Analysis of RNA-protein networks with RNP-MaP defines functional hubs on RNA.
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ABSTRACT:
SUBMITTER: Weidmann CA
PROVIDER: S-EPMC7956044 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:RNAs interact with networks of proteins to form complexes (RNPs) that govern many biological processes, but inter-protein networks on RNA are currently impossible to examine in a comprehensive way. We developed a live-cell RNP-MaP (RNP network analysis by mutational profiling) chemical probing strategy for mapping simultaneous binding by and cooperative interactions among multiple proteins with single RNA molecules at nucleotide resolution. RNP-MaP revealed that two structurally related, but sequence-divergent noncoding RNAs, RNase P and RMRP, share nearly identical RNP networks and, further, that protein-mediated structural communication identifies function-critical network hubs in these RNAs. RNP-MaP identified previously unknown protein interaction networks within the XIST long noncoding RNA that are conserved between mouse and human RNAs and defined silencing, compartmentalization and splicing communities of proteins whose binding sites are networked together on XIST. The XIST E region contains a dense network of protein interactions, and including PTBP1, MATR3, and TIA1 proteins , which RNP-MaP revealed to each bind the XIST E region via two distinct interaction modes.; Depletion of PTBP1 and MATR3 caused native XIST particles to disperse and disappear in a human cell line. the The highly networked XIST E region was sufficient to mediate XIST RNA-like foci formation in cells. RNP-MaP enables discovery and prioritization of in-cell protein interaction networks critical for function in long RNAs, in the absence of pre-existing knowledge about protein binding sites.
2020-07-05 | GSE152483 | GEO
Project description:RNP-MaP: In-cell analysis of protein interaction networks defines functional hubs in RNA
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Project description:Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting-state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: (1) finding network nodes that participate in multiple subnetworks of the brain, and (2) finding spatial locations in which several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity.
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Project description:Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches.
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Project description:Functional networks are usually accessed with "resting-state" functional magnetic resonance imaging using preselected "seeds" regions. Frequently, however, the selection of the seed locations is arbitrary. Recently, we proposed local functional connectivity density mapping (FCDM), an ultrafast data-driven to locate highly connected brain regions (functional hubs). Here, we used the functional hubs obtained from local FCDM to determine the functional networks of the resting state in 979 healthy subjects without a priori hypotheses on seed locations. In addition, we computed the global functional connectivity hubs. Seven networks covering 80% of the gray matter volume were identified. Four major cortical hubs (ventral precuneus/posterior cingulate, inferior parietal cortex, cuneus, and postcentral gyrus) were linked to 4 cortical networks (default mode, dorsal attention, visual, and somatosensory). Three subcortical networks were associated to the major subcortical hubs (cerebellum, thalamus, and amygdala). The networks differed in their resting activity and topology. The higher coupling and overlap of subcortical networks was associated to higher contribution of short-range functional connectivity in thalamus and cerebellum. Whereas cortical local FCD hubs were also hubs of long-range connectivity, which corroborates the key role of cortical hubs in network architecture, subcortical hubs had minimal long-range connectivity. The significant variability among functional networks may underlie their sensitivity/resilience to neuropathology.
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Project description:BACKGROUND: Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking. RESULTS: We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups. CONCLUSIONS: In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/
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Project description:Human brain networks have topological properties in common with many other complex systems, prompting the following question: what aspects of brain network organization are critical for distinctive functional properties of the brain, such as consciousness? To address this question, we used graph theoretical methods to explore brain network topology in resting state functional MRI data acquired from 17 patients with severely impaired consciousness and 20 healthy volunteers. We found that many global network properties were conserved in comatose patients. Specifically, there was no significant abnormality of global efficiency, clustering, small-worldness, modularity, or degree distribution in the patient group. However, in every patient, we found evidence for a radical reorganization of high degree or highly efficient "hub" nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of comatose brain networks and vice versa. These results indicate that global topological properties of complex brain networks may be homeostatically conserved under extremely different clinical conditions and that consciousness likely depends on the anatomical location of hub nodes in human brain networks.
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Project description:The protein-protein interaction (PPI) network has a small number of highly connected protein nodes (known as hubs) and many poorly connected nodes. Genome-wide studies show that deletion of a hub protein is more likely to be lethal than deletion of a non-hub protein, a phenomenon known as the centrality-lethality rule. This rule is widely believed to reflect the special importance of hubs in organizing the network, which in turn suggests the biological significance of network architectures, a key notion of systems biology. Despite the popularity of this explanation, the underlying cause of the centrality-lethality rule has never been critically examined. We here propose the concept of essential PPIs, which are PPIs that are indispensable for the survival or reproduction of an organism. Our network analysis suggests that the centrality-lethality rule is unrelated to the network architecture, but is explained by the simple fact that hubs have large numbers of PPIs, therefore high probabilities of engaging in essential PPIs. We estimate that approximately 3% of PPIs are essential in the yeast, accounting for approximately 43% of essential genes. As expected, essential PPIs are evolutionarily more conserved than nonessential PPIs. Considering the role of essential PPIs in determining gene essentiality, we find the yeast PPI network functionally more robust than random networks, yet far less robust than the potential optimum. These and other findings provide new perspectives on the biological relevance of network structure and robustness.
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Project description:The human brain develops rapidly in the first postnatal year, in which rewired functional brain networks could shape later behavioral and cognitive performance. Resting-state functional magnetic resonances imaging (rs-fMRI) and complex network analysis have been widely used for characterizing the developmental brain functional connectome. Yet, such studies focusing on the first year of postnatal life are still very limited. Leveraging normally developing longitudinal infant rs-fMRI scans from neonate to one year of age, we investigated how brain functional networks develop at a fine temporal scale (every 3 months). Considering challenges in the infant fMRI-based network analysis, we developed a novel algorithm to construct the robust, temporally consistent and modular structure augmented group-level network based on which functional modules were detected at each age. Our study reveals that the brain functional network is gradually subdivided into an increasing number of functional modules accompanied by the strengthened intra- and inter-modular connectivities. Based on the developing modules, we found connector hubs (the high-centrality regions connecting different modules) emerging and increasing, while provincial hubs (the high-centrality regions connecting regions in the same module) diminishing. Further region-wise longitudinal analysis validates that different hubs have distinct developmental trajectories of the intra- and inter-modular connections suggesting different types of role transitions in network, such as non-hubs to hubs or provincial hubs to connector hubs et al. All findings indicate that functional segregation and integration are both increased in the first year of postnatal life. The module reorganization and hub transition lead to more efficient brain networks, featuring increasingly segregated modular structure and more connector hubs. This study provides the first comprehensive report of the development of functional brain networks at a 3-month interval throughout the first postnatal year of life, which provides essential information to the future neurodevelopmental and developmental disorder studies.
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Project description:It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity.
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