Prediction stability in a data-based, mechanistic model of σF regulation during sporulation in Bacillus subtilis

Enzymatic deamination of the epigenetic nucleoside N6-methyladenosine regulates gene expression

N⁶-methyladenosine (m⁶A) modification is the most extensively studied epigenetic modification due to its crucial role in regulating an array of biological processes. Herein, Bsu06560, formerly annotated as an adenine deaminase derived from Bacillus subtilis 168, was recognized as the first enzyme capable of metabolizing the epigenetic nucleoside N⁶-methyladenosine. A model of Bsu06560 was constructed, and several critical residues were putatively identified via mutational screening. Two mutants, F91L and Q150W, provided a superiorly enhanced conversion ratio of adenosine and N⁶-methyladenosine. The CRISPR-Cas9 system generated Bsu06560-knockout, F91L, and Q150W mutations from the B. subtilis 168 genome. Transcriptional profiling revealed a higher global gene expression level in BS-F91L and BS-Q150W strains with enhanced N⁶-methyladenosine deaminase activity. The differentially expressed genes were categorized using GO, COG, KEGG and verified through RT-qPCR. This study assessed the crucial roles of Bsu06560 in regulating adenosine and N⁶-methyladenosine metabolism, which influence a myriad of biological processes. This is the first systematic research to identify and functionally annotate an enzyme capable of metabolizing N⁶-methyladenosine and highlight its significant roles in regulation of bacterial metabolism. Besides, this study provides a novel method for controlling gene expression through the mutations of critical residues.

Heterogeneous dynamics, robustness/fragility trade-offs, and the eradication of the macroparasitic disease, lymphatic filariasis

BACKGROUND

The current WHO-led initiative to eradicate the macroparasitic disease, lymphatic filariasis (LF), based on single-dose annual mass drug administration (MDA) represents one of the largest health programs devised to reduce the burden of tropical diseases. However, despite the advances made in instituting large-scale MDA programs in affected countries, a challenge to meeting the goal of global eradication is the heterogeneous transmission of LF across endemic regions, and the impact that such complexity may have on the effort required to interrupt transmission in all socioecological settings.

METHODS

Here, we apply a Bayesian computer simulation procedure to fit transmission models of LF to field data assembled from 18 sites across the major LF endemic regions of Africa, Asia and Papua New Guinea, reflecting different ecological and vector characteristics, to investigate the impacts and implications of transmission heterogeneity and complexity on filarial infection dynamics, system robustness and control.

RESULTS

We find firstly that LF elimination thresholds varied significantly between the 18 study communities owing to site variations in transmission and initial ecological parameters. We highlight how this variation in thresholds lead to the need for applying variable durations of interventions across endemic communities for achieving LF elimination; however, a major new result is the finding that filarial population responses to interventions ultimately reflect outcomes of interplays between dynamics and the biological architectures and processes that generate robustness/fragility trade-offs in parasite transmission. Intervention simulations carried out in this study further show how understanding these factors is also key to the design of options that would effectively eliminate LF from all settings. In this regard, we find how including vector control into MDA programs may not only offer a countermeasure that will reliably increase system fragility globally across all settings and hence provide a control option robust to differential locality-specific transmission dynamics, but by simultaneously reducing transmission regime variability also permit more reliable macroscopic predictions of intervention effects.

CONCLUSIONS

Our results imply that a new approach, combining adaptive modelling of parasite transmission with the use of biological robustness as a design principle, is required if we are to both enhance understanding of complex parasitic infections and delineate options to facilitate their elimination effectively.

Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis

Background

Mathematical models of parasite transmission can help integrate a large body of information into a consistent framework, which can then be used for gaining mechanistic insights and making predictions. However, uncertainty, spatial variability and complexity, can hamper the use of such models for decision making in parasite management programs.

Methods

We have adapted a Bayesian melding framework for calibrating simulation models to address the need for robust modelling tools that can effectively support management of lymphatic filariasis (LF) elimination in diverse endemic settings. We applied this methodology to LF infection and vector biting data from sites across the major LF endemic regions in order to quantify model parameters, and generate reliable predictions of infection dynamics along with credible intervals for modelled output variables. We used the locally calibrated models to estimate breakpoint values for various indicators of parasite transmission, and simulate timelines to parasite extinction as a function of local variations in infection dynamics and breakpoints, and effects of various currently applied and proposed LF intervention strategies.

Results

We demonstrate that as a result of parameter constraining by local data, breakpoint values for all the major indicators of LF transmission varied significantly between the sites investigated. Intervention simulations using the fitted models showed that as a result of heterogeneity in local transmission and extinction dynamics, timelines to parasite elimination in response to the current Mass Drug Administration (MDA) and various proposed MDA with vector control strategies also varied significantly between the study sites. Including vector control, however, markedly reduced the duration of interventions required to achieve elimination as well as decreased the risk of recrudescence following stopping of MDA.

Conclusions

We have demonstrated how a Bayesian data-model assimilation framework can enhance the use of transmission models for supporting reliable decision making in the management of LF elimination. Extending this framework for delivering predictions in settings either lacking or with only sparse data to inform the modelling process, however, will require development of procedures to estimate and use spatio-temporal variations in model parameters and inputs directly, and forms the next stage of the work reported here.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-015-1132-7) contains supplementary material, which is available to authorized users.