Kojima, Y. | Search bioRxiv

bioRxiv2018

Comparative evaluation of the GeeniusTM HIV 1/2 Confirmatory Assay and the HIV-1 and HIV-2 Western blots in the Japanese population

Accurate diagnosis of earlier HIV infection is essential for treatment and prevention. Currently, confirmation tests of HIV infection in Japan are performed using Western blot (WB), but WB has several limitations including low sensitivity and cross-reactivity between HIV-1 and HIV-2 antibodies. To address these problems, a new HIV testing algorithm and a more reliable confirmation and HIV-1/2 differentiation assay are required. The Bio-Rad Geenius HIV-1/2 Confirmatory Assay (Geenius) has recently been approved and recommended for use in the revised guidelines for diagnosis of HIV infection by the Center for Disease Control and Prevention (USA). We made comprehensive comparison of the performance of Geenius and the Bio-Rad NEW LAV BLOT 1 and 2 (NLB 1 and 2) which are WB kits for HIV-1 and HIV-2, respectively, to examine if Geenius is a suitable alternative to these WB assays which are now being used in HIV testing in Japan. A total of 166 HIV-1 positive samples (146 from patients with established HIV-1 infection and 20 from patients with acute infection), five HIV-1 seroconversion panels containing 21 samples and 30 HIV-2 positive samples were used. In addition, a total of 140 HIV negative samples containing 10 false-positives on screening tests were examined. The sensitivity of Geenius and NLB 1 for HIV-1 positive samples was 99.3% and 98.6%, respectively. Geenius provided more positive results in the samples from acute infections and detected positivity 0 to 32 days earlier in seroconversion panels than NLB 1. NLB 2 gave positive results in 12.3% of HIV-1 positive samples. The sensitivity of both Geenius and NLB 2 for HIV-2 positive samples was 100%. The specificity of Geenius, NLB 1 and NLB 2 was 98.5%, 81.5% and 90.0%, respectively. Geenius is an attractive alternative to WB for confirmation and differentiation of HIV-1 and HIV-2 infections. The adaptation of Geenius to the HIV testing algorithm may be advantageous for rapid diagnosis and the reduction of testing costs.

molecular biology↗

bioRxiv2017

An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis

The spatial patterns of gene expression in early Drosophila melanogaster embryogenesis have been studied experimentally and theoretically to reveal the molecular basis of morphogenesis. In particular, the gene regulatory network (GRN) of gap genes has been investigated through mathematical modeling and simulation. Although these simulation-based approaches are useful for describing complex dynamics and have revealed several important regulations in spatial patterning, they are computationally intensive because they optimize GRN with iterative simulation. Recently, the advance of experimental technologies is enabling the acquisition of comprehensive spatial expression data, and an efficient algorithm will be necessary to analyze such large-scale data. In this research, we developed an efficient algorithm to infer the GRN based on a linear reaction-diffusion model. First, we qualitatively analyzed the GRNs of gap genes and pair-rule genes based on our algorithm and showed that two mutual repressions are fundamental regulations. Then, we inferred the GRN from gap gene data, and identified asymmetric regulations in addition to the two mutual repressions. We analyzed the effect of these asymmetric regulations on spatial patterns, and showed that they have the potential to adjust peak position. Our algorithm runs in sub-second time, which is significantly smaller than the runtime of simulation-based approaches (between 8 and 160 h, for exmaple). Neverthe-less, our inferred GRN was highly correlated with the simulation-based GRNs. We also analyzed the gap gene network of Clogmia albipunctata and showed that different mutual repression regulations might be important in comparison with those of Drosophila melanogaster. As our algorithm can infer GRNs efficiently and can be applied to several different network analysis, it will be a valuable approach for analyzing large-scale data.

bioinformatics↗