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The Landscape of Microbial Phenotypic Traits and Associated Genes Nucleic Acids Research Advance Access published October 24, 2016 Nucleic Acids Research, 2016 1 doi: 10.1093/nar/gkw964 The landscape of microbial phenotypic traits and associated genes Maria Brbic´ 1, Matija Piskorecˇ 1, Vedrana Vidulin1,AnitaKriskoˇ 2, Tomislav Smucˇ 1 and Fran Supek1,3,4,* 1Division of Electronics, Ruder Boskovic Institute, 10000 Zagreb, Croatia, 2Mediterranean Institute of Life Sciences, 21000 Split, Croatia, 3EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003 Barcelona, Spain and 4Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain Received June 12, 2016; Revised September 21, 2016; Editorial Decision October 06, 2016; Accepted October 11, 2016 Downloaded from ABSTRACT INTRODUCTION Bacteria and Archaea display a variety of phenotypic Bacteria and Archaea inhabit a wide spectrum of ecologi- traits and can adapt to diverse ecological niches. cal niches, including growth in extreme environments and http://nar.oxfordjournals.org/ However, systematic annotation of prokaryotic phe- association to plant or animal hosts, whether mutualistic, notypes is lacking. We have therefore developed Pro- commensal or parasitic. This is made possible by a plethora Traits, a resource containing ∼545 000 novel phe- of physiological adaptations observed in prokaryotes, such as the use of different carbon sources and electron ac- notype inferences, spanning 424 traits assigned to ceptors, resistance to stressors and molecular interactions 3046 bacterial and archaeal species. These anno- with host cells. Broadly construed, the notion of a micro- tations were assigned by a computational pipeline bial phenotypic trait encompasses all of the above facili- that associates microbes with phenotypes by text- ties – the ability to colonize ecological niches and the un- mining the scientific literature and the broader World derlying physiological features. A deeper characterization at Ruder Boskovic Institute on December 9, 2016 Wide Web, while also being able to define novel of such traits can be obtained by linking them to the ge- concepts from unstructured text. Moreover, the Pro- netic makeup of the microbes (1,2). Statistical associations Traits pipeline assigns phenotypes by drawing exten- of genes to phenotypes can implicate certain proteins or sively on comparative genomics, capturing patterns pathways, providing insight into the mechanistic basis of in gene repertoires, codon usage biases, proteome phenotypic traits (3,4), as demonstrated for adaptation to composition and co-occurrence in metagenomes. stress (5,6), host-association (7,8), pathogenesis (9,10), drug resistance (11,12) and relevance to biotechnological appli- Notably, we find that gene synteny is highly predic- cations (13,14). tive of many phenotypes, and highlight examples of The amount of prokaryotic genomes is increasing rapidly gene neighborhoods associated with spore-forming (15), aided by single-cell sequencing (16) and metagenomics ability. A global analysis of trait interrelatedness out- (17). However, efforts to obtain systematic, high-quality lined clusters in the microbial phenotype network, phenotype annotation of microbes are not keeping pace, suggesting common genetic underpinnings. Our ex- meaning that the potential for comprehensive gene-trait tended set of phenotype annotations allows detec- association studies cannot be realized (1,18). A thorough tion of 57 088 high confidence gene-trait links, which characterization of phenotypes of phylogenetically diverse recover many known associations involving sporula- microbial taxa could implicate genes in phenotypic traits tion, flagella, catalase activity, aerobicity, photosyn- by capturing the evolutionary signal across the prokary- thesis and other traits. Over 99% of the commonly otic tree of life. Moreover, multiple traits could be consid- ered jointly, as they have been for mammalian phenotypes occurring gene families are involved in genetic inter- (19,20), thus boosting statistical power and elucidating rela- actions conditional on at least one phenotype, sug- tionships between the traits (21). Comprehensive resources gesting that epistasis has a major role in shaping that link phenotypes to genes and pathways exist for several microbial gene content. eukaryotic model organisms (22,23), but not so for prokary- otes. Current databases of microbial genome sequencing projects (24,25) do supply a certain amount of annotated *To whom correspondence should be addressed. Tel: +385 1 4561 080; Email: [email protected] C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] 2 Nucleic Acids Research, 2016 traits, however, these important resources do not focus on combination as a missing value (n = 147, list in Supplemen- phenotype data and thus the coverage is not extensive. In tary Table S1). contrast, the scientific literature abounds with trait descrip- Second, we included descriptions of the ecosystems where tions stored as unstructured text, which are therefore not microbes were isolated from, as provided in the GOLD directly accessible to automated analyses. Relying on man- database of genome sequencing projects (25). Since GOLD ual curation to organize these data does not scale with provides only positive assertions, we have provisionally an- the increasing volume of scientific publications. Motivated notated as negative examples for a certain ecosystem all by the above, we have developed the ProTraits resource, those organisms which were not explicitly assigned to that a comprehensive atlas of 424 microbial phenotypes, cov- ecosystem type. For instance, organisms annotated as ‘ma- ering 3046 Bacterial and Archaeal species, each receiving rine’ were used as provisional negatives for ‘soil’ or for ’ther- on average 165 novel high-confidence annotations in ad- mal springs’. In GOLD, each organism can have more than dition to 23 previously known labels; available online at one assigned value, e.g. being annotated as both ‘marine’ http://protraits.irb.hr/. and ‘freshwater’ and thus receiving positive labels for these The pipeline behind ProTraits relies on automated text two ecosystems, and negative labels for all remaining ones. mining of diverse corpora of biological literature, anno- Such provisional negative annotations were used to train tating microbes with existing phenotypic traits, while ad- the classification models (see below). Downloaded from ditionally being able to define novel phenotypic concepts Third, we further considered a set of biochemical phe- from free text in an unsupervised manner. Furthermore, notypes, which here implies the experimental data we man- our approach draws extensively on genomic data, includ- ually collected from 265 articles describing new microbial ing novel methods of automated phenotype inference from strains, published in the IJSEM journal (26) during the conserved gene neighborhoods and from evolution of syn- years 2013 and 2014. These publications describe series of onymous codon biases. The broad coverage with phenotype laboratory tests meant to discriminate novel species, such as http://nar.oxfordjournals.org/ annotations allows us to systematically discover thousands the ability to grow on a particular substrate. Next, we col- of high-confidence statistical associations that link genes to lapsed together synonymous biochemical traits, further re- traits, thereby informing about their genetic basis. Finally, taining the trait only if at least 30 species were covered with our analyses suggest that epistatic interactions are ubiqui- annotations (full list of synonymous names in Supplemen- tous within bacterial and archaeal gene repertoires, affect- tary Table S1). We merged this data with a smaller set of ex- ing almost all commonly-occurring gene families. perimental measurements of growth on various substrates for 40 species (27), measured using Biolog phenotype ar- MATERIALS AND METHODS rays, a technique with broad application to metabolic mod- at Ruder Boskovic Institute on December 9, 2016 eling (28). Definitions Here, we adopt a broad definition of a phenotypic trait (or, Text sources and preprocessing simply, trait) of a microorganism. Firstly, this encompasses the common meanings of this term in the field of micro- We analyzed text documents describing 1640 bacterial and biology (metabolic capabilities, morphology, growth condi- archaeal species from (i) Wikipedia, (ii) MicrobeWiki, (iii) tions). Secondly, this also encompasses the microbe’s ability HAMAP proteomes, (iv) PubMed abstracts retrieved upon to colonize certain ecological niches, which includes the as- searching for the name of each species (setting ‘sort by rel- sociation to a particular host and an organ/tissue thereof, evance’; first 100 items kept); (v) a set of PubMed Cen- and moreover the type of this association (pathogenic, sym- tral publications that were looked up via the ‘Reference’ biotic). While a trait is any attribute of an organism which field in the KEGG Organisms repository; and (vi) amixed matches the above definition, the term ‘phenotype’ also im- collection of smaller resources that were pooled together
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