Biology subjects

Krag, A.

Publications and source records attributed to Krag, A..

9 recordsLinked to original sources

bioRxiv2026

Sex-stratified adipose-liver circuits in human MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common, clinically heterogeneous condition associated with liver-related and cardiometabolic morbidity. Sex differences in MASLD prevalence and progression are well recognized, but how they extend across the liver and visceral adipose tissue (VAT), a major source of lipid and endocrine signals to the liver, remains unclear. Here, we profiled paired liver and VAT biopsies from women and men with and without MASLD, generating single-nucleus datasets of 34 liver samples and 40 VAT samples, including 32 patient-matched pairs. In liver, women with MASLD showed a higher Kupffer-cell triglyceride-catabolism score (1.30-SD female-male difference; FDR=0.0022), accompanied by directionally consistent hepatocyte and endothelial programs. In VAT, women with MASLD showed higher adipocyte lipid and oxidative metabolism (0.87-1.15 SD; FDR<0.05 across seven pathways) together with macrophage immune activation, whereas men with MASLD showed stronger stromal communication routes. Across matched patients, gene modules linked VAT adipocyte and macrophage states to liver hepatocyte stress and lipid-receipt states. Bulk RNA-seq module scoring and paired liver-VAT analyses in 293 patients supported hepatic stress in both sexes, lower adipocyte signaling in women with increasing steatosis, and cross-tissue adipocyte-hepatocyte coupling (r=0.30; FDR=2.1e-6). Together, these findings identify distinct sex-associated transcriptional states in liver and VAT in MASLD and show that these states are coordinated across both tissues within the same individuals.

physiology↗

bioRxiv2025

Adipose tissue cell states linked to progression and clinical subtypes of MASLD in human obesity

Adipose tissue dysfunction is a key determinant of inter-individual variability in obesity-associated comorbidities, yet the underlying tissue-level mechanisms linking adipose remodeling to progression of metabolic dysfunction-associated steatotic liver disease (MASLD) remain poorly defined. We applied single-nucleus RNA sequencing to abdominal subcutaneous adipose tissue (SAT) from individuals with severe obesity, stratified by histologically defined MASLD stage, and integrated these data with bulk RNA-seq from [~]200 individuals. MASLD was associated with adipocyte hypertrophy and near-depletion of an ADH1BHI adipocyte subset linked to improved lipid buffering and systemic metabolic health. Conversely, lipid-associated macrophages (LAMs) expanded in MASLD, coordinating lipid-handling and inflammatory programs in the myeloid compartment. In advanced metabolic dysfunction-associated steatohepatitis (MASH), we identified a senescence-associated PLAUHI progenitor subpopulation with pro-inflammatory signaling potential. Whereas LAMs were broadly enriched in MASLD subtypes, PLAUHI progenitors specifically marked a cardiometabolic MASLD endotype, characterized by type 2 diabetes and cardiovascular disease. These findings highlight cellular remodeling of adipose tissue as a potential driver of MASLD heterogeneity and progression.

cell biology↗

bioRxiv2025

Single cell-resolved transcriptional dynamics of human subcutaneous adipose tissue during lifestyle- and bariatric surgery-induced weight loss

During sustained weight gain, human white adipose tissue undergoes dramatic remodeling that may compromise adipose tissue function and lead to obesity comorbidities such as cardiometabolic disease. These comorbidities can be at least partially reversed by weight loss; however, the degree to which human adipose tissue is remodeled during weight loss is currently unclear. Here, we have used snRNA-seq combined with bulk RNA-seq and 3D light microscopy to investigate the cellular and transcriptional alterations in abdominal subcutaneous adipose tissue (ASAT) from a unique cohort of obese individuals undergoing initially modest (8-10%) lifestyle-induced weight loss followed by a more dramatic (20-45%) bariatric surgery-induced weight loss. We show that in response to this weight loss, ASAT in both males and females underwent dramatic compositional and transcriptional remodeling. Most notably, surgery-indued weight loss led to an increase in the adipose vascular compartment, a dramatic reduction in proinflammatory immune cells, and a tissue-wide reduction in inflammatory gene signatures. In response to modest weight loss, we observed an increase in specific progenitor populations and an induction of proadipogenic genes, indicating that this process is important for the early adaptation of AT to weight loss that might contribute to the early overall beneficial effects on insulin sensitivity and systemic inflammation.

cell biology↗

bioRxiv2024

The proteomic landscape of proteotoxic stress in a fibrogenic liver disease

Protein misfolding diseases, including alpha-1 antitrypsin deficiency (AATD), pose significant health challenges, with their cellular progression still poorly understood1-3. We utilize spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudo-time across fibrosis stages. We achieve unprecedented proteome depth of up to 3,800 proteins from a third of a single cell in formalin-fixed, paraffin-embedded (FFPE) tissue. This dataset revealed a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show alpha-1 antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with AI-guided image-based phenotyping across multiple disease stages, revealing a terminal hepatocyte state characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10/TRAIL expression. This phenotype may represent a critical disease progression stage. Our study offers novel insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.

cell biology↗

bioRxiv2024

Deep Proteome Profiling of Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects roughly 1 in 3 adults and is a leading cause of liver transplants and liver related mortality. A deeper understanding of disease pathogenesis is essential to assist in developing blood-based biomarkers. Here, we use data-independent acquisition mass spectrometry to assess disease-state associated protein profiles in human liver, blood plasma, and white adipose tissue (WAT). In liver, we find that MASLD is associated with an increased abundance of proteins involved in immune response and extracellular matrix (ECM) and a decrease in proteins involved in metabolism. Cell type deconvolution of the proteome indicate liver endothelial and hepatic stellate cells as main source of the ECM rearrangements, and hepatocytes as the major contributor to the changes in liver metabolism. In the blood, profiles of several MASLD-associated proteins that correlate with their expression in WAT rather than liver yet could serve as suitable liver disease predictors in a multi-protein panel marker. Moreover, our proteomics-based logistic regression models consistently outperform existing methods for predicting MASLD and liver fibrosis from human blood samples.

systems biology↗

bioRxiv2024

Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

The microbiota in individual habitats differ both in relative composition and absolute abundance. While sequencing approaches determine only the relative abundances of taxa and genes, experimental techniques for absolute abundance determination are rarely applied to large-scale microbiome studies. Here, we developed a machine learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applied to large-scale datasets (n = 34,539), we demonstrate that microbial load is the major determinant of gut microbiome variation and associated with numerous host factors. We found that for several diseases, the altered microbial load, not the disease itself, was the main driver of the gut microbiome changes. Adjusting for this effect substantially reduced the significance of more than half of the disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

microbiology↗

bioRxiv2021

High-throughput UHPLC-MS to screen metabolites in feces for gut metabolic health

(1) BackgroundFeces are the product of our diets and have been linked to diseases of the gut, including Chrons disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. (2) MethodsAs sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n= 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. (3) ResultsMS analyses of the prepared fecal samples have shown reproducibility and coverage of n=28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD, LOQ and range of linearity. The average concen-trations for 135 healthy participants are reported here for clinical applications. (4) Conclusionsour high-throughput method provides an efficient tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

biochemistry↗

bioRxiv2020

A paired liver biopsy and plasma proteomics study reveals circulating biomarkers for alcohol-related liver disease

Existing tests for detecting liver fibrosis, inflammation and steatosis, three stages of liver disease that are still reversible are severely hampered by limited accuracy or invasive nature. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to identify biomarkers in a cross-sectional alcohol-related liver disease cohort of nearly 600 individuals. Metabolic functions were downregulated whereas fibrosis-associated signaling and novel immune responses were upregulated, but only half of tissue proteome changes were transmitted to the circulation. Machine learning models based on our biomarker panels outperformed existing tests, laying the foundation for a generic proteomic liver health assessment.

biochemistry↗

bioRxiv2019

Describing the fecal metabolome in cryogenically collected samples from healthy participants.

IntroductionThe chemical composition of feces plays an important role in human metabolism. Metabolomics and lipidomics are valuable tools for screening the metabolite composition in feces. Here we set out to describe fecal metabolite composition in healthy participants in frozen stools.\n\nMethodsFrozen stool samples were collected from 10 healthy volunteers and cryogenically drilled in four areas along the specimen. Polar metabolites were analyzed using derivatization followed by two-dimensional gas chromatography and time of flight mass spectrometry. Lipids were detected using ultra high-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry. The technical variation threshold was set to 30% in pooled quality control samples and metabolite variation was then assessed in four areas per specimen. A data-generated network using metabolites found in all areas was computed for healthy participants.\n\nResults2326 metabolic features were detected. Out of a total of 298 metabolites that were annotated we report here 185 that showed a technical variation of x< 30%. These metabolites included amino acids, fatty acid derivatives, carboxylic acids and phenolic compounds. Lipids predominantly belonged to the groups of diacylglycerols, triacylglycerols and ceramides. Metabolites varied between sampling areas (14%-80%). A network using metabolites present in all areas showed two main clusters, DAG lipids and phenyllactic acid.\n\nConclusionsIn feces from healthy participants, the main groups detected were phenolic compounds, ceramides, diacylglycerols and triacylglycerols. Metabolite levels differed considerably depending on the sampling area.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC=\"FIGDIR/small/708685v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@17db665org.highwire.dtl.DTLVardef@51e140org.highwire.dtl.DTLVardef@19b55d7org.highwire.dtl.DTLVardef@120d399_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗