Incidence of Hereditary Gastric Cancer May Be Much Higher than Reported.

Hereditary gastric cancers (HGCs) are supposed to be rare and difficult to identify. Nonetheless, many cases of young patients with gastric cancer (GC) fulfill the clinical criteria for considering this diagnosis but do not present the defined pathogenic mutations necessary to meet a formal diagnosis of HGC. Moreover, GC in young people is a challenging medical situation due to the usual aggressiveness of such cases and the potential risk for their relatives when related to a germline variant. Aiming to identify additional germline alterations that might contribute to the early onset of GC, a complete exome sequence of blood samples from 95 GC patients under 50 and 94 blood samples from non-cancer patients was performed and compared in this study. The number of identified germline mutations in GC patients was found to be much higher than that from individuals without a cancer diagnosis. Specifically, the number of high functional impact mutations, including those affecting genes involved in medical diseases, cancer hallmark genes, and DNA replication and repair processes, was much higher, strengthening the hypothesis of the potential causal role of such mutations in hereditary cancers. Conversely, classically related HGC mutations were not found and the number of mutations in genes in the CDH1 pathway was not found to be relevant among the young GC patients, reinforcing the hypothesis that existing alternative germline contributions favor the early onset of GC. The LILRB1 gene variants, absent in the world's cancer datasets but present in high frequencies among the studied GC patients, may represent essential cancer variants specific to the Amerindian ancestry's contributions. Identifying non-reported GC variants, potentially originating from under-studied populations, may pave the way for additional discoveries and translations to clinical interventions for GC management. The newly proposed approaches may reduce the discrepancy between clinically suspected and molecularly proven hereditary GC and shed light on similar inconsistencies among other cancer types. Additionally, the results of this study may support the development of new blood tests for evaluating cancer risk that can be used in clinical practice, helping physicians make decisions about strategies for surveillance and risk-reduction interventions.

PipeCoV: a pipeline for SARS-CoV-2 genome assembly, annotation and variant identification.

Motivation: Since the identification of the novel coronavirus (SARS-CoV-2), the scientific community has made a huge effort to understand the virus biology and to develop vaccines. Next-generation sequencing strategies have been successful in understanding the evolution of infectious diseases as well as facilitating the development of molecular diagnostics and treatments. Thousands of genomes are being generated weekly to understand the genetic characteristics of this virus. Efficient pipelines are needed to analyze the vast amount of data generated. Here we present a new pipeline designed for genomic analysis and variant identification of the SARS-CoV-2 virus. Results: PipeCoV shows better performance when compared to well-established SARS-CoV-2 pipelines, with a lower content of Ns and higher genome coverage when compared to the Wuhan reference. It also provides a variant report not offered by other tested pipelines. Availability: https://github.com/alvesrco/pipecov.

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.