Early mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variants in Chile.

Genomic surveillance (GS) programmes were crucial in identifying and quantifying the mutating patterns of SARS-CoV-2 during the COVID-19 pandemic. In this work, we develop a Bayesian framework to quantify the relative transmissibility of different variants tailored for regions with limited GS. We use it to study the relative transmissibility of SARS-CoV-2 variants in Chile. Among the 3443 SARS-CoV-2 genomes collected between January and June 2021, where sampling was designed to be representative, the Gamma (P.1), Lambda (C.37), Alpha (B.1.1.7), B.1.1.348, and B.1.1 lineages were predominant. We found that Lambda and Gamma variants' reproduction numbers were 5% (95% CI: [1%, 14%]) and 16% (95% CI: [11%, 21%]) larger than Alpha's, respectively. Besides, we observed a systematic mutation enrichment in the Spike gene for all circulating variants, which strongly correlated with variants' transmissibility during the studied period (r = 0.93, p-value = 0.025). We also characterised the mutational signatures of local samples and their evolution over time and with the progress of vaccination, comparing them with those of samples collected in other regions worldwide. Altogether, our work provides a reliable method for quantifying variant transmissibility under subsampling and emphasises the importance of continuous genomic surveillance.

A Comprehensive Analysis of the Effect of A>I(G) RNA-Editing Sites on Genotoxic Drug Response and Progression in Breast Cancer.

Dysregulated A>I(G) RNA editing, which is mainly catalyzed by ADAR1 and is a type of post-transcriptional modification, has been linked to cancer. A low response to therapy in breast cancer (BC) is a significant contributor to mortality. However, it remains unclear if there is an association between A>I(G) RNA-edited sites and sensitivity to genotoxic drugs. To address this issue, we employed a stringent bioinformatics approach to identify differentially RNA-edited sites (DESs) associated with low or high sensitivity (FDR 0.1, log2 fold change 2.5) according to the IC50 of PARP inhibitors, anthracyclines, and alkylating agents using WGS/RNA-seq data in BC cell lines. We then validated these findings in patients with basal subtype BC. These DESs are mainly located in non-coding regions, but a lesser proportion in coding regions showed predicted deleterious consequences. Notably, some of these DESs are previously reported as oncogenic variants, and in genes related to DNA damage repair, drug metabolism, gene regulation, the cell cycle, and immune response. In patients with BC, we uncovered DESs predominantly in immune response genes, and a subset with a significant association (log-rank test p < 0.05) between RNA editing level in LSR, SMPDL3B, HTRA4, and LL22NC03-80A10.6 genes, and progression-free survival. Our findings provide a landscape of RNA-edited sites that may be involved in drug response mechanisms, highlighting the value of A>I(G) RNA editing in clinical outcomes for BC.

Longitudinal study of wound healing status and bacterial colonisation of Staphylococcus aureus and Corynebacterium diphtheriae in epidermolysis bullosa patients.

Epidermolysis bullosa (EB) is an inherited disorder characterised by skin fragility and the appearance of blisters and wounds. Patient wounds are often colonised or infected with bacteria, leading to impaired healing, pain and high risk of death by sepsis. Little is known about the impact of bacterial composition and susceptibility in wound resolution, and there is a need for longitudinal studies to understand healing outcomes with different types of bacterial colonisation. A prospective longitudinal study of 70 wounds from 15 severe EB patients (Junctional and Recessive Dystrophic EB) from Chile. Wounds were selected independently of their infected status. Wound cultures, including bacterial species identification, composition and Staphylococcus aureus (SA) antibiotic susceptibility were registered. Wounds were separated into categories according to their healing capacity, recognising chronic, and healing wounds. Hundred-one of the 102 wound cultures were positive for bacterial growth. From these, 100 were SA-positive; 31 were resistant to Ciprofloxacin (31%) and only seven were methicillin-resistant SA (7%). Ciprofloxacin-resistant SA was found significantly predominant in chronic wounds (**P < .01). Interestingly, atoxigenic Corynebacterium diphtheriae (CD) was identified and found to be the second most abundant recovered bacteria (31/101), present almost always in combination with SA (30/31). CD was only found in Recessive Dystrophic EB patients and not related to wound chronicity. Other less frequent bacterial species found included Pseudomonas aeruginosa, Streptococus spp. and Proteus spp. Infection was negatively associated with the healing status of wounds.

Early-life nutrition interacts with developmental genes to shape the brain and sleep behavior in Drosophila melanogaster.

The mechanisms by which the genotype interacts with nutrition during development to contribute to the variation of complex behaviors and brain morphology of adults are not well understood. Here we use the Drosophila Genetic Reference Panel to identify genes and pathways underlying these interactions in sleep behavior and mushroom body morphology. We show that early-life nutritional restriction effects on sleep behavior and brain morphology depends on the genotype. We mapped genes associated with sleep sensitivity to early-life nutrition, which were enriched for protein-protein interactions responsible for translation, endocytosis regulation, ubiquitination, lipid metabolism, and neural development. By manipulating the expression of candidate genes in the mushroom bodies (MBs) and all neurons, we confirm that genes regulating neural development, translation and insulin signaling contribute to the variable response of sleep and brain morphology to early-life nutrition. We show that the interaction between differential expression of candidate genes with nutritional restriction in early life resides in the MBs or other neurons and that these effects are sex-specific. Natural variations in genes that control the systemic response to nutrition and brain development and function interact with early-life nutrition in different types of neurons to contribute to the variation of brain morphology and adult sleep behavior.

Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients.

BACKGROUND: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohorts of cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), summarises the mutational and clinical profiles of different subtypes of Lung Cancer (LC). Mutational and clinical signatures have been used independently for tumour typification and prediction of metastasis in LC patients. Is it then possible to achieve better typifications and predictions when combining both data streams? METHODS: In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for different classes of patients. Using the TML and different sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classification models that calculate the likelihood of developing metastasis. RESULTS: We found that LC patients different in age, smoking status, and tumour type had significantly different mean TMLs. Although TML was an informative feature, its effect was secondary to the "tumour stage" feature. However, its contribution to the classification is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest performance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]). CONCLUSIONS: Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evidence of the need for comprehensive diagnostic tools for metastasis.

BEST: a Shiny/R web-based application to easily retrieve cross-related enzyme functional parameters and information from BRENDA.

MOTIVATION: BRENDA is the largest enzyme functional database, containing information of 84 000 experimentally characterized enzyme entries. This database is an invaluable resource for researchers in the biological field, which classifies enzyme-related information in categories that are very useful to obtain specific functional and protein engineering information for enzyme families. However, the BRENDA web interface, the most used by researchers with a non-informatic background, does not allow the user to cross-reference data from different categories or sub-categories in the database. Obtaining information in an easy and fast way, in a friendly web interface, without the necessity to have a deep informatics knowledge, will facilitate and improve research in the enzymology and protein engineering field. RESULTS: We developed the Brenda Easy Search Tool (BEST), an interactive Shiny/R application that enables querying the BRENDA database for complex cross-tabulated characteristics, and retrieving enzyme-related parameters and information readily and efficiently, which can be used for the study of enzyme function or as an input for other bioinformatics tools. AVAILABILITY AND IMPLEMENTATION: BEST and its tutorial are freely available from https://pesb2.cl/best/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Visualization of Results from Systems Genetics Studies in Chromosomal Context.

This chapter describes methods currently available for visualizing results from systems genetics experiments. Here, we abstract from the statistical methods used for genetic mapping, which are dependent on the specific resource being used, i.e. F2, RILs, or outbred populations among others. We use a public dataset with results from a mouse eQTL experiment for three examples of visualization: genome-wide dot plots of marker-by-gene association, karyotype-like plots, and circos plots. Dot plots give a first overview of the results from eQTL mapping, allowing detecting genome-wide patterns of cis- and trans-genetic association to transcription level. Karyotype-like plots provide chromosomal context and allow integrating multiple tracks of information in a single plot. Circos plots can, in addition, display long-range interactions to provide an overview of genetic connectivity at the genome level. All examples are developed and explained using R code, an open-source language with powerful statistical and graphical capabilities. The principles reviewed here, however, can be applied with other software options, organisms, and to any type of molecular phenotype that can be assigned to a genomic position.

chromPlot: visualization of genomic data in chromosomal context.

UNLABELLED: : Visualizing genomic data in chromosomal context can help detecting errors in data processing and may suggest new hypotheses to be tested. Here, we report a new tool for displaying large and diverse genomic data along chromosomes. The software is implemented in R so that visualization can be easily integrated with its numerous packages for processing genomic data. It supports simultaneous visualization of multiple tracks of data. Large genomic regions such as QTLs or synteny tracts may be shown along histograms of number of genes, genetic variants, or any other type of genomic element. Tracks can also contain values for continuous or categorical variables and the user can choose among points, connected lines, colored segments, or histograms for representing data. chromPlot takes data from tables in data.frame in GRanges formats. The information necessary to draw chromosomes for mouse and human is included with the package. For other organisms, chromPlot can read Gap and cytoBandIdeo tables from the UCSC Genome Browser. We present common use cases here, and a full tutorial is included as the package's vignette. AVAILABILITY AND IMPLEMENTATION: chromPlot is distributed under a GLP2 licence at http://www.bioconductor.org CONTACT: raverdugo@u.uchile.cl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.