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Background: Pancreatic ductal adenocarcinoma (PDAC) remains a formidable health challenge due to its detection at a late stage and a lack of reliable biomarkers for early detection. Although levels of carbohydrate antigen 19-9 are often used in conjunction with imaging-based tests to aid in the diagnosis of PDAC, there is still a need for more sensitive and specific biomarkers for early detection of PDAC., Methods: We obtained serum samples from 88 subjects (patients with PDAC (n = 58) and controls (n = 30)). We carried out a multi-omics analysis to measure cytokines and related proteins using proximity extension technology and lipidomics and metabolomics using tandem mass spectrometry. Statistical analysis was carried out to find molecular alterations in patients with PDAC and a machine learning model was used to derive a molecular signature of PDAC., Results: We quantified 1,462 circulatory proteins along with 873 lipids and 1,001 metabolites. A total of 505 proteins, 186 metabolites and 33 lipids including bone marrow stromal antigen 2 (BST2), keratin 18 (KRT18), and cholesteryl ester(20:5) were found to be significantly altered in patients. We identified different levels of sphingosine, sphinganine, urobilinogen and lactose indicating that glycosphingolipid and galactose metabolisms were significantly altered in patients compared to controls. In addition, elevated levels of diacylglycerols and decreased cholesteryl esters were observed in patients. Using a machine learning model, we identified a signature of 38 biomarkers for PDAC, composed of 21 proteins, 4 lipids, and 13 metabolites., Conclusions: Overall, this study identified several proteins, metabolites and lipids involved in various pathways including cholesterol and lipid metabolism to be changing in patients. In addition, we discovered a multi-analyte signature that could be further tested for detection of PDAC., Competing Interests: Declarations. Conflict of interest: The authors declare that they have no conflict of interest., (© 2024. Japanese Society of Gastroenterology.)

Metabolic dysfunction-associated steatotic liver disease (MASLD) and steatohepatitis (MASH) are associated with a high prevalence of type 2 diabetes (T2D). Individuals with MASLD exhibit insulin resistance (IR) and hyperglycemia, but it is unclear whether hepatic glucose production (HGP) is increased with MASLD severity. We evaluated HGP in a cohort of histologically characterized individuals with MASL/MASH using stable isotope infusion (6,6- 2 H 2 -glucose, U- 2 H 5 -glycerol) and liver-specific genome-scale metabolic models (GEMs). Tracer-measured HGP is increased with liver fibrosis and inflammation, but not steatosis, and is associated with lipolysis and IR. The GEM-derived gluconeogenesis is elevated due to high glucogenic/energy metabolite uptakes (lactate, glycerol, and free fatty acid [FFA]), and the expression of insulin action genes (IRS1, IRS2, and AKT2) is reduced in MASH with fibrosis F2-F4, with/without T2D, suggesting these as putative mechanisms for increased fasting HGP and hyperglycemia. In conclusion, elevated HGP, lipolysis, and IR help to explain the mechanisms for the increased risk of hyperglycemia and T2D in MASH., Competing Interests: Declaration of interests G.M. reports consulting fees from Novo Nordisk, Fractyl Inc, and Recor Inc. She is also scientific advisor of Metadeq Inc, Keyron Ltd, GHP Scientific Ltd, and Jemyll Ltd. G.M. reports receiving research grants from Metadeq Inc and Fractyl Inc. A.G. has served as a consultant for: Boehringer Ingelheim, Eli Lilly and Company, Metadeq Diagnostics, and Fractyl Health; has participated in advisory boards for Boehringer Ingelheim, Merck Sharp & Dohme, Novo Nordisk, Metadeq Diagnostics, and Pfizer; and has received speaker’s honorarium and other fees from Eli Lilly and Company, Merck Sharp & Dohme, and Novo Nordisk. Q.M.A. reports grants and/or personal fees from Allergan/Tobira, E3Bio, Eli Lilly & Company Ltd, Galmed, Genfit SA, Gilead, Grunthal, Imperial Innovations, Intercept Pharma Europe Ltd, Inventiva, Janssen, Kenes, MedImmune, NewGene, Pfizer Ltd, Raptor Pharma, Novartis Pharma AG, AbbVie, BMS, GSK, NGMBio, Madrigal, Servier, EcoR1, 89Bio, Altimmune, AstraZeneca, Axcella, Blade, BNN Cardio, Celgene, Cirius, CymaBay, Genentech, HistoIndex, Indalo, IQVIA,Metacrine, North Sea Therapeutics, Novo Nordisk, Poxel, Terns, Viking Therapeutics, Glympse Bio, and PathAI, outside the submitted work., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Regulatory T cells (Tregs) are central in controlling immune responses, and dysregulation of their function can lead to autoimmune disorders or cancer. Despite extensive studies on Tregs, the basis of epigenetic regulation of human Treg development and function is incompletely understood. Long intergenic noncoding RNAs (lincRNA)s are important for shaping and maintaining the epigenetic landscape in different cell types. In this study, we identified a gene on the chromosome 6p25.3 locus, encoding a lincRNA, that was up-regulated during early differentiation of human Tregs. The lincRNA regulated the expression of interleukin-2 receptor alpha (IL2RA), and we named it the lincRNA regulator of IL2RA (LIRIL2R). Through transcriptomics, epigenomics, and proteomics analysis of LIRIL2R-deficient Tregs, coupled with global profiling of LIRIL2R binding sites using chromatin isolation by RNA purification, followed by sequencing, we identified IL2RA as a target of LIRIL2R. This nuclear lincRNA binds upstream of the IL2RA locus and regulates its epigenetic landscape and transcription. CRISPR-mediated deletion of the LIRIL2R-bound region at the IL2RA locus resulted in reduced IL2RA expression. Notably, LIRIL2R deficiency led to reduced expression of Treg-signature genes (e.g., FOXP3 , CTLA4 , and PDCD1 ), upregulation of genes associated with effector T cells (e.g., SATB1 and GATA3 ), and loss of Treg-mediated suppression., Competing Interests: Competing interests statement:A.M. is a cofounder of Arsenal Biosciences, Function Bio, Spotlight Therapeutics, and Survey Genomics; serves on the boards of directors at Function Bio, Spotlight Therapeutics, and Survey Genomics; is a member of the scientific advisory boards of Arsenal Biosciences, Function Bio, Spotlight Therapeutics, Survey Genomics, NewLimit, Amgen, Tenaya, and Lightcast; owns stock in Arsenal Biosciences, Function Bio, Spotlight Therapeutics, NewLimit, Survey Genomics, Tenaya, and Lightcast; and has received fees from Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Amgen, 23andMe, PACT Pharma, Juno Therapeutics, Tenaya, Survey Genomics, Lightcast, Gilead, Trizell, Vertex, Merck, Genentech, AlphaSights, Rupert Case Management, Bernstein, GLG, ClearView Healthcare Partners, and ALDA. A.M. is an investor in and informal advisor to Offline Ventures and a client of EPIQ. S.B.A.A., U.U.K., S.P., V.K., O.R., and R.L. are inventors in a European patent (Oligonucleotides for Modulating Regulatory T Cell Mediated Immunosuppression, patent number: WO2023242469A1) related to this manuscript. All other authors declare no competing interests.

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/m 2 ) 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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Matej Oresic reports financial support was provided by Research Council of Finland. Tuulia Hyotylainen reports financial support was provided by Novo Nordisk Foundation. Tuulia Hyotylainen reports financial support was provided by Swedish Research Council Formas. Tuulia Hyotylainen reports financial support was provided by Swedish Research Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The gut microbiome has been implicated in the pathogenesis of mental disorders where the gut-brain axis acts as a bidirectional communication network., Methods: Herein, we investigated the compositional and functional differences of gut microbiome between patients with first-episode psychosis (FEP) (n = 26) and healthy control participants (n = 22) using whole-genome shotgun sequencing. In addition, we assessed the oral microbiome in patients with FEP (n = 13) and listed their taxonomic diversity., Results: Our findings suggest that there is a dysbiosis of gut microbiota in patients with FEP. Relative abundance of Bifidobacterium adolescentis, Prevotella copri, and Turicibacter sanguinis was markedly increased (linear discriminant analysis scores [log 10 ] > 1, and Mann-Whitney U test; false discovery rate-adjusted p values < .05) in the FEP group compared with the healthy control participants. Pathway analysis indicated that several metabolic pathways, particularly deoxyribonucleotide biosynthesis, branched-chain amino acid biosynthesis, tricarboxylic acid cycle, and fatty acid elongation and biosynthesis, were dysregulated in the FEP group compared with the healthy control group. In addition, this preliminary study was able to identify specific gut microbes (at baseline) that were predictive of weight gain in the FEP group at a 1-year follow-up. Bacteroides dorei, Bifidobacterium adolescentis, Turicibacter sanguinis, Roseburia spp., and Ruminococcus lactaris were positively associated (eXtreme gradient boosting, XGBoost regression model, Shapley additive explanations, R 2  = 0.82) with weight gain., Conclusions: Our findings may suggest the involvement of gut microbiota in the pathogenesis of psychosis. The benefit of modulation of the gut microbiome in the treatment of psychotic disorders should be explored further., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lamichhane, Sen, Dickens, Kråkström, Ilonen, Lempainen, Hyöty, Lahesmaa, Veijola, Toppari, Hyötyläinen, Knip and Orešič.)

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.

Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation). HIC data has the potential to inform relevant aspects of the healthcare systems. Although HIC data inherit limitations, large sample sizes and long-term follow-up provides enormous predictive power. Herein, we highlight the benefits and limitations of HIC data and provide examples from the cardiovascular field, i.e. how HIC data is supporting healthcare, focusing on the demographical and epidemiological differences, pharmacotherapy, healthcare utilization, cost-effectiveness and outcomes of different treatments. As an outlook we discuss the potential of using HIC-based big data and modern artificial intelligence (AI) algorithms to guide patient education and care, which could lead to the development of a learning healthcare system and support a medically relevant legislation in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Krefting, Sen, David-Rus, Güldener, Hawe, Cassese, von Scheidt and Schunkert.)

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., Competing Interests: None., (© 2023 The Authors.)

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: J.M.S. declares Consultancy: Boehringer Ingelheim, BMS, Genfit, Gilead Sciences, Intercept Pharmaceuticals, Madrigal, Novartis, Novo Nordisk, Nordic Bioscience, Pfizer, Roche, Sanofi, Siemens Healthcare GmbH. Research Funding: Gilead Sciences, Boehringer Ingelheim. Speakers Bureau: Falk Foundation MSD Sharp & Dohme GmbH. M.A. reports consultancy/advisory with MedImmune/Astra Zeneca and E3Bio, as well as honoraria from Intercept and grant support from GSK and Takeda. E.B. reports advisory/consulting for BMS, Genfit SA, Gilead, Intercept, and Novartis. Q.M.A. reports grants, personal fees and other from Allergan/Tobira, other from E3Bio, other from Eli Lilly & Company Ltd, other from Galmed, grants, personal fees and other from Genfit SA, personal fees and other from Gilead, other from Grunthal, other from Imperial Innovations, grants and other from Intercept Pharma Europe Ltd, other from Inventiva, other from Janssen, personal fees from Kenes, other from MedImmune, other from NewGene, grants, personal fees and other from Pfizer Ltd, other from Raptor Pharma, grants and other from Novartis Pharma AG, grants from Abbvie, personal fees and other from BMS, grants from GSK, other from NGMBio, other from Madrigal, other from Servier, other from EcoR1, other from 89Bio, other from Altimmune, grants and other from AstraZeneca, other from Axcella, other from Blade, other from BNN Cardio, other from Celgene, other from Cirius, other from CymaBay, other from Genentech, other from HistoIndex, other from Indalo, other from IQVIA, other from Metacrine, other from North Sea Therapeutics, personal fees and other from Novo Nordisk, other from Poxel, other from Terns, other from Viking Therapeutics, grants from Glympse Bio, other from PathAI, outside the submitted work. All other authors declare that they have no competing interests., (© 2022 The Author(s).)

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., Competing Interests: Conflict of interest The authors declare no conflicts of interest that pertain to this work. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Knowledge about in vivo effects of human circulating C-6 hydroxylated bile acids (BAs), also called muricholic acids, is sparse. It is unsettled if the gut microbiome might contribute to their biosynthesis. Here, we measured a range of serum BAs and related them to markers of human metabolic health and the gut microbiome. We examined 283 non-obese and obese Danish adults from the MetaHit study. Fasting concentrations of serum BAs were quantified using ultra-performance liquid chromatography-tandem mass-spectrometry. The gut microbiome was characterized with shotgun metagenomic sequencing and genome-scale metabolic modeling. We find that tauro- and glycohyocholic acid correlated inversely with body mass index (P = 4.1e-03, P = 1.9e-05, respectively), waist circumference (P = 0.017, P = 1.1e-04, respectively), body fat percentage (P = 2.5e-03, P = 2.3e-06, respectively), insulin resistance (P = 0.051, P = 4.6e-4, respectively), fasting concentrations of triglycerides (P = 0.06, P = 9.2e-4, respectively) and leptin (P = 0.067, P = 9.2e-4). Tauro- and glycohyocholic acids, and tauro-a-muricholic acid were directly linked with a distinct gut microbial community primarily composed of Clostridia species (P = 0.037, P = 0.013, P = 0.027, respectively). We conclude that serum conjugated C-6-hydroxylated BAs associate with measures of human metabolic health and gut communities of Clostridia species. The findings merit preclinical interventions and human feasibility studies to explore the therapeutic potential of these BAs in obesity and type 2 diabetes.

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., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.

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., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Celiac disease (CD) is a systemic immune-mediated disorder with increased frequency in the developed countries over the last decades implicating the potential causal role of various environmental triggers in addition to gluten. Herein, we apply determination of perfluorinated alkyl substances (PFAS) and combine the results with the determination of bile acids (BAs) and molecular lipids, with the aim to elucidate the impact of prenatal exposure on risk of progression to CD in a prospective series of children prior the first exposure to gluten (at birth and at 3 months of age). Here we analyzed PFAS, BAs and lipidomic profiles in 66 plasma samples at birth and at 3 months of age in the Type 1 Diabetes Prediction and Prevention (DIPP) study (n = 17 progressors to CD, n = 16 healthy controls, HCs). Plasma PFAS levels showed a significant inverse association with the age of CD diagnosis in infants who later progressed to the disease. Associations between BAs and triacylglycerols (TGs) showed different patterns already at birth in CD progressors, indicative of different absorption of lipids in these infants. In conclusion, PFAS exposure may modulate lipid and BA metabolism, and the impact is different in the infants who develop CD later in life, in comparison to HCs. The results indicate more efficient uptake of PFAS in such infants. Higher PFAS exposure during prenatal and early life may accelerate the progression to CD in the genetically predisposed children., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Aims/hypothesis: Previous metabolomics studies suggest that type 1 diabetes is preceded by specific metabolic disturbances. The aim of this study was to investigate whether distinct metabolic patterns occur in peripheral blood mononuclear cells (PBMCs) of children who later develop pancreatic beta cell autoimmunity or overt type 1 diabetes., Methods: In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who (1) progressed to type 1 diabetes (PT1D, n = 34), (2) seroconverted to ≥1 islet autoantibody without progressing to type 1 diabetes (P1Ab, n = 27) or (3) remained autoantibody negative during follow-up (CTRL, n = 10)., Results: During the first year of life, levels of most lipids and polar metabolites were lower in the PT1D and P1Ab groups compared with the CTRL group. Pathway over-representation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were over-represented in PT1D. Genome-scale metabolic models of PBMCs during type 1 diabetes progression were developed by using publicly available transcriptomics data and constrained with metabolomics data from our study. Metabolic modelling confirmed altered ceramide pathways, known to play an important role in immune regulation, as specifically associated with type 1 diabetes progression., Conclusions/interpretation: Our data suggest that systemic dysregulation of lipid metabolism, as observed in plasma, may impact the metabolism and function of immune cells during progression to overt type 1 diabetes., Data Availability: The GEMs for PBMCs have been submitted to BioModels (www.ebi.ac.uk/biomodels/), under accession number MODEL1905270001. The metabolomics datasets and the clinical metadata generated in this study were submitted to MetaboLights (https://www.ebi.ac.uk/metabolights/), under accession number MTBLS1015.

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.

Objectives: Celiac disease (CD) is a chronic enteropathy characterized by an autoimmune reaction in the small intestine of genetically susceptible individuals. The underlying causes of autoimmune reaction and its effect on host metabolism remain largely unknown. Herein, we apply lipidomics to elucidate the early events preceding clinical CD in a cohort of Finnish children, followed up in the Type 1 Diabetes Prediction and Prevention study., Methods: Mass spectrometry-based lipidomics profiling was applied to a longitudinal/prospective series of 233 plasma samples obtained from CD progressors (n = 23) and healthy controls (n = 23), matched for human leukocyte antigen (HLA) risk, sex, and age. The children were followed from birth until diagnosis of clinical CD and subsequent introduction of a gluten-free diet., Results: Twenty-three children progressed to CD at a mean age of 4.8 years. They showed increased amounts of triacylglycerols (TGs) of low carbon number and double bond count and a decreased level of phosphatidylcholines by age 3 months as compared to controls. These differences were exacerbated with age but were not observed at birth (cord blood). No significant differences were observed in the essential TGs., Discussion: Our preliminary findings suggest that abnormal lipid metabolism associates with the development of clinical CD and occurs already before the first introduction of gluten to the diet. Moreover, our data suggest that the specific TGs found elevated in CD progressors may be due to a host response to compromised intake of essential lipids in the small intestine, requiring de novo lipogenesis.

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.

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.

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 (~[2-10] folds), riboflavin (~[4-10] folds) and ascorbic acid (<2 folds) contents of Asian and African foods were markedly lower. In order to extend the search for foods that includes similar dietary constituents as human milk, we designed a strategy of screening 8654 foods. 12 foods were identified and these foods were evaluated for their ability to meet the daily nutritional requirement of breastfed and non-breastfed infants during their first year of life. Genome-scale models of infant's hepatocytes, adipocytes and myocytes were then used to simulate in vitro growth of tissues when subjected to these foods. Key findings were that pork ham cured, fish pudding, and egg lean white induced better tissue growth, and quark with fruit, cheese quarg 45% and cheese cream 60% had similar lactose content as human milk.

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 (36bp, 50bp, 72bp, 100bp, 125bp, 150bp, 200bp, 250bp and 300bp) 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 (>100bp) mapping. On the other hand NovoAlign showed higher sensitivity towards both short reads (36bp, 50bp, 72bp) and long reads (>100bp) 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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

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., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., Competing Interests: P.A. is coinventor on a patent application covering the use of H56 and CAF01 in vaccines. All rights have been assigned to Statens Serum Institut, a Danish nonprofit governmental institute. P.A., DE.CH. and E.M.A. are employees at Statens Serum Institut. R.C., DA.CA. and S.G.R. are employees at the Infectious Disease Research Institute, Seattle. R.R. is an employee of GSK vaccines. A.M. is an employee of Valneva Austria GmbH.

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., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Plasmodium is the causal parasite of malaria, infectious disease responsible for the death of up to one million people each year. Glycerophospholipid and consequently membrane biosynthesis are essential for the survival of the parasite and are targeted by a new class of antimalarial drugs developed in our lab. In order to understand the highly redundant phospholipid synthethic pathways and eventual mechanism of resistance to various drugs, an organism specific kinetic model of these metabolic pathways need to be developed in Plasmodium species., Results: Fluxomic data were used to build a quantitative kinetic model of glycerophospholipid pathways in Plasmodium knowlesi. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimization method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors.The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway.In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis.The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas major part of phosphatidylserine (PS) is formed by base-exchange reactions.Sensitivity analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest sensitivity coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step., Conclusion: We provide a fully parametrized kinetic model for the multiple phospholipid synthetic pathways in P. knowlesi. This model has been used to clarify the relative importance of the various reactions in these metabolic pathways. Future work extensions of this modelling strategy will serve to elucidate the regulatory mechanisms governing the development of Plasmodium during its blood stages, as well as the mechanisms of action of drugs on membrane biosynthetic pathways and eventual mechanisms of resistance.