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HVTN 505 is a preventative vaccine efficacy trial testing DNA followed by recombinant adenovirus serotype 5 (rAd5) in circumcised, Ad5-seronegative men and transgendered persons who have sex with men in the United States. Identified immune correlates of lower HIV-1 risk and a virus sieve analysis revealed that, despite lacking overall efficacy, vaccine-elicited responses exerted pressure on infecting HIV-1 viruses. To interrogate the mechanism of the antibody correlate of HIV-1 risk, we examined antigen-specific antibody recruitment of Fc[gamma] receptors (Fc[gamma]Rs), antibody-dependent cellular phagocytosis (ADCP), and the role of anti-envelope (anti-Env) IgG3. In a prespecified immune correlates analysis, antibody-dependent monocyte phagocytosis and antibody binding to Fc[gamma]RIIa correlated with decreased HIV-1 risk. Follow-up analyses revealed that anti-Env IgG3 breadth correlated with reduced HIV-1 risk, anti-Env IgA negatively modified infection risk by Fc effector functions, and that vaccine recipients with a specific Fc[gamma]RIIa single-nucleotide polymorphism locus had a stronger correlation with decreased HIV-1 risk when ADCP, Env-Fc[gamma]RIIa, and IgG3 binding were high. Additionally, Fc[gamma]RIIa engagement correlated with decreased viral load setpoint in vaccine recipients who acquired HIV-1. These data support a role for vaccine-elicited anti-HIV-1 Env IgG3, antibody engagement of FcRs, and phagocytosis as potential mechanisms for HIV-1 prevention., Introduction An efficacious vaccine that provides durable immunity remains a key priority in the fight against HIV-1. One strategy to identify an efficacious vaccine regimen is the identification of immune [...]

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver. [ABSTRACT FROM AUTHOR]

Inferring 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 computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series 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 (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. 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/). [ABSTRACT FROM AUTHOR]

The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution, leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.