Vagococcus allomyrinae sp. nov. and Enterococcus larvae sp. nov., isolated from larvae of Allomyrina dichotoma.

The taxonomic positions of two novel strains isolated from larvae of an insect (Allomyrina dichotoma) collected in Jeju, Republic of Korea, were determined by a polyphasic approach. Strain BWB3-3T was closely related to the type strain of Vagococcus salmoninarum, having 97.2 % 16S rRNA gene sequence similarity, whereas strain BWM-S5T formed an independent cluster within the genus Enterococcus in the 16S rRNA gene phylogeny and the closest relative was the type strain of Enterococcus canis (98.1 % sequence similarity). The core gene analysis supported the phylogenetic positions of the isolates revealed by 16S rRNA gene phylogeny. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between strain BWB3-3T and the type strain of V. salmoninarum were 73.2 and 20.0 %, respectively, whereas strain BWM-S5 T showed an ANI value of 70.9 % with the type strain of Enterococcus canis. The dDDH values between strain BWM-S5T and all the type strains of Enterococcus species were ≤25.1 %. On the basis of the results obtained here, the two isolates are considered to constitute two novel species of the family Enterococcaceae, for which the names Vagococcus allomyrineae sp. nov. and Enterococcus larvae sp. nov. are proposed, with the type strains BWB3-3T (=KCTC 43277T=CCM 9080T) and BWM-S5T (=KACC 22156T=CCM 9075T), respectively.

Aeromicrobium stalagmiti sp. nov., isolated from a lava cave.

A Gram-reaction-positive, strictly aerobic, non-sporulating, non-motile, rod-shaped bacterium, designated YC3-14T, was isolated from pieces of stalagmite collected in a lava cave in Jeju, Republic of Korea. Cells showed growth at 15-35 °C, pH 6.0-9.0 and with 0-3 % (w/v) NaCl. Colonies of the cells were circular, smooth, convex and cream in colour. A 16S rRNA gene-based neighbour-joining tree indicated that the organism belonged to the genus Aeromicrobium and formed a sublineage between an Aeromicrobium endophyticum-Aeromicrobium fastidiosum cluster and an Aeromicrobium yanjiei-Aeromicrobium chenweiae cluster. The highest 16S rRNA gene similarity values of strain YC3-14T were with the type strains of A. yanjiei (99.2 %), A. endophyticum (99.1 %), A. fastidiosum (98.8 %), A. ginsengisoli (98.8 %) and A. chenweiae (98.7 %). The cell-wall peptidoglycan contained ll-diaminopimelic acid as the diagnostic diamino acid. The major menaquinone was MK-9(H4). The predominant fatty acids were C18 : 0.10-methyl, C18 : 1 ω9c and C16 : 0. The polar lipids comprised diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol, an unidentified phospholipid and two unidentified lipids. The G+C content of the genome DNA was 69.9 mol%. These chemotaxonomic features of the isolate were typical for the genus Aeromicrobium. The genome-based phylogeny showed the same tree topology as the 16S rRNA gene phylogeny. The average nucleotide identity (≤84.5 %) and digital DNA-DNA hybridization (≤27.5 %) values supported that the isolate belongs to a novel species of the genus Aeromicrobium. On the basis of data obtained by a polyphasic approach, strain YC3-14T (=KCTC 49469T=NBRC 114653T) represents a novel species of the genus Aeromicrobium, for which the name Aeromicrobium stalagmiti sp. nov. is proposed.

Tenebrionicola larvae gen. nov., sp. nov., isolated from larvae of mealworm Tenebrio molitor L., and a proposal to transfer Erwinia teleogrylli Liu et al. 2016 to a new genus Entomohabitans as Entomohabitans teleogrylli comb. nov.

Two enterobacterial strains, designated YMB-R21T and YMB-R22, were isolated from larvae of mealworm Tenebrio molitor L. and examined for their taxonomic characteristics. A 16S rRNA gene-based neighbour-joining tree showed that the two isolates formed two distinct sublineages within the family Enterobacteriaceae and were separated from other genera of the family. The isolates showed 16S rRNA gene sequence similarity of 98.9 % to each other and ≤96.5 % to members of the order Enterobacteriales. The phylogenomic analysis based on 92 singly-copy core genes showed that the two isolates belonged to the family Enterobacteriaceae and formed a distinct sublineage at a position located remotely from the genera of the family. The loosely associated members were the type strain of Erwinia teleogrylli and members of the genus Shimwellia. Average nucleotide identity and digital DNA-DNA hybridization values showed that the isolates represented members of a novel species in the family Enterobacteriaceae. The values of amino acid identity between the two isolates and the closest relatives were 74.5-75.0 % with the type strain of E. teleogrylli and 74.5-74.8 % with the type strains of two Shimwellia species, while E. teleogrylli showed the amino acid identity values of 76.3-76.5 % with two Shimwellia species. Based on the results obtained here, we propose a new genus Tenebrionicola with the description of Tenebrionicola larvae sp. nov. (type strain YMB-R21T=KCTC 82597T=CCM 9152T and strain YMB-R22=KCTC 82598=CCM 9153), with the transfer of Erwinia teleogrylli Liu et al. 2016 to a new genus Entomohabitans as Entomohabitans teleogrylli comb. nov. (type strain SCU-B244T=CGMCC 1.12772T=DSM 28222T=KCTC 42022T).

Individual Identification by Late Information Fusion of EmgCNN and EmgLSTM from Electromyogram Signals.

This paper is concerned with individual identification by late fusion of two-stream deep networks from Electromyogram (EMG) signals. EMG signal has more advantages on security compared to other biosignals exposed visually, such as the face, iris, and fingerprints, when used for biometrics, at least in the aspect of visual exposure, because it is measured through contact without any visual exposure. Thus, we propose an ensemble deep learning model by late information fusion of convolutional neural networks (CNN) and long short-term memory (LSTM) from EMG signals for robust and discriminative biometrics. For this purpose, in the ensemble model's first stream, one-dimensional EMG signals were converted into time-frequency representation to train a two-dimensional convolutional neural network (EmgCNN). In the second stream, statistical features were extracted from one-dimensional EMG signals to train a long short-term memory (EmgLSTM) that uses sequence input. Here, the EMG signals were divided into fixed lengths, and feature values were calculated for each interval. A late information fusion is performed by the output scores of two deep learning models to obtain a final classification result. To confirm the superiority of the proposed method, we use an EMG database constructed at Chosun University and a public EMG database. The experimental results revealed that the proposed method showed performance improvement by 10.76% on average compared to a single stream and the previous methods.

Hongsoonwoonella zoysiae gen. nov., sp. nov., a new member of the family Stappiaceae isolated from a tidal mudflat.

A Gram stain-negative bacterial strain, designated SY4-7T, was isolated from rhizosphere mudflat of a halophyte (Zoysia sinica) collected around Seonyu Island, Republic of Korea. Cells of the organism were strictly aerobic, non-sporulating, non-motile rods and grew at 20-42 °C, pH 6-8 and 1-6% (w/v) NaCl. The 16S rRNA gene-based phylogenetic analyses revealed that strain SY4-7T formed an independent cluster separated from the recognized genera of the family Stappiaceae, which was also supported by phylogenomic analysis-based 92-core gene sequences. The type stains of the phylogenetically closest relatives were Stappia indica (95.6% sequence similarity), Stappia stellulata (95.1%) and Roseibium hamelinense (95.1%). The isoprenoid quinone was Q-10. The polar lipids consisted of phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, three unidentified aminophospholipids, an unidentified phosphoglycolipid, an unidentified aminolipid, two unidentified phospholipids and an unidentified lipid. The major cellular fatty acids are C18:1ω7c and C19:1 cyclo ω8c. The G + C content of the genomic DNA is 60.7%. Discrimination of the organism from all the recognized genera of the family Stappiaceae was apparent by the chemotaxonomic and phylogenetic features. Based on the results presented here, strain SY4-7T (= KCTC 72226T = NBRC 113902T) represents a novel species of a new genus in the family Stappiaceae, for which the name Hongsoonwoonella zoysiae sp. nov. is proposed.

Jinshanibacter allomyrinae sp. nov., isolated from larvae of Allomyrina dichotoma, proposal of Insectihabitans xujianqingii gen. nov., comb. nov. and emended descriptions of the genera Jinshanibacter, Limnobaculum and Pragia.

Taxonomic positions of four Gram-negative bacterial strains, which were isolated from larvae of two insects in Jeju, Republic of Korea, were determined by a polyphasic approach. Strains CWB-B4, CWB-B41 and CWB-B43 were recovered from larvae of Protaetia brevitarsis seulensis, whereas strain BWR-B9T was from larvae of Allomyrina dichotoma. All the isolates grew at 10-37 °C, at pH 5.0-9.0 and in the presence of 4 % (w/v) NaCl. The 16S rRNA gene phylogeny showed that the four isolates formed two distinct sublines within the order Enterobacteriales and closely associated with members of the genus Jinshanibacter. The first group represented by strain CWB-B4 formed a tight cluster with Jinshanibacter xujianqingii CF-1111T (99.3 % sequence similarity), whereas strain BWR-B9T was most closely related to Jinshanibacter zhutongyuii CF-458T (99.5 % sequence similarity). The 92 core gene analysis showed that the isolates belonged to the family Budviciaceae and supported the clustering shown in 16S rRNA gene phylogeny. The genomic DNA G+C content of the isolates was 45.2 mol%. A combination of overall genomic relatedness and phenotypic distinctness supported that three isolates from Protaetia brevitarsis seulensis are different strains of Jinshanibacter xujianqingii, whereas one isolate from Allomyrina dichotoma represents a new species of the genus Jinshanibacter. On the basis of results obtained here, Jinshanibacter allomyrinae sp. nov. (type strain BWR-B9T=KACC 22153T=NBRC 114879T) and Insectihabitans xujianqingii gen. nov., comb. nov. are proposed, with the emended descriptions of the genera Jinshanibacter, Limnobaculum and Pragia.

Body and Hand-Object ROI-Based Behavior Recognition Using Deep Learning.

Behavior recognition has applications in automatic crime monitoring, automatic sports video analysis, and context awareness of so-called silver robots. In this study, we employ deep learning to recognize behavior based on body and hand-object interaction regions of interest (ROIs). We propose an ROI-based four-stream ensemble convolutional neural network (CNN). Behavior recognition data are mainly composed of images and skeletons. The first stream uses a pre-trained 2D-CNN by converting the 3D skeleton sequence into pose evolution images (PEIs). The second stream inputs the RGB video into the 3D-CNN to extract temporal and spatial features. The most important information in behavior recognition is identification of the person performing the action. Therefore, if the neural network is trained by removing ambient noise and placing the ROI on the person, feature analysis can be performed by focusing on the behavior itself rather than learning the entire region. Therefore, the third stream inputs the RGB video limited to the body-ROI into the 3D-CNN. The fourth stream inputs the RGB video limited to ROIs of hand-object interactions into the 3D-CNN. Finally, because better performance is expected by combining the information of the models trained with attention to these ROIs, better recognition will be possible through late fusion of the four stream scores. The Electronics and Telecommunications Research Institute (ETRI)-Activity3D dataset was used for the experiments. This dataset contains color images, images of skeletons, and depth images of 55 daily behaviors of 50 elderly and 50 young individuals. The experimental results showed that the proposed model improved recognition by at least 4.27% and up to 20.97% compared to other behavior recognition methods.

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics.

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%-0.27% higher than AlexNet or GoogLeNet on PTB-ECG-and the ResNet was 0.94%-0.12% higher than AlexNet or GoogLeNet on CU-ECG.

An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal.

We herein propose an EigenECG Network (EECGNet) based on the principal component analysis network (PCANet) for the personal identification of electrocardiogram (ECG) from human biosignal data. The EECGNet consists of three stages. In the first stage, ECG signals are preprocessed by normalization and spike removal. The R peak points in the preprocessed ECG signals are detected. Subsequently, ECG signals are transformed into two-dimensional images to use as the input to the EECGNet. Further, we perform patch-mean removal and PCA algorithm similar to the PCANet from the transformed two-dimensional images. The second stage is almost the same as the first stage, where the mean removal and PCA process are repeatedly performed in the cascaded network. In the final stage, the binary quantization, block sliding, and histogram computation are performed. Thus, this EECGNet performs well without the use of back-propagation to obtain features from the visual content. We constructed a Chosun University (CU)-ECG database from an ECG sensor implemented by ourselves. Further, we used the well-known MIT Beth Israel Hospital (BIH) ECG database. The experimental results clearly reveal the good performance and effectiveness of the proposed method compared with conventional algorithms such as PCA, auto-encoder (AE), extreme learning machine (ELM), and ensemble extreme learning machine (EELM).

Low melatonin production by suppression of either serotonin N-acetyltransferase or N-acetylserotonin methyltransferase in rice causes seedling growth retardation with yield penalty, abiotic stress susceptibility, and enhanced coleoptile growth under anoxic conditions.

Serotonin N-acetyltransferase (SNAT) and N-acetylserotonin methyltransferase (ASMT) are the last two key enzymes for melatonin biosynthesis in living organisms. In this study, we demonstrated that transgenic rice (Oryza sativa L.) plants, in which expression of either endogenous SNAT or ASMT was suppressed, had reduced melatonin synthesis, confirming that both SNAT and ASMT are functionally involved in melatonin synthesis. The melatonin-deficient SNAT rice had retarded seedling growth, which was partially restored by exogenous melatonin application, suggesting melatonin's role in seedling growth. In addition, the plants were more sensitive to various abiotic stresses, including salt and cold, compared with the wild type. Melatonin-deficient SNAT rice had increased coleoptile growth under anoxic conditions, indicating that melatonin also inversely regulates plant growth under anaerobic conditions with the concomitant high expression of alcohol dehydrogenase genes. Similarly, the melatonin-deficient ASMT rice exhibited accelerated senescence in detached flag leaves, as well as significantly reduced yield. These loss-of-function studies on the melatonin biosynthetic genes confirmed most previous pharmacological reports that melatonin not only promotes plant growth but also mitigates various abiotic stresses.

Cloning and functional characterization of the Arabidopsis N-acetylserotonin O-methyltransferase responsible for melatonin synthesis.

The N-acetylserotonin O-methyltransferase (ASMT) gene encodes the enzyme that catalyzes the conversion of N-acetylserotonin to melatonin as the last step in melatonin biosynthesis. The first plant ASMT gene to be cloned was from rice. An orthologous gene encoding a protein with ASMT activity and only 39.7% amino acid sequence identity to the rice ASMT protein was recently isolated from apple (Malus zumi). The low homology of the apple ASMT sequence prompted us to screen the Arabidopsis genome for a homologous ASMT gene. The At4g35160 gene exhibited the highest sequence identity (31%) to the rice ASMT gene, followed by the At1g76790 gene with 29% sequence identity. We purified recombinant proteins expressed from the two Arabidopsis genes. The At4g35160 recombinant protein exhibited ASMT enzyme activity, but the At1g76790 recombinant protein did not; thus, we designated At4g35160 as an Arabidopsis thaliana ASMT (AtASMT) gene. The AtASMT protein catalyzed the conversion of N-acetylserotonin to melatonin and serotonin to 5-methoxytryptamine with Vmax values of 0.11 and 0.29 pkat/mg protein, respectively. However, AtASMT exhibited no caffeic acid O-methyltransferase activity, suggesting that its function was highly specific to melatonin synthesis. AtASMT transcripts were induced by cadmium treatment in Arabidopsis followed by increased melatonin synthesis. Similar to other ASMT proteins, AtASMT was localized in the cytoplasm and its ectopic overexpression in rice resulted in increased ASMT enzyme activity and melatonin production, indicating the involvement of AtASMT in melatonin synthesis.

Cloning and characterization of the serotonin N-acetyltransferase-2 gene (SNAT2) in rice (Oryza sativa).

The penultimate enzyme in melatonin synthesis is serotonin N-acetyltransferase (SNAT), which exists as a single copy in mammals and plants. Our recent studies of the Arabidopsis snat-knockout mutant and SNAT RNAi rice (Oryza sativa) plants predicted the presence of at least one other SNAT isogene in plants; that is, the snat-knockout mutant of Arabidopsis and the SNAT RNAi rice plants still produced melatonin, even in the absence or the suppression of SNAT expression. Here, we report a molecular cloning of an SNAT isogene (OsSNAT2) from rice. The mature amino acid sequences of SNAT proteins indicated that OsSNAT2 and OsSNAT1 proteins had 39% identity values and 60% similarity. The Km and Vmax values of the purified recombinant OsSNAT2 were 371 µm and 4700 pmol/min/mg protein, respectively; the enzyme's optimal activity temperature was 45°C. Confocal microscopy showed that the OsSNAT2 protein was localized to both the cytoplasm and chloroplasts. The in vitro enzyme activity of OsSNAT2 was severely inhibited by melatonin, but the activities of sheep SNAT (OaSNAT) and rice OsSNAT1 proteins were not. The enzyme activity of OsSNAT2 was threefold higher than that of OsSNAT1, but 232-fold lower than that of OaSNAT. The OsSNAT1 and OsSNAT2 transcripts were similarly suppressed in rice leaves during the melatonin induction after cadmium treatment. Phylogenetic analyses indicated that OsSNAT1 and OsSNAT2 are distantly related, suggesting that they evolved independently from Cyanobacteria prior to the endosymbiosis event.

Melatonin production in Escherichia coli by dual expression of serotonin N-acetyltransferase and caffeic acid O-methyltransferase.

Melatonin is a well-known bioactive molecule produced in animals and plants and a well-studied natural compound. Two enzymatic steps are required for the biosynthesis of melatonin from serotonin. First, serotonin N-acetyltransferase (SNAT) catalyzes serotonin to N-acetylserotonin (NAS) followed by the action of N-acetylserotonin O-methyltransferase (ASMT), resulting in the synthesis of O-methylated NAS, also known as melatonin. Attempts to document melatonin production in Escherichia coli have been unsuccessful to date due to either low enzyme activity or inactive ASMT expression. Here, we employed caffeic acid O-methyltransferase (COMT) instead of ASMT, as COMT is a multifunctional enzyme that has ASMT activity as well. Among several combinations of dual expression cassettes, recombinant E. coli that expressed sheep SNAT with rice COMT produced a high quantity of melatonin, which was measured in a culture medium (1.46 mg/L in response to 1 mM serotonin). This level was several orders of magnitude higher than that produced in transgenic rice and tomato overexpressing sheep SNAT and ASMT, respectively. This heterologous expression system can be widely employed to screen various putative SNAT or ASMT genes from animals and plants as well as to overproduce melatonin in various useful microorganisms.

Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor.

In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider's hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse's gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider's motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country's top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data.

Chloroplast-encoded serotonin N-acetyltransferase in the red alga Pyropia yezoensis: gene transition to the nucleus from chloroplasts.

Melatonin biosynthesis involves the N-acetylation of arylalkylamines such as serotonin, which is catalysed by serotonin N-acetyltransferase (SNAT), the penultimate enzyme of melatonin biosynthesis in both animals and plants. Here, we report the functional characterization of a putative N-acetyltransferase gene in the chloroplast genome of the alga laver (Pyropia yezoensis, formerly known as Porphyra yezoensis) with homology to the rice SNAT gene. To confirm that the putative Pyropia yezoensis SNAT (PySNAT) gene encodes an SNAT, we cloned the full-length chloroplastidic PySNAT gene by PCR and purified the recombinant PySNAT protein from Escherichia coli. PySNAT was 174 aa and had 50% amino acid identity with cyanobacteria SNAT. Purified recombinant PySNAT showed a peak activity at 55 °C with a K m of 467 µM and V max of 28 nmol min-1 mg(-1) of protein. Unlike other plant SNATs, PySNAT localized to the cytoplasm due to a lack of N-terminal chloroplast transit peptides. Melatonin was present at 0.16ng g(-1) of fresh mass but increased during heat stress. Phylogenetic analysis of the sequence suggested that PySNAT has evolved from the cyanobacteria SNAT gene via endosymbiotic gene transfer. Additionally, the chloroplast transit peptides of plant SNATs were acquired 1500 million years ago, concurrent with the appearance of green algae.

Melatonin biosynthesis requires N-acetylserotonin methyltransferase activity of caffeic acid O-methyltransferase in rice.

Caffeic acid O-methyltransferase (COMT) methylates N-acetylserotonin into melatonin; that is, it has N-acetylserotonin O-methyltransferase (ASMT) activity. The ASMT activity of COMT was first detected in Arabidopsis thaliana COMT (AtCOMT). To confirm the involvement of COMT on melatonin synthesis in other plant species, the ASMT activity of a COMT from rice (Oryza sativa) (OsCOMT) was evaluated. Purified recombinant OsCOMT protein from Escherichia coli was used to validate the high ASMT activity of OsCOMT, similar to that of AtCOMT. The K m and V max values for the ASMT activity of OsCOMT were 243 µM and 2400 pmol min(-1) mg protein(-1), which were similar to those of AtCOMT. Similar to AtCOMT, OsCOMT was localized in the cytoplasm. In vitro ASMT activity was significantly inhibited by either caffeic acid or quercetin in a dose-dependent manner. Analogously, in vivo production of melatonin was significantly inhibited by quercetin in 4-week-old detached rice leaves. Lastly, the transgenic rice plants overexpressing rice COMT showed an increase in melatonin levels whereas transgenic rice plants suppressing the rice COMT had a significant decrease on melatonin levels, suggestive of the direct role of COMT in melatonin biosynthesis in plants.

Molecular cloning of melatonin 2-hydroxylase responsible for 2-hydroxymelatonin production in rice (Oryza sativa).

Although melatonin biosynthetic genes from plants have been cloned, the melatonin catabolism mechanisms remain unclear. To clone the genes responsible for melatonin metabolism, we ectopically expressed 35 full-length cDNAs of rice 2-oxoglutarate-dependent dioxygenase (2-ODD) in Escherichia coli and purified the corresponding recombinant proteins. In vitro 2-ODD assays showed four independent 2-ODD proteins that were able to catalyze melatonin into 2-hydroxymelatonin, exhibiting melatonin 2-hydroxylase (M2H). These M2H proteins had peak activities at pH 8.0 and 30°C. The Km ranged from 121 µm to 371 µm with the Vmax ranging from 1.7 to 18.5 pkat/mg protein, respectively. The M2H enzyme activities were dependent on cofactors such as α-ketoglutarate, ascorbate, and Fe(2+), similar to the 2-ODD enzymes. M2H activity was inhibited by prohexadione-Ca, an inhibitor of 2-ODD, in a dose-dependent manner. M2H activity was high in the roots of rice seedlings, concurrent with high transcription levels of 2-ODD 21, suggesting that 2-ODD 21 was a major gene for M2H activity. Analogous to the high M2H activity in the roots, 2-hydroxymelatonin was found in large quantities in roots treated with melatonin. These results suggest that melatonin was metabolized into 2-hydroxymelatonin by the M2H genes in plants, but the physiological significance of 2-hydroxymelatonin remains to be examined in the future.

Predominance of 2-hydroxymelatonin over melatonin in plants.

The cloning of the gene encoding melatonin 2-hydroxylase (M2H), which is responsible for the synthesis of 2-hydroxymelatonin, has expanded the study of melatonin metabolism in plants. Kinetic analysis of M2H enzymatic activity demonstrated that the catalytic efficiency of M2H is much higher than those of other melatonin biosynthetic enzymes such as serotonin N-acetyltransferase (SNAT) and N-acetylserotonin O-methyltransferase (ASMT), suggesting that melatonin metabolism is rapid in plants. To test this prediction, we selected 24 plant species belonging to 16 families and quantified the levels of melatonin and 2-hydroxymelatonin using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The melatonin levels in most of the species were <1 ng/g fresh weight (FW), while those in leaves from radish and feverfew were 3.5 and 3.3 ng/g FW, respectively. In contrast, the average levels of 2-hydroxymelatonin were much higher at 6.2 ng/g FW. The average ratio of 2-hydroxymelatonin to melatonin in plants was approximately 368:1, indicating that the accumulation of 2-hydroxymelatonin predominates over that of melatonin. These data were consistent with previous results on the kinetics of the corresponding enzymes, as well as with in vivo melatonin conversion data. Among several melatonin metabolites in plants, the most abundant metabolite was found to be 2-hydroxymelatonin (99%) followed by 4-hydroxymelatonin (0.05%), but 6-hydroxymelatonin was not detected in rice seedlings.

Chloroplastic and cytoplasmic overexpression of sheep serotonin N-acetyltransferase in transgenic rice plants is associated with low melatonin production despite high enzyme activity.

Serotonin N-acetyltransferase (SNAT), the penultimate enzyme in melatonin biosynthesis, catalyzes the conversion of serotonin into N-acetylserotonin. Plant SNAT is localized in chloroplasts. To test SNAT localization effects on melatonin synthesis, we generated transgenic rice plants overexpressing a sheep (Ovis aries) SNAT (OaSNAT) in their chloroplasts and compared melatonin biosynthesis with that of transgenic rice plants overexpressing OaSNAT in their cytoplasm. To localize the OaSNAT in chloroplasts, we used a chloroplast targeting sequence (CTS) from tobacco protoporphyrinogen IX oxidase (PPO), which expresses in chloroplasts. The purified recombinant CTS:OaSNAT fusion protein was enzymatically functional and localized in chloroplasts as confirmed by confocal microscopic analysis. The chloroplast-targeted CTS:OaSNAT lines and cytoplasm-expressed OaSNAT lines had similarly high SNAT enzyme activities. However, after cadmium and butafenacil treatments, melatonin production in rice leaves was severalfold lower in the CTS:OaSNAT lines than in the OaSNAT lines. Notably, enhanced SNAT enzyme activity was not directly proportional to the production of N-acetylserotonin, melatonin, or 2-hydroxymelatonin, suggesting that plant SNAT has a role in the homeostatic regulation of melatonin rather than in accelerating melatonin synthesis.

Arabidopsis serotonin N-acetyltransferase knockout mutant plants exhibit decreased melatonin and salicylic acid levels resulting in susceptibility to an avirulent pathogen.

Serotonin N-acetyltransferase (SNAT) is the penultimate enzyme in the melatonin biosynthesis pathway in plants. We examined the effects of SNAT gene inactivation in two Arabidopsis T-DNA insertion mutant lines. After inoculation with the avirulent pathogen Pseudomonas syringe pv. tomato DC3000 harboring the elicitor avrRpt2 (Pst-avrRpt2), melatonin levels in the snat knockout mutant lines were 50% less than in wild-type Arabidopsis Col-0 plants. The snat knockout mutant lines exhibited susceptibility to pathogen infection that coincided with decreased induction of defense genes including PR1, ICS1, and PDF1.2. Because melatonin acts upstream of salicylic acid (SA) synthesis, the reduced melatonin levels in the snat mutant lines led to decreased SA levels compared to wild-type, suggesting that the increased pathogen susceptibility of the snat mutant lines could be attributed to decreased SA levels and subsequent attenuation of defense gene induction. Exogenous melatonin treatment failed to induce defense gene expression in nahG Arabidopsis plants, but restored the induction of defense gene expression in the snat mutant lines. In addition, melatonin caused translocation of NPR1 (nonexpressor of PR1) protein from the cytoplasm into the nucleus indicating that melatonin-elicited pathogen resistance in response to avirulent pathogen attack is SA-dependent in Arabidopsis.