Feature selection using Haar wavelet power spectrum

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem [1]. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special softwares or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design

Mitochondria localization induced self-assembly of peptide amphiphiles for cellular dysfunction

Achieving spatiotemporal control of molecular self-assembly associated with actuation of biological functions inside living cells remains a challenge owing to the complexity of the cellular environments and the lack of characterization tools. We present, for the first time, the organelle-localized self-assembly of a peptide amphiphile as a powerful strategy for controlling cellular fate. A phenylalanine dipeptide (FF) with a mitochondria-targeting moiety, triphenyl phosphonium (Mito-FF), preferentially accumulates inside mitochondria and reaches the critical aggregation concentration to form a fibrous nanostructure, which is monitored by confocal laser scanning microscopy and transmission electron microscopy. The Mito-FF fibrils induce mitochondrial dysfunction via membrane disruption to cause apoptosis. The organelle-specific supramolecular system provides a new opportunity for therapeutics and in-depth investigations of cellular functions.clos

Interferon-α Regulates Glutaminase 1 Promoter through STAT1 Phosphorylation: Relevance to HIV-1 Associated Neurocognitive Disorders

HIV-1 associated neurocognitive disorders (HAND) develop during progressive HIV-1 infection and affect up to 50% of infected individuals. Activated microglia and macrophages are critical cell populations that are involved in the pathogenesis of HAND, which is specifically related to the production and release of various soluble neurotoxic factors including glutamate. In the central nervous system (CNS), glutamate is typically derived from glutamine by mitochondrial enzyme glutaminase. Our previous study has shown that glutaminase is upregulated in HIV-1 infected monocyte-derived-macrophages (MDM) and microglia. However, how HIV-1 leads to glutaminase upregulation, or how glutaminase expression is regulated in general, remains unclear. In this study, using a dual-luciferase reporter assay system, we demonstrated that interferon (IFN) α specifically activated the glutaminase 1 (GLS1) promoter. Furthermore, IFN-α treatment increased signal transducer and activator of transcription 1 (STAT1) phosphorylation and glutaminase mRNA and protein levels. IFN-α stimulation of GLS1 promoter activity correlated to STAT1 phosphorylation and was reduced by fludarabine, a chemical that inhibits STAT1 phosphorylation. Interestingly, STAT1 was found to directly bind to the GLS1 promoter in MDM, an effect that was dependent on STAT1 phosphorylation and significantly enhanced by IFN-α treatment. More importantly, HIV-1 infection increased STAT1 phosphorylation and STAT1 binding to the GLS1 promoter, which was associated with increased glutamate levels. The clinical relevance of these findings was further corroborated with investigation of post-mortem brain tissues. The glutaminase C (GAC, one isoform of GLS1) mRNA levels in HIV associated-dementia (HAD) individuals correlate with STAT1 (p<0.01), IFN-α (p<0.05) and IFN-β (p<0.01). Together, these data indicate that both HIV-1 infection and IFN-α treatment increase glutaminase expression through STAT1 phosphorylation and by binding to the GLS1 promoter. Since glutaminase is a potential component of elevated glutamate production during the pathogenesis of HAND, our data will help to identify additional therapeutic targets for the treatment of HAND