1 / 44

Data Analysis – Part 2: The Initial Questions of the AFCS Madhu Natarajan, Rama Ranganathan

Data Analysis – Part 2: The Initial Questions of the AFCS Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2003. O 1. L 1. Signaling Network. O 2. L 2. O 3. L 3. O m. L n. Data Analysis: The Initial Questions of the AFCS.

kobe
Download Presentation

Data Analysis – Part 2: The Initial Questions of the AFCS Madhu Natarajan, Rama Ranganathan

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Analysis – Part 2:The Initial Questions of the AFCS Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2003

  2. O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln Data Analysis:The Initial Questions of the AFCS • What are the goals of the analysis? Again AFCS question 1…how complex is signaling in cells?

  3. O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln Data Analysis:The Initial Questions of the AFCS • What are the goals of the analysis? • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • Quantitative measurement of the density of interactions between pairs of ligands.

  4. O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln Data Analysis:The Initial Questions of the AFCS • What are the goals of the analysis? • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • The density of interactions between pairs of ligands. What is a good experimental way to think about such interaction? • Apply each ligand singly and in combination, and ask whether the response to the combined application is the additive effect of the single ligand treatments. That is…is the effect of one ligand different in the presence of another?

  5. O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln Data Analysis:The Initial Questions of the AFCS • What are the goals of the analysis? • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • The density of interactions between pairs of ligands. What is a good experimental way to think about such interaction? • Apply each ligand singly and in combination, and ask whether the response to the combined application is the additive effect of the single ligand treatments. That is…is the effect of one ligand different in the presence of another? • Seems sensible…so does non-additivity happen and how is it interpreted?

  6. Does non-additivity happen in cell signaling? Yes…. J. Trimmer, Science STKE (2002), Unexpected crosstalk: Small GTPase regulation of calcium channel trafficking. Y.Q. Xiao et al., JBC (2002), Crosstalk between ERK and p38 MAPK in TGF-b signaling. T. Jun et al. , Science STKE (1999), Tangled webs: Evidence of crosstalk between c-Raf1 and Akt. Y.M. Agazie et al., Am J Physiol Heart Circ Physiol, Synergistic stimulation of smooth muscle growth by ATP and insulin. A.R. Asthagiri et al., J. Cell Sci. (2000), The role of ERK2 signals in fibronectin- and insulin-mediated DNA synthesis. R. Laufer and J.P. Changeux, JBC (1989), Interaction between two second messenger systems in skeletal muscle. S. Fanayan et al., JBC (2002), Interaction between IGFBP-3 and TGF-b signaling in breast cancer cells. L. Szanto and C.R. Kahn (2000), PNAS, Leptin and insulin signaling pathways interact in a hepatic cell line. J.M. Fredricksson et al. (2000), JBC, Interaction of b-receptor signaling and a pathway involving src in adipocytes. A. Fatatis et al. (1994), PNAS, Synergy between VIP and a-adrenergic receptors in astroglia. B. Gonalez et al. (2001), Endocrinology, Cooperation between LDL receptor and IGF-1 in smooth muscle proliferation. Etc……(many many papers). The interpretation? Non-additivity implies interaction in the signaling network.

  7. Does additivity happen in cell signaling? Yes… E.L. Greene et al. (2001) Hypertension, Additive effects of Angiotensin II and Oleic acid in muscle cell migration. S. Shen et al. (2001) Diabetes, Additivity in PKC-d activation by insulin and IGF-1. S. Lobert et al. (1999), Cancer Research, Additivity of Dilantin and Vinblastine effects on microtubule assembly. S. Seraskeris et al. (2002) JCB, Additivity in a1-adrenergic receptor mediated calcium mobilization and bulk changes in intracellular calcium. J.D. Johnson and J.P. Chang (2000), Mol. Cell. Endocrin., Additivity of different calcium pools in pituitary neurons. J.W. Reed et al. (2000), Plant Physiol., Addtivity of several genes in controlling light-dependent growth in Arabidopsis. G. Hiller and R. Sundler, (1999), Cell Signal., Additivity in c-PLA2 activity by several MAPKs. Etc….many, many papers. Additivity is taken as implying lack of interaction in the signaling network.

  8. O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln Data Analysis:The Initial Questions of the AFCS • What are the goals of the analysis? • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • The density of interactions between pairs of ligands.What is a good conceptual way to think about such interaction? Is the effect of one ligand different in the presence of another? • Non-additivity of inputs implies interaction in the signaling network during transduction of the two signals. • Additivity of inputs implies the of lack of such interaction.

  9. O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln Data Analysis:The Initial Questions of the AFCS • What are the goals of the analysis? • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • The density of interactions between pairs of ligands.What is a good conceptual way to think about such interaction? Is the effect of one ligand different in the presence of another? • Non-additivity of inputs implies interaction in the signaling network during transduction of the two signals. • Additivity of inputs implies the of lack of such interaction. • How can we use our signaling parameter (S) to represent interaction between ligands?

  10. Quantitative measurement of similarity in ligand screen data The Experiment Space • What can we learn from this representation? • The response profile for each ligand is the final S vector.

  11. All the dimensions (minus gene expression): 2.5’ 5.0’ 30’ 15’ 3 8 20 1 2 3 4 5 .5 1

  12. Quantitative measurement of similarity in ligand screen data The Experiment Space • What can we learn from this representation? • The response profile for each ligand is the final S vector. • Differences between ligand responses have a natural meaning… DS1,2

  13. 5.0’ 2.5’ 30’ 15’ 1 2 3 4 5 .5 1 3 8 20 Clustering the experiment space:

  14. Quantitative measurement of interactions between ligands Say we put on both ligands 1 and 2 together. In the case they don’t interact at all, and none of our output variables has saturated, what should we expect?

  15. Quantitative measurement of interactions between ligands Say we put on both ligands 1 and 2 together. In the case they don’t interact at all, and none of our output measurement has saturated, what should we expect?

  16. Quantitative measurement of interactions between ligands Say we put on both ligands 1 and 2 together. In the case they don’t interact at all, and none of our output measurement has saturated, what should we expect? But what if the effect of ligand 1 changes in the background of ligand 2?

  17. Quantitative measurement of interactions between ligands Say we put on both ligands 1 and 2 together. In the case they don’t interact at all, and none of our output measurement has saturated, what should we expect? But what if the effect of ligand 1 changes in the background of ligand 2? The so-called “lack-of-closure” error is the interaction between the two ligands. DDS1,2

  18. Quantitative measurement of interactions between ligands Say we put on both ligands 1 and 2 together. In the case they don’t interact at all, and none of our output measurement has saturated, what should we expect? But what if the effect of ligand 1 changes in the background of ligand 2? The so-called “lack-of-closure” error is the interaction between the two ligands. This is not the same thing as the difference between two ligands (DS1,2)! This is the interaction between two ligands (DDS1,2)…the degree to which one influences the other. DDS1,2

  19. Quantitative measurement of interactions between ligands • A few key points… • the interaction vector preserves all the dimensions along which the two ligands interact. Thus, we can determine what experimental variables carry the interaction between two ligands. DDS1,2

  20. Quantitative measurement of interactions between ligands • A few key points… • the interaction vector preserves all the dimensions along which the two ligands interact. • What conditions seem most likely to generate additive responses? DDS1,2

  21. Quantitative measurement of interactions between ligands • A few key points… • the interaction vector preserves all the dimensions along which the two ligands interact. • What conditions seem most likely to generate additive responses? Two scenarios… • (a) one ligand has no effect at all…

  22. Quantitative measurement of interactions between ligands • A few key points… • the interaction vector preserves all the dimensions along which the two ligands interact. • What conditions seem most likely to generate additive responses? Two scenarios… • (a) one ligand has no effect at all… • (b) the two ligands induce fully orthogonal responses.

  23. Quantitative measurement of interactions between ligands • A few key points… • the interaction vector preserves all the dimensions along which the two ligands interact. • What conditions seem most likely to generate additive responses? • A trivial reason for non-additivity is output saturation… DDS1,2

  24. Quantitative measurement of interactions between ligands • A few key points… • the interaction vector preserves all the dimensions along which the two ligands interact. • What conditions seem most likely to generate additive responses? • A trivial reason for non-additivity is output saturation… • Let’s now look at our double ligand data for B cells. DDS1,2

  25. Examples from the double ligand screen • Let us start with the intuitive cases. • Ligand pairs that show very similar patterns in their single ligand responses seem like good candidates for non-additivity.

  26. 5.0’ 2.5’ 30’ 15’ BLC-ELC… less than additive Ca2+ cAMP S Time (seconds)

  27. 5.0’ 2.5’ 30’ 15’ AIG-BLC… greater than additive Ca2+ cAMP S Time (seconds)

  28. 5.0’ 2.5’ 30’ 15’ LPA-TER… greater than additive Ca2+ cAMP S Time (minutes)

  29. Examples from the double ligand screen • Let us start with the intuitive cases. • Ligand pairs that show very similar patterns in their single ligand responses seem like good candidates for non-additivity. • Ligand pairs that show very different patterns in their single ligand responses might be expected to be additive,

  30. 5.0’ 2.5’ 30’ 15’ AIG-LPS… Additive Ca2+ cAMP S Time (seconds)

  31. Examples from the double ligand screen • Let us start with the intuitive cases. • Ligand pairs that show very similar patterns in their single ligand responses seem like good candidates for non-additivity. • Ligand pairs that show very different patterns in their single ligand responses might be expected to be additive, • But there are rather non-intuitive cases as well… • Ligand pairs that show very similar patterns in single ligand responses that are nevertheless additive

  32. 5.0’ 2.5’ 30’ 15’ BLC-S1P… Additive Ca2+ cAMP S Time (seconds)

  33. 5.0’ 2.5’ 30’ 15’ AIG-M3A… Additive Ca2+ cAMP S Time (seconds)

  34. An clear case of additivity in the same signaling network…

  35. An clear case of additivity in the same signaling network…

  36. Examples from the double ligand screen • Let us start with the intuitive cases. • Ligand pairs that show very similar patterns in their single ligand responses seem like good candidates for non-additivity. • Ligand pairs that show very different patterns in their single ligand responses might be expected to be additive, • But there are rather non-intuitive cases as well… • Ligand pairs that show very similar patterns in single ligand responses that are nevertheless additive. • Ligand pairs that show very different patterns in single ligand responses that are non-additive.

  37. 5.0’ 2.5’ 30’ 15’ ELC-LPS… Non-additive, even though LPS has no single ligand response Ca2+ cAMP S Time (seconds)

  38. 5.0’ 2.5’ 30’ 15’ AIG-LPA and AIG-IL4… Non-additive, but little overlap in single ligand responses Ca2+ cAMP S

  39. Conclusions: • A new parameter of the S variable space (DDS1,2) provides a quantitative representation of the interaction between two stimuli. The calculation of the DDS1,2 vector is a complete analysis of all our data for a pair of ligands.

  40. Conclusions: • A new parameter of the S variable space (DDS1,2) provides a quantitative representation of the interaction between two stimuli. The calculation of the DDS1,2 vector is a complete analysis of all our data for a pair of ligands. • The analysis of interaction is now automated and should be used for at least a first pass analysis of data quickly. This should facilitate the experimental cycle in the AFCS.

  41. Conclusions: • A new parameter of the S variable space (DDS1,2) provides a quantitative representation of the interaction between two stimuli. The calculation of the DDS1,2 vector is a complete analysis of all our data for a pair of ligands. • The analysis of interaction is now automated and should be used for at least a first pass analysis of data quickly. This should facilitate the experimental cycle in the AFCS. • Out of 116 double ligand experiments analyzed, we find 42% that show statistically significant non-additivity in at least two experimental dimensions.

  42. Conclusions: • A new parameter of the S variable space (DDS1,2) provides a quantitative representation of the interaction between two stimuli. The calculation of the DDS1,2 vector is a complete analysis of all our data for a pair of ligands. • The analysis of interaction is now automated and should be used for at least a first pass analysis of data quickly. This should facilitate the experimental cycle in the AFCS. • Out of 116 double ligand experiments analyzed, we find 42% that show statistically significant non-additivity in at least two experimental dimensions. • How did our intuition do?

  43. Conclusions: • A new parameter of the S variable space (DDS1,2) provides a quantitative representation of the interaction between two stimuli. The calculation of the DDS1,2 vector is a complete analysis of all our data for a pair of ligands. • The analysis of interaction is now automated and should be used for at least a first pass analysis of data quickly. This should facilitate the experimental cycle in the AFCS. • Out of 116 double ligand experiments analyzed, we find 42% that show statistically significant non-additivity in at least two experimental dimensions. • How did our intuition do? • Future work: (1) automated construction of interaction maps to display these data, (2) incorporation of gene expression data, and (3) generation of reasonable hypotheses for the cellular mechanisms for observed interactions…the basis for future experimentation.

  44. Acknowledgements: Madhu Natarajan Paul Sternweis Elliott Ross Mel Simon Al Gilman

More Related