Genome Resource of Podosphaera xanthii, the Host-Specific Fungal Pathogen That Causes Cucurbit Powdery Mildew.

Approximately 33 types of commonly consumed fruits and vegetables are members of the family Cucurbitaceae, making it an important crop family worldwide. However, pathogen resistance to pesticides and fungicides has become a growing problem in cultivation practices. The identification of the effector proteins in each unique fungus-host pair would help toward the development of strategies for preventing the infection of important crops. In this study, we characterized the genome of Podosphaera xanthii, the fungal pathogen that causes powdery mildew disease in cucurbitaceous plants. A first-draft genome of 209.08 MB was assembled and compared with those of 25 other fungal pathogens, particularly for identifying candidate secreted effector proteins. This draft genome can serve as a valuable resource for future genomic and proteomic studies of P. xanthii and its host-specific pathogenesis.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Extremely-randomized-tree-based Prediction of N6-Methyladenosine Sites in Saccharomyces cerevisiae.

INTRODUCTION: N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. METHODOLOGY: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. RESULTS: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. CONCLUSION: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

Preparation of Polyethylene Glycol-Ginsenoside Rh1 and Rh2 Conjugates and Their Efficacy against Lung Cancer and Inflammation.

Low solubility and tumor-targeted delivery of ginsenosides to avoid off-target cytotoxicity are challenges for clinical trials. In the present study, we report on a methodology for the synthesis of polyethylene glycol (PEG)-ginsenoside conjugates through a hydrolysable ester bond using the hydrophilic polymer polyethylene glycol with the hydrophobic ginsenosides Rh1 and Rh2 to enhance water solubility and passive targeted delivery. The resulting conjugates were characterized by 1H nuclear magnetic resonance (1H NMR) and Fourier-transform infrared spectroscopy (FT-IR). 1H NMR revealed that the C-6 and C-3 sugar hydroxyl groups of Rh1 and Rh2 were esterified. The conjugates showed spherical shapes that were monitored by field-emission transmission electron microscopy (FE-TEM), and the average sizes of the particles were 62 ± 5.72 nm and 134 ± 8.75 nm for PEG-Rh1and PEG-Rh2, respectively (measured using a particle size analyzer). Owing to the hydrophilic enhancing properties of PEG, PEG-Rh1 and PEG-Rh2 solubility was greatly enhanced compared to Rh1 and Rh2 alone. The release rates of Rh1 and Rh2 were increased in lower pH conditions (pH 5.0), that for pathophysiological sites as well as for intracellular endosomes and lysosomes, compared to normal-cell pH conditions (pH 7.4). In vitro cytotoxicity assays showed that the PEG-Rh1conjugates had greater anticancer activity in a human non-small cell lung cancer cell line (A549) compared to Rh1 alone, whereas PEG-Rh2 showed lower cytotoxicity in lung cancer cells. On the other hand, both PEG-Rh1 and PEG-Rh2 showed non-cytotoxicity in a nondiseased murine macrophage cell line (RAW 264.7) compared to free Rh1 and Rh2, but PEG-Rh2 exhibited increased efficacy against inflammation by greatly inhibiting nitric oxide production. Thus, the overall conclusion of our study is that PEG conjugation promotes the properties of Rh1 for anticancer and Rh2 for inflammation treatments. Depends on the disease models, they could be potential drug candidates for further studies.

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy.

Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition-transition-distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP prediction with an accuracy of 0.896 when tested on independent data sets, whereas in the second layer, hybrid features obtained through feature selection protocol and random forest produced an accuracy of 0.725 that is better than state-of-the-art predictors. We anticipate that our method MLCPP will become a valuable tool for predicting CPPs and their uptake efficiency and might facilitate hypothesis-driven experimental design. The MLCPP server interface along with the benchmarking and independent data sets are freely accessible at www.thegleelab.org/MLCPP .

Stage-Wise Identification and Analysis of miRNA from Root-Knot Nematode Meloidogyne incognita.

In this study, we investigated global changes in miRNAs of Meloidogyne incognita throughout its life cycle. Small RNA sequencing resulted in approximately 62, 38, 38, 35, and 39 Mb reads in the egg, J2, J3, J4, and female stages, respectively. Overall, we identified 2724 known and 383 novel miRNAs (read count > 10) from all stages, of which 169 known and 13 novel miRNA were common to all the five stages. Among the stage-specific miRNAs, miR-286 was highly expressed in eggs, miR-2401 in J2, miR-8 and miR-187 in J3, miR-6736 in J4, and miR-17 in the female stages. These miRNAs are reported to be involved in embryo and neural development, muscular function, and control of apoptosis. Cluster analysis indicated the presence of 91 miRNA clusters, of which 36 clusters were novel and identified in this study. Comparison of miRNA families with other nematodes showed 17 families to be commonly absent in animal parasitic nematodes and M. incognita. Validation of 43 predicted common and stage-specific miRNA by quantitative PCR (qPCR) indicated their expression in the nematode. Stage-wise exploration of M. incognita miRNAs has not been carried out before and this work presents information on common and stage-specific miRNAs of the root-knot nematode.

Discovery of Gene Sources for Economic Traits in Hanwoo by Whole-genome Resequencing.

Hanwoo, a Korean native cattle (Bos taurus coreana), has great economic value due to high meat quality. Also, the breed has genetic variations that are associated with production traits such as health, disease resistance, reproduction, growth as well as carcass quality. In this study, next generation sequencing technologies and the availability of an appropriate reference genome were applied to discover a large amount of single nucleotide polymorphisms (SNPs) in ten Hanwoo bulls. Analysis of whole-genome resequencing generated a total of 26.5 Gb data, of which 594,716,859 and 592,990,750 reads covered 98.73% and 93.79% of the bovine reference genomes of UMD 3.1 and Btau 4.6.1, respectively. In total, 2,473,884 and 2,402,997 putative SNPs were discovered, of which 1,095,922 (44.3%) and 982,674 (40.9%) novel SNPs were discovered against UMD3.1 and Btau 4.6.1, respectively. Among the SNPs, the 46,301 (UMD 3.1) and 28,613 SNPs (Btau 4.6.1) that were identified as Hanwoo-specific SNPs were included in the functional genes that may be involved in the mechanisms of milk production, tenderness, juiciness, marbling of Hanwoo beef and yellow hair. Most of the Hanwoo-specific SNPs were identified in the promoter region, suggesting that the SNPs influence differential expression of the regulated genes relative to the relevant traits. In particular, the non-synonymous (ns) SNPs found in CORIN, which is a negative regulator of Agouti, might be a causal variant to determine yellow hair of Hanwoo. Our results will provide abundant genetic sources of variation to characterize Hanwoo genetics and for subsequent breeding.

Overexpression of Panax ginseng sesquiterpene synthase gene confers tolerance against Pseudomonas syringae pv. tomato in Arabidopsis thaliana.

Sesquiterpenes are an abundant group belonging to the terpenoid family, with a C15 structure comprise of three isoprene units. Many sesquiterpenes are volatile compounds and it act as chemical messenger in plant signalling, particularly in the defense mechanism against biotic and abiotic stresses. Panax ginseng Meyer is important medicinal herbs with various reported pharmacological efficacies in which its triterpenoid saponins, called ginsenosides, were mostly studied. However, there have been few studies on volatile sesquiterpenes compounds regulation on P. ginseng. As slow-growing perennial plant, P. ginseng received many kind of stresses during its cultivation. The pathogen attack is one of the most devastated perturbation for ginseng yield. Thus, we aimed to analyze P. ginseng STS gene (PgSTS) expressions in ginseng organs as well as mono-, sesquiterpenes contents from ginseng seedlings treated with elicitors. qRT-PCR and GC-MS analysis showed that two elicitors- salicylic acid (SA) and methyl jasmonate (MeJA) triggered PgSTS expression at different time points and significantly induced mono-, sesquiterpene yield. Overexpression of PgSTS in Arabidopsis also induced high terpene content and conferred tolerance against Pseudomonas syringae pv. tomato infection. These results suggested that PgSTS transcripts are involved in terpenoid biosynthesis in response to environmental stress mediated by MeJA and SA elicitors; thus, generate tolerance against pathogen attack.

Synthesis and pharmacokinetic characterization of a pH-sensitive polyethylene glycol ginsenoside CK (PEG-CK) conjugate.

The ginsenosides in Panax ginseng have vast structural and pharmacological efficacies. We covalently conjugated polyethylene glycol on the surface of CK (PEG-CK) through an acid-labile ester-linkage that showed increased solubility of CK. HPLC analysis showed that the release of CK was enhanced at acidic pH 5, whereas it was dramatically decreased at physiological pH 7.4. This might enhance the efficacy of CK.

In silico and in vitro inhibition of host-based viral entry targets and cytokine storm in COVID-19 by ginsenoside compound K.

SARS-CoV-2 is a novel coronavirus that emerged as an epidemic, causing a respiratory disease with multiple severe symptoms and deadly consequences. ACE-2 and TMPRSS2 play crucial and synergistic roles in the membrane fusion and viral entry of SARS-CoV-2 (COVID-19). The spike (S) protein of SARS-CoV-2 binds to the ACE-2 receptor for viral entry, while TMPRSS2 proteolytically cleaves the S protein into S1 and S2 subunits, promoting membrane fusion. Therefore, ACE-2 and TMPRSS2 are potential drug targets for treating COVID-19, and their inhibition is a promising strategy for treatment and prevention. This study proposes that ginsenoside compound K (G-CK), a triterpenoid saponin abundant in Panax Ginseng, a dietary and medicinal herb highly consumed in Korea and China, effectively binds to and inhibits ACE-2 and TMPRSS2 expression. We initially conducted an in-silico evaluation where G-CK showed a high affinity for the binding sites of the two target proteins of SARS-CoV-2. Additionally, we evaluated the stability of G-CK using molecular dynamics (MD) simulations for 100 ns, followed by MM-PBSA calculations. The MD simulations and free energy calculations revealed that G-CK has stable and favorable energies, leading to strong binding with the targets. Furthermore, G-CK suppressed ACE2 and TMPRSS2 mRNA expression in A549, Caco-2, and MCF7 cells at a concentration of 12.5 µg/mL and in LPS-induced RAW 264.7 cells at a concentration of 6.5 µg/mL, without significant cytotoxicity.ACE2 and TMPRSS2 expression were significantly lower in A549 and RAW 264.7 cells following G-CK treatment. These findings suggest that G-CK may evolve as a promising therapeutic against COVID-19.

SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.

Sortase enzymes are cysteine transpeptidases that embellish the surface of Gram-positive bacteria with various proteins thereby allowing these microorganisms to interact with their neighboring environment. It is known that several of their substrates can cause pathological implications, so researchers have focused on the development of sortase inhibitors. Currently, six different classes of sortases (A-F) are recognized. However, with the extensive application of bacterial genome sequencing projects, the number of potential sortases in the public databases has exploded, presenting considerable challenges in annotating these sequences. It is very laborious and time-consuming to characterize these sortase classes experimentally. Therefore, this study developed the first machine-learning-based two-layer predictor called SortPred, where the first layer predicts the sortase from the given sequence and the second layer predicts their class from the predicted sortase. To develop SortPred, we constructed an original benchmarking dataset and investigated 31 feature descriptors, primarily on five feature encoding algorithms. Afterward, each of these descriptors were trained using a random forest classifier and their robustness was evaluated with an independent dataset. Finally, we selected the final model independently for both layers depending on the performance consistency between cross-validation and independent evaluation. SortPred is expected to be an effective tool for identifying bacterial sortases, which in turn may aid in designing sortase inhibitors and exploring their functions. The SortPred webserver and a standalone version are freely accessible at: https://procarb.org/sortpred.

Immune-enhancing effects of postbiotic produced by Bacillus velezensis Kh2-2 isolated from Korea Foods.

Postbiotics defined as soluble factors (products or metabolic byproducts) that are released after bacterial lysis or secreted by live bacteria, have attracted considerable attention because of their long shelf life, safety, and beneficial effects. In this study, we investigated the immune-enhancing activities of squid jeotgal (a traditional Korean fermented seafood)-derived Bacillus velezensis Kh2-2 (Kh2-2) postbiotics in vitro, ex vivo, and in vivo. Cell lysates of four Bacillus species were prepared by sonication. In particular, Kh2-2 lysates induced NO production by upregulating iNOS expression in RAW264.7 cells compared with the lysates of B. subtilis Kh2-1, B. vallismortis Kh8-3, and B. amyloliquefaciens Kh3-1. Furthermore, Kh2-2 lysates stimulated immune activation of macrophages by upregulating the NF-κB and MAPK signaling pathways and promoting immune-related cytokine secretion. In the ex vivo study, Kh2-2 lysates stimulated proliferation and polarized Th1 response by inducing the production of IL-2 and IFN-γ and inhibiting IL-10 expression in splenocytes. The in vivo immune-enhancing effects of Kh2-2 lysates and Kh2-2 were further evaluated using a cyclophosphamide (CTX)-induced immunosuppression mouse model. The results showed that oral administration of Kh2-2 lysates improved CTX-induced immunosuppression by enhancing innate and adaptive immunity, stimulating immune-related cytokine secretion, and modulating gut microbiota dysbiosis in mice. Thus, we concluded that Kh2-2 lysates have potential as a functional material for postbiotics with immune-enhancing effects.

Transcriptome profiling and insilico analysis of Gynostemma pentaphyllum using a next generation sequencer.

Gynosaponins (Gypenosides) are major phyto-chemicals in Gynostemma pentaphyllum (Thunb.), with similarities to the ginsenosides present in Panax ginseng. Gynosaponins are classified as terpenoid compounds. In G. pentaphyllum, 25% of the total gynosaponins are similar to ginsenosides. In this study, we analyzed the transcriptional levels of the G. pentaphyllum genome to identify secondary metabolite genes. The complete transcriptomes for the roots and leaves were obtained using a GS-FLX pyro-sequencer. In total, we obtained 265,340 and all reads were well annotated according to biological databases. Using insilico analysis, 84% of sequence were well annotated and we obtained most of the secondary metabolite genes that represent mono-, di-, tri- and sesquiterpenoids. From our EST, most of the terpenoid genes were noted, among those few similar genes were studied in P. ginseng and these transcripts will help to characterize more triterpenoid genes in G. pentaphyllum. Also help to compare P. ginseng and G. pentaphyllum at transcriptome level.

Screening and optimization of pectin lyase and polygalacturonase activity from ginseng pathogen Cylindrocarpon Destructans.

Cylindrocarpon destructans isolated from ginseng field was found to produce pectinolytic enzymes. A Taguchi's orthogonal array experimental design was applied to optimize the preliminary production of polygalacturonase (PG) and pectin lyase (PL) using submerged culture condition. This method was applied to evaluate the significant parameters for the production of enzymes. The process variables were pH, pectin concentration, incubation time and temperature. Optimization of process parameters resulted in high levels of enzyme (PG and PL) production after ten days of incubation at a pH of 5.0 at 25°C in the presence of 1.5% pectin. Among different nitrogen sources, urea and peptone showed high production of PG and PL, respectively. The enzyme production and mycelial growth seems to have direct influence on the culture conditions; therefore, at stationary state high enzyme production and mycelial growth were obtained than agitation state. Along with this, optimization of enzyme activity was also determined using various physiological parameters like, temperature, incubation time and pH. Taguchi's data was also analyzed using one step ANOVA statistical method.

Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus.

Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

Genome-Wide Differential Methylation Profiles from Two Terpene-Rich Medicinal Plant Extracts Administered in Osteoarthritis Rats.

Extracts from the plants Phlomis umbrosa and Dipsacus asperoides-which are widely used in Korean and Chinese traditional medicine to treat osteoarthritis and other bone diseases-were used to treat experimental osteoarthritis (OA) rats. Genome-wide differential methylation regions (DMRs) of these medicinal-plant-treated rats were profiled as therapeutic evidence associated with traditional medicine, and they need to be investigated further using detailed molecular research to extrapolate traditional practices to modern medicine. In total, 49 protein-encoding genes whose expression is differentially regulated during disease progression and recovery have been discovered via systematic bioinformatic analysis and have been approved/proposed as druggable targets for various bone diseases by the US food and drug administration. Genes encoding proteins involved in the PI3K/AKT pathway were found to be enriched, likely as this pathway plays a crucial role during OA progression as well as during the recovery process after treatment with the aforementioned plant extracts. The four sub-networks of PI3K/AKT were highly regulated by these plant extracts. Overall, 29 genes were seen in level 2 (51-75%) DMRs and were correlated highly with OA pathogenesis. Here, we propose that these genes could serve as targets to study OA; moreover, the iridoid and triterpenoid phytochemicals obtained from these two plants may serve as potential therapeutic agents.

Till 2018: a survey of biomolecular sequences in genus Panax.

Ginseng is popularly known to be the king of ancient medicines and is used widely in most of the traditional medicinal compositions due to its various pharmaceutical properties. Numerous studies are being focused on this plant's curative effects to discover their potential health benefits in most human diseases, including cancer- the most life-threatening disease worldwide. Modern pharmacological research has focused mainly on ginsenosides, the major bioactive compounds of ginseng, because of their multiple therapeutic applications. Various issues on ginseng plant development, physiological processes, and agricultural issues have also been studied widely through state-of-the-art, high-throughput sequencing technologies. Since the beginning of the 21st century, the number of publications on ginseng has rapidly increased, with a recent count of more than 6,000 articles and reviews focusing notably on ginseng. Owing to the implementation of various technologies and continuous efforts, the ginseng plant genomes have been decoded effectively in recent years. Therefore, this review focuses mainly on the cellular biomolecular sequences in ginseng plants from the perspective of the central molecular dogma, with an emphasis on genomes, transcriptomes, and proteomes, together with a few other related studies.

The transcriptome data from the leaves of four Papaver species captured at the plant's three developmental life cycles.

The plants in the Papaver genus are widely known as Poppies, which is used for ornamental and medicinal purposes, to utilize its plants derived alkaloids and attractive flowers. From this genus, we have sequenced the transcriptomes of four species's (Papaver rhoeas (two cultivar), Papaver nudicaule (five cultivar), Papaver fauriei, and Papaver somniferum) leaves at three developmental stages (i.e., leaf rosette (30 days), elongation and branching (60 days), and blossom and seed formations (90 days)), to elucidate the secondary metabolite biosynthesis gene expression profiles at respective plant stages.

Identification of transcriptome-wide, nut weight-associated SNPs in Castanea crenata.

Nut weight is one of the most important traits that can affect a chestnut grower's returns. Due to the long juvenile phase of chestnut trees, the selection of desired characteristics at early developmental stages represents a major challenge for chestnut breeding. In this study, we identified single nucleotide polymorphisms (SNPs) in transcriptomic regions, which were significantly associated with nut weight in chestnuts (Castanea crenata), using a genome-wide association study (GWAS). RNA-sequencing (RNA-seq) data were generated from large and small nut-bearing trees, using an Illumina HiSeq. 2000 system, and 3,271,142 SNPs were identified. A total of 21 putative SNPs were significantly associated with chestnut weight (false discovery rate [FDR] < 10-5), based on further analyses. We also applied five machine learning (ML) algorithms, support vector machine (SVM), C5.0, k-nearest neighbour (k-NN), partial least squares (PLS), and random forest (RF), using the 21 SNPs to predict the nut weights of a second population. The average accuracy of the ML algorithms for the prediction of chestnut weights was greater than 68%. Taken together, we suggest that these SNPs have the potential to be used during marker-assisted selection to facilitate the breeding of large chestnut-bearing varieties.

Expression of Heat Shock Proteins in Thermally Challenged Pacific Abalone Haliotis discus hannai.

Summer mortality, caused by thermal conditions, is the biggest threat to abalone aquaculture production industries. Various measures have been taken to mitigate this issue by adjusting the environment; however, the cellular processes of Pacific abalone (Haliotis discus hannai) have been overlooked due to the paucity of genetic information. The draft genome of H. discus hannai has recently been reported, prompting exploration of the genes responsible for thermal regulation in Pacific abalone. In this study, 413 proteins were systematically annotated as members of the heat shock protein (HSP) super families, and among them 26 HSP genes from four Pacific abalone tissues (hemocytes, gill, mantle, and muscle) were differentially expressed under cold and heat stress conditions. The co-expression network revealed that HSP expression patterns were tissue-specific and similar to those of other shellfish inhabiting intertidal zones. Finally, representative HSPs were selected at random and their expression patterns were identified by RNA sequencing and validated by qRT-PCR to assess expression significance. The HSPs expressed in hemocytes were highly similar in both analyses, suggesting that hemocytes could be more reliable samples for validating thermal condition markers compared to other tissues.