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Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource.

Schuette S, Piatkowski B, Corley A, Lang D, Geisler M - BMC Bioinformatics (2015)

Bottom Line: This method has been used to successfully predict interactions for a number of angiosperm plants.Most conserved interactions among proteins were those associated with metabolic processes.Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Biology, Southern Illinois University, Carbondale, IL, USA. swschuette@gmail.com.

ABSTRACT

Background: Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results: The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion: Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of "guilt-by-association" models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

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A frequency distribution of interactions by experiment type and organism was analyzed. Histogram showing numbers of interactions by type of experimental system. Major contributions shown in (A) and a smaller scale of contributions shown in (B). Code abbreviations are as follows: NR, Not reported; AC, Affinity Capture; NG, Negative Genetic; Y2H, Yeast-two-hybrid; PE, Phenotypic Enhancement; SL, Synthetic Lethality; SGD, Synthetic Growth Defect; DR, Dosage Rescue; PS, Phenotypic Suppression; PG, Positive Genetic; RC, Reconstituted Complex; Co-P, Co-purification; SR, Synthetic Rescue; BA, Biochemical Activity. PCA, Principal Components Analysis; Co-F, Co-fractionation; Co-CS, Co-crystal Structure; Co-L, Co-localization; DL, Dosage Lethality; FW, Far Western; SH, Synthetic Haploinsufficiency; DGD, Dosage Growth Defect; Prot, Protein/Peptide; FRET. (C) Histogram of interactions by organism type.
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Fig4: A frequency distribution of interactions by experiment type and organism was analyzed. Histogram showing numbers of interactions by type of experimental system. Major contributions shown in (A) and a smaller scale of contributions shown in (B). Code abbreviations are as follows: NR, Not reported; AC, Affinity Capture; NG, Negative Genetic; Y2H, Yeast-two-hybrid; PE, Phenotypic Enhancement; SL, Synthetic Lethality; SGD, Synthetic Growth Defect; DR, Dosage Rescue; PS, Phenotypic Suppression; PG, Positive Genetic; RC, Reconstituted Complex; Co-P, Co-purification; SR, Synthetic Rescue; BA, Biochemical Activity. PCA, Principal Components Analysis; Co-F, Co-fractionation; Co-CS, Co-crystal Structure; Co-L, Co-localization; DL, Dosage Lethality; FW, Far Western; SH, Synthetic Haploinsufficiency; DGD, Dosage Growth Defect; Prot, Protein/Peptide; FRET. (C) Histogram of interactions by organism type.

Mentions: Large hub sizes for proteins in both predicted and experimentally determined interactomes may be attributed to several factors. The most common high throughput experimental techniques used in reference organisms included Affinity Capture and Yeast Two Hybrid (Figure 4a,b), which can generate experimental false positive results [34]. This prompted the use of an experiment multiplier (E) when calculating confidence values. Additionally, conserved proteins tend to have more interacting partners and tend to be more essential [35], and this might skew the structure of the network composed of mostly conserved proteins towards mid to large hub sizes. Finally, it might indeed be true that this many potential interactions are possible, but that only a subset of these interactions actually occur in any given living cell or tissue due to differential expression of genes encoding the proteins. The distribution of species investigated was determined by counting the number experiments for each species, most coming from fungi (yeast) or animal references (Figure 4c). Approximately 5,130 moss predicted interactions that come from plant and cyanobacterial reference organisms (i.e. Arabidopsis and Synechocystis) were detected. Many of these interactions are highly conserved across eukaryotes (Additional file 5: Table S4), making evolutionary comparisons of plant networks feasible.Figure 4


Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource.

Schuette S, Piatkowski B, Corley A, Lang D, Geisler M - BMC Bioinformatics (2015)

A frequency distribution of interactions by experiment type and organism was analyzed. Histogram showing numbers of interactions by type of experimental system. Major contributions shown in (A) and a smaller scale of contributions shown in (B). Code abbreviations are as follows: NR, Not reported; AC, Affinity Capture; NG, Negative Genetic; Y2H, Yeast-two-hybrid; PE, Phenotypic Enhancement; SL, Synthetic Lethality; SGD, Synthetic Growth Defect; DR, Dosage Rescue; PS, Phenotypic Suppression; PG, Positive Genetic; RC, Reconstituted Complex; Co-P, Co-purification; SR, Synthetic Rescue; BA, Biochemical Activity. PCA, Principal Components Analysis; Co-F, Co-fractionation; Co-CS, Co-crystal Structure; Co-L, Co-localization; DL, Dosage Lethality; FW, Far Western; SH, Synthetic Haploinsufficiency; DGD, Dosage Growth Defect; Prot, Protein/Peptide; FRET. (C) Histogram of interactions by organism type.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4384322&req=5

Fig4: A frequency distribution of interactions by experiment type and organism was analyzed. Histogram showing numbers of interactions by type of experimental system. Major contributions shown in (A) and a smaller scale of contributions shown in (B). Code abbreviations are as follows: NR, Not reported; AC, Affinity Capture; NG, Negative Genetic; Y2H, Yeast-two-hybrid; PE, Phenotypic Enhancement; SL, Synthetic Lethality; SGD, Synthetic Growth Defect; DR, Dosage Rescue; PS, Phenotypic Suppression; PG, Positive Genetic; RC, Reconstituted Complex; Co-P, Co-purification; SR, Synthetic Rescue; BA, Biochemical Activity. PCA, Principal Components Analysis; Co-F, Co-fractionation; Co-CS, Co-crystal Structure; Co-L, Co-localization; DL, Dosage Lethality; FW, Far Western; SH, Synthetic Haploinsufficiency; DGD, Dosage Growth Defect; Prot, Protein/Peptide; FRET. (C) Histogram of interactions by organism type.
Mentions: Large hub sizes for proteins in both predicted and experimentally determined interactomes may be attributed to several factors. The most common high throughput experimental techniques used in reference organisms included Affinity Capture and Yeast Two Hybrid (Figure 4a,b), which can generate experimental false positive results [34]. This prompted the use of an experiment multiplier (E) when calculating confidence values. Additionally, conserved proteins tend to have more interacting partners and tend to be more essential [35], and this might skew the structure of the network composed of mostly conserved proteins towards mid to large hub sizes. Finally, it might indeed be true that this many potential interactions are possible, but that only a subset of these interactions actually occur in any given living cell or tissue due to differential expression of genes encoding the proteins. The distribution of species investigated was determined by counting the number experiments for each species, most coming from fungi (yeast) or animal references (Figure 4c). Approximately 5,130 moss predicted interactions that come from plant and cyanobacterial reference organisms (i.e. Arabidopsis and Synechocystis) were detected. Many of these interactions are highly conserved across eukaryotes (Additional file 5: Table S4), making evolutionary comparisons of plant networks feasible.Figure 4

Bottom Line: This method has been used to successfully predict interactions for a number of angiosperm plants.Most conserved interactions among proteins were those associated with metabolic processes.Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

View Article: PubMed Central - PubMed

Affiliation: Department of Plant Biology, Southern Illinois University, Carbondale, IL, USA. swschuette@gmail.com.

ABSTRACT

Background: Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results: The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion: Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of "guilt-by-association" models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

Show MeSH
Related in: MedlinePlus