Biology subjects

Karlsson, C.

Publications and source records attributed to Karlsson, C..

9 recordsLinked to original sources

bioRxiv2026

Learning proteomic disease trajectories with flow matching

High-throughput proteomics has enabled detailed characterization of molecular states across health and disease. However, biological systems are inherently dynamic and methods for reconstructing continuous proteome changes remain limited. Here, we introduce proteome velocity, a framework for inferring continuous proteome trajectories from cross-sectional or sparsely sampled proteomics data using flow matching, in which a neural network learns velocity fields over proteome space. Proteome velocity estimates how rapidly and in which direction protein abundances change along a biological progression, such as disease. In mouse sepsis, covariate-conditioned velocity models resolved tissue- and pathogen-specific proteome trajectories and identified inflammatory proteins with distinct temporal activation patterns across infection routes and organ systems. In clinical COVID-19 plasma proteomes, inferred trajectories separated into distinct velocity programs associated with disease severity. These results show how generative trajectory models can transform cross-sectional proteomics data into interpretable, protein-resolved representations of molecular progression.

bioinformatics↗

bioRxiv2025

Hierarchical organization of mechano-nociceptive pathways revealed by activity labeling

Noxious mechanical stimuli give rise to distinct percepts, from sharp cutaneous pain to diffuse visceral discomfort, yet the nociceptor ensembles that underlie these differences remain poorly defined. We mapped the peripheral architecture of nociceptive signaling by combining in vivo activity labeling of pelvic nerve afferents with single-cell RNA sequencing. Noxious stimuli recruited diverse classes of mechano-nociceptors, but stimulus type exerted modest influence on the composition of activated ensembles. Instead, tissue identity imposed the dominant organizational structure: stimulation of cutaneous and deep pelvic tissues engaged distinct subsets of both myelinated and unmyelinated neurons, revealing a clear domain-level division. Within this architecture, we identified a bladder-innervating myelinated nociceptor subtype with distinctive molecular features, illustrating an additional layer of refinement. Functional imaging and anatomical tracing corroborated this multilevel organization. These findings reveal a hierarchical organization of peripheral mechanical pain encoding, in which mechano-nociceptor populations are differentially engaged according to tissue domain and organ context.

neuroscience↗

bioRxiv2025

Design of a Streptolysin O Epitope-Centric Nanoparticle Vaccine Against Streptococcus pyogenes

Streptococcus pyogenes (Group A Streptococcus, GAS) is a significant human pathogen for which no licensed vaccine is currently available. Here, we report a de novo designed epitope-centric protein-based nanoparticle vaccine against GAS. By integrating structural mass spectrometry techniques and deep learning approaches, we re-engineered a protective epitope (D3m) present in domain 3 of streptolysin O, a prominent pore-forming toxin produced by GAS. D3m was displayed on the surface of a self-assembling icosahedral nanoparticle (D3m-NP) to enhance epitope presentation and immunogenicity. Mice immunised with D3m-NP mounted haemolysis-neutralising titres and displayed a more uniform, epitope-centric antibody response than those receiving the community-standard detoxified full-length streptolysin O. Our findings highlight a promising strategy for GAS vaccine development by combining multimodal protein mass spectrometry, protein design and a versatile protein-based nanoparticle vaccine platform.

immunology↗

bioRxiv2022

Generalized peakgroup scoring boosts identification rates and accuracy in mass spectrometry based discovery proteomics

The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (https://github.com/InfectionMedicineProteomics/gscore).

bioinformatics↗

bioRxiv2022

Distance estimation in the Goldfish (Carassius auratus)

Neurophysiological advances have given us exciting insights into the systems responsible for spatial mapping in mammals. However, we are still lacking information on the evolution of these systems and whether the underlying mechanisms identified are universal across phyla, or specific to the species studied. Here we address these questions by exploring whether a species that is evolutionarily distant from mammals can perform a task central to mammalian spatial mapping - distance estimation. We developed a behavioural paradigm allowing us to test whether goldfish (Carassius auratus) can estimate distance and explored the behavioural mechanisms that underpin this ability. Fish were trained to swim a set distance within a narrow tank covered with striped pattern. After changing the background pattern, we found that goldfish use the spatial frequency of their visual environment to estimate distance; doubling the spatial frequency of optic flow pattern resulted in a large overestimation of the swimming distance. These results provide robust evidence that goldfish can accurately estimate distance, and show that they use optic flow to do so. These results provide a compelling basis to utilise goldfish as a model system to interrogate the evolution of the mechanisms that underpin spatial cognition, from brain to behaviour.

animal behavior and cognition↗

bioRxiv2022

Abundance and expression of hgcAB genes and mercury availability jointly explain methylmercury formation in stratified brackish waters

Neurotoxic methylmercury (MeHg) is formed by microbial methylation of inorganic divalent Hg (HgII) and constitutes severe environmental and human health risks. The methylation is enabled by hgcA and hgcB genes, but it is not known if the associated molecular-level processes are rate-limiting or enable accurate prediction of MeHg formation in nature. In this study, we investigated the relationships between hgcA genes and MeHg across redox stratified water columns in the brackish Baltic Sea. We found that the abundance of hgcA genes and transcripts combined with the concentration of dissolved HgII-sulfide species were strong predictors of both HgII methylation rate and MeHg concentration, implying their roles as principal joint drivers of MeHg formation in these systems. In establishing relationships between hgcA genes and MeHg, we advance the fundamental understanding of mechanistic principles governing MeHg formation in nature and enable refined predictions of MeHg levels in coastal seas in response to the accelerating spread of oxygen deficient zones.

ecology↗

bioRxiv2020

Divergent gene expression responses in two Baltic Sea heterotrophic model bacteria to dinoflagellate dissolved organic matter

Phytoplankton release massive amounts of dissolved organic matter (DOM) into the water column during recurring blooms in coastal waters and inland seas. The released DOM encompasses a complex mixture of both known and unknown compounds, and is a rich nutrient source for heterotrophic bacteria. The metabolic activity of bacteria during and after phytoplankton blooms can hence be expected to reflect the characteristics of the released DOM. We therefore investigated if bacterioplankton could be used as "living sensors" of phytoplankton DOM quantity and/or quality, by applying gene expression analyses to identify bacterial metabolisms induced by DOM. We used transcriptional analysis of two Baltic Sea bacterial isolates (Polaribacter sp. BAL334 [Flavobacteriia] and Brevundimonas sp. BAL450 [Alphaproteobacteria]) growing with DOM from axenic cultures of the dinoflagellate Prorocentrum minimum. We observed pronounced differences between the two bacteria both in bacterial growth and the expressed metabolic pathways in cultures exposed to dinoflagellate DOM compared with controls. Differences in metabolic responses between the two isolates were caused both by differences in gene repertoire between them (e.g. in the SEED categories for membrane transport, motility and photoheterotrophy) and the regulation of expression (e.g. fatty acid metabolism), emphasizing the importance of separating the responses of different taxa in analyses of community sequence data. Similarities between the bacteria included substantially increased expression of genes for Ton and Tol transport systems in both isolates, which are commonly associated with uptake of complex organic molecules. Polaribacter sp. BAL334 showed stronger metabolic responses to DOM harvested from exponential than stationary phase dinoflagellates (128 compared to 26 differentially expressed genes), whereas Brevundimonas sp. BAL450 responded more to the DOM from stationary than exponential phase dinoflagellates (33 compared to 6 differentially expressed genes). These findings suggest that shifts in bacterial metabolisms during different phases of phytoplankton blooms can be detected in individual bacterial species and can provide insights into their involvement in DOM transformations.

microbiology↗

bioRxiv2019

Teleost fish can accurately estimate distance travelled

Terrestrial animals compute shortcuts through their environment by integrating self-motion vectors containing distance and direction information. The sensory and neural mechanisms underlying this navigational feat have been extensively documented, but their evolutionary origins remain unexplored. Among extant vertebrates, the teleost fish make up one of the most diverse and earliest-branching phylogenetic groups, and provide a powerful system to study the origins of vertebrate spatial processing. However, how freely-swimming teleost fish collect and compute metric spatial information underwater are unknown. Using the Picasso triggerfish, Rhinecanthus aculeatus, we investigate the functional and mechanistic basis of distance estimation in teleost fish for the first time. We show that a fish can learn and remember distance travelled with remarkable accuracy. By analysing swimming trajectories, we form hypotheses about how distance is represented in the teleost brain, and propose that distance may be encoded by dedicated neural structures in a similar way to terrestrial vertebrates. Finally, we begin exploring the sensory mechanisms underlying distance estimation in fish. Many walking animals use a step counter for odometry. By quantifying finbeat use during our distance task, we show that a functionally equivalent finbeat counter is unlikely to provide reliable and precise distance information in an aquatic environment.

animal behavior and cognition↗

bioRxiv2019

Sweet revenge - Streptococcus pyogenes showcases first example of immune evasion through specific IgG glycan hydrolysis

Streptococcus pyogenes (Group A streptococcus, GAS) is an important human pathogen responsible for a wide variety of diseases from uncomplicated tonsillitis to life-threatening invasive infections. GAS secretes EndoS, an endoglycosidase able to specifically cleave the conserved N-glycan on human IgG antibodies. In vitro, removal of this glycan impairs IgG effector functions but its relevance to GAS infection in vivo is unclear. Using targeted mass spectrometry, we were able to characterize the effects of EndoS on host IgG glycosylation during the course of natural infections in human patients. We found substantial IgG glycan hydrolysis locally at site of infection as well as systemically in the most severe cases. Using these findings we were able to set up appropriate model systems to demonstrate decreased resistance to phagocytic killing of GAS lacking EndoS in vitro, as well as decreased virulence in a mouse model of invasive infection. This study represents the first described example of specific bacterial IgG glycan hydrolysis during infection and highlights the importance of IgG glycan hydrolysis for streptococcal pathogenesis. We thereby offer new insights into the mechanism of immune evasion employed by this pathogen with clear implications for treatment of severe GAS infections and future efforts at vaccine development.

microbiology↗