Identification of plasmids from Brazilian Chromobacterium violaceum strains.

Chromobacterium violaceum is an opportunistic pathogen found in tropical and subtropical regions worldwide. Chromobacterium violaceum infections are difficult to treat, and many strains are resistant to antibiotics. Recently, a novel plasmid (pChV1) was discovered in the type strain ATCC 12472, suggesting that other C. violaceum strains may harbor extra-chromosomal DNA. The aim of the present study was to detect and compare new plasmids in Brazilian strains of C. violaceum using next-generation sequencing techniques. We obtained draft genomes of six plasmids from strains isolated from the Amazon region and aligned them with pChV1. At least three plasmids, CVAC05, CVACO2, and CVT8, were similar to pChV1. Phylogenetic analysis suggested that these new extra-chromosomal DNA sequences have a common origin with pChV1 but have diverged. Many of the ORFs detected were related to plasmid segregation/maintenance, viral structural proteins, and proteins with unknown functions. These findings may enable better genetic manipulation of C. violaceum, which will enhance our ability to exploit this valuable microorganism in industrial and clinical applications.

Cyclic alternation of quiet and active sleep states in the octopus.

Previous observations suggest the existence of 'Active sleep' in cephalopods. To investigate in detail the behavioral structure of cephalopod sleep, we video-recorded four adult specimens of Octopus insularis and quantified their distinct states and transitions. Changes in skin color and texture and movements of eyes and mantle were assessed using automated image processing tools, and arousal threshold was measured using sensory stimulation. Two distinct states unresponsive to stimulation occurred in tandem. The first was a 'Quiet sleep' state with uniformly pale skin, closed pupils, and long episode durations (median 415.2 s). The second was an 'Active sleep' state with dynamic skin patterns of color and texture, rapid eye movements, and short episode durations (median 40.8 s). 'Active sleep' was periodic (60% of recurrences between 26 and 39 min) and occurred mostly after 'Quiet sleep' (82% of transitions). These results suggest that cephalopods have an ultradian sleep cycle analogous to that of amniotes.

A small interfering RNA (siRNA) database for SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) rapidly transformed into a global pandemic, for which a demand for developing antivirals capable of targeting the SARS-CoV-2 RNA genome and blocking the activity of its genes has emerged. In this work, we presented a database of SARS-CoV-2 targets for small interference RNA (siRNA) based approaches, aiming to speed the design process by providing a broad set of possible targets and siRNA sequences. The siRNAs sequences are characterized and evaluated by more than 170 features, including thermodynamic information, base context, target genes and alignment information of sequences against the human genome, and diverse SARS-CoV-2 strains, to assess possible bindings to off-target sequences. This dataset is available as a set of four tables, available in a spreadsheet and CSV (Comma-Separated Values) formats, each one corresponding to sequences of 18, 19, 20, and 21 nucleotides length, aiming to meet the diversity of technology and expertise among laboratories around the world. A metadata table (Supplementary Table S1), which describes each feature, is also provided in the aforementioned formats. We hope that this database helps to speed up the development of new target antivirals for SARS-CoV-2, contributing to a possible strategy for a faster and effective response to the COVID-19 pandemic.

A decision tree to improve identification of pathogenic mutations in clinical practice.

BACKGROUND: A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic. METHODS: In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machine-learning (ML) algorithms. RESULTS: The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for False Neutrals, 9% for False Pathogenic, and 92% for True Pathogenic. CONCLUSIONS: The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms. Besides, the decision tree algorithm is easier to apply in clinical practice by non-IT experts. From the cancer research community perspective, this approach can be successfully applied as an alternative for the determination of potential pathogenicity of VOUS.