(p)ppGpp-mediated stress response induced by defects in outer membrane biogenesis and ATP production promotes survival in <i>Escherichia coli</i>

Abstract Cellular growth requires a high level of coordination to ensure that all processes run in concert. The role of the nucleotide alarmone (p)ppGpp has been extensively studied in response to external stresses, such as amino acid starvation, in Escherichia coli, but much less is known about the involvement of (p)ppGpp in response to perturbations in intracellular processes. We therefore employed CRISPRi to transcriptionally repress essential genes involved in 14 vital processes and investigated whether a (p)ppGpp-mediated response would be induced. We show that (p)ppGpp is produced and required for a pertinent stress response during interference with outer membrane biogenesis and ADP synthesis specifically. When these processes were perturbed via the transcriptional repression of essential genes, wild type E. coli MG1655 ceased growing and entered a semi-dormant state, whereas isogenic (p)ppGpp0 cells continued to grow uncontrollably to the point of lysis. Furthermore, in vivo measurements revealed that the ATP levels were intrinsically offset in (p)ppGpp0 cells, further indicating a role for the alarmone in cellular energy homeostasis. In summary, our investigation suggests that (p)ppGpp acts as a coordinator of cell growth in response to imbalances in outer membrane biogenesis and adenosine ribonucleotide synthesis, elucidating novel roles for (p)ppGpp in bacterial physiology

Stochastic induction of persister cells by HipA through (p)ppGpp-mediated activation of mRNA endonucleases

The model organism Escherichia coli codes for at least 11 type II toxin–antitoxin (TA) modules, all implicated in bacterial persistence (multidrug tolerance). Ten of these encode messenger RNA endonucleases (mRNases) inhibiting translation by catalytic degradation of mRNA, and the 11th module, hipBA, encodes HipA (high persister protein A) kinase, which inhibits glutamyl tRNA synthetase (GltX). In turn, inhibition of GltX inhibits translation and induces the stringent response and persistence. Previously, we presented strong support for a model proposing (p)ppGpp (guanosine tetra and penta-phosphate) as the master regulator of persistence. Stochastic variation of [(p)ppGpp] in single cells induced TA-encoded mRNases via a pathway involving polyphosphate and Lon protease. Polyphosphate activated Lon to degrade all known type II antitoxins of E. coli. In turn, the activated mRNases induced persistence and multidrug tolerance. However, even though it was known that activation of HipA stimulated (p)ppGpp synthesis, our model did not explain how hipBA induced persistence. Here we show that, in support of and consistent with our initial model, HipA-induced persistence depends not only on (p)ppGpp but also on the 10 mRNase-encoding TA modules, Lon protease, and polyphosphate. Importantly, observations with single cells convincingly show that the high level of (p)ppGpp caused by activation of HipA does not induce persistence in the absence of TA-encoded mRNases. Thus, slow growth per se does not induce persistence in the absence of TA-encoded toxins, placing these genes as central effectors of bacterial persistence

Intramolecular Interactions Dominate the Autoregulation of Escherichia coli Stringent Factor RelA

Amino acid starvation in Escherichia coli activates the enzymatic activity of the stringent factor RelA, leading to accumulation of the alarmone nucleotide (p)ppGpp. The alarmone acts as an intercellular messenger to regulate transcription, translation and metabolism to mediate bacterial stress adaptation. The enzymatic activity of RelA is subject to multi-layered allosteric control executed both by ligands - such as "starved" ribosomal complexes, deacylated tRNA and pppGpp - and by individual RelA domains. The auto-regulation of RelA is proposed to act either in cis (inhibition of the enzymatic activity of the N-terminal region, NTD, by regulatory C-terminal region, CTD) or in trans (CTD-mediated dimerization leading to enzyme inhibition). In this report, we probed the regulatory roles of the individual domains of E. coli RelA and our results are not indicative of RelA dimerization being the key regulatory mechanism. First, at growth-permitting levels, ectopic expression of RelA CTD does not interfere with activation of native ReIA, indicating lack of regulation via inhibitory complex formation in the cell. Second, in our biochemical assays, increasing RelA concentration does not decrease the enzyme activity, as would be expected in the case of efficient auto-inhibition via dimerization. Third, while high-level CTD expression efficiently inhibits the growth, the effect is independent of native RelA and is mediated by direct inhibition of protein synthesis, likely via direct interaction with the ribosomal A-site. Finally, deletion of the RRM domain of the CTD region leads to growth inhibition mediated by accumulation of (p)ppGpp, suggesting de-regulation of the synthetic activity in this mutant

Bayesian inference in nonlinear univariate time series: Investigation of GSTUR and SB models.

In the literature, many statistical models have been used to investigate the existence of a deterministic time trend, changing persistence and nonlinearity in macroeconomic and financial data. Good understanding of these properties in a univariate time series model is crucial when making forecasts. Forecasts are used in various ways, such as helping to control risks in financial institutions and to assist in setting monetary policies in central banks. Hence, evaluating the forecast capacities of statistical models, quantifying and reducing forecast uncertainties are the main concerns of forecast practitioners. In this thesis, we propose two flexible parametric models that allow for autoregressive parameters to be time varying. One is a novel Generalised Stochastic Unit Root (GSTUR) model and the other is a Stationary Bilinear (SR) model. Bayesian inference in these two models are developed using methods on the frontier of numerical analysis. Programs, including model estimation with Markov chain Monte Carlo (MCMC), model comparison with Bayes Factors, model forecasting and Forecast Model Averaging, are developed and made available to meet the demand of economic modelers. With an application to the S&P 500 series, we found strong evidences of a deterministic trend when we allow the persistence to change with time. By fitting the GSTUR model to monthly UK/US real exchange rate data, the Purchasing Power Parity (PPP) theory is revisited. Our findings of a changing persistence in the data suggest that the GSTUR model may reconcile the empirical findings of nonstationarity in real exchange rates with the PPP theory. The forecasting capacities of a group of nonlinear and linear models are evaluated with an application to UK inflation rates. We propose a GSTUR model to be applied with data, which contains as much information as possible, for forecasting near-term inflation rates