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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.


Predictability of the probabilistic spliceosome. a PS-networks visualized at different cutoffs: Pin ≥0.001, Pin ≥0.01, Pin ≥0.1, Pin ≥0.5, and Pin ≥0.9 along with a deterministic network of PPIs detected by Y2H. b-d Cross-validation results. b Predictability by protein family. The height of the column indicates the percent of correctly predicted PPIs for SR proteins (red), hnRNPs (blue), snRNPs (purple), and LSm proteins (yellow). c Sensitivity (dark gray) and specificity (light gray). d Mathew’s correlation coefficient. e Distribution of Pin values in the PS-network. Dark gray indicates values above the threshold Pin ≥0.1. f Independent contribution of transitivity and co-expression. The plot shows the percent of correctly predicted PPIs for the full model, using: a combination of transitivity and co-expression (black); transitivity only (dark gray); co-expression (light gray); and as predicted by chance (white)
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Fig3: Predictability of the probabilistic spliceosome. a PS-networks visualized at different cutoffs: Pin ≥0.001, Pin ≥0.01, Pin ≥0.1, Pin ≥0.5, and Pin ≥0.9 along with a deterministic network of PPIs detected by Y2H. b-d Cross-validation results. b Predictability by protein family. The height of the column indicates the percent of correctly predicted PPIs for SR proteins (red), hnRNPs (blue), snRNPs (purple), and LSm proteins (yellow). c Sensitivity (dark gray) and specificity (light gray). d Mathew’s correlation coefficient. e Distribution of Pin values in the PS-network. Dark gray indicates values above the threshold Pin ≥0.1. f Independent contribution of transitivity and co-expression. The plot shows the percent of correctly predicted PPIs for the full model, using: a combination of transitivity and co-expression (black); transitivity only (dark gray); co-expression (light gray); and as predicted by chance (white)

Mentions: We conducted a cross-validation analysis to compare the predictability of the PS-network to that of a deterministic network (DET). We used the PPI network from [23] as a test set, and the Human Protein Reference Database (HPRD, [25]) as a training set (see Methods for details). The PS-network was trained as tresholded at Pin ≥0.001, Pin ≥0.01, Pin ≥0.1, Pin ≥0.5, and Pin ≥0.9. Direct PPIs present in the test set were removed from the training set, leaving neighboring PPIs as the sole evidence for probabilistic prediction. We quantified the effect of ignoring direct PPIs for transitivity scoring, and observed that their exclusion left 99.8 % of the estimated Pin probabilities unaffected; only 80/198,135 Pin scores showed residuals ≥0.1 (Additional file 4: Figure S1). Hence, in this work we treat direct and neighboring PPIs equally. Finally, to predict DET PPIs, we counted the net overlap between direct PPIs in the training and test sets. The resulting networks are shown in Fig. 3a.Fig. 3


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)

Predictability of the probabilistic spliceosome. a PS-networks visualized at different cutoffs: Pin ≥0.001, Pin ≥0.01, Pin ≥0.1, Pin ≥0.5, and Pin ≥0.9 along with a deterministic network of PPIs detected by Y2H. b-d Cross-validation results. b Predictability by protein family. The height of the column indicates the percent of correctly predicted PPIs for SR proteins (red), hnRNPs (blue), snRNPs (purple), and LSm proteins (yellow). c Sensitivity (dark gray) and specificity (light gray). d Mathew’s correlation coefficient. e Distribution of Pin values in the PS-network. Dark gray indicates values above the threshold Pin ≥0.1. f Independent contribution of transitivity and co-expression. The plot shows the percent of correctly predicted PPIs for the full model, using: a combination of transitivity and co-expression (black); transitivity only (dark gray); co-expression (light gray); and as predicted by chance (white)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Predictability of the probabilistic spliceosome. a PS-networks visualized at different cutoffs: Pin ≥0.001, Pin ≥0.01, Pin ≥0.1, Pin ≥0.5, and Pin ≥0.9 along with a deterministic network of PPIs detected by Y2H. b-d Cross-validation results. b Predictability by protein family. The height of the column indicates the percent of correctly predicted PPIs for SR proteins (red), hnRNPs (blue), snRNPs (purple), and LSm proteins (yellow). c Sensitivity (dark gray) and specificity (light gray). d Mathew’s correlation coefficient. e Distribution of Pin values in the PS-network. Dark gray indicates values above the threshold Pin ≥0.1. f Independent contribution of transitivity and co-expression. The plot shows the percent of correctly predicted PPIs for the full model, using: a combination of transitivity and co-expression (black); transitivity only (dark gray); co-expression (light gray); and as predicted by chance (white)
Mentions: We conducted a cross-validation analysis to compare the predictability of the PS-network to that of a deterministic network (DET). We used the PPI network from [23] as a test set, and the Human Protein Reference Database (HPRD, [25]) as a training set (see Methods for details). The PS-network was trained as tresholded at Pin ≥0.001, Pin ≥0.01, Pin ≥0.1, Pin ≥0.5, and Pin ≥0.9. Direct PPIs present in the test set were removed from the training set, leaving neighboring PPIs as the sole evidence for probabilistic prediction. We quantified the effect of ignoring direct PPIs for transitivity scoring, and observed that their exclusion left 99.8 % of the estimated Pin probabilities unaffected; only 80/198,135 Pin scores showed residuals ≥0.1 (Additional file 4: Figure S1). Hence, in this work we treat direct and neighboring PPIs equally. Finally, to predict DET PPIs, we counted the net overlap between direct PPIs in the training and test sets. The resulting networks are shown in Fig. 3a.Fig. 3

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.