Gut microbiome composition and strain-sharing in multiplex autism spectrum disorder families

Domain: chemistry
Published: 2026-02-26
Editorial outcome: major_revision

Abstract
Autism spectrum disorder (ASD) is associated with alteration of gut microbiome, but the influence of familial structure on it remains poorly understood. We investigate gut microbiota across 429 children from multiplex families with multiple affected children, simplex families with one affected child, and single-child ASD families, alongside typically developing controls. We found that children from multiplex families exhibit the most distinct microbiome compositions. Cohabiting siblings in ASD families display higher microbiome similarity than those in healthy families, with a clear gradient in strain-sharing rates that is highest in multiplex, intermediate in simplex, and lowest in healthy siblings. This increased sharing involves specific taxa with reported opportunistic pathogenic potential, such as Eubacterium rectale, alongside reduced sharing of the commensal bacterium Bacteroides xylanisolvens. This suggests that their gut microbiome configurations, which are potentially influenced by shared environmental and host factors, are associated with increased persistence or detectability of specific bacterial strains. Our results underscore the significant contribution of family type to microbial heterogeneity in ASD and provide a hypothesis-generating context for future studies to explore the role of the shared microbial environment in a familial context.

Introduction
Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions defined as impaired social communication and interactions and restricted repetitive behaviour1-4. Although the etiology of ASD remains unclear, it appears to involve a complicated interaction of genetic and environmental factors5,6. In the past years, different family types of ASD, including multiplex families with multiple affected children and simplex families with only one affected child have captured the interest of researchers 7-12. Previous studies indicate that children with ASD have altered gut microbiome composition and function compared with typically developing (TD) children13,14, and the gut microbiome has been shown to play a crucial role in the gut-brain axis which may contribute to the neurobehavioral and intestinal dysfunction in ASD15,16. Various factors, such as lifestyle and diet, have been widely recognized to influence the human gut microbiome17,18. However, the majority of microorganisms are acquired from other individuals19,20, and understanding the acquisition and development of the gut microbiome is a crucial component for the clinical translation and intervention of microbiome research. So far, studies focused on the transmission of the microbiome highlight the contribution of intra-household transmission by the assessment of the bacteria strain-sharing across individuals, and cohabitation appears to be more strongly linked to the transmission and co-acquisition than age and genetics did20-22. These findings sparked our curiosity about whether the gut microbiome in children with ASD would be affected by their cohabitation siblings among different family types.

In this study, we performed metagenomic analysis of fecal samples of 429 children, characterized the gut microbiome composition and quantified the patterns of microbiome strain sharing in different ASD families.

Results
Study characteristics
A total of 429 well-characterized children were recruited in this study. Within this cohort, 312 children represented three types of multi-child families: “multiplex” families (two or more children diagnosed with ASD), “simplex” families (one affected child with at least one unaffected male sibling), and “TD” families (two or more typically developing children and no children with ASD). Another 117 children from only-child ASD families were included for broader comparison. Amongst the 429 children (mean age: 7.05) studied, there were 37 multiplex ASD families (ASD children, n = 75, males 74.6%, namely ASD-M), 50 simplex ASD families (ASD children, n = 50; males 94%, namely ASD-S; healthy siblings, n = 54, Male: 96.3%), 66 TD families (typically developing children, n = 133, males 59.6%; namely TD), and 117 only-child families (ASD, n = 117, males 87.1%, namely ASD-O). Age, Gender, and presence of attention-deficit/hyperactivity disorder (ADHD) are significantly different among these groups, thus, these factors were adjusted in the following association analyses. We performed metagenomic sequencing on 429 faecal samples. The bacterial dataset was filtered by 5% prevalence and contained 509 taxa at the specie level. For the strain-level profiling, we have detected 368 strains for the strain-sharing assessment.

Alterations of taxonomic profiling in ASD-M
Bray-Curtis dissimilarity-based principal coordinate analysis (PCoA) revealed that the gut microbiome difference between ASD-M and TD children is larger than that between ASD-S and TD children (PERMANOVA test: p = 0.001 and p = 0.242, Figure 1a). When compared to TD children, bacteria alpha diversity and richness were significantly increased in ASD-M (p = 0.017 and p = 0.022) but not in ASD-S (p = 0.97 and p = 0.64, Figure 1b). To further confirm this finding, we further tested the gut microbiome difference between ASD children from ASD-O and TD children, and we found it lies between the ASD-M and ASD-S. This finding indicates the microbiome in ASD-S was closer to TD but ASD-M was far from TD when compared with ASD-O.

We further analyzed the above findings at the species level. To account for potential confounding factors, all species-level analyses were conducted using MaAslin2 with adjustment for age, gender, and ADHD comorbidity as fixed effects in the statistical models. 18 bacteria species were identified to be associated with ASD children from ASD-M (p < 0.05, FDR < 0.2, MaAslin2, Figure 1c, Supplementary Data 4), whereas only 3 species was significantly associated with ASD children from ASD-S (p < 0.05, FDR < 0.2, MaAslin2, Figure 1c, Supplementary Data 9), when compared to TD children, respectively. In detail, a total of 18 bacteria species including pathogens Coprobacillus cateniformis, Alistipes finegoldii, Murimonas intestine, Romboutsia timonensis, and Eisenbergiella tayi were enriched in ASD-M. In contrast, 3 beneficial species Faecalibacterium prausnitzii, Bacteroides xylanisolvens, and Agathobaculum butyriciproducens were enriched in the TD group. For better comparison, we also analyzed ASD children from ASD-O and TD children, but we did not observe similar results like ASD-M versus TD, and no significant differences in microbial taxa were found (p < 0.05, FDR < 0.2, MaAslin2, Figure 1c). Furthermore, although the overall dietary patterns showed no significant differences between groups based on PCA analysis of daily diet intake (Supplementary Figure 1), we have conducted sensitivity analyses adjusting for dietary factors. First, we performed Kruskal-Wallis tests for overall differences of dietary factors (Supplementary Data 2), followed by pairwise Wilcoxon tests with FDR correction for each significantly different factor (Supplementary Data 3). After incorporating these significant dietary factors into MaAaslin2 models, the main findings remained robust. Specifically, 10 and 3 differentially abundant bacterial species were associated with ASD-M and ASD-S, respectively (Supplementary Figure 2b and 2h, Supplementary Data 5 and 10), and 2 associated species emerged in the ASD-O group (Supplementary Figure 2n, Supplementary Data 13). We also conducted a stratified analysis regarding the comorbidity of ADHD by categorizing ASD children into four subgroups: ASD-M (ADHD+), ASD-M (ADHD-), ASD-S (ADHD+), and ASD-S (ADHD-) (n = 37, 38, 11, and 39, respectively). The key finding that ASD-M children had the most distinct gut microbiome from TD children remained significant even when including only participants without an ADHD diagnosis. Notably, ASD-M (ADHD-) children consistently exhibited a higher number of differential bacterial species than ASD-S (ADHD-) children, both before (Supplementary Figure 2c and 2i, Supplementary Data 6 and 11) and after adjusting for dietary factors (Supplementary Figure 2d and 2j, Supplementary Data 17 and 12). To further evaluate the specific effect of ADHD comorbidity within the ASD families, we compared ASD-M (ADHD+) versus ASD-M (ADHD-) and ASD-S (ADHD+) versus ASD-S (ADHD-), adjusting for age, gender, and dietary factors (Supplementary Figure 2f and 2i). Notably, no significant bacterial species were identified in either comparison after multiple testing correction, suggesting that ADHD status alone does not yield significant microbiome differences within the same familial ASD subtype when controlling for other confounding factors.

These results suggest that the gut microbiome of children from ASD-M is more distinct from that of TD children than the microbiome of children from ASD-S. This indicates a more pronounced gut dysbiosis in the ASD-M than ASD-S. One possible explanation for these observations is that cohabitation with an affected sibling may exacerbate gut microbial alterations in ASD.

The dissimilarity between ASD children and their cohabitation siblings within families
In this study, all the sibling pairs shared a household. Similar to previous reports about the widely shared microbiome among cohabitants23,24, there were higher Bray-Curtis similarities at species levels in cohabiting sibling pairs from three multi-child families, when compared to unrelated random non-cohabiting pairs and random ASD pairs from ASD-O group (Kruskal-Wallis, p = 2.6× 10−15, χ²(4) = 74.449, Figure 2 and Supplementary Data 14). Moreover, among the cohabiting sibling pairs from the three types of families, simplex ASD sibling pairs and multiplex ASD sibling pairs showed higher Bray-Curtis similarities in bacteria species levels (p = 0.022 and p = 0.13, two-sided Wilcoxon rank-sum test, Figure 2 and Supplementary Data 14) compared with TD sibling pairs. These findings further illustrated that ASD families were likely to have a more similar gut microbiome, indicating that ASD children may be more easily affected by their siblings than TD children.

Microorganism strain-sharing between cohabitating siblings
Previous research has examined gut microbiome similarities among household members and strain transmission in cohabiting populations and more distant contacts22. Our cross-sectional study investigates strain sharing among sibling pairs in different family types. At the bacterial strain level, we observed significantly elevated strain-sharing rates in sibling pairs from both multiplex and simplex ASD families relative to TD families, with sibling pairs from multiplex families sharing significantly more strains than those from simplex families (Kruskal-Wallis test, p = 0.00053, χ²(2) = 15.098; Figure 3, Supplementary Data 15 and 16). Consistent with previous observations that microbiome transmission occurs beyond close contacts and reflects population structure, we observed distinct strain-sharing patterns across family types. This was evidenced by both the density distributions of sharing rates (Supplementary Figure 4a, Supplementary Data 17) and a PERMANOVA performed on the unsupervised strain-sharing network (Euclidean distance matrix; R² = 10.6%, F = 44.08, p = 0.001; based on 999 permutations; Supplementary Figure 4b and Methods). Network topology analysis revealed that the extensive distribution of Multiplex families in the visualization corresponds to a highly interconnected structure rather than a loose association, followed by simplex ASD families, and then TD families. Sibling pairs from multiplex ASD families exhibited significantly higher clustering coefficient compared to other families (Kruskal-Wallis test, χ²(2) = 159.4, p < 2× 10−16; Supplementary Figure 4c), suggesting the formation of tight-knit strain-sharing communities. These topological metrics confirm that strain transmission in multiplex ASD families is more robust and pervasive.

To evaluate gut microbiome sharing between siblings across different family types, we employed the chi-squared test with Yates' continuity correction or Fisher’s exact test. Notably, several bacterial strains showed significantly higher sharing rates in multiplex ASD families compared to TD controls, including taxa with reported opportunistic or context-dependent pathogenic potential, such as Eubacterium rectale (SGB4933), Dorea formicigenerans (SGB4575), and Acidaminococcus intestini (SGB5736) (p < 0.05, Figure 4; Supplementary Data 20). In contrast, the sharing of Bacteroides xylanisolvens (SGB1867) was markedly lower in multiplex ASD families than in the other two groups. Interestingly, we also observed an increased prevalence of sharing for several common gut commensals in ASD families. For instance, Bifidobacterium pseudocatenulatum (SGB17237) and Faecalibacterium prausnitzii (SGB15342) exhibited their highest sharing percentages in multiplex ASD families, while Sellimonas intestinalis (SGB4617) showed a significantly increased sharing rate specifically in simplex ASD families (p < 0.05, Figure 4; Supplementary Data 20). These differential sharing patterns underscore a strong association between familial context and gut microbiome configuration in ASD. Rather than direct transmission, these findings indicate that children from ASD families exhibit distinct strain-sharing patterns compared with TD families. These patterns, which may be influenced by shared environmental and host factors, suggest that the gut microbiome configurations in ASD families are associated with the increased persistence or detectability of specific bacterial strains. This identifies the shared familial microbiome as a hypothesis-generating context that may be relevant for future interventional studies, pending longitudinal and mechanistic validation.

Gut microbiome is associated with ASD clinical scores
To extend the findings and explore the clinical relevance. We processed the association analysis of the markers and clinical parameters. We found 7 multiplex ASD families enriched markers based on the MaAslin2 were significantly positively correlated with several ASD-related clinical parameters, including Social Responsiveness Scale (SRS), Social Experience Questionnaire (SEQ), Child Behaviour Checklist (CBCL), Anxiety scale for children-ASD (ASC-ASD) (Spearman’s correlation, p<0.05, Supplementary Figure 5), including Coprobacillus cateniformis, Murimonas intestini, Eisenbergiella tayi, Alistipes finegoldii, Lachnospira pectinoschiza, Sanguibacter SGB15121, and Eubacterium siraeum. In contrast, the TD enriched species Faecalibacterium prausnitzii, GGB51510_SGB71883, Agathobaculum butyriciproducens, and Bacteroides xylanisolvens were negatively correlated with the ASD clinical parameters (Spearman’s correlation, p<0.05, Supplementary Figure 5).

Methods
Ethics statement
The study was approved by the Joint Chinese University of Hong Kong of New Territories East Cluster Clinical Research Ethics Committee (CUHK-NTEC CREC). Written informed consent was obtained from the parents or legal guardians of all participating children prior to their inclusion in the study.

Study Population
A cohort consisting of different family types was recruited from the Child and Adolescent Psychiatric Clinic of the New Territories East Cluster (NTEC) of the Hospital Authority and the community. In addition to a self-reported diagnosis of ASD, parents were required to provide a formal medical certificate from a qualified psychiatrist, pediatrician, or psychologist confirming the ASD diagnosis before their child was included in the case group for recruitment. Exclusion criteria included the use of probiotics, antidepressants, anti-epileptics, or antibiotics within the one month prior to enrollment. A total of 429 children were recruited in this study with phenotypic data (Supplementary Data 1) and diet. For the typically developing (TD) children, individuals with a positive screening on the Autism Spectrum Quotient-10 (AQ-10) will be excluded from the recruitment. Among the 429 children, 312 of them were from three types of multi-child families and the other 117 children were from only-child ASD families. Among the multi-child families, the “multiplex” ASD families were defined as families with two or more children with a confirmed diagnosis of ASD, “simplex” families were defined as families with only one child diagnosed with ASD and at least one unaffected male sibling, and “TD” families were defined as families without children with ASD.

Stool sample collection
Feacal samples were collected at home by all subjects using stool specimen collection tubes prepared by investigators with preservative media (cat. 63700, Norgen Biotek Corp, Ontario Canada). The Norgen preservative can effectively preserve microbial DNA and RNA during transportation at room temperature, ensuring sample integrity and eliminating variability. Stool samples were delivered to the hospital within 24 hours after collection and stored at -80°C refrigerators until further processing. In our previous study, we have demonstrated that the composition of gut microbiota data obtained from fecal samples collected using this preservative medium is comparable to data derived from fresh samples that were promptly stored at -80°C46. Also, the storage duration exhibited comparability between cases and controls across all individuals involved in the study.

Stool DNA extraction and sequencing
All feacal samples from this study were handled in a random sequence according to the same protocol to avoid potential batch effects. In brief, following the removal of the preservative media, microbial DNA isolation was performed using the Qiagen (Hilden, Germany) DNeasy PowerSoil Pro Kit, under the instructions provided by the manufacturer. After the quality control procedures by Qubit 2.0, agarose gel electrophoresis, and Agilent 2100, the extracted DNA was utilized for constructing DNA libraries. Library construction involved end repairing, the addition of A to tails, purification, and PCR amplification, using the Illumina® DNA Prep with (M) Tagmentation kit (Illumina, San Diego, CA). Libraries were subsequently sequenced on our in-house Illumina NovaSeq (150 base pairs paired-end). To ensure the reliability of the results, ZymoBIOMICS Microbial Community Standard (Cat: D6300, ZYMO Research, USA) and ZymoBIOMICS Microbial Community DNA Standard (Cat: D6306) were used as positive controls throughout the DNA extraction, library construction, sequencing, and quality assessment processes. In cases where abnormal signals were detected, resequencing was performed to ensure data accuracy.

Sequencing data preprocessing
The raw sequence data were quality filterted using Trimmomatic (v39) to eliminate adaptor sequences, low-quality reads (quality score <20), and reads shorter than 50 base pairs47. The remaining reads were mapped to the mammalian genome (hg38, felCat8, canFam3, mm10, rn6, susScr3, galGal4, and bosTau8; UCSC Genome Browser), bacterial plasmids (accessed from the National Center for Biotechnology Information (NCBI) RefSeq database in January 2023), complete plastomes (accessed from the NCBI RefSeq database in January 2023), and UniVec sequences (accessed from the NCBI RefSeq database in January 2023) using bowtie2 v.2.4.248. Reads that potentially originated from the host or laboratory-associated sequences were removed as contaminant reads using KneadData v.0.6 . To expedite data processing, GNU parallel was employed for parallel analysis tasks.

Shotgun metagenomic sequencing was performed on the Illumina platform for all 429 stool samples with an average depth of 6 Gb. While the yield varied across samples, we generated a total of approximately 3.6 Tb of raw data, with a targeted coverage of approximately 2X (assuming a baseline of 3 Gb for 1X coverage of the human gut metagenome). Although sequencing yield varied across samples, our downstream analytical pipeline was designed to ensure high-resolution profiling across this range.

Microbial taxonomic profiles
Microbial composition profiles were analyzed from the quality-filtered forward reads using MetaPhlAn449 (version 4.1.4) with default parameters and the vOct22_202212 database. To accelerate processing, all analyses were run in parallel using GNU parallel (version 2018). Taxonomic profiling at the strain levels was performed using StrainPhlAn450 (version 4.1.4) with default parameters.

Then, the strain-level profiling was performed with StrainPhlAn4 using the custom species-level genome bins (SGBs) marker database with the following parameters: "marker_in_n_samples_perc 50 -sample_with_n_markers 10 -phylophlan_mode accurate -mutation_rates". The SGBs detected with "-print_clade_only” were selected for strain-level profiling. This marker-gene-based approach has been benchmarked to provide robust strain-level identification even at an average coverage of 1.3× per SGB, consistently outperforming assembly-based methods in samples with moderate sequencing depth.

Detection of strain-sharing event
Strain sharing was assessed by profiling each sample, with the dominant strain of SGBs with a set of SGB-specific normalized phylogenetic distance (nGD) thresholds in each strain by a previous study from five published metagenomic datasets in four countries22,51-55. nGDs were computed as the ratio of leaf-to-leaf branch length normalized by the total branch length of the phylogenetic trees generated by StrainPhlAn. These trees were constructed based on the marker gene alignments, specifically targeting positions with a minimum variability of 1%. For the SGB-specific nGD that were not estimated previously, the nGD value corresponding to the 3rd percentile of the distribution of nGD values among individuals was used. To enhance the reliability of strain identity threshold selection, we derived an alternative threshold for the 105 SGBs with n_related ≥ 5 (i.e., detected in at least 5 sibling pairs). The species-specific strain identity threshold was calculated based on Youden's index and compared against the threshold obtained from the method described earlier; the more conservative of the two was selected as the final strain identity threshold. For the remaining strains with insufficient sharing events, we retained the SGB-specific nGD thresholds derived from previous studies or the 3rd percentile of the nGD distribution across individuals.

In detail, to evaluate the reproducibility of the species-specific strain-identity thresholds within our dataset, we included all 429 samples from related sibling pairs and unrelated subjects. This validation assessed the separation between the distributions of nGD distances for strains from the same SGB in these two groups. The analysis was performed for the 105 SGBs detected in at least 5 sibling pairs in this cohort. Given the absence of longitudinal samples, we defined true positives as pairwise phylogenetic distances (nGD) between sibling pairs that fell below the species-specific strain identity threshold—obtained either (1) from independent longitudinal datasets of previous studies or as the 3rd percentile of the inter-individual nGD distribution, or (2) computed using Youden's index from our dataset. False positives were defined as nGD values from balanced unrelated pairs below the threshold, true negatives as those from balanced unrelated pairs above the threshold, and false negatives as nGD values from sibling pairs above the threshold. Performance metrics for the two thresholds in distinguishing strains from sibling pairs versus unrelated pairs were as follows: median precision = 0.97 and 0.98, and median F-score = 0.72 and 0.77 (Supplementary Figure 3; Supplementary Data 19).

Strain–sharing networks
Unsupervised strain‑sharing networks were constructed and visualized using the R packages tidygraph (v1.2.3), igraph (v1.4.3), and plotted with ggraph (v2.1.0). Networks were laid out using a stress‑minimization algorithm. Nodes represent individuals, and edges connect pairs that share ≥10 bacterial strains. Only edges meeting this threshold are displayed. We calculated the Local Clustering Coefficient for each node using the igraph package in R.

Statistics & Reproducibility
No statistical method was used to predetermine sample size. The sample size (n = 429) was determined by the available recruitment from the Child and Adolescent Psychiatric Clinic and the community during the study period. No data were excluded from the analyses after the recruitment phase based on the predefined exclusion criteria (e.g., use of antibiotics or probiotics within one month prior to enrollment). The experiments were not randomized; subjects were categorized into groups (Multiplex, Simplex, TD families, and ASD-O group) based on their clinical diagnosis and family structure. The Investigators were not blinded to allocation during experiments and outcome assessment, although all fecal samples were processed in a random sequence using standardized protocols to minimize potential batch effects.

The reproducibility of the metagenomic pipeline was ensured through the inclusion of internal positive controls (ZymoBIOMICS Microbial Community Standard and DNA Standard) throughout DNA extraction, library construction, and sequencing. In cases of abnormal signals, resequencing was performed to ensure data integrity. The robustness of the strain-level profiling was validated by benchmarking against unrelated subject pairs, achieving a median precision of 0.97 and a median F-score of 0.72. Statistical analyses, including the calculation of local clustering coefficients and network visualizations using stress-minimization algorithms, were performed using R with the tidygraph, igraph, and ggraph packages.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study analyzed 429 fecal metagenomic samples from children in various family configurations with or without autism\nspectrum disorder (ASD), including ASD-Multiplex (ASD-M), ASD-Simplex (ASD-S), ASD-Only (ASD-O) and Typically\nDeveloping (TD) children.\nAmong the main findings, the authors report that children with autism spectrum disorder from multiplex families (ASD-M)\nhave the largest differences in gut microbiome composition when compared to typically developing (TD) children, with these\ndifferences characterized by increased diversity and a greater abundance of pathogenic bacteria. They also find that siblings\nin both multiplex (ASD-M) and simplex (ASD-S) ASD families share more similar microbiomes and show greater microbial\nstrain sharing than siblings from TD households. Furthermore, in multiplex ASD families, the strains exchanged among\nsiblings tended to be potentially harmful, such as Dorea formicigenerans and Acidaminococcus intestini. In contrast, among\nsimplex ASD families, the sharing of strains was more often associated with bacteria considered beneficial, such as\nFaecalibacterium prausnitzii.\nFinally, the authors hypothesize that cohabitation with ASD-affected siblings worsens gut dysbiosis in ASD-M, whereas\nhealthy siblings may have protective effects in ASD-S.\nWhile the study might provide valuable insights into the dynamic of the gut microbiota in ASD, several methodological\nlimitations raise concerns about the robustness and interpretability of the findings\nMajor Concerns\nSequencing statistics:\nThe study lacks details related to the sequencing phase, including targeted coverage and number of bases sequenced in\ntotal and per sample. hese details would allow the reader to interpret the results more carefully and cautiously. This is\nparticularly relevant for the strain transmission section (see below).\nAccounting for confounders:\nThere is a lack of transparency in the adjustment for potential confounders: while the authors claim to adjust for age, gender,\nand ADHD in their models, they do not clearly describe the statistical models used to do so.\nThere is no stratified analysis, sensitivity testing, or inclusion of interaction terms to show how these strongly imbalanced\ndemographic variables influence the results.\nThe ADHD diagnosis, present in nearly half of ASD-M cases, is known to affect the microbiome but is not disentangled from\nASD-related patterns.\nStatistical thresholds\nThe use of FDR < 0.2 as a significance threshold in MaAsLin2 is not standard and increases risk of false positives. This\nweakens the reliability of most species and pathway associations reported.\nNo effect sizes or confidence intervals are provided alongside p-values, making it difficult to interpret the magnitude and\nclinical relevance of the associations.\nStrain sharing assessment\nLimited Robustness and Validation of Strain Sharing Claims: the study uses SGB-specific nGD thresholds from a previous\nmeta-analysis, a reasonable baseline, but provides no internal validation (e.g., longitudinal replicates, F1 score\nbenchmarks) specific to their own cohort. There is no evaluation of false positive strain-sharing or precision estimates, and\nno filtering of potentially co-ingested probiotic strains.\nDiscussion: the conclusions about \"shared pathogenic or beneficial strains\" across siblings lack adequate technical rigor\nand may conflate co-acquisition with actual transmission. The statement that \"cohabitation with ASD siblings worsens gut\ndysbiosis\" implies directionality and causation in a study that is entirely cross-sectional. The possibility of reverse causation,\nshared environmental exposures, or common genetic susceptibility is acknowledged only briefly in limitations and not\nreflected in the interpretations drawn in results and discussion.\nStrainPhlAn parameters and questionable settings:\nUpon examining figure 5, an abnormal level of strain transmission is reported for two species which are normally found in\nthe gut at a high abundance. Also, the Methods report that StrainPhlAn parameters were tuned to “marker_in_n_samples 1 -\nsample_with_n_markers 10”. In fact, StrainPhlAn does not have a parameter “marker_in_n_samples”, but has\n“marker_in_n_samples_perc”, which is normally set to 50, meaning only markers present in 50% of the samples are used.\nTuning this parameter down to 1% would practically inflate the total diversity in the tree, potentially producing erroneous\nconclusions on strain similarity.\nThis parameter change increases the number of low-frequency markers in alignments, making downstream phylogenetic\ncalls potentially unstable and distances less reliable.\nAlso, while it is in principle “correct” to adopt previously established thresholds, these must be placed in the context of the\nsame StrainPhlAn parameters used in the original publications. If the authors want to modify the algorithm settings, they\nmust provide evidence that the updated parameters correctly segregate same-individual samples from inter-individual\ndistances.\nMinor Concerns\nThe discussion on host genetics and microbiome convergence opens interesting avenues but remains speculative. No\nactual genetic data is presented, and the manuscript mixes correlation with mechanism throughout.\nDietary factors are briefly addressed via PCA, but only at a population level (no macronutrient or intake detail is shown), and\nthese can be powerful confounders.\nTechnical details on sample storage, sequencing depth, and quality control metrics (e.g., coverage thresholds for strain\n strain\ncalling) are minimal; replications or reproducibility checks are not discussed.\nThe figures are not as detailed as the complexity of the study design warrants. For example, no visualization of strain-\nsharing networks or transmission matrices is provided. The authors should specify which MetaPhlAn/StrainPhlAn database\nwas used (and which version, for transparency and reproducibility).\nThe manuscript presents an interesting study concept ie dissecting microbial profiles in ASD subtypes by family structure,\nand executes it using up-to-date shotgun metagenomics tools. The addition of strain-level sharing data is timely. However,\nsubstantial analytical and interpretational weaknesses limit the strength of the conclusions:\n● Confounder control is opaque and likely inadequate.\n● The statistical thresholds are lenient and unaccompanied by effect size reporting.\n● The strain transmission analysis is not robustly validated, especially in comparison to recent seminal work (e.g., Valles-\nColomer et al., Nature 2023).\n● There is a tendency to overstate causality, especially regarding environmental or familial influence.", "reviewer_2": "The study addresses the question about a potential pathogenic role of the gut microbiome in ASD, but fail to provide\nconvincing evidence for a causal role of the microbiome. The authors recruited a total of 429 children with a diagnosis of\nASD with a mean age of 7 years. The aim of the study was to identify variations in gut microbial composition between\nfamilies with more than 1 child with ASD (ASD-M), with 1 child with ASD and siblings without ASD (ASD-S), with 1 child\nwith ASD but not siblings (ASD-O) and typically developing children (TD). ASD-M children exhibited a statistically different\ngut microbiome composition than TD (increased alpha diversity and richness, while ASD-S showed no differences and 12\npathogenic organism were observed compared to only 1 species if ASD-S. Cohabitation with affected siblings (like in the\nASD-M) families was linked to more gut dysbisosis. ASD-M shared more pathogenic strains while ASD-S shared more\nbeneficial strains.\nThe strength of the study include: 1)The size of the sample and the standardized microbiome analyses. 2) The sequencing\nat the strain level and identification of pathogenic and beneficial strains. 3.The results confirm the lateral transmission\nbetween affected family members. Weaknesses include: 1) The different families showed significant differences in age,\nsex and ADHD diagnosis (unclear why ADHD was assessed). Likely differences in factors important in ASD symptomatology\nand clinical presentation including dietary habits, number of pets, ASD severity, GI symptoms were not addressed. 2.\nResults do not prove a causal role of altered gut microbial composition in ASD, and other than confirming the lateral\ntransmission of gut microbial features.\nDoes the work support the conclusions and claims? the results support some of the conclusions, except for the claimed\nclinical implications\nAre there any flaws in the data analysis, interpretation and conclusions? No\nIs the methodology sound? Does the work meet the expected standards in your field?\nIs there enough detail provided in the methods for the work to be reproduced? Yes\nIn summary, despite the size of the study and high quality of analyses, I do not see a major progress or an actionable finding\nfrom this well executed study for the treatment of children with ASD.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript has substantially improved in methodological rigor and transparency. The remaining issues are\ninterpretational rather than analytical and can be resolved through targeted textual revisions. The requested changes below\ndo not require additional analyses but are necessary to ensure that conclusions remain strictly aligned with the\nobservational, cross-sectional design of the study.\n1. Causal and directional language\nRephrase any statements implying influence, susceptibility, receptivity, or directionality to strictly associational language, as\nno causal inference can be made from the current study design.\nSection: Results (strain-sharing summary)\nSentence to be replaced or modified:\n“These differential sharing patterns underscore the significant influence of the familial context on the ASD microbiome.”\nSuggested replacement:\n“These differential sharing patterns underscore a strong association between familial context and gut microbiome\nconfiguration in ASD.”\n2. Strain-sharing interpretation (transmission vs association)\nStrain sharing observed in a cross-sectional cohort cannot distinguish direct microbial transmission from co-acquisition or\nconvergence and should therefore be framed as association or co-occurrence only.\nSection: Results (strain-level comparison across family types)\nSentence to be replaced:\n“These findings indicate that the gut environment in children from ASD families facilitates the transmission of specific\nbacterial strains.”\nSuggested replacement or modified:\n“These findings indicate that children from ASD families exhibit distinct strain-sharing patterns compared with TD families,\nwithout implying direct microbial transmission.”\n3. Use of “opportunistic pathogen” terminology\nLabeling taxa as pathogens risks clinical overinterpretation, particularly when pathogenicity is context-dependent and not\nassessed in this study.\nSection: Results (description of shared taxa)\nSentence to be replaced or modified:\n“including the opportunistic pathogens Eubacterium rectale, Dorea formicigenerans, and Acidaminococcus intestini.”\nSuggested replacement:\n“including taxa with reported opportunistic or context-dependent pathogenic potential, such as Eubacterium rectale,\nDorea formicigenerans, and Acidaminococcus intestini.”\n4. Host “susceptibility” or “receptivity” framing\nPhrasing that implies intrinsic host susceptibility or biological receptivity is not supported without direct host-level functional\nmeasurements.\nSection: Discussion (interpretation of strain-sharing patterns)\nSentence to be replaced or modified:\n“The gut environment in children from ASD families may be more receptive to the stable colonization of specific bacterial\nstrains.”\nSuggested replacement:\n“The gut microbiome configurations observed in children from ASD families may be associated with increased persistence\nor detectability of specific bacterial strains.”\n5. Therapeutic and interventional implications\nTranslational or interventional relevance cannot be inferred from cross-sectional observational data and must be framed as\nhypothesis-generating only.\nSection: Discussion (future directions or concluding paragraph)\nSentence to be replaced or modified:\n“This identifies the shared familial microbiome as a promising, adjustable target for future intervention strategies.”\nSuggested replacement:\n“This identifies the shared familial microbiome as a hypothesis-generating context that may be relevant for future\ninterventional studies, pending longitudinal and mechanistic validation.”\n6. Genetics discussion\nHost genetic contributions are discussed without corresponding genetic data and must be clearly identified as speculative.\nSection: Discussion (genetics paragraph)\n7. Directionality and causation disclaimer\nThe manuscript should include an explicit and unambiguous statement clarifying that directionality cannot be inferred, as\nfrom a cross-sectional study, one cannot distinguish between direct microbial transmission, co-acquisition from shared\nenvironments, or microbiome convergence driven by unmeasured host factors, and no inference regarding\ndirectionality can be made.\nSection: Limitations\nAlso: As all participants were Hong Kong Chinese, the generalisability of these findings to other populations remains to be\nestablished", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "POINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear reviewers,\nWe are deeply grateful to the reviewers for their continued engagement and for providing such\ninsightful and constructive feedback, which has been invaluable in further strengthening our\nmanuscript. We have carefully addressed reviewers’ comments and suggestions point-by-point\nin our responses below. The revised manuscript is attached in both a tracked-changes version\nand a clean version, with all modifications highlighted in yellow for your convenience.\nWe are truly grateful for the opportunity to resubmit our work and hope that the revisions and\nclarifications provided will make the manuscript suitable for publication in your esteemed\njournal.\nYours sincerely\nSiew Ng on behalf of co-authors\nReviewers' Comments:\nReviewer #1:\nRemarks to the Author:\nMajor Concerns\nSequencing statistics:\nThe study lacks details related to the sequencing phase, including targeted coverage and number\nof bases sequenced in total and per sample. These details would allow the reader to interpret\nthe results more carefully and cautiously. This is particularly relevant for the strain transmission\nsection (see below)\nResponse: We sincerely appreciate the reviewer's suggestion regarding sequencing\ntransparency. We have now provided comprehensive details of our sequencing statistics in the\nrevised Methods section.\nSpecifically, each of the 429 stool samples was sequenced to an average depth of 6 Gb. While\nthe yield varied across samples, we generated a total of approximately 3.6 Tb of raw sequencing\ndata, providing an average targeted coverage of approximately 2X (assuming a baseline of 3\nGb for 1X coverage of the human gut metagenome).\nWhile this depth is moderate, we utilized StrainPhlAn 4 with the Species-level Genome Bin\n(SGB) marker database. As demonstrated in recent benchmarks, this approach allows for robust\nstrain-level profiling even at an average coverage of 1.3× per SGB, which is well within our\nsequencing range. To further ensure data quality, we applied stringent filtering (e.g., requiring\nat least 10 markers per SGB) to include a sample in the strain-sharing analysis.\nLine 375: Shotgun metagenomic sequencing was performed on the Illumina platform\nfor all 429 stool samples with an average depth of 6 Gb. While the yield varied across\nsamples, we generated a total of approximately 3.6 Tb of raw data, with a targeted\ncoverage of approximately 2X (assuming a baseline of 3 Gb for 1X coverage of the\nhuman gut metagenome). Although sequencing yield varied across samples, our\ndownstream analytical pipeline was designed to ensure high-resolution profiling\nacross this range.\nLine 392: This marker-gene-based approach has been benchmarked to provide robust\nstrain-level identification even at an average coverage of 1.3× per SGB, consistently\noutperforming assembly-based methods in samples with moderate sequencing depth.\nReviewers' Comments:\nReviewer #1:\nRemarks to the Author:\nAccounting for confounders:\nThere is a lack of transparency in the adjustment for potential confounders: while the authors\nclaim to adjust for age, gender, and ADHD in their models, they do not clearly describe the\nstatistical models used to do so.\nThere is no stratified analysis, sensitivity testing, or inclusion of interaction terms to show how\nthese strongly imbalanced demographic variables influence the results.\nThe ADHD diagnosis, present in nearly half of ASD-M cases, is known to affect the\nmicrobiome but is not disentangled from ASD-related patterns.\nResponse: We thank the reviewer for this valuable comment. In response, we have revised our\nanalytical approach accordingly. The MaAsLin2 models have been updated to explicitly\ninclude age, gender, and ADHD diagnosis as fixed effects. Furthermore, we performed the\nsuggested stratified analysis by comparing the gut microbiome of ASD children withoutan\nADHD diagnosis (the ADHD- subgroup) to the TD control group. This refined analysis\nconfirmed that our core findings remained stable. The specific influence of ADHD comorbidity\nwithin ASD families was also assessed and found to be minimal. These methodological\nclarifications and the results of the stratified analysis have been incorporated into the revised\nmanuscript.\nLine 111: We further analyzed the above findings at the species level. To account for potential\nconfounding factors, all species-level analyses were conducted using MaAsLin2 with\nadjustment for age, gender, and ADHD comorbidity as fixed effects in the statistical models.\nLine 134: The key finding that ASD-M children had the most distinct gut microbiome from TD\nchildren remained significant even when including only participants without an ADHD\ndiagnosis (Supplementary Figure 2c). To further evaluate the specific effect of ADHD\ncomorbidity within the ASD families, we compared ASD-M (ADHD+) versus ASD-M\n(ADHD-) and ASD-S (ADHD+) versus ASD-S (ADHD-), adjusting for age, gender, and\ndietary factors (Supplementary Figure 2f and 2i). Notably, no significant bacterial species were\nidentified in either comparison after multiple testing correction, suggesting that ADHD status\nalone does not yield significant microbiome differences within the same familial ASD subtype\nwhen controlling for other confounding factors.\nLine 661: (Figure legend)\nSupplementary Figure 2. Differential bacterial species associated with ASD in different family\ntypes. Associations between gut microbiota and children from different family types (ASD-M,\nASD-S, ASD-O, TD) were assessed using multivariate linear models (MaAsLin2; significance:\np < 0.05, FDR < 0.2). (a), (c), (e), (g), (i), (k), (m): Comparisons of ASD family types versus\nTD, and stratified analyses within the same family type by ADHD status (+/-), adjusting for\nconfounders except dietary factors; (b), (d), (f), (h), (j), (l), (n): The same comparisons,\nadjusting for confounders including dietary factors.\nReviewer #1:\nRemarks to the Author:\nStatistical thresholds\nThe use of FDR < 0.2 as a significance threshold in MaAsLin2 is not standard and increases\nrisk of false positives. This weakens the reliability of most species and pathway associations\nreported.\nNo effect sizes or confidence intervals are provided alongside p-values, making it difficult to\ninterpret the magnitude and clinical relevance of the associations.\nResponse: We thank the reviewer for raising these important statistical considerations. In\nresponse, we have revised the manuscript to enhance the clarity and robustness of our statistical\nreporting, specifically addressing the two key points on significance thresholds and effect size\nreporting. Below, we detail the specific changes made.\n1. Clarification on the statistical threshold in MaAsLin2\nThe reviewer rightly points out the trade-off in false discovery control. In this exploratory, high-\ndimensional analysis, we selected an FDR threshold of 0.2 to balance sensitivity and specificity,\na practice documented in other microbiome studies (e.g., Stražar et al. Genome Biol2021,\nPFDR < 0.25; Martin et al. Microbiome2022, FDR < 0.2; Huang et al. Cell Reports2024, FDR\n≤ 0.25). While we acknowledge that a less stringent threshold can increase the risk of false\npositives, we have been cautious in our interpretation by emphasizing consistent patterns and\nstronger effect sizes rather than isolated hits. Importantly, the core findings relating to increased\ndysbiosis in multiplex families are supported by multiple lines of evidence (e.g.,\nPERMANOVA, α-diversity) and are not reliant solely on species-level MaAsLin2 results.\n2. Addition of effect sizes and confidence intervals\nWe fully agree on the importance of reporting effect sizes. In the revised manuscript, we have\nnow included MaAsLin2 coefficients (as effect size estimates) for all significant species- and\npathway-level associations in Supplementary Tables S4–S17. Moreover, we have consistently\nreported appropriate effect size measures throughout the manuscript, such as epsilon-squared\nfor Kruskal–Wallis tests and Cramer’s V for chi-squared tests, provided in figure legends and\nSupplementary Tables S2–S3 and S18, S19, S20, S23. These additions allow readers to better\nassess the magnitude and potential clinical relevance of the reported associations.\nOverall, these revisions improve the statistical transparency of the study and strengthen the\nreliability of our conclusions, while preserving the main findings of the paper. We are grateful\nfor the reviewer’s suggestions, which have undoubtedly enhanced the rigor of our work.\nReviewer #1:\nRemarks to the Author:\nStrain sharing assessment\nLimited Robustness and Validation of Strain Sharing Claims: the study uses SGB-specific nGD\nthresholds from a previous meta-analysis, a reasonable baseline, but provides no internal\nvalidation (e.g., longitudinal replicates, F1 score benchmarks) specific to their own cohort.\nThere is no evaluation of false positive strain-sharing or precision estimates, and no filtering of\npotentially co-ingested probiotic strains.\nResponse: We sincerely thank the reviewer for the expert comments regarding the validation\nof our strain-sharing analysis. In response, we have significantly refined our analytical approach\nand reporting to strengthen the robustness of our claims.\n1. Internal validation of SGB-specific nGD thresholds\nWe acknowledge the importance of cohort-specific validation. While we initially used\npreviously published SGB-specific nGD thresholds—which were validated in an independent\nFMT cohort and showed high reproducibility (F1 ≈ 0.94–0.98)—we have now performed\nadditional internal validation within our own cohort. Given the absence of longitudinal\nreplicates from the same individual, we used sibling pairs as a natural mixture of positive\n(shared) and negative (non-shared) references to evaluate threshold performance.\n2. Derivation and evaluation of new thresholds\nFor 105 SGBs with sufficient sibling pairs (n ≥ 5), we manually calculated new strain-sharing\nthresholds by optimizing Youden’s index. Both the original (published or 3rd percentile of the\nnGD distribution) thresholds and our newly derived thresholds were evaluated. The resulting\nprecision values were consistently high (median precision = 0.97 and 0.98 for original and new\nthresholds, respectively), indicating that the thresholds reliably distinguish shared from non-\nshared strains. The observed, slightly lower recall reflects the biological reality that siblings do\nnot always share the same strain, rather than a limitation of the threshold itself.\n3. Conservative implementation and visualization\nTo ensure conservative calls, the more stringent threshold (original vs. new) was selected for\ndownstream analysis. We have also provided density plots for the top 35 most abundant SGBs\n(Supplement Figure 3) and detailed performance metrics (Supplementary Tables S22) for the\ndistance distributions, visually confirming that the selected thresholds can effectively\ndiscriminate between the two states.\nOverall, the revised analysis demonstrates that the choice of threshold has minimal impact on\nthe core conclusion: strain sharing is significantly higher in ASD sibling pairs than in TD sibling\npairs. We are grateful for the reviewer’s suggestions, which have led to a more rigorous and\ntransparent validation of our strain-sharing assessment.\nLine 397: Detection of strain-sharing event\nStrain sharing was assessed by profiling each sample, with the dominant strain of SGBs with a\nset of SGB-specific normalized phylogenetic distance (nGD) thresholds in each strain by a\nprevious study from five published metagenomic datasets in four countries22,51-55. nGDs\nwere computed as the ratio of leaf-to-leaf branch length normalized by the total branch length\nof the phylogenetic trees generated by StrainPhlAn. These trees were constructed based on the\nmarker gene alignments, specifically targeting positions with a minimum variability of 1%. For\nthe SGB-specific nGD that were not estimated previously, the nGD value corresponding to the\n3rd percentile of the distribution of nGD values among individuals was used. To enhance the\nreliability of strain identity threshold selection, we derived an alternative threshold for the 105\nSGBs with n_related ≥ 5 (i.e., detected in at least 5 sibling pairs). The species-specific strain\nidentity threshold was calculated based on Youden's index and compared against the threshold\nobtained from the method described earlier; the more conservative of the two was selected as\nthe final strain identity threshold. For the remaining strains with insufficient sharing events, we\nretained the SGB-specific nGD thresholds derived from previous studies or the 3rd percentile\nof the nGD distribution across individuals.\nIn detail, to evaluate the reproducibility of the species-specific strain-identity thresholds within\nour dataset, we included all 429 samples from related sibling pairs and unrelated subjects. This\nvalidation assessed the separation between the distributions of nGD distances for strains from\nthe same SGB in these two groups. The analysis was performed for the 105 SGBs detected in\nat least 5 sibling pairs in this cohort. Given the absence of longitudinal samples, we defined\ntrue positives as pairwise phylogenetic distances (nGD) between sibling pairs that fell below\nthe species-specific strain identity threshold—obtained either (1) from independent\nlongitudinal datasets of previous studies or as the 3rd percentile of the inter-individual nGD\ndistribution, or (2) computed using Youden's index from our dataset. False positives were\ndefined as nGD values from balanced unrelated pairs below the threshold, true negatives as\nthose from balanced unrelated pairs above the threshold, and false negatives as nGD values\nfrom sibling pairs above the threshold. Performance metrics for the two thresholds in\ndistinguishing strains from sibling pairs versus unrelated pairs were as follows: median\nprecision = 0.97 and 0.98, and median F-score = 0.72 and 0.77 (Supplementary Figure 3;\nSupplementary Table S19).\nResponse: Regarding the potential concern of co-ingested probiotic strains, the exclusion\ncriteria of our study proactively addressed this confounder rather than relying on post hoc\nbioinformatic filtering. Furthermore, the top-ranked shared bacterial strains identified in our\nanalysis are not typical food-grade or commercially used probiotic species, suggesting they are\nmore likely to originate from environmental or communal household exposures.\nLine 318: Exclusion criteria included the use of probiotics, antidepressants, anti-epileptics, or\nantibiotics within the one month prior to enrollment.\nReviewer #1:\nRemarks to the Author:\nDiscussion: the conclusions about \"shared pathogenic or beneficial strains\" across siblings lack\nadequate technical rigor and may conflate co-acquisition with actual transmission. The\nstatement that \"cohabitation with ASD siblings worsens gut dysbiosis\" implies directionality and\ncausation in a study that is entirely cross-sectional. The possibility of reverse causation,\nshared environmental exposures, or common genetic susceptibility is acknowledged only\nbriefly in limitations and not reflected in the interpretations drawn in results and discussion.\nResponse: We thank the reviewer for highlighting the importance of technical rigor and caution\nwhen interpreting strain-sharing data. In the revised manuscript, we have revised the manuscript\nto remove any language that might imply causation or directionality, ensuring our discussion\nremains strictly observational.\nSpecifically, we first note that children in ASD-M families show the most pronounced species-\nlevel dysbiosis and the highest strain-sharing rates with their siblings. We then report which\nbacterial strains are shared more frequently in ASD families compared to TD families. When\ndiscussing these strains, we focus on their annotated functions as potential traits of interest. We\nhave also expanded the Discussion and Limitations sections to more thoroughly discuss other\npossible explanations. Such as shared environmental exposure, common genetic susceptibility,\nand have removed statements that could be misread as suggesting that cohabitation worsens\ndysbiosis. These revisions ensure our conclusions align with the cross-sectional nature of our\nstudy and improve the overall rigor of the manuscript. We are grateful for the reviewer's\nguidance in clarifying these points.\nLine 193 (results): To evaluate gut microbiome sharing between siblings across different family\ntypes, we employed the chi-squared test with Yates' continuity correction or Fisher’s exact test.\nNotably, several bacterial strains showed significantly higher sharing rates in multiplex ASD\nfamilies compared to TD controls, including the opportunistic pathogens Eubacterium rectale\n(SGB4933), Dorea formicigenerans (SGB4575), and Acidaminococcus intestini (SGB5736)\n(p < 0.05, Figure 4; Supplementary Table S20). In contrast, the sharing of Bacteroides\nadj\nxylanisolvens (SGB1867) was markedly lower in multiplex ASD families than in the other two\ngroups. Interestingly, we also observed an increased prevalence of sharing for several common\ngut commensals in ASD families. For instance, Bifidobacterium pseudocatenulatum\n(SGB17237) and Faecalibacterium prausnitzii (SGB15342) exhibited their highest sharing\npercentages in multiplex ASD families, while Sellimonas intestinalis (SGB4617) showed a\nsignificantly increased sharing rate specifically in simplex ASD families (padj < 0.05, Figure\n4). These differential sharing patterns underscore the significant influence of the familial\ncontext on the ASD microbiome. Rather than direct transmission alone, these findings suggest\nthat the gut environment in children from ASD families—potentially shaped by shared\nenvironmental exposures and common genetic susceptibility—may be more receptive to the\nstable colonization of specific bacterial strains. This identifies the shared familial microbiome\nas a promising, adjustable target for future intervention strategies.\nLine 256 (discussion): These observations suggest that the high strain-sharing rate observed in\nASD-M families may be associated with cohabitation or their shared environmental exposures,\nparticularly given that our initial species-level findings demonstrated more severe gut dysbiosis\nand a higher abundance of opportunistic pathogens in ASD-M individuals. Furthermore, the\ndifferential bacteria identified between ASD-M and TD groups in our results showed correlative\ntrends with ASD-related clinical scores, highlighting a potential clinical relevance. While we\ninterpret the apparent reduced resistance in their gut microbiota as an observational finding, it\nmay also be linked to potential common genetic susceptibility factors. Moreover, the sharing\nrates of three beneficial bacterial strains—Faecalibacterium prausnitzii (SGB15342),\nBifidobacterium pseudocatenulatum (SGB17237), and Sellimonas intestinalis (SGB4617)—\nwere significantly higher in ASD families compared to TD families. This indicates that\nintestinal susceptibility may not solely present disadvantages. The characteristic ease of\ncolonization and high environmental transmissibility of these specific strains highlight a\npotential avenue for intervention strategies from a different perspective. These results point out\nthe potential of microbiome-based therapeutics in alleviating the symptoms of ASD. Targeting\nshared beneficial bacteria in ASD families might be an effective intervention approach because\nthese bacteria might be easily absorbed or colonized in the intestines.\nReviewer #1:\nRemarks to the Author:\nStrainPhlAn parameters and questionable settings:\nUpon examining figure 5, an abnormal level of strain transmission is reported for two species\nwhich are normally found in the gut at a high abundance. Also, the Methods report that\nStrainPhlAn parameters were tuned to “marker_in_n_samples 1 -sample_with_n_markers 10”.\nIn fact, StrainPhlAn does not have a parameter “marker_in_n_samples”, but has\n“marker_in_n_samples_perc”, which is normally set to 50, meaning only markers present in\n50% of the samples are used. Tuning this parameter down to 1% would practically inflate the\ntotal diversity in the tree, potentially producing erroneous conclusions on strain similarity.\nThis parameter change increases the number of low-frequency markers in alignments, making\ndownstream phylogenetic calls potentially unstable and distances less reliable.\nResponse: Thank you for the expert comment regarding the StrainPhlAn parameters. The\nreviewer correctly identified that we had used an inappropriately low value\n(marker_in_n_samples_perc 1) in our initial analysis. In accordance with this feedback, we\nhave revised our methodology and re-ran the entire StrainPhlAn4 analysis pipeline using the\nrecommended standard parameter (marker_in_n_samples_perc 50). This re-analysis,\nconducted with the correct settings, confirmed that our core finding—significantly higher\nstrain-sharing rates within ASD sibling pairs compared to TD sibling pairs—remains robust\n(Figure 3, please see below). While the adjustment resulted in subtle quantitative variations in\nthe resulting phylogenetic trees and distances, leading to expected variations in certain\nintermediate results, such as specific p-values or strain-sharing rates for individual species, the\nprimary conclusions of the study remain unchanged. We have updated the Methods section to\naccurately reflect the use of the correct parameter and value. We sincerely appreciate the\nreviewer’s vigilance, which has strengthened the methodological rigor of our strain-level\nanalysis:\nLine 388: Then, the strain-level profiling was performed with StrainPhlAn4 using the custom\nspecies-level genome bins (SGBs) marker database with the following parameters: \"\nmarker_in_n_samples_perc 50 -sample_with_n_markers 10 -phylophlan_mode accurate -\nmutation_rates\". The SGBs detected with \"-print_clade_only” were selected for strain-level\nprofiling.\nReviewer #1:\nRemarks to the Author:\nAlso, while it is in principle “correct” to adopt previously established thresholds, these must be\nplaced in the context of the same StrainPhlAn parameters used in the original publications. If\nthe authors want to modify the algorithm settings, they must provide evidence that the updated\nparameters correctly segregate same-individual samples from inter-individual distances.\nResponse: We agree that strain-sharing thresholds should be validated under the specific\nStrainPhlAn parameters used. After correcting the parameter to the standard\nmarker_in_n_samples_perc 50 as noted in our previous response, we re-evaluated the\nperformance of the original published thresholds alongside our cohort-derived thresholds\ncalculated from the 3rd percentile of the distribution of nGD values. We also calculated new\nYouden-optimized thresholds where sufficient data were available. The performance of all\nthresholds remained high, and the density plots confirmed clear separation between shared and\nnon-shared distances. For downstream analysis, we consistently applied the most conservative\nthreshold for each SGB. This re-validation under the corrected parameters ensures the\nmethodological consistency and reliability of our strain-sharing conclusions. A more detailed\ndescription of this process, including the associated density plots (Supplementary Figure 3,\nplease see the figure in the above response), has been provided in our response to your earlier\ncomment on strain sharing assessment.\nReviewer #1:\nRemarks to the Author:\nMinor Concerns\nThe discussion on host genetics and microbiome convergence opens interesting avenues but\nremains speculative. No actual genetic data is presented, and the manuscript mixes correlation\nwith mechanism throughout.\nDietary factors are briefly addressed via PCA, but only at a population level (no macronutrient\nor intake detail is shown), and these can be powerful confounders.\nTechnical details on sample storage, sequencing depth, and quality control metrics (e.g.,\ncoverage thresholds for strain calling) are minimal; replications or reproducibility checks are\nnot discussed.\nResponse: We thank the reviewer for recognizing the value of our study and for noting the\nlimitation regarding the lack of genetic data. In our subsequent follow-up research, we have\ncollected relevant biological samples and will consider conducting genetic-related studies.\nRegarding dietary factors, in addition to the overall PCA analysis, we have incorporated further\nadjustments as detailed below:\nLine 125: Furthermore, although the overall dietary patterns showed no significant differences\nbetween groups based on PCA analysis of daily diet intake (Supplementary Figure 1), we have\nconducted sensitivity analyses adjusting for dietary factors. First, we performed Kruskal-Wallis\ntests for overall differences of dietary factors (Supplementary Table S2), followed by pairwise\nWilcoxon tests with FDR correction for each significantly different factor (Supplementary\nTable S3). After incorporating these significant dietary factors into Maaslin2 models, the main\nfindings remained robust. Specifically, 10 and 3 differentially abundant bacterial species were\nassociated with ASD-M and ASD-S, respectively (Supplementary Figure 2b and 2h), and 2\nassociated species emerged in the ASD-O group (Supplementary Figure 2n).\nLine 605: (Figure legend) Supplementary Figure 1. Principal component analysis\n(PCA) of dietary daily intake among individuals from different groups. Dietary\npatterns assessed by daily food consumption. Kruskal–Wallis test, PC1, p =0.198. PC2,\np=0.0867.\nLine 609: (Figure legend) Supplementary Figure 2. Differential bacterial species associated\nwith ASD in different family types. Associations between gut microbiota and children from\ndifferent family types (ASD-M, ASD-S, ASD-O, TD) were assessed using multivariate linear\nmodels (MaAsLin2; significance: p < 0.05, FDR < 0.2). (a), (c), (e), (g), (i), (k), (m):\nComparisons of ASD family types versus TD, and stratified analyses within the same family\ntype by ADHD status (+/-), adjusting for confounders except dietary factors; (b), (d), (f), (h),\n(j), (l), (n): The same comparisons, adjusting for confounders including dietary factors.\nReviewer #1:\nRemarks to the Author:\nThe figures are not as detailed as the complexity of the study design warrants. For example, no\nvisualization of strain-sharing networks or transmission matrices is provided. The authors\nshould specify which MetaPhlAn/StrainPhlAn database was used (and which version, for\ntransparency and reproducibility).\nResponse: We sincerely appreciate the reviewer's suggestion to enhance the visualization of\nthe study's complexity, particularly concerning strain-sharing dynamics. To address this, we\nhave significantly expanded our supplementary figures to provide the requested detailed\nvisualization and analysis:\nVisualization of Strain-Sharing: We have added a comprehensive visualization of the strain-\nsharing network (Supplementary Figure 4b), which depicts the connections among individuals\nbased on shared strains.\nTo further support our findings on the distinct patterns across family types, we have also added\ntwo complementary visualizations: A strain-sharing Density Plot (Supplementary Figure 4a) to\nshow the distribution of sharing rates and a Clustering Coefficient Plot (Supplementary Figure\n4c) derived from the network topology analysis, which quantitatively demonstrates the highly\ninterconnected structure within Multiplex ASD families.\nThese new figures and the associated network topology metrics confirm that strain transmission\nin Multiplex ASD families is more robust and pervasive, aligning with the pattern of dysbiosis\nobserved.\nLine 177: Consistent with previous observations that microbiome transmission occurs beyond\nclose contacts and reflects population structure, we observed distinct strain-sharing patterns\nacross family types. This was evidenced by both the density distributions of sharing rates\n(Supplementary Figure 4a, Supplementary Table S17) and a PERMANOVA performed on the\nstrain-sharing network (Euclidean distance matrix; n = 747, R² = 10.6%, p = 0.001;\nSupplementary Figure 4b and Methods). Network topology analysis revealed that the extensive\ndistribution of Multiplex families in the visualization corresponds to a highly interconnected\nstructure rather than a loose association, followed by simplex ASD families, and then TD\nfamilies. Sibling pairs from multiplex ASD families exhibited significantly higher clustering\ncoefficient compared to other families (Kruskal-Wallis test, p = 0.00053, χ² = 15.098;\nSupplementary Figure 4c), suggesting the formation of tight-knit strain-sharing communities.\nThese topological metrics confirm that strain transmission in multiplex ASD families is more\nrobust and pervasive.\nLine 432: (Methods) Strain–sharing networks\nUnsupervised strain‑sharing networks were constructed and visualized using the R packages\ntidygraph (v1.2.3), igraph (v1.4.3), and plotted with ggraph (v2.1.0). Networks were laid out\nusing a stress‑minimization algorithm. Nodes represent individuals, and edges connect pairs\nthat share ≥10 bacterial strains. Only edges meeting this threshold are displayed. We calculated\nthe Local Clustering Coefficient for each node using the igraph package in R.\nSupplementary Figure 4\nResponse: Thank you for this suggestion regarding the database and version. In response, we\nhave updated the Methods section to specify the exact database versions used for\nreproducibility.\nLine 382: Microbial taxonomic profiles\nMicrobial composition profiles were analyzed from the quality-filtered forward reads using\nMetaPhlAn449 (version 4.1.4) with default parameters and the vOct22_202212 database. To\naccelerate processing, all analyses were run in parallel using GNU parallel (version 2018).\nTaxonomic profiling at the strain levels was performed using StrainPhlAn450 (version 4.1.4)\nwith default parameters.\nThen, the strain-level profiling was performed with StrainPhlAn4 using the custom species-\nlevel genome bins (SGBs) marker database with the following parameters: \"\nmarker_in_n_samples_perc 50 -sample_with_n_markers 10 -phylophlan_mode accurate -\nmutation_rates\". The SGBs detected with \"-print_clade_only” were selected for strain-level\nprofiling. This marker-gene-based approach has been benchmarked to provide robust strain-\nlevel identification even at an average coverage of 1.3× per SGB, consistently outperforming\nassembly-based methods in samples with moderate sequencing depth.\nReviewer #2:\nRemarks to the Author:\nWeaknesses include: 1) The different families showed significant differences in age, sex and\nADHD diagnosis (unclear why ADHD was assessed). Likely differences in factors important\nin ASD symptomatology and clinical presentation including dietary habits, number of pets,\nASD severity, GI symptoms were not addressed.\nResponse: We sincerely appreciate the reviewer's insightful comments regarding host factors\nand potential confounders. We have addressed these concerns point-by-point below:\n1.Regarding Age, Sex, and ADHD: ADHD was assessed and included in our analysis due to\nits high comorbidity rate with ASD. To rigorously account for the demographic differences\nobserved, we treated age, sex, and ADHD diagnosis as covariates in our statistical models.\nFurthermore, we conducted stratified analyses based on ADHD status to ensure our findings\nwere not driven by this comorbidity. The results remained robust across these adjustments.\n2.Regarding Dietary Habits: We concur that diet is a critical factor. We performed a PCA\nanalysis of overall dietary patterns, which showed no significant clustering or differences\nbetween the groups (Supplementary Figure 1). Additionally, we performed sensitivity analyses\nadjusting for dietary factors, which further confirmed the stability of our results (Supplementary\nFigure 2).\nLine 125: Furthermore, although the overall dietary patterns showed no significant differences\nbetween groups based on PCA analysis of daily diet intake (Supplementary Figure 1), we have\nconducted sensitivity analyses adjusting for dietary factors. First, we performed Kruskal-Wallis\ntests for overall differences of dietary factors (Supplementary Table S2), followed by pairwise\nWilcoxon tests with FDR correction for each significantly different factor (Supplementary\nTable S3). After incorporating these significant dietary factors into Maaslin2 models, the main\nfindings remained robust. Specifically, 10 and 3 differentially abundant bacterial species were\nassociated with ASD-M and ASD-S, respectively (Supplementary Figure 2b and 2h), and 2\nassociated species emerged in the ASD-O group (Supplementary Figure 2n).\nSupplementary Figure 2:\n3.Regarding Other Environmental Factors (e.g., Pets): We acknowledge the reviewer’s valid\npoint regarding other potential confounders, such as the number of pets, which were not\nincluded in our original questionnaire. While we comprehensively adjusted for measured\nconfounders (age, sex, ADHD, and diet), we recognize that data on certain environmental\nexposures were unavailable. We have explicitly addressed this constraint in the revised\nLimitation section of the manuscript to ensure transparency regarding potential unmeasured\nconfounding.\nLine 300: Finally, while we meticulously adjusted for collected covariates, observational\nhuman microbiome studies are inherently subject to unmeasured confounders. We lacked data\non specific environmental factors, such as the number of pets in the household.\n4. Concerning ASD severity, given the various complex measurements and standards for\nASD-related clinical scores, we were concerned that including all scores in the analytical\nmodels might lead to overfitting. Therefore, we conducted additional correlation analyses\nbetween differential bacteria identified in the ASD-M vs. TD comparison and ASD-related\nscores.\nLine 215: Gut microbiome is associated with ASD clinical scores\nTo extend the findings and explore the clinical relevance. We processed the association analysis\nof the markers and clinical parameters. We found 7 multiplex ASD families enriched markers\nbased on the MaAslin2 were significantly positively correlated with several ASD-related\nclinical parameters, including Social Responsiveness Scale (SRS), Social Experience\nQuestionnaire (SEQ), Child Behaviour Checklist (CBCL), Anxiety scale for children-ASD\n(ASC-ASD) (Spearman’s correlation, p<0.05, Supplementary Figure 5), including\nCoprobacillus cateniformis, Murimonas intestini, Eisenbergiella tayi, Alistipes finegoldii,\nLachnospira pectinoschiza, Sanguibacter SGB15121, and Eubacterium siraeum. In contrast,\nthe TD enriched species Faecalibacterium prausnitzii, GGB51510_SGB71883,\nAgathobaculum butyriciproducens, and Bacteroides xylanisolvens were negatively correlated\nwith the ASD clinical parameters (Spearman’s correlation, p<0.05, Supplementary Figure 5)\nLine 238: Our correlation analysis also indicated that differential bacteria between ASD-M and\nTD are associated with ASD-related scores, thereby enhancing the reference value of our\nclinical observations.\nSupplement Figure 5:\n5. Regarding the GI symptoms: In our cohort, the proportion of children with GI symptoms\nwas below 15%, so we considered its potential impact to be limited and did not perform specific\nadjustments.\nReviewer #2:\nRemarks to the Author:\n2. Results do not prove a causal role of altered gut microbial composition in ASD, and other\nthan confirming the lateral transmission of gut microbial features.\nIn summary, despite the size of the study and high quality of analyses, I do not see a major\nprogress or an actionable finding from this well executed study for the treatment of children\nwith ASD.\nResponse: Thank you for your valuable feedback regarding the clinical and mechanistic\nimplications of our work. We sincerely appreciate the positive assessment of our study's quality\nand size. We have refined our discussion to address the points. We fully acknowledge that, as\na cross-sectional observational study, our data cannot establish a causal link between the altered\ngut microbiome and ASD. Our primary aim was to characterize the distinct gut microbial\npatterns and differing degrees of bacterial strain sharing across diverse ASD family types. The\ngut microbiome shows the most significant dysbiosis at the species level in ASD children from\nMultiplex families. Strain sharing is highest in Multiplex ASD sibling pairs, intermediate in\nSimplex pairs, and lowest in TD pairs. This pattern underscores the influence of the familial\ncontext on the ASD microbiome. While this does not prove causation, it strongly suggests that\nthe gut environment in some children with ASD may be more receptive to colonization by\ncertain bacterial strains within a shared household environment. While we acknowledge that\nthe distance between identifying these microbial features and developing actionable treatments\nis considerable, our findings nonetheless establish the gut microbiota as a promising, adjustable\ntarget. This observation could inform future studies exploring whether modifying the shared\nmicrobial environment within families represents a viable intervention strategy. We believe\nthese adjustments provide a clearer and more measured interpretation of our findings, and we\nappreciate your guidance in strengthening the manuscript.\nReviewer #3:\nRemarks to the Author:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We thank the reviewer for their participation in the peer-review process. We\nsincerely appreciate the time and effort invested in evaluating our manuscript, and we are\ngrateful for the valuable contribution made through this collaborative review initiative by\nNature Communications.\nPOINT-BY-POINT REPLY TO EDITORS AND REVIEWERS\nDear reviewers,\nWe are deeply grateful for your continued engagement and for the additional insightful\nfeedback, which has further strengthened our work, particularly in refining the precision of our\nterminology. We have provided a point-by-point response to all comments below. The revised\nmanuscript is attached in both tracked and clean versions, with all changes highlighted in yellow\nfor your convenience.\nWe appreciate the opportunity to resubmit and hope this revision is acceptable for publication.\nYours sincerely\nSiew Ng on behalf of co-authors\nReviewers' Comments:\nReviewer #1:\nRemarks to the Author:\n1. Causal and directional language\nRephrase any statements implying influence, susceptibility, receptivity, or directionality to\nstrictly associational language, as no causal inference can be made from the current study design.\nSection: Results (strain-sharing summary)\nSentence to be replaced or modified:\n“These differential sharing patterns underscore the significant influence of the familial context\non the ASD microbiome.”\nSuggested replacement:\n“These differential sharing patterns underscore a strong association between familial context\nand gut microbiome configuration in ASD.”\nResponse: We sincerely appreciate the reviewer's critical reminder regarding the causal and\ndirectional language. We fully agree that as a cross-sectional study, our data support\nassociations rather than causal influences. We have replaced the word \"influence\" with \"strong\nassociation\" and \"ASD microbiome\" with \"gut microbiome configuration in ASD,\" exactly as\nsuggested.\nLine 211: These differential sharing patterns underscore a strong association between familial\ncontext and gut microbiome configuration in ASD.\n2. Strain-sharing interpretation (transmission vs association)\nStrain sharing observed in a cross-sectional cohort cannot distinguish direct microbial\ntransmission from co-acquisition or convergence and should therefore be framed as association\nor co-occurrence only.\nSection: Results (strain-level comparison across family types)\nSentence to be replaced:\n“These findings indicate that the gut environment in children from ASD families facilitates the\ntransmission of specific bacterial strains.”\nSuggested replacement or modified:\n“These findings indicate that children from ASD families exhibit distinct strain-sharing patterns\ncompared with TD families, without implying direct microbial transmission.”\n4. Host “susceptibility” or “receptivity” framing\nPhrasing that implies intrinsic host susceptibility or biological receptivity is not supported\nwithout direct host-level functional measurements.\nSection: Discussion (interpretation of strain-sharing patterns)\nSentence to be replaced or modified:\n“The gut environment in children from ASD families may be more receptive to the stable\ncolonization of specific bacterial strains.”\nSuggested replacement:\n“The gut microbiome configurations observed in children from ASD families may be associated\nwith increased persistence or detectability of specific bacterial strains.”\nResponse: We sincerely thank the reviewer for these critical insights. We agree that a cross-\nsectional study design limits our ability to distinguish between direct transmission and other\necological processes. As suggested, we have revised the text in the Results section. This new\nphrasing accurately reflects the associative nature of our data while avoiding unsupported\nimplications of direct microbial transmission or intrinsic host receptivity.\nIn accordance with your suggestions from comment 2 & 4, we have:\n1. Re-framed our findings in the Results as \"distinct strain-sharing patterns\" rather than\n\"facilitating transmission.\"\n2. Replaced terms like \"receptivity\" with more objective descriptions such as \"increased\npersistence or detectability\" of specific strains.\n3. Clarified that these configurations are \"associated with\" microbiome patterns without\nimplying a direct change in microbial transmission.\nLine 212: Rather than direct transmission, these findings indicate that children from ASD\nfamilies exhibit distinct strain-sharing patterns compared with TD families. These patterns,\nwhich may be influenced by shared environmental and host factors, suggest that the gut\nmicrobiome configurations in ASD families are associated with the increased persistence or\ndetectability of specific bacterial strains.\n3. Use of “opportunistic pathogen” terminology\nLabeling taxa as pathogens risks clinical overinterpretation, particularly when pathogenicity is\ncontext-dependent and not assessed in this study.\nSection: Results (description of shared taxa)\nSentence to be replaced or modified:\n“including the opportunistic pathogens Eubacterium rectale, Dorea formicigenerans, and\nAcidaminococcus intestini.”\nSuggested replacement:\n“including taxa with reported opportunistic or context-dependent pathogenic potential, such as\nEubacterium rectale, Dorea formicigenerans, and Acidaminococcus intestini.”\nResponse: We agree with the reviewer that the pathogenicity of these taxa is context-dependent\nand was not directly assessed in this study. To avoid clinical overinterpretation, we have\nadopted the more nuanced phrasing suggested by the reviewer to describe these taxa.\nLine 199: Notably, several bacterial strains showed significantly higher sharing rates in\nmultiplex ASD families compared to TD controls, including taxa with reported opportunistic\nor context-dependent pathogenic potential, such as Eubacterium rectale (SGB4933), Dorea\nformicigenerans (SGB4575), and Acidaminococcus intestini (SGB5736) (p < 0.05, Figure 4;\nadj\nSupplementary Table S20).\nLine 259: This elevated sharing is characterized by a group of microorganisms typically\nrecognized for their opportunistic or context-dependent pathogenic potential, including\nEubacterium rectale (SGB4933), Dorea formicigenerans (SGB4575) and Acidaminococcus\nintestini (SGB5736).\n5. Therapeutic and interventional implications\nTranslational or interventional relevance cannot be inferred from cross-sectional observational\ndata and must be framed as hypothesis-generating only.\nSection: Discussion (future directions or concluding paragraph)\nSentence to be replaced or modified:\n“This identifies the shared familial microbiome as a promising, adjustable target for future\nintervention strategies.”\nSuggested replacement:\n“This identifies the shared familial microbiome as a hypothesis-generating context that may be\nrelevant for future interventional studies, pending longitudinal and mechanistic validation.”\nResponse: We completely agree that translational implications must be approached with\ncaution in the absence of longitudinal or mechanistic data. We have revised our conclusion to\nemphasize that our findings serve as a foundation for hypothesis generation rather than direct\nclinical recommendations.\nLine 217: This identifies the shared familial microbiome as a hypothesis-generating context\nthat may be relevant for future interventional studies, pending longitudinal and mechanistic\nvalidation.\n6. Genetics discussion\nHost genetic contributions are discussed without corresponding genetic data and must be clearly\nidentified as speculative.\nSection: Discussion (genetics paragraph)\nResponse: We agree that host genetic contributions remain speculative without direct\nsequencing data. We have revised the relevant paragraph in the Discussion to explicitly identify\nthese points as hypotheses. Specifically, we have added a clarifying statement that \"host\ngenetics were not directly assessed in the current study\" and ensured that all related conclusions\nare framed using speculative language (e.g., \"might potentially\" and \"remain speculative\").\nLine 297: In the context of our study, host genetics were not directly assessed in the current\nprogress. Therefore, we speculatively suggest that a shared genetic background in multiplex\nfamilies might potentially contribute to the observed strain-sharing patterns and microbiome\nconvergence, alongside shared environmental exposures. However, without corresponding host\nfunctional or genetic data, these potential links remain speculative. Further integrative studies\nare needed to elucidate the potential associations between host genetic backgrounds and the\nheterogeneity of the gut microbiome across different ASD family types.\n7. Directionality and causation disclaimer\nThe manuscript should include an explicit and unambiguous statement clarifying that\ndirectionality cannot be inferred, as from a cross-sectional study, one cannot distinguish\nbetween direct microbial transmission, co-acquisition from shared environments, or\nmicrobiome convergence driven by unmeasured host factors, and no inference regarding\ndirectionality can be made.\nSection: Limitations\nAlso: As all participants were Hong Kong Chinese, the generalisability of these findings to\nother populations remains to be established\nResponse: We have added an explicit and unambiguous disclaimer in the Limitations section\nto address the inherent constraints of our study design. The revised text now clarifies that our\nstudy cannot distinguish between \"direct microbial transmission, co-acquisition from shared\nenvironments, or microbiome convergence driven by unmeasured host factors.\" Furthermore,\nwe have highlighted the limited generalizability of our findings, noting that the cohort consisted\nexclusively of Hong Kong Chinese participants.\nLine 309: Crucially, no inference regarding the directionality of our findings can be made. As\na cross-sectional study, our design cannot distinguish between direct microbial transmission,\nco-acquisition from shared environments, or microbiome convergence driven by unmeasured\nhost factors or common genetic susceptibility; therefore, directionality and causality cannot be\ninferred. Second, while we adjusted for collected covariates, observational human microbiome\nstudies are inherently subject to unmeasured confounders, such as specific household\nenvironmental factors (e.g., the number of pets) that were not recorded. Finally, as all\nparticipants in this study were Hong Kong Chinese, the generalizability of these findings to\nother ethnic or geographical populations remains to be established. Further longitudinal studies\nand mechanistic investigations are required to distinguish between these ecological processes\nand validate our findings in more diverse cohorts.\nReviewer #3:\nRemarks to the Author:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We thank the reviewer for their participation in the peer-review process as part of\nthe Nature Communications co-review initiative. We sincerely appreciate the professional and\nconstructive feedback provided by the team."}]

Decision letter