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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

Comparative transcriptome analyses of the third and fourth stage larvae of Anisakis simplex (Nematoda: Anisakidae).

We analyzed transcriptome profiles of Anisakis simplex (Nematoda: Anisakidae) 3rd (ASL3) and 4th larvae (ASL4) obtained by RNA-seq, to understand the molecular pathways linked to parasite survival and discover stage-enriched gene expressions. ASL3 were collected from chum salmon and ASL4 were obtained by in vitro culture. Whole transcriptome sequencing was conducted with Illumina sequencer, and de novo assembly was conducted. 47,179 and 41,934 genes were expressed in ASL3 and ASL4 transcriptomes. Of them, 17,633 were known and 29,546 were unmapped sequence for ASL3. 17,126 were known and 24,808 were unmapped sequence for ASL4. Polyubiquitins-related genes and collagen-related genes were the most abundantly expressed in ASL3 and ASL4. Mitochondrial enzyme-related genes were highly expressed both in ASL3 and ASL4. Among the transcripts, 675 were up-regulated in ASL3, while 1015 were up-regulated in ASL4. Several protease-related and protein biosynthesis-related genes were highly expressed in ASL3, all of which are thought to be crucial for invading host tissues. Collagen synthesis-related genes were highly expressed in ASL4, reflecting active biosynthesis of collagens during molting process. This information will extend our understanding of biology of the fish-borne zoonotic parasite A. simplex.

Identification of differentially expressed miRNAs and miRNA-targeted genes in bladder cancer.

BACKGROUND: Differentially expressed genes and their post-transcriptional regulator-microRNAs (miRNAs), are potential keys to pioneering cancer diagnosis and treatment. The aim of this study was to investigate how the miRNA-mRNA interactions might affect the tumorigenesis of bladder cancer (BC) and to identify specific miRNA and mRNA genetic markers in the two BC types: non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). RESULTS: We identified 227 genes that interacted with 54 miRNAs in NMIBC, and 14 genes that interacted with 10 miRNAs in MIBC. Based on this data, we found extracellular matrix-related genes are highly enriched in NMIBC while genes related to core nuclear division are highly enriched in MIBC. Furthermore, using a transcriptional regulatory element database, we found indirect regulatory transcription factors (TFs) for enriched genes could regulate tumorigenesis with or without miRNAs. MATERIALS AND METHODS: Tissue samples from 234 patients histologically diagnosed with BC and 83 individuals without BC were analyzed using microarray and next-generation sequencing technology, and we used different cut-offs to identify differentially expressed mRNAs and miRNAs in NMIBC and MIBC. The selected mRNAs and miRNAs were paired using validated target datasets and according to inverse expression relationships. MiRNA interacted genes were compared with the TF-regulating genes in BC. Meanwhile, pathway enrichment analysis was performed to identify the functions of selected miRNAs and genes. CONCLUSIONS: Identification of differential gene expression in specific tumor types could facilitate development of cancer diagnosis and aid in the early detection of BC.

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 .

Molecular signaling of ginsenosides Rb1, Rg1, and Rg3 and their mode of actions.

Ginseng has gained its popularity as an adaptogen since ancient days because of its triterpenoid saponins, known as ginsenosides. These triterpenoid saponins are unique and classified as protopanaxatriol and protopanaxadiol saponins based on their glycosylation patterns. They play many protective roles in humans and are under intense research as various groups continue to study their efficacy at the molecular level in various disorders. Ginsenosides Rb1 and Rg1 are the most abundant ginsenosides present in ginseng roots, and they confer the pharmacological properties of the plant, whereas ginsenoside Rg3 is abundantly present in Korean Red Ginseng preparation, which is highly known for its anticancer effects. These ginsenosides have a unique mode of action in modulating various signaling cascades and networks in different tissues. Their effect depends on the bioavailability and the physiological status of the cell. Mostly they amplify the response by stimulating phosphotidylinositol-4,5-bisphosphate 3-kinase/protein kinase B pathway, caspase-3/caspase-9-mediated apoptotic pathway, adenosine monophosphate-activated protein kinase, and nuclear factor kappa-light-chain-enhancer of activated B cells signaling. Furthermore, they trigger receptors such as estrogen receptor, glucocorticoid receptor, and N-methyl-d-aspartate receptor. This review critically evaluates the signaling pathways attenuated by ginsenosides Rb1, Rg1, and Rg3 in various tissues with emphasis on cancer, diabetes, cardiovascular diseases, and neurodegenerative disorders.