RNA editing regulates host immune response and T cell homeostasis in SARS-CoV-2 infection

Adenosine to inosine (A-to-I) RNA editing by ADAR1 has been implicated in maintaining self-tolerance, preventing autoimmunity, and mediating antiviral immunity. Foreign viral double-stranded RNA triggers rapid interferon response and activates ADAR1 in the host immune system. Emerging data points to a role of ADAR1 A-to-I editing in the inflammatory response associated with severe COVID-19 disease. We identify A-to-I editing events within human whole transcriptome data from SARS-CoV-2 infected individuals, non-infected individuals, and individuals with other viral illnesses from nasopharyngeal swabs. High levels of RNA editing in host cells are associated with low SARS-CoV-2 viral load (p = 9.27 E-06), suggesting an inhibitory effect of ADAR1 on viral infection. Additionally, we find differentially expressed genes associated with RNA-modifications and interferon response. Single cell RNA-sequencing analysis of SARS-CoV-2 infected nasopharyngeal swabs reveals that cytotoxic CD8 T cells upregulate ADAR1 in COVID-19 positive samples (p = 0.0269). We further reveal ADAR1 expression increases with CD4 and CD8 T cell activation, and knockdown of ADAR1 leads to apoptosis and aberrant IL-2 secretion. Together, our data suggests A-to-I RNA editing is required to maintain healthy homeostasis of activated T cells to combat SARS-CoV-2 infection

An RNA editing fingerprint of cancer stem cell reprogramming

BackgroundDeregulation of RNA editing by adenosine deaminases acting on dsRNA (ADARs) has been implicated in the progression of diverse human cancers including hematopoietic malignancies such as chronic myeloid leukemia (CML). Inflammation-associated activation of ADAR1 occurs in leukemia stem cells specifically in the advanced, often drug-resistant stage of CML known as blast crisis. However, detection of cancer stem cell-associated RNA editing by RNA sequencing in these rare cell populations can be technically challenging, costly and requires PCR validation. The objectives of this study were to validate RNA editing of a subset of cancer stem cell-associated transcripts, and to develop a quantitative RNA editing fingerprint assay for rapid detection of aberrant RNA editing in human malignancies.MethodsTo facilitate quantification of cancer stem cell-associated RNA editing in exons and intronic or 3'UTR primate-specific Alu sequences using a sensitive, cost-effective method, we established an in vitro RNA editing model and developed a sensitive RNA editing fingerprint assay that employs a site-specific quantitative PCR (RESSq-PCR) strategy. This assay was validated in a stably-transduced human leukemia cell line, lentiviral-ADAR1 transduced primary hematopoietic stem and progenitor cells, and in primary human chronic myeloid leukemia stem cells.ResultsIn lentiviral ADAR1-expressing cells, increased RNA editing of MDM2, APOBEC3D, GLI1 and AZIN1 transcripts was detected by RESSq-PCR with improved sensitivity over sequencing chromatogram analysis. This method accurately detected cancer stem cell-associated RNA editing in primary chronic myeloid leukemia samples, establishing a cancer stem cell-specific RNA editing fingerprint of leukemic transformation that will support clinical development of novel diagnostic tools to predict and prevent cancer progression.ConclusionsRNA editing quantification enables rapid detection of malignant progenitors signifying cancer progression and therapeutic resistance, and will aid future RNA editing inhibitor development efforts

Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS)

NOTCH1 Signaling Promotes Human T-Cell Acute Lymphoblastic Leukemia Initiating Cell Regeneration in Supportive Niches

Leukemia initiating cells (LIC) contribute to therapeutic resistance through acquisition of mutations in signaling pathways, such as NOTCH1, that promote self-renewal and survival within supportive niches. Activating mutations in NOTCH1 occur commonly in T cell acute lymphoblastic leukemia (T-ALL) and have been implicated in therapeutic resistance. However, the cell type and context specific consequences of NOTCH1 activation, its role in human LIC regeneration, and sensitivity to NOTCH1 inhibition in hematopoietic microenvironments had not been elucidated.We established humanized bioluminescent T-ALL LIC mouse models transplanted with pediatric T-ALL samples that were sequenced for NOTCH1 and other common T-ALL mutations. In this study, CD34(+) cells from NOTCH1(Mutated) T-ALL samples had higher leukemic engraftment and serial transplantation capacity than NOTCH1(Wild-type) CD34(+) cells in hematopoietic niches, suggesting that self-renewing LIC were enriched within the NOTCH1(Mutated) CD34(+) fraction. Humanized NOTCH1 monoclonal antibody treatment reduced LIC survival and self-renewal in NOTCH1(Mutated) T-ALL LIC-engrafted mice and resulted in depletion of CD34(+)CD2(+)CD7(+) cells that harbor serial transplantation capacity.These results reveal a functional hierarchy within the LIC population based on NOTCH1 activation, which renders LIC susceptible to targeted NOTCH1 inhibition and highlights the utility of NOTCH1 antibody targeting as a key component of malignant stem cell eradication strategies

User Switching Behavior: AI Chatbots or Human Agents?

Although Artificial intelligence (AI) chatbots have been widely used as the first supports in online consumer service, many customers intend to switch from chatbots to human agents. However, there is a lack of knowledge about the reasons for such switching intention. To fill this gap, we build on the push-pull-mooring (PPM) model to theorize on user switching intention from AI chatbots to human agents. Based on the extensive review of the literature, we propose that the push and pull factors will positively affect individual switching intention and the mooring dimension will have a significant negative impact on the intention. Meanwhile, the relationships among push, pull category and switching intention are moderated by the mooring effects. Items scales will be developed and data will be collected through a laboratory experiment. Then ANOVA and structural equation model (SEM) will be applied to validate the model

The Generalization Gap in Offline Reinforcement Learning

Despite recent progress in offline learning, these methods are still trained and tested on the same environment. In this paper, we compare the generalization abilities of widely used online and offline learning methods such as online reinforcement learning (RL), offline RL, sequence modeling, and behavioral cloning. Our experiments show that offline learning algorithms perform worse on new environments than online learning ones. We also introduce the first benchmark for evaluating generalization in offline learning, collecting datasets of varying sizes and skill-levels from Procgen (2D video games) and WebShop (e-commerce websites). The datasets contain trajectories for a limited number of game levels or natural language instructions and at test time, the agent has to generalize to new levels or instructions. Our experiments reveal that existing offline learning algorithms struggle to match the performance of online RL on both train and test environments. Behavioral cloning is a strong baseline, outperforming state-of-the-art offline RL and sequence modeling approaches when trained on data from multiple environments and tested on new ones. Finally, we find that increasing the diversity of the data, rather than its size, improves performance on new environments for all offline learning algorithms. Our study demonstrates the limited generalization of current offline learning algorithms highlighting the need for more research in this area.Comment: Published as a conference paper at ICLR 2024; First two authors contributed equall

Experimental and Numerical Investigation of the Anti-Overturning Theory of Single-Column Pier Bridges

In recent years, overturning accidents at single-pier bridges have occurred frequently, resulting in significant property losses and safety accidents. Overturning accidents show that there are still many hidden dangers in the design, operation, and management of existing single-pier bridges. Therefore, this paper takes the K503 + 647.4 separated overpass of the Hegang–Dalian Expressway as the research object and carries out an onsite anti-overturning stability test of a single-column pier bridge. Through loading under various working conditions, the displacement changes of each support are measured, and the reaction changes of the supports are calculated. In the process of simulating the field test using the finite element program ANSYS, a rigid model based on ideal support and an elastic model considering beam deformation are established, and the accuracy of the elastic model is verified by comparison with the field-measured data. Furthermore, a series of parameters, such as the bridge side-span ratio, bridge span number, bearing spacing, loading position, and torsional rigidity, are varied, and finite element simulation is carried out on the basis of the elastic model. Through comparison of the results, a relationship between the parameters of the single-pier bridge and the anti-overturning ability is obtained, which provides a theoretical basis for anti-overturning design research and the effective reinforcement of single-pier bridges

OsCpn60β1 is Essential for Chloroplast Development in Rice (Oryza sativa L.)

The chaperonin 60 (Cpn60) protein is of great importance to plants due to its involvement in modulating the folding of numerous chloroplast protein polypeptides. In chloroplasts, Cpn60 is differentiated into two subunit types—Cpn60α and Cpn60β and the rice genome encodes three α and three β plastid chaperonin subunits. However, the functions of Cpn60 family members in rice were poorly understood. In order to investigate the molecular mechanism of OsCpn60β1, we attempted to disrupt the OsCpn60β1 gene by CRISPR/Cas9-mediated targeted mutagenesis in this study. We succeeded in the production of homozygous OsCpn60β1 knockout rice plants. The OsCpn60β1 mutant displayed a striking albino leaf phenotype and was seedling lethal. Electron microscopy observation demonstrated that chloroplasts were severely disrupted in the OsCpn60β1 mutant. In addition, OsCpn60β1 was located in the chloroplast and OsCpn60β1 is constitutively expressed in various tissues particularly in the green tissues. The label-free qualitative proteomics showed that photosynthesis-related pathways and ribosomal pathways were significantly inhibited in OsCpn60β1 mutants. These results indicate that OsCpn60β1 is essential for chloroplast development in rice