Methanobacterium formicicum as a target rumen methanogen for the development of new methane mitigation interventions: A review.

Methanobacterium formicicum (Methanobacteriaceae family) is an endosymbiotic methanogenic Archaean found in the digestive tracts of ruminants and elsewhere. It has been significantly implicated in global CH4 emission during enteric fermentation processes. In this review, we discuss current genomic and metabolic aspects of this microorganism for the purpose of the discovery of novel veterinary therapeutics. This microorganism encompasses a typical H2 scavenging system, which facilitates a metabolic symbiosis across the H2 producing cellulolytic bacteria and fumarate reducing bacteria. To date, five genome-scale metabolic models (iAF692, iMG746, iMB745, iVS941 and iMM518) have been developed. These metabolic reconstructions revealed the cellular and metabolic behaviors of methanogenic archaea. The characteristics of its symbiotic behavior and metabolic crosstalk with competitive rumen anaerobes support understanding of the physiological function and metabolic fate of shared metabolites in the rumen ecosystem. Thus, systems biological characterization of this microorganism may provide a new insight to realize its metabolic significance for the development of a healthy microbiota in ruminants. An in-depth knowledge of this microorganism may allow us to ensure a long term sustainability of ruminant-based agriculture.

Introduction

CH4 is the second largest anthropogenic greenhouse gas and its global warming potential is 25 times more than that of CO2 (Forster et al., 2007; IPCC 2007). The US-Energy Protection Agency (EPA) stated that China, India, the United States, Brazil, Russia, Mexico, Ukraine and Australia are the major CH4 emitters in the world. CH4 emission is projected to increase by 15% to 7904 MMT (Million Metric Ton)-CO2Eq. by 2020 (US-EPA, 2014). About 25% of enteric CH4 emission accounted globally from the ruminants represents a loss of 5–7% of dietary energy (Hristov et al., 2013, Thorpe, 2009).

A total CH4 emission is estimated to be 163.3 MMT-CO2 Eq. from enteric fermentation and 61.2 MM-TCO2 Eq. from manure management (US-EPA, 2014). Beef (116.7 MMT-CO2 Eq.) and dairy cattle (41.6 MMT-CO2 Eq.) are being as main sources for enteric CH4 emission (Table 1). The management of manure from anaerobic digester (61.2 MMT-CO2 Eq.), dairy cattle (32.2 MMT-CO2 Eq.) and swine (22.4 MMT-CO2 Eq.) is also contributed to global CH4 emission. Cattle (77.3 kg × 106) and buffalo (12.1 kg × 106) will be the major source for the projected global CH4 emission from enteric fermentation in 2025. CH4 emission budget (105 kg ×106) will be 107 times more from manure management (12,849 kg ×106) (Table 2). Hence, reducing CH4 emissions from ruminants is not only benefits for the environment, but also to ensure the long-term sustainability of ruminant-based agriculture (Zhang et al., 2015).

Table 1

CH4 emissions (MMT-CO2 Eq.) from ruminant-based agriculture (US EPA report 2016).

Gas/Livestock	2005		2010		2011		2012		2013		2014
	EF	MM	EF	MM	EF	MM	EF	MM	EF	MM	EF	MM
Beef cattle	125.2	3.3	124.6	3.3	121.8	3.3	119.1	3.2	118	3	116.7	3
Anaerobic digester*	–	56.3	–	60.9	–	61.5	–	63.7	–	61.4	–	61.2
Dairy cattle	37.6	26.4	40.7	30.4	41.1	31.1	41.7	32.6	41.6	31.8	41.9	32.2
Poultry	–	3.2	–	3.2	–	3.2	–	3.2	–	3.2	–	3.2
Horses	2.3	0.3	2.4	0.2	2.5	0.2	2.5	0.2	2.5	0.2	2.4	0.2
Sheep	1.7	0.1	1.7	0.1	1.7	0.1	1.6	0.1	1.6	0.1	1.6	0.1
Swine	1.2	22.9	1.1	23.6	1.1	23.6	1.1	24.3	1.1	23	1	22.4
Goats	0.4	–	0.4	–	0.3	–	0.3	–	0.3	–	0.3	–
American bison	0.4	–	0.4	–	0.3	–	0.3	–	0.3	–	0.3	–
Mules and asses	0.1	–	0.1	–	0.1	–	0.1	–	0.1	–	0.1	–
Gross total	168.9	56.3	171.3	60.9	168.9	61.5	166.7	63.7	165.5	61.4	164.3	61.2

*Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.

EF: Enteric fermentation; MM: Manure management.

Table 2

The projected CH4 emissions (kg ×106) in the year of 2025 from ruminant-based agriculture.

Livestock	Enteric fermentation	Manure management
Cattle	77.3	7002
Buffalo	12.1	933
Sheep	6.18	183
Goat	5.19	222
Swine	1.29	3700
Poultry	–	537
Camel	1.17	62
Horse	1.03	–
Ass	0.45	40
Mule	0.09	9
Alpaca	0.13	86
Total	105	12,849

Rumen microbiota

The typical rumen microbiota consists of 10–50 billion bacteria, 1 million protozoa and variable numbers of yeasts and fungi in each milliliter of rumen content (Ekarius, 2010). Anaerobic microbes are degrading polysaccharides (cellulose, hemicellulose, starch and pectin), proteins, and lipids from food/feed and of producing organic acids (formate, pyruvate, acetate, propionate, butyrate, and succinate) from which CH4 gas is produced by rumen methanogenic archaea (Chellapandi, Prabaharan, & Uma, 2010). The rumen microbes rapidly ferment amino acids and soluble proteins and form various acidic fermentation products (NH3, H2 and CO2). The turnover rate of fermentation products cannot be measured due to exchange reactions across the microorganisms (Andrade-Montemayor, Gasca, & Kawas, 2009). Pyruvate produced from anaerobes is carboxylated to form oxaloacetate and further converted to malate, fumarate and succinate. The rumen microbes that are not producing succinate can use CO2 as a sole carbon source (Ungerfeld, 2015). Gut microbiota are able to synthesis indispensable amino acids and vitamins from the non-protein nitrogen sources and offer them as nutrient supplements to the host animal (Morowitz, Carlisle, & Alverdy, 2011). These processes clearly indicate that the rumen is a dynamic ecosystem wherein the gut microbiota may interact and support one another in a complex food web.

Rumen methanogenic archaea

Methanogenic archaea are capable of producing CH4 from the low carbon substrates such as formate, pyruvate, methylamine, acetate, and CO2 through methanogenesis. This process depends on the availability of ATP derived from enteric fermentation of rumen anaerobic bacteria (Balch, Fox and Magrum, 1979, Hook, Wright and McBride, 2010). The overall effects of methanogenic archaea play a crucial role in the physiology and health of the ruminants (Delzenne & Cani, 2011). Methanomicrobium mobile, Methanobacterium lacus, Methanobacterium formicicum (MFI), Methanomicrobium bryantii, Methanobrevibacter ruminantium, Methanobrevibacter smithi, Methanosarcina barkeri and Methanosarcina mazei are culturable rumen methanogenic archaea have been studied in detail (Henderson et al., 2015). Methanobacterium and Methanobrevibacter are predominant genera usually inhabited in the rumen ecosystem.

Methanobacterium formicicum

MFI is a representative species of methanogenic archaea found in the gut of ruminants and humans (Pimentel, Gunsalus, Rao and Zhang, 2012, Sirohi, Goel and Pandey, 2012). This microorganism can utilize CO2 with H2, fermented by-products of the rumen bacteria, for CH4 production in rumen gut environment. CO2 is released by the animal into the atmosphere. MFI is a clinically important microorganism because it can cause gastrointestinal and metabolic disorders in animals and humans (Kelly et al., 2014, Mitsumori and Sun, 2008). This microorganism is able to ferment acetate, carbohydrate, amino acid, ethanol, methanol, propionate, butyrate and lactate. MFI contains all essential genes need for methanogenic process with exception of [Fe]-hydrogenase dehydrogenase (hmd). Both CH4 production and formate consumption are linear functions of its growth rate. The molar growth yield of MFI for CH4 of formate cultures is 4.8 g dry weight per mol, and that of H2—CO2 culture was 3.5 g dry weight per mol (Schauer & Ferry, 1980). Pseudomurein and polysaccharide biosynthesis genes are similar to those found in M. ruminantium (Leahy et al., 2010). MFI strain BRM9 does not have homologs of N-acylneuraminatecytidyl transferase coding genes (neuA/neuB) as in the strain DSM 3637 (Kandiba & Eichler, 2013). MFI consists of 3 ectoine biosynthetic genes usually existed in halo-tolerant microorganisms, but ectoine is not yet reported to be produced by methanogenic archaea (Lo et al., 2009, Moe et al., 2009). The genome of this microorganism contains a large number of genes for two-component signal transduction system. This system helps to monitor the changes in the redox potential, oxygen and overall cellular energy level of MFI (Taylor & Zhulin, 1999). It has a characteristic metabolism of nitrogen, particularly in ammonium transporters and glutamine synthase/glutamate synthase pathway.

Genomic features

Currently, 7 complete genome sequences are available for different species of Methanobacterium and Methanobrevibacter (Gutiérrez, 2012, Leahy et al., 2010, Leahy et al., 2013a) (Fig. 1; Maus et al., 2013a). Researchers have identified 3 different strains (DSM3637, DSM1535 and BRM9) of MFI from the bovine rumen (Kelly et al., 2014, Maus et al., 2013, Maus et al., 2014). Comparison of 16S rRNA gene sequences indicate 99.8% sequence similarity between strain BRM9 and strain DSM1535 (Bryant & Boone, 1987). MFI strain KOR-1 strain isolated from an anaerobic digester using pig slurry has shown 98% rRNA gene and 97% mcrA gene sequence similarities to other strains (Battumur, Yoon, & Kim, 2016). The genome of strain DSM3637 (2.47 Mbp) comprises of a total of 2556 protein-coding genes in which 643 proteins assigned to be hypothetical proteins (Gutiérrez, 2012). The strain DSM1535 is 2.4 Mbp in genome size with 41.23% GC content and encoded for several adaptation genes responsible for abiotic stress (Maus et al., 2014). The BRM9 strain consists of a single 2.44 Mbp circular chromosome with 2352 protein coding genes (83%). A putative function is assigned to 1715 of the protein-coding genes, with the remainder annotated as hypothetical proteins (Kelly et al., 2014). Conserved hypothetical proteins are ranged from 413 to 736 in the genera of Methanobacterium and Methanobrevibacter.

Fig. 1

Genome-scale metabolic information of MFI and related rumen methanogens, collected from the MetaCyc database (https://metacyc.org/). We compared genome (a), metabolome (b), anabolism (c) and catabolism (d) to infer genomic similarities and dissimilarities across them. (MEL: Methanobacterium lacus, MEW: Methanobacterium sp. SWAN-1, METH: Methanobacterium sp.MB1, MSI: Methanobrevibacter smithii, MRU: Methanobrevibacter ruminantium, MEB: Methanobrevibacter sp. AbM4).

Transcription regulatory systems

The MFI genome possesses a maximum number of transcription units compared to the genera of Methanobacterium and Methanobrevibacter (Kelly et al., 2014, Kern, Linge and Rother, 2015, Worm, Stams, Cheng and Plugge, 2011). Transcriptional regulation of coenzyme F420-dependent formate dehydrogenase (fdhCAB) (Patel and Ferry, 1988, Schauer and Ferry, 1982, White and Ferry, 1992), glyceraldehyde-3-phosphate dehydrogenase (Fabry, Lang, Niermann, Vingron, & Hensel, 1989), archaeal histones (hfoAB) (Darcy, Sandman and Reeve, 1995, Zhu et al., 1998) has been extensively characterized in this microorganism. NrpR is a transcriptional regulator that represses transcription of nitrogen fixation genes, glutamine synthase and ammonium transporters. This regulator binds to inverted repeat operators in the promoter regions located upstream from the starts of glnA,nifH, pdxT, amt1 and amt2 (Andrade-Montemayor, Gasca and Kawas, 2009, Lie et al., 2010, Magingo and Stumm, 1991). MFI contains nif operon, nitrogenase and nitrogenase cofactor biosynthesis genes as similar to Methanococcus maripaludis (Lie et al., 2010, Magingo and Stumm, 1991). An intensive analysis of the current genomic data of MFI provided a new avenue for the development of veterinary vaccines and small-molecule inhibitors for CH4 mitigation (Leahy et al., 2013b, Wedlock et al., 2010). Hence, the MFI genome is considered as a suitable candidate for studying systems biological characterization of rumen methanogens. Such systems-level information is currently useful to discover new veterinary vaccines and chemogenomic targets for new CH4 mitigation interventions (Bharathi and Chellapandi, 2017, Sedano-Núñez, Boeren, Stams and Plugge, 2018).

Metabolic regulatory systems

Metabolic pathway data including reactions, enzymes, and metabolites provide insight into the growth and metabolic physiology of MFI (Fig. 1b). This genome consists of abundant genes for the biosynthesis of carbohydrate and nucleotides (Fig. 1c). Genes involved in anabolism of this microorganism are considerably lower than that of other species in Methanobacterium genus, indicative of a characteristic system exists for carbohydrate biosynthesis. It has been well-established that systems for catabolism of amino acids and nucleic acids, which are relatively low to related genera (Fig. 1d). Methanobacterium lacus and Methanobacterium sp. SWAN-1 have 5 more additional genes for energetic CH4 biosynthesis, compared to the MFI genome. MFI is a target rumen methanogen for the development of new CH4 mitigation interventions owing to the existence of conserved nature of genes required for methanogenesis, central metabolism and Pseudomurein cell wall formation (Kelly et al., 2014).

Gut microbial symbiosis

Gut microbiota are shaped by both genetic background and lifestyle, which in turns impairs intestinal barrier function (Burcelin, 2010, Stenman, Burcelin and Lahtinen, 2015) and modulates epithelial cell proliferation (Sommer & Bäckhed, 2013) and metabolic inflammation (Stenman et al., 2015). It is well known that healthy gut microbiota are essential one to protect against the pathogenic microorganisms in the intestine (Tremaroli & Bäckhed, 2012) and modulate gut-brain axis (Hsiao et al., 2013). Several studies have focused on the metabolic crosstalk between gut microbiota and host to reveal the metabolic disorders of human (Burcelin, 2010, Cani and Delzenne, 2009, Koeth, Wang and Levison, 2013, Stenman, Burcelin and Lahtinen, 2015), but none has been reported for animals. A gut microbial composition may restrict the production of certain bacterial metabolites (Heinken, Sahoo, Fleming, & Thiele, 2013). Microbial mutualism can occur through metabolic interactions between host and gut microbe and between microbe and microbe (Bath et al., 2012, Morgavi et al., 2015). Endo-symbiotic methanogenic archaea are usually habituated in the gastrointestinal tracts of ruminants, which are contributing in the syntrophic degradation and improved metabolic function. MFI is an endosymbiotic methanogenic archaea of free-living anaerobic flagellate Psalteriomonas vulgaris (Broers et al., 1993) associating syntrophically with Syntrophomonas zehnderi (Sousa, Smidt, Alves, & Stams, 2007).

H2 scavenging systems

Interspecies H2 transfer is a metabolic process occurring between hydrogenotrophic methanogenic archaea and cellulolytic/acetogenic bacteria. Hydrogenotrophic methanogenic archaea maintain the partial pressure of H2 by utilizing H2 produced by cellulolytic bacteria. Ruminococcus albus and R. flavefaciens are H2-producing cellulolytic anaerobe for interspecies H2 transfer of MFI (Chaucheyras-Durand, Masséglia, Fonty and Forano, 2010, Joblin, Naylor and Williams, 1990, Pavlostathis, Miller and Wolin, 1990, Williams, Withers and Joblin, 1994, Wolin, Miller and Stewart, 1997). Fibrobacter succinogenes, Wolinella succinogenes and Mitsuokella jalaludinii are fumarate reducing rumen anaerobic bacteria. These microorganisms are able to reduce CH4 production either by competing with hydrogenotrophic methanogenic archaea for H2 as well as formate or by increasing succinate (Asanuma, Iwamoto and Hino, 1999, Mamuad et al., 2012, Mamuad et al., 2014). Therefore, cellulolytic and fumarate reducing bacteria are extensively studied symbiotic anaerobes for interspecies H2 transfer of MFI. Comparative metabolic analysis shows that 237 metabolic enzymes are shared across the MFI, F. succinogens and R. albus and 210 enzymes are common between F. succinogens and R. albus(Fig. 2). MFI has 126 unique enzymes across F. succinogens and R. albus, 38 enzymes shared with R. albus and 44 enzymes with F. succinogens. Hence, studying metabolic symbiosis of these genomes is important to comprehend the gut physiology and metabolic disorders of veterinary animals.

Fig. 2

Comparison of metabolic enzymes sharing across MFI, F. succinogens and R. albus. Complete metabolic enzymes (E.C.) were collected from the MetaCyc database (https://metacyc.org/), compared and then viewd in Venn diagram.

Systems biology paradigm

The biochemical function of individual genes and proteins of microorganisms has been investigated by traditional molecular approaches. A complexity of microbial symbiosis and metabolic crosstalk has been reconciled by recent quantitative systems biology advances. Genome-scale metabolic models are being as the promising computational platforms for studying intracellular metabolism and interspecies interactions of microbial communities and for hypotheis testing (Liu, Agren, Bordel, & Nielsen, 2010).

Genome-scale reconstructions for methanogenic archaea

Systematic analysis of methanogenic archaea and mutualistic anaerobic bacteria provides an opportunity to capture growth parameters and bacterial community composition (Durmus, Cakır, Özgür and Guthke, 2015, Stolyar, Van Dien, Hillesland and Pinel, 2007). In silico models for M. barkeri (iAF692; iMG746) (Feist et al., 2006, Gonnerman et al., 2013), M. acetivorans (iMB745; iVS941) (Benedict, Gonnerman, Metcalf and Price, 2012, Kumar, Ferry and Maranas, 2011) and Methanococcus maripaludis (iMM518) (Goyal, Widiastuti, Karimi, & Zhou, 2014) have been previously developed for studying their metabolic behaviors on different growth substrates. In addition to that, a metabolic flux model has been reconstructed for understanding a microbial mutualism between M. maripaludis (Goyal et al., 2014) and Desulfovibrio vulgaris (Stolyar et al., 2007).

Genome-scale reconstructions for gut-microbe interactions

Several genome-scale models have been developed for evaluating the mechanistic details of gut-microbe interactions (Ding et al., 2016, Gao, Zhao and Huang, 2014, Sadhukhan and Raghunathan, 2014, Shoaie and Nielsen, 2014, Shoaie et al., 2013, Shoaie, Ghaffari, Kovatcheva-Datchary and Mardinoglu, 2015) and host-microbe metabolic symbiosis (Heinken, Sahoo, Fleming and Thiele, 2013, Ji and Nielsen, 2015, Singer, 2010). PHIDIAS (Xiang, Tian, & He, 2007), HPIDB (Kumar & Nanduri, 2010), PHISTO (Durmus et al., 2013), PATRIC (Wattam, Gabbard, Shukla, & Sobral, 2014), PHI-base (Urban, Irvine, Cuzick, & Hammond-Kosack, 2015), CASINO (Shoaie et al., 2015), HMI™ module (Marzorati et al., 2014), and NetCooperate (Levy, Carr, Kreimer, Freilich, & Borenstein, 2015) are web-based tools and databases accessible for studying the microbe-microbe, diet-microbe and microbe-host interactions. GeoSymbio (Franklin, Stat, Pochon, Putnam, & Gates, 2012) and SymbioGenomesDB (Reyes-Prieto, Vargas-Chávez, Latorre, & Moya, 2015) are specialized computational resources developed for learning the host-microbiome interactions and microbial symbiosis (Table 3).

Table 3

Systems biology tools and databases used for studying microbial mutualism in the gut environments.

Tool	Purpose	Reference
PHIDIAS	Molecular functions of pathogen and host genes	Xiang et al. (2007)
HPIDB	Microbial infections and drug targets discovery	Xiang et al. (2007)
GeoSymbio	Symbiodinium-host symbioses	Franklin et al. (2012)
PHISTO	Therapeutic targets discovery for microbial infections	Durmus et al. (2013)
PATRIC	Comparative genomic or transcriptomic analysis	Wattam et al. (2014)
HMI™ module	Mechanistic understanding of host-microbiome interactions	Marzorati et al. (2014)
PHI-base	Catalogues experimentally verified molecular virulence factors	Urban et al. (2015)
CASINO	Analysis of microbial communities through metabolic modeling	Shoaie et al. (2015)
NetCooperate	Host-microbe and microbe-microbe cooperation	Levy et al. (2015)
SymbioGenomesDB	Host-symbiont relationships	Reyes-Prieto et al. (2015)

CH4 mitigation interventions

CH4 mitigation stratagies should ideally target features that are conserved across all rumen methanogenic archaea. Consequently, other beneficial anaerobes continue their normal digestive functions in the ruminants (Gottlieb, Wacher, Sliman and Pimentel, 2016, Weimer, Stevenson, Mertens and Thomas, 2008). Several CH4 mitigation interventions have been investigated such as change in dietary composition like use of fatty acids (Agarwal, Kamra, Chatterjee, Ravindra, & Chaudhary, 2008), tannin (Kumar et al., 2009), monensin (Weimer et al., 2008), plant extracts (Goel, Makkar and Becker, 2008, Sirohi, Goel and Pandey, 2012), fumarate and chemical inhibitors (Chidthaisong and Conrad, 2000, Miller and Wolin, 2001; Ungerfeld, Rust, Boone, & Liu, 2004), and anti-methanogenic vaccines (Wedlock et al., 2013, Williams, Popovski and Rea, 2009). So far, only a small percentage of CH4 mitigation has been successfully implemented by dietary changing. Some chemical inhibitors have been investigated to destroy the pathogenic bacteria, and those inhibitors may be beneficial to the host, which in turn affects the rumen microbiota. Thus, it is important to access the effect of methanogenic inhibitors on the stability of rumen healthy microbiota and also to discover new chemogenomic targets for CH4 mitigation.

Methanogenic antibiotics and inhibitors

Chemical inhibitors or enzymes targeting essential functions of methanogenic archaea are delivered via slow-release capsules administered to the rumen. Neomycin, pseudomonic acid (Boccazzi, Zhang and Metcalf, 2000, Jenal, Rechsteiner and Tan, 1991), puromycin (Gernhardt, Possot, & Foglino, 1990), 8-aza-2, 6-diaminopurine (Pritchett, Zhang, & Metcalf, 2004) and 8-aza-hypoxanthine (Moore & Leigh, 2005) are methanogenic antibiotics, and inhibitors are presently used against M. mariplaudis and M. barkeri. Ethyl 2-butynoate, lovastatin, mevastatin, fluoroacetate, chloroform, 2-bromoethanesulphonate, and 2-nitroethanol are potential methanogenic inhibitors investigated to inhibit the methanogenesis of Methanobrevibacter and Methanobacterium (Chidthaisong and Conrad, 2000, Miller and Wolin, 2001, Ungerfeld, Rust, Boone and Liu, 2004). The growth of methanogenic archaea and persistence of 2-bromoethanesulfonate resistance increased with administration of it in bovine (Van Nevel & Demeyer, 1996). M. ruminantium, M. mazei and M. mobile found to be resistant to 3-bromopropanesulfonate up to 250 μmol/L in pure cultures (Ungerfeld et al., 2004). Therefore, it is consistent with the limited efficacy of 2-bromoethanesulfonate and 3-bromopropanesulfonate in lowering CH4 production by rumen microbiome (Karnati et al., 2009, Patra, Park, Kim and Yu, 2017).

Monensin inhibits the methanogenesis from formate, but not from H2—CO2 in ruminants (Dellinger & Ferry, 1984). Bovine somatotrophin, monensin and lasalocid have been extensively used in beef and cattle farming to improve growth rates (Abrar et al., 2016, Appuhamy et al., 2013, Etherton, 2013). Monensin affects electrolyte transport of methanogenic and propionate-producing bacteria. It also inhibits some bacteria responsible for proteolysis and deamination. A long term supplementation of monensin does not have an implementation in CH4 reduction efficacy (Hook, Northwood, Wright, & McBride, 2009). Interestingly, Thermoplasmata archaea are methylotrophic (methylamine degrading) methanogens found in bovine rumen, which are able to mitigate methane emissions from lactating cows upon dietary supplementation with rapeseed oil (Poulsen et al., 2013). Thus, methanogenic inhibitors have been investigated in regard to their affect on the total population of Methanobacterium, Methanobrevibacter, Methanosphaera and Thermoplasmata making unbalanced microbial ecosystem in the rumen gut (Witzig, Zeder and Rodehutscord, 2018, Zhou, Meng and Yu, 2011, Zhu et al., 2017).

Chemogenomic targets

Generally, methanogenic antibiotics and inhibitors that target the key enzymes involved in the biosynthesis of cell wall, protein, vitamins and cofactors of MFI. Hydroxymethylglutaryl-SCoA reductase, aconitase, coenzyme M are common targets for many methanogenic inhibitors (Chidthaisong and Conrad, 2000, Miller and Wolin, 2001, Ungerfeld, Rust, Boone and Liu, 2004) (Table 4; Supplementary). Some of the methanogenic inhibitors have shown to decrease the proton gradient across the membrane, loss of digestible energy for ruminants, regulation of formate and H2 oxidation, carbohydrate-fermentation and acetate metabolism (Chen and Wolin, 1979, Chidthaisong and Conrad, 2000, Liu, Wang, Wang and Chen, 2011, Zhou, Meng and Yu, 2011, Ungerfeld, Rust, Boone and Liu, 2004). However, the inhibitory effect is lost or reverted following long term administration in ruminants. It suggests the discovery of new therapeutic targets to be intended to resolve such crises.

Table 4

Methanogenic antibiotics and inhibitors used for reducing enteric methane emission from Methanobacterium and Methanobrevibacter genera resident in ruminants.

Compound	Conc. (mM/ml)	CH4 inhibition (%)	Targets to be inhibited
2-Nitroethanol	0.012	99	Formate and H2 oxidation
Sodium nitrate	0.012	70	Alternative electron acceptors
Acetylene	7.2e−6	50	Proton gradient across the membrane
Ethylene	1e−4	50	Proton gradient across the membrane
2-Bromoethanesulphonate	0.00025	100	Coenzyme M
Propynoic acid	0.004	96	Carbohydrate-fermentation pathway
Ethyl 2-butynoate	0.008	100	Loss of digestible energy for ruminants
Lovastatin	1e−5	100	Hydroxymethylglutaryl-SCoAreductase
Mevastatin	5.8e−6	100	Hydroxymethylglutaryl-SCoAreductase
Fluoroacetate	0.001	100	Aconitase
Chloroform	0.1	100	Acetate metabolism
Monensin	5.9e−5	83	Loss of digestible energy for ruminants
lasalocid	6.7e−5	89	Loss of digestible energy for ruminants

Veterinary vaccination

Immunization is one of the novel CH4 mitigation stratagies in which the animals acquire immunity against a particular rumen methanogenic Archaean (Iqbal, Cheng, Zhu and Zeshan, 2008, Mitsumori and Sun, 2008, Ulyatt, Lassey, Shelton and Walker, 2002). When animals are vaccinated, salivary antibodies are produced in the animal against rumen methanogenic archaea. A vaccine developed against Streptococcus bovis and Lactobacillus species causes a lactic acidosis, which elicits an immune response against rumen methanogenic archaea (Gill, Shu and Leng, 2000, Shu et al., 2000). Using VF3 and VF7 antigens, anti-methanogenic vaccines have been investigated for the reduction of CH4 emission from enteric fermentation (Williams, Popovski and Rea, 2009, Wright et al., 2004). Vaccination of sheep with methanogenic archaeal fractions has been developed for effective CH4 mitigation (Wedlock et al., 2010). Genome sequence of M. ruminantium M1 was compared with closely related methanogenic archaea to identify conserved methanogen surface proteins as suitable candidates for the development of vaccines (Leahy et al., 2010). Energy metabolism (EC 2.1.1.86, 3.6.3.14), protein fate (EC 3.4.23.43) and adhesion/cell surface proteins are identified as vaccine targets for MFI (Table 5; Supplementary). Yet, new CH4 mitigation interventions should be addressed in the development of alternative veterinary vaccines against MFI. Any veterinary vaccine should be targeted methanogen-specific proteins and should not affect the growth of other beneficial microorganisms, which can be resolved by systems-biology approach.

Table 5

Chemogenomic and vaccine targets identified in M. formicicum formethane mitigation.

Metabolism	Gene/Locus tag	EC	Molecular function
Amino acid metabolism	RS11100	4.2.3.4	3-Dehydroquinate synthase
	RS02945	2.5.1.19	3-Phosphoshikimate 1-carboxyvinyltransferase
	RS01470	4.1.1.20	Diaminopimelate decarboxylase
Cell cycle	RS05130| RS05135	5.99.1.3	DNA topoisomerase VI subunit AB
Cell envelop	glmU	2.3.1.157	Glucosamine-1-phosphate N-acetyltransferase
	glmU	2.7.7.23	UDP-N-acetylglucosaminediphosphorylase
Central carbon metabolism	sdhA/RS12465	1.3.5.1	Succinate dehydrogenase
	RS07380	1.5.98.2	5,10-methylenetetrahydromethanopterin reductase
	RS01770	1.5.98.1	Methylenetetrahydromethanopterin dehydrogenase
	RS10770| RS00440	2.3.1.101	Formylmethanofuran–tetrahydromethanopterinformyltransferase
	RS09605	3.5.4.27	N(5),N(10)-methenyltetrahydromethanopterincyclohydrolase
	RS09415| RS09420| RS09425| RS09430| RS09435| RS07735| RS00350| RS00355| RS00360| RS00365	2.8.4.1	Coenzyme-B sulfoethylthiotransferase
	RS09460| RS09455| RS09450| RS09445| RS09440| RS09465| RS09470| RS09475	2.1.1.86	Tetrahydromethanopterin S-methyltransferase subunit ABCDEFGH
	RS01320|RS02480	1.2.99.5	Formylmethanofuran dehydrogenase subunit E
Lipid metabolism	ubiA|RS11985|RS06695|RS06235|RS06250|RS02925	2.5.1.1	Dimethylallyltranstransferase
	RS05485	5.3.3.2	Isopentenyl pyrophosphate isomerase
Protein biosynthesis	RS10560|RS04105|RS04110	6.3.5.6	Asparaginyl-tRNA synthase subunit CDE
	RS01925|RS09115|RS10560|RS04105|RS04110	6.3.5.7	Glutaminyl-tRNA synthase subunit ABCDE
Vitamins and cofactors	RS10335	2.4.2.52	Triphosphoribosyl-dephospho-CoA synthase
Energy metabolism	RS06810|RS06805|RS06820|RS06800|RS06825|RS06815|RS06835|RS06830	3.6.3.14*	ATP synthase subunit ABCDFIK
	selD	2.7.9.3	Selenide, water dikinase
	bcrA, bcrB, bcrC, bcrD	1.3.7.8	Benzoyl-CoA reductase
	RS09460| RS09455| RS09450| RS09445| RS09440| RS09465| RS09470| RS09475	2.1.1.86*	Tetrahydromethanopterin S-methyltransferase subunit ABCDEFGH
Protein fate	gspO	3.4.23.43*	Prepilin peptidase

*Vaccine targets

Conclusions

Grazing ruminant animals are important contributors to the CH4 pool and account for 25% of greenhouse gas emission in the world. A genome-scale metabolic network of MFI could be reconstructed to elucidate its metabolic symbiosis across the gut microbiota and host. The mechanisms underlying syntrophic and competitive behaviors of this microorganism can be explored with experiment-driven molecular hypotheses. Metabolic modeling process may serve as a platform to discover and prioritize the potential chemogenomic and vaccine targets from MFI for CH4 mitigation interventions. To resolve this issue effectively at the systems-scale, we should address the following questions; 1. What are the key metabolites to be produced from MFI to ensure its growth and methanogenesis in a rumen ecosystem? 2. How does the core metabolic network of MFI determined its symbiotic behavior across the physiologically distinct anaerobes? and 3. How is MFI interacting with gut microbiota via metabolic crosstalk in response to microbial symbiosis, drugs and nutrients?

The development of CH4 mitigation interventions is a great concern for improving animal production efficiency because of the demand for increased meat and milk products. The H2 scavenging action of MFI is an essential function, since it prevents accumulation of H2 produced as a result of enteric fermentation. Moreover, it is imperative for us to know that if the rumen methanogenic archaea are inhibited, what would be the alternate ways in which to reduce the accumualtion of H2. Metabolic symbiosis of MFI in response to different environmental stimuli has resulted from the action of syntrophic and competitive bacteria in the ruminants, and it is important to understand its growth physiology in the gut environment. CH4 mitigation strategies should be developed, but without affecting the beneficial rumen microbiome and microbiota, and without compromise to the digestive function of ruminants. Methanogenic antibiotics, inhibitors and vaccines have been narrow spectrum and species-specific activity, reflecting the demand for the potential target discovery for wide-range of methanogenic archaea.

Conflict of interest

The authors declare that this article's content has no conflict of interest.