Metastasis is the major driver of cancer deaths and begins when cancer cells invade surrounding tissues. Invasion and metastasis have been proposed to initiate following loss of the intercellular adhesion protein, E-cadherin (E-cad) 1,2 , based upon inverse correlations between in vitro migration and E-cad levels 3. This hypothesis is inconsistent, however, with the observation that most breast cancers are invasive ductal carcinomas (IDC) and express E-cad in primary tumors and metastases 4. To resolve this discrepancy, we tested the genetic requirement for E-cad in metastasis using murine and human models of both luminal and basal IDC. Here we show that E-cad promotes metastasis in IDC. While loss of E-cad increased invasion, it also reduced cancer cell proliferation and survival, circulating tumor cell number, seeding of cancer cells in distant organs, Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Efforts to decipher chronic lung disease and to reconstitute functional lung tissue through regenerative medicine have been hampered by an incomplete understanding of cell-cell interactions governing tissue homeostasis. Because the structure of mammalian lungs is highly conserved at the histologic level, we hypothesized that there are evolutionarily conserved homeostatic mechanisms that keep the fine architecture of the lung in balance. We have leveraged single-cell RNA sequencing techniques to identify conserved patterns of cell-cell cross-talk in adult mammalian lungs, analyzing mouse, rat, pig, and human pulmonary tissues. Specific stereotyped functional roles for each cell type in the distal lung are observed, with alveolar type I cells having a major role in the regulation of tissue homeostasis. This paper provides a systems-level portrait of signaling between alveolar cell populations. These methods may be applicable to other organs, providing a roadmap for identifying key pathways governing pathophysiology and informing regenerative efforts.

Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.

The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.

The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to "friends" and genetic interactions analogous to "enemies" from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data.[Supplemental material is available online at www.genome.org.]Genetics establishes links between genotype and phenotype. Many genes are pleiotropic, carrying out multiple functions in different pathways under different environmental conditions and can have partially redundant function with other genes. Genetic buffering can be evolutionarily stable (Nowak et al. 1997). While individual gene perturbations may have little or no effect, combined perturbations can generate a phenotype. This is the rationale of genetic interaction screens, which test for phenotypes from two-gene perturbations that differ from the singlegene effects. Pairwise genetic interaction screens have been valuable in understanding functional relationships between genes and assigning functions to genes in a pathway-dependent manner.With the completion of the Yeast Knockout deletion collection (Giaever et al. 2002), high-throughput studies of pairwise lethal or growth defect interactions between null or hypomorph alleles of Saccharomyces cerevisiae (budding yeast) have made vast progress in the past few years. "Synthetic growth defect" or "synthetic sickness" describes a genetic interaction between two genes whose individual deletion mutants have minimal growth defects, while the double knockout results in a significant growth defect under a given condition. A subset of those pairs whose double knockouts lead to diminished growth or death are called "synthetic lethal" (Dobzhansky 1946). We will refer to the union of "synthetic sickness" and "synthetic lethal" as a "synthetic fitness or lethal interaction," or SFL. Multiple studies have screened a subset of deletions or hypomorph alleles against the entire set of viable yeast deletion mutants using methods including synthetic genetic array (SGA) (Tong et al. 2001, synthetic lethality analyzed by microarray (SLAM), and diploid-based SLAM (dSLAM) (Ooi et al. 2003;Pan et al. 2004Pan et al. , 2006. A second approach, termed an epistatic miniarray profile, searches for both positive and negative interactions among a subset of genes (Collins et al. 2007).Large . To achieve these goals, especially prediction of pathway membership, algorithms have progressed from counting the number of shared neighbo...

Voltage-gated Ca2+ (Ca(V)) channels are central to the biology of excitable cells, and therefore regulating their activity has widespread applications. We describe genetically encoded molecules for inducibly inhibiting Ca(V) channels (GEMIICCs). GEMIICCs are derivatives of Rem, a Ras-like GTPase that constitutively inhibits Ca2+ currents (I(Ca)). C terminus-truncated Rem(1-265) lost the ability to inhibit I(Ca) owing to loss of membrane targeting. Fusing the C1 domain of protein kinase Cgamma to yellow fluorescent protein (YFP)-Rem(1-265) generated a molecule that rapidly translocated from cytosol to plasma membrane with phorbol-12,13-dibutyrate in human embryonic kidney cells. Recombinant Ca(V)2.2 and Ca(V)1.2 channels were inhibited concomitantly with C1(PKCgamma)-YFP-Rem(1-265) membrane translocation. The generality of the approach was confirmed by creating a GEMIICC using rapamycin-dependent heterodimerization of YFP-FKBP-Rem(1-265) and a constitutively membrane-targeted rapamycin-binding domain. GEMIICCs reduced I(Ca) without diminishing gating charge, thereby ruling out decreased number of surface channels and voltage-sensor immobilization as mechanisms for inhibition. We introduce small-molecule-regulated GEMIICCs as potent tools for rapidly manipulating Ca2+ signals in excitable cells.

First identified as histone-modifying proteins, lysine acetyltranferases (KATs) and deacetylases (KDACs) antagonize each other through modification of the side chains of lysine residues in histone proteins1. (De)acetylation of many non-histone proteins involved in chromatin, metabolism or cytoskeleton regulation were further identified in eukaryotic organisms2–6, but the corresponding modifying enzymes and substrate-specific functions of the modification are unclear. Moreover, mechanisms underlying functional specificity of individual KDACs7 remain enigmatic, and the substrate spectra of each KDAC lack comprehensive definition. Here we dissect the functional specificity of twelve critical human KDACs using a genome-wide synthetic lethality screen8–13 in cultured human cells. The genetic interaction profiles revealed enzyme-substrate relationships between individual KDACs and many important substrates governing a wide array of biological processes including metabolism, development and cell cycle progression. We further confirmed that (de)acetylation of the catalytic subunit of the adenosine monophosphate-activated protein kinase (AMPK), a critical cellular energy-sensing protein kinase complex, is controlled by the opposing catalytic activities of HDAC1 and p300. Its deacetylation enhances physical interaction with the upstream kinase LKB1, in turn leading to AMPK phosphorylation and activation, resulting in lipid breakdown in human liver cells. These findings provide new insights into previously underappreciated metabolism-regulatory roles of HDAC1 in coordinating nutrient availability and cellular responses upstream of AMPK, and demonstrate the importance of high-throughput genetic interaction profiling to elucidate functional specificity and critical substrates of individual human KDACs potentially valuable for therapeutic applications.

Abstract-This paper describes an area and power-efficient VLSI approach for implementing the discrete wavelet transform on streaming multielectrode neurophysiological data in real time. The VLSI implementation is based on the lifting scheme for wavelet computation using the symmlet4 basis with quantized coefficients and integer fixed-point data precision to minimize hardware demands. The proposed design is driven by the need to compress neural signals recorded with high-density microelectrode arrays implanted in the cortex prior to data telemetry. Our results indicate that signal integrity is not compromised by quantization down to 5-bit filter coefficient and 10-bit data precision at intermediate stages. Furthermore, results from analog simulation and modeling show that a hardware-minimized computational core executing filter steps sequentially is advantageous over the pipeline approach commonly used in DWT implementations. The design is compared to that of a B-spline approach that minimizes the number of multipliers at the expense of increasing the number of adders. The performance demonstrates that in vivo real-time DWT computation is feasible prior to data telemetry, permitting large savings in bandwidth requirements and communication costs given the severe limitations on size, energy consumption and power dissipation of an implantable device.Index Terms-B-spline, brain machine interface, lifting, microelectrode arrays, neural signal processing, neuroprosthetic devices, wavelet transform.