JQ-1 ameliorates schistosomiasis liver granuloma in mice by suppressing male and female reproductive systems and egg development of Schistosoma japonicum

Schistosomiasis is a serious and widespread parasitic disease caused by infection with Schistosoma. Because the parasite’s eggs are primarily responsible for schistosomiasis dissemination and pathogenesis, inhibiting egg production is a potential approach to control the spread and severity of the disease. The bromodomain and extra-terminal (BET) proteins represent promising targets for the development of epigenetic drugs against Schistosoma. JQ-1 is a selective inhibitor of the BET protein family. In the present study, JQ-1 was applied to S. japonicum in vitro. By using laser confocal scanning microscopy and EdU incorporation assays, we showed that application of JQ-1 to worms in vitro affected egg laying and the development of both the male and female reproductive systems. JQ-1 also inhibited the expression of the reproductive-related genes SjPlk1 and SjNanos1 in S. japonicum. Mice infected with S. japonicum were treated with JQ-1 during egg granuloma formation. JQ-1 treatment significantly reduced the size of the liver granulomas and levels of serum alanine aminotransferase and aspartate aminotransferase in mice and suppressed both egg laying and the development of male and female S. japonicum reproductive systems in vivo. Moreover, the mRNA expression levels of some proinflammatory cytokines were decreased in the parasites. Our findings suggest that JQ-1 treatment attenuates S. japonicum egg–induced hepatic granuloma due at least in part to suppressing the development of the reproductive system and egg production of S. japonicum. These findings further suggest that JQ-1 or other BET inhibitors warrant additional study as a new approach for the treatment or prevention of schistosomiasis.     

Electrospun Scaffold of Collagen and Polycaprolactone Containing ZnO Quantum Dots for Skin Wound Regeneration

Nanofibers(NFs)have been widely used in tissue engineering such as wound healing.In this work,the antibacterial ZnO quantum dots(ZnO QDs)have been incorporated into the biocompatible poly(ε-caprolactone)/collagen(PCL/Col)fibrous scaffolds for wound healing.The as-fabricated PCL-Col/ZnO fibrous scaffolds exhibited good swelling,antibacterial activity,and biodegradation behaviors,which were beneficial for the applications as a wound dressing.Moreover,the PCL-Col/ZnO fibrous scaffolds showed excellent cytocompatibility for promoting cell proliferation.The resultant PCL-Col/ZnO fibrous scaffolds containing vascular endothelial growth factor(VEGF)also exhibited promoted wound-healing effect through promoting expression of transforming growth factor-β(TGF-β)and the vascular factor(CD31)in tissues in the early stages of wound healing.This new electrospun fibrous scaffolds with wound-healing promotion and antibacterial property should be convenient for treating wound healing.     

Single-cell transcriptomic atlas of primate cardiopulmonary aging

Aging is a major risk factor for many diseases,especially in highly prevalent cardiopulmonary comorbidities and infectious diseases including Coronavirus Diseas...     

Global biogeography of warning coloration in the butterfly Danaus chrysippus

Warning coloration provides a textbook example of natural selection, but the frequent observation of polymorphism in aposematic species presents an evolutionary puzzle. We investigated biogeography and polymorphism of warning patterns in the widespread butterfly Danaus chrysippus using records from citizen science (n = 5467), museums (n = 8864) and fieldwork (n = 2586). We find that polymorphism in three traits controlled by known mendelian loci is extensive. Broad allele frequency clines, hundreds of kilometres wide, suggest a balance between long-range dispersal and predation of unfamiliar morphs. Mismatched clines for the white hindwing and forewing tip in East Africa are consistent with a previous finding that the black wingtip allele has spread recently in the region through hitchhiking with a heritable endosymbiont. Light/dark background coloration shows more extensive polymorphism. The darker genotype is more common in cooler regions, possibly reflecting a trade-off between thermoregulation and predator warning. Overall, our findings show how studying local adaptation at the global scale provides a more complete picture of the evolutionary forces involved.     

Definition of germ layer cell lineage alternative splicing programs reveals a critical role for Quaking in specifying cardiac cell fate

Alternative splicing is critical for development; however, its role in the specification of the three embryonic germ layers is poorly understood. By performing RNA-Seq on human embryonic stem cells (hESCs) and derived definitive endoderm, cardiac mesoderm, and ectoderm cell lineages, we detect distinct alternative splicing programs associated with each lineage. The most prominent splicing program differences are observed between definitive endoderm and cardiac mesoderm. Integrative multi-omics analyses link each program with lineage-enriched RNA binding protein regulators, and further suggest a widespread role for Quaking (QKI) in the specification of cardiac mesoderm. Remarkably, knockout of QKI disrupts the cardiac mesoderm-associated alternative splicing program and formation of myocytes. These changes arise in part through reduced expression of BIN1 splice variants linked to cardiac development. Mechanistically, we find that QKI represses inclusion of exon 7 in BIN1 pre-mRNA via an exonic ACUAA motif, and this is concomitant with intron removal and cleavage from chromatin. Collectively, our results uncover alternative splicing programs associated with the three germ lineages and demonstrate an important role for QKI in the formation of cardiac mesoderm.     

Chromosome-level genome assembly and characterization of Sophora Japonica

Sophora japonica is a medium-size deciduous tree belonging to Leguminosae family and famous for its high ecological, economic and medicinal value. Here, we reveal a draft genome of S. japonica, which was ∼511.49 Mb long (contig N50 size of 17.34 Mb) based on Illumina, Nanopore and Hi-C data. We reliably assembled 110 contigs into 14 chromosomes, representing 91.62% of the total genome, with an improved N50 size of 31.32 Mb based on Hi-C data. Further investigation identified 271.76 Mb (53.13%) of repetitive sequences and 31,000 protein-coding genes, of which 30,721 (99.1%) were functionally annotated. Phylogenetic analysis indicates that S. japonica separated from Arabidopsis thaliana and Glycine max ∼107.53 and 61.24 million years ago, respectively. We detected evidence of species-specific and common-legume whole-genome duplication events in S. japonica. We further found that multiple TF families (e.g. BBX and PAL) have expanded in S. japonica, which might have led to its enhanced tolerance to abiotic stress. In addition, S. japonica harbours more genes involved in the lignin and cellulose biosynthesis pathways than the other two species. Finally, population genomic analyses revealed no obvious differentiation among geographical groups and the effective population size continuously declined since 2 Ma. Our genomic data provide a powerful comparative framework to study the adaptation, evolution and active ingredients biosynthesis in S. japonica. More importantly, our high-quality S. japonica genome is important for elucidating the biosynthesis of its main bioactive components, and improving its production and/or processing.     

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.