Genomic Insights Into the Acid Adaptation of Novel Methanotrophs Enriched From Acidic Forest Soils

Abstract

Soil acidification is accelerated by anthropogenic and agricultural activities, which could significantly affect global methane cycles. However, detailed knowledge of the genomic properties of methanotrophs adapted to acidic soils remains scarce. Using metagenomic approaches, we analyzed methane-utilizing communities enriched from acidic forest soils with pH 3 and 4, and recovered near-complete genomes of proteobacterial methanotrophs. Novel methanotroph genomes designated KS32 and KS41, belonging to two representative clades of methanotrophs (Methylocystis of Alphaproteobacteria and Methylobacter of Gammaproteobacteria), were dominant. Comparative genomic analysis revealed diverse systems of membrane transporters for ensuring pH homeostasis and defense against toxic chemicals. Various potassium transporter systems, sodium/proton antiporters, and two copies of proton-translocating F1F0-type ATP synthase genes were identified, which might participate in the key pH homeostasis mechanisms in KS32. In addition, the V-type ATP synthase and urea assimilation genes might be used for pH homeostasis in KS41. Genes involved in the modification of membranes by incorporation of cyclopropane fatty acids and hopanoid lipids might be used for reducing proton influx into cells. The two methanotroph genomes possess genes for elaborate heavy metal efflux pumping systems, possibly owing to increased heavy metal toxicity in acidic conditions. Phylogenies of key genes involved in acid adaptation, methane oxidation, and antiviral defense in KS41 were incongruent with that of 16S rRNA. Thus, the detailed analysis of the genome sequences provides new insights into the ecology of methanotrophs responding to soil acidification.

Methylococcus geothermalis sp. nov., a methanotroph isolated from a geothermal field in the Republic of Korea

“…This was supported by the consumption of methane in the headspace, measured using gas chromatograph (GC-2010 Plus, Shimadzu). The gas chromatograph was equipped with an Rtx1 capillary column (30 m×0.25 mm×0.25 µm, Restek) coupled to a flame ionization detector and operated as described previously [22]. To obtain a pure culture, the enriched culture was repeatedly diluted to extinction until a culture with uniform cell morphology was observed under the light microscope (Eclipse 80i, Nikon) [23].…”

Microorganisms and electron acceptors affecting methane oxidation in freshwater and marine systems

“…KS41 of the Methylobacter clade 2 (see Fig. 3) but the phylogeny is not well supported (58% bootstrap support) similarly to what was observed previously by Nguyen et al (2018). OTU LL-pmoA-1 is not identical to the pmoA gene coding sequence retrieved from the MAG bin-63, but as this OTU represented 97% of the total pmoA gene sequences in the sample from which the metagenome was obtained, it is likely that both the pmoA of MAG bin-63 and the OTU LL-pmoA-1 are the same.…”

Investigation of microbial community interactions between Lake Washington methanotrophs using ­­­­­­­genome-scale metabolic modeling

“…The remaining imbalanced reactions were stoichiometrically inaccurate and required the atoms on both sides of the reaction equation to be balanced. The metabolites consumed and produced by models were established by referring to literature and known transporter information (Boden et al, 2011;Caspi et al, 2016;Elbourne et al, 2017;Henry et al, 2010;Kanehisa, 2008;Nguyen et al, 2018;Orata, Kits & Stein, 2018;Svenning et al, 2011;Szklarczyk et al, 2017;UniProt Consortium, 2018;Wartiainen et al, 2006).…”

“…Metabolic pathways and individual reactions that are fundamental to the growth of these organisms such as the ribulose monophosphate pathway, pentose phosphate pathway, methane metabolism, amino acid synthesis and utilization, serine cycle, and Coenzyme B12 biosynthesis were manually scrutinized and then integrated into the models. The metabolites exchanged by the two species models were established by referring to literature and known transporter information (Boden et al, 2011;Caspi et al, 2016;Elbourne et al, 2017;Henry et al, 2010;Kanehisa, 2008;Nguyen et al, 2018;Orata, Kits & Stein, 2018;Svenning et al, 2011;Szklarczyk et al, 2017;UniProt Consortium, 2018;Wartiainen et al, 2006). The curated models of Methylobacter (704 genes, 1,329 metabolites, and 1,404 reactions) and Methylomonas (658 genes, 1,378 metabolites, and 1,391 reactions) were then utilized to develop a community model using a multi-level optimization framework, which was used to estimate biologically feasible metabolite secretion profiles and community compositions (Zomorrodi & Maranas, 2012;Zomorrodi, Islam & Maranas, 2014;Islam et al, 2019).…”