VELIZY-VILLACOUBLAY, France and MUNICH February 1, 2024 Dassault Systmes (Euronext Paris: FR0014003TT8, DSY.PA) and BMW Group today embarked on a long-term strategic partnership to develop BMW Groups future engineering platform featuring Dassault Systmes 3DEXPERIENCE platform at its core. More than 17,000 employees across multiple engineering disciplines at the premium automobile manufacturer will rely on the 3DEXPERIENCE platform to accelerate the development of all vehicles, from their ideation to their production.

In an industry where quick time to market of sustainable mobility solutions with advanced technology is a competitive differentiator, the partnership between Dassault Systmes and BMW Group is testimony to the fundamental role of the 3DEXPERIENCE platform in enabling companies to deliver products faster. The platforms virtual twin experiences streamline enterprise-wide collaboration and deliver data-driven approaches to manage the exponential complexity carmakers are facing in connected, autonomous vehicle engineering.

We will only optimize our engineering process if we think digital, work connected and rely on an integrated data. For the BMW Group the 3DEXPERIENCE platform will support this approach and help to reach a higher level of quality in our processes, said Julien Hohenstein, Vice President Processes, Digitalization, Governance Idea to Offer at the BMW Group research and development.

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BMW Group Partners with Dassault Systmes to Bring the 3DEXPERIENCE Platform to Its Future Engineering Platform - Dassault Systmes

Enhancing lysine production guided by the iML1515 model

To increase the lysine production in a lysine-producing strain E. coli Lys (CCTCC M2019435, Supplementary Fig.1, Supplementary Tables1, 2, Supplementary Note1), we constructed E. coli Lys1 according to previous well-known metabolic engineering strategies, including (i) knocking out aspA (encoding aspartate ammonia-lyase) to minimize the carbon metabolic flux diversion from lysine biosynthesis16, (ii) overexpressing asd (encoding aspartate-semialdehyde dehydrogenase) to strengthen the rate-limiting enzyme in the lysine synthetic pathway17, and (iii) changing the start codon of icd (encoding isocitrate dehydrogenase) from ATG to GTG to balance cell growth and lysine production4 (Fig.1a). After fed-batch fermentation using the defined medium AM1, E. coli Lys1 exhibited a 50.4% increase in lysine titer, a 30.3% increase in yield, and a 60.0% increase in productivity compared to E. coli Lys (Supplementary Fig.2).

a Construction of E. coli Lys1 using established metabolic engineering strategies. b Screening of targets guided by the iML1515 model. c Schematic representation of genes identified in lysine production. Genes encoding high-demand proteins are highlighted in red, while genes for low-demand proteins are shown in blue. GLC glucose, 6-P-GLC 6-Phosphoglucose, PYR pyruvate, OAA oxaloacetate, ASP L-aspartate, ASPS L-aspartate phosphate, HOM L-homoserine, MED Meso diaminopimelic acid, LYS lysine. d The combination of OmpF and OmpN with different RBS strengths. e Fermentation parameters of strain E. coli Lys5 using AM1 medium in a 5-L fermenter. n=3 independent experiments. Data are presented as mean valuesSD. Source data are provided as a Source Data file.

To further increase lysine production, the genome-scale metabolic model iML1515 was employed to identify the potential gene targets for promoting lysine synthesis18 (Fig.1b). From the simulation database, we extracted fifty proteins, ultimately selecting nine potential targets directly affecting lysine synthesis for metabolic manipulation: (i) eight proteins (encoded by dapD, dapE, dapF, lysA, ompC, ompF, ompN, and phoE) to be strengthened and (ii) one protein (encoded by pgi) to be attenuated (Fig.1c). Based on these targets, E. coli Lys1 was engineered from three aspects: (i) increasing NADPH supply, (ii) enhancing lysine core pathway efficiency, and (iii) strengthening ammonia transport.

Initially, the NADPH supply was enhanced by increasing the pentose phosphate pathway flux by genomic alteration of the start codon of pgi (encoding glucose-6-phosphate isomerase) from ATG to GTG, generating E. coli Lys2. Consequently, E. coli Lys2 exhibited a 33% higher intracellular NADPH level than E. coli Lys1 (Supplementary Fig.3). The lysine titer, yield, and productivity of E. coli Lys2 increased by 99.2%, 36.4%, and 120.0%, respectively, compared with those of E. coli Lys (Table1, Supplementary Fig.4).

Next, to achieve optimal lysine pathway efficiency, the native promoter of the lysA operon was replaced with a stronger promoter Ptrc in E. coli Lys2 to construct the E. coli Lys3 strain. Three promoters, including PJ23119 of high expression strength (H), PJ23105 with moderate expression strength (M), and PJ23115 with low expression strength (L), were used to fine-tune the expression levels of dapD, dapE, and dapF. Twenty-seven expression cassettes were constructed and introduced into E. coli Lys3 to identify the optimal combination for lysine production in shake-flask fermentation. Among these engineered strains, E. coli Lys3-6 (DapD[H]-DapE[M]-DapF[L]) exhibited the optimal lysine titer (Supplementary Fig.5). Subsequently, this expression cassette was integrated into E. coli Lys3s genome to obtain E. coli Lys4. The lysine titer, yield, and productivity of E. coli Lys4 increased by 4.2-fold, 0.7-fold, and 4.6-fold compared with those of E. coli Lys (Table1, Supplementary Fig.6).

Finally, to provide sufficient ammonium ions for lysine biosynthesis in E. coli Lys4, four engineered strains were constructed by individually overexpressing potential ammonia transporters OmpC, OmpF, OmpN, and PhoE. In the shake-flask fermentation test, strains overexpressing OmpF and OmpN exhibited positive effects on lysine production (Supplementary Fig.7). Thus, both genes were co-expressed with different strengths of RBS (RBS10: high strength, RBS09: medium strength, and RBS03: low strength) in E. coli Lys4. The optimal combination strain, E. coli Lys4-4 (RBS09: ompF/RBS10: ompN), showed the best lysine production (Fig.1d). Subsequently, this expression cassette was integrated into the genome of E. coli Lys4 to construct E. coli Lys5. The lysine titer, yield, and productivity in the engineered E. coli Lys5 reached 163.2g/L, 0.60g/g glucose, and 3.9g/Lh, which were increased by 5.3-fold, 0.8-fold, and 6.8-fold compared to E. coli Lys (Fig.1e). The total glucose consumption of E. coli Lys5 increased by 2.5-fold to 271.5g/L, and the fermentation time was shortened by nearly 6h, suggesting that ammonia transport was critical for improving lysine production.

To validate the effectiveness of the models predictions, we evaluated the impact of several gene targets associated with lysine synthesis (lysC, thrA, metL, ppc, aspC, and panB) on lysine production in E. coli strain Lys5 (Supplementary Figs.810). However, no significant target genes for lysine production were identified (Supplementary Note2). These findings suggest that the metabolic flux responsible for lysine synthesis in strain E. coli Lys5 reached an optimal state through refined metabolic regulation guided by the iML1515 model. To assess the effect of genetic modifications on cellular metabolism, the carbon abundance of key metabolites in E. coli Lys5 was calculated using 13C-labeled glucose in the AM1 medium. The findings also indicated the redirection of carbon metabolic flux toward the lysine synthesis pathway in strain E. coli Lys5 compared to the control strain E. coli Lys (Fig.2, Supplementary Fig.11).

a 13C-abundance analysis of key metabolites of strain E. coli Lys. b 13C-abundance analysis of key metabolites of strain E. coli Lys5. Glu glucose, G6P glucose-6-phosphate, 6PG 6-phosphogluconate, RL5P Ribulose-5-phosphate, R5P ribose 5-phosphate, Xu5P xylulose 5-phosphate, E4P erythrose 4-phosphate, F6P fructose-6-phosphate, FBP fructose-1,6-diphosphate, GAP glyceraldehyde 3-phosphate, PEP phosphoenolpyruvate, PYR pyruvate, AcCoA acetyl-CoA, CIT citrate, OXO 2-oxoglutarate, SuCoA Succinyl-CoA, SUC succinate, FUM fumarate, MAL malate, OAA oxaloacetate, ASP Aspartic acid, LYS Lysine. n=3 independent experiments. Data are presented as mean valuesSD.

To evaluate the production robustness of E. coli Lys5 under different fermentation medium conditions, we conducted fermentation using the nutrient-rich medium. Consequently, the engineered strain E. coli Lys5 exhibited a lysine titer, yield, and productivity of 195.9g/L, 0.67g/g glucose, and 5.4g/Lh, respectively (Supplementary Fig.12).

To design an artificial glutarate synthetic pathway starting from lysine, a retro-synthesis workflow comprising four key steps was developed (Fig.3a): (i) Analysis of the functional groups in lysine, which include two amino groups and one carboxyl group. (ii) Identification of initial reactions stemming from l-lysine, encompassing six distinct reactions: decarboxylation, monooxygenation, oxidation, decarboxylative oxidation, oxidative deamination, and acyl-transfer reactions. (iii) Discovery of enzymes capable of catalyzing the initial products through enzyme mining using the MetaCyc database15. (iv) Assembly and evaluation of the complete pathways. A total of six potential pathways for glutarate synthesis were identified (Supplementary Fig.13). We selected the AMA pathway, which involved the fewest catalytic steps, for experimental validation. Enzymes in the AMA pathway included aromatic aldehyde synthase (AAS), monoamine oxidase (MAO), and aldehyde dehydrogenase (ALDH) (Fig.3b, Supplementary Figs.1416). As shown in Table2, compared to other reported glutarate biosynthetic pathways19, the AMA pathway exhibits several advantages: (i) High thermodynamic favorability, indicated by maximum driving force (MDF)20 and total Gibbs energy change (rGm); (ii) Minimal catalytic steps and cofactors involved; and (iii) Avoidance of -ketoglutarate, a key intermediate in the TCA cycle. These characteristics make the AMA pathway a promising option for glutarate biosynthesis.

a Retro-synthesis workflow for artificial glutarate synthetic pathway design. b The enzyme composition of the AMA pathway. c Schematic representation of the in vitro reconstructed system. d HPLC detection: The blue profile represents the reaction sample and the red profile represents glutarate standard samples. e LC-MS detection was conducted with the ESI negative mode. Glutarate was noted in red. f Fermentation parameters of strain E. coli AMA01 in a 5-L fermenter using nutrient-rich medium. n=3 independent experiments. Data are presented as mean valuesSD. Source data are provided as a Source Data file.

Due to the instability and unavailability of 5-aminoglutaraldehyde, the AMA pathway was divided into two modules. Module I contained two enzymes for converting lysine to glutaraldehyde, while Module II contained the last enzyme for converting glutaraldehyde to glutarate. In Module I, five AAS candidates were selected based on the structural similarities between 5-aminoglutaraldehyde and 3,4-dihidroxyphenylacetaldehyde21. Additionally, four MAO candidates were screened based on the structural similarities between glutaraldehyde and 4-droxyphenylacetaldehyde22 (Supplementary Tables3, 4). As a result, twenty plasmid combinations, termed pGA1-pGA20, were constructed to express the AAS-MAO operons. The optimal strain harboring pGA1 (AAS from Petroselinum crispum and MAO from Homo sapiens) could produce 18.0g/L of glutaraldehyde from 20g/L of lysine (Supplementary Fig.17). In Module II, we selected 11 potential ALDH enzymes from the BRENDA database to construct the plasmids pGA21-pGA31. Whole-cell bioconversion experiments revealed that the optimal strain harboring pGA21 (ALDH from Klebsiella pneumoniae) could produce 2.5g/L glutarate from 20g/L glutaraldehyde (Supplementary Table5 and Supplementary Fig.18).

To verify the feasibility of directly producing glutarate from lysine, the three selected enzymes were purified and added into an in vitro reconstruction system at an equimolar ratio (Fig.3c). As shown in Fig.3d, e, the final product, glutarate, was detected using both HPLC and LC-MS (Supplementary Figs.19, 20). This finding proved the viability of the AMA pathway for converting lysine into glutarate. In addition, the AMA pathway displayed excellent transferability across various lysine-producing microorganisms (Supplementary Figs.2122, Supplementary Note3).

The introduction of the AMA pathway into E. coli Lys5 resulted in the development of E. coli AMA01, which produced 51.6g/L of glutarate with a yield of 0.30g/g and a productivity rate of 1.1g/Lh using nutrient-rich medium (Fig.3f). However, the limited glutarate titer achieved and the accumulation of high concentrations of intermediate glutaraldehyde (24.8g/L) indicated the presence of a rate-limiting step in glutarate production (Fig.3f).

ALDH was identified as the rate-limiting enzyme in the AMA pathway based on three experiments: (i) Enzyme activity assay: Despite being more highly expressed than the other two enzymes, ALDH exhibited the lowest enzyme activity (Supplementary Fig.23, Supplementary Table6). (ii) Catalytic efficiency assay: Among the three enzymes, increasing the concentration of ALDH proved to be the most effective method for enhancing the overall catalytic efficiency of the AMA pathway in the in vitro reconstruction system (Supplementary Fig.24). (iii) Fermentation conditions assay: Increasing the stirring rate and aeration ratio during fermentation did not improve the catalytic efficiency of oxygen-dependent AAS and MAO (Supplementary Figs.25, 26).

Subsequently, ALDH was crystallized to obtain the protein crystal structure with a resolution of 2.28 (Fig.4a, Supplementary Table7). Each ALDH monomer was found to comprise three domains: an oligomerization domain, a catalytic domain, and an NAD+-binding domain. The ternary conformation was determined by molecular docking of the substrate glutaraldehyde and cofactor NAD+ with ALDH (Fig.4b).

a The structure of ALDH (PBD ID:8IXI) is shown with subunit 1 in light orange and subunit 2 in purple. b ALDH comprises three domains: the substrate-binding domain (residues 1-99, fuchsia), the NAD+-binding domain (residues 100-280, green), and the helical domain (residues 280-294, cyan). c Detection of 5-oxopentanoic acid in HPLC. The red and purple profiles represented the standard sample of glutarate and glutaraldehyde, while the green profile represented the sample of whole-cell catalysis, with the peak of 5-oxopentanoic acid indicated by an arrow. d Concentration changes of the substrate (glutaraldehyde: blue), intermediate (5-oxopentanoic acid: red), and product (glutarate: green) during in vitro catalysis of pure enzymes. e Initial reaction rate using different pH conditions. f Tyr88 residue was mutated to alanine to verify its role in the catalytic reaction. g Reaction mechanism for the oxidation of glutaraldehyde by ALDH. GLD Glutaraldehyde, GLT Glutarate. h DFT-computed Gibbs free energies (in kcal/mol) at the CPCM (water) level of theory and transition-state structures (carbon: gray, hydrogen: white, oxygen: red, nitrogen: blue, angles are shown in o, and distances are shown in ). n=3 independent experiments. Data are presented as mean valuesSD. Source data are provided as a Source Data file.

Based on the catalytic mechanism of aldehyde dehydrogenase on single-aldehyde substrates, a putative catalytic mechanism of ALDH was proposed: Tyr-88 initiates a nucleophilic attack on the carbonyl group of glutaraldehyde; Subsequently, the hydrogen (H) on the synthesized hemiacetal hydroxyl (OH) is deprotonated. Simultaneously, the hydrogen (H) on the central carbon of the hemiacetal is transferred from the substrate to the carbon of the amide neighbor of the cofactor NAD+; Finally, the ester bond is hydrolyzed, resulting in the formation of glutarates. To confirm this catalytic mechanism, four experimental strategies were implemented: (i) Intermediate detection: We detected the presence of the intermediate, 5-oxopentanoic acid, when using glutaraldehyde as a substrate. The intermediate from the aldehyde oxidation reaction was isolated (Fig.4c), purified using preparative high-performance liquid chromatography, and confirmed through 1HNMR spectroscopy and LC-MS, thus confirming the presence of 5-oxopentanoic acid (Supplementary Figs.27,28); (ii) Chemical concentration changes: During the reaction process, we observed a decrease in the concentration of the substrate, glutaraldehyde, along with an increase in glutarate production. Importantly, the intermediate displayed an initial increase followed by a decrease in concentration during the reaction process (Fig.4d); (iii) Reaction microenvironment verification: Given that the entire reaction requires a neutral environment for deprotonation, we investigated the initial reaction rate under various pH conditions. Our findings indicated that the reaction could not proceed under acidic conditions (Fig.4e); and (iv) Key residue validation: When Tyr88 residue was mutated to alanine, its catalytic efficiency was significantly reduced, nearly reaching zero. This suggests that the mutated residue has a strong affinity for attacking the aldehyde key residue of the substrate glutaraldehyde (Fig.4f).

Furthermore, transition state theory calculations were performed to determine the catalytic mechanism of ALDH (Fig.4g), where the entire reaction was divided into six steps (Fig.4h). In step 1, the substrate glutaraldehyde is nucleophilically attacked by one molecule of hydroxyl and water, representing the active site as Tyr (TyrM: Tyr truncation model). The substrate S1-CHO takes a proton from Tyr to generate intermediate IN1 via the transition state [TS1], which requires an activation-free energy of 13.9kcal/mol. In step 2, the C1H (hydride ion: H-) of IN1 is transferred to the carbon of the amide neighbor of the cofactor NAD+M (NAD+M: NAD+ truncation model). Simultaneously, the H on C1OH of IN1 is transferred to the O (C=O) of the amide branch chain of the cofactor NAD+M through a transition state, forming IN2 and reducing NAD+ (NADH) via the transition state [TS2]. This process requires an activation-free energy of 28.8kcal/mol. In step 3, IN2 hydroxide hydrolyzes the ester to produce the carboxylic acid IN3, which also requires 30.9kcal/mol of energy. In step 4, the S5-CHO in IN4 is nucleophilically attacked by Tyr and water molecules to form the IN4 via the transition state [TS4], which requires an activation-free energy of 14.1kcal/mol. In step 5, C5H (hydride ion: H-) of IN4 is transferred to the carbon of the amide neighbor of the cofactor NADM to form the IN5 and reduced NAD+ (NADH) via the transition state [TS5], which requires 32.2kcal/mol of activation free energy. In step 6, similar to step 3, the C5H (hydride ion: H-) of IN5 is transferred to the carbon of the amide neighbor of the cofactor NAD+M (NAD+M: NAD+ truncation model). At the same time, H on C1OH of IN5 is transferred to O (C=O) of the amide branch chain of cofactor NAD+M through a transition state, which requires 30.8kcal/mol of energy. In general, the overall steps collectively release 8.7kcal/mol of energy, indicating the feasibility of this reaction under enzymatic conditions.

In summary, these results support the proposed mechanism for glutarate formation from glutaraldehyde. However, two primary challenges limit the speed of the catalytic process. One is the start-up rate of the catalytic process, which includes steps 1 and 4; the other is the catalytic process has a high energy barrier, which includes steps 2, 3, 5, and 6. The high-energy barriers in steps 3 and 6 can be reduced by introducing water molecules23. Ultimately, four key steps are determined, namely S[TS1] (13.9kcal/mol) and IN3[TS4] (14.1kcal/mol) in steps 1 and 4, as well as IN1[TS2] (28.8kcal/mol) and IN4[TS5] (32.2kcal/mol) in steps 2 and 5. Thus, lowering the energy barrier by reprogramming the transition states [TS1], [TS4], [TS2], and [TS5] may be a strategy to further improve the catalytic efficiency of ALDH.

To improve catalytic efficiency, ALDH was rationally modified at different stages. In steps 1 and 4, the Y88 residue and water molecules within the loop ring region were identified as potential nucleophilic groups capable of initiating a nucleophilic attack on the substrates carbonyl group to form IN1 and IN4. However, the nucleophilic capabilities of these residues were found to be relatively weak, leading to a substantial energy barrier in steps 1 and 4. Monoaldol biocatalysis often relies on the presence of Cys as a critical residue in the catalytic mechanism24,25. Therefore, we constructed six single ALDH mutations (I90C, L91C, K92C, G210C, V211C, and I212C) near the Y88 loop (Fig.5a). Whole-cell conversion experiments showed that two single mutants, I90C and I212C, increased glutarate conversion to 22.0% and 23.0%, respectively (Supplementary Fig.29). On this basis, a double mutant Mu1 (ALDHI90C/I212C) was constructed to increase the glutarate titer to 6.5g/L from 20g/L glutaraldehyde, which was 2.6-fold than that of the wild-type ALDH (Fig.5b).

a Creation of the protein model introducing CYS residues (I90C, L91C, K92C, G210C, V211C, and I212C) visualized using Pymol. b. Glutarate production by different mutants under whole-cell conversion. Reactions were performed with recombinant E. coli (20g/L whole cell catalyst) in 50mL air-saturated PBS buffer (50mM, pH 7.4) at 30C for 30h (220rpm). Glutarate titers were determined using HPLC. c Identification of residue sites in mutant Mu5 and its associated protein structure. d The distance between C1H, C5H, and NAD+ in both the WT and variant Mu5. e. DFT-computed Gibbs free energies (in kcal/mol) at the CPCM (water) level of theory and transition-state structures (Carbon: gray, hydrogen: white, Oxygen: red, Nitrogen: blue, angles are shown in o, and distances are shown in ). The WT is shown in the black line, while mutant Mu5 is shown in the red line. n=3 independent experiments. Data are presented as mean valuesSD. Source data are provided as a Source Data file.

The high energy potentials of steps 2 and 5 were caused by the suboptimal orientation of IN1 and IN4 toward the cofactor NAD+. To lower the energy barriers of steps 2 and 5, the binding posture of the substrate close to [TS2] and [TS5] was adjusted by releasing the spatial site resistance and enhancing substrate affinity. The interactions between glutaraldehyde and the ALDH complexes were analyzed, and three residues (N94, P95, and G210) in step 2 that affected the energy potential were identified. To reduce spatial hindrance, the large-volume residue (N94) near the substrate-binding pocket was mutated to a small-volume residue (S94) to bring the substrate closer to NAD+. The resulting mutant, Mu2 (ALDHN94S), produced 5.8g/L glutarate, which was 2.3-fold than that produced by wild-type ALDH in whole-cell conversion. To enhance substrate affinity, P95 and G210 were mutated into slightly smaller (L/I/N) and slightly smaller polar residues (S/T/C), respectively. Two highly active mutants, ALDHP95N and ALDHG210T were identified by establishing mutant libraries (P95L, P95I, P95N, G210C, G210S, and G210T) (Supplementary Fig.30). After two rounds of iterative mutation, the optimal mutant Mu3 (ALDHP95N/G210T) was obtained, displaying a 3.0-fold improvement over the wild type ALDH, producing 7.4g/L glutarate through whole-cell conversion. Subsequently, a combinatorial mutation approach was employed to create the mutant, Mu4 (ALDHN94S/P95N/G210T). Whole-cell conversion of Mu4 produced 9.9g/L of glutarate, which was 4.0-fold than that produced by wild-type ALDH. Finally, the above mutant sites were combined to generate the mutant Mu5 (ALDHI90C/I212C/N94S/P95N/G210T) (Fig.5c), capable of producing 13.9g/L glutarate from 20g/L glutaraldehyde in 30h, representing a 5.6-fold improvement over wild-type ALDH.

The increase in the catalytic activity of the Mu5 mutant could be explained in three ways: (i) The kcat, KM, and kcat/KM values of Mu5 were 27.9-fold, 1.5-fold, and 51.0-fold compared to the corresponding values for wild-type ALDH (Table3). (ii) Following Molecular Dynamics analysis, the catalytic distance between the substrate C1H and C5H and the carbon of the amide neighbor of the cofactor NADM shortened from approximately 3.5 and 6.0 to 2.5 and 2.6, respectively (Fig.5d, Supplementary Note4). (iii) The energy barriers of steps 1, 4, 2, and 5 in the final mutant Mu5 decreased to 11.4, 12.8, 26.5, and 27.0kcal/mol, respectively (Fig.5e).

A fed-batch fermentation experiment was performed on strain E. coli AMA02 containing the Mu5 mutant strain, and the glutarate titer increased to 72.5g/L with a yield of 0.40g/g glucose and a productivity of 1.5g/Lh. These values were 40.5%, 33.3%, and 36.4% higher than those of strain E. coli AMA01 (Supplementary Fig.31). However, its worth noting that the survival rate of E. coli AMA02 decreased by 59.3% at the end of fermentation.

The spot assay results revealed that E. coli AMA02 exhibited a limited tolerance to glutarate, with a maximum tolerance observed at a concentration of 70g/L (Fig.6a). At this concentration, the maximum optical density (OD) and cell survival rate in shake flask fermentation decreased by 34.0% and 40.4%, respectively (Fig.6b). The half-maximal inhibitory concentration (IC50) was determined to be 61.2g/L glutarate, causing severe damage to the cell morphology of strain E. coli AMA02 (Fig.6c).

a Strain E. coli AMA02 spotted on LB plates at different glutarate concentrations. b Maximum biomass and cell survival of strain E. coli AMA02 in LB medium (0 and 70g/L glutarate, p=0.001069, 0.000012). c Cell morphology of E. coli AMA02 under 70g/L glutarate. Images were taken after 6h of cultivation in the LB medium containing 70g/L glutarate. d Effects of different potential tolerance genes overexpression on cell survival and glutarate production in shaking fermentation with medium supplemented with 70g/L glutarate. e. Comparison of the maximum OD562 and cell survival of the three strains (E. coli AMA03, AMA02cbpA, and AMA02cbpA/cbpA) in shake flask fermentation (p=0.000024, 0.001282, 0.081595, 0.017024). f IC50 of strains E. coli AMA02 and AMA04 after cultivating 6h in the LB medium with varying concentrations of glutarate. g 5-L fermentation test of strain E. coli AMA04 using nutrient-rich medium. h Cell morphology of E. coli AMA04 under 70g/L glutarate. Images were taken after 6h of cultivation in the LB medium containing 70g/L glutarate. Statistical significance was indicated as *P<0.05, ** for P<0.01 and *** for P<0.001, respectively. n=3 independent experiments. Data are presented as mean valuesSD. Similar results were obtained from three biological independent samples, and a representative result was displayed for Fig.6c, h. Source data are provided as a Source Data file.

To elucidate the underlying mechanisms, RNA sequencing was performed to compare global gene expression in E. coli AMA02 in the absence and presence of 70g/L glutarate. The transcriptional profiling revealed significant alterations in the expression of 882 genes, with 476 genes upregulated and 406 genes downregulated. Based on the KEGG classification, most of these targets belonged to the metabolism and microbial metabolism in diverse environments pathways (Supplementary Figs.32, 33). Subsequently, the seven top-upregulated genes were selected (Supplementary Table8) and then individually overexpressed in E. coli AMA02 to examine their resistance to high concentrations of glutarate. Among them, the strain overexpressing cbpA (referred to as E. coli AMA03) exhibited good resistance (cell survival rate of 85.9%) and the optimal glutarate production (10.4g/L) when exposed to 70g/L glutarate (Fig.6d).

To further confirm that cbpA plays an important role in resisting glutarate stress, the maximum biomass, cell survival, and electron microscopy of the three strains (overexpressing strain E. coli AMA03, knockout strain E. coli AMA02 cbpA, and backup strain E. coli AMA02 cbpA/cbpA) were compared in shake flask fermentation. At 70g/L glutarate, compared with strains E. coli AMA02 cbpA/cbpA and E. coli AMA02 cbpA, the E. coli AMA03 strain exhibited a 15.0% and 43.0% increase in maximum OD, and a 64.6% and 205.7% increase in cell survival, respectively (Fig.6e).

To test the effect of cbpA on glutarate production, cbpA was genomically integrated into the glutarate degradation gene csiD in the engineered strain E. coli AMA02 with different RBS strengths. Among them, the strain with cbpA expression controlled by RBS07 exhibited the optimal cell survival rates and glutarate production. This strain was termed E. coli AMA04 and selected for the subsequent study. Its worth mentioning that there was a positive correlation between cell survival rates and glutarate production (Supplementary Figs.3437). The IC50 of strain E. coli AMA04 was 28.3% higher than that of strain E. coli AMA02 (Fig.6f). With 5-L fed-batch fermentation using the nutrient-rich medium, the glutarate titer, yield, and productivity of strain E. coli AMA04 reached 82.6g/L, 0.40g/g glucose, and 1.7g/Lh, respectively (Fig.6g). Furthermore, cell morphology observations showed that E. coli AMA04 cells displayed a more complete and regular form than the swollen E. coli AMA02 cells (Fig.6h). Compared to E. coli AMA02, the glutarate titer and productivity of E. coli AMA04 increased by 13.9% and 13.3%, respectively, suggesting that the toxicity associated with higher concentrations of glutarate was alleviated through the expression of the tolerance gene cbpA. Additionally, we evaluated the glutarate-tolerance gene cbpA in various glutarate-producing microorganisms, highlighting the robust transferability of the cbpA gene (Supplementary Figs.38-39, Supplementary Note5, Supplementary Table9).

To further increase glutarate production in strain E. coli AMA04, the metabolic burden and enzyme expression levels were optimized. Compared with that of strain E. coli Lys5, E. coli AMA04 displayed a decrease of 44.7% in maximum biomass, a 40.0% reduction in specific growth rate, and a 27.5% decrease in total sugar consumption. These results indicated that the dual-vector expression system caused a metabolic burden on the growth of E. coli AMA04. Thus, we constructed a single vector (pETM6R1-ALDH-AAS-MAO) to replace the dual-vector system in E. coli AMA04 to generate the engineered strain E. coli AMA05. As shown in Fig.7a, the glutarate titer of E. coli AMA05 reached 84.3g/L, with a yield of 0.32g/g and a productivity of 1.8g/Lh. Notably, the maximum biomass, specific growth rate, and total sugar consumption of strain E. coli AMA05 were increased by 0.4- fold, 5.5- fold, and 0.2-fold than that of strain E. coli AMA04, reaching 32.5, 1.3h1, and 260.0g/L (Fig.7b).

a Fermentation parameters of strain E. coli AMA05 in a 5-L fermenter using nutrient-rich medium. b Strain E. coli AMA05 was constructed by replacing the two-vector system with a single-vector system. Comparison of maximum biomass, specific growth rate, and total sugar consumption of strains E. coli AMA04 and E. coli AMA05 using nutrient-rich medium. c The effects of promoter optimization on glutarate production in the shake flask experiments. d Fermentation parameters of strain E. coli AMA06 using nutrient-rich medium in a 5-L fermenter. n=3 independent experiments. Data are presented as mean valuesSD. Source data are provided as a Source Data file.

Furthermore, to determine the potential enzyme synergy, the expression levels of AAS and MAO were optimized using three promoters of different strengths in a single-vector system. Among the nine engineered strains, E. coli AMA05-3 exhibited the optimal glutarate production in the shake flask fermentation and was termed as E. coli AMA06 (Fig.7c). The fermentation performance of strain E. coli AMA06 was evaluated on AM1 medium, yielding a glutarate titer, yield, and productivity of 74.3g/L, 0.37g/g, and 1.46g/Lh, respectively (Supplementary Fig.40). Subsequently, it was further evaluated using a nutrient-rich medium, which led to a glutarate production of 88.4g/L, with a yield and productivity of 0.42g/g and 1.8g/Lh, respectively (Fig.7d, Supplementary Figs.41, 42).

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In Clean Architecture, Robert Martin adds some commentary to Kent Becks famous Make it work, make it right, make it fast quote:

First, make it work. You are out of business if it doesnt work.

Then make it right. Refactor the code so that you and others can understand and evolve it as needs change or are better understood.

Then make it fast. Refactor the code for needed performance.

One of my old teams did this so right. Another one did it so, so wrong

I used to work for a company that built a B2B SaaS product. Their old, monolithic, Java-based product was a cash cow, bringing in $15 million annually for eight straight years. They hired a new engineering director who promised to build a next-gen product that would take them even further using the latest tech (at the time, that meant Kubernetes solves all problems). He just needed 30 engineers and permission to do it as a complete rewrite.

The product was an engineering marvel. The director got the 30 engineers he wanted and they built and deployed 162 microservices (picoservices might be a better name) supporting everything from CAN-SPAM to a chatbot with some early NLP features.

There was only one problem: it didnt work.

After two years of development and $27 million spent on R&D, no customers could migrate from the Java monolith to the new system. Pages loaded in minutes. Events were stuck in queues for hours as calls fanned out and validated data.

The director was, unsurprisingly, let go. The project was written off as a total loss. After two years, the company returned to iterating on the Java monolith, a system they still use today.

There was another team I worked for that had a single driving principle: Delight the users. That core principle drove all our decisions, especially when building new features. We took user feedback, implemented it quickly, and shipped it in a few days.

We cared about clean architecture, passing tests, and scalable services. But our primary focus was a working product.

The result of this team was just that: a product that worked. We iterated on a great product, delighted users, and built a profitable system that continues to run ten years later. (You go, SyncTimes!)

Maybe its boring that there wasnt much drama about this project. But Boring is beautiful (credit to Ben "The Hosk" Hosking in Boring is Beautiful in Software Development, worth the read).

Originally posted here:

A tale of two engineering teams. Hi, this is Jacob with this weeks | by Jacob Bennett | The Atomic Engineer | Feb ... - Medium

Clearing trees and brush is one of the challenges in eastern Kansas, where woods will naturally encroach upon fields and sometimes need to be removed. Today, well meet a rural-preneur who has designed better equipment to cut trees and clear fields.

Kelly Coover is the co-owner of CVR Manufacturing in Galesburg, Kansas. Coover and his brothers had been involved with a feed mill growing up in the small town.

He went to school in Erie, where he was active in agricultural education classes. In 1976, he became the state champion in the Future Farmers of America Structures and Environment competition.

Coover earned a degree in agricultural engineering from K-State and returned to southeast Kansas. For a time, he worked as an engineer for the U.S. Army ammunition plant in Parsons.

Coover always looked for a better way to do things. He joined his brother in creating a company called CVR Manufacturing to do research and development of new and improved agriculture-related products.

The company was named CVR, which is a contraction of the family name, and was also the cattle brand that Kellys father had acquired years ago.

CVR Manufacturing explored making different kinds of projects: a biodegradable injection-moldable plastic material made from wheat straw and starch.

That made it useful for making animal feed containers.

Another product was an outdoor furnace called Heatsource 1 that uses wood, corn or pellets for heating.

They then came across tree-choppers, which are a real need in forested southeast Kansas.

They licensed a product called the tree chopper, designed to be mounted on a four wheeler.

As the business expanded, Coover brought on board his son Kyle, also a K-State engineering graduate.

We saw cutting trees was a good market, Coover said.

The Sawfish line uses an appropriately named long narrow blade. The Coovers designed another tree cutting device of their own with overlapping circular disk blades.

They continued to upgrade and improve their design over time.

If the blades overlap, it only takes half the energy to cut a tree, Coover said.

Their first redesigned model included ten-inch disks to cut four-inch diameter trees, which had been created with a mount plate to go on four-wheelers.

Next, they upscaled the device to include 16-inch disks with bucket clamps that could go on the front-end loader of a tractor.

Kyle suggested another improvement on the product line: The Sawtilus trimmer, which uses a spiral-shaped blade to cut smaller trees in one revolution. This can be mounted on a string trimmer.

It keeps torque constant and minimizes hydraulics, Coover said.

CVR Manufacturing earned a patent on that product in May of 2023.

There was nothing he couldnt fix, Kyle said about his father.

It runs in the family. Kelly Coover has three sisters and three brothers: Don, a veterinarian; Brian, an ag engineer; and Dave, an ag education major who is back on the Coover family farm.

Coover continues to look for better ways to get things done.

I can see alternate uses and other ways to do things, he said. I take a notebook and when I have an idea, I write it down. I have 25 or 30 notebooks with ideas in them. I just need the time and money to get them done.

The disk tree cutter has proven especially popular for cutting trees and clearing brush.

In Texas, they are using them to cut mesquite, Coover said. Our small model is used on yucca in the west.

CVR products have gone as far away as Georgia, Oregon and West Virginia. Its an impressive record for a business located in the rural community of Galesburg, population 149 people. Now, thats rural.

For more information on the companys outdoor furnaces, go to http://www.heatsource1.com.

For information on other products, go to http://www.cvrmanufacturing.com.

Clearing trees and brush is a real need in certain parts of the country, and Kansas-based CVR Manufacturing is finding better ways to make this possible.

We salute Kelly and Kyle Coover and all those involved with CVR Manufacturing for making a difference with ingenuity and engineering.

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Kansas Profile: Family of engineers develops new strategies for the family business to succeed - The Mercury - Manhattan, Kansas