DNA MICROARRAYS

Differential impact of nutrition on developmental and metabolic gene expression during fruiting body development in Neurospora crassa

by Jeffrey Townsend

Fungal fruiting body size and form are influenced by the ecology of the species, including diverse environmental... more Fungal fruiting body size and form are influenced by the ecology of the species, including diverse environmental stimuli. Accordingly, nutritional resources available to the fungus during development can be vital to successful production of fruiting bodies. To investigate the effect of nutrition, perithecial development of Neurospora crassa was induced on two different media, a chemically sparsely nutritive Synthetic Crossing Medium (SCM) and a natural Carrot Agar (CA). Protoperithecia were collected before crossing, and perithecia were collected at 2, 24, 48, 72, 96, 120, and at full maturity 144h after crossing. No differences in fruiting body morphology were observed between the two media at any time point. A circuit of microarray hybridizations comparing cDNA from all neighboring stages was performed. For a majority of differentially expressed genes, expression was higher in SCM than in CA, and expression of core metabolic genes was particularly affected. Effects of nutrition were highest in magnitude before crossing, lowering in magnitude during early perithecial development. Interestingly, metabolic effects of the media were also large in magnitude during late perithecial development, at which stage the lower expression in CA presumably reflected the continued intake of diverse complex initial compounds, diminishing the need for expression of anabolic pathways. However, for genes with key regulatory roles in sexual development, including pheromone precursor ccg-4 and poi2, expression patterns were similar between treatments. When possible, a common nutritional environment is ideal for comparing transcriptional profiles between different fungi. Nevertheless, the observed consistency of the developmental program across media, despite considerable metabolic differentiation is reassuring. This result facilitates comparative studies that will require different nutritional resources for sexual development in different fungi.

Genome-Wide Patterns of Arabidopsis Gene Expression in Nature

by Christina Richards

Richards*, C.L., U. Rosas*, J.A. Banta, N. Bhambra & M.D. Purugganan. 2012. Genome-wide patterns of Arabidopsis gene expression in nature. PLoS Genetics. *shared first authorship.

Organisms in the wild are subject to multiple, fluctuating environmental factors, and it is in complex natural... more Organisms in the wild are subject to multiple, fluctuating environmental factors, and it is in complex natural environments that genetic regulatory networks actually function and evolve. We assessed genome-wide gene expression patterns in the wild in two natural accessions of the model plant Arabidopsis thaliana and examined the nature of transcriptional variation throughout its life cycle and gene expression correlations with natural environmental fluctuations. We grew plants in a natural field environment and measured genome-wide time-series gene expression from the plant shoot every three days, spanning the seedling to reproductive stages. We find that 15,352 genes were expressed in the A. thaliana shoot in the field, and accession and flowering status (vegetative versus flowering) were strong components of transcriptional variation in this plant. We identified between ,110 and 190 time-varying gene expression clusters in the field, many of which were significantly overrepresented by genes regulated by abiotic and biotic environmental stresses. The two main principal components of vegetative shoot gene expression (PCveg) correlate to temperature and precipitation occurrence in the field. The largest PCveg axes included thermoregulatory genes while the second major PCveg was associated with precipitation and contained drought-responsive genes. By exposing A. thaliana to natural environments in an open field, we provide a framework for further understanding the genetic networks that are deployed in natural environments, and we connect plant molecular genetics in the laboratory to plant organismal ecology in the wild.

Manifold Anomalies In Gene Expression In a Vineyard Isolate of Saccharomyces Cerevisiae Revealed by DNA Microarray Analysis

by Jeffrey Townsend

Genome-wide transcriptional profiling has important applications in evolutionary biology for assaying the extent of... more Genome-wide transcriptional profiling has important applications in evolutionary biology for assaying the extent of heterozygosity for alleles showing quantitative variation in gene expression in natural populations. We have used DNA microarray analysis to study the global pattern of transcription in a homothallic strain of Saccharomyces cerevisiae isolated from wine grapes in a Tuscan vineyard, along with the diploid progeny obtained after sporulation. The parental strain shows 2:2 segregation (heterozygosity) for three unlinked loci. One determines resistance to trifluoroleucine; another, resistance to copper sulfate; and the third is associated with a morphological phenotype observed as colonies with a ridged surface resembling a filigree. Global expression analysis of the progeny with the filigreed and smooth colony phenotypes revealed a greater than 2-fold difference in transcription for 378 genes (6% of the genome). A large number of the overexpressed genes function in pathways of amino acid biosynthesis (particularly methionine) and sulfur or nitrogen assimilation, whereas many of the underexpressed genes are amino acid permeases. These wholesale changes in amino acid metabolism segregate as a suite of traits resulting from a single gene or a small number of genes. We conclude that natural vineyard populations of S. cerevisiae can harbor alleles that cause massive alterations in the global patterns of gene expression. Hence, studies of expression variation in natural populations, without accompanying segregation analysis, may give a false picture of the number of segregating genes underlying the variation.

Long-Oligomer Microarray Profiling In Neurospora Crassa Reveals the Transcriptional Program Underlying Biochemical and Physiological Events of Conidial Germination

by Jeffrey Townsend

To test the inferences of spotted microarray technology against a biochemically well-studied process, we performed... more To test the inferences of spotted microarray technology against a biochemically well-studied process, we performed transcriptional profiling of conidial germination in the filamentous fungus, Neurospora crassa. We first constructed a 70 base oligomer microarray that assays 3366 predicted genes. To estimate the relative gene expression levels and changes in gene expression during conidial germination, we analyzed a circuit design of competitive hybridizations throughout a time course using a Bayesian analysis of gene expression level. Remarkable consistency of mRNA profiles with previously published northern data was observed. Genes were hierarchically clustered into groups with respect to their expression profiles over the time course of conidial germination. A functional classification database was employed to characterize the global picture of gene expression. Consensus motif searches identified a putative regulatory component associated with genes involved in ribosomal biogenesis. Our transcriptional profiling data correlate well with biochemical and physiological processes associated with conidial germination and will facilitate functional predictions of novel genes in N.crassa and other filamentous ascomycete species. Furthermore, our dataset on conidial germination allowed comparisons to transcriptional mechanisms associated with germination processes of diverse propagules, such as teliospores of the phytopathogenic fungus Ustilago maydis and spores of the social amoeba Dictyostelium discoideum.

Bayesian Analysis of Gene Expression Levels: Statistical Quantification of Relative MRNA Level Across Multiple Strains or Treatments

by Jeffrey Townsend

BACKGROUND: Methods of microarray analysis that suit experimentalists using the technology are vital. Many... more BACKGROUND: Methods of microarray analysis that suit experimentalists using the technology are vital. Many methodologies discard the quantitative results inherent in cDNA microarray comparisons or cannot be flexibly applied to multifactorial experimental design. Here we present a flexible, quantitative Bayesian framework. This framework can be used to analyze normalized microarray data acquired by any replicated experimental design in which any number of treatments, genotypes, or developmental states are studied using a continuous chain of comparisons. RESULTS: We apply this method to Saccharomyces cerevisiae microarray datasets on the transcriptional response to ethanol shock, to SNF2 and SWI1 deletion in rich and minimal media, and to wild-type and zap1 expression in media with high, medium, and low levels of zinc. The method is highly robust to missing data, and yields estimates of the magnitude of expression differences and experimental error variances on a per-gene basis. It reveals genes of interest that are differentially expressed at below the twofold level, genes with high 'fold-change' that are not statistically significantly different, and genes differentially regulated in quantitatively unanticipated ways. CONCLUSIONS: Anyone with replicated normalized cDNA microarray ratio datasets can use the freely available MacOS and Windows software, which yields increased biological insight by taking advantage of replication to discern important changes in expression level both above and below a twofold threshold. Not only does the method have utility at the moment, but also, within the Bayesian framework, there will be considerable opportunity for future development.

Multifactorial Experimental Design and the Transitivity of Ratios With Spotted DNA Microarrays

by Jeffrey Townsend

Background

Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but... more Background

Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored.
Results

A strong correlation between direct and transitive inference for significantly differentially expressed genes is demonstrated, using subsets of a dye-swap loop design.
Conclusions

In experimental design, opportunities for transitive inference should be exploited, while always ensuring that comparisons of greatest interest comprise direct hybridizations.

Designing Experiments Using Spotted Microarrays to Detect Gene Regulation Differences Within and Among Species

by Jeffrey Townsend

Comparative studies of genome-wide gene expression must account for variation not only among species, but also within... more Comparative studies of genome-wide gene expression must account for variation not only among species, but also within species. Such studies are necessarily large in scale, because they incorporate experiments on multiple individuals of multiple species in multiple developmental stages in multiple environmental conditions. If the experiments are carefully designed and performed, the data they provide are worth the effort. We describe the utility of spotted microarrays for these studies and highlight experimental design criteria that will maximize inferential and statistical power. We conclude with a discussion of experimental protocols that are designed for investigations of differential gene expression and their pitfalls.

Resolution of Large and Small Differences In Gene Expression Using Models for the Bayesian Analysis of Gene Expression Levels and Spotted DNA Microarrays

by Jeffrey Townsend

Background

The detection of small yet statistically significant differences in gene expression in spotted... more Background

The detection of small yet statistically significant differences in gene expression in spotted DNA microarray studies is an ongoing challenge. Meeting this challenge requires careful examination of the performance of a range of statistical models, as well as empirical examination of the effect of replication on the power to resolve these differences.
Results

New models are derived and software is developed for the analysis of microarray ratio data. These models incorporate multiplicative small error terms, and error standard deviations that are proportional to expression level. The fastest and most powerful method incorporates additive small error terms and error standard deviations proportional to expression level. Data from four studies are profiled for the degree to which they reveal statistically significant differences in gene expression. The gene expression level at which there is an empirical 50% probability of a significant call is presented as a summary statistic for the power to detect small differences in gene expression.
Conclusions

Understanding the resolution of difference in gene expression that is detectable as significant is a vital component of experimental design and evaluation. These small differences in gene expression level are readily detected with a Bayesian analysis of gene expression level that has additive error terms and constrains samples to have a common error coefficient of variation. The power to detect small differences in a study may then be determined by logistic regression.

A fully electronic sensor for the measurement of cDNA hybridization kinetics

by Stefano Cagnin

Molecular Representations: reflections on microarrays and prostate cancer

by Marko Monteiro

In this article the author debates the concept of representation and its changes related to the emergence of genetics... more In this article the author debates the concept of representation and its changes related to the emergence of genetics through the example of the microarray. The microarray is discussed in the context of its uses to define a molecular biomarker for prostate cancer. The contrast between the current research with microarrays and the traditional accepted form of defining prostate cancer, the Gleason score, is used to define the difference between an analogical body and a digital body. This difference hinges on the fact that the Gleason score was a form of representing the prostate anatomy and defining cancer on the basis of physiological differences and visual observation. The shift to a molecular representation stems from the wish to determine markers for disease in the DNA itself. The uniqueness of microarray technology is that it leads to a questioning of the concept of representation, as it allows for the process of naming disease to be conjugated with technologies of intervention.

Computational Sequence Design Techniques for DNA Microarray Technologies.

by Dan Tulpan

Tulpan, D., Ghiggi, A., Montemanni, R. (2011). Computational Sequence Design Techniques for DNA Microarray Technologies. IGI Global, book chapter (accepted).

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete... more In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

Herpesvirus of Turkeys: microarray analysis of host gene responses to infection

by Carl Schmidt

The microarray manual curation tool (MMCT): A web server for microarray probe evaluations

by Dan Tulpan

D. Tulpan, L. Belliveau, S. Leger, Bioinformation, 4(8):344-346, 2010.

Recent Patents and Challenges on DNA Microarray Probe Design Technologies

by Dan Tulpan

D. Tulpan, Recent Patents and Challenges on DNA Microarray Probe Design Technologies, Recent Patents on DNA & Gene Sequences, Bentham Science Publishers, Feb 2011, http://www.benthamscience.com/dnag/E-Pub-Ahead-of-Schedule.htm#3

Effective heuristic methods for DNA strand design

by Dan Tulpan

Ph.D. Thesis

Sets of DNA strands that satisfy combinatorial and thermodynamic properties play
an important role in various... more Sets of DNA strands that satisfy combinatorial and thermodynamic properties play
an important role in various approaches to biomolecular computations, nano structure
design, molecular tagging, and DNA microarrays. The problem of designing
such sets of DNA strands appears to be computationally hard.
This thesis introduces new algorithms for design of DNA strand sets that satisfy
any of several combinatorial and thermodynamic constraints, which aim to maximize
desired hybridization between strands and their complements, while minimizing
undesired cross-hybridizations. To heuristically search for good strand sets
for bio-computing applications, our algorithms use a conflict-driven stochastic local
search approach, which is known to be effective in solving comparable search
problems.
We describe new and improved thermodynamic measures of the quality of
strand sets. With respect to these measures of quality, our algorithms consistently
find, within reasonable time, sets that are significantly better than previously published
sets in the literature. We also present a detailed analysis and selection of
heuristics for improving the quality of DNA strand selection criteria with direct
applications in microarray probe design.

A Systematic Review of Large Scale and Heterogeneous Gene Array Data In Heart Failure

by Chris Evelo

Umesh C Sharma, Saraswati Pokharel, Chris T Evelo, Jos G Maessen (2005) A systematic review of large scale and heterogeneous gene array data in heart failure. J Mol Cell Cardiol 38: 3. 425-432 Mar

Microarray analysis has become a widely available tool for the generation of gene expression data on a genomic scale.... more Microarray analysis has become a widely available tool for the generation of gene expression data on a genomic scale. Since the studies with similar protocols are growing, it has become necessary to systematically revise the large body of literature to decipher the gene expression data. In this review, we analyzed and critically discussed the database presented from 14 published studies that showed the gene expression profile in heart failure (HF) using microarray as a primary tool. After comparing the diverse database from these studies, we explain the protein translational, matri-cellular, immunological and fibrosis-related mechanisms in HF. In addition to previously annotated genes, we analyzed two differentially expressed expressed sequence tags (ESTs) (KIAA0152 and Suppressor of GTwo allele of the suppressor of kinetochore protein-1, SGT1) in HF and showed how bio-informatic analysis of ESTs can lead to the identification of novel pathways active in HF. We have also discussed the new publicly accessible tools that link the gene expression data to gene ontogeny (GO) and functionality. Finally, we have systematically revised the chromosomal localization of the genes that are specifically up-regulated in HF. We have thus spotted chromosome 1, 2, 11 and 12 as the chromosomal hotspots of HF. This methodical approach will simplify the existing concepts on the evolution and progression of HF and lead us toward the development of newer diagnostic and therapeutic tools. Although modeled to HF, this approach should be of broader scientific interest to elaborate multiple genes and complex pathways.

Biologically Relevant Effects of mRNA Amplification on Gene Expression Profiles

by Chris Evelo

Rachel I van Haaften, Blanche Schroen, Ben J Janssen, Arie van Erk, Jacques J Debets, Hubert J Smeets, Jos F Smits, Arthur van den Wijngaard, Yigal M Pinto, Chris T Evelo (2006) Biologically relevant effects of mRNA amplification on gene expression profiles. BMC Bioinformatics 7: 04

Background

Gene expression microarray technology permits the analysis of global gene expression profiles.... more Background

Gene expression microarray technology permits the analysis of global gene expression profiles. The amount of sample needed limits the use of small excision biopsies and/or needle biopsies from human or animal tissues. Linear amplification techniques have been developed to increase the amount of sample derived cDNA. These amplified samples can be hybridised on microarrays. However, little information is available whether microarrays based on amplified and unamplified material yield comparable results.

In the present study we compared microarray data obtained from amplified mRNA derived from biopsies of rat cardiac left ventricle and non-amplified mRNA derived from the same organ. Biopsies were linearly amplified to acquire enough material for a microarray experiment. Both amplified and unamplified samples were hybridized to the Rat Expression Set 230 Array of Affymetrix.

Results

Analysis of the microarray data showed that unamplified material of two different left ventricles had 99.6% identical gene expression. Gene expression patterns of two biopsies obtained from the same parental organ were 96.3% identical. Similarly, gene expression pattern of two biopsies from dissimilar organs were 92.8% identical to each other.

Twenty-one percent of reporters called present in parental left ventricular tissue disappeared after amplification in the biopsies. Those reporters were predominantly seen in the low intensity range.

Sequence analysis showed that reporters that disappeared after amplification had a GC-content of 53.7+/-4.0%, while reporters called present in biopsy- and whole LV-samples had an average GC content of 47.8+/-5.5% (P <0.001). Those reporters were also predicted to form significantly more (0.76+/-0.07 versus 0.38+/-0.1) and longer (9.4+/-0.3 versus 8.4+/-0.4) hairpins as compared to representative control reporters present before and after amplification.

Conclusion

This study establishes that the gene expression profile obtained after amplification of mRNA of left ventricular biopsies is representative for the whole left ventricle of the rat heart. However, specific gene transcripts present in parental tissues were undetectable in the minute left ventricular biopsies. Transcripts that were lost due to the amplification process were not randomly distributed, but had higher GC-content and hairpins in the sequence and were mainly found in the lower intensity range which includes many transcription factors from specific signalling pathways.

An Integrated Bioinformatics Approach to Improve Two-Color Microarray Quality-Control: Impact on Biological Conclusions

by Chris Evelo

Rachel I van Haaften, Cristina Luceri, Arie van Erk, Chris T Evelo (2009) Genes Nutr 4: 2. 123-127 Jun

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the... more Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

GBV-C acute infected cells reveal IL-16 cell mediated downregulation of critical hosts proteins involved in HIV-1

by Richard Allen White III