Until recently, rare-earth elements (REEs) had been thought to be biologically inactive. This view changed with the discovery of the methanol dehydrogenase XoxF that strictly relies on REEs for its activity. Some methylotrophs only contain xoxF, while others, including the model phyllosphere colonizer Methylobacterium extorquens PA1, harbor this gene in addition to mxaFI encoding a Ca 2+ -dependent enzyme. Here we found that REEs induce the expression of xoxF in M. extorquens PA1, while repressing mxaFI, suggesting that XoxF is the preferred methanol dehydrogenase in the presence of sufficient amounts of REE. Using reporter assays and a suppressor screen, we found that lanthanum (La 3+ ) is sensed both in a XoxF-dependent and independent manner. Furthermore, we investigated the role of REEs during Arabidopsis thaliana colonization. Element analysis of the phyllosphere revealed the presence of several REEs at concentrations up to 10 μg per g dry weight. Complementary proteome analyses of M. extorquens PA1 identified XoxF as a top induced protein in planta and a core set of La 3+regulated proteins under defined artificial media conditions. Among these was a REE-binding protein that is encoded next to a gene for a TonB-dependent transporter.The latter was essential for REE-dependent growth on methanol indicating chelator-assisted uptake of REEs.

Methylotrophy is the ability to use reduced one-carbon compounds, such as methanol, as a single source of carbon and energy. Methanol is, due to its availability and potential for production from renewable resources, a valuable feedstock for biotechnology. Nature offers a variety of methylotrophic microorganisms that differ in their metabolism and represent resources for engineering of value-added products from methanol. The most extensively studied methylotroph is the Alphaproteobacterium Methylobacterium extorquens. Over the past five decades, the metabolism of M. extorquens has been investigated physiologically, biochemically, and more recently, using complementary omics technologies such as transcriptomics, proteomics, metabolomics, and fluxomics. These approaches, together with a genome-scale metabolic model, facilitate system-wide studies and the development of rational strategies for the successful generation of desired products from methanol. This review summarizes the knowledge of methylotrophy in M. extorquens, as well as the available tools and biotechnological applications.

a b s t r a c tIn the Gram-positive methylotroph Bacillus methanolicus, methanol oxidation is catalyzed by an NAD-dependent methanol dehydrogenase (Mdh) that belongs to the type III alcohol dehydrogenase (Adh) family. It was previously shown that the in vitro activity of B. methanolicus Mdh is increased by the endogenous activator protein Act, a Nudix hydrolase. Here we show that this feature is not unique, but more widespread among type III Adhs in combination with Act or other Act-like Nudix hydrolases. In addition, we studied the effect of site directed mutations in the predicted active site of Mdh and two other type III Adhs with regard to activity and activation by Act.

Methylotrophy is the ability of organisms to grow at the expense of reduced one-carbon compounds, such as methanol or methane. Here, we used transposon sequencing combining hyper-saturated transposon mutagenesis with high-throughput sequencing to define the essential methylotrophy genome of Methylobacterium extorquens PA1, a model methylotroph. To distinguish genomic regions required for growth only on methanol from general required genes, we contrasted growth on methanol with growth on succinate, a non-methylotrophic reference substrate. About 500,000 insertions were mapped for each condition, resulting in a median insertion distance of five base pairs. We identified 147 genes and 76 genes as specific for growth on methanol and succinate, respectively, and a set of 590 genes as required under both growth conditions. For the integration of metabolic functions, we reconstructed a genome-scale metabolic model and performed in silico essentiality analysis. In total, the approach uncovered 95 genes not previously described as crucial for methylotrophy, including genes involved in respiration, carbon metabolism, transport, and regulation. Strikingly, regardless of the absence of the Calvin cycle in the methylotroph, the screen led to the identification of the gene for phosphoribulokinase as essential during growth on methanol, but not during growth on succinate. Genetic experiments in addition to metabolomics and proteomics revealed that phosphoribulokinase serves a key regulatory function. Our data support a model according to which ribulose-1,5-bisphosphate is an essential metabolite that induces a transcriptional regulator driving one-carbon assimilation.

Methylobacterium extorquens AM1 uses dedicated cofactors for one-carbon unit conversion. Based on the sequence identities of enzymes and activity determinations, a methanofuran analog was proposed to be involved in formaldehyde oxidation in Alphaproteobacteria. Here, we report the structure of the cofactor, which we termed methylofuran. Using an in vitro enzyme assay and LC-MS, methylofuran was identified in cell extracts and further purified. From the exact mass and MS-MS fragmentation pattern, the structure of the cofactor was determined to consist of a polyglutamic acid side chain linked to a core structure similar to the one present in archaeal methanofuran variants. NMR analyses showed that the core structure contains a furan ring. However, instead of the tyramine moiety that is present in methanofuran cofactors, a tyrosine residue is present in methylofuran, which was further confirmed by MS through the incorporation of a 13 C-labeled precursor. Methylofuran was present as a mixture of different species with varying numbers of glutamic acid residues in the side chain ranging from 12 to 24. Notably, the glutamic acid residues were not solely ␥-linked, as is the case for all known methanofurans, but were identified by NMR as a mixture of ␣-and ␥-linked amino acids. Considering the unusual peptide chain, the elucidation of the structure presented here sets the basis for further research on this cofactor, which is probably the largest cofactor known so far.

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Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model description of the metabolic phenomena inside the cell, the extrapolation to a broader operation domain (e.g., different feeding concentrations and dilution rates) may present mismatches between the predictions and the actual process outcomes. Such detailed models may require almost perfect knowledge of the cell metabolism and omic-level measurements, hampering their development. On the other hand, purely data-driven models that learn correlations from experimental data do not require any prior knowledge of the process and are therefore unbiased and flexible. However, many more data are required for their development and their extrapolation ability is limited to conditions that are similar to the ones used for training. An attractive alternative is the combination of the extrapolation power of first principles knowledge with the flexibility of machine learning methods. This approach results in a hybrid model for the growth and uptake rates that can be used to predict the dynamic operation of the bioreactor. Here we develop a hybrid model to describe the continuous production of PHA by Pseudomonas putida GPo1 culture. After training, the model with experimental data gained under different dilution rates and medium compositions, we demonstrate how the model can describe the process in a wide range of operating conditions, including both single and dual nutrient-limited growth.

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