Decoding the genome of bloodsucking midge Forcipomyia taiwana (Diptera: Ceratopogonidae): Insights into odorant receptor expansion.

Biting midges, notably those within the Ceratopogonidae family, have long been recognized for their epidemiological significance, both as nuisances and vectors for disease transmission in vertebrates. Despite their impact, genomic insights into these insects, particularly beyond the Culicoides genus, remain limited. In this study, we assembled the Forcipomyia taiwana (Shiraki) genome, comprising 113 scaffolds covering 130.4 Mbps-with the longest scaffold reaching 7.6 Mbps and an N50 value of 2.6 Mbps-marking a pivotal advancement in understanding the genetic architecture of ceratopogonid biting midges. Phylogenomic analyses reveal a shared ancestry between F. taiwana and Culicoides sonorensis Wirth & Jones, dating back approximately 124 million years, and highlight a dynamic history of gene family expansions and contractions within the Ceratopogonidae family. Notably, a substantial expansion of the odorant receptor (OR) gene family was observed, which is crucial for the chemosensory capabilities that govern biting midges' interactions with their environment, including host seeking and oviposition behaviors. The distribution of OR genes across the F. taiwana genome displays notable clusters on scaffolds, indicating localized tandem gene duplication events. Additionally, several collinear regions were identified, hinting at segmental duplications, inversions, and translocations, contributing to the olfactory system's evolutionary complexity. Among the 156 ORs identified in F. taiwana, 134 are biting midge-specific ORs, distributed across three distinct clades, each exhibiting unique motif features that distinguish them from the others. Through weighted gene co-expression network analysis, we correlated distinct gene modules with sex and reproductive status, laying the groundwork for future investigations into the interplay between gene expression and adaptive behaviors in F. taiwana. In conclusion, our study not only highlights the unique olfactory repertoire of ceratopogonid biting midges but also sets the stage for future studies into the genetic underpinnings of their unique biological traits and ecological strategies.

Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains.

Because of the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) may be ideal for next-generation antibiotics. This study trained a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based on 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 selected by an AMP Artificial Intelligence (AI) classifier and synthesized for further experiments. Disc diffusion testing and minimum inhibitory concentration (MIC) determinations were used to identify the antibacterial effects of the synthesized GAN-designed peptides. Seven of the eight synthesized GAN-designed peptides displayed antibacterial activity. Additionally, GAN-pep 3 and GAN-pep 8 presented a broad spectrum of antibacterial effects and were effective against antibiotic-resistant bacteria strains, such as methicillin-resistant Staphylococcus aureus and carbapenem-resistant Pseudomonas aeruginosa. GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria. In brief, our approach shows an efficient way to discover AMPs effective against general and antibiotic-resistant bacteria strains. In addition, such a strategy also allows other novel functional peptides to be quickly designed, identified, and synthesized for validation on the wet bench.

Elegancy: Digitizing the wisdom from laboratories to the cloud with free no-code platform.

One of the top priorities in any laboratory is archiving experimental data in the most secure, efficient, and errorless way. It is especially important to those in chemical and biological research, for it is more likely to damage experiment records. In addition, the transmission of experiment results from paper to electronic devices is time-consuming and redundant. Therefore, we introduce an open-source no-code electronic laboratory notebook, Elegancy, a cloud-based/standalone web service distributed as a Docker image. Elegancy fits all laboratories but is specially equipped with several features benefitting biochemical laboratories. It can be accessed via various web browsers, allowing researchers to upload photos or audio recordings directly from their mobile devices. Elegancy also contains a meeting arrangement module, audit/revision control, and laboratory supply management system. We believe Elegancy could help the scientific research community gather evidence, share information, reorganize knowledge, and digitize laboratory works with greater ease and security.

A metagenomics study of hexabromocyclododecane degradation with a soil microbial community.

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-1 after the first treatment and 0.104 day-1 after second exposure. With additional C-sources, the rate constants decreased to 0.054-0.097 day-1. The hydroxylic debromination metabolites and ring cleavage long-chain alkane metabolites were identified to support the potential metabolic pathways utilized by the soil microbial communities. The metagenome established by Nanopore sequencing showed significant compositional alteration in the soil microbial community after the HBCD treatment. After ranking, comparing relative abundances, and performing network analyses, several novel bacterial taxa were identified to contribute to HBCD biotransformation, including Herbaspirillum, Sphingomonas, Brevundimonas, Azospirillum, Caulobacter, and Microvirga, through halogenated / aromatic compound degradation, glutathione-S-transferase, and hydrolase activity. We present a compelling and applicable approach combining metagenomics research, degradation kinetics, and metabolomics strategies, which allowed us to decipher the natural attenuation and remediation mechanisms of HBCDs.

MetaSquare: an integrated metadatabase of 16S rRNA gene amplicon for microbiome taxonomic classification.

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.

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.

Piwi reduction in the aged niche eliminates germline stem cells via Toll-GSK3 signaling.

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 ß-catenin. Disruption of ß-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 ß-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.

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/.

Association Between the Communication Skills of Physicians and the Signing of Do-Not-Resuscitate Consent for Terminally Ill Patients in Emergency Rooms (Cross-Sectional Study).

BACKGROUND: The signing of do-not-resuscitate (DNR) consent is mandatory in providing a palliative approach in the end-of-life care for the terminally ill patients and requires an effective communication between the physician and the patients or their family members. This study aimed to investigate the association between the communication skills of physicians who participated in the SHARE (supportive environment, how to deliver the bad news, additional information, reassurance, and emotional support) model course on the patient notification and the signing of do-not-resuscitate (DNR) consent by the terminally ill patients at emergency rooms. METHODS: Between May 1, 2017 and April 30, 2018, a total of 109 terminally ill patients were enrolled in this study, of which 70 had signed a DNR and 39 had not. Data regarding the patients' medical records, a questionnaire survey completed by family members, and patient observation forms were used for the assessment of physicians' communication skills during patient notification. The observation form was designed based on the SHARE model. A multivariate logistic regression model was applied to identify the independent significant factors of the patient and family member variables as well as the four main components of the observation form. RESULTS: The results revealed that knowing how to convey bad news and providing reassurance and emotional support were significantly correlated with a higher rate of signing DNR consent. Additionally, physician-initiated discussion with family members and a predicted limited life expectancy were negative independent significant factors for signing DNR consent. CONCLUSION: This study revealed that good communication skills help to increase the signing of DNR consent. The learning of such skills from attendance of the SHARE model course is encouraged for the physicians in the palliative care of terminally ill patients in an emergency room.

Using high-throughput transcriptome sequencing to investigate the biotransformation mechanism of hexabromocyclododecane with Rhodopseudomonas palustris in water.

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.

SQUAT: a Sequencing Quality Assessment Tool for data quality assessments of genome assemblies.

BACKGROUND: With the rapid increase in genome sequencing projects for non-model organisms, numerous genome assemblies are currently in progress or available as drafts, but not made available as satisfactory, usable genomes. Data quality assessment of genome assemblies is gaining importance not only for people who perform the assembly/re-assembly processes, but also for those who attempt to use assemblies as maps in downstream analyses. Recent studies of the quality control, quality evaluation/ assessment of genome assemblies have focused on either quality control of reads before assemblies or evaluation of the assemblies with respect to their contiguity and correctness. However, correctness assessment depends on a reference and is not applicable for de novo assembly projects. Hence, development of methods providing both post-assembly and pre-assembly quality assessment reports for examining the quality/correctness of de novo assemblies and the input reads is worth studying. RESULTS: We present SQUAT, an efficient tool for both pre-assembly and post-assembly quality assessment of de novo genome assemblies. The pre-assembly module of SQUAT computes quality statistics of reads and presents the analysis in a well-designed interface to visualize the distribution of high- and poor-quality reads in a portable HTML report. The post-assembly module of SQUAT provides read mapping analytics in an HTML format. We categorized reads into several groups including uniquely mapped reads, multiply mapped, unmapped reads; for uniquely mapped reads, we further categorized them into perfectly matched, with substitutions, containing clips, and the others. We carefully defined the poorly mapped (PM) reads into several groups to prevent the underestimation of unmapped reads; indeed, a high PM% would be a sign of a poor assembly that requires researchers' attention for further examination or improvements before using the assembly. Finally, we evaluate SQUAT with six datasets, including the genome assemblies for eel, worm, mushroom, and three bacteria. The results show that SQUAT reports provide useful information with details for assessing the quality of assemblies and reads. AVAILABILITY: The SQUAT software with links to both its docker image and the on-line manual is freely available at https://github.com/luke831215/SQUAT .

Modeling spinocerebellar ataxias 2 and 3 with iPSCs reveals a role for glutamate in disease pathology.

Spinocerebellar ataxias 2 and 3 (SCA2 and SCA3) are dominantly inherited neurodegenerative diseases caused by expansion of polyglutamine-encoding CAG repeats in the affected genes. The etiology of these disorders is known to involve widespread loss of neuronal cells in the cerebellum, however, the mechanisms that contribute to cell death are still elusive. Here we established SCA2 and SCA3 induced pluripotent stem cells (iPSCs) and demonstrated that SCA-associated pathological features can be recapitulated in SCA-iPSC-derived neurons. Importantly, our results also revealed that glutamate stimulation promotes the development of disease-related phenotypes in SCA-iPSC-derived neurons, including altered composition of glutamatergic receptors, destabilized intracellular calcium, and eventual cell death. Furthermore, anti-glutamate drugs and calcium stabilizer treatment protected the SCA-iPSC-derived neurons and reduced cell death. Collectively, our study demonstrates that the SCA-iPSC-derived neurons can recapitulate SCA-associated pathological features, providing a valuable tool to explore SCA pathogenic mechanisms and screen drugs to identify potential SCA therapeutics.

A gene profiling deconvolution approach to estimating immune cell composition from complex tissues.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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 .

Exploring Discrepancies in Perceived Nursing Competence Between Postgraduate-Year Nurses and Their Preceptors.

BACKGROUND: Postgraduate clinical training programs improve the core competence of nurses. How postgraduate-year (PGY) nurses perceive their clinical competence and their preceptors' perceptions may affect program effectiveness. This study compared the perspectives of clinical competencies of PGY nurses engaged in a residency program in Taiwan with their preceptors' perspectives. METHOD: A cross-sectional study was conducted at a medical center in Taiwan. The Nursing Competence Questionnaire was used to obtain data from 99 pairs of PGY nurses and preceptors. RESULTS: PGY nurses' scores were higher than their preceptors' for communication, patient education, and management competencies (p <.05). Preceptors with more years of clinical experience exhibited greater assessment discrepancies for clinical care, communication, patient education, research awareness, and overall competence (p <.05). CONCLUSION: Preceptor development courses should be grounded in a strong pedagogical framework. An assessment tool with explicit behavioral indicators would be needed for objective evaluation from both perspectives. J Contin Educ Nurs. 2017;48(4):190-196.

Predictor of Self-Perceived Nursing Competency Among New Nurses in Taiwan.

The aim of this article is to identify the factors that predict self-perceived nursing competency among new nurses in Taiwan. This quantitative cross-sectional survey was performed with a convenience sample of 105 new nurses. Data were collected with questionnaires. Multiple linear regression showed perceived benefit of preceptor policies to nursing capacity and age accounted for 21.1% of the variance in dependent variables for self-perceived nursing competency importance. Satisfaction with current preceptor, satisfaction with current nursing job, and participation in interprofessional education conferences accounted for 22% of the variance in dependent variables for self-perceived nursing competency adequacy. The results could be a reference to design nursing education curricula that improve clinical training and retention of new nurses. J Contin Educ Nurs. 2017;48(3):129-137.

Dual-compartmental transcriptomic + proteomic analysis of a marine endosymbiosis exposed to environmental change.

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.

Characterization of FN1-FGFR1 and novel FN1-FGF1 fusion genes in a large series of phosphaturic mesenchymal tumors.

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.

Over-expression of AURKA, SKA3 and DSN1 contributes to colorectal adenoma to carcinoma progression.

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.