A multivariate prediction model for microarray cross-hybridization

Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays

The reliability of differential gene expression analysis on formalin-fixed, paraffin-embedded (FFPE) expression profiles generated using Affymetrix arrays is questionable, due to the high range of percent-present values reported in studies which profiled FFPE samples using this technology. Moreover, the validity of gene-modules derived from external datasets in FFPE microarray expression profiles is unknown. By generating matched gene expression profiles using RNAs derived from fresh-frozen (FF) and FFPE preserved breast tumors with Affymetrix arrays and FF/FFPE RNA specific amplification-and-labeling kits, the reliability of differential expression analysis and the validity of gene modules derived from external datasets were investigated. Specifically, the reliability of differential expression analysis was investigated by developing de-novo ER/HER2 pathway gene-modules from the matched datasets and validating them on external FF/FFPE gene expression datasets using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE/frozen datasets was used to measure the reliability of gene-modules derived from external datasets in FFPE expression profiles. Independent of the array/amplification-kit/sample preservation method used, de-novo ER/HER2 gene-modules derived from all matched datasets showed similar prediction performance in the independent validation (AUC range in FFPE dataset; ER: 0.93-0.95, HER2: 0.85-0.91), except for the de-novo ER/HER2 gene-module derived from the FFPE dataset using the 3'IVT kit (AUC range in FFPE dataset; ER: 0.79-0.81, HER2: 0.78). Among the external gene modules considered, roughly ~50% gene modules showed high concordance between expression profiles derived from matching FF and FFPE RNA. The remaining discordant gene modules between FF and FFPE expression profiles showed high concordance within matching FF datasets and within matching FFPE datasets independently, implying that microarrays still require improved amplification-and-sample-preparation protocols for deriving 100% concordant expression profiles from matching FF and FFPE RNA.

Transcriptome sequencing of the human pathogen Corynebacterium diphtheriae NCTC 13129 provides detailed insights into its transcriptional landscape and into DtxR-mediated transcriptional regulation

Background

The human pathogen Corynebacterium diphtheriae is the causative agent of diphtheria. In the 1990s a large diphtheria outbreak in Eastern Europe was caused by the strain C. diphtheriae NCTC 13129. Although the genome was sequenced more than a decade ago, not much is known about its transcriptome. Our aim was to use transcriptome sequencing (RNA-Seq) to close this knowledge gap and gain insights into the transcriptional landscape of a C. diphtheriae tox⁺ strain.

Results

We applied two different RNA-Seq techniques, one to retrieve 5′-ends of primary transcripts and the other to characterize the whole transcriptional landscape in order to gain insights into various features of the C. diphtheriae NCTC 13129 transcriptome. By examining the data we identified 1656 transcription start sites (TSS), of which 1202 were assigned to genes and 454 to putative novel transcripts. By using the TSS data promoter regions recognized by the housekeeping sigma factor σ^A and its motifs were analyzed in detail, revealing a well conserved −10 but an only weakly conserved −35 motif, respectively. Furthermore, with the TSS data 5’-UTR lengths were explored. The observed 5’-UTRs range from zero length (leaderless transcripts), which make up 20% of all genes, up to over 450 nt long leaders, which may harbor regulatory functions. The C. diphtheriae transcriptome consists of 471 operons which are further divided into 167 sub-operon structures. In a differential expression analysis approach, we discovered that genetic disruption of the iron-sensing transcription regulator DtxR, which controls expression of diphtheria toxin (DT), causes a strong influence on general gene expression. Nearly 15% of the genome is differentially transcribed, indicating that DtxR might have other regulatory functions in addition to regulation of iron metabolism and DT. Furthermore, our findings shed light on the transcriptional landscape of the DT encoding gene tox and present evidence for two tox antisense RNAs, which point to a new way of transcriptional regulation of toxin production.

Conclusions

This study presents extensive insights into the transcriptome of C. diphtheriae and provides a basis for future studies regarding gene characterization, transcriptional regulatory networks, and regulation of the tox gene in particular.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4481-8) contains supplementary material, which is available to authorized users.

Design of custom oligonucleotide microarrays for single species or interspecies hybrids using Array Oligo Selector

New technologies for DNA sequencing have made it feasible to determine the genome sequence of any organism of interest. This sequence is the resource required to create tools for downstream studies, including DNA microarrays. A number of vendors can produce DNA microarrays containing customer-specified sequences, allowing investigators to design and order arrays customized for any species of interest. Freely available, user-friendly computer programs are available for designing microarray probes. These design programs can be used to create probes that distinguish between two related genomes, allowing investigation of gene expression or gene representation in intra- or interspecies hybrids or in samples containing DNA from multiple species.

The effect of tert-butyl hydroperoxide on hepatic transcriptome expression patterns in the striped sea bream (Lithognathus mormyrus; Teleostei)

The study was aimed at examining the effects of tert-butyl hydroperoxide (tBHP) on hepatic transcriptome expression patterns of the teleost fish Lithognathus mormyrus. tBHP is an organic hydro-peroxide, widely used as a model pro-oxidant. It generates the reactive oxygen species (ROS) tert-butoxyl and tert-butylperoxyl. Complementary DNAs of tBHP-treated vs control fish were applied onto a previously produced cDNA microarray of approximately 1500 unique sequences. The effects of the tBHP application were demonstrated by leukocyte infiltration into the liver and by differential expression of various genes, some already known to be involved in ROS-related responses. Indicator genes of putative ROS effects were: aldehyde dehydrogenase 3A2, Heme oxygenase and the hemopexin-like protein. Putative indicators of transendothelial leukocyte migration and function were: p22phox, Rac1 and CD63-like genes. Interestingly, 7-dehydrocholesterol reductase was significantly down-regulated in response to all treatments. Several non-annotated genes revealed uniform directions of differential expression in response to all treatments.

Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays

Background

Tiling arrays have been the tool of choice for probing an organism's transcriptome without prior assumptions about the transcribed regions, but RNA-Seq is becoming a viable alternative as the costs of sequencing continue to decrease. Understanding the relative merits of these technologies will help researchers select the appropriate technology for their needs.

Results

Here, we compare these two platforms using a matched sample of poly(A)-enriched RNA isolated from the second larval stage of C. elegans. We find that the raw signals from these two technologies are reasonably well correlated but that RNA-Seq outperforms tiling arrays in several respects, notably in exon boundary detection and dynamic range of expression. By exploring the accuracy of sequencing as a function of depth of coverage, we found that about 4 million reads are required to match the sensitivity of two tiling array replicates. The effects of cross-hybridization were analyzed using a "nearest neighbor" classifier applied to array probes; we describe a method for determining potential "black list" regions whose signals are unreliable. Finally, we propose a strategy for using RNA-Seq data as a gold standard set to calibrate tiling array data. All tiling array and RNA-Seq data sets have been submitted to the modENCODE Data Coordinating Center.

Conclusions

Tiling arrays effectively detect transcript expression levels at a low cost for many species while RNA-Seq provides greater accuracy in several regards. Researchers will need to carefully select the technology appropriate to the biological investigations they are undertaking. It will also be important to reconsider a comparison such as ours as sequencing technologies continue to evolve.

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater efficiency. In this paper we report comparative classification of Pyruvate Dehydrogenase protein sequences from bacterial sources based on 28 different physicochemical parameters (such as bulkiness, hydrophobicity, total positively and negatively charged residues, α helices, β strand etc.) and 20 type amino acid compositions. Logistic, MLP (Multi Layer Perceptron), SMO (Sequential Minimal Optimization), RBFN (Radial Basis Function Network) and SL (simple logistic) methods were compared in this study. MLP was found to be the best method with maximum average accuracy of 88.20%. Same dataset was subjected for clustering using 2*2 grid of a two dimensional SOM (Self Organizing Maps). Clustering analysis revealed the proximity of the unannotated sequences with the Mycobacterium and Synechococcus genus.

Compressive sensing DNA microarrays

Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints from CS theory and the biochemistry of probe-target DNA hybridization. An appropriate cross-hybridization model is proposed for CSMs, and several methods are developed for probe design and CS signal recovery based on the new model. Lab experiments suggest that in order to achieve accurate hybridization profiling, consensus probe sequences are required to have sequence homology of at least 80% with all targets to be detected. Furthermore, out-of-equilibrium datasets are usually as accurate as those obtained from equilibrium conditions. Consequently, one can use CSMs in applications in which only short hybridization times are allowed.

CrossHybDetector: detection of cross-hybridization events in DNA microarray experiments

BACKGROUND

DNA microarrays contain thousands of different probe sequences represented on their surface. These are designed in such a way that potential cross-hybridization reactions with non-target sequences are minimized. However, given the large number of probes, the occurrence of cross hybridization events cannot be excluded. This problem can dramatically affect the data quality and cause false positive/false negative results.

RESULTS

CrossHybDetector is a software package aimed at the identification of cross-hybridization events occurred during individual array hybridization, by using the probe sequences and the array intensity values. As output, the software provides the user with a list of array spots potentially 'corrupted' and their associated p-values calculated by Monte Carlo simulations. Graphical plots are also generated, which provide a visual and global overview of the quality of the microarray experiment with respect to cross-hybridization issues.

CONCLUSION

CrossHybDetector is implemented as a package for the statistical computing environment R and is freely available under the LGPL license within the CRAN project.

Origin of co-expression patterns in E. coli and S. cerevisiae emerging from reverse engineering algorithms

Background

The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative gene-gene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of “physical” networks of interactions among genes or gene products.

Results

This paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E.coli and S.cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor–binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, co-localization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E.coli.

Conclusion

The gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E.coli to S.cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies.

PRIMEGENS-v2: genome-wide primer design for analyzing DNA methylation patterns of CpG islands

MOTIVATION

DNA methylation plays important roles in biological processes and human diseases, especially cancers. High-throughput bisulfite genomic sequencing based on new generation of sequencers, such as the 454-sequencing system provides an efficient method for analyzing DNA methylation patterns. The successful implementation of this approach depends on the use of primer design software capable of performing genome-wide scan for optimal primers from in silico bisulfite-treated genome sequences. We have developed a method, which fulfills this requirement and conduct primer design for sequences including regions of given promoter CpG islands.

RESULTS

The developed method has been implemented using the C and JAVA programming languages. The primer design results were tested in the PCR experiments of 96 selected human DNA sequences containing CpG islands in the promoter regions. The results indicate that this method is efficient and reliable for designing sequence-specific primers.

AVAILABILITY

The sequence-specific primer design for DNA meth-ylated sequences including CpG islands has been integrated into the second version of PRIMEGENS as one of the primer design features. The software is freely available for academic use at http://digbio.missouri.edu/primegens/.

Pollution-affected fish hepatic transcriptome and its expression patterns on exposure to cadmium

Individuals of the fish Lithognathus mormyrus were exposed to a series of pollutants including: benzo[a]pyrene, pp-DDE, Aroclor 1254, perfluorooctanoic acid, tributyl-tin chloride, lindane, estradiol, 4-nonylphenol, methyl mercury chloride, and cadmium chloride. Five mixtures of the pollutants were injected. Each mixture included one to three compounds. A microarray was constructed using 4608 L. mormyrus hepatic cDNAs cloned from the pollutant-exposed fish. Most clones (4456) were sequenced and assembled into 1494 annotated unique clones. The constructed microarray was used to identify changes in hepatic gene expression profile on exposure to cadmium administered to the fish by feeding or injections. Thirty-one unique clones showed altered expression levels on exposure to cadmium. Prominently differentially expressed genes included elastase 4, carboxypeptidase B, trypsinogen, perforin, complement C31, cytochrome P450 2K5, ceruloplasmin, carboxyl ester lipase, and metallothionein. Twelve sequences have no available annotation. Most genes (23) were downregulated and hypothesized to be affected by general toxicity due to the intensive cadmium exposure regime. The concept of an operational multigene cDNA microarray, aimed at routine and fast biomonitoring of multiple environmental threats, is outlined and the cadmium exposure experiment has been used to demonstrate functional and methodological aspects of the biomonitoring tool. The components of the outlined system include: (1) spotted array, composed of both pollution-affected and constitutively expressed genes, the latter are used for normalization; (2) standard, repeatable labeling procedure of a reference transcript population; and (3) biomarker indices derived from the profile of expression ratio across the pollution-affected genes, between the field-sampled transcript populations and the reference.

Specificity of DNA microarray hybridization: characterization, effectors and approaches for data correction

Microarray-hybridization specificity is one of the main effectors of microarray result quality. In the present review, we suggest a definition for specificity that spans four hybridization levels, from the single probe to the microarray platform. For increased hybridization specificity, it is important to quantify the extent of the specificity at each of these levels, and correct the data accordingly. We outline possible effects of low hybridization specificity on the obtained results and list possible effectors of hybridization specificity. In addition, we discuss several studies in which theoretical approaches, empirical means or data filtration were used to identify specificity effectors, and increase the specificity of the hybridization results. However, these various approaches may not yet provide an ultimate solution; rather, further tool development is needed to enhance microarray-hybridization specificity.

Characterization of DNA bio-bonds for meso-scale self-assembly

In this paper, we have investigated the use of DNA hybridization as the basis for the production of new mesoscale components. AFM experimental results are studied and compared to two theoretical approaches: molecular and thermodynamic. We explain how and why DNA hybridization process can provide a good bond to self assemble components, and how molecular modelling methods allow further understanding of the physical mechanism of this process. Furthermore, the strength interaction of DNA complementary strands is measured and analyzed using statistical tools. These results are then compared to the theoretical approaches.

Hepatopancreatic multi-transcript expression patterns in the crayfish Cherax quadricarinatus during the moult cycle

Alterations of hepatopancreatic multi-transcript expression patterns, related to induced moult cycle, were identified in male Cherax quadricarinatus through cDNA microarray hybridizations of hepatopancreatic transcript populations. Moult was induced by X-organ sinus gland extirpation or by repeated injections of 20-hydroxyecdysone. Manipulated males were sacrificed at premoult or early postmoult, and a reference population was sacrificed at intermoult. Differentially expressed genes among the four combinations of two induction methods and two moult stages were identified. Biologically interesting clusters revealing concurrently changing transcript expressions across treatments were selected, characterized by a general shift of expression throughout premoult and early postmoult vs. intermoult, or by different premoult vs. postmoult expressions. A number of genes were differentially expressed in 20-hydroxyecdysone-injected crayfish vs. X-organ sinus gland extirpated males.

In situ analysis of cross-hybridisation on microarrays and the inference of expression correlation

BACKGROUND

Microarray co-expression signatures are an important tool for studying gene function and relations between genes. In addition to genuine biological co-expression, correlated signals can result from technical deficiencies like hybridization of reporters with off-target transcripts. An approach that is able to distinguish these factors permits the detection of more biologically relevant co-expression signatures.

RESULTS

We demonstrate a positive relation between off-target reporter alignment strength and expression correlation in data from oligonucleotide genechips. Furthermore, we describe a method that allows the identification, from their expression data, of individual probe sets affected by off-target hybridization.

CONCLUSION

The effects of off-target hybridization on expression correlation coefficients can be substantial, and can be alleviated by more accurate mapping between microarray reporters and the target transcriptome. We recommend attention to the mapping for any microarray analysis of gene expression patterns.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards

Background

The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards.

Results

We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.

Conclusion

The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

Optimization and clinical validation of a pathogen detection microarray

New design and optimization of pathogen detection microarrays is shown to allow robust and accurate detection of a range of pathogens. The customized microarray platform includes a method for reducing PCR bias during DNA amplification.

DNA microarrays used as 'genomic sensors' have great potential in clinical diagnostics. Biases inherent in random PCR-amplification, cross-hybridization effects, and inadequate microarray analysis, however, limit detection sensitivity and specificity. Here, we have studied the relationships between viral amplification efficiency, hybridization signal, and target-probe annealing specificity using a customized microarray platform. Novel features of this platform include the development of a robust algorithm that accurately predicts PCR bias during DNA amplification and can be used to improve PCR primer design, as well as a powerful statistical concept for inferring pathogen identity from probe recognition signatures. Compared to real-time PCR, the microarray platform identified pathogens with 94% accuracy (76% sensitivity and 100% specificity) in a panel of 36 patient specimens. Our findings show that microarrays can be used for the robust and accurate diagnosis of pathogens, and further substantiate the use of microarray technology in clinical diagnostics.

A cDNA microarray for Crassostrea virginica and C. gigas

The eastern oyster, Crassostrea virginica, and the Pacific oyster, C. gigas, are species of global economic significance as well as important components of estuarine ecosystems and models for genetic and environmental studies. To enhance the molecular tools available for oyster research, an international group of collaborators has constructed a 27,496-feature cDNA microarray containing 4460 sequences derived from C. virginica, 2320 from C. gigas, and 16 non-oyster DNAs serving as positive and negative controls. The performance of the array was assessed by gene expression profiling using gill and digestive gland RNA derived from both C. gigas and C. virginica, and digestive gland RNA from C. ariakensis. The utility of the microarray for detection of homologous genes by cross-hybridization between species was also assessed and the correlation between hybridization intensity and sequence homology for selected genes determined. The oyster cDNA microarray is publicly available to the research community on a cost-recovery basis.

Estimating the efficiency of fish cross-species cDNA microarray hybridization

Using an available cross-species cDNA microarray is advantageous for examining multigene expression patterns in non-model organisms, saving the need for construction of species-specific arrays. The aim of the present study was to estimate relative efficiency of cross-species hybridizations across bony fishes, using bioinformatics tools. The methodology may serve also as a model for similar evaluations in other taxa. The theoretical evaluation was done by substituting comparative whole-transcriptome sequence similarity information into the thermodynamic hybridization equation. Complementary DNA sequence assemblages of nine fish species belonging to common families or suborders and distributed across the bony fish taxonomic branch were selected for transcriptome-wise comparisons. Actual cross-species hybridizations among fish of different taxonomic distances were used to validate and eventually to calibrate the theoretically computed relative efficiencies.

Improvement in the reproducibility and accuracy of DNA microarray quantification by optimizing hybridization conditions

Background

DNA microarrays, which have been increasingly used to monitor mRNA transcripts at a global level, can provide detailed insight into cellular processes involved in response to drugs and toxins. This is leading to new understandings of signaling networks that operate in the cell, and the molecular basis of diseases. Custom printed oligonucleotide arrays have proven to be an effective way to facilitate the applications of DNA microarray technology. A successful microarray experiment, however, involves many steps: well-designed oligonucleotide probes, printing, RNA extraction and labeling, hybridization, and imaging. Optimization is essential to generate reliable microarray data.

Results

Hybridization and washing steps are crucial for a successful microarray experiment. By following the hybridization and washing conditions recommended by an oligonucleotide provider, it was found that the expression ratios were compressed greater than expected and data analysis revealed a high degree of non-specific binding. A series of experiments was conducted using rat mixed tissue RNA reference material (MTRRM) and other RNA samples to optimize the hybridization and washing conditions. The optimized hybridization and washing conditions greatly reduced the non-specific binding and improved the accuracy of spot intensity measurements.

Conclusion

The results from the optimized hybridization and washing conditions greatly improved the reproducibility and accuracy of expression ratios. These experiments also suggested the importance of probe designs using better bioinformatics approaches and the need for common reference RNA samples for platform performance evaluation in order to fulfill the potential of DNA microarray technology.