Biological Reaction Engineering for the Preparation of C9 Chemicals from Oleic Acid: 9‑Aminononanoic Acid, 1,9-Nonanediol, 9‑Amino-1-nonanol, and 1,9-Diaminononane

Engineering of native and recombinant enzyme reactions in whole-cell biocatalysis may allow the production of a variety of chemicals. In particular, fine-tuning of the reaction selectivity may enable the preparation of a desired product to a high conversion. Here, we demonstrated that various C9 chemicals such as 9-aminononanoic acid, 1,9-nonanediol, 9-amino-1-nonanol, and 1,9-diaminononane could be produced from renewable C18 oleic acid. As a representative example, activation of six recombinant enzyme reactions (e.g., fatty acid double bond hydratase, long-chain secondary alcohol dehydrogenase, Baeyer–Villiger monooxygenase, lipase, primary alcohol dehydrogenase, and ω-aminotransferases) with repression of one native enzyme reaction (i.e., aldehyde dehydrogenase) in Escherichia coli-based biocatalysis led to the formation of 9-aminononanoic acid with an isolation yield of 54% from oleic acid via 10-hydroxyoctadecanoic acid, 10-keto-octadecanoic acid, 9-(nonanoyloxy)nonanoic acid, 9-hydroxynonanoic acid, and 9-oxo-nonanoic acid. This study will contribute to biosynthesis of not only ω-aminoalkanoic acids but also ω-amino-1-alkanols and α,ω-diaminoalkanes from renewable fatty acids (e.g., oleic acid and ricinoleic acid)

Simultaneous Enzyme/Whole-Cell Biotransformation of Plant Oils into C9 Carboxylic Acids

Oxyfunctionalization of plant oils such as olive oil and soybean oil into C9 carboxylic acids (e.g., <i>n-</i>nonanoic acid and 9-hydroxynonanoic acid) was investigated. The biotransformation was composed of hydrolysis of plant oils by the <i>Thermomyces lanuginosus</i> lipase (TLL) and C9–C10 double-bond cleavage in unsaturated fatty acids by a serial reaction of a fatty acid double bond-hydratase of <i>Stenotrophomonas maltophilia</i>, an alcohol dehydrogenase of <i>Micrococcus luteus</i>, and a Baeyer–Villiger monooxygenase (BVMO) of <i>Pseudomonas putida</i> KT2440 expressed in <i>Escherichia coli</i>. The newly cloned oleate hydratase allowed one to produce 10-hydroxyoctadecanoic acid and 10-hydroxyoctadec-12-enoic acid at a high rate from oleic acid and linoleic acid, respectively, which are major fatty acid constituents of many plant oils. Furthermore, overexpression of a long chain fatty acid transporter FadL in the recombinant <i>E. coli</i> led to a significant increase of whole-cell biotransformation rates of oleic acid and linoleic acid into the corresponding esters. The resulting esters (the BVMO reaction products) were hydrolyzed in situ by TLL, generating nonanoic acid, non-3-enoic acid, and 9-hydroxynonanoic acid, which can be further oxidized to 1,9-nonanedioic acid. This study demonstrated that industrially relevant C9 carboxylic acids could be produced from olive oil or soybean oil by simultaneous enzyme/whole-cell biocatalysis

Different organization of upstream regulatory region between <i>E. coli</i> and <i>K. pneumoniae</i>.

<p>(A) Venn diagram showing orthologous genes and species-specific genes between <i>E. coli</i> and <i>K. pneumoniae</i>. (B) 4 different types of promoter regions, and their numbers identified in two species. (C) Schematic drawing of annotated TSSs and sequence comparison of regulatory region upstream of <i>lpd</i>. (D) Length difference between the pairs of comparable 5′ UTR. (E) Comparison of sequence conservation of promoter, 5′ UTR, and ORF regions. (F) Sequence conservation of genomic regions surrounding translation start sites.</p

TSS annotation and structure of promoter region and 5′ UTR.

<p>(A) Number of TSSs assigned per annotated genes. (B) Distribution of 5′ UTR lengths for <i>E. coli</i> and <i>K. pneumoniae</i>, and the Shine-Dalgarno sequence motif. (C) Sequence motif of promoter region containing −10 and −35 boxes. (D) Conservation of RpoD amino acid sequences of 5 species in gammaproteobacteria and 3 other species belonging to proteobacteria. (E) Di-nucleotide preference near the TSS site.</p

Experimentally determined TSSs and their association with annotated genes.

<p>(A) Genome-wide TSS mapped onto <i>E. coli</i> and <i>K. pneumoniae</i> genome annotation. (B) Number of <i>E. coli</i> sRNAs detected with 5 TSS datasets generated by different methods. (C) Number of sRNAs detected from <i>E. coli</i> and <i>K. pneumoniae</i> during the exponential growth. (D) Schematic drawing of annotated TSSs assigned to orthologous <i>micF</i> sRNA and coding genes surrounding <i>micF</i> in <i>E. coli</i> and <i>K. pneumoniae</i>. (E) Schematic drawing of annotated TSSs assigned to <i>K. pneumoniae</i> sRNA, <i>rnai</i>, and coding genes near <i>rnai</i>.</p

Comparison analysis of orthologous sRNAs.

<p>(A) Expression of RNA-binding protein <i>hfq</i> (B) Sequence conservation of regulatory region upstream of <i>hfq</i> ORF, including promoter, TSS and 5′ UTR. (C) Conservation and expression of non-coding regulatory sRNAs, <i>rprA</i>, <i>arcZ</i> and <i>sgrS</i>. (D) Sequence comparison analysis of <i>rprA</i> and <i>arcZ</i> regulating translation of <i>rpoS</i>. (E) Sequence comparison analysis of <i>sgrS</i> regulating translation of <i>ptsG</i> and <i>manX</i>.</p

Identifying Key Residues in Lysine Decarboxylase for Soluble Expression Using Consensus Design Soluble Mutant Screening (ConsenSing)

Although recent advances in deep learning approaches for protein engineering have enabled quick prediction of hot spot residues improving protein solubility, the predictions do not always correspond to an actual increase in solubility under experimental conditions. Therefore, developing methods that rapidly confirm the linkage between computational predictions and empirical results is essential to the success of improving protein solubility of target proteins. Here, we present a simple hybrid approach to computationally predict hot spots possibly improving protein solubility by sequence-based analysis and empirically explore valuable mutants using split GFP as a reporter system. Our approach, Consensus design Soluble Mutant Screening (ConsenSing), utilizes consensus sequence prediction to find hot spots for improvement of protein solubility and constructs a mutant library using Darwin assembly to cover all possible mutations in one pot but still keeps the library as compact as possible. This approach allowed us to identify multiple mutants of Escherichia coli lysine decarboxylase, LdcC, with substantial increases in soluble expression. Further investigation led us to pinpoint a single critical residue for the soluble expression of LdcC and unveiled its mechanism for such improvement. Our approach demonstrated that following a protein’s natural evolutionary path provides insights to improve protein solubility and/or increase protein expression by a single residue mutation, which can significantly change the profile of protein solubility