Estudio comparativo de la determinación de HPV entre HC2 y Cervista™ / Comparative study of HC2 and Cervista™ for the detection of HPV

Antecedentes. La determinación del virus del papiloma humano (VPH) en muestras ginecológicas es en la actualidad un elemento clave para la toma de decisiones en patología cervical. El método más contrastado clínicamente es la captura de híbridos de segunda generación (HC2). Métodos. Estudio comparativo de los resultados obtenidos, mediante HC2 y Cervista™, para la detección de VPH de alto riesgo (VPH-AR) en muestras ginecológicas. Resultado. La comparación de resultados muestra una concordancia del 96,4%, con una sensibilidad de Cervista™ para CIN2+ del 98,3% y una especificidad del 98,4%. Conclusiones. Creemos que Cervista™ es un buen método para la detección de VPH-AR, aplicable tanto en el cribado como en la clínica(AU)

Introduction. HPV detection in gynaecological samples currently provides key data in cervical pathology for diagnostic and management decisions. At present HC2 is the most clinically proven method for this purpose. Methods. Comparative study of HPV-HR detection between HC2 and Cervista™. Results. The concordance between the two methods is 96.4%. The sensitivity of Cervista™ for CIN2+ is 98.3% and specificity exceeds 98.4%. Conclusions. We conclude that Cervista™ is an appropriate method for the detection of HPV-HR in gynecological samples for screening and clinical purposes(AU)

The social network of a cell: recent advances in interactome mapping.

Proteins very rarely act in isolation. Biomolecular interactions are central to all biological functions. In human, for example, interference with biomolecular networks often lead to disease. Protein-protein and protein-metabolite interactions have traditionally been studied one by one. Recently, significant progresses have been made in adapting suitable tools for the global analysis of biomolecular interactions. Here we review this suite of powerful technologies that enable an exponentially growing number of large-scale interaction datasets. These new technologies have already contributed to a more comprehensive cartography of several pathways relevant to human pathologies, offering a broader choice for therapeutic targets. Genome-wide scale analyses in model organisms reveal general organizational principles of eukaryotic proteomes. We also review the biochemical approaches that have been used in the past on a smaller scale for the quantification of the binding constant and the thermodynamics parameters governing biomolecular interaction. The adaptation of these technologies to the large-scale measurement of biomolecular interactions in (semi-)quantitative terms represents an important challenge.

Resolving the network of cell signaling pathways using the evolving yeast two-hybrid system.

In 1983, while investigators had identified a few human proteins as important regulators of specific biological outcomes, how these proteins acted in the cell was essentially unknown in almost all cases. Twenty-five years later, our knowledge of the mechanistic basis of protein action has been transformed by our increasingly detailed understanding of protein-protein interactions, which have allowed us to define cellular machines. The advent of the yeast two-hybrid (Y2H) system in 1989 marked a milestone in the field of proteomics. Exploiting the modular nature of transcription factors, the Y2H system allows facile measurement of the activation of reporter genes based on interactions between two chimeric or "hybrid" proteins of interest. After a decade of service as a leading platform for individual investigators to use in exploring the interaction properties of interesting target proteins, the Y2H system has increasingly been applied in high-throughput applications intended to map genome-scale protein-protein interactions for model organisms and humans. Although some significant technical limitations apply, Y2H has made a great contribution to our general understanding of the topology of cellular signaling networks.

Interactive proteomics: what lies ahead?

Interactive proteomics addresses the physical associations among proteins and establishes global, disease-, and pathway-specific protein interaction networks. The inherent chemical and structural diversity of proteins, their different expression levels, and their distinct subcellular localizations pose unique challenges for the exploration of these networks, necessitating the use of a variety of innovative and ingenious approaches. Consequently, recent years have seen exciting developments in protein interaction mapping and the establishment of very large interaction networks, especially in model organisms. In the near future, attention will shift to the establishment of interaction networks in humans and their application in drug discovery and understanding of diseases. In this review, we present an impressive toolbox of different technologies that we expect to be crucial for interactive proteomics in the coming years.

Advanced technologies for studies on protein interactomes.

One of the key challenges of biology in the post-genomic era is to assign function to the many genes revealed by large-scale sequencing programmes, since only a small fraction of gene function can be directly inferred from the coding sequence. Identifying interactions between proteins is a substantial part in understanding their function. The main technologies for investigating protein-protein interactions and assigning functions to proteins include direct detection intermolecular interactions through protein microarray, yeast two-hybrid system, mass spectrometry fluorescent techniques to visualize protein complexes or pull-down assays, as well as technologies detecting functional interactions between genes, such as RNAi knock down or functional screening of cDNA libraries. Over recent years, considerable advances have been made in the above techniques. In this review, we discuss some recent developments and their impact on the gene function annotation.

Motor-cargo interactions: the key to transport specificity.

Eukaryotic cells organize their cytoplasm by moving different organelles and macromolecular complexes along microtubules and actin filaments. These movements are powered by numerous motor proteins that must recognize their respective cargoes in order to function. Recently, several proteins that interact with motors have been identified by yeast two-hybrid and biochemical analyses, and their roles in transport are now being elucidated. In several cases, analysis of the binding partners helped to identify new transport pathways, new types of cargo, and transport regulated at the level of motor-cargo binding. We discuss here how different motors of the kinesin, dynein and myosin families recognize their cargo and how motor-cargo interactions are regulated.

Identification of transcription factor in the promoter region of rat regucalcin gene: binding of nuclear factor I-A1 to TTGGC motif.

Hepatic nuclear protein has been reported to bind specifically to the TTGGC sequence of the rat regucalcin gene promoter region in stimulating the promoter activity (Misawa and Yamaguchi [2000] Biochem. Biophys. Res. Commun. 279: 275-281). The present study was undertaken to identify transcription factor, which binds to TTGGC motif in the rat regucalcin gene promoter, using the yeast one-hybrid system. The sequence between -525 and -504, which has been defined as a functional promoter element II-b, was used as bait to screen a rat liver cDNA library. Two cDNA clones were identified as a nuclear factor I-A1 (NF1-A1). The results of gel mobility shift assay and mutation analysis using recombinant NF1-A1 protein showed that this protein could specifically bind to TTGGC motif of the II-b oligonucleotide in promoter region. The expression of NF1-A1 mRNA was found in the liver, kidney, heart, spleen, and brain of rats. This study demonstrates that NF1-A1 is a transcription factor in stimulating the rat regucalcin gene promoter activity.

Use of protein-interaction maps to formulate biological questions.

Protein-interaction mapping approaches generate functional information for large numbers of genes that are predicted from complete genome sequences. This information, released as databases available on the Internet, is likely to transform the way biologists formulate and then address their questions of interest.

Encroaching genomics: adapting large-scale science to small academic laboratories.

The process of conducting biological research is undergoing a profound metamorphosis due to the technological innovations and torrent of information resulting from the execution of multiple species genome projects. The further tasks of mapping polymorphisms and characterizing genome-wide protein-protein interaction (the characterization of the proteome) will continue to garner resources, talent, and public attention. Although some elements of these whole genome size projects can only be addressed by large research groups, consortia, or industry, the impact of these projects has already begun to transform the process of research in many small laboratories. Although the impact of this transformation is generally positive, laboratories engaged in types of research destined to be dominated by the efforts of a genomic consortium may be negatively impacted if they cannot rapidly adjust strategies in the face of new large-scale competition. The focus of this report is to outline a series of strategies that have been productively utilized by a number of small academic laboratories that have attempted to integrate such genomic resources into research plans with the goal of developing novel physiological insights.