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37 results for “interaction rewiring”
Food web rewiring drives long-term compositional differences and late-disturbance interactions at the community level
<p><strong>Abstract</strong></p> <p>Ecological communities are constantly exposed to multiple natural and anthropogenic disturbances. Multivariate composition (if recovered) has been found to need significantly more time to be regained after pulsed disturbance compared to univariate diversity metrics and functional endpoints. However, the mechanisms driving the different recovery times of communities to single and multiple disturbances remain unexplored. Here, we apply for the first time quantitative ecological network analyses to try to elucidate the mechanisms driving long-term community composition dissimilarity and late-stage disturbance interactions at the community level. For this, we evaluate the effects of two pesticides, nutrients enrichment and their interactions in outdoor mesocosms containing a complex freshwater community. We found changes in interactions strength to be strongly related to compositional changes and identified post-disturbance interaction strength rewiring to be responsible for most of the observed compositional changes. Additionally, we found pesticides interactions to be significant in the long term only when both interactions strength and food web architecture are reshaped by the disturbances. We suggest that quantitative network analysis has the potential to unveil ecological processes that prevent long-term community recovery.</p> <p><strong>Significance Statement</strong></p> <p>Multiple anthropogenic disturbances affect the structure and functioning of communities. Recent evidence highlighted that, after pulse disturbance, the functioning a community performs may be recovered fast due to functional redundancy, whereas community multivariate composition needs longer time. Yet, the mechanisms that drive the different community recovery times have not been quantified empirically. We use quantitative food web analysis to assess the influence of species interactions on community recovery. We found species interactions strength to be the main mechanism driving differences between structural and functional recovery. Additionally, we show that interactions between multiple disturbances appear in the long term only when both species interaction strength and food web architecture change significantly.</p> <p>Please see the "readme" sheet in the datafile for a description of the file structure and treatments abreviations.</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>
Data for: The pathogenic T42A mutation in SHP2 rewires interaction specificity and enhances signaling
<p>Mutations in the tyrosine phosphatase SHP2 are associated with various human diseases. Most of these mutations increase the basal catalytic activity of SHP2 by disrupting auto-inhibitory interactions between its phosphatase domain and N-terminal SH2 (phosphotyrosine recognition) domain. By contrast, several other disease-associated mutations located in the ligand-binding pockets of the N- or C-terminal SH2 domains do not increase basal activity and likely exert their pathogenicity through alternative mechanisms. We lack a molecular understanding of how these SH2 mutations impact SHP2 structure, activity, and signaling. Here, we characterize five SHP2 SH2 domain ligand-binding pocket mutants through a combination of high-throughput biochemical screens, biophysical and biochemical measurements, molecular dynamics simulations, and cellular assays. We show that, while several of these mutants alter binding affinity to phosphorylation sites, the T42A mutation in the N-SH2 domain is unique in that it also alters ligand-binding specificity. The functional consequence of this altered specificity is that the T42A mutant has biased sensitivity toward a subset of activating ligands. Our study highlights an example of a nuanced mechanism of action for a disease-associated mutation, characterized by a change in protein-protein interaction specificity that alters enzyme activation.</p>
Data for: The pathogenic T42A mutation in SHP2 rewires interaction specificity and enhances signaling
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Data from: Interaction rewiring and the rapid turnover of plant-pollinator networks
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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>
Data from: A new tool to improve the estimates of interaction rewiring considering the whole community composition
<p>Understanding temporal dynamics in ecological networks is crucial to predicting their capability to cope with global changes. Despite this, proper quantification of network dynamics still remains a challenge. Temporal dynamics are typically studied using data of interaction networks over time, through the evaluation of interaction turnover and its two components: changes related to species turnover (species gains and losses) or rewiring (switching partners among the set of species shared over time). However, with this approach based exclusively on network data, dynamics are computed similarly for species that are truly missing from the community at a given temporal period, and for species occurring in the community but that do not interact with any other. This might lead to an underestimation of the real extent of rewiring, while overestimating the species turnover component of interaction turnover.</p> <p>We used data on 20 plant-pollinator communities to calculate interaction turnover components accounting also for the species that occurred in the communities at different temporal periods but did not appear in some of the temporal interaction networks (non-interacting species), and then compared these estimates with conventional ones. Besides, we used empirical data and simulations to evaluate the extent to which dynamics estimates were affected by sampling effort when including and excluding non-interacting species.</p> <p>As expected, disregarding the non-interacting species that occur in the communities at different temporal periods leads to the underestimation of rewiring and the overestimation of species turnover as components of interaction turnover. Effect size was moderate when independent pollinator data were included, and large when including plants or both trophic levels. Simulations indicated that, in general, considering the non-interacting species reduced biases at the time of identifying changes due to the different interaction turnover components. Accounting for non-interacting species was particularly important to reduce bias when sampling effort was low and when dynamics were calculated seasonally. Despite sampling effort effects, phenology was the main determinant of species' rewiring frequencies.</p> <p>Our approach contributes to reducing biases and improving the estimates of interaction flexibility in networks, which are necessary to comprehend the response of communities in the face of global change.</p>
Habitat loss increases seasonal interaction rewiring in plant-pollinator networks
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Data from: A new tool to improve the estimates of interaction rewiring considering the whole community composition
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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>
Epigenetic therapy suppresses tumour growth by rewiring ER-mediated long-range chromatin interactions in ER+ endocrine-resistant breast cancer [methylation GAR15-13]
GEO Series GSE171066. Homo sapiens. 8 samples. Type: Methylation profiling by array.
Hyperosmotic stress induces a large-scale rewiring of 3D chromatin interactions [Cut & Tag]
GEO Series GSE310047. Homo sapiens. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A bacterial macrolide acts as signalling molecule and rewires the fungal metabolism in the Streptomyces – Aspergillus interaction
GEO Series GSE185285. Aspergillus nidulans. 6 samples. Type: Expression profiling by high throughput sequencing.
Epigenetic therapy suppresses tumour growth by rewiring ER-mediated long-range chromatin interactions in ER+ endocrine-resistant breast cancer [methylation HCI-005]
GEO Series GSE171069. Homo sapiens. 8 samples. Type: Methylation profiling by array.
Epigenetic therapy suppresses tumour growth by rewiring ER-mediated long-range chromatin interactions in ER+ endocrine-resistant breast cancer [RNA-seq]
GEO Series GSE171073. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Cellular reprogramming is driven by widespread rewiring of promote-enhancer interactions
GEO Series GSE221737. Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing; Other.
Epigenetic therapy suppresses tumour growth by rewiring ER-mediated long-range chromatin interactions in ER+ endocrine-resistant breast cancer [HiC]
GEO Series GSE171071. Homo sapiens. 6 samples. Type: Other.
Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring
GEO Series GSE65124. Mus musculus. 24 samples. Type: Expression profiling by array.
Hyperosmotic stress induces a large-scale rewiring of 3D chromatin interactions [Hi-C]
GEO Series GSE310051. Homo sapiens. 32 samples. Type: Other.
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