Eli Zamir

Systems Biology of Cell-Matrix Adhesion


Table of Content

  • Molecular and functional diversity of cell-matrix adhesion sites
  • Resolving the building blocks of adhesion sites and their spatio-temporal dynamics
  • Diversity and noise in the assembly paths of focal adhesions
  • Constructing and modeling the integrin adhesome hypernetwork
  • Reverse-engineering mixture of protein-network topologies
  • Optical and computational methods for studying intracellular biochemical networks

  • Molecular and functional diversity of cell-matrix adhesion sites

    My work on cell adhesion started with revealing the molecular diversity of cell-matrix adhesions sites (Zamir et al., 1999). Along this line, I identified a new type of adhesion sites, fibrillar adhesions and the mechanism of its formation (Zamir et al., 2000) (Fig. 1).
    Figure 1. Model for the involvement of actomyosin-driven forces in the formation and segregation of fibrillar adhesions and focal contacts. a, Before segregation of focal contacts and fibrillar adhesions, the adhesion site contains avb3 integrin bound mainly to the ECM protein vitronectin, together with a5b1 integrin bound mainly to fibronectin. Both integrins are associated through different plaque proteins with actin filaments (F-actin), and are subject to actomyosin-driven contraction forces. b, As vitronectin provides a rigid substrate, avb3 integrin does not move as a result of these contractile forces, and high tension develops, resulting in the recruitment of proteins typical of focal contacts, such as paxillin and vinculin,and in an increase in levels of phosphorylated tyrosine residues. In contrast, a5b1, which is bound to the moveable fibronectin matrix, is moved by actomyosin pulling. Because tension is low, tension-dependent recruitment of paxillin, vinculin and phosphotyrosine residues does not occur. The association of fibrillar-adhesion components, such as tensin, is tension-independent; such components remain associated and translocate with a5b1 integrin and the associated fibronectin. Reproduced from: Zamir et al., 2000, Nature Cell Biology.
    Conceptually, a fundamental implication of these results is that they indicated the tight coupling between the dynamics, molecular composition and functions of cell-matrix adhesion sites (Zamir and Geiger, 2001a and Zamir, 2016).

    In order to visualize and resolve comprehansively the molcular diversity of adhesion sites I developed compositional imaging (Fig. 1) and thus revealed compositional heterogeneity within focal adhesions (Zamir et al., 2008).
    Figure 1. Compositional imaging. Reproduced from: Zamir et al.,2008, Plos One.
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    Resolving the building blocks of adhesion sites and their spatio-temporal dynamics

    We study how the integrin adheosme can give rise to diverse adheison sites, locally, rapidly and correctly -- solely be self-organization prcosses. To address this question we investigated the state of the integrin adhesome network in the cytosol and its spatiotemporal relation with focal adhesions. Using systematic fluorescence cross-correlation spectroscopy (FCCS), fluorescence recovery after photobleaching (FRAP) and fluorescence lifetime imaging microscopy (FLIM) measurements we found that the integrin adhesome is extensively pre-assembled in the cytosol, thereby forming multi-protein building blocks for adhesion sites (Hoffmann et al.,2014, eLife 3:e02257). These building blocks are combinatorially diversified, confined in their size and correlate with the structural and functional organization of proteins across focal adhesions (Fig. 1). The building blocks enter and exit focal adhesions without being altered, thus preserving their specifications and the assembly logic of the system (Fig. 1).
    Figure 1. A model of switchable formation of adhesion sites via pre-assembled multi-protein building blocks. A model of switchable formation of adhesion sites via pre-assembled multi-protein building blocks. The integrin adhesome is pre-assembled in the cytosol as multi-protein building blocks for adhesion sites. These building blocks are combinatorially diversified but confined in their size. In the cytosol, the pre-assembled building blocks cannot further assemble to form bigger structures due to mutual exclusiveness between protein interactions and allosteric regulations. On the plasma membrane, the system can get locally switched on to assemble an adhesion site by passing through checkpoints that enable additional protein interactions in the integrin adhesome. Symmetric material exchange between adhesion sites and cytosol retains the wiring of the building blocks and therefore retains the assembly logic and switchability of the system. From: Hoffmann et al.,2014, eLife 3:e02257.
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    Diversity and noise in the assembly paths of focal adhesions

    How can diverse adhesion sites assemble correctly despite the alternative ways their components can interact with each other and stochastic biochemical noise ? To address this question, we (in collaboration with Hernan Grecco) established high-throughput cyclic immunofluoescence (cycIF) imaging of intracellular proteins which, by serial cycles of immunolabeling, imaging and bleaching, enables to image unprecedented large number of proteins and phosphorylation states in thousands of individual adhesion sites (Fig. 2) (Harizanova et al., 2016, Plos One).
    Figure 2. High-throughput CycIF imaging of cell-matrix adhesion sites. (a) Imaging procedure and an example of the images obtained for a cell. Scale bar, 10 μm. From: Harizanova et al., 2016, Plos One.
    By implementing these CycIF and live cell imaging in a high-throughput manner, thousands of individual focal adhesions were imaged obtaining an unprecedented multiplexed quantification of the molecular content of these structures. Based on changes in the variances of the densities of these components and in the correlations between them, we inferred changes in noise levels in focal adhesions (Fig. 3).
    Figure 3. Inferring changes in noise levels in the molecular content of focal adhesions. (a) An assembly process with competing binding interactions. (b) Higher diversity in the local levels of a recruiting protein leads to a stronger correlation between the recruited proteins, while higher noise causes the opposite. From: Harizanova et al., 2016, Plos One.
    To reveal high-order statistical relations between the densities of components in focal adhesions, we examined how well artificial neural networks can predict the densities of a target component based on the densities of the other components in these structures (Fig. 4).

    Figure 4. Searching for high-dimensional statistical relations between the levels of proteins in focal adhesions by high-throughput artificial neural-network analysis of 10-components CycIF data. From: Harizanova et al., 2016, Plos One.
    Our results revealed that the noise in the molecular content of focal adhesions is suppressed as they grow. This noise suppression is coupled with the size and internal density of focal adhesions, but not with their age or shape. The levels of two components, zyxin and paxillin, are particularly tuned, creating a hub of order within the focal adhesion structure. Our results indicate a mechanism for the emergence of quality control as a system property of assembling focal adhesions (Fig. 5)(Harizanova et al., 2016, Plos One).

    Figure 5. A positive feedback model for the emergence of noise suppression in focal adhesions. The model suggests that increasing internal density within assembling focal adhesions promotes high-order integration of interactions that tunes the levels of paxillin and zyxin and thus suppresses compositional noiseFrom: Harizanova et al., 2016, Plos One.
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    Constructing and modeling the integrin adhesome hypernetwork

    The integrin adhesome is interconnected by a dense network of interactions in which most proteins have multiple potential binding partners. While this enables the integrin adhesome to construct different adhesive devises with different properties, it also creates a fundamental challenge for confining the set of possible assembly and regulations models. Particularly, a network description of such biochemical systems (with nodes and edges corresponding to proteins and interactions, respectively) does not incorporate information about the dependencies of each interaction and therefore is not sufficient for inferring assembly and regulation mechanisms. Boolean dependencies between protein interactions can arise for example if two proteins compete on the same binding domain along a third protein or if one interaction induces a conformational change on a protein and thereby affecting allosterically its other interactions. To address this, we (in collaboration with Johannes Koester and ven Rahmann) developed approaches for system-level integration of information about interaction dependencies toward predictive protein hyper-networks. Specifically, we applied these approaches for the construction of the integrin adhesome hypernetwork and inference of potential protein complexes, paths of assembly and regulatory switches (Fig. 6).
    integrin adhesome hypernetwork
    Figure 6. System-level integration of interaction inter-dependencies in the integrin adhesome. (a) A network-type description of a system containing proteins (nodes) and their interactions (edges). This description does not contain information about the dependencies between protein interactions (e.g. the interactions of the red and blue colored proteins with the green one). Boolean interaction dependencies can arise due to allosteric regulations (left) or competition on the same binding domain. (b) Pipeline of the computational approach that we developed for curating information about protein-interaction dependencies from the scientific literature. (c) The current reconstruction state of the integrin adhesome hypernetwork of interaction dependencies. (d) We developed approaches to formulate information about interaction dependencies based on propositional logic (left) and integrate their effects over a large biochemical system to efficiently confine the sets of possible protein complexes and paths of adhesion-sites assembly as well as to elucidate potential regulatory switches. From: Koester et al., 2012, Integrative Biology..
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    Reverse-engineering mixture of protein-network topologies

    Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we (in collaboration with Katja Ickstadt and Hernan Grecco) developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation.
    Unmixing network topologies by UNPBN
    Figure 7. UNPBN addresses the challenge of studying intracellular protein networks caused by unmeasured proteins and inter-cellular heterogeneity. a A biochemical system for which three proteins (x, y, z) are being measured in the same cell while the other proteins are unmeasured. Note that the effects of z on x are mediated by unmeasured proteins (α and β). b Depending on the level and state of these unmeasured proteins, the measured causality between x and z can differ qualitatively between cells. For example, normal and cancer cells have different activity levels of oncogenes and tumor suppressors which here lead to a negative or a positive causal effect of z on x, respectively, thereby to a controlled growth or a constitutive growth. c Left, multiparametric high-throughput single-cell measurements (e.g., flow-cytometry) of a heterogenous sample of cells (e.g., cancer and normal cells). Middle, attempts to statistically infer a single set of relations (here, causal topology) between the measured proteins fail because there are two distinct subpopulations having two distinct sets of relations. At the same time, it is also impossible to identify the two distinct subpopulations as two distinct proximity-based clusters. Right, UNPBN performs unmixing and inference of statistical relations as one process, thus finds the set of sets-of-relations (network topologies) that explains best the observations. From: Wieczorek et al., 2015, BMC Systems Biology.
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    Optical and computational methods for studying intracellular biochemical networks

    The study of cell-matrix adhesion sites is fundamentally challenged by: (1) their molecular and functional diversity, (2) their large number of components (3) the complex interaction network between their components. Therefore, we develop methods that address these challenges and allow to study large protein networks at high spatiotemporal resolution in intact cells.
  • Perturbations: genetic, pharmacological and chemical-biology perturbations.

  • Optical methods: Multi-color live cell imaging, fluorescence correlation spectroscopy (FCS), fluorescence lifetime imaging (FLIM), fluorescence recovery after photobleaching (FRAP), fluorescence lifetime correlation spectroscopy (FLCS), cyclic immunofluorescence (cycIF) and other approaches.

  • Computational methods: Compositional imaging, multi-dimensional data analyses, reverse-engineering, protein hypernetworks analysis.
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    © 2018 Eli Zamir, www.cell-adhesion.com