LC-MS metabolomics from study design to data-analysis - using a versatile pathogen as a test case

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

1H NMR metabolomics analysis of oil palm stem tissue infected by Ganoderma boninense based on field severity Indices

Basal stem rot disease (BSR) caused by G. boninense affects most oil palm plants in Southeast Asia. This disease can be fatal to palm oil production. BSR shows no signs on the tree in the early stages of infection. Therefore, it is essential to find an approach that can detect BSR disease in oil palm, especially at any level of disease severity in the field. This study aims to identify biomarkers of BSR disease in oil palm stem tissue based on various disease severity indices in the field using ¹H NMR-based metabolomics analysis. The crude extract of oil palm stem tissue with four disease severity indices was analyzed by ¹H NMR metabolomics. Approximately 90 metabolites from oil palm stem tissue were identified.Twenty of these were identified as metabolites that significantly differentiated the four disease severity indices. These metabolites include the organic acid group, the carbohydrate group, the organoheterocyclic compound group, and the benzoid group. In addition, different tentative biomarkers for different disease severity indices were also identified. These tentative biomarkers consist of groups of organic acids, carbohydrates, organoheterocyclic compounds, nitrogenous organic compounds, and benzene. There are five pathways in oil palm that are potentially affected by BSR disease.

Recent Developments in Metabolomics Studies of Endophytic Fungi

Endophytic fungi are microorganisms that colonize living plants' tissues without causing any harm. They are known as a natural source of bioactive metabolites with diverse pharmacological functions. Many structurally different chemical metabolites were isolated from endophytic fungi. Recently, the increasing trends in human health problems and diseases have escalated the search for bioactive metabolites from endophytic fungi. The conventional bioassay-guided study is known as laborious due to chemical complexity. Thus, metabolomics studies have attracted extensive research interest owing to their potential in dealing with a vast number of metabolites. Metabolomics coupled with advanced analytical tools provides a comprehensive insight into systems biology. Despite its wide scientific attention, endophytic fungi metabolomics are relatively unexploited. This review highlights the recent developments in metabolomics studies of endophytic fungi in obtaining the global metabolites picture.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

Untargeted serum metabolites profiling in high-fat diet mice supplemented with enhanced palm tocotrienol-rich fraction using UHPLC-MS

Excessive high fat dietary intake promotes risk of developing non-alcoholic fatty liver disease (NAFLD) and predisposed with oxidative stress. Palm based tocotrienol-rich fraction (TRF) has been reported able to ameliorate oxidative stress but exhibited poor bioavailability. Thus, we investigated whether an enhanced formulation of TRF in combination with palm kernel oil (medium-chain triglycerides) (ETRF) could ameliorate the effect of high-fat diet (HFD) on leptin-deficient male mice. All the animals were divided into HFD only (HFD group), HFD supplemented with ETRF (ETRF group) and HFD supplemented with TRF (TRF group) and HFD supplemented with PKO (PKO group). After 6 weeks, sera were collected for untargeted metabolite profiling using UHPLC-Orbitrap MS. Univariate analysis unveiled alternation in metabolites for bile acids, amino acids, fatty acids, sphingolipids, and alkaloids. Bile acids, lysine, arachidonic acid, and sphingolipids were downregulated while xanthine and hypoxanthine were upregulated in TRF and ETRF group. The regulation of these metabolites suggests that ETRF may promote better fatty acid oxidation, reduce oxidative stress and pro-inflammatory metabolites and acts as anti-inflammatory in fatty liver compared to TRF. Metabolites regulated by ETRF also provide insight of its role in fatty liver. However, further investigation is warranted to identify the mechanisms involved.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Experimental Strategies to Explore Drug Action and Resistance in Kinetoplastid Parasites

Kinetoplastids are the causative agents of leishmaniasis, human African trypanosomiasis, and American trypanosomiasis. They are responsible for high mortality and morbidity in (sub)tropical regions. Adequate treatment options are limited and have several drawbacks, such as toxicity, need for parenteral administration, and occurrence of treatment failure and drug resistance. Therefore, there is an urgency for the development of new drugs. Phenotypic screening already allowed the identification of promising new chemical entities with anti-kinetoplastid activity potential, but knowledge on their mode-of-action (MoA) is lacking due to the generally applied whole-cell based approach. However, identification of the drug target is essential to steer further drug discovery and development. Multiple complementary techniques have indeed been used for MoA elucidation. In this review, the different 'omics' approaches employed to define the MoA or mode-of-resistance of current reference drugs and some new anti-kinetoplastid compounds are discussed.

Chemical Barcoding: A Nuclear-Magnetic-Resonance-Based Approach To Ensure the Quality and Safety of Natural Ingredients

One of the greatest challenges facing the functional food and natural health product (NHP) industries is sourcing high-quality, functional, natural ingredients for their finished products. Unfortunately, the lack of ingredient standards, modernized analytical methodologies, and industry oversight creates the potential for low quality and, in some cases, deliberate adulteration of ingredients. By exploring a diverse library of NHPs provided by the independent certification organization ISURA, we demonstrated that nuclear magnetic resonance (NMR) spectroscopy provides an innovative solution to authenticate botanicals and warrant the quality and safety of processed foods and manufactured functional ingredients. Two-dimensional NMR experiments were shown to be a robust and reproducible approach to capture the content of complex chemical mixtures, while a binary normalization step allows for emphasizing the chemical diversity in each sample, and unsupervised statistical methodologies provide key advantages to classify, authenticate, and highlight the potential presence of additives and adulterants.

Tissue-Specific Sample Dilution: An Important Parameter to Optimise Prior to Untargeted LC-MS Metabolomics

When developing a sample preparation protocol for LC–MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC–MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC–MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI−) linear features from liver, 698 (ESI+) and 498 (ESI−) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI−) linear features from liver, 114 (ESI+) and 108 (ESI−) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (<50) compared to hundreds of reproducible features from the optimum concentration used. Our study highlights on multiple matrices and multiple extract and chromatography types, the critical importance of determining a suitable injected concentration prior to untargeted LC–MS metabolomics, with the described workflow applicable to any matrix and LC–MS system.

Evaluation of an untargeted nano-liquid chromatography-mass spectrometry approach to expand coverage of low molecular weight dissolved organic matter in Arctic soil

Characterizing low molecular weight (LMW) dissolved organic matter (DOM) in soils and evaluating the availability of this labile pool is critical to understanding the underlying mechanisms that control carbon storage or release across terrestrial systems. However, due to wide-ranging physicochemical diversity, characterizing this complex mixture of small molecules and how it varies across space remains an analytical challenge. Here, we evaluate an untargeted approach to detect qualitative and relative-quantitative variations in LMW DOM with depth using water extracts from a soil core from the Alaskan Arctic, a unique system that contains nearly half the Earth's terrestrial carbon and is rapidly warming due to climate change. We combined reversed-phase and hydrophilic interaction liquid chromatography, and nano-electrospray ionization coupled with high-resolution tandem mass spectrometry in positive- and negative-ionization mode. The optimized conditions were sensitive, robust, highly complementary, and enabled detection and putative annotations of a wide range of compounds (e.g. amino acids, plant/microbial metabolites, sugars, lipids, peptides). Furthermore, multivariate statistical analyses revealed subtle but consistent and significant variations with depth. Thus, this platform is useful not only for characterizing LMW DOM, but also for quantifying relative variations in LMW DOM availability across space, revealing hotspots of biogeochemical activity for further evaluation.

Lipidomics and Biomarker Discovery in Kidney Disease

Technological advances in mass spectrometry-based lipidomic platforms have provided the opportunity for comprehensive profiling of lipids in biological samples and shown alterations in the lipidome that occur in metabolic disorders. A lipidomic approach serves as a powerful tool for biomarker discovery and gaining insight to molecular mechanisms of disease, especially when integrated with other -omics platforms (ie, transcriptomics, proteomics, and metabolomics) in the context of systems biology. In this review, we describe the workflow commonly applied to the conduct of lipidomic studies including important aspects of study design, sample preparation, biomarker identification and quantification, and data processing and analysis, as well as crucial considerations in clinical applications. We also review some recent studies of the application of lipidomic platforms that highlight the potential of lipid biomarkers and add to our understanding of the molecular basis of kidney disease.

Differences among Unique Nanoparticle Protein Corona Constructs: A Case Study Using Data Analytics and Multi-Variant Visualization to Describe Physicochemical Characteristics

Gold nanoparticles (AuNPs) used in pharmaceutical treatments have been shown to effectively deliver a payload, such as an active pharmaceutical ingredient or image contrast agent, to targeted tissues in need of therapy or diagnostics while minimizing exposure, availability, and accumulation to surrounding biological compartments. Data sets collected in this field of study include some toxico- and pharmacodynamic properties (e.g., distribution and metabolism) but many studies lack information about adsorption of biological molecules or absorption into cells. When nanoparticles are suspended in blood serum, a protein corona cloud forms around its surface. The extent of the applications and implications of this formed cloud are unknown. Some researchers have speculated that the successful use of nanoparticles in pharmaceutical treatments relies on a comprehensive understanding of the protein corona composition. The work presented in this paper uses a suite of data analytics and multi-variant visualization techniques to elucidate particle-to-protein interactions at the molecular level. Through mass spectrometry analyses, corona proteins were identified through large and complex datasets. With such high-output analyses, complex datasets pose a challenge when visualizing and communicating nanoparticle-protein interactions. Thus, the creation of a streamlined visualization method is necessary. A series of user-friendly data informatics techniques were used to demonstrate the data flow of protein corona characteristics. Multi-variant heat maps, pie charts, tables, and three-dimensional regression analyses were used to improve results interpretation, facilitate an iterative data transfer process, and emphasize features of the nanoparticle-protein corona system that might be controllable. Data informatics successfully highlights the differences between protein corona compositions and how they relate to nanoparticle surface charge.

Identification and comprehensive evaluation of a novel biocontrol agent Bacillus atrophaeus JZB120050

Bacillus spp. have long been used as biocontrol agents because of their efficient broad-spectrum antimicrobial activity. We identified a novel strain of Bacillus atrophaeus, named JZB120050, from soil. B. atrophaeus JZB120050 had a strong inhibitory effect against Botrytis cinerea and many other phytopathogens. Gas chromatography-mass spectrometry showed that B. atrophaeus JZB120050 produced many secondary metabolites, such as alkanes, alkenes and acids; some of which were related to pathogen inhibition. Enzyme activity analysis showed that B. atrophaeus JZB120050 secreted cell-wall-degrading enzymes, including chitinase, glucanase and protease, which degraded fungal cell walls. Both the novel glucanase gene bglu and chitinase gene chit1 were cloned and heterologously expressed in Escherichia coli and the products showed strong enzyme activity. In addition, B. atrophaeus JZB120050 secreted siderophores and formed a significant biofilm. Future studies should focus on these antimicrobial factors to facilitate widespread application in the field of agricultural biocontrol.

Integrated genomic and metabolomic profiling of ISC1, an emerging Leishmania donovani population in the Indian subcontinent

Leishmania donovani is the responsible agent for visceral leishmaniasis (VL) in the Indian subcontinent (ISC). The disease is lethal without treatment and causes 0.2 to 0.4 million cases each year. Recently, reports of VL in Nepalese hilly districts have increased as well as VL cases caused by L. donovani from the ISC1 genetic group, a new and emerging genotype. In this study, we perform for the first time an integrated, untargeted genomics and metabolomics approach to characterize ISC1, in comparison with the Core Group (CG), main population that drove the most recent outbreak of VL in the ISC. We show that the ISC1 population is very different from the CG, both at genome and metabolome levels. The genomic differences include SNPs, CNV and small indels in genes coding for known virulence factors, immunogens and surface proteins. Both genomic and metabolic approaches highlighted dissimilarities related to membrane lipids, the nucleotide salvage pathway and the urea cycle in ISC1 versus CG. Many of these pathways and molecules are important for the interaction with the host/extracellular environment. Altogether, our data predict major functional differences in ISC1 versus CG parasites, including virulence. Therefore, particular attention is required to monitor the fate of this emerging ISC1 population in the ISC, especially in a post-VL elimination context.

Multiomics tools for the diagnosis and treatment of rare neurological disease

Conventional workup of rare neurological disease is frequently hampered by diagnostic delay or lack of diagnosis. While biomarkers have been established for many neurometabolic disorders, improved methods are required for diagnosis of previously unidentified or underreported causes of rare neurological disease. This would result in a higher diagnostic yield and increased patient numbers required for interventional studies. Recent studies using next-generation sequencing and metabolomics have led to identification of novel disease-causing genes and biomarkers. This combined approach can assist in overcoming challenges associated with analyzing and interpreting the large amount of data obtained from each technique. In particular, metabolomics can support the pathogenicity of sequence variants in genes encoding enzymes or transporters involved in metabolic pathways. Moreover, metabolomics can show the broader perturbation caused by inborn errors of metabolism and identify a metabolic fingerprint of metabolic disorders. As such, using "omics" has great potential to meet the current needs for improved diagnosis and elucidation of rare neurological disease.

Molecular Preadaptation to Antimony Resistance in Leishmania donovani on the Indian Subcontinent

The “antibiotic resistance crisis” is a major challenge for scientists and medical professionals. This steady rise in drug-resistant pathogens also extends to parasitic diseases, with antimony being the first anti-Leishmania drug that fell in the Indian subcontinent (ISC). Leishmaniasis is a major but neglected infectious disease with limited therapeutic options. Therefore, understanding how parasites became resistant to antimonials is of commanding importance. In this study, we experimentally characterized the dynamics of this resistance acquisition and show for the first time that some Leishmania populations of the ISC were preadapted to antimony resistance, likely driven by environmental factors or by drugs used in the 19th century.

Antimonials (Sb) were used for decades for chemotherapy of visceral leishmaniasis (VL). Now abandoned in the Indian subcontinent (ISC) because of Leishmania donovani resistance, this drug offers a unique model for understanding drug resistance dynamics. In a previous phylogenomic study, we found two distinct populations of L. donovani: the core group (CG) in the Gangetic plains and ISC1 in the Nepalese highlands. Sb resistance was only encountered within the CG, and a series of potential markers were identified. Here, we analyzed the development of resistance to trivalent antimonials (Sb^III) upon experimental selection in ISC1 and CG strains. We observed that (i) baseline Sb^III susceptibility of parasites was higher in ISC1 than in the CG, (ii) time to Sb^III resistance was higher for ISC1 parasites than for CG strains, and (iii) untargeted genomic and metabolomic analyses revealed molecular changes along the selection process: these were more numerous in ISC1 than in the CG. Altogether these observations led to the hypothesis that CG parasites are preadapted to Sb^III resistance. This hypothesis was experimentally confirmed by showing that only wild-type CG strains could survive a direct exposure to the maximal concentration of Sb^III. The main driver of this preadaptation was shown to be MRPA, a gene involved in Sb^III sequestration and amplified in an intrachromosomal amplicon in all CG strains characterized so far. This amplicon emerged around 1850 in the CG, well before the implementation of antimonials for VL chemotherapy, and we discuss here several hypotheses of selective pressure that could have accompanied its emergence.

IMPORTANCE The “antibiotic resistance crisis” is a major challenge for scientists and medical professionals. This steady rise in drug-resistant pathogens also extends to parasitic diseases, with antimony being the first anti-Leishmania drug that fell in the Indian subcontinent (ISC). Leishmaniasis is a major but neglected infectious disease with limited therapeutic options. Therefore, understanding how parasites became resistant to antimonials is of commanding importance. In this study, we experimentally characterized the dynamics of this resistance acquisition and show for the first time that some Leishmania populations of the ISC were preadapted to antimony resistance, likely driven by environmental factors or by drugs used in the 19th century.

Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics

Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in all data sets. This work highlights the importance of technical replication in mass spectrometry-based studies, and presents a new application of HCA as a tool for evaluating the effectiveness of data filtering prior to statistical analysis.

Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD

Breath analysis holds the promise of a non-invasive technique for the diagnosis of diverse respiratory conditions including chronic obstructive pulmonary disease (COPD) and lung cancer. Breath contains small metabolites that may be putative biomarkers of these conditions. However, the discovery of reliable biomarkers is a considerable challenge in the presence of both clinical and instrumental confounding factors. Among the latter, instrumental time drifts are highly relevant, as since question the short and long-term validity of predictive models. In this work we present a methodology to counter instrumental drifts using information from interleaved blanks for a case study of GC-MS data from breath samples. The proposed method includes feature filtering, and additive, multiplicative and multivariate drift corrections, the latter being based on component correction. Biomarker discovery was based on genetic algorithms in a filter configuration using Fisher's ratio computed in the partial least squares-discriminant analysis subspace as a figure of merit. Using our protocol, we have been able to find nine peaks that provide a statistically significant area under the ROC curve of 0.75 for COPD discrimination. The method developed has been successfully validated using blind samples in short-term temporal validation. However, the attempt to use this model for patient screening six months later was not successful. This negative result highlights the importance of increasing validation rigor when reporting biomarker discovery results.

The Chemistry of Plant-Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance

Plant roots communicate with microbes in a sophisticated manner through chemical communication within the rhizosphere, thereby leading to biofilm formation of beneficial microbes and, in the case of plant growth-promoting rhizomicrobes/-bacteria (PGPR), resulting in priming of defense, or induced resistance in the plant host. The knowledge of plant-plant and plant-microbe interactions have been greatly extended over recent years; however, the chemical communication leading to priming is far from being well understood. Furthermore, linkage between below- and above-ground plant physiological processes adds to the complexity. In metabolomics studies, the main aim is to profile and annotate all exo- and endo-metabolites in a biological system that drive and participate in physiological processes. Recent advances in this field has enabled researchers to analyze 100s of compounds in one sample over a short time period. Here, from a metabolomics viewpoint, we review the interactions within the rhizosphere and subsequent above-ground 'signalomics', and emphasize the contributions that mass spectrometric-based metabolomic approaches can bring to the study of plant-beneficial - and priming events.

Applications of Metabolomics in Cancer Studies

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Macromolecular biosynthetic parameters and metabolic profile in different life stages of Leishmania braziliensis: Amastigotes as a functionally less active stage

It was recently hypothesized that Leishmania amastigotes could constitute a semi-quiescent stage characterized by low replication and reduced metabolic activity. This concept developed with Leishmania (Leishmania) mexicana and Leishmania (Leishmania) major models might explain numerous clinical and sub-clinical features of Leishmania (Viannia) braziliensis infections, like reactivation of the disease, non-response to chemotherapy or asymptomatic infections. We compared here in vitro the proliferative capability of L. (V.) braziliensis amastigotes and promastigotes, assessed the expression of key molecular parameters and performed metabolomic analysis. We found that contrary to the highly proliferative promastigotes, amastigotes (axenic and intracellular) do not show evidence of extensive proliferation. In parallel, amastigotes showed a significant decrease of (i) the kDNA mini-circle abundance, (ii) the intracellular ATP level, (iii) the ribosomal components: rRNA subunits 18S and 28S α and ribosomal proteins RPS15 and RPL19, (iv) total RNA and protein levels. An untargeted metabolomic study identified clear differences between the different life stages: in comparison to logarithmic promastigotes, axenic amastigotes showed (a) a strong decrease of 14 essential and non-essential amino acids and eight metabolites involved in polyamine synthesis, (b) extensive changes in the phospholipids composition and (c) increased levels of several endogenous and exogenous sterols. Altogether, our results show that L. (V.) braziliensis amastigotes can show a phenotype with negligible rate of proliferation, a lower capacity of biosynthesis, a reduced bio-energetic level and a strongly altered metabolism. Our results pave the way for further exploration of quiescence among amastigotes of this species.

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

Background

More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies.

Methods

We propose that higher-order functional representation of metabolomics data, such as pathway-based metabolomic features, can be used as robust biomarkers for breast cancer. Towards this, we have developed a new computational method that uses personalized pathway dysregulation scores for disease diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods.

Results

The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under the Curve, a receiver operating characteristic curve) of 0.968 and 0.934, sensitivities of 0.946 and 0.954, and specificities of 0.934 and 0.918. These two metabolomics-based pathway models are further validated by RNA-Seq-based TCGA (The Cancer Genome Atlas) breast cancer data, with AUCs of 0.995 and 0.993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer.

Conclusions

We have successfully developed a new type of pathway-based model to study metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease diagnosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0289-9) contains supplementary material, which is available to authorized users.

Experimental resistance to drug combinations in Leishmania donovani: metabolic and phenotypic adaptations

Together with vector control, chemotherapy is an essential tool for the control of visceral leishmaniasis (VL), but its efficacy is jeopardized by growing resistance and treatment failure against first-line drugs. To delay the emergence of resistance, the use of drug combinations of existing antileishmanial agents has been tested systematically in clinical trials for the treatment of visceral leishmaniasis (VL). In vitro, Leishmania donovani promastigotes are able to develop experimental resistance to several combinations of different antileishmanial drugs after 10 weeks of drug pressure. Using an untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics approach, we identified metabolic changes in lines that were experimentally resistant to drug combinations and their respective single-resistant lines. This highlighted both collective metabolic changes (found in all combination therapy-resistant [CTR] lines) and specific ones (found in certain CTR lines). We demonstrated that single-resistant and CTR parasite cell lines show distinct metabolic adaptations, which all converge on the same defensive mechanisms that were experimentally validated: protection against drug-induced and external oxidative stress and changes in membrane fluidity. The membrane fluidity changes were accompanied by changes in drug uptake only in the lines that were resistant against drug combinations with antimonials, and surprisingly, drug accumulation was higher in these lines. Together, these results highlight the importance and the central role of protection against oxidative stress in the different resistant lines. Ultimately, these phenotypic changes might interfere with the mode of action of all drugs that are currently used for the treatment of VL and should be taken into account in drug development.

Metabolomics for secondary metabolite research

Metabolomics, the global characterization of metabolite profiles, is becoming an increasingly powerful tool for research on secondary metabolite discovery and production. In this review we discuss examples of recent technological advances and biological applications of metabolomics in the search for chemical novelty and the engineered production of bioactive secondary metabolites.

Metabolic adaptations of Leishmania donovani in relation to differentiation, drug resistance, and drug pressure

Antimonial (sodium stibogluconate, SSG) resistance and differentiation have been shown to be closely linked in Leishmania donovani, with SSG-resistant strains showing an increased capacity to generate infectious (metacyclic) forms. This is the first untargeted LC-MS metabolomics study which integrated both phenomena in one experimental design and provided insights into metabolic differences between three clinical L. donovani strains with a similar genetic background but different SSG-susceptibilities. We performed this analysis at different stages during promastigote growth and in the absence or presence of drug pressure. When comparing SSG-resistant and SSG-sensitive strains, a number of metabolic changes appeared to be constitutively present in all growth stages, pointing towards a clear link with SSG-resistance, whereas most metabolic changes were only detected in the stationary stage. These changes reflect the close intertwinement between SSG-resistance and an increased metacyclogenesis in resistant parasites. The metabolic changes suggest that SSG-resistant parasites have (i) an increased capacity for protection against oxidative stress; (ii) a higher fluidity of the plasma membrane; and (iii) a metabolic survival kit to better endure infection. These changes were even more pronounced in a resistant strain kept under Sb(III) drug pressure.

Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments

Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven, based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this article, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs. In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows the removal of systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods, if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments.

(Post-) Genomic approaches to tackle drug resistance in Leishmania

Leishmaniasis, like other neglected diseases is characterized by a small arsenal of drugs for its control. To safeguard the efficacy of current drugs and guide the development of new ones it is thus of utmost importance to acquire a deep understanding of the phenomenon of drug resistance and its link with treatment outcome. We discuss here how (post-)genomic approaches may contribute to this purpose. We highlight the need for a clear definition of the phenotypes under consideration: innate and acquired resistance versus treatment failure. We provide a recent update of our knowledge on the Leishmania genome structure and dynamics, and compare the contribution of targeted and untargeted methods for the understanding of drug resistance and show their limits. We also present the main assays allowing the experimental validation of the genes putatively involved in drug resistance. The importance of analysing information downstream of the genome is stressed and further illustrated by recent metabolomics findings. Finally, the attention is called onto the challenges for implementing the acquired knowledge to the benefit of the patients and the population at risk.