Topology and dynamics of an artificial genetic regulatory network model

This thesis presents some of the methods of studying models of regulatory networks using mathematical and computational formalisms. A basic review of the biology behind gene regulation is introduced along with the formalisms used for modelling networks of such regulatory interactions. Topological measures of large-scale complex networks are discussed and then applied to a specific artificial regulatory network model created through a duplication and divergence mechanism. Such networks share topological features with natural transcriptional regulatory networks. Thus, it may be the case that the topologies inherent in natural networks may be primarily due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks are also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model

Evolutionary divergence in the fungal response to fluconazole revealed by soft clustering

Background: Fungal infections are an emerging health risk, especially those involving yeast that are resistant to antifungal agents. To understand the range of mechanisms by which yeasts can respond to anti-fungals, we compared gene expression patterns across three evolutionarily distant species- Saccharomyces cerevisiae, Candida glabrata and Kluyveromyces lactis- over time following fluconazole exposure. Results: Conserved and diverged expression patterns were identified using a novel soft clustering algorithm that concurrently clusters data from all species while incorporating sequence orthology. The analysis suggests complementary strategies for coping with ergosterol depletion by azoles- Saccharomyces imports exogenous ergosterol, Candida exports fluconazole, while Kluyveromyces does neither, leading to extreme sensitivity. In support of this hypothesis we find that only Saccharomyces becomes more azole resistant in ergosterol-supplemented media; that this depends on sterol importers Aus1 and Pdr11; and that transgenic expression of sterol importers in Kluyveromyces alleviates its drug sensitivity. Conclusions: We have compared the dynamic transcriptional responses of three diverse yeast species to fluconazole treatment using a novel clustering algorithm. This approach revealed significant divergence among regulatory programs associated with fluconazole sensitivity. In future, such approaches might be used to survey

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Evolution of transcriptional regulatory circuits in yeasts

A central challenge to post-genomic biology is to elucidate the cellular networks that underlie biological form and function. Such networks of transcriptional, post- transcriptional and post-translation regulation form an essential part of the cellular repertoire. The recent explosion of high-throughput genome-wide technologies has allowed us to begin to elucidate the structure and function of such networks. Additionally, these technologies also allow direct comparisons of networks to be made across species -- a subject that has received comparatively less attention. Simultaneous study of multiple species at appropriate evolutionary distances allows us to make more general statements about the robustness, evolvability, modularity and evolutionary redundancy of cellular networks than can otherwise be made when studying a single species in isolation. In this thesis, I describe the generation and analysis of genome- wide mRNA expression and transcription factor localization data across four diverse species of yeast separated by hundreds of millions of years of evolution. In Chapter 3, I generate genome-wide transcription factor localization data for the budding yeasts S. cerevisiae, and C. glabrata discovering a system of tightly coupled compensatory trans and cis mutations in the AP-1 transcriptional network. These compensatory mutations allow for conserved transcriptional regulation despite continued genetic change. Such systems of tightly coupled compensatory mutations might serve to counter the widespread divergence observed in transcriptional networks, and may constitute a general evolutionary mechanism maintaining the regulation of transcriptional networks. In Chapter 4, I generate genome-wide transcription factor localization data for several cell-cycle transcription factors, but in the fission yeast S. pombe. Similarly to previous studies, I find relatively poor conservation of binding between orthologous transcription factors. However, further analysis of our data along with that of previous studies suggests that transcription factors while not being particularly well conserved at the level of the binding of target genes show stronger conservation in other ways such as DNA binding motif, the functional enrichment of target genes, transcription factor expression and transcription factor activit

Evolution of transcriptional regulatory circuits in yeasts

A central challenge to post-genomic biology is to elucidate the cellular networks that underlie biological form and function. Such networks of transcriptional, post- transcriptional and post-translation regulation form an essential part of the cellular repertoire. The recent explosion of high-throughput genome-wide technologies has allowed us to begin to elucidate the structure and function of such networks. Additionally, these technologies also allow direct comparisons of networks to be made across species -- a subject that has received comparatively less attention. Simultaneous study of multiple species at appropriate evolutionary distances allows us to make more general statements about the robustness, evolvability, modularity and evolutionary redundancy of cellular networks than can otherwise be made when studying a single species in isolation. In this thesis, I describe the generation and analysis of genome- wide mRNA expression and transcription factor localization data across four diverse species of yeast separated by hundreds of millions of years of evolution. In Chapter 3, I generate genome-wide transcription factor localization data for the budding yeasts S. cerevisiae, and C. glabrata discovering a system of tightly coupled compensatory trans and cis mutations in the AP-1 transcriptional network. These compensatory mutations allow for conserved transcriptional regulation despite continued genetic change. Such systems of tightly coupled compensatory mutations might serve to counter the widespread divergence observed in transcriptional networks, and may constitute a general evolutionary mechanism maintaining the regulation of transcriptional networks. In Chapter 4, I generate genome-wide transcription factor localization data for several cell-cycle transcription factors, but in the fission yeast S. pombe. Similarly to previous studies, I find relatively poor conservation of binding between orthologous transcription factors. However, further analysis of our data along with that of previous studies suggests that transcription factors while not being particularly well conserved at the level of the binding of target genes show stronger conservation in other ways such as DNA binding motif, the functional enrichment of target genes, transcription factor expression and transcription factor activit

Integrating Behavioral and Neural Data in a Model of Zebrafish Network Interaction

The spinal neural networks of larval zebrafish generate a variety of movements such as escape, struggling, and swimming. Various mechanisms at the neural and network levels have been proposed to account for switches between these behaviors

Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence

Topological measures of large-scale complex networks are applied to a specific artificial regulatory network model created through a whole genome duplication and divergence mechanism. This class of networks share topological features with natural transcriptional regulatory networks. Specifically, these networks display scale-free and small-world topology and possess subgraph distributions similar to those of natural networks. Thus, the topologies inherent in natural networks may be in part due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks is also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model. (c) 2006 Elsevier Ireland Ltd. All rights reserved