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Ceramides are important intermediates in the metabolism of sphingolipids. High-throughput liquid chromatography-mass spectrometry has been used extensively for monitoring the levels of serological ceramides, but is still limited by inadequate coverage or lack of sensitivity. Herein, a rapid, sensitive, and high-throughput isotope dilution liquid chromatography-negative ion electrospray tandem mass spectrometry (IDLC-nESI-MS/MS) method was developed and verified for accurate quantification of 41 ceramides, involving ceramides with C16-20 sphingosine, dihydro-ceramide, and dehydro-ceramide. This method was validated with excellent linearity (R

The current challenges and opportunities for machine learning in the interdisciplinary fields of metabolomics and environmental science.

Ganglioside is an important class of lipid species involved in intercellular signaling and various diseases, especially for neurodegenerative diseases. Systematic ganglioside profiling is challenging because of their naturally low abundance and highly diverse species. Herein, a new data-independent acquisition and parallel reaction monitoring (DIA/PRM) method with superior sensitivity was developed. The untargeted DIA acquisition consecutively records all the precursor ion and fragment ions at the same time, while the targeted PRM analysis with versatile higher collisional dissociation generates full MS/MS spectra for structure elucidation and verification. As compared with traditional data-dependent acquisition (DDA), the DIA/PRM method unbiasedly detected the majority of abundant ganglioside species and as low as 50 pg of ganglioside in an untargeted manner. Gangliosides in four kinds of biological samples including the mouse brain, mouse plasma, HeLa cell, and human colon cancer tissue were systematically identified, and low-abundance ganglioside species were further extended on the basis of linear chromatography retention rules of the most frequently detected ganglioside species. A total of 383 ganglioside features were defined with 329 of them derived from 32 ganglioside species. Taking advantage of the high-resolution MS analysis, rare ganglioside species were further elucidated according to their characteristic fragment ions and neutral losses. In total, 18 gangliosides with a ceramide carbon number from 20 to 25 and modified gangliosides, including 18 acetylated, 8 diacetylated, 1 phosphorylated, 36 N-glycolyneuraminic acid (NeuGc)-containing, and 7 di-NeuGc-containing gangliosides, were newly identified. The developed DIA/PRM method therefore generated a rich ganglioside resource for further functional exploration and is a unique alternative for DDA analysis for global ganglioside profiling in various biological systems.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the COVID-19 pandemic, is rapidly evolving. Due to the limited efficacy of vaccination in prevention of SARS-CoV-2 transmission and continuous emergence of variants of concern (VOC), including the currently most prevalent Delta variant, orally bioavailable and broadly efficacious antiviral drugs are urgently needed. Previously we showed that adenosine analogue 69-0 (also known as GS-441524), possesses potent anti-SARS-CoV-2 activity. Herein, we report that esterification of the 5’-hydroxyl moieties of 69-0 markedly improved the antiviral potency. The 5’-hydroxyl-isobutyryl prodrug, ATV006, showed excellent oral bioavailability in rats and cynomolgus monkeys and potent antiviral efficacy against different VOCs of SARS-CoV-2 in cell culture and three mouse models. Oral administration of ATV006 significantly reduced viral loads, alleviated lung damage and rescued mice from death in the K18-hACE2 mouse model challenged with the Delta variant. Moreover, ATV006 showed broad antiviral efficacy against different mammal-infecting coronaviruses. These indicate that ATV006 represents a promising oral drug candidate against SARS-CoV-2 VOCs and other coronaviruses.

Gestational diabetes mellitus (GDM) is a high-prevalence disease and diagnosed in middle pregnancy. Acylcarnitines are a series of fatty acid esters of carnitine and play important roles in fatty acid and carbohydrate metabolism. However, the role of acylcarnitine on the development of GDM remains unclear. This case-control study involving 214 study participants (107 GDM cases and 107 matched controls) was conducted in a cohort, in China, from 2013 to 2015. The levels of carnitine and 36 acylcarnitines in serum samples collected at the early stage of pregnancy were determined by using ultra-high performance liquid chromatography coupled with tandem mass spectrometry. The associations of the levels of the 37 targeted compounds with GDM risk were investigated by using binary conditional logistic regression models. Alterations in acylcarnitine levels were observed 9–17 weeks before GDM diagnosis. The increases in levels of propionyl-carnitine, malonyl-carnitine, isovaleryl-carnitine, palmitoyl-carnitine and linoleoyl-carnitine were associated with GDM risk with odds ratios (ORs) per standard deviation (SD) increment greater than 1 ( p p

Maternal exposure to phthalates may cause some adverse health effects on both mother and fetus, but variations of phthalate exposure and metabolism during pregnancy have not been thoroughly characterized. A total of 946 participants were selected from a cohort study conducted in Wuhan between 2014 and 2015 through which they had provided a complete set of urine samples at three trimesters. Eight phthalate metabolites were analyzed in 2838 urine samples. Based on urinary concentrations, various parameters (i.e. phthalate metabolite concentrations, ratios of metabolites of bis(2-ethylhexyl) phthalate (DEHP) in DEHP, and percentages of individual metabolites in total phthalates) were compared over three visits. We observed that levels of phthalate metabolites showed a U-shaped trend across three trimesters. The significant variations in the ratios of DEHP metabolites indicated that the efficiency in metabolizing DEHP declined during pregnancy and less recent exposure occurred in mid-pregnancy. The changes of percentages of individual compound in total phthalates suggested the inconsistent pattern over trimesters. This longitudinal study found that the exposure pattern, exposure timing and metabolic susceptibility varied by trimesters, which suggests that urine samples should be collected at multiple time points and mothers should be especially careful in the early pregnancy.

Endocrine-disrupting chemicals (EDCs) are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy. However, the adverse effects of EDCs in the body of pregnant women were underestimated. The interaction between dynamic concentration of EDCs and endogenous hormones (EHs) on gestational age and delivery time remains unclear. To define a temporal interaction between the EDCs and EHs during pregnancy, comprehensive, unbiased, and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women. We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women. The optimal combination of EHs and EDCs can identify when labor occurs (time to delivery within two and four weeks, AUROC of 0.82). Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time. This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs’ mixture effect on pregnancy with potential clinical utilities.

Type 2 diabetes mellitus (T2DM) is characterized by hyperinsulinemia, hyperglycemia and insulin resistance, which correlated with high mortality worldwide. Exercise is one of the effective lifestyle interventions in maintaining blood glucose level in the normal range and lowering risk factors. Metabolomics approaches are powerful tools in systematic study of overall metabolic changes in response to disease or interventions. In this study, mass spectrometry-based metabolomics studies were performed to investigate the regulatory effect of moderate intensity of exercise on db/db diabetic mice in skeletal muscle. Both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) have been carried out to monitor a wide range of regulated metabolites. Ninety-five metabolites were identified which contributing to the discrimination of db/m + control and db/db diabetic mice. The regulatory effects of exercise on these metabolites were mainly focusing on attenuating the levels of long-chain fatty acids (C14 to C18) and medium-to long-chain acylcarnitines (C12 to C18), indicated that exercise might play a positive role in inhibiting the accumulation of excessive lipids, which is positively related to insulin resistance. In addition, uric acid, which is a risk factor for inflammation, cardiovascular complications, and fatty liver in diabetic patients, together with its intermediates (such as inosinic acid, hypoxanthine, etc.) in purine metabolism pathway, were also substantially down regulated after exercise, indicating exercise might also be protective against hyperuricemia related risks in T2DM. These findings reveal that moderate intensity of exercise might play a positive role in improving the efficiency of lipid metabolism in skeletal muscle and meanwhile enhancing uric acid clearance to prevent lipid accumulation, which might contribute to improved body fitness and body muscle composition.

Large-scale quantitative mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter- and intra-batch variations. The unwanted variations can lead to poor inter- and intra-day reproducibility, which is a hindrance to discover real significance. The use of quality control samples and data treatment strategies in the quality assurance procedure provides a mechanism to evaluate the quality and remove the analytical variance of the data. The statTarget we developed is a streamlined tool with an easy-to-use graphical user interface and an integrated suite of algorithms specifically developed for the evaluation of data quality and removal of unwanted variations for quantitative mass spectrometry-based omics data. A novel quality control-based random forest signal correction algorithm, which can remove inter- and intra-batch unwanted variations at feature-level was implanted in the statTarget. Our evaluation based on real samples showed the developed algorithm could improve the data precision and statistical accuracy for mass spectrometry-based metabolomics and proteomics data. Additionally, the statTarget offers the streamlined procedures for data imputation, data normalization, univariate analysis, multivariate analysis, and feature selection. To conclude, the statTarget allows user-friendly the improvement of the data precision for uncovering the biologically differences, which largely facilitates quantitative mass spectrometry-based omics data processing and statistical analysis.

Polybrominated diphenyl ethers (PBDE), as one kind of the major persistent organic pollutants (POP), have potential adverse effects on human health. 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) has been identified as one of the dominant PBDE congener in environmental and human samples. In this paper, liquid chromatography–orbitrap mass spectrometry (LC-Orbitrap MS) was applied for the profiling analysis of small metabolites in mice serum and striatum. The metabolic characteristics of adult male C57BL/6 mice exposed to BDE-47 were investigated with a non-targeted metabolomics method. The partial least-squares discriminant analysis (PLS-DA) indicated that the metabolites profile was significantly changed due to the exposure. Fifty-seven differential metabolites that significantly altered in mice serum and striatum were identified through databases searching and authentic standards confirmation. The related metabolic pathways mainly involved purine metabolism, alanine, aspartate and glutamate metabolism, tryptophan metabolism, phenylalanine metabolism, and glutathione metabolism. On the basis of metabolic variations, molecular mechanisms including disturbance of dopaminergic system, neurotransmitters regulation, DNA methylation, and oxidative stress were proposed. The obtained results suggested that LC-Orbitrap MS-based metabolomics had the great potential for molecular understanding of metabolic regulation linked to the exposure of environment pollutants.

Polychlorinated biphenyls (PCBs) remain a relatively high level in e-waste recycling regions 3 decades after ban on use. Illegal recycling activities cunningly moved under the environmental law enforcement. Here, we analyzed PCBs in soils and plants from Guiyu, China (one of the world's largest recycling areas) to understand the relationship between PCBs pollution and the transition of recycling activities (locations and techniques). High concentrations of PCBs were found in soil and plant samples from emerging recycling sites, up to 234 ng g−1 and 236 ng g−1 (dry weight), respectively. The recycling activities, specifically the open burning process, would obviously aggravate the PCB pollution levels in its environment. The calculated values of estimated daily intake and hazard ratios of PCBs in dietary routes showed that health risks should be taken seriously.

Alkyl chloroformate have been wildly used for the fast derivatization of metabolites with amino and/or carboxyl groups, coupling of powerful separation and detection systems, such as GC-MS, which allows the comprehensive analysis of non-amino organic acids and amino acids. The reagents involving n-alkyl chloroformate and n-alcohol are generally employed for providing symmetric labeling terminal alkyl chain with the same length. Here, we developed an asymmetric labeling strategy and positive chemical ionization gas chromatography-tandem mass spectrometry (PCI-GC-MS-MS) approach for determination of non-amino organic acids and amino acids, as well as the short chain fatty acids. Carboxylic and amino groups could be selectively labelled by propyl and ethyl groups, respectively. The specific neutral loss of C3H8O (60Da), C3H5O2 (74Da) and C4H8O2 (88Da) were useful in the selective identification for qualitative analysis of organic acids and amino acid derivatives. PCI-GC-MS-MS using multiple reaction monitoring (MRM) was applied for semi-quantification of typical non-amino organic acids and amino acids. This method exhibited a wide range of linear range, good regression coefficient (R2) and repeatability. The relative standard deviation (RSD) of targeted metabolites showed excellent intra- and inter-day precision (

As the predominant congener of polybrominated diphenyl ethers (PBDEs) detected in human serum, 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) has been reported to induce neurotoxicity. However, the possible linkage between BDE-47 and typical neurodegenerative diseases such as Parkinson's disease (PD) is still unclear. Here we carried out omics studies using liquid chromatography-orbitrap mass spectrometry (LC-orbitrap MS) to depict the BDE-47 induced metabolic changes in C57BJ/L mice to explore the possible contribution of BDE-47 exposure to PD pathology. BDE-47 dissolved in corn oil was orally administered to mice for 30 consecutive days. Results of metabolomics and lipidomics studies of PD-related brain regions revealed significant metabolite changes in pathways involved in oxidative stress and neurotransmitter production. Moreover, isobaric tags for relative and absolute quantitation (iTRAQ) proteomics study of the striatum, which is the part of brain that is most intensively studied in PD pathogenesis, revealed that BDE-47 could induce neurotransmitter system disturbance, abnormal phosphorylation, mitochondrial dysfunction and oxidative stress. Overall, this study depicts the possible contribution of BDE-47 exposure to PD pathology and highlights the powerfulness of omics platforms to deepen the mechanistic understanding of environmental pollutant-caused toxicity.

Despite the increasing research attention paid to gestational diabetes mellitus (GDM) due to its high prevalence, limited knowledge is available about its pathogenesis. In this study, 428 serum samples were collected from 107 pregnant women suffering from GDM and 107 matched healthy controls. The nontargeted metabolomics data of maternal serum samples from the first (T1, n = 214) and second trimesters (T2, n = 214) were acquired by using ultrahigh performance liquid chromatography coupled with Orbitrap mass spectrometry (MS). A total of 93 differential metabolites were identified on the basis of the accurate mass and MS/MS fragmentation. After false discovery rate correction, the levels of 31 metabolites in GDM group were significantly altered in the first trimester. The differential metabolites were mainly attributed to purine metabolism, fatty acid β-oxidation, urea cycle, and tricarboxylic acid cycle pathways. The fold changes across pregnancy (T2/T1) of six amino acids (serine, proline, leucine/isoleucine, glutamic acid, tyrosine, and ornithine), a lysophosphatidylcholine (LysoPC(20:4)), and uric acid in GDM group were significantly different from those in the control groups, suggesting that these 8 metabolites might have contributed to the occurrence and progression of GDM. The findings revealed that the amino acid metabolism, lipid metabolism, and other pathways might be disturbed prior to GDM onset and during the period from the first to the second trimester of pregnancy.

Large-scale mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter‐ and intra‐ batch variations. The unwanted variations can lead to poor inter‐ and intra-day reproducibility, which is a hindrance to discover real significance. We developed a novel quality control-based random forest signal correction algorithm, being ensemble learning approach to remove inter‐ and intra‐ batches of unwanted variations at feature-level. Our evaluation based on real samples showed the developed algorithm improved the data precision and statistical accuracy for metabolomics and proteomics, which was superior to other common correction methods. We have been able to improve its performance for interpretations of large-scale metabolomics and proteomics data, and to allow the improvement of the data precision for uncovering the real biologically differences.

Metabolomics seeks to take a "snapshot" in a time of the levels, activities, regulation and interactions of all small molecule metabolites in response to a biological system with genetic or environmental changes. The emerging development in mass spectrometry technologies has shown promise in the discovery and quantitation of neuroactive small molecule metabolites associated with gut microbiota and brain. Significant progress has been made recently in the characterization of intermediate role of small molecule metabolites linked to neural development and neurodegenerative disorder, showing its potential in understanding the crosstalk between gut microbiota and the host brain. More evidence reveals that small molecule metabolites may play a critical role in mediating microbial effects on neurotransmission and disease development. Mass spectrometry-based metabolomics is uniquely suitable for obtaining the metabolic signals in bidirectional communication between gut microbiota and brain. In this review, we summarized major mass spectrometry technologies including liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and imaging mass spectrometry for metabolomics studies of neurodegenerative disorders. We also reviewed the recent advances in the identification of new metabolites by mass spectrometry and metabolic pathways involved in the connection of intestinal microbiota and brain. These metabolic pathways allowed the microbiota to impact the regular function of the brain, which can in turn affect the composition of microbiota via the neurotransmitter substances. The dysfunctional interaction of this crosstalk connects neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease and Huntington's disease. The mass spectrometry-based metabolomics analysis provides information for targeting dysfunctional pathways of small molecule metabolites in the development of the neurodegenerative diseases, which may be valuable for the investigation of underlying mechanism of therapeutic strategies.

Epidemiological and animal studies have revealed a possible linkage between 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) exposure and neurodegenerative disease such as Parkinson's disease (PD). However, whether or how BDE-47 would affect the PD progression remains unclear. Here, we carried out a metabolomics study based on liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) to investigate the possible contribution of BDE-47 exposure to PD progression in Drosophila (fly) model. Transgenic PD flies were exposed to BDE-47 through diet for 30 days. Global metabolomic analysis identified 48 altered metabolites after the exposure. These metabolites were mainly involved in tryptophan metabolism, phenylalanine metabolism, purine metabolism, and alanine, aspartate and glutamate metabolism. Further, by quantifying metabolites of interest using LC-MS/MS, we confirmed that the formation of neuro-protector kynurenic acid was slowed down while the formation of neurotoxin 3-hydroxy-kynurenine was speeded up on the 20th exposure day. Moreover, the levels of SAM/SAH (an index of methylation potential) and GSH/GSSG (an indicator of oxidative stress) were found to decrease on the 30th exposure day. Our results suggest that BDE-47 could induce imbalance of kynurenine metabolism and methylation potential, and oxidative stress, which might further accelerate PD progression.

Metabolic variations occur during normal pregnancy to provide the growing fetus with a supply of nutrients required for its development and to ensure the health of the woman during gestation. Mass spectrometry-based metabolomics was employed to study the metabolic phenotype variations in the maternal plasma that are induced by pregnancy in each of its three trimesters. Nontargeted metabolomics analysis showed that pregnancy significantly altered the profile of metabolites in maternal plasma. The levels of six metabolites were found to change significantly throughout pregnancy, with related metabolic pathway variations observed in biopterin metabolism, phospholipid metabolism, amino acid derivatives, and fatty acid oxidation. In particular, there was a pronounced elevation of dihydrobiopterin (BH₂), a compound produced in the synthesis of dopa, dopamine, norepinephrine, and epinephrine, in the second trimester, whereas it was markedly decreased in the third trimester. The turnover of BH₂ and tryptophan catabolites indicated that the fluctuations of neurotransmitters throughout pregnancy might reveal the metabolic adaption in the maternal body for the growth of the fetus. Furthermore, 11 lipid classes and 41 carnitine species were also determined and this showed variations in the presence of long-chain acylcarnitines and lysophospholipids in later pregnancy, suggesting changes of acylcarnitines and lysophospholipids to meet the energy demands in pregnant women. To our knowledge, this work is the first report of dynamic metabolic signatures and proposed related metabolic pathways in the maternal plasma for normal pregnancies and provided the basis for time-dependent metabolic trajectory against which disease-related disorders may be contrasted.

The aim of this pilot study is to find discriminating signals from the patient's congestive heart failure (HF) caused by coronary artery disease (CAD) through a non-target metabolomics method and test their usefulness in progress of human HF diseases. Multivariate data analysis was used to identify the discriminating signals. Interestingly, 12 metabolites contributing to the complete separation of HF from matched CAD were identified. Metabolic pathways including free fatty acids, sphingolipids and amino acid derivatives were found to be disturbed in HF patients compared with CAD patients. Lipid molecules associated with energy metabolism and signaling pathways may play key roles in the development of failing heart.

Perfluoroalkyl substances (PFASs) have been detected in wildlife and human samples worldwide. Toxicology research showed that PFASs could interfere with thyroid hormone homeostasis. In this study, eight PFASs, fifteen PFAS precursors and five thyroid hormones were analyzed in 157 paired maternal and cord serum samples collected in Beijing around delivery. Seven PFASs and two precursors were detected in both maternal and cord sera with significant maternal-fetal correlations (r = 0.336 to 0.806, all P P P

Urine metabolic phenotyping has been associated with the development of Parkinson’s disease (PD). However, few studies using a comprehensive metabolomics approach have investigated the correlation between changes in the urinary markers and the progression of clinical symptoms in PD. A comprehensive metabolomic study with robust quality control procedures was performed using gas chromatography - mass spectrometry (GC - MS) and liquid chromatography - mass spectrometry (LC - MS) to characterize the urinary metabolic phenotypes of idiopathic PD patients at three stages (early, middle and advanced) and normal control subjects, with the aim of discovering potential urinary metabolite markers for the diagnosis of idiopathic PD. Both GC-MS and LC-MS metabolic profiles of idiopathic PD patients differed significantly from those of normal control subjects. 18 differentially expressed metabolites were identified as constituting a unique metabolic marker associated with the progression of idiopathic PD. Related metabolic pathway variations were observed in branched chain amino acid metabolism, glycine derivation, steroid hormone biosynthesis, tryptophan metabolism and phenylalanine metabolism. Comprehensive, successive metabolomic profiling revealed changes in the urinary markers associated with progression of idiopathic PD. This profiling relies on noninvasive sampling and is complementary to existing clinical modalities.

Breast cancer is the leading cause of cancer death among women worldwide. Identification of lipid targets that play a role in breast cancer invasion may advance our understanding of the rapid progression of cancer and may lead to the development of new biomarkers for the disease.Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) was applied for the lipidomic profiling of two poorly invasive and two highly invasive breast cancer cell lines to identify the differentially accumulated lipids related to the invasive phenotype. The four cell lines were individually grown on indium tin oxide (ITO)-coated glass slides, analyzed as cell cultures. The raster width and matrix for detection were optimized to improve detection sensitivity.Optimized MSI measurements were performed directly on the cell culture with 9-aminoacridine as matrix, resulting in 215 endogenous compounds detected in positive ion mode and 267 endogenous compounds in negative ion mode in all the four cell lines, representing the largest group of analytes that have been analyzed from cells by a single MSI study. In highly invasive cell lines, 31 lipids including phosphatidylglycerol (PG) and phosphatidic acids were found upregulated and eight lipids including sphingomyelin (SM) downregulated in negative ion mode. The products of de novo fatty acid synthesis incorporated into membrane phospholipids, like oleic-acid-containing PG, may be involved in mitochondrial dysfunction and thus affect the invasion of breast cancer cells. The deficiency of SM may be related to the disruption of apoptosis in highly invasive cancer cells.This work uncovered more analytes in cells by MSI than previous reports, providing a better visualization and novel insights to advance our understanding of the relationship between rapid progression of breast cancer and lipid metabolism. The most altered lipids may aid the discovery of diagnostic markers and therapeutic targets of breast cancer.

Increasing evidence has shown that abnormal metabolic phenotypes in body fluids reflect the pathogenesis and pathophysiology of Parkinson's disease (PD). These body fluids include urine; however, the relationship between, specifically, urinary metabolic phenotypes and PD is not fully understood. In this study, urinary metabolites from a total of 401 clinical urine samples collected from 106 idiopathic PD patients and 104 normal control subjects were profiled by using high-performance liquid chromatography coupled to high-resolution mass spectrometry. Our study revealed significant correlation between clinical phenotype and urinary metabolite profile. Metabolic profiles of idiopathic PD patients differed significantly and consistently from normal controls, with related metabolic pathway variations observed in steroidogenesis, fatty acid beta-oxidation, histidine metabolism, phenylalanine metabolism, tryptophan metabolism, nucleotide metabolism, and tyrosine metabolism. In the fruit fly Drosophila melanogaster, the alteration of the kynurenine pathway in tryptophan metabolism corresponded with pathogenic changes in the alpha-synuclein overexpressed Drosophila model of PD. The results suggest that LC-MS-based urinary metabolomic profiling can reveal the metabolite signatures and related variations in metabolic pathways that characterize PD. Consistent PD-related changes across species may provide the basis for understanding metabolic regulation of PD at the molecular level.

As an economic crop, pepper satisfies people's spicy taste and has medicinal uses worldwide. To gain a better understanding of Capsicum evolution, domestication, and specialization, we present here the genome sequence of the cultivated pepper Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum). We estimate that the pepper genome expanded similar to 0.3 Mya (with respect to the genome of other Solanaceae) by a rapid amplification of retrotransposons elements, resulting in a genome comprised of similar to 81% repetitive sequences. Approximately 79% of 3.48-Gb scaffolds containing 34,476 protein-coding genes were anchored to chromosomes by a high-density genetic map. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular footprints of artificial selection, providing us with a list of candidate domestication genes. We also found that dosage compensation effect of tandem duplication genes probably contributed to the pungent diversification in pepper. The Capsicum reference genome provides crucial information for the study of not only the evolution of the pepper genome but also, the Solanaceae family, and it will facilitate the establishment of more effective pepper breeding programs.

Aqueous (ET1) and alcoholic (ET2) extracts from Mactra veneriformis were studied for their antioxidant potentials using various in vitro assays. The ET2 was fractioned into four parts according to the polarity of the extractive medium, viz. ET2-p (petroleum ether), ET2-e (ethyl acetate), ET2-n (n-butanol) and ET2-w (water). Among the four fractions, ET2-w showed the strongest reducing power, and highest 2,2 diphenyl-1-picrylhydrazyl (DPPH) scavenging and metal chelating activities, while the ET2-p possessed higher hydroxyl radical scavenging activities. It was found that ET2-w had large amounts of free amino acids and oligosaccharides, while ET2-p contained a large amount of unsaturated fatty acids. We conclude that amino acids, oligosaccharides and unsaturated fatty acids all contribute to the antioxidant activity of M. veneriformis. In order to elucidate the above, antioxidative components involved in the antioxidant activities were also detected. It was found that amino acids and carbohydrates were the major components of ET2-w. The amino acids were Tau (0.26%), Tyr (0.38%), Met (0.31%), Cys (0.38%), Arg (1.79%) and so on, while carbohydrates were oligosaccharides that were composed of disaccharides and trisaccharides. The unsaturated fatty acids were the major components of ET2-p, which contained a high amount of docosahexaenoic acid (9.03%) and eicosapentaenoic acid (18.49%).

Marine ecosystems can be considered as a rather unexplored source of drugs as well as a surprising reservoir ofbioactive oligosaccharide. Marine oligosaccharides could be obtained by hydrolysis of polysaccharide existing ina variety of biomass sources or synthesized by simple oligosaccharides. In recent years, marine oligosaccharideswhich degraded by polysaccharides are attracting increasing interest in developing potential drugs for chronicdiseases. Significant efforts have been made to find and confirm their bioactivity such as anti-tumor, antimicrobial,antioxidant, anti-inflammatory and so on. In this review, the mechanisms of bio-function eliciting kinds ofapplications of different marine oligosaccharides are summarized.

In the process of curating and describing the data and methods of this publication for submission as a Data Note in the journal GigaScience, certain discrepancies between the actual methods and the reported methods came to light. In most cases, these are minor and do not affect the results as described in the original article. Yet one discrepancy involving randomization of the samples in one analysis could be of more concern when interpreting those results. The full details of each run of mass spectrometry can be visualized fully in the figure presented in the supplemental file to the Data Note. The corrections to be noted concern distribution of quality control (QC) samples within the run and between batches of samples and randomization of samples in the positive ion mode, nontargeted, global metabolomics analysis.

Background: To identify metabolic biomarkers that can be used to differentiate sepsis from systemic inflammatory response syndrome (SIRS), assess severity and predict outcomes. Methods: 65 patients were involved in this study, including 35 patients with sepsis, 15 patients with SIRS and 15 normal patients. Small metabolites that were present in patient serum samples were measured by liquid chromatography mass spectrometry techniques and analysed using multivariate statistical methods. Results: The metabolic profiling of normal patients and patients with SIRS or sepsis was markedly different. A significant decrease in the levels of lactitol dehydrate and S-phenyl-D-cysteine and an increase in the levels of S-(3-methylbutanoyl)-dihydrolipoamide-E and N-nonanoyl glycine were observed in patients with sepsis in comparison to patients with SIRS (p

Background Metabolomics has the potential to be a powerful and sensitive approach for investigating the low molecular weight metabolite profiles present in maternal fluids and their role in pregnancy. Findings In this Data Note, LC–MS metabolome, lipidome and carnitine profiling data were collected from 180 healthy pregnant women, representing six time points spanning all three trimesters, and providing sufficient coverage to model the progression of normal pregnancy. Conclusions As a relatively large scale, real-world dataset with robust numbers of quality control samples, the data are expected to prove useful for algorithm optimization and development, with the potential to augment studies into abnormal pregnancy. All data and ISA-TAB format enriched metadata are available for download in the MetaboLights and GigaScience databases. Electronic supplementary material The online version of this article (doi:10.1186/s13742-015-0054-9) contains supplementary material, which is available to authorized users.