Quantifying diet-induced metabolic changes of the human gut microbiome

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

Prediction of breast cancer risk based on profiling with common genetic variants

BACKGROUND: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. METHODS: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. RESULTS: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. CONCLUSIONS: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

New insights into functional regulation in MS-based drug profiling

We present a novel data analysis strategy which combined with subcellular fractionation and liquid chromatography-mass spectrometry (LC-MS) based proteomics provides a simple and effective workflow for global drug profiling. Five subcellular fractions were obtained by differential centrifugation followed by high resolution LC-MS and complete functional regulation analysis. The methodology combines functional regulation and enrichment analysis into a single visual summary. The workflow enables improved insight into perturbations caused by drugs. We provide a statistical argument to demonstrate that even crude subcellular fractions leads to improved functional characterization. We demonstrate this data analysis strategy on data obtained in a MS-based global drug profiling study. However, this strategy can also be performed on other types of large scale biological data.

Cardiorespiratory fitness is negatively associated with metabolic risk factors independently of the adherence to a healthy dietary pattern

Background and aim: Cardiorespiratory fitness (CRF) and diet have been involved as significant factors towards the prevention of cardio-metabolic diseases. This study aimed to assess the impact of the combined associations of CRF and adherence to the Southern European Atlantic Diet (SEADiet) on the clustering of metabolic risk factors in adolescents. Methods and Results: A cross-sectional school-based study was conducted on 468 adolescents aged 15-18, from the Azorean Islands, Portugal. We measured fasting glucose, insulin, total cholesterol (TC), HDL-cholesterol, triglycerides, systolic blood pressure, waits circumference and height. HOMA, TC/HDL-C ratio and waist-to-height ratio were calculated. For each of these variables, a Z-score was computed by age and sex. A metabolic risk score (MRS) was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had 1SD of this score. CRF was measured with the 20 m-Shuttle-Run- Test. Adherence to SEADiet was assessed with a semi-quantitative food frequency questionnaire. Logistic regression showed that, after adjusting for potential confounders, unfit adolescents with low adherence to SEADiet had the highest odds of having MRS (OR Z 9.4; 95%CI:2.6e33.3) followed by the unfit ones with high adherence to the SEADiet (OR Z 6.6; 95% CI: 1.9e22.5) when compared to those who were fit and had higher adherence to SEADiet.

Assessment of nutritional and metabolic profiles of pea shoots: The new ready-to-eat baby-leaf vegetable

Pea-shoots are a new option as ready-to-eat baby-leaf vegetable. However, data about the nutritional composition and the shelf-life stability of these leaves, especially their phytonutrient composition is scarce. In this work, the macronutrient, micronutrient and phytonutrients profile of minimally processed pea shoots were evaluated at the beginning and at the end of a 10-day storage period. Several physicochemical characteristics (color, pH, total soluble solids, and total titratable acidity) were also monitored. Standard AOAC methods were applied in the nutritional value evaluation, while chromatographic methods with UV–vis and mass detection were used to analyze free forms of vitamins (HPLC-DAD-ESI-MS/MS), carotenoids (HPLC-DAD-APCI-MSn) and flavonoid compounds (HPLC-DAD-ESI-MSn). Atomic absorption spectrometry (HR-CS-AAS) was employed to characterize the mineral content of the leaves. As expected, pea leaves had a high water (91.5%) and low fat (0.3%) and carbohydrate (1.9%) contents, being a good source of dietary fiber (2.1%). Pea shoots showed a high content of vitamins C, E and A, potassium and phosphorous compared to other ready-to-eat green leafy vegetables. The carotenoid profile revealed a high content of β-carotene and lutein, typical from green leafy vegetables. The leaves had a mean flavonoid content of 329 mg/100 g of fresh product, mainly composed by glycosylated quercetin and kaempferol derivatives. Pea shoots kept their fresh appearance during the storage being color maintained throughout the shelf-life. The nutritional composition was in general stable during storage, showing some significant (p < 0.05) variation in certain water-soluble vitamins.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

Metabolic and morphogenetic engineering of Bacillus subtilis: biotechnology for industry

Dissertation presented to obtain the Ph.D. degree in “Biology” at the Institute of Chemical and Biological Technology of the New University of Lisbon

Full-scale biological phosphorus removal: quantification of storage polymers, microbial performance and metabolic modelling

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica

User Profiling and Knowledge Extraction For Tourism

O sector do turismo é uma área francamente em crescimento em Portugal e que tem desenvolvido a sua divulgação e estratégia de marketing. Contudo, apenas se prende com indicadores de desempenho e de oferta instalada (número de quartos, hotéis, voos, estadias), deixando os indicadores estatísticos em segundo plano. De acordo com o “ Travel & tourism Competitiveness Report 2013”, do World Economic Forum, classifica Portugal em 72º lugar no que respeita à qualidade e cobertura da informação estatística, disponível para o sector do Turismo. Refira-se que Espanha ocupa o 3º lugar. Uma estratégia de mercado, sem base analítica, que sustente um quadro de orientações específico e objetivo, com relevante conhecimento dos mercados alvo, dificilmente é compreensível ou até mesmo materializável. A implementação de uma estrutura de Business Intelligence que permita a realização de um levantamento e tratamento de dados que possibilite relacionar e sustentar os resultados obtidos no sector do turismo revela-se fundamental e crucial, para que sejam criadas estratégias de mercado. Essas estratégias são realizadas a partir da informação dos turistas que nos visitam, e dos potenciais turistas, para que possam ser cativados no futuro. A análise das características e dos padrões comportamentais dos turistas permite definir perfis distintos e assim detetar as tendências de mercado, de forma a promover a oferta dos produtos e serviços mais adequados. O conhecimento obtido permite, por um lado criar e disponibilizar os produtos mais atrativos para oferecer aos turistas e por outro informá-los, de uma forma direcionada, da existência desses produtos. Assim, a associação de uma recomendação personalizada que, com base no conhecimento de perfis do turista proceda ao aconselhamento dos melhores produtos, revela-se como uma ferramenta essencial na captação e expansão de mercado.

Development of metabolic labeling strategies to study changes in protein expression

Résumé : Les progrès techniques de la spectrométrie de masse (MS) ont contribué au récent développement de la protéomique. Cette technique peut actuellement détecter, identifier et quantifier des milliers de protéines. Toutefois, elle n'est pas encore assez puissante pour fournir une analyse complète des modifications du protéome corrélées à des phénomènes biologiques. Notre objectif était le développement d'une nouvelle stratégie pour la détection spécifique et la quantification des variations du protéome, basée sur la mesure de la synthèse des protéines plutôt que sur celle de la quantité de protéines totale. Pour cela, nous volions associer le marquage pulsé des protéines par des isotopes stables avec une méthode d'acquisition MS basée sur le balayage des ions précurseurs (precursor ion scan, ou PIS), afin de détecter spécifiquement les protéines ayant intégré les isotopes et d'estimer leur abondance par rapport aux protéines non marquées. Une telle approche peut identifier les protéines avec les plus hauts taux de synthèse dans une période de temps donnée, y compris les protéines dont l'expression augmente spécifiquement suite à un événement précis. Nous avons tout d'abord testé différents acides aminés marqués en combinaison avec des méthodes PIS spécifiques. Ces essais ont permis la détection spécifique des protéines marquées. Cependant, en raison des limitations instrumentales du spectromètre de masse utilisé pour les méthodes PIS, la sensibilité de cette approche s'est révélée être inférieure à une analyse non ciblée réalisée sur un instrument plus récent (Chapitre 2.1). Toutefois, pour l'analyse différentielle de deux milieux de culture conditionnés par des cellules cancéreuses humaines, nous avons utilisé le marquage métabolique pour distinguer les protéines d'origine cellulaire des protéines non marquées du sérum présentes dans les milieux de culture (Chapitre 2.2). Parallèlement, nous avons développé une nouvelle méthode de quantification nommée IBIS, qui utilise des paires d'isotopes stables d'acides aminés capables de produire des ions spécifiques qui peuvent être utilisés pour la quantification relative. La méthode IBIS a été appliquée à l'analyse de deux lignées cellulaires cancéreuses complètement marquées, mais de manière différenciée, par des paires d'acides aminés (Chapitre 2.3). Ensuite, conformément à l'objectif initial de cette thèse, nous avons utilisé une variante pulsée de l'IBIS pour détecter des modifications du protéome dans des cellules HeLa infectée par le virus humain Herpes Simplex-1 (Chapitre 2.4). Ce virus réprime la synthèse des protéines des cellules hôtes afin d'exploiter leur mécanisme de traduction pour la production massive de virions. Comme prévu, de hauts taux de synthèse ont été mesurés pour les protéines virales détectées, attestant de leur haut niveau d'expression. Nous avons de plus identifié un certain nombre de protéines humaines dont le rapport de synthèse et de dégradation (S/D) a été modifié par l'infection virale, ce qui peut donner des indications sur les stratégies utilisées par les virus pour détourner la machinerie cellulaire. En conclusion, nous avons montré dans ce travail que le marquage métabolique peut être employé de façon non conventionnelle pour étudier des dimensions peu explorées en protéomique. Summary : In recent years major technical advancements greatly supported the development of mass spectrometry (MS)-based proteomics. Currently, this technique can efficiently detect, identify and quantify thousands of proteins. However, it is not yet sufficiently powerful to provide a comprehensive analysis of the proteome changes correlated with biological phenomena. The aim of our project was the development of ~a new strategy for the specific detection and quantification of proteomé variations based on measurements of protein synthesis rather than total protein amounts. The rationale for this approach was that changes in protein synthesis more closely reflect dynamic cellular responses than changes in total protein concentrations. Our starting idea was to couple "pulsed" stable-isotope labeling of proteins with a specific MS acquisition method based on precursor ion scan (PIS), to specifically detect proteins that incorporated the label and to simultaneously estimate their abundance, relative to the unlabeled protein isoform. Such approach could highlight proteins with the highest synthesis rate in a given time frame, including proteins specifically up-regulated by a given biological stimulus. As a first step, we tested different isotope-labeled amino acids in combination with dedicated PIS methods and showed that this leads to specific detection of labeled proteins. Sensitivity, however, turned out to be lower than an untargeted analysis run on a more recent instrument, due to MS hardware limitations (Chapter 2.1). We next used metabolic labeling to distinguish the proteins of cellular origin from a high background of unlabeled (serum) proteins, for the differential analysis of two serum-containing culture media conditioned by labeled human cancer cells (Chapter 2.2). As a parallel project we developed a new quantification method (named ISIS), which uses pairs of stable-isotope labeled amino acids able to produce specific reporter ions, which can be used for relative quantification. The ISIS method was applied to the analysis of two fully, yet differentially labeled cancer cell lines, as described in Chapter 2.3. Next, in line with the original purpose of this thesis, we used a "pulsed" variant of ISIS to detect proteome changes in HeLa cells after the infection with human Herpes Simplex Virus-1 (Chapter 2.4). This virus is known to repress the synthesis of host cell proteins to exploit the translation machinery for the massive production of virions. As expected, high synthesis rates were measured for the detected viral proteins, confirming their up-regulation. Moreover, we identified a number of human proteins whose synthesis/degradation ratio (S/D) was affected by the viral infection and which could provide clues on the strategies used by the virus to hijack the cellular machinery. Overall, in this work, we showed that metabolic labeling can be employed in alternative ways to investigate poorly explored dimensions in proteomics.

Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links.

Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on (1)H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10(-8)) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10(-44)) and lysine (rs8101881, P = 1.2×10(-33)), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.

Resting metabolic rate and protein turnover in apparently healthy elderly Gambian men.

Body composition, resting energy expenditure (REE), and whole body protein metabolism were studied in 26 young and 28 elderly Gambian men matched for body mass index during the dry season in a rural village in The Gambia. REE was measured by indirect calorimetry (hood system) in the fasting state and after five successive meals. Rates of whole body nitrogen flux, protein synthesis, and protein breakdown were determined in the fed state from the level of isotopic enrichment of urinary ammonia over a period of 12 h after a single oral dose of [15N]glycine. Expressed in absolute value, REE was significantly lower in the elderly compared with the young group (3.21 +/- 0.07 vs. 4.04 +/- 0.07 kJ/min, P < 0.001) and when adjusted to body weight (3.29 +/- 0.05 vs. 3.96 +/- 0.05 kJ/min, P < 0.0001) and fat-free mass (FFM; 3.38 +/- 0.01 vs. 3.87 +/- 0.01 kJ/min, P < 0.0001). The rate of protein synthesis averaged 207 +/- 13 g protein/day in the elderly and 230 +/- 13 g protein/day in the young group, whereas protein breakdown averaged 184 +/- 13 g protein/day in the elderly and 203 +/- 13 g protein/day in the young group (nonsignificant). When values were adjusted for body weight or FFM, they did not reveal any difference between the two groups. It is concluded that the reduced REE adjusted for body composition observed in elderly Gambian men is not explained by a decrease in protein turnover.