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Transposons are known to participate in tissue aging, but their effects on aged stem cells remain unclear. Here, we report that in the Drosophila ovarian germline stem cell (GSC) niche, aging-related reductions in expression of Piwi (a transposon silencer) derepress retrotransposons and cause GSC loss. Suppression of Piwi expression in the young niche mimics the aged niche, causing retrotransposon depression and coincident activation of Toll-mediated signaling, which promotes Glycogen synthase kinase 3 activity to degrade β-catenin. Disruption of β-catenin-E-cadherin-mediated GSC anchorage then results in GSC loss. Knocking down gypsy (a highly active retrotransposon) or toll, or inhibiting reverse transcription in the piwi-deficient niche, suppresses GSK3 activity and β-catenin degradation, restoring GSC-niche attachment. This retrotransposon-mediated impairment of aged stem cell maintenance may have relevance in many tissues, and could represent a viable therapeutic target for aging-related tissue degeneration., Retrotransposons are known to be involved in aging. Here the authors show that the retrotransposon silencer Piwi contributes to germline stem cell loss during aging in Drosophila.

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/.

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.

There are numerous means to improve the tilapia aquaculture industry, and one is to develop disease resistance through selective breeding using molecular markers. In this study, 11 disease-resistance-associated microsatellite markers including 3 markers linked to hamp2, 4 linked to hamp1, 1 linked to pgrn2, 2 linked to pgrn1, and 1 linked to piscidin 4 (TP4) genes were established for tilapia strains farmed in Taiwan after challenge with Streptococcus inae. The correlation analysis of genotypes and survival revealed a total of 55 genotypes related to survival by the chi-square and Z-test. Although fewer markers were found in B and N2 strains compared with A strain, they performed well in terms of disease resistance. It suggested that this may be due to the low potency of some genotypes and the combinatorial arrangement between them. Therefore, a predictive model was built by the genotypes of the parental generation and the mortality rate of different combinations was calculated. The results show the same trend of predicted mortality in the offspring of three new disease-resistant strains as in the challenge experiment. The present findings is a nonkilling method without requiring the selection by challenge with bacteria or viruses and might increase the possibility of utilization of selective breeding using SSR markers in farms.

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.

MotivationNew antiviral drugs are urgently needed because of emerging viral pathogens’ increasing severity and drug resistance. Antiviral peptides (AVPs) have multiple antiviral properties and are appealing candidates for antiviral drug development. We developed a sequence-based binary classifier to identify whether an unknown short peptide has AVP activity. We collected AVP sequence data from six existing databases. We used a generative adversarial network to augment the number of AVPs in the positive training dataset and allow our deep convolutional neural network model to train on more data.ResultsOur classifier achieved outstanding performance on the testing dataset compared with other state-of-the-art classifiers. We deployed our trained classifier on a user-friendly web server.Availability and implementationAI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/Contactcylin@iis.sinica.edu.twSupplementary informationSupplementary data is also available.

Hexabromocyclododecanes (HBCDs) are globally prevalent and persistent organic pollutants (POPs) listed by the Stockholm Convention in 2013. They have been detected in many environmental media from waterbodies to Plantae and even in the human body. Due to their highly bioaccumulative characterization, they pose an urgent public health issue. Here, we demonstrate that the indigenous microbial community in the agricultural soil in Taiwan could decompose HBCDs with no additional carbon source incentive. The degradation kinetics reached 0.173 day

Due to the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) can become ideal for next-generation antibiotics. This study trained a deep convolutional generative adversarial network (GAN) with known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them named GAN-pep 1∼8 were chosen to be synthesized for further experiments. Disk diffusion testing and minimum inhibitory concentration (MIC) determination were used to determine the antibacterial effects of the synthesized GAN-designed peptides. Seven out of the eight synthesized GAN-designed peptides showed antibacterial activities.Additionally, GAN-pep 3 and GAN-pep 8 had a broad spectrum of antibacterial effects. Both of them were also effective against antibiotic-resistant bacteria strains such as methicillin-resistant Staphylococcus aureus (S. aureus) and carbapenem-resistant Pseudomonas aeruginosa (P. aeruginosa). GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria.

Motivation Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. Results MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. Availability and implementation Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare Supplementary information Supplementary data are available at Bioinformatics online.

Androgens stimulate primary ovarian development in Vertebrate. Japanese eels underwent operation to sample the pre- and post-treated ovarian tissues from the same individual. Ovarian phenotypic or genotypic data were mined in a pair. A correlation between the initial ovarian status (determined by kernel density estimation (KDE), presented as a probability density of oocyte size) and the consequence of androgen (17MT) treatment (change in ovary) has been showed. The initial ovarian status appeared to be important to influence ovarian androgenic sensitivity. The initial ovary was important to the outcomes of androgen treatments, and ePAV (expression presence-absence variation) is existing in Japanese eel by analyze DEGs; core, unique, or accessory genes were identified, the sensitivities of initial ovaries were correlated with their gene expression profiles. We speculated the importance of genetic differential expression on the variations of phenotypes by 17MT, and transcriptomic approach seems to allow extracting multiple layers of genomic data.

The pan-genome was defined as the complete gene set across strains, and it is built upon genes displaying presence-absence variations (PAVs); the pan-transcriptome is defined by recalling the pan-genome. Indeed, a PAV is reflected from the expression presence-absence variation (ePAV). In this study, treated with androgen, eels, which are a primitive fish from the basal lineage of Teleost, with different ovarian developments were chosen and submitted to RAN-sequencing. Transcriptomes were the assembly against eel genome scaffolds; a pair was the unit (the same eel before and after treatment) to analyze DEGs (differentially expressed genes); the core, unique, or accessory genes were identified, and the list of DEGs was analyzed to investigate ePAV. The results suggest that there was ePAV in Japanese eel, and the ePAV of eel was analyzed by pathway enrichment. These results signify the importance of genetic differential expression on the variations of phenotypes by androgen, and a transcriptomic approach appears to enable extracting multiple layers of genomic data.

We discovered one purple photosynthetic bacterium, Rhodopseudomonas palustris YSC3, which has a specific ability to degrade 1, 2, 5, 6, 9, 10-hexabromocyclododecane (HBCD). The whole transcriptome of R. palustris YSC3 was analyzed using the RNA-based sequencing technology in illumina and was compared as well as discussed through Multi-Omics onLine Analysis System (MOLAS, http://molas.iis.sinica.edu.tw/NTUIOBYSC3/) platform we built. By using genome based mapping approach, we can align the trimmed reads on the genome of R. palustris and estimate the expression profiling for each transcript. A total of 341 differentially expressed genes (DEGs) in HBCD-treated R. palustris (RPH) versus control R. palustris (RPC) was identified by 2-fold changes, among which 305 genes were up-regulated and 36 genes were down-regulated. The regulated genes were mapped to the database of Gene Ontology (GO) and Genes and Genomes Encyclopedia of Kyoto (KEGG), resulting in 78 pathways being identified. Among those DEGs which annotated to important functions in several metabolic pathways, including those involved in two-component system (13.6%), ribosome assembly (10.7%), glyoxylate and dicarboxylate metabolism (5.3%), fatty acid degradation (4.7%), drug metabolism-cytochrome P450 (2.3%), and chlorocyclohexane and chlorobenzene degradation (3.0%) were differentially expressed in RPH and RPC samples. We also identified one transcript annotated as dehalogenase and other genes involved in the HBCD biotransformation in R. palustris. Furthermore, the putative HBCD biotransformation mechanism in R. palustris was proposed.

MotivationAntimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitution for antibiotics. However, AMPs discovery through traditional wet-lab research is expensive and inefficient. Thus, we developed AI4AMP, a user-friendly web-server that provides an accurate prediction of the antimicrobial activity of a given protein sequence, to accelerate the process of AMP discovery.ResultsOur results show that our prediction model is superior to the existing AMP predictors.AvailabilityAI4AMP is freely accessible at http://symbiosis.iis.sinica.edu.tw/PC_6/Contactcylin@iis.sinica.edu.tw

Cancer immunotherapy reaches a remarkable achievement in various cancer types and brings new possibilities to improve cancer patients’ long-term survival. However, outcomes vary from case to case, and the present protocol benefits a small fraction of patients. One notable factor is the tumor microenvironment, especially the immune cell components, that may reflect the immune response's status quo on site. Thus, understanding the content of infiltrating immune cells in tumors is not only for research interesting but also a crucial subject toward precision medicine. We implement an algorithm for resolving relative proportions of twenty-one immune cell subclasses from a human tissue profiled transcriptome by microarray technology to reach the goal above. By selecting gene features and then adopting ?-Support Vector Regression, we can construct a deconvolution model and resolve the immune cell context. The excellent consistency between the estimated values and the correct immune-cell composition further demonstrates this approach provides a more natural alternative to revealing samples' immune cell content and reliable results like recent single-cell technologies. Based on this algorithm, the web-based deconvolution tool implemented named mySORT provides a user-friendly interface for estimating the immune cell content by uploading gene expression profiling. We also present comprehensive visualization 2D/3D plots in mySORT so that users can easily make a comparison between different samples. Finally, we synthesized pseudo-bulk expression data from single-cell transcriptomic datasets of 17 melanoma and 16 head and neck cancer patients. The deconvolution results of microarray-based data in the previous study and synthetic pseudo-bulk data all proved the excellent performance of mySORT. We believe that mySORT can help researchers in all fields easily understand complex immune microenvironment. The website of mySORT is freely accessible on https://symbiosis.iis.sinica.edu.tw/mySORT/.

Background Extraintestinal pathogenic E. coli (ExPEC) remains one of the most prevalent bacterial pathogens that cause extraintestinal infections, including neonatal meningitis, septicemia, and urinary tract (UT) infections (UTIs). Antibiotic therapy has been the conventional treatment for such infections, but its efficacy has decreased due to the emergence of antibiotic-resistant bacteria. Identification and characterization of bacterial factors that contribute to the severity of infection would facilitate the development of novel therapeutic strategies. The ExPEC periplasmic protease Prc contributes to the pathogen’s ability to evade complement-mediated killing in the serum. Here, we further investigated the role of the Prc protease in ExPEC-induced UTIs and the underlying mechanism. Methods The uropathogenic role of Prc was determined in a mouse model of UTIs. Using global quantitative proteomic analyses, we revealed that the expression of FliC and other outer membrane-associated proteins was altered by Prc deficiency. Comparative transcriptome analyses identified that Prc deficiency affected expression of the flagellar regulon and genes that are regulated by five extracytoplasmic signaling systems. Results A mutant ExPEC with a prc deletion was attenuated in bladder and kidney colonization. Global quantitative proteomic analyses of the prc mutant and wild-type ExPEC strains revealed significantly reduced flagellum expression in the absence of Prc, consequently impairing bacterial motility. The prc deletion triggered downregulation of the flhDC operon encoding the master transcriptional regulator of flagellum biogenesis. Overexpressing flhDC restored the prc mutant’s motility and ability to colonize the UT, suggesting that the impaired motility is responsible for attenuated UT colonization of the mutant. Further comparative transcriptome analyses revealed that Prc deficiency activated the σE and RcsCDB signaling pathways. These pathways were responsible for the diminished flhDC expression. Finally, the activation of the RcsCDB system was attributed to the intracellular accumulation of a known Prc substrate Spr in the prc mutant. Spr is a peptidoglycan hydrolase and its accumulation destabilizes the bacterial envelope. Conclusions We demonstrated for the first time that Prc is essential for full ExPEC virulence in UTIs. Our results collectively support the idea that Prc is essential for bacterial envelope integrity, thus explaining how Prc deficiency results in an attenuated ExPEC.

Additional file 1. This supplementary file provides the description and usage of DocMethyl and EpiMOLAS_web, including the installation steps on how to execute the workflow in DocMethyl and the usage guides on analyzing WGBS data through the modules in EpiMOLAS_web.

Additional file 1: Table S1. Primers used in this study

With the development of robotics, there are growing needs for real time motion planning. However, due to obstacles in the environment, the planning problem is highly non-convex, which makes it difficult to achieve real time computation using existing non-convex optimization algorithms. This paper introduces the convex feasible set algorithm (CFS) which is a fast algorithm for non-convex optimization problems that have convex costs and non-convex constraints. The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex constraints. The feasibility and the convergence of the proposed algorithm are proved in the paper. The application of this method on motion planning for mobile robots is discussed. The simulations demonstrate the effectiveness of the proposed algorithm., in SIAM Journal on Control and Optimization

Spontaneous regression of an osteochondroma of the distal femur is unusual. This report highlights the spontaneous regression of a sessile osteochondroma of the distal femur in a 9-year-old boy which resolved over a 4-year period. The mechanism underlying regression of the tumor is discussed with a review of previous reports. Since this type of osteochondroma can spontaneously resolve, conservative treatment is always the first choice to avoid unnecessary surgery.

Hexabromocyclododecane (HBCD) is one of the most frequently used brominated flame retardants (BFRs). However, the HBCD degradation method's development has become vital because it readily bioaccumulates and is persistent in the environment. A previous study showed Rhodopseudomonas palustris degrades HBCD through several possible metabolic pathways based on transcriptomic analysis of compared samples. This study introduces multiple time-course transcriptomic analysis approaches to identify the specific HBCD metabolic pathway in R. palustris inbubated at different temperatures. The transcriptome profiles revealed that the addition of HBCD triggered 126 transcripts in cells at 25°C and 35°C. Further KEGG analysis showed several HBCD induced metabolic pathways, including ABC transporter, butanoate metabolism, dephosphorylation, lipid glycosylation pathways, etc. The principal component analysis further provides evidence of genes directly affected by HBCD. The increased expression level of transcriptional regulator LysR, two-component system regulators, HBCD degradation enzymes, including haloacid dehalogenases, glutathione-S-transferase, cytochrome p450, hydrolases, and dioxygenases in R. palustris were confirmed by qRT-PCR analysis. Combining the transcriptomic profiles and gene expression level analysis, we proposed the HBCD metabolic pathway in R. palustris. Briefly, HBCD signal transferred from cell membrane to transcriptional regulator LysR, then further to downstream degradation working enzymes. Overall, our results highlight the value of systematic transcriptomic approaches to discover and elucidate the intrinsic microbial metabolisms for HBCD degradation in R. palustris. The results of this study provide a novel perspective on the degradation of persistent organic pollutants (POPs) such as HBCD using a bio-omics approach.

Hepatitis C virus (HCV) encodes mechanisms to evade the multilayered antiviral actions of the host immune system. Great progress has been made in elucidating the strategies HCV employs to down-regulate interferon (IFN) production, impede IFN signaling transduction, and impair IFN-stimulated gene (ISG) expression. However, there is a limited understanding of the mechanisms governing how viral proteins counteract the antiviral functions of downstream IFN effectors due to the lack of an efficient approach to identify such interactions systematically. To study the mechanisms by which HCV antagonizes the IFN responses, we have developed a high-throughput profiling platform that enables mapping of HCV sequences critical for anti-IFN function at high resolution. Genome-wide profiling performed with a 15-nt insertion mutant library of HCV showed that mutations in the p7 region conferred high levels of IFN sensitivity, which could be alleviated by the expression of WT p7 protein. This finding suggests that p7 protein of HCV has an immune evasion function. By screening a liver-specific ISG library, we identified that IFI6-16 significantly inhibits the replication of p7 mutant viruses without affecting WT virus replication. In contrast, knockout of IFI6-16 reversed the IFN hypersensitivity of p7 mutant virus. In addition, p7 was found to be coimmunoprecipitated with IFI6-16 and to counteract the function of IFI6-16 by depolarizing the mitochondria potential. Our data suggest that p7 is a critical immune evasion protein that suppresses the antiviral IFN function by counteracting the function of IFI6-16.

The detailed workflow of post-assembly read type labelling. (PDF 498 kb)

The details of SQUAT runtime and resource consumption and the results of the wheat dataset. (PDF 364 kb)

As significant anthropogenic pressures are putting undue stress on the world's oceans, there has been a concerted effort to understand how marine organisms respond to environmental change. Transcriptomic approaches, in particular, have been readily employed to document the mRNA-level response of a plethora of marine invertebrates exposed to an array of simulated stress scenarios, with the tacit and untested assumption being that the respective proteins show a corresponding trend. To better understand the degree of congruency between mRNA and protein expression in an endosymbiotic marine invertebrate, mRNAs and proteins were sequenced from the same samples of the common, Indo-Pacific coral Seriatopora hystrix exposed to stable or upwelling-simulating conditions for 1 week. Of the 167 proteins downregulated at variable temperature, only two were associated with mRNAs that were also differentially expressed between treatments. Of the 378 differentially expressed genes, none were associated with a differentially expressed protein. Collectively, these results highlight the inherent risk of inferring cellular behaviour based on mRNA expression data alone and challenge the current, mRNA-focused approach taken by most marine and many molecular biologists.

Phosphaturic mesenchymal tumors typically cause paraneoplastic osteomalacia, chiefly as a result of FGF23 secretion. In a prior study, we identified FN1-FGFR1 fusion in 9 of 15 phosphaturic mesenchymal tumors. In this study, a total of 66 phosphaturic mesenchymal tumors and 7 tumors resembling phosphaturic mesenchymal tumor but without known phosphaturia were studied. A novel FN1-FGF1 fusion gene was identified in two cases without FN1-FGFR1 fusion by RNA sequencing and cross-validated with direct sequencing and western blot. Fluorescence in situ hybridization analyses revealed FN1-FGFR1 fusion in 16 of 39 (41%) phosphaturic mesenchymal tumors and identified an additional case with FN1-FGF1 fusion. The two fusion genes were mutually exclusive. Combined with previous data, the overall prevalence of FN1-FGFR1 and FN1-FGF1 fusions was 42% (21/50) and 6% (3/50), respectively. FGFR1 immunohistochemistry was positive in 82% (45/55) of phosphaturic mesenchymal tumors regardless of fusion status. By contrast, 121 cases of potential morphologic mimics (belonging to 13 tumor types) rarely expressed FGFR1, the main exceptions being solitary fibrous tumors (positive in 40%), chondroblastomas (40%), and giant cell tumors of bone (38%), suggesting a possible role for FGFR1 immunohistochemistry in the diagnosis of phosphaturic mesenchymal tumor. With the exception of one case reported in our prior study, none of the remaining tumors resembling phosphaturic mesenchymal tumor had either fusion type or expressed significant FGFR1. Our findings provide insight into possible mechanisms underlying the pathogenesis of phosphaturic mesenchymal tumor and imply a central role of the FGF1-FGFR1 signaling pathway. The novel FN1-FGF1 protein is expected to be secreted and serves as a ligand that binds and activates FGFR1 to achieve an autocrine loop. Further study is required to determine the functions of these fusion proteins.

Development of colorectal cancer (CRC) involves sequential transformation of normal mucosal tissues into benign adenomas and then adenomas into malignant tumors. The identification of genes crucial for malignant transformation in colorectal adenomas (CRAs) has been based primarily on cross-sectional observations. In this study, we identified relevant genes using autologous samples. By performing genome-wide SNP genotyping and RNA sequencing analysis of adenocarcinomas, adenomatous polyps, and non-neoplastic colon tissues (referred as tri-part samples) from individual patients, we identified 68 genes with differential copy number alterations and progressively dysregulated expression. Aurora A, SKA3, and DSN1 protein levels were sequentially up-regulated in the samples, and this overexpression was associated with chromosome instability (CIN). Knockdown of SKA3 in CRC cells dramatically reduced cell growth rates and increased apoptosis. Depletion of SKA3 or DSN1 induced G2/M arrest and decreased migration, invasion, and anchorage-independent growth. AURKA and DSN1 are thus critical for chromosome 20q amplification-associated malignant transformation in CRA. Moreover, SKA3 at chromosome 13q was identified as a novel gene involved in promoting malignant transformation. Evaluating the expression of these genes may help identify patients with progressive adenomas, helping to improve treatment.

In many solid tumors, tissue of the mesenchymal subtype is frequently associated with epithelial&ndash, mesenchymal transition (EMT), strong stromal infiltration, and poor prognosis. Emerging evidence from tumor ecosystem studies has revealed that the two main components of tumor stroma, namely, infiltrated immune cells and cancer-associated fibroblasts (CAFs), also express certain typical EMT genes and are not distinguishable from intrinsic tumor EMT, where bulk tissue is concerned. Transcriptomic analysis of xenograft tissues provides a unique advantage in dissecting genes of tumor (human) or stroma (murine) origins. By transcriptomic analysis of xenograft tissues, we found that oral squamous cell carcinoma (OSCC) tumor cells with a high EMT score, the computed mesenchymal likelihood based on the expression signature of canonical EMT markers, are associated with elevated stromal contents featured with fibronectin 1 (Fn1) and transforming growth factor-&beta, (Tgf&beta, ) axis gene expression. In conjugation with meta-analysis of these genes in clinical OSCC datasets, we further extracted a four-gene index, comprising FN1, TGFB2, TGFBR2, and TGFBI, as an indicator of CAF abundance. The CAF index is more powerful than the EMT score in predicting survival outcomes, not only for oral cancer but also for the cancer genome atlas (TCGA) pan-cancer cohort comprising 9356 patients from 32 cancer subtypes. Collectively, our results suggest that a further distinction and integration of the EMT score with the CAF index will enhance prognosis prediction, thus paving the way for curative medicine in clinical oncology.

A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v -Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a .

Due to advancement in next-generation sequencing (NGS) technology, ultra-large datasets have been generated for further studies. These datasets contain a large amount of erroneous data. It is a computational problem to detect and correct the errors to improve performance of future applications, e.g., de novo assembly. In this article, we present a MapReduce-based algorithm, CloudEC, to correct sequencing errors in NGS data. We use five datasets and GAGE benchmark to compare efficiency and efficacy of CloudEC with CloudRS and error correction module of ALLPATHS-LG. Experiments show that CloudEC is 1.6 times faster than CloudRS. The corrected reads are then assembled by Velvet assembler, and the assembled results are examined by GAGE benchmark for correctness of assembly. It is shown that the correct N50 of the assembled contigs with CloudEC as the error corrector is larger than that with CloudRS as the error corrector, and is comparable to that with ALLPATHS-LG as the error corrector.

This paper deals with the problem of modeling and identification for industrial robots with indirect drive mechanisms (e.g., gear transmissions), where the motor-side behavior may deviate from the load-side (i.e., end effector) behavior due to the robot joint dynamics. In this case, the motor-side sensors alone are not sufficient for directly obtaining the load-side behavior. We call such problems incomplete data problems since some key information (i.e., the load-side behaviors) is missing during the identification process. This paper, thus, presents a procedure for identifying robot models even with limited load-side information. It begins by describing the relationship between the missing information and the available measurements using a Gaussian dynamic model. Then, the expectation-maximization algorithm is applied to handle the parameter estimation problem in the presence of missing information. Various relaxation techniques are utilized to alleviate the computational complexity of the algorithm. The effectiveness of the proposed method is demonstrated on three real-world problems in industrial automation, namely the gain tuning of the Kalman filter, the friction identification, and the self-calibration of load-side inertial sensors.

Phosphaturic mesenchymal tumours (PMTs) are uncommon soft tissue and bone tumours that typically cause hypophosphataemia and tumour-induced osteomalacia (TIO) through secretion of phosphatonins including fibroblast growth factor 23 (FGF23). PMT has recently been accepted by the World Health Organization as a formal tumour entity. The genetic basis and oncogenic pathways underlying its tumourigenesis remain obscure. In this study, we identified a novel FN1–FGFR1 fusion gene in three out of four PMTs by next-generation RNA sequencing. The fusion transcripts and proteins were subsequently confirmed with RT-PCR and western blotting. Fluorescence in situ hybridization analysis showed six cases with FN1–FGFR1 fusion out of an additional 11 PMTs. Overall, nine out of 15 PMTs (60%) harboured this fusion. The FN1 gene possibly provides its constitutively active promoter and the encoded protein's oligomerization domains to overexpress and facilitate the activation of the FGFR1 kinase domain. Interestingly, unlike the prototypical leukaemia-inducing FGFR1 fusion genes, which are ligand-independent, the FN1–FGFR1 chimeric protein was predicted to preserve its ligand-binding domains, suggesting an advantage of the presence of its ligands (such as FGF23 secreted at high levels by the tumour) in the activation of the chimeric receptor tyrosine kinase, thus effecting an autocrine or a paracrine mechanism of tumourigenesis. Copyright © 2014 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Mammalian herbivores rely on microbial activities in an expanded gut chamber to convert plant biomass into absorbable nutrients. Distinct from ruminants, small herbivores typically have a simple stomach but an enlarged cecum to harbor symbiotic microbes; however, knowledge of this specialized gut structure and characteristics of its microbial contents is limited. Here, we used leaf-eating flying squirrels as a model to explore functional characteristics of the cecal microbiota adapted to a high-fiber, toxin-rich diet. Specifically, environmental conditions across gut regions were evaluated by measuring mass, pH, feed particle size, and metabolomes. Then, parallel metagenomes and metatranscriptomes were used to detect microbial functions corresponding to the cecal environment. Based on metabolomic profiles, >600 phytochemical compounds were detected, although many were present only in the foregut and probably degraded or transformed by gut microbes in the hindgut. Based on metagenomic (DNA) and metatranscriptomic (RNA) profiles, taxonomic compositions of the cecal microbiota were dominated by bacteria of the Firmicutes taxa; they contained major gene functions related to degradation and fermentation of leaf-derived compounds. Based on functional compositions, genes related to multidrug exporters were rich in microbial genomes, whereas genes involved in nutrient importers were rich in microbial transcriptomes. In addition, genes encoding chemotaxis-associated components and glycoside hydrolases specific for plant beta-glycosidic linkages were abundant in both DNA and RNA. This exploratory study provides findings which may help to form molecular-based hypotheses regarding functional contributions of symbiotic gut microbiota in small herbivores with folivorous dietary habits.

Trajectory smoothing is an important step in robot motion planning, where optimization methods are usually employed. However, the optimization problem for trajectory smoothing in a clustered environment is highly non-convex, and is hard to solve in real time using conventional non-convex optimization solvers. This paper discusses a fast online optimization algorithm for trajectory smoothing, which transforms the original non-convex problem to a convex problem so that it can be solved efficiently online. The performance of the algorithm is illustrated in various cases, and is compared to that of conventional sequential quadratic programming (SQP). It is shown that the computation time is greatly reduced using the proposed algorithm.

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. 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. 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 .

Although rising ocean temperatures threaten scleractinian corals and the reefs they construct, certain reef corals can acclimate to elevated temperatures to which they are rarely exposed in situ. Specimens of the model Indo-Pacific reef coral Pocillopora damicornis collected from upwelling reefs of Southern Taiwan were previously found to have survived a 36-week exposure to 30°C, a temperature they encounter infrequently and one that can elicit the breakdown of the coral–dinoflagellate (genus Symbiodinium) endosymbiosis in many corals of the Pacific Ocean. To gain insight into the subcellular pathways utilized by both the coral hosts and their mutualistic Symbiodinium populations to acclimate to this temperature, mRNAs from both control (27°C) and high (30°C)-temperature samples were sequenced on an Illumina platform and assembled into a 236 435-contig transcriptome. These P. damicornis specimens were found to be ~60% anthozoan and 40% microbe (Symbiodinium, other eukaryotic microbes, and bacteria), from an mRNA-perspective. Furthermore, a significantly higher proportion of genes from the Symbiodinium compartment were differentially expressed after two weeks of exposure. Specifically, at elevated temperatures, Symbiodinium populations residing within the coral gastrodermal tissues were more likely to up-regulate the expression of genes encoding proteins involved in metabolism than their coral hosts. Collectively, these transcriptome-scale data suggest that the two members of this endosymbiosis have distinct strategies for acclimating to elevated temperatures that are expected to characterize many of Earth's coral reefs in the coming decades.

e14199 Background: Prior study stratified Taiwanese oral cancer specimens (n = 40) and cell lines (n = 7) into three distinct molecular subtypes, namely classical (CL), mesenchymal (MS), and basal (BA). In a cell line derived xenograft (CDX) model, we observed MS grew at least 10-fold slower than CL did in immunodeficient mice. By using RNA-seq to portrait the transcriptomes of human tumor and mouse stroma simultaneously, contribution of mouse innate immunity in restricting the growth of human oral cancer cells was assessed. Methods: A half millions of mycoplasma-free OC3 (MS) or TW2.6 (CL) cells with matrigel were subcutaneously implanted into the flank of 10 to 16 weeks old NOG (NOD/SCID/Il2rgtm). RNAs of CDX tissues from OC3-NOG (n = 7) and TW2.6-NOG (n = 5) were extracted and subjected to stranded mRNA sequencing. Clean reads were aligned to GRCh38 (human) and GRCm38 (mouse), respectively, followed by identifying differentially expressed genes and gene set enrichment analysis. Results: Up-regulation of signature genes for dendritic cells and macrophages and enrichment of innate immunity, including Tnfa-Nfkb signaling, interferon alpha response, interferon gamma response and inflammation were detected in OC3- but not in TW2.6-CDXs. These results suggested that OC3 (MS) was more immunogenic than TW2.6 (CL), which is reminiscent of the inflammatory MS (IMS) subtype of head and neck cancer recently described by Keck et al (n = 938, Clin Cancer Res 2015, 21: p870. PMID: 25492084). Conclusions: By RNA-seq/xenome analysis of CDXs, we provide evidence that compared to CL, MS subtype of oral cancer cells elicited a stronger innate immunity in NOG mouse. Alternatively, CL subtype might have evolved as a prototype with superiority in immune escape.

As is generally assumed, clusters in protein–protein interaction (PPI) networks perform specific, crucial functions in biological systems. Various network community detection methods have been developed to exploit PPI networks in order to identify protein complexes and functional modules. Due to the potential role of various regulatory modes in biological networks, a single method may just apply a single graph property and neglect communities highlighted by other network properties. This work presents a novel integration method to capture protein modules/protein complexes by multiple network features detected by different algorithms. The integration method is further implemented in a web-based platform with a highly effective interactive network analyzer. Conventionally adopted methods with different perspectives on network community detection (e.g., CPM, FastGreedy, HUNTER, MCL, LE, SpinGlass, and WalkTrap) are also executed simultaneously. Analytical results indicate that the proposed method performs better than the conventional ones. The proposed approach can capture the transcription and RNA splicing machineries from the yeast protein network. Meanwhile, proteins that are highly associated with each other, yet not described in both machineries are also identified. In sum, a protein that is closely connected to components of a known module or a complex in the network view implies the functional association among them. Importantly, our method can detect these unique network features, thus facilitating efforts to discover unknown components of functional modules/protein complexes. Availability: Spotlight is freely accessible at http://hub.iis.sinica.edu.tw/spotlight . Video clips for a quick view of usage are available in the website online help page.

For many desirable applications of vision guided industrial robots, real-time visual servoing is necessary but also challenging. Difficulty comes from the limited sampling rate and response time of typical machine vision systems equipped on industrial robots. These factors are addressed as the dynamics of visual sensing. In addition, robot dynamics should also be fully considered when designing the control law. Considering these aspects, this paper presents a control scheme of visual servoing. A dual-rate adaptive tracking filter is presented to compensate the visual sensing dynamics. Based on the compensated vision feedback, the techniques of multi-surface sliding control and dynamic surface control are used to formulate a two-layer control law for target tracking. System kinematics and dynamics are decoupled and dealt with by the two layers of the control law respectively. The proposed method is validated through experiments on a SCARA robot.

Real-time object tracking for manipulators has been applied to industrial automation such as assembly and human robot collaboration. During the tracking process, the vision sensor is used as a feedback means to the robot controller. It means that the imperfect sensing and stability issues must be carefully considered. This paper deals with the modeling of sensor physics (e.g. limited sampling rate, irregular sampling time, packet loss, noise, and latency), and realizes a globally asymptotically stable tracking controller. First, an varying rate Kalman filter is applied to estimate the markers' positions and the corresponding error co-variances. Then, a maximum likelihood estimation problem is solved to estimate the transformation between the target frame and the world frame. In addition, a dynamic tracking controller is implemented to realize object tracking. The stability of the tracking controller under model uncertainties is also discussed. The proposed tracking algorithm is experimentally verified on an industrial robot.