Molecular targeting of proteins by homocysteine: implications in familial and clinical hypercholesterolemia

Cardiovascular diseases (CVDs) are one of the leading causes of death and disability worldwide and one of its underlying causes is hypercholesterolemia. Hypercholesterolemia can have genetic (familial hypercholesterolemia, FH) and non-genetic causes (clinical hypercholesterolemia, CH), the first much more severe, with occurrence of premature atherosclerosis. While the pathophysiological role of homocysteine (Hcy) on CVD is still controversial, molecular targeting of protein by S and N-homocysteinylation offers a new paradigm to be considered in the vascular pathogenesis of hypercholesterolemia. On this regard, the present study aims to give new insights on protein targeting by Hcy in both CH and FH conditions. A total of 187 subjects were included: 65 normolipidemic and 122 hypercholesterolemic. Total (tHcy) and free (fHcy) fractions were quantified in serum samples after validation of an HPLCFD method, to assess S-homocysteinylation. Also, the lactonase (LACase) activity of paraoxonase-1 (PON1) was quantified by a colorimetric assay, as a surrogate of N-homocysteinylation. tHcy does not differ among groups. Nevertheless, fHcy declines in the hypercholesterolemic groups, with more evidence to the FH population. Consequently, there seems to be an increase of Shomocysteinylation, regardless of lipid lowering therapy (LLT). Also, despite of LLT use, LACase activity is lower in FH, thus the risk for protein N-homocysteinylation seems to be higher. Moreover, the decrease in LACase/ApoA1 and LACase/HDL ratios in FH, shows that HDL is dysfunctional in this population, despite its normal concentration values. Data supports that the pathophysiological role of Hcy on hypercholesterolemia may reside in its ability to post-translationally modify proteins. This role is particularly evident in FH condition. In the future, it will be interesting to identify which target proteins are modified and thus involved in vascular pathology progression.

Friedreich's ataxia: clinical and molecular study of 25 Brazilian cases

INTRODUCTION: Friedreich's ataxia is a neurodegenerative disorder whose clinical diagnostic criteria for typical cases basically include: a) early age of onset (< 20 or 25 years), b) autosomal recessive inheritance, c) progressive ataxia of limbs and gait, and d) absence of lower limb tendon reflexes. METHODS: We studied the frequency and the size of expanded GAA and their influence on neurologic findings, age at onset, and disease progression in 25 Brazilian patients with clinical diagnosis of Friedreich's ataxia - 19 typical and 6 atypical - using a long-range PCR test. RESULTS: Abnormalities in cerebellar signs, in electrocardiography, and pes cavus occurred more frequently in typical cases; however, plantar response and speech were more frequently normal in this group when the both typical and atypical cases were compared. Homozygous GAA expansion repeats were detected in 17 cases (68%) - all typical cases. In 8 patients (32%) (6 atypical and 2 typical), no expansion was observed, ruling out the diagnosis of Friedreich's ataxia. In cases with GAA expansions, foot deformity, cardiac abnormalities, and some neurologic findings occurred more frequently; however, abnormalities in cranial nerves and in tomographic findings were detected less frequently than in patients without GAA expansions. DISCUSSION: Molecular analysis was imperative for the diagnosis of Friedreich's ataxia, not only for typical cases but also for atypical ones. There was no genotype-phenotype correlation. Diagnosis based only on clinical findings is limited; however, it aids in better screening for suspected cases that should be tested. Evaluation for vitamin E deficiency is recommended, especially in cases without GAA expansion.

Molecular tools in the diagnostic and epidemiology of infections caused by members of Mycobacterium avium Complex

Mycobacterium avium Complex (MAC) comprises microorganisms that affect a wide range of animals including humans. The most relevant are Mycobacterium avium subspecies hominissuis (Mah) with a high impact on public health affecting mainly immunocompromised individuals and Mycobacterium avium subspecies paratuberculosis (Map) causing paratuberculosis in animals with a high economic impact worldwide. In this work, we characterized 28 human and 67 porcine Mah isolates and evaluated the relationship among them by Multiple-Locus Variable number tandem repeat Analysis (MLVA). We concluded that Mah population presented a high genetic diversity and no correlations were inferred based on geographical origin, host or biological sample. For the first time in Portugal Map strains, from asymptomatic bovine faecal samples were isolated highlighting the need of more reliable and rapid diagnostic methods for Map direct detection. Therefore, we developed an IS900 nested real time PCR with high sensitivity and specificity associated with optimized DNA extraction methodologies for faecal and milk samples. We detected 83% of 155 faecal samples from goats, cattle and sheep, and 26% of 98 milk samples from cattle, positive for Map IS900 nested real time PCR. A novel SNPs (single nucleotide polymorphisms) assay to Map characterization based on a Whole Genome Sequencing analysis was developed to elucidate the genetic relationship between strains. Based on sequential detection of 14 SNPs and on a decision tree we were able to differentiate 14 phylogenetic groups with a higher discriminatory power compared to other typing methods. A pigmented Map strain was isolated and characterized evidencing for the first time to our knowledge the existence of pigmented Type C strains. With this work, we intended to improve the ante mortem direct molecular detection of Map, to conscientiously aware for the existence of Map animal infections widespread in Portugal and to contribute to the improvement of Map and Mah epidemiological studies.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

Aging and Alzheimer's disease: Searching for novel molecular cues

Tese de Doutoramento em Ciências da Saúde

The effects of solvent composition on the affinity of a peptide towards hair keratin: experimental and molecular dynamics data

The study of the interaction between hair filaments and formulations or peptides is of utmost importance in fields like cosmetic research. Keratin intermediate filaments structure is not fully described, limiting the molecular dynamics (MD) studies in this field although its high potential to improve the area. We developed a computational model of a truncated protofibril, simulated its behavior in alcoholic based formulations and with one peptide. The simulations showed a strong interaction between the benzyl alcohol molecules of the formulations and the model, leading to the disorganization of the keratin chains, which regress with the removal of the alcohol molecules. This behavior can explain the increase of peptide uptake in hair shafts evidenced in fluorescence microscopy pictures. The model developed is valid to computationally reproduce the interaction between hair and alcoholic formulations and provide a robust base for new MD studies about hair properties. It is shown that the MD simulations can improve hair cosmetic research, improving the uptake of a compound of interest.

On line pre-concentration for simultaneous determination of low molecular weight organic acids and inorganic anions in Amazonian river water samples employing ion chromatography with conductivity detection

An ion chromatography procedure, employing an IonPac AC15 concentrator column was used to investigate on line preconcentration for the simultaneous determination of inorganic anions and organic acids in river water. Twelve organic acids and nine inorganic anions were separated without any interference from other compounds and carry-over problems between samples. The injection loop was replaced by a Dionex AC15 concentrator column. The proposed procedure employed an auto-sampler that injected 1.5 ml of sample into a KOH mobile phase, generated by an Eluent Generator, at 1.5 mL min-1, which carried the sample to the chromatographic columns (one guard column, model AG-15, and one analytical column, model AS15, with 250 x 4mm i.d.). The gradient elution concentrations consisted of a 10.0 mmol l-1 KOH solution from 0 to 6.5 min, gradually increased to 45.0 mmol l-1 KOH at 21 min., and immediatelly returned and maintained at the initial concentrations until 24 min. of total run. The compounds were eluted and transported to an electro-conductivity detection cell that was attached to an electrochemical detector. The advantage of using concentrator column was the capability of performing routine simultaneous determinations for ions from 0.01 to 1.0 mg l-1 organic acids (acetate, propionic acid, formic acid, butyric acid, glycolic acid, pyruvate, tartaric acid, phthalic acid, methanesulfonic acid, valeric acid, maleic acid, oxalic acid, chlorate and citric acid) and 0.01 to 5.0 mg l-1 inorganic anions (fluoride, chloride, nitrite, nitrate, bromide, sulfate and phosphate), without extensive sample pretreatment and with an analysis time of only 24 minutes.

A Gß protein and the TupA Co-Regulator bind to protein kinase A Tpk2 to act as antagonistic molecular switches of fungal morphological changes

A Gß protein and the TupA Co-Regulator Bind to Protein Kinase A Tpk2 to Act as Antagonistic Molecular Switches of Fungal Morphological Changes

To be or not to be a pseudogene: a molecular epidemiological approach to the mclx genes and its impact in tuberculosis

Tuberculosis presents a myriad of symptoms, progression routes and propagation patterns not yet fully understood. Whereas for a long time research has focused solely on the patient immunity and overall susceptibility, it is nowadays widely accepted that the genetic diversity of its causative agent, Mycobacterium tuberculosis, plays a key role in this dynamic. This study focuses on a particular family of genes, the mclxs (Mycobacterium cyclase/LuxR-like genes), which codify for a particular and nearly mycobacterial-exclusive combination of protein domains. mclxs genes were found to be pseudogenized by frameshift-causing insertion(s)/deletion(s) in a considerable number of M. tuberculosis complex strains and clinical isolates. To discern the functional implications of the pseudogenization, we have analysed the pattern of frameshift-causing mutations in a group of M. tuberculosis isolates while taking into account their microbial-, patient- and disease-related traits. Our logistic regression-based analyses have revealed disparate effects associated with the transcriptional inactivation of two mclx genes. In fact, mclx2 (Rv1358) pseudogenization appears to be primarily driven by the microbial phylogenetic background, being mainly related to the Euro-American (EAm) lineage; on the other hand, mclx3 (Rv2488c) presents a higher tendency for pseudogenization among isolates from patients born on the Western Pacific area, and from isolates causing extra-pulmonary infections. These results contribute to the overall knowledge on the biology of M. tuberculosis infection, whereas at the same time launch the necessary basis for the functional assessment of these so far overlooked genes.

Widespread environmental contamination with Mycobacterium tuberculosis complex revealed by a molecular detection protocol

Environmental contamination with Mycobacterium tuberculosis complex (MTC) has been considered crucial for bovine tuberculosis persistence in multi-host-pathogen systems. However, MTC contamination has been difficult to detect due to methodological issues. In an attempt to overcome this limitation we developed an improved protocol for the detection of MTC DNA. MTC DNA concentration was estimated by the Most Probable Number (MPN) method. Making use of this protocol we showed that MTC contamination is widespread in different types of environmental samples from the Iberian Peninsula, which supports indirect transmission as a contributing mechanism for the maintenance of bovine tuberculosis in this multi-host-pathogen system. The proportion of MTC DNA positive samples was higher in the bovine tuberculosis-infected than in presumed negative area (0.32 and 0.18, respectively). Detection varied with the type of environmental sample and was more frequent in sediment from dams and less frequent in water also from dams (0.22 and 0.05, respectively). The proportion of MTC-positive samples was significantly higher in spring (p<0.001), but MTC DNA concentration per sample was higher in autumn and lower in summer. The average MTC DNA concentration in positive samples was 0.82 MPN/g (CI95 0.70-0.98 MPN/g). We were further able to amplify a DNA sequence specific of Mycobacterium bovis/caprae in 4 environmental samples from the bTB-infected area.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.