Biology subjects

Kjolby, M.

Publications and source records attributed to Kjolby, M..

7 recordsLinked to original sources

bioRxiv2024

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex Diseases

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the models performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR models ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits, potentially leading to tailored therapeutic interventions.

genomics↗

bioRxiv2024

Evaluation of Bayesian Linear Regression Derived Gene Set Test Methods

Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations. Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. This suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.

genomics↗

bioRxiv2023

Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Our aim was to evaluate Bayesian Linear Regression (BLR) models with BayesC and BayesR priors as a fine mapping tool and compare them to the state-of-the-art external models: FINEMAP, SuSIE-RSS, SuSIE-Inf and FINEMAP-Inf. Based on extensive simulations, we evaluated the different models based on F1 classification score. The different models were applied on quantitative and binary UK Biobank (UKB) phenotypes and evaluated based upon predictive accuracy and features of credible sets (CSs). We used over 533K genotyped and 6.6 million imputed single nucleotide polymorphisms (SNPs) for simulations and UKB phenotypes respectively, from over 335K UKB White British Unrelated samples. We simulated phenotypes from low (GA1) to moderate (GA2) polygenicity, heritability (h2) of 10% and 30%, causal SNPs ({pi}) of 0.1% and 1% sampled genome-wide, and disease prevalence (PV) of 5% and 15%. Single marker summary statistics and in-sample linkage disequilibrium were used to fit models in regions defined by lead SNPs. BayesR improved the F1 score, averaged across all simulations, between 27.26% and 13.32% relative to the external models. Predictive accuracy quantified as variance explained (R2), averaged across all the UKB quantitative phenotypes, with BayesR was decreased by 5.32% (SuSIE-Inf) and 3.71% (FINEMAP-Inf), and was increased by 7.93% (SuSIE-RSS) and 8.3% (BayesC). Area under the receiver operating characteristic curve averaged across all the UKB binary phenotypes, with BayesR was increased between 0.40% and 0.05% relative to the external models. SuSIE-RSS and BayesR, demonstrated the highest number of CSs, with BayesC and BayesR exhibiting the smallest average median size CSs in the UKB phenotypes. The BLR models performed similar to the external models. Specifically, BayesRs performance closely aligned with SuSIE-Inf and FINEMAP-Inf models. Collectively, our findings from both simulations and application of the models in the UKB phenotypes support that the BLR models are efficient fine mapping tools.

genomics↗

bioRxiv2021

Distinct SARS-CoV-2 sensing pathways in pDCs driving TLR7-antiviral vs. TLR2-immunopathological responses in COVID-19

Understanding the molecular pathways driving the acute antiviral and inflammatory response to SARS-CoV-2 infection is critical for developing treatments for severe COVID-19. Here we show that in COVID-19 patients, circulating plasmacytoid dendritic cells (pDCs) decline early after symptom onset and this correlated with COVID-19 disease severity. This transient depletion coincides with decreased expression of antiviral type I IFN and the systemic inflammatory cytokines CXCL10 and IL-6. Importantly, COVID-19 disease severity correlated with decreased pDC frequency in peripheral blood. Using an in vitro stem cell-based human pDC model, we demonstrate that pDCs directly sense SARS-CoV-2 and in response produce multiple antiviral (IFN and IFN{lambda}1) and inflammatory (IL-6, IL-8, CXCL10) cytokines. This immune response is sufficient to protect epithelial cells from de novo SARS-CoV-2 infection. Targeted deletion of specific sensing pathways identified TLR7-MyD88 signaling as being crucial for production of the antiviral IFNs, whereas TLR2 is responsible for the inflammatory IL-6 response. Surprisingly, we found that SARS-CoV-2 engages the neuropilin-1 receptor on pDCs to mitigate the antiviral IFNs but not the IL-6 response. These results demonstrate distinct sensing pathways used by pDCs to elicit antiviral vs. immunopathological responses to SARS-CoV-2 and suggest that targeting neuropilin-1 on pDCs may be clinically relevant for mounting TLR7-mediated antiviral protection. One Sentence SummarypDCs sense SARS-CoV-2 and elicit antiviral protection of lung epithelial cells through TLR7, while recognition of TLR2 elicits an IL-6 inflammatory response associated with immunopathology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/469755v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@fe64daorg.highwire.dtl.DTLVardef@18f4278org.highwire.dtl.DTLVardef@54de50org.highwire.dtl.DTLVardef@1cf67cb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract:C_FLOATNO SARS-CoV-2 sensing by plasmacytoid dendritic cells. SARS-CoV-2 is internalized by pDCs via a yet unknown endocytic mechanism. The intracellular TLR7 sensor detects viral RNA and induces a signaling cascade involving MyD88-IRAK4-TRAF6 (1) to induce CXCL10 and, via IRF7 phosphorylation and translocation, inducing type I and III Interferons (2). Once secreted, type I and III IFNs initiate autocrine and paracrine signals that induce the expression of IFN stimulated genes (ISGs), thereby facilitating an antiviral response that can protect the cell against infection. However, SARS-CoV-2, has the intrinsic property to facilitate CD304 signaling, potentially by interfering with IRF7 nuclear translocation, thereby inhibiting type I IFN production and thus reducing the antiviral response generated by the pDC (4). Furthermore, the SARS-CoV-2 envelope (E) glycoprotein is sensed by the extracellular TLR2/6 heterodimer and this facilitates production of the inflammatory IL-6 cytokine (5). Illustration was created with BioRender.com C_FIG

immunology↗

bioRxiv2021

SorCS2 dynamically interacts with TrkB and GluN2B to control neurotransmission and Huntington's disease progression

BackgroundHuntingtons disease (HD) is a fatal neurodegenerative disorder characterized by progressive motor dysfunction and loss of medium spiny neurons (MSNs) in dorsal striatum. Brain-derived neurotrophic factor (BDNF) sustains functionality and integrity of MSNs, and thus reduced BDNF signaling is integral to the disease. Mutations in BDNF receptor SorCS2 were recently identified in HD patients. Our study investigates the role of SorCS2 in MSNs biology and in HD progression. MethodsWe derived a double transgenic line by crossbreeding SorCS2 deficient (KO) mice with the HD mouse model R6/1. Subsequently, we characterized the SorCS2 KO; R6/1 line by a set of behavioral and biochemical studies to evaluate phenotypes related to HD. Moreover, in combination with electrophysiology and super resolution microscopy techniques, we addressed the molecular mechanism by which SorCS2 controls synaptic activity in MSNs neurons. ResultsWe show that SorCS2 is expressed in MSNs with reduced levels in R6/1 HD model, and that SorCS2 deficiency exacerbates the disease progression in R6/1 mice. Furthermore, we find that SorCS2 binds TrkB and the NMDA receptor subunit GluN2B, which is required to control neurotransmission in corticostriatal synapses. While BDNF stimulates SorCS2-TrkB complex formation to enable TrkB signaling, it disengages SorCS2 from GluN2B, leading to enrichment of the subunit at postsynaptic densities. Consequently, long-term potentiation (LTP) is abolished in SorCS2 deficient mice, despite increased striatal TrkB and unaltered BDNF expression. However, the addition of exogenous BDNF rescues the phenotype. Finally, GluN2B, but not GluN2A, currents are also severely impaired in the SorCS2 KO mice. ConclusionsWe formulate a novel molecular mechanism by which SorCS2 acts as a molecular switch. SorCS2 targets TrkB and GluN2B into postsynaptic densities to enable BDNF signaling and NMDAR dependent neurotransmission in the dorsal striatum. Remarkably, the binding between SorCS2 and TrkB or GluN2B, respectively, is mutually exclusive and controlled by BDNF. This mechanism provides an explanation why deficient SorCS2 signaling severely aggravates HD progression in mice. Moreover, we provide evidence that this finding might represent a general mechanism of SorCS2 signaling found in other brain areas, thus increasing its relevance for other neurological and psychiatric impairments.

neuroscience↗

bioRxiv2020

SorCS1 binds the insulin receptor to enhance insulin sensitivity

Type 2 diabetes has reached endemic proportions and is a substantial burden for the affected patients and the society. Along with lifestyle factors, a number of genetic loci predisposing to type 2 diabetes have been identified, including SORCS1 that encodes the transmembrane receptor SorCS1. The ectodomain of SorCS1 (sol-SorCS1) is shed from plasma membranes but the biological function of this fragment is unknown. Here we show that sol-SorCS1 acts as a high-affinity binding partner for the insulin receptor to stabilize the receptor and increase insulin affinity, protein kinase B activation, and glucose uptake in myocytes. Sol-SorCS1 is liberated from adipocytes, and in diabetic patients the plasma concentration positively correlates with body mass index, but inversely with plasma glucose. In mouse models of insulin resistance, exogenous sol-SorCS1 restored insulin sensitivity. We conclude that sol-SorCS1 increases peripheral insulin sensitivity and propose sol-SorCS1 as a novel insulin sensitizing adipokine and potential antidiabetic agent.

physiology↗

bioRxiv2020

Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity

Antiviral therapy is urgently needed to combat the coronavirus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The protease inhibitor camostat mesylate inhibits SARS-CoV-2 infection of lung cells by blocking the virus-activating host cell protease TMPRSS2. Camostat mesylate has been approved for treatment of pancreatitis in Japan and is currently being repurposed for COVID-19 treatment. However, potential mechanisms of viral resistance as well as camostat mesylate metabolization and antiviral activity of metabolites are unclear. Here, we show that SARS-CoV-2 can employ TMPRSS2-related host cell proteases for activation and that several of them are expressed in viral target cells. However, entry mediated by these proteases was blocked by camostat mesylate. The camostat metabolite GBPA inhibited the activity of recombinant TMPRSS2 with reduced efficiency as compared to camostat mesylate and was rapidly generated in the presence of serum. Importantly, the infection experiments in which camostat mesylate was identified as a SARS-CoV-2 inhibitor involved preincubation of target cells with camostat mesylate in the presence of serum for 2 h and thus allowed conversion of camostat mesylate into GBPA. Indeed, when the antiviral activities of GBPA and camostat mesylate were compared in this setting, no major differences were identified. Our results indicate that use of TMPRSS2-related proteases for entry into target cells will not render SARS-CoV-2 camostat mesylate resistant. Moreover, the present and previous findings suggest that the peak concentrations of GBPA established after the clinically approved camostat mesylate dose (600 mg/day) will result in antiviral activity.

molecular biology↗