Use of short term test systems for the prediction of the hazard represented by potential chemical carcinogens

It has been hypothesized that results from the short term bioassays will ultimately provide information that will be useful for human health hazard assessment. Although toxicologic test systems have become increasingly refined, to date, no investigator has been able to provide qualitative or quantitative methods which would support the use of short term tests in this capacity.^ Historically, the validity of the short term tests have been assessed using the framework of the epidemiologic/medical screens. In this context, the results of the carcinogen (long term) bioassay is generally used as the standard. However, this approach is widely recognized as being biased and, because it employs qualitative data, cannot be used in the setting of priorities. In contrast, the goal of this research was to address the problem of evaluating the utility of the short term tests for hazard assessment using an alternative method of investigation.^ Chemical carcinogens were selected from the list of carcinogens published by the International Agency for Research on Carcinogens (IARC). Tumorigenicity and mutagenicity data on fifty-two chemicals were obtained from the Registry of Toxic Effects of Chemical Substances (RTECS) and were analyzed using a relative potency approach. The relative potency framework allows for the standardization of data "relative" to a reference compound. To avoid any bias associated with the choice of the reference compound, fourteen different compounds were used.^ The data were evaluated in a format which allowed for a comparison of the ranking of the mutagenic relative potencies of the compounds (as estimated using short term data) vs. the ranking of the tumorigenic relative potencies (as estimated from the chronic bioassays). The results were statistically significant (p $<$.05) for data standardized to thirteen of the fourteen reference compounds. Although this was a preliminary investigation, it offers evidence that the short term test systems may be of utility in ranking the hazards represented by chemicals which may be human carcinogens. ^

Systems Biology Approaches to Probe Gene Regulation in Bacteria

Mechanisms that allow pathogens to colonize the host are not the product of isolated genes, but instead emerge from the concerted operation of regulatory networks. Therefore, identifying components and the systemic behavior of networks is necessary to a better understanding of gene regulation and pathogenesis. To this end, I have developed systems biology approaches to study transcriptional and post-transcriptional gene regulation in bacteria, with an emphasis in the human pathogen Mycobacterium tuberculosis (Mtb). First, I developed a network response method to identify parts of the Mtb global transcriptional regulatory network utilized by the pathogen to counteract phagosomal stresses and survive within resting macrophages. As a result, the method unveiled transcriptional regulators and associated regulons utilized by Mtb to establish a successful infection of macrophages throughout the first 14 days of infection. Additionally, this network-based analysis identified the production of Fe-S proteins coupled to lipid metabolism through the alkane hydroxylase complex as a possible strategy employed by Mtb to survive in the host. Second, I developed a network inference method to infer the small non-coding RNA (sRNA) regulatory network in Mtb. The method identifies sRNA-mRNA interactions by integrating a priori knowledge of possible binding sites with structure-driven identification of binding sites. The reconstructed network was useful to predict functional roles for the multitude of sRNAs recently discovered in the pathogen, being that several sRNAs were postulated to be involved in virulence-related processes. Finally, I applied a combined experimental and computational approach to study post-transcriptional repression mediated by small non-coding RNAs in bacteria. Specifically, a probabilistic ranking methodology termed rank-conciliation was developed to infer sRNA-mRNA interactions based on multiple types of data. The method was shown to improve target prediction in Escherichia coli, and therefore is useful to prioritize candidate targets for experimental validation.