The uncertain consequences of transferring bacterial strains between laboratories - rpoS instability as an example

Abstract\ud \ud \ud \ud Background\ud \ud Microbiological studies frequently involve exchanges of strains between laboratories and/or stock centers. The integrity of exchanged strains is vital for archival reasons and to ensure reproducible experimental results. For at least 50 years, one of the most common means of shipping bacteria was by inoculating bacterial samples in agar stabs. Long-term cultures in stabs exhibit genetic instabilities and one common instability is in rpoS. The sigma factor RpoS accumulates in response to several stresses and in the stationary phase. One consequence of RpoS accumulation is the competition with the vegetative sigma factor σ70. Under nutrient limiting conditions mutations in rpoS or in genes that regulate its expression tend to accumulate. Here, we investigate whether short-term storage and mailing of cultures in stabs results in genetic heterogeneity.\ud \ud \ud \ud Results\ud \ud We found that samples of the E. coli K-12 strain MC4100TF exchanged on three separate occasions by mail between our laboratories became heterogeneous. Reconstruction studies indicated that LB-stabs exhibited mutations previously found in GASP studies in stationary phase LB broth. At least 40% of reconstructed stocks and an equivalent proportion of actually mailed stock contained these mutations. Mutants with low RpoS levels emerged within 7 days of incubation in the stabs. Sequence analysis of ten of these segregants revealed that they harboured each of three different rpoS mutations. These mutants displayed the classical phenotypes of bacteria lacking rpoS. The genetic stability of MC4100TF was also tested in filter disks embedded in glycerol. Under these conditions, GASP mutants emerge only after a 3-week period. We also confirm that the intrinsic high RpoS level in MC4100TF is mainly due to the presence of an IS1 insertion in rssB.\ud \ud \ud \ud Conclusions\ud \ud Given that many E. coli strains contain high RpoS levels similar to MC4100TF, the integrity of such strains during transfers and storage is questionable. Variations in important collections may be due to storage-transfer related issues. These results raise important questions on the integrity of bacterial archives and transferred strains, explain variation like in the ECOR collection between laboratories and indicate a need for the development of better methods of strain transfer.We are grateful to Fundação de Amparo á Pesquisa do Estado de São Paulo (FAPESP-Brazil), who supported this study and provided a travel allowance for TF. TF was also supported by the the Australian Research Council and the US Army Research Office. We also thank K. C. Murphy and S. Kushner for respectively providing strain KM32 and plasmid pWKS130

Bordetella pertussis Clones Identified by Multilocus Variable-Number Tandem-Repeat Analysis

Multilocus variable-number tandem-repeat analysis (MLVA) of 316 Bordetella pertussis isolates collected over 40 years from Australia and 3 other continents identified 66 MLVA types (MTs), including 6 predominant MTs. Typing of genes encoding acellular vaccine antigens showed changes that may be vaccine driven in 2 MTs prevalent in Australia

Divergence Involving Global Regulatory Gene Mutations in an Escherichia coli Population Evolving under Phosphate Limitation

Many of the important changes in evolution are regulatory in nature. Sequenced bacterial genomes point to flexibility in regulatory circuits but we do not know how regulation is remodeled in evolving bacteria. Here, we study the regulatory changes that emerge in populations evolving under controlled conditions during experimental evolution of Escherichia coli in a phosphate-limited chemostat culture. Genomes were sequenced from five clones with different combinations of phenotypic properties that coexisted in a population after 37 days. Each of the distinct isolates contained a different mutation in 1 of 3 highly pleiotropic regulatory genes (hfq, spoT, or rpoS). The mutations resulted in dissimilar proteomic changes, consistent with the documented effects of hfq, spoT, and rpoS mutations. The different mutations do share a common benefit, however, in that the mutations each redirect cellular resources away from stress responses that are redundant in a constant selection environment. The hfq mutation lowers several individual stress responses as well the small RNA–dependent activation of rpoS translation and hence general stress resistance. The spoT mutation reduces ppGpp levels, decreasing the stringent response as well as rpoS expression. The mutations in and upstream of rpoS resulted in partial or complete loss of general stress resistance. Our observations suggest that the degeneracy at the core of bacterial stress regulation provides alternative solutions to a common evolutionary challenge. These results can explain phenotypic divergence in a constant environment and also how evolutionary jumps and adaptive radiations involve altered gene regulation

Global population structure and evolution of Bordetella pertussis and their relationship with vaccination.

Bordetella pertussis causes pertussis, a respiratory disease that is most severe for infants. Vaccination was introduced in the 1950s, and in recent years, a resurgence of disease was observed worldwide, with significant mortality in infants. Possible causes for this include the switch from whole-cell vaccines (WCVs) to less effective acellular vaccines (ACVs), waning immunity, and pathogen adaptation. Pathogen adaptation is suggested by antigenic divergence between vaccine strains and circulating strains and by the emergence of strains with increased pertussis toxin production. We applied comparative genomics to a worldwide collection of 343 B. pertussis strains isolated between 1920 and 2010. The global phylogeny showed two deep branches; the largest of these contained 98% of all strains, and its expansion correlated temporally with the first descriptions of pertussis outbreaks in Europe in the 16th century. We found little evidence of recent geographical clustering of the strains within this lineage, suggesting rapid strain flow between countries. We observed that changes in genes encoding proteins implicated in protective immunity that are included in ACVs occurred after the introduction of WCVs but before the switch to ACVs. Furthermore, our analyses consistently suggested that virulence-associated genes and genes coding for surface-exposed proteins were involved in adaptation. However, many of the putative adaptive loci identified have a physiological role, and further studies of these loci may reveal less obvious ways in which B. pertussis and the host interact. This work provides insight into ways in which pathogens may adapt to vaccination and suggests ways to improve pertussis vaccines. IMPORTANCE Whooping cough is mainly caused by Bordetella pertussis, and current vaccines are targeted against this organism. Recently, there have been increasing outbreaks of whooping cough, even where vaccine coverage is high. Analysis of the genomes of 343 B. pertussis isolates from around the world over the last 100 years suggests that the organism has emerged within the last 500 years, consistent with historical records. We show that global transmission of new strains is very rapid and that the worldwide population of B. pertussis is evolving in response to vaccine introduction, potentially enabling vaccine escape

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

A shifting mutational landscape in 6 nutritional states: Stress-induced mutagenesis as a series of distinct stress input-mutation output relationships.

Environmental stresses increase genetic variation in bacteria, plants, and human cancer cells. The linkage between various environments and mutational outcomes has not been systematically investigated, however. Here, we established the influence of nutritional stresses commonly found in the biosphere (carbon, phosphate, nitrogen, oxygen, or iron limitation) on both the rate and spectrum of mutations in Escherichia coli. We found that each limitation was associated with a remarkably distinct mutational profile. Overall mutation rates were not always elevated, and nitrogen, iron, and oxygen limitation resulted in major spectral changes but no net increase in rate. Our results thus suggest that stress-induced mutagenesis is a diverse series of stress input-mutation output linkages that is distinct in every condition. Environment-specific spectra resulted in the differential emergence of traits needing particular mutations in these settings. Mutations requiring transpositions were highest under iron and oxygen limitation, whereas base-pair substitutions and indels were highest under phosphate limitation. The unexpected diversity of input-output effects explains some important phenomena in the mutational biases of evolving genomes. The prevalence of bacterial insertion sequence transpositions in the mammalian gut or in anaerobically stored cultures is due to environmentally determined mutation availability. Likewise, the much-discussed genomic bias towards transition base substitutions in evolving genomes can now be explained as an environment-specific output. Altogether, our conclusion is that environments influence genetic variation as well as selection

The effect of 6 nutritional states on base substitution patterns in <i>lacZ</i>.

<p>Mutation rates for each of 6 different possible base pairs, AT→GC, GC→AT, GC→TA, GC→CG, AT→TA, and AT→CG were assayed by using the tester <i>E</i>. <i>coli</i> strains CC106, CC102, CC104, CC103, CC105, and CC101, respectively. The transition to transversion (Ti/Tv) substitution ratios amongst all base-pair substitution (BPS) changes in the 6 environments are shown in the right panel. The environments and axis labels are as defined in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.g002" target="_blank">Fig 2</a>. Box-and-whisker plots are shown, in which whiskers represent minimum and maximum values, the box represents top 75 and bottom 25 percentiles, and the horizontal line represents median value. Two-tailed <i>t</i> test <i>P</i> values were based on assuming 2-sample unequal variance. In plots, * represents <i>P</i> < 0.05; ** represents <i>P</i> < 0.01, and *** represents <i>P</i> < 0.001. ns, not significant. The numerical data for all parts of the figure are given in supplementary file <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.s002" target="_blank">S1 Data</a>.</p

Mutation rates in <i>Escherichia coli</i> obtained by different laboratories and methods.

<p>Mutation rates in <i>Escherichia coli</i> obtained by different laboratories and methods.</p

The distribution of fitness effects and mutation locations in <i>cycA</i>.

<p>(a) The fitness effect of cycloserine resistance (Cyc^R) mutations are shown for examples of the 4 mutation classes: base-pair substitution (BPS, <i>cycA</i> G→T at position 298), insertion sequence (IS, <i>cycA</i> IS<i>150</i> at 848), deletion and insertion indels >1bp (LI, 19 base-pair deletion at 918), and single base pair indel (SI, -1G at 226), relative to the Cyc^S ancestor. Error bars are standard deviations from at least 2 replicate experiments. (b) The position of mutations in <i>cycA</i> in Cyc^R colonies. The plot includes sequence changes in 1,399 Cyc^R mutants, 228 from Un (nutrient-unlimited), 249 from iron (Fe)-limited, 234 from oxygen (O)-limited, 240 from nitrogen (N)-limited, 245 from phosphate (P)-limited, and 203 from carbon (C)- or glucose-limited cultures. (c) The location of large insertion and deletion mutations in <i>cycA</i>. Positions of deleted or inserted nucleotides are based on the sequence of <i>cycA</i> of wild-type <i>E</i>. <i>coli</i> MC4100 used in this study. Bold-typed nucleotides are short repeat sequences that we suspect promote insertion or deletion mutations. LDR1, a deletion of 12 bp at base positions 96–108 of <i>cycA</i>; LIR1, an insertion of 12 bp at base positions 96–108 of <i>cycA</i>. LDR2, a deletion of 18 bp in the 918–948 region of <i>cycA</i>; an insertion of 18 bp in the 918–948 region of <i>cycA</i>. Because there are only a few insertion mutations at region 1, LIR1 is combined with other deletion and insertion indels > 1bp (Other-LIs), which occurred across <i>cycA</i> as shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.g004" target="_blank">Fig 4</a> in the main text. Locations of Other-LIs are not shown here but can be found in Supplementary material, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.s001" target="_blank">S1 Table</a>. (d) Rates of the 4 major classes of mutations (BPS, SI, LI, and IS transpositions) in 6 to 8 replicate cultures. Individual points and statistics of measured mutation rates in replicate cultures are shown for each class. The plots and statistics are presented as described in (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.g002" target="_blank">Fig 2A</a>). The numerical data for all parts of the figure are given in supplementary file <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.s002" target="_blank">S1 Data</a>.</p

The mutational landscape in 6 nutritional states.

<p>(A) The landscape is based on the mean mutation rates of the 16 different types of mutation estimated in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.g004" target="_blank">Fig 4A–4D</a> plus 2 composite rates (other insertion sequences [Other-ISs] and other deletion and insertion indels > 1bp [Other-LIs]) in the 6 nutritional states. In (B), the relationship of mutational patterns is related by the Unweighted Pair-Group Method with Arithmetic Mean (UPGMA [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001477#pbio.2001477.ref044" target="_blank">44</a>]). The bootstrap values were obtained from 1,000 replicate analyses.</p