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:BackgroundProtein-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.ResultsWe 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.ConclusionsIn 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:Healthy aging is usually accompanied by changes in the functional modular organization of the human brain, which may result in the decline of cognition and underlying brain dysfunction. However, the relationship between age-related brain functional modular structure differences and cognition remain debatable. In this study, we investigated the age-associated differences of modules and hubs from young, middle and old age groups, using resting-state fMRI data from a large cross-sectional adulthood sample. We first divided the subjects into three age groups and constructed an individual-level network for each subject. Subsequently, a module-guided group-level network construction method was applied to form a weighted network for each group from which functional modules were detected. The intra- and inter-modular connectivities were observed negatively correlated with age. According to the detected modules, we found the number of connector hubs in the young group was more than middle-age and old group, while the quantity of provincial hubs in middle-age group was discovered more than other two groups. Further ROI-wise analysis shows that different hubs have distinct age-associated trajectories of intra- and inter-modular connections, which suggests the different types of topological role transitions in functional networks across age groups. Our results indicated an inverse association between functional segregation/integration with age, which demonstrated age-associated differences in communication effeciency. This study provides a new perspective and useful information to better understand the normal aging of 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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