| 1. |
- 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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| 2. |
- Dickens, Alex M., et al.
(författare)
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Dysregulated Lipid Metabolism Precedes Onset of Psychosis
- 2021
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Ingår i: Biological Psychiatry. - : Elsevier. - 0006-3223 .- 1873-2402. ; 89:3, s. 288-297
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group.METHODS: Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy control subjects, who were then clinically monitored for up to 5 years. Machine learning was used to identify lipid profiles that discriminated between CHR and control subjects, and between subgroups of CHR subjects with distinct clinical outcomes.RESULTS: At baseline, compared with control subjects, CHR subjects (independent of outcome) had higher levels of triacylglycerols with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n = 50) were distinguished from those that did not (n = 213) on the basis of lipid profile at baseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p < .01).CONCLUSIONS: Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.
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| 3. |
- 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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| 4. |
- Lamichhane, Santosh, et al.
(författare)
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An Overview of Metabolomics Data Analysis : Current Tools and Future Perspectives
- 2018
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Ingår i: Data Analysis for Omic Sciences. - : Elsevier. - 9780444640444 ; , s. 387-413
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Bokkapitel (refereegranskat)abstract
- Metabolomics is a study of small molecules in the body and the associated metabolic pathways and is considered to provide a close link between organism's genotype and phenotype. As with other ‘omics’ techniques, metabolomic analysis generates large-scale and complex datasets. Therefore, various data analysis tools are needed to extract biologically relevant information. The data analysis workflows in metabolomics studies are generally complex and involve several steps. In this chapter, we highlight the concept of metabolomics workflow and discuss the data analysis strategies for metabolomics experiments. We also discuss the available tools that can assist in biological interpretation of metabolomics data. We also present an emerging approach of developing genome-scale metabolic models to study cellular metabolism.
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| 5. |
- Lamichhane, Santosh, et al.
(författare)
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Association Between Circulating Lipids and Future Weight Gain in Individuals With an At-Risk Mental State and in First-Episode Psychosis
- 2021
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Ingår i: Schizophrenia Bulletin. - : Oxford University Press. - 0586-7614 .- 1745-1701. ; 47:1, s. 160-169
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Tidskriftsartikel (refereegranskat)abstract
- Patients with schizophrenia have a lower than average life span, largely due to the increased prevalence of cardiometabolic comorbidities. There is an unmet public health need to identify individuals with psychotic disorders who have a high risk of rapid weight gain and who are at risk of developing metabolic complications. Here, we applied mass spectrometry-based lipidomics in a prospective study comprising 48 healthy controls (CTR), 44 first-episode psychosis (FEP) patients, and 22 individuals at clinical high risk (CHR) for psychosis, from 2 study centers (Turku, Finland and London, UK). Baseline serum samples were analyzed using lipidomics, and body mass index (BMI) was assessed at baseline and after 12 months. We found that baseline triacylglycerols (TGs) with low double-bond counts and carbon numbers were positively associated with the change in BMI at follow-up. In addition, a molecular signature comprised of 2 TGs (TG[48:0] and TG[45:0]) was predictive of weight gain in individuals with a psychotic disorder, with an area under the receiver operating characteristic curve (AUROC) of 0.74 (95% CI: 0.60-0.85). When independently tested in the CHR group, this molecular signature predicted said weight change with AUROC = 0.73 (95% CI: 0.61-0.83). We conclude that molecular lipids may serve as a predictor of weight gain in psychotic disorders in at-risk individuals and may thus provide a useful marker for identifying individuals who are most prone to developing cardiometabolic comorbidities.
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| 6. |
- Lamichhane, Santosh, et al.
(författare)
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Circulating metabolic signatures of rapid and slow progression to type 1 diabetes in islet autoantibody-positive children
- 2023
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Ingår i: Frontiers in Endocrinology. - : Frontiers Media S.A.. - 1664-2392. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- AIMS/HYPOTHESIS: Appearance of multiple islet cell autoantibodies in early life is indicative of future progression to overt type 1 diabetes, however, at varying rates. Here, we aimed to study whether distinct metabolic patterns could be identified in rapid progressors (RP, disease manifestation within 18 months after the initial seroconversion to autoantibody positivity) vs. slow progressors (SP, disease manifestation at 60 months or later from the appearance of the first autoantibody).METHODS: Longitudinal samples were collected from RP (n=25) and SP (n=41) groups at the ages of 3, 6, 12, 18, 24, or ≥ 36 months. We performed a comprehensive metabolomics study, analyzing both polar metabolites and lipids. The sample series included a total of 239 samples for lipidomics and 213 for polar metabolites.RESULTS: We observed that metabolites mediated by gut microbiome, such as those involved in tryptophan metabolism, were the main discriminators between RP and SP. The study identified specific circulating molecules and pathways, including amino acid (threonine), sugar derivatives (hexose), and quinic acid that may define rapid vs. slow progression to type 1 diabetes. However, the circulating lipidome did not appear to play a major role in differentiating between RP and SP.CONCLUSION/INTERPRETATION: Our study suggests that a distinct metabolic profile is linked with the type 1 diabetes progression. The identification of specific metabolites and pathways that differentiate RP from SP may have implications for early intervention strategies to delay the development of type 1 diabetes.
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| 7. |
- 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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| 8. |
- Lamichhane, Santosh, et al.
(författare)
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Gut metabolome meets microbiome : A methodological perspective to understand the relationship between host and microbe
- 2018
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Ingår i: Methods. - : Academic Press. - 1046-2023 .- 1095-9130. ; 149, s. 3-12
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Forskningsöversikt (refereegranskat)abstract
- It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host-microbe interactions are highlighted.
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| 9. |
- 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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| 10. |
- 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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