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Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge.

Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S - Bioinformatics (2014)

Bottom Line: In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers.The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible.However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality.

View Article: PubMed Central - PubMed

Affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.

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Comparison of signaling pathways in rat and human. (A) Heatmap showing the clustering for directionality of phosphoprotein activation (columns) after a given stimulus (rows) for the two species, showing which phosphoproteins were activated early or late in each species by the different stimuli. Only stimuli with at least one non-zero entry according to Table 2 are shown. (B and C) Top: in orange are shown potential pathway activation diagrams for phosphoproteins activated by RPKB6S1 (B) and AKT1 (C). Bottom: left heatmaps show the clustering of the rat phosphorylation activation status of the phosphoproteins shown in the diagrams for all active stimuli. Right heatmaps display human phosphorylation activation status of the phosphoproteins shown in the diagrams for stimuli using the same clustering structure obtained from the rat data to ease comparison among species. Only stimuli where activation is present in at least one species are shown. Protein phosphorylation states are defined as inactive, active early (active only at 5 min), active at both time points (active at 5 and 25 min) and active late (active only at 25 min)
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btu407-F3: Comparison of signaling pathways in rat and human. (A) Heatmap showing the clustering for directionality of phosphoprotein activation (columns) after a given stimulus (rows) for the two species, showing which phosphoproteins were activated early or late in each species by the different stimuli. Only stimuli with at least one non-zero entry according to Table 2 are shown. (B and C) Top: in orange are shown potential pathway activation diagrams for phosphoproteins activated by RPKB6S1 (B) and AKT1 (C). Bottom: left heatmaps show the clustering of the rat phosphorylation activation status of the phosphoproteins shown in the diagrams for all active stimuli. Right heatmaps display human phosphorylation activation status of the phosphoproteins shown in the diagrams for stimuli using the same clustering structure obtained from the rat data to ease comparison among species. Only stimuli where activation is present in at least one species are shown. Protein phosphorylation states are defined as inactive, active early (active only at 5 min), active at both time points (active at 5 and 25 min) and active late (active only at 25 min)

Mentions: To detect phosphorylation patterns that differ in timing and activity in rat and human, the challenge organizers looked for changes in phosphorylation at the two different time points, 5 min and 25 min, after the cells’ exposure to a stimulus and computed the state of phosphorylation of the 16 measured proteins for each of the 52 stimuli (training and test sets) at both time points. Figure 3 shows that, overall, stimuli cause more activation in rat than in human cells except for two specific phosphoproteins KS6A1 and HSPB1. Differences between the kinetics of activation at time points 5 and 25 min are minimal. This difference could be owing to a more homogeneous biological sample in rats than in humans, or simply to higher sensitivity and faster signaling of the NRBE cells compared with NHBE cells.Fig. 3.


Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge.

Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S - Bioinformatics (2014)

Comparison of signaling pathways in rat and human. (A) Heatmap showing the clustering for directionality of phosphoprotein activation (columns) after a given stimulus (rows) for the two species, showing which phosphoproteins were activated early or late in each species by the different stimuli. Only stimuli with at least one non-zero entry according to Table 2 are shown. (B and C) Top: in orange are shown potential pathway activation diagrams for phosphoproteins activated by RPKB6S1 (B) and AKT1 (C). Bottom: left heatmaps show the clustering of the rat phosphorylation activation status of the phosphoproteins shown in the diagrams for all active stimuli. Right heatmaps display human phosphorylation activation status of the phosphoproteins shown in the diagrams for stimuli using the same clustering structure obtained from the rat data to ease comparison among species. Only stimuli where activation is present in at least one species are shown. Protein phosphorylation states are defined as inactive, active early (active only at 5 min), active at both time points (active at 5 and 25 min) and active late (active only at 25 min)
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4325536&req=5

btu407-F3: Comparison of signaling pathways in rat and human. (A) Heatmap showing the clustering for directionality of phosphoprotein activation (columns) after a given stimulus (rows) for the two species, showing which phosphoproteins were activated early or late in each species by the different stimuli. Only stimuli with at least one non-zero entry according to Table 2 are shown. (B and C) Top: in orange are shown potential pathway activation diagrams for phosphoproteins activated by RPKB6S1 (B) and AKT1 (C). Bottom: left heatmaps show the clustering of the rat phosphorylation activation status of the phosphoproteins shown in the diagrams for all active stimuli. Right heatmaps display human phosphorylation activation status of the phosphoproteins shown in the diagrams for stimuli using the same clustering structure obtained from the rat data to ease comparison among species. Only stimuli where activation is present in at least one species are shown. Protein phosphorylation states are defined as inactive, active early (active only at 5 min), active at both time points (active at 5 and 25 min) and active late (active only at 25 min)
Mentions: To detect phosphorylation patterns that differ in timing and activity in rat and human, the challenge organizers looked for changes in phosphorylation at the two different time points, 5 min and 25 min, after the cells’ exposure to a stimulus and computed the state of phosphorylation of the 16 measured proteins for each of the 52 stimuli (training and test sets) at both time points. Figure 3 shows that, overall, stimuli cause more activation in rat than in human cells except for two specific phosphoproteins KS6A1 and HSPB1. Differences between the kinetics of activation at time points 5 and 25 min are minimal. This difference could be owing to a more homogeneous biological sample in rats than in humans, or simply to higher sensitivity and faster signaling of the NRBE cells compared with NHBE cells.Fig. 3.

Bottom Line: In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers.The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible.However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality.

View Article: PubMed Central - PubMed

Affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.

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