A Novel Role for Stat1 in Phagosome Acidification and Natural Host Resistance to Intracellular Infection by Leishmania major

Intracellular parasites of the genus Leishmania generate severe diseases in humans, which are associated with a failure of the infected host to induce a protective interferon γ (IFNγ)-mediated immune response. We tested the role of the JAK/STAT1 signaling pathway in Leishmania pathogenesis by utilizing knockout mice lacking the signal transducer and activator of transcription 1 (Stat1) and derived macrophages. Unexpectedly, infection of Stat1-deficient macrophages in vitro with promastigotes from Leishmania major and attenuated LPG1 knockout mutants (lpg−) specifically lacking lipophosphoglycan (LPG) resulted in a twofold increased intracellular growth, which was independent of IFNγ and associated with a substantial increase in phagosomal pH. Phagosomes in Stat1−/− macrophages showed normal maturation as judged by the accumulation of the lysosomal marker protein rab7, and provided normal vATPase activity, but were defective in the anion conductive pathway required for full vesicular acidification. Our results suggest a role of acidic pH in the control of intracellular Leishmania growth early during infection and identify for the first time an unexpected role of Stat1 in natural anti-microbial resistance independent from its function as IFNγ-induced signal transducer. This novel Stat1 function may have important implications to studies of other pathogens, as the acidic phagolysosomal pH plays an important role in antigen processing and the uncoating process of many viruses

Transgenic analysis of the Leishmania MAP kinase MPK10 reveals an auto-inhibitory mechanism crucial for stage-regulated activity and parasite viability

Protozoan pathogens of the genus Leishmania have evolved unique signaling mechanisms that can sense changes in the host environment and trigger adaptive stage differentiation essential for host cell infection. The signaling mechanisms underlying parasite development remain largely elusive even though Leishmania mitogen-activated protein kinases (MAPKs) have been linked previously to environmentally induced differentiation and virulence. Here, we unravel highly unusual regulatory mechanisms for Leishmania MAP kinase 10 (MPK10). Using a transgenic approach, we demonstrate that MPK10 is stage-specifically regulated, as its kinase activity increases during the promastigote to amastigote conversion. However, unlike canonical MAPKs that are activated by dual phosphorylation of the regulatory TxY motif in the activation loop, MPK10 activation is independent from the phosphorylation of the tyrosine residue, which is largely constitutive. Removal of the last 46 amino acids resulted in significantly enhanced MPK10 activity both for the recombinant and transgenic protein, revealing that MPK10 is regulated by an auto-inhibitory mechanism. Over-expression of this hyperactive mutant in transgenic parasites led to a dominant negative effect causing massive cell death during amastigote differentiation, demonstrating the essential nature of MPK10 auto-inhibition for parasite viability. Moreover, phosphoproteomics analyses identified a novel regulatory phospho-serine residue in the C-terminal auto-inhibitory domain at position 395 that could be implicated in kinase regulation. Finally, we uncovered a feedback loop that limits MPK10 activity through dephosphorylation of the tyrosine residue of the TxY motif. Together our data reveal novel aspects of protein kinase regulation in Leishmania, and propose MPK10 as a potential signal sensor of the mammalian host environment, whose intrinsic pre-activated conformation is regulated by auto-inhibition

Reverse Epidemiology: An Experimental Framework to Drive Leishmania Biomarker Discovery in situ by Functional Genetic Screening Using Relevant Animal Models

Leishmania biomarker discovery remains an important challenge that needs to be revisited in light of our increasing knowledge on parasite-specific biology, notably its genome instability. In the absence of classical transcriptional regulation in these early-branching eukaryotes, fluctuations in transcript abundance can be generated by gene and chromosome amplifications, which have been linked to parasite phenotypic variability with respect to virulence, tissue tropism, and drug resistance. Conducting in vitro evolutionary experiments to study mechanisms of Leishmania environmental adaptation, we recently validated the link between parasite genetic amplification and fitness gain, thus defining gene and chromosome copy number variations (CNVs) as important Leishmania biomarkers. These experiments also demonstrated that long-term Leishmania culture adaptation can strongly interfere with epidemiologically relevant, genetic signals, which challenges current protocols for biomarker discovery, all of which rely on in vitro expansion of clinical isolates. Here we propose an experimental framework independent of long-term culture termed “reverse” epidemiology, which applies established protocols for functional genetic screening of cosmid-transfected parasites in animal models for the identification of clinically relevant genetic loci that then inform targeted field studies for their validation as Leishmania biomarkers

Smart Eco-CityDevelopment in Europe and China: Opportunities, Drivers and Challenges

The policy pointers presented in this report are the result of a three-year (2015-18) research project led by Federico Caprotti at the University of Exeter. The project, Smart Eco-Cities for a Green Economy: A Comparative Analysis of Europe and China, was delivered by a research consortium comprising scholars and researchers in the UK, China, the Netherlands, France, and Germany. The aim of the project was to investigate the way in which smart city and eco-city strategies are used to enable a transition towards digital and green economies. While previous work has considered smart cities and eco-cities as separate urban development models, the project considers them together for the first time. We use the term ‘the smart eco-city’ to focus on how green targets are now included in smart city development policies and strategies. This report presents a summary of policy pointers, or ‘lessons’, learned through our work on the cities we studied in the UK, China, the Netherlands, France and Germany. Specifically, we studied, in depth, the cities of Manchester, Amsterdam, Hamburg, Bordeaux, Shanghai, Shenzhen, Ningbo and Wuhan. This work included interviews with policymakers, urban municipal authorities, tech firm executives, and grassroots and community representatives and stakeholders. Our work also included intensive and in-depth qualitative analysis of documentary sources including policy and corporate reports and other materials.The research undertaken to produce this report was supported by funding from: the Economic and Social Research Council (ESRC) through research grant ES/ L015978/1; the National Natural Science Foundation of China, project number 71461137005; the Netherlands Organisation for Scientific Research (NWO) through research grant 467-14-153 and the Dutch Academy of Sciences (KNAW) through research grant 530-6CD108; the French National Research Agency (ANR) through research grant ANR-14-02; and the German Research Foundation DFG through research grant SP 1545/1-1

A Subset of Liver NK T Cells is Activated During \u3cem\u3eLeishmania donovani\u3c/em\u3e Infection by CD1d-Bound Lipophosphoglycan

Natural killer (NK) T cells are activated by synthetic or self-glycolipids and implicated in innate host resistance to a range of viral, bacterial, and protozoan pathogens. Despite the immunogenicity of microbial lipoglycans and their promiscuous binding to CD1d, no pathogen-derived glycolipid antigen presented by this pathway has been identified to date. In the current work, we show increased susceptibility of NK T cell–deficient CD1d−/− mice to Leishmania donovani infection and Leishmania-induced CD1d-dependent activation of NK T cells in wild-type animals. The elicited response was Th1 polarized, occurred as early as 2 h after infection, and was independent from IL-12. The Leishmania surface glycoconjugate lipophosphoglycan, as well as related glycoinositol phospholipids, bound with high affinity to CD1d and induced a CD1d-dependent IFNγ response in naive intrahepatic lymphocytes. Together, these data identify Leishmania surface glycoconjugates as potential glycolipid antigens and suggest an important role for the CD1d–NK T cell immune axis in the early response to visceral Leishmania infection

A Subset of Liver NK T Cells Is Activated during Leishmania donovani Infection by CD1d-bound Lipophosphoglycan

Natural killer (NK) T cells are activated by synthetic or self-glycolipids and implicated in innate host resistance to a range of viral, bacterial, and protozoan pathogens. Despite the immunogenicity of microbial lipoglycans and their promiscuous binding to CD1d, no pathogen-derived glycolipid antigen presented by this pathway has been identified to date. In the current work, we show increased susceptibility of NK T cell–deficient CD1d−/− mice to Leishmania donovani infection and Leishmania-induced CD1d-dependent activation of NK T cells in wild-type animals. The elicited response was Th1 polarized, occurred as early as 2 h after infection, and was independent from IL-12. The Leishmania surface glycoconjugate lipophosphoglycan, as well as related glycoinositol phospholipids, bound with high affinity to CD1d and induced a CD1d-dependent IFNγ response in naive intrahepatic lymphocytes. Together, these data identify Leishmania surface glycoconjugates as potential glycolipid antigens and suggest an important role for the CD1d–NK T cell immune axis in the early response to visceral Leishmania infection

lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers

We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spherically symmetric distribution within the ambient space (for simplicity we may assume that all distributions are uniform on their corresponding unit spheres). We also assume mixture weights for the different components. We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces. We study the recovery of the most significant subspace by minimizing the lp-averaged distances of data points from d-dimensional subspaces, where p>0. Unlike other lp minimization problems, this minimization is non-convex for all p>0 and thus requires different methods for its analysis. We show that if 0<p<=1, then for any fraction of outliers the most significant subspace can be recovered by lp minimization with overwhelming probability (which depends on the generating distribution and its parameters). We show that when adding small noise around the underlying subspaces the most significant subspace can be nearly recovered by lp minimization for any 0<p<=1 with an error proportional to the noise level. On the other hand, if p>1 and there is more than one underlying subspace, then with overwhelming probability the most significant subspace cannot be recovered or nearly recovered. This last result does not require spherically symmetric outliers.Comment: This is a revised version of the part of 1002.1994 that deals with single subspace recovery. V3: Improved estimates (in particular for Lemma 3.1 and for estimates relying on it), asymptotic dependence of probabilities and constants on D and d and further clarifications; for simplicity it assumes uniform distributions on spheres. V4: minor revision for the published versio

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196