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July, 2003. Vol.3, No.2

RAW264.7 Macrophage

New Cell Line of Choice

RAW264.7 cell engulfs bacteria (photo by Adrian Ozinsky)

It is official. The macrophage cell line RAW264.7 has been adopted as the new cell line to be studied by the Alliance for Cellular Signaling. The need to explore alternative cells was realized by the AfCS in March 2003, when we encountered apparently insurmountable difficulties in applying RNA interference (RNAi) techniques to our chosen cells, particularly including the WEHI-231 B cell line. The ability to inhibit gene expression with RNAi is a powerful technique that permits knock-out of proteins, singly and in combinations, within a few days of transfection of cells. Such perturbation of signaling pathways is a requirement for the long-term goals of the AfCS, and as Al Gilman stated in the May, 2003 Newsletter, “we must have this technology; it is way too good to miss.”

Yellow = challenge Red = major problem

Upon the recommendation of the AfCS Steering Committee, laboratories moved quickly to narrow a list of seven potential cells lines to three high priority lines: two macrophage lines (J774A.1 and RAW264.7) and one pituitary line (AtT-20). In less than two months AfCS laboratories successfully produced a great deal of high quality data for presentation at the 3rd annual meeting of the AfCS in order to evaluate the suitability of the alternative cells. Presentations were made by AfCS Labs to address the following issues:

1. gene transcription profiles to identify receptors and thus a list of potentially useful ligands [PPT]

2. ability to express exogenous proteins following transfection or viral infection (required for protein interaction, microscopy, and other studies) [PPT] [PPT]

3. utility of RNAi approaches for knockout of particular gene products [PPT]

4. suitability for microscopy [PPT] 5. ligand responses in functional assays,

such as quantification of intracellular calcium, cyclic AMP, andphosphoproteins [PPT]

Mel Simon (Cal Tech), Mike Rogers (NIGMS), Rochelle Long (NIGMS), Alison Deckhut (NIAID

NEWS OF THE

ALLIANCE FOR

CELLULAR SIGNALING

)

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Dierdre Brekken (Protein Chemistry Lab, UTSW) demonstrated expression of YFP in 50-70% of viable RAW264.7 cells after transfection with Lipofectamine 2000. Tamara Roach (Assay Lab, UCSF) also showed that virtually all RAW264.7 macrophage-like cells could be transduced with lentivirus. Very importantly, Iain Fraser (Molecular Biology Lab, Cal Tech) showed convincing evidence for the effectiveness of RNAi in RAW264.7 and other cells using both lentivirus and plasmids.

Iain Fraser discusses RNAi macrophage successes.

With the approval of both the AfCS External Advisory Committee and our sponsors, a newly formed Macrophage Committee now replaces the B Cell and Cardiac Myocyte Committees. Chaired by Henry Bourne (UCSF), with Jim Stull (UTSW) serving as Vice-Chair, six of the seven current Macrophage Committee members come from the two former System Committees. In addition to Bourne and Stull, the membership includes Michael Berridge (Babraham), Lew Cantley (Harvard), Peter Devreotes (Johns Hopkins), and Mike Gold (U of British Columbia). Alan Aderem from the Institute for Systems Biology in Seattle joins the AfCS as a new member of the Committee and brings extensive expertise in macrophage physiology. Kelly Smith (U of Washington), formerly a member of the Aderem lab, will serve as a consultant to the committee. The Macrophage Committee is busy developing a new ligand list, considering new assays to evaluate phagocytosis and secretion of various cytokines and related molecules, and making decisions about yeast two-hybrid baits and libraries and possible focus modules, such as calcium or PIP3.

Robert Hsueh, Kelly Smith, Tamara Roach discuss the transition from B cells to macrophages.

3rd Annual AfCS Meeting Highlights

Beckman Institute, Cal Tech

The 3rd annual meeting of the AfCS was held May 19-21, 2003 in Pasadena, California. Al Gilman opened the meeting by reviewing the goals of the AfCS and highlighting accomplishments in terms of people and capabilities, data and analysis, and products for the signaling community. [PPT] Highlights of AfCS Data and Analysis • The B cell single ligand screen is complete

and the raw and processed data are available on the website.

• The B cell double ligand screen, which looks for non-additive interactions between receptor agonists, is yielding interesting results that speak to a high density of interactions between individual signaling pathways (see details in article on p. 4).

• The yeast two-hybrid screen for protein –protein interactions is proving to be a goldmine (see details in article on p. 6).

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• A large number of phosphoproteins and phophorylation sites have been identified in WEHI-231 cells. (LINK)

• Studies involving subcellular localization and translocation of families of signaling proteins are contributing valuble information.

Al Gilman, Grischa Chandy & Ron Taussig

check out the new Microscopy Image Database. AfCS products for the Signaling Community: • The AfCS-Nature Signaling Gateway

http://www.signaling-gateway.org/ As of April 2003, the Gateway has 40,000 registered users and serves over 250,000 successful HTLM page requests every month.

• The Molecule Page Database (LINK) (see details in article p. 8 ).

• The Antibody Database contains detailed information about the AfCS experience with more than 200 commercial antibodies. (LINK)

• A database of Covalent Modifications of Signaling Proteins is a cooperative project with the AfCS membership. (LINK)

• DNA reagents will be available shortly from the ATCC. These are displayed in the Plasmid Database (LINK), which includes 600 fully verified cDNA sequences, 100 Gateway-ready expression vector backbones, and 1500 expression constructs.

• The Microscopy Image Database, which currently contains images documenting subcellular localization of proteins and PH domains in WEHI-231 cells. (LINK)

• Protocols, including 60 procedures and 200 ligand and solution protocols. (LINK)

The AfCS welcomed our Gateway collaborators from The Nature Publishing Group: Brenda Riley, Barbara

Marte, and Timo Hannay. A major focus of the AfCS this year has been to meet the demanding challenge of analyzing AfCS data. A new data analysis team has been created by recruitment of several computationally sophisticated individuals who are interested in applying their talents to complex biological systems. Madhusudan Natarajan (UTSW), working together with Rama Ranganathan (UTSW), is the member of the data analysis team who has taken on the responsibility for evaluating, analyzing, and interpreting ligand screen data. For the single ligand screen, Natarajan and Ranganathan have developed methods for evaluating, quantitatively, the similarity or dissimilarity of responses to different ligands. These methods were presented at the annual meeting. [PPT] Ranganathan also gave a second presentation at the meeting [PPT] on analysis of the double ligand screen. He described methods for evaluating the interactions between ligands to estimate the total density of interactions within the system. To accomplish this, Natarajan and Ranganathan have learned to combine multivariate output data into general parameters that represent signaling and to collapse non-independent outputs into a workable number of data points that describe a response adequately. They have also developed formulas for calculation of the similarity of ligand responses and have evaluated interactions between ligand pairs in B cell ligand screen experiments (see details in article on p. 10 ).

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Double Ligand Screen in B Cells Yields Abundant

Interactions

One of the high points of this year’s annual meeting was a first look at interactions among signaling pathways that are evoked by multiple ligands. Paul Sternweis, Director of the AfCS Cell Preparation and Analysis lab, reported on the dual ligand screen that is currently nearing completion in primary B cells. Screening was initiated with three assays (cyclic AMP, calcium, and phosphoproteins), and the strategy for detection of interactions included both simultaneous and delayed addition of ligands for the first two assays. Sixteen ligands that elicited a response in at least one of the three assays and three additional ligands that were expected to affect primary B cells in other ways were included in this dual screen (see Fig. 1).

Many non-additive interactions between ligands have been observed. Differences from additivity are seen in the magnitude of peak response, the level of the sustained response, and the kinetics of response. For example, both terbutaline and lysophosphatidic acid stimulate accumulation of cyclic AMP ~2-4 fold. However, when added together, the two ligands evoke a 15-25 fold increase in the concentration of the second messenger – much more than that predicted by addition of the responses to the two ligands alone (Fig. 2). In several cases ligands that have no effect on an output by themselves do have an effect on other ligands that elicit the response. For example, LPS has no measurable effect on the basal concentrations of cyclic AMP, calcium, or selected phosphoproteins. Nevertheless, LPS abolishes the elevation of cytosolic calcium by two of the chemokines (Fig. 3). This appears to be an early, transient effect of LPS. If cells are exposed to ELC 10 min after treatment with LPS, a normal calcium response is observed (Fig. 3, red trace). Another example of time-dependent responses is the inhibition by LPA of the calcium response evoked by stimulation of the B cell receptor (AIG). Even though LPA produces a small stimulation of calcium by itself, the lysolipid inhibits the peak response to AIG (Fig. 4). This inhibition is transient because the

Figure 1. Ligands used in the dual ligand screen for B cells 2MA 2-Methylthio-Adenosine Triphosphate (purinergic R) AIG anti-IgM BLC B-lymphocyte chemoattractant 40L anti-CD40 CGS CGS-21680 (adenosine analogue – A2aR -selective) DIM dimaprit (H2 receptor agonist) ELC Epstein Barr Virus-induced molecule-1 Ligand Chemokine IFB Interferon-beta I10 Interleukin 10 I04 Interleukin 4 LPS Lipopolysaccharide LPA Lysophosphatidic Acid M3A MIP3-alpha (Macrophage inflammatory protein-3) PGE Prostaglandin E2 SDF SDF1 alpha (Stromal cell derived factor-1) SLC Secondary lymphoid-organ chemokine S1P D-erythro-Sphingosine-1-Phosphate TER Terbutaline TNF Tumor necrosis factor-alpha

Figure 2. Stimulation of cAMP by terbutaline (TER) and lysophosphatidic acid

Time (sec) Figure 3. Stimulation of calcium by ELC n the absence or presence of LPS

i

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sustained response of calcium in response to AIG is the same in the presence or absence of LPA. The transient nature of the LPA response is readily observed when the ligand is added to cells that have already been stimulated with AIG (Fig. 4, blue trace).

In summary, more than 40 interactions among ligand pairs were found with calcium as the output and 15-20 interactions were found when cyclic AMP was measured. In both cases, this represents about 30 % of the ligand pairs tested. Thus, we observe a high density of interactions between ligands, even when measuring two single outputs presumed to represent early events in signal transduction cascades. A connection map, which illustrates the potential interactions among some ligands that regulate cytosolic calcium concentrations, is shown in Fig. 5. This map is a work in progress. Replication is required before definitive conclusions can be dawn.

B cell interaction data is discussed by Adam Arkin, Joan Brugge, Henry Bourne, & Bob Lefkowitz

Figure 4. The effect of lysophosphatidic acid (LPA) on the increase in calcium caused by stimulation of the B cell receptor.

Figure 5

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Yeast Two Hybrid Data— A Goldmine of Interactions

In order to produce a large body of information on protein-protein interactions, the AfCS is collaborating with Myriad Genetics to perform large-scale yeast two-hybrid screens. Using mRNA isolated by the AfCS Cell Preparation Laboratory, Myriad has prepared B cell- and myocyte-specific activation domain libraries. Over the past year, the AfCS System Committees selected B cell and myocyte proteins that they consider to be priority bait candidates for yeast two-hybrid screening. Among other criteria, initial baits used in the screen were selected for potential involvement in GPCR or BCR signaling leading to PIP3 generation. The choices were made in coordination with the Focus on PIP3 Module (FPM), an attempt to explore a smaller, better defined signaling module in parallel with more global goals.

At last year’s meeting, 170 interactions had been derived from just 16 bait proteins and suggested limited connectivity. There are now 473 interactions from 55 bait proteins posted on the Gateway website. All 473 interactions can be accessed by registered users through the Data Center on the HomePage of the Gateway. Once the protein of interest has been identified, the user can easily access information about the number of baits

designed, the number of hits/bait, and the identity of proteins named as prey. At the AfCS meeting in Pasadena, Lead Scientist Iain Fraser (Molecular Biology Laboratory, Cal Tech) presented a map of interactions based on those preys that had been identified by at least two different bait proteins. Although subjective, this approach highlights the increasing interconnectivity of the signaling network downstream of the BCR (see figure below). Data is freely available to the cell signaling community on the AfCS-Nature Signaling Gateway website. Of the interaction data reported, some of the “hits” have already been documented in the literature, while others are novel interactions.

Iain Fraser discusses verified hits vs. novel interactions.

Project Status-One Year Later

473170Total Interactions

55 (90)16 (26)Bait Proteins with Released Data

-119Bait Fragments in Preparation

706302Screens Initiated

16398Bait Proteins Approved

May 2003May 2002

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• e now have 473 interactions from 55 bait proteins • How can the average biologist analyze this data set?

Fraser used the Ruk 1 protein as a case in point. Earlier AfCS screens have identified the Ruk1 protein (a.k.a. CIN85) as a bait with PLC gamma 2, BLNK, and Grb2. The literature suggests that Ruk 1 is an adapter protein that regulates PI3 kinase activity, and it has been shown to influence apoptotic pathways in neuronal cells. A recent paper (J. Biol. Chem. 2002 Oct 18;277(42):39666-72) shows that Ruk1/CIN85 binds to the tyrosine kinase Cbl-b. This ties in nicely with AfCS yeast-two hybrid data, since we have detected Cbl-b as a bait with Grb2 (which also binds to Ruk1-see above). AfCS data also suggests several additional interactions between proteins implicated here and Ruk1 (PLC gamma 2, BLNK, Grb2, Cbl-b), which provide clues to an expanded protein interaction network. One of the goals of the AfCS is to make available to the signaling community knowledge of the protein-protein interactions that that are suggested by the AfCS/Myriad yeast two-hybrid screen. However, we depend on individual researchers and laboratories to validate these data and to determine the physiological significance of the interactions, if any.

CD19CD19

BtkBtk

ActinCytoskeleton

SOS2SOS2

WISHWISH

CAPCAP

DblDbl

CamK IICamK II

NdkBNdkB

cdc42cdc42

PDK1PDK1

CD22CD22

FynFyn

PI3Kγ (p110)PI3Kγ (p110)

1907019719070197

67553996755399

66715386671538

46335144633514

85673258567325

13542677135426772089443020894430

2632696826326968

30642623064262

Tested as Y2H bait

Not yet tested as Y2H baitPrey not represented in AfCS protein list (Number=NCBI GI#)

Legacy dataY2H and LegacyY2H NovelRecent Y2H Novel

CD19CD19

BtkBtk

ActinCytoskeleton

SOS2SOS2

WISHWISH

CAPCAP

DblDbl

CamK IICamK II

NdkBNdkB

cdc42cdc42

PDK1PDK1

CD22CD22

FynFyn

PI3Kγ (p110)PI3Kγ (p110)

1907019719070197

67553996755399

66715386671538

46335144633514

85673258567325

13542677135426772089443020894430

2632696826326968

30642623064262

19070197190701971907019719070197

675539967553996755399675539967553996755399

6671538667153866715386671538

4633514463351446335144633514

8567325856732585673258567325

1354267713542677135426771354267720894430208944302089443020894430

26326968263269682632696826326968

3064262306426230642623064262

Tested as Y2H bait

Not yet tested as Y2H baitPrey not represented in AfCS protein list (Number=NCBI GI#)

Legacy dataY2H and LegacyY2H NovelRecent Y2H Novel

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AfCS Molecule Pages States, Transitions, and Pathways

Ron Taussig and Pat Casey recruit Gil Sambrano as a Molecule Page alpha tester. The AfCS Molecule Page Database continues to evolve. The Molecule Pages are now a database of over 3,400 proteins involved in cellular signaling. A Molecule Page for each protein is linked to many public databases, which provide a large amount of ‘automated’ data that can be accessed through links provided on the display page (the “Molecule Page Overview”). From this Overview display, summaries or “MiniMolecule Pages” can also be viewed for over 800 of the 3,400 proteins. These Minimolecule Pages contain written descriptions submitted by assigned Molecule Page authors detailing protein function, protein regulation, concentration, and subcellular localization, as well as splice variants, antibodies, and phenotypes associated with mouse knockouts. At the annual meeting in Pasadena, AfCS Associate Director Ron Taussig and Membership and Editorial Committee Chair Pat Casey gave a live, web-based demonstration of the user interfaces that will enable entry into the database of highly structured information on

protein states, interactions, subcellular localization, and function. This information will then be used to derive relationships and create pathway maps. Molecule Page authors will enter data from the literature. Once complete, individual Molecule Pages will be peer-reviewed by referees selected by the Nature Publishing Group editorial team and will be formally citable.

Authors will be asked to create each state in which their protein “resides”. They will then be asked to describe: 1) covalent modifications, 2) binding of ligands, 3) binding of other proteins, and 4) subcellular localization. When all of the states have been defined, the author will then define all the known transitions that exist between each pair of states and the agent that catalyzes the transition between these states (i.e. the specific phosphatase that catalyzes removal of a phosphate group).

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The author also has options to define thermodynamic or kinetic parameters for the transitions, as well as the functional consequences associated with the transition from one state to the next (e.g., stimulation of enzymatic activity). Available outputs will allow the viewer to examine the defined parameters for each state of the molecule (e.g, protein X phosphorylated at residue xxx and bound to protein Y at the plasma membrane). Similarly the viewer can examine the transition of a protein from state 1 to state 2. When the data entered is sufficiently complete, the Molecule Page program will automatically calculate and display the connections and hence the pathways that underlie the signaling system. For example, when states and transitions were entered for Galpha-s, the figure below was displayed, modeling a G protein cycle.

Furthermore, the viewer can navigate from one Molecule Page to a connected Molecule Page (e.g., move quickly from receptor to G protein to effector). It is anticipated that these tools will be an invaluable resource for the signaling community. The extent and scope of such pathway mapping will be limited only by the number of molecules that are described by Molecule Page authors. Please go to the author application page at www.signaling-gateway.org to apply to write a Molecule Page. In addition, the Membership and Editorial Committee (afcsed@mc.duke.edu) is always open to suggestions for additional molecules to add to the Protein List and for nominations of appropriate Molecule Page authors.

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Single and Double B Cell Ligand Screen Data Analysis

Madhu Natarajan and Rama Ranganathan examined the results of the single and double ligand screens in the context of the question “How complex is signaling in cells?” Ranganathan presented the methods used and the results of the study of single and double ligand data in two separate talks. The single ligand screen. The single ligand screen was designed to provide a signature or a pattern of responses for a ligand sampled at several points in the signaling network (see presentation by E. Ross). [PPT] While these results may or may not shed light on the mechanistic basis of such responses, they do allow us to ask questions about the similarity or dissimilarity of response, i.e., comparisons between ligands or across groups of ligands. Ranganathan’s first talk addressed the question, “The single ligand screen measures responses across very different cellular parameters; how can we quantitatively measure the similarity (or dissimilarity) of the responses to different ligands?” The single ligand screen is comprised of four different assays (calcium, cyclic AMP, phosphoproteins, and cDNA microarrays). To combine all the multivariate output data into general parameters that represent signaling, a Gaussian error model was generated for the unstimulated value of each variable. Each ligand response for that variable was then converted into a dimensionless unit that represents the “significance” of the response given the basal (unstimulated) value and the error (σ),

S units describe the observed value as a certain number of (basal) standard deviations from the unstimulated. In this context, we refer to S as signaling units. Clearly, a larger observed value yields a higher significance score. Also, a noisy unstimulated case with a large error about the basal state should downweight the significance of a large observed value because it cannot be clearly distinguished from the unstimulated case. All the data (calcium, cyclic AMP, and phosphoproteins) were converted into a corresponding S score. For instance, the calcium response to application of one ligand, lysophophatidic acid (LPA), is shown in blue. The calcium response to application of buffer (error bar: mean ± s.d.) is shown in red.

As the S score is dimensionless, all the variables (calcium, cyclic AMP, phosphoproteins) are reduced to a common scale. The variables were collated to create a representation of the complete dataset for each ligand. This array of S scores is treated as a vector, with each time point for each variable being a separate dimension along which the response is characterized. As some of these datasets include non-independent measurements (best characterized by the calcium trace where sample rate dictates the

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number of time points: 200), a hierarchical cluster-based approach was used to reduce dimensionality. For example, the matrix of S scores for calcium was reduced to five values (see image) that represent concomitant portions of the calcium trace. The combined calcium, cyclic AMP, and phosphoprotein S vector matrix describes the entire experimental space of the single ligand screen (microarrays to be incorporated soon). The experimental space was clustered to examine similarities (and differences) between ligand responses. Groups of ligands that clustered together were examined to see if they indicated other common biological themes like compound classification, specificity of action on receptor subtype, etc. To summarize, an analysis technique was identified to transform raw data variables into dimensionless S variables (units of significance). This allowed construction of a unified experiment space of all data, regardless of source or differences in intrinsic dynamic range and signal to noise ratio. The experimental space was clustered to identify similarities (and dissimilarities) between ligands. The differences between ligand responses can be quantified as differences in the arrays of S scores or the vectors making up a ligand response along different variable dimensions. The double ligand screen. The double ligand screen examines the changes in the response patterns of a ligand in the presence of another ligand. In his second talk, Ranganathan addressed the question, “How can we quantitatively evaluate the interactions between pairs of ligands? Secondly, given the quantitative evaluation of the interactions between pairs of ligand responses, what is the estimate of total interaction density?” The S vector for each ligand describes the cellular measurements in response to the ligand. Addition of two S vectors describes the expected response when two ligands are applied simultaneously. This prediction or expectation is not necessarily accurate, especially in cases where the intracellular pathways acted upon by the two ligands interact. As a rule of thumb, pathways that interact are most likely to yield non-additive responses. The presence of additivity or non-additivity can be examined by making the comparison:

2121 ligandligandligandligand SwithSS ++ When the expected response (i.e., vectorial addition of Sligand 1 + Sligand 2) does not equal the experimen lly observed response (Sligand1+ligand 2), pathways involving the two ligands are predicted to interact. he illustration below shows an example of the vectorial addition of two ligands (L1, L2) across th

taT

ree

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dimensions (var 1 to var 3). The expected response L1+2 (in black) is contrasted to the observed response L1+2, obs. The difference in the responses or the vector ∆∆S1,2 indicates the extent of non-additivity.

The analysis was carried out for all available ligand combinations (Sternweis’ talk) and the outputs tabulated. We can determine the extent of additivity or non-additivity across each of the individual dimensions. In summary, the methods used to analyze the single ligand screen (generating S scores from raw data) were used to quantify the change in the effects of a ligand in the presence of a second ligand. Results of all

Madhu Natarajan, Paul Sternweis & Rama Ranganathan on the way home . . . still at it after all these days.

ligand pairs tested experimentally to date were used to quantify the net amount of additivity/non-additivity within the experimental space. This study is the first attempt to describe, systematically, the level of complexity of a cell’s receptor-mediated intracellular signaling network.

SPONSORS We acknowledge our sponsors with gratitude: The National Institutes of Health The National Institute of General Medical Sciences. The Alliance Project was conceived under the NIGMS Glue Grant Initiative. See: www.nigms.nih.gov/funding/gluegrants.html The National Institute of Allergy and Infectious Diseases The National Cancer Institute The Pharmaceutical Industry Eli Lilly and Co. The Merck Genome Research Institute Aventis Pharmaceuticals, Inc. Johnson & Johnson Novartis Pharma AG

It is notable that these corporate sponsors support the investigative aims of the Alliance in compliance with our policy on Intellectual Property. No sponsor views Alliance data before it is posted on the public Internet. Philanthropic Foundations: The Agouron Institute Anonymous Foundation, Dallas TX Others: The University of Texas Southwestern Medical Center

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We have established collaborative relationships with the following entities: Myriad Genetics, Inc. Cell Signaling Technology Agilent Technologies Isis Pharmaceuticals, Inc. Bio-Rad Laboratories, Inc. These relationships involve either (1) collaborative research agreements in which services are performed for the Alliance at cost or below (with full and prompt disclosure of data) or (2) substantial reductions in pricing for equipment and/or reagents with the hope that successful use of these technologies by the Alliance (and promulgation of such data) will encourage widespread adoption of relevant technologies.

POWER POINT PRESENTATIONS

Slides from many of the presentations made at the 3rd Annual Meeting can be accessed through the Meetings and Reports link on the AfCS-Nature Signaling Gateway Home Page or via the links provided below.

Overview Introduction Al Gilman [PPT] Single ligand screen in B cells Goals and plans for the ligand screen experiments Elliott Ross [PPT] Single ligand screen: What did we do; what did we learn about protocols, ligands, and cells? Signaling experiments Rama Ranganathan [PPT] Single ligand screen: What did we do; what did we learn about protocols, ligands, and cells? Microarray experiments Mel Simon [PPT],

Double ligand screen in B cells Double ligand screen: experimental design and initial patterns of interaction Paul Sternweis [[PPT] Analysis and results of double ligand screen data Rama Ranganathan [PPT] What comes next? Elliott Ross [PPT] Other ongoing measurements Phopholipids and potential interactions with LIPID MAPS project Alex Brown [PPT] Protein location and translocation; domain translocation Tobias Meyer [PPT] Ligand –stimulated protein phosphorylation: Antibodies and other techniques

Susanne Mumby [PPT]; Tyr vrs. ser/thr. and target–specific vrs. non-specific Marc Mumby [PPT] Yeast two hybrid Iain Fraser [PPT] Protein complementation assays Steve Michnick [PPT] Pull-downs Paul Sternweis[PPT] Criteria for choice of an additional cell line Criteria for choice Ron Taussig [PPT] Macrophages and macrophage cell lines The macrophage Kelly Smith [PPT] AfCS Experimental observations on new candidate cell lines Transfection Deirdre Brekken [PPT] Infection Tamara Roach [PPT] Microscopy Grischa Chandy [PPT] RNAi Iain Fraser [PPT] Transcript analysis Sangdun Choi [PPT] Cyclic AMP & calcium responses Robert Hsueh [PPT] Phosphoproteins Heping Han [PPT] Further challenges Problem of isoform specificity David Fruman [PPT] Parts list, database, isoform detection Shankar Subramaniam [PPT] Using databases and extracting pathways from AfCS data Shankar Subramaniam [PPT] I. Extracting pathway information from experimental data and databases Bruce Conklin [PPT] II. Extracting pathways Adam Arkin [PPT]

Bruce Conklin continues his talk about Gene Microarray Pathway Profiler(GenMAPP), a graphic tool to view data.