Sandra Castillo

MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

BackgroundMass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.ResultsA key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.ConclusionsMZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Activation of lipid metabolism is an early event in carcinogenesis and a central hallmark of many cancers. However, the precise molecular composition of lipids in tumors remains generally poorly characterized. The aim of the present study was to analyze the global lipid profiles of breast cancer, integrate the results to protein expression, and validate the findings by functional experiments. Comprehensive lipidomics was conducted in 267 human breast tissues using ultraperformance liquid chromatography/ mass spectrometry. The products of de novo fatty acid synthesis incorporated into membrane phospholipids, such as palmitate-containing phosphatidylcholines, were increased in tumors as compared with normal breast tissues. These lipids were associated with cancer progression and patient survival, as their concentration was highest in estrogen receptor-negative and grade 3 tumors. In silico transcriptomics database was utilized in investigating the expression of lipid metabolism related genes in breast cancer, and on the basis of these results, the expression of specific proteins was studied by immunohistochemistry. Immunohistochemical analyses showed that several genes regulating lipid metabolism were highly expressed in clinical breast cancer samples and supported also the lipidomics results. Gene silencing experiments with seven genes [ACACA (acetyl-CoA carboxylase α), ELOVL1 (elongation of very long chain fatty acid-like 1), FASN (fatty acid synthase), INSIG1 (insulin-induced gene 1), SCAP (sterol regulatory element-binding protein cleavage-activating protein), SCD (stearoyl-CoA desaturase), and THRSP (thyroid hormone-responsive protein)] indicated that silencing of multiple lipid metabolism-regulating genes reduced the lipidomic profiles and viability of the breast cancer cells. Taken together, our results imply that phospholipids may have diagnostic potential as well as that modulation of their metabolism may provide therapeutic opportunities in breast cancer treatment.

Data analysis tool for comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry.

Data processing and identification of unknown compounds in comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC/TOFMS) analysis is a major challenge, particularly when large sample sets are analyzed. Herein, we present a method for efficient treatment of large data sets produced by GC×GC/TOFMS implemented as a freely available open source software package, Guineu. To handle large data sets and to efficiently utilize all the features available in the vendor software (baseline correction, mass spectral deconvolution, peak picking, integration, library search, and signal-to-noise filtering), data preprocessed by instrument software are used as a starting point for further processing. Our software affords alignment of the data, normalization, data filtering, and utilization of retention indexes in the verification of identification as well as a novel tool for automated group-type identification of the compounds. Herein, different features of the software are studied in detail and the performance of the system is verified by the analysis of a large set of standard samples as well as of a large set of authentic biological samples, including the control samples. The quantitative features of our GC×GC/TOFMS methodology are also studied to further demonstrate the method performance and the experimental results confirm the reliability of the developed procedure. The methodology has already been successfully used for the analysis of several thousand samples in the field of metabolomics.

Liquid Chromatography-Mass Spectrometry (LC-MS)-Based Lipidomics for Studies of Body Fluids and Tissues

In this paper, analytical methodologies for the global profiling of lipids in serum and tissue samples are reported. The sample preparation is based on a modified Folch extraction, and the analysis is carried out with ultrahigh-performance liquid chromatography combined with mass spectrometry (UPLC-MS). For further identification, MS(n) mass spectrometry is carried out utilizing an LTQ-Orbitrap mass spectrometry as the detector. Such a system affords determination of accurate masses and is thus a highly useful tool for lipid identification. The repeatability of the analysis proved to be good, with relative standard errors for spiked samples being between 4.51 and 10.44%. The throughput of the methodology described here is over 100 samples a day.

Bioinformatics and computational methods for lipidomics.

Large amounts of lipidomics data are rapidly becoming available. However, there is a lack of tools capable of taking the full advantage of the wealth of new information. Lipid bioinformatics is thus an emerging need as well as challenge for lipid research. Lipid concentration changes in biological systems reflect regulation at multiple spatial and dynamic scales, e.g., biochemical reactions in the cells, intercellular lipid trafficking, changes in cell membrane composition, systemic lipid metabolism or lipid oxidation. In order to address the complexity of lipids and their regulation, four areas of bioinformatics need to be developed: (1) data processing and lipid identification, (2) statistical data analysis, (3) pathway analysis, and (4) lipid modeling in systems and biophysical contexts. In this paper we overview the current state of the lipid bioinformatics field as well as suggest few potential new areas of research.

Novel Transcriptional Targets of the SRY-HMG Box Transcription Factor SOX4 Link Its Expression to the Development of Small Cell Lung Cancer

The HMG box transcription factor SOX4 involved in neuronal development is amplified and overexpressed in a subset of lung cancers, suggesting that it may be a driver oncogene. In this study, we sought to develop this hypothesis including by defining targets of SOX4 that may mediate its involvement in lung cancer. Ablating SOX4 expression in SOX4-amplified lung cancer cells revealed a gene expression signature that included genes involved in neuronal development such as PCDHB, MYB, RBP1, and TEAD2. Direct recruitment of SOX4 to gene promoters was associated with their upregulation upon ectopic overexpression of SOX4. We confirmed upregulation of the SOX4 expression signature in a panel of primary lung tumors, validating their specific response by a comparison using embryonic fibroblasts from Sox4-deficient mice. Interestingly, we found that small cell lung cancer (SCLC), a subtype of lung cancer with neuroendocrine characteristics, was generally characterized by high levels of SOX2, SOX4, and SOX11 along with the SOX4-specific gene expression signature identified. Taken together, our findings identify a functional role for SOX genes in SCLC, particularly for SOX4 and several novel targets defined in this study.

Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids

Aims/hypothesisWe examined whether analysis of lipids by ultra-performance liquid chromatography (UPLC) coupled to MS allows the development of a laboratory test for non-alcoholic fatty-liver disease (NAFLD), and how a lipid-profile biomarker compares with the prediction of NAFLD and liver-fat content based on routinely available clinical and laboratory data.MethodsWe analysed the concentrations of molecular lipids by UPLC-MS in blood samples of 679 well-characterised individuals in whom liver-fat content was measured using proton magnetic resonance spectroscopy (1H-MRS) or liver biopsy. The participants were divided into biomarker-discovery (n = 287) and validation (n = 392) groups to build and validate the diagnostic models, respectively.ResultsIndividuals with NAFLD had increased triacylglycerols with low carbon number and double-bond content while lysophosphatidylcholines and ether phospholipids were diminished in those with NAFLD. A serum-lipid signature comprising three molecular lipids (‘lipid triplet’) was developed to estimate the percentage of liver fat. It had a sensitivity of 69.1% and specificity of 73.8% when applied for diagnosis of NAFLD in the validation series. The usefulness of the lipid triplet was demonstrated in a weight-loss intervention study.Conclusions/interpretationThe liver-fat-biomarker signature based on molecular lipids may provide a non-invasive tool to diagnose NAFLD, in addition to highlighting lipid molecular pathways involved in the disease.

The SOX family of genes in cancer development: biological relevance and opportunities for therapy.

Introduction: It has been more that 20 years since the first SOX genes were discovered. Twenty SOX genes have now been identified in mammals and classified into groups with respect to protein identity. SOX family genes code for transcription factors that either activate or repress lineage-specific genes during embryonic development. Furthermore, SOX genes are altered in human genetic syndromes and malignancies, highlighting their involvement in development. Areas covered: This paper reviews the role of SOX genes in embryonic development and human diseases, and describe their involvement in human cancers and possible use in cancer therapeutics. Expert opinion: Since most SOX genes behave as oncogenes in many human cancers, their targeting has great therapeutic potential. However, novel specific therapies such as those recently developed against growth factor receptors based on monoclonal antibodies, small inhibitors and even small interfering RNA strategies are difficult to implement for transcriptional factors. Novel strategies are being developed to overcome some of these obstacles. Alternative approaches could indirectly tackle altered SOX genes by exploiting the related molecular networks.

Statistical methods for handling unwanted variation in metabolomics data

Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available.

Splanchnic balance of free fatty acids, endocannabinoids, and lipids in subjects with nonalcoholic fatty liver disease.

BACKGROUND & AIMS Animal studies suggest that endocannabinoids could contribute to the development of nonalcoholic fatty liver disease (NAFLD). In addition, NAFLD has been shown to be associated with multiple changes in lipid concentrations in liver biopsies. There are no data on splanchnic free fatty acid (FFA), glycerol, ketone body, endocannabinoid, and lipid fluxes in vivo in subjects with NAFLD. METHODS We performed hepatic venous catheterization studies in combination with [(2)H(2)]palmitate infusion in the fasting state and during a low-dose insulin infusion in 9 subjects with various degrees of hepatic steatosis as determined using liver biopsy. Splanchnic balance of endocannabinoids and individual lipids was determined using ultra performance liquid chromatography coupled to mass spectrometry. RESULTS Concentrations of the endocannabinoid 2-arachidonoylglycerol were higher in arterialized (91 ± 33 μg/L basally) than in hepatic venous (51 ± 19 μg/L; P < .05) plasma. Fasting arterial (r = 0.72; P = .031) and hepatic venous (r = 0.70; P = .037) concentrations of 2-arachidonoylglycerol were related positively to liver fat content. Analysis of fluxes of 85 different triglycerides showed that the fatty liver overproduces saturated triglycerides. In the plasma FFA fraction in the basal state, the relative amounts of palmitoleate and linoleate were lower and those of stearate and oleate were higher in the hepatic vein than in the artery. Absolute concentrations of all nontriglyceride lipids were comparable in arterialized venous plasma and the hepatic vein both in the basal and insulin-stimulated states. CONCLUSIONS The human fatty liver takes up 2-arachidonoylglycerol and overproduces triacylglycerols containing saturated fatty acids, which might reflect increased de novo lipogenesis.