28 research outputs found
Soluble and multivalent Jag1 DNA origami nanopatterns activate Notch without pulling force
No full textFunding Information: The authors would like to acknowledge support from the NIH grant number R35GM133482 for V.C.L., the Knut and Alice Wallenberg Foundation (Grants KAW 2017.0114 for B.H. and A.I.T. and KAW 2017.0276 for B.H.), from the European Research Council ERC for B.H. (Acronym: Cell Track GA No. 724872) and A.I.T (Acronym: MechComm GA No. 617711), and from the Swedish Research Council for B.H. (grant no. 2019-01474) and from the Göran Gustafsson Foundation for B.H. And from the Academy of Finland for B.S. (grant no. 341908). lt-NES samples were obtained from, and initial culture protocols was made possible with the help of Anna Falk’s team and the iPS Core facility at Karolinska Institutet. Part of this work was performed at the Karolinska Institutet/SciLifeLab Protein Science Core Facility (PSF). Part of this work was performed at the Karolinska Institutet Biomedicum Imaging Core (BIC). EM data was collected at the Karolinska Institutet 3D-EM facility. Publisher Copyright: © 2024, The Author(s).The Notch signaling pathway has fundamental roles in embryonic development and in the nervous system. The current model of receptor activation involves initiation via a force-induced conformational change. Here, we define conditions that reveal pulling force-independent Notch activation using soluble multivalent constructs. We treat neuroepithelial stem-like cells with molecularly precise ligand nanopatterns displayed from solution using DNA origami. Notch signaling follows with clusters of Jag1, and with chimeric structures where most Jag1 proteins are replaced by other binders not targeting Notch. Our data rule out several confounding factors and suggest a model where Jag1 activates Notch upon prolonged binding without appearing to need a pulling force. These findings reveal a distinct mode of activation of Notch and lay the foundation for the development of soluble agonists.Peer reviewe
An intersectional analysis of sociodemographic disparities in Covid-19 vaccination: A nationwide register-based study in Sweden
No full textBACKGROUND: Studies on sociodemographic disparities in Covid-19 vaccination uptake in the general population are still limited and mostly focused on older adults. This study examined sociodemographic differences in Covid-19 vaccination uptake in the total Swedish population aged 18–64 years. METHODS: National Swedish register data within the SCIFI-PEARL project were used to cross-sectionally investigate sociodemographic differences in Covid-19 vaccination among Swedish adults aged 18–64 years (n = 5,987,189) by 12 October 2021. Using logistic regression models, analyses were adjusted for sociodemographic factors, region of residence, history of Covid-19, and comorbidities. An intersectional analysis approach including several cross-classified subgroups was used to further address the complexity of sociodemographic disparities in vaccination uptake. FINDINGS: By 12 October 2021, 76·0% of the Swedish population 18–64 years old had received at least two doses of Covid-19 vaccine, an additional 5·5% had received only one dose, and 18·5% were non-vaccinated. Non-vaccinated individuals were, compared to vaccinated, more often younger, male, had a lower income, were not gainfully employed, and/or were born outside Sweden. The social patterning for vaccine dose two was similar, but weaker, than for dose one. After multivariable adjustments, findings remained but were attenuated indicating the need to consider different sociodemographic factors simultaneously. The intersectional analysis showed a large variation in vaccine uptake ranging from 32% to 96% in cross-classified subgroups, reflecting considerable sociodemographic heterogeneity in vaccination coverage. INTERPRETATION: Our study, addressing the entire Swedish population aged 18–64 years, showed broad sociodemographic disparities in Covid-19 vaccine uptake but also wide heterogeneities in coverage. The intersectional analysis approach indicates that focusing on specific sociodemographic factors in isolation and group average risks without considering the heterogeneity within such groups will risk missing the full variability of vaccine coverage. FUNDING: SciLifeLab / Knut & Alice Wallenberg Foundation, Swedish Research Council, Swedish government ALF agreement, FORMAS
Novel origins of copy number variation in the dog genome
No full textBACKGROUND: Copy number variants (CNVs) account for substantial variation between genomes and are a major source of normal and pathogenic phenotypic differences. The dog is an ideal model to investigate mutational mechanisms that generate CNVs as its genome lacks a functional ortholog of the PRDM9 gene implicated in recombination and CNV formation in humans. Here we comprehensively assay CNVs using high-density array comparative genomic hybridization in 50 dogs from 17 dog breeds and 3 gray wolves. RESULTS: We use a stringent new method to identify a total of 430 high-confidence CNV loci, which range in size from 9 kb to 1.6 Mb and span 26.4 Mb, or 1.08%, of the assayed dog genome, overlapping 413 annotated genes. Of CNVs observed in each breed, 98% are also observed in multiple breeds. CNVs predicted to disrupt gene function are significantly less common than expected by chance. We identify a significant overrepresentation of peaks of GC content, previously shown to be enriched in dog recombination hotspots, in the vicinity of CNV breakpoints. CONCLUSIONS: A number of the CNVs identified by this study are candidates for generating breed-specific phenotypes. Purifying selection seems to be a major factor shaping structural variation in the dog genome, suggesting that many CNVs are deleterious. Localized peaks of GC content appear to be novel sites of CNV formation in the dog genome by non-allelic homologous recombination, potentially activated by the loss of PRDM9. These sequence features may have driven genome instability and chromosomal rearrangements throughout canid evolution.Additional author: The LUPA Consortium (www.eurolupa.org)</p
Impact of systemic SARS-CoV-2 vaccination on mucosal IgA responses to subsequent breakthrough infection
No full textBackground Mucosal IgA responses are central to protection against SARS-CoV-2 infection and viral transmission. While systemic immunity following SARS-CoV-2 infection and vaccination is thoroughly investigated, we have limited understanding of factors affecting the generation and boosting of mucosal IgA. Methods In this cohort study, we investigated factors influencing mucosal SARS-CoV-2 IgA responses among 879 healthcare workers enrolled in the longitudinal COMMUNITY study. Blood samples and clinical data were collected from all participants every four months since April 2020. SARS-CoV-2 immune histories are well characterized through national vaccine and infection registries along with regular monitoring of seroconversion of spike and/or nucleocapsid antigen. Regression models were developed to assess the influence of vaccinations and prior infections on the magnitude of SARS-CoV-2 spike-specific IgA in nasal secretions collected from the cohort in October 2022. Findings Mucosal SARS-CoV-2 spike-specific IgA was detected in 81% of participants, with a positive association with number of prior infections, indicating a booster effect by reinfection. The increased odds ratio of detectable mucosal IgA remained for at least 22 months post infection. There was a strong association between repeated systemic vaccinations and a lower magnitude of mucosal IgA responses. Moreover, the temporal sequence of infection and vaccination influenced mucosal IgA responses, with higher levels among participants with infection prior to systemic vaccination as compared to those with breakthrough infection as the first viral encounter. Interpretation The observation that repeated mucosal exposures elicit enhanced and long-lasting mucosal IgA responses strengthens the rationale for developing effective mucosal vaccines. While systemic vaccination remains essential for preventing severe disease, our findings suggest that it may influence subsequent generation of mucosal IgA trough a reduction of viral load and inflammation in the mucosa. This is highly relevant for both understanding the development of population immunity and for optimizing the timing of a sequential systemic and mucosal vaccination approach. Funding This study was supported by grants from Region Stockholm, and SciLifeLab and the Knut and Alice Wallenberg Foundation, SSMF and European Research Council. We thank the Public Health Agency of Sweden for support. Copyright (c) 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Funding Agencies|Region Stockholm; SciLifeLab; Knut and Alice Wallenberg Foundation; SSMF; European Research Council; Public Health Agency of Sweden</p
Open Science Pathways in the Earth, Space, and Life Sciences: Your Journey Towards Open Science
No full textPresented during the Open Science Pathways in the Earth, Space, and Life Sciences sponsored by AGU and SciLifeLab on 9 May 2022.
Resources:
AGU Position Statement on Data: https://www.agu.org/Share-and-Advocate/Share/Policymakers/Position-Statements/Position_Data
Nature's 2019 "150 Years of Nature" Interactive Graph: https://www.nature.com/immersive/d42859-019-00121-0/index.html
International Collaborations: Monastersky, R., & Van Noorden, R. (2019). 150 years of Nature: a data graphic charts our evolution. Nature, 575(7781), 22–23. https://doi.org/10.1038/d41586-019-03305-w
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016).
FAIR Guiding Principles: https://www.ands.org.au/working-with-data/fairdata/training
Is FAIR Open? Higman, Rosie, Daniel Bangert, and Sarah Jones. 2019. “Three Camps, One Destination: The Intersections of Research Data Management, FAIR and Open”. Insights 32 (1): 18. DOI: http://doi.org/10.1629/uksg.468
Researchers Love Data Management: Stall, Shelley. (2016). Researchers and scientists love data management. Huh?. Zenodo. https://doi.org/10.5281/zenodo.5504418
UNESCO Recommendation on Open Science; adopted November 2021: https://en.unesco.org/science-sustainable-future/open-science
Digital Presence Checklist:
Checklist http://doi.org/10.5281/zenodo.4706118 (English)
Tutorial – 15 min https://doi.org/10.5281/zenodo.4706146 (slides, link to recording)
AGU Data & Software Sharing Guidance: https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Data-and-Software-for-Authors
AGU Data Leadership Resources (and blog): https://data.agu.org/resources
SysBioChalmers/RAVEN: v2.8.7
No full text<ul>
<li>Fix:<ul>
<li><code>ftINIT</code> could have removed metabolites that are required for specific tasks.</li>
<li><code>importModel</code> allows SBML without a specified objective function (this is not valid SBML, but will be tolerated).</li>
<li>Swap <code>soplex</code> with <code>scip</code> as solver. The <code>soplex</code> option introduced in release 2.8.5 should not be used for MILP, <code>scip</code> is dedicated for these problems. Precompiled binaries for Windows are provided (downloaded upon request), further installation instructions are mentioned in the Wiki.</li>
<li><code>ravenCobraWrapper</code> properly makes <code>comps</code> and <code>compNames</code> fields when lacking in COBRA model.</li>
<li><code>writeYAMLmodel</code> can deal with mixture of nested and not-nested cell arrays of subsystems (as COBRA models can have).</li>
<li><code>extractMiriam</code> should allow cell array of multiple miriamNames as input.</li>
<li><code>checkInstallation</code> correctly makes binaries executable on Ubuntu (solves #527)</li>
</ul>
</li>
<li>Refactor:<ul>
<li>Reduce the default verbosity and number of messages to the command window for a few functions.</li>
</ul>
</li>
</ul>
SysBioChalmers/RAVEN: v2.7.8
No full textfix:
findGeneDeletions report zero flux and growth ratio for essential genes
GitHub workflow parsing of testing output
feat:
removeRavenFromPath can quickly remove RAVEN folders from the MATLAB path
graph layout improvements for runPhenotypePhasePlane
runRobustnessAnalysis optional reduced cost plot
refactor:
setParam minor speed improvemen
SysBioChalmers/RAVEN: v2.9.1
No full text<ul>
<li>refactor:<ul>
<li>when running a unit test and a solver is not installed, report the absence of the solver, not the general failing of the test</li>
</ul>
</li>
<li>fix:<ul>
<li>prevent <code>glpk</code> timeout when running selected large FBA calculations</li>
<li>avoid an error when running <code>solveLP</code> in parallel and COBRA toolbox is not installed</li>
<li><code>getAllowedBounds</code> returned inconsistent results when running parallelization</li>
<li> various changes in relation to <code>solveLP</code> swapping the sign of reported objective function (<code>sol.f</code>), as already announced with RAVEN release <a href="https://github.com/SysBioChalmers/RAVEN/releases/tag/v2.7.12">2.7.12</a>, but not actually done at that point</li>
<li>ensure that RAVEN provided libSBML binaries are used, by using unique filenames</li>
<li><code>removeMets</code> also considers <code>metNotes</code> field if available</li>
<li><code>setParam</code> error message if incorrect paramType is specified</li>
</ul>
</li>
<li>feat:<ul>
<li><code>checkInstallation</code> reports how RAVEN was installed, as described in the <a href="https://github.com/SysBioChalmers/RAVEN/wiki/Installation#obtain-raven-toolbox">Wiki</a></li>
</ul>
</li>
<li>doc:<ul>
<li>minor changes in formatting of function documentation</li>
</ul>
</li>
</ul>
SysBioChalmers/RAVEN: v2.5.1
No full textMain improvements in this release:
fix:
ravenCobraWrapper parsing of PubMed IDs and other reaction references (PR #347)
writeYaml avoid empty entries (PR #347
SysBioChalmers/RAVEN: v2.9.0
No full text<ul>
<li>chore:<ul>
<li>update libSBML to version 5.20.2, now including support for Apple Silicon .mexmaca64. The macOS Intel .mexmaci64 is kept at version 5.19.0, as the required compiled mex file is not included in the 5.20.2 release.</li>
<li>add .mexmaca64 glpk binary for Apple Silicon.</li>
</ul>
</li>
<li>fix:<ul>
<li><code>simplifyModel</code> with irreversible backwards-only reactions (solves #529)</li>
<li><code>writeYAMLmodel</code> do not write lines with empty entries (e.g. reactions without subsystems)</li>
<li><code>getModelFromKEGG</code> includes <code>model.annotation.defaultLB</code> and <code>model.annotation.defaultUB</code> fields</li>
<li><code>getGenesFromGrRules</code> can handle genes with '|'</li>
<li><code>getModelFromHomology</code> remove geneFrom field (solves #533)</li>
<li><code>getMinNrFluxes</code> reduce default verbosity</li>
<li><code>writeYAMLmodel</code> allow empty id and name fields, in line with https://github.com/SysBioChalmers/RAVEN/wiki/RAVEN-Model-Structure</li>
<li><code>ravenCobraWrapper</code> prefers to use grRules in COBRA models if present (solves GECKO issue <a href="https://github.com/SysBioChalmers/GECKO/discussions/367">#367</a>)</li>
<li><code>optimizeProb</code> will throw error when trying to solve MILP with glpk (also if glpk is set via cobra)</li>
<li><code>writeYAMLmodel</code> will throw an informative error if it cannot write the file to the intended directory</li>
<li><code>mapCompartments</code> correct horizontal concatenation of cell array</li>
<li><code>checkInstallation</code> during first installation on unix machines, <code>makeBinaryExecutables</code> threw an error.</li>
<li><code>importModel</code> correctly parses SBML file that has some missing SBO terms</li>
</ul>
</li>
<li>feat:<ul>
<li><code>randomSampling</code> can run in parallel with MATLAB Parallel Computing Toolbox installed</li>
<li><code>setParam</code> has an additional option, 'unc' if a reaction's lower and upper bound should be set as unconstrained. If available, this will use the default bounds in <code>model.annotation</code> (otherwise [-1000, 1000]), and considers reversibility (in which case lower bound = 0)</li>
<li>auxiliary <code>parallelPoolRAVEN</code> function to check if function should be running in parallel</li>
<li>use alternative <a href="https://github.com/elgar328/matlab-code-examples/tree/main/tools/ProgressBar">ProgressBar</a>, particularly in functions containing <code>parfor</code> calls</li>
<li>give execution rights in Terminal for new RAVEN functions</li>
</ul>
</li>
<li>refactor:<ul>
<li>remove mentions to soplex (which has been replaced by scip).</li>
<li>avoid cmd windows output by <code>parallelPoolRaven</code>.</li>
<li>allow <code>randomSampling</code> with <code>nsamples</code> set to 0, to only get <code>goodRxns</code>.</li>
</ul>
</li>
</ul>
