Probing Hybridization parameters from microarray experiments: nearest neighbor model and beyond

In this article it is shown how optimized and dedicated microarray experiments can be used to study the thermodynamics of DNA hybridization for a large number of different conformations in a highly parallel fashion. In particular, free energy penalties for mismatches are obtained in two independent ways and are shown to be correlated with values from melting experiments in solution reported in the literature. The additivity principle, which is at the basis of the nearest-neighbor model, and according to which the penalty for two isolated mismatches is equal to the sum of the independent penalties, is thoroughly tested. Additivity is shown to break down for a mismatch distance below 5 nt. The behavior of mismatches in the vicinity of the helix edges, and the behavior of tandem mismatches are also investigated. Finally, some thermodynamic outlying sequences are observed and highlighted. These sequences contain combinations of GA mismatches. The analysis of the microarray data reported in this article provides new insights on the DNA hybridization parameters and can help to increase the accuracy of hybridization-based technologies.Comment: 13 pages, 11 figures, 1 table, Supplementary Data available in Appendi

Thermodynamic framework to assess low abundance DNA mutation detection by hybridization

The knowledge of genomic DNA variations in patient samples has a high and increasing value for human diagnostics in its broadest sense. Although many methods and sensors to detect or quantify these variations are available or under development, the number of underlying physico-chemical detection principles is limited. One of these principles is the hybridization of sample target DNA versus nucleic acid probes. We introduce a novel thermodynamics approach and develop a framework to exploit the specific detection capabilities of nucleic acid hybridization, using generic principles applicable to any platform. As a case study, we detect point mutations in the KRAS oncogene on a microarray platform. For the given platform and hybridization conditions, we demonstrate the multiplex detection capability of hybridization and assess the detection limit using thermodynamic considerations; DNA containing point mutations in a background of wild type sequences can be identified down to at least 1% relative concentration. In order to show the clinical relevance, the detection capabilities are confirmed on challenging formalin-fixed paraffin-embedded clinical tumor samples. This enzyme-free detection framework contains the accuracy and efficiency to screen for hundreds of mutations in a single run with many potential applications in molecular diagnostics and the field of personalised medicine

2021-10-05 Lecture JUB

Lecture slide of "Current Topics in Data Engineering Colloquium", JUB, 5 October 202

CONSTANd : a normalization method for isobaric labeled spectra by constrained optimization

In quantitative proteomics applications, the use of isobaric labels is a very popular concept as they allow for multiplexing, such that peptides from multiple biological samples are quantified simultaneously in one mass spectrometry experiment. Although this multiplexing allows that peptide intensities are affected by the same amount of instrument variability, systematic effects during sample preparation can also introduce a bias in the quantitation measurements. Therefore, normalization methods are required to remove this systematic error. At present, a few dedicated normalization methods for isobaric labeled data are at hand. Most of these normalization methods include a framework for statistical data analysis and rely on ANOVA or linear mixed models. However, for swift quality control of the samples or data visualization a simple normalization technique is sufficient. To this aim, we present a new and easy-to-use data-driven normalization method, named CONSTANd. The CONSTANd method employs constrained optimization and prior information about the labeling strategy to normalize the peptide intensities. Further, it allows maintaining the connection to any biological effect while reducing the systematic and technical errors. As a result, peptides can not only be compared directly within a multiplexed experiment, but are also comparable between other isobaric labeled datasets from multiple experimental designs that are normalized by the CONSTANd method, without the need to include a reference sample in every experimental setup. The latter property is especially useful when more than six, eight or ten (TMT/iTRAQ) biological samples are required to detect differential peptides with sufficient statistical power and to optimally make use of the multiplexing capacity of isobaric labels

Wild type and mutants sequences.

<p>Wild type and mutants sequences.</p

Plot of concentration profile similar to Fig 3 for all 12 KRAS mutations, based on experiment set 2.

<p>The profile is known to be linear, hence a single experiment per mutation type suffices. A higher slope corresponds to a stronger mutation. The bold dotted line is 10C→G (G12A in amino acid notation), the same mutation we used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177384#pone.0177384.g003" target="_blank">Fig 3</a>. The horizontal dashed line corresponds to the threshold value <i>ρ</i>_<i>t</i> ≈ 0.5 from which we can derive the concentration threshold for each mutation.</p

Epigenetic Targeting to Overcome Radioresistance in Head and Neck Cancer

(1) Background: The sensitivity of head and neck squamous cell carcinoma (HNSCC) to ionizing radiation, among others, is determined by the number of cells with high clonogenic potential and stem-like features. These cellular characteristics are dynamically regulated in response to treatment and may lead to an enrichment of radioresistant cells with a cancer stem cell (CSC) phenotype. Epigenetic mechanisms, particularly DNA and histone methylation, are key regulators of gene-specific transcription and cellular plasticity. Therefore, we hypothesized that specific epigenetic targeting may prevent irradiation-induced plasticity and may sensitize HNSCC cells to radiotherapy. (2) Methods: We compared the DNA methylome and intracellular concentrations of tricarboxylic acid cycle metabolites in radioresistant FaDu and Cal33 cell lines with their parental controls, as well as aldehyde dehydrogenase (ALDH)-positive CSCs with negative controls. Moreover, we conducted a screen of a chemical library targeting enzymes involved in epigenetic regulation in combination with irradiation and analyzed the clonogenic potential, sphere formation, and DNA repair capacity to identify compounds with both radiosensitizing and CSC-targeting potential. (3) Results: We identified the histone demethylase inhibitor GSK-J1, which targets UTX (KDM6A) and JMJD3 (KDM6B), leading to increased H3K27 trimethylation, heterochromatin formation, and gene silencing. The clonogenic survival assay after siRNA-mediated knock-down of both genes radiosensitized Cal33 and SAS cell lines. Moreover, high KDM6A expression in tissue sections of patients with HNSCC was associated with improved locoregional control after primary (n = 137) and post-operative (n = 187) radio/chemotherapy. Conversely, high KDM6B expression was a prognostic factor for reduced overall survival. (4) Conclusions: Within this study, we investigated cellular and molecular mechanisms underlying irradiation-induced cellular plasticity, a key inducer of radioresistance, with a focus on epigenetic alterations. We identified UTX (KDM6A) as a putative prognostic and therapeutic target for HNSCC patients treated with radiotherapy

Wild type and mutants sequences.

<p>Wild type and mutants sequences.</p

A summary of the statistical results of all three experiment sets.

<p>The <i>p</i>′-value of the most significant hypothesis (the true mutation) is indicated by a square. The <i>p</i>′-values of all the hypotheses at the other three nucleotides are indicated with a dot. Raw data can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177384#pone.0177384.s002" target="_blank">S1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177384#pone.0177384.s003" target="_blank">S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177384#pone.0177384.s004" target="_blank">S3</a> Tables.</p