Search

Summary: Atoh7 is transiently expressed in retinal progenitor cells (RPCs) and is required for retinal ganglion cell (RGC) differentiation. In humans, a deletion in a distal non-coding regulatory region upstream of ATOH7 is associated with optic nerve atrophy and blindness. Here, we functionally interrogate the significance of the Atoh7 regulatory landscape to retinogenesis in mice. Deletion of the Atoh7 enhancer structure leads to RGC deficiency, optic nerve hypoplasia, and retinal blood vascular abnormalities, phenocopying inactivation of Atoh7. Further, loss of the Atoh7 remote enhancer impacts ipsilaterally projecting RGCs and disrupts proper axonal projections to the visual thalamus. Deletion of the Atoh7 remote enhancer is also associated with the dysregulation of axonogenesis genes, including the derepression of the axon repulsive cue Robo3. Our data provide insights into how Atoh7 enhancer elements function to promote RGC development and optic nerve formation and highlight a key role of Atoh7 in the transcriptional control of axon guidance molecules.

Summary: Background: An intergenic region at chromosome 4q31 is one of the most significant regions associated with COPD susceptibility and lung function in GWAS. In this region, the implicated causal gene HHIP has a unique epithelial expression pattern in adult human lungs, in contrast to dominant expression in fibroblasts in murine lungs. However, the mechanism underlying the species-dependent cell type-specific regulation of HHIP remains largely unknown. Methods: We employed snATAC-seq analysis to identify open chromatin regions within the COPD GWAS region in various human lung cell types. ChIP-quantitative PCR, reporter assays, chromatin conformation capture assays and Hi-C assays were conducted to characterize the regulatory element in this region. CRISPR/Cas9-editing was performed in BEAS-2B cells to generate single colonies with stable knockout of the regulatory element. RT-PCR and Western blot assays were used to evaluate expression of HHIP and epithelial–mesenchymal transition (EMT)-related marker genes. Findings: We identified a distal enhancer within the COPD 4q31 GWAS locus that regulates HHIP transcription at baseline and after TGFβ treatment in a SMAD3-dependent, but Hedgehog-independent manner in human bronchial epithelial cells. The distal enhancer also maintains chromatin topological domains near 4q31 locus and HHIP gene. Reduced HHIP expression led to increased EMT induced by TGFβ in human bronchial epithelial cells. Interpretation: A distal enhancer regulates HHIP expression both under homeostatic condition and upon TGFβ treatment in human bronchial epithelial cells. The interaction between HHIP and TGFβ signalling possibly contributes to COPD pathogenesis. Funding: Supported by NIH grants R01HL127200, R01HL148667 and R01HL162783 (to X. Z).

The quantitative, accurate and efficient acquisition of tree phenotypes is the basis for forest “gene-phenotype-environment” studies. It also offers significant support for clarifying the genetic control mechanisms of tree traits. The application of unmanned aerial vehicle (UAV) remote sensing technology to the collection of phenotypic traits at an individual tree level quantitatively analyses tree phenology and directionally evaluates tree growth, as well as accelerating the process of forest genetics and breeding. In this study, with the help of high-resolution, high-overlap, multispectral images obtained by an UAV, combined with digital elevation models (DEMs) extracted from point clouds acquired by a backpack LiDAR, a high-throughput tree structure and spectral phenotypic traits extraction and a genetic selection were conducted in a trial of Eucalyptus clones in the State-owned Dongmen Forest Farm in the Guangxi Zhuang Autonomous Region. Firstly, we validated the accuracy of extracting the phenotypic parameters of individual tree growth based on aerial stereo photogrammetry point clouds. Secondly, on this basis, the repeatability of the tree growth traits and vegetation indices (VIs), the genetic correlation coefficients between the traits were calculated. Finally, the eucalypt clones were ranked by integrating a selection index of traits, and the superior genotypes were selected and their genetic gain predicted. The results showed a high accuracy of the tree height (H) extracted from the digital aerial photogrammetry (DAP) point cloud based on UAV images (R2 = 0.91, and RMSE = 0.56 m), and the accuracy of estimating the diameter at breast height (DBH) was R2 = 0.71, and RMSE = 0.75 cm. All the extracted traits were significantly different within the tree species and among the clones. Except for the crown width (CW), the clonal repeatability (Rc) of the traits were all above 0.9, and the individual repeatability values (Ri) were all above 0.5. The genetic correlation coefficient between the tree growth traits and VIs fluctuated from 0.3 to 0.5, while the best clones were EA14-15, EA14-09, EC184, and EC183 when the selection proportion was 10%. The purpose of this study was to construct a technical framework for phenotypic traits extraction and genetic analysis of trees based on unmanned aerial stereo photography point clouds and high-resolution multispectral images, while also exploring the application potential of this approach in the selective breeding of eucalypt clones.

Pan-cancer strategy, an integrative analysis of different cancer types, can be used to explain oncogenesis and identify biomarkers using a larger statistical power and robustness. Fine-mapping defines the casual loci, whereas genome-wide association studies (GWASs) typically identify thousands of cancer-related loci and not necessarily have a fine-mapping component. In this study, we develop a novel strategy to identify the causal loci using a pan-cancer and fine-mapping assumption, constructing the CAusal Pan-cancER gene (CAPER) score and validating its performance using internal and external validation on 1,287 individuals and 985 cell lines. Summary statistics of 15 cancer types were used to define 54 causal loci in 15 potential genes. Using the Cancer Genome Atlas (TCGA) training set, we constructed the CAPER score and divided cancer patients into two groups. Using the three validation sets, we found that 19 cancer-related variables were statistically significant between the two CAPER score groups and that 81 drugs had significantly different drug sensitivity between the two CAPER score groups. We hope that our strategies for selecting causal genes and for constructing CAPER score would provide valuable clues for guiding the management of different types of cancers.