Characterization of constitutive and putative differentially expressed mRNAs by means of expressed sequence tags, differential display reverse transcriptase-PCR and randomly amplified polymorphic DNA-PCR from the sand fly vector Lutzomyia longipalpis

Molecular studies of insect disease vectors are of paramount importance for understanding parasite-vector relationship. Advances in this area have led to important findings regarding changes in vectors' physiology upon blood feeding and parasite infection. Mechanisms for interfering with the vectorial capacity of insects responsible for the transmission of diseases such as malaria, Chagas disease and dengue fever are being devised with the ultimate goal of developing transgenic insects. A primary necessity for this goal is information on gene expression and control in the target insect. Our group is investigating molecular aspects of the interaction between Leishmania parasites and Lutzomyia sand flies. As an initial step in our studies we have used random sequencing of cDNA clones from two expression libraries made from head/thorax and abdomen of sugar fed L. longipalpis for the identification of expressed sequence tags (EST). We applied differential display reverse transcriptase-PCR and randomly amplified polymorphic DNA-PCR to characterize differentially expressed mRNA from sugar and blood fed insects, and, in one case, from a L. (V.) braziliensis-infected L. longipalpis. We identified 37 cDNAs that have shown homology to known sequences from GeneBank. Of these, 32 cDNAs code for constitutive proteins such as zinc finger protein, glutamine synthetase, G binding protein, ubiquitin conjugating enzyme. Three are putative differentially expressed cDNAs from blood fed and Leishmania-infected midgut, a chitinase, a V-ATPase and a MAP kinase. Finally, two sequences are homologous to Drosophila melanogaster gene products recently discovered through the Drosophila genome initiative.

Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex.

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by Class I major histocompatibility complexes (MHC) is the key event in the immune response against virus-infected cells or tumor cells. A study of the 2C TCR/SIYR/H-2K(b) system using a computational alanine scanning and a much faster binding free energy decomposition based on the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is presented. The results show that the TCR-p-MHC binding free energy decomposition using this approach and including entropic terms provides a detailed and reliable description of the interactions between the molecules at an atomistic level. Comparison of the decomposition results with experimentally determined activity differences for alanine mutants yields a correlation of 0.67 when the entropy is neglected and 0.72 when the entropy is taken into account. Similarly, comparison of experimental activities with variations in binding free energies determined by computational alanine scanning yields correlations of 0.72 and 0.74 when the entropy is neglected or taken into account, respectively. Some key interactions for the TCR-p-MHC binding are analyzed and some possible side chains replacements are proposed in the context of TCR protein engineering. In addition, a comparison of the two theoretical approaches for estimating the role of each side chain in the complexation is given, and a new ad hoc approach to decompose the vibrational entropy term into atomic contributions, the linear decomposition of the vibrational entropy (LDVE), is introduced. The latter allows the rapid calculation of the entropic contribution of interesting side chains to the binding. This new method is based on the idea that the most important contributions to the vibrational entropy of a molecule originate from residues that contribute most to the vibrational amplitude of the normal modes. The LDVE approach is shown to provide results very similar to those of the exact but highly computationally demanding method.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Characterization of the murine high Km glucose transporter GLUT2 gene and its transcriptional regulation by glucose in a differentiated insulin-secreting cell line.

In pancreatic beta-cells, the high Km glucose transporter GLUT2 catalyzes the first step in glucose-induced insulin secretion by glucose uptake. Expression of the transporter has been reported to be modulated by glucose either at the protein or mRNA levels. In this study we used the differentiated insulinoma cell line INS-1 which expresses high levels of GLUT2 and show that the expression of GLUT2 is regulated by glucose at the transcriptional level. By run-on transcription assays we showed that glucose induced GLUT2 gene transcription 3-4-fold in INS-1 cells which was paralleled by a 1.7-2.3-fold increase in cytoplasmic GLUT2 mRNA levels. To determine whether glucose regulatory sequences were present in the promoter region of GLUT2, we cloned and characterized a 1.4-kilobase region of mouse genomic DNA located 5' of the translation initiation site. By RNase protection assays and primer extension, we determined that multiple transcription initiation sites were present at positions -55, -64, and -115 from the first coding ATG and which were identified in liver, intestine, kidney, and beta-cells mRNAs. Plasmids were constructed with the mouse promoter region linked to the reporter gene chloramphenicol acetyltransferase (CAT), and transiently and stably transfected in the INS-1 cells. Glucose induced a concentration-dependent increase in CAT activity which reached a maximum of 3.6-fold at 20 mM glucose. Similar CAT constructs made of the human GLUT2 promoter region and the CAT gene displayed the same glucose-dependent increase in transcriptional activity when transfected into INS-1 cells. Comparison of the mouse and human promoter regions revealed sequence identity restricted to a few stretches of sequences which suggests that the glucose responsive element(s) may be conserved in these common sequences.

Sound Texture Modelling

In the PhD thesis “Sound Texture Modeling” we deal with statistical modelling or textural sounds like water, wind, rain, etc. For synthesis and classification. Our initial model is based on a wavelet tree signal decomposition and the modeling of the resulting sequence by means of a parametric probabilistic model, that can be situated within the family of models trainable via expectation maximization (hidden Markov tree model ). Our model is able to capture key characteristics of the source textures (water, rain, fire, applause, crowd chatter ), and faithfully reproduces some of the sound classes. In terms of a more general taxonomy of natural events proposed by Graver, we worked on models for natural event classification and segmentation. While the event labels comprise physical interactions between materials that do not have textural propierties in their enterity, those segmentation models can help in identifying textural portions of an audio recording useful for analysis and resynthesis. Following our work on concatenative synthesis of musical instruments, we have developed a pattern-based synthesis system, that allows to sonically explore a database of units by means of their representation in a perceptual feature space. Concatenative syntyhesis with “molecules” built from sparse atomic representations also allows capture low-level correlations in perceptual audio features, while facilitating the manipulation of textural sounds based on their physical and perceptual properties. We have approached the problem of sound texture modelling for synthesis from different directions, namely a low-level signal-theoretic point of view through a wavelet transform, and a more high-level point of view driven by perceptual audio features in the concatenative synthesis setting. The developed framework provides unified approach to the high-quality resynthesis of natural texture sounds. Our research is embedded within the Metaverse 1 European project (2008-2011), where our models are contributting as low level building blocks within a semi-automated soundscape generation system.

HLA photoaffinity labeling reveals overlapping binding of homologous melanoma-associated gene peptides by HLA-A1, HLA-A29, and HLA-B44.

Melanoma-associated genes (MAGEs) encode tumor-specific antigens that can be recognized by CD8+ cytotoxic T lymphocytes. To investigate the interaction of the HLA-A1-restricted MAGE-1 peptide 161-169 (EADPT-GHSY) with HLA class I molecules, photoreactive derivatives were prepared by single amino acid substitution with N beta-[iodo-4-azidosalicyloyl]-L-2,3-diaminopropionic acid. These derivatives were tested for their ability to bind to, and to photoaffinity-label, HLA-A1 on C1R.A1 cells. Only the derivatives containing the photoreactive amino acid in position 1 or 7 fulfilled both criteria. Testing the former derivative on 14 lymphoid cell lines expressing over 44 different HLA class I molecules indicated that it efficiently photoaffinity-labeled not only HLA-A1, but possibility also HLA-A29 and HLA-B44. MAGE peptide binding by HLA-A29 and HLA-B44 was confirmed by photoaffinity labeling with photoreactive MAGE-3 peptide derivatives on C1R.A29 and C1R.B44 cells, respectively. The different photoaffinity labeling systems were used to access the ability of the homologous peptides derived from MAGE-1, -2, -3, -4a, -4b, -6, and -12 to bind to HLA-A1, HLA-A29, and HLA-B44. All but the MAGE-2 and MAGE-12 nonapeptides efficiently inhibited photoaffinity labeling of HLA-A1, which is in agreement with the known HLA-A1 peptide-binding motif (acidic residue in P3 and C-terminal tyrosine). In contrast, photoaffinity labeling of HLA-A29 was efficiently inhibited by these as well as by the MAGE-3 and MAGE-6 nonapeptides. Finally, the HLA-B44 photoaffinity labeling, unlike the HLA-A1 and HLA-A29 labeling, was inhibited more efficiently by the corresponding MAGE decapeptides, which is consistent with the reported HLA-B44 peptide-binding motif (glutamic acid in P2, and C-terminal tyrosine or phenylalanine). The overlapping binding of homologous MAGE peptides by HLA-A1, A29, and B44 is based on different binding principles and may have implications for immunotherapy of MAGE-positive tumors.

Computational quest for understanding the role of astrocyte signaling in synaptic transmission and plasticity.

The complexity of the signaling network that underlies astrocyte-synapse interactions may seem discouraging when tackled from a theoretical perspective. Computational modeling is challenged by the fact that many details remain hitherto unknown and conventional approaches to describe synaptic function are unsuitable to explain experimental observations when astrocytic signaling is taken into account. Supported by experimental evidence is the possibility that astrocytes perform genuine information processing by means of their calcium signaling and are players in the physiological setting of the basal tone of synaptic transmission. Here we consider the plausibility of this scenario from a theoretical perspective, focusing on the modulation of synaptic release probability by the astrocyte and its implications on synaptic plasticity. The analysis of the signaling pathways underlying such modulation refines our notion of tripartite synapse and has profound implications on our understanding of brain function.

Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach

Protecting native biodiversity against alien invasive species requires powerful methods to anticipate these invasions and to protect native species assumed to be at risk. Here, we describe how species distribution models (SDMs) can be used to identify areas predicted as suitable for rare native species and also predicted as highly susceptible to invasion by alien species, at present and under future climate and land-use scenarios. To assess the condition and dynamics of such conflicts, we developed a combined predictive modelling (CPM) approach, which predicts species distributions by combining two SDMs fitted using subsets of predictors classified as acting at either regional or local scales. We illustrate the CPM approach for an alien invader and a rare species associated to similar habitats in northwest Portugal. Combined models predict a wider variety of potential species responses, providing more informative projections of species distributions and future dynamics than traditional, non-combined models. They also provide more informative insight regarding current and future rare-invasive conflict areas. For our studied species, conflict areas of highest conservation relevance are predicted to decrease over the next decade, supporting previous reports that some invasive species may contract their geographic range and impact due to climate change. More generally, our results highlight the more informative character of the combined approach to address practical issues in conservation and management programs, especially those aimed at mitigating the impact of invasive plants, land-use and climate changes in sensitive regions

Regulation of membrane trafficking and organ separation by the NEVERSHED ARF-GAP protein.

Cell separation, or abscission, is a highly specialized process in plants that facilitates remodeling of their architecture and reproductive success. Because few genes are known to be essential for organ abscission, we conducted a screen for mutations that alter floral organ shedding in Arabidopsis. Nine recessive mutations that block shedding were found to disrupt the function of an ADP-ribosylation factor-GTPase-activating protein (ARF-GAP) we have named NEVERSHED (NEV). As predicted by its homology to the yeast Age2 ARF-GAP and transcriptional profile, NEV influences other aspects of plant development, including fruit growth. Co-localization experiments carried out with NEV-specific antiserum and a set of plant endomembrane markers revealed that NEV localizes to the trans-Golgi network and endosomes in Arabidopsis root epidermal cells. Interestingly, transmission electron micrographs of abscission zone regions from wild-type and nev flowers reveal defects in the structure of the Golgi apparatus and extensive accumulation of vesicles adjacent to the cell walls. Our results suggest that NEV ARF-GAP activity at the trans-Golgi network and distinct endosomal compartments is required for the proper trafficking of cargo molecules required for cell separation.

Characterization of Sm14 related components in different helminths by sodium dodecyl sulphate-polyacrylamide gel electrophoresis and Western blotting analysis

Sm14 was the first fatty acid-binding protein homologue identified in helminths. Thereafter, members of the same family were identified in several helminth species, with high aminoacid sequence homology between them. In addition, immune crossprotection was also reported against Fasciola hepatica infection, in animals previously immunized with the Schistosoma mansoni vaccine candidate, r-Sm14. In the present study, data on preliminary sodium dodecyl sulphate-polyacrylamide gel electrophoresis and Western blotting analysis of nine different helminth extracts focusing the identification of Sm14 related proteins, is reported. Out of these, three extracts - Ascaris suum (males and females), Echinostoma paraensei, and Taenia saginata - presented components that comigrated with Sm14 in SDS-PAGE, and that were recognized by anti-rSm14 policlonal serum, in Western blotting tests.

Combining probabilistic land-use change and tree population dynamics modelling to simulate responses in mountain forests

Altitudinal tree lines are mainly constrained by temperature, but can also be influenced by factors such as human activity, particularly in the European Alps, where centuries of agricultural use have affected the tree-line. Over the last decades this trend has been reversed due to changing agricultural practices and land-abandonment. We aimed to combine a statistical land-abandonment model with a forest dynamics model, to take into account the combined effects of climate and human land-use on the Alpine tree-line in Switzerland. Land-abandonment probability was expressed by a logistic regression function of degree-day sum, distance from forest edge, soil stoniness, slope, proportion of employees in the secondary and tertiary sectors, proportion of commuters and proportion of full-time farms. This was implemented in the TreeMig spatio-temporal forest model. Distance from forest edge and degree-day sum vary through feed-back from the dynamics part of TreeMig and climate change scenarios, while the other variables remain constant for each grid cell over time. The new model, TreeMig-LAb, was tested on theoretical landscapes, where the variables in the land-abandonment model were varied one by one. This confirmed the strong influence of distance from forest and slope on the abandonment probability. Degree-day sum has a more complex role, with opposite influences on land-abandonment and forest growth. TreeMig-LAb was also applied to a case study area in the Upper Engadine (Swiss Alps), along with a model where abandonment probability was a constant. Two scenarios were used: natural succession only (100% probability) and a probability of abandonment based on past transition proportions in that area (2.1% per decade). The former showed new forest growing in all but the highest-altitude locations. The latter was more realistic as to numbers of newly forested cells, but their location was random and the resulting landscape heterogeneous. Using the logistic regression model gave results consistent with observed patterns of land-abandonment: existing forests expanded and gaps closed, leading to an increasingly homogeneous landscape.

Quantification of in vivo short echo-time proton magnetic resonance spectra at 14.1 T using two different approaches of modelling the macromolecule spectrum

Reliable quantification of the macromolecule signals in short echo-time H-1 MRS spectra is particularly important at high magnetic fields for an accurate quantification of metabolite concentrations (the neurochemical profile) due to effectively increased spectral resolution of the macromolecule components. The purpose of the present study was to assess two approaches of quantification, which take the contribution of macromolecules into account in the quantification step. H-1 spectra were acquired on a 14.1 T/26 cm horizontal scanner on five rats using the ultra-short echo-time SPECIAL (spin echo full intensity acquired localization) spectroscopy sequence. Metabolite concentrations were estimated using LCModel, combined with a simulated basis set of metabolites using published spectral parameters and either the spectrum of macromolecules measured in vivo, using an inversion recovery technique, or baseline simulated by the built-in spline function. The fitted spline function resulted in a smooth approximation of the in vivo macromolecules, but in accordance with previous studies using Subtract-QUEST could not reproduce completely all features of the in vivo spectrum of macromolecules at 14.1 T. As a consequence, the measured macromolecular 'baseline' led to a more accurate and reliable quantification at higher field strengths.

Modelling the mechanical aspects of the curing process of magneto-sensitive elastomeric materials

In this paper, a phenomenologically motivated magneto-mechanically coupled finite strain elastic framework for simulating the curing process of polymers in the presence of a magnetic load is proposed. This approach is in line with previous works by Hossain and co-workers on finite strain curing modelling framework for the purely mechanical polymer curing (Hossain et al., 2009b). The proposed thermodynamically consistent approach is independent of any particular free energy function that may be used for the fully-cured magneto-sensitive polymer modelling, i.e. any phenomenological or micromechanical-inspired free energy can be inserted into the main modelling framework. For the fabrication of magneto-sensitive polymers, micron-size ferromagnetic particles are mixed with the liquid matrix material in the uncured stage. The particles align in a preferred direction with the application of a magnetic field during the curing process. The polymer curing process is a complex (visco) elastic process that transforms a fluid to a solid with time. Such transformation process is modelled by an appropriate constitutive relation which takes into account the temporal evolution of the material parameters appearing in a particular energy function. For demonstration in this work, a frequently used energy function is chosen, i.e. the classical Mooney-Rivlin free energy enhanced by coupling terms. Several representative numerical examples are demonstrated that prove the capability of our approach to correctly capture common features in polymers undergoing curing processes in the presence of a magneto-mechanical coupled load.

The fine-scale architecture of structural variants in 17 mouse genomes.

BACKGROUND: Accurate catalogs of structural variants (SVs) in mammalian genomes are necessary to elucidate the potential mechanisms that drive SV formation and to assess their functional impact. Next generation sequencing methods for SV detection are an advance on array-based methods, but are almost exclusively limited to four basic types: deletions, insertions, inversions and copy number gains. RESULTS: By visual inspection of 100 Mbp of genome to which next generation sequence data from 17 inbred mouse strains had been aligned, we identify and interpret 21 paired-end mapping patterns, which we validate by PCR. These paired-end mapping patterns reveal a greater diversity and complexity in SVs than previously recognized. In addition, Sanger-based sequence analysis of 4,176 breakpoints at 261 SV sites reveal additional complexity at approximately a quarter of structural variants analyzed. We find micro-deletions and micro-insertions at SV breakpoints, ranging from 1 to 107 bp, and SNPs that extend breakpoint micro-homology and may catalyze SV formation. CONCLUSIONS: An integrative approach using experimental analyses to train computational SV calling is essential for the accurate resolution of the architecture of SVs. We find considerable complexity in SV formation; about a quarter of SVs in the mouse are composed of a complex mixture of deletion, insertion, inversion and copy number gain. Computational methods can be adapted to identify most paired-end mapping patterns.

Detection of Brazilian hantavirus by reverse transcription polymerase chain reaction amplification of N gene in patients with hantavirus cardiopulmonary syndrome

We report a nested reverse transcription-polymerase chain reaction (RT-PCR) assay for hantavirus using primers selected to match high homology regions of hantavirus genomes detected from the whole blood of hantavirus cardiopulmonary syndrome (HCPS) patients from Brazil, also including the N gene nucleotide sequence of Araraquara virus. Hantavirus genomes were detected in eight out of nine blood samples from the HCPS patients by RT-PCR (88.9% positivity) and in all 9 blood samples (100% positivity) by nested-PCR. The eight amplicons obtained by RT-PCR (P1, P3-P9), including one obtained by nested-PCR (P-2) and not obtained by RT-PCR, were sequenced and showed high homology (94.8% to 99.1%) with the N gene of Araraquara hantavirus. Although the serologic method ELISA is the most appropriate test for HCPS diagnosis, the use of nested RT-PCR for hantavirus in Brazil would contribute to the diagnosis of acute hantavirus disease detecting viral genomes in patient specimens as well as initial genomic characterization of circulating hantaviruses.