The majority of cells transferred from stationary-phase culture into fresh medium resume growth quickly, while a few remain in a nongrowing state for longer. These temporarily nonproliferating bacteria are tolerant of several bactericidal antibiotics and constitute a main source of persisters. Several genes have been shown to influence the frequency of persisters in Escherichia coli, although the exact mechanism underlying persister formation is unknown. This study demonstrates that the frequency of persisters is highly dependent on the age of the inoculum and the medium in which it has been grown. The hipA7 mutant had 1,000 times more persisters than the wild type when inocula were sampled from younger stationary-phase cultures. When started after a long stationary phase, the two displayed equal and elevated persister frequencies. The lower persister frequencies of glpD, dnaJ, and surA knockout strains were increased to the level of the wild type when inocula aged. The mqsR and phoU deletions showed decreased persister levels only when the inocula were from aged cultures, while sucB and ygfA deletions had decreased persister levels irrespective of the age of the inocula. A dependency on culture conditions underlines the notion that during screening for mutants with altered persister frequencies, the exact experimental details are of great importance. Unlike ampicillin and norfloxacin, which always leave a fraction of bacteria alive, amikacin killed all cells in the growth resumption experiment. It was concluded that the frequency of persisters depends on the conditions of inoculum cultivation, particularly its age, and the choice of antibiotic.Genetically homogeneous bacterial cultures can give rise to subpopulations with different physiological properties (38). This kind of heterogeneity can be demonstrated in terms of growth resumption when stationary-phase bacteria are diluted in fresh medium: some cells start growth immediately, some later, and some do not recover during the period of observation (2, 15, 33). Variation in recovery could reflect the random nature of cell damage (8). Alternatively, as rapidly recovering bacteria are more vulnerable to harmful environmental conditions than dormant ones, the wide range of growth resumption times could represent an ecological strategy (11,21,39). For example, the majority of bactericidal antibiotics kill growing bacteria, and nongrowing cells survive (41). Furthermore, antibiotic-sensitive growing cultures cannot be sterilized by bactericidal drugs, suggesting the existence of a small subpopulation of nongrowing bacteria (4). These bacteria, called persisters, can survive antibiotic treatment and resume growth after removal of the drug (23).Several mutations lead to increased or decreased persister levels (9,10,13,19,24,25,27,40). A classical mutant with increased persister frequency is the hipA7 strain (27), which carries mutations in the gene coding for the toxin in the HipAB toxin-antitoxin pair (21). This mutation decreases the affinity of the toxin for anti...

Background: A fundamental characteristic of cells is the ability to divide. To date, most parameters of bacterial cultures, including cell division, have been measured as cell population averages, assuming that all bacteria divide at a uniform rate.

In the absence of extensive transcription control mechanisms the pathogenic parasite Trypanosoma brucei crucially depends on translation regulation to orchestrate gene expression. However, molecular insight into regulating protein biosynthesis is sparse. Here we analyze the small non-coding RNA (ncRNA) interactome of ribosomes in T. brucei during different growth conditions and life stages. Ribosome-associated ncRNAs have recently been recognized as unprecedented regulators of ribosome functions. Our data show that the tRNAThr 3´half is produced during nutrient deprivation and becomes one of the most abundant tRNA-derived RNA fragments (tdRs). tRNAThr halves associate with ribosomes and polysomes and stimulate translation by facilitating mRNA loading during stress recovery once starvation conditions ceased. Blocking or depleting the endogenous tRNAThr halves mitigates this stimulatory effect both in vivo and in vitro. T. brucei and its close relatives lack the well-described mammalian enzymes for tRNA half processing, thus hinting at a unique tdR biogenesis in these parasites.

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Almost two-thirds of the microbiome’s biomass has been predicted to be in a non-proliferating, and thus dormant, growth state. It is assumed that dormancy goes hand in hand with global downregulation of gene expression. However, it remains largely unknown how bacteria manage to establish this resting phenotype at the molecular level. Recently small non-protein-coding RNAs (sRNAs or ncRNAs) have been suggested to be involved in establishing the non-proliferating state in bacteria. Here, we have deep sequenced the small transcriptome of Escherichia coli in the exponential and stationary phases and analyzed the resulting reads by a novel biocomputational pipeline STARPA (Stable RNA Processing Product Analyzer). Our analysis reveals over 12,000 small transcripts enriched during both growth stages. Differential expression analysis reveals distinct sRNAs enriched in the stationary phase that originate from various genomic regions, including transfer RNA (tRNA) fragments. Furthermore, expression profiling by Northern blot and RT-qPCR analyses confirms the growth phase-dependent expression of several enriched sRNAs. Our study adds to the existing repertoire of bacterial sRNAs and suggests a role for some of these small molecules in establishing and maintaining stationary phase as well as the bacterial stress response. Functional characterization of these detected sRNAs has the potential of unraveling novel regulatory networks central for stationary phase biology.

Stationary-phase bacterial cells are characterized by vastly reduced metabolic activities yielding a dormant-like phenotype. Several hibernation programs ensure the establishment and maintenance of this resting growth state. Some of the stationary phase-specific modulations affect the ribosome and its translational activity directly. In stationary-phase Escherichia coli, we observed the appearance of a 16S rRNA fragmentation event at the tip of helix 6 within the small ribosomal subunit (30S). Stationary-phase 30S subunits showed markedly reduced activities in protein biosynthesis. On the other hand, the functional performance of stationary-phase large ribosomal subunits (50S) was indistinguishable from particles isolated from exponentially growing cells. Introduction of the 16S rRNA cut in vitro at helix 6 of exponential phase 30S subunits renders them less efficient in protein biosynthesis. This indicates that the helix 6 fragmentation is necessary and sufficient to attenuate translational activities of 30S ribosomal subunits. These results suggest that stationary phase-specific cleavage of 16S rRNA within the 30S subunit is an efficient means to reduce global translation activities under non-proliferating growth conditions.

We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.

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