Independent Bottlenecks Characterize Colonization of Systemic Compartments and Gut Lymphoid Tissue by <i>Salmonella</i>

<div><p>Vaccination represents an important instrument to control typhoid fever in humans and protects mice from lethal infection with mouse pathogenic serovars of <i>Salmonella</i> species. Mixed infections with tagged <i>Salmonella</i> can be used in combination with probabilistic models to describe the dynamics of the infection process. Here we used mixed oral infections with tagged <i>Salmonella</i> strains to identify bottlenecks in the infection process in naïve and vaccinated mice. We established a next generation sequencing based method to characterize the composition of tagged <i>Salmonella</i> strains which offers a fast and reliable method to characterise the composition of genome-tagged <i>Salmonella</i> strains. We show that initial colonization of <i>Salmonella</i> was distinguished by a non-Darwinian selection of few bacteria setting up the infection independently in gut associated lymphoid tissue and systemic compartments. Colonization of Peyer's patches fuels the sustained spread of bacteria into mesenteric lymph nodes via dendritic cells. In contrast, infection of liver and spleen originated from an independent pool of bacteria. Vaccination only moderately reduced invasion of Peyer's patches but potently uncoupled bacterial populations present in different systemic compartments. Our data indicate that vaccination differentially skews the capacity of <i>Salmonella</i> to colonize systemic and gut immune compartments and provide a framework for the further dissection of infection dynamics.</p></div

Infected organs show different levels of vaccination-mediated protection.

<p>C57BL/6 mice were orally vaccinated with attenuated SL1344Δ<i>aro</i>A <i>Salmonella</i> Typhimurium. 40–50 day later vaccinated mice and age-matched controls were orally infected with 1×10⁹ of either SL1344 or SL1344Δ<i>aro</i>A. CFU were determined after two days for single PP, mLN, liver and spleen (spl). Symbols indicate individual organs and bold lines median CFU from 9 or more mice pooled from 2 independent experiments. Numbers indicate organs displaying less than 10 CFU.</p

Individual WITS disproportionally contribute to the infection.

<p>(A) C57BL/6 mice were orally infected with a mixture of 10 individually tagged WITS. Two days after infection mice were sacrificed and cecal content, spleen, liver and mLN plated. Individual colonies were picked and sequenced. (B) Pie diagrams depict the contribution of the 10 different WITS in each compartment and numbers indicate the number of colonies analysed. WITS clone 5 has intentionally been excluded because this clone did not grow well in this particular experiment. The five mice depicted were infected in two independent experiments (first experiment Ia, Ib, Ic and second experiment IIa and IIb). For the characterization of the WITS composition, we selected five out of eight mice infected in two independent experiments, which showed colonisation levels fairly close to the median CFU of the entire group. CFU for the five mice included in this figure were as follows (median (minimum - maximum)): spleen: 104 (42–1080); liver: 305 (110–635) and mLN: 3720 (1880–5260). All WITS appeared fairly evenly represented when all 766 sequences were pooled and all 10 WITS were occasionally absent from individual compartments analysed.</p

WITS similarity reflects routes of <i>S.</i> Typhimurium dissemination.

<p>Mice were treated as described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004270#ppat-1004270-g003" target="_blank">Fig. 3A</a>. (A) The frequency of individual WITS in proximal and distal PP (left panel) or middle and distal PP (right panel) was compared. Numbers indicate Pearson correlation coefficients. Symbols represent individual PP pooled from 3 independent experiments. (B) Similarity of WITS representation was calculated as Morisita-Horn index. Lines indicate pairwise comparisons. Numbers indicate the MHI±SD. Line thickness/style depicts average MHI as indicated. Pearson correlation coefficients are higher comparing middle to distal compared to proximal to middle PP. Still, no significance was detected using 2-way ANOVA.</p

Vaccination modulates routes of <i>S.</i> Typhimurium dissemination.

<p>Mice were vaccinated with a SL1344Δ<i>aroA</i> strain 40–50 days or left untreated before infection with the WITS library. WITS composition was determined as described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004270#ppat-1004270-g003" target="_blank">Fig. 3A</a>. (A) Pie diagrams depict representative examples of WITS composition in pooled PP, mLN, liver and spleen of one non-vaccinated and one vaccinated mouse. (B) Similarity of WITS composition was compared between various compartments. Numbers indicate MHI for the various comparisons and line width/style depicts MHI as indicated in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004270#ppat-1004270-g004" target="_blank">Fig. 4B</a>. 2-way ANOVA reveals that the effect of vaccination on the similarity between compartments (determined by MHI) is significant (p<0.001). Mann-Whitney t test revealed significant effects on distinct compartments. Vaccination significantly affected similarity of the WITS composition between PP and mLN (p = 0.016), mLN and liver (p = 0.026), mLN and spleen (p = 0.016) and liver and spleen (p = 0.004). Vaccination had no significant effect on the similarity in WITS composition observed for the comparison of PP and liver or PP and spleen. (C) Vaccination only moderately reduced invasion of PP. Symbols indicate TSU predicted for individual PP, mLN, liver and spleen of non-vaccinated and vaccinated mice pooled from 3 or more independent experiments. Horizontal bars indicate the mean.</p

Additional file 1: Figure S1. of The potential of circulating tumor DNA methylation analysis for the early detection and management of ovarian cancer

Design of the nested case-control study based on the UKCTOCS Cohort. Figure S2. DMR discovery with Illumina 450 K methylation arrays. Figure S3. Pattern counts for informative regions. Figure S4. Pattern frequencies for the different regions analyzed in serum set 1 samples. Figure S5. Pattern frequencies for the different regions analyzed in serum set 2 samples. Figure S6. DNA methylation for regions #144, #204, and #228 according to OC stages. Figure S7. Coverage (number of reads) for the three different regions analyzed in serum set 3 samples. Figure S8. CA125 levels measured in NACT serum set samples. Figure S9. Pattern frequencies for the top three reactions measured in NACT serum set samples. Figure S10. Coverage (number of reads) for the top three reactions measured in NACT serum set samples. Figure S11. Average DNA amount extracted correlates with average UK temperature. Figure S12 The fraction (%) of small fragment (50–250 bp) DNA in the serum DNA preparation for 171 UKCTOCS samples analyzed in the study. Figure S13. Box plots comparing the average beta values for 450 k array probes within regions #204 and #228 between each normal (N), cancer (C) group, and white blood cell (WBC) data for OC and other 19 TCGA cancer types. (DOCX 3024 kb

Additional file 1: Figure S1. of Methylation patterns in serum DNA for early identification of disseminated breast cancer

Samples from the SUCCESS trial analyzed within this study. Figure S2. Samples from the UKCTOCS cohort analyzed within this study (nested case/control setting). Figure S3. Absolute pattern counts for all patterns detected in the region of marker EFC#93 in Serum Set 1 samples. Figure S4. Pattern frequency of EFC#93 serum DNAme in two prospectively independently collected cohorts. Figure S5. DNA amount per mL serum in the prospectively collected serum (Set 1 and 2), SUCCESS cohort, and UKCTOCS cohort. Figure S6. Pattern frequency for EFC#93 measured in SUCCESS serum set samples from women with no, 1–4 or ≥ 5 CTCs in the matched blood sample before (A) or after (B) chemotherapy. Figure S7. Impact of the presence (+ve, ≥ 1 cancer cell in blood sample) or absence (-ve) of CTCs on patient outcome. Figure S8. Impact of the presence (+ve, EFC#93 pattern frequency ≥ 0.00008) or absence (-ve) of serum DNA methylation in CTC + ve (≥1 cancer cell in pre-chemotherapy blood sample) or absence CTC-ve patients. Figure S9. Relapse-free and overall survival according to samples taken after chemotherapy. Figure S10. Relapse-free and overall survival according to samples taken after chemotherapy. Figure S11. Average serum DNA amount correlates with average UK temperature. Figure S12. Average serum DNA fragment size correlates with average UK temperature. Figure S13. Correlation of DNA fragment size and DNA amount. Figure S14. Overall survival of women whose samples were taken before and after chemotherapy and before anti-hormonal treatment in hormone receptor-negative and -positive SUCCESS participants. Figure S15. Overall survival of women whose samples were taken before and after chemotherapy and before anti-hormonal treatment in hormone receptor-negative and -positive SUCCESS participants. (PDF 2123 kb

Additional file 3: Table S1. of Methylation patterns in serum DNA for early identification of disseminated breast cancer

Coordinates and primers used to amplify the identified target regions using bisulfite sequencing. (PDF 578 kb