Chemometric strategy for modeling metabolic biological space along the gastrointestinal tract and assessing microbial influences

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

Cancer Cell Surface Negative Charges: A Bio-Physical Manifestation of the Warburg Effect

The early detection of circulating tumor cells (CTCs) in blood as part of medical diagnosis will give the doctors a head start in the provision and treatment of cancer, and therefore, with the advance in Nano technology, there is an increasing expectation of some form of early detection of circulating tumor cells at a highly sensitive level, without any biomarkers, for both early cancer diagnosis and monitoring disease progression after medical intervention. This technical note reports on the recent development in detection of highly sensitive detection of cancer cells without biomarkers. This novel concept is developed based on a hallmark cancer metabolic pattern: high glycolysis rate. Secretion of high level of lactate acid by cancer cells ultimately results in negative electrical charges on their surfaces, enabling strong binding and capturing by the positively-charged nanoprobes, and subsequent magnetic separation. When nanoprobes are incubated with cancer cells in suspension, binding takes place due to charge differences, and cancer cells are then magnetically separated. The separated cells are enumerated using a flow cytometry and identified by pathological and genome sequencing methods. Preliminary results using the approach have shown exceptionally high cancer cell capture rates, therefore potentially applicable in cancer cell detection in clinical settings.

Systems medicine of inflammaging

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.

Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research

Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Reprint of: Musculoskeletal system in the old age and the demand for healthy ageing biomarkers

Population ageing has emerged as a major demographic trend worldwide due to improved health and longevity. This global ageing phenomenon will have a major impact on health-care systems worldwide due to increased morbidity and greater needs for hospitalization/institutionalization. As the ageing population increases worldwide, there is an increasing awareness not only of increased longevity but also of the importance of "healthy ageing" and "quality of life". Yet, the age related chronic inflammation is believed to be pathogenic with regards to its contribution to frailty and degenerative disorders. In particular, the frailty syndrome is increasingly being considered as a key risk indicator of adverse health outcomes. In addition, elderly may be also prone to be resistant to anabolic stimuli which is likely a key factor in the loss of skeletal muscle mass with ageing. Vital to understand these key biological processes is the development of biological markers, through system biology approaches, aiding at strategies for tailored therapeutic and personalized nutritional program. Overall aim is to prevent or attenuate decline of key physiological functions required to live an active, independent life. This review focus on core indicators of health and functions in older adults, where nutrition and tailored personalized programs could exhibit preventive roles, and where the aid of metabolomics technologies are increasingly displaying potential in revealing key molecular mechanisms/targets linked to specific ageing and/or healthy ageing processes.

Clinical metabolomics paves the way towards future healthcare strategies

Metabolomics is recognized as a powerful top-down system biological approach to understand genetic-environment-health paradigms paving new avenues to identify clinically relevant biomarkers. It is nowadays commonly used in clinical applications shedding new light on physiological regulatory processes of complex mammalian systems with regard to disease aetiology, diagnostic stratification and, potentially, mechanism of action of therapeutic solutions. A key feature of metabolomics lies in its ability to underpin the complex metabolic interactions of the host with its commensal microbial partners providing a new way to define individual and population phenotypes. This review aims at describing recent applications of metabolomics in clinical fields with insight into diseases, diagnostics/monitoring and improvement of homeostatic metabolic regulation.

Specific dietary preferences are linked to differing gut microbial metabolic activity in response to dark chocolate intake

Systems biology approaches are providing novel insights into the role of nutrition for the management of health and disease. In the present study, we investigated if dietary preference for dark chocolate in healthy subjects may lead to different metabolic response to daily chocolate consumption. Using NMR- and MS-based metabolic profiling of blood plasma and urine, we monitored the metabolic response of 10 participants stratified as chocolate desiring and eating regularly dark chocolate (CD) and 10 participants stratified as chocolate indifferent and eating rarely dark chocolate (CI) to a daily consumption of 50 g of dark chocolate as part of a standardized diet over a one week period. We demonstrated that preference for chocolate leads to different metabolic response to chocolate consumption. Daily intake of dark chocolate significantly increased HDL cholesterol by 6% and decreased polyunsaturated acyl ether phospholipids. Dark chocolate intake could also induce an improvement in the metabolism of long chain fatty acid, as noted by a compositional change in plasma fatty acyl carnitines. Moreover, a relationship between regular long-term dietary exposure to a small amount of dark chocolate, gut microbiota, and phenolics was highlighted, providing novel insights into biological processes associated with cocoa bioactives.

Metabolomic applications to decipher gut microbial metabolic influence in health and disease

Dietary preferences and nutrients composition have been shown to influence human and gut microbial metabolism, which ultimately has specific effects on health and diseases’ risk. Increasingly, results from molecular biology and microbiology demonstrate the key role of the gut microbiota metabolic interface to the overall mammalian host’s health status. There is therefore raising interest in nutrition research to characterize the molecular foundations of the gut microbial–mammalian cross talk at both physiological and biochemical pathway levels. Tackling these challenges can be achieved through systems biology approaches, such as metabolomics, to underpin the highly complex metabolic exchanges between diverse biological compartments, including organs, systemic biofluids, and microbial symbionts. By the development of specific biomarkers for prediction of health and disease, metabolomics is increasingly used in clinical applications as regard to disease etiology, diagnostic stratification, and potentially mechanism of action of therapeutical and nutraceutical solutions. Surprisingly, an increasing number of metabolomics investigations in pre-clinical and clinical studies based on proton nuclear magnetic resonance (¹H NMR) spectroscopy and mass spectrometry provided compelling evidence that system wide and organ-specific biochemical processes are under the influence of gut microbial metabolism. This review aims at describing recent applications of metabolomics in clinical fields where main objective is to discern the biochemical mechanisms under the influence of the gut microbiota, with insight into gastrointestinal health and diseases diagnostics and improvement of homeostasis metabolic regulation.

1H NMR-based metabonomic applications to decipher gut microbial metabolic influence on mammalian health

Recent advances in molecular biology and microbiology have increased awareness on the importance of the gut microbiota to the overall mammalian host's health status. There is therefore increasing interest in nutrition research to characterise the molecular foundations of the gut microbial mammalian crosstalk at both physiological and biochemical pathway levels. Tackling these challenges can be achieved through systems biology strategies based on the measurement of metabolites to assess the highly complex metabolic exchanges between diverse biological compartments, including organs, biofluids and microbial symbionts. By opening a direct biochemical window into the metabolome, metabonomics is uniquely suited for the identification of biomarkers providing better understanding of these complex metabolic processes. Recent applications of top-down system biology based on (1)H NMR spectroscopy coupled to advanced chemometric modelling approaches provided compelling evidence that system-wide and organ-specific changes in biochemical processes may be finely tuned by gut microbial activities. This review aims at describing current advances in NMR-based metabonomics where the main objective is to discern the molecular pathways and biochemical mechanisms under the influence of the gut microbiota. Furthermore, emphasis is given on nutritional approaches, where the quest for homeostatic balance is dependent not only on the host but also on the nutritional modulation of the gut microbiota-host metabolic interactions, using, for instance, probiotics and prebiotics.

Metabolic phenotyping of the Crohn's disease-like IBD etiopathology in the TNF(ΔARE/WT) mouse model

The underlying biochemical consequences of inflammatory bowel disease (IBD) on the systemic and gastrointestinal metabolism have not yet been fully elucidated but could help to better understand the disease pathogenesis and to identify tissue-specific markers associated with the different disease stages. Here, we applied a metabonomic approach to monitor metabolic events associated with the gradual development of Crohn's disease (CD)-like ileitis in the TNF(ΔARE/WT) mouse model. Metabolic profiles of different intestinal compartments from the age of 4 up to 24 weeks were generated by combining proton nuclear magnetic resonance ((1)H NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). From 8 weeks onward, mice developed CD similar to the immune and tissue-related phenotype of human CD with ileal involvement, including ileal histological abnormalities, reduced fat mass and body weight, as well as hallmarks of malabsorption with higher energy wasting. The metabonomic approach highlighted shifts in the intestinal lipid metabolism concomitant to the histological onset of inflammation. Moreover, the advanced disease status was characterized by a significantly altered metabolism of cholesterol, triglycerides, phospholipids, plasmalogens, and sphingomyelins in the inflamed tissue (ileum) and the adjacent intestinal parts (proximal colon). These results describe different biological processes associated with the disease onset, including modifications of the general cell membrane composition, alteration of energy homeostasis, and finally the generation of inflammatory lipid mediators. Taken together, this provides novel insights into IBD-related alterations of specific lipid-dependant processes during inflammatory states.