Person:

Zhou, Xiaobo

Background: Prior studies suggest a role for a variant (rs5743836) in the promoter of toll-like receptor 9 (TLR9) in asthma and other inflammatory diseases. We performed detailed genetic association studies of the functional variant rs5743836 with asthma susceptibility and asthma-related phenotypes in three independent cohorts. Methods: rs5743836 was genotyped in two family-based cohorts of children with asthma and a case-control study of adult asthmatics. Association analyses were performed using chi square, family-based and population-based testing. A luciferase assay was performed to investigate whether rs5743836 genotype influences TLR9 promoter activity. Results: Contrary to prior reports, rs5743836 was not associated with asthma in any of the three cohorts. Marginally significant associations were found with FEV1 and FVC (p = 0.003 and p = 0.008, respectively) in one of the family-based cohorts, but these associations were not significant after correcting for multiple comparisons. Higher promoter activity of the CC genotype was demonstrated by luciferase assay, confirming the functional importance of this variant. Conclusion: Although rs5743836 confers regulatory effects on TLR9 transcription, this variant does not appear to be an important asthma-susceptibility locus.

Background: Human peripheral blood monocytes (Mo) consist of subsets distinguished by expression of CD16 (FCγRIII) and chemokine receptors. Classical CD16- Mo express CCR2 and migrate in response to CCL2, while a minor CD16+ Mo subset expresses CD16 and CX3CR1 and migrates into tissues expressing CX3CL1. CD16+ Mo produce pro-inflammatory cytokines and are expanded in certain inflammatory conditions including sepsis and HIV infection. Results: To gain insight into the developmental relationship and functions of CD16+ and CD16- Mo, we examined transcriptional profiles of these Mo subsets in peripheral blood from healthy individuals. Of 16,328 expressed genes, 2,759 genes were differentially expressed and 228 and 250 were >2-fold upregulated and downregulated, respectively, in CD16+ compared to CD16- Mo. CD16+ Mo were distinguished by upregulation of transcripts for dendritic cell (DC) (SIGLEC10, CD43, RARA) and macrophage (MΦ) (CSF1R/CD115, MafB, CD97, C3aR) markers together with transcripts relevant for DC-T cell interaction (CXCL16, ICAM-2, LFA-1), cell activation (LTB, TNFRSF8, LST1, IFITM1-3, HMOX1, SOD-1, WARS, MGLL), and negative regulation of the cell cycle (CDKN1C, MTSS1), whereas CD16- Mo were distinguished by upregulation of transcripts for myeloid (CD14, MNDA, TREM1, CD1d, C1qR/CD93) and granulocyte markers (FPR1, GCSFR/CD114, S100A8-9/12). Differential expression of CSF1R, CSF3R, C1QR1, C3AR1, CD1d, CD43, CXCL16, and CX3CR1 was confirmed by flow cytometry. Furthermore, increased expression of RARA and KLF2 transcripts in CD16+ Mo coincided with absence of cell surface cutaneous lymphocyte associated antigen (CLA) expression, indicating potential imprinting for non-skin homing. Conclusion: These results suggest that CD16+ and CD16- Mo originate from a common myeloid precursor, with CD16+ Mo having a more MΦ – and DC-like transcription program suggesting a more advanced stage of differentiation. Distinct transcriptional programs, together with their recruitment into tissues via different mechanisms, also suggest that CD16+ and CD16- Mo give rise to functionally distinct DC and MΦ in vivo.

Background: The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. Results: Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. Conclusion: We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.

Chronic lymphocytic leukemia deletion gene 7 (Clld7) is a candidate tumor suppressor on chromosome 13q14. Clld7 encodes an evolutionarily conserved protein that contains an RCC1 domain plus broad complex, tramtrack, bric-a-brac (BTB) and POZ domains. In this study, we investigated the biological functions of Clld7 protein in inducible osteosarcoma cell lines. Clld7 induction inhibited cell growth, decreased cell viability, and increased gamma-H2AX staining under conditions of caspase inhibition, indicating activation of the DNA damage/repair pathway. Real-time PCR analysis in tumor cells and normal human epithelial cells revealed Clld7 target genes that regulate DNA repair responses. Furthermore, depletion of Clld7 in normal human epithelial cells conferred resistance to apoptosis triggered by DNA damage. Taken together, the biological actions of Clld7 are consistent with those of a tumor suppressor.

The Nrf2 (nuclear erythroid 2 p45-related factor-2) signaling pathway is known to play a pivotal role in a variety of oxidative stress-related human disorders. It has been reported recently that the plasma membrane resident protein caveolin-1 (Cav-1) can regulate expression of certain antioxidant enzymes and involves in the pathogenesis of oxidative lung injury, but the detailed molecular mechanisms remain incompletely understood. Here, we demonstrated that Cav-1 inhibited the expression of antioxidant enzymes through direct interaction with Nrf2 and subsequent suppression of its transcriptional activity in lung epithelial Beas-2B cells. Cav-1 deficiency cells exhibited higher levels of antioxidant enzymes and were more resistant to oxidative stress induced cytotoxicity, whereas overexpression of Cav-1 suppressed the induction of these enzymes and further augmented the oxidative cell death. Cav-1 constitutively interacted with Nrf2 in both cytosol and nucleus. Stimulation of 4-hydroxynonenol increased the Cav-1-Nrf2 interaction in cytosol but disrupted their association in the nucleus. Knockdown of Cav-1 also disassociated the interaction between Nrf2 and its cytoplasmic inhibitor Keap1 (Kelch-like ECH-associated protein 1) and increased the Nrf2 transcription activity. Mutation of the resembling Cav-1 binding motif on Nrf2 effectively attenuated their interaction, which exhibited higher transcription activity and induced higher levels of antioxidant enzymes relative to the wild-type control. Altogether, these studies clearly demonstrate that Cav-1 inhibits cellular antioxidant capacity through direct interaction with Nrf2 and subsequent suppression of its activity, thereby implicating in certain oxidative stress-related human pathologies.

Background: Genome-wide association studies have yet to identify the majority of genetic variants involved in asthma. We hypothesized that expression quantitative trait locus (eQTL) mapping can identify novel asthma genes by enabling prioritization of putative functional variants for association testing. Objective: We evaluated 6,706 cis-acting expression-associated variants (eSNP) identified through a genome-wide eQTL survey of CD4+ lymphocytes for association with asthma. Methods: eSNP were tested for association with asthma in 359 asthma cases and 846 controls from the Childhood Asthma Management Program, with verification using family-based testing. Significant associations were tested for replication in 579 parent-child trios with asthma from Costa Rica. Further functional validation was performed by Formaldehyde Assisted Isolation of Regulatory Elements (FAIRE)-qPCR and Chromatin-Immunoprecipitation (ChIP)-PCR in lung derived epithelial cell lines (Beas-2B and A549) and Jurkat cells, a leukemia cell line derived from T lymphocytes. Results: Cis-acting eSNP demonstrated associations with asthma in both cohorts. We confirmed the previously-reported association of ORMDL3/GSDMB variants with asthma (combined p=2.9 × 108). Reproducible associations were also observed for eSNP in three additional genes: FADS2 (p=0.002), NAGA (p=0.0002), and F13A1 (p=0.0001). We subsequently demonstrated that FADS2 mRNA is increased in CD4+ lymphocytes in asthmatics, and that the associated eSNPs reside within DNA segments with histone modifications that denote open chromatin status and confer enhancer activity. Conclusions: Our results demonstrate the utility of eQTL mapping in the identification of novel asthma genes, and provide evidence for the importance of FADS2, NAGA, and F13A1 in the pathogenesis of asthma.

We applied the alternating decision trees (ADTrees) method to the last 3 replicates from the Aipotu, Danacca, Karangar, and NYC populations in the Problem 2 simulated Genetic Analysis Workshop dataset. Using information from the 12 binary phenotypes and sex as input and Kofendrerd Personality Disorder disease status as the outcome of ADTrees-based classifiers, we obtained a new quantitative trait based on average prediction scores, which was then used for genome-wide quantitative trait linkage (QTL) analysis. ADTrees are machine learning methods that combine boosting and decision trees algorithms to generate smaller and easier-to-interpret classification rules. In this application, we compared four modeling strategies from the combinations of two boosting iterations (log or exponential loss functions) coupled with two choices of tree generation types (a full alternating decision tree or a classic boosting decision tree). These four different strategies were applied to the founders in each population to construct four classifiers, which were then applied to each study participant. To compute average prediction score for each subject with a specific trait profile, such a process was repeated with 10 runs of 10-fold cross validation, and standardized prediction scores obtained from the 10 runs were averaged and used in subsequent expectation-maximization Haseman-Elston QTL analyses (implemented in GENEHUNTER) with the approximate 900 SNPs in Hardy-Weinberg equilibrium provided for each population. Our QTL analyses on the basis of four models (a full alternating decision tree and a classic boosting decision tree paired with either log or exponential loss function) detected evidence for linkage (Z ≥ 1.96, p less than 0.01) on chromosomes 1, 3, 5, and 9. Moreover, using average iteration and abundance scores for the 12 phenotypes and sex as their relevancy measurements, we found all relevant phenotypes for all four populations except phenotype b for the Karangar population, with suggested subgroup structure consistent with latent traits used in the model. In conclusion, our findings suggest that the ADTrees method may offer a more accurate representation of the disease status that allows for better detection of linkage evidence.

Background High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study.Results The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system.Conclusion The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.

The abnormality of serine/threonine kinase Aurora-A is seen in many types of cancers. Although in physiological context it has been shown to play a vital role in cellular mitosis, how this oncogene contributes to tumorigenesis remains unclear. Here we demonstrate that Aurora-A overexpression enhances both the expression level and transcriptional activity of c-Myc. The inhibition of c-Myc expression by RNA interference significantly impaired the oncogenic potential of Aurora-A, resulting in attenuated cellular proliferation and transformation rates as well as fewer centrosomal aberrations. Furthermore, downregulation of c-Myc effectively overcame Aurora-A-induced resistance to cisplatin in esophageal cancer cells. Taken together, our results suggest an important role for c-Myc in mediating the oncogenic activity of Aurora-A, which may in turn allow for future targeting of c-Myc as a potential therapeutic strategy for tumors with Aurora-A overexpression.