Alterations in fecal virome and bacteriome virome interplay in children with autism spectrum disorder

Yating Wan; Lin Zhang; Zhilu Xu; Qi Su; Ting-Fan Leung; Dorothy Chan; Oscar WH Wong; Sandra Chan; Francis KL Chan; Hein M Tun; Siew C Ng

doi:10.1016/j.xcrm.2024.101409

. 2024 Feb 1;5(2):101409. doi: 10.1016/j.xcrm.2024.101409

Alterations in fecal virome and bacteriome virome interplay in children with autism spectrum disorder

Yating Wan ^1,^2,^3,⁸, Lin Zhang ^1,^2,^3,⁸, Zhilu Xu ^1,^2,³, Qi Su ^1,^2,³, Ting-Fan Leung ^6,⁸, Dorothy Chan ⁶, Oscar WH Wong ^7,⁸, Sandra Chan ^7,⁸, Francis KL Chan ^1,^4,⁸, Hein M Tun ^1,^3,^5,⁹, Siew C Ng ^1,^2,^3,^8,^9,^10,^∗

PMCID: PMC10897546 PMID: 38307030

Summary

Emerging evidence suggests autism spectrum disorder (ASD) is associated with altered gut bacteria. However, less is known about the gut viral community and its role in shaping microbiota in neurodevelopmental disorders. Herein, we perform a metagenomic analysis of gut-DNA viruses in 60 children with ASD and 64 age- and gender-matched typically developing children to investigate the effect of the gut virome on host bacteria in children with ASD. ASD is associated with altered gut virome composition accompanied by the enrichment of Clostridium phage, Bacillus phage, and Enterobacteria phage. These ASD-enriched phages are largely associated with disrupted viral ecology in ASD. Importantly, changes in the interplay between the gut bacteriome and virome seen in ASD may influence the encoding capacity of microbial pathways for neuroactive metabolite biosynthesis. These findings suggest an impaired bacteriome-virome ecology in ASD, which sheds light on the importance of bacteriophages in pathogenesis and the development of microbial therapeutics in ASD.

Keywords: autism spectrum disorder, autism, virome, bacteriome, interplay, functional pathways, metagenomics, diet, phage, bacteriophage

Graphical abstract

Highlights

•
ASD is a key factor contributing to gut-DNA virome compositional variation
•
ASD-enriched phages are largely associated with altered viral ecology in ASD
•
Gut bacterial-viral networks are disrupted in ASD
•
Mediation of phages on pathways coding for neuroactive metabolites is impaired in ASD

Wan et al. depict that children with ASD harbor an altered gut viral community and increased abundances of Clostridium phage, Bacillus phage, and Enterobacteria phage. Concomitantly, the bacterial-viral interplay changes in ASD may influence the mediation effect of phages on the encoding capacity of microbial pathways for neuroactive metabolite biosynthesis.

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social communication and repetitive behaviors.¹ It is responsible for nearly 10% of developmental disabilities in children and adolescents, and the prevalence of ASD has increased dramatically over the past decade.² Although the etiology of ASD remains unclear, experimental and clinical studies have shown that children diagnosed with ASD displayed alterations in the gut microbiota.³^,⁴^,⁵ Among the trillions of microbes in the gut, the bacterial communities have been shown to play an important role in modulating the microbiota-gut-brain axis.⁵^,⁶^,⁷ A reduction in gut bacteria diversity, altered gut bacteria composition, and changes in bacteria neuroactive metabolites biosynthesis pathways have been reported in children with ASD.⁴^,⁸^,⁹^,¹⁰^,¹¹ However, the bacterial associations reported in ASD, which may due to ASD itself, restricted dietary preferences, or other characteristics that occur in children with ASD, remain controversial.¹²

Apart from bacteria, humans are also colonized by a remarkable diversity of viruses. The most predominant members of the gut viruses are DNA nature bacteriophages (phages), constituting 90% of gut viruses,¹³^,¹⁴^,¹⁵ while RNA viruses are a small proportion, most of which consist of plant-derived RNA viruses.¹⁶ Phages are a key factor in shaping human microbiome diversity and functions by shifting bacterial composition and modulating bacterial metabolisms.¹⁷^,¹⁸^,¹⁹^,²⁰^,²¹ Altered phage composition has been reported to be associated with impaired mood, brain function, and memory.²²^,²³ Siphoviridae and Caudovirales phages have been shown to enhance the capacity for novel object recognition and memory via up-regulating memory-involved brain genes in flies and mice, respectively.²³ In addition to the gut phage composition, phage-encoded auxiliary functions showed a potential role in promoting a human healthy lifespan by contributing to bacterial sulfate metabolic pathways.²⁴ However, the gut viruses are relatively underexplored in existing microbiota studies of ASD. This study therefore investigated gut virome composition and the bacteriome-virome interplay in children with ASD. We characterized the role of the gut-DNA virome in children with ASD using metagenome-assembled genomes. Herein, we depict changes in the viral microbiome community in children with ASD compared to typically developing (TD) children. We further demonstrate altered interkingdom microbial interactions in ASD by characterizing the relationship between bacteria and viruses and comparing the mediation relationship of phages and bacterial pathways encoding for neuroactive metabolites biosynthesis in children with and without ASD. These observations reveal that children with ASD harbor an altered gut-DNA virome configuration with an impaired modulation ability in the biosynthesis of neuroactive metabolites which provides a rationale for considering the reconstruction of the enteric virome as a novel therapeutic strategy for ASD.

Results

Subjects’ clinical characteristics

A total of 124 children aged 3 to 6 years (mean age ± standard deviation: 4.8 ± 1.0) were recruited including 60 with confirmed ASD and 64 age- and gender-matched TD children. Most of the children were male (81.7% in ASD, 84.3% in TD). There were no differences between ASD and TD children in terms of body mass index (BMI), diet diversity, gastrointestinal symptoms, or early childhood characteristics such as mode of delivery, gestational age at birth, and duration of breastfeeding. Subjects’ demographics, dietary records, and early-childhood characteristics are shown in Table 1.

Table 1.

Demographic summary of participants in the metagenomics study

Characteristic	ASD (n = 60)	TD (n = 64)
Subjects (n)	60	64
Age, months (1st–3rd quartile)	59.0 (51.0–66.0)	56.0 (44.0–66.0)
Gender, male %	81.7	84.30
Male, n	49	54
Female, n	11	10
Body mass index (1st–3rd quartile)	14.95 (14.32–15.93)	15.18 (14.69–15.96)
Weight, kg	17.8 (15.6–19.2)	16.7 (15.1–19.4)
Height, cm	107.9 (103.3–112.1)	104.6 (98.8–112.7)

Dietary score

DDSsum FFQ	4.0 (3.0–5.0)	4.5 (3.5–5.0)
DDSsum DR	6.0 (4.5–7.0)	6.0 (5.5–7.0)

GI symptoms

Constipation, n (%)	8 (13.1)	5 (7.8%)
Diarrhea, n	0	0

Mode delivery

Vaginal delivery, n (%)	42 (70.0)	45 (70.3)
Cesarean delivery, n (%)	18 (30.0)	19 (29.7)

Gestational age

Preterm, n (%)	7 (11.7)	2 (3.1)
Term, n (%)	53 (88.3)	62 (96.9)

Breastfeeding

Period of breastfeeding, months	6.0 (0.5–11.3)	10.0 (2.0–22.3)

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Data are presented as median and interquartile range or as n/out of total n (%). DDSsum FFQ, dietary diversity sum score from Food Frequency Questionnaire; DDSsum DR, dietary diversity sum score from short-term diet record; GI, gastrointenstinal.

ASD represents a key determinant factor contributing to altered gut-DNA virome composition

First, we included ASD diagnosis, gender, age, BMI, diet, and early-life characteristics to identify covariates of the gut-DNA virome. Gut-DNA virome variation was measured at the level of species and at a high-resolution level of virus-like sequences (VLSs; see STAR Methods). At the VLS level, we found that children with ASD showed altered gut-DNA virome composition and diversity compared to TD children (Figure 1). In addition, a diagnosis of ASD, age, and delivery mode were found to have greater associations with gut-DNA virome composition using VLSs compared to taxonomic viral species (e.g., ASD diagnosis: R² = 1.40%, p = 0.007 in VLSs, R² = 1.27%, p = 0.87 in species level; age: R² estimates: 1.18%, p = 0.02 in VLSs, R² estimates: 0.71%, p = 0.07 in species level; Figures 1A and 1B), suggesting that the associations of VLSs and taxonomic species with host phenotypes were different. After generating a high-resolution level of assembly of the gut virome, ASD was shown to be the top covariate with the largest effect size accounting for the variation in the gut-DNA virome (Figure 1A). Gut virome composition in children with ASD was significantly different from that of TD children (p = 0.007, based on the Bray-Curtis dissimilarities; Figure 1C). Apart from the diagnosis of ASD, we found that BMI also showed an association with gut-DNA virome changes in all children. However, there was no difference in the BMIs between ASD and TD children and no evidence for a two-way interaction (ASD × BMI; p = 0.173). Dietary diversity also did not show significant associations with gut-DNA virome compositional variation, and the effect of ASD on the gut-DNA virome was independent of diet (Figures 1A and S1A; Table S1). Furthermore, we further explored whether virome diversity may be a downstream consequence of diet, but diet was not correlated with gut-DNA virome richness or Shannon diversity (Figure S1B). These findings indicated that BMI and diet were unlikely to have been confounding factors in the relationship between ASD and the gut virome. Other characteristics including age, gender, and early-childhood characteristics showed no significant association with the gut-DNA virome at the VLS level (VLSs: R² = 0.5%–1.1%; all p > 0.05; Figures 1A and S1). Overall, these data suggested that ASD represented a key determinant contributing to gut-DNA virome compositional variation.

The proportion of gut-DNA virome variance associated with host phenotypes and virome diversity analysis

(A and B) Phenotypes, including ASD, intrinsic-host factors (age, gender, BMI), dietary traits (short- and long-term diet diversity), and early-life characteristics (breastfed period, gestational age, and delivery mode) in fecal VLSs (A) and viral taxonomic species-level (B) analysis. The x axis shows the proportion of gut-DNA virome compositional variance associated with the host phenotypes. Effect size estimates for analysis with and without covariates are indicated by circles and squares, respectively. ∗p<0.05 and ∗∗p<0.01.

(C) Redundancy analysis (RDA) of the gut VLS composition responding to host factors. Arrows in the RDA denote the magnitudes and directions of the effect of host factors in shaping the gut-DNA virome in children.

(D and E) Boxplots of VLS counts (D) and Shannon diversity (E) in children with ASD and TD children. Statistical significance was determined by Welch’s t test.

(F) Heatmap of correlation between host factors and VLS richness and Shannon diversity. Correlations were calculated through Spearman’s rank correlation analysis. The color intensity of the bottom bar is proportional to the correlation coefficient, where blue indicates inverse correlations and red indicates positive correlations. ∗p<0.05.

The gut-DNA virome richness (number of VLS counts) was lower and more heterogeneous across children with ASD compared to TD children (mean VLSs: 20,251 in ASD and 22,340 in TD, p = 0.031; Figure 1D). Viral richness was significantly correlated with ASD diagnosis and age, while viral Shannon diversity did not show any correlation with these host factors (Spearman’s rank correlation; Figures 1E and 1F). Among all children, the gut-DNA virome was dominated by bacteriophages (>70%), followed by eukaryotic viruses (∼24%; Figure S1C).

Children with ASD have increased abundances of gut Clostridium phage, Bacillus phage, and Enterobacteria phage

We next tested the differential abundance of VLSs using analysis of composition of microbiome²⁵ to identify the ASD-specific virome. After adjusting for age, gender, and BMI, 177 VLSs showed significantly differential abundances between children with ASD and TD children at a conventional detection threshold >0.7 (Figures 2A and S2A). Most differential abundant VLSs (96.0%) showed higher prevalence in the ASD group, while only 7 VLSs were more prevalent in the TD group (Figure 2B). Around 80.0% of differential VLSs were identified as phages, of which many differential VLSs (27.1%) were annotated to Clostridium phage, Bacillus phage, and Enterobacteria phage (Figure S2A). The abundance and prevalence of these phages were all significantly higher in children with ASD than in TD children, particularly an increased number of Clostridium phage c-st, which was reported to carry neurotoxin genes (Figures 2C–2E and S2B). The above results suggest that gut-DNA virome compositional alterations were largely attributed to ASD-enriched viruses, and Clostridium phage, Bacillus phage, and Enterobacteria phage identified as ASD-specific VLSs had a significant association with ASD. In ASD, we also observed a reduction of Caudovirales (Figure S2C), which has been reported to be associated with improved performance in brain executive processes and verbal memory in humans.²³ In addition, Anelloviridae, a potential human pathogen associated with changes in host immune status, was found to be increased in the gut of children with ASD (Figure S2D).²⁶ We found that phage lysogeny was marginally increased in ASD but that the equilibrium between lytic and lysogenic states of phages was not significantly different between the two groups (Figure S2E).

Abundances of *Clostridium* phage, *Bacillus* phage, and *Enterobacteria* phage were enriched in ASD

(A) VLS results of analysis of composition of microbiome (ANCOM) between children with ASD and TD children, adjusting for covariates (age, gender, BMI). A threshold >0.7 is considered significantly differentially abundant.

(B) Distribution of 177 differential VLSs prevalent in children with and without ASD. Red line and area under the curve denote the distribution of differential abundant VLS prevalence in the ASD group, and the blue line and area under the curve denote the distribution of differential abundant VLS prevalence in the TD group.

(C–E) Dot plots comparing the abundances and prevalence of the differential VLSs annotated to *Clostridium* phage (C), *Bacillus* phage (D), and *Enterobacteria* phage (E) between 2 groups.

Compared to the bulk metagenome,³ the enriched viral-like particle metagenome (VLPM) showed higher efficiency and sensitivity in viral identification. Bulk metagenomes showed a lower level of reads recruitment in the viral catalog (median: 56.5% vs. 90.6% in enriched VLPM) and a lower sensitivity in identifying rare and low-abundance fecal VLSs (Figure S3). Besides, the differential VLSs identified in enriched VLPM cannot be fully reproduced in bulk metagenome data (Figure S3C). We also obtained fecal bulk metagenomes from an independent Hong Kong cohort consisting of 78 children with ASD and 90 TD children.¹¹ Gut-DNA virome richness was significantly lower (p = 0.025) in children with ASD compared with TD children, though changes in compositional configuration were not observed. Clostridium phage and Bacillus phage consistently showed higher abundance in children with ASD when compared to TD children (Figure S4). Differences in Enterobacteria phage and Caudovirales between children with ASD and TD children have not been observed in this independent cohort, and the inconsistency may due to the lower sensitivity of viral identification of the bulk metagenome. These findings suggest that our decontaminating strategy was effective and ensured that most of the data were included in the analysis. Viral identification in the bulk metagenome was not as sensitive as in enriched VLPM.

Gut viral-viral interactions are altered in children with ASD

Gut microbes do not exist in isolation but form complex ecological webs in the human intestine, and the interactions within microbial communities play important roles in maintaining human health.²⁷ To further assess ecological interactions in the gut viral community in ASD, we tracked the relationship alterations of common VLSs (VLS prevalence >80%) between the two groups. Among these VLSs, the viral-viral interactions were altered in children with ASD, characterized by many VLS relationships being inverse in children with ASD compared to TD children (VLS relationship alterations: inverse correlation: 41.1%, strength of correlation higher in ASD: 25%, strength of correlation higher in TD: 33.3%; Figure 3A). The correlation between Bacillus virus Pookie and Escherichia virus was negative in TD children but shifted to a positive correlation in children with ASD (Figures 3A; Table S2). Besides, the ASD-enriched Bacillus phage was identified to have a key role in viral interactions dysbiosis and was responsible for 24.5% of altered viral-viral relationships, followed by Enterobacteria phages and then phages of Clostridium, which accounted for 22.5% and 8.8% of the altered viral-viral relationship, respectively (Figure 3B). These results suggest that ASD-related VLSs associate with viral-viral interaction dysbiosis.

Gut viral-viral ecology altered in children with ASD

(A) The significantly altered gut viral community network between children with ASD and TD children. Red line denotes that the microbial relationship is inverse between 2 groups, blue dash line denotes that the microbial relationship is stronger in children with ASD, and red dotted line denotes that the microbial relationship is stronger in controls. Only statistically significant altered correlations were plotted; p < 0.05.

(B) The proportion of the VLSs in the altered viral community network.

Gut bacterial-viral networks are disrupted in ASD

Gut bacteriophage shape the composition and evolution of bacterial communities in humans.²⁸ To reveal a comprehensive perspective of the gut microbiome ecological interaction in children with ASD, we investigated the bacterial-viral networks in two groups. There was a significant difference in children with ASD and TD children in the overall interplay of bacterial and viral communities between the two groups (Figure 4). We observed a significant positive correlation between viral and bacterial richness in TD children, but the association was not observed in children with ASD (Figure 4A), suggesting that a closely intertwined virus-bacteria community existed in healthy children but not in children with ASD. The robustness of the bacterial-viral network was assessed, and the bacterial-viral ecosystem was fragile in children with ASD. When the relationships among microbes were randomly removed, the interactions between the remaining relationships in the microbial network were largely affected and disrupted in children with ASD compared to TD children (Figure 4B). Furthermore, we found that the number of significant associations of ASD-related bacteria and bacteriophages were more in children with ASD than in TD children, with an increase of 1.4-fold in Clostridium, 1.4-fold in Alistipes, and 5.8-fold in Enterococcus (Figure 4C), whereas the number of significant correlations of bacteriophages with Coprococcus and with Pseudoramibacter were more in TD children (increased 1.6-fold in Coprococcus and increased 1.8-fold in Pseudoramibacter) than in children with ASD. Coprococcus was reported to have a lower abundance in children with ASD.²⁹ In contrast to TD children, the relationships between VLSs and Bacteroides were dominated by negative correlations (87.2%), and the relationships between VLSs and Ruminococcus were dominated by positive correlations (87.1%) in children with ASD. Such changes in the gut microbiome ecological network suggest that interkingdom communication and interplay are significantly altered in children with ASD.

Interkingdom correlations of gut bacteria and viruses altered in children with ASD

(A) Correlations between the α-diversity (Shannon diversity, richness) of fecal bacteria and VLSs in children with ASD and TD children, respectively. Spearman’s correlation coefficient was calculated, while statistical significance was determined for all pairwise comparisons. Significant correlations are displayed with an asterisk; ∗p < 0.05, ∗∗p < 0.01and ∗∗∗p<0.001.

(B) Robustness curves for bacterial-viral networks. The robustness of a network was measured by removing nodes (based on degree) and edges (randomly selected) and measuring the percentage of nodes/edges that remain in the central connected component. The results are plotted here with the percentage of nodes/edges removed on the x axis and the percentage of remaining nodes/edges in the central connected component on the y axis. A larger area under the curve indicates a more robust network.

(C) Interkingdom correlations between gut VLSs (tan circle) and bacterial genus (blue diamond). Correlations between bacterial genus and VLSs were calculated through the sparse inverse covariance estimation (SpiecEasi) correlation test in each group. The correlation coefficient was calculated, while statistical significance was determined for all pairwise comparisons. Only statistically significant correlations were plotted. The size of nodes corresponding to bacteria genus/VLSs is proportional to the number of significant connections. The color intensity of the connective line is proportional to the correlation coefficient, where blue lines indicate negative correlations and red lines indicate positive correlations.

Previously, we found that Faecalibacterium was reduced in children with ASD, whereas Clostridium showed higher abundance.³ Herein, we found that Faecalibacterium phages positively correlated with the host Faecalibacterium (coefficient r: 0.22, p = 0.0132). However, the significant correlations between Faecalibacterium phage Epona, Faecalibacterium phage Lugh, and host Faecalibacterium prausnitzii in TD children disappeared in children with ASD (Figure S5, left). Besides, many more significant positive correlations between phages and their host Clostridium perfringens were observed in children with ASD than in TD children (Figure S5, right). These findings suggest that the relationship between the ASD-related bacterial species and their bacteriophages was altered in children with ASD compared to TD children.

Mediation relationship of bacteriophages and microbial pathways encoding for neuroactive metabolites biosynthesis is altered in ASD

First, we assessed the role of phages in host cell metabolism modulation by identifying phages encoding auxiliary metabolic genes (AMGs) in children with ASD and TD children.³⁰^,³¹ A diagnosis of ASD had the largest association with viral function variation (ASD: R² estimates = 1.2%, p = 0.091; Figure S6A), and the viral-encoded AMGs involved in overall energy metabolism and the antibiotic resistance pathway were significantly increased in children with ASD compared to TD children (Figures S6B and S6C).

Then, we dissected the link between bacteria function diversity and phage AMG diversity. As shown in Figure 5, AMG richness and Shannon diversity were positively associated with Shannon diversity of the bacteria functional pathway in reciprocal regression analyses (b = 0.005, p = 0.009 and b = 0.09, p = 0.03, respectively), suggesting a positive reciprocal effect of phage AMG diversity and bacterial functional coding diversity.

Relationships between phage AMG diversity and bacteria functional pathway diversity

Linear model plots to visualize model outputs, showing effect sizes (95% confidence interval) and p value for the independent variable.

(A) Linear model coefficients taking bacterial pathway richness as the dependent variable.

(B) Linear model coefficients taking bacterial pathway Shannon diversity as the dependent variable.

Dysfunction in the biosynthesis of neuroactive amino acids and neuroactive metabolite precursors was thought to link to ASD.³^,³²^,³³ Our data suggest that phages may influence host bacterial metabolism via mediating microbial encoding capacity. Next, we explored whether phages are involved in mediating the coding capacity of neuroactive amino acid metabolism in their host and whether these processes are altered in children with ASD. We previously found that bacterial functional pathways involved in glycine, tryptophan, thiamin, threonine, and chorismite biosynthesis were significantly decreased in children with ASD compared to TD children.³ Among them, Faecalibacterium prausnitzii was identified as one of the main contributors to serine and glycine biosynthesis as well as threonine biosynthesis (Figures S7A). Interestingly, Faecalibacterium virus Lugh, one of the phages that infected Faecalibacterium prausnitzii, was positively correlated with these amino acid biosynthesis pathways (SER-GLYSYN-PWY: r: 0.19, p = 0.03; THRESYN-PWY: r: 0.12, p = 0.17; Figure S7B). These observations prompted us to further pursue the link between phages and bacterial neuroactive amino acid biosynthesis pathways. The direct and indirect effects of bacteria and phages on functional pathways were calculated after adjusting for age, gender, and BMI. Bacteria predominated their respective functional pathways (Figure 6), but concomitantly, the several neuroactive amino acid biosynthesis pathways encoded in Faecalibacterium prausnitzii and Escherichia coli were subsequently affected by the presence of their phages, respectively. These significant phage modulations were only observed in TD children but disappeared in children with ASD (Figure 6). The presence of Faecalibacterium virus Lugh significantly affected the host bacteria coding capacity of serine and glycine biosynthesis in healthy controls, but the effect was not significant in children with ASD (Figure 6A). In addition, phages of Escherichia contributed to tyrosine, valine, and isoleucine biosynthesis pathways in their host bacteria in healthy controls but not in children with ASD (Figure 6B). This trend suggested that the effect of phages to modulate and mediate host cell neuroactive metabolite biosynthesis coding capacity was impaired in ASD. These amino acids are viewed as important compounds involved in neurodevelopment and synaptogenesis; hence, the disrupted modulation capacity of these amino acid metabolisms is believed to be associated with ASD.

Mediation linkages among the gut bacteria, phages, and microbial functional pathways

Mediation effects of Faecalibacterium phage (A) and Escherichia phage (B) after adjusting for age, gender, and BMI. The red arrows indicate the bacteriophages’ effect on the neurotransmitter biosynthesis-related pathways owned by their respective host bacteria. p_asd indicates the p value of regression analysis assessed in the ASD group, and p_td indicates the p value of regression analysis assessed in the TD group.

Discussion

To our knowledge, this study represents the most in-depth shotgun metagenomic analysis to delineate the role of the gut virome and bacteriome-virome interplay in children with ASD. Characterizing gut viral community, in particular phage, composition and functions in ASD contributes to our understanding of disease-associated gut microbiome alterations. We focused on virus-like particles to avoid extracting and sequencing non-viral nucleic acids. An increased proportion of viral reads improved viral genome assemblies that enable sensitive detection even of low-abundance and rare viruses.³⁴ Consistently, a higher efficiency of viral identification was observed in the enriched VLPM when compared with the bulk metagenome (Figure S3) in this study. Due to limitations in existing reference genome databases for gut viruses, we adopted a database-independent approach by de novo assembly and binning to allow for high-level resolution of virome (VLSs) analysis that can more accurately capture and characterize alterations in the gut virome in children with ASD.¹²^,³⁵ By integrating the composition and functionality of the gut virome in association with the children’s characteristics, we demonstrate gut-DNA virome compositional variation driven by ASD that was not confounded by other factors that were available; in particular, the mediation effect of bacteriophages on the microbial coding capacity of neuroactive amino acid biosynthesis is impaired in children with ASD.

To address the possibility of dietary differences among children surveyed in this study, we assessed associations between their dietary intake and gut virome profile, taking into account other parameters. The majority of children (>90%) with and without ASD did not have a restrictive or special diet (e.g., vegetarian, metabolic, or diabetic diet), and there was no significant difference in the diversity of food intake between the two groups (p = 0.064), suggesting that variety in food was similar in both groups of children. Consistent with the result in the bacterial community, diet did not have a statistically significant effect on the DNA viral community (α- and β-diversity), suggesting that the gut bacteriome and virome shared a similar response to environmental stimuli³⁶ and that diet was unlikely to have a confounding factor in this cohort.

Recently, emerging evidence has suggested that gut microbiota, microbiota-derived metabolites, and virulence factors were associated with certain brain diseases.³⁷^,³⁸^,³⁹^,⁴⁰ In children with ASD, a higher abundance of Clostridium in the gut has been reported in studies from Europe and Asia.³^,⁴¹^,⁴²^,⁴³^,⁴⁴ Coincidently, our findings also identified Clostridium phages to be enriched in the gut of children with ASD. Other ASD-specific VLSs such as Bacillus phage and Enterobacteria phage were also identified as main contributors to viral-viral ecological interaction alterations in children with ASD (Figure 3B), suggesting that changes in the abundance of these phages may be triggers of gut viral ecological system dysbiosis. Besides, the close associations among these ASD-specific phages may accelerate disease progression.⁴⁵ Zhan Tong et al. reported that Streptococcal phages were significantly enriched in the mouths of children with ASD,⁴⁶ and consistently, there are 4 gut Streptococcal phages (Figure S2A) that were identified as ASD-enriched phages in our analysis. Streptococcus and the interplay with its phages may affect normal brain normal function via releasing inflammatory signals.⁴⁶^,⁴⁷

Microbial interactions are known to help maintain homeostasis of the microbial community structure and their functions in humans,⁴⁸ and disruption of this equilibrium is associated with various human diseases.⁴⁹^,⁵⁰^,⁵¹ An altered interkingdom network between the viral and bacterial communities at the gut mucosa level has been shown in inflammatory diseases including ulcerative colitis, whereby viruses and bacteria became less intertwined.⁵² We also observed a weaker correlation between the diversity of the bacteriome and virome in children with ASD compared to TD children. This observation suggests that children with ASD have more dispersed bacteria-viruses interplay, and it may lead to the impairment in phage-mediated physiological processes including facilitating horizontal gene transfer and reprogramming microbial metabolisms.⁵³ Some experimental studies have demonstrated the importance of the relationship between bacteriophages and bacterial hosts in health.⁵⁴^,⁵⁵^,⁵⁶^,⁵⁷ Recently, Johansen et al. highlighted the potential of phages in supporting mucosal integrity and resistance to pathobionts by contributing to sulfate metabolic pathways in centenarians.²⁴ The mediation effect of phages on metabolic pathways has been evidenced in an in vitro study whereby the phage Xccφ1 ameliorated antibiotic resistance by regulating amino acid pathways that interfered with biofilm formation.⁵⁸ In this study, the mediation effect of phages on the coding capacity of glycine, valine, isoleucine, and tyrosine biosynthesis in their respective host bacteria was impaired in children with ASD. Notably, lower levels of serine, glycine, valine, isoleucine and tyrosine were reported in ASD serum and urine specimens,³³^,⁵⁹^,⁶⁰ and these amino acids enable the crossing of the blood-brain barrier, as shown in animal experiments and clinical trials.⁶¹^,⁶²^,⁶³ These findings indicated that bacteriophages likely had an impact on human health via their mediation effects on coding capacity of bacterial functions.

In conclusion, our study provides new insights into the potential role of the gut virome and virome-bacteriome interplay in the pathogenesis of ASD via metagenomic whole-virome analysis. Our data highlight that the gut virome and its interplay with the bacteriome are linked to health and that alterations in the abundance of ASD-specific virome may drive gut viral dysbiosis in children with ASD. The phage-associated mechanism of ASD pathogenesis deserves further investigations and validation, which can potentially provide valuable clues and open new avenues for prevention and management strategies for ASD.

Limitations of the study

This study has several limitations. First, the relatively small sample size of the ASD stool microbiome may lead to sampling biases that require larger studies to overcome. Second, we did not study the gut-RNA virome, including some diet-originated plant RNA viruses that may be sensitive to diet. Third, the fecal metabolite and cause-consequence mechanisms behind the link between phages and ASD are still unclear. The bacteria and its phages identified from children with ASD and the intriguing phage-associated mediation mechanism in ASD pathogenesis deserve further experimental validation. Fourth, data on ASD clinical parameters in this cohort were not systematically collected and could not account for the gut-DNA virome variation with ASD severity.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Chemicals

Magnesium Sulfate (MgSO4·7H2O)	Sigma	230391
Tris	Sigma	10708976001
Gelatin	Sigma	G1393
Sodium Chloride (NaCl)	Sigma	S7653
Lysozyme from Chicken Egg White (100 MG/ML)	Sigma	L6876-1G
Chloroform	Sigma	C2432
Baseline-Zero™ DNase Reaction Buffer (10X)	Epicentre	DB0715K
Baseline-Zero™ DNase Stop Solution (10X)	Epicentre	DB0715K
Sodium Dodecyl Sulfate (SDS)	Sigma	L3771
Proteinase K from Tritirachium Album (20 MG/ML)	Sigma	P6556-100MG
Hexadecyltrimethylammonium Bromide (CTAB)	Sigma	52365-50G
Phenol-Chloroform-Isoamyl Alcohol Mixture (PCI)	Sigma	77617
Tris-EDTA Buffer (1X)	Promega	V6231
β-Mercaptoethanol	Sigma	M3148
Lyticase	Sigma	L2524

Critical Commercial Assays

DNA Clean & Concentrator™-5	Zymo Research	D4014
easyPure viral DNA/RNA kit	Transgene	ER201-01
GenomiPhi V2 DNA Amplification Kit	Illustra	25-6600-32
Zirconia Beads 0.5 mm	Sigma	11079195z
Zirconia Beads 0.1 mm	Sigma	11079191z

Deposited data

Fastq. Files from metagenomics sequencing	This paper	PRJNA1037036

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Resource availability

Lead contact

Further information and requests for reagents may be directed to and will be fulfilled by Lead Contact, Siew C. Ng (siewchienng@cuhk.edu.hk).

Materials availability

This study did not generate new unique reagents.

Data and code availability

•
Sequence data for the virome metagenome were deposited to the NCBI Sequence Read Archive under BioProject accession numbers PRJNA1037036.
•
This paper does not report original code.
•
Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.

Experimental model and subject details

Subject recruitment and sample collection

Sixty-one children (3–6 years old) with a diagnosis of ASD were recruited from a regional teaching hospital (Prince of Wales Hospital, Shatin, Hong Kong), and 64 TD children matched by age and gender were recruited from the community within the same period. As described in our previous study,³ the protocol was performed in compliance with the Declaration of Helsinki and approved by the Clinical Research Ethics Committee of The Chinese University of Hong Kong (CREC Ref. No.: 2014.026 and 2016.607), and the legal guardian of all subjects provided written informed consent.

Children with ASD were diagnosed by pediatricians or clinical psychologists according to the standard of the fourth or fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV or DSM-5). TD children are free of developmental delay and do not have any first-degree relatives with ASD. Parents of TD children were first asked to complete the Chinese-validated version of the Social Responsiveness Scale 2nd edition (SRS-2).⁶⁴ Only those screened negative with SRS-2 were included in this study. Children who met any of the following criteria were excluded from the study: children with a diagnosis of chronic seizures, suffering from recent infection 1-month prior to data and sample collection, having disorders that affect dietary/physical activity habits, usage of antibiotics and antifungal medications as well as prebiotics and probiotics supplements 1-month prior to data and sample collection, concurrent or recent (i.e., one month) participation in any trials or dietary intervention programs, and having other conditions as judged by the investigators as ineligible to participate. A structured interview was arranged for the recruited families for data and sample collection. The participants provided stool samples were stored at −80°C in the laboratory until processing.

Questionnaires and measurements

Detailed information as described in our previous study.³ Briefly, a standardized questionnaire was used to capture data on family demographics and other aspects of children’s health, including allergy, medication and early-life characterize (mode of delivery, period of breastfeeding and gestational age at birth).

Children’s current and usual dietary intake were assessed using a 3-day food record and a validated Chinese version of the food frequency questionnaire (FFQ). Parents were asked to complete both questionnaires in a face-to-face interview with assistance from trained research staff. Daily nutrient intake and consumption of food were calculated using the nutrition analysis software Food Processor Nutrition analysis and Fitness software version 8.0 (ESHA Research, Salem, USA). Dietary and nutrient data were used to generate the Chinese Children Dietary Index (CCDI). Since data on children’s vitamin A and detailed fatty acid intake, whether the children had breakfast or dinner with parents, and children’s sedentary behaviors were not available in the present study, these components were removed from the calculation of the CCDI. A component of CCDI diet variety score (CCDI-DVS) was used to measure children’s diet variety in the present study. Daily consumption of at least one serving from each of the food groups (grains, vegetables, fruits, dairy/beans, and meat/poultry/fish/eggs) was used to calculate diet variety. A total score of 0–10 was generated and a higher score indicates higher diet variety.

Method details

Extraction of fecal virus-like particles (VLPs) DNA and shotgun sequencing

Fecal VLPs DNA was prepared from 0.2g aliquots of feces, according to the previously described protocol.⁵²^,⁶⁵ The stool was homogenized in saline-magnesium buffer. Suspensions were collected after centrifugation (6,000 x g for 10 min) and then passed through 0.45 mm and 0.22 mm filters successively to remove residual host and bacterial cells. Filtrates were treated with lysozyme (1 mg/mL at 37°C for 30 min) followed by chloroform (0.2x volume at room temperature for 10 min) to degrade bacterial and host cell membranes. Non-viral and non-encapsulated nucleic acids were degraded by DNase cocktail treatment (10U Tubro DNaseI (Ambion), 1U Baseline zero DNase) followed by heat inactivation of DNases at 65°C for 10 min. Afterward, fecal VLPs were lysed with 4% SDS and 38 mg/mL Proteinase K at 56°C for 20 min and followed by CTAB (2.5% CTAB plus 0.5 M NaCl at 65°C for 10 min). Nucleic acid of VLPs was extracted with phenol:chloroform (pH 8.0). The aqueous fraction was washed once with an equal volume of chloroform, and extracted DNA was purified and concentrated by easyPure viral DNA/RNA kit (Transgene) and DNA Clean & Concentrator kit (Zymo Research), according to the manufacturer’s protocol. The concentration of the purified DNA was measured and amplified for 30min using Phi29 polymerase (GenomiPhi V2 kit, GE Healthcare) prior to sequencing. To reduce amplification bias, three independent reactions were performed for each sample and pooled together afterward. The extracted fecal VLPs DNA was used for library construction and ultra-deep metagenomics sequencing on an Ilumina Novoseq 6000 (Novogene, Beijing, China). An average of 45 ± 2.6 million reads per sample were obtained.

Virome sequence processing and gene catalog generation

Raw sequence reads from the DNA viral metagenomics were filtered utilizing Trimmomatic⁶⁶ using the following parameters; SLIDINGWINDOW:4:20, MINLEN:60 HEADCROP15; CROP225. Contaminating human reads were filtered using Kneaddata (Reference database: GRCh38p12) with default parameters. Megahit with default parameters was chosen to assemble the filtered reads into contigs in each sample. Assemblies were subsequently pooled together and retained if longer than 1kb. Redundancy was removed with 90% identity over 90% of the length via mmseqs2⁶⁷ and the longest contig retained to generate a unique contig consortium. Bacteria-contaminated sequences were removed by using a set of previously described inclusion criteria to select viral sequences only.³⁵^,⁶⁸ Briefly, the virus-like sequences (VLSs) were required to fulfill one of the following criteria; 1)Categories 1–6 positive from VirSorter when run with default parameters and Refseqdb (–db1), 2) circular, 3) greater than 3kb with no BLASTn alignments to the NT database (e-value threshold:1e−10), 4) a minimum of 2 pVogs with at least 3 per 1kb, 5) BLASTn alignments to viral RefSeq database (v.89) (e-value threshold:1e−10), and 6) less than 3 ribosomal proteins as predicted using the COG database. 7) BLASTn alignment to a crAssphage database. HMMscan was used to search the pVOGs hmm profile database using predicted protein sequences on VLS with and e-value filter of 1e−5, retaining the top hit in each case. This viral database includes the virus-like sequences recovered by the screening criteria from the bulk metagenomic assemblies. Then the filtered paired reads were mapped to the viral contig database with BWA.⁶⁹ The viral operational taxonomic unit (OTU) table of viral abundance was pulled from SAMtools.⁷⁰ The viral contig abundances were normalized by TPM (transcripts per million).

Virus-like sequences taxonomy annotation

The viral contigs were analyzed according to their open reading frames (ORFs). The ORFs on the contigs were predicted by using MetaProdigal (v2.6.3)⁷¹ with the metagenomics procedure (-p meta). To annotate the predicted ORFs, the amino acid sequences of the ORFs were queried by Diamond against the viral RefSeq protein (v84) with an E-value<10⁻⁵ and a bitscore >50. The viral Refseq proteins with the top closest homologies (E-value<10⁻⁵ and bitscore >50) were considered for each ORF. Contigs were taxonomically binned according to the predominant assignment of their constituent ORFs to a taxon.

Fecal bacteriome analysis

For the fecal bacteriome analysis, raw shotgun fecal bacteria metagenomes were acquired from our previous study.³ Raw sequence reads were trimmed using Trimmomatic to remove adapters and low-quality regions (Trimmomatic-0.36) and then removed contaminating human reads by using KneadData (Reference database: GRCh38 p12). Paired-end reads were concatenated. Profiling of the composition of bacterial communities was performed on trimmed reads using MetaPhlAn2.⁷² Bacterial functions were predicted using HUMANN2 (chocophlan).⁷³

Quantification and statistical analysis

The subjects’ metadata including age, gender, BMI, ASD diagnosis status, diet scores, and early-life characteristics were explored to identify covariates of gut-DNA virome compositional variation by using PERMANOVA⁷⁴ (adonis) in the vegan R package (999 permutations; FDR<0.05). Each host factor was calculated according to its explanation rate (R²), and p values were generated based on 999 permutations. Calculations of VLSs richness (Chao1 index) and diversity (Shannon indices) were performed by using phyloseq in R. Gut viral compositional data and its response to metadata were analyzed and visualized via RDA (Redundancy analysis) in the vegan R package based on Bray-Curtis dissimilarities. Metagenomics data are a form of compositional data, which violate the assumption of independence as proportionality imposes negative correlations within the dataset. Hence, we applied centered-log-ratio (clr) transformation⁷⁵ in the variance component analyses. We filtered out ultra-low prevalence features (<20%) and performed ASD-specific differential-abundance analysis in virome abundance dataset via ANCOMv2.1 package.²⁵ The benchmarking traits age, gender and BMI were adjusted.

We conducted an analysis of the co-occurrence networks for bacteriome and virome. The SpiecEasi method was used to generate the networks in a compositional dataset to a high degree of precision. Default parameters and the SpiecEasi package (version 1.1.0) were employed to run, and the correlation matrix we obtained was filtered using an absolute correlation score greater than or equal to 0.4. The networks were subsequently visualized using the Cytoscape (3.8.1) where each node represents one bacteria/VLS and each edge indicates the correlations between the bacteria/VLSs abundances. The correlations of α diversity (richness and Shannon diversity) of fecal bacteria - viruses and phage – metabolic pathways were performed with the corr.test from the stats R package (v3.6.1) using the Spearman’s rank correlation. The significantly altered co-occurrence network in gut viral community in ASD compared with TD group was assessed by using multinomial logistic regression with Markov Chain Monte Carlo sampling method.⁷⁶ To assess the microbial network robustness, we follow the methods developed by Jun, W. U. et al.⁷⁷ The influence of microbes and microbial relationship loss on network connectivity was assessed using robustness in the pulsar R package. The “attack robustness” of the networks is measured by sequentially removing nodes or randomly removing edges from the network and calculating the size of the remaining largest connected component relative to its starting size.

Viral community function, including viral auxiliary metabolic genes and metabolic pathways, was characterized by metagenomic assemblies via neural networks of protein signatures and a newly developed v-score metric.⁷⁸ Furthermore, PropagAtE (Prophage Activity Estimator) was used to estimate the lysogenic and lytic stage of prophage infection based on genomic/scaffold viral profile.⁷⁹ The prophage:host read coverage ratio and corresponding effect size is used to estimate if the prophage was actively replicating its genome. The default threshold for effect size was used in PropagAtE for the determination of prophage activity and significance. In addition, we applied the mediation analysis using the mediate function⁸⁰ from the R package mediation to infer the causal role of the bacteriophages in contributing to their respective bacteria host functions. We estimated the total effect, direct effect, and indirect effect of the bacteria and bacteriophages on the bacterial functional pathways from these models. The results were confirmed by the simulation exercises bootstrapped 1000 times.⁸⁰

Acknowledgments

We are grateful to the late Ruth Chan for child recruitment and collection of fecal samples and metadata. We acknowledge the contributions of Fen Zhang, Whitney Tang, Jessica Ching, and C.P. Cheung in their help with expert advice and sample processing. This study was funded by InnoHK, the government of Hong Kong, Special Administrative Region of the People’s Republic of China, and The D.H. Chen Foundation.

Author contributions

Y.W. conducted the study, performed gut viral DNA extraction and data analysis, and drafted the manuscript. D.C. was responsible for the ethical application, subject recruitment, collection of all questionnaire data, and clinical samples. H.M.T., L.Z., F.K.L.C., O.W.H.W., S.C., and Z.X. provided significant intellectual contributions to the manuscript. T.-F.L. provided support and clinical advice on subject recruitment. S.C.N. supervised the study and revised the manuscript.

Declaration of interests

F.K.L.C. is board member of CUHK Medical Center. He is a co-founder, non-executive board chairman, non-executive scientific advisor, chief medical officer, and shareholder of GenieBiome, Ltd. He receives patent royalties through his affiliated institutions. He has received fees as an advisor and honoraria as a speaker for Eisai Co., Ltd., AstraZeneca, Pfizer, Inc., Takeda Pharmaceutical Co., and Takeda (China) Holdings Co., Ltd. S.C.N. has served as an advisory board member for Pfizer, Ferring, Janssen, and Abbvie and received honoraria as a speaker for Ferring, Tillotts, Menarini, Janssen, Abbvie, and Takeda. S.C.N. has received research grants through her affiliated institutions from Olympus, Ferring, and Abbvie. S.C.N. is a founder member, non-executive director, non-executive scientific advisor, and shareholder of GenieBiome, Ltd. S.C.N. receives patent royalties through her affiliated institutions. F.K.L.C., S.C.N., H.M.T., and Q.S. are named inventors of patent applications held by the CUHK and MagIC that cover the therapeutic and diagnostic use of microbiome.

Published: February 1, 2024

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2024.101409.

Supplemental information

Document S1. Figure S1–S7 and Tables S1 and S2

mmc1.pdf^{(3.2MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(7.5MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figure S1–S7 and Tables S1 and S2

mmc1.pdf^{(3.2MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(7.5MB, pdf)}

Data Availability Statement

•
Sequence data for the virome metagenome were deposited to the NCBI Sequence Read Archive under BioProject accession numbers PRJNA1037036.
•
This paper does not report original code.
•
Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.

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PERMALINK

Alterations in fecal virome and bacteriome virome interplay in children with autism spectrum disorder

Yating Wan

Lin Zhang

Zhilu Xu

Qi Su

Ting-Fan Leung

Dorothy Chan

Oscar WH Wong

Sandra Chan

Francis KL Chan

Hein M Tun

Siew C Ng

Summary

Graphical abstract

Highlights

Introduction

Results

Subjects’ clinical characteristics

Table 1.

ASD represents a key determinant factor contributing to altered gut-DNA virome composition

Figure 1.

Children with ASD have increased abundances of gut Clostridium phage, Bacillus phage, and Enterobacteria phage

Figure 2.

Gut viral-viral interactions are altered in children with ASD

Figure 3.

Gut bacterial-viral networks are disrupted in ASD

Figure 4.

Mediation relationship of bacteriophages and microbial pathways encoding for neuroactive metabolites biosynthesis is altered in ASD

Figure 5.

Figure 6.

Discussion

Limitations of the study

STAR★Methods

Key resources table

Resource availability

Lead contact

Materials availability

Data and code availability

Experimental model and subject details

Subject recruitment and sample collection

Questionnaires and measurements

Method details

Extraction of fecal virus-like particles (VLPs) DNA and shotgun sequencing

Virome sequence processing and gene catalog generation

Virus-like sequences taxonomy annotation

Fecal bacteriome analysis

Quantification and statistical analysis

Acknowledgments

Author contributions

Declaration of interests

Footnotes

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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