Biology subjects

Xiao, G.

Publications and source records attributed to Xiao, G..

5 recordsLinked to original sources

bioRxiv2018

Screening of FDA-approved Drugs and Identification of Novel Lassa Virus Entry Inhibitors

Lassa virus (LASV) belongs to the Mammarenavirus genus (family Arenaviridae) and causes severe hemorrhagic fever in humans. At present, there are no Food and Drug Administration (FDA)-approved drugs or vaccines specific for LASV. Herein, high-throughput screening of an FDA-approved drug library was performed against LASV entry using a pseudo-type virus enveloping LASV glycoproteins. Two hit drugs, lacidipine and phenothrin, were identified as LASV entry inhibitors in the micromolar range. A mechanistic study revealed that both drugs inhibited LASV entry by blocking low-pH-induced membrane fusion. Moreover, lacidipine irreversibly bound to the LASV glycoprotein complex (GPC), resulting in virucidal activity. Adaptive mutant analyses demonstrated that replacement of T40, located in the ectodomain of the stable-signal peptide (SSP), with lysine (K) conferred LASV resistance to lacidipine without apparent loss of the viral growth profile. Furthermore, lacidipine showed antiviral activity and specificity against both LASV and the Guanarito virus (GTOV), which is also a category A new world arenavirus. Drug-resistant variants indicate that the V36M in ectodomain of SSP mutant and V436A in the transmembrane domain of GP2 mutant conferred GTOV resistance to lacidipine, suggesting that lacidipine might act via a novel mechanism other than calcium inhibition. This study shows that both lacidipine and phenothrin are candidates for LASV therapy, and the membrane-proximal external region of the GPC might provide an entry-targeted platform for inhibitors.

microbiology↗

bioRxiv2018

Obesity blocks oligodendrocyte precursor cell differentiation and impedes repair after white matter stroke

Obesity is a growing public health problem that increases rates of white matter atrophy and increases the likelihood of ischemic lesions within white matter. However, the cellular and molecular mechanisms that regulate these changes are unknown. We hypothesized that obesity may alter oligodendrocytes and myelin priming white matter for worsening injury and repair responses after ischemia. C57Bl/6 mice fed a high fat diet (60% kcal from fat) show increased numbers of oligodendrocyte precursor cells (OPCs), decreased myelin thickness with elevated g-ratios, and shorter paranodal axonal segments, indicating accelerated myelin turnover. Fate mapping of OPCs in PDGFR-CreERT;Rpl22tm1.1Psam mice demonstrated that OPC differentiation rates are enhanced by obesity. Gene expression analyses using a novel oligodendrocyte staging assay demonstrated OPC differentiation is blocked by obesity in between the pre-myelinating and myelinating stage. Using a model of subcortical white matter stroke, the number of stroke-responsive OPCs in obese mice was increased after stroke. At early time points after ischemic white matter stroke, spatial mapping of stroke-responsive OPCs indicates that obesity leads to increased OPCs at the edge of ischemic white matter lesions. At later time points, obesity results in increased OPCs within the ischemic lesion while reducing the number of GST-{pi}-positive mature oligodendrocytes in the lesion core. These data indicate that obesity disrupts normal white matter biology by blocking oligodendrocyte differentiation, leads to an exaggerated response of OPCs to white matter ischemia, and limits remyelination after stroke.\n\nSignificance StatementObesity is a growing public health crisis that specifically increases the development of white matter lesions and silent brain infarcts. This relationship implies a direct cellular effect on white matter yet direct evidence is limited. We modeled diet-induced obesity in mice and studied the effect on oligodendrocyte biology and the response to focal white matter stroke. Adult-onset obesity results in thinner myelin, compromised axonal microdomain structure, and blocks the differentiation of OPCs, leaving the white matter with increased numbers of OPCs. After focal white matter stroke in obese mice, the early OPC response to stroke is exaggerated while late reparative OPC differentiation is blocked. These results suggest that obesity specifically blocks OPC differentiation with consequence on brain repair after stroke.

neuroscience↗

bioRxiv2018

Development and Validation of a Nomogram Prognostic Model for Small-Cell Lung Cancer Patients

BackgroundSmall-cell lung cancer (SCLC) accounts for almost 15% of lung cancer cases in the United States. Nomogram prognostic models could greatly facilitate risk stratification and treatment planning, as well as more refined enrollment criteria for clinical trials. We developed and validated a new nomogram prognostic model for SCLC patients using a large SCLC patient cohort from the National Cancer Database (NCDB).\n\nMethodsClinical data of 24,680 SCLC patients diagnosed from 2004 to 2011 were used to develop the nomogram prognostic model. The model was then validated using an independent cohort of 9,700 SCLC patients diagnosed from 2012 to 2013. The prognostic performance was evaluated using p value, concordance index and integrated Area Under the (time dependent Receiver Operating Characteristic) Curve.\n\nResultsThe following variables were contained in the final prognostic model: age, gender, race, ethnicity, Charlson/Deyo Score, TNM Stage (assigned according to the AJCC 8th edition), treatment type (combination of surgery, radiation therapy and chemotherapy), and laterality. The model was validated in an independent testing group with a concordance index of 0.722 {+/-} 0.004 and an integrated AUC of 0.79. The nomogram model has a significantly higher prognostic accuracy than previously developed models, including the AJCC 8th edition TNM-staging system. We implemented the proposed nomogram and two previously published nomograms in an online webserver.\n\nConclusionsWe developed a nomogram prognostic model for SCLC patients, and validated the model using an independent patient cohort. The nomogram performs better than earlier models, including AJCC staging.

cancer biology↗

bioRxiv2018

Comprehensive analysis of lung cancer pathology images to discover tumor shape features that predict survival outcome

Pathology slide images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology slides is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology slides. From the identified regions, we extracted 22 well-defined tumor shape features and found that 15 of them were significantly associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial. A tumor shape-based prognostic model was developed and validated in an independent patient cohort (n=389). The predicted high-risk group had significantly worse survival than the low-risk group (p value = 0.0029). Predicted risk group serves as an independent prognostic factor (high-risk vs. low-risk, hazard ratio = 2.25, 95% CI 1.34-3.77, p value = 0.0022) after adjusting for age, gender, smoking status, and stage. This study provides new insights into the relationship between tumor shape and patient prognosis.

cancer biology↗

bioRxiv2018

Lung Cancer Explorer (LCE): an open web portal to explore gene expression and clinical associations in lung cancer

We constructed a lung cancer-specific database housing expression data and clinical data from over 6,700 patients in 56 studies. Expression data from 23 \"whole-genome\" based platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource - the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs normal differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.

cancer biology↗