An improved physico-chemical model of hybridization on high-density oligonucleotide microarrays

Inference of transcriptome signatures of Escherichia coli in long-term stationary phase

"Non-growing" is a dominant life form of microorganisms in nature, where available nutrients and resources are limited. In laboratory culture systems, Escherichia coli can survive for years under starvation, denoted as long-term stationary phase, where a small fraction of cells manages to survive by recycling resources released from nonviable cells. Although the physiology by which viable cells in long-term stationary phase adapt to prolonged starvation is of great interest, their genome-wide response has not been fully understood. In this study, we analyzed transcriptional profiles of cells exposed to the supernatant of 30-day long-term stationary phase culture and found that their transcriptome profiles displayed several similar responses to those of cells in the 16-h short-term stationary phase. Nevertheless, our results revealed that cells in long-term stationary phase supernatant exhibit higher expressions of stress-response genes such as phage shock proteins (psp), and lower expressions of growth-related genes such as ribosomal proteins than those in the short-term stationary phase. We confirmed that the mutant lacking the psp operon showed lower survival and growth rate in the long-term stationary phase culture. This study identified transcriptional responses for stress-resistant physiology in the long-term stationary phase environment.

The highly conserved chromosomal periodicity of transcriptomes and the correlation of its amplitude with the growth rate in Escherichia coli

The growth rate, representing the fitness of a bacterial population, is determined by the transcriptome. Chromosomal periodicity, which is known as the periodic spatial pattern of a preferred chromosomal distance in microbial genomes, is a representative overall feature of the transcriptome; however, whether and how it is associated with the bacterial growth rate are unknown. To address these questions, we analysed a total of 213 transcriptomes of multiple Escherichia coli strains growing in an assortment of culture conditions varying in terms of temperature, nutrition level and osmotic pressure. Intriguingly, Fourier transform analyses of the transcriptome identified a common chromosomal periodicity of transcriptomes, which was independent of the variation in genomes and environments. In addition, fitting of the data to a theoretical model, we found that the amplitudes of the periodic transcriptomes were significantly correlated with the growth rates. These results indicated that the amplitude of periodic transcriptomes is a parameter representing the global pattern of gene expression in correlation with the bacterial growth rate. Thus, our study provides a novel parameter for evaluating the adaptiveness of a growing bacterial population and quantitatively predicting the growth dynamics according to the global expression pattern.

Enhancing the Performance of DNA Surface-Hybridization Biosensors through Target Depletion

DNA surface-hybridization biosensors utilize the selective hybridization of target sequences in solution to surface-immobilized probes. In this process, the target is usually assumed to be in excess, so that its concentration does not significantly vary while hybridizing to the surface-bound probes. If the target is initially at low concentrations and/or if the number of probes is very large, and they have high affinity for the target, the DNA in solution may become depleted. In this paper we analyze the equilibrium and kinetics of hybridization of DNA biosensors in the case of strong target depletion, by extending the Langmuir adsorption model. We focus, in particular, on the detection of a small amount of a single-nucleotide "mutant" sequence (concentration c₂) in a solution, which differs by one or more nucleotides from an abundant "wild-type" sequence (concentration c₁ ≫ c₂). We show that depletion can give rise to a strongly enhanced sensitivity of the biosensors. Using representative values of rate constants and hybridization free energies, we find that in the depletion regime one could detect relative concentrations c₂/c₁ that are up to 3 orders of magnitude smaller than in the conventional approach. The kinetics is surprisingly rich and exhibits a nonmonotonic adsorption with no counterpart in the no-depletion case. Finally, we show that, alongside enhanced detection sensitivity, this approach offers the possibility of sample enrichment, by substantially increasing the relative amount of the mutant over the wild-type sequence.

Two-dimensional Ising model for microarray hybridization: cooperative interactions between bound target molecules

The Langmuir adsorption model is widely used for description and quantification of microarray oligo-target hybridization. According to the model, the binding centers for adsorption of target molecules from solution are represented by oligo-probes. However, the Langmuir model does not consider the interactions between the targets adsorbed at the neighboring binding centers, which are possible due to high-density of array-bound probes. We have shown that the two-dimensional Ising model, which takes into account the nearest neighboring target molecules interactions, better describes the experimental data of oligo-target hybridization in comparison with the Langmuir model. Thus, we found an evidence for existence of positive cooperative interactions between adsorbed target molecules: so, binding of the first target molecules facilitates the binding of subsequent ones to the neighboring probes. Communicated by Ramaswamy H. Sarma.

A comprehensive hybridization model allows whole HERV transcriptome profiling using high density microarray

Background

Human endogenous retroviruses (HERVs) have received much attention for their implications in the etiology of many human diseases and their profound effect on evolution. Notably, recent studies have highlighted associations between HERVs expression and cancers (Yu et al., Int J Mol Med 32, 2013), autoimmunity (Balada et al., Int Rev Immunol 29:351–370, 2010) and neurological (Christensen, J Neuroimmune Pharmacol 5:326–335, 2010) conditions. Their repetitive nature makes their study particularly challenging, where expression studies have largely focused on individual loci (De Parseval et al., J Virol 77:10414–10422, 2003) or general trends within families (Forsman et al., J Virol Methods 129:16–30, 2005; Seifarth et al., J Virol 79:341–352, 2005; Pichon et al., Nucleic Acids Res 34:e46, 2006).

Methods

To refine our understanding of HERVs activity, we introduce here a new microarray, HERV-V3. This work was made possible by the careful detection and annotation of genomic HERV/MaLR sequences as well as the development of a new hybridization model, allowing the optimization of probe performances and the control of cross-reactions.

Results

HERV-V3 offers an almost complete coverage of HERVs and their ancestors (mammalian apparent LTR-retrotransposons, MaLRs) at the locus level along with four other repertoires (active LINE-1 elements, lncRNA, a selection of 1559 human genes and common infectious viruses). We demonstrate that HERV-V3 analytical performances are comparable with commercial Affymetrix arrays, and that for a selection of tissue/pathological specific loci, the patterns of expression measured on HERV-V3 is consistent with those reported in the literature.

Conclusions

Given its large HERVs/MaLRs coverage and additional repertoires, HERV-V3 opens the door to multiple applications such as enhancers and alternative promoters identification, biomarkers identification as well as the characterization of genes and HERVs/MaLRs modulation caused by viral infection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3669-7) contains supplementary material, which is available to authorized users.

Expression Profiling of Antibiotic-Resistant Bacteria Obtained by Laboratory Evolution

To elucidate the mechanisms of antibiotic resistance, integrating phenotypic and genotypic features in resistant strains is important. Here, we describe the expression profiling of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution, and a method for extracting a small number of genes whose expression changes can contribute to the acquisition of resistance.

Bacterial transcriptome reorganization in thermal adaptive evolution

Background

Evolution optimizes a living system at both the genome and transcriptome levels. Few studies have investigated transcriptome evolution, whereas many studies have explored genome evolution in experimentally evolved cells. However, a comprehensive understanding of evolutionary mechanisms requires knowledge of how evolution shapes gene expression. Here, we analyzed Escherichia coli strains acquired during long-term thermal adaptive evolution.

Results

Evolved and ancestor Escherichia coli cells were exponentially grown under normal and high temperatures for subsequent transcriptome analysis. We found that both the ancestor and evolved cells had comparable magnitudes of transcriptional change in response to heat shock, although the evolutionary progression of their expression patterns during exponential growth was different at either normal or high temperatures. We also identified inverse transcriptional changes that were mediated by differences in growth temperatures and genotypes, as well as negative epistasis between genotype—and heat shock-induced transcriptional changes. Principal component analysis revealed that transcriptome evolution neither approached the responsive state at the high temperature nor returned to the steady state at the regular temperature. We propose that the molecular mechanisms of thermal adaptive evolution involve the optimization of steady-state transcriptomes at high temperatures without disturbing the heat shock response.

Conclusions

Our results suggest that transcriptome evolution works to maintain steady-state gene expression during constrained differentiation at various evolutionary stages, while also maintaining responsiveness to environmental stimuli and transcriptome homeostasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1999-x) contains supplementary material, which is available to authorized users.

Phenotypic convergence in bacterial adaptive evolution to ethanol stress

BACKGROUND

Bacterial cells have a remarkable ability to adapt to environmental changes, a phenomenon known as adaptive evolution. During adaptive evolution, phenotype and genotype dynamically changes; however, the relationship between these changes and associated constraints is yet to be fully elucidated.

RESULTS

In this study, we analyzed phenotypic and genotypic changes in Escherichia coli cells during adaptive evolution to ethanol stress. Phenotypic changes were quantified by transcriptome and metabolome analyses and were similar among independently evolved ethanol tolerant populations, which indicate the existence of evolutionary constraints in the dynamics of adaptive evolution. Furthermore, the contribution of identified mutations in one of the tolerant strains was evaluated using site-directed mutagenesis. The result demonstrated that the introduction of all identified mutations cannot fully explain the observed tolerance in the tolerant strain.

CONCLUSIONS

The results demonstrated that the convergence of adaptive phenotypic changes and diverse genotypic changes, which suggested that the phenotype-genotype mapping is complex. The integration of transcriptome and genome data provides a quantitative understanding of evolutionary constraints.

Global coordination in adaptation to gene rewiring

Gene rewiring is a common evolutionary phenomenon in nature that may lead to extinction for living organisms. Recent studies on synthetic biology demonstrate that cells can survive genetic rewiring. This survival (adaptation) is often linked to the stochastic expression of rewired genes with random transcriptional changes. However, the probability of adaptation and the underlying common principles are not clear. We performed a systematic survey of an assortment of gene-rewired Escherichia coli strains to address these questions. Three different cell fates, designated good survivors, poor survivors and failures, were observed when the strains starved. Large fluctuations in the expression of the rewired gene were commonly observed with increasing cell size, but these changes were insufficient for adaptation. Cooperative reorganizations in the corresponding operon and genome-wide gene expression largely contributed to the final success. Transcriptome reorganizations that generally showed high-dimensional dynamic changes were restricted within a one-dimensional trajectory for adaptation to gene rewiring, indicating a general path directed toward cellular plasticity for a successful cell fate. This finding of global coordination supports a mechanism of stochastic adaptation and provides novel insights into the design and application of complex genetic or metabolic networks.

A revised design for microarray experiments to account for experimental noise and uncertainty of probe response

Background

Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance.

Results

Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements.

Conclusion

The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.

Transcriptome analysis of a microbial coculture in which the cell populations are separated by a membrane

The microbial coculture of multiple cell populations is used to study community evolution and for bioengineering applications. The cells in coculture undergo dynamic changes because of cell-cell and cell-environment interactions. Transcriptome analysis allows us to study the molecular basis of these changes in cell physiology. For transcriptome analysis, it is essential that the cell populations in the coculture are harvested separately. Here, we describe a method for transcriptome analysis of a microbial coculture in which two different cell populations are separated by a porous membrane.

Growth rate-coordinated transcriptome reorganization in bacteria

Background

Cell growth rate reflects an organism’s physiological state and largely relies on the ability of gene expression to respond to the environment. The relationship between cellular growth rate and gene expression remains unknown.

Results

Growth rate-coordinated changes in gene expression were discovered by analyzing exponentially growing Escherichia coli cells cultured under multiple defined environments, in which osmotic pressure, temperature and starvation status were varied. Gene expression analyses showed that all 3,740 genes in the genome could be simply divided into three clusters (C1, C2 and C3), which were accompanied by a generic trend in the growth rate that was coordinated with transcriptional changes. The direction of transcriptional change in C1 indicated environmental specificity, whereas those in C2 and C3 were correlated negatively and positively with growth rates, respectively. The three clusters exhibited differentiated gene functions and gene regulation task division.

Conclusions

We identified three gene clusters, exhibiting differential gene functions and distinct directions in their correlations with growth rates. Reverses in the direction of the growth rate correlated transcriptional changes and the distinguished duties of the three clusters indicated how transcriptome homeostasis is maintained to balance the total expression cost for sustaining life in new habitats.

Development of a physical model-based algorithm for the detection of single-nucleotide substitutions by using tiling microarrays

High-density DNA microarrays are useful tools for analyzing sequence changes in DNA samples. Although microarray analysis provides informative signals from a large number of probes, the analysis and interpretation of these signals have certain inherent limitations, namely, complex dependency of signals on the probe sequences and the existence of false signals arising from non-specific binding between probe and target. In this study, we have developed a novel algorithm to detect the single-base substitutions by using microarray data based on a thermodynamic model of hybridization. We modified the thermodynamic model by introducing a penalty for mismatches that represent the effects of substitutions on hybridization affinity. This penalty results in significantly higher detection accuracy than other methods, indicating that the incorporation of hybridization free energy can improve the analysis of sequence variants by using microarray data.

Multilevel comparative analysis of the contributions of genome reduction and heat shock to the Escherichia coli transcriptome

Background

Both large deletions in genome and heat shock stress would lead to alterations in the gene expression profile; however, whether there is any potential linkage between these disturbances to the transcriptome have not been discovered. Here, the relationship between the genomic and environmental contributions to the transcriptome was analyzed by comparing the transcriptomes of the bacterium Escherichia coli (strain MG1655 and its extensive genomic deletion derivative, MDS42) grown in regular and transient heat shock conditions.

Results

The transcriptome analysis showed the following: (i) there was a reorganization of the transcriptome in accordance with preferred chromosomal periodicity upon genomic or heat shock perturbation; (ii) there was a considerable overlap between the perturbed regulatory networks and the categories enriched for differentially expressed genes (DEGs) following genome reduction and heat shock; (iii) the genes sensitive to genome reduction tended to be located close to genomic scars, and some were also highly responsive to heat shock; and (iv) the genomic and environmental contributions to the transcriptome displayed not only a positive correlation but also a negatively compensated relationship (i.e., antagonistic epistasis).

Conclusion

The contributions of genome reduction and heat shock to the Escherichia coli transcriptome were evaluated at multiple levels. The observations of overlapping perturbed networks, directional similarity in transcriptional changes, positive correlation and epistatic nature linked the two contributions and suggest somehow a crosstalk guiding transcriptional reorganization in response to both genetic and environmental disturbances in bacterium E. coli.

Hybridization and amplification rate correction for affymetrix SNP arrays

BACKGROUND

Copy number variation (CNV) is essential to understand the pathology of many complex diseases at the DNA level. Affymetrix SNP arrays, which are widely used for CNV studies, significantly depend on accurate copy number (CN) estimation. Nevertheless, CN estimation may be biased by several factors, including cross-hybridization and training sample batch, as well as genomic waves of intensities induced by sequence-dependent hybridization rate and amplification efficiency. Since many available algorithms only address one or two of the three factors, a high false discovery rate (FDR) often results when identifying CNV. Therefore, we have developed a new CNV detection pipeline which is based on hybridization and amplification rate correction (CNVhac).

METHODS

CNVhac first estimates the allelic concentrations (ACs) of target sequences by using the sample independent parameters trained through physicochemical hybridization law. Then the raw CN is estimated by taking the ratio of AC to the corresponding average AC from a reference sample set for one specific site. Finally, a hidden Markov model (HMM) segmentation process is implemented to detect CNV regions.

RESULTS

Based on public HapMap data, the results show that CNVhac effectively smoothes the genomic waves and facilitates more accurate raw CN estimates compared to other methods. Moreover, CNVhac alleviates, to a certain extent, the sample dependence of inference and makes CNV calling with appreciable low FDRs.

CONCLUSION

CNVhac is an effective approach to address the common difficulties in SNP array analysis, and the working principles of CNVhac can be easily extended to other platforms.

Adaptation by stochastic switching of a monostable genetic circuit in Escherichia coli

Stochastic switching of a bistable genetic circuit represents a potential cost-saving strategy for adaptation to environmental challenges. This study reports that stochastic switching of a monostable circuit can be sufficient to mediate reversible adaptation in E. coli.

Stochastic switching of a monostable circuit mediated the adaptation of the engineered OSU12-hisC Escherichia coli strain to histidine starvation.

The population shift of OSU12-hisC was accompanied by growth recovery and was reversible upon histidine addition. This is the first report of adaptation mediated by stochastic switching based on a monostable structure.

Weak directionality in stochastic switching initiated the population shift and the fast growth of the occasionally appearing fit cells drove the later stages of adaptation.

Adaptation of OSU12-hisC was resulted from the enhanced expression of the structural genes within the native His operon, along with the transcriptional reorganization of a large number of genes.

The fundamental mechanisms underlying adaptations can be divided into responsive switching and stochastic switching (Kussell and Leibler, 2005). Responsive switching is generally considered as resulting from evolved regulatory units, such as operons and regulons, which enable immediate adaptation (Jacob and Monod, 1961). However, as cells are subject to a wide range of both genetic and environmental perturbations that damage the specificity or efficiency of regulatory systems (Carroll, 2005; Crombach and Hogeweg, 2008), the limited number of regulatory units that can evolve and remain functional may not be sufficient to completely protect cell populations from the danger of extinction. Whether and how cells are able to survive external perturbations, when the corresponding regulatory units are absent or have been genetically disrupted, is an open question of great importance.

Recent studies showed the stochastic switching provided cells a huge potential for sustenance under severe conditions via a so-called ‘bet-hedging' strategy. The experimental evidence was generally based on a bistable genetic structure that fixed stochastically appearing fit state thus limiting further random switching (Kussell and Leibler, 2005; Acar et al, 2008). In contrast to bistable gene expression, monostable gene expression is much more common (Newman et al, 2006) and does not rely on a specific complex genetic architecture. Since a monostable structure has no fixation effect, the fit cells that would appear stochastically tend to return to the original steady state (i.e., unfit state). To achieve a population shift from a maladaptive state (but stable) to an adaptive state (but unstable), a significant increase in fitness (i.e., growth rate) of the fit cells is necessary. Otherwise, the random switching will mask occasionally occurring adaptive transitions and lead to an unchanged population at the stable but maladaptive state. Whether adaptation can be achieved by stochastic switching based on a monostable structure is however an open issue.

To address this question, we applied an engineered E. coli strain, OSU12-hisC, carrying a foreign gene circuit encompassing a physiologically functional gene, hisC, replaced from its native chromosomal locus (Figure 1A). Consequently, hisC in OSU12-hisC is no longer responsive to the native regulation (His operon) that senses histidine depletion. Instead, the foreign gene circuit provided a monostable structure for hisC's stochastic switching. The green fluorescent protein (gfpuv5) was co-expressed with hisC for the quantitative evaluation of HisC in single cells. The upstream regulation of TetR, whose expression level was reported by the red fluorescent protein (dsred.T4), was introduced to achieve the inducible GFP (HisC) level. The full induction of TetR by IPTG was applied to avoid any possible upstream noise that caused by the abundance of endogenous LacI.

Microscopic observation revealed that the OSU12-hisC cells showed stronger green fluorescence after histidine depletion (Figure 1B), which suggested an increased expression level of hisC. Population analysis using flow cytometry showed that the distributions of both GFP concentration and GFP bias (GFP/RFP ratio) in OSU12-hisC shifted towards a higher level in histidine-free conditions (Figure 1C and D), whereas, the depletion caused only a slight change in distributions of OSU11, a control strain carrying both the same engineered genetic circuit and an intact His operon, including the hisC gene in its native context. Repeated experiments revealed that the increases in both GFP concentration (∼2.1 folds) and GFP bias (∼1.5 folds) due to histidine depletion were highly significant (P<0.005, N=6) in OSU12-hisC. In particular, the increased GFP bias strongly suggested that the change in gene expression occurred specifically in the rewired hisC (i.e., GFP) but not in all genes (e.g., RFP). Furthermore, both the growth recovery accompanied population shift and the stress relaxation triggered restoration were clearly observed. It strongly indicated that the adaptation was mediated by stochastic switching of hisC under the monostable control.

Analysis on microcolonies' formation (Figure 4A) showed stochastic behaviour and directionality in individual cells. Variation in cellular GFP level was clearly observed in individual cells. Stochastic switching of hisC was verified according to the random changes in GFP bias along with the cell division under histidine-rich conditions (Figure 4B). On the other hand, the microcolonies formed under the histidine-free conditions tended to the higher level of GFP bias were observed (Figure 4B). The directional tendency favoured the high GFP (HisC) level was evidently detected in the first 2 h after histidine depletion, which resulted in a population shift (Figure 4C). In contrast, the distributions of microcolonies grown in histidine-rich conditions kept steady, due to the randomized directions of stochastic switching (Figure 4C). Further analysis showed that the stochastic fluctuations in the initial state had an important role not only in fate decision (i.e., whether to grow) but also in the directionality of the stochastic switch.

Microarray analysis showed the adaptation of OSU12-hisC was resulted from the enhanced expression of the structural genes within the native His operon, along with the transcriptional reorganization of a large number of genes. In summary, in contrast to bistable structures, the monostable structure used here did not fix the phenotype but allowed the cells to decide where to go. Taken together, the findings suggest that bacteria do not necessarily need to evolve signalling mechanisms to control gene expression appropriately, even for essential genes.

Stochastic switching is considered as a cost-saving strategy for adaptation to environmental challenges. We show here that stochastic switching of a monostable circuit can mediate the adaptation of the engineered OSU12-hisC Escherichia coli strain to histidine starvation. In this strain, the hisC gene was deleted from the His operon and placed under the control of a monostable foreign promoter. In response to histidine depletion, the OSU12-hisC population shifted to a higher HisC expression level, which is beneficial under starving conditions but is not favoured by the monostable circuit. The population shift was accompanied by growth recovery and was reversible upon histidine addition. A weak directionality in stochastic switching of hisC was observed in growing microcolonies under histidine-free conditions. Directionality and fate decision were in part dependent on the initial cellular status. Finally, microarray analysis indicated that OSU12-hisC reorganized its transcriptome to reach the appropriate physiological state upon starvation. These findings suggest that bacteria do not necessarily need to evolve signalling mechanisms to control gene expression appropriately, even for essential genes.

Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion

BACKGROUND

The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained.

METHODOLOGY/PRINCIPAL FINDINGS

This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias.

CONCLUSIONS

Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.

Nonequilibrium effects in DNA microarrays: a multiplatform study

It has recently been shown that in some DNA microarrays the time needed to reach thermal equilibrium may largely exceed the typical experimental time, which is about 15 h in standard protocols (Hooyberghs et al. Phys. Rev. E2010, 81, 012901). In this paper we discuss how this breakdown of thermodynamic equilibrium could be detected in microarray experiments without resorting to real time hybridization data, which are difficult to implement in standard experimental conditions. The method is based on the analysis of the distribution of fluorescence intensities I from different spots for probes carrying base mismatches. In thermal equilibrium and at sufficiently low concentrations, log I is expected to be linearly related to the hybridization free energy ΔG with a slope equal to 1/RT(exp), where T(exp) is the experimental temperature and R is the gas constant. The breakdown of equilibrium results in the deviation from this law. A model for hybridization kinetics explaining the observed experimental behavior is discussed, the so-called 3-state model. It predicts that deviations from equilibrium yield a proportionality of log I to ΔG/RT(eff). Here, T(eff) is an "effective" temperature, higher than the experimental one. This behavior is indeed observed in some experiments on Agilent arrays [Hooyberghs et al. Phys. Rev. E2010, 81, 012901 and Hooyberghs et al. Nucleic Acids Res. 2009, 37, e53]. We analyze experimental data from two other microarray platforms and discuss, on the basis of the results, the attainment of equilibrium in these cases. Interestingly, the same 3-state model predicts a (dynamical) saturation of the signal at values below the expected one at equilibrium.

Cooperative adaptation to establishment of a synthetic bacterial mutualism

To understand how two organisms that have not previously been in contact can establish mutualism, it is first necessary to examine temporal changes in their phenotypes during the establishment of mutualism. Instead of tracing back the history of known, well-established, natural mutualisms, we experimentally simulated the development of mutualism using two genetically-engineered auxotrophic strains of Escherichia coli, which mimic two organisms that have never met before but later establish mutualism. In the development of this synthetic mutualism, one strain, approximately 10 hours after meeting the partner strain, started oversupplying a metabolite essential for the partner's growth, eventually leading to the successive growth of both strains. This cooperative phenotype adaptively appeared only after encountering the partner strain but before the growth of the strain itself. By transcriptome analysis, we found that the cooperative phenotype of the strain was not accompanied by the local activation of the biosynthesis and transport of the oversupplied metabolite but rather by the global activation of anabolic metabolism. This study demonstrates that an organism has the potential to adapt its phenotype after the first encounter with another organism to establish mutualism before its extinction. As diverse organisms inevitably encounter each other in nature, this potential would play an important role in the establishment of a nascent mutualism in nature.

Transition from positive to neutral in mutation fixation along with continuing rising fitness in thermal adaptive evolution

It remains to be determined experimentally whether increasing fitness is related to positive selection, while stationary fitness is related to neutral evolution. Long-term laboratory evolution in Escherichia coli was performed under conditions of thermal stress under defined laboratory conditions. The complete cell growth data showed common continuous fitness recovery to every 2°C or 4°C stepwise temperature upshift, finally resulting in an evolved E. coli strain with an improved upper temperature limit as high as 45.9°C after 523 days of serial transfer, equivalent to 7,560 generations, in minimal medium. Two-phase fitness dynamics, a rapid growth recovery phase followed by a gradual increasing growth phase, was clearly observed at diverse temperatures throughout the entire evolutionary process. Whole-genome sequence analysis revealed the transition from positive to neutral in mutation fixation, accompanied with a considerable escalation of spontaneous substitution rate in the late fitness recovery phase. It suggested that continually increasing fitness not always resulted in the reduction of genetic diversity due to the sequential takeovers by fit mutants, but caused the accumulation of a considerable number of mutations that facilitated the neutral evolution.

The detailed results of a two-year in vitro thermal adaptive evolution experiment are described. A laboratory-evolved E. coli strain with an improved upper temperature limit, as high as 45.9°C, was acquired after 523 days of serial transfer, equivalent to 7,560 generations, in nutrient-limited medium. The complete daily records of cell growth exhibited universal two-phase fitness dynamics, a rapid growth recovery phase followed by a gradual increasing growth phase, throughout the entire evolutionary process. Genome-sequence analysis not only showed considerable escalation of the spontaneous substitution rate, but also revealed the transition from positive to nearly neutral in mutation fixation. Particularly, even with the rising fitness of bacterial cells, neutrality was observed in molecular evolution. These observations suggested that a discrete evolutionary mode occurred in the continuous evolutionary route, linking Darwinian adaptive selection with Kimura's neutral evolution. Such transition from beneficial to neutral path may be adopted as an evolutionary strategy robust to rigorous environmental changes.

Transcriptome analysis of parallel-evolved Escherichia coli strains under ethanol stress

Background

Understanding ethanol tolerance in microorganisms is important for the improvement of bioethanol production. Hence, we performed parallel-evolution experiments using Escherichia coli cells under ethanol stress to determine the phenotypic changes necessary for ethanol tolerance.

Results

After cultivation of 1,000 generations under 5% ethanol stress, we obtained 6 ethanol-tolerant strains that showed an approximately 2-fold increase in their specific growth rate in comparison with their ancestor. Expression analysis using microarrays revealed that common expression changes occurred during the adaptive evolution to the ethanol stress environment. Biosynthetic pathways of amino acids, including tryptophan, histidine, and branched-chain amino acids, were commonly up-regulated in the tolerant strains, suggesting that activating these pathways is involved in the development of ethanol tolerance. In support of this hypothesis, supplementation of isoleucine, tryptophan, and histidine to the culture medium increased the specific growth rate under ethanol stress. Furthermore, genes related to iron ion metabolism were commonly up-regulated in the tolerant strains, which suggests the change in intracellular redox state during adaptive evolution.

Conclusions

The common phenotypic changes in the ethanol-tolerant strains we identified could provide a fundamental basis for designing ethanol-tolerant strains for industrial purposes.

Washing scaling of GeneChip microarray expression

BACKGROUND

Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities.

RESULTS

We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values.

CONCLUSIONS

Washing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.

G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration

Background

The brightness of the probe spots on expression microarrays intends to measure the abundance of specific mRNA targets. Probes with runs of at least three guanines (G) in their sequence show abnormal high intensities which reflect rather probe effects than target concentrations. This G-bias requires correction prior to downstream expression analysis.

Results

Longer runs of three or more consecutive G along the probe sequence and in particular triple degenerated G at its solution end ((GGG)₁-effect) are associated with exceptionally large probe intensities on GeneChip expression arrays. This intensity bias is related to non-specific hybridization and affects both perfect match and mismatch probes. The (GGG)₁-effect tends to increase gradually for microarrays of later GeneChip generations. It was found for DNA/RNA as well as for DNA/DNA probe/target-hybridization chemistries. Amplification of sample RNA using T7-primers is associated with strong positive amplitudes of the G-bias whereas alternative amplification protocols using random primers give rise to much smaller and partly even negative amplitudes.

We applied positional dependent sensitivity models to analyze the specifics of probe intensities in the context of all possible short sequence motifs of one to four adjacent nucleotides along the 25meric probe sequence. Most of the longer motifs are adequately described using a nearest-neighbor (NN) model. In contrast, runs of degenerated guanines require explicit consideration of next nearest neighbors (GGG terms). Preprocessing methods such as vsn, RMA, dChip, MAS5 and gcRMA only insufficiently remove the G-bias from data.

Conclusions

Positional and motif dependent sensitivity models accounts for sequence effects of oligonucleotide probe intensities. We propose a positional dependent NN+GGG hybrid model to correct the intensity bias associated with probes containing poly-G motifs. It is implemented as a single-chip based calibration algorithm for GeneChips which can be applied in a pre-correction step prior to standard preprocessing.

Hybridization thermodynamics of NimbleGen microarrays

Background

While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets.

Results

We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature T_m was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex.

Conclusions

This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals.

Beyond Affymetrix arrays: expanding the set of known hybridization isotherms and observing pre-wash signal intensities

Microarray hybridization studies have attributed the nonlinearity of hybridization isotherms to probe saturation and post-hybridization washing. Both processes are thought to distort ‘true’ target abundance because immobilized probes are saturated with excess target and stringent washing removes loosely bound targets. Yet the paucity of studies aimed at understanding hybridization and dissociation makes it difficult to align physicochemical theory to microarray results. To fill the void, we first examined hybridization isotherms generated on different microarray platforms using a ribosomal RNA target and then investigated hybridization signals at equilibrium and after stringent wash. Hybridization signal at equilibrium was achieved by treating the microarray with isopropanol, which prevents nucleic acids from dissolving into solution. Our results suggest that (i) the shape of hybridization isotherms varied by microarray platform with some being hyperbolic or linear, and others following a power-law; (ii) at equilibrium, fluorescent signal of different probes hybridized to the same target were not similar even with excess of target and (iii) the amount of target removed by stringent washing depended upon the hybridization time, the probe sequence and the presence/absence of nonspecific targets. Possible physicochemical interpretations of the results and future studies are discussed.

Linear model for fast background subtraction in oligonucleotide microarrays

Background

One important preprocessing step in the analysis of microarray data is background subtraction. In high-density oligonucleotide arrays this is recognized as a crucial step for the global performance of the data analysis from raw intensities to expression values.

Results

We propose here an algorithm for background estimation based on a model in which the cost function is quadratic in a set of fitting parameters such that minimization can be performed through linear algebra. The model incorporates two effects: 1) Correlated intensities between neighboring features in the chip and 2) sequence-dependent affinities for non-specific hybridization fitted by an extended nearest-neighbor model.

Conclusion

The algorithm has been tested on 360 GeneChips from publicly available data of recent expression experiments. The algorithm is fast and accurate. Strong correlations between the fitted values for different experiments as well as between the free-energy parameters and their counterparts in aqueous solution indicate that the model captures a significant part of the underlying physical chemistry.

A novel single nucleotide polymorphism detection of a double-stranded DNA target by a ribonucleotide-carrying molecular beacon and thermostable RNase HII

Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation. SNPs are important markers that link sequence variations to phenotypic changes. Because of the importance of SNPs in the life and medical sciences, a great deal of effort has been devoted to developing accurate, rapid, and cost-effective technologies for SNP analysis. In this article, we describe a novel method for SNP genotyping based on differential fluorescence emission due to cleavage by Thermus thermophilus RNase HII (TthRNase HII) of DNA heteroduplexes containing an SNP site-specific chimeric DNA-rN(1)-DNA molecular beacon (cMB). We constructed a loop sequence for a cMB that contains a single SNP-specific ribonucleotide at the central site. When the cMB probe is hybridized to a target double-stranded DNA (dsDNA), a perfect match of the cMB/DNA duplex permits efficient cleavage with TthRNase HII, whereas a mismatch in the duplex due to an SNP greatly reduces efficiency. Cleavage efficiency is measured by the incremental difference of fluorescence emission of the beacon. We show that the genotypes of 10 individuals at 12 SNP sites across a series of human leukocyte antigen (HLA) can be determined correctly with respect to conventional DNA sequencing. This novel TthRNase HII-based method offers a platform for easy and accurate SNP analysis.

Probes containing runs of guanines provide insights into the biophysics and bioinformatics of Affymetrix GeneChips

The reliable interpretation of Affymetrix GeneChip data is a multi-faceted problem. The interplay between biophysics, bioinformatics and mining of GeneChip surveys is leading to new insights into how best to analyse the data. Many of the molecular processes occurring on the surfaces of GeneChips result from the high surface density of probes. Interactions between neighbouring adjacent probes affect their rate and strength of hybridization to targets. Competing targets may hybridize to the same probe, and targets may partially bind to more than one probe. The formation of these partial hybrids results in a number of probes not reaching thermodynamic equilibrium during hybridization. Moreover, some targets fold up, or cross-hybridize to other targets. Furthermore, probes may fold and can undergo chemical saturation. There are also sequence-dependent differences in the rates of target desorption during the washing stage. Improvements in the mappings between probe sequence and biological databases are leading to more accurate gene expression profiles. Moreover, algorithms that combine the intensities of multiple probes into single measures of expression are increasingly dependent upon models of the hybridization processes occurring on GeneChips. The large repositories of GeneChip data can be searched for systematic effects across many experiments. This data mining has led to the discovery of a family of thousands of probes, which show correlated expression across thousands of GeneChip experiments. These probes contain runs of guanines, suggesting that G-quadruplexes are able to form on GeneChips. We discuss the impact of these structures on the interpretation of data from GeneChip experiments.

Model-based analysis of non-specific binding for background correction of high-density oligonucleotide microarrays

MOTIVATION

High-density DNA microarrays provide us with useful tools for analyzing DNA and RNA comprehensively. However, the background signal caused by the non-specific binding (NSB) between probe and target makes it difficult to obtain accurate measurements. To remove the background signal, there is a set of background probes on Affymetrix Exon arrays to represent the amount of non-specific signals, and an accurate estimation of non-specific signals using these background probes is desirable for improvement of microarray analyses.

RESULTS

We developed a thermodynamic model of NSB on short nucleotide microarrays in which the NSBs are modeled by duplex formation of probes and multiple hypothetical targets. We fitted the observed signal intensities of the background probes with those expected by the model to obtain the model parameters. As a result, we found that the presented model can improve the accuracy of prediction of non-specific signals in comparison with previously proposed methods. This result will provide a useful method to correct for the background signal in oligonucleotide microarray analysis.

AVAILABILITY

The software is implemented in the R language and can be downloaded from our website (http://www-shimizu.ist.osaka-u.ac.jp/shimizu_lab/MSNS/).

A competitive hybridization model predicts probe signal intensity on high density DNA microarrays

A central, unresolved problem of DNA microarray technology is the interpretation of different signal intensities from multiple probes targeting the same transcript. We propose a competitive hybridization model for DNA microarray hybridization. Our model uses a probe-specific dissociation constant that is computed with current nearest neighbor model and existing parameters, and only four global parameters that are fitted to Affymetrix Latin Square data. This model can successfully predict signal intensities of individual probes, therefore makes it possible to quantify the absolute concentration of targets. Our results offer critical insights into the design and data interpretation of DNA microarrays.