Metabolomic systems biology of trypanosomes

Metabolomics analysis, which aims at the systematic identification and quantification of all metabolites in biological systems, is emerging as a powerful new tool to identify biomarkers of disease, report on cellular responses to environmental perturbation, and to identify the targets of drugs. Here we discuss recent developments in metabolomic analysis, from the perspective of trypanosome research, highlighting remaining challenges and the most promising areas for future research

Metabolic fingerprinting to assess the impact of salinity on carotenoid content in developing tomato fruits

As the presence of health-promoting substances has become a significant aspect of tomato fruit appreciation, this study investigated nutrient solution salinity as a tool to enhance carotenoid accumulation in cherry tomato fruit (Solanum lycopersicum L. cv. Juanita). Hereby, a key objective was to uncover the underlying mechanisms of carotenoid metabolism, moving away from typical black box research strategies. To this end, a greenhouse experiment with five salinity treatments (ranging from 2.0 to 5.0 decisiemens (dS) m(-1)) was carried out and a metabolomic fingerprinting approach was applied to obtain valuable insights on the complicated interactions between salinity treatments, environmental conditions, and the plant's genetic background. Hereby, several hundreds of metabolites were attributed a role in the plant's salinity response (at the fruit level), whereby the overall impact turned out to be highly depending on the developmental stage. In addition, 46 of these metabolites embraced a dual significance as they were ascribed a prominent role in carotenoid metabolism as well. Based on the specific mediating actions of the retained metabolites, it could be determined that altered salinity had only marginal potential to enhance carotenoid accumulation in the concerned tomato fruit cultivar. This study invigorates the usefulness of metabolomics in modern agriculture, for instance in modeling tomato fruit quality. Moreover, the metabolome changes that were caused by the different salinity levels may enclose valuable information towards other salinity-related plant processes as well

Simulated microgravity promotes the formation of tridimensional cultures and stimulates pluripotency and a glycolytic metabolism in human hepatic and biliary tree stem/progenitor cells

Many pivotal biological cell processes are affected by gravity. The aim of our study was to evaluate biological and functional effects, differentiation potential and exo-metabolome profile of simulated microgravity (SMG) on human hepatic cell line (HepG2) and human biliary tree stem/progenitor cells (hBTSCs). Both hBTSCs and HepG2 were cultured in a weightless and protected environment SGM produced by the Rotary Cell Culture System (Synthecon) and control condition in normal gravity (NG). Self-replication and differentiation toward mature cells were determined by culturing hBTSCs in Kubota's Medium (KM) and in hormonally defined medium (HDM) tailored for hepatocyte differentiation. The effects on the expression and cell exo-metabolome profiles of SMG versus NG cultures were analyzed. SMG promotes tridimensional (3D) cultures of hBTSCs and HepG2. Significative increase of stemness gene expression (p < 0.05) has been observed in hBTSCs cultured in SMG when compared to NG condition. At the same time, the expression of hepatocyte lineage markers in hBTSCs differentiated by HDM was significantly lower (p < 0.05) in SMG compared to NG, demonstrating an impaired capability of hBTSCs to differentiate in vitro toward mature hepatocytes when cultured in SMG condition. Furthermore, in HepG2 cells the SMG caused a lower (p < 0.05 vs controls) transcription of CYP3A4, a marker of late-stage (i.e. Zone 3) hepatocytes. Exo-metabolome NMR-analysis showed that both cell cultures consumed a higher amount of glucose and lower glutamate in SMG respect to NG (p < 0.05). Moreover, hBTSCs media cultures resulted richer of released fermentation (lactate, acetate) and ketogenesis products (B-hydroxybutyrate) in SGM (p < 0.05) than NG. While, HepG2 cells showed higher consumption of amino acids and release of ketoacids (3-Methyl-2-oxovalerate, 2-oxo-4-methyl-valerate) and formiate with respect to normogravity condition (p < 0.05). Based on our results, SMG could be helpful for developing hBTSCs-derived liver devices. In conclusion, SMG favored the formation of hBTSCs and HepG2 3D cultures and the maintenance of stemness contrasting cell differentiation; these effects being associated with stimulation of glycolytic metabolism. Interestingly, the impact of SMG on stem cell biology should be taken into consideration for workers involved in space medicine programs

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Visceral leishmaniasis is caused by a parasite called Leishmania donovani, which every year infects about half a million people and claims several thousand lives. Existing treatments are now becoming less effective due to the emergence of drug resistance. Improving our understanding of the mechanisms used by the parasite to adapt to drugs and achieve resistance is crucial for developing future treatment strategies. Unfortunately, the biological mechanism whereby Leishmania acquires drug resistance is poorly understood. Recent years have brought new technologies with the potential to increase greatly our understanding of drug resistance mechanisms. The latest mass spectrometry techniques allow the metabolome of parasites to be studied rapidly and in great detail. We have applied this approach to determine the metabolome of drug-sensitive and drug-resistant parasites isolated from patients with leishmaniasis. The data show that there are wholesale differences between the isolates and that the membrane composition has been drastically modified in drug-resistant parasites compared with drug-sensitive parasites. Our findings demonstrate that untargeted metabolomics has great potential to identify major metabolic differences between closely related parasite strains and thus should find many applications in distinguishing parasite phenotypes of clinical relevance

MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Our previous work revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. Through this work, which consists of a protocol, a toolbox, and tutorials of two use cases, we make our methods available to the broader scientific community. The protocol describes, in a step-wise manner, the workflow of data integration and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, this protocol constitutes a comprehensive guide to the intra-model analysis of extracellular metabolomic data and a resource offering a broad set of computational analysis tools for a wide biomedical and non-biomedical research community

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or “awakening” of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu