Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families.

Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks.

Characterization of rheumatoid arthritis subtypes using symptom profiles, clinical chemistry and metabolomics measurements.

OBJECTIVE: The aim is to characterize subgroups or phenotypes of rheumatoid arthritis (RA) patients using a systems biology approach. The discovery of subtypes of rheumatoid arthritis patients is an essential research area for the improvement of response to therapy and the development of personalized medicine strategies. METHODS: In this study, 39 RA patients are phenotyped using clinical chemistry measurements, urine and plasma metabolomics analysis and symptom profiles. In addition, a Chinese medicine expert classified each RA patient as a Cold or Heat type according to Chinese medicine theory. Multivariate data analysis techniques are employed to detect and validate biochemical and symptom relationships with the classification. RESULTS: The questionnaire items 'Red joints', 'Swollen joints', 'Warm joints' suggest differences in the level of inflammation between the groups although c-reactive protein (CRP) and rheumatoid factor (RHF) levels were equal. Multivariate analysis of the urine metabolomics data revealed that the levels of 11 acylcarnitines were lower in the Cold RA than in the Heat RA patients, suggesting differences in muscle breakdown. Additionally, higher dehydroepiandrosterone sulfate (DHEAS) levels in Heat patients compared to Cold patients were found suggesting that the Cold RA group has a more suppressed hypothalamic-pituitary-adrenal (HPA) axis function. CONCLUSION: Significant and relevant biochemical differences are found between Cold and Heat RA patients. Differences in immune function, HPA axis involvement and muscle breakdown point towards opportunities to tailor disease management strategies to each of the subgroups RA patient.

Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis.

Experimental Autoimmune Encephalomyelitis (EAE) is the most commonly used animal model for Multiple Sclerosis (MScl). CSF metabolomics in an acute EAE rat model was investigated using targetted LC-MS and GC-MS. Acute EAE in Lewis rats was induced by co-injection of Myelin Basic Protein with Complete Freund's Adjuvant. CSF samples were collected at two time points: 10 days after inoculation, which was during the onset of the disease, and 14 days after inoculation, which was during the peak of the disease. The obtained metabolite profiles from the two time points of EAE development show profound differences between onset and the peak of the disease, suggesting significant changes in CNS metabolism over the course of MBP-induced neuroinflammation. Around the onset of EAE the metabolome profile shows significant decreases in arginine, alanine and branched amino acid levels, relative to controls. At the peak of the disease, significant increases in concentrations of multiple metabolites are observed, including glutamine, O-phosphoethanolamine, branched-chain amino acids and putrescine. Observed changes in metabolite levels suggest profound changes in CNS metabolism over the course of EAE. Affected pathways include nitric oxide synthesis, altered energy metabolism, polyamine synthesis and levels of endogenous antioxidants. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0306-3) contains supplementary material, which is available to authorized users.

Lipidomics reveals multiple pathway effects of a multi-components preparation on lipid biochemistry in ApoE*3Leiden.CETP mice.

BACKGROUND: Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses. METHODOLOGY/PRINCIPAL FINDINGS: ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (-49%, p<0.001), CETP levels (-31%, p<0.001), CETP activity (-74%, p<0.001) and increased HDL-C (39%, p<0.05). It influenced lipidomics classes of cholesterol esters and triglycerides the most. Rimonabant induced a weight loss (-9%, p<0.05), but only a moderate improvement of lipid profiles. In vitro, SUB885C extract caused adipolysis stimulation and adipogenesis inhibition in 3T3-L1 cells. CONCLUSIONS: SUB885C, a multi-components preparation, is able to produce anti-atherogenic changes in lipids of the ApoE*3Leiden.CETP mice, which are comparable to those obtained with compounds belonging to known drugs (e.g. rimonabant, atorvastatin, niacin). This study successfully illustrated the power of lipidomics in unraveling intervention effects and to help finding new targets or ingredients for lifestyle-related metabolic abnormality.

Linking biological activity with herbal constituents by systems biology-based approaches: effects of Panax ginseng in type 2 diabetic Goto-Kakizaki rats.

Although a number of animal experiments and clinical trials have investigated the effects of ginseng roots on diabetes, the relationship between their therapeutic effects on diabetes and the quality and the growth age of this herb have not yet been reported. This study systematically investigated the effects of 3- to 6-year-old ginseng roots on glycemic and plasma lipid control in a rat model of type 2 diabetes. Six groups of male Goto-Kakizaki (GK) rats received either metformin, 3- to 6-year-old ginseng roots, or no treatment. The treatments were administered twice daily for 9 weeks. A combined approach was used that involved applying liquid chromatography-mass spectrometry-based lipidomics, measuring biochemical parameters and profiling the components of ginseng roots of different ages. Compared to the untreated controls, treatment with 4- and 6-year-old ginseng roots significantly improved glucose disposal, and 5-year-old ginseng treatment significantly increased high density lipoprotein cholesterol. Treatment with 6-year-old ginseng significantly decreased total plasma triacylglyceride (TG) and very-low-density lipoprotein cholesterol and improved plasma glycated hemoglobin (HbA1c). In addition, treatment with 4- to 6-year-old ginseng influenced plasma lipidomics in diabetic GK rats by reducing TG lipid species. Metformin significantly reduced fasting blood glucose by 41% and reduced HbA1c by 11%, but showed no effects on the plasma lipid parameters. The present study demonstrates that ginseng roots show growth age-dependent therapeutic effects on hyperlipidemia and hyperglycemia in diabetic GK rats. These age-dependent effects may be linked with the variation in both the ratios and concentrations of specific bioactive ginsenosides in ginseng roots of different growth ages. This study introduced novel systems biology-based approaches for linking biological activities with potential active components in herbal mixtures.

Sub-typing of rheumatic diseases based on a systems diagnosis questionnaire.

BACKGROUND: The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups. METHODOLOGY: 49 patients with a rheumatic disease were characterized using a systems diagnosis questionnaire containing 106 questions based on Chinese and Western medicine symptoms. Categorical principal component analysis (CATPCA) was used to discover differences in symptom patterns between the patients. Two Chinese medicine experts where subsequently asked to rank the Cold and Heat status of all the patients based on the questionnaires. These rankings were used to study the Cold and Heat symptoms used by these practitioners. FINDINGS: The CATPCA analysis results in three dimensions. The first dimension is a general factor (40.2% explained variance). In the second dimension (12.5% explained variance) 'anxious', 'worrying', 'uneasy feeling' and 'distressed' were interpreted as the Internal disease stage, and 'aggravate in wind', 'fear of wind' and 'aversion to cold' as the External disease stage. In the third dimension (10.4% explained variance) 'panting s', 'superficial breathing', 'shortness of breath s', 'shortness of breath f' and 'aversion to cold' were interpreted as Cold and 'restless', 'nervous', 'warm feeling', 'dry mouth s' and 'thirst' as Heat related. 'Aversion to cold', 'fear of wind' and 'pain aggravates with cold' are most related to the experts Cold rankings and 'aversion to heat', 'fullness of chest' and 'dry mouth' to the Heat rankings. CONCLUSIONS: This study shows that the presented systems diagnosis questionnaire is able to identify groups of symptoms that are relevant for sub-typing patients with a rheumatic disease.

Elemental composition determination based on MS(n).

MOTIVATION: Identification of metabolites is essential for its use as biomarkers, for research in systems biology and for drug discovery. The first step before a structure can be elucidated is to determine its elemental composition. High-resolution mass spectrometry, which provides the exact mass, together with common constraint rules, for rejecting false proposed elemental compositions, cannot always provide one unique elemental composition solution. RESULTS: The Multistage Elemental Formula (MEF) tool is presented in this article to enable the correct assignment of elemental composition to compounds, their fragment ions and neutral losses that originate from the molecular ion by using multistage mass spectrometry (MS(n)). The method provided by MEF reduces the list of predicted elemental compositions for each ion by analyzing the elemental compositions of its parent (precursor ion) and descendants (fragments). MS(n) data of several metabolites were processed using the MEF tool to assign the correct elemental composition and validate the efficacy of the method. Especially, the link between the mass accuracy needed to generate one unique elemental composition and the topology of the MS(n) tree (the width and the depth of the tree) was addressed. This method makes an important step toward semi-automatic de novo identification of metabolites using MS(n) data. AVAILABILITY: Software available at: http://abs.lacdr.gorlaeus.net/people/rojas-cherto CONTACT: m.rojas@lacdr.leidenuniv.nl; t.reijmers@lacdr.leidenuniv.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plasma and liver lipidomics response to an intervention of rimonabant in ApoE*3Leiden.CETP transgenic mice.

BACKGROUND: Lipids are known to play crucial roles in the development of life-style related risk factors such as obesity, dyslipoproteinemia, hypertension and diabetes. The first selective cannabinoid-1 receptor blocker rimonabant, an anorectic anti-obesity drug, was frequently used in conjunction with diet and exercise for patients with a body mass index greater than 30 kg/m(2) with associated risk factors such as type II diabetes and dyslipidaemia in the past. Less is known about the impact of this drug on the regulation of lipid metabolism in plasma and liver in the early stage of obesity. METHODOLOGY/PRINCIPAL FINDINGS: We designed a four-week parallel controlled intervention on apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) transgenic mice with mild overweight and hypercholesterolemia. A liquid chromatography-linear ion trap-Fourier transform ion cyclotron resonance-mass spectrometric approach was employed to investigate plasma and liver lipid responses to the rimonabant intervention. Rimonabant was found to induce a significant body weight loss (9.4%, p<0.05) and a significant plasma total cholesterol reduction (24%, p<0.05). Six plasma and three liver lipids in ApoE*3Leiden.CETP transgenic mice were detected to most significantly respond to rimonabant treatment. Distinct lipid patterns between the mice were observed for both plasma and liver samples in rimonabant treatment vs. non-treated controls. This study successfully applied, for the first time, systems biology based lipidomics approaches to evaluate treatment effects of rimonabant in the early stage of obesity. CONCLUSION: The effects of rimonabant on lipid metabolism and body weight reduction in the early stage obesity were shown to be moderate in ApoE*3Leiden.CETP mice on high-fat diet.

Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets.

Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets.

Influence of clotting time on the protein composition of serum samples based on LC-MS data.

Many large, disease-related biobanks of serum samples have been established prior to the widespread use of proteomics in biomarker research. These biobanks may contain relevant information about the disease process, response to therapy or patient classifications especially with respect to long-term follow-up that is otherwise very difficult to obtain based on newly initiated studies, particularly in the case of slowly developing diseases. An important parameter that may influence the composition of serum but that is often not exactly known is clotting time. We therefore investigated the influence of clotting time on the protein and peptide composition of serum by label-free and stable-isotope labeling techniques. The label-free analysis of trypsin-digested serum showed that the overall pattern of LC-MS data is not affected by clotting times varying from 2 to 8h. However, univariate and multivariate statistical analyses revealed that proteins that are directly involved in blood clot formation, such as the clotting-derived fibrinopeptides, change significantly. This is most easily detected in the supernatant of acid-precipitated, immunodepleted serum. Stable-isotope labeling techniques show that truncated or phosphorylated forms of fibrinopeptides A and B increase or decrease depending on clotting time. These patterns can be easily recognized and should be taken into consideration when analyzing LC-MS data using serum sample collections of which the clotting time is not known. Next to the fibrinopeptides, leucine-rich alpha-2-glycoprotein (P02750) was shown to be consistently decreased in samples with clotting times of more than 1h. For prospective studies, we recommend to let blood clot for at least 2h at room temperature using glass tubes with a separation gel and micronized silica to accelerate blood clotting.

RPLC-ion-trap-FTMS method for lipid profiling of plasma: method validation and application to p53 mutant mouse model.

A reversed-phase liquid chromatography-linear ion trap-Fourier transform ion cyclotron resonance-mass spectrometric method was developed for the profiling of lipids in human and mouse plasma. With the use of a fused-core C 8 column and a binary gradient, more than 160 lipids belonging to eight different classes were detected in a single LC-MS run. The method was fully validated and the analytical characteristics such as linearity ( R (2), 0.994-1.000), limit of detection (0.08-1.28 microg/mL plasma), repeatability (RSD, 2.7-7.9%) and intermediate precision (RSD, 2.7-15.6%) were satisfactory. The method was successfully applied to p53 mutant mice plasma for studying some phenotypic effects of p53 expression.

Similarities and differences in lipidomics profiles among healthy monozygotic twin pairs.

Differences in genetic background and/or environmental exposure among individuals are expected to give rise to differences in measurable characteristics, or phenotypes. Consequently, genetic resemblance and similarities in environment should manifest as similarities in phenotypes. The metabolome reflects many of the system properties, and is therefore an important part of the phenotype. Nevertheless, it has not yet been examined to what extent individuals sharing part of their genome and/or environment indeed have similar metabolomes. Here we present the results of hierarchical clustering of blood plasma lipid profile data obtained by liquid chromatography-mass spectrometry from 23 healthy, 18-year-old twin pairs, of which 21 pairs were monozygotic, and 8 of their siblings. For 13 monozygotic twin pairs, within-pair similarities in relative concentrations of the detected lipids were indeed larger than the similarities with any other study participant. We demonstrate such high coclustering to be unexpected on basis of chance. The similarities between dizygotic twins and between nontwin siblings, as well as between nonfamilial participants, were less pronounced. In a number of twin pairs, within-pair dissimilarity of lipid profiles positively correlated with increased blood plasma concentrations of C-reactive protein in one twin. In conclusion, this study demonstrates that in healthy individuals, the individual genetic background contributes to the blood plasma lipid profile. Furthermore, lipid profiling may prove useful in monitoring health status, for example, in the context of personalized medicine.

Chip-LC-MS for label-free profiling of human serum.

The discovery of biomarkers in easily accessible body fluids such as serum is one of the most challenging topics in proteomics requiring highly efficient separation and detection methodologies. Here, we present the application of a microfluidics-based LC-MS system (chip-LC-MS) to the label-free profiling of immunodepleted, trypsin-digested serum in comparison to conventional capillary LC-MS (cap-LC-MS). Both systems proved to have a repeatability of approximately 20% RSD for peak area, all sample preparation steps included, while repeatability of the LC-MS part by itself was less than 10% RSD for the chip-LC-MS system. Importantly, the chip-LC-MS system had a two times higher resolution in the LC dimension and resulted in a lower average charge state of the tryptic peptide ions generated in the ESI interface when compared to cap-LC-MS while requiring approximately 30 times less (~5 pmol) sample. In order to characterize both systems for their capability to find discriminating peptides in trypsin-digested serum samples, five out of ten individually prepared, identical sera were spiked with horse heart cytochrome c. A comprehensive data processing methodology was applied including 2-D smoothing, resolution reduction, peak picking, time alignment, and matching of the individual peak lists to create an aligned peak matrix amenable for statistical analysis. Statistical analysis by supervised classification and variable selection showed that both LC-MS systems could discriminate the two sample groups. However, the chip-LC-MS system allowed to assign 55% of the overall signal to selected peaks against 32% for the cap-LC-MS system.

Multiple testing issues in discriminating compound-related peaks and chromatograms from high frequency noise, spikes and solvent-based noise in LC-MS data sets.

Liquid Chromatography--Mass Spectrometry (LC-MS) is a powerful method for sensitive detection and quantification of proteins and peptides in complex biological fluids like serum. LC-MS produces complex data sets, consisting of some hundreds of millions of data points per sample at a resolution of 0.1 amu in the m/z domain and 7000 data points in the time domain. However, the detection of the lower abundance proteins from this data is hampered by the presence of artefacts, such as high frequency noise and spikes. Moreover, not all of the tens of thousands of the chromatograms produced per sample are relevant for the pursuit of the biomarkers. Thus in analysing the LC-MS data, two critical pre-processing issues arise. Which of the thousands of the: 1. chromatograms per sample are relevant for the detection of the biomarkers?, and 2. signals per chromatogram are truly compound-related? Each of these issues involves assessing the significance (deviation from noise) of multiple observations and the issue of multiple comparisons arises. Current methods disregard the multiplicity and provide no concrete threshold for significance. However, with such procedures, the probability of one or more false-positives is high as the number of tests to be performed is large, and must be controlled. Realizing that the cut-offs for declaring a chromatogram (or a signal) to be compound-related can hugely influence which proteins are detected, it seems natural to define thresholds that are neither arbitrary nor subjective. We suggest the choice of thresholds guided by the critical aim of controlling the False Discovery Rate (FDR) in multiple hypotheses testing for significance over a large set of features produced per sample. This involves the use of the regression diagnostics to characterize the signals of a chromatogram (e.g. as outliers or influential) and to suggest suitable tests statistics for the multiple testing procedures (MTP) for discriminating noise and spikes from true signals. The role of the Generalized Linear Models (GLM) in this MTP is investigated. The method is applied to LC-MS datasets from trypsin-digested serum spiked with varying levels of horse heart cytochrome C (cytoc).

Profiling of endogenous peptides in human synovial fluid by NanoLC-MS: method validation and peptide identification.

Synovial fluid potentially contains markers for early diagnosis and disease progression in degenerative joint diseases such as osteoarthritis. Here, a method is described for profiling endogenous peptides in human synovial fluid, using ultrafiltration, solid-phase extraction, nanoscale liquid chromatography, and high-resolution mass spectrometry. Synovial fluid is characterized by its high viscosity, caused by the presence of the lubricant hyaluronic acid. The method proved to be capable of eliminating the high concentrations of hyaluronic acid, which appeared to be necessary to obtain satisfactory analytical performance, that is, within-day relative standard deviations of 5-15%, between-day relative standard deviations of 6-16%, a linear response of R2=0.994, a limit of detection in the femtomole range, and reproducible recoveries of 14-67%. With the developed method, in a synovial fluid sample from an osteoarthritis patient and a healthy control, in total, 501 peptides originating from 40 proteins were identified. Peptide cleavage products from six proteins that have been associated with osteoarthritis in earlier studies (collagen II, proteoglcycan 4, serum amyloid A, tubulin, vimentin, and Matrix Gla) could also be identified with our profiling method. The robustness of the method indicates that it can be applied in systems biology approaches for further studies on degenerative joint disease, eventually leading to a better understanding of the disease and its therapy, as well as the development of novel biomarkers to monitor these processes.

How to distinguish healthy from diseased? Classification strategy for mass spectrometry-based clinical proteomics.

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.

Comparative urine analysis by liquid chromatography-mass spectrometry and multivariate statistics: method development, evaluation, and application to proteinuria.

We describe a platform for the comparative profiling of urine using reversed-phase liquid chromatography-mass spectrometry (LC-MS) and multivariate statistical data analysis. Urinary compounds were separated by gradient elution and subsequently detected by electrospray Ion-Trap MS. The lower limit of detection (5.7-21 nmol/L), within-day (2.9-19%) and between-day (4.8-19%) analytical variation of peak areas, linearity (R2: 0.918-0.999), and standard deviation for retention time (<0.52 min) of the method were assessed by means of addition of seven 3-8 amino acid peptides (0-500 nmol/L). Relating the amount of injected urine to the area under the curve (AUC) of the chromatographic trace at 214 nm better reduced the coefficient of variation (CV) of the AUC of the total ion chromatogram (CV = 10.1%) than relating it to creatinine (CV = 38.4%). LC-MS data were processed, and the common peak matrix was analyzed by principal component analysis (PCA) after supervised classification by the nearest shrunken centroid algorithm. The feasibility of the method to discriminate urine samples of differing compositions was evaluated by (i) addition of seven peptides at nanomolar concentrations to blank urine samples of different origin and (ii) a study of urine from kidney patients with and without proteinuria. (i) The added peptides were ranked as highly discriminatory peaks despite significant biological variation. (ii) Ninety-two peaks were selected best discriminating proteinuric from nonproteinuric samples, of which 6 were more intense in the majority of the proteinuric samples. Two of these 6 peaks were identified as albumin-derived peptides, which is in accordance with the early rise of albumin during glomerular proteinuria. Interestingly, other albumin-derived peptides were nondiscriminatory indicating preferential proteolysis at some cleavage sites.