Biology subjects

Carlsson, M.

Publications and source records attributed to Carlsson, M..

4 recordsLinked to original sources

bioRxiv2026

ChEBI-N - Enrichment Analysis with the ChEBI Ontology

Introduction: Biological and chemical ontologies are structured representations that can be used to map entities such as metabolites onto broader chemical and biological categories. Enrichment analysis applied to such ontologies identifies classes whose representation within a set deviates from an expected background distribution, helping to interpret complex metabolomics measurements. Objectives: This study aimed to develop a tool for ChEBI (Chemical Entities of Biological Interest) ontology-based enrichment analysis, named ChEBI-N (ChEBI eNrichment). It is a successor of the previous tool BiNChE, with new features that broaden its applicability. Methods: We reimplemented BiNChE and extended it to accept SMILES or InChI strings as well as ChEBI IDs. These broaden applicability to datasets with entities that are not yet present in ChEBI. We also constructed organism-specific background populations. Results: We demonstrate the tool on three published metabolomics datasets covering wine, cheese, and human plasma samples. ChEBI-N correctly highlighted relevant enriched classes in all three datasets, such as flavonoids in the wine dataset and fatty acid classes in the cheese dataset. Comparing organism-specific and full-ChEBI backgrounds shows that restricted backgrounds reduce the inflated significance of generic classes, although the most enriched classes remain largely consistent. Conclusion: ChEBI-N is an extended successor to BiNChE for ontology-based enrichment analysis of metabolomics data, offering a hypothesis-generating complement to existing metabolomics analysis pipelines. Availability: ChEBI-N is freely available at chebin.hastingslab.org or as a Python package (pypi.org/project/chebin), with source code at github.com/ontology-tools/chebin, archived at Zenodo (doi:10.5281/zenodo.23264219) and Software Heritage (swh:1:rev:133a41792704eb7f1db746f36a5ae97e9247b2b7).

bioinformatics↗

bioRxiv2025

Activin receptor type IIA/B blockade increases muscle mass and strength, but compromises glycemic control in mice

Short abstractO_ST_ABSPurposeC_ST_ABSBlocking the Activin receptor type IIA and B (ActRIIA/IIB) has clinical potential to increase muscle mass and improve glycemic control in obesity, cancer, and aging. However, the impact of blocking ActRIIA/IIB on strength, metabolic regulation and insulin action remains unclear. MethodsHere, we investigated the effect of short- (10 mg/kg once, 40h) or long-term (10 mg/kg twice weekly, 21 days) antibody targeting ActRIIA/IIB (ActRIIA/IIBab) in lean and diet-induced obese mice and engineered human muscle tissue. ResultsShort-term ActRIIA/IIB administration in lean mice increased insulin-stimulated glucose uptake in skeletal muscle by 76-105%. Despite this, ActRIIA/IIB-treated mice exhibited 33% elevated fasting blood glucose and glucose intolerance. Moreover, long-term ActRIIA/IIB treatment increased average muscle mass (20%) and reduced fat mass (-8%) in obese mice but did not change insulin-stimulated glucose uptake in skeletal muscle or adipose tissue, yet induced marked glucose intolerance, and increased hepatic glucose output in response to pyruvate. Concomitantly, long-term ActRIIA/IIBab treatment increased strength (30%) in mouse soleus muscle and prevented activin A-induced loss of tissue strength in engineered human muscle tissue. Surprisingly, long-term ActRIIA/IIBab treatment lowered volitional running (-250%). ConclusionOur findings demonstrate that, in accordance with human studies, ActRIIA/IIB blockade holds promise for increasing muscle mass, strength, and insulin sensitivity. However, contrary to the improved glycemic control in humans, ActRIIA/IIB blockade in mice causes severe glucose intolerance and lowers voluntary physical activity. Our study underscores the complex metabolic and functional consequences of ActRIIA/IIB blockade, and highlight species differences on glycemic control, which warrant further investigation.

molecular biology↗

bioRxiv2019

Tumor-bearing mice display reduced insulin-stimulated glucose uptake and microvascular perfusion and exhibit increased hepatic glucose production

BackgroundRedirecting glucose from skeletal muscle and adipose tissue, likely benefits the tumors energy demand to support tumor growth, as cancer patients with type 2 diabetes have 30% increased mortality rates. The aim of this study was to elucidate tissue-specific contributions and molecular mechanisms underlying cancer-induced metabolic perturbations. MethodsGlucose uptake in skeletal muscle and white adipose tissue (WAT), as well as hepatic glucose production, were determined in control and Lewis lung carcinoma (LLC) tumor-bearing C57BL/6 mice using isotopic tracers. Skeletal muscle microvascular perfusion was analyzed via a real-time contrast-enhanced ultrasound technique. Finally, the role of fatty acid turnover on glycemic control was determined by treating tumor-bearing insulin-resistant mice with nicotinic acid or etomoxir. ResultsLLC tumor-bearing mice displayed reduced insulin-induced blood-glucose-lowering and glucose intolerance, which was restored by etomoxir or nicotinic acid. Insulin-stimulated glucose uptake was 30-40% reduced in skeletal muscle and WAT of mice carrying large tumors. Despite compromised glucose uptake, tumor-bearing mice displayed upregulated insulin-stimulated phosphorylation of TBC1D4Thr642 (+18%), AKTSer474 (+65%), and AKTThr309 (+86%) in muscle. Insulin caused a 70% increase in muscle microvascular perfusion in control mice, which was abolished in tumor-bearing mice. Additionally, tumor-bearing mice displayed increased (+45%) basal (not insulin-stimulated) hepatic glucose production. ConclusionsCancer can result in marked perturbations on at least six metabolically essential functions; i) insulins blood-glucose-lowering effect, ii) glucose tolerance, iii) skeletal muscle and WAT insulin-stimulated glucose uptake, iv) intramyocellular insulin signaling, v) muscle microvascular perfusion, and vi) basal hepatic glucose production in mice. The mechanism causing cancer-induced insulin resistance may relate to fatty acid metabolism.

cancer biology↗

bioRxiv2018

SonHi-C: a set of non-procedural approaches for predicting 3D genome organization from Hi-C data

1BackgroundMany computational methods have been developed that leverage the results from biological experiments (such as Hi-C) to infer the 3D organization of the genome. Formally, this is referred to as the 3D genome reconstruction problem (3D-GRP). None of the existing methods for solving the 3D-GRP have utilized a non-procedural programming approach (such as constraint programming or integer programming) despite the established advantages and successful applications of such approaches for predicting the 3D structure of other biomolecules. Our objective was to develop a set of mathematical models and corresponding non-procedural implementations for solving the 3D-GRP to realize the same advantages.\n\nResultsWe present a set of non-procedural approaches for predicting 3D genome organization from Hi-C data (collectively referred to as SonHi-C and pronounced \"sonic\"). Specifically, this set is comprised of three mathematical models based on constraint programming (CP), graph matching (GM) and integer programming (IP). All of the mathematical models were implemented using non-procedural languages and tested with Hi-C data from Schizosaccharomyces pombe (fission yeast). The CP implementation could not optimally solve the problem posed by the fission yeast data after several days of execution time. The GM and IP implementations were able to predict a 3D model of the fission yeast genome in 1.088 and 294.44 seconds, respectively. These 3D models were then biologically validated through literature search which verified that the predictions were able to recapitulate key documented features of the yeast genome.\n\nConclusionsOverall, the mathematical models and programs developed here demonstrate the power of non-procedural programming and graph theoretic techniques for quickly and accurately modelling the 3D genome from Hi-C data. Additionally, they highlight the practical differences observed when differing non-procedural approaches are utilized to solve the 3D-GRP.

bioinformatics↗