Biology subjects

Lu, W.

Publications and source records attributed to Lu, W..

166 records · Page 10Linked to original sources

bioRxiv2018

N6-methyladenosine Binding Proteins Negatively Regulate HIV-1 Infectivity and Viral Production

BackgroundThe internal N6-methyladenosine (m6A) modification of cellular mRNA regulates post-transcriptional gene expression. The YTH domain family proteins (YTHDF1-3, or Y1-3) bind to m6A-modified cellular mRNA and modulate its metabolism and processing, thereby affecting protein translation in cells. We previously reported that HIV-1 RNA contains m6A modification and that Y1-3 proteins inhibit HIV-1 infection by decreasing HIV-1 reverse transcription. Here we extended our studies to further understand the mechanisms of Y1-3-mediated inhibition of HIV-1 infection and viral production.\n\nResultsOverexpression of Y1-3 proteins in HIV-1 target cells decreased viral genome RNA (gRNA) levels and inhibited early and late reverse transcription. Purified recombinant Y1-3 proteins preferentially bound to the m6A-modified 5 leader sequence of gRNA compared with its unmodified RNA counterpart, consistent with the strong binding of Y1-3 to HIV-1 gRNA in infected cells. HIV-1 mutants with two altered m6A modification sites in the 5 leader sequence of gRNA demonstrated significantly lower infectivity compared with wild-type HIV-1, confirming that these sites are important for viral infection. HIV-1 produced from cells with knockdown of endogenous Y1, Y3, or Y1-3 proteins together showed increased viral infectivity compared with HIV-1 produced from control cells. Interestingly, we found that Y1-3 proteins and HIV-1 Gag formed a complex with RNA in HIV-1-infected target cells.\n\nConclusionsOur results suggest the inhibitory effects of Y1-3 proteins on HIV-1 infection and provide new insight into the mechanisms of m6A modification of HIV-1 RNA in regulating viral replication, which clarify some discrepancies in the previously published studies in this area.

microbiology↗

bioRxiv2017

DEET as a feeding deterrent

The insect repellent N,N-diethyl-3-methylbenzamide (DEET), is a multimodal compound that acts as a spatial repellent as well as an irritant (contact repellent), thus being perceived by the insects olfactory and gustatory systems as an odorant and a tastant, respectively. Soon after DEET was developed, almost 6 decades ago, it was reported that it reduced mosquito feeding on blood mixed with this repellent. It is now known that the mosquito proboscis senses contact repellents with the tips (labella) of the labium, which remain in direct contact with the outer layers of the skin, while the stylets, including the feeding deterrent sensor (labrum), penetrate the skin. We designed a behavioral assay that allowed us to tease apart contact repellency from feeding deterrence. First, we demonstrate here that when DEET was mixed with blood and covered by Parafilm(R) layers, it did not leak to the outer surface. In our assays, the mean number of landings and duration of contacts with surfaces covering blood mixed with DEET or blood plus solvent (dimethyl sulfoxide) did not differ significantly. The feeding times, however, were significantly different. When blood was mixed either with 0.1 or 1% DEET, female southern house mosquitoes spent significantly less time feeding than the time spent feeding on blood mixed only with the solvent. By contrast, there were no significant differences in the mean times of feeding on blood containing 1% picaridin and blood plus solvent. Like DEET, the contact repellent and insecticide, permethrin, caused a significant reduction in feeding time. We, therefore, concluded, that in this context, DEET and permethrin act as feeding deterrents.

animal behavior and cognition↗

bioRxiv2017

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step towards linking tumor transcriptomic data with clinical outcomes. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.

bioinformatics↗

bioRxiv2016

Statistical testing and power analysis for brain-wide association study

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

neuroscience↗