Free fatty acids profiling in response to carnitine synergize with lutein in diabetic rats

Background: The objective of this study was to investigate the fatty acids profiling in diabetic rats induced by sterptozocine (STZ) and their response to administration of lutein and carnitine.Materials and methods: Ninety male albino rats were divided into 6 groups as follows: Normal control. The remaining rats were injected i.p a single dose of STZ (65 mg /kg bw) for induction of diabetes. Diabetic rats were grouped as: GP II: (Untreated): GP III: Rats were given orally with L-lutein (100 mg/kg bw).GP IV: Rats were given carnitine (30 μg/kg) i.p. GP V: Rats were given carnitine and lutein GP VI were given metformin (100mg/kg bw/d) for 6 weeks.Results: Treatment of diabetic rats with lutein, L-carnitine, combined decreased the levels of glucose, HA1C compared with untreated diabetic (p<0.001). Administration of L-lutein, carnitine, combined to normal rats significantly decreased the levels of myristic, palmitice, palmitoleic, stearic, linoleic, α-linolenic, arachidic and eicosadienoic when compared with control normal rats (p<0.001).Conclusion: Abnormalities of fatty acids composition was observed in diabetic rats. Combination treatment with lutein and carnitine could ameliorate deleterious effect induced by STZ and attenuate the changed fatty acid compositionKeywords: Fatty acids profiling- lutein-carnitine-rat

Recent advances in plant metabolomics and greener pastures

Metabolomics is an extension of the omics concept and experimental approaches. However, is metabolomics just another trendy omics fashion perturbation or is metabolomics actually delivering novel content and value? This article highlights some recent advances that definitely support the role of plant metabolomics in the movement toward greener pastures

Facilitating the development of controlled vocabularies for metabolomics technologies with text mining

BACKGROUND: Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public resources. Experimental data sets from metabolomics studies need to be integrated with one another, but also with data produced by other types of omics studies in the spirit of systems biology, hence the pressing need for vocabularies and ontologies in metabolomics. However, it is time-consuming and non trivial to construct these resources manually. RESULTS: We describe a methodology for rapid development of controlled vocabularies, a study originally motivated by the needs for vocabularies describing metabolomics technologies. We present case studies involving two controlled vocabularies (for nuclear magnetic resonance spectroscopy and gas chromatography) whose development is currently underway as part of the Metabolomics Standards Initiative. The initial vocabularies were compiled manually, providing a total of 243 and 152 terms. A total of 5,699 and 2,612 new terms were acquired automatically from the literature. The analysis of the results showed that full-text articles (especially the Materials and Methods sections) are the major source of technology-specific terms as opposed to paper abstracts. CONCLUSIONS: We suggest a text mining method for efficient corpus-based term acquisition as a way of rapidly expanding a set of controlled vocabularies with the terms used in the scientific literature. We adopted an integrative approach, combining relatively generic software and data resources for time- and cost-effective development of a text mining tool for expansion of controlled vocabularies across various domains, as a practical alternative to both manual term collection and tailor-made named entity recognition methods

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCore(TM), MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages

پزشکی آینده پزشکی سیستمی : پزشکی P4

برای درک جهان بیولوژیک، دانشمندان ناچارند که به سوی ناشناخته¬ها، به ویژه به سوی چالش‌های سترگ پیش روند. چالش‌هایی که با پرسش‌هایی بس سترگ‌تر و پیچیده همراه هستند. سیستم‌های بیولوژیک از قوانین حاکم بر سیستم‌های پیچیده پیروی می‌کنند. یک سیستم پیچیده دارای تعداد زیادی اجزای بر هم کنش است که فعالیت انباشتی آنها نمایی غیر خطی داشته و به شکل آشکار تحت فشارهای خاصی نیز رفتار ”خود سازماندهی“ از خود نشان می‌دهند. برای مدل سازی هر رفتار پیچیده، ما می‌بایست از اجزاء تشکیل دهنده‌ی جدا از هم آن (زیرسیستم‌ها) و نیز الگوی پیچیده‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌¬ی ”خود سازماندهی“ که از بر هم کنش این اجزاء خاص می‌آیند، آگاهی داشته باشیم. تاکنون اساس روش شناخت فرایندها بر پایه‌ی روش‌های استقرایی و خرد کردن سیستم به اجزاء تشکیل دهنده‌ی آن و بررسی روابط خطی آنها بوده است. هر چند در پنج سده‌ی گذشته، بشر توانسته است بر این پایه به پیشرفت‌های بسیار بزرگی نایل شود ولی طیّ چند دهه‌ی گذشته‌ پی برده است که این شیوه، پاسخگوی شناخت سیستم‌های پیچیده مانند سیستم‌های بیولوژیک نیست و از این رو کم کم تفکر سیستمی در کاوش‌های علمی، راه خود را باز نموده است. در حقیقت تفکر سیستمی، رهیافتی بسیار فرا دقیق برای دریافت روابط غیر خطی است که روش‌های استقرایی در علم، توان دریافت آنها را ندارند. بنابراین تفکر سیستمی، بینش درک ماهیت کل سیستم را امکان پذیر می‌سازد؛ با درکی که نمی‌توان بر پایه‌ی مطالعه‌ی مجزای اجزای سیستم به دست آورد. بدین سان تفکر سیستمی یک پارادایم است که پیوستگی‌های میان اجزاء گوناگون و بر هم کنش آنها را تحت رصد قرار می‌دهد

Emerging pollutants in water: innovative approaches of study and treatment

Water quality is one of the major challenges that human-ity has to face. Tackling the problem of pollution needs the use of all the resources and expertise available to fill the lack of knowledge and technology. Concern is grow-ing over the many emerging contaminants, including heavy metals ions and plastics, which are omnipresent and poorly managed. In this contest, the presence of micro- and nanoplastics in the marine environment is raising strong concerns. The lack of appropriate methodologies to collect the nanoplastics from water systems imposes the use of engi-neered model nanoparticles to explore their main charac-teristics and behaviour. In order to develop a nanoplastic model more reliable and realistic compared to the com-mon polystyrene nanospheres, in this thesis, laser abla-tion has been applied to induce the formation of plastic nanoparticles in water starting from a bulk polymer film. The process was performed on Polyethylene Tereph-thalate, a commercial polymer used to produce beverage bottles, widespread in the environment. PET nanoparti-cles with an average size <100 nm, were carefully charac-terized in terms of chemical/physical properties. Size, shape, surface chemistry and colloidal stability were ana-lyzed and compared with what expected from a real sam-ple. As the oral route has been defined as the main route for human exposure to nanoplastics, their biological in-teractions and the effects on single intestinal epithelial cells and on a model of intestinal barrier have been as-sessed. The aquatic environment exposes the nanoplastics to a great variety of substances and contaminants. The nanoplastics can therefore act as carriers for many toxic sub-stances with risks for aquatic organisms but also for hu-mans. The nanoplastic model was studied in presence of three model contaminants a pesticide, a drug and a heavy metal ion (glyphosate, levofloxacin and Hg2+ respec-tively). The binding capacity toward these contaminants, was demonstrated and characterized quantitatively and qualitatively. The synergic biological effect of the contam-inant-nanoplastic complexes was investigated in vitro on macrophages and intestinal epithelial cells. The conven-tional toxicological assays have been implemented with a preliminary metabolomic analysis. Concerning heavy metal ions pollution, considerable at-tention is being devoted to the development of low-cost and environmentally safe materials for their removal from polluted waters. Several strategies have been ap-plied to solve the problem of toxic metal ions contamina-tion in water, where the development of nanotechnol-ogy, and in particular of novel metal oxide nano-sorbents provides a promising and efficient alternative. The appli-cation of these technologies is however limited by the dif-ficult management of nanomaterials in the environment.Therefore, the use of a bionanocomposite made of titan-ate nanosheets embedded in a silk fibroin matrix was pro-posed as eco-friendly approach for water treatment ap-plications. The nanocomposite has been characterized and its ion exchange performances have been analysed under various conditions. The nanocomposites capacity to efficiently retain and adsorbed ions, with no release of titanate nanosheets has been proved. By modifying the nanocomposite formulation, it was also possible to en-hance the materials selectivity towards the lead ions

Metabolomic approaches for the identification of metabolic pathways in Trypanosoma brucei

Trypanosoma brucei is a parasitic protozoan that can cause human African trypanosomiasis (HAT) and Nagana in cattle. Human African trypanosomiasis is deadly when left untreated, and thus there is an urgent need to develop new drugs against this disease. As trypanosomes are early diverged eukaryotes, it is anticipated that studying their metabolism can identify novel drug targets. The main drug currently in use against the late encephalitic stage, Eflornithine, was shown to inhibit an essential pathway in trypanosomes (Yarlett and Bacchi, 1989). In this Thesis three approaches were used to apply metabolomic and proteomic techniques for protein function identification and to investigate metabolic pathways. The genome of T. brucei has been published (Berriman et al., 2005) and data is available via databases, such as TriTrypDB, a database dedicated to the trypanosomatids (Aslett et al., 2009). An estimated 40% of the identified genes in this organism are annotated with an unknown or putative function. In 2006, Saito et al. developed a systematic method to ascertain enzyme function based on an in vitro assay, in combination with metabolite profiling. This approach was successfully applied in several other studies. Here, I investigate the use of this method for its application in a high throughput approach for unknown enzyme identification in trypanosomes. Seven putative identified enzymes were randomly selected from TriTrypDB, cloned and expressed in E. coli and a function could be attributed to at least one of the enzymes. Furthermore, the amino acid metabolism in trypanosomes was investigated; using stable isotope labelling combined with metabolomics. The flux of labelled compounds could be traced through the organism showing the active metabolic pathways of L-methionine, L-proline and L-arginine in T. brucei. Two T. b. brucei strains used in this study, GVR35 and 427, cause different forms of infections in their mammalian host. GVR35 causes a chronic infection and invades the central nervous system (CNS) with varying parasitemia in mice, whereas infection with strain 427 presents an acute form with high parasitaemia, causing high mortality, without invading the CNS. What causes this difference in the progression of infection? Secreted or excreted proteins from the parasites, referred to as the secretome, have been described as being important factor for virulence and avoiding the host immune response (Geiger et al., 2010) and Garzon et al. (2006) showed that excreted/secreted proteins can inhibit the maturation of dentritic cells and stop them from inducing a lymphocytic allogenic response. Significant differences in proteins secreted from these two strains are discussed; although the results are preliminary