Indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) catalyze the commitment step of the kynurenine (KYN) metabolic pathway. Traditionally the immunosuppressive effect of IDO1 ...has been attributed mainly to reduced levels of tryptophan, which activates the kinase general control nonderepressible 2 (GCN2). Emerging data have shed light on an unexpected role of the ligand-activated transcription factor aryl hydrocarbon receptor (AhR) in transducing the tumor immune escape function imparted by IDO1 and TDO2. AhR activation by the IDO1/TDO2 product KYN leads to the generation of immune-tolerant dendritic cells (DCs) and regulatory T cells, which collectively foster a tumor immunological microenvironment that is defective in recognizing and eradicating cancer cells. Multiple IDO1 inhibitors have been evaluated in clinical trials. There are novel modalities downstream of IDO1/TDO2 for pharmacological interventions. We review recent progress and future perspectives in targeting the IDO1/TDO2–KYN–AhR signaling pathway for the development of novel cancer immunotherapies.
IDO1 and TDO2 catalyze the commitment and rate-limiting step of the KYN metabolic pathway that produces the endogenous AhR agonist KYN. Activation of AhR by KYN contributes to the immunosuppressive tumor microenvironment and supports cancer immune escape.
Three differential therapeutic modalities, each intercepting a unique node of the IDO1/TDO2–KYN–AhR enzymatic/signaling cascade, have been pursued for the development of novel cancer immunotherapies.
Inhibition of IDO1/TDO2 prevents the formation of the endogenous AhR ligand KYN. At least four small-molecule selective IDO1 inhibitors are in clinical development for the treatment of advanced cancers.
Systemic depletion of KYN prevents its engagement with AhR. Engineered kynureninase degrades extracellular KYN and shows remarkable efficacy in mouse tumor models.
Inhibition of AhR activation by synthetic AhR modulators: an advantage of AhR antagonists is to broadly inhibit the immunosuppressive effect of any endogenous and exogenous AhR ligands.
The recognition of microbial nucleic acids is a major mechanism by which the immune system detects pathogens. Cyclic GMP-AMP (cGAMP) synthase (cGAS) is a cytosolic DNA sensor that activates innate ...immune responses through production of the second messenger cGAMP, which activates the adaptor STING. The cGAS-STING pathway not only mediates protective immune defense against infection by a large variety of DNA-containing pathogens but also detects tumor-derived DNA and generates intrinsic antitumor immunity. However, aberrant activation of the cGAS pathway by self DNA can also lead to autoimmune and inflammatory disease. Thus, the cGAS pathway must be properly regulated. Here we review the recent advances in understanding of the cGAS-STING pathway, focusing on the regulatory mechanisms and roles of this pathway in heath and disease.
The small protein ubiquitin is a central regulator of a cell's life and death. Ubiquitin is best known for targeting protein destruction by the 26S proteasome. In the past few years, however, ...nonproteolytic functions of ubiquitin have been uncovered at a rapid pace. These functions include membrane trafficking, protein kinase activation, DNA repair, and chromatin dynamics. A common mechanism underlying these functions is that ubiquitin, or polyubiquitin chains, serves as a signal to recruit proteins harboring ubiquitin-binding domains, thereby bringing together ubiquitinated proteins and ubiquitin receptors to execute specific biological functions. Recent advances in understanding ubiquitination in protein kinase activation and DNA repair are discussed to illustrate the nonproteolytic functions of ubiquitin in cell signaling.
•Applied the LDA model to discover transportation research themes from 17163 journal articles.•Quantified general patterns of research topics at the level of journal, year, and country/region.•The ...results could benefit different parties in transportation research community.
Transportation research is a key area in both science and engineering. In this paper, we present an empirical analysis of 17,163 articles published in 22 leading transportation journals from 1990 to 2015. We apply a latent Dirichlet allocation (LDA) model on article abstracts to infer 50 key topics. We show that those characterized topics are both representative and meaningful, mostly corresponding to established sub-fields in transportation research. These identified fields reveal a research landscape for transportation. Based on the results of LDA, we quantify the similarity of journals and countries/regions in terms of their aggregated topic distributions. By measuring the variation of topic distributions over time, we find some general research trends, such as topics on sustainability, travel behavior and non-motorized mobility are becoming increasingly popular over time. We also carry out this temporal analysis for each journal, observing a high degree of consistency for most journals. However, some interesting anomaly, such as special issues on particular topics, are detected from temporal variation as well. By quantifying the temporal trends at the country/region level, we find that countries/regions display clearly distinguishable patterns, suggesting that research communities in different regions tend to focus on different sub-fields. Our results could benefit different parties in the academic community—including researchers, journal editors and funding agencies—in terms of identifying promising research topics/projects, seeking for candidate journals for a submission, and realigning focus for journal development.
Microbial infections are recognized by the innate immune system through germline-encoded pattern recognition receptors (PRRs). As most microbial pathogens contain DNA and or RNA during their life ...cycle, nucleic acid sensing has evolved as an essential strategy for host innate immune defense. Pathogen-derived nucleic acids with distinct features are recognized by specific host PRRs localized in endolysosomes and the cytosol. Activation of these PRRs triggers signaling cascades that culminate in the production of type I interferons and proinflammatory cytokines, leading to induction of an antimicrobial state, activation of adaptive immunity, and eventual clearance of the infection. Here, we review recent progress in innate immune recognition of nucleic acids upon microbial infection, including pathways involving endosomal Toll-like receptors, cytosolic RNA sensors, and cytosolic DNA sensors. We also discuss the mechanisms by which infectious microbes counteract host nucleic acid sensing to evade immune surveillance.
•Presented a probabilistic tensor learning approach for SHM data imputation and forecasting.•Proposed an efficient incremental learning scheme to deal with streaming data.•Developed a Bayesian tensor ...decomposition framework for missing data recovery.•Integrated tensor decomposition and vector autoregression for response forecasting.•Demonstrated effectiveness of the approach on a concrete bridge with over three-year records.
There has been increased interest in missing sensor data imputation, which is ubiquitous in the field of structural health monitoring (SHM) due to discontinuous sensing caused by sensor malfunction. Recent development in Bayesian temporal factorization models for high-dimensional time series analysis has provided an effective tool to solve both imputation and prediction problems. However, for large datasets, the default Bayesian temporal factorization model becomes inefficient since the model has to be fully retrained when new data arrives. A potential solution is to train the model using a short time window covering only most recent data; however, by doing so, we may miss some critical dynamics and long-term dependencies which can only be identified from a longer time window. To address this fundamental issue in temporal factorization models, this paper presents an incremental Bayesian matrix/tensor learning scheme to achieve efficient imputation and prediction of structural response in long-term SHM. In particular, a spatiotemporal tensor is first constructed followed by Bayesian tensor factorization that extracts latent features for missing data imputation. To enable structural response forecasting based on long-term and incomplete sensing data, we develop an incremental learning scheme to effectively update the Bayesian temporal factorization model. The performance of the proposed approach is validated on continuous field-sensing data (including strain and temperature records) of a concrete bridge, based on the assumption that strain time histories are highly correlated to temperature recordings. The results indicate that the proposed probabilistic tensor learning framework is accurate and robust even in the presence of large rates of random missing, structured missing and their combination. The effect of rank selection on the imputation and prediction performance is also investigated. The results show that a better estimation accuracy can be achieved with a higher rank for random missing whereas a lower rank for structured missing.
Non-alcoholic fatty liver disease (NAFLD) is recognized as the most frequent classification of liver disease around the globe. Along with the sequencing technologies, gut microbiota has been regarded ...as a vital factor for the maintenance of human and animal health and the mediation of multiple diseases. The modulation of gut microbiota as a mechanism affecting the pathogenesis of NAFLD is becoming a growing area of concern. Recent advances in the communication between gut and hepatic tissue pave novel ways to better explain the molecular mechanisms regarding the pathological physiology of NAFLD. In this review, we recapitulate the current knowledge of the mechanisms correlated with the development and progression of NAFLD regulated by the gut microbiome and gut-liver axis, which may provide crucial therapeutic strategies for NAFLD. These mechanisms predominantly involve: (1) the alteration in gut microbiome profile; (2) the effects of components and metabolites from gut bacteria (e.g., lipopolysaccharides (LPS), trimethylamine-N-oxide (TMAO), and N,N,N-trimethyl-5-aminovaleric acid (TMAVA)); and (3) the impairment of intestinal barrier function and bile acid homeostasis. In particular, the prevention and therapy of NAFLD assisted by nutritional strategies are highlighted, including probiotics, functional oligosaccharides, dietary fibers, ω-3 polyunsaturated fatty acids, functional amino acids (L-tryptophan and L-glutamine), carotenoids, and polyphenols, based on the targets excavated from the gut-liver axis.
•YAP delayed the lipid oxidation and colour changes of GCS during cold storage.•Chlorogenic acid was the primary component that showed the preservative effects.•YAP delayed the degradation of SMP and ...protected its functional properties.•YAP protected the gel properties of GCS and extended its sensory shelf life.
The aim of this study was to investigate the effects of young apple polyphenols (YAP) on the quality of grass cap surimi (GCS) during storage at 4°C. The addition of YAP into GCS was found to be effective in delaying lipid oxidation, soluble myofibrillar protein (SMP) degradation and changes of L∗, a∗ and b∗ values of GCS. Chlorogenic acid was screened to be the primary component showing preservative effects. YAP was shown to protect the functional properties of SMP during cold storage, retarding both the decrease in emulsifying activity and stability, and the increase in surface hydrophobicity of SMP. Additionally, the loss of gel strength and texture of GCS with YAP were significantly (P<0.05) lower than that of GCS without YAP during cold storage. Therefore, YAP may be developed as a natural antioxidant to maintain the quality and to extend the shelf life of freshwater fish surimi.
cGMP-AMP (cGAMP) synthase (cGAS) is a cytosolic DNA sensor that activates innate immune responses. cGAS catalyzes the synthesis of cGAMP, which functions as a second messenger that binds and ...activates the adaptor protein STING to induce type I interferons (IFNs) and other immune modulatory molecules. Here we show that cGAS is indispensable for the antitumor effect of immune checkpoint blockade in mice. Wild-type, but not cGAS-deficient, mice exhibited slower growth of B16 melanomas in response to a PD-L1 antibody treatment. Consistently, intramuscular delivery of cGAMP inhibited melanoma growth and prolonged the survival of the tumor-bearing mice. The combination of cGAMP and PD-L1 antibody exerted stronger antitumor effects than did either treatment alone. cGAMP treatment activated dendritic cells and enhanced cross-presentation of tumor-associated antigens to CD8 T cells. These results indicate that activation of the cGAS pathway is important for intrinsic antitumor immunity and that cGAMP may be used directly for cancer immunotherapy.
•We develop a new tensor completion framework-LSTC-for large-scale traffic data.•LSTC transforms the large problem into a series of small subproblems for each day.•We introduce unitary linear ...transforms to preserve the correlation among subproblems.•We use Tubal nuclear norm minimization to achieve global consistency.•We use quadratic variation minimization to achieve local smoothness.
Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data. However, despite the promising results, these approaches do not scale well to large data tensors. In this paper, we focus on addressing the missing data imputation problem for large-scale spatiotemporal traffic data. To achieve both high accuracy and efficiency, we develop a scalable tensor learning model—Low-Tubal-Rank Smoothing Tensor Completion (LSTC-Tubal)—based on the existing framework of Low-Rank Tensor Completion, which is well-suited for spatiotemporal traffic data that is characterized by multidimensional structure of location × time of day × day. In particular, the proposed LSTC-Tubal model involves a scalable tensor nuclear norm minimization scheme by integrating linear unitary transformation. Therefore, tensor nuclear norm minimization can be solved by singular value thresholding on the transformed matrix of each day while the day-to-day correlation can be effectively preserved by the unitary transform matrix. Before setting up the experiment, we consider some real-world data sets, including two large-scale 5-min traffic speed data sets collected by the California PeMS system with 11160 sensors: 1) PeMS-4W covers the data over 4 weeks (i.e., 288×28 time points), and 2) PeMS-8W covers the data over 8 weeks (i.e., 288×56 time points). We compare LSTC-Tubal with some state-of-the-art baseline models, and find that LSTC-Tubal can achieve competitively accuracy with a significantly lower computational cost. In addition, the LSTC-Tubal will also benefit other tasks in modeling large-scale spatiotemporal traffic data, such as network-level traffic forecasting.
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