Evolution of H9N2 influenza viruses from domestic poultry in Mainland China

AbstractH9N2 viruses have circulated in domestic poultry in Mainland China since 1994, and an inactivated vaccine has been used in chickens to control the disease since 1998. The present study analyzed 27 H9N2 avian influenza viruses that were isolated from chickens and ducks from 1996 to 2002. Infection studies indicated that most of the viruses replicate efficiently but none of them is lethal for SPF chickens. However, these viruses exhibit different phenotypes of replication in a mouse model. Five viruses, including 4 early isolates and one 2000 isolate, are not able to replicate in mice; 14 viruses replicate to moderate titers in mouse lungs and cause less than 5% weight loss, while other 8 viruses could replicate to high titers in the lungs and 7 of them induce 10–20% weight loss of the mice on day 5 after inoculation. Most of the viruses isolated after 1996 are antigenically different from the vaccine strain that is currently used in China. Three viruses isolated in central China in 1998 are resistant to adamantanes. Phylogenetic analysis revealed that all of the viruses originated from CK/BJ/1/94-like virus and formed multiple genotypes through complicated reassortment with QA/HK/G1/97-, CK/HK/G9/97-, CK/SH/F/98-, and TY/WI/66-like viruses. This study is a description of the previously uncharacterized H9N2 avian influenza viruses recently circulating in chickens and ducks in Mainland China. Our findings suggest that urgent attention should be paid to the control of H9N2 influenza viruses in animals and to the human's influenza pandemic preparedness

Identification of SNPs in MITF associated with beak color of duck

Introduction: Beak color—a pigment-related trait—is an important feature of duck breeds. Recently, little research has addressed genetic mechanism of the beak colors in poultry, whereas the process and the regulation factors of melanin deposition have been well described.Methods: To investigate the genetic mechanism of beak colors, we conducted an integrated analysis of genomic selection signatures to identify a candidate site associated with beak color. For this, we used black-billed (Yiyang I meat duck synthetic line H1, H2, H3&HF) and yellow-billed ducks (Cherry Valley ducks and white feather Putian black duck). Quantitative real-time PCR and genotyping approaches were used to verify the function of the candidate site.Results: We identified 3,895 windows containing 509 genes. After GO and KEGG enrichment analysis, nine genes were selected. Ultimately, MITF was selected by comparing the genomic differentiation (FST). After loci information selection, 41 extreme significantly different loci were selected, which are all located in intron regions of MITF and are in almost complete linkage disequilibrium. Subsequently, the site ASM874695v1:10:g.17814522T > A in MITF was selected as the marker site. Furthermore, we found that MITF expression is significantly higher in black-beaked ducks than in yellow-beaked ducks of the F2 generation (p < 0.01). After genotyping, most yellow-billed individuals are found with homozygous variant; at the same time, there are no birds with homozygous variant in black-billed populations, while the birds with homozygous and heterozygous variant share the same proportion.Conclusion:MITF plays a very critical role in the melanogenesis and melanin deposition of duck beaks, which can effectively affect the beak color. The MITF site, ASM874695v1:10:g.17814522T > A could be selected as a marker site for the duck beak color phenotype

Uncovering the potential role of oxidative stress in the development of periodontitis and establishing a stable diagnostic model via combining single-cell and machine learning analysis

BackgroundThe primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis.MethodsWe used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations.ResultsWe discovered 3 periodontitis-associated genes (CASP3, IL-1β, and TXN). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1β differed with the change of cell status.ConclusionThis study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM.MethodsIn this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals.Results4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy.Conclusions4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies

Identification of Amino Acids in HA and PB2 Critical for the Transmission of H5N1 Avian Influenza Viruses in a Mammalian Host

Since 2003, H5N1 influenza viruses have caused over 400 known cases of human infection with a mortality rate greater than 60%. Most of these cases resulted from direct contact with virus-contaminated poultry or poultry products. Although only limited human-to-human transmission has been reported to date, it is feared that efficient human-to-human transmission of H5N1 viruses has the potential to cause a pandemic of disastrous proportions. The genetic basis for H5N1 viral transmission among humans is largely unknown. In this study, we used guinea pigs as a mammalian model to study the transmission of six different H5N1 avian influenza viruses. We found that two viruses, A/duck/Guangxi/35/2001 (DKGX/35) and A/bar-headed goose/Qinghai/3/2005(BHGQH/05), were transmitted from inoculated animals to naïve contact animals. Our mutagenesis analysis revealed that the amino acid asparagine (Asn) at position 701 in the PB2 protein was a prerequisite for DKGX/35 transmission in guinea pigs. In addition, an amino acid change in the hemagglutinin (HA) protein (Thr160Ala), resulting in the loss of glycosylation at 158–160, was responsible for HA binding to sialylated glycans and was critical for H5N1 virus transmission in guinea pigs. These amino acids changes in PB2 and HA could serve as important molecular markers for assessing the pandemic potential of H5N1 field isolates

Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI

Objectives. To investigate the predictors of telomerase reverse transcriptase (TERT) promoter mutations in adults suffered from high-grade glioma (HGG) through radiomics analysis, develop a noninvasive approach to evaluate TERT promoter mutations. Methods. 126 adult patients with HGG (88 in the training cohort and 38 in the validation cohort) were retrospectively enrolled. Totally 5064 radiomics features were, respectively, extracted from three VOIs (necrosis, enhanced, and edema) in MRI. Firstly, an optimal radiomics signature (Radscore) was established based on LASSO regression. Secondly, univariate and multivariate logistic regression analyses were performed to investigate important potential variables as predictors of TERT promoter mutations. Besides, multiparameter models were established and evaluated. Eventually, an optimal model was visualized as radiomics nomogram for clinical evaluations. Results. 6 radiomics features were selected to build Radscore signature through LASSO regression. Among them, 5 were from necrotic VOIs and 1 was from enhanced ones. With univariate and multivariate analysis, necrotic volume percentages of core (CNV), Age, Cho/Cr, Lac, and Radscore were significantly higher in TERTm than in TERTw (p<0.05). 4 models were built in our study. Compared with Model B (Age, Cho/Cr, Lac, and Radscore), Model A (Age, Cho/Cr, Lac, Radscore, and CNV) has a larger AUC in both training (0.955 vs. 0.917, p=0.049) and validation (0.889 vs. 0.868, p=0.039) cohorts. It also has higher performances in net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) evaluation. Conclusively, Model A was visualized as a radiomics nomogram. Calibration curve shows a good agreement between estimated and actual probabilities. Conclusions. Age, Cho/Cr, Lac, CNV, and Radscore are important indicators for TERT promoter mutation predictions in HGG. Tumor necrosis seems to be closely related to TERT promoter mutations. Radiomics nomogram based on multiparameter MRI and CNV has higher prediction accuracies