A model of binding on DNA microarrays: understanding the combined effect of probe synthesis failure, cross-hybridization, DNA fragmentation and other experimental details of affymetrix arrays

CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level

Detecting cyanobacteria in environments is an important concern due to their crucial roles in ecosystems, and they can form blooms with the potential to harm humans and nonhuman entities. However, the most widely used methods for high-throughput detection of environmental cyanobacteria, such as 16S rRNA sequencing, typically provide above-species-level resolution, thereby disregarding intraspecific variation. To address this, we developed a novel DNA microarray tool, termed the CyanoStrainChip, that enables strain-level comprehensive profiling of environmental cyanobacteria. The CyanoStrainChip was designed to target 1277 strains; nearly all major groups of cyanobacteria are included by implementing 43,666 genome-wide, strain-specific probes. It demonstrated strong specificity by in vitro mock community experiments. The high correlation (Pearson's R > 0.97) between probe fluorescence intensities and the corresponding DNA amounts (ranging from 1-100 ng) indicated excellent quantitative capability. Consistent cyanobacterial profiles of field samples were observed by both the CyanoStrainChip and next-generation sequencing methods. Furthermore, CyanoStrainChip analysis of surface water samples in Lake Chaohu uncovered a high intraspecific variation of abundance change within the genus Microcystis between different severity levels of cyanobacterial blooms, highlighting two toxic Microcystis strains that are of critical concern for Lake Chaohu harmful blooms suppression. Overall, these results suggest a potential for CyanoStrainChip as a valuable tool for cyanobacterial ecological research and harmful bloom monitoring to supplement existing techniques.

Sequence characteristics define trade-offs between on-target and genome-wide off-target hybridization of oligoprobes

Off-target oligoprobe's interaction with partially complementary nucleotide sequences represents a problem for many bio-techniques. The goal of the study was to identify oligoprobe sequence characteristics that control the ratio between on-target and off-target hybridization. To understand the complex interplay between specific and genome-wide off-target (cross-hybridization) signals, we analyzed a database derived from genomic comparison hybridization experiments performed with an Affymetrix tiling array. The database included two types of probes with signals derived from (i) a combination of specific signal and cross-hybridization and (ii) genomic cross-hybridization only. All probes from the database were grouped into bins according to their sequence characteristics, where both hybridization signals were averaged separately. For selection of specific probes, we analyzed the following sequence characteristics: vulnerability to self-folding, nucleotide composition bias, numbers of G nucleotides and GGG-blocks, and occurrence of probe's k-mers in the human genome. Increases in bin ranges for these characteristics are simultaneously accompanied by a decrease in hybridization specificity-the ratio between specific and cross-hybridization signals. However, both averaged hybridization signals exhibit growing trends along with an increase of probes' binding energy, where the hybridization specific signal increases significantly faster in comparison to the cross-hybridization. The same trend is evident for the S function, which serves as a combined evaluation of probe binding energy and occurrence of probe's k-mers in the genome. Application of S allows extracting a larger number of specific probes, as compared to using only binding energy. Thus, we showed that high values of specific and cross-hybridization signals are not mutually exclusive for probes with high values of binding energy and S. In this study, the application of a new set of sequence characteristics allows detection of probes that are highly specific to their targets for array design and other bio-techniques that require selection of specific probes.

Modeling Hybridization Kinetics of Gene Probes in a DNA Biochip Using FEMLAB

Microfluidic DNA biochips capable of detecting specific DNA sequences are useful in medical diagnostics, drug discovery, food safety monitoring and agriculture. They are used as miniaturized platforms for analysis of nucleic acids-based biomarkers. Binding kinetics between immobilized single stranded DNA on the surface and its complementary strand present in the sample are of interest. To achieve optimal sensitivity with minimum sample size and rapid hybridization, ability to predict the kinetics of hybridization based on the thermodynamic characteristics of the probe is crucial. In this study, a computer aided numerical model for the design and optimization of a flow-through biochip was developed using a finite element technique packaged software tool (FEMLAB; package included in COMSOL Multiphysics) to simulate the transport of DNA through a microfluidic chamber to the reaction surface. The model accounts for fluid flow, convection and diffusion in the channel and on the reaction surface. Concentration, association rate constant, dissociation rate constant, recirculation flow rate, and temperature were key parameters affecting the rate of hybridization. The model predicted the kinetic profile and signal intensities of eighteen 20-mer probes targeting vancomycin resistance genes (VRGs). Predicted signal intensities and hybridization kinetics strongly correlated with experimental data in the biochip (R² = 0.8131).

Optimizing a custom tiling microarray for low input detection and identification of unamplified virus targets

Viruses are major pathogens causing foodborne illnesses and are often present at low levels in foods, thus requiring sensitive techniques for their detection in contaminated foods. The lack of efficient culture methods for many foodborne viruses and the potential for multi-species viral contamination have driven investigation toward non-amplification based methods for virus detection and identification. A custom DNA microarray (FDA_EVIR) was assessed for its sensitivity in the detection and identification of low-input virus targets, human hepatitis A virus, norovirus, and coxsackievirus, individually and in combination. Modifications to sample processing were made to accommodate low input levels of unamplified virus targets, which included addition of carrier cDNA, RNase treatment, and optimization of DNase I-mediated target fragmentation. Amplification-free detection and identification of foodborne viruses were achieved in the range of 250-500 copies of virus RNA. Alternative data analysis methods were employed to distinguish the genotypes of the viruses particularly at lower levels of target input and the single probe-based analysis approach made it possible to identify a minority species in a multi-virus complex. The oligonucleotide array is shown to be a promising platform to detect foodborne viruses at low levels close to what are anticipated in food or environmental samples.

Expression Comparison of Oil Biosynthesis Genes in Oil Palm Mesocarp Tissue Using Custom Array

Gene expression changes that occur during mesocarp development are a major research focus in oil palm research due to the economic importance of this tissue and the relatively rapid increase in lipid content to very high levels at fruit ripeness. Here, we report the development of a transcriptome-based 105,000-probe oil palm mesocarp microarray. The expression of genes involved in fatty acid (FA) and triacylglycerol (TAG) assembly, along with the tricarboxylic acid cycle (TCA) and glycolysis pathway at 16 Weeks After Anthesis (WAA) exhibited significantly higher signals compared to those obtained from a cross-species hybridization to the Arabidopsis (p-value < 0.01), and rice (p-value < 0.01) arrays. The oil palm microarray data also showed comparable correlation of expression (r² = 0.569, p < 0.01) throughout mesocarp development to transcriptome (RNA sequencing) data, and improved correlation over quantitative real-time PCR (qPCR) (r² = 0.721, p < 0.01) of the same RNA samples. The results confirm the advantage of the custom microarray over commercially available arrays derived from model species. We demonstrate the utility of this custom microarray to gain a better understanding of gene expression patterns in the oil palm mesocarp that may lead to increasing future oil yield.

Key genes and pathways in thyroid cancer based on gene set enrichment analysis

The incidence of thyroid cancer and its associated morbidity has shown the most rapid increase among all cancers since 1982, but the mechanisms involved in thyroid cancer, particularly significant key genes induced in thyroid cancer, remain undefined. In many studies, gene probes have been used to search for key genes involved in causing and facilitating thyroid cancer. As a result, many possible virulence genes and pathways have been identified. However, these studies lack a case contrast for selecting the most possible virulence genes and pathways, as well as conclusive results with which to clarify the mechanisms of cancer development. In the present study, we used gene set enrichment and meta-analysis to select key genes and pathways. Based on gene set enrichment, we identified 5 downregulated and 4 upregulated mixed pathways in 6 tissue datasets. Based on the meta-analysis, there were 17 common pathways in the tissue datasets. One pathway, the p53 signaling pathway, which includes 13 genes, was identified by both the gene set enrichment analysis and meta-analysis. Genes are important elements that form key pathways. These pathways can induce the development of thyroid cancer later in life. The key pathways and genes identified in the present study can be used in the next stage of research, which will involve gene elimination and other methods of experimentation.