Biology subjects

Larsson, P.

Publications and source records attributed to Larsson, P..

8 recordsLinked to original sources

bioRxiv2025

Predictive encoding of auditory sequences in the human prefrontal cortex

Humans extract regularities from the environment to form expectations that guide perception and optimize behavior. Although the prefrontal cortex (PFC) is central to this process, the relative contributions of orbitofrontal (OFC) and lateral PFC (LPFC) remain unclear. Here, we show that the brain tracks sound regularities in an auditory deviance detection task to predict when a target deviant will occur. Intracranial EEG in epilepsy patients reveals prefrontal engagement, with earlier expectancy-related modulation in OFC and later modulation in LPFC. Connectivity analyses indicate bidirectional and asymmetrical expectancy-related information exchange between the two areas, with a first lead by OFC, consistent with its role in initiating predictive encoding. Converging causal evidence shows that OFC lesions abolish sensitivity to expectancy, whereas LPFC lesions yield only modest effects not significantly different from controls. Together, these results provide electrophysiological and causal evidence for distinct, temporally organized contributions of prefrontal subregions to predictive processing. Significance StatementThe human brain builds probabilistic associations capturing the dynamic environmental structure in a predictive manner. The ability to encode and deploy predictive information to anticipate future events relies on the prefrontal cortex (PFC). However, the specific roles of PFC subregions in this process remain unclear. Here, we provide converging electrophysiological and causal lesion evidence on the distinct involvement of the orbital and lateral PFC in generating anticipatory neural signals based on probabilistic auditory structure.

neuroscience↗

bioRxiv2025

Proteomic and lipidomic profiling of immune cell-derived subpopulations of extracellular vesicles

IntroductionExtracellular vesicles (EVs) are a heterogeneous group of membrane-enclosed vesicles released by cells. They play important roles in intercellular communication and contribute to several physiological and pathological processes. Cells release subpopulations of EVs with distinct biogenesis and functions, however, we currently have few markers to differentiate them. This study, therefore, aimed to determine proteomic and lipidomic differences among four EV subpopulations of varying sizes and densities. MethodsLarge and small EVs (L-EVs and S-EVs) were isolated from two immune cell lines by differential ultracentrifugation at 16,500 x g and 118,000 x g, respectively. The crude EVs were then further separated by density cushion centrifugation. EVs were isolated from the interphase between 1.079-1.146 and 1.146-1.185 g/ml, hereafter referred to as low density (LD) and high density (HD), respectively. This resulted in four subpopulations of EVs: L-EVs LD, L-EVs HD, S-EVs LD, and S-EVs HD. The morphology, size, and yield of EVs were determined by nanoparticle tracking analysis, electron microscopy, and western blot. The proteome and lipidome of the four subpopulations of EVs were determined with mass spectrometry. ResultsA total of 5364 proteins were quantified in the dataset. L-EV and S-EVs as well as LD and HD were well separated. Briefly, L-EVs LD were enriched in mitochondrial proteins such as the TIMM/TOMM complex, MICOS, and ATP5 proteins. In contrast, L-EVs HD were enriched in proteins associated with the cytoskeleton, such as KIF proteins. Furthermore, S-EVs LD were enriched in tetraspanins and ESCRT machinery proteins, while S-EVs HD were enriched in histones, CCT proteins, and proteins from the complement pathway. Proteins such as flotillins, RABs, annexins, and integrins were enriched in two or several subpopulations. Furthermore, 107 lipids were quantified, and the most abundant lipids in EVs were phosphatidylcholine (PC), sphingomyelin, and phosphatidylethanolamine (PE). The most profound difference was that PE was less abundant in L-EVs LD as compared to the other EV subtypes, and ceramides were enriched in L-EVs as compared to S-EVs. ConclusionThis study demonstrates that the proteome and lipidome differ in EV subpopulations separated based on size and density. Furthermore, it validates several protein groups that have previously been suggested to be enriched in either S-EVs or L-EVs.

molecular biology↗

bioRxiv2024

Binding of PUFA stabilizes a conductive state of the selectivity filter in IKs channels

In cardiomyocytes, the KCNQ1/KCNE1 channel complex mediates the slow delayed-rectifier current (IKs), pivotal during the repolarization phase of the ventricular action potential. Mutations in IKs cause Long QT Syndrome (LQTS), a syndrome with a prolonged QT interval on the ECG, which increases the risk of ventricular arrhythmia and sudden cardiac death. One potential therapeutical intervention for LQTS is based on targeting IKs channels to restore channel function and/or the physiological QT interval. Polyunsaturated fatty acids (PUFAs) are potent activators of KCNQ1 channels and activate IKs channels by binding to two different sites, one in the voltage sensor domain (VSD) - which shifts the voltage dependence to more negative voltages- and the other in the pore domain (PD) - which increases the maximal conductance of the channels (Gmax). However, the mechanism by which PUFAs increase the Gmax of the IKs channels is still poorly understood. In addition, it is unclear why IKs channels have a very small single channel conductance and a low open probability or whether PUFAs affect any of these properties of IKs channels. Our results suggest that the selectivity filter in KCNQ1 is normally unstable, contributing to the low open probability, and that the PUFA-induced increase in Gmax is caused by a stabilization of the selectivity filter in an open-conductive state.

biophysics↗

bioRxiv2023

Finding and Following: A deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo

The last decade has seen increasing use of advanced imaging techniques in platelet research. However, there has been a lag in the development of image analysis methods, leaving much of the information trapped in images. Herein, we present a robust analytical pipeline for finding and following individual platelets over time in growing thrombi. Our pipeline covers four steps: detection, tracking, estimation of tracking accuracy, and quantification of platelet metrics. We detect platelets using a deep learning network for image segmentation, which we validated with proofreading by multiple experts. We then track platelets using a standard particle tracking algorithm and validate the tracks with custom image sampling -- essential when following platelets within a dense thrombus. We show that our pipeline is more accurate than previously described methods. To demonstrate the utility of our analytical platform, we use it to show that in vivo thrombus formation is much faster than that ex vivo. Furthermore, platelets in vivo exhibit less passive movement in the direction of blood flow. Our tools are free and open source and written in the popular and user-friendly Python programming language. They empower researchers to accurately find and follow platelets in fluorescence microscopy experiments.x Plain language summaryIn this paper we describe computational tools to find and follow individual platelets in blood clots recorded with fluorescence microscopy. Our tools work in a diverse range of conditions, both in living animals and in artificial flow chamber models of thrombosis. Our work uses deep learning methods to achieve excellent accuracy. We also provide tools for visualising data and estimating error rates, so you dont have to just trust the output. Our workflow measures platelet density, shape, and speed, which we use to demonstrate differences in the kinetics of clotting in living vessels versus a synthetic environment. The tools we wrote are open source, written in the popular Python programming language, and freely available to all. We hope they will be of use to other platelet researchers.

bioinformatics↗

bioRxiv2022

Convergent evolution of skim feeding in baleen whales

The origin of pygmy right whales (Caperea marginata), the smallest and most enigmatic of the living baleen whales, remains contentious. Morphological analyses largely continue to ally Caperea with right whales (balaenids) based on shared cranial features like a tall braincase and a narrow, highly arched rostrum. By contrast, molecular data and some anatomical evidence suggest a closer relationship with rorquals (balaenopterids), but fail to explain "the substantial issue of convergence posed by the many balaenid features of Caperea" (Berta & Demere, 2017). To resolve this question, we sequenced the nuclear genome of C. marginata (812,269,251 paired reads; 47X average depth of coverage, with 89.33% of the genome covered at [≥]10X) and subjected it to a multispecies coalescent analysis including representatives of all baleen whale families. Our results confirm Caperea as sister to rorquals and, thus, the convergent origin of its right whale-like anatomy. Considering this overwhelming molecular evidence, we propose that the traditional taxonomic grouping of Caperea with right whales be abandoned.

evolutionary biology↗

bioRxiv2019

GLP-1R is downregulated in beta cells of NOD mice and T1D patients

Glucagon-like peptide 1 (GLP-1) is produced by L cells in the small intestine in response to ingested glucose and increases insulin release from pancreatic beta cells by activation of its cognate receptor (GLP-1R). Stimulation of this receptor also contributes to increased beta cell survival and regeneration. We have found that pancreatic beta cells from Non Obese Diabetic (NOD) mice express significantly lower levels of GLP-1R than C57BL/6 mice, leaving the NOD beta cells with an impaired response to GLP-1 stimulation. The lower expression appears to be caused by accelerated degradation of GLP-1R in the beta cells, a process that can be reversed by inhibiting trafficking to the lysosome. Importantly, our results appear to translate to the human disease since we also observed significantly lower expression of the GLP-1R in pancreatic islets from donors with type 1 diabetes. These results suggest that beta cell physiology may play a role in susceptibility to autoimmune inflammation.

physiology↗

bioRxiv2019

Replication Stress Generates Multiple Distinct Classes of Copy Number Alterations

BackgroundWe previously showed that a major driver of cancer chromosomal instability (CIN) is replication stress, the slowing or stalling of DNA replication. However, the precise drivers of replication stress in cancer and the mechanisms by which these cause CIN and influence tumour evolution remain unclear. Common fragile sites are well-known genomic locations of breakage after aphidicolin-induced replication stress, but their precise causes of fragility are debated, and additional genomic consequences of replication stress are not fully explored. ResultsUsing single cell sequencing we detected DNA copy number alterations (CNAs) caused by one cell cycle under replication stress in diploid non-transformed cells. Aphidicolin-induced replication stress caused multiple types of CNAs associated with different genomic regions and features. Coupling cell type-specific analysis of CNAs to gene expression and single cell replication timing analyses allowed us to pinpoint the causative large genes of the most recurrent chromosome-scale CNAs. In RPE1 cells these were largely confined to three sites on chromosomes 1, 2 and 7 and generated acentric lagging chromatin and micronuclei containing these chromosomes. Different replicative stresses generated distinct profiles of CNAs providing the potential to interpret specific replication stress mechanisms from cancer cells. ConclusionsChromosomal instability driven by replication stress occurs via focal CNAs and chromosome arm-scale changes, with the latter confined to a very small subset of chromosome regions, potentially heavily skewing cancer genome evolution trajectories. Single cell CNA analysis thus reveals new insights into the impact of replication stress on the genome and provides a platform to further dissect molecular mechanisms involved in the replication stress response and to gain insights into how replication stress fuels chromosomal instability in cancer.

molecular biology↗

bioRxiv2017

Arteria: An automation system for a sequencing core facility

Arteria is an automation system aimed at sequencing core facilities. It is built on existing open source technologies, with a modular design allowing for a community-driven effort to create plug-and-play micro-services. Herein we describe the Arteria system and elaborate on the underlying conceptual framework. The Arteria system breaks down into three conceptual levels; orchestration, process and execution. At the orchestration level it utilizes an event-based model of automation. It models processes, e.g. the steps involved in processing sequencing data, as workflows and executes these in a micro-service based environment. This creates a system which is both flexible and scalable. The Arteria Project code is available as open source software at http://www.github.com/arteria-project.

bioinformatics↗