The bacterial cell wall (CW) is an essential protective barrier and the frontline of cellular interactions with the environment and also a target for numerous antimicrobial agents. Accordingly, its integrity and homeostasis are closely monitored and rapid adaptive responses by transcriptional reprogramming induce appropriate counter-measures against perturbations. Here, we report a comprehensive and comparative transcriptional profiling of the primary cell envelope stress responses (CESR), based on combining RNAseq and high-resolution tiling array studies of the Gram-positive model bacterium Bacillus subtilis exposed to a range of antimicrobial compounds that interfere with cytoplasmic, membrane-coupled or extracellular steps of peptidoglycan (PG) biosynthesis. It revealed the complexity of the CESR of B. subtilis and unraveled the contribution of extracytoplasmic function sigma factors (ECFs) and two-component signal transduction systems (TCSs) to protect the cell envelope. While membrane-anchored steps are tightly controlled, early cytoplasmic and late extracellular steps of PG biosynthesis are hardly monitored at all. The ECF σ factors σ W and particularly σ M provide a general CESR, while σ V is almost exclusively induced by lysozyme, against which it provides specific resistance. Remarkably, σ X was slightly repressed by most antibiotics, pointing towards a role in envelope homeostasis rather than CESR. It shares this role with the WalRK TCS, which balances CW growth with controlled autolysis. In contrast, all remaining TCSs are envelope stress-inducible systems. LiaRS is induced by a wide range of PG synthesis inhibitors, while the three paralogous systems BceAB, PsdRS and ApeRS are more compound-specific detoxification modules. Induction of the CssRS TCS by all antibiotics interfering with membrane-anchored steps of PG biosynthesis points towards a physiological link between CESR and secretion stress. Based on the expression signatures, a suite of CESR-specific B. subtilis whole cell biosensors were developed and carefully evaluated. This is the first comprehensive transcriptomic study focusing exclusively on the primary effects of envelope perturbances that shall provide a reference point for future studies on Gram-positive CESR.

Extracytoplasmic function σ factors (ECFs) represent one of the major bacterial signal transduction mechanisms in terms of abundance, diversity and importance, particularly in mediating stress responses. Here, we performed a comprehensive phylogenetic analysis of this protein family by scrutinizing all proteins in the NCBI database. As result, we identified ∼10 ECFs per bacterial genome on average and classified them into 157 phylogenetic ECF groups that feature a conserved genetic neighborhood and a similar regulation mechanism. Our analysis expands the number of unique ECF sequences ∼50-fold relative to previous classification efforts, enriches many original ECF groups with previously unclassified proteins and identifies 22 entirely new ECF groups. The ECF groups are hierarchically related to each other and are further composed of subgroups with closely related sequences. This two-tiered classification allows for the accurate prediction of common promoter motifs and the inference of putative regulatory mechanisms across subgroups composing an ECF group. This comprehensive, high-resolution description of the phylogenetic distribution of the ECF family, together with the massive expansion of classified ECF sequences, enables the application of in silico tools for the prediction of important functional residues, and serves as a powerful hypothesis-generator to guide future research in the field.

Owing greatly to the advancement of next-generation sequencing (NGS), the amount of NGS data is increasing rapidly. Although there are many NGS applications, one of the most commonly used techniques 'RNA sequencing (RNA-seq)' is rapidly replacing microarray-based techniques in laboratories around the world. As more and more of such techniques are standardized, allowing technicians to perform these experiments with minimal hands-on time and reduced experimental/operator-dependent biases, the bottleneck of such techniques is clearly visible; that is, data analysis. Further complicating the matter, increasing evidence suggests most of the genome is transcribed into RNA; however, the majority of these RNAs are not translated into proteins. These RNAs that do not become proteins are called 'noncoding RNAs (ncRNAs)'. Although some time has passed since the discovery of ncRNAs, their annotations remain poor, making analysis of RNA-seq data challenging. Here, we examine the current limitations of RNA-seq analysis using case studies focused on the detection of novel transcripts and examination of their characteristics. Finally, we validate the presence of novel transcripts using biological experiments, showing novel transcripts can be accurately identified when a series of filters is applied. In conclusion, novel transcripts that are identified from RNA-seq must be examined carefully before proceeding to biological experiments.