Charles Langelier’s research while affiliated with Chan Zuckerberg Biohub San Francisco and other places

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Publications (237)


Proteomic profiling of the local and systemic immune response to pediatric respiratory viral infections
  • Article

November 2024

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8 Reads

Emily Lydon

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Christina M Osborne

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[...]

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Peter M Mourani

Viral lower respiratory tract infection (vLRTI) is a leading cause of hospitalization and death in children worldwide. Despite this, no studies have employed proteomics to characterize host immune responses to severe pediatric vLRTI in both the lower airway and systemic circulation. To address this gap, gain insights into vLRTI pathophysiology, and test a novel diagnostic approach, we assayed 1,305 proteins in tracheal aspirate (TA) and plasma from 62 critically ill children using SomaScan. We performed differential expression (DE) and pathway analyses comparing vLRTI ( n = 40) to controls with non-infectious acute respiratory failure ( n = 22), developed a diagnostic classifier using LASSO regression, and analyzed matched TA and plasma samples. We further investigated the impact of viral load and bacterial coinfection on the proteome. The TA signature of vLRTI was characterized by 200 DE proteins (P adj <0.05) with upregulation of interferons and T cell responses and downregulation of inflammation-modulating proteins including FABP and MIP-5. A nine-protein TA classifier achieved an area under the receiver operator curve (AUC) of 0.96 (95% CI: 0.90–1.00) for identifying vLRTI. In plasma, the host response to vLRTI was more muted with 56 DE proteins. Correlation between TA and plasma was limited, although ISG15 was elevated in both compartments. In bacterial coinfection, we observed increases in the TNF-stimulated protein TSG-6, as well as CRP, and interferon-related proteins. Viral load correlated positively with interferon signaling and negatively with neutrophil-activation pathways. Taken together, our study provides fresh insights into the lower airway and systemic proteome of severe pediatric vLRTI and identifies novel protein biomarkers with diagnostic potential. IMPORTANCE We describe the first proteomic profiling of the lower airway and blood in critically ill children with severe viral lower respiratory tract infection (vLRTI). From tracheal aspirate (TA), we defined a proteomic signature of vLRTI characterized by increased expression of interferon signaling proteins and decreased expression of proteins involved in immune modulation including FABP and MIP-5. Using machine learning, we developed a parsimonious diagnostic classifier that distinguished vLRTI from non-infectious respiratory failure with high accuracy. Comparative analysis of paired TA and plasma specimens demonstrated limited concordance, although the interferon-stimulated protein ISG15 was significantly upregulated with vLRTI in both compartments. We further identified TSG-6 and CRP as airway biomarkers of bacterial-viral coinfection, and viral load analyses demonstrated a positive correlation with interferon-related protein expression and a negative correlation with the expression of neutrophil activation proteins. Taken together, our study provides new insights into the lower airway and systemic proteome of severe pediatric vLRTI.


Figure 1: Analysis pipeline and acute primary clinical outcomes. (A) Timeline showing study design, patient hospitalization (N=1,152), biosample collection and processing (n=23,421), and PASC group (m=587) based on self-reported PASC surveys monitored up to one-year post-discharge. (B) Measured omics assays across various biosamples, including transcriptomics, metabolomics, proteomics, viral loads, serology and CyTOF. (C) Pipeline for constructing disease subtypes among patients using multi-omics molecular profiles and downstream analysis. (D) Network plot illustrating the multi-dimensional scaling of the distance between participants. Each ellipse encompasses 60% of the points from the centroid of its respective cluster. (E) Comparisons of acute severity level across subtypes using ordinal regression test and TG group assignment with TG1 being the least severe and TG5 being the most severe, with barplot presenting the empirical TG proportions within each subtype and its row legend presenting the ordinal regression coefficients and significance levels (*p<=0.05, **p<=0.01, ***p<=0.001). (F) Survival curve and forest plot showing the log-transformed hazard ratios for acute mortality for each subtype. In the left panel, shaded areas indicate the 95% confidence intervals from Kaplan-Meier estimates (thick line). In the right panel, horizontal lines represent the 95% confidence intervals for log hazard ratios from Cox proportional-hazards model controlling for age and sex. Intervals covering 0 suggest nonsignificant differences compared to other subtypes.
Figure 2: Patient molecular subtypes exhibit distinct acute clinical characteristics. (A) Heatmap of coefficients and BH adjusted p-values comparing clinical characteristics across subtypes, with clinical characteristics including demographics, comorbidities, baseline laboratory tests, complications and hospital course. Each subtype was compared against all others using generalized linear regressions (ordinal for admission age quantiles, BMI, and radiographic findings on chest imaging, and logistic regression for others). Except for demographics, models have been adjusted for age and sex. (B) Boxplot of admission age distribution. (C) Donut plots for subtype demographics, including sex, BMI, and ethnicity. (D) Bar plots of top differentially enriched comorbidities across subtypes (Chi-squared test adj.p<=0.001). (E) Normalized frequency plot showing top differentially enriched complications across subtypes (Chi-squared test adj.p<=.001) grouped by bodily systems and functions. Vertical bars indicate the mean frequency and the corresponding standard error. Horizontal lines marked with asterisks show the significance of group-wise comparisons, with the grey line indicating the comparison between EF and ABC. Lines and asterisks matching the colors of A, B, and C denote comparisons of each group against the other two -A vs BC, B vs AC, and C vs AB respectively. The red line matching the color of F represents the comparison between F and E. (*adj.p<=0.05, **adj.p<=0.01, ***adj.p<=0.001).
Figure 3: Molecular subtypes exhibit distinct cell composition and serum protein characterization. (A) Heatmap of comparisons in cell composition, viral load and antibody for visits 1-3 (less than 10 days after admission) and visits 4-6 (more than 10 days after admission) among 5 major subtypes using linear mixed effect modeling after further adjusting for participant ID and enrollment site as random effects, and age, sex, and admission date as fixed effects, with heatmap cell colored based on regression coefficient (white if adj.p > 0.2) and annotated by its significance. (B) Longitudinal trajectories of negative N1-CT, AUC RBD IgG, neutrophil, monocytes, CD4+ T cell, CD8+ T cell, B cell and basophil, colored by subtypes. Shaded region denotes 95% confidence interval from generalized additive mixed model of the fitted trajectory (thick line). (C) Circular heatmap of comparisons in whole blood (CyTOF) of both parent and child populations for visits 1-3 and visits 4-6 among severe subtypes. (D) Heatmaps of comparisons in serum protein profiles for visits 1-3 and visits 4-6 among 5 major subtypes, with proteins grouped based on SPmod and heatmap cells colored by the regression coefficients (white if adj.p > 0.2). (E) Longitudinal trajectories of a small subset of most notable serum proteins IL-6, IL-10, IL17C, MMP10, IL12B, FLT3LG, CD274 and FGF23, colored by subtypes. Shaded region denotes 95% confidence interval from generalized additive mixed model of the fitted trajectory (thick line). (*adj.p<=0.05, **adj.p<=0.01, ***adj.p<=0.001).
Figure 4: Blood and upper-airway inflammation underpins subtype heterogeneity. (A) Longitudinal trajectories of Factor 1 (right), colored by subtypes and with shaded region denoting 95% confidence interval, and heatmap of comparisons in Factor 1 for visits 1-3 (<10 days after hospitalization) and visits 4-6 among subtypes (left), colored by regression coefficients from the linear mixed effect model. (B) Pathway Enrichment of Factor 1, where column names PPT/PPG=Plasma Proteomics Targeted/Global, SPT =Serum Proteomics Targeted, PMG = Plasma Metabolomics Global, NGX/PGX=Nasal/PBMC gene expression, adj.joint = aggregated pvalue across omics after BH correction. For Factor 1, only top 30 pathways measured by adj,joint out of 99 all pathways enriched with adj.joint < 0.05. (C) Longitudinal trajectories of Factor 10 and heatmaps of comparisons in Factor 10 for visits 1-3 and visits 4-6 among subtypes. (D) Pathway enrichment of Factor 10 (adj.joint < 0.05). The top table showed the high-contributing features and number of all pathways with unadjusted joint p.value < 0.05, which were all from NGX. (E) Regression coefficients from linear mixed effect model between nasal viral load and Factor 1, Factor 10, antibody and significant serum protein modules, for severe subtypes (SubA & B & C), critical subtypes (SubE & F), and the difference between critical and severe subtypes displaying interactions of critical subtypes compared to severe subtypes, respectively. The results for multiomics factors Factor 1/10 were further adjusted for the more specific immune components on the right panels. (*adj.p<=0.05, **adj.p<=0.01, ***adj.p<=0.001).
Figure 5: Dysregulated coagulation, disrupted fatty acid metabolism, and increased cardiomyopathy and amino acid catabolism identify COVID subtypes with increased complications. (A) Longitudinal trajectories of Factor 2 (left), colored by subtypes with shaded region denoting 95% confidence interval, and heatmaps of comparisons in Factor 2 for visits 1-3 and visits 4-6 among subtypes (right), colored by regression coefficients from the linear mixed effect modeling. (B) Pathway Enrichment of Factor 2 (adj.joint < 0.05). (C) Coefficient comparison for proteins enriched in complement and coagulation in Factor 1 and Factor 2. (D) Longitudinal trajectories of complement and coagulation and polyunsaturated fatty acid indicated in Factor 2, colored by subtypes. (E) Longitudinal trajectories of Factor 3, colored by subtypes, and heatmaps of comparisons in Factor 2 for visits 1-3 and visits 4-6 among subtypes. (F) Pathway Enrichment of Factor 3 (adj.joint < 0.05). (G) Longitudinal trajectories of cardiac muscle contraction, tryptophan metabolism, and acetylated peptides indicated in Factor 3, colored by subtypes. (*adj.p<=0.05, **adj.p<=0.01, ***adj.p<=0.001).

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Unraveling SARS-CoV-2 Host-Response Heterogeneity through Longitudinal Molecular Subtyping
  • Preprint
  • File available

November 2024

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130 Reads

Hospitalized COVID-19 patients exhibit diverse immune responses during acute infection, which are associated with a wide range of clinical outcomes. However, understanding these immune heterogeneities and their links to various clinical complications, especially long COVID, remains a challenge. In this study, we performed unsupervised subtyping of longitudinal multi-omics immunophenotyping in over 1,000 hospitalized patients, identifying two critical subtypes linked to mortality or mechanical ventilation with prolonged hospital stay and three severe subtypes associated with timely acute recovery. We confirmed that unresolved systemic inflammation and T-cell dysfunctions were hallmarks of increased severity and further distinguished patients with similar acute respiratory severity by their distinct immune profiles, which correlated with differences in demographic and clinical complications. Notably, one critical subtype (SubF) was uniquely characterized by early excessive inflammation, insufficient anticoagulation, and fatty acid dysregulation, alongside higher incidences of hematologic, cardiac, and renal complications, and an elevated risk of long COVID. Among the severe subtypes, significant differences in viral clearance and early antiviral responses were observed, with one subtype (SubC) showing strong early T-cell cytotoxicity but a poor humoral response, slower viral clearance, and greater risks of chronic organ dysfunction and long COVID. These findings provide crucial insights into the complex and context-dependent nature of COVID-19 immune responses, highlighting the importance of personalized therapeutic strategies to improve both acute and long-term outcomes.

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Figure 6 -Chronic Viral Reactivation is Associated with Alterations in Host Gene Expression. A) Top enriched pathways in the PBMC transcriptomics associated with reactivation of the identified latently infecting viruses. B) Top enriched pathways in the nasal transcriptomics associated with reactivation of the identified latently infecting viruses. Results for (A) and (B) calculated using hypergeometric enrichment of pathways from Reactome, separately on the positive and negative differentially expressed genes. Dot only shown for a pathway if adjusted p-value < 0.01. Source data are provided as a Source Data file.
Chronic Viral Reactivation and Associated Host Immune Response and Clinical Outcomes in Acute COVID-19 and Post-Acute Sequelae of COVID-19

November 2024

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81 Reads

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2 Citations

Chronic viral infections are ubiquitous in humans, with individuals harboring multiple latent viruses that can reactivate during acute illnesses. Recent studies have suggested that SARS- CoV-2 infection can lead to reactivation of latent viruses such as Epstein-Barr Virus (EBV) and cytomegalovirus (CMV), yet, the extent and impact of viral reactivation in COVID-19 and its effect on the host immune system remain incompletely understood. Here we present a comprehensive multi-omic analysis of viral reactivation of all known chronically infecting viruses in 1,154 hospitalized COVID-19 patients, from the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study, who were followed prospectively for twelve months. We reveal significant reactivation of Herpesviridae , Enteroviridae , and Anelloviridae families during acute stage of COVID-19 (0-40 days post- hospitalization), each exhibiting distinct temporal dynamics. We also show that viral reactivation correlated with COVID-19 severity, demographic characteristics, and clinical outcomes, including mortality. Integration of cytokine profiling, cellular immunophenotyping, metabolomics, transcriptomics, and proteomics demonstrated virus-specific host responses, including elevated pro-inflammatory cytokines (e.g. IL-6, CXCL10, and TNF), increased activated CD4+ and CD8+ T-cells, and upregulation of cellular replication genes, independent of COVID-19 severity and SARS-CoV-2 viral load. Notably, persistent Anelloviridae reactivation during convalescence (≥3 months post-hospitalization) was associated with Post-Acute Sequelae of COVID-19 (PASC) symptoms, particularly physical function and fatigue. Our findings highlight a remarkable prevalence and potential impact of chronic viral reactivation on host responses and clinical outcomes during acute COVID-19 and long term PASC sequelae. Our data provide novel immune, transcriptomic, and metabolomic biomarkers of viral reactivation that may inform novel approaches to prognosticate, prevent, or treat acute COVID- 19 and PASC.


Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery

November 2024

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62 Reads

Tools for rapid identification of novel and/or emerging viruses are urgently needed for clinical diagnosis of unexplained infections and pandemic preparedness. Here we developed and clinically validated a largely automated metagenomic next-generation sequencing (mNGS) assay for agnostic detection of respiratory viral pathogens from upper respiratory swab and bronchoalveolar lavage samples in <24 h. The mNGS assay achieved mean limits of detection of 543 copies/mL, viral load quantification with 100% linearity, and 93.6% sensitivity, 93.8% specificity, and 93.7% accuracy compared to gold-standard clinical multiplex RT-PCR testing. Performance increased to 97.9% overall predictive agreement after discrepancy testing and clinical adjudication, which was superior to that of RT-PCR (95.0% agreement). To enable discovery of novel, sequence-divergent human viruses with pandemic potential, de novo assembly and translated nucleotide algorithms were incorporated into the automated SURPI+ computational pipeline used by the mNGS assay for pathogen detection. Using in silico analysis, we showed that after removal of all human viral sequences from the reference database, 70 (100%) of 70 representative human viral pathogens could still be identified based on homology to related animal or plant viruses. Our assay, which was granted breakthrough device designation from the US Food and Drug Administration (FDA) in August of 2023, demonstrates the feasibility of routine mNGS testing in clinical and public health laboratories, thus facilitating a robust and rapid response to the next viral pandemic.


Fig. 1: Study overview. Patients with systemic lupus erythematosus (SLE) enrolled in the California Lupus Epidemiology Study (CLUES) were compared based on physical activity status (active versus sedentary). Single cell RNA sequencing of PBMCs was carried out to identify and profile immune cell populations. Cell frequencies, gene expression, biological pathways, and gene networks were compared between physically active and sedentary groups.
Fig. 2: scRNA-seq identifies differences in immune cell frequencies and gene expression based on physical inactivity. (A) UMAP plot of all single cells used in the study, coloured by the cell types. There are 11 cell types: CD4+ T cells and CD8+ T cells, B cells, classical and nonclassical monocytes (cM and ncM), natural killer cells (NK), plasmablasts (PB), conventional and plasmacytoid dendritic cells (cDC and pDC), proliferating lymphocytes (Prolif), and CD34 progenitors (Progen). 10 (B) UMAP plots of single cells from physically inactive (left) and physically active (right)
Fig. 3: Physical inactivity drives proinflammatory gene expression in T cells. (A) Bar plot showing the number of differentially expressed (DE) genes between the physically active (n = 81) and inactive (n = 42) groups at an adjusted P-value (Padj) < 0.1 for each of the 6 most abundant cell types. Data regarding more finely resolved cell subtypes are presented in Supplemental Figure S1. (B) Volcano plots of differential expression analysis in CD4+ T cells and CD8+ T cells. There were 686 and 445 DE genes (FDR <0.1) in CD4+ T cells and CD8+ T cells, respectively. A positive log 2 (fold change) indicates that a gene is upregulated in physically inactive patients compared to active patients. (C) Dot plots showing Hallmark pathways that are statistically significantly associated with physical inactivity in CD4+ T cells and CD8+ T cells (FDR <0.1). (D, E) Bar plots showing the cytokines predicted by Ingenuity Pathway Analysis to be activated in (D) CD4+ T cells and (E) CD8+ T cells of physically inactive patients compared to active patients.
Fig. 4: A gene network drives proinflammatory signalling in CD4+ T cells. A gene-concept network plot of 4 immune-related Hallmark pathways in CD4+ T cells (TNF-α signalling via NF-kB, IFN-γ response, IL6 JAK STAT3 signalling, and IL2 STAT5 signalling pathways). The gene dots are coloured by the genes' log 2 (fold change), and a positive and negative log 2 (fold change) indicate that the gene is upregulated and downregulated in physically inactive patients, respectively.
Physical inactivity exacerbates pathologic inflammatory signalling at the single cell level in patients with systemic lupus

November 2024

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7 Reads

EBioMedicine

Background Physical activity is an adjunctive therapy that improves symptoms in people living with systemic lupus erythematosus (SLE), yet the mechanisms underlying this benefit remain unclear. Methods We carried out a cohort study of 123 patients with SLE enrolled in the California Lupus Epidemiology Study (CLUES). The primary predictor variable was self-reported physical activity, which was measured using a previously validated instrument. We analyzed peripheral blood mononuclear cell (PBMC) single-cell RNA sequencing (scRNA-seq) data available from the cohort. From the scRNA-seq data, we compared immune cell frequencies, cell-specific gene expression, biological signalling pathways, and upstream cytokine activation states between physically active and inactive patients, adjusting for age, sex and race. Findings We found that physical activity influenced immune cell frequencies, with sedentary patients most notably demonstrating greater CD4+ T cell lymphopenia (Padj = 0.028). Differential gene expression analysis identified a transcriptional signature of physical inactivity across five cell types. In CD4+ and CD8+ T cells, this signature was characterized by 686 and 445 differentially expressed genes (Padj < 0.1). Gene set enrichment analysis demonstrated enrichment of proinflammatory genes in the TNF-α signalling through NF-kB, interferon-γ (IFN-γ), IL2/STAT5, and IL6/JAK/STAT3 signalling pathways. Computational prediction of upstream cytokine activation states suggested CD4+ T cells from physically inactive patients exhibited increased activation of TNF-α, IFN-γ, IL1Β, and other proinflammatory cytokines. Network analysis demonstrated interconnectivity of genes driving the proinflammatory state of sedentary patients. Findings were consistent in sensitivity analyses adjusting for corticosteroid treatment and physical function. Interpretation Taken together, our findings suggest a mechanistic explanation for the observed benefits of physical activity in patients with SLE. Specifically, we find that physical inactivity is associated with altered frequencies and transcriptional profiles of immune cell populations and may exacerbate pathologic inflammatory signalling via CD4+ and CD8+ T cells. Funding This work was supported by the US 10.13039/100000002National Institutes of Health (NIH) (R01 AR069616, K23HL138461-01A1, K23AT011768) the US 10.13039/100000030CDC (U01DP0670), and the CZ Biohub.


BiP/GRP78 is a pro-viral factor for diverse dsDNA viruses that promotes the survival and proliferation of cells upon KSHV infection

October 2024

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25 Reads

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1 Citation

The Endoplasmic Reticulum (ER)-resident HSP70 chaperone BiP (HSPA5) plays a crucial role in maintaining and restoring protein folding homeostasis in the ER. BiP’s function is often dysregulated in cancer and virus-infected cells, conferring pro-oncogenic and pro-viral advantages. We explored BiP’s functions during infection by the Kaposi’s sarcoma-associated herpesvirus (KSHV), an oncogenic gamma-herpesvirus associated with cancers of immunocompromised patients. Our findings reveal that BiP protein levels are upregulated in infected epithelial cells during the lytic phase of KSHV infection. This upregulation occurs independently of the unfolded protein response (UPR), a major signaling pathway that regulates BiP availability. Genetic and pharmacological inhibition of BiP halts KSHV viral replication and reduces the proliferation and survival of KSHV-infected cells. Notably, inhibition of BiP limits the spread of other alpha- and beta-herpesviruses and poxviruses with minimal toxicity for normal cells. Our work suggests that BiP is a potential target for developing broad-spectrum antiviral therapies against double-stranded DNA viruses and a promising candidate for therapeutic intervention in KSHV-related malignancies.


Fig. 1 | Study overview. a Patient flow after recruitment within 48 hours of admission. *Intubated for reasons other than COVID-19 or transferred to an outside hospital within 48 h of recruitment. b Pathogens detected by tracheal aspirate (TA) bacterial culture at time of secondary bacterial pneumonia (2°BP) diagnosis. Three patients had >1 pathogen detected. Created with BioRender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en).
Fig. 3 | Dynamics of 2°BP pathogen over time relative to the date of clinical diagnosis highlighting examples of unique pathogen trajectories. The top row includes plots of genus level 2°BP pathogen rank based on bacterial reads per million (rpM) in the lung microbiome for each of four pathogen trajectories (expansion, reduction, persistence and resistance). The middle row includes plots of pathogen mass. The bottom row consists of plots of pathogen mass normalized to the total RNA mass of sample. Days relative to 2°BP clinical diagnosis are plotted on the X axis. Days during which patient received antibiotics to which 2°BP pathogen was phenotypically susceptible (light gray bar) or resistant (dark gray bar) are plotted below. The patient ID and pathogen genus are listed above each plot. Open circles denote samples in which the pathogen was not detected by metatranscriptomics.
Fig. 4 | Nasal microbiome differences in patients with or without 2°BP and relationship to the lung microbiome. a Nasal swab (NS) bacterial RNA mass or (b) Shannon Diversity Index (SDI) in COVID-19 patients with 2°BP (purple, N = 15) or No-BP (pink, N = 20). P values based on two-sided Wilcoxon tests. c Bar plot showing the number of 2°BP patients (y axis) in which a specific rank by reads per million (rpM) of the culture-confirmed pathogen (x axis) was observed within 7 days of clinical diagnosis. d Stacked bar plots highlighting the taxonomic composition of paired NS and tracheal aspirate (TA) samples from 2°BP patients (top) and No-BP patients (bottom). Bacterial taxa present at <0.5% relative abundance across all samples were included in the "other" category, except if the microbe had been cultured as a 2°BP pathogen. Spearman's rho tests were performed to assess taxonomic concordance between paired samples from each patient. IQR, interquartile range. Boxes in (a, b) show median and 25th-75th percentiles, with whiskers from min to max. Source data for (a-c) provided in the Source Data file.
Fig. 6 | Lower respiratory tract gene expression differs based on 2°BP status and is influenced by corticosteroid treatment. a Volcano plot of differentially expressed genes between 2°BP (N = 27) and No-BP (N = 29). b Bar plot of GSEA analysis showing the Hallmark pathways that are downregulated in 2°BP patients. c Bar plot of GSEA analysis limited to steroid recipients (N = 25 2°BP patients; N = 19 No-BP patients) showing the same Hallmark pathways as in (b). In the analyses in (a-c), we controlled for SARS-CoV-2 viral load in our differential expression analysis. d Bar plot of GSEA analysis demonstrating Hallmark pathways associated with days of corticosteroid treatment in 2°BP patients. e Bar plot of GSEA analysis demonstrating Hallmark pathways associated with days of corticosteroid treatment in No-BP patients. The two-sided P values in (a) were calculated using linear modeling (limma package) and Benjamini-Hochberg correction. The two-sided P values in (b-e) were calculated using the fgsea package and Benjamini-Hochberg correction.
Fig. 7 | Lower respiratory tract immune gene expression inversely correlates with bacterial mass. a Volcano plots of genes that are associated with bacterial mass in 2°BP patients (N = 27). b Scatter plots showing the relationship between HLA-DRA and C1QC gene expression and bacterial RNA mass in 2°BP patients. The black lines indicate the linear regression fit, and the ribbons indicate the 95% confidence interval of the fits. c Volcano plots of genes associated with bacterial mass in No-BP patients (N = 29). Bar plot showing Hallmark pathways associated with bacterial mass in (d) all 2°BP patients and (e) all No-BP patients. Hallmark pathways associated with bacterial mass in (f) 2°BP and (g) No-BP patients, but limited to only steroid recipients (N = 25 and N = 19; respectively). h Hallmark pathways associated with bacterial mass in the 10 No-BP patients who did not receive steroids prior to sample collection. The two-sided P values in (a, c) were calculated using linear modeling (limma package) and Benjamini-Hochberg correction. The two-sided P values in (d-h) were calculated using the fgsea package and Benjamini-Hochberg correction.
Microbial dynamics and pulmonary immune responses in COVID-19 secondary bacterial pneumonia

October 2024

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19 Reads

Secondary bacterial pneumonia (2°BP) is associated with significant morbidity following respiratory viral infection, yet remains incompletely understood. In a prospective cohort of 112 critically ill adults intubated for COVID-19, we comparatively assess longitudinal airway microbiome dynamics and the pulmonary transcriptome of patients who developed 2°BP versus controls who did not. We find that 2°BP is significantly associated with both mortality and corticosteroid treatment. The pulmonary microbiome in 2°BP is characterized by increased bacterial RNA mass and dominance of culture-confirmed pathogens, detectable days prior to 2°BP clinical diagnosis, and frequently also present in nasal swabs. Assessment of the pulmonary transcriptome reveals suppressed TNFα signaling in patients with 2°BP, and sensitivity analyses suggest this finding is mediated by corticosteroid treatment. Further, we find that increased bacterial RNA mass correlates with reduced expression of innate and adaptive immunity genes in both 2°BP patients and controls. Taken together, our findings provide fresh insights into the microbial dynamics and host immune features of COVID-19-associated 2°BP, and suggest that suppressed immune signaling, potentially mediated by corticosteroid treatment, permits expansion of opportunistic bacterial pathogens.


Figure 1: Study overview. A) From a prospectively enrolled multicenter cohort of pediatric patients
Figure 2: Comparison of host protein expression between vLRTI and No LRTI cohorts in tracheal
Figure 3: Host protein expression in plasma and comparative proteomic analysis between plasma
Figure 4: Tracheal aspirate protein and pathway expression in bacterial-viral coinfection.
Figure 5: Correlation of lower respiratory protein expression and viral load. For each vLRTI subject
Proteomic profiling of the local and systemic immune response to pediatric respiratory viral infections

October 2024

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34 Reads

Viral lower respiratory tract infection (vLRTI) is a leading cause of hospitalization and death in children worldwide. Despite this, no studies have employed proteomics to characterize host immune responses to severe pediatric vLRTI in both the lower airway and systemic circulation. To address this gap, gain insights into vLRTI pathophysiology, and test a novel diagnostic approach, we assayed 1,305 proteins in tracheal aspirate (TA) and plasma from 62 critically ill children using SomaScan. We performed differential expression (DE) and pathway analyses comparing vLRTI (n=40) to controls with non-infectious acute respiratory failure (n=22), developed a diagnostic classifier using LASSO regression, and analyzed matched TA and plasma samples. We further investigated the impact of viral load and bacterial coinfection on the proteome. The TA signature of vLRTI was characterized by 200 DE proteins (P adj <0.05) with upregulation of interferons and T cell responses and downregulation of inflammation-modulating proteins including FABP and MIP-5. A nine-protein TA classifier achieved an AUC of 0.96 (95% CI 0.90-1.00) for identifying vLRTI. In plasma, the host response to vLRTI was more muted with 56 DE proteins. Correlation between TA and plasma was limited, although ISG15 was elevated in both compartments. In bacterial coinfection, we observed increases in the TNF-stimulated protein TSG-6, as well as CRP, and interferon-related proteins. Viral load correlated positively with interferon signaling and negatively with neutrophil-activation pathways. Taken together, our study provides fresh insight into the lower airway and systemic proteome of severe pediatric vLRTI, and identifies novel protein biomarkers with diagnostic potential.


Impact of doxycycline post-exposure prophylaxis for sexually transmitted infections on the gut microbiome and antimicrobial resistome

October 2024

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46 Reads

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1 Citation

Nature Medicine

Doxycycline post-exposure prophylaxis (doxy-PEP) reduces bacterial sexually transmitted infections among men who have sex with men and transgender women. Although poised for widespread clinical implementation, the impact of doxy-PEP on antimicrobial resistance remains a primary concern as its effects on the gut microbiome and resistome, or the antimicrobial resistance genes (ARGs) present in the gut microbiome, are unknown. To investigate these effects, we studied participants from the DoxyPEP trial, a randomized clinical trial comparing doxy-PEP use, a one-time doxycycline 200-mg dose taken after condomless sex (DP arm, n = 100), to standard of care (SOC arm, n = 50) among men who have sex with men and transgender women. From self-collected rectal swabs at enrollment (day-0) and after 6 months (month-6), we performed metagenomic DNA sequencing (DNA-seq) or metatranscriptomic RNA sequencing (RNA-seq). DNA-seq data were analyzable from 127 samples derived from 89 participants, and RNA-seq data were analyzable from 86 samples derived from 70 participants. We compared the bacterial microbiome and resistome between the two study arms and over time. The median number of doxycycline doses taken since enrollment by participants with DNA-seq data was zero (interquartile range (IQR): 0–7 doses) for the SOC arm and 42 (IQR: 27–64 doses) for the DP arm. Tetracycline ARGs were detected in all day-0 DNA-seq samples and in 85% of day-0 RNA-seq samples. The proportional mass of tetracycline ARGs in the resistome increased between day-0 and month-6 in DP participants from 46% to 51% in the metagenome (P = 2.3 × 10⁻²) and from 4% to 15% in the metatranscriptome (P = 4.5 × 10⁻⁶), but no statistically significant increases in other ARG classes were observed. Exposure to a higher number of doxycycline doses correlated with proportional enrichment of tetracycline ARGs in the metagenome (Spearman’s ρ = 0.23, P = 9.0 × 10⁻³) and metatranscriptome (Spearman’s ρ = 0.55, P = 3.7 × 10⁻⁸). Bacterial microbiome alpha diversity, beta diversity and total bacterial mass did not differ between day-0 and month-6 samples from DP participants when assessed by either DNA-seq or RNA-seq. In an abundance-based correlation analysis, we observed an increase over time in the strength of the correlation between tetracycline ARGs and specific bacterial taxa, including some common human pathogens. In sum, doxy-PEP use over a 6-month period was associated with an increase in the proportion of tetracycline ARGs comprising the gut resistome and an increase in the expression of tetracycline ARGs. At 6 months of doxy-PEP use, no residual differences were observed in alpha and beta diversity or taxonomic composition of the gut microbiome. As doxy-PEP is implemented as a public health strategy, further studies and population-level surveillance of doxycycline-resistant pathogens are needed to understand the implications of these findings. ClinicalTrials.gov registration number: NCT03980223.


FIGURE 2 The key events in allergic bronchopulmonary aspergillosis (ABPA) pathogenesis include Aspergillus colonisation followed by skewed type-2 immune responses. Polymorphisms in airway epithelial receptors and innate and adaptive immune pathways prevent elimination of A. fumigatus and promote the development of an aberrant type-2 immune response. Pathogen-associated molecular patterns (PAMPs) from Aspergillus (glucan, galactomannan, galactosaminogalactan, proteases) are recognised by pattern recognition receptors (PRRs), including dectin-1 and toll-like receptors (TLRs), at the lung epithelial cell surface. Fungal proteases can damage the respiratory epithelium and result in release of alarmins (interleukin (IL)-33, IL-25 and thymic stromal lymphopoietin (TSLP)), which in turn stimulate the type 2 innate lymphoid cells (ILC2) and CD4 + type 2 lymphocytes. The dendritic cells also recognise fungal proteins and activate allergen-specific type 2 T-cells (Th2 cells). Eosinophils remain the primary mediators of inflammation in ABPA, with the interaction between eosinophils and A. fumigatus releasing galectin-10 and forming Charcot-Leyden crystals (CLCs). Subsequently, eosinophils undergo cell death forming histone-rich extracellular traps (EETs) and increase the viscosity of mucus plugs, which contribute to ABPA pathogenesis. The skewed type 2 responses lead to secretion of IL-4, IL-5 and IL-13. IL-4 mediates the class switching and production of IgE antibodies, which attach to mast cells and cause mast cell degranulation on allergen exposure. IL-5 is pivotal for eosinophil recruitment, maturation and survival and is central to eosinophilic inflammation. IL-13 from ILC2 and Th2 cells promotes mucus hypersecretion. Finally, immune activation leads to airway inflammation, mucus plugging and bronchiectasis. HAM: high-attenuation mucus. Figure created with Biorender.com.
Revised International Society for Human and Animal Mycology ABPA working group consensus criteria for diagnosing ABPA
Fungal signatures in chronic lung disease and associated clinical outcomes with potential treatment approaches
Fungal Lung Disease

European Respiratory Journal

Fungal lung disease encompasses a wide spectrum of organisms and associated clinical conditions presenting a significant global health challenge with type and severity determined by underlying host immunity and infecting fungal strain. The most common group of diseases are associated with the filamentous fungus Aspergillus spp. and include allergic bronchopulmonary aspergillosis (ABPA), sensitization, aspergilloma, and chronic and invasive pulmonary aspergillosis. Fungal lung disease remains epidemiologically heterogenous and is influenced by geography, environment, and host comorbidities. Diagnostic modalities continue to evolve and now include novel molecular assays and biomarkers, however, persisting challenges include achieving rapid and accurate diagnosis, particularly in resource-limited settings and in differentiating fungal infection from other pulmonary conditions. Treatment strategies for fungal lung diseases rely mainly on antifungal agents, however, the emergence of drug-resistant strains poses a substantial global threat and adds complexity to existing therapeutic challenges. Emerging antifungal agents and increasing insight into lung mycobiome via may offer fresh and personalized approaches to diagnosis and treatment, while innovative methodologies are required to mitigate drug resistance and adverse effects of treatment. This state-of-the-art review describes the current landscape of fungal lung disease, highlighting key clinical insights, current challenges, and emerging approaches for its diagnosis and treatment.


Citations (49)


... En las publicaciones realizadas, a este aumento en la carga viral en la sangre y plasma se le denomina en algunos casos como reactivación (de virus latente) [6], sobrerrepresentación [7], sobrecrecimiento (de virus que no eran patógenos mientras la carga viral era baja, eran comensales), expansión [8,9], aumento de su actividad o mayor carga de anellovirus [10]. En todos estos casos, lo que ocurre es una pérdida del equilibrio que existía en el microbioma, generándose una disbiosis sanguínea con un sobrecrecimiento importante de los virus de la familia Anelloviridae entre otros microorganismos. ...

Reference:

DISBIOSIS SANGUÍNEA Y DISBIOSIS MULTIORGÁNICA CON AUMENTO DE LA CARGA VIRAL TOTAL DE ANELLOVIRIDAE Y DEL VIRUS TORQUE TENO (TTV) EN COVID PERSISTENTE O LONG COVID, HIV, HEPATITIS, ENCEFALOMIELITIS MIÁLGICA/SÍNDROME DE FATIGA CRÓNICA (EM/SFC), CARDIOPATÍA ISQUÉMICA, ESCLEROSIS MÚLTIPLE, ESCLEROSIS LATERAL AMIOTRÓFICA (ELA), ENFERMEDADES AUTOINMUNES, ENFERMEDADES HEPÁTICAS, INTESTINALES, CEREBRALES Y OTRAS
Chronic Viral Reactivation and Associated Host Immune Response and Clinical Outcomes in Acute COVID-19 and Post-Acute Sequelae of COVID-19

... /2024 we utilize influenza to infect fibroblasts derived from control and DBR1 mutant patients to confirm that this connection between infection and decreased intron recycling can be extended across multiple viruses (Supplementary Figure S1A, 20-30% increase in lariats post infection in mutant lines, I120T p = 0.015 and Y17H p = 0.0059). Moreover, after analyzing the RNA-seq data from cells infected with Kaposi's Sarcoma-Associated Herpesvirus (KSHV) (Najarro et al., 2024), we found that the lariats are 2.48-fold higher (Supplementary Figure S1B, p = 0.034) in the lytic phase compared to the latent phase. As the HSV-1, influenza and KSHV genomes are composed of intron-containing genes, it is possible that some feature of viral introns could dominantly inhibit lariat turnover. ...

BiP/GRP78 is a pro-viral factor for diverse dsDNA viruses that promotes the survival and proliferation of cells upon KSHV infection

... This epitope is involved in binding to the so-called attachment receptors, lectins DC-SIGN, L-SIGN, and SIGLEC1, which facilitate SARS-CoV-2 infection via the canonical ACE2 pathway (Corti et al., 2021). The role of these attachment receptors, prominently expressed on lung myeloid cells, explains the efficiency of lower respiratory tract infection, despite the paradoxically low level of ACE2 expression (Looney et al., 2022). This finding indicated that besides the beneficial SARS-CoV-2-neutralizing effect of SOT and its capacity to normalize biomarkers that could predict severity and progression of SARS-CoV-2 infection compared to placebo (Maher et al., 2022), the blocking of lectin-facilitated infection could hinder the immune responses triggered by leukocyte cells, including lung myeloid cells. ...

Immediate myeloid depot for SARS-CoV-2 in the human lung
  • Citing Article
  • July 2024

Science Advances

... Overall, dexamethasone is only recommended for use in critically ill patients requiring oxygen or mechanical ventilation [133]. Dexamethasone treatment decreases IL-6 and IFN-γ and increases IL-10, which is likely a mechanism of action along with other general changes in inflammatory signaling [10,134]. However, not all trials showed an effect on cytokine levels even when there was still a clinically significant improvement in treated patients [37,135]. ...

Distinct pulmonary and systemic effects of dexamethasone in severe COVID-19

... 240 Recent animal data support these results. 251 Together, these studies suggest that in select patients with severe CAP, some of whom meet criteria for ARDS, corticosteroids are likely to provide benefit for preventing progression to mechanical ventilation and possibly for preventing death. ...

Biological effects of corticosteroids on pneumococcal pneumonia in Mice-translational significance

Critical Care

... Immune signatures associated with poor COVID-19 outcomes have been identified at both the acute and convalescent stages [6][7][8][9][10][11][12] . Hallmarks of acute disease severity, measured by respiratory status and mortality, include over-production of proinflammatory cytokines 8,13,14 , lymphopenia 8,[15][16][17] , formation of neutrophil extracellular traps (NETs) 18 , dysregulation of complement and coagulation 8,19,20 , impaired interferon (IFN) signaling 7,21-23 , immune senescence 24 , delayed onset of neutralizing antibodies 25 , and signatures of apoptosis and tissue damage 17,26 , which have been confirmed in our prior studies focusing on an inpatient cohort exclusively 6,7 . ...

Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality

The Journal of clinical investigation

... Type II alveolar epithelial cells are key in maintaining the pulmonary respiratory function, synthesizing and secreting pulmonary surfactant, and participating in water regulation, injury repair, and inflammatory responses (Rana et al. 2023). In the pathophysiological mechanism of ARDS, damage and death of type II alveolar epithelial cells may lead to the destruction of the alveolar epithelial barrier, thereby causing extensive pulmonary inflammation and edema (Valda Toro et al. 2024;Wen et al. 2023;Li et al. 2024). Observations of the ultrastructure of lung tissues from various groups using a transmission electron microscope revealed that HS can cause significant changes in targets. ...

Rapidly improving ARDS differs clinically and biologically from persistent ARDS

Critical Care

... A subset of the IMPACC cohort has previously been analyzed to identify immune correlates or multi-omics immune programs predictive and associated with acute mortality, respiratory severity, and specific demographic or clinical characteristics such as aging 6,7,98 . However, while its large data volume enabled us to reach more conclusive statements identifying immune profiles associated with specific clinical endpoints of interest, this rich and largescale database also offers an underexplored opportunity to conduct deep analysis on immune heterogeneity and identify immune-based patient subtypes. ...

Host-microbe multiomic profiling reveals age-dependent immune dysregulation associated with COVID-19 immunopathology
  • Citing Article
  • April 2024

Science Translational Medicine

... Read subsampling, depth, and coverage was calculated using SAMtools [81]. Raw reads were directly submitted to the CZID mNGS Illumina pipeline [85] for microbial composition characterization within samples. Further data analyses and visualizations were carried out in Rstudio (v.2024.04.2+764) [86] using the tidyverse suite (v.2.0.0) [87]. ...

Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline

... This dynamic can ultimately lead to LV dysfunction, a critical factor in conditions such as septic shock. If untreated, impaired cardiac function can lead to cardiovascular collapse and multi-organ failure (Chotalia et al., 2023a;Chotalia et al., 2023b;Sinha et al., 2024). Dammassa et al. (2023) evaluated serum levels of catecholamines in patients with COVID-19-related ARDS, examining their relationships with clinical, inflammatory, and echocardiographic parameters. ...

Molecular Phenotypes of ARDS in the ROSE Trial have Differential Outcomes and Gene Expression Patterns That Differ at Baseline and Longitudinally Over Time
  • Citing Article
  • February 2024

American Journal of Respiratory and Critical Care Medicine