Uncovering the metabolic response of abalone (<Emphasis Type="Italic">Haliotis midae)</Emphasis> to environmental hypoxia through metabolomics

Introduction

Oxygen is essential for metabolic processes and in the absence thereof alternative metabolic pathways are required for energy production, as seen in marine invertebrates like abalone. Even though hypoxia has been responsible for significant losses to the aquaculture industry, the overall metabolic adaptations of abalone in response to environmental hypoxia are as yet, not fully elucidated.

Objective

To use a multiplatform metabolomics approach to characterize the metabolic changes associated with energy production in abalone (Haliotis midae) when exposed to environmental hypoxia.

Methods

Metabolomics analysis of abalone adductor and foot muscle, left and right gill, hemolymph, and epipodial tissue samples were conducted using a multiplatform approach, which included untargeted NMR spectroscopy, untargeted and targeted LC–MS spectrometry, and untargeted and semi-targeted GC-MS spectrometric analyses.

Results

Increased levels of anaerobic end-products specific to marine animals were found which include alanopine, strombine, tauropine and octopine. These were accompanied by elevated lactate, succinate and arginine, of which the latter is a product of phosphoarginine breakdown in abalone. Primarily amino acid metabolism was affected, with carbohydrate and lipid metabolism assisting with anaerobic energy production to a lesser extent. Different tissues showed varied metabolic responses to hypoxia, with the largest metabolic changes in the adductor muscle.

Conclusions

From this investigation, it becomes evident that abalone have well-developed (yet understudied) metabolic mechanisms for surviving hypoxic periods. Furthermore, metabolomics serves as a powerful tool for investigating the altered metabolic processes in abalone.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

<Superscript>1</Superscript>H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

Introduction

Gastric cancer (GC) is a malignant tumor worldwide. As primary pathway for metastasis, the lymphatic system is an important prognostic factor for GC patients. Although the metabolic changes of gastric cancer have been investigated in extensive studies, little effort focused on the metabolic profiling of lymph node metastasis (LNM)-positive or negative GC patients.

Objectives

We performed ¹H NMR spectrum of GC tissue samples with and without LNM to identify novel potential metabolic biomarkers in the process of LNM of GC.

Methods

¹H NMR-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of tissue samples from LNM-positive GC patients (n?=?40), LNM-negative GC patients (n?=?40) and normal controls (n?=?40).

Results

There was a clear separation between GC patients and normal controls, and 33 differential metabolites were identified in the study. Moreover, GC patients were also well-classified according to LNM-positive or negative. Totally eight distinguishing metabolites were selected in the metabolic profiling of GC patients with LNM-positive or negative, suggesting the metabolic dysfunction in the process of LNM. According to further validation and analysis, especially BCAAs metabolism (leucine, isoleucine, valine), GSH and betaine may be as potential factors of diagnose and prognosis of GC patients with or without LNM.

Conclusion

To our knowledge, this is the first metabolomics study focusing on LNM of GC. The identified distinguishing metabolites showed a promising application on clinical diagnose and therapy prediction, and understanding the mechanism underlying the carcinogenesis, invasion and metastasis of GC.

     

Serum metabolites associated with feed efficiency in black angus steers

Introduction

Improving feed utilization in cattle is required to reduce input costs, increase production, and ultimately improve sustainability of the beef cattle industry. Characterizing metabolic differences between efficient and non-efficient animals will allow stakeholders to identify more efficient cattle during backgrounding.

Objectives

This study used an untargeted metabolomics approach to determine differences in serum metabolites between animals of low and high residual feed intake.

Methods

Residual feed intake was determined for 50 purebred Angus steers and 29 steers were selected for the study steers based on low versus high feed efficiency. Blood samples were collected from steers and analyzed using untargeted metabolomics via mass spectrometry. Metabolite data was analyzed using Metaboanalyst, visualized using orthogonal partial least squares discriminant analysis, and p-values derived from permutation testing. Non-esterified fatty acids, urea nitrogen, and glucose were measured using commercially available calorimetric assay kits. Differences in metabolites measured were grouped by residual feed intake was measured using one-way analysis of variance in SAS 9.4.

Results

Four metabolites were found to be associated with differences in feed efficiency. No differences were found in other serum metabolites, including serum urea nitrogen, non-esterified fatty acids, and glucose.

Conclusions

Four metabolites that differed between low and high residual feed intake have important functions related to nutrient utilization, among other functions, in cattle. This information will allow identification of more efficient steers during backgrounding.

     

Metabolome variability for two Mediterranean sponge species of the genus <Emphasis Type="Italic">Haliclona</Emphasis>: specificity,time, and space

Introduction

The study of natural variation of metabolites brings valuable information on the physiological state of the organisms as well as their phenotypic traits. In marine organisms, metabolome variability has mostly been addressed through targeted studies on metabolites of ecological or pharmaceutical interest. However, comparative metabolomics has demonstrated its potential to address the overall and complex metabolic variability of organisms.

Objectives

In this study, the intraspecific (temporal and spatial) variability of two Mediterranean Haliclona sponges (H. fulva and H. mucosa) was investigated through an untargeted and then targeted metabolomics approach and further compared to their interspecific variability.

Methods

Samples of both species were collected monthly during 1 year in the coralligenous habitat of the Northwestern Mediterranean sae at Marseille and Nice. Their metabolomic profiles were obtained by UHPLC-QqToF analyses.

Results

Marked variations were noticed in April and May for both species including a decrease in Shannon’s diversity and concentration in specialized metabolites together with an increase in fatty acids and lyso-PAF like molecules. Spatial variations across different sampling sites could also be observed for both species, however in a lesser extent.

Conclusions

Synchronous metabolic changes possibly triggered by physiological factors like reproduction and/or environmental factors like an increase in the water temperature were highlighted for both Mediterranean Haliclona species inhabiting close habitats but displaying different biosynthetic pathways. Despite significative intraspecific variations, metabolomic variability remains minor when compared to interspecific variations for these congenerous species, therefore suggesting the predominance of genetic information of the holobiont in the observed metabolome.

     

Untargeted and stable isotope-assisted metabolomic analysis of MDA-MB-231 cells under hypoxia

Introduction

Hypoxia commonly occurs in cancers and is highly related with the occurrence, development and metastasis of cancer. Treatment of triple negative breast cancer remains challenge. Knowledge about the metabolic status of triple negative breast cancer cell lines in hypoxia is valuable for the understanding of molecular mechanisms of this tumor subtype to develop effective therapeutics.

Objectives

Comprehensively characterize the metabolic profiles of triple negative breast cancer cell line MDA-MB-231 in normoxia and hypoxia and the pathways involved in metabolic changes in hypoxia.

Methods

Differences in metabolic profiles affected pathways of MDA-MB-231 cells in normoxia and hypoxia were characterized using GC–MS based untargeted and stable isotope assisted metabolomic techniques.

Results

Thirty-three metabolites were significantly changed in hypoxia and nine pathways were involved. Hypoxia increased glycolysis, inhibited TCA cycle, pentose phosphate pathway and pyruvate carboxylation, while increased glutaminolysis in MDA-MB-231 cells.

Conclusion

The current results provide metabolic differences of MDA-MB-231 cells in normoxia and hypoxia conditions as well as the involved metabolic pathways, demonstrating the power of combined use of untargeted and stable isotope-assisted metabolomic methods in comprehensive metabolomic analysis.

     

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.

     

Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

Introduction

Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population.

Objective

This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response.

Methods

Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools.

Results

Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC–MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc.

Conclusions

Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.

     

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.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

Metabolic profiling of <Emphasis Type="Italic">Drosophila melanogaster</Emphasis> metamorphosis: a new insight into the central metabolic pathways

Introduction

Metamorphosis is a complicated process in which cell proliferation, differentiation, and death are orchestrated to form the mature structures of insects. In Drosophila, this process is controlled by ecdysone, a steroid hormone responsible for tissue remodeling and organogenesis that gives rise to the adult fly.

Objective

By using a metabolomics approach, this study aimed to elucidate global changes in the central metabolic pathways in Drosophila throughout metamorphosis and then further examine the effects of temperature and origin on metabolic profiles.

Methods

Targeted and non-targeted metabolic profiling of time-course samples from Drosophila were constructed to cover a wide range of cellular metabolites during metamorphosis.

Results

This was the first wide-scale metabolomics study of Drosophila metamorphosis focusing on central metabolism. The abundance of detected metabolites changed drastically and correlated strongly with the development of Drosophila pupae. In non-stress conditions, temperature affected the developmental time, but the metabolic state at a certain stage of metamorphosis remained stable. Between D. melanogaster Canton S and Oregon R, similar metabolic profiles throughout metamorphosis was observed. However, there were still differences in purine and pyrimidine metabolism at an early stage in the pupal period, which was matched by differences in ecdysteroid levels.

Conclusion

This study supported the strength of metabolomics in the field of developmental biology. The results provided a general view on the metabolic profile of Drosophila during metamorphosis, which provides basic 3 knowledge for future metabolomics studies using Drosophila.

     

The altered human serum metabolome induced by a marathon

Introduction

Endurance races have been associated with a substantial amount of adverse effects which could lead to chronic disease and long-term performance impairment. However, little is known about the holistic metabolic changes occurring within the serum metabolome of athletes after the completion of a marathon.

Objectives

Considering this, the aim of this study was to better characterize the acute metabolic changes induced by a marathon.

Methods

Using an untargeted two dimensional gas chromatography time-of-flight mass spectrometry metabolomics approach, pre- and post-marathon serum samples of 31 athletes were analyzed and compared to identify those metabolites varying the most after the marathon perturbation.

Results

Principle component analysis of the comparative groups indicated natural differentiation due to variation in the total metabolite profiles. Elevated concentrations of carbohydrates, fatty acids, tricarboxylic acid cycle intermediates, ketones and reduced concentrations of amino acids indicated a metabolic shift between various fuel substrate systems. Additionally, elevated odd-chain fatty acids and α-hydroxy acids indicated the utilization of α-oxidation and autophagy as alternative energy-producing mechanisms. Adaptations in gut microbe-associated markers were also observed and correlated with the metabolic flexibility of the athlete.

Conclusion

From these results it is evident that a marathon places immense strain on the energy-producing pathways of the athlete, leading to extensive protein degradation, oxidative stress, mammalian target of rapamycin complex 1 inhibition and autophagy. A better understanding of this metabolic shift could provide new insights for optimizing athletic performance, developing more efficient nutrition regimens and identify strategies to improve recovery.

     

Metabolic profiling of isolated mitochondria and cytoplasm reveals compartment-specific metabolic responses

Introduction

Subcellular compartmentalization enables eukaryotic cells to carry out different reactions at the same time, resulting in different metabolite pools in the subcellular compartments. Thus, mutations affecting the mitochondrial energy metabolism could cause different metabolic alterations in mitochondria compared to the cytoplasm. Given that the metabolite pool in the cytosol is larger than that of other subcellular compartments, metabolic profiling of total cells could miss these compartment-specific metabolic alterations.

Objectives

To reveal compartment-specific metabolic differences, mitochondria and the cytoplasmic fraction of baker’s yeast Saccharomyces cerevisiae were isolated and subjected to metabolic profiling.

Methods

Mitochondria were isolated through differential centrifugation and were analyzed together with the remaining cytoplasm by gas chromatography–mass spectrometry (GC–MS) based metabolic profiling.

Results

Seventy-two metabolites were identified, of which eight were found exclusively in mitochondria and sixteen exclusively in the cytoplasm. Based on the metabolic signature of mitochondria and of the cytoplasm, mutants of the succinate dehydrogenase (respiratory chain complex II) and of the F_OF₁-ATP-synthase (complex V) can be discriminated in both compartments by principal component analysis from wild-type and each other. These mitochondrial oxidative phosphorylation machinery mutants altered not only citric acid cycle related metabolites but also amino acids, fatty acids, purine and pyrimidine intermediates and others.

Conclusion

By applying metabolomics to isolated mitochondria and the corresponding cytoplasm, compartment-specific metabolic signatures can be identified. This subcellular metabolomics analysis is a powerful tool to study the molecular mechanism of compartment-specific metabolic homeostasis in response to mutations affecting the mitochondrial metabolism.

     

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.

     

Biological effects of amphiphilic copolymer nanoparticle-encapsulated multi-target chemotherapeutic drugs on MCF-7 human breast cancer cells

Introduction

Amphiphilic copolymer nanoparticle-encapsulated multi-target chemotherapeutic drugs have attracted considerable attention due to their favorable drug efficiency and potential application prospect. Studies have shown that an amphiphilic copolymer, methoxypoly(ethylene glycol)-poly(lactide-co-glycolide) modified with ε-polylysine, and encapsulated with hydrophilic doxorubicin, hydrophobic paclitaxel and survivin siRNA profoundly improved the therapeutic effect both in vitro and in vivo.

Objectives

To investigate how MCF-7 cells would response to the exposure of these nanoparticles over with time and assess the biological effects of these nanoparticles and their encapsulated drugs in a holistic manner.

Methods

MCF-7 cells were treated with PBS, nanocarrier and three encapsulated drugs, respectively. Metabolic alterations associated with nano-drugs exposure were investigated by performing untargeted NMR metabolomics with combination of targeted fatty acids analysis by GC-MS on cell extracts. Altered metabolic pathways were further validated by qRT-PCR approach.

Results

Copolymers showed great biocompatibility with cells as it induced transit metabolic disruptions without affecting cell survival rate. The rapid release of encapsulated doxorubicin resulted in inhibition of glycolysis and DNA synthesis, active proteolysis; these metabolic alternations were recovered after 10 h exposure. However, the combination use of multiple drugs consistently induced cell cycle arrest and apoptosis evidenced by reduction in glycolysis, active proteolysis, stimulated O-GlcNAcylation, reduced the PC:GPC ratio and fatty acids accumulation. Prolonged exposure to encapsulated-multiple-drugs also induced oxidative stress to cells.

Conclusion

These findings provide important insight into the biological effects of nanoparticles and their encapsulated drugs while demonstrate that metabolomics is a powerful approach to evaluate the biological effects of nano-drugs.

     

Untargeted metabolomics approach using UPLC-ESI-QTOF-MS to explore the metabolome of fresh-cut iceberg lettuce

Introduction

The shelf-life of fresh-cut lettuce after storage is limited by several factors that affect its quality and lead to consumer rejection. Different metabolic events occur after cutting as an abiotic stress response.

Objectives

This study aims to explore the metabolome of iceberg lettuce and to understand the changes related to storage time and genetics applying an untargeted metabolomics approach.

Methods

Two cultivars with different browning susceptibility, fast-browning (FB) and slow-browning (SB), were analyzed by UPLC-ESI-QTOF-MS just after cutting (d0) and after five days of storage (d5). Extraction, metabolic profiling, and data-pretreatment procedures were optimized to obtain a robust and reliable data set.

Results

Preliminary principal component analysis and hierarchical cluster analysis of the full dataset [around 8551 extracted, aligned and filtered molecular features (MFs)] showed a clear separation between the different samples (FB-d0, FB-d5, SB-d0, and SB-d5), highlighting a clear storage time-dependent effect. After statistical analysis applying Student’s t test, 536 MFs were detected as significantly different between d0 and d5 of storage in FB and 633 in SB. Some of them (221) were common to both cultivars. Out of these significant compounds, 22 were tentatively identified by matching their molecular formulae with those previously reported in the literature. Five families of metabolites were detected: amino acids, phenolic compounds, sesquiterpene lactones, fatty acids, and lysophospholipids. All compounds showed a clear trend to decrease at d5 except phenolic compounds that increased after storage.

Conclusion

The untargeted metabolomics analysis is a powerful tool for characterizing the changes on lettuce metabolome associated with cultivar and especially with storage time. Some families of compounds affected by storage time were reported to be closely related to quality loss.

     

Investigation of species and environmental effects on rhubarb roots metabolome using <Superscript>1</Superscript>H NMR combined with high performance thin layer chromatography

Introduction

The pharmacological activities of medicinal plants are reported to be due to a wide range of metabolites, therein, the concentrations of which are greatly affected by many genetic and/or environmental factors. In this context, a metabolomics approach has been applied to reveal these relationships. The investigation of such complex networks that involve the correlation between multiple biotic and abiotic factors and the metabolome, requires the input of information acquired by more than one analytical platform. Thus, development of new metabolomics techniques or hyphenations is continuously needed.

Objectives

Feasibility of high performance thin-layer chromatography (HPTLC) were investigated as a supplementary tool for medicinal plants metabolomics supporting ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

Method

The overall metabolic difference of plant material collected from two species (Rheum palmatum and Rheum tanguticum) in different geographical locations and altitudes were analyzed by ¹H NMR- and HPTLC-based metabolic profiling. Both NMR and HPTLC data were submitted to multivariate data analysis including principal component analysis and orthogonal partial least square analysis.

Results

The NMR and HPTLC profiles showed that while chemical variations of rhubarb are in some degree affected by all the factors tested in this study, the most influential factor was altitude of growth. The metabolites responsible for altitude differentiation were chrysophanol, emodin and sennoside A, whereas aloe emodin, catechin, and rhein were the key species-specific markers.

Conclusion

These results demonstrated the potential of HTPLC as a supporting tool for metabolomics due to its high profiling capacity of targeted metabolic groups and preparative capability.

     

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

Background

Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.

Methods

We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n?=?1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.

Results

Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.

Conclusion

Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.