism for safe and effective obesity treatment.

Harnessing the power of proteomics in precision diabetes medicine.

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Integrated Liver and Plasma Proteomics in Obese Mice Reveals Complex Metabolic Regulation.

Obesity leads to the development of nonalcoholic fatty liver disease (NAFLD) and associated alterations to the plasma proteome. To elucidate the underlying changes associated with obesity, we performed liquid chromatography-tandem mass spectrometry in the liver and plasma of obese leptin-deficient ob/ob mice and integrated these data with publicly available transcriptomic and proteomic datasets of obesity and metabolic diseases in preclinical and clinical cohorts. We quantified 7173 and 555 proteins in the liver and plasma proteomes, respectively. The abundance of proteins related to fatty acid metabolism were increased, alongside peroxisomal proliferation in ob/ob liver. Putatively secreted proteins and the secretory machinery were also dysregulated in the liver, which was mirrored by a substantial alteration of the plasma proteome. Greater than 50% of the plasma proteins were differentially regulated, including NAFLD biomarkers, lipoproteins, the 20S proteasome, and the complement and coagulation cascades of the immune system. Integration of the liver and plasma proteomes identified proteins that were concomitantly regulated in the liver and plasma in obesity, suggesting that the systemic abundance of these plasma proteins is regulated by secretion from the liver. Many of these proteins are systemically regulated during type 2 diabetes and/or NAFLD in humans, indicating the clinical importance of liver-plasma cross talk and the relevance of our investigations in ob/ob mice. Together, these analyses yield a comprehensive insight into obesity and provide an extensive resource for obesity research in a prevailing model organism.

Mass spectrometry-based proteomics approaches to interrogate skeletal muscle adaptations to exercise.

Acute exercise and chronic exercise training elicit beneficial whole-body changes in physiology that ultimately depend on profound alterations to the dynamics of tissue-specific proteins. Since the work accomplished during exercise owes predominantly to skeletal muscle, it has received the majority of interest from exercise scientists that attempt to unravel adaptive mechanisms accounting for salutary metabolic effects and performance improvements that arise from training. Contemporary scientists are also beginning to use mass spectrometry-based proteomics, which is emerging as a powerful approach to interrogate the muscle protein signature in a more comprehensive manner. Collectively, these technologies facilitate the analysis of skeletal muscle protein dynamics from several viewpoints, including changes to intracellular proteins (expression proteomics), secreted proteins (secretomics), post-translational modifications as well as fiber-, cell-, and organelle-specific changes. This review aims to highlight recent literature that has leveraged new workflows and advances in mass spectrometry-based proteomics to further our understanding of training-related changes in skeletal muscle. We call attention to untapped areas in skeletal muscle proteomics research relating to exercise training and metabolism, as well as basic points of contention when applying mass spectrometry-based analyses, particularly in the study of human biology. We further encourage researchers to couple the hypothesis-generating and descriptive nature of omics data with functional analyses that propel our understanding of the complex adaptive responses in skeletal muscle that occur with acute and chronic exercise.

Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer.

We report a novel method with higher than 90% accuracy in diagnosing buccal mucosa cancer. We use Fourier transform infrared spectroscopic analysis of human serum by suppressing confounding high molecular weight signals, thus relatively enhancing the biomarkers' signals. A narrower range molecular weight window of the serum was also investigated that yielded even higher accuracy on diagnosis. The most accurate results were produced in the serum's 10-30 kDa molecular weight region to distinguish between the two hardest to discern classes, i.e., premalignant and cancer patients. This work promises an avenue for earlier diagnosis with high accuracy as well as greater insight into the molecular origins of these signals by identifying a key molecular weight region to focus on.

Discovery of thymosin ß4 as a human exerkine and growth factor.

Skeletal muscle is an endocrine organ secreting exercise-induced factors (exerkines), which play a pivotal role in interorgan cross talk. Using mass spectrometry (MS)-based proteomics, we characterized the secretome and identified thymosin ß4 (TMSB4X) as the most upregulated secreted protein in the media of contracting C2C12 myotubes. TMSB4X was also acutely increased in the plasma of exercising humans irrespective of the insulin resistance condition or exercise mode. Treatment of mice with TMSB4X did not ameliorate the metabolic disruptions associated with diet induced-obesity, nor did it enhance muscle regeneration in vivo. However, TMSB4X increased osteoblast proliferation and neurite outgrowth, consistent with its WADA classification as a prohibited growth factor. Therefore, we report TMSB4X as a human exerkine with a potential role in cellular cross talk.

Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation.

Universal proteomics sample preparation is challenging because of the high heterogeneity of biological samples. Here we describe a novel mechanism that exploits the inherent instability of denatured proteins for nonspecific immobilization on microparticles by protein aggregation capture. To demonstrate the general applicability of this mechanism, we analyzed phosphoproteomes, tissue proteomes, and interaction proteomes as well as dilute secretomes. The findings present a practical, sensitive and cost-effective proteomics sample preparation method.

Risk prediction by Raman spectroscopy for disease-free survival in oral cancers.

In the present study, the potential of Raman spectroscopy (RS) in predicting disease-free survival (DFS) in oral cancer patients has been explored. Raman spectra were obtained from the tumor and contralateral regions of 94 oral squamous cell carcinoma patients. These patients were managed surgically and recommended for adjuvant therapy. The Cox proportional survival analysis was carried out to identify the spectral regions that can be correlated to DFS. The survival analysis was performed with 95% confidence intervals, hazard ratio, and p-values in the 1200-1800 cm-1 spectral region. Out of a total of 182 spectral points, 76 were found to be correlating with DFS, suggesting their utility to predict the patient outcome. The cut-off points of each correlating RS-point values were defined and tested towards predicting the DFS. The performance of predicting the power of spectral points was validated through Brier value, and it was found to be closer to the actual progression. The 76 spectral points identified from the tumors have the potential to accurately predict DFS in oral squamous cell carcinoma through a relatively simplistic prediction model in the absence of confounding factors.

Proteomics Analysis of Skeletal Muscle from Leptin-Deficient ob/ob Mice Reveals Adaptive Remodeling of Metabolic Characteristics and Fiber Type Composition.

Skeletal muscle insulin resistance, an early metabolic defect in the pathogenesis of type 2 diabetes (T2D), may be a cause or consequence of altered protein expression profiles. Proteomics technology offers enormous promise to investigate molecular mechanisms underlying pathologies, however, the analysis of skeletal muscle is challenging. Using state-of-the-art multienzyme digestion and filter-aided sample preparation (MED-FASP) and a mass spectrometry (MS)-based workflow, we performed a global proteomics analysis of skeletal muscle from leptin-deficient, obese, insulin resistant (ob/ob) and lean mice in mere two fractions in a short time (8 h per sample). We identified more than 6000 proteins with 118 proteins differentially regulated in obesity. This included protein kinases, phosphatases, and secreted and fiber type associated proteins. Enzymes involved in lipid metabolism in skeletal muscle from ob/ob mice were increased, providing evidence against reduced fatty acid oxidation in lipid-induced insulin resistance. Mitochondrial and peroxisomal proteins, as well as components of pyruvate and lactate metabolism, were increased. Finally, the skeletal muscle proteome from ob/ob mice displayed a shift toward the "slow fiber type." This detailed characterization of an obese rodent model of T2D demonstrates an efficient workflow for skeletal muscle proteomics, which may easily be adapted to other complex tissues.

Single muscle fiber proteomics reveals unexpected mitochondrial specialization.

Mammalian skeletal muscles are composed of multinucleated cells termed slow or fast fibers according to their contractile and metabolic properties. Here, we developed a high-sensitivity workflow to characterize the proteome of single fibers. Analysis of segments of the same fiber by traditional and unbiased proteomics methods yielded the same subtype assignment. We discovered novel subtype-specific features, most prominently mitochondrial specialization of fiber types in substrate utilization. The fiber type-resolved proteomes can be applied to a variety of physiological and pathological conditions and illustrate the utility of single cell type analysis for dissecting proteomic heterogeneity.

Deep proteomics of mouse skeletal muscle enables quantitation of protein isoforms, metabolic pathways, and transcription factors.

Skeletal muscle constitutes 40% of individual body mass and plays vital roles in locomotion and whole-body metabolism. Proteomics of skeletal muscle is challenging because of highly abundant contractile proteins that interfere with detection of regulatory proteins. Using a state-of-the art MS workflow and a strategy to map identifications from the C2C12 cell line model to tissues, we identified a total of 10,218 proteins, including skeletal muscle specific transcription factors like myod1 and myogenin and circadian clock proteins. We obtain absolute abundances for proteins expressed in a muscle cell line and skeletal muscle, which should serve as a valuable resource. Quantitation of protein isoforms of glucose uptake signaling pathways and in glucose and lipid metabolic pathways provides a detailed metabolic map of the cell line compared with tissue. This revealed unexpectedly complex regulation of AMP-activated protein kinase and insulin signaling in muscle tissue at the level of enzyme isoforms.

Diacylglycerol kinase-δ regulates AMPK signaling, lipid metabolism, and skeletal muscle energetics.

Decrease of AMPK-related signal transduction and insufficient lipid oxidation contributes to the pathogenesis of obesity and type 2 diabetes. Previously, we identified that diacylglycerol kinase-δ (DGKδ), an enzyme involved in triglyceride biosynthesis, is reduced in skeletal muscle from type 2 diabetic patients. Here, we tested the hypothesis that DGKδ plays a role in maintaining appropriate AMPK action in skeletal muscle and energetic aspects of contraction. Voluntary running activity was reduced in DGKδ(+/-) mice, but glycogen content and mitochondrial markers were unaltered, suggesting that DGKδ deficiency affects skeletal muscle energetics but not mitochondrial protein abundance. We next determined the role of DGKδ in AMPK-related signal transduction and lipid metabolism in isolated skeletal muscle. AMPK activation and signaling were reduced in DGKδ(+/-) mice, concomitant with impaired lipid oxidation and elevated incorporation of free fatty acids into triglycerides. Strikingly, DGKδ deficiency impaired work performance, as evident by altered force production and relaxation dynamics in response to repeated contractions. In conclusion, DGKδ deficiency impairs AMPK signaling and lipid metabolism, thereby highlighting the deleterious role of excessive lipid metabolites in the development of peripheral insulin resistance and type 2 diabetes pathogenesis. DGKδ deficiency also influences skeletal muscle energetics, which may lead to low physical activity levels in type 2 diabetes.

Absolute quantitative profiling of the key metabolic pathways in slow and fast skeletal muscle.

Slow and fast skeletal muscles are composed of, respectively, mainly oxidative and glycolytic muscle fibers, which are the basic cellular motor units of the motility apparatus. They largely differ in excitability, contraction mechanism, and metabolism. Because of their pivotal role in body motion and homeostasis, the skeletal muscles have been extensively studied using biochemical and molecular biology approaches. Here we describe a simple analytical and computational approach to estimate titers of enzymes of basic metabolic pathways and proteins of the contractile machinery in the skeletal muscles. Proteomic analysis of mouse slow and fast muscles allowed estimation of the titers of enzymes involved in the carbohydrate, lipid, and energy metabolism. Notably, we observed that differences observed between the two muscle types occur simultaneously for all proteins involved in a specific process such as glycolysis, free fatty acid catabolism, Krebs cycle, or oxidative phosphorylation. These differences are in a good agreement with the well-established biochemical picture of the muscle types. We show a correlation between maximal activity and the enzyme titer, suggesting that change in enzyme concentration is a good proxy for its catalytic potential in vivo. As a consequence, proteomic profiling of enzyme titers can be used to monitor metabolic changes in cells. Additionally, quantitative data of structural proteins allowed studying muscle type specific cell architecture and its remodeling. The presented proteomic approach can be applied to study metabolism in any other tissue or cell line.

Secretome Analysis of Lipid-Induced Insulin Resistance in Skeletal Muscle Cells by a Combined Experimental and Bioinformatics Workflow.

Skeletal muscle has emerged as an important secretory organ that produces so-called myokines, regulating energy metabolism via autocrine, paracrine, and endocrine actions; however, the nature and extent of the muscle secretome has not been fully elucidated. Mass spectrometry (MS)-based proteomics, in principle, allows an unbiased and comprehensive analysis of cellular secretomes; however, the distinction of bona fide secreted proteins from proteins released upon lysis of a small fraction of dying cells remains challenging. Here we applied highly sensitive MS and streamlined bioinformatics to analyze the secretome of lipid-induced insulin-resistant skeletal muscle cells. Our workflow identified 1073 putative secreted proteins including 32 growth factors, 25 cytokines, and 29 metalloproteinases. In addition to previously reported proteins, we report hundreds of novel ones. Intriguingly, ∼40% of the secreted proteins were regulated under insulin-resistant conditions, including a protein family with signal peptide and EGF-like domain structure that had not yet been associated with insulin resistance. Finally, we report that secretion of IGF and IGF-binding proteins was down-regulated under insulin-resistant conditions. Our study demonstrates an efficient combined experimental and bioinformatics workflow to identify putative secreted proteins from insulin-resistant skeletal muscle cells, which could easily be adapted to other cellular models.

Plasma proteome profiling reveals metabolic and immunologic differences between Anorexia Nervosa subtypes.

AIMS/HYPOTHESIS: Anorexia Nervosa (AN) is a severe psychiatric disorder of an unknown etiology with a crude mortality rate of about 5 % per decade, making it one of the deadliest of all psychiatric illnesses. AN is broadly classified into two main subtypes, restricting and binge/purging disorder. Despite extensive research efforts during several decades, the underlying pathophysiology of AN remains poorly understood. In this study, we aimed to identify novel protein biomarkers for AN by performing a proteomics analysis of fasting plasma samples from 78 females with AN (57 restrictive and 21 binge/purge type) and 70 healthy controls. METHODS: Using state-of-the-art mass spectrometry-based proteomics technology in conjunction with an advanced bioinformatics pipeline, we quantify >500 plasma proteins. RESULTS: Differential expression analysis and correlation of proteomics data with clinical variables led to identification of a panel of novel protein biomarkers with potential pathophysiological significance for AN. Our findings demonstrate evidence of a humoral immune system response, altered lipid metabolism and potential alteration of plasma cells in AN patients. Additionally, we stratified AN patients based on the quantified proteins and suggest a potential autoimmune nature in the restrictive subtype of AN. CONCLUSIONS/INTERPRETATION: In summary, on top of biomarkers of AN subtypes, this study provides a comprehensive map of plasma proteins that constitute a resource for further studies of the pathophysiology of AN.

Exercise-induced crosstalk between immune cells and adipocytes in humans: Role of oncostatin-M.

The discovery of exercise-regulated circulatory factors has fueled interest in organ crosstalk, especially between skeletal muscle and adipose tissue, and the role in mediating beneficial effects of exercise. We studied the adipose tissue transcriptome in men and women with normal glucose tolerance or type 2 diabetes following an acute exercise bout, revealing substantial exercise- and time-dependent changes, with sustained increase in inflammatory genes in type 2 diabetes. We identify oncostatin-M as one of the most upregulated adipose-tissue-secreted factors post-exercise. In cultured human adipocytes, oncostatin-M enhances MAPK signaling and regulates lipolysis. Oncostatin-M expression arises predominantly from adipose tissue immune cell fractions, while the corresponding receptors are expressed in adipocytes. Oncostatin-M expression increases in cultured human Thp1 macrophages following exercise-like stimuli. Our results suggest that immune cells, via secreted factors such as oncostatin-M, mediate a crosstalk between skeletal muscle and adipose tissue during exercise to regulate adipocyte metabolism and adaptation.

Targeting postsynaptic glutamate receptor scaffolding proteins PSD-95 and PICK1 for obesity treatment.

Human genome-wide association studies (GWAS) suggest a functional role for central glutamate receptor signaling and plasticity in body weight regulation. Here, we use UK Biobank GWAS summary statistics of body mass index (BMI) and body fat percentage (BF%) to identify genes encoding proteins known to interact with postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-d-aspartate (NMDA) receptors. Loci in/near discs large homolog 4 (DLG4) and protein interacting with C kinase 1 (PICK1) reached genome-wide significance (P < 5 × 10-8) for BF% and/or BMI. To further evaluate the functional role of postsynaptic density protein-95 (PSD-95; gene name: DLG4) and PICK1 in energy homeostasis, we used dimeric PSD-95/disc large/ZO-1 (PDZ) domain-targeting peptides of PSD-95 and PICK1 to demonstrate that pharmacological inhibition of PSD-95 and PICK1 induces prolonged weight-lowering effects in obese mice. Collectively, these data demonstrate that the glutamate receptor scaffolding proteins, PICK1 and PSD-95, are genetically linked to obesity and that pharmacological targeting of their PDZ domains represents a promising therapeutic avenue for sustained weight loss.

In vivo Raman spectroscopy of oral buccal mucosa: a study on malignancy associated changes (MAC)/cancer field effects (CFE).

Occurrence of metachronous and synchronous secondary tumors in oral cavities has been associated with poor prognosis and decreased 5-year disease-free survival rates. The origin of secondary tumors in the oral cavity has been primarily attributed to cancer field effects (CFE) or malignancy-associated changes (MAC) in uninvolved areas. Classification of normal, cancerous and pre-cancerous oral lesions by in vivo Raman spectroscopy (RS) has already been demonstrated. In the present study, MAC/CFE in oral buccal mucosa were explored. In vivo Raman spectra from 84 subjects (722 spectra) under five categories - cancer and contralateral normal (opposite side of tumor), healthy controls (no tobacco habit, no cancer), habitués healthy controls (tobacco habit, no cancer) and non-habitués contralateral normal (no tobacco habit with cancer) were acquired. Mean and difference spectra suggest that loss of lipids and additional features representing proteins and DNA are characteristics of all pathological conditions, with respect to healthy controls. Spectral data were analyzed by PC-LDA followed by leave-one-out cross-validation. Results suggest that Raman characteristics of mucosa of healthy controls are exclusive, while those of habitués healthy controls are similar to those of contralateral normal mucosa. It was observed that the cluster of non-habitués contralateral normal mucosa is different from habitués healthy controls, suggesting that malignancy associated changes can be identified and also indicating that transformation of uninvolved oral mucosa due to tobacco habit or malignancy is different. The findings of the study demonstrate the potential of RS in identifying early transformation changes in oral mucosa and the efficacy of this approach in oral cancer applications.

Serum Raman spectroscopy: Prognostic applications in oral cancers.

BACKGROUND: Loco-regional recurrences attributable to field cancerization and minimal residual cancer, remain prime causes of mortality in oral cancer (OC) subjects. The current study evaluates potential of serum Raman spectroscopy (SRS) to identify recurrence-prone OC subjects. METHODS: Raman spectra of serum from eight healthy subjects (H) and 57 OC subjects (with-recurrence [R], without-recurrence [NR], and with suspicious-lesions [S]), before (BS) and after (AS) surgical excision of tumor were recorded. OC subjects were followed-up for 7-years. RESULTS: DNA and protein alterations were observed in AS sera of all groups. 4-, 3-, and 2-model multivariate analyses were used to stratify BS and AS groups. H spectra were 100% distinguishable from all other groups. AS, R and NR were distinguished with high accuracy (84%) in all models. No stratification (~50%) was observed BS. CONCLUSION: SRS shows potential to identify recurrence prone subjects, post-surgery, using serum collected as early as 1 week after surgery.

A Comparative Evaluation of Collagen in Ameloblastoma and Oral Squamous Cell Carcinoma using Picrosirius Red Staining with Polarizing Microscopy and CD44v6 Immunoreactivity.

Background: Solid multicystic ameloblastoma (SMA) is a locally aggressive, benign odontogenic tumor of odontogenic origin with greater rate of recurrence. Epithelial-mesenchymal interaction plays an important role in tooth morphogenesis that shows complete differentiation of epithelial and ectomesenchymal components to the level of tooth formation. Tumor stroma in ameloblastoma is normal mature collagen that prevents differentiation to the level of tooth formation. Current study evaluates the role of stromal elements in aggressive behavior of SMA using picrosirius red staining with polarizing microscopy and CD44v6 immunohistochemistry (IHC). Objectives: To compare nature of collagen using picrosirius red staining under polarized microscope and IHC expression of CD44v6 marker in SMA and oral squamous cell carcinoma (OSCC). Methods: Thirty blocks were retrieved from departmental archives and subjected to picrosirius red staining and CD44v6 IHC staining. Slides stained with picrosirius red were observed under polarized microscope to report the birefringence pattern. IHC slides were annotated for intensity of staining of tumor cells. Results: In contrast to OSCC's 40% red, 40% yellowish-red, and 20% greenish-yellow birefringence, SMA displayed 87% red, 13% yellowish-red, and 0% greenish-yellow. Compared to OSCC, which had tumor cells stained 9% strongly, 64% moderately, 27% mildly, and 0% negatively, SMA revealed 0% strong, 10% moderate, 60% weak, and 30% negative staining. Conclusion: As opposed to OSCC, which exhibited a greater quantity of greenish-yellow birefringence of immature collagen, SMA showed predominantly red birefringence, which is suggestive of mature collagen with a lack of metastasis. Comparing SMA to OSCC, the lack of significant CD44v6 positivity suggests that there has not been perineural invasion or regional metastases in SMA.