Identification of Sexually Dimorphic Genes in Pectoral Fin as Molecular Markers for Assessing the Sex of Japanese Silver Eels (Anguilla japonica).

The Japanese eel (Anguilla japonica) is an important species in East Asian aquaculture. However, the production of seedlings for this purpose still depends on natural resources, as the commercial production of glass eels is not yet possible. Confusion about the sex of silver eels is one of the factors affecting the success rate of artificial maturation. This study sought to devise a harmless method to precisely assess the sex of silver eels. Partial pectoral fins were collected from females and males and the total RNA was extracted for transcriptomic analysis to identify sexually dimorphic genes as molecular markers for sex typing. An online database was constructed to integrate the annotations of transcripts and perform comparative transcriptome analysis. This analysis identified a total of 29 candidate sexually dimorphic genes. Ten were selected for a real-time quantitative polymerase chain reaction (RT-qPCR) to validate the transcriptomic data and evaluate their feasibility as markers. The transcriptomic analysis and RT-qPCR data implicated three potential markers (LOC111853410, kera, and dcn) in sex typing. The expression of LOC111853410 was higher in females than in males. In contrast, the expression of kera and dcn was higher in males than in females. The ΔCT values of three markers were analyzed to determine their inferred thresholds, which can be used to determine the sex of Japanese eels. The results suggested that if a silver eel had a pectoral fin with the pectoral fin having the ΔCT of LOC111853410 < 11.3, the ΔCT of kera > 11.4, or the ΔCT of dcn > 6.5 can be assessed it could be assessed as female. Males could be assessed by the ΔCT of LOC111853410 > 11.3, the ΔCT of kera < 11.4, or the ΔCT of dcn < 6.5 in their pectoral fins. The molecular functions of these markers and the biological significance of their differential expression require further exploration.

Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning.

Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates' anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly.

AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation.

Motivation: Antiviral peptides (AVPs) from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified AVPs and the advances in deep learning theory, it is reasonable to experiment with peptide drug design using in silico methods. Results: We collected the most up-to-date AVPs and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of AVPs in the positive training dataset and enable our deep learning convolutional neural network (CNN) model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community. Availability and implementation: AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/; codes and datasets for the peptide GAN and the AVP predictor CNN are available at https://github.com/lsbnb/amp_gan and https://github.com/LinTzuTang/AI4AVP_predictor. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

AI4AMP: an Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning.

Antimicrobial peptides (AMPs) are innate immune components that have recently stimulated considerable interest among drug developers due to their potential as antibiotic substitutes. AMPs are notable for their fundamental properties of microbial membrane structural interference and the biomedical applications of killing or suppressing microbes. New AMP candidates must be developed to oppose antibiotic resistance. However, the discovery of novel AMPs through wet-lab screening approaches is inefficient and expensive. The prediction model investigated in this study may help accelerate this process. We collected both the up-to-date AMP data set and unbiased negatives based on which the protein-encoding methods and deep learning model for AMPs were investigated. The external testing results indicated that our trained model achieved 90% precision, outperforming current methods. We implemented our model on a user-friendly web server, AI4AMP, to accurately predict the antimicrobial potential of a given protein sequence and perform proteome screening. IMPORTANCE Antimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitute for antibiotics. New candidates need to fight antibiotic resistance, while discovering novel AMPs through wet-lab screening approaches is inefficient and expensive. To accelerate the discovery of new AMPs, we both collected the up-to-date antimicrobial peptide data set and integrated the protein-encoding methods with a deep learning model. The trained model outperforms the current methods and is implemented into a user-friendly web server, AI4AMP, to accurately predict the antimicrobial properties of a given protein sequence and perform proteome screening. Author Video: An author video summary of this article is available.

EpiMOLAS: an intuitive web-based framework for genome-wide DNA methylation analysis.

BACKGROUND: DNA methylation is a crucial epigenomic mechanism in various biological processes. Using whole-genome bisulfite sequencing (WGBS) technology, methylated cytosine sites can be revealed at the single nucleotide level. However, the WGBS data analysis process is usually complicated and challenging. RESULTS: To alleviate the associated difficulties, we integrated the WGBS data processing steps and downstream analysis into a two-phase approach. First, we set up the required tools in Galaxy and developed workflows to calculate the methylation level from raw WGBS data and generate a methylation status summary, the mtable. This computation environment is wrapped into the Docker container image DocMethyl, which allows users to rapidly deploy an executable environment without tedious software installation and library dependency problems. Next, the mtable files were uploaded to the web server EpiMOLAS_web to link with the gene annotation databases that enable rapid data retrieval and analyses. CONCLUSION: To our knowledge, the EpiMOLAS framework, consisting of DocMethyl and EpiMOLAS_web, is the first approach to include containerization technology and a web-based system for WGBS data analysis from raw data processing to downstream analysis. EpiMOLAS will help users cope with their WGBS data and also conduct reproducible analyses of publicly available data, thereby gaining insights into the mechanisms underlying complex biological phenomenon. The Galaxy Docker image DocMethyl is available at https://hub.docker.com/r/lsbnb/docmethyl/. EpiMOLAS_web is publicly accessible at http://symbiosis.iis.sinica.edu.tw/epimolas/.

TEA: the epigenome platform for Arabidopsis methylome study.

BACKGROUND: Bisulfite sequencing (BS-seq) has become a standard technology to profile genome-wide DNA methylation at single-base resolution. It allows researchers to conduct genome-wise cytosine methylation analyses on issues about genomic imprinting, transcriptional regulation, cellular development and differentiation. One single data from a BS-Seq experiment is resolved into many features according to the sequence contexts, making methylome data analysis and data visualization a complex task. RESULTS: We developed a streamlined platform, TEA, for analyzing and visualizing data from whole-genome BS-Seq (WGBS) experiments conducted in the model plant Arabidopsis thaliana. To capture the essence of the genome methylation level and to meet the efficiency for running online, we introduce a straightforward method for measuring genome methylation in each sequence context by gene. The method is scripted in Java to process BS-Seq mapping results. Through a simple data uploading process, the TEA server deploys a web-based platform for deep analysis by linking data to an updated Arabidopsis annotation database and toolkits. CONCLUSIONS: TEA is an intuitive and efficient online platform for analyzing the Arabidopsis genomic DNA methylation landscape. It provides several ways to help users exploit WGBS data. TEA is freely accessible for academic users at: http://tea.iis.sinica.edu.tw .

The effect of red light and far-red light conditions on secondary metabolism in agarwood.

BACKGROUND: Agarwood, a heartwood derived from Aquilaria trees, is a valuable commodity that has seen prevalent use among many cultures. In particular, it is widely used in herbal medicine and many compounds in agarwood are known to exhibit medicinal properties. Although there exists much research into medicinal herbs and extraction of high value compounds, few have focused on increasing the quantity of target compounds through stimulation of its related pathways in this species. RESULTS: In this study, we observed that cucurbitacin yield can be increased through the use of different light conditions to stimulate related pathways and conducted three types of high-throughput sequencing experiments in order to study the effect of light conditions on secondary metabolism in agarwood. We constructed genome-wide profiles of RNA expression, small RNA, and DNA methylation under red light and far-red light conditions. With these profiles, we identified a set of small RNA which potentially regulates gene expression via the RNA-directed DNA methylation pathway. CONCLUSIONS: We demonstrate that light conditions can be used to stimulate pathways related to secondary metabolism, increasing the yield of cucurbitacins. The genome-wide expression and methylation profiles from our study provide insight into the effect of light on gene expression for secondary metabolism in agarwood and provide compelling new candidates towards the study of functional secondary metabolic components.

Sequencing and analysis of the transcriptome of the acorn worm Ptychodera flava, an indirect developing hemichordate.

Hemichordates are the sister group of echinoderms, and together they are closely related to chordates within the deuterostome lineage. Therefore, hemichordates represent an important animal group for the understanding of both the evolution of developmental mechanisms in deuterostome animals and the origin of chordates. Recently, the majority of studies investigating hemichordates have focused on the direct-developing enteropneust hemichordate Saccoglossus kowalevskii; few have focused on the indirect-developing hemichordates, partly because of the lack of extensive genomic resources in these animals. In this study, we report the sequencing and analysis of a transcriptome from an indirect-developing enteropneust hemichordate Ptychodera flava. We sequenced a mixed cDNA library from six developmental stages using the Roche GS FLX Titanium System to generate more than 879,000 reads. These reads were assembled into 17,990 contigs with an average length of 1316bp. We found that 60% of the assembled contigs, along with 28% of the unassembled singleton reads, had significant hits to sequences in the NCBI database by a BLASTx search, and we also annotated these sequences and obtained Gene Ontology (GO) terms for 6744 contigs and 5802 singletons. We further identified candidate P. flava transcripts corresponding to genes involved in major developmental signaling pathways, including the Wnt, Notch and TGF-ß signaling pathways. Using available genome/transcriptome datasets from the direct-developing hemichordate S. kowalevskii, the echinoderm Strongylocentrotus purpuratus and the chordate Branchiostoma floridae, we found that 90%, 80% and 73% of the annotated protein sequences in these respective species matched our P. flava transcriptome in a homology search. We also constructed a database for the P. flava transcriptome, and researchers can easily access this dataset online. Our dataset significantly increases the amount of available P. flava sequence data and can serve as a reference transcriptome for future studies using this species.

High-throughput profiling of influenza A virus hemagglutinin gene at single-nucleotide resolution.

Genetic research on influenza virus biology has been informed in large part by nucleotide variants present in seasonal or pandemic samples, or individual mutants generated in the laboratory, leaving a substantial part of the genome uncharacterized. Here, we have developed a single-nucleotide resolution genetic approach to interrogate the fitness effect of point mutations in 98% of the amino acid positions in the influenza A virus hemagglutinin (HA) gene. Our HA fitness map provides a reference to identify indispensable regions to aid in drug and vaccine design as targeting these regions will increase the genetic barrier for the emergence of escape mutations. This study offers a new platform for studying genome dynamics, structure-function relationships, virus-host interactions, and can further rational drug and vaccine design. Our approach can also be applied to any virus that can be genetically manipulated.

Lycopene inhibits the proliferation of androgen-dependent human prostate tumor cells through activation of PPARγ-LXRα-ABCA1 pathway.

The activation of nuclear receptors, peroxisome proliferator-activated receptor gamma (PPARγ) and liver X receptor alpha (LXRα), has been shown to inhibit the growth of prostate cancer cells. This study examined whether the anti-proliferative effect of lycopene on androgen-dependent human prostate cancer (LNCaP) cells involves the up-regulation of the expression of PPARγ and LXRα. As expected, lycopene treatment (2.5-10 µM) significantly inhibited the proliferation of LNCaP cells during incubation for 96 h. Lycopene significantly increased the protein and mRNA expression of PPARγ and LXRα at 24 and 48 h, while the increased in the expression of ATP-binding cassette transporter 1 (ABCA1) was only evident 96 h. In addition, lycopene significantly decreased cellular total cholesterol levels and increased apoA1 protein expression at 96 h. Incubation of LNCaP cells with lycopene (10 µM) in the presence (20 µM) of a specific antagonist of PPARγ (GW9662) and LXRα (GGPP) restored the proliferation of LNCaP cells to the control levels and significantly suppressed protein expression of PPARγ and LXRα as well as increased cellular total cholesterol levels. LXRα knockdown by siRNA against LXRα significantly enhanced the proliferation of LNCaP cells, whereas si-LXRα knockdown followed by incubation with lycopene (10 µM) restored the proliferation to the control level. The present study is the first to demonstrate that the anti-proliferative effect of lycopene on LNCaP cells involves the activation of the PPARγ-LXRα-ABCA1 pathway, leading to reduced cellular total cholesterol levels.

Inhibition of glutathione S-transferase M1 by new gabosine analogues is essential for overcoming cisplatin resistance in lung cancer cells.

A new class of human GST inhibitors has been identified via rational design approach; we report their discovery, synthesis, inhibitory activity, and synergetic effect in combination with cisplatin against A549 lung cancer cell line. The results of this effort show that the lead 4-O-decyl-gabosine D (24) has optimum synergetic effect in A549 human lung adenocarcinoma epithelial cell and that this activity involves inhibition of glutathione S-transferase M1, apparently consistent with siRNA-mediated knockdown of GSTM1 gene.

Isomalyngamide A, A-1 and their analogs suppress cancer cell migration in vitro.

Isomalyngamide A (1) and A-1 (2) were isolated from the Taiwanese Lyngbya majuscule and the latter structure was elucidated by a combination of NMR spectroscopic analysis and HRESIMS measurement. We report the isolation of isomalyngamide A (1), discovery of isomalyngamide A-1 (2) and their synthetic analogs (3-9), which are further demonstrated to have therapeutic potential against tumor cell migration at the level of nanomolar to micromolar ranges, perhaps, by inactivating the expression of p-FAK, FAK, p-Akt and Akt through ß1 integrin-mediated antimetastatic pathway.

HLA-B*1502-bound peptides: implications for the pathogenesis of carbamazepine-induced Stevens-Johnson syndrome.

BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) can involve MHC-restricted presentation of a drug or its metabolites for T-cell activation. HLA-B(*)1502 tightly associated with carbamazepine (CBZ) induced these conditions in a Han Chinese population. OBJECTIVE: We sought to identify HLA-B(*)1502-bound peptides that might be involved in CBZ-induced SJS/TEN. METHODS: Soluble HLA-B(*)1502 was used to identify bound peptides in the presence and absence of CBZ by using liquid chromatography-tandem mass spectrometry. Peptide-binding assays were performed to detect the specific interaction between the HLA molecule and the identified peptides. Mass spectra were compared to detect CBZ-modified peptides. RESULTS: We identified more than 145 peptides bound to HLA-B(*)1502. In 13 of 15 peptides examined, we functionally confirmed their specificity with binding assays. Preferable uses of these peptides at the anchoring residues P2 and P9 were similar to those observed in other HLA-B alleles in the Han Chinese population. However, the preferable use of serine residues at the nonanchoring position (P) 5, P6, P7, and P8 appeared to be unique for the B(*)1502 peptides. No specific CBZ-modified peptides were detected when we compared the mass spectra of peptides detected in the presence or absence of the drug. CONCLUSION: Noncovalent interaction between a drug and an HLA complex might contribute to cytotoxic T cell-mediated cell death in patients with SJS/TEN. CLINICAL IMPLICATIONS: An understanding of pharmacologic interaction of drugs with an HLA complex might lead to safer drugs that avoid SJS/TEN.