Etics Early Online, Published on September 16, 2015 As Doi:10.1534/G3.115.020180
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G3: Genes|Genomes|Genetics Early Online, published on September 16, 2015 as doi:10.1534/g3.115.020180 1 Long-lasting gene conversion shapes the convergent evolution of the critical 2 methanogenesis genes 3 Sishuo Wang1, 2, *, Youhua Chen3, 4, Qinhong Cao1, Huiqiang Lou1, * 4 5 Running title: Long-term effects of gene conversion 6 7 1 State Key Laboratory of Agro-Biotechnology and Ministry of Agriculture Key Laboratory 8 of Soil Microbiology, College of Biological Sciences, China Agricultural University, 2 9 Yuan-Ming-Yuan West Road, Beijing 100193, China 10 2 Department of Botany, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada 11 3 Department of Zoology, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada 12 4 Department of Renewable Resources, University of Alberta, Edmonton, T6G 2H1, Canada 13 14 15 16 * To whom correspondence should be addressed. Email: [email protected] (S. Wang) 17 Email: [email protected] (H. Lou) 18 1 © The Author(s) 2013. Published by the Genetics Society of America. 1 Abstract 2 Methanogenesis and its key small-molecule methyltransferase Mtr complex are poorly 3 understood despite their pivotal role in Earth’s global carbon cycle. Mtr complex is encoded 4 by a conserved mtrEDCBAFGH operon in most methanogens. Here we report that two 5 discrete lineages, Methanococcales and Methanomicrobiales, have a non-canonical mtr 6 operon carrying two copies of mtrA resulted from an ancient duplication. Compared to 7 MtrA-1, MtrA-2 acquires a distinct transmembrane domain through domain shuffling and 8 gene fusion. However, the non-transmembrane domains (MtrA domain) of MtrA-1 and 9 MtrA-2 are homogenized by gene conversion events lasting throughout the long history of 10 these extant methanogens (over 2,410 million years). Furthermore, we identified a possible 11 recruitment of ancient non-methanogenic methyltransferase genes to establish the 12 methanogenesis pathway. These results not only provide novel evolutionary insight into the 13 methanogenesis pathway and methyltransferase superfamily, but also suggest an unanticipated 14 long-lasting effect of gene conversion on gene evolution in a convergent pattern. 15 Keywords 16 gene conversion, concerted evolution, gene fusion, methyltransferase, methanogenesis 17 2 1 Introduction 2 Mtr is the key methyltransferase in the biological methanogenesis process, which plays 3 crucial role in Earth’s global carbon cycle and has significant contribution to global warming 4 (GOTTSCHALK and THAUER 2001; HOUGHTON and INTERGOVERNMENTAL PANEL ON 5 CLIMATE CHANGE. WORKING GROUP I. 2001). However, our knowledge of methanogenesis 6 and its key small molecular methyltransferase remains very limited. 7 Methanogenesis is an ancient process performed exclusively by a particular group of 8 anaerobic archaea called methanogens (WEISS and THAUER 1993; BAPTESTE et al. 2005; 9 GRIBALDO and BROCHIER-ARMANET 2006; LIU and WHITMAN 2008). These methanogens 10 are capable to catalyze the formation of methane from low molecular organic compounds like 11 carbon dioxide and acetic acid, which is usually the final step in the decay of organic matter 12 (LELIEVELD et al. 1998; HOUGHTON and INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE. 13 WORKING GROUP I. 2001). Methane (CH4) is not only the major component of clean natural 14 gas, but also a potent greenhouse gas. On the Earth, approximately 109-10 tons of methane are 15 biologically produced through methanogenesis each year, while 1-10% is estimated to be 16 released into the atmosphere (THAUER 1998; WELANDER and METCALF 2005). 5 17 Mtr, N -methyltetrahydromethanopterin (CH3-H4MPT): Coenzyme M (CoM), catalyzes 18 the second-to-last step of methanogenesis C1 pathway (GOTTSCHALK and THAUER 2001). 19 Mtr complex is composed of eight subunits (named as MtrA-H) with no homology between 20 them (HARMS et al. 1995). Genes encoding the eight subunits are located in the 21 mtrEDCBAFGH operon (HARMS et al. 1995). Among the eight subunits, MtrH is thought to 22 be responsible for the energy-consuming transfer of the methyl-group from 3 1 methyl-tetrahydromethanopterin (H4MPT) (in some species 2 N5-methyltetrahydrosarcinapterin is the methyl donor (THAUER 1998)) to the corrinoid 3 prosthetic group harbored by MtrA (HIPPLER and THAUER 1999), a protein anchored to cell 4 membrane (HARMS and THAUER 1996). The conformational change induced in MtrA via 5 methylation and demethylation in turn enables the sodium pumper (probably MtrE) to pump 6 Na+ extracellularly and helps establish the sodium ion gradient utilized in the ATP synthesis 7 (BECHER et al. 1992; GOTTSCHALK and THAUER 2001; LANE and MARTIN 2012). 8 In this study, through comprehensive phylogenetic and comparative genomic study of 9 Mtr methyltransferase complex, we revealed an unexpected recurrent concerted evolution of 10 mtrA genes, likely lasting throughout the whole history of the extant methanogens (over 2,410 11 million years), after the convergent evolution of gene structure in three different archaeal 12 lineages. Strikingly, the transmembrane domain of the same gene undoes significant divergent 13 evolution very likely through domain shuffling and gene fusion. Additionally, we found an 14 ancient non-methanogenic origin of mtrH from a cobalamin-dependent 15 methyltetrahydrofolate-homocysteine methyltransferase. Besides, phylogenomic analyses 16 detected homologs of mtrA and mtrH in non-methanogens which are probably involved in 17 novel methyl-transfer pathways, implicating neo-functionalization after horizontal gene 18 transfer (HGT) between methanogens and non-methanogens. Our findings reveal the 19 important roles of gene conversion and gene fusion in shaping the convergent evolution of 20 methanogenesis genes and shed new insight into the evolution of small molecular 21 methyltransferase gene superfamily. 22 Methods 4 1 Identification of homologous sequences 2 Protein sequences from Uniprot-Swiss annotated as subunits of Mtr were chosen as the seed 3 sequences in BLASTP (ALTSCHUL et al. 1997) search. Homologs of mtr genes were identified 4 with the E-value threshold of 1e-10 and BLOSUM62 matrix in all species with complete 5 genome sequences available in RefSeq database (PRUITT et al. 2007). PSI-BLAST 6 (ALTSCHUL et al. 1997) was used to search for remote homologs with default parameters in 7 bacteria. The search was iterated until convergence. For homologs of Mtr proteins with a 8 typical length less than 100 amino acids (MtrB, MtrF, MtrG), we used a different strategy to 9 enhance the sensitivity of the search. We used seed sequences as the bait and searched for 10 homologs in each order of methanogenic archaea. In this case, the hit with the lowest E value 11 was then used to search for homologs in the same order and candidates with an E value lower 12 than 1e-5 were considered as homologs. The taxonomic information of methanogenic archaea 13 at order level was based on (BORREL et al. 2013) where methanogens are classified into seven 14 orders including six traditional orders and a recently proposed order referred to Mx in this 15 study for current communication (BORREL et al. 2013). The putative false positive homologs 16 were minimized with hmmscan implemented in HMMER3 (EDDY 1998) using the HMM 17 profile of the corresponding domain. MeTr domain containing proteins shown in the 18 phylogenetic tree were selected based on the list of representative species across different 19 kingdoms of life (RICHARDS et al. 2011) and clustered using Cd-hit (LI and GODZIK 2006) to 20 filter out highly redundant sequences. Sequence processing and format conversion were 21 performed using custom Perl and Ruby (GOTO et al. 2010) scripts. 22 Phylogenetic reconstruction 5 1 Sequence alignment was performed using MUSCLE v3.8.31 (EDGAR 2004) with default 2 settings and edited manually with Bioedit (HALL 1999). Alignment was visualized with 3 UGENE (OKONECHNIKOV et al. 2012). Prior to phylogenetic reconstruction, jModelTest v2.1 4 (DARRIBA et al. 2012) and ProtTest v3.2 (DARRIBA et al. 2011) were used to find the most 5 proper substitution model for DNA sequence alignment and protein sequence alignment using 6 the Akaike Information Criterion (AIC), respectively. 7 Phylogenetic analyses were generated using maximum likelihood and Bayesian methods 8 separately. The phylogenetic tree of each gene encoded by mtr operon was built based on 9 protein sequences alignments using RAxML v7.3.9 (STAMATAKIS 2006) with 500 pseudo 10 replicates of bootstrap sampling. To infer gene conversion events, maximum likelihood trees 11 were reconstructed based on DNA sequences alignments with maximum likelihood and 12 Bayesian methods, respectively. Maximum likelihood was reconstructed using Phyml 3.0 13 (GUINDON and GASCUEL 2003) with 1,000 pseudo replicates of bootstrap sampling Bayesian 14 phylogenetic analyses were performed with Mrbayes v3.1 (RONQUIST and HUELSENBECK 15 2003). MCMC chains were run for 10 million generations with sampling every 16 1,000 generations. The average standard deviation of split frequencies reached less than 0.002 17 when chains were summarized. The chains were thought to reach convergence. The first 25% 18 of the sampled generations were discarded as burn-in and the rest were used in the calculation 19 of posterior probabilities and consensus tree construction. Phylogenetic trees were visualized 20 with MEGA v5 (TAMURA et al. 2011), TreeGraph v2.0 (STOVER and MULLER 2010) and 21 Figtree v3.1 (http://tree.bio.ed.ac.uk/software/figtree/). For DNA sequences utilized in gene 22 conversion detection, maximum likelihood bootstrap support values and Bayesian inference 6 1 posterior probability for each node were mapped using TreeGraph (STOVER and MULLER 2 2010) and checked manually. The phylogenetic history of all methanogens is based on 3 (BORREL et al. 2013) where all of the seven orders of methanogens are included. The 4 divergence time of species was obtained from TimeTree (BATTISTUZZI and HEDGES 2009; 5 KUMAR and HEDGES 2011). 6 Detection of gene conversion 7 To detect gene conversion events, phylogenetic trees were built using maximum likelihood 8 and Bayesian methods based on DNA sequences separately as described above.