Subtype C avian metapneumovirus (aMPV-C), is an important pathogen that can cause egg-drop and acute respiratory diseases in poultry. To date, aMPV-C infection has not been documented in Muscovy ducks in China. Here, we isolated and characterized an aMPV-C, designated S-01, which has caused severe respiratory disease and noticeable egg drop in Muscovy duck flocks in south China since 2010. Electron microscopy showed that the isolate was an enveloped virus exhibiting multiple morphologies with a diameter of 20–500 nm. The S-01 strain was able to produce a typical cytopathic effect (CPE) on Vero cells and cause death in 10- to 11-day-old Muscovy duck embryos. In vivo infection of layer Muscovy ducks with the isolate resulted in typical clinical signs and pathological lesions similar to those seen in the original infected cases. We report the first complete genomic sequence of aMPV-C from Muscovy ducks. A phylogenetic analysis strongly suggested that the S-01 virus belongs to the aMPV-C family, sharing 92.3%-94.3% of nucleotide identity with that of aMPV-C, and was most closely related to the aMPV-C strains isolated from Muscovy ducks in France. The deduced eight main proteins (N, P, M, F, M2, SH, G and L) of the novel isolate shared higher identity with hMPV than with other aMPV (subtypes A, B and D). S-01 could bind a monoclonal antibody against the F protein of hMPV. Together, our results indicate that subtype-C aMPV has been circulating in Muscovy duck flocks in South China, and it is urgent for companies to develop new vaccines to control the spread of the virus in China.
Abstract Antibodies play critical roles in neutralizing viral infections and are increasingly used as therapeutic drugs and diagnostic tools. Structural studies on virus-antibody immune complexes are important for better understanding the molecular mechanisms of antibody-mediated neutralization and also provide valuable information for structure-based vaccine design. Cryo-electron microscopy (cryo-EM) has recently matured as a powerful structural technique for studying bio-macromolecular complexes. When combined with X-ray crystallography, cryo-EM provides a routine approach for structurally characterizing the immune complexes formed between icosahedral viruses and their antibodies. In this review, recent advances in the structural understanding of virus-antibody interactions are outlined for whole virions with icosahedral T = pseudo 3 (picornaviruses) and T = 3 (flaviviruses) architectures, focusing on the dynamic nature of viral shells in different functional states. Glycoprotein complexes from pleomorphic enveloped viruses are also discussed as immune complex antigens. Improving our understanding of viral epitope structures using virus-based platforms would provide a fundamental road map for future vaccine development.
As the most conspicuous structure of Eastern China, the Tan-Lu fault arouses the most controversies on its initial time of the sinistral strike-slip movement. In the northern Anhui Province, the Bengbu uplift, which consists of basement rocks of the North China Craton, is situated to the west of the Tan-Lu fault, and the Weigang granitic gneiss is located to the east. Three samples were analyzed by in situ zircon U-Pb dating and P-T calculation from the Bengbu uplift: WS055-1 (albite granitic gneiss), WS055-3 (amphibolite) and WS021-1 (granitic gneiss). All of them have experienced medium-high grade metamorphism with the P-T condition about 0.53±0.01 GPa and 742±5℃. LA-ICP-MS U-Pb zircon dating results suggest a main crystallization age at Late Mesoarchean to Paleoproterozoic. New geochronological data from zircons of the basement rocks can be preliminarily divided into four groups around (1) 2886±28 Ma, (2) 22481±18 Ma to 2440±28 Ma, (3) 2163±55 Ma, and (4) 1944±89 Ma, respectively. The oldest age indicates the presence of the oldest juvenile crustal materials at the late Mesoarchean (2.89 Ga). These age groups correspond well to the tectonic events of the North China Craton. One granitic gneiss (FD019-1) was collected from the west of the Zhangbaling group. Zircons of the Sample FD019-1 have structure of core-mantle-rim, which yield ages at 667-271 Ma for core, 228±3 Ma for mantle, and 211±2 Ma for rim, respectively. According to the regional tectonic framework, FD019-1 can be considered as derived from the Qinling-Dabie-Sulu orogenic belt along the northern margin of the Yangtze Block. Hence, as the boundary between the North China Craton and the Yangtze Block, the western boundary of the Tan-Lu fault in northern Anhui Province is the Zhuding-Shimenshan fault. Combined with previous work, the initial time of the Tan-Lu wrench fault is also discussed.
Fat deposition is a complex economic trait regulated by polygenic genetic basis and environmental factors. Therefore, integrating multi-omics data to uncover its internal regulatory mechanism has attracted extensive attention. Here, we performed genomics and transcriptomics analysis to detect candidates affecting subcutaneous fat (SCF) deposition in beef cattle. The association of 770K SNPs with the backfat thickness captured nine significant SNPs within or near 11 genes. Additionally, 13 overlapping genes regarding fat deposition were determined via the analysis of differentially expressed genes and weighted gene co-expression network analysis (WGCNA). We then calculated the correlations of these genes with BFT and constructed their interaction network. Finally, seven biomarkers including ACACA, SCD, FASN, ACOX1, ELOVL5, HACD2, and HSD17B12 were screened. Notably, ACACA, identified by the integration of genomics and transcriptomics, was more likely to exert profound effects on SCF deposition. These findings provided novel insights into the regulation mechanism underlying bovine fat accumulation.
The Global Anticoagulant Registry in the FIELD–Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012–2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3–2.3) versus 2.3 (IQR 1.8–2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32–9.35) vs 4.34 (4.16–4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. http://www.clinicaltrials.gov. Unique identifier: NCT01090362.
Single-stranded DNA-binding proteins (SSBs) interact with single-stranded DNA (ssDNA) to form filamentous structures with various degrees of cooperativity, as a result of intermolecular interactions between neighboring SSB subunits on ssDNA. However, it is still challenging to perform structural studies on SSB-ssDNA filaments at high resolution using the most studied SSB models, largely due to the intrinsic flexibility of these nucleoprotein complexes. In this study, HaLEF-3, an SSB protein from Helicoverpa armigera nucleopolyhedrovirus, was used for in vitro assembly of SSB-ssDNA filaments, which were structurally studied at atomic resolution using cryo-electron microscopy. Combined with the crystal structure of ssDNA-free HaLEF-3 octamers, our results revealed that the three-dimensional rearrangement of HaLEF-3 induced by an internal hinge-bending movement is essential for the formation of helical SSB-ssDNA complexes, while the contacting interface between adjacent HaLEF-3 subunits remains basically intact. We proposed a local cooperative SSB-ssDNA binding model, in which, triggered by exposure to oligonucleotides, HaLEF-3 molecules undergo ring-to-helix transition to initiate continuous SSB-SSB interactions along ssDNA. Unique structural features revealed by the assembly of HaLEF-3 on ssDNA suggest that HaLEF-3 may represent a new class of SSB.
Depending on excellent prediction ability, machine learning has been considered the most powerful implement to analyze high-throughput sequencing genome data. However, the sophisticated process of tuning hyperparameters tremendously impedes the wider application of machine learning in animal and plant breeding programs. Therefore, we integrated an automatic tuning hyperparameters algorithm, tree-structured Parzen estimator (TPE), with machine learning to simplify the process of using machine learning for genomic prediction. In this study, we applied TPE to optimize the hyperparameters of Kernel ridge regression (KRR) and support vector regression (SVR). To evaluate the performance of TPE, we compared the prediction accuracy of KRR-TPE and SVR-TPE with the genomic best linear unbiased prediction (GBLUP) and KRR-RS, KRR-Grid, SVR-RS, and SVR-Grid, which tuned the hyperparameters of KRR and SVR by using random search (RS) and grid search (Gird) in a simulation dataset and the real datasets. The results indicated that KRR-TPE achieved the most powerful prediction ability considering all populations and was the most convenient. Especially for the Chinese Simmental beef cattle and Loblolly pine populations, the prediction accuracy of KRR-TPE had an 8.73% and 6.08% average improvement compared with GBLUP, respectively. Our study will greatly promote the application of machine learning in GP and further accelerate breeding progress.
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