Host–microbiome archetypes differentiate infection from pathogen carriage in the human lower airway

Domain: biology
Published: 2026-04-13
Editorial outcome: minor_revision

Abstract
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.

Introduction
The upper and lower airways harbor robust microbial communities which together represent the human respiratory microbiome1. While primarily comprised of commensal bacteria during states of health, it has been well established that respiratory pathobionts, or microbes with the potential to cause disease, can be incidentally carried within the airway microbiome without eliciting signs or symptoms of infection2. This phenomenon of colonization, or incidental pathogen carriage (IPC), which involves a dynamic relationship between pathobiont, microbiome, and host immune response, remains incompletely understood.

IPC frequently complicates the management of acute respiratory illness by confounding accurate lower respiratory tract infection (LRTI) diagnosis. For instance, in patients hospitalized for respiratory failure, the underlying cause often remains unclear for days, as both infectious and non-infectious conditions can present with overlapping clinical features. LRTI diagnostic tests that rely exclusively on pathogen detection cannot differentiate between true infection and IPC, nor discern the presence of a non-infectious etiology3,4. Early postmortem studies of direct lung tissue demonstrated poor concordance between lung culture and histologically confirmed pneumonia, underscoring the limitations of microbiology-based studies alone.5 However, in contemporary clinical practice, detection of any potential pathogen, particularly in the setting of respiratory failure, can reflexively lead to an LRTI diagnosis, even in the absence of true infection6,7. This contributes to unnecessary antimicrobial use and missed opportunities to diagnose and treat alternative causes of respiratory failure, such as cardiac conditions or autoinflammatory diseases8–10.

While IPC occurs across the age spectrum, the incidence is highest in children11–13. For instance, an estimated 33-90% of children incidentally carry Streptococcus pneumoniae in the airway, compared to <5% of adults14–17. Similarly, Moraxella catarrhalis colonizes the nasopharynx in 30-100% of infants but only 1-5% of adults18–20. Viral IPC is also far more common among children; based on population surveillance studies, an estimated 25% of asymptomatic young children incidentally carry at least one viral pathogen, in contrast to only 2% of adults21–23.

The high baseline rates of respiratory IPC in children are further increased in the setting of hospitalization and critical illness. The physiological disturbances of critical illness, including disruption of epithelial barriers, immune dysregulation, and introduction of endotracheal tubes, reshapes the respiratory microenvironment, promoting shifts in microbial community composition and facilitating colonization by opportunistic pathogens24,25. Among patients who require mechanical ventilation for non-infectious indications, airway colonization with potentially pathogenic organisms occurs in nearly half within the first 24 hours26,27.

Despite its clinical relevance and frequent occurrence, the host and microbial factors that distinguish IPC from LRTI remain incompletely understood. No studies have yet evaluated IPC in the context of the lower airway microbiome and host response, and tests capable of accurately distinguishing LRTI from IPC do not yet exist. To address these gaps, we studied a prospective cohort of critically ill children hospitalized for acute respiratory failure and performed metatranscriptomic RNA sequencing on lower respiratory samples to simultaneously profile both host and microbe. We identify striking host and microbial biosignatures that distinguish the two states and then leverage findings to build accurate diagnostic classifiers with the potential to advance acute respiratory illness management.

Results
Patient cohort, clinical adjudication, and pathogen detection
Critically ill children with all-cause respiratory failure requiring mechanical ventilation (n=457) were prospectively enrolled at eight U.S. hospitals between 2/2015 and 12/2017 (Figure S1).28–30 Tracheal aspirate (TA) was obtained within 24 hours of intubation and stored in an RNA stabilizing agent. High-quality RNA sequencing data capturing both the respiratory microbiome and host transcriptome were generated on 343 patients.

LRTI cases were identified by structured, retrospective clinical adjudication following ICU discharge performed by ≥2 physicians trained in critical care medicine or infectious diseases using the CDC/NHSN PNU1 criteria31. Adjudicators, who had access to all clinical data in the electronic health record and were blinded to metatranscriptomic results, identified 224 patients (65.3%) with LRTI. Alternative, non-infectious etiologies of respiratory failure were adjudicated in 119 (34.7%) patients, and included neurologic conditions, anatomic airway abnormalities, toxin exposures, cardiac conditions, trauma and autoimmune disease. To comprehensively identify respiratory pathogens in the lower airway, a combination of standard-of-care clinical microbiologic testing and respiratory metatranscriptomics was performed. Following this process, 207 patients received a clinical diagnosis of LRTI and had a respiratory pathogen detected (“LRTI” group). Of those with a clear non-infectious cause of respiratory failure, 70 had a pathogen detected (“IPC” group) and 49 patients did not (“CTRL” group). Seventeen patients with clinically adjudicated LRTI but negative microbiologic testing were excluded from the analysis.

We found no differences in sex, race, ethnicity, or comorbidities between groups (Table 1). Patients in the LRTI group had a younger median age of 0.6 years (interquartile range (IQR) 0.2-2.1), compared to 1.6 years (IQR 0.9-6.4) in the IPC group and 9.5 years (IQR 1.3-14.4) in the CTRL group, reflecting the typical epidemiologic differences in pediatric respiratory failure.4,32 Ventilator days and ICU length of stay were slightly longer in LRTI versus IPC, though hospital length of stay was similar and mortality was lower. Antibiotic usage prior to intubation was similar across all groups, and notably, most patients received antibiotics during their hospital course (LRTI 99.0%, IPC 91.4%, CTRL 83.7%).

Respiratory syncytial virus (RSV) was the most common respiratory pathogen in the cohort, identified in 52.7% of the LRTI group and 12.9% of the IPC group (P<0.001, Fisher’s exact test) (Figure 1; detection stratified by clinical versus metatranscriptomics testing modality shown in Figure S2). Other pathogens that statistically differed in prevalence between groups included human metapneumovirus (LRTI 6.8%, IPC 0.0%, P=0.02), Haemophilus influenzae (LRTI 30.0%, IPC 17.1%, P=0.04), and Pseudomonas aeruginosa (LRTI 1.9%, IPC 10.0%, P=0.01). Several other pathogens were identified at similar rates between groups, including rhinovirus, Moraxella catarrhalis, Staphylococcus aureus, and Streptococcus pneumoniae. Co-detection of both bacteria and viruses was common, comprising 59.4% of LRTI cases and 25.7% of IPC cases.

The respiratory microbiome differs between LRTI, IPC, and controls
We first sought to compare the composition and function of the lung microbiome between LRTI and IPC, hypothesizing that both biologically relevant and diagnostically useful distinctions may exist. Alpha diversity demonstrated notable differences, with LRTI characterized by a lower Shannon Diversity Index (SDI) compared to either IPC (P =2.8e-8, Wilcoxon rank-sum test) or CTRL (P =2.6e-14). In contrast, SDI did not differ between IPC and CTRL groups (P =0.59) (Figure 2a). Similarly, we found that community richness (total number of unique species in the lung microbiome) was significantly higher in both IPC and CTRL groups compared to those with LRTI (P =1.2e-11 and 1.3e-9, respectively) (Figure 2b). Community composition also differed between LRTI, IPC, and CTRL groups based on the Bray-Curtis index (P<0.001, PERMANOVA) (Figure 2c). Despite these distinct microbiome archetypes, the microbe designated as the pathogen by our combined clinical-metagenomic framework was typically the most abundant microbe in the airway microbiome in both LRTI and IPC (Figure S3).

We next compared total bacterial abundance (measured in reads per million, RPM) between groups. Surprisingly, total bacterial abundance was highest in IPC compared to both LRTI (P =2.2e-3, Wilcoxon rank-sum test) and CTRL (P =2.7e-3) (Figure 2d). In contrast, total viral RPM was highest in LRTI, while the IPC group exhibited an intermediate state with greater viral abundance compared to the CTRL group (P =1.3e-9) (Figure 2e). Sensitivity analyses subsetting by viral-bacterial co-detection did not markedly change the observed relationships between LRTI, IPC, and CTRL groups (Figure S4).

We next examined pathogen abundance and found that rhinovirus RPM was significantly higher in LRTI compared to IPC (P=0.047, Wilcoxon rank-sum test), while mean RSV abundance trended higher in LRTI, but the difference did not reach statistical significance (P=0.060) (Figure S5). H. influenzae abundance did not differ between groups (P=0.84), while M. catarrhalis RPM was unexpectedly higher in IPC compared to LRTI (P=9.1e-3).

To further characterize the microbiome features of LRTI and IPC, we carried out differential taxonomic abundance analysis using ANCOM-BC33. While RSV was the only microbe with significantly greater abundance in LRTI, we found that IPC was enriched with classically commensal and anaerobic taxa including Prevotella, Neisseria, Porphyromonas, Gemella, and Streptococcus species (Figure 2f, Supp. Data 1). We subsequently evaluated pathogen burden, quantified as the proportion of non-human sequencing reads assigned to the implicated respiratory pathogen. A ratio near 1.0 indicates that the pathogen comprised near all microbial reads, while lower values (e.g., <0.2) indicate that the pathogen represented only a minority of the community. Although both groups demonstrated a wide range of pathogen burden, patients with LRTI were more likely to exhibit more pathogen-dominant communities, whereas in IPC, the pathogen more often comprised only a small fraction of the microbial community (63.4% versus 36.6% mean pathogen proportion, P=5.9e-7, Wilcoxon rank-sum test) (Figure 2g).

We hypothesized that virulence factor expression might differ between LRTI and IPC, and thus carried out an exploratory assessment using the MetaVF database34. We found that the expression of two H. influenzae virulence factors, HMW1/2 and HxuABC, were higher in LRTI (P=0.02 and P=0.03, respectively, ANCOM-BC) (Figure 2h, Figure S6, Supp. Data 2). HMW1/2 are adhesin proteins that facilitate H. influenzae adherence to the respiratory epithelium35, and hxuABC is a specialized ATP-binding cassette transporter for iron acquisition from the host36.

We additionally assessed microbiome functional differences by profiling metabolic pathways using HUMAnN37. We found that compared to LRTI, IPC was characterized by higher expression of metabolic pathways essential for fatty acid beta-oxidation, citrulline biosynthesis, and arginine degradation (Figure 2i, Supp. Data 3). Taken together, our findings suggested that IPC is characterized by a more diverse, taxonomically rich, abundant, and metabolically active respiratory microbiome compared to LRTI.

Host airway transcriptional responses distinguish LRTI from IPC and controls.
We next tested the hypothesis that the pulmonary host response would differ between LRTI, IPC, and CTRL groups by evaluating the lower airway transcriptome. Principal component analysis demonstrated that LRTI was characterized by a distinct transcriptional signature compared to IPC or CTRL groups (P =0.002, PERMANOVA), but did not differ between IPC and CTRL (P =0.25) (Figure 3a, Figure S7). This finding was underscored by hierarchical clustering of the top 20 most differentially expressed (DE) genes between LRTI and CTRL groups, which generally separated LRTI from non-LRTI cases, though several IPC cases clustered among LRTI cases (Figure 3b). IPC and CTRL patients did not clearly separate based on hierarchical clustering.

To better understand host gene expression differences between the three groups at a more granular level, we performed pairwise differential expression analyses, adjusting for age and sex. We identified distinct host signatures that differentiated LRTI from IPC and CTRL groups, with 3517 and 2856 DE genes, respectively (Figure 3c, Figure 3d, Supp. Data 4). In contrast, IPC and CTRL groups demonstrated minimal differences, with only 2 DE genes (Figure 3e, Supp. Data 4). Among the genes DE between LRTI and both CTRL and IPC groups, we noted that FABP4, which is expressed in macrophages and encodes a lipid chaperone that modulates leukotriene stability38, was a clear outlier in both fold change and statistical significance (Figure 3f, Figure S8).

To characterize biological pathways encompassing the DE genes, we carried out gene set enrichment analyses (GSEA) (Figure 3g, Supp. Data 5). Canonical infection-related pathways, including interferon signaling, antigen presentation, adaptive immune signaling, and neutrophil degranulation, were all upregulated in LRTI versus IPC, as expected. However, we also noted that interferon signaling pathways were upregulated in IPC compared to CTRL patients, though to a lesser extent, suggesting that some, albeit subtle, induction of innate immune signaling occurs in the setting of pathogen presence in the airway.

Given that interferon signaling is a central feature of the anti-viral host immune response, we hypothesized that the immunologic features of IPC may differ between viral and bacterial pathogens. To investigate this, we performed differential expression analyses within patients who had viral (n=223) or bacterial (n=195) pathogens detected. Aligning with our primary composite analysis, both viral and bacterial LRTI were characterized by distinct airway transcriptional signatures with respect to the CTRL group (3424 and 3140 DE genes, respectively) (Figure 4a, Supp. Data 6). Compared to IPC, viral and bacterial LRTI also exhibited distinct host signatures, although with fewer DE genes (1860 and 1991, respectively). Few transcriptomic differences were observed between IPC and CTRL groups, although a subtle signature of 29 DE genes, primarily interferon-stimulated genes (ISGs, e.g., ISG15, IFIH1, OAS3) characterized viral IPC (Figure 4b). In contrast, there were zero DE genes between bacterial IPC and CTRL groups, suggesting fundamental differences in immune activation between patients incidentally carrying viral versus bacterial pathogens.

We next examined expression of individual genes classically associated with anti-viral and anti-bacterial defense. The viral subgroups demonstrated a gradation in ISG expression39, ranging from highest in viral LRTI to lowest in CTRL (Figure 4c). Given the similar pattern with viral abundance in our microbiome analysis (Figure 2e), we hypothesized that the differences in interferon signaling might simply be related to viral load differences between groups. A regression of ISG expression against viral RPM showed that ISG expression did positively correlate with interferon expression, as expected (adjusted R2=0.45), though interestingly, when stratified by group, IPC patients demonstrated a proportionally attenuated response compared to those with LRTI for any given viral load (P=1.8e-3, linear regression interaction test for the ISG IFIH1) (Figure 4d). When age was added as a covariate, this finding did not change (P=2.0e-3), and other ISGs (ISG15, IFI44) exhibited the same pattern (Figure S9).

While these findings could be due entirely to intrinsic differences in innate immune response activation between individuals, we considered the possibility that the lung microbiome might modulate, at least to some extent, inflammatory gene expression in the setting of pathogen exposure. To investigate this, we evaluated the relationship between SDI and ISG expression and found that as lung microbiome diversity increased, ISG expression decreased (adjusted R2=0.29, linear regression P=1.3e-17) (Figure 4e). Furthermore, we found that after adjusting for SDI, between-group differences in the relationship between viral load and ISG expression disappeared (P=0.50, linear regression interaction test), suggesting that the lung microbiome modulates anti-viral host responses (Figure 4f). Mediation analysis suggested that lower SDI was independently associated with higher IFIH1 expression, and that microbiome composition may partially mediate the relationship between viral pathogen presence and interferon signaling, explaining ~43% of the group effect on IFIH1 expression (Figure 4g).

We performed a parallel analysis focused on bacterial LRTI and IPC (Figure S10). In contrast to our observations with viral pathogens, the expression of canonical anti-bacterial innate immunity genes (GZMB40, CD6441, and TLR142) remained relatively constant across a range of bacterial pathogen loads (e.g., for GZMB, adjusted R2=0.07). However, as with viral pathogens, the bacterial IPC group exhibited consistently lower innate immunity gene expression compared to the LRTI group (e.g., for GZMB, P =3.0e-04). Applying the same mediation analysis to GZMB demonstrated that lower lung microbiome alpha diversity was independently associated with higher innate immunity gene expression, explaining ~31% of the group effect on GZMB expression (Figure 4h).

Lastly, we assessed the impact of adjusting for SDI in our original pairwise differential expression analyses and found that doing so markedly reduced the lower airway transcriptional differences between LRTI and IPC (Figure 4i, Supp. Data 7).

Integration of host and microbial features enables accurate LRTI diagnosis
Having identified such distinct microbiome and host immune response differences between groups, we next sought to translate our findings into proof-of-concept diagnostic tests. Using LASSO regularized regression, we built diagnostic classifiers to distinguish true infection from the alternative clinically encountered states of IPC or non-infectious acute respiratory illness (Figure 5a). Given prior work demonstrating the utility of FABP4 as a pneumonia diagnostic biomarker43,44, we evaluated its performance alone or in combination with alpha diversity. Both FABP4 and SDI performed well individually, although the combination achieved even better classification performance with an area under the receiver operator curve (AUC) of 0.87 (95% confidence interval (CI) 0.83–0.91) based on 5-fold cross validation (CV). A multi-gene host transcriptional classifier in combination with SDI performed comparably with an AUC of 0.89 (95% CI 0.85–0.92, Figure 5b, Table S1) and yielded classifier scores that effectively distinguished LRTI from patients in either the IPC or CTRL groups (Figure 5c).

Considering that a single protein biomarker could have distinct practical utility as a clinical diagnostic, we tested whether protein levels of FABP4 could also effectively differentiate LRTI from IPC and CTRL groups in a subset of patients with FABP4 protein measurements from the lower airway (n=134). Indeed, the LRTI group had markedly different levels of FABP4 compared to both IPC (P=6.0e-4, Wilcoxon rank-sum test) and CTRL (P=2.3e-9) groups (Figure 5d). Consistent with our findings at the transcriptional level, no difference in FABP4 levels between IPC and CTRL groups was observed (Figure 3f). Notably, we found that respiratory FABP4 alone performed as well as the integrated host/microbe metatranscriptomic classifier (AUC=0.88, 95% CI 0.82–0.93) (Figure 5e), suggesting promise as a clinical biomarker for both LRTI diagnosis and distinguishing LRTI from IPC.

Methods
Study cohort
We studied a prospective multicenter cohort of 457 critically ill children with acute respiratory illnesses requiring mechanical ventilation who were admitted to eight U.S. intensive care units (ICUs) in the National Institute of Child Health and Human Development’s Collaborative Pediatric Care Research Network (CPCCRN) between February 2015 and December 201728–30. Enrollment sites included: Children’s Hospital Colorado, Aurora, CO USA; University of California San Francisco, San Francisco, CA; Nationwide Children’s Hospital, Columbus, OH, USA; The Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pittsburgh, Pittsburgh, PA, USA; Children’s Hospital of Michigan, Detroit, MI, USA; University of California Los Angeles, Los Angeles, CA, USA; Children’s National Medical Center and George Washington School of Medicine and Health Sciences, Washington, DC, USA.

Children aged 31 days to 17 years who were expected to require mechanical ventilation for at least 72 hours and had TA sampling performed within 24 hours of intubation were approached for enrollment. Exclusion criteria included TA sample collection > 24 hours after intubation, presence of a tracheostomy tube, any condition in which deep tracheal suctioning was contraindicated, prior mechanical ventilation during the hospitalization, goals of care dictating a do not resuscitate order and/or a request for limited support, or previous enrollment in the study.

Eligible patients were identified, and their guardians were approached for consent by clinical research coordinator staff as soon as possible following intubation. Written informed consent for study participation was obtained from legal guardians. An initial waiver of consent was granted for TA samples to be obtained from standard of care suctioning of the endotracheal tube and stored until the parents or other legal guardians could be approached for informed consent. Samples from unconsented subjects were subsequently destroyed. The study was approved by the University of Utah central IRB #00088656.

Adjudication of infection status and definition of subgroups
Adjudication of LRTI status was carried out retrospectively by study-site clinicians with access to all clinical, laboratory, microbiology, and radiology data available up to the end of admission, without knowledge of metatranscriptomic sequencing results. Each patient was reviewed independently by two adjudicators with expertise in pediatric infectious disease and/or critical care to determine the presence or absence of clinical LRTI; disagreements were discussed and resolved by a panel. For this study, patients were classified into three groups: 1) LRTI if they were clinically adjudicated as having LRTI and had supportive microbiology, 2) IPC if they were clinically adjudicated as not having LRTI but had positive microbiology, and 3) CTRL if they were clinically adjudicated as not having LRTI and had negative microbiology. Microbiology included standard-of-care clinical microbiology (multiplex polymerase chain reaction (PCR) and semiquantitative bacterial respiratory cultures) and/or metagenomic detection for pathogenic bacteria and viruses (implementing a validated, rules-based computational model, described in detail below)28,29,45. Additional detail regarding clinical adjudication and interpretation of upper versus lower respiratory tract microbe detection can be found in the Supplementary Methods.

RNA sequencing
Tracheal aspirate (TA) collected within 24 hours of intubation was mixed equi-volume with DNA/RNA shield (Zymo Research, Cat. No R1100) and stored at -80 °C. Following bead-bashing, samples underwent extraction using the Qiagen AllPrep Kit (Qiagen, Cat. No R2145), followed by DNAse treatment. All sample handling and RNA extractions were performed using a rigorous ultra-clean protocol designed for low biomass specimens, including use of personal protective equipment, RNase-decontaminated work surfaces and instruments, strict workflow separation, and processing of water-only controls in parallel to monitor and account for background contamination. Sequencing libraries were prepared from purified RNA using the NEBNext Ultra II Library Prep Kit (New England Biolabs, Cat. No E7770L) and dual index barcodes. Human ribosomal RNA depletion was carried out prior to library amplification and pooling using the Cas9-based Depletion of Abundant Sequences by Hybridization (DASH) method66. Libraries underwent 150-base pair paired-end sequencing on an Illumina NovaSeq 6000 sequencer.

Measurement of FABP4 protein levels
FABP4 was measured from TA specimens collected within 24 hours of intubation using the SomaScan 7k assay (SomaLogic)67–69 in a subset of this cohort. Following collection, TA specimens underwent centrifugation at 4°C at 15,000 × g for 5 min, subsequently the supernatant was frozen at −80°C within 30 minutes.

Taxonomic mapping from RNA-seq data
We employed the CZ ID Illumina mNGS pipeline (v7.1) for taxonomic mapping of microbial sequence data70,71. This incorporates initial removal of human reads using Kallisto72, adapter sequence trimming with fastp73, filtering low quality and low complexity reads using PriceSeq74 and the Lempel-Ziv-Welch algorithm, respectively, and a final scrub of any residual human reads using Bowtie275. Taxonomic classification was then performed on both short reads and assembled contigs using the NCBI nucleotide (NT) and nonredundant (NAR) databases. Background and batch correction was performed on species level taxon matrices (see below).

Identification and mitigation of background contaminants
Thirty-four negative water controls were processed and sequenced alongside the patient samples to enable characterization and subtraction of background contamination (Supp. Data 8). A previously developed negative binomial model55 was used to model the distribution of reads of microbial taxa in the negative controls. Mean and dispersion parameters were fit to the data and estimates of the mean were generated for each batch:taxon pair. A single dispersion parameter was generated across all taxa using the MASS package (R, v7.3.58.1). P values were adjusted for multiple testing using the Benjamini-Hochberg False Discovery Rate method. Microbial taxa that were present at a significantly higher average abundance in participant samples than in negative controls (P <0.05) were retained for downstream analyses. Microbial taxa were included in downstream analysis if they met these criteria: (1) ≥1 hit to the NT database (2) ≥1 hit to the NR database (3) a minimum alignment length of 70 bases to the NT database.

Clinical detection of respiratory pathogens
Standard-of-care clinical respiratory microbiologic testing was performed at the discretion of the treating clinicians at each study site. Diagnostics included nasopharyngeal swab respiratory pathogen testing by multiplex PCR and/or TA bacterial semiquantitative cultures. Clinical diagnostic tests on samples obtained within 48 hours of intubation were included in the analyses. Microbes reported by the clinical laboratory as representing laboratory, skin, or environmental contaminants, or reported as mixed upper respiratory flora were excluded.

Detection of respiratory pathogens by metatranscriptomics
For bacterial taxa that remained after background filtering, we applied an established rules-based model (RBM) to identify potential respiratory pathogens. In prior studies, the RBM identified 82-96% of clinically-confirmed lower respiratory pathogens compared to standard of care clinical diagnostics, and permitted detection of otherwise missed potential pathogens in >50% of patients with clinically adjudicated LRTI but negative standard testing28,29,45. The RBM operates by first retaining the most abundant species from each mapped genus, and any lower-abundance species within that genus with known pathogenicity in the respiratory tract based on a curated reference list from epidemiologic surveillance studies.76–79 Species were then ranked by abundance (reads per million values aligned to NT database, sum NT RPM), limiting to the top 15. The largest drop in abundance among this ranked list was identified, and any species above the largest drop in abundance with known ability to cause LRTI as a potential pathogen were counted as bacterial hits. Viruses detected with an abundance > 0.1 RPM with established human respiratory pathogenicity were subsequently identified.

Microbial abundance calculations
Microbial abundance/load was approximated for each sample by calculating the sum NT RPM. Statistical comparison of sum NT RPM across groups was performed using the wilcox_test() function (rstatix v0.7.2). Resulting P values were adjusted using the Benjamini-Hochberg False Discovery Rate algorithm via the p.adjust() function of the stats package. Generalized linear modeling of these relationships was performed using the glm() function of the stats package, specifying a Gaussian distribution and identity link function, and adjusted for both sex and age. These methods were applied to the entire microbial profile as well as subsets of the profile (e.g., bacterial, viral) based on NCBI lineage data. Differences in abundance (sum NT RPM) of individual species of interest between groups were also performed using this approach.

Microbiome diversity analyses
Alpha diversity (Shannon Diversity Index, or SDI) was calculated using the diversity() function of the vegan package (v2.7-1). Beta diversity (Bray-Curtis dissimilarity) was calculated using the functions vegdist(), betadisper(), permutest() and adonis2() of the vegan package. Principal Coordinate Analysis (PCoA) was performed using the cmdscale() function of the stats package.

Differential microbial abundance analysis
Differential abundance analysis was performed using the ANCOM-BC package33 (v2.8.1) using a library filter of 0, prevalence filter of 10%, alpha level of 0.05 and a pseudo-count of 1. The analysis was adjusted for age and sex, and P values were adjusted for multiple testing using the Benjamini-Hochberg correction.

Virulence factor screening
Virulence factors were identified from transcriptomic data using the MetaVF toolkit34, its associated virulence factor database VFDB2.0, and BLAST (v2.16.0). Called virulence factors with an associated e-value less than 1e-10 were retained for downstream analysis. Differentially expressed virulence factors were identified using ANCOM-BC33 with a library filter of 0, prevalence filter of 5%, alpha level of 0.05 and a pseudo-count of 1.

Generation of host gene counts
RNA-seq reads were pseudoaligned using Kallisto72 against an index consisting of all transcripts associated with human protein coding genes (GRCh38-based). We excluded samples with less than one million exon counts. Gene-level counts were generated using the tximport package, with the scaled TPM method (v1.28, Bioconductor 3.18)80. Genes were retained for subsequent analysis if they had at least 10 counts in at least 20% of the samples in the cohort. The gene counts table underwent variance-stabilizing transformation (VST) using the R package DESeq2 (v1.50.2)81, and VST-transformed counts were used in the principal component analysis, hierarchical clustering, assessment of individual genes, and classifier development.

Principal component analysis
Principal component analysis (PCA) was performed on the complete gene expression matrix using the prcomp() function of the stats package. For data visualization, we plotted PC1 versus PC3, which provided the greatest apparent separation in two dimensions, and ellipses depict 68% confidence regions around group centroids. PC1 versus PC2 and PC2 versus PC3 are shown in the supplement. To formally assess group separation, we ran pairwise PERMANOVA (adonis2, vegan package v2.7-1) on Euclidean distances computed from the full expression matrix (i.e., testing differences in group centroids). P values from the pairwise contrasts were adjusted by the Benjamini-Hochberg method, with P <0.05 considered significant.

Heat map and hierarchical clustering
For display, we selected the top 20 most significantly differentially expressed genes based in the LRTI and CTRL comparison, based on P (see below for DE methods). Each gene was standardized across samples (z-score; mean = 0, SD = 1). Genes were ordered by unsupervised hierarchical clustering using Euclidean distance and complete linkage (ComplexHeatmap v2.26.1 defaults). Samples were clustered using correlation distance with Ward.D2 to emphasize similarity of gene expression profiles. Dendrograms were computed for ordering but omitted in final visual panel.

Differential expression and gene set enrichment analyses
DE analyses were performed with the R package limma-voom (v3.66.0) on raw gene-level counts82. The design matrix included age and sex as covariates, and where noted, Shannon Diversity Index (SDI). Counts were transformed with voom (mean-variance modeling with precision weights) and quantile normalized across samples. Gene-wise statistics used empirical-Bayes moderated two-sided t-tests; multiple testing was accounted for by Benjamin i-Hochberg with P <0.05 considered significant. Where individual genes are displayed (e.g., boxplots for FABP4, IFIH1, and GZMB), the adjusted P values from the respective limma DE analyses are displayed. For pathway analysis, we performed pre-ranked gene set enrichment analysis (GSEA) using ReactomePA (v1.54.0) on Reactome gene sets with a minimum pathway size of 10 genes and a maximum size of 1500 genes83. All genes from each limma DE comparison, ranked by the limma t-statistic, were included as input. For visualization, we displayed the top 20 DE pathways between LRTI and IPC (all statistically significant with P <0.05), then overlaid results from the IPC and CTRL comparison, only displaying the significant pathways.

Regression and mediation analyses
For both viral and bacterial subgroup analyses (run in parallel with identical pipeline), we modeled gene expression using linear regression with log pathogen abundance (sum NT RPM) and group (LRTI vs IPC) as predictors, including an interaction term (gene expression ~ log pathogen abundance * group). Group differences were evaluated using a global F-test comparing this model to the null model (gene expression ~ log pathogen abundance), and sensitivity models additionally adjusted for age. For visualization, raw data points are plotted alongside model-predicted regression lines. The same structure was applied for microbiome diversity (gene expression ~ SDI * group). SDI-adjusted associations were visualized by plotting residuals from the gene expression ~ SDI model against pathogen abundance with group-specific linear fits. To evaluate modulation of innate host gene expression by diversity, we used the mediation package with SDI as the mediator and included pathogen abundance as a covariate in both models (mediator: SDI ~ group + log10 pathogen abundance; outcome: expression ~ group + SDI + log10 pathogen abundance) and calculated average causal mediated effect (ACME) and average direct effect (ADE) with 1000 simulations using the mediation package (v4.5.1)84. The mediation models used simpler additive models to maintain the interpretability of the effect estimates and because the interaction terms were not significant in any of the regression models. Viral outcomes focused on interferon-stimulated genes (IFIH1, ISG15, IFI44) and bacterial outcomes on canonical anti-bacterial defense genes (GZMB, CD64, TLR1).

Classifier development
Binary classifiers were developed to distinguish LRTI from non-LRTI (IPC + CTRL combined). We performed stratified five-fold cross-validation (same folds re-used across the different models, with a minimum IPC and CTRL counts to keep each fold balanced) and generated out-of-fold predictions for performance assessment. Single-feature models (i.e. FABP4 gene, FABP4 protein, SDI) used logistic regression, as did FABP4 + SDI. Multi-gene models used LASSO logistic regression on all genes (glmnet v4.1-10), with the regularization parameter lambda selected by internal cross-validation85. Non-zero coefficients selected by the LASSO model are provided in Table S1. For performance metrics, the reported AUC reflects the mean AUC of each of the five folds computed with the pROC package (v1.19.0.1)86, and the confidence intervals were obtained by bootstrapping the out-of-fold predictions with 1000 resamples.

Statistics and reproducibility
No statistical method was used to predetermine sample size; sample size was determined by the number of eligible participants enrolled in the parent cohort and for whom high-quality sequencing data were available. Sample exclusions occurred only for eligibility or quality control criteria, as described in the study flow diagram (Figure S1). No additional data were excluded from the analyses. The study was observational and experiments were not randomized. Clinical adjudication of infection status was performed retrospectively by two independent physician reviewers who were blinded to metatranscriptomic sequencing results, with disagreements resolved by consensus review. Statistical analyses were conducted in R v4.4.2 and v4.5.0. Differential gene expression analyses used limma-voom with empirical Bayes moderated two-sided t-tests and Benjamini–Hochberg correction for multiple testing. Group comparisons used Wilcoxon rank-sum or Fisher’s exact tests as appropriate. Classifier development used logistic regression or LASSO-regularized logistic regression with stratified five-fold cross-validation. Two-sided tests were used throughout and adjusted P values <0.05 were considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript comes from one of the World’s leading groups in this specific field of infectious diseases. Its authors asked whether a combined metagenomic investigation of both host and pathogen in 326 ventilated children could offer mechanistic and diagnostic insights into whether their respiratory failure was considered to represent pneumonia driven by pathogenic organisms (LRTI), or merely accompanied by them (IPC). A third control group consisted of children whose respiratory failure was associated with explainable pathologies other than infection and no evidence of pathogen (CTRL).\nThese children were indexed into these three groups by clinical assessors blinded to these metagenomic results.\nThey demonstrate that LRTI is associated with both reduced taxonomic richness and lower transcriptional signatures of innate and adaptive response, whilst IPC was associated with greater bacterial abundance, to include anaerobes as well as higher transcriptional innate immune and adaptive responses, the latter similar to controls. They also went on to demonstrate significant associations between elements of the airway microbiome and the host response to pathogens. Finally, they demonstrated that a combination of host and pathogen metagenomic analysis led to impressive discrimination between LRTI and both IPC and control patients, and that measurement of a single macrophage gene product, FABP4, was equally capable of differentiating between “true” lung infection and that caused by other factors, whether or not there was evidence of “bystander” pathogens.\nThis is truly a Magnum opus, and could have been split into possibly 3 separate publications. The dataset these authors gathered and display is truly gargantuan and catholic in its origins and breadth. I suspect that the authors wished to keep the considerably different research elements within this study together, to build a more coherent story of the very complicated landscape of both the microbiome and how local immune responses relate to it, both when pathogens are present and not.\nIt is a difficult paper to read and this referee took three attempts at it to fully digest its contents as it is so data-dense. It is thankfully very well written.\nIt also represents in my opinion a very significant development in our understanding of the pathology of infectious diseases, both bacterial and viral, as they come to be associated with (or cause) respiratory failure.\nThis is an exceptional paper, all the more so because it addresses a real “block” in our understanding of pulmonary infectious pathology.\nThe “block” relates to the unresolved question of whether patients may be “dying of” a pulmonary pathogen, or “dying with” a relative bystander, not responsible for the underlying respiratory failure. This is a perennial question, asked almost daily in ICUs worldwide. Because we don’t know (as an example) what a heavy growth of Klebsiella spp. returned from a ventilated patient’s lungs means that we treat with antibiotics “just in case”. I note that 99% of patients enrolled in this study were given empiric antibiotics – despite little if any evidence that Koch’s postulates have ever been fulfilled for this and many related organisms, almost all them Gram negative, in the setting of ICU ventilation. Such is the current Gestalt of this clinical scenario. Such prescriptions are typical, traditional “ICU culture” in such a setting and are doubtless responsible for a very significant contribution to antimicrobial resistance. The issue is that such prescriptions may not be necessary in the first place for at least some patients. The authors address this specifically in the particularly interesting group they describe as “incidental pathogen carriage” – a group which will almost by definition receive empiric antibiotics as whether or not the pathogens isolated are causing trouble is not known.\nThis paper begins to shed light on a more evidence-based approach to this very big problem, with clear implications for both clinical practice and the advancement of knowledge in this area.\nThis study demonstrates that a decision boundary between “true pathogens” and the same organisms being described as “colonisers” can be objectively constructed.\nIt furthermore highlights the loss of diversity associated with true infection, as well as its relationship to innate and adaptive immune response.\nThere are also other data relating to both the association between H influenzae virulence factors and true LRTI, as well as differences in the metabolomics of their three clinically indexed groups.\nSome data relating to a “metabolic analysis”, offers interesting and additional insight into the condition of “incidental pathogen carriage”.\nThe discussion is (for the huge amount of data presented) in my opinion very well “distilled” and not only very readable, but appropriate in that it always retains a connection to the clinical challenges posed by this condition, often detecting pathogens whose role in the process is “inferred” but actually unknown and with a remarkably poor level of mechanistic insight and understanding.\nI cannot comment on the appropriateness, correct deployment and interpretation of some of the statistical methods, however.\nI have no major criticisms of this manuscript from my perspective as an academic clinician. But I confirm that with this study, the authors have addressed one of the most important and unresolved issues in the field of crtical pulmonary illness and infection - namely, the relasioships between these two and the association with the ever increasing evidence of the importance of the microbiome in such processes.\nPerhaps the biggest area for uncertainty relates to the clinical indexing of the cases – as whether or not the detection of a particular pathogen implies that it is driving, as opposed to accompanying, disease remains currently a decision down to clinical interpretation of the case in the round. In that sense, I suspect that this represents the weakest part of the study – but paradoxically it is because this is a difficult area, that this work was performed in the first place. Always difficult to tighten up a study with a better “gold standard” which is why the work is being done in the first place.\nAs concerns other matters, these are all minor and more suggestions:\n1. Perhaps the authors might consider describing their interpretation of the bimodal distributions in Figure 2 (g)? Might this somehow relate to antibiotic effect? Or have I misinterpreted the graph? Either way, this might need a bit more explanation.\n2. This study was in children – the authors correctly point out that adults with the same conditions might behave differently – and I think the authors are right to point this out very clearly.\n3. They ask the fascinating question as to whether diversity collapse before, during or after infection. Some very early (maybe about 30 years ago) ICU UK studies with conventional microbiology demonstrated that the appearance of “abnormal” bacterial dominance in the airways preceded clinical deterioration by a few days. This was a tantalising finding at the tie – and I wonder whether this might be referenced? This was not my work, by the way and this is a suggestion only.\n4. Equally, a very early study of trucut lung biopsies in people who had just died of VAP were cultured conventionally, alongside histology and the last Chest X ray available. This paper was considered counterintuitive at the time and demonstrated no relationship between pulmonary shadowing, organism density and histological evidence of infection at specific lung sites. There may be some elements of these findings that may be of interest to consider here. I appreciate that these are old data and in adults, and I am simply mentioning these for the authors to consider. The paper is at: DOI: 10.1097/00000542-199604000-00002. I note that at the time, the paper was largely \"ignored\" - as its findings made those who belived that Kich's postulates for any bacterium finding itself in the lung were satisfied and that antibiotics were necessary. Such a paradigm sadly continues to this day.\n5. Finally, the authors highlight the appearance of anaerobes in their IPC cohort. Some intensivists “believe” in the role of these organisms as true lung pathogens in the absence of evidence of aspiration and others don’t. They might perhaps consider discussing this a bit further – specially since anaerobic LPS if anything antagonises macrophage activation when compared to “ordinary” LPS.\nOther than these minor points, the authors are to be congratulated for the very considerable clinical and academic relevance of this work. I look forward to the same approach being applied to an adult population as I struggle to consider whether results will be significantly different, or not. Either way, I suspect that this this will also be a very important publication.\nFinally, the work prompts the possibility of macrophage FABP4 as a differential diagnostic in thsi clinical space. Shooudl this prove clicnially successful, thsi will be a very major advance in the limitation of inapprorpiate antibitois in this setting; such a finding would be an excellent example of successful translation from CLinical Science to Practice.", "reviewer_2": "In this study, Lydon et al., evaluated the metatranscriptome of tracheal aspirate from 326 mechanically ventilated children with clinically adjudicated LRTI (n=207), IPC (n=70), and non-infectious acute respiratory illnesses (n=49).\nThere are several strengths here. This study tries to address an important problem, where incidental pathogen carriage frequently happens and we do not know how to interpret or what to do. This culture independent method seems to provide a new view on this issue. The use of metatranscriptomic RNA sequencing is a cutting edge unfrequently used technique for lower respiratory tract studies.\nBelow are my point-by-point comments.\nMajor:\n1. In abstract: “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).”. A mention of what FABP4 would facilitate the reading of the abstract\n2. A key factor here is the presence or absence of clinical LRTI as described in the methods. One can probably infer what that means but there might be some room for difference of criteria. For example, was this only based on imaging, or changes in oxygeneation patter, fever, leukocytosis, etc., were also considered. They should add this criteria in the methods.\n3. In some patients nasopharyngeal swabs were utilized for identification of respiratory pathogen. Those could have nasal incidental pathogen carriage and not lower respiratory tract incidental pathogen carriage. Those are two distinct scenarios. Did the authors try to separate the two?\n4. Line 118: “Antibiotic usage prior to intubation was similar across…” A different an important data is to evaluate antibiotic usage prior to sampling as this could modify the evaluation of the metatranscriptome. This could then be used as a confounder to adjust some analyses.\n5. Figure 1 shows “Distribution of pathogens among patients with clinically adjudicated lower respiratory tract infection (LRTI) or incidental pathogen carriage (IPC). Bar plot demonstrating the proportion of participants (…) with each detected pathogen as determined by combined clinical testing and metatranscriptomics”. This is bit problematic since there is likely variability of the clinical testing performed across different patients (for example, not every patient might have gotten a respiratory viral panel, or anaerobic cultures, etc.). While this is un avoidable, I would recommend to report the results of clinical testing separately from metatranscriptome.\n6. There is no data shown on background (technical) control samples which is commonly needed in low biomass studies.\n7. Line 147: “Community composition also differed between LRTI, IPC, and CTRL groups based on the Bray-Curtis index (P<0.001) (Figure 2c).” In the supplement in fig S6 they also report a beta diversity plot comparing these three groups but the distribution is very different. Please clarify.\n8. Among the control patients, some might have had a pathogen identified as such based on their criteria on metatranscriptome data. How many patients had that and how does that compare with IPC and LRTI?\n9. Line 148: “Despite these distinct microbiome archetypes, the detected pathogen was typically the most abundant microbe in the airway microbiome in both LRTI and IPC (Figure S2)”. Is the detection of pathogen here based on clinical results or based on metatranscriptome relative abundance. If the later were included then these results are kind of obvious, no?\n10. For Fig 3b I would expect to see some sort of dendogram supporting the clustering.\n11. Line 208: “Among the genes DE between LRTI and both CTRL and IPC groups, we noted that FABP4, which is expressed in macrophages and encodes a lipid chaperone that modulates leukotriene stability38, was a clear outlier in both fold change and statistical significance (Figure 3f).” What criteria supports that FABP4 is a clear outlier (or extreme value). This could be shown by indicating where this gene fall within the volcano plot\n12. Line 337: “Unexpectedly, total bacterial abundance was greater in IPC than in LRTI or controls, which may be explained by the enrichment of commensal taxa such as Prevotella, Neisseria, and Porphyromonas and reflect a more resilient and balanced barrier microbiota.” A quantitative method such as ddPCR with a clear target (e.g., 16S rRNA gene) would be more desirable to address this issue.\nMinor:\n13. In Abstract: “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.” Maybe “that incorporate host and microbe signatures” or “biomarkers”\n14. Table 1 should be referenced in the text, probably in the third paragraph of the results."}, "author_response": "Response to Reviewers’ Comments\nReviewer #1:\nC1. This manuscript comes from one of the World’s leading groups in this specific field of\ninfectious diseases. Its authors asked whether a combined metagenomic investigation of both\nhost and pathogen in 326 ventilated children could offer mechanistic and diagnostic insights into\nwhether their respiratory failure was considered to represent pneumonia driven by pathogenic\norganisms (LRTI), or merely accompanied by them (IPC). A third control group consisted of\nchildren whose respiratory failure was associated with explainable pathologies other than\ninfection and no evidence of pathogen (CTRL).\nThese children were indexed into these three groups by clinical assessors blinded to these\nmetagenomic results.\nThey demonstrate that LRTI is associated with both reduced taxonomic richness and lower\ntranscriptional signatures of innate and adaptive response, whilst IPC was associated with\ngreater bacterial abundance, to include anaerobes as well as higher transcriptional innate\nimmune and adaptive responses, the latter similar to controls. They also went on to\ndemonstrate significant associations between elements of the airway microbiome and the host\nresponse to pathogens. Finally, they demonstrated that a combination of host and pathogen\nmetagenomic analysis led to impressive discrimination between LRTI and both IPC and control\npatients, and that measurement of a single macrophage gene product, FABP4, was equally\ncapable of differentiating between “true” lung infection and that caused by other factors, whether\nor not there was evidence of “bystander” pathogens.\nThis is truly a Magnum opus, and could have been split into possibly 3 separate publications.\nThe dataset these authors gathered and display is truly gargantuan and catholic in its origins\nand breadth. I suspect that the authors wished to keep the considerably different research\nelements within this study together, to build a more coherent story of the very complicated\nlandscape of both the microbiome and how local immune responses relate to it, both when\npathogens are present and not.\nIt is a difficult paper to read and this referee took three attempts at it to fully digest its contents\nas it is so data-dense. It is thankfully very well written.\nIt also represents in my opinion a very significant development in our understanding of the\npathology of infectious diseases, both bacterial and viral, as they come to be associated with (or\ncause) respiratory failure.\nThis is an exceptional paper, all the more so because it addresses a real “block” in our\nunderstanding of pulmonary infectious pathology.\nThe “block” relates to the unresolved question of whether patients may be “dying of” a\npulmonary pathogen, or “dying with” a relative bystander, not responsible for the underlying\nrespiratory failure. This is a perennial question, asked almost daily in ICUs worldwide. Because\nwe don’t know (as an example) what a heavy growth of Klebsiella spp. returned from a\nventilated patient’s lungs means that we treat with antibiotics “just in case”. I note that 99% of\npatients enrolled in this study were given empiric antibiotics – despite little if any evidence that\nKoch’s postulates have ever been fulfilled for this and many related organisms, almost all them\nGram negative, in the setting of ICU ventilation. Such is the current Gestalt of this clinical\nscenario. Such prescriptions are typical, traditional “ICU culture” in such a setting and are\ndoubtless responsible for a very significant contribution to antimicrobial resistance. The issue is\nthat such prescriptions may not be necessary in the first place for at least some patients. The\nauthors address this specifically in the particularly interesting group they describe as “incidental\npathogen carriage” – a group which will almost by definition receive empiric antibiotics as\nwhether or not the pathogens isolated are causing trouble is not known.\nThis paper begins to shed light on a more evidence-based approach to this very big problem,\nwith clear implications for both clinical practice and the advancement of knowledge in this area.\nThis study demonstrates that a decision boundary between “true pathogens” and the same\norganisms being described as “colonisers” can be objectively constructed.\nIt furthermore highlights the loss of diversity associated with true infection, as well as its\nrelationship to innate and adaptive immune response.\nThere are also other data relating to both the association between H influenzae virulence factors\nand true LRTI, as well as differences in the metabolomics of their three clinically indexed\ngroups.\nSome data relating to a “metabolic analysis”, offers interesting and additional insight into the\ncondition of “incidental pathogen carriage”.\nThe discussion is (for the huge amount of data presented) in my opinion very well “distilled” and\nnot only very readable, but appropriate in that it always retains a connection to the clinical\nchallenges posed by this condition, often detecting pathogens whose role in the process is\n“inferred” but actually unknown and with a remarkably poor level of mechanistic insight and\nunderstanding.\nI cannot comment on the appropriateness, correct deployment and interpretation of some of the\nstatistical methods, however.\nI have no major criticisms of this manuscript from my perspective as an academic clinician. But I\nconfirm that with this study, the authors have addressed one of the most important and\nunresolved issues in the field of crtical pulmonary illness and infection - namely, the relasioships\nbetween these two and the association with the ever increasing evidence of the importance of\nthe microbiome in such processes.\nResponse. We thank Reviewer 1 for their thoughtful and detailed evaluation of our manuscript.\nAs the reviewer aptly notes, distinguishing whether critically ill patients are “dying of” a\npulmonary pathogen or “dying with” a detected bystander organism is a ubiquitous yet\nunresolved problem in the ICU, with major implications for clinical decision-making and\nantimicrobial stewardship. Despite its frequency in practice, this question has remained\nrelatively understudied using integrated, biology-informed approaches.\nWe appreciate that our objective of moving beyond pathogen detection alone and toward an\nobjective framework for differentiating true LRTI from incidental pathogen carriage resonated\nwith the reviewer. We agree that this is a multifaceted problem and intentionally retained\ncomplementary analyses within a single manuscript to present an integrated view of host–\nmicrobe interactions in this clinical context.\nC2. Perhaps the biggest area for uncertainty relates to the clinical indexing of the cases – as\nwhether or not the detection of a particular pathogen implies that it is driving, as opposed to\naccompanying, disease remains currently a decision down to clinical interpretation of the case\nin the round. In that sense, I suspect that this represents the weakest part of the study – but\nparadoxically it is because this is a difficult area, that this work was performed in the first place.\nAlways difficult to tighten up a study with a better “gold standard” which is why the work is being\ndone in the first place.\nResponse. We agree with the reviewer that clinical adjudication of LRTI versus non-infectious\nrespiratory failure is inherently imperfect and that no true gold standard exists for diagnosing\npneumonia in critically ill patients. In the absence of an objective reference standard, we\nemployed a rigorous retrospective clinical adjudication process involving independent review by\n≥2 physicians with expertise in pediatric critical care and infectious diseases, performed after\nICU discharge, with an additional adjudicator added if needed to obtain consensus on any\ndisagreements.\nTo further enhance transparency and to also address a similar point raised by Reviewer 2, we\nhave added a new Supplementary Methods section detailing the clinical features considered\nduring adjudication, which align with the CDC/NHSN PNEU 1 guidelines that we have cited in\nour methods and results sections. We have also added a statement to the discussion explicitly\nacknowledging the limitations of clinical adjudication alongside with a citation that critically\nexamines the use of adjudicated clinical syndromes in diagnostic studies (PMID 36262037, now\nreference #64):\nLine 417: “First, LRTI status was defined using retrospective clinical adjudication\nperformed following ICU discharge, an approach taken due to the absence of a true gold\nstandard for pneumonia diagnosis64. Although adjudication was performed rigorously by\n≥2 independent expert reviewers and consensus resolution, some misclassification is\nunavoidable and reflects the clinical uncertainty this study seeks to address”.\nWe agree with the reviewer that this uncertainty represents a both a limitation of our study and a\ncentral reason why data-driven, biology-informed approaches are needed to distinguish true\ninfection from incidental pathogen carriage.\nC3. Perhaps the authors might consider describing their interpretation of the bimodal\ndistributions in Figure 2 (g)? Might this somehow relate to antibiotic effect? Or have I\nmisinterpreted the graph? Either way, this might need a bit more explanation.\nResponse. We appreciate the reviewer drawing attention to this point and agree that additional\nclarification is helpful. Figure 2g is a raincloud plot that depicts pathogen burden, defined as the\nproportion of non-human sequencing reads assigned to the implicated respiratory pathogen(s).\nA value near 1.0 indicates that the pathogen makes up nearly the entire microbial community\n(i.e. ecological dominance), whereas lower values near 0.0 indicate that the pathogen\nrepresented only a minority of the community among a background microbiome.\nAlthough both LRTI and IPC spanned the full range of pathogen burden/dominance, the\ndistributions differed. LRTI more frequently demonstrated pathogen-dominant communities, and\nIPC more often reflected scenarios in which the pathogen was just one member of a broader\nmicrobial ecosystem. We interpret this to mean even when the same pathogen is detected, the\necological context in which it exists differs between groups, with LRTI characterized by\nmicrobiome collapse and loss of diversity, while IPC characterized by a more intact and\nbalanced microbial community. We view this dominance metric as providing complementary but\ndistinct information from our other microbiome analyses, including abundance (Fig 2d-e, Fig\nS4), alpha diversity (Fig 2a), and pathogen rank within the community (Fig S3).\nThe reviewer queried whether preceding antibiotic administration could explain this pattern.\nSimilar proportions of patients received antibiotics prior to intubation in each group (34.8% in\nLRTI vs 24.3% in IPC, p=0.14, as noted in Table 1), so we suspect that antibiotics alone do not\nexplain the observed pattern. However, preceding antibiotics could contribute to variability seen\nin each group.\nTo improve clarity and interpretation, we have revised and expanded upon the following\nproportion of our results section:\nLine 173: “We subsequently evaluated pathogen burden, quantified as the proportion of\nnon-human sequencing reads assigned to the implicated respiratory pathogen. A value\nnear 1.0 indicates that the pathogen comprised nearly all microbial reads, while lower\nvalues (e.g. <0.2) indicate that the pathogen represented only a minority of the\ncommunity. Although both groups demonstrated a wide range of pathogen burden,\npatients with LRTI were more likely to exhibit more pathogen-dominant communities,\nwhereas in IPC, the pathogen more often comprised only a small fraction of the\nmicrobial community (63.4% versus 36.6% mean pathogen proportion, P=5.9e-7)\n(Figure 2g).”\nC4: This study was in children – the authors correctly point out that adults with the same\nconditions might behave differently – and I think the authors are right to point this out very\nclearly.\nResponse. We thank the reviewer for this comment and agree. As we note in the discussion,\nbecause we focused exclusively on children (a population with high baseline prevalence of IPC),\nit remains known whether our findings generalize to adults. We have now also emphasized this\nas a necessary future direction and are planning parallel analyses in adult cohorts.\nLine 440: “Because this work was conducted in a pediatric cohort, future studies in adult\npopulations will be important to determine whether the microbial and host response\npatterns that define LRTI and IPC are conserved across the age spectrum.”\nC5. They ask the fascinating question as to whether diversity collapse before, during or after\ninfection. Some very early (maybe about 30 years ago) ICU UK studies with conventional\nmicrobiology demonstrated that the appearance of “abnormal” bacterial dominance in the\nairways preceded clinical deterioration by a few days. This was a tantalising finding at the tie –\nand I wonder whether this might be referenced? This was not my work, by the way and this is a\nsuggestion only.\nResponse. We appreciate the reviewer bringing this important work to our attention. The early\nICU study referenced (A’Court et al., 1993, PMID 8255961) used conventional culture methods\nand found abnormal bacterial dominance in the airways leading up to clinical pneumonia\ndiagnosis, a result that resonates with our observation that pathogen dominance differentiates\nLRTI from incidental bacterial carriage. Although the methods differ from our sequencing-based\napproach, the conceptual parallel is striking and adds important historical context to our\nfindings.\nFurthermore, this question has also been explored using contemporary molecular profiling. In a\nprior study from our group examining ventilator-associated pneumonia (VAP), longitudinal\nrespiratory sampling demonstrated that early shifts in community composition and reduced\ndiversity preceded clinical VAP diagnosis (Mourani et al., 2021, PMID 33008935). This study\nwas already cited in the Introduction in the context of critical illness-associated microbiome\ndisruption; in response to the reviewer’s comment, we now also reference it in the Discussion to\nhighlight its relevance to the temporal relationship between microbiome changes and infection\nonset.\nTogether, these studies support the biological plausibility that microbiome disruption may occur\nprior to overt infection, while underscoring the need for dedicated longitudinal designs to more\nprecisely define the timing and causality of these changes. We have now referenced both\nstudies in our discussion section, as suggested by the reviewer:\nLine 429: “Longitudinal sampling could help determine whether diversity collapse\nprecedes, accompanies, or follows infection onset, a question that has been raised by\nearly ICU studies using conventional microbiology that suggested abnormal bacterial\ndominance may precede clinical LRTI diagnosis by several days65, and supported by\nmore recent sequencing-based studies demonstrating decreased community diversity\nprior to ventilator-associated pneumonia diagnosis25.”\nC6. Equally, a very early study of trucut lung biopsies in people who had just died of VAP were\ncultured conventionally, alongside histology and the last Chest X ray available. This paper was\nconsidered counterintuitive at the time and demonstrated no relationship between pulmonary\nshadowing, organism density and histological evidence of infection at specific lung sites. There\nmay be some elements of these findings that may be of interest to consider here. I appreciate\nthat these are old data and in adults, and I am simply mentioning these for the authors to\nconsider. The paper is at: DOI: 10.1097/00000542-199604000-00002. I note that at the time, the\npaper was largely \"ignored\" - as its findings made those who belived that Kich's postulates for\nany bacterium finding itself in the lung were satisfied and that antibiotics were necessary. Such\na paradigm sadly continues to this day.\nResponse: We thank the reviewer for calling this important and underappreciated study to our\nattention. In that study (Fàbregas et al., 1996, PMID 8638829), conventional lung cultures\ndemonstrated poor correlation with histologically confirmed pneumonia, even in the absence of\nprior antibiotic exposure. The limitations of pathogen-focused pneumonia diagnostics\nunderscored by this study, namely the inability to distinguish true infection from incidental\npathogen carriage, were a key motivation for our study. We have now referenced this work in\nour introduction to underscore the longstanding diagnostic challenge:\nLine 66: “Early postmortem studies of direct lung tissue demonstrated poor concordance\nbetween lung culture and histologically confirmed pneumonia, underscoring the\nlimitations of microbiology-based studies alone.5 However, in contemporary clinical\npractice, detection of any potential pathogen, particularly in the setting of respiratory\nfailure, can reflexively lead to an LRTI diagnosis, even in the absence of true\ninfection6,7.”\nC7. Finally, the authors highlight the appearance of anaerobes in their IPC cohort. Some\nintensivists “believe” in the role of these organisms as true lung pathogens in the absence of\nevidence of aspiration and others don’t. They might perhaps consider discussing this a bit\nfurther – specially since anaerobic LPS if anything antagonises macrophage activation when\ncompared to “ordinary” LPS.\nResponse. We thank the reviewer for raising this important point. We agree that the detection of\nanaerobic taxa in the lower airway raises clinically relevant questions regarding their pathogenic\nsignificance. To address this, we have expanded the discussion to explicitly note that many of\nthe taxa enriched in IPC were oral-respiratory anaerobes and to situate this finding within the\nbroader debate regarding the role of anaerobes as true pneumonia pathogens. We now cite two\nstudies regarding the minimal role of anaerobes in VAP (Marik and Careau, 1999, PMID\n9925081) and the harms associated with anaerobic coverage in aspiration pneumonia (Bai et al,\n2024, PMID 38387648), as well as the IDSA/ATS 2019 Community Acquired Pneumonia\ntreatment guidelines.\nLine 352: “Many of the taxa enriched in IPC were oral-respiratory anaerobes,\nintersecting with the ongoing debate regarding the role of anaerobes as true pneumonia\npathogens47,48. In this context, our findings suggest that enrichment of anaerobic taxa in\nthe lower airway more often reflects innocuous presence within a diverse microbial\ncommunity rather than invasive infection, aligning with clinical pneumonia guidelines that\ngenerally do not recommend empiric anaerobic coverage49.”\nC8. Other than these minor points, the authors are to be congratulated for the very considerable\nclinical and academic relevance of this work. I look forward to the same approach being applied\nto an adult population as I struggle to consider whether results will be significantly different, or\nnot. Either way, I suspect that this this will also be a very important publication.\nResponse. We thank the reviewer for their kind and encouraging comments. We agree that\nextending this approach in adult populations is a key next step, and as noted above in our\nresponse to C5, we are planning parallel analyses in adult cohorts.\nC9. Finally, the work prompts the possibility of macrophage FABP4 as a differential diagnostic in\nthsi clinical space. Shooudl this prove clicnially successful, thsi will be a very major advance in\nthe limitation of inapprorpiate antibitois in this setting; such a finding would be an excellent\nexample of successful translation from CLinical Science to Practice.\nResponse. We thank the reviewer for this comment and agree that FABP4 holds promise as a\nclinically translatable biomarker in this setting. As noted in our manuscript, these findings build\nupon prior work demonstrating the utility of FABP4 as a pneumonia biomarker, and we very\nrecently reported additional supporting data combining FABP4 with a large language model to\neven more accurately diagnose pneumonia (Phan et al., 2025, PMID 41402257). We have now\nadded this study as a reference in the manuscript. Translation of FABP4 onto a practical gene-\nor protein-based diagnostic assay is an area of ongoing work in our laboratory.\nReviewer #2:\nC10. In this study, Lydon et al., evaluated the metatranscriptome of tracheal aspirate from 326\nmechanically ventilated children with clinically adjudicated LRTI (n=207), IPC (n=70), and non-\ninfectious acute respiratory illnesses (n=49). There are several strengths here. This study tries\nto address an important problem, where incidental pathogen carriage frequently happens and\nwe do not know how to interpret or what to do. This culture independent method seems to\nprovide a new view on this issue. The use of metatranscriptomic RNA sequencing is a cutting\nedge unfrequently used technique for lower respiratory tract studies.\nResponse. We thank the reviewer for the thoughtful comments about the strengths of our study.\nWe agree that incidental pathogen carriage represents a major unresolved challenge in the\ndiagnosis and management of respiratory infections as well as a major gap in the literature. We\nappreciate the reviewer’s recognition of novelty and potential clinical relevance of our integrated\nhost-microbial metatranscriptomics approach.\nC11. In abstract: “The single gene FABP4, when combined with alpha diversity, performed\nsimilarly, and FABP4 protein alone achieved an AUC=0.88 (95% CI 0.82–0.93).”. A mention of\nwhat FABP4 would facilitate the reading of the abstract.\nResponse. We thank the reviewer for this suggestion and agree. We have revised the abstract\nto briefly describe FABP4:\nLine 47: “The single gene FABP4, which encodes a macrophage-associated lipid\nchaperone and is a recently described pneumonia biomarker, performed similarly when\ncombined with alpha diversity; FABP4 protein alone achieved an AUC=0.88 (95% CI\n0.82–0.93).”\nC12. A key factor here is the presence or absence of clinical LRTI as described in the methods.\nOne can probably infer what that means but there might be some room for difference of criteria.\nFor example, was this only based on imaging, or changes in oxygeneation patter, fever,\nleukocytosis, etc., were also considered. They should add this criteria in the methods.\nResponse: We appreciate the reviewer raising this excellent point, which overlaps with\nComment C2 raised by Reviewer 1. We agree that explicit description of the clinical\ncriteria/definition used for adjudicating LRTI versus non-infectious respiratory failure is key for\ntransparency and reproducibility.\nAs recommended by the reviewer, we have added a new Supplementary Methods section that\ndetails the clinical adjudication process and the specific features considered by adjudicators.\nAdjudication was performed retrospectively by ≥2 independent clinicians with expertise in\npediatric infectious diseases and/or critical care, and adjudicators were instructed to utilize the\nCenter for Diseases Control (CDC)/National Healthcare Safety Network (NHSN) Pneumonia\n(PNEU) 1 definition. Specifically, respiratory symptoms, vital signs, physical exam findings,\nlaboratory values (including white blood cell indices and blood gas parameters), and radiology\nfindings are all incorporated in the PNEU1 definition.\nC13. In some patients nasopharyngeal swabs were utilized for identification of respiratory\npathogen. Those could have nasal incidental pathogen carriage and not lower respiratory tract\nincidental pathogen carriage. Those are two distinct scenarios. Did the authors try to separate\nthe two?\nResponse. We appreciate this important and nuanced point raised by the reviewer. We agree\nthat pathogen detection in the upper versus lower respiratory tract represents distinct biologic\nscenarios, and that upper airway carriage does not necessarily imply lower respiratory tract\ncarriage.\nIn this study, microbiologic positivity was defined as a composite of standard-of-care clinical\ntesting and a validated method for lower respiratory tract pathogen detection using\nmetatranscriptomics that has been found to accurately identify culture or PCR-confirmed\npathogens while enabling additional detection in culture-negative cases of LRTI (Langelier and\nKalantar et al., 2018, PMID 30482864; Tsitsiklis et al., 2022, PMID 35544065; Mick et al., 2023,\nPMID 37009900). Importantly, all clinically identified bacterial pathogens were detected via\nlower respiratory tract sampling, so this concern applies primarily to the viral pathogens\nidentified by nasopharyngeal respiratory viral panels.\nTo explore this, we reviewed concordance between upper respiratory clinical testing and lower\nrespiratory tract metatranscriptomics data. Only 9 patients (2.9% of the LRTI cases and 4.3% of\nthe IPC cases) had viral pathogens detected exclusively by nasopharyngeal respiratory viral\npanels. Notably, in 4 out of 9 of these cases, the same virus was present in the tracheal aspirate\nsequencing data but fell below our predefined abundance and/or background-filtering\nthresholds. These data indicate that discordant upper-only viral detection only occurred in a\nsmall minority of cases and is unlikely to meaningfully impact our study conclusions. We have\nadded this detail to our new Supplementary Methods section:\nLine 177: “Standard-of-care clinical viral testing was frequently performed on upper\nrespiratory tract specimens, whereas host transcriptomic and microbiome profiling in this\nstudy was conducted exclusively on lower respiratory tract samples (tracheal aspirates).\nBecause microbial detection and carriage can differ between upper and lower airway\ncompartments, and because the biological focus of this study was the lower respiratory\ntract, we specifically reviewed discordance between upper and lower respiratory tract\npathogen detection that would have altered microbiologic classification. Such\ndiscordance was rare, occurring in only 9 patients (6 LRTI and 3 IPC; 2.9% of LRTI and\n4.3% of IPC cases). Notably, in 4 of these 9 cases, the same viral pathogen was\ndetected in lower respiratory tract metatranscriptomic data but fell below predefined\nabundance and/or background-filtering thresholds. These findings suggest that our\nmicrobiologic definitions were robust, reflected lower airway biology, and aligned with\nhow respiratory pathogen detection is interpreted in routine clinical practice.”\nFor transparency, our clinical microbiology and metatranscriptomic pathogen detection is\npublicly available in the metadata file of our GitHub repository. In addition, our research group\nhas previously examined concordance between the upper respiratory tract PCR testing and\ntracheal aspirate metatranscriptomics in this same cohort, demonstrating generally high\nagreement (Osborne et al, 2024, PMID 37732845).\nC14. Line 121: “Antibiotic usage prior to intubation was similar across…” A different an\nimportant data is to evaluate antibiotic usage prior to sampling as this could modify the\nevaluation of the metatranscriptome. This could then be used as a confounder to adjust some\nanalyses.\nResponse. We appreciate the reviewer raising this important point and agree that prior antibiotic\nexposure can influence airway microbial communities and metatranscriptomic profiles. In this\ncohort, some patients received antibiotics prior to intubation, either in the outpatient setting or\nduring earlier inpatient care. As shown in Table 1, however, antibiotic use prior to intubation was\nstatistically similar across LRTI, IPC, and CTRL groups (approximately 30% in each), indicating\nthat this was unlikely contributing to any confounding of analyses.\nImportantly, enrollment and lower respiratory tract sampling occurred within 24 hours of\nintubation, thereby minimizing prolonged or differential antibiotic exposure immediately\npreceding sample collection. Given the comparable prevalence of pre-intubation antibiotic use\nacross groups and the short interval between intubation and sampling, we did not include\nantibiotic exposure as an adjustment variable in primary analyses. We agree that antibiotic\ntiming and duration are critical considerations for longitudinal microbiome studies and note this\nas an important factor when interpreting cross-sectional airway microbiome data.\nC15. Figure 1 shows “Distribution of pathogens among patients with clinically adjudicated lower\nrespiratory tract infection (LRTI) or incidental pathogen carriage (IPC). Bar plot demonstrating\nthe proportion of participants (…) with each detected pathogen as determined by combined\nclinical testing and metatranscriptomics”. This is bit problematic since there is likely variability of\nthe clinical testing performed across different patients (for example, not every patient might\nhave gotten a respiratory viral panel, or anaerobic cultures, etc.). While this is un avoidable, I\nwould recommend to report the results of clinical testing separately from metatranscriptome.\nResponse. We thank the reviewer for this helpful suggestion. We added a new Supplementary\nFigure S2 that delineates whether each pathogen was identified by clinical testing alone,\nmetatranscriptomics alone, or by both methods. We believe this approach balances readability\nwith detail, allowing interested readers to examine detection modality in greater detail without\novercomplicating the main figure. Detailed per-patient clinical microbiology and\nmetatranscriptomic data are also available in our public GitHub repository metadata file, for\nreaders who wish to further explore these relationships.\nFor ease of reviewing, the new Figure S2 and caption are shown below:\nFigure S2. Pathogen distribution stratified by detection modality in lower respiratory tract\ninfection (LRTI) and incidental pathogen carriage (IPC). Bar plot demonstrating the proportion\nof participants in the LRTI (n = 207, orange) and IPC (n = 70, teal) groups with pathogen detection\nstratified by clinical testing, metatranscriptomics, or both. Bar pattern denotes detection modality.\nOdds ratio (OR) with 95% confidence interval (CI) tabulated and plotted on the right. Filled circles\nrepresent statistically significant pathogens (P<0.05). Asterisks indicate pathogens detected\nexclusively in one group, for which an OR could not be estimated. Arrows indicate where the lower\nor upper confidence interval exceeds the plotting area. Pathogens detected only once in the entire\ncohort were excluded from plotting. Statistical significance for between‑group differences was\nassessed with Fisher’s exact test.\nC16. There is no data shown on background (technical) control samples which is commonly\nneeded in low biomass studies.\nResponse. We agree with the reviewer that background and reagent contamination is an\nimportant consideration in low-biomass respiratory microbiome samples, particularly when using\nhighly sensitive metatranscriptomic sequencing approaches.\nFirst, we would like to note that all tracheal aspirate samples were processed using a rigorous\nultra-clean protocol designed to minimize contamination. This included wearing personal\nprotective equipment (gown, sterile gloves, and face shield) when handling samples, wiping\ndown pipettes and work surfaces with RNaseZap (Ambion) to remove RNase contamination,\nfollowed by 70% ethanol for additional disinfection, and maintaining strict physical separation of\nwork areas. Samples were thawed by extraction batch, and all tubes and plates were kept\nsealed or covered at all times until use. We have clarified these procedures in the Methods\nsection:\nLine 499: “All sample handling and RNA extractions were performed using a rigorous\nultra-clean protocol designed for low biomass specimens, including use of personal\nprotective equipment, RNase-decontaminated work surfaces and instruments, strict\nworkflow separation, and processing of water-only controls in parallel to monitor and\naccount for background contamination.”\nDespite this ultraclean protocol, some degree of contamination is unavoidable. We therefore\nprocessed double-distilled water samples as technical controls in parallel with our clinical\nsamples. For this study, we included 34 water controls. We incorporated these background\ncontrols into our analytic pipeline in order to estimate taxon-specific background distributions,\nwhich are then included in our previously validated negative-binomial model to determine\nwhether a given taxon was present at an abundance above the expected background in each\nclinical sample. Only taxa exceeding this background threshold were retained for downstream\nanalyses. This is described in further detail in the Methods section “Identification and mitigation\nof background contaminants”.\nWe have added a new Supplemental Data file (Supplementary Data File 8) tabulating the\nreads (counts based on the NCBI nucleotide database at the species level) in the water control\nsamples that passed our described QC filters and exceeded modeled background\ncontamination thresholds. Of the 24 water control samples processed, only 6 contained taxa\nexceeding background thresholds.\nFor reviewers and readers interested in additional detail, the complete background-model output\nincluding species level taxonomic hits, full NT/NR alignment metrics, and statistical testing\nresults from the background contamination model for both clinical samples and water controls is\npublicly available on our project GitHub page (file name: microbial_data_bg_corrected.csv). To\nfurther facilitate reviewer evaluation, we have now added a simple CSV file listing the water\ncontrol sample identifiers (file name: water_sample_names.csv) to our GitHub.\nC17. Line 147: “Community composition also differed between LRTI, IPC, and CTRL groups\nbased on the Bray-Curtis index (P<0.001) (Figure 2c).” In the supplement in fig S6 they also\nreport a beta diversity plot comparing these three groups but the distribution is very different.\nPlease clarify.\nResponse. We appreciate the reviewer’s comment and the opportunity to clarify this point.\nFigure 2c and Figure S7 (previously Figure S6) represent different types of analyses applied to\ndifferent data modalities.\nFigure 2c shows microbiome beta diversity, calculated using Bray-Curtis dissimilarity and\nvisualized by principal-coordinate analysis (PCoA), and therefore reflects differences in lower\nairway microbial community composition between LRTI, IPC, and CTRL groups.\nIn contrast, Figure S7 displays principal component analysis (PCA) of the lower airway host\ntranscriptome, showing variation in global host gene expression across samples. These panels\nwere included in the supplement to provide additional views of the host transcriptomic ordination\n(PC1–PC2 and PC2–PC3), complementing the PC1–PC3 plot shown in the main text (Figure\n3a), which was selected because it provided the clearest visual separation between groups.\nBecause these figures depict different biological data (microbiome versus host response) and\nuse different distance metrics and ordination methods, their visual distributions are not expected\nto align. To improve clarity, we have updated the Figure S7 legend to more explicitly explain that\nthis reflects host gene expression, not microbiome diversity.\nC18. Among the control patients, some might have had a pathogen identified as such based on\ntheir criteria on metatranscriptome data. How many patients had that and how does that\ncompare with IPC and LRTI?\nResponse. We appreciate the reviewer’s question. We have added a new figure, Figure S2, to\nexplicitly distinguish pathogens detected by clinical microbiology alone, metatranscriptomics\nalone, or both.\nWe also appreciate the opportunity to clarify how metatranscriptomic pathogen detection was\nincorporated into cohort definitions. As noted by the reviewer, metatranscriptomics was part of\nthe microbiologic reference standard used to define groups, together with standard-of-care\nclinical testing. A validated rules-based model for detecting lower respiratory tract pathogens\nusing metatranscriptomics that accurately identifies culture or PCR-confirmed pathogens while\nenabling additional detection in culture-negative LRTI cases was utilized in this study (Langelier\nand Kalantar et al., 2018, PMID 30482864; Tsitsiklis et al., 2022, PMID 35544065; Mick et al.,\n2023, PMID 37009900). These supportive validation studies are cited in our Methods section.\nPatients were classified as:\n1. LRTI if clinically adjudicated as LRTI and a pathogen was detected by clinical testing\nand/or metatranscriptomics;\n2. IPC if clinically adjudicated as not having LRTI but a pathogen was detected by clinical\ntesting and/or metatranscriptomics; or\n3. CTRL if clinically adjudicated as not having LRTI and no pathogen was detected by\neither clinical testing or metatranscriptomics.\nAccordingly, by definition, control patients had no pathogens detected by either culture or the\nrules-based model. We have added a new Supplemental Methods section to help clarify group\ndefinitions. To further enhance transparency, we provide in the GitHub metadata file the\npathogen detection status for each participant by clinical testing and metatranscriptomics.\nC19. Line 148: “Despite these distinct microbiome archetypes, the detected pathogen was\ntypically the most abundant microbe in the airway microbiome in both LRTI and IPC (Figure\nS2)”. Is the detection of pathogen here based on clinical results or based on metatranscriptome\nrelative abundance. If the later were included then these results are kind of obvious, no?\nResponse. We thank the reviewer for this thoughtful question and agree that this point warrants\nclarification. In our study, “detected pathogen” refers to pathogens identified using our combined\nmicrobiologic framework, which integrates standard-of-care clinical testing and\nmetatranscriptomic pathogen calling using a previously validated rules-based model, which we\nhave now described more clearly in the Methods/Supplementary Methods (see responses\nabove).\nFor viruses, metatranscriptomic pathogen detection is based on identifying established\nrespiratory viral pathogens that exceed a conservative abundance threshold (>0.1 reads per\nmillion) after background filtering. For bacteria, pathogen calling is more nuanced and does not\nsimply assign the organism with the highest read count as the pathogen. Instead, the rules-\nbased model described above evaluates each microbe’s relative abundance and ecological\ndominance within the respiratory microbiome, and considers whether a taxon is on a reference\nlist of established respiratory pathogens derived from large epidemiological surveillance studies\n(Jain et al., 2015, PMID 26650159; Jain et al., 2015, PMID 25714161; Iwai et al., 2014, PMID\n24752365; Magill et al., 2014, PMID 24670166). These studies are cited in our Methods section.\nNotably, the model does not simply select the top-ranked microbe by abundance, rather it\nidentifies the largest abundance gap among the top 15 most abundant species, if one exists.\nFor example, in some cases, more than one organism may be present at a comparably high\nabundance, each of which is significantly higher in abundance than all remaining detected\nspecies. In such cases, the most abundant microbe may be a commensal organism, while the\nother microbe present at slightly lower abundance may be an established respiratory pathogen.\nIn this circumstance, only the pathogen will be returned by the model, even though both are\ndominant over all the other microbes detected. As noted above and in our Methods section, this\nmodel has been found to accurately identify culture or PCR-confirmed pathogens while enabling\nadditional detection in culture-negative LRTI cases (Langelier and Kalantar et al., 2018, PMID\n30482864; Tsitsiklis et al., 2022, PMID 35544065; Mick et al., 2023, PMID 37009900).\nWe have included Figure S3 (previously Figure S2) to highlight the frequency at which the\npathogen identified by our combined clinical-metagenomic framework was also the most\nabundant microbe in the community; notably the results were similar across both LRTI and IPC\ngroups. We have now clarified the above as follows:\nLine 153: “Despite these distinct microbiome archetypes, the microbe designated as the\npathogen by our combined clinical-metagenomic framework was typically, but not\nalways, the most abundant microbe in the airway microbiome in both LRTI and IPC\n(Figure S3).”\nC20. For Fig 3b I would expect to see some sort of dendogram supporting the clustering.\nResponse: We thank the reviewer for this suggestion. We agree that displaying the dendrogram\nhelps clarify the hierarchical clustering shown in Figure 3b. We have now added the patient\n(column) dendrogram above the heatmap to explicitly show the clustering structure:\nIn the original version, the dendrogram was omitted to maximize readability and preserve space\nin this highly multi-paneled figure. Of note, we describe the clustering methodology in the\nMethods section “Heat map and hierarchical clustering”.\nC21. Line 208: “Among the genes DE between LRTI and both CTRL and IPC groups, we noted\nthat FABP4, which is expressed in macrophages and encodes a lipid chaperone that modulates\nleukotriene stability38, was a clear outlier in both fold change and statistical significance (Figure\n3f).” What criteria supports that FABP4 is a clear outlier (or extreme value). This could be shown\nby indicating where this gene fall within the volcano plot.\nResponse: We thank the reviewer for this helpful suggestion. To explicitly demonstrate that\nFABP4 is an extreme outlier in both effect size and statistical significance, we have added a new\nSupplementary Figure (Figure S8) showing the same volcano plots as in Figure 3c (LRTI vs\nCTRL) and Figure 3d (LRTI vs IPC), with FABP4 labeled.\nAs demonstrated in this new figure, FABP4 lies among the most highly significant and most\nstrongly differentially expressed genes in both comparisons, supporting its designation as a\nclear outlier.\nFigure S8. FABP4 is an outlier in differential expression analyses comparing LRTI to\nCTRL or IPC groups. Volcano plots showing differential gene expression between a. LRTI and\nCTRL and b. LRTI and IPC groups. Each point represents a gene, plotted by log₂(fold change)\nand -log₁₀(Benjamini-Hochberg adjusted P value). Genes significantly upregulated in LRTI\n(orange), CTRL (gray), or IPC (teal) are highlighted. The gene FABP4 is labeled and shown as a\nmarked outlier in both comparisons, with among the largest effect sizes and highest statistical\nsignificance relative to other differentially expressed genes.\nC22. Line 337: “Unexpectedly, total bacterial abundance was greater in IPC than in LRTI or\ncontrols, which may be explained by the enrichment of commensal taxa such as Prevotella,\nNeisseria, and Porphyromonas and reflect a more resilient and balanced barrier microbiota.” A\nquantitative method such as ddPCR with a clear target (e.g., 16S rRNA gene) would be more\ndesirable to address this issue.\nResponse: We agree with the reviewer that absolute quantification of bacterial mass using 16S\nrRNA ddPCR would be a preferable approach for assessing total bacterial load. Such\nmeasurements unfortunately are unavailable. We agree that future studies incorporating\northogonal approaches, such as DNA-based absolute quantification (e.g., 16S PCR) or the use\nof external RNA spike-in mass standards, would allow for absolute quantification and the ability\nto truly assess differences in bacterial mass/burden between groups. We have added this as a\nfuture direction in our Discussion section.\nLine 436: “Our metatranscriptomic measurements reflect relative microbial abundance\nrather than absolute microbial mass or burden; future studies incorporating more\nquantitative orthogonal approaches (e.g., 16S rRNA gene quantification or spike-in mass\nstandards66 will be useful to more precisely estimate microbial burden.\nMinor:\nC23. In Abstract: “Together, our findings reveal distinct ecological and immunologic archetypes\nthat define LRTI and IPC, and support data-driven, biology-informed LRTI diagnostics that\nincorporate host and microbe.” Maybe “that incorporate host and microbe signatures” or\n“biomarkers”\nResponse: We thank the reviewer for this helpful suggestion. We agree that specifying the\nnature of what is incorporated improves clarity. We have revised the wording as follows:\nLine 50: “Together, our findings reveal distinct ecological and immunologic archetypes\nthat define LRTI and IPC, and support data-driven, biology-informed LRTI diagnostics\nthat incorporate host and microbial features.”\nC24. Table 1 should be referenced in the text, probably in the third paragraph of the results.\nResponse. We thank the reviewer for noting this omission. We have now added a citation to\nTable 1 in the third paragraph of the Results section, where the baseline clinical and\ndemographic characteristics of the cohort are described.\nC25. (Remarks on code availability): seems appropriate thought I did not try to run it\nResponse. We thank the reviewer for taking the time to review our code and data on GitHub."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for their responses to my questions and suggestions. I am happy with their responses and their altered MS text and additions. This MS is in my opinion ready for publication, at least from my standpoint as a clinician rather than a statistician.", "reviewer_2": "The authors have addressed my comments."}, "author_response": ""}]

Decision letter