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Phylogenomic proximity and metabolic discrepancy of Methanosarcina mazei Go1 across methanosarcinal genomes
Accepted Manuscript Title: Phylogenomic Proximity and Metabolic Discrepancy of Methanosarcina mazei Go1 across Methanosarcinal Genomes Authors: M. Bharathi, P. Chellapandi PII: DOI: Reference:
S0303-2647(16)30231-3 http://dx.doi.org/doi:10.1016/j.biosystems.2017.03.002 BIO 3733
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BioSystems
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17-9-2016 15-3-2017 20-3-2017
Please cite this article as: Bharathi, M., Chellapandi, P., Phylogenomic Proximity and Metabolic Discrepancy of Methanosarcina mazei Go1 across Methanosarcinal Genomes.BioSystems http://dx.doi.org/10.1016/j.biosystems.2017.03.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Phylogenomic Proximity and Metabolic Discrepancy of Methanosarcina mazei Go1 across Methanosarcinal Genomes
Running title: Phylogenomic proximity...............methanosarcinal genomes
Bharathi M and Chellapandi P*
Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli-620 024, Tamil Nadu, India Tel: +91-431-2407071 Fax:+91-431-2407045 Email: [email protected] *Corresponding author Highlights
Current genomic data of Methanosarcina mazei Go1 provide an important of its phylogenomic origin for the evolution of metabolic core. This study speculates its metabolic similarity and dissimilarity across the methanosarcinal genomes. Comparative metabolic data discovered of four novel putative metabolic pathways in this genome.
Abstract Methanosarcina mazei Go1 is a heterotrophic methanogenic archaean contributing a significant role in global methane cycling and biomethanation process. Phylogenomic relatedness and metabolic discrepancy of this genome were described herein by comparing its whole genome sequence, intergenomic distance, genome function, synteny homologs and origin of replication, and marker genes with very closely related genomes, Methanosarcina acetivorans and Methanosarcina barkeri. Phylogenomic analysis of this study revealed that genome functional feature and metabolic core of M. mazei and M. barkeri could be originated from M. acetivorans. The metabolic core of these genomes shares a common evolutionary origin to perform the metabolic activity at different environmental niches. Genome expansion, dynamics and gene collinearity were constrained and restrained the conservation of the metabolic core genes by duplication events occurring across methanosarcinal genomes. The Darwinian positive selection was an evolutionary constraint to purify the function of core metabolic genes. Using genome-wide metabolic survey, we found the existence of four novel putative metabolic pathways such as complete methanogenesis from acetate, indole-3-acetate biosynthesis V, 4-aminobutyrate degradation III, galactosamine biosynthesis I and siroheme biosynthesis. Overall, the present study would provide a stand point to revisit its phylogenomic status in order to understand the origin and evolution history of this organism. 1
Keywords: Methanosarcina; Methane; Genome evolution; Methanogenesis; Biogas; Metabolomics
1. Introduction Methanogenic archaea play an energetic role in the global carbon cycle by producing methane with the measured amount of one billion metric tons (Thauer et al. 2008; McInerney et al. 2009). Methanosarcina mazei Go1 (MMA) is a marine methanogenic archaean belonging to the Methanosarcina genus. It is able to use all methanogenic substrates in an anaerobic environment. It has diverse physiological functions capable of converting methanogenic substrates or reducing CO2 to methane, as revealed by global proteomic and DNA microarray experiments (Hovey et al. 2005; Li et al. 2005; Lie et al. 2005). This organism creates a favorable thermodynamic condition by scavenging H2 and keeping its partial pressure low, leading to allow the growth of sulfate reducing bacteria (Li et al. 2005; Stolyar et al. 2007). Genetic, metabolic process and biochemistry of many Methanosarcina species have been studied extensively in recent years (Chellapandi 2011a; 2011b; 2013). A huge quantity of available high-throughput data and genome sequences of methanogenic archaea has been provided the systems-level understanding of its growth physiology and metabolism (Chellapandi 2010). The genome sequence of MMA was completed and revealed the lateral gene transfer event acted as a major evolutionary constraint for its genomic expansion (Deppenmeier et al. 2002). Extensive gene rearrangements and a high degree of conservation were compared to describe its mechanism of the structural modification and functional organization across methanosarcinal genomes (Maeder et al. 2006). Intergenomic evolution across methanogenic archaea was influenced on the genomic distribution of haloarchaeal rRNA genes, tryptophan synthase and archaeal conjugative plasmids (Boucher et al. 2004; Basta et al. 2009; Lane 2014). Salt adaptation, osmoregulatory network and glycine betaine transporters of MMA were genetically identified by genome-wide gene expression profiling (Pflüger et al. 2007; Spanheimer and Müller, 2008; Saum et al. 2009a; Saum et al. 2009b). Metabolic constituents of the cell and methanogenic growth capabilities were computationally reconciled from genome-scale metabolic models of M. barkeri (MBA) iAF692 (Feist et al. 2006), iMG746 (Gonnerman et al. 2013), M. acetivorans (MAC) iVS941 (Kumar et al. 2011), iMB745 (Benedict et al. 2012), iMAC868 (Nazem‑Bokaee et al. 2016) and Methanococcus maripaludis iMM518 (Goyal et al. 2014). Inference of intergenomic distance, and whole genome phylogeny are used as general computational parameters for studying the gene duplication event and the gene distribution of closely related genomes (Maeder et al. 2006, Gao 2014). Phylogenomic studies are also employed to estimate the overall similarity and dissimilarity between the genomes (Wei et al. 2002; Auch et al. 2010). Phylogenetic markers such as 5s rRNA, 16s rRNA, 23s rRNA and SRP (Signal Recognition Particle) are used to deduce the genetic and biochemical systematics of methanogenic archaea in order to correct many major clades in the tree (Lai et al. 1999; Stantscheff et al. 2014; Petitjean et al. 2015). Secondary structure-based on rRNA such as ykok, group II intron and RNaseP sequences were also used for accurate prediction of 2
the phylogenetic status of prokaryotic genomes (Juana and Wilsona 1999). Methyl-CoM reductase A (mcrA) gene was served as a functional marker for the taxonomic classification of all methanogenic archaea (Ufnar et al. 2007; Wrede et al. 2013). Comparison of genomic features is a secondary layer of biological description, which affords a significant and effective flowchart for the metabolic pathways among methanogenic archaea (Maeder et al. 2006; Zhang et al. 2009). We have undertaken such approach to explain or to resolve the genomic proximity and metabolic discrepancy of MMA from its phylogenetic neighbors such as MAC and MBA. We inferred how intergenomic evolutionary constraints attribute its genome dynamics and metabolic adjustment across methanosarcinal genomes. Using this approach, genes are analyzed case-by-case to make genome annotation in a more complete and significant way. 2. Materials and Methods 2.1. Genome-scale tree construction The complete genome sequence of MMA was retrieved from the National Center for Biotechnology Information (NCBI) -FTP site for finding its current phylogenomic status and evolutionary relatedness among all methanogenic archaea (http://www.ncbi.nlm.nih.gov/). A whole-genome based tree was constructed by a composition vector algorithm (K-mer length = 6) using CVtree3 server (Xu and Hao 2009). Each organism is represented by a composition vector made of K-mer length obtained from a given genome. To suppress the effect of neutral mutations and to highlight the shaping role of natural selection, a subtraction procedure based on (K−6)-th order Markov prediction is introduced in the CVtree method. The collection of distances for all species pairs comprises a dissimilarity matrix, which has been calculated by using the neighbor-joining (NJ) algorithm (Saitou and Nei 1987) to construct phylogenomic trees. In the CVtree, time-consuming statistical re-sampling tests such as Jackknife and bootstrap tests can be neglected as it is an alignment-free and parameter-free method. 2.2. Analysis of genome synteny Syntenic regions in the genome of MMA were identified by comparing with those found in MAC and MBA using CoGe:SynMap (Lyons et al. 2008). Nucleotide-nucleotide search between two genomes was performed with a LastZ algorithm (http://last.cbrc.jp). A collinear set of genes was identified and merged by using interactive DAGChainer (Haas et al. 2004). False positive synteny was removed by Quota Align algorithm and then merged all refined syntenic blocks (Tang et al. 2011). Synonymous and non-synonymous substitutions between syntenic gene pairs were computed by PAML package using CodeML algorithm. Synteny blocks and evolutionary events resulted due to synonymous substitution were represented as syntenic histogram (Yang 2007). 2.3. Analysis of intergenomic distance Genomic variations in the functional genes in the MMA were detected by measuring Pearson correlation coefficients for a cluster of orthologs profiling of its closely related genomes (Benesty et al. 2009). Pearson correlation coefficients determine the intergenomic relatedness of selected genomes. Intergenomic distances were inferred between MMA and its phylogenetic neighbors using Genome-to-Genome Distance Calculator (Meier-Kolthoff et al. 2013). The genomic compositional dissimilarity between MMA and its phylogenetic 3
neighbors was measured by Bray-Curtis dissimilarity coefficient at a non-metric multidimensional scale (Bray and Curtis 1957). 2.4. Analysis of origin of replication (oriC) We detected oriCs and genes encoding for DNA replication proteins from the genomes of MMA, MAC and MBA by Ori Finder 2.0 (Luo et al., 2014). The common ORB (the origin recognition boxes), a specific motif within oriC in archaeal genome, was used to predict the sequence of oriCs across these genomes. The predicted oriCs and DNA replication protein sequences were aligned separately by ClustalX 2.0 software (Thompson et al. 1997) for detecting a sequence-specific conservation pattern across these genomes. 2.5. Analysis of molecular markers We retrieved nucleotide sequences of phylogenetic markers (5s, 16s and 23s rRNAs) and amino acid sequences of functional markers (mcrA and SRP) from Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/). Sequence similarity hits for these sequences were obtained by the BLAST tool by searching against KEGG database (Altschul et al. 1997). Multiple sequence alignments were carried out separately by ClustalX 2.0 software (Thompson et al. 1997). After complete alignment, the aligned sequences were edited manually to remove improperly aligned sequences and then phylogenetic trees constructed with MEGA 5.0 software (Tamura et al. 2011). Kimura-2parameter was chosen as an evolutionary model with 1000 bootstrap replicates and 5 Gamma distribution rates (Lukacs, 1955). Segregation sites, recombination/scalable-mutation rate, nucleotide diversity were estimated for each marker across different species of methanogenic archaea. Tajima's D test performed to determine genetic drift and genetic diversity among methanogenic archaea (Tajima 1989). All of these evolutionary genetic analyses were conducted by MEGA software. The free energy of rRNA secondary structure was calculated by MFold server at the folding temperature of 37oC with 1M NaCl ionic condition (Zuker et al. 2003). Homology models for mcrA were generated by SWISS-MODEL based a structural template (pdb id: 1E6Y: A) (Biasini et al. 2014). The reliability of modelled structures was determined with template by means of QMEAN Z-Score and sequence identity. Structural alignment and superimposition of each model were performed with 3D-SS server to calculate the disparity in the structural motifs (Sumathi et al. 2006). Superimposed structure was visualized in the UCSF Chimera 1.10 software (Pettersen et al. 2004). 2.6. Analysis of metabolic features We have carried out comparative metabolomic analysis to discover unique genes and to novel putative metabolic pathways based on the metabolic relatedness with MBA and MAC. Metabolic information for each genome was collected from the MetaCyc database (Caspi et al. 2014) and individual metabolic features, genes, enzymes and pathways compared manually. Enzymes involved in the central metabolism and methanogenesis were searched for MMA and then compared its metabolic feature with MBA and MAC. Novel putative metabolic pathways were identified by merging common genes, and enzyme shared across methanosarcinal genomes. 3. Results 3.1. Whole genome phylogeny 4
A whole genome phylogenetic tree was constructed with 39 completed genome sequences as represented in Fig 1. Methanobacterium SWAN1 and AL21 are found as out-group organisms for the taxonomic classification of the MMA genome in the tree. There are two major clusters formed separately with monophyletic groups of different physiological species. In the first cluster, many Methanococcus genomes are grouped with Methanothermococcus Methanobacterium (Methanobrevibacter smithi and Methanobrevibacter ruminantium). The genera of Methanosarcina, Methanothermobacter and Methanosaeta are placed in the second cluster. A phylogenetic proximity is observed between MMA and MAC and then with MBA, which are further grouped with Methanoregula formicicum, Methanothermobacter marburgensis and Methanothermobacter thermoautotrophicus. 3.2. Intergenomic distance The results of the genome correlation coefficient analysis indicate that MMA genome is closely related with MAC (0.80) and MBA (0.83) and all three of them are shared their genomic function with distantly related methanogenic archaea (0.67-0.56) (Table 1). Similarly, intergenomic distance analysis demonstrates that MMA genome is very close to the MAC genome (0.6), but it was slightly far away from MBA (0.7), and distantly related to other methanogenic archaea. The rank order correlation of a dissimilarity matrix describes that MMA is highly correlated with MAC and followed by MBA. Non-methanogenic archaea have shown high rank order dissimilarities to the MMA while comparing with other methanogenic archaea (Supplementary Fig. S1). 3.3. Genome synteny of homologs Genome synteny analysis shows that whole genome duplication events occurred between the MMA and MBA is higher than those events occurred between MMA and MAC (Fig 2). Syntenic orthologs in the MMA are originated from MBA, which was relatively more than those originated from MAC. It may be resulted from low rate of synonymous substitutions. A collinearity of either gene orders or gene families may be adjusted by syntenic blocks at the location of syntenic out-paralogs. 3.4. Origin of replication In the genome of MMA, we noted two different oriC sites in the MMA genome as represented in Fig 3. The first oriC region is located in upstream of cdc6 gene at the genomic position of 1564657-1566241 bp (GC cont. 26.50%). The second oriC region is located in upstream of AAA+ ATPase gene that is required for initiation of DNA replication. Both oriC regions identified in the MMA are similar to those found in the MAC, but oriC regions in the MBA are located at different positions. The ORB identified from MMA has shown 95% similarity with MAC and MBA, indicative of evolutionary conservation of the DNA boxes across methanosarcinal genomes. 3.5. Genetic diversity of marker genes Phylogenetic and metabolic markers of this genome compared with those markers similarly found in the MAC and MBA as shown in Fig 4. Phylogenetic diversity of methanogenic archaea is associated with segregation sites in the 16s rRNA and mcrA (Supplementary Fig S2 & S3). At the low evolutionary frequency rate, segregation sites determine the conservation of marker genes across methanosarcianal genomes. SRP and fdhD of 5
Methanosarciana are highly mutable during evolutionary events. Estimates of nucleotide diveristy demonstrate its genetic polymorphism explicating the moderate diversity in patterns of metabolic markers (fdhD, hemC, ogt, glyS, coaE and ahaD). There are fewer discrepancy observed in metabolic markers (fdhD, coaE and hemC) among methanogenic archaeal lineage. Apart from phylogenetic markers, all of the markers have shown to diverge functionally by imposing Darwinian positive selection. 3.6. Metabolic diversity Genome-wide metabolic mining of our study points out that MMA genome covers 123 pathways consisting of 670 metabolic reactions and 134 pathway holes. Totally, 243 genes and 137 enzymes are shared across methanosarcinal genomes and 97 genes are unique to the MMA. A total of 123 genes is shared between MMA and MAC compared to MBA as shown in Fig 5. Similarly, an increased number of enzymes are shared between MMA and MAC. Genes involved in central metabolism are common in all methanosarcinal genomes. Nevertheless, MMA consists of 3 unique genes involved in methanogenesis, suggesting the existence of a complete system for methane biosynthesis. 3.7. Discovery of novel putative metabolic pathways Genes coding for carbon monoxide dehydrogenase (EC 1.2.7.4), corrinoid/iron-sulfur protein Co-methyltransferase (EC 2.1.1.245) and co-methylating acetyl-CoA synthase (EC 2.3.1.169; α-subunit: MM_0358; β-subunit: MM_0493; acetyl CoA synthase: MM_3180) are exclusively found in the MMA, which are not present in MAC and MBA. All of these enzymes are involved in hydrogenotrophic methanogenesis. Indole-3-acetate biosynthesis V pathway is a novel putative metabolic pathway identified from MMA that can be mediated by gene MM_1229. Similarly, MM_0047, MM_0838 and MM_2341 are unique genes involved in the 4-aminobutyrate degradation. MMA also exclusively consists of unique genes MM_1162 and MM_1740 to be involved in galactosamine biosynthesis I and siroheme biosynthesis, respectively (Fig 6). 4. Discussion Our comparative genomic study was aimed to address its phylogenomic relationship and metabolic discrepancy from closely related methanosarcinal genomes (MAC and MBA). Similarly, comparative genomic analysis was used to infer its evolutionary dynamics of nickel, cobalt and vitamin B12 assimilation systems (Wei et al. 2002; Zhang et al. 2009). The results of phylogenetic analysis indicated that methanosarcinal genomes are closely related together and their genomic features and some of the metabolic core genes are shared with the genome of halophilic archaea in accordance to the previous work (Chellapandi et al. 2009). It suggested that MMA has a typical molecular system for salt adaptation and osmotolerance was found in the MMA, as evidenced earlier by gene expression profiling (Pflüger et al. 2007; Spanheimer and Müller, 2008; Spanheimer et al. 2008; Saum et al. 2009a; 2009b). MMA strain N2M9705 was isolated from aquaculture fishpond, which has shown the methylotrophic growth capability (Lai et al. 1999). We found three unique genes coding for enzymes (EC 1.2.7.4; 2.1.1.245; 2.3.1.169) involved in the hydrogenotrophic methanogenesis. Therefore, our study provides additional support for the existence of a typical system for methylotrophic capability. 6
Analysis of intergenomic distances stated that some of its metabolic features may be originated from distantly related genomes rather than methanosarcinal genomes, as suggested by the earlier hypothesis of molecular evolution of methanogens (Chellapandi 2010). Synonymous substitution is a major evolutionary event for the duplication of the syntenic orthologs and syntenic out-paralogs, which might be occurred by either -event or -event. The majority of duplicated genes in MMA was arisen from MBA through MAC. Such duplicated genes were major cause-effect in its genome complexity and species diversity through mutation and genomic drift (Zhu et al. 2013). Consequently, its metabolic function may be restrained to the new environmental niche. Gene products in the chromosomal oriC region of MMA are approximately 93-95% identical across the methanosarcinal species (Deppenmeier et al. 2002; Maeder et al. 2006). Several origin recognition boxes are flanked by oriC region, which have shown homology towards hypothetical protein MA_4646 from MAC and hypothetical protein Mbar_A2231 from MBA. It implied that replication initiation gene products (cdc6 and AAA+ ATPase) in the oriC regions share a common archaeal ancestor for all methanosarcinal species, but the genomic location of oriC and sequences are evolutionarily conserved within species. Very often, its genome seems to be expanded due to the copy number of rRNA genes and exclusive existence of ykok and group II intron. Ribosomal RNA and mcrA genes are well known molecular markers for the phylogenetic classification of methanogenic archaea and to study the evolutionary relationships (Zhang et al. 2009; Chellapandi et al. 2009; Stantscheff et al. 2014; Petitjean et al. 2015). Many metabolic marker genes are highly conserved across methanogenic archaea, but at the structural level, each marker was faintly varied within species. It may be resulted due to the Darwinian positive selection acting on metabolic markers or gene shuffling/gene duplication events. Horizontal gene transfer (HGT) plays a major role in the evolution of Methanosarcina genomes. The largest set of metabolic pathways and the large genome size in Methanosarcina genus are caused by massive HGT from bacteria (Maeder et al. 2006; Chellapandi, 2011a, 2011b, 2013). About 5% of genes in Methanosarcina are horizontally transferred from bacterial origin. About 9.4% of metabolic enzymes and 11.5% of transporters in MMA are horizontally transferred from bacteria, but the regulation of these genes is unknown (Garushyants et al. 2015). The distribution of enzymes and transporters among horizontally transferred genes was slightly varied from MMA to MBA; MMA to MAC. Hence, intergenomic evolution is a major constraint to determine its metabolic function from ancestral species by massive HGT from bacterial origins. Complete methanogenesis from acetate, indole-3-acetate biosynthesis V, 4aminobutyrate degradation III, galactosamine biosynthesis I and siroheme biosynthesis are novel putative metabolic pathways disclosed from this genome (Supplementary Fig. S4). This organism can adapt to utilize different methanogenic substrates using substrate-dependent gene expression system (Hovey et al. 2005). It is capable of utilizing acetate as a sole carbon source for its growth and methanogenesis. As MMA contains acetyl-CoA decarbonylase/synthase with all five subunits, it may able to grow on carbon monoxide as similar to those found in MMA (Matschiavelli et al. 2012), MBA (Hutten et al. 1980) and autotrophic methanogenic archaea (Chellapandi, 2011a, 2011b, 2013; Cheng et al. 2011; 7
Goyal et al. 2014). Qualitative analysis of CO dehydrogenase/acetyl coenzyme A synthase multienzyme complex (CODH/ACS)-encoding transcripts of MMA supported that CODH/ACS isoform catalyzed both CO oxidation/CO2 reduction and cleavage/synthesis of acetyl-CoA (Eggen et al. 1996). Moreover, indole-3-acetate produced from MMA via indole3-acetate biosynthesis V may indirectly support the growth of plants as a phyto-regulator. Similar pathway was reported in the plant-associated fungi such as Taphrina wiesneri, T. deformans and T. pruni (Yamada et al. 1990) and an opportunistic vertebrate pathogen, Alcaligenes faecalis (Kobayashi et al. 1993). 4-Aminobutyrate is a four-carbon non-protein amino acid that may be derived as a product of plant, animal tissue decay and bacteria. It is produced as an intermediate during bacterial degradation of putrescin. MMA consists of three unique gene coding enzymes involved in the 4-aminobutyrate degradation III as similar to Saccharomyces cerevisiae (Ramos et al. 1985) and Ralstonia eutropha H16 (Mayer and Cook 2009). Acetylornithine aminotransferase from MMA is closely related to validamycin aminotransferase from Streptomyces hygroscopicus 5008 and the putative 4-aminobutyrate aminotransferase from Deinococcus radiodurans (Bai et al. 2006). UDP-N-Acetyl-D-galactosamine is the amino sugar nucleotide donor of N-acetyl-Dgalactosamine residues for the biosynthesis of a component of several different kinds of cell surface structures and glycosylated proteins (Milewski et al. 2006). It is produced from UDPN-acetyl-D-glucosamine by action of UDP-N-acetyl-D-glucosamine 4-epimerase (EC 5.1.3.7) in the UDP-N-acetyl-D-galactosamine biosynthesis I. This enzyme encoded gene MM_1162 may be useful as a distinct cell wall marker for identification of closely related methanosarcinal species as it is not found in MAC and MBA. Jain et al (2014)., described the stability and robustness of archaeal membrane ether lipids and evolutionary aspects of the membrane composition of the last universal common ancestor. MMA uses siroheme, an iron-containing isobacteriochlorin, for heme b biosynthesis found in many archaea and sulfate reducing bacteria (Bali et al. 2014). Siroheme synthase is a single trifunctional enzyme exclusively found in the MMA, which catalyzes all four reactions in siroheme biosynthesis. In vitro enzyme activity assay demonstrated its functional characterization for the conversion of iron-coproporphyrin III into heme during the alternative heme biosynthesis pathway in MBA (Kühner et al. 2014). Nevertheless, the late heme genes are missing from the genomes of MMA, MBA and MAC (Deppenmeier et al. 2002; Galagan et al. 2008). The core genes involved in the biosynthetic pathways for methane (Bapteste et al. 2005), acetamido sugar (Namboori and Graham 2008) and N-linked glycoprotein (Larkin et al. 2013) have shown to share a common evolutionary origin in the euryarchaeal lineage. 5. Conclusions Comparative genomic analysis is useful for better understanding of its phylogenomic relatedness and metabolome complexity across methanosarcinal genomes. A strong genomic and metabolic proximity is noted between MMA and MAC at the molecular level. Core metabolic genes of MMA and MBA might be originated from MAC and shared within methanosarcinal genomes at a slow evolution rate. Current phylogenetic and functional markers would discriminate its recent ancestral status in the tree. Comparative metabolic 8
analysis disclosed the four novel putative metabolic pathways, including complete methanogenesis from acetate, indole-3-acetate biosynthesis V, 4-aminobutyrate degradation III, galactosamine biosynthesis I and siroheme biosynthesis in this genome. As the results, we revealed its methylotorphic capability, phyto-regulatory activity, unique cell wall composition (UDP-N-acetyl-D-galactosamine) and siroheme biosynthesis. Quantitative transcriptome and proteome data would provide a warrent for studying the functional evolution of its genome and to infer molecular regulatory networks to be conserved within methanosarcial genomes. Hence, our approach would provide a hint to discover novel putative metabolic pathways for its genome refinement and genome-scale metabolic reconstruction in the future. Acknowledgement The corresponding author is thankful to the University Grants Commission, New Delhi, India, for financial assistance (42-864/2013 (SR)) to carry out the work. Conflict of interest The authors confirm that this article content has no conflict of interest. References [1]. Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., et al. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:3389-3402. doi: 10.1093/nar/25.17.3389. PMID: 9254694. [2]. Auch, A.F., Auch, A.F., Klenk, H.P., and Göker M. 2010. Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs. Stand Genomic Sci. 2:142–148. doi: 10.4056/sigs.541628. PMID: 21304686. [3]. Bai, L., Li, L., Xu, H., Minagawa, K., Yu, Y., Zhang, Y., Zhou, X., Floss, H.G., Mahmud, T., Deng, Z. 2006. Functional analysis of the validamycin biosynthetic gene cluster and engineered production of validoxylamine A. Chem Biol. 13:387-397. PMID: 16632251. [4]. Bali, S., Rollauer, S., Roversi, P., Raux-Deery, E., Lea, S.M., Warren, M.J., et al. 2014 Identification and characterization of the 'missing' terminal enzyme for siroheme biosynthesis in α-proteobacteria. Mol Microbiol. 92:153-163. doi: 10.1111/mmi.12542. PMID: 24673795. [5]. Bapteste, E., Brochier, C., and Boucher, Y. 2005. Higher-level classification of the Archaea: evolution of methanogenesis and methanogenic archaea. Archaea. 1:353-363. PMID: 15876569. [6]. Basta, T., Smyth, J., Forterre, P., Prangishvili, D., and Peng, X. 2009. Novel archaeal plasmid pAH1 and its interactions with the lipothrixvirus AFV1. Mol Microbiol. 71:23– 34. doi: 10.1111/j.1365-2958.2008.06488.x. PMID: 19007417. [7]. Benedict, M.N., Gonnerman, M.C., Metcalf, W.W., and Price, N.D. 2012. Genomescale metabolic reconstruction and hypothesis testing in the methanogenic archaeon Methanosarcina acetivorans C2A. J Bacteriol. 194:855–865. doi: 10.1128/JB.0604011. PMID: 22139506.
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Fig.1 Phylogenomic tree constructed by a compositional vector algorithm using the whole genome sequences of MMA and other closely related methanogenic archaea. Branch lengths are proportional to evolutionary distances. Out-group organism was selected randomly for the classification.
15
Fig.2 Synonymous substitution histograms for analysis of syntenic homologs between MMA and MAC; MMA and MBA
16
Fig.3 Z-curve (AT, GC, RY and MK disparity curve) representation for comparing the location of DNA replication proteins and OriC across the genomes of MMA, MAC and MBA. Short vertical red line indicates the location of DNA replication proteins. The black 17
arrows pointing below are the oriCs with origin recognition box sequences. OriC sequence alignment is shown in below left.
Fig.4 Estimates of genetic diversity and Darwinian selection of phylogenetic and metabolic markers 18
Fig.5 Comparative genomic analyses to identify the metabolic relationships of genes (a), enzymes (b), central metabolism (c), and methanogenesis (d) across the MMA, MAC and MBA. 19
MMA: Methanosarcina mazei Go1; MAC: Methanosarcina acetivorans C2A; MBA: Methanosarcina barkeri strain fusaro
Fig.6 The novel putative metabolic pathways discovered in MMA based on the unique genes. In siroheme biosynthesis (6a), successive methylation at positions 2 and 7 of uroporphyrinogen III forms precorrin-1 and precorrin-2, respectively. Precorrin-2 is oxidized to sirohydrochlorin by precorrin-2 dehydrogenase (EC 1.3.1.76) and then chelated with 20
ferrous iron for the synthesis of siroheme by sirohydrochlorin ferrochelatase (EC 4.99.1.4). 4Aminobutyrate can be degraded into succinate semialdehyde and L-glutamate by acetylornithine aminotransferase (EC 2.6.1.19) in the presence of 2-oxoglutarate (6b). Succinate-semialdehyde dehydrogenase [NADP+] (EC 1.2.1.16) catalyses succinate semialdehyde into succinate, which further assimilated via incomplete reductive TCA cycle for energy-driven process.
Table 1 Correlation coefficient calculated for comparing intergenomic distance of MMA across the phylogenomic neighbors 21
Genome AFU MMA MAC MBA MBU MMH MRU MSI MTP NPH
AFU 1 0.43 0.33 0.35 0.45 0.51 0.35 0.38 0.48 0.5
MMA 0.43 1 0.8 0.83 0.64 0.67 0.49 0.49 0.56 0.4
MAC 0.33 0.8 1 0.87 0.53 0.58 0.42 0.38 0.49 0.33
MBA 0.35 0.83 0.87 1 0.53 0.58 0.42 0.41 0.55 0.37
MBU 0.45 0.64 0.53 0.53 1 0.77 0.4 0.42 0.55 0.48
22
MMH 0.51 0.67 0.58 0.58 0.77 1 0.48 0.5 0.61 0.49
MRU 0.35 0.49 0.42 0.42 0.4 0.48 1 0.77 0.49 0.27
MSI 0.38 0.49 0.38 0.41 0.42 0.5 0.77 1 0.52 0.26
MTP 0.48 0.56 0.49 0.55 0.55 0.61 0.49 0.52 1 0.37
NPH 0.5 0.4 0.33 0.37 0.48 0.49 0.27 0.26 0.37 1
S0303-2647(16)30231-3 http://dx.doi.org/doi:10.1016/j.biosystems.2017.03.002 BIO 3733
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Please cite this article as: Bharathi, M., Chellapandi, P., Phylogenomic Proximity and Metabolic Discrepancy of Methanosarcina mazei Go1 across Methanosarcinal Genomes.BioSystems http://dx.doi.org/10.1016/j.biosystems.2017.03.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Phylogenomic Proximity and Metabolic Discrepancy of Methanosarcina mazei Go1 across Methanosarcinal Genomes
Running title: Phylogenomic proximity...............methanosarcinal genomes
Bharathi M and Chellapandi P*
Molecular Systems Engineering Lab, Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli-620 024, Tamil Nadu, India Tel: +91-431-2407071 Fax:+91-431-2407045 Email: [email protected] *Corresponding author Highlights
Current genomic data of Methanosarcina mazei Go1 provide an important of its phylogenomic origin for the evolution of metabolic core. This study speculates its metabolic similarity and dissimilarity across the methanosarcinal genomes. Comparative metabolic data discovered of four novel putative metabolic pathways in this genome.
Abstract Methanosarcina mazei Go1 is a heterotrophic methanogenic archaean contributing a significant role in global methane cycling and biomethanation process. Phylogenomic relatedness and metabolic discrepancy of this genome were described herein by comparing its whole genome sequence, intergenomic distance, genome function, synteny homologs and origin of replication, and marker genes with very closely related genomes, Methanosarcina acetivorans and Methanosarcina barkeri. Phylogenomic analysis of this study revealed that genome functional feature and metabolic core of M. mazei and M. barkeri could be originated from M. acetivorans. The metabolic core of these genomes shares a common evolutionary origin to perform the metabolic activity at different environmental niches. Genome expansion, dynamics and gene collinearity were constrained and restrained the conservation of the metabolic core genes by duplication events occurring across methanosarcinal genomes. The Darwinian positive selection was an evolutionary constraint to purify the function of core metabolic genes. Using genome-wide metabolic survey, we found the existence of four novel putative metabolic pathways such as complete methanogenesis from acetate, indole-3-acetate biosynthesis V, 4-aminobutyrate degradation III, galactosamine biosynthesis I and siroheme biosynthesis. Overall, the present study would provide a stand point to revisit its phylogenomic status in order to understand the origin and evolution history of this organism. 1
Keywords: Methanosarcina; Methane; Genome evolution; Methanogenesis; Biogas; Metabolomics
1. Introduction Methanogenic archaea play an energetic role in the global carbon cycle by producing methane with the measured amount of one billion metric tons (Thauer et al. 2008; McInerney et al. 2009). Methanosarcina mazei Go1 (MMA) is a marine methanogenic archaean belonging to the Methanosarcina genus. It is able to use all methanogenic substrates in an anaerobic environment. It has diverse physiological functions capable of converting methanogenic substrates or reducing CO2 to methane, as revealed by global proteomic and DNA microarray experiments (Hovey et al. 2005; Li et al. 2005; Lie et al. 2005). This organism creates a favorable thermodynamic condition by scavenging H2 and keeping its partial pressure low, leading to allow the growth of sulfate reducing bacteria (Li et al. 2005; Stolyar et al. 2007). Genetic, metabolic process and biochemistry of many Methanosarcina species have been studied extensively in recent years (Chellapandi 2011a; 2011b; 2013). A huge quantity of available high-throughput data and genome sequences of methanogenic archaea has been provided the systems-level understanding of its growth physiology and metabolism (Chellapandi 2010). The genome sequence of MMA was completed and revealed the lateral gene transfer event acted as a major evolutionary constraint for its genomic expansion (Deppenmeier et al. 2002). Extensive gene rearrangements and a high degree of conservation were compared to describe its mechanism of the structural modification and functional organization across methanosarcinal genomes (Maeder et al. 2006). Intergenomic evolution across methanogenic archaea was influenced on the genomic distribution of haloarchaeal rRNA genes, tryptophan synthase and archaeal conjugative plasmids (Boucher et al. 2004; Basta et al. 2009; Lane 2014). Salt adaptation, osmoregulatory network and glycine betaine transporters of MMA were genetically identified by genome-wide gene expression profiling (Pflüger et al. 2007; Spanheimer and Müller, 2008; Saum et al. 2009a; Saum et al. 2009b). Metabolic constituents of the cell and methanogenic growth capabilities were computationally reconciled from genome-scale metabolic models of M. barkeri (MBA) iAF692 (Feist et al. 2006), iMG746 (Gonnerman et al. 2013), M. acetivorans (MAC) iVS941 (Kumar et al. 2011), iMB745 (Benedict et al. 2012), iMAC868 (Nazem‑Bokaee et al. 2016) and Methanococcus maripaludis iMM518 (Goyal et al. 2014). Inference of intergenomic distance, and whole genome phylogeny are used as general computational parameters for studying the gene duplication event and the gene distribution of closely related genomes (Maeder et al. 2006, Gao 2014). Phylogenomic studies are also employed to estimate the overall similarity and dissimilarity between the genomes (Wei et al. 2002; Auch et al. 2010). Phylogenetic markers such as 5s rRNA, 16s rRNA, 23s rRNA and SRP (Signal Recognition Particle) are used to deduce the genetic and biochemical systematics of methanogenic archaea in order to correct many major clades in the tree (Lai et al. 1999; Stantscheff et al. 2014; Petitjean et al. 2015). Secondary structure-based on rRNA such as ykok, group II intron and RNaseP sequences were also used for accurate prediction of 2
the phylogenetic status of prokaryotic genomes (Juana and Wilsona 1999). Methyl-CoM reductase A (mcrA) gene was served as a functional marker for the taxonomic classification of all methanogenic archaea (Ufnar et al. 2007; Wrede et al. 2013). Comparison of genomic features is a secondary layer of biological description, which affords a significant and effective flowchart for the metabolic pathways among methanogenic archaea (Maeder et al. 2006; Zhang et al. 2009). We have undertaken such approach to explain or to resolve the genomic proximity and metabolic discrepancy of MMA from its phylogenetic neighbors such as MAC and MBA. We inferred how intergenomic evolutionary constraints attribute its genome dynamics and metabolic adjustment across methanosarcinal genomes. Using this approach, genes are analyzed case-by-case to make genome annotation in a more complete and significant way. 2. Materials and Methods 2.1. Genome-scale tree construction The complete genome sequence of MMA was retrieved from the National Center for Biotechnology Information (NCBI) -FTP site for finding its current phylogenomic status and evolutionary relatedness among all methanogenic archaea (http://www.ncbi.nlm.nih.gov/). A whole-genome based tree was constructed by a composition vector algorithm (K-mer length = 6) using CVtree3 server (Xu and Hao 2009). Each organism is represented by a composition vector made of K-mer length obtained from a given genome. To suppress the effect of neutral mutations and to highlight the shaping role of natural selection, a subtraction procedure based on (K−6)-th order Markov prediction is introduced in the CVtree method. The collection of distances for all species pairs comprises a dissimilarity matrix, which has been calculated by using the neighbor-joining (NJ) algorithm (Saitou and Nei 1987) to construct phylogenomic trees. In the CVtree, time-consuming statistical re-sampling tests such as Jackknife and bootstrap tests can be neglected as it is an alignment-free and parameter-free method. 2.2. Analysis of genome synteny Syntenic regions in the genome of MMA were identified by comparing with those found in MAC and MBA using CoGe:SynMap (Lyons et al. 2008). Nucleotide-nucleotide search between two genomes was performed with a LastZ algorithm (http://last.cbrc.jp). A collinear set of genes was identified and merged by using interactive DAGChainer (Haas et al. 2004). False positive synteny was removed by Quota Align algorithm and then merged all refined syntenic blocks (Tang et al. 2011). Synonymous and non-synonymous substitutions between syntenic gene pairs were computed by PAML package using CodeML algorithm. Synteny blocks and evolutionary events resulted due to synonymous substitution were represented as syntenic histogram (Yang 2007). 2.3. Analysis of intergenomic distance Genomic variations in the functional genes in the MMA were detected by measuring Pearson correlation coefficients for a cluster of orthologs profiling of its closely related genomes (Benesty et al. 2009). Pearson correlation coefficients determine the intergenomic relatedness of selected genomes. Intergenomic distances were inferred between MMA and its phylogenetic neighbors using Genome-to-Genome Distance Calculator (Meier-Kolthoff et al. 2013). The genomic compositional dissimilarity between MMA and its phylogenetic 3
neighbors was measured by Bray-Curtis dissimilarity coefficient at a non-metric multidimensional scale (Bray and Curtis 1957). 2.4. Analysis of origin of replication (oriC) We detected oriCs and genes encoding for DNA replication proteins from the genomes of MMA, MAC and MBA by Ori Finder 2.0 (Luo et al., 2014). The common ORB (the origin recognition boxes), a specific motif within oriC in archaeal genome, was used to predict the sequence of oriCs across these genomes. The predicted oriCs and DNA replication protein sequences were aligned separately by ClustalX 2.0 software (Thompson et al. 1997) for detecting a sequence-specific conservation pattern across these genomes. 2.5. Analysis of molecular markers We retrieved nucleotide sequences of phylogenetic markers (5s, 16s and 23s rRNAs) and amino acid sequences of functional markers (mcrA and SRP) from Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/). Sequence similarity hits for these sequences were obtained by the BLAST tool by searching against KEGG database (Altschul et al. 1997). Multiple sequence alignments were carried out separately by ClustalX 2.0 software (Thompson et al. 1997). After complete alignment, the aligned sequences were edited manually to remove improperly aligned sequences and then phylogenetic trees constructed with MEGA 5.0 software (Tamura et al. 2011). Kimura-2parameter was chosen as an evolutionary model with 1000 bootstrap replicates and 5 Gamma distribution rates (Lukacs, 1955). Segregation sites, recombination/scalable-mutation rate, nucleotide diversity were estimated for each marker across different species of methanogenic archaea. Tajima's D test performed to determine genetic drift and genetic diversity among methanogenic archaea (Tajima 1989). All of these evolutionary genetic analyses were conducted by MEGA software. The free energy of rRNA secondary structure was calculated by MFold server at the folding temperature of 37oC with 1M NaCl ionic condition (Zuker et al. 2003). Homology models for mcrA were generated by SWISS-MODEL based a structural template (pdb id: 1E6Y: A) (Biasini et al. 2014). The reliability of modelled structures was determined with template by means of QMEAN Z-Score and sequence identity. Structural alignment and superimposition of each model were performed with 3D-SS server to calculate the disparity in the structural motifs (Sumathi et al. 2006). Superimposed structure was visualized in the UCSF Chimera 1.10 software (Pettersen et al. 2004). 2.6. Analysis of metabolic features We have carried out comparative metabolomic analysis to discover unique genes and to novel putative metabolic pathways based on the metabolic relatedness with MBA and MAC. Metabolic information for each genome was collected from the MetaCyc database (Caspi et al. 2014) and individual metabolic features, genes, enzymes and pathways compared manually. Enzymes involved in the central metabolism and methanogenesis were searched for MMA and then compared its metabolic feature with MBA and MAC. Novel putative metabolic pathways were identified by merging common genes, and enzyme shared across methanosarcinal genomes. 3. Results 3.1. Whole genome phylogeny 4
A whole genome phylogenetic tree was constructed with 39 completed genome sequences as represented in Fig 1. Methanobacterium SWAN1 and AL21 are found as out-group organisms for the taxonomic classification of the MMA genome in the tree. There are two major clusters formed separately with monophyletic groups of different physiological species. In the first cluster, many Methanococcus genomes are grouped with Methanothermococcus Methanobacterium (Methanobrevibacter smithi and Methanobrevibacter ruminantium). The genera of Methanosarcina, Methanothermobacter and Methanosaeta are placed in the second cluster. A phylogenetic proximity is observed between MMA and MAC and then with MBA, which are further grouped with Methanoregula formicicum, Methanothermobacter marburgensis and Methanothermobacter thermoautotrophicus. 3.2. Intergenomic distance The results of the genome correlation coefficient analysis indicate that MMA genome is closely related with MAC (0.80) and MBA (0.83) and all three of them are shared their genomic function with distantly related methanogenic archaea (0.67-0.56) (Table 1). Similarly, intergenomic distance analysis demonstrates that MMA genome is very close to the MAC genome (0.6), but it was slightly far away from MBA (0.7), and distantly related to other methanogenic archaea. The rank order correlation of a dissimilarity matrix describes that MMA is highly correlated with MAC and followed by MBA. Non-methanogenic archaea have shown high rank order dissimilarities to the MMA while comparing with other methanogenic archaea (Supplementary Fig. S1). 3.3. Genome synteny of homologs Genome synteny analysis shows that whole genome duplication events occurred between the MMA and MBA is higher than those events occurred between MMA and MAC (Fig 2). Syntenic orthologs in the MMA are originated from MBA, which was relatively more than those originated from MAC. It may be resulted from low rate of synonymous substitutions. A collinearity of either gene orders or gene families may be adjusted by syntenic blocks at the location of syntenic out-paralogs. 3.4. Origin of replication In the genome of MMA, we noted two different oriC sites in the MMA genome as represented in Fig 3. The first oriC region is located in upstream of cdc6 gene at the genomic position of 1564657-1566241 bp (GC cont. 26.50%). The second oriC region is located in upstream of AAA+ ATPase gene that is required for initiation of DNA replication. Both oriC regions identified in the MMA are similar to those found in the MAC, but oriC regions in the MBA are located at different positions. The ORB identified from MMA has shown 95% similarity with MAC and MBA, indicative of evolutionary conservation of the DNA boxes across methanosarcinal genomes. 3.5. Genetic diversity of marker genes Phylogenetic and metabolic markers of this genome compared with those markers similarly found in the MAC and MBA as shown in Fig 4. Phylogenetic diversity of methanogenic archaea is associated with segregation sites in the 16s rRNA and mcrA (Supplementary Fig S2 & S3). At the low evolutionary frequency rate, segregation sites determine the conservation of marker genes across methanosarcianal genomes. SRP and fdhD of 5
Methanosarciana are highly mutable during evolutionary events. Estimates of nucleotide diveristy demonstrate its genetic polymorphism explicating the moderate diversity in patterns of metabolic markers (fdhD, hemC, ogt, glyS, coaE and ahaD). There are fewer discrepancy observed in metabolic markers (fdhD, coaE and hemC) among methanogenic archaeal lineage. Apart from phylogenetic markers, all of the markers have shown to diverge functionally by imposing Darwinian positive selection. 3.6. Metabolic diversity Genome-wide metabolic mining of our study points out that MMA genome covers 123 pathways consisting of 670 metabolic reactions and 134 pathway holes. Totally, 243 genes and 137 enzymes are shared across methanosarcinal genomes and 97 genes are unique to the MMA. A total of 123 genes is shared between MMA and MAC compared to MBA as shown in Fig 5. Similarly, an increased number of enzymes are shared between MMA and MAC. Genes involved in central metabolism are common in all methanosarcinal genomes. Nevertheless, MMA consists of 3 unique genes involved in methanogenesis, suggesting the existence of a complete system for methane biosynthesis. 3.7. Discovery of novel putative metabolic pathways Genes coding for carbon monoxide dehydrogenase (EC 1.2.7.4), corrinoid/iron-sulfur protein Co-methyltransferase (EC 2.1.1.245) and co-methylating acetyl-CoA synthase (EC 2.3.1.169; α-subunit: MM_0358; β-subunit: MM_0493; acetyl CoA synthase: MM_3180) are exclusively found in the MMA, which are not present in MAC and MBA. All of these enzymes are involved in hydrogenotrophic methanogenesis. Indole-3-acetate biosynthesis V pathway is a novel putative metabolic pathway identified from MMA that can be mediated by gene MM_1229. Similarly, MM_0047, MM_0838 and MM_2341 are unique genes involved in the 4-aminobutyrate degradation. MMA also exclusively consists of unique genes MM_1162 and MM_1740 to be involved in galactosamine biosynthesis I and siroheme biosynthesis, respectively (Fig 6). 4. Discussion Our comparative genomic study was aimed to address its phylogenomic relationship and metabolic discrepancy from closely related methanosarcinal genomes (MAC and MBA). Similarly, comparative genomic analysis was used to infer its evolutionary dynamics of nickel, cobalt and vitamin B12 assimilation systems (Wei et al. 2002; Zhang et al. 2009). The results of phylogenetic analysis indicated that methanosarcinal genomes are closely related together and their genomic features and some of the metabolic core genes are shared with the genome of halophilic archaea in accordance to the previous work (Chellapandi et al. 2009). It suggested that MMA has a typical molecular system for salt adaptation and osmotolerance was found in the MMA, as evidenced earlier by gene expression profiling (Pflüger et al. 2007; Spanheimer and Müller, 2008; Spanheimer et al. 2008; Saum et al. 2009a; 2009b). MMA strain N2M9705 was isolated from aquaculture fishpond, which has shown the methylotrophic growth capability (Lai et al. 1999). We found three unique genes coding for enzymes (EC 1.2.7.4; 2.1.1.245; 2.3.1.169) involved in the hydrogenotrophic methanogenesis. Therefore, our study provides additional support for the existence of a typical system for methylotrophic capability. 6
Analysis of intergenomic distances stated that some of its metabolic features may be originated from distantly related genomes rather than methanosarcinal genomes, as suggested by the earlier hypothesis of molecular evolution of methanogens (Chellapandi 2010). Synonymous substitution is a major evolutionary event for the duplication of the syntenic orthologs and syntenic out-paralogs, which might be occurred by either -event or -event. The majority of duplicated genes in MMA was arisen from MBA through MAC. Such duplicated genes were major cause-effect in its genome complexity and species diversity through mutation and genomic drift (Zhu et al. 2013). Consequently, its metabolic function may be restrained to the new environmental niche. Gene products in the chromosomal oriC region of MMA are approximately 93-95% identical across the methanosarcinal species (Deppenmeier et al. 2002; Maeder et al. 2006). Several origin recognition boxes are flanked by oriC region, which have shown homology towards hypothetical protein MA_4646 from MAC and hypothetical protein Mbar_A2231 from MBA. It implied that replication initiation gene products (cdc6 and AAA+ ATPase) in the oriC regions share a common archaeal ancestor for all methanosarcinal species, but the genomic location of oriC and sequences are evolutionarily conserved within species. Very often, its genome seems to be expanded due to the copy number of rRNA genes and exclusive existence of ykok and group II intron. Ribosomal RNA and mcrA genes are well known molecular markers for the phylogenetic classification of methanogenic archaea and to study the evolutionary relationships (Zhang et al. 2009; Chellapandi et al. 2009; Stantscheff et al. 2014; Petitjean et al. 2015). Many metabolic marker genes are highly conserved across methanogenic archaea, but at the structural level, each marker was faintly varied within species. It may be resulted due to the Darwinian positive selection acting on metabolic markers or gene shuffling/gene duplication events. Horizontal gene transfer (HGT) plays a major role in the evolution of Methanosarcina genomes. The largest set of metabolic pathways and the large genome size in Methanosarcina genus are caused by massive HGT from bacteria (Maeder et al. 2006; Chellapandi, 2011a, 2011b, 2013). About 5% of genes in Methanosarcina are horizontally transferred from bacterial origin. About 9.4% of metabolic enzymes and 11.5% of transporters in MMA are horizontally transferred from bacteria, but the regulation of these genes is unknown (Garushyants et al. 2015). The distribution of enzymes and transporters among horizontally transferred genes was slightly varied from MMA to MBA; MMA to MAC. Hence, intergenomic evolution is a major constraint to determine its metabolic function from ancestral species by massive HGT from bacterial origins. Complete methanogenesis from acetate, indole-3-acetate biosynthesis V, 4aminobutyrate degradation III, galactosamine biosynthesis I and siroheme biosynthesis are novel putative metabolic pathways disclosed from this genome (Supplementary Fig. S4). This organism can adapt to utilize different methanogenic substrates using substrate-dependent gene expression system (Hovey et al. 2005). It is capable of utilizing acetate as a sole carbon source for its growth and methanogenesis. As MMA contains acetyl-CoA decarbonylase/synthase with all five subunits, it may able to grow on carbon monoxide as similar to those found in MMA (Matschiavelli et al. 2012), MBA (Hutten et al. 1980) and autotrophic methanogenic archaea (Chellapandi, 2011a, 2011b, 2013; Cheng et al. 2011; 7
Goyal et al. 2014). Qualitative analysis of CO dehydrogenase/acetyl coenzyme A synthase multienzyme complex (CODH/ACS)-encoding transcripts of MMA supported that CODH/ACS isoform catalyzed both CO oxidation/CO2 reduction and cleavage/synthesis of acetyl-CoA (Eggen et al. 1996). Moreover, indole-3-acetate produced from MMA via indole3-acetate biosynthesis V may indirectly support the growth of plants as a phyto-regulator. Similar pathway was reported in the plant-associated fungi such as Taphrina wiesneri, T. deformans and T. pruni (Yamada et al. 1990) and an opportunistic vertebrate pathogen, Alcaligenes faecalis (Kobayashi et al. 1993). 4-Aminobutyrate is a four-carbon non-protein amino acid that may be derived as a product of plant, animal tissue decay and bacteria. It is produced as an intermediate during bacterial degradation of putrescin. MMA consists of three unique gene coding enzymes involved in the 4-aminobutyrate degradation III as similar to Saccharomyces cerevisiae (Ramos et al. 1985) and Ralstonia eutropha H16 (Mayer and Cook 2009). Acetylornithine aminotransferase from MMA is closely related to validamycin aminotransferase from Streptomyces hygroscopicus 5008 and the putative 4-aminobutyrate aminotransferase from Deinococcus radiodurans (Bai et al. 2006). UDP-N-Acetyl-D-galactosamine is the amino sugar nucleotide donor of N-acetyl-Dgalactosamine residues for the biosynthesis of a component of several different kinds of cell surface structures and glycosylated proteins (Milewski et al. 2006). It is produced from UDPN-acetyl-D-glucosamine by action of UDP-N-acetyl-D-glucosamine 4-epimerase (EC 5.1.3.7) in the UDP-N-acetyl-D-galactosamine biosynthesis I. This enzyme encoded gene MM_1162 may be useful as a distinct cell wall marker for identification of closely related methanosarcinal species as it is not found in MAC and MBA. Jain et al (2014)., described the stability and robustness of archaeal membrane ether lipids and evolutionary aspects of the membrane composition of the last universal common ancestor. MMA uses siroheme, an iron-containing isobacteriochlorin, for heme b biosynthesis found in many archaea and sulfate reducing bacteria (Bali et al. 2014). Siroheme synthase is a single trifunctional enzyme exclusively found in the MMA, which catalyzes all four reactions in siroheme biosynthesis. In vitro enzyme activity assay demonstrated its functional characterization for the conversion of iron-coproporphyrin III into heme during the alternative heme biosynthesis pathway in MBA (Kühner et al. 2014). Nevertheless, the late heme genes are missing from the genomes of MMA, MBA and MAC (Deppenmeier et al. 2002; Galagan et al. 2008). The core genes involved in the biosynthetic pathways for methane (Bapteste et al. 2005), acetamido sugar (Namboori and Graham 2008) and N-linked glycoprotein (Larkin et al. 2013) have shown to share a common evolutionary origin in the euryarchaeal lineage. 5. Conclusions Comparative genomic analysis is useful for better understanding of its phylogenomic relatedness and metabolome complexity across methanosarcinal genomes. A strong genomic and metabolic proximity is noted between MMA and MAC at the molecular level. Core metabolic genes of MMA and MBA might be originated from MAC and shared within methanosarcinal genomes at a slow evolution rate. Current phylogenetic and functional markers would discriminate its recent ancestral status in the tree. Comparative metabolic 8
analysis disclosed the four novel putative metabolic pathways, including complete methanogenesis from acetate, indole-3-acetate biosynthesis V, 4-aminobutyrate degradation III, galactosamine biosynthesis I and siroheme biosynthesis in this genome. As the results, we revealed its methylotorphic capability, phyto-regulatory activity, unique cell wall composition (UDP-N-acetyl-D-galactosamine) and siroheme biosynthesis. Quantitative transcriptome and proteome data would provide a warrent for studying the functional evolution of its genome and to infer molecular regulatory networks to be conserved within methanosarcial genomes. Hence, our approach would provide a hint to discover novel putative metabolic pathways for its genome refinement and genome-scale metabolic reconstruction in the future. Acknowledgement The corresponding author is thankful to the University Grants Commission, New Delhi, India, for financial assistance (42-864/2013 (SR)) to carry out the work. Conflict of interest The authors confirm that this article content has no conflict of interest. References [1]. Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., et al. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:3389-3402. doi: 10.1093/nar/25.17.3389. PMID: 9254694. [2]. Auch, A.F., Auch, A.F., Klenk, H.P., and Göker M. 2010. Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs. Stand Genomic Sci. 2:142–148. doi: 10.4056/sigs.541628. PMID: 21304686. [3]. Bai, L., Li, L., Xu, H., Minagawa, K., Yu, Y., Zhang, Y., Zhou, X., Floss, H.G., Mahmud, T., Deng, Z. 2006. Functional analysis of the validamycin biosynthetic gene cluster and engineered production of validoxylamine A. Chem Biol. 13:387-397. PMID: 16632251. [4]. Bali, S., Rollauer, S., Roversi, P., Raux-Deery, E., Lea, S.M., Warren, M.J., et al. 2014 Identification and characterization of the 'missing' terminal enzyme for siroheme biosynthesis in α-proteobacteria. Mol Microbiol. 92:153-163. doi: 10.1111/mmi.12542. PMID: 24673795. [5]. Bapteste, E., Brochier, C., and Boucher, Y. 2005. Higher-level classification of the Archaea: evolution of methanogenesis and methanogenic archaea. Archaea. 1:353-363. PMID: 15876569. [6]. Basta, T., Smyth, J., Forterre, P., Prangishvili, D., and Peng, X. 2009. Novel archaeal plasmid pAH1 and its interactions with the lipothrixvirus AFV1. Mol Microbiol. 71:23– 34. doi: 10.1111/j.1365-2958.2008.06488.x. PMID: 19007417. [7]. Benedict, M.N., Gonnerman, M.C., Metcalf, W.W., and Price, N.D. 2012. Genomescale metabolic reconstruction and hypothesis testing in the methanogenic archaeon Methanosarcina acetivorans C2A. J Bacteriol. 194:855–865. doi: 10.1128/JB.0604011. PMID: 22139506.
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Fig.1 Phylogenomic tree constructed by a compositional vector algorithm using the whole genome sequences of MMA and other closely related methanogenic archaea. Branch lengths are proportional to evolutionary distances. Out-group organism was selected randomly for the classification.
15
Fig.2 Synonymous substitution histograms for analysis of syntenic homologs between MMA and MAC; MMA and MBA
16
Fig.3 Z-curve (AT, GC, RY and MK disparity curve) representation for comparing the location of DNA replication proteins and OriC across the genomes of MMA, MAC and MBA. Short vertical red line indicates the location of DNA replication proteins. The black 17
arrows pointing below are the oriCs with origin recognition box sequences. OriC sequence alignment is shown in below left.
Fig.4 Estimates of genetic diversity and Darwinian selection of phylogenetic and metabolic markers 18
Fig.5 Comparative genomic analyses to identify the metabolic relationships of genes (a), enzymes (b), central metabolism (c), and methanogenesis (d) across the MMA, MAC and MBA. 19
MMA: Methanosarcina mazei Go1; MAC: Methanosarcina acetivorans C2A; MBA: Methanosarcina barkeri strain fusaro
Fig.6 The novel putative metabolic pathways discovered in MMA based on the unique genes. In siroheme biosynthesis (6a), successive methylation at positions 2 and 7 of uroporphyrinogen III forms precorrin-1 and precorrin-2, respectively. Precorrin-2 is oxidized to sirohydrochlorin by precorrin-2 dehydrogenase (EC 1.3.1.76) and then chelated with 20
ferrous iron for the synthesis of siroheme by sirohydrochlorin ferrochelatase (EC 4.99.1.4). 4Aminobutyrate can be degraded into succinate semialdehyde and L-glutamate by acetylornithine aminotransferase (EC 2.6.1.19) in the presence of 2-oxoglutarate (6b). Succinate-semialdehyde dehydrogenase [NADP+] (EC 1.2.1.16) catalyses succinate semialdehyde into succinate, which further assimilated via incomplete reductive TCA cycle for energy-driven process.
Table 1 Correlation coefficient calculated for comparing intergenomic distance of MMA across the phylogenomic neighbors 21
Genome AFU MMA MAC MBA MBU MMH MRU MSI MTP NPH
AFU 1 0.43 0.33 0.35 0.45 0.51 0.35 0.38 0.48 0.5
MMA 0.43 1 0.8 0.83 0.64 0.67 0.49 0.49 0.56 0.4
MAC 0.33 0.8 1 0.87 0.53 0.58 0.42 0.38 0.49 0.33
MBA 0.35 0.83 0.87 1 0.53 0.58 0.42 0.41 0.55 0.37
MBU 0.45 0.64 0.53 0.53 1 0.77 0.4 0.42 0.55 0.48
22
MMH 0.51 0.67 0.58 0.58 0.77 1 0.48 0.5 0.61 0.49
MRU 0.35 0.49 0.42 0.42 0.4 0.48 1 0.77 0.49 0.27
MSI 0.38 0.49 0.38 0.41 0.42 0.5 0.77 1 0.52 0.26
MTP 0.48 0.56 0.49 0.55 0.55 0.61 0.49 0.52 1 0.37
NPH 0.5 0.4 0.33 0.37 0.48 0.49 0.27 0.26 0.37 1