Search

Pancreatic ductal adenocarcinoma (PDA) is characterized by aggressive local invasion and metastatic spread, leading to high lethality. Although driver gene mutations during PDA progression are conserved, no specific mutation is correlated with the dissemination of metastases1–3. Here we analysed RNA splicing data of a large cohort of primary and metastatic PDA tumours to identify differentially spliced events that correlate with PDA progression. De novo motif analysis of these events detected enrichment of motifs with high similarity to the RBFOX2 motif. Overexpression of RBFOX2 in a patient-derived xenograft (PDX) metastatic PDA cell line drastically reduced the metastatic potential of these cells in vitro and in vivo, whereas depletion of RBFOX2 in primary pancreatic tumour cell lines increased the metastatic potential of these cells. These findings support the role of RBFOX2 as a potent metastatic suppressor in PDA. RNA-sequencing and splicing analysis of RBFOX2 target genes revealed enrichment of genes in the RHO GTPase pathways, suggesting a role of RBFOX2 splicing activity in cytoskeletal organization and focal adhesion formation. Modulation of RBFOX2-regulated splicing events, such as via myosin phosphatase RHO-interacting protein (MPRIP), is associated with PDA metastases, altered cytoskeletal organization and the induction of focal adhesion formation. Our results implicate the splicing-regulatory function of RBFOX2 as a tumour suppressor in PDA and suggest a therapeutic approach for metastatic PDA.

Background Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a clinically critical pathogen that causes severe infection. Due to improper antibiotic administration, the prevalence of CRKP infection has been increasing considerably. In recent years, the utilization of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has enabled the identification of bacterial isolates at the families and species level. Moreover, machine learning (ML) classifiers based on MALDI-TOF MS have been recently considered a novel method to detect clinical antimicrobial-resistant pathogens. Methods A total of 2683 isolates (369 CRKP cases and 2314 carbapenem-susceptible Klebsiella pneumoniae [CSKP]) collected in the clinical laboratories of Taipei Medical University Hospital (TMUH) were included in this study, and 80% of data was split into the training data set that were submitted for the ML model. The remaining 20% of data was used as the independent data set for external validation. In this study, we established an artificial neural network (ANN) model to analyze all potential peaks on mass spectrum simultaneously. Results Our artificial neural network model for detecting CRKP isolates showed the best performance of area under the receiver operating characteristic curve (AUROC = 0.91) and of area under precision–recall curve (AUPRC = 0.90). Furthermore, we proposed the top 15 potential biomarkers in probable CRKP isolates at 2480, 4967, 12,362, 12,506, 12,855, 14,790, 15,730, 16,176, 16,218, 16,758, 16,919, 17,091, 18,142, 18,998, and 19,095 Da. Conclusions Compared with the prior MALDI-TOF and machine learning studies of CRKP, the amount of data in our study was more sufficient and allowing us to conduct external validation. With better generalization abilities, our artificial neural network model can serve as a reliable screening tool for CRKP isolates in clinical practice. Integrating our model into the current workflow of clinical laboratories can assist the rapid identification of CRKP before the completion of traditional antimicrobial susceptibility testing. The combination of MADLI-TOF MS and machine learning techniques can support physicians in selecting suitable antibiotics, which has the potential to enhance the patients’ outcomes and lower the prevalence of antimicrobial resistance.

The use of nitrogen fertilizers in agriculture produces significant quantities of nitrogenous gases including ammonia, nitric oxide, and nitrous oxide. Through better farmland management practices, the emission of nitrogenous gases can be reduced while realizing clean water environment and climate-smart agriculture. In this article, we first provided an overview of the international movements on reducing nitrogenous gas emissions from farmlands. Then, we summarized the effect of agricultural management practices on nitrogen use efficiency for various crops, and evaluated their effect on nitrogenous gas emissions. The results indicated the importance of implementing site-specific sustainable management practices to enhance nitrogen use efficiency, and thus mitigate nitrogenous gas emissions. We also addressed the impact of agricultural activities on cropland nitrogen cycles, and highlighted the need to perform systematic trade-off evaluations with a well-defined scope to maximize environmental benefits and maintain ecosystem services. Lastly, we proposed three priority directions by moving toward a low-emission agriculture.

Transcription and pre-mRNA splicing are key steps in the control of gene expression and mutations in genes regulating each of these processes are common in leukaemia1,2. Despite the frequent overlap of mutations affecting epigenetic regulation and splicing in leukaemia, how these processes influence one another to promote leukaemogenesis is not understood and, to our knowledge, there is no functional evidence that mutations in RNA splicing factors initiate leukaemia. Here, through analyses of transcriptomes from 982 patients with acute myeloid leukaemia, we identified frequent overlap of mutations in IDH2 and SRSF2 that together promote leukaemogenesis through coordinated effects on the epigenome and RNA splicing. Whereas mutations in either IDH2 or SRSF2 imparted distinct splicing changes, co-expression of mutant IDH2 altered the splicing effects of mutant SRSF2 and resulted in more profound splicing changes than either mutation alone. Consistent with this, co-expression of mutant IDH2 and SRSF2 resulted in lethal myelodysplasia with proliferative features in vivo and enhanced self-renewal in a manner not observed with either mutation alone. IDH2 and SRSF2 double-mutant cells exhibited aberrant splicing and reduced expression of INTS3, a member of the integrator complex3, concordant with increased stalling of RNA polymerase II (RNAPII). Aberrant INTS3 splicing contributed to leukaemogenesis in concert with mutant IDH2 and was dependent on mutant SRSF2 binding to cis elements in INTS3 mRNA and increased DNA methylation of INTS3. These data identify a pathogenic crosstalk between altered epigenetic state and splicing in a subset of leukaemias, provide functional evidence that mutations in splicing factors drive myeloid malignancy development, and identify spliceosomal changes as a mediator of IDH2-mutant leukaemogenesis. Analyses of transcriptomes from patients with acute myeloid leukaemia identified frequently co-occurring mutations of IDH2 and SRSF2, which functional analyses showed to have distinct and coordinated leukaemogenic effects on the epigenome and RNA splicing.

In Taiwan, the hillside is about 70 % of total area. These areas also have steep topography and geological vulnerability. When an event of torrential rain comes during a typhoon, the landslide disasters usually occur at these areas due to the long duration and high intensity of rainfall. Therefore, a design which considers the potential landslide has become an important issue in Taiwan. In this study, a temporal characteristic of landslide fragility curve (LFC) was developed, based on the geomorphological and vegetation factors using landslides at the Chen-Yu-Lan watershed in Taiwan, during Typhoon Sinlaku (September 2008) and Typhoon Morakot (August 2009). This study addressed an effective landslide hazard assessment process, linking together the post-landslide damage and post-rainfall data for LFC model. The Kriging method was used to interpolate the rainfall indices (R 0, R, I) for numerical analysis. Remote sensing data from SPOT images were applied to analyze the landslide ratio and vegetation conditions. The 40-m digital elevation model was used for slope variation analysis in the watershed, and the maximum likelihood estimate was conducted to determine the mean and standard deviation parameters of the proposed empirical LFC model. This empirical model can express the probability of exceeding a damage state for a certain classification (or conditions) of landslides by considering a specific hazard index for a given event. Finally, the vulnerability functions can be used to assess the loss from landslides, and, in the future, to manage the risk of debris flow in the watershed.

Although alloying and heat treatments are common industrial practices to obtain ductile irons with desired mechanical properties, related information on how the two practices affect corrosion behavior is scarce. In this study, two ductile irons—with and without 1 wt pct copper addition—were austempered to obtain austempered ductile irons (ADIs). Polarization tests and salt spray tests were conducted to explore how both copper-alloying and austempering heat treatments influenced the corrosion behavior of ductile irons. The results showed that the corrosion resistance of 1 wt pct copper-alloyed ductile iron was better than that of the unalloyed one, while ADI had improved corrosion resistance compared with the as-cast. In particular, the ductile iron combined with the copper-alloying and austempering treatments increased the corrosion inhibition efficiency up to 84 pct as tested in 3.5 wt pct NaCl solution.

Background The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. Results Here, we build an integrative platform, the E ncyclopedia of H epatocellular C arcinoma genes O nline, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. Conclusion This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.