DNA microarray technology in toxicogenomics of aquatic models: methods and applications

Toxicogenomics represents the merging of toxicology with genomics and bioinformatics to investigate biological functions of genome in response to environmental contaminants. Aquatic species have traditionally been used as models in toxicology to characterize the actions of environmental stresses. Recent completion of the DNA sequencing for several fish species has spurred the development of DNA microarrays allowing investigators access to toxicogenomic approaches. However, since microarray technology is thus far limited to only a few aquatic species and derivation of biological meaning from microarray data is highly dependent on statistical arguments, the full potential of microarray in aquatic species research has yet to be realized. Herein we review some of the issues related to construction, probe design, statistical and bioinformatical data analyses, and current applications of DNA microarrays. As a model a recently developed medaka (Oryzias latipes) oligonucleotide microarray was described to highlight some of the issues related to array technology and its application in aquatic species exposed to hypoxia. Although there are known non-biological variations present in microarray data, it remains unquestionable that array technology will have a great impact on aquatic toxicology. Microarray applications in aquatic toxicogenomics will range from the discovery of diagnostic biomarkers, to establishment of stress-specific signatures and molecular pathways hallmarking the adaptation to new environmental conditions.     

High-Throughput Analysis of Ammonia Oxidiser Community Composition via a Novel,amoA-Based Functional Gene Array

Advances in microbial ecology research are more often than not limited by the capabilities of available methodologies. Aerobic autotrophic nitrification is one of the most important and well studied microbiological processes in terrestrial and aquatic ecosystems. We have developed and validated a microbial diagnostic microarray based on the ammonia-monooxygenase subunit A (amoA) gene, enabling the in-depth analysis of the community structure of bacterial and archaeal ammonia oxidisers. The amoA microarray has been successfully applied to analyse nitrifier diversity in marine, estuarine, soil and wastewater treatment plant environments. The microarray has moderate costs for labour and consumables and enables the analysis of hundreds of environmental DNA or RNA samples per week per person. The array has been thoroughly validated with a range of individual and complex targets (amoA clones and environmental samples, respectively), combined with parallel analysis using traditional sequencing methods. The moderate cost and high throughput of the microarray makes it possible to adequately address broader questions of the ecology of microbial ammonia oxidation requiring high sample numbers and high resolution of the community composition.     

基因芯片及其在环境微生物研究中的应用

基因芯片因其具有高密度、高灵敏度、快速 (实时 )检测、经济、自动化和低背景水平等特点 ,而广泛应用于不同的研究领域。目前 ,应用于环境微生物研究的基因芯片主要有功能基因芯片 (FGAs)、系统发育的寡核苷酸芯片 (POAs)和群落基因组芯片 (CGAs)。综述了基因芯片在环境微生物研究中的应用 ,包括自然环境中微生物的基因表达分析、比较基因组分析和混合微生物群落的分析等。讨论了基因芯片面临的挑战和前景展望     

Development and validation of a diagnostic microbial microarray for methanotrophs

The potential of DNA microarray technology in high-throughput detection of bacteria and quantitative assessment of their community structures is widely acknowledged but has not been fully realised yet. A generally applicable set of techniques, based on readily available technologies and materials, was developed for the design, production and application of diagnostic microbial microarrays. A microarray targeting the particulate methane monooxygenase (pmoA) gene was developed for the detection and quantification of methanotrophs and functionally related bacteria. A microarray consisting of a set of 59 probes that covers the whole known diversity of these bacteria was validated with a representative set of extant strains and environmental clones. The potential of the pmoA microarray was tested with environmental samples. The results were in good agreement with those of clone library sequence analyses. The approach can currently detect less dominant bacteria down to 5% of the total community targeted. Initial tests assessing the quantification potential of this system with artificial PCR mixtures showed very good correlation with the expected results with standard deviations in the range of 0.4-17.2%. Quantification of environmental samples with this method requires the design of a reference mixture consisting of very close relatives of the strains within the sample and is currently limited by biases inherent in environmental DNA extraction and universal PCR amplification.     

Development and validation of a prototype 16S rRNA-based taxonomic microarray for Alphaproteobacteria

The microarray approach has been proposed for high throughput analysis of the microbial community by providing snapshots of the microbial diversity under different environmental conditions. For this purpose, a prototype of a 16S rRNA-based taxonomic microarray was developed and evaluated for assessing bacterial community diversity. The prototype microarray is composed of 122 probes that target bacteria at various taxonomic levels from phyla to species (mostly Alphaproteobacteria). The prototype microarray was first validated using bacteria in pure culture. Differences in the sequences of probes and potential target DNAs were quantified as weighted mismatches (WMM) in order to evaluate hybridization reliability. As a general feature, probes having a WMM > 2 with target DNA displayed only 2.8% false positives. The prototype microarray was subsequently tested with an environmental sample, which consisted of an Agrobacterium-related polymerase chain reaction amplicon from a maize rhizosphere bacterial community. Microarray results were compared to results obtained by cloning-sequencing with the same DNA. Microarray analysis enabled the detection of all 16S rRNA gene sequences found by cloning-sequencing. Sequences representing only 1.7% of the clone library were detected. In conclusion, this prototype 16S rRNA-based taxonomic microarray appears to be a promising tool for the analysis of Alphaproteobacteria in complex ecosystems.     

Exploitation of genomic sequences in a systematic analysis to access how cyanobacteria sense environmental stress

The perception and subsequent transduction of environmental signals are primary events in the acclimation of living organisms to changes in their environment. Many of the molecular sensors and transducers of environmental stress cannot be identified by traditional and conventional methods. Therefore, the genomic information has been exploited in a systematic approach to this problem, performing systematic mutagenesis of potential sensors and transducers, namely, histidine kinases and response regulators, respectively, in combination with DNA microarray analysis, to examine the genome-wide expression of genes in the unicellular cyanobacterium Synechocystis sp. PCC 6803. Using targeted mutagenesis, 44 out of the 47 histidine kinases and 42 out of the 45 response regulators of this organism have successfully been inactivated. The resultant mutant libraries were screened by genome-wide DNA microarray analysis and by slot-blot hybridization analysis under various stress and non-stress conditions. Histidine kinases have been identified that perceive and transduce signals of low-temperature, hyperosmotic, and salt stress, as well as manganese deficiency.     

Taking arrays from the lab to the field: trying to make sense of the unknown

With the rapid pace of nucleic acid microarray technology development and a renewed national emphasis on detecting and characterizing microorganisms in environmental samples, there is a rush to operationalize existing microarray technologies and apply them to uncharacterized environmental backgrounds. The purpose of this article is to pause and ask a basic question: what do microarray data actually mean in the face of uncharacterized sample backgrounds? In attempting to answer this question, we draw a clear distinction between hypothesis-driven fundamental science and operational uses of microarray technology; assess microarray technology assumptions in the face of uncharacterized environments; offer an environmental microbiologist's perspective on technology needs and requirements for quantitatively analyzing microbial communities; and hopefully stimulate a scientific and technical dialogue around the concept of analytical environmental microbiology and future technology development.     

An oligonucleotide prokaryotic acidophile microarray: its validation and its use to monitor seasonal variations in extreme acidic environments with total environmental RNA

An oligonucleotide microarray that monitors prokaryotic diversity in extremely acidic environments has been developed. The oligonucleotide probes target most known acidophilic microorganisms, including members of the Nitrospira phylum, Acidithiobacillus genus, acidobacteria, sulfur reducing bacteria, Actinobacteria and Archaea of the Ferroplasma and Thermoplasma genera. The probes were tested for their specificity against the corresponding type strain by microarray hybridization using PCR-amplified fluorescent DNA of the 16S rRNA genes. The microarray was tested and validated against well-established molecular ecology techniques such as molecular cloning and sequencing and FISH by using samples obtained from a natural extremely acidic environment, the Río Tinto (SW Spain). Also, fluorescent labelled total environmental RNA from Río Tinto samples were used as targets for microarray hybridizations. This approach allowed the detection of the most metabolically active prokaryotes of the ecosystem by simultaneously checking probes against 16S and 23S rRNAs as well as other functional genes. Seasonal and spatial variations in the relative expression of specific rRNA genes have been detected between two sampling sites that differ in several physicochemical parameters, mainly iron and sulfur content.     

High-Density Universal 16S rRNA Microarray Analysis Reveals Broader Diversity than Typical Clone Library When Sampling the Environment

Molecular approaches aimed at detection of a broad-range of prokaryotes in the environment routinely rely on classifying heterogeneous 16S rRNA genes amplified by polymerase chain reaction (PCR) using primers with broad specificity. The general method of sampling and categorizing DNA has been to clone then sequence the PCR products. However, the number of clones required to adequately catalog the majority of taxa in a sample is unwieldy. Alternatively, hybridizing target sequences to a universal 16S rRNA gene microarray may provide a more rapid and comprehensive view of prokaryotic community composition. This study investigated the breadth and accuracy of a microarray in detecting diverse 16S rRNA gene sequence types compared to clone-and-sequencing using three environmental samples: urban aerosol, subsurface soil, and subsurface water. PCR products generated from universal 16S rRNA gene-targeted primers were classified by using either the clone-and-sequence method or by hybridization to a novel high-density microarray of 297,851 probes complementary to 842 prokaryotic subfamilies. The three clone libraries comprised 1391 high-quality sequences. Approximately 8% of the clones could not be placed into a known subfamily and were considered novel. The microarray results confirmed the majority of clone-detected subfamilies and additionally demonstrated greater amplicon diversity extending into phyla not observed by the cloning method. Sequences matching operational taxonomic units within the phyla Nitrospira, Planctomycetes, and TM7, which were uniquely detected by the array, were verified with specific primers and subsequent amplicon sequencing. Subfamily richness detected by the array corresponded well with nonparametric richness predictions extrapolated from clone libraries except in the water community where clone-based richness predictions were greatly exceeded. It was concluded that although the microarray is unreliable in identifying novel prokaryotic taxa, it reveals greater diversity in environmental samples than sequencing a typically sized clone library. Furthermore, the microarray allowed samples to be rapidly evaluated with replication, a significant advantage in studies of microbial ecology.     

Psychoactive pharmaceuticals induce fish gene expression profiles associated with human idiopathic autism

Idiopathic autism, caused by genetic susceptibility interacting with unknown environmental triggers, has increased dramatically in the past 25 years. Identifying environmental triggers has been difficult due to poorly understood pathophysiology and subjective definitions of autism. The use of antidepressants by pregnant women has been associated with autism. These and other unmetabolized psychoactive pharmaceuticals (UPPs) have also been found in drinking water from surface sources, providing another possible exposure route and raising questions about human health consequences. Here, we examined gene expression patterns of fathead minnows treated with a mixture of three psychoactive pharmaceuticals (fluoxetine, venlafaxine & carbamazepine) in dosages intended to be similar to the highest observed conservative estimates of environmental concentrations. We conducted microarray experiments examining brain tissue of fish exposed to individual pharmaceuticals and a mixture of all three. We used gene-class analysis to test for enrichment of gene sets involved with ten human neurological disorders. Only sets associated with idiopathic autism were unambiguously enriched. We found that UPPs induce autism-like gene expression patterns in fish. Our findings suggest a new potential trigger for idiopathic autism in genetically susceptible individuals involving an overlooked source of environmental contamination.     

Promise and progress in environmental genomics: a status report on the applications of gene expression-based microarray studies in ecologically relevant fish species

The advent of any new technology is typically met with great excitement. So it was a few years ago, when the combination of advances in sequencing technology and the development of microarray technology made measurements of global gene expression in ecologically relevant species possible. Many of the review papers published around that time promised that these new technologies would revolutionize environmental biology as they had revolutionized medicine and related fields. A few years have passed since these technological advancements have been made, and the use of microarray studies in non‐model fish species has been adopted in many laboratories internationally. Has the relatively widespread adoption of this technology really revolutionized the fields of environmental biology, including ecotoxicology, aquaculture and ecology, as promised? Or have these studies merely become a novelty and a potential distraction for scientists addressing environmentally relevant questions? In this review, the promises made in early review papers, in particular about the advances that the use of microarrays would enable, are summarized; these claims are compared to the results of recent studies to determine whether the forecasted changes have materialized. Some applications, as discussed in the paper, have been realized and have led to advances in their field, others are still under development.     

Root development--branching into novel spheres

Recent progress in deciphering the genetics of Arabidopsis root development has been driven by the availability of novel molecular tools. For instance, combining enhancer trap lines and microarray analyses enabled the creation of an expression map for over 22000 genes at cellular resolution. Such expression profiles often suggest redundant action of homologous genes, which has indeed been observed for several pivotal factors that are required for the organization and maintenance of root meristems. Additional regulators of root development are also being identified by analysis of natural genetic variation. Moreover, microRNA control of gene expression has recently been implicated in root development, and progress has been made in understanding the interplay between environmental and genetic factors in root branching.     

Evaluation of 50-mer oligonucleotide arrays for detecting microbial populations in environmental samples

Microarrays fabricated with oligonucleotides longer than 40 bp have been introduced for monitoring whole genome expression but have not been evaluated with environmental samples. To determine the potential of this type of microarray for environmental studies, a 50-mer oligonucleotide microarray was constructed using 763 genes involved in nitrogen cycling: nitrite reductase (nirS and nirK), ammonia monooxygenase (amoA), nitrogenase (nifH), methane monooxygenase (pmoA), and sulfite reductase (dsrAB) from public databases and our own sequence collections. The comparison of the sequences from pure cultures indicated that the developed microarrays could provide species-level resolution for analyzing microorganisms involved in nitrification, denitrification, nitrogen fixation, methane oxidation, and sulfite reduction. Sensitivity tests suggested that the 50-mer oligonucleotide arrays could detect dominant populations in the environments, although sensitivity still needs to be improved. A significant quantitative relationship was also obtained with a mixture of DNAs from eight different bacteria. These results suggest that the 50-mer oligonucleotide array can be used as a specific and quantitative parallel tool for the detection of microbial populations in environmental samples.     

Adding confidence to gene expression clustering

It has been well established that gene expression data contain large amounts of random variation that affects both the analysis and the results of microarray experiments. Typically, microarray data are either tested for differential expression between conditions or grouped on the basis of profiles that are assessed temporally or across genetic or environmental conditions. While testing differential expression relies on levels of certainty to evaluate the relative worth of various analyses, cluster analysis is exploratory in nature and has not had the benefit of any judgment of statistical inference. By using a novel dissimilarity function to ascertain gene expression clusters and conditional randomization of the data space to illuminate distinctions between statistically significant clusters of gene expression patterns, we aim to provide a level of confidence to inferred clusters of gene expression data. We apply both permutation and convex hull approaches for randomization of the data space and show that both methods can provide an effective assessment of gene expression profiles whose coregulation is statistically different from that expected by random chance alone.