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Biology subjects

Shi, K.

Publications and source records attributed to Shi, K..

2 recordsLinked to original sources

The Antiviral and Cancer Genomic DNA Deaminase APOBEC3H Is Regulated by a RNA-Mediated Dimerization Mechanism

Human APOBEC3H and homologous single-stranded DNA cytosine deaminases are unique to mammals. These DNA editing enzymes function in innate immunity by restricting the replication of viruses and transposons. Misregulated APOBEC3H also contributes to cancer mutagenesis. Here we address the role of RNA in APOBEC3H regulation. APOBEC3H co-purifies with RNA as an inactive protein, and RNase A treatment yields enzyme preparations with stronger DNA deaminase activity. RNA binding-defective mutants are DNA hypermutators. Chromatography profiles and crystallographic data demonstrate a mechanism in which double-stranded RNA mediates enzyme dimerization. RNA binding is required for APOBEC3H cytoplasmic localization and for packaging into HIV-1 particles and antiviral activity. Related DNA deaminases including other APOBEC3 family members and the antibody gene diversification enzyme AID also bind RNA and are predicted to have a similar RNA binding motif suggesting mechanistic conservation and relevance to innate and adaptive immunity and to multiple diseases.\n\nHIGHLIGHTSRNA inhibits human APOBEC3H DNA cytosine deaminase activity\n\nRNA binding mutants are DNA hypermutators\n\nX-ray structure demonstrates an RNA duplex-mediated APOBEC3H dimerization mechanism\n\nRNA binding is required for packaging into HIV-1 particles and antiviral activity

microbiology

fastBMA: Scalable Network Inference and Transitive Reduction

BACKGROUNDInferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a novel and computationally efficient method for eliminating redundant indirect edges in the network.\n\nFINDINGSWe evaluated the performance of fastBMA on synthetic data and experimental genome-wide yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory efficient, parallel and distributed application that scales to human genome wide expression data. A 10,000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster.\n\nCONCLUSIONSfastBMA is a significant improvement over its predecessor ScanBMA. It is orders of magnitude faster and more accurate than other fast network inference methods such as LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable timeframe. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/).

systems biology