Abstract
Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogen biology. In this paper, we apply a gene regulatory network approach to analyze transcriptomic data of the parasite Toxoplasma gondii. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of T. gondii were embedded into the dynamics of a gene regulatory network. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 338 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small gene circuit that drives a series of phenotypic transitions that characterize the life cycle of this pathogen. These new findings can contribute to the understanding of parasite pathogenesis.
Highlights
Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases
In order to model the T. gondii gene regulatory networks (GRNs) we assume that the state of the system at time t can be represented by a N-dimensional vector x(t) associated with the expression levels of N clusters of genes, or nodes of the network
Further to consider the available expression data, we take into account known biological facts: (i) the life cycle of parasite has seven stages, but we include in our analysis transcriptional data available for five stages: immature oocysts, mature oocysts, tachyzoite, bradyzoite and merozoite; (ii) there are four possible transitions between these five stages, promoted by different environmental cues; (iii) The system fluctuates around one of attraction basins associated with the parasite stage; and (iv) the connectivity matrix is a sparse matrix
Summary
Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. Systems biology is an emergent and multidisciplinary field that proposes new and rational approaches for the analysis of HTT-derived information in the field of infectious diseases[9] One of these goals involves the inference of gene regulatory networks (GRNs) from large amounts of information, since it allows modeling the dynamics of complex systems in a single conceptual framework[10,11,12]. Despite the wide range of experimental conditions studied with different HTT, the only one that provides data on the life cycle of the parasite in a more comprehensive manner is still the microarray technology This limitation could be overcome in the near future, allowing the integration of complementary data (for example, epigenetics) in more complete studies of this type. While genes that are co-expressed tend to take part in the same processes and perform similar or complementary functions[18], the inference of communities in the network allows to predict putative functions within the network
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