Longitudinal metabolomic analysis of plasma enables modeling disease progression in Duchenne muscular dystrophy mouse models

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

The power of effective study design in animal Experimentation: Exploring the statistical and ethical implications of asking multiple questions of a data set

One of the chief advantages of using highly standardised biological models including model organisms is that multiple variables can be precisely controlled so that the variable of interest is more easily studied. However, such an approach often obscures effects in sub-populations resulting from natural population heterogeneity. Efforts to expand our fundamental understanding of multiple sub-populations are in progress. However, such stratified or personalised approaches require fundamental modifications of our usual study designs that should be implemented in Brain, Behavior and Immunity (BBI) research going forward. Here we explore the statistical feasibility of asking multiple questions (including incorporating sex) within the same experimental cohort using statistical simulations of real data. We illustrate and discuss the large explosion in sample numbers necessary to detect effects with appropriate power for every additional question posed using the same data set. This exploration highlights the strong likelihood of type II errors (false negatives) for standard data and type I errors when dealing with complex genomic data, where studies are too under-powered to appropriately test these interactions. We show this power may differ for males and females in high throughput data sets such as RNA sequencing. We offer a rationale for the use of alternative experimental and statistical strategies based on interdisciplinary insights and discuss the real-world implications of increasing the complexities of our experimental designs, and the implications of not attempting to alter our experimental designs going forward.

Alterations of the Gut Microbiota and Metabolomics Associated with the Different Growth Performances of Macrobrachium rosenbergii Families

To investigate the key gut microbiota and metabolites associated with the growth performance of Macrobrachium rosenbergii families, 16S rRNA sequencing and LC-MS metabolomic methods were used. In this study, 90 M. rosenbergii families were bred to evaluate growth performance. After 92 days of culture, high (H), medium (M), and low (L) experimental groups representing three levels of growth performance, respectively, were collected according to the weight gain and specific growth rate of families. The composition of gut microbiota showed that the relative abundance of Firmicutes, Lachnospiraceae, Lactobacillus, and Blautia were much higher in Group H than those in M and L groups. Meanwhile, compared to the M and L groups, Group H had significantly higher levels of spermidine, adenosine, and creatinine, and lower levels of L-citrulline. Correlation analysis showed that the abundances of Lactobacillus and Blautia were positively correlated with the levels of alpha-ketoglutaric acid and L-arginine. The abundance of Blautia was also positively correlated with the levels of adenosine, taurine, and spermidine. Notably, lots of metabolites related to the metabolism and biosynthesis of arginine, taurine, hypotaurine, and fatty acid were upregulated in Group H. This study contributes to figuring out the landscape of the gut microbiota and metabolites associated with prawn growth performance and provides a basis for selective breeding.

Plasma metabolomics and network pharmacology identified glutamate, glutamine, and arginine as biomarkers of depression under Shuganjieyu capsule treatment

Depression is a psychiatric disorder and confers an enormous burden on society. Mild to moderate forms of depression (MMD) are particularly common. Our previous studies showed that the Shuganjieyu (SGJY) capsule might improve depressive and cognitive symptoms in patients with MMD. However, biomarkers evaluating the efficacy of SGJY and the underlying mechanism remains unclear. The aim of the present study was to discover efficacy biomarkers and explore the underlying mechanisms of SGJY as antidepression treatment. Twenty-three patients with MMD were recruited and administered with SGJY for 8 weeks. Results showed that the content of 19 metabolites changed significantly in the plasma of patients with MMD, among which 8 metabolites improved significantly after SGJY treatment. Network pharmacology analysis showed that 19 active compounds, 102 potential targets, and 73 enzymes were related to the mechanistic action of SGJY. Through a comprehensive analysis, we identified four hub enzymes (GLS2, GLS, GLUL, and ADC), three key differential metabolites (glutamine, glutamate, and arginine), and two shared pathways (alanine, aspartate, and glutamate metabolism; and arginine biosynthesis). Receiver operating characteristic curve (ROC) analysis showed that the three metabolites had a high diagnostic ability. The expression of hub enzymes was validated using RT-qPCR in animal models. Overall, glutamate, glutamine, and arginine may be potential biomarkers for evaluating the efficacy of SGJY. The present study provides a new strategy for pharmacodynamic evaluation and mechanistic study of SGJY, and offers new information for clinical practice and treatment research.

Gas chromatography-mass spectrometry-based untargeted metabolomics analysis reveals circulating biomarkers related to wooden breast myopathy in broilers: a preliminary study

Approaches for the diagnosis of wooden breast (WB) myopathy in live birds are urgently required before applying intervention strategies to reduce occurrence and severity for the poultry industry. The objective of this study was to characterize the serum metabolic profiles in male broilers affected by WB and to identify biomarkers related to this myopathy. Broilers were categorized into normal (CON) and WB groups based on gross scoring and histological evaluation. Gas chromatography-mass spectrometry-based metabolomics, multivariate analysis, and orthogonal partial least squares discriminant analysis revealed a clear separation between CON and WB. A total of 73 significantly different (P < 0.05) metabolites with 17 upregulated and 56 downregulated were identified, which were mainly involved in pathways of alanine, aspartate, and glutamate metabolism, carbohydrate metabolism, and taurine and hypotaurine metabolism. By using the nested cross-validation function of random forest analysis, 9 significantly altered (P < 0.05) metabolites (cerotinic acid, arabitol, phosphoenolpyruvate, terephthalic acid, cis-gondoic acid, N-acetyl-d-glucosamine, 4-hydroxymandelic acid, caffeine, and xanthurenic acid) were identified as biomarkers with an excellent discriminant performance for WB myopathy. Collectively, this study provides new insights for a deeper understanding of the pathogenesis and provides metabolites as biomarkers for diagnostic utilization of WB myopathy.

Multiomic characterization of disease progression in mice lacking dystrophin

Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. A better understanding of how objective biomarkers for DMD vary across subjects and over time is needed to model disease progression and response to therapy more effectively, both in pre-clinical and clinical research. We present an in-depth characterization of disease progression in 3 murine models of DMD by multiomic analysis of longitudinal trajectories between 6 and 30 weeks of age. Integration of RNA-seq, mass spectrometry-based metabolomic and lipidomic data obtained in muscle and blood samples by Multi-Omics Factor Analysis (MOFA) led to the identification of 8 latent factors that explained 78.8% of the variance in the multiomic dataset. Latent factors could discriminate dystrophic and healthy mice, as well as different time-points. MOFA enabled to connect the gene expression signature in dystrophic muscles, characterized by pro-fibrotic and energy metabolism alterations, to inflammation and lipid signatures in blood. Our results show that omic observations in blood can be directly related to skeletal muscle pathology in dystrophic muscle.

Biomarkers in Duchenne Muscular Dystrophy: Current Status and Future Directions

 Duchenne muscular dystrophy is a severe, X-linked disease characterized by decreased muscle mass and function in children. Genetic and biochemical research over the years has led to the characterization of the cause and the pathophysiology of the disease. Moreover, the elucidation of genetic mechanisms underlining Duchenne muscular dystrophy has allowed for the design of innovative personalized therapies.The identification of specific, accurate, and sensitive biomarkers is becoming crucial for evaluating muscle disease progression and response to therapies, disease monitoring, and the acceleration of drug development and related regulatory processes.This review illustrated the up-to-date progress in the development of candidate biomarkers in DMD at the level of proteins, metabolites, micro-RNAs (miRNAs) and genetic modifiers also highlighting the complexity of translating research results to clinical practice.We highlighted the challenges encountered in translating biomarkers into the clinical context and the existing bottlenecks hampering the adoption of biomarkers as surrogate endpoints. These challenges could be overcome by national and international collaborative efforts, multicenter data sharing, definition of public biobanks and patients' registries, and creation of large cohorts of patients. Novel statistical tools/ models suitable to analyze small patient numbers are also required.Finally, collaborations with pharmaceutical companies would greatly benefit biomarker discovery and their translation in clinical trials.

Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Biomarkers in Duchenne Muscular Dystrophy

PURPOSE OF REVIEW

This review highlights the key studies investigating various types of biomarkers in Duchenne muscular dystrophy (DMD).

RECENT FINDINGS

Several proteomic and metabolomic studies have been undertaken in both human DMD patients and animal models of DMD that have identified potential biomarkers in DMD. Although there have been a number of proteomic and metabolomic studies that have identified various potential biomarkers in DMD, more definitive studies still need to be undertaken in DMD patients to firmly correlate these biomarkers with diagnosis, disease progression, and monitoring the effects of novel treatment strategies being developed.

A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data

This review presents an overview of the statistical methods on differential abundance (DA) analysis for mass spectrometry (MS)-based metabolomic data. MS has been widely used for metabolomic abundance profiling in biological samples. The high-throughput data produced by MS often contain a large fraction of zero values caused by the absence of certain metabolites and the technical detection limits of MS. Various statistical methods have been developed to characterize the zero-inflated metabolomic data and perform DA analysis, ranging from simple tests to more complex models including parametric, semi-parametric, and non-parametric approaches. In this article, we discuss and compare DA analysis methods regarding their assumptions and statistical modeling techniques.

Complexity of skeletal muscle degeneration: multi-systems pathophysiology and organ crosstalk in dystrophinopathy

Duchenne muscular dystrophy is a highly progressive muscle wasting disorder due to primary abnormalities in one of the largest genes in the human genome, the DMD gene, which encodes various tissue-specific isoforms of the protein dystrophin. Although dystrophinopathies are classified as primary neuromuscular disorders, the body-wide abnormalities that are associated with this disorder and the occurrence of organ crosstalk suggest that a multi-systems pathophysiological view should be taken for a better overall understanding of the complex aetiology of X-linked muscular dystrophy. This article reviews the molecular and cellular effects of deficiency in dystrophin isoforms in relation to voluntary striated muscles, the cardio-respiratory system, the kidney, the liver, the gastrointestinal tract, the nervous system and the immune system. Based on the establishment of comprehensive biomarker signatures of X-linked muscular dystrophy using large-scale screening of both patient specimens and genetic animal models, this article also discusses the potential usefulness of novel disease markers for more inclusive approaches to differential diagnosis, prognosis and therapy monitoring that also take into account multi-systems aspects of dystrophinopathy. Current therapeutic approaches to combat muscular dystrophy are summarised.

Platelet Derived Growth Factor-AA Correlates With Muscle Function Tests and Quantitative Muscle Magnetic Resonance in Dystrophinopathies

Introduction: Duchenne (DMD) and Becker (BMD) muscular dystrophy are X-linked muscular disorders produced by mutations in the DMD gene which encodes the protein dystrophin. Both diseases are characterized by progressive involvement of skeletal, cardiac, and respiratory muscles. As new treatment strategies become available, reliable biomarkers and outcome measures that can monitor disease progression are needed for clinical trials.

Methods: We collected clinical and functional data and blood samples from 19 DMD patients, 13 BMD patients, and 66 healthy controls (8 pediatric and 58 adult controls), and blood samples from 15 patients with dysferlinopathy (DYSF) and studied the serum concentration of 4 growth factors involved in the process of muscle fibrosis. We correlated the serum concentration of these growth factors with several muscle function tests, spirometry results and fat fraction identified by quantitative Dixon muscle MRI.

Results: We found significant differences in the serum concentration of Platelet Derived Growth Factor-AA (PDGF-AA) between DMD patients and pediatric controls, in Connective Tissue Growth Factor (CTGF) between BMD patients and adult controls, and in and Transforming Growth Factor- β1 (TGF-β1) between BMD and DYSF patients. PDGF-AA showed a good correlation with several muscle function tests for both DMD and BMD patients and with thigh fat fraction in BMD patients. Moreover, PDGF-AA levels were increased in muscle biopsies of patients with DMD and BMD as was demonstrated by immunohistochemistry and Real-Time PCR studies.

Conclusion: Our study suggests that PDGF-AA should be further investigated in a larger cohort of DMD and BMD patients because it might be a good biomarker candidate to monitor the progression of these diseases.

Peripheral blood transcriptome profiling enables monitoring disease progression in dystrophic mice and patients

DMD is a rare disorder characterized by progressive muscle degeneration and premature death. Therapy development is delayed by difficulties to monitor efficacy non-invasively in clinical trials. In this study, we used RNA-sequencing to describe the pathophysiological changes in skeletal muscle of 3 dystrophic mouse models. We show how dystrophic changes in muscle are reflected in blood by analyzing paired muscle and blood samples. Analysis of repeated blood measurements followed the dystrophic signature at five equally spaced time points over a period of seven months. Treatment with two antisense drugs harboring different levels of dystrophin recovery identified genes associated with safety and efficacy. Evaluation of the blood gene expression in a cohort of DMD patients enabled the comparison between preclinical models and patients, and the identification of genes associated with physical performance, treatment with corticosteroids and body measures. The presented results provide evidence that blood RNA-sequencing can serve as a tool to evaluate disease progression in dystrophic mice and patients, as well as to monitor response to (dystrophin-restoring) therapies in preclinical drug development and in clinical trials.

Pathway testing for longitudinal metabolomics

We propose a top‐down approach for pathway analysis of longitudinal metabolite data. We apply a score test based on a shared latent process mixed model which can identify pathways with differentially progressing metabolites. The strength of our approach is that it can handle unbalanced designs, deals with potential missing values in the longitudinal markers, and gives valid results even with small sample sizes. Contrary to bottom‐up approaches, correlations between metabolites are explicitly modeled leveraging power gains. For large pathway sizes, a computationally efficient solution is proposed based on pseudo‐likelihood methodology. We demonstrate the advantages of the proposed method in identification of differentially expressed pathways through simulation studies. Finally, longitudinal metabolite data from a mice experiment is analyzed to demonstrate our methodology.

Circulating Biomarkers in Muscular Dystrophies: Disease and Therapy Monitoring

Muscular dystrophies are a group of inherited disorders that primarily affect the muscle tissues. Across the muscular dystrophies, symptoms commonly compromise the quality of life in all areas of functioning. It is well noted that muscular dystrophies need reliable and measurable biomarkers that will monitor the progress of the disease and evaluate the potential therapeutic approaches. In this review, we analyze the current findings regarding the development of blood-based circulating biomarkers for different types of muscular dystrophies. We emphasize those muscular dystrophies that gained particular interest for the development of biomarkers, including Duchenne muscular dystrophy, Becker muscular dystrophy, myotonic dystrophy types 1 and 2, Ullrich congenital muscular dystrophy, congenital muscular dystrophy type 1A, Facioscapulohumeral muscular dystrophy, and limb-girdle muscular dystrophy types 2A, 2B, 2C, and 2D, recently renamed as limb-girdle muscular dystrophy R1 calpain3-related, R2 dysferlin-related, R5 γ-sarcoglycan-related, and R3 α-sarcoglycan-related. This review highlights the up-to-date progress in the development of biomarkers at the level of proteins, lipids, and metabolites, as well as microRNAs (miRNAs) that currently are the main potential biomarker candidates in muscular dystrophies.

Duchenne Muscular Dystrophy: recent advances in protein biomarkers and the clinical application

INTRODUCTION

Early biomarker discovery studies have praised the value of their emerging results, predicting an unprecedented impact on health care. Biomarkers are expected to provide tests with increased specificity and sensitivity compared to existing measures, improve the decision-making process, and accelerate the development of therapies. For rare disorders, like Duchenne Muscular Dystrophy (DMD) such biomarkers can assist the development of therapies, therefore also helping to find a cure for the disease.

AREA COVERED

State-of-the-art technologies have been used to identify blood biomarkers for DMD and efforts have been coordinated to develop and promote translation of biomarkers for clinical practice. Biomarker translation to clinical practice is however, adjoined by challenges related to the complexity of the disease, involving numerous biological processes, and the limited sample resources. This review highlights the current progress on the development of biomarkers, describing the proteomics technologies used, the most promising findings and the challenges encountered.

EXPERT OPINION

Strategies for effective use of samples combined with orthogonal proteomics methods for protein quantification are essential for translating biomarkers to the patient's bed side. Progress is achieved only if strong evidence is provided that the biomarker constitutes a reliable indicator of the patient's health status for a specific context of use.