Imperial College London


Faculty of MedicineDepartment of Infectious Disease

Honorary Lecturer







Huxley BuildingSouth Kensington Campus





Publication Type

16 results found

Boonyasiri A, Myall AC, Wan Y, Bolt F, Ledda A, Mookerjee S, Weiße AY, Turton JF, Abbas H, Prakapaite R, Sabnis A, Abdolrasouli A, Malpartida-Cardenas K, Miglietta L, Donaldson H, Gilchrist M, Hopkins KL, Ellington MJ, Otter JA, Larrouy-Maumus G, Edwards AM, Rodriguez-Manzano J, Didelot X, Barahona M, Holmes AH, Jauneikaite E, Davies Fet al., 2021, Integrated patient network and genomic plasmid analysis reveal a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both blaIMP and mcr-9 genes

<jats:title>Abstract</jats:title><jats:p>The incidence of carbapenemase-producing Enterobacterales (CPE) is rising globally, yet Imipenemase (IMP) carbapenemases remain relatively rare. This study describes an investigation of the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 in patients across a London regional hospital network.</jats:p><jats:p>A network analysis approach to patient pathways, using routinely collected electronic health records, identified previously unrecognised contacts between patients who were IMP CPE positive on screening, implying potential bacterial transmission events. Whole genome sequencing of 85 Enterobacterales isolates from these patients revealed that 86% (73/85) were diverse species (predominantly <jats:italic>Klebsiella</jats:italic> spp, <jats:italic>Enterobacter</jats:italic> spp, <jats:italic>E. coli</jats:italic>) and harboured an IncHI2 plasmid, which carried both <jats:italic>bla</jats:italic><jats:sub>IMP</jats:sub> and the putative mobile colistin resistance gene <jats:italic>mcr-9</jats:italic>. Detailed phylogenetic analysis identified two distinct IncHI2 plasmid lineages, A and B, both of which showed significant association with patient movements between four hospital sites and across medical specialities.</jats:p><jats:p>Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of <jats:italic>bla</jats:italic><jats:sub>IMP</jats:sub>CPE, which remained unidentified during standard microbiology and infection control investigations. With whole genome sequencing (WGS) technologies and large-data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Analysing outbreaks at the plasmid level reveal

Journal article

Ming DKY, Myall A, Hernandez B, Weisse A, Peach R, Barahona M, Rawson T, Holmes Aet al., 2021, Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin, BMC Infectious Diseases, Vol: 21, ISSN: 1471-2334

Background:To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making.Methods:Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT. Pairwise comparisons were performed between: (i) those with or without significant bacterial growth from cultures, and (ii) those who survived or died in hospital.Results:CRP concentrations were higher over time in COVID-19 patients with positive microbiology (day 9: 236 vs 123 mg/L, p < 0.0001) and in those who died (day 8: 226 vs 152 mg/L, p < 0.0001) but only after day 7 of COVID-related symptom onset. Failure for CRP to reduce in the first week of hospital admission was associated with significantly higher odds of death. PCT concentrations were higher in patients with COVID-19 and positive microbiology or in those who died, although these differences were not statistically significant.Conclusions:Both the absolute CRP concentration and the trajectory during the first week of hospital admission are important factors predicting microbiology culture positivity and outcome in patients hospitalised with COVID-19. Further work is needed to describe the role of PCT for co-infection. Understanding relationships of these biomarkers can support development of risk models and inform optimal antimicrobial strategies.

Journal article

Myall AC, Peach RL, Weiße AY, Davies F, Mookerjee S, Holmes A, Barahona Met al., 2021, Network memory in the movement of hospital patients carrying drug-resistant bacteria, Applied Network Science, Vol: 6, ISSN: 2364-8228

Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.

Journal article

Myall A, Peach RL, Wan Y, Mookerjee S, Jauneikaite E, Bolt F, Price J, Davies F, Weiße AY, Holmes A, Barahona Met al., 2021, Characterising contact in disease outbreaks via a network model of spatial-temporal proximity

<jats:title>ABSTRACT</jats:title><jats:p>Contact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce contact maps of individuals based on a binary definition of contact which can be hampered by missing data and indirect contacts. Here, we present a Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. Using real-world data we showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacteria and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP model reveals missing contacts that connect seemingly separate outbreaks. Comparison with genomic data further confirmed that these recovered contacts indeed improve characterisation of disease transmission and so highlights how the StEP framework can inform effective strategies of infection control and prevention.</jats:p>

Journal article

Price JR, Mookerjee S, Dyakova E, Myall A, Leung W, Weiße AY, Shersing Y, Brannigan ET, Galletly T, Muir D, Randell P, Davies F, Bolt F, Barahona M, Otter JA, Holmes AHet al., 2021, Development and delivery of a real-time hospital-onset COVID-19 surveillance system using network analysis, Clinical Infectious Diseases, Vol: 72, Pages: 82-89, ISSN: 1058-4838

BackgroundUnderstanding nosocomial acquisition, outbreaks and transmission chains in real-time will be fundamental to ensuring infection prevention measures are effective in controlling COVID-19 in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions.MethodsThe study took place in a large teaching hospital group in London, UK. All patients tested for SARS-CoV-2 between 4th March and 14th April 2020 were included. Utilising data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation, and generated geo-temporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users.ResultsReal-time surveillance reports revealed: changing rates of HOCI throughout the course of the COVID-19 epidemic; key wards fuelling probable transmission events; HOCIs over-represented in particular specialities managing high-risk patients; the importance of integrating analysis of individual prior pathways; and the value of co-design in producing data visualisation. Our surveillance system can effectively support national surveillance.ConclusionsThrough early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterising risk of transmission and targeting infection control interventions.

Journal article

Nikolados E-M, Weiße AY, Oyarzún DA, 2021, Prediction of cellular burden with host-circuit models., Methods in Molecular Biology, Publisher: Springer, Pages: 267-291

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.

Book chapter

Otter JA, Mookerjee S, Davies F, Bolt F, Dyakova E, Shersing Y, Boonyasiri A, Weisse AY, Gilchrist M, Galletly TJ, Brannigan ET, Holmes AHet al., 2020, Detecting carbapenemase-producing Enterobacterales (CPE): an evaluation of an enhanced CPE infection control and screening programme in acute care, JOURNAL OF ANTIMICROBIAL CHEMOTHERAPY, Vol: 75, Pages: 2670-2676, ISSN: 0305-7453

Journal article

Nikolados E, Weisse A, Ceroni F, Oyarzun Det al., 2019, Growth defects and loss-of-function in synthetic gene circuits, ACS Synthetic Biology, Vol: 8, Pages: 1231-1240, ISSN: 2161-5063

Synthetic gene circuits perturb the physiology of their cellular host. The extra load on endogenous processes shifts the equilibrium of resource allocation in the host, leading to slow growth and reduced biosynthesis. Here we built integrated host-circuit models to quantify growth defects caused by synthetic gene circuits. Simulations reveal a complex relation between circuit output and cellular capacity for gene expression. For weak induction of heterologous genes, protein output can be increased at the expense of growth defects. Yet for stronger induction, cellular capacity reaches a tipping point, beyond which both gene expression and growth rate drop sharply. Extensive simulations across various growth conditions and large regions of the design space suggest that the critical capacity is a result of ribosomal scarcity. We studied the impact of growth defects on various gene circuits and transcriptional logic gates, which highlights the extent to which cellular burden can limit, shape, and even break down circuit function. Our approach offers a comprehensive framework to assess the impact of host-circuit interactions in silico, with wide-ranging implications for the design and optimization of bacterial gene circuits.

Journal article

Thomas P, Terradot G, Danos V, Weisse Aet al., 2018, Sources, propagation and consequences of stochasticity in cellular growth, Nature Communications, Vol: 9, ISSN: 2041-1723

Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. Alongside we provide a theory to analyse stochastic chemical reactions coupled with cell divisions, enabling efficient parameter estimation, sensitivity analysis and hypothesis testing. The cell model recovers population-averaged data on growth-dependence of bacterial physiology and how growth variations in single cells change across conditions. We identify processes responsible for this variation and reconstruct the propagation of initial fluctuations to growth and other processes. Finally, we study drug-nutrient interactions and find that antibiotics can both enhance and suppress growth heterogeneity. Our results provide a predictive framework to integrate heterogeneous data and draw testable predictions with implications for antibiotic tolerance, evolutionary and synthetic biology.

Journal article

Terradot G, Beica A, Weisse A, Danos Vet al., 2018, Survival of the Fattest: Evolutionary Trade-offs in Cellular Resource Storage, ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE, Vol: 335, Pages: 91-112, ISSN: 1571-0661

Journal article

Weisse AY, Mannan AA, Oyarzun DA, 2016, Signaling tug-of-war delivers the whole message, Cell Systems, Vol: 3, Pages: 414-46, ISSN: 2405-4720

How do cells transmit biochemical signals accurately? It turns out,pushing and pulling can go a long way.

Journal article

Weisse AY, Oyarzun DA, Danos V, Swain PSet al., 2015, Mechanistic links between cellular trade-offs, gene expression, and growth, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 112, Pages: E1038-E1047, ISSN: 0027-8424

Journal article

Karwacki-Neisius V, Goeke J, Osorno R, Halbritter F, Ng JH, Weisse AY, Wong FCK, Gagliardi A, Mullin NP, Festuccia N, Colby D, Tomlinson SR, Ng H-H, Chambers Iet al., 2013, Reduced Oct4 Expression Directs a Robust Pluripotent State with Distinct Signaling Activity and Increased Enhancer Occupancy by Oct4 and Nanog, CELL STEM CELL, Vol: 12, Pages: 531-545, ISSN: 1934-5909

Journal article

Oyarzun DA, Lopez-Caamal F, Garcia MR, Middleton RH, Weisse AYet al., 2013, Cumulative signal transmission in nonlinear reaction-diffusion networks, Plos One, Vol: 5

Journal article

Weiße AY, Middleton RH, Huisinga W, 2010, Quantifying uncertainty, variability and likelihood for ordinary differential equation models, BMC Systems Biology, Vol: 4

Journal article

Veron N, Bauer H, Weisse AY, Luder G, Werber M, Herrmann BGet al., 2009, Retention of gene products in syncytial spermatids promotes non-Mendelian inheritance as revealed by the t complex responder, Genes & Development, Vol: 23, Pages: 2705-2710, ISSN: 0890-9369

Journal article

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