The ISME Journal (2018) 12, 411–423 © 2018 International Society for Microbial Ecology All rights reserved 1751-7362/18 www.nature.com/ismej ORIGINAL ARTICLE Metabolic shift at the class level sheds light on adaptation of methanogens to oxidative environments Zhe Lyu1,2 and Yahai Lu1,3 1College of Resources and Environmental Sciences, China Agricultural University, Beijing, PR China; 2Department of Microbiology, University of Georgia, Athens, GA, USA and 3College of Urban and Environmental Sciences, Peking University, Beijing, PR China Methanogens have long been considered strictly anaerobic and oxygen-sensitive microorganisms, but their ability to survive oxygen stress has also been documented. Indeed, methanogens have been found in oxidative environments, and antioxidant genes have been detected in their genomes. How methanogens adapt to oxidative environments, however, remain poorly understood. Here, we systematically predicted and annotated antioxidant features from representative genomes across six well-established methanogen orders. Based on functional gene content involved in production of reactive oxygen species, Hierarchical Clustering analyses grouped methanogens into two distinct clusters, corresponding to the Class I and II methanogens, respectively. Comparative genomics suggested a systematic shift in metabolisms across the two classes, resulting in an enrichment of antioxidant features in the Class II. Moreover, meta-analysis of 16 S rRNA gene sequences obtained from EnvDB indicated that members of Class II were more frequently recovered from microaerophilic and even oxic environments than the Class I members. Phylogenomic analysis suggested that the Class I and II methanogens might have evolved before and around the Great Oxygenation Event, respectively. The enrichment of antioxidant features in the Class II methanogens may have played a key role in the adaption of this group to oxidative environments today and historically. The ISME Journal (2018) 12, 411–423; doi:10.1038/ismej.2017.173; published online 14 November 2017 Introduction et al., 2001). However, populations of methanogens appeared to decline greatly at the end of the Archean Methane has been a key component in the atmosphere Eon, likely due to the depletion of oceanic nickel, since the dawn of life on Earth (Kasting, 1993). Being which is an essential cofactor for many key enzymes one of the most potent greenhouse gases, methane has in the methanogenesis pathway and required for a crucial role in regulating the modern as well as growth (Thauer et al., 2010), and the increasing ancient climate of Earth (Pavlov et al., 2000). Snowball environmental oxygenation during the Great Oxida- events in the ancient Earth could be linked to declines tion Event at ~ 2.5–2.3 Ga (Konhauser et al., 2009). in the concentration of methane, and modern Earth is Oxygenation could be detrimental directly to metha- now experiencing global warming at least in part nogens via the formation of deadly derivatives of O2 or owing to excess emissions of anthropogenic methane 2 − reactive oxygen species (ROS), such as H2O2 and O (Kasting, 2004; Bousquet et al., 2006). The biological radicals (Imlay, 2008). production of methane in the atmosphere has been Previous reports have demonstrated that methano- largely attributed to the activity of methanogens, a gens neither abandon their overall dependence on group of strictly anaerobic microbes which likely nickel nor develop nickel-specific ligands to cope with originated at some point in the mid to early-Archean low nickel availability when facing nickel famine (Battistuzzi et al., 2004; Ueno et al., 2006; Wordsworth (Hausrath et al., 2007; Thauer et al., 2010). Likewise, it and Pierrehumbert, 2013). It is suggested that metha- remains elusive how ancestors of methanogens coped nogens could have dominated the biosphere in most of with the ROS damage. Nevertheless, they appeared to the Archean Eon, and methane emissions may have prevented the rapid cooling of the early Earth (Catling have survived those catastrophic events and prolifer- ated into the modern era. Today, modern methano- gens are virtually found in all types of anaerobic Correspondence: Y Lu, College of Urban and Environmental environments, and most of them are sensitive to Sciences, Peking University, Beijing 100871, PR China. oxygen (Fetzer et al., 1993; Fetzer and Conrad, 1993; E-mail: [email protected] Received 19 April 2017; revised 31 July 2017; accepted 9 August Thauer et al., 2010; Yuan et al., 2011). However, it has 2017; published online 14 November 2017 been shown that some methanogens could survive Metabolic shift across two methanogen classes Z Lyu and Y Lu 412 oxygen stress for several hours to days (Kiener and from the sampled genomes (Supplementary Table 1). Leisinger, 1983; Fetzer et al., 1993; Ueki et al., 1997; Ribosomal proteins were aligned by MAFFT (v7.107) Ma and Lu, 2011), and they have also been found in (Katoh and Standley, 2013). Concatenation of typical oxidative environments such as upland soils sequence data was done by Geneious R8.1 (Kearse (Angel et al.,2011;Angelet al., 2012). Likewise, et al., 2012). The E-INS-I algorithm was used for physiological, ecological and genomic analyses have MAFFT. The alignments were filtered by eliminating revealed that at least certain methanogen lineages any position that was missing data from 90% or more have evolved strong antioxidant features (Fetzer and out of the total aligned taxa unless otherwise Conrad, 1993; Erkel et al., 2006; Angel et al., 2012; Lyu mentioned. Phylogenetic trees were constructed by and Lu, 2015). However, how methanogens adapt to either RAxML (v8.2.9) constrained by the bootstrap- oxidative environments remain poorly understood. To ping autoMRE stopping criteria or MrBayes (v3.2.6) gain more insight into this puzzle, here we sampled through Bayesian inference (BI) (Ronquist and genomes from six well-established methanogen orders Huelsenbeck, 2003; Stamatakis et al., 2008). Both (Methanobacteriales, Methanocellales, Methanococ- algorithms were hosted by the CIPRES Science Gate- cales, Methanomicrobiales, Methanosarcinales and way (Miller et al., 2010), where a WAG protein matrix Methanopyrales) and predicted their antioxidant model with corrections for a gamma distribution with features by analyzing functional genes relevant to four discrete rate categories and a proportion of ROS production, O2/ROS elimination and self- invariant sites (WAG+G+I) were implemented for the repairing systems. A survey of environmental 16 S phylogenetic analyses. Whenever necessary, another rRNA genes of those methanogen lineages was also protein model (LG+G+I) was also implemented in both conducted to determine their distribution in oxidative RAxML and MrBayes, testing if a tree topology environments. At last, a phylogenomic analysis was remained robust after changing the substitution performed to estimate major diversification events of model. This model was chosen out of the 56 different those methanogens and to put our results into an substitution models examined by Mega 7 based on the evolutionary perspective. best fit Bayesian Information Criterion (Kumar et al., 2016). For BI, the following parameters were also implemented, nrun = 3, ngen = 1 000 000, nchains = 4, Subjects and Methods samplefreq = 10, nst = 6, Nucmodel = protein, rates = invgamma, sump burnin = 50 000, sumt burnin Comparative genomics Functional genes associated to antioxidant potentials = 50 000 and burin frac = 0.25. A few pre-runs were were extracted from the sampled methanogen gen- performed to optimize the parameters to ensure the omes (Supplementary Table 1) and manually curated log probability plateaued after the burnin setting and and re-annotated based on experimental evidence the Markov Chain Monte Carlo samplings converged (Supplementary Methods). The presence of [4Fe-4S] before the ngen setting. clusters in methanogenesis redox enzymes was Both RAxML and MrBayes trees were subjected to summarized from various experimental sources and molecular dating with the RelTime module in Mega 7 verified via examining conserved [4Fe-4S] binding (Tamura et al., 2012; Kumar et al., 2016). Relative node motifs for all strains by sequence alignments (Major ages were first obtained by using the ribosomal protein et al., 2004). Hierarchical clustering of gene content alignment data with the same aforementioned WAG+G was done by the Ward’s minimum variance method +I or LG+G+I model in a maximum-likelihood frame- with JMP Pro, Version 12, SAS Institute Inc. work. Reliable calibration anchors reported previously Nonparametric tests were performed with Kruskal– were then used to scale the relative node ages into Wallis analysis of variance followed by Mann– absolute dates (Battistuzii and Hedges, 2009; Marin Whitney to investigate the statistical differences in et al., 2017). RelTime belongs to the latest version of gene abundance among methanogen classes. molecular dating algorithms, which appears to outper- form the sophisticated and time-consuming Bayesian methods while retaining comparable accuracy (Tamura Environmental gene survey et al., 2012; Kumar and Hedges, 2016). Environmental 16 S rRNA gene sequences were extracted from EnvDB and classified by RDP10 (Cole et al., 2009; Tamames et al., 2009). Habitat Results and discussion information was further extracted and verified from the literature as well as GenBank for each sequence. Antioxidant features across methanogens Complete methods and datasets were summarized in Three