Biology subjects

Zheng, J.-J.

Publications and source records attributed to Zheng, J.-J..

5 recordsLinked to original sources

bioRxiv2024

Exercise-stimulated Resolvin Biosynthesis in Adipose Tissue is Abrogated by High Fat Diet-induced Adrenergic Deficiency

ObjectiveDiet-induced white adipose tissue inflammation is associated with insulin resistance and metabolic perturbations. Conversely, exercise (Exe) protects against the development of chronic inflammation and insulin resistance independent of changes in weight; however, the mechanisms remain largely unknown. We have recently shown that, through adrenergic stimulation of macrophages, exercise promotes resolution of acute peritoneal inflammation by enhancing the biosynthesis of specialized pro-resolving lipid mediators (SPMs). In this study, we sought to determine if exercise stimulates pro-resolving pathways in adipose tissue and whether this response is modified by diet. Specifically, we hypothesized that high fat diet feeding disrupts exercise-stimulated resolution by inhibiting adrenergic signaling, priming the development of chronic inflammation in adipose tissue (AT). Approach and ResultsTo explore the dietary dependence of the pro-resolving effects of Exe, mice were fed either a control or high-fat diet (HFD) for 2 weeks prior to, and throughout, a 4 wk period of daily treadmill running. Glucose handling, body weight and composition, and exercise performance were evaluated at the end of the feeding and exercise interventions. Likewise, catecholamines and their biosynthetic enzymes were measured along with AT SPM biosynthesis and macrophage phenotype and abundance. When compared with sedentary controls (Sed), macrophages isolated from mice exposed to 4 wk of exercise display elevated expression of the SPM biosynthetic enzyme Alox15, while whole AT SPM levels and anti-inflammatory CD301+ M2 macrophages increased. These changes were dependent upon diet as 6 wk of feeding with HFD abrogated the pro-resolving effect of exercise when compared with control diet-fed animals. Interestingly, exercise-induced epinephrine production was inhibited by HFD, which diminished expression of the epinephrine biosynthetic enzyme phenylethanolamine N-methyltransferase (PNMT) in adrenal glands. ConclusionTaken together, these results suggest that a diet high in fat diminishes the pro-resolving effects of exercise in adipose tissue via decreasing the biosynthesis of catecholamines. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=192 SRC="FIGDIR/small/608014v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@1b508e7org.highwire.dtl.DTLVardef@5b9d8corg.highwire.dtl.DTLVardef@c46707org.highwire.dtl.DTLVardef@48083e_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Structure and dynamics of human disease-complication network

A complication is an unanticipated disease arisen following, induced by a disease, a treatment or a procedure. We compile the Human Disease-Complication Network from the medical data and investigate the characteristics of the network. It is observed that the modules of the network are dominated by the classes of diseases. The relations between modules are unveiled in detail. Three nontrivial motifs are identified from the network. We further simulate the dynamics of motifs with the Boolean dynamic model. Each motif represents a specific dynamic behavior, which is potentially functional in the disease system, such as generating temporal progressions and governing the responses to fluctuating external stimuli. Author summaryAdvances in molecular biology lead to a new discipline of network medicine, investigating human diseases in a networked structure perspective. Recently, clinical records have been introduced to the research of complex networks of diseases. An important available medical dataset that has been overlooked so far is the complications of diseases, which are vital for human beings. We compile the Human Disease-Complication Network, representing the causality between the upstream diseases and their downstream complications. This work not only helps us to comprehend why certain groups of diseases appear collectively, but also provides a new paradigm to investigate the dynamics of disease progression. For clinical applications, the investigation of complications may yield new approaches to disease prevention, diagnosis and treatment.

bioinformatics↗

bioRxiv2020

Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China

Emerging and re-emerging infectious diseases, such as SARS, MERS, Zika and highly pathogenic influenza present a major threat to public health1-3. Despite intense research effort, how, when and where novel diseases appear are still the source of considerable uncertainly. A severe respiratory disease was recently reported in the city of Wuhan, Hubei province, China. At the time of writing, at least 62 suspected cases have been reported since the first patient was hospitalized on December 12nd 2019. Epidemiological investigation by the local Center for Disease Control and Prevention (CDC) suggested that the outbreak was associated with a sea food market in Wuhan. We studied seven patients who were workers at the market, and collected bronchoalveolar lavage fluid (BALF) from one patient who exhibited a severe respiratory syndrome including fever, dizziness and cough, and who was admitted to Wuhan Central Hospital on December 26th 2019. Next generation metagenomic RNA sequencing4 identified a novel RNA virus from the family Coronaviridae designed WH-Human-1 coronavirus (WHCV). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that WHCV was most closely related (89.1% nucleotide similarity similarity) to a group of Severe Acute Respiratory Syndrome (SARS)-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) previously sampled from bats in China and that have a history of genomic recombination. This outbreak highlights the ongoing capacity of viral spill-over from animals to cause severe disease in humans.

pathology↗

bioRxiv2017

Anti-HIV activity prediction and enrichment identify novel human targets for Anti-HIV therapy

Human immunodeficiency virus (HIV) relies heavily on the host proteins to facilitate its entry and replication. Currently, more than 4000 human proteins are recorded to be involved in the HIV-1 life cycle. Identifying appropriate anti-HIV targets from so many host proteins is crucial to anti-HIV drug development, but a challenging work. Here we combined anti-HIV activity prediction and enrichment analysis to identify novel human targets for anti-HIV therapy. We firstly developed an accurate prediction tool named Anti-HIV-Predictor (AUC>0.96) to predict the anti-HIV activity of given compounds. Using this tool, we predicted 10488 anti-HIV compounds from ChEMBLdb. Then, based on this result and relationships of targets and compounds, we inferred 73 anti-HIV targets that enriched with anti-HIV compounds. The functional annotation and network analysis revealed that they directly or indirectly interact with 20 HIV proteins through neuropeptide signaling, GPCR signaling, cell surface signaling pathway, and so on. Nearly half of these targets overlap with the NCBI HIV dataset. However, the percentage of known therapeutic targets in these targets is significantly higher than that in the NCBI HIV dataset. After a series of feature analysis, we identified 13 novel human targets with high potential as anti-HIV targets, the inhibitors of which have experimentally confirmed anti-HIV activity. It is noteworthy that the inhibitors of REN and CALCA have better anti-HIV activity than CCR5 inhibitors. Taken together, our findings provide novel human targets for the host-oriented anti-HIV drug development and should significantly advance current anti-HIV research.

bioinformatics↗

bioRxiv2016

Efficient repositioning of approved drugs as anti-HIV agents using Anti-HIV-Predictor

Development of new, effective and affordable drugs against HIV is urgently needed. In this study, we developed a worlds first web server called Anti-HIV-Predictor (http://bsb.kiz.ac.cn:70/hivpre) for predicting anti-HIV activity of given compounds. This server is rapid and accurate (accuracy >93% and AUC > 0.958). We applied the server to screen 1835 approved drugs for anti-HIV therapy. Totally 67 drugs were predicted to have anti-HIV activity, 25 of which are anti-HIV drugs. Then we experimentally evaluated 35 predicted new anti-HIV compounds by assays of syncytia formation, p24 quantification, cytotoxicity. Finally, we repurposed 7 approved drugs (cetrorelix, dalbavancin, daunorubicin, doxorubicin, epirubicin, idarubicin and valrubicin) as new anti-HIV agents. The original indication of these drugs is involved in a variety of diseases such as female infertility and cancer. Anti-HIV-Predictor and the 7 repurposed anti-HIV agents provided here demonstrate the efficacy of this strategy for discovery of new anti-HIV agents.

bioinformatics↗