The Thai reference exome (T-REx) variant database.

To maximize the potential of genomics in medicine, it is essential to establish databases of genomic variants for ethno-geographic groups that can be used for filtering and prioritizing candidate pathogenic variants. Populations with non-European ancestry are poorly represented among current genomic variant databases. Here, we report the first high-density survey of genomic variants for the Thai population, the Thai Reference Exome (T-REx) variant database. T-REx comprises exome sequencing data of 1092 unrelated Thai individuals. The targeted exome regions common among four capture platforms cover 30.04 Mbp on autosomes and chromosome X. 345 681 short variants (18.27% of which are novel) and 34 907 copy number variations were found. Principal component analysis on 38 469 single nucleotide variants present worldwide showed that the Thai population is most genetically similar to East and Southeast Asian populations. Moreover, unsupervised clustering revealed six Thai subpopulations consistent with the evidence of gene flow from neighboring populations. The prevalence of common pathogenic variants in T-REx was investigated in detail, which revealed subpopulation-specific patterns, in particular variants associated with erythrocyte disorders such as the HbE variant in HBB and the Viangchan variant in G6PD. T-REx serves as a pivotal addition to the current databases for genomic medicine.

Four novel mutations of FAM20A in amelogenesis imperfecta type IG and review of literature for its genotype and phenotype spectra.

Amelogenesis imperfecta type IG (AI1G) is caused by mutations in FAM20A. Genotypic and phenotypic features of AI1G are diverse and their full spectra remain to be characterized. The aim of this study was to identify and summarize variants in FAM20A in a broad population of patients with AI1G. We identified a Thai female (Pt-1) and a Saudi male (Pt-2) affected with AI1G. Both had hypoplastic enamel, gingival hyperplasia, and intrapulpal calcification. Pt-1 also had rapidly progressive embedding of unerupted teeth, early eruption of permanent teeth, and spontaneous dental infection. Uniquely, Pt-2 had all permanent teeth erupted which was uncommon in AI1G patients. Whole exome sequencing (WES) identified that Pt-1 was heterozygous for FAM20A, c.758A > G (p.Tyr253Cys), inherited from her father. The mutation on maternal allele was not detected by WES. Pt-2 possessed compound heterozygous mutations, c.1248dupG (p.Phe417Valfs*7); c.1081C > T (p.Arg361Cys) in FAM20A. Array comparative genomic hybridization (aCGH), cDNA sequencing, and whole genome sequencing successfully identified 7531 bp deletion on Pt-1's maternal allele. This was the largest FAM20A deletion ever found. A review of all 70 patients from 50 independent families with AI1G (including two families in this study) showed that the penetrance of hypoplastic enamel and gingival hyperplasia was complete. Unerupted permanent teeth were found in all 70 patients except Pt-2. Exons 1 and 11 were mutation-prone. Most mutations were frameshift. Certain variants showed founder effect. To conclude, this study reviews and expands phenotypic and genotypic spectra of AI1G. A large deletion missed by WES can be detected by WGS. Hypoplastic enamel, gingival hyperplasia, and unerupted permanent teeth prompt genetic testing of FAM20A. Screening of nephrocalcinosis, early removal of embedded teeth, and monitoring of dental infection are recommended.

Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations.

BACKGROUND: The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology. Some previous investigations conducted the study by applying feature-based models; others have presented deep-learning-based models such as convolutional and recurrent neural networks used with the shortest dependency paths (SDPs). Although SDPs contain valuable and concise information, some parts of crucial information that is required to define bacterial location relationships are often neglected. Moreover, the traditional word-embedding used in previous studies may suffer from word ambiguation across linguistic contexts. RESULTS: Here, we present a deep learning model for biomedical RE. The model incorporates feature combinations of SDPs and full sentences with various attention mechanisms. We also used pre-trained contextual representations based on domain-specific vocabularies. To assess the model's robustness, we introduced a mean F1 score on many models using different random seeds. The experiments were conducted on the standard BB corpus in BioNLP-ST'16. Our experimental results revealed that the model performed better (in terms of both maximum and average F1 scores; 60.77% and 57.63%, respectively) compared with other existing models. CONCLUSIONS: We demonstrated that our proposed contributions to this task can be used to extract rich lexical, syntactic, and semantic features that effectively boost the model's performance. Moreover, we analyzed the trade-off between precision and recall to choose the proper cut-off to use in real-world applications.

Identification and evaluation of novel anchoring proteins for cell surface display on Saccharomyces cerevisiae.

The development of arming yeast strains as whole-cell biocatalysts involves a selection of effective anchoring proteins to display enzymes and proteins on yeast cell surface. To screen for novel anchoring proteins with improved efficiency, a bioinformatics pipeline for the identification of glycosylphosphatidylinositol-anchored cell wall proteins (GPI-CWPs) suitable for attaching passenger proteins to the cell surface of Saccharomyces cerevisiae has been developed. Here, the C-terminal sequences (CTSs) of putative GPI-CWPs were selected based on the criteria that the sequence must contain a serine/threonine-rich (S/T) region of at least 30% S/T content, a total threonine content of at least 10%, a continuous S/T stretch of at least 130 amino acids in length, and a continuous T-rich region of at least 10 amino acids in length. Of the predicted 790 proteins, 37 putative GPI-CWPs were selected from different yeast and fungal species to be evaluated for their performance in displaying yeast-enhanced green fluorescent protein and ß-glucosidase enzyme. This led to the identification of five novel anchoring proteins with higher performance compared to α-agglutinin used as benchmark. In particular, the CTS of uncharacterized protein in Kluyveromyces lactis, namely 6_Kl, is the most efficient anchoring protein of the group. The CTS of 6_Kl protein provided a ß-glucosidase activity of up to 23.5 U/g cell dry weight, which is 2.8 times higher than that of the CTS of α-agglutinin. These identified CTSs could be potential novel anchoring protein candidates for construction of efficient arming yeasts for biotechnology applications in the future.

A genome scan of diversifying selection in Ophiocordyceps zombie-ant fungi suggests a role for enterotoxins in co-evolution and host specificity.

Identification of the genes underlying adaptation sheds light on the biological functions targeted by natural selection. Searches for footprints of positive selection, in the form of rapid amino acid substitutions, and the identification of species-specific genes have proved to be powerful approaches to identifying the genes involved in host specialization in plant-pathogenic fungi. We used an evolutionary comparative genomic approach to identify genes underlying host adaptation in the ant-infecting genus Ophiocordyceps, which manipulates ant behaviour. A comparison of the predicted genes in the genomes of species from three species complexes-O. unilateralis, O. australis and O. subramanianii-revealed an enrichment in pathogenesis-associated functions, including heat-labile enterotoxins, among species-specific genes. Furthermore, these genes were overrepresented among those displaying significant footprints of positive selection. Other categories of genes suspected to be important for virulence and pathogenicity in entomopathogenic fungi (e.g., chitinases, lipases, proteases, core secondary metabolism genes) were much less represented, although a few candidate genes were found to evolve under positive selection. An analysis including orthologs from other entomopathogenic fungi in a broader context showed that positive selection on enterotoxins was specific to the ant-infecting genus Ophiocordyceps. Together with previous studies reporting the overexpression of an enterotoxin during behavioural manipulation in diseased ants, our findings suggest that heat-labile enterotoxins are important effectors in host adaptation and co-evolution in the Ophiocordyceps entomopathogenic fungi.

Insights from the genome of Ophiocordyceps polyrhachis-furcata to pathogenicity and host specificity in insect fungi.

BACKGROUND: Ophiocordyceps unilateralis is an outstanding insect fungus for its biology to manipulate host ants' behavior and for its extreme host-specificity. Through the sequencing and annotation of Ophiocordyceps polyrhachis-furcata, a species in the O. unilateralis species complex specific to the ant Polyrhachis furcata, comparative analyses on genes involved in pathogenicity and virulence between this fungus and other fungi were undertaken in order to gain insights into its biology and the emergence of host specificity. RESULTS: O. polyrhachis-furcata possesses various genes implicated in pathogenicity and virulence common with other fungi. Overall, this fungus possesses protein-coding genes similar to those found on other insect fungi with available genomic resources (Beauveria bassiana, Metarhizium robertsii (formerly classified as M. anisopliae s.l.), Metarhizium acridum, Cordyceps militaris, Ophiocordyceps sinensis). Comparative analyses in regard of the host ranges of insect fungi showed a tendency toward contractions of various gene families for narrow host-range species, including cuticle-degrading genes (proteases, carbohydrate esterases) and some families of pathogen-host interaction (PHI) genes. For many families of genes, O. polyrhachis-furcata had the least number of genes found; some genes commonly found in other insect fungi are even absent (e.g. Class 1 hydrophobin). However, there are expansions of genes involved in 1) the production of bacterial-like toxins in O. polyrhachis-furcata, compared with other entomopathogenic fungi, and 2) retrotransposable elements. CONCLUSIONS: The gain and loss of gene families helps us understand how fungal pathogenicity in insect hosts evolved. The loss of various genes involved throughout the pathogenesis for O. unilateralis would result in a reduced capacity to exploit larger ranges of hosts and therefore in the different level of host specificity, while the expansions of other gene families suggest an adaptation to particular environments with unexpected strategies like oral toxicity, through the production of bacterial-like toxins, or sophisticated mechanisms underlying pathogenicity through retrotransposons.

Potential anti-Pythium insidiosum therapeutics identified through screening of agricultural fungicides.

Pythiosis is a life-threatening infectious disease caused by the oomycete Pythium insidiosum. Clinical manifestations of pythiosis include an eye, blood vessel, skin, or gastrointestinal tract infection. Pythiosis has been increasingly reported worldwide, with an overall mortality rate of 28%. Radical surgery is required to save patients' lives due to the limited efficacy of antimicrobial drugs. Effective medical treatments are urgently needed for pythiosis. This study aims to find anti-P. insidiosum agents by screening 17 agricultural fungicides that inhibit plant-pathogenic oomycetes and validating their efficacy and safety. Cyazofamid outperformed other fungicides as it can potently inhibit genetically diverse P. insidiosum isolates while exhibiting minimal cellular toxicities. The calculated therapeutic scores determined that the concentration of cyazofamid causing significant cellular toxicities was eight times greater than the concentration of the drug effectively inhibiting P. insidiosum. Furthermore, other studies showed that cyazofamid exhibits low-to-moderate toxicities in animals. The mechanism of cyazofamid action is likely the inhibition of cytochrome b, an essential component in ATP synthesis. Molecular docking and dynamic analyses depicted a stable binding of cyazofamid to the Qi site of the P. insidiosum's cytochrome b orthologous protein. In conclusion, our search for an effective anti-P. insidiosum drug indicated that cyazofamid is a promising candidate for treating pythiosis. With its high efficacy and low toxicity, cyazofamid is a potential chemical for treating pythiosis, reducing the need for radical surgeries, and improving recovery rates. Our findings could pave the way for the development of new and effective treatments for pythiosis.IMPORTANCEPythiosis is a severe infection caused by Pythium insidiosum. The disease is prevalent in tropical/subtropical regions. This infectious condition is challenging to treat with antifungal drugs and often requires surgical removal of the infected tissue. Pythiosis can be fatal if not treated promptly. There is a need for a new treatment that effectively inhibits P. insidiosum. This study screened 17 agricultural fungicides that target plant-pathogenic oomycetes and found that cyazofamid was the most potent in inhibiting P. insidiosum. Cyazofamid showed low toxicity to mammalian cells and high affinity to the P. insidiosum's cytochrome b, which is involved in energy production. Cyazofamid could be a promising candidate for the treatment of pythiosis, as it could reduce the need for surgery and improve the survival rate of patients. This study provides valuable insights into the biology and drug susceptibility of P. insidiosum and opens new avenues for developing effective therapies for pythiosis.

iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction.

Artificial intelligence has become more prevalent in broad fields, including drug discovery, in which the process is costly and time-consuming when conducted through wet experiments. As a result, drug repurposing, which tries to utilize approved and low-risk drugs for a new purpose, becomes more attractive. However, screening candidates from many drugs for specific protein targets is still expensive and tedious. This study aims to leverage computational resources to aid drug discovery by utilizing drug-protein interaction data and estimating their interaction strength, so-called binding affinity. Our estimation approach addresses multiple challenges encountered in the field. First, we employed a graph-based deep learning technique to overcome the limitations of drug compounds represented in string format by incorporating background knowledge of node and edge information as separate multi-dimensional features. Second, we tackled the complexities associated with extracting the representation and structure of proteins by utilizing a pre-trained model for feature extraction. Also, we employed graph operations over the 1D representation of a protein sequence to overcome the fixed-length problem typically encountered in language model tasks. In addition, we conducted a comparative analysis with a baseline model that creates a protein graph from a contact map prediction model, giving valuable insights into the performance and effectiveness of our proposed method. We evaluated the performance of our model using the same benchmark datasets with a variety of matrices as other previous work, and the results show that our model achieved the best prediction results while requiring no contact map information compared to other graph-based methods.

Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets.

Drug discovery is a process that finds new potential drug candidates for curing diseases and is also vital to improving the wellness of people. Enhancing deep learning approaches, e.g., molecular generation models, increases the drug discovery process's efficiency. However, there is a problem in this field in creating drug candidates with desired properties such as the quantitative estimate of druglikeness (QED), synthetic accessibility (SA), and binding affinity (BA), and there is a challenge for training a generative model for specific protein targets that has less pharmaceutical data. In this research, we present Mol-Zero-GAN, a framework that aims to solve the problem based on Bayesian optimization (BO) to find the model optimal weights' singular values, factorized by singular value decomposition, and generate drug candidates with desired properties with no additional data. The proposed framework can produce drugs with the desired properties on protein targets of interest by optimizing the model's weights. Our framework outperforms the state-of-the-art methods sharing the same objectives. Mol-Zero-GAN is publicly available at https://github.com/cucpbioinfo/Mol-Zero-GAN.

STRategy: A support system for collecting and analyzing next-generation sequencing data of short tandem repeats for forensic science.

Short tandem repeats (STRs) are short repeated sequences commonly found in the human genome and valuable in forensic science, used for human identity and relatedness markers. Next-generation sequencing (NGS) technologies, e.g., ForenSeq Signature Prep, can sequence STRs, inferring length-based alleles and single nucleotide polymorphisms (SNPs) and providing valuable insights into population and sub-population structures. Despite the potential benefits of NGS for STRs, no open-source software platform integrates the collection, management, and analysis of STR data from NGS into one place. Users must use multiple programs to process their STR data and then collect the results into a separate database or a file system folder. Moreover, analyzing repeat structures (STR repeat motifs) may require learning multiple software tools, making the process inefficient and cumbersome. To address this gap, we introduce the STRategy, a standalone web-based application supporting essential STR data management and analysis capabilities. The STRategy allows users to collect their data into its database, automatically calculates forensic parameters, and visualizes the analyzed data in various forms. Users can search the database using different options, such as by profile, loci, and genotypes, with and without a specific test kit. Moreover, users can also find the nucleotide variants of a locus among the samples. We designed the STRategy for internal use in a laboratory or an organization. Hence, our system includes role-based access control that allows users to search for or access specific data based on their responsibilities. The administrator role can customize the system, for example, configure maps according to the samples' geographic data, and manage reference STR repeat motifs. A laboratory or an organization can download and install a copy of STRategy on their local system using Docker, as described in https://github.com/cucpbioinfo/STRategy. In summary, the STRategy is an end-to-end system that provides users with a database to collect the analyzed STR data from NGS, the dynamic analyses of forensic parameters, and the variants of STR patterns according to the newly added samples, which are then explorable via various search options and visualizations. The system is helpful for both forensic investigations and forensic genetics.

ChemEx: information extraction system for chemical data curation.

BACKGROUND: Manual chemical data curation from publications is error-prone, time consuming, and hard to maintain up-to-date data sets. Automatic information extraction can be used as a tool to reduce these problems. Since chemical structures usually described in images, information extraction needs to combine structure image recognition and text mining together. RESULTS: We have developed ChemEx, a chemical information extraction system. ChemEx processes both text and images in publications. Text annotator is able to extract compound, organism, and assay entities from text content while structure image recognition enables translation of chemical raster images to machine readable format. A user can view annotated text along with summarized information of compounds, organism that produces those compounds, and assay tests. CONCLUSIONS: ChemEx facilitates and speeds up chemical data curation by extracting compounds, organisms, and assays from a large collection of publications. The software and corpus can be downloaded from http://www.biotec.or.th/isl/ChemEx.

A computational tool for the design of live attenuated virus vaccine based on microRNA-mediated gene silencing.

BACKGROUND: The microRNA-based gene-silencing machinery has been recognized as a promising approach to control viral replication and used for improving safety for the live attenuated virus vaccines. The effective host microRNA response elements (MREs) have been incorporated into a virus sequence mainly based on the experimental trials for identifying both microRNA binding sites and effective mutations. The design of MREs for viral genomes or with multiple host microRNAs of interest, then, will be time and cost consuming. RESULTS: In this paper, we introduced a computational flow that could be used to design MREs of human microRNAs within Influenza A H1N1 virus gene segments. The main steps of the flow includes locating possible binding sites; MREs, of human microRNAs within the viral sequences using a miRNA target prediction tool (miranda), performing various mutations among mismatched binding positions, calculating the binding energy, score, identity, and the effects of changed physical properties of amino acids according to the changed bases in RNA level, and prioritizing the mutated binding sites. The top ranked MREs of human microRNA hsa-miR-93 is consistent with previous literature while other results waited to be experimentally verified. To make the computational flow easily accessible by virologists, we also developed MicroLive, a web server version of the MRE design flow together with the database of miranda-predicted MREs within gene sequences of seven RNA viruses including Influenza A, dengue, hepatitis C, measles, mumps, poliovirus, and rabies. Users may design MREs of specific human microRNAs for their input viral sequences using MRE design tool or optimize the miranda-predicted MREs of seven viruses available on the system. Also, users could design varied number of MREs for multiple human microRNAs to modulate the degree of live vaccine attenuation and reduce the likelihood of escape mutants. CONCLUSIONS: The computational design of MREs helps reduce time and cost for experimental trials. While the flow was demonstrated using human microRNAs and Influenza A H1N1 virus, it could be flexibly applied to other hosts (e.g., animals) and viruses of interest for constructing host-specific live attenuated vaccines. Also, it could be deployed for engineering tissue-specific oncolytic viruses in cancer virotherapeutics. The MicroLive web server is freely accessible at http://www.biotec.or.th/isl/microlive.

C-mii: a tool for plant miRNA and target identification.

BACKGROUND: MicroRNAs (miRNAs) have been known to play an important role in several biological processes in both animals and plants. Although several tools for miRNA and target identification are available, the number of tools tailored towards plants is limited, and those that are available have specific functionality, lack graphical user interfaces, and restrict the number of input sequences. Large-scale computational identifications of miRNAs and/or targets of several plants have been also reported. Their methods, however, are only described as flow diagrams, which require programming skills and the understanding of input and output of the connected programs to reproduce. RESULTS: To overcome these limitations and programming complexities, we proposed C-mii as a ready-made software package for both plant miRNA and target identification. C-mii was designed and implemented based on established computational steps and criteria derived from previous literature with the following distinguishing features. First, software is easy to install with all-in-one programs and packaged databases. Second, it comes with graphical user interfaces (GUIs) for ease of use. Users can identify plant miRNAs and targets via step-by-step execution, explore the detailed results from each step, filter the results according to proposed constraints in plant miRNA and target biogenesis, and export sequences and structures of interest. Third, it supplies bird's eye views of the identification results with infographics and grouping information. Fourth, in terms of functionality, it extends the standard computational steps of miRNA target identification with miRNA-target folding and GO annotation. Fifth, it provides helper functions for the update of pre-installed databases and automatic recovery. Finally, it supports multi-project and multi-thread management. CONCLUSIONS: C-mii constitutes the first complete software package with graphical user interfaces enabling computational identification of both plant miRNA genes and miRNA targets. With the provided functionalities, it can help accelerate the study of plant miRNAs and targets, especially for small and medium plant molecular labs without bioinformaticians. C-mii is freely available at http://www.biotec.or.th/isl/c-mii for both Windows and Ubuntu Linux platforms.

LinkinPath: from sequence to interconnected pathway.

SUMMARY: LinkinPath is a pathway mapping and analysis tool that enables users to explore and visualize the list of gene/protein sequences through various Flash-driven interactive web interfaces including KEGG pathway maps, functional composition maps (TreeMaps), molecular interaction/reaction networks and pathway-to-pathway networks. Users can submit single or multiple datasets of gene/protein sequences to LinkinPath to (i) determine the co-occurrence and co-absence of genes/proteins on animated KEGG pathway maps; (ii) compare functional compositions within and among the datasets using TreeMaps; (iii) analyze the statistically enriched pathways across the datasets; (iv) build the pathway-to-pathway networks for each dataset; (v) explore potential interaction/reaction paths between pathways; and (vi) identify common pathway-to-pathway networks across the datasets. AVAILABILITY: LinkinPath is freely available to all interested users at http://www.biotec.or.th/isl/linkinpath/.

Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-Coding RNA Identification.

Long non-coding RNAs (lncRNAs) play crucial roles in many biological processes and are implicated in several diseases. With the next-generation sequencing technologies, substantial unannotated transcripts have been discovered. Classifying unannotated transcripts using biological experiments are more time-consuming and expensive than computational approaches. Several tools are available for identifying long non-coding RNAs. These tools, however, did not explain the features in their tools that contributed to the prediction results. Here, we present Xlnc1DCNN, a tool for distinguishing long non-coding RNAs (lncRNAs) from protein-coding transcripts (PCTs) using a one-dimensional convolutional neural network with prediction explanations. The evaluation results of the human test set showed that Xlnc1DCNN outperformed other state-of-the-art tools in terms of accuracy and F1-score. The explanation results revealed that lncRNA transcripts were mainly identified as sequences with no conserved regions, short patterns with unknown functions, or only regions of transmembrane helices while protein-coding transcripts were mostly classified by conserved protein domains or families. The explanation results also conveyed the probably inconsistent annotations among the public databases, lncRNA transcripts which contain protein domains, protein families, or intrinsically disordered regions (IDRs). Xlnc1DCNN is freely available at https://github.com/cucpbioinfo/Xlnc1DCNN.

d-Omix: a mixer of generic protein domain analysis tools.

Domain combination provides important clues to the roles of protein domains in protein function, interaction and evolution. We have developed a web server d-Omix (a Mixer of Protein Domain Analysis Tools) aiming as a unified platform to analyze, compare and visualize protein data sets in various aspects of protein domain combinations. With InterProScan files for protein sets of interest provided by users, the server incorporates four services for domain analyses. First, it constructs protein phylogenetic tree based on a distance matrix calculated from protein domain architectures (DAs), allowing the comparison with a sequence-based tree. Second, it calculates and visualizes the versatility, abundance and co-presence of protein domains via a domain graph. Third, it compares the similarity of proteins based on DA alignment. Fourth, it builds a putative protein network derived from domain-domain interactions from DOMINE. Users may select a variety of input data files and flexibly choose domain search tools (e.g. hmmpfam, superfamily) for a specific analysis. Results from the d-Omix could be interactively explored and exported into various formats such as SVG, JPG, BMP and CSV. Users with only protein sequences could prepare an InterProScan file using a service provided by the server as well. The d-Omix web server is freely available at http://www.biotec.or.th/isl/Domix.

Norcycloartocarpin targets Akt and suppresses Akt-dependent survival and epithelial-mesenchymal transition in lung cancer cells.

In searching for novel targeted therapeutic agents for lung cancer treatment, norcycloartocarpin from Artocarpus gomezianus was reported in this study to promisingly interacted with Akt and exerted the apoptosis induction and epithelial-to-mesenchymal transition suppression. Selective cytotoxic profile of norcycloartocarpin was evidenced with approximately 2-fold higher IC50 in normal dermal papilla cells (DPCs) compared with human lung cancer A549, H460, H23, and H292 cells. We found that norcycloartocarpin suppressed anchorage-independent growth, cell migration, invasion, filopodia formation, and decreased EMT in a dose-dependent manner at 24 h, which were correlated with reduced protein levels of N-cadherin, Vimentin, Slug, p-FAK, p-Akt, as well as Cdc42. In addition, norcycloartocarpin activated apoptosis caspase cascade associating with restoration of p53, down-regulated Bcl-2 and augmented Bax in A549 and H460 cells. Interestingly, norcycloartocarpin showed potential inhibitory role on protein kinase B (Akt) the up-stream dominant molecule controlling EMT and apoptosis. Computational molecular docking analysis further confirmed that norcycloartocarpin has the best binding affinity of -12.52 kcal/mol with Akt protein at its critical active site. As Akt has recently recognized as an attractive molecular target for therapeutic approaches, these findings support its use as a plant-derived anticancer agent in cancer therapy.

Hydroquinone 5-O-Cinnamoyl Ester of Renieramycin M Suppresses Lung Cancer Stem Cells by Targeting Akt and Destabilizes c-Myc.

Cancer stem cells (CSCs) are distinct cancer populations with tumorigenic and self-renewal abilities. CSCs are drivers of cancer initiation, progression, therapeutic failure, and disease recurrence. Thereby, novel compounds targeting CSCs offer a promising way to control cancer. In this study, the hydroquinone 5-O-cinnamoyl ester of renieramycin M (CIN-RM) was demonstrated to suppress lung cancer CSCs. CIN-RM was toxic to lung cancer cells with a half-maximal inhibitory concentration of around 15 µM. CIN-RM suppressed CSCs by inhibiting colony and tumor spheroid formation. In addition, the CSC population was isolated and treated and the CSCs were dispatched in response to CIN-RM within 24 h. CIN-RM was shown to abolish cellular c-Myc, a central survival and stem cell regulatory protein, with the depletion of CSC markers and stem cell transcription factors ALDH1A1, Oct4, Nanog, and Sox2. For up-stream regulation, we found that CIN-RM significantly inhibited Akt and consequently decreased the pluripotent transcription factors. CIN-RM also inhibited mTOR, while slightly decreasing p-GSK3ß (Ser9) but rarely affected the protein kinase C (PKC) signal. Inhibiting Akt/mTOR induced ubiquitination of c-Myc and promoted degradation. The mechanism of how Akt regulates the stability of c-Myc was validated with the Akt inhibitor wortmannin. The computational analysis further confirmed the strong interaction between CIN-RM and the Akt protein with a binding affinity of -10.9 kcal/mol at its critical active site. Taken together, we utilized molecular experiments, the CSC phenotype, and molecular docking methods to reveal the novel suppressing the activity of this compound on CSCs to benefit CSC-targeted therapy for lung cancer treatment.

Artocarpin Targets Focal Adhesion Kinase-Dependent Epithelial to Mesenchymal Transition and Suppresses Migratory-Associated Integrins in Lung Cancer Cells.

Focal adhesion kinase (FAK) controls several cancer aggressive potentials of cell movement and dissemination. As epithelial-mesenchymal transition (EMT) and the migratory-associated integrins, known influencers of metastasis, have been found to be linked with FAK activity, this study unraveled the potential pharmacological effect of artocarpin in targeting FAK resulting in the suppression of EMT and migratory behaviors of lung cancer cells. Treatment with artocarpin was applied at concentrations of 0-10 µM, and the results showed non-cytotoxicity in lung cancer cell lines (A549 and H460), normal lung (BEAS-2B) cells and primary metastatic lung cancer cells (ELC12, ELC16, and ELC20). We also found that artocarpin (0-10 µM) had no effect on cell viability, proliferation, and migration in BEAS-2B cells. For metastasis-related approaches, artocarpin significantly inhibited cell migration, invasion, and filopodia formation. Artocarpin also dramatically suppressed anchorage-independent growth, cancer stem cell (CSC) spheroid formation, and viability of CSC-rich spheroids. For molecular targets of artocarpin action, computational molecular docking revealed that artocarpin had the best binding affinity of -8.0 kcal/mol with FAK protein. Consistently, FAK-downstream proteins, namely active Akt (phosphorylated Akt), active mTOR (phosphorylated mTOR), and Cdc42, and EMT marker and transcription factor (N-cadherin, Vimentin, and Slug), were found to be significantly depleted in response to artocarpin treatment. Furthermore, we found the decrease of Caveolin-1 (Cav-1) accompanied by the reduction of integrin-αν and integrin-ß3. Taken together, these findings support the anti-metastasis potentials of the compound to be further developed for cancer therapy.

inCNV: An Integrated Analysis Tool for Copy Number Variation on Whole Exome Sequencing.

The detection of copy number variations (CNVs) on whole-exome sequencing (WES) represents a cost-effective technique for the study of genetic variants. This approach, however, has encountered an obstacle with high false-positive rates due to biases from exome sequencing capture kits and GC contents. Although plenty of CNV detection tools have been developed, they do not perform well with all types of CNVs. In addition, most tools lack features of genetic annotation, CNV visualization, and flexible installation, requiring users to put much effort into CNV interpretation. Here, we present "inCNV," a web-based application that can accept multiple CNV-tool results, then integrate and prioritize them with user-friendly interfaces. This application helps users analyze the importance of called CNVs by generating CNV annotations from Ensembl, Database of Genomic Variants (DGV), ClinVar, and Online Mendelian Inheritance in Man (OMIM). Moreover, users can select and export CNVs of interest including their flanking sequences for primer design and experimental verification. We demonstrated how inCNV could help users filter and narrow down the called CNVs to a potentially novel CNV, a common CNV within a group of samples of the same disease, or a de novo CNV of a sample within the same family. Besides, we have provided in CNV as a docker image for ease of installation (https://github.com/saowwapark/inCNV).