Secondary structure in the target as a confounding factor in synthetic oligomer microarray design

BACKGROUND: Secondary structure in the target is a property not usually considered in software applications for design of optimal custom oligonucleotide probes. It is frequently assumed that eliminating self-complementarity, or screening for secondary structure in the probe, is sufficient to avoid interference with hybridization by stable secondary structures in the probe binding site. Prediction and thermodynamic analysis of secondary structure formation in a genome-wide set of transcripts from Brucella suis 1330 demonstrates that the properties of the target molecule have the potential to strongly influence the rate and extent of hybridization between transcript and tethered oligonucleotide probe in a microarray experiment. RESULTS: Despite the relatively high hybridization temperatures and 1M monovalent salt imposed in the modeling process to approximate hybridization conditions used in the laboratory, we find that parts of the target molecules are likely to be inaccessible to intermolecular hybridization due to the formation of stable intramolecular secondary structure. For example, at 65°C, 28 ± 7% of the average cDNA target sequence is predicted to be inaccessible to hybridization. We also analyzed the specific binding sites of a set of 70mer probes previously designed for Brucella using a freely available oligo design software package. 21 ± 13% of the nucleotides in each probe binding site are within a double-stranded structure in over half of the folds predicted for the cDNA target at 65°C. The intramolecular structures formed are more stable and extensive when an RNA target is modeled rather than cDNA. When random shearing of the target is modeled for fragments of 200, 100 and 50 nt, an overall destabilization of secondary structure is predicted, but shearing does not eliminate secondary structure. CONCLUSION: Secondary structure in the target is pervasive, and a significant fraction of the target is found in double stranded conformations even at high temperature. Stable structure in the target has the potential to interfere with hybridization and should be a factor in interpretation of microarray results, as well as an explicit criterion in array design. Inclusion of this property in an oligonucleotide design procedure would change the definition of an optimal oligonucleotide significantly

Comparative Genomic Analysis of Two Vibrio toranzoniae Strains with Different Virulence Capacity Reveals Clues on Its Pathogenicity for Fish

Vibrio toranzoniae is a Gram-negative bacterium of the Splendidus clade within the Vibrio genus. V. toranzoniae was first isolated from healthy clams in Galicia (Spain) but recently was also identified associated to disease outbreaks of red conger eel in Chile. Experimental challenges showed that the Chilean isolates were able to produce fish mortalities but not the strains isolated from clams. The aim of the present study was to determine the differences at the genomic level between the type strain of the species (CECT 7225T) and the strain R17, isolated from red conger eel in Chile, which could explain their different virulent capacity. The genome-based comparison showed high homology between both strains but differences were observed in certain gene clusters that include some virulence factors. Among these, we found that iron acquisition systems and capsule synthesis genes were the main differential features between both genomes that could explain the differences in the pathogenicity of the strains. Besides, the studied genomes presented genomic islands and toxins, and the R17 strain presented CRISPR sequences that are absent on the type strain. Taken together, this analysis provided important insights into virulence factors of V. toranzoniae that will lead to a better understanding of the pathogenic processThis research was supported in part by grants AGL2013-42628R and AGL2016-77539R from Ministerio de Economia y Competitividad (Spain)S

Background correction using dinucleotide affinities improves the performance of GCRMA

BACKGROUND: High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly. RESULTS: We introduce an approach to calculate probe affinity based on sequence composition, incorporating nearest-neighbor (NN) information. Our model uses position-specific dinucleotide information, instead of the original single nucleotide approach, and adds up to 10% to the total variance explained (R(2)) when compared to the previously published model. We demonstrate that correcting for background noise using this approach enhances the performance of the GCRMA preprocessing algorithm when applied to control datasets, especially for detecting low intensity targets. CONCLUSION: Modifying the previously published position-dependent affinity model to incorporate dinucleotide information significantly improves the performance of the model. The dinucleotide affinity model enhances the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This is conceptually consistent with physical models of binding affinity, which depend on the nearest-neighbor stacking interactions in addition to base-pairing

Molecular targets for rapid identification of Brucella spp

BACKGROUND: Brucella is an intracellular pathogen capable of infecting animals and humans. There are six recognized species of Brucella that differ in their host preference. The genomes of the three Brucella species have been recently sequenced. Comparison of the three revealed over 98% sequence similarity at the protein level and enabled computational identification of common and differentiating genes. We validated these computational predictions and examined the expression patterns of the putative unique and differentiating genes, using genomic and reverse transcription PCR. We then screened a set of differentiating genes against classical Brucella biovars and showed the applicability of these regions in the design of diagnostic tests. RESULTS: We have identified and tested set of molecular targets that are associated in unique patterns with each of the sequenced Brucella spp. A comprehensive comparison was made among the published genome sequences of B. abortus, B. melitensis and B. suis. The comparison confirmed published differences between the three Brucella genomes, and identified subsets of features that were predicted to be of interest in a functional comparison of B. melitensis and B. suis to B. abortus. Differentiating sequence regions from B. abortus, B. melitensis and B. suis were used to develop PCR primers to test for the existence and in vitro transcription of these genes in these species. Only B. suis is found to have a significant number of unique genes, but combinations of genes and regions that exist in only two out of three genomes and are therefore useful for diagnostics were identified and confirmed. CONCLUSION: Although not all of the differentiating genes identified were transcribed under steady state conditions, a group of genes sufficient to discriminate unambiguously between B. suis, B. melitensis, and B. abortus was identified. We present an overview of these genomic differences and the use of these features to discriminate among a number of Brucella biovars

Sequencing by Hybridization of Long Targets

Sequencing by Hybridization (SBH) reconstructs an n-long target DNA sequence from its biochemically determined l-long subsequences. In the standard approach, the length of a uniformly random sequence that can be unambiguously reconstructed is limited to due to repetitive subsequences causing reconstruction degeneracies. We present a modified sequencing method that overcomes this limitation without the need for different types of biochemical assays and is robust to error

Accurate Estimates of Microarray Target Concentration from a Simple Sequence-Independent Langmuir Model

Background: Microarray technology is a commonly used tool for assessing global gene expression. Many models for estimation of target concentration based on observed microarray signal have been proposed, but, in general, these models have been complex and platform-dependent. Principal Findings: We introduce a universal Langmuir model for estimation of absolute target concentration from microarray experiments. We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray. The model also accurately predicts concentration for the MAQC data sets. This approach significantly reduces the computational complexity of quantitative target concentration estimates. Conclusions: Using a simple form of the Langmuir isotherm model, with a minimum of parameters and assumptions, and without explicit modeling of individual probe properties, we were able to recover absolute transcript concentrations with high R 2 on four different array platforms. The results obtained here suggest that with a ‘‘spiked-in’ ’ concentration serie

Measures of Association for Identifying MicroRNA-mRNA Pairs of Biological Interest

MicroRNAs are a class of small non-protein coding RNAs that play an important role in the regulation of gene expression. Most studies on the identification of microRNA-mRNA pairs utilize the correlation coefficient as a measure of association. The use of correlation coefficient is appropriate if the expression data are available for several conditions and, for a given condition, both microRNA and mRNA expression profiles are obtained from the same set of individuals. However, there are many instances where one of the requirements is not satisfied. Therefore, there is a need for new measures of association to identify the microRNA-mRNA pairs of interest and we present two such measures. The first measure requires expression data for multiple conditions but, for a given condition, the microRNA and mRNA expression may be obtained from different individuals. The new measure, unlike the correlation coefficient, is suitable for analyzing large data sets which are obtained by combining several independent studies on microRNAs and mRNAs. Our second measure is able to handle expression data that correspond to just two conditions but, for a given condition, the microRNA and mRNA expression must be obtained from the same set of individuals. This measure, unlike the correlation coefficient, is appropriate for analyzing data sets with a small number of conditions. We apply our new measures of association to multiple myeloma data sets, which cannot be analyzed using the correlation coefficient, and identify several microRNA-mRNA pairs involved in apoptosis and cell proliferation

Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

Background: The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms. Principal Findings: 50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods. Conclusions: Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe ‘‘percent bound’ ’ value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for shor

Discovering Dysfunction of Multiple MicroRNAs Cooperation in Disease by a Conserved MicroRNA Co-Expression Network

MicroRNAs, a new class of key regulators of gene expression, have been shown to be involved in diverse biological processes and linked to many human diseases. To elucidate miRNA function from a global perspective, we constructed a conserved miRNA co-expression network by integrating multiple human and mouse miRNA expression data. We found that these conserved co-expressed miRNA pairs tend to reside in close genomic proximity, belong to common families, share common transcription factors, and regulate common biological processes by targeting common components of those processes based on miRNA targets and miRNA knockout/transfection expression data, suggesting their strong functional associations. We also identified several co-expressed miRNA sub-networks. Our analysis reveals that many miRNAs in the same sub-network are associated with the same diseases. By mapping known disease miRNAs to the network, we identified three cancer-related miRNA sub-networks. Functional analyses based on targets and miRNA knockout/transfection data consistently show that these sub-networks are significantly involved in cancer-related biological processes, such as apoptosis and cell cycle. Our results imply that multiple co-expressed miRNAs can cooperatively regulate a given biological process by targeting common components of that process, and the pathogenesis of disease may be associated with the abnormality of multiple functionally cooperative miRNAs rather than individual miRNAs. In addition, many of these co-expression relationships provide strong evidence for the involvement of new miRNAs in important biological processes, such as apoptosis, differentiation and cell cycle, indicating their potential disease links

Sequence-Dependent Fluorescence of Cyanine Dyes on Microarrays

Cy3 and Cy5 are among the most commonly used oligonucleotide labeling molecules. Studies of nucleic acid structure and dynamics use these dyes, and they are ubiquitous in microarray experiments. They are sensitive to their environment and have higher quantum yield when bound to DNA. The fluorescent intensity of terminal cyanine dyes is also known to be significantly dependent on the base sequence of the oligonucleotide. We have developed a very precise and high-throughput method to evaluate the sequence dependence of oligonucleotide labeling dyes using microarrays and have applied the method to Cy3 and Cy5. We used light-directed in-situ synthesis of terminally-labeled microarrays to determine the fluorescence intensity of each dye on all 1024 possible 5′-labeled 5-mers. Their intensity is sensitive to all five bases. Their fluorescence is higher with 5′ guanines, and adenines in subsequent positions. Cytosine suppresses fluorescence. Intensity falls by half over the range of all 5-mers for Cy3, and two-thirds for Cy5. Labeling with 5′-biotin-streptavidin-Cy3/-Cy5 gives a completely different sequence dependence and greatly reduces fluorescence compared with direct terminal labeling