Jason D. Lee

Ubiquitination-dependent mechanisms regulate synaptic growth and function

The covalent attachment of ubiquitin to cellular proteins is a powerful mechanism for controlling protein activity and localization. Ubiquitination is a reversible modification promoted by ubiquitin ligases and antagonized by deubiquitinating proteases. Ubiquitin-dependent mechanisms regulate many important processes including cell-cycle progression, apoptosis and transcriptional regulation. Here we show that ubiquitin-dependent mechanisms regulate synaptic development at the Drosophila neuromuscular junction (NMJ). Neuronal overexpression of the deubiquitinating protease fat facets leads to a profound disruption of synaptic growth control; there is a large increase in the number of synaptic boutons, an elaboration of the synaptic branching pattern, and a disruption of synaptic function. Antagonizing the ubiquitination pathway in neurons by expression of the yeast deubiquitinating protease UBP2 (ref. 5) also produces synaptic overgrowth and dysfunction. Genetic interactions between fat facets and highwire, a negative regulator of synaptic growth that has structural homology to a family of ubiquitin ligases, suggest that synaptic development may be controlled by the balance between positive and negative regulators of ubiquitination.

The type III TGF-β receptor suppresses breast cancer progression

TheTGF-β�signalingpathwayhasacomplexroleinregulatingmammarycarcinogenesis.�Herewedemon- stratethatthetypeIIITGF-β�receptor�(TβRIII,�orbetaglycan),�aubiquitouslyexpressedTGF-β�coreceptor,� regulatedbreastcancerprogressionandmetastasis.�MosthumanbreastcancerslostTβRIIIexpression,�with� lossofheterozygosityoftheTGFBR3�genelocuscorrelatingwithdecreasedTβRIIIexpression.�TβRIIIexpres- siondecreasedduringbreastcancerprogression,�andlowTβRIIIlevelspredicteddecreasedrecurrence-free� survivalinbreastcancerpatients.�RestoringTβRIIIexpressioninbreastcancercellsdramaticallyinhibited� tumorinvasivenessinvitroandtumorinvasion,�angiogenesis,�andmetastasisinvivo.�TβRIIIappearedto� inhibittumorinvasionbyundergoingectodomainsheddingandproducingsolubleTβRIII,�whichbinds� andsequestersTGF-β�todecreaseTGF-β�signalingandreducebreastcancercellinvasionandtumor-induced� angiogenesis.�OurresultsindicatethatlossofTβRIIIthroughallelicimbalanceisafrequentgeneticevent� duringhumanbreastcancerdevelopmentthatincreasesmetastaticpotential.

Exact post-selection inference, with application to the lasso

We develop a general approach to valid inference after model selection. In a nutshell, our approach produces post-selection inferences with the same frequency guarantees as those given by data splitting but are more powerful. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid condence intervals for the selected coecients and test whether all relevant variables have been included in the model.

PROXIMAL NEWTON-TYPE METHODS FOR MINIMIZING COMPOSITE FUNCTIONS

We generalize Newton-type methods for minimizing smooth functions to handle a sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping. We show that the resulting proximal Newton-type methods inherit the desirable convergence behavior of Newton-type methods for minimizing smooth functions, even when search directions are computed inexactly. Many popular methods tailored to problems arising in bioinformatics, signal processing, and statistical learning are special cases of proximal Newton-type methods, and our analysis yields new convergence results for some of these methods.

Treating disseminated fusariosis : amphotericin B, voriconazole or both?

Disseminated Fusarium infection can cause significant morbidity and mortality in immunocompromised patients. We present a case of disseminated fusariosis in a patient with neutropenic fever successfully treated using both liposomal amphotericin B and voriconazole. Combination anti‐fungal therapy may be considered for such patients, particularly for those failing single‐drug therapy.

Learning the Structure of Mixed Graphical Models

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme involves a novel symmetric use of the group-lasso norm and follows naturally from a particular parameterization of the model. Supplementary materials for this article are available online.

Enabling network-aware applications

Many high-performance distributed applications use only a small fraction of their available bandwidth. A common cause of this problem is not a flaw in the application design, but rather improperly tuned network settings. Proper tuning techniques, such as setting the correct TCP buffers and using parallel streams, are well-known in the networking community, but outside this community they are infrequently applied. In this paper, we describe a service that makes the task of network tuning trivial for application developers and users. Widespread use of this service should virtually eliminate a common stumbling block for high-performance distributed applications.

The type III TGF-β receptor suppresses breast cancer progression through GIPC-mediated inhibition of TGF-β signaling

Loss of expression of the type III transforming growth factor-beta receptor (TbetaRIII or betaglycan), a transforming growth factor-beta (TGF-beta) superfamily co-receptor, is common in human breast cancers. TbetaRIII suppresses cancer progression in vivo by reducing cancer cell migration and invasion by largely unknown mechanisms. Here, we demonstrate that the cytoplasmic domain of TbetaRIII is essential for TbetaRIII-mediated downregulation of migration and invasion in vitro and TbetaRIII-mediated inhibition of breast cancer progression in vivo. Functionally, the cytoplasmic domain of TbetaRIII is required to attenuate TGF-beta signaling, whereas TbetaRIII-mediated attenuation of TGF-beta signaling is required for TbetaRIII-mediated inhibition of migration and invasion. Mechanistically, both TbetaRIII-mediated inhibition of TGF-beta signaling and TbetaRIII-mediated inhibition of invasion occur through the interaction of the cytoplasmic domain of TbetaRIII with the scaffolding protein GAIP-interacting protein C-terminus (GIPC). Taken together, these studies support a functional role for the TbetaRIII cytoplasmic domain interacting with GIPC to suppress breast cancer progression.

Molecular Characterization of the Tumor-Suppressive Function of Nischarin in Breast Cancer

BACKGROUND Nischarin (encoded by NISCH), an α5 integrin-binding protein, has been identified as a regulator of breast cancer cell invasion. We hypothesized that it might be a tumor suppressor and were interested in its regulation. METHODS We examined nischarin expression in approximately 300 human breast cancer and normal tissues using quantitative polymerase chain reaction and immunohistochemistry. Loss of heterozygosity analysis was performed by examining three microsatellite markers located near the NISCH locus in normal and tumor tissues. We generated derivatives of MDA-MB-231 human metastatic breast cancer cells that overexpressed nischarin and measured tumor growth from these cells as xenografts in mice; metastasis by these cells after tail vein injection; and α5 integrin expression, Rac, and focal adhesion kinase (FAK) signaling using western blotting. We also generated clones of MCF-7 human breast cancer cells in which nischarin expression was silenced and measured tumor growth in mouse xenograft models (n = 5 for all mouse experiments). P values were from two-sided Student t tests in pairwise comparisons. RESULTS Normal human breast tissue samples had statistically significantly higher expression of nischarin mRNA compared with tumor tissue samples (mean level in normal breast = 50.7 [arbitrary units], in breast tumor = 16.49 [arbitrary units], difference = 34.21, 95% confidence interval [CI] = 11.63 to 56.79, P = .003), and loss of heterozygosity was associated with loss of nischarin expression. MDA-MB-231 cells in which nischarin was overexpressed had statistically significantly reduced tumor growth and metastasis compared with parental MDA-MB-231 cells (mean volume at day 40, control vs nischarin-expressing tumors, 1977 vs 42.27 mm(3), difference = 1935 mm(3), 95% CI = 395 to 3475 mm(3), P = .025). Moreover, MCF-7 tumor xenografts in which nischarin expression was silenced grew statistically significantly faster than parental cells (mean volume at day 63, tumors with scrambled short hairpin RNA [shRNA] vs with nischarin shRNA, 224 vs 1262 mm(3), difference = 1038 mm(3), 95% CI = 899.6 to 1176 mm(3), P < .001). Overexpression of nischarin was associated with decreased α5 integrin expression, FAK phosphorylation, and Rac activation. CONCLUSION Nischarin may be a novel tumor suppressor that limits breast cancer progression by regulating α5 integrin expression and subsequently α5 integrin-, FAK-, and Rac-mediated signaling.

Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale SVD

The problem of estimating the intrinsic dimensionality of certain point clouds is of interest in many applications in statistics and analysis of high-dimensional data sets. Our setting is the following: the points are sampled from a manifold M of dimension k, embedded in ℝD, with k ≪ D, and corrupted by D-dimensional noise. When M is a linear manifold (hyperplane), one may analyse this situation by SVD, hoping the noise would perturb the rank k covariance matrix. When M is a nonlinear manifold, SVD performed globally may dramatically overestimate the intrinsic dimensionality. We discuss a multiscale version SVD that is useful in estimating the intrinsic dimensionality of nonlinear manifolds.