Effects of deletion of the Streptococcus pneumoniae lipoprotein diacylglyceryl transferase gene lgt on ABC transporter function and on growth in vivo

Lipoproteins are an important class of surface associated proteins that have diverse roles and frequently are involved in the virulence of bacterial pathogens. As prolipoproteins are attached to the cell membrane by a single enzyme, prolipoprotein diacylglyceryl transferase (Lgt), deletion of the corresponding gene potentially allows the characterisation of the overall importance of lipoproteins for specific bacterial functions. We have used a Δlgt mutant strain of Streptococcus pneumoniae to investigate the effects of loss of lipoprotein attachment on cation acquisition, growth in media containing specific carbon sources, and virulence in different infection models. Immunoblots of triton X-114 extracts, flow cytometry and immuno-fluorescence microscopy confirmed the Δlgt mutant had markedly reduced lipoprotein expression on the cell surface. The Δlgt mutant had reduced growth in cation depleted medium, increased sensitivity to oxidative stress, reduced zinc uptake, and reduced intracellular levels of several cations. Doubling time of the Δlgt mutant was also increased slightly when grown in medium with glucose, raffinose and maltotriose as sole carbon sources. These multiple defects in cation and sugar ABC transporter function for the Δlgt mutant were associated with only slightly delayed growth in complete medium. However the Δlgt mutant had significantly reduced growth in blood or bronchoalveolar lavage fluid and a marked impairment in virulence in mouse models of nasopharyngeal colonisation, sepsis and pneumonia. These data suggest that for S. pneumoniae loss of surface localisation of lipoproteins has widespread effects on ABC transporter functions that collectively prevent the Δlgt mutant from establishing invasive infection

Can we achieve better recruitment by providing better information? : Meta-analysis of 'studies within a trial' (SWATs) of optimised participant information sheets

BACKGROUND: The information given to people considering taking part in a trial needs to be easy to understand if those people are to become, and then remain, trial participants. However, there is a tension between providing comprehensive information and providing information that is comprehensible. User-testing is one method of developing better participant information, and there is evidence that user-tested information is better at informing participants about key issues relating to trials. However, it is not clear if user-testing also leads to changes in the rates of recruitment in trials, compared to standard trial information. As part of a programme of research, we embedded 'studies within a trial' (SWATs) across multiple ongoing trials to see if user-tested materials led to better rates of recruitment. METHODS: Seven 'host' trials included a SWAT evaluation and randomised their participants to receive routine information sheets generated by the research teams, or information sheets optimised through user-testing. We collected data on trial recruitment and analysed the results across these trials using random effects meta-analysis, with the primary outcome defined as the proportion of participants randomised in a host trial following an invitation to take part. RESULTS: Six SWATs (n=27,805) provided data on recruitment. Optimised participant information sheets likely result in little or no difference in recruitment rates (7.2% versus 6.8%, pooled odds ratio = 1.03, 95% CI 0.90 to 1.19, p-value = 0.63, I2 = 0%). CONCLUSIONS: Participant information sheets developed through user testing did not improve recruitment rates. The programme of work showed that co-ordinated testing of recruitment strategies using SWATs is feasible and can provide both definitive and timely evidence on the effectiveness of recruitment strategies. TRIAL REGISTRATION: Healthlines Depression (ISRCTN14172341) Healthlines CVD (ISRCTN27508731) CASPER (ISRCTN02202951) ISDR (ISRCTN87561257) ECLS (NCT01925625) REFORM (ISRCTN68240461) HeLP Diabetes (ISRCTN02123133)

Deconvoluting Post-Transplant Immunity: Cell Subset-Specific Mapping Reveals Pathways for Activation and Expansion of Memory T, Monocytes and B Cells

A major challenge for the field of transplantation is the lack of understanding of genomic and molecular drivers of early post-transplant immunity. The early immune response creates a complex milieu that determines the course of ensuing immune events and the ultimate outcome of the transplant. The objective of the current study was to mechanistically deconvolute the early immune response by purifying and profiling the constituent cell subsets of the peripheral blood. We employed genome-wide profiling of whole blood and purified CD4, CD8, B cells and monocytes in tandem with high-throughput laser-scanning cytometry in 10 kidney transplants sampled serially pre-transplant, 1, 2, 4, 8 and 12 weeks. Cytometry confirmed early cell subset depletion by antibody induction and immunosuppression. Multiple markers revealed the activation and proliferative expansion of CD45RO+CD62L− effector memory CD4/CD8 T cells as well as progressive activation of monocytes and B cells. Next, we mechanistically deconvoluted early post-transplant immunity by serial monitoring of whole blood using DNA microarrays. Parallel analysis of cell subset-specific gene expression revealed a unique spectrum of time-dependent changes and functional pathways. Gene expression profiling results were validated with 157 different probesets matching all 65 antigens detected by cytometry. Thus, serial blood cell monitoring reflects the profound changes in blood cell composition and immune activation early post-transplant. Each cell subset reveals distinct pathways and functional programs. These changes illuminate a complex, early phase of immunity and inflammation that includes activation and proliferative expansion of the memory effector and regulatory cells that may determine the phenotype and outcome of the kidney transplant

An optimised patient information sheet did not significantly increase recruitment or retention in a falls prevention study: an embedded randomised recruitment trial.

BACKGROUND: Randomised controlled trials are generally regarded as the 'gold standard' experimental design to determine the effectiveness of an intervention. Unfortunately, many trials either fail to recruit sufficient numbers of participants, or recruitment takes longer than anticipated. The current embedded trial evaluates the effectiveness of optimised patient information sheets on recruitment of participants in a falls prevention trial. METHODS: A three-arm, embedded randomised methodology trial was conducted within the National Institute for Health Research-funded REducing Falls with ORthoses and a Multifaceted podiatry intervention (REFORM) cohort randomised controlled trial. Routine National Health Service podiatry patients over the age of 65 were randomised to receive either the control patient information sheet (PIS) for the host trial or one of two optimised versions, a bespoke user-tested PIS or a template-developed PIS. The primary outcome was the proportion of patients in each group who went on to be randomised to the host trial. RESULTS: Six thousand and nine hundred patients were randomised 1:1:1 into the embedded trial. A total of 193 (2.8%) went on to be randomised into the main REFORM trial (control n = 62, template-developed n = 68; bespoke user-tested n = 63). Information sheet allocation did not improve recruitment to the trial (odds ratios for the three pairwise comparisons: template vs control 1.10 (95% CI 0.77-1.56, p = 0.60); user-tested vs control 1.01 (95% CI 0.71-1.45, p = 0.94); and user-tested vs template 0.92 (95% CI 0.65-1.31, p = 0.65)). CONCLUSIONS: This embedded methodology trial has demonstrated limited evidence as to the benefit of using optimised information materials on recruitment and retention rates in the REFORM study. TRIAL REGISTRATION: International Standard Randomised Controlled Trials Number registry, ISRCTN68240461 . Registered on 01 July 2011

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

Probabilistic gradients for fast calibration of differential equation models

Calibration of large-scale differential equation models to observational or experimental data is a widespread challenge throughout applied sciences and engineering. A crucial bottleneck in state-of-the art calibration methods is the calculation of local sensitivities, i.e. derivatives of the loss function with respect to the estimated parameters, which often necessitates several numerical solves of the underlying system of partial or ordinary differential equations. In this paper we present a new probabilistic approach to computing local sensitivities. The proposed method has several advantages over classical methods. Firstly, it operates within a constrained computational budget and provides a probabilistic quantification of uncertainty incurred in the sensitivities from this constraint. Secondly, information from previous sensitivity estimates can be recycled in subsequent computations, reducing the overall computational effort for iterative gradient-based calibration methods. The methodology presented is applied to two challenging test problems and compared against classical methods

On the Bayesian solution of differential equations

The interpretation of numerical methods, such as finite difference methods for differential equations, as point estimators allows for formal statistical quantification of the error due to discretisation in the numerical context. Competing statistical paradigms can be considered and Bayesian probabilistic numerical methods (PNMs) are obtained when Bayesian statistical principles are deployed. Bayesian PNM are closed under composition, such that uncertainty due to different sources of discretisation can be jointly modelled and rigorously propagated. However, we argue that no strictly Bayesian PNM for the numerical solution of ordinary differential equations (ODEs) have yet been developed. To address this gap, we work at a foundational level, where a novel Bayesian PNM is proposed as a proof-of-concept. Our proposal is a synthesis of classical Lie group methods, to exploit the underlying structure of the gradient field, and non-parametric regression in a transformed solution space for the ODE. The procedure is presented in detail for first order ODEs and relies on a certain technica l condition -- existence of a solvable Lie algebra -- being satisfied. Numerical illustrations are provided

A role for symmetry in the Bayesian solution of differential equations

The interpretation of numerical methods, such as finite difference methods for differential equations, as point estimators suggests that formal uncertainty quantification can also be performed in this context. Competing statistical paradigms can be considered and Bayesian probabilistic numerical methods (PNMs) are obtained when Bayesian statistical principles are deployed. Bayesian PNM have the appealing property of being closed under composition, such that uncertainty due to different sources of discretisation in a numerical method can be jointly modelled and rigorously propagated. Despite recent attention, no exact Bayesian PNM for the numerical solution of ordinary differential equations (ODEs) has been proposed. This raises the fundamental question of whether exact Bayesian methods for (in general nonlinear) ODEs even exist. The purpose of this paper is to provide a positive answer for a limited class of ODE. To this end, we work at a foundational level, where a novel Bayesian PNM is proposed as a proof-of-concept. Our proposal is a synthesis of classical Lie group methods, to exploit underlying symmetries in the gradient field, and non-parametric regression in a transformed solution space for the ODE. The procedure is presented in detail for first and second order ODEs and relies on a certain strong technical condition – existence of a solvable Lie algebra – being satisfied. Numerical illustrations are provided

Probabilistic linear solvers: a unifying view

Several recent works have developed a new, probabilistic interpretation for numerical algorithms solving linear systems in which the solution is inferred in a Bayesian framework, either directly or by inferring the unknown action of the matrix inverse. These approaches have typically focused on replicating the behaviour of the conjugate gradient method as a prototypical iterative method. In this work,surprisingly general conditions for equivalence of these disparate methods arepresented. We also describe connections between probabilistic linear solvers andprojection methods for linear systems, providing a probabilistic interpretation of afar more general class of iterative methods. In particular, this provides such aninterpretation of the generalised minimum residual method. A probabilistic view ofpreconditioning is also introduced. These developments unify the literature onprobabilistic linear solvers and provide foundational connections to the literatureon iterative solvers for linear systems

A probabilistic Taylor expansion with applications in filtering and differential equations

We study a class of Gaussian processes for which the posterior mean, for a particular choice of data, replicates a truncated Taylor expansion of any order. The data consists of derivative evaluations at the expansion point and the prior covariance kernel belongs to the class of Taylor kernels, which can be written in a certain power series form. This permits statistical modelling of the uncertainty in a variety of algorithms that exploit first and second order Taylor expansions. To demonstrate the utility of this Gaussian process model we introduce new probabilistic versions of the classical extended Kalman filter for non-linear state estimation and the Euler method for solving ordinary differential equations