PubMed

Arbuscular Mycorrhizal Fungi and Plant Chemical Defence: Effects of Colonisation on Aboveground and Belowground Metabolomes. J Chem Ecol. 2018 Feb 02;: Authors: Hill EM, Robinson LA, Abdul-Sada A, Vanbergen AJ, Hodge A, Hartley SE Abstract Arbuscular mycorrhizal fungal (AMF) colonisation of plant roots is one of the most ancient and widespread interactions in ecology, yet the systemic consequences for plant secondary chemistry remain unclear. We performed the first metabolomic investigation into the impact of AMF colonisation by Rhizophagus irregularis on the chemical defences, spanning above- and below-ground tissues, in its host-plant ragwort (Senecio jacobaea). We used a non-targeted metabolomics approach to profile, and where possible identify, compounds induced by AMF colonisation in both roots and shoots. Metabolomics analyses revealed that 33 compounds were significantly increased in the root tissue of AMF colonised plants, including seven blumenols, plant-derived compounds known to be associated with AMF colonisation. One of these was a novel structure conjugated with a malonyl-sugar and uronic acid moiety, hitherto an unreported combination. Such structural modifications of blumenols could be significant for their previously reported functional roles associated with the establishment and maintenance of AM colonisation. Pyrrolizidine alkaloids (PAs), key anti-herbivore defence compounds in ragwort, dominated the metabolomic profiles of root and shoot extracts. Analyses of the metabolomic profiles revealed an increase in four PAs in roots (but not shoots) of AMF colonised plants, with the potential to protect colonised plants from below-ground organisms. PMID: 29392532 [PubMed - as supplied by publisher]

Occlusion of left atrial appendage affects metabolomic profile: focus on glycolysis, tricarboxylic acid and urea metabolism. Metabolomics. 2017;13(11):127 Authors: Sattler K, Behnes M, Barth C, Wenke A, Sartorius B, El-Battrawy I, Mashayekhi K, Kuschyk J, Hoffmann U, Papavasiliu T, Fastner C, Baumann S, Lang S, Zhou X, Yücel G, Borggrefe M, Akin I Abstract Background: Left atrial appendage (LAA) closure (LAAC) by implantation of an occlusion device is an established cardiac intervention to reduce risk of stroke while avoiding intake of oral anticoagulation medication during atrial fibrillation. Cardiac interventions can alter local or systemic gene and protein expression. Effects of LAAC on systemic metabolism have not been studied yet. Objectives: We aimed to study the effects of interventional LAAC on systemic metabolism. Methods: Products of glycolysis, tricarboxylic acid and urea metabolism were analyzed by ESI-LC-MS/MS and MS/MS using the AbsoluteIDQ™ p180 Kit in plasma of 44 patients undergoing successful interventional LAAC at baseline (T0) and after 6 months (T1). Results: During follow up, plasma concentrations of several parameters of glycolysis and tricarboxylic acid cycle (TCA) and urea metabolism increased (alanine, hexose, proline, sarcosine), while others decreased (aspartate, glycine, SDMA, serine). Multivariate linear regression analysis showed that time after interventional LAAC was an independent predictor for metabolite changes, including the decrease of SDMA (beta -0.19, p < 0.01) and the increase of sarcosine (beta 0.16, p < 0.01). Conclusions: Successful interventional LAAC affects different pathways of the metabolome, which are probably related to cardiac remodeling. The underlying mechanisms as well as the long term effects have to be studied in the future. PMID: 29391863 [PubMed]

Metabolomics in COPD Acute Respiratory Failure Requiring Noninvasive Positive Pressure Ventilation. Can Respir J. 2017;2017:9480346 Authors: Fortis S, Lusczek ER, Weinert CR, Beilman GJ Abstract We aimed to investigate whether metabolomic analysis can discriminate acute respiratory failure due to COPD exacerbation from respiratory failure due to heart failure and pneumonia. Since COPD exacerbation is often overdiagnosed, we focused on those COPD exacerbations that were severe enough to require noninvasive mechanical ventilation. We enrolled stable COPD subjects and patients with acute respiratory failure requiring noninvasive mechanical ventilation due to COPD, heart failure, and pneumonia. We excluded subjects with history of both COPD and heart failure and patients with obstructive sleep apnea and obstructive lung disease other than COPD. We performed metabolomics analysis using NMR. We constructed partial least squares discriminant analysis (PLS-DA) models to distinguish metabolic profiles. Serum (p=0.001, R2 = 0.397, Q2 = 0.058) and urine metabolic profiles (p < 0.001, R2 = 0.419, Q2 = 0.142) were significantly different between the four diagnosis groups by PLS-DA. After excluding stable COPD patients, the metabolomes of the various respiratory failure groups did not cluster separately in serum (p=0.2, R2 = 0.631, Q2 = 0.246) or urine (p=0.065, R2 = 0.602, Q2 = -0.134). However, several metabolites in the serum were reduced in patients with COPD exacerbation and pneumonia. We did not find a metabolic profile unique to COPD exacerbation, but we were able to clearly and reliably distinguish stable COPD patients from patients with respiratory failure in both serum and urine. PMID: 29391845 [PubMed - in process]

Asymmetric dimethylarginine (ADMA) is identified as a potential biomarker of insulin resistance in skeletal muscle. Sci Rep. 2018 Feb 01;8(1):2133 Authors: Lee W, Lee HJ, Jang HB, Kim HJ, Ban HJ, Kim KY, Nam MS, Choi JS, Lee KT, Cho SB, Park SI, Lee HJ Abstract To unravel metabolic determinats of insulin resistance, we performed a targeted metabolomics analysis in Korean Children-Adolescent Cohort Study (KoCAS, n = 430). Sixty-seven metabolites were associated with insulin resistance in adolescents and the association also found in an adult population (KoGES, n = 2,485). Functional interactions of metabolites with gene/proteins using biological pathway with insulin resistance were not identified biological significance and regulatory effects of asymmetric dimethylarginine (ADMA). However, ADMA showed a higher association with adolescent obesity (P < 0.001) and adult diabetes (P = 0.007) and decreased after obesity intervention program. Functional studies in cellular and mouse models demonstrated that an accumulation of ADMA is associated with the regulation of obesity-induced insulin resistance in skeletal muscle. ADMA treatment inhibited dimethylarginine-dimethylaminohydrolase (DDAH) activity and mRNA expression in insulin resistance muscle cell. Moreover, the treatment led to decrease of phosphorylation of insulin receptor (IR), AKT, and GLUT4 but increase of protein-tyrosine phosphatase 1B (PTP1B). Accordingly, increased ADMA significantly inhibited glucose uptake in myotube cell. We suggest that accumulation of ADMA is associated with modulation of insulin signaling and insulin resistance. ADMA might expand the possibilities of new therapeutic target for functional and clinical implications in the control of energy and metabolic homeostasis in humans. PMID: 29391561 [PubMed - in process]

Related Articles Application of 1H NMR spectroscopy to the metabolic phenotyping of rodent brain extracts: A metabonomic study of gut microbial influence on host brain metabolism. J Pharm Biomed Anal. 2017 Sep 05;143:141-146 Authors: Swann JR, Garcia-Perez I, Braniste V, Wilson ID, Sidaway JE, Nicholson JK, Pettersson S, Holmes E Abstract 1H NMR Spectroscopy has been applied to determine the neurochemical profiles of brain extracts from the frontal cortex and hippocampal regions of germ free and normal mice and rats. The results revealed a number of differences between germ free (GF) and conventional (CV) rats or specific pathogen-free (SPF) mice with microbiome-associated metabolic variation found to be both species- and region-dependent. In the mouse, the GF frontal cortex contained lower amounts of creatine, N-acetyl-aspartate (NAA), glycerophosphocholine and lactate, but greater amounts of choline compared to that of specific pathogen free (SPF) mice. In the hippocampus, the GF mice had greater creatine, NAA, lactate and taurine content compared to those of the SPF animals, but lower relative quantities of succinate and an unidentified lipid-related component. The GF rat frontal cortex contained higher relative quantities of lactate, creatine and NAA compared to the CV animals whilst the GF hippocampus was characterized by higher taurine and phosphocholine concentrations and lower quantities of NAA, N-acetylaspartylglutamate and choline compared to the CV animals. Of note is that, in both rat and mouse brain extracts, concentrations of hippocampal taurine were found to be greater in the absence of an established microbiome. The results provide further evidence that brain biochemistry can be influenced by gut microbial status, specifically metabolites involved in energy metabolism demonstrating biochemical dialogue between the microbiome and brain. PMID: 28595107 [PubMed - indexed for MEDLINE]

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2018/02/02PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2018/02/02PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Related Articles GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies. PLoS Comput Biol. 2018 Jan 31;14(1):e1005973 Authors: Wei R, Wang J, Jia E, Chen T, Ni Y, Jia W Abstract Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp. PMID: 29385130 [PubMed - as supplied by publisher]

Related Articles Metabolomics and Biomarkers for Drug Discovery. Metabolites. 2018 Jan 31;8(1): Authors: Yeung PK Abstract Metabolomics and biomarkers are increasingly used in drug discovery and development, and are applied to personalized medicine. Progress in these research areas has increased our understanding of disease pathology and improved therapeutic strategies for many diseases with unmet challenges. Further advances will ultimately result in the development of better drugs and breakthrough therapies, which will benefit millions of patients suffering from chronic and life-threatening diseases worldwide. PMID: 29385049 [PubMed]

Related Articles Mass Spectrometric Methodologies for Investigating the Metabolic Signatures of Parkinson's disease: Current Progress and Future Perspectives. Anal Chem. 2018 Jan 31;: Authors: Gill EL, Koelmel JP, Yost RA, Okun MS, Vedam-Mai V, Garrett TJ Abstract Parkinson's disease (PD) is a neurodegenerative disorder resulting from the loss of dopaminergic neurons of the substantia nigra as well as degeneration of motor and non-motor basal ganglia circuitries. Typically known for classical motor deficits (tremor, rigidity, bradykinesia), early stages of the disease are associated with a large non-motor component (depression, anxiety, apathy, etc.). Currently there are no definitive biomarkers of PD, and the measurement of dopamine metabolites does not allow for detection of prodromal PD, nor does it aid in long-term monitoring of disease progression. Given that PD is increasingly recognized as complex and heterogeneous, involving several neurotransmitters and proteins, it is of importance that we advance interdisciplinary studies to further our knowledge of the molecular and cellular pathways that are affected in PD. This approach will yield useful biomarkers for early diagnosis and will ultimately result in the development of disease-modifying therapies. Here, we discuss pre-analytical factors associated with metabolomics studies, summarize current mass spectrometric methodologies used to evaluate the metabolic signature of PD, and provide future perspectives of the rapidly developing field of MS in the context of PD. PMID: 29384654 [PubMed - as supplied by publisher]

Related Articles From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites. Nat Prod Rep. 2018 Jan 31;: Authors: Hautbergue T, Jamin EL, Debrauwer L, Puel O, Oswald IP Abstract Fungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully. PMID: 29384544 [PubMed - as supplied by publisher]

Related Articles Septic Shock Non-Survivors have Persistently Elevated Acylcarnitines Following Carnitine Supplementation. Shock. 2017 Sep 20;: Authors: Puskarich MA, Evans CR, Karnovsky A, Das AK, Jones AE, Stringer KA Abstract INTRODUCTION: Sepsis-induced metabolic disturbances include hyperlactatemia, disruption of glycolysis, protein catabolism, and altered fatty acid metabolism. It may also lower serum L-carnitine which supports the use of L-carnitine supplementation as a treatment to ameliorate several of these metabolic consequences. METHODS: To further understand the association between L-carnitine-induced changes in serum acylcarnitines, fatty acid metabolism and survival, serum samples from (T0), 12 hours following completion (T24) of L-carnitine (n = 16) or placebo (n = 15) administration, and 48 hours (T48) after enrollment from patients with septic shock enrolled in a randomized control trial were assayed for acylcarnitines, free fatty acids and insulin. Data were analyzed comparing 1-year survivors and non-survivors within treatment groups. RESULTS: Mortality was 8/16 (50%) and 12/15 (80%) at one-year, for L-carnitine and placebo-treated patients, respectively. Free carnitine, C2, C3, and C8 acylcarnitines were higher among non-survivors at enrollment. L-carnitine treatment increased levels of all measured acylcarnitines; an effect that was sustained for at least 36 hours following completion of the infusion and was more prominent among non-survivors. Several fatty acids followed a similar, though less consistent pattern. Glucose, lactate, and insulin levels did not differ based on survival or treatment arm. CONCLUSIONS: In human patients with septic shock, L-carnitine supplementation increases a broad range of acylcarnitine concentrations that persist after cessation of infusion, demonstrating both immediate and sustained effects on the serum metabolome. Non-survivors demonstrate a distinct metabolic response to L-carnitine compared to survivors, which may indicate pre-existing or more profound metabolic derangement that constrains any beneficial response to treatment. PMID: 29384504 [PubMed - as supplied by publisher]

Related Articles Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review. Oncotarget. 2017 Dec 29;8(70):115774-115786 Authors: Yu L, Li K, Zhang X Abstract Lung cancer is the leading cause of cancer-related death. Next-generation metabolomics is becoming a powerful emerging technology for studying the systems biology and chemistry of health and disease. This mini review summarized the main platforms of next-generation metabolomics and its main applications in lung cancer including early diagnosis, pathogenesis, classifications and precision medicine. The period covers between 2009 and August, 2017. The major issues and future directions of metabolomics in lung cancer research and clinical applications were also discussed. PMID: 29383200 [PubMed]

Related Articles Metabolite identification in fecal microbiota transplantation mouse livers and combined proteomics with chronic unpredictive mild stress mouse livers. Transl Psychiatry. 2018 Jan 31;8(1):34 Authors: Li B, Guo K, Zeng L, Zeng B, Huo R, Luo Y, Wang H, Dong M, Zheng P, Zhou C, Chen J, Liu Y, Liu Z, Fang L, Wei H, Xie P Abstract Major depressive disorder (MDD) is a common mood disorder. Gut microbiota may be involved in the pathogenesis of depression via the microbe-gut-brain axis. Liver is vulnerable to exposure of bacterial products translocated from the gut via the portal vein and may be involved in the axis. In this study, germ-free mice underwent fecal microbiota transplantation from MDD patients and healthy controls. Behavioral tests verified the depression model. Metabolomics using gas chromatography-mass spectrometry, nuclear magnetic resonance, and liquid chromatography-mass spectrometry determined the influence of microbes on liver metabolism. With multivariate statistical analysis, 191 metabolites were distinguishable in MDD mice from control (CON) mice. Compared with CON mice, MDD mice showed lower levels for 106 metabolites and higher levels for 85 metabolites. These metabolites are associated with lipid and energy metabolism and oxidative stress. Combined analyses of significantly changed proteins in livers from another depression model induced by chronic unpredictive mild stress returned a high score for the Lipid Metabolism, Free Radical Scavenging, and Molecule Transports network, and canonical pathways were involved in energy metabolism and tryptophan degradation. The two mouse models of depression suggest that changes in liver metabolism might be involved in the pathogenesis of MDD. Conjoint analyses of fecal, serum, liver, and hippocampal metabolites from fecal microbiota transplantation mice suggested that aminoacyl-tRNA biosynthesis significantly changed and fecal metabolites showed a close relationship with the liver. These findings may help determine the biological mechanisms of depression and provide evidence about "depression microbes" impacting on liver metabolism. PMID: 29382834 [PubMed - in process]

Related Articles Pyruvate cycle increases aminoglycoside efficacy and provides respiratory energy in bacteria. Proc Natl Acad Sci U S A. 2018 Jan 30;: Authors: Su YB, Peng B, Li H, Cheng ZX, Zhang TT, Zhu JX, Li D, Li MY, Ye JZ, Du CC, Zhang S, Zhao XL, Yang MJ, Peng XX Abstract The emergence and ongoing spread of multidrug-resistant bacteria puts humans and other species at risk for potentially lethal infections. Thus, novel antibiotics or alternative approaches are needed to target drug-resistant bacteria, and metabolic modulation has been documented to improve antibiotic efficacy, but the relevant metabolic mechanisms require more studies. Here, we show that glutamate potentiates aminoglycoside antibiotics, resulting in improved elimination of antibiotic-resistant pathogens. When exploring the metabolic flux of glutamate, it was found that the enzymes that link the phosphoenolpyruvate (PEP)-pyruvate-AcCoA pathway to the TCA cycle were key players in this increased efficacy. Together, the PEP-pyruvate-AcCoA pathway and TCA cycle can be considered the pyruvate cycle (P cycle). Our results show that inhibition or gene depletion of the enzymes in the P cycle shut down the TCA cycle even in the presence of excess carbon sources, and that the P cycle operates routinely as a general mechanism for energy production and regulation in Escherichia coli and Edwardsiella tarda These findings address metabolic mechanisms of metabolite-induced potentiation and fundamental questions about bacterial biochemistry and energy metabolism. PMID: 29382755 [PubMed - as supplied by publisher]

Related Articles Omics techniques and biobanks to find new biomarkers for the early detection of acute lymphoblastic leukemia in middle-income countries: a perspective from Mexico. Bol Med Hosp Infant Mex. 2017 May - Jun;74(3):227-232 Authors: Aguirre-Guillén WA, Angeles-Floriano T, López-Martínez B, Reyes-Morales H, Zlotnik A, Valle-Rios R Abstract Acute lymphoblastic leukemia (ALL) affects the quality of life of many children in the world and particularly in Mexico, where a high incidence has been reported. With a proper financial investment and with well-organized institutions caring for those patients, together with solid platforms to perform high-throughput analyses, we propose the creation of a Mexican repository system of serum and cells from bone marrow and blood samples derived from tissues of pediatric patients with ALL diagnosis. This resource, in combination with omics technologies, particularly proteomics and metabolomics, would allow longitudinal studies, offering an opportunity to design and apply personalized ALL treatments. Importantly, it would accelerate the development of translational science and will lead us to further discoveries, including the identification of new biomarkers for the early detection of leukemia. PMID: 29382491 [PubMed - in process]

Related Articles Omics-based biomarkers: current status and potential use in the clinic. Bol Med Hosp Infant Mex. 2017 May - Jun;74(3):219-226 Authors: Quezada H, Guzmán-Ortiz AL, Díaz-Sánchez H, Valle-Rios R, Aguirre-Hernández J Abstract In recent years, the use of high-throughput omics technologies has led to the rapid discovery of many candidate biomarkers. However, few of them have made the transition to the clinic. In this review, the promise of omics technologies to contribute to the process of biomarker development is described. An overview of the current state in this area is presented with examples of genomics, proteomics, transcriptomics, metabolomics and microbiomics biomarkers in the field of oncology, along with some proposed strategies to accelerate their validation and translation to improve the care of patients with neoplasms. The inherent complexity underlying neoplasms combined with the requirement of developing well-designed biomarker discovery processes based on omics technologies present a challenge for the effective development of biomarkers that may be useful in guiding therapies, addressing disease risks, and predicting clinical outcomes. PMID: 29382490 [PubMed - in process]

Related Articles Synchronous characterization of carbohydrates and ginsenosides yields deeper insights into the processing chemistry of ginseng. J Pharm Biomed Anal. 2017 Oct 25;145:59-70 Authors: Zhou SS, Xu J, Kong M, Yip KM, Xu JD, Shen H, Zhao ZZ, Li SL, Chen HB Abstract Carbohydrates and ginsenosides in ginseng are biologically interrelated. Their synchronous analysis is therefore essential in chemical research on ginseng to characterize its "holistic" quality. Here we investigated the processing chemistry of red ginseng (RG), a ginseng product processed by water-steaming, for which both carbohydrates and ginsenosides were qualitatively and quantitatively determined through multiple analytical techniques. Results revealed that the steam-processing not only qualitatively and quantitatively altered the ginsenosides but also affected the polymeric carbohydrates via changing their physiochemical parameters, i.e. water-solubility, molecular size, types and ratios of constituent monosaccharides. Potential mechanisms involved in the transformation of ginseng chemicals are proposed and discussed, including hydrolysis (deglycosylation, demalonylation, deacetylation), dehydration, polymerization, volatilization, reduction and the Maillard reaction. The study strengthens the research on the processing chemistry of RG, and therefore should be helpful for elucidating the scientific basis of RG preparation and application. PMID: 28651108 [PubMed - indexed for MEDLINE]

Metabolomic response to coffee consumption: Application to a three-stage clinical trial. J Intern Med. 2018 Jan 30;: Authors: Cornelis MC, Erlund I, Michelotti GA, Herder C, Westerhuis JA, Tuomilehto J Abstract BACKGROUND: Coffee is widely consumed and contains many bioactive compounds, any of which may impact pathways related to disease development. OBJECTIVE: To identify individual metabolite changes in response to coffee. METHODS: We profiled the metabolome of fasting serum samples collected from a previously reported single blinded, 3-stage clinical trial. 47 habitual coffee consumers refrained from drinking coffee for 1 month, consumed 4 cups of coffee/d in the second month and 8 cups/d in the third month. Samples collected after each coffee stage were subject to nontargeted metabolomic profiling using UPLC-ESI-MS/MS. 733 metabolites were included for univariate and multivariate analysis. RESULTS: A total of 115 metabolites were significantly associated with coffee intake (P<0.05 and Q<0.05). 82 were of known identity and mapped to one of 33 pre-defined biological pathways. We observed a significant enrichment of metabolite members of 5 pathways (P<0.05): (1) xanthine metabolism- includes caffeine metabolites, (2) benzoate metabolism- reflects polyphenol metabolite products of gut-microbiota metabolism, (3) steroid- novel but may reflect phytosterol content of coffee, (4) fatty acid metabolism (acyl choline)- novel link to coffee and (5) endocannabinoid- novel link to coffee. CONCLUSIONS: The novel metabolites and candidate pathways we have identified may provide new insight to the mechanisms by which coffee may be exerting its health effects. This article is protected by copyright. All rights reserved. PMID: 29381822 [PubMed - as supplied by publisher]

Tests for comparison of multiple endpoints with application to omics data. Stat Appl Genet Mol Biol. 2018 Jan 30;: Authors: Marozzi M Abstract In biomedical research, multiple endpoints are commonly analyzed in "omics" fields like genomics, proteomics and metabolomics. Traditional methods designed for low-dimensional data either perform poorly or are not applicable when analyzing high-dimensional data whose dimension is generally similar to, or even much larger than, the number of subjects. The complex biochemical interplay between hundreds (or thousands) of endpoints is reflected by complex dependence relations. The aim of the paper is to propose tests that are very suitable for analyzing omics data because they do not require the normality assumption, are powerful also for small sample sizes, in the presence of complex dependence relations among endpoints, and when the number of endpoints is much larger than the number of subjects. Unbiasedness and consistency of the tests are proved and their size and power are assessed numerically. It is shown that the proposed approach based on the nonparametric combination of dependent interpoint distance tests is very effective. Applications to genomics and metabolomics are discussed. PMID: 29381476 [PubMed - as supplied by publisher]