A multilayer network approach for guiding drug repositioning in neglected diseases

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.Fil: Berenstein, Ariel José. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Magariños, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Chernomoretz, Ariel. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Fernandez Aguero, Maria Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentin

La renovación de la palabra en el bicentenario de la Argentina : los colores de la mirada lingüística

El libro reúne trabajos en los que se exponen resultados de investigaciones presentadas por investigadores de Argentina, Chile, Brasil, España, Italia y Alemania en el XII Congreso de la Sociedad Argentina de Lingüística (SAL), Bicentenario: la renovación de la palabra, realizado en Mendoza, Argentina, entre el 6 y el 9 de abril de 2010. Las temáticas abordadas en los 167 capítulos muestran las grandes líneas de investigación que se desarrollan fundamentalmente en nuestro país, pero también en los otros países mencionados arriba, y señalan además las áreas que recién se inician, con poca tradición en nuestro país y que deberían fomentarse. Los trabajos aquí publicados se enmarcan dentro de las siguientes disciplinas y/o campos de investigación: Fonología, Sintaxis, Semántica y Pragmática, Lingüística Cognitiva, Análisis del Discurso, Psicolingüística, Adquisición de la Lengua, Sociolingüística y Dialectología, Didáctica de la lengua, Lingüística Aplicada, Lingüística Computacional, Historia de la Lengua y la Lingüística, Lenguas Aborígenes, Filosofía del Lenguaje, Lexicología y Terminología

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Leptin promotes cell proliferation and survival of trophoblastic cells

Leptin, the 16-kDa protein product of the obese gene, was originally considered as an adipocyte-derived signaling molecule for the central control of metabolism. However, leptin has been suggested to be involved in other functions during pregnancy, particularly in placenta. In the present work, we studied a possible effect of leptin on trophoblastic cell proliferation, survival, and apoptosis. Recombinant human leptin added to JEG-3 and BeWo choriocarcinoma cell lines showed a stimulatory effect on cell proliferation up to 3 and 2.4 times, respectively, measured by 3H-thymidine incorporation and cell counting. These effects were time and dose dependent. Maximal effect was achieved at 250 ng leptin/ml for JEG-3 cells and 50 ng leptin/ml for BeWo cells. Moreover, by inhibiting endogenous leptin expression with 2 μM of an antisense oligonucleotide (AS), cell proliferation was diminished. We analyzed cell population distribution during the different stages of cell cycle by fluorescence-activated cell sorting, and we found that leptin treatment displaced the cells towards a G2/M phase. We also found that leptin upregulated cyclin D1 expression, one of the key cell cycle-signaling proteins. Since proliferation and death processes are intimately related, the effect of leptin on cell apoptosis was investigated. Treatment with 2 μM leptin AS increased the number of apoptotic cells 60 times, as assessed by annexin V-fluorescein isothiocyanate/propidium iodide staining, and the caspase-3 activity was increased more than 2 fold. This effect was prevented by the addition of 100 ng leptin/ml. In conclusion, we provide evidence that suggests that leptin is a trophic and mitogenic factor for trophoblastic cells by virtue of its inhibiting apoptosis and promoting proliferation.Fil: Magariños, María Paula. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; ArgentinaFil: Sánchez Margalet, Víctor. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Universidad de Sevilla; EspañaFil: Kotler, Monica Lidia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; ArgentinaFil: Calvo, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; ArgentinaFil: Varone, Cecilia Laura. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentin

Suggesting targets for orphan compounds: example 2, peptide deformylase.

<p>Following the strategy described in the text, and visualized in Figs <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004300#pntd.0004300.g004" target="_blank">4</a> and <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004300#pntd.0004300.g005" target="_blank">5</a>, based on the functional affiliations of the chemical similarity neighborhood of orphan compound 599594 (TDR Targets ID), the target of this compound is proposed to be a peptide deformylase.</p

Performance at the task of recovery of the correct target for artificially orphaned compounds.

<p><b>A:</b> Number of recovered proteins, <i>ρ</i>(<i>r</i>_<i>G</i>) (left scale, blue line), and protein recovery rate <i>λ</i>(<i>r</i>_<i>G</i>) (right scale, orange dashed line) as a function of global ranking threshold values, r_G. The horizontal black dashed line represents 3 standard deviations (3σ) above the mean asymptotic noise level (see text). <b>B</b>: Distribution of species-specific ranking positions, r_ss, for the 703 recovered true-targets which presented global ranking values lower than r*_G = 38 estimated in panel A. Cumulative fraction of recovered targets is shown above bars.</p

Suggesting targets for orphan compounds: example 1, N-myristoyltransferase.

<p>The compound shown in the upper panel (TDR Targets ID 606689, ChEMBL ID 688510) is an orphan compound (no known target) that was shown to be active against P. falciparum. A similar compound (Tanimoto similarity coefficient = 0.804), shown at the right, is active against a glycylpeptide N-tetradecanoyltransferase of Candida albicans [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004300#pntd.0004300.ref078" target="_blank">78</a>] which belongs to the same ortholog group, and shares 2 Pfam domains with the P. falciparum N-myristoyltransferase (PlasmoDB ID PF14_0127).</p

Suggesting targets for orphan compounds: alanyl aminopeptidase.

<p>A series of 13 structurally related orphan compounds (EC50 values range = 0.03–0.74 uM against <i>P</i>. <i>falciparum</i>, <i>data from</i> [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004300#pntd.0004300.ref050" target="_blank">50</a>], available at TDR Targets) are connected in our network model to the PfA-M1 <i>Plasmodium</i> enzyme. In the figure we show the Markush structure for the series, and the corresponding R-groups and their database IDs. We also show one representative of other active similar compounds (see [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004300#pntd.0004300.ref095" target="_blank">95</a>]) with activity against defined targets in the network.</p

Schematic representation of data and workflow.

<p><b>a)</b> Multilayer representation of drug-target data. First layer (bottom) contains drugs as nodes and chemical similarity relations as edges. Second layer contains proteins as nodes. Links between these two layers represent known and significant bioactivity of a compound against a defined protein target. The top layer contains functional annotation-type objects as nodes (Pfam domains, green circles; Ortholog groups, orange diamonds; and metabolic pathways, yellow stars). Links between the second and third layers represent affiliations of proteins to each of these annotation classes. For clarituy, the representation shows a partial view of the whole network corresponding to objects and connections related to the example shown in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004300#pntd.0004300.g005" target="_blank">Fig 5</a>. <b>b)</b> Bipartite projection of the two upper layers, into a protein-protein monopartite network after weighting of informative affiliations as described in the main text.</p

Inference of targets of orphaned compounds.

<p><b>a, top:</b> Schematic view of two different ways in which the algorithm can find the correct target for artificially orphaned compounds. O = orphan compound; D = bioactive drug/compound which is a first neighbor of O in the D-layer; T_o, known target of the artificially orphaned compound O; T_d, known target of compound D. Arrows represent significant similarity relationships between compounds or significant bioactivity links between a target and a compound. Dashed lines connecting compounds and targets represent the original E_DP edges that were removed for the cross-validation procedure. <b>a, left:</b> direct inference, compound D has a bioactivity link to T_o (special case, T_o = T_d). <b>a, right:</b> indirect inference, compound D lacks bioactivity links against T_o, but a high-scoring path connects T_d to T_o in the projected PP-layer. <b>b, bottom:</b> boxplots showing the distribution of the position of T_o targets in the rankings for 703 orphaned compounds. <b>b, left:</b> boxplot for cases that fell in the direct inference class (478 compounds). <b>b, right:</b> boxplot for cases in the indirect inference class (225 compounds).</p