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Jia Huang Xiaobo Zhou Wenbo Wang Guangdong Zhou WenJie Zhang Zhen Gao Xiaoli Wu Wei Liu 《PLoS genetics》2022,18(6)
Keloid disorder is a tumour-like disease with invasive growth and a high recurrence rate. Genetic contribution is well expected due to the presence of autosomal dominant inheritance and various genetic mutations in keloid lesions. However, GWAS failed to reveal functional variants in exon regions but single nucleotide polymorphisms in the non-coding regions, suggesting the necessity of innovative genetic investigation. This study employed combined GWAS, RNA-sequence and Hi-C analyses to dissect keloid disorder genetic mechanisms using paired keloid tissues and normal skins. Differentially expressed genes, miRNAs and lncRNAs mined by RNA-sequence were identified to construct a network. From which, 8 significant pathways involved in keloid disorder pathogenesis were enriched and 6 of them were verified. Furthermore, topologically associated domains at susceptible loci were located via the Hi-C database and ten differentially expressed RNAs were identified. Among them, the functions of six molecules for cell proliferation, cell cycle and apoptosis were particularly examined and confirmed by overexpressing and knocking-down assays. This study firstly revealed unknown key biomarkers and pathways in keloid lesions using RNA-sequence and previously reported mutation loci, indicating a feasible approach to reveal the genetic contribution to keloid disorder and possibly to other diseases that are failed by GWAS analysis alone. 相似文献
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Tapio Schneider Oliver R. A. Dunbar Jinlong Wu Lucas Bttcher Dmitry Burov Alfredo Garbuno-Inigo Gregory L. Wagner Sen Pei Chiara Daraio Raffaele Ferrari Jeffrey Shaman 《PLoS computational biology》2022,18(6)
Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption. 相似文献