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Metabolomics of Complex Traits II

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A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 32325

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Prof. Dr. Guangju Zhai

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Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL‎ A1B 3V6, Canada
Interests: population-based studies; genomics; metabolomics; biomarker discovery; musculoskeletal diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolomics is a relatively young member of the -omics family, which uses state-of-the-art analytical chemistry techniques and advanced computational methods to comprehensively characterize small molecules (metabolites) in biological fluids and tissues. Metabolites represent both the downstream output of the genome and the upstream input from the environment and are directly linked to the cellular function and phenotypes. The study of metabolites not only enables the identification of disease biomarkers but also provides unique insights into the fundamental causes of disease. Recent advances in metabolomics technologies result in a growing number of applications in biomedical research of complex traits, and such applications have already identified a number of unexpected chemical causes or metabolic pathways for several important complex diseases, including atherosclerosis, diabetes, cancer, and osteoarthritis. In this Special issue, we seek both review articles and original research with a focus on studies of metabolomics in complex diseases and traits, which will provide all readers with an overview of the application of metabolomics in complex disease and summarize the most recent new knowledge and advances in the field.

Prof. Guangju Zhai
Guest Editor

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Keywords

Metabolomics (MS-based and NMR-based)
Targeted and Untargeted Metabolomics
Biomarker Discovery
Complex Diseases and Traits
Pharmacometabolomics
Precision Medicine

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Published Papers (9 papers)

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Research

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17 pages, 2103 KiB

Open AccessArticle

Trans- and Multigenerational Maternal Social Isolation Stress Programs the Blood Plasma Metabolome in the F3 Generation

by Joshua P. Heynen, Eric J. Paxman, Prachi Sanghavi, J. Keiko McCreary, Tony Montina and Gerlinde A. S. Metz

Metabolites 2022, 12(7), 572; https://doi.org/10.3390/metabo12070572 - 22 Jun 2022

Cited by 4 | Viewed by 2155

Abstract

Metabolic risk factors are among the most common causes of noncommunicable diseases, and stress critically contributes to metabolic risk. In particular, social isolation during pregnancy may represent a salient stressor that affects offspring metabolic health, with potentially adverse consequences for future generations. Here, we used proton nuclear magnetic resonance (¹H NMR) spectroscopy to analyze the blood plasma metabolomes of the third filial (F3) generation of rats born to lineages that experienced either transgenerational or multigenerational maternal social isolation stress. We show that maternal social isolation induces distinct and robust metabolic profiles in the blood plasma of adult F3 offspring, which are characterized by critical switches in energy metabolism, such as upregulated formate and creatine phosphate metabolisms and downregulated glucose metabolism. Both trans- and multigenerational stress altered plasma metabolomic profiles in adult offspring when compared to controls. Social isolation stress increasingly affected pathways involved in energy metabolism and protein biosynthesis, particularly in branched-chain amino acid synthesis, the tricarboxylic acid cycle (lactate, citrate), muscle performance (alanine, creatine phosphate), and immunoregulation (serine, threonine). Levels of creatine phosphate, leucine, and isoleucine were associated with changes in anxiety-like behaviours in open field exploration. The findings reveal the metabolic underpinnings of epigenetically heritable diseases and suggest that even remote maternal social stress may become a risk factor for metabolic diseases, such as diabetes, and adverse mental health outcomes. Metabolomic signatures of transgenerational stress may aid in the risk prediction and early diagnosis of non-communicable diseases in precision medicine approaches. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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14 pages, 894 KiB

Open AccessArticle

Metabolomic Associations of Asthma in the Hispanic Community Health Study/Study of Latinos

by Yura Lee, Han Chen, Wei Chen, Qibin Qi, Majid Afshar, Jianwen Cai, Martha L. Daviglus, Bharat Thyagarajan, Kari E. North, Stephanie J. London, Eric Boerwinkle, Juan C. Celedón, Robert C. Kaplan and Bing Yu

Metabolites 2022, 12(4), 359; https://doi.org/10.3390/metabo12040359 - 16 Apr 2022

Cited by 2 | Viewed by 3152

Abstract

Asthma disproportionally affects Hispanic and/or Latino backgrounds; however, the relation between circulating metabolites and asthma remains unclear. We conducted a cross-sectional study associating 640 individual serum metabolites, as well as twelve metabolite modules, with asthma in 3347 Hispanic/Latino background participants (514 asthmatics, 15.36%) from the Hispanic/Latino Community Health Study/Study of Latinos. Using survey logistic regression, per standard deviation (SD) increase in 1-arachidonoyl-GPA (20:4) was significantly associated with 32% high odds of asthma after accounting for clinical risk factors (p = 6.27 × 10⁻⁵), and per SD of the green module, constructed using weighted gene co-expression network, was suggestively associated with 25% high odds of asthma (p = 0.006). In the stratified analyses by sex and Hispanic and/or Latino backgrounds, the effect of 1-arachidonoyl-GPA (20:4) and the green module was predominantly observed in women (OR = 1.24 and 1.37, p < 0.001) and people of Cuban and Puerto-Rican backgrounds (OR = 1.25 and 1.27, p < 0.01). Mutations in Fatty Acid Desaturase 2 (FADS2) affected the levels of 1-arachidonoyl-GPA (20:4), and Mendelian Randomization analyses revealed that high genetically regulated 1-arachidonoyl-GPA (20:4) levels were associated with increased odds of asthma (p < 0.001). The findings reinforce a molecular basis for asthma etiology, and the potential causal effect of 1-arachidonoyl-GPA (20:4) on asthma provides an opportunity for future intervention. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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16 pages, 4312 KiB

Open AccessArticle

Restricting Branched-Chain Amino Acids within a High-Fat Diet Prevents Obesity

by Ming Liu, Yiheng Huang, Hongwei Zhang, Dawn Aitken, Michael C. Nevitt, Jason S. Rockel, Jean-Pierre Pelletier, Cora E. Lewis, James Torner, Yoga Raja Rampersaud, Anthony V. Perruccio, Nizar N. Mahomed, Andrew Furey, Edward W. Randell, Proton Rahman, Guang Sun, Johanne Martel-Pelletier, Mohit Kapoor, Graeme Jones, David Felson, Dake Qi and Guangju Zhai Show full author list Hide full author list

Metabolites 2022, 12(4), 334; https://doi.org/10.3390/metabo12040334 - 7 Apr 2022

Cited by 19 | Viewed by 4659

Abstract

Obesity is a global pandemic, but there is yet no effective measure to control it. Recent metabolomics studies have identified a signature of altered amino acid profiles to be associated with obesity, but it is unclear whether these findings have actionable clinical potential. The aims of this study were to reveal the metabolic alterations of obesity and to explore potential strategies to mitigate obesity. We performed targeted metabolomic profiling of the plasma/serum samples collected from six independent cohorts and conducted an individual data meta-analysis of metabolomics for body mass index (BMI) and obesity. Based on the findings, we hypothesized that restriction of branched-chain amino acids (BCAAs), phenylalanine, or tryptophan may prevent obesity and tested our hypothesis in a dietary restriction trial with eight groups of 4-week-old male C57BL/6J mice (n = 5/group) on eight different types of diets, respectively, for 16 weeks. A total of 3397 individuals were included in the meta-analysis. The mean BMI was 30.7 ± 6.1 kg/m², and 49% of participants were obese. Fifty-eight metabolites were associated with BMI and obesity (all p ≤ 2.58 × 10⁻⁴), linked to alterations of the BCAA, phenylalanine, tryptophan, and phospholipid metabolic pathways. The restriction of BCAAs within a high-fat diet (HFD) maintained the mice’s weight, fat and lean volume, subcutaneous and visceral adipose tissue weight, and serum glucose and insulin at levels similar to those in the standard chow group, and prevented obesity, adipocyte hypertrophy, adipose inflammation, and insulin resistance induced by HFD. Our data suggest that four metabolic pathways, BCAA, phenylalanine, tryptophan, and phospholipid metabolic pathways, are altered in obesity and restriction of BCAAs within a HFD can prevent the development of obesity and insulin resistance in mice, providing a promising strategy to potentially mitigate diet-induced obesity. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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11 pages, 902 KiB

Open AccessArticle

Lipidomic Profiling Identifies Serum Lipids Associated with Persistent Multisite Musculoskeletal Pain

by Canchen Ma, Ming Liu, Jing Tian, Guangju Zhai, Flavia Cicuttini, Yvette L. Schooneveldt, Peter J. Meikle, Graeme Jones and Feng Pan

Metabolites 2022, 12(3), 206; https://doi.org/10.3390/metabo12030206 - 25 Feb 2022

Cited by 3 | Viewed by 2873

Abstract

Lipid mediators have been suggested to have a role in pain sensitivity and response; however, longitudinal data on lipid metabolites and persistent multisite musculoskeletal pain (MSMP) are lacking. This study was to identify lipid metabolic markers for persistent MSMP. Lipidomic profiling of 807 lipid species was performed on serum samples of 536 participants from a cohort study. MSMP was measured by a questionnaire and defined as painful sites ≥4. Persistent MSMP was defined as having MSMP at every visit. Logistic regression was used with adjustment for potential confounders. The Benjamini–Hochberg method was used to control for multiple testing. A total of 530 samples with 807 lipid metabolites passed quality control. Mean age at baseline was 61.54 ± 6.57 years and 50% were females. In total, 112 (21%) of the participants had persistent MSMP. Persistent MSMP was significantly associated with lower levels of monohexosylceramide (HexCer)(d18:1/22:0 and d18:1/24:0), acylcarnitine (AC)(26:0) and lysophosphatidylcholine (LPC)(18:1 [sn1], 18:2 [sn1], 18:2 [sn2], and 15-MHDA[sn1] [104_sn1]) after controlling for multiple testing. After adjustment for age, sex, body mass index, comorbidities, and physical activity, HexCer(d18:1/22:0 and d18:1/24:0) and LPC(15-MHDA [sn1] [104_sn1]) were significantly associated with persistent MSMP [Odds Ratio (OR) ranging from 0.25–0.36]. Two lipid classes—HexCer and LPC—were negatively associated with persistent MSMP after adjustment for covariates (OR = 0.22 and 0.27, respectively). This study identified three novel lipid signatures of persistent MSMP, suggesting that lipid metabolism is involved in the pathogenesis of persistent pain. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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17 pages, 2436 KiB

Open AccessArticle

Diethyl Succinate Modulates Microglial Polarization and Activation by Reducing Mitochondrial Fission and Cellular ROS

by Lixiang Wang, Yanli Zhang, Magdalena Kiprowska, Yuqi Guo, Ken Yamamoto and Xin Li

Metabolites 2021, 11(12), 854; https://doi.org/10.3390/metabo11120854 - 8 Dec 2021

Cited by 8 | Viewed by 2926

Abstract

Succinate is a metabolite in the tricarboxylic acid cycle (TCA) which plays a central role in mitochondrial activity. Excess succinate is known to be transported out of the cytosol, where it activates a succinate receptor (SUCNR1) to enhance inflammation through macrophages in various contexts. In addition, the intracellular role of succinate beyond an intermediate metabolite and prior to its extracellular release is also important to the polarization of macrophages. However, the role of succinate in microglial cells has not been characterized. Lipopolysaccharide (LPS) stimulates the elevation of intracellular succinate levels. To reveal the function of intracellular succinate associated with LPS-stimulated inflammatory response in microglial cells, we assessed the levels of ROS, cytokine production and mitochondrial fission in the primary microglia pretreated with cell-permeable diethyl succinate mimicking increased intracellular succinate. Our results suggest that elevated intracellular succinate exerts a protective role in the primary microglia by preventing their conversion into the pro-inflammatory M1 phenotype induced by LPS. This protective effect is SUCNR1-independent and mediated by reduced mitochondrial fission and cellular ROS production. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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16 pages, 790 KiB

Open AccessArticle

Amino Acid and Phospholipid Metabolism as an Indicator of Inflammation and Subtle Cardiomyopathy in Patients with Marfan Syndrome

by Lisa Bartenbach, Thomas Karall, Jakob Koch, Markus Andreas Keller, Herbert Oberacher, Sabine Scholl-Bürgi, Daniela Karall, Gregor Oemer, Daniela Baumgartner, Katharina Meinel, Safwat Aly, Irena Odri-Komazec, Ralf Geiger and Miriam Michel

Metabolites 2021, 11(12), 805; https://doi.org/10.3390/metabo11120805 - 27 Nov 2021

Cited by 5 | Viewed by 2949

Abstract

Patients with Marfan syndrome (MFS) have an increased risk of aortic aneurysm formation, dissection and development of a subtle cardiomyopathy. We analyzed amino acid and lipid metabolic pathways in MFS patients, seeking biomarker patterns as potential monitoring tools of cardiovascular risk with deterioration of myocardial function. We assessed myocardial function in 24 adult MFS patients and compared traditional laboratory values and mass spectrometry-based amino acid, phospholipid and acylcarnitine metabolomes in patients with those in healthy controls. Analytes for which values differed between patients and controls were subjected to regression analysis. A high proportion of patients had signs of impaired diastolic function and elevated serum levels of NT-proBNP. Patients had lower serum levels of taurine, histidine and PCaeC42:3 than controls. The evidence of diastolic dysfunction, aortic root dimensions and history of aortic root surgery correlated with NT-proBNP and taurine levels. Alterations in serum levels of metabolism derived analytes link MFS pathophysiology with inflammation, oxidative stress and incipient cardiomyopathy. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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16 pages, 1471 KiB

Open AccessArticle

Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation

by Nancy McBride, Paul Yousefi, Ulla Sovio, Kurt Taylor, Yassaman Vafai, Tiffany Yang, Bo Hou, Matthew Suderman, Caroline Relton, Gordon C. S. Smith and Deborah A. Lawlor

Metabolites 2021, 11(8), 530; https://doi.org/10.3390/metabo11080530 - 10 Aug 2021

Cited by 9 | Viewed by 4658

Abstract

Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26–28 weeks’ gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models’ area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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13 pages, 930 KiB

Open AccessArticle

Lipoprotein Proteomics and Aortic Valve Transcriptomics Identify Biological Pathways Linking Lipoprotein(a) Levels to Aortic Stenosis

by Raphaëlle Bourgeois, Jérôme Bourgault, Audrey-Anne Despres, Nicolas Perrot, Jakie Guertin, Arnaud Girard, Patricia L. Mitchell, Clarisse Gotti, Sylvie Bourassa, Corey A. Scipione, Nathalie Gaudreault, Michael B. Boffa, Marlys L. Koschinsky, Philippe Pibarot, Arnaud Droit, Sébastien Thériault, Patrick Mathieu, Yohan Bossé and Benoit J. Arsenault

Metabolites 2021, 11(7), 459; https://doi.org/10.3390/metabo11070459 - 16 Jul 2021

Cited by 16 | Viewed by 4344

Abstract

Lipoprotein(a) (Lp(a)) is one of the most important risk factors for the development of calcific aortic valve stenosis (CAVS). However, the mechanisms through which Lp(a) causes CAVS are currently unknown. Our objectives were to characterize the Lp(a) proteome and to identify proteins that may be differentially associated with Lp(a) in patients with versus without CAVS. Our second objective was to identify genes that may be differentially regulated by exposure to high versus low Lp(a) levels in explanted aortic valves from patients with CAVS. We isolated Lp(a) from the blood of 21 patients with CAVS and 22 volunteers and performed untargeted label-free analysis of the Lp(a) proteome. We also investigated the transcriptomic signature of calcified aortic valves from patients who underwent aortic valve replacement with high versus low Lp(a) levels (n = 118). Proteins involved in the protein activation cascade, platelet degranulation, leukocyte migration, and response to wounding may be associated with Lp(a) depending on CAVS status. The transcriptomic analysis identified genes involved in cardiac aging, chondrocyte development, and inflammation as potentially influenced by Lp(a). Our multi-omic analyses identified biological pathways through which Lp(a) may cause CAVS, as well as key molecular events that could be triggered by Lp(a) in CAVS development. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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Review

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21 pages, 2052 KiB

Open AccessReview

Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

by Lauren Katz, Alessandra Tata, Michael Woolman and Arash Zarrine-Afsar

Metabolites 2021, 11(10), 660; https://doi.org/10.3390/metabo11100660 - 28 Sep 2021

Cited by 14 | Viewed by 2960

Abstract

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies. Full article

(This article belongs to the Special Issue Metabolomics of Complex Traits II)

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