Role of the dimerization domain of black carp STING during the antiviral innate immunity

Stimulator of interferon gene (STING) functions importantly as antiviral adaptor protein during the innate immune activation. The role of the dimerization domain (DD) of STING remains obscure although other domains of this molecule have been studied extensively. To clarify the mechanism of black carp STING (bcSTING) in the innate immunity, bcSTING-ΔDD (bcSTING without DD) and bcSTING-ΔCTT (bcSTING without CTT domain) were constructed and analyzed in this manuscript. The reporter assays revealed that the induced transcription of IFN promoters mediated by bcSTING-ΔDD were much higher than that of the wild type bcSTING; however, bcSTING-ΔCTT almost lost the activities to trigger the interferon (IFN) promoters transcription. The mRNA transcriptional levels of IFN and interferon stimulated genes (ISGs) in EPC cells expressing bcSTING-ΔDD were obviously higher than those of EPC cells expressing wild type bcSTING; however, the transcriptional levels of the above cytokines of EPC cells expressing bcSTING-ΔCTT were basically the same as those of control cells. EPC cells overexpressing bcSTING-DD showed stronger antiviral activity than those overexpressing wild-type bcSTING. Furthermore, the co-immunoprecipitation assay identified the self-interaction between bcSTING-ΔDD molecules. And it was interesting that the affinity between bcSTING-ΔDD and bcTBK1 was obviously stronger than that between bcTBK1 and wild-type bcSTING. Thus, our data suggests that DD of black carp STING plays a negative regulatory role in STING-mediated antiviral immunity, which provides a new perspective for further researching the function of fish STING in the innate immunity

DDX19 inhibits RLR/IRF3 mediated type I interferon signaling of black carp Mylopharyngodon piceus by restricting IRF3 from entering nucleus

Members of DExD/H-box helicase family are important receptors for detecting viral nucleic acids. Mammalian DExD/H-box RNA helicase 19 (DDX19) has been reported to play as a negative regulator of type I interferon (IFN). However, the role of teleost DDX19 during the innate immune response is still obscure. In this study, the DDX19 homolog of black carp (bcDDX19) has been cloned and characterized. The open reading frame (ORF) of bcDDX19 consists of 1440 nucleotides and encodes contains 479 amino acids. bcDDX19 migrates around 54 kDa in immunoblotting assay and is identified as a cytosolic protein by immunofluorescence staining. The qPCR results show that the transcription of bcDDX19 in host cells rises in response to Lipopolysaccharide (LPS), Poly (I:C), spring viremia of carp virus (SVCV) or grass carp reovirus (GCRV) stimulation. In the report assay, bcDDX19 suppressed bcIRF3-, but not bcIRF7-mediated transcription activities of bcIFNa and DrIFNφ1 promoter. Co-immunoprecipitation assays identify that bcDDX19 interacts with bcIRF3 but not bcIRF7. According, plaque assay results demonstrate that bcDDX19 dampens bcIRF3- but not bcIRF7-induced antiviral activity of EPC cells. Knockdown of bcDDX19 offers host cells the increase of mRNA levels of bcIFNa, bcMx1 and bcViperin, and the improved antiviral ability against SVCV. In addition, the nuclear translocation of bcIRF3 is dampened when co-expressed with bcDDX19. All of these findings demonstrate that bcDDX19 negatively regulates RLR/IRF3 mediated type I IFN signaling in the innate immune activation through restricting IRF3 from entering nucleus. •DDX19 negatively regulates the interferon signaling of black carp.•DDX19 targets IRF3 to regulate RLRs/IFN signaling.•DDX19 restricts IRF3 from entering nucleus

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset