| 1. |
- Sen, Partho, 1983-, et al.
(författare)
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Genome-scale metabolic modeling of human hepatocytes reveals dysregulation of glycosphingolipid pathways in progressive non-alcoholic fatty liver disease
- 2021
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 75:Suppl. 2, s. S256-S256
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background and aims: Non-alcoholic fatty liver disease (NAFLD) is a spectrum of chronic liver diseases intertwined with the metabolic disorders. The prevalence of NAFLD is rapidly increasing worldwide, while the pathologyand the underlying mechanism driving NAFLD is not fully understood. In NAFLD, a series of metabolic changes takes place in the liver. However, the alteration of the metabolic pathways in the human liver along the progression of NAFLD,i.e., transition from non-alcoholic steatosis (NAFL) to steatohepatitis (NASH) through cirrhosis remains to be discovered. Here, we sought to examine the metabolic pathways of the human liver across the full histological spectrum of NAFLD.Method: We analyzed the whole liver tissue transcriptomic (RNA-Seq)1 and serum metabolomics data obtained from a large cohort of histologically characterized patients derived from the European NAFLD Registry (n = 206), and developed genome-scale metabolic models (GEMs) of human hepatocytes at different stages of NAFLD. The integrative approach employed in this study has enabled us to understand the regulation of the metabolic pathways of human liver in NAFL, and with progressive NASH-associated fibrosis (F0-F4).Results: Our study identified several metabolic signatures in the liver and blood of these patients, specifically highlighting the alteration of vitamins (A, E) and glycosphingolipids, and their link with complex glycosaminoglycans in advanced fibrosis. Furthermore, by applying genome-scale metabolic modeling, we were able to identify the metabolic differences among carriers of widely validated genetic variants associated with NAFLD/NASH disease severity in three genes (PNPLA3,TM6SF2andHSD17B13).Conclusion: The study provides insights into the underlying pathways of the progressive-fibrosing steatohepatitis. Of note, there is a marked dysregulation of the glycosphingolipid metabolism in the liver of the patients with advanced fibrosis.
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| 2. |
- Sen, Partho, 1983-, et al.
(författare)
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Quantitative modeling of human liver reveals dysregulation of glycosphingolipid pathways in nonalcoholic fatty liver disease
- 2022
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Ingår i: iScience. - : Cell Press. - 2589-0042. ; 25:9
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Tidskriftsartikel (refereegranskat)abstract
- Nonalcoholic fatty liver disease (NAFLD) is an increasingly prevalent disease that is associated with multiple metabolic disturbances, yet the metabolic pathways underlying its progression are poorly understood. Here, we studied metabolic pathways of the human liver across the full histological spectrum of NAFLD. We analyzed whole liver tissue transcriptomics and serum metabolomics data obtained from a large, prospectively enrolled cohort of 206 histologically characterized patients derived from the European NAFLD Registry and developed genome-scale metabolic models (GEMs) of human hepatocytes at different stages of NAFLD. We identified several metabolic signatures in the liver and blood of these patients, specifically highlighting the alteration of vitamins (A, E) and glycosphingolipids, and their link with complex glycosaminoglycans in advanced fibrosis. Furthermore, we derived GEMs and identified metabolic signatures of three common NAFLD-associated gene variants (PNPLA3, TM6SF2, and HSD17B13). The study demonstrates dysregulated liver metabolic pathways which may contribute to the progression of NAFLD.
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| 3. |
- Alves, Marina Amaral, et al.
(författare)
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Systems biology approaches to study lipidomes in health and disease
- 2021
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Ingår i: Biochimica et Biophysica Acta - Molecular and Cell Biology of Lipids. - : Elsevier. - 1388-1981 .- 1879-2618. ; 1866:2
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Forskningsöversikt (refereegranskat)abstract
- Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.
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| 4. |
- Khoomrung, Sakda, 1978, et al.
(författare)
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Metabolic Profiling and Compound-Class Identification Reveal Alterations in Serum Triglyceride Levels in Mice Immunized with Human Vaccine Adjuvant Alum
- 2020
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3907 .- 1535-3893. ; 19:1, s. 269-278
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Tidskriftsartikel (refereegranskat)abstract
- Alum has been widely used as an adjuvant for human vaccines; however, the impact of Alum on host metabolism remains largely unknown. Herein, we applied mass spectrometry (MS) (liquid chromatography-MS)-based metabolic and lipid profiling to monitor the effects of the Alum adjuvant on mouse serum at 6, 24, 72, and 168 h post-vaccination. We propose a new strategy termed subclass identification and annotation for metabolomics for class-wise identification of untargeted metabolomics data generated from high-resolution MS. Using this approach, we identified and validated the levels of several lipids in mouse serum that were significantly altered following Alum administration. These lipids showed a biphasic response even 168 h after vaccination. The majority of the lipids were triglycerides (TAGs), where TAGs with long-chain unsaturated fatty acids (FAs) decreased at 24 h and TAGs with short-chain FAs decreased at 168 h. To our knowledge, this is the first report on the impact of human vaccine adjuvant Alum on the host metabolome, which may provide new insights into the mechanism of action of Alum. ©
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| 5. |
- Lamichhane, Santosh, et al.
(författare)
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Dysregulation of secondary bile acid metabolism precedes islet autoimmunity and type 1 diabetes
- 2022
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Ingår i: Cell Reports Medicine. - : Cell Press. - 2666-3791. ; 3:10
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Tidskriftsartikel (refereegranskat)abstract
- The gut microbiota is crucial in the regulation of bile acid (BA) metabolism. However, not much is known about the regulation of BAs during progression to type 1 diabetes (T1D). Here, we analyzed serum and stool BAs in longitudinal samples collected at 3, 6, 12, 18, 24, and 36 months of age from children who developed a single islet autoantibody (AAb) (P1Ab; n = 23) or multiple islet AAbs (P2Ab; n = 13) and controls (CTRs; n = 38) who remained AAb negative. We also analyzed the stool microbiome in a subgroup of these children. Factor analysis showed that age had the strongest impact on both BA and microbiome profiles. We found that at an early age, systemic BAs and microbial secondary BA pathways were altered in the P2Ab group compared with the P1Ab and CTR groups. Our findings thus suggest that dysregulated BA metabolism in early life may contribute to the risk and pathogenesis of T1D.
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| 6. |
- Lamichhane, Santosh, et al.
(författare)
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Linking Gut Microbiome and Lipid Metabolism : Moving beyond Associations
- 2021
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Ingår i: Metabolites. - : MDPI. - 2218-1989 .- 2218-1989. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Various studies aiming to elucidate the role of the gut microbiome-metabolome co-axis in health and disease have primarily focused on water-soluble polar metabolites, whilst non-polar microbial lipids have received less attention. The concept of microbiota-dependent lipid biotransformation is over a century old. However, only recently, several studies have shown how microbial lipids alter intestinal and circulating lipid concentrations in the host, thus impacting human lipid homeostasis. There is emerging evidence that gut microbial communities play a particularly significant role in the regulation of host cholesterol and sphingolipid homeostasis. Here, we review and discuss recent research focusing on microbe-host-lipid co-metabolism. We also discuss the interplay of human gut microbiota and molecular lipids entering host systemic circulation, and its role in health and disease.
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| 7. |
- Mathema, Vivek Bhakta, et al.
(författare)
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Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
- 2023
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Ingår i: Computational and Structural Biotechnology Journal. - : Elsevier. - 2001-0370. ; 21, s. 1372-1382
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Forskningsöversikt (refereegranskat)abstract
- Cancer progression is linked to gene-environment interactions that alter cellular homeostasis. The use of biomarkers as early indicators of disease manifestation and progression can substantially improve diagnosis and treatment. Large omics datasets generated by high-throughput profiling technologies, such as microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry, have enabled data-driven biomarker discoveries. The identification of differentially expressed traits as molecular markers has traditionally relied on statistical techniques that are often limited to linear parametric modeling. The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep learning (DL), a major subunit of machine learning (ML), has been increasingly utilized in recent years to investigate various diseases. The combination of ML/DL approaches for performance optimization across multi-omics datasets produces robust ensemble-learning prediction models, which are becoming useful in precision medicine. This review focuses on the recent development of ML/DL methods to provide integrative solutions in discovering cancer-related biomarkers, and their utilization in precision medicine.
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| 8. |
- McGlinchey, Aidan J, 1984-, et al.
(författare)
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Prenatal exposure to perfluoroalkyl substances modulates neonatal serum phospholipids, increasing risk of type 1 diabetes
- 2020
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Ingår i: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 143
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Tidskriftsartikel (refereegranskat)abstract
- In the last decade, increasing incidence of type 1 diabetes (T1D) stabilized in Finland, a phenomenon that coincides with tighter regulation of perfluoroalkyl substances (PFAS). Here, we quantified PFAS to examine their effects, during pregnancy, on lipid and immune-related markers of T1D risk in children. In a mother-infant cohort (264 dyads), high PFAS exposure during pregnancy associated with decreased cord serum phospholipids and progression to T1D-associated islet autoantibodies in the offspring. This PFAS-lipid association appears exacerbated by increased human leukocyte antigen-conferred risk of T1D in infants. Exposure to a single PFAS compound or a mixture of organic pollutants in non-obese diabetic mice resulted in a lipid profile characterized by a similar decrease in phospholipids, a marked increase of lithocholic acid, and accelerated insulitis. Our findings suggest that PFAS exposure during pregnancy contributes to risk and pathogenesis of T1D in offspring.
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| 9. |
- Olafsdottir, Torunn, et al.
(författare)
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Comparative Systems Analyses Reveal Molecular Signatures of Clinically tested Vaccine Adjuvants
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- A better understanding of the mechanisms of action of human adjuvants could inform a rational development of next generation vaccines for human use. Here, we exploited a genome wide transcriptomics analysis combined with a systems biology approach to determine the molecular signatures induced by four clinically tested vaccine adjuvants, namely CAF01, IC31, GLA-SE and Alum in mice. We report signature molecules, pathways, gene modules and networks, which are shared by or otherwise exclusive to these clinical-grade adjuvants in whole blood and draining lymph nodes of mice. Intriguingly, co-expression analysis revealed blood gene modules highly enriched for molecules with documented roles in T follicular helper (TFH) and germinal center (GC) responses. We could show that all adjuvants enhanced, although with different magnitude and kinetics, TFH and GC B cell responses in draining lymph nodes. These results represent, to our knowledge, the first comparative systems analysis of clinically tested vaccine adjuvants that may provide new insights into the mechanisms of action of human adjuvants.
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| 10. |
- Ribeiro, Henrique Caracho, et al.
(författare)
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Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis
- 2022
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Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 18:8
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities.OBJECTIVES: This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease.METHODS: Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites.RESULTS: Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914).CONCLUSION: From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.
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| 11. |
- Sen, Partho, 1983-, et al.
(författare)
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Deep learning meets metabolomics : a methodological perspective
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press. - 1467-5463 .- 1477-4054. ; 22:2, s. 1531-1542
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Forskningsöversikt (refereegranskat)abstract
- Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.
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| 12. |
- Sen, Partho, 1983-, et al.
(författare)
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Exposure to environmental contaminants is associated with altered hepatic lipid metabolism in non-alcoholic fatty liver disease
- 2022
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 76:2, s. 283-293
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Tidskriftsartikel (refereegranskat)abstract
- Background & aims: Recent experimental models and epidemiological studies suggest that specific environmental contaminants (ECs) contribute to the initiation and pathology of non-alcoholic fatty liver disease (NAFLD). However, the underlying mechanisms linking EC exposure with NAFLD remain poorly understood and there is no data on their impact on the human liver metabolome. Herein, we hypothesized that exposure to ECs, particularly perfluorinated alkyl substances (PFAS), impacts liver metabolism, specifically bile acid metabolism.Methods: In a well-characterized human NAFLD cohort of 105 individuals, we investigated the effects of EC exposure on liver metabolism. We characterized the liver (via biopsy) and circulating metabolomes using 4 mass spectrometry-based analytical platforms, and measured PFAS and other ECs in serum. We subsequently compared these results with an exposure study in a PPARa-humanized mouse model.Results: PFAS exposure appears associated with perturbation of key hepatic metabolic pathways previously found altered in NAFLD, particularly those related to bile acid and lipid metabolism. We identified stronger associations between the liver metabolome, chemical exposure and NAFLD-associated clinical variables (liver fat content, HOMA-IR), in females than males. Specifically, we observed PFAS-associated upregulation of bile acids, triacylglycerols and ceramides, and association between chemical exposure and dysregulated glucose metabolism in females. The murine exposure study further corroborated our findings, vis-à-vis a sex-specific association between PFAS exposure and NAFLD-associated lipid changes.Conclusions: Females may be more sensitive to the harmful impacts of PFAS. Lipid-related changes subsequent to PFAS exposure may be secondary to the interplay between PFAS and bile acid metabolism.Lay summary: There is increasing evidence that specific environmental contaminants, such as perfluorinated alkyl substances (PFAS), contribute to the progression of non-alcoholic fatty liver disease (NAFLD). However, it is poorly understood how these chemicals impact human liver metabolism. Here we show that human exposure to PFAS impacts metabolic processes associated with NAFLD, and that the effect is different in females and males.
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| 13. |
- Sen, Partho, 1983-, et al.
(författare)
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Exposure to environmental toxicants is associated with gut microbiome dysbiosis, insulin resistance and obesity
- 2024
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Ingår i: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 186
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Tidskriftsartikel (refereegranskat)abstract
- Environmental toxicants (ETs) are associated with adverse health outcomes. Here we hypothesized that exposures to ETs are linked with obesity and insulin resistance partly through a dysbiotic gut microbiota and changes in the serum levels of secondary bile acids (BAs). Serum BAs, per- and polyfluoroalkyl substances (PFAS) and additional twenty-seven ETs were measured by mass spectrometry in 264 Danes (121 men and 143 women, aged 56.6 ± 7.3 years, BMI 29.7 ± 6.0 kg/m2) using a combination of targeted and suspect screening approaches. Bacterial species were identified based on whole-genome shotgun sequencing (WGS) of DNA extracted from stool samples. Personalized genome-scale metabolic models (GEMs) of gut microbial communities were developed to elucidate regulation of BA pathways. Subsequently, we compared findings from the human study with metabolic implications of exposure to perfluorooctanoic acid (PFOA) in PPARα-humanized mice. Serum levels of twelve ETs were associated with obesity and insulin resistance. High chemical exposure was associated with increased abundance of several bacterial species (spp.) of genus (Anaerotruncus, Alistipes, Bacteroides, Bifidobacterium, Clostridium, Dorea, Eubacterium, Escherichia, Prevotella, Ruminococcus, Roseburia, Subdoligranulum, and Veillonella), particularly in men. Conversely, females in the higher exposure group, showed a decrease abundance of Prevotella copri. High concentrations of ETs were correlated with increased levels of secondary BAs including lithocholic acid (LCA), and decreased levels of ursodeoxycholic acid (UDCA). In silico causal inference analyses suggested that microbiome-derived secondary BAs may act as mediators between ETs and obesity or insulin resistance. Furthermore, these findings were substantiated by the outcome of the murine exposure study. Our combined epidemiological and mechanistic studies suggest that multiple ETs may play a role in the etiology of obesity and insulin resistance. These effects may arise from disruptions in the microbial biosynthesis of secondary BAs.
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| 14. |
- Sen, Partho, 1983-, et al.
(författare)
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Integrating Omics Data in Genome-Scale Metabolic Modeling : A Methodological Perspective for Precision Medicine
- 2023
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Ingår i: Metabolites. - : MDPI. - 2218-1989 .- 2218-1989. ; 13:7
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Forskningsöversikt (refereegranskat)abstract
- Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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| 15. |
- Sen, Partho, 1983-, et al.
(författare)
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Metabolic Modeling of Human Gut Microbiota on a Genome Scale : An Overview
- 2019
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Ingår i: Metabolites. - : MDPI. - 2218-1989 .- 2218-1989. ; 9:2
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Forskningsöversikt (refereegranskat)abstract
- There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.
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| 16. |
- Sen, Partho, 1983-, et al.
(författare)
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Quantitative genome-scale metabolic modeling of human CD4+ T cell differentiation reveals subset-specific regulation of glycosphingolipid pathways
- 2021
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Ingår i: Cell Reports. - : Cell Press. - 2211-1247. ; 37:6
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Tidskriftsartikel (refereegranskat)abstract
- T cell activation, proliferation, and differentiation involve metabolic reprogramming resulting from the interplay of genes, proteins, and metabolites. Here, we aim to understand the metabolic pathways involved in the activation and functional differentiation of human CD4+ T cell subsets (T helper [Th]1, Th2, Th17, and induced regulatory T [iTreg] cells). Here, we combine genome-scale metabolic modeling, gene expression data, and targeted and non-targeted lipidomics experiments, together with in vitro gene knockdown experiments, and show that human CD4+ T cells undergo specific metabolic changes during activation and functional differentiation. In addition, we confirm the importance of ceramide and glycosphingolipid biosynthesis pathways in Th17 differentiation and effector functions. Through in vitro gene knockdown experiments, we substantiate the requirement of serine palmitoyltransferase (SPT), a de novo sphingolipid pathway in the expression of proinflammatory cytokines (interleukin [IL]-17A and IL17F) by Th17 cells. Our findings provide a comprehensive resource for selective manipulation of CD4+ T cells under disease conditions characterized by an imbalance of Th17/natural Treg (nTreg) cells.
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| 17. |
- Sen, Partho, 1983, et al.
(författare)
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Selection of complementary foods based on optimal nutritional values
- 2017
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 7:1, s. Article no 5413 -
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Tidskriftsartikel (refereegranskat)abstract
- Human milk is beneficial for growth and development of infants. Several factors result in mothers ceasing breastfeeding which leads to introduction of breast-milk substitutes (BMS). In some communities traditional foods are given as BMS, in others they are given as complementary foods during weaning. Improper food selection at this stage is associated with a high prevalence of malnutrition in children under 5 years. Here we listed the traditional foods from four continents and compared them with human milk based on their dietary contents. Vitamins such as thiamine (similar to[2-10] folds), riboflavin (similar to[4-10] folds) and ascorbic acid (
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| 18. |
- Sen, Partho, 1983-, et al.
(författare)
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The Role of Omic Technologies in the Study of the Human Gut Microbiome
- 2019
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Ingår i: Reference Module in Food Science. - : Elsevier. - 9780081005965
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Tidskriftsartikel (refereegranskat)abstract
- Human gut is colonized by a vast number of microbes known as gut microbiota. The microbiota plays a significant role in the maintenance of health and well-being. A dysbiosis in the microbiota has been associated with the altered metabolism and health disorders. Next-generation sequencing (NGS) aided in taxonomic and functional profiling of the gut microbiome. It has unveiled its richness and diversity. However, little is known about the regulation of microbes in the gut ecosystem, and the underlying interactions with the host. In this chapter, we review recent progress in high-throughput (HT) meta-omics technologies and integrative approaches, with special focus on the utilization of metabolic modeling applied in human along the diet-gut-host axis. We also discuss, how meta-omics and microbiome abundances can be integrated to develop condition-specific gut microbiota models on a genome-scale.
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| 19. |
- Shoaie, Saeed, 1985, et al.
(författare)
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Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome
- 2015
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Ingår i: Cell Metabolism. - : Elsevier BV. - 1550-4131 .- 1932-7420. ; 22:2, s. 320-331
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Tidskriftsartikel (refereegranskat)abstract
- The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.
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| 20. |
- Thankaswamy, Subazini, 1980, et al.
(författare)
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Evaluation and assessment of read-mapping by multiple next-generation sequencing aligners based on genome-wide characteristics
- 2017
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Ingår i: Genomics. - : Elsevier BV. - 1089-8646 .- 0888-7543. ; 109:3-4, s. 186-191
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Tidskriftsartikel (refereegranskat)abstract
- Massive data produced due to the advent of next-generation sequencing (NGS) technology is widely used for biological researches and medical diagnosis. The crucial step in NGS analysis is read alignment or mapping which is computationally intensive and complex. The mapping bias tends to affect the downstream analysis, including detection of polymorphisms. In order to provide guidelines to the biologist for suitable selection of aligners; we have evaluated and benchmarked 5 different aligners (BWA, Bowtie2, NovoAlign, Smalt and Stampy) and their mapping bias based on characteristics of 5 microbial genomes. Two million simulated read pairs of various sizes (36 bp, 50 bp, 72 bp, 100 bp, 125 bp, 150 bp, 200 bp, 250 bp and 300 bp) were aligned. Specific alignment features such as sensitivity of mapping, percentage of properly paired reads, alignment time and effect of tandem repeats on incorrectly mapped reads were evaluated. BWA showed faster alignment followed by Bowtie2 and Smalt. NovoAlign and Stampy were comparatively slower. Most of the aligners showed high sensitivity towards long reads (> 100 bp) mapping. On the other hand NovoAlign showed higher sensitivity towards both short reads (36 bp, 50 bp, 72 bp) and long reads (> 100 bp) mappings; It also showed higher sensitivity towards mapping a complex genome like Plasmodium falciparum. The percentage of properly paired reads aligned by NovoAlign, BWA and Stampy were markedly higher. None of the aligners outperforms the others in the benchmark, however the aligners perform differently with genome characteristics. We expect that the results from this study will be useful for the end user to choose aligner, thus enhance the accuracy of read mapping. (C) 2017 Elsevier Inc. All rights reserved.
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| 21. |
- VINCENT, ANDREW, 1981, et al.
(författare)
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Herring and chicken/pork meals lead to differences in plasma levels of TCA intermediates and arginine metabolites in overweight and obese men and women
- 2017
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Ingår i: Molecular Nutrition & Food Research. - : Wiley. - 1613-4125 .- 1613-4133. ; 61:3
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Tidskriftsartikel (refereegranskat)abstract
- Scope: What effect does replacing chicken or pork with herring as the main dietary source of protein have on the human plasma metabolome? Method and results: A randomised crossover trial with 15 healthy obese men and women (age 24-70 years). Subjects were randomly assigned to four weeks of herring diet or a reference diet of chicken and lean pork, five meals per week, followed by a washout and the other intervention arm. Fasting blood serum metabolites were analysed at 0, 2 and 4 weeks for eleven subjects with available samples, using GC-MS based metabolomics. The herring diet decreased plasma citrate, fumarate, isocitrate, glycolate, oxalate, agmatine and methyhistidine and increased asparagine, ornithine, glutamine and the hexosamine glucosamine. Modelling found that the tricarboxylic acid cycle, glyoxylate, and arginine metabolism were affected by the intervention. The effect on arginine metabolism was supported by an increase in blood nitric oxide in males on the herring diet. Conclusion: The results suggest that eating herring instead of chicken and lean pork leads to important metabolic effects, particularly on energy and amino acid metabolism. Our findings support the hypothesis that there are metabolic effects of herring intake unrelated to the long chain n-3 polyunsaturated fatty acid content.
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