Systems-level analysis provides insights on
methanol-based production of l-glutamate and its
decarboxylation product γ-aminobutyric acid by Bacillus
methanolicus

Marta Irla, Ingemar Nærdal, David Virant, Trygve Brautaset, Tobias Busche, Dušan Goranovič, Carsten Haupka, Stéphanie Heux, Gregor Kosec, Christian Rückert-Reed, et al.

► To cite this version:

Marta Irla, Ingemar Nærdal, David Virant, Trygve Brautaset, Tobias Busche, et al. Systems-level analysis provides insights on methanol-based production of l-glutamate and its decarboxylation product γ-aminobutyric acid by Bacillus methanolicus. Metabolic Engineering, 2025, 91, pp.389-404. 10.1016/j.ymben.2025.06.002. hal-05151404

HAL Id: hal-05151404

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# Systems-level analysis provides insights on methanol-based production of L-glutamate and its decarboxylation product γ-aminobutyric acid by *Bacillus methanolicus*

Marta Irla<sup>a,1</sup>, Ingemar Nærdal<sup>b,1</sup>, David Virant<sup>c,1</sup>, Trygve Brautaset<sup>d</sup>, Tobias Busche<sup>e</sup>, Dušan Goranovič<sup>c</sup>, Carsten Haupka<sup>e,f</sup>, Stéphanie Heux<sup>g</sup>, Gregor Kosec<sup>c</sup>, Christian Rückert-Reed<sup>e</sup>, Volker F. Wendisch<sup>e,f</sup>, Luciana F. Brito<sup>d,*,1 ID</sup>, Cláudia M. Vicente<sup>g,**,1,2 ID</sup>

## ARTICLE INFO

### Keywords:

*Bacillus methanolicus*

L-Glutamate

GABA

metabolic engineering

Genomics

Transcriptomic

Fluxomic

Genome-scale-model

Systems biology

## ABSTRACT

*Bacillus methanolicus* is the next workhorse in biotechnology using methanol, an alternative and economical one-carbon feedstock that can be obtained directly from carbon dioxide, as both carbon and energy source for the production of value-added chemicals. The wild-type strain *B. methanolicus* MGA3 naturally overproduces L-glutamate in methanol-based fed-batch fermentations. Here we generated a *B. methanolicus* strain exhibiting enhanced L-glutamate production capability through induced mutagenesis. To showcase the potential of this mutant strain, further metabolic engineering enabled the production of γ-aminobutyric acid (GABA) directly from L-glutamate during methanol fed-batch fermentations. Using a systems-level analysis, encompassing whole-genome sequencing, RNA sequencing, fluxome analysis and genome-scale metabolic modelling, we were able to elucidate the metabolic and regulatory adaptations that sustain the biosynthesis of these products. The metabolism of the mutant strain specifically evolved to prioritize energy conservation and efficient carbon utilization, culminating in increased product formation. These results and insights provide a foundation for further rational metabolic engineering and bioprocess optimization, enhancing the industrial viability of *B. methanolicus* for sustainable production of L-glutamate and its derivatives.

## 1. Background

Microbial bioprocesses are designed to decrease reliance on fossil-derived resources for value-added chemical production. The development of bio-based production processes is driven by the limited reserves of fossil fuels and the necessary shift towards sustainable production of important platform chemicals needed to support the growing global

population. Microbial cell factories have profound value for a bio-based economy because they can selectively convert renewable feedstocks into desired products at a high yield and productivity. Such industrial workhorses include the bacterial species *Escherichia coli*, *Bacillus subtilis* and *Corynebacterium glutamicum* that are, among others, currently used for the biosynthesis of various products, including amino acids, d-amines, alcohols, organic acids, enzymes and therapeutic proteins

<footnote><sup>a</sup> Department of Biological and Chemical Engineering, Aarhus University, 8000, Aarhus, Denmark</footnote>

<footnote><sup>b</sup> Department of Biotechnology and Nanomedicine, SINTEF Industry, 7465, Trondheim, Norway</footnote>

<footnote><sup>c</sup> Acies Bio d.o.o., Tehnološki Park 21, SI-1000, Ljubljana, Slovenia</footnote>

<footnote><sup>d</sup> Department of Biotechnology and Food Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway</footnote>

<footnote><sup>e</sup> Technology Platform Genomics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany</footnote>

<footnote><sup>f</sup> Genetics of Prokaryotes, Faculty of Biology, Bielefeld University, Bielefeld, Germany</footnote>

<footnote><sup>g</sup> TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: luciana.f.d.brito@ntnu.no (L.F. Brito), claudia.vicente@inrae.fr (C.M. Vicente).</footnote>

<footnote n="1">¹ Contributed equally.</footnote>

<footnote n="2">² New address (CMV): GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet-Tolosan, France.</footnote>
(Becker et al., 2018; McElwain et al., 2022; Su et al., 2020; Yang et al., 2021).

Bacillus methanolicus MGA3 possesses many features that make it a candidate to become the next industrial workhorse for sustainable bio-production. Key traits include its natural thermophilic and methylo-trophic lifestyle, and its documented high methanol consumption rate and product formation rates, including the amino acids L-glutamate and L-lysine (Brautaset et al., 2007). Methanol (CH3OH), a liquid and water-miscible reduced derivative of carbon dioxide (CO2) that can be captured from industrial emissions, is completely utilized during mi-crobial fermentation. During methanol-based fed-batch fermentation, B. methanolicus MGA3 is known to naturally produce and excrete up to 60 g/L of L-glutamate (Brautaset et al., 2010; Irla and Wendisch, 2022). The L-glutamate biosynthetic pathways and associated enzymes were previously characterized with a significant role of its two glutamate synthases (GLUSy), α-ketoglutarate dehydrogenase and citrate synthase in regulating L-glutamate production (Brautaset et al., 2003; Krog et al., 2013) (Fig. 1A). L-Glutamate accumulation can also be attributed to the carbon flux distribution in the central metabolism of B. methanolicus when grown in methanol. In this condition, methylotrophs normally do not need a complete tricarboxylic acid (TCA) cycle to fulfil their energy requirements as the reaction of methanol oxidation generates energy in the form of NADH (Chistoserdova et al., 2009). Although B. methanolicus is equipped with all the genes necessary for a functional TCA cycle (Drejer et al., 2020; Heggeset et al., 2012; Müller et al., 2015b), the levels of certain enzymes of its lower part were found to be sparse during growth on methanol (Delépine et al., 2020; Müller et al., 2014). The synthesis of L-glutamate is reported to be triggered by external physio-logical factors including magnesium limitation (Krog et al., 2013) and the supplementation with surfactant Tween 80 (Schendel et al., 2000). Finally, conversion of L-glutamate to γ-aminobutyric acid (GABA) was established in B. methanolicus through heterologous expression of a *Sulfobacillus thermosulfidooxidans*-derived glutamate decarboxylase gene from a GABA biosynthesis plasmid and the employment of a two-phase production strategy comprising a pH reduction from 6.5 to 4.6 (Irla et al., 2017).

Both L-glutamate and GABA are widely applied in food and beverage industries. The market for L-glutamate was valued at USD 7.58 billion in 2023 and is projected to grow, reaching USD 11.52 billion by 2030. Meanwhile, the GABA supplement market is also growing, with a fore-casted increase from USD 45.31 million in 2024 to USD 69.32 million by 2031 (source: Research and Markets). Moreover, the current price of methanol stands at approximately USD 891 per metric ton (source: Methanex). Methanol's economic performance is superior to that of glucose, hence, making it a promising feedstock for future industrial applications (Orfali et al., 2025). In this study, we applied induced mutagenesis, screening and selection to improve L-glutamate biosyn-thesis without using external factors as a trigger for production (Irla et al., 2016, 2020). In selected strains, L-glutamate was used as a pre-cursor for GABA production through heterologous expression of its decarboxylase gene. The resulting strains were then subjected to a comprehensive multi-omics and modelling analysis to gain a systems-level understanding of their genetic backgrounds, cell meta-bolism and functions. Such knowledge is central in the design-build-test-learn cycle for microbial cell factory improvement while providing a rational roadmap for future work to solve bottlenecks and further boost production.

## 2. Results and discussion

### 2.1. Induced mutagenesis led to a L-glutamate overproducing B. methanolicus strain with improved GABA production

B. methanolicus MGA3 mutants were obtained using classical muta-genesis combined with a high-throughput screening to identify L-gluta-mate overproducing clones. First, a reproducible protocol was

established for growing B. methanolicus as single colonies on agar plates using rich complex medium supplemented with mannitol, instead of methanol, to circumvent evaporation at 50 °C. In addition, a liquid pre-culture step with cells in exponential growth phase was added prior to spreading cells onto agar plates to ensure consistent cell growth on the plates.

Different mutagenesis protocols were applied and included the mutagenic agents N-methyl-N'-nitro-N-nitroso-guanidine (NTG), ethyl-methane sulphonate (EMS) and ultraviolet (UV) light. Under these conditions, cell "kill rate" was used as a proxy measurement for DNA damage and mutagenesis rate. Parameters such as mutagenesis time, chemical mutagen concentration, UV light source distance and intensity as well as biomass input were adjusted to achieve a kill rate of <99 % to avoid selection of atypical resistant variants which occur spontaneously in cell populations (Lawrence, 1991). The desired kill rate threshold was achieved for all tested mutagens. Since each mutagen causes different types of mutations (Glaab et al., 1999; Ikehata and Ono, 2011; Ohnishi et al., 2008), we used all three in subsequent experiments. The assay for strain mutagenesis and screening was designed as depicted in Fig S1, and described in the "Materials and methods" section.

Initially, 2500 mutant clones were screened by deep-well plate cul-tivations, in which roughly 30 clones exhibited a 2-fold increased L-glutamate titer compared to the parental strain B. methanolicus MGA3 (Fig. 1B). For such screening, cultures were sampled after 24 h of in-cubation, at which point they were in late exponential to stationary phase. The timed approach instead of a growth stage-based approach was used to increase throughput. Previous experiments showed that no additional production takes place during late stationary phase. Strain ABBM741 was selected for a second round of mutagenesis as it exhibited the most consistent improvement in L-glutamate production in different cultivation media. We repeated the mutagenesis protocol and screened an additional 800 clones for further improvements in L-glutamate pro-duction (Fig. 1C).

To achieve high throughput, colonies of mutagenized cultures were screened in deep-well plates as an initial pre-screening phase without biological replicates. This introduced significant experimental noise, which was an accepted trade-off for higher throughput. In the second stage of screening, the strains were tested in 250 mL Erlenmeyer flasks in different media. Strain ABBM741 consistently overproduced L-gluta-mate in these experiments across all tested media, while other strains' performance was not transferable to the Erlenmeyer flask setup. Lack of transferability between deep-well plates and shake flasks is a well-known phenomenon (Duetz and Witholt, 2001; Wewetzer et al., 2015), in most cases due to the difference in oxygen transfer rate and agitation between the two setups. While several strains generated in the second round of mutagenesis produced up to 50 % higher L-glutamate titers than the parental strain ABBM741, only strains ABBM4224, ABBM4266 and ABBM4307 performed consistently and were further tested in biological triplicates in shake flasks instead of deep-well plates (Fig. 1D). While all strains maintained increased L-glutamate production in comparison to MGA3, with strains ABBM4266 and ABBM4307 pro-ducing the highest L-glutamate titers out of the tested strains, strain ABBM4307 was finally chosen for further tests and characterization due to its reliable growth in all experiments.

To test the capability of the evolved ABBM4307 strain to produce GABA from L-glutamate, it was transformed with the pBV2xp-gadSt plasmid which contains a *S. thermosulfidooxidans*-derived glutamate decarboxylase gene (i.e., GABA biosynthesis plasmid) to create the ABBM4307gad strain (Irla et al., 2017). While very low GABA titers were observed after xylose induction of gadSt expression at near-neutral pH (Fig. 2), significant GABA accumulation of up to 13.2 g/L was achieved for ABBM4307gad after culture acidification to pH 4.6. Only one third of the available L-glutamate was converted into GABA suggesting that this conversion process is a limiting step. However, the GABA titer achieved in this study represents a 44 % increase in comparison to the GABA titer previously achieved in methanol-based fed-batch fermentations
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** L-Glutamate production during high-throughput screening and in shake flasks.</figcaption></figure>

A) Putative pathways towards L-glutamate and GABA biosynthesis. GLNSy (glutamine synthase), GLUSy (glutamate synthases). B) The first generation of mutants, descending from *B. methanolicus* MGA3. The x axis represents the serial number of the mutant strains tested and the y axis represents the L-glutamate titer in the broth supernatant relative to the parental strain MGA3. MGA3 control cultures were present on each plate to account for variability. C) 800 clones from the second generation of mutants, descending from the top candidate ABBM741, were tested. D) L-Glutamate titer in strains ABBM4224, ABBM4266 and ABBM4307 relative to their parental strains MGA3 and ABBM741. GAD<sup>St</sup>-glutamate decarboxylase.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Fed-batch cultivation of strain ABBM4307gad and its methanol-based production of L-glutamate (GLU) and subsequent conversion to GABA.</figcaption></figure>

l-Glutamate and GABA production titers were corrected for the bioreactor dilution factor due to feeding.

employing *B. methanolicus* MGA3(pTH1mp-gad<sup>St</sup>) (Irla et al., 2017). This is, to our knowledge, the highest GABA titer reported using methanol as substrate.

## 2.2. Cultivation of L-glutamate and GABA overproducing strains for multi-omics characterization

To explore how overproduction of L-glutamate and its derivative GABA affect ABBM4307 and ABBM4307gad on different cellular levels (i.e., genome, transcriptome and fluxome), a multi-omics analysis was performed (Fig. 3). The three selected strains MGA3gad (MGA3 transformed with pBV2xp-gad<sup>St</sup> plasmid), ABBM4307 and ABBM4307gad were cultivated in a methanol-based growth medium as three biological replicates in bioreactors at controlled conditions. The multi-omics sampling was executed during the exponential growth phase, and within the optical density (OD<sub>600nm</sub>) interval of 0.6–2.2 for strain MGA3gad and 0.6–2.8 for strains ABBM4307 and ABBM4307gad (Fig. 3A). During this period of approximately 5 h, the biomass growth was monitored at four timepoints. The multi-omics sampling was initiated by transcriptomics sampling before and 1.5–2 h after xylose induction of gad<sup>St</sup> expression, followed by fluxome sampling subsequent to a pulse of 3.2 g/L (100 mM) of <sup>13</sup>C-labelled methanol. Xylose could be considered a gratuitous inducer for *B. methanolicus* as it is not metabolized by this bacterium (Schendel et al., 1990). The specific growth rates (μ) of the strains during the exponential phase varied and were in the range of 0.28 h<sup>-1</sup>-0.35 h<sup>-1</sup> for all tested strains. During the multi-omics sampling phase, the triplicate cultures of all three strains displayed highly reproducible growth characteristics (Fig. 3B). However, in the case of the MGA3gad strain, after completing the multi-omics sampling the growth of the triplicate cultures increasingly deviated with time which is not typically observed for *B. methanolicus*. After the completion of the multi-omics sampling the cultures were acidified to a pH of approximately 4.6 to initiate the pH-dependent conversion of produced L-glutamate to GABA by Gad<sup>St</sup>. Upon this pH shift the cell respiratory activity halted immediately as dissolved oxygen rapidly increased. This conversion phase lasted for nearly 10 h and samples were taken to
<figure><img src="image_6.png" /></figure>

<table><thead><tr><td>Condition vs Control</td><td>Method applied</td><td>Query</td></tr></thead><tbody><tr><td>ABBM4307gad vs MGA3gad</td><td>G*,T,F</td><td>Mutagenesis-specific response</td></tr><tr><td>AMMB4307gad vs ABBM4307</td><td>G*,T,F</td><td>Plasmid-specific response</td></tr><tr><td>ABBM4307gad vs ABBM4307gad NI</td><td>G*,T</td><td>Plasmid induction-specific response</td></tr></tbody></table>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 3.** Multi-omics cultures and sampling.</figcaption></figure>

A) Schematic representation of the strategy of multi-omics sampling throughout bacterial growth. The table represents the control and test conditions for each query adopted in this study. G\*-genomics to detect single nucleotide polymorphisms (SNPs) in each studied strain in comparison to the MGA3 GenBank references, T-transcriptomics, F-fluxomics, NI-non-induced. B) Plots display bacterial growth (OD<sub>600nm</sub>), pH and dissolved oxygen (DO) evolution. The growth curves are means of three biological replicates and the error bars represent standard deviation.

determine the final L-glutamate and GABA titers. A separate inoculated bioreactor, with otherwise identical conditions, was used as a control to determine the methanol evaporation rate needed for flux analysis. Sampling was performed as technical triplicates every hour. In the control bioreactor, a pulse of 3.2 g/L (100 mM) of unlabeled methanol was added at the same time when the ¹³C-methanol pulse was performed in the inoculated bioreactors.

The exometabolome samples collected during the cultivation were analyzed by NMR to assess physiological parameters such as methanol and product titers, the uptake/production rates and the evaporation constant (Table 1). The MGA3gad strain showed higher growth and biomass yield values, as well as methanol uptake rate, than the ABBM4307 and ABBM4307gad strains, in the studied conditions. As expected, the L-glutamate titer and its specific production rate were up to 5-fold higher in the evolved strains compared to the parental strain. In the evolved strains, the presence of the GABA production plasmid boosted L-glutamate titer, as well as the L-glutamate production rate (Table 1).

While acetate production for *B. methanolicus* during non-methylotrophic growth has only been reported once before (Delépine et al., 2020), all three strains produced acetate at a different titer and rate. The evolved strain ABBM4307 produced acetate at a 3.5-fold higher rate than the MGA3gad strain. Here again, the introduction of the GABA production plasmid had a metabolic impact since the ABBM4307gad strain showed a 6.8-fold increase in acetate production rate compared to ABBM4307 (Table 1). To our knowledge, these results constitute the first observation of acetate production by *B. methanolicus* under methanol growth conditions (Carnicer et al., 2016; Müller et al., 2015b). Redirection of carbon flux from α-ketoglutarate (AKG) to L-glutamate instead of the TCA cycle leads to a decrease in energy supply in the mutant strains. It could be speculated that acetate formation compensates for that energy loss, as one ATP molecule is generated per one molecule of acetate during its biosynthesis.

**Table 1**

**Physiological parameters of the *B. methanolicus* cultures during the exponential phase and final GABA product titer.**

The results are presented as mean values of biological triplicates with standard deviations.

<table><thead><tr><th>Parameter</th><th>MGA3gad</th><th>ABBM4307</th><th>ABBM4307gad</th></tr></thead><tbody><tr><td>Biomass yield (gDCW<sup>a</sup>/g)</td><td>0.49 ± 0.052</td><td>0.42 ± 0.02</td><td>0.43 ± 0.05</td></tr><tr><td>Growth rate, μ (h<sup>-1</sup>)</td><td>0.37 ± 0.01</td><td>0.31 ± 0.01</td><td>0.31 ± 0.01</td></tr><tr><td>Substrate uptake rate (mmol/gDCW/h)</td><td>24.0 ± 2.35</td><td>23.2 ± 0.92</td><td>22.8 ± 3.36</td></tr><tr><td>Glutamate product titer (g/L)<sup>b</sup></td><td>0.03 ± 0.00</td><td>0.10 ± 0.0303</td><td>0.16 ± 0.0101</td></tr><tr><td>Glutamate product titer (g/mol methanol)<sup>b</sup></td><td>0.22 ± 0.04</td><td>0.74 ± 0.20</td><td>1.16 ± 0.10</td></tr><tr><td>Glutamate production rate (mmol/gDCW/h)</td><td>0.17 ± 0.02</td><td>0.64 ± 0.12</td><td>0.81 ± 0.01</td></tr><tr><td>Acetate product titer (g/L)<sup>b</sup></td><td>0.01 ± 0.004</td><td>0.03 ± 0.01</td><td>0.07 ± 0.01</td></tr><tr><td>Acetate product titer (g/mol methanol)<sup>b</sup></td><td>0.24 ± 0.04</td><td>0.22 ± 0.06</td><td>0.50 ± 0.05</td></tr><tr><td>Acetate production rate (mmol/gDCW/h)</td><td>0.13 ± 0.07</td><td>0.46 ± 0.18</td><td>0.89 ± 0.18</td></tr><tr><td>Final GABA product titer (g/L)<sup>c</sup></td><td>0.12 ± 0.09</td><td>ND<sup>d</sup></td><td>0.16 ± 0.01</td></tr><tr><td>Final GABA product titer (g/mol methanol)<sup>c</sup></td><td>0.89 ± 0.64</td><td>ND<sup>d</sup></td><td>1.19 ± 0.07</td></tr><tr><td>Evaporation constant (h<sup>-1</sup>)</td><td colspan="3">0.0471</td></tr></tbody></table>

**2.3. Genome and transcriptome changes cause increased L-glutamate and GABA production**

To investigate the genetic basis of the induced mutagenesis and the influence of the GABA producing plasmid on L-glutamate overproduction, the genomic and the transcriptomic data of the three selected strains MGA3gad, ABBM4307 and ABBM4307gad were analyzed and compared.

<footnote>a gDCW- grams dry cell weight.</footnote>

<footnote>b Values measured at the end of the exponential phase.</footnote>

<footnote>c Values measured at the end of the cultivation.</footnote>

<footnote>d Not detected.</footnote>
First, the whole genomes from four samples, MGA3gad, ABBM4307, and ABBM4307gad, as well as ABBM4307gad NI (i.e., strain harvested in non-induced conditions) were sequenced. The shared and unique SNPs identified in the genomes of the analyzed strains are presented as a Venn diagram in Fig. 4. The analysis revealed a total of 229 SNPs across the four conditions. The unique genetic variants identified in ABBM4307gad NI were predominantly located in 23S rRNA genes, whereas those detected in ABBM4307gad were synonymous variants within hypothetical proteins (Table S1). Hence, even though transformation with the GABA biosynthesis plasmid and induction of gadSt expression in ABBM4307 resulted in unique SNPs, these variants do not appear to impact genotypic features related to L-glutamate production. The stable mutations detected in strains ABBM4307 and ABBM4307gad, both before and after induction, were present in various aspects of B. methanolicus metabolism. Notably, mutations were identified in genes related to flagellar assembly, amino acid biosynthesis, and fatty acid and lipid biosynthesis (Table 2). Key mutations included a gene annotated as a cryptic catabolic NAD-specific glutamate dehydrogenase YweB, and genes belonging to the fatty acid biosynthetic family (fab), which are putatively linked to L-glutamate production (Table 2). Those mutations are discussed in detail later in this study.

Notably, the mutagenesis of MGA3 to the mutant strain ABBM4307 resulted in 11 unique genetic variants within five genes encoding enzymes that belong to insertional mutagenesis process and DNA repair: recombinase (BMMGA3_02090 and BMMGA3_13340), transposase (BMMGA3_05495), and reverse transcriptase (BMMGA3_05100 and BMMGA3_06980) (Table S1). Alteration of such DNA repair systems increase mutation rates in the Gram-positive bacterium B. subtilis (Dervyn et al., 2023). Thus, ABBM4307 may have a higher potential for genetic plasticity via mutation than MGA3. This is evidenced by the fact that the genome of MGA3gad exhibited only a single unique SNP while ABBM4307gad and ABBM4307gad NI had a total of 10 unique SNPs between both conditions indicating a 10-fold increase in mutation frequency (Fig. 4, Table S1). Mutations in DNA repair genes commonly occur during random mutagenesis of bacterial strains. For example, studies have demonstrated that mutations in DNA repair genes such as *mutS*, *mutY*, and *mutM* can lead to increased mutation rates in bacteria like *Bacillus anthracis* (Zeibell et al., 2007). These mutations impair the bacterium's ability to correct replication errors, resulting in a higher

frequency of mutations across the genome. Additionally, research on enterobacteria has shown that mutagenic DNA repair systems, such as the *umuDC* operon in *E. coli*, are present and can influence mutation rates. Variations in these systems can affect the overall mutagenic response of bacterial populations (Sedgwick et al., 1991).

Next, a comprehensive analysis of the transcriptomic data was conducted for the following strains and conditions: MGA3gad, ABBM4307, ABBM4307gad and ABBM4307gad NI. Following read trimming, the total number of paired reads across all four RNA samples remained consistently at around 1.2 million reads (Table S2). The generated trimmed reads were mapped to the reference sequences of the *B. methanolicus* MGA3 chromosome and its two plasmids, pBM69 and pBM19 (respective GenBank accession numbers CP007739, CP007740, and CP007741). The alignment mapping percentages on the chromosome (~90 %), plasmids pBM19 (~6 %), and pBM69 (~1 %) remained comparable among the strains. When examining the GABA production plasmid in the plasmid-carrying strains, the mapping percentage on the GABA biosynthesis plasmid was 0.6 % for ABBM4307gad NI, and significantly higher 3.2 % ± 0.3 % and 3.4 % ± 0.1 % for conditions MGA3gad and ABBM4307gad, respectively.

The transcriptomic data of the parental strain MGA3gad and the mutant strain ABBM4307gad were compared by a differential expression analysis leading to the identification of a set of 421 differentially expressed genes (DEGs) that were downregulated and 553 that were upregulated (Table S3). We also compared whole transcriptomes of ABBM4307gad NI to the data obtained from ABBM4307gad induced with 10 g/L xylose, revealing 194 upregulated and 206 downregulated DEGs in response to gadSt gene induction (Table S3). Finally, 17 DEGs were downregulated while 31 DEGs were upregulated when comparing the transcriptome of ABBM4307gad to ABBM4307 strain (Table S3). Overall, these results highlight several changes at both the genomic and transcriptomic levels, encompassing key metabolic processes resulting from our metabolic engineering and growth conditions: the central carbon metabolism from the substrate methanol to L-glutamate, the electron transport chain and ATP generation, cell wall and fatty acids biosynthesis, sporulation, chemotaxis and motility, and vitamin biosynthesis. A full correlation of these genomic and transcriptomic events with the metabolic responses to glutamate and GABA overproduction is detailed in the sections that follow in this study.

## 2.4. Overproduction of L-glutamate affected the fluxes in the central carbohydrate network

In order to study the *in vivo* reaction rates associated with the increased L-glutamate accumulation in *B. methanolicus*, a¹³C-metabolic flux analysis (MFA) was performed after xylose induction in the three selected strains, MGA3gad, ABBM4307 and ABBM4307gad (Fig. 5). A statistical analysis was performed on the flux values obtained for each strain (Table S4).

The methanol uptake rate was not significantly different between analyzed strains (Table 1), and all strains showed a similar flux through the hexulose-phosphate synthase (Hps) (21.8, 22.5 and 21.1 mmol/gDCW/h in strains ABBM4307, ABBM4307gad and MGA3gad, respectively), as well as through the other reactions immediately after methanol assimilation, methanol dehydrogenase (Mdh) and 6-phospho-3-hexuloisomerase (Phi). As expected, a high flux towards the regeneration of the C₁ acceptor pool (i.e., ribulose 5-phophosphate, Ribu5P) through the non-oxidative pentose phosphate pathway (PPP) was observed in the three strains. A total flux through transketolase (Tkt), fructose-bisphosphate aldolase (Sda) and transaldolase (Ta, i.e., Tala) of 17.3, 17.6 and 20.5 mmol/gDCW/h were measured, corresponding to 70, 75 and 84 % of the methanol uptake rate in strains MGA3gad, ABBM4307 and ABBM4307gad, respectively (Fig. 5; Table S4). The alternative route to recycle Ribu5P from fructose 6-phosphate (Fru6P) takes place through the cyclic dissimilatory ribulose monophosphate (RuMP) pathway (Chistoserdova et al., 1991; Heggeset et al., 2012) which

<figure><img src="image_9.png" /><figcaption>Fig. 4. A study of genetic variants found in four L-glutamate overproduction and/or GABA production strains of *B. methanolicus*, MGA3gad, ABBM4307, ABBM4307gad and ABBM4307gad NI.</figcaption></figure>

The Venn diagram displays the shared and unique SNPs detected in the four whole genomes.
<caption>**Table 2**
Summary of stable mutations in the genomes of the mutant strain ABBM4307.</caption>

The table lists mutations across the genomes of ABBM4307, ABBM4307gad, and ABBM4307gad NI. Asterisks (*) indicate the insertion of stop codons and FS denotes frameshift insertions.

<table><thead><tr><th>Locus tag</th><th>Gene</th><th>Function</th><th>SNP Position</th><th>Mutation</th></tr></thead><tbody><tr><td>BMMGA3_01740</td><td>rocA</td><td>1-Pyrroline-5-carboxylate dehydrogenase</td><td>322941</td><td>Gly263Gly</td></tr><tr><td>BMMGA3_03225</td><td>hutH</td><td>Histidine ammonia-lyase</td><td>634134</td><td>Asn119Asn</td></tr><tr><td>BMMGA3_04495</td><td>fabF</td><td>3-Oxoacyl-[acyl-carrier-protein] synthase 2</td><td>886520</td><td>Ala275Thr</td></tr><tr><td>BMMGA3_04555</td><td>cls</td><td>Cardiolipin synthase</td><td>900108</td><td>Ile247Ile</td></tr><tr><td>BMMGA3_04620</td><td>fabI</td><td>Enoyl-[acyl-carrier-protein] reductase [NADH] FabI</td><td>912024</td><td>Arg195Lys</td></tr><tr><td>BMMGA3_05085</td><td>spoOA</td><td>Histidine kinase phosphorylating SpoOA</td><td>997567</td><td>Arg91Lys</td></tr><tr><td>BMMGA3_05955</td><td>plsX</td><td>Phosphate acyltransferase</td><td>1168802</td><td>Lys231Arg</td></tr><tr><td>BMMGA3_05960</td><td>fabD</td><td>Malonyl CoA-acyl carrier protein transacylase</td><td>1169371</td><td>Ser80Ser</td></tr><tr><td>BMMGA3_06150</td><td>flgD</td><td>Flagellar basal body rod modification protein</td><td>1205541</td><td>Thr95Ile</td></tr><tr><td>BMMGA3_06155</td><td>flgG</td><td>Flagellar basal-body rod protein FlgG</td><td>1206377</td><td>Thr208Thr</td></tr><tr><td>BMMGA3_06395</td><td>dapA</td><td>4-Hydroxy-tetrahydropicolinate synthase</td><td>1255890</td><td>Asp243Gly</td></tr><tr><td>BMMGA3_06830</td><td>-</td><td>Hypothetical protein</td><td>1344179</td><td>Gly151Gly</td></tr><tr><td>BMMGA3_08610</td><td>jitJ</td><td>Bifunctional homocysteine S-methyltransferase/5,10-methylenetetrahydrofolate reductase</td><td>1704427</td><td>Gly407Arg</td></tr><tr><td>BMMGA3_09070</td><td>queE</td><td>7-Carboxy-7-deazaguanine synthase</td><td>1804475</td><td>Val242Ile</td></tr><tr><td>BMMGA3_09385</td><td>oxdD</td><td>Oxalate decarboxylase OxdD</td><td>1877514</td><td>Gly304Arg</td></tr><tr><td>BMMGA3_09485</td><td>yvfT</td><td>Sensor histidine kinase YvfT</td><td>1895436</td><td>Ile149Ile</td></tr><tr><td>BMMGA3_09610</td><td>trpC</td><td>Indole-3-glycerol phosphate synthase</td><td>1917486</td><td>Gly2Glu</td></tr><tr><td>BMMGA3_09670</td><td>fruA</td><td>PTS system fructose-specific EIIBC component</td><td>1929684</td><td>Trp498*</td></tr><tr><td>BMMGA3_09955</td><td>uppP4</td><td>Undecaprenyl-diphosphatase 4</td><td>1985431</td><td>Gly114Arg</td></tr><tr><td>BMMGA3_10895</td><td>yweB</td><td>Cryptic catabolic NAD-specific glutamate dehydrogenase YweB</td><td>2163086</td><td>Cys297Tyr</td></tr><tr><td>BMMGA3_10975</td><td>resD</td><td>Transcriptional regulatory protein ResD</td><td>2180140</td><td>Asp4FS</td></tr><tr><td>BMMGA3_11265</td><td>yqjD</td><td>Putative propionyl-CoA carboxylase beta chain</td><td>2234448</td><td>Glu212Glu</td></tr><tr><td>BMMGA3_11330</td><td>bfmBC</td><td>Dihydrolipoyl dehydrogenase</td><td>2249633</td><td>Gly253Asp</td></tr><tr><td>BMMGA3_12280</td><td>mnmA2</td><td>tRNA-specific 2-thiouridylase MnmA 2</td><td>2432455</td><td>Val167Ile</td></tr><tr><td>BMMGA3_12335</td><td>apt</td><td>Adenine phosphoribosyltransferase</td><td>2445918</td><td>Gly139Arg</td></tr><tr><td>BMMGA3_13025</td><td>polA</td><td>DNA polymerase I</td><td>2595290</td><td>Gly582Glu</td></tr><tr><td>BMMGA3_14735</td><td>hisA</td><td>1-(5-Phosphoribosyl)-5-[(5-phosphoribosylamino)methylideneamino] imidazole-4-carboxamide isomerase</td><td>2913454</td><td>Ala127Ala</td></tr><tr><td>BMMGA3_14790</td><td>nagA</td><td>N-Acetylglucosamine-6-phosphate deacetylase</td><td>2922992</td><td>Gly67Glu</td></tr><tr><td>BMMGA3_15055</td><td>-</td><td>Cell envelope transcriptional attenuator</td><td>2983126</td><td>Ala285Val</td></tr><tr><td>BMMGA3_15060</td><td>-</td><td>Hypothetical protein</td><td>2985037</td><td>Ser528Leu</td></tr><tr><td>BMMGA3_16115</td><td>murA2</td><td>UDP-N-acetylglucosamine 1-carboxyvinyltransferase 2</td><td>3213401</td><td>Ala208Val</td></tr><tr><td>BMMGA3_16595</td><td>yycG</td><td>Sensor histidine kinase YycG</td><td>3308480</td><td>Ser87Pro</td></tr><tr><td>BMMGA3_16635</td><td>purA</td><td>Adenylosuccinate synthetase</td><td>3313064</td><td>Gly179Ser</td></tr></tbody></table>

operates within the oxidative PPP. The flux through the glucose-6-phosphate dehydrogenase (Zwf) was at 4.1, 1.9 and 3.9 mmol/gDCW/h in the strains ABBM4307, ABBM4307gad and MGA3gad, respectively (Fig. 5; Table S4). At the same time there is an increased flux through the non-oxidative PPP in ABBM4307gad suggesting that in this strain Ribu5P regeneration, which does not involve carbon loss, is favored. This may have an impact on the final titer of compounds overproduced from a single-carbon substrate such as methanol. Meanwhile, regeneration of the C₁ acceptor in MGA3gad occurred chiefly through the cyclic dissimilation pathway, likely leading to carbon loss in the form of CO₂ (Fig. 5).

In *B. methanolicus*, glyceraldehyde 3-phosphate (GAP) that is not used for recycling of the C₁ acceptor Ribu5P is converted into pyruvate (Pyr) via a linear pathway. A significantly increased flux through the last reaction by the pyruvate kinase (Pyk) was measured in the evolved strains (on average 4.5 and 5.4 mmol/gDCW/h in the ABBM4307 and ABBM4307gad strains, respectively, compared to 4.0 mmol/gDCW/h in MGA3gad) (Table S4). In these strains, Pyr was converted into acetyl-CoA (AcCoA) by a pyruvate dehydrogenase (Pdh), with a flux of up to 2.7 and 2.2 mmol/gDCW/h in the ABBM4307gad and ABBM4307 strains, respectively, compared to 1.6 mmol/gDCW/h in the MGA3gad strain. Interestingly, the build-up of AcCoA might have contributed to the significant increase of acetate production in the evolved strains with up to 3.8 and 4.8-fold more flux towards acetate production in the ABBM4307 and ABBM4307gad strains compared to MGA3gad strain, almost exactly matching the variations in the measured acetate production rates (Table 1).

Consistent with the increased flux towards Pyr and AcCoA in ABBM4307gad, the carbon flux through the pyruvate carboxylase (Pyc, i.e., Pc), which replenishes oxaloacetate (OAA) in the TCA cycle from Pyr, shows 2.0 mmol/gDCW/h and 1.7 mmol/gDCW/h in the

ABBM4307gad and MGA3gad strains, respectively. Unlike some methylotrophs, *B. methanolicus* carries the complete gene set for a functional TCA cycle and glyoxylate shunt (Heggeset et al., 2012; Müller et al., 2015a). The MGA3gad strain showed a low flux through the TCA with an absolute flux to citrate (Cit) of 0.7 mmol/gDCW/h and very little flux afterwards, consistent with previous findings in methylotrophic conditions (Delépine et al., 2020; Müller et al., 2015b). Interestingly, both evolved strains, ABBM4307 and ABBM4307gad, presented a higher flux not only to Cit but through the TCA upper reactions from OAA to AKG, particularly when transformed with the GABA biosynthesis plasmid (in total, an increase of 30 % and 90 % in the ABBM4307 and ABBM4307gad strains compared with the MGA3gad strain, respectively). In addition, little to no flux was observed in the remaining lower reactions from AKG to malate (Mal) in these strains (Fig. 5). The flux through the glyoxylate shunt was negligible, as described previously (Delépine et al., 2020), with only a very low flux in the ABBM4307 strain (0.1 mmol/gDCW/h). The obtained data confirm that the TCA operates both as an oxidative and a reductive branch, with a small portion of the flux at 0.3 mmol/gDCW/h through the malate dehydrogenase (Maldh) reducing OAA to Mal in MGA3gad, and only 0.1 and 0.2 mmol/gDCW/h in the ABBM4307 and ABBM4307gad strains, respectively. Overall, it seems that in the evolved strains the metabolic flux is directed towards AKG supporting the high production of its derivative, L-glutamate. Indeed, the flux towards L-glutamate production was 3.6-fold and 6.9-fold higher in the ABBM4307 and ABBM4307gad strains, respectively.

## 2.5. Integration of fluxome with genome and transcriptome data revealed a strain-specific altered regulatory landscape

In order to identify the potential regulatory mechanisms involved in
<figure><img src="image_10.png" /><figcaption>Fig. 5. Flux maps of the central carbon metabolism illustrate the metabolic state of each strain.</figcaption></figure>

The thickness of the reaction lines represents the absolute flux values in mmol/gDCW/h. Flux values were averaged among three biological replicates. The direction of the reaction arrows represents the estimated net flux directionality. Large grey arrows represent a flux directed toward amino acid biosynthesis and biomass requirements for growth. Pathways discussed in the text are highlighted with a background of patch color. Metabolites and reactions are represented following the recommendations of the Systems Biology Graphical Notation (SBGN). Metabolites are represented by circles and reactions by arrows. Metabolites which are duplicated in the same panel have a solid bottom half. Metabolites subjected to experimental measurement of ¹³C-labelling are marked with yellow boxes. Reactions towards amino acid pools and biomass are not represented. 3PG (3-phospho-D-glycerate), AcCoA (acetyl-CoA), Ace (acetate), Aco (aconitate), AKG (α-ketoglutarate), Cit (citrate), Ery4P (erythrose 4-phosphate), For (formaldehyde), Fru6P (fructose 6-phosphate), FruBP (fructose 1,6-bisphosphate), Fum (fumarate), GAP (glyceraldehyde 3-phosphate), Glc6P (glucose 6-phosphate), Glu (glutamate), Glyox (glyoxylate), Gnt6P (6-phosphogluconate), Hex6P (hexulose 6-phosphate), Icit (isocitrate), Mal (malate), MeOH (methanol), OAA (oxaloacetate), PEP (phosphoenolpyruvate), Pyr (pyruvate), Rib5P (ribose 5-phosphate), Ribu5P (ribulose 5-phosphate), SBP (sedoheptulose 1,7-bisphosphate), Sed7P (sedoheptulose 7-phosphate), Suc (succinate), SucCoA (succinyl-CoA), akgdh (α-ketoglutarate dehydrogenase), ald (fructose bisphosphate aldolase), detox (methanol linear detoxification), fum (fumarate reductase), glpx (sedoheptulose bisphosphatase), gnd (6-phosphogluconate dehydrogenase), hps (hexulose-phosphate synthase), idh (isocitrate dehydrogenase), mae (malic enzyme), maldh (malate synthase), mdh (methanol dehydrogenase), pfk (phosphofructokinase), pgi (glucose-6-phosphate isomerase), phi (6-phospho-3-hexuloisomerase), pyc (pyruvate carboxylase), pyk (pyruvate kinase), rpe (ribulose phosphate epimerase), rpi (ribose phosphate isomerase), sda (sedoheptulose-bisphosphate aldolase), ta (transaldolase), tkt (transketolase), zwf (glucose-6-phosphate dehydrogenase). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

the increased L-glutamate accumulation in the evolved strain transformed with the GABA biosynthesis plasmid, genome-wide flux data predicted with a genome-scale model (GSM) of *B. methanolicus* and fluxes measured with the MFA were integrated with the genome and transcriptome data for strains MGA3gad and ABBM4307gad. Since a GSM of *B. methanolicus* was not available, it was constructed in the course of this study as described in the “Materials and methods” section. A strong correlation between simulated (predicted with the GSM) and estimated (measured by MFA) fluxes was obtained (Fig. S2) demonstrating a high accuracy of the GSM. Next, the measured (Table S5) and simulated (Table S6) flux distributions were combined and used to calculate the log2 fold change values for each of the 1020 reactions belonging to the GSM. This dataset was combined with the table containing the list of 974 genes, which were significantly (*p*-value <0.01) and differentially (log2 fold change value outside the range [−0.25; 0.25]) expressed in ABBM4307gad in comparison to MGA3gad (Table S7) and the table containing the 27 genes which showed a stable (and not silent) mutation in ABBM4307gad in comparison to MGA3gad (Table 2). To ensure biological relevance and to avoid biases arising from inactive parts of the network, reactions carrying a non-zero flux (i. e., active reactions) were considered for subsequent analyses (Table S7). Out of the 327 active reactions, only 8 were associated with a mutated gene (Table S8). This includes: (i) one reaction associated with only differential change at the flux level (log2 fold change value outside the range [−0.25; 0.25]); (ii) two reactions associated with only changes at the transcript level and (iii) five reactions with no changes at the flux

and transcript level. Of the 327 reactions, 149 were not associated with any differential change, either at gene level or at flux level (Table S9). The rest of the dataset was composed of: (i) 26 reactions with changes both in the flux and expression indicating that the regulation of these reactions is occurring at a transcriptional level (i.e., transcriptional regulation) (Table S10); (ii) 34 reactions with changes in flux but not in expression (Table S11); and (iii) 118 reactions with changes in expression but not in the flux (Table S12). In the latter two cases, it is suggested that the regulation of these reactions occurs after gene expression, at levels beyond transcription, specifically post-transcriptional and post-translational regulation, collectively referred to as post-expression regulation. Post-transcriptional regulation refers to the control of gene expression after transcription but before translation, involving processes like mRNA splicing, stability, and translation efficiency. It modulates protein production by influencing mRNA maturation, degradation, and translation initiation. Post-translational regulation involves the modification of proteins, with changes in the enzymatic activity by phosphorylation or acetylation and/or by the binding of inhibitors or activators. Such modifications can increase or decrease enzyme activity, thus modifying flux in the absence of changes in gene expression, or conversely counterbalance changes in enzyme levels due to gene expression, thus preventing any change in flux. Regardless of the regulation type, all these metabolic reactions play an important role in the L-glutamate and GABA overproduction which is further described in the following sections.
## 2.6. The central carbohydrate network is tightly regulated in the ABBM4307gad strain harboring the GABA biosynthesis plasmid

Here, all central carbohydrate metabolism reactions displayed regulatory changes occurring at both the transcriptional level and at stages following gene expression (post-expression regulation) (Fig. 6). While the fluxes of all the reactions involved in formaldehyde assimilation were increased in ABBM4307gad compared to MGA3gad, only five of them (Hps, Ah6pi (i.e., Phi), Tkt, Fba and Rpi) showed increased fluxes

along with an increase of the transcript levels of their respective genes. This is consistent with the upregulation of the *hxlR* gene encoding a transcriptional regulator which controls the expression of the chromosomal genes *hps* (BMMGA3_06845) and *phi* (BMMGA3_06840) (Klein et al., 2022). Surprisingly, the flux through the Rpe reaction was increased despite the downregulation of one of its encoding genes (Fig. 6). Metabolic reactions involved in the cyclic dissimilation of the RuMP showed changes in fluxes in ABBM4307gad compared to MGA3gad but were unaffected at the gene expression levels, except for

<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Integrative analysis of the central carbohydrate metabolism and related pathways in ABBM4307gad vs MGA3gad.</figcaption></figure>

Red and green indicate increased and decreased fluxes and up- and down-expressed genes, respectively. Black indicates non-active reactions. 2PG (2-phosphoglycerate), 3mob (3-methyl-2-oxobutanoate), AcP (acetyl-phosphate), BPG (1,3-bisphospho-glycerate), DHAP (dihydroxyacetone phosphate), FAD (formaldehyde), Gnl6P (6-phosphogluconolactone), Val (valine), Xyl5P (xylose 5-phosphate), Ackr (acetate kinase), Acont (aconitate hydratase), Acs (acetyl-CoA synthetase), Act2r (acetate transport), Ah6pi (hexulose-phosphate synthase), Akgs (α-ketoglutarate dehydrogenase), Alcd1 (methanol dehydrogenase), Aldd1 (formaldehyde dehydrogenase), Cs (citrate synthase), Eno (enolase), Fdh (formate dehydrogenase), G6pdh2r (glucose 6-phosphate dehydrogenase), Gapd (glyceraldehyde-3-phosphate dehydrogenase), Glut2r (L-glutamate transport), Icdhyr (isocitrate dehydrogenase), Ici (isocitrate transport), Mals (malate synthase), Mdh (malate dehydrogenase), Me2 (malic enzyme), Pgk (phosphoglycerate kinase), Pgl (6-phosphogluconolactonase), Pgm (phosphoglycerate mutase), Ptar (phosphate acetyltransferase), Sucd1 (succinate dehydrogenase), Sucoas (succinyl-CoA synthetase), Tpi (triose-phosphate isomerase), Valta (valine transaminase). Other abbreviations can be found in Fig. 5. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
G6pdh2r (i.e., Zwf) which is encoded by two alternative genes in the genome of *B. methanolicus*. The BMMGA3_06850 gene is co-localized with the *hps-phi* operon in the chromosome and was upregulated in ABBM4307gad, while BMMGA3_11235 is located elsewhere in the chromosome and was downregulated in ABBM4307gad compared to MGA3gad (Heggeset et al., 2012). It is not known which one of the two genes is responsible for the Zwf activity. However, the decrease of the flux through Zwf corresponding to the downregulation of BMMGA3_11235 seems to indicate a role of this gene in the regulation of the G6pdh2r-catalyzed reaction.

Interestingly, the regulation of reactions involved in the conversion of GAP into Pyr is governed by regulatory processes occurring during and after gene expression. The conversion of Pyr into AcCoA by Pdh showed coordinated changes between the flux and the transcription of its encoding operon (i.e., BMMGA3_05295-BMMGA3_05305). Our data highlights transcriptional regulation of Pdh, as well as almost all reactions of the TCA cycle (Table S13). The coordinated response of transcript abundances and fluxes in the TCA cycle observed in the evolved strain compared to the parental strain seem to be adapted to promote AKG and hence L-glutamate accumulation.

In *B. methanolicus*, L-glutamate synthesis has been described to occur from AKG through the activity of two glutamate synthases (GLUSy) that are encoded by BMMGA3_01940-BMMGA3_01945 and BMMGA3_03480 while the role of glutamate dehydrogenase (GLUDxi) encoded by BMMGA3_10895 is unclear in this process (Krog et al., 2013). In the present study, predicted fluxes through these reactions were null and transcripts levels were similar in all tested strains for the 3 genes encoding for GLUSy. In addition, the gene encoding GLUDxi was downregulated which is consistent with the identified SNP (890G > A), resulting in the missense mutation Cys297Tyr (Table 2), which based on an InterProScan analysis is in an active site of the enzyme. Our GSM analysis identified a valine transaminase (Valta) putatively encoded by BMMGA3_07960 and BMMGA3_12770 as responsible for 85 % of the L-glutamate production in both ABBM4307gad and ABBM4307. Interestingly, this reaction showed increased fluxes along with the upregulation of both encoding genes in ABBM4307gad compared to MGA3gad. It has been shown before that L-glutamate can be synthesized from AKG through the activity of GLUDxi transaminases, functioning in the opposite direction of the typical branched-chain amino acid biosynthetic process. Such transaminases convert AKG to L-glutamate, utilizing a range of amino acids as amino group donors, with a particular preference for isoleucine, leucine, and valine (Lapujade et al., 1998). While our data does not unequivocally indicate a reaction through which α-ketocarboxylic acids primarily undergo amination, we propose that L-glutamate is synthesized largely through transamination reactions (Fig. 1).

In contrast to some of the reactions involved in the L-glutamate production which are transcriptionally regulated, the ones involved in the production of acetate i.e., acetate kinase AckR (encoded by BMMGA3_13190) and phosphate acetyltransferase Ptar (encoded by BMMGA3_16265), were regulated after the gene expression with the exception of the transporter Act2r (encoded by BMMGA3_13490). The metabolic capacities of these enzymes were not limited by the levels of their mRNAs since ABBM4307gad in comparison to MGA3gad was able to direct an up to 52 log2 fold change increase in flux through these enzymes without a differential change in the transcript level. Notably, a similar type of regulation has been recently demonstrated for acetate production in *E. coli* (Millard et al., 2021).

Overall, these findings underscore the coordinated regulation of gene expression encoding enzymes of the central carbohydrate metabolism, which appears to be an important process governing the observed metabolic behavior. From an evolutionary perspective, such coordinated changes can only be explained by mutations affecting global regulators involved in the transcriptional control of many genes. According to the transcriptomic data, 26 transcriptional regulators are differentially expressed between the ABBM4307gad and MGA3gad

strains (Table S13). In addition, genomic data showed a mutation in the transcriptional regulatory protein ResD (Table 2). In *B. subtilis*, ResD activates transcription of diverse genes in response to oxygen limitation (Henares et al., 2014), however its role in *B. methanolicus* has not been described yet.

## 2.7. Regulatory processes occurring after gene expression affect reactions beyond the central carbohydrate metabolic network in the ABBM4307gad strain

More than 90 % of the reactions regulated at the post-expression level are outside of the set of core biochemical reactions involved in

<table><caption>**Table 3**

Functional classification of post-transcriptionally and post-translationally regulated reactions in strain ABBM4307gad compared to strain MGA3gad. The KEGG and BioCyc pathways were used for grouping.</caption><thead><tr><th></th><th>Pathway</th><th>Number of reactions</th><th>Reactions<sup>a</sup></th></tr></thead><tbody><tr><td colspan="4"><strong>Reactions with changes in flux but not in expression (n = 34)</strong></td></tr><tr><td rowspan="3">Central carbohydrate metabolism (13)</td><td>Glycolysis/ Gluconeogenesis</td><td>6</td><td>Pfk, Tpi, Pgk, Eno, Pgm, Pyk</td></tr><tr><td>Citric acid cycle</td><td>2</td><td>Fum, Akgdh</td></tr><tr><td>Anaplerotic reactions</td><td>3</td><td>Icl, Mals, Me2</td></tr><tr><td rowspan="3">Methanol metabolism (8)</td><td>Pyruvate metabolism</td><td>2</td><td>Ackr, Ptar</td></tr><tr><td>Assimilatory RumP Cycle</td><td>3</td><td>Fba3 (Sda), Glpx, Tala</td></tr><tr><td>Dissimilatory RumP cycle</td><td>3</td><td>Pgi, Pgl, Gnd</td></tr><tr><td rowspan="3">Amino acid metabolism (6)</td><td>Oxidation</td><td>2</td><td>Aldd1, Fdh</td></tr><tr><td>Cysteine and methionine metabolism</td><td>4</td><td>Shsl2r, Metgl, Hserta, Ahserl</td></tr><tr><td>Threonine and lysine metabolism</td><td>2</td><td>Hsk, Thrs</td></tr><tr><td colspan="4"><strong>Reactions with changes in expression but not in flux (n = 118)</strong></td></tr><tr><td rowspan="2">Cell wall biosynthesis (36)</td><td rowspan="2">Cell wall biosynthesis</td><td rowspan="2">36</td><td>3had100, 3oar100, Ear100x, Glpact, Gf6pta, Pappt3, Pgamt, Pgmt, Uagdp, Uamags</td></tr><tr><td>Alata1, Asnn, Asns1</td></tr><tr><td rowspan="6">Amino acid metabolism (32)</td><td>Alanine and aspartate metabolism</td><td>2</td><td>Alata1, Asnn, Asns1</td></tr><tr><td>Cysteine and methionine metabolism</td><td>1</td><td>Argss</td></tr><tr><td>Glycine and serine metabolism</td><td>2</td><td>Apta1i, Mets</td></tr><tr><td>Histidine metabolism</td><td>4</td><td>Ghmt2r, Pgcd, Psert, Psp_L, Histp, Hstpt</td></tr><tr><td>Threonine and lysine metabolism</td><td>5</td><td>Asad, Aspk, Dape, Dhdpry, Dhdps</td></tr><tr><td>Tyrosine, tryptophan, and phenylalanine metabolism</td><td>5</td><td>Chorm, Ddpa, Pheta1, Shk3dr, Tyrta</td></tr><tr><td rowspan="2">Vitamins, cofactors, coenzymes and prosthetic groups biosynthesis (13)</td><td rowspan="2">Vitamins, cofactors, coenzymes and prosthetic groups biosynthesis</td><td rowspan="2">13</td><td>Achbs, Acls, Dhad1, Ipmd, Ippmia, Ippmib, Ipps, Kara1, Kara2, Leutai</td></tr><tr><td>Pants, Asp1dc, Mohmt</td></tr><tr><td rowspan="2">Nucleotides metabolism (23)</td><td>Nucleotide salvage pathway</td><td>12</td><td>Adnk1, Durik1, Ndpk1, Nrdr1</td></tr><tr><td>Purine and pyrimidine biosynthesis</td><td>11</td><td>Adsl1r, Aicart, Garft, Gluprt, Orpt, Pragsr, Prais, Prfgs</td></tr><tr><td rowspan="2">Fatty acid metabolism (10)</td><td>Fatty acid metabolism</td><td>4</td><td>3oar160, 3oas60, Ear160x, Mcoata</td></tr><tr><td>Membrane lipid metabolism</td><td>3</td><td>Acoata, Kas14, Mcoata, Accoac</td></tr></tbody></table>

<footnote>a Reaction ID from BiGG Models.</footnote>
the central carbohydrate metabolism (Table 3). Interestingly, several reactions of the same metabolic pathway were co-regulated. The metabolic pathways with the most pronounced co-regulated metabolic reactions include the biosynthesis of cell wall components, amino acids, vitamins, fatty acids, nucleotides, cofactors, coenzymes and prosthetic groups (Table 3).

Our results demonstrated that the 36 reactions involved in the teichoic acid, nucleotide sugars and peptidoglycan metabolism, which are crucial components of the cell wall in Gram-positive bacteria, were regulated at the post-expression level in the mutant strain ABBM4307-gad vs MGA3gad (Brown et al., 2015). Interestingly, 83 % of the reactions showed an increased level of transcripts in the corresponding encoding genes indicating preference towards cell wall preservation in ABBM4307gad. However, none of the reactions associated with mutated genes showed changes in the expression of the encoding genes (Table 2; Table S12).

Proteins constitute about half of the dry biomass of *B. methanolicus*, and expression of amino acid biosynthesis genes is tightly regulated to meet that demand during growth (Delépine et al., 2020). L-Glutamate plays a central role in this process since GLUDxi that catalyzes the amination of AKG to l-glutamate is an initial step of amino acid biosynthesis wherein amino groups are delivered to α-ketoacids by transamination. Therefore, its overproduction is expected to strongly impact the biosynthesis of the other 19 amino acids. This is indeed the case since 32 metabolic reactions involved in the synthesis of all amino acids showed changes in the expression of their encoding genes but not in the fluxes, while six metabolic reactions showed changes in the flux but not in the expression of their encoding genes (Table 3). Interestingly, the leucine, isoleucine and valine biosynthesis pathway genes in the ABBM4307gad strain were upregulated in comparison to the parental strain MGA3gad (Table S13). It is worth noting that valine, isoleucine, and leucine are converted by the aminotransferases into α-ketoisovalerate, α-keto-β-methylvalerate, and α-ketoisocaproate, respectively, with AKG as an acceptor of amino group. These metabolic reactions result in the production of l-glutamate, and pyridoxine is suggested to serve as a potential cofactor in these processes. As mentioned before, the genes BMMGA3_07960 and BMMGA3_12770 encoding the valine transaminase Valta and involved in the overproduction of l-glutamate in ABBM4307gad were upregulated in ABBM4307gad in comparison to MGA3gad but not in ABBM4307gad when compared to ABBM4307 (Tables S13–S14). In addition, the gene encoding another transaminase (BMMGA3_05040), putatively involved in the transfer of the amino group between l-glutamate and 4-methylthio-2-oxobutanoate to yield AKG and l-methionine, was downregulated in ABBM4307gad compared to ABBM4707 (Fig. 6, Table S14). This could explain why ABBM4307gad produces more l-glutamate than ABBM4307. Catabolism of leucine, isoleucine and valine leads to pantothenate and coenzyme A (CoA) formation, and the genes encoding the corresponding enzymes for pantothenate biosynthetic reactions (Asp1dc and Mohmt encoded by BMMGA3_10610 and BMMGA3_10620, respectively) were upregulated in ABBM4307gad compared to MGA3gad (Table 3). Mutations occurred in two genes encoding the enzymes in the reactions of amino acid metabolism (BMMGA3_06395, BMMGA3_09610) (Table S7). The mutation in BMMGA3_06395, which was upregulated in ABBM4307gad compared to MGA3gad, is consistent with an InterProScan analysis showing that it does not affect the enzyme’s active site. Various reactions involved in the biosynthesis of vitamins, cofactors, coenzymes, and prosthetic groups showed changes in the expression of their encoding genes, but not at the flux level. In particular, genes BMMGA3_10610, BMMGA3_10615, BMMGA3_10620 encoding the enzymes in reactions Asp1dc, Pants and Mohmt, which are involved in the biosynthesis of vitamin B₅, were highly upregulated in the strain ABBM4307gad compared to MGA3gad (Table S13). Vitamin B₅ is essential for the synthesis of coenzyme A, which plays a key role in the conversion of pyruvate to acetyl-CoA before entering the TCA cycle. Interestingly, genes BMMGA3_16300, BMMGA3_04410,

BMMGA3_01115, encoding the enzymes in the reactions involved in molybdenum cofactor (Moco) synthesis were upregulated in ABBM4307gad (Table S13). While these reactions are absent in the GSM, it is interesting to notice that Moco is required for the functioning of the transcriptionally regulated reaction of formaldehyde dehydrogenase (Fdh) involved in the detoxification of formaldehyde (Fig. 6) (Iobbi-Nivol and Leimkühler, 2013). The upregulation of vitamin B₅ is consistent with the increased need for acetyl-CoA for producing glutamate, such as the above mentioned Pdh whereof subunit E1 requires thiamine pyrophosphate derived from vitamin B₁ as a cofactor.

Purines and pyrimidines are the building blocks for the synthesis of nucleotides. They are also needed to produce riboflavin and flavoproteins like flavin mononucleotide and flavin adenine dinucleotide, which are important cofactors used in the electron transfer chain in bacteria (Light et al., 2018; Liu et al., 2020). Several reactions involved in the purine biosynthetic pathway showed increased levels of transcripts in the corresponding enzyme-encoding genes (Table S13). Among all the reactions involved in nucleotide metabolism only a few showed a stronger increase in the gene transcript’s levels that encode the corresponding enzyme in comparison to others. For instance, the four reactions (Rndr1-4) which catalyze the conversion of nucleoside 5'-diphosphate to deoxynucleoside 5'-diphosphate (a basic component of DNA), showed a 4 log2 fold change in their enzymes encoded gene’s expression (BMMGA3_09290 and BMMGA3_09295) in ABBM4307gad in comparison to MGA3gad. This typically indicates that DNA replication is increased in ABBM4307gad although the fluxes through those reactions remain stable. One mutation occurred in one gene involved in a reaction of purine and pyrimidine metabolism but without consequence either on the flux or on the expression (Table S8).

Changes in the expression of genes involved in reactions of fatty acid metabolism were visible in the ABBM4307gad strain. In particular, enzymes involved in glycerophospholipid metabolism showed a downregulation in the encoding genes while the genes encoding membrane lipid and fatty acid metabolism showed an upregulation. Fatty acids are key building blocks for the phospholipid components of cell membranes, and most of the reactions involved in the initiation and elongation of fatty acids (Table 3) showed increased levels of the corresponding gene transcripts (i.e., fabDFGHILZ genes, Table S13). In addition, two reactions (EAR160x and 30AS60) had mutations in their enzyme encoding genes, BMMGA3_04620 and BMMGA3_04495, concurrent with the upregulation of one of them (Table S7). Glycerophospholipids are the main components of the lipid bilayer that constitutes cell membranes, contributing to their fluidity and flexibility. Hence, the changes in the glycerophospholipid biosynthesis process can consequently drive structural and functional changes in the cell membrane, affecting the l-glutamate export in the ABBM4307gad strain. This is similar to mechanisms observed in *C. glutamicum*, where membrane alterations influence the export through mechanosensitive transmembrane channel proteins activated by the force-from-lipids (Kawasaki and Martinac, 2020). Prokaryotic small-conductance mechanosensitive channels are sensitive to physical changes in the cell membrane. Their gating is activated by force-from-lipids, in which membrane structural changes cause lipids to undergo conformational shifts. These shifts directly influence the channel’s structure, causing it to open or close in response to the mechanical force (Reddy et al., 2019).

Our data demonstrates that most of these reactions exhibited changes at the gene expression level but not at the flux level, in ABBM4307gad compared to MGA3gad, while affecting various parts of the metabolism. From an evolutionary perspective, a change in gene expression without a corresponding change in metabolic flux can be explained by several mechanisms that reflect the robustness and adaptability of the biological systems. It is possible that the genomic mutations generated via induced mutagenesis result in many transcriptional changes that are buffered due to redundancy in the system (e.g., multiple enzymes catalyzing the same reaction) and regulatory mechanisms (i.e., post-translational modifications or allosteric
regulations) ensuring steady fluxes even when there are changes in gene expression. It is conceivable that laboratorial evolution has also favored phenotypic plasticity in the ABBM4307*gad* strain, enabling it to regulate certain genes (e.g., transaminases) in anticipation of increased metabolic activity (e.g., production of L-glutamate to sustain GABA synthesis), but without immediately increasing flux. This enables the cell to rapidly increase its activity when needed, without wasting resources when it does not.

## 2.8. Important cellular processes are affected by L-glutamate overproduction

As described above, L-glutamate overproduction in *B. methanolicus* significantly alters carbon flux, affecting energy balance and the electron transport chain (ETC) in the ABBM4307*gad* strain. Specifically, the coordinated downregulation of the expression level and metabolic flux of succinate dehydrogenase (Sucd1, Fig. 6) observed in the ABBM4307*gad* strain compared to MGA3*gad* might reduce electron transfer to ubiquinone, which is converted to ubiquinol in the ETC. Additionally, reduced flux through the TCA cycle decreases NADH formation, limiting the electron supply to complex I of the ETC. These changes impact the expression of genes encoding ETC components (Fig. S3, Table S13), leading to a broad effect on oxidative phosphorylation. The ETC of *B. methanolicus* has not been described in detail yet but the inspection of genomic sequence suggests similarities with the ETC of *B. subtilis*. The ETC in *B. methanolicus* includes NADH dehydrogenases NDH-I, encoded by the *nuo* locus and NDH-II encoded by either *yutJ* or *yumB*, and branches ending in either quinol or cytochrome c terminal oxidases similarly to *B. subtilis* (Falk-Krzesinski and Wolfe, 1998; Gyan et al., 2006). Despite the overall downregulation of ETC-related genes, the *cydABCD* gene cluster, encoding cytochrome *bd* quinol oxidase and a heterodimeric ATP-binding cassette transporter, was upregulated (Table S13) suggesting a compensatory mechanism under low oxygen conditions as it has been observed in *E. coli* (Cotter et al., 1997).

The transcriptomic data indicates that L-glutamate overproduction correlates with decreased synthesis and activity of flagella together with the downregulation of chemotaxis genes and upregulation of genes related to sporulation and biofilm formation. Genes involved in flagella structure and function (e.g., *flgD*, *fli*, *flg* and *flh*) were downregulated in ABBM4307*gad* compared with MGA3*gad* (Table S13), and a mutation in the *flgD* gene was identified in ABBM4307 (Table 2). It is worth noticing that the transcriptomic response of the flagella-related processes persisted in the evolved strain expressing the GABA biosynthesis plasmid both induced and non-induced (Tables S14–S15). Chemotaxis genes, including a cluster coding for the chemotaxis response regulator protein-glutamate methylesterase (*cheB*) and chemotaxis central regulator (*cheA*), were also downregulated. In addition, the *spoA* gene, a master regulator of sporulation and biofilm formation in *B. methanolicus*, was upregulated in ABBM4307*gad* compared to MGA3*gad* (Table S13), enhancing sporulation while downregulating biofilm formation (Schultenkämper et al., 2019). These changes in gene expression reflect a strategic shift to minimize energy-intensive process (i.e., flagella assembly and chemotaxis) (Guttenplan et al., 2013; Sourjik and Armitage, 2010) and carbon intensive process (i.e., biofilm formation) (Schultenkämper et al., 2019), reallocating resources towards L-glutamate production.

## 3. Conclusions

By engineering *B. methanolicus* MGA3, we significantly enhanced its L-glutamate production through induced mutagenesis and subsequently extended the metabolic pathway to synthesize GABA at high titers. System-level analyses combining genomic, transcriptomic, fluxomic and modelling revealed that regulation of various central carbohydrate reactions at the transcriptional level plays an important role in adjusting

fluxes from methanol to L-glutamate, thus ensuring a high titer of its derivative GABA. However, post-transcriptional and post-translational regulation of reactions across different parts of the metabolism are equally important to ensure readiness while optimizing resource use.

The integration of multi-omics data suggests several regulatory mechanisms of *B. methanolicus*, laying the groundwork for targeted strain improvement. However, further experimental validation is needed, particularly for post-transcriptional and post-translational regulation. These mechanisms offer promising targets for future strain optimization, including modulating transcriptional regulators, stabilizing/destabilizing mRNAs, and engineering protein modifications. Importantly, our findings highlight that metabolic adaptation for increased L-glutamate production involves not only genomic modifications but also additional layers of regulation. A deeper understanding of these regulatory processes will be essential for developing more dynamic and adaptable microbial strains. Overall, our results demonstrate the potential of *B. methanolicus* as an emerging platform for the biotechnological production of value-added chemicals from methanol.

## 4. Materials and methods

### 4.1. Random mutagenesis

Cultures of *B. methanolicus* were grown in MVcMY medium containing 6.5 g/L (200 mM) methanol (Tables S16–S18) at 50 °C and 180 rpm for 8–10 h to late exponential phase. Cells were harvested by centrifugation at 4000×g and the cell pellet was resuspended in 0.1 M sterile phosphate buffer at pH 7. Cultures were then agitated for 5–90 min at 50 °C in the dark or irradiated with ultraviolet (UV) at 254 nm for 0.5–30 min in a shallow Petri dish with a constant agitation at room temperature. The mutagen was removed by two washes in 0.1 M phosphate buffer. Cell pellets were ultimately resuspended in a 20 % glycerol solution in MVcMY medium and plated onto SOB medium agar plates (Brito et al., 2021) containing 10 g/L mannitol as serial dilutions for kill rate and colony-forming units (CFU) determination. The mutagenesis conditions resulting in a <99 % kill rate (Fig. S1) were chosen for screening and selected mutants were stored at –20 °C.

### 4.2. High-throughput mutant screening

Liquid MVcMY medium was inoculated with frozen mutagenized cultures at 0.5 % (v/v) inoculum size and grown at 50 °C and 180 rpm for 8–10 h. Cultures were then serially diluted and streaked to single colonies onto SOB plates containing 10 g/L mannitol and incubated at 50 °C for 48 h. Single colonies were picked with sterile toothpicks into 48-deep-well plates containing 0.7 mL of MvcMY medium and incubated at 50 °C and 180 rpm until an OD<sub>600nm</sub> of 0.7–2.5. The seed cultures were then used to inoculate 0.6 mL of ABBM-PM10 production media (Tables S16–S18) with a 10 % (v/v) inoculum size and incubated for 24 h at 50 °C and 180 rpm. For GABA production, the cultures were induced with xylose to a 10 g/L final concentration at 4 h into cultivation and acidified to pH 4.6 with 1 M HCl after 24 h of cultivation, then incubated for a further 24 h period. Following incubation, cultures were adjusted to pH 2 with 1 M HCl to lyse cells and release any intracellular product, then centrifuged at 20000×g for 10 min.

### 4.3. Molecular cloning

*E. coli* DH5α competent cells were prepared according to the calcium chloride protocol (Green and Rogers, 2013). All standard molecular cloning procedures were carried out as described previously (Sambrook and Russell, 2001), or according to manuals provided by the manufacturer. The *gad*<sup>St</sup> gene was amplified using CloneAmp HiFi PCR Premix (Takara) and purified using a QIAquick PCR Purification kit from QiaGen. pBV2xp-*gad*<sup>St</sup> was constructed by amplifying the *gad*<sup>St</sup> gene from the plasmid pTH1mp-*gad*<sup>St</sup> with primers PS56 and PS57 (Table S19) and
joining the resulting PCR product with the *SacI* and *BamHI* digested pBV2xp by means of the isothermal DNA assembly method (Gibson et al., 2009). The plasmid pBV2xp is a derivative of pHCMC04 for gene expression under control of the *Bacillus megaterium*-derived *xylA* (xylose inducible) promoter (Drejer et al., 2020). Colony PCR was performed using GoTaq DNA Polymerase (Promega) using primers PXPF and BVXR (Table S19). The sequence correctness of cloned DNA fragments was confirmed by Sanger sequencing (Eurofins Genomics). *B. methanolicus* MGA3 was made electrocompetent and transformed by electroporation as described by Jakobsen et al. (2006) (Jakobsen et al., 2006). Recombinant *B. methanolicus* strains containing the pBV2xp plasmid were cultivated with supplementation of 20 µg/mL kanamycin.

### 4.4. Assessing *L*-glutamate and GABA production in fed-batch methanol fermentations

For bioreactor experiments testing our newly engineered strains, a two-stage seed strategy was employed. First-stage seed cultures were first inoculated from -20 °C working cell banks stored in 20 % glycerol into shake flasks with liquid SOB media containing 10 g/L mannitol and incubated for 8–10 h at 50 °C and 180 rpm. Shake flasks with ABBM-PM-BR media and without antifoam were inoculated with the second-stage seed with a 10 % inoculum size and incubated to an OD<sub>600nm</sub> of 2–2.5. The cultures were then used to inoculate the bioreactor pre-filled with 2 L of ABBM-PM-BR (Tables S16–S18) and pre-warmed to 50 °C via sterile tubing with a 10 % inoculum size. Methanol concentration in the bioreactor was maintained between 4.0 and 6.1 g/L by automatic feeding of methanol. This was orchestrated via a peristaltic pump controlled by an Arduino microcontroller. The microcontroller was connected to a silicone tubing methanol sensor submerged into the fermentation vessel (Wagner et al., 1997). Culture pH was maintained by an ammonia solution (20 %), which also served as the nitrogen source. Dissolved oxygen was kept at 30 % by supplementation of pure oxygen on demand. Reactors were regularly sampled for OD<sub>600nm</sub>, pH and ion content (PO<sub>4</sub><sup>3-</sup>, SO<sub>4</sub><sup>2-</sup>, NH<sub>4</sub><sup>+</sup>), and for *L*-glutamate and GABA measurements using the same extraction procedure as described above in the "Materials and methods, High-throughput mutant screening" section. To produce GABA, xylose was added to a final concentration of 10 g/L, at 30–40 h post-inoculation. To lower the medium pH, the ammonia feed was shut off until pH 4.5 was reached or the culture was acidified with HCl or H<sub>2</sub>SO<sub>4</sub>.

### 4.5. Batch methanol fermentations for multi-omics sampling

*B. methanolicus* strains ABBM4307, ABBM4307gad and MGA3gad were cultivated in biological triplicates in 3 L Applikon bioreactors (Getinge, USA) filled with 0.75 L of the multi-omics batch fermentation medium (Table S20). An aeration rate of 0.67 VVM was used and dissolved oxygen (DO) was maintained above 30 % throughout the fermentation. The culture temperature and pH were controlled at 50 °C and 6.8, respectively. The pH was controlled by the automatic addition of 1 M KOH solution upon demand. The seed cultures were prepared in 2 L baffled glass shake flasks using 400 mL MvCMy medium. Flasks were incubated at 50 °C with agitation at 150 rpm until OD<sub>600nm</sub> of 1.5–2.5 before cells were harvested by centrifugation for 10 min at 3200×g and 40 °C. The cells were resuspended in a small volume of pre-warmed (50 °C) batch methanol fermentation medium and the concentrated cell suspension was used to inoculate the bioreactors (1.3 %) to a start OD<sub>600nm</sub> of 0.15. All strains were induced at OD<sub>600nm</sub> of ca 0.6 by adding xylose to 10 g/L final concentration. The metabolite labelling experiment was initiated by adding 3.2 g/L<sup>13</sup>C-methanol (99 %<sup>13</sup>C; Eurisotop, France) at OD<sub>600nm</sub> of 1.6–2.2.

### 4.6. Multi-omics sampling

Samples for RNA-seq were collected from batch fermentation

cultures before and after xylose induction, at approximately OD<sub>600nm</sub> of 0.5–0.7 and 1.0–1.3, respectively. At the latter RNA-seq sampling point, samples for genomic DNA sequencing were also harvested. For genome sequencing, 50 mL Falcon tubes were filled with 20 mL of culture broth, the tubes were centrifuged for 10 min at 8000×g and 25 °C, and cell pellets were stored at -20 °C. For RNA-seq sampling, 50 mL Falcon tubes were pre-filled with ice and stored at -80 °C for 16 h prior to use, to which 20 mL of culture broth samples were immediately added. The tubes were centrifuged for 5 min at 8000×g and 4 °C, and the cell pellets were snap frozen in liquid nitrogen and stored at -80 °C.

Supernatant samples were taken to analyze substrate consumption as well as product formation using Nuclear Magnetic Resonance (NMR) measurements. Culture samples of 1.5 mL were taken twelve times throughout the batch cultivation followed by centrifugation for 5 min at 18000×g and 4 °C. Supernatant was then collected and stored at -80 °C.

Ion chromatography tandem mass spectrometry (IC-MS/MS) quantification was used to analyze the isotopologues of each metabolite as described previously (Kiefer et al., 2007). To determine metabolic pool sizes, blank control samples were taken prior to inoculation of the bioreactors. Later, directly before adding labelled <sup>13</sup>C-methanol at culture OD<sub>600nm</sub> of 1.4–1.7, metabolome samples from whole broth (intra- and extracellular pools; WB) and culture filtrate (extracellular pools; CF) were collected using the previously described optimized method (Carnicer et al., 2016). CF samples were taken from the bioreactor using a cold 3 mL syringe holding 21 pre-cooled (-20 °C) stainless steel beads, attached to a 0.20 µm filter (Whatman Puradisc). WB samples were collected with the same approach, but without the filter. Samples were transferred to ice cold 1.5 mL tubes from which technical triplicates of 150 µL were dispensed into new 5 mL extraction tubes containing 3 mL extraction solution (acetonitrile:methanol:0.1 M formic acid in 40:40:20 (v/v/v)) pre-chilled to -20 °C prior to use. Samples were homogenized by vortexing. To each tube (except for blank control samples), 200 µL of internal standard was added (cell extract of *E. coli* cultivated on 99 % [U-<sup>13</sup>C<sub>6</sub>]-glucose, Eurisotop, France). The extracted samples were stored at -80 °C. The average time between sample collection and extraction of technical triplicates was 18 ± 2 s for WB samples and 25 ± 3 s for CF samples.

Label enrichments in the intracellular metabolites were sampled directly after performing the addition of 3.2 g/L<sup>13</sup>C-methanol. WB and CF samples were collected as described above but using cold 1 mL syringes with 7 stainless steel beads, and without adding any internal standard. From each bioreactor, technical duplicates of 13 WB and 3 CF samples were collected within 3.5 min average. Samples of strain ABBM4307 were taken at intervals of 11–16 s, and strains ABBM4307-gad and MGA3gad were sampled at intervals of 12–15 s. Detailed sampling points can be found in the input files used in the flux calculations (see Data availability). Samples were stored at -80 °C.

The dry cell weight (DCW) was determined by sampling 20 mL of culture from all bioreactors when OD<sub>600nm</sub> reached 2.5–3.0. The samples were centrifuged for 10 min at 3200×g and 4 °C, pellets thoroughly washed and eventually transferred to pre-weighted aluminum cups. The samples were dried in an oven at 105 °C for 24 h, along with blank controls. A conversion factor of 0.24 g/L DCW per OD<sub>600nm</sub> unit was obtained.

### 4.7. Analytical procedures

In the fed-batch fermentations, sampled supernatants were routinely analyzed for *L*-glutamate and GABA content by high performance liquid chromatography (HPLC) in an AkzoNobel Kromasil 100-5-C<sub>18</sub> 200 x 4.6 column, using 0.1 % HFBA in a water:methanol mixture (80:20 (v/v)) as the mobile phase.

Metabolite concentration in culture supernatants used in MFA were measured by 1D 1H NMR at 280K using a 30° angle pulse 16 scans, and a pre-saturation of the water signal (zgpr30) was applied during a relaxation delay of 3s (total repetition time of 7s) in an Avance III 800 MHz
spectrometer (Bruker, Germany) equipped with a QCI-P 5 mm cryo-
probe (Bruker, Germany). Deuterated trimethylsilyl propionate (TSP-
d4) dissolved in D₂O was used as an internal standard for quantification.
The spectra were processed using Topspin 3.5. Metabolite pool sizes
were quantified by IC-MS/MS using the U-¹³C-labelled *E. coli* cell extract
as an internal standard (Mashego et al., 2004).

Metabolite quantification for fluxomics analysis was performed with liquid anion-exchange chromatography as described previously (Kiefer et al., 2007). In short, high-resolution experiments were performed with an Ultimate 3000 high-performance liquid chromatography (HPLC) system (Dionex, CA, USA) coupled to an LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a heated electrospray ionization probe. MS analyses were performed in positive Fourier transform MS (FTMS) mode at a resolution of 60000 (at 400 m/z) in full-scan mode, with the following source parameters: capillary temperature, 275 °C; source heater temperature, 250 °C; sheath gas flow rate, 45 AU (arbitrary units); auxiliary gas flow rate, 20 AU; S-lens radio frequency (RF) level, 40 %; source voltage, 5 kV. Isotopic clusters were determined by extracting the exact mass of all isotopologues, with a tolerance of 5 ppm. The following metabolites analyzed were used for MFA analysis: FruBP, AKG, Mal, Cit, 3PG plus 2-phosphoglycerate (PGA), Rib5P plus Ribu5P and Xyl5, Glc6P, Fru6P, Fum, PEP and Aco. After peak integration, the raw peak areas were corrected for the contribution of naturally abundant isotopes using the IsoCor software version v2.1.3 (Millard et al., 2012).

4.8. *Calculation of growth, substrate uptake and degradation rates*

Quantitative cell growth parameters, extracellular flux and evapo-
ration constant from the measured time-dependent concentrations of
biomass and extracellular metabolites quantified by NMR were inferred
using Physiofit version v3.3.6 (Le Grégam et al., 2024). A conversion
factor was used to obtain cellular DCW from OD<sub>600nm</sub> units (0.24
gDCW/OD<sub>600nm</sub>) and calculate specific substrate and production rates.
Averages and standard deviations were calculated based on three bio-
logical replicates.

4.9. Variant calling

Genomic DNA was isolated using the NucleoSpin® Microbial DNA
kit (Macherey-Nagel). Long and short DNA reads were generated by
Nanopore and Illumina sequencing, respectively. For library prepara-
tion, the TruSeq DNA PCR-free high-throughput library prep kit (Illu-
mina) and the SQK-LSK109 sequencing kit (Oxford Nanopore
Technologies [ONT]) were used without prior shearing of the DNA. To
generate the short reads, a 2 × 300-nucleotide run (MiSeq reagent kit v3,
600 cycles) was executed. The long reads were generated on a GridION
platform using a R9.4.1 flow cell. Base calling and demultiplexing were
performed using GUPPY v3.2.10 with the high accuracy base calling
model. Assemblies were done using CANU v1.8 (Koren et al., 2017) for
the Nanopore long read data, followed by polishing with medaka v.1.0.1
(https://github.com/nanoporetech/medaka) and pilon v1.22 (Walker
et al., 2014). The polished genomes were then used to identify SNPs with
snippy v.4.0-dev2 (https://github.com/tseemann/snippy) in contig
mode. To interpret the effect of SNPs in the reaction sites of altered
proteins, their amino acid sequences were submitted to functional
analysis in the InterProScan software (Jones et al., 2014).

4.10. RNA-seq analysis

For the total RNA extraction from the tested *B. methanolicus* strains, we employed the NucleoSpin RNA isolation kit (Macherey-Nagel) and the RNase-free DNase set (Qiagen), following the manufacturers' protocols. Snap frozen bacterial pellets were thawed on ice, and total RNA isolation was carried out individually for each replicate of the cultivation condition. To ensure the quality of the resulting RNA material and

confirm the absence of DNA contamination, PCR was performed
employing gene-specific forward primer KATFW and reverse primer
KATREV (Table S19). They bind specifically to the catalase encoding
gene *katA* in *B. methanolicus*. Furthermore, the concentration and quality
of the samples were assessed by measuring the 260/280 nm and 260/
230 nm ratios using a NanoDrop™ One (Thermo Scientific™). The RNA
quality was checked by Trinean Xpose (Trinean NV, Gentbrugge,
Belgium) and Agilent RNA 6000 Nano Kit with an Agilent 2100 Bio-
analyzer (Agilent Technologies, Böblingen, Germany). rRNA was
removed from total RNA with Ribo-Zero rRNA Removal Kit (Illumina,
San Diego, CA, USA), and removal of rRNA was checked using Agilent
RNA 6000 Pico Kit and an Agilent 2100 Bioanalyzer (Agilent Technol-
ogies). cDNA libraries were prepared with TruSeq Stranded mRNA Li-
brary Prep Kit (Illumina), and the resulting cDNA was paired-end
sequenced using an Illumina NextSeq 500 system with a 2 × 75 bp read
length.

Paired-end reads generated from the RNA-seq were aligned to the *B. methanolicus* MGA3 genome reference sequences, encompassing the chromosome as well as the two plasmids pBM19 and pBM69 (GenBank accession numbers CP007739, CP007741, and CP007740, respectively). Before the alignment, the reads underwent trimming with a minimum length of 36 base pairs using the Trimmomatic v0.33 tool (Bolger et al., 2014). The trimmed reads were then aligned to the reference sequences using the short-read alignment software Bowtie 2 (Langmead and Salzberg, 2012). The ReadXplorer software v2 (Hilker et al., 2016) was employed for visualizing the mapped reads, conducting operon structure analysis, and determining reads per kilobase per million mapped reads (RPKM) values. Furthermore, the differential gene expression analysis was executed with the statistical method DESeq2 (Love et al., 2014) within the same software framework. To classify a gene as differentially expressed, the criteria included a change in expression level exceeding 50, with the adjusted *p*-value set at or below 0.01. For differentially expressed genes (DEGs) encoding proteins of unknown function, BLASTx analysis (Altschul et al., 1990) was applied to identify conserved protein family. Venn diagrams were generated using InteractiVenn software (Heberle et al., 2015).

4.11. *Metabolic flux analysis model and simulations*

A model previously designed that covers the central carbon meta-
bolism and biomass needs of *B. methanolicus* MGA3 (Delépine et al.,
2020) was modified to adjust to the present study, i.e., network topology
changes to include L-glutamate production and the GABA synthetic
pathway. The influx i programme uses a nonstandard legacy file format
(FTBL) to encode the metabolic network and the associated atom-atom
transitions. This format centralizes the metabolic network with all bio-
logical measurements, i.e., metabolite pool sizes, fluxes, and carbon
isotopologue distributions. Simulations needed for non-stationary
¹³C-MFA were performed with influx si v5.3.0 in non-stationary mode
(Sokol et al., 2012). The influx software has the advantage of allowing
for the integration of labelling data coming from different experimental
setups (MS and NMR) and even supporting several integration strategies
(stationary, nonstationary, and parallel labelling).

Experimental data were fitted to our models as described above. For each culture replicate, a chi-squared goodness-of-fit statistical test was performed to ensure that simulated data for each biological replicate was significantly close to experimental data. All tests were significant with a significance level (α) of 0.05.

Statistical analysis comparing the different strains were performed in R (version 4.2.0). Data were analyzed with linear models. A Benjamini-Hochberg correction was used to calculate false discovery rate adjustments of *p*-values. Groups were compared pairwise for each reaction with the Tukey post-hoc test. A principal component analysis was performed with the mixOmics package.
## 4.12. Genome-scale model analysis

To reconstruct the GSM of *B. methanolicus*, a bioinformatic pipeline described by Peyraud et al. (2016) was used (Peyraud et al., 2016). In this approach, orthologs, i.e., genes which are likely to perform the same function, are identified between a query genome and a reference genome for which there is already a GSM available. Then the gene protein reaction (GPR) is extracted from the reference GSM and combined with ortholog data allowing the propagation of GPR toward the query genome. Genomes of two microorganisms (*E. coli* and *B. subtilis*) for which GSM were available were used as references. *E. coli* was selected because its GSM is well annotated. The "iAF1260" model of *E. coli* (Feist et al., 2007) containing 2382 reactions for 1972 metabolites was chosen as the first reference GSM for the reconstruction. However, *E. coli*, being a Gram-negative bacterium, does not have the membrane specificities associated with Gram-positive bacteria, and for that reason *B. subtilis* was chosen as the second reference. The "iBsu1103" GSM was used, which includes 1681 reactions for 1625 metabolites (Henry et al., 2009). Since these metabolic models use different ontologies, the identifiers were standardized using a tool called SAMIR (Semi Automatic Metabolic Identifier Reconciliation) (Peyraud et al., 2016).

The orthology prediction being based on protein sequence in order to avoid codon bias, protein sequences of the entire genome (i.e., proteome) of the reference and source organisms were downloaded from UniPROT (The UniProt Consortium, 2023). The orthology prediction was made using a tuned version of Inparanoid (Remm et al., 2001). Inparanoid found orthologs between the proteome of *B. methanolicus* and each of the two reference proteomes. As a result, out of the 3301 protein sequences of the *B. methanolicus* proteome, 2131 orthologues were detected against the proteome of *E. coli* (containing 10530 sequences) and 2154 against that of *B. subtilis* (containing 8308 sequences). Next, the Autograph method (Notebaart et al., 2006) was used to automatically transfer by orthology the GPRs of the two reference GSM in one draft model. In this step, each query gene for which there is an orthology with the reference gene(s) is associated with the corresponding reaction (s) from the reference model. By combining the orthology data with the GPRs from the reference GSM, 764 reactions were propagated from "iBsu1103" and 1642 from "iAF1260". Among these reactions, reactions with complete (918), incomplete (806) or without (682) GPR can be distinguished. The so-called full GPR reactions are reactions where all the genes involved in a reaction in the reference model have been associated with genes present in *B. methanolicus*. Conversely, reactions without GPR are reactions for which no orthology has been identified between the reference and the target. These reactions are still propagated to avoid losing information. 87.1 % of these reactions were spontaneous exchange and transport reactions that are not encoded by genes. Finally, the so-called incomplete GPR reactions are those for which all the genes attributed to a reaction in the reference model have not been associated with genes in *B. methanolicus*. These GPRs relate to reactions catalyzed by enzymatic complexes or isoenzymes. At this stage the draft reconstruction enclosed 1642 and 764 reaction propagated from *E. coli* and *B. subtilis*, respectively. Finally, a manual refinement was performed. First, the draft reconstruction was cleaned by removing the redundant GPRs, the non-functional reactions with incomplete or no GPRs, the dubious homology and the periplasmic transport and exchange reactions (originating from the *E. coli* model). Unlike *E. coli*, *B. methanolicus* does not possess a periplasm and therefore no periplasmic exchange and transport reactions either. Then reconstruction was further improved by adding specific GPRs (e.g., methylotrophic metabolism, biomass reactions). The biomass reactions were adapted from those in the model "iBsu1103". Metexplore (Cottret et al., 2018) was used to identify the "dead ends" (i.e., metabolites which are produced but not consume or inversely) and visualize the metabolic pathways. MetaCyc (Caspi et al., 2014) was used to identify the reactions and BLASTp (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins) to associate these with the corresponding genes. Reactions and metabolites

identifiers were updated to the latest BiGG identifier and genes were associated with a RefSeq number. The "iBmeth613" model of *B. methanolicus* contains 1020 reactions, 899 metabolites, 613 genes and 2 compartments (cytosol and extracellular space). The quality of this model was tested by performing a mass balance analysis of all the reactions except biomass and "MAGIC" reactions that synthesize abstract metabolites for the biomass.

The functions "parsimonious flux balance analysis" (pFBA) and "flux variability analysis" (FVA) of the COBRA toolbox for Python version 3.8.8 (Ebrahim et al., 2013) and the "iBmeth613" model of *B. methanolicus* (this study) were used to simulate the flux distribution and flux changes. The objective function was the growth rate, and the model was constrained with the uptake and production rates (in mmol/gDCW/h) calculated from the MFA analysis for MGA3gad and ABBM4307gad (Table S4). pFBA was used to identify a realistic flux distribution within the "iBmeth613" model by minimizing the squared sum of all fluxes while preserving the achieved optimum. FVA was used to, on the other hand, assess the minimum and maximum bounds for each flux reaction that can still meet the constraints, employing a maximization followed by a minimization for each reaction in the GSM. Specifically, FVA data were used in Fig. S2 to compare the variability of the predicted fluxes with that of the estimated fluxes obtained from MFA.

## CRediT authorship contribution statement

**Marta Irla:** Writing – original draft, Validation, Project administration, Investigation. **Ingemar Nærdal:** Writing – original draft, Visualization, Investigation, Funding acquisition. **David Virant:** Writing – original draft, Validation, Investigation. **Trygve Brautaset:** Funding acquisition, Conceptualization. **Tobias Busche:** Formal analysis. **Dusan Goranovic:** Investigation, Formal analysis. **Carsten Haupka:** Investigation, Formal analysis. **Stéphanie Heux:** Writing – original draft, Visualization, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Gregor Kosec:** Project administration, Funding acquisition. **Christian Rückert-Reed:** Formal analysis. **Volker F. Wendisch:** Writing – original draft, Validation, Project administration, Funding acquisition, Conceptualization. **Luciana F. Brito:** Writing – review & editing, Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Data curation. **Cláudia M. Vicente:** Writing – review & editing, Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Data curation.

## Declaration of interest

The authors declare no conflict of interest.

## Acknowledgements

We thank MetaToul-FluxoMet (Toulouse, France), which is part of the French National Infrastructure for Metabolomics and Fluxomics (http://www.metabohub.fr) and funded by the ANR (MetaboHUB-ANR-11-INBS-0010), for their help in collecting, processing, and interpreting ¹³C NMR and MS data. We also thank Pierre Millard (INRAE, Toulouse, France) for insightful discussions on MFA, as well as Baudoin Delépine for his help with the first draft of the GSM. We would also like to thank the support of the entire Acies Bio lab team, especially Petra Kralj with the random mutagenesis and screening, Dr. Jaka Horvat with the analytics and processing of samples, as well as Toni Nagode and Filips Oleskovs with the bioreactor fermentations. This research was funded by ERA CoBioTech, an ERA-Net Cofund Action under H2020, grant number 285794 ERA-NET.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ymben.2025.06.002.

## Data availability

The datasets and computer code produced in this study are available in the following databases:•

Genomic data: BioProject PRJNA1167228 (https://www.ncbi.nlm.nih.gov/search/all/?term=PRJNA1167228)•

Transcriptome data: Gene Expression Omnibus GSE277855•

MFA raw model, input and output files and statistical analysis results: FAIRDOM (https://fairdomhub.org/data_files/7513?version=1)•

GSM raw model, input and output files: FAIRDOM (https://fairdomhub.org/data_files/7516?version=1)

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