Quantifying engineered nanomaterial toxicity: comparison of common cytotoxicity and gene expression measurements

Background

When evaluating the toxicity of engineered nanomaterials (ENMS) it is important to use multiple bioassays based on different mechanisms of action. In this regard we evaluated the use of gene expression and common cytotoxicity measurements using as test materials, two selected nanoparticles with known differences in toxicity, 5 nm mercaptoundecanoic acid (MUA)-capped InP and CdSe quantum dots (QDs). We tested the effects of these QDs at concentrations ranging from 0.5 to 160 µg/mL on cultured normal human bronchial epithelial (NHBE) cells using four common cytotoxicity assays: the dichlorofluorescein assay for reactive oxygen species (ROS), the lactate dehydrogenase assay for membrane viability (LDH), the mitochondrial dehydrogenase assay for mitochondrial function, and the Comet assay for DNA strand breaks.

Results

The cytotoxicity assays showed similar trends when exposed to nanoparticles for 24 h at 80 µg/mL with a threefold increase in ROS with exposure to CdSe QDs compared to an insignificant change in ROS levels after exposure to InP QDs, a twofold increase in the LDH necrosis assay in NHBE cells with exposure to CdSe QDs compared to a 50% decrease for InP QDs, a 60% decrease in the mitochondrial function assay upon exposure to CdSe QDs compared to a minimal increase in the case of InP and significant DNA strand breaks after exposure to CdSe QDs compared to no significant DNA strand breaks with InP. High-throughput quantitative real-time polymerase chain reaction (qRT-PCR) data for cells exposed for 6 h at a concentration of 80 µg/mL were consistent with the cytotoxicity assays showing major differences in DNA damage, DNA repair and mitochondrial function gene regulatory responses to the CdSe and InP QDs. The BRCA2, CYP1A1, CYP1B1, CDK1, SFN and VEGFA genes were observed to be upregulated specifically from increased CdSe exposure and suggests their possible utility as biomarkers for toxicity.

Conclusions

This study can serve as a model for comparing traditional cytotoxicity assays and gene expression measurements and to determine candidate biomarkers for assessing the biocompatibility of ENMs.

     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Quantifying stability in gene list ranking across microarray derived clinical biomarkers

Background

Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.

Methods

To address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.

Results

The algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.

Conclusions

We developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.     

Quantifying bacterial population dynamics in compost using 16S rRNA gene probes

Composting provides a dynamic setting for studying ecological topics such as succession, competition, and community stability in a relatively short period of time. This study used hierarchical small sub-unit-based rRNA gene probes to quantify the change in the relative abundance of phylogenetic groups common to compost in laboratory scale reactors. Bacterial 16S rRNA gene targets accounted for only 37% of all small subunit (SSU) rRNA genes initially, but increased to a maximum of 83% of the total at 84 h. The sum of rRNA genes detected using probes specific to Pseudomonas and low-G+C Gram-positive rRNA genes represented between 16% and 87% of the total. The lack of hybridization to the taxon-specific probes was most pronounced between 36 h and 60 h, when the pH was between 4.6 and 4.8. During this period the relative abundance of taxon-specific gene targets accounted for only 17–33% of the total bacterial rRNA gene targets. Pseudomonas-type 16S rRNA genes were the most abundant of the groups measured until 72 h. Those genes had their highest relative abundance at 12 h (78% of bacterial rRNA genes; 30% of all rRNA genes), after which time their relative abundance began to decline as the temperature increased. Prior to 72 h, 16S rRNA genes from low-G+C Gram-positive bacteria (LGC-GPB) represented less than 7% of the bacterial rRNA genes. However, by 84 h the relative abundance of LGC-GPB and Bacillus rRNA genes had increased to 60% and 18% of the bacterial rRNA gene targets, respectively (50% and 15% of all rRNA genes, respectively).     

Post-meiotic gene expression

Evolutionary arguments and well-designed experiments (based on false premises, however) had suggested that post-meiotic gene expression did not occur in animals. The techniques of molecular genetics have now clearly demonstrated such genetic activity in mammalian testes. The current problem is to understand why some classes of genes, such as Zfy and many oncogenes, are expressed in this manner.     

Auxin-regulated gene expression

During the 1960s a wide range of studies provided an information base that led to the suggestion that auxin-regulated cell processes--especially cell elongation--may be mediated by auxin-regulated gene expression. Indirect evidence from our work, based on the influence of inhibitors of RNA synthesis (e.g. actinomycin D) and of protein synthesis (e.g. cycloheximide) on auxin-induced cell elongation, coupled with correlations of the influence of auxin on RNA synthesis and cell elongation, provided the basis for this suggestion. With the availability of techniques for DNA-DNA and DNA-RNA hybridization, mRNA isolation-translation, in vitro 2D gel analysis of the translation products, and ultimately the cloning by recombinant DNA technologies of genomic DNA and copy DNAs (cDNAs) made to poly(A)+ mRNAs, we and others have provided direct evidence for the influence of auxin on the expression of a few genes (i.e. poly(A)+ RNA levels). Our laboratory has provided evidence for auxin's both down-regulating and up-regulating the level of a few poly(A)+ mRNAs out of a population of about 4 X 10(4) sequences that are not significantly affected by auxin. In our studies on auxin-regulated cell elongation, two cDNA clones (pJCW1 and pJCW2) were isolated which corresponded to poly(A)+ mRNAs that responded during growth transitions in a way consistent with a potential role of their protein products in cell elongation. These mRNAs are most abundant in the elongating zone of the soybean hypocotyl. Upon excision and incubation in the absence of auxin, these mRNAs deplete in concert with a decreasing rate of cell elongation. Addition of auxin to the medium results in both increased levels of these mRNAs and enhanced rates of cell elongation. These mRNAs do not deplete if auxin is added to the medium at the onset of excised incubation, and cell elongation rates remain high. We have isolated and sequenced genomic clones that are homologous to these cDNAs. Of the two genes sequenced, both genes are members of small multigene families. There are regions of high amino acid homology even though the nucleotide sequences are sufficiently different in these regions for cross-hybridization of the clones not to be observed. More recently others, especially Guilfoyle's laboratory, have shown that auxin selectively and rapidly influences the level of certain mRNAs and proteins. We have worked on other gene systems such as ribosomal proteins and possible cell wall proteins that are responsive to auxin; again the nature of regulation of expression of these genes is not known.(ABSTRACT TRUNCATED AT 400 WORDS)     

转录后水平沉默与基因表达

基因沉默是1个非常复杂和普遍的现象。转录后水平的基因沉默是指转基因在细胞核里能稳定转录,细胞质里却无相应的稳定态mRNA存在的现象。它往往被称为共抑制、静息作用或RNA干预等。本文介绍了转录后水平的基因沉默现象的发现、分子机理和应用等方面的进展。提出了克服转录后水平基因沉默的一些对策。     

Dimerizer-regulated gene expression

Control of gene expression using small molecules is a powerful research tool and has clinical utility in the context of regulated gene therapy. Use of chemical inducers of dimerization, or dimerizers, for this purpose has several advantages, including tight regulation, modularity to facilitate iterative improvements, and assembly from human proteins to minimize immune responses in clinical applications. Recent developments include the use of the rapamycin-based dimerizer system to regulate the expression of endogenous genes, the generation of new chemical dimerizers based on FK506, dexamethasone and methotrexate, and progress towards the clinical use of adeno-associated virus and adenovirus vectors regulated by rapamycin analogs.