Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks
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ABSTRACT:
PROVIDER: PRJNA553196 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9652871.fastq.gz | Fastqsanger.gz | |||
| SRR9652872.fastq.gz | Fastqsanger.gz | |||
| SRR9652873.fastq.gz | Fastqsanger.gz | |||
| SRR9652874.fastq.gz | Fastqsanger.gz | |||
| SRR9652875.fastq.gz | Fastqsanger.gz |
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Project description:Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks to detect active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. scPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, scPPIN also identifies proteins that are not differentially expressed but of crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis. Human primary hepatocytes from a mixture of 10 donors grown in a 3D spheroid, were purchased from InSphero AG (Switzerland) and maintained in the culture medial provided by the company. Single cell libraries were prepare with a 10X Genomics 3’ v2 kit and sequenced in an Illumina NextSeq 500. Sequencing data demultiplexing and alignment was carried out with CellRanger with default parameters.
2019-09-09 | GSE133948 | GEO
Project description:Complex scientific experiments provide researchers with a wealth of data and knowledge from heterogeneous sources. Analyzed in its entirety, OMICs data provide a deep insight into the overall biological processes of organisms. However, the integration of data from different cellular levels (e.g., transcriptomics and proteomics) is challenging. Analyzing lists of differentially abundant molecules from different cellular levels often results in a small overlap, which can be accounted to, e.g., different regulatory mechanisms, different temporal scales as well as inherent properties of the measurement method. Thus, there is a need for approaches that allow efficient integration of OMICs data from different cellular levels. In this study, we make use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall leading to a compensatory stress response. We analyze the experimental data based on two different approaches. First, we apply a simple approach based on the comparison of differentially regulated genes and proteins with subsequent pathway analysis. Second, we compare the cellular levels based on the identification of regulatory or functional modules by two module-detecting algorithms from protein-protein interaction networks in conjunction with transcriptomic and proteomic data. Our results show that both approaches associate the fungal caspofungin response with biological pathways like cell wall biosynthesis, fatty acid metabolism as well as carbohydrate metabolism. Compared to results of the simple approach, the use of regulatory modules shows a notably higher agreement between the different cellular levels. The additional structural information of the networks provided by the module-based approach allows for topological analysis as well as the analysis of the temporal evolution of cellular response at a molecular level. However, we also found that quality of the module-based results depends on the comprehensiveness of the underlying protein-protein interaction network itself. Thus, while our results highlight the benefits and potential provided by a module-based analysis of OMICs data from different cellular levels, future studies will have to focus on the expansion of organism specific protein-protein interaction networks.
2018-10-25 | PXD008153 | Pride
Project description:BackgroundRecent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states.ResultsIn this study, we present SCPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein-protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With SCPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein-protein interaction networks significantly enriched which represent biological pathways. In these pathways, SCPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis.ConclusionsThe introduced SCPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.
| S-EPMC7607865 | biostudies-literature
Project description:MotivationFunctional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.ResultsHere we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity-based network alignment display little overlap (<15%) with sequence similarity-based alignment. Our combined approach integrating sequence and function-based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.AvailabilityProgram binaries and source code is freely available at http://www.stats.ox.ac.uk/research/bioinfo/resources.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2778333 | biostudies-literature
Project description:The combination of four proteins and their paralogues including MBD2/3, GATAD2A/B, CDK2AP1, and CHD3/4/5, which we refer to as the MGCC module, form the chromatin remodeling module of the Nucleosome Remodeling and Deacetylase (NuRD) complex. To date, mechanisms by which the MGCC module acquires paralogue-specific function and specificity have not been addressed. Understanding the protein-protein interaction (PPI) network of the MGCC subunits is essential in defining underlying mechanisms of gene regulation. Therefore, using pulldown followed by mass spectrometry analysis (PD-MS) we report a proteome-wide interaction network of the MGCC module in a paralogue-specific manner. Our data also demonstrate that the disordered C-terminal region of CHD3/4/5 is a gateway to incorporate remodeling activity into both the ChAHP (CHD4, ADNP, HP1γ) and NuRD complexes in a mutually exclusive manner. We define a short aggregation prone region (APR) within the C-terminal segment of GATAD2B that is essential for the interaction of CHD4 and CDK2AP1 with the NuRD complex. Finally, we also report an association of CDK2AP1 with the Nuclear Receptor Co-Repressor (NCOR) complex. Overall, this study provides insight into the possible mechanisms through which the MGCC module can achieve specificity and diverse biological functions.
2023-03-11 | PXD025263 | Pride
Project description:BackgroundStudies of functional modules in a Protein-Protein Interaction (PPI) network contribute greatly to the understanding of biological mechanisms. With the development of computing science, computational approaches have played an important role in detecting functional modules.ResultsWe present a new approach using multi-agent evolution for detection of functional modules in PPI networks. The proposed approach consists of two stages: the solution construction for agents in a population and the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the detection problem of functional modules in a PPI network. First, the approach utilizes a connection-based encoding scheme to model an agent, and employs a random-walk behavior merged topological characteristics with functional information to construct a solution. Next, it applies several evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents as well as solution evolution. Systematic experiments have been conducted on three benchmark testing sets of yeast networks. Experimental results show that the approach is more effective compared to several other existing algorithms.ConclusionsThe algorithm has the characteristics of outstanding recall, F-measure, sensitivity and accuracy while keeping other competitive performances, so it can be applied to the biological study which requires high accuracy.
| S-EPMC4262229 | biostudies-literature
Project description:A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.
| S-EPMC4369690 | biostudies-literature
Project description:MotivationIdentifying functional modules in protein-protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of 'higher than expected connectivity', those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks.ResultsIn this article, we propose a novel optimization formulation LCP(2) (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP(2) by random walk. A spectral approximate algorithm SLCP(2) is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP(2) to a new algorithm (greedy algorithm for LCP(2)) GLCP(2) to identify overlapping functional modules. We compare SLCP(2) and GLCP(2) with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP(2) outperform all other compared algorithms.Availability and implementationAll data and code are available at http://www.cse.usf.edu/~xqian/fmi/slcp2hop/.
| S-EPMC3924044 | biostudies-literature
Project description:BackgroundPrevious studies have shown modular structures in PPI (protein-protein interaction) networks. More recently, many genome and metagenome investigations have focused on identifying modules in PPI networks. However, most of the existing methods are insufficient when applied to networks with overlapping modular structures. In our study, we describe a novel overlapping module identification method (OMIM) to address this problem.ResultsOur method is an agglomerative clustering method merging modules according to their contributions to modularity. Nodes that have positive effects on more than two modules are defined as overlapping parts. As well, we designed de-noising steps based on a clustering coefficient and hub finding steps based on nodal weight.ConclusionsThe low computational complexity and few control parameters prove that our method is suitable for large scale PPI network analysis. First, we verified OMIM on a small artificial word association network which was able to provide us with a comprehensive evaluation. Then experiments on real PPI networks from the MIPS Saccharomyces Cerevisiae dataset were carried out. The results show that OMIM outperforms several other popular methods in identifying high quality modular structures.
| S-EPMC3348045 | biostudies-literature
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