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Phenotypic heterogeneity at the cellular level in response to various stresses, e.g., antibiotic treatment has been reported for a number of bacteria. In a clonal population, cell-to-cell variation may result in phenotypic heterogeneity that is a mechanism to survive changing environments including antibiotic therapy. Stenotrophomonas rnaltophilia has been frequently isolated from cystic fibrosis patients, can cause numerous infections in other organs and tissues, and is difficult to treat due to antibiotic resistances. S. maltophilia K279a produces the Li and L2 beta-lactamases in response to beta-lactam treatment. Here we report that the patient isolate S. rnaltophilia K279a diverges into cellular subpopulations with distinct but reversible morphotypes of small and big colonies when challenged with ampicillin. This observation is consistent with the formation of elongated chains of bacteria during exponential growth phase and the occurrence of mainly rod-shaped cells in liquid media. RNA-seq analysis of small versus big colonies revealed differential regulation of at least seven genes among the colony morphotypes. Among those, bleu and bla(L2) were transcriptionally the most strongly upregulated genes. Promoter fusions of b/a(L1) and b/a(L2) genes indicated that expression of both genes is also subject to high levels of phenotypic heterogeneous expression on a single cell level. Additionally, the comE homolog was found to be differentially expressed in homogenously versus heterogeneously bla(L2) expressing cells as identified by RNA(seq) analysis. Overexpression of cornE in S. maltophilia K279a reduced the level of cells that were in a bla(L2)-ON mode to 1% or lower. Taken together, our data provide strong evidence that S. maltophilia K279a populations develop phenotypic heterogeneity in an ampicillin challenged model. This cellular variability is triggered by regulation networks including b/a(L1), b/a(L2), and comE.
The topic of my doctorial research was the computational analysis of metagenomic data. A metagenome comprises the genomic information from all the microorganisms within a certain environment. The currently available metagenomic data sets cover only parts of these usually huge metagenomes due to the high technical and financial effort of such sequencing endeavors. During my thesis I developed bioinformatic tools and applied them to analyse genomic features of different metagenomic data sets and to search for enzymes of importance for biotechnology or pharmaceutical applications in those sequence collections. In these studies nine metagenomic projects (with up to 41 subsamples) were analysed. These samples originated from diverse environments like farm soil, acid mine drainage, microbial mats on whale bones, marine water, fresh water, water treatment sludges and the human gut flora. Additionally, data sets of conventionally retrieved sequence data were taken into account and compared with each other
Background
Differential RNA-sequencing (dRNA-seq) is indispensable for determination of primary transcriptomes. However, using dRNA-seq data to map transcriptional start sites (TSSs) and promoters genome-wide is a bioinformatics challenge. We performed dRNA-seq of Bradyrhizobium japonicum USDA 110, the nitrogen-fixing symbiont of soybean, and developed algorithms to map TSSs and promoters.
Results
A specialized machine learning procedure for TSS recognition allowed us to map 15,923 TSSs: 14,360 in free-living bacteria, 4329 in symbiosis with soybean and 2766 in both conditions. Further, we provide proteomic evidence for 4090 proteins, among them 107 proteins corresponding to new genes and 178 proteins with N-termini different from the existing annotation (72 and 109 of them with TSS support, respectively). Guided by proteomics evidence, previously identified TSSs and TSSs experimentally validated here, we assign a score threshold to flag 14 % of the mapped TSSs as a class of lower confidence. However, this class of lower confidence contains valid TSSs of low-abundant transcripts. Moreover, we developed a de novo algorithm to identify promoter motifs upstream of mapped TSSs, which is publicly available, and found motifs mainly used in symbiosis (similar to RpoN-dependent promoters) or under both conditions (similar to RpoD-dependent promoters). Mapped TSSs and putative promoters, proteomic evidence and updated gene annotation were combined into an annotation file.
Conclusions
The genome-wide TSS and promoter maps along with the extended genome annotation of B. japonicum represent a valuable resource for future systems biology studies and for detailed analyses of individual non-coding transcripts and ORFs. Our data will also provide new insights into bacterial gene regulation during the agriculturally important symbiosis between rhizobia and legumes.