IMPORTANCE: EV-A71 is one of many viruses that cause HFMD, a common syndrome that largely affects infants and children. HFMD usually causes only mild illness with no long-term consequences. Occasionally, however, severe infection may arise, especially in very young children, causing neurological complications and even death. EV-A71 is highly contagious and is associated with the most severe HFMD cases, with large and frequent epidemics of the virus recorded worldwide. Although major advances have been made in the development of a potential EV-A71 vaccine, there is no current prevention and little is known about the patterns and dynamics of EV-A71 spread. In this study, we utilize full-length genome sequence data obtained from HFMD patients in Viet Nam, a geographical region where the disease has been endemic since 2003, to characterize the phylodynamics of this important emerging virus.
CONCLUSION: All mutations are private except one mutation; p.Ile1254Phe was found in three unrelated families. Identification of a recurrent p.Ile1254Phe mutation suggests the presence of a common and unique mutation in our population. Our study also expands the mutational spectrum of the CPS1 gene.
Methods: A total of 3843 participants (7,020 healthy eyes) were enrolled from the Singapore Epidemiology of Eye Diseases (SEED) study, a population-based study composing of three major ethnic groups-Malay, Indian, and Chinese-in Singapore. Ocular examinations were performed, and spectral-domain optical coherence tomography (SD-OCT) was used to measure circumpapillary RNFL thickness. We selected 35 independent glaucoma-associated genetic loci for analysis. An linear regression model was conducted to determine the association of these variants with circumpapillary RNFL, assuming an additive genetic model. We conducted association analysis in each of the three ethnic groups, followed by a meta-analysis of them.
Results: The mean age of the included participants was 59.4 ± 8.9 years, and the mean RFNL thickesss is 92.3 ± 11.2 µm. In the meta-analyses, of the 35 glacuoma loci, we found that only SIX6 was significantly associated with reduction in global RNFL thickness (rs33912345; β = -1.116 um per risk allele, P = 1.64E-05), and the effect size was larger in the inferior RNFL quadrant (β = -2.015 µm, P = 2.9E-6), and superior RNFL quadrant (β = -1.646 µm, P = 6.54E-5). The SIX6 association were consistently observed across all three ethnic groups. Other than RNFL, we also found several genetic varaints associated with vertical cuo-to-disc ratio (ATOH7, CDKN2B-AS1, and TGFBR3-CDC7), rim area (SIX6 and CDKN2B-AS1), and disc area (SIX6, ATOH7, and TGFBR3-CDC7). The association of SIX6 rs33912345 with NRFL thickness remained similar after further adjusting for disc area and 3 other disc parameter associated SNPs (ATOH7, CDKN2B-AS1, and TGFBR3-CDC7).
Conclusions: Of the 35 glaucoma identified risk loci, only SIX6 is significantly and independently associated with thinner RNFL. Our study further supports the involvement of SIX6 with RNFL thickness and pathogensis of glaucoma.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.