Biology subjects

Cheng, W.

Publications and source records attributed to Cheng, W..

4 recordsLinked to original sources

bioRxiv2018

HiAlc Klebsiella pneumonia, one of potential chief culprits of non-alcoholic fatty liver disease: through generation of endogenous ethanol

Non-alcoholic fatty liver disease (NAFLD), a prelude of cirrhosis and hepatocellular carcinoma, is the most common chronic liver disease worldwide. NAFLD has been considerated to be associated with the composition of gut microbiota. However, causal relationship between change of gut microbiome and NAFLD remains unclear. Here we show that Klebsiella pneumoniae was significantly associated with NAFLD through inducing generation of endogenous ethanol. A strain of high alcohol-producing Klebsiella pneumoniae (HiAlc Kpn) was initially isolated from fecal samples of patient with non-alcoholic steatohepatitis (NASH) accompanied with auto-brewery syndrome (ABS). Gavage of HiAlc Kpn was capable of inducing murine model of fatty liver disease (FLD) in which had typical pathological changes of hepatic steatosis and similar liver gene expression profiles to those of alcohol intake in mice. Data derived from germ-free mice by gnotobiotic gavage further demonstrated that the HiAlc Kpn is the major cause of the changes in FLD mice. Furthermore, using proteomic and metabolitic analysis, we found that HiAlc Kpn induced generation of endogenous alcohol through the 2,3-butanediol fermentation pathway. More interestingly, the blood alcohol concentration was elevated in FLD mice induced by HiAlc Kpn after glucose intake. Clinical analysis showed that HiAlc Kpn were observed in up to 60% of patients with NAFLD. Our results suggested that HiAlc Kpn make important contribution to NAFLD, possibly through generation of the endogenous alcohol. Thus, targeting these bacteria might provide a novel therapeutic for clinical treatment of NAFLD.\n\nIn BriefFatty liver disease induced by high alcohol-producing Klebsiella pneumoniae\n\nCompeting Financial Interest StatementThe authors declare no conflicts of interest.

microbiology↗

bioRxiv2018

Long noncoding RNA ANRIL supports proliferation of adult T-cell leukemia cells through cooperation with EZH2

Adult T-cell leukemia (ATL) is a highly aggressive T-cell malignancy induced by human T-cell leukemia virus type 1 (HTLV-1) infection. Long noncoding RNA (lncRNA) plays a critical role in the development and progression of multiple human cancers. However, the function of lncRNA on HTLV-1-induced oncogenesis has not been elucidated. In the present study, we show that the expression of the lncRNA ANRIL was elevated in HTLV-1 infected cell lines and clinical ATL samples. E2F1 induced ANRIL transcription by enhancing its promoter activity. Knocking down of ANRIL in ATL cells repressed cellular proliferation and increased apoptosis in vitro and in vivo. As a mechanism for these actions, we found that ANRIL targeted EZH2, and activated the NF-{kappa}B pathway in ATL cells. This activation was independent of the histone methyltransferase (HMT) activity of EZH2, but required the formation of an ANRIL/EZH2/p65 ternary complex. Chromatin immunoprecipitation assay revealed that ANRIL/EZH2 enhanced p65 DNA binding capability. In addition, we observed that ANRIL/EZH2 complex repressed p21/CDKN1A transcription through H3K27 trimethylation of the p21/CDKN1A promoter. Taken together, our results implicate that lncRNA ANRIL, by cooperating with EZH2, supports the proliferation of HTLV-1 infected cells, which is thought to be critical for oncogenesis.\n\nIMPORTANCEHuman T-cell leukemia virus type 1 (HTLV-1) is the pathogen that causes adult T-cell leukemia (ATL), which is a unique malignancy of CD4+ T cells. A role for long noncoding RNA (lncRNA) in HTLV-1-mediated cellular transformation has not been described. In this study, we demonstrated that lncRNA ANRIL was important for maintaining proliferation of ATL cells in vitro and in vivo. ANRIL was shown to activate NF-{kappa}B signaling through forming a ternary complex with EZH2 and p65. Further, epigenetic inactivation of p21/CDKN1A was involved in the oncogenic function of ANRIL. To the best of our knowledge, this is the first study to address the regulatory role of the lncRNA ANRIL in ATL and provides an important clue to prevent or treat HTLV-1 associated human diseases.

cancer biology↗

bioRxiv2017

The functional and genetic associations of neuroimaging data: a toolbox

Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and to identify the roots of psychiatric diseases. However, the results generated by most neuroimaging studies, i.e., activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a Brain Annotation Toolbox (BAT), a toolbox that helps to generate functional and genetic annotations for neuroimaging results. The toolbox can take data from brain regions identified with an atlas, or from brain regions identified as activated in tasks, or from functional connectivity links or networks of links. Then, the voxel-level functional description from the Neurosynth database and the gene expression profile from the Allen Brain Atlas are used to generate functional and genetic knowledge for such region-level data. Parametric (Fishers exact test) or non-parametric (permutation test) statistical tests are adopted to identify significantly related functional descriptors and genes for the neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and gene co-expression networks are highly correlated for many major brain atlases. One application of BAT is to help provide functional and genetic annotations for the newly discovered regions with unknown functions, e.g., the 97 new regions identified in the Human Connectome Project. Importantly too, this toolbox can help understand differences between patients with psychiatric disorders and controls, and this is demonstrated using data for schizophrenia and autism, for which the functional and genetic annotations for the neuroimaging data differences between patients and controls are consistent with each other and help with the interpretation of the differences.

neuroscience↗

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↗