Soluciones de Agujeros Negros

5.1 Abstract

This chapter studies the mechanisms behind reduced biofilm formation and increased c-di- GMP levels, using a network based approach to analyse transcriptome data. RNAseq data are verified with the use of GFP-promoter fusions and RT-qPCR. We found that the increase in c-di-GMP levels are probably driven by an increase in purine biosynthesis. Furthermore, biofilm inhibition occurs via inhibition of csgD transcription, possibly through the action of Fis and RpoS.

5.2 Introduction

As described in chapter 1, Van Puvelde (2014) showed that Salmonella SL1344 biofilm development in nutrient-poor liquid media could be divided into three distinct phases: (i) The planktonic pre-switch phase, (ii) the interphase-switch and (ii) post-switch biofilm growth. In this thesis it was reported that the same pattern was observed for strain wild type strain ATCC14028, and that a pyrimidine starved ΔcarA mutant does not undergo such a switch. Transcriptome analysis by Van Puyvelde indicated that core biofilm genes (such as csgDCBA and bapABCD) were already activated in the planktonic phase. In some cases, there was a time-shift in the activation/repression of genes when comparing biofilm and planktonic samples at the time points 7, 8, 9 and 10 hours (the pre-switch and switch timepoints). In other words, biofilm cells had gene expression levels at a certain time-point that were comparable to expression levels in planktonic cells at a later time-point. Van Puyvelde concluded that genes which were differentially expressed in this fashion might be necessary for sub- populations of planktonic cells to switch to biofilm mode (in other words, some planktonic cells switch to a biofilm lifestyle before others, and this switch happens after the genes’ expression levels change). The set of regulatory genes that were predicted to play a role in this switch consist of PrpR, PhoB, BaeR, KdgR RpoE, OmrA and OmrB (Van Puyvelde, 2014). What is striking is that some of these genes are part of the outer membrane (OM) stress systems. OM remodelling and biofilm formation are related processes, due to the role that the cell surface plays in biofilm formation. Many of the biofilm components, such as curli,

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fimbriae and lipopolysaccharides are associated with the OM. Several components involved in environmental signalling are also shared between the OM and biofilm regulatory pathways. Thus, one can conclude that the aforementioned regulators probably play a role in changing the OM in a way that enables bacteria to attach to the surface and produce a biofilm matrix. During the post-switch phase, pathways involved in oxidative stress, carbohydrate metabolism, starvation-related systems, virulence genes and iron-sulphur cluster assembly are activated (Van Puyvelde, 2014).

Based on our observations that pyrimidine starved cells have impaired biofilm formation, we were interested in determining how pyrimidine starvation prevents the switch to biofilm-based growth. Although a number of insights were obtained in the previous chapter, some very important questions remained unanswered: (i) Why are c-di-GMP levels increased in the ΔcarA mutant supplemented with low levels of uracil (17.5 µM)? (ii) By which mechanism is biofilm formation down regulated? (iii) What are the other effects of pyrimidine starvation Thus, to get a global overview of the transcriptional differences between the pyrimidine starved cells compared to the wild type planktonic cells that are preparing to enter the biofilm- mode, we sampled RNA 10 hours past inoculation when planktonic phase cells switch to biofilm based growth. Further validations were done using GFP-promoter fusions, RT-qPCR and mutant studies.

5.3 Results

In order to study the transcriptional changes that affect the switch from free-living to biofilm growth in the wild type and pyrimidine starved ΔcarA mtant, RNA samples were taken from the planktonic phase at the time of the switch between lifestyles. The optical densities of the planktonic phases were determined and the biofilms on the surface of the plates were coloured and measured (to verify that the pyrimidine starved mutant did not make biofilm and that it does not have a growth defect). As expected, the pyrimidine-starved samples exhibited a severely reduced biofilm phenotype and slightly increased planktonic culture densities. RNA sequencing of the planktonic samples was performed by BGI. Reads were mapped to the S. Typhimurium LT2 genome and gene expression levels were quantified using Rockhopper (McClure et al., 2013). More than 96% of the reads coming from each sample aligned to the S. Typhimurium LT2 chromosome and approximately 1% of the reads aligned to the LT2 plasmid (Supp. Table 3, Supp. Table 4) . We defined genes that are significantly up/down-regulated as those with a log2fold change of 1 or -1 (expression levels are 2x

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these selection criteria, 849 genes are significantly differentially regulated (roughly 20% of the genome) with 450 being up-regulated and 399 being down-regulated. The LT2 genome was used as a reference for assembly because subsequent analyses were done on a publicly available LT2 interactions network. As a control the ATCC14028 genome was also used as a reference: assembly results and gene expression predictions were similar. Genes with very high differential expression (absolute log2 fold change >2 and low q-values) are listed in

(Supp. Table 5).

The expression data were analysed using a network-based approach. The networks that were generated by PheNetic (DeMaeyer et al., 2013) were vizualised in Cytoscape (Shannon et al., 2003). PheNetic predicts the most probable interaction and regulatory networks that lead to the observed transcriptional changes. For analysis, a reduced gene list (size 427) was submitted to Phenetic after increasing the log2fold cutoff to 1.5. Due to the way in which the algorithm

works, genes that do not appear in the gene list but that are part of the connection path between other genes in the list are also added as nodes in the network. Thus, important interactions involving genes with smaller absolute log2 fold changes were also visible. To identify

downstream pathways that are triggered by differentially repressed/ activated in the pyrimidine starved strain, PheNetic was run in “Downstream” mode. The network produced by the “Downstream mode” mostly contains genes with similar functionalities or that are involved in the same pathways and are together differentially up or down regulated. From these results, it was possible to identify affected pathways which include, purine metabolism, pyrimidine metabolism, curli biosynthesis, chemotaxis, invasion, the TCA-cycle and lipopolysaccharide biosynthesis amongst others.

In order to identify and prioritize the regulatory mechanisms potentiating the observed differential expression of genes in the pyrimidine starved ΔcarA strain, PheNetic was run in “Upstream mode” to identify the regulatory mechanisms behind the global transcription profile. Some of the most interesting/relevant pathways and their regulatory mechanisms are discussed in the rest of this Chapter. Figure 33 presents a global view of both networks produced with PheNetic.

In the following sections, we will first discuss the effects that pyrimidine starvation has on the highest hierarchical levels of the gene regulatory networks and the processes that are affected, and then we will shed more light on the mechanisms behind the increase in c-di-GMP and biofilm inhibition.

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Figure 33 Global overviews of the upstream and downstream networks predicted by Phenetic.

Networks were visualized in Cytoscape. The upstream network shows regulator pathways that can explain the observed changes in transcription. The size of a node correlates to the amount of targets connected to that particular node. The downstream network shows pathways that are activated or deactivated because of changes in transcription. Genes that are down-regulated are shown in blue and genes that are upregulated are shown in yellow.

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