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Virtue S.,University of Cambridge | Masoodi M.,Medical Research Council | Masoodi M.,Nestle | De Weijer B.A.M.,University of Amsterdam | And 9 more authors.
International Journal of Obesity | Year: 2015

Background/Objectives:Obesity has been associated with both changes in adipose tissue lipid metabolism and inflammation. A key class of lipid-derived signalling molecules involved in inflammation are the prostaglandins. In this study, we aimed to determine how obesity affects the levels of prostaglandins within white adipose tissue (WAT) and determine which cells within adipose tissue produce them. To avoid the effects of cellular stress on prostaglandin levels, we developed a multivariate statistical approach in which metabolite concentrations and transcriptomic data were integrated, allowing the assignment of metabolites to cell types.Subjects/Methods:Eicosanoids were measured by liquid chromatography-tandem mass spectrometry and mRNA levels using real-time PCR. Eicosanoid levels and transcriptomic data were combined using principal component analysis and hierarchical clustering in order to associate metabolites with cell types. Samples were obtained from C57Bl/6 mice aged 16 weeks. We studied the ob/ob genetically obese mouse model and diet-induced obesity model. We extended our results in mice to a cohort of morbidly obese humans undergoing bariatric surgery. Results: Using our modelling approach, we determined that prostglandin D 2 (PGD 2) in adipose tissue was predominantly produced in macrophages by the haematopoietic isoform of prostaglandin D synthase (H-Pgds). Analysis of sub-fractionated WAT confirmed that H-Pgds was expressed in adipose tissue macrophages (ATMs). Furthermore, H-Pgds expression in ATMs isolated from lean and obese mice was consistent with it affecting macrophage polarisation. Functionally, we demonstrated that H-PGDS-produced PGD 2 polarised macrophages toward an M2, anti-inflammatory state. In line with a potential anti-inflammatory role, we found that H-PGDS expression in ATMs was positively correlated with both peripheral insulin and adipose tissue insulin sensitivity in humans.Conclusions:In this study, we have developed a method to determine the cellular source of metabolites within an organ and used it to identify a new role for PGD 2 in the control of ATM polarisation. © 2015 Macmillan Publishers Limited All rights reserved. Source


Beisken S.,European Bioinformatics Institute | Eiden M.,Metabolomx | Salek R.M.,European Bioinformatics Institute | Salek R.M.,University of Cambridge
Expert Review of Molecular Diagnostics | Year: 2015

Small molecules within biological systems provide powerful insights into the biological roles, processes and states of organisms. Metabolomics is the study of the concentrations, structures and interactions of these thousands of small molecules, collectively known as the metabolome. Metabolomics is at the interface between chemistry, biology, statistics and computer science, requiring multidisciplinary skillsets. This presents unique challenges for researchers to fully utilize the information produced and to capture its potential diagnostic power. A good understanding of study design, sample preparation, analysis methods and data analysis is essential to get the right answers for the right questions. We outline the current state of the art, benefits and challenges of metabolomics to create an understanding of metabolomics studies from the experimental design to data analysis. © 2015 Informa UK, Ltd. Source


News Article | March 1, 2012
Site: www.bloomberg.com

A few years ago researchers in California received widespread attention for showing that dogs can smell cancer on a human’s breath. With 99 percent accuracy the canines could detect if a person had lung or breast cancer, beating the best figures from standard laboratory tests. Subsequent studies confirmed the results and provided further evidence that dogs really are man’s best friend. The problem with cancer-detecting dogs is that, well, they’re dogs. Hospitals haven’t embraced the idea of a diagnostic tool that poops, barks, and requires feeding. With such concerns in mind, technology startups have hustled to build digital devices that can mimic the dogs’ olfactory sense and reduce the need for biopsies and CAT scans. Metabolomx, a 12-person outfit in Mountain View, Calif., now appears on the fast track—insofar as such a thing exists in the heavily regulated medical field—to bringing a cancer-sniffing device to market. The Metabolomx machine looks like a desktop PC with a hose attached. It sits on a cart that can be wheeled up to a patient, who is instructed to breathe in and out for about four minutes. The machine analyzes the breath and its volatile organic compounds, or VOCs—aerosolized molecules that, among other things, determine how something smells. Tumors produce their own VOCs, which pass into the bloodstream. The lungs create a bridge between the bloodstream and airways, so the breath exhaled by a patient will carry the VOC signatures of a tumor if one is present. “It may seem surprising, but it’s actually very straightforward,” says Paul Rhodes, the co-founder and chief executive officer at Metabolomx. Dr. Peter Mazzone, a lung cancer expert at the Cleveland Clinic, recently published results from a trial he ran with an early version of the Metabolomx machine. He studied 229 people and found that the machine could detect lung cancer more than 80 percent of the time. Just as intriguing, the machine outdid the dogs by distinguishing between different forms of lung cancer with about 85 percent accuracy, giving the doctor insight into whether a patient had an aggressive case. The goal now is to use a far more sensitive, updated version of the machine in new trials and see if it can get to 93 percent accuracy—a figure doctors say would make the device viable for widespread use. Much of the technology behind the Metabolomx machine came from research done by co-founder Kenneth Suslick, a professor of materials science and engineering at the University of Illinois. Suslick and his team created a way to form sponges made of silicon, each about half a millimeter across, that are combined with a pigment. Dozens are laid on a plastic film. As VOCs such as toluene (a lung cancer indicator) interact with the film, the sponges change color to show how strongly they are reacting to the various compounds. The scent of an orange will throw off a pattern of multicolored balls distinct from that of a lemon, for example. Having a bit of fun with the technology, Suslick has published scientific papers showing his ability to distinguish between very similar products. The sensors prove that dark sodas like Coke and Pepsi share many similarities but enough unique characteristics to tell them apart. Suslick’s technology can even tell the difference between various Starbucks blends, while also disclosing that Folgers decaf smells almost identical to original Maxwell House. The newer version of the Metabolomx machine quintuples the number of sensors and improves upon the underlying chemistry, making it 100 to 1,000 times more sensitive, though it’s unclear what the impact on accuracy will be. “The new machine is a big improvement and has really got me excited,” says Dr. James Jett, a professor of medicine at National Jewish Health hospital in Denver and one of the world’s leading lung cancer experts. This month, Jett will join Mazzone in launching a new lung cancer study using data from the revamped machine. (The Mayo Clinic may soon join the study.) The grand goal this time is to collect data on thousands of patients’ breath signatures and analyze the data with computer algorithms. “This system needs to be trained on people’s age, smoking history, and other health conditions,” Mazzone says. “Then we can say, ‘Your breath matches most closely with this 60-year-old woman in our signature library.’” Early indications show that the Metabolomx technology should work in detecting multiple types of cancer, including breast and colon. But the company has opted to focus on lung cancer initially because it’s complicated to diagnose. Patients will often display spots on CT scans of their lungs, but it’s difficult to tell whether the underlying nodules are cancerous or what type of cancer is present without a biopsy, which can be both painful and dangerous. “It’s a ways off before you replace a test like a biopsy, but it’s now conceivable that we would get there,” says Jett. Metabolomx faces competition from other companies that perform blood and genetic tests to detect cancer. The result of such tests, however, must often be sent off to a lab, keeping the doctor and patient waiting a couple of weeks for results, and they are not yet as accurate as doctors would like. “The idea of applying a breath test at the patient’s bedside and getting a result without even requiring a stick of a needle would be the ultimate in noninvasiveness,” says Mazzone. Menssana Research has a system similar to Metabolomx’s called BreathLink that can detect diseases such as pulmonary tuberculosis. Other researchers are applying the illness-sniffing idea to pediatric asthma. At the Metabolomx offices, Rhodes shows off the company’s chemistry wet lab and waxes optimistic. “One day this could possibly be applied during chemotherapy to see if the tumor changes and gives off different signals, so that you know if the medicine is working,” he says. A serial entrepreneur, Rhodes has spent decades trying to build computing systems that mimic neural circuitry and has funded Metabolomx out of his pocket and through government grants. He hopes to apply for Food and Drug Administration approval for lung-cancer detection in 12 months and is already exploring other ways the smell sensors can be applied, including sniffing out dangerous chemicals at the airport. While equally encouraged, Mazzone is a bit more cautious. “I’ve received letters from people asking to come in for a breath test because their dog has been hanging around them more, and they’re worried it smells cancer,” he says. “Well, we’re just not there yet. The new sensor needs to be trained.” Just like the dogs.


Masoodi M.,Nestle | Masoodi M.,Medical Research Council | Masoodi M.,University of Toronto | Lee E.,Sunnybrook Research Institute | And 10 more authors.
Leukemia | Year: 2014

Oleoylethanolamide (OEA) is a bioactive lipid that stimulates nuclear and G protein-coupled receptors and regulates appetite and fat metabolism. It has not previously been shown to have a role in cancer. However, a mass spectrometry-based lipidomics platform revealed the presence of high amounts of OEA in the plasma of chronic lymphocytic leukemia (CLL) patients compared with normal donors. CLL cells produced OEA and the magnitude of plasma OEA levels was related directly to the circulating leukemic cell number. OEA from CLL cells was increased by URB-597, an inhibitor of fatty acid amide hydrolase (FAAH), and decreased by inflammatory mediators that downregulate expression of N-acylphosphatidylethanolamine-specific phospholipase D (NAPE-PLD). These enzymes degrade and synthesize OEA, respectively. Nonphysiologic doses of OEA prevented spontaneous apoptosis of CLL cells in a receptor-independent manner that was mimicked by its free fatty acid (FFA) derivative oleate. However, OEA-containing supernatants from CLL cells induced lipolysis in adipocytes, lipid products from adipocytes protected CLL cells from cytotoxic chemotherapy, and increased levels of FFAs were found in CLL plasma that correlated with OEA. We suggest OEA is a lipolytic factor produced by CLL cells to fuel their growth with a potential role in drug resistance and cancer cachexia. © 2014 Macmillan Publishers Limited. Source


Mazzone P.J.,Cleveland Clinic | Wang X.-F.,Cleveland Clinic | Lim S.,Metabolomx | Choi H.,Cleveland Clinic | And 7 more authors.
BMC Cancer | Year: 2015

Background: The mixture of volatile organic compounds in the headspace gas of urine may be able to distinguish lung cancer patients from relevant control populations. Methods: Subjects with biopsy confirmed untreated lung cancer, and others at risk for developing lung cancer, provided a urine sample. A colorimetric sensor array was exposed to the headspace gas of neat and pre-treated urine samples. Random forest models were trained from the sensor output of 70 % of the study subjects and were tested against the remaining 30 %. Models were developed to separate cancer and cancer subgroups from control, and to characterize the cancer. An additional model was developed on the largest clinical subgroup. Results: 90 subjects with lung cancer and 55 control subjects participated. The accuracies, reported as C-statistics, for models of cancer or cancer subgroups vs. control ranged from 0.795 - 0.917. A model of lung cancer vs. control built using only subjects from the largest available clinical subgroup (30 subjects) had a C-statistic of 0.970. Models developed and tested to characterize cancer histology, and to compare early to late stage cancer, had C-statistics of 0.849 and 0.922 respectively. Conclusions: The colorimetric sensor array signature of volatile organic compounds in the urine headspace may be capable of distinguishing lung cancer patients from clinically relevant controls. The incorporation of clinical phenotypes into the development of this biomarker may optimize its accuracy. © 2015 Mazzone et al. Source

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