Gaelle Rondeau

Development of a DNA Microarray to Detect Antimicrobial Resistance Genes Identified in the National Center for Biotechnology Information Database

To understand the mechanisms and epidemiology of antimicrobial resistance (AR), the genetic elements responsible must be identified. Due to the myriad of possible genes, a high-density genotyping technique is needed for initial screening. To achieve this, AR genes in the National Center for Biotechnology Information GenBank database were identified by their annotations and compiled into a nonredundant list of 775 genes. A DNA microarray was constructed of 70mer oligonucelotide probes designed to detect these genes encoding resistances to aminoglycosides, beta-lactams, chloramphenicols, glycopeptides, heavy metals, lincosamides, macrolides, metronidazoles, polyketides, quaternary ammonium compounds, streptogramins, sulfonamides, tetracyclines, and trimethoprims as well as resistance transfer genes. The microarray was validated with two fully sequenced control strains of Salmonella enterica: Typhimurium LT2 (sensitive) and Typhi CT18 (multidrug resistance [MDR]). All resistance genes encoded on the MDR plasmid, pHCM1, harbored by CT18 were detected in that strain, whereas no resistance genes were detected in LT2. The microarray was also tested with a variety of bacteria, including MDR Salmonella enterica serovars, Escherichia coli, Campylobacter spp., Enterococcus spp., methicillin-resistant Staphylococcus aureus, Listeria spp., and Clostridium difficile. The results presented here demonstrate that a microarray can be designed to detect virtually all AR genes found in the National Center for Biotechnology Information database, thus reducing the subsequent assays necessary to identify specific resistance gene alleles.

Combinatorial mRNA binding by AUF1 and Argonaute 2 controls decay of selected target mRNAs

The RNA-binding protein AUF1 binds AU-rich elements in 3′-untranslated regions to regulate mRNA degradation and/or translation. Many of these mRNAs are predicted microRNA targets as well. An emerging theme in post-transcriptional control of gene expression is that RNA-binding proteins and microRNAs co-regulate mRNAs. Recent experiments and bioinformatic analyses suggest this type of co-regulation may be widespread across the transcriptome. Here, we identified mRNA targets of AUF1 from a complex pool of cellular mRNAs and examined a subset of these mRNAs to explore the links between RNA binding and mRNA degradation for both AUF1 and Argonaute 2 (AGO2), which is an essential effector of microRNA-induced gene silencing. Depending on the specific mRNA examined, AUF1 and AGO2 binding is proportional/cooperative, reciprocal/competitive or independent. For most mRNAs in which AUF1 affects their decay rates, mRNA degradation requires AGO2. Thus, AUF1 and AGO2 present mRNA-specific allosteric binding relationships for co-regulation of mRNA degradation.

The Accuracy of Survival Time Prediction for Patients with Glioma Is Improved by Measuring Mitotic Spindle Checkpoint Gene Expression

Identification of gene expression changes that improve prediction of survival time across all glioma grades would be clinically useful. Four Affymetrix GeneChip datasets from the literature, containing data from 771 glioma samples representing all WHO grades and eight normal brain samples, were used in an ANOVA model to screen for transcript changes that correlated with grade. Observations were confirmed and extended using qPCR assays on RNA derived from 38 additional glioma samples and eight normal samples for which survival data were available. RNA levels of eight major mitotic spindle assembly checkpoint (SAC) genes (BUB1, BUB1B, BUB3, CENPE, MAD1L1, MAD2L1, CDC20, TTK) significantly correlated with glioma grade and six also significantly correlated with survival time. In particular, the level of BUB1B expression was highly correlated with survival time (p<0.0001), and significantly outperformed all other measured parameters, including two standards; WHO grade and MIB-1 (Ki-67) labeling index. Measurement of the expression levels of a small set of SAC genes may complement histological grade and other clinical parameters for predicting survival time.

“Promoter Array” Studies Identify Cohorts of Genes Directly Regulated by Methylation, Copy Number Change, or Transcription Factor Binding in Human Cancer Cells

DNA microarrays of promoter sequences have been developed in order to identify the profile of genes bound and activated by DNA regulatory proteins such as the transcription factors c‐Jun and ATF2 as well as DNA‐modifying methylases. The arrays contain 3083 unique human promoter sequences from +500 to −1000 nts from the transcription start site. Cisplatin‐induced DNA damage rapidly leads to specific activation of the Jun kinase pathway leading to increased phosphorylation of c‐Jun and ATF2‐DNA complexes at hundreds of sites within 3 hours. Using three statistical criteria, approximately 269 most commonly phosphorylated c‐Jun/ATF2‐DNA complexes were identified and representative cases were verified by qPCR measurement of ChIP‐captured DNA. Expression was correlated at the mRNA and protein levels. The largest functional cohort was 24 genes of known DNA repair function, most of which exhibited increased protein expression indicated coordinate gene regulation. In addition, cell lines of prostate cancer exhibit stable methylation or copy number changes that reflect the alterations of the corresponding primary tumors. 504 (18.5%) promoters showed differential hybridization between immortalized control prostate epithelial and cancer cell lines. Among candidate hypermethylated genes in cancer‐derived lines, eight had previously been observed in prostate cancer, and 13 were previously determined methylation targets in other cancers. The vast majority of genes that appear to be both differentially methylated and differentially regulated between prostate epithelial and cancer cell lines are novel methylation targets, including PAK6, RAD50, TLX3, PIR51, MAP2K5, INSR, FBN1, GG2‐1, representing a rich new source of candidate genes to study the role of DNA methylation in prostate tumors. Earlier studies using prototype promoter arrays examine approximately 7% of the proximal regulatory sequences while the current gene regulatory events surveyed here occur on a large scale and may rapidly effect the coordinated expression of a large number of genes.

Enhanced microarray performance using low complexity representations of the transcriptome

Low abundance mRNAs are more difficult to examine using microarrays than high abundance mRNAs due to the effect of concentration on hybridization kinetics and signal-to-noise ratios. This report describes the use of low complexity representations (LCRs) of mRNA as the targets for cDNA microarrays. Individual sequences in LCRs are more highly represented than in the mRNA populations from which they are derived, leading to favorable hybridization kinetics. LCR targets permit the measurement of abundance changes that are difficult to measure using oligo(dT) priming for target synthesis. An oligo(dT)-primed target and three LCRs detect twice as many differentially regulated genes as could be detected by the oligo(dT)-primed target alone, in an experiment in which serum-starved fibroblasts responded to the reintroduction of serum. Thus, this target preparation strategy considerably increases the sensitivity of cDNA microarrays.

A single step multiplex PCR for identification of six diarrheagenic E. coli pathotypes and Salmonella

E. coli is generally a commensal but includes some highly pathogenic strains carrying additional genes in plasmids and/or the chromosome. Based on these genes the pathogenic strains are divided into pathotypes including enteropathogenic (EPEC), enterohemorrhagic (EHEC), enterotoxigenic (ETEC), enteroaggregative (EAEC), enteroinvasive (EIEC) and diffusely adherent (DAEC) E. coli. Here, previously developed multiplex PCR strategies for these strains were integrated into one single step multiplex that differentiates all these E. coli pathotypes, usually based on multiple characteristic PCR products. This multiplex PCR works reliably for colony PCR. Two additional markers were added: one to detect most Enterobacteriacea, which acts as a positive control for successful PCR, and one to distinguish Salmonella. The multiplex correctly classified a set of 45 reference strains by colony PCR and 71 (45+26) strains by in silico PCR. It was then used to interrogate 44 clinical strains from bovine hosts resulting in detection of EAEC and DAEC determinants.

Effects of Different Tissue Microenvironments on Gene Expression in Breast Cancer Cells

In metastasis, circulating tumor cells penetrate the walls of blood vessels and enter the metastatic target tissue, thereby becoming exposed to novel and relatively unsupportive microenvironments. In the new microenvironments, the tumor cells often remain in a dormant state indefinitely and must adapt before they are able to successfully colonize the tissue. Very little is known about this adaptive process. We studied temporal changes in gene expression when breast cancer cells adapt to survive and grow on brain, bone marrow, and lung tissue maintained in an in vivo culture system, as models of the metastatic colonization of these tissues. We observed the transient activation of genes typically associated with homeostasis and stress during the initial stages of adaptation, followed by the activation of genes that mediate more advanced functions, such as elaboration of cell morphology and cell division, as the cells adapted to thrive in the host tissue microenvironment. We also observed the temporary induction of genes characteristic of the host tissue, which was particularly evident when tumor cells were grown on brain tissue. These early transient gene expression events suggest potential points of therapeutic intervention that are not evident in data from well-established tumors.

Assessment of gene expression in many samples using vertical arrays

Microarrays and high-throughput sequencing methods can be used to measure the expression of thousands of genes in a biological sample in a few days, whereas PCR-based methods can be used to measure the expression of a few genes in thousands of samples in about the same amount of time. These methods become more costly as the number of biological samples increases or as the number of genes of interest increases, respectively, and these factors constrain experimental design. To address these issues, we introduced ‘vertical arrays’ in which RNA from each biological sample is converted into multiple, overlapping cDNA subsets and spotted on glass slides. These vertical arrays can be queried with single gene probes to assess the expression behavior in thousands of biological samples in a single hybridization reaction. The spotted subsets are less complex than the original RNA from which they derive, which improves signal-to-noise ratios. Here, we demonstrate the quantitative capabilities of vertical arrays, including the sensitivity and accuracy of the method and the number of subsets needed to achieve this accuracy for most expressed genes.

Plumbagin improves the efficacy of androgen deprivation therapy in prostate cancer: A pre-clinical study

Plumbagin is a candidate drug for the treatment of prostate cancer. Previous observations indicated that it may improve the efficacy of androgen deprivation therapy (ADT). This study evaluates the effectiveness of treatment with combinations of plumbagin and alternative strategies for ADT in mouse models of prostate cancer to support its clinical use.

Vertical arrays: microarrays of complex mixtures of nucleic acids.

Vertical arrays are microarrays that have complex mixtures of nucleic acids as array elements, and that are hybridized with single sequence probes. Like dot blots, many different experiments can be spotted on a single vertical array, allowing single genes to be compared across many conditions. Vertical arrays have two additional advantages over dot blots. First, they are printed on glass slides, allowing the use of low-volume, high-concentration hybridization reactions. Second, they can be made using low-complexity representations of the original nucleic acid population. This increases signal-to-noise relative to the usual use of dot blots, wherein the entire complexity of the population is usually spotted. Whereas standard microarrays achieve horizontal coverage of many genes and are repeated to cover many experiments, vertical arrays achieve vertical coverage of many experiments and are repeated to cover many genes. In cases where the number of genes is limited, but the number of experiments is very large, vertical arrays may be advantageous.