A detailed depiction of the 'integrin adhesome', consisting of a complex network of 156 components linked together and modified by 690 interactions is presented. Different views of the network reveal several functional 'subnets' that are involved in switching on or off many of the molecular interactions within the network, consequently affecting cell adhesion, migration and cytoskeletal organization. Examination of the adhesome network motifs reveals a relatively small number of key motifs, dominated by three-component complexes in which a scaffolding molecule recruits both a signalling molecule and its downstream target. We discuss the role of the different network modules in regulating the structural and signalling functions of cell-matrix adhesions. Top-down and bottom-up approaches for studying the integrin adhesomeCell-extracellular matrix (ECM) interactions are mediated through specialized subcellular sites that contain specific adhesion receptors, cytoskeletal elements and a wide variety of interconnecting adaptor proteins [1][2][3] . These adhesion complexes permit cells to sense multiple extracellular signals that specify the chemical identity, geometry and physical properties of the ECM 4,5 . Thus, cells behave differently on two-and three-dimensional matrices 6 , distinguish between different ECM components 7 , can detect differences in adhesive ligand density 8 , and respond to mechanical perturbation and surface rigidity 9,10 . Competing Financial Interests:The authors declare no competing financial interests.Website -www.adhesome.org contains the adhesome database of components and interactions with an interface that allows dynamical navigation between hyperlinked subnets created for each component and for many network motifs within the adhesome network.Publisher's Disclaimer: Disclaimer: Nature Publishing Group has a collaboration with the Cell Migration Consortium for the creation and maintenance of the Cell Migration gateway (http://www.cellmigration.org/), but has no role in generating or curating the Cell Migration Consortium database content. As always, Nature Cell Biology Editors have been fully independent and solely responsible for the editorial content and peer review of this Analysis article. NIH Public Access Author ManuscriptNat Cell Biol. Author manuscript; available in PMC 2009 August 31. Published in final edited form as:Nat Cell Biol. 2007 August ; 9(8): 858-867. doi:10.1038/ncb0807-858. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptTo understand the mechanisms underlying these diverse responses, in-depth characterization of individual proteins or pathways 11,12 , and collection of information about multiple components that concertedly form the presumed adhesome 13,14 , have been undertaken. Each of these approaches has limitations, and individually is unlikely to explain how the adhesion machinery senses environmental cues and responds to them. However, combining data from the two approaches could produce new mechanistic insights into the structu...

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.

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.

MicroRNAs (miRNAs) in body fluids are candidate diagnostics for a variety of conditions and diseases, including breast cancer. One premise for using extracellular miRNAs to diagnose disease is the notion that the abundance of the miRNAs in body fluids reflects their abundance in the abnormal cells causing the disease. As a result, the search for such diagnostics in body fluids has focused on miRNAs that are abundant in the cells of origin. Here we report that released miRNAs do not necessarily reflect the abundance of miRNA in the cell of origin. We find that release of miRNAs from cells into blood, milk and ductal fluids is selective and that the selection of released miRNAs may correlate with malignancy. In particular, the bulk of miR-451 and miR-1246 produced by malignant mammary epithelial cells was released, but the majority of these miRNAs produced by non-malignant mammary epithelial cells was retained. Our findings suggest the existence of a cellular selection mechanism for miRNA release and indicate that the extracellular and cellular miRNA profiles differ. This selective release of miRNAs is an important consideration for the identification of circulating miRNAs as biomarkers of disease.

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.

N-myristoylation is a common form of co-translational protein fatty acylation resulting from the attachment of myristate to a required N-terminal glycine residue.1,2 We show that aberrantly acquired N-myristoylation of SHOC2, a leucine-rich repeat-containing protein that positively modulates RAS-MAPK signal flow,3–6 underlies a clinically distinctive condition of the neuro-cardio-facial-cutaneous disorders family. Twenty-five subjects with a relatively consistent phenotype previously termed Noonan-like syndrome with loose anagen hair [OMIM 607721]7 shared the 4A>G missense change (Ser2Gly) in SHOC2 that introduces an N-myristoylation site, resulting in aberrant targeting of SHOC2 to the plasma membrane and impaired translocation to the nucleus upon growth factor stimulation. Expression of SHOC2S2G in vitro enhanced MAPK activation in a cell type-specific fashion. Induction of SHOC2S2G in Caenorhabditis elegans engendered protruding vulva, a neomorphic phenotype previously associated with aberrant signaling. These results document the first example of an acquired N-terminal lipid modification of a protein causing human disease.

Both the ␣ and ␤␥ subunits of heterotrimeric guanine nucleotide-binding proteins (G proteins) communicate signals from receptors to effectors. G␤␥ subunits can regulate a diverse array of effectors, including ion channels and enzymes. G␣ subunits bound to guanine diphosphate (G␣-GDP) inhibit signal transduction through G␤␥ subunits, suggesting a common interface on G␤␥ subunits for G␣ binding and effector interaction. The molecular basis for interaction of G␤␥ with effectors was characterized by mutational analysis of G␤ residues that make contact with G␣-GDP. Analysis of the ability of these mutants to regulate the activity of calcium and potassium channels, adenylyl cyclase 2, phospholipase C-␤2, and ␤-adrenergic receptor kinase revealed the G␤ residues required for activation of each effector and provides evidence for partially overlapping domains on G␤ for regulation of these effectors. This organization of interaction regions on G␤ for different effectors and G␣ explains why subunit dissociation is crucial for signal transmission through G␤␥ subunits.Upon receptor activation, G proteins dissociate into free G␣ and G␤␥ subunits that can activate various effectors (1). Effector proteins of the G␤␥ complex include phospholipases (2), adenylyl cyclases (3), ion channels (4), G protein-coupled receptor kinases (5) and phosphoinositide 3-kinases (6). Other potential G␤␥ effectors include dynamin I and the nonreceptor protein tyrosine kinases Btk and Tsk (7). GDP-bound G␣ subunits (G␣-GDP) can compete with G␤␥ effectors and deactivate G␤␥-dependent signaling, suggesting that G␤␥ may use a common binding surface for interaction with G␣ and with its diverse effectors. Two regions on G␤␥ that interact with G␣ have been defined by the crystal structures of heterotrimeric G␣␤␥ (8), the switch interface (G␤ residues 57, 59, 98, 99, 101, 117, 119, 143, 186, 228, and 332) and the NH 2 -terminal interface (G␤ residues 55, 78, 80 and 89). Each of these residues on retinal G␤ (G␤1) was substituted with alanine, and each G␤1 mutant was expressed with either G␥1 or G␥2, two isoforms of the G␥ subunit. All mutated G␤1␥1 dimers were folded properly, were post-translationally modified appropriately, and were expressed at similar amounts as in the wild type (9). The G␤␥ mutants were tested for their ability to assemble into heterotrimers with G␣, to be activated by rhodopsin, and to interact with G␤␥ downstream signaling partners: ␤-adrenergic receptor kinase (␤ARK), phospholipase C-␤2 (PLC-␤2), adenylyl cyclase 2 (AC2), muscarinic potassium channel (GIRK1/GIRK4), and the calcium channel ␣1B subunit (CC␣1B).To determine whether purified G␤1H 6 ␥1 mutants could form heterotrimers, we measured the ability of the G␤␥ mutants to facilitate pertussis toxin-catalyzed adenosine diphosphate (ADP) ribosylation of transducin G␣-GDP (Gt␣) (10). All mutants could support some level of ADP ribosylation, although G␤ mutants Ile 80 3 Ala 80 (I80A), K89A, L117A, and W332A (11) showed reduced ability to form heterotrimers (Fig. 1A).Because G␤␥ ...

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 in biological systems occurs at multiple levels. In its broad sense, one could use the term "signaling" to describe events ranging from interactions between single molecules to interactions between species in ecological systems. The aim here is to deal with complexity in signaling at a single level: intracellular signaling within a cell. We will outline how current and forthcoming tools in biochemistry, cell and molecular biology, and physiology, as well as theoretical analysis and simulation methods, may be used to study this complex system. In a general sense, the adjective "complex" describes a system or component that by design or function or both is difficult to understand and verify. In the past decade, analysis of complex systems (the field of complexity) has emerged as a distinct facet of mathematical and physical sciences. Understanding of biological systems may be enhanced by analysis of their complex nature. In physical systems, complexity is determined by such factors as the number of components and the intricacy of the interfaces between them, the number and intricacy of conditional branches, the degree of nesting, and the types of data structures. Biological signaling networks possess many of these attributes, as well as dynamic assembly, translocation, degradation, and channeling of chemical reactions. All of these activities occur simultaneously, and each component participates in several different activities.One approach to understanding complexity is to start with a conceptually simple view of signaling and add details that introduce new levels of complexity. As this process unfolds, it becomes clear where experimental data end and how progressively more difficult it becomes to understand the system as a whole in terms of the functional details of individual components. A Signaling WireThe simplest description of signaling may be in terms of elementary chemistry in a homogenous well-stirred cell where all molecules have equal access to each other. Here, the most upstream component of the signaling pathway interacts with an external source and