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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.

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., This study was supported by the Novo Nordisk Foundation (NNF18OC0034506, to MO), Juvenile Diabetes Research Foundation (2-SRA-2014-159-Q-R, to MO, TH, RL, and MKn), Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research – SyMMyS, Decision No. 250114, to RL, JT, MO, RL, and MKn; and Personalised Health 2014 programme project, Decision No. 292568, to RL, JT, MO, RL, and MKn). Further support was received by the Academy of Finland postdoctoral grant (No. 323171 to SL) and (grant no. 333981 to MO), “Inflammation in human early life: targeting impacts on life-course health” (INITIALISE) consortium funded by the Horizon Europe Program of the European Union under Grant Agreement 101094099 (to MO), the Medical Research Funds, Tampere and Helsinki University Hospitals (to MKn), InFLAMES Flagship Programme of the Academy of Finland (decision number: 337530) and Turku University Hospital (to MO, RL, and JT).

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., This work was supported by the Research Council of Finland (grant no. 333981 to M.O.) and by the “Inflammation in human early life: targeting impacts on life-course health” (INITIALISE) consortium funded by the Horizon Europe Program of the European Union under Grant Agreement 101094099.

Background & Aims: Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages.Methods: We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR.Results: Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen within individual NAFLD stages, but miR-193a-5p consistently showed increased levels in all comparisons. Relative to NAFL/non-alcoholic steatohepatitis (NASH) with mild fibrosis (stage 0/1), 3 miRNAs (miR-193a-5p, miR-378d, and miR378d) were increased in cases with NASH and clinically significant fibrosis (stages 2-4), 7 (miR193a-5p, miR-378d, miR-378e, miR-320b, miR-320c, miR-320d, and miR-320e) increased in cases with NAFLD activity score (NAS) 5-8 compared with lower NAS, and 3 (miR-193a-5p, miR-378d, and miR-378e) increased but 1 (miR-19b-3p) decreased in steatosis, activity, and fibrosis (SAF) activity score 2-4 compared with lower SAF activity. The significant findings for miR-193a-5p were replicated in the additional cohort with NAFLD. Studies in Hep G2 cells showed that following palmitic acid treatment, miR-193a-5p expression decreased significantly. Gene targets for miR-193a-5p were investigated in liver RNAseq data for a case subgroup (n = 80); liver GPX8 levels correlated positively with serum miR-193a-5p.Conclusions: Serum miR-193a-5p levels correlate strongly with NAFLD activity grade and fibrosis stage. MiR-193a-5p may have a role in the hepatic response to oxidative stress and is a potential clinically tractable circulating biomarker for progressive NAFLD.Lay su

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., Funding agencies:Academy of Finland postdoctoral grant 323171Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research 250114Medical Research Funds, Helsinki University Hospital

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.

T‐cells are sentinels of adaptive immune responses. T‐cell activation, proliferation and differentiation involves metabolic reprogramming involving 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 (Th1, Th2, Th17 and iTregs). We combined genome‐scale metabolic modeling, gene expression data, targeted and non‐targeted lipidomics experiments, together with in vitro gene knockdown experiments and showed that human CD4+ T cells undergo specific metabolic changes during activation and functional differentiation. In addition, we identified and confirmed the importance of ceramide and glycosphingolipid biosynthesis pathways in Th17 differentiation and effector functions. Through in vitro gene knockdown experiments, we substantiated the requirement of serine palmitoyl transferase, a de novo sphingolipid pathway in the expression of proinflammatory cytokine (IL17A and IL17F) by Th17 cells. Our findings may provide a comprehensive resource for identifying CD4+ T‐cell‐specific targets for their selective manipulation under disease conditions, particularly, diseases characterized by an imbalance of Th17/nTreg cells.

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.

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.

Background and aims: Liver has a vital role in metabolism, distribution, and excretion of exogenous chemicals. The endocrine disrupting chemicals (EDCs) may act as a‘second hit’in the progression of NAFLD, advancing the earlier stages of liver pathology such as steatosis to more severe stages. A specific class of ECDs that have been linked with NAFLD are perfluorinated alkyl substances (PFAS), a class of commonly used industrial chemicals that humans are widelyexposed to. Due to the their structural similarity with fatty acids, PFAS may disrupt hepatic lipid metabolism. Furthermore, functionally, PFAS share some features with bile acids, including similar enterohepatic circulation. Nevertheless, human data linking PFAS exposure and lipid metabolism in the liver are currently lacking. The principal aim of our study was to define the impact of PFAS exposure on hepatic metabolism, with specific focus on bile acid and lipid metabolism. Method: In a well-characterized human NAFLD cohort of 105 individuals, we investigated the impact of PFAS exposure on liver metabolism in the individuals with NAFLD. Average BMI was 45.65 ± 5.99 kg/m2, with liver fat content varying between 0% and 80%. We comprehensively characterized both hepatic (liver biopsy) and serum metabolome using four analytical platforms, and measured PFAS in serum. We investigated the association between the NAFLD (liver fat %, NASH grade, fibrosis stage, insulin resistance), PFAS exposure, and metabolome. Results: PFAS exposurewas associated with NAFLD (Figure) as well as with changes in hepatic lipid and bile acid metabolism. Importantly, we observed sex-specific association between chemical exposure and NAFLD, linked with sex-specific changes in both hepatic and circulating metabolome. We noticed differences not only in the exposure profiles between the males and females, but, notably, also the impact of the exposure, as characterized both with the impact on metabolome but also on clinical parameters was cle

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.

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.

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.

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.

Ceramides are bioactive sphingolipids, comprised of sphingosine and a fatty acyl chain. They have been recognized as key mediators of lipotoxicity; a phenomenon where excess fat in adipose tissue leads to de novo synthesis of ceramides and their precursor dihydroceramides (DHCer). This occurs in adipose tissue as well as in the periphery. Accumulation of these ceramides is associated with insulin resistance, de novo lipogenesis, and inflammation1 , thus increasing the risk of cardiometabolic diseases such as type 2 diabetes (T2D) and atherosclerosis. Recently, in this journal, Hannish and colleagues reported that another, non-canonical class of ceramides, the 1-deoxyceramides (DoxCer), is highly enriched in visceral adipose tissue (VAT) as well as in serum of obese patients with T2D2 . This study also clarified previously-reported discrepancies in the literature concerning the association of DHCer with the risk of T2D, as the signals from DHCer can be easily confused with the signals from DoxCer in mass spectrometric (MS) analysis of lipids.