A methanotrophic bacterium to enable methane removal for climate mitigation.

The rapid increase of the potent greenhouse gas methane in the atmosphere creates great urgency to develop and deploy technologies for methane mitigation. One approach to removing methane is to use bacteria for which methane is their carbon and energy source (methanotrophs). Such bacteria naturally convert methane to CO2 and biomass, a value-added product and a cobenefit of methane removal. Typically, methanotrophs grow best at around 5,000 to 10,000 ppm methane, but methane in the atmosphere is 1.9 ppm. Air above emission sites such as landfills, anaerobic digestor effluents, rice paddy effluents, and oil and gas wells contains elevated methane in the 500 ppm range. If such sites are targeted for methane removal, technology harnessing aerobic methanotroph metabolism has the potential to become economically and environmentally viable. The first step in developing such methane removal technology is to identify methanotrophs with enhanced ability to grow and consume methane at 500 ppm and lower. We report here that some existing methanotrophic strains grow well at 500 ppm methane, and one of them, Methylotuvimicrobium buryatense 5GB1C, consumes such low methane at enhanced rates compared to previously published values. Analyses of bioreactor-based performance and RNAseq-based transcriptomics suggest that this ability to utilize low methane is based at least in part on extremely low non-growth-associated maintenance energy and on high methane specific affinity. This bacterium is a candidate to develop technology for methane removal at emission sites. If appropriately scaled, such technology has the potential to slow global warming by 2050.

High-fidelity predictions of diffusion in the brain microenvironment.

Multiple-particle tracking (MPT) is a microscopy technique capable of simultaneously tracking hundreds to thousands of nanoparticles in a biological sample and has been used extensively to characterize biological microenvironments, including the brain extracellular space (ECS). Machine learning techniques have been applied to MPT data sets to predict the diffusion mode of nanoparticle trajectories as well as more complex biological variables, such as biological age. In this study, we develop a machine learning pipeline to predict and investigate changes to the brain ECS due to injury using supervised classification and feature importance calculations. We first validate the pipeline on three related but distinct MPT data sets from the living brain ECS-age differences, region differences, and enzymatic degradation of ECS structure. We predict three ages with 86% accuracy, three regions with 90% accuracy, and healthy versus enzyme-treated tissue with 69% accuracy. Since injury across groups is normally compared with traditional statistical approaches, we first used linear mixed effects models to compare features between healthy control conditions and injury induced by two different oxygen glucose deprivation exposure times. We then used machine learning to predict injury state using MPT features. We show that the pipeline predicts between the healthy control, 0.5 h OGD treatment, and 1.5 h OGD treatment with 59% accuracy in the cortex and 66% in the striatum, and identifies nonlinear relationships between trajectory features that were not evident from traditional linear models. Our work demonstrates that machine learning applied to MPT data is effective across multiple experimental conditions and can find unique biologically relevant features of nanoparticle diffusion.

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE.

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. We show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

Water-Accelerated Photooxidation of CH3NH3PbI3 Perovskite.

Halide perovskites have the potential to disrupt the photovoltaics market based on their high performance and low cost. However, the decomposition of perovskites under moisture, oxygen, and light raises concerns about service lifetime, especially because degradation mechanisms and the corresponding rate laws that fit the observed data have thus far eluded researchers. Here, we report a water-accelerated photooxidation mechanism dominating the degradation kinetics of archetypal perovskite CH3NH3PbI3 in air under >1% relative humidity at 25 °C. From this mechanism, we develop a kinetic model that quantitatively predicts the degradation rate as a function of temperature, ambient O2 and H2O levels, and illumination. Because water is a possible product of dry photooxidation, these results highlight the need for encapsulation schemes that rigorously block oxygen ingress, as product water may accumulate beneath the encapsulant and initiate the more rapid water-accelerated photooxidative decomposition.

Lanthanide-dependent cross-feeding of methane-derived carbon is linked by microbial community interactions.

The utilization of methane, a potent greenhouse gas, is an important component of local and global carbon cycles that is characterized by tight linkages between methane-utilizing (methanotrophic) and nonmethanotrophic bacteria. It has been suggested that the methanotroph sustains these nonmethanotrophs by cross-feeding, because subsequent products of the methane oxidation pathway, such as methanol, represent alternative carbon sources. We established cocultures in a microcosm model system to determine the mechanism and substrate that underlay the observed cross-feeding in the environment. Lanthanum, a rare earth element, was applied because of its increasing importance in methylotrophy. We used co-occurring strains isolated from Lake Washington sediment that are involved in methane utilization: a methanotroph and two nonmethanotrophic methylotrophs. Gene-expression profiles and mutant analyses suggest that methanol is the dominant carbon and energy source the methanotroph provides to support growth of the nonmethanotrophs. However, in the presence of the nonmethanotroph, gene expression of the dominant methanol dehydrogenase (MDH) shifts from the lanthanide-dependent MDH (XoxF)-type, to the calcium-dependent MDH (MxaF)-type. Correspondingly, methanol is released into the medium only when the methanotroph expresses the MxaF-type MDH. These results suggest a cross-feeding mechanism in which the nonmethanotrophic partner induces a change in expression of methanotroph MDHs, resulting in release of methanol for its growth. This partner-induced change in gene expression that benefits the partner is a paradigm for microbial interactions that cannot be observed in studies of pure cultures, underscoring the importance of synthetic microbial community approaches to understand environmental microbiomes.

Mosaic plasmids are abundant and unevenly distributed across prokaryotic taxa.

Mosaic plasmids, plasmids composed of genetic elements from distinct sources, are associated with the spread of antibiotic resistance genes. Transposons are considered the primary mechanism for mosaic plasmid formation, though other mechanisms have been observed in specific instances. The frequency with which mosaic plasmids have been described suggests they may play an important role in plasmid population dynamics. Our survey of the confirmed plasmid sequences available from complete and draft genomes in the RefSeq database shows that 46% of them fit a strict definition of mosaic. Mosaic plasmids are also not evenly distributed over the taxa represented in the database. Plasmids from some genera, including Piscirickettsia and Yersinia, are almost all mosaic, while plasmids from other genera, including Borrelia, are rarely mosaic. While some mosaic plasmids share identical regions with hundreds of others, the median mosaic plasmid only shares with 8 other plasmids. When considering only plasmids from finished genomes (51.6% of the total), mosaic plasmids have significantly higher proportions of transposase and antibiotic resistance genes. Conversely, only 56.6% of mosaic fragments (DNA fragments shared between mosaic plasmids) contain a recognizable transposase gene, and only 1.2% of mosaic fragments are flanked by inverted repeats. Mosaic fragments associated with the IS26 transposase gene are 3.8-fold more abundant than any other sequence shared between mosaic plasmids in the database, though this is at least partly due to overrepresentation of Enterobacteriaceae plasmids. Mosaic plasmids are a complicated trait of some plasmid populations, only partly explained by transposition. Though antibiotic resistance genes led to the identification of many mosaic plasmids, mosaic plasmids are a broad phenomenon encompassing many more traits than just antibiotic resistance. Further research will be required to determine the influence of ecology, host repair mechanisms, conjugation, and plasmid host range on the formation and influence of mosaic plasmids. AUTHOR SUMMARY: Plasmids are extrachromosomal genetic entities that are found in many prokaryotes. They serve as flexible storage for genes, and individual cells can make substantial changes to their characteristics by acquiring, losing, or modifying a plasmid. In some pathogenic bacteria, such as Escherichia coli, antibiotic resistance genes are known to spread primarily on plasmids. By analyzing a database of 8592 plasmid sequences we determined that many of these plasmids have exchanged genes with each other, becoming mosaics of genes from different sources. We next separated these plasmids into groups based on the organism they were isolated from and found that different groups had different fractions of mosaic plasmids. This result was unexpected and suggests that the mechanisms and selective pressures causing mosaic plasmids do not occur evenly over all species. It also suggests that plasmids may provide different levels of potential variation to different species. This work uncovers a previously unrecognized pattern in plasmids across prokaryotes, that could lead to new insights into the evolutionary role that plasmids play.

Quorum Sensing in a Methane-Oxidizing Bacterium.

Aerobic methanotrophic bacteria use methane as their sole source of carbon and energy and serve as a major sink for the potent greenhouse gas methane in freshwater ecosystems. Dissecting the molecular details of how these organisms interact in the environment may increase our understanding of how they perform this important ecological role. Many bacterial species use quorum sensing (QS) systems to regulate gene expression in a cell density-dependent manner. We have identified a QS system in the genome of Methylobacter tundripaludum, a dominant methane oxidizer in methane enrichments of sediment from Lake Washington (Seattle, WA). We determined that M. tundripaludum produces primarily N-3-hydroxydecanoyl-l-homoserine lactone (3-OH-C10-HSL) and that its production is governed by a positive feedback loop. We then further characterized this system by determining which genes are regulated by QS in this methane oxidizer using transcriptome sequencing (RNA-seq) and discovered that this system regulates the expression of a putative nonribosomal peptide synthetase biosynthetic gene cluster. Finally, we detected an extracellular factor that is produced by M. tundripaludum in a QS-dependent manner. These results identify and characterize a mode of cellular communication in an aerobic methane-oxidizing bacterium.IMPORTANCE Aerobic methanotrophs are critical for sequestering carbon from the potent greenhouse gas methane in the environment, yet the mechanistic details of chemical interactions in methane-oxidizing bacterial communities are not well understood. Understanding these interactions is important in order to maintain, and potentially optimize, the functional potential of the bacteria that perform this vital ecosystem function. In this work, we identify a quorum sensing system in the aerobic methanotroph Methylobacter tundripaludum and use both chemical and genetic methods to characterize this system at the molecular level.

Difference in C3-C4 metabolism underlies tradeoff between growth rate and biomass yield in Methylobacterium extorquens AM1.

BACKGROUND: Two variants of Methylobacterium extorquens AM1 demonstrated a trade-off between growth rate and biomass yield. In addition, growth rate and biomass yield were also affected by supplementation of growth medium with different amounts of cobalt. The metabolism changes relating to these growth phenomena as well as the trade-off were investigated in this study. (13)C metabolic flux analysis was used to generate a detailed central carbon metabolic flux map with both absolute and normalized flux values. RESULTS: The major differences between the two variants occurred at the formate node as well as within C3-C4 inter-conversion pathways. Higher relative fluxes through formyltetrahydrofolate ligase, phosphoenolpyruvate carboxylase, and malic enzyme led to higher biomass yield, while higher relative fluxes through pyruvate kinase and pyruvate dehydrogenase led to higher growth rate. These results were then tested by phenotypic studies on three mutants (null pyk, null pck mutant and null dme mutant) in both variants, which agreed with the model prediction. CONCLUSIONS: In this study, (13)C metabolic flux analysis for two strain variants of M. extorquens AM1 successfully identified metabolic pathways contributing to the trade-off between cell growth and biomass yield. Phenotypic analysis of mutants deficient in corresponding genes supported the conclusion that C3-C4 inter-conversion strategies were the major response to the trade-off.

Ethylmalonyl coenzyme A mutase operates as a metabolic control point in Methylobacterium extorquens AM1.

The metabolism of one- and two-carbon compounds by the methylotrophic bacterium Methylobacterium extorquens AM1 involves high carbon flux through the ethylmalonyl coenzyme A (ethylmalonyl-CoA) pathway (EMC pathway). During growth on ethylamine, the EMC pathway operates as a linear pathway carrying the full assimilatory flux to produce glyoxylate, malate, and succinate. Assimilatory carbon enters the ethylmalonyl-CoA pathway directly as acetyl-CoA, bypassing pathways for formaldehyde oxidation/assimilation and the regulatory mechanisms controlling them, making ethylamine growth a useful condition to study the regulation of the EMC pathway. Wild-type M. extorquens cells were grown at steady state on a limiting concentration of succinate, and the growth substrate was then switched to ethylamine, a condition where the cell must make a sudden switch from utilizing the tricarboxylic acid (TCA) cycle to using the ethylmalonyl-CoA pathway for assimilation, which has been an effective strategy for identifying metabolic control points. A 9-h lag in growth was observed, during which butyryl-CoA, a degradation product of ethylmalonyl-CoA, accumulated, suggesting a metabolic imbalance. Ethylmalonyl-CoA mutase activity increased to a level sufficient for the observed growth rate at 9 h, which correlated with an upregulation of RNA transcripts for ecm and a decrease in the levels of ethylmalonyl-CoA. When the wild-type strain overexpressing ecm was tested with the same substrate switchover experiment, ethylmalonyl-CoA did not accumulate, growth resumed earlier, and, after a transient period of slow growth, the culture grew at a higher rate than that of the control. These findings demonstrate that ethylmalonyl-CoA mutase is a metabolic control point in the EMC pathway, expanding our understanding of its regulation.

Multiphyletic origins of methylotrophy in Alphaproteobacteria, exemplified by comparative genomics of Lake Washington isolates.

We sequenced the genomes of 19 methylotrophic isolates from Lake Washington, which belong to nine genera within eight families of the Alphaproteobacteria, two of the families being the newly proposed families. Comparative genomic analysis with a focus on methylotrophy metabolism classifies these strains into heterotrophic and obligately or facultatively autotrophic methylotrophs. The most persistent metabolic modules enabling methylotrophy within this group are the N-methylglutamate pathway, the two types of methanol dehydrogenase (MxaFI and XoxF), the tetrahydromethanopterin pathway for formaldehyde oxidation, the serine cycle and the ethylmalonyl-CoA pathway. At the same time, a great potential for metabolic flexibility within this group is uncovered, with different combinations of these modules present. Phylogenetic analysis of key methylotrophy functions reveals that the serine cycle must have evolved independently in at least four lineages of Alphaproteobacteria and that all methylotrophy modules seem to be prone to lateral transfers as well as deletions.

Genetic tools for the industrially promising methanotroph Methylomicrobium buryatense.

Aerobic methanotrophs oxidize methane at ambient temperatures and pressures and are therefore attractive systems for methane-based bioconversions. In this work, we developed and validated genetic tools for Methylomicrobium buryatense, a haloalkaliphilic gammaproteobacterial (type I) methanotroph. M. buryatense was isolated directly on natural gas and grows robustly in pure culture with a 3-h doubling time, enabling rapid genetic manipulation compared to many other methanotrophic species. As a proof of concept, we used a sucrose counterselection system to eliminate glycogen production in M. buryatense by constructing unmarked deletions in two redundant glycogen synthase genes. We also selected for a more genetically tractable variant strain that can be conjugated with small incompatibility group P (IncP)-based broad-host-range vectors and determined that this capability is due to loss of the native plasmid. These tools make M. buryatense a promising model system for studying aerobic methanotroph physiology and enable metabolic engineering in this bacterium for industrial biocatalysis of methane.

Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1).

BACKGROUND: Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration. RESULTS: A stoichiometric flux balance model of Methylomicrobium buryatense strain 5G(B1) was constructed and used for evaluating metabolic engineering strategies for biofuels and chemical production with a methanotrophic bacterium as the catalytic platform. The initial metabolic reconstruction was based on whole-genome predictions. Each metabolic step was manually verified, gapfilled, and modified in accordance with genome-wide expression data. The final model incorporates a total of 841 reactions (in 167 metabolic pathways). Of these, up to 400 reactions were recruited to produce 118 intracellular metabolites. The flux balance simulations suggest that only the transfer of electrons from methanol oxidation to methane oxidation steps can support measured growth and methane/oxygen consumption parameters, while the scenario employing NADH as a possible source of electrons for particulate methane monooxygenase cannot. Direct coupling between methane oxidation and methanol oxidation accounts for most of the membrane-associated methane monooxygenase activity. However the best fit to experimental results is achieved only after assuming that the efficiency of direct coupling depends on growth conditions and additional NADH input (about 0.1-0.2 mol of incremental NADH per one mol of methane oxidized). The additional input is proposed to cover loss of electrons through inefficiency and to sustain methane oxidation at perturbations or support uphill electron transfer. Finally, the model was used for testing the carbon conversion efficiency of different pathways for C1-utilization, including different variants of the ribulose monophosphate pathway and the serine cycle. CONCLUSION: We demonstrate that the metabolic model can provide an effective tool for predicting metabolic parameters for different nutrients and genetic perturbations, and as such, should be valuable for metabolic engineering of the central metabolism of M. buryatense strains.

Multimolecule test-tube simulations of protein unfolding and aggregation.

Molecular dynamics simulations of protein folding or unfolding, unlike most in vitro experimental methods, are performed on a single molecule. The effects of neighboring molecules on the unfolding/folding pathway are largely ignored experimentally and simply not modeled computationally. Here, we present two all-atom, explicit solvent molecular dynamics simulations of 32 copies of the Engrailed homeodomain (EnHD), an ultrafast-folding and -unfolding protein for which the folding/unfolding pathway is well-characterized. These multimolecule simulations, in comparison with single-molecule simulations and experimental data, show that intermolecular interactions have little effect on the folding/unfolding pathway. EnHD unfolded by the same mechanism whether it was simulated in only water or also in the presence of other EnHD molecules. It populated the same native state, transition state, and folding intermediate in both simulation systems, and was in good agreement with experimental data available for each of the three states. Unfolding was slowed slightly by interactions with neighboring proteins, which were mostly hydrophobic in nature and ultimately caused the proteins to aggregate. Protein-water hydrogen bonds were also replaced with protein-protein hydrogen bonds, additionally contributing to aggregation. Despite the increase in protein-protein interactions, the protein aggregates formed in simulation did not do so at the total exclusion of water. These simulations support the use of single-molecule techniques to study protein unfolding and also provide insight into the types of interactions that occur as proteins aggregate at high temperature at an atomic level.

Membrane protein complex of APS reductase and Qmo is present in Desulfovibrio vulgaris and Desulfovibrio alaskensis.

Due to their adjacent location in the genomes of Desulfovibrio species and their potential for formation of an electron transfer pathway in sulfate-reducing prokaryotes, adenosyl phosphosulfate (APS) reductase (Apr) and quinone-interacting membrane-bound oxidoreductase (Qmo) have been thought to interact together during the reduction of APS. This interaction was recently verified in Desulfovibrio desulfuricans. Membrane proteins of Desulfovibrio vulgaris Hildenborough ΔqmoABCD JW9021, a deletion mutant, were compared to the parent strain using blue-native PAGE to determine whether Qmo formed a complex with Apr or other proteins. In the parent strain of D. vulgaris, a unique band was observed that contained all four Qmo subunits, and another band contained three subunits of Qmo, as well as subunits of AprA and AprB. Similar results were observed with bands excised from membrane preparations of Desulfovibrio alaskensis strain G20. These results are in support of the formation of a physical complex between the two proteins; a result that was further confirmed by the co-purification of QmoA/B and AprA/B from affinity-tagged D. vulgaris Hildenborough strains (AprA, QmoA and QmoB) regardless of which subunit had been tagged. This provides clear evidence for the presence of a Qmo-Apr complex that is at least partially stable in protein extracts of D. vulgaris and D. alaskensis.

Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe.

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis.

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Novel methylotrophic isolates from lake sediment, description of Methylotenera versatilis sp. nov. and emended description of the genus Methylotenera.

Phylogenetic positions, and genotypic and phenotypic characteristics of three novel methylotrophic isolates, strains 301(T), 30S and SIP3-4, from sediment of Lake Washington, Seattle, USA, are described. The strains were restricted facultative methylotrophs capable of growth on single carbon compounds (methylamine and methanol) in addition to a limited range of multicarbon compounds. All strains used the N-methylglutamate pathway for methylamine oxidation. Strain SIP3-4 possessed the canonical (MxaFI) methanol dehydrogenase, but strains 301(T) and 30S did not. All three strains used the ribulose monophosphate pathway for C1 assimilation. The major fatty acids in the three strains were C(16:0) and C(16:1)ω7c. The DNA G+C contents of strains 301(T) and SIP3-4 were 42.6 and 54.6 mol%, respectively. Based on 16S rRNA gene sequence phylogeny and the relevant phenotypic characteristics, strain SIP3-4 was assigned to the previously defined species Methylovorus glucosotrophus. Strains 301(T) and 30S were closely related to each other (100% 16S rRNA gene sequence similarity) and shared 96.6% 16S rRNA gene sequence similarity with a previously described isolate, Methylotenera mobilis JLW8(T). Based on significant genomic and phenotypic divergence with the latter, strains 301(T) and 30S represent a novel species within the genus Methylotenera, for which the name Methylotenera versatilis sp. nov. is proposed; the type strain is 301(T) (=VKM B-2679(T)=JCM 17579(T)). An emended description of the genus Methylotenera is provided.

Quick and Accurate Estimates of Mutation Effects on Transition-State Stabilization of Enzymes from Molecular Simulations with Restrained Transition States.

Data science and machine learning are revolutionizing enzyme engineering; however, high-throughput simulations for screening large libraries of enzyme variants remain a challenge. Here, we present a novel but highly simple approach to comparing enzyme variants with fully atomistic classical molecular dynamics (MD) simulations on a tractable timescale. Our method greatly simplifies the problem by restricting sampling only to the reaction transition state, and we show that the resulting measurements of transition-state stability are well correlated with experimental activity measurements across two highly distinct enzymes, even for mutations with effects too small to resolve with free energy methods. This method will enable atomistic simulations to achieve sampling coverage for enzyme variant prescreening and machine learning model training on a scale that was previously not possible.

An integrated proteomics/transcriptomics approach points to oxygen as the main electron sink for methanol metabolism in Methylotenera mobilis.

Methylotenera species, unlike their close relatives in the genera Methylophilus, Methylobacillus, and Methylovorus, neither exhibit the activity of methanol dehydrogenase nor possess mxaFI genes encoding this enzyme, yet they are able to grow on methanol. In this work, we integrated a genome-wide proteomics approach, shotgun proteomics, and a genome-wide transcriptomics approach, shotgun transcriptome sequencing (RNA-seq), of Methylotenera mobilis JLW8 to identify genes and enzymes potentially involved in methanol oxidation, with special attention to alternative nitrogen sources, to address the question of whether nitrate could play a role as an electron acceptor in place of oxygen. Both proteomics and transcriptomics identified a limited number of genes and enzymes specifically responding to methanol. This set includes genes involved in oxidative stress response systems, a number of oxidoreductases, including XoxF-type alcohol dehydrogenases, a type II secretion system, and proteins without a predicted function. Nitrate stimulated expression of some genes in assimilatory nitrate reduction and denitrification pathways, while ammonium downregulated some of the nitrogen metabolism genes. However, none of these genes appeared to respond to methanol, which suggests that oxygen may be the main electron sink during growth on methanol. This study identifies initial targets for future focused physiological studies, including mutant analysis, which will provide further details into this novel process.

Genomes of three methylotrophs from a single niche reveal the genetic and metabolic divergence of the methylophilaceae.

The genomes of three representatives of the family Methylophilaceae, Methylotenera mobilis JLW8, Methylotenera versatilis 301, and Methylovorus glucosetrophus SIP3-4, all isolated from a single study site, Lake Washington in Seattle, WA, were completely sequenced. These were compared to each other and to the previously published genomes of Methylobacillus flagellatus KT and an unclassified Methylophilales strain, HTCC2181. Comparative analysis revealed that the core genome of Methylophilaceae may be as small as approximately 600 genes, while the pangenome may be as large as approximately 6,000 genes. Significant divergence between the genomes in terms of both gene content and gene and protein conservation was uncovered, including the varied presence of certain genes involved in methylotrophy. Overall, our data demonstrate that metabolic potentials can vary significantly between different species of Methylophilaceae, including organisms inhabiting the very same environment. These data suggest that genetic divergence among the members of this family may be responsible for their specialized and nonredundant functions in C1 cycling, which in turn suggests means for their successful coexistence in their specific ecological niches.