Abstract: A critical step in drug development is the optimization of the efficacy and specificity of candidate therapeutic compounds. Ideally, optimization is carried out using knowledge of the drug’s mode of action, i.e., the gene products with which a drug functionally interacts (drug targets). These drug targets may include genes that mediate the therapeutic effects of the drug, as well as genes that mediate undesirable side-effects. However, for many drug candidates the targets are unknown and difficult to identify among the thousands of genes in a typical genome. Previously, we developed an algorithm to identify drug targets in yeast using multiple perturbations to a cell and by measuring the response at steady-state (di Bernardo et al, Nature Biotechnology, in press). Here, we report a novel computational approach for rapidly identifying drug targets using time-course gene expression profiles. The approach filters expression profiles using a reverse-engineered gene-network model to distinguish the targets of compounds from the genes that exhibit only secondary responses. We tested this approach experimentally in E coli and show that it can overcome some of the experimental and computational limitations of existing chemogenetic approach for identifying a drug’s mode of action.

Abstract: Neoplastic transformation is a multistep process in which gene products of specific regulatory pathways are involved at each stage. Identification of these over-expressed or mutated gene products provides an unprecedented opportunity to address the immune system against defined antigens and eliminate transformed cells. Mice transgenic for these oncogenes (e.g. HER-2/neu, a prototype of deregulated oncogenic protein kinase membrane receptors) are ideal experimental models for assessing the potential of active immunization. The demonstration that vaccines can cure HER-2/neu transplantable tumors, prevent their onset and delay the progression of preneoplastic lesions in mice at risk suggests that efficient immunological inhibition of HER- 2/neu carcinogenesis can be achieved by specific vaccination. Since an important issue of mammary cancer immunoprevention is the definition of a set target genes other that HER-2/neu in order to make broader the coverage of the efficacy of the vaccination we decided to use part of the data previously described by us (Quaglino et al. J. Clical. Invest. 2004; Astolfi et al. Cancer Immunol., Immunot.2005) to identify a set of new putative targets to be used as TAA.

Abstract: Computational analysis of genomic sequence databases has recently revealed a striking abundance of Natural Antisense Transcripts within the mouse and human genomes. Since antisense transcription is increasingly recognized as a molecular mechanism involved in the regulation of gene expression, a significant proportion of human disease genes could potentially display antisense-mediated abnormal patterns of gene expression. Cancer is a pathological phenotype that could represent a potential target for such gene regulation mechanism, given the high number of genes governing cancer-related cellular functions such as proliferation, differentiation and apoptosis. Preliminary experimental evidence has been reported recently for the occurrence of natural antisense transcript for several genes whose function has been implicated in cancer pathogenesis. Therefore, a targeted in silico survey of antisense transcription, coupled with a detailed inspection of annotated gene features, could represent a novel tool for the identification of candidate cancer-related genes. We have performed an in silico search for “sense-antisense gene clusters” within two regions from human chromosome 6 (6q21 and 6q27) that have long been reported to carry cancer-associated deletions and rearrangements, but for which no tumor suppressor genes has been unambiguously identified. Experimental validation of each sense-antisense cluster detected in this study, followed by definition of bona fide tumor suppressor candidates based on the available annotation features, confirmed the feasibility of this approach to better define candidate cancer-associated genes.

Abstract: One of the major challenges in the post-genomic era is the improvemnt of the capability to recognize the disease related molecular targets. Even if the new experimental methods have greatly enhanced the analytical capability,the application of NLP (natural language processing) play a relevant role in the selection and extraction of relevant information. The published paper still constitute the main entry point to bridge togheter molcular biology, chemical and medical information. The text processing gives a great effort in the speed up the data warehousing. In this paper we present an interactive tool that allow to conceptually organize organize the Medline.