Generation of high-affinity monoclonal antibodies of IgG class against native β-d-glucans from basidiomycete mushrooms

β-d-glucans from basidiomycete strains are powerful immunomodulatory agents in several clinical conditions. Therefore, their assay, purification and characterization are of great interest to understand their structure-function relationship. Hybridoma cell fusion was used to raise monoclonal antibodies (Mabs) against extracellular β-d-glucans (EBGs) from Pleurotus ostreatus. Two of the hybridoma clones (1E6-1E8-B5 and 3E8-3B4) secreting Mabs against EBGs were selected. This hybridoma cell line secreted Mabs of the IgG class which were then purified by hydroxyapatite chromatography to apparent homogeneity on native and SDS-PAGE. Mabs secreted by 1E6-1E8-B5 clone were found to recognize a common epitope on several β-d-glucans from different basidiomycete strains. This Mab exhibited high affinity constant (KA) for β-d-glucans from several mushroom strains in the range of 3.20 × 109 ± 3.32 × 103-1.51 × 1013 ± 3.58 × 107 L/mol. Moreover, they reacted to some heat-treated β-d-glucans in a different mode when compared with the native forms; these data suggest that this Mab binds to a conformational epitope on the β-d-glucan molecule. The epitope-binding studies of Mabs obtained from 1E6-1E8-B5 and 3E8-3B4 revealed that the Mabs bind to the same epitope on some β-d-glucans and to different epitopes in other antigen molecules. Therefore, these Mabs can be used to assay for β-d-glucan from basidiomycete mushrooms. © 2015 Elsevier Ltd. All rights reserved.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Effect of skin colour and selected physical characteristics on Schistosoma mansoni dependent morbidity

The effect of the colour group on the morbidity due to Schistosoma mansoni was examined in two endemic areas situated in the State of Minas Gerais, Brazil. Of the 2773 eligible inhabitants, 1971 (71.1%) participated in the study: 545 (27.6%) were classified as white, 719 (36.5%) as intermediate and 707 (35.9%) as black. For each colour group, signs and symptoms of individuals who eliminated S.mansoni eggs (cases) were compared to those who did not present eggs in the faeces (controls). The odds ratios were adjusted by age, gender, previous treatment for schistosomiasis, endemic area and quality of the household. There was no evidence of a modifier effect of colour on diarrhea, bloody faeces or abdominal pain. A modifier effect of colour on hepatomegaly was evident among those heaviest infected (> 400 epg): the adjusted odds ratios for palpable liver at the middle clavicular and the middle sternal lines were smaller among blacks (5.4 and 6.5, respectively) and higher among whites (10.6 and 12.9) and intermediates (10.4 and 10.1, respectively). These results point out the existence of some degree of protection against hepatomegaly among blacks heaviest infected in the studied areas.

Lettuce (Lactuca sativa L.) leaf-proteome profiles after exposure to cylindrospermopsin and a microcystin-LR/cylindrospermopsin mixture: A concentration-dependent response

The intensification of agricultural productivity is an important challenge worldwide. However, environmental stressors can provide challenges to this intensification. The progressive occurrence of the cyanotoxins cylindrospermopsin (CYN) and microcystin-LR (MC-LR) as a potential consequence of eutrophication and climate change is of increasing concern in the agricultural sector because it has been reported that these cyanotoxins exert harmful effects in crop plants. A proteomic-based approach has been shown to be a suitable tool for the detection and identification of the primary responses of organisms exposed to cyanotoxins. The aim of this study was to compare the leaf-proteome profiles of lettuce plants exposed to environmentally relevant concentrations of CYN and a MC-LR/CYN mixture. Lettuce plants were exposed to 1, 10, and 100 lg/l CYN and a MC-LR/CYN mixture for five days. The proteins of lettuce leaves were separated by twodimensional electrophoresis (2-DE), and those that were differentially abundant were then identified by matrix-assisted laser desorption/ionization time of flight-mass spectrometry (MALDI-TOF/TOF MS). The biological functions of the proteins that were most represented in both experiments were photosynthesis and carbon metabolism and stress/defense response. Proteins involved in protein synthesis and signal transduction were also highly observed in the MC-LR/CYN experiment. Although distinct protein abundance patterns were observed in both experiments, the effects appear to be concentration-dependent, and the effects of the mixture were clearly stronger than those of CYN alone. The obtained results highlight the putative tolerance of lettuce to CYN at concentrations up to 100 lg/l. Furthermore, the combination of CYN with MC-LR at low concentrations (1 lg/l) stimulated a significant increase in the fresh weight (fr. wt) of lettuce leaves and at the proteomic level resulted in the increase in abundance of a high number of proteins. In contrast, many proteins exhibited a decrease in abundance or were absent in the gels of the simultaneous exposure to 10 and 100 lg/l MC-LR/CYN. In the latter, also a significant decrease in the fr. wt of lettuce leaves was obtained. These findings provide important insights into the molecular mechanisms of the lettuce response to CYN and MC-LR/CYN and may contribute to the identification of potential protein markers of exposure and proteins that may confer tolerance to CYN and MC-LR/CYN. Furthermore, because lettuce is an important crop worldwide, this study may improve our understanding of the potential impact of these cyanotoxins on its quality traits (e.g., presence of allergenic proteins).

Culture-dependent characterization of cyanobacterial diversity in the intertidal zones of the Portuguese coast: A polyphasic study

Cyanobacteria are important primary producers, and many are able to fix atmospheric nitrogen playing a key role in the marine environment. However, not much is known about the diversity of cyanobacteria in Portuguese marine waters. This paper describes the diversity of 60 strains isolated from benthic habitats in 9 sites (intertidal zones) on the Portuguese South and West coasts. The strains were characterized by a morphological study (light and electron microscopy) and by a molecular characterization (partial 16S rRNA, nifH, nifK, mcyA, mcyE/ndaF, sxtI genes). The morphological analyses revealed 35 morphotypes (15 genera and 16 species) belonging to 4 cyanobacterial Orders/Subsections. The dominant groups among the isolates were the Oscillatoriales. There is a broad congruence between morphological and molecular assignments. The 16S rRNA gene sequences of 9 strains have less than 97% similarity compared to the sequences in the databases, revealing novel cyanobacterial diversity. Phylogenetic analysis, based on partial 16S rRNA gene sequences showed at least 12 clusters. One-third of the isolates are potential N2-fixers, as they exhibit heterocysts or the presence of nif genes was demonstrated by PCR. Additionally, no conventional freshwater toxins genes were detected by PCR screening.

Chlorella sp. coagulation‐flocculation by inducing a modification on the pH broth medium

Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Bioenergia

The genetic basis of morphological change in convergent evolution of natural populations: identifying candidate genes behind convergent evolution in blind cavefish astyanax mexicanus

Dissertation presented to obtain the Ph.D. degree in Biology at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa.

Biochemistry of grape berries: post-genomics approaches to uncover the effects of water deficits on ripening

Dissertation presented to obtain the Ph.D degree in Biochemistry, Plant Physiology

Desenvolvimento e aplicação de um biossensor para monitorização de creatinina, um biomarcador associado a doenças emergentes

Este trabalho descreve o desenvolvimento de um material sensor para creatinina por impressão molecular em estrutura polimérica (MIP) e a sua aplicação no desenvolvimento de um dispositivo de natureza potenciométrica para a determinação da molécula alvo em fluidos biológicos. A creatinina é um dos biomarcadores mais utilizados no acompanhamento da doença renal, já que é um bom indicador da taxa de filtração glomerular (TFG). Os materiais biomiméticos desenhados para interação com a creatinina foram obtidos por polimerização radicalar, recorrendo a monómeros de ácido metacríclico ou de vinilpiridina e a um agente de reticulação apropriado. De modo a aferir o efeito da impressão da creatinina na resposta dos materiais MIP à sua presença, foram também preparados e avaliados materiais de controlo, obtidos sem impressão molecular (NIP). O controlo da constituição química destes materiais, incluindo a extração da molécula impressa, foi realizado por Espectroscopia de Raman e de Infravermelho com Transformada de Fourrier. A afinidade de ligação entre estes materiais e a creatinina foi também avaliada com base em estudos cinéticos. Todos os materiais descritos foram integrados em membranas selectivas de elétrodos seletivos de ião, preparadas sem ou com aditivo iónico lipófilo, de carga negativa ou positiva. A avaliação das características gerais de funcionamento destes elétrodos, em meios de composição e pH diferentes, indicaram que as membranas com materiais impressos e aditivo aniónico eram as únicas com utilidade analítica. Os melhores resultados foram obtidos em solução tampão Piperazine-N,N′-bis(2- ethanesulfonic acid), PIPES, de pH 2,8, condição que permitiu obter uma resposta quasi-Nernstiana, a partir de 1,6×10-5 mol L-1. Estes elétrodos demonstraram ainda uma boa selectividade ao apresentaram uma resposta preferencial para a creatinina quando na presença de ureia, carnitina, glucose, ácido ascórbico, albumina, cloreto de cálcio, cloreto de potássio, cloreto de sódio e sulfato de magnésio. Os elétrodos foram ainda aplicados com sucesso na análise de amostras sintéticas de urina, quando os materiais sensores eram baseados em ácido metacrilico, e soro, quando os materiais sensores utilizados eram baseados em vinilpiridina.

Alterações de pH após adição de pulsos de glucose a Saccharomyces cerevisiae: estimativa de parâmetros a partir da modelação matemática do comportamento cinéticometabólico

Dissertação para a obtenção do grau de mestre em Bioquímica Estrutural e Funcional

Crystallographic studies on molybdopterin-dependent enzymes

Dissertação para obtenção do Grau de Doutor em Bioquímica, Especialidade Bioquímica Estrutural

New modified electrochemical conductive paper support for BSA detection

Chemical sensors and biosensors are widely used to detect various kinds of protein target biomolecules. Molecularly Imprinted Polymers (MIPs) have raised great interest in this area, because these act as antibody-like recognition materials, with high affinity to the template molecule. Compared to natural antibodies, these are also of lower cost and higher stability. There are different types of supports used to carry MIP materials, mostly of these made of gold, favourably assembled on a Screen Printed Electrode (SPE) strategy. For this work a new kind of support for the sensing layer was developed: conductive paper. This support was made by modifying first cellulose paper with paraffin wax (to make it waterproof), and casting a carbon-ink on it afterwards, to turn it conductive. The SPAM approach previously reported in1 was employed herein to assemble to MIP sensing material on the conductive paper. The selected charged monomers were (vinylbenzyl) trimethlammonium chloride (positive charge) or vinylbenzoic acid (negative charge), used to generate binding positions with single-type charge (positive or negative). The non-specific binding area of the MIP layer was assembled by chronoamperometry-assisted polymerization (at 1 V, for 60, 120 or 180 seconds) of vinylbenzoate, cross-linked with ethylene glycol vinyl ether. The BSA biomolecules lying within the polymeric matrix were removed by Proteinase K action. All preparation stages of the MIP assembly were followed by FTIR, Raman spectroscopy and, electrochemical analysis. In general, the best results were obtained for longer polymerization times and positively charged binding sites (which was consistent with a negatively-charged protein under physiological pH, as BSA). Linear responses against BSA concentration ranged from 0.005 to 100 mg/mL, in PBS buffer standard solutions. The sensor was further calibrated in standard solutions that were prepared in synthetic or real urine, and the analytical response became more sensitive and stable. Compared to the literature, the detection capability of the developed device is better than most of the reported electrodes. Overall, the simplicity, low cost and good analytical performance of the BSA SPE device, prepared with positively charged binding positions, seems a suitable approach for practical application in clinical context. Further studies with real samples are required, as well as gathering with electronic-supporting devices to allow on-site readings.

Smart Plastic Antibody Material for Hemoglobin Tailored by Silica Surface Imprinting and with Charged Binding Sites: Its use as Ionophore in Potentiometric Transduction

JORNADAS DE ELECTROQUÍMICA E INOVAÇÃO 2013