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Differential connectivity of splicing activators and repressors to the human spliceosome.

Akerman M, Fregoso OI, Das S, Ruse C, Jensen MA, Pappin DJ, Zhang MQ, Krainer AR - Genome Biol. (2015)

Bottom Line: In addition, we discover and experimentally validate PPIs between the oncoprotein SRSF1 and members of the anti-tumor drug target SF3 complex.Our findings suggest that activators promote the formation of PPIs between spliceosomal sub-complexes, whereas repressors mostly operate through protein-RNA interactions.This study demonstrates that combining in-silico modeling with biochemistry can significantly advance the understanding of structure and function relationships in the human spliceosome.

View Article: PubMed Central - PubMed

Affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

ABSTRACT

Background: During spliceosome assembly, protein-protein interactions (PPI) are sequentially formed and disrupted to accommodate the spatial requirements of pre-mRNA substrate recognition and catalysis. Splicing activators and repressors, such as SR proteins and hnRNPs, modulate spliceosome assembly and regulate alternative splicing. However, it remains unclear how they differentially interact with the core spliceosome to perform their functions.

Results: Here, we investigate the protein connectivity of SR and hnRNP proteins to the core spliceosome using probabilistic network reconstruction based on the integration of interactome and gene expression data. We validate our model by immunoprecipitation and mass spectrometry of the prototypical splicing factors SRSF1 and hnRNPA1. Network analysis reveals that a factor's properties as an activator or repressor can be predicted from its overall connectivity to the rest of the spliceosome. In addition, we discover and experimentally validate PPIs between the oncoprotein SRSF1 and members of the anti-tumor drug target SF3 complex. Our findings suggest that activators promote the formation of PPIs between spliceosomal sub-complexes, whereas repressors mostly operate through protein-RNA interactions.

Conclusions: This study demonstrates that combining in-silico modeling with biochemistry can significantly advance the understanding of structure and function relationships in the human spliceosome.

No MeSH data available.


Related in: MedlinePlus

Workflow of the Bayesian probability model to predict protein-protein interactions. Example of how the probability of direct interaction (Pin) between SRSF1 and TRA2B was calculated. a We first extracted all known PPIs formed by SRSF1 or TRA2B from a PPI database. b We used the number of shared PPIs between both proteins (blue nodes) and exclusive PPIs (white nodes) to calculate the Transitivity (T). c We then extracted their co-expression profile from the BioGPS microarray database and computed the Pearson correlation coefficient (C). d By transforming the calculated values of T and C through conditional-probability models, we estimated the probability that both T and C may occur in a true PPI network (e = 1, left network) and a false (that is, shuffled) interactome (e = 0, right network). e Finally, the probability Pin was calculated using the Bayes rule, as the posterior probability that SRSF1 and TRA2B directly bind each other, given T and C as evidence
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Fig1: Workflow of the Bayesian probability model to predict protein-protein interactions. Example of how the probability of direct interaction (Pin) between SRSF1 and TRA2B was calculated. a We first extracted all known PPIs formed by SRSF1 or TRA2B from a PPI database. b We used the number of shared PPIs between both proteins (blue nodes) and exclusive PPIs (white nodes) to calculate the Transitivity (T). c We then extracted their co-expression profile from the BioGPS microarray database and computed the Pearson correlation coefficient (C). d By transforming the calculated values of T and C through conditional-probability models, we estimated the probability that both T and C may occur in a true PPI network (e = 1, left network) and a false (that is, shuffled) interactome (e = 0, right network). e Finally, the probability Pin was calculated using the Bayes rule, as the posterior probability that SRSF1 and TRA2B directly bind each other, given T and C as evidence

Mentions: The amount of high-quality yeast two-hybrid (Y2H) data has grown remarkably in the last two decades [15], as has the number of analytical methods to interpret PPI networks. Probabilistic modeling is an increasingly popular approach to interrogate PPI data, allowing the integration of diverse types of evidence to prioritize biological associations and demote spurious PPIs [16–18]. To investigate the differential connectivity and relative network occupancy of spliceosomal proteins, we modeled PPIs in the spliceosome as probabilistic events, and built a Bayesian probability model using transitivity and co-expression as supporting evidence (Fig. 1 and Additional file 1). In graph theory, transitivity (also known as clustering coefficient) measures the extent to which a pair of nodes in a network share common interactions with other nodes [19]. This concept was successfully applied to study the organization of other biological networks, such as metabolic networks [20]. In a PPI network, the existence or lack of third-party PPIs can serve as evidence to predict new PPIs or reject false PPIs [21].Fig. 1


Differential connectivity of splicing activators and repressors to the human spliceosome.

Akerman M, Fregoso OI, Das S, Ruse C, Jensen MA, Pappin DJ, Zhang MQ, Krainer AR - Genome Biol. (2015)

Workflow of the Bayesian probability model to predict protein-protein interactions. Example of how the probability of direct interaction (Pin) between SRSF1 and TRA2B was calculated. a We first extracted all known PPIs formed by SRSF1 or TRA2B from a PPI database. b We used the number of shared PPIs between both proteins (blue nodes) and exclusive PPIs (white nodes) to calculate the Transitivity (T). c We then extracted their co-expression profile from the BioGPS microarray database and computed the Pearson correlation coefficient (C). d By transforming the calculated values of T and C through conditional-probability models, we estimated the probability that both T and C may occur in a true PPI network (e = 1, left network) and a false (that is, shuffled) interactome (e = 0, right network). e Finally, the probability Pin was calculated using the Bayes rule, as the posterior probability that SRSF1 and TRA2B directly bind each other, given T and C as evidence
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4502471&req=5

Fig1: Workflow of the Bayesian probability model to predict protein-protein interactions. Example of how the probability of direct interaction (Pin) between SRSF1 and TRA2B was calculated. a We first extracted all known PPIs formed by SRSF1 or TRA2B from a PPI database. b We used the number of shared PPIs between both proteins (blue nodes) and exclusive PPIs (white nodes) to calculate the Transitivity (T). c We then extracted their co-expression profile from the BioGPS microarray database and computed the Pearson correlation coefficient (C). d By transforming the calculated values of T and C through conditional-probability models, we estimated the probability that both T and C may occur in a true PPI network (e = 1, left network) and a false (that is, shuffled) interactome (e = 0, right network). e Finally, the probability Pin was calculated using the Bayes rule, as the posterior probability that SRSF1 and TRA2B directly bind each other, given T and C as evidence
Mentions: The amount of high-quality yeast two-hybrid (Y2H) data has grown remarkably in the last two decades [15], as has the number of analytical methods to interpret PPI networks. Probabilistic modeling is an increasingly popular approach to interrogate PPI data, allowing the integration of diverse types of evidence to prioritize biological associations and demote spurious PPIs [16–18]. To investigate the differential connectivity and relative network occupancy of spliceosomal proteins, we modeled PPIs in the spliceosome as probabilistic events, and built a Bayesian probability model using transitivity and co-expression as supporting evidence (Fig. 1 and Additional file 1). In graph theory, transitivity (also known as clustering coefficient) measures the extent to which a pair of nodes in a network share common interactions with other nodes [19]. This concept was successfully applied to study the organization of other biological networks, such as metabolic networks [20]. In a PPI network, the existence or lack of third-party PPIs can serve as evidence to predict new PPIs or reject false PPIs [21].Fig. 1

Bottom Line: In addition, we discover and experimentally validate PPIs between the oncoprotein SRSF1 and members of the anti-tumor drug target SF3 complex.Our findings suggest that activators promote the formation of PPIs between spliceosomal sub-complexes, whereas repressors mostly operate through protein-RNA interactions.This study demonstrates that combining in-silico modeling with biochemistry can significantly advance the understanding of structure and function relationships in the human spliceosome.

View Article: PubMed Central - PubMed

Affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

ABSTRACT

Background: During spliceosome assembly, protein-protein interactions (PPI) are sequentially formed and disrupted to accommodate the spatial requirements of pre-mRNA substrate recognition and catalysis. Splicing activators and repressors, such as SR proteins and hnRNPs, modulate spliceosome assembly and regulate alternative splicing. However, it remains unclear how they differentially interact with the core spliceosome to perform their functions.

Results: Here, we investigate the protein connectivity of SR and hnRNP proteins to the core spliceosome using probabilistic network reconstruction based on the integration of interactome and gene expression data. We validate our model by immunoprecipitation and mass spectrometry of the prototypical splicing factors SRSF1 and hnRNPA1. Network analysis reveals that a factor's properties as an activator or repressor can be predicted from its overall connectivity to the rest of the spliceosome. In addition, we discover and experimentally validate PPIs between the oncoprotein SRSF1 and members of the anti-tumor drug target SF3 complex. Our findings suggest that activators promote the formation of PPIs between spliceosomal sub-complexes, whereas repressors mostly operate through protein-RNA interactions.

Conclusions: This study demonstrates that combining in-silico modeling with biochemistry can significantly advance the understanding of structure and function relationships in the human spliceosome.

No MeSH data available.


Related in: MedlinePlus