Biology subjects

Andersson, D.

Publications and source records attributed to Andersson, D..

7 recordsLinked to original sources

bioRxiv2026

Germline genomic and methylomic dynamics following three generations of early-life metabolic challenges

Environmental and dietary factors can exert multigenerational effects on health and development. In this study, we investigated whether early-life metabolic challenge affects the germline genome and epigenome across three generations. Using a murine model of early life obesity via litter size reduction (overnutrition group, ON) and a control group (CT), we followed the paternal lineage focusing on germline genomic and methylation changes employing Genotyping-by-Sequencing (GBS) coupled with methyl-immunoprecipitation (GBS-MeDIP). We found that unrelated ON families clustered together based on identified Single-Nucleotide Polymorphism (SNP), suggesting that the treatment may have genomic impact. Copy number variations (CNVs) events were identified in ON individuals, being enriched in Long Interspersed Nuclear Elements (LINEs) and Long Terminal Repeats (LTRs). While Principal Component Analysis (PCA) of the methylome showed no clear treatment effect, pathway enrichment and regional analyses revealed methylation changes associated with transposable elements and developmental genes. Notably, the ON group exhibited a disruption in the methylation of Repetitive Elements (RE), which was significant in the same type of RE that were also enriched in the observed CNVs. The ON also showed reduced emergence of novel SNPs in offspring compared to the CT group. These findings suggest that multigenerational metabolic challenge can constrain genetic variability and induce genome instability, potentially mediated by transposable element activity rather than widespread changes in DNA methylation. This work highlights the importance of studying both genome and epigenome dynamics under realistic, multigenerational exposure scenarios and suggests that early metabolic challenges can have long-lasting impacts on genomic architecture and evolutionary potential.

genomics↗

bioRxiv2025

A Hybrid Modeling Framework for Predictive Digital Twins of CHO Cell Culture

Digital twins of mammalian cell cultures hold great potential for predictive bioprocess modeling, yet their development is challenged by the nonlinear dynamics and metabolic complexity of these systems. We present a hybrid computational framework that integrates mechanistic and data-driven modeling to construct predictive digital twins for Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies. The framework couples ordinary differential equation (ODE) models with constraint-based metabolic modeling and machine learning components trained on Bayesian-estimated metabolic rates. Applied to 23 CHO fed-batch cultures, viable cell density, product titer, and key metabolite concentrations are accurately predicted under varying feeding and media conditions within a unified simulation engine, where empirical variability is incorporated through multivariate statistical constraints derived from experimental data. Cross-validation analyses demonstrated strong generalization across process variations, highlighting the frameworks capacity to capture both biochemical constraints and adaptive cellular behavior. This hybrid modeling approach provides a mechanistically interpretable yet data-adaptive foundation for constructing bioprocess digital twins. By bridging statistical, mechanistic, and machine learning methodologies, it advances the computational representation of CHO cell culture systems and offers a generalizable strategy for predictive modeling in complex biological production processes.

bioengineering↗

bioRxiv2025

Overcoming the Limits of Traditional Rate Calculations from Sparse Concentration Data: A Probabilistic Framework for Bioprocess Modeling

Accurate estimation of growth and metabolic rates is essential for understanding and optimizing bioprocesses, yet traditional methods often fail when faced with sparse or noisy concentration data. We present a probabilistic framework based on Bayesian inference and Nested Sampling that addresses these challenges by integrating biological knowledge directly into the model structure. The approach transforms raw concentration measurements into pseudo-concentrations that account for distortions caused by bioreactor volume changes (e.g., feed additions, sample withdrawals), and models metabolic rates as linear combinations of basis functions to yield continuous rate profiles from discrete data. Using in-silico simulations, we evaluated the framework under a range of experimental conditions and compared its performance with a conventional rate calculation method. We further analyzed the influence of key experimental design parameters - sampling frequency, sample volume, and measurement noise - on both rate estimation accuracy and concentration reconstruction quality. Results demonstrate that the proposed framework delivers accurate, robust metabolic rate estimates even under severe data sparsity and noise, offering a powerful tool for improving bioprocess characterization and optimization.

bioengineering↗

bioRxiv2025

The sensitising effect of IgG in fibromyalgia syndrome is mediated by Mrgprb2 in mast cells

Fibromyalgia syndrome (FMS) is characterized by elevated levels of immunoglobulin G (IgG), altered bowel habits, and increased pain sensitivity, suggesting immune dysregulation, but the exact mechanism remains unclear. Here, we found that FMS-IgG binds to mast cells in a MRGPRX2/b2-dependent manner, leading to mast cell recruitment and IL-6 secretion. Transferring serum-IgG from FMS patients to mice induced FMS-like symptoms and increased skin mast cells, indicating that FMS-IgG acts through mast cell activation. The ablation of mice Mrgprb2 mast cells or deleting Mrgprb2 receptors prevented IgG-induced heightened sensitivity to mechanical and cold stimuli. Stimulating human LAD2 cells with FMS IgG elicited MRGPRX2-dependent IL-6 production. Consistent with mice findings, mast cell density and tryptase levels increased in human FMS skin samples compared to healthy controls. Taken together our results suggests that FMS IgG mediates hypersensitivity via activation of mast cells bearing the MRGPRX2 receptor and that these cells are a potential therapeutic target.

immunology↗

bioRxiv2024

Personalized Molecular Signatures of Insulin Resistance and Type 2 Diabetes

HighlightsO_LIAdvanced proteomics analysis reveals personalized signatures of insulin resistance C_LIO_LIFasting muscle proteome and phosphoproteome predicts whole-body insulin sensitivity C_LIO_LIInsulin-stimulated phosphoproteome reveals selective insulin resistance signatures C_LIO_LIPhosphoproteome and proteome atlas explains sex-specific muscle metabolism C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/578994v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@a83000org.highwire.dtl.DTLVardef@1e77336org.highwire.dtl.DTLVardef@3d1ecdorg.highwire.dtl.DTLVardef@b1592b_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG Insulin resistance is a hallmark of type 2 diabetes, which is a highly heterogeneous disease with diverse pathology. Understanding the molecular signatures of insulin resistance and its association with individual phenotypic traits is crucial for advancing precision medicine in type 2 diabetes. Utilizing cutting-edge proteomics technology, we mapped the proteome and phosphoproteome of skeletal muscle from >120 men and women with normal glucose tolerance or type 2 diabetes, with varying degrees of insulin sensitivity. Leveraging deep in vivo phenotyping, we reveal that fasting proteome and phosphoproteome signatures strongly predict insulin sensitivity. Furthermore, the insulin-stimulated phosphoproteome revealed both dysregulated and preserved signaling nodes - even in individuals with severe insulin resistance. While substantial sex-specific differences in the proteome and phosphoproteome were identified, molecular signatures of insulin resistance remained largely similar between men and women. These findings underscore the need for precision medicine approaches in type 2 diabetes care, acknowledging disease heterogeneity.

biochemistry↗

bioRxiv2022

Rescue of Escherichia coli auxotrophy by de novo small proteins

Increasing numbers of small proteins with diverse physiological roles are being identified and characterized in both prokaryotic and eukaryotic systems, but the origins and evolution of these proteins remain unclear. Recent genomic sequence analyses in several organisms suggest that new functions encoded by small open reading frames (sORFs) may emerge de novo from noncoding sequences. However, experimental data demonstrating if and how randomly generated sORFs can confer beneficial effects to cells are limited. Here we show that by up-regulating hisB expression, de novo small proteins ([≤] 50 amino acids in length) selected from random sequence libraries can rescue Escherichia coli cells that lack the conditionally essential SerB enzyme. The recovered small proteins are hydrophobic and confer their rescue effect by binding to the 5 end regulatory region of the his operon mRNA, suggesting that protein binding promotes structural rearrangements of the RNA that allow increased hisB expression. This study adds RNA regulatory elements as another interacting partner for de novo proteins isolated from random sequence libraries, and provides further experimental evidence that small proteins with selective benefits can originate from the expression of nonfunctional sequences.

evolutionary biology↗

bioRxiv2020

Structure and mechanism of a phage-encoded SAM lyase revises catalytic function of enzyme family

The first SAM degrading enzyme (SAMase) was discovered in bacteriophage T3, as a counter-defense against the bacterial restriction-modification system, and annotated as an S-adenosyl-L-methionine (SAM) hydrolase forming 5-methyl-thioadenosine (MTA) and L-homoserine. From environmental phages, we recently discovered three SAMases with barely detectable sequence similarity to T3 SAMase and without homology to proteins of known structure. Here, we present the very first phage SAMase structures, in complex with a substrate analogue and the product MTA. The structure shows a trimer of alpha-beta sandwiches similar to the GlnB-like superfamily, with active sites formed at the trimer interfaces. Quantum-mechanical calculations, thin-layer chromatography and NMR spectroscopy demonstrate that this family of enzymes are not hydrolases but lyases forming MTA and L-homoserine lactone in a unimolecular reaction mechanism. Sequence analysis, in vitro and in vivo mutagenesis support that T3 SAMase belongs to the same structural family and utilizes the same reaction mechanism.

biochemistry↗