Biomedical subjects

Ron Milo

Publications and source records attributed to Ron Milo.

9 recordsLinked to original sources

PubMed2006

A paxillin tyrosine phosphorylation switch regulates the assembly and form of cell-matrix adhesions.

Diverse cellular processes are carried out by distinct integrin-mediated adhesions. Cell spreading and migration are driven by focal complexes; robust adhesion to the extracellular matrix by focal adhesions; and matrix remodeling by fibrillar adhesions. The mechanism(s) regulating the spatio-temporal distribution and dynamics of the three types of adhesion are unknown. Here, we combine live-cell imaging, labeling with phosphospecific-antibodies and overexpression of a novel tyrosine phosphomimetic mutant of paxillin, to demonstrate that the modulation of tyrosine phosphorylation of paxillin regulates both the assembly and turnover of adhesion sites. Moreover, phosphorylated paxillin enhanced lamellipodial protrusions, whereas non-phosphorylated paxillin was essential for fibrillar adhesion formation and for fibronectin fibrillogenesis. We further show that focal adhesion kinase preferentially interacted with the tyrosine phosphomimetic paxillin and its recruitment is implicated in high turnover of focal complexes and translocation of focal adhesions. We created a mathematical model that recapitulates the salient features of the measured dynamics, and conclude that tyrosine phosphorylation of the adaptor protein paxillin functions as a major switch, regulating the adhesive phenotype of cells.

Animals↗

PubMed2006

Variability and memory of protein levels in human cells.

Protein expression is a stochastic process that leads to phenotypic variation among cells. The cell-cell distribution of protein levels in microorganisms has been well characterized but little is known about such variability in human cells. Here, we studied the variability of protein levels in human cells, as well as the temporal dynamics of this variability, and addressed whether cells with higher than average protein levels eventually have lower than average levels, and if so, over what timescale does this mixing occur. We measured fluctuations over time in the levels of 20 endogenous proteins in living human cells, tagged by the gene for yellow fluorescent protein at their chromosomal loci. We found variability with a standard deviation that ranged, for different proteins, from about 15% to 30% of the mean. Mixing between high and low levels occurred for all proteins, but the mixing time was longer than two cell generations (more than 40 h) for many proteins. We also tagged pairs of proteins with two colours, and found that the levels of proteins in the same biological pathway were far more correlated than those of proteins in different pathways. The persistent memory for protein levels that we found might underlie individuality in cell behaviour and could set a timescale needed for signals to affect fully every member of a cell population.

Bacterial Proteins↗

PubMed2006

Oscillations and variability in the p53 system.

Understanding the dynamics and variability of protein circuitry requires accurate measurements in living cells as well as theoretical models. To address this, we employed one of the best-studied protein circuits in human cells, the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We measured the dynamics of fluorescently tagged p53 and Mdm2 over several days in individual living cells. We found that isogenic cells in the same environment behaved in highly variable ways following DNA-damaging gamma irradiation: some cells showed undamped oscillations for at least 3 days (more than 10 peaks). The amplitude of the oscillations was much more variable than the period. Sister cells continued to oscillate in a correlated way after cell division, but lost correlation after about 11 h on average. Other cells showed low-frequency fluctuations that did not resemble oscillations. We also analyzed different families of mathematical models of the system, including a novel checkpoint mechanism. The models point to the possible source of the variability in the oscillations: low-frequency noise in protein production rates, rather than noise in other parameters such as degradation rates. This study provides a view of the extensive variability of the behavior of a protein circuit in living human cells, both from cell to cell and in the same cell over time.

Biological Clocks↗

PubMed2006

Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins.

We examined cell cycle-dependent changes in the proteome of human cells by systematically measuring protein dynamics in individual living cells. We used time-lapse microscopy to measure the dynamics of a random subset of 20 nuclear proteins, each tagged with yellow fluorescent protein (YFP) at its endogenous chromosomal location. We synchronized the cells in silico by aligning protein dynamics in each cell between consecutive divisions. We observed widespread (40%) cell-cycle dependence of nuclear protein levels and detected previously unknown cell cycle-dependent localization changes. This approach to dynamic proteomics can aid in discovery and accurate quantification of the extensive regulation of protein concentration and localization in individual living cells.

Bacterial Proteins↗

PubMed2005

Coarse-graining and self-dissimilarity of complex networks.

Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units as connectivity patterns which can serve as the nodes of a coarse-grained network and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit module made of many gates. We apply our approach also to a mammalian protein signal-transduction network, to find a simplified coarse-grained network with three main signaling channels that resemble multi-layered perceptrons made of cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are "self-dissimilar," with different network motifs at each level. The present approach may be used to simplify a variety of directed and nondirected, natural and designed networks.

Algorithms↗

PubMed2004

Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction.

Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

Amino Acid Motifs↗

PubMed2004

Superfamilies of evolved and designed networks.

Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.

Animals↗

PubMed2004

The utility of the Visual Analogue Scale for the assessment of depressive mood in cognitively impaired patients.

BACKGROUND: Early detection of depression in elderly demented patients may assist in adequate treatment. The aim of this study was to evaluate the utility of Visual Analogue Scale for depression among demented and mild cognitively impaired elderly patients. METHODS: 157 Patients, aged from 65 to 92 years, in a memory clinic were divided into two groups according to scores on the Mini Mental State Examination (MMSE): Demented group (62 patients) with MMSE scores from 16 to 23, and Mild Cognitively Impaired group (MCI-95 patients) with MMSE scores from 24 to 29. Subjects were diagnosed for depression according to DSM-IV criteria, using Hamilton Depression Rating Scale (HDRS) and a comprehensive clinical evaluation. All were administered a Visual Analogue Scale (VAS) for depression. RESULTS: In the demented group, 25 subjects were diagnosed as depressive. In the MCI group, 46 subjects were diagnosed as depressive. Mean VAS scores for the demented and MCI groups were similar, both for depressive and non-depressive patients. Correlations between VAS scores with HDRS scores and with the clinical diagnosis were high, although somewhat lower for the demented group. Analyzing data with ROC curve technique yielded significantly different ROC curves. Optimal cutoff point for the demented group was VAS value of 60, and 50 for the MCI group. CONCLUSIONS: VAS seems to be a useful tool for evaluation of depression among cognitively impaired patients. Severity of cognitive decline in the elderly may influence the cutoff points on VAS for detecting depression. Further large-scale studies are needed to substantiate our observation.

Aged↗

PubMed2002

Network motifs in the transcriptional regulation network of Escherichia coli.

Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

Algorithms↗