B-Cell Ligand Screen: Complexity Estimation and Future Applications

The B-Cell Ligand Screen (the Final Report) and Next Steps Madhu Natarajan, Rama Ranganathan AFCS Annual Meeting 2004

The B-Cell Ligand Screen and Next Steps • Talk Outline: • Statement of the first question of the AFCS…the estimation of the level of complexity in the signaling network. • Accomplishments and conclusions from the B cell work: • Development of a general method for analysis of multivariate data. • Classification of ligand responses and estimation of interaction density. • Production of a substantial high quality dataset for the signaling community. • The future…application of the analytic concepts and methods for the next steps in the AFCS project.

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln I. The Initial Question of the AFCS. • How complex is signaling in cells? That is, what is the density of interactions between input signals in producing outputs?

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln I. The Initial Question of the AFCS. • The single and double ligand screens… • The ligand screen… • (1) Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands.

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln I. The Initial Question of the AFCS. • The single and double ligand screens… • The ligand screen… • (1) Quantitativemeasurement of the similarity (or dissimilarity) of the responses to different ligands. • Quantitative evaluation of the interactions between pairs of ligand responses, and an estimation of total interaction density.

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln II. The Analytic Methods and Results • Main issues… Calcium time points cAMP time points . . . • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • How can we get all the multivariate output data into general parameters that represent signaling? S-variable transformation. s Observed value basal

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln II. The Analytic Methods and Results • Main issues… Calcium time points cAMP time points . . . • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • How can we get all the multivariate output data into general parameters that represent signaling? S-variable transformation. • Parameter reduction to appropriately represent the signaling system…as Madhu showed.

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln II. The Analytic Methods and Results • Main issues… Calcium time points cAMP time points . . . • Quantitative measurement of the similarity (or dissimilarity) of the responses to different ligands. • How can we get all the multivariate output data into general parameters that represent signaling? S-variable transformation. • Parameter reduction to appropriately represent the signaling system. • A formalism for calculating the similarity of ligand responses…the S space.

Quantitative measurement of similarity in ligand screen data The Experiment Space A highly multi-dimensional space, but one that behaves just like three-dimensional space. Each variable gets an independent dimension, and so a complete single ligand dataset is one vector in this space.

Quantitative measurement of similarity in ligand screen data The Experiment Space What can we learn from this representation?

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.

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…

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

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…and this preserves the dimensions along which the differences occur. The cosine DS1,2

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…and this preserves the dimensions along which the differences occur. • How to represent dynamics?

P-Proteins (min) Gene expression (hrs) cAMP Ca2+ 2.5 5.0 15 30 0.5 1.0 2.0 4.0 The Experiment Space

P-Proteins (min) Gene expression (hrs) cAMP Ca2+ 2.5 5.0 15 30 0.5 1.0 2.0 4.0 The Clustered Experiment Space

Conclusions: • A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise. Experiment space now contains everything we measured…Ca2+, cAMP, P-proteins, gene expression changes.

Conclusions: • A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise. • The work in the past year has addressed two fundamental problems…(1) over-parameterization, the usage of many non-independent variables to represent a biological process.

Conclusions: • A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise. • The work in the past year has addressed two fundamental problems…(1) over-parameterization, the usage of many non-independent variables to represent a biological process….and (2) increasing the reliability of our experiments, something particularly important in quantifying interactions between ligands.

Conclusions: • A simple transformation of raw data variables into dimensionless S variables (units of significance) permits construction of an unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise. • The work in the past year has helped to address two fundamental problems with this kind of analysis. One is over-parameterization, the usage of many non-independent variables to represent a biological process. The second is reliability of the measurements, a factor that becomes particularly important in evaluating interactions between ligands. • We showed that 27 out of 32 ligands applied to the B cell showed some significant response in at least some of the 134 experiment space dimensions. • The 27 positive ligands show at least 19 distinct response patterns in our experiment space.

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . Om Ln I. The Initial Question of the AFCS. • The single and double ligand screens… • The ligand screen… • (1) Quantitativemeasurement of the similarity (or dissimilarity) of the responses to different ligands. • Quantitative evaluation of the interactions between pairs of ligand responses, and an estimation of total interaction density.

O1 L1 Signaling Network O2 L2 O3 L3 . . . . . . On Ln The Initial Questions of the AFCS – Conclusions from Question 1 and Next Steps • Quantitative measurement of the “interaction” between pairs of stimuli. What experiment tests for 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?

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.

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.

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.

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?

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.

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?

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?

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?

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

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

Quantitative measurement of interactions between ligands • 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

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.

The AIG-ELC interaction (1) The single-ligand profiles…

The AIG-ELC interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series… observed expected

2 3 4 5 1 The AIG-ELC interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series… observed (3) And the DDSAIG,ELC variables… expected

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 where one has no response at all seem like good candidates for additivity.

observed expected The LPA-TNF interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series…

observed expected 2 3 4 5 1 The LPA-TNF interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series… (3) And the DDSLPA,TNF variables…

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 where one has no response at all seem like good candidates for additivity. • Now some not-so-intuitive cases. • Ligand pairs that show only weak overlap in response pattern but are still non-additive.

observed expected The AIG-TER interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series…

observed expected 2 3 4 5 1 The AIG-TER interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series… (3) And the DDSAIG,TER variables…

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 where one has no response at all seem like good candidates for additivity. • Now some not-so-intuitive cases. • Ligand pairs that show only weak overlap in response pattern but are still non-additive. • Ligand pairs that show both positive and negative interactions.

observed expected The ELC-LPA interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series…

observed expected 2 3 4 5 1 The ELC-LPA interaction (1) The single-ligand profiles… (2) Scaled but not reduced time series… (3) And the DDSELC,LPA variables…

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. A crutch for developing intuition…

B-Cell Ligand Screen: Complexity Estimation and Future Applications