Biology subjects

Svensson, D.

Publications and source records attributed to Svensson, D..

5 recordsLinked to original sources

bioRxiv2025

CD5L insufficiency exacerbates skeletal joint damage in rheumatoid arthritis

CD5L is an immunoregulatory protein induced during inflammation and makes a life-saving contribution in infection and sepsis. Here, we explored the overall impact of CD5L in rheumatoid arthritis (RA). Using experimental RA models, we demonstrated an early surge of CD5L expression in wild-type mice, as well as a higher incidence and increased severity of arthritis in CD5L-deficient mice. In the blood, CD5L-deficient mice exhibited enhanced inflammation, a higher proportion of CD11b mononuclear cells, and elevated levels of IL-1{beta}, IL-17, and IFN-{psi}, particularly at the pre-clinical stage of arthritis. In human RA, mononuclear cells conditioned with CD5L, as well as endogenous CD5L production, were associated with reduced CD11b expression, increased IL-10 and PD-L1 production, and an enrichment of non-classical CD16+ monocytes. Analysis of the CD5L-dependent transcriptome in CD14 cells from RA patients revealed the acquisition of an efferocytosis profile, with upregulation of C1Q subunits, GAS6, AXL, and ALOX15B. It also showed signs of insufficient cargo processing, which dampens the resolution of inflammation, permits the expansion of IFN-primed non-classical monocytes, and correlates strongly with joint damage accrual. Despite markedly low CD5L levels in the synovium, RA synovial tissue was enriched with non-classical monocytes carrying the GAS6-AXL signature, which fosters osteoclast progenitors. In conclusion, this study demonstrates that the anti-inflammatory and inflammation-resolving properties of CD5L in experimental and human RA are mediated through the induction of efferocytosis-related and IFN-primed monocyte programs. This harbors a potential risk of uncontrolled invasion of non-classical monocytes into the synovial tissue, leading to joint structural damage. The complex role of CD5L in inflammation and disease outcomes requires careful consideration of the disease phase and experimental context.

immunology↗

bioRxiv2025

Insulin exerts epigenetic control of joint-specific memory T cells in rheumatoid arthritis

BackgroundInsulin has epigenetic effect influencing gene expression. High peripheral insulin concentrations promote insulin resistance in autoimmunity. Oncoprotein survivin/BIRC5 modulates glucose metabolism through chromatin binding and propagates IFNg effects in CD4+ cells. In this study, we explored how insulin influences chromatin binding and metabolic activity in autoimmune CD4+ cells of patients with rheumatoid arthritis (RA). MethodsWe profiled the metabolic activity of CD4+ cell clusters using single-cell transcriptome analysis in blood, synovial fluid and synovial tissue of RA patients. Through chromatin immunoprecipitation and sequencing, we identified the genes controlled by deposition of survivin and acetylated lysine 27 on histone H3 (H3K27ac) in CD4+ cells. Treating CD4+ cells with insulin and histone deacetylase inhibitors (HDACi), we identified changes in H3K27ac, linked those to transcription of the H3K27-survivin-controlled genes and the pathogenic phenotype of CD4+ cells using flow cytometry. Finally, we explored if anti-diabetic and anti-rheumatic drugs affect the metabolic profile and memory phenotype of the metabolic active CD4+ cells. ResultsTranscription of survivin/BIRC5 and histone acetylation enzymes strongly correlate with active metabolism in blood CD4+ cells of RA patients. In RA synovial tissue, these BIRC5hi active T cell clusters are inflammatory, exhausted, and memory-like. Genome co-deposition of H3K27ac-survivin pinpointed the insulin-dependent genes in metabolic active CD4+ cells. These genes favored histone acetylation by suppressing methylating enzymes EZH2 and KMT2A, and T cell development by activating CD27, CD3G, and SCIMP. Inhibition of histone deacetylation reverted these transcriptional effects and supported cellular sensitivity to insulin. Insulin stimulation increased H3K27ac and together with HDACi, suppressed PDCD1 and IFNg transcription and production in CD4+CD27+CD45RO+ memory T cells. Immune modulation impacted metabolic activity and synergized with the effect of histone acetylation on insulin responsiveness in RA patients. ConclusionsRA synovia is enriched with the metabolic active BIRC5hiCD4+ T cell clusters. The metabolic activity of these cells is histone acetylation-dependent and mediates insulin effects through the H3K27ac-survivin epigenetic mechanism. Increasing plasma insulin levels when combined with insulin sensitivity, can be protective in RA dearmoring effector T cell function. Hence, increasing the insulin sensitivity by enabling histone acetylation presents a reasonable interventional goal to restore immune cell homeostasis in RA.

immunology↗

bioRxiv2023

Airborne eDNA captures three decades of ecosystem biodiversity

Conserving biodiversity is a global imperative, yet our capacity to quantify and understand species occurrences has been limited. To help address this challenge, we develop a novel monitoring approach based on deep sequencing of airborne eDNA. When applied to a 34-year archive of weekly filters from an aerosol sampling station in northern Sweden, our methods enabled robust detection of over 2,700 genera across all domains of life and estimates of eDNA catchment areas. Reconstructed time series revealed regional biodiversity declines consistent with contemporary, large-scale transformations of forest composition and structure. Our results show airborne eDNA can reliably monitor biodiversity and underscore the immense latent potential in the thousands of aerosol monitoring stations deployed worldwide. One-Sentence SummaryDNA captured from air reveals organisms from all domains of life and their long-term trends.

ecology↗

bioRxiv2020

A whole-genome sequenced control population in northern Sweden reveals subregional genetic differences.

The number of national reference populations that are whole-genome sequenced are rapidly increasing. Partly driving this development is the fact that genetic disease studies benefit from knowing the genetic variation typical for the geographical area of interest. A whole-genome sequenced Swedish national reference population (n=1000) has been recently published but with few samples from northern Sweden. In the present study we have whole-genome sequenced a control population (n=300) (ACpop) from Vasterbotten County, a sparsely populated region in northern Sweden previously shown to be genetically different from southern Sweden. The aggregated variant frequencies within ACpop are publicly available (DOI 10.17044/NBIS/G000005) to function as a basic resource in clinical genetics and for genetic studies. Our analysis of ACpop, representing approximately 0.11% of the population in Vasterbotten, indicates the presence of a genetic substructure within the county. Furthermore, a demographic analysis showed that the population from which samples were drawn was to a large extent geographically stationary, a finding that was corroborated in the genetic analysis down to the level of municipalities. Including ACpop in the reference population when imputing unknown variants in a Vasterbotten cohort resulted in a strong increase in the number of high-confidence imputed variants (up to 81% for variants with minor allele frequency < 5%). ACpop was initially designed for cancer disease studies, but the genetic structure within the cohort will be of general interest for all genetic disease studies in northern Sweden.

genetics↗

bioRxiv2018

doepipeline: a systematic approach for optimizing multi-level and multi-step data processing workflows

BackgroundSelecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.\n\nResultsWe present doepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling. doepipeline was used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases, doepipeline found parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python package doepipeline.\n\nConclusionsOur proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and it can be installed through conda-forge.

bioinformatics↗