Environmental and skin–nasal microbiome variation in South African children with atopic dermatitis

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Skin and nasal microbiomes have been linked to AD severity, but not yet in an African cohort. Here, we aimed to explore how urban and rural stratification, disease severity, and inter-site bacterial overlap shape the skin and nasal microbiomes of children with AD in South Africa (ZA). Methods Children were recruited from urban Cape Town (CT) and rural Umtata (UM), ZA. We profiled the skin and nasal microbiomes of 197 children (87 healthy, 110 with AD; ages 12–36 months), totaling 502 samples, including both lesional and non-lesional skin sites in children with AD, in a cross-sectional study design. We used 16S rRNA V4–V5 sequencing for its accessibility and scalability to large sample sets. To address the limited species- and strain-level resolution of 16S data, we applied random forest (RF) machine-learning models to classify AD status with amplicon sequence variants (ASVs). We analyzed microbiome composition and diversity stratified by environment. Results We found that RF models could predict AD status using both skin and nasal microbiomes ( AUCs: skin = 0.70–0.82; nasal = 0.68 ), strongly driven by both Streptococcus and Staphylococcus . The correlations between skin–nasal microbiome were significantly stronger in children with AD compared to healthy controls, with overall higher correlations observed in rural UM (healthy r = 0.44 to AD r = 0.69 ) compared to urban CT (healthy r = 0.34 to AD r = 0.65 ). The skin microbiome diversity was higher in children from rural UM with healthy skin than those from urban CT ( p = 0.0012 ). However, children with AD in both groups showed significant alterations in their microbiome, with those in rural UM exhibiting greater beta diversity ( p = 0.001 ) than their urban CT counterparts ( p = 0.002 ). Conclusion In children with AD in South Africa, the environmental context is associated with microbial dysbiosis, and skin–nasal microbiomes reflect shared reservoirs. These findings highlight the value of geographically diverse studies with skin and mucocutaneous sampling in understanding pediatric AD. Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Atopic dermatitis (AD) is a chronic inflammatory skin disease that typically begins in early childhood and affects up to 20% of children worldwide 1 , with rising rates in many African countries 2 – 4 . The International Study of Asthma and Allergies in Childhood estimated a 23.3% lifetime prevalence of AD among African children aged 6–7, significantly higher than the global average of 14.2% 3 . Given that African skin is different from that of Western populations, region-specific microbiome studies are essential to better understand how geographic and environmental factors influence AD in children. The skin microbiome plays a central role in AD by modulating immune responses and maintaining barrier integrity 5 – 9 . Colonization by Staphylococcus is known to promote inflammation through cytotoxins and proteases that impair the skin barrier and activate type 2 immunity 7 , 10 . Pediatric AD is distinct in skin physiology and microbial colonization from adult AD, reflecting changes in skin with age 11 . In children, AD is often characterized by increased Streptococcus abundance and variable S. aureus colonization, whereas adult AD is more consistently driven by S. aureus or other Staphylococcus species 11 . Beyond the skin, evidence suggests that the nasal microbiome may also contribute to AD. The nasal cavity contains colonizing microbes commonly found on the skin and may serve as a reservoir for AD-associated taxa 12 , 13 . Individuals with AD are over five times as likely to be nasally colonized by S. aureus as healthy controls 14 . Longitudinal data from a large birth cohort have further linked early and frequent nasal colonization with S. aureus during infancy with increased risk and severity of AD 15 . It has also been observed that changes in the skin and nasal microbiome of Staphylococcus species occurred following treatment of AD with Dupliumab 16 . Totté et al. previously identified that both the skin and nasal microbiomes are associated with AD severity in children, particularly driven by Staphylococcus 17 . However, the study did not include healthy controls or non-lesional skin samples, and the association was observed in a Dutch cohort 17 . Here, we expand upon this knowledge through our unique dataset of skin and nasal microbiomes of 197 South African toddlers (87 healthy, 110 with AD) using 16S rRNA V4–V5 sequencing of 502 samples (Fig. 1 ). We employed 16S sequencing for its accessibility and scalability in this setting. Recognizing the limited resolution of 16S at the species and strain levels, we applied random forest models to classify AD status from ASVs. Leveraging this geographically stratified cohort, including both lesional and non-lesional sites from an urban and rural region, we explored how geography, disease severity, and skin/nasal microbial overlap associate with the microbiome in early-life AD. RESULTS Random forest models classify AD status from skin and nasal microbiomes Using 16S relative abundances of ASV bacterial features from both skin and nasal samples, we used RF models to evaluate whether bacterial composition can distinguish AD status 18 , 19 . As expected, skin and nasal microbiomes were very highly distinguishable, with high classification accuracy ( AUC = 0.95 ± 0.03 ) (Fig. 2 A, left ). Dolosigranulum was the top-ranked feature distinguishing skin from nares, consistent with its well-documented presence in the nasal cavity 20 , 21 (Fig. 2 B). RF models could accurately predict AD status, particularly when comparing lesional AD (ADL) and healthy (H) skin ( AUC = 0.82 ± 0.01 ), and to a lesser extent, AD non-lesional (ADNL) vs H ( AUC = 0.76 ± 0.01 ), and ADL vs ADNL ( AUC = 0.70 ± 0.03 ) (Fig. 2 A, middle ). Across all classification tasks related to skin disease prediction, H vs ADL, H vs ADNL, and ADL vs ADNL, Streptococcus consistently ranked as the top feature (Fig. 2 B). A key finding in this study was that models trained on nasal microbiomes only were also able to predict AD status with moderate accuracy ( AUC = 0.68 ± 0.03 ) (Fig. 2 A, right ), aligning with previous findings that disease-associated bacterial signatures are not limited to the skin but are also detectable in the nasal cavity 16 , 17 . Notably, Staphylococcus emerged as the top predictive feature, followed by Streptococcus , with Dolosigranulum ranking third (Fig. 2 B). To further distinguish these findings across geographic regions, we performed RF classifications using skin and nasal microbiomes from the UM and CT cohorts separately ( Fig. S1) . Notably, skin-based models from UM exhibited stronger stratification of AD status ( AUCs = 0.74–0.89 ), particularly for ADL vs H and ADNL vs H comparisons, compared to CT ( AUCs = 0.70–0.74 ), suggesting that the greater microbiome disruption in UM enhances classification accuracy. Nares-based models showed moderate predictive ability in both cohorts (UM AUC = 0.68 ± 0.06 ; CT AUC = 0.68 ± 0.06 ). AD is associated with increased skin–nasal bacterial sharing We examined bacterial overlap between the skin and nares by analyzing ASV sharing within individuals with paired samples. Children with AD demonstrated markedly higher skin–nasal ASV overlap compared to healthy controls (Fisher’s exact test; p < 0.001, OR = 1.86 ). Among children with AD, a total of 369 ASVs were shared between skin and nasal samples, whereas 249 ASVs were unique to the skin and 50 were unique to the nares (Fig. 3 A, left ). In contrast, healthy individuals exhibited fewer shared ASVs (n = 254), with a greater number of taxa uniquely detected in either the skin (n = 323) or nares (n = 60) (Fig. 3 A, right ). To evaluate the relative abundance concordance of shared ASVs, we compared log-transformed mean relative abundances of shared ASVs between skin and nares across four groups: CT healthy, CT AD, UM healthy, and UM AD. We observed significant positive correlations between skin and nasal ASV abundances in all groups. However, the strength of these correlations was markedly higher in children with AD than in healthy children, and in the rural than the urban region. In CT, the correlation increased from (Pearson r = 0.34, p = 8.7e-05 in healthy children to ( r = 0.65, p = 2.0e-26 ) in those with AD (Fig. 3 B). A similar pattern was observed in UM, where correlations increased from ( r = 0.44, p = 1.7e-12 ) in healthy children to ( r = 0.69, p = 4.9e-42 ) in children with AD (Fig. 3 B). AD-associated microbiome changes in rural Umtata versus urban Cape Town We compared children with AD from urban CT and rural UM to investigate regional differences in disease presentation and skin microbiome composition. Children in UM more frequently presented with severe AD (oSCORAD > 40, n = 32) compared to those in CT (oSCORAD > 40, n = 21). Moderate cases (15 ≤ oSCORAD ≤ 40) were observed at similar frequencies in both regions (UM: n = 27; CT: n = 24) ( Fig. S2A-B ). To separate regional effects from those driven by disease severity, we restricted alpha and beta diversity analyses to samples with oSCORAD < 40 (see Fig. S3 for the complete dataset). Skin microbiome diversity among healthy children was significantly higher in Umtata (n = 22) compared to Cape Town (n = 22) (Mann-Whitney U-test; p = 0.0012, U = 104 ) (Fig. 4 A). Principal coordinates analysis (PCoA) of robust Aitchison (RPCA) further revealed significant differences in beta diversity between H, ADNL, and ADL samples in both regions. In UM (Fig. 4 B), clear separation was observed among the three skin types ( PERMANOVA p = 0.001, F = 25.72 ), suggesting greater microbiome disruption. In CT (Fig. 4 C), the separation was also significant but less pronounced ( p = 0.002, F = 6.20 ), indicating milder dysbiosis. In both settings, ADNL samples clustered between H and ADL samples, consistent with a potential transitional microbial state. A Streptococcus ASV-1 exhibited significantly higher RCLR-transformed abundances in both ADNL and ADL skin relative to healthy skin in both CT and UM (Fig. 4 D–E). A second Streptococcus ASV (ASV-2) was significantly higher in UM only. Neither ASV significantly correlated with oSCORAD scores, suggesting that Streptococcus enrichment may be a hallmark of AD skin independent of disease severity. A Staphylococcus ASV (ASV-1) was differentially abundant across severity groups, whereas no other taxa correlated significantly with severity scores. In UM, it was also considerably elevated in lesional skin compared to healthy controls ( FDR-adjusted p = 2.2e-03 ), whereas in CT, we did not see this trend (Fig. 4 D–E). Broader taxonomic shifts were more pronounced in UM than in CT, underscoring region-specific differences (Fig. 4 D–E). In UM, Micrococcus ASV-1 was significantly reduced in both ADL and ADNL skin, while Veillonella_A ASV-1 was enriched in both. Neither ASV correlated with oSCORAD, suggesting these shifts reflect broader dysbiosis rather than AD severity. These findings support greater microbial disruption in UM AD skin, possibly driven by environmental or geographic factors. To explore region-specific differences in nasal microbiota and their association with disease severity, we also assessed ASVs that were differentially abundant in the nares of children with and without AD in Cape Town and Umtata ( Fig. S4 ). In Cape Town, Staphylococcus ASV-1 abundance in the nares was positively correlated with AD severity ( Spearman r = 0.58, p = 1.9e − 02). Additionally, Micrococcus ASV-1 in the nares was significantly increased in AD samples compared to healthy controls ( Mann–Whitney p = 3.0e − 02). In Umtata, Streptococcus ASV-1 abundance was significantly reduced in AD samples relative to healthy children ( Mann–Whitney p = 5.2e − 03). DISCUSSION Most microbiome research focused on AD has been conducted in a limited number of high-income Western countries 22 . This geographical skew raises questions about the broader applicability of current microbiome-derived insights in the global population, especially regarding health disparities and differing environmental exposures in the Global South 23 , 24 . This study helps address this gap by reporting observational findings of skin and nasal microbiomes in South African children stratified by rural and urban location. We found that environmental context was associated with AD severity and microbial dysbiosis. Machine learning has previously been used to identify disease associations from microbial signatures 18 , including in atopic dermatitis in children 19 . One of the most interesting aspects of this study was our RF model trained solely on nasal samples, which predicted AD status with moderate accuracy ( AUC = 0.68 ± 0.03 ), and models using skin microbiomes could distinguish lesional and non-lesional skin from healthy skin. These findings suggest that skin and nasal microbiome profiles, even from short-read 16S data, can capture disease-related patterns in pediatric AD. Notably, classification accuracy varied by environment: predictions for Umtata were more accurate than for Cape Town, likely reflecting the greater shifts in microbial diversity observed in the rural cohort. While these AUCs are likely insufficient for diagnostic screening, the results underscore the potential of microbiome-based classification approaches, particularly if combined with higher-resolution multi-omics data in future studies, which may be used towards pre-disease prediction. A notable observation was the significant similarity between skin and nasal microbiomes in AD patients, supporting the hypothesis that bacterial exchange occurs between these sites. This aligns with the idea that the nasal cavity may act as a reservoir for strains capable of exacerbating skin inflammation, such as S. aureus . In our cohort, Streptococcus ASVs were consistently enriched in AD skin, yet showed no correlation with oSCORAD scores. Whether Streptococcus is more associated with disease presence than progression, and whether it can facilitate colonization by Staphylococcus species such as S. aureus , warrants further investigation. Assessing the nasal microbiome could therefore be important for evaluating the risk of cutaneous colonization by potentially pathogenic species. Additional research is needed to clarify the mechanisms and directionality of this exchange and whether the nasal microbiome plays a causal role in establishing cutaneous dysbiosis. We acknowledge several methodological limitations. 16S rRNA sequencing restricts taxonomic resolution to the genus level, limiting species- and strain-level insights. Our study used the 16S V4–V5 region, which underrepresents Cutibacterium , an abundant skin bacterial genus that is more accurately captured by V1–V3 primers 25 . Lesional body sites were not recorded for each sample, though sampling was conducted from typical pediatric AD predilection sites (e.g., cheeks, forearms) using standardized protocols across both locations. Although we cannot exclude the possibility that differences in sampling location influenced regional microbiome variation, all skin and nasal swabs were collected, handled, and stored under identical procedures by trained personnel to minimize technical bias. The skin microbiome is influenced by urbanization 26 , with rural and less urbanized environments typically associated with greater bacterial diversity 27 – 30 , which may be protective in early life. Additionally, African skin is compositionally distinct from that of Western populations, underscoring the need for region-specific studies to better understand their roles in health and disease 31 , 32 . While our study design, sampling one urban and one rural site, limits broader generalizability, our findings expand available data on pediatric AD microbiomes in South Africa and provide evidence that environmental context shapes microbial dysbiosis. Our findings also need to be considered within the context of environmental and immunologic differences. Contrary to the conventional “Hygiene Hypothesis,” which suggests rural environments protect against allergic diseases by promoting greater early-life microbial exposures, children in rural Umtata exhibited greater microbial dysbiosis than those in urban Cape Town, even when controlling for severity. Previous published work in a subset of this same cohort 33 , 34 found that rural children, regardless of AD status, had heightened innate immune activation and AD-associated gene expression, correlated with exposures such as farm animal contact during pregnancy. While AD was associated with downregulated lymphocyte and innate signaling pathways, the environment exerted a substantial influence on immune transcriptional profiles 34 . Rural children showed marked upregulation of chemokines, cytokines, GPCRs, and regulatory pathways, including IL-10, a key anti-inflammatory cytokine known to suppress antigen-presenting cells and lymphocyte effector functions. Together, these environmental, immunologic, and healthcare-access differences may help explain the greater microbiome alterations observed in rural children with AD. METHODS Clinical Study Design and Sample Collection We collected samples from 197 South African toddlers (ages 12–36 months), including 110 children clinically diagnosed with AD and 87 healthy controls between February 2015 and May 2016. AD diagnosis followed the UK Working Party’s criteria 35 by trained clinicians. We aimed to enroll and collect as many samples as possible from children fitting the study criteria. Severity was assessed using the objective SCORAD (oSCORAD) index, which ranges from 0 to 83 and is based on the extent of affected body surface area and the intensity of six clinical signs (erythema, edema/papulation, oozing/crusts, excoriation, lichenification, and dryness), each scored from 0 to 3. The final score was computed using the formula: oSCORAD = A/5 + 7B/2 36 . Children with chronic illness, immunomodulatory treatments, or recent antibiotic use were excluded. Participants were recruited from two socio-environmentally distinct locations: Cape Town (CT; ~5 million population), a highly urbanized setting, and Umtata (UM; ~200,000 population), a rural region. Samples from urban AD cases were collected at Red Cross Children’s Hospital in CT, and healthy controls from local preschools. Rural samples were collected at Nelson Mandela Academic Hospital in UM, primarily from the Mqanduli district. All participants were of Black (AmaXhosa ethno-linguistic) descent. Written informed consent was obtained from parents or legal guardians. Skin and nasal swabs were collected using standardized protocols 37 , 38 and performed consistently across both sites. For AD children, lesional skin swabs were taken from commonly affected sites (e.g., cheeks, forearms), and non-lesional swabs from the mid-back. Specific lesional body site locations were not recorded. Healthy controls were swabbed on the mid-back for consistency. Nasal swabs were taken from the anterior nares. All samples were stored at − 80°C in skim milk-tryptone-glycerol-glucose buffer until processing. DNA Extraction, 16S rRNA Sequencing, and Sequence Data Processing DNA was extracted from skin and nasal swabs using the DSP Virus/Pathogen Mini Kit® (Qiagen, Germany) with a 70 µL elution volume at the University of Cape Town. The V4–V5 region of the 16S rRNA gene was amplified using previously described primers 515F (TATGGTAATTGTGTGYCAGCMGCCGCGGTAA) and 926R (CGGCATACGAGATAGTCAGCCAGGGCCGYCAATTYMTTTRAGTTT) 39 – 41 . While this primer pair underrepresents Cutibacterium relative to V1–V3 primers 25 , it provides broader coverage of Staphylococcus and Streptococcus sequencing depth. Amplicons were barcoded, pooled in batches of 300, and sequenced on an Illumina MiSeq v3 (300-cycle) run, generating ~ 13 Gbp per run. Raw data were processed via the Qiita platform 42 using a standardized workflow. Sequences were demultiplexed, PHRED 43 quality-filtered, and denoised using Deblur 44 in QIIME 2 (v2024.2.0). ASVs were assigned taxonomy using the Greengenes 2 database (v2022.10) 45 , and feature tables were generated at both ASV and genus levels. To reduce noise and sparsity, a 5% sample depth threshold was applied to filtered tables, retaining over 95% of samples. Samples below this threshold were excluded from rarefied alpha diversity analyses but retained in the complete dataset for descriptive and statistical comparisons. Random Forest Classification RF classifiers were implemented using the scikit-learn Python package (v1.4.2) to evaluate the predictive power of 16S rRNA ASV-level features (n = 2283) and their observed abundances across multiple classification tasks. Models were trained to distinguish (1) skin and nasal samples, (2) ADL, ADNL, and H skin samples in pairwise comparisons (ADL vs H, ADNL vs H, ADL vs ADNL), and (3) AD vs H status in nasal samples. To prevent overfitting and account for repeated measures, we ensured all samples from a participant were assigned exclusively to either the training (80%) or testing (20%) set in each fold (GroupKFold n_splits = 5). RF models with 1,000 estimators were trained for each classification task, and performance was evaluated using the area under the ROC curve. Feature importance was measured by the mean decrease in Gini impurity across folds, and the top 10 ASVs were retained to identify key bacterial drivers of predictive performance. To explore geographic variation in classification performance, the above analyses were repeated independently for CT and UM cohorts using the same classification framework ( Fig. S4 ). ROC curves shaded regions denoting ± 1 SD around the mean true positive rate. This comprehensive framework enabled us to assess cross-site consistency and regional specificity in microbiome-based disease status and anatomical site classification. Microbiome Diversity Analyses Alpha diversity was assessed using the Faith PD, a phylogenetically informed metric 46 . ASV count tables were processed using the BIOM (v.2.1.15) package via Python (v.3.10.14). This analysis included only AD samples with oSCORAD 15 to 40 and healthy controls to reduce potential confounding from extreme disease severity. Faith PD values were computed for each sample using the skbio.diversity.alpha_diversity function. Statistical significance was determined using the non-parametric Mann–Whitney U-test for all pairwise comparisons. All p-values were FDR-adjusted using BH. The same analysis with all samples (including oSCORAD > 40) is in the supplementary. Beta diversity was analyzed using RPCA 47 , a compositionality-aware ordination technique within gemelli (v.0.0.12). Before RPCA, samples were filtered to exclude nasal swabs, and the feature table was converted back into BIOM format to ensure compatibility with the gemelli.rpca function. Ordination was performed on this filtered table, and sample coordinates from the first two RPCA axes were used to visualize group separations. Statistical differences in beta diversity were tested using PERMANOVA on the RPCA-based distance matrix, with significance evaluated between pairwise combinations of skin sample groups. Declarations Acknowledgements: We are grateful to the clinicians at Red Cross Children’s Hospital, Cape Town, and at the Nelson Mandela Academic Hospital, Umtata, for collecting the samples in this project. We thank our collaborators at UC San Diego and the J. Craig Venter Institute for the microbiome analysis. Funding sources: This research was funded by the National Research Foundation of South Africa (UID: 150539). Conflicts of Interest: R.L.G. serves as a co-founder, scientific advisor, consultant, and equity holder in MatriSys Biosciences, and also holds a position as a consultant, receiving income, and equity holder in Sente Inc. R.K . is a scientific advisory board member and consultant for BiomeSense, Inc., holding equity and receiving income. He is a member of the scientific advisory board and has equity in GenCirq. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma, holds equity, and serves on the scientific advisory board. He is a board member of Microbiome Vault, Inc. He is a N=1 IBS advisory board member and receives income. He is a Senior Visiting Fellow of the HKUST Jockey Club Institute for Advanced Study. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, by its conflict-of-interest policies. C.C. is the founder of Phiome and receives income from Dr. Armpit. He is a scientific advisor to NAOS, MyMicrobiome, and Parallel Health, Inc. C.L.D. is a co-founder, president, chief strategic officer, and equity holder in Biorentis, Inc. Data availability: Sequencing 16S data is deposited in the NCBI Sequence Read Archive (SRA) under Bioproject ID PRJNA1269634. The data processing can be found on QIITA via study ID 15564. Code used in this manuscript, along with the figures, can be found at our GitHub repository: https://github.com/yangchen2/16S_AD_South-Africa. Ethics statement: The Human Research and Ethics Committee of the Faculty of Health Science, University of Cape Town (HREC/REF: 451/2014) approved the parent study, and additional ethical approval for the bacteriome study, Human Research and Ethics Committee (HREC/REC: 668/2020), was obtained. Patient consent: Written informed consent was obtained from all parents or legal guardians of children included in this study. References Nutten, S. Atopic dermatitis: global epidemiology and risk factors. Ann Nutr Metab 66 Suppl 1 , 8–16 (2015). Atopic dermatitis. The Lancet 387 , 1109–1122 (2016). Obeng, B. B., Hartgers, F., Boakye, D. & Yazdanbakhsh, M. Out of Africa: what can be learned from the studies of allergic disorders in Africa and Africans? Curr Opin Allergy Clin Immunol 8 , 391–397 (2008). Deckers, I. A. G. et al. 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R.K. is a scientific advisory board member and consultant for BiomeSense, Inc., holding equity and receiving income. He is a member of the scientific advisory board and has equity in GenCirq. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma, holds equity, and serves on the scientific advisory board. He is a board member of Microbiome Vault, Inc. He is a N=1 IBS advisory board member and receives income. He is a Senior Visiting Fellow of the HKUST Jockey Club Institute for Advanced Study. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. C.L.D. is a co-founder, president, chief strategic officer, and equity holder in Biorentis, Inc. C.C. is the founder of Phiome and receives income from Dr. Armpit. He is a scientific advisor to NAOS, MyMicrobiome, and Parallel Health, Inc. Supplementary Files SUPPLEMENTARYFIGURES.docx Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 30 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers invited by journal 03 Oct, 2025 Editor assigned by journal 28 Aug, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 20 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":104200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract. Environmental context influences AD severity and microbiome composition across skin and nasal sites in South African Children.\u003c/strong\u003e Skin and nasal swabs were collected from 197 South African children aged 12–36 months, including 87 healthy controls and 110 with AD. Lesional and non-lesional skin sites were sampled from AD participants, with non-lesional swabs taken from the mid-back. Healthy controls were swabbed at the same mid-back location. Nasal swabs were collected from all participants. AD severity was assessed using the oSCORAD scale. Samples were collected from urban CT (n=212) and rural Umtata (n=290), totaling 502 samples (305 skin and 197 nasal) for 16S rRNA (V4–V5) sequencing. The diagram summarizes the study design and hypothesized microbial connectivity between skin and nasal sites in AD.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7419827/v1/5cdfdf94cfe5e64177458bac.jpg"},{"id":93728085,"identity":"a453bdfe-06eb-4de7-afd8-8901f03908cb","added_by":"auto","created_at":"2025-10-17 02:10:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRandom forest classifiers distinguish AD status using skin and nasal microbiome. (A)\u003c/strong\u003e ROC curves from RF models trained on 16S ASV-level relative abundances. Left: Classifier accurately distinguishes skin from nares samples (\u003cem\u003eAUC = 0.95 ± 0.03\u003c/em\u003e). Middle: Classifiers trained on skin microbiome differentiate AD lesional (ADL), AD non-lesional (ADNL), and healthy (H) samples (\u003cem\u003eAUCs=0.82 ± 0.01, 0.76 ± 0.01, and 0.70 ± 0.03\u003c/em\u003e, respectively). Right: Nares microbiome moderately predicts AD status (\u003cem\u003eAUC = 0.68 ± 0.04\u003c/em\u003e). Shaded regions denote ±1 standard deviation from 5-fold cross-validation. \u003cstrong\u003e(B)\u003c/strong\u003e Top ASV-level features contributing to classification across all models. Heatmap shows the rank of each ASV within the top 10 most important features per model (lower ranks indicate greater importance, 1=highest rank). ASVs belonging to \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003ewere consistently among the most predictive taxa within AD classifications.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7419827/v1/99cf2d76e77c53a4ff631b35.jpg"},{"id":93728109,"identity":"ca4758cc-0b84-45b7-96f2-cf8366dee681","added_by":"auto","created_at":"2025-10-17 02:10:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":159294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAD is associated with increased skin–nasal bacterial sharing. \u003c/strong\u003e\u0026nbsp;\u003cstrong\u003e(A) \u003c/strong\u003eVenn diagrams showing the number of unique ASVs detected exclusively in the skin (purple), exclusively in the nares (orange), or both sites (pink), aggregated across all skin and nares samples from AD (top) and H (bottom) individuals. \u003cstrong\u003e(B)\u003c/strong\u003e Scatter plots comparing log-transformed mean relative abundances of ASVs shared between skin and nares in four groups: CT healthy, CT AD, Umtata healthy, and Umtata AD. Each point represents an ASV with shared prevalence in both body sites across disease status and region-stratified groups. Pearson correlation coefficients (\u003cem\u003er\u003c/em\u003e) and corresponding p-values reflect the concordance in abundance between skin and nasal sites.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7419827/v1/c962c3809a96bff86b5430bb.jpg"},{"id":93728128,"identity":"bbdd0b04-431d-40ed-9050-64ff897d10e4","added_by":"auto","created_at":"2025-10-17 02:10:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":128560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAD-associated microbiome changes in rural Umtata and urban Cape Town. \u003c/strong\u003e\u0026nbsp;\u003cstrong\u003e(A) \u003c/strong\u003eAmong healthy children, Faith PD is significantly higher in Umtata (n=22) compared to Cape Town (n=22) (\u003cem\u003ep = 0.0012, U = 104\u003c/em\u003e). \u003cstrong\u003e(B–C) \u003c/strong\u003eRPCA beta diversity among children with oSCORAD \u0026lt; 40 shows stronger microbial separation across groups in Umtata (\u003cem\u003ep=0.001, F=22.03\u003c/em\u003e) than in CT (\u003cem\u003ep=0.004, F=4.77\u003c/em\u003e). Ellipses denote 90% confidence intervals. \u003cstrong\u003e(D–E)\u003c/strong\u003e box plots (left) and scatterplots (right) display Robust Center Log Ratio (RCLR)-transformed relative abundances of differentially abundant ASVs in urban CT \u003cstrong\u003e(D)\u003c/strong\u003e and rural Umtata \u003cstrong\u003e(E)\u003c/strong\u003eskin samples. Panels include \u003cem\u003eStreptococcus\u003c/em\u003eASVs, \u003cem\u003eStaphylococcus\u003c/em\u003e ASV, and other taxa (\u003cem\u003eMicrococcus\u003c/em\u003e, \u003cem\u003eVeillonella_A\u003c/em\u003e). Box plots compare abundance across healthy (H), non-lesional AD (ADNL), and lesional AD (ADL) groups. Box plots show medians and interquartile ranges. FDR-corrected p-values from the Mann-Whitney U-test are shown for pairwise groups. Scatterplots show correlations between ranked relative abundance and ranked oSCORAD scores per sample. Spearman correlation coefficients (ρ) and \u003cem\u003ep\u003c/em\u003e-values are shown in each scatterplot; grey shading denotes non-significance (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7419827/v1/da07cb5c6f9f71f4d122f5b1.jpg"},{"id":98244134,"identity":"7334b695-4187-4623-9dfe-1b47259d1ad9","added_by":"auto","created_at":"2025-12-15 16:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1552805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7419827/v1/d6a85727-d985-4535-ad1a-f9bfd0e8d1c0.pdf"},{"id":93728265,"identity":"67585b98-0209-4d3e-aa97-1c0a010af134","added_by":"auto","created_at":"2025-10-17 02:10:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2490512,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYFIGURES.docx","url":"https://assets-eu.researchsquare.com/files/rs-7419827/v1/d65f7aa97a0f1206e764e7f7.docx"}],"financialInterests":"Competing interest reported. R.L.G. serves as a co-founder, scientific advisor, consultant, and equity holder in MatriSys Biosciences, and also holds a position as a consultant, receiving income, and equity holder in Sente Inc. \nR.K. is a scientific advisory board member and consultant for BiomeSense, Inc., holding equity and receiving income. He is a member of the scientific advisory board and has equity in GenCirq. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma, holds equity, and serves on the scientific advisory board. He is a board member of Microbiome Vault, Inc. He is a N=1 IBS advisory board member and receives income. He is a Senior Visiting Fellow of the HKUST Jockey Club Institute for Advanced Study. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. \nC.L.D. is a co-founder, president, chief strategic officer, and equity holder in Biorentis, Inc. \nC.C. is the founder of Phiome and receives income from Dr. Armpit. He is a scientific advisor to NAOS, MyMicrobiome, and Parallel Health, Inc.","formattedTitle":"Environmental and skin–nasal microbiome variation in South African children with atopic dermatitis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAtopic dermatitis (AD) is a chronic inflammatory skin disease that typically begins in early childhood and affects up to 20% of children worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, with rising rates in many African countries\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The International Study of Asthma and Allergies in Childhood estimated a 23.3% lifetime prevalence of AD among African children aged 6\u0026ndash;7, significantly higher than the global average of 14.2%\u003csup\u003e3\u003c/sup\u003e. Given that African skin is different from that of Western populations, region-specific microbiome studies are essential to better understand how geographic and environmental factors influence AD in children.\u003c/p\u003e\u003cp\u003eThe skin microbiome plays a central role in AD by modulating immune responses and maintaining barrier integrity\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Colonization by \u003cem\u003eStaphylococcus\u003c/em\u003e is known to promote inflammation through cytotoxins and proteases that impair the skin barrier and activate type 2 immunity\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Pediatric AD is distinct in skin physiology and microbial colonization from adult AD, reflecting changes in skin with age\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In children, AD is often characterized by increased \u003cem\u003eStreptococcus\u003c/em\u003e abundance and variable \u003cem\u003eS. aureus\u003c/em\u003e colonization, whereas adult AD is more consistently driven by \u003cem\u003eS. aureus\u003c/em\u003e or other \u003cem\u003eStaphylococcus\u003c/em\u003e species\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond the skin, evidence suggests that the nasal microbiome may also contribute to AD. The nasal cavity contains colonizing microbes commonly found on the skin and may serve as a reservoir for AD-associated taxa\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Individuals with AD are over five times as likely to be nasally colonized by \u003cem\u003eS. aureus\u003c/em\u003e as healthy controls\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Longitudinal data from a large birth cohort have further linked early and frequent nasal colonization with \u003cem\u003eS. aureus\u003c/em\u003e during infancy with increased risk and severity of AD\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It has also been observed that changes in the skin and nasal microbiome of \u003cem\u003eStaphylococcus\u003c/em\u003e species occurred following treatment of AD with Dupliumab\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Tott\u0026eacute; et al. previously identified that both the skin and nasal microbiomes are associated with AD severity in children, particularly driven by \u003cem\u003eStaphylococcus\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. However, the study did not include healthy controls or non-lesional skin samples, and the association was observed in a Dutch cohort\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, we expand upon this knowledge through our unique dataset of skin and nasal microbiomes of 197 South African toddlers (87 healthy, 110 with AD) using 16S rRNA V4\u0026ndash;V5 sequencing of 502 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We employed 16S sequencing for its accessibility and scalability in this setting. Recognizing the limited resolution of 16S at the species and strain levels, we applied random forest models to classify AD status from ASVs. Leveraging this geographically stratified cohort, including both lesional and non-lesional sites from an urban and rural region, we explored how geography, disease severity, and skin/nasal microbial overlap associate with the microbiome in early-life AD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eRandom forest models classify AD status from skin and nasal microbiomes\u003c/h2\u003e\u003cp\u003eUsing 16S relative abundances of ASV bacterial features from both skin and nasal samples, we used RF models to evaluate whether bacterial composition can distinguish AD status\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. As expected, skin and nasal microbiomes were very highly distinguishable, with high classification accuracy (\u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003eleft\u003c/b\u003e). \u003cem\u003eDolosigranulum\u003c/em\u003e was the top-ranked feature distinguishing skin from nares, consistent with its well-documented presence in the nasal cavity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRF models could accurately predict AD status, particularly when comparing lesional AD (ADL) and healthy (H) skin (\u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/em\u003e), and to a lesser extent, AD non-lesional (ADNL) vs H (\u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/em\u003e), and ADL vs ADNL (\u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003emiddle\u003c/b\u003e). Across all classification tasks related to skin disease prediction, H vs ADL, H vs ADNL, and ADL vs ADNL, \u003cem\u003eStreptococcus\u003c/em\u003e consistently ranked as the top feature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eA key finding in this study was that models trained on nasal microbiomes only were also able to predict AD status with moderate accuracy (\u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003eright\u003c/b\u003e), aligning with previous findings that disease-associated bacterial signatures are not limited to the skin but are also detectable in the nasal cavity\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Notably, \u003cem\u003eStaphylococcus\u003c/em\u003e emerged as the top predictive feature, followed by \u003cem\u003eStreptococcus\u003c/em\u003e, with \u003cem\u003eDolosigranulum\u003c/em\u003e ranking third (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTo further distinguish these findings across geographic regions, we performed RF classifications using skin and nasal microbiomes from the UM and CT cohorts separately (\u003cb\u003eFig. S1)\u003c/b\u003e. Notably, skin-based models from UM exhibited stronger stratification of AD status (\u003cem\u003eAUCs\u0026thinsp;=\u0026thinsp;0.74\u0026ndash;0.89\u003c/em\u003e), particularly for ADL vs H and ADNL vs H comparisons, compared to CT (\u003cem\u003eAUCs\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;0.74\u003c/em\u003e), suggesting that the greater microbiome disruption in UM enhances classification accuracy. Nares-based models showed moderate predictive ability in both cohorts (UM \u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/em\u003e; CT \u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/em\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAD is associated with increased skin–nasal bacterial sharing\u003c/h3\u003e\n\u003cp\u003eWe examined bacterial overlap between the skin and nares by analyzing ASV sharing within individuals with paired samples. Children with AD demonstrated markedly higher skin\u0026ndash;nasal ASV overlap compared to healthy controls (Fisher\u0026rsquo;s exact test; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.86\u003c/em\u003e). Among children with AD, a total of 369 ASVs were shared between skin and nasal samples, whereas 249 ASVs were unique to the skin and 50 were unique to the nares (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003eleft\u003c/b\u003e). In contrast, healthy individuals exhibited fewer shared ASVs (n\u0026thinsp;=\u0026thinsp;254), with a greater number of taxa uniquely detected in either the skin (n\u0026thinsp;=\u0026thinsp;323) or nares (n\u0026thinsp;=\u0026thinsp;60) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003eright\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the relative abundance concordance of shared ASVs, we compared log-transformed mean relative abundances of shared ASVs between skin and nares across four groups: CT healthy, CT AD, UM healthy, and UM AD. We observed significant positive correlations between skin and nasal ASV abundances in all groups. However, the strength of these correlations was markedly higher in children with AD than in healthy children, and in the rural than the urban region. In CT, the correlation increased from (Pearson \u003cem\u003er\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;8.7e-05\u003c/em\u003e in healthy children to (\u003cem\u003er\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;=\u0026thinsp;2.0e-26\u003c/em\u003e) in those with AD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). A similar pattern was observed in UM, where correlations increased from (\u003cem\u003er\u0026thinsp;=\u0026thinsp;0.44, p\u0026thinsp;=\u0026thinsp;1.7e-12\u003c/em\u003e) in healthy children to (\u003cem\u003er\u0026thinsp;=\u0026thinsp;0.69, p\u0026thinsp;=\u0026thinsp;4.9e-42\u003c/em\u003e) in children with AD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003eAD-associated microbiome changes in rural Umtata versus urban Cape Town\u003c/h3\u003e\n\u003cp\u003e We compared children with AD from urban CT and rural UM to investigate regional differences in disease presentation and skin microbiome composition. Children in UM more frequently presented with severe AD (oSCORAD\u0026thinsp;\u0026gt;\u0026thinsp;40, n\u0026thinsp;=\u0026thinsp;32) compared to those in CT (oSCORAD\u0026thinsp;\u0026gt;\u0026thinsp;40, n\u0026thinsp;=\u0026thinsp;21). Moderate cases (15\u0026thinsp;\u0026le;\u0026thinsp;oSCORAD\u0026thinsp;\u0026le;\u0026thinsp;40) were observed at similar frequencies in both regions (UM: n\u0026thinsp;=\u0026thinsp;27; CT: n\u0026thinsp;=\u0026thinsp;24) (\u003cb\u003eFig. S2A-B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo separate regional effects from those driven by disease severity, we restricted alpha and beta diversity analyses to samples with oSCORAD\u0026thinsp;\u0026lt;\u0026thinsp;40 (see \u003cb\u003eFig. S3\u003c/b\u003e for the complete dataset). Skin microbiome diversity among healthy children was significantly higher in Umtata (n\u0026thinsp;=\u0026thinsp;22) compared to Cape Town (n\u0026thinsp;=\u0026thinsp;22) (Mann-Whitney U-test; \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0012, U\u0026thinsp;=\u0026thinsp;104\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Principal coordinates analysis (PCoA) of robust Aitchison (RPCA) further revealed significant differences in beta diversity between H, ADNL, and ADL samples in both regions. In UM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), clear separation was observed among the three skin types (\u003cem\u003ePERMANOVA p\u0026thinsp;=\u0026thinsp;0.001, F\u0026thinsp;=\u0026thinsp;25.72\u003c/em\u003e), suggesting greater microbiome disruption. In CT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), the separation was also significant but less pronounced (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002, F\u0026thinsp;=\u0026thinsp;6.20\u003c/em\u003e), indicating milder dysbiosis. In both settings, ADNL samples clustered between H and ADL samples, consistent with a potential transitional microbial state.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA \u003cem\u003eStreptococcus\u003c/em\u003e ASV-1 exhibited significantly higher RCLR-transformed abundances in both ADNL and ADL skin relative to healthy skin in both CT and UM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;E). A second \u003cem\u003eStreptococcus\u003c/em\u003e ASV (ASV-2) was significantly higher in UM only. Neither ASV significantly correlated with oSCORAD scores, suggesting that \u003cem\u003eStreptococcus\u003c/em\u003e enrichment may be a hallmark of AD skin independent of disease severity. A \u003cem\u003eStaphylococcus\u003c/em\u003e ASV (ASV-1) was differentially abundant across severity groups, whereas no other taxa correlated significantly with severity scores. In UM, it was also considerably elevated in lesional skin compared to healthy controls (\u003cem\u003eFDR-adjusted p\u0026thinsp;=\u0026thinsp;2.2e-03\u003c/em\u003e), whereas in CT, we did not see this trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;E). Broader taxonomic shifts were more pronounced in UM than in CT, underscoring region-specific differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;E). In UM, \u003cem\u003eMicrococcus\u003c/em\u003e ASV-1 was significantly reduced in both ADL and ADNL skin, while \u003cem\u003eVeillonella_A\u003c/em\u003e ASV-1 was enriched in both. Neither ASV correlated with oSCORAD, suggesting these shifts reflect broader dysbiosis rather than AD severity. These findings support greater microbial disruption in UM AD skin, possibly driven by environmental or geographic factors.\u003c/p\u003e\u003cp\u003eTo explore region-specific differences in nasal microbiota and their association with disease severity, we also assessed ASVs that were differentially abundant in the nares of children with and without AD in Cape Town and Umtata (\u003cb\u003eFig. S4\u003c/b\u003e). In Cape Town, \u003cem\u003eStaphylococcus\u003c/em\u003e ASV-1 abundance in the nares was positively correlated with AD severity (\u003cem\u003eSpearman r\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.9e\u0026thinsp;\u0026minus;\u0026thinsp;02). Additionally, \u003cem\u003eMicrococcus\u003c/em\u003e ASV-1 in the nares was significantly increased in AD samples compared to healthy controls (\u003cem\u003eMann\u0026ndash;Whitney p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.0e\u0026thinsp;\u0026minus;\u0026thinsp;02). In Umtata, \u003cem\u003eStreptococcus\u003c/em\u003e ASV-1 abundance was significantly reduced in AD samples relative to healthy children (\u003cem\u003eMann\u0026ndash;Whitney p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.2e\u0026thinsp;\u0026minus;\u0026thinsp;03).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMost microbiome research focused on AD has been conducted in a limited number of high-income Western countries\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This geographical skew raises questions about the broader applicability of current microbiome-derived insights in the global population, especially regarding health disparities and differing environmental exposures in the Global South\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This study helps address this gap by reporting observational findings of skin and nasal microbiomes in South African children stratified by rural and urban location. We found that environmental context was associated with AD severity and microbial dysbiosis.\u003c/p\u003e\u003cp\u003eMachine learning has previously been used to identify disease associations from microbial signatures\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, including in atopic dermatitis in children\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. One of the most interesting aspects of this study was our RF model trained solely on nasal samples, which predicted AD status with moderate accuracy (\u003cem\u003eAUC\u0026thinsp;=\u0026thinsp;0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/em\u003e), and models using skin microbiomes could distinguish lesional and non-lesional skin from healthy skin. These findings suggest that skin and nasal microbiome profiles, even from short-read 16S data, can capture disease-related patterns in pediatric AD. Notably, classification accuracy varied by environment: predictions for Umtata were more accurate than for Cape Town, likely reflecting the greater shifts in microbial diversity observed in the rural cohort. While these AUCs are likely insufficient for diagnostic screening, the results underscore the potential of microbiome-based classification approaches, particularly if combined with higher-resolution multi-omics data in future studies, which may be used towards pre-disease prediction.\u003c/p\u003e\u003cp\u003eA notable observation was the significant similarity between skin and nasal microbiomes in AD patients, supporting the hypothesis that bacterial exchange occurs between these sites. This aligns with the idea that the nasal cavity may act as a reservoir for strains capable of exacerbating skin inflammation, such as \u003cem\u003eS. aureus\u003c/em\u003e. In our cohort, \u003cem\u003eStreptococcus\u003c/em\u003e ASVs were consistently enriched in AD skin, yet showed no correlation with oSCORAD scores. Whether \u003cem\u003eStreptococcus\u003c/em\u003e is more associated with disease presence than progression, and whether it can facilitate colonization by \u003cem\u003eStaphylococcus\u003c/em\u003e species such as \u003cem\u003eS. aureus\u003c/em\u003e, warrants further investigation. Assessing the nasal microbiome could therefore be important for evaluating the risk of cutaneous colonization by potentially pathogenic species. Additional research is needed to clarify the mechanisms and directionality of this exchange and whether the nasal microbiome plays a causal role in establishing cutaneous dysbiosis.\u003c/p\u003e\u003cp\u003eWe acknowledge several methodological limitations. 16S rRNA sequencing restricts taxonomic resolution to the genus level, limiting species- and strain-level insights. Our study used the 16S V4\u0026ndash;V5 region, which underrepresents \u003cem\u003eCutibacterium\u003c/em\u003e, an abundant skin bacterial genus that is more accurately captured by V1\u0026ndash;V3 primers\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Lesional body sites were not recorded for each sample, though sampling was conducted from typical pediatric AD predilection sites (e.g., cheeks, forearms) using standardized protocols across both locations. Although we cannot exclude the possibility that differences in sampling location influenced regional microbiome variation, all skin and nasal swabs were collected, handled, and stored under identical procedures by trained personnel to minimize technical bias.\u003c/p\u003e\u003cp\u003eThe skin microbiome is influenced by urbanization\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, with rural and less urbanized environments typically associated with greater bacterial diversity\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which may be protective in early life. Additionally, African skin is compositionally distinct from that of Western populations, underscoring the need for region-specific studies to better understand their roles in health and disease\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. While our study design, sampling one urban and one rural site, limits broader generalizability, our findings expand available data on pediatric AD microbiomes in South Africa and provide evidence that environmental context shapes microbial dysbiosis.\u003c/p\u003e\u003cp\u003eOur findings also need to be considered within the context of environmental and immunologic differences. Contrary to the conventional \u0026ldquo;Hygiene Hypothesis,\u0026rdquo; which suggests rural environments protect against allergic diseases by promoting greater early-life microbial exposures, children in rural Umtata exhibited greater microbial dysbiosis than those in urban Cape Town, even when controlling for severity. Previous published work in a subset of this same cohort\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e found that rural children, regardless of AD status, had heightened innate immune activation and AD-associated gene expression, correlated with exposures such as farm animal contact during pregnancy. While AD was associated with downregulated lymphocyte and innate signaling pathways, the environment exerted a substantial influence on immune transcriptional profiles\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Rural children showed marked upregulation of chemokines, cytokines, GPCRs, and regulatory pathways, including IL-10, a key anti-inflammatory cytokine known to suppress antigen-presenting cells and lymphocyte effector functions. Together, these environmental, immunologic, and healthcare-access differences may help explain the greater microbiome alterations observed in rural children with AD.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eClinical Study Design and Sample Collection\u003c/p\u003e\u003cp\u003eWe collected samples from 197 South African toddlers (ages 12\u0026ndash;36 months), including 110 children clinically diagnosed with AD and 87 healthy controls between February 2015 and May 2016. AD diagnosis followed the UK Working Party\u0026rsquo;s criteria\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e by trained clinicians. We aimed to enroll and collect as many samples as possible from children fitting the study criteria. Severity was assessed using the objective SCORAD (oSCORAD) index, which ranges from 0 to 83 and is based on the extent of affected body surface area and the intensity of six clinical signs (erythema, edema/papulation, oozing/crusts, excoriation, lichenification, and dryness), each scored from 0 to 3. The final score was computed using the formula: oSCORAD\u0026thinsp;=\u0026thinsp;A/5\u0026thinsp;+\u0026thinsp;7B/2\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eChildren with chronic illness, immunomodulatory treatments, or recent antibiotic use were excluded. Participants were recruited from two socio-environmentally distinct locations: Cape Town (CT; ~5\u0026nbsp;million population), a highly urbanized setting, and Umtata (UM; ~200,000 population), a rural region. Samples from urban AD cases were collected at Red Cross Children\u0026rsquo;s Hospital in CT, and healthy controls from local preschools. Rural samples were collected at Nelson Mandela Academic Hospital in UM, primarily from the Mqanduli district. All participants were of Black (AmaXhosa ethno-linguistic) descent. Written informed consent was obtained from parents or legal guardians.\u003c/p\u003e\u003cp\u003eSkin and nasal swabs were collected using standardized protocols\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and performed consistently across both sites. For AD children, lesional skin swabs were taken from commonly affected sites (e.g., cheeks, forearms), and non-lesional swabs from the mid-back. Specific lesional body site locations were not recorded. Healthy controls were swabbed on the mid-back for consistency. Nasal swabs were taken from the anterior nares. All samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C in skim milk-tryptone-glycerol-glucose buffer until processing.\u003c/p\u003e\u003cp\u003eDNA Extraction, 16S rRNA Sequencing, and Sequence Data Processing\u003c/p\u003e\u003cp\u003eDNA was extracted from skin and nasal swabs using the DSP Virus/Pathogen Mini Kit\u0026reg; (Qiagen, Germany) with a 70 \u0026micro;L elution volume at the University of Cape Town. The V4\u0026ndash;V5 region of the 16S rRNA gene was amplified using previously described primers 515F (TATGGTAATTGTGTGYCAGCMGCCGCGGTAA) and 926R (CGGCATACGAGATAGTCAGCCAGGGCCGYCAATTYMTTTRAGTTT)\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. While this primer pair underrepresents \u003cem\u003eCutibacterium\u003c/em\u003e relative to V1\u0026ndash;V3 primers\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, it provides broader coverage of \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e sequencing depth.\u003c/p\u003e\u003cp\u003eAmplicons were barcoded, pooled in batches of 300, and sequenced on an Illumina MiSeq v3 (300-cycle) run, generating\u0026thinsp;~\u0026thinsp;13 Gbp per run. Raw data were processed via the Qiita platform\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e using a standardized workflow. Sequences were demultiplexed, PHRED\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e quality-filtered, and denoised using Deblur\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e in QIIME 2 (v2024.2.0). ASVs were assigned taxonomy using the Greengenes 2 database (v2022.10)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and feature tables were generated at both ASV and genus levels. To reduce noise and sparsity, a 5% sample depth threshold was applied to filtered tables, retaining over 95% of samples. Samples below this threshold were excluded from rarefied alpha diversity analyses but retained in the complete dataset for descriptive and statistical comparisons.\u003c/p\u003e\u003cp\u003eRandom Forest Classification\u003c/p\u003e\u003cp\u003eRF classifiers were implemented using the scikit-learn Python package (v1.4.2) to evaluate the predictive power of 16S rRNA ASV-level features (n\u0026thinsp;=\u0026thinsp;2283) and their observed abundances across multiple classification tasks. Models were trained to distinguish (1) skin and nasal samples, (2) ADL, ADNL, and H skin samples in pairwise comparisons (ADL vs H, ADNL vs H, ADL vs ADNL), and (3) AD vs H status in nasal samples.\u003c/p\u003e\u003cp\u003e To prevent overfitting and account for repeated measures, we ensured all samples from a participant were assigned exclusively to either the training (80%) or testing (20%) set in each fold (GroupKFold n_splits\u0026thinsp;=\u0026thinsp;5). RF models with 1,000 estimators were trained for each classification task, and performance was evaluated using the area under the ROC curve. Feature importance was measured by the mean decrease in Gini impurity across folds, and the top 10 ASVs were retained to identify key bacterial drivers of predictive performance.\u003c/p\u003e\u003cp\u003eTo explore geographic variation in classification performance, the above analyses were repeated independently for CT and UM cohorts using the same classification framework (\u003cb\u003eFig. S4\u003c/b\u003e). ROC curves shaded regions denoting\u0026thinsp;\u0026plusmn;\u0026thinsp;1 SD around the mean true positive rate. This comprehensive framework enabled us to assess cross-site consistency and regional specificity in microbiome-based disease status and anatomical site classification.\u003c/p\u003e\u003cp\u003eMicrobiome Diversity Analyses\u003c/p\u003e\u003cp\u003eAlpha diversity was assessed using the Faith PD, a phylogenetically informed metric \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. ASV count tables were processed using the BIOM (v.2.1.15) package via Python (v.3.10.14). This analysis included only AD samples with oSCORAD 15 to 40 and healthy controls to reduce potential confounding from extreme disease severity. Faith PD values were computed for each sample using the skbio.diversity.alpha_diversity function. Statistical significance was determined using the non-parametric Mann\u0026ndash;Whitney U-test for all pairwise comparisons. All p-values were FDR-adjusted using BH. The same analysis with all samples (including oSCORAD\u0026thinsp;\u0026gt;\u0026thinsp;40) is in the supplementary.\u003c/p\u003e\u003cp\u003eBeta diversity was analyzed using RPCA \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, a compositionality-aware ordination technique within gemelli (v.0.0.12). Before RPCA, samples were filtered to exclude nasal swabs, and the feature table was converted back into BIOM format to ensure compatibility with the gemelli.rpca function. Ordination was performed on this filtered table, and sample coordinates from the first two RPCA axes were used to visualize group separations. Statistical differences in beta diversity were tested using PERMANOVA on the RPCA-based distance matrix, with significance evaluated between pairwise combinations of skin sample groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe are grateful to the clinicians at Red Cross Children\u0026rsquo;s Hospital, Cape Town, and at the Nelson Mandela Academic Hospital, Umtata, for collecting the samples in this project. We thank our collaborators at UC San Diego and the J. Craig Venter Institute for the microbiome analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources:\u003c/strong\u003e This research was funded by the National Research Foundation of South Africa (UID: 150539).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eR.L.G.\u003c/em\u003e serves as a co-founder, scientific advisor, consultant, and equity holder in MatriSys Biosciences, and also holds a position as a consultant, receiving income, and equity holder in Sente Inc.\u0026nbsp;\u003cem\u003eR.K\u003c/em\u003e.\u0026nbsp;is\u0026nbsp;a\u0026nbsp;scientific\u0026nbsp;advisory\u0026nbsp;board\u0026nbsp;member\u0026nbsp;and\u0026nbsp;consultant\u0026nbsp;for\u0026nbsp;BiomeSense,\u0026nbsp;Inc.,\u0026nbsp;holding\u0026nbsp;equity\u0026nbsp;and\u0026nbsp;receiving income. He is a member of the scientific advisory board and has equity in GenCirq. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma, holds equity, and serves on the scientific advisory board. He is a board member of Microbiome Vault, Inc. He is a N=1 IBS advisory board member and receives income. He is a Senior Visiting Fellow of the HKUST Jockey Club Institute for Advanced Study. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, by its conflict-of-interest policies.\u0026nbsp;\u003cem\u003eC.C.\u003c/em\u003e is the founder of Phiome and receives income from Dr. Armpit. He is a scientific advisor to NAOS, MyMicrobiome, and Parallel Health, Inc. \u003cem\u003eC.L.D.\u003c/em\u003e is a co-founder, president, chief strategic officer, and equity holder in Biorentis, Inc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eSequencing 16S data is deposited in the NCBI Sequence Read Archive (SRA) under Bioproject ID PRJNA1269634. The data processing can be found on QIITA via study ID 15564. Code used in this manuscript, along with the figures, can be found at our GitHub repository: https://github.com/yangchen2/16S_AD_South-Africa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement:\u003c/strong\u003e The Human Research and Ethics Committee of the Faculty of Health Science, University of Cape Town (HREC/REF: 451/2014) approved the parent study, and additional ethical approval for the bacteriome study, Human Research and Ethics Committee (HREC/REC: 668/2020), was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent:\u003c/strong\u003e Written informed consent was obtained from all parents or legal guardians of children included in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNutten, S. 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Skin and nasal microbiomes have been linked to AD severity, but not yet in an African cohort. Here, we aimed to explore how urban and rural stratification, disease severity, and inter-site bacterial overlap shape the skin and nasal microbiomes of children with AD in South Africa (ZA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eChildren were recruited from urban Cape Town (CT) and rural Umtata (UM), ZA. We profiled the skin and nasal microbiomes of 197 children (87 healthy, 110 with AD; ages 12\u0026ndash;36 months), totaling 502 samples, including both lesional and non-lesional skin sites in children with AD, in a cross-sectional study design. We used 16S rRNA V4\u0026ndash;V5 sequencing for its accessibility and scalability to large sample sets. To address the limited species- and strain-level resolution of 16S data, we applied random forest (RF) machine-learning models to classify AD status with amplicon sequence variants (ASVs). We analyzed microbiome composition and diversity stratified by environment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe found that RF models could predict AD status using both skin and nasal microbiomes (\u003cem\u003eAUCs: skin\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;0.82; nasal\u0026thinsp;=\u0026thinsp;0.68\u003c/em\u003e), strongly driven by both \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e. The correlations between skin\u0026ndash;nasal microbiome were significantly stronger in children with AD compared to healthy controls, with overall higher correlations observed in rural UM (healthy \u003cem\u003er\u0026thinsp;=\u0026thinsp;0.44\u003c/em\u003e to AD \u003cem\u003er\u0026thinsp;=\u0026thinsp;0.69\u003c/em\u003e) compared to urban CT (healthy \u003cem\u003er\u0026thinsp;=\u0026thinsp;0.34\u003c/em\u003e to AD \u003cem\u003er\u0026thinsp;=\u0026thinsp;0.65\u003c/em\u003e). The skin microbiome diversity was higher in children from rural UM with healthy skin than those from urban CT (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0012\u003c/em\u003e). However, children with AD in both groups showed significant alterations in their microbiome, with those in rural UM exhibiting greater beta diversity (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/em\u003e) than their urban CT counterparts (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn children with AD in South Africa, the environmental context is associated with microbial dysbiosis, and skin\u0026ndash;nasal microbiomes reflect shared reservoirs. These findings highlight the value of geographically diverse studies with skin and mucocutaneous sampling in understanding pediatric AD.\u003c/p\u003e","manuscriptTitle":"Environmental and skin–nasal microbiome variation in South African children with atopic dermatitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:09:18","doi":"10.21203/rs.3.rs-7419827/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T04:28:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T14:18:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T13:35:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T22:15:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T11:54:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280266109144373087789323039975468962570","date":"2025-10-08T06:58:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259306207367649211291063265396190214302","date":"2025-10-06T13:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179137191433955466242051277681605095183","date":"2025-10-06T08:41:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157871723428166551726006659613461565661","date":"2025-10-03T09:01:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-03T08:52:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-28T11:55:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T11:55:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-08-20T17:38:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04b6440f-3533-4364-99ce-ee0ee638af40","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:06:18+00:00","versionOfRecord":{"articleIdentity":"rs-7419827","link":"https://doi.org/10.1186/s12866-025-04589-x","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-12-12 15:59:41","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-10-17 02:09:18","video":"","vorDoi":"10.1186/s12866-025-04589-x","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04589-x","workflowStages":[]},"version":"v1","identity":"rs-7419827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7419827","identity":"rs-7419827","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}