610 Medizin und Gesundheit
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Our body is colonized by a vast array of bacteria the sum of which forms our microbiota. The gut alone harbors >1,000 bacterial species. An understanding of their individual or synergistic contributions to human health and disease demands means to interfere with their functions on the species level. Most of the currently available antibiotics are broad‐spectrum, thus too unspecific for a selective depletion of a single species of interest from the microbiota. Programmable RNA antibiotics in the form of short antisense oligonucleotides (ASOs) promise to achieve precision manipulation of bacterial communities. These ASOs are coupled to small peptides that carry them inside the bacteria to silence mRNAs of essential genes, for example, to target antibiotic‐resistant pathogens as an alternative to standard antibiotics. There is already proof‐of‐principle with diverse bacteria, but many open questions remain with respect to true species specificity, potential off‐targeting, choice of peptides for delivery, bacterial resistance mechanisms and the host response. While there is unlikely a one‐fits‐all solution for all microbiome species, I will discuss how recent progress in bacterial RNA biology may help to accelerate the development of programmable RNA antibiotics for microbiome editing and other applications.
Enterococcus faecalis and faecium are two major representative clinical strains of the Enterococcus genus and are sadly notorious to be part of the top agents responsible for nosocomial infections. Despite their critical implication in worldwide public healthcare, essential and available resources such as deep transcriptome annotations remain poor, which also limits our understanding of post-transcriptional control small regulatory RNA (sRNA) functions in these bacteria. Here, using the dRNA-seq technique in combination with ANNOgesic analysis, we successfully mapped and annotated transcription start sites (TSS) of both E. faecalis V583 and E. faecium AUS0004 at single nucleotide resolution. Analyzing bacteria in late exponential phase, we capture ~40% (E. faecalis) and 43% (E. faecium) of the annotated protein-coding genes, determine 5′ and 3′ UTR (untranslated region) length, and detect instances of leaderless mRNAs. The transcriptome maps revealed sRNA candidates in both bacteria, some found in previous studies and new ones. Expression of candidate sRNAs is being confirmed under biologically relevant environmental conditions. This comprehensive global TSS mapping atlas provides a valuable resource for RNA biology and gene expression analysis in the Enterococci. It can be accessed online at www.helmholtz-hiri.de/en/datasets/enterococcus through an instance of the genomic viewer JBrowse.
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.
To understand the gene regulation of an organism of interest, a comprehensive genome annotation is essential. While some features, such as coding sequences, can be computationally predicted with high accuracy based purely on the genomic sequence, others, such as promoter elements or noncoding RNAs, are harder to detect. RNA sequencing (RNA-seq) has proven to be an efficient method to identify these genomic features and to improve genome annotations. However, processing and integrating RNA-seq data in order to generate high-resolution annotations is challenging, time consuming, and requires numerous steps. We have constructed a powerful and modular tool called ANNOgesic that provides the required analyses and simplifies RNA-seq-based bacterial and archaeal genome annotation. It can integrate data from conventional RNA-seq and differential RNA-seq and predicts and annotates numerous features, including small noncoding RNAs, with high precision. The software is available under an open source license (ISCL) at https://pypi.org/project/ANNOgesic/.
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.
Background: RNA-seq and its variant differential RNA-seq (dRNA-seq) are today routine methods for transcriptome analysis in bacteria. While expression profiling and transcriptional start site prediction are standard tasks today, the problem of identifying transcriptional units in a genome-wide fashion is still not solved for prokaryotic systems.
Results: We present RNASEG, an algorithm for the prediction of transcriptional units based on dRNA-seq data. A key feature of the algorithm is that, based on the data, it distinguishes between transcribed and un-transcribed genomic segments. Furthermore, the program provides many different predictions in a single run, which can be used to infer the significance of transcriptional units in a consensus procedure. We show the performance of our method based on a well-studied dRNA-seq data set for Helicobacter pylori.
Conclusions: With our algorithm it is possible to identify operons and 5'- and 3'-UTRs in an automated fashion. This alleviates the need for labour intensive manual inspection and enables large-scale studies in the area of comparative transcriptomics.