Transcriptional profiling reveals genes in the human pathogen Trichophyton rubrum that are expressed in response to pH signaling

Trichophyton rubrum is a dermatophyte that infects human skin and nails. Its growth on keratin as its carbon source shifts the ambient pH from acidic to alkaline, which may be an efficient strategy for its successful infection and maintenance in the host. In this study, we used suppression subtractive hybridization to identify genes preferentially expressed in T rubrum incubated at either pH 5.0 or pH 8.0. The functional grouping of the 341 overexpressed unigenes indicated proteins putatively involved in diverse cellular processes, such as membrane remodeling, cellular transport, metabolism, cellular protection, fungal pathogenesis, gene regulation, interaction with the environment, and iron uptake. Although the basic metabolic machinery identified under both growth conditions seems to be functionally similar, distinct genes are upregulated at acidic or alkaline pHs. We also isolated a large number of genes of unknown function, probably unique to T rubrum or dermatophytes. Interestingly, the transcriptional profiling of several genes in a pacC mutant suggests that, in T rubrum, the transcription factor PacC has a diversity of metabolic functions, in response to either acidic or alkaline ambient pH. (C) 2009 Elsevier Ltd. All rights reserved.

Analysis of the Nicotiana tabacum Stigma/Style Transcriptome Reveals Gene Expression Differences between Wet and Dry Stigma Species

The success of plant reproduction depends on pollen-pistil interactions occurring at the stigma/style. These interactions vary depending on the stigma type: wet or dry. Tobacco (Nicotiana tabacum) represents a model of wet stigma, and its stigmas/styles express genes to accomplish the appropriate functions. For a large-scale study of gene expression during tobacco pistil development and preparation for pollination, we generated 11,216 high-quality expressed sequence tags (ESTs) from stigmas/styles and created the TOBEST database. These ESTs were assembled in 6,177 clusters, from which 52.1% are pistil transcripts/genes of unknown function. The 21 clusters with the highest number of ESTs (putative higher expression levels) correspond to genes associated with defense mechanisms or pollen-pistil interactions. The database analysis unraveled tobacco sequences homologous to the Arabidopsis (Arabidopsis thaliana) genes involved in specifying pistil identity or determining normal pistil morphology and function. Additionally, 782 independent clusters were examined by macroarray, revealing 46 stigma/style preferentially expressed genes. Real-time reverse transcription-polymerase chain reaction experiments validated the pistil-preferential expression for nine out of 10 genes tested. A search for these 46 genes in the Arabidopsis pistil data sets demonstrated that only 11 sequences, with putative equivalent molecular functions, are expressed in this dry stigma species. The reverse search for the Arabidopsis pistil genes in the TOBEST exposed a partial overlap between these dry and wet stigma transcriptomes. The TOBEST represents the most extensive survey of gene expression in the stigmas/styles of wet stigma plants, and our results indicate that wet and dry stigmas/styles express common as well as distinct genes in preparation for the pollination process.

Comprehensive gene expression profiling in lungs of mice infected with Mycobacterium tuberculosis following DNAhsp65 immunotherapy

Background The continued increase in tuberculosis (TB) rates and the appearance of extremely resistant Mycobacterium tuberculosis strains (XDR-TB) worldwide are some of the great problems of public health. In this context, DNA immunotherapy has been proposed as an effective alternative that could circumvent the limitations of conventional drugs. Nonetheless, the molecular events underlying these therapeutic effects are poorly understood. Methods We characterized the transcriptional signature of lungs from mice infected with M. tuberculosis and treated with heat shock protein 65 as a genetic vaccine (DNAhsp65) combining microarray and real-time polymerase chain reaction analysis. The gene expression data were correlated with the histopathological analysis of lungs. Results The differential modulation of a high number of genes allowed us to distinguish DNAhsp65-treated from nontreated animals (saline and vector-injected mice). Functional analysis of this group of genes suggests that DNAhsp65 therapy could not only boost the T helper (Th)1 immune response, but also could inhibit Th2 cytokines and regulate the intensity of inflammation through fine tuning of gene expression of various genes, including those of interleukin-17, lymphotoxin A, tumour necrosis factor-cl, interleukin-6, transforming growth factor-beta, inducible nitric oxide synthase and Foxp3. In addition, a large number of genes and expressed sequence tags previously unrelated to DNA-therapy were identified. All these findings were well correlated with the histopathological lesions presented in the lungs. Conclusions The effects of DNA therapy are reflected in gene expression modulation; therefore, the genes identified as differentially expressed could be considered as transcriptional biomarkers of DNAhsp65 immunotherapy against TB. The data have important implications for achieving a better understanding of gene-based therapies. Copyright (C) 2008 John Wiley & Sons, Ltd.

Proteomics of breast cancer for marker discovery and signal pathway profiling

Breast cancer is the most common form of cancer among women and the identification of markers to discriminate tumorigenic from normal cells, as well as the different stages of this pathology, is of critical importance. Two-dimensional electrophoresis has been used before for studying breast cancer, but the progressive completion of human genomic sequencing and the introduction of mass spectrometry, combined with advanced bioinformatics for protein identification, have considerably increased the possibilities for characterizing new markers and therapeutic targets. Breast cancer proteomics has already identified markers of potential clinical interest (such as the molecular chaperone 14-3-3 sigma) and technological innovations such as large scale and high throughput analysis are now driving the field. Methods in functional proteomics have also been developed to study the intracellular signaling pathways that underlie the development of breast cancer. As illustrated with fibroblast growth factor-2, a mitogen and motogen factor for breast cancer cells, proteomics is a powerful approach to identify signaling proteins and to decipher the complex signaling circuitry involved in tumor growth. Together with genomics, proteomics is well on the way to molecularly characterizing the different types of breast tumor, and thus defining new therapeutic targets for future treatment.

Profiling the Senior Traveler: An Australia Perspective

Recently, the behavior of senior travelers has become an important area of interest because of its market size and potential for growth. This study describes a study profiling senior travelers according to their demographic and psychographic characteristics. Six market segments were used to highlight the differences that exist in terms of holiday attractions, travel motivations, and information sources used among senior travelers when planning and choosing a holiday. Seniors are shown not to be a uniform conservative market, which has implications for marketing and product development.

Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs

Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts. There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones. Here we report the manual annotation of 60,770 full-length mouse complementary DNA sequences. These are clustered into 33,409 'transcriptional units', contributing 90.1% of a newly established mouse transcriptome database. Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome. 41% of all transcriptional units showed evidence of alternative splicing. In protein-coding transcripts, 79% of splice variations altered the protein product. Whole-transcriptome analyses resulted in the identification of 2,431 sense-antisense pairs. The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics.

Profiling "new" communication technologies in adolescence

In an overview of some of the central issues concerning the impact and effects of new technology in adolescence, this article questions the reality of the net generation before considering the interplay of new and old technologies, the internet as both communication and lifestyle resource, and newer technologies like text messaging and webcams.

Ex vivo profiling of CD8(+)-T-cell responses to human cytomegalovirus reveals broad and multispecific reactivities in healthy virus carriers

Human cytomegalovirus (HCMV) can establish both nonproductive (latent) and productive (lytic) infections. Many of the proteins expressed during these phases of infection could be expected to be targets of the immune response; however, much of our understanding of the CD8(+)-T-cell response to HCMV is mainly based on the pp65 antigen. Very little is known about T-cell control over other antigens expressed during the different stages of virus infection; this imbalance in our understanding undermines the importance of these antigens in several aspects of HCMV disease pathogenesis. In the present study, an efficient and rapid strategy based on predictive bioinformatics and ex vivo functional T-cell assays was adopted to profile CD8(+)-T-cell responses to a large panel of HCMV antigens expressed during different phases of replication. These studies revealed that CD8(+)-T-cell responses to HCMV often contained multiple antigen-specific reactivities, which were not just constrained to the previously identified pp65 or IE-1 antigens. Unexpectedly, a number of viral proteins including structural, early/late antigens and HCMV-encoded immunomodulators (pp28, pp50, gH, gB, US2, US3, US6, and UL18) were also identified as potential targets for HCMV-specific CD8(+)-T-cell immunity. Based on this extensive analysis, numerous novel HCMV peptide epitopes and their HLA-restricting determinants recognized by these T cells have been defined. These observations contrast with previous findings that viral interference with the antigen-processing pathway during lytic infection would render immediate-early and early/late proteins less immunogenic. This work strongly suggests that successful HCMV-specific immune control in healthy virus carriers is dependent on a strong T-cell response towards a broad repertoire of antigens.

Analysis of the mouse transcriptome for genes involved in the function of the nervous system

We analyzed the mouse Representative Transcript and Protein Set for molecules involved in brain function. We found full-length cDNAs of many known brain genes and discovered new members of known brain gene families, including Family 3 G-protein coupled receptors, voltage-gated channels, and connexins. We also identified previously unknown candidates for secreted neuroactive molecules. The existence of a large number of unique brain ESTs suggests an additional molecular complexity that remains to be explored. A list of genes containing CAG stretches in the coding region represents a first step in the potential identification of candidates for hereditary neurological disorders.

Profiling consumer behaviour in cross-border tourism destinations

Tourism is a phenomenon that moves millions of people around the world, taking as a major driver of the global economy. Such relevance is reflected in the proliferation of studies in the overall area known as tourism, under various perspectives and backgrounds. In the light of such multitude of insights our study aims at gaining a deeper understanding of customer profiling and behavior in cross-border tourism destinations. Previous studies conducted in such contexts suggest that cross-border regions (CBRs) are an attractive and desirable idea, yet requiring further theoretical and empirical research. The new configuration of many CBRs calls for a debate on issues concerning its development, raising up important dimensions, such as, organization and planning of common tourism destinations. There is still a gap in the understanding of destination management in CBRs and the customer profile and motivations. Overall this research aims at attaining a deeper understanding of the profile and behavior of consumers in tourism settings, addressing the predisposition for the destination. The study addresses the following research question: “What factors influence customer behavior and attitudes in a CBRs tourism destination?” To address our question we will take an interdisciplinary perspective bringing together inputs from marketing, tourism and local economics. When addressing consumer behavior in tourism previous studies considered the following constructs: involvement, place attachment, satisfaction and destination loyalty. In order to establish the causal relationships in our theoretical model, we intend to develop a predominant quantitative design, yet we plan to conduct exploratory interviews. In the analysis and discussion of results, we intend to use Structural Equation Modeling. It will further allow understanding how the constructs in the research model relate to each other in the specified context. Results are also expected to have managerial implications. Consequently our results may assist decision makers in developing their local policies.

New insights into functional regulation in MS-based drug profiling

We present a novel data analysis strategy which combined with subcellular fractionation and liquid chromatography-mass spectrometry (LC-MS) based proteomics provides a simple and effective workflow for global drug profiling. Five subcellular fractions were obtained by differential centrifugation followed by high resolution LC-MS and complete functional regulation analysis. The methodology combines functional regulation and enrichment analysis into a single visual summary. The workflow enables improved insight into perturbations caused by drugs. We provide a statistical argument to demonstrate that even crude subcellular fractions leads to improved functional characterization. We demonstrate this data analysis strategy on data obtained in a MS-based global drug profiling study. However, this strategy can also be performed on other types of large scale biological data.

Epigenetic and transcriptional signatures of stable versus plastic differentiation of proinflammatory gd T cell subsets

Two distinct subsets of γδ T cells that produce interleukin 17 (IL-17) (CD27(-) γδ T cells) or interferon-γ (IFN-γ) (CD27(+) γδ T cells) develop in the mouse thymus, but the molecular determinants of their functional potential in the periphery remain unknown. Here we conducted a genome-wide characterization of the methylation patterns of histone H3, along with analysis of mRNA encoding transcription factors, to identify the regulatory networks of peripheral IFN-γ-producing or IL-17-producing γδ T cell subsets in vivo. We found that CD27(+) γδ T cells were committed to the expression of Ifng but not Il17, whereas CD27(-) γδ T cells displayed permissive chromatin configurations at loci encoding both cytokines and their regulatory transcription factors and differentiated into cells that produced both IL-17 and IFN-γ in a tumor microenvironment.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.