BACKGROUND: Equol is a major isoflavone metabolite, and equol-producing bacteria have been isolated and characterized; however, fermentation has been performed with soybean-based products as substrates. Pueraria lobata has been reported as a plant with higher content of isoflavones. RESULTS:The genome of new equol-producing bacteria, Lactobacillus paracasei JS1, was analyzed. Also, the effect of P. lobata extract fermented with L. paracasei JS1 (FPE) on the skin and intestinal immune response was examined. With gene expression analysis, it was proven that seven skin-related proteins, hyaluronan synthase-1, -2, -3, collagen, elastin, epidermal growth factor, and epidermal growth factor receptor were differentially expressed upon FPE treatment. The messenger RNA expression increased with treatment with the FPE, and a skin moisturizing effect was confirmed by a hematoxylin-eosin staining experiment. In addition, such an experiment showed that proinflammatory cytokines, tumor necrosis factor-, cyclooxygenase-2, inducible nitric oxide synthase, interleukin-1 , -4, and -6, were reduced in large intestine when treated with FPE. CONCLUSION: L. paracasei JS1 has the ability to produce equol having beneficial effects on the skin. Moreover, FPE also has an inhibitory effect on inflammation cytokines in the large intestine. Thus, the novel and edible equol-producing L. paracasei JS1 and FPE have thepotential to be developed as nutricosmetic resources. Experimental animalsSix-week-old male C57BL/6J mice (RaonBio, Yongin, Korea) were housed in an air-conditioned animal room over a 12-h light/dark cycle at a temperature of 22 ± 1 ∘ C and humidity of 50 ± 10%. The J Sci Food Agric 2019; 99: 4200-4210

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In this study, whole‐cell biotransformation was conducted to produce nonanedioic acid from nonanoic acid by expressing the alkane hydroxylating system (AlkBGT) from Pseudomonas putida GPo1 in Escherichia coli. Following adaptive laboratory evolution, an efficient E. coli mutant strain, designated as MRE, was successfully obtained, demonstrating the fastest growth (27‐fold higher) on nonanoic acid as the sole carbon source compared to the wild‐type strain. Additionally, the MRE strain was engineered to block nonanoic acid degradation by deleting fadE. The resulting strain exhibited a 12.8‐fold increase in nonanedioic acid production compared to the wild‐type strain. Six mutations in acrR, Pcrp, dppA, PfadD, e14, and yeaR were identified in the mutant MRE strain, which was characterized using genomic modifications and RNA‐sequencing. The acquired mutations were found to be beneficial for rapid growth and nonanedioic acid production.

Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users’ specific needs.