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Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

Dynamic models can offer deep understanding of information processing mechanisms in physiology, cell signaling, and biological regulation when they are appropriately detailed. Here, we describe some of the key aspects of the model-building process, including proper parameterization and error analysis, as well as common mistakes, such as model-tweaking and oversimplification, which can decrease the value of the models., (Copyright © 2015, American Association for the Advancement of Science.)

Podocytes are kidney cells with specialized morphology that is required for glomerular filtration. Diseases, such as diabetes, or drug exposure that causes disruption of the podocyte foot process morphology results in kidney pathophysiology. Proteomic analysis of glomeruli isolated from rats with puromycin-induced kidney disease and control rats indicated that protein kinase A (PKA), which is activated by adenosine 3',5'-monophosphate (cAMP), is a key regulator of podocyte morphology and function. In podocytes, cAMP signaling activates cAMP response element-binding protein (CREB) to enhance expression of the gene encoding a differentiation marker, synaptopodin, a protein that associates with actin and promotes its bundling. We constructed and experimentally verified a β-adrenergic receptor-driven network with multiple feedback and feedforward motifs that controls CREB activity. To determine how the motifs interacted to regulate gene expression, we mapped multicompartment dynamical models, including information about protein subcellular localization, onto the network topology using Petri net formalisms. These computational analyses indicated that the juxtaposition of multiple feedback and feedforward motifs enabled the prolonged CREB activation necessary for synaptopodin expression and actin bundling. Drug-induced modulation of these motifs in diseased rats led to recovery of normal morphology and physiological function in vivo. Thus, analysis of regulatory motifs using network dynamics can provide insights into pathophysiology that enable predictions for drug intervention strategies to treat kidney disease.

Drugs are designed for therapy, but medication-related adverse events are common, and risk/benefit analysis is critical for determining clinical use. Rosiglitazone, an efficacious antidiabetic drug, is associated with increased myocardial infarctions (MIs), thus limiting its usage. Because diabetic patients are often prescribed multiple drugs, we searched for usage of a second drug ("drug B") in the Food and Drug Administration's Adverse Event Reporting System (FAERS) that could mitigate the risk of rosiglitazone ("drug A")-associated MI. In FAERS, rosiglitazone usage is associated with increased occurrence of MI, but its combination with exenatide significantly reduces rosiglitazone-associated MI. Clinical data from the Mount Sinai Data Warehouse support the observations from FAERS. Analysis for confounding factors using logistic regression showed that they were not responsible for the observed effect. Using cell biological networks, we predicted that the mitigating effect of exenatide on rosiglitazone-associated MI could occur through clotting regulation. Data we obtained from the db/db mouse model agreed with the network prediction. To determine whether polypharmacology could generally be a basis for adverse event mitigation, we analyzed the FAERS database for other drug combinations wherein drug B reduced serious adverse events reported with drug A usage such as anaphylactic shock and suicidality. This analysis revealed 19,133 combinations that could be further studied. We conclude that this type of crowdsourced approach of using databases like FAERS can help to identify drugs that could potentially be repurposed for mitigation of serious adverse events.

The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug's multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.

Because of the complexity inherent in biological systems, many researchers frequently rely on a combination of global analysis and computational approaches to gain insight into both (i) how interacting components can produce complex system behaviors, and (ii) how changes in conditions may alter these behaviors. Because the biological details of a particular system are generally not taught along with the quantitative approaches that enable hypothesis generation and analysis of the system, we developed a course at Mount Sinai School of Medicine that introduces first-year graduate students to these computational principles and approaches. We anticipate that such approaches will apply throughout the biomedical sciences and that courses such as the one described here will become a core requirement of many graduate programs in the biological and biomedical sciences.

Long QT syndrome (LQTS) is a congenital or drug-induced change in electrical activity of the heart that can lead to fatal arrhythmias. Mutations in 12 genes encoding ion channels and associated proteins are linked with congenital LQTS. With a computational systems biology approach, we found that gene products involved in LQTS formed a distinct functional neighborhood within the human interactome. Other diseases form similarly selective neighborhoods, and comparison of the LQTS neighborhood with other disease-centered neighborhoods suggested a molecular basis for associations between seemingly unrelated diseases that have increased risk of cardiac complications. By combining the LQTS neighborhood with published genome-wide association study data, we identified previously unknown single-nucleotide polymorphisms likely to affect the QT interval. We found that targets of U.S. Food and Drug Administration (FDA)-approved drugs that cause LQTS as an adverse event were enriched in the LQTS neighborhood. With the LQTS neighborhood as a classifier, we predicted drugs likely to have risks for QT effects and we validated these predictions with the FDA's Adverse Events Reporting System, illustrating how network analysis can enhance the detection of adverse drug effects associated with drugs in clinical use. Thus, the identification of disease-selective neighborhoods within the human interactome can be useful for predicting new gene variants involved in disease, explaining the complexity underlying adverse drug side effects, and predicting adverse event susceptibility for new drugs.

Signaling from G(i/o)-coupled G protein-coupled receptors (GPCRs), such as the serotonin 1B, cannabinoid 1, and dopamine D2 receptors, inhibits cAMP production by adenylyl cyclases and activates protein kinases, such as Src, mitogen-activated protein kinases 1 and 2, and Akt. Activation of these protein kinases results in stimulation of neurite outgrowth in the central nervous system (CNS) and in neuronal cell lines. This Connections Map traces downstream signaling pathways from G(i/o)-coupled GPCRs to key protein kinases and key transcription factors involved in neuronal differentiation. Components in the Science Signaling Connections Map are linked to Nature Molecule Pages. This interoperability provides ready access to detail that includes information about specific states for the nodes.

Cannabinoid receptor 1 (CB1R) regulates neuronal differentiation. To understand the logic underlying decision-making in the signaling network controlling CB1R-induced neurite outgrowth, we profiled the activation of several hundred transcription factors after cell stimulation. We assembled an in silico signaling network by connecting CB1R to 23 activated transcription factors. Statistical analyses of this network predicted a role for the breast cancer 1 protein BRCA1 in neuronal differentiation and a new pathway from CB1R through phosphoinositol 3-kinase to the transcription factor paired box 6 (PAX6). Both predictions were experimentally confirmed. Results of transcription factor activation experiments that used pharmacological inhibitors of kinases revealed a network organization of partial OR gates regulating kinases stacked above AND gates that control transcription factors, which together allow for distributed decision-making in CB1R-induced neurite outgrowth.

This teaching resource provides lecture notes and slides for a class on mathematical modeling of mammalian signaling pathways. This lecture was part of the course . Cell Signaling Systems: A Course for Graduate Students. and focused on quantitative modeling. The lecture describes how different types of mathematical representations are applied to different signaling systems to provide insight into how a signaling system controls cell behavior. The development of an ordinary differential equation.based model for a bistable switch and the predictions that can be obtained from the model are described. The lecture concludes with a description of how models can be tested experimentally and modified when the details of the models and the resultant predictive behavior are not confirmed by experimentation.

This Teaching Resource describes how to use an online asynchronous discussion as a mechanism to introduce students to the peer-review process, as well as to assess student performance and understanding. This method was applied to a graduate course on signal transduction and the Teaching Resource includes a syllabus, detailed plan for incorporating the online discussion, sample journal club questions, and sample student responses to the discussion forum, faculty responses, and student revisions.

We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.

This Teaching Resource provides lecture notes and slides for a class covering the structure and function of G protein-coupled receptors and is part of the course "Cell Signaling Systems: A Course for Graduate Students." The lecture begins with a discussion of the crystal structure of rhodopsin and G protein subunits and then proceeds to describe the molecular mechanisms of receptor activation and the subsequent release of G proteins.

This Teaching Resource provides the overview to the course "Cell Signaling Systems: A Course for Graduate Students" and lays out the general principles that can be deduced from the current understanding of the organization of cell signaling pathways and networks and how information flows through these pathways and networks. In addition, the lecture provides an overview of G protein-coupled receptor (GPCR) signaling. A description of the lecture, along with a set of slides (http://stke.sciencemag.org/cgi/content/full/sigtrans;2005/270/tr4/DC1) used to present this information, is provided.

This course uses the primary literature to develop a systems-level understanding of the information flow through the various cell signaling pathways and networks. Current areas of research activities in the experimental and theoretical understanding of cell signaling research are highlighted. This Teaching Resource describes a sample syllabus and organization for this course. Each lecture is 2 hours. After each set of lectures, there is a 2-hour discussion period during which students present an in-depth analysis of a primary literature article, selected in consultation with the lecturer.

Understanding complex protein networks within cells requires the ability to develop quantitative models and to numerically compute the properties and behavior of the networks. To carry out such computational analysis, it is necessary to use modeling tools and information management systems (IMSs) where the quantitative data, associated to its biological context, can be stored, curated, and reliably retrieved. We have focused on the biochemical computation of cellular interactions and developed an IMS that stores both quantitative information on the cellular components and their interactions, and the basic reactions governing those interactions. This information can be used to construct pathways and eventually large-scale networks. This system, SigPath, is available on the Internet (http://www.sigpath.org). Key features of the approach include (i) the use of background information (for example, names of molecules, aliases, and accession codes) to ease data submission and link this quantitative database with other qualitative databases, (ii) a strategy to allow refinement of information over time by multiple users, (iii) the development of a data representation that stores both qualitative and quantitative information, and (iv) features to assist contributors and users in assembling custom quantitative models from the information stored in the IMS. Currently, models assembled in SigPath can be automatically exported to several computing environments, such as Kinetikit/Genesis, Virtual Cell, Jarnac/JDesigner, and JSim. We anticipate that, when appropriately populated, such a system will be useful for large-scale quantitative studies of cell-signaling networks and other cellular networks. SigPath is distributed under the GNU General Public License.

Information transfer from activated heterotrimeric guanine nucleotide-binding proteins (G proteins) to downstream effectors occurs through noncovalent protein-protein interactions. Such interactions involve multiple regions of contact between the G protein and the effector. Some of these regions mediate information transfer, as defined by their ability to change the activity of their downstream binding partners, whereas other interactions appear to contribute solely to binding affinity. Such modular configurations occur in functionally diverse proteins such as myosin and a regulator of the double-stranded DNA stimulated protein kinase (PKR) called PACT. In most cases, it appears that both charge complementarity and the architecture of the interacting surfaces provide the appropriate balance between specificity of interactions and their reversibility. Information transfer regions appear to display conformational flexibility in interactions. Such flexible interactions may be essential for the local conformational changes necessary to induce change in activity by an induced fit-type mechanism. Thus, a general mechanism for information transfer by protein-protein interactions could use specific regions that induce conformation changes in the downstream partner. Other binding regions may be arranged within the protein to impart specificity of recognition and thereby maintain overall contact between the partners during the conformational dynamics that occur in the signal-transfer process.

Intracellular signaling networks receive and process information to control cellular machines. The mitogen-activated protein kinase (MAPK) 1,2/protein kinase C (PKC) system is one such network that regulates many cellular machines, including the cell cycle machinery and autocrine/paracrine factor synthesizing machinery. We used a combination of computational analysis and experiments in mouse NIH-3T3 fibroblasts to understand the design principles of this controller network. We find that the growth factor-stimulated signaling network containing MAPK 1, 2/PKC can operate with one (monostable) or two (bistable) stable states. At low concentrations of MAPK phosphatase, the system exhibits bistable behavior, such that brief stimulus results in sustained MAPK activation. The MAPK-induced increase in the amounts of MAPK phosphatase eliminates the prolonged response capability and moves the network to a monostable state, in which it behaves as a proportional response system responding acutely to stimulus. Thus, the MAPK 1, 2/PKC controller network is flexibly designed, and MAPK phosphatase may be critical for this flexible response.

The heterotrimeric guanine nucleotide-binding proteins (G proteins) are signal transducers that communicate signals from many hormones, neurotransmitters, chemokines, and autocrine and paracrine factors. The extracellular signals are received by members of a large superfamily of receptors with seven membrane-spanning regions that activate the G proteins, which route the signals to several distinct intracellular signaling pathways. These pathways interact with one another to form a network that regulates metabolic enzymes, ion channels, transporters, and other components of the cellular machinery controlling a broad range of cellular processes, including transcription, motility, contractility, and secretion. These cellular processes in turn regulate systemic functions such as embryonic development, gonadal development, learning and memory, and organismal homeostasis.

Expression of Q205L Galphao (Galphao*), an alpha subunit of heterotrimeric guanine nucleotide-binding proteins (G proteins) that lacks guanosine triphosphatase (GTPase) activity in NIH-3T3 cells, results in transformation. Expression of Galphao* in NIH-3T3 cells activated signal transducer and activator of transcription 3 (Stat3) but not mitogen-activated protein (MAP) kinases 1 or 2. Coexpression of dominant negative Stat3 inhibited Galphao*-induced transformation of NIH-3T3 cells and activation of endogenous Stat3. Furthermore, Galphao* expression increased activity of the tyrosine kinase c-Src, and the Galphao*-induced activation of Stat3 was blocked by expression of Csk (carboxyl-terminal Src kinase), which inactivates c-Src. The results indicate that Stat3 can function as a downstream effector for Galphao* and mediate its biological effects.

Biological signaling pathways interact with one another to form complex networks. Complexity arises from the large number of components, many with isoforms that have partially overlapping functions; from the connections among components; and from the spatial relationship between components. The origins of the complex behavior of signaling networks and analytical approaches to deal with the emergent complexity are discussed here.

Signaling by guanine nucleotide-binding proteins (G proteins) involves sequential protein-protein interactions. G protein-betagamma subunit (Gbetagamma) interactions with phospholipase C-beta2 (PLC-beta2) were studied to determine if all Gbeta contacts are required for signaling. A peptide encoding Gbeta amino acid residues 86 to 105 stimulated PLC-beta2. Six residues (96 to 101) within this sequence could transfer signals and thus constitute a core signal transfer region. Another peptide, encoding Gbeta amino acid residues 115 to 135, did not substantially stimulate PLC-beta2 by itself but inhibited Gbetagamma stimulation, indicating that residues 115 to 135 constitute a general binding domain. Resolution of signal transfer regions from general binding domains indicates that all protein-protein contacts are not required for signal transfer and that it may be feasible to synthesize agonists and antagonists that regulate intracellular signal flow.

Many distinct signaling pathways allow the cell to receive, process, and respond to information. Often, components of different pathways interact, resulting in signaling networks. Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Feedback can result in bistable behavior with discrete steady-state activities, well-defined input thresholds for transition between states and prolonged signal output, and signal modulation in response to transient stimuli. These properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.

Long-term potentiation (LTP) at the Schaffer collateral-CA1 synapse involves interacting signaling components, including calcium (Ca2+)/calmodulin-dependent protein kinase II (CaMKII) and cyclic adenosine monophosphate (cAMP) pathways. Postsynaptic injection of thiophosphorylated inhibitor-1 protein, a specific inhibitor of protein phosphatase-1 (PP1), substituted for cAMP pathway activation in LTP. Stimulation that induced LTP triggered cAMP-dependent phosphorylation of endogenous inhibitor-1 and a decrease in PP1 activity. This stimulation also increased phosphorylation of CaMKII at Thr286 and Ca2+-independent CaMKII activity in a cAMP-dependent manner. The blockade of LTP by a CaMKII inhibitor was not overcome by thiophosphorylated inhibitor-1. Thus, the cAMP pathway uses PP1 to gate CaMKII signaling in LTP.

Both the alpha and betagamma subunits of heterotrimeric guanine nucleotide-binding proteins (G proteins) communicate signals from receptors to effectors. Gbetagamma subunits can regulate a diverse array of effectors, including ion channels and enzymes. Galpha subunits bound to guanine diphosphate (Galpha-GDP) inhibit signal transduction through Gbetagamma subunits, suggesting a common interface on Gbetagamma subunits for Galpha binding and effector interaction. The molecular basis for interaction of Gbetagamma with effectors was characterized by mutational analysis of Gbeta residues that make contact with Galpha-GDP. Analysis of the ability of these mutants to regulate the activity of calcium and potassium channels, adenylyl cyclase 2, phospholipase C-beta2, and beta-adrenergic receptor kinase revealed the Gbeta residues required for activation of each effector and provides evidence for partially overlapping domains on Gbeta for regulation of these effectors. This organization of interaction regions on Gbeta for different effectors and Galpha explains why subunit dissociation is crucial for signal transmission through Gbetagamma subunits.

Conversion of external signals into proliferative responses may be mediated by interactions between signaling pathways that control cell proliferation. Interactions between G alpha s, the alpha subunit of the heterotrimeric guanine nucleotide binding protein that stimulates adenylyl cyclase, and Ras, an important element in growth factor signaling, were studied. Expression of activated G alpha s in NIH 3T3 cells increased intracellular concentrations of adenosine 3',5'-monophosphate (cAMP) and inhibited H-Ras-stimulated DNA synthesis and mitogen-activated protein kinase activity. Activated G alpha s and 8-Br-cAMP suppressed H-Ras-induced transformation of NIH 3T3 cells. Apparently, G alpha s inhibits proliferative signals from Ras by stimulating cAMP production and activating protein kinase A.