Imperial College London

DrLachlanCoin

Faculty of MedicineDepartment of Infectious Disease

Honorary Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1930l.coin

 
 
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Location

 

172Medical SchoolSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
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189 results found

Schlapbach LJ, Ganesamoorthy D, Wilson C, Raman S, George S, Snelling PJ, Phillips N, Irwin A, Sharp N, Le Marsney R, Chavan A, Hempenstall A, Bialasiewicz S, MacDonald AD, Grimwood K, Cling JC, McPherson SJ, Blumenthal A, Kaforou M, Levin M, Herberg JA, Gibbons KS, Coin LJM, EUCLIDS consortium, RAPIDS Study Groupet al., 2024, Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study., Lancet Child Adolesc Health

BACKGROUND: Sepsis is defined as dysregulated host response to infection that leads to life-threatening organ dysfunction. Biomarkers characterising the dysregulated host response in sepsis are lacking. We aimed to develop host gene expression signatures to predict organ dysfunction in children with bacterial or viral infection. METHODS: This cohort study was done in emergency departments and intensive care units of four hospitals in Queensland, Australia, and recruited children aged 1 month to 17 years who, upon admission, underwent a diagnostic test, including blood cultures, for suspected sepsis. Whole-blood RNA sequencing of blood was performed with Illumina NovaSeq (San Diego, CA, USA). Samples with completed phenotyping, monitoring, and RNA extraction by March 31, 2020, were included in the discovery cohort; samples collected or completed thereafter and by Oct 27, 2021, constituted the Rapid Paediatric Infection Diagnosis in Sepsis (RAPIDS) internal validation cohort. An external validation cohort was assembled from RNA sequencing gene expression count data from the observational European Childhood Life-threatening Infectious Disease Study (EUCLIDS), which recruited children with severe infection in nine European countries between 2012 and 2016. Feature selection approaches were applied to derive novel gene signatures for disease class (bacterial vs viral infection) and disease severity (presence vs absence of organ dysfunction 24 h post-sampling). The primary endpoint was the presence of organ dysfunction 24 h after blood sampling in the presence of confirmed bacterial versus viral infection. Gene signature performance is reported as area under the receiver operating characteristic curves (AUCs) and 95% CI. FINDINGS: Between Sept 25, 2017, and Oct 27, 2021, 907 patients were enrolled. Blood samples from 595 patients were included in the discovery cohort, and samples from 312 children were included in the RAPIDS validation cohort. We derived a ten-gene disease

Journal article

Chai GG, Tu Q, Cotta MO, Bauer MJ, Balch R, Okafor C, Comans T, Kruger P, Meyer J, Shekar K, Brady K, Fourie C, Sharp N, Vlad L, Whiley D, Ungerer JPJ, Mcwhinney BC, Farkas A, Paterson DL, Clark JE, Hajkowicz K, Raman S, Bialasiewicz S, Lipman J, Forde BM, Harris PNA, Schlapbach LJ, Coin L, Roberts JA, Irwin ADet al., 2024, Achievement of therapeutic antibiotic exposures using Bayesian dosing software in critically unwell children and adults with sepsis., Intensive Care Med

PURPOSE: Early recognition and effective treatment of sepsis improves outcomes in critically ill patients. However, antibiotic exposures are frequently suboptimal in the intensive care unit (ICU) setting. We describe the feasibility of the Bayesian dosing software Individually Designed Optimum Dosing Strategies (ID-ODS™), to reduce time to effective antibiotic exposure in children and adults with sepsis in ICU. METHODS: A multi-centre prospective, non-randomised interventional trial in three adult ICUs and one paediatric ICU. In a pre-intervention Phase 1, we measured the time to target antibiotic exposure in participants. In Phase 2, antibiotic dosing recommendations were made using ID-ODS™, and time to target antibiotic concentrations were compared to patients in Phase 1 (a pre-post-design). RESULTS: 175 antibiotic courses (Phase 1 = 123, Phase 2 = 52) were analysed from 156 participants. Across all patients, there was no difference in the time to achieve target exposures (8.7 h vs 14.3 h in Phase 1 and Phase 2, respectively, p = 0.45). Sixty-one courses in 54 participants failed to achieve target exposures within 24 h of antibiotic commencement (n = 36 in Phase 1, n = 18 in Phase 2). In these participants, ID-ODS™ was associated with a reduction in time to target antibiotic exposure (96 vs 36.4 h in Phase 1 and Phase 2, respectively, p < 0.01). These patients were less likely to exhibit subtherapeutic antibiotic exposures at 96 h (hazard ratio (HR) 0.02, 95% confidence interval (CI) 0.01-0.05, p < 0.01). There was no difference observed in in-hospital mortality. CONCLUSIONS: Dosing software may reduce the time to achieve target antibiotic exposures. It should be evaluated further in trials to establish its impact on clinical outcomes.

Journal article

Yeoh S, Estrada-Rivadeneyra D, Jackson H, Keren I, Galassini R, Cooray S, Shah P, Agyeman P, Basmaci R, Carrol E, Emonts M, Fink C, Kuijpers T, Martinon-Torres F, Mommert-Tripon M, Paulus S, Pokorn M, Rojo P, Romani L, Schlapbach L, Schweintzger N, Shen C-F, Tsolia M, Usuf E, van der Flier M, Vermont C, von Both U, Yeung S, Zavadska D, Coin L, Cunnington A, Herberg J, Levin M, Kaforou M, Hamilton S, PERFORM, DIAMONDS and UK KD Genetic Consortiaet al., 2024, Plasma Protein Biomarkers Distinguish Multisystem Inflammatory Syndrome in Children From Other Pediatric Infectious and Inflammatory Diseases., Pediatr Infect Dis J

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious hyperinflammatory complication following infection with severe acute respiratory syndrome coronavirus 2. The mechanisms underpinning the pathophysiology of MIS-C are poorly understood. Moreover, clinically distinguishing MIS-C from other childhood infectious and inflammatory conditions, such as Kawasaki disease or severe bacterial and viral infections, is challenging due to overlapping clinical and laboratory features. We aimed to determine a set of plasma protein biomarkers that could discriminate MIS-C from those other diseases. METHODS: Seven candidate protein biomarkers for MIS-C were selected based on literature and from whole blood RNA sequencing data from patients with MIS-C and other diseases. Plasma concentrations of ARG1, CCL20, CD163, CORIN, CXCL9, PCSK9 and ADAMTS2 were quantified in MIS-C (n = 22), Kawasaki disease (n = 23), definite bacterial (n = 28) and viral (n = 27) disease and healthy controls (n = 8). Logistic regression models were used to determine the discriminatory ability of individual proteins and protein combinations to identify MIS-C and association with severity of illness. RESULTS: Plasma levels of CD163, CXCL9 and PCSK9 were significantly elevated in MIS-C with a combined area under the receiver operating characteristic curve of 85.7% (95% confidence interval: 76.6%-94.8%) for discriminating MIS-C from other childhood diseases. Lower ARG1 and CORIN plasma levels were significantly associated with severe MIS-C cases requiring inotropes, pediatric intensive care unit admission or with shock. CONCLUSION: Our findings demonstrate the feasibility of a host protein biomarker signature for MIS-C and may provide new insight into its pathophysiology.

Journal article

Harris PNA, Bauer MJ, Lüftinger L, Beisken S, Forde BM, Balch R, Cotta M, Schlapbach L, Raman S, Shekar K, Kruger P, Lipman J, Bialasiewicz S, Coin L, Roberts JA, Paterson DL, Irwin ADet al., 2024, Rapid nanopore sequencing and predictive susceptibility testing of positive blood cultures from intensive care patients with sepsis., Microbiol Spectr, Vol: 12

We aimed to evaluate the performance of Oxford Nanopore Technologies (ONT) sequencing from positive blood culture (BC) broths for bacterial identification and antimicrobial susceptibility prediction. Patients with suspected sepsis in four intensive care units were prospectively enrolled. Human-depleted DNA was extracted from positive BC broths and sequenced using ONT (MinION). Species abundance was estimated using Kraken2, and a cloud-based system (AREScloud) provided in silico predictive antimicrobial susceptibility testing (AST) from assembled contigs. Results were compared to conventional identification and phenotypic AST. Species-level agreement between conventional methods and AST predicted from sequencing was 94.2% (49/52), increasing to 100% in monomicrobial infections. In 262 high-quality AREScloud AST predictions across 24 samples, categorical agreement (CA) was 89.3%, with major error (ME) and very major error (VME) rates of 10.5% and 12.1%, respectively. Over 90% CA was achieved for some taxa (e.g., Staphylococcus aureus) but was suboptimal for Pseudomonas aeruginosa. In 470 AST predictions across 42 samples, with both high quality and exploratory-only predictions, overall CA, ME, and VME rates were 87.7%, 8.3%, and 28.4%. VME rates were inflated by false susceptibility calls in a small number of species/antibiotic combinations with few representative resistant isolates. Time to reporting from sequencing could be achieved within 8-16 h from BC positivity. Direct sequencing from positive BC broths is feasible and can provide accurate predictive AST for some species. ONT-based approaches may be faster but significant improvements in accuracy are required before it can be considered for clinical use.IMPORTANCESepsis and bloodstream infections carry a high risk of morbidity and mortality. Rapid identification and susceptibility prediction of causative pathogens, using Nanopore sequencing direct from blood cultures, may offer clinical benefit. We assessed this

Journal article

McWhinney B, Ungerer J, LeMarsey R, Phillips N, Raman S, Gibbons K, Schlapbach LJ, Rapid Acute Paediatric Infection Diagnosis in Suspected Sepsis RAPIDS Study Investigatorset al., 2024, Serum Levels of Vitamin C and Thiamin in Children With Suspected Sepsis: A Prospective Observational Cohort Study., Pediatr Crit Care Med, Vol: 25, Pages: 171-176, ISSN: 1529-7535

OBJECTIVES: Vitamin C and thiamin have been trialed as adjunctive therapies in adults with septic shock but their role in critically ill children is unclear. We assessed serum levels of vitamin C and thiamin in children evaluated for sepsis. DESIGN: Single-center prospective observational study. Serum levels of vitamin C and thiamin were measured on admission and association with multiple organ dysfunction syndrome (MODS) was explored using logistic regression. SETTING: Emergency department and PICU in a tertiary children's hospital, Queensland, Australia. PATIENTS: Children greater than 1 month and less than 17 years evaluated for sepsis. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Vitamin levels were determined in 221 children with a median age of 3.5 (interquartile range [IQR] 1.6, 8.3) years. Vitamin C levels were inversely correlated with severity as measured by pediatric Sequential Organ Failure Assessment (Spearman's rho = -0.16, p = 0.018). Median (IQR) vitamin C levels on admission were 35.7 (17.9, 54.1) µmol/L, 36.1 (21.4, 53.7) µmol/L, and 17.9 (6.6, 43.0) µmol/L in children without organ dysfunction, single organ dysfunction, and MODS, respectively (p = 0.017). In multivariable analyses, low levels of vitamin C at the time of sampling were associated with greater odds of MODS (adjusted odds ratio [aOR] 3.04; 95% CI, 1.51-6.12), and vitamin C deficiency was associated with greater odds of MODS at 24 hours after sampling (aOR 3.38; 95% CI, 1.53-7.47). Median (IQR) thiamin levels were 162 (138, 192) nmol/L, 185 (143, 200) nmol/L, and 136 (110, 179) nmol/L in children without organ dysfunction, single organ dysfunction, and MODS, respectively (p = 0.061). We failed to identify an association between thiamin deficiency and either MODS at sampling (OR 2.52; 95% CI, 0.15-40.86) or MODS at 24 hours (OR 2.96; 95% CI, 0.18-48.18). CONCLUSIONS: Critically ill children evaluated for sepsis frequently manifest decreased levels of v

Journal article

Wang C, He Z, Jia R, Pan S, Coin LJ, Song J, Li Fet al., 2024, PLANNER: a multi-scale deep language model for the origins of replication site prediction., IEEE J Biomed Health Inform, Vol: PP

Origins of replication sites (ORIs) are crucial genomic regions where DNA replication initiation takes place, playing pivotal roles in fundamental biological processes like cell division, gene expression regulation, and DNA integrity. Accurate identification of ORIs is essential for comprehending cell replication, gene expression, and mutation-related diseases. However, experimental approaches for ORI identification are often expensive and time-consuming, leading to the growing popularity of computational methods. In this study, we present PLANNER (DeeP LeArNiNg prEdictor for ORI), a novel approach for species-specific and cell-specific prediction of eukaryotic ORIs. PLANNER uses the multi-scale ktuple sequences as input and employs the DNABERT pre-training model with transfer learning and ensemble learning strategies to train accurate predictive models. Extensive empirical test results demonstrate that PLANNER achieved superior predictive performance compared to state-of-the-art approaches, including iOri-Euk, Stack-ORI, and ORI-Deep, within specific cell types and across different cell types. Furthermore, by incorporating an interpretable analysis mechanism, we provide insights into the learned patterns, facilitating the mapping from discovering important sequential determinants to comprehensively analysing their biological functions. To facilitate the widespread utilisation of PLANNER, we developed an online webserver and local stand-alone software, available at http://planner.unimelb-biotools.cloud.edu.au/ and https://github.com/CongWang3/PLANNER, respectively.

Journal article

Zhang S, Shi J, Li X, Tiwari A, Gao S, Zhou X, Sun X, O'Brien JW, Coin L, Hai F, Jiang Get al., 2023, Wastewater-based epidemiology of Campylobacter spp.: A systematic review and meta-analysis of influent, effluent, and removal of wastewater treatment plants, SCIENCE OF THE TOTAL ENVIRONMENT, Vol: 903, ISSN: 0048-9697

Journal article

Jackson HR, Zandstra J, Menikou S, Hamilton MS, McArdle AJ, Fischer R, Thorne AM, Huang H, Tanck MW, Jansen MH, De T, Agyeman PKA, Von Both U, Carrol ED, Emonts M, Eleftheriou I, Van der Flier M, Fink C, Gloerich J, De Groot R, Moll HA, Pokorn M, Pollard AJ, Schlapbach LJ, Tsolia MN, Usuf E, Wright VJ, Yeung S, Zavadska D, Zenz W, Coin LJM, Casals-Pascual C, Cunnington AJ, Martinon-Torres F, Herberg JA, de Jonge MI, Levin M, Kuijpers TW, Kaforou M, PERFORM consortiumet al., 2023, A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study, The Lancet: Digital Health, Vol: 5, Pages: e774-e785, ISSN: 2589-7500

BACKGROUND: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-

Journal article

Li F, Wang C, Guo X, Akutsu T, Webb GI, Coin LJM, Kurgan L, Song Jet al., 2023, ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction., Brief Bioinform, Vol: 24

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.

Journal article

Zhang S, Shi J, Li X, Coin L, O'Brien JW, Sivakumar M, Hai F, Jiang Get al., 2023, Triplex qPCR assay for Campylobacter jejuni and Campylobacter coli monitoring in wastewater, SCIENCE OF THE TOTAL ENVIRONMENT, Vol: 892, ISSN: 0048-9697

Journal article

Habgood-Coote D, Wilson C, Shimizu C, Barendregt AM, Philipsen R, Galassini R, Calle IR, Workman L, Agyeman PKA, Ferwerda G, Anderson ST, van den Berg JM, Emonts M, Carrol ED, Fink CG, de Groot R, Hibberd ML, Kanegaye J, Nicol MP, Paulus S, Pollard AJ, Salas A, Secka F, Schlapbach LJ, Tremoulet AH, Walther M, Zenz W, Pediatric Emergency Medicine Kawasaki Disease Research Group PEMKDRG, UK Kawasaki Genetics consortium, GENDRES consortium, EUCLIDS consortium, PERFORM consortium, Van der Flier M, Zar HJ, Kuijpers T, Burns JC, Martinón-Torres F, Wright VJ, Coin LJM, Cunnington AJ, Herberg JA, Levin M, Kaforou Met al., 2023, Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature, Med, Vol: 4, Pages: 635-654.e5, ISSN: 2666-6340

BACKGROUND: Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood. METHODS: A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children. FINDINGS: We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures. CONCLUSIONS: Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis. FUNDING: European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC.

Journal article

Li F, Guo X, Bi Y, Jia R, Pitt ME, Pan S, Li S, Gasser RB, Coin LJM, Song Jet al., 2023, Digerati-A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins, COMPUTERS IN BIOLOGY AND MEDICINE, Vol: 163, ISSN: 0010-4825

Journal article

Herberg J, Shah P, Voice M, Calvo-Bado L, Rivero Calle I, Morris S, Nijman R, Broderick C, De T, Eleftheriou I, Galassini R, Khanijau A, Kolberg L, Kolnik M, Rudzate A, Sagmeister M, Schweintzger N, Secka F, Thakker C, van der Velden F, Vermont C, Vincek K, Agyeman P, Cunnington A, de Groot R, Emonts M, Fidler K, Kuijpers T, Mommert-Tripon M, Brengel-Pesce K, Mallet F, Moll H, Paulus S, Pokorn M, Pollard A, Schlapbach L, Shen C-F, Tsolia M, Usuf E, Van Der Flier M, von Both U, Yeung S, Zavadska D, Zenz W, Wright V, Carrol E, Kaforou M, Martinon-Torres F, Fink C, Levin M, PERFORM consortiumet al., 2023, Relationship between molecular pathogen detection and clinical disease in febrile children across Europe: a multicentre, prospective observational study, The Lancet Regional Health. Europe, Vol: 32, Pages: 1-17, ISSN: 2666-7762

The PERFORM study aimed to understand causes of febrile childhood illness by comparing molecular pathogen detection with current clinical practice. Methods. Febrile children and controls were recruited on presentation to hospital in 9 European countries 2016-2020. Each child was assigned a standardized diagnostic category based on retrospective review of local clinical and microbiological data. Subsequently, centralised molecular tests (CMTs) for 19 respiratory and 27 blood pathogens were performed.Findings. Of 4,611 febrile children, 643 (14%) were classified as definite bacterial infection (DB), 491 (11%) as definite viral infection (DV), and 3,477 (75%) had uncertain aetiology. 1,061 controls without infection were recruited. CMTs detected blood bacteria more frequently in DB than DV cases for N.meningitidis (OR: 3.37, 95% CI: 1.92 – 5.99), S.pneumoniae (OR: 3.89, 95% CI: 2.07 – 7.59), Group A streptococcus (OR 2.73, 95% CI 1.13 – 6.09) and E.coli (OR 2.7, 95% CI 1.02 – 6.71). Respiratory viruses were more common in febrile children than controls, but only influenza A (OR 0.24, 95% CI 0.11 – 0.46), Influenza B (OR 0.12, 95% CI 0.02 – 0.37) and RSV (OR 0.16, 95% CI: 0.06 – 0.36) were less common in DB than DV cases. Of 16 blood viruses, enterovirus (OR 0.43, 95% CI 0.23 – 0.72) and EBV (OR 0.71, 95% CI 0.56 – 0.90) were detected less often in DB than DV cases. Combined local diagnostics and CMTs respectively detected blood viruses and respiratory viruses in 360 (56%) and 161 (25%) of DB cases, and virus detection ruled-out bacterial infection poorly, with predictive values of 0.64 and 0.68 respectively. Interpretation. Most febrile children cannot be conclusively defined as having bacterial or viral infection when molecular tests supplement conventional approaches. Viruses are detected in most patients with bacterial infections, and the clinical value of individual pathogen detection in determining treatment is

Journal article

Bader SM, Cooney JP, Sheerin D, Taiaroa G, Harty L, Davidson KC, Mackiewicz L, Dayton M, Wilcox S, Whitehead L, Rogers KL, Georgy SR, Coussens AK, Grimley SL, Corbin V, Pitt M, Coin L, Pickering R, Thomas M, Allison CC, McAuley J, Purcell DFJ, Doerflinger M, Pellegrini Met al., 2023, SARS-CoV-2 mouse adaptation selects virulence mutations that cause TNF-driven age-dependent severe disease with human correlates., Proc Natl Acad Sci U S A, Vol: 120

The diversity of COVID-19 disease in otherwise healthy people, from seemingly asymptomatic infection to severe life-threatening disease, is not clearly understood. We passaged a naturally occurring near-ancestral SARS-CoV-2 variant, capable of infecting wild-type mice, and identified viral genomic mutations coinciding with the acquisition of severe disease in young adult mice and lethality in aged animals. Transcriptomic analysis of lung tissues from mice with severe disease elucidated a host antiviral response dominated mainly by interferon and IL-6 pathway activation in young mice, while in aged animals, a fatal outcome was dominated by TNF and TGF-β signaling. Congruent with our pathway analysis, we showed that young TNF-deficient mice had mild disease compared to controls and aged TNF-deficient animals were more likely to survive infection. Emerging clinical correlates of disease are consistent with our preclinical studies, and our model may provide value in defining aberrant host responses that are causative of severe COVID-19.

Journal article

Hall MB, Lima L, Coin LJM, Iqbal Zet al., 2023, Drug resistance prediction for Mycobacterium tuberculosis with reference graphs, MICROBIAL GENOMICS, Vol: 9, ISSN: 2057-5858

Journal article

Zhu Y, Li F, Guo X, Wang X, Coin LJM, Webb G, Song J, Jia Cet al., 2023, TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters, BRIEFINGS IN BIOINFORMATICS, ISSN: 1467-5463

Journal article

Jackson HR, Miglietta L, Habgood-Coote D, D'Souza G, Shah P, Nichols S, Vito O, Powell O, Davidson MS, Shimizu C, Agyeman PKA, Beudeker CR, Brengel-Pesce K, Carrol ED, Carter MJ, De T, Eleftheriou I, Emonts M, Epalza C, Georgiou P, De Groot R, Fidler K, Fink C, van Keulen D, Kuijpers T, Moll H, Papatheodorou I, Paulus S, Pokorn M, Pollard AJ, Rivero-Calle I, Rojo P, Secka F, Schlapbach LJ, Tremoulet AH, Tsolia M, Usuf E, Van Der Flier M, Von Both U, Vermont C, Yeung S, Zavadska D, Zenz W, Coin LJM, Cunnington A, Burns JC, Wright V, Martinon-Torres F, Herberg JA, Rodriguez-Manzano J, Kaforou M, Levin Met al., 2023, Diagnosis of multisystem inflammatory syndrome in children by a whole-blood transcriptional signature, Journal of the Pediatric Infectious Diseases Society, Vol: 12, Pages: 322-331, ISSN: 2048-7207

BACKGROUND: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

Journal article

Chen A, Sun J, Viljoen A, Mostert D, Xie Y, Mangila L, Bothma S, Lyons R, Hribova E, Christelova P, Uwimana B, Amah D, Pearce S, Chen N, Batley J, Edwards D, Dolezel J, Crisp P, Brown AF, Martin G, Yahiaoui N, D'Hont A, Coin L, Swennen R, Aitken EABet al., 2023, Genetic Mapping, Candidate Gene Identification and Marker Validation for Host Plant Resistance to the Race 4 of <i>Fusarium oxysporum</i> f. sp. <i>cubense</i> Using <i>Musa acuminata</i> ssp. <i>malaccensis</i>, PATHOGENS, Vol: 12

Journal article

Chen R, Li F, Guo X, Bi Y, Li C, Pan S, Coin LJM, Song Jet al., 2023, ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species, BRIEFINGS IN BIOINFORMATICS, Vol: 24, ISSN: 1467-5463

Journal article

Zhang S, Shi J, Sharma E, Li X, Gao S, Zhou X, O'Brien J, Coin L, Liu Y, Sivakumar M, Hai F, Jiang Get al., 2023, In-sewer decay and partitioning of<i> Campylobacter</i><i> jejuni</i> and<i> Campylobacter</i><i> coli</i> and implications for their wastewater surveillance, WATER RESEARCH, Vol: 233, ISSN: 0043-1354

Journal article

Davies MRR, Keller N, Brouwer S, Jespersen MGG, Cork AJJ, Hayes AJ, Pitt MEE, De Oliveira DMP, Harbison-Price N, Bertolla OMM, Mediati DGG, Curren BFF, Taiaroa G, Lacey JAA, Smith HVV, Fang N-X, Coin LJM, Stevens K, Tong SYC, Sanderson-Smith M, Tree JJJ, Irwin ADD, Grimwood K, Howden BPP, Jennison AVV, Walker MJJet al., 2023, Detection of <i>Streptococcus pyogenes</i> M1<sub>UK</sub> in Australia and characterization of the mutation driving enhanced expression of superantigen SpeA, NATURE COMMUNICATIONS, Vol: 14

Journal article

Chen A, Sun J, Martin G, Gray L-A, Hribova E, Christelova P, Yahiaoui N, Rounsley S, Lyons R, Batley J, Chen N, Hamill S, Rai SK, Coin L, Uwimana B, D'Hont A, Dolezel J, Edwards D, Swennen R, Aitken EABet al., 2023, Identification of a Major QTL-Controlling Resistance to the Subtropical Race 4 of <i>Fusarium oxysporum</i> f. sp. <i>cubense</i> in <i>Musa acuminata</i> ssp. <i>malaccensis</i>, PATHOGENS, Vol: 12

Journal article

Boeddha NP, Atkins L, de Groot R, Driessen G, Hazelzet J, Zenz W, Carrol ED, Anderson ST, Martinon-Torres F, Agyeman PKA, Galassini R, Herberg J, Levin M, Schlapbach LJ, Emonts Met al., 2023, Group A streptococcal disease in paediatric inpatients: a European perspective (Vol 182, pg 697, 2023), EUROPEAN JOURNAL OF PEDIATRICS, ISSN: 0340-6199

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