Genome-scale metabolic model of the human pathogen Candida albicans: a promising platform for drug target prediction

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.“Fundação para a Ciência e a Tecnologia” (FCT) [Contract PTDC /BII-BIO/28216/2017] and by Programa Operacional Regional de Lisboa 2020 [LISBOA-01-0145-FEDER-022231], through the Biodata.pt Research Infrastructure. Funding received by iBB-Institute for Bioengineering and Biosciences from FCT [Contract UIDB/04565/2020]info:eu-repo/semantics/publishedVersio

Genome-scale metabolic model of the human pathogen C. albicans: aiming the identification of promising new drug targets

Candida albicans is the most common cause of invasive candidiasis, partly due to its ability to acquire drug resistance. With the rise in frequency of multidrug resistant clinical isolates, therapeutic options are running low. The identification of new drug targets and new drugs is crucial to overcome the increase in therapeutic failure. Currently, genome-scale metabolic models can be considered established tools for drug targeting. In this study, we propose the first genome-scale metabolic model for Candida albicans, iRV1930. The model consists of 1556 reactions, 1344 metabolites, 1053 genes, and 5 compartments. This model, currently under validation, proved accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. This model was reconstructed using open source software tool, merlin 3.9.6, and is provided in the well-established systems biology markup language (SBML) format, thus, it can be used in most metabolic engineering platforms, such as OptFlux or Cobra. Altogether, this model provides a promising platform for global elucidation of the metabolism of C. albicans, currently being used to guide the identification of new drug targets to tackle human candidiasis.info:eu-repo/semantics/publishedVersio

merlin v4: an updated platform for reconstructing genome-scale metabolic models

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) software is an open source user-friendly Java application developed for Windows and Unix, aimed towards the reconstruction of genome-scale metabolic models. The development of merlin follows a design philosophy of automating time-consuming steps in the reconstruction of genome-scale metabolic models, while allowing users to control the parameters of operations and manually curate the results. All major steps involved in the reconstruction of a metabolic model are implemented in merlin, including genome retrieval and its functional annotation, construction of the reactions set and associated entities, model compartmentalization and conversion to standard SBML formats. The fourth iteration of merlin includes a major overhaul of the user interface, implementation of new features, improvements to existing features, and most notably, the implementation of the object-relational mapping framework Hibernate. The graphical layout has been significantly streamlined, while supporting the latest version of AiBench, providing users with an intuitive and responsive interface. Development was also focused at new quality of life improvements, aimed mainly towards importing, exporting and duplicating merlin user projects. The development of the latest version of merlin followed a modular approach, culminating in the implementation of a plugin manager which simplifies and hastens the process of updating and debugging the various features of merlin. In addition, TranSyT, a state-of-the-art genome-wide transmembrane transport system annotation tool has been implemented to overcome the limitations of the previously available TRIAGE module. Finally, it is noteworthy to mention the implementation of BioISO, a tool aimed at evaluating a genome-scale metabolic network or biomass formulation, based on the previously available COBRA and FBA frameworks.info:eu-repo/semantics/publishedVersio

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.Centre of Biological Engineering (CEB, UMinho); Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit; this work is a result of the project 22231/01/SAICT/2016: Biodata.pt Infraestrutura Portuguesa de Dados Biologicos, supported by the ´ PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF); FCT for providing PhD scholarships [DFA/BD/08789/2021 J.C, DFA/BD/8076/2020 E.C., SFRH/BD/139198/2018 to F.C., SFRH/BD/131916/2017 R. Rodrigues]; FCT for the Assistant Research contract of Oscar Dias obtained under CEEC Individual 2018.info:eu-repo/semantics/publishedVersio

Development of bioinformatics tools for the classification of transporter systems

Dissertação de mestrado em BioinformáticaThe Transport Systems Tracker (TranSyT) is a new approach to the problem of identifying genome-wide transmembrane transport systems, annotating these with reactions. TranSyT is the next iteration of TRIAGE (29), though more efficient and designed to overcome its limitations. This new approach still relies on the TCDB (118) to perform the annotation of transporters systems; however, TranSyT automatically retrieves and processes information from this source. The information available in TCDB allows determining which metabolites are carried by each transporter system and the respective reactions. In TranSyT, these metabolites are assigned with identifiers and hierarchies through Biosynth (81), which combines in formation retrieved from several sources such as ModelSEED, KEGG, MetaCyc and BiGG. TranSyT generates new transport reactions using automatic text processing to determine the direction (in/out or out/in), reversibility and most suitable transport type (e.g. symport, antiport, etc) for each reaction retrieved from TCDB. All information is stored in a Neo4j graph database. The identification of the genes encoding transporter systems is the only module with user’s interaction, as the database is available for remote access, without the need of storing it in the users’ machine. Users start the identification of the transporters af ter loading a genome and the respective taxonomy identifier. TranSyT uploads the genome to its remote service and performs the homology search using BLAST against all records available in TCDB. Afterwards, it selects the correct Transporter Classification (TC) family to annotate each transport system and associates transport reactions to the encoded pro tein, creating Gene-Protein-Reaction (GPR) associations. The integration with genome-scale models filters metabolites not available in the reconstructed network. The localization of the reactions can be inferred from third-party tools such as PSORTb 3.0 or LocTree3. Other tools that determine whether the genes encoding transport proteins have transmembrane domains, allow assigning confidence levels to the systems. The iAF1260 Escherichia coli model (38) was used to validate the developed framework. TranSyT was able to automatically create reactions for nearly 75% of the metabolites de scribed in iAF1260 model transporters. Moreover, it allowed identifying transport reactions incorrectly assigned to genes that do not encode reactions transporting such metabolites. TranSyT is an open-source JavaTM software available at https://gitlab.bio.di.uminho. pt/TranSyT, currently being implemented in merlin and KBase.O Transport Systems Tracker (TranSyT) é uma nova abordagem ao problema de identificação de sistemas de transporte transmembranares em genomas. O TranSyT e a próxima iteração da ferramenta TRIAGE (29), embora mais eficiente e desenvolvido para ultrapassar as limitações existentes. Tal como o TRIAGE, ainda depende da TCDB (118) para a anotação de sistemas, no entanto, o TranSyT recolhe e processa automaticamente a informação desta fonte. A informação disponível na TCDB permite determinar quais os metabolitos que são transportados por cada sistema de transporte e quais as respectivas reacções. No TranSyT, identificadores e descendentes hierárquicos são atribuídos aos metabolitos utilizando o Biosynth (81), que combina informação de várias fontes, como o ModelSEED, KEGG, MetaCyc e BiGG. O TranSyT gera reações de transporte utilizando processamento automático de texto para determinar a direção (interior/exterior ou exterior/interior), reversibilidade, e o tipo de transporte adequado (e.g. symport, antiport, etc) para cada reacção recolhida da TCDB. Todas as informações recolhidas são guardadas numa base de dados de grafos Neo4j. A identificação de sistemas transportadores codificados por genes e o único módulo com o qual o utilizador tem interação, visto que a base de dados está disponível remotamente, não havendo a necessidade de armazená-la na máquina do utilizador. Ao carregar um genoma e o respectivo identificador taxonómico, o utilizador é capaz de começar a identificação dos transportadores. O TranSyT carrega o genoma para o seu serviço remoto e executa a procura de homologias utilizando BLAST (3) contra todos os registos da TCDB. De seguida, selecciona a família TC correcta para anotar cada sistema transportador e associa reacções de transporte à proteína codificada, criando associações Gene-Proteína-Reacção (GPR). A integração com o modelo a escala genómica usada para filtrar metabolitos que não estão indisponíveis na rede. A localização dos sistemas pode ser inferida utilizando ferramentas de terceiros, tais como PSORTb 3.0 ou LocTree3. Outras ferramentas que determinam se os genes que codificam proteínas de transporte têm domínios transmembranares, permitem a atribuição de graus de confiança às classificações. O modelo iAF1260 da Escherichia coli (38) foi utilizado para validar a plataforma desenvolvida. O TranSyT foi capaz de criar automaticamente reacções para aproximadamente 75% dos metabolitos associados a transportadores do modelo. Permitiu ainda identificar reacções de transporte associadas a genes que não codificam reacções que transportam os metabolitos que lhes são atribuídos. TranSyT é um software open-source JavaTM disponível em https://gitlab.bio.di.uminho. pt/TranSyT, actualmente a ser implementado no merlin e na KBase

TranSyT, an innovative framework for identifying transport systems

The importance and rate of development of genome-scale metabolic models have been growing for the last years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGEs release, software development for automatic integration of transport reactions into models has stalled. Here we present the Transport Systems Tracker (TranSyT), the next iteration of TRIAGE. Unlike its predecessor, TranSyT does not rely on manual curation to expand its internal database, derived from highly-curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporters families, transport proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.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. This work is supported by the Office of Biological and Environmental Research’s Genomic Science program within the US Department of Energy Office of Science, under award numbers DE-AC02-05CH11231, DE-AC02-06CH11357, DE AC05-00OR22725, and DE-AC02-98CH10886.info:eu-repo/semantics/publishedVersio

TranSyT, an innovative framework for identifying transport systems

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGEâs release, software development for the automatic integration of transport reactions into models has stalled.Here, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.TranSyT web service: https://transyt.bio.di.uminho.pt/; GitHub for the tool: https://github.com/BioSystemsUM/transyt; GitHub with examples and instructions to run TranSyT: https://github.com/ecunha1996/transyt\_paper.The submitted manuscript has been created by UChicago Argonne, LLC as Operator of Argonne National Laboratory (‘Argonne’) under Contract No. DE-AC02-06CH11357 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. The authors would like to acknowledge project 22231/01/SAICT/ 2016: “Biodata.pt—Infraestrutura Portuguesa de Dados Biolo´ gicos,” supported by Lisboa Portugal Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Oscar Dias acknowledges FCT for the Assistant Research contract obtained under CEEC Individual 2018. The authors would also like to acknowledge the Portuguese Foundation for Science and Technology (FCT) for providing a PhD scholarship to E. Cunha (DFA/BD/8076/2020).info:eu-repo/semantics/publishedVersio

The first multi-tissue diel cycle genome-scale metabolic model of a woody plant highlights the role of the secondary metabolism pathways in Quercus suber

In the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behavior at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6230 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights into other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.The authors would like to acknowledge project 22231/01/SAICT/2016: “Biodata.pt – Infraestrutura Portuguesa de Dados Biológicos”, supported by Lisboa Portugal Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors would also like to acknowledge the Portuguese Foundation for Science and Technology (FCT) for providing a PhD scholarship to E. Cunha (DFA/BD/8076/2020). Oscar Dias acknowledges FCT for the Assistant Research contract obtained under CEEC Individual 2018. Inês Chaves was funded by DL 57/2016/CP1351/CT0003.info:eu-repo/semantics/publishedVersio

SamPler - a novel method for selecting parameters for gene functional annotation routines

Background: As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins. Results: Here SamPler, a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm’s parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters. Conclusions: The potential of this methodology is demonstrated with four genome functional annotations performed in merlin, an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UID/BIO/ 04469] unit and COMPETE 2020 [POCI-01-0145-FEDER-006684] 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 thank the project DDDeCaF - Bioinformatics Services for Data-Driven Design of Cell Factories and Communities, Ref. H2020-LEIT-BIO-2015-1 686070–1, funded by the European Commission.info:eu-repo/semantics/publishedVersio