Emily C. Lydon

We report a 7,456-bp near-complete norovirus GII.P7-GII.7 genome recovered by metagenomic RNA sequencing from a pediatric stool swab collected during a 2026 gastroenteritis outbreak in Northern California. The genome expands the public genomic record of norovirus diversity and is available under GenBank accession PZ705759 .

Background: Plasma metagenomic next-generation sequencing (mNGS) may detect pathogens in solid organ transplant (SOT) recipients, but optimal patient selection and result interpretation remain uncertain. Methods: We studied 145 SOT recipients who received Karius plasma mNGS testing between 2017-2025. Retrospective multi-physician adjudication assessed diagnostic accuracy, clinical impact, and outcomes. We examined whether detection of atypical bacteria, invasive fungi, adenovirus/parvovirus and parasites, termed pre-specified organisms of presumed significance (POPS), predicted positive clinical impact. We applied a GPT-4o large language model (LLM) to limited electronic medical record (EMR) data to identify patients with POPS diagnoses and positive-impact testing. Results: Of 145 SOT recipients, 119 (82.1%) had positive tests, and 42 (29.0%) had ≥1 POPS organism detected. Twenty-seven of 133 diagnoses (20.3%) were made by mNGS first or mNGS only. Diagnostic performance versus a gold standard of all microbiologic testing varied by organism, ranging from 100% sensitivity and specificity ( Bartonella, Nocardia ) to 60.0% and 53.3% sensitivity for Coccidioides and Aspergillus, respectively. Of 141 patients with interpretable test impact, 27 (19.1%) had positive clinical impact, associated with POPS detection (P<0.001). The LLM identified patients with POPS diagnoses (area under the receiver operating characteristic curve [AUC] 0.86), and patients with positive-impact testing (AUC 0.71). Conclusions: Plasma mNGS can aid diagnosis and management of infections in SOT recipients, but negative tests do not exclude invasive fungal disease. Positive clinical impact is greatest when POPS organisms are detected, and patients at risk of POPS diagnoses may be identified by an LLM given limited EMR data. Summary: Optimal use of plasma mNGS in solid organ transplant recipients remains uncertain. We find mNGS is most impactful when atypical bacteria, fungi, key viruses, or parasites are detected, and develop a method to identify patients at risk of those infections.

Distinguishing lower respiratory tract infection (LRTI) from incidental pathogen carriage (IPC) is clinically challenging. The immunologic and microbial factors defining the states of LRTI and IPC are poorly understood. Here, we perform host-microbe metatranscriptomic profiling of tracheal aspirates from 326 mechanically ventilated children with clinically adjudicated LRTI ( n = 207), IPC ( n = 70), or non-infectious respiratory failure ( n = 49). In the airway microbiome, LRTI shows reduced alpha diversity and taxonomic richness, while IPC displays greater bacterial abundance, enrichment in respiratory anaerobes, and increased metabolic activity. At the host level, patients with LRTI exhibit a distinct lower airway transcriptional signature of innate and adaptive immune activation compared to those with IPC, who have similar transcriptional profiles to uninfected controls. Mediation analyses suggest the airway microbiome influences the host response to pathogens. An integrated host-microbe metatranscriptomic classifier accurately discriminates LRTI from IPC and controls (AUC = 0.89, 95% confidence interval (CI) 0.85–0.92). The single gene FABP4 , encoding a macrophage-associated lipid chaperone and recently described pneumonia biomarker, performs similarly when combined with alpha diversity; FABP4 protein alone achieves an AUC = 0.88 (95% CI 0.82–0.93). Together, our findings reveal distinct ecological and immunologic archetypes defining LRTI and IPC, and support data-driven, biology-informed LRTI diagnostics incorporating host and microbial features.

Accurately distinguishing lower respiratory tract infection (LRTI) from incidental pathogen carriage (IPC) is clinically challenging. The host immunologic and microbial factors that define the states of LRTI and IPC are poorly understood. We performed host-microbe metatranscriptomic profiling of tracheal aspirate from 326 mechanically ventilated children with clinically adjudicated LRTI (n=207), IPC (n=70), or non-infectious acute respiratory illnesses (n=49). In the airway microbiome, LRTI was characterized by reduced alpha diversity and taxonomic richness, while IPC was characterized greater total bacterial abundance, enrichment in respiratory anaerobes and increased metabolic activity. In terms of host response, patients with LRTI exhibited a distinct lower airway transcriptional signature of innate and adaptive immune activation compared to those with IPC, who had similar transcriptional profiles as uninfected controls. Mediation analyses suggested that the airway microbiome influences the host response to pathogens. An integrated host-microbe metatranscriptomic classifier discriminated LRTI from IPC and controls with an AUC=0.89 (95% confidence interval (CI) 0.85-0.92). The single gene FABP4, when combined with alpha diversity, performed similarly, and FABP4 protein alone achieved an AUC=0.88 (95% CI 0.82-0.93). Together, our findings reveal distinct ecological and immunologic archetypes that define LRTI and IPC, and support data-driven, biology-informed LRTI diagnostics that incorporate host and microbe.

In the general population, aging is associated with an increase in systemic inflammation and chronic disease burden. However, in systemic lupus erythematosus (SLE), older age is uniquely associated with a decrease in disease activity, suggesting a distinct relationship between aging and inflammation. Using a multiomic approach, we compared aging-related changes in the peripheral blood immune profiles of 287 patients with SLE and 928 healthy controls. In patients with SLE, aging correlated with lower expression of interferon (IFN)–stimulated genes (ISGs) across multiple cell types, decreased plasma IFN-α2, and differential genome methylation. Both patients with SLE and controls demonstrated age-related declines in naïve T cells, but only patients with SLE exhibited increases in CD56 ^dim natural killer cells with older age. Of the genes both down-regulated and hypermethylated with older age, ISGs were disproportionately represented, suggesting a role for epigenetic silencing. Altogether, we found that patients with SLE exhibit unique aging-related decreases in IFN signaling that correlate with improved disease activity.

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.

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. 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 positive correlation with interferon-related protein expression and negative correlation with the expression of neutrophil activation proteins. Taken together, our study provides new insight into the lower airway and systemic proteome of severe pediatric vLRTI.

Asthma exacerbations often occur due to infectious triggers, but determining whether infection is present and whether it is bacterial or viral remains clinically challenging. A diagnostic strategy that clarifies these uncertainties could enable personalized asthma treatment and mitigate antibiotic overuse. To explore the performance of validated peripheral blood gene expression signatures in discriminating bacterial, viral, and noninfectious triggers in subjects with asthma exacerbations. Subjects with suspected asthma exacerbations of various etiologies were retrospectively selected for peripheral blood gene expression analysis from a pool of subjects previously enrolled in emergency departments with acute respiratory illness. RT-PCR quantified 87 gene targets, selected from microarray-based studies, followed by logistic regression modeling to define bacterial, viral, or noninfectious class. The model-predicted class was compared to clinical adjudication and procalcitonin. Of 46 subjects enrolled, 7 were clinically adjudicated as bacterial, 18 as viral, and 21 as noninfectious. Model prediction was congruent with clinical adjudication in 15/18 viral and 13/21 noninfectious cases, but only 1/7 bacterial cases. None of the adjudicated bacterial cases had confirmatory microbiology; the precise etiology in this group was uncertain. Procalcitonin classified only one subject in the cohort as bacterial. 47.8% of subjects received antibiotics. Our model classified asthma exacerbations by the underlying bacterial, viral, and noninfectious host response. Compared to clinical adjudication, the majority of discordances occurred in the bacterial group, due to either imperfect adjudication or model misclassification. Bacterial infection was identified infrequently by all classification schemes, but nearly half of subjects were prescribed antibiotics. A gene expression-based approach may offer useful diagnostic information in this population and guide appropriate antibiotic use.