Mutants of common bean alpha-amylase inhibitor-2 as an approach to investigate binding specificity to alpha-amylases

Despite the presence of a family of defense proteins, Phaseolus vulgaris can be attacked by bruchid insects resulting in serious damage to stored grains. The two distinct active forms of a-amylase inhibitors, a-AI1 and a-AI2, in P. vulgaris show different specificity toward a-amylases. Zabrotes subfasciatus a-amylase is inhibited by a-AI2 but not by a-AI1. In contrast, porcine a-amylase is inhibited by a-AI1 but not by a-AI2. The objective of this work was to understand the molecular basis of the specificity of two inhibitors in P. vulgaris (a-AI1 and a-AI2) in relation to a-amylases. Mutants of a-AI2 were made and expressed in tobacco plants. The results showed that all the a-AI2 mutant inhibitors lost their activity against the insect a-amylases but none exhibited activity toward the mammalian a-amylase. The replacement of His33 of a-AI2 with the a-AI1-like sequence Ser-Tyr-Asn abolished inhibition of Z. subfasciatus a-amylase. From structural modeling, the conclusion is that the size and complexity of the amylase-inhibitor interface explain why mutation of the N-terminal loop and resultant abolition of Z. subfasciatus a-amylase inhibition are not accompanied by gain of inhibitory activity against porcine a-amylase.

Characterization of HbpR binding by site-directed mutagenesis of its DNA-binding site and by deletion of the effector domain.

In the presence of 2-hydroxybiphenyl, the enhancer binding protein, HbpR, activates the sigma54-dependent P(hbpC) promoter and controls the initial steps of 2-hydroxybiphenyl degradation in Pseudomonas azelaica. In the activation process, an oligomeric HbpR complex of unknown subunit composition binds to an operator region containing two imperfect palindromic sequences. Here, the HbpR-DNA binding interactions were investigated by site-directed mutagenesis of the operator region and by DNA-binding assays using purified HbpR. Mutations that disrupted the twofold symmetry in the palindromes did not affect the binding affinity of HbpR, but various mutations along a 60 bp region, and also outside the direct palindromic sequences, decreased the binding affinity. Footprints of HbpR on mutant operator fragments showed that a partial loss of binding contacts occurs, suggesting that the binding of one HbpR 'protomer' in the oligomeric complex is impaired whilst leaving the other contacts intact. An HbpR variant, devoid of its N-terminal sensing A-domain, was unable to activate transcription from the hbpC promoter while maintaining protection of the operator DNA in footprints. Wild-type HbpR was unable to activate transcription from the hbpC promoter when delta A-HbpR was expressed in the same cell, suggesting the formation of (repressing) hetero-oligomers. This model implies that HbpR can self-associate on its operator DNA without effector recognition or ATP binding. Furthermore, our findings suggest that the N-terminal sensing domain of HbpR is needed to activate the central ATPase domain rather than to repress a constitutively active C domain, as is the case for the related regulatory protein XylR.

Sublimation of new matrix candidates for high spatial resolution imaging mass spectrometry of lipids: enhanced information in both positive and negative polarities after 1,5-diaminonapthalene deposition.

Matrix sublimation has demonstrated to be a powerful approach for high-resolution matrix-assisted laser desorption ionization (MALDI) imaging of lipids, providing very homogeneous solvent-free deposition. This work presents a comprehensive study aiming to evaluate current and novel matrix candidates for high spatial resolution MALDI imaging mass spectrometry of lipids from tissue section after deposition by sublimation. For this purpose, 12 matrices including 2,5-dihydroxybenzoic acid (DHB), sinapinic acid (SA), α-cyano-4-hydroxycinnamic acid (CHCA), 2,6-dihydroxyacetphenone (DHA), 2',4',6'-trihydroxyacetophenone (THAP), 3-hydroxypicolinic acid (3-HPA), 1,8-bis(dimethylamino)naphthalene (DMAN), 1,8,9-anthracentriol (DIT), 1,5-diaminonapthalene (DAN), p-nitroaniline (NIT), 9-aminoacridine (9-AA), and 2-mercaptobenzothiazole (MBT) were investigated for lipid detection efficiency in both positive and negative ionization modes, matrix interferences, and stability under vacuum. For the most relevant matrices, ion maps of the different lipid species were obtained from tissue sections at high spatial resolution and the detected peaks were characterized by matrix-assisted laser desorption ionization time-of-flight/time-of-flight (MALDI-TOF/TOF) mass spectrometry. First proposed for imaging mass spectrometry (IMS) after sublimation, DAN has demonstrated to be of high efficiency providing rich lipid signatures in both positive and negative polarities with high vacuum stability and sub-20 μm resolution capacity. Ion images from adult mouse brain were generated with a 10 μm scanning resolution. Furthermore, ion images from adult mouse brain and whole-body fish tissue sections were also acquired in both polarity modes from the same tissue section at 100 μm spatial resolution. Sublimation of DAN represents an interesting approach to improve information with respect to currently employed matrices providing a deeper analysis of the lipidome by IMS.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Families of periodic orbits for the spatial isosceles 3-body problem

We study the families of periodic orbits of the spatial isosceles 3-body problem (for small enough values of the mass lying on the symmetry axis) coming via the analytic continuation method from periodic orbits of the circular Sitnikov problem. Using the first integral of the angular momentum, we reduce the dimension of the phase space of the problem by two units. Since periodic orbits of the reduced isosceles problem generate invariant two-dimensional tori of the nonreduced problem, the analytic continuation of periodic orbits of the (reduced) circular Sitnikov problem at this level becomes the continuation of invariant two-dimensional tori from the circular Sitnikov problem to the nonreduced isosceles problem, each one filled with periodic or quasi-periodic orbits. These tori are not KAM tori but just isotropic, since we are dealing with a three-degrees-of-freedom system. The continuation of periodic orbits is done in two different ways, the first going directly from the reduced circular Sitnikov problem to the reduced isosceles problem, and the second one using two steps: first we continue the periodic orbits from the reduced circular Sitnikov problem to the reduced elliptic Sitnikov problem, and then we continue those periodic orbits of the reduced elliptic Sitnikov problem to the reduced isosceles problem. The continuation in one or two steps produces different results. This work is merely analytic and uses the variational equations in order to apply Poincar´e’s continuation method.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.