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84 results for “network rewiring”
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
Functional precision profiling reveals non-mutational rewiring of kinase signaling networks in colorectal cancer
<p>Multi-omics profiling of colorectal cancer (CRC) patients and associated patient-derived organoids. Tumor organoids were characterized in steady-state and perturbed using kinase inhibitors.</p>
Dataset supporting: Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks
<p>This dataset supports the Figures in the preprint: 'Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks' by Anil et. al., <a href="https://www.biorxiv.org/content/10.1101/2023.03.20.533396v1">2023</a>.</p> <p>The code used to genrate this data can be found on the github repository: <a href="https://github.com/swathianil">swathianil </a><a href="https://github.com/swathianil/homeostatic_structural_plasticity_rTMS"> /homeostatic_structural_plasticity_rTMS</a>.</p> <p>To interactively regenerate the figures, follow the Jupyter notebook, GraphPlotter, available in the repository.</p>
Data from: Interaction rewiring and the rapid turnover of plant-pollinator networks
Whether species interactions are static or change over time has wide-reaching ecological and evolutionary consequences. However, species interaction networks are typically constructed from temporally aggregated interaction data, thereby implicitly assuming that interactions are fixed. This approach has advanced our understanding of communities, but it obscures the timescale at which interactions form (or dissolve) and the drivers and consequences of such dynamics. We address this knowledge gap by quantifying the within-season turnover of plant–pollinator interactions from weekly censuses across 3 years in a subalpine ecosystem. Week-to-week turnover of interactions (1) was high, (2) followed a consistent seasonal progression in all years of study and (3) was dominated by interaction rewiring (the reassembly of interactions among species). Simulation models revealed that species' phenologies and relative abundances constrained both total interaction turnover and rewiring. Our findings reveal the diversity of species interactions that may be missed when the temporal dynamics of networks are ignored.
Protein interaction networks are substantially rewired across evolution
<p>Complete data and source code deposit for Protein interaction networks are substantially rewired across evolution.</p>
Dynamic rewiring of electrophysiological brain networks during learning
<p>This space is dedicated to the dataset used in the manuscript "Dynamic rewiring of electrophysiological brain networks during learning" under peer-reviewing process.</p>
Plant–hummingbird pollination networks exhibit limited rewiring after experimental removal of a locally abundant plant species
<p>In this study, we simulated the local extinction of a hummingbird-pollinated understory plant, <em>Heliconia</em> <em>tortuosa</em>, from tropical forest fragments using a replicated Before-After-Control-Impact (BACI) experimental design while quantifying plant-hummingbird interactions through two parallel techniques: pollen collected from individual hummingbirds ('pollen networks', created from >300 pollen samples) and observations of hummingbirds visiting focal plants ('camera networks', created from >19,000 observation hours). Each response variable was measured during each experimental period (pre and post) in sites with and without <em>H. tortuosa</em> removal (treatment and control).</p>
Plant–hummingbird pollination networks exhibit limited rewiring after experimental removal of a locally abundant plant species
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Data from: Interaction rewiring and the rapid turnover of plant-pollinator networks
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Data from: Influence of the honeybee and trait similarity on the effect of a non-native plant on pollination and network rewiring
Introduced entomophilous non-native plants usually become well integrated into the diet of generalist pollinators. This integration can affect the entire recipient plant–pollinator network. Effects vary from facilitative to competitive, and understanding the factors that govern such variability is one of the fundamental goals in invasion ecology. Species traits determine the linking patterns between plant and pollinator species. Therefore, trait similarity among plants or among pollinators might modulate how they affect each other. We conducted a flower removal experiment to investigate the effects of the non-native entomophilous legume Hedysarum coronarium on the pollination patterns of a Mediterranean shrubland plant–pollinator network. Specifically, we explored whether effects were influenced by similarity with the resident plant species in flower morphology (papilionate vs. non-papilionate), and whether effects on the pollinator community were influenced by similarity in functional group with its main visitor species (bees vs. non-bees). In addition, we explored whether Hedysarum had an effect on the identity of interactions. For this purpose, we calculated the interaction rewiring, that is the number of plant–pollinator interactions that were gained or lost after invasion. Hedysarum was well integrated into the diet of 15 generalist pollinators having the honeybee as its main visitor species. Such integration did not affect visitation rates, normalized degree (i.e. proportion of pollinators they are visited by) nor niche overlap (i.e. proportion of plant species they share pollinators with) of plants, irrespective of their flower morphology. Only the proportion of honeybee visits to resident plants decreased with invasion. On the other hand, Hedysarum reduced visitation rates and niche overlap of pollinators, mainly those of bee species. Finally, we observed that changes in the foraging behaviour of the honeybee were positively associated with the interaction rewiring involving the rest (92 taxa) of pollinators. In conclusion, pollinators show a plastic use of floral resources, responding to the presence of non-native plants. When the non-native attracts highly competitive pollinators such as the honeybee, plasticity is especially significant in pollinators that are functionally close to that competitive pollinator. The result is an interaction rewiring, probably due to pollinators avoiding competition with the honeybee. Though this plasticity might not quantitatively affect the pollination of plants, consequences on their reproduction and the functioning of the network can derive from the interaction rewiring.
Habitat loss increases seasonal interaction rewiring in plant-pollinator networks
<p>Understanding the flexibility of interactions and network rewiring (i.e. reassembly of interactions due to partner-switching) is necessary to comprehend how future anthropogenic changes will affect interspecific interactions and the functioning of communities. A higher rewiring could be expected in more disturbed landscapes because these landscapes contain fewer and more generalist species with more homogeneous traits. We sampled pollination interactions in 20 wild Olea europaea communities along a disturbance gradient to evaluate the hypothesis that the loss of natural habitats increases seasonal (within-year) interaction rewiring in plant-pollinator communities, influencing their functional structure. For this, we particularly tested whether rewiring frequency was negatively related to the extent of natural habitats surrounding the communities, whether interaction rewiring influenced the static structure of networks (nestedness, network specialization –H2'–), and whether a high generalization (low specialization –d'–) and abundance of species in communities made them more prone to rewiring. We show that habitat loss increased seasonal interaction rewiring in networks. Changes in rewiring were related to changes in the cumulative static structure of pollination networks. Nestedness decreased and network specialization (H2') also tended to decrease as interaction rewiring increased, suggesting an indirect effect of habitat loss on the robustness of networks through their dynamics. As expected, generalist insect and plant species were more prone to rewiring. However, flower abundance had different effects on the rewiring probability of plant species depending on the extent of habitat loss, with abundant species rewiring more in disturbed communities and rewiring less in more natural communities. Likely, this is related to the context-dependent foraging behaviour of pollinators, which may switch to more abundant species if the cost of searching for trait-matching resources is high in disturbed habitats. Our work shows the role of partner-switching in generalist species to adapt to new conditions. It also highlights the importance of going beyond general network metrics to understand the underlying processes of community-level interaction assembly, and predict and anticipate the effects of anthropogenic disturbances on pollination services.</p>
Rewired SSCx network model with 2nd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 2nd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 3rd-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 3rd-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 1st-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 1st-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Rewired SSCx network model with 4th-order simplified connectome
<p>This dataset is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). The dataset is part of the main accompanying dataset (DOI: <a href="https://doi.org/10.5281/zenodo.11402579" target="_blank" rel="noopener">10.5281/zenodo.11402579</a>) and contains the rewired SSCx network model with simplified E-to-E connectivity within the central column based on a 4th-order simplified model of connectivity which had been fit against the original connectome beforehand.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Habitat loss increases seasonal interaction rewiring in plant-pollinator networks
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Data from: Influence of the honeybee and trait similarity on the effect of a non-native plant on pollination and network rewiring
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Data from: Comparative developmental transcriptomics reveals rewiring of a highly conserved gene regulatory network during a major life history switch in the sea urchin genus Heliocidaris
The ecologically significant shift in developmental strategy from planktotrophic (feeding) to lecithotrophic (nonfeeding) development in the sea urchin genus Heliocidaris is one of the most comprehensively studied life history transitions in any animal. Although the evolution of lecithotrophy involved substantial changes to larval development and morphology, it is not known to what extent changes in gene expression underlie the developmental differences between species, nor do we understand how these changes evolved within the context of the well-defined gene regulatory network (GRN) underlying sea urchin development. To address these questions, we used RNA-seq to measure expression dynamics across development in three species: the lecithotroph Heliocidaris erythrogramma, the closely related planktotroph H. tuberculata, and an outgroup planktotroph Lytechinus variegatus. Using well-established statistical methods, we developed a novel framework for identifying, quantifying, and polarizing evolutionary changes in gene expression profiles across the transcriptome and within the GRN. We found that major changes in gene expression profiles were more numerous during the evolution of lecithotrophy than during the persistence of planktotrophy, and that genes with derived expression profiles in the lecithotroph displayed specific characteristics as a group that are consistent with the dramatically altered developmental program in this species. Compared to the transcriptome, changes in gene expression profiles within the GRN were even more pronounced in the lecithotroph. We found evidence for conservation and likely divergence of particular GRN regulatory interactions in the lecithotroph, as well as significant changes in the expression of genes with known roles in larval skeletogenesis. We further use coexpression analysis to identify genes of unknown function that may contribute to both conserved and derived developmental traits between species. Collectively, our results indicate that distinct evolutionary processes operate on gene expression during periods of life history conservation and periods of life history divergence, and that this contrast is even more pronounced within the GRN than across the transcriptome as a whole.
Splitpea: quantifying protein interaction network rewiring changes due to alternative splicing in cancer
<p>This serves as the supplementary data repo for the paper, <em><a href="https://doi.org/10.1101/2023.09.04.556262">Splitpea: quantifying protein interaction network rewiring changes due to alternative splicing in cancer</a>. </em></p> <p>The file structure is as follows:</p> <ul> <li>Splitpea rewired PPI networks for individual patient samples (files ending in `patient-rewired-networks.zip`). The networks are stored as dat files, which are tab delimited with one row per edge. Gene ids are reported as entrez id, the edge weights determine the directionality of the interaction (positive weights are potential gains, negative weights are edges likely lost). Chaos edges can have a positive or negative weight but will be indicated by a boolean value in the `chaos` column.</li> <li>The consensus networks (files ending in `consensus_networks`) are separated into two networks one for positive edges and one for negative edges present in at least 80% of tumor samples. These files are tab delimited files with one row per edge in the corresponding undirected weighted network. The following columns are included: <ul> <li>node1, node2: genes incident on the edge as entrez gene IDs</li> <li>mean_weight: average weight of edge across networks (where edge is present) used to build the consensus</li> <li>total_weight: sum of weights across networks (where edge is present) used to build the consensus</li> <li>num_graphs: # of networks where edge was present</li> <li>prop_graphs: # of networks where edge was present / total # of networks used to build the consensus</li> </ul> </li> <li>IRIS.zip: Splicing matrix spliced exon files from IRIS (Pan et al. <a href="https://doi.org/10.1073/pnas.2221116120">https://doi.org/10.1073/pnas.2221116120</a>). These are the initial inputs for the Splitpea run described in the paper.</li> <li>BRCA-psi.zip: precalculated PSI values for breast cancer samples as described in the paper</li> <li>PAAD-psi.zip: precalculated PSI values for pancreatic cancer samples as described in the paper</li> <li>*-centralities.zip: precalculated centralities for each patient network</li> <li>*-pickle.zip: network representations as pickle files</li> </ul>
Data from: Comparative developmental transcriptomics reveals rewiring of a highly conserved gene regulatory network during a major life history switch in the sea urchin genus Heliocidaris
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