Cohort-based strategies as an in-house tool to evaluate and improve phenotyping robustness of LC-MS/MS lipidomics platforms.

In recent years, instrumental improvements have enabled the spread of mass spectrometry-based lipidomics platforms in biomedical research. In mass spectrometry, the reliability of generated data varies for each compound, contingent on, among other factors, the availability of labeled internal standards. It is challenging to evaluate the data for lipids without specific labeled internal standards, especially when dozens to hundreds of lipids are measured simultaneously. Thus, evaluation of the performance of these platforms at the individual lipid level in interlaboratory studies is generally not feasible in a time-effective manner. Herein, using a focused subset of sphingolipids, we present an in-house validation methodology for individual lipid reliability assessment, tailored to the statistical analysis to be applied. Moreover, this approach enables the evaluation of various methodological aspects, including discerning coelutions sharing identical selected reaction monitoring transitions, pinpointing optimal labeled internal standards and their concentrations, and evaluating different extraction techniques. While the full validation according to analytical guidelines for all lipids included in a lipidomics method is currently not possible, this process shows areas to focus on for subsequent method development iterations as well as the robustness of data generated across diverse methodologies.

Effects of Indoor Dust Exposure on Lung Cells: Association of Chemical Composition with Phenotypic and Lipid Changes in a 3D Lung Cancer Cell Model.

Indoor dust is a key contributor to the global human exposome in urban areas since the population develops most of its activities in private and public buildings. To gain insight into the health risks associated with this chronic exposure, it is necessary to characterize the chemical composition of dust and understand its biological impacts using reliable physiological models. The present study investigated the biological effects of chemically characterized indoor dust extracts using three-dimensional (3D) lung cancer cell cultures combining phenotypic and lipidomic analyses. Apart from the assessment of cell viability, reactive oxygen species (ROS) induction, and interleukin-8 release, lipidomics was applied to capture the main lipid changes induced as a cellular response to the extracted dust compounds. The application of chemometric tools enabled the finding of associations between chemical compounds present in dust and lipidic and phenotypic profiles in the cells. This study contributes to a better understanding of the toxicity mechanisms associated with exposure to chemical pollutants present in indoor dust.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis-ASCA, regularized MANOVA-rMANOVA, and Group-wise ANOVA-simultaneous component analysis-GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets.

BACKGROUND: The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field. RESULTS: Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications. CONCLUSIONS: The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography.

The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis.

Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution.

In this work, a new strategy for the chemometric analysis of two-dimensional liquid chromatography-high-resolution mass spectrometry (LC × LC-HRMS) data is proposed. This approach consists of a preliminary compression step along the mass spectrometry (MS) spectral dimension based on the selection of the regions of interest (ROI), followed by a further data compression along the chromatographic dimension by wavelet transforms. In a secondary step, the multivariate curve resolution alternating least squares (MCR-ALS) method is applied to previously compressed data sets obtained in the simultaneous analysis of multiple LC × LC-HRMS chromatographic runs from multiple samples. The feasibility of the proposed approach is demonstrated by its application to a large experimental data set obtained in the untargeted LC × LC-HRMS study of the effects of different environmental conditions (watering and harvesting time) on the metabolism of multiple rice samples. An untargeted chromatographic setup coupling two different liquid chromatography (LC) columns [hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography (RPLC)] together with an HRMS detector was developed and applied to analyze the metabolites extracted from rice samples at the different experimental conditions. In the case of the metabolomics study taken as example in this work, a total number of 154 metabolites from 15 different families were properly resolved after the application of MCR-ALS. A total of 139 of these metabolites could be identified by their HRMS spectra. Statistical analysis of their concentration changes showed that both watering and harvest time experimental factors had significant effects on rice metabolism. The biochemical insight of the effects of watering and harvesting experimental factors on the changes in concentration of these detected metabolites in the investigated rice samples is attempted.

Metabolic profiling for the identification of Huntington biomarkers by on-line solid-phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools.

In this work, an untargeted metabolomic approach based on sensitive analysis by on-line solid-phase extraction capillary electrophoresis mass spectrometry (SPE-CE-MS) in combination with multivariate data analysis is proposed as an efficient method for the identification of biomarkers of Huntington's disease (HD) progression in plasma. For this purpose, plasma samples from wild-type (wt) and HD (R6/1) mice of different ages (8, 12, and 30 weeks), were analyzed by C18 -SPE-CE-MS in order to obtain the characteristic electrophoretic profiles of low molecular mass compounds. Then, multivariate curve resolution alternating least squares (MCR-ALS) was applied to the multiple full scan MS datasets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. A total number of 29 compounds were relevant to discriminate between wt and HD plasma samples, as well as to follow-up the HD progression. The intracellular signaling was found to be the most affected metabolic pathway in HD mice after 12 weeks of birth, when mice already showed motor coordination deficiencies and cognitive decline. This fact agreed with the atrophy and dysfunction of specific neurons, loss of several types of receptors, and changed expression of neurotransmitters.

Solution equilibria of cytosine- and guanine-rich sequences near the promoter region of the n-myc gene that contain stable hairpins within lateral loops.

BACKGROUND: Cytosine- and guanine-rich regions of DNA are capable of forming complex structures named i-motifs and G-quadruplexes, respectively. In the present study the solution equilibria at nearly physiological conditions of a 34-base long cytosine-rich sequence and its complementary guanine-rich strand corresponding to the first intron of the n-myc gene were studied. Both sequences, not yet studied, contain a 12-base tract capable of forming stable hairpins inside the i-motif and G-quadruplex structures, respectively. METHODS: Spectroscopic, mass spectrometry and separation techniques, as well as multivariate data analysis methods, were used to unravel the species and conformations present. RESULTS: The cytosine-rich sequence forms two i-motifs that differ in the protonation of bases located in the loops. A stable Watson-Crick hairpin is formed by the bases in the first loop, stabilizing the i-motif structure. The guanine-rich sequence adopts a parallel G-quadruplex structure that is stable throughout the pH range 3-7, despite the protonation of cytosine and adenine bases at lower pH values. The presence of G-quadruplex aggregates was confirmed using separation techniques. When mixed, G-quadruplex and i-motif coexist with the Watson-Crick duplex across a pH range from approximately 3.0 to 6.5. CONCLUSIONS: Two cytosine- and guanine-rich sequences in n-myc gene may form stable i-motif and G-quadruplex structures even in the presence of long loops. pH modulates the equilibria involving the intramolecular structures and the intermolecular Watson-Crick duplex. GENERAL SIGNIFICANCE: Watson-Crick hairpins located in the intramolecular G-quadruplexes and i-motifs in the promoter regions of oncogenes could play a role in stabilizing these structures.

qRT-PCR evaluation of the transcriptional response of zebra mussel to heavy metals.

BACKGROUND: The transcriptional response of adult zebra mussels (Dreissena polymorpha) to heavy metals (mercury, copper, and cadmium) was analyzed by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) to study the coordinated regulation of different metal-, oxidative stress- and xenobiotic defence-related genes in gills and digestive gland. Regulatory network analyses allowed the comparison of this response between different species and taxa. RESULTS: Chemometric analyses allowed identifying the effects of these metals clearly separating control and treated samples of both tissues. Interactions between the different genes, either in the same or between both tissues, were analysed to identify correlations and to propose stress-related genes' regulatory networks. These networks were finally compared with existing data from human, mouse, zebrafish, Drosophila and the roundworm to evaluate their mechanistically-known response to metals (and to stressors in general) with the correlations observed in the still poorly-known, invasive zebra mussel. CONCLUSIONS: Our analyses found a general conservation of regulation genes and of their interactions among the different considered species, and may serve as a guide to extrapolate regulatory data from model species to lesser-known environmentally (or medically) relevant species.

Combination of CE-MS and advanced chemometric methods for high-throughput metabolic profiling.

In this work, an untargeted approach based on capillary electrophoresis-mass spectrometry (CE-MS) in combination with multivariate data analyses is proposed as a high-throughput general methodology for metabolomic studies. First, total ion electropherograms (TIEs) were considered for exploratory and classification purposes by means of principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Then, multivariate curve resolution alternating least squares (MCR-ALS) was applied to the multiple full scan MS data sets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. The proposed approach allowed solving additional electrophoretic issues, such as background noise contributions, low signal-to-noise ratios, asymmetric peaks and migration time shifts. The usefulness of the proposed methodology is demonstrated in a comparative study of the metabolic profiles from baker's yeast (Saccharomyces cerevisiae) samples cultured at two temperatures, 30°C and 37°C. A total number of 80 metabolites were relevant to yeast samples differentiation at the two temperatures and almost 50 of them were tentatively identified based on their accurate experimental molecular mass. The results show that changes in amino acid, nucleotide and lipid metabolic pathways participated in the acclimatization of yeast cells to grow at 37°C.

Potential use of multivariate curve resolution for the analysis of mass spectrometry images.

In this work the application of multivariate curve resolution is proposed for the analysis of Mass Spectrometry Imaging (MSI) data. Recently, developments in the ionization of samples have dramatically expanded the number of applications of MSI due to the possibility of collecting the mass spectrum for each pixel of a considered surface in a reasonable time. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. However, there are major drawbacks inherent to MSI related to the high complexity of the data obtained from real samples and to the extremely huge size of the datasets generated by this technique. Therefore, the potential of chemometrical tools in different steps of the analysis process is unquestionable, from data compression to data resolution of the different components present at each pixel of the image. In this work, this data analysis is carried out by means of the multivariate curve resolution method. The benefits of the application of this method are shown for two examples consisting of a MS image of two platted microbes and a MS image of a mouse lung section. The results show that multivariate curve resolution allows us to obtain distribution maps of different components and their identification from resolved high-resolution mass spectra.

Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies.

The comprehensive analysis of untargeted metabolomics data acquired using LC-MS is still a major challenge. Different data analysis tools have been developed in recent years such as XCMS (various forms (X) of chromatography mass spectrometry) and multivariate curve resolution alternating least squares (MCR-ALS)-based strategies. In this work, metabolites extracted from rice tissues cultivated in an environmental test chamber were subjected to untargeted full-scan LC-MS analysis, and the obtained data sets were analyzed using XCMS and MCR-ALS. These approaches were compared in the investigation of the effects of copper and cadmium exposure on rice tissue (roots and aerial parts) samples. Both methods give, as a result of their application, the whole set of resolved elution and spectra profiles of the extracted metabolites in control and metal-treated samples, as well as the values of their corresponding chromatographic peak areas. The effects caused by the two considered metals on rice samples were assessed by further chemometric analysis and statistical evaluation of these peak area values. Results showed that there was a statistically significant interaction between the considered factors (type of metal of treatment and tissue). Also, the discrimination of the samples according to both factors was possible. A tentative identification of the most discriminant metabolites (biomarkers) was assessed. It is finally concluded that both XCMS- and MCR-ALS-based strategies provided similar results in all the considered cases despite the completely different approaches used by these two methods in the chromatographic peak resolution and detection strategies. Finally, advantages and disadvantages of using these two methods are discussed. Graphical Abstract Summary of the workflow for untargeted metabolomics using the compared approaches.

Phenotypic malignant changes and untargeted lipidomic analysis of long-term exposed prostate cancer cells to endocrine disruptors.

Endocrine disruptors (EDs) are a class of environmental toxic molecules able to interfere with the normal hormone metabolism. Numerous studies involve EDs exposure to initiation and development of cancers, including prostate cancer. In this work, three different EDs (aldrin, aroclor 1254 and chlorpyrifos (CPF)) were investigated as potential inducers of a malignant phenotype in DU145 prostate cancer cells after a chronic exposure. Epithelial to mesenchymal transition (EMT) induction, proliferation, migration, colony formation and release of metalloproteinase 2 (MMP-2) were analyzed in 50-day exposed cells to the selected EDs. As a result, aldrin and CPF exposure led to an EMT induction (loss of 16% and 14% of E-cadherin levels, respectively, compared to the unexposed cells). Aroclor and CPF presented an increased migration (134% and 126%, respectively), colony formation (204% and 144%, respectively) and MMP-2 release (137% in both cases) compared to the unexposed cells. An untargeted lipidomic analysis was performed to decipher the lipids involved in the observed transformations. As general results, aldrin exposure showed a global decrease in phospholipids and sphingolipids, and aroclor and CPF showed an increase of certain phospholipids, glycosphingolipids as well as a remarkable increase of some cardiolipin species. Furthermore, the three exposures resulted in an increase of some triglyceride species. In conclusion, some significant changes in lipids were identified and thus we postulate that some lipid compounds and lipid metabolic pathways could be involved in the acquisition of the malignant phenotype in exposed prostate cancer cells to the selected EDs.

Porphyrin binding mechanism is altered by protonation at the loops in G-quadruplex DNA formed near the transcriptional activation site of the human c-kit gene.

BACKGROUND: G-quadruplex DNA structures are hypothesized to be involved in the regulation of gene expression and telomere homeostasis. The development of small molecules that modulate the stability of G-quadruplex structures has a potential therapeutic interest in cancer treatment and prevention of aging. METHODS: Molecular absorption and circular dichroism spectra were used to monitor thermal denaturation, acid base titration and mole ratio experiments. The resulting data were analyzed by multivariate data analysis methods. Surface plasmon resonance was also used to probe the kinetics and affinity of the DNA-drug interactions. RESULTS: We investigated the interaction between a G-quadruplex-forming sequence in the human c-kit proto-oncogene and the water soluble porphyrin TMPyP4. The role of cytosine and adenine residues at the loops of G-quadruplex was studied by substitution of these residues by thymidines. CONCLUSIONS: Here, we show the existence of two binding modes between TMPyP4 and the considered G-quadruplex. The stronger binding mode (formation constant around 107) involves end-stacking, while the weaker binding mode (formation constant around 106) is probably due to external loop binding. Evidence for the release of TMPyP4 upon protonation of bases at the loops has been observed. GENERAL SIGNIFICANCE: The results may be used for the design of porphyrin-based anti-cancer molecules with a higher affinity to G-quadruplex structures which may have anticancer properties.

Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches.

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.

Lipidomic analysis of single and combined effects of polyethylene microplastics and polychlorinated biphenyls on human hepatoma cells.

Microplastics are an emerging environmental issue as a result of their ubiquity, persistence, and intrinsic toxic potential. In addition, their ability to sorb and transport a wide variety of environmental pollutants (i.e. "Trojan Horse" effect) exerts significant adverse impacts upon ecosystems. The toxicological evaluation of the single and combined effects produced by polyethylene microplastics and two polychlorinated biphenyl congeners was performed on the human hepatoma cell line HepG2 by cell viability assessment and an untargeted lipidomic study. The cell lethality evaluation evinced that MPs did not induce relevant cell lethality at any of the concentration range tested, while both PCBs presented a hormetic behavior. The lipidomic analysis suggested that both single PCB exposures induced significant lipidomic changes, especially for glycerophospholipids and glycerolipids. In contrast, for MPs single exposure, the most remarkable change was the substantial enhancement of triglyceride content. Regarding combined exposures, results showed that MPs could induce even more harmful effects than those produced intrinsically as a result of desorbing previously sorbed toxic pollutants. To the best of our knowledge, this is the first study assessing the toxicity of microplastics and their possible "Trojan Horse" effect by applying an untargeted lipidomic methodology.

Chemical equilibria studies using multivariate analysis methods.

Chemical multiequilibria systems can be monitored efficiently with the aid of spectroscopic techniques. Both hard- and soft-modeling are effective and powerful tools to extract chemical information from spectroscopic data. Recently, hybrid approaches that combine the flexibility of soft-modeling with the precise solutions provided by hard-modeling have been proposed. Here, we tested the performance of these three chemometric approaches for the analysis of several simulated data sets. In addition, experimental data recorded during the study of the acid-base equilibria of two DNA structures (G-quadruplex and i-motif) corresponding to two short sequences of the k-ras oncogene were studied. Finally, we also analyzed the interaction of the two DNA sequences with the model ligand TMPyP4. The results obtained from the analysis of these data sets may be useful to determine the most appropriate use of each approach. Whenever the presence of optically active interferences or unknown drifts can be neglected and a chemical model can easily be proposed and fitted, the hard-modeling method shows the best performance. If any of these conditions is not fulfilled, a hybrid-modeling approach may be a better option because all the contributions (chemical and unknown) can be modeled and the ambiguities inherent to soft-modeling methods show minor effects.

Interaction of Environmental Pollutants with Microplastics: A Critical Review of Sorption Factors, Bioaccumulation and Ecotoxicological Effects.

Microplastics have become one of the leading environmental threats due to their persistence, ubiquity and intrinsic toxic potential. The potential harm that microplastics impose on ecosystems varies from direct effects (i.e., entanglement and ingestion) to their ability to sorb a diversity of environmental pollutants (e.g., heavy metals, persistent organic compounds or pharmaceuticals). Therefore, the toxicological assessment of the combined effects of microplastics and sorbed pollutants can produce in biota is one of the hottest topics on the environmental toxicology field. This review aims to clarify the main impacts that this interaction could have on ecosystems by (1) highlighting the principal factors that influence the microplastics sorption capacities; (2) discussing the potential scenarios in which microplastics may have an essential role on the bioaccumulation and transfer of chemicals; and (3) reviewing the recently published studies describing toxicological effects caused by the combination of microplastics and their sorbed chemicals. Finally, a discussion regarding the need for a new generation of toxicological studies is presented.

Partial characterization of the lipidome of the cold-water scallop, Chlamys islandica.

Fingerprinting of the main lipid components of the digestive gland of the Icelandic scallop-Chlamys islandica-has been performed by ultra-high-performance liquid chromatography coupled with time of flight high-resolution mass spectrometry, UHPLC-HRMS/ToF. This method allowed the identification of 224 lipids, including phosphatidylcholines (PC), plasmanyl (PC-O)/plasmenyl (PC-P) phosphatidylcholines, lyso-phosphatidylcholines (LPC), and their plasmanyl/plasmenyl forms (LPC-O/LPC-P). Diacylglycerols (DG), triacylglycerols (TG), and cholesteryl esters (CE) were the neutral lipids (NL) analyzed. While all of the lipids showed a strong seasonal dependence in terms of quantity, only NLs presented significant qualitative changes. Principal component analysis (PCA) of TG and DG profiles evidenced a prevalence of low unsaturated TGs and DGs in spring, which were replaced by species with a higher degree of unsaturations in summer. In autumn, long and highly unsaturated TGs constitute the lipid fraction of the digestive gland of the scallop, while DG species offer a mixed profile. This study contributes to the characterization and the elucidation of the lipidome of Chlamys islandica and provides baseline data for further study of the effects of pollutants on the lipidome of the Icelandic scallop, often used as a sentinel species in biomonitoring programs.

pH-Modulated Watson-Crick duplex-quadruplex equilibria of guanine-rich and cytosine-rich DNA sequences 140 base pairs upstream of the c-kit transcription initiation site.

Guanine-rich regions of DNA are sequences capable of forming G-quadruplex structures. The formation of a G-quadruplex structure in a region 140 base pairs (bp) upstream of the c-kit transcription initiation site was recently proposed (Fernando et al., Biochemistry, 2006, 45, 7854). In the present study, the acid-base equilibria and the thermally induced unfolding of the structures formed by a guanine-rich region and by its complementary cytosine-rich strand in c-kit were studied by means of circular dichroism and molecular absorption spectroscopies. In addition, competition between the Watson-Crick duplex and the isolated structures was studied as a function of pH value and temperature. Multivariate data analysis methods based on both hard and soft modeling were used to allow accurate quantification of the various acid-base species present in the mixtures. Results showed that the G-quadruplex and i-motif coexist with the Watson-Crick duplex over the pH range from 3.0 to 6.5, approximately, under the experimental conditions tested in this study. At pH 7.0, the duplex is practically the only species present.