Shawn M. Gomez

Arp2/3 Is Critical for Lamellipodia and Response to Extracellular Matrix Cues but Is Dispensable for Chemotaxis

Lamellipodia are sheet-like, leading edge protrusions in firmly adherent cells that contain Arp2/3-generated dendritic actin networks. Although lamellipodia are widely believed to be critical for directional cell motility, this notion has not been rigorously tested. Using fibroblasts derived from Ink4a/Arf-deficient mice, we generated a stable line depleted of Arp2/3 complex that lacks lamellipodia. This line shows defective random cell motility and relies on a filopodia-based protrusion system. Utilizing a microfluidic gradient generation system, we tested the role of Arp2/3 complex and lamellipodia in directional cell migration. Surprisingly, Arp2/3-depleted cells respond normally to shallow gradients of PDGF, indicating that lamellipodia are not required for fibroblast chemotaxis. Conversely, these cells cannot respond to a surface-bound gradient of extracellular matrix (haptotaxis). Consistent with this finding, cells depleted of Arp2/3 fail to globally align focal adhesions, suggesting that one principle function of lamellipodia is to organize cell-matrix adhesions in a spatially coherent manner.

Learning to predict protein–protein interactions from protein sequences

In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.

Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome

MOTIVATION Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes. RESULTS We suggest a stochastic model capable of describing the evolutionary growth of metabolic or signal-transduction networks. This model generates networks that share important statistical properties (so-called scale-free behavior) with real molecular networks. In particular, the frequency of vertices connected to exactly k other vertices follows a power-law distribution. The shape of this distribution remains invariant to changes in network scale: a small subgraph has the same distribution as the complete graph from which it is derived. Furthermore, the model correctly predicts that the frequencies of distinct DNA and protein domains also follow a power-law distribution. Finally, the model leads to a simple equation linking the total number of different DNA and protein domains in a genome with both the total number of genes and the overall network topology. AVAILABILITY MatLab (MathWorks, Inc.) programs described in this manuscript are available on request from the authors. CONTACT ar345@columbia.edu.

High-Resolution Quantification of Focal Adhesion Spatiotemporal Dynamics in Living Cells

Focal adhesions (FAs) are macromolecular complexes that provide a linkage between the cell and its external environment. In a motile cell, focal adhesions change size and position to govern cell migration, through the dynamic processes of assembly and disassembly. To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones. As a proof-of-concept, we show how a single point mutation in Paxillin at the Jun-kinase phosphorylation site Serine 178 changes FA size, distribution, and rate of assembly. This study provides a detailed, quantitative picture of FA spatiotemporal dynamics as well as a set of tools and methodologies for advancing our understanding of how focal adhesions are dynamically regulated in living cells. A full, open-source software implementation of this pipeline is provided at http://gomezlab.bme.unc.edu/tools.

Mapping Protein Interactions between Dengue Virus and Its Human and Insect Hosts

Background Dengue fever is an increasingly significant arthropod-borne viral disease, with at least 50 million cases per year worldwide. As with other viral pathogens, dengue virus is dependent on its host to perform the bulk of functions necessary for viral survival and replication. To be successful, dengue must manipulate host cell biological processes towards its own ends, while avoiding elimination by the immune system. Protein-protein interactions between the virus and its host are one avenue through which dengue can connect and exploit these host cellular pathways and processes. Methodology/Principal Findings We implemented a computational approach to predict interactions between Dengue virus (DENV) and both of its hosts, Homo sapiens and the insect vector Aedes aegypti. Our approach is based on structural similarity between DENV and host proteins and incorporates knowledge from the literature to further support a subset of the predictions. We predict over 4,000 interactions between DENV and humans, as well as 176 interactions between DENV and A. aegypti. Additional filtering based on shared Gene Ontology cellular component annotation reduced the number of predictions to approximately 2,000 for humans and 18 for A. aegypti. Of 19 experimentally validated interactions between DENV and humans extracted from the literature, this method was able to predict nearly half (9). Additional predictions suggest specific interactions between virus and host proteins relevant to interferon signaling, transcriptional regulation, stress, and the unfolded protein response. Conclusions/Significance Dengue virus manipulates cellular processes to its advantage through specific interactions with the hosts protein interaction network. The interaction networks presented here provide a set of hypothesis for further experimental investigation into the DENV life cycle as well as potential therapeutic targets.

MAP3K4/CBP-Regulated H2B Acetylation Controls Epithelial-Mesenchymal Transition in Trophoblast Stem Cells

Epithelial stem cells self-renew while maintaining multipotency, but the dependence of stem cell properties on maintenance of the epithelial phenotype is unclear. We previously showed that trophoblast stem (TS) cells lacking the protein kinase MAP3K4 maintain properties of both stemness and epithelial-mesenchymal transition (EMT). Here, we show that MAP3K4 controls the activity of the histone acetyltransferase CBP, and that acetylation of histones H2A and H2B by CBP is required to maintain the epithelial phenotype. Combined loss of MAP3K4/CBP activity represses expression of epithelial genes and causes TS cells to undergo EMT while maintaining their self-renewal and multipotency properties. The expression profile of MAP3K4-deficient TS cells defines an H2B acetylation-regulated gene signature that closely overlaps with that of human breast cancer cells. Taken together, our data define an epigenetic switch that maintains the epithelial phenotype in TS cells and reveals previously unrecognized genes potentially contributing to breast cancer.

Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

BackgroundIn the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogens ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes.ResultsWe developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence.ConclusionsUnderstanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.

Towards the prediction of complete protein--protein interaction networks.

We present a statistical method for the prediction of protein--protein interactions within an organism. This approach is based on the treatment of proteins as collections of conserved domains, where each domain is responsible for a specific interaction with another domain. By characterizing the frequency with which specific domain--domain interactions occur within known interactions, our model can assign a probability to an arbitrary interaction between any two proteins with defined domains. Domain interaction data is complemented with information on the topology of a network and is incorporated into the model by assigning greater probabilities to networks displaying more biologically realistic topologies. We use Markov chain Monte Carlo techniques for the prediction of posterior probabilities of interaction between a set of proteins; allowing its application to large data sets. In this work we attempt to predict interactions in a set of 40 human proteins, known to form a connected network, and discuss methods for future improvement.

SAGE analysis of mosquito salivary gland transcriptomes during Plasmodium invasion.

Invasion of the vector salivary glands by Plasmodium is a critical step for malaria transmission. To describe salivary gland cellular responses to sporozoite invasion, we have undertaken the analysis of Anopheles gambiae salivary gland transcriptome using Serial Analysis of Gene Expression (SAGE). Statistical analysis of the more than 160 000 sequenced tags generated from four libraries, two from glands infected by Plasmodium berghei, two from glands of controls, revealed that at least 57 Anopheles genes are differentially expressed in infected salivary glands. Among the 37 immune‐related genes identified by SAGE tags, four (Defensin1, GNBP, Serpin6 and Cecropin2) were found to be upregulated during salivary gland invasion, while five genes encoding small secreted proteins display induction patterns strongly reminiscent of that of Cecropin2. Invasion by Plasmodium has also an impact on the expression of genes involved in transport, lipid and energy metabolism, suggesting that the sporozoite may exploit the metabolism of its host. In contrast, protein composition of saliva is predicted to be only slightly modified after infection. This study, which is the first transcriptome analysis of the salivary gland response to Plasmodium infection, provides a basis for a better understanding of Plasmodium/Anopheles salivary gland interactions.

The Focal Adhesion Analysis Server: a web tool for analyzing focal adhesion dynamics

The Focal Adhesion Analysis Server (FAAS) is a web-based implementation of a set of computer vision algorithms designed to quantify the behavior of focal adhesions in cells imaged in 2D cultures. The input consists of one or more images of a labeled focal adhesion protein. The outputs of the system include a range of static and dynamic measurements for the adhesions present in each image as well as how these properties change over time. The user is able to adjust several parameters important for proper focal adhesion identification. This system provides a straightforward tool for the global, unbiased assessment of focal adhesion behavior common in optical microscopy studies. The webserver is available at: http://faas.bme.unc.edu/.