Bioinformatics and HIV Latency

Despite effective treatment, HIV is not completely eliminated from the infected organism because of the existence of viral reservoirs. A major reservoir consists of infected resting CD4+ T cells, mostly of memory type, that persist over time due to the stable proviral insertion and a long cellular lifespan. Resting cells do not produce viral particles and are protected from viral-induced cytotoxicity or immune killing. However, these latently infected cells can be reactivated by stochastic events or by external stimuli. The present review focuses on novel genome-wide technologies applied to the study of integration, transcriptome, and proteome characteristics and their recent contribution to the understanding of HIV latency

AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact

In recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel. AnophelesModel is an online, open-access R package that quantifies the impact of vector control interventions depending on mosquito species and location-specific characteristics. In addition, it includes a previously published, comprehensive, curated database of field entomological data from over 50 Anopheles species, field data on mosquito and human behaviour, and estimates of vector control effectiveness. Using the input data, the package parameterizes a discrete-time, state transition model of the mosquito oviposition cycle and infers species-specific impacts of various interventions on vectorial capacity. In addition, it offers formatted outputs ready to use in downstream analyses and by other models of malaria transmission for accurate representation of the vector-specific components. Using AnophelesModel, we show how the key implications for intervention impact change for various vectors and locations. The package facilitates quantitative comparisons of likely intervention impacts in different geographical settings varying in vector compositions, and can thus guide towards more robust and efficient malaria control recommendations. The AnophelesModel R package is available under a GPL-3.0 license at https://github.com/SwissTPH/AnophelesModel

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation

Whole genome mapping of 5' RNA ends in bacteria by tagged sequencing : A comprehensive view in Enterococcus faecalis

Enterococcus faecalis is the third cause of nosocomial infections. To obtain the first comprehensive view of transcriptional organizations in this bacterium, we used a modified RNA-seq approach enabling to discriminate primary from processed 5'RNA ends. We also validated our approach by confirming known features in Escherichia coli. We mapped 559 transcription start sites and 352 processing sites in E. faecalis. A blind motif search retrieved canonical features of SigA- and SigN-dependent promoters preceding TSSs mapped. We discovered 95 novel putative regulatory RNAs, small- and antisense RNAs, and 72 transcriptional antisense organisations. Presented data constitute a significant insight into bacterial RNA landscapes and a step towards the inference of regulatory processes at transcriptional and post-transcriptional levels in a comprehensive manner

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

Background Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts. Methods Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017–2019. The reported council-level prevalence classification in school children aged 5–16 years (PfPR5–16) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum. Results Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding. Conclusion The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions

Statistical analysis of PAR-CLIP data

From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis. The present work has two main goals. First, to develop a modular pipeline for pre-processing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the Signals extracted in the pre-processing step

Applying Hidden Markov Models to RNA-seq data

Enterococcus faecalis is one of the most controversial commensal bacteria of the human intestinal flora that is also responsible for lethal nosocomial infections. Determining the factors that influence its pathogenicity is at present a great challenge. Cutting-edge approaches analyze the E. faecalis bacterium trough the next generation RNA-sequencing technology. Since next generation sequencing is recent and yields a large amount of data, there is a continuous need for appropriate statistical methods to interpret its output. We propose an approach based on hidden Markov models to explore RNA-seq data and show an example of how we can apply this statistical tool to detect transcription start sites. We compare this application with a previously developed method based on signal processing.Individual Project in Computational Biology. QC 20130911</p

Bioinformatics and HIV Latency

Despite effective treatment, HIV is not completely eliminated from the infected organism because of the existence of viral reservoirs. A major reservoir consists of infected resting CD4+ T cells, mostly of memory type, that persist over time due to the stable proviral insertion and a longcellular lifespan. Resting cells do not produce viral particles and are protected from viral-induced cytotoxicity or immune killing. However, these latently infected cells can be reactivated by stochastic events or by external stimuli. The present review focuses on novel genome-wide technologies applied to the study of integration, transcriptome, and proteome characteristics and their recent contribution to the understanding of HIV latency

Proteo-Transcriptomic Dynamics of Cellular Response to HIV-1 Infection

Throughout the HIV-1 replication cycle, complex host-pathogen interactions take place in the infected cell, leading to the production of new virions. The virus modulates the host cellular machinery in order to support its life cycle, while counteracting intracellular defense mechanisms. We investigated the dynamic host response to HIV-1 infection by systematically measuring transcriptomic, proteomic, and phosphoproteomic expression changes in infected and uninfected SupT1 CD4+ T cells at five time points of the viral replication process. By means of a Gaussian mixed-effects model implemented in the new R/Bioconductor package TMixClust, we clustered host genes based on their temporal expression patterns. We identified a proteo-transcriptomic gene expression signature of 388 host genes specific for HIV-1 replication. Comprehensive functional analyses of these genes confirmed the previously described roles of some of the genes and revealed novel key virus-host interactions affecting multiple molecular processes within the host cell, including signal transduction, metabolism, cell cycle, and immune system. The results of our analysis are accessible through a freely available, dedicated and user-friendly R/Shiny application, called PEACHi2.0. This resource constitutes a catalogue of dynamic host responses to HIV-1 infection that provides a basis for a more comprehensive understanding of virus-host interactions.ISSN:2045-232