HMDB 5.0: the Human Metabolome Database for 2022.

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

Estudo do efeito de estatinas e antiplaquetários na evolução do infarto agudo do miocárdio: uma abordagem metabolômica multiplataform / Effect of statins and antiplatelets in the evolution of acute myocardium infarction: a multiplatform metabolomics approach

O infarto agudo do miocárdio (IAM) é a maior causa de mortalidade no mundo. A oclusão coronária determina a necrose completa de cardiomiócitos (células musculares cardíacas) durante as primeiras horas do IAM. Porém, mesmo após a perda de massa de miocárdio viável cessar, a região infartada pode se expandir ou contrair no decorrer das primeiras semanas, afetando o prognóstico dos pacientes. Alguns tratamentos podem auxiliar na recuperação e melhoria do prognóstico desses pacientes, como o uso de estatinas e antiplaquetários, que quando utilizados em conjunto, proporcionam efeitos sinérgicos. O presente estudo investigou e comparou, através da óptica da metabolômica global multiplataforma, tratamentos concomitantes de estatinas (sinvastatina ou rosuvastatina) e antiplaquetários bloqueadores do receptor de ADP (clopidogrel ou ticagrelor), em pacientes que sofreram IAM. Foram coletadas amostras de plasma e urina de cerca 40 pacientes tratados com clopidrogrel e sinvastatina ou ticagrelor e rosuvastatina no Hospital São Paulo em diferentes períodos (basal, 1 mês e 6 meses após IAM). Amostras de plasma (basal e 1 mês) foram analisadas por RPLC-MS nos modos de ionização positivo e negativo, GC-MS e CEMS. Amostras de urina (basal, 1 mês e 6 meses) foram analisadas por RPLC-MS no modo de ionização positivo e HILIC-MS nos modos de ionização positivo e negativo. A abordagem metabolomica global multiplataforma evidenciou alterações no metabolismo de diferentes vias pelos dois tratamentos. Os dois tratamentos proporcionaram um efeito pronunciado no metabolismo de diferentes lipídios, como glicerolipídios, esfingolipídios, glicerofosfolipídios e ácidos graxos, sendo que a combinação rosuvastatina e ticagrelor resultou num efeito mais acentuado. Já o tratamento com clopidogrel e sinvastatina alterou de maneira mais pronunciada o metabolismo de aminoácidos ramificados e de acilcarnitinas de cadeia curta. Observou-se ainda a alteração de possíveis biomarcadores relatados na literatura como associados a problemas cardiovasculares, como hipoxantina, ácido 2-hidroxibutírico, algumas espécies de ceramidas, fosfatidilcolinas e acilcarnitinas de cadeia curta

cute myocardium infarction (AMI) is the main mortality cause in the world. The coronary occlusion determines the complete necrosis of cardiomyocytes (cardiac muscle cells) during the first hours of AMI. However, even after the loss of viable myocardial mass ceases, the infarcted area may still expand or contract during the first weeks after AMI, affecting the patient prognosis. Some treatments may assist patient recovery and improve prognostic, such as statins and antiplatelets which, when combined, provide synergic effects. This study investigated and compared, by untargeted multiplatform metabolomics, simultaneous treatments of statins (simvastatin or rosuvastatin) and ADP receptor antagonist antiplatelets (clopidogrel or ticagrelor) in patients that suffered AMI. Plasma and urine samples from around 40 patients treated with clopidogrel and simvastatin or ticagrelor and rosuvastatin were collected in Hospital Sao Paulo at different time points (basal, 1 month, 6 months after AMI). Plasma samples (basal and 1 month) were analyzed by RPLC-MS in positive and negative ionization modes, GC-MS and CE-MS. Urine samples (basal, 1 month, 6 months) were analyzed by RPLC-MS in positive ionization mode and by HILIC-MS in positive and negative ionization modes. The untargeted multiplatform metabolomics approach has shown that different metabolic pathways have been altered by the two treatments. Both treatments had a profound impact on the metabolism of different lipids, such as glycerolipids, sphingolipids, glycerophospholipids, and fatty acids. However, the combined treatment using rosuvastatin and ticagrelor impacted the most the lipid pathways. On the other hand, clopidogrel and simvastatin treatment affected more intensily the branched chain amino acids and short chain acylcarnitines metabolisms. Reported biomarkers in the literature related to cardiovascular diseases were also observed in this study, such as hypoxanthine, 2-hydroxybutyric acid, some species of ceramides, phosphatidylcholines and short chain acylcarnitines

A abordagem metabolômica na indicação do tratamento de estenose de junção ureteropélvica em crianças / Metabolomics approach in the treatment of ureteropelvic junction stenosis in children

Estenose de Junção Ureteropélvica (JUP) é uma doença caracterizada pelo bloqueio do fluxo de urina da pelve renal (porção proximal do ureter no rim) ao ureter, tubo que liga o rim à bexiga. Essa formação congênita é uma das maiores causas de dilatação do rim (hidronefrose) em recém-nascidos e, em alguns casos, pode causar danos mais severos ao órgão. A hidronefrose causada pela estenose de JUP pode desaparecer espontaneamente sem perda da função renal, entretanto, é preciso um acompanhamento clínico. Por outro lado, em casos mais severos, onde a dilatação pode causar danos maiores ao rim, um tratamento cirúrgico se faz necessário. Embora existam métodos para o diagnóstico da estenose de JUP, como ultrassonografia, tomografia computadorizada, ressonância e cintilografia, é um grande desafio diferenciar os pacientes que requerem um tratamento cirúrgico e os que necessitam apenas de um acompanhamento convencional. A metabolômica global, que investiga de modo comparativo o conjunto de metabólitos de baixa massa molecular expressos em indivíduos em condições pré-selecionadas, tem o potencial de servir como ferramenta diagnóstica para os pacientes com estenose de JUP e, consequentemente, auxiliar na tomada de decisão entre um acompanhamento clínico ou tratamento cirúrgico. Assim sendo, no presente trabalho, três grupos de pacientes com estenose de JUP, pré-diagnosticados por métodos convencionais, foram investigados sob a perspectiva da metabolômica global, por meio de análises de urina, utilizando cromatografia gasosa e cromatografia líquida, ambas acoplada à espectrometria de massas (GC-MS e RPLC10 MS, respectivamente): pacientes que requerem tratamento cirúrgico (CIR), pacientes que requerem acompanhamento clínico (CLI), e indivíduos sãos (CON). Os resultados mostram que é possível encontrar metabólitos discriminantes entre todas as comparações (CON x CLI, CON x CIR e CLI x CIR); os metabólitos encontrados nas análises multivariada e univariada foram utilizados para construção da curva ROC, para confirmar a possibilidade de utilização desses compostos como biomarcadores. Foram observadas alterações em rotas metabólicas importantes para o bom funcionamento das funções renais, principalmente entre a comparação mais desafiadora (CLI x CIR), como o metabolismo da fenilalanina, da tirosina, da beta-alanina, dos aminoaçúcares e dos nucleotídeos. Há indícios de que o ciclo de Krebs também sofre alteração. Os resultados obtidos podem servir como ponto de partida para uma futura análise alvo e validação biológica

Ureteropelvic junction (UPJ) stenosis is a disease characterized by the interruption of the flow of urine from the renal pelvis (proximal part of ureter in the kidney) to the ureter, tube that links the kidney to bladder. That congenital formation is one of the main causes of kidney dilation (hydronephrosis) in newborns and, sometimes, can cause more serious damage to the organ. The hydronephrosis caused by UPJ stenosis can disappear spontaneously without compromising renal function, although a clinical follow-up is required. On the other hand, in more serious cases, when dilation can induce larger damage to the kidney, surgery intervention is necessary. Although there are methods to diagnose UPJ stenosis, such as ultrasound, computed tomography, resonance and scintigraphy, it is still a great challenge to distinguish patients that require surgery from those whose a clinical follow-up suffices. Global metabolomics, a method that investigates in a comparative manner the set of low molecular mass metabolites expressed by an individual in pre-selected conditions, has the potential to function as a diagnostic tool for patients with UPJ stenosis to support decisions about patient treatment, i.e., surgery versus clinical follow-up. In this work, three groups of UPJ stenosis patients were investigated with the aid of global metabolomics using urine analysis by gas chromatography and liquid chromatography coupled to mass spectrometry (GC-MS and RPLC-MS, respectively): one group consisted of UPJ stenosis patients requiring clinical follow-up (CLI), other group UPJ stenosis patients requiring surgery intervention (CIR) and a third group comprising healthy individuals, the control group (CON).12 The results show that it was possible to find discriminant metabolites among all pairwise comparisons (CON versus CLI, CON versus CIR and CLI versus CIR). The metabolites found by multivariate and univariate analyses were used to build ROC curves, to confirm whether it is possible to use them as biomarkers. Alterations in metabolic pathways that are important for the good maintenance of kidney functions were found, especially in the most challenged comparison (CLI versus CIR), such as the metabolism of phenylalanine, tyrosine, beta-alanine, amino acids and nucleotides. There are evidences that Krebs cycle was also impacted. The results obtained here can serve as a starting point to future targeted analysis and biological validation

Metabolic adaptation to calorie restriction.

Calorie restriction (CR) enhances health span (the length of time that an organism remains healthy) and increases longevity across species. In mice, these beneficial effects are partly mediated by the lowering of core body temperature that occurs during CR. Conversely, the favorable effects of CR on health span are mitigated by elevating ambient temperature to thermoneutrality (30°C), a condition in which hypothermia is blunted. In this study, we compared the global metabolic response to CR of mice housed at 22°C (the standard housing temperature) or at 30°C and found that thermoneutrality reverted 39 and 78% of total systemic or hypothalamic metabolic variations caused by CR, respectively. Systemic changes included pathways that control fuel use and energy expenditure during CR. Cognitive computing-assisted analysis of these metabolomics results helped to prioritize potential active metabolites that modulated the hypothermic response to CR. Last, we demonstrated with pharmacological approaches that nitric oxide (NO) produced through the citrulline-NO pathway promotes CR-triggered hypothermia and that leucine enkephalin directly controls core body temperature when exogenously injected into the hypothalamus. Because thermoneutrality counteracts CR-enhanced health span, the multiple metabolites and pathways altered by thermoneutrality may represent targets for mimicking CR-associated effects.

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.

INTRODUCTION: Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models. OBJECTIVES: We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis. METHODS: We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks. RESULTS: There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice. CONCLUSION: The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm.

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies.

INTRODUCTION: The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data. OBJECTIVES: To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies. METHODS: The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment. RESULTS: Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories. CONCLUSION: RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis.

INTRODUCTION: Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately. OBJECTIVES: TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface. METHODS: TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically. RESULTS: TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well. CONCLUSION: TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

Marsdenia tenacissima extract alters crucial metabolites in cancer, determined by 1 H NMR based metabolomics approach

ABSTRACT Altered metabolites level in the biosystems, is the potential cause of cancer, the primary reason of alteration of metabolism is change in nutrient consumption and waste excretion, as a result genetic mutation leads to cancer initiation and progression. Aberration of specific metabolites such as fumarate, succinate, 2-hydroxyglutarate may alter cell signaling. We collected liver and kidney samples and prepared for 1H NMR analysis, then executed NMR spectroscopy. We used a set of domestic R scripts to perform an unsupervised principal component analysis (PCA) and a supervised orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA). It signifies class discrimination for getting a clear separation, whereas PCA scores plot signifies the model group kept further away from the control group than drug group on the horizontal axis. In another PCA scores plots, most parts of the control group was overlapping with the drug group but was distant from the model group. Marsdenia tenacissima extract (MTE) (Chines name: Xiao-Ai-Ping, XAP) modulates level of crucial metabolites such as fumarate, lactate, succinate, determined by 1H NMR spectroscopy and their altered level contributes major role in cancer

Chemotaxonomic Classification Applied to the Identification of Two Closely-Related Citrus TCMs Using UPLC-Q-TOF-MS-Based Metabolomics.

This manuscript elaborates on the establishment of a chemotaxonomic classification strategy for closely-related Citrus fruits in Traditional Chinese Medicines (TCMs). UPLC-Q-TOF-MS-based metabolomics was applied to depict the variable chemotaxonomic markers and elucidate the metabolic mechanism of Citrus TCMs from different species and at different ripening stages. Metabolomics can capture a comprehensive analysis of small molecule metabolites and can provide a powerful approach to establish metabolic profiling, creating a bridge between genotype and phenotype. To further investigate the different metabolites in four closely-related Citrus TCMs, non-targeted metabolite profiling analysis was employed as an efficient technique to profile the primary and secondary metabolites. The results presented in this manuscript indicate that primary metabolites enable the discrimination of species, whereas secondary metabolites are associated with species and the ripening process. In addition, analysis of the biosynthetic pathway highlighted that the syntheses of flavone and flavone glycosides are deeply affected in Citrus ripening stages. Ultimately, this work might provide a feasible strategy for the authentication of Citrus fruits from different species and ripening stages and facilitate a better understanding of their different medicinal uses.

Avaliação de terapias imunossupressoras em transplantes renais com uma abordagem metabolômica / Assessment of immunosuppressive therapies with a metabolomic approach

O aprimoramento das técnicas analíticas viabilizou a metabolômica, uma área da ciência que busca compreender, de forma comparativa, os metabólitos envolvidos nas vias bioquímicas. A metabolômica está inserida no contexto das "ômicas", que teve início na "Era Genômica", a qual permitiu a identificação de diversos genes. Em seguida, o interesse dos pesquisadores centrou no estudo dos metabólitos (metabolômica) mostrando ser uma ferramenta valiosa na pesquisa do transplante renal, que exige um tratamento medicamentoso por meio de imunossupressores. A combinação destes imunossupressores pode minimizar a rejeição do órgão transplantado, reforçando a necessidade de um estudo metabolômico, a fim de avaliar e comparar as mudanças ocorridas após o transplante em nível molecular, melhorando o conhecimento sobre a influência destes regimes e dando subsídios sobre prognósticos possíveis na área de transplante renal. Nesta tese foram avaliadas 2 terapias: Everolimo/ Prednisona/Tacrolimo (grupo 1) e Micofenolato mofetil/Prednisona/Tacrolimo (grupo 2) a partir de uma abordagem untargeted. No presente trabalho foram coletadas amostras de urina de pacientes ao longo de 6 meses. Foi necessário determinar a melhor condição para análise das amostras de urina dos pacientes. Desta forma, foram realizados estudos sobre alguns parâmetros que impactam no preparo de amostra abordando a influência da urease, tipos e proporção de solventes para precipitação de proteína, seleção do melhor agente derivatizante e tratamento de dados. A avaliação da medida de qualidade dos tratamentos com urease foi feita a partir do desvio padrão relativo (RSD) dos valores de intensidade de pico. A concentração de 10 mg mL-1 apresentou o melhor resultado. O estudo mostrou também que o teor de ureia na urina pode influenciar na identificação dos compostos. O número de compostos identificados foi menor quando a urina não foi tratada com urease, com aproximadamente 10 compostos a menos em relação à amostra tratada com a enzima, na mesma concentração de ureia adicionada. Dos solventes orgânicos testados para precipitação de proteínas nas amostras de urina, o isopropanol mostrou ser o solvente mais adequado na proporção 1:6 urina:solvente (v/v), utilizando-se 100 µL de urina. Foram testados dois protocolos de derivatização para análises por GC-MS: metoximação e sililação utilizando BSTFA e cloroformiato de metila. A comparação mostrou que o procedimento por BSTFA, com 40 metabólitos identificados, foi superior ao cloroformiato de metila, com 13 compostos identificados. No tratamento de dados com o software XCMS, os seguintes parâmetros foram avaliados: largura a meia altura do pico (fwhm), largura da banda (bw) e threshold (sntresh). Para avaliar a melhor combinação destes parâmetros, foi feita uma variação univariada destes valores. A qualidade do resultado de cada combinação foi monitorada pelos valores gerados de número de missing values, quantidade de picos com RSD <15% e número de valores duplicados. Os valores ótimos foram obtidos para a combinação: fwhm=4, bw=2 e threshold=5. A abordagem do estudo dos dois grupos de pacientes baseou-se inicialmente na comparação entre o dia 7 da terapia com os demais períodos (dia 14, mês 1, mês 3 e mês 6) e posteriormente avaliou-se a evolução temporal. A partir do mês 3 os valores de correlação e predição dos modelos de PLS-DA são melhores e já é eficaz na diferenciação entre os dois grupos. Foram observadas perturbações no metabolismo de carboidratos em ambos os grupos, como açúcares, glicerol e N-acetil-D-manosamina. No grupo 1, foram observados metabólitos discriminantes da classe dos poliois e das vias do ciclo do ácido cítrico e degradação de xenobióticos, enquanto que, no grupo 2, foi observada alteração do hidroxibutirato, um corpo cetônico. Neste grupo, foi observado também um aumento do ácido hipúrico, ácido acetamido butírico, ácido benzoico, entre outros. Nesta tese foi possível demonstrar que a metabolômica é uma ferramenta importante para comparar metabólitos discriminantes entre dois regimes imunossupressores, sendo um estudo piloto que visa fornecer subsídios para avaliação da influência destas terapias no prognóstico de transplante renal

The improvement of analytical techniques enabled the emergence of metabolomics, which aims to compare the metabolites involved in biochemical pathways, in certain biological conditions. Metabolomics is inserted in the "omics" context, which began in the "Genomic Age", and allowed the identification of several genes. After that, the researchers focused on the study of metabolites. Among several applications, metabolomics can be a valuable tool in renal transplant research, which requires a drug treatment through immunosuppressants. The combination of these immunosuppressants can minimize toxicity and rejection of the transplanted organ, reinforcing the need for a metabolomic study, in order to evaluate and compare changes after transplantation at the molecular level, improving knowledge about the influence of these regimens and giving subsidies on prognosis in the area of renal transplantation. In this thesis two immunosuppressive therapies were evaluated by an untargeted approach: Everolimus/Prednisone/Tacrolimus (group 1) and Mycophenolate mofetil/Prednisone/Tacrolimus (group 2). In this study, urine samples were collected from patients over 6 months. It was necessary to determine the best condition for analysis of patients' urine samples. Thus, studies were carried out on some parameters that impact on sample preparation, evaluating the influence of urease, types and proportion of solvents for protein precipitation, selection of the best derivatizing agent, and data treatment. The evaluation of the quality measure of the urease treatments was made from the relative standard deviation (RSD) of the peak intensity values. The concentration of 10 mg mL-1 presented the best result. The study also showed that urine urea content may influence the identification of the compounds. The number of identified compounds was lower when urine was not treated with urease, with approximately 10 compounds less than the enzyme-treated sample, at the same concentration of urea added. In the evaluation of the organic solvents tested for protein precipitation in the urine samples, isopropanol was the most suitable solvent in the ratio 1: 6 urine:solvent (v/v), using 100 µL of urine. Two derivatization protocols were tested for GC-MS analysis: metoximation and silylation with BSTFA and methyl chloroformate. The comparison between the two derivatization protocols showed that the BSTFA procedure, with 40 identified metabolites, was superior to methyl chloroformate with 13 compounds identified. In data processing with the XCMS software, the following parameters were evaluated: full width at half maximum of the peak (fwhm), bandwidth (bw) and threshold (sntresh). To evaluate the best combination of these parameters, a univariate variation of these values was made. The quality of the result of each combination was monitored by the number of missing values, number of peaks with RSD <15%, and number of duplicate values. The optimal values were obtained for the combination: fwhm=4, bw=2 and threshold =5. The study of the two groups of patients was initially based on the comparison between day 7 of the therapy with the other periods (day 14, month 1, month 3 and month 6) and later the temporal evolution was evaluated. From month 3 the values of correlation and prediction of the PLS-DA models are better and already effective in the differentiation between the two groups. Disorders in carbohydrate metabolism were observed in both groups with sugars and glycerol and N-acetyl-D-mannosamine as discriminant metabolites. In group 1, discriminant metabolites of the class of polyols and citric acid cycle pathways and degradation of xenobiotics were observed, and in group 2 alteration of hydroxybutyrate, a ketone body, was observed. In this group an increase of hippuric acid, acetamido butyric acid, benzoic acid, among others, was also observed. In this thesis it was possible to demonstrate that metabolomics is an important tool to compare discriminant metabolites between two immunosuppressive regimens, being a pilot study that aims to provide future subsidies to evaluate the influence of these therapies on the renal transplant prognosis

Yeast metabolic chassis designs for diverse biotechnological products.

The diversity of industrially important molecules for which microbial production routes have been experimentally demonstrated is rapidly increasing. The development of economically viable producer cells is, however, lagging behind, as it requires substantial engineering of the host metabolism. A chassis strain suitable for production of a range of molecules is therefore highly sought after but remains elusive. Here, we propose a genome-scale metabolic modeling approach to design chassis strains of Saccharomyces cerevisiae - a widely used microbial cell factory. For a group of 29 products covering a broad range of biochemistry and applications, we identified modular metabolic engineering strategies for re-routing carbon flux towards the desired product. We find distinct product families with shared targets forming the basis for the corresponding chassis cells. The design strategies include overexpression targets that group products by similarity in precursor and cofactor requirements, as well as gene deletion strategies for growth-product coupling that lead to non-intuitive product groups. Our results reveal the extent and the nature of flux re-routing necessary for producing a diverse range of products in a widely used cell factory and provide blueprints for constructing pre-optimized chassis strains.

Metabolómica de la lágrima / Human tear metabolome

No disponible

Variable selection for binary classification using error rate p-values applied to metabolomics data.

BACKGROUND: Metabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups. Two tasks need to be performed: (i) finding variables which differ between the two groups; and (ii) determining how the selected variables can be used to classify new subjects. We introduce an approach using minimum classification error rates as test statistics to find discriminatory and therefore informative variables. The thresholds resulting in the minimum error rates can be used to classify new subjects. This approach transforms error rates into p-values and is referred to as ERp. RESULTS: We show that non-parametric hypothesis testing, based on minimum classification error rates as test statistics, can find statistically significantly shifted variables. The discriminatory ability of variables becomes more apparent when error rates are evaluated based on their corresponding p-values, as relatively high error rates can still be statistically significant. ERp can handle unequal and small group sizes, as well as account for the cost of misclassification. ERp retains (if known) or reveals (if unknown) the shift direction, aiding in biological interpretation. The threshold resulting in the minimum error rate can immediately be used to classify new subjects. We use NMR generated metabolomics data to illustrate how ERp is able to discriminate subjects diagnosed with Mycobacterium tuberculosis infected meningitis from a control group. The list of discriminatory variables produced by ERp contains all biologically relevant variables with appropriate shift directions discussed in the original paper from which this data is taken. CONCLUSIONS: ERp performs variable selection and classification, is non-parametric and aids biological interpretation while handling unequal group sizes and misclassification costs. All this is achieved by a single approach which is easy to perform and interpret. ERp has the potential to address many other characteristics of metabolomics data. Future research aims to extend ERp to account for a large proportion of observations below the detection limit, as well as expand on interactions between variables.

Distinct Metabolomic Profiles of Papillary Thyroid Carcinoma and Benign Thyroid Adenoma.

Papillary thyroid carcinoma (PTC) and benign thyroid adenoma (BTA) are the most common head and neck tumors. However, the metabolic differences between PTC and BTA have not been characterized. The aim of this study was to identify the metabolic profiles of these two types of tumors using a metabolomics approach. Tumors and adjacent nontumor specimens collected from 57 patients with PTC and 48 patients with BTA were profiled using gas chromatography-time-of-flight mass spectrometry and ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. A panel of 46 and 44 differentially expressed metabolites were identified in the PTC and BTA specimens, respetively, and compared with nontumor tissues. Common metabolic signatures, as characterized by increased glycolysis, amino acid metabolism, one carbon metabolism and tryptophan metabolism, were found in both types of tumors. Purine and pyrimidine metabolism was significantly elevated in the PTC specimens, and taurine and hypotaurine levels were also higher in the PTC tissues. Increased fatty acid and bile acid levels were found, especially in the BTA tissues. The metabolic profiles of the PTC and BTA tissues include both similar and remarkably different metabolites, suggesting the presence of common and unique mechanistic pathways in these types of tumors during tumorigenesis.

Metabolite profiling elucidates communalities and differences in the polyphenol biosynthetic pathways of red and white Muscat genotypes.

The chemical composition of grape berries is varietal dependent and influenced by the environment and viticulture practices. In Muscat grapes, phenolic compounds play a significant role in the organoleptic property of the wine. In the present study, we investigated the chemical diversity of berries in a Muscat collection. Metabolite profiling was performed on 18 Moscato bianco clones and 43 different red and white grape varieties of Muscat using ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UPLC-QTOF-MS/MS) coupled with SNP genotyping. Principle component analysis and hierarchical clustering showed a separation of the genotypes into six main groups, three red and three white. Anthocyanins mainly explained the variance between the different groups. Additionally, within the white varieties mainly flavonols and flavanols contributed to the chemical diversity identified. A genotype-specific rootstock effect was identified when separately analyzing the skin of the clones, and it was attributed mainly to resveratrol, quercetin 3-O-galactoside, citrate and malate. The metabolite profile of the varieties investigated reveals the chemical diversity existing among different groups of Muscat genotypes. The distribution pattern of metabolites among the groups dictates the abundance of precursors and intermediate metabolite classes, which contribute to the organoleptic properties of Muscat berries.

Functional divergence in gastrointestinal microbiota in physically-separated genetically identical mice.

Despite the fundamental contribution of the gut microbiota to host physiology, the extent of its variation in genetically-identical animals used in research is not known. We report significant divergence in both the composition and metabolism of gut microbiota in genetically-identical adult C57BL/6 mice housed in separate controlled units within a single commercial production facility. The reported divergence in gut microbiota has the potential to confound experimental studies using mammalian models.

Dehydrin, alcohol dehydrogenase, and central metabolite levels are associated with cold tolerance in diploid strawberry (Fragaria spp.).

The use of artificial freezing tests, identification of biomarkers linked to or directly involved in the low-temperature tolerance processes, could prove useful in applied strawberry breeding. This study was conducted to identify genotypes of diploid strawberry that differ in their tolerance to low-temperature stress and to investigate whether a set of candidate proteins and metabolites correlate with the level of tolerance. 17 Fragaria vesca, 2 F. nilgerrensis, 2 F. nubicola, and 1 F. pentaphylla genotypes were evaluated for low-temperature tolerance. Estimates of temperatures where 50 % of the plants survived (LT50) ranged from -4.7 to -12.0 °C between the genotypes. Among the F. vesca genotypes, the LT50 varied from -7.7 °C to -12.0 °C. Among the most tolerant were three F. vesca ssp. bracteata genotypes (FDP821, NCGR424, and NCGR502), while a F. vesca ssp. californica genotype (FDP817) was the least tolerant (LT50) -7.7 °C). Alcohol dehydrogenase (ADH), total dehydrin expression, and content of central metabolism constituents were assayed in select plants acclimated at 2 °C. The LT50 estimates and the expression of ADH and total dehydrins were highly correlated (r(adh) = -0.87, r (dehyd) = -0.82). Compounds related to the citric acid cycle were quantified in the leaves during acclimation. While several sugars and acids were significantly correlated to the LT50 estimates early in the acclimation period, only galactinol proved to be a good LT50 predictor after 28 days of acclimation (r(galact) = 0.79). It is concluded that ADH, dehydrins, and galactinol show great potential to serve as biomarkers for cold tolerance in diploid strawberry.

Understanding and classifying metabolite space and metabolite-likeness.

While the entirety of 'Chemical Space' is huge (and assumed to contain between 10(63) and 10(200) 'small molecules'), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as 'naturally occurring' products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a 'metabolite-likeness' score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more 'synthetic' and the other being more 'metabolite-like'. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.

Ultra-high performance liquid chromatography tandem mass spectrometry for comprehensive analysis of urinary acylcarnitines.

We report an enabling mass spectrometric method for the analysis of lipid metabolites in order to define better the lipid metabolome in terms of chemical diversity and generate fragment ion spectra of these metabolites as a potential resource for unknown metabolite identification. This work focuses on the analysis of one important class of lipid metabolites, the acylcarnitines. Current analytical methods have only detected and identified a limited number of these metabolites. The method described herein provides the most comprehensive acylcarnitine profile in urine of healthy individuals up to date. It involves an optimized solid phase extraction technique for selective analyte extraction using cartridges containing both lipophilic and cation-exchange properties. The captured analytes are then subjected to ultra-high performance liquid chromatography (UPLC) separation, followed by tandem mass spectrometry (MS/MS) analysis using information-dependent acquisitions and selected reaction monitoring (SRM). The urine of six healthy individuals was analyzed using this method. A total of 355 acylcarnitines were detected; only 43 of them have been previously reported in the urine of healthy individuals. Detection of this large number of acylcarnitines illustrates the great diversity of the lipid metabolome as well as the usefulness of the method for profiling acylcarnitines. Furthermore, the MS/MS spectra of the 355 acylcarnitines will be uploaded to a public human metabolome database as a mass spectrometric resource for unknown metabolite identification.