Biology subjects

Yu, T.

Publications and source records attributed to Yu, T..

6 recordsLinked to original sources

bioRxiv2018

Genetically modified pigs are protected from classical swine fever virus

Classical swine fever (CSF) caused by classical swine fever virus (CSFV) is among the most detrimental diseases, and leads to significant economic losses in the swine industry. Despite efforts by many government authorities try to stamp out the disease from national pig populations, the disease remains widespread. Here, antiviral small hairpin RNAs (shRNAs) were selected and then inserted at the porcine ROSA26 (pROSA26) locus via a CRISPR/Cas9-mediated knock-in strategy. Finally, anti-CSFV transgenic (TG) pigs were produced by somatic nuclear transfer (SCNT). Importantly, in vitro and in vivo viral challenge assays demonstrated that these TG pigs could effectively limit the growth of CSFV and reduced CSFV-associated clinical signs and mortality, and the disease resistance was stably transmitted to F1-generation. The use of these TG pigs can improve the well-being of livestock and substantially reduce virus-related economic losses. Additionally, this antiviral approach may provide a reference for future antiviral research.\n\nAuthor summaryClassical swine fever (CSF), caused by classical swine fever virus (CSFV), and is a highly contagious, often fatal porcine disease with significant economic losses. Due to its economic importance to the pig industry, the biology and pathogenesis of CSFV have been investigated extensively. Despite efforts by many government authorities to stamp out the disease from national pig populations, the disease remains widespread in some regions and seems to be waiting for the reintroduction and the next round of disease outbreaks. These highlight the necessity and urgency of developing more effective approaches to eradicate the challenging CSFV. In this study, we successfully produced anti-CSFV transgenic pigs and confirmed that these transgenic pigs could effectively limit the growth of CSFV in vivo and in vitro and that the disease resistance traits in the TG founders can be stably transmitted to their F1-generation offspring. This study suggests that these TG pigs can improve the well-being of livestock and contribute to offer potential benefits over commercial vaccination. The use of these TG pigs can improve the well-being of livestock and substantially reduce CSFV-related economic losses.

genomics↗

bioRxiv2018

Transcription initiation defines kinetoplast RNA boundaries

Mitochondrial genomes are often transcribed into polycistronic primary RNAs punctuated by tRNAs whose excision defines mature RNA boundaries. Although kinetoplast DNA lacks tRNA genes, it is commonly held that monophosphorylated 5'-ends of functional molecules typify precursor partitioning by an unknown endonuclease. To the contrary, we demonstrate that in Trypanosoma brucei individual mRNAs and rRNAs are independently synthesized as 3' extended precursors. The transcription-defined 5' terminus is converted into monophosphorylated state by the 5' pyrophosphohydrolase complex, termed PPsome, which is activated by RNA editing substrate binding complex (RESC). Most guide RNAs lack PPsome recognition sites and, therefore, remain triphosphorylated. We provide evidence that both 5' pyrophosphate removal and 3' adenylation are essential for mRNA stabilization. Furthermore, we uncover a mechanism by which antisense RNA-controlled 3'-5' exonucleolytic trimming defines mRNA 3'-end. We conclude that mitochondrial mRNAs and rRNAs are transcribed and processed as insulated units irrespective of their genomic location.\n\nSignificanceIt is commonly held that in trypanosomes both mitochondrial DNA strands are transcribed into polycistronic precursors. These primary RNAs are presumably partitioned into individual pre-mRNAs by a \"cryptic\" endonuclease. We challenged the polycistronic transcription/ endonuclease model after revealing precursor processing by 3'-5' degradation. This work demonstrates individual transcription of each gene and mRNA 5'-end definition by the first incorporated nucleotide triphosphate. We have uncovered the stabilizing role of 5' triphosphate to monophosphate conversion and identified a protein complex responsible for this reaction. We have discovered antisense noncoding RNA originating near mRNA 3' end and showed that a duplex formation modulates exonuclease activity to delimit the mature 3' end. Collectively, our findings reveal mechanisms by which transcription defines both mRNA termini.

biochemistry↗

bioRxiv2018

Template switching causes artificial junction formation and false identification of circular RNAs

Hundreds of thousands of putative circular RNAs have been identified through deep sequencing and bioinformatic analyses. However, the circularity of these putative RNA circles has not been experimentally validated due to limited methodologies currently available. We reported here that the template-switching capability of commonly used reverse transcriptases (e.g., SuperScript II) leads to the formation of artificial junction sequences, and consequently misclassification of large linear RNAs as RNA circles. Use of reverse transcriptases without terminal transferase activity (e.g., MonsterScript) for cDNA synthesis is critical for the identification of physiological circular RNAs. We also report two methods, MonsterScript junction PCR and high-resolution melting curve analyses, which can reliably distinguish circular RNAs from their linear forms and thus, can be used to discover and validate true circular RNAs.\n\nSignificance StatementThe vast majority of circular RNAs were identified through computational detection of junction sequences in the deep sequencing reads because these unique fusion sequences represent back-splicing events. We found that artificial junction sequences could be formed through template switching (TS) when MMLV-derived reverse transcriptases, e.g., SuperScript II, are used to synthesize cDNAs. Thus, many of the reported circular RNAs may not be RNA circles, but rather experimental artifacts. Fake circular RNAs can be avoided by using reverse transcriptases without terminal transferase activity (e.g., MonsterScript) for cDNA synthesis. We developed two novel methods, MonsterScript junction PCR and high-resolution melting curve analyses, for distinguishing circular RNAs from their linear form.

molecular biology↗

bioRxiv2018

Sub-voxel light-sheet microscopy for high-resolution, high-throughput volumetric imaging of large biomedical specimens

A key challenge when imaging whole biomedical specimens is how to quickly obtain massive cellular information over a large field of view (FOV). Here, we report a sub-voxel light-sheet microscopy (SLSM) method enabling high-throughput volumetric imaging of mesoscale specimens at cellular-resolution. A non-axial, continuous scanning strategy is used to rapidly acquire a stack of large-FOV images with three-dimensional (3-D) nanoscale shifts encoded. Then by adopting a sub-voxel-resolving procedure, the SLSM method models these low-resolution, cross-correlated images in the spatial domain and iteratively recovers a 3-D image with improved resolution throughout the sample. This technique can surpass the optical limit of a conventional light-sheet microscope by more than three times, with high acquisition speeds of gigavoxels per minute. As demonstrated by quick reconstruction (minutes to hours) of various samples, e.g., 3-D cultured cells, an intact mouse heart, mouse brain, and live zebrafish embryo, the SLSM method presents a high-throughput way to circumvent the tradeoff between intoto mapping of large-scale tissue (>100 mm3) and isotropic imaging of single-cell (~1-m resolution). It also eliminates the need of complicated mechanical stitching or precisely modulated illumination, using a simple light-sheet setup and fast graphics-processing-unit (GPU)-based computation to achieve high-throughput, high-resolution 3-D microscopy, which could be tailored for a wide range of biomedical applications in pathology, histology, neuroscience, etc.

bioengineering↗

bioRxiv2017

Community assessment of cancer drug combination screens identifies strategies for synergy prediction

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patients tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.

bioinformatics↗

bioRxiv2016

A community-based collaboration to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer patients

BackgroundDocetaxel has a demonstrated survival benefit for metastatic castration-resistant prostate cancer (mCRPC). However, 10-20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and managing risk factors for toxicity remains an ongoing challenge for health care providers and patients. Prospective identification of high-risk patients for early discontinuation has the potential to assist clinical decision-making and can improve the design of more efficient clinical trials. In partnership with Project Data Sphere (PDS), a non-profit initiative facilitating clinical trial data-sharing, we designed an open-data, crowdsourced DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge for developing models to predict early discontinuation of docetaxel\n\nMethodsData from the comparator arms of four phase III clinical trials in first-line mCRPC were obtained from PDS, including 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 598 patients treated with docetaxel, prednisone/prednisolone, and placebo in the VENICE trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, and 528 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Early discontinuation was defined as treatment stoppage within three months due to adverse treatment effects. Over 150 clinical features including laboratory values, medical history, lesion measures, prior treatment, and demographic variables were curated and made freely available for model building for all four trials. The ASCENT2, VENICE, and MAINSAIL trial data sets formed the training set that also included patient discontinuation status. The ENTHUSE 33 trial, with patient discontinuation status hidden, was used as an independent validation set to evaluate model performance. Prediction performance was assessed using area under the precision-recall curve (AUPRC) and the Bayes factor was used to compare the performance between prediction models.\n\nResultsThe frequency of early discontinuation was similar between training (ASCENT2, VENICE, and MAINSAIL) and validation (ENTHUSE 33) sets, 12.3% versus 10.4% of docetaxel-treated patients, respectively. In total, 34 independent teams submitted predictions from 61 different models. AUPRC ranged from 0.088 to 0.178 across submissions with a random model performance of 0.104. Seven models with comparable AUPRC scores (Bayes factor [≤]; 3) were observed to outperform all other models. A post-challenge analysis of risk predictions generated by these seven models revealed three distinct patient subgroups: patients consistently predicted to be at high-risk or low-risk for early discontinuation and those with discordant risk predictions. Early discontinuation events were two-times higher in the high-versus low-risk subgroup and baseline clinical features such as presence/absence of metastatic liver lesions, and prior treatment with analgesics and ACE inhibitors exhibited statistically significant differences between the high- and low-risk subgroups (adjusted P < 0.05). An ensemble-based model constructed from a post-Challenge community collaboration resulted in the best overall prediction performance (AUPRC = 0.230) and represented a marked improvement over any individual Challenge submission. A\n\nFindingsOur results demonstrate that routinely collected clinical features can be used to prospectively inform clinicians of mCRPC patients risk to discontinue docetaxel treatment early due to adverse events and to the best of our knowledge is the first to establish performance benchmarks in this area. This work also underscores the \"wisdom of crowds\" approach by demonstrating that improved prediction of patient outcomes is obtainable by combining methods across an extended community. These findings were made possible because data from separate trials were made publicly available and centrally compiled through PDS.

bioinformatics↗