OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes.

João Pedro Saraiva,Oscar Dias,Antonis Chatzinotas,Jonas Coelho Kasmanas,René Kallies,Alexandre Bartholomäus,Carsten Vogt,Ulisses Nunes Da Rocha,Peter Stadler,Marta Gomes,Marcos Bicalho

doi:10.26508/lsa.202101167

Abstract

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96% of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy-to-use workflow that allows for rapid functional annotation based on a user-curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

Highlights

In environments where microorganisms play a crucial role, the microbial community functional potential encompasses the building blocks for all possible interspecies interactions (Mulder et al, 2001; Maestre et al, 2012)
Several tools have been developed to improve the reconstruction of metagenome-assembled genomes (MAGs), the same cannot be said for predicting interspecies interactions (Morin et al, 2018)
Additional control is given to the user with the option to establish thresholds in the minimum e-values

Summary

Introduction

In environments where microorganisms play a crucial role, the microbial community functional potential encompasses the building blocks for all possible interspecies interactions (Mulder et al, 2001; Maestre et al, 2012). In environments rich in methane, microbial communities are dominated by species with genes encoding proteins involved in methanogenesis (Lyu et al, 2018) Soil microbes, especially those in the rhizosphere, are genetically adapted to support plants in the resistance against pathogens and tolerance to stress (Mendes et al, 2018). The exponential increase in high-throughput sequencing data and the development of computational sciences and bioinformatics pipelines have advanced our understanding of microbial community composition and distribution in complex ecosystems (Roh et al, 2010) This knowledge increased our ability to reconstruct and functionally characterize genomes in complex communities, for example, by recovering metagenome-assembled genomes (MAGs) (Parks et al, 2017; Tully et al, 2018; Pasolli et al, 2019). The sheer number of representative genomes (4,930) and the lack of focus on specific ecosystem processes make predicting interspecies interactions challenging

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