UPIMAPI, reCOGnizer and KEGGCharter: three tools for functional annotation

Omics technologies generate large datasets from which biological information must be extracted by using bioinformatics tools. Although web services provide easier to use interfaces, large datasets are difficult to handle. This is not a limitation of command-line tools and programmatic modules, but these may be challenging to use. In this work, three command-line tools were developed, aimed for speed and automation. The tools are available through Bioconda for Unix systems and were developed in Python 3, making use of multithreading/multiprocessing in computationally demanding steps. UPIMAPI integrates annotation with reference to the UniProt database with automatic retrieval of internal and cross-reference information from other databases (e.g., KEGG, BRENDA and RefSeq) through UniProts API, accessed with urllib package. The input is a FASTA file containing protein sequences, and the outputs are EXCEL or TSV files containing taxonomic, functional, and cross-reference information. reCOGnizer performs domain-based annotation of protein sequences with CDD, Pfam, NCBIfam, Protein Clusters, TIGRFAM, SMART, COG and KOG as reference databases, and obtains EC numbers and taxonomic assignments per domain identified. The results are outputted in TSV and EXCEL files. KEGGCharter is a command line implementation of KEGG Pathways mapping service, while also obtaining additional KOs and EC numbers, through the methods available in BioPython for accessing KEGGs API. KEGGCharter takes as input a table (TSV or EXCEL), containing either KEGG IDs, KOs or EC numbers. KEGGCharter represents identified KOs in metabolic maps and includes information on differential gene expression. When data from more than one organism is uploaded, KEGGCharter links function to taxonomic identification, which can be visualized in the maps. Differential expression of genes/proteins can be visualized in metabolic maps, by showing mini heatmaps. UPIMAPI and reCOGnizer are complementary tools, providing functional annotation based on protein sequencing homology and on identification of protein conserved domains, respectively. Both tools retrieve the IDs (KEGG IDs, EC numbers and KOs) necessary to run KEGGCharter. Together, these tools provide a complete characterization and visualization of results, facilitating the interpretation of omics experiments, and requiring minimal bioinformatics expertise.info:eu-repo/semantics/publishedVersio

Towards a multivariate analysis of genome-scale metabolic models derived from the BiGG models database

First Online: 28 August 2021Genome-Scale metabolic models (GEMs) are a relevant tool in systems biology for in silico strain optimisation and drug discovery. An easier way to reconstruct a model is to use available GEMs as templates to create the initial draft, which can be curated up until a simulation-ready model is obtained. This approach is implemented in merlin's BiGG Integration Tool, which reconstructs models from existing GEMs present in the BiGG Models database. This study aims to assess draft models generated using models from BiGG as templates for three distinct organisms, namely, Streptococcus thermophilus, Xylella fastidiosa and Mycobacterium tuberculosis. Several draft models were reconstructed using the BiGG Integration Tool and different templates (all, selected and random). The variability of the models was assessed using the reactions and metabolic functions associated with the model's genes. This analysis showed that, even though the models shared a significant portion of reactions and metabolic functions, models from different organisms are still differentiated. Moreover, there also seems to be variability among the templates used to generate the draft models to a lower extent. This study concluded that the BiGG Integration Tool provides a fast and reliable alternative for draft reconstruction for bacteria.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit. A. Oliveira (DFA/BD/10205/2020), E. Cunha (DFA/BD/8076/2020), F. Cruz (SFRH /BD/139198/2018), J. Sequeira (SFRH/BD/147271/2019), and M. Sampaio (SFRH/BD/144643/2019) hold a doctoral fellowship provided by the FCT. Oscar Dias acknowledge FCT for the Assistant Research contract obtained under CEEC Individual 2018.info:eu-repo/semantics/publishedVersio

Effect of sub-stoichiometric Fe(III) amounts on LCFA degradation by methanogenic communities

Long-chain fatty acids (LCFA) are common contaminants in municipal and industrial wastewater that can be converted anaerobically to methane. A low hydrogen partial pressure is required for LCFA degradation by anaerobic bacteria, requiring the establishment of syntrophic relationships with hydrogenotrophic methanogens. However, high LCFA loads can inhibit methanogens, hindering biodegradation. Because it has been suggested that anaerobic degradation of these compounds may be enhanced by the presence of alternative electron acceptors, such as iron, we investigated the effect of sub-stoichiometric amounts of Fe(III) on oleate (C18:1 LCFA) degradation by suspended and granular methanogenic sludge. Fe(III) accelerated oleate biodegradation and hydrogenotrophic methanogenesis in the assays with suspended sludge, with H2-consuming methanogens coexisting with iron-reducing bacteria. On the other hand, acetoclastic methanogenesis was delayed by Fe(III). These effects were less evident with granular sludge, possibly due to its higher initial methanogenic activity relative to suspended sludge. Enrichments with close-to-stoichiometric amounts of Fe(III) resulted in a microbial community mainly composed of Geobacter, Syntrophomonas, and Methanobacterium genera, with relative abundances of 83–89%, 3–6%, and 0.2–10%, respectively. In these enrichments, oleate was biodegraded to acetate and coupled to iron-reduction and methane production, revealing novel microbial interactions between syntrophic LCFA-degrading bacteria, iron-reducing bacteria, and methanogens.Portuguese Foundation for Science and Technology (FCT) under the scope of project MORE (POCI-01-0145-FEDER-016575), of the strategic funding of UIDB/04469/2020 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte. The authors also acknowledge the financial support of FCT and European Social Fund through the grants attributed to S.A. Silva (SFRH/BD/122623/2016), A.L. Arantes (PD/BD/128030/2016), and J.C. Sequeira (SFRH/BD/147271/2019)info:eu-repo/semantics/publishedVersio

Plasma Extracellular Vesicle-Derived TIMP-1 mRNA as a Prognostic Biomarker in Clear Cell Renal Cell Carcinoma:A Pilot Study

The tumor microenvironment has gained a lot of attention from the scientific community since it has a proven impact in the development of tumor progression and metastasis. Extracellular vesicles (EVs) are now considered one of the key players of tumor microenvironment modulation. Clear cell renal cell carcinoma (ccRCC) is the most lethal urological neoplasia and presents a high metastatic potential, which reinforces the need for the development of more effective predictive biomarkers. Our goal was to evaluate the applicability of EV-derived matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) as prognostic biomarkers for ccRCC. To do so, we studied the plasma EV content of 32 patients with localized ccRCC and 29 patients with metastatic ccRCC. We observed that patients with localized disease and tumors larger than 7 cm presented higher levels of plasma EV-derived TIMP-1 mRNA when compared with patients presenting smaller tumors (p = 0.020). Moreover, patients with metastatic disease presented higher levels of EV-derived TIMP-1 mRNA when compared with patients with localized disease (p = 0.002) and when we stratified those patients in high and low levels of TIMP-1 EV-derived mRNA, the ones presenting higher levels had a lower overall survival (p = 0.030). EV-derived TIMP-1 mRNA may be a good prognostic biomarker candidate for ccRCC

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Atividade biológica de extratos acetato de etila, etanólico e aquoso de timbó (Lonchocarpus floribundus) sobre carrapato bovino

Os extratos acetato de etila, etanólico e aquoso de raízes de Lonchocarpus floribundus foram utilizados, a fim de avaliar a atividade biológica sobre carrapato bovino. Carrapatos adultos foram coletados em bovinos infestados artificialmente, separados em grupos de dez indivíduos, pesados e imersos, separadamente, nos extratos de raízes de L. Floribundus, nas concentrações de 5, 25, 50, 75 e 100 mg mL-1. Para a avaliação em larvas, foram utilizados indivíduos de 14 a 21 dias, os quais foram imersos nos extratos nas concentrações de 1, 5, 10, 15 e 20 mg mL-1. Após o tratamento, cada grupo foi colocado em placa de Petri e incubado a 27 ± 1 ºC e umidade relativa de 80 ± 5%. Os extratos avaliados não foram eficazes para induzir, acima de 50%, a mortalidade de fêmeas ingurgitadas. Os extratos acetato de etila e etanólico induziram 100% de mortalidade de larvas. Entretanto, quanto aos valores de concentração letal mediana (CL50), o extrato etanólico (CL50 = 2,1 mg mL-1) foi mais tóxico que o extrato acetato de etila (CL50 = 4,1 mg mL-1). O extrato etanólico estimou concentração inibitória mediana (CI50) de 3,0 mg mL-1 e foi mais tóxico que os demais extratos quanto a este parâmetro de avaliação. Entre os três extratos avaliados, os extratos acetato de etila e etanólico apresentaram os melhores resultados quanto ao controle de reprodução de R. (B.) microplus, atingindo 100% na concentração de 5 mg mL-1. Os extratos de raízes de L. Floribundus apresentaram atividade biológica sobre carrapato bovino

Development of an automated pipeline for meta-omics data analysis

Dissertação de mestrado em Computer ScienceKnowing what lies around us has been a goal for many decades now, and the new advances in sequencing technologies and in meta-omics approaches have permitted to start answering some of the main questions of microbiology - what is there, and what is it doing? The exponential growth of omics studies has been answered by the development of some bioinformatic tools capable of handling Metagenomics (MG) analysis, with a scarce few integrating such analysis with Metatranscriptomics (MT) or Metaproteomics (MP) studies. Furthermore, the existing tools for meta-omics analysis are usually not user friendly, usually limited to command-line usage. Because of the variety in meta-omics approaches, a standard workflow is not possible, but some routines exist, which may be implemented in a single tool, thereby facilitating the work of laboratory professionals. In the framework of this master thesis, a pipeline for integrative MG and MT data analysis was developed. This pipeline aims to retrieve comprehensive comparative gene/transcript expression results obtained from different biological samples. The user can access the data at the end of each step and summaries containing several parameters of evaluation of the previous step, and final graphical representations, like Krona plots and Differential Expression (DE) heatmaps. Several quality reports are also generated. The pipeline was constructed with tools tested and validated for meta-omics data analysis. Selected tools include FastQC, Trimmomatic and SortMeRNA for preprocessing, MetaSPAdes and Megahit for assembly, MetaQUAST and Bowtie2 for reporting on the quality of the assembly, FragGeneScan and DIAMOND for annotation and DeSEQ2 for DE analysis. Firstly, the tools were tested separately and then integrated in several python wrappers to construct the software Meta-Omics Software for Community Analysis (MOSCA). MOSCA performs preprocessing of MG and MT reads, assembly of the reads, annotation of the assembled contigs, and a final data analysis. Real datasets were used to test the capabilities of the tool. Since different types of files can be obtained along the workflow, it is possible to perform further analyses to obtain additional information and/or additional data representations, such as metabolic pathway mapping.O objectivo da microbiologia, e em particular daqueles que se dedicam ao estudo de comunidades microbianas, é descobrir o que compõe as comunidades, e a função de cada microrganismo no seio da comunidade. Graças aos avanços nas técnicas de sequenciação, em particular no desenvolvimento de tecnologias de Next Generation Sequencing, surgiram abordagens de meta-ómicas que têm vindo a ajudar a responder a estas questões. Várias ferramentas foram desenvolvidas para lidar com estas questões, nomeadamente lidando com dados de Metagenómica (MG), e algumas poucas integrando esse tipo de análise com estudos de Metatranscriptómica (MT) e Metaproteómica (MP). Além da escassez de ferramentas bioinformáticas, as que já existem não costumam ser facilmente manipuláveis por utilizadores com pouca experiencia em informática, e estão frequentemente limitadas a uso por linha de comando. Um formato geral para uma ferramenta de análise meta-ómica não é possível devido à grande variedade de aplicações. No entanto, certas aplicações possuem certas rotinas, que são passíveis de serem implementadas numa ferramenta, facilitando assim o trabalho dos profissionais de laboratório. Nesta tese, uma pipeline integrada para análise de dados de MG e MT foi desenvolvida, pretendendo determinar a expressão de genes/transcriptos entre diferentes amostras biológicas. O utilizador tem disponíveis os resultados de cada passo, sumários com vários parâmetros para avaliação do procedimento, e representações gráficas como gráficos Krona e heatmaps de expressão diferencial. Vários relatórios sobre a qualidade dos resultados obtidos também são gerados. A ferramenta foi construída baseada em ferramentas e procedimentos testados e validados com análise de dados de meta-ómica. Essas ferramentas são FastQC, Trimmomatic e SortMeRNA para pré-processamento, Megahit e MetaSPAdes para assemblagem, MetaQUAST e Bowtie2 para controlo da qualidade dos contigs obtidos na assemblagem, FragGeneScan e DIAMOND para anotação e DeSEQ2 para análise de expressão diferencial. As ferramentas foram testadas uma a uma, e depois integradas em diferentes wrappers de python para compôr a Meta-Omics Software for Community Analysis (MOSCA). A MOSCA executa pré-processamento de reads de MG e MT, assemblagem das reads, anotação dos contigs assemblados, e uma análise de dados final Foram usados dados reais para testar as capacidades da MOSCA. Como podem ser obtidos diferentes tipos de ficheiros ao longo da execução da MOSCA, é possível levar a cabo análises posteriores para obter informação adicional e/ou representações de dados adicionais, como mapeamento de vias metabólicas