An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value.

INTRODUCTION: Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics. AREAS COVERED: In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value. EXPERT OPINION: A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis.

INTRODUCTION: Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities. OBJECTIVES: This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease. METHODS: Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites. RESULTS: Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914). CONCLUSION: From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.

Foodomics for agroecology: Differentiation of volatile profile in mint (Mentha × gracilis Sole) from permaculture, organic and conventional agricultural systems using HS-SPME/GC-MS.

In the present study, foodomics approach was employed to investigate changes in the metabolism from the volatile terpenoids profile of mint(Mentha × gracillis Sole)from conventional, organic and permaculture (a type of agroecological agriculture system) farms using headspace solid-phase microextraction (HS-SPME) associated to gas chromatography coupled to mass spectrometry (GC-MS) and chemometric tools. The discrimination among the three types of mint was successfully achieved and demonstrated evidence of ecological interaction impact in the food metabolism. The agroecological mint presented as differential compounds: α-terpineol, bornyl formate, cis-carvyl propionate, cis-carveol, camphor, dihydrocarvyl acetate, dihydrocarveol, karahanaenone, nonanal, 3-octyl acetate, and trans-3-hexenyl-2 methylbutyrate. While organic and conventional mint presented as differential compounds: α-cedrene, ß -pinene, γ-muurolene, δ-cadinene, germacrene, terpinolene, and elemol. The majority of differential metabolites from agroecological mint are oxygenated monoterpenes, which have more intense flavor and biological activities than hydrocarbons monoterpenes and sesquiterpenes found in organic and conventional mint. Furthermore, the discrimination between organic and conventional mint was effectively performed, which demonstrated different terpenoid profiles though without implying benefits for one or another agriculture system.

Systems biology approaches to study lipidomes in health and disease.

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Metabolomics: Definitions and Significance in Systems Biology.

Nowadays, there is a growing interest in deeply understanding biological mechanisms not only at the molecular level (biological components) but also the effects of an ongoing biological process in the organism as a whole (biological functionality), as established by the concept of systems biology. Within this context, metabolomics is one of the most powerful bioanalytical strategies that allow obtaining a picture of the metabolites of an organism in the course of a biological process, being considered as a phenotyping tool. Briefly, metabolomics approach consists in identifying and determining the set of metabolites (or specific metabolites) in biological samples (tissues, cells, fluids, or organisms) under normal conditions in comparison with altered states promoted by disease, drug treatment, dietary intervention, or environmental modulation. The aim of this chapter is to review the fundamentals and definitions used in the metabolomics field, as well as to emphasize its importance in systems biology and clinical studies.