Risks and Etiology of Bacterial Vaginosis Revealed by Species Dominance Network Analysis

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

5 Risks and etiology of bacterial vaginosis revealed by species dominance network analysis

Zhanshan (Sam) Ma1,2,* Aaron M. Ellison3 10 1Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences

2CAS Center for Excellence in Animal Evolution and Genetics 15 Chinese Academy of Sciences, Kunming, 650223, China *For all correspondence: [email protected]

3Harvard University, Harvard Forest, 324 North Main Street, Petersham, 20 Massachusetts 01366, USA

Running Head: Risks and etiology of BV

25 Keywords: BV (Bacterial vaginosis); Community dominance; Species dominance; Species dominance networks (SDN); Diversity-stability relationship (DSR); Core/periphery network (CPN); High-salience skeleton networks (HSN); BV-associated anaerobic bacteria (BVAB)

30 Single sentence summary: 15 trio motifs unique to the BV (bacterial vaginosis) may act as indicators for personalized BV diagnosis, risk prediction and etiological study.

1 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract

BV (bacterial vaginosis) influences 20-40% of women but its etiology is still poorly understood. 35 An open question about the BV is which of the hundreds of bacteria found in the human vaginal microbiome (HVM) are the causal pathogens of BV? Existing attempts to identify them have failed for at least two reasons: (i) a focus on species per se that ignores species interactions; (ii) a lack of systems-level understanding of the HVM. Here, we recast the question of microbial causality of BV by asking if there are any reliable ‘signatures’ of community composition (or 40 poly-microbial ‘cults’) associated with it? We apply a new framework (species dominance network analysis by Ma & Ellison (2019: Ecological Monographs) to detect critical structures in HVM networks associated with BV risks and etiology. We reanalyzed the metagenomic datasets of a mixed-cohort of 25 BV patients and healthy women. In these datasets, we detected 15 trio- motifs that occurred exclusively in BV patients. We failed to find any of these 15 trio-motifs in 45 three additional cohorts of 1535 healthy women. Most member-species of the 15 trio motifs are BV-associated anaerobic bacteria (BVAB), Ravel’s community-state type indicators, or the most dominant species; virtually all species interactions in these trios are high-salience skeletons, suggesting that those trios are strongly connected ‘cults.’ The presence of trio motifs unique to BV may act as indicators for its personalized diagnosis and could help elucidate a more 50 mechanistic interpretation of its risks and etiology.

Introduction BV is a high-recurrence vaginal disease that occurs in 20–40% of sampled women (Koumans et 55 al. 2001). It is associated with genital tract infections and many pregnancy complications, including pelvic inflammatory disease, premature rupture of membranes, intrauterine growth restriction, intrauterine fetal demise, chorioamnionitis, endometritis, preterm labor and delivery, postpartum infection, ectopic pregnancy, and tubal factor infertility (White et al. 2011). BV is also an independent risk factor for the acquisition and transmission of STDs (sexually- 60 transmitted diseases) and HIV (Ma et al. 2012). Beginning in the late 1990s, clinical microbiologists using culture-dependent technology (that could only cultivate and identify very limited number of bacteria) used ecological interpretations (particularly community diversity- stability relationships and species dominance) to interpret BV etiology (Sobel 1999). The advent of metagenomics has made it possible to detect large number of uncultivable microbes in the 65 human vaginal microbiome (HVM) but has focused attention on identifying a single causal agent

2 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. of BV rather than a more holistic, ecological process (Fredricks et al. 2005, 2011, White et al. 2011, Ma et al. 2012). The recent characterization of BV as “a syndrome linked to various community types that cause somewhat similar physiological symptoms” by Ma et al. (2012) reflects the limited ecological aspect of the state-of-the-art of research on BV etiology. 70 The focus on a causal agent for BV asks: which of the hundreds of bacteria found in the HVM are the pathogens of BV? Even though Koch’s postulate of one pathogen to one disease has in some cases given way to a paradigm of polymicrobial disease etiology, studies of BV have neither identified a single causal pathogen nor a definite polymicrobial ‘signature’ (or what we call here a ‘cult’) of community composition associated with either a woman with a healthy 75 vagina or one with BV. We were tempted to designate some cults of anaerobes in species dominance networks (SDNs) of the HVM as polymicrobial pathogens, but we detected the same cults in the SDN of healthy peers in the same investigated cohort (see Results and Discussion, below) (Ravel et al. 2011, 2013, White et al. 2011, Gajer et al. 2012, Hickey et al. 2012, Ma et al. 2012, Romero et al. 2014, Ma et al. 2011, 2015, Ma & Ellison 2018, 2019). 80 A series of longitudinal studies (Ravel et al. 2011, 2013, Gajer et al. 2012) together with other global efforts during the last decade (Fredricks et al. 2005, 2011, Bradshaw et al 2006, Gibbs 2007, Spear et al. 2007, Menard et al. 2008, Mania-Pramanik et al. 2009, Srinivasan et al. 2010, Lamont et al. 2010, Ling et al. 2010, White et al. 2011, Aagaard et al. 2012, Guedou et al. 2012, Jespers et al. 2012, Brotman et al. 2012, Romero et al. 2014, Doyle et al. 2018), have 85 greatly expanded our knowledge on HVM and BV. It had been argued that a lack of ‘good’ dominant species in the HVM, especially Lactobacillus iners, led to increased risk of BV. This argument was extended to associate HVMs with high community “evenness” (no singularly dominant species) with elevated risk of BV. However, L. iners may still be abundant in BV patients and more even HVMs have been reported from asymptomatic women (Ma et al. 2012). 90 Ma & Ellison (2018, 2019) re-examined the classic dominance (diversity)-stability relationship (DSR) paradigm (Costello et al. 2012, Donohue et al. 2016, Ma et al. 2019), distinguished between stability and resilience, and introduced a new SDN-based framework for investigating the DSR in the healthy HVM. We suggest that a universal pathogen(s) or a single diagnostic taxon for BV is unlikely. 95 Furthermore, a single, universal dysbiotic community state is unlikely to cause all BV states. Rather, we hypothesize that BV corresponds to one or more non-equilibrium states in the complex ecosystem that is the HVM (Ma et al. 2012) and that there may be multiple paths to one or more types of BV states (henceforth “BV states”). We further hypothesize that despite their multiplicity, BV states are likely to be ecologically equivalent in terms of their decreased 3 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. 100 ecological stability or resilience (Ma & Ellison 2019). With these hypotheses as a starting point, we predict that it is feasible to detect certain ‘signatures’ of BV states. If the HVM is modeled as a complex network, finding a signature of BV states is reducible to finding a network motif (see Materials and Methods, below for a brief presentation of motifs and other network characteristics; and Ma & Ellison 2019 for a more detailed discussion of them). We follow the 105 principle of parsimony and search for simple trio motifs (motifs consisting of three taxa) in complex networks (Ma & Ye 2017). We also harness other characteristics of complex ecological networks (including core/periphery and high-salience structures) point at underlying processes (mechanisms) driving dynamics of vaginal microbiomes and effects of BV as ecological disturbances or perturbations. Integrated together, our motif detection and SDN analyses may be 110 developable as predictors of BV risks and its etiology. Figure 1 illustrates the primary objectives and approaches of this study.

115 Identifying network structures (trio-motifs, core/periphery species and backbones of species interactions) for assessing and interpreting the BV risks and etiology

Global & local network structures of critical significance for the stability/resilience 125

Special trio- Core/Periphery Network High-salience skeleton reveals the heterogeneous network reveals the motifs of 135 (asymmetrical) species asymmetrical species BVAB, MDO, interactions and interactions interactions & interactions etc, in silico agents for BV with host and diseases from with host environment risks and the node perspective. from the link perspective. etiology.

145 A framework for studying the diversity (dominance)-stability relationship (DSR) based on the unified concepts of community dominance and species dominance as well as species dominance network (SDN) analysis (Ma & Ellison 2018, 2019) 155 Fig 1. A diagram showing the primary objectives and approaches to achieving the objectives

4 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Results Basic properties of the SDNs 160 The MDO, MAO, and hubs of each subject are given in Table S1-1. In the whole cohort, the MDO of 11 (out of 25 or 44%) individuals was Lactobacillus iners. In the ABV group, the MDO of 5 (out of 6 or 83%) individuals was Gardnerella vaginalis, but in the SBV group, the MDO of 9 (out of 15 or 60%) individuals was L. iners. The MDOs of the four HEA individuals were all different: L. iners, L. crispatus, L. jensenii and Bifidobacterium bifidum. The MAO of 11 (44%) 165 subjects also was L. iners, and the MAO and MDO were the same OTU in 23 of the 25 individuals (all but #s112 and #s17). The hub OUTs differed among individuals and the various groups. Network graphs for all 25 subjects are displayed in Fig. S1. Here, we illustrate network graphs for three exemplar subjects (Figs 1-3) to illustrate basic network topology of the HVM. In 170 these figures, we distinguish the MDO, MAO, and hub; one OTU may play two or all three of these roles. We also distinguish core (in cyan) and periphery (in azure blue) nodes; negative interactions (red) and positive ones (green). Thick edges identify the high-salience skeletons (salience-value S ≥ 0.25, which measures the strengthen of species interactions). Individual #s112 had different OTUs for its hub (Peptoniphilus), MDO (Gardnerella 175 vaginalis), and MAO (L. iners) (Fig. 2). Although her MAO was L. iners, she also had BV (Ravel et al. 2013), in contrast with the current interpretation of BV etiology that suggests that high abundance of L. iners should lower susceptibility to BV (Ma et al. 2012). However, the MDO of individual #s112 was Gardnerella vaginalis, an anaerobic species indicative of an altered vaginal environment. Some even consider Gardnerella vaginalis as the predominant 180 cause of BV (Bretelle et al. 2015), including asymptomatic BV (ABV) (Eren et al. 2011, Pleckaityte et al. 2012). The hub OTU of individual #s112 was Peptoniphilus, considered an indicator of higher risk of BV and of BV state type IV-B (Gajer et al. 2012). This example illustrates the utility of distinguishing among different roles (MDO, MAO, hub) in interacting networks of OTUs in the HVM (Ma & Ellison 2018, 2019). 185 Individual #s23 had different dominant OTUs: the hub was Prevotella buccalis and Gardnerella vaginalis was both the MAO and MDO (Fig. 3). This individual was diagnosed with ABV. Prevotella also is one of the indicator species of the BV state type IV-B (Gajer et al. 2012). Increased abundance of Prevotella in the HVM has been associated to BV, and Randis & Ratner (2019) asserted that Gardnerella and Prevotella are “co-conspirators in the pathogenesis 190 of bacterial vaginosis”.

5 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Individual #s52, who was identified as healthy (HEA), had Lactobacillus jensenii as her MAO and MDO, and Clostridiales Family XI (incertae sedis) as her hub species (Fig. 4). Among the four sampled HEA women, the MDOs of three were L. jensenii. All negative interactions in the HVM network of #s52 were linked to L. jensenii and L. gasseri, which 195 suggests their suppressive effects on BVABs and other opportunistic pathogens. Both Lactobacillus species are normal inhabitants of the lower reproductive tract in healthy women, whereas Clostridiales Family XI (incertae sedis) can be an opportunistic pathogen found in women diagnosed with BV (Lambert et al. 2013). The basic network properties of the individual SDNs of the 25-mixed cohort and the 200 associated P-value from a Kruskal-Wallis one-way ANOVA are given in Table S1-2. Five network properties, average degree, diameter, average path length, network density and modularity were significantly different between ABV and SBV individuals (P< 0.05). All other network properties were not significantly different among various groups (P> 0.05). Across individuals, SDNs are variable, heterogeneous, and specific to individual women 205 (Figs 2-4, Fig S1, Table S1-2). As each woman has a unique HVM, HVM-associated BV requires personalized diagnosis and treatment. Individual SDN networks can reveal some unique aspects of individual subjects, but from standard network properties (Table S1-2), we can derive limited insights on either HVM heterogeneity or BV etiology.

6 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Prevotella.buccalis

Megasphaera Bacteria Roseburia Prevotella Prevotella.bivia Eggerthella

Anaerococcus.tetradius Dialister.sp..type.2 Staphylococcus.hominis

Peptoniphilus.lacrimalis Prevotella.genogroup.1 Staphylococcus.capitis Enterococcus.faecalis Anaerococcus Parvimonas.micra

Lachnospiraceae Gardnerella.vaginalis Lactobacillus.coleohominis Staphylococcus

Actinobacteridae Veillonellaceae Clostridiales

Prevotella.genogroup.2 Actinobacteria..class.

Clostridiales.Family.XI..Incertae.Sedis

Finegoldia.magna Veillonella.montpellierensis

Firmicutes

Actinomycetales

Peptoniphilus.asaccharolyticus Leptotrichia.amnionii Veillonella

Mobiluncus.curtisii

Peptoniphilus

Lactobacillales

Aerococcus.christensenii

Dialister.sp..type.3 BVAB3 BVAB2 Mobiluncus Streptococcus.salivarius Bacteroides Bacillales Megasphaera.sp..type.1 Enterobacteriaceae Atopobium Lactobacillus.iners Streptococcus

Varibaculum.cambriense Streptococcus.mitis Atopobium.vaginae Streptococcus.oralis Bifidobacteriaceae Porphyromonas Streptococcus.parasanguinis Anaerococcus.vaginalis Streptococcus.sanguinis Bacteroides.coagulans Fusobacteriaceae Peptostreptococcus Peptoniphilus.harei Campylobacter.ureolyticus Porphyromonas.bennonis Porphyromonas.asaccharolytica Actinomyces Dialister Proteobacteria 210 Fig 2. The SDN network graph for individual #s112 in the 25-mixed cohort (hub=Peptoniphilus, MAO=Lactobacillus iners; MDO= Gardnerella vaginalis; BV Status=SBV; hub, MAO and MDO all belong to the core; core nodes are in cyan color, and periphery nodes are in azure)

7 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Bacilli Anaerococcus.vaginalis Veillonella.parvula

Streptococcus.parasanguinis

Lactobacillales Roseburia Fusobacterium Eggerthella Veillonella.atypica Firmicutes Prevotella.buccalis Bacteroides.coagulans Gemella Enterobacteriaceae

Porphyromonas.bennonis Prevotella

Porphyromonas.asaccharolytica Peptostreptococcus candidate.division.TM7 Fusobacterium.gonidiaformans Bacteria

Anaerococcus Escherichia.coli Porphyromonas.sp..type.1

Porphyromonas Peptoniphilus

Finegoldia.magna Pseudomonas.aeruginosa BVAB1

Atopobium.vaginae Prevotella.genogroup.3

Gardnerella.vaginalis Aerococcus Clostridiales

Megasphaera Dialister.sp..type.2 Pseudomonas Aerococcus.christensenii Dialister.sp..type.3

Lactobacillus.jensenii Sneathia.sanguinegens Parvimonas.micra

BVAB2 Leptotrichia.amnionii Sneathia

Anaerococcus.tetradius Bacteroidales

Lachnospiraceae Bacteroides Mobiluncus Megasphaera.sp..type.1 Corynebacterium.accolens Veillonellaceae

Clostridiales.Family.XI..Incertae.Sedis Mycoplasma.hominis

Megasphaera.sp..type.2 Prevotella.bivia

Actinomycetales Staphylococcus.haemolyticus Peptoniphilus.asaccharolyticus

Staphylococcus Lactobacillus.salivarius Prevotella.disiens Eubacterium.rectale Prevotella.genogroup.4 Streptococcus Sutterella Prevotella.melaninogenica Bifidobacterium Coriobacteriaceae Bacteroides.uniformis Bifidobacterium.bifidum Streptococcus.salivarius Eubacterium Veillonella Bacillales Streptococcus.mitis

Streptococcus.oralis

Veillonella.montpellierensis 215 Fig 3. SDN network graph for individual #s23 in the 25-subjects cohort (hub, MDO & MAO are the same

node, i.e., Gardnerella vaginalis, hub= Prevotella buccalis; MDO, MAO and hub all belong to the core; core nodes are in cyan color, and periphery nodes are in azure; BV-status=ABV)

8 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Firmicutes Bacteroides Bacteria Clostridiales Mobiluncus.curtisii Bacilli Roseburia Lactobacillales Prevotella.buccalis Dialister.sp..type.3 Peptoniphilus.asaccharolyticus Atopobium Veillonellaceae Dialister Peptoniphilus.lacrimalis Porphyromonas Anaerococcus

Porphyromonas.bennonis Actinobacteria..class.

Arcanobacterium Prevotella

Peptoniphilus.harei Clostridiales.Family.XI..Incertae.Sedis

Anaerococcus.vaginalis Finegoldia.magna

Bacteroides.coagulans Actinomycetales

Campylobacter.ureolyticus Peptoniphilus

Varibaculum.cambriense Streptococcus.anginosus

Actinobacteridae Prevotella.genogroup.2

Proteobacteria Lactobacillus.gasseri

Dialister.propionicifaciens Lactobacillus.jensenii Eubacterium Sutterella Lactobacillus.OTU1

Actinomyces Bifidobacteriaceae

Veillonella Bacillales

Gammaproteobacteria Actinomyces.urogenitalis Streptococcus

Lachnospiraceae Streptococcus.salivarius

Fusobacteriaceae BVAB2

Prevotella.melaninogenica Parvimonas.micra Lactobacillus

Streptococcus.oralis Porphyromonas.asaccharolytica

Streptococcus.parasanguinis Lactobacillus.iners

Veillonella.parvula Gardnerella.vaginalis

Veillonella.atypica Peptostreptococcus Aerococcus.christensenii Coriobacteriaceae

Streptococcus.mitis Veillonella.montpellierensis

Enterobacteriaceae Streptococcus.agalactiae

Corynebacterium.accolens Staphylococcus.capitis

Bifidobacterium.breve Escherichia.coli Streptococcus.anginosus.group Staphylococcus.haemolyticus Actinomyces.neuii Enterococcus.faecalis Fusobacterium.nucleatum Staphylococcus Prevotella.genogroup.1 Prevotella.genogroup.3 Staphylococcus.hominis Dialister.sp..type.2 Megasphaera.sp..type.1 Bacteroidales Gemella Pseudomonas Mycoplasma.hominis Prevotella.biviaBVAB3 Eggerthella Megasphaera.sp..type.2 Bifidobacterium

Eubacterium.rectale Anaerococcus.tetradius 220 Fig 4. SDN network graph for individual #s52 in the 25-subjects cohort (hub, MDO & MAO are the same

node, i.e., Lactobacillus jensenii, hub= Clostridiales Family XI (incertae sedis); MDO, MAO and hub all belong to the core; core nodes are in cyan color, and periphery nodes are in azure; BV-status=HEA)

225 Trio motifs and indicators of BV risk We searched for the trio motifs in the individual SDNs by considering (i) both positive and negative interactions; (ii) the MDO, MAO, and BVAB; and (iii) two types of trio motifs: 3-node “Trios without handle” (the three-node trios, see Tables S2-1A) and 4-node “Trios with MDO handle” (in which the MDO is connected to a three-node trio with one, two, or three links, see 230 Table S2-1B). The total number of double-linked MDO (DLM) trios (in which the MDO is connected to three-nodes trio with two links) was significantly different only between ABV and SBV individuals (P<0.05). There were no significant differences in all other types of trios in any other comparisons (P>0.05). We draw the following findings from this trio motif analysis:

235 (i) 951 trios occurred simultaneously in four or more women in the 25-mixed cohort. The reason we chose counting the trios that occurred in four or more subjects was that the minimal group 9 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. size of BV, ABV and HEA groups in the cohort was 4 for the HEA group. Among these 951 trios, one trio occurred in 15 women, another trio occurred in 12 women, four trios occurred in 10 women, six occurred in nine women, 16 occurred in eight women, 43 occurred in seven 240 women, 114 occurred in six women, 255 occurred in five women, and 511 occurred in four women (see Fig. S2, Tables S2-2, S2-3 for more detailed information on the distribution of these 951 trios among ABV, BV and HEA groups). As shown in Fig S2, 32% (301) of these 951 trios occurred exclusively in the SBV group, in which 12 trios occurred in near half of the subjects in the SBV group (Table 1). However, no 245 trios were detected exclusively in ABV or HEA without also being detected in another group(s). Another 232 (24%) of the trios were detected in both SBV and ABV groups, in which three trios occurred in ten (50%) of the subjects in the BV (SBV+ABV) groups (Table 1). Finally, 340 trios (36%) occurred in all three groups. Note that when we state a trio occurred in a group (e.g., SBV), it means that this trio 250 occurred in at least one of the subjects in the group (SBV).

(ii) The 12 trios that occurred only in women with SBV potentially could be used as indicators of risk for BV (see Tables S2-3, S2-4 for their biomedical implications). Among these 12 trios, all three OTUs (nodes of the trio) in ten trios were BVABs and most were core species (see below 255 for further explanation of the core species) of the individual SDNs. For example, trio #17 occurred in eight women with SBV subjects; its three nodes were Atopobium vaginae, BVAB2 and Parvimonas micra. Interactions between three BVABs were positive (cooperative), but the trio itself was inhibited by the MDO in seven of the eight women. Previous studies also have identified Atopobium and Parvimonas as indicators of BV-state CST (community state type) 4-B 260 or CST-4B (Gajer et al. 2012, Ma & Li 2017). The two others of these 12 trios included L. iners and two BVABs. L. iners interacted negatively with the two BVABs in all the SDNs that contained these two trios, corroborating previous observations of inhibitory effects of L. iners on BVABs.

265 (iii) Three trios were BV-only (BV=SBV+ABV) trios, occurring in 10 of the individuals diagnosed with either SBV or ABV (see Table S2-3 for their biomedical implications). Interactions among the three OTUs in these three trios were all positive. Trio #1—consisting of Dialister sp. type.2, Eggerthella, and Veillonellaceae—occurred in 11 women with SBV subjects and 4 women with ABV. These three species also were the core species in the SDNs of all four 270 women with ABV and 8 of the 11 with SBV. Trio #3—Anaerococcus, Anaerococcus tetradius, 10 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. and Peptoniphilus—occurred in 6 women with SBV and 4 with ABV. These three species were the core species in 7 of the 10 SDNs. Studies have identified Anaerococcus and Peptoniphilus as two other indicator species of the BV-state CST4-B (Gajer et al. 2012). Trio #5—Atopobium vaginae, Bifidobacteriaceae and Gardnerella vaginalis—was detected in 7 women with SBV and 275 3 with ABV. Among their 10 SDNs, Atopobium vaginae, Bifidobacteriaceae and Gardnerella vaginalis were detected, respectively, as the core species in 7, 8, and 5 of the SDNs. These three BV-only trios merit additional investigation as promising indicators of BV risk (Table S2-4). However, we consider they are more ambiguous indicators than the previous 12 SBV-only trios because they occurred in both SBV and ABV. 280 (iv) We further identified the trios occurred in 50% or more subjects in each of the three diagnostic groups (see Table S2-5). Note that the term occurrence is used non-exclusively here. In fact, there is no single trio that was detected in all-individual SDNs of the 25-mixed cohort, which simply displays enormous inter-subject heterogeneity of the HVM. For this, here we 285 choose to detect trios occurred in simple majority (≥50%) of each diagnostic group (SVB, ABV or HEA). In the SBV group, there were 22 trios detected in >50% of the women (≥7 out of 15), in which only one trio (trio #1) occurred in 73% (11/15) women, and 3 occurred in 53% (8/15) women. Among these 22 trios detected in the SBV group: 12 trios occurred exclusively in the 290 SBV group, as described in (ii) previously; nine trios occurred in both the SBV and ABV groups, including trios #1, #3 and #5 described in (iii) previously; and one trio (trio #6) occurred in all three diagnostic groups including HEA group. In the ABV group, there were 21 trios detected in 50% or more subjects (≥3 out of 6), in which one trio (#2) occurred in 80% (5/6) subjects, three trios (#1, #3 and #4) occurred in 70% 295 (4/6) subjects. Among these 21 trios, 10 trios also occurred in both the SBV and ABV groups, including # 1, #3 and #5 discussed in (iii), and 11 trios occurred in all three diagnostic groups. In the HEA (healthy) group, there were 244 trios detected in ≥50% (≥ 2 out of 4) subjects. However, only 2 trios (#4 & #72) out of the 244 were detected in 75% (3/4) subjects. Among these 244 trios, 201 trios including (# 4 & #72) were also detected in the SBV and ABV 300 groups, and the remaining 43 occurred in both the HEA and SBV groups.

(v) In summary, we identified 15 special trios, including 12 SBV-only trios occurred exclusively in the SBV group and 3 BV-only trios occurred in ≥50% of the BV subjects only (BV=ABV+SBV groups). Table S2-4 provides brief biomedical information on the members of 11 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. 305 the 15 trios. These trios are of potentially significant importance for assessing BV risks and for investigating BV etiology.

Table 1. The 15 special trios, including the 12 trios exclusively occurred in the SBV group (SBV-only) and the 3 trios occurred in the BV-only (BV=SBV+ABV) (see Table S2-4 for 310 biomedical information on the 15 trios), as well as the relevant core/periphery/skeleton information Average Trios BV Trio members Subject ID Salience No# Status Value**

The 12 special trios occurred exclusively in the SBV group (occurred in 50% or more SBV subjects)

Atopobium vaginae 17* BVAB2 SBV s128, s130, s15, s17, s40, s5, s53, s59 0.130 Parvimonas micra Bifidobacteriaceae 22 Parvimonas micra SBV s128, s130, s135, s15, s17, s40, s53, s59 0.114 Peptoniphilus Anaerococcus tetradius 34* BVAB2 SBV s112, s116, s128, s135, s40, s5, s53 0.081 Peptoniphilus Anaerococcus tetradius 35 Gemella SBV s127, s128, s135, s17, s40, s5, s53 0.078 Peptoniphilus Atopobium vaginae 39* BVAB2 SBV s128, s130, s135, s40, s5, s53, s59 0.092 Eggerthella Atopobium vaginae 40* BVAB2 SBV s112, s116, s128, s130, s17, s40, s53 0.086 Peptoniphilus BVAB2 47* Dialister sp. type 2 SBV s128, s130, s135, s40, s5, s53, s59 0.073 Eggerthella BVAB2 48* Dialister sp. type 2 SBV s128, s130, s135, s40, s5, s53, s59 0.059 Peptoniphilus BVAB2 50 Lactobacillus iners SBV s112, s130, s135, s15, s17, s53, s59 0.084 Peptoniphilus Dialister sp. type 2 58* Parvimonas micra SBV s128, s130, s135, s35, s40, s53, s59 0.066 Peptoniphilus Dialister sp. type 2 59* Parvimonas micra SBV s112, s128, s135, s35, s40, s53, s59 0.056 Prevotella genogroup 1 Lactobacillus iners 65 Parvimonas micra SBV s130, s135, s15, s17, s35, s53, s59 0.083 Peptoniphilus The 3 special trios occurred in BV-only (BV=SBV+ABV) in 50% or more BV subjects

s116, s127, s128, s130, s135, s15, s17, Dialister sp. type 2 SBV 1 Eggerthella s35, s40, s5, s59 0.162 Veillonellaceae ABV s27, s60, s77, s82 Anaerococcus SBV s112, s127, s128, s135, s15, s5 3* Anaerococcus tetradius 0.121 Peptoniphilus ABV s12, s27, s60, s77 Atopobium vaginae SBV s127, s128, s130, s3, s35, s40, s59 5 Bifidobacteriaceae 0.275 Gardnerella vaginalis ABV s27, s60, s77

The OTU frequencies of the core/periphery nodes*** CST Periphery OTU ID Core Frequency (community Is BVAB? Frequency state type)

12 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Dialister sp. type 2 19 5 - BVAB Peptoniphilus 17 7 CST4-B BVAB Veillonellaceae 17 6 - BVAB Bifidobacteriaceae 14 8 - - Eggerthella 14 6 - BVAB Atopobium vaginae 13 10 CST4-B BVAB Anaerococcus 13 9 CST4-A BVAB BVAB2 11 12 - - Anaerococcus tetradius 11 8 CST4-A BVAB Parvimonas micra 10 11 CST4-B BVAB Gemella 10 9 - - Lactobacillus iners 10 8 CST1~CST3 - Gardnerella vaginalis 9 11 CST4-B BVAB Prevotella genogroup 1 9 7 CST4-B BVAB Mean 12.642 8.357 Standard Error 3.225 2.098 χ2 test*** χ2 =13.141 P=0.437 (i)*These 9 trios are all-BVAB (BV associated anaerobic bacteria) trios, i.e., all their three nodes are BVABs. **The average skeleton salience (interaction strength) values of these special trios are significantly higher than (ii) the average salience values at the whole network level (see Table S4-4) for the test results. 315 ***The 15 trios consisted of 14 OTUs, 10 out of which are BVABs, and 8 out of which are CST indicators. (iii) It appears that these OTUs occurred more frequently in core than in the periphery (approximately 13 vs. 8), but the χ2-test indicates that the difference is not statistically significant.

Core/periphery species in the SDNs 320 Properties of the core/periphery structures for the individual SDNs of the 25-mixed cohort are given in Table S3-1. There were no significant differences in the network properties between women with BV (SBV+ABV) and those without (HEA). The core strength (ρ), its size as the proportion of connected network nodes (C/[C+P]), and degree of nestedness (S) were significantly larger in SBV than ABV individuals (P ≤ 0.05).

325 Regression analysis of overall network dominance as a function of core/periphery dominance revealed the following relationship: ln(Network) =1.97 + 0.05ln(Core) + 0.48ln(Periphery) (r=0.85, P<<0.001) (1) The above model clearly indicates that periphery has a larger effect on community-wide dominance given that the scaling parameter of periphery is nearly 10 times that of core (0.48 vs. € 330 0.05). Back-transforming these models gives the exponential relationship between network dominance and core/periphery dominance: Network = 7.17(Periphery)0.48(Core)0.05 . (2) The core/periphery status of individual OTUs varied among individuals (Tables S3-2A, S3-2B). Examination of frequency distributions of OTUs of the HVM in the 25-mixed cohort € 335 revealed that although many species occurred in the core (104 OTUs) or periphery (146 OTUs),

13 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. only a small number of them were exclusive to either (3 exclusive to the core, 42 exclusive to the periphery). Note that the core/periphery status is not fixed in the cohort and can be individual- specific. Interestingly, the most commonly observed species L. iners, arguably the most ‘prominent’ species in the HVM is only the 16th most frequent core species and it occurred in 10 340 individuals (SDNs) only. It also occurred in the periphery of 8 SDNs (8 subjects) and ranked after the 50th. This indicates that L. iners may not be a universal ‘power’ player in the HVM. Distribution in the core and periphery of BVAB differed significantly (P<0.0001, test; Table S3-3) and were more frequent in the periphery. Table S5 lists all the BVABs recovered from the samples (see also Ravel et al. 2012). Table S3-3 also shows that although the BVABs 345 may occur in either core or periphery, they occurred more frequently in the periphery, 314 times in cores vs. 429 in peripheries, or 37% more in the periphery. This finding echoes the previous finding that the periphery is more influential than the core to the network dominance as a whole. Finally, we analyzed shared core/periphery networks by comparing pairs of subjects from two different groups (ABV, SBV, or HEA; Table S3-4). Using two different algorithms (Ma et 350 al. 2019)—the A1 algorithm that reshuffles reads and the A2 algorithm that reshuffles samples, we found that the observed numbers of shared core (or periphery) species were significantly lower than those expected by chance among all three groups (P<0.001 all cases). That is, there exist group-specific, unique core (or periphery) species for each of the SBV, ABV and HEA groups (Table S3-5).

355 High-salience skeletons in the SDNs The salience of skeletons measures the strength of interactions in terms of species co-dominance. Table S4-1 gives the properties of the high-salience skeletons in all the SDNs in the 25-mixed cohort; those with salience value ≥0.5 are reported in Table S4-2. In the latter, the two nodes (OTUs) connected by the skeleton are MDOs, MAOs, hubs, BV-state indicators, or BVABs. We 360 also tested the number of shared skeletons between each pair of subjects from the three diagnostic groups (Table S4-3). The average of shared skeletons between the HEA and SBV group, between the HEA and ABV group, between the ABV and SBV group, was 79, 91, and 129, respectively (Table S4-3B), with relative similarity ordered as ABV vs. SBV > ABV vs. HEA > SBV vs. HEA. This suggests that BV can change the strength of network links relative to 365 those observed in healthy individuals.

14 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Conclusions and Discussion Basic SDN analysis confirmed that human vaginal microbiomes are highly heterogeneous among individuals (see also Ma & Ellison 2019) even among women diagnosed with SBV or ABV. This 370 result suggests that it is unlikely that either a universal BV pathogen (diagnostic OTUs or their cults) or a universal dysbiotic BV state will be discovered. Core/periphery and high-salience skeleton network analyses found additional complexities and did not identify any universal clear- cut network structures or properties for diagnosing BV or interpreting BV etiology. Nonetheless, the 15 BV-only trio motifs (especially the 12 found exclusively in women with SBV) have 375 promise for assessing and predicting BV risks and provide new insights about its mechanisms and etiology. In addition to using these special trio motifs as indicators of BV risk, individualized (personalized) analysis of each SDN can offer further mechanistic interpretations for the BV status of a particular individual (Figs. 2-4, Fig S1). Although the presence of L. iners, certain BVABs, or their co-occurrence may not be sufficient evidence for determining BV status, 380 the existence of their interactions (special trio motifs) and interaction strengths (measured by skeleton salience) do provide supporting diagnostic evidence. Further insights can be shed on the ecological mechanisms of BV by the distinguishing core and periphery OTUs, or by the identifications of critical pathway of species interactions (network backbones consisting of high-salience skeletons). Although the vaginal microbiomes of 385 different individuals may have different levels of stability or resilience to various disturbances including BV, BV may indeed change the core strength/size and nestedness of SDNs and the strength of species interactions within an SDN, resulting in a change in its stability or resilience. Therefore, the core/periphery network and high-salience skeleton network (Ma & Ellison 2019) further complemented the findings and insights from special trio motif and inductive inspections 390 on individual SDNs. In conclusion, the 15 BV-only trio motifs, their nodes and links, and the core/periphery/high-salience skeleton structures of their associated SDNs are sufficiently unique to act as indicators of community composition and signaling the potential of dysbiosis associated with BV. Although we are still far from revealing clear-cut risk or diagnostic indicators, our study presents important material candidates (15 BV-only trio motifs) and tools (trio-motif 395 detection technique, augmented with core /periphery/skeleton network analyses) for further deepening our understanding on BV risks and etiology. Finally, we asked how specific are the 15 BV-only trio-motifs? We answered this question by testing whether the 15 special trio-motifs (listed in Table 1) could be detected in cohorts of healthy women. We used three additional 16S-rRNA datasets of 1535 healthy women 400 to assess specificity. Although there were differeces among the three healthy-cohort datatsets 15 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (e.g., longitudinal vs. cross-sectional sampling designs), we were only interested in determining if we detected the special BV-only trio motifs in species dominance networks of healthy individuals. None of the 15 special trio-motifs were detected in any of the species doimnance networks of the healthy women, regardless of sampling design (Table 2). Thus, it appears that 405 the 15 trio-motifs could be a highly specific indicator of BV to be used for its personalized diagnosis and treatment.

Table 2. The number of 15 special trios (originally detected in the BV patients and listed in Table 1) that were detected in the networks of HVM-32, HVM-400, and HVM-UK-1107 cohorts ID# Nodes Edges #17 #22 #34 #35 #39 #40 #47 #48 #50 #58 #59 #65 #1 #3 #5 32-Healthy Cohort (Gajer et al. 2012) #400 43 196 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #401 69 263 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #402 73 523 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #403 19 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #404 65 203 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #405 30 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #406 62 131 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #407 71 183 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #408 22 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #410 41 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #411 46 98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #412 23 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #413 22 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #414 24 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #415 35 112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #416 39 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #418 54 133 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #420 49 117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #423 59 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #424 84 439 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #429 20 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #430 41 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #431 11 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #432 65 317 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #435 22 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #436 74 210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #437 70 316 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #439 15 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #443 75 178 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #444 41 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #445 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #446 51 272 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 400-Healthy Cohort (Ravel et al. 2011)

16 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Asian 165 1008 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Black 165 851 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hispanic 157 782 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 White 160 785 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Whole 244 2494 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1107-Healthy Cohort (Doyle et al. 2018) UK-1107 461 6008 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 410

Transparent Methods The HVM datasets 415 We used the “25-mixed cohort” dataset originally described by Ravel et al. (2013) for primary species dominance network (SDN) analysis. Briefly, Ravel et al. (2013) sequenced samples from vaginal communities collected daily for ten weeks from 25 women who were diagnosed as symptomatic BV (SBV: n = 15 women), asymptomatic BV (ABV: n = 6), or healthy (HEA: n = 4). In total, Ravel et al. (2013) sequenced 1,657 samples (median = 67 per woman) and obtained 420 8,757,681 high-quality sequenced reads of the V1–V3 hypervariable region of 16S-rRNA genes, with a median of 5,093 reads per sample.

We used three additional 16S-rRNA datasets of healthy women originally collected by Ravel et al. (2010), Gajer et al. (2012), and Doyle et al. (2018), respectively, to test the specificity of the 425 15 special trio motifs.. A total of 1535 healthy women were sampled in these three additional datasets. The dataset of “32-healthy cohort” (Gajer et al. 2012) consisted of the longitudinal sampling of 32 healthy women and has virtually the same data structure as the previously described “25-mixed cohort”. For this dataset, 32 SDNs were constructed to verify the findings from the primary dataset (25-mixed cohort). The “400-healthy cohort” dataset (Ravel et al. 2011) 430 consisted of the cross-sectional samples of 397 healthy women from four ethnic groups (Asian, Black, Hispanic, and White). For this dataset, we built a SDN for each of the four ethnic groups as well as a fifth SDN of the combined groups. Finally, the dataset of “1107-healthy cohort” (Doyle et al. 2018) consisted of the cross-sectional samples of 1107 healthy, postpartum women. For this dataset, we built a single SDN for all individuals in the cohort. 435 SDN analysis The species dominance network (SDN) analysis we used was detailed in Ma & Ye (2017) and Ma & Ellison (2018, 2019). Briefly, by noting that dominance (in terms of numbers of 17 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. individuals or total biomass) of a one or a few taxa in a multi-species assemblage results in an 440 uneven distribution of all the taxa in the assemblage, measures of dominance can be equated to measures of unevenness, and by inversion, evenness of the assemblage. Whereas species diversity and species evenness have been applied routinely to multi-species assemblages or “communities”, we have shown that measures of dominance can be applied to both communities and individual species (Ma & Ellison 2018, 2019). The dominance metric that we derived 445 provides a unified mathematical approach to measuring overall community dominance (≡ diversity), dominance by individual species (Ma & Ellison 2018, 2019), and relationships between dominance and community-wide stability and resilience. Additional insights were gained by developing techniques to detect trio motifs, core/periphery networks, and high- salience skeleton networks, and an approach to model network structure phenomenologically. 450 Trio motifs A trio motif is the simplest local network of species interactions. In the present study, we detect the trios primarily consisting of BV-associated anaerobic bacteria (BVAB), and the structure and composition of trio motifs can have significant implications for the stability of the HVMC and BV etiology (Ma & Ye 2017, Ma & Ellison 2018, 2019). Unlike standard correlation analysis of 455 networks, descriptions and analyses of trio motifs can take into accounts multiple properties of network “nodes” (taxa or operational taxonomic units [OTUs]) or “edges” (links between taxa or OTUs). These properties include node types—the most dominant OTU (MDO), the most abundant OTU (MAO), or the “hub” (most connections to other OTUs)—or edge types— positive or negative between OTUs. 460 Core/periphery and high-salience skeleton networks Core/periphery (Csermely et al. 2013, Ma & Ellison 2019) and skeleton network analyses (Grady et al. 2012, Shekhtman et al. 2014, Ma & Ellison 2019) detect global (network- or community-wide) topological structures with significant implications for community stability or resilience. Core/periphery analyses focuses on nodes, identifying more stable and highly 465 connected “core” OTUs and less stable and often loosely-connected “peripheral” OTUs. In contrast, skeleton analyses address the edges, including the interaction paths connecting OTUs that form the “backbone” or high-salience skeletons in a SDN. Because the sets of nodes and edges fully determine the topology of a network, our SDN analyses (Ma & Ellison 2018, 2019, Ma & Ye 2017) are complete—they address both nodes and edges (Fig. 1). 470 Analysis We used the algorithms and computational procedures for describing and analyzing SDNs (Ma & Ellison 2018, 2019) to analyze the HVM datasets originally described by Ravel et al. (2013). 18 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Specifically, we used Spearman’s rank correlation coefficient (R) and for testing its significance, set α=0.001 after FDR (false discovery rate) adjustment. A SDN was considered valid if absolute 475 |R| ≥ 0.5. In addition, we set α =0.05 for testing significance of data analyzed using a Kruskal- Wallis one-way analysis of variance (ANOVA).

References

480 Aagaard K, Riehle K, Ma J, Segata N, Mistretta TA, Coarfa C, et al. (2012) A metagenomic approach to characterization of the vaginal microbiome signature in pregnancy. PloS One 2012, 7:e36466

Bradshaw CS, Tabrizi SN, Fairley CK, Morton AN, Rudland E, Garland SM (2006) The association of Atopobium vaginae and Gardnerella vaginalis with bacterial vaginosis and recurrence after oral 485 metronidazole therapy. J Infect Dis. 2006, 194:828-836.

Bretelle, F, P Rozenberg, A. Pascal, R Favre, C Bohec, A Loundou et al. (2015). High Atopobium vaginae and Gardnerella vaginalis Vaginal Loads Are Associated With Preterm Birth. Clinical Infectious Diseases 60(6):860–7 490 Brotman RM, Bradford LL, Conrad M, Gajer P, Ault K, Peralta L, et al. (2012) Association between Trichomonas vaginalis and vaginal bacterial community composition among reproductive-age women. Sex Transm Dis 2012, 39:807-812

495 Csermely, P, A London, LY Wu, et al (2013) Structure and dynamics of core/periphery networks. Journal of Complex Networks, 1:93-123.

Costello EK, Stagaman K, Dethlefsen L, Bohannan BJ, Relman DA. (2012). The application of ecological theory toward an understanding of the human microbiome. Science 336(6086):1255–1262 500 Donohue, I., H. Hillebrand, J. M. Montoya, O. L. Petchey, S. L. Pimm, M. S. Fowler, K. Healy, A. L. Jackson, M. Lurgi, D. McClean, N. E. O’Connor, E. J. O’Gorman, and Q. Yang. 2016. Navigating the complexity of ecological stability. Ecology Letters 19: 1172-1185.

505 Doyle R, Gondwe A, Fan Y, et al. (2018) A Lactobacillus-Deficient Vaginal Microbiota Dominates Postpartum Women in Rural Malawi. Applied and Environmental Microbiology, 84(6): e02150-17

Eren AM, Zozaya M, Taylor CM, Dowd SE, Martin DH, Ferris MJ. (2011). Exploring the diversity of Gardnerella vaginalis in the genitourinary tract microbiota of monogamous couples through subtle 510 nucleotide variation. PLoS One 6: e26732.

Fredricks DN, Fiedler TL, Marrazzo JM. (2005). Molecular identification of bacteria associated with bacterial vaginosis. N. Engl. J. Med. 353:1899–911

515 Fredricks DN (2011) Molecular Methods to Describe the Spectrum and Dynamics of the Vaginal Microbiota. Anaerobe, 17(4): 191–195. doi:10.1016/j.anaerobe.2011.01.001.

Gajer, P., R. M. Brotman, G. Bail, J. Sakamoto, U. M. E. Schütte, X. Zhong, S. S. Koeng, L. Fu, Z. Ma, X. Zhou, Z. Abdo, L. J. Forney and J. Ravel. (2012). Temporal Dynamics of the Human Vaginal 520 Microbiota. Science Translational Medicine, 4(132):132ra52.

Gibbs RS (2007) Asymptomatic bacterial vaginosis: is it time to treat? Am J Obstet Gynecol 2007, 196:495-496.

19 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

525 Grady, D, C Thiemann, D Brockmann (2012) Robust classification of salient links in complex networks. Nature Communications, DOI: 10.1038/ncomms1847

Guedou FA, Van Damme L, Mirembe F, Solomon S, Becker M, Deese J, et al. (2012) Intermediate vaginal flora is associated with HIV prevalence as strongly as bacterial vaginosis in a cross-sectional 530 study of participants screened for a randomized controlled trial. Sex Transm Infect 2012, 88:545-551.

Hickey RJ, Zhou X, Pierson JD, Ravel J, Forney LJ. (2012). Understanding vaginal microbiome complexity from an ecological perspective. Transl. Res, 2012, 160:267-282.

535 Jespers V, Menten J, Smet H, Poradosu S, Abdellati S, Verhelst R, et al. (2012) Quantification of bacterial species of the vaginal microbiome in different groups of women, using nucleic acid amplification tests. BMC Microbiol 2012, 12:83.

Koumans, EH, Kendrick, JS. 2001. Preventing adverse sequelae of bacterial vaginosis: a public health 540 program and research agenda. Sexually Transmitted Diseases, 28(5):292–297.

Lambert JA, Kalra A, Dodge CT, et al. (2013) Novel PCR-Based Methods Enhance Characterization of Vaginal Microbiota in a Bacterial Vaginosis Patient before and after Treatment. Applied and Environmental Microbiology, 79(13): 4181–4185 545 Lamont RF, Taylor-Robinson D (2010) The role of bacterial vaginosis, aerobic vaginitis, abnormal vaginal flora and the risk of preterm birth. BJOG 2010, 117:119-120.

Ling Z, Kong J, Liu F, Zhu H, Chen X, Wang Y, et al. (2010) Molecular analysis of the diversity of 550 vaginal microbiota associated with bacterial vaginosis. BMC Genom 2010, 11:488.

Ma, B, LJ Forney, J. Ravel. 2012. The Vaginal Microbiome: Rethinking Health and Disease. Annual Review of Microbiology. 66:371-389. doi:10.1146/annurev-micro-092611-150157

555 Ma, ZS, Abdo Z, Forney L (2011) Caring about trees in the forest: incorporating frailty in risk analysis for personalized medicine. Personalized Medicine 8: 681-688.

Ma, Z.S. et al (2015) Network analysis suggests a potentially ‘evil’ alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities. Sci. Rep. 5, 8275; 560 DOI:10.1038/srep08275 (2015).

Ma ZS & DD Ye (2017) Trios—promising in silico biomarkers for differentiating the effect of disease on the human microbiome network. Scientific Reports, vol. 7. Article No. 13259

565 Ma ZS & LW Li (2017) Quantifying the human vaginal community state types (CSTs) with the species specificity index. PeerJ, 5:e3366 https://doi.org/10.7717/peerj.3366

Ma ZS and AM Ellison (2018) A unified concept of dominance applicable at both community and species scales. Ecosphere 9(10):e02477. 10.1002/ecs2.2477 570 Ma ZS and AM Ellison (2019). Dominance network analysis provides a new framework for studying the diversity-stability relationship. Ecological Monographs, vol. 89(2): e01358, https://doi.org/10.1002/ecm.1358

Ma ZS, Li LW, Gotelli NJ (2019) Diversity-disease relationships and shared species analyses for human 575 microbiome-associated diseases, The ISME Journal. https://www.nature.com/articles/s41396-019-0395-y

Mania-Pramanik J, Kerkar SC, Salvi VS (2009) Bacterial vaginosis: a cause of infertility? Int J Std Aids 2009, 20:778-781

20 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

580 Menard JP, Fenollar F, Henry M, Bretelle F, Raoult D (2008) Molecular quantification of Gardnerella vaginalis and Atopobium vaginae loads to predict bacterial vaginosis. Clin Infect Dis 2008, 47:33-43.

Pleckaityte M, M. Zilnyte and A. Zvirbliene. (2012) Insights into the CRISPR/Cas system of Gardnerella vaginalis. BMC Microbiology 12:301. doi:10.1186/1471-2180-12-301 585 Randis TM, Ratner AJ. (2019) Gardnerella and Prevotella: Co-conspirators in the Pathogenesis of Bacterial Vaginosis. The Journal of Infectious Diseases, doi: 10.1093/infdis/jiy705

Ravel J., Gajer, P., Abdo, Z., Schneider, G. M., Koenig, S. S. K., McCulle, S. L., Karlebach, S., et al. 590 (2011). Vaginal microbiome of reproductive-age women. Proceedings of the US National Academy of Sciences, 108 Suppl. 1, 4680–4687.

Ravel, J. Jacques Ravel, Rebecca M Brotman, Pawel Gajer, Bing Ma, Melissa Nandy, Douglas W Fadrosh, Joyce Sakamoto, Sara SK Koenig, Li Fu, Xia Zhou, Roxana J Hickey, Jane R Schwebke, Larry J 595 Forney (2013) Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis. Microbiome 2013, 1:29

Romero, R, SS Hassan, P. Gajer, AL Tarca, DW Fadrosh, L. Nikita, M. Galuppi, RF Lamont, P. Chaemsaithong, J. Miranda, T. Chaiworapongsa and J. Ravel (2014) The composition and stability of the 600 vaginal microbiota of normal pregnant women is different from that of non-pregnant women. Microbiome, 2014, 2:4 doi:10.1186/2049-2618-2-4

Shekhtman, LM, JP Bagrow, & D Brockmann (2014) Robustness of skeletons and salient features in networks. Journal of Complex Networks, doi:10.1093/comnet/cnt019 605 Sobel JD (1999) Is there a protective role for vaginal flora? Curr. Infect. Dis. Rep. 1:379–83

Spear GT, St John E, Zariffard MR (2007) Bacterial vaginosis and human immunodeficiency virus infection. AIDS Res Ther 2007, 4:25 610 Srinivasan S, Liu C, Mitchell CM, Fiedler TL, Thomas KK, Agnew KJ, et al. (2010) Temporal variability of human vaginal bacteria and relationship with bacterial vaginosis. 2010, PloS One 2010, 5:e10197.

White, BA, D. J. Creedon, K. E. Nelson, B. A. Wilson (2011) The vaginal microbiome in health and 615 disease. Trends in Endocrinology and Metabolism, 2011, 22(1): 389-393

Abbreviations ABV (asymptomatic bacterial vaginosis) 620 BV (bacterial vaginosis) BVAB (BV-associated anaerobic bacteria) CPN (core/periphery network) CST (community state type) DSR (diversity-stability relationship) 625 HEA (healthy treatment) HSN (high-salience skeleton network) HVM (human vaginal microbiome) MAO (most abundant species or OTU) MDO (most dominant species or OTU) 630 OTU (operational taxonomic unit) SBV (symptomatic BV) SDN (species dominance network)

21 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Acknowledgements 635 This study received funding from the following sources: A National Natural Science Foundation (NSFC) Grant (No. 31970116); Cloud-Ridge Industry Technology Leader Grant; A China-US International Cooperation Project on Genomics/Metagenomics Big Data. We appreciate Wendy Li and Lianwei Li of the Chinese Academy of Sciences for their computational support.

640 Author contributions ZSM conceived and performed the analysis, interpreted the results and wrote the paper. AME participated in interpretation of the results and revised the paper. All authors approved the submission.

645 Correspondence should be addressed to ZSM ([email protected])

Competing interests: The author declares no competing interests.

Data availability: The datasets are available in public domain and the source are given in Ravel 650 et al. (2013) Microbiome 2013, Ravel et al. (2011) PNAS, Gajer et al. (2012) Science Translational Medicine, and Doyle et al. (2018) Applied Environmental Microbiology.

22 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

655 Figure Captions

Fig 1. A diagram showing the primary objectives and approaches to achieving the objectives

660 Fig 2. The SDN network graph for individual #s112 in the 25-mixed cohort (hub=Peptoniphilus, MAO=Lactobacillus iners; MDO= Gardnerella vaginalis; BV Status=SBV; hub, MAO and MDO all belong to the core; core nodes are in cyan color, and periphery nodes are in azure)

Fig 3. SDN network graph for individual #s23 in the 25-subjects cohort (hub, MDO & MAO are 665 the same node, i.e., Gardnerella vaginalis, hub= Prevotella buccalis; MDO, MAO and hub all belong to the core; core nodes are in cyan color, and periphery nodes are in azure; BV- status=ABV)

Fig 4. SDN network graph for individual #s52 in the 25-subjects cohort (hub, MDO & MAO are 670 the same node, i.e., Lactobacillus jensenii, hub= Clostridiales Family XI (incertae sedis); MDO, MAO and hub all belong to the core; core nodes are in cyan color, and periphery nodes are in azure; BV-status=HEA)

675 Table Captions

Table 1. The 15 special trios, including the 12 trios exclusively occurred in the SBV group (SBV-only) and the 3 trios occurred in the BV-only (BV=SBV+ABV) (see Table S2-4 for biomedical information on the 15 trios), as well as the relevant core/periphery/skeleton 680 information

Table 2. The number of 15 special trios (originally detected in the BV patients and listed in Table 1) that were detected in the networks of HVM-32, HVM-400, and HVM-UK-1107 cohorts

685

23 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

685 Online Supplementary Information (OSI) 3 OSI files (2 PDF & 1 Excel files) included.

File-A: “HVM OSI-File-A (Figures).pdf” contains: Fig S0 (Legend for the SDN graphs) and Fig S1 (the SDN graphs for 25 individuals in the 25-mixed cohort).

690 File-B: “HVM-OSI-File-B (Tables).pdf” contains: Table S1-1, Table S1-2, Table S2-1A, Table S2-1B, Table S3-1, Table S3-4B, Table S3-5B, Table S4-1, Table S4-3B, Table S4-4

File-C: “HVM-OSI-File-C (Excel Tables).xlsx” contains: Table S2-2, Table S2-3, Table S2-4, Table S2-5, Table S3-2A, Table S3-2B, Table S3-3, Table S3-4A, Table S3-5A, Table S4-2, Table S4-3A. 695

List of Online Supplementary Tables (included in “HVM-OSI-File-B (Tables).pdf” & “HVM-OSI-File-C (Excel Tables).xlsx”:

700 (1) Basic properties of the SDNs Table S1-1. The MDO, MAO and hub in the SDNs of the 25-mixed cohort

Table S1-2. The basic network properties (parameters) of the SDNs for the 25-mixed cohort

705 (2) Trio motifs and indicators of BV risk Table S2-1A. The occurrence of MDO-trios in the SDNs of the 25-mixed cohorts

Table S2-1B The occurrence of “MDO-trios with a handle” in the SDNs of the 25-mixed cohorts

710 Table S2-2. The 951 trios that occurred across 4 or more SDNs (subjects) of the 25-mixed cohort (Excel Tables)

Table S2-3. The 71 trios occurred in 7 or more SDNs, and their relationships with MDO in the 25-mixed cohort. Note there were 26 BVAB-trios (all their nodes are BVAB), 12 trios occurred 715 only in the SBV group, and 43 occurred only in SBV and ABV groups. (Excel Tables)

Table S2-4. The 15 special trios, including the 12 trios occurred exclusively in the SBV group and 3 trios occurred in 50% or more BV subjects (BV=SBV+ABV) (Excel Tables)

720 Table S2-5. The trios occurred in 50% or more subjects in each of three groups (Excel Tables)

(3) Core/periphery species in the SDNs Table S3-1. The core/periphery properties in the SDNs of the 25-mixed cohort

725 Table S3-2A. The OTU frequencies of the core and periphery nodes, respectively (Excel Tables)

Table S3-2B. The OTU occurrence frequency in the core and periphery, respectively (Excel Tables)

730 Table S3-3. The frequency of BVAB as core or periphery species (Excel Tables)

Table S3-4A. Shared core (or periphery) OUT analysis between two SDNs from different groups (Excel Tables)

24 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. 735 Table S3-4B. Summary results of the shared core (periphery) species between each paired SDNs, from two of the SBV, ABV and HEA groups (summarized from Table S3-4A)

Table S3-5A. The observed numbers of shared core (periphery) OTUs between each paired SDNs, from two of the SBV, ABV and HEA groups (Excel Tables) 740 Table S3-5B. The observed number of shared core (periphery) OTUs among the SBV, ABV and HEA groups (Summarized from Table S3-5A)

(4) High-salience skeletons in the SDNs 745 Table S4-1. Statistical properties of the high-salience skeletons in the SDNs

Table S4-2. The list of high-salience skeletons that are incident on the two special OTUs they connect (e.g., MDO, MAO, hub, CST-indicator species or BVAB) with salience value ≥ 0.5 in the SDNs of the 25-mixed cohort (Excel Tables) 750 Table S4-3A. The observed number of shared skeletons between each paired SDNs, from two of the HEA, ABV and SBV groups (Excel Tables)

Table S4-3B. The observed number of shared skeletons among HEA, ABV and SBV groups 755 (Summarized from Table S4-3A)

Table S4-4. The differential significant test for the salience values between the special trio- motifs and the whole SDN

760

25 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Online Supplementary Figures for: Ma & Ellison (2020) “Risks and etiology of bacterial vaginosis revealed by species dominance network analysis”

Figure S0. The legends of network symbols for the SDNs of the 25-mixed cohort Type Shape & Color Note Hub The node with the highest degree; Hexagon in Green

MDO The most dominant OTU (MDO); Square in Iris blue

MAO The most abundant OTU (MAO); Diamond in Pink

Hub + MDO + MAO Hub, MDO, and MAO are the same node; Octagon in Red

MDO + MAO MDO and MAO are the same node (using the shape of MDO, and the color of MAO) Hub + MDO Hub and MDO are the same node (using the shape of hub, and the Iris blue color of MDO)

Regular OTU node The regular OTU node; Circle in Azure Blue

Core OTU node The Core node; Circle in Cyan

Core + MAO MAO and Core are the same node (using the shape of MAO, and the color of Core))

Core + MDO MDO and Core are the same node (using the shape of MDO, and the color of Core)) Core + Hub Hub and Core are the same node (using the shape of Hub, and the color of Core)

Core + Hub + MDO + Core, Hub, MDO, and MAO are MAO the same node (using the shape of overloaded hub, MDO and MAO, but the color of Core)

Core + MDO + MAO Core, MDO and MAO are the same node (using the shape of MDO, but with red color) medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

2 Core + Hub + MDO Core, Hub and MDO are the same node (using Hexagon shape of hub, but with red color) Edge (Positive Regular positive edge, thinner Relationship) line in green

Edge (Positive Positive skeleton edge, thicker Relationship, Skeleton) line in green Edge (Negative Regular negative, red line Relationship) Edge (Negative Skeleton (negative), thickened Relationship, Skeleton) red line The following legends are possible but did not occur Hub + MAO Hub and MAO are the same node (using the shape of hub, and the Pink color of MAO)

Core + Hub + MAO Core, Hub and MAO are the same node (using the shape of hub, but with blue color)

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

3

Lactobacillus.jensenii Prevotella.melaninogenica

Lactobacillus.iners Streptococcus Veillonella Bifidobacteriaceae

Streptococcus.mitis Atopobium.vaginae Aerococcus.christensenii Finegoldia.magna

Gardnerella.vaginalis Streptococcus.agalactiae

Fig S1-1. SDN network graph for Subject #s3 of the 25-mixed cohort

Bacteroidales Prevotella.bivia

Prevotella BVAB3 Atopobium Lactobacillales Peptoniphilus.lacrimalis Peptoniphilus

Leptotrichia.amnionii BVAB2 Dialister Prevotella.genogroup.1

Anaerococcus candidate.division.TM7

Gemella Atopobium.vaginae

Veillonellaceae Parvimonas.micra

Lactobacillus.jensenii

Eggerthella Lactobacillus.iners

Dialister.sp..type.2 Lactobacillus.gasseri

Lactobacillus.vaginalis Peptoniphilus.harei Aerococcus.christensenii

Anaerococcus.vaginalis Bifidobacteriaceae

Prevotella.buccalis Finegoldia.magna

Prevotella.melaninogenica Veillonella.montpellierensis

Sneathia.sanguinegens Peptostreptococcus Prevotella.genogroup.2

Lactobacillus.OTU7 Bacillales Streptococcus.agalactiae

Anaerococcus.tetradius Streptococcus.parasanguinis

Lactobacillus.OTU6 Porphyromonas Streptococcus.anginosus Escherichia.coli

Fusobacterium Streptococcus.salivarius Actinomycetales Enterococcus.faecalis Lactobacillus.OTU2 Staphylococcus.aureus Corynebacterium.accolens Ureaplasma Staphylococcus Veillonella FirmicutesStreptococcus Proteobacteria Actinobacteria..class.

Lactobacillus Streptococcus.mitis Staphylococcus.hominis

Fig S1-2. SDN network graph for Subject #s5 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

4

Dialister Firmicutes BacteriaVeillonellaceae Atopobium Peptoniphilus Dialister.sp..type.3 Prevotella.bivia Mobiluncus.curtisii Prevotella

Porphyromonas.asaccharolytica Finegoldia.magna

Streptococcus.parasanguinis Corynebacterium.accolens

Proteobacteria Streptococcus.anginosus

Porphyromonas.bennonis Peptoniphilus.lacrimalis

Prevotella.buccalis Lactobacillus.gasseri

Peptoniphilus.asaccharolyticus Prevotella.genogroup.2

Lactobacillus.OTU2 Actinomyces.neuii Aerococcus.christensenii

Anaerococcus Lactobacillus.iners

Actinomycetaceae Actinomycetales

Campylobacter.ureolyticus Enterococcus.faecalis

Peptoniphilus.harei Staphylococcus Actinobacteria..class.

Varibaculum.cambriense Dialister.sp..type.2

Prevotella.genogroup.7 Atopobium.vaginae

Gardnerella.vaginalis Atopobium.minutum Clostridiales

Anaerococcus.vaginalis Megasphaera.sp..type.2

Lactobacillus.jensenii Bifidobacterium.breve

Streptococcus.salivarius Veillonella.parvula BVAB2

Staphylococcus.hominis Porphyromonas

Gammaproteobacteria Veillonella.atypica

Lactobacillales Bifidobacterium.bifidum

Staphylococcus.haemolyticus Clostridiales.Family.XI..Incertae.Sedis

Streptococcus Streptococcus.mutans Streptococcus.oralis Streptococcus.sanguinis Streptococcus.mitis Actinomyces Bacteroides Bacillales Bacteroides.coagulans BVAB1 Veillonella Aerococcus Roseburia

Staphylococcus.lugdunensis

Lactobacillus.coleohominis

Fig S1-3. SDN network graph for Subject #s7 of the 25-mixed cohort

Lactobacillales

Dialister Veillonella Gemella Enterobacteriaceae Bacteria Aerococcus Dialister.sp..type.3 Enterococcus.faecalis Bacteroides.coagulans Mobiluncus.curtisii Corynebacterium.accolens Peptostreptococcus Acinetobacter.calcoaceticus Prevotella.disiens Veillonella.montpellierensis Peptoniphilus.asaccharolyticus Staphylococcus.haemolyticus Raoultella.planticola Actinobacteria..class. Bacteroides

Peptoniphilus.harei Staphylococcus.aureus

Staphylococcus.hominis Actinomyces.neuii Veillonellaceae Porphyromonas.bennonis

Clostridiales.Family.XI..Incertae.Sedis Anaerococcus.vaginalis

Peptoniphilus.lacrimalis

Lachnospiraceae BVAB1 Anaerococcus.tetradius

Porphyromonas.asaccharolytica Anaerococcus Staphylococcus.capitis

Veillonella.atypica Porphyromonas Prevotella Lactobacillus.gasseri Clostridiales Firmicutes Dialister.sp..type.2

Gammaproteobacteria Actinobacteridae Finegoldia.magna

Varibaculum.cambriense Staphylococcus

Lactobacillus.OTU2 Parvimonas.micra Actinomycetales

Megasphaera.sp..type.1 Streptococcus.anginosus

BVAB2 Prevotella.genogroup.2

Megasphaera.sp..type.2 Lactobacillus.vaginalis Peptoniphilus

BVAB3 Streptococcus.agalactiae

Prevotella.genogroup.1 Aerococcus.christensenii Lactobacillus.jensenii Sneathia.sanguinegens Bifidobacteriaceae

Coriobacteriaceae Leptotrichia.amnionii Lactobacillus.iners

Streptococcus.salivarius Lactobacillus.crispatus Gardnerella.vaginalis Prevotella.buccalis Atopobium.vaginae

Megasphaera Prevotella.bivia

Roseburia Prevotella.genogroup.7

Streptococcus.mitis Campylobacter.ureolyticus

Actinomyces Alloscardovia.omnicolens Actinomycetaceae Fusobacteriaceae Actinomyces.urogenitalis Mycoplasma Bifidobacterium.longum Streptococcus Bacilli Eggerthella Ureaplasma Fig S1-4. SDN network graph for Subject #s12 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

5

Fusobacterium.nucleatum

Lactobacillales Bacillales Atopobium Gemella Bacteria Eggerthella Prevotella candidate.division.TM7 BVAB3 Streptococcus.agalactiae Aerococcus Anaerococcus Prevotella.genogroup.2 Parvimonas.micra

Firmicutes Dialister.sp..type.2

Anaerococcus.tetradius Prevotella.genogroup.1

Peptostreptococcus Lactobacillus.vaginalis

Peptoniphilus Lactobacillus.iners Peptoniphilus.lacrimalis

BVAB2 Clostridiales.Family.XI..Incertae.Sedis

Mobiluncus.curtisii Gardnerella.vaginalis Roseburia

Sneathia.sanguinegens Atopobium.vaginae

Actinomycetales Lactobacillus.crispatus Porphyromonas

Enterococcus.faecalis Lactobacillus.gasseri

Staphylococcus Leptotrichia.amnionii

Eubacterium.rectale Bifidobacteriaceae

Bifidobacterium Finegoldia.magna

Corynebacterium.accolens

Veillonellaceae

Actinobacteria..class.

Anaerococcus.vaginalis Prevotella.buccalis

Peptoniphilus.asaccharolyticus

Proteobacteria Lachnospiraceae

Actinomycetaceae Porphyromonas.bennonis Streptococcus

Lactobacillus.OTU2 Actinomyces

Escherichia.coli Lactobacillus.OTU3

Mycoplasma.hominis Alloscardovia.omnicolens Prevotella.bivia Eubacterium Aerococcus.christensenii Mycoplasma Veillonella.montpellierensis Enterobacteriaceae Sutterella Lactobacillus Bacteroides.coagulans BVAB1 Prevotella.disiens Dialister.sp..type.3 Peptoniphilus.harei Veillonella Bacilli

Staphylococcus.hominis Bacteroides

Fig S1-5. SDN network graph for Subject #s15 of the 25-mixed cohort

Peptoniphilus BVAB2 Clostridiales Dialister.sp..type.2

Gemella Leptotrichia.amnionii Peptoniphilus.harei Anaerococcus Prevotella.genogroup.2 Eubacterium

Bifidobacteriaceae Parvimonas.micra

Peptoniphilus.lacrimalis Megasphaera.sp..type.1 Eubacterium.rectale

Atopobium.vaginae Bacteroides.coagulans Prevotella.bivia

Prevotella Lactobacillus.iners

Coriobacteriaceae Mobiluncus.curtisii Staphylococcus Gardnerella.vaginalis Dialister.sp..type.3

Streptococcus.mitis Veillonellaceae Megasphaera Peptoniphilus.asaccharolyticus

Sneathia.sanguinegens Clostridiales.Family.XI..Incertae.Sedis Aerococcus.christensenii Porphyromonas

Anaerococcus.tetradius Atopobium Firmicutes Peptostreptococcus Finegoldia.magna Aerococcus Bacteroidales

Mycoplasma.hominis Prevotella.disiens Lactobacillales Prevotella.buccalis EggerthellaBacteroides

Fig S1-6. SDN network graph for Subject #s17 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

6

Bacilli Anaerococcus.vaginalis Veillonella.parvula

Streptococcus.parasanguinis

Lactobacillales Roseburia Fusobacterium Eggerthella Veillonella.atypica Firmicutes Prevotella.buccalis Bacteroides.coagulans Gemella Enterobacteriaceae

Porphyromonas.bennonis Prevotella

Porphyromonas.asaccharolytica Peptostreptococcus candidate.division.TM7 Fusobacterium.gonidiaformans Bacteria

Anaerococcus Escherichia.coli Porphyromonas.sp..type.1

Porphyromonas Peptoniphilus

Finegoldia.magna Pseudomonas.aeruginosa BVAB1

Atopobium.vaginae Prevotella.genogroup.3

Gardnerella.vaginalis Aerococcus Clostridiales

Megasphaera Dialister.sp..type.2 Pseudomonas Aerococcus.christensenii Dialister.sp..type.3

Lactobacillus.jensenii Sneathia.sanguinegens Parvimonas.micra

BVAB2 Leptotrichia.amnionii Sneathia

Anaerococcus.tetradius Bacteroidales

Lachnospiraceae Bacteroides Mobiluncus Megasphaera.sp..type.1 Corynebacterium.accolens Veillonellaceae

Clostridiales.Family.XI..Incertae.Sedis Mycoplasma.hominis

Megasphaera.sp..type.2 Prevotella.bivia

Actinomycetales Staphylococcus.haemolyticus Peptoniphilus.asaccharolyticus

Staphylococcus Lactobacillus.salivarius Prevotella.disiens Eubacterium.rectale Prevotella.genogroup.4 Streptococcus Sutterella Prevotella.melaninogenica Bifidobacterium Coriobacteriaceae Bacteroides.uniformis Bifidobacterium.bifidum Streptococcus.salivarius Eubacterium Veillonella Bacillales Streptococcus.mitis

Streptococcus.oralis

Veillonella.montpellierensis

Fig S1-7. SDN network graph for Subject #s23 of the 25-mixed cohort

Streptococcus.anginosus Peptoniphilus.harei

Anaerococcus.vaginalis

Bacteria Bacilli Bacteroides Anaerococcus Roseburia Sneathia Actinomycetales Finegoldia.magna Anaerococcus.tetradius BVAB2 Lactobacillus.crispatus Atopobium.vaginae Peptostreptococcus Aerococcus

Aerococcus.christensenii Bifidobacteriaceae

Gardnerella.vaginalis Eggerthella

Lactobacillus.iners Dialister Staphylococcus

Porphyromonas.uenonis Sneathia.sanguinegens

Prevotella.genogroup.2 Porphyromonas

Escherichia.coli Prevotella Porphyromonas.sp..type.1

BVAB1 Clostridiales.Family.XI..Incertae.Sedis

Megasphaera.sp..type.2 Mobiluncus.curtisii

Porphyromonas.asaccharolytica Peptoniphilus.lacrimalis

Peptoniphilus.asaccharolyticus Gemella

Prevotella.genogroup.1 Clostridiales Veillonella.atypica

Dialister.sp..type.2 Streptococcus.salivarius Veillonellaceae Streptococcus.parasanguinis

Leptotrichia.amnionii Eubacterium Megasphaera Streptococcus Streptococcus.mitis Streptococcus.agalactiae Staphylococcus.aureus candidate.division.TM7 Bacillales Actinomycetaceae Streptococcus.oralis Actinobacteria..class. Atopobium Parvimonas.micraPeptoniphilus BVAB3 Mobiluncus

Fig S1-8. SDN network graph for Subject #s27 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

7

Aerococcus.christensenii

Aerococcus

BVAB1 Clostridiales Gemella BVAB3 BVAB2 Megasphaera.sp..type.2 Bacillales Peptoniphilus Streptococcus.salivarius Parvimonas.micra Gammaproteobacteria

Megasphaera.sp..type.1 Veillonella.montpellierensis

Prevotella.genogroup.1 Actinobacteria..class.

Prevotella.genogroup.2 Staphylococcus.lugdunensis

Lactobacillus.crispatus Streptococcus

Staphylococcus.aureus Finegoldia.magna

Bifidobacteriaceae Actinomycetales

Bifidobacterium.bifidum Streptococcus.agalactiae

Atopobium.vaginae Streptococcus.anginosus

Enterococcus.faecalis Anaerococcus.tetradius

Anaerococcus Staphylococcus

Dialister.sp..type.2 Gardnerella.vaginalis

Leptotrichia.amnionii Lactobacillus.iners

Veillonellaceae Porphyromonas.uenonis

Sneathia.sanguinegens Clostridiales.Family.XI..Incertae.Sedis

Mobiluncus Prevotella.disiens

Lactobacillus.coleohominis Peptoniphilus.lacrimalis Actinomyces.neuii

Atopobium Bifidobacterium

Firmicutes Prevotella

Prevotella.buccalis candidate.division.TM7

Mobiluncus.curtisii Eggerthella

Actinomycetaceae Corynebacterium.accolens

Mycoplasma.hominis Staphylococcus.haemolyticus Prevotella.genogroup.7 Peptoniphilus.asaccharolyticus Staphylococcus.capitis

Megasphaera Veillonella.atypica

Porphyromonas Veillonella.parvula

Peptoniphilus.harei Staphylococcus.epidermidis

Roseburia Streptococcus.parasanguinis Actinomyces Proteobacteria Streptococcus.mitis

Porphyromonas.asaccharolytica Streptococcus.oralis Sutterella Staphylococcus.hominis Bacilli Actinobacteridae Lachnospiraceae Lactobacillales Dialister Veillonella

Peptostreptococcus Fig S1-9. SDN network graph for Subject #s35 of the 25-mixed cohort

Ureaplasma Sutterella

Gemella Clostridiales Dialister.sp..type.2 Actinomyces Veillonellaceae Peptoniphilus

Anaerococcus.tetradius Streptococcus.agalactiae Lactobacillus.gasseri Parvimonas.micra BVAB2 Atopobium Proteobacteria Bifidobacteriaceae

Sneathia.sanguinegens

Aerococcus.christensenii Bifidobacterium.breve

Lactobacillus.vaginalis Prevotella.genogroup.4 Eggerthella

Lactobacillus.OTU1 Megasphaera.sp..type.1 Peptostreptococcus Lactobacillus.jensenii

Veillonella.montpellierensis Atopobium.vaginae Lactobacillus.coleohominis

Corynebacterium.accolens Lactobacillus.crispatus

Streptococcus.oralis Lactobacillus.iners

Streptococcus.salivarius Gardnerella.vaginalis Mobiluncus

Prevotella.genogroup.2 Peptoniphilus.lacrimalis Prevotella.genogroup.1

Streptococcus.mitis Prevotella.genogroup.3 Streptococcus.anginosus Actinomycetales Finegoldia.magna BVAB3 Bacteria Prevotella.bivia Bacillales Streptococcus Bacilli Staphylococcus.capitis

Staphylococcus

Staphylococcus.haemolyticus Staphylococcus.aureus

Fig S1-10. SDN network graph for Subject #s40 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

8

Veillonellaceae Prevotella Lachnospiraceae Clostridiales

Prevotella.genogroup.2

Dialister.sp..type.2 Clostridium.colicanis Anaerococcus.vaginalis

Streptococcus.anginosus Finegoldia.magna

Varibaculum.cambriense Lactobacillus.jensenii Dialister Peptoniphilus Peptoniphilus.harei

Bacilli Prevotella.buccalis Anaerococcus

Actinomycetales Lactobacillus.crispatus

Dialister.sp..type.3 Staphylococcus Lactobacillus.vaginalis Atopobium.vaginae

Fig S1-11. SDN network graph for Subject #s49 of the 25-mixed cohort

Firmicutes Bacteroides Bacteria Clostridiales Mobiluncus.curtisii Bacilli Roseburia Lactobacillales Prevotella.buccalis Dialister.sp..type.3 Peptoniphilus.asaccharolyticus Atopobium Veillonellaceae Dialister Peptoniphilus.lacrimalis Porphyromonas Anaerococcus

Porphyromonas.bennonis Actinobacteria..class.

Arcanobacterium Prevotella

Peptoniphilus.harei Clostridiales.Family.XI..Incertae.Sedis

Anaerococcus.vaginalis Finegoldia.magna

Bacteroides.coagulans Actinomycetales

Campylobacter.ureolyticus Peptoniphilus

Varibaculum.cambriense Streptococcus.anginosus

Actinobacteridae Prevotella.genogroup.2

Proteobacteria Lactobacillus.gasseri

Dialister.propionicifaciens Lactobacillus.jensenii Eubacterium Sutterella Lactobacillus.OTU1

Actinomyces Bifidobacteriaceae

Veillonella Bacillales

Gammaproteobacteria Actinomyces.urogenitalis Streptococcus

Lachnospiraceae Streptococcus.salivarius

Fusobacteriaceae BVAB2

Prevotella.melaninogenica Parvimonas.micra Lactobacillus

Streptococcus.oralis Porphyromonas.asaccharolytica

Streptococcus.parasanguinis Lactobacillus.iners

Veillonella.parvula Gardnerella.vaginalis

Veillonella.atypica Peptostreptococcus Aerococcus.christensenii Coriobacteriaceae

Streptococcus.mitis Veillonella.montpellierensis

Enterobacteriaceae Streptococcus.agalactiae

Corynebacterium.accolens Staphylococcus.capitis

Bifidobacterium.breve Escherichia.coli Streptococcus.anginosus.group Staphylococcus.haemolyticus Actinomyces.neuii Enterococcus.faecalis Fusobacterium.nucleatum Staphylococcus Prevotella.genogroup.1 Prevotella.genogroup.3 Staphylococcus.hominis Dialister.sp..type.2 Megasphaera.sp..type.1 Bacteroidales Gemella Pseudomonas Mycoplasma.hominis Prevotella.biviaBVAB3 Eggerthella Megasphaera.sp..type.2 Bifidobacterium

Eubacterium.rectale Anaerococcus.tetradius

Fig S1-12. SDN network graph for Subject #s52 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

9

Fusobacteriaceae Lachnospiraceae Sneathia

Bifidobacterium.breve Prevotella.bivia

BVAB1 Lactobacillus.gasseri Gemella BVAB3 Bacteroidales Peptoniphilus Bacteria Megasphaera.sp..type.2 Gardnerella.vaginalis BVAB2 Leptotrichia.amnionii Megasphaera.sp..type.1 Clostridiales Corynebacterium.accolens Atopobium.vaginae Lactobacillus.jensenii Dialister.sp..type.2

Aerococcus.christensenii Bacilli Prevotella.genogroup.1 Mycoplasma.hominis Lactobacillus.crispatus

Veillonellaceae

Lactobacillus.iners Peptoniphilus.lacrimalis

Parvimonas.micra

Aerococcus candidate.division.TM7 Sneathia.sanguinegens Lactobacillus.vaginalis Actinomyces.neuii Prevotella Finegoldia.magna Bacteroides Megasphaera

Prevotella.genogroup.4 Actinomyces Anaerococcus.tetradius Mobiluncus Mobiluncus.curtisii Firmicutes Atopobium Bifidobacteriaceae Prevotella.genogroup.3 Lactobacillales Eggerthella Clostridiales.Family.XI..Incertae.Sedis Dialister

Actinobacteridae Streptococcus

Bacillales Staphylococcus.aureus

Fig S1-13. SDN network graph for Subject #s53 of the 25-mixed cohort

Lactobacillus.crispatus Peptostreptococcus

Bacteroides.coagulans

Veillonella Streptococcus.parasanguinis BacteroidesLactobacillales Peptoniphilus Aerococcus Campylobacter.ureolyticus Anaerococcus.tetradius Gammaproteobacteria

Dialister Veillonella.montpellierensis

Finegoldia.magna Bacteria Staphylococcus.hominis Sutterella Veillonellaceae Anaerococcus

Atopobium Mobiluncus.curtisii

candidate.division.TM7 Peptoniphilus.asaccharolyticus Porphyromonas.bennonis Mobiluncus.mulieris Prevotella.disiens Sneathia.sanguinegens

Mobiluncus Leptotrichia.amnionii

Mycoplasma.hominis Prevotella.genogroup.2

Prevotella Dialister.sp..type.2 Streptococcus.anginosus

Prevotella.buccalis Gardnerella.vaginalis

Bifidobacterium.breve Megasphaera.sp..type.2

Megasphaera.sp..type.1 Porphyromonas Lactobacillus.jensenii

Prevotella.genogroup.4 Clostridiales

Actinomycetaceae Peptoniphilus.lacrimalis

Porphyromonas.sp..type.1 Anaerococcus.vaginalis

Eubacterium Clostridiales.Family.XI..Incertae.Sedis Lactobacillus.gasseri

Atopobium.vaginae Prevotella.bivia

Actinobacteria..class. Staphylococcus

Actinomycetales Enterococcus.faecalis Sneathia Staphylococcus.haemolyticus Bifidobacteriaceae Corynebacterium.accolens

Bacilli BVAB3 Streptococcus.agalactiae BVAB2 Firmicutes Lactobacillus.iners Parvimonas.micra Lachnospiraceae Eubacterium.rectale Roseburia Gemella

Aerococcus.christensenii Prevotella.genogroup.3 Fig S1-14. SDN network graph for Subject #s55 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

10

Actinomyces Dialister Bacteroides Eggerthella Gemella Veillonellaceae Prevotella.bivia Veillonella.montpellierensis Prevotella Veillonella Anaerococcus Peptostreptococcus Fusobacteriaceae Firmicutes Bifidobacteriaceae Peptoniphilus.asaccharolyticus Clostridiales

Streptococcus Sneathia.sanguinegens Leptotrichia.amnionii

Porphyromonas.asaccharolytica Dialister.sp..type.2

Streptococcus.mitis Anaerococcus.tetradius Peptoniphilus

Prevotella.genogroup.2 Finegoldia.magna

Bacteroides.coagulans Actinomycetales Sneathia Clostridiales.Family.XI..Incertae.Sedis Lactobacillus.iners

Actinobacteria..class. Parvimonas.micra

Staphylococcus.aureus Peptoniphilus.lacrimalis Aerococcus.christensenii

Staphylococcus.haemolyticus BVAB2

Corynebacterium.accolens Gardnerella.vaginalis Mobiluncus Dialister.sp..type.3 Atopobium.vaginae

Porphyromonas.bennonis Streptococcus.anginosus Lachnospiraceae Prevotella.genogroup.1 Aerococcus Veillonella.parvula Enterococcus.faecalis Lactobacillus.jensenii Peptoniphilus.harei Atopobium Staphylococcus Campylobacter.ureolyticus Anaerococcus.vaginalis Peptostreptococcus.stomatis Bacteria Roseburia Bacillales

Lactobacillales Lactobacillus.OTU1

Bifidobacterium.bifidum Porphyromonas

Fig S1-15. SDN network graph for Subject #s59 of the 25-mixed cohort

Bifidobacterium

Enterobacteriaceae Roseburia Lachnospiraceae Fusobacterium.gonidiaformans

Eubacterium Coriobacteriaceae Eubacterium.rectale Aerococcus Sutterella Firmicutes Megasphaera.sp..type.2 Peptostreptococcus Lactobacillus.fermentum Peptoniphilus.asaccharolyticus Actinomyces Staphylococcus.aureus Porphyromonas Anaerococcus Anaerococcus.vaginalis Veillonella.atypica Ureaplasma.urealyticum Dialister.sp..type.2 Peptoniphilus.lacrimalis Actinomycetaceae Bacillales Veillonella.parvula Prevotella Streptococcus.parasanguinis Anaerococcus.tetradius Streptococcus.agalactiae Escherichia.coli Aerococcus.christensenii Prevotella.genogroup.1 Staphylococcus Actinobacteria..class. Streptococcus.anginosus.group Gemella Leptotrichia.amnionii

Actinomycetales Prevotella.genogroup.4 Gammaproteobacteria Bifidobacteriaceae Sneathia.sanguinegens

Streptococcus.anginosus Streptococcus.mitis Atopobium.vaginae Streptococcus Streptococcus.oralis Gardnerella.vaginalis Veillonella Streptococcus.salivarius Dialister Mobiluncus.curtisii

Veillonellaceae Enterococcus Bacteroidales Mycoplasma.hominis Veillonella.montpellierensis Prevotella.buccalis Megasphaera.sp..type.1 Prevotella.genogroup.3 Megasphaera Bacteria Eggerthella Fusobacterium Porphyromonas.asaccharolytica Peptoniphilus candidate.division.TM7 Prevotella.disiens Parvimonas.micra Lactobacillus.crispatus Prevotella.genogroup.2 BVAB2 BVAB3 Clostridiales Prevotella.bivia Bacteroides Lactobacillus.gasseri Lactobacillus.jensenii

Lactobacillus.OTU2 Clostridiales.Family.XI..Incertae.Sedis Staphylococcus.lugdunensis

Bifidobacterium.breve

Fig S1-16. SDN network graph for Subject #s60 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

11

Enterococcus.faecalis BVAB1

Atopobium Peptoniphilus Prevotella.disiens Finegoldia.magna Clostridiales Mobiluncus Gardnerella.vaginalis Megasphaera.sp..type.1 Anaerococcus.tetradius Prevotella.buccalis Bacilli Bifidobacteriaceae BVAB2 Dialister.sp..type.3 Peptoniphilus.asaccharolyticus Lactobacillus.crispatus Parvimonas.micra Anaerococcus.vaginalis Actinomyces Prevotella.genogroup.2 Prevotella Actinobacteria..class. Mobiluncus.curtisii Peptoniphilus.harei Atopobium.vaginae Bacteroides.coagulans Prevotella.bivia Dialister.sp..type.2 Varibaculum.cambriense

Streptococcus Campylobacter.ureolyticus Veillonellaceae Veillonella.montpellierensis

ProteobacteriaBifidobacterium.bifidum Porphyromonas Sneathia.sanguinegens Porphyromonas.asaccharolytica Veillonella Bifidobacterium Bacteroides.uniformis Bacteroides Streptococcus.anginosus Clostridiales.Family.XI..Incertae.Sedis Gemella Megasphaera Lachnospiraceae Bifidobacterium.longum Streptococcus.agalactiae Megasphaera.sp..type.2 Peptostreptococcus Firmicutes Roseburia Staphylococcus Bacteroidales Eubacterium.rectale Coriobacteriaceae Anaerococcus Aerococcus.christensenii Eubacterium Eggerthella Bacteria Staphylococcus.haemolyticus Dialister

Fig S1-17. SDN network graph for Subject #s77 of the 25-mixed cohort

Clostridiales Dialister Peptoniphilus Finegoldia.magna Aerococcus Bifidobacteriaceae

Megasphaera Parvimonas.micra Megasphaera.sp..type.1 Enterococcus Streptococcus Eggerthella Lactobacillus.crispatus

Anaerococcus Staphylococcus.hominis Atopobium.vaginae Leptotrichia.amnionii Actinomycetales Enterococcus.faecalis

BacilliStreptococcus.salivarius Sneathia.sanguinegens Veillonellaceae Lactobacillus.jensenii

Prevotella.bivia Aerococcus.christensenii

Prevotella.buccalis Peptostreptococcus Bifidobacterium Prevotella.genogroup.4

Staphylococcus.lugdunensis Prevotella.genogroup.2 Peptoniphilus.lacrimalis Anaerococcus.vaginalis

Dialister.sp..type.2 candidate.division.TM7 Bifidobacterium.bifidum

Prevotella.genogroup.3 Staphylococcus.aureus BVAB2 Streptococcus.anginosus Atopobium Prevotella.genogroup.1 Prevotella Gemella BVAB1

Fig S1-18. SDN network graph for Subject #s82 of the 25-mixed cohort

Acinetobacter.calcoaceticus Gammaproteobacteria Streptococcus.salivarius

Sutterella candidate.division.TM7 Escherichia.coli Enterococcus.faecalis Bacillales Enterobacteriaceae

Streptococcus.agalactiae Mycoplasma Bacilli Bifidobacterium

Streptococcus.anginosus Bacteria Proteobacteria Lactobacillus.gasseri Porphyromonas.sp..type.1

Bifidobacterium.bifidum Bacteroidales Porphyromonas Staphylococcus.epidermidis

Staphylococcus Firmicutes Clostridiales Staphylococcus.lugdunensis Staphylococcus.capitis Actinobacteria..class. Gardnerella.vaginalis Anaerococcus Staphylococcus.hominis Megasphaera.sp..type.1 Actinomycetales Corynebacterium.accolens Mobiluncus Pseudomonadales

Bifidobacteriaceae Lactobacillus Finegoldia.magna Peptoniphilus BVAB1 Lactobacillus.OTU2 Parvimonas.micra Dialister.sp..type.2 Peptoniphilus.harei BVAB2 Aerococcus Megasphaera.sp..type.2 Aerococcus.christensenii Dialister.sp..type.3 Acinetobacter.calcoaceticus.baumannii.complex Clostridiales.Family.XI..Incertae.Sedis

Fig S1-19. SDN network graph for Subject #s96 of the 25-mixed cohort medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

12

Prevotella.buccalis

Megasphaera Bacteria Roseburia Prevotella Prevotella.bivia Eggerthella

Anaerococcus.tetradius Dialister.sp..type.2 Staphylococcus.hominis

Peptoniphilus.lacrimalis Prevotella.genogroup.1 Staphylococcus.capitis Enterococcus.faecalis Anaerococcus Parvimonas.micra

Lachnospiraceae Gardnerella.vaginalis Lactobacillus.coleohominis Staphylococcus

Actinobacteridae Veillonellaceae Clostridiales

Prevotella.genogroup.2 Actinobacteria..class.

Clostridiales.Family.XI..Incertae.Sedis

Finegoldia.magna Veillonella.montpellierensis

Firmicutes

Actinomycetales

Peptoniphilus.asaccharolyticus Leptotrichia.amnionii Veillonella

Mobiluncus.curtisii

Peptoniphilus

Lactobacillales

Aerococcus.christensenii

Dialister.sp..type.3 BVAB3 BVAB2 Mobiluncus Streptococcus.salivarius Bacteroides Bacillales Megasphaera.sp..type.1 Enterobacteriaceae Atopobium Lactobacillus.iners Streptococcus

Varibaculum.cambriense Streptococcus.mitis Atopobium.vaginae Streptococcus.oralis Bifidobacteriaceae Porphyromonas Streptococcus.parasanguinis Anaerococcus.vaginalis Streptococcus.sanguinis Bacteroides.coagulans Fusobacteriaceae Peptostreptococcus Peptoniphilus.harei Campylobacter.ureolyticus Porphyromonas.bennonis Porphyromonas.asaccharolytica Actinomyces Dialister Proteobacteria

Fig S1-20. SDN network graph for Subject #s112 of the 25-mixed cohort

Dialister.sp..type.2

Prevotella Lactobacillus.gasseri Lactobacillus.OTU2 Prevotella.genogroup.1 Veillonellaceae Prevotella.genogroup.2 Eggerthella Lactobacillus.coleohominis Sutterella

Mobiluncus.mulieris BVAB3 Gemella Parvimonas.micra Mobiluncus Clostridiales

Actinomycetaceae candidate.division.TM7 Staphylococcus.haemolyticus Sneathia Peptoniphilus.lacrimalis BVAB2

Staphylococcus.warneri Peptoniphilus Gardnerella.vaginalis

Atopobium.vaginae Finegoldia.magna Aerococcus Veillonella

Actinomycetales Megasphaera.sp..type.1 Veillonella.montpellierensis

Anaerococcus Bifidobacteriaceae

Anaerococcus.tetradius Peptoniphilus.asaccharolyticus Enterococcus.faecalis

Actinomyces Peptostreptococcus Clostridiales.Family.XI..Incertae.Sedis

Leptotrichia.amnionii Campylobacter.ureolyticus Sneathia.sanguinegens Peptoniphilus.harei

Actinobacteridae Bacteria Bacteroides.coagulans Roseburia Firmicutes Bacteroides

Fig S1-21. SDN network graph for Subject #s116 of the 25-mixed cohort

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

13

Bacteria Corynebacterium.accolens

Gemella Prevotella Peptoniphilus Lactobacillus.OTU2 Actinomycetales Gardnerella.vaginalis Streptococcus.agalactiae Clostridiales Aerococcus.christensenii Dialister.sp..type.2 Escherichia.coli Enterococcus Actinomyces.neuii

Finegoldia.magna Staphylococcus.capitisStreptococcus Prevotella.genogroup.2 Staphylococcus Anaerococcus Enterococcus.faecalis

Lactobacillus.crispatus Atopobium.vaginae

Staphylococcus.haemolyticus Lactobacillus.OTU4 Anaerococcus.tetradius Lactobacillus.coleohominisLactobacillus.vaginalis Lactobacillus.iners Prevotella.bivia Dialister Lactobacillus.gasseri

Lactobacillus.jensenii Lactobacillus.OTU5 Bifidobacteriaceae

Actinomyces

Clostridiales.Family.XI..Incertae.Sedis Bacteroidales Prevotella.disiens

Veillonellaceae

Anaerococcus.vaginalis Veillonella.atypica Eggerthella Atopobium

Peptoniphilus.lacrimalis Veillonella.parvula Veillonella Porphyromonas Peptoniphilus.asaccharolyticus Prevotella.buccalis Mobiluncus Coriobacteriaceae Dialister.sp..type.3 Mycoplasmataceae Streptococcus.salivarius Varibaculum.cambriense Peptoniphilus.harei Peptostreptococcus Lactobacillus Firmicutes AerococcusUreaplasma Bacteroides.coagulans BVAB2 BVAB3

candidate.division.TM7 Bifidobacterium.breve Proteobacteria Enterobacteriaceae Acinetobacter.calcoaceticus

Fig S1-22. SDN network graph for Subject #s127 of the 25-mixed cohort

Lactobacillus.gasseri Lactobacillus.coleohominis Bacillales Streptococcus Streptococcus.parasanguinis Megasphaera Streptococcus.salivarius BifidobacteriaceaeAerococcus.christensenii BVAB2 Parvimonas.micra Streptococcus.oralis Atopobium.vaginae Peptoniphilus Staphylococcus.aureus Megasphaera.sp..type.1 Streptococcus.agalactiae Prevotella.genogroup.1 Lactobacillus.crispatus Staphylococcus Enterococcus.faecalis Lactobacillus.iners Dialister.sp..type.2

Gardnerella.vaginalis Prevotella.genogroup.2 Streptococcus.mitis Lactobacillus.OTU2 Anaerococcus Clostridiales BVAB1 Leptotrichia.amnionii

Finegoldia.magna BVAB3 Streptococcus.anginosus Gemella Prevotella.genogroup.3 Mycoplasma.hominis Lactobacillales Anaerococcus.tetradius

Prevotella Proteobacteria Prevotella.bivia Dialister.sp..type.3 Fusobacterium Staphylococcus.haemolyticus Prevotella.disiens Veillonella.montpellierensis Fusobacterium.gonidiaformans Sneathia.sanguinegens Actinomycetaceae Dialister Veillonellaceae Peptostreptococcus Staphylococcus.hominis Actinomyces.neuii Eggerthella Veillonella

Aerococcus Porphyromonas Actinomycetales

Actinobacteria..class.

Fig S1-23. SDN network graph for Subject #s128 of the 25-mixed cohort

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

14

BVAB2 Peptoniphilus BVAB3 Parvimonas.micra BVAB1 Bifidobacteriaceae Dialister.sp..type.2 Bifidobacterium.breve Lactobacillus.crispatus Aerococcus.christensenii Clostridiales

Prevotella.genogroup.2 Prevotella.bivia Proteobacteria Prevotella.genogroup.1

Gardnerella.vaginalis

Leptotrichia.amnionii Atopobium.vaginae Anaerococcus

Enterococcus.faecalis Veillonellaceae Staphylococcus.aureus Lactobacillus.iners Streptococcus.agalactiae Peptoniphilus.lacrimalis Streptococcus

Actinomycetaceae Megasphaera.sp..type.2

Arcanobacterium Clostridiales.Family.XI..Incertae.Sedis Sneathia.sanguinegens Porphyromonas

Megasphaera.sp..type.1 Dialister Dialister.sp..type.3 Escherichia.coli Megasphaera Prevotella.genogroup.3 Prevotella.buccalis Eggerthella Firmicutes Prevotella

Fig S1-24. SDN network graph for Subject #s130 of the 25-mixed cohort

Gammaproteobacteria Prevotella.melaninogenica

Streptococcus Bacillales Actinobacteria..class.Staphylococcus.lugdunensis Dialister Prevotella.disiens Atopobium Eggerthella Staphylococcus.hominis Porphyromonas Streptococcus.oralis Porphyromonas.asaccharolytica Streptococcus.agalactiae Lactobacillus.coleohominis Streptococcus.salivarius Gemella Enterobacteriaceae Anaerococcus.tetradius

Peptoniphilus.lacrimalis Mycoplasma.hominis Peptoniphilus.asaccharolyticus Raoultella.planticola Veillonella Gardnerella.vaginalis Prevotella.buccalis Proteobacteria Lactobacillus.iners Actinomyces.neuii

Lactobacillus.vaginalis Bacteroidales Streptococcus.mitis Lactobacillus.crispatus Veillonella.parvulaBacteroides Veillonella.montpellierensis Mobiluncus.curtisii

Lactobacillus.jensenii Peptoniphilus.harei Sneathia.sanguinegens Fusobacterium.nucleatum Sutterella Atopobium.vaginae Firmicutes Lactobacillus.delbrueckii Actinobacteridae Veillonellaceae Bifidobacterium.breve Megasphaera.sp..type.1 Lactobacillus.gasseri Alloscardovia.omnicolens Bacteroides.coagulans Prevotella RoseburiaPrevotella.bivia Dialister.sp..type.2

Leptotrichia.amnionii Aerococcus.christensenii Corynebacterium.accolens Staphylococcus.aureus Porphyromonas.bennonis Prevotella.genogroup.2 candidate.division.TM7 Bifidobacteriaceae Porphyromonas.uenonisDialister.sp..type.3 Parvimonas.micra Peptoniphilus Prevotella.genogroup.1 BVAB2 Anaerococcus Actinomycetales Staphylococcus Actinomyces Prevotella.genogroup.4 Ureaplasma.urealyticumBacilli

Fig S1-25. SDN network graph for Subject #s135of the 25-mixed cohort

Fig S1. The 25 SDN graphs for the 25 subjects in the 25-mixed cohort. Among the 25 SDNs, there are 23 subjects (i.e., s3, s5, s23, s49, s55, s59, s60, s77, s130) whose MDO and MAO are the same overloaded node. There is only two subject (S112 & S17) whose MDO and MAO are different nodes and nothing is overloaded See previous Suppl. Table S1-1 for the symbol legends. medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

15

(a) (b)

Fig S2. The Venn diagram and pie plot that display the distribution of 951 trios that occurred 4 or more subjects among SBV, ABV and healthy (HEA) groups: • Among 951 trios, there was no trio occurring only in ABV or HEA groups without also occurring in the other one (or two) group(s); • 301 (32%) of 951 trios occurred exclusively in SBV group; • 78 (8%) of 951 trios occurred in both SBV and HEA groups; • 232 (24%) of 951 trios occurred in both SBV and ABV groups; • 340 (36%) of 951 trios occurred in all three groups.

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

1 Online Supplementary Figures for: Ma & Ellison (2020) “Risks and etiology of bacterial vaginosis revealed by species dominance network analysis”

1. Basic network properties (parameters) of the SDNs

Table S1-1. The MDO, MAO and hub in the SDNs of the 25-mixed cohort Subject ID MDO MAO Hub

SBV Group S112 Gardnerella vaginalis Lactobacillus iners Peptoniphilus BVAB2 S116 Atopobium vaginae Atopobium vaginae Peptoniphilus S127 Lactobacillus iners Lactobacillus iners Prevotella.genogroup.2 Anaerococcus.tetradius S128 Lactobacillus iners Lactobacillus iners Peptoniphilus BVAB3 S130 Lactobacillus iners Lactobacillus iners Dialister Leptotrichia.amnionii S135 Lactobacillus iners Lactobacillus iners Gemella S15 Gardnerella vaginalis Gardnerella vaginalis Lactobacillus.OTU2 S17 Lactobacillus iners Gardnerella vaginalis Prevotella.bivia S3 Lactobacillus iners Lactobacillus iners Prevotella Bifidobacteriaceae S35 Gardnerella vaginalis Gardnerella vaginalis Firmicutes Staphylococcus Aerococcus.christensenii S40 Lactobacillus crispatus Lactobacillus crispatus Atopobium.vaginae Bifidobacteriaceae Prevotella.genogroup.1 Anaerococcus S5 Lactobacillus iners Lactobacillus iners Eggerthella Gemella Peptoniphilus S53 Lactobacillus iners Lactobacillus iners Bacteroides BVAB2 S55 Gardnerella vaginalis Gardnerella vaginalis Prevotella.genogroup.3 S59 Lactobacillus iners Lactobacillus iners Clostridiales ABV Group Anaerococcus.tetradius S12 Lactobacillus iners Lactobacillus iners Veillonellaceae S23 Gardnerella vaginalis Gardnerella vaginalis Prevotella.buccalis Dialister S27 Gardnerella vaginalis Gardnerella vaginalis Eggerthella Anaerococcus S60 Gardnerella vaginalis Gardnerella vaginalis Peptoniphilus Peptoniphilus.asaccharolyticus S77 Gardnerella vaginalis Gardnerella vaginalis Gardnerella.vaginalis Parvimonas.micra S82 Gardnerella vaginalis Gardnerella vaginalis Veillonellaceae Healthy Group S7 Lactobacillus iners Lactobacillus iners Anaerococcus S49 Lactobacillus crispatus Lactobacillus crispatus Dialister.sp..type.3 S52 Lactobacillus jensenii Lactobacillus jensenii Clostridiales.Family.XI..Incertae.Sedis S96 Bifidobacterium bifidum Bifidobacterium bifidum Aerococcus

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

2

Table S1-2. The basic network properties (parameters) of the SDNs for the 25-mixed cohort Avg. Local Num. of Num. of Average Average Path Connected Network Network Num. of SDN ID Cluster Diameter Nodes Edges Degree Length Components Density Modularity Communities Coefficient SBV (Symptomatic BV) Group S112 68 626 18.412 0.806 4 1.617 4 0.275 0.155 19 S116 50 155 6.200 0.626 7 2.691 6 0.127 0.315 8 S127 71 287 8.085 0.710 6 2.772 6 0.115 0.263 12 S128 61 242 7.934 0.592 10 3.664 3 0.132 0.300 7 S130 42 342 16.286 0.869 5 1.467 4 0.397 0.057 5 S135 78 422 10.821 0.779 5 1.728 7 0.141 0.347 8 S15 76 284 7.474 0.568 7 3.044 2 0.100 0.454 5 S17 46 203 8.826 0.669 5 1.793 3 0.196 0.156 3 S3 12 19 3.167 0.907 2 1.136 3 0.288 0.499 3 S35 83 492 11.855 0.697 7 2.819 2 0.145 0.513 6 S40 54 185 6.852 0.771 8 3.208 3 0.129 0.370 10 S5 61 178 5.836 0.489 7 3.081 4 0.097 0.344 11 S53 57 342 12.000 0.791 6 1.861 7 0.214 0.117 8 S55 74 261 7.054 0.473 7 2.884 4 0.097 0.290 11 S59 65 328 10.092 0.604 7 2.593 4 0.158 0.204 23 Mean 59.867 291.067 9.393 0.690 6.200 2.424 4.133 0.174 0.292 9.267 Std. Err. 4.622 38.045 1.037 0.034 0.480 0.195 0.424 0.022 0.035 1.436 ABV (Asymptomatic BV) Group S12 90 609 13.533 0.739 10 3.887 3 0.152 0.388 8 S23 79 203 5.139 0.637 11 4.012 4 0.066 0.637 10 S27 63 130 4.127 0.548 10 3.697 4 0.067 0.660 8 S60 84 159 3.786 0.491 13 4.152 6 0.046 0.656 13 S77 66 175 5.303 0.689 10 3.953 5 0.082 0.629 12 S82 46 109 4.739 0.694 6 2.625 4 0.105 0.620 6 Mean 71.333 230.833 6.105 0.633 10.000 3.721 4.333 0.086 0.598 9.500 Std. Err. 6.601 76.827 1.505 0.039 0.931 0.228 0.422 0.015 0.043 1.088 HEA Group (Healthy) S7 71 413 11.634 0.624 10 3.178 1 0.166 0.243 12 S49 24 31 2.583 0.646 5 2.321 3 0.112 0.584 5 S52 94 511 10.872 0.616 11 3.800 3 0.117 0.347 7 S96 52 56 2.154 0.410 8 2.898 8 0.042 0.785 11 Mean 60.250 252.750 6.811 0.574 8.500 3.049 3.750 0.109 0.490 8.750 Std. Err. 14.823 122.562 2.571 0.055 1.323 0.307 1.493 0.025 0.122 1.652 The p-value of Kruskal-Wallis one-way analysis of variance (ANOVA) SBV vs. HEA 0.960 0.841 0.424 0.162 0.092 0.134 0.472 0.194 0.110 0.840 SBV vs. ABV 0.150 0.110 0.036 0.350 0.006 0.004 0.545 0.010 0.001 0.456 ABV vs. HEA 0.670 0.670 0.670 0.286 0.377 0.088 0.274 0.394 0.286 0.592 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

3

2. The trio motifs in the SDNs—searching for BV risk indicators

Table S2-1A. The occurrence of MDO-trios in the SDNs of the 25-mixed cohorts Trios with MDO Trios without MDO

SDN ID MDO + – – + – + + +

+ ∑ – – + + ∑ ∑ + – – – + SBV (Symptomatic BV) Group S112 Gardnerella vaginalis 103 318 421 6 427 0 1382 0 2678 4060 S116 Atopobium vaginae 31 35 66 6 72 0 131 0 100 231 S127 Lactobacillus iners 0 3 3 0 3 0 57 0 896 953 S128 Lactobacillus iners 0 19 19 0 19 0 0 0 690 690 S130 Lactobacillus iners 0 282 282 0 282 0 6 0 1995 2001 S135 Lactobacillus iners 74 195 269 6 275 0 727 0 1150 1877 S15 Gardnerella vaginalis 5 6 11 1 12 0 47 0 407 454 S17 Lactobacillus iners 0 89 89 0 89 0 0 0 470 470 S3 Lactobacillus iners 0 5 5 0 5 0 3 0 6 9 S35 Gardnerella vaginalis 2 1 3 1 4 0 170 0 1653 1823 S40 Lactobacillus crispatus 0 9 9 0 9 0 0 0 457 457 S5 Lactobacillus iners 0 37 37 0 37 0 0 0 285 285 S53 Lactobacillus iners 17 233 250 0 250 0 325 0 1375 1700 S55 Gardnerella vaginalis 4 111 115 0 115 0 24 0 432 456 S59 Lactobacillus iners 0 169 169 0 169 0 0 0 856 856 Mean 15.733 100.800 116.533 1.333 117.867 0 191.467 0 896.667 1088.133 Std. Err. 8.067 28.657 34.084 0.630 34.399 0 98.714 0 194.226 272.196 ABV (Asymptomatic BV) Group S12 Lactobacillus iners 0 184 184 0 184 0 0 0 3057 3057 S23 Gardnerella vaginalis 6 19 25 0 25 0 13 0 184 197 S27 Gardnerella vaginalis 1 6 7 1 8 0 5 0 83 88 S60 Gardnerella vaginalis 1 10 11 1 12 0 19 0 87 106 S77 Gardnerella vaginalis 18 34 52 1 53 0 43 0 148 191 S82 Gardnerella vaginalis 0 0 0 0 0 0 0 0 122 122 Mean 4.333 42.167 46.500 0.500 47.000 0 13.333 0 613.500 626.833 Std. Err. 2.883 28.775 28.508 0.224 28.428 0 6.677 0 488.946 486.377 HEA Group (Healthy) S7 Lactobacillus iners 5 25 30 0 30 0 12 0 1714 1726 S49 Lactobacillus crispatus 0 5 5 0 5 0 0 0 6 6 S52 Lactobacillus jensenii 13 147 160 0 160 0 75 0 1961 2036 S96 Bifidobacterium bifidum 0 0 0 0 0 0 1 0 12 13 Mean 4.500 44.250 48.750 0 48.750 0 22.000 0 923.250 945.250 Std. Err. 3.069 34.673 37.659 0 37.659 0 17.875 0 530.246 543.951 The p-value of Kruskal-Wallis one-way analysis of variance (ANOVA) SBV vs. HEA 0.957 0.250 0.250 0.195 0.250 - 0.576 - 0.803 0.689 SBV vs. ABV 0.869 0.275 0.293 0.787 0.293 - 0.341 - 0.087 0.043 ABV vs. HEA 0.825 0.748 0.748 0.109 0.748 - 1.000 - 0.670 0.670 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

4

Table S2-1B The occurrence of “MDO-trios with a handle” in the SDNs of the 25-mixed cohorts Single-Link MDO Double-Link MDO Triple-Link MDO

SDN ID – + + – + + + – + ∑ – – + ∑ – – + + ∑ – – – + SBV (Symptomatic BV) Group S112 48 8 56 449 117 4 570 2074 1109 144 4 3331 S116 12 1 13 30 10 1 41 43 82 32 4 161 S127 374 7 381 56 0 0 56 1 0 0 0 1 S128 283 0 283 128 0 0 128 25 0 0 0 25 S130 0 0 0 58 0 0 58 1940 0 0 0 1940 S135 0 0 0 90 45 4 139 956 584 94 4 1638 S15 78 8 86 26 4 1 31 4 4 2 0 10 S17 56 0 56 147 0 0 147 246 0 0 0 246 S3 0 0 0 3 0 0 3 2 0 0 0 2 S35 241 131 372 17 34 3 54 0 1 0 0 1 S40 165 0 165 34 0 0 34 7 0 0 0 7 S5 68 0 68 130 0 0 130 64 0 0 0 64 S53 2 3 5 218 18 0 236 1322 126 0 0 1448 S55 40 0 40 49 0 0 49 334 6 0 0 340 S59 42 0 42 176 0 0 176 606 0 0 0 606 Mean 93.933 10.533 104.467 107.400 15.200 0.867 123.467 508.267 127.467 18.133 0.800 654.667 Std. Err. 30.283 8.642 34.435 29.439 8.119 0.389 36.039 187.688 80.175 11.056 0.428 256.201 ABV (Asymptomatic BV) Group S12 324 0 324 1286 0 0 1286 1017 0 0 0 1017 S23 25 2 27 18 2 0 20 21 7 0 0 28 S27 20 0 20 19 0 1 20 4 0 0 0 4 S60 30 0 30 14 0 0 14 4 0 0 0 4 S77 13 0 13 10 1 1 12 38 36 5 0 79 S82 0 0 0 0 0 0 0 0 0 0 0 0 Mean 68.667 0.333 69.000 224.500 0.500 0.333 225.333 180.667 7.167 0.833 0 188.667 Std. Err. 51.244 0.333 51.189 212.319 0.342 0.211 212.154 167.368 5.879 0.833 0 166.110 HEA Group (Healthy) S7 691 0 691 243 4 0 247 34 8 0 0 42 S49 2 0 2 1 0 0 1 1 0 0 0 1 S52 359 0 359 818 13 0 831 648 62 0 0 710 S96 1 1 2 0 0 0 0 0 0 0 0 0 Mean 263.250 0.250 263.500 265.500 4.250 0 269.750 170.750 17.500 0 0 188.250 Std. Err. 165.621 0.250 165.489 192.833 3.065 0 195.898 159.279 14.953 0 0 174.192 The p-value of Kruskal-Wallis one-way analysis of variance (ANOVA) SBV vs. HEA 0.581 0.418 0.688 0.920 0.956 0.196 0.920 0.271 1.000 0.262 0.343 0.229 SBV vs. ABV 0.458 0.246 0.390 0.062 0.423 0.780 0.043 0.258 0.546 0.567 0.248 0.243 ABV vs. HEA 0.670 0.879 0.669 0.915 0.337 0.221 0.915 0.748 0.471 0.414 - 0.748

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

5

Table S2-2. The 951 trios that occurred across 4 or more SDNs (subjects) of the 25-mixed cohort This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S2-3. The 71 trios occurred in 7 or more SDNs, and their relationships with MDO in the 25-mixed cohort. Note there were 26 BVAB-trios (all their nodes are BVAB), 12 trios occurred only in the SBV group, and 43 occurred only in SBV and ABV groups. This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S2-4. The 15 special trios, including the 12 trios occurred exclusively in the SBV group and 3 trios occurred in 50% or more BV subjects (BV=SBV+ABV) This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S2-5. The trios occurred in 50% or more subjects in each of three groups This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

6

3. Core/periphery species in the SDNs

Table S3-1. The core/periphery properties in the SDNs of the 25-mixed cohort Mean of Core/Periphery Density Matrix P/N Ratio Nested Ratio of Dominance SDN ρ -ness C/(C+P) Peri- Peri- B11 B12(21) B22 Whole Core C-P (S) Core Whole phery phery SBV (Symptomatic BV) Group S112 0.742 0.500 0.854 0.069 0.118 3.340 2.685 Inf 4.714 0.400 7.946 9.944 17.249 S116 0.602 0.360 0.654 0.040 0.063 2.020 1.326 5.200 6.667 0.187 5.705 8.016 16.062 S127 0.660 0.352 0.683 0.027 0.048 15.824 17.636 49.000 5.200 0.179 16.801 29.171 42.852 S128 0.650 0.361 0.710 0.035 0.063 29.125 Inf 46.000 3.286 0.191 9.467 28.488 47.815 S130 0.868 0.619 0.942 0.060 0.083 11.179 11.750 Inf 5.250 0.531 13.177 14.879 28.295 S135 0.739 0.364 0.817 0.019 0.073 3.087 2.153 Inf 4.200 0.199 11.260 34.525 30.761 S15 0.405 0.276 0.529 0.047 0.079 9.481 21.200 8.833 4.400 0.164 10.237 33.014 45.829 S17 0.612 0.391 0.778 0.097 0.090 10.882 7.500 Inf 15.333 0.301 7.625 11.580 29.668 S3 0.561 0.500 0.733 0.000 0.467 2.000 0.833 Inf Inf 0.298 3.332 1.354 11.418 S35 0.444 0.470 0.440 0.049 0.086 15.367 16.158 Inf 6.636 0.214 18.974 22.059 30.328 S40 0.682 0.296 0.883 0.043 0.074 29.667 Inf 25.000 5.500 0.196 5.402 31.989 44.623 S5 0.570 0.279 0.691 0.048 0.050 12.615 9.444 46.000 11.000 0.165 14.204 17.868 47.320 S53 0.814 0.456 0.874 0.042 0.049 4.413 3.733 Inf 10.333 0.338 18.472 27.109 43.226 S55 0.555 0.243 0.765 0.064 0.051 4.652 5.882 3.875 4.000 0.166 14.517 23.137 34.011 S59 0.579 0.338 0.753 0.076 0.090 12.625 8.158 Inf 13.400 0.230 8.516 9.545 15.042 Mean 0.632 0.387 0.740 0.048 0.099 11.085 - - - 0.250 15.444 19.450 52.615 Std. Err. 0.033 0.027 0.034 0.006 0.027 2.282 - - - 0.027 11.317 20.133 33.569 ABV (Asymptomatic BV) Group S12 0.646 0.333 0.816 0.048 0.094 27.952 16.750 Inf 86.000 0.224 22.907 35.829 66.665 S23 0.318 0.342 0.285 0.024 0.052 8.619 6.143 68.000 4.500 0.103 13.759 11.593 26.341 S27 0.398 0.161 0.711 0.029 0.062 6.588 7.000 9.250 2.000 0.096 7.672 14.722 23.087 S60 0.327 0.250 0.314 0.020 0.034 7.316 3.714 65.000 5.500 0.073 11.476 20.688 32.920 S77 0.446 0.212 0.659 0.029 0.070 4.118 1.143 92.000 3.200 0.119 6.819 11.676 31.531 S82 0.410 0.391 0.405 0.034 0.077 53.000 61.000 28.000 Inf 0.159 4.010 17.162 29.250 Mean 0.424 0.282 0.532 0.030 0.065 17.932 15.958 - - 0.129 11.107 18.612 34.966 Std. Err. 0.049 0.036 0.092 0.004 0.008 7.851 9.265 - - 0.022 2.750 3.721 6.504 HEA Group (Healthy) S7 0.676 0.338 0.877 0.063 0.092 21.889 Inf 8.900 7.875 0.238 9.184 30.422 46.183 S49 0.431 0.208 0.800 0.074 0.088 5.000 1.667 Inf 2.500 0.175 4.636 9.673 29.856 S52 0.602 0.383 0.587 0.014 0.067 13.571 10.563 54.500 28.000 0.176 18.472 27.109 43.226 S96 0.281 0.235 0.288 0.015 0.039 12.750 Inf 8.667 6.000 0.072 5.872 19.804 33.996 Mean 0.497 0.291 0.638 0.041 0.071 13.303 - - 11.094 0.165 9.541 21.752 38.315 Std. Err. 0.088 0.041 0.132 0.016 0.012 3.452 - - 5.744 0.034 3.128 4.597 3.832 The p-value of Kruskal-Wallis one-way analysis of variance (ANOVA) SBV vs. HEA 0.230 0.089 0.617 0.689 1.000 0.271 0.192 0.290 0.549 0.162 0.549 0.764 0.549 SBV vs. ABV 0.008 0.047 0.043 0.073 0.436 0.697 0.533 0.029 0.275 0.005 0.815 0.938 0.938 ABV vs. HEA 0.522 1.000 0.522 1.000 0.670 0.831 0.134 0.165 0.286 0.394 0.670 0.670 0.201 medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

7

Table S3-2A. The OTU frequencies of the core and periphery nodes, respectively This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S3-2B. The OTU occurrence frequency in the core and periphery, respectively This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S3-3. The frequency of BVAB as core or periphery species This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S3-4A. Shared core (or periphery) OUT analysis between two SDNs from different groups This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S3-4B. Summary results of the shared core (periphery) species between each paired SDNs, from two of the SBV, ABV and HEA groups (summarized from Table S3-4A) Core

Algorithm A1 Algorithm A2 Observed Treatment Shared OTUs Expected Expected p-value p-value Shared OTUs shared OTUs Mean 5.33 32.86 <0.001 33.34 <0.001 HEA vs. ABV Std. Err. 0.99 2.20 <0.001 2.13 <0.001 Mean 5.18 34.92 <0.001 35.50 <0.001 HEA vs. SBV Std. Err. 0.60 1.53 <0.001 1.47 <0.001 Mean 7.96 34.02 <0.001 34.07 <0.001 ABV vs. SBV Std. Err. 0.40 0.94 <0.001 0.91 <0.001 Periphery Algorithm A1 Algorithm A2 Observed Treatment Expected Expected Shared OTUs p-value p-value Shared OTUs shared OTUs Mean 17.38 65.66 <0.001 59.59 <0.001 HEA vs. ABV Std. Err. 1.74 2.61 <0.001 2.18 <0.001 Mean 12.77 53.01 <0.001 50.85 <0.001 HEA vs. SBV Std. Err. 0.91 2.49 <0.001 1.65 <0.001 Mean 16.29 64.27 <0.001 56.36 <0.001 ABV vs. SBV Std. Err. 0.74 1.78 <0.001 1.19 <0.001

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

8

Table S3-5A. The observed numbers of shared core (periphery) OTUs between each paired SDNs, from two of the SBV, ABV and HEA groups This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S3-5B. The observed number of shared core (periphery) OTUs among the SBV, ABV and HEA groups (Summarized from Table S3-5A) Core Num. of cores in Num. of cores in % of cores in % of cores in Treatments Shared cores HEA ABV terms of HEA terms of ABV Mean 19.25 20 5.33 25.21 25.86 HEA vs. ABV Std. Err. 2.46 1.45 0.99 4.04 3.90 Num. of cores in Num. of cores in % of cores in % of cores in Treatments Shared cores HEA SBV terms of HEA terms of SBV Mean 19.25 22.40 5.18 27.72 22.62 HEA vs. SBV Std. Err. 1.54 0.99 0.60 2.03 2.13 Num. of cores in Num. of cores in % of cores in % of cores in Treatments Shared cores ABV SBV terms of ABV terms of SBV Mean 20 22.40 7.96 42.20 36.95 ABV vs. SBV Std. Err. 0.74 0.81 0.40 2.08 1.66

Periphery Num. of Num. of periphery Shared % of periphery in % of periphery in Treatments periphery in HEA in ABV peripheries terms of HEA terms of ABV Mean 40.75 51.17 17.38 41.91 33.61 HEA vs. ABV Std. Err. 2.97 2.34 1.74 2.45 2.68 Num. of Num. of periphery Shared % of periphery in % of periphery Treatments periphery in HEA in SBV peripheries terms of HEA in terms of SBV Mean 40.75 37.40 12.77 31.17 34.38 HEA vs. SBV Std. Err. 1.86 1.71 0.91 1.70 2.10 Num. of Num. of periphery Shared % of periphery in % of periphery in Treatments periphery in ABV in SBV peripheries terms of ABV terms of SBV Mean 51.17 37.40 16.29 32.31 44.46 ABV vs. SBV Std. Err. 1.19 1.40 0.74 1.39 1.30

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

9 4. High-salience skeletons in the SDNs

Table S4-1. Statistical properties of the high-salience skeletons in the SDNs Statistics of HSS Assortativity SDN ID

Links (%) Max Mean Std. Err Skewness Kurtosis rHSS SBV (Symptomatic BV) Group S112 72.695 0.721 0.029 0.042 8.569 110.280 -0.015 S116 60.000 0.720 0.040 0.080 5.648 38.514 -0.020 S127 50.785 0.775 0.028 0.081 7.211 57.985 -0.014 S128 44.863 0.820 0.033 0.096 6.211 42.610 -0.017 S130 76.887 0.786 0.048 0.057 7.516 81.850 -0.024 S135 86.398 0.455 0.026 0.023 8.988 132.938 -0.013 S15 15.123 0.974 0.026 0.104 5.602 36.294 -0.013 S17 66.667 0.696 0.043 0.057 4.038 27.054 -0.022 S3 93.939 0.417 0.167 0.057 0.000 8.738 -0.091 S35 19.189 0.976 0.024 0.096 6.237 44.972 -0.012 S40 36.688 0.870 0.037 0.114 5.411 31.574 -0.019 S5 31.202 0.885 0.033 0.112 5.774 36.245 -0.017 S53 75.376 0.667 0.035 0.051 8.450 92.874 -0.018 S55 29.915 0.892 0.027 0.093 6.055 42.750 -0.014 S59 33.317 0.908 0.031 0.092 5.639 39.306 -0.016 Mean 52.870 0.771 0.042 0.077 6.090 54.932 -0.022 Std. Err. 6.472 0.043 0.009 0.007 0.560 8.834 0.005 ABV (Asymptomatic BV) Group S12 23.870 0.956 0.022 0.095 7.146 56.701 -0.011 S23 27.783 0.886 0.025 0.104 6.241 41.197 -0.013 S27 40.719 0.806 0.032 0.099 5.329 30.835 -0.016 S60 57.631 0.655 0.024 0.068 7.361 58.656 -0.012 S77 35.897 0.848 0.030 0.111 5.909 35.856 -0.015 S82 62.512 0.674 0.043 0.079 4.752 26.448 -0.022 Mean 41.402 0.804 0.030 0.093 6.123 41.616 -0.015 Std. Err. 6.410 0.049 0.003 0.007 0.414 5.470 0.002 HEA Group (Healthy) S7 16.579 1.000 0.028 0.111 6.078 41.799 -0.014 S49 70.290 0.500 0.083 0.103 2.761 8.124 -0.043 S52 19.995 0.957 0.021 0.095 6.703 50.042 -0.011 S96 83.922 0.314 0.039 0.041 4.932 28.569 -0.020 Mean 47.697 0.693 0.043 0.087 5.118 32.133 -0.022 Std. Err. 17.220 0.170 0.014 0.016 0.867 9.144 0.007 The p-value of Kruskal-Wallis one-way analysis of variance (ANOVA) SBV vs. HEA 0.689 1.000 1.000 0.549 0.368 0.271 1.000 SBV vs. ABV 0.312 0.938 0.161 0.276 0.815 0.392 0.161 ABV vs. HEA 1.000 1.000 0.670 1.000 0.394 0.522 0.670

Table S4-2. The list of high-salience skeletons that are incident on the two special OTUs they connect (e.g., MDO, MAO, hub, CST-indicator species or BVAB) with salience value ≥ 0.5 in the SDNs of the 25-mixed cohort This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

Table S4-3A. The observed number of shared skeletons between each paired SDNs, from two of the HEA, ABV and SBV groups This table was listed in Excel File: HVM-OSI-File-C (Excel Tables).xlsx

medRxiv preprint doi: https://doi.org/10.1101/2020.05.23.20104208; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

10 Table S4-3B. The observed number of shared skeletons among HEA, ABV and SBV groups (Summarized from Table S4-3A) Num. of skeleton Num. of skeleton % of skeletons in % of skeletons in Treatments Shared skeletons in HEA in ABV terms of HEA terms of ABV Mean 637.50 1001.33 91.29 16.50 9.75 HEA vs. ABV Std. Err. 73.02 95.98 10.95 1.77 1.03 Num. of skeleton Num. of skeleton % of skeletons in % of skeletons in Treatments Shared skeletons in HEA in SBV terms of HEA terms of SBV Mean 637.50 886.67 79.47 13.73 9.28 HEA vs. SBV Std. Err. 45.59 74.10 7.72 1.18 0.63 Num. of skeleton Num. of skeleton % of skeletons in % of skeletons in Treatments Shared skeletons in ABV in SBV terms of ABV terms of SBV Mean 1001.33 886.67 129.28 13.73 15.39 ABV vs. SBV Std. Err. 48.79 60.33 10.34 0.93 0.73

Table S4-4. The differential significant test for the salience values between the special trio-motifs and the whole SDN

Special Trio Whole Network p-value of SDN ID Special trio ID Wilcoxon test Mean Std. Err Mean Std. Err SBV (Symptomatic BV) Group s112 #3 #34 #40 #50 #59 0.047 0.014 0.029 0.042 0.000 s116 #1 #34 #40 0.248 0.288 0.040 0.080 0.000 s127 #1 #3 #5 #35 0.097 0.110 0.028 0.081 0.000 #1 #3 #5 #17 #22 s128 #34 #35 #39 #40 0.109 0.101 0.033 0.096 0.000 #47 #48 #58 #59 #1 #5 #17 #22 #39 s130 #40 #47 #48 #50 0.091 0.095 0.048 0.057 0.000 #58 #65 #1 #3 #22 #34 #35 s135 #39 #47 #48 #50 0.038 0.019 0.026 0.023 0.000 #58 #59 #65 #1 #3 #17 #22 #50 s15 0.317 0.222 0.026 0.104 0.000 #65 #1 #17 #22 #35 #40 s17 0.128 0.101 0.043 0.057 0.000 #50 #65 s3 #5 0.194 0.048 0.167 0.057 0.000 s35 #1 #5 #58 #59 #65 0.245 0.284 0.024 0.096 0.000 #1 #5 #17 #22 #34 s40 #35 #39 #40 #47 0.111 0.109 0.037 0.114 0.000 #48 #58 #59 #1 #3 #17 #34 #35 s5 0.106 0.066 0.033 0.112 0.000 #39 #47 #48 #17 #22 #34 #35 s53 #39 #40 #47 #48 0.050 0.016 0.035 0.051 0.000 #50 #58 #59 #65 #1 #5 #17 #22 #39 s59 #47 #48 #50 #58 0.095 0.071 0.031 0.092 0.000 #59 #65 ABV (Asymptomatic BV) Group s12 #3 0.037 0.017 0.022 0.095 0.003 s27 #1 #3 #5 0.301 0.149 0.032 0.099 0.000 s60 #1 #3 #5 0.169 0.232 0.024 0.068 0.000 s77 #1 #3 #5 0.135 0.199 0.030 0.111 0.000 s82 #1 0.065 0.022 0.043 0.079 0.110