In vivo analysis of Caenorhabditis elegans noncoding RNA promoter motifs

<p>Abstract</p> <p>Background</p> <p>Noncoding RNAs (ncRNAs) play important roles in a variety of cellular processes. Characterizing the transcriptional activity of ncRNA promoters is therefore a critical step toward understanding the complex cellular roles of ncRNAs.</p> <p>Results</p> <p>Here we present an <it>in vivo </it>transcriptional analysis of three <it>C. elegans </it>ncRNA upstream motifs (UM1-3). Transcriptional activity of all three motifs has been demonstrated, and mutational analysis revealed differential contributions of different parts of each motif. We showed that upstream motif 1 (UM1) can drive the expression of green fluorescent protein (GFP), and utilized this for detailed analysis of temporal and spatial expression patterns of 5 SL2 RNAs. Upstream motifs 2 and 3 do not drive GFP expression, and termination at consecutive T runs suggests transcription by RNA polymerase III. The UM2 sequence resembles the tRNA promoter, and is actually embedded within its own short-lived, primary transcript. This is a structure which is also found at a few plant and yeast loci, and may indicate an evolutionarily very old dicistronic transcription pattern in which a tRNA serves as a promoter for an adjacent snoRNA.</p> <p>Conclusion</p> <p>The study has demonstrated that the three upstream motifs UM1-3 have promoter activity. The UM1 sequence can drive expression of GFP, which allows for the use of UM1::GFP fusion constructs to study temporal-spatial expression patterns of UM1 ncRNA loci. The UM1 loci appear to act in concert with other upstream sequences, whereas the transcriptional activities of the UM2 and UM3 are confined to the motifs themselves.</p

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: \textit{how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment?} Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive and time-consuming, and the collections can only cover limited scenarios. To this end, we propose the first open adversarial scene generator V2XP-ASG that can produce realistic, challenging scenes for modern LiDAR-based multi-agent perception systems. V2XP-ASG learns to construct an adversarial collaboration graph and simultaneously perturb multiple agents' poses in an adversarial and plausible manner. The experiments demonstrate that V2XP-ASG can effectively identify challenging scenes for a large range of V2X perception systems. Meanwhile, by training on the limited number of generated challenging scenes, the accuracy of V2X perception systems can be further improved by 12.3\% on challenging and 4\% on normal scenes. Our code will be released at https://github.com/XHwind/V2XP-ASG.Comment: ICRA 2023, see https://github.com/XHwind/V2XP-AS

Pharmacoproteomic characterisation of human colon and rectal cancer

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. © 2017 The Authors. Published under the terms of the CC BY 4.0 licens

Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS

Nano-flow liquid chromatography tandem mass spectrometry (nano-flow LC-MS/MS) is the mainstay in proteome research because of its excellent sensitivity but often comes at the expense of robustness. Here we show that micro-flow LC-MS/MS using a 1x150 mm column shows excellent reproducibility of chromatographic retention time (2000 samples of human cell lines, tissues and body fluids. Deep proteome analysis identifies >9000 proteins and >120,000 peptides in 16 h and sample multiplexing using tandem mass tags increases throughput to 11 proteomes in 16 h. The system identifies >30,000 phosphopeptides in 12 h and protein-protein or protein-drug interaction experiments can be analyzed in 20 min per sample. We show that the same column can be used to analyze >7500 samples without apparent loss of performance. This study demonstrates that micro-flow LC-MS/MS is suitable for a broad range of proteomic applications

Systematic identification and characterization of chicken (Gallus gallus) ncRNAs

Recent studies have demonstrated that non-coding RNAs (ncRNAs) play important roles during development and evolution. Chicken, the first genome-sequenced non-mammalian amniote, possesses unique features for developmental and evolutionary studies. However, apart from microRNAs, information on chicken ncRNAs has mainly been obtained from computational predictions without experimental validation. In the present study, we performed a systematic identification of intermediate size ncRNAs (50–500 nt) by ncRNA library construction and identified 125 chicken ncRNAs. Importantly, through the bioinformatics and expression analysis, we found the chicken ncRNAs has several novel features: (i) comparative genomic analysis against 18 sequenced vertebrate genomes revealed that the majority of the newly identified ncRNA candidates is not conserved and most are potentially bird/chicken specific, suggesting that ncRNAs play roles in lineage/species specification during evolution. (ii) The expression pattern analysis of intronic snoRNAs and their host genes suggested the coordinated expression between snoRNAs and their host genes. (iii) Several spatio-temporal specific expression patterns suggest involvement of ncRNAs in tissue development. Together, these findings provide new clues for future functional study of ncRNAs during development and evolution

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte

Sig2GRN : a software tool linking signaling pathway with gene regulatory network for dynamic simulation

Background: Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. Methods: A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Results: Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. Conclusions: As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design.MOE (Min. of Education, S’pore)Published versio

Facile Synthesis for Benzo-1,4-Oxazepine Derivatives by Tandem Transformation of C-N Coupling/C-H Carbonylation

A tandem transformation of C-N coupling/C-H carbonylation has been developed for the synthesis of benzo-1,4-oxazepine pharmaceutically derivatives. Notably, this reaction was accomplished by various phenylamine with ally halides under carbon dioxide atmosphere employing 2-(2-dimethylamino-vinyl)-1H-inden-1-olcatalyzed. Furthermore, under the optimized conditions, various benzo-1,4-oxazepine derivatives were obtained in good yields. Finally, a plausible CuI/CuIII mechanism of C-N coupling/C-H carbonylation transformation was proposed