Victoria J. Wright’s research while affiliated with Imperial College London and other places


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Publications (46)


Clinical test results of children with MIS-C
A Graphic summary of the study. Children with MIS-C or COVID-19 pneumonia were studied alongside control paediatric patients and healthy volunteers. There were 4 major parts to the study: 48-plex biomarker assay of plasma samples, high dimensional CyToF study of cell surface markers, scRNA-Seq, and whole blood functional assays. Child icon was from Microsoft PowerPoint 2020. B Clinical test results of MIS-C patients including white blood cell, neutrophil, lymphocyte and monocyte counts, C-Reactive Protein (CRP), ferritin, D-dimer, alanine transaminase (ALT) and troponin levels. Green area indicates the reference range¹. C Correlation matrix of clinical test results. Statistical significance (p < 0.05) is indicated with star symbols (without adjustment for multiple comparisons by FDR method unless there was a black box outline), with red and blue colours indicating positive and negative correlation respectively. D Table of demographic and ethnicity information for the study subjects. Median and interquartile range (IQR) are provided for age and weight data. Source data are provided as a Source Data file.
Multiplex cytokine analysis identifies prominent Th1 plasma inflammatory markers in acute MIS-C
Plasma cytokine and chemokine levels were measured for 3 groups of acutely ill paediatric patients: MIS-C, PICU COVID-19 pneumonia and LRTI patients (n = 38, n = 8 and n = 22 respectively). Horizontal line indicates the median value of each group. A PCA analysis of 48-plex Luminex assay results showing the ten most variable analytes; different shapes & colours are used for each patient group. B–R Results for 17 of the 48 cytokines and chemokines analysed, including IL-12 p40, IL-18 and IFN-γ. Star symbols above a straight line denoted significant changes for the initial Kruskal–Wallis test while those above the capped line denote significant changes for the subsequent Dunn’s multiple comparison test. S LBP levels in acute plasma samples of MIS-C and LRTI groups (n = 5 and 7, respectively. P = 0.0025). T LPS levels in acute plasma samples of MIS-C and respiratory groups (n = 14 and 22, respectively. P = 0.65). U IL-18BPa levels in acute plasma samples of MIS-C and LRTI groups. (n = 15 and 17, respectively. P < 0.0001). V Free IL-18 levels calculated by comparing IL-18 and IL-18 BPa levels for the samples analysed in (U); p = 0.77. Data shown in (S–V) were analysed by Mann–Whitney test. Horizontal lines in the graphs indicate the median for each group. Source data are provided as a Source Data file.
Deep CyToF phenotyping of TCR Vβ21.3 T cells in MIS-C
Cryopreserved PBMCs from healthy volunteers (n = 7) or patients with: acute MIS-C (n = 17), MIS-C follow-up (n = 7), PICU COVID-19 pneumonia (n = 10) or acute paediatric infection (n = 14, mixed chest, gastrointestinal and systemic infections) were analysed by CyTOF. A Opt-SNE plotting of non-naïve CD3+ T cells (selected by Boolean gating of CD27+ CD45RA+ cells) using a maximum of 10,000 cells per sample. Note that the TCR Vβ21.3 parameter was not used to construct the tSNE projection. For each plot, colours show expression levels of the following markers from each group: TCR Vβ21.3, CD38, HLA-DR, ICOS, CD28, IL-18R, PD-1 and CD39. B Median metal staining intensities of CD38, HLA-DR, ICOS and IL-18R were quantified in TCR Vβ21.3± non-naïve CD4 and CD8 T cells identified by human gating of the data. Two-way ANOVA was conducted with Šídák’s multiple comparisons test to compare between TCR Vβ21.3+ and TCR Vβ21.3– cells for each group. C Comparison of IL-18R, PD-1, ICOS and CD28 levels between TCR Vβ21.3+ and TCR Vβ21.3 activated T cells (cells positive for HLA-DR+ and CD38+) Two-way ANOVA was conducted with Šídák’s multiple comparisons test to compare between TCR Vβ21.3+ and – cells for each group. D Percentage of TCR Vβ21.3+ cells in CD8 T cells (divided by differentiation state, based on CD45RA and CD27 levels) subsets. Ordinary one-way ANOVA test was used to compare the five groups, with Dunn’s multiple comparisons test to compare each group to the MIS-C group. Naïve T cells were CD45RA+ CD27+, central memory (CM) CD45RA− CD27+, effector memory (EM) CD45RA− CD27− and terminal effector (TE) CD45RA+ CD27. MIS-C (F.Up): MIS-C follow-up. E Percentage of TCR Vβ21.3+ cells in CD4+ T cells (divided into Th1, Th2, Th17 and Treg subsets based on chemokine receptor expression). Ordinary one-way ANOVA test was used to compare the five groups, with Dunn’s multiple comparisons test to compare each group to the MIS-C group. Horizontal lines in graphs in (B–E) indicate the median for each group. Source data are provided as a Source Data file.
CD16+ NK cells and monocytes of acute MIS-C patients have increased cell surface levels of IL-18R and CD95
Monocyte and NK cell populations were analysed in (A–K) in the same experiment groups shown in Fig. 3: acute MIS-C (n = 17), MIS-C follow-up (n = 7), PICU COVID-19 pneumonia (n = 10) or acute paediatric infection (n = 14, mixed chest, gastrointestinal and systemic infections). Frequency in PBMCs of A total NK cells or B CD16+ NK cells and median metal staining intensity of C CD95 or D IL-18R on CD16+ NK cells. E–G show the proportion of each subject’s monocytes classified into the three canonical subsets: classical (CD14++ CD16−), intermediate (CD14+ CD16+), or non-classical (CD14+ CD16++) monocytes. H–O show the MFI of the indicated proteins on the surface of classical monocytes using data from the T cell (H–K) or monocyte (L–O) CyTOF antibody panels (healthy children (n = 7), acute MIS-C (n = 13), MIS-C follow-up (n = 6), acute paediatric COVID-19 pneumonia (n = 9) or acute paediatric infection patients (n = 12, mixed chest, gastrointestinal and systemic infections)). Horizontal line indicates the median value of each group tested. Statistical testing was performed using ordinary one-way ANOVA; results of Dunnett’s multiple comparisons test comparing the acute MIS-C group to all other groups is shown in (A–D, K) and comparing healthy children to all other groups in (H–J, L–O). P Receiver operating characteristic curves of the indicated markers on particular cell subsets for MIS-C diagnosis (n = 17, acute samples) with infection samples as control (n = 14). Area under the curve values are shown in each graph. All four marker/cell combinations were significantly different between the two groups. Q Correlation between plasma IL-18 levels and frequency of the indicated cell populations. Pearson correlation coefficient and significance are shown on each graph. N = 9. Source data are provided as a Source Data file.
Lack of overaction of NLRP3 inflammation activation in MIS-C and increased active caspase 8 activity in MIS-C monocytes
Whole blood samples from children with MIS-C (n = 14), acute COVID-19 pneumonia (COVID-19, n = 15), other paediatric admissions (Paediatric Admission, n = 16) and MIS-C follow-up patients (MIS-C_Follow, collected about 1 month post hospital discharge, n = 6) were stimulated in vitro with LPS (50 ng/ml), ATP (5 mM), or LPS plus ATP (50 ng/ml and 5 mM respectively, ATP was added 3 h after the LPS addition). Cytokines were measured 4 h and 24 h after stimulation. Levels of IL-1β, IL-18 and TNF-α levels were shown in (A–C). The lower limit of quantification of IL-1β is about 18 pg/ml, 16.5 pg/ml for IL-18 and 7.3 pg/ml for TNF-α (grey-shaded areas). In the MIS-C group, samples with glucocorticoids/IVIG treatment in the last 24 h before sampling were marked as unfilled circle. Horizontal line and error bars show the mean and standard deviation. Results of two-way ANOVA with post hoc Tukey’s multiple comparisons test between the first 3 groups (all collected at the acute stage) within each treatment condition are shown. There were no significant differences between the MIS-C-Follow and paediatric admission group by multiple Mann–Whitney tests with each treatment condition. D Monocyte active caspase staining signals from 5 MIS-C and 6 COVID-19 blood monocyte samples. Left: histograms of active caspase 8 staining (red lines indicate MIS-C, blue COVID-19). Right: Quantification of active caspase 8 median fluorescence intensity. Mann–Whitney test was used to compare the 2 groups. Source data are provided as a Source Data file.

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Enhanced CD95 and interleukin 18 signalling accompany T cell receptor Vβ21.3+ activation in multi-inflammatory syndrome in children
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May 2024

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89 Reads

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1 Citation

Nature Communications

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Iain R. L. Kean

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Multisystem inflammatory syndrome in children is a post-infectious presentation SARS-CoV-2 associated with expansion of the T cell receptor Vβ21.3+ T-cell subgroup. Here we apply muti-single cell omics to compare the inflammatory process in children with acute respiratory COVID-19 and those presenting with non SARS-CoV-2 infections in children. Here we show that in Multi-Inflammatory Syndrome in Children (MIS-C), the natural killer cell and monocyte population demonstrate heightened CD95 (Fas) and Interleuking 18 receptor expression. Additionally, TCR Vβ21.3+ CD4+ T-cells exhibit skewed differentiation towards T helper 1, 17 and regulatory T cells, with increased expression of the co-stimulation receptors ICOS, CD28 and interleukin 18 receptor. We observe no functional evidence for NLRP3 inflammasome pathway overactivation, though MIS-C monocytes show elevated active caspase 8. This, coupled with raised IL18 mRNA expression in CD16- NK cells on single cell RNA sequencing analysis, suggests interleukin 18 and CD95 signalling may trigger activation of TCR Vβ21.3+ T-cells in MIS-C, driven by increased IL-18 production from activated monocytes and CD16- Natural Killer cells.

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Figure 4 A, B, C Number of febrile episodes with 'bacterial' and 'viral' phenotype receiving antibiotics in relation to the presumed etiology of the initial syndrome classification
Raising AWaRe-ness of Antimicrobial Stewardship Challenges in Pediatric Emergency Care: Results from the PERFORM Study Assessing Consistency and Appropriateness of Antibiotic Prescribing Across Europe

October 2023

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152 Reads

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2 Citations

Clinical Infectious Diseases

Objectives Optimization of antimicrobial stewardship (AMS) is key to tackling antimicrobial resistance (AMR), which is exacerbated by over-prescription of antibiotics in pediatric Emergency Departments (EDs). We described patterns of empiric antibiotic use in European EDs, and characterized appropriateness and consistency of prescribing. Methods Between August 2016 and December 2019 febrile children attending the ED in nine European countries with suspected infection were recruited into the PERFORM (Personalised Risk assessment in Febrile illness to Optimise Real-life Management) study. Empiric systemic antibiotic use was determined in view of assigned final ‘bacterial’ or ‘viral’ phenotype. Antibiotics were classified according to WHO AWaRe. Results Of 2130 febrile episodes (excluding children with non-bacterial/non-viral phenotypes), 1549 (72.7%) were assigned a ‘bacterial’ and 581 (27.3%) a ‘viral’ phenotype. A total of 1318/1549 (85.1%) episodes with a ‘bacterial’ and 269/581 (46.3%) with a ‘viral’ phenotype received empiric systemic antibiotics (first two days of admission). Of those, the majority (87.8% in ‘bacterial’ and 87.0% in ‘viral’ group) received parenteral antibiotics. The top three antibiotics prescribed were third-generation cephalosporins, penicillins and penicillin/beta-lactamase inhibitor combinations. Of those treated with empiric systemic antibiotics in the ‘viral’ group 216/269 (80.3%) received ≥ one Watch antibiotic. Conclusions Differentiating bacterial from viral etiology in febrile illness on initial ED presentation remains challenging, resulting in a substantial over-prescription of antibiotics. A significant proportion of patients with a ‘viral’ phenotype received systemic antibiotics, predominantly classified as WHO Watch. Rapid and accurate point-of-care tests in the ED differentiating between bacterial and viral etiology, could significantly improve AMS.


Competitive Amplification Networks enable molecular pattern recognition with PCR

July 2023

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26 Reads

Gene expression has great potential to be used as a clinical diagnostic tool. However, despite the progress in identifying these gene expression signatures, clinical translation has been hampered by a lack of purpose-built, readily deployable testing platforms. We have developed Competitive Amplification Networks (CANs) to enable analysis of an entire gene expression signature in a single PCR reaction. CANs consist of natural and synthetic amplicons that compete for shared primers during amplification, forming a reaction network that leverages the molecular machinery of PCR. These reaction components are tuned such that the final fluorescent signal from the assay is exactly calibrated to the conclusion of a statistical model. In essence, the reaction acts as a biological computer, simultaneously detecting the RNA targets, interpreting their level in the context of the gene expression signature, and aggregating their contributions to the final diagnosis. We illustrate the clinical validity of this technique, demonstrating perfect diagnostic agreement with the gold-standard approach of measuring each gene independently. Crucially, CAN assays are compatible with existing qPCR instruments and workflows. CANs hold the potential to enable rapid deployment and massive scalability of gene expression analysis to clinical laboratories around the world, in highly developed and low-resource settings alike. Abstract Figure


Figure 3: Performance of the FS-PLS signature in the pre-COVID-19 prospective validation cohort Boxplots showing the FS-PLS signature score (A), CRP (C), and leukocyte count (E) in the prospective validation cohort comparing different infection categories. ROC curves of the FS-PLS signature (B) CRP (D), and leukocyte count (F) for definite bacterial versus definite viral comparison. Boxplots show mean and IQR and the horizontal dashed line corresponds to the threshold that maximises the Youden's J statistic. In the ROC plots, the shaded areas represent 95% CIs plotted for sensitivity at given in-sample specificities. AUC=area under the curve. CRP=C-reactive protein. FS-PLS=forward selection-partial least squares. ROC=receiver operating characteristic.
Figure 4: Decision curve analysis in the pre-COVID-19 prospective validation cohort Net benefit for FS-PLS, CRP, or leukocyte count measurements to discriminate between definite bacterial versus others (A), definite and probable bacterial versus others (B), definite viral versus others (C), and definite and probable viral versus others (D). In each analysis, these biomarkers are benchmarked against a treat all or treat none approach. All curves are smoothed using locally estimated scatterplot smoothing. The analysis includes complete cases for which data are available for all three measurements (n=186). CRP=C-reactive protein. FS-PLS=forward selection-partial least squares.
Figure 5: Performance of the FS-PLS signature in the COVID-19 validation cohort Boxplot showing the FS-PLS signature score (A), CRP (C), and leukocyte count (E) in the COVID-19 validation cohort comparing definite bacterial and COVID-19 groups. ROC curve of the FS-PLS signature (B), CRP (D), and leukocyte count (F) for the definite bacterial versus definite COVID-19 comparison. Boxplots show mean and IQR and the horizontal dashed line corresponds to the threshold that maximises the Youden's J statistic. In the ROC plots, the shaded areas represent 95% CIs plotted for sensitivity at given in-sample specificities. AUC=area under the curve. CRP=C-reactive protein. FS-PLS=forward selection-partial least squares. ROC=receiver operating characteristic. Definite bacterial infection (n=35) SARS-CoV-2 (n=34)
Discovery and validation of a three-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations: a case-control and observational cohort study

August 2021

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105 Reads

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23 Citations

The Lancet Microbe

Background Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We aimed to derive and validate a blood transcriptional signature to detect viral infections, including COVID-19, among adults with suspected infection who presented to the emergency department. Methods Individuals (aged ≥18 years) presenting with suspected infection to an emergency department at a major teaching hospital in the UK were prospectively recruited as part of the Bioresource in Adult Infectious Diseases (BioAID) discovery cohort. Whole-blood RNA sequencing was done on samples from participants with subsequently confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes that met additional filtering criteria were subjected to feature selection to derive the most parsimonious discriminating signature. We validated the signature via RT-qPCR in a prospective validation cohort of participants who presented to an emergency department with undifferentiated fever, and a second case-control validation cohort of emergency department participants with PCR-positive COVID-19 or bacterial infection. We assessed signature performance by calculating the area under receiver operating characteristic curves (AUROCs), sensitivities, and specificities. Findings A three-gene transcript signature, comprising HERC6, IGF1R, and NAGK, was derived from the discovery cohort of 56 participants with bacterial infections and 27 with viral infections. In the validation cohort of 200 participants, the signature differentiated bacterial from viral infections with an AUROC of 0·976 (95% CI 0·919−1·000), sensitivity of 97·3% (85·8−99·9), and specificity of 100% (63·1−100). The AUROC for C-reactive protein (CRP) was 0·833 (0·694−0·944) and for leukocyte count was 0·938 (0·840−0·986). The signature achieved higher net benefit in decision curve analysis than either CRP or leukocyte count for discriminating viral infections from all other infections. In the second validation analysis, which included SARS-CoV-2-positive participants, the signature discriminated 35 bacterial infections from 34 SARS-CoV-2-positive COVID-19 infections with AUROC of 0·953 (0·893−0·992), sensitivity 88·6%, and specificity of 94·1%. Interpretation This novel three-gene signature discriminates viral infections, including COVID-19, from other emergency infection presentations in adults, outperforming both leukocyte count and CRP, thus potentially providing substantial clinical utility in managing acute presentations with infection. Funding National Institute for Health Research, Medical Research Council, Wellcome Trust, and EU-FP7.



Figure 1 Flow chart of recruitment to the Biomarkers of Acute Serious Illness in Children study.
Table 3 Course during intensive care stay (N=674)
Cohort profile of the Biomarkers of Acute Serious Illness in Children (BASIC) study: a prospective multicentre cohort study in critically ill children

November 2018

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100 Reads

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4 Citations

BMJ Open

Purpose Despite significant progress, challenges remain in the management of critically ill children, including early identification of infection and organ failure and robust early risk stratification to predict poor outcome. The Biomarkers of Acute Serious Illness in Children study aims to identify genetic and biological pathways underlying the development of critical illness in infections and organ failure and those leading to poor outcome (death or severe disability) in children requiring emergency intensive care. Participants We recruited a prospective cohort of critically ill children undergoing emergency transport to four paediatric intensive care units (PICUs) in Southeast England between April 2014 and December 2016. Findings to date During the study period, 1017 patients were recruited by the regional PICU transport team, and blood and urine samples were obtained at/around first contact with the patient by the transport team. Consent for participation in the study was deferred until after PICU admission and 674 parents/carers were consented. Further samples (blood, urine, stool and throat swabs) were collected after consent. Samples were processed and stored for genomic, transcriptomic, proteomic and metabolomic analyses. Demographic, clinical and laboratory data at first contact, during PICU stay and at discharge, were collected, as were detailed data regarding infectious or non-infectious aetiology. In addition, 115 families have completed 12-month validated follow-up questionnaires to assess quality of life and child behaviour. The first phase of sample analyses (transcriptomic profiling) is currently in progress. Future plans Stored samples will be analysed using genomic, proteomic and metabolic profiling. Advanced bioinformatics techniques will be used to identify biomarkers for early diagnosis of infection, identification of organ failure and risk stratification to predict poor outcome (death/severe disability). Trial registration number NCT03238040 .


Modelled temporal changes in gene expression
A. Heat map showing modelled changes in expression of the significant gene transcripts in TBM patients from the time of diagnosis (0) to 180 days. Green represents lower transcript abundance, red represents higher transcript abundance and black represents no difference in expression as compared to healthy children with a past history of TB sampled at least one year after diagnosis and treatment. The relative degree of transcript abundance is indicated by the colour intensity derived from the fitted mean expression levels over time (see methods). Genes showing similar temporal patterns of expression have been clustered together. The apparent linear change in colour is derived from the statistical model that interpolates the observed time points and can therefore be represented as a continuum. B and C. Example plots of two significantly differentially expressed gene transcripts. Expression levels for each TBM patient (red circles n = 9) are shown from diagnosis (time 0) to day 180. Blue circles are expression levels for healthy children (n = 9) with a past history of TB sampled at least one year after diagnosis and treatment. M = “minus” and denotes the log2 ratio of the red and green channels. The line represents the fitted mean gene expression level over time, from linear mixed-effects model (see methods). 1b = TARP; 1c = IL1R2.
Confirmation of significantly differentially expressed genes from Cohort 1 in Cohort 2
A. Average log fold change in the SDE transcripts identified in the time-course study (cohort 1) and their corresponding log fold change in the single time-point study (cohort 2). 140/262 transcripts identified in cohort 1 were measured in cohort 2. 129 transcripts followed the same regulation pattern (purple crosses); and 11 showed opposite regulation (represented by red crosses, annotated by gene symbol). Correlation coefficient was r2 = 0.78, 95% CI = [0.71, 0.82] p<2x10-16. The y-axis shows log fold change of SDE gene transcripts in cohort 1 relative to cohort 1 Healthy Controls (HC), and the x-axis shows their log fold change in cohort 2 relative to cohort 2 HC. B. Average log fold change in TBM patients relative to cohort 2 HC (x-axis) plotted against average log fold change in PTB patients relative to cohort 2 HC (y-axis) of the significant transcripts (140) that were identified in cohort 1 and common to both cohorts. Least-squares fitted line is shown in dashes. Correlation coefficient was r² = 0.71, 95% CI = [0.62, 0.79] p<2x10⁻¹⁶. C. Heat map showing almost complete discrimination between TBM cases from cohort 1 and cohort 2 and healthy controls (cohort 2) using 129 transcripts significantly differentially expressed in both cohorts. Gene list is provided in Table C in S1 File. Hierarchical clustering was performed by the complete linkage method to identify similar clusters. Solid red bar (top) shows cases, green bar shows controls. Intensity of colour indicates degree of reduced (green) or elevated (red) abundance of each transcript relative to healthy controls. White indicates no expression.
Gene expression of T-cell receptor signalling pathway and validation
A. Transcripts that were SDE in TBM patients at admission compared to the 6 month time point that mapped to the T-cell receptor signalling pathway. After activation of the T-cell receptor, a cascade of signalling events is initiated leading to gene induction. Gene products highlighted green are significantly less abundant in TBM patients at admission compared to the 6 month time point. Corrected p value on Ingenuity Pathways Analysis = 1.47E⁻¹¹. Gene list provided in Tables D and E in S1 File. B. Validation of T-cell signalling pathway genes by RT-PCR in TBM patients (cohort 1). Selected genes in the T-cell signalling pathway were validated by RT-PCR including seven that were significantly less abundant at admission compared to post treatment (TRA, ZAP70, CD3G, CD3D, LAT, LCK, NFATC2) and one showing no change (NFATC3). Two genes were also included that were more abundant at admission compared to post treatment (AREG, SLC7A5) that acted as the positive controls. Fold change between TBM patients at admission and post treatment (n = 8) are shown relative to Beta actin control. Boxes show 25th and 75th percentile. Whiskers show lowest and highest data point and horizontal lines show medians. * p<0.05, ** p<0.01 shows significance using paired Wilcoxon rank test.
Functional T-cell responses in cohort 3
A. Adjusted T-cell proliferative responses (cpm) to PHA in acute TB (TBM n = 19, EPTB n = 29, PTB n = 27) and healthy Mantoux positive controls n = 26. Normalised proliferative responses were determined by deducting the value for the unstimulated well from that of the PHA well. Means are shown by horizontal bars together with standard error of the mean. Asterisk denotes significant differences in corrected p values. PTB vs HC * p = 0.018, TBM vs HC ** p = 0.001, EPTB vs HC *** p<0.0003. B. IFNγ production in response to PHA in acute TB (TBM n = 36, other EPTB n = 57, PTB n = 55) and healthy Mantoux positive controls (HC) n = 75. Medians are shown by horizontal bars together with their interquartile ranges. Asterisk denotes significant difference in corrected p value between TBM and controls * p<0.0003.
Childhood tuberculosis is associated with decreased abundance of T cell gene transcripts and impaired T cell function

November 2017

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123 Reads

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18 Citations

The WHO estimates around a million children contract tuberculosis (TB) annually with over 80 000 deaths from dissemination of infection outside of the lungs. The insidious onset and association with skin test anergy suggests failure of the immune system to both recognise and respond to infection. To understand the immune mechanisms, we studied genome-wide whole blood RNA expression in children with TB meningitis (TBM). Findings were validated in a second cohort of children with TBM and pulmonary TB (PTB), and functional T-cell responses studied in a third cohort of children with TBM, other extrapulmonary TB (EPTB) and PTB. The predominant RNA transcriptional response in children with TBM was decreased abundance of multiple genes, with 140/204 (68%) of all differentially regulated genes showing reduced abundance compared to healthy controls. Findings were validated in a second cohort with concordance of the direction of differential expression in both TBM (r² = 0.78 p = 2x10⁻¹⁶) and PTB patients (r² = 0.71 p = 2x10⁻¹⁶) when compared to a second group of healthy controls. Although the direction of expression of these significant genes was similar in the PTB patients, the magnitude of differential transcript abundance was less in PTB than in TBM. The majority of genes were involved in activation of leucocytes (p = 2.67E⁻¹¹) and T-cell receptor signalling (p = 6.56E⁻⁰⁷). Less abundant gene expression in immune cells was associated with a functional defect in T-cell proliferation that recovered after full TB treatment (p<0.0003). Multiple genes involved in T-cell activation show decreased abundance in children with acute TB, who also have impaired functional T-cell responses. Our data suggest that childhood TB is associated with an acquired immune defect, potentially resulting in failure to contain the pathogen. Elucidation of the mechanism causing the immune paresis may identify new treatment and prevention strategies.


Diagnosis of Bacterial Infection Using a 2-Transcript Host RNA Signature in Febrile Infants 60 Days or Younger

April 2017

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27 Reads

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42 Citations

JAMA The Journal of the American Medical Association

Distinguishing children with potentially life-threatening bacterial infections from febrile children with viral infections remains a major challenge. Herberg and colleagues,¹ in a preliminary, cross-sectional study of 370 febrile children (aged <17 years) in Europe and the United States, reported that children with bacterial infection may be characterized by the difference in blood RNA expression values of 2 genes. In a recent study, Mahajan and colleagues² reported a 66-transcript blood RNA signature that distinguished bacterial from viral infection in 279 febrile infants younger than 60 days. Young infants are at high risk of bacterial infection; diagnosis is difficult and prompt treatment important. To provide further validation of the 2-gene signature and to evaluate its performance in the infant population, we applied the signature to the RNA expression data of Mahajan et al.


PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS

January 2017

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147 Reads

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105 Citations

Bioinformatics

Motivation: We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information. Results: To demonstrate PRINCESS' performance and feasibility, we conducted a family-based allelic association study for Kawasaki Disease, with data hosted in three different continents. The experimental results show that PRINCESS provides secure and accurate analyses much faster than alternative solutions, such as homomorphic encryption and garbled circuits (over 40 000× faster). Availability and implementation: https://github.com/achenfengb/PRINCESS_opensource CONTACT: shw070@ucsd.eduSupplementary information: Supplementary data are available at Bioinformatics online.


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Citations (16)


... Other potential TCR binding sites were also identified, including a site with homology to known neurotoxins. Modeling and sequence analyses work reaffirmed the TCR binding hypothesis (3)(4)(5), and recent work exploring the link between MIS-C and SARS-CoV-2 infection in children theorized activation by a viral superantigen (6). Further commentary has even raised the possibility of a link between the proposed TCR interaction and severe acute hepatitis in children (7). ...

Reference:

SARS-CoV-2 spike does not interact with the T cell receptor or directly activate T cells
Enhanced CD95 and interleukin 18 signalling accompany T cell receptor Vβ21.3+ activation in multi-inflammatory syndrome in children

... For example, extraction of features from unstructured clinical notes through natural language processing (NLP) techniques as well as the use of photo or video data as training features (e.g. for predicting/detecting middle ear infections, diabetic foot ulcers, or surgical site infections) have started to be increasingly explored over the past few years [106][107][108][109][110][111][112][113][114][115]. In addition, automated streaming or recording of heart rate variability, respiration, and/or body motion, or also of raw biosignals have been explored as relevant features for training ML models to recognize development of sepsis in neonates [116][117][118][119][120]. Finally, a future increase in the availability of omics data from routine clinical practice holds promises to further improve the predictive accuracy of ML-based models for the early detection of bacterial infections or their imminent/rapid worsening, in turn impacting the use of antibiotics [121,122]. ...

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
  • Citing Article
  • November 2023

The Lancet Digital Health

... We also compared the host transcriptional profile of patients with LRTI caused by SARS-CoV-2, other viral etiologies, and bacterial etiology, in order to identify differentially expressed genes including those involved in the activation of host immune pathways. Finally, we evaluated promising published gene signatures (29)(30)(31)(32) that effectively discriminate between bacterial and viral etiology in our transcriptional data set. ...

Discovery and validation of a three-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations: a case-control and observational cohort study

The Lancet Microbe

... A growing body of research suggests that individual infectious and inflammatory diseases are characterised by unique patterns of host RNA abundance in whole blood. Sparse gene signatures, based on small numbers of transcripts have been reported for several diseases including tuberculosis disease [16][17][18] , malaria [19], bacterial and viral infections [20,21], and KD [22]. MIS-C has already been shown to elicit specific changes in gene expression compared to healthy controls and paediatric COVID-19 using targeted and untargeted transcriptomic approaches, respectively [23,24]. ...

Discovery and Validation of a 3-Gene Transcriptional Signature to Distinguish COVID-19 and Other Viral Infections from Bacterial Sepsis in Adults; A Case-Control then Observational Cohort Study

SSRN Electronic Journal

... Blood samples from the multicentre Biomarkers of Acute Serious Illness in Children (BASIC) biobank were used to measure NFL. The methods and cohort profiles of children enrolled in BASIC have been previously described [21]. Briefly, children 0-16 years of age (excluding those < 36 weeks of gestation) were enrolled into BASIC if they (i) were being transported to one of three participating PICUs by the regional intensive care transport service as an emergency admission and (ii) had an indwelling arterial or venous catheter for sampling. ...

Cohort profile of the Biomarkers of Acute Serious Illness in Children (BASIC) study: a prospective multicentre cohort study in critically ill children

BMJ Open

... Previous studies have shown lower numbers of T cells, reduced ability to respond to Mycobacterium tuberculosis antigens or reduced expression of activation markers and cytokine production in TBM compared to PTB and healthy individuals (van Laarhoven et al., 2019;Davoudi et al., 2008;Shridhar et al., 2022. This impaired T cell function has correlated with disease severity and poor clinical outcomes in participants with PTB and TBM van Laarhoven et al., 2019;An et al., 2022;Hemingway et al., 2017). ...

Childhood tuberculosis is associated with decreased abundance of T cell gene transcripts and impaired T cell function

... This recruits FH to the bacterial cell surface, whose normal function is to protect surfaces from AP-driven complement attack, which negates APdriven complement activation and cell killing. In support of the importance of this role of N. meningitidis immune evasion through binding FH is the genome-wide association study linking CFH variants with meningococcal disease susceptibility (Davila et al., 2010;Martinon-Torres et al., 2016). Additionally, FHR-3 also binds FHbp (Fig. 2) and competitively inhibits FHbp binding to FH (Caesar et al., 2014;Schneider et al., 2009). ...

Natural resistance to Meningococcal Disease related to CFH loci: Meta-analysis of genome-wide association studies

Scientific Reports

... Among these, transcriptomics presents itself as a particularly promising approach. Host RNA expression signatures have been proven capable of discriminating bacterial infections from viral infections in young infants with high sensitivity and specificity [9][10][11]. Furthermore, clinical implementation of a bedside two-gene signature is showing promising results [12]. ...

Diagnosis of Bacterial Infection Using a 2-Transcript Host RNA Signature in Febrile Infants 60 Days or Younger
  • Citing Article
  • April 2017

JAMA The Journal of the American Medical Association

... As such a common use case of SGX is for secure outsourced computation, in which a user with limited computational capabilities sends private data to a remote SGX enclave, controlled by an untrusted party, for secure data processing. This enticing possibility has been gathering increasing attention in the bioinformatics community in recent years [22,23,24,25,26,27,16,28,29,30] due to its potential to accelerate scientific discoveries by facilitating the sharing of sensitive biomedical data. For security in the outsourcing scenario, SGX usually employs a cryptographic protocol called remote attestation to provide proof to a remote user that both the enclave environment and the application running inside the enclave would not be tampered with; and that the communication channel for the transfer of sensitive data is secure. ...

PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS
  • Citing Article
  • January 2017

Bioinformatics

... Furthermore, mutation of the miR-223-3p binding sites within the Slc8a1 3'-UTR abolished this effect, substantiating the specificity of this interaction (Fig. 2H). Previous reports that Slc8a1 and its encoded protein NCX1 are highly associated with arrhythmias in humans and cardiovascular inflammation in infants [23,24], we believe that the enrichment of the miRNAs and their downstream target genes in the heart rate pathway, as analyzed here, substantiates their potential binding functionality. Table 2 listed the top DEmiRNAs and their various targets. ...

Genetic Variation in the SLC8A1 Calcium Signaling Pathway Is Associated With Susceptibility to Kawasaki Disease and Coronary Artery Abnormalities
  • Citing Article
  • November 2016

Circulation Cardiovascular Genetics