High-fat diet, microbiome-gut-brain axis signaling, and anxiety-like behavior in male rats.

Obesity, associated with the intake of a high-fat diet (HFD), and anxiety are common among those living in modern urban societies. Recent studies suggest a role of microbiome-gut-brain axis signaling, including a role for brain serotonergic systems in the relationship between HFD and anxiety. Evidence suggests the gut microbiome and the serotonergic brain system together may play an important role in this response. Here we conducted a nine-week HFD protocol in male rats, followed by an analysis of the gut microbiome diversity and community composition, brainstem serotonergic gene expression (tph2, htr1a, and slc6a4), and anxiety-related defensive behavioral responses. We show that HFD intake decreased alpha diversity and altered the community composition of the gut microbiome in association with obesity, increased brainstem tph2, htr1a and slc6a4 mRNA expression, including in the caudal part of the dorsomedial dorsal raphe nucleus (cDRD), a subregion previously associated with stress- and anxiety-related behavioral responses, and, finally, increased anxiety-related defensive behavioral responses. The HFD increased the Firmicutes/Bacteroidetes ratio relative to control diet, as well as higher relative abundances of Blautia, and decreases in Prevotella. We found that tph2, htr1a and slc6a4 mRNA expression were increased in subregions of the dorsal raphe nucleus in the HFD, relative to control diet. Specific bacterial taxa were associated with increased serotonergic gene expression in the cDRD. Thus, we propose that HFD-induced obesity is associated with altered microbiome-gut-serotonergic brain axis signaling, leading to increased anxiety-related defensive behavioral responses in rats.

Selected cachaça yeast strains share a genomic profile related to traits relevant to industrial fermentation processes.

The isolation and selection of yeast strains to improve the quality of the cachaça-Brazilian Spirit-have been studied in our research group. Our strategy considers Saccharomyces cerevisiae as the predominant species involved in sugarcane juice fermentation and the presence of different stressors (osmolarity, temperature, ethanol content, and competition with other microorganisms). It also considers producing balanced concentrations of volatile compounds (higher alcohols and acetate and/or ethyl esters), flocculation capacity, and ethanol production. Since the genetic bases behind these traits of interest are not fully established, the whole genome sequencing of 11 different Saccharomyces cerevisiae strains isolated and selected from different places was analyzed to identify the presence of a specific genetic variation common to cachaça yeast strains. We have identified 20,128 single-nucleotide variants shared by all genomes. Of these shared variants, 37 were new variants (being six missenses), and 4,451 were identified as missenses. We performed a detailed functional annotation (using enrichment analysis, protein-protein interaction network analysis, and database and in-depth literature searches) of these new and missense variants. Many genes carrying these variations were involved in the phenotypes of flocculation, tolerance to fermentative stresses, and production of volatile compounds and ethanol. These results demonstrate the existence of a genetic profile shared by the 11 strains under study that could be associated with the applied selective strategy. Thus, this study points out genes and variants that may be used as molecular markers for selecting strains well suited to the fermentation process, including genetic improvement by genome editing, ultimately producing high-quality beverages and adding value.IMPORTANCEThis work demonstrates the existence of new genetic markers related to different phenotypes used to select yeast strains and mutations in genes directly involved in producing flavoring compounds and ethanol, and others related to flocculation and stress resistance.

Genetic diversity and relatedness in captive collared peccaries Dicotyles tajacu (Linnaeus, 1758) (Cetartiodactyla: Tayassuidae) estimated by microsatellite genotyping using high-throughput sequencing: Implications for their conservation and reintroduction.

The global population of Dicotyles tajacu (Linnaeus, 1758) (Cetartiodactyla: Tayassuidae), commonly known as the collared peccary and distributed in the Neotropics, is currently in decline due to anthropogenic pressures. In this study, five microsatellite loci were used to genetically characterize a group of 20 captive-born collared peccaries intended for reintroduction. This study aimed to evaluate the genetic diversity and relatedness of captive individuals using microsatellite markers. The genetic data generated were used to evaluate the viability of the reintroduction and to propose measures for the management and conservation of this species. In this study, we found relatively high genetic diversity indices, indicating that the group was genetically diverse. Inbreeding coefficients with negative values were observed, indicating an excess of alleles in heterozygosis and an absence of inbreeding. One locus showed deviation from Hardy-Weinberg equilibrium, which may have been caused by the mixing of individuals from different origins. Relatedness analysis indicated that some individuals were highly related, with coefficients indicating they may be first-degree relatives. Our findings indicate that the studied group has enough genetic diversity to be released into nature, but the high individual relatedness found would require the adoption of strategies after the release of animals in the wild to ensure their persistence.

Neuroserpin: A potential biomarker for early-onset severe preeclampsia.

Preeclampsia is a hypertensive disease of pregnancy associated with intense inflammatory and pro-coagulant responses. Neuroserpin is a serine protease inhibitor that has been involved in neurological and immune processes and has not yet been investigated in preeclampsia. Herein, we evaluated neuroserpin levels in association with other inflammatory mediators (IL-17A, IL-33, and CXCL-16) during severe preeclampsia. The mediators' plasma levels were measured by immunoassays in 24 pregnant women with severe preeclampsia (early preeclampsia: N = 17, late preeclampsia: N = 7), 34 normotensive pregnant women, and 32 non-pregnant women. In general, pregnancy was associated with higher levels of neuroserpin, IL-17A, IL-33, and CXCL-16 than the non-pregnant state. However, this increase was attenuated in pregnancies complicated by severe preeclampsia. Although neuroserpin levels did not differ between normotensive pregnant women and pregnant women with severe preeclampsia, neuroserpin levels tended to be lower in early-onset than in late-onset severe preeclampsia. There were positive correlations between neuroserpin and IL-17A, neuroserpin and CXCL-16, and IL-17A and CXCL-16 levels in women with severe preeclampsia. In addition, although the risk for developing severe preeclampsia was higher in older women in this study, maternal age did not significantly influence the mediators' levels, nor their correlations in the preeclampsia group. In summary, our data suggest that neuroserpin might be a potential biomarker for early-onset severe preeclampsia and, that the imbalance among neuroserpin, IL-17A, IL-33, and CXCL-16 levels may be associated with the pathogenesis of preeclampsia, regardless of the maternal age.

CapsProm: a capsule network for promoter prediction.

Locating the promoter region in DNA sequences is of paramount importance in bioinformatics. This problem has been widely studied in the literature, but it has not yet been fully resolved. Some researchers have shown remarkable results using convolutional networks that allowed the automatic extraction of features from a DNA chain. However, a single architecture schema that could learn the promoter prediction task competitively for several organisms has not yet been achieved. Thus, researchers must seek new architectures by hand-designing or by Neural Architecture Search for each new evaluated organism dataset. This work proposes a versatile architecture based on a capsule network that can accurately identify promoter sequences in raw DNA data from five different organisms, eukaryotic and prokaryotic. Our architecture, the CapsProm, could help create models with minimum effort to learn the promoter identification task between different datasets. Furthermore, the CapsProm showed competitive results, overcoming the baseline method in five out of seven tested datasets (F1-score). The models and source code are made available at https://github.com/lauromoraes/CapsNet-promoter.

A deep descriptor for cross-tasking EEG-based recognition.

Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.

The effectiveness of surfactants applied with essential oil of Lippia alba in the anesthesia of Nile tilapia (Oreochromis niloticus) and their toxicity assessment for fish and mammals.

The Lippia alba essential oil (EO) is a fish anesthetic immiscible in water and commonly used diluted in ethanol. We evaluated the effectiveness of surfactant use with Lippia alba EO in the anesthesia of Oreochromis niloticus, as well as its toxicity in fish and mammals. The EO was extracted by hydrodistillation and the fish were exposed to anesthesia at the concentration of 250 µL/L for 10 min with the surfactants polysorbate 20 (T20), polysorbate 80 (T80), polyethylene glycol (PEG), and ethanol. We also evaluated fish recovery and anesthetic safety margin after exposure for 10, 20, and 30 min. To assess the surfactants' toxicity in mammals, Mus musculus (mice) received the same treatments by gavage. The main constituents of the Lippia alba EO were linalool (42.36%), geraniol (12.46%), neral (10.7%), and limonene (7.45%). Deeper anesthesia was faster in the T20 (60 ± 2.9 s) and T80 (272 ± 21 s) treatment groups, while recovery time for T80 was longer (596 ± 47 s). All treatments showed a good safety margin, without mortality. The genotoxic effects caused by surfactants in mammals and fish were at similar levels to those found in the ethanol treatment. Therefore, this study demonstrated that the use of surfactants T20 and T80 in Oreochromis niloticus anesthesia presented neither a reduction nor a considerable increase of the toxicity when compared to the commonly used ethanol; however, an increase in anesthetic effectiveness was observed throughout the experiment.

Serratia liquefaciens FG3 isolated from a metallophyte plant sheds light on the evolution and mechanisms of adaptive traits in extreme environments.

Serratia liquefaciens strain FG3 (SlFG3), isolated from the flower of Stachytarpheta glabra in the Brazilian ferruginous fields, has distinctive genomic, adaptive, and biotechnological potential. Herein, using a combination of genomics and molecular approaches, we unlocked the evolution of the adaptive traits acquired by S1FG3, which exhibits the second largest chromosome containing the largest conjugative plasmids described for Serratia. Comparative analysis revealed the presence of 18 genomic islands and 311 unique protein families involved in distinct adaptive features. S1FG3 has a diversified repertoire of genes associated with Nonribosomal peptides (NRPs/PKS), a complete and functional cluster related to cellulose synthesis, and an extensive and functional repertoire of oxidative metabolism genes. In addition, S1FG3 possesses a complete pathway related to protocatecuate and chloroaromatic degradation, and a complete repertoire of genes related to DNA repair and protection that includes mechanisms related to UV light tolerance, redox process resistance, and a laterally acquired capacity to protect DNA using phosphorothioation. These findings summarize that SlFG3 is well-adapted to different biotic and abiotic stress situations imposed by extreme conditions associated with ferruginous fields, unlocking the impact of the lateral gene transfer to adjust the genome for extreme environments, and providing insight into the evolution of prokaryotes.

ChimericalDataset Creation Protocol Based on Doddington Zoo: A Biometric Application with Face, Eye, and ECG.

Multimodal systems are a workaround to enhance the robustness and effectiveness of biometric systems. A proper multimodal dataset is of the utmost importance to build such systems. The literature presents some multimodal datasets, although, to the best of our knowledge, there are no previous studies combining face, iris/eye, and vital signals such as the Electrocardiogram (ECG). Moreover, there is no methodology to guide the construction and evaluation of a chimeric dataset. Taking that fact into account, we propose to create a chimeric dataset from three modalities in this work: ECG, eye, and face. Based on the Doddington Zoo criteria, we also propose a generic and systematic protocol imposing constraints for the creation of homogeneous chimeric individuals, which allow us to perform a fair and reproducible benchmark. Moreover, we have proposed a multimodal approach for these modalities based on state-of-the-art deep representations built by convolutional neural networks. We conduct the experiments in the open-world verification mode and on two different scenarios (intra-session and inter-session), using three modalities from two datasets: CYBHi (ECG) and FRGC (eye and face). Our multimodal approach achieves impressive decidability of 7.20 ± 0.18, yielding an almost perfect verification system (i.e., Equal Error Rate (EER) of 0.20% ± 0.06) on the intra-session scenario with unknown data. On the inter-session scenario, we achieve a decidability of 7.78 ± 0.78 and an EER of 0.06% ± 0.06. In summary, these figures represent a gain of over 28% in decidability and a reduction over 11% of the EER on the intra-session scenario for unknown data compared to the best-known unimodal approach. Besides, we achieve an improvement greater than 22% in decidability and an EER reduction over 6% in the inter-session scenario.

TabPath: interactive tables for metabolic pathway analysis.

Motivation: Information about metabolic pathways in a comparative context is one of the most powerful tool to help the understanding of genome-based differences in phenotypes among organisms. Although several platforms exist that provide a wealth of information on metabolic pathways of diverse organisms, the comparison among organisms using metabolic pathways is still a difficult task. Results: We present TabPath (Tables for Metabolic Pathway), a web-based tool to facilitate comparison of metabolic pathways in genomes based on KEGG. From a selection of pathways and genomes of interest on the menu, TabPath generates user-friendly tables that facilitate analysis of variations in metabolism among the selected organisms. Availability and implementation: TabPath is available at http://200.239.132.160:8686. Contact: lmmorei@gmail.com.

DNMT3A Mutations in Patients with Acute Myeloid Leukemia in South Brazil.

Acute myeloid leukemia (AML) is a complex and heterogeneous hematopoietic tissue neoplasm. Several molecular markers have been described that help to classify AML patients into risk groups. DNA methyltransferase 3A (DNMT3A) gene mutations have been recently identified in about 22% of AML patients and associated with poor prognosis as an independent risk factor. Our aims were to determine the frequency of somatic mutations in the gene DNMT3A and major chromosomal translocations in a sample of patients with AML. We investigated in 82 samples of bone marrow from patients with AML for somatic mutations in DNMT3A gene by sequencing and sought major fusion transcripts by RT-PCR. We found mutations in the DNMT3A gene in 5 patients (6%); 3 were type R882H [corrected]. We found fusion transcripts in 19 patients, namely, AML1/ETO (n = 5; 6.1%), PML/RARα (n = 12; 14.6%), MLL/AF9 (0; 0%), and CBFß/MYH11 (n = 2; 2.4%). The identification of recurrent mutations in the DNMT3A gene and their possible prognostic implications can be a valuable tool for making treatment decisions. This is the first study on the presence of somatic mutations of the DNMT3A gene in patients with AML in Brazil. The frequency of these mutations suggests a possible ethnogeographic variation.