Microarray analysis of high light intensity stress on hydrogen production metabolism of Rhodobacter capsulatus

Biohydrogen obtained from purple non sulfur bacteria (PNSB) is an environmentally friendly alternative for hydrogen production. PNSB can be employed in large scale outdoor photobioreactors to produce hydrogen by photofermentation with sunlight as the light source. In external environmental conditions, however, bacteria can experience stress due to high light intensities, which can inhibit or slow down hydrogen production. Previous studies with other PNSB showed varying responses to light intensities (above 4000 lux), in some cases improving, and in others adversely affecting hydrogen production.In this study, Rhodobacter capsulatus, a PNSB species that produce hydrogen efficiently from dark fermenter effluents containing acetate, was used to investigate the effects of high light intensity stress on the hydrogen production metabolism at the gene expression level. A microarray analysis was carried out using a custom-design Affymetrix GeneChip TR_RCH2a520699F. R. capsulatus DSM1710 was grown under a cyclic illumination of 2000 and 7000 lux (12 h light/12 h dark) in a hydrogen production medium having 30 mM acetate and 2 mM glutamate, and was exposed to a high light intensity (10,000 lux) for 1 h in the middle of a light period. The results reveal that photosynthetic reaction center genes were down-regulated in order to protect the photosynthetic membrane from damage. On the other hand, the expression of nitrogenase and electron transport system genes were enhanced by high light intensity. These results show that a high light intensity stress drives R. capsulatus to direct gene expression towards hydrogen production, which supports the hypothesis that hydrogen production is a way for the disposal of excess reducing equivalents to maintain the internal redox balance.     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.     

Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

Microarray data classification is a task involving high dimensionality and small samples sizes. A common criterion to decide on the number of selected genes is maximizing the accuracy, which risks overfitting and usually selects more genes than actually needed. We propose, relaxing the maximum accuracy criterion, to select the combination of attribute selection and classification algorithm that using less attributes has an accuracy not statistically significantly worst that the best. Also we give some advice to choose a suitable combination of attribute selection and classifying algorithms for a good accuracy when using a low number of gene expressions. We used some well known attribute selection methods (FCBF, ReliefF and SVM-RFE, plus a Random selection, used as a base line technique) and classifying techniques (Naive Bayes, 3 Nearest Neighbor and SVM with linear kernel) applied to 30 data sets involving different cancer types.     

Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.     

A dynamic and complex monochloramine stress response in Escherichia coli revealed by transcriptome analysis

Despite the widespread use of monochloramine in drinking water treatment, there is surprisingly little information about its mode of action. In this study, DNA microarrays were used to investigate the global gene expression of Escherichia coli cells exposed to sub-lethal concentrations of monochloramine, with a focus on temporal dynamics. Genes induced by monochloramine were associated with several stress response functions, including oxidative stress, DNA repair, multidrug efflux, biofilm formation, antibiotic resistance, and cell wall repair. The diversity of functional associations supports a model of monochloramine action involving multiple cellular targets including cell membranes, nucleic acids, and proteins. These data suggest that E. coli responds to monochloramine exposure by activating diverse defense responses rather than a single antioxidant system and the exposure may also induce biofilm formation. The induction of multidrug efflux pumps and specific antibiotic resistance genes further suggests that exposure to monochloramine may contribute to reduced susceptibility to some antibiotics.     

Application of multivariate curve resolution to the analysis of yeast genome-wide screens

In this work, the application of Multivariate Curve Resolution to the analysis of yeast genome-wide screens obtained by means of DNA microarray technology is shown. In order to perform the analysis of this type of data, two algorithms based on Alternating Least Squares (MCR-ALS) and on its maximum likelihood weighted projection (MCR-WALS) variant are compared. The utilization of the modified weighted alternating least (WALS) squares algorithm is motivated by the rather poor quality, uncertainties and experimental noise associated to DNA microarray data. Moreover, a large number of missing values are usually present in these data sets and the weighted WALS approach allowed circumventing this problem. Two different experimental datasets were used for this comparison. In the first dataset, gene expression values in budding yeast were monitored in-response to glucose limitation. In the second dataset, the changes in the gene expression caused by the daunorubicin drug were monitored as a function of time. Results obtained by application of Multivariate Curve Resolution in the two cases allowed a good recovery of the evolving gene expression profiles and the identification of metabolic pathways and individual genes involved in these gene expression changes.     

Sparse and stable gene selection with consensus SVM-RFE

A method is described for performing sparse and stable gene selection from a number of unstable, but low cost, SVM-RFE units referred to as SVM-RFE subunits. Using a comprehensive simulation study, we show that the introduction of a consensus constraint with respect to variations in the policy of gene removal and a stability constraint with respect to perturbations in the training data can remarkably improve gene selection precision, dimensionality reduction ratio and stability of low cost SVM-RFE subunits still guaranteeing affordable computational costs. The method, which does not require the preselection of the number of selected genes, is divided into two stages. Multiple rough gene removal policies are first applied to multiple surrogate training datasets (spreading). Multiple consensus gene sets with respect to variations in the gene removal policy are then obtained and passed through a stability filter which selects the best performing gene set (despreading). Hence, while the consensus constraint performs strong dimensionality reduction at affordable computational costs, the stability constraint ensures acceptable indexes of gene selection stability and further dimensionality reduction. The method is validated on three benchmark microarray datasets.     

A Quick and Parallel Analytical Method Based on Quantum Dots Labeling for ToRCH-Related Antibodies

Quantum dot is a special kind of nanomaterial composed of periodic groups of II–VI, III–V or IV–VI materials. Their high quantum yield, broad absorption with narrow photoluminescence spectra and high resistance to photobleaching, make them become a promising labeling substance in biological analysis. Here, we report a quick and parallel analytical method based on quantum dots for ToRCH-related antibodies including Toxoplasma gondii, Rubella virus, Cytomegalovirus and Herpes simplex virus type 1 (HSV1) and 2 (HSV2). Firstly, we fabricated the microarrays with the five kinds of ToRCH-related antigens and used CdTe quantum dots to label secondary antibody and then analyzed 100 specimens of randomly selected clinical sera from obstetric outpatients. The currently prevalent enzyme-linked immunosorbent assay (ELISA) kits were considered as “golden standard” for comparison. The results show that the quantum dots labeling-based ToRCH microarrays have comparable sensitivity and specificity with ELISA. Besides, the microarrays hold distinct advantages over ELISA test format in detection time, cost, operation and signal stability. Validated by the clinical assay, our quantum dots-based ToRCH microarrays have great potential in the detection of ToRCH-related pathogens.     

采用喷墨打印机进行低成本微阵列印刷的可行性研究

根据网点形成技术,微阵列制作方法可归结为接触式印刷和非接触式印刷.接触式印刷中,如印刷生物样本时,设备与基底是直接接触.而非接触式印刷中,设备与基底之间不会直接接触.在非接触式印刷中,喷墨印刷技术是相对灵活、简便、便宜的一种方式,同时还减少了污染且避免了更高的数据吞吐量.但是,喷墨印刷需要有能喷射儿微微升墨滴的喷嘴和能够精确喷射墨滴的喷嘴位置控制单元.若使用喷墨方法制作微阵列就需要复杂的机构系统,以致成本增加.因此,本论文提出使用已具有喷墨系统的商用喷墨打印机来制作微阵列.     

Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis

The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity, both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way, a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis of our approach using two and three objectives, and with other existing algorithms, suggest that our proposal is highly appropriate for solving this problem.