Cholera toxin-induced modulation of gene expression: elucidation via cDNA microarray for rational cell-based sensor design

Cell-based biosensors utilize functional changes in cellular response to identify the biological threats in a physiological relevant manner. Cell-based sensors have been used for a wide array of applications including toxicological assessment and drug-screening. In this paper, we utilize DNA arrays to identify differential gene expression events induced by toxin exposure for the purpose of developing a reporter gene assay system compatible with insertion into a cell-based sensor platform. HT29, an intestine epithelial cell line, was used as a cell model to study the cholera toxin (CT)-induced host cell modulation using DNA array analysis. A false positive model was generated from analysis of housekeeping genes in untreated control experiments to characterize our system and to minimize the number of false positives in the data. Threshold probability scores (−3.72), which gives <0.02% false positives for up/down regulation from the false positive model, were used to identify 73 and 25 known genes/expression tag sequences (ESTs) that were up- and down-regulated, respectively, in cells exposed 23 nM of CT. Using quantitative multiplex PCR assay, the gene expression levels for several genes shown to be modulated according to the microarray experiments, such as apolipoprotein D (Apol D), E-cadherin, and cyclin A2, were confirmed. The differential expression of genes encoding cytochrome P450, glutathione transferase (GST), and MGAT2 were noteworthy and consistent with previous studies. Our study provides an approach to analyze cDNA microarray data with defined false positive rates. The utility of cDNA microarray information for the design of cell-based sensor using a reporter gene approach is discussed.     

Cranberry proanthocyanidins mediate growth arrest of lung cancer cells through modulation of gene expression and rapid induction of apoptosis

Cranberries are rich in bioactive constituents purported to enhance immune function, improve urinary tract health, reduce cardiovascular disease and more recently, inhibit cancer in preclinical models. However, identification of the cranberry constituents with the strongest cancer inhibitory potential and the mechanism associated with cancer inhibition by cranberries remains to be elucidated. This study investigated the ability of a proanthocyanidin rich cranberry fraction (PAC) to alter gene expression, induce apoptosis and impact the cell cycle machinery of human NCI-H460 lung cancer cells. Lung cancer is the leading cause of cancer-related deaths in the United States and five year survival rates remain poor at 16%. Thus, assessing potential inhibitors of lung cancer-linked signaling pathways is an active area of investigation.     

Differential gene expression in response to copper in Acidithiobacillus ferrooxidans analyzed by RNA arbitrarily primed polymerase chain reaction

Acidithiobacillus ferrooxidans is a chemoautotrophic bacterium that plays an important role in metal bioleaching processes. Despite the high level of tolerance to heavy metals shown by A. ferrooxidans, the genetic basis of copper resistance in this species remains unknown. We investigated the gene expression in response to copper in A. ferrooxidans LR using RNA arbitrarily primed polymerase chain reaction (RAP-PCR). One hundred and four differentially expressed genes were identified using eight arbitrary primers. Differential gene expression was confirmed by DNA slot blot hybridization, and approximately 70% of the RAP-PCR products were positive. The RAP-PCR products that presented the highest levels of induction or repression were cloned, sequenced and the sequences were compared with those in databases using the BLAST search algorithm. Seventeen sequences were obtained. The RAP-PCR product with the highest induction ratio showed similarity with the A. ferrooxidans cytochrome c. A high similarity with the thiamin biosynthesis gene thiC from Caulobacter crescentus was observed for another RAP-PCR product induced by copper. An RAP-PCR product repressed by copper showed significant similarity with the carboxysome operon that includes the ribulose-1,5-bisphosphate carboxylase/oxygenase complex from A. ferrooxidans and another copper-repressed product was significantly similar to the XyIN outer membrane protein from Pseudomonas putida. Finally, RAP-PCR products of unknown similarities were also present.     

A label-free, quadruplex-based functional molecular beacon (LFG4-MB) for fluorescence turn-on detection of DNA and nuclease

We demonstrate a novel concept for the construction of a label-free, quadruplex-based functional molecular beacon (LFG4-MB) by using G-quadruplex motif as a substitute for Watson-Crick base pairing in the MB stem and a specific G-quadruplex binder, N-methyl mesoporphyrin IX (NMM) as a reporter. It shows high sensitivity in assays for UDG activity/inhibition and detection of DNA sequence based on the unique fluorescence increase that occurs as a result of the strong interaction between NMM and the folded quadruplex upon removal of uracil by UDG or displacement of block sequence by target DNA. The LFG4-MB is simple in design, fast in operation and could be easily transposed to other biological relevant target analysis by simply changing the recognition portion. The LFG4-MB does not require any chemical modification for DNA, which offers the advantages of simplicity and cost efficiency and obviates the possible interference with the affinity and specificity of the MB as well as the kinetic behavior of the catalysts caused by the bulky fluorescent groups. More importantly, the LFG4-MB offers great extent of freedom to tune the experimental conditions for the general applicability in bioanalysis.     

Programmed vesicle fusion triggers gene expression

The membrane properties of phospholipid vesicles can be manipulated to both regulate and initiate encapsulated biochemical reactions and networks. We present evidence for the inhibition and activation of reactions encapsulated in vesicles by the exogenous addition of charged amphiphiles. While the incorporation of cationic amphiphile exerts an inhibitory effect, complementation of additional anionic amphiphiles revitalize the reaction. We demonstrated both the simple hydrolysis reaction of β-glucuronidase and the in vitro gene expression of this enzyme from a DNA template. Furthermore, we show that two vesicle populations decorated separately with positive and negative amphiphiles can fuse selectively to supply feeding components to initiate encapsulated reactions. This mechanism could be one of the rudimentary but effective means to regulate and maintain metabolism in dynamic artificial cell models.     

A new vision of evaluating gene expression signatures

Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures.     

Regulation of macrophage-specific gene expression by degenerated lipoproteins

The effect of aggregated low-density lipoprotein (agLDL) on cell viability and macrophage-specific gene expression using human peripheral blood monocytes in culture was investigated. AgLDL suppressed activation-induced cell death of phorbol ester-treated macrophages. The inhibition of apoptosis was accompanied by downregulation of apoptosis-promoting proteases, including interleukin-1beta-converting enzyme (ICE) and CPP32 and upregulation of anti-apoptotic cytokine (interleukin-1beta (IL-1beta)). In contrast, macrophage-colony stimulating factor (M-CSF) enhanced cell death of lipid-bearing macrophages, suggesting that the anti-atherogenic action of M-CSF is at least in part mediated through apoptotic elimination of macrophages. Then, we attempted to isolate the genes specifically induced by agLDL in macrophages using a subtraction-based cloning strategy. One of the genes isolated, termed LIG (LDL-inducible gene), encodes a human homolog of E2 ubiquitin-conjugating enzyme. Ubiquitination of multiple intracellular proteins was observed in agLDL-treated macrophages, which coincided with upregulation of LIG. These results suggest that LIG acts as a direct mediator of foam cell formation through polyubiquitination and subsequent degradation of cellular proteins with apoptosis-inducing properties. The regulation of apoptosis by macrophage-specific gene expression may contribute to foam cell formation and atherosclerosis.     

Cell-like systems with riboswitch controlled gene expression

Synthetic riboswitches can be used to control protein expression under fully defined conditions in vitro, in water-in-oil emulsions, and in vesicles. The developed system could serve as a foundation for the construction of cellular mimics that respond to molecules of our choosing.     

Multi-group cancer outlier differential gene expression detection

It has recently been shown that cancer genes (oncogenes) tend to have heterogeneous expressions across disease samples. So it is reasonable to assume that in a microarray data only a subset of disease samples will be activated (often referred to as outliers), which presents some new challenges for statistical analysis. In this paper, we study the multi-class cancer outlier differential gene expression detection. Statistical methods will be proposed to take into account the expression heterogeneity. Through simulation studies and application to public microarray data, we will show that the proposed methods could provide more comprehensive analysis results and improve upon the traditional differential gene expression detection methods, which often ignore the expression heterogeneity and may loss power. Supplementary information can be found at http://www.biostat.umn.edu/~baolin/research/orf.html.     

Using gene expression programming to infer gene regulatory networks from time-series data

Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively.     

Identification of differential gene expression by high throughput analysis

High throughput analysis of differential gene expression is a powerful tool that can be applied to many areas in molecular cell biology, including differentiation, development, physiology, and pharmacology. In recent years, a variety of techniques have been developed to analyze differential gene expression, including comparative expressed sequence tag sequencing, differential display, representational difference analysis, cDNA or oligonucleotide arrays, and serial analysis of gene expression. This review explains the technologies, their scopes, impact on science, as well as their costs and possible limitations. The application of differential display is presented as a tool to identify genes induced by darkness or yellowing process in rice leaves.