Emerging and Traditional Organic Markers in Areas with Multiple Anthropogenic Activities: Development of an Analytical Protocol and Its Application in Environmental Assessment Studies.

This work describes the development of an analytical protocol combining cleanup by liquid-solid extraction and GC-MS for the determination of emerging and traditional multi-molecular markers. The procedure was used for the environmental assessment of a coastal region with multiple human activities. Global recovery rates ranged from 45.49% to 119.4% for the 46 substances analyzed: pesticides (73.7%-97.7%), PAHs (52.5%-93.7%), sterols (66.7%-119.4%) and natural and synthetic hormones (45.5%-119.1%) and the rates were compared to those reported in studies on both individual classes and multi-classes of contaminants. The analytical protocol demonstrated satisfactory efficiency and could be used successfully in environmental assessments and source assignment studies. The environmental assessment study revealed that the Acaraú River in northeastern Brazil is influenced by the combination of urban and rural activities. The sources of PAHs are vehicular traffic and the burning of biomass; pesticides stem from pest control in agribusiness and public health campaigns; sterols and hormones stem from a combination of natural inputs, human sewage (treated and raw) and animal husbandry activities.

Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler.

A primary obstacle in translating genetic associations with disease into therapeutic strategies is elucidating the cellular programs affected by genetic risk variants and effector genes. Here, we introduce LipocyteProfiler, a cardiometabolic-disease-oriented high-content image-based profiling tool that enables evaluation of thousands of morphological and cellular profiles that can be systematically linked to genes and genetic variants relevant to cardiometabolic disease. We show that LipocyteProfiler allows surveillance of diverse cellular programs by generating rich context- and process-specific cellular profiles across hepatocyte and adipocyte cell-state transitions. We use LipocyteProfiler to identify known and novel cellular mechanisms altered by polygenic risk of metabolic disease, including insulin resistance, fat distribution, and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse deep phenotypic profiling in lipocytes and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

Molecular Dynamics Analysis of Fast-Spreading Severe Acute Respiratory Syndrome Coronavirus 2 Variants and Their Effects on the Interaction with Human Angiotensin-Converting Enzyme 2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is evolving with mutations in the spike protein, especially in the receptor-binding domain (RBD). The failure of public health measures in some countries to contain the spread of the disease has given rise to novel viral variants with increased transmissibility. However, key questions about how quickly the variants can spread remain unclear. Herein, we performed a structural investigation using molecular dynamics simulations and determined dissociation constant (K D) values using surface plasmon resonance assays of three fast-spreading SARS-CoV-2 variants, alpha, beta, and gamma, as well as genetic factors in host cells that may be related to the viral infection. Our results suggest that the SARS-CoV-2 variants facilitate their entry into the host cell by moderately increased binding affinities to the human ACE2 receptor, different torsions in hACE2 mediated by RBD variants, and an increased spike exposure time to proteolytic enzymes. We also found that other host cell aspects, such as gene and isoform expression of key genes for the infection (ACE2, FURIN, and TMPRSS2), may have few contributions to the SARS-CoV-2 variant infectivity. In summary, we concluded that a combination of viral and host cell factors allows SARS-CoV-2 variants to increase their abilities to spread faster than the wild type.

Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach.

Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks.

Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis.

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients' prognosis. Similarly, Reboot was able to find a seven-splicing isoforms signature related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive and straightforward way of finding genes or splicing isoforms signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated cancer-related genes and splicing isoforms.

Characterization of Cu2O/TiO2NTs nanomaterials using EDXRF, XRD and DRS for photocatalytic applications.

TiO2 nanotube arrays (TiO2NTs) were decorated with Cu2O nanoparticles by pulse electrochemical deposition. The Cu2O nanoparticles were uniformly distributed onto TiO2NTs surface and with diameter varying between 50 and 200 nm. The effects of the independent variables on the photocatalytic response were simultaneously assessed by a multivariate statistical design. Energy-Dispersive X-Ray Fluorescence, X-Ray Diffraction, FEG-SEM and Diffuse Reflectance Spectroscopy were employed to Cu2O/TiO2NTs characterization. The Cu2O films obtained on TiO2NTs surface were crystalline. DRS analysis confirmed that the modification of TiO2NTs with Cu2O nanoparticles improved the quantum efficiency of the material. The Cu2O/TiO2NTs photoelectrode shows an intense absorption peak in the visible region decreasing the band gap energy from 3.20eV for TiO2NTs to 2.96 eV for Cu2O/TiO2NTs.

Quick analysis of organic matter in soil by energy-dispersive X-ray fluorescence and multivariate analysis.

The rapid, simple and accurate determination of soil quality indicators is fundamental for improvements in precision agriculture and consequently in production efficiency. The objectives of this study were to determine the organic matter (OM) and total organic carbon (TOC) concentrations in agricultural soil and to discriminate soil provenance by energy-dispersive X-ray fluorescence (EDXRF) combined with principal component analysis and partial least square regression. The conventional methods used for the determination of OM and TOC concentrations are the gravimetric and Walkley-Black methods, respectively. Figures of merit such as sensitivity, detection and quantification limits, accuracy and precision were evaluated. Samples were differentiated by their provenance, and the quality of the prediction model shows that EDXRF combined with multivariate analysis is a promising methodology to fulfil the lack of rapid and accurate analytical methods for the assessment of OM and TOC concentrations in agricultural soils.

MiRIAD update: using alternative polyadenylation, protein interaction network analysis and additional species to enhance exploration of the role of intragenic miRNAs and their host genes.

Database URL: http://www.miriad-database.org.