Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence

Detection of populations in single-cell RNA sequencing data via coexpression modules

O advento do sequenciamento de RNA de células únicas trouxe avanços científicos e desafios técnicos. Desenvolver novos métodos de análise é uma etapa crucial paramaximizar a extração de conhecimento desses dados. Neste trabalho, exploramos o reposicionamentode um algoritmo de seleção de características para tratar desafios de dados públicos de RNA de células únicas. Adaptamos o método FCBF (Filtro Rápido Baseado em Correlação, Fast Correlation-Based Filter) para selecionar genes relevantes para distinguir tipos celulares, e encontrar módulos de genes coexpressos. Em dados de células de sangue, notamos que os módulos encontrados correspondiam a programas transcricionais característicos de grupos celulares conhecidos. Em decorrência disso, implementamos uma pipeline capaz de utilizar os módulos de coexpressão para inferir os tipos celulares presentes em conjuntos de dados de forma multinível, evadindo os limites das rotulações únicas tradicionais. Processamos con-juntos de dados de células mononucleares de sangue humano e células embrionárias de peixe-zebra,observando que os módulos e populações encontradas traziam a luz informações biologicamente relevantes. Os algoritmos foram implementados em dois pacotes do Bioconductor, FCBF e fcoex, e estão disponíveis para comunidade, aumentando o arsenal para análise de dados de sequenciamento de RNA de células únicas.The advent of single-cell RNA sequencing has brought scientific advances and technical challenges. The development of new analysis methodologies is a crucial step to maximize the extraction of knowledge from this data. In this work, we explore the repositioning of a feature selection algorithm to address the analytical challenges of public single-cell RNA data. We adapted the FCBF method(Fast Correlation-Based Filter) to select relevant genes to distinguish cell types, and, from this list of genes, find modules of coexpressed genes. In blood cell data, we noted that the modules found corresponded to transcriptional programs characteristic of specific cell groups. As a result, we implemented a pipeline capable of using the coexpression modules to infer the cell types present in datasets in a multilevel way, avoiding the limits of traditional single labels. We processed datasets from human blood mononuclear cells and zebrafish embryonic cells, noting that the modules and populations found brought to light biologically relevant information. The algorithms were implemented in two Bioconductor packages, FCBF and fcoex, and are available to the community,increasing the arsenal for analyzing single-cell RNA sequencing data

The opportunistic pathogen Stenotrophomonas maltophilia utilizes a type IV secretion system for interbacterial killing.

Bacterial type IV secretion systems (T4SS) are a highly diversified but evolutionarily related family of macromolecule transporters that can secrete proteins and DNA into the extracellular medium or into target cells. It was recently shown that a subtype of T4SS harboured by the plant pathogen Xanthomonas citri transfers toxins into target cells. Here, we show that a similar T4SS from the multi-drug-resistant opportunistic pathogen Stenotrophomonas maltophilia is proficient in killing competitor bacterial species. T4SS-dependent duelling between S. maltophilia and X. citri was observed by time-lapse fluorescence microscopy. A bioinformatic search of the S. maltophilia K279a genome for proteins containing a C-terminal domain conserved in X. citri T4SS effectors (XVIPCD) identified twelve putative effectors and their cognate immunity proteins. We selected a putative S. maltophilia effector with unknown function (Smlt3024) for further characterization and confirmed that it is indeed secreted in a T4SS-dependent manner. Expression of Smlt3024 in the periplasm of E. coli or its contact-dependent delivery via T4SS into E. coli by X. citri resulted in reduced growth rates, which could be counteracted by expression of its cognate inhibitor Smlt3025 in the target cell. Furthermore, expression of the VirD4 coupling protein of X. citri can restore the function of S. maltophilia ΔvirD4, demonstrating that effectors from one species can be recognized for transfer by T4SSs from another species. Interestingly, Smlt3024 is homologous to the N-terminal domain of large Ca2+-binding RTX proteins and the crystal structure of Smlt3025 revealed a topology similar to the iron-regulated protein FrpD from Neisseria meningitidis which has been shown to interact with the RTX protein FrpC. This work expands our current knowledge about the function of bacteria-killing T4SSs and increases the panel of effectors known to be involved in T4SS-mediated interbacterial competition, which possibly contribute to the establishment of S. maltophilia in clinical and environmental settings

Chimeric spider silk production in microalgae: a modular bionanomaterial

In this project, we propose to explore the modular characteristic of spider silk proteins, through synthetic biology techniques, by combining and directing its properties to the desired application. The aim of this project is to generate a modular bionanomaterial able to immobilize proteins. This bionanomaterial will be composed of modular recombinant proteins from spider silk, which will be the immobilization support to other proteins, in this project an antimicrobial protein (enzybiotic). By combining these proteins and their properties, the primary focus will be the use of this technology for the development of artificial skin for burn victims. The recombinant proteins, spider silk proteins and enzybiotics, will be expressed in Chlamydomonas reinhardtii strains by nuclear transformation. Each recombinant strain will express a different protein, which will contain the N- and C-terminal polymerization domains from native spider silk proteins. These domains are essential to the polymerization step and, subsequently, for production of a material very similar to silk. This material will be evaluated regarding its antimicrobial and mechanical properties, as well as the system productivity. These results may shed some light on spider silk-based immobilization support effectiveness, even for other biotechnological applications, such as the one idealized here

No-Budget Science Hack Week 2021

Repositório para os projetos desenvolvidos na edição remota da No-Budget Science Hack Week 2021 - https://www.reprodutibilidade.bio.br/hack-week-202

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence