Complex regulation of two target genes encoding SPX-MFS proteins by rice miR827 in response to phosphate starvation

Here we report on the characterization of rice osa-miR827 and its two target genes, OsSPX-MFS1 and OsSPX-MFS2, which encode SPX-MFS proteins predicted to be implicated in phosphate (Pi) sensing or transport. We first show by Northern blot analysis that osa-miR827 is strongly induced by Pi starvation in both shoots and roots. Hybridization of osa-miR827 in situ confirms its strong induction by Pi starvation, with signals concentrated in mesophyll, epidermis and ground tissues of roots. In parallel, we analyzed the responses of the two OsSPX-MFS1 and OsSPX-MFS2 gene targets to Pi starvation. OsSPX-MFS1 mRNA is mainly expressed in shoots under sufficient Pi supply while its expression is reduced on Pi starvation, revealing a direct relationship between induction of osa-miR827 and down-regulation of OsSPX-MFS1. In contrast, OsSPX-MFS2 responds in a diametrically opposed manner to Pi starvation. The accumulation of OsSPX-MFS2 mRNA is dramatically enhanced under Pi starvation, suggesting the involvement of complex regulation of osa-miR827 and its two target genes. We further produced transgenic rice lines overexpressing osa-miR827 and T-DNA knockout mutant lines in which the expression of osa-miR827 is abolished. Compared with wild-type controls, both target mRNAs exhibit similar changes, their expression being reduced and increased in overexpressing and knockout lines, respectively. This suggests that OsSPX-MFS1 and OsSPX-MFS2 are both negatively regulated by osa-miR827 abundance although they respond differently to external Pi conditions. We propose that this is a complex mechanism comprising fine tuning of spatial or temporal regulation of both targets by osa-miR827.     

Androgen receptor gene polymorphism may affect the risk of urothelial carcinoma

The study sought to explore if androgen receptor gene (AR) polymorphisms are associated with the risk of urothelial carcinoma (UC) which is male-predominant. AR CAG and GGN repeat lengths were analyzed in 277 UC cases and 280 age and sex-matched controls by direct sequencing of leukocyte DNA. Smoking habits were obtained using a structured questionnaire interview. Relative risks were compared between groups categorized by all possible cutoffs of AR CAG and GGN repeat lengths. Men and women who had 23 and 44 (cumulative) CAG repeats had a significantly greater risk of UC, respectively (OR 2.09, 95% CI: 1.05–4.17, p = 0.036 and OR 4.95, 95% CI: 1.56–15.73, p = 0.007). Amongst males who were medium-dose cigarette smokers, those who had 23 CAG and shorter GGN (<22) repeats, had an elevated risk than those with longer CAG and GGN (OR 4.32 and 4.57, p = 0.034 and 0.042, respectively). However, neither CAG nor GGN affected the UC risk in non-smokers or heavy smokers (≥25 packs per day-years). AR CAG polymorphism may affect the risk of UC in both genders. In addition, AR polymorphisms may influence carcinogenic effect of medium-dose of cigarette smoking in men.     

Inferring gene expression dynamics via functional regression analysis

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.     

The ribosomal stalk is required for ribosome binding,depurination of the rRNA and cytotoxicity of ricin A chain in Saccharomyces cerevisiae

Ribosome inactivating proteins (RIPs) like ricin, pokeweed antiviral protein (PAP) and Shiga‐like toxins 1 and 2 (Stx1 and Stx2) share the same substrate, the α‐sarcin/ricin loop, but differ in their specificities towards prokaryotic and eukaryotic ribosomes. Ricin depurinates the eukaryotic ribosomes more efficiently than the prokaryotic ribosomes, while PAP can depurinate both types of ribosomes. Accumulating evidence suggests that different docking sites on the ribosome might be used by different RIPs, providing a basis for understanding the mechanism underlying their kingdom specificity. Our previous results demonstrated that PAP binds to the ribosomal protein L3 to depurinate the α‐sarcin/ricin loop and binding of PAP to L3 was critical for its cytotoxicity. Here, we used surface plasmon resonance to demonstrate that ricin toxin A chain (RTA) binds to the P1 and P2 proteins of the ribosomal stalk in Saccharomyces cerevisiae. Ribosomes from the P protein mutants were depurinated less than the wild‐type ribosomes when treated with RTA in vitro. Ribosome depurination was reduced when RTA was expressed in the ΔP1 and ΔP2 mutants in vivo and these mutants were more resistant to the cytotoxicity of RTA than the wild‐type cells. We further show that while RTA, Stx1 and Stx2 have similar requirements for ribosome depurination, PAP has different requirements, providing evidence that the interaction of RIPs with different ribosomal proteins is responsible for their ribosome specificity.     

Oct-4 expression maintained cancer stem-like properties in lung cancer-derived CD133-positive cells

CD133 (prominin-1), a 5-transmembrane glycoprotein, has recently been considered to be an important marker that represents the subset population of cancer stem-like cells. Herein we report the isolation of CD133-positive cells (LC-CD133(+)) and CD133-negative cells (LC-CD133(-)) from tissue samples of ten patients with non-small cell lung cancer (LC) and five LC cell lines. LC-CD133(+) displayed higher Oct-4 expressions with the ability to self-renew and may represent a reservoir with proliferative potential for generating lung cancer cells. Furthermore, LC-CD133(+), unlike LC-CD133(-), highly co-expressed the multiple drug-resistant marker ABCG2 and showed significant resistance to chemotherapy agents (i.e., cisplatin, etoposide, doxorubicin, and paclitaxel) and radiotherapy. The treatment of Oct-4 siRNA with lentiviral vector can specifically block the capability of LC-CD133(+) to form spheres and can further facilitate LC-CD133(+) to differentiate into LC-CD133(-). In addition, knock-down of Oct-4 expression in LC-CD133(+) can significantly inhibit the abilities of tumor invasion and colony formation, and increase apoptotic activities of caspase 3 and poly (ADP-ribose) polymerase (PARP). Finally, in vitro and in vivo studies further confirm that the treatment effect of chemoradiotherapy for LC-CD133(+) can be improved by the treatment of Oct-4 siRNA. In conclusion, we demonstrated that Oct-4 expression plays a crucial role in maintaining the self-renewing, cancer stem-like, and chemoradioresistant properties of LC-CD133(+). Future research is warranted regarding the up-regulated expression of Oct-4 in LC-CD133(+) and malignant lung cancer.     

Selective COX-2 inhibitors. Part 2: synthesis and biological evaluation of 4-benzylideneamino- and 4-phenyliminomethyl-benzenesulfonamides

Two series of 4-benzylideneamino- and 4-phenyliminomethyl-benzenesulfonamide derivatives were designed and synthesized for the evaluation as selective cyclooxygenase-2 (COX-2) inhibitors in a cellular assay using human whole blood (HWB). Extensive structure-activity relationships (SAR) were studied within these series. Several compounds were found to be novel and selective COX-2 inhibitors. Among them, the most potent and selective was 4-(3-carboxy-4-hydroxy-benzylideneamino)benzenesulfonamide (20, LA2135), (IC(50)'s for COX-1: 85.13 microM; COX-2: 0.74 microM; SI: 114.5), being more active COX-2 selective than celecoxib.