Lung injury-induced skeletal muscle wasting in aged mice is linked to alterations in long chain fatty acid metabolism

Introduction

Older patients are more likely to acquire and die from acute respiratory distress syndrome (ARDS) and muscle weakness may be more clinically significant in older persons. Recent data implicate muscle ring finger protein 1 (MuRF1) in lung injury-induced skeletal muscle atrophy in young mice and identify an alternative role for MuRF1 in cardiac metabolism regulation through inhibition of fatty acid oxidation.

Objectives

To develop a model of lung injury-induced muscle wasting in old mice and to evaluate the skeletal muscle metabolomic profile of adult and old acute lung injury (ALI) mice.

Methods

Young (2 month), adult (6 month) and old (20 month) male C57Bl6 J mice underwent Sham (intratracheal H₂O) or ALI [intratracheal E. coli lipopolysaccharide (i.t. LPS)] conditions and muscle functional testing. Metabolomic analysis on gastrocnemius muscle was performed using gas chromatography-mass spectrometry (GC–MS).

Results

Old ALI mice had increased mortality and failed to recover skeletal muscle function compared to adult ALI mice. Muscle MuRF1 expression was increased in old ALI mice at day 3. Non-targeted muscle metabolomics revealed alterations in amino acid biosynthesis and fatty acid metabolism in old ALI mice. Targeted metabolomics of fatty acid intermediates (acyl-carnitines) and amino acids revealed a reduction in long chain acyl-carnitines in old ALI mice.

Conclusion

This study demonstrates age-associated susceptibility to ALI-induced muscle wasting which parallels a metabolomic profile suggestive of altered muscle fatty acid metabolism. MuRF1 activation may contribute to both atrophy and impaired fatty acid oxidation, which may synergistically impair muscle function in old ALI mice.

     

Metabolomic signatures of increases in temperature and ocean acidification from the reef-building coral, <Emphasis Type="Italic">Pocillopora damicornis</Emphasis>

Introduction

As a changing climate threatens the persistence of terrestrial and marine ecosystems by altering community composition and function, differential performance of taxa highlights the need for predictive metrics and mechanistic understanding of the factors underlying positive performance in the face of environmental disturbances. Biochemical reactions within cells provide a snapshot of molecular regulation and flexibility during exposure to environmental stressors. However, because the organism is the unit of selection there is a need for the integration of metabolite data with organism physiology to understand mechanisms responsible for individual success under a changing climate.

Objectives

Our study aims to characterize the molecular response of reef corals to simulated global climate change stressors. Furthermore, we seek to relate changes in the molecular physiology to observations in overall colony response.

Methods

To this end, we applied a non-targeted metabolomic approach to describe lipid and primary metabolite composition after exposure of the reef-building coral Pocillopora damicornis to ambient and elevated experimental climate change conditions. We compared these metabolite data to organism physiology, specifically the key processes of photosynthesis, respiration, and calcification.

Results

Corals significantly altered their lipid and primary metabolite profiles in response to experimental treatments. Primary metabolite profiles predicted organisms’ net photosynthesis, but not calcification or respiration measures. Despite challenges in metabolome annotation, our data indicated corals alter carbohydrate composition, cell structural lipids, and signaling compounds in response to elevated treatment conditions.

Conclusions

The integration of metabolite and physiological data highlights the predictive power of metabolomics in defining organism performance and provides biomarkers for future studies. Here, we present a multivariate biomarker approach to assess climate change impacts and advance our mechanistic understanding of stress response in this keystone species.

     

24-Epibrassinolide regulates carbohydrate metabolism and increases polyamine content in cucumber exposed to Ca(NO3)2 stress

Key message

This study focuses on the impact of carbohydrate metabolism and endogenous polyamines levels in leaves of cucumber seedlings under salt stress by exogenous BRs.

Abstract

The effects of 24-epibrassinolide (EBL) on carbohydrate metabolism and endogenous content of polyamines were investigated in cucumber seedlings (Cucumis sativus L. cv. Jinyou No. 4) exposed to salinity stress [80 mM Ca(NO₃)₂]. Spraying of exogenous EBL partially enhanced the enzyme activities of sucrose phosphate synthase, sucrose synthase and acid invertase; thus, raising the level of sucrose, fructose and total soluble sugars. The amylase activity was also increased by EBL, companied by the rising of sucrose level. These results indicated that EBL improved the carbohydrate metabolism of cucumber under Ca(NO₃)₂ stress. Moreover, EBL raised the levels of soluble conjugated and insoluble bound polyamines while lowered the free polyamines content, particularly putrescine. Our experiment demonstrated that exogenous EBL elevated stability of cellular membrane and positively improve the carbohydrate metabolism in cucumber growing under Ca(NO₃)₂ stress.     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes

Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes.

Specific metabolic pathways (especially those from amino acid and carbohydrate metabolism) underlie heterosis of six agronomic traits in rice.     

A metabolomics guided exploration of marine natural product chemical space

Introduction

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.

Objective

Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.

Method

In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.

Result

This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.

Conclusion

Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background

The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.

Results

In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.

Conclusion

Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.