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Background and aims: Liver fibrosis is the main determinant of clinical outcomes in advanced chronic liver disease (ACLD) of any etiology. The introduction of effective etiological therapies has shown that liver fibrosis and even cirrhosis is reversible after eliminating the cause of ACLD. However, not all patients experience fibrosis regression, evidencing that other factors modulate this process. In order to better understand the genetic and molecular mechanisms controlling fibrosis regression we analysed the genetic and tran- scriptomic pathways in ACLD with or without regression of fibrosis using high-throughput technologies. Method: We conducted a case-control pilot study of 60 patients with ACLD of different etiology, 30 with histological and/or clinical evidence of regression (Regressors, Re) and 30 without (Non- regressors, NRe), after a minimum of 24 months of etiological therapy. In all patients we performed genotyping (TaqMan assay) of gene variants associated with liver fibrosis (PNPLA3, TM6SF2, SERPINA1, GCKR, TMC4, HSD17B13). Transcriptomic analysis (RNAseq) was done in 30 frozen paired liver biopsies (before and after therapy) of 15 patients. Differentially Expressed Genes (DEGs) analysis were determined using DESeq2 and selected for FDR

Background and aims: Liver fibrosis is the main determinant of clinical outcomes in advanced chronic liver disease (ACLD) of any etiology. Regression of bridging fibrosis and cirrhosis can take place after effective etiological treatment, but not in all cases, suggesting that factors other than the etiology play a role in the modulation of this important outcome. In patients achieving regression of ACLD, liver function markedly improves, suggesting that several metabolic pathways are influenced by regression of fibrosis. However, the metabolic pathways associated with fibrosis regression in ACLD have not been characterized. We hypothesize that thorough assessment of liver metabolism could provide evidence-based data for better characterization of cirrhosis patients with regression following effective etiological treatment, thus providing a rational basis for personalized interventions. Method: We performed a case-control pilot study of 60 patients with ACLD of different etiologies, 30 with histological and/or clinical evidence of regression of ACLD (Regressors) and 30 without any improvement (Non-regressors) after a minimum of 24 months of successful etiological therapy. We used the combination of metabolic modelling and metabolic profiling to define metabolic pathways and associated signature that differentiate Regressors from Non-regres- sors. We have developed a transcriptomics-driven metabolic model- ling approach to assess the regression of ACLD. We further investigated the associated metabolic signature using mass spec- trometry-based metabolomics. Results: Although Regressors and Non-regressors showed similar profiles at baseline, our observation is pointing to fatty acid β- oxidation and arachidonic acid metabolism (associated to inflamma- tion and oxidative stress) as key metabolic pathways differentiating Regressors from Non-regressors after therapy, suggesting that lipid metabolism plays a central role in the modulation of the extracellular matrix in ACLD. In addition, Using Least Absolute Shrinkage and Selection Operator (LASSO) analyses, the Regressor phenotype was predicted with 80% accuracy based on the levels of identified lipid markers after etiologic therapy.

Additional file 1. Supplementary methods used in this study, including additional details on omics techniques used for quantification, validation of the MOFA model and association with clinical measurements.

Additional file 2: Additional Figures, including a correlation matrix analysis of latent factors (Figure S1) and prediction of CSF AD biomarkers using the trained MOFA model (Figure S2).

Additional file 3: Additional Tables, including coarse-grain categories used for pathway enrichment (Table S1), relationship between selected CSF molecules and latent factors (Table S2), correlations between selected proteins and lipids with CDR-SoB and MMSE scores (Tables S3) and CSF proteins presenting an association with cognitive impairment (Table S4).

In this study, we define a new role for lipocalin prostaglandin D synthase (L-PGDS) in the control of metabolic fuel utilization by brown adipose tissue (BAT). We demonstrate that L-PGDS expression in BAT is positively correlated with BAT activity, upregulated by peroxisome proliferator-activated receptor γ coactivator 1α or 1β and repressed by receptor-interacting protein 140. Under cold-acclimated conditions, mice lacking L-PGDS had elevated reliance on carbohydrate to provide fuel for thermogenesis and had increased expression of genes regulating glycolysis and de novo lipogenesis in BAT. These transcriptional differences were associated with increased lipid content in BAT and a BAT lipid composition enriched with de novo synthesized lipids. Consistent with the concept that lack of L-PGDS increases glucose utilization, mice lacking L-PGDS had improved glucose tolerance after high-fat feeding. The improved glucose tolerance appeared to be independent of changes in insulin sensitivity, as insulin levels during the glucose tolerance test and insulin, leptin, and adiponectin levels were unchanged. Moreover, L-PGDS knockout mice exhibited increased expression of genes involved in thermogenesis and increased norepinephrine-stimulated glucose uptake to BAT, suggesting that sympathetically mediated changes in glucose uptake may have improved glucose tolerance. Taken together, these results suggest that L-PGDS plays an important role in the regulation of glucose utilization in vivo.

BACKGROUND Multiple pathophysiological processes have been described in Alzheimer's disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology. METHODS We performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analyzed at single-omic level with correlation and regression approaches. Multi-omics factor analysis was used to integrate all biological levels. Identified analytes were used to construct best predictive models of the presence of AD pathology and of cognitive decline with multifactorial regression analysis. Pathway enrichment analysis identified pathway alterations in AD. RESULTS Multi-omics integration identified five major dimensions of heterogeneity explaining the variance within the cohort and differentially associated with AD. Further analysis exposed multiple interactions between single 'omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response, and extracellular matrix signaling pathways in association with AD. Finally, combinations of four molecules improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1). CONCLUSIONS Applying an integrative multi-omics approach we report novel molecular and pathways alterations associated with AD pathology. These findings are relevant for the development of personalized diagnosis and treatment approaches in AD.

Background: The brain consumes a considerable amount of glucose relative to its size. Brain glucose is a source of energy, mainly via oxidative phosphorylation, as well as a source of building blocks for synthesising macromolecules. Little is known about the spatial distribution of this biosynthesis. Aim: To develop an imaging method of cerebral biosynthesis from the carbon backbone of glucose molecules at the nanometer scale Method: Injection of [U-13C]glucose into awake mice followed by correlated electron microscopy imaging and nanoscale secondary ion mass-spectroscopy (NanoSIMS) mapping of the13C/12C isotopic ratio in brain thin sections. Results: Large 13C signals were detected in somata, nucleoli, Golgi apparatus, lysosomes, dendrites and synapses. The 13C signal intensity exhibited a highly inhomogeneous spatial distribution. In particular, it was not the same in neurons and astrocytes. The time evolution of the 13C-enrichment was also strikingly different between subcellular compartments. Discussion & Conclusion: Given the fact that glucose, as well all other small, soluble metabolites, are lost in the sample preparation process, the observed 13C atoms are those incorporated into macromolecules such as nucleotides, proteins and lipids produced via biosynthetic pathways. The information obtained at the subcellular level gives a high-resolution, time-resolved map of the biosynthetic processes taking place in the brain.