The experience of teaching introductory programming skills to bioscientists in Brazil.

Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year's experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants' assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners' feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term.

Network Analysis Combining Proteomics and Metabolomics Reveals New Insights Into Early Responses of Eucalyptus grandis During Rust Infection.

Eucalyptus rust is caused by the biotrophic fungus, Austropuccinia psidii, which affects commercial plantations of Eucalyptus, a major raw material for the pulp and paper industry in Brazil. In this manuscript we aimed to uncover the molecular mechanisms involved in rust resistance and susceptibility in Eucalyptus grandis. Epifluorescence microscopy was used to follow the fungus development inside the leaves of two contrasting half-sibling genotypes (rust-resistance and rust-susceptible), and also determine the comparative time-course of changes in metabolites and proteins in plants inoculated with rust. Within 24 h of complete fungal invasion, the analysis of 709 metabolomic features showed the suppression of many metabolites 6 h after inoculation (hai) in the rust-resistant genotype, with responses being induced after 12 hai. In contrast, the rust-susceptible genotype displayed more induced metabolites from 0 to 18 hai time-points, but a strong suppression occurred at 24 hai. Multivariate analyses of genotypes and time points were used to select 16 differential metabolites mostly classified as phenylpropanoid-related compounds. Applying the Weighted Gene Co-Expression Network Analysis (WGCNA), rust-resistant and rust-susceptible genotypes had, respectively, 871 and 852 proteins grouped into 5 and 6 modules, of which 5 and 4 of them were significantly correlated to the selected metabolites. Functional analyses revealed roles for photosynthesis and oxidative-dependent responses leading to temporal activity of metabolites and related enzymes after 12 hai in rust-resistance; while the initial over-accumulation of those molecules and suppression of supporting mechanisms at 12 hai caused a lack of progressive metabolite-enzyme responses after 12 hai in rust-susceptible genotype. This study provides some insights on how E. grandis plants are functionally modulated to integrate secondary metabolites and related enzymes from phenylpropanoid pathway and lead to temporal divergences of resistance and susceptibility responses to rust.