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Zhu Y, Li L, Jin X, Li Z, Wang C, Teng L, Li Y, Zhang Y, Wang D. Structure characterisation of polysaccharides purified from Boletus aereus Bull. and its improvement on AD-like behaviours via reliving neuroinflammation in APP/PS1 mice. Int J Biol Macromol 2024; 258:128819. [PMID: 38104691 DOI: 10.1016/j.ijbiomac.2023.128819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
The water-soluble neutral polysaccharide BEP2, with a molecular weight of 26.65 kDa, was isolated from the aqueous extract obtained from the fruiting bodies of Boletus aereus Bull. BEP2 primarily comprises Gal, with specific site substitutions speculated at partial positions, such as the substitution of -OCH3 at position H-3 or the branch at position C-2 including α-L-Fucp-(1→, α-D-Manp-(1 → and α-D-Manp-(1 → 3)-α-L-Fucp-(1 → 6)-β-D-Glcp-(1→. Treatment with BEP2 significantly enhanced learning, memory, and cognitive function, while concurrently reducing the accumulation of β-amyloid and suppressing neuroinflammation within the brains of APP/PS1 mice. Based on the results of biochemical detection, gut microbiota analysis, and metabolomic profiling, we found that BEP2 significantly upregulated the abundance of two bacterial families while downregulation that of seven bacterial families within the intestinal ecosystem. Notably, the abundance of the S24-7 family was significantly increased. Treatment with BEP2 upregulated five metabolites, while downregulating three metabolites, including norepinephrine. Additionally, BEP2 decreased the levels of interleukin (IL)-1β and IL-6, regulated the activities of microglial cells and astrocytes and increased the levels of the chemokine fractalkine (CX3CL1) and its receptor on microglia (CX3CR1), as well as that of transforming growth factor (TGF)-β1. These findings confirmed the suppressive effects of BEP2 on neuroinflammation.
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Affiliation(s)
- Yanfeng Zhu
- School of Life Sciences, Jilin University, Changchun 130012, China.
| | - Lanzhou Li
- Engineering Research Center of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China.
| | - Xinghui Jin
- School of Life Sciences, Jilin University, Changchun 130012, China.
| | - Zhige Li
- School of Life Sciences, Jilin University, Changchun 130012, China.
| | - Chunyue Wang
- Engineering Research Center of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China.
| | - Lirong Teng
- School of Life Sciences, Jilin University, Changchun 130012, China.
| | - Yu Li
- Engineering Research Center of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China
| | - Yongfeng Zhang
- School of Life Sciences, Jilin University, Changchun 130012, China; Engineering Research Center of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China.
| | - Di Wang
- School of Life Sciences, Jilin University, Changchun 130012, China; Engineering Research Center of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China.
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Demicheva E, Dordiuk V, Polanco Espino F, Ushenin K, Aboushanab S, Shevyrin V, Buhler A, Mukhlynina E, Solovyova O, Danilova I, Kovaleva E. Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review. Metabolites 2024; 14:54. [PMID: 38248857 PMCID: PMC10820779 DOI: 10.3390/metabo14010054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/05/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.
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Affiliation(s)
- Ekaterina Demicheva
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Vladislav Dordiuk
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
| | - Fernando Polanco Espino
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
| | - Konstantin Ushenin
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
| | - Saied Aboushanab
- Institute of Chemical Engineering, Ural Federal University, Ekaterinburg 620002, Russia; (S.A.); (V.S.); (E.K.)
| | - Vadim Shevyrin
- Institute of Chemical Engineering, Ural Federal University, Ekaterinburg 620002, Russia; (S.A.); (V.S.); (E.K.)
| | - Aleksey Buhler
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
| | - Elena Mukhlynina
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Olga Solovyova
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Irina Danilova
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg 620075, Russia; (V.D.); (F.P.E.); (K.U.); (A.B.); (E.M.); (O.S.); (I.D.)
- Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg 620049, Russia
| | - Elena Kovaleva
- Institute of Chemical Engineering, Ural Federal University, Ekaterinburg 620002, Russia; (S.A.); (V.S.); (E.K.)
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Menezes A, Peixoto M, Silva M, Costa-Bartuli E, Oliveira CL, Walter-Nuno AB, Kistenmacker NDC, Pereira J, Ramos I, Paiva-Silva GO, Atella GC, Zancan P, Sola-Penna M, Gomes FM. Western diet consumption by host vertebrate promotes altered gene expression on Aedes aegypti reducing its lifespan and increasing fertility following blood feeding. Parasit Vectors 2024; 17:12. [PMID: 38184590 PMCID: PMC10770904 DOI: 10.1186/s13071-023-06095-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/12/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND The high prevalence of metabolic syndrome in low- and middle-income countries is linked to an increase in Western diet consumption, characterized by a high intake of processed foods, which impacts the levels of blood sugar and lipids, hormones, and cytokines. Hematophagous insect vectors, such as the yellow fever mosquito Aedes aegypti, rely on blood meals for reproduction and development and are therefore exposed to the components of blood plasma. However, the impact of the alteration of blood composition due to malnutrition and metabolic conditions on mosquito biology remains understudied. METHODS In this study, we investigated the impact of whole-blood alterations resulting from a Western-type diet on the biology of Ae. aegypti. We kept C57Bl6/J mice on a high-fat, high-sucrose (HFHS) diet for 20 weeks and followed biological parameters, including plasma insulin and lipid levels, insulin tolerance, and weight gain, to validate the development of metabolic syndrome. We further allowed Ae. aegypti mosquitoes to feed on mice and tracked how altered host blood composition modulated parameters of vector capacity. RESULTS Our findings identified that HFHS-fed mice resulted in reduced mosquito longevity and increased fecundity upon mosquito feeding, which correlated with alteration in the gene expression profile of nutrient sensing and physiological and metabolic markers as studied up to several days after blood ingestion. CONCLUSIONS Our study provides new insights into the overall effect of alterations of blood components on mosquito biology and its implications for the transmission of infectious diseases in conditions where the frequency of Western diet-induced metabolic syndromes is becoming more frequent. These findings highlight the importance of addressing metabolic health to further understand the spread of mosquito-borne illnesses in endemic areas.
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Affiliation(s)
- Alexandre Menezes
- Laboratório de Ultraestrutura Celular Hertha Meyer, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marilia Peixoto
- Laboratório de Ultraestrutura Celular Hertha Meyer, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Melissa Silva
- Laboratório de Ultraestrutura Celular Hertha Meyer, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Emylle Costa-Bartuli
- The Metabolizsm' Group, Departamento de Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Cinara Lima Oliveira
- Laboratório de Bioquímica de Lipídeos e Lipoproteínas, Instituto de Bioquímica Médica Leopoldo De Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ana Beatriz Walter-Nuno
- Laboratório de Bioquímica e Biologia Molecular de Artrópodes Hematófagos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Rio de Janeiro, Brazil
| | - Nathan da Cruz Kistenmacker
- Laboratório de Ultraestrutura Celular Hertha Meyer, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jessica Pereira
- Laboratorio de Ovogênese Molecular de Insetos Vetores, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Isabela Ramos
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Rio de Janeiro, Brazil
- Laboratorio de Ovogênese Molecular de Insetos Vetores, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gabriela O Paiva-Silva
- Laboratório de Bioquímica e Biologia Molecular de Artrópodes Hematófagos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratorio de Ovogênese Molecular de Insetos Vetores, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Geórgia C Atella
- Laboratório de Bioquímica de Lipídeos e Lipoproteínas, Instituto de Bioquímica Médica Leopoldo De Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratorio de Ovogênese Molecular de Insetos Vetores, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Patricia Zancan
- The Metabolizsm' Group, Departamento de Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Mauro Sola-Penna
- The Metabolizsm' Group, Departamento de Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fabio M Gomes
- Laboratório de Ultraestrutura Celular Hertha Meyer, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
- Laboratorio de Ovogênese Molecular de Insetos Vetores, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
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Hou X, Zhang R, Yang M, Niu N, Zong W, Yang L, Li H, Hou R, Wang X, Wang L, Liu X, Shi L, Zhao F, Wang L, Zhang L. Characteristics of Transcriptome and Metabolome Concerning Intramuscular Fat Content in Beijing Black Pigs. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:15874-15883. [PMID: 37847170 DOI: 10.1021/acs.jafc.3c02669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
To study the characteristics of genes and metabolites related to intramuscular fat (IMF) content with less influence by breed background and individual differences, the skeletal muscle samples from 40 Beijing black pigs with either high or low IMF content were used to perform transcriptome and metabolome analyses. About 99 genes (twofold-change) were differentially expressed. Up-regulated genes in the high IMF pigs were mainly related to fat metabolism. The key genes in charge of IMF deposition are ADIPOQ, CIDEC, CYP4B1, DGAT2, LEP, OPRL1, PLIN1, SCD, and THRSP. KLHL40, TRAFD1, and HSPA6 were novel candidate genes for the IMF trait due to their high abundances. In the low IMF pigs, the differentially expressed genes involved in virus resistance were up-regulated. About 16 and 18 differential metabolites (1.5 fold-change) were obtained in the positive and negative modes, respectively. Pigs with low IMF had weaker fatty acid oxidation due to the down-regulation of various carnitines. Differentially expressed genes were more important in determining IMF deposition than differential metabolites because relatively few differential metabolites were obtained, and they were merely the products under the physiological status of diverged IMF content. This study provided valuable information for further studies on IMF deposition.
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Affiliation(s)
- Xinhua Hou
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Run Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Man Yang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Naiqi Niu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Wencheng Zong
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Liyu Yang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Huihui Li
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Renda Hou
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Xiaoqing Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Ligang Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Xin Liu
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Lijun Shi
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Fuping Zhao
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Lixian Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Longchao Zhang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
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Huang Y, Kong Y, Li B, Zhao C, Loor JJ, Tan P, Yuan Y, Zeng F, Zhu X, Qi S, Zhao B, Wang J. Effects of perinatal stress on the metabolites and lipids in plasma of dairy goats. STRESS BIOLOGY 2023; 3:11. [PMID: 37676623 PMCID: PMC10441998 DOI: 10.1007/s44154-023-00088-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/13/2023] [Indexed: 09/08/2023]
Abstract
Dairy goats experience metabolic stress during the peripartal period, and their ability to navigate this stage of lactation is related to the occurrence and development of metabolic diseases. Unlike dairy cows, there is a lack of comprehensive analysis of changes in the plasma profiles of peripartal dairy goats, particularly using high-throughput techniques. A subset of 9 clinically-healthy dairy goats were used from a cohort of 96 primiparous Guanzhong dairy goats (BCS, 2.75 ± 0.15). Blood samples were collected at seven time points around parturition (d 21, 14, 7 before parturition, the day of kidding, and d 7, 14, 21 postpartum), were analyzed using untargeted metabolomics and targeted lipidomics. The orthogonal partial least squares discriminant analysis model revealed a total of 31 differential metabolites including p-cresol sulfate, pyruvic acid, cholic acid, and oxoglutaric acid. The pathway enrichment analysis identified phenylalanine metabolism, aminoacyl-tRNA biosynthesis, and citrate cycle as the top three significantly-altered pathways. The Limma package identified a total of 123 differentially expressed lipids. Phosphatidylserine (PS), free fatty acids (FFA), and acylcarnitines (ACs) were significantly increased on the day of kidding, while diacylglycerols (DAG) and triacylglycerols (TAG) decreased. Ceramides (Cer) and lyso-phosphatidylinositols (LPI) were significantly increased during postpartum period, while PS, FFA, and ACs decreased postpartum and gradually returned to antepartum levels. Individual species of FFA and phosphatidylcholines (PC) were segregated based on the differences in the saturation and length of the carbon chain. Overall, this work generated the largest repository of the plasma lipidome and metabolome in dairy goats across the peripartal period, which contributed to our understanding of the multifaceted adaptations of transition dairy goats.
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Affiliation(s)
- Yan Huang
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Yezi Kong
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Bowen Li
- LipidALL Technologies Company Limited, Changzhou, 213022, Jiangsu, China
| | - Chenxu Zhao
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Juan J Loor
- Department of Animal Sciences, Division of Nutritional Sciences, University of Illinois, Urbana, IL, 61801, USA
| | - Panpan Tan
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Yang Yuan
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Fangyuan Zeng
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Xiaoyan Zhu
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Simeng Qi
- LipidALL Technologies Company Limited, Changzhou, 213022, Jiangsu, China
| | - Baoyu Zhao
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Jianguo Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling, 712100, Shaanxi, China.
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Zhang L, Guo K, Tian Q, Ye J, Ding Z, Zhou Q, Li X, Zhou Z, Yang L. Serum Metabolomics Reveals a Potential Benefit of Methionine in Type 1 Diabetes Patients with Poor Glycemic Control and High Glycemic Variability. Nutrients 2023; 15:nu15030518. [PMID: 36771224 PMCID: PMC9921163 DOI: 10.3390/nu15030518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/12/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023] Open
Abstract
Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Lin Yang
- Correspondence: ; Tel.: +86-731-8529-2154
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Zhang B, Wan Y, Zhou X, Zhang H, Zhao H, Ma L, Dong X, Yan M, Zhao T, Li P. Characteristics of Serum Metabolites and Gut Microbiota in Diabetic Kidney Disease. Front Pharmacol 2022; 13:872988. [PMID: 35548353 PMCID: PMC9084235 DOI: 10.3389/fphar.2022.872988] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/18/2022] [Indexed: 01/11/2023] Open
Abstract
Disturbance of circulating metabolites and disorders of the gut microbiota are involved in the progression of diabetic kidney disease (DKD). However, there is limited research on the relationship between serum metabolites and gut microbiota, and their involvement in DKD. In this study, using an experimental DKD rat model induced by combining streptozotocin injection and unilateral nephrectomy, we employed untargeted metabolomics and 16S rRNA gene sequencing to explore the relationship between the metabolic profile and the structure and function of gut microbiota. Striking alterations took place in 140 serum metabolites, as well as in the composition and function of rat gut microbiota. These changes were mainly associated with carbohydrate, lipid, and amino acid metabolism. In these pathways, isomaltose, D-mannose, galactonic acid, citramalic acid, and prostaglandin B2 were significantly upregulated. 3-(2-Hydroxyethyl)indole, 3-methylindole, and indoleacrylic acid were downregulated and were the critical metabolites in the DKD model. Furthermore, the levels of these three indoles were restored after treatment with the traditional Chinese herbal medicine Tangshen Formula. At the genera level, g_Eubacterium_nodatum_group, g_Lactobacillus, and g_Faecalibaculum were most involved in metabolic disorders in the progression of DKD. Notably, the circulating lipid metabolites had a strong relationship with DKD-related parameters and were especially negatively related to the mesangial matrix area. Serum lipid indices (TG and TC) and UACR were directly associated with certain microbial genera. In conclusion, the present research verified the anomalous circulating metabolites and gut microbiota in DKD progression. We also identified the potential metabolic and microbial targets for the treatment of DKD.
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Affiliation(s)
- Bo Zhang
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Yuzhou Wan
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Xuefeng Zhou
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Haojun Zhang
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Hailing Zhao
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Liang Ma
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Xi Dong
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Meihua Yan
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Tingting Zhao
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
| | - Ping Li
- Beijing Key Laboratory for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
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Yu S, Drton M, Promislow DEL, Shojaie A. CorDiffViz: an R package for visualizing multi-omics differential correlation networks. BMC Bioinformatics 2021; 22:486. [PMID: 34627139 PMCID: PMC8501646 DOI: 10.1186/s12859-021-04383-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 09/20/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression. RESULTS We present a new R package, CorDiffViz, that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in R, HTML and Javascript, and is available at https://github.com/sqyu/CorDiffViz . Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html . CONCLUSIONS Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.
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Affiliation(s)
- Shiqing Yu
- Department of Statistics, University of Washington, NE Stevens Way, Seattle, WA, 98195, USA.
| | - Mathias Drton
- Department of Mathematics, Technical University of Munich, Boltzmannstraße, 85748, Garching bei München, Germany
| | - Daniel E L Promislow
- Departments of Pathology and Biology, University of Washington, NE Pacific St, Seattle, WA, 98195, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, NE Pacific St, Seattle, WA, 98195, USA
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9
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Hellstern M, Ma J, Yue K, Shojaie A. netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis. PLoS Comput Biol 2021; 17:e1008979. [PMID: 34115744 PMCID: PMC8221786 DOI: 10.1371/journal.pcbi.1008979] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 06/23/2021] [Accepted: 04/18/2021] [Indexed: 01/26/2023] Open
Abstract
Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods' capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization.
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Affiliation(s)
- Michael Hellstern
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Jing Ma
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Kun Yue
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, Washington
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10
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Borrego SL, Fahrmann J, Hou J, Lin DW, Tromberg BJ, Fiehn O, Kaiser P. Lipid remodeling in response to methionine stress in MDA-MBA-468 triple-negative breast cancer cells. J Lipid Res 2021; 62:100056. [PMID: 33647277 PMCID: PMC8042402 DOI: 10.1016/j.jlr.2021.100056] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 02/12/2021] [Accepted: 02/19/2021] [Indexed: 02/08/2023] Open
Abstract
Methionine (Met) is an essential amino acid and critical precursor to the cellular methyl donor S-adenosylmethionine. Unlike nontransformed cells, cancer cells have a unique metabolic requirement for Met and are unable to proliferate in growth media where Met is replaced with its metabolic precursor, homocysteine. This metabolic vulnerability is common among cancer cells regardless of tissue origin and is known as "methionine dependence", "methionine stress sensitivity", or the Hoffman effect. The response of lipids to Met stress, however, is not well-understood. Using mass spectroscopy, label-free vibrational microscopy, and next-generation sequencing, we characterize the response of lipids to Met stress in the triple-negative breast cancer cell line MDA-MB-468 and its Met stress insensitive derivative, MDA-MB-468res-R8. Lipidome analysis identified an immediate, global decrease in lipid abundances with the exception of triglycerides and an increase in lipid droplets in response to Met stress specifically in MDA-MB-468 cells. Furthermore, specific gene expression changes were observed as a secondary response to Met stress in MDA-MB-468, resulting in a downregulation of fatty acid metabolic genes and an upregulation of genes in the unfolded protein response pathway. We conclude that the extensive changes in lipid abundance during Met stress is a direct consequence of the modified metabolic profile previously described in Met stress-sensitive cells. The changes in lipid abundance likely results in changes in membrane composition inducing the unfolded protein response we observe.
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Affiliation(s)
- Stacey L Borrego
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, USA
| | - Johannes Fahrmann
- West Coast Metabolomics Center, University of California, Davis, Davis, CA, USA; Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jue Hou
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Da-Wei Lin
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, USA
| | - Bruce J Tromberg
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA; National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, Davis, CA, USA
| | - Peter Kaiser
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, USA.
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11
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Balzano-Nogueira L, Ramirez R, Zamkovaya T, Dailey J, Ardissone AN, Chamala S, Serrano-Quílez J, Rubio T, Haller MJ, Concannon P, Atkinson MA, Schatz DA, Triplett EW, Conesa A. Integrative analyses of TEDDY Omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes. Genome Biol 2021; 22:39. [PMID: 33478573 PMCID: PMC7818777 DOI: 10.1186/s13059-021-02262-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 01/04/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and understand progression over time. RESULTS We identify a multi-omics signature that was predictive of islet autoimmunity (IA) as early as 1 year before seroconversion. At this time, abnormalities in lipid metabolism, decreased capacity for nutrient absorption, and intracellular ROS accumulation are detected in children progressing towards IA. Additionally, extracellular matrix remodeling, inflammation, cytotoxicity, angiogenesis, and increased activity of antigen-presenting cells are observed, which may contribute to beta cell destruction. Our results indicate that altered molecular homeostasis is present in IA-developing children months before the actual detection of islet autoantibodies, which opens an interesting window of opportunity for therapeutic intervention. CONCLUSIONS The approach employed herein for assessment of the TEDDY cohort showcases the utilization of multi-omics data for the modeling of complex, multifactorial diseases, like T1D.
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Affiliation(s)
- Leandro Balzano-Nogueira
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, USA
| | - Ricardo Ramirez
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, USA
| | - Tatyana Zamkovaya
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, USA
| | - Jordan Dailey
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, USA
| | - Alexandria N Ardissone
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, USA
| | - Srikar Chamala
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Joan Serrano-Quílez
- Gene Expression and RNA Metabolism Laboratory, Instituto de Biomedicina de Valencia (CSIC), Jaume Roig, 11, 46010, Valencia, Spain
| | - Teresa Rubio
- Laboratory of Neurobiology, Prince Felipe Research Center, Valencia, Spain
| | - Michael J Haller
- Department of Pediatrics, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Patrick Concannon
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
- University of Florida Genetics Institute, Gainesville, FL, USA
| | - Mark A Atkinson
- Department of Pediatrics, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Desmond A Schatz
- Department of Pediatrics, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Eric W Triplett
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, USA
| | - Ana Conesa
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, USA.
- University of Florida Genetics Institute, Gainesville, FL, USA.
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12
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Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations. Metabolites 2020; 10:metabo10120479. [PMID: 33255384 PMCID: PMC7761243 DOI: 10.3390/metabo10120479] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.
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13
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Cas MD, Roda G, Li F, Secundo F. Functional Lipids in Autoimmune Inflammatory Diseases. Int J Mol Sci 2020; 21:E3074. [PMID: 32349258 PMCID: PMC7246500 DOI: 10.3390/ijms21093074] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 12/27/2022] Open
Abstract
Lipids are apolar small molecules known not only as components of cell membranes but also, in recent literature, as modulators of different biological functions. Herein, we focused on the bioactive lipids that can influence the immune responses and inflammatory processes regulating vascular hyperreactivity, pain, leukocyte trafficking, and clearance. In the case of excessive pro-inflammatory lipid activity, these lipids also contribute to the transition from acute to chronic inflammation. Based on their biochemical function, these lipids can be divided into different families, including eicosanoids, specialized pro-resolving mediators, lysoglycerophospholipids, sphingolipids, and endocannabinoids. These bioactive lipids are involved in all phases of the inflammatory process and the pathophysiology of different chronic autoimmune diseases such as rheumatoid arthritis, multiple sclerosis, type-1 diabetes, and systemic lupus erythematosus.
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Affiliation(s)
- Michele Dei Cas
- Department of Health Sciences, Università degli Studi di Milano, 20142 Milan, Italy
| | - Gabriella Roda
- Department of Pharmaceutical Sciences, Università degli Studi di Milano, 20133 Milan, Italy
| | - Feng Li
- State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China
| | - Francesco Secundo
- Istituto di Scienze e Tecnologie Chimiche “Giulio Natta”, Consiglio Nazionale delle Ricerche, 20131 Milan, Italy
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14
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Schlueter RJ, Al-Akwaa FM, Benny PA, Gurary A, Xie G, Jia W, Chun SJ, Chern I, Garmire LX. Prepregnant Obesity of Mothers in a Multiethnic Cohort Is Associated with Cord Blood Metabolomic Changes in Offspring. J Proteome Res 2020; 19:1361-1374. [PMID: 31975597 DOI: 10.1021/acs.jproteome.9b00319] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal prepregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using a coupled untargeted and targeted metabolomic approach from the newborn cord blood metabolomes associated with a matched maternal prepregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multiethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using an elastic net regularization-based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age, ethnicity, parity, and gravidity). We validated the model results in a subsequent set of samples (N = 30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese and normal-weight groups. Interestingly, hydroxy-3-methylbutyric acid showed significantly higher levels in cord blood from the NHPI group compared to that from Asian and Caucasian groups. In summary, significant associations were observed between maternal prepregnant obesity and offspring metabolomic alternation at birth, revealing the intergenerational impact of maternal obesity.
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Affiliation(s)
- Ryan J Schlueter
- Department of Obstetrics and Gynecology, University of Hawaii, 1319 Punahou St Ste 824, Honolulu, Hawaii 96826, United States
| | - Fadhl M Al-Akwaa
- Department of Computational Medicine and Bioinformatics, North Campus Research Complex, University of Michigan, 1600 Huron Parkway, Ann Arbor, Michigan 48105, United States
| | - Paula A Benny
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, Hawaii 96813, United States
| | - Alexandra Gurary
- John A. Burns School of Medicine, Department of Tropical Medicine, Medical Microbiology and Pharmacology, University of Hawaii, 651 Ilalo Street, Bioscience Building 320, Honolulu, Hawaii 96813, United States
| | - Guoxiang Xie
- Metabolomics Shared Resource, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, Hawaii 96813, United States
| | - Wei Jia
- Metabolomics Shared Resource, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, Hawaii 96813, United States
| | - Shaw J Chun
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, Hawaii 96813, United States
| | - Ingrid Chern
- Department of Obstetrics and Gynecology, University of Hawaii, 1319 Punahou St Ste 824, Honolulu, Hawaii 96826, United States
| | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, North Campus Research Complex, University of Michigan, 1600 Huron Parkway, Ann Arbor, Michigan 48105, United States
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15
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Wang R, Li B, Lam SM, Shui G. Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression. J Genet Genomics 2019; 47:69-83. [PMID: 32178981 DOI: 10.1016/j.jgg.2019.11.009] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/19/2019] [Accepted: 11/25/2019] [Indexed: 12/17/2022]
Abstract
Mass spectrometry (MS)-based omics technologies are now widely used to profile small molecules in multiple matrices to confer comprehensive snapshots of cellular metabolic phenotypes. The metabolomes of cells, tissues, and organisms comprise a variety of molecules including lipids, amino acids, sugars, organic acids, and so on. Metabolomics mainly focus on the hydrophilic classes, while lipidomics has emerged as an independent omics owing to the complexities of the organismal lipidomes. The potential roles of lipids and small metabolites in disease pathogenesis have been widely investigated in various human diseases, but system-level understanding is largely lacking, which could be partly attributed to the insufficiency in terms of metabolite coverage and quantitation accuracy in current analytical technologies. While scientists are continuously striving to develop high-coverage omics approaches, integration of metabolomics and lipidomics is becoming an emerging approach to mechanistic investigation. Integration of metabolome and lipidome offers a complete atlas of the metabolic landscape, enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology, facilitating the study of interconnection between lipids and other metabolites in disease progression. In this review, we summarize omics-based findings on the roles of lipids and metabolites in the pathogenesis of selected major diseases threatening public health. We also discuss the advantages of integrating lipidomics and metabolomics for in-depth understanding of molecular mechanism in disease pathogenesis.
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Affiliation(s)
- Raoxu Wang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Bowen Li
- Lipidall Technologies Company Limited, Changzhou, 213000, China
| | - Sin Man Lam
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China; Lipidall Technologies Company Limited, Changzhou, 213000, China.
| | - Guanghou Shui
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100101, China.
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16
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Ma J, Shojaie A, Michailidis G. A comparative study of topology-based pathway enrichment analysis methods. BMC Bioinformatics 2019; 20:546. [PMID: 31684881 PMCID: PMC6829999 DOI: 10.1186/s12859-019-3146-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 10/02/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Pathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples. RESULTS The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment. CONCLUSION The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.
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Affiliation(s)
- Jing Ma
- Texas A&M University, Department of Statistics, College Station, 77840 USA
- Fred Hutchinson Cancer Research Center, Public Health Sciences Division, Seattle, 98107 USA
| | - Ali Shojaie
- University of Washington, Department of Biostatistics, Seattle, 98105 USA
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17
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Ash JR, Kuenemann MA, Rotroff D, Motsinger-Reif A, Fourches D. Cheminformatics approach to exploring and modeling trait-associated metabolite profiles. J Cheminform 2019; 11:43. [PMID: 31236709 PMCID: PMC6591908 DOI: 10.1186/s13321-019-0366-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 06/17/2019] [Indexed: 12/17/2022] Open
Abstract
Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers. ![]()
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Affiliation(s)
- Jeremy R Ash
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Melaine A Kuenemann
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Daniel Rotroff
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Alison Motsinger-Reif
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA. .,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
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18
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Eckert MA, Coscia F, Chryplewicz A, Chang JW, Hernandez KM, Pan S, Tienda SM, Nahotko DA, Li G, Blaženović I, Lastra RR, Curtis M, Yamada SD, Perets R, McGregor SM, Andrade J, Fiehn O, Moellering RE, Mann M, Lengyel E. Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts. Nature 2019; 569:723-728. [PMID: 31043742 PMCID: PMC6690743 DOI: 10.1038/s41586-019-1173-8] [Citation(s) in RCA: 277] [Impact Index Per Article: 55.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 03/27/2019] [Indexed: 12/23/2022]
Abstract
High-grade serous carcinoma has a poor prognosis, owing primarily to its early dissemination throughout the abdominal cavity. Genomic and proteomic approaches have provided snapshots of the proteogenomics of ovarian cancer1,2, but a systematic examination of both the tumour and stromal compartments is critical in understanding ovarian cancer metastasis. Here we develop a label-free proteomic workflow to analyse as few as 5,000 formalin-fixed, paraffin-embedded cells microdissected from each compartment. The tumour proteome was stable during progression from in situ lesions to metastatic disease; however, the metastasis-associated stroma was characterized by a highly conserved proteomic signature, prominently including the methyltransferase nicotinamide N-methyltransferase (NNMT) and several of the proteins that it regulates. Stromal NNMT expression was necessary and sufficient for functional aspects of the cancer-associated fibroblast (CAF) phenotype, including the expression of CAF markers and the secretion of cytokines and oncogenic extracellular matrix. Stromal NNMT expression supported ovarian cancer migration, proliferation and in vivo growth and metastasis. Expression of NNMT in CAFs led to depletion of S-adenosyl methionine and reduction in histone methylation associated with widespread gene expression changes in the tumour stroma. This work supports the use of ultra-low-input proteomics to identify candidate drivers of disease phenotypes. NNMT is a central, metabolic regulator of CAF differentiation and cancer progression in the stroma that may be therapeutically targeted.
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Affiliation(s)
- Mark A Eckert
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Fabian Coscia
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Clinical Proteomics Group, Proteomics Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Agnieszka Chryplewicz
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Jae Won Chang
- Department of Chemistry, University of Chicago, Chicago, IL, USA
| | - Kyle M Hernandez
- Center for Research Informatics, University of Chicago, Chicago, IL, USA
| | - Shawn Pan
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Samantha M Tienda
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Dominik A Nahotko
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Gang Li
- Department of Chemistry, University of Chicago, Chicago, IL, USA
| | - Ivana Blaženović
- West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA, USA
| | - Ricardo R Lastra
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | - Marion Curtis
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - S Diane Yamada
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Ruth Perets
- Division of Oncology, Clinical Research Institute at Rambam, Rambam Health Care Campus, Haifa, Israel
| | | | - Jorge Andrade
- Center for Research Informatics, University of Chicago, Chicago, IL, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA, USA
| | | | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Clinical Proteomics Group, Proteomics Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Ernst Lengyel
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA.
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19
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Generation and quality control of lipidomics data for the alzheimer's disease neuroimaging initiative cohort. Sci Data 2018; 5:180263. [PMID: 30457571 PMCID: PMC6244184 DOI: 10.1038/sdata.2018.263] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 10/04/2018] [Indexed: 11/30/2022] Open
Abstract
Alzheimer’s disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/
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20
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La Frano MR, Fahrmann JF, Grapov D, Pedersen TL, Newman JW, Fiehn O, Underwood MA, Mestan K, Steinhorn RH, Wedgwood S. Umbilical cord blood metabolomics reveal distinct signatures of dyslipidemia prior to bronchopulmonary dysplasia and pulmonary hypertension. Am J Physiol Lung Cell Mol Physiol 2018; 315:L870-L881. [PMID: 30113229 PMCID: PMC6295510 DOI: 10.1152/ajplung.00283.2017] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 07/31/2018] [Accepted: 08/15/2018] [Indexed: 01/27/2023] Open
Abstract
Pulmonary hypertension (PH) is a common consequence of bronchopulmonary dysplasia (BPD) and remains a primary contributor to increased morbidity and mortality among preterm infants. Unfortunately, at the present time, there are no reliable early predictive markers for BPD-associated PH. Considering its health consequences, understanding in utero perturbations that lead to the development of BPD and BPD-associated PH and identifying early predictive markers is of utmost importance. As part of the discovery phase, we applied a multiplatform metabolomics approach consisting of untargeted and targeted methodologies to screen for metabolic perturbations in umbilical cord blood (UCB) plasma from preterm infants that did ( n = 21; cases) or did not ( n = 21; controls) develop subsequent PH. A total of 1,656 features were detected, of which 407 were annotated by metabolite structures. PH-associated metabolic perturbations were characterized by reductions in major choline-containing phospholipids, such as phosphatidylcholines and sphingomyelins, indicating altered lipid metabolism. The reduction in UCB abundances of major choline-containing phospholipids was confirmed in an independent validation cohort consisting of UCB plasmas from 10 cases and 10 controls matched for gestational age and BPD status. Subanalyses in the discovery cohort indicated that elevations in the oxylipins PGE1, PGE2, PGF2a, 9- and 13-HOTE, 9- and 13-HODE, and 9- and 13-KODE were positively associated with BPD presence and severity. This expansive evaluation of cord blood plasma identifies compounds reflecting dyslipidemia and suggests altered metabolite provision associated with metabolic immaturity that differentiate subjects, both by BPD severity and PH development.
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Affiliation(s)
- Michael R La Frano
- West Coast Metabolomics Center, University of California, Davis Genome Center, University of California , Davis, California
- Department of Nutrition, University of California , Davis, California
- Department of Food Science and Nutrition, California Polytechnic State University , San Luis Obispo, California
| | - Johannes F Fahrmann
- West Coast Metabolomics Center, University of California, Davis Genome Center, University of California , Davis, California
- Department of Clinical Cancer Prevention, University of Texas M. D. Anderson Cancer Center , Houston, Texas
| | | | - Theresa L Pedersen
- Obesity and Metabolism Research Unit, United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center , Davis, California
| | - John W Newman
- West Coast Metabolomics Center, University of California, Davis Genome Center, University of California , Davis, California
- Department of Nutrition, University of California , Davis, California
- Obesity and Metabolism Research Unit, United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center , Davis, California
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis Genome Center, University of California , Davis, California
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia
| | - Mark A Underwood
- Department of Pediatrics, University of California, Davis Medical Center , Sacramento, California
| | - Karen Mestan
- Department of Pediatrics, Division of Neonatology, Northwestern University Feinberg School of Medicine , Chicago, Illinois
| | - Robin H Steinhorn
- Department of Pediatrics, Children's National Medical Center, George Washington University , Washington, District of Columbia
| | - Stephen Wedgwood
- Department of Pediatrics, University of California, Davis Medical Center , Sacramento, California
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21
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Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics. Sci Rep 2018; 8:10056. [PMID: 29968805 PMCID: PMC6030047 DOI: 10.1038/s41598-018-28477-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 06/25/2018] [Indexed: 12/12/2022] Open
Abstract
The pathogenesis of ME/CFS, a disease characterized by fatigue, cognitive dysfunction, sleep disturbances, orthostatic intolerance, fever, irritable bowel syndrome (IBS), and lymphadenopathy, is poorly understood. We report biomarker discovery and topological analysis of plasma metabolomic, fecal bacterial metagenomic, and clinical data from 50 ME/CFS patients and 50 healthy controls. We confirm reports of altered plasma levels of choline, carnitine and complex lipid metabolites and demonstrate that patients with ME/CFS and IBS have increased plasma levels of ceramide. Integration of fecal metagenomic and plasma metabolomic data resulted in a stronger predictive model of ME/CFS (cross-validated AUC = 0.836) than either metagenomic (cross-validated AUC = 0.745) or metabolomic (cross-validated AUC = 0.820) analysis alone. Our findings may provide insights into the pathogenesis of ME/CFS and its subtypes and suggest pathways for the development of diagnostic and therapeutic strategies.
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22
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Basu S, Duren W, Evans CR, Burant CF, Michailidis G, Karnovsky A. Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics 2018; 33:1545-1553. [PMID: 28137712 DOI: 10.1093/bioinformatics/btx012] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Accepted: 01/11/2017] [Indexed: 02/01/2023] Open
Abstract
Motivation Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. Results Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. Availability and Implementation http://metscape.med.umich.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sumanta Basu
- Department of Statistics, University of California, Berkeley, CA, USA.,Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - William Duren
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Charles R Evans
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
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23
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Murfitt SA, Zaccone P, Wang X, Acharjee A, Sawyer Y, Koulman A, Roberts LD, Cooke A, Griffin JL. Metabolomics and Lipidomics Study of Mouse Models of Type 1 Diabetes Highlights Divergent Metabolism in Purine and Tryptophan Metabolism Prior to Disease Onset. J Proteome Res 2018; 17:946-960. [PMID: 28994599 DOI: 10.1021/acs.jproteome.7b00489] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
With the increase in incidence of type 1 diabetes (T1DM), there is an urgent need to understand the early molecular and metabolic alterations that accompany the autoimmune disease. This is not least because in murine models early intervention can prevent the development of disease. We have applied a liquid chromatography (LC-) and gas chromatography (GC-) mass spectrometry (MS) metabolomics and lipidomics analysis of blood plasma and pancreas tissue to follow the progression of disease in three models related to autoimmune diabetes: the nonobese diabetic (NOD) mouse, susceptible to the development of autoimmune diabetes, and the NOD-E (transgenic NOD mice that express the I-E heterodimer of the major histocompatibility complex II) and NOD-severe combined immunodeficiency (SCID) mouse strains, two models protected from the development of diabetes. All three analyses highlighted the metabolic differences between the NOD-SCID mouse and the other two strains, regardless of diabetic status indicating that NOD-SCID mice are poor controls for metabolic changes in NOD mice. By comparing NOD and NOD-E mice, we show the development of T1DM in NOD mice is associated with changes in lipid, purine, and tryptophan metabolism, including an increase in kynurenic acid and a decrease in lysophospholipids, metabolites previously associated with inflammation.
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Affiliation(s)
- Steven A Murfitt
- Department of Biochemistry, University of Cambridge , The Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, U.K
| | - Paola Zaccone
- Department of Pathology, University of Cambridge , Tennis Court Road, Cambridge CB2 1QP, U.K
| | - Xinzhu Wang
- Department of Biochemistry, University of Cambridge , The Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, U.K
| | - Animesh Acharjee
- Medical Research Council Human Nutrition Research, The Elsie Widdowson Laboratory , 120 Fulbourn Road, Cambridge CB1 9NL, U.K
| | - Yvonne Sawyer
- Department of Pathology, University of Cambridge , Tennis Court Road, Cambridge CB2 1QP, U.K
| | - Albert Koulman
- Medical Research Council Human Nutrition Research, The Elsie Widdowson Laboratory , 120 Fulbourn Road, Cambridge CB1 9NL, U.K
| | - Lee D Roberts
- Medical Research Council Human Nutrition Research, The Elsie Widdowson Laboratory , 120 Fulbourn Road, Cambridge CB1 9NL, U.K
| | - Anne Cooke
- Department of Pathology, University of Cambridge , Tennis Court Road, Cambridge CB2 1QP, U.K
| | - Julian Leether Griffin
- Department of Biochemistry, University of Cambridge , The Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, U.K.,Medical Research Council Human Nutrition Research, The Elsie Widdowson Laboratory , 120 Fulbourn Road, Cambridge CB1 9NL, U.K
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24
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Barupal DK, Fan S, Fiehn O. Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets. Curr Opin Biotechnol 2018; 54:1-9. [PMID: 29413745 DOI: 10.1016/j.copbio.2018.01.010] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/09/2018] [Accepted: 01/11/2018] [Indexed: 12/28/2022]
Abstract
Access to high quality metabolomics data has become a routine component for biological studies. However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases. Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text mining. Integrating these approaches into comprehensive and unbiased interpretations must carefully consider both caveats of the metabolomics dataset itself as well as the structure and properties of the biological study design. Special considerations need to be taken when adopting approaches from genomics for use in metabolomics. R and Python programming language are enabling an easier exchange of diverse tools to deploy integrated workflows. This review summarizes the key ideas and latest developments in regards to these approaches.
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Affiliation(s)
- Dinesh Kumar Barupal
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, United States
| | - Sili Fan
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, United States
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, United States; Biochemistry Department, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia.
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25
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Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics. Metabolites 2017; 7:metabo7040058. [PMID: 29137180 PMCID: PMC5746738 DOI: 10.3390/metabo7040058] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 10/24/2017] [Accepted: 11/08/2017] [Indexed: 11/28/2022] Open
Abstract
Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.
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26
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Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci Rep 2017; 7:14567. [PMID: 29109515 PMCID: PMC5673929 DOI: 10.1038/s41598-017-15231-w] [Citation(s) in RCA: 210] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 10/23/2017] [Indexed: 12/28/2022] Open
Abstract
Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. We demonstrate ChemRICH’s efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at www.chemrich.fiehnlab.ucdavis.edu
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27
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Omega-6 and omega-3 oxylipins are implicated in soybean oil-induced obesity in mice. Sci Rep 2017; 7:12488. [PMID: 28970503 PMCID: PMC5624939 DOI: 10.1038/s41598-017-12624-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 09/14/2017] [Indexed: 12/11/2022] Open
Abstract
Soybean oil consumption is increasing worldwide and parallels a rise in obesity. Rich in unsaturated fats, especially linoleic acid, soybean oil is assumed to be healthy, and yet it induces obesity, diabetes, insulin resistance, and fatty liver in mice. Here, we show that the genetically modified soybean oil Plenish, which came on the U.S. market in 2014 and is low in linoleic acid, induces less obesity than conventional soybean oil in C57BL/6 male mice. Proteomic analysis of the liver reveals global differences in hepatic proteins when comparing diets rich in the two soybean oils, coconut oil, and a low-fat diet. Metabolomic analysis of the liver and plasma shows a positive correlation between obesity and hepatic C18 oxylipin metabolites of omega-6 (ω6) and omega-3 (ω3) fatty acids (linoleic and α-linolenic acid, respectively) in the cytochrome P450/soluble epoxide hydrolase pathway. While Plenish induced less insulin resistance than conventional soybean oil, it resulted in hepatomegaly and liver dysfunction as did olive oil, which has a similar fatty acid composition. These results implicate a new class of compounds in diet-induced obesity–C18 epoxide and diol oxylipins.
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28
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Agrawal K, Melliou E, Li X, Pedersen TL, Wang SC, Magiatis P, Newman JW, Holt RR. Oleocanthal-rich extra virgin olive oil demonstrates acute anti-platelet effects in healthy men in a randomized trial. J Funct Foods 2017; 36:84-93. [PMID: 29904393 PMCID: PMC5995573 DOI: 10.1016/j.jff.2017.06.046] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
The phenolic profiles of extra virgin olive oils (EVOOs) may influence their cardiovascular benefits. In a randomized crossover of acute EVOO intake on platelet function, participants (n=9) consumed 40 mL of EVOO weekly. EVOOs were matched for total phenolic content and were either tyrosol-poor with 1:2 oleacein/oleocanthal (D2i0.5), or 2:1 oleacein/oleocanthal (D2i2), or predominantly tyrosol (D2i0). Ibuprofen provided a platelet inhibition control. Blood was collected pre- and 2 hr post-EVOO intake. D2i0.5 and D2i2 reduced 1 µg/mL collagen-stimulated maximum platelet aggregation (Pmax), with effects best correlated to oleocanthal intake (R=0.56, P=0.002). Total phenolic intake was independently correlated to eicosanoid production inhibition, suggesting that cyclooxygenase blockade was not responsible for the Pmax inhibition. Five participants exhibited >25% ΔPmax declines with D2i0.5 and D2i2 intake and plasma metabolomic profiles discriminated subjects by oil responsivity. Platelet responses to acute EVOO intake are associated with oil phenolic composition and may be influenced by diet.
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Affiliation(s)
- Karan Agrawal
- Department of Nutrition, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA
- West Coast Metabolomics Center, Genome Center, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Eleni Melliou
- Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou 15 771, Athens, Greece
| | - Xueqi Li
- UC Davis Olive Center, University of California-Davis, 392 Old Davis Road, Davis, CA 95616, USA
| | - Theresa L. Pedersen
- Obesity and Metabolism Research Unit, USDA - Agricultural Research Service - Western Human Nutrition Research Center, 430 W Health Sciences Drive, Davis, CA 95616, USA
| | - Selina C. Wang
- UC Davis Olive Center, University of California-Davis, 392 Old Davis Road, Davis, CA 95616, USA
- Department of Food Science and Technology, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Prokopios Magiatis
- Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou 15 771, Athens, Greece
| | - John W. Newman
- Department of Nutrition, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA
- West Coast Metabolomics Center, Genome Center, 451 Health Sciences Drive, Davis, CA 95616, USA
- Obesity and Metabolism Research Unit, USDA - Agricultural Research Service - Western Human Nutrition Research Center, 430 W Health Sciences Drive, Davis, CA 95616, USA
| | - Roberta R. Holt
- Department of Nutrition, University of California-Davis, One Shields Avenue, Davis, CA 95616, USA
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29
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Buchwald P, Tamayo-Garcia A, Ramamoorthy S, Garcia-Contreras M, Mendez AJ, Ricordi C. Comprehensive Metabolomics Study To Assess Longitudinal Biochemical Changes and Potential Early Biomarkers in Nonobese Diabetic Mice That Progress to Diabetes. J Proteome Res 2017; 16:3873-3890. [PMID: 28799767 DOI: 10.1021/acs.jproteome.7b00512] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
A global nontargeted longitudinal metabolomics study was carried out in male and female NOD mice to characterize the time-profile of the changes in the metabolic signature caused by onset of type 1 diabetes (T1D) and identify possible early biomarkers in T1D progressors. Metabolomics profiling of samples collected at five different time-points identified 676 and 706 biochemicals in blood and feces, respectively. Several metabolites were expressed at significantly different levels in progressors at all time-points, and their proportion increased strongly following onset of hyperglycemia. At the last time-point, when all progressors were diabetic, a large percentage of metabolites had significantly different levels: 57.8% in blood and 27.8% in feces. Metabolic pathways most strongly affected included the carbohydrate, lipid, branched-chain amino acid, and oxidative ones. Several biochemicals showed considerable (>4×) change. Maltose, 3-hydroxybutyric acid, and kojibiose increased, while 1,5-anhydroglucitol decreased more than 10-fold. At the earliest time-point (6-week), differences between the metabolic signatures of progressors and nonprogressors were relatively modest. Nevertheless, several compounds had significantly different levels and show promise as possible early T1D biomarkers. They include fatty acid phosphocholine derivatives from the phosphatidylcholine subpathway (elevated in both blood and feces) as well as serotonin, ribose, and arabinose (increased) in blood plus 13-HODE, tocopherol (increased), diaminopimelate, valerate, hydroxymethylpyrimidine, and dulcitol (decreased) in feces. A combined metabolic signature based on these compounds might serve as an early predictor of T1D-progressors.
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30
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Diet-induced obesity and weight loss alter bile acid concentrations and bile acid-sensitive gene expression in insulin target tissues of C57BL/6J mice. Nutr Res 2017; 46:11-21. [PMID: 29173647 DOI: 10.1016/j.nutres.2017.07.006] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 06/13/2017] [Accepted: 07/20/2017] [Indexed: 12/31/2022]
Abstract
Bile acids (BAs) influence the metabolism of glucose, lipids, and energy expenditure. We hypothesized that BA concentrations and related gene expression would be altered in lean (low-fat diet fed; LFD) vs diet-induced obese (high-fat diet fed; HFD) groups of mice and that some detected changes would remain after weight loss in an HFD group switched to the LFD (SW). Taurine conjugates dominated the bile acid composition of the liver, epididymal white adipose tissue (eWAT), and hypothalamus, with the latter having lower levels (~95%, ~95%, and ~80%, respectively; P<.05). Plasma conjugated bile acids were elevated in the HFD relative to the LFD and SW animals. Total hepatic BA concentrations decreased in obese mice fed HFD, and levels returned to preobese levels in the SW group. Subtle changes in unconjugated bile acids were detected in the eWAT, hypothalamus, and muscle. Liver expression of a variety of enzymes involved in BA synthesis (eg, Cyp27a1, Acox2), BA transport (eg, Slc22a8), and BA-sensitive receptors (Fxr, Tgr5) were unchanged by HFD feeding but decreased with SW. Other hepatic enzymes were induced in the SW group (eg, Amacr and Bal). In eWAT, Cyp27a1 and Acox2 also declined in the SW group, whereas the HFD group showed reduced expression of BA transporters (eg, Abcc3), and changes in Fxr and Tgr5 were unclear. Therefore, although most detectable changes in BA metabolism associated with diet-induced obesity are reversed by diet-induced weight loss, some effects on BA composition, concentrations, and gene expression can persist after weight loss.
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31
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Khoomrung S, Wanichthanarak K, Nookaew I, Thamsermsang O, Seubnooch P, Laohapand T, Akarasereenont P. Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine. Front Pharmacol 2017; 8:474. [PMID: 28769804 PMCID: PMC5513896 DOI: 10.3389/fphar.2017.00474] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Accepted: 07/03/2017] [Indexed: 12/28/2022] Open
Abstract
In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.
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Affiliation(s)
- Sakda Khoomrung
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden
| | - Kwanjeera Wanichthanarak
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Intawat Nookaew
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden.,Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical SciencesLittle Rock, AR, United States
| | - Onusa Thamsermsang
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Patcharamon Seubnooch
- Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Tawee Laohapand
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Pravit Akarasereenont
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
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32
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Regnell SE, Hessner MJ, Jia S, Åkesson L, Stenlund H, Moritz T, La Torre D, Lernmark Å. Longitudinal analysis of hepatic transcriptome and serum metabolome demonstrates altered lipid metabolism following the onset of hyperglycemia in spontaneously diabetic biobreeding rats. PLoS One 2017; 12:e0171372. [PMID: 28192442 PMCID: PMC5305198 DOI: 10.1371/journal.pone.0171372] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2016] [Accepted: 01/18/2017] [Indexed: 12/30/2022] Open
Abstract
Type 1 diabetes is associated with abberations of fat metabolism before and after the clinical onset of disease. It has been hypothesized that the absence of the effect of insulin in the liver contributes to reduced hepatic fat synthesis. We measured hepatic gene expression and serum metabolites before and after the onset of hyperglycemia in a BioBreeding rat model of type 1 diabetes. Functional pathway annotation identified that lipid metabolism was differentially expressed in hyperglycemic rats and that these pathways significantly overlapped with genes regulated by insulin. 17 serum metabolites significantly changed in concentration. All but 2 of the identified metabolites had previously been reported in type 1 diabetes, and carbohydrates were overall the most upregulated class of metabolites. We conclude that lack of insulin in the liver contributes to the changes in fat metabolism observed in type 1 diabetes. Further studies are needed to understand the clinical consequences of a lack of insulin in the liver in patients with type 1 diabetes.
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Affiliation(s)
- Simon E. Regnell
- Diabetes and Celiac Disease Unit, Department of Clinical Sciences, Lund University/Clinical Research Centre and Skåne University Hospital, Malmö, Sweden
- * E-mail:
| | - Martin J. Hessner
- Max McGee National Research Center for Juvenile Diabetes, Children's Research Institute of Children's Hospital of Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Shuang Jia
- Max McGee National Research Center for Juvenile Diabetes, Children's Research Institute of Children's Hospital of Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Lina Åkesson
- Diabetes and Celiac Disease Unit, Department of Clinical Sciences, Lund University/Clinical Research Centre and Skåne University Hospital, Malmö, Sweden
| | - Hans Stenlund
- Swedish Metabolomics Centre, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Thomas Moritz
- Swedish Metabolomics Centre, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Daria La Torre
- Diabetes and Celiac Disease Unit, Department of Clinical Sciences, Lund University/Clinical Research Centre and Skåne University Hospital, Malmö, Sweden
| | - Åke Lernmark
- Diabetes and Celiac Disease Unit, Department of Clinical Sciences, Lund University/Clinical Research Centre and Skåne University Hospital, Malmö, Sweden
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Abstract
Metabolomics is the snapshot of all detectable metabolites and lipids in biological materials and has potential in reflecting genetic and environmental factors contributing to the development of complex diseases, such as type 1 diabetes. The progression to seroconversion to development of type 1 diabetes has been studied using this technique, although in relatively small cohorts and at limited time points. Overall, three observations have been consistently reported; phospholipids at birth are lower in children developing type 1 diabetes early in childhood, methionine levels are lower in children at seroconversion, and triglycerides are increased at seroconversion and associated to microbiome diversity, indicating an association between the metabolome and microbiome in type 1 diabetes progression.
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Affiliation(s)
- Anne Julie Overgaard
- Department of Pediatrics, Herlev University Hospital, Herlev Ringvej 75, DK-2730, Herlev, Denmark.
| | - Simranjeet Kaur
- Department of Pediatrics, Herlev University Hospital, Herlev Ringvej 75, DK-2730, Herlev, Denmark
| | - Flemming Pociot
- Department of Pediatrics, Herlev University Hospital, Herlev Ringvej 75, DK-2730, Herlev, Denmark
- Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
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Wang Y, Deng GG, Davies KP. Novel insights into development of diabetic bladder disorder provided by metabolomic analysis of the rat nondiabetic and diabetic detrusor and urothelial layer. Am J Physiol Endocrinol Metab 2016; 311:E471-9. [PMID: 27354236 PMCID: PMC5005965 DOI: 10.1152/ajpendo.00134.2016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 06/23/2016] [Indexed: 11/22/2022]
Abstract
There are at present no published studies providing a global overview of changes in bladder metabolism resulting from diabetes. Such studies have the potential to provide mechanistic insight into the development of diabetic bladder disorder (DBD). In the present study, we compared the metabolome of detrusor and urothelial layer in a 1-mo streptozotocin-induced rat model of type 1 diabetes with nondiabetic controls. Our studies revealed that diabetes caused both common and differential changes in the detrusor and urothelial layer's metabolome. Diabetes resulted in similar changes in the levels of previously described diabetic markers in both tissues, such as glucose, lactate, 2-hydroxybutyrate, branched-chain amino acid degradation products, bile acids, and 1,5-anhydroglucitol, as well as markers of oxidative stress. In the detrusor (but not the urothelial layer), diabetes caused activation of the pentose-phosphate and polyol pathways, concomitant with a reduction in the TCA cycle and β-oxidation. Changes in detrusor energy-generating pathways resulted in an accumulation of sorbitol that, through generation of advanced glycation end products, is likely to play a central role in the development of DBD. In the diabetic urothelial layer there was decreased flux of glucose via glycolysis and changes in lipid metabolism, particularly prostaglandin synthesis, which also potentially contributes to detrusor dysfunction.
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Affiliation(s)
- Yi Wang
- Department of Urology, Albert Einstein College of Medicine, Bronx, New York
| | - Gary G Deng
- Endocrine/Cardiovascular Research, Lilly Research Laboratories, Indianapolis, Indiana; and
| | - Kelvin P Davies
- Department of Urology, Albert Einstein College of Medicine, Bronx, New York; Department of Physiology and Biophysics, Albert Einstein College of Medicine, Bronx, New York
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Frohnert BI, Rewers MJ. Metabolomics in childhood diabetes. Pediatr Diabetes 2016; 17:3-14. [PMID: 26420304 PMCID: PMC4703499 DOI: 10.1111/pedi.12323] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 08/20/2015] [Accepted: 08/21/2015] [Indexed: 12/30/2022] Open
Abstract
Recent increases in the incidence of both type 1 (T1D) and type 2 diabetes (T2D) in children and adolescents point to the importance of environmental factors in the development of these diseases. Metabolomic analysis explores the integrated response of the organism to environmental changes. Metabolic profiling can identify biomarkers that are predictive of disease incidence and development, potentially providing insight into disease pathogenesis. This review provides an overview of the role of metabolomic analysis in diabetes research and summarizes recent research relating to the development of T1D and T2D in children.
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Affiliation(s)
- Brigitte I Frohnert
- Barbara Davis Center for Childhood Diabetes; University of Colorado; Aurora CO 80045 USA
| | - Marian J Rewers
- Barbara Davis Center for Childhood Diabetes; University of Colorado; Aurora CO 80045 USA
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Hara M, Fowler JL, Bell GI, Philipson LH. Resting beta-cells - A functional reserve? DIABETES & METABOLISM 2016; 42:157-61. [PMID: 26827115 DOI: 10.1016/j.diabet.2016.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 12/28/2015] [Accepted: 01/01/2016] [Indexed: 01/09/2023]
Abstract
Pancreatic beta-cells play a pivotal role to synthesize and secrete insulin, as the solo source of the body. Physical as well as functional loss of beta-cells over a certain threshold result in diabetes. While the mechanisms underlying beta-cell loss in various types of diabetes have been extensively studied, less is known about residual beta-cells, found even in autoimmune type 1 diabetes and type 2 diabetes with a substantial amount. Why have these beta-cells been spared? Some patients with neonatal diabetes have demonstrated the life-changing restoration of functional beta-cells that were inactive for decades but awakened in several weeks following specific treatment. The recent striking outcomes of bariatric surgery in many obese diabetic patients indicate that their beta-cells are likely "preserved" rather than irreversibly lost even in the multifactorial polygenic state that is type 2 diabetes. Collectively, the preservation of residual beta-cells in various diabetic conditions challenges us regarding our understanding of beta-cell death and survival, where their sustenance may stem from the existence of resting beta-cells under physiological conditions. We posit that beta-cells rest and that studies of this normal feature of beta-cells could lead to new approaches for potentially reactivating and preserving beta-cell mass in order to treat diabetes.
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Affiliation(s)
- M Hara
- Department of Medicine, The University of Chicago, 5841, South Maryland avenue, MC1027, 60637 Chicago, IL, USA.
| | - J L Fowler
- Department of Medicine, The University of Chicago, 5841, South Maryland avenue, MC1027, 60637 Chicago, IL, USA
| | - G I Bell
- Department of Medicine, The University of Chicago, 5841, South Maryland avenue, MC1027, 60637 Chicago, IL, USA
| | - L H Philipson
- Department of Medicine, The University of Chicago, 5841, South Maryland avenue, MC1027, 60637 Chicago, IL, USA
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Misra BB, van der Hooft JJJ. Updates in metabolomics tools and resources: 2014-2015. Electrophoresis 2015; 37:86-110. [DOI: 10.1002/elps.201500417] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2015] [Revised: 10/04/2015] [Accepted: 10/05/2015] [Indexed: 12/12/2022]
Affiliation(s)
- Biswapriya B. Misra
- Department of Biology, Genetics Institute; University of Florida; Gainesville FL USA
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Wanichthanarak K, Fahrmann JF, Grapov D. Genomic, Proteomic, and Metabolomic Data Integration Strategies. Biomark Insights 2015; 10:1-6. [PMID: 26396492 PMCID: PMC4562606 DOI: 10.4137/bmi.s29511] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Revised: 07/21/2015] [Accepted: 07/22/2015] [Indexed: 12/31/2022] Open
Abstract
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these challenges. This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and empirical-correlation-based methods.
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