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The conflict between hosts and non-hosts changes the severity of diseases in degraded grassland plant communities
<p>load: disease severity of plant community</p> <p>richnes: species richness</p> <p>Nonhost: Non-hosts richness</p> <p>coverage: relative coverage</p> <p>RHost: relative hosts richness</p> <p>PD: Faith's phylogenetic distance</p> <p>beta: beta diversity of plant community</p> <p>SLA: CWM SLA</p> <p>LN: CWM leaf N content</p> <p>LP:CWM leaf P content</p> <p>NP: CWM leaf N:P</p> <p>group: degree of grassland degradation</p> <p>Shannon: Shannon index of plant community</p> <p>Simpson: Simpsion index of plant community</p> <p>Pielou: Pielou index of plant community</p> <p>marglef: Marglef index of plant community</p>
Processed data supporting the manuscript "Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution"
<div><strong>Processed data supporting the manuscript: </strong>Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution</div> <div> </div> <div><strong>By:</strong> Diego de S. Souza 1, 2, Rowan L. K. French 3, José O. Silva Júnior 4, Eugenio H. Nearns 5, Luciane Marinoni 4, Ian P. Swift 6, Kelly B. Miller 7, Felix A. H. Sperling 2 & Marcela L. Monné 1</div> <div> </div> <div>1 Department of Entomology, National Museum, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.</div> <div>2 Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.</div> <div>3 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.</div> <div>4 Department of Zoology, Federal University of Paraná, Curitiba, Paraná, Brazil.</div> <div>5 National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.</div> <div>6 California State Collection of Arthropods, Sacramento, California, USA.</div> <div>7 Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico, USA.</div> <div> </div> <div>Corresponding author: Diego de S. Souza, dsouza@fieldmuseum.org. Current affiliation: Field Museum of Natural History, Chicago, Illinois, USA.</div> <div> </div> <div> </div> <div><strong>List of Contents: </strong></div> <div> </div> <div><strong>Onciderini_concat_matrix.phy</strong></div> <div>Concatenated matrix (cox1, Wg and CPS) used for the phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini). </div> <div> </div> <div><strong>PartitionFinder_AICc_best_scheme.txt</strong></div> <div>Results from PartitionFinder v2.1.1, containing the best partitioning scheme for the concatenated matrix of Onciderini, identified using the corrected Akaike Information Criterion (AICc), with model definitions for use in the phylogenetic analyses.</div> <div> </div> <div><strong>RAxML_Onciderini_concat_matrix (zip file)</strong></div> <div>- Onciderini_concat_matrix.phy: concatenated matrix (cox1, Wg and CPS) used in the RAxML phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini).</div> <div>- Partitions_AICc_RAxML.txt: partitioning scheme used in the RAxML analysis as predefined by PartitionFinder v2.1.1 using the corrected Akaike Information Criterion (AICc).</div> <div>- RAxML_bestTree.Onciderini_concat_matrix_ML: best-scoring maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini.</div> <div>- RAxML_bipartitions.Onciderini_concat_matrix_final: bipartitions (clades) of the maximum likelihood tree inferred by RAxML with support values estimated from 1,000 pseudoreplicates.</div> <div>- RAxML_bipartitionsBranchLabels.Onciderini_concat_matrix_final: final maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini, with labeled branches showing bootstrap support values.</div> <div>- RAxML_bootstrap.Onciderini_concat_matrix_bootstrap: bootstrap trees generated from a non-parametric bootstrap analysis in RAxML based on 1,000 pseudoreplicates.</div> <div>- RAxML_info.Onciderini_concat_matrix_bootstrap: log file containing details of the bootstrap analysis, including the settings and parameters used in the non-parametric bootstrap runs in RAxML.</div> <div>- RAxML_info.Onciderini_concat_matrix_final: log file summarizing the RAxML analysis, including settings and convergence statistics for the final maximum likelihood tree.</div> <div>- RAxML_info.Onciderini_concat_matrix_ML: log file containing details of the maximum likelihood tree search, including the parameters and models applied during the maximum likelihood analysis conducted by RAxML.</div> <div>- RAxML_log.Onciderini_concat_matrix_ML: log file of the maximum likelihood tree search for the concatenated matrix of Onciderini.</div> <div>- RAxML_parsimonyTree.Onciderini_concat_matrix_ML: parsimony starting tree used by RAxML during the maximum likelihood analysis for the concatenated matrix of Onciderini.</div> <div>- RAxML_result.Onciderini_concat_matrix_ML: maximum likelihood tree inferred by RAxML from the concatenated matrix of Onciderini, summarizing the tree topology and likelihood score for the best tree obtained.</div> <div> </div> <div><strong>BI_AICc_Onciderini_concat_matrix (zip file)</strong></div> <div>- BI_AICc_Onciderini_concat_matrix.nex: nexus file containing the concatenated matrix of Onciderini used for Bayesian Inference (BI), including the best-fit model scheme identified by PartitionFinder and MCMC parameters for running the analysis in MrBayes.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex_r1_r2_combined_consensus.tree: consensus tree from two combined independent Bayesian Inference (BI) runs based on the concatenated matrix of Onciderini, after discarding the first 25% of initial generations as burn-in.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run1.p: log file containing parameter values and likelihood scores from the first run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run2.p: log file containing parameter values and likelihood scores from the second run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_lognormal (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_lognormal.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- BEAST2_Onciderini_BD_lognormal_run[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_exponential (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_exponential.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- BEAST2_Onciderini_BD_exponential_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_uniform (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_uniform.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- BEAST2_Onciderini_BD_uniform_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>Comparative_analyses (zip file)</strong></div> <div><strong>RawData (folder):</strong> raw morphometric and girdling data, plus tree that was later pruned for downstream comparative analyses; these data were used as input for the OncidHeadDimorphism-DatasetPREP-FINAL.R data cleaning script. </div> <div>- Onciderini_BD_lognormal_run1-run8_consensus.nwk: newick version of TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre (outputted as a newick file by importing the .tre file into FigTree and exporting in newick format).</div> <div>- Measurements_Onciderini_Raw_Final.csv: individual-level raw morphometric data for Onciderini.</div> <div>- Behav_Matrix_2_states_trimmed_2022-12-15.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as girdlers (2 behavioral states across Onciderini species). </div> <div>- Matrix_3_states_trimmed_final.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as facultative girdlers (3 behavioral states across Onciderini species). </div> <div>- Matrix_2_states_trimmed_1LochGirdler_Final.csv: species-level data on girdling status for Onciderini species, with only one Lochmaeocles species (L. tessellatus) classified as a girdler (2 behavioral states across Onciderini species). </div> <div> </div> <div><strong>ProcessedData (folder): </strong>filtered data and pruned trees outputted by the OncidHeadDimorphism-DatasetPREP-FINAL.R script</div> <div>- oncid_f_36spp_clean.csv: dataset of species means and log-ratios for morphometric traits in females, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_m_42spp_clean.csv: dataset of species means and log-ratios for morphometric traits in males, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_sd_35spp_clean.csv: dataset of species means for sexual dimorphism in morphometric traits, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_girdlingbehav_allingroupspp_clean.csv: full dataset of girdling behavior for 56 Onciderini species that are in the phylogenetic tree; includes separate columns for the three alternative girdling classification schemes</div> <div>- oncid_tree_behavfull_56spp.nwk: pruned phylogenetic tree for the full girdling dataset (56 species)</div> <div>- oncid_tree_f_36spp.nwk: pruned phylogenetic tree for the female morphometric dataset (36 species)</div> <div>- oncid_tree_m_42spp.nwk: pruned phylogenetic tree for the male morphometric dataset (42 species)</div> <div>- oncid_tree_mf_43spp.nwk: pruned phylogenetic tree for all species with morphometric data for males or females; used for the stochastic character map next to the heatmap plot (Fig 4)</div> <div>- oncid_tree_sd_35spp.nwk: pruned phylogenetic tree for the sexual dimorphism dataset (35 species)</div> <div> </div> <div><strong>FittedModels (folder): </strong>fitted models (mvgls, model comparison analyses, OUM models, simmaps) outputted by the OncidHeadDimorphism-Analysis-FINAL.R script </div> <div>- MacroModelFits_logRtraits_f_36spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to female morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits_logRtraits_m_42spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to male morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits-SDDI-35spp_2024-10-11.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to sexual dimorphism data across 100 stochastic character maps of girdling behavior</div> <div>- mvgls-results-headsize-mf-2024-10-12.Rdata: fitted mvgls regression models for male and female traits (analyzed separately)</div> <div>- mvgls-results-sddi-2024-10-12.Rdata: fitted mvgls regression models for sexual dimorphism</div> <div>- OUM_headtraits_f_36spp-2024-10-12.Rdata: fitted OUM models and summary statistics for female head traits</div> <div>- OUM_headtraits_m_42spp-2024-10-12.Rdata: fitted OUM models and summary statistics for male head traits</div> <div>- OUM-SDDI-35spp-2024-10-12.Rdata: fitted OUM models and summary statistics for sexual dimorphism in head traits</div> <div>- simmaps_ard_full_2state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - all Lochmaeocles species are classified as girdlers</div> <div>- simmaps_ard_full_3state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with three behavioral states (girdling, non-girdling, or facultative girdling)</div> <div>- simmaps_ard_full_1Loch.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - only one Lochmaeocles species (L. tessellatus) is classified as a girdler</div> <div>- simmaps_ard_m.RDS: stochastic character map for the 42 species used in the analyses of male morphometric traits</div> <div>- simmaps_ard_f.RDS: stochastic character map for the 36 species used in the analyses of female morphometric traits</div> <div>- simmaps_ard.RDS: stochastic character map for the 35 species used in the sexual dimorphism analyses; 2 behavioral states.</div> <div> </div> <div><strong>Rscripts (folder): </strong>R scripts used to process data and run phylogenetic comparative analyses of head size and girdling behavior.</div> <div>- OncidHeadDimorphism-DatasetPREP-FINAL.R: R script used to filter data and prune trees from the RawData folder for downstream phylogenetic comparative analyses; outputs of this script are in the ProcessedData folder.</div> <div>- OncidHeadDimorphism-Analysis-FINAL.R: R script used to analyze data in the ProcessedData folder to answer questions about the origin and evolution of girdling behavior and the relationship between girdling and head size or head size sexual dimorphism; fitted models outputted by this script are in the FittedModels folder</div> <div>- OncidHeadDimorphism-Plots-FINAL.R: R script used to generate plots for the manuscript<br><br></div>
Curated data for phage-bacteria hosts for BioML Hackathon
<p>This is the accompanying raw data for this github: https://github.com/havu73/hackathonBio</p> <p>A brief summary of the datasets:</p> <p>For every dataset we have genome sequences for hosts and phages and all positive pairs between phages and hosts (majority of the possible pairs are negative, so we do not save them explicitly). </p> <ul> <li><strong>E.coli: </strong>325 bacterial hosts and 96 phages<strong> </strong>(source study link: https://doi.org/10.1101/2023.11.22.567924)</li> <li><strong>Vibrio: </strong>259 bacterial hosts and 239 phages (source study link: https://doi.org/10.1038/s41467-021-27583-z)</li> <li><strong>Klebsiella:</strong> 149 bacterial hosts and 115 phages (source study link: https://doi.org/10.1038/s41467-024-48675-6)</li> <li><strong>phageDB: </strong>74 bacterial hosts and 4766 phages<strong> </strong>(source link: https://phagesdb.org/)</li> <li><strong>phageScope: </strong>180 bacterial hosts and 4434 phages (source link: https://phagescope.deepomics.org/database)</li> </ul>
Data for Marshall et al, "Microbial metabolism disrupts cytokine activity to impact host immune response"
<p>Raw data for publication "Microbial metabolism disrupts cytokine activity to impact host immune response", Marshall EKP et al. The archive is organized into folders containing raw data pertaining to each figure.</p>
Skin autonomous antibody production regulates host-microbiota interactions
<p>Supplemental Tables containing bulk BCR-sequencing clonotype results for all samples and for sequences with somatic hypermutations. Data, analysis and results are described in more detail in the accompaning publication Gribonika et al., "Host-microbiota interaction is regulated by autonomous skin-intrinsic germinal centers", Nature, 2024</p>
Additional tables: Obliquities of exoplanet host stars. Nineteen new, updated measurements for the sample of 205 measurements and observed trends.
<p>Here we provide some additional tables as supporting material to Knudstrup et al. (2024).</p> <p>The tables are given as .tex files and can be compiled by including the additional files (.bib, .cls, etc.).</p> <p><strong>mcmc_post.tex</strong> contains a selection of the posteriors resulting from the MCMCs we carried out in Appendix A of Knudstrup et al. (2024).</p> <p><strong>literature.tex</strong> contains extensions to Tables A1 and A2 of Albrecht et al. (2022), which we have used for our analyses in Section 4 of Knudstrup et al. (2024).</p> <p><strong>dfm_post.tex</strong> contains the posteriors from the different runs presented in Section 4.1 of Knudstrup et al. (2024).</p> <p><strong>rvs.tex</strong> contains an example of the format for the radial velocities used in Knudstrup et al. (2024), which are available at CDS <a title="RVs" href="https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/690/A379" target="_blank" rel="noopener">here</a>.</p> <p> </p> <h2>Update v3:</h2> <p>Added .csv files for the extensions to Tables A1 (<strong>planets.csv</strong>) and A2 (<strong>stars.csv</strong>) of Albrecht et al. (2022).</p> <p> </p> <p><strong>References:</strong></p> <div> <div> <div> <p>Albrecht, S. H., Dawson, R. I., & Winn, J. N. 2022, PASP, 134, 082001</p> <p>Knudstrup, E., Albrecht, S. H., Winn, J. N., et al., 2024, A&A, 690, A379</p> </div> </div> </div>
DDoS and host background traffic - Pcap traces
<p>This dataset includes Pcap files with DDoS and background traffic related to a host machine. Data can be used to test host-based DDoS detection solutions. DDoS traffic has been generated using the following tool: https://github.com/ricardojoserf/ddos simulation/tree/master</p> <p>Each archive include a different number of malicious IP addresses, specified in the .zip file name.</p> <p><br>If you use this dataset, please credit us by citing our paper:</p> <p><code>M. Zang, F. De Iaco, J. Wu, M. Savi, <em>In-Kernel Traffic Sketching for Volumetric DDoS Detection</em>, in IEEE International Conference on Communications (ICC), Jun. 2025</code></p> <p>If using LaTeX, you can use the following BibTeX:<br><br><code>@inproceedings{zang2025ebpfsketching,</code><br><code> title={In-Kernel Traffic Sketching for Volumetric DDoS Detection},</code><br><code> author={Zang, Mingyuan and De-Iaco, Federico and Wu, Jie and Savi, Marco},</code><br><code> booktitle={IEEE International Conference on Communications (ICC)},</code><br><code> year={2025},</code><br><code>}</code></p>
Supplementary videos of "Description of a novel extremophile green algae, Chlamydomonas pacifica, and its potential as a biotechnology host" Molino et al
<p><strong>Supplementary Video 1: </strong>Motility tracking of <em>C. pacifica</em> - (Raw_402_Video_1.mp4)</p> <p> </p> <p><strong>Supplementary Video 2: </strong>Motility tracking of <em>C. pacifica</em> - (Raw_402_Video_2.mp4)</p> <p> </p> <p><strong>Supplementary Video 3:</strong> Motility tracking of <em>C. pacifica</em> - (Raw_402_Video_3.mp4)</p> <p> </p> <p><strong>Supplementary Video 4:</strong> Motility tracking of <em>C. pacifica</em> - (TrackMate capture of Video_1_402.avi)</p> <p> </p> <p><strong>Supplementary Video 5: </strong>Motility tracking of <em>C. pacifica</em> - (TrackMate capture of Video_2_402.avi)</p> <p> </p> <p><strong>Supplementary Video 6:</strong> Motility tracking of <em>C. pacifica</em> - (TrackMate capture of Video_3_402.avi)</p> <p> </p> <p><strong>Supplementary Video 7:</strong> Mating video 1 - (Starting to aggregate 402 403.avi)</p> <p> </p> <p><strong>Supplementary Video 8:</strong> Mating video 2 - (Fusing_cell_wall_motility.avi)</p> <p> </p> <p> </p>
Theoretical Design and Synthesis of Metal-Inorganic Frameworks Using Host Atom-Centered Building Blocks for Efficient Catalysis with Diverse Reactive Sites
<p><strong>ML_ABN</strong> is a necessary file for training machine learning potentials using VASP. It contains the structural details and energies of the training set. <strong>ML_FFN</strong> is the force field file obtained after training. <strong>Test_DataSet.zip</strong> contains the test set with 900 structures, and <strong>Test_DataSet_Energy.xlsx</strong> stores the energies of these structures after being evaluated by DFT.</p>
Docking data for "The evolution of the SARS-CoV-2 spike protein for differential usage of the host transmembrane serine proteases entry pathway"
<p><br>The dataset includes predicted complexes of the SARS-CoV-2 Spike protein (specifically at the S2' cleavage site) with Hepsin and TMPRSS2 proteins. It contains data on three variants: Wuhan, Delta, and Omicron BA.1.</p> <p><strong>Compressed folders:</strong></p> <p>-357596-DeltaHepsin.tgz</p> <p>-357597-DeltaTMPRSS2.tgz</p> <p>-360039-WuhanHepsin.tgz</p> <p>-360042-TMPRSSWuhan.tgz</p> <p>-392981-TMPRSS-BA1_all.tgz</p> <p>-392982-Hepsin-BA-all.tgz</p> <p><strong>Each compressed folder contains the following:</strong></p> <p>-Initial structures in pdb format</p> <p>-Output complexes in pdb format</p> <p>-Clusters in pdb format</p> <p>-Protocols</p> <p>-Parameters</p> <p>-Scoring files</p> <p> </p> <p><strong>Protein-protein docking </strong><br>Molecular docking between the SARS-CoV-2 S protein of Wuhan, Delta (PDB: 7W92, [DOI: 10.1038/s41467-022-28528-w]), and BA.1 (PDB: 7XO5, [DOI: 10.1038/s41422-022-00672-4]) and the human proteases TMPRSS2 (PDB: 8HD8, [DOI: 10.1038/s41467-023-42527-5]) and Hepsin (PDB: 1Z8G, [DOI: 10.1042/BJ20041955]) was performed using the HADDOCK v2.5-2024.03 webserver ([DOI: 10.1021/ja026939x], [DOI: 10.1016/j.jmb.2015.09.014]). Missing loops in the protein structures were reconstructed using Modeller v10.5 ([DOI: 10.1006/jmbi.1993.1626]). Every heteroatom was removed from the reference structures. The relaxed atomistic coordinates for each S protein variant were derived via all-atom molecular dynamics (MD) simulations. These simulations were performed using AMBER22 with the FF19SB force fields and the pmemd.cuda module for enhanced performance ([DOI: 10.1021/acs.jcim.3c01153], [DOI: 10.1021/jz501780a], [DOI:10.1021/ct400314y]). For the Wuhan variant the S protein was retrieved from our previous modeling study [DOI: 10.1039/D0NR03969A] where for Delta and BA.1, ecah S protein was placed in a dodecahedral box, extending 20 Å beyond the solute in every cartesian direction, and solvated with the four-site OPC water model ([DOI: 10.1021/jz501780a]). The systems were neutralized with counterions, specifically one Cl− ion for the Delta variant and three Cl- ions for the BA.1 variant. To remove local clashes, a geometric optimization was performed using the steepest descent algorithm for 5000 cycles. The MD equilibration process consisted of several stages. First, temperature equilibration in the NVT ensemble was performed by gradually increasing the temperature through steps of 150, 200, 250, 300, and finally 310 K, each lasting 200 ps. During this phase, position restraints were applied to the heavy atoms of the proteins, with progressively decreasing spring constants of 5.0, 4.0, 3.0, and 1.0 kcal mol−1 Å−2, facilitating gradual relaxation. This was followed by a 1 ns equilibration at 310 K in the NPT ensemble without restraints. For production MD, the simulations were run in the NPT ensemble with periodic boundary conditions and Particle Mesh Ewald (PME) method ([DOI: 10.1063/5.0040966], [DOI: 10.1021/ct9001015]) using a grid spacing of 1.0 Å for long-range electrostatics. Non-bonded interactions were modeled with a Lennard-Jones potential using a 9Å cutoff. Temperature control was maintained using Langevin dynamics ([DOI: 10.1021/ct800573m]) with a collision frequency of 4.0 ps−1, and pressure control was managed by the Monte Carlo barostat ([DOI: 10.1016/j.cplett.2003.12.039]) with a 2.0 ps relaxation time at 1 bar. Bond constraints on hydrogen atoms were applied using the SHAKE algorithm ([DOI: 10.1016/0021-9991(77)90098-5]), and the hydrogen mass repartitioning scheme was applied via ParmEd ([DOI: 10.1371/journal.pcbi.1005659]), enabling a 4 fs integration time step ([DOI: 10.1021/ct5010406]). Each protein complex was simulated for a total of 20 ns. For the Wuhan variant, the 3D coordinates were retrieved from [DOI: 10.5281/zenodo.3817446].<br>The active interaction region on the spike protein was defined as the cleavage site (residues P809-R815). For TMPRSS2 and Hepsin, the active sites were defined based on their catalytic residues: H296, D345, D435, S441, S460, and G462 for TMPRSS2, and H203, D257, D347, A348, and S353 for Hepsin. These specific regions were selected to guide the docking process and maximize biologically relevant interactions. Docking clusters were analyzed by selecting those with the lowest interaction energies for further structural analysis. To evaluate binding accuracy, native contacts between the S protein and proteases were computed using the contact map analysis based on the OV+rCSU method ([DOI: 10.12693/APhysPolA.145.S9, 10.1021/acs.jctc.6b00986]), which allows for a precise identification of critical stabilizing interactions, both specific and non-specifics. High-frequency contacts, defined as those appearing in over 70% of the generated models, were highlighted as key determinants of protein-protein recognition, providing insight into the most stable and consistent interactions across docking configurations.</p>
Data from: Host association influences variation at salivary protein genes in the bat ectoparasite Cimex adjunctus
Parasite-host relationships create strong selection pressures that can lead to adaptation and increasing specialization of parasites to their hosts. Even in relatively loose host-parasite relationships, such as between generalist ectoparasites and their hosts, we may observe some degree of specialization of parasite populations to one of the multiple potential hosts. Salivary proteins are used by blood-feeding ectoparasites to prevent hemostasis in the host and maximize energy intake. We investigated the influence of association with specific host species on allele frequencies of salivary protein genes in Cimex adjunctus, a generalist blood-feeding ectoparasite of bats in North America. We analysed two salivary protein genes: an apyrase, which hydrolyses ATP at the feeding site and thus inhibits platelet aggregation, and a nitrophorin, which brings nitrous oxide to the feeding site, inhibiting platelet aggregation and vasoconstriction. We observed more variation at both salivary protein genes among parasite populations associated with different host species than among populations from different spatial locations associated with the same host species. The variation in salivary protein genes among populations on different host species was also greater than expected under a neutral scenario of genetic drift and gene flow. Finally, host species was an important predictor of allelic divergence in genotypes of individual C. adjunctus at both salivary protein genes. Our results suggest differing selection pressures on these two salivary protein genes in C. adjunctus depending on the host species.
Data from: Host genotype and age shape the leaf and root microbiomes of a wild perennial plant
Bacteria living on and in leaves and roots influence many aspects of plant health, so the extent of a plant's genetic control over its microbiota is of great interest to crop breeders and evolutionary biologists. Laboratory-based studies, because they poorly simulate true environmental heterogeneity, may misestimate or totally miss the influence of certain host genes on the microbiome. Here we report a large-scale field experiment to disentangle the effects of genotype, environment, age and year of harvest on bacterial communities associated with leaves and roots of Boechera stricta (Brassicaceae), a perennial wild mustard. Host genetic control of the microbiome is evident in leaves but not roots, and varies substantially among sites. Microbiome composition also shifts as plants age. Furthermore, a large proportion of leaf bacterial groups are shared with roots, suggesting inoculation from soil. Our results demonstrate how genotype-by-environment interactions contribute to the complexity of microbiome assembly in natural environments.
Data from: Differential host responses to parasitism shape divergent fitness costs of infection
Fitness costs of infection are fundamental to understanding the ecology and evolution of host-parasite interactions. However, these costs, and particularly their underlying mechanisms, are challenging to evaluate in wild populations. Here, we quantified total and species-specific costs of gastrointestinal worms on African buffalo, by combining the power of an anthelmintic treatment experiment that perturbed the entire worm community with a longitudinal study that tracked the two most dominant community members. Reducing all worms improved buffalo body condition, which was strongly associated with increases in survival and reproduction. Species-specific analyses revealed that condition-mediated fitness costs of infection differed between parasite species. Hosts that gained the blood-sucking worm Haemonchus, lost condition, and this loss may have been mediated by reductions in forage intake. Hosts that resisted Haemonchus by elevating IL-4 and eosinophil immune defences were able to reduce their parasite loads and gain back condition. Conversely, hosts that gained Cooperia, a less pathogenic worm, gained condition and had higher survival and reproductive success. Elevating immune defences had no effect on Cooperia abundance. Coupled with the positive relationship observed between Cooperia and host condition, our data suggest that hosts might benefit from tolerating Cooperia rather than incurring the costs of resistance. Overall, our study reveals that differential host responses to parasites play a key role in mediating the costs of infection.
Data from: Forecasting potential emergence of zoonotic diseases in Southeast Asia: network analysis identifies key rodent hosts
1. Within complex ecological systems, identifying animal species likely to play a key role in the emergence of infectious zoonotic diseases remains a major challenge. One approach consists of using information on current ecological and parasitological similarities among host species in order to predict the most likely pathways for future pathogen spillover. 2. Using field data acquired from 15 sympatric rodent species in various habitats in Thailand, Cambodia and Laos, we built networks based on shared parasites (17 helminth and 15 microparasite species) and shared habitats among rodent species and humans. We investigated the architectures of bipartite and unipartite networks using modularity, subgroups partitioning or node centrality, to assess the relative epidemiological importance of particular rodent species. 3. Our results showed that Rattus tanezumi, Bandicota savilei and R. exulans were consistently found to be members of subgroups that included humans in unipartite and bipartite networks on zoonotic agents and shared habitats. High values of centrality in shared zoonotic agents were found for the same three rodent species, whereas high values of shared habitats were observed for two of them. Although phylogenetically related rodent species likely shared both habitats and parasites, a lack of habitat specialisation was associated with increased zoonotic parasite sharing. 4. Our results emphasize the disproportionate importance of these three rodent species, through their high degree of connectivity with humans, which may represent a high risk for direct zoonotic spillover. Moreover, due to its high centrality in habitats, R. tanezumi may also play a key role as a bridge host. 5. The recent discovery of new arenaviruses in rodents in Southeast Asia, with associated disease in humans in Cambodia, provides an opportunity to test this empirically. The three rodent species identified using our network approach are some of the potential maintenance hosts for these new emerging arenaviruses. 6. Synthesis and applications. Our results on rodents and their pathogens in Southeast Asia show that network analysis has a high potential to improve the surveillance of emerging zoonotic pathogens by targeting key host species and potential "emerging' pathogen–rodent interactions in complex and heterogeneous landscapes.
Data from: Parallel Pleistocene amphitropical disjunctions in a parasitic plant and its host
PREMISE OF THE STUDY: Aphyllon is a clade of holoparasites that includes closely related North American and South American species parasitic on Grindelia. Both Aphyllon (Orobanchaceae) and Grindelia (Asteraceae) have amphitropical disjunctions between North America and South America; however, the timing of these patterns and the processes to explain them are unknown. METHODS: Chronograms for the Orobanchaceae and Grindelia and their relatives were constructed using fossil and secondary calibration points, one of which was based on the inferred timing of horizontal gene transfer from a papilionoid legume into the common ancestor of Orobanche and Phelipanche. Elevated rates of molecular evolution in the Orobanchaceae have hindered efforts to determine reliable divergence time estimates in the absence of a fossil record. However, using a horizontal gene transfer event as a secondary calibration overcomes this limitation. These chronograms were used to reconstruct the biogeography of Aphyllon, Grindelia, and relatives using a DEC+J model implemented in RevBayes. KEY RESULTS: Aphyllon had two amphitropical dispersals from North America to South America, while Grindelia had a single dispersal. The dispersal of the Aphyllon lineage that is parasitic on Grindelia (0.40 Ma) took place somewhat after Grindelia began to diversify in South America (0.93 Ma). Using a secondary calibration based on horizontal gene transfer, we infer more recent divergence dates of holoparasitic Orobancheae than previous studies. CONCLUSIONS: Parallel host–parasite amphitropical disjunctions in Grindelia and Aphyllon illustrate one means by which ecological specialization may result in nonindependent patterns of diversity in distantly related lineages. Although Grindelia and Aphyllon both dispersed to South America recently, Grindelia appears to have diversified more extensively following colonization. More broadly, recent Pleistocene glaciations probably have also contributed to patterns of diversity and biogeography of temperate northern hemisphere Orobancheae. We also demonstrate the utility of using horizontal gene transfer events from well-dated clades to calibrate parasite phylogenies in the absence of a fossil record.
Data from: The socially parasitic ant Polyergus mexicanus has host-associated genetic population structure and related neighboring nests
<p>The genetic structure of populations can be both a cause and a consequence of ecological interactions. For parasites, genetic structure may be a consequence of preferences for host species or of mating behavior. Conversely, genetic structure can determine where conspecific interactions among parasites lay on a spectrum from cooperation to conflict. We used microsatellite loci to characterize the genetic structure of a population of the socially parasitic dulotic (aka "slave-making") ant (<i>Polyergus mexicanus</i>), which is known for its host-specificity and conspecific aggression. First, we assessed whether the pattern of host species use by the parasite has influenced parasite population structure. We found that host species use was correlated with subpopulation structure, but this correlation was imperfect: some subpopulations used one host species exclusively, while others used several. Second, we examined the viscosity of the parasite population by measuring the relatedness of pairs of neighboring parasitic ant nests at varying distances from each other. Although natural history observations of local dispersal by queens suggested the potential for viscosity, there was no strong correlation between relatedness and distance between nests. However, 35% of nests had a closely related neighboring nest, indicating that kinship could potentially affect the nature of some interactions between nests of this social parasite. Our findings confirm that ecological forces like host species selection can shape the genetic structure of parasite populations, and that such genetic structure has the potential to influence parasite-parasite interactions in social parasites via inclusive fitness.</p>
Data from: Standing geographic variation in eclosion time and the genomics of host race formation in Rhagoletis pomonella fruit flies
Taxa harboring high levels of standing variation may be more likely to adapt to rapid environmental shifts and experience ecological speciation. Here, we characterize geographic and host-related differentiation for 10,241 single nucleotide polymorphisms in Rhagoletis pomonella fruit flies to infer if standing genetic variation in adult eclosion time in the ancestral hawthorn (Crataegus spp.)-infesting host race, as opposed to new mutations, contributed substantially to its recent shift to earlier fruiting apple (Malus domestica). Allele frequency differences associated with early versus late eclosion time within each host race were significantly related to geographic genetic variation and host race differentiation across four sites, arrayed from north to south along a 430 km transect, where the host races co-occur in sympatry in the Midwest USA. Host fruiting phenology is clinal, with both apple and hawthorn trees fruiting earlier in the North and later in the South. Thus, we expected alleles associated with earlier eclosion to be at higher frequencies in northern populations. This pattern was observed in the hawthorn race across all four populations; however, allele frequency patterns in the apple race were more complex. Despite the generally earlier eclosion timing of apple flies and corresponding apple fruiting phenology, alleles on chromosomes 2 and 3 associated with earlier emergence were paradoxically at lower frequency in the apple than hawthorn host race across all four sympatric sites. However, loci on chromosome 1 did show higher frequencies of early eclosion associated alleles in the apple than hawthorn host race at the two southern sites, potentially accounting for their earlier eclosion phenotype. Thus, although extensive clinal genetic variation in the ancestral hawthorn race exists and contributed to the host shift to apple, further study is needed to resolve details of how this standing variation was selected to generate earlier eclosing apple fly populations in the North.
Data from: Host plant-related genomic differentiation in the European cherry fruit fly, Rhagoletis cerasi (L., 1758) (Diptera: Tephritidae)
<p>Elucidating the mechanisms and conditions facilitating the formation of biodiversity are central topics in evolutionary biology. A growing number of studies imply that divergent ecological selection may often play a critical role in speciation by counteracting the homogenising effects of gene flow. Several examples involve phytophagous insects, where divergent selection pressures associated with host plant shifts may generate reproductive isolation, promoting speciation. Here, we use ddRADseq to assess the population structure and to test for host-related genomic differentiation in the European cherry fruit fly, Rhagoletis cerasi (L., 1758) (Diptera: Tephritidae). This tephritid is distributed throughout Europe and western Asia, and has adapted to two different genera of host plants, Prunus spp. (cherries) and Lonicera spp. (honeysuckle). Our data imply that geographic distance and geomorphic barriers serve as the primary factors shaping genetic population structure across the species range. Locally, however, flies genetically cluster according to host plant, with consistent allele frequency differences displayed by a subset of loci between Prunus and Lonicera flies across four sites surveyed in Germany and Norway. These 17 loci display significantly higher FST values between host plants than others. They also showed high levels of linkage disequilibrium within and between Prunus and Lonicera flies, supporting host-related selection and reduced gene flow. Our findings support the existence of sympatric host races in R. cerasi embedded within broader patterns of geographic variation in the fly, similar to the related apple maggot, Rhagoletis pomonella, in North America.</p>
Figure 1 in New adult host records for three Buprestidae (Coleoptera) rarely encountered in the United States and significant extension of the known geographic range of Agrilus pilosicollis Fisher
Figure 1. Agrilus langei Obenberger ♂. a) Dorsal view. b) Ventral view. c) Lat-
Data from: Genomic differentiation during speciation-with-gene-flow: comparing geographic and host-related variation in divergent life history adaptation in Rhagoletis pomonella
A major goal of evolutionary biology is to understand how variation within populations gets partitioned into differences between reproductively isolated species. Here, we examine the degree to which diapause life history timing, a critical adaptation promoting population divergence, explains geographic and host-related genetic variation in ancestral hawthorn and recently derived apple-infesting races of Rhagoletis pomonella. Our strategy involved combining experiments on two different aspects of diapause (initial diapause intensity and adult eclosion time) with a geographic survey of genomic variation across four sites where apple and hawthorn flies co-occur from north to south in the Midwestern USA. The results demonstrated that the majority of the genome showing significant geographic and host-related variation can be accounted for by initial diapause intensity and eclosion time. Local genomic differences between sympatric apple and hawthorn flies were subsumed within broader geographic clines; allele frequency differences within the races across the Midwest were 2 to 3-fold greater than those between the races in sympatry. As a result, sympatric apple and hawthorn populations displayed more limited genomic clustering compared to geographic populations within the races. The findings suggest that with reduced gene flow and increased selection on diapause equivalent to that seen between geographic sites, the host races may be recognized as different genotypic entities in sympatry, and perhaps species, a hypothesis requiring future genomic analysis of related sibling species to R. pomonella to test. Our findings concerning the way selection and geography interplay could be of broad significance for many cases of earlier stages of divergence-with-gene flow, including (1) where only modest increases in geographic isolation and the strength of selection may greatly impact genetic coupling and (2) the dynamics of how spatial and temporal standing variation is extracted by selection to generate differences between new and discrete units of biodiversity.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.