Rapid dissemination of Staphylococcus aureus in the neonatal intensive care unit is associated with invasive infection

Domain: neuroscience
Published: 2026-02-09
Editorial outcome: major_revision

Abstract
Staphylococcus aureus is a leading cause of severe infections in neonatal intensive care units (NICUs). We present results of precision surveillance of S. aureus carriage and invasive infection in a cohort study of high-risk infants in the NICU with the goal of identifying novel strategies for prevention and control. Using whole genome sequence guided epidemiology over a 3-year time frame, we identify spatial and temporal links for transmission between babies, suggesting shared spaces, caregivers, and physical proximity as major risks for transmission in the NICU. Moreover, environmental surveillance reveals potential environmental reservoirs of S. aureus. Remarkably, specific clusters of S. aureus strains that are associated with invasive infection are also detected in more infants over time during routine surveillance, suggesting a strong link between the rate of dissemination and disease in this vulnerable population. Overall, our findings demonstrate a strong association amongst colonization, transmission, persistence, and the development of invasive infections, underscoring the importance of targeted measures to prevent S. aureus infections in the NICU setting.

Introduction
Infants hospitalized in Neonatal Intensive Care Units (NICUs) are at increased risk for healthcare-associated infections (HAIs), which are a major cause of morbidity, mortality, adverse long-term neurodevelopmental outcomes, and a significant contributor to the escalating cost of neonatal care1–3. Staphylococcus aureus colonizes and persists on the skin and mucous membranes of infants early in life and is a major cause of invasive infection4–6. A recent study of 100,000 very preterm infants born across the US between 2018 and 2020 revealed that one-quarter of HAIs were attributed to S. aureus7. Single and multicenter investigations of S. aureus infections in NICUs have identified significant rates of infection-specific morbidity and mortality, with one study reporting an ~10% case fatality rate7,8. There is an urgent need for improved strategies and effective interventions to combat S. aureus colonization, transmission, and infection.

Currently, there are no standard approaches for NICU surveillance of S. aureus colonization, and there is little evidence for when to initiate or stop surveillance, how frequently to test, and which populations to target9. Guidelines suggest that surveillance is likely to be most useful in outbreaks or ongoing healthcare-associated transmission, especially when results will be used to direct infection prevention strategies such as cohorting, isolation, and decolonization10. However, the true impact of surveillance-guided prevention strategies for S. aureus on HAI outcomes remains unclear11.

At the heart of addressing these gaps in knowledge is the ability to measure the persistence and transmission of specific strains of S. aureus in the NICU setting over time, especially while interventions are being implemented. Traditional molecular epidemiology methods, such as multi-locus sequence typing (MLST), spa-typing, or SCCmec-typing, lack the necessary granularity as genetically distinct S. aureus clones may differ by as few as ~100 single-nucleotide polymorphisms (SNPs)12–16. Whole-genome sequencing (WGS) offers an advantage by enabling direct assessment of the clonality of bacterial isolates. While prior WGS studies have primarily focused on outbreak investigations of methicillin-resistant S. aureus (MRSA), few have monitored the colonization dynamics of both MRSA and methicillin-susceptible S. aureus (MSSA) over extended periods of time17–20.

Here we report the results of a 3-year, unit-wide surveillance screening for S. aureus across the Children’s Hospital of Philadelphia (CHOP) NICU, encompassing WGS of surveillance isolates of NICU patients, blood cultures from both NICU and non-NICU patients at CHOP, and NICU environmental samples. We aimed to measure the introduction, transmission, persistence, and the risk of invasive infection from S. aureus (MSSA and MRSA) using WGS of samples from serial surveillance screening, bloodstream infections, and environmental sampling in the NICU. Our findings provide evidence of S. aureus transmission and persistence within the NICU and suggest that clusters with documented bloodstream infection display higher rates of dissemination, highlighting new opportunities to prevent serious invasive infections through targeted interventions.

Results
High prevalence of S. aureus colonization and infections in the NICU
S. aureus is a critical threat to the health of high-risk infants, and novel surveillance approaches are needed to track, prevent, and control invasive S. aureus infections in the NICU setting. Here, we designed a precision surveillance approach tracking S. aureus carriage and invasive infection across a 3-year time period in the NICU. Throughout the study surveillance period (October 2021–June 2024), S. aureus was consistently detected with recurrent positive swabs from patients across all sections of the NICU (Fig. 1a). S. aureus was also isolated in 43 blood cultures from 41 infants across 38 distinct bed locations, spanning all seven NICU sections (Fig. 1d). Stable MSSA and MRSA colonization rates were observed over time across all NICU locations. MSSA colonization rates (mean rate, 25.97% colonized patients/sampled patients; [95% CI, 19.41–34.62%]; median rate, 25% [Q1–Q3, 23.79–28.07%]) significantly exceeded those of MRSA (mean rate, 4.98% colonized patients/sampled patients [95% CI, median rate, 4.58% [Q1–Q3, 1.27–9.43%]) (Fig. 1d). This pattern was mirrored in bloodstream infection (BSI) rates, where MSSA BSIs (mean rate, 0.37 BSIs/1000 patient-days [95% CI, 0.27–0.47]; median rate, 0.33 [Q1–Q3, 0.32–0.36]) occurred more frequently than MRSA BSIs (mean rate, 0.06 BSIs/1000 patient-days [95% CI, 0.014–0.105]; median rate, 0 [Q1–Q3, 0–0]) (Fig. 1d).

Genomic diversity and clonality among NICU S. aureus isolates
We performed WGS on 1670 isolates, retaining 1446 high-quality genomes after assembly (982 surveillance, 449 blood cultures, and 15 environmental isolates). These genomes spanned 10 clonal complexes (CCs) and 66 sequence types (STs), with CC398, CC8, and CC5 predominating. A core-genome ML tree (Fig. 1b) indicated substantial genomic diversity. Notably, genomes from surveillance swabs (colonizing), positive blood cultures (invasive), and environmental samples were distributed across all major phylogenetic clades, showing that the ability to cause invasive infections was not restricted to specific lineages.

We identified 328 patients with two or more genomes, a total of 1142 genomes. While most genomes from the same patient formed monophyletic groups (median, 3 genomes/patient [Q1–Q3, 2–4]), 158 genomes from 36 patients (median, 4 genomes/patient [Q1–Q3, 2–5]) were in distinct phylogenetic clades. Twenty-four distinct phylogenetic clades of highly related genomes were identified, encompassing 1442 genomes (median, 26 genomes/clade [Q1–Q3, 4–73]) from 657 patients (median, 11 patients/clade [Q1–Q3, 2–36]). Clonal relationships were further determined through comprehensive SNP analysis using three datasets. We constructed pairwise SNP distance matrices comparing NICU genomes against (1) themselves (NICU matrix), (2) a local database of 349 pediatric S. aureus genomes collected in Philadelphia (Local matrix)21, and (3) a curated global database of 68,298 high-quality publicly available S. aureus genomes22. The NICU matrix uniquely demonstrated a significant cluster of closely related genomes (SNP distance < 100) (Fig. 1c).

Based on the phylogenetic pattern, clonality, and high rates of S. aureus colonization across the NICU, we hypothesized that at least some infants may have acquired S. aureus through transmission within the NICU. To rigorously test for transmission within the NICU, we used a dynamic, phylogeny-corrected SNP threshold (phylothreshold), which allowed for flexible SNP thresholds across different groups (Extended Data Table S1). Briefly, this approach first groups genomes using hierarchical clustering based on average silhouette scores calculated from branch lengths on an inclusive genome tree. Within each group, we determine the number and composition of strains by single-linkage clustering, imposing a threshold value for SNP distance. To determine the optimal threshold, we sequentially increase the SNP distance threshold until reaching a plateau where further increases no longer change the number or composition of strains within the group. This approach revealed that single-SNP thresholds occasionally did not capture clonal relationships within phylogenetic trees, as putatively linked genomes were not always monophyletic (Extended Data Fig. S1). Thus, we incorporated a phylogenetic correction step that checks for monophyly and bootstrap support within each strain. Specifically, genomes are added to plateau-defined groups through this phylogenetic correction step if they belong to the same clade and the clade has bootstrap support ≥70. By employing “phylothresholds”, our approach better reflects phylogenetic inference, and it avoids the limitations of using a single threshold across all genomes from different genetic backgrounds. While phylothresholds are tailored to fit specific groups, they are also consistent with strict thresholds defined across numerous other studies14. For example, when we compared the SNP distances within our phylothreshold-defined strains against published S. aureus transmission studies reporting SNP thresholds or SNP distance ranges within transmission clusters, there was strong concordance (Extended Data Table S3; Extended Data Fig. S2).

Widespread transmission linked to infections and source
We defined a “strain” as either a group of genomes defined by a phylo-threshold or singletons with no SNP distance matches. A “clone” was designated as a strain detected in at least two distinct samples on more than one date or patient, while a “transmission cluster” refers to a clone found in more than one patient. Using the above phylothresholds, we identified 490 strains (360 NICU, 130 non-NICU), 209 clones (150 NICU, 59 non-NICU), and 69 transmission clusters (69 NICU) (Fig. 1d, Extended Data Table S2). The 355 non-NICU blood culture genomes from 140 patients comprised 134 non-cluster strains, with only 19 genomes belonging to 7 transmission clusters (Clusters 1, 2, 10, 29, 37, 51, and 58). All cluster-associated non-NICU genomes originated from patients with prior NICU admission, and 97% of non-cluster strains detected outside NICU (130/134) were genomically distinct from NICU strains. This differentiation validated NICU-specific transmission clusters and supported our hypothesis that most infants who acquired S. aureus did so within the NICU rather than from hospital-wide circulation. We quantified colonization events by noting the first time that a strain of S. aureus was detected in a patient’s surveillance sample. We observed 528 NICU colonization events, and 176 (33.33%) were identified as transmission-acquired after excluding 69 index patient cases (the first patient in which the cluster was observed) (Extended Data Fig. S3). CC398 MSSA was most prevalent, with 62/490 strains and 14/69 clusters. This observation aligns with many studies reporting the recent global spread of the CC398 MSSA lineage23, as well as findings from other NICUs in the US17. Of 69 transmission clusters, 19 “invasive clusters” were identified based on the detection of the strain from any BSI (Fig. 2a). Among these, CC398 MSSA accounted for 5/19 (26%), highlighting the association between CC398 MSSA and HAIs in the NICU setting.

We captured the progression of 19 patients (19/618) from colonization to invasive infection with the same strain. The median interval was 19 days (Q1–Q3, 9–138, mean, 66.95). At the strain level, for the 19 strains associated with both colonization and invasive infection (10 of which were transmission clusters), the median interval from earliest colonization to earliest invasive infection was 147 days (Q1–Q3, 14–245, mean, 156.47). Among the 69 transmission clusters identified, the median length from earliest detection to detection in a new patient was 91 days (Q1–Q3, 18–210, mean: 131.10).

A total of 44 BSIs were associated with strains detected in the NICU, including 26 from 19 invasive clusters. After excluding 11 index patient cases, 15 infections (34.10%) were attributed to transmission (Extended Data Fig. S3). Cluster 1 was the largest transmission cluster, involving 30 patients, and four patients were diagnosed with Cluster 1 BSI. To investigate whether transmission clusters originated from putative index patients pre-colonized by the respective strains upon admission, we compared admission dates of the index patients in the clusters with genomic emergence dates estimated through (i) time to the most recent common ancestor (tMRCA) and (ii) transmission index dates from TransPhylo analysis24. The medoid of dates of transmission index inferred by TransPhylo v1.4.5 fell within the 95% highest posterior density (HPD) intervals of the tMRCA, and the 95% confidence intervals of transmission index dates overlapped with the 95% HPD for all 48 clusters (Fig. 2b). This concordance indicates that tMRCA serves as both an indicator of genomic emergence and a reliable proxy for transmission index cases. Among the 48 evaluable transmission clusters, 50% (24/48) had putative index patient admission dates that fell within the 95% HPD of tMRCA, suggesting that these pre-colonized patients likely introduced the clusters upon admission rather than acquiring them within the NICU. In the remaining 50% (24/48) of clusters, transmission index dates either predated or postdated the putative index patients’ admission dates, indicating alternative introduction routes. We also employed TransPhylo and a Hidden Markov Model (HMM; msm v1.8.225; Extended Data Fig. S10) to estimate rates of unsampled genomes in each transmission cluster.

These findings suggest that at least half of transmission clusters were introduced through the admission of pre-colonized patients captured in our NICU. Alternative introduction routes for the remaining clusters may include introduction by unsampled patients, healthcare workers, visitors, or environmental reservoirs. Together, these results illuminate the primary mechanisms of cluster introduction while underscoring the complexity of S. aureus transmission dynamics in the NICU setting.

Spatial and temporal proximity between patients is associated with transmission
Only eight of the 69 transmission clusters were MRSA, with three being invasive clusters and five colonizing clusters, suggesting that antibiotic resistance alone does not fully explain transmission and persistence (Fig. 2a). We hypothesized that spatial and temporal proximity between patients would be a strong determinant of transmission. As exemplified in Cluster 1 (Fig. 3a, c), patients shared overlapping lengths of stay and locations across all seven NICU sections, generating a strong epidemiological link in the cluster.

Complementing the epidemiological link in the cluster and serving as a potential proxy for unsampled healthcare workers, shared treatment team assignments revealed potential healthcare worker-mediated transmission routes. For instance, despite physical separation in distinct NICU sections, patients 229 and 235, as well as patients 414 and 399, were linked through concurrent care by the same NICU treatment team (Fig. 3b). This pattern observed in Cluster 1, where strong epidemiological links emerged through both spatial proximity and overlapping treatment team assignments, was seen in 60.9% (42/69) of transmission clusters, as exemplified by Cluster 1 (Supplementary Data S1–S3). An additional 27.5% (19/69) of clusters demonstrated links only through shared NICU sections, while one cluster (1.4%) showed overlapping treatment teams without a shared NICU section (Supplementary Data S1–S3).

Notably, 10.1% of clusters (7/69) exhibited no epidemiological links in our analysis. The SNP distances within these clusters confirmed accurate strain determination by our pipeline, suggesting that analytical artifacts are unlikely (Extended Data Fig. S2d). These transmission clusters lacking epidemiological links suggest transmission may have occurred through unsampled intermediaries, shared equipment, environmental reservoirs, or transmission from parents not observed in this study. Collectively, these findings suggest that shared NICU spaces and treatment teams represent the primary drivers of transmission, but also highlight a role of additional cryptic transmission that was not detected by our study.

Isolates recovered from environmental sampling represented three transmission clusters
To further detect potential reservoirs of transmission, we collected 210 environmental samples from the surfaces in all seven NICU sections using swabs and broad-based culture methods. Of the 19 S. aureus isolated from these surfaces, 15 produced high-quality genomes (sampling locations in Extended Data Fig. S4). Remarkably, we recovered environmental isolates matching three of the previously defined transmission clusters (Clusters 22, 58, 67) (Fig. 4). There was high spatial proximity between environmental isolate collection sites and patient beds for all three (Fig. 4b). The time intervals between the most recent cluster detections in patients and the corresponding environmental recovery (30–42 days) suggest the possibility of persistent environmental reservoirs despite routine NICU sterilization procedures. This also could be explained by high abundance or prevalence in the NICU, and shedding from colonized patients, healthcare workers, or visitors. Regardless of the possible scenarios, our findings demonstrate that environmental surfaces in NICU settings could serve as critical intermediary reservoirs facilitating transmission dynamics between patients, healthcare workers, and the NICU environment.

Persistence in the NICU is associated with increased invasive infection
We examined the length of cluster persistence in the NICU by measuring the time between the first isolation date of a transmission cluster and the most recent date of detection. Invasive clusters, defined by at least one BSI isolate in the cluster, demonstrated significantly higher NICU persistence (median persistence, 421 days [Q1–Q3, 212–570]) than the 50 colonizing clusters (median persistence, 128 days [Q1–Q3, 35.25–394]) (Fig. 6a). To further assess persistence of transmission clusters, we calculated the tMRCA for 48 transmission clusters with more than four genomes (18 invasive, 30 colonizing). The molecular clock persistence times of 18 invasive clusters (median persistence, 576 days [Q1–Q3, 304.5–907.25]) were higher than those for the 30 colonizing clusters (median persistence, 383 days [Q1–Q3, 195.5–655.25]) (Extended Data Table S2). These results suggest that persistence in the NICU is highly associated with subsequent invasive infection. Additionally, our analyses revealed transitions from colonizing to invasive states in several invasive clusters. For instance, Clusters 1, 2, and 4, which were the top three most persistent invasive clusters, initially manifested as colonizing clusters for a few months before causing invasive disease (Fig. 5). These findings suggest that strains with enhanced persistence and transmission capabilities pose a significantly higher risk for invasive infections in the NICU setting. This increased risk may be mediated by a variety of bacterial factors that promote both persistence, spread, or invasive disease, unmeasured host characteristics that render the infant more vulnerable to persistent colonization and infection, or the overall abundance or prevalence of specific clones.

Enhanced dissemination is associated with the risk of invasive infections
Invasive clusters persisted in the NICU longer than colonizing clusters (median difference, 293 days; P=0.01) and, on average, were detected in more patients (median difference, 2 patients; P=0.01) (Fig. 6a, b). To test if this difference applies across equivalent time periods, we calculated the correlation between the number of patients and the duration of persistence. Invasive clusters exhibited a slope that was approximately five-fold higher than colonizing clusters (Fig. 6c). Next, we did a temporal analysis of the accumulated patient numbers over days post-initial detection throughout the study. We confirmed that invasive clusters were detected in more patients over time (interaction term P<2e−16). Although Cluster 1 exhibited exceptional dissemination across all metrics and could potentially cause bias, analyses excluding this cluster confirmed that invasive clusters remained significantly higher in accumulated patient numbers and persistence duration (Extended Data Fig. S5). To account for bias introduced by combining genomes from blood cultures with genomes collected during surveillance, we also recalculated NICU persistence and accumulated patient detection, limiting our analysis to genomes collected during surveillance (Extended Data Fig. S8). In this analysis, transmission clusters that had been independently seen in blood cultures were still more likely to be found in more patients and demonstrated higher inferred rates of dissemination. Furthermore, when clusters were stratified by the number of patients with bloodstream infections, we observed a graded relationship, in which dissemination rates were highest in clusters with two or more patients with bloodstream infections, intermediate in those with a single patient, and lowest in those with none (Extended Data Fig. S9). The findings suggest that strains with the ability to persist and disseminate are potentially more invasive, providing a clear rationale for prioritizing surveillance and prevention targeting these particular strains. Together, this comprehensive study of S. aureus in the NICU setting creates a new paradigm for our understanding of colonization, transmission, and invasive infection of S. aureus in high-risk neonates and provides a roadmap for future surveillance and intervention strategies.

Methods
Study setting and surveillance
This study was approved by the CHOP Institutional Review Board (IRB 022889 and 17-014648). Informed consent was waived, and no participant compensation was provided. Sex and gender were not considered in the study design, as dissemination of S. aureus is expected to be independent of patient sex and gender. The CHOP NICU system comprises two facilities: a 102-bed quaternary care unit (main campus) and an 18-bed Level III NICU at the King of Prussia (KOP) campus. These units manage approximately 2900 patient-days monthly and 12,000 central line-days annually.

A comprehensive S. aureus colonization surveillance program was implemented across both facilities. At the main campus NICU, we initiated quarterly MSSA nares screening in addition to monthly MRSA surveillance between October 2021 and July 2022, followed by monthly MRSA/MSSA nares surveillance, collecting 5466 samples from 2092 patients across seven distinct NICU sections. The KOP NICU initiated MRSA/MSSA monthly surveillance in December 2023, with 133 samples collected from 95 patients by June 2024. Given the monthly point prevalence sampling approach and the prolonged hospitalization experienced by many preterm and term infants with complex medical conditions, patients were re-sampled if they remained admitted in the NICU for additional point prevalence sampling dates. As long as samples met quality control parameters, repeat samples (surveillance or positive blood culture) from the same patients were included in the analysis.

During the timeframe of the study, the NICU and Infection Prevention and Control teams were actively engaged in quality improvement and infection prevention efforts to decrease S. aureus colonization and infection rates, in addition to ongoing efforts to prevent healthcare-associated infections. All infants identified to be positive with either MSSA or MRSA based on surveillance or results of diagnostic testing for clinical indication, underwent decolonization with a 5-day course of mupirocin, starting in July 2022. All infants with central venous catheters (term corrected age or older) received daily chlorhexidine bathing. The NICU also embarked on a hand hygiene and ‘bare below the elbows’ campaign in July 2022 to re-engage staff in infection prevention efforts. Finally, the NICU added initiatives to enhance cleaning procedures for NICU patient rooms through the course of the study.

Concurrent with NICU colonization surveillance, S. aureus-positive blood culture isolates were collected hospital-wide from patients with bloodstream infections as part of routine sampling at CHOP. Between October 2021 and June 2024, 94 isolates from 34 patients within the NICU and 355 isolates from 140 patients outside the NICU were obtained. These isolates served as geographically proximate controls to assess NICU-specific transmission and enabled longitudinal tracking of patients transferred from the NICU to other locations.

Study definitions
Bloodstream infections (BSI) and central line-associated bloodstream infections (CLABSI) were defined as per the Centers for Disease Control National Healthcare Safety Network (NHSN) definitions for lab-confirmed bloodstream infections36. BSI was defined as primary when no secondary source was identified, or secondary when identified to be seeded from a localized infection. CLABSI was defined as a BSI in the setting of a central line in place for more than two consecutive calendar days, with no secondary source, in concordance with NHSN definitions. For S. aureus, a single positive blood culture is sufficient to diagnose BSI.

Clinical sampling and isolate processing
Blood culture: During cases of suspected bacteremia, Bactec Peds Plus/F (BD, New Jersey, USA) bottles were loaded onto a Bactec FX automated analyzer for up to 5 days of incubation at the CHOP Infectious Disease Diagnostics Laboratory (IDDL). Positive blood samples are then plated to 5% sheep blood Tryptic Soy Agar (BD), Chocolate agar (BD), and MacConkey (BD) agar for aerobic growth. All plates are grown at 5% CO2 and 35-37°C. Plates are analyzed daily for growth. Species-level identification is confirmed using a Biotyper CA (Bruker, MA, USA), matrix-assisted LASER desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) platform. Single colonies were subcultured and archived for long-term storage and downstream sequencing. From subcultures, a sterile half-loop (10 µL inoculating loop) of growth from the plate was transferred into the Qiagen PowerSoil 0.5 mm glass bead tube (cat# 13116-50) containing 500 µL of molecular-grade water for the Illumina direct from colony DNA extraction. Tubes were briefly vortexed to mix and either processed immediately or stored at −80°C. A fresh loop is then used to collect remaining growth and transfer it into 1 mL of 20% LB-glycerol solution for cryobanking.

S. aureus nasal surveillance: BBL™ CultureSwab™ EZ collection (BD) from the anterior nares were sent to the CHOP IDDL and plated onto Chromagar™ MRSA II (Chromagar, St-Denis, France) and Mannitol salt agar (BD). Each surveillance collection involved all infants in the NICU and was performed over 2–3 days. Plates were incubated in 5% CO2 at 35°C and analyzed following overnight growth. Identification as S. aureus was confirmed using a Staphaurex (Thermo Fisher Scientific, MA, USA) latex agglutination assay and as MRSA by the PBP2a SA Culture Colony Test. Subcultured single colonies of confirmed isolates were then processed for cryobanking and direct from colony DNA extraction following the same methods described above.

Environmental sampling
A pilot environmental sampling was conducted across all seven sections of the neonatal intensive care unit (NICU) to identify potential environmental reservoirs of transmission. A total of 210 surfaces representing high-touch and high-traffic areas (e.g., cribs, monitors, sinks, chairs, toys, closets) were systematically sampled using a standardized protocol. A Neogen® Sponge-Stick with Neutralizing Buffer (Neogen cat.# SSL10NB) for culturing, ensuring consistent order of sampling from the crib area outward to the general room environment. Samples were transported immediately to the laboratory and processed the same day. Sponge samples were homogenized using a BagMixer 400 to extract the liquid fraction, which was then aliquoted into sterile conical tubes. Aliquots (10 µL) were plated onto a panel of these selective CHROMagar media (cat.# TA672) for the detection of S. aureus amongst other media for organisms outside the scope of this study. In parallel, 100 µL of each homogenized sample was inoculated into BHI broth for enrichment to facilitate the recovery of low-abundance organisms. All plates and enrichment cultures were incubated at 37°C for 18–24 h. Plates were examined for colony growth and morphology after incubation. Distinct colonies were subcultured using sterile 10 µL loops onto LB agar plates for isolation. Representative colonies were archived in 20% LB–glycerol solution and stored at −80°C. Downstream whole-genome sequencing was performed on single colonies from all S. aureus isolates.

Whole genome sequencing
Single colony isolates from the CHOP IDDL were stored at −80°C at the CHOP microbial ARchive and Cryo-collection (microbialARC) strain bank and were sequenced at the CHOP Microbiome Center. DNA from all isolates was extracted using the Illumina Direct from Colony protocol. Briefly, for lysis, bead tubes were shaken at 20 Hz for 10 min, then centrifuged at 4500 × g for 2 min to pellet the beads. Twenty microliters of the supernatant were transferred into a fully skirted PCR plate. Crude DNA was purified using the AMPure XP SPRI bead cleanup, with two washes in 80% ethanol and final elution in 80 µL of elution buffer. The resulting DNA was stored at −20°C until sequencing. Extracted DNA was quantified with the Quant-iT PicoGreen® dsDNA assay kit (Thermo Fisher Scientific) before library generation. Libraries were generated from 7.5 ng DNA using the Illumina DNA Prep kit and IDT for Illumina unique dual indexes at 1:4 scale reaction volumes. Library success was assessed by Quant-iT PicoGreen® dsDNA assay kit, and samples with library yields <1 ng/µl were re-prepped as needed. After all samples for a given pool were prepped, an equal volume of library was pooled from every sample, and then the pool was sequenced using a 300-cycle Nano kit on the Illumina MiSeq. This low-cost pilot run provided preliminary read counts and indexing performance, allowing us to assess library balance across samples. Libraries were subsequently re-normalized and re-pooled based on the demultiplexing statistics of the MiSeq Nano run to ensure more even representation in the final sequencing pool. Final libraries were sequenced on an Illumina NovaSeq 6000 v1.5 flow cell, producing 2×150 bp paired-end reads. Extraction blanks and nucleic acid-free water were processed along with experimental samples to empirically assess environmental and reagent contamination. A laboratory-generated mock community consisting of DNA from Vibrio campbellii and Lambda phage was included as a positive sequencing control.

Genome assembly and quality control
Raw reads underwent quality control using Sunbeam 4.3.7, including adapter trimming and host decontamination37. Isolate genomes were assembled using SPAdes v3.15.538. To obtain clean genomes, 102 isolate genomes were processed through the Anvi’o v8 pipeline using MEGAHIT v1.2.9 for contig filtering and removal of contaminating sequences39,40. Assembly quality was assessed using CheckM v1.2.2 with the following criteria: ≥95% completeness, ≤5% contamination, and “Staphylococcus (UID301)” lineage classification41. Species-level contamination was evaluated using Mash version 2.342. Assemblies were filtered to retain genomes between 2.55 and 3.15 Mb. Of 1670 assembled genomes, 1446 passed quality criteria and were used for downstream analyses. Genomes rejected from quality control appeared to be a random subset of the data; we found no temporal, patient-based, location, sequence type, or anatomical collection site associations (https://github.com/qianxuans/CHOP-NICU-Scripts/blob/main/Genome_assembly_and_quality_control/failed_genomes.xlsx). All passed assemblies are available on NCBI (accession numbers available in https://github.com/qianxuans/CHOP-NICU-Scripts/blob/main/Genome_assembly_and_quality_control/passed_genomes.xlsx).

The genome assemblies demonstrated robust sequencing depth with a mean coverage of 90.22X (median, 76.79X [Q1–Q3, 42.78−115.92X]) and assembly contiguity as reflected by contig length averaging 18,541.07 base pairs (median, 18.361 bp [Q1–Q3, 13,357.5–22,566.5 bp]). This comprehensive genomic dataset allowed high-resolution investigation of clonality and transmission dynamics. The genome assemblies deposited in the NCBI GenBank database may exhibit minor variations from those employed in our analyses, due to GenBank’s standard processing pipeline, which includes sequence trimming and the exclusion of contigs below 200 base pairs. Accession numbers are available in the Data Availability section.

Core-genome phylogeny inference
The 1446 high-quality genome assemblies were annotated using Bakta v1.9.2 with Database v5.143. The resulting gff3 files were utilized for core-genome alignment, which was carried out using panaroo 1.5.044. This alignment was then used to infer the maximum-likelihood phylogeny in IQ-TREE version 2.3.045, employing the general time-reversible (GTR) substitution model46 and accounting for among-site rate heterogeneity using the Gamma distribution and four rate categories47.

The phylogenetic inference was performed with 100 initial parsimony trees. Unbiased branch support values were provided by UFBoot using 10,000 bootstrap replicates48. SH-like approximate-likelihood ratio test was implemented with 10,000 replicates to assess branch support49. The resulting phylogeny was midpoint-rooted and visualized using a customized R script with packages ggplot2 and ggtree50,51.

Clonality determination
To test for and determine clonal strain composition, we first examined pairwise SNP distances calculated using the wrapper dnadiff in MUMmer 4.0.0rc52, using the gSNP in the dnadiff output as the number for SNP distance. We constructed pairwise SNP distance matrices comparing NICU genomes against: (1) themselves (NICU matrix), (2) a local database of 349 pediatric S. aureus genomes collected in Philadelphia (Local matrix)21, and (3) a curated global database of 68,298 high-quality publicly available S. aureus genomes53.

Next, we used a hierarchical clustering approach using silhouette scores to partition all genomes into 28 groups using the phylogenetic tree described previously in core-genome phylogeny inference. For the hierarchical clustering process, we started by computing the pairwise distances between any pair of genomes using branch lengths in the phylogenetic tree using the cophenetic.phylo() function in R package ape54. The generated distance matrix was used to test every possible grouping pattern using the function hclust() from stats, a built-in R package. To evaluate the quality of the grouping pattern, we calculated the average silhouette scores using the function silhouette from the R package cluster55, and the grouping pattern with the highest average silhouette scores was used to partition the genomes. Every group with two or more genomes was supported with 100 ultrafast bootstrap support48 and 100 SH-like approximate-likelihood ratio test (SH-aLRT) support49, expect for Group 2 with 97 SH-aLRT support (Extended Data Table S1).

In total, 28 groups were identified by hierarchical clustering. If there was only one genome in a group, that genome was considered a singleton. For groups with 2 or more genomes, we also filtered out any genomes that were more than 1000 SNPs away from every member of the group. These were treated as singletons, which minimizes the possibility of disruptive long branches in subsequent phylogenetic inference.

Twenty-four out of 28 groups consisted of two or more genomes, averaging 60 genomes (median, 26 [Q1–Q3, 4–73]). After excluding three singletons from three groups, we implemented a fine-grained phylogenetic analysis for each of the 24 groups. We used the curated database of 68,298 high-quality S. aureus genomes mentioned previously to identify the five most similar publicly available genomes for every study genome in the group. Those publicly available genomes limited the thresholds to focus on potential transmission clusters within the NICU. We then produced multi-sequence alignments containing study genomes and the most similar publicly available genomes using Snippy 4.6.056, with the study genome with the highest N50 in each group used as the reference genome. The resulting alignments were used as the input for inferring the reference-based maximum-likelihood phylogenetic trees with IQ-TREE version 2.3.045, employing the general time-reversible (GTR) substitution model46 and accounting for among-site rate heterogeneity using the Gamma distribution and four rate categories47, and tested for robustness with standard bootstrap supports57. We implemented a single linkage clustering (SLC) algorithm to determine closely related strains at every possible potential SNP threshold, from the minimal distance in the group to a threshold of 500. We then selected the SNP threshold at which the number and composition of strains plateaued. The plateaus of 24 groups averaged 21.58 SNPs (median, 18.5 [Q1–Q3, 10.75−27.25]), and 486 strains were determined (223 singletons and 263 clones). At each SNP threshold tested, the strain compositions determined by pairwise SNP distances were mapped to the corresponding reference-based phylogenetic tree of the group. Interestingly, even at conservative SNP thresholds (<25), the strains determined by SNP thresholds were sometimes not monophyletic on the phylogenetic tree (multi-SNP-threshold plot, Extended Data Fig. S1). To correct for this problem, we expanded each group to include the smallest clade that included all SLC members as well as any non-SLC members in that clade, when the bootstrap support for the clade ≥70. We compared our SNP thresholds to those used in 40 other studies in NICU (Extended Data Table S3; Extended Data Fig. S2), in ongoing work, we are formally validating our pipeline with simulated and other real-world genomic datasets.

Cluster analysis and visualizations
If any bacteremia isolate was present in a transmission cluster, it was defined as an invasive cluster; otherwise, it was referred to as a colonizing cluster.

Methicillin resistance in transmission clusters was confirmed through clinical laboratory testing and detection of the mecA gene using BLASTx 2.14.0+ against a customized protein database containing 13 PBP2a family β-lactam-resistant peptidoglycan transpeptidase amino acid sequences from the NCBI protein database58,59. The accession numbers of the sequences were WP_057521704.1, WP_063852670.1, WP_063852677.1, WP_063852683.1, WP_000721309.1, WP_000721306.1, WP_063852626.1, WP_063852617.1, WP_000721310.1, WP_063851348.1, WP_012655867.1, WP_063852710.1, and WP_000725529.1.

We used customized R scripts to create visualizations. These included plots depicting patient metadata, invasive status, methicillin resistance, and cluster persistence. Additionally, we generated swimmer plots and annotated NICU floor plans using separate customized R scripts.

Transmission cluster source analysis
We conducted two complementary analyses to infer the sources and timing of transmission clusters. Molecular clock analysis was performed to estimate the genomic emergence of transmission clusters. For genomes identified as belonging to the same strain, we generated whole-genome alignments and masked putative recombination regions using ClonalFrameML v1.1360. Time to the most recent common ancestor (tMRCA) was estimated using BEAST v2.7.661, employing an HKY substitution model and a strict molecular clock. The analysis incorporated a coalescent constant population model with clock rates following log-normal distributions (1.0E−6 to 1.0E−7) as Bayesian priors. Markov Chain Monte Carlo (MCMC) chains were run up to 500 million steps to ensure effective sample sizes exceeded 200 for all parameters. The resulting phylogenetic trees were generated in NEXUS format and can be accessed at https://github.com/qianxuans/CHOP-NICU-Scripts/tree/main/Molecular_Clock_Analysis/BEAST2_Tree_Visualization/nexus.

To complement the inference of cluster source, we used TransPhylo24 to infer the transmission trees and to estimate the medoids using the timed phylogenies inferred with BEAST v2.7.661. The 95% confidence intervals were calculated as the 2.5th and 97.5th percentiles of inferred transmission index dates, weighted by sampling frequency in the posterior distribution. We estimated the shape and scale parameters of the gamma-distributed generation time by fitting observed time intervals between primary colonization events and subsequent secondary colonization events in previously uncolonized patients within identified transmission clusters (Extended Data Fig. S7). MCMC chains were run for 1,000,000 iterations to ensure effective sample sizes exceeded 100 for all parameters. Transmission index date confidence intervals and medoids were calculated from inferTTree() results after discarding the first 10% as burn-in. Colored phylogenetic trees, transmission trees, and outbreak plots showing sampled versus unsampled cases are provided in Supplementary Data 5–7.

Spatial distance rescaled phylogenetic analysis
For each cluster containing at least four genomes with a genomic maximum-likelihood (ML) phylogeny, pairwise spatial distances between all genomes were calculated using the Pythagorean theorem based on collection site coordinates from floor plans, generating a Naïve spatial distance matrix measured in meters.

A spatial-distance-rescaled phylogeny was constructed using the nnls.tree() function from the phangorn package62, with the Naïve spatial distance matrix and genomic ML phylogeny as inputs. This function preserves the ML phylogeny topology while rescaling branch lengths with spatial distances using ordinary least squares (OLS) regression to minimize the residual sum of squares (RSS).

The phylogenetic rescaled spatial distance matrix for each cluster was computed using the cophenetic.phylo() function in R package ape54. To calculate the accumulated distance through days post-initial detection, samples collected each day were compared to all previously collected samples using phylogenetic rescaled spatial distances. Single linkage clustering was applied to determine new distances added daily, where the minimum distance between each new sample and all previously collected samples represented the new distance contribution for that day. This approach corrects for genomes in a cluster that were isolated close to each other in the NICU but are not the closest relatives in the phylogeny.

Statistical analysis
We examined the persistence in the NICU, patient numbers, and travel distances of transmission clusters within the NICU. To evaluate statistical significance for patient numbers, persistence, and the total spatial distance-rescaled branch length between colonizing and invasive clusters, we applied the two-sided nonparametric Mann–Whitney–Wilcoxon test with continuity correction using R’s built-in functions, without adjustment for multiple comparisons. Linear regression models were fitted using the lm() function from the R stats package to assess the relationships between total spatial distance-rescaled branch length, persistence in the NICU, and patient count. Additionally, we employed linear mixed-effects models via the lmer() function from the lme4 package to model the relationships between accumulated distance, accumulated patient numbers, and days post-initial detection, with cluster as a random effect63. Models were fitted using restricted maximum likelihood (REML), and likelihood ratio tests with maximum-likelihood estimation were used to compare models with and without interaction terms.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This work by She et al. provides a comprehensive longitudinal examination of S. aureus (SA) colonization and infection\namong patients in the Children’s Hospital of Philadelphia NICU. Progress in infection control and mitigation has stalled over\nthe past 10 years pointing to severe knowledge gaps in our understanding of carriage and transmission. This work provides\na significant contribution to that end. The rationale for the study is sound, the methods are solid, and the results and\nconclusion are mostly appropriate.\nMethodological strengths of this work include a large number of children sampled over multiple time points, collection from\nasymptomatic carriage as well as infections, focus on all SA (not just MRSA), extensive patient metadata data, and use of\nwhole genome sequencing (WGS) for high-resolution assessment of evolutionary links. Such large-scale sampling that\nincludes longitudinal samples and WGS is extremely rare!\nThe attempt to tie strain types to transmission, invasive disease, and persistence is noteworthy with extremely valuable\npotential to translate surveillance efforts into mitigation, and disease prevention. While there have been a small number of\nstudies that explored genetic features associated with the transition from colonization to invasion, pathogenicity, and\ninfection persistence (https://doi.org/10.1101/gr.165415.113, https://doi.org/10.1128/msystems.00378-22,\nhttps://doi.org/10.7554/eLife.77195, https://doi.org/10.1128/jb.01000-07, https://doi.org/10.1016/J.CHOM.2023.03.009), I am\nnot aware of studies that link strains (or their components) to asymptomatic persistence and transmission. This is an\nimportant line of current research efforts to which this work will make a valuable contribution.\nInclusion of spatial data is also a strength (but see my next point) as it adds to evidence that patient-patient transmission is\noccurring, although it is difficult to understand how direct patient-to-patient transmission occurs among infants in such an\nenvironment (with no direct interactions). I appreciate the authors pointing out that transmission could be mediated by\ncontaminated environments/equipment, and caregivers, but what other mechanisms of transmission seem possible? Could\ndirect transmission be occurring or transition from a parent to infants that are not theirs?\nIn my opinion, author’s use of spatial data to further parse the effects of distance on transmission is not warranted or needed.\nWhile the distance of spread within each NICU section may provide some biological insight into mechanisms of spread, it is\nhard for me to imagine why distances between NICU sections are considered when an infant may be transported just as\neasily between distant and adjacent sections. As such, while the presence of a colonized/infected individual within a section\nincreases risk to others in the same section, the impact of larger distances (between sections) on transmission is likely\ncompletely anthropogenic. I suggest that the authors remove these additional analyses (including the rescaled phylogeny)\nas they complicated this work and don’t seem to be needed to support the conclusions – Alternatively, provide better\njustification for their inclusion.\nPerhaps the most important weakness of this work is that there is limited discussion of an alternative (null) hypothesis: By\nchance alone (not due to any intrinsic genetic characteristic or adaptive evolution), some clones will have better fitness (at\nsome point they were able to proliferate – perhaps shed in higher numbers by a single source, or were in an infant that was\nplaced near others – in other words, they were in the right place at the right time). A high-fitness clone would be expected to\nbe more likely to colonize additional persons, be shed into the environment, and persist for longer periods of time - even in\nthe absence of any adaptive differences between strains. Importantly, such clones would also be more likely to cause\ninvasive disease simply by persisting in an environment with many immunosuppressed individuals subject to invasive\nprocedures. As such, while transmission seems to predict invasive disease (as stated in the title), this may not be due to the\npresence of certain strains or genes, but rather because SA is widely circulating. Authors need to better modulate the\ndiscussion of their results in light of this – the single sentence in line 270 is not sufficient. If indeed this null hypothesis is\ncorrect, then simply monitoring the load of SA around the NICU may be easier and more useful than monitoring for specific\nstrains or genes. These two scenarios are not mutually exclusive.\nA few comments:\nAbout SNP thresholds: I see these as being a guide for identifying transmission events or even intra-host diversity. It is\nunfortunate that the term “threshold” is commonly used as it suggests a hard cut-off rather than a guide. It is widely\nrecognized that the resolution in any clade is maximized when SNP discovery and phylogenetic analysis is restricted to\nmembers of that clade, and thus a hierarchical approach to zoom in on a given clade is widely practiced. However, the\napplication of this concept has been slow in guiding transmission determination. While this method is not novel, I have never\nseen such a thorough demonstration of this concept – and this will be of value to a broad set of readers. In short, the authors\nshow that the concept of a “threshold” is not valid. I would thus encourage the authors to use their analyses as a justification\nfor moving beyond the “threshold” concept. Can the authors think of a more appropriate descriptor than a “threshold\napproach”? I think that authors can state that the number of SNPs found within a clone varied between x and y and this was\nin line with previous work, instead of applying a “threshold”. Also, your hierarchical snp threshold approach may be better\ndescribed as being “clade-specific” rather than “corrected for phylogenetic structure” (Line 113).\nThe recovery of an isolate in the environment that belongs to the same clone as a person does not strongly implicate an\nenvironmental reservoir as claimed in line 187 or indicate the resilience of potential environmental reservoirs (line 190). A\nmore likely scenario is that colonized persons are shedding SA into the environment – explaining why SA can be found in\nthe environment despite persistent cleaning.\nIn the discussion of potential limitations (line 280), authors could include the limited number of isolates sampled from each\nindividual which does not provide a representative sample of the within-host population. Population sequencing is becoming\nmore attainable and can show directionality of spread, identification of sources, and knowledge of missing transmission\nlinks. Recent work has shown that mixed strains within a single host are more common than previously thought and\nsampling only a single isolate to infer transmission will underestimate transmission.\nMinor points:\nWhy use the term “geospatial” instead of just “spatial”?\nLine 253 and 406 – define what is meant by a “conservative” cutoff to eliminate any ambiguity. I presume that this is a low\nnumber?\nLine 388 – “any of the rest members” should presumably be “any of the other members”.\nLine 397 is not clear - Do you mean an excessively high SNP distance to the external genome? What do you mean by\nextend beyond the scope of intra-NICU transmission? The point here is to use a close genome so that the genomic real\nestate (core genome) in which to find SNPs is maximized. So what did you do if the external genomes were > 1000 SNPs\naway?\nLine 408 – “Multi-SNP-Threshold Plot” – I’m assuming that you meant to reference figure S1?\nSigned: Talima Pearson", "reviewer_2": "Thank you for the opportunity to review the manuscript submitted by Planet and colleagues that reports on the\ngenomic relationships between environmental, human colonisation and human infection isolates of Staphylococcus aureus in a high-\nvolume two-centre Neonatal Intensive Care Unit over a three-year period. As the authors report, whole-genome sequencing\n(WGS) has been a useful tool to investigate outbreaks involving transmission of Staphylococcus aureus, though there are\nfewer data available on the transmission dynamics involving colonisation isolates outside of identified outbreak contexts.\nI will acknowledge that I am a clinician who oversees our hospital infection control program and have extensive experience\nusing genomics to infer transmission, although I have not assessed the bioinformatic methods in detail by downloading the\ncode and source data to replicate the analyses. However, the authors have presented sufficient detail in their methods that I\nwas able to follow how their analysis was undertaken and bioinformatic tools used.\nMy main criticism is that the aim or the purpose of the study was not explicitly stated and consequently, there is a sense that\nthe study could have been better designed had the research questions been clearly defined at the start of the study.\nAlthough the authors pose some interesting thoughts for consideration, it is not clear what new information from this study\ncould be directly used to inform clinical practice or subsequent genomics investigations. In the discussion, the authors point\nto their inclusion of both MRSA and MSSA as a novel aspect of this study, though the analyses were not particularly targeted\ntowards comparisons between MRSA and MSSA and similar to other studies, seemed to be intended to be generalisable to\nall isolates of Staphylococcus aureus.\nI have included some other points for consideration intended to help improve the quality of the manuscript.\nMajor comments:\n1. I could not find a statement of approval from an ethics committee or IRB for this study - could this please be provided in\nline with journal policy?\n2. The sampling strategy was possibly designed out of convenience rather than directed towards answering a particular\nresearch question - possibly due to the unclear aims of the study. For example, if aiming to compare persistence and\ninvasive isolates, this would be better assessed with a systematic sampling strategy to rigorously identify all colonisation at\na minimum (e.g. all patients at regular intervals, multiple sample collection sites), with inferences around transmission\nincluding all potential sources of incursion (patients, staff, visitors, environment), but could have been undertaken over a\nshorter period of time. The limitations of the sampling strategy resulting in a large amount of missing data should be\nacknowledged and discussed in detail:\na) Due to the intermittent (3-monthly or monthly) sampling, it is difficult to know how many index patients were unsampled, or\nwhether the transmission could have been introduced through alternative sources e.g. staff, visitors, environmental sources.\nb) The interesting aspects around clusters involving invasive isolates being more persistent than pure colonisation clusters\nis possibly influenced by ascertainment bias - given colonisation isolates were only obtained monthly, many of the clusters\nmay have only contained a few isolates as shown in Figure 6a and 6b. More routine sampling for colonisation may have\nidentified additional patients to inform persistence.\nc) From an epidemiological perspective, it is difficult to compare and interpret colonisation rates when surveillance/sampling\nrates differ. The authors have presented colonisation rates as a number of colonised patients per 1000 patient-days.\nHowever, this is typically only done when there is routine longitudinal surveillance of every patient or where every incidence\nis captured rather than intermittent point-prevalence surveys - these cross-sectional data would be better presented as a\nproportion of admitted patients colonised instead.\nd) Some studies have previously attempted to impute missing data to understand transmission e.g.\nhttps://pubmed.ncbi.nlm.nih.gov/40256930/\n3. The inference of transmission from the date of their most recent common genomic ancestor for transmission clusters\n(Figure 2) is based on the assumption that genomic emergence is equivalent to epidemiological emergence - however,\nphylogenetic trees are not transmission trees (https://pubmed.ncbi.nlm.nih.gov/28100788/). For example, the authors have\nmapped dates of the most recent common ancestor (tMRCA) for transmission clusters against admission dates for putative\nindex cases for each cluster. However, the date a bacterial strain \"emerges\" through genetic evolution is independent of the\nmovement of the bacterium's host (the patient). Furthermore, the first patient detected with S aureus colonisation is not\nnecessarily the source of transmission in an outbreak. Given the timespan, how would transmission within the NICU be\ndistinguished from vertical transmission (e.g. two neonates acquiring S aureus from the same mother, or transmission\nbetween mothers rather than between neonates)?\n4. The underlying message that patients who are closer in proximity and in time are more likely to have the same strain is not\na particularly novel message. I think the more interesting messages lie in the exceptions to the rule - why were some isolates\nfrom different patients separated in time and space (e.g. by 3 years in different sections) part of the same clone? Were these\npatients related to others in the cluster or were there opportunities for transmission outside the NICU?\n5. More detail is needed on sample collection: How were environmental surfaces sampled? Was this after routine cleaning?\nWere surfaces immediately adjacent to current patients excluded? It is interesting that these strains were isolated from the\nenvironment so long after patients colonised with isolates - e.g. Mar 2024 (patient) -> May 2024 (environmental), Jan 2024\n(patient) -> May 2024 (environmental). Many ICU surfaces are cleaned on a regular basis daily and persistent detection\nsuggests another undetected persistent reservoir e.g. staff, other patients. I think a result subheading that \"Transmission\nclusters CAN persist in environmental reservoirs\" would be better representation of the data - <10% of of isolates recovered\nfrom environmental sampling were S aureus, and only 3/69 (<5%) of transmission clusters were represented.\n6. More information on the infection prevention and control measures used within the NICUs during the study would assist\nreaders for context and interpretation. Were there any changes in policy during the study? For patients identified with\nMSSA/MRSA colonisation, what measures were undertaken? In the discussion, lines 272-274 suggest that infection\nprevention and control measures could reduce the risk of invasive infection - this should be the risk of transmission. The only\nmeasure that has been shown to reduce the risk of invasive infection is decolonisation (in adults colonised with S aureus),\nthough to my knowledge this has not been demonstrated in neonates. Note there are also risks associated with\nchlorhexidine administration in neonates.\n7. From an implementation in clinical practice perspective, what was the median time from colonisation to invasive infection\nand from colonisation to onwards transmission? Would it have been feasible to identify colonisation, sequence the isolate\nand implement decolonisation measures to reduce the risk of invasive infection, or control measures to reduce the risk of\nonwards transmission? If not feasible, some of the discussion could be reframed around more realistic implementations of\nthe approach - for example, triggering infection control interventions such as point prevalence surveys across the unit,\nuniversal decolonisation, enhanced unit cleaning and staff re-education around hand hygiene to interrupt persistence of\nparticular strains associated with a high incidence of invasive disease.\n8. Figures - in general the figures are detailed and contain a great deal of information. However, I feel the details are lost in\nthe interpretation of the figures. Firstly, many of the panels were too small at standard resolution to read the text or discern\ndifferences between colours or points on plots. I took a long time to interpret the meaning of several panels within the figures\nand how they contributed to the main messages of the manuscript. For example, Figure 3 appears interesting, but I am\nstruggling to understand panel 3e and what conclusions should be drawn from this. I understand the concept is that over\ntime with repeated transmission events, there is greater dispersion of a strain/clone, but I think this is already implied. The\ndepiction of this in the figure as a cumulative distance seems a bit farcical given the dimensions of the NICU physical\nenvironment, and inaccurate due to the inability to track the colonisation status of individual patients post discharge from the\nNICU.\nMinor comments:\nPlease provide additional information around inclusion/exclusion of isolates. For example, were patients known to be\nalready colonised re-sampled? When patients were re-sampled e.g. re-screened for colonisation or had screening and\ninvasive isolates, were all isolates included in the analysis? e.g. multiple screening samples, multiple positive blood\ncultures, mix of blood culture/screening/urine/wound/sputum/other sample types\nLine 107: What is the purpose of comparing to the local database of S aureus genomes in Philadelphia? The plot displayed\nin Figure 1c suggests there are genomes in the global database that are more similar to the local NICU dataset than the\nPhiladelphia dataset.\nLine 113: Although setting variable SNP thresholds corrected for phylogenetic grouping makes sense, was there any\nvalidation against real-world epidemiological data to validate the inferences from genomic data?\nLine 125: What were the non-NICU strains/clones in this study referred to in this paragraph? The study methods only refer to\ncollection of patient or environmental isolates of S aureus from the NICU.\nLine 298: Please define KOP\nLine 311: Why did you need to define CLABSI for this study? I note that the BSI definition is adopted from NHSN protocols,\nbut the CLABSI definition differs - please provide a rationale for this.\nLine 322: Why were libraries prepared from individual isolates pooled and sequenced on the MiSeq before repooling and\nresequencing on the NovaSeq?\nLine 330: What metagenomic samples were obtained? The sample collection methods do not mention this, and the results\ndo not report any metagenomic data."}, "author_response": "Response to Reviewers for Nature Communications manuscript\nWe thank the Reviewers for their constructive feedback, which has significantly strengthened our manuscript.\nWe have thoroughly revised the manuscript to address all concerns raised.\nIn response to the Reviewers' comments, we have (1) clarified the study design and explicitly\nacknowledged sampling limitations; (2) incorporated transmission tree analysis to substantiate our inference of\ntransmission cluster sources (as suggested by Reviewer 1); (3) moved the spatial distance rescaled phylogeny\nanalysis to the supplementary materials with accompanying discussion, following both Reviewers'\nrecommendations; (4) enhanced figure clarity throughout the manuscript; and (5) described comparative\nanalysis of SNP thresholds and within-cluster SNP distance distributions against published studies, providing\npreliminary validation for our Thresher approaches.\nReviewer 1:\nThis work by She et al. provides a comprehensive longitudinal examination of S. aureus (SA) colonization and\ninfection among patients in the Children’s Hospital of Philadelphia NICU. Progress in infection control and\nmitigation has stalled over the past 10 years pointing to severe knowledge gaps in our understanding of\ncarriage and transmission. This work provides a significant contribution to that end. The rationale for the study\nis sound, the methods are solid, and the results and conclusion are mostly appropriate.\nMethodological strengths of this work include a large number of children sampled over multiple time points,\ncollection from asymptomatic carriage as well as infections, focus on all SA (not just MRSA), extensive patient\nmetadata data, and use of whole genome sequencing (WGS) for high-resolution assessment of evolutionary\nlinks. Such large-scale sampling that includes longitudinal samples and WGS is extremely rare!\nThe attempt to tie strain types to transmission, invasive disease, and persistence is noteworthy with extremely\nvaluable potential to translate surveillance efforts into mitigation, and disease prevention. While there have\nbeen a small number of studies that explored genetic features associated with the transition from colonization to\ninvasion, pathogenicity, and infection persistence\n(https://doi.org/10.1101/gr.165415.113, https://doi.org/10.1128/msystems.00378-\n22, https://doi.org/10.7554/eLife.77195, https://doi.org/10.1128/jb.01000-\n07, https://doi.org/10.1016/J.CHOM.2023.03.009), I am not aware of studies that link strains (or their\ncomponents) to asymptomatic persistence and transmission. This is an important line of current research efforts\nto which this work will make a valuable contribution.\nAuthor response: We thank the Reviewer for the comments. We have added specific responses to each\ncomment below.\nSpecific comments:\n1. I appreciate the authors pointing out that transmission could be mediated by contaminated\nenvironments/equipment, and caregivers, but what other mechanisms of transmission seem possible?\nCould direct transmission be occurring or transition from a parent to infants that are not theirs?\nAuthor response:\nWe thank the Reviewer for this important observation. We agree that multiple transmission mechanisms\nare possible. Parent-to-infant transmission, including cross-transmission where parents may transfer S.\naureus between their own baby and others, represents another potential route that cannot be excluded\nbased on our current analysis. We acknowledge this as a limitation of our study, as we did not collect\nsamples from parents or other family members who may serve as reservoirs for transmission.\nAdditionally, direct transmission is theoretically possible, but it would likely still be mediated by\nhealthcare workers, as infants in the NICU have limited mobility and remain primarily in their\nindividual bed spaces. Finally, contamination of the patients’ immediate environment, shared spaces for\ncaregivers of patients, and shared equipment that is moved around the NICU from patient to patient\ncould be additional sources of transmission. We have expanded our Discussion section (lines 308-313)\nto explicitly address alternative transmission routes.\n2. In my opinion, author’s use of spatial data to further parse the effects of distance on transmission is not\nwarranted or needed. While the distance of spread within each NICU section may provide some\nbiological insight into mechanisms of spread, it is hard for me to imagine why distances between NICU\nsections are considered when an infant may be transported just as easily between distant and adjacent\nsections. As such, while the presence of a colonized/infected individual within a section increases risk to\nothers in the same section, the impact of larger distances (between sections) on transmission is likely\ncompletely anthropogenic. I suggest that the authors remove these additional analyses (including the\nrescaled phylogeny) as they complicated this work and don’t seem to be needed to support the\nconclusions – Alternatively, provide better justification for their inclusion.\nAuthor response:\nWe thank the Reviewer for this insightful critique regarding the spatial distance analysis. Following the\nReviewer's recommendation, we have relocated the spatial distance rescaled phylogeny analysis to the\nExtended Data Figure S8,9 with a brief mention in the Discussion (lines 291-295). We now frame this\nanalysis as an exploratory approach that may have utility for understanding transmission dynamics but\nrequires further studies to validate the epidemiological significance. We have streamlined the main text\nto focus on our core findings, particularly the correlation between patient numbers and persistence as a\nmeasure of transmissibility, which remains robust independent of the spatial analysis.\n3. Perhaps the most important weakness of this work is that there is limited discussion of an alternative\n(null) hypothesis: By chance alone (not due to any intrinsic genetic characteristic or adaptive\nevolution), some clones will have better fitness (at some point they were able to proliferate – perhaps\nshed in higher numbers by a single source, or were in an infant that was placed near others – in other\nwords, they were in the right place at the right time). A high-fitness clone would be expected to be more\nlikely to colonize additional persons, be shed into the environment, and persist for longer periods of time\n- even in the absence of any adaptive differences between strains. Importantly, such clones would also\nbe more likely to cause invasive disease simply by persisting in an environment with many\nimmunosuppressed individuals subject to invasive procedures. As such, while transmission seems to\npredict invasive disease (as stated in the title), this may not be due to the presence of certain strains or\ngenes, but rather because SA is widely circulating. Authors need to better modulate the discussion of\ntheir results in light of this – the single sentence in line 270 is not sufficient. If indeed this null\nhypothesis is correct, then simply monitoring the load of SA around the NICU may be easier and more\nuseful than monitoring for specific strains or genes. These two scenarios are not mutually exclusive.\nAuthor response:\nWe thank the Reviewer for this excellent and fundamental observation, which identifies a critical\nalternative hypothesis that the observed correlation between transmission and invasive infections may\nresult from stochastic events rather than intrinsic genetic characteristics. Our current data cannot\ndistinguish between the two hypotheses, and the hypotheses, as Reviewer suggests, likely operate\nsimultaneously and are not mutually exclusive. Future studies are needed to investigate the contributions\nof those factors to transmission dynamics in the NICU settings. We have substantially expanded our\nDiscussion (lines 327-333) to address this alternative hypothesis.\n4. About SNP thresholds: I see these as being a guide for identifying transmission events or even intra-host\ndiversity. It is unfortunate that the term “threshold” is commonly used as it suggests a hard cut-off\nrather than a guide. It is widely recognized that the resolution in any clade is maximized when SNP\ndiscovery and phylogenetic analysis is restricted to members of that clade, and thus a hierarchical\napproach to zoom in on a given clade is widely practiced. However, the application of this concept has\nbeen slow in guiding transmission determination. While this method is not novel, I have never seen such\na thorough demonstration of this concept – and this will be of value to a broad set of readers. In short,\nthe authors show that the concept of a “threshold” is not valid. I would thus encourage the authors to\nuse their analyses as a justification for moving beyond the “threshold” concept. Can the authors think\nof a more appropriate descriptor than a “threshold approach”? I think that authors can state that the\nnumber of SNPs found within a clone varied between x and y and this was in line with previous work,\ninstead of applying a “threshold”. Also, your hierarchical snp threshold approach may be better\ndescribed as being “clade-specific” rather than “corrected for phylogenetic structure” (Line 113).\nAuthor response:\nWe thank the Reviewer for recognizing the value of our approach and for this insightful critique of the\n\"threshold\" terminology. We agree that \"threshold\" might inappropriately imply a hard cutoff rather than\nthe dynamic, clade-specific approach we employ. We would also like to point out that our approach is\nboth “clade-specific” and “phylogeny corrected”. That is, we first limit our threshold-finding algorithm\nto each subclade of the broad phylogeny. Then, once a strain has been determined (in this case by\nfinding a “plateau” where no new genomes are added to a group as the threshold is increased) we then\nmap these genomes back to the phylogenetic tree to ensure that groups are monophyletic and have\nstrong bootstrap support. In our revised manuscript, we have rewritten this section to clarify this point,\nand we have introduced the term “phylothreshold” to summarize this approach. We have automated this\napproach in the “Thresher” pipeline that is available on GitHub\n(github.com/microbialARC/THRESHER).\nWe quantified within-cluster SNP distances across all 69 transmission clusters. A mean distance of 23.9\nSNPs (SD,12.60; median 22 [Q1-Q3, 15-30]) was found. Individual cluster distributions are shown in\nFigure R1d. We then compared our results to 40 published S. aureus transmission studies to validate our\napproach. We have incorporated Extended Data Table S3 with details of all 40 studies. The results\ndemonstrated that the phylothresholds determined using our approach align well with those reported in\nthe literature when grouped by clonal complex (Figure R1a). Studies without hard cutoffs also showed\nSNP distributions consistent with ours (Figure R1b,c).\nWe note that some studies report narrower SNP ranges due to shorter study periods. For example,\nGideskog et al. found 0-7 SNPs over one month (Study ID 34) and Eyre et al. found 0-3 SNPs over three\nmonths (Study ID 39). Additionally, methodological differences in SNP calling tools and reference\ngenomes can affect absolute SNP values.\nThis comparative analysis provides preliminary validation that our clade-specific approach captures\ngenetic relatedness that is consistent with established transmission studies. In our revised manuscript, we\nhave added Extended Data Figure S2(Figure R1) to visualize this comparative validation. We are\ncurrently working on systematic validation through simulated outbreak and reanalysis of publicly\navailable datasets using our approach to further confirm the accuracy.\nFigure R1 Validation of Thresher plateau approach against S. aureus transmission studies\n(a) Comparison of SNP thresholds between Thresher plateau approach and published studies stratified\nby clonal complex. Each point represents either a Thresher plateau approach (circles)or a literature-\nreported cutoff value (stars). (b) SNP distance ranges reported for outbreak strains in published studies,\ngrouped by clonal complex. Each bar represents the range observed in a single study. (c) Distribution of\npairwise SNP distances within transmission clusters identified by Thresher plateau approach, stratified\nby clonal complex. Each point represents a single pairwise comparison between genomes within the\nsame cluster. Boxes indicate interquartile ranges with median lines. (d) Overall distribution of pairwise\nSNP distances across all 69 transmission clusters. Visualization format as in panel (c), with each cluster\nshown individually on the x-axis.\n5. The recovery of an isolate in the environment that belongs to the same clone as a person does not\nstrongly implicate an environmental reservoir as claimed in line 187 or indicate the resilience of\npotential environmental reservoirs (line 190). A more likely scenario is that colonized persons are\nshedding SA into the environment – explaining why SA can be found in the environment despite\npersistent cleaning.\nAuthor response:\nWe thank the Reviewer for this insightful comment regarding environmental reservoirs. We have\nchanged the subtitle and revised our interpretation of these findings in the manuscript (lines 194-205),\nas also suggested by Reviewer 2. We have also added a section in the methods (lines 387-401) to describe\nthe procedures used for recovery of isolates from the NICU environment.\n6. In the discussion of potential limitations (line 280), authors could include the limited number of isolates\nsampled from each individual which does not provide a representative sample of the within-host\npopulation. Population sequencing is becoming more attainable and can show directionality of spread,\nidentification of sources, and knowledge of missing transmission links. Recent work has shown that\nmixed strains within a single host are more common than previously thought and sampling only a single\nisolate to infer transmission will underestimate transmission.\nAuthor response:\nWe thank the Reviewer for this important observation. We have now acknowledged and incorporated\nthis limitation into our discussion (lines 317-325).\n7. Why use the term “geospatial” instead of just “spatial”?\nAuthor response:\nWe have changed “geospatial” to “spatial” in the manuscript.\n8. Line 253 and 406 – define what is meant by a “conservative” cutoff to eliminate any ambiguity. I\npresume that this is a low number?\nAuthor response:\nWe thank the Reviewer for pointing out this ambiguity. We have now clarified that our \"conservative\"\ncutoff refers to a threshold of ≤25 SNPs.\n9. Line 388 – “any of the rest members” should presumably be “any of the other members”.\nAuthor response:\nWe have changed “any of the rest members” to “any of the other members”.\n10. Line 397 is not clear - Do you mean an excessively high SNP distance to the external genome? What do\nyou mean by extend beyond the scope of intra-NICU transmission? The point here is to use a close\ngenome so that the genomic real estate (core genome) in which to find SNPs is maximized. So what did\nyou do if the external genomes were > 1000 SNPs away?\nAuthor response:\nWe thank the Reviewer for highlighting this. We have revised this text at lines 484-485 to improve\nclarity, and we provide our rationale for using publicly available genomes below.\nThe Thresher pipeline tests SNP thresholds from the minimum SNP distance up to 500 SNPs within\neach clade determined by hierarchical clustering, using a plateau method to determine optimal strain\ncompositions. However, if the plateau is reached at excessively high values, the resulting clusters may\ninclude genetically distant genomes that represent broader epidemiological connections rather than\ntransmission events within our NICU setting.\nOur approach uses publicly available S. aureus genomes as checkpoints to validate the SNP thresholds\nused for defining the compositions of transmission clusters. We assume that publicly available S. aureus\ngenomes should not be part of NICU transmission clusters. When strain compositions begin\nincorporating public genomes, this indicates the SNP threshold has become too high and is capturing\ngenetic relatedness beyond our study scope. We used a 1000 SNP threshold to exclude genomes from\ndownstream fine-grained analysis, which create long phylogenetic branches in the clade-specific\nphylogeny and could compromise accuracy of the analysis. The excluded genomes are considered\nsingletons. This 1000-SNP threshold was informed by our SNP distance matrix analysis (Figure R2),\nwhich showed that distances of >1000 SNPs typically represent comparisons between genomes that\nbelong to different sequence types, making the compared genomes highly unlikely within the same\ntransmission cluster.\nFigure R2 Rationale for 1000 SNP threshold based on pairwise genetic distance analysis. (a)\nOverview of pairwise SNP distances for NICU genomes against three datasets: (1) NICU genomes\n(NICU), (2) a local Philadelphia pediatric database (Local), and (3) a curated global publicly available\ndatabase (Global) (expanded view of Figure 1c). (b) Distribution of pairwise SNP distance comparing\nNICU genomes against themselves, stratified by genetic relatedness: isolates within the same sequence\ntype (Same ST), same clonal complex but different sequence types (Same CC), and different clonal\ncomplexes (Different CC). The majority of pairwise comparisons within the same sequence type fall\nbelow 1000 SNPs, supporting this threshold for excluding genetically distant outliers from transmission\ncluster analysis.\n11. Line 408 – “Multi-SNP-Threshold Plot” – I’m assuming that you meant to reference figure S1?\nAuthor response:\nWe thank the Reviewer for this correction. We have now properly referenced Extended Data Figure S1\nin the manuscript.\nReviewer 2:\nThank you for the opportunity to review the manuscript submitted by Planet and colleagues that reports on the\ngenomic relationships between environmental, human colonisation and human infection isolates of\nStaphylococcus aureus in a high-volume two-centre Neonatal Intensive Care Unit over a three-year period. As\nthe authors report, whole-genome sequencing (WGS) has been a useful tool to investigate outbreaks involving\ntransmission of Staphylococcus aureus, though there are fewer data available on the transmission dynamics\ninvolving colonisation isolates outside of identified outbreak contexts.\nI will acknowledge that I am a clinician who oversees our hospital infection control program and have\nextensive experience using genomics to infer transmission, although I have not assessed the bioinformatic\nmethods in detail by downloading the code and source data to replicate the analyses. However, the authors\nhave presented sufficient detail in their methods that I was able to follow how their analysis was undertaken\nand bioinformatic tools used.\nAuthor response:\nWe thank the Reviewer for the thoughtful evaluation of our manuscript and for the valuable clinical perspective\non infection control and transmission analysis. We have carefully addressed each of the specific comments\nbelow and believe these revisions have significantly strengthened the manuscript.\nSpecific comments:\n1. My main criticism is that the aim or the purpose of the study was not explicitly stated and consequently,\nthere is a sense that the study could have been better designed had the research questions been clearly\ndefined at the start of the study\nAuthor response:\nWe have now included the following aim statement to more clearly state the study objective (lines 73-\n75). We aimed to measure the introduction, transmission, persistence, and the risk of invasive infection\nfrom S. aureus (MSSA and MRSA) using WGS of samples from surveillance screening, and\nbloodstream infections and environmental sampling in the NICU.\n2. Although the authors pose some interesting thoughts for consideration, it is not clear what new\ninformation from this study could be directly used to inform clinical practice or subsequent genomics\ninvestigations.\nAuthor response:\nWe thank the Reviewer for this comment. We have added text to the revised manuscript describing how\nthese findings could inform clinical practice (lines 276-285 and lines 301-303). Namely, the NICU has\nleveraged our analyses and recently implemented targeted eradication strategies (enhanced\nenvironmental cleaning and decolonization) aimed at highly transmissible, invasive strains identified by\nthis study. Specifically, data on transmission in the NICU has helped inform infection prevention efforts\nto improve the quality of hand hygiene. Mupirocin decolonization has been introduced in the NICU to\nattempt to decrease prolonged persistence of S. aureus. Spatial localization of clusters has helped\nuncover that transmission is occurring unit-wide and not limited to specific sections or staffing groups.\nEnvironmental sampling has pinpointed that the patient environment is likely a reservoir for S. aureus\nand has led to an increased focus on environmental cleaning efforts, such as UV cleaning, to reduce\nbacterial burden in the NICU. The timing of initiation of each of these interventions is being precisely\ndocumented. Ongoing genomic investigations will help measure the impact of each of these\ninterventions on rates of colonization and transmission of various strains of S. aureus, as well as\ninvasive infections. Additionally, WGS is helping us define S. aureus strains at highest risk of\ntransmission and infection in the specific context of the NICU. As next steps, we are designing new\napproaches to rapid detection of high-risk S. aureus strains, to facilitate deployment of targeted infection\nprevention approaches against high-risk strains. The precision surveillance approach proposed in this\nstudy will provide us with the ability to accurately measure the impact of targeted interventions on\ndecreasing transmission of high-risk strains. Identification of the biological and genomic properties of\nmore invasive and transmissible strains will be a major focus of future work.\n3. I could not find a statement of approval from an ethics committee or IRB for this study - could this\nplease be provided in line with journal policy?\nAuthor response:\nWe have provided the statement of approval from IRB for this study.\n4. The sampling strategy was possibly designed out of convenience rather than directed towards answering\na particular research question - possibly due to the unclear aims of the study. The limitations of the\nsampling strategy resulting in a large amount of missing data should be acknowledged and discussed in\ndetail.\nAuthor response:\nThe study collected and utilized samples obtained as part of routine surveillance per NICU infection\nprevention and control policies and was therefore reliant on the unit's sampling frequency. However, the\nNICU did establish universal, routine surveillance for both MRSA and MSSA early in the course of this\nstudy, which provided a reliable frequency of samples.\nWe further address the sampling strategy and missing data concerned below in point 6.\n5. Due to the intermittent (3-monthly or monthly) sampling, it is difficult to know how many index patients\nwere unsampled, or whether the transmission could have been introduced through alternative sources\ne.g. staff, visitors, environmental sources.\nAuthor response:\nWe thank the Reviewer for highlighting this important limitation. We acknowledge and have expanded\nour discussion to address this limitation (lines 308-315 and lines 322-323).\n6. The interesting aspects around clusters involving invasive isolates being more persistent than pure\ncolonisation clusters is possibly influenced by ascertainment bias - given colonisation isolates were only\nobtained monthly, many of the clusters may have only contained a few isolates as shown in Figure 6a\nand 6b. More routine sampling for colonisation may have identified additional patients to inform\npersistence.\nAuthor response:\nWe thank the Reviewer for this insightful observation. We acknowledge that our monthly sampling may\nhave underestimated persistence.\nTo address this concern, we performed two analyses to assess sampling bias. First, using TransPhylo\ntransmission tree inference, we calculated the probability of unsampled cases for each cluster,\nnormalized across the time in the posterior sample of transmission tree by segmenting into ten equal\ntime bins and then averaging these ten values to obtain a single probability estimate per cluster. We\nfound no significant difference in unsampled case probabilities between colonizing clusters (n=30; mean\n3.76, median 3.31[Q1-Q3, 2.52-4.71]) and invasive clusters (n=18; mean 4.64, median 4.58 [Q1-Q3,\n2.75-6.09]) using Wilcoxon testing.\nAdditionally, we performed Hidden Markov Model (HMM) analysis using the R package msm.\nPersistence periods were stratified into monthly time units across the entire surveillance period (October\n2021 – June 2024), and we inferred the probability of failing to detect the presence of cluster when\nclusters actually persisted during the defined persistence periods in the study. The HMM analysis\nrevealed very low false negative rates, indicating that for months when clusters were not detected, there\nwas less than 4×10⁻⁵ % probability that colonizing clusters persisted and 2×10⁻⁵ % probability that\ninvasive clusters persisted. When we used invasive status as a covariate on the misclassification\nprobabilities, the odds ratio confidence interval included 1 (odds ratio: 1.7, 95% CI: 2.34×10⁻¹⁴⁴-\n1.2×10¹⁴⁴), the large interval indicates no evidence from this data suggesting invasive status impacts\nfalse negative rates\nThese analyses provide no evidence that sampling differences significantly bias persistence comparisons\nbetween colonizing and invasive clusters, though we acknowledge that more frequent colonization\nsampling would strengthen future investigations.\nFigure R3. Unsampled case probabilities and false negative rates between colonizing and invasive\nclusters. (a) Comparison of unsampled case probabilities between colonizing and invasive clusters using\nTransPhylo transmission tree inference. Each dot represents the mean probability across ten temporal\nbins for each cluster, with color indicating cluster persistence length in days. No significant difference\nwas observed between invasive and colonizing clusters (Wilcoxon test, P > 0.05). (b) False negative\nrates for cluster persistence detection inferred using Hidden Markov Model analysis show similarly low\nrates for both invasive and colonizing clusters, with not enough information to provide evidence of bias\nbased on invasive status.\n7. From an epidemiological perspective, it is difficult to compare and interpret colonisation rates when\nsurveillance/sampling rates differ. The authors have presented colonisation rates as a number of\ncolonised patients per 1000 patient-days. However, this is typically only done when there is routine\nlongitudinal surveillance of every patient or where every incidence is captured rather than intermittent\npoint-prevalence surveys - these cross-sectional data would be better presented as a proportion of\nadmitted patients colonised instead.\nAuthor response:\nWe appreciate the Reviewer's comment. Our NICU established routine MRSA and MSSA surveillance\nearly in the study for all admitted infants, ensuring reliable sampling frequency. We have recalculated\nthe colonization rates using sampled patients (which was identical to the number of admitted patients on\nsampling dates) as the denominator and updated Figure 1d, its legend, and the results section\naccordingly.\n8. The inference of transmission from the date of their most recent common genomic ancestor for\ntransmission clusters (Figure 2) is based on the assumption that genomic emergence is equivalent to\nepidemiological emergence - however, phylogenetic trees are not transmission trees\n(https://pubmed.ncbi.nlm.nih.gov/28100788/). For example, the authors have mapped dates of the most\nrecent common ancestor (tMRCA) for transmission clusters against admission dates for putative index\ncases for each cluster. However, the date a bacterial strain \"emerges\" through genetic evolution is\nindependent of the movement of the bacterium's host (the patient). Furthermore, the first patient\ndetected with S aureus colonisation is not necessarily the source of transmission in an outbreak. Given\nthe timespan, how would transmission within the NICU be distinguished from vertical transmission (e.g.\ntwo neonates acquiring S aureus from the same mother, or transmission between mothers rather than\nbetween neonates)?\nAuthor response:\nWe thank the Reviewer for this important methodological point. We acknowledge that genomic\nemergence does not necessarily correspond to epidemiological emergence or transmission index case.\nWe implemented TransPhylo analysis to address these concerns. The results revealed that the medoid\ntransmission index dates fell within the 95% highest posterior density intervals of tMRCA, with 95%\nconfidence intervals of transmission index dates overlapping with the 95% HPD of tMRCA for all 48\nclusters. This concordance suggests that tMRCA serves as both an indicator of genomic emergence and\na reliable proxy for transmission index cases at least in our dataset.\nWe have updated Figure 2b with TransPhylo results, added detailed TransPhylo methodology in the\nMethods section, revised the Results section to incorporate transmission tree analysis, and made all\nanalysis scripts publicly available in our GitHub repository. These analyses strengthen our interpretation\nwhile acknowledging the complexity of distinguishing healthcare-associated transmission from other\npotential routes, including vertical transmission between mothers and neonates.\n9. The underlying message that patients who are closer in proximity and in time are more likely to have the\nsame strain is not a particularly novel message. I think the more interesting messages lie in the\nexceptions to the rule - why were some isolates from different patients separated in time and space (e.g.\nby 3 years in different sections) part of the same clone? Were these patients related to others in the\ncluster or were there opportunities for transmission outside the NICU?\nAuthor response:\nWe thank the Reviewer for this insightful comment. We agree that the exceptions to the rule are worth\ndeeper analysis and have expanded our discussion of these findings in the results section (lines 186-\n192).\nOur analysis revealed that 60.9% (42/69) of transmission clusters demonstrated strong epidemiological\nlinks through both spatial proximity and overlapping treatment team assignments. About 27.5% (19/69:\nClusters 16, 29, 32, 33, 35, 37, 39, 40, 41, 42, 43, 44, 46, 47, 49, 54, 62, 64, and 66) of clusters showed\nlinks only through shared NICU sections, while one cluster (1.4%: Cluster 55) had overlapping\ntreatment teams without shared NICU section observed.\nNotably, 10.1% of clusters (7/69: Clusters 34, 38, 49, 57, 58, 59, and 63) exhibited no epidemiological\nlinks in our analysis (Extended Data Figures S3-S5). The SNP distances within these clusters confirmed\naccurate strain determination by our pipeline, suggesting that analytical artifacts are unlikely (Extended\nData Figure S2d).\nAs the Reviewer suggested, these exceptions likely reflect transmission routes not captured in our study.\nPotential explanations include transmission through unsampled intermediary patients, contaminated\nshared equipment, environmental reservoirs, or vertical transmission from parents—mechanisms that\nwould not be apparent from spatial proximity or treatment team assignments alone.\n10. More detail is needed on sample collection: How were environmental surfaces sampled? Was this after\nroutine cleaning? Were surfaces immediately adjacent to current patients excluded? It is interesting that\nthese strains were isolated from the environment so long after patients colonised with isolates - e.g.\nMar 2024 (patient) -> May 2024 (environmental), Jan 2024 (patient) -> May 2024 (environmental). Many\nICU surfaces are cleaned on a regular basis daily and persistent detection suggests another undetected\npersistent reservoir e.g. staff, other patients. I think a result subheading that \"Transmission clusters\nCAN persist in environmental reservoirs\" would be better representation of the data - <10% of of\nisolates recovered from environmental sampling were S aureus, and only 3/69 (<5%) of transmission\nclusters were represented.\nAuthor response:\nWe thank the Reviewer for their constructive feedback regarding the environmental sampling\nmethodology and data interpretation. We have added the environmental sampling methods (lines 387-\n401), revised the result subheading (line 194), and updated the interpretation of the results (lines 195-\n205).\n11. More information on the infection prevention and control measures used within the NICUs during the\nstudy would assist readers for context and interpretation. Were there any changes in policy during the\nstudy? For patients identified with MSSA/MRSA colonisation, what measures were undertaken? In the\ndiscussion, lines 272-274 suggest that infection prevention and control measures could reduce the risk\nof invasive infection - this should be the risk of transmission. The only measure that has been shown to\nreduce the risk of invasive infection is decolonisation (in adults colonised with S aureus), though to my\nknowledge this has not been demonstrated in neonates. Note there are also risks associated with\nchlorhexidine administration in neonates.\nAuthor response:\nDuring the timeframe of the study, the NICU and Infection Prevention and Control teams were actively\nengaged in quality improvement and infection prevention efforts to decrease S. aureus colonization and\ninfection rates, in addition to ongoing efforts to prevent healthcare associated infections. All infants\nidentified to be positive with either MSSA or MRSA based on surveillance or results of diagnostic\ntesting for clinical indication, underwent decolonization with a 5-day course of mupirocin, starting in\nJuly 2022. All infants with central venous catheters (term corrected age or older) received daily\nchlorhexidine bathing. The NICU also embarked on a hand hygiene and ‘bare below the elbows’\ncampaign in July 2022 to re-engage staff in infection prevention efforts. Finally, the NICU added\ninitiatives to enhance cleaning procedures for NICU patient rooms through the course of the study.\n12. From an implementation in clinical practice perspective, what was the median time from colonisation to\ninvasive infection and from colonisation to onwards transmission? Would it have been feasible to\nidentify colonisation, sequence the isolate and implement decolonisation measures to reduce the risk of\ninvasive infection, or control measures to reduce the risk of onwards transmission? If not feasible, some\nof the discussion could be reframed around more realistic implementations of the approach - for\nexample, triggering infection control interventions such as point prevalence surveys across the unit,\nuniversal decolonisation, enhanced unit cleaning and staff re-education around hand hygiene to\ninterrupt persistence of particular strains associated with a high incidence of invasive disease.\nAuthor response:\nWe thank the Reviewer for this important clinical perspective on implementation feasibility. We\nanalyzed the temporal patterns and present the following findings:\n(1) We captured the progression of 19 patients (19/618) from colonization to invasive infection with the\nsame strain. The median interval was 19 days (Q1-Q3, 9-138, mean, 66.95).\n(2) At the strain level, for the 19 strains associated with both colonization and invasive infection (10 of\nwhich were transmission clusters), the median interval from earliest colonization to earliest invasive\ninfection was 147 days (Q1-Q3, 14-245, mean, 156.47).\n(3) Among the 69 transmission clusters identified, the median length from earliest detection to earliest\ntransmission event was 91 days (Q1-Q3, 18-210, mean: 131.10).\nWe acknowledge that the variable intervals likely reflect multiple confounding factors, including strain-\nspecific genetic traits and individual patient susceptibility, which requires further investigation for better\ninterpret these results. However, these results suggest that there is likely an adequate interval of time\navailable in many cases, once the colonizing strain is identified, to feasibly implement measures to\ndecolonize patients, reduce environmental bioburden, or both. We hypothesize that implementation of\nthis targeted approach for strains known to be at high risk of transmission and invasive infection, above\nand beyond general infection control measures, may improve the effectiveness of efforts to decrease\ntransmission and invasive infections.\n13. Figures - in general the figures are detailed and contain a great deal of information. However, I feel the\ndetails are lost in the interpretation of the figures. Firstly, many of the panels were too small at standard\nresolution to read the text or discern differences between colours or points on plots. I took a long time to\ninterpret the meaning of several panels within the figures and how they contributed to the main\nmessages of the manuscript. For example, Figure 3 appears interesting, but I am\nstruggling to understand panel 3e and what conclusions should be drawn from this. I understand the concept is that\nover time with repeated transmission events, there is greater dispersion of a strain/clone, but I think this\nis already implied. The depiction of this in the figure as a cumulative distance seems a bit farcical given\nthe dimensions of the NICU physical environment, and inaccurate due to the inability to track the\ncolonisation status of individual patients post discharge from the NICU.\nAuthor response:\nWe thank the Reviewer for the feedback regarding figure clarity and interpretation. We relocated the\nSpatial Distance Rescaled Phylogeny Analysis (previously Figure 3e, 6d, 6h, 6i, and 6j) to the\nSupplementary Figure S9. The remaining panels in Figures 3 and 6 have been enlarged to improve\nreadability of text and color differentiation. Additionally, we enhanced the readability of Figure 2b and\nexpanded the figure legends to provide clearer interpretation of key findings.\n14. Please provide additional information around inclusion/exclusion of isolates. For example, were\npatients known to be already colonised re-sampled? When patients were re-sampled e.g. re-screened for\ncolonisation or had screening and invasive isolates, were all isolates included in the analysis? e.g.\nmultiple screening samples, multiple positive blood cultures, mix of blood\nculture/screening/urine/wound/sputum/other sample types.\nAuthor response:\nGiven the monthly point prevalence sampling approach adopted by the NICU, and the prolonged\nhospitalization experienced by many preterm and term infants with complex medical conditions, patients\nwere re-sampled if they remained admitted in the NICU for additional point prevalence sampling dates\nAs long as samples met quality control parameters, repeat samples (surveillance or positive blood\nculture) from the same patients were included in the analysis.\n15. Line 107: What is the purpose of comparing to the local database of S aureus genomes in Philadelphia?\nThe plot displayed in Figure 1c suggests there are genomes in the global database that are more similar\nto the local NICU dataset than the Philadelphia dataset.\nAuthor response:\nWe thank the Reviewer for seeking clarification on our methodology. The global database comprised\n68,299 publicly available genomes from NCBI GenBank (as of April 2024), which contained limited\ngenomes from the Philadelphia area. Notably, the genomes with close genetic relatedness (≤100 SNP\ndistance) to our NICU isolates were collected from the Hospital of the University of Pennsylvania,\nlocated adjacent to our NICU.\nThe local Philadelphia database consisted of unpublished genomes not available in NCBI, serving as an\nimportant comparator to demonstrate that the transmission events observed in our NICU were not\ncirculating more broadly in the local Philadelphia area. This comparison strengthens our evidence for\ncryptic transmission events confined to the NICU setting, as these closely related strains were not\ndetected even outside our NICU in the community.\nThe inclusion of both global and local databases provides comprehensive context for interpreting the\ngenomic relatedness and transmission dynamics within our study.\n16. Line 113: Although setting variable SNP thresholds corrected for phylogenetic grouping makes sense,\nwas there any validation against real-world epidemiological data to validate the inferences from\ngenomic data?\nAuthor response:\nWe thank the Reviewer for this important question regarding validation of our approach. Our approach\nhas been validated through comparison with 40 published S. aureus transmission studies, as detailed in\nour response to Reviewer 1, Comment 4. We have added Extended Data Figure S2 (Figure R1) to\nvisualize this comparative validation and Extended Data Table S3 with details of all 40 studies.\nWhile we did not perform formal validation against real-world epidemiological data in this study, we are\ncurrently conducting systematic validation analysis using real-world data and simulated genomic data,\nwhich will be presented in our forthcoming manuscript describing the Thresher pipeline.\n17. Line 125: What were the non-NICU strains/clones in this study referred to in this paragraph? The study\nmethods only refer to collection of patient or environmental isolates of S aureus from the NICU.\nAuthor response:\nWe thank the Reviewer for this comment. We have expanded the description of non-NICU isolates in\nthe Methods (lines 354-357) and Results (lines 137-142) and provided rationale for including non-NICU\nisolates in the Methods (lines 355-357) and Discussion (lines 271-273) sections of our revised\nmanuscript, as detailed below.\nWhile surveillance samples were restricted to NICU patients, S. aureus-positive blood culture isolates\ncollected hospital-wide as part of concurrent surveillance efforts at CHOP were included in our analysis.\nThese genomes served as \"very local\" controls to detect potential transmission events between the NICU\nand other hospital locations. Additionally, including non-NICU isolates enabled continuous monitoring\nof patients initially admitted to the NICU who subsequently transferred to other units, allowing us to\ntrack potential NICU-acquired strains beyond NICU.\nA total of 355 isolates from S. aureus-positive blood cultures were collected from 140 patients outside\nthe NICU. Of these 140 patients, 109 had no prior NICU admission, while 31 had been previously\nadmitted to the NICU. Among these 31 patients, only 12 had high-quality NICU genomes available for\nanalysis prior to transfer outside NICU.\nFrom a clonality perspective, the 355 non-NICU genomes comprised 134 non-cluster strains: 57\nsingletons (57 genomes) and 77 clones (279 genomes). Notably, only 19 genomes belonged to 7\ntransmission clusters (Clusters 1, 2, 10, 29, 37, 51, and 58). None of the 57 singletons were observed in\nthe NICU. Of the 77 clones from 76 patients, only 4 clones were epidemiologically linked to the NICU.\nSpecifically, these 4 clones were isolated from 4 patients who had NICU admissions with positive\nsamples collected during their NICU stay, prior to transfer to other hospital units where subsequent non-\nNICU isolates were obtained.\nAll 19 genomes involved in transmission clusters were from patients with prior NICU admission, and\nimportantly, each of these 7 clusters contained at least one genome collected within the NICU.\nIn Cluster 1, all 4 non-NICU isolates were from Patient 111, who had been admitted to the NICU despite\nhaving no positive surveillance samples. In Cluster 2, 2 non-NICU isolates were from Patient 257, who\nhad both positive surveillance and blood culture isolates from the NICU period, and 1 isolate was from\nPatient 161 during their NICU admission. The inclusion of these non-NICU isolates in Clusters 1 and 2\ndid not alter cluster composition or the classification of strains as invasive or colonizing. For smaller\nclusters, single or multiple non-NICU isolates were identified: Cluster 10 (1 isolate, Patient 245),\nCluster 29 (1 genome, Patient 534), Cluster 37 (3 genomes, Patient 467), Cluster 51 (2 genomes from\nPatient 330 and 1 genome from Patient 331), and Cluster 58 (4 genomes, Patient 36).\nCollectively, the hospital-wide sampling including non-NICU isolates provided essential\nepidemiological context without compromising the integrity of our NICU-focused transmission analysis.\nThe predominance of non-overlapping strains outside the NICU supports our hypothesis that most\ninfants acquired S. aureus through transmission within the NICU rather than from hospital-wide\ncirculation, while the small subset of NICU-linked isolates detected post-transfer reveals clinically\nimportant patterns of strain persistence that would remain invisible under NICU-restricted sampling. Far\nfrom introducing noise, this comprehensive approach strengthens both the specificity of our\ntransmission inference and the clinical relevance of our findings.\n18. Line 298: Please define KOP\nAuthor response:\nWe have added the definition for KOP (King of Prussia; census-designated place (CDP) in\nPennsylvania, USA) in the manuscript to clarify the location of the second NICU facility.\n19. Line 311: Why did you need to define CLABSI for this study? I note that the BSI definition is adopted\nfrom NHSN protocols, but the CLABSI definition differs - please provide a rationale for this.\nAuthor response:\nWe would like to clarify that CLABSI was strictly adjudicated by the hospital Infection Prevention and\nControl team as per the NHSN protocol and this has been updated in our revised manuscript (lines 360-\n365) to ensure clarity.\n20. Line 322: Why were libraries prepared from individual isolates pooled and sequenced on the MiSeq\nbefore repooling and resequencing on the NovaSeq?\nAuthor response:\nWe thank the Reviewer for this comment. Libraries were first pooled and sequenced on the MiSeq Nano\nto obtain preliminary read counts and assess library balance and indexing success at low cost. This\nallows us to empirically determine the relative representation of each isolate in the pool and identify any\nunder- or over-represented libraries before committing to NovaSeq sequencing. Based on these\ndemultiplexing statistics, libraries were re-normalized and re-pooled to ensure more even representation\nof samples in the final high-throughput sequencing run. We have updated the manuscript (lines 413-416)\nto ensure this is clear.\n21. Line 330: What metagenomic samples were obtained? The sample collection methods do not mention\nthis, and the results do not report any metagenomic data.\nAuthor response:\nWe apologize for this confusion. We did not collect or analyze metagenomic samples in this study. We\napplied metagenomic analytical tools (Anvi'o v8 pipeline with MEGAHIT v1.2.9) to process 102 isolate\ngenomes for quality control purposes, specifically to filter and exclude potential contaminating contigs.\nWe have corrected this terminology in the revised Methods section to clarify that only isolate genomes\nwere analyzed (lines 425-426)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Authors have mostly addressed my concerns. However, there remain areas where alternative explanations are not included,\ngiving the reader the false impression that the given explanation is more likely than the alternatives that are only brought up\nin the discussion. These are easy but important fixes.\nI appreciate the added discussion on the potential of unsampled staff and parents to contribute to transmission. However,\nthis is not included in the abstract which states that the results suggest that shared spaces and physical proximity are the\nmajor risk factors for transmission. Without explicitly mentioning the possibility of transmission by staff and parents, the\nreader may assume that transmission is limited to patient to patient or environment to patient transmission.\nAuthors have included text in the discussion to address my previous concern that the observed correlation between\ntransmission and invasive infections may be simply due to abundance of circulating clones rather than intrinsic genetic\ncharacteristics. However, in the results section (Lines 218-222 and Lines 238-242), this alternative hypothesis is not\nmentioned, leading the reader to mistakenly believe that phenotypic or genetic differences are the only or most likely\nexplanation for this correlation.\nThe following are additional minor observations that may help to further improve this paper:\nLine 134: The distinction between a clone and a strain is not completely clear.\nLine 142: A quick definition of “colonization event” would be helpful.\nLines 161-164: Considering the 50% of clusters where estimated genomic emergence dates pre or post-date index patient\nadmission date, those that pre-date index patient admission dates could still be due to introduction through the admission of\ncolonized patients. As such, line 164 should read “These findings suggest that AT LEAST half of transmission clusters were\nintroduced through the admission…”. Such a change would require slight editing of the next sentence to more precisely\nclarify the role of unsampled patients.\nLine 170: Spatial and temporal proximity between patients are associated with transmission, but have not been shown to\n“drive” transmission as this term is suggestive of a mechanism of transmission. See also Line 173.\nLine 196: Should this read “Of the 19 S. aureus isolates, 15 ....”?\nLine 330: Another example of a likely scenario would make this argument stronger – such as continued introductions by a\nstaff or healthcare worker.\nLine 331: I don’t see how longer periods of time would cause stochastic factors associated with strain persistence and\ntransmissibility to be diminished. A healthcare worker who is a carrier could constantly be exposing patient with the same\nclone, increasing the prevalence, persistence, and transmissibility of that clone even without intrinsic genetic characteristics\nthat may enhance persistence and transmission.\nLine 380: Were samples cultured the same day (as with the environmental samples)?\nSigned: Talima Pearson", "reviewer_2": "Thank you for the opportunity to review the revised manuscript submitted by Planet and colleagues. The authors have made\nsignificant revisions with additional analyses and details of methods to enhance the manuscript and I commend them for\ntheir significant efforts. I also thank them for responding to my comments and providing further details around the study\ndesign, sampling methods, and the data they present in their manuscript.\nHowever, I would respectfully disagree with this statement (abstract): \"specific clusters of S. aureus strains that were\nassociated with invasive infection had higher rates of transmission\" and the subtitle in the results: \"Enhanced transmissibility\npredicts risk of invasive infections\":\n1. To conclude there was a higher rate of transmission would require comparable denominators – for example, in\nepidemiology, rates of transmission would be assessed by comparing the number of new infections per number of\nsusceptible people exposed. However, the sampling denominators for invasive and colonising clusters is different -\ncolonising-cluster isolates were only captured monthly, but invasive-cluster isolates were obtained continuously. To\nhighlight this imbalance with a more extreme example, would the same conclusions have been drawn (that invasive-cluster\nisolates are associated with higher rates of transmission) if the screening had only been performed annually? I believe that\nthe likelihood of many colonising-cluster isolates being missed between annual screening samples would have clouded\nthose conclusions – the same applies for 3-monthly and monthly screening. Did invasive-cluster isolates really transmit to\nmore patients, or could patients with colonisation-only isolates have transmitted to the same number of patients but not get\npicked up as frequently because they stayed for shorter periods in the NICU, were sampled less frequently, and were\ndischarged home without being sampled?\n2. There is also a temporal bias and confounder that needs to be accounted for in epidemiological studies of transmission\nrates. Under the definitions used in the study, any cluster that included a single BSI isolate was considered an \"invasive\ncluster\". Multiple studies have shown an association between bacteraemia and length of stay in NICU - infants who stay\nlonger provide more opportunities to transmit to other individuals in the same environment who will be sampled as part of the\nstudy, compared to those who are discharged early from the NICU and transmit to other individuals, but who are not\ncaptured in the study. Furthermore, the larger a cluster grows, the greater the chance of a single invasive isolate – consider a\nscenario where there were 99 colonising isolates in a cluster that result in 100 transmission events, but the 100th\nisolate is an invasive isolate. Following the 100th isolate, there are no further transmission events because the neonate is\ncommenced on antibiotics and discharged from NICU. In this scenario, the cluster is labelled an \"invasive cluster\", even\nthough transmission resulted from predominantly colonising isolates. Perhaps another approach to consider would be to\ndefine clusters as \"invasive\" or \"colonising\" on proportion of bloodstream isolates in the cluster with clusters having a higher\nproportion of invasive isolates labelled as \"invasive clusters\". Although this will still miss some unsampled colonising\nisolates, there will be less temporal bias.\n3. Although I recognise the additional analyses undertaken by the authors to try and demonstrate the absence of bias in\nresponse to my previous comment #6, I think these analyses raise additional questions:\ni. Firstly, regarding the use of TransPhylo to compare unsampled populations - as the main aim of TransPhylo is to infer the\ntransmission tree between sampled individuals, any unsampled individual that does not lead to one sampled individual is\npruned from the transmission tree (https://pubmed.ncbi.nlm.nih.gov/33617114/). Given the genetic variation among\ncolonisation isolates is similar to the variation among invasive isolates over a similar period of time, I would expect\nTransPhylo to underestimate the proportion of unsampled individuals contributing to transmission.\nii. Secondly, although I am a clinician and not familiar with the msm package in R, I presume that the HMM depends heavily\non the priors supplied to the model. Would this mean that the model performance is dependent on the observed data, and\nthus difficult to accurately infer the influence of unsampled patients?\n4. The wording used in several sections is biased towards these conclusions and overstates the findings based on the data\npresented. For example in the discussion, the text states \"In our analyses, strains that colonized or infected more patients\nover time were more likely to be invasive.\" However, this particular wording is misleading. My interpretation of the data is\nthat isolates that were part of a cluster with \"invasive potential\" were more frequently detected in the NICU, than isolates part\nof clusters that were not observed to have \"invasive potential\".\nUnfortunately, I think it is difficult to completely avoid the limitations of the study design\n(retrospective observational cohort study), but I believe the accuracy of the manuscript would be improved by acknowledging\nthe sources of bias, adding caution against over-interpretation of the data, and proposing more rigorous assessment in\nappropriately designed studies. The genomic and transmission analyses are very comprehensive and should be commended,\nbut these cannot completely cover for limitations introduced by sampling methodology. Given the issues outlined above with\nassessing and comparing rates of transmission with the reported study design, I believe the title, discussion and conclusions\nof the manuscript should be tempered to better match the data presented.\nAdditional comments:\nComment #11: Thank you for this additional information. As stated, I believe this information would help readers interpret the\nstudy and give it context, and I would suggest this information is added to the methods. The use of chlorhexidine in neonates\nis controversial and could be a confounder in this study in causing persistence.\nComment #12: Thank you for this information. Some of this could potentially be added to the discussion to enhance\nrelevance for readers with a clinical background, but I will leave this to the authors' discretion.\nComment #14: Could you please add this information to the Methods?\nComment #16: For transparency, could you please mention in the methods that formal validation against real-world\nepidemiological data was not undertaken?\nThe remainder of comments have been adequately addressed and I thank the authors for their responses."}, "author_response": "Response to Reviewers for Nature Communications manuscript\nWe thank the Reviewers for their continued thoughtful and constructive feedback on our manuscr\nipt, which has significantly strengthened both the clarity and rigor of our study. We are grateful f\nor the opportunity to address their remaining concerns. In response to the\nsuggestions, we have made several key changes: (1) revised the title to avoid overstating claims\nabout transmission rates, acknowledging the Reviewer's valid concern; (2) added context\nthroughout the manuscript to acknowledge alternative explanations for observed correlations,\nincluding the potential role of clone abundance and unsampled individuals in transmission\ndynamics; (3) conducted additional analyses to address concerns about sampling bias (Extended\nData Figures S15, S16, S17); (4) tempered our language regarding transmission rates and\ncausality, including substituting \"dissemination\" for \"transmission\" where appropriate; (5) added\nmethod details requested by the Reviewers. We provide point-by-point responses below, with our\nrevisions indicated in the manuscript with tracked changes. All line numbers refer to the\nmanuscript with tracked changes visible.\nReviewer #1 (Remarks to the Author):\n1. Authors have mostly addressed my concerns. However, there remain areas where alternative\nexplanations are not included, giving the reader the false impression that the given explanation\nis more likely than the alternatives that are only brought up in the discussion. These are easy but\nimportant fixes.\nI appreciate the added discussion on the potential of unsampled staff and parents to contribute to\ntransmission. However, this is not included in the abstract which states that the results suggest\nthat shared spaces and physical proximity are the major risk factors for transmission. Without\nexplicitly mentioning the possibility of transmission by staff and parents, the reader may assume\nthat transmission is limited to patient to patient or environment to patient transmission.\nAuthor response:\nWe thank the Reviewer for this important observation. We have added \"caregivers\" to the\nabstract to clarify that transmission may occur through staff and parents, not solely through\npatient-to-patient or environment-to-patient routes.\n2. Authors have included text in the discussion to address my previous concern that the observed\ncorrelation between transmission and invasive infections may be simply due to abundance of\ncirculating clones rather than intrinsic genetic characteristics. However, in the results section\n(Lines 218-222 and Lines 238-242), this alternative hypothesis is not mentioned, leading the\nreader to mistakenly believe that phenotypic or genetic differences are the only or most likely\nexplanation for this correlation.\nAuthor response:\nWe appreciate the Reviewer's careful reading. We have added language to acknowledge the\nabundance and prevalence of clones to both of these sections (lines 251, 276).\n3. The following are additional minor observations that may help to further improve this paper:\nLine 134: The distinction between a clone and a strain is not completely clear.\nAuthor response:\nWe appreciate the Reviewer’s suggestion for improved clarity. We have updated our definitions\nas such “We defined a \"strain\" as either a group of genomes defined by a phylothreshold or\nsingletons with no SNP distance matches (lines 166). A \"clone\" was designated as a strain\ndetected in at least two distinct samples on more than one date or patient, while a \"transmission\ncluster\" refers to a clone found in more than one patient.\n4. Line 142: A quick definition of “colonization event” would be helpful.\nAuthor response:\nWe appreciate this helpful suggestion. We have now defined how we enumerated colonization\nevents in the sentence before we first use the term (lines 174-175)\n5. Lines 161-164: Considering the 50% of clusters where estimated genomic emergence dates\npre or post-date index patient admission date, those that pre-date index patient admission dates\ncould still be due to introduction through the admission of colonized patients. As such, line 164\nshould read “These findings suggest that AT LEAST half of transmission clusters were\nintroduced through the admission…”. Such a change would require slight editing of the next\nsentence to more precisely clarify the role of unsampled patients.\nAuthor response:\nWe appreciate the Reviewer's attention to this important nuance. We have added this wording\nand clarification (line 210).\n6. Line 170: Spatial and temporal proximity between patients are associated with transmission,\nbut have not been shown to “drive” transmission as this term is suggestive of a mechanism of\ntransmission. See also Line 173.\nAuthor response:\nWe appreciate this careful attention to language and agree that \"drive\" overstates our findings.\nWe have changed the wording, and taken out the word “drive”.\n7. Line 196: Should this read “Of the 19 S. aureus isolates, 15 ....”?\nAuthor response:\nWe thank the Reviewer for catching this. We changed this but altered form the reviewer’s\nsuggestion for clarity to “Of the 19 S. aureus isolated from these surfaces…”\n8. Line 330: Another example of a likely scenario would make this argument stronger – such as\ncontinued introductions by a staff or healthcare worker.\nAuthor response:\nWe thank the Reviewer for this helpful suggestion that improves the balance of our discussion.\nWe have reworked this paragraph and added the idea of repeated introductions by staff (lines\n452-453).\n9. Line 331: I don’t see how longer periods of time would cause stochastic factors associated\nwith strain persistence and transmissibility to be diminished. A healthcare worker who is a\ncarrier could constantly be exposing patient with the same clone, increasing the prevalence,\npersistence, and transmissibility of that clone even without intrinsic genetic characteristics\nthat may enhance persistence and transmission.\nAuthor response:\nThis is a well taken point, and we appreciate the Reviewer's clear counterexample. We have\neliminated this sentence.\n10. Line 380: Were samples cultured the same day (as with the environmental samples)?\nAuthor response:\nWe thank the Reviewer for this clarifying question. We added that surveillance sampling for each\nmonthly surveillance time point was performed over 2-3 days (line 540-541).\nSigned: Talima Pearson\nReviewer #2 (Remarks to the Author):\n1. Thank you for the opportunity to review the revised manuscript submitted by Planet and\ncolleagues. The authors have made significant revisions with additional analyses and details of\nmethods to enhance the manuscript and I commend them for their significant efforts. I also thank\nthem for responding to my comments and providing further details around the study design,\nsampling methods, and the data they present in their manuscript.\nHowever, I would respectfully disagree with this statement (abstract): \"specific clusters of S.\naureus strains that were associated with invasive infection had higher rates of transmission\" and\nthe subtitle in the results: \"Enhanced transmissibility predicts risk of invasive infections\":\n1. To conclude there was a higher rate of transmission would require comparable denominators\n– for example, in epidemiology, rates of transmission would be assessed by comparing the\nnumber of new infections per number of susceptible people exposed. However, the sampling\ndenominators for invasive and colonising clusters is different - colonising-cluster isolates were\nonly captured monthly, but invasive-cluster isolates were obtained continuously. To highlight this\nimbalance with a more extreme example, would the same conclusions have been drawn (that\ninvasive-cluster isolates are associated with higher rates of transmission) if the screening had\nonly been performed annually? I believe that the likelihood of many colonising-cluster isolates\nbeing missed between annual screening samples would have clouded those conclusions – the\nsame applies for 3-monthly and monthly screening. Did invasive-cluster isolates really transmit\nto more patients, or could patients with colonisation-only isolates have transmitted to the same\nnumber of patients but not get picked up as frequently because they stayed for shorter periods in\nthe NICU, were sampled less frequently, and were discharged home without being sampled?\nAuthor response:\nWe are grateful for the Reviewer's rigorous assessment of potential sampling bias. This is a very\ngood point. Indeed, the denominator for colonizing clusters is smaller by a small amount\ncompared to the invasive clusters since invasive clusters were detected in both monthly\nsurveillance and from blood cultures. To test the impact of this potential bias on our data, we\nexcluded all blood culture samples from our analyses (lines 289-293). The information about\nblood cultures was used only to distinguish between invasive and colonizing clusters. This made\nthe denominators for both groups exactly the same. The trends and conclusions remained the\nsame for these new analyses (Extended data Figure S15). We have also clarified that our\npersistence measurements are based on persistence in the NICU, not in a given patient (lines\n256, 259, 279, 284, 290, and throughout).\n2. There is also a temporal bias and confounder that needs to be accounted for in\nepidemiological studies of transmission rates. Under the definitions used in the study, any cluster\nthat included a single BSI isolate was considered an \"invasive cluster\". Multiple studies have\nshown an association between bacteraemia and length of stay in NICU - infants who stay longer\nprovide more opportunities to transmit to other individuals in the same environment who will be\nsampled as part of the study, compared to those who are discharged early from the NICU and\ntransmit to other individuals, but who are not captured in the study. Furthermore, the larger a\ncluster grows, the greater the chance of a single invasive isolate – consider a scenario where\nthere were 99 colonising isolates in a cluster that result in 100 transmission events, but the 100th\nisolate is an invasive isolate. Following the 100th isolate, there are no further transmission\nevents because the neonate is commenced on antibiotics and discharged from NICU. In this\nscenario, the cluster is labelled an \"invasive cluster\", even though transmission resulted from\npredominantly colonising isolates. Perhaps another approach to consider would be to\ndefine clusters as \"invasive\" or \"colonising\" on proportion of bloodstream isolates in the cluster with\nclusters having a higher proportion of invasive isolates labelled as \"invasive clusters\". Although\nthis will still miss some unsampled colonising isolates, there will be less temporal bias.\nAuthor response:\nWe appreciate the Reviewer's careful consideration of potential confounders in our study design.\nThese are important points and we have updated text throughout the manuscript to be more\ncautious and in line with these suggestions. Analytically, we addressed this question by\nrerunning our analyses stratifying clusters by the numbers of patients with blood stream\ninfections detected for each cluster (lines 293-296, Extended Data Figure S16). For these\nanalyses, the trends remained the same, and clusters with only one patient with bloodstream\ninfection were intermediate between those that had more than 2 and those that had 0.\n3. Although I recognise the additional analyses undertaken by the authors to try and demonstrate\nthe absence of bias in response to my previous comment #6, I think these analyses raise\nadditional questions:\ni. Firstly, regarding the use of TransPhylo to compare unsampled populations - as the main aim\nof TransPhylo is to infer the transmission tree between sampled individuals, any unsampled\nindividual that does not lead to one sampled individual is pruned from the transmission tree\n(https://pubmed.ncbi.nlm.nih.gov/33617114/). Given the genetic variation among colonisation\nisolates is similar to the variation among invasive isolates over a similar period of time, I would\nexpect TransPhylo to underestimate the proportion of unsampled individuals contributing to\ntransmission.\nAuthor response:\nWe appreciate the Reviewer's insightful observation regarding the limitations of TransPhylo. The\nReviewer is correct that unsampled individuals that do not transmit to a sampled individual are\npruned from the transmission tree. In the TransPhylo publication this led to an underestimation\nof the rate of unsampled individuals for simulated data where the sampling probability (pi) was\nset at a specific rate. Therefore, TransPhylo will have a tendency to underestimate unsampled\nrates. Theoretically, the additional sampling of invasive clusters from blood culture could have\nled to higher rates of detection compared to colonizing clusters collected only from surveillance.\nThus, to examine this more thoroughly, we excluded all blood culture data and re-ran the\nanalysis (lines 289-293, Extended Data Figure S15). The estimated unsampled probability was\nthe same between invasive and colonizing clusters. While the actual unsampled rate between the\ncolonizing and invasive clusters may be higher because of pruning, we have no reason to believe\nthat invasive and colonizing clusters would be any different since they were collected in the same\nway during monthly surveillance.\nii. Secondly, although I am a clinician and not familiar with the msm package in R, I presume\nthat the HMM depends heavily on the priors supplied to the model. Would this mean that the\nmodel performance is dependent on the observed data, and thus difficult to accurately infer the\ninfluence of unsampled patients?\nAuthor response:\nWe thank the Reviewer for this thoughtful question. The msm model uses a maximum\nlikelihood, and not a Bayesian approach, so there are no prior probabilities involved. However,\nthe author makes an excellent point that maximum likelihood model-based inference will find\nthe explanation that maximizes the probability of observing the data at hand given a specified\nmodel. Therefore, the model is critical and potentially misleading if the assumptions behind the\nmodel are very far from the truth. This is true for all likelihood models. To make sure that our\nstarting matrices did not affect our final results we reran the analysis with different matrices, and\nall converged on the same result. We also used a bootstrap procedure to test the robustness of\nour estimates. We also limited our analyses to surveillance isolates (excluding blood stream\nisolates)(lines 289-293, Extended Data Figure S15) and obtained the same results. Because\ninvasive and colonizing clusters detected on surveillance were sampled in exactly the same way,\nthere is no reason to believe that there is an ascertainment bias at play. As with the Transphylo\nanalysis it is difficult to estimate the true rate and impact of unsampled isolates. We have added\nan explicit statement of this limitation in the discussion.\n4. The wording used in several sections is biased towards these conclusions and overstates the\nfindings based on the data presented. For example in the discussion, the text states \"In our\nanalyses, strains that colonized or infected more patients over time were more likely to be\ninvasive.\" However, this particular wording is misleading. My interpretation of the data is that\nisolates that were part of a cluster with \"invasive potential\" were more frequently detected in the\nNICU, than isolates part of clusters that were not observed to have \"invasive potential\".\nUnfortunately, I think it is difficult to completely avoid the limitations of the study design\n(retrospective observational cohort study), but I believe the accuracy of the manuscript would be\nimproved by acknowledging the sources of bias, adding caution against over-interpretation of the\ndata, and proposing more rigorous assessment in appropriately designed studies. The\ngenomic and transmission analyses are very comprehensive and should be commended, but these\ncannot completely cover for limitations introduced by sampling methodology. Given the issues\noutlined above with assessing and comparing rates of transmission with the reported study\ndesign, I believe the title, discussion and conclusions of the manuscript should be tempered to\nbetter match the data presented.\nAuthor response:\nWe are grateful for the Reviewer's careful attention to language and interpretation throughout the\nmanuscript. We have carefully looked through the text and changed the language to reflect this\nReviewer point. We have made a concerted effort to separate the reporting of results from\npossible interpretations of those results, and we have added additional possibilities to make sure\nthat other interpretations are considered. We very much appreciate the reviewer’s careful\napproach to language and interpretation.\nWe have been especially careful in our use of the word “transmission”. Where appropriate, we\nhave occasionally substituted the word “dissemination” which we take to be a more inclusive\nterm that includes transmission and successful persistence in individuals and the environment.\n5. Comment #11: Thank you for this additional information. As stated, I believe this information\nwould help readers interpret the study and give it context, and I would suggest this information is\nadded to the methods. The use of chlorhexidine in neonates is controversial and could be a\nconfounder in this study in causing persistence.\nAuthor response:\nWe thank the Reviewer for this suggestion. We added this comment to the methods. (lines 498-\n505)\n6. Comment #12: Thank you for this information. Some of this could potentially be added to the\ndiscussion to enhance relevance for readers with a clinical background, but I will leave this to\nthe authors' discretion.\nAuthor response:\nWe appreciate this suggestion and agree it enhances clinical relevance. We have added these\nfindings to the results (lines 184-189).\n7. Comment #14: Could you please add this information to the Methods?\nAuthor response:\nWe thank the Reviewer for this suggestion for completeness. We added this to the methods (lines\n492-496).\n8. Comment #16: For transparency, could you please mention in the methods that formal\nvalidation against real-world epidemiological data was not undertaken?\nAuthor response:\nWe appreciate this point regarding transparency. We added this sentence to the methods (lines\n691-693): “We compared our SNP thresholds to those used in 40 other studies in NICU\n(Extended Data Table S3; Extended Data Figure S2), in ongoing work we are formally validating\nour pipeline with simulated and other real-world genomic datasets.”\nThe remainder of comments have been adequately addressed and I thank the authors for their\nresponses.\nWe thank the Reviewer for confirming that the remaining comments have been addressed, and\nwe appreciate their constructive engagement throughout the review process."}]

Decision letter