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Interactome analysis brings splicing into focus.

Dominguez D, Burge CB - Genome Biol. (2015)

Bottom Line: The spliceosome is a huge molecular machine that assembles dynamically onto its pre-mRNA substrates.A new study based on interactome analysis provides clues about how splicing-regulatory proteins modulate assembly of the spliceosome to either activate or repress splicing.Please see related Research article: http://www.genomebiology.com/2015/16/1/119/abstract.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.

ABSTRACT
The spliceosome is a huge molecular machine that assembles dynamically onto its pre-mRNA substrates. A new study based on interactome analysis provides clues about how splicing-regulatory proteins modulate assembly of the spliceosome to either activate or repress splicing.Please see related Research article: http://www.genomebiology.com/2015/16/1/119/abstract.

No MeSH data available.


Representation of the interactomes of a prototypical splicing activator, SRSF1, and a prototypical splicing repressor, HNRNPA1. Akerman and colleagues [3] report that activators — which promote exon inclusion (shown at right) — form more interactions with components of the spliceosome than repressors, which promote exon exclusion (left). EJC exon junction complex; hnRNP heterogeneous nuclear ribonucleoprotein; RBM RNA binding motif; SRSF serine/arginine-rich splicing factor
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Fig1: Representation of the interactomes of a prototypical splicing activator, SRSF1, and a prototypical splicing repressor, HNRNPA1. Akerman and colleagues [3] report that activators — which promote exon inclusion (shown at right) — form more interactions with components of the spliceosome than repressors, which promote exon exclusion (left). EJC exon junction complex; hnRNP heterogeneous nuclear ribonucleoprotein; RBM RNA binding motif; SRSF serine/arginine-rich splicing factor

Mentions: In an effort to make sense of this complexity and to understand the functions of splicing regulators, Akerman and colleagues [3] build a probabilistic model of the protein–protein interactions (PPIs) among splicing factors called the ‘probabilistic spliceosome’ (or PS-network) (Fig. 1). They use the Human Protein Reference Database (HPRD) as a source of data on interactions, which is based mostly on large-scale yeast two-hybrid (Y2H) experiments. Central to their analysis is the graph theory concept of ‘transitivity’, or clustering coefficient, which measures the extent to which a pair of nodes in a network share interactions with other nodes. For example, Y2H might have failed to detect an interaction between two splicing factors ‘A’ and ‘B’ but successfully detected that each interacts with several of the same spliceosomal proteins, yielding a high transitivity score that enables the inference that A and B are often in proximity and likely interact. The authors also use gene-expression data, following the logic that interacting proteins should tend to be coexpressed. The Y2H and expression data were combined in a Bayesian approach to produce a composite ‘probability of interaction’ (Pin) for each pair of proteins, and PS-networks were built from interactions predicted at each of several Pin cutoffs. These networks were then tested on an independently generated Y2H interaction matrix screen of splicing factors that detected over 600 interactions between approximately 200 proteins [4]. The results were promising, detecting 55 % of spliceosome interactions at a moderate threshold (Pin ≥ 0.1) that retained a high prediction specificity of 85 %.Fig. 1


Interactome analysis brings splicing into focus.

Dominguez D, Burge CB - Genome Biol. (2015)

Representation of the interactomes of a prototypical splicing activator, SRSF1, and a prototypical splicing repressor, HNRNPA1. Akerman and colleagues [3] report that activators — which promote exon inclusion (shown at right) — form more interactions with components of the spliceosome than repressors, which promote exon exclusion (left). EJC exon junction complex; hnRNP heterogeneous nuclear ribonucleoprotein; RBM RNA binding motif; SRSF serine/arginine-rich splicing factor
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4493805&req=5

Fig1: Representation of the interactomes of a prototypical splicing activator, SRSF1, and a prototypical splicing repressor, HNRNPA1. Akerman and colleagues [3] report that activators — which promote exon inclusion (shown at right) — form more interactions with components of the spliceosome than repressors, which promote exon exclusion (left). EJC exon junction complex; hnRNP heterogeneous nuclear ribonucleoprotein; RBM RNA binding motif; SRSF serine/arginine-rich splicing factor
Mentions: In an effort to make sense of this complexity and to understand the functions of splicing regulators, Akerman and colleagues [3] build a probabilistic model of the protein–protein interactions (PPIs) among splicing factors called the ‘probabilistic spliceosome’ (or PS-network) (Fig. 1). They use the Human Protein Reference Database (HPRD) as a source of data on interactions, which is based mostly on large-scale yeast two-hybrid (Y2H) experiments. Central to their analysis is the graph theory concept of ‘transitivity’, or clustering coefficient, which measures the extent to which a pair of nodes in a network share interactions with other nodes. For example, Y2H might have failed to detect an interaction between two splicing factors ‘A’ and ‘B’ but successfully detected that each interacts with several of the same spliceosomal proteins, yielding a high transitivity score that enables the inference that A and B are often in proximity and likely interact. The authors also use gene-expression data, following the logic that interacting proteins should tend to be coexpressed. The Y2H and expression data were combined in a Bayesian approach to produce a composite ‘probability of interaction’ (Pin) for each pair of proteins, and PS-networks were built from interactions predicted at each of several Pin cutoffs. These networks were then tested on an independently generated Y2H interaction matrix screen of splicing factors that detected over 600 interactions between approximately 200 proteins [4]. The results were promising, detecting 55 % of spliceosome interactions at a moderate threshold (Pin ≥ 0.1) that retained a high prediction specificity of 85 %.Fig. 1

Bottom Line: The spliceosome is a huge molecular machine that assembles dynamically onto its pre-mRNA substrates.A new study based on interactome analysis provides clues about how splicing-regulatory proteins modulate assembly of the spliceosome to either activate or repress splicing.Please see related Research article: http://www.genomebiology.com/2015/16/1/119/abstract.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.

ABSTRACT
The spliceosome is a huge molecular machine that assembles dynamically onto its pre-mRNA substrates. A new study based on interactome analysis provides clues about how splicing-regulatory proteins modulate assembly of the spliceosome to either activate or repress splicing.Please see related Research article: http://www.genomebiology.com/2015/16/1/119/abstract.

No MeSH data available.