Dr Mario Recker
Stella Turk Building
University of Exeter, Penryn Campus, Penryn, TR10 9FE
I am a mathematical biologist with a research focus on the evolutionary ecology and epidemiology of infectious diseases, such as malaria, dengue and (drug resistant) Staphylococcus aureus. I am particularly interested in multi-scale host-pathogen interactions and how these affect infection pathologies, pathogen evolution and disease incidence across time and space. For this I employ a wide variety of mathematical and computational techniques, from individual-based and population-level modelling to advanced statistical methods and machine learning approaches.
- DPhil, Zoology (2000 - 2003, University of Oxford)
- MSc, Nonlinear Dynamics & Chaos (1999 - 2000, UCL)
- Diplom Mathematiker (1995 - 1999, TFH Berlin)
Many pathogens utilise antigenic and phenotypic diversity as a means to avoid detection and clearance by the host’s immune system. This enables them to remain within the host for prolonged periods of time, allows the pathogen to establish infections in previously exposed individuals and can lead to highly varied infection outcomes. My research focuses on the multifaceted epidemiology of antigenically and phenotypically diverse pathogens, such as Plasmodium falciparum, dengue or Staphylococcus aureus. With the help of mathematical and data analytic frameworks and in close collaboration with field and laboratory scientists I investigate the pathology and evolutionary epidemiology of these pathogens, focusing on host-pathogen interactions at multiple ecological scales that link within-host processes of gene expression and immune selection to between-host epidemiological patterns of infection and disease.
The causative agent of severe malaria in humans, Plasmodium falciparum, employs a sophisticated immune evasion strategy, called antigenic variation, to circumvent the host's immune pressure and maintain long-lasting infections. Central to this process is the var multi-gene family, which encode the cell-surface antigens PfEMP1. Mutually exclusive switching between ~60 members of these highly polymorphic genes ensures that only a small fraction of the whole antigenic repertoire is exposed to the immune system at a time. PfEMP1 are also involved in malaria virulence. They mediate the attachment of parasitised red blood cells to host tissues, which can then lead to parasite sequestration and obstruction of blood flow in vital organs, such as the brain or placenta. Different PfEMP1 variants adhere to different host tissues, and antigenic switches between var genes can therefore also lead to a phenotypic change during the course of an infection. The involvement of var genes and var gene switching is therefore central for our understanding of the infection dynamics, pathology and epidemiology of P. falciparum malaria.
I am particularly interested in the underlying patterns of var gene switching, and how these relate to observed gene expression pattern in individuals growing up in malaria endemic regions. E.g. we have shown that antigenic switching is a highly non-random process in which different genes have different, hard-wired switch characteristics in terms of the rates at which they are activated or silenced [Noble et al. (2013), Recker et al. (2011)]. Furthermore, we could show the resulting switch hierarchy is inherently linked to gene recombination and therefore the generation of further antigenic diversity in these parasites. My work currently focuses on developing multi-scale, mathematical frameworks that integrate molecular genetic processes with the dynamics of within-host infections and between-host epidemiologies.
Recent / selected Publications
Andrade CM, Fleckenstein H, Thomson-Luque R, [...], Recker M, Traore B, Crompton PD, Portugal S (2020). Increased circulation time of Plasmodium falciparum underlies persistent asymptomatic infection in the dry season. Nat Med, 26(12), 1929-1940
Bediako Y, Adams R, Reid AJ, [...], Recker M, Newbold CI, Berriman M, Bejon P Marsh K, Langhorne J (2019). Repeated clinical malaria episodes are associated with modification of the immune system in children. BMC Med, 17(1)
Holding T, Valletta JJ, Recker M (2018). Multiscale Immune Selection and the Transmission-Diversity Feedback in Antigenically Diverse Pathogen Systems. Am Nat, 192(6):E189-E201
Valletta JJ, & Recker M (2017). Identication of immune signatures predictive of clinical protection from malaria. PLoS Comput Biol, 13(10):e1005812
Noble R, Christodoulou Z, Kyes S, Pinches R , Newbold CI, & Recker M (2013). The antigenic switching network of Plasmodium falciparum and its implications for the immuno-epidemiology of malaria. eLife 2013;2:e01074
Portugal S, Carret C, Recker M, Armitage AE, Goncalves LA, Epiphanio S, Sullivan D, Roy C, Newbold CI, Drakesmith H, & Mota MM (2011). Host-mediated regulation of superinfection in malaria. Nat Med, 17(6):732-737
Recker M, Buckee CO, Serazin A, Kyes S, Pinches R, Christodoulou Z, Springer AL, Gupta S, Newbold CI (2011). Antigenic variation in Plasmodium falciparum malaria involves a highly structured switching pattern. PLoS Pathog, 7(3):e1001306
Recker M, Nee S, Bull PC, Kinyanjui S, Marsh K, Newbold C, & Gupta S (2004). Transient cross-reactive immune responses can orchestrate antigenic variation in malaria. Nature, 429(6991):555-8
In less than six decades dengue has emerged from South East Asia to become the most widespread arbovirus affecting human populations. A recent dramatic increase in epidemic dengue fever has mainly been attributed to factors such as vector expansion and ongoing ecological, climate and socio-demographic changes. The lack of antivirals or vaccines and the current failure to control the pathogen in endemic regions and to prevent globalized distribution of the vector-species and viral variants underlines the urgency for reassessment of previous research methods, hypothesis and empirical observations.
Previous modelling approaches have mostly focused on the impact of immunological competition between dengue’s four serotypes (DENV1-4), which can generate a frequency-dependent mechanism that partially explains dengue's temporal epidemiological patterns. We have developed a spatially explicit, individual-based model to investigate the effects of demographic and ecological stochasticities. Our model demonstrated that amplification of natural stochastic differences in disease transmission can give rise to persistent oscillations comprising semi-regular epidemic outbreaks and sequential serotype dominance that are characteristic of dengue's epidemiological dynamics. Work is currently under way to address such questions as if and how host ecological and demographic heterogeneities are shaping the viral evolution of dengue, and how different population structures (small-world, lattice, scale-free, etc) can affect the spatial epidemiology of dengue, including persistence and synchrony? This work also extends to other arborival diseases, such as chikungunya and zika.
Recent / selected Publications
Tennant W, Recker M (2018). Robustness of the reproductive number estimates in vector-borne disease systems. PLoS Negl Trop Dis, 12(12):e0006999
Lourenco J, de Lima MM, Faria NR, Walker A, Kraemer MU, Villabona-Arenas CJ, Lambert B, Marques de Cerqueira E, Pybus OG, Alcantara LC, & Recker M (2017). Epidemiological and ecological determinants of Zika virus transmission in an urban setting. eLife, 6:e29820
Flasche S, Jit M, Rodríguez-Barraquer I, Coudeville L, Recker M*, et al. (2016). The long term safety, public health impact, and cost effectiveness of routine vaccination with a recombinant, live-attenuated dengue vaccine (Dengvaxia): a model comparison study. PLoS Med, 13(11):e1002181
Lourenço J, & Recker M (2016). Dengue serotype immune-interactions and their consequences for vaccine impact predictions. Epidemics, 16: 40-48
Lourenço J, & Recker M (2014). The 2012 Madeira dengue outbreak: epidemiological determinants and future epidemic potential. PLoS Negl Trop Dis, 8(8):e3083
Lourenço J, & Recker M (2013). Natural, persistent oscillations in a spatial multi-strain disease system with application to dengue. PLoS Comp Biol, 9(10): e1003308
Antimicrobial resistance (AMR) is a major global public health issue, making first-line treatments of many bacterial infections ineffective. One of the best known examples is the methicillin-resistant Staphylococcus aureus, or MRSA. It is the most common cause of hospital-acquired infections although community-acquired MRSA (CA-MRSA) is also becoming of increasing concern. S. aureus is an opportunistic pathogen. It colonises around 30% of the human population, where its interactions with the human hosts are largely asymptomatic. Infections most commonly result from breaches in the host’s innate immunity and can result in both acute and chronic disease. The most severe form of infection occurs when S. aureus gains access to the blood stream, which is referred to as bacteraemia. This is often aided by breakages in the skin or mucosal membranes, for example due to surgery or the use of catheters, and can lead to very high fatality rate in the absence of antibiotic treatment.
The genes expressed by S. aureus and their interaction with the human immune system facilitating such varied infections are not fully understood, and my work on MRSA focuses on understanding this bacterial virulence as a complex phenotype. In collaboration with researchers at Bath University and by using whole genome approaches, combined with functional genomics, mathematical and statistical modelling we have made important headway in seeking to map phenotype directly from genotype (Laabei et al. (2014)) and to understand how bacterial virulence evolved as a trade-off between maintaining fitness at the within-host level and at the between-host level (Laabei et al. (2015)). By analysing fully sequenced and phenotyped, clinical isolates we are currently investigating if and to what degree severe infection outcomes can be predicted using machine learning algorithms, which would be an important step towards personalised medicine and infectious disease management.
Recent / selected Publications
Yokoyama M, Stevens E, Laabei M, Bacon L, Heesom K, Bayliss S, Ooi N, O'Neill AJ, Murray E, Williams P, Lubben A, Reeksting S, Meric G, Pascoe B, Sheppard SK, Recker M, Hurst LD, Massey RC (2018). Epistasis analysis uncovers hidden antibiotic resistance-associated fitness costs hampering the evolution of MRSA. Genome Biol, 19(1)
Recker, M, Laabei M, Toleman MS, Reuter S, Saunderson RB, Blane B, Torok ME, Ouadi K, Stevens E, Yokoyama M, Steventon J, Thompson L, Milne G, Bayliss S, Bacon L, Peacock SJ, & Massey RC (2017). Clonal dierences in Staphylococcus aureus bacteraemia-associated mortality. Nat Microbiol, 2(10):1381-1388
Laabei M, Uhlemann AC, Lowy FD, Austin ED, Yokoyama M, Ouadi K, Feil E, Thorpe HA, Williams B, Perkins M, Peacock SJ, Clarke SR, Dordel J, Holden M, Votintseva AA, Bowden R, Crook DW, Young BC, Wilson DJ, Recker M, Massey RC (2015) Evolutionary Trade-Offs Underlie the Multi-faceted Virulence of Staphylococcus aureus. PLoS Biol, 13(9):e1002229
Laabei M, Recker M, Rudkin J, Sloan T, Williams P, Lewis K, Scowen L, Peacock S, van den Elsen J, Priest N, Feil E, Josefsson E, & Massey RC (2014). Predicting the virulence of MRSA from its genome sequence. Genome Res, 24: 839-849
Priest NK, RudkinJ, Feil EJ, van den Elsen J, Cheung A, Peacock JP, Laabei M, Lucks DA, Recker M, & Massey RC (2012). From genotype to phenotype: can systems biology be used to predict Staphylococcus aureus virulence. Nat Rev Microbiol, 10(11):791-7
Collins J, Rudkin J, Recker M, Pozzi C, O'Gara JP, & Massey RC (2010). Offsetting virulence and antibiotic resistance costs by MRSA. ISME J, 4(4):577-84
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