Multi-kingdom microbiota analysis reveals bacteria-viral interplay in IBS with depression and anxiety
多域微生物组分析揭示了伴有抑郁和焦虑的肠易激综合症中的细菌-病毒相互作用

Irritable Bowel Syndrome (IBS) is a common gastrointestinal disorder frequently accompanied by psychological symptoms. Bacterial microbiota plays a critical role in mediating local and systemic immunity, and alterations in these microbial communities have been linked to IBS. Emerging data indicate that other intestinal organisms, including bacteriophages, are closely interlinked with the bacterial microbiota and their host, yet their collective role remains to be elucidated. Here, we analyze the gut multi-kingdom microbiota of 360 IBS patients from a prospective cohort study in Hong Kong, with participants phenotyped through psychological assessment. Our findings reveal significantly lower intra-community correlations in IBS patients compared to healthy controls and highlight unique taxa patterns associated with IBS and mental disorders. Utilizing multi-omic data alongside machine learning techniques, we successfully predicted psychiatric comorbidities in IBS, achieving an average AUC of 0.78. Notably, gut viruses emerged as significant contributors to our predictive model, indicating a vital role for bacteriophages in the gut microbiome of IBS patients. We found that lysogenic phages in IBS displayed a broader host range, with Bilophia containing the most abundant prophages. Our analysis further indicates that IBS patients with depression exhibited a higher prevalence of viralencoded auxiliary metabolic genes, specifically those involved in the sulfur metabolic pathway related to ubiquinone biosynthesis. The gut virome is increasingly reported to play an important role in the pathogenesis of many diseases. The study provides a systematic characterization of the drivers of the gut viral community and further expands our knowledge of the distinct interaction of gut viruses with their prokaryotic hosts, which is critical for understanding the viral-bacterial environment in IBS.
肠易激综合征（IBS）是一种常见的胃肠道疾病，经常伴随有心理症状。细菌微生物群在介导局部和全身免疫中起着关键作用，这些微生物群落的改变与 IBS 有关。新出现的证据表明，其他肠道生物，包括噬菌体，与细菌微生物群及其宿主密切相关，但它们共同的作用尚未阐明。在这里，我们分析了来自香港前瞻性队列研究的 360 名 IBS 患者的肠道多界微生物群，通过心理评估对参与者进行表型分类。我们的发现显示，与健康对照相比，IBS 患者的群落内相关性显著降低，并突出了与 IBS 和精神疾病相关的独特分类群模式。利用多组学数据以及机器学习技术，我们成功预测了 IBS 的精神共病情况，平均 AUC 达到 0.78。值得注意的是，肠道病毒成为我们预测模型中的显著贡献者，表明噬菌体在 IBS 患者的肠道微生物群中发挥着重要作用。 我们发现，肠易激综合征（IBS）中的溶原性噬菌体具有更广泛的宿主范围，其中 Bilophia 含有最丰富的前噬菌体。我们的分析进一步表明，伴有抑郁症状的 IBS 患者中，病毒编码的辅助代谢基因的流行率更高，特别是那些与辅酶 Q 生物合成相关的硫代谢途径的基因。越来越多的报道指出，肠道病毒组在许多疾病的发病机制中发挥着重要作用。该研究对驱动肠道病毒群落的关键因素进行了系统表征，并进一步扩展了我们对肠道病毒与其原核宿主独特相互作用的知识，这对于理解 IBS 中的病毒-细菌环境至关重要。

Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder with an estimated local prevalence of $6.6\mathrm{\%}$$6.6\mathrm{\%}$6.6%6.6 \% in Hong Kong ${}^1$${}^1$^(1){ }^{1}. The symptoms of IBS can seriously affect a person’s quality of life. Under the Rome IV guidelines, IBS is divided into four key subtypes based on stool patterns: IBS-D (diarrhea-predominant), IBS-C (constipation-predominant), IBS-M (mixed), and unclassified IBS. Despite the gastrointestinal symptoms, IBS patients suffer a very high frequency of psychological problems, such as anxiety and depression ${}^2$${}^2$^(2){ }^{2}. With advances in gut microbiota analyses, abnormalities in intestinal microflora, known as gut dysbiosis, have been implicated in IBS ${}^{3,4}$${}^{3,4}$^(3,4){ }^{3,4}. Gut dysbiosis is involved in the
肠易激综合征（IBS）是一种常见功能性胃肠道疾病，据估计在香港的局部发病率为 $6.6\mathrm{\%}$$6.6\mathrm{\%}$6.6%6.6 \% ${}^1$${}^1$^(1){ }^{1} 。IBS 的症状会严重影响一个人的生活质量。根据罗马 IV 指南，IBS 根据粪便模式分为四个关键亚型：IBS-D（腹泻型）、IBS-C（便秘型）、IBS-M（混合型）以及未分类 IBS。尽管有胃肠道症状，IBS 患者仍会非常频繁地遭受心理问题，如焦虑和抑郁 ${}^2$${}^2$^(2){ }^{2} 。随着肠道微生物群分析技术的进步，肠道微生态的异常，即肠道微生态失衡，被认为与 IBS 有关 ${}^{3,4}$${}^{3,4}$^(3,4){ }^{3,4} 。肠道微生态失衡参与在
pathogenesis of IBS including gastrointestinal and psychiatric symptoms ${}^5$${}^5$^(5){ }^{5}. While much of the research focuses on the bacterial components, it is crucial to recognize that the human gut microbiome is an ecosystem of diverse microorganisms. Among these, viruses have recently garnered increasing attention due to their significant role in shaping the microbiota and their potential therapeutic implications ${}^6$${}^6$^(6){ }^{6}. Advancements in sequencing technologies and bioinformatics tools have particularly driven this heightened interest.
IBS 的发病机制，包括胃肠道和精神病症状 ${}^5$${}^5$^(5){ }^{5} 。虽然大部分研究集中在细菌成分上，但认识到人类肠道微生物组是一个由多种微生物组成的生态系统至关重要。在这些微生物中，病毒最近引起了越来越多的关注，因为它们在塑造微生物群及其潜在的治疗意义上具有重要作用 ${}^6$${}^6$^(6){ }^{6} 。测序技术和生物信息学工具的进步尤其推动了这种兴趣的增加。

The human gut virome is thought to significantly impact the microbiome and human health. The viral sequences detected in the human gut are
人类肠道病毒组被认为对微生物群和人类健康有重大影响。在人类肠道中检测到的病毒序列是

dominated by bacteriophages, which are viruses that specifically infect and replicate within bacteria. Despite most of the phage sequences corresponding to “dark matter” remaining to be characterized, phages have gained increasing attention in recent years as potential modulating tools against bacterial infections, including drug-resistant infections ${}^{7,8}$${}^{7,8}$^(7,8){ }^{7,8}, and those targeting the gastrointestinal tract ${}^9$${}^9$^(9){ }^{9}. Several studies have highlighted the relationship between bacteriophages and psychiatric disorders such as depression and anxiety ${}^{10,11}$${}^{10,11}$^(10,11){ }^{10,11}. Shotgun metagenomics captures genomic DNA from all gut organisms, not only prokaryotes, making it an optimal tool for studying gut multi-kingdom profiles ${}^{12,13}$${}^{12,13}$^(12,13){ }^{12,13}. The well-benchmarked assemblybased approaches overcome the fundamental limitation of the lack of reference genomes for the majority of phages, enabling the discovery of tens of thousands of viral species ${}^{14-16}$${}^{14-16}$^(14-16){ }^{14-16} and their functional capacities ${}^{15,17}$${}^{15,17}$^(15,17){ }^{15,17}, and providing virome researchers with great insights into the structure and composition of the human gut virome. We hypothesize that gut virome dysbiosis may drive IBS heterogeneity and psychiatric comorbidities through bacteriophage-mediated microbial community shifts. Distinct virome signatures could serve as diagnostic biomarkers for IBS stratification and associated mental disorders.
由噬菌体主导，噬菌体是一种专门感染并在细菌内复制的水生病毒。尽管大多数噬菌体序列对应的“暗物质”尚未被鉴定，但近年来，噬菌体作为对抗细菌感染的潜在调节工具受到了越来越多的关注，包括对抗耐药感染 ${}^{7,8}$${}^{7,8}$^(7,8){ }^{7,8} ，以及针对胃肠道感染的 ${}^9$${}^9$^(9){ }^{9} 。多项研究强调了噬菌体与抑郁症和焦虑等精神疾病之间的关联 ${}^{10,11}$${}^{10,11}$^(10,11){ }^{10,11} 。鸟枪宏基因组学捕获了所有肠道生物的基因组 DNA，而不仅仅是原核生物，使其成为研究肠道多界别谱的最优工具 ${}^{12,13}$${}^{12,13}$^(12,13){ }^{12,13} 。经过良好基准的基于组装的方法克服了大多数噬菌体缺乏参考基因组的根本限制，使得能够发现数以万计的病毒物种 ${}^{14-16}$${}^{14-16}$^(14-16){ }^{14-16} 及其功能容量 ${}^{15,17}$${}^{15,17}$^(15,17){ }^{15,17} ，并为病毒组研究者提供了深入了解人类肠道病毒组的结构和组成的宝贵信息。 我们假设肠道病毒群落的失衡可能通过噬菌体介导的微生物群落变化驱动肠易激综合征（IBS）的异质性和精神共病。独特的病毒群落特征可作为肠易激综合征分型和相关精神障碍的诊断生物标志物。

In this study, we aim to elucidate the intricate relationships between gut virome-bacteriome interactions and IBS. Furthermore, we focus on a subgroup of IBS patients who exhibit comorbid psychiatric conditions, offering detailed insights into how these mental health issues may correlate with gut viral dynamics. By examining these interactions, we thus expect to uncover novel insights that could pave the way for innovative intervention strategies targeting the gut virome of IBS, particularly in those with concurrent mental health disorders.
在本研究中，我们旨在阐明肠道病毒-细菌群相互作用与肠易激综合征之间的复杂关系。此外，我们专注于那些表现出精神共病的肠易激综合征患者亚组，提供这些心理健康问题如何与肠道病毒动态相关联的详细见解。通过检查这些相互作用，我们期望揭示能够为针对肠易激综合征患者肠道病毒的创新干预策略铺平道路的新见解，尤其是那些同时存在心理健康障碍的患者。

A total of 444 stool samples were obtained from IBS ( $n=360$$n=360$n=360n=360 ) and HC ( $n=84$$n=84$n=84n=84 ) controls and sequenced using shotgun metagenomics (Fig. 1a). The IBS patients were further categorized according to the Rome IV criteria based on their predominant stool pattern(Supplemental Fig. 1a). While IBS is recognized as a multifactorial and heterogeneous disorder, its gut microbiome characteristics remain elusive due to this inherent variability, highlighting the need for more nuanced approaches to accurately capture IBS-associated microbial signatures. To assess IBS microbiome heterogeneity, we performed Hopkins clustering analysis on multi-kingdom microbial profiles, suggesting significant clustering tendency (Hopkins statistic $=0.97$$=0.97$=0.97=0.97 ), indicating substantial heterogeneity among IBS patients. The quality of clustering, as represented with a silhouette plot (Supplementary Fig. 1b), was highest (0.93) with $k=2$$k=2$k=2k=2, suggesting that was the optimal number of clusters. To further resolve this heterogeneity, we integrated multi-kingdom microbial data (205 bacteria, 91 fungi, and 242 bacteriophages) using weighted similarity network fusion (WSNF), which identified two robust clusters (Cluster 1 [C1]: $n=185$$n=185$n=185n=185; Cluster 2 [C2]: $n=132$$n=132$n=132n=132; Fig. 1a). Comparative analysis of clinical characteristics revealed a significantly higher Bristol stool score in C2 (mean $\pm$$\pm$+-\pm SD: $4.66\pm 1.59$$4.66\pm 1.59$4.66+-1.594.66 \pm 1.59 ) compared to $\mathrm{C}1(5.06\pm 1.30;p=0.012$$\mathrm{C}1(5.06\pm 1.30;p=0.012$C1(5.06+-1.30;p=0.012\mathrm{C} 1(5.06 \pm 1.30 ; p=0.012, Fig. 1b), suggesting distinct phenotypic profiles between clusters.
共收集了来自肠易激综合征（ $n=360$$n=360$n=360n=360 ）和健康对照（ $n=84$$n=84$n=84n=84 ）的 444 份粪便样本，并使用鸟枪宏基因组测序技术进行测序（图 1a）。根据罗马 IV 标准，依据患者的主要粪便模式，进一步将肠易激综合征患者进行分类（补充图 1a）。尽管肠易激综合征被认为是一种多因素、异质性疾病，但由于其内在的变异性，其肠道微生物组特征仍然难以捉摸，这突显了需要更细致的方法来准确捕捉与肠易激综合征相关的微生物标志。为了评估肠易激综合征微生物组的异质性，我们对多界微生物谱进行了霍普金斯聚类分析，表明有显著的聚类趋势（霍普金斯统计量 $=0.97$$=0.97$=0.97=0.97 ），说明肠易激综合征患者之间存在大量异质性。聚类的质量，如轮廓图（补充图 1b）所示，在 $k=2$$k=2$k=2k=2 时最高（0.93），表明这是最佳的聚类数量。 为进一步解析这种异质性，我们采用加权相似性网络融合（WSNF）方法整合了多界微生物数据（205 种细菌、91 种真菌和 242 种噬菌体），确定了两个稳健的聚类（聚类 1 [C1]: $n=185$$n=185$n=185n=185 ；聚类 2 [C2]: $n=132$$n=132$n=132n=132 ；图 1a）。对临床特征的比较分析显示，C2 的布里斯托尔粪便评分显著高于 $\mathrm{C}1(5.06\pm 1.30;p=0.012$$\mathrm{C}1(5.06\pm 1.30;p=0.012$C1(5.06+-1.30;p=0.012\mathrm{C} 1(5.06 \pm 1.30 ; p=0.012 （平均值 $\pm$$\pm$+-\pm SD: $4.66\pm 1.59$$4.66\pm 1.59$4.66+-1.594.66 \pm 1.59 ，图 1b），这表明不同聚类间存在不同的表型特征。

The two WSNF-derived clusters exhibited significant differences in their multi-kingdom microbiome profiles, as demonstrated by $\beta$$\beta$beta\beta-diversity analysis (PERMANOVA: $R^2=0.07,p<0.01$$R^2=0.07,p<0.01$R^(2)=0.07,p < 0.01R^{2}=0.07, p<0.01; Fig. 1c, Supplementary Fig. 1d-f). The comparison of alpha-diversity (Shannon index and richness) between the two clusters revealed no significant differences in the multikingdom profile. However, when examining bacteria, fungi, and viruses separately, we observed compensatory dynamics within the multi-kingdom communities (Supplementary Fig. 1g-j). Specifically, while the alphadiversity of bacteria and fungi decreased, viral diversity increased, suggesting potential coevolutionary dynamics among these microbial groups.
由 WSNF 衍生的两个聚类在多界微生物群谱方面表现出显著差异，这一点通过 $\beta$$\beta$beta\beta -多样性分析（PERMANOVA: $R^2=0.07,p<0.01$$R^2=0.07,p<0.01$R^(2)=0.07,p < 0.01R^{2}=0.07, p<0.01 ；图 1c，补充图 1d-f）得到证实。两个聚类之间的α多样性（香农指数和丰富度）在多界谱方面没有显著差异。然而，当分别检查细菌、真菌和病毒时，我们发现多界群落内部存在补偿性动态（补充图 1g-j）。具体而言，尽管细菌和真菌的α多样性降低，但病毒多样性增加，这表明这些微生物群体之间可能存在潜在的协同进化动态。

To evaluate the potential of microbial clusters for IBS stratification, we constructed multi-kingdom random forest models, achieving discriminative AUCs of 0.73-0.93 across IBS clusters (IBS-C1: 0.897-0.898;
为了评估微生物聚类对肠易激综合征分层的潜在价值，我们构建了多界别随机森林模型，在肠易激综合征不同聚类中获得了 0.73-0.93 的区分性 AUC 值（IBS-C1：0.897-0.898；

IBS-C2: 0.930-0.931; HC: 0.726-0.731), demonstrating their utility in subtyping (Fig.1d). We ranked microbial contributions across multiple taxonomic levels by assessing feature importance in the random forest model. We found gut viruses had a much more important effect (54%) than the other taxa in the analysis (bacteria $26\mathrm{\%}$$26\mathrm{\%}$26%26 \%, fungi $20\mathrm{\%}$$20\mathrm{\%}$20%20 \% ) (Fig.1e). These results pointed to a crucial role of gut virome in shaping the division between IBS two clusters and healthy controls. To understand the interaction among gut microbiota, the co-occurrence of multi-kingdom taxa was measured and depicted with relative abundances in both IBS and healthy controls (Fig. 1f and g ). The strong interactions among bacteriophages were observed in healthy controls (Fig. 1f). Interestingly, Escherichia coli, Escherichia phage HK639, and Escherichia phage TL-2011b had an inverse relationship with Bacteroides uniformis, which is known as indicators of healthy intestines ${}^{18}$${}^{18}$^(18){ }^{18}, only in healthy controls. Nevertheless, gut multi-kingdom taxa interactions were more noticeable in healthy controls than in IBS subjects (HC 423 vs. C1-IBS 168, C2-IBS 228) (Fig. 1g, Supplementary Fig. 2).
IBS-C2：0.930-0.931；HC：0.726-0.731)，证明了它们在分型中的实用性（图 1d）。我们通过评估随机森林模型中的特征重要性，对多个分类水平上的微生物贡献进行了排序。我们发现肠道病毒的影响（54%）比分析中的其他分类群（细菌 $26\mathrm{\%}$$26\mathrm{\%}$26%26 \% ，真菌 $20\mathrm{\%}$$20\mathrm{\%}$20%20 \% ）更为重要（图 1e）。这些结果指向了肠道病毒在塑造 IBS 两个簇群和健康对照之间的划分中起着关键作用。为了理解肠道微生物群之间的相互作用，我们测量了多界分类群的共现性，并在 IBS 和健康对照中用相对丰度进行了描述（图 1f 和 g）。在健康对照中观察到了噬菌菌之间的强烈相互作用（图 1f）。有趣的是，大肠杆菌、大肠杆菌噬菌体 HK639 和大肠杆菌噬菌体 TL-2011b 与健康肠道指标菌——拟杆菌均匀菌呈现负相关，仅在健康对照中观察到这一点 ${}^{18}$${}^{18}$^(18){ }^{18} 。然而，与健康对照相比，IBS 受试者中的肠道多界分类群相互作用更为明显（HC 423 vs. C1-IBS 168，C2-IBS 228）（图 1g，补充图 2）。

Previous epidemiological studies showed that the incidence of anxiety and depression in IBS patients is much higher than that in the general population. The frequent co-occurrence of mental disorders and IBS is well established, more than a quarter of individuals with IBS had depressive symptoms, and over a third had anxiety symptoms, which was considerably higher than in healthy individuals ${}^{2,19}$${}^{2,19}$^(2,19){ }^{2,19}. A recent meta-analysis revealed that the prevalence of depressive and anxiety symptoms in IBS patients was $28.8\mathrm{\%}$$28.8\mathrm{\%}$28.8%28.8 \% and $39.1\mathrm{\%}$$39.1\mathrm{\%}$39.1%39.1 \%, respectively ${}^2$${}^2$^(2){ }^{2}. Growing evidence suggests that the gut microbiota is closely linked to mental health ${}^{20,21}$${}^{20,21}$^(20,21){ }^{20,21}, and microbial imbalance may contribute to psychiatric conditions. However, most gut microbiota studies in IBS have focused exclusively on bacteria, neglecting the potential roles of other kingdoms (e.g. viruses) within the microbiome ecosystem. Here, we investigated the associations between gut microbiota, especially viruses, and psychiatric comorbidities in IBS.
先前流行病学研究显示，肠易激综合征患者中焦虑和抑郁的发病率远高于普通人群。精神障碍与肠易激综合征的频繁共病现象已得到充分证实，超过四分之一的肠易激综合征患者有抑郁症状，超过三分之一有焦虑症状，这比健康个体要高得多 ${}^{2,19}$${}^{2,19}$^(2,19){ }^{2,19} 。最近的一项荟萃分析显示，肠易激综合征患者中抑郁症状和焦虑症状的患病率分别为 $28.8\mathrm{\%}$$28.8\mathrm{\%}$28.8%28.8 \% 和 $39.1\mathrm{\%}$$39.1\mathrm{\%}$39.1%39.1 \% ，分别 ${}^2$${}^2$^(2){ }^{2} 。越来越多的证据表明，肠道微生物群与心理健康密切相关，微生物失衡可能促使精神疾病的发生。然而，大多数关于肠易激综合征的肠道微生物群研究仅专注于细菌，忽略了微生物群生态系统中其他界（例如病毒）的潜在作用。在此，我们研究了肠道微生物群，特别是病毒与肠易激综合征的精神病共病之间的关联。

To investigate the potential association between microbiome signatures and psychological symptoms, we classified samples into five groups: healthy controls (HC), regular IBS patients without depression (rIBS, SAS < 50 and SDS < 53), IBS with depression (dIBS, SDS $\ge 53$$\ge 53$>= 53\geq 53 and SAS < 50), IBS with anxiety (aIBS, SAS $\ge 50$$\ge 50$>= 50\geq 50 and SDS < 53), and IBS with both depression and anxiety (adIBS, SDS $\ge 53$$\ge 53$>= 53\geq 53 and SAS $\ge 50$$\ge 50$>= 50\geq 50 ). We tested the differential abundance of viral species using microbiome multivariable association with linear models (MaAsLin2) with the individual participant as a random factor. We observed differential abundance patterns of gut viruses in IBS with depression and anxiety when compared with the HC (Fig. 2a). Notably, the changes observed in dIBS and adIBS were more pronounced compared to aIBS and rIBS. These alterations may signify the strong correlations of the gut viral population with depression. We found increased Enterobacteria phages and Escherichia phages in dIBS and adIBS, including Enterobacteria_phage_cdtI, Enterobacteria_phage_HK225, and Escherichia_phage_Cartapus. We found shared decreased levels of Salmonella phage 118970_sal3, Clostridium phage phiCD211, and Mannheimia phage vB_MhS_587AP2 in aIBS and adIBS. MaAsLin2 analysis revealed distinct bacterial signatures of IBS with psychiatric co-morbidity (Supplementary Fig. 3a). Compared to rIBS, adIBS showed the most pronounced dysbiosis, with significant depletion of butyrate producers Eubacterium rectale ${}^{22}$${}^{22}$^(22){ }^{22} and next-generation beneficial microbe Akkermansia muciniphila (FDR<0.05) ${}^{23}$${}^{23}$^(23){ }^{23}, alongside enrichment in pathobionts (Klebsiella pneumoniae, Escherichia coli, FDR < 0.05). Notably, both adIBS and dIBS groups exhibited depletion of Eubacterium eligens, a putative gut-brain axis modulator implicated in energy homeostasis, colonic motility, and anti-inflammatory immunoregulation. These findings suggest psychiatric comorbidities in IBS are associated with taxon-specific ecological imbalances, affecting short chain fatty acid producers and immunomodulatory species ${}^{24}$${}^{24}$^(24){ }^{24}. However, the causal direction of these associations requires further validation through longitudinal studies and mechanistic experiments. Distinct phage-bacteria interaction patterns are illustrated across IBS subtypes in
为探究微生物组特征与心理症状之间的潜在关联，我们将样本分为五组：健康对照（HC）、无抑郁的常规肠易激综合征患者（rIBS，焦虑自评量表

the Supplementary Fig. 3b-e, with the strongest negative correlations (e.g., bacteroides-phage pairs) being observed in adIBS, while minimal associations are displayed in rIBS. These differential networks suggest that psychiatric comorbidities may exacerbate phage-mediated dysbiosis, particularly affecting key bacterial taxa involved in gut-brain axis regulation.
补充图 3b-e 显示，最强负相关性（例如，拟杆菌-噬菌体对）出现在伴有精神症状的肠易激综合征（adIBS）中，而在缓解型肠易激综合征（rIBS）中显示出的关联性最小。这些差异网络表明，精神合并症可能会加剧噬菌体介导的菌群失调，特别是影响参与肠道-大脑轴调节的关键细菌分类群。

Next, we explored whether the gut microbiome could predict psychiatric comorbidities in IBS. Given that anxiety and depression (adIBS, $42\mathrm{\%},134$$42\mathrm{\%},134$42%,13442 \%, 134 out of 317) are the most common subgroups, we conducted machine learning training and evaluated the prediction performance for this group. We constructed a machine learning classifier with ten repeats of fivefold cross-validation to differentiate adIBS from regular IBS. Combining
接下来，我们探讨了肠道微生物组是否可以预测肠易激综合征中的精神合并症。鉴于焦虑和抑郁（adIBS，317 例中有 $42\mathrm{\%},134$$42\mathrm{\%},134$42%,13442 \%, 134 例）是最常见的亚组，我们对这一群体进行了机器学习训练，并评估了对此组的预测性能。我们构建了一个机器学习分类器，进行了十次重复的五折交叉验证，以区分 adIBS 和普通肠易激综合征。结合

Fig. 1 | Gut viruses drive the differentiation of IBS from HC. a Integration of gut multi-biome data through weighted similarity network fusion (WSNF) approach. Heatmap of similarity scores derived from WSNF analysis in IBS patients, with spectral clustering identifying two distinct patient subgroups (labeled Cluster 1 and Cluster 2). Color gradient represents pairwise similarity strength (scale bar at right). b Comparison of Bristol score of patients between two identified patient clusters. c Principal coordinate analysis (PCoA) of gut multi-biome of based on Bray-Curtis dissimilarity illustrates two patient clusters. d Random forest classifier model trained on multi-biome can predict the clustering of IBS patients. e Features from gut viruses
图 1 | 肠道病毒驱动肠易激综合征（IBS）与对照（HC）的区别。a 通过加权相似性网络融合（WSNF）方法整合肠道多生物群数据。基于 WSNF 分析的 IBS 患者相似度得分热图，通过谱聚类识别出两个不同的患者亚群（标记为簇 1 和簇 2）。颜色渐变代表成对相似性强度的变化（右侧标尺）。b 比较两个识别出的患者簇之间的布里斯托尔评分。c 基于 Bray-Curtis 相异性分析的肠道多生物群主坐标分析（PCoA）展示了两个患者簇。d 基于多生物群的随机森林分类器模型可以预测 IBS 患者的聚类。e 来自肠道病毒的特征。
contribute most to differentiating clusters in the random forest models. $\mathbf{f},\mathbf{g}$$\mathbf{f},\mathbf{g}$f,g\mathbf{f}, \mathbf{g} Network visualization of key taxa in two clusters in HC (f) and IBS clusters (g). Each node represents a microbial taxon (e.g., a bacterial, viral or fungal species) included in the co-occurrence network. The shape of a node is a visual attribute used to distinguish between different types of microbes (e.g., bacteria vs. viruses vs. fungi). Circles, triangle, and inverted triangle represent microbes and lines represent their associated interactions. Node size (degree) reflects the number of direct interactions for a given microbe. Border thickness represents calculated stress centrality for each microbe, while color depth reflects the positive or negative of the interactions.
在随机森林模型中，对区分不同簇的贡献最大的关键菌群的网络可视化。（f）HC 簇中的两个簇的关键菌群网络可视化；（g）IBS 簇中的关键菌群网络可视化。每个节点代表一个微生物分类群（例如，细菌、病毒或真菌物种），包含在共现网络中。节点的形状是一种视觉属性，用于区分不同类型的微生物（例如，细菌与病毒与真菌）。圆形、三角形和倒三角形代表微生物，线条代表它们之间的相互作用。节点大小（度）反映了给定微生物的直接交互数量。边框的粗细代表了为每个微生物计算的压力中心性，而颜色深浅反映了相互作用的正负。

Fig. 2 | Multi-omics based machine learning for mental disorder diagnosis in IBS. a MaAslin2 analysis of IBS with mental disorder illustrating discriminant taxa compared to HC. b The area under the receiver operating characteristic curve
图 2 | 基于多组学的机器学习在 IBS 中精神障碍诊断的应用。a MaAslin2 分析显示 IBS 伴有精神障碍的区分性分类群与 HC 的比较。b 受试者工作特征曲线下的面积
(AUROC, center for the error bands is median). c Feature importance summary of top 30 features from random forest classifier.
(AUROC，误差带中心为中位数)。c 随机森林分类器中前 30 个特征的特征重要性总结。
this with patient stratification based on the microbiome could offer a focused and precision-based diagnostic strategy. Using gut multi-kingdom microbiome and metabolites data, we trained a random forest machine learning model to differentiate adIBS from rIBS. The random forest model achieved a mean AUROC of 0.78 (Fig. 2b), suggesting that multi-class disease classification based on the fecal microbiome was feasible. To further test whether a simplified panel of biomarkers could be used as biomarkers to distinguish the mental disorder in IBS according to the multi-omics data, we performed predictions using top-ranked features contributing to the random forest classifier (Fig. 2b). We tested how many of the features representatives of the mental disorder in IBS were necessary to achieve the
将此与基于微生物组的患者分层相结合，可能提供一种专注且基于精准的诊断策略。利用肠道多界微生物群和代谢物数据，我们训练了一个随机森林机器学习模型，以区分 adIBS 和 rIBS。随机森林模型达到了平均 AUROC 为 0.78（图 2b），表明基于粪便微生物群的多类疾病分类是可行的。为了进一步测试是否可以根据多组学数据使用简化的生物标志物面板作为生物标志物来区分 IBS 中的精神障碍，我们使用随机森林分类器中贡献最大的顶级特征进行了预测（图 2b）。我们测试了需要多少代表 IBS 中精神障碍的特征才能达到
comparable predictive performance by training the classifier model with different number of top-ranking features that were chosen based on the mean decrease in GINI from the classifier trained with the full set of features (Supplementary Fig. 4a). The results showed that using as few as 100 features (Supplementary Fig. 4b) achieved best average AUC 0.84 for IBS with mental disorder. Applying the variable selection strategy based on random forest, important features were identified based on the importance score. Particularly, important features of random forests in cluster 2 are distinct with healthy control (Fig. 2c). Viruses have the highest importance ( $36.2\mathrm{\%}$$36.2\mathrm{\%}$36.2%36.2 \% ) among the top 100 features, suggesting the model relies heavily on gut viruses, with fungi ( $31.1\mathrm{\%}$$31.1\mathrm{\%}$31.1%31.1 \% ), bacteria ( $21.5\mathrm{\%}$$21.5\mathrm{\%}$21.5%21.5 \% ), and metabolites ( $11.2\mathrm{\%}$$11.2\mathrm{\%}$11.2%11.2 \% )
通过使用基于随机森林的特征选择策略，根据重要性评分识别重要特征。特别是，聚类 2 中随机森林的重要特征与健康对照组有显著差异（图 2c）。在前 100 个特征中，病毒的重要性最高（ $36.2\mathrm{\%}$$36.2\mathrm{\%}$36.2%36.2 \% ），表明模型严重依赖肠道病毒，其次是真菌（ $31.1\mathrm{\%}$$31.1\mathrm{\%}$31.1%31.1 \% ）、细菌（ $21.5\mathrm{\%}$$21.5\mathrm{\%}$21.5%21.5 \% ）和代谢物（ $11.2\mathrm{\%}$$11.2\mathrm{\%}$11.2%11.2 \% ）。
playing supporting roles in the model’s predictions (Supplementary Fig. 4c). Biomarker discovery in the context of mental disorders in IBS has the potential to revolutionize patient care by offering more precise diagnostics, personalized treatment strategies, and a deeper understanding of the complex interplay between gut health and mental well-being. This knowledge can inform preventive measures and interventions, potentially reducing the overall burden of mental health issues within the population. Gut viruses, particularly bacteriophages can influence the composition and function of the gut microbiome, which in turn affect host metabolism. A recent study from our group found that impaired bile acid synthesis regulation and excessive bile acid excretion, driven by a Clostridia-rich microbiota, are linked to the severity of diarrheal symptoms in IBS-D patients ${}^{25}$${}^{25}$^(25){ }^{25}. Given the contribution of gut viruses to prediction of the mental disorders in IBS, we test the relationship between the gut virus composition and the bile acid concentrations. We performed MaAsLin2 analyses of 37 bile acid metabolites in HC, rIBS, and adIBS, and found 9 bile acid metabolites that were significantly decreased in IBS patients (Supplementary Fig. 4d). The correlation between bile acids and gut viruses varied across the HC, rIBS, and adIBS groups (Supplementary Fig. 4d). In general, the adIBS group exhibited fewer positive and negative correlations, indicating a potential disruption or weakening of the interplay between bile acids and gut viruses in these patients (Supplementary Fig. 4e).
在模型预测中扮演辅助角色的生物标志物（补充图 4c）。在肠易激综合征（IBS）背景下，针对精神障碍的生物标志物发现有可能通过提供更精确的诊断、个性化的治疗策略以及对肠道健康与精神福祉复杂互动的更深入理解，从而彻底改变患者的护理方式。这些知识可以指导预防措施和干预，可能减少人群中精神健康问题的总体负担。肠道病毒，尤其是噬菌体，可以影响肠道微生物组的组成和功能，进而影响宿主代谢。我们团队最近的一项研究发现，由富含梭状芽孢杆菌的微生物群驱动的胆汁酸合成调节受损和胆汁酸过度排泄与 IBS-D 患者腹泻症状的严重程度相关 ${}^{25}$${}^{25}$^(25){ }^{25} 。鉴于肠道病毒对预测 IBS 中精神障碍的贡献，我们测试了肠道病毒组成与胆汁酸浓度之间的关系。 我们对健康对照组（HC）、缓解型肠易激综合征（rIBS）和伴有焦虑抑郁的肠易激综合征（adIBS）中的 37 种胆汁酸代谢物进行了 MaAsLin2 分析，发现 IBS 患者中有 9 种胆汁酸代谢物显著降低（补充图 4d）。胆汁酸与肠道病毒之间的相关性在 HC、rIBS 和 adIBS 组间存在差异（补充图 4d）。总体而言，adIBS 组表现出较少的正相关和负相关，这表明这些患者胆汁酸与肠道病毒之间的相互作用可能受到破坏或减弱（补充图 4e）。

Improvements in high-throughput sequencing and bioinformatic technologies have allowed the virome to be analyzed in unprecedented detail. Using state-of-the-art bioinformatics tools, we characterized the global virome in IBS by analyzing 1.28 TB of sequences derived from 444 fecal metagenomes. Rarefaction analysis (Supplementary Fig. 5a) revealed that more gut viruses remain to be identified. The average read count per sample was $20,436,661.5$$20,436,661.5$20,436,661.520,436,661.5 and per-sample alignment of bacteria, fungi and viruses has been shown in Supplementary Fig.5c. Following recent viromics benchmarking approaches and stringent criteria, we identified 626,559 putative viral contigs. Assessing with CheckV revealed 1302 complete viral genomes, 4843 high quality and 7026 medium quality viral genomes. We were able to classify 29,254 viral genomes ( $4.67\mathrm{\%}$$4.67\mathrm{\%}$4.67%4.67 \% of the total) using PhaGCN as belonging to existing families. We have collected intrinsic and extrinsic factors (e.g. age, sex, body mass index [BMI], lifestyle, dietary habits, diseases, and medications) through questionnaires, face-to-face interviews, and medical records. The influences of these factors on the composition of gut microbiota from IBS patients were determined by the Adonis test. We assessed the confounding effects of these factors on the IBS gut microbiome. The results indicated that although none of the trends were statistically significant ( $p>0.05$$p>0.05$p > 0.05p>0.05 ), the IBS-SSS score was the primary factor influencing gut microbiome composition across all IBS samples (Supplementary Fig. 5b).
高通量测序和生物信息学技术的改进使得病毒组得以以前所未有的细节进行分析。利用最先进的生物信息学工具，我们通过分析来自 444 个粪便宏基因组样本的 1.28 TB 序列，表征了肠易激综合症（IBS）中的全球病毒组。稀有度分析（补充图 5a）显示，还有更多的肠道病毒有待识别。每个样本的平均读取计数为 $20,436,661.5$$20,436,661.5$20,436,661.520,436,661.5 ，样本中细菌、真菌和病毒的比对已在补充图 5c 中展示。遵循最近的病毒组学基准方法及严格标准，我们鉴定出 626,559 个可能的病毒片段。通过 CheckV 评估，发现 1302 个完整的病毒基因组，4843 个高质量和 7026 个中等质量的病毒基因组。我们能够使用 PhaGCN 将 29,254 个病毒基因组（占总数的 $4.67\mathrm{\%}$$4.67\mathrm{\%}$4.67%4.67 \% ）分类到现有家族中。我们通过问卷、面对面访谈和病历收集了内在和外在因素（例如年龄、性别、体重指数（BMI）、生活方式、饮食习惯、疾病和药物）。 这些因素对肠易激综合征（IBS）患者肠道微生物群组成的影响通过 Adonis 检验确定。我们评估了这些因素对 IBS 肠道微生物组的混杂效应。结果显示，尽管这些趋势均未达到统计学显著性（ $p>0.05$$p>0.05$p > 0.05p>0.05 ），但 IBS-SSS 评分是影响所有 IBS 样本肠道微生物群组成的主要因素（补充图 5b）。

To examine the taxonomic spread covered by metagenome-assembled viral genomes, we compared them against the RefSeq prokaryotic virus database using vConTACT2, a network-based method to classify viral contigs, with all viral sequences from bulk metagenomes as input, a network-based method to classify viral contigs. Clustering of the viral sequences with $95\mathrm{\%}$$95\mathrm{\%}$95%95 \% sequence similarity generated 105,365 viral genomes (corresponding to the species level) (Fig. 3a). The majority of the phages were predicted to belong to the Caudoviricetes, among which Sepvirinae ( $n=2388$$n=2388$n=2388n=2388 ), Peduoviridae ( $n=2312$$n=2312$n=2312n=2312 ) were most abundant (Fig. 3b). Viral families Tectiviridae, Hendrixvirinae, Adenoviridae, Phycodnaviridae, Fernvirus in HC were more abundant than that in IBS (Fig. 3b). Virus clustering patterns generated by vContact2, revealing distinct virome compositions across the IBS with depression and anxiety (Fig. 3c). In addition, dominant families such as Kostyavirus, Eucampyvirinae, and Boydwoodruffvirinae exhibited differential distributions, with rIBS showing unique viral signatures compared to psychologically distressed subgroups (aIBS, dIBS, adIBS) (Fig. 3d). These findings suggest a potential link between gut virome profiles and IBS patients with anxiety or depression.
为检测宏基因组组装病毒基因组所涵盖的分类分布，我们使用 vConTACT2 对其进行比较，这是一种基于网络的病毒片段分类方法，输入的是来自宏基因组总体的所有病毒序列。序列相似性为 $95\mathrm{\%}$$95\mathrm{\%}$95%95 \% 的病毒序列聚类生成了 105,365 个病毒基因组（对应于物种水平）（图 3a）。大多数噬菌体被预测属于尾病毒目，其中 Sepvirinae（ $n=2388$$n=2388$n=2388n=2388 ）、Peduoviridae（ $n=2312$$n=2312$n=2312n=2312 ）最为丰富（图 3b）。在健康对照组（HC）中，Tectiviridae、Hendrixvirinae、Adenoviridae、Phycodnaviridae、Fernvirus 的数量比肠易激综合征（IBS）组更多（图 3b）。由 vContact2 生成的病毒聚类模式揭示了肠易激综合征伴抑郁和焦虑患者的独特病毒组组成（图 3c）。此外，主要家族如 Kostyavirus、Eucampyvirinae 和 Boydwoodruffvirinae 表现出差异性分布，rIBS 显示出与心理压力亚组（aIBS、dIBS、adIBS）相比独特的病毒特征（图 3d）。 这些发现提示肠道病毒组谱与患有焦虑或抑郁的肠易激综合征（IBS）患者之间可能存在潜在联系。

Applying the integrated phage-host prediction, we were able to identify 8517 (8.08%) DNA vOTUs associated with a host genus or family (Supplementary Fig. 5d). Among the most abundant host families, a higher proportion of vOTU mapped to Bacteroidaceae and Ruminococcaceae in HC, whereas higher number of viral population with host families Lachnospiraceae, Acutalibacteraceae, and Enterobacteriaceae were observed in IBS (Supplementary Fig. 5e).
应用整合的噬菌体-宿主预测方法，我们能够识别出 8517 个（8.08%）与宿主属或科相关的 DNA 病毒操作分类单元（vOTUs）（补充图 5d）。在最丰富的宿主科中，健康对照组（HC）中更多的 vOTU 映射到拟杆菌科（Bacteroidaceae）和瘤胃球菌科（Ruminococcaceae），而在 IBS 组中观察到更多与拟杆菌科、阿克塔菌科（Acutalibacteraceae）和肠杆菌科（Enterobacteriaceae）宿主科相关的病毒群体（补充图 5e）。

The host range is one of the central traits to understand in phages, which is governed by intricate molecular interactions between phages and host throughout the infection cycle. Host prediction is important to understand the potential roles of gut viruses in an ecosystem. While many well studied model phages seem to display a narrow host specificity, recent ecological and metagenomic evidence suggests that phage host ranges can vary considerably ${}^{26,27}$${}^{26,27}$^(26,27){ }^{26,27}, from narrow to broad. Recent investigations have elucidated key molecular mechanisms underpinning phage multiple-host infectivity ${}^{28,29}$${}^{28,29}$^(28,29){ }^{28,29}, a phenomenon with profound implications for phage therapy, microbial community dynamics, and biotechnological applications. Here, we systematically characterize the host ranges of gut bacteriophages in IBS, revealing distinct patterns of phage-bacteria interactions. By predicting the probable hosts of the gut viruses, we identified 52,690 bacteriophages from the MAG. Host prediction of the vOTUs revealed that the most common phylum of the predicted hosts was Firmicutes (IBS:67.2%, HC: $66.5\mathrm{\%}$$66.5\mathrm{\%}$66.5%66.5 \% ), followed by Bacteroidota (IBS:17.11%, HC: 19.77%), and Pseudomonadota (IBS:4.48%, HC: 6.46%) (Fig. 4a, b). Phage-host interactions differed notably between IBS patients and healthy controls (Fig. 4c). Comparative assessment of phage abundance per host phylum further highlighted elevated phage predation on Bacillota (e.g., Bacillota I, mean $\pm$$\pm$+-\pm SD $2.43\pm 7.81$$2.43\pm 7.81$2.43+-7.812.43 \pm 7.81 vs. $7.32\pm 18.27,p<0.001$$7.32\pm 18.27,p<0.001$7.32+-18.27,p < 0.0017.32 \pm 18.27, p<0.001 ) in IBS, whereas diminished predation on Bacteroidota was observed relative to HC (mean $\pm \mathrm{SD}$$\pm \mathrm{SD}$+-SD\pm \mathrm{SD} $20.52\pm 17.51$$20.52\pm 17.51$20.52+-17.5120.52 \pm 17.51 vs. $24.99\pm 18.82,p=0.049$$24.99\pm 18.82,p=0.049$24.99+-18.82,p=0.04924.99 \pm 18.82, p=0.049 ) (Fig. 4d). These findings suggest phage-bacterial interactions may contribute to microbial dysbiosis associated with IBS. At the genus level, the commonly assigned hosts were Parabacteroides, Bilophila, and Faecalibacillus. Notably, bacteriophages were predicted to infect a broader range of host species in IBS compared to HC. We calculated the average number of phages per host genus, and the average number of phages per individual host genome for bacteriophage (Fig. 4a, b, e). The number of phages per host genome varied, with Bilophia having the most abundant prophages in IBS (Fig. 4e). A Unique host genome was defined as a MAG that served as the sole predicted bacterial host for a given phage based on our metagenomic analyses. 2237 phages were identified in the genus Bilophia (77 unique host genome) (Fig. 4e). In comparison, only 394 gut phages could infect Bilophia ( 13 unique host genome). The unique host genome of prophages varied among host genera, and IBS has a greater number of unique genomes than HC (Fig. 4c). Following the viral-host pattern via matched prophages in viral families, the host genera of IBS and HC virus were mainly from 7 and 5 bacteria phyla, respectively (Fig. 4f). We found number of bacteria host for Peduoviridae ( $p=0.02$$p=0.02$p=0.02p=0.02 ) was significantly enriched in IBS, reinforcing the unique hostspecific associations in IBS (Supplementary Fig. 5f). IBS virus comprised of more abundant host genera in the bacteria community. For example, the viral contigs belonging to Peduoviridae spanned 10 bacteria genera in IBS while only 3 host genera linked in HC (Fig. 4f). Viruses in HC seemed to be specific with viral contig family linked to one specific bacterial genus, whereas a considerable fraction of viruses in IBS have broad host ranges with phage contigs predicted to infect multiple genera.
宿主范围是理解噬菌体特性的核心特征之一，它由噬菌体与宿主在整个感染周期中的复杂分子相互作用所决定。宿主预测对于理解肠道病毒在生态系统中的潜在作用非常重要。虽然许多研究充分的模式噬菌体似乎显示出狭窄的宿主特异性，但最近的生态学和宏基因组学证据表明，噬菌体的宿主范围可能会有相当大的变化 ${}^{26,27}$${}^{26,27}$^(26,27){ }^{26,27} ，从狭窄到广泛。最近的研究阐明了噬菌体多宿主感染性的关键分子机制 ${}^{28,29}$${}^{28,29}$^(28,29){ }^{28,29} ，这一现象对噬菌体疗法、微生物群落动态和生物技术应用具有深远的影响。在这里，我们系统地描述了肠易激综合征(IBS)中肠道噬菌体的宿主范围，揭示了噬菌体-细菌相互作用的独特模式。通过预测肠道病毒的潜在宿主，我们从 MAG 中鉴定出 52,690 个噬菌体。对 vOTU 的宿主预测显示，预测宿主中最常见的门类是厚壁菌门（IBS：67.2%，HC： $66.5\mathrm{\%}$$66.5\mathrm{\%}$66.5%66.5 \% ），其次是拟杆菌门（IBS：17.11%，HC：19.77%）和假单胞菌门（IBS：4.48%，HC：6.46%）（图 4a，b）。 噬菌体-宿主相互作用在肠易激综合症（IBS）患者和健康对照之间显著不同（图 4c）。对每个宿主门类中噬菌体丰度的比较评估进一步突显了 IBS 中噬菌体对拟杆菌门（例如，拟杆菌门 I，平均数 $\pm$$\pm$+-\pm SD $2.43\pm 7.81$$2.43\pm 7.81$2.43+-7.812.43 \pm 7.81 与 $7.32\pm 18.27,p<0.001$$7.32\pm 18.27,p<0.001$7.32+-18.27,p < 0.0017.32 \pm 18.27, p<0.001 ）的捕食增加，而相对于健康对照（HC），观察到对拟杆菌门的捕食减少（平均数 $\pm \mathrm{SD}$$\pm \mathrm{SD}$+-SD\pm \mathrm{SD} $20.52\pm 17.51$$20.52\pm 17.51$20.52+-17.5120.52 \pm 17.51 与 $24.99\pm 18.82,p=0.049$$24.99\pm 18.82,p=0.049$24.99+-18.82,p=0.04924.99 \pm 18.82, p=0.049 ）（图 4d）。这些发现表明噬菌体-细菌相互作用可能有助于与 IBS 相关的微生物群失调。在属水平上，通常指定的宿主是副拟杆菌属、 Bilophila 和 Faecalibacillus。值得注意的是，与 HC 相比，肠易激综合症患者的噬菌体预测感染宿主物种范围更广。我们计算了每个宿主属的平均噬菌体数量，以及每个宿主基因组平均噬菌体数量（图 4a，b，e）。每个宿主基因组的噬菌体数量各不相同，其中 Bilophia 在 IBS 中拥有最丰富的前噬菌体（图 4e）。一个独特的宿主基因组被定义为基于我们的宏基因组分析，作为特定噬菌体唯一预测的细菌宿主的 MAG。在 Bilophia 属中鉴定出 2237 个噬菌体（77 个独特宿主基因组）（图 4e）。 与之相比，仅有 394 种肠道噬菌体能感染 Bilophia（13 个独特宿主基因组）。前噬菌体的独特宿主基因组在宿主属间有所不同，且肠易激综合征（IBS）的独特基因组数量多于健康对照（HC）（图 4c）。通过匹配前噬菌体在病毒家族中的宿主模式，发现 IBS 和 HC 病毒的宿主属主要分别来自 7 个和 5 个细菌门（图 4f）。我们发现 Peduoviridae（ $p=0.02$$p=0.02$p=0.02p=0.02 ）的细菌宿主数量在 IBS 中显著富集，强化了 IBS 中独特的宿主特异性关联（补充图 5f）。IBS 病毒包含的细菌群落中的宿主属更为丰富。例如，属于 Peduoviridae 的病毒连续体在 IBS 中跨越了 10 个细菌属，而在 HC 中仅与 3 个宿主属相关联（图 4f）。HC 中的病毒似乎具有特异性，病毒连续体家族与一个特定的细菌属相关联，而在 IBS 中相当一部分病毒具有广泛的宿主范围，预测噬菌体连续体能够感染多个属。

Gut viral infection can affect host metabolism via the expression of viral AMGs. In addition to their physical impact on microbial communities, phages carry host-derived AMGs, which are used to manipulate the host cell metabolism during infection. To better understand the ecological effects of viruses in IBS, we searched the AMGs by VIBRANT ${}^{30}$${}^{30}$^(30){ }^{30} in IBS viral genomes
肠道病毒感染可以通过病毒来源的微生物组相关基因（AMGs）的表达影响宿主代谢。除了对微生物群体产生物理影响外，噬菌体携带宿主来源的 AMGs，这些 AMGs 在感染过程中被用来操纵宿主细胞代谢。为了更好地理解病毒在肠易激综合症（IBS）中的生态效应，我们使用 VIBRANT ${}^{30}$${}^{30}$^(30){ }^{30} 在 IBS 病毒基因组中搜索了 AMGs
a HC

C

d

Fig. 3 | Taxonomic diversity of gut viruses in IBS. a Gene-sharing network of the metagenome-assembled viral genomes in IBS and healthy controls obtained from vConTACT2 and visualized using Cytoscape, with the viral genomes being colored according to the family assignment. Nodes represent viral genomes and edges indicate
图 3 | IBS 患者肠道病毒的分类多样性。a 通过 vConTACT2 获得的 IBS 和健康对照的宏基因组组装病毒基因组的基因共享网络，并使用 Cytoscape 进行可视化，病毒基因组根据家族分类进行着色。节点代表病毒基因组，边缘表示
similarity based on shared protein clusters. $\mathbf{b}$$\mathbf{b}$b\mathbf{b} Comparison of the viral family distribution of viral genomes in HC and IBS. c Gene-sharing network of the metagenome-assembled viral genomes in IBS with mental disorders. d Significantly altered viral families of viral genomes in IBS with mental disorders (Wilcoxon test, $p<0.05$$p<0.05$p < 0.05p<0.05 ).
基于共享蛋白簇的相似性。 $\mathbf{b}$$\mathbf{b}$b\mathbf{b} 比较 HC 和 IBS 中病毒基因组病毒家族分布。c IBS 中伴有精神障碍的宏基因组组装病毒基因组的基因共享网络。d IBS 中伴有精神障碍的病毒基因组显著改变的病毒家族（Wilcoxon 检验， $p<0.05$$p<0.05$p < 0.05p<0.05 ）。
and calculated the relative abundances. A total of 31,819 genes were annotated to be AMGs (Fig. 5a). According to the KEGG annotation, the identified AMGs of gut viruses were involved in a variety of metabolic pathways, including those related to amino acid metabolism ( $29.9\mathrm{\%}$$29.9\mathrm{\%}$29.9%29.9 \% ),
并计算了相对丰度。共注释得到 31,819 个基因为 AMGs（图 5a）。根据 KEGG 注释，鉴定出的肠道病毒 AMGs 参与了多种代谢途径，包括与氨基酸代谢相关的途径（ $29.9\mathrm{\%}$$29.9\mathrm{\%}$29.9%29.9 \% ），
cofactors and vitamins metabolism (20.3%), carbohydrate metabolism ( $14.6\mathrm{\%}$$14.6\mathrm{\%}$14.6%14.6 \% ) (Fig. 5a). The most frequently annotated AMGs across all vOTUs were related to sulfur metabolic pathways (dcm, cysH, the most widespread AMGs) and could be found in $37.81\mathrm{\%}$$37.81\mathrm{\%}$37.81%37.81 \% of AMGs encoding vOTUs
辅因子和维生素代谢（20.3%）、碳水化合物代谢（ $14.6\mathrm{\%}$$14.6\mathrm{\%}$14.6%14.6 \% ）（图 5a）。在所有 vOTU 中最常注释的 AMG 与硫代谢途径相关（dcm、cysH，最普遍的 AMG），并且在编码 vOTU 的 AMG 中可以找到 $37.81\mathrm{\%}$$37.81\mathrm{\%}$37.81%37.81 \% 。

Fig. 4 | Bacterial host range of the gut viruses in IBS. $\mathbf{a}$$\mathbf{a}$a\mathbf{a} and $\mathbf{b}$$\mathbf{b}$b\mathbf{b} Genome-based phylogenetic trees of bacterial genera contained the predicted hosts of viral genomes. c Percentage of phage infections (host phylum) in IBS and healthy controls.
图 4 | IBS 患者肠道病毒的细菌宿主范围。 $\mathbf{a}$$\mathbf{a}$a\mathbf{a} 和 $\mathbf{b}$$\mathbf{b}$b\mathbf{b} 基于基因组的细菌属的系谱树包含了病毒基因组的预测宿主。c IBS 与健康对照中噬菌体感染（宿主门）的百分比。
d Numbers of phages per phylum of host in IBS and healthy controls. e Comparison of lysogeny rates, numbers of phages per genus of host in IBS and HC. f Sankey plot showing the virus-host linkages in IBS and HC.
d IBS 组和健康对照组宿主每个门类中噬菌体的数量。e IBS 组和 HC 组溶源性率比较，以及宿主每个属中噬菌体的数量。f 显示 IBS 组和 HC 组病毒-宿主关联的桑基图。
(Supplementary Fig. 5). Notably, the most prevalent AMG was DNMT1, a DNA (cytosine-5)-methyltransferase that protects viruses from their hosts’ antiviral restriction-modification systems, which was detected in $27.5\mathrm{\%}$$27.5\mathrm{\%}$27.5%27.5 \% of all AMGs. The high proportion of such an AMG probably represents a
(补充图 5)。值得注意的是，最常见的 AMG 是 DNMT1，这是一种 DNA（胞嘧啶-5）-甲基转移酶，能够保护病毒免受宿主抗病毒限制-修饰系统的影响，在所有 AMG 中检测到 $27.5\mathrm{\%}$$27.5\mathrm{\%}$27.5%27.5 \% 。这种 AMG 的高比例可能代表了一种
defense mechanism of the gut virome. Several previous studies have suggested a link between gut viruses and depression ${}^{11,31}$${}^{11,31}$^(11,31){ }^{11,31}.
肠道病毒的防御机制。先前的一些研究已经暗示了肠道病毒与抑郁之间的关联 ${}^{11,31}$${}^{11,31}$^(11,31){ }^{11,31} 。

Previous work has shown more altered gut viruses than gut bacteria in depression, suggesting that gut virome may play a role at least
先前的研究表明，在抑郁症中，改变的肠道病毒比肠道细菌更多，这表明肠道病毒组至少起着一定的作用

Fig. 5 | Viral auxiliary metabolic genes (AMG) on host metabolism. a Auxiliary metabolic categories identified in virome bacteriophage contigs, with amino acid metabolism representing the highest proportion of overall AMGs in the assembled
图 5 | 病毒辅助代谢基因（AMG）对宿主代谢的影响。a 在病毒噬菌体 contigs 中识别的辅助代谢类别，其中氨基酸代谢在组装的 AMG 中占比最高。
bacteriophage dataset. b Diversity and richness of AMGs in IBS with and without psychiatric comorbidities. c Volcano plot of AMG showing differential abundance between HC and IBS patients with psychiatric comorbidities.
细菌噬菌体数据集。b IBS 患者有无精神共病时 AMG 的多样性和丰富度。c AMG 的火山图，显示 HC 与伴有精神共病的 IBS 患者之间的差异丰度。
equivalent to that of the gut bacteriome in the pathology of depression ${}^{10}$${}^{10}$^(10){ }^{10}. Gut viruses may influence host behaviors by regulating their host bacteria and their metabolism. We next compared the AMG levels between IBS individuals with and without psychiatric comorbidities to explore the potential link between gut viral functions and mental disorders in IBS. We found higher AMG diversity in IBS with depression and anxiety (Fig. 5b), indicating more metabolic functions on the viral genome. The volcano plot (Fig. 5c) illustrated the comparison of AMG levels of IBS with or without mental disorders, showing a significant decrease in ubiG and ahcY in the IBS with depression, with ubiG also exhibiting a consistent trend of decrease in individuals who have both anxiety and depression. Notably, the ubiG gene encodes an S-adenosyl-l-methionine (SAM-e)-
相当于肠道细菌组在抑郁症病理学中的作用 ${}^{10}$${}^{10}$^(10){ }^{10} 。肠道病毒可能通过调节宿主细菌及其代谢来影响宿主行为。接下来，我们比较了有和无精神合并症的肠易激综合征（IBS）个体的 AMG 水平，以探索肠道病毒功能与 IBS 中精神障碍之间的潜在联系。我们发现 IBS 伴有抑郁和焦虑的 AMG 多样性更高（图 5b），表明病毒基因组上的代谢功能更多。火山图（图 5c）说明了有无精神障碍的 IBS 的 AMG 水平比较，显示在伴有抑郁的 IBS 中 ubiG 和 ahcY 显著降低，ubiG 在同时具有焦虑和抑郁的个体中也表现出一致性的降低趋势。值得注意的是，ubiG 基因编码 S-腺苷-L-甲硫氨酸（SAM-e）-
dependent methyltransferase enzyme involved in ubiquinone biosynthesis, a process reported to be crucial in various neurological diseases. SAMe is one of the most extensively studied supplements for treating depression, with low levels reported in individuals with depressive disorders ${}^{32,33}$${}^{32,33}$^(32,33){ }^{32,33}. Interestingly, the IBS with anxiety exhibits a markedly different AMG pattern, characterized by an increase in the expression of pcaC (xenobiotic metabolism), UGDH (carbohydrate), asnB (amino acid metabolism), while coaD (cofactors and vitamins), acpP (secondary metabolites), gala(glycan), EARS (cofactors and vitamins), and guaA (nucleotide) showed decreased levels than the HC. This contrasting AMG profile underscored the distinct viral function underpinning associated with IBS when comorbid with either depression or anxiety.
依赖性甲基转移酶酶参与辅酶 Q 的生物合成，该过程据报道在多种神经性疾病中至关重要。SAMe 是研究最为广泛的用于治疗抑郁的补充剂之一，据报道在抑郁症患者中其水平较低 ${}^{32,33}$${}^{32,33}$^(32,33){ }^{32,33} 。有趣的是，伴有焦虑的 IBS 表现出明显不同的 AMG 模式，特征为 pcaC（外来物代谢）、UGDH（碳水化合物）、asnB（氨基酸代谢）的表达增加，而 coaD（辅因子和维生素）、acpP（次生代谢物）、gala（糖甘）、EARS（辅因子和维生素）以及 guaA（核苷酸）的水平低于健康对照。这种对比鲜明的 AMG 谱揭示了与 IBS 共病抑郁或焦虑时，与之相关的不同病毒功能。

In the present study, we have performed a large-scale analysis for viral profiles of deeply phenotyped IBS individuals ( $n=317$$n=317$n=317n=317 ) and shown extensive virome variation and its association with the bacteriome and mental disorders. This study is, to the best of our knowledge, the largest single cohort analysis for the human gut virome of IBS population. The analysis uncovered novel viral clades, interactions between the virome and bacterial viral functional genes, and clinical features strongly associated with the virome, expanding our knowledge of the gut virome structure and variation and offering potential biomarkers for diagnosis and new insights into the pathogenesis of the disorder.
在当前研究中，我们对深度表型的肠易激综合征（ $n=317$$n=317$n=317n=317 ）个体的病毒谱进行了大规模分析，并展示了广泛的病毒组变异及其与菌组和精神疾病的关联。据我们所知，本研究是对肠易激综合征人群人肠道病毒组进行的最大单一队列分析。该分析揭示了新的病毒族群、病毒组与细菌病毒功能基因之间的相互作用，以及与病毒组强烈相关的临床特征，扩展了我们对肠道病毒组结构和变异的了解，并为诊断提供了潜在的生物标志物，以及对疾病发病机制的新见解。

In addition to delineating the general virome features of IBS, we have conducted a focused investigation into the virome of IBS patients who also suffer from depression and anxiety. Our findings reveal distinct viral compositions and interactions within this subgroup, suggesting that the gut virome may play a significant role in the gut-brain axis and the manifestation of psychiatric comorbidities in IBS patients. The observed increase in viral-host interactions in IBS patients with depression or anxiety may be driven by several interconnected pathways. Depression and anxiety are associated with low-grade systemic inflammation (e.g., elevated IL-6, TNF$\alpha$$\alpha$alpha\alpha ), which may amplify host susceptibility to viral activity or persistence ${}^{34,35}$${}^{34,35}$^(34,35){ }^{34,35}. The mental disorder may increase intestinal permeability (“leaky gut”) could facilitate viral particle translocation or immune activation ${}^{11,36}$${}^{11,36}$^(11,36){ }^{11,36}. Dysbiosis of the gut microbiota-a hallmark of both IBS and mood disordersmight indirectly promote viral replication by disrupting colonization resistance or altering mucosal immunity ${}^{37,38}$${}^{37,38}$^(37,38){ }^{37,38}. While further research is needed to validate these mechanisms, their convergence in comorbid IBS and psychiatric conditions provides a plausible framework for our findings. The identification of specific viruses and their interactions in IBS patients with mental health issues underscores the complexity of IBS as a multifactorial disorder and highlights the need for tailored therapeutic approaches that address both gastrointestinal and mental health symptoms.
除了描绘肠易激综合征（IBS）的一般病毒组特征外，我们还对同时患有抑郁和焦虑的 IBS 患者的病毒组进行了专门研究。我们的发现揭示了这一亚组中独特的病毒组成和相互作用，表明肠道病毒组可能在肠道-大脑轴和 IBS 患者的精神并发症表现中扮演重要角色。观察到 IBS 患者中伴有抑郁或焦虑的病毒-宿主相互作用增加可能是由于几个相互连接的途径驱动。抑郁和焦虑与低度系统性炎症相关（例如，IL-6、TNF 升高 $\alpha$$\alpha$alpha\alpha ），这可能会增强宿主对病毒活动或持续性的易感性 ${}^{34,35}$${}^{34,35}$^(34,35){ }^{34,35} 。精神障碍可能增加肠道渗透性（“肠漏”）可能促进病毒粒子的转位或免疫激活 ${}^{11,36}$${}^{11,36}$^(11,36){ }^{11,36} 。肠道微生物群的失衡——IBS 和情绪障碍的共同特征——可能通过破坏定植抗力或改变粘膜免疫间接促进病毒复制 ${}^{37,38}$${}^{37,38}$^(37,38){ }^{37,38} 。 尽管需要进一步研究来验证这些机制，但它们在共病肠易激综合征和精神病状况中的汇聚为我们发现提供了合理的框架。在患有精神健康问题的肠易激综合征患者中识别特定的病毒及其相互作用，凸显了肠易激综合征作为多因素疾病的复杂性，并强调了需要采取针对性的治疗方法，同时解决胃肠道和精神健康症状。

Our analysis indicates that the gut virome in IBS patients exhibits a broader host range compared to healthy controls, suggesting a higher prevalence of phage-bacteria interactions in IBS. This expanded host range may facilitate more dynamic and extensive interactions within the gut microbiome, potentially exacerbating the dysbiotic state observed in IBS. Moreover, we have identified distinct AMGs in the IBS virome, which are not only involved in the modulation of bacterial metabolism but also in pathways potentially linked to mental disorders. These findings suggest that gut viruses may contribute to the metabolic and neuropsychiatric dimensions of IBS, providing new avenues for research into the viral contributions to IBS pathophysiology and its psychiatric comorbidities.
我们的分析表明，与健康对照相比，肠病毒组在肠易激综合症（IBS）患者中表现出更广泛的宿主范围，这表明 IBS 中噬菌体-细菌相互作用的发生率更高。这种扩展的宿主范围可能促进了肠道微生物群内更动态和广泛的相互作用，可能会加剧 IBS 中观察到的菌群失调状态。此外，我们在 IBS 病毒组中鉴定出了独特的抗微生物基因（AMGs），这些基因不仅参与调节细菌代谢，还与可能关联精神障碍的途径有关。这些发现表明，肠道病毒可能对 IBS 的代谢和精神病性维度有所贡献，为研究病毒在 IBS 病理生理学及其精神并发症中的作用提供了新的研究方向。

In summary, our study provides a comprehensive analysis of the gut virome in IBS, uncovering novel insights into its complexity and potential implications for both gastrointestinal and mental health. The identification of unique viral signatures, expanded host ranges, and specific AMGs highlights the intricate interplay between gut viruses and the broader microbiome, paving the way for future studies aimed at elucidating the role of the virome in IBS and its associated comorbidities.
总结而言，我们的研究对肠易激综合征（IBS）患者的肠道病毒组进行了全面分析，揭示了其复杂性的新见解以及可能对胃肠道和心理健康的影响。独特病毒特征的识别、宿主范围的扩大以及特定的抗微生物组分子（AMGs）突显了肠道病毒与更广泛的微生物群之间的精细相互作用，为未来旨在阐明病毒组在 IBS 及其相关并发症中作用的研究铺平了道路。

Individuals meeting the Rome IV criteria for IBS ${}^{12,13}$${}^{12,13}$^(12,13){ }^{12,13} were prospectively recruited from two Chinese medicine clinics affiliated with the School of Chinese Medicine at Hong Kong Baptist University. A total of 360 IBS patients (meeting Rome IV diagnostic criteria, including 317 with complete psychiatric comorbidity assessments), and 84 healthy controls (HC) were recruited for fecal metagenomic sequencing. Among the 360 IBS patients, subtype distribution was as follows: $67.22\mathrm{\%}$$67.22\mathrm{\%}$67.22%67.22 \% IBS-D (diarrhea-predominant), $8.05\mathrm{\%}$$8.05\mathrm{\%}$8.05%8.05 \% IBS-C (constipation-predominant), $8.33\mathrm{\%}$$8.33\mathrm{\%}$8.33%8.33 \% IBS-M (mixed), and $16.39\mathrm{\%}$$16.39\mathrm{\%}$16.39%16.39 \% IBS-U (undefined). Specifically, IBS patients were included if they were 18-65 years, and met Rome IV criteria; had an IBS symptom severity scale (IBS-SSS) score over 75 points at baseline; had a normal colonic evaluation within the past 5 years via colonoscopy or barium enema. Bowel
符合罗马 IV 标准的功能性肠病（IBS） ${}^{12,13}$${}^{12,13}$^(12,13){ }^{12,13} 患者前瞻性地从香港浸会大学中医学院下属的两个中医诊所招募。共招募了 360 名 IBS 患者（符合罗马 IV 诊断标准，其中 317 名完成了精神共病评估），以及 84 名健康对照（HC）进行粪便宏基因组测序。在 360 名 IBS 患者中，亚型分布如下： $67.22\mathrm{\%}$$67.22\mathrm{\%}$67.22%67.22 \% IBS-D（腹泻型）， $8.05\mathrm{\%}$$8.05\mathrm{\%}$8.05%8.05 \% IBS-C（便秘型）， $8.33\mathrm{\%}$$8.33\mathrm{\%}$8.33%8.33 \% IBS-M（混合型），以及 $16.39\mathrm{\%}$$16.39\mathrm{\%}$16.39%16.39 \% IBS-U（未定型）。具体而言，纳入的 IBS 患者需年龄在 18-65 岁之间，且符合罗马 IV 标准；基线时 IBS 症状严重程度量表（IBS-SSS）评分超过 75 分；过去 5 年内通过结肠镜或钡剂灌肠进行了正常的结肠评估。肠道
movement frequency was recorded as number per day and consistency was scored on a 7-point scale and stool consistency was evaluated using the Bristol stool scale ${}^{39}$${}^{39}$^(39){ }^{39}. Subjects were excluded if they were pregnant; had a medical history of IBD; surgical histories involving gallbladder removal, the gastrointestinal (GI) tract, or cerebral cranium; had a medications know to influenc GI function, antidepressants and anxiolytics. Individuals without the medical history of GI diseases, neurodegenerative diseases, cardiovascular diseases, metabolic disorders were also recruited as control. All participants were required to stop using microbiota-related supplements. Probiotics, antibiotics, prebiotics, at least three weeks before stool sampling. Specimens (stool) were transported to the laboratory on dry ice and stored at $-{80}^{\circ }\mathrm{C}$$-{80}^{\circ }\mathrm{C}$-80^(@)C-80^{\circ} \mathrm{C} until DNA extraction. Details of patients’ diagnoses, subject recruitment, and sampling are described in the previous work ${}^{25,40}$${}^{25,40}$^(25,40){ }^{25,40}. The study was conducted in accordance with the declaration of Helsinki. This study was approved by the Ethics Committee of Hong Kong Baptist University (HASC/15-16/0300 and HASC/16-17/0027). Written informed consent was signed and obtained from all participants.
排便频率以每天次数记录，一致性评分采用 7 分制，大便一致性使用布里斯托尔大便量表 ${}^{39}$${}^{39}$^(39){ }^{39} 进行评估。如果受试者怀孕；有炎症性肠病（IBD）的病史；有涉及胆囊切除、胃肠道（GI）或颅脑手术的手术史；或使用已知影响胃肠道功能的药物，如抗抑郁药和镇静剂，则将这些受试者排除。同时招募了没有胃肠道疾病、神经退行性疾病、心血管疾病、代谢障碍病史的健康个体作为对照。所有参与者均需在粪便采样前至少三周停止使用与微生物群相关的补充剂，包括益生菌、抗生素、益生元。标本（粪便）在干冰上运送到实验室，并在 $-{80}^{\circ }\mathrm{C}$$-{80}^{\circ }\mathrm{C}$-80^(@)C-80^{\circ} \mathrm{C} 条件下保存直至提取 DNA。患者诊断详情、受试者招募和采样过程在之前的工作 ${}^{25,40}$${}^{25,40}$^(25,40){ }^{25,40} 中有描述。本研究按照赫尔辛基宣言进行。该研究得到了香港浸会大学伦理委员会的批准（HASC/15-16/0300 和 HASC/16-17/0027）。 所有参与者均签署并获取了书面知情同意书。

Phenol/chloroform/isoamyl alcohol method was applied to to extract microbial DNA from stool samples ( 200 mg ) of included subjects. The DNA that passed quality control was then subjected to library construction using the TruSeq DNA HT Sample Prep Kit. Paired-end sequencing ( $2\times 150\mathrm{bp}$$2\times 150\mathrm{bp}$2xx150bp2 \times 150 \mathrm{bp} ) was carried out using Illumina Hi-Seq platform.
采用酚/氯仿/异戊醇法从纳入对象的粪便样本（200 毫克）中提取微生物 DNA。通过质量控制的 DNA 随后使用 TruSeq DNA HT Sample Prep Kit 进行文库构建。使用 Illumina Hi-Seq 平台进行配对末端测序（ $2\times 150\mathrm{bp}$$2\times 150\mathrm{bp}$2xx150bp2 \times 150 \mathrm{bp} ）。

IBS patients with depression and anxiety were identified using a combination of the Zung self-rating depression scale (SDS), the 17-item Hamilton depression rating scale (HAMD-17), and the Zung self-rating anxiety scale (SAS). Firstly, all subjects were requested to complete the SDS and SAS, with a cut-off index score of 50 . Patients scoring above 53 on the SDS were subsequently evaluated with the HAMD-17 for a professional diagnosis ${}^{40}$${}^{40}$^(40){ }^{40}.
采用 Zung 自评抑郁量表（SDS）、17 项汉密尔顿抑郁评定量表（HAMD-17）和 Zung 自评焦虑量表（SAS）相结合的方式，识别出伴有抑郁和焦虑的肠易激综合征（IBS）患者。首先，要求所有受试者完成 SDS 和 SAS 问卷，以 50 分为临界指数分数。SDS 评分超过 53 分的患者随后接受 HAMD-17 的专业诊断 ${}^{40}$${}^{40}$^(40){ }^{40} 。

We first performed short-read taxonomic classification of metagenomic sequencing using Metaphlan3 ${}^{41}$${}^{41}$^(41){ }^{41} and Kraken v1.1.1 with default settings was performed as previously described (database, NCBI RefSeq, release 202) ${}^{42,43}$${}^{42,43}$^(42,43){ }^{42,43}. The relative abundance were estimated with Bracken ${}^{44}$${}^{44}$^(44){ }^{44}. Abundance profiles across samples with species with medium abundance $\le 0.01\mathrm{\%}$$\le 0.01\mathrm{\%}$<= 0.01%\leq 0.01 \% were filtered out.
我们首先使用 Metaphlan3 ${}^{41}$${}^{41}$^(41){ }^{41} 和默认设置的 Kraken v1.1.1 对宏基因组测序的短读段进行分类学分类，如前所述（数据库，NCBI RefSeq，发布 202） ${}^{42,43}$${}^{42,43}$^(42,43){ }^{42,43} 。使用 Bracken ${}^{44}$${}^{44}$^(44){ }^{44} 估计相对丰度。对样本间中等丰度的物种的丰度轮廓进行过滤 $\le 0.01\mathrm{\%}$$\le 0.01\mathrm{\%}$<= 0.01%\leq 0.01 \% 。

Integration of bacterial, fungal, and viral profiles was achieved through weighted SNF (online tool (WSNF, https://integrative-microbiomics.ntu. edu.sg)), with biome-specific weighting based on the observed richness of each biome. WSNF integrates multiple data types by constructing and fusing similarity networks, assigning adaptive weights to each data source to optimize the combined representation. This approach offers advantages over traditional clustering methods by effectively capturing complementary information across omics layers while reducing noise and bias from individual datasets. Using the merged dataset of microbiome profile, the tool generates a corresponding patient similarity network using a clustering algorithm (Bray-Curtis metric, default settings), outputting the cluster assignments for each patient. The optimal clustering configuration ( $n=2$$n=2$n=2n=2 ) was established through eigengap method and silhouette-optimized $k$$k$kk nearest neighbors (KNN) parameters.
通过加权 SNF（在线工具（WSNF，https://integrative-microbiomics.ntu.edu.sg））实现了细菌、真菌和病毒图谱的整合，基于每个生物群观察到的丰富度进行生物群特定的加权。WSNF 通过构建和融合相似性网络来整合多种数据类型，为每个数据源分配自适应权重以优化组合表示。这种方法通过有效捕获跨组学层的互补信息，同时减少单个数据集的噪声和偏差，优于传统的聚类方法。使用合并后的微生物组剖面数据集，该工具使用聚类算法（Bray-Curtis 指标，默认设置）生成相应的患者相似性网络，输出每位患者的聚类分配。通过特征间隙方法和轮廓优化的 $k$$k$kk 最近邻（KNN）参数，建立了最佳的聚类配置（ $n=2$$n=2$n=2n=2 ）。

Metagenomes (metagenome-assembled genomes, or MAGs) were assembled by using MEGAHIT v1.2.9 ${}^{45}$${}^{45}$^(45){ }^{45} to investigate the genomic diversity of bacteria species ${}^{46}$${}^{46}$^(46){ }^{46}. Sequence coverage profiles were then generated by aligning quality-filtered reads to their respective assemblies by BWA v0.7.17 ${}^{47}$${}^{47}$^(47){ }^{47}. Contigs were binned with MetaBAT2 ${}^{48}$${}^{48}$^(48){ }^{48} (v2.15, default parameters). The quality of assembly bins were evaluated with CheckM ${}^{49}$${}^{49}$^(49){ }^{49},
利用 MEGAHIT v1.2.9 ${}^{45}$${}^{45}$^(45){ }^{45} 对细菌物种的基因组多样性进行了宏基因组（宏基因组组装基因组，即 MAGs）组装 ${}^{46}$${}^{46}$^(46){ }^{46} 。然后通过 BWA v0.7.17 ${}^{47}$${}^{47}$^(47){ }^{47} 将质量过滤后的读段与其相应的组装对齐，生成序列覆盖度轮廓。使用 MetaBAT2 ${}^{48}$${}^{48}$^(48){ }^{48} （v2.15，默认参数）对组装的 contigs 进行分类。使用 CheckM ${}^{49}$${}^{49}$^(49){ }^{49} 对组装箱的质量进行了评估。
retaining those with over $90\mathrm{\%}$$90\mathrm{\%}$90%90 \% completeness and $<5\mathrm{\%}$$<5\mathrm{\%}$< 5%<5 \% contamination. The taxonomy of MAGs were inferre to SGBs by applying ‘phylophlan_metagenomic’, a subroutine of PhyloPhlAn v3.0.67 ${}^{50}$${}^{50}$^(50){ }^{50}. PhyloPhlan was employed to establish taxonomic classifications of MAGs, which were crucial for subsequent host-phage association analysis.
保留那些完整性超过 $90\mathrm{\%}$$90\mathrm{\%}$90%90 \% 且污染度小于 $<5\mathrm{\%}$$<5\mathrm{\%}$< 5%<5 \% 的 MAGs。利用 PhyloPhlAn v3.0.67 的一个子程序‘phylophlan_metagenomic’将 MAGs 的分类推断至 SGBs ${}^{50}$${}^{50}$^(50){ }^{50} 。PhyloPhlan 用于建立 MAGs 的分类学分类，这对于后续宿主-噬菌体关联分析至关重要。

Viral sequences were identified from metagenomic assemblies using VirSorter2 (v2.2.4) with the parameters --exclude-lt2gene --db 2. Scaffolds longer than 1 kbp were considered putative viral sequences if they met at least one of the following criteria ${}^{51,52}$${}^{51,52}$^(51,52){ }^{51,52} : (1) VirSorter-positive (including all six categories), (2) BLASTn alignments to NCBI viral dereplicated sequences ( $e\le {10}^{-10},>90\mathrm{\%}$$e\le {10}^{-10},>90\mathrm{\%}$e <= 10^(-10), > 90%e \leq 10^{-10},>90 \% query coverage, $>50\mathrm{\%}\mathrm{ANI}$$>50\mathrm{\%}\mathrm{ANI}$> 50%ANI>50 \% \mathrm{ANI} ), (3) being circular ${}^{53}$${}^{53}$^(53){ }^{53}, (4) longer than 3 kbp with no hits ( $e$$e$ee value of ${10}^{-10},90\mathrm{\%}$${10}^{-10},90\mathrm{\%}$10^(-10),90%10^{-10}, 90 \% ANI, alignments $>100$$>100$> 100>100 nucleotides) to the nt database (release 249). A total of $10,458,078$$10,458,078$10,458,07810,458,078 scaffolds were initially classified as viral. Putative viral scaffolds were subjected to quality control using CheckV ${}^{54}$${}^{54}$^(54){ }^{54} to removed potential contaminations. Taxonomic classification at the family level was performed using PhaGCN v2.1 ${}^{55}$${}^{55}$^(55){ }^{55}. The viral contigs were next analyzed based on the predominant assignment of their open reading frames (ORFs). The ORFs were predicted with MetaProdigal v2.6.3 ${}^{56}$${}^{56}$^(56){ }^{56}. To annotate the predicted ORFs, their amino acid sequences against the viral RefSeq protein database (v84) were queried using Diamond ${}^{57}$${}^{57}$^(57){ }^{57} ( $e$$e$ee-value threshold of $<{10}^{-5}$$<{10}^{-5}$< 10^(-5)<10^{-5} and a bitscore $>50$$>50$> 50>50 ). Contigs were taxonomically annotated according to the predominant assignment of their constituent ORFs to a taxon. Viral abundance was estimated by mapping reads to viral contigs using BWA, and the resulting alignments were processed using custom scripts. Abundances were normalized as reads per kilobase per million mapped reads (RPKM). Viral contigs were clustered into viral clusters (VCs) using vContact2 (v0.11.3) ${}^{58}$${}^{58}$^(58){ }^{58}.
病毒序列是通过使用 VirSorter2（v2.2.4）从宏基因组组装中识别出来的，参数设置为--exclude-lt2gene --db 2。如果超过 1 kbp 的支架满足以下至少一个标准 ${}^{51,52}$${}^{51,52}$^(51,52){ }^{51,52} ，则被认为是潜在的病毒序列：（1）VirSorter 检测结果为阳性（包括所有六个类别），（2）与 NCBI 病毒去重复序列的 BLASTn 比对（ $e\le {10}^{-10},>90\mathrm{\%}$$e\le {10}^{-10},>90\mathrm{\%}$e <= 10^(-10), > 90%e \leq 10^{-10},>90 \% 查询覆盖度， $>50\mathrm{\%}\mathrm{ANI}$$>50\mathrm{\%}\mathrm{ANI}$> 50%ANI>50 \% \mathrm{ANI} ），（3）呈环状 ${}^{53}$${}^{53}$^(53){ }^{53} ，（4）超过 3 kbp 且在 nt 数据库（发布版 249）中没有比对（ $e$$e$ee 的 ${10}^{-10},90\mathrm{\%}$${10}^{-10},90\mathrm{\%}$10^(-10),90%10^{-10}, 90 \% ANI 值，比对 $>100$$>100$> 100>100 核苷酸）。共有 $10,458,078$$10,458,078$10,458,07810,458,078 个支架最初被归类为病毒。疑似病毒支架经过 CheckV ${}^{54}$${}^{54}$^(54){ }^{54} 的质量控制以去除潜在的污染。在家族水平上进行分类使用的是 PhaGCN v2.1 ${}^{55}$${}^{55}$^(55){ }^{55} 。接着，基于开放阅读框（ORFs）的主要分配对病毒连续体进行分析。ORFs 使用 MetaProdigal v2.6.3 ${}^{56}$${}^{56}$^(56){ }^{56} 进行预测。为了注释预测的 ORFs，使用 Diamond ${}^{57}$${}^{57}$^(57){ }^{57} 将其氨基酸序列与病毒 RefSeq 蛋白数据库（v84）进行比对（ $e$$e$ee -值阈值为 $<{10}^{-5}$$<{10}^{-5}$< 10^(-5)<10^{-5} ，位得分为 $>50$$>50$> 50>50 ）。 组装的连续序列根据其组成开放阅读框（ORF）的主要分类学归属进行分类注释。病毒丰度是通过将读段映射到病毒连续序列上使用 BWA 进行估计的，然后使用自定义脚本来处理生成的比对结果。丰度被标准化为每千碱基每百万映射读段（RPKM）。病毒连续序列使用 vContact2（版本 0.11.3） ${}^{58}$${}^{58}$^(58){ }^{58} 进行聚类，形成病毒簇（VCs）。

Host prediction were achieved by CRISPR spacers and prophage sequence alignment to MAGs. We manually constructed a database of MAG and viral sequences for each independent metagenomic sample. For CRISPR spacer hit, we used BLASTn v2.12.0+ to compare the CRISPR spacers ${}^{59}$${}^{59}$^(59){ }^{59} identified from the MAG to the database built with the viral genomes with parameters: -task blastn-short -perc_identity 100 -qcov_hsp_perc 100. For prophage sequence alignment, we used BLASTn v2.12.0+ to align all viral genomes into MAG with parameters: -task megablast -perc_identity 90 -qcov_hsp_perc 75, and results with MAG length>2500 bp were retained. To reveal the infection patterns of IBS and HC gut phage, we established genome-level phylogenetic trees on MAGs reconstructed from the IBS and HC gut metagenomes, respectively. For each genus, we used a script to randomly select a MAG and count the infection status of all MAGs in the genus (the number of times each genus was infected, the average number of times the MAGs in the genus were infected). Afterwards, phylogenetic trees were constructed using IQ-TREE v2.3.3 ${}^{60}$${}^{60}$^(60){ }^{60} (-bb 1000 -m MFP -nt 128) and then visualized using the R package ggtree v3.8.2.
通过 CRISPR 间隔区和噬菌体序列与 MAGs 的比对实现了宿主预测。我们手动构建了每个独立宏基因组样本的 MAG 和病毒序列数据库。对于 CRISPR 间隔区命中，我们使用 BLASTn v2.12.0+将 MAG 中识别的 CRISPR 间隔区 ${}^{59}$${}^{59}$^(59){ }^{59} 与使用病毒基因组构建的数据库进行比较，参数设置为：-task blastn-short -perc_identity 100 -qcov_hsp_perc 100。对于噬菌体序列比对，我们使用 BLASTn v2.12.0+将所有病毒基因组比对到 MAG，参数设置为：-task megablast -perc_identity 90 -qcov_hsp_perc 75，并保留 MAG 长度>2500 bp 的结果。为了揭示 IBS 和 HC 肠道噬菌体的感染模式，我们在 IBS 和 HC 肠道宏基因组重建的 MAG 上建立了基因组水平的系统发育树。对于每个属，我们使用脚本随机选择一个 MAG，并计算该属中所有 MAG 的感染状态（每个属被感染的次数，该属中 MAG 的平均感染次数）。之后，使用 IQ-TREE v2.3.3 ${}^{60}$${}^{60}$^(60){ }^{60} (-bb 1000 -m MFP -nt 128)构建系统发育树，并使用 R 包 ggtree v3.8.2 进行可视化。

AMGs were predicted for all metagenome-assembled viral genomes using VIBRANT ${}^{30}$${}^{30}$^(30){ }^{30}, which employs a rigorous pipeline including viral contig identification, Prodigal-based gene prediction, and HMM searches against KEGG, Pfam and VOG databases with stringent filters ( $E$$E$EE-value, viral-like score). The relative abundance of AMGs is determined by calculating the relative abundance of viral genomes carrying those AMGs in each sample. Differential abundance analysis of AMGs between groups was performed using DESeq2 ${}^{61}$${}^{61}$^(61){ }^{61} with default parameters.
使用 VIBRANT ${}^{30}$${}^{30}$^(30){ }^{30} 对所有宏基因组组装的病毒基因组预测 AMGs，该工具采用严格的流程，包括病毒片段识别、基于 Prodigal 的基因预测以及使用严格过滤器（ $E$$E$EE -值，病毒相似度评分）对 KEGG、Pfam 和 VOG 数据库进行 HMM 搜索。通过计算携带这些 AMGs 的病毒基因组在每个样本中的相对丰度来确定 AMGs 的相对丰度。使用 DESeq2 ${}^{61}$${}^{61}$^(61){ }^{61} 和默认参数进行组间 AMGs 的差异丰度分析。

To determine the interaction among gut microbiota in IBS and HC, a multikingdom interaction network were constructed. Co-occurrence analysis were performed with statistical significance testing using SparCC network analysis ${}^{62}$${}^{62}$^(62){ }^{62}. SparCC analysis were performed using the R package “SpiecEasi
为了确定肠易激综合征（IBS）和健康对照（HC）中肠道微生物群之间的相互作用，构建了多界相互作用网络。使用 SparCC 网络分析 ${}^{62}$${}^{62}$^(62){ }^{62} 进行共发生分析并进行统计学显著性测试。使用 R 包“SpiecEasi”进行 SparCC 分析。
v1.1.1” with 20 iterations in the outer loop and 10 iterations in the inner loop ${}^{63}$${}^{63}$^(63){ }^{63}. The correlation strength exclusion threshold is 0.1 , using SparCC default settings. Correlations with an absolute value $<0.1$$<0.1$< 0.1<0.1 were considered zero by the inner SparCC loop, and $p$$p$pp-values $<0.05$$<0.05$< 0.05<0.05 were considered significant. Network metrics such as node degree (busy), stress centrality (critical), and betweenness centrality (influential) were calculated, and visualized in cytoscape ${}^{63}$${}^{63}$^(63){ }^{63}.
“v1.1.1”在外循环中迭代 20 次，内循环中迭代 10 次 ${}^{63}$${}^{63}$^(63){ }^{63} 。相关性强度排除阈值为 0.1，使用 SparCC 默认设置。内层 SparCC 循环将绝对值 $<0.1$$<0.1$< 0.1<0.1 的相关性视为零， $p$$p$pp -值 $<0.05$$<0.05$< 0.05<0.05 被视为显著。计算了网络指标，如节点度（繁忙）、压力中心性（关键）和介数中心性（有影响力），并在 Cytoscape ${}^{63}$${}^{63}$^(63){ }^{63} 中可视化。