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Biosynthesis of novel non-proteinogenic amino acids β-hydroxyenduracididine and β-methylphenylalanine in Escherichia coli

ABSTRACT: Non-proteinogenic amino Q7 Q8 acids (npAAs) are valuable building blocks for the development of advanced pharmaceuticals and agrochemicals. The surge in interest in their synthesis is primarily due to the potential to enhance and diversify existing bioactive molecules. This can be achieved by altering these bioactive molecules to improve their effectiveness, reducing resistance compared to their natural counterparts or generating molecules with novel functions. Traditional production of npAAs in native hosts requires specialized conditions and complex cultivation media. Furthermore, these compounds are often found in organisms that challenge genetic manipulation. Thus, the recombinant production of these npAAs in a model organism like Escherichia coli paves the way for groundbreaking advancements in synthetic biology. For the first time, we report the synthesis of npAAs β-methylphenylalanine (BmePhe), β- hydroxyenduracididine (BhEnd), and enduracididine (End) from Streptomyces hygroscopicus in E. coli. Through proteomic and metabolomic analyses, we achieved the production of these compounds from two synthetic operons, comprising five heterologous proteins. Interestingly, we discovered that the exogenous addition of pathway precursors to the E. coli system enhanced the yield of BmePhe by 2.5 times, whereas it concurrently attenuated the production of BhEnd and End, signifying a selective precursor-dependent yield enhancement. The synthetic biology landscape is broadened in this study by expanding the repertoire of amino acids beyond the conventional set of 22 standard proteinogenic amino acids. The biosynthesized npAAs, End, BhEnd, and BmePhe hold promise for engineering proteins with modified functions, integrating into novel metabolites and/or enhancing biological stability and activity. Additionally, these amino acids’ biological production and subsequent purification present an alternative to traditional chemical synthesis methods, paving a direct pathway for pharmacological evaluation.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Escherichia Coli

SUBMITTER: Rosemary Gillane

LAB HEAD: Esteban Marcellin

PROVIDER: PXD056235 | Pride | 2024-10-01

REPOSITORIES: Pride

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Project description:For over 30 years, serine hydroxamate has been used to chemically stimulate a stringent response in Escherichia coli and other bacteria. These studies have elucidated numerous characteristics of the classical stringent response beyond the simple cellular response to an amino acid shortage, including phospholipid synthesis and protease up-regulation. In this study, the effects of a serine hydroxamate addition on high cell density recombinant E. coli were examined and compared to the effects of recombinant protein production to determine overlaps, as recombinant protein production stress has often been attributed to amino acid shortages. Both the transcriptome and growth characteristics were evaluated and compared. The serine hydroxamate addition profoundly decreased the culture growth rate, whereas, recombinant protein production did not. Conversely, the transcriptome profile of the recombinant E. coli cultures were relatively unaffected by the serine hydroxamate addition, yet recombinant protein production dramatically changed the transcriptome profile. A subset of the classical stringent response genes were effected by the serine hydroxamate addition, whereas, recombinant protein production regulated numerous classical stringent response genes; however, not all. The genes that were regulated by the serine hydroxamate addition include numerous fatty acid synthesis genes, in agreement with altered phospholipids synthesis reports. These results indicate that recombinant protein production and the stringent response have many overlapping responses; however, are far from identical. It was hypothesized that recombinant protein production leads to a stringent response due to the high amino acid synthesis demands related to recombinant protein synthesis. A comparison of the transcriptomes during recombinant protein production and a chemical imposed stringent response would assist with determining what portion of the “metabolic burden” associated with recombinant protein production is due to amino acid shortages. In this study, the transcriptome profiles of recombinant E. coli were examined and compared for the three culture conditions: 1) Normal growth, no external stress; 2) L-serine hydroxamate addition (to mediate a stringent response); and 3) IPTG-induction to produce the recombinant protein chloramphenicol acetyltransferase (CAT). The transcriptome profiles from these three conditions were analyzed using Affymetrix Anti-sense E. coli GeneChip® microarrays. Experiment Overall Design: For the serine hydroxamate fermentations, cells were harvested immediately prior to the serine hydroxamate-addition (Time S0) and 1-hour post serine hydroxamate-addition (Time S1). For the IPTG-induced fermentations, cells were harvested immediately prior to the IPTG-addition (Time S0) and 1-hour post-induction (Time S1). For the uninduced (unstressed) fermentations, cells were harvested at Time S0 and Time S1, for which the timing was synchronized with the serine hydroxamate and IPTG fermentations based on OD for Time S0 and time for Time S1. Each fermentation condition was repeated (two biological replicates). RNA from each biological replicates was purified and processed independently. Three biological replicates were obtained for the control condition (Time S0), since prior the serine hydroxamate- or IPTG- addition all fermentations were replicates. Prior to DNA microarray hybridization, where only two biological replicates existed, one of the processed samples was divided into two technical replicates, resulting in three separate hybridized chips. All Time S1 samples contained three technical replicates from two biological duplicates, whereas the control Time S0 measurement contained three biological replicates.

Project description:Background: The biotechnology industry has extensively exploited Escherichia coli for producing recombinant proteins, biofuels etc. However, high growth rate aerobic E. coli cultivations are accompanied by acetate excretion i.e. overflow metabolism which is harmful as it inhibits growth, diverts valuable carbon from biomass formation and is detrimental for target product synthesis. Although overflow metabolism has been studied for decades, its regulation mechanisms still remain unclear. Results: In the current work, growth rate dependent acetate overflow metabolism of E. coli was continuously monitored using advanced continuous cultivation methods (A-stat and D-stat). The first step in acetate overflow switch (at μ = 0.27 ± 0.02 1/h) is the repression of acetyl-CoA synthethase (Acs) activity triggered by carbon catabolite repression resulting in decreased assimilation of acetate produced by phosphotransacetylase (Pta), and disruption of the PTA-ACS node. This was indicated by acetate synthesis pathways PTA-ACKA and POXB component expression down-regulation before the overflow switch at μ = 0.27 ± 0.02 1/h with concurrent 5-fold stronger repression of acetate-consuming Acs. This in turn suggests insufficient Acs activity for consuming all the acetate produced by Pta, leading to disruption of the acetate cycling process in PTA-ACS node where constant acetyl phosphate or acetate regeneration is essential for E. coli chemotaxis, proteolysis, pathogenesis etc. regulation. In addition, two-substrate A-stat and D-stat experiments showed that acetate consumption capability of E. coli decreased drastically, just as Acs expression, before the start of overflow metabolism. The second step in overflow switch is the sharp decline in cAMP production at μ = 0.45 1/h leading to total Acs inhibition and fast accumulation of acetate. Accumulation of acetate was also coupled to excretion of products such as orotate and N-carbomoyl-L-aspartate making it a novel carbon spilling mechanism in E. coli. Conclusion: This study is an example of how a systems biology approach allowed to propose a new regulation mechanism for overflow metabolism in E. coli shown by proteomic, transcriptomic and metabolomic levels coupled to two-phase acetate accumulation: acetate overflow metabolism in E. coli is triggered by Acs down-regulation resulting in decreased assimilation of acetic acid produced by Pta, and disruption of the PTA-ACS node. Reference samples at specific growth rate (μ) 0.11 1/h were compared to the ones acquired at μ 0.21, 0.26, 0.31, 0.36, 0.40 and 0.48 1/h

Project description:Significant insight about biological networks arises from the study of network motifs âoverly abundant network subgraphs, but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions. We generated a set of 13 time courses by measuring gene expression after a metabolic change. Yeast strain KCN118 (MATalpha ade2) was grown at 28 Â°C in 400 ml of synthetic complete media with 2% dextrose (SCD) to an OD600 of 0.6. Synthetic complete was prepared using the standard recipe, except 75 uM inositol was included. At OD600 of 0.6, 100 ml of cells were collected by centrifugation and frozen as a reference sample, and the remaining cells were rapidly collected by filtration, washed with distilled water and resuspended in 300 ml of one of the following media: SCE (SC + 2% ethanol), SCG (SC + 2% galactose), SM1 (SCD lacking amino acids A, R, N, C, Q, G, K, P, S, F and T), SM2 (SCD lacking amino acids L, I, V, W, H and M), S0 (SCD lacking all amino acids), S0G (no amino acids, 2% galactose) or S0E (no amino acids, 2% ethanol). The data appears in Figures 2 and 4 of the manuscript, as it relates to the global analysis of all the arrays used in the dataset. All time courses consist of the following time points (in min): 15, 30, 60, 120, 240, and were hybridized against the t = 0 time point of cells grown in SCD. Each time course was performed as one single biological replicate and one technological replicate, except where noted below. Specifically, the 13 time courses break down into the following groups: Media key: SCD (synthetic complete, not including inositol) SCE (SC + 2% ethanol) SCG (SC + 2% galactose) SM1 (SCD lacking amino acids A, R, N, C, Q, G, K, P, S, F and T) SM2 (SCD lacking amino acids L, I, V, W, H and M) S0 (SCD lacking all amino acids) S0G (no amino acids, 2% galactose) S0E (no amino acids, 2% ethanol). ino = inositol aa = all amino acids supplemented The following group descriptions are the media as described above, followed by the description as indicated in the long title of the individual arrays: 1. S0 (5 arrays) = SD 2. SCD (6 arrays, t = 240 min has 2 tech replicates) = SD+aa 3. SM2 (5 arrays) = SD+aa:ARNCQGKPSDEFTY+ino 4. SM1 + ino (5 arrays) = SD+aa:LIVWHM+ino 5. S0 + ino (6 arrays, t = 240 min has 2 tech replicates) = SD+ino 6. S0E (5 arrays) = SEtOH 7. S0E + aa (7 arrays, t = 240 min and 60 min each have 2 tech replicates) = SEtOH+aa 8. S0E + aa + ino (5 arrays) = SEtOH+aa+ino 9. S0E + ino (8 arrays, t = 240 min, 15 min and 30 min each have 2 tech replicates) = SEtOH+ino 10. S0G (5 arrays) = Sgal 11. S0G + aa (5 arrays) = Sgal+aa 12. S0G + aa + ino (5 arrays) = Sgal+aa+ino 13. S0G + ino (6 arrays, t = 60 min has 2 tech replicates) = Sgal+ino

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Biosynthesis of novel non-proteinogenic amino acids β-hydroxyenduracididine and β-methylphenylalanine in Escherichia coli

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