Metabolic profiling of <Emphasis Type="Italic">Daphnia magna</Emphasis> exposed to environmental stressors by GC–MS and chemometric tools

Introduction

Climate change is a major concern for the scientific community, demanding novel information about the effects of environmental stressors on living organisms. Metabolic profiling is required for achieving the most extensive possible range of compounds and their concentration changes on stressed conditions.

Objectives

Individuals of the crustacean species Daphnia magna were exposed to three different abiotic factors linked to global climate change: high salinity, high temperature levels and hypoxia. Advanced chemometric tools were used to characterize the metabolites affected by the exposure.

Method

An exploratory analysis of gas chromatography-mass spectrometry (GC–MS) data was performed to discriminate between control and exposed daphnid samples. Due to the complexity of these GC–MS data sets, a comprehensive untargeted analysis of the full scan data was performed using multivariate curve resolution-alternating least squares (MCR-ALS) method. This approach enabled to resolve most of the metabolite signals from interference peaks caused by derivatization reactions. Metabolites with significant changes in their peak areas were tentatively identified and the involved metabolic pathways explored.

Results

D. magna metabolic biomarkers are proposed for the considered physical factors. Metabolites related with energy metabolic pathways including some amino acids, carbohydrates, organic acids and nucleosides were identified as potential biomarkers of the investigated treatments.

Conclusions

The proposed untargeted GC–MS metabolomics strategy and multivariate data analysis tools were useful to investigate D. magna metabolome under environmental stressed conditions.

     

Interferon γ induced compositional changes in human bone marrow derived mesenchymal stem/stromal cells

Background

Mesenchymal stem/stromal cells (MSC) display a range of immunoregulatory properties which can be enhanced by the exposure to cytokines such interferon γ (IFN-γ). However the compositional changes associated with the ‘licensing’ of these cells have not been clearly defined. The present study was undertaken to provide a detailed comparative proteomic analysis of the compositional changes that occur in human bone marrow derived MSC following 20 h treatment with IFN-γ.

Methods

2D LC MSMS analysis of control and IFN-γ treated cells from 5 different healthy donors provided confident identification of more than 8400 proteins.

Results

In total 210 proteins were shown to be significantly altered in their expression levels (≥|2SD|) following IFN-γ treatment. The changes for several of these proteins were confirmed by flow cytometry. STRING analysis determined that approximately 30% of the altered proteins physically interacted in described interferon mediated processes. Comparison of the list of proteins that were identified as changed in the proteomic analysis with data for the same proteins in the Interferome DB indicated that ~35% of these proteins have not been reported to be IFN-γ responsive in a range of cell types.

Conclusions

This data provides an in depth analysis of the proteome of basal and IFN-γ treated human mesenchymal stem cells and it identifies a number of novel proteins that may contribute to the immunoregulatory capacity if IFN-γ licensed cells.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

MIDAS-G: a computational platform for investigating fragmentation rules of tandem mass spectrometry in metabolomics

Introduction

Tandem mass spectrometry (MS/MS) has been widely used for identifying metabolites in many areas. However, computationally identifying metabolites from MS/MS data is challenging due to the unknown of fragmentation rules, which determine the precedence of chemical bond dissociation. Although this problem has been tackled by different ways, the lack of computational tools to flexibly represent adjacent structures of chemical bonds is still a long-term bottleneck for studying fragmentation rules.

Objectives

This study aimed to develop computational methods for investigating fragmentation rules by analyzing annotated MS/MS data.

Methods

We implemented a computational platform, MIDAS-G, for investigating fragmentation rules. MIDAS-G processes a metabolite as a simple graph and uses graph grammars to recognize specific chemical bonds and their adjacent structures. We can apply MIDAS-G to investigate fragmentation rules by adjusting bond weights in the scoring model of the metabolite identification tool and comparing metabolite identification performances.

Results

We used MIDAS-G to investigate four bond types on real annotated MS/MS data in experiments. The experimental results matched data collected from wet labs and literature. The effectiveness of MIDAS-G was confirmed.

Conclusion

We developed a computational platform for investigating fragmentation rules of tandem mass spectrometry. This platform is freely available for download.

     

Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome

Background

Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.

Aim

Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.

Methods

Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.

Results

Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.

Conclusion

Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.

     

Pathway-based analyses

Background

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.

Methods

The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.

Results

Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.

Conclusions

The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

     

Combined effects of aspirin and vitamin D3 on two OSCC cell lines: a preliminary study

Objectives

We evaluated the potential effects of aspirin combined with vitamin D3 on cell proliferation and apoptosis in oral cancer cells.

Results

Compared to the untreated control or individual drug, the combinations of aspirin and vitamin D3 significantly decreased the rates of cell proliferation by CCK-8 assay, and caused higher rates of cell apoptosis in both CAL-27 and SCC-15 cells by Annexin V-FITC apoptosis assay and flow cytometry. Remarkably, the combined treatment with aspirin and vitamin D3 significantly suppressed the expression of Bcl-2 protein and p-Erk1/2 protein, examined by western blot analysis.

Conclusions

Our study demonstrates that aspirin and vitamin D3 have biological activity against two human OSCC cell lines and their activity is synergistic or additive when two drugs used in combination with therapeutic concentrations. The combination of aspirin and vitamin D3 may be an effective approach for inducing cell death in OSCC.

     

Machine learning analysis of microbial flow cytometry data from nanoparticles,antibiotics and carbon sources perturbed anaerobic microbiomes

Background

Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments.

Results

Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics.

Conclusion

Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.

     

Logistic regression model training based on the approximate homomorphic encryption

Background

Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives.

Methods

This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier.

Results

Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable.

Conclusions

We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality.

     

Hyper response to ovarian stimulation affects the follicular fluid metabolomic profile of women undergoing IVF similarly to polycystic ovary syndrome

Introduction

During in vitro fertilization (IVF), the hyper response to controlled ovarian stimulation (COS) is a common characteristic among patients diagnosed with polycystic ovary syndrome (PCOS), although non-diagnosed patients may also demonstrate this response.

Objectives

In an effort to investigate follicular metabolic characteristics associated with hyper response to COS, the present study analyzed follicular fluid (FF) samples from patients undergoing IVF.

Methods

FF samples were obtained from patients with PCOS and hyper response during IVF (PCOS group, N?=?15), patients without PCOS but with hyper response during IVF (HR group, N?=?44), and normo-responder patients receiving IVF (control group, N?=?22). FF samples underwent Bligh and Dyer extraction, followed by metabolomic analysis by ultra-performance liquid chromatography mass spectrometry, considering two technical replicates. Clinical data was analyzed by ANOVA and chi-square tests. The metabolomic dataset was analyzed by multivariate statistics, and the significance of biomarkers was confirmed by ANOVA.

Results

Clinical data showed differences regarding follicles production, oocyte and embryo quality. From the 15 proposed biomarkers, 14 were of increased abundance in the control group and attributed as fatty acids, diacylglycerol, triacylglycerol, ceramide, ceramide-phosphate, phosphatidylcholine, and sphingomyelin. The PCOS patients showed increased abundance of a metabolite of m/z 144.0023 that was not attributed to a class.

Conclusion

The clinical and metabolic similarities observed in the FF of hyper responders with and without PCOS diagnosis indicate common biomarkers that could assist on the development of accessory tools for assessment of IVF parameters.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

The EcoSpold 2 format—why a new format?

Purpose

This paper introduces the new EcoSpold data format for life cycle inventory (LCI).

Methods

A short historical retrospect on data formats in the life cycle assessment (LCA) field is given. The guiding principles for the revision and implementation are explained. Some technical basics of the data format are described, and changes to the previous data format are explained.

Results

The EcoSpold 2 data format caters for new requirements that have arisen in the LCA field in recent years.

Conclusions

The new data format is the basis for the Ecoinvent v3 database, but since it is an open data format, it is expected to be adopted by other LCI databases. Several new concepts used in the new EcoSpold 2 data format open the way for new possibilities for the LCA practitioners and to expand the application of the datasets in other fields beyond LCA (e.g., Material Flow Analysis, Energy Balancing).

     

DeltaMS: a tool to track isotopologues in GC- and LC-MS data

Introduction

Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing.

Objective

To introduce a software tool for the identification of isotopologues from mass spectrometry data.

Methods

DeltaMS relies on XCMS peak detection and X¹³CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X¹³CMS.

Results

To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures.

Conclusion

DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.

Graphical Abstract

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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.

     

Metabolic response of porcine colon explants to in vitro infection by <Emphasis Type="Italic">Brachyspira hyodysenteriae</Emphasis>: a leap into disease pathophysiology

Introduction

Swine dysentery caused by Brachyspira hyodysenteriae is a production limiting disease in pig farming. Currently antimicrobial therapy is the only treatment and control method available.

Objective

The aim of this study was to characterize the metabolic response of porcine colon explants to infection by B. hyodysenteriae.

Methods

Porcine colon explants exposed to B. hyodysenteriae were analyzed for histopathological, metabolic and pro-inflammatory gene expression changes.

Results

Significant epithelial necrosis, increased levels of l-citrulline and IL-1α were observed on explants infected with B. hyodysenteriae.

Conclusions

The spirochete induces necrosis in vitro likely through an inflammatory process mediated by IL-1α and NO.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

     

Propofol alleviates hypoxia-induced nerve injury in PC-12 cells by up-regulation of microRNA-153

Background

Although the neuroprotective role of propofol has been identified recently, the regulatory mechanism associated with microRNAs (miRNAs/miRs) in neuronal cells remains to be poorly understood. We aimed to explore the regulatory mechanism of propofol in hypoxia-injured rat pheochromocytoma (PC-12) cells.

Methods

PC-12 cells were exposed to hypoxia, and cell viability and apoptosis were assessed by CCK-8 assay and flow cytometry assay/Western blot analysis, respectively. Effects of propofol on hypoxia-injured cells were measured, and the expression of miR-153 was determined by stem-loop RT-PCR. After that, whether propofol affected PC-12 cells under hypoxia via miR-153 was verified, and the downstream protein of miR-153 as well as the involved signaling cascade was finally explored.

Results

Hypoxia-induced decrease of cell viability and increase of apoptosis were attenuated by propofol. Then, we found hypoxia exposure up-regulated miR-153 expression, and the level of miR-153 was further elevated by propofol in hypoxia-injured PC-12 cells. Following experiments showed miR-153 inhibition reversed the effects of propofol on hypoxia-treated PC-12 cells. Afterwards, we found BTG3 expression was negatively regulated by miR-153 expression, and BTG3 overexpression inhibited the mTOR pathway and AMPK activation. Besides, hypoxia inhibited the mTOR pathway and AMPK, and these inhibitory effects could be attenuated by propofol.

Conclusion

Propofol protected hypoxia-injured PC-12 cells through miR-153-mediataed down-regulation of BTG3. BTG3 could inhibit the mTOR pathway and AMPK activation.

     

PPIRank - an advanced method for ranking protein-protein interations in TAP/MS data

Background

Tandem affinity purification coupled with mass-spectrometry (TAP/MS) analysis is a popular method for the identification of novel endogenous protein-protein interactions (PPIs) in large-scale. Computational analysis of TAP/MS data is a critical step, particularly for high-throughput datasets, yet it remains challenging due to the noisy nature of TAP/MS data.

Results

We investigated several major TAP/MS data analysis methods for identifying PPIs, and developed an advanced method, which incorporates an improved statistical method to filter out false positives from the negative controls. Our method is named PPIRank that stands for PPI rank ing in TAP/MS data. We compared PPIRank with several other existing methods in analyzing two pathway-specific TAP/MS PPI datasets from Drosophila.

Conclusion

Experimental results show that PPIRank is more capable than other approaches in terms of identifying known interactions collected in the BioGRID PPI database. Specifically, PPIRank is able to capture more true interactions and simultaneously less false positives in both Insulin and Hippo pathways of Drosophila Melanogaster.