Biology subjects

Ou, W.

Publications and source records attributed to Ou, W..

3 recordsLinked to original sources

bioRxiv2018

Rapid CD4 cell loss is caused by specific CRF01_AE cluster with V3 signatures favoring CXCR4 usage

HIV-1 evolved into various genetic subtypes and circulating recombinant forms (CRFs) in the global epidemic, with the same subtype or CRF usually having similar phenotype. Being one of the worlds major CRFs, CRF01_AE infection was reported to associate with higher prevalence of CXCR4 (X4) viruses and faster CD4 decline. However, the underlying mechanisms remain unclear. We identified eight phylogenetic clusters of CRF01_AE in China and hypothesized that they may have different phenotypes. In the national HIV molecular epidemiology survey, we discovered that people infected by CRF01_AE cluster 4 had significantly lower CD4 count (391 vs. 470, p < 0.0001) and higher prevalence of predicted X4-using viruses (17.1% vs. 4.4%, p < 0.0001) compared to those infected by cluster 5. In a MSM cohort, X4-using viruses were only isolated from sero-convertors infected by cluster 4, which associated with rapid CD4 loss within the first year of infection (141 vs. 440, p = 0.01). Using co-receptor binding model, we identified unique V3 signatures in cluster 4 that favor CXCR4 usage. We demonstrate for the first time that HIV-1 phenotype and pathogenicity can be determined at the phylogenetic cluster level in a single subtype. Since its initial spread to human from chimpanzee in 1930s, HIV-1 remains undergoing rapid evolution in larger and more diverse population. The divergent phenotype evolution of two major CRF01_AE clusters highlights the importance in monitoring the genetic evolution and phenotypic shift of HIV-1 to provide early warning for the appearance of more pathogenic strains such as CRF01_AE cluster 4.\n\nSignificance StatementPast studies on HIV-1 evolution were mainly at the genetic level. This study provides well-matched genotype and phenotype data and demonstrates disparate pathogenicity of two major CRF01_AE clusters. While both CRF01_AE cluster 4 and cluster 5 are mainly spread through the MSM route, cluster 4 but not cluster 5 causes fast CD4 loss, which is associated with the higher prevalence CXCR4 viruses in cluster 4. The higher CXCR4 use tendency in cluster 4 is derived from its unique V3 loop favoring CXCR4 binding. This study for the first time demonstrates disparate HIV-1 phenotype between different phylogenetic clusters. It is important to monitor HIV-1 evolution at both the genotype and phenotype level to identify and control more pathogenic HIV-1 strains.

microbiology↗

bioRxiv2018

Mate-pair Library Construction with Controlled Polymerization Enables Comprehensive Structural Rearrangement Detection

The diversity of disease presentations warrants one single assay for detection and delineation of various genomic disorders. Herein, we describe a gel-free and biotin-capture-free mate-pair method through coupling Controlled Polymerizations by Adapter-Ligation (CP-AL). We first demonstrated the feasibility and ease-of-use in monitoring DNA nick-translation and primer extension by limiting the nucleotide input. By coupling these two controlled polymerizations by a reported non-conventional adapter ligation reaction 3 branch ligation, we evidenced that CP-AL significantly increased DNA-circularization efficiency (by 4-fold) and was applicable for different sequencing methods but at a faction of current cost. Its advantages were further demonstrated by fully elimination of small-insert-contaminated (by 39.3-fold) with a ~50% increment of physical coverage, and producing uniform genome/exome coverage and the lowest chimeric rate. It achieved single-nucleotide variants detection with sensitivity and specificity up to 97.3 and 99.7%, respectively, compared with data from small-insert libraries. In addition, this method can provide a comprehensive delineation of structural rearrangements, evidenced by a potential diagnosis in a patient with oligo-atheno-terato-spermia. Moreover, it enables accurate mutation identification by integration of genomic variants from different aberration types. Overall, it provides a potential single-integrated solution for detecting various genomic variants, facilitating a genetic diagnosis in human diseases.

genomics↗

bioRxiv2018

Combined use of procalcitonin and C-reactive protein levels can help clinically diagnose bacterial co-infections in children infected with H1N1 influenza

ObjectiveThis study evaluated the diagnostic value of measuring the levels of procalcitonin (PCT) and C-reactive protein (CRP) to differentiate children co-infected with H1N1 influenza and bacteria from children infected with H1N1 influenza alone and to provide a reliable clinical diagnostic support system with improved accuracy and precision control.\n\nMethodsConsecutive patients (children aged <5 years) with laboratory-confirmed H1N1 influenza who were hospitalized or received outpatient care from a tertiary-care hospital in Canton, China between 1 January 2012 and 1 September 2017 were included in the present study. Laboratory results, including serum PCT and CRP levels, white blood cell (WBC) counts, and blood and sputum cultures, were analyzed. The predictive value of the combination of biomarkers versus either biomarker alone for diagnosing bacterial co-infections was evaluated using logistic regression analyses.\n\nResultsOf 3180 children infected with H1N1 influenza, 226 (7.1%) met the bacterial co-infection criteria, with Staphylococcus pneumoniae being the most commonly identified bacteria (36.28%). Significantly higher PCT (1.46 vs 0.21 ng/ml, p<0.001) and CRP (19.20 vs 5.10 mg/dl, p<0.001) levels were detected in the bacterial co-infection group than in the H1N1 infection only group. Multivariate logistic regression analysis showed independent associations between PCT (odds ratio [OR]: 1.73, 95% confidence interval [CI],1.34-2.42, p<0.001) and CRP levels (OR:1.09, 95% CI, 1.06-1.13, p<0.001) with bacterial co-infections. Using PCT or CRP levels alone, the areas under the curves (AUCs) for predicting bacterial co-infections were 0.801 (95%CI, 0.772-0.855) and 0.762 (95%CI, 0.722-0.803), respectively. Using a combination of PCT and CRP, the logistic regression-based model, Logit(P)=-1.912+0.546 PCT+0.087 CRP, showed significantly greater accuracy (AUC: 0.893, 95%CI: 0.842-0.934) than did the other three biomarkers.\n\nConclusionsThe combination of PCT and CRP levels could provide a useful method of distinguishing bacterial co-infections from an H1N1 influenza infection alone in children during the early disease phase. After further validation, the flexible model derived here could assist clinicians in decision-making processes.

microbiology↗