Qianxing Mo

Blocking PGE2-induced tumour repopulation abrogates bladder cancer chemoresistance

Cytotoxic chemotherapy is effective in debulking tumour masses initially; however, in some patients tumours become progressively unresponsive after multiple treatment cycles. Previous studies have demonstrated that cancer stem cells (CSCs) are selectively enriched after chemotherapy through enhanced survival. Here we reveal a new mechanism by which bladder CSCs actively contribute to therapeutic resistance via an unexpected proliferative response to repopulate residual tumours between chemotherapy cycles, using human bladder cancer xenografts. Further analyses demonstrate the recruitment of a quiescent label-retaining pool of CSCs into cell division in response to chemotherapy-induced damages, similar to mobilization of normal stem cells during wound repair. While chemotherapy effectively induces apoptosis, associated prostaglandin E2 (PGE2) release paradoxically promotes neighbouring CSC repopulation. This repopulation can be abrogated by a PGE2-neutralizing antibody and celecoxib drug-mediated blockade of PGE2 signalling. In vivo administration of the cyclooxygenase-2 (COX2) inhibitor celecoxib effectively abolishes a PGE2- and COX2-mediated wound response gene signature, and attenuates progressive manifestation of chemoresistance in xenograft tumours, including primary xenografts derived from a patient who was resistant to chemotherapy. Collectively, these findings uncover a new underlying mechanism that models the progressive development of clinical chemoresistance, and implicate an adjunctive therapy to enhance chemotherapeutic response of bladder urothelial carcinomas by abrogating early tumour repopulation.

A microarray analysis of sex- and gonad-biased gene expression in the zebrafish: Evidence for masculinization of the transcriptome

BackgroundIn many taxa, males and females are very distinct phenotypically, and these differences often reflect divergent selective pressures acting on the sexes. Phenotypic sexual dimorphism almost certainly reflects differing patterns of gene expression between the sexes, and microarray studies have documented widespread sexually dimorphic gene expression. Although the evolutionary significance of sexual dimorphism in gene expression remains unresolved, these studies have led to the formulation of a hypothesis that male-driven evolution has resulted in the masculinization of animal transcriptomes. Here we use a microarray assessment of sex- and gonad-biased gene expression to test this hypothesis in zebrafish.ResultsBy using zebrafish Affymetrix microarrays to compare gene expression patterns in male and female somatic and gonadal tissues, we identified a large number of genes (5899) demonstrating differences in transcript abundance between male and female Danio rerio. Under conservative statistical significance criteria, all sex-biases in gene expression were due to differences between testes and ovaries. Male-enriched genes were more abundant than female-enriched genes, and expression bias for male-enriched genes was greater in magnitude than that for female-enriched genes. We also identified a large number of genes demonstrating elevated transcript abundance in testes and ovaries relative to male body and female body, respectively.ConclusionOverall our results support the hypothesis that male-biased evolutionary pressures have resulted in male-biased patterns of gene expression. Interestingly, our results seem to be at odds with a handful of other microarray-based studies of sex-specific gene expression patterns in zebrafish. However, ours was the only study designed to address this specific hypothesis, and major methodological differences among studies could explain the discrepancies. Regardless, all of these studies agree that transcriptomic sex differences in D. rerio are widespread despite the apparent absence of heterogamety. These differences likely make important contributions to phenotypic sexual dimorphism in adult zebrafish; thus, from an evolutionary standpoint, the precise roles of sex-specific selection and sexual conflict in the evolution of sexually dimorphic gene expression are very important. The results of our study and others like it set the stage for further work aimed at directly addressing this exciting issue in comparative genomics.

Oncogenic mTOR signalling recruits myeloid-derived suppressor cells to promote tumour initiation

Myeloid-derived suppressor cells (MDSCs) play critical roles in primary and metastatic cancer progression. MDSC regulation is widely variable even among patients harbouring the same type of malignancy, and the mechanisms governing such heterogeneity are largely unknown. Here, integrating human tumour genomics and syngeneic mammary tumour models, we demonstrate that mTOR signalling in cancer cells dictates a mammary tumour’s ability to stimulate MDSC accumulation through regulating G-CSF. Inhibiting this pathway or its activators (for example, FGFR) impairs tumour progression, which is partially rescued by restoring MDSCs or G-CSF. Tumour-initiating cells (TICs) exhibit elevated G-CSF. MDSCs reciprocally increase TIC frequency through activating Notch in tumour cells, forming a feedforward loop. Analyses of primary breast cancers and patient-derived xenografts corroborate these mechanisms in patients. These findings establish a non-canonical oncogenic role of mTOR signalling in recruiting pro-tumorigenic MDSCs and show how defined cancer subsets may evolve to promote and depend on a distinct immune microenvironment.

The REGγ Proteasome Regulates Hepatic Lipid Metabolism through Inhibition of Autophagy

The ubiquitin-proteasome and autophagy-lysosome systems are major proteolytic pathways, whereas function of the Ub-independent proteasome pathway is yet to be clarified. Here, we investigated roles of the Ub-independent REGγ-proteasome proteolytic system in regulating metabolism. We demonstrate that mice deficient for the proteasome activator REGγ exhibit dramatic autophagy induction and are protected against high-fat diet (HFD)-induced liver steatosis through autophagy. Molecularly, prevention of steatosis in the absence of REGγ entails elevated SirT1, a deacetylase regulating autophagy and metabolism. REGγ physically binds to SirT1, promotes its Ub-independent degradation, and inhibits its activity to deacetylate autophagy-related proteins, thereby inhibiting autophagy under normal conditions. Moreover, REGγ and SirT1 dissociate from each other through a phosphorylation-dependent mechanism under energy-deprived conditions, unleashing SirT1 to stimulate autophagy. These observations provide a function of the REGγ proteasome in autophagy and hepatosteatosis, underscoring mechanistically a crosstalk between the proteasome and autophagy degradation system in the regulation of lipid homeostasis.

Assessing Side-Chain Perturbations of the Protein Backbone: A Knowledge-Based Classification of Residue Ramachandran Space

Grouping the 20 residues is a classic strategy to discover ordered patterns and insights about the fundamental nature of proteins, their structure, and how they fold. Usually, this categorization is based on the biophysical and/or structural properties of a residues side-chain group. We extend this approach to understand the effects of side chains on backbone conformation and to perform a knowledge-based classification of amino acids by comparing their backbone phi, psi distributions in different types of secondary structure. At this finer, more specific resolution, torsion angle data are often sparse and discontinuous (especially for nonhelical classes) even though a comprehensive set of protein structures is used. To ensure the precision of Ramachandran plot comparisons, we applied a rigorous Bayesian density estimation method that produces continuous estimates of the backbone phi, psi distributions. Based on this statistical modeling, a robust hierarchical clustering was performed using a divergence score to measure the similarity between plots. There were seven general groups based on the clusters from the complete Ramachandran data: nonpolar/beta-branched (Ile and Val), AsX (Asn and Asp), long (Met, Gln, Arg, Glu, Lys, and Leu), aromatic (Phe, Tyr, His, and Cys), small (Ala and Ser), bulky (Thr and Trp), and, lastly, the singletons of Gly and Pro. At the level of secondary structure (helix, sheet, turn, and coil), these groups remain somewhat consistent, although there are a few significant variations. Besides the expected uniqueness of the Gly and Pro distributions, the nonpolar/beta-branched and AsX clusters were very consistent across all types of secondary structure. Effectively, this consistency across the secondary structure classes implies that side-chain steric effects strongly influence a residues backbone torsion angle conformation. These results help to explain the plasticity of amino acid substitutions on protein structure and should help in protein design and structure evaluation.

Transforming growth factor-β signaling in hypertensive remodeling of porcine aorta

A porcine aortic coarctation model was used to examine regulation of gene expression in early hypertensive vascular remodeling. Aortic segments were collected proximal (high pressure) and distal (low pressure) to the coarctation after 2 wk of sustained hypertension (mean arterial pressure>150 mmHg). Porcine 10K oligoarrays used for gene expression profiling of the two regions of aorta revealed downregulation of cytoskeletal and upregulation of extracellular region genes relative to the whole genome. A genomic database search for transforming growth factor-beta (TGF-beta) control elements showed that 19% of the genes that changed expression due to hypertension contained putative TGF-beta control elements. Real-time RT-PCR and microarray analysis showed no change in expression of TGF-beta1, TGF-beta2, TGF-beta3, or bone morphogenetic proteins-2 and -4, yet immunohistochemical staining for phosphorylated SMAD2, an indicator of TGF-beta signaling, and for phosphorylated SMAD1/5/8, an indicator of signaling through the bone morphogenetic proteins, showed the highest percentage of positively stained cells in the proximal aortic segments of occluded animals. For TGF-beta signaling, this increase was significantly different than for sham-operated controls. Western blot analysis showed no difference in total TGF-beta1 protein levels with respect to treatment or aortic segment. Immunohistochemistry showed that the protein levels of latency-associated peptide was decreased in proximal segments of occluded animals. Collectively, these results suggest that activation of TGF-beta, but not altered expression, may be a major mechanism regulating early hypertensive vascular remodeling.

Simultaneous inference for multiple testing and clustering via a Dirichlet process mixture model

We propose a Bayesian nonparametric regression model that exploits clustering for increased sensitivity in multiple hypothesis testing. We build on the recently proposed BEMMA (Bayesian Effects Models for Microarrays) method which is able to model dependence among objects through clustering and then estimates hypothesis-testing parameters averaged over clustering uncertainty. We propose several improvements. First, we separate the clustering of the regression coefficients from the part of the model that accommodates heteroscedasticity. Second, our model accommodates a wider class of experimental designs, such as permitting covariates and not requiring independent sampling. Third, we provide a more satisfactory treatment of nuisance parameters and some hyperparameters. Finally, we do not require the arbitrary designation of a reference treatment. The proposed method is compared in a simulation study to ANOVA and the BEMMA methods.

Bayesian variable selection in clustering high-dimensional data with substructure

In this article we focus on clustering techniques recently proposed for high-dimensional data that incorporate variable selection and extend them to the modeling of data with a known substructure, such as the structure imposed by an experimental design. Our method essentially approximates the within-group covariance by facilitating clustering without disrupting the groups defined by the experimenter. The method we adopt simultaneously determines which expression patterns are important, and which genes contribute to such patterns. We evaluate performance on simulated data and on microarray data from a colon carcinogenesis study. Selected genes are biologically consistent with current research and provide strong biological validation of the cluster configuration identified by the method.

Positive association of collagen type I with non-muscle invasive bladder cancer progression

PURPOSE Non-muscle invasive bladder cancers (NMIBC) are generally curable, while ~15% progresses into muscle-invasive cancer with poor prognosis. While efforts have been made to identify genetic alternations associated with progression, the extracellular matrix (ECM) microenvironment remains largely unexplored. Type I collagen is a major component of the bladder ECM, and can be altered during cancer progression. We set out to explore the association of type I collagen with NMIBC progression. EXPERIMENTAL DESIGN The associations of COL1A1 and COL1A2 mRNA levels with progression were evaluated in a multi-center cohort of 189 patients with NMIBCs. Type I collagen protein expression and structure were evaluated in an independent single-center cohort of 80 patients with NMIBCs. Immunohistochemical analysis was performed and state-of-the-art multi-photon microscopy was used to evaluate collagen structure via second harmonic generation imaging. Progression to muscle invasion was the primary outcome. Kaplan-Meier method, Cox regression, and Wilcoxon rank-sum were used for statistical analysis. RESULTS There is a significant association of high COL1A1 and COL1A2 mRNA expression in patients with poor progression-free survival (P=0.0037 and P=0.011, respectively) and overall survival (P=0.024 and P=0.012, respectively). Additionally, immunohistochemistry analysis of type I collagen protein deposition revealed a significant association with progression (P=0.0145); Second-harmonic generation imaging revealed a significant lower collagen fiber curvature ratio in patients with invasive progression (P = 0.0018). CONCLUSIONS Alterations in the ECM microenvironment, particularly type I collagen, likely contributes to bladder cancer progression. These findings will open avenues to future functional studies to investigate ECM-tumor interaction as a potential therapeutic intervention to treat NMIBCs.

A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data

Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features.