Accurate Estimates of Microarray Target Concentration from a Simple Sequence-Independent Langmuir Model

Background: Microarray technology is a commonly used tool for assessing global gene expression. Many models for estimation of target concentration based on observed microarray signal have been proposed, but, in general, these models have been complex and platform-dependent. Principal Findings: We introduce a universal Langmuir model for estimation of absolute target concentration from microarray experiments. We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray. The model also accurately predicts concentration for the MAQC data sets. This approach significantly reduces the computational complexity of quantitative target concentration estimates. Conclusions: Using a simple form of the Langmuir isotherm model, with a minimum of parameters and assumptions, and without explicit modeling of individual probe properties, we were able to recover absolute transcript concentrations with high R 2 on four different array platforms. The results obtained here suggest that with a ‘‘spiked-in’ ’ concentration serie

Identification of a biomarker panel for colorectal cancer diagnosis

<p>Abstract</p> <p>Background</p> <p>Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries.</p> <p>Methods</p> <p>A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables.</p> <p>Results</p> <p>After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples.</p> <p>Conclusions</p> <p>We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).</p

FRET Detection of Lymphocyte Function-Associated Antigen-1 Conformational Extension

Lymphocyte function-associated antigen 1 (LFA-1, CD11a/CD18, αLβ2-integrin) and its ligands are essential for adhesion between T-cells and antigen-presenting cells, formation of the immunological synapse, and other immune cell interactions. LFA-1 function is regulated through conformational changes that include the modulation of ligand binding affinity and molecular extension. However, the relationship between molecular conformation and function is unclear. Here fluorescence resonance energy transfer (FRET) with new LFA-1-specific fluorescent probes showed that triggering of the pathway used for T-cell activation induced rapid unquenching of the FRET signal consistent with extension of the molecule. Analysis of the FRET quenching at rest revealed an unexpected result that can be interpreted as a previously unknown LFA-1 conformation