Acylcarnitines are anticoagulants that inhibit factor Xa and are reduced in venous thrombosis, based on metabolomics data.

In many patients with deep vein thrombosis and pulmonary embolism (venous thromboembolism, VTE), biomarkers or genetic risk factors have not been identified. To discover novel plasma metabolites associated with VTE risk, we employed liquid chromatography-mass spectrometry-based untargeted metabolomics, which do not target any specific metabolites. Using the Scripps Venous Thrombosis Registry population for a case-control study, we discovered that 10:1 and 16:1 acylcarnitines were low in plasmas of the VTE patient group compared with matched controls, respectively. Data from targeted metabolomics studies showed that several long-chain acylcarnitines (10:1, 12:0, 12:2, 18:1, and 18:2) were lower in the VTE group. Clotting assays were used to evaluate a causal relationship for low acylcarnitines in patients with VTE. Various acylcarnitines inhibited factor Xa-initiated clotting. Inhibition of factor Xa by acylcarnitines was greater for longer acyl chains. Mechanistic studies showed that 16:0 acylcarnitine had anticoagulant activity in the absence of factor Va or phospholipids. Surface plasmon resonance investigations revealed that 16:0 acylcarnitine was bound to factor Xa and that binding did not require the γ-carboxy glutamic acid domain. In summary, our study identified low plasma levels of acylcarnitines in patients with VTE and showed that acylcarnitines have anticoagulant activity related to an ability to bind and inhibit factor Xa.

Meta-analysis of global metabolomic data identifies metabolites associated with life-span extension.

The manipulation of distinct signaling pathways and transcription factors has been shown to influence life span in a cell-non-autonomous manner in multicellular model organisms such as Caenorhabditis elegans. These data suggest that coordination of whole-organism aging involves endocrine signaling, however, the molecular identities of such signals have not yet been determined and their potential relevance in humans is unknown. Here we describe a novel metabolomic approach to identify molecules directly associated with extended life span in C. elegans that represent candidate compounds for age-related endocrine signals. To identify metabolic perturbations directly linked to longevity, we developed metabolomic software for meta-analysis that enabled intelligent comparisons of multiple different mutants. Simple pairwise comparisons of long-lived glp-1, daf-2, and isp-1 mutants to their respective controls resulted in more than 11,000 dysregulated metabolite features of statistical significance. By using meta-analysis, we were able to reduce this number to six compounds most likely to be associated with life-span extension. Mass spectrometry-based imaging studies suggested that these metabolites might be localized to C. elegans muscle. We extended the metabolomic analysis to humans by comparing quadricep muscle tissue from young and old individuals and found that two of the same compounds associated with longevity in worms were also altered in human muscle with age. These findings provide candidate compounds that may serve as age-related endocrine signals and implicate muscle as a potential tissue regulating their levels in humans.

A computational framework for proteome-wide pursuit and prediction of metalloproteins using ICP-MS and MS/MS data.

BACKGROUND: Metal-containing proteins comprise a diverse and sizable category within the proteomes of organisms, ranging from proteins that use metals to catalyze reactions to proteins in which metals play key structural roles. Unfortunately, reliably predicting that a protein will contain a specific metal from its amino acid sequence is not currently possible. We recently developed a generally-applicable experimental technique for finding metalloproteins on a genome-wide scale. Applying this metal-directed protein purification approach (ICP-MS and MS/MS based) to the prototypical microbe Pyrococcus furiosus conclusively demonstrated the extent and diversity of the uncharacterized portion of microbial metalloproteomes since a majority of the observed metal peaks could not be assigned to known or predicted metalloproteins. However, even using this technique, it is not technically feasible to purify to homogeneity all metalloproteins in an organism. In order to address these limitations and complement the metal-directed protein purification, we developed a computational infrastructure and statistical methodology to aid in the pursuit and identification of novel metalloproteins. RESULTS: We demonstrate that our methodology enables predictions of metal-protein interactions using an experimental data set derived from a chromatography fractionation experiment in which 870 proteins and 10 metals were measured over 2,589 fractions. For each of the 10 metals, cobalt, iron, manganese, molybdenum, nickel, lead, tungsten, uranium, vanadium, and zinc, clusters of proteins frequently occurring in metal peaks (of a specific metal) within the fractionation space were defined. This resulted in predictions that there are from 5 undiscovered vanadium- to 13 undiscovered cobalt-containing proteins in Pyrococcus furiosus. Molybdenum and nickel were chosen for additional assessment producing lists of genes predicted to encode metalloproteins or metalloprotein subunits, 22 for nickel including seven from known nickel-proteins, and 20 for molybdenum including two from known molybdo-proteins. The uncharacterized proteins are prime candidates for metal-based purification or recombinant approaches to validate these predictions. CONCLUSIONS: We conclude that the largely uncharacterized extent of native metalloproteomes can be revealed through analysis of the co-occurrence of metals and proteins across a fractionation space. This can significantly impact our understanding of metallobiochemistry, disease mechanisms, and metal toxicity, with implications for bioremediation, medicine and other fields.

metaXCMS: second-order analysis of untargeted metabolomics data.

Mass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distinguishing metabolites that are causally associated with the phenotype of interest from those that are unrelated but altered in downstream pathways as an effect. To facilitate this distinction, here we describe new software called metaXCMS for performing second-order ("meta") analysis of untargeted metabolomics data from multiple sample groups representing different models of the same phenotype. While the original version of XCMS was designed for the direct comparison of two sample groups, metaXCMS enables meta-analysis of an unlimited number of sample classes to facilitate prioritization of the data and increase the probability of identifying metabolites causally related to the phenotype of interest. metaXCMS is used to import XCMS results that are subsequently filtered, realigned, and ultimately compared to identify shared metabolites that are up- or down-regulated across all sample groups. We demonstrate the software's utility by identifying histamine as a metabolite that is commonly altered in three different models of pain. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

Microbial metalloproteomes are largely uncharacterized.

Metal ion cofactors afford proteins virtually unlimited catalytic potential, enable electron transfer reactions and have a great impact on protein stability. Consequently, metalloproteins have key roles in most biological processes, including respiration (iron and copper), photosynthesis (manganese) and drug metabolism (iron). Yet, predicting from genome sequence the numbers and types of metal an organism assimilates from its environment or uses in its metalloproteome is currently impossible because metal coordination sites are diverse and poorly recognized. We present here a robust, metal-based approach to determine all metals an organism assimilates and identify its metalloproteins on a genome-wide scale. This shifts the focus from classical protein-based purification to metal-based identification and purification by liquid chromatography, high-throughput tandem mass spectrometry (HT-MS/MS) and inductively coupled plasma mass spectrometry (ICP-MS) to characterize cytoplasmic metalloproteins from an exemplary microorganism (Pyrococcus furiosus). Of 343 metal peaks in chromatography fractions, 158 did not match any predicted metalloprotein. Unassigned peaks included metals known to be used (cobalt, iron, nickel, tungsten and zinc; 83 peaks) plus metals the organism was not thought to assimilate (lead, manganese, molybdenum, uranium and vanadium; 75 peaks). Purification of eight of 158 unexpected metal peaks yielded four novel nickel- and molybdenum-containing proteins, whereas four purified proteins contained sub-stoichiometric amounts of misincorporated lead and uranium. Analyses of two additional microorganisms (Escherichia coli and Sulfolobus solfataricus) revealed species-specific assimilation of yet more unexpected metals. Metalloproteomes are therefore much more extensive and diverse than previously recognized, and promise to provide key insights for cell biology, microbial growth and toxicity mechanisms.

Large scale physiological readjustment during growth enables rapid, comprehensive and inexpensive systems analysis.

BACKGROUND: Rapidly characterizing the operational interrelationships among all genes in a given organism is a critical bottleneck to significantly advancing our understanding of thousands of newly sequenced microbial and eukaryotic species. While evolving technologies for global profiling of transcripts, proteins, and metabolites are making it possible to comprehensively survey cellular physiology in newly sequenced organisms, these experimental techniques have not kept pace with sequencing efforts. Compounding these technological challenges is the fact that individual experiments typically only stimulate relatively small-scale cellular responses, thus requiring numerous expensive experiments to survey the operational relationships among nearly all genetic elements. Therefore, a relatively quick and inexpensive strategy for observing changes in large fractions of the genetic elements is highly desirable. RESULTS: We have discovered in the model organism Halobacterium salinarum NRC-1 that batch culturing in complex medium stimulates meaningful changes in the expression of approximately two thirds of all genes. While the majority of these changes occur during transition from rapid exponential growth to the stationary phase, several transient physiological states were detected beyond what has been previously observed. In sum, integrated analysis of transcript and metabolite changes has helped uncover growth phase-associated physiologies, operational interrelationships among two thirds of all genes, specialized functions for gene family members, waves of transcription factor activities, and growth phase associated cell morphology control. CONCLUSIONS: Simple laboratory culturing in complex medium can be enormously informative regarding the activities of and interrelationships among a large fraction of all genes in an organism. This also yields important baseline physiological context for designing specific perturbation experiments at different phases of growth. The integration of such growth and perturbation studies with measurements of associated environmental factor changes is a practical and economical route for the elucidation of comprehensive systems-level models of biological systems.

Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data.

Analytical and biological variability are issues of central importance to human metabolomics studies. Here both types of variation are examined in human plasma and cerebrospinal fluid (CSF) using a global liquid chromatography/mass spectrometry (LC/MS) metabolomics strategy. The platform shows small analytical variation with a median coefficient of variation (CV) of 15-16% for both plasma and CSF sample matrixes when the integrated area of each peak in the mass spectra is considered. Analysis of biological variation shows that human CSF has a median CV of 35% and plasma has a median CV of 46%. To understand the difference in CV between the biofluids, we compared plasma and CSF independently obtained from different healthy humans. Additionally, we analyzed another group of patients from whom we compared matched CSF and plasma (plasma and CSF obtained from the same human subject). A similar number of features was observed in both biofluids, although the majority of features appeared with greater intensity in plasma. More than a dozen metabolites shared between the human CSF and plasma metabolomes were identified based on accurate mass measurements, retention times, and MS/MS spectra. The fold change in these metabolites was consistent with the median biological CV determined for all peaks. The measured median biological CV together with analysis of intragroup variation of healthy individuals suggests that fold changes above 2 in metabolomics studies investigating plasma or CSF are statistically relevant with respect to the inherent variability of a healthy control group. These data demonstrate the reproducibility of the global metabolomics platform using LC/MS and reveal the robustness of the approach for biomarker discovery.

Cerebrospinal fluid proteomics reveals potential pathogenic changes in the brains of SIV-infected monkeys.

The HIV-1-associated neurocognitive disorder occurs in approximately one-third of infected individuals. It has persisted in the current era of antiretroviral therapy, and its study is complicated by the lack of biomarkers for this condition. Since the cerebrospinal fluid is the most proximal biofluid to the site of pathology, we studied the cerebrospinal fluid in a nonhuman primate model for HIV-1-associated neurocognitive disorder. Here we present a simple and efficient liquid chromatography-coupled mass spectrometry-based proteomics approach that utilizes small amounts of cerebrospinal fluid. First, we demonstrate the validity of the methodology using human cerebrospinal fluid. Next, using the simian immunodeficiency virus-infected monkey model, we show its efficacy in identifying proteins such as alpha-1-antitrypsin, complement C3, hemopexin, IgM heavy chain, and plasminogen, whose increased expression is linked to disease. Finally, we find that the increase in cerebrospinal fluid proteins is linked to increased expression of their genes in the brain parenchyma, revealing that the cerebrospinal fluid alterations identified reflect changes in the brain itself and not merely leakage of the blood-brain or blood-cerebrospinal fluid barriers. This study reveals new central nervous system alterations in lentivirus-induced neurological disease, and this technique can be applied to other systems in which limited amounts of biofluids can be obtained.

Identification of a new endogenous metabolite and the characterization of its protein interactions through an immobilization approach.

The emerging field of global mass-based metabolomics provides a platform for discovering unknown metabolites and their specific biochemical pathways. We report the identification of a new endogenous metabolite, N(4)-(N-acetylaminopropyl)spermidine and the use of a novel proteomics based method for the investigation of its protein interaction using metabolite immobilization on agarose beads. The metabolite was isolated from the organism Pyrococcus furiosus, and structurally characterized through an iterative process of synthesizing candidate molecules and comparative analysis using accurate mass LC-MS/MS. An approach developed for the selective preparation of N(1)-acetylthermospermine, one of the possible structures of the unknown metabolite, provides a convenient route to new polyamine derivatives through methylation on the N(8) and N(4) of the thermospermine scaffold. The biochemical role of the novel metabolite as well as that of two other polyamines: spermidine and agmatine is investigated through metabolite immobilization and incubation with native proteins. The identification of eleven proteins that uniquely bind with N(4)-(N-acetylaminopropyl)spermidine, provides information on the role of this novel metabolite in the native organism. Identified proteins included hypothetical ones such as PF0607 and PF1199, and those involved in translation, DNA synthesis and the urea cycle like translation initiation factor IF-2, 50S ribosomal protein L14e, DNA-directed RNA polymerase, and ornithine carbamoyltransferase. The immobilization approach demonstrated here has the potential for application to other newly discovered endogenous metabolites found through untargeted metabolomics, as a preliminary screen for generating a list of proteins that could be further investigated for specific activity.

Novel multiprotein complexes identified in the hyperthermophilic archaeon Pyrococcus furiosus by non-denaturing fractionation of the native proteome.

Virtually all cellular processes are carried out by dynamic molecular assemblies or multiprotein complexes, the compositions of which are largely undefined. They cannot be predicted solely from bioinformatics analyses nor are there well defined techniques currently available to unequivocally identify protein complexes (PCs). To address this issue, we attempted to directly determine the identity of PCs from native microbial biomass using Pyrococcus furiosus, a hyperthermophilic archaeon that grows optimally at 100 degrees C, as the model organism. Novel PCs were identified by large scale fractionation of the native proteome using non-denaturing, sequential column chromatography under anaerobic, reducing conditions. A total of 967 distinct P. furiosus proteins were identified by mass spectrometry (nano LC-ESI-MS/MS), representing approximately 80% of the cytoplasmic proteins. Based on the co-fractionation of proteins that are encoded by adjacent genes on the chromosome, 106 potential heteromeric PCs containing 243 proteins were identified, only 20 of which were known or expected. In addition to those of unknown function, novel and uncharacterized PCs were identified that are proposed to be involved in the metabolism of amino acids (10), carbohydrates (four), lipids (two), vitamins and metals (three), and DNA and RNA (nine). A further 30 potential PCs were classified as tentative, and the remaining potential PCs (13) were classified as weakly interacting. Some major advantages of native biomass fractionation for PC identification are that it provides a road map for the (partial) purification of native forms of novel and uncharacterized PCs, and the results can be utilized for the recombinant production of low abundance PCs to provide enough material for detailed structural and biochemical analyses.