microbial function and genital inflammation in young south ... · 3/10/2020 · 1 microbial...

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Microbial function and genital inflammation in young South African women at high risk of HIV 1

infection 2

3

Arghavan Alisoltani1,2, Monalisa T. Manhanzva1, Matthys Potgieter3,4, Christina Balle5, Liam Bell6, 4

Elizabeth Ross6, Arash Iranzadeh3, Michelle du Plessis6, Nina Radzey1, Zac McDonald6, Bridget 5

Calder4, Imane Allali3,7, Nicola Mulder3,8,9, Smritee Dabee1,10, Shaun Barnabas1, Hoyam Gamieldien1, 6

Adam Godzik2, Jonathan M. Blackburn4,8, David L. Tabb8,11,12, Linda-Gail Bekker8,13, Heather B. 7

Jaspan5,8,10, Jo-Ann S. Passmore1,8,14,15, Lindi Masson1,8,14,16, 17* 8

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1Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town 7925, South 10

Africa; 2Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA 11

92521, USA; 3Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape 12

Town, Cape Town 7925, South Africa; 4Division of Chemical and Systems Biology, Department of Integrative 13

Biomedical Sciences, University of Cape Town, Cape Town 7925, South Africa. 5Division of Immunology, 14

Department of Pathology, University of Cape Town, Cape Town 7925, South Africa; 6Centre for Proteomic and 15

Genomic Research, Cape Town 7925, South Africa; 7Laboratory of Human Pathologies Biology, Department of 16

Biology and Genomic Center of Human Pathologies, Mohammed V University in Rabat, Morocco; 8Institute of 17

Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town 7925, South Africa; 18 9Centre for Infectious Diseases Research (CIDRI) in Africa Wellcome Trust Centre, University of Cape Town, 19

Cape Town 7925, South Africa; 10Seattle Children’s Research Institute, University of Washington, WA 98101, 20

USA; 11Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Stellenbosch University, 21

Stellenbosch 7602, South Africa; 12DST–NRF Centre of Excellence for Biomedical Tuberculosis Research, 22

Stellenbosch University, Stellenbosch 7602, South Africa; 13Desmond Tutu HIV Centre, Cape Town 7925, 23

University of Cape Town, South Africa; 14Centre for the AIDS Programme of Research in South Africa, Durban 24

4013, South Africa; 15National Health Laboratory Service, Cape Town 7925, South Africa; 16Disease Elimination 25

Program, Life Sciences Discipline, Burnet Institute, Melbourne 3004, Australia; 17Central Clinical School, Monash 26

University, Melbourne 3004, Australia 27

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*Corresponding author: Dr Lindi Masson; 85 Commercial Road, Melbourne, Victoria, Australia 3004; GPO Box 29

2284, Melbourne, Victoria, Australia 3001; Email: [email protected] 30

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted September 27, 2020. ; https://doi.org/10.1101/2020.03.10.986646doi: bioRxiv preprint

https://doi.org/10.1101/2020.03.10.986646

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Abstract 1

Background: Female genital tract (FGT) inflammation is an important risk factor for HIV acquisition. 2

The FGT microbiome is closely associated with inflammatory profile, however, the relative importance 3

of microbial activities has not been established. Since proteins are key elements representing actual 4

microbial functions, this study utilized metaproteomics to evaluate the relationship between FGT 5

microbial function and inflammation in 113 young and adolescent South African women at high risk of 6

HIV infection. Women were grouped as having low, medium or high FGT inflammation by K-means 7

clustering according to pro-inflammatory cytokine concentrations. 8

Results: A total of 3,186 microbial and human proteins were identified in lateral vaginal wall swabs 9

using liquid chromatography-tandem mass spectrometry, while 94 microbial taxa were included in the 10

taxonomic analysis. Both metaproteomics and 16S rRNA gene sequencing analyses showed 11

increased non-optimal bacteria and decreased lactobacilli in women with FGT inflammatory profiles. 12

However, differences in the predicted relative abundance of most bacteria were observed between 13

16S rRNA gene sequencing and metaproteomics analyses. Bacterial protein functional annotations 14

(gene ontology) predicted inflammatory cytokine profiles more accurately than bacterial relative 15

abundance determined by 16S rRNA gene sequence analysis, as well as functional predictions based 16

on 16S rRNA gene sequence data (p<0.0001). The majority of microbial biological processes were 17

underrepresented in women with high inflammation compared to those with low inflammation, 18

including a Lactobacillus-associated signature of reduced cell wall organization and peptidoglycan 19

biosynthesis. This signature remained associated with high FGT inflammation in a subset of 74 20

women nine weeks later, was upheld after adjusting for Lactobacillus relative abundance, and was 21

associated with in vitro inflammatory cytokine responses to Lactobacillus isolates from the same 22

women. Reduced cell wall organization and peptidoglycan biosynthesis were also associated with 23

high FGT inflammation in an independent sample of ten women. 24

Conclusions: Both the presence of specific microbial taxa in the FGT and their properties and 25

activities are critical determinants of FGT inflammation. Our findings support those of previous studies 26

suggesting that peptidoglycan is directly immunosuppressive, and identify a possible avenue for 27

biotherapeutic development to reduce inflammation in the FGT. To facilitate further investigations of 28

microbial activities, we have developed the FGT-METAP application that is available at 29

(http://immunodb.org/FGTMetap/). 30


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Keywords 1

Metaproteomics; microbiome; microbial function; female genital tract; inflammation; cytokine 2


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Background 1

Despite the large reduction in AIDS-related deaths as a result of the expansion of HIV antiretroviral 2

programs, global HIV incidence has declined by only 16% since 2010 [1]. Of particular concern are 3

the extremely high rates of HIV infection in young South African women [1]. The behavioral and 4

biological factors underlying this increased risk are not fully understood, however, a key predisposing 5

factor that has been identified in this population is genital inflammation [2–4]. We have previously 6

shown that women with elevated concentrations of pro-inflammatory cytokines and chemokines in 7

their genital tracts were at higher risk of becoming infected with HIV [2]. Furthermore, the protection 8

offered by a topical antiretroviral tenofovir microbicide was compromised in women with evidence of 9

genital inflammation [3]. 10

11

The mechanisms by which female genital tract (FGT) inflammation increases HIV risk are likely 12

multifactorial, and increased genital inflammatory cytokine concentrations may facilitate the 13

establishment of a productive HIV infection by recruiting and activating HIV target cells, directly 14

promoting HIV transcription and reducing epithelial barrier integrity [5–7]. Utilizing proteomics, Arnold, 15

et al. showed that proteins associated with tissue remodeling processes were up-regulated in the 16

FGT, while protein biomarkers of mucosal barrier integrity were down-regulated in individuals with an 17

inflammatory profile [7]. This suggests that inflammation increases tissue remodeling at the expense 18

of mucosal barrier integrity, which may in turn increase HIV acquisition risk [7]. Furthermore, 19

neutrophil-associated proteins, especially certain proteases, were positively associated with 20

inflammation and may be involved in disrupting the mucosal barrier [7]. This theory was further 21

validated by the finding that antiproteases associated with HIV resistance were increased in a cohort 22

of sex workers from Kenya [8]. 23

24

Bacterial vaginosis (BV), non-optimal cervicovaginal bacteria, and sexually transmitted infections 25

(STIs), which are highly prevalent in South African women, are likely important drivers of genital 26

inflammation in this population [9,10]. BV and non-optimal bacteria have been consistently associated 27

with a marked increase in pro-inflammatory cytokine concentrations [9–12]. Conversely, women who 28

have “optimal” vaginal microbiota, primarily consisting of Lactobacillus spp., have low levels of genital 29

inflammation and a reduced risk of acquiring HIV [13]. Lactobacilli and the lactic acid that they 30


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produce may actively suppress inflammatory responses and thus may play a significant role in 1

modulating immune profiles in the FGT [14–16]. However, partly due to the complexity and diversity of 2

the microbiome, the immunomodulatory mechanisms of specific vaginal bacterial species are not fully 3

understood [17]. Further adding to this complexity is the fact that substantial differences in the 4

cervicovaginal microbiota exist by geographical location and ethnicity and that the properties of 5

different strains within particular microbial species are also highly variable [15,18]. 6

7

In addition to providing insight into host mucosal barrier function, metaproteomic studies of the FGT 8

can evaluate microbial activities and functions that may influence inflammatory profiles [19]. A recent 9

study highlighted the importance of vaginal microbial function by demonstrating that FGT bacteria, 10

such as Gardnerella vaginalis, modulate the efficacy of the tenofovir microbicide by active metabolism 11

of the drug [20]. Additionally, Zevin et al. found that bacterial functional profiles were associated with 12

epithelial barrier integrity and wound healing in the FGT [21], suggesting that microbial function may 13

also be closely linked to genital inflammation. The aim of the present study was to utilize both 14

metaproteomics and 16S rRNA gene sequencing to improve our understanding of the relationship 15

between microbial function and inflammatory profiles in the FGTs of South African women. 16

17

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Results 1

This study included 113 young and adolescent HIV-uninfected women (aged 16-22 years) residing in 2

Cape Town, South Africa [22]. Seventy-four of these women had samples and metadata available for 3

analysis at a second time-point nine weeks later. Ten women had samples available at the second 4

time-point, but not the first, and were included to validate the functional signature identified in the 5

primary analysis of this study. STI and BV prevalence were high in this cohort, with 48/113 (42%) of 6

the women having at least one STI and 56/111 (50%) of the women being BV positive by Nugent 7

scoring (Additional file 1: Table S1). The use of injectable contraceptives, BV, and chlamydia were 8

associated with increased genital inflammatory cytokines, as previously described [10,23]. 9

10

FGT metaproteome associates with BV and inflammatory cytokine profiles 11

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was conducted on the total 12

protein acquired from lateral vaginal wall swabs for all participants. Using principal component 13

analysis (PCA), it was found that women with BV clustered separately from women who were BV 14

negative according to the relative abundance of all host and microbial proteins identified (Additional 15

file 1: Fig. S1a). Women who were considered to have high levels of genital inflammation based on 16

pro-inflammatory cytokine concentrations (Additional file 1: Fig. S2) tended to cluster separately from 17

women with low inflammation, while no clear grouping was observed by STI status or chemokine 18

profiles (Additional file 1: Fig. S1b-d). After adjusting for potentially confounding variables including 19

age, contraceptives, prostate specific antigen (PSA) and co-infections, BV was associated with the 20

largest number of differentially abundant proteins, followed by inflammatory cytokine profiles, while 21

fewer associations were observed between chemokine profiles and protein relative abundance and 22

none between STI status and protein relative abundance (Additional file 1: Fig. S1e). Individual STIs 23

were also not associated with marked changes in the metaproteome, with Chlamydia trachomatis, 24

Neisseria gonorrhoeae, Trichomonas vaginalis and Mycoplasma genitalium associated with 25

significant changes in zero, five, seven and eleven proteins, respectively (after adjustment for 26

potential confounders and multiple comparisons). However, this stratified analysis is limited by the 27

small number of STI cases for some infections and the prevalence of co-infections. 28

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Differences in taxonomic assignment using metaproteomics and 16S rRNA gene sequencing 1

Of the proteins identified in lateral vaginal wall swab samples, 38.8% were human, 55.8% were 2

bacterial, 2.7% fungal, 0.4% archaeal, 0.09% of viral origin, and 2% grouped as “other” (not shown). 3

However, as the majority of taxa identified had <2 proteins detected, a more stringent cut-off was 4

applied to include only taxa with >3 detected proteins, or 2 proteins detected in multiple taxa. The final 5

curated dataset included 44% human, 55% bacterial (n=81 taxa), and 1% fungal proteins (n=13 taxa) 6

(Fig. 1a-d; Additional file 2: Table S2). When the relative abundance of the most abundant bacterial 7

taxa identified using metaproteomics was compared to the relative abundance of the most abundant 8

bacteria identified using 16S rRNA gene sequencing, a large degree of similarity was found at the 9

genus level. A total of 6/9 of the genera identified using 16S rRNA gene sequence analysis were also 10

identified using metaproteomics (Fig. 1e, f; Additional file 1: Fig. S3). As expected, both approaches 11

showed that multiple Lactobacillus species were less abundant in women with high versus low 12

inflammation, while BV-associated bacteria (including G. vaginalis, Prevotella species, Megasphaera 13

species, Sneathia amnii and Atopobium vaginae) were more abundant in women with high 14

inflammation compared to those with low inflammation (Additional file 2: Table S3). However, 15

species-level annotation differed between the 16S rRNA gene sequencing and metaproteomics 16

analyses, with metaproteomics identifying more lactobacilli at the species level, while 17

Lachnovaginosum genomospecies (previously known as BVAB1) was not identified using 18

metaproteomics. Additionally, the relative abundance of the taxa differed between 16S rRNA gene 19

sequencing and metaproteomics analyses. 20

21

Protein profiles differ between women defined as having high, medium or low FGT 22

inflammation 23

A total of 449, 165, and 39 host and microbial proteins were differentially abundant between women 24

with high versus low inflammation, medium versus high inflammation and medium versus low 25

inflammation, respectively (Additional file 2: Table S4). Microbial proteins that were more abundant in 26

women with high or medium inflammation versus low inflammation were mostly assigned to non-27

optimal bacterial taxa (including G. vaginalis, Prevotella species, Megasphaera species, S. amnii and 28

A. vaginae). Less abundant microbial proteins in women with high inflammation were primarily of 29

Lactobacillus origin (Additional file 1: Fig. S4). Of the human proteins that were differentially abundant 30


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according to inflammation status, 93 were less abundant and 132 were more abundant in women with 1

high compared to those with low inflammation. Human biological process gene ontologies (GOs) that 2

were significantly underrepresented in women with high versus low inflammation included positive 3

regulation of apoptotic signaling, establishment of endothelial intestinal barrier, ectoderm 4

development and cornification [false discovery rate (FDR) adj. p<0.0001 for all; Additional file 1: Fig. 5

S5]. Significantly overrepresented pathways in women with high versus low inflammation included 6

multiple inflammatory processes - such as chronic response to antigenic stimulus, positive regulation 7

of IL-6 production and inflammatory response (FDR adj. p<0.0001 for all; Additional file 1: Fig. S5). 8

9

Weighted correlation network analysis of microbial and host proteins identified five modules (clusters) 10

representing co-correlations between microbial and host proteins (Fig. 2a, b). The yellow module 11

included primarily L. iners proteins, while the turquoise module primarily consisted of L. crispatus and 12

host proteins. The grey and brown modules consisted entirely of host proteins and the blue module 13

included non-optimal bacteria and host proteins. Pro-inflammatory cytokines correlated inversely with 14

the Lactobacillus modules (yellow and turquoise) and positively with the grey, brown and blue 15

modules. Chemokines were significantly positively correlated with the brown and grey modules and 16

inversely with the turquoise module (Fig. 2c). Blue, yellow, turquoise and grey modules correlated 17

with BV Nugent score, while BV showed no significant correlation with the brown module (Fig. 2d). 18

The finding that the brown module did not include any microbial proteins and was not associated with 19

BV or STIs, suggests the presence of inflammatory processes independent of the microbiota. For 20

STIs, no significant correlations (p-value <0.05) with any of the five modules were detected (Fig. 2d). 21

To determine whether the co-correlations of microbial proteins with host proteins had functional 22

meaning, we profiled the top biological processes of proteins as shown in the row sidebar in Fig. 2a. 23

Host proteins that correlated negatively with the blue module (consisting of non-optimal bacteria) were 24

mostly involved in cell adhesion, cell-cell adhesion, cell-cell junction assembly, and regulation of cell-25

adhesion pathways. This suggests reduced epithelial barrier function associated with G. vaginalis, 26

Prevotella spp., Megasphaera, and A. vaginae. Host proteins involved in immune system and 27

inflammatory response pathways were positively correlated with brown and grey modules and 28

inversely associated with the turquoise module including L. crispatus. Despite variability in the 29

microbial proteins and taxa, redundant microbial functional pathways were identified across different 30


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modules, indicating that microbiota share a set of common metabolic functions (e.g. glucose 1

metabolism, protein translation and synthesis), as expected. Although most lactobacilli proteins were 2

negatively associated with BV and pro-inflammatory cytokines (see turquoise and yellow modules in 3

Fig. 2a), almost all L. iners proteins were clustered in the yellow module with slightly different 4

functional pathways from lactobacilli proteins in the turquoise module. 5

6

Microbial function predicts genital inflammation with greater accuracy than taxa relative 7

abundance 8

The accuracies of (i) microbial relative abundance (determined using 16S rRNA gene sequencing), (ii) 9

functional prediction based on 16S rRNA gene sequence data, (iii) microbial protein relative 10

abundance (determined using metaproteomics) and (iv) microbial molecular function (determined by 11

aggregation of GOs of proteins identified using metaproteomics) for prediction of genital inflammation 12

status (low, medium and high groups) were evaluated using random forest analysis. The three most 13

important species predicting genital inflammation grouping by 16S rRNA gene sequence data were 14

Sneathia sanguinegens, A. vaginae, and Lactobacillus iners (Fig. 3a). The top functional pathways 15

based on 16S rRNA gene sequence data for the identification of women with inflammation were 16

prolactin signaling, biofilm formation, and tyrosine metabolism (Fig. 3b). Similarly, the most important 17

proteins predicting inflammation grouping by metaproteomics included two L. iners proteins 18

(chaperone protein DnaK and pyrophosphate phospho-hydrolase), two A. vaginae proteins 19

(glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase), Megasphaera 20

genomosp. type 1 cold-shock DNA-binding domain protein, and L. crispatus ATP synthase subunit 21

alpha (Fig. 3c). The top molecular functions associated with genital inflammation included 22

oxidoreductase activity (of multiple proteins expressed primarily by lactobacilli, but also Prevotella and 23

Atopobium species), beta-phosphoglucomutase activity (expressed by lactobacilli) and GTPase 24

activity (of multiple proteins produced by mainly Prevotella, G. vaginalis, and some lactobacilli) as 25

illustrated in Fig. 3d. These findings suggest that the functions of key BV-associated bacteria and 26

lactobacilli are critical for determining the level of genital inflammation. It was also found that the out-27

of-bag (OOB) error rate distribution was significantly lower for molecular function, followed by protein 28

relative abundance, functional prediction based on 16S rRNA gene sequence data and then taxa 29

relative abundance (Fig. 3e). This suggests that the activities of the bacteria present in the FGT play a 30


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critical role in driving genital inflammation, in addition to the presence and relative abundance of 1

particular taxa. 2

3

Differences in microbial stress response, lactate dehydrogenase and metabolic pathways 4

between women with high versus low FGT inflammation 5

Microbial functional profiles were further investigated by comparing molecular functions, biological 6

processes and cellular component GO enrichment for detected proteins between women with low 7

versus high inflammation using differential expression analysis, adjusting for potentially confounding 8

variables including age, contraceptives, PSA and STIs (Fig. 4; Additional file 2: Table S5). The 9

majority of microbial molecular functions (90/107), biological processes (57/83) and cellular 10

components (14/23) were underrepresented in women with high compared to low genital 11

inflammation, with most of the differentially abundant metabolic processes, including peptide, 12

nucleoside, glutamine, and glycerophospholipid metabolic processes, decreased in women with 13

inflammation. The phosphoenolpyruvate-dependent sugar phosphotransferase system (a major 14

microbial carbohydrate uptake system including proteins assigned to Lactobacillus species, S. amniii, 15

A. vaginae, and G. vaginalis) was also underrepresented in women with evidence of genital 16

inflammation compared to women with low inflammation (FDR adj. p<0.0001; Fig. 4a). Additionally, L-17

lactate dehydrogenase activity was inversely associated with inflammation (Additional file 2: Table S5) 18

and both large and small ribosomal subunit cellular components were underrepresented in those with 19

high FGT inflammation (Fig. 4b). The only metabolic processes that were overabundant in women 20

with high versus low inflammation were malate and ATP metabolic processes. Overrepresented 21

microbial biological processes in women with genital inflammation also included response to oxidative 22

stress and cellular components associated with cell division (e.g. mitotic spindle midzone) and 23

ubiquitination (e.g. Doa10p ubiquitin ligase complex, Hrd1p ubiquitin ligase ERAD-L complex, RQC 24

complex; Fig. 4). 25

26

Underrepresented Lactobacillus peptidoglycan, cell wall and membrane pathways in women 27

with high versus low FGT inflammation 28

Among the top biological processes inversely associated with inflammation were cell wall 29

organization, regulation of cell shape and peptidoglycan biosynthetic process (all adj. p<0.0001; Fig. 30


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4a). The GO terms, regulation of cell shape, and peptidoglycan biosynthetic process included 1

identical proteins (e.g. D-alanylalanine synthetase, D-alanyl-D-alanine-adding enzyme and 2

Bifunctional protein GlmU), while cell wall organization included the same proteins plus D-alanyl 3

carrier protein. Although proteins involved in these processes were only detected for Lactobacillus 4

species and may thus be biomarkers of the increased relative abundance of lactobacilli, each 5

remained significantly associated with inflammation after adjusting for the relative abundance of L. 6

iners and non-iners Lactobacillus species (determined using 16S rRNA gene sequencing), as well as 7

STI status, PSA detection and hormonal contraceptive use [beta-coefficient: -1.68; 95% confidence 8

interval (CI): -2.84 to -0.52; p=0.004 for cell wall organization; beta-coefficient: -1.71; 95% CI: -2.91 to 9

-0.53; p=0.005 for both regulation of cell shape and peptidoglycan biosynthetic process]. This 10

suggests that the relationship between these processes and inflammation is independent of the 11

relative abundance of Lactobacillus species and that the cell wall and membrane properties of 12

lactobacilli may play a role in modulating FGT inflammation. Furthermore, we found that multiple cell 13

wall and membrane cellular components, including the S-layer, peptidoglycan-based cell wall, cell 14

wall and membrane, were similarly underrepresented in women with high versus low FGT 15

inflammation (Fig. 4b). Using metaproteomic data from an independent group of 10 women, we 16

similarly found that peptidoglycan biosynthetic process, cell wall organization, and regulation of cell 17

shape GOs were underrepresented in women with high inflammation (Fig. 5a). 18

19

To further investigate whether the associations between FGT inflammation and Lactobacillus 20

biological processes and cellular components were independent of Lactobacillus relative abundance, 21

we compared these GOs in BV negative women with Lactobacillus dominant communities who were 22

grouped (median splitting of samples based on the first principal component of nine pro-inflammatory 23

cytokine concentrations) as having low (n=20) versus high (n=20) inflammation (Fig. S6). 24

Interestingly, it was found that, even though only non-significant trends towards decreased relative 25

abundance of lactobacilli determined by 16S rRNA gene sequencing (p=0.73) and metaproteomics 26

(p=0.14) were observed, several previously identified GOs, including peptidoglycan-based cell wall 27

(p=0.01), cell wall organization (p=0.03), and S-layer (p=0.01), remained associated with the level of 28

inflammation (Fig. S6). This suggests an association between these biological processes and cellular 29

components that is not fully explained by the relative abundance of the lactobacilli themselves. 30


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1

In vitro confirmation of the role of Lactobacillus cell wall and membrane pathways in 2

regulating genital inflammation 3

As previous studies have suggested that the cell wall, as well as the cell membrane, may influence 4

the immunomodulatory properties of Lactobacillus species and that peptidoglycan may be directly 5

immunosuppressive [14,24], we further investigated the relationships between Lactobacillus cell wall 6

and membrane properties and inflammation using 64 Lactobacillus species that were isolated from 7

the same women [25]. From these lactobacilli, the 22 isolates that induced the lowest levels of pro-8

inflammatory cytokine production by vaginal epithelial cells and the 22 isolates that induced the 9

greatest cytokine responses were selected and their protein profiles evaluated using LC-MS/MS. Cell 10

wall organization, regulation of cell shape, and peptidoglycan biosynthetic process that were 11

associated with genital inflammation in the FGT metaproteomic analysis of vaginal swab samples 12

were also significantly overrepresented in isolates that induced low compared to high levels of 13

inflammatory cytokines in vitro (Fig. 5b-d). Furthermore, plasma membrane, cell wall and membrane 14

cellular components were similarly overrepresented in isolates that induced low versus high levels of 15

inflammation (Fig. 5e-i). The cell wall cellular component included multiple proteins involved in 16

adhesion (adhesion exoprotein, LPXTG-motif cell wall anchor domain protein, collagen-binding 17

protein, mannose-specific adhesin, putative mucin binding protein, sortase-anchored surface protein 18

and surface anchor protein). The relative abundance of this cell wall cellular component correlated 19

positively with the level of adhesion of the isolates to vaginal epithelial cells in vitro (rho=0.30, 20

p=0.0476), which in turn correlated inversely with in vitro inflammatory cytokine production by these 21

cells in response to the isolates [25]. Importantly, the cell wall organization biological process included 22

multiple proteins involved in peptidoglycan biosynthesis, which has the potential to dampen immune 23

responses in a strain-dependent manner [14,26]. 24

25

Changes in FGT metaproteomic profiles over time 26

To evaluate variations in FGT metaproteomic profiles associated with inflammatory profiles over time, 27

we compared proteins, taxa and biological process and cellular component GOs between two time-28

points nine weeks apart (n=74). The grouping according to inflammatory cytokine profile was 29

consistent at both visits for 41/74 (55%) participants, including 7/12 (58%) women who received 30


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antibiotic treatment between visits (Fig. 6a). The findings based on the second visit also confirmed the 1

role of inflammation as one of the drivers of variation in complex metaproteome data, and similar 2

patterns of data distribution according to inflammation status were observed at both visits (Fig. 6b). 3

When the proteins, taxa and functions that were most closely associated with inflammation grouping 4

(high vs. low) at the first visit, were evaluated at the second visit, overall profiles remained similar 5

between visits (Fig. 6c-e). Significantly lower relative abundance of Lactobacillus species and 6

increased non-optimal bacteria were observed in women with inflammation at the second visit (Fig. 7

6d). Similarly, cell wall organization, regulation of cell shape, and peptidoglycan biosynthetic process 8

were among the top biological processes associated with low versus high inflammation at both visits 9

(for other top GO terms see Fig. 6e). Among women assigned to the same inflammatory profile group 10

at both visits, the relative abundance of cell wall-related GO terms, malate metabolic process and 11

response to oxidative stress were highly consistent (Additional file 1: Fig. S7a-c). However, decreased 12

levels of inflammation between visits were associated with an increase in the relative abundance of 13

cell wall GOs and decreased response to oxidative stress and malate metabolic process GOs 14

(Additional file 1: Fig. S7d-f). On the other hand, increased levels of inflammation were associated 15

with decreased relative abundance of cell wall GOs and increased response to oxidative stress and 16

malate metabolic process GOs (Additional file1: Fig. S7g-i). 17

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Discussion 1

Understanding biological risk factors for HIV acquisition is critical for the development of effective HIV 2

prevention strategies. FGT inflammation, defined by elevated inflammatory cytokine levels, has been 3

identified as a key factor associated with HIV infection risk. However, the causes and underlying 4

mechanisms are not fully understood. In this study, we demonstrate that, while the presence of 5

particular microorganisms, including non-optimal bacteria and Lactobacillus species, is important for 6

modulating FGT inflammation, their activities and functions are likely of greater importance. We 7

showed that the molecular functions of bacterial proteins predicted FGT inflammation status more 8

accurately than taxa relative abundance determined using 16S rRNA gene sequencing, as well as 9

functional predictions based on 16S rRNA gene sequencing analysis. The majority of microbial 10

biological processes were underrepresented in women with high compared to low inflammation 11

(57/83), including metabolic pathways and a strong signature of reduced Lactobacillus-associated cell 12

wall organization and peptidoglycan biosynthesis. This signature remained associated with FGT 13

inflammation after adjusting for the relative abundance of lactobacilli, as well as other potential 14

confounders (such as hormone contraceptive use, semen exposure, STIs), and was also associated 15

with inflammatory cytokine induction in vaginal epithelial cells in response to Lactobacillus isolates in 16

vitro. We observed a high level of consistency between FGT metaproteomics profiles of the same 17

women at two time points nine weeks apart, particularly among those with the same level of 18

inflammation at both visits. However, cell wall organization and peptidoglycan biosynthesis increased 19

between visits in women who moved from a higher to a lower inflammation grouping and decreased in 20

women who moved from a lower to a higher inflammation grouping. 21

22

While we found a large degree of similarity between metaproteomics and 16S rRNA gene sequence 23

taxonomic assignment determined for the same women [27], important differences in the predicted 24

relative abundance of most bacteria were observed. Differences in the relative abundance of certain 25

taxa observed using metaproteomics and 16S rRNA gene sequence data is not surprising as 26

taxonomic assignment from metaproteomic approaches also indicates functional activity (measured 27

by the amount of expressed protein), rather than simply the abundance of a particular bacterial taxon. 28

Additionally, others have noted that 16S rRNA gene sequencing does not have the ability to 29

distinguish between live bacteria and transient DNA [28]. We also observed differences that are likely 30


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due to database limitations. For example, Lachnovaginosum genomospecies (previously known as 1

BVAB1) was not identified in the metaproteomics analysis. This is due to the fact that this species 2

was not present in the UniProt or NCBI databases [29]. However, the Clostridium and Ruminococcus 3

species which were identified are likely to be Lachnovaginosum genomospecies since these taxa fall 4

into the same Clostridiales order. Additionally, Clostridium and Ruminococcus species were more 5

abundant in women with high compared to low inflammation, as expected for 6

Lachnovaginosum genomospecies [10]. Metaproteomic analysis was able to identify lactobacilli to 7

species level, while sequencing of the V4 region of the 16S rRNA gene had more limited resolution for 8

this genus, as expected. 9

10

The finding that fungal protein relative abundance was substantially lower than bacterial protein 11

relative abundance is not surprising as it has been estimated that the total number of fungal cells is 12

orders of magnitude lower than the number of bacterial cells in the human body [30]. Although 13

Candida proteins were detected, the curated dataset (following removal of taxa that had only 1 protein 14

detected or 2 proteins detected in only a single sample) did not include any Candida species. This 15

may be due to the relative rarity of fungal cells in these samples and it would be interesting to 16

evaluate alternative methodology for sample processing to better resolve this population in the future 17

[31]. A limitation of microbial functional analysis using metaproteomics is that, at present, there is 18

generally sparse population of functional information in the databases for the majority of the 19

microbiome. Thus, the findings of this study will be biased toward the microbes that are better 20

annotated. Nonetheless, microbial function was closely associated with genital inflammatory profiles 21

and it is possible that this relationship may be even stronger should better annotation exist. Another 22

limitation of this study is that the in vitro Lactobacillus analysis was conducted using a transformed 23

cell line system that is a greatly simplified model and cannot recapitulate the complexity of the FGT 24

and the microbiome. It is however significant to note that similar proteomic signatures were noted 25

both in vitro and in vivo, regardless of this limitation. To enable researchers to further study 26

associations between microbial composition and function with BV, STIs, and FGT inflammation by 27

conducting detailed analysis of specific proteins, taxa and GO terms of interest, we have developed 28

the “FGT-METAP” online application. The application is available at (http://immunodb.org/FGTMetap/) 29


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and allows users to repeat the analyses presented here, as well as obtain additional information by 1

analyzing individual proteins, species, and pathways. 2

3

A significant functional signature associated with FGT inflammation grouping included Lactobacillus-4

associated cell wall, peptidoglycan and cell membrane biological processes and cellular components. 5

Although these functions were linked exclusively to Lactobacillus species, the associations with FGT 6

inflammation were upheld after adjusting for Lactobacillus relative abundance determined by 16S 7

rRNA gene sequencing and were also evident in an analysis including only women with Lactobacillus-8

dominant microbiota. This profile was confirmed when these GOs were compared between 9

Lactobacillus isolates that induced high versus low levels of cytokine production in vitro. Previous 10

studies have suggested that the peptidoglycan structure, the proteins present in the cell wall, as well 11

as the cell membrane, may influence the immunomodulatory properties of Lactobacillus species 12

[14,24]. In a murine model, administration of peptidoglycan extracted from gut lactobacilli was able to 13

rescue the mice from colitis [26]. This activity was dependent on the NOD2 pathway and correlated 14

with an upregulation of the indoleamine 2,3-dioxygenase immunosuppressive pathway. The 15

immunomodulatory properties of peptidoglycan were furthermore dependent on the strain of 16

lactobacilli from which the peptidoglycan was isolated [26]. The increase in cell wall organization and 17

peptidoglycan biosynthesis that accompanied a reduction in local inflammation suggests an increase 18

in Lactobacillus strains producing relatively large amounts of proteins involved in these processes. In 19

a previous study, we showed that adhesion of lactobacilli to vaginal epithelial cells in vitro was 20

inversely associated with cytokine responses, suggesting that direct interaction between the isolates 21

and vaginal epithelial cells is critical for immunoregulation [25]. In addition to cell wall and membrane 22

properties, L-lactate dehydrogenase activity was identified as a molecular function inversely 23

associated with inflammation. Similarly, D-lactate dehydrogenase relative abundance and D-lactate 24

production by the Lactobacillus isolates included in this study were also associated with inflammatory 25

profiles in an in vitro analysis [25]. These findings support the results of previous studies showing that 26

lactic acid has immunoregulatory properties [16]. Taken together, these findings suggest that 27

lactobacilli may modulate the inflammatory environment in the FGT through multiple mechanisms. It 28

was further found that L. crispatus was more strongly associated with low inflammation in the 29

differential abundance analysis and that L. iners was the Lactobacillus sp. most frequently detected in 30


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women with high inflammation. The co-expression analysis revealed that the majority of L. crispatus 1

proteins grouped separately to L. iners proteins. However, both the L. crispatus and the L. iners 2

modules were strongly inversely associated with inflammatory cytokine and chemokine 3

concentrations. This is interesting since L. crispatus dominance is considered optimal, while the role 4

of L. iners, the most prevalent Lactobacillus species in African women [10,11], is poorly understood 5

and it has been associated with compositional instability and transition to a non-optimal microbiota, as 6

well as increased risk of STI acquisition [32,33]. 7

8

In this study, we observed a similar host proteome profile associated with high FGT inflammation 9

compared to previous studies [7]. Multiple inflammatory pathways were overrepresented in women 10

with high versus low FGT inflammation, while signatures of reduced barrier function were observed in 11

women with high inflammation, with underabundant endothelial, ectoderm and tight junction biological 12

processes. These findings similarly suggest that genital inflammation may be associated with 13

epithelial barrier function. 14

15

Conclusions 16

The link between FGT microbial function and local inflammatory responses described in this study 17

suggests that both the presence of specific microbial taxa in the FGT and their properties and 18

activities likely play a critical role in modulating inflammation. Currently, the annotation of microbial 19

functions is sparse, but with ever-increasing amounts of high-quality, high-throughput data, the 20

available information will improve steadily. The findings of the present study contribute to our 21

understanding of the mechanisms by which the microbiota may influence local immunity, and in turn 22

alter the risk of HIV infection. Additionally, the analyses described herein identify specific microbial 23

properties that may be harnessed for biotherapeutic development. 24

25


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Methods 1

Participants 2

This study included sexually experienced HIV-negative adolescent girls and young women (16-22 3

years) from the Women’s Initiative in Sexual Health (WISH) study in Cape Town, South Africa [22]. 4

While the parent study cohort comprised 149 women, the present sub-study included 113 women who 5

had both vaginal swabs available for metaproteomics analysis and menstrual cup (MC) cervicovaginal 6

secretions available for cytokine profiling. Seventy-four of these women had specimens available at a 7

second time-point 9 weeks later (interquartile range: 9-11 weeks) for metaproteomics analysis. Of 8

these women, 12 received antibiotic treatment for laboratory-diagnosed or symptomatic STIs and/or 9

BV (doxycycline, metronidazole, azithromycin, cefixime, ceftriaxone and/or amoxicillin) between study 10

visits, while 2 received topical antifungal treatment (clotrimazole vaginal cream). Ten additional 11

women who did not have visit 1 samples available for analysis were included for validation purposes. 12

The University of Cape Town Faculty of Health Sciences Human Research Ethics Committee (HREC 13

REF: 267/2013) approved this study. Women ≥18 years provided written informed consent, those <18 14

years provided informed assent and informed consent was obtained from parents/guardians. 15

16

Definition of genital inflammation based on cytokine concentrations 17

For cytokine measurement, MCs (Softcup®, Evofem Inc, USA) were inserted by the clinician and kept 18

in place for an hour, after which they were transported to the lab at 4ºC and processed within 4 hours 19

of removal from the participants. MCs were centrifuged and the cervicovaginal secretions were re-20

suspended in phosphate buffered saline (PBS) at a ratio of 1ml of mucus: 4 ml of PBS and stored at -21

80ºC until cytokine measurement. Prior to cytokine measurement, MC secretions were pre-filtered 22

using 0.2 μm cellulose acetate filters (Sigma-Aldrich, MO, USA). The concentrations of 48 cytokines 23

were measured in MC samples using Luminex (Bio-Rad Laboratories Inc®, CA, USA) [23]. K-means 24

clustering was used to identify women with low, medium and high pro-inflammatory cytokine 25

[interleukin (IL)-1α, IL-1β, IL-6, IL-12p40, IL-12p70, tumor necrosis factor (TNF)-α, TNF-β, TNF-26

related apoptosis-inducing ligand (TRAIL), interferon (IFN)-γ] and chemokine profiles [cutaneous T-27

cell-attracting chemokine (CTACK), eotaxin, growth regulated oncogene (GRO)-α, IL-8, IL-16, IFN-γ-28

induced protein (IP)-10, monocyte chemoattractant protein (MCP)-1, MCP-3, monokine induced by 29


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IFN-γ (MIG), macrophage inflammatory protein (MIP)-1α, MIP-1β, regulated on activation, normal T 1

cell expressed and secreted (RANTES)] (Additional file 1: Fig. S2). 2

3

STI and BV diagnosis and evaluation of semen contamination 4

Vulvovaginal swab samples were screened for common STIs, including Chlamydia trachomatis, 5

Neisseria gonorrhoeae, Trichomonas vaginalis, Mycoplasma genitalium, herpes simplex virus (HSV) 6

types 1 and 2, Treponema pallidum and Haemophilus ducreyi, using real-time multiplex PCR assays 7

[22]. Lateral vaginal wall swabs were collected for BV assessment by Nugent scoring. Blood was 8

collected for HIV rapid testing (Alere Determine™ HIV-1/2 Ag/Ab Combo, Alere, USA). PSA was 9

measured in lateral vaginal wall swabs using Human Kallikrein 3/PSA Quantikine ELISA kits (R&D 10

Systems, MN, USA). 11

12

Discovery metaproteomics and analysis 13

For shotgun LC-MS/MS, lateral vaginal wall swabs were collected, placed in 1 ml PBS, transported to 14

the laboratory at 4ºC and stored at -80°C. Samples were computationally randomized for processing 15

and analysis. The stored swabs were thawed overnight at 4°C before each swab sample was 16

vortexed for 30 seconds, the mucus scraped off the swab on the side of the tube and vortexed for an 17

additional 10 seconds. The samples were then clarified by centrifugation and the protein content of 18

each supernatant was determined using the Quanti-Pro bicinchoninic acid (BCA) assay kit (Sigma-19

Aldrich, MO, USA). Equal protein amounts (100 μg) were denatured with urea exchange buffer, 20

filtered, reduced with dithiothreitol, alkylated with iodoacetamide and washed with hydroxyethyl 21

piperazineethanesulfonic acid (HEPES). Acetone precipitation/formic acid (FA; Sigma-Aldrich, MO, 22

USA) solubilisation was added as a further sample clean-up procedure. The samples were then 23

incubated overnight with trypsin (Promega, WI, USA) and the peptides were eluted with HEPES and 24

dried via vacuum centrifugation. Reversed-phase liquid chromatography was used for desalting using 25

a step-function gradient. The eluted fractions were dried via vacuum centrifugation and kept at -80°C 26

until analysis. LC-MS/MS analysis was conducted on a Q-Exactive quadrupole-Orbitrap MS (Thermo 27

Fisher Scientific, MA, USA) coupled with a Dionex UltiMate 3000 nano-UPLC system (120min per 28

sample). The solvent system included Solvent A: 0.1% Formic Acid (FA) in LC grade water (Burdick 29

and Jackson, NJ, USA); and Solvent B: 0.1% FA in Acetonitrile (ACN; Burdick & Jackson, NJ, USA). 30


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The MS was operated in positive ion mode with a capillary temperature of 320°C, and 1.95 kV 1

electrospray voltage was applied. All 187 samples were evaluated in 11 batches over a period of 3 2

months. A quality control consisting of pooled lateral vaginal swab eluants from the study participants 3

was run at least twice with every batch. Preliminary quality control analysis was conducted, and 4

flagged samples/batches were rerun. 5

6

Due to the important role of database in metaproteomic analysis, we compared two different 7

databases to identify proteins in our dataset: (i) UniProt database restricted to human and microbial 8

entries (73,910,451, release August-2017) and filtered using the MetaNovo pipeline [34]; (ii) vaginal 9

metagenome-based database with human proteins obtained from the study of Afiuni-Zadeh et al. [35]. 10

The raw data were processed using MaxQuant version 1.5.7.4 against each database, separately. 11

The detailed parameters are provided in Additional file 1: Table S6. In brief, methionine oxidation and 12

acetylation of the protein N-terminal amino acid were considered as variable modifications and 13

carbamidomethyl (C) as a fixed modification. The digestion enzyme was trypsin with a maximum of 14

two missed cleavages. The taxonomic analysis of the proteins identified using both of the databases 15

was comparable, however, the first database (filtered UniProt human and microbial entries) resulted 16

in the identification of a greater number of proteins compared to the vaginal metagenome database, 17

whilst exhibiting a high degree of similarity compared to the 16S rRNA gene sequence data. 18

Therefore, the data generated using the first database was used for downstream analysis. 19

20

For quality control analysis, raw protein intensities and log10-transformed iBAQ intensities of the 21

quality control pool, as well as the raw protein intensities of the clinical samples, were compared 22

between batches (not shown). As variation in raw intensity was observed, all data were adjusted for 23

batch number prior to downstream analysis. The cleaned and transformed iBAQ values were used to 24

identify significantly differentially abundant proteins according to inflammatory cytokine profiles, BV 25

status or STIs using the limma R package [36]. The p-values were obtained from the moderated t-test 26

after adjusting for confounding variables including age, contraceptives, PSA, and STIs. Proteins with 27

FDR adjusted p-values <0.05 and log2-transformed fold change > 1.2 or < -1.2 were considered as 28

significantly differentially abundant. To define significantly differentially abundant taxa and GOs, 29

aggregated iBAQ values of proteins of the same species or GO IDs were compared between women 30


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with low, medium and high inflammation using the limma R package. The aggregation was performed 1

separately for host and microbial proteins. The p-values along with GO IDs were uploaded to 2

REVIGO (http://revigo.irb.hr/) to visualize the top 50 biological processes with the lowest p-values. 3

4

The effects of BV, STIs and chemokine and pro-inflammatory cytokine profiles on the metaproteome 5

were investigated by PCA using the mixOmics R package [37]. The ComplexHeatmap package [38] 6

was used to generate heatmaps and cluster the samples and metaproteomes based on the 7

hierarchical and k-means clustering methods. Additionally, R packages EnhancedVolcano, weighted 8

correlation network analysis (WGCNA) [39], and FlipPlots were applied for plotting volcano plots, co-9

correlation analysis, and Sankey diagrams, respectively. Basic R functions and the ggplot2 R 10

package were used for data manipulation, transformation, normalization and generation of graphics. 11

To identify the key factors distinguishing women defined as having low, medium and high 12

inflammation in their FGTs, we used random forest analysis using the R package randomForest [40] 13

using the following settings: (i) the type of random forest was classification, (ii) the number of trees 14

was 2000, and (iii) the number of variables evaluated at each split was one-third of the total number of 15

variables [proteins/molecular function GO IDs/16S rRNA gene-based operational taxonomic units 16

(OTUs)]. To compare the accuracy of the protein-, function- and species-based models, we iterated 17

each random forest model 100 times for each of the input datasets separately. The distributions of the 18

OOBs for 100 models for each data type were then compared using t-tests. For the WGCNA analysis, 19

a microbial-host weighted co-correlation network was built using iBAQ values for all proteins and 20

modules were identified using dynamic tree cut. Correlations between proteins and modules were 21

then determined and the top taxa and biological processes assigned to each of the proteins identified. 22

Spearman’s rank correlation was used to determine correlation coefficients between individual 23

cytokines/chemokines and module eigengenes (the first principal component of the expression matrix 24

of the corresponding module) for all samples. Average Spearman’s correlation coefficients for all pro-25

inflammatory cytokines and chemokines were then determined. Similarly, Spearman’s correlation 26

coefficients were calculated between module eigengenes and Nugent scores and STIs (including C. 27

trachomatis, N. gonorrhoeae, T. vaginalis, M. genitalium and active HSV-2). 28

29

30


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16S rRNA gene sequencing and analysis 1

Bacterial 16S rRNA gene sequencing and analysis was conducted as previously described [27]. 2

Briefly, the V4 hypervariable region of the 16S rRNA gene was amplified using modified universal 3

primers [41]. Duplicate samples were pooled, purified using Agencourt AMPure XP beads (Beckman 4

Coulter, CA, USA) and quantified using the Qubit dsDNA HS Assay (Life Technologies, CA, USA). 5

Illumina sequencing adapters and dual-index barcodes were added to the purified amplicon products 6

using limited cycle PCR and the Nextera XT Index Kit (Illumina, CA, USA). Amplicons from 96 7

samples and controls were pooled in equimolar amounts and the resultant libraries were purified by 8

gel extraction and quantified (Qiagen, Germany). The libraries were sequenced on an Illumina MiSeq 9

platform (300 bp paired-end with v3 chemistry). Following de-multiplexing, raw reads were 10

preprocessed, merged and filtered using DADA2 [42]. Primer sequences were removed using a 11

custom python script and the reads truncated at 250 bp. Taxonomic annotation was based on a 12

customized version of SILVA reference database. Only samples with ≥ 2000 reads were included in 13

further analyses. The representative sequences for each amplicon sequence variant were BLAST 14

searched against NCBI non-redundant protein and 16S rRNA gene sequence databases for further 15

validation, as well as enrichment of the taxonomic classifications. Microbial functional predictions 16

were performed using Piphillin server [43] against the Kyoto Encyclopedia of Genes and Genomes 17

(KEGG) database (99% identity cutoff). The relative abundance of functional pathways (KEGG level 18

3) was used for RF analysis. 19

20

Lactobacillus isolation 21

Lactobacilli were isolated from cervicovaginal secretions collected using MCs by culturing in de Man 22

Rogosa and Sharpe (MRS) broth for 48 hours at 37°C under anaerobic conditions. The cultures were 23

streaked onto MRS agar plates under the same culture conditions, single colonies were picked and 24

then pre-screened microscopically by Gram staining. Matrix Assisted Laser Desorption Ionization 25

Time of Flight (MALDI-TOF) was conducted to identify the bacteria to species level. A total of 115 26

lactobacilli were isolated and stored at -80°C in a final concentration of 60% glycerol. From these, 64 27

isolates from 25 of the study participants were selected for evaluation of inflammatory profiles. 28

29

Vaginal epithelial cell stimulation and measurement of cytokine concentrations 30


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As described previously [25], vaginal epithelial cells (VK2/E6E7 ATCC® CRL-2616™; RRID:CVCL_6471) 1

were maintained in complete keratinocyte serum free media (KSFM) supplemented with 0.4 mM 2

calcium chloride, 0.05 mg/ml of bovine pituitary extract, 0.1 ng/ml human recombinant epithelial 3

growth factor and 50 U/ml penicillin and 50 U/ml streptomycin (Sigma-Aldrich, MO, USA). The VK2 4

cells were seeded into 24-well tissue culture plates, incubated at 37°C in the presence of 5% carbon 5

dioxide and grown to confluency. Lactobacilli, adjusted to 4.18 x 106 colony forming units (CFU)/ml in 6

antibiotic-free KSFM, were used to stimulate VK2 cells for 24 hours at 37°C in the presence of 5% 7

carbon dioxide. As previously described, G. vaginalis ATCC 14018 cultures standardized to 1 x 107 8

CFU/ml in antibiotic-free KSFM were used as a positive control [25]. Production of IL-6, IL-8, IL-1α, IL-9

1β, IP-10, MIP-3α, MIP-1α, MIP-1β and IL-1RA were measured using a Magnetic Luminex Screening 10

Assay kit (R&D Systems, MN, USA) and a Bio-PlexTM Suspension Array Reader (Bio-Rad 11

Laboratories Inc®, CA, USA). VK2 cell viability following bacterial stimulation was confirmed using the 12

Trypan blue exclusion assay. PCA was used to compare the overall inflammatory profiles of the 13

isolates and to select the 22 most inflammatory and 22 least inflammatory isolates for characterization 14

using proteomics. 15

16

Characterization of Lactobacillus protein expression in vitro using mass spectrometry 17

Forty-four Lactobacillus isolates were adjusted to 4.18 x 106 CFU/ml in MRS and incubated for 24 18

hours under anaerobic conditions. Following incubation, the cultures were centrifuged and the pellets 19

washed 3x with PBS. Protein was extracted by resuspending the pellets in 100 mM triethylammonium 20

bicarbonate (TEAB; Sigma-Aldrich, MO, USA) 4% sodium dodecyl sulfate (SDS; Sigma-Aldrich, MO, 21

USA). Samples were sonicated in a sonicating water bath and subsequently incubated at 95°C for 22

10min. Nucleic acids were degraded using benzonase nuclease (Sigma-Aldrich, MO, USA) and 23

samples were clarified by centrifugation at 10 000xg for 10 min. Quantification was performed using 24

the Quanti-Pro BCA assay kit (Sigma-Aldrich, MO, USA). HILIC beads (ReSyn Biosciences, South 25

Africa) were washed with 250 μl wash buffer [15% ACN, 100 mM Ammonium acetate (Sigma-Aldrich, 26

MO, USA) pH 4.5]. The beads were then resuspended in loading buffer (30% ACN, 200mM 27

Ammonium acetate pH 4.5). A total of 50 μg of protein from each sample was transferred to a protein 28

LoBind plate (Merck, NJ, USA). Protein was reduced with tris (2-carboxyethyl) phosphine (Sigma-29

Aldrich, MO, USA) and alkylated with methylmethanethiosulphonate (MMTS; Sigma-Aldrich, MO, 30


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USA). HILIC magnetic beads were added at an equal volume to that of the sample and a ratio of 5:1 1

total protein and incubated on the shaker at 900 rpm for 30 min. After binding, the beads were 2

washed four times with 95% ACN. Protein was digested by incubation with trypsin for four hours and 3

the supernatant containing peptides was removed and dried down in a vacuum centrifuge. LC-MS/MS 4

analysis was conducted with a Q-Exactive quadrupole-Orbitrap MS as described above. Raw files 5

were processed with MaxQuant version 1.5.7.4 against a database including the Lactobacillus genus 6

and common contaminants. Log10-transformed iBAQ intensities were compared using Mann-Whitney 7

U test and p-values were adjusted for multiple comparisons using an FDR step-down procedure. 8

9

FGT-METAP application 10

We have developed FGT-METAP (Female Genital Tract Metaproteomics), an online application for 11

exploring and mining the FGT microbial composition and function of South African women at high risk 12

of HIV infection. The app is available at (http://immunodb.org/FGTMetap/) and allows users to repeat 13

the analyses presented here, as well as perform additional analyses and investigation using the 14

metaproteomic data generated in this study. 15

16


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Abbreviations 1

BP: biological processes 2

BV: Bacterial vaginosis 3

CC: cellular component 4

FDR: false discovery rate 5

FGT: Female genital tract 6

GO: gene ontology 7

iBAQ: intensity-based absolute quantification 8

LC-MS/MS: liquid chromatography-tandem mass spectrometry 9

MF: molecular function 10

OOB: out-of-bag 11

PCA: principal component analysis 12

PSA: prostate specific antigen 13

RF: random forest 14

STIs: sexually transmitted infections 15

WGCNA: Weighted correlation network analysis 16

17


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Declarations 1

Ethics approval and consent to participate 2

The University of Cape Town Faculty of Health Sciences Human Research Ethics Committee (HREC 3

REF: 267/2013) approved this study. Women ≥18 years provided written informed consent, those <18 4

years provided informed assent and informed consent was obtained from parents/guardians. 5

6

Consent for publication 7

Not applicable. 8

9

Availability of data and materials 10

The dataset supporting the conclusions of this article is available in the FGT-METAP application, 11

http://immunodb.org/FGTMetap/. Data are available from the corresponding author upon reasonable 12

request. The protein accession IDs provided at http://immunodb.org/FGTMetap/ reflect UniProt IDs of 13

each protein and additional information for each protein is provided based on UniProt and NCBI 14

databases. 15

16

Competing interests 17

The authors declare that they have no competing interests. 18

19

Funding 20

The laboratory work, data analysis and writing were supported by the Carnegie Corporation of New 21

York, South African National Research Foundation (NRF), the Poliomyelitis Research Foundation, 22

and the South African Medical Research Council (PI: L. Masson). The WISH cohort was supported by 23

the European and Developing Countries Clinical Trials Partnership Strategic Primer grant 24

(SP.2011.41304.038; PI J.S. Passmore) and the South African Department of Science and 25

Technology (DST/CON 0260/2012; PI J.S. Passmore). J.M.B. thanks the NRF for a SARChI Chair. 26

D.L.T. was supported by the South African Tuberculosis Bioinformatics Initiative (SATBBI), a Strategic 27

Health Innovation Partnership grant from the South African Medical Research Council and South 28

African Department of Science and Technology. 29

30


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Authors' contributions 1

AA analyzed the data, developed the FGT-METAP application, and wrote the manuscript; MTM, CB, 2

SD, and SB conducted some of the laboratory experiments, analyzed the data and contributed to 3

manuscript preparation; MP, AI, NR, IA, NM, BC and DLT analyzed the data and contributed to 4

manuscript preparation; LB, EW, MP, ZM, HG conducted some of the laboratory experiments and 5

contributed to manuscript preparation; NM and JMB supervised the data analysis and contributed to 6

manuscript preparation; AG supervised the development of FGT-METAP and hosting the application 7

and contributed to manuscript preparation; LGB managed the clinical site for the WISH study, 8

collected some of the clinical data and contributed to manuscript preparation; HJ supervised the data 9

collection, analyzed the data and contributed to manuscript preparation; JSP was Principal 10

Investigator of the WISH cohort, supervised the collection of some of the data and contributed to 11

manuscript preparation; LM conceptualized the study, supervised the data collection, analyzed some 12

of the data and wrote the manuscript. 13

14

Acknowledgements 15

We would like to acknowledge the participants of the Women’s Initiative in Health Study. 16

17

18

19

20


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Figure legends 1

2

Figure 1 Bacterial relative abundance determined using metaproteomics versus 16S rRNA 3

gene sequencing by inflammation cytokine profile. Liquid chromatography-tandem mass 4

spectrometry was used to evaluate the metaproteome in lateral vaginal wall swabs from 113 women 5

from Cape Town, South Africa. Proteins were identified using MaxQuant and a custom database 6

generated using de novo sequencing to filter the UniProt database. Taxonomy was assigned using 7

UniProt and relative abundance of each taxon was determined by aggregating the intensity-based 8

absolute quantification (iBAQ) values of all proteins identified for each taxon. (a) Proteins identified 9

were assigned to the Eukaryota and Bacteria domains. Eukaryota proteins included those assigned to 10

the fungi kingdom and metazoan subkingdom, while the bacteria domain included actinobacteria, 11

firmicutes, fusobacteria, bacteriodetes and gammaproteobacter phyla. (b) Distribution of taxa 12

identified is shown as a pie chart. (c) Number of proteins detected for taxa for which the greatest 13

number of proteins were identified. (d) Protein relative abundance per taxon for taxa with the highest 14

relative abundance. The relative abundance of the top 20 most abundant bacterial taxa identified 15

using (e) 16S rRNA gene sequencing and (f) metaproteomics is shown for all participants for whom 16

both 16S rRNA gene sequence data and metaproteomics data were generated (n=74). For 16S rRNA 17

gene sequence analysis, the V4 region was amplified and libraries sequenced on an Illumina MiSeq 18

platform. Inflammation groups were defined based on hierarchical followed by K-means clustering of 19

all women according to the concentrations of nine pro-inflammatory cytokine concentrations 20


related apoptosis-inducing ligand (TRAIL), interferon (IFN)-γ]. OTU: Operational taxonomic unit. 22

23

Figure 2 Weighted co-correlation network analysis of microbial, and human proteins. The 24

weighted correlation network analysis (WGCNA) R package was used to build a microbial-host 25

functional weighted co-correlation network using intensity-based absolute quantification (iBAQ) 26

values. a) Correlations between proteins and modules are shown by the heatmap, with positive 27

correlations shown in red and negative correlations shown in blue. Row sidebars represent the top 28

taxa and biological processes assigned to each of the proteins (no separate correlation coefficients 29

were calculated for taxa and biological processes). b) The protein dendrogram and module 30


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29

assignment are displayed, with five modules identified using dynamic tree cut. c) Spearman’s rank 1

correlation was used to determine correlation coefficients between individual cytokines/chemokines 2

and module eigengenes (the first principal component of the expression matrix of the corresponding 3

module) for all samples. Finally, we reported the average Spearman’s correlation coefficients for all 4

pro-inflammatory cytokines and chemokines. d) Similarly, Spearman’s correlation coefficients were 5

calculated between module eigengenes and Nugent scores [bacterial vaginosis (BV)] and sexually 6

transmitted infections (STIs) as a categorical variable. 7

8

Figure 3 Comparison of bacterial, bacterial protein and bacterial function relative abundance 9

for prediction of genital inflammation. Random forest analysis was used to evaluate the accuracy 10

of (a) bacterial relative abundance (determined using 16S rRNA gene sequencing; n=74), (b) 11

bacterial functional predictions based on 16 rRNA data (n=74), (c) bacterial protein relative 12

abundance (determined using metaproteomics; n=74) and (d) bacterial protein molecular function 13

relative abundance (determined by metaproteomics and aggregation of protein values assigned to the 14

same gene ontology term; n=74) for determining the presence of genital inflammation (low, medium 15

and high groups). Inflammation groups were defined based on hierarchical followed by K-means 16

clustering of women according to the concentrations of nine pro-inflammatory cytokine concentrations 17


related apoptosis-inducing ligand (TRAIL), interferon (IFN)-γ]. The bars and numbers within the bars 19

indicate the relative importance of each taxon, protein or function based on the Mean Decrease in 20

Gini Value. The sizes of bars in each panel differ based on the length of the labels. (e) Each random 21

forest model was iterated 100 times for each of the input datasets separately and the distribution of 22

the out-of-bag error rates for the 100 models were then compared using t-tests. OTU: Operational 23

taxonomic unit. 24

25

Figure 4 Microbial biological process and cellular component gene ontologies associated with 26

genital inflammatory cytokine profiles. Unsupervised hierarchical clustering of aggregated 27

intensity-based absolute quantification (iBAQ) values for microbial protein (a) biological process (BP) 28

or (b) cellular component (CC) gene ontology (GO) relative abundance (n=113). GO relative 29

abundance was determined by metaproteomics and aggregation of microbial protein iBAQ values 30


https://doi.org/10.1101/2020.03.10.986646

30

assigned to the same GO term. The heatmaps show aggregated microbial GO relative abundance 1

and the bar graphs show fold changes in aggregated log2-transformed iBAQ values (LOGFC) for each 2

microbial protein with the same (a) BP or (b) CC GO in women with high versus low inflammation. 3

Inflammation groups were defined based on hierarchical followed by K-means clustering of women 4

according to the concentrations of nine pro-inflammatory cytokine concentrations [interleukin (IL)-1α, 5

IL-1β, IL-6, IL-12p40, IL-12p70, tumor necrosis factor (TNF)-α, TNF-β, TNF-related apoptosis-6

inducing ligand (TRAIL), interferon (IFN)-γ]. Red bars indicate positive and blue bars indicate negative 7

fold changes in women with high versus low inflammation. False discovery rate adjusted p-values are 8

shown by dots, with red dots indicating low p-values. Red arrows indicate cell wall and membrane 9

processes and components. BV: Bacterial vaginosis. Proinflam cyt: Pro-inflammatory cytokine 10

11

Figure 5 Relative abundance of gene ontologies in independent samples and in inflammatory 12

versus non-inflammatory Lactobacillus isolates. (a) The top 14 microbial biological process (BP) 13

and cellular component (CC) gene ontology (GO) terms that distinguished women with low versus 14

high inflammation in the full cohort (n=113) were validated in an independent sample of ten women 15

from Cape Town, South Africa. Liquid chromatography-tandem mass spectrometry was used to 16

evaluate the metaproteome in lateral vaginal wall swabs from these women. Inflammation groups 17

were defined based on hierarchical followed by K-means clustering of these ten women according to 18

the concentrations of nine pro-inflammatory cytokine concentrations [interleukin (IL)-1α, IL-1β, IL-6, 19

IL-12p40, IL-12p70, tumor necrosis factor (TNF)-α, TNF-β, TNF-related apoptosis-inducing ligand 20

(TRAIL), interferon (IFN)-γ]. GO relative abundance was determined by aggregation of microbial 21

protein intensity-based absolute quantification (iBAQ) values assigned to a same GO term. The 22

relative abundance of the top BPs and CCs (except ATP binding cassette transporter complex which 23

was not detected in these samples) are shown as bar graphs, with blue indicating women with low 24

inflammation (n=5) and red indicating women with high inflammation (n=5). Welch’s t-test was used 25

for comparisons. *P<0.05. (b-i) Twenty-two Lactobacillus isolates that induced relatively high 26

inflammatory responses and 22 isolates that induced lower inflammatory responses were adjusted to 27

4.18 x 106 CFU/ml in bacterial culture medium and incubated for 24 hours under anaerobic 28

conditions. Following incubation, protein was extracted, digested and liquid chromatography-tandem 29

mass spectrometry was conducted. Raw files were processed with MaxQuant against a database 30


https://doi.org/10.1101/2020.03.10.986646

31

including the Lactobacillus genus and common contaminants. The iBAQ values for proteins with the 1

same gene ontologies were aggregated, log10-transformed and compared using Mann-Whitney U test. 2

Box-and-whisker plots show log10-transformed iBAQ values, with lines indicating medians and 3

whiskers extending to 1.5 times the interquartile range from the box. A false discovery rate step-down 4

procedure was used to adjust for multiple comparisons and adjusted p-values <0.05 were considered 5

statistically significant. 6

7

Figure 6 Longitudinal changes in FGT metaproteomic profiles. Liquid chromatography-tandem 8

mass spectrometry was used to evaluate the metaproteome in lateral vaginal wall swabs from 74 9

women from Cape Town, South Africa at two visits nine weeks (interquartile range: 9-11 weeks) 10

apart. (a) Sankey diagram was used to visualize changes in inflammatory cytokine profiles between 11

visits. (b) Principal component analysis (mixOmics R package) was used to group women based on 12

the log2-transformed intensity-based absolute quantification (iBAQ) values of all proteins identified at 13

both visits. Grouping is based on vaginal pro-inflammatory cytokine concentrations and each point 14

represents an individual woman. (c) Top 20 proteins (UniProt IDs) distinguishing women with and 15

without inflammation at both visits determined by moderated t-test (limma R package) and random 16

forest analysis (randomForest R package). (d) Top 10 taxa distinguishing women with and without 17

inflammation at both visits determined by moderated t-test (limma R package) and random forest 18

algorithm (randomForest R package). (e) Top 14 biological process and cellular component gene 19

ontology terms distinguishing women with and without inflammation at both visits determined by 20

moderated t-test (limma R package) and random forest algorithm (randomForest R package). 21

Positions of participants in each heatmap are fixed and the inflammation status of each participant 22

across the visits can be tracked using the row sidebars. Inflammation groups were defined based on 23

hierarchical followed by K-means clustering of nine pro-inflammatory cytokine concentrations 24


related apoptosis-inducing ligand (TRAIL), interferon (IFN)-γ]. ABC: ATP-binding cassette. Agg: 26

aggregated. BP: biological process. CC: cellular component. Infla: inflammation. PC: principal 27

component. PEP-PTS: phosphoenolpyruvate-dependent phosphotransferase system. RNDP 28

complex: ribonucleoside-diphosphate reductase complex. 29

30


https://doi.org/10.1101/2020.03.10.986646

32

1

2

Supplementary table titles and legends 3

Additional file 1: Table S1 Demographic and clinical characteristics of study participants stratified by 4

female genital tract pro-inflammatory cytokine levels 5

6

Additional file 2: Table S2 List of protein BLAST results against UniProt and NCBI nr databases 7

(DBs). Liquid chromatography-tandem mass spectrometry was used to evaluate the metaproteome in 8

lateral vaginal wall swabs from 113 women from Cape Town, South Africa. Primary assigned taxa, 9

obtained based on MaxQuant version 1.5.7.4, are presented in the second column. The curated taxa 10

(third column) were obtained after considering the top similar and frequent BLAST hits for each 11

protein. IDs: UniProt Identity codes 12

13

Additional file 2: Table S3 List of differentially abundant taxa distinguishing women with low and 14

high inflammation. Liquid chromatography-tandem mass spectrometry was used to evaluate the 15

metaproteome in lateral vaginal wall swabs from 113 women from Cape Town, South Africa. The 16

false discovery rate (FDR) corrected p-values were obtained based on the aggregated log2-17

transformed intensity-based absolute quantification of all proteins assigned to the same taxa applying 18

the moderated t-test (limma R package), after adjusting for confounding variables including age, 19

contraceptives, prostate specific antigen (PSA) and sexually transmitted infections (STIs). Taxa with 20

FDR adjusted p-values <0.05 and log2-transformed fold change > 1.2 or < -1.2 were considered as 21

differentially abundant taxa. 22

23

Additional file 2: Table S4 List of differentially abundant proteins distinguishing women with low, 24

medium and high inflammation. Liquid chromatography-tandem mass spectrometry was used to 25

evaluate the metaproteome in lateral vaginal wall swabs from 113 women from Cape Town, South 26

Africa. The false discovery rate (FDR) corrected p-values were obtained based on the log2-27

transformed intensity-based absolute quantification of each protein applying the moderated t-test 28

(limma R package), after adjusting for confounding variables including age, contraceptives, prostate 29

specific antigen (PSA) and sexually transmitted infections (STIs). Proteins with FDR adjusted p-30


https://doi.org/10.1101/2020.03.10.986646

33

values <0.05 and log2-transformed fold change > 1.2 or < -1.2 were considered as differentially 1

abundant proteins. IDs: UniProt Identity codes 2

3

Additional file 2: Table S5 List of differentially abundant molecular function (MF) gene ontology 4

terms distinguishing women with low and high inflammation. Liquid chromatography-tandem mass 5

spectrometry was used to evaluate the metaproteome in lateral vaginal wall swabs from 113 women 6

from Cape Town, South Africa. The false discovery rate (FDR) corrected p-values were obtained 7

based on the aggregated log2-transformed intensity-based absolute quantification of all proteins 8

assigned to the same MF term applying the moderated t-test (limma R package), after adjusting for 9

confounding variables including age, contraceptives, prostate specific antigen (PSA) and sexually 10

transmitted infections (STIs). Taxa with FDR adjusted p-values <0.05 and log2-transformed fold 11

change > 1.2 or < -1.2 were considered as significantly abundant MFs. 12

13

Additional file 1: Table S6 Details of MaxQuant version 1.5.7.4 parameters used for analysis of 14

metaproteomic data obtained from lateral vaginal wall swabs from 113 women from Cape Town, 15

South Africa. 16

17


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34

References 1

1. UNAIDS Data 2019. Available at: https://www.unaids.org/sites/default/files/media_asset/2019-2

UNAIDS-data_en.pdf. Accessed 20/02/20. 3

2. Masson L, Passmore JAS, Liebenberg LJ, Werner L, Baxter C, Arnold KB, et al. Genital 4

Inflammation and the Risk of HIV Acquisition in Women. Clin Infect Dis. 2015;61:260-269. 5

3. McKinnon LR, Liebenberg LJ, Yende-Zuma N, Archary D, Ngcapu S, Sivro A, et al. Genital 6

inflammation undermines the effectiveness of tenofovir gel in preventing HIV acquisition in women. 7

Nat Med. 2018;24:491. 8

4. Morrison C, Fichorova RN, Mauck C, Chen PL, Kwok C, Chipato T, et al. Cervical inflammation and 9

immunity associated with hormonal contraception, pregnancy, and HIV-1 seroconversion. J Acquir 10

Immune Defic Syndr. 2014;66:109-117. 11

5. Li Q, Estes JD, Schlievert PM, Duan L, Brosnahan AJ, Southern PJ, et al. Glycerol monolaurate 12

prevents mucosal SIV transmission. Nature. 2009;458:1034-1038. 13

6. Osborn L, Kunkel S, Nabel GJ. Tumor necrosis factor α and interleukin 1 stimulate the human 14

immunodeficiency virus enhancer by activation of the nuclear factor κB. Proc Natl Acad Sci U S A. 15

1989;86:2336-2340. 16

7. Arnold KB, Burgener A, Birse K, Romas L, Dunphy LJ, Shahabi K, et al. Increased levels of 17

inflammatory cytokines in the female reproductive tract are associated with altered expression of 18

proteases, mucosal barrier proteins, and an influx of HIV-susceptible target cells. Mucosal Immunol. 19

2016;9:194-205. 20

8. Burgener A, Rahman S, Ahmad R, Lajoie J, Ramdahin S, Mesa C, et al. Comprehensive proteomic 21

study identifies serpin and cystatin antiproteases as novel correlates of HIV-1 resistance in the 22

cervicovaginal mucosa of female Sex workers. J Proteome Res. 2011;11:5139-5149. 23

9. Masson L, Mlisana K, Little F, Werner L, Mkhize NN, Ronacher K, et al. Defining genital tract 24

cytokine signatures of sexually transmitted infections and bacterial vaginosis in women at high risk of 25

HIV infection: A cross-sectional study. Sex Transm Infect. 2014;90:580-587. 26

10. Lennard K, Dabee S, Barnabas SL, Havyarimana E, Blakney A, Jaumdally SZ, et al. Microbial 27

composition predicts genital tract inflammation and persistent bacterial vaginosis in South African 28

adolescent females. Infect Immun. 2018;86:e00410-17. 29


https://doi.org/10.1101/2020.03.10.986646

35

11. Anahtar MN, Byrne EH, Doherty KE, Bowman BA, Yamamoto HS, Soumillon M, et al. 1

Cervicovaginal bacteria are a major modulator of host inflammatory responses in the female genital 2

tract. Immunity. 2015;42: 965-976. 3

12. Deese J, Masson L, Miller W, Cohen M, Morrison C, Wang M, et al. Injectable progestin-only 4

contraception is associated with increased levels of pro-inflammatory cytokines in the female genital 5

tract. Am J Reprod Immunol. 2015;74:357-367. 6

13. Gosmann C, Anahtar MN, Handley SA, Farcasanu M, Abu-Ali G, Bowman BA, et al. 7

Lactobacillus-deficient cervicovaginal bacterial communities are associated with increased HIV 8

acquisition in young South African women. Immunity. 2017;46:29-37. 9

14. Chapot-Chartier MP, Kulakauskas S. Cell wall structure and function in lactic acid bacteria. Microb 10

Cell Fact. 2014;13:S9. 11

15. Chetwin E, Manhanzva MT, Abrahams AG, Froissart R, Gamieldien H, Jaspan H, et al. 12

Antimicrobial and inflammatory properties of South African clinical Lactobacillus isolates and vaginal 13

probiotics. Sci Rep. 2019; 9:1-15. 14

16. Hearps AC, Tyssen D, Srbinovski D, Bayigga L, Diaz DJD, Aldunate M, et al. Vaginal lactic acid 15

elicits an anti-inflammatory response from human cervicovaginal epithelial cells and inhibits 16

production of pro-inflammatory mediators associated with HIV acquisition. Mucosal Immunol. 17

2017;10:1480-1490. 18

17. McKinnon LR, Achilles SL, Bradshaw CS, Burgener A, Crucitti T, Fredricks DN, et al. The 19

Evolving Facets of Bacterial Vaginosis: Implications for HIV Transmission. AIDS Res. Hum. 20

Retroviruses. 2019. 21

18. Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SSK, McCulle SL, et al. Vaginal microbiome of 22

reproductive-age women. Proc Natl Acad Sci U S A. 2011;35:219-228. 23

19. Xiong W, Abraham PE, Li Z, Pan C, Hettich RL. Microbial metaproteomics for characterizing the 24

range of metabolic functions and activities of human gut microbiota. Proteomics. 2015;15:3424-38. 25

20. Klatt NR, Cheu R, Birse K, Zevin AS, Perner M, Noël-Romas L, et al. Vaginal bacteria modify HIV 26

tenofovir microbicide efficacy in African women. Science. 2017;356:938-945. 27

21. Zevin AS, Xie IY, Birse K, Arnold K, Romas L, Westmacott G, et al. Microbiome Composition and 28

Function Drives Wound-Healing Impairment in the Female Genital Tract. PLoS Pathog. 2016;12: 29

e1005889. 30


https://doi.org/10.1101/2020.03.10.986646

36

22. Barnabas SL, Dabee S, Passmore JAS, Jaspan HB, Lewis DA, Jaumdally SZ, et al. Converging 1

epidemics of sexually transmitted infections and bacterial vaginosis in southern African female 2

adolescents at risk of HIV. Int J STD AIDS. 2018;29:531-539. 3

23. Dabee S, Barnabas SL, Lennard KS, Jaumdally SZ, Gamieldien H, Balle C, et al. Defining 4

characteristics of genital health in South African adolescent girls and young women at high risk for 5

HIV infection. PLoS One. 2019;14:e0213975. 6

24. Fernandez EM, Valenti V, Rockel C, Hermann C, Pot B, Boneca IG, et al. Anti-inflammatory 7

capacity of selected lactobacilli in experimental colitis is driven by NOD2-mediated recognition of a 8

specific peptidoglycan-derived muropeptide. Gut. 2011;60:1050-1059. 9

25. Manhanzva MT, Abrahams AG, Gamieldien H, Froissart R, Jaspan H, Jaumdally SZ, et al. 10

Inflammatory and antimicrobial properties differ between vaginal Lactobacillus isolates from South 11

African women with non-optimal versus optimal microbiota. Sci Rep. 2020;10:1-13. 12

26. Macho Fernandez E, Pot B, Grangette C. Beneficial effect of probiotics in IBD: Are peptidogycan 13

and NOD2 the molecular key effectors? Gut Microbes. 2011;2:280-6. 14

27. Balle C, Lennard K, Dabee S, Barnabas SL, Jaumdally SZ, Gasper MA, et al. Endocervical and 15

vaginal microbiota in South African adolescents with asymptomatic Chlamydia trachomatis infection. 16

Sci Rep. 2018;8:1-9. 17

28. Lagier JC, Dubourg G, Million M, Cadoret F, Bilen M, Fenollar F, et al. Culturing the human 18

microbiota and culturomics. Nat. Rev. Microbiol. 2018;16:540-550. 19

29. Holm JB, France M, Ma B, Robinson CK, McComb E, Brotman RM, et al. Comparative 20

metagenome-assembled genome analysis of Lachnovaginosum genomospecies, formerly known as 21

BVAB1. bioRxiv. 2019;657197. 22

30. Huffnagle GB, Noverr MC. The emerging world of the fungal microbiome. Trends Microbiol. 2013. 23

31. Bianco L, Perrotta G. Methodologies and perspectives of proteomics applied to filamentous fungi: 24

From sample preparation to secretome analysis. Int. J. Mol. Sci. 2015;21:334-41. 25

32. Verstraelen H, Verhelst R, Claeys G, De Backer E, Temmerman M, Vaneechoutte M. Longitudinal 26

analysis of the vaginal microflora in pregnancy suggests that L. crispatus promotes the stability of the 27

normal vaginal microflora and that L. gasseri and/or L. iners are more conducive to the occurrence of 28

abnormal vaginal microflora. BMC Microbiol. 2009;9:116. 29


https://doi.org/10.1101/2020.03.10.986646

37

33. van Houdt R, Ma B, Bruisten SM, Ravel AGCLSJ, Vries HJC de. Lactobacillus iners-dominated 1

vaginal microbiota is associated with increased susceptibility to Chlamydia trachomatis infection in 2

Dutch women: a case–control study. Sex Transm Infect. 2018;94(2):117-23. 3

34. Potgieter MG, Nel AJM, Tabb DL, Fortuin S, Garnett S, Blackburn JM, et al. 4

<em>MetaNovo</em>: a probabilistic approach to peptide and polymorphism discovery in complex 5

mass spectrometry datasets. bioRxiv. 2019;605550. 6

35. Afiuni-Zadeh S, Boylan KLM, Jagtap PD, Griffin TJ, Rudney JD, Peterson ML, et al. Evaluating 7

the potential of residual Pap test fluid as a resource for the metaproteomic analysis of the cervical-8

vaginal microbiome. Sci Rep. 2018;8:10868. 9

36. Smyth GK. limma: Linear Models for Microarray Data. Bioinforma Comput Biol Solut Using R 10

Bioconductor. 2005. 11

37. Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: An R package for ‘omics feature selection 12

and multiple data integration. PLoS Comput Biol. 2017;13:e1005752. 13

38. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in 14

multidimensional genomic data. Bioinformatics. 2016;32:2847-9. 15

39. Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC 16

Bioinformatics. 2008;9:559. 17

40. Liaw A, Wiener M. Classification and Regression by randomForest. R News. 2002;2:18-22. 18

41. Pearce MM, Hilt EE, Rosenfeld AB, Zilliox MJ, Thomas-White K, Fok C, et al. The female urinary 19

microbiome: A comparison of women with and without urgency urinary incontinence. MBio. 2014;5, 20

e01283-14. 21

42. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-22

resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581. 23

43. Narayan NR, Weinmaier T, Laserna-Mendieta EJ, Claesson MJ, Shanahan F, Dabbagh K, et al. 24

Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA 25

sequences. BMC Genomics. 2020;21:1-12. 26

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https://doi.org/10.1101/2020.03.10.986646

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Mod

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Biological process

Modules

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BVModule−trait relationships

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MEturquoise

MEyellow

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−0.62(4e−07)

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Modules

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MEgrey

0.43(7e−06)

0.21(0.2)

0.44(4e−04)

0.42(0.004)

−0.52(5e−08)

−0.37(0.04)

−0.44(5e−06)

−0.33(0.07)

0.45(2e−04)

0.43(0.001)


https://doi.org/10.1101/2020.03.10.986646

OTU Function based on OTUs Protein Function based on proteins

3540

4550

OO

B es

timat

e of

erro

r rat

e (%

)

a bO

OB

est

imat

e of

err

or ra

te (%

)

OTU OTU-based function Protein Protein-based function

p=1.7E-94

p=9.4E-25

p=0.79

c d

e

1.631.321.030.670.650.640.630.530.530.520.520.520.490.470.460.450.440.430.420.39

Prolactin signaling Biofilm formationTyrosine metabolism Carbon metabolismPrimary bile acid biosynthesis Secondary bile acid biosynthesis Sulfur relay system Oxidative phosphorylation GABAergic synapse Photosynthesis Alanine aspartate and glutamate metabolism Epithelial cell signaling PPAR signaling Tuberculosis Chloroalkane and chloroalkene degradation Prodigiosin biosynthesis Primary immunodeficiency Neomycin kanamycin and gentamicin biosynthesis Biosynthesis of ansamycins Sphingolipid metabolism

−1 0 12.642.4

2.231.891.711.181.040.750.670.670.670.650.620.540.540.530.520.520.510.5

Sneathia sanguinegensAtopobium vaginaeLactobacillus inersGardnerella vaginalisSneathia amniiAerococcus christenseniiLachnovaginosum BVAB1CorynebacteriumLactobacillusGemella asaccharolyticaGardnerella(OTU7)Ureaplasma parvumDialisterPeptostreptococcus anaerobiusDialister micraerophilusMegasphaeraPrevotella(OTU336)FastidiosipilaMycoplasma hominisPrevotella

−2 −1 0 1 2

2.051.871.721.281.050.920.910.90.90.750.590.570.560.530.490.480.470.470.460.44

Chaperone protein DnaK Glyceraldehyde−3−phosphate dehydrogenase Phosphoglycerate kinase(A0A0E9DXN4)Cold−shock DNA−binding domain proteinPyrophosphate phospho−hydrolaseATP synthase subunit alpha Phosphocarrier, HPr familyActin, cytoplasmic 2Elongation factor Tu(C8PAS6)Triosephosphate isomerase Glutamine synthetase 2,3−bisphosphoglyceratePhosphoglycerate kinaseFructose−1,6−bisphosphate aldolase, class II Threonine−−tRNA ligase Elongation factor Tu Transcription elongation factor GreA Formate C−acetyltransferase 60 kDa chaperonin 30S ribosomal protein S13

−2 −1 0 1 23.272.921.511.221.180.840.770.70.680.650.640.640.630.630.620.540.530.520.470.47

oxidoreductase activitybeta−phosphoglucomutase activityGTPase activityglutamate−ammonia ligase activityhydrolase activityadenylosuccinate synthase activityDNA bindingamidase activityphosphoglycerate kinase activitystructural constituent of ribosometranslation elongation factor activityphosphoglucosamine mutase activityribosome bindingGTP binding5S rRNA bindingadenylate kinase activityaminoacyl−tRNA editing activityrRNA bindingD−alanine−poly(phosphoribitol) ligase activityATP binding

−2 0 2

p=6.4E-101

2.642.4

2.231.891.711.181.040.750.670.670.670.650.620.540.540.530.520.520.510.5

Sneathia sanguinegensAtopobium vaginaeLactobacillus inersGardnerella vaginalisSneathia amniiAerococcus christenseniiLachnovaginosum BVAB1CorynebacteriumLactobacillusGemella asaccharolyticaGardnerella(OTU7)Ureaplasma parvumDialisterPeptostreptococcus anaerobiusDialister micraerophilusMegasphaeraPrevotella(OTU336)FastidiosipilaMycoplasma hominisPrevotella

−2 −1 0 1 2


https://doi.org/10.1101/2020.03.10.986646

1 2

phosphoenolpyruvate−dependent sugar phosphotransferase systemcell wall organization

transcription, DNA−templatedregulation of cell shape

peptidoglycan biosynthetic processcell division

nitrogen fixationglutamine biosynthetic process

lipoteichoic acid biosynthetic processnucleoside metabolic process

'de novo' CTP biosynthetic processregulation of DNA−templated transcription, elongation

cell cycle'de novo' AMP biosynthetic process'de novo' UMP biosynthetic process

glutamine metabolic processthreonyl−tRNA aminoacylation

NAD biosynthetic processregulation of translation

deoxyribonucleotide biosynthetic process'de novo' pyrimidine nucleobase biosynthetic process

D−gluconate metabolic processtRNA threonylcarbamoyladenosine modification

carbohydrate derivative biosynthetic processprotein initiator methionine removal

tetrahydrofolate interconversionisoprenoid biosynthetic process

DNA replicationprotein dephosphorylation

glycine biosynthetic process from serinenucleotide biosynthetic process

selenocysteinyl−tRNA(Sec) biosynthetic processselenocysteine biosynthetic process

seryl−tRNA aminoacylationtRNA processing

glycerol−3−phosphate catabolic processglycerol−3−phosphate biosynthetic process

phospholipid biosynthetic processglycerophospholipid metabolic process

DNA−templated transcription, elongationalanyl−tRNA aminoacylation

actin filament reorganization involved in cell cycleER to Golgi vesicle−mediated transport

retrograde vesicle−mediated transport, Golgi to ERGolgi to plasma membrane protein transport

dTTP biosynthetic processpeptide metabolic process

tyrosyl−tRNA aminoacylationprolyl−tRNA aminoacylationdTMP biosynthetic process

D−alanine biosynthetic processone−carbon metabolic process

tetrahydrofolate biosynthetic processcellular phosphate ion homeostasis

negative regulation of phosphate metabolic processphosphate ion transport

removal of superoxide radicalsdiaminopimelate biosynthetic process

ribophagyestablishment of endoplasmic reticulum localization

positive regulation of histone H2B ubiquitinationendoplasmic reticulum membrane fusion

retrograde protein transport, ER to cytosolER−associated ubiquitin−dependent protein catabolic process

ER−associated misfolded protein catabolic processcytoplasm−proteasomal ubiquitin−dependent protein catabolic process

nucleus−proteasomal ubiquitin−dependent protein catabolic processsterol regulatory element binding protein cleavage

cellular protein complex disassemblyribosome−associated ubiquitin−dependent protein catabolic process

positive regulation of protein localization to nucleusnegative regulation of telomerase activity

nonfunctional rRNA decaymitochondria−associated ubiquitin−dependent protein catabolic process

piecemeal microautophagy of nucleusSCF complex disassembly in response to cadmium stress

protein localization to Golgi apparatusmitotic sister chromatid separation

mitotic spindle disassemblysister chromatid biorientation

ATP metabolic processmalate metabolic process

response to oxidative stress

ProinflaBV

−50 0 50

LOGFC

0

0.02

0.04

Adj.P.Val

050100150200

ProinflaHighLow

BVPositiveNotNegativeIntermediate

a

Aggregated LOG2iBAQ value

P-value

LOGFC

Proinflammatory cytokines

1 2

31

2

ProinflaBV

−20 0 20

LOGFC

0

0.02

0.04

Adj.P.Val

Spec

ies

BP

log2−iBAQ

010203040

SpeciesAerococcus christenseniiAtopobium vaginaeCoriobacteriales bacterium DNF00809Gardnerella vaginalisHomo sapiens (Human)Lactobacillus crispatusLactobacillus delbrueckiiLactobacillus hamsteriLactobacillus helveticus (Lactobacillus suntoryeus)Lactobacillus inersLactobacillus jenseniiLactobacillus johnsoniiLactobacillus kefiranofaciensMegasphaera genomosp. type_1Prevotella amniiPrevotella sp. BV3P1Prevotella timonensisRhizoctonia solaniRoseburia inulinivoransSchizosaccharomyces japonicus yFS275Sneathia amniiuncultured Ruminococcus sp.Wallemia ichthyophaga EXF−994

BPactin cytoskeleton organizationAMP salvageangiogenesisantibacterial humoral responseantimicrobial humoral immune responseantimicrobial humoral responseapoptotic processArp2/3 complex−mediated actin nucleationATP synthesis coupled proton transportbarbed−end actin filament cappingbiosynthetic processcarbohydrate derivative biosynthetic processcarbohydrate metabolic processcarboxylic acid metabolic processcell adhesioncell cyclecell divisioncell proliferationcell redox homeostasiscell wall organizationcell−cell adhesioncellular iron ion homeostasischromosome condensationcyanate catabolic processD−gluconate metabolic processde novo' AMP biosynthetic processde novo' CTP biosynthetic processde novo' pyrimidine nucleobase biosynthetic processdeoxyribonucleotide biosynthetic processfructose 1,6−bisphosphate metabolic processgluconeogenesisglucose metabolic processglutamine biosynthetic processglutamyl−tRNA aminoacylationglycine biosynthetic process from serineglycolytic processimmune responseintracellular protein transportlipoteichoic acid biosynthetic processmetabolic processNAD biosynthesis via nicotinamide riboside salvage pathwayNAD biosynthetic processnegative regulation of coagulationneutrophil degranulationnucleoside metabolic processOthersphosphoenolpyruvate−dependent sugar phosphotransferase systempost−translational protein modificationprotein dephosphorylationprotein foldingprotein initiator methionine removalprotein refoldingregulation of DNA−templated transcription, elongationregulation of transcription, DNA−templatedribosome biogenesistranscription, DNA−templatedtranslationtranslational terminationNA

ProinflaHighLowMedium


1 2

31

2

ProinflaBV

−20 0 20

LOGFC

0

0.02

0.04

Adj.P.Val

Spec

ies

BP

log2−iBAQ

010203040





BV

1 2

cell wall organizationtranscription, DNA−templated

regulation of cell shapepeptidoglycan biosynthetic process

phosphoenolpyruvate−dependent sugar phosphotransferase systemcell division

nitrogen fixationglutamine biosynthetic process

lipoteichoic acid biosynthetic processnucleoside metabolic process

'de novo' CTP biosynthetic processregulation of DNA−templated transcription, elongation

cell cycle'de novo' AMP biosynthetic process'de novo' UMP biosynthetic process

glutamine metabolic processthreonyl−tRNA aminoacylation

NAD biosynthetic processregulation of translation

deoxyribonucleotide biosynthetic process'de novo' pyrimidine nucleobase biosynthetic process

D−gluconate metabolic processtRNA threonylcarbamoyladenosine modification

carbohydrate derivative biosynthetic processprotein initiator methionine removal

tetrahydrofolate interconversionisoprenoid biosynthetic process

DNA replicationprotein dephosphorylation

glycine biosynthetic process from serinenucleotide biosynthetic process

selenocysteinyl−tRNA(Sec) biosynthetic processselenocysteine biosynthetic process

seryl−tRNA aminoacylationtRNA processing

glycerol−3−phosphate catabolic processglycerol−3−phosphate biosynthetic process

phospholipid biosynthetic processglycerophospholipid metabolic process

DNA−templated transcription, elongationalanyl−tRNA aminoacylation

actin filament reorganization involved in cell cycleER to Golgi vesicle−mediated transport

retrograde vesicle−mediated transport, Golgi to ERGolgi to plasma membrane protein transport

dTTP biosynthetic processpeptide metabolic process

tyrosyl−tRNA aminoacylationprolyl−tRNA aminoacylationdTMP biosynthetic process

D−alanine biosynthetic processone−carbon metabolic process

tetrahydrofolate biosynthetic processcellular phosphate ion homeostasis

negative regulation of phosphate metabolic processphosphate ion transport

removal of superoxide radicalsdiaminopimelate biosynthetic process

ribophagyestablishment of endoplasmic reticulum localization

positive regulation of histone H2B ubiquitinationendoplasmic reticulum membrane fusion

retrograde protein transport, ER to cytosolER−associated ubiquitin−dependent protein catabolic process

ER−associated misfolded protein catabolic processcytoplasm−proteasomal ubiquitin−dependent protein catabolic process

nucleus−proteasomal ubiquitin−dependent protein catabolic processsterol regulatory element binding protein cleavage

cellular protein complex disassemblyribosome−associated ubiquitin−dependent protein catabolic process

positive regulation of protein localization to nucleusnegative regulation of telomerase activity

nonfunctional rRNA decaymitochondria−associated ubiquitin−dependent protein catabolic process

piecemeal microautophagy of nucleusSCF complex disassembly in response to cadmium stress

protein localization to Golgi apparatusmitotic sister chromatid separation

mitotic spindle disassemblysister chromatid biorientation

ATP metabolic processmalate metabolic process

response to oxidative stress

ProinflaBV

−60

−40

−20 0 20 40

LOGFC

0

0.02

0.04

Adj.P.Val

050100150200

ProinflaHighLow


Proinflam cytBV

1 2

large ribosomal subunit

small ribosomal subunit

peptidoglycan−based cell wall

S−layer

ribonucleoside−diphosphate reductase complex

glutamyl−tRNA(Gln) amidotransferase complex

cell wall

membrane

GMP reductase complex

glycerol−3−phosphate dehydrogenase complex

endoplasmic reticulum−Golgi intermediate compartment

trans−Golgi network

cytoskeleton

RQC complex

Cdc48p−Npl4p−Vms1p AAA ATPase complex

cytosolic large ribosomal subunit

nuclear chromatin

Doa10p ubiquitin ligase complex

Hrd1p ubiquitin ligase ERAD−L complex

VCP−NPL4−UFD1 AAA ATPase complex

mitotic spindle midzone

ATP−binding cassette (ABC) transporter complex

ProinflaBV

−60

−40

−20 0

LOGFC0

0.02

0.04

Adj.P.Val

050100150200

ProinflaHighLow


P-value

LOGFC

1 2

31

2

ProinflaBV

−20 0 20

LOGFC

0

0.02

0.04

Adj.P.Val

Spec

ies

BP

log2−iBAQ

010203040





Proinflam cytBV

b

1 2

31

2

ProinflaBV

−20 0 20

LOGFC

0

0.02

0.04

Adj.P.Val

Spec

ies

BP

log2−iBAQ

010203040




BVPositiveNotNegativeIntermediateNA


https://doi.org/10.1101/2020.03.10.986646

Lactobacillus isolates inducing low inflammatory responses (n=22)Lactobacillus isolates inducing high inflammatory responses (n=22)

Reg

ulat

ion

of c

ell s

hape

BP

Pept

idog

lyca

n bi

osyn

thet

ic p

roce

ss B

P

Plas

ma

mem

bran

e C

C

Cel

l wal

l CC

Pept

idog

lyca

n-ba

sed

cell

wal

l CC

p=0.0172* p=0.0272 p=0.0327 p=0.0002*

p=0.0092* p=0.0327 p=0.1372 p=0.1372

0 1

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Inflammatory response

Mem

bran

e re

lativ

e ab

unda

nce

0 1

1.0

1.2

1.4

1.6

1.8


Cel

l wal

l rel

ative

abu

ndan

ce

0 1

0.8

1.0

1.2

1.4

1.6


Pept

idog

lyca

n−ba

sed

cell

wall

rela

tive

abun

danc

e

0 1

0.8

1.0

1.2

1.4

1.6


Pept

idog

lyca

n−ba

sed

cell

wall

rela

tive

abun

danc

e

0 1

4.0

4.2

4.4

4.6

4.8


Mem

bran

e re

lativ

e ab

unda

nce

0 1

2.0

2.1

2.2

2.3


Pept

idog

lyca

n bi

osyn

thet

ic p

roce

ss re

lativ

e ab

unda

nce

0 1

2.1

2.2

2.3

2.4


Reg

ulat

ion

of c

ell s

hape

rela

tive

abun

danc

e

S-la

yer C

C

0 1

1.9

2.0

2.1

2.2

2.3


Cel

l wal

l org

aniz

atio

n re

lativ

e ab

unda

nce

Cel

l wal

l org

aniz

atio

n BP

Mem

bran

e C

C

Log 10-

trans

form

ed G

O iB

AQva

lue

0 100 200 300

malate metabolic process

regulation of cell shape

response to oxidative stressATP metabolic process

PEP-PTS

transcription, DNA-templated

cell wall organization

peptidoglycan biosynthetic process

peptidoglycan-based cell wall

large ribosomal subunit

small ribosomal subunitS-layer

ribonucleoside-diphosphate reductase complex

CC

BP

Log2-transformed GO iBAQ value

Low (n=5)High (n=5)

**

*

*

*

a

b c d e

f g h i


https://doi.org/10.1101/2020.03.10.986646

V1 V2a b

cStandardized PC1 (29.8%)

Stan

dard

ized

PC

2 (2

0.6%

)

Visit 1 (n=74)Low Medium High

Visit 2 (n=74)

Standardized PC1 (35.5%)

Stan

dard

ized

PC

2 18

.3%

)

E3BT

Z6A0

A0E9

DWT8

A0A0

E9D

XN4

A0A0

U0X

F48

A0A0

U0X

LH1

A0A1

20D

II0A0

A135

P9T8

A0A2

28ZS

I6C

8PAE

9C

8PAS

6C

8PAV

5C

8PBF

6C

8PBW

2C

8PC

W6

D3L

UD

9D

3LVS

9D

6S62

4E1

NM

B0E1

NQ

Z0K1

MY9

8E3

BTZ6

.1A0

A0E9

DWT8

.1A0

A0E9

DXN

4.1

A0A0

U0X

F48.

1A0

A0U

0XLH

1.1

A0A1

20D

II0.1

A0A1

35P9

T8.1

A0A2

28ZS

I6.1

C8P

AE9.

1C

8PAS

6.1

C8P

AV5.

1C

8PBF

6.1

C8P

BW2.

1C

8PC

W6.

1D

3LU

D9.

1D

3LVS

9.1

D6S

624.

1E1

NM

B0.1

E1N

QZ0

.1K1

MY9

8.1

Infla

V1

Infla

V2

010203040

Infla V1HighLowMedium


Inflammation

LOG2 iBAQVisit 1 Visit 2

d

E3BT

Z6A0

A0E9

DWT8

A0A0

E9D

XN4

A0A0

U0X

F48

A0A0

U0X

LH1

A0A1

20D

II0A0

A135

P9T8

A0A2

28ZS

I6C

8PAE

9C

8PAS

6C

8PAV

5C

8PBF

6C

8PBW

2C

8PC

W6

D3L

UD

9D

3LVS

9D

6S62

4E1

NM

B0E1

NQ

Z0K1

MY9

8E3

BTZ6

.1A0

A0E9

DWT8

.1A0

A0E9

DXN

4.1

A0A0

U0X

F48.

1A0

A0U

0XLH

1.1

A0A1

20D

II0.1

A0A1

35P9

T8.1

A0A2

28ZS

I6.1

C8P

AE9.

1C

8PAS

6.1

C8P

AV5.

1C

8PBF

6.1

C8P

BW2.

1C

8PC

W6.

1D

3LU

D9.

1D

3LVS

9.1

D6S

624.

1E1

NM

B0.1

E1N

QZ0

.1K1

MY9

8.1

Infla

V1

Infla

V2

010203040


Infla V2HighLowMediumVisit 1 Visit 2

E3BT

Z6A0

A0E9

DWT8

A0A0

E9D

XN4

A0A0

U0X

F48

A0A0

U0X

LH1

A0A1

20D

II0A0

A135

P9T8

A0A2

28ZS

I6C

8PAE

9C

8PAS

6C

8PAV

5C

8PBF

6C

8PBW

2C

8PC

W6

D3L

UD

9D

3LVS

9D

6S62

4E1

NM

B0E1

NQ

Z0K1

MY9

8E3

BTZ6

.1A0

A0E9

DWT8

.1A0

A0E9

DXN

4.1

A0A0

U0X

F48.

1A0

A0U

0XLH

1.1

A0A1

20D

II0.1

A0A1

35P9

T8.1

A0A2

28ZS

I6.1

C8P

AE9.

1C

8PAS

6.1

C8P

AV5.

1C

8PBF

6.1

C8P

BW2.

1C

8PC

W6.

1D

3LU

D9.

1D

3LVS

9.1

D6S

624.

1E1

NM

B0.1

E1N

QZ0

.1K1

MY9

8.1

Infla

V1

Infla

V2

010203040



Inflammation

Agg. LOG2 iBAQ

e

Infla

V1

Infla

V2

Infla

V1

Infla

V2

Infla

V1

Infla

V2

E3BT

Z6A0

A0E9

DWT8

A0A0

E9D

XN4

A0A0

U0X

F48

A0A0

U0X

LH1

A0A1

20D

II0A0

A135

P9T8

A0A2

28ZS

I6C

8PAE

9C

8PAS

6C

8PAV

5C

8PBF

6C

8PBW

2C

8PC

W6

D3L

UD

9D

3LVS

9D

6S62

4E1

NM

B0E1

NQ

Z0K1

MY9

8E3

BTZ6

.1A0

A0E9

DWT8

.1A0

A0E9

DXN

4.1

A0A0

U0X

F48.

1A0

A0U

0XLH

1.1

A0A1

20D

II0.1

A0A1

35P9

T8.1

A0A2

28ZS

I6.1

C8P

AE9.

1C

8PAS

6.1

C8P

AV5.

1C

8PBF

6.1

C8P

BW2.

1C

8PC

W6.

1D

3LU

D9.

1D

3LVS

9.1

D6S

624.

1E1

NM

B0.1

E1N

QZ0

.1K1

MY9

8.1

Infla

V1

Infla

V2

010203040



Inflammation

Agg. LOG2 iBAQ

malate.

metabo

lic.pro

cess

respo

nse.t

o.oxid

ative

.stres

s

PEP.PTS

trans

cripti

on..D

NA.templa

ted

regula

tion.o

f.cell.

shap

e

cell.w

all.org

aniza

tion

pepti

dogly

can.b

iosyn

thetic.

proce

ss

membra

ne

pepti

dogly

can.b

ased

.cell.w

all

cell.w

all

S.laye

r

ABC.trans

porte

r.com

plex

malate.

metabo

lic.pro

cess.

1

respo

nse.t

o.oxid

ative

.stres

s.1

PEP.PTS.1

trans

cripti

on..D

NA.templa

ted.1

regula

tion.o

f.cell.

shap

e.1

cell.w

all.org

aniza

tion.1

pepti

dogly

can.b

iosyn

thetic.

proce

ss.1

membra

ne.1

pepti

dogly

can.b

ased

.cell.w

all.1

cell.w

all.1

S.laye

r.1

ABC.trans

porte

r.com

plex.1

Infla

V1

Infla

V2

050100150200



Visit 1 Visit 2

−2

−1

0

1

2

−1 0 1 2standardized PC1 (29.8% explained var.)

stan

dard

ized

PC2

(20.

6% e

xpla

ined

var

.)

High

Low

Medium

−2

−1

0

1

2

−2 −1 0 1standardized PC1 (35.5% explained var.)

stan

dard

ized

PC2

(18.

3% e

xpla

ined

var

.)

High

Low

Medium

A0A0

E9E2

G5

A0A0

J8F9

A7A0

A0U0

XF48

A0A0

U0XL

H1A0

A0U0

XMC3

A0A0

U0XX

74A0

A120

DII0

A0A1

33NZ

Q8

A0A1

35Z8

S8A0

A1C2

D6A4

C8PA

E9D3

LTX3

D3LU

F4D3

LVS9

D3LW

49D6

S624

E1G

VQ0

E1G

WR9

E1NP

90K1

NQK4

A0A0

E9E2

G5.

1A0

A0J8

F9A7

.1A0

A0U0

XF48

.1A0

A0U0

XLH1

.1A0

A0U0

XMC3

.1A0

A0U0

XX74

.1A0

A120

DII0

.1A0

A133

NZQ

8.1

A0A1

35Z8

S8.1

A0A1

C2D6

A4.1

C8PA

E9.1

D3LT

X3.1

D3LU

F4.1

D3LV

S9.1

D3LW

49.1

D6S6

24.1

E1G

VQ0.

1E1

GW

R9.1

E1NP

90.1

K1NQ

K4.1

Infla

V1

Infla

V2

010203040


Infla V2HighLowMediumA0

A0E9

E2G

5A0

A0J8

F9A7

A0A0

U0XF

48A0

A0U0

XLH1

A0A0

U0XM

C3A0

A0U0

XX74

A0A1

20DI

I0A0

A133

NZQ

8A0

A135

Z8S8

A0A1

C2D6

A4C8

PAE9

D3LT

X3D3

LUF4

D3LV

S9D3

LW49

D6S6

24E1

GVQ

0E1

GW

R9E1

NP90

K1NQ

K4A0

A0E9

E2G

5.1

A0A0

J8F9

A7.1

A0A0

U0XF

48.1

A0A0

U0XL

H1.1

A0A0

U0XM

C3.1

A0A0

U0XX

74.1

A0A1

20DI

I0.1

A0A1

33NZ

Q8.

1A0

A135

Z8S8

.1A0

A1C2

D6A4

.1C8

PAE9

.1D3

LTX3

.1D3

LUF4

.1D3

LVS9

.1D3

LW49

.1D6

S624

.1E1

GVQ

0.1

E1G

WR9

.1E1

NP90

.1K1

NQK4

.1

Infla

V1

Infla

V2

010203040



Lacto

bacill

us in

ers

Lacto

bacill

us cr

ispatu

s

Lacto

bacill

us de

lbrue

ckii

Lacto

bacill

us je

nsen

ii

Lacto

bacill

us jo

hnso

nii

Gardne

rella

vagin

alis

Megas

phae

rasp

.

Prevote

llaam

nii

Atopob

iumva

ginae

Sneath

iaam

nii

Lacto

bacill

us in

ers

Lacto

bacill

us cr

ispatu

s

Lacto

bacill

us de

lbrue

ckii

Lacto

bacill

us je

nsen

ii

Lacto

bacill

us jo

hnso

nii

Gardne

rella

vagin

alis

Megas

phae

rasp

.

Prevote

llaam

nii

Atopob

iumva

ginae

Sneath

iaam

nii

Lact

obac

illus.

iner

sLa

ctob

acillu

s.cr

ispa

tus

Lact

obac

illus.

jens

enii

Lact

obac

illus.

john

soni

iLa

ctob

acillu

s.de

lbru

ecki

iG

ardn

erel

la.v

agin

alis

Meg

asph

aera

.gen

omos

p..ty

pe_1

Prev

otel

la.a

mni

iAt

opob

ium

.vag

inae

Snea

thia

.am

nii

Lact

obac

illus.

iner

s.1

Lact

obac

illus.

cris

patu

s.1

Lact

obac

illus.

jens

enii.

1La

ctob

acillu

s.jo

hnso

nii.1

Lact

obac

illus.

delb

ruec

kii.1

Gar

dner

ella

.vag

inal

is.1

Meg

asph

aera

.gen

omos

p..ty

pe_1

.1Pr

evot

ella

.am

nii.1

Atop

obiu

m.v

agin

ae.1

Snea

thia

.am

nii.1

Infla

V1

Infla

V2

0200040006000



mala

te m

etab

olic p

roce

ss

resp

onse

to o

xidat

ive st

ress

PEP-

PTS

ATP

met

aboli

c pro

cess

regu

lation

of c

ell sh

ape

cell w

all o

rgan

izatio

n

pept

idogly

can

biosy

nthe

tic p

roce

ss

pept

idogly

can-

base

d ce

ll wall

S-lay

er

ABC

trans

porte

r com

plex

large

ribo

som

al su

bunit

RNDP

com

plex

trans

cript

ion, D

NA-te

mpla

ted

small

ribo

som

al su

bunit

mala

te m

etab

olic p

roce

ss

resp

onse

to o

xidat

ive st

ress

PEP-

PTS

ATP

met

aboli

c pro

cess

regu

lation

of c

ell sh

ape

cell w

all o

rgan

izatio

n

pept

idogly

can

biosy

nthe

tic p

roce

ss

pept

idogly

can-

base

d ce

ll wall

S-lay

er

ABC

trans

porte

r com

plex

large

ribo

som

al su

bunit

RNDP

com

plex

trans

cript

ion, D

NA-te

mpla

ted

small

ribo

som

al su

bunit

CCBP

Text color

mala

te.m

eta

bolic

.pro

cess

resp

onse

.to.

oxid

ativ

e.st

ress

ATP

.meta

bolic

.pro

cess

regula

tion.o

f.cell.

shape

phosp

hoenolp

yruva

te.d

ependent.su

gar.phosp

hotr

ansf

era

se.s

yste

mtr

ansc

riptio

n..D

NA

.tem

pla

ted

cell.

wall.

org

aniz

atio

npeptid

ogly

can.b

iosy

nth

etic

.pro

cess

ATP

.bin

din

g.c

ass

ette.

.AB

C..tr

ansp

ort

er.co

mple

xpeptid

ogly

can.b

ase

d.c

ell.

wall

larg

e.ri

boso

mal.s

ubu

nit

small.

riboso

mal.s

ubu

nit

S.la

yer

ribonu

cleosi

de.

dip

hosp

hate

.reduct

ase

.com

ple

xm

ala

te.m

eta

bolic

.pro

cess

.1re

sponse

.to.

oxid

ativ

e.st

ress

.1AT

P.m

eta

bolic

.pro

cess

.1re

gula

tion.o

f.cell.

shape.

1

phosp

hoenolp

yruva

te.d

ependent.su

gar.phosp

hotr

ansf

era

se.s

yste

m.1

transc

riptio

n..D

NA

.tem

pla

ted.1

cell.

wall.

org

aniz

atio

n.1

peptid

ogly

can.b

iosy

nth

etic

.pro

cess

.1

ATP

.bin

din

g.c

ass

ette.

.AB

C..tr

ansp

ort

er.co

mple

x.1

peptid

ogly

can.b

ase

d.c

ell.

wall.

1la

rge.

riboso

mal.s

ubu

nit.

1sm

all.

riboso

mal.s

ubu

nit.

1S

.laye

r.1

ribonu

cleosi

de.

dip

hosp

hate

.reduct

ase

.com

ple

x.1

Infla

V1

Infla

V2

Tre

atm

en

t

050100150200



TreatmentAntibiotic treatedAntifungal treatedNo treatment

Trea

tmen

tTr

eatm

ent

No Antibiotic treatment

High High

Medium

Low

Medium

Low

11

519

13

8

4

10

1

V1 V2Antibiotic treatment

High High

Medium

Low

Medium

Low

1

111

6

1

1

Lacto

bacil

lus.in

ers

Lacto

bacil

lus.cr

ispatu

sLa

ctoba

cillus

.jens

enii

Lacto

bacil

lus.jo

hnso

niiLa

ctoba

cillus

.delbr

ueck

iiGa

rdne

rella

.vagin

alis

Mega

spha

era.g

enom

osp..

type_

1Pr

evote

lla.am

niiAt

opob

ium.va

ginae

Snea

thia.a

mnii

Lacto

bacil

lus.in

ers.1

Lacto

bacil

lus.cr

ispatu

s.1La

ctoba

cillus

.jens

enii.1

Lacto

bacil

lus.jo

hnso

nii.1

Lacto

bacil

lus.de

lbrue

ckii.1

Gard

nere

lla.va

ginali

s.1Me

gasp

haer

a.gen

omos

p..typ

e_1.1

Prev

otella

.amnii

.1At

opob

ium.va

ginae

.1Sn

eathi

a.amn

ii.1

Infla

V1

Infla

V2

Treatm

ent

0200040006000



TreatmentAntibiotic treatmentAntifungal treatedNo treatment

Lact

obac

illus.

iner

sLa

ctob

acillu

s.cr

ispa

tus

Lact

obac

illus.

jens

enii

Lact

obac

illus.

john

soni

iLa

ctob

acillu

s.de

lbru

ecki

iG

ardn

erel

la.v

agin

alis

Meg

asph

aera

.gen

omos

p..ty

pe_1

Prev

otel

la.a

mni

iAt

opob

ium

.vag

inae

Snea

thia

.am

nii

Lact

obac

illus.

iner

s.1

Lact

obac

illus.

cris

patu

s.1

Lact

obac

illus.

jens

enii.

1La

ctob

acillu

s.jo

hnso

nii.1

Lact

obac

illus.

delb

ruec

kii.1

Gar

dner

ella

.vag

inal

is.1

Meg

asph

aera

.gen

omos

p..ty

pe_1

.1Pr

evot

ella

.am

nii.1

Atop

obiu

m.v

agin

ae.1

Snea

thia

.am

nii.1

Infla

V1

Infla

V2

Trea

tmen

t

0200040006000




Trea

tmen

t

A0A0E9

E2G5

A0A0J8

F9A7

A0A0U0

XF48

A0A0U0

XLH1

A0A0U0

XMC3

A0A0U0

XX74

A0A120

DII0A0A

133NZQ

8A0A

135Z8S

8A0A

1C2D6A

4C8P

AE9D3L

TX3D3L

UF4D3L

VS9D3L

W49D6S

624E1G

VQ0

E1GWR9

E1NP90

K1NQK

4A0A

0E9E2G

5.1A0A

0J8F9A

7.1A0A

0U0XF4

8.1A0A

0U0XLH

1.1A0A

0U0XM

C3.1

A0A0U0

XX74.1

A0A120

DII0.1

A0A133

NZQ8.1

A0A135

Z8S8.1

A0A1C2

D6A4.1

C8PAE9

.1D3L

TX3.1

D3LUF4

.1D3L

VS9.1

D3LW49.1

D6S624

.1E1G

VQ0.1

E1GWR9.

1E1N

P90.1

K1NQK

4.1

Infla V1

Infla V2

Treatme

nt

010203040




A0A0

E9E2

G5A0

A0J8F

9A7

A0A0

U0XF

48A0

A0U0

XLH1

A0A0

U0XM

C3A0

A0U0

XX74

A0A1

20DII0

A0A1

33NZQ

8A0

A135Z

8S8

A0A1

C2D6

A4C8

PAE9

D3LTX

3D3

LUF4

D3LVS

9D3

LW49

D6S6

24E1

GVQ0

E1GW

R9E1

NP90

K1NQ

K4A0

A0E9

E2G5

.1A0

A0J8F

9A7.1

A0A0

U0XF

48.1

A0A0

U0XL

H1.1

A0A0

U0XM

C3.1

A0A0

U0XX

74.1

A0A1

20DII0.

1A0

A133N

ZQ8.1

A0A1

35Z8S

8.1A0

A1C2

D6A4

.1C8

PAE9

.1D3

LTX3.1

D3LU

F4.1

D3LVS

9.1D3

LW49.

1D6

S624.

1E1

GVQ0

.1E1

GWR9

.1E1

NP90.

1K1

NQK4

.1

Infla V

1

Infla V

2

Treatm

ent

010203040




Treatment

Lact

obac

illus.

iner

sLa

ctob

acillu

s.cr

ispa

tus

Lact

obac

illus.

jens

enii

Lact

obac

illus.

john

soni

iLa

ctob

acillu

s.de

lbru

ecki

iG

ardn

erel

la.v

agin

alis

Meg

asph

aera

.gen

omos

p..ty

pe_1

Prev

otel

la.a

mni

iAt

opob

ium

.vag

inae

Snea

thia

.am

nii

Lact

obac

illus.

iner

s.1

Lact

obac

illus.

cris

patu

s.1

Lact

obac

illus.

jens

enii.

1La

ctob

acillu

s.jo

hnso

nii.1

Lact

obac

illus.

delb

ruec

kii.1

Gar

dner

ella

.vag

inal

is.1

Meg

asph

aera

.gen

omos

p..ty

pe_1

.1Pr

evot

ella

.am

nii.1

Atop

obiu

m.v

agin

ae.1

Snea

thia

.am

nii.1

Infla

V1

Infla

V2

Trea

tmen

t

0200040006000




Antibiotic treatmentAntifungal treatmentNo treatment

Treatment

Lact

obac

illus.

iner

sLa

ctob

acillu

s.cr

ispat

usLa

ctob

acillu

s.je

nsen

iiLa

ctob

acillu

s.jo

hnso

nii

Lact

obac

illus.

delb

ruec

kiiG

ardn

erel

la.va

gina

lisM

egas

phae

ra.g

enom

osp.

.type

_1Pr

evot

ella

.am

nii

Atop

obiu

m.va

gina

eSn

eath

ia.a

mni

iLa

ctob

acillu

s.in

ers.

1La

ctob

acillu

s.cr

ispat

us.1

Lact

obac

illus.

jens

enii.1

Lact

obac

illus.

john

soni

i.1La

ctob

acillu

s.de

lbru

eckii

.1G

ardn

erel

la.va

gina

lis.1

Meg

asph

aera

.gen

omos

p..ty

pe_1

.1Pr

evot

ella

.am

nii.1

Atop

obiu

m.va

gina

e.1

Snea

thia

.am

nii.1

Infla

V1

Infla

V2

Trea

tmen

t

0200040006000





Treatment

Lact

obac

illus.

iner

sLa

ctob

acillu

s.cr

ispat

usLa

ctob

acillu

s.je

nsen

iiLa

ctob

acillu

s.jo

hnso

nii

Lact

obac

illus.

delb

ruec

kiiG

ardn

erel

la.v

agin

alis

Meg

asph

aera

.gen

omos

p..ty

pe_1

Prev

otel

la.a

mni

iAt

opob

ium

.vag

inae

Snea

thia

.am

nii

Lact

obac

illus.

iner

s.1

Lact

obac

illus.

crisp

atus

.1La

ctob

acillu

s.je

nsen

ii.1La

ctob

acillu

s.jo

hnso

nii.1

Lact

obac

illus.

delb

ruec

kii.1

Gar

dner

ella

.vag

inal

is.1

Meg

asph

aera

.gen

omos

p..ty

pe_1

.1Pr

evot

ella

.am

nii.1

Atop

obiu

m.v

agin

ae.1

Snea

thia

.am

nii.1

Infla

V1

Infla

V2

Trea

tmen

t

0200040006000






https://doi.org/10.1101/2020.03.10.986646

microbial function and genital inflammation in young south ... · 3/10/2020 · 1 microbial...

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