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Microbiology and Biotechnology Letters

Research Article(보문)

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Molecular and Cellular Microbiology (MCM)  |  Microbiome

Microbiol. Biotechnol. Lett. 2023; 51(1): 109-123

https://doi.org/10.48022/mbl.2210.10008

Received: October 18, 2022; Revised: January 10, 2023; Accepted: January 18, 2023

Identification of Distinct Vaginal Microbiota Signatures Contributing Toward Preterm Birth Using an Integrative Computational Approach

Sudeepti Kulshreshtha1†, Priyanka Narad1†, Brojen Singh2 , Deepak Modi3, and Abhishek Sengupta1*
†These authors contributed equally to this work.

1Amity Institute of Biotechnology, Amity University, Uttar Pradesh 201301, India
2School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
3Department of Molecular and Cellular Biology, National Institute for Research in Reproductive Health and Child Health, Mumbai 400012, India

Correspondence to :
Abhishek Sengupta,       asengupta@amity.edu

†These authors contributed equally to this work.

Abstract

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Preterm birth (PTB) is defined as giving birth prior to the 37th week of pregnancy and is a major cause of infant mortality. Studies have indicated that the vaginal microbiota's composition and its dysbiosis, particularly during pregnancy, may play a major role in PTB. While previous research work concentrated on well-studied microorganisms such as Lactobacillus, Prevotella, Gardnerella, various other microbes, and their significance in the vaginal microbiota's stability remain unknown. Moreover, current studies have focused primarily on the relative abundances of the microbes found, without considering their interactions with other members of the vaginal microbiota. In this work, we developed a novel computational approach and performed taxonomic classification of vaginal microbiota samples stratified longitudinally (Term/PTB) to observe compositional disparities and find underexamined microbes that may be contributing to PTB. Furthermore, we carried out a correlational analysis to build a microbial co-interaction network and investigated the functional implications of the genes present in both Term and PTB samples. The co-occurrence network revealed that Lactobacillus acts in solidarity to maintain the stability of the vaginal microbiota and did not have strong co-interactions with any of the other microbes. Similarly, microbes with strong interactions with Atopobium, a well-known marker microbe of PTB, were also observed. Additionally, several genes such as PTXA, FANCM, GPX, and DUSP were found to be playing an important role in the occurrence of PTB. This study provides a novel conceptual framework revealing distinct vaginal microbiota signatures that could be potential therapeutic targets for the prevention of PTB.

Keywords: Vaginal microbiota, preterm birth, pregnancy-related complications, bioinformatics, reproductive medicine

Graphical Abstract


Introduction

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Every year, around 15 million Preterm births (PTB) occur at less than 37 weeks of pregnancy [1]. Although maternal genetics plays a role in gestation duration, environmental influences like the microbiota, are the most significant contributors to PTB [1]. An increasing amount of research is now focusing on the role of the vaginal microbiota in determining PTB risk. Reports indicate that the vaginal microbial populations are critical in the etiology and pathogenesis of PTB transmission [2] with microbes such as Lactobacillus, Bifidobacteria, and more, associated with increased vaginal microenvironmental stability, lower risks of pregnancy-related complications (such as PTB), and a healthy full-Term Birth Delivery [1, 3−7]. On the other hand, pathogenic microorganisms found in the vaginal microbiota, such as Gardnerella, Atopobium, Prevotella, and more, are known to disturb the vaginal microenvironment and increase the risk of pregnancy-related problems such as PTB [8, 9]. The variation in the makeup of the vaginal microbiota among women of various races/ethnicities is also crucial in increasing the chance of pregnancyrelated complications [10, 11].

A majority of the previous studies have focussed on taxonomic classification to identify microbial signatures associated with healthy and diseased states. However, comprehending the complexities and involvement of the entire microbial community that inhabits the host is far more perplexing than studying individual microbes that cause a specific disease or infection. To begin with, one of the major challenges in microbiome research is the diversity of methodologies used to detect individual microorganisms and characterize the microbiota. Such discrepancies have the potential to result in significant disparities in analysis and data interpretation. Additionally, the majority of current research focuses on certain microorganisms (for example, Lactobacillus, Gardnerella, and Prevotella) because they are either highly prevalent in the vaginal microbiota or occur in significant abundance only in healthy or diseased conditions. Microbes seldom function in isolation, and hence their linkages and interactions with one another must also be considered. Co-interactions between the members of the vaginal microbiota may disclose reveal critical details about how the vaginal microenvironment operates, particularly in a dysbiotic condition. Because the vaginal microbiota's composition has been linked to a variety of diseases and pregnancy complications, the next step should be to investigate the underlying molecular pathways to better understand the vaginal microbiota's impact on a pregnant woman's reproductive health.

In this study, we develop a novel computational approach for functional profiling and correlational analysis to establish a co-occurrence network among different microbes present in the vaginal microbiota. The representative results in this study were produced using data from a 2020 Alabama study on the vaginal microbiota of pregnant women aged 15−34. 39 vaginal microbiota samples were obtained, divided into two broad groups based on ethnicity (White = 20N, Black = 19N). The samples were further stratified based on their birth timing (White = 10 Term, 10 PTB, and Black = 9 Term, 10 PTB). We performed taxonomic classification to investigate the differences between the two vaginal microbiota environments by comparing the taxonomic profiles of Term Birth Delivery vs PTB Delivery samples. The resultant microbial relative abundances were utilized to establish a microbial co-interaction network by performing a correlational analysis among the various microorganisms that comprise the vaginal microbiota. Subsequently, we performed functional profiling to estimate the functional composition of the samples based on 16S rRNA gene OTUs to gain a deeper insight into the functions and pathways that differentiate the Term Birth Delivery state from the PTB Delivery condition. This approach can be applied to numerous microbiome samples to articulate the mechanisms and complex interactions in the vaginal microbiome unique to a dysbiotic condition, which culminates in an elevated risk of PTB in pregnant women.

Material and Methods

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The computational approach followed for this study is summarised in Fig. 1.

Figure 1.The general workflow for the taxonomic, functional, and correlational analysis of the vaginal microbiota.

Data collection and retrieval

Vaginal microbiota samples of women stratified by birth timing (Term/Preterm) and race (black/white) were downloaded from NCBI Bioproject Project ID: PRJNA600021. The vaginal swabs considered for this study were originally collected at 21 to 25 weeks (stored at -80℃). The V4 region of the 16S rDNA gene was amplified from individual samples to produce an “amplicon library” [12]. In our work, a total of 39 raw samples were downloaded in FASTQ file format in Ubuntu 20.04.4 [13], stratified by ethnicity (black/white) and birth timing (Preterm/term), as shown in Fig. 2. The quality of the samples was checked using FASTQC software.

Figure 2.Sample size and its categorization into ethnicities, and delivery timing.

Microbial taxonomic classification

The pre-processed samples were subjected to taxonomic classification using the Parallel-Meta 3 tool (version 3.3.2) [14], which generates comparable findings to QIIME (version 1.9.0) [15] and PICRUSt (version 1.0) [16], but at a considerably quicker speed and with much less memory use, demonstrating its capacity to decipher taxonomic and functional dynamics patterns across huge datasets and reveal ecological linkages between the microbiota and environment [13]. Parallel-Meta 3 uses Bowtie2 to align all gene sequences to its reference database, to parse out an Operational Taxonomic Units (OTUs) table along with their relative abundances. To produce the OTU/taxa feature table files, the vaginal microbiota samples (FASTQ files) were taxonomically profiled using the “PM-select-taxa” command used for multi-sample feature selection (with a specified taxonomical level). Greengenes (version 13-8) [17] was used as the reference database (16s rRNA, 97% level identity), with the following parameters: ASV denoising, Chimera removal, level of taxonomic classification desired (level 5 for Genus), and sequence alignment threshold of 0.99, parsing out relative abundances and count files of the microbes present in each sample as the output. The taxonomic classification results were visualized using the ggplot2 library [18] in R (version 4.1.2). DiVenn 2.0 [19] was used to visualize the microbes unique to Term Birth Delivery and PTB Delivery conditions.

Microbial functional profiling

For functional profiling, Parallel-META 3 re-implements the PICRUSt algorithm using the KEGG database to estimate all of the functional genes found in a microbiota using 16S rRNA gene OTUs. The functional genes are annotated by KEGG Ontology (KO) and the KEGG pathway database (Release 103.0) [20]. So, the “PMselect- func” command was used to carry out 16S rRNAbased functional profiling on the vaginal samples. The PM-select-func command further extracts the sequence counts and normalized relative abundances (from 0– 100%) for all OTUs and estimates the same information for identified genes and pathways based on the specified KEGG pathway level parameter.

The results are formatted into tables (with KEGG Gene IDs, their names and functions, their abundance, and the pathways the genes are involved in) that are appropriate for further analysis and manual user investigation.

Correlational analysis

Based on the results of the PM-select-taxa command used earlier, Parallel-Meta 3 can evaluate correlations between taxa using Spearman's correlation metrics (p-value < 0.05). The “PM-comp-corr” command was used to compute the correlation and interaction probabilities between each of the microorganisms using the previously constructed abundance table, producing a cooccurrence matrix among the taxa. Weak interactions were manually excluded and only correlations between 0.5 and 1 (positive co-interactions) and -0.5 and -1 (negative co-interactions) were considered for further analysis.

Building co-occurrence abundance network in cytoscape

The correlation abundance table was reformatted as a list with all the co-interactions and their scores enumerated individually. The list was then imported by the “Network from File” option in Cytoscape 3.9.0 [21] to visualize the microbial co-interactions. The microbes present in the samples are visualized as nodes, and the interactions that occur between them are visualized as the edges. ClusterViz (version 1.0.3) [22] was used to elucidate the clusters of the PTB condition for both ethnicities using the MCODE algorithm. The cluster with the highest score indicated the highest degree of interconnectivity and was visualized using the yFiles Radial Layout option of the yFiles plug-in (version 1.1.2) [23] in Cytoscape.

Exploring the microbial genetic variation of the vaginal microbiota

The results of the PM-select-func command used earlier have information related to the KEGG Gene IDs, their names and functions, their abundance, and the pathways the genes are involved in. These results were manually examined for both Term Birth Delivery and PTB Delivery samples to probe further into the genes and pathways that are unique to each condition and to understand the phenotypic implications of the unique genes found in PTB Delivery samples. Subsequently, the gene counts of common genes were compared in both circumstances to determine if there was any upregulation or downregulation of essential genes that resulted in substantial changes in the phenotype of the vaginal microbiota, contributing to PTB Delivery. DiVenn 2.0 [19] was used to visualize the genes unique to Term Birth Delivery and PTB Delivery conditions, the common genes, as well as the upregulated and downregulated genes based on their gene counts.

Results

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Taxonomic classification of the vaginal microbiota

Taxonomic classification using parallel-meta 3 revealed the microbial composition of the vaginal microbiota in terms of the microbial genus identified and their relative abundances. A comprehensive list of microbes found in the vaginal microbiota samples of women stratified by birth timing (Term/Preterm) and race (black/white), along with their relative abundances can be found in Table 1 of the Supplementary File.

A total of 67 genera (Table S1) were found in Term Birth Delivery samples (Nblack = 33; Nwhite = 34). Dorea, Delftia, Achromobacter, Alistipes, Turicibacter, Thalassospira, Akkermansia, and more were found to be unique to Term Birth samples across both ethnicities. (Nblack = 5; Nwhite = 3).

Similarly, a total of 95 genera (Table S1) were found in PTB Delivery samples (Nblack = 47; Nwhite = 48). 36 microbes were found to be unique to the Preterm Birth Delivery condition (Nblack = 19; Nwhite = 17), which mostly belonged to the Actinobacteria, Firmicutes, Proteobacteria, and Tenericutes phyla. Therefore, an increase in microbial diversity was observed in PTB conditions suggesting instability in the vaginal microenvironment.

Amongst the microbes common in both delivery conditions, the relative abundances of Blautia, Coprococcus, Faecalibacterium, Megasphaera, Paenibacillus, Roseburia, Shuttleworthia, and Streptococcus were found to be higher in Term Birth Delivery samples. Similarly, the relative abundances of microbes such as Acinetobacter, Aerococcus, Atopobium, Bacteroides, Bifidobacterium, Clostridium, Dialister, Eubacterium, Fusicatenibacter, Lachnospiraceae, Prevotella, Ruminococcaceae, Ruminococcus, and Sneathia, were found to be higher in PTB samples as compared to Term Birth samples, out of which numerous microbes are pathogenic in nature. This suggests that the presence of pathogenic microorganisms may contribute to PTB. Fig. 3A and Fig. 3B depict the comparison of relative abundances in Term Birth Delivery and PTB Delivery conditions for Black and White women, respectively.

Figure 3.Relative abundances of microbes present in the vaginal microbiota based on taxonomic classification of 16s rRNA sequences of the vaginal samples retrieved from NCBI Bioproject PRJNA600021 using Parallel-META 3 and visualized using ggplot2. (A) Comparison of relative abundances in Term Birth Delivery and Preterm Birth Delivery conditions in Black women. (B) Comparison of relative abundances in Term Birth Delivery and Preterm Birth Delivery conditions in White women. (C) Comparison of vaginal microbiota composition based on ethnicities (Black and White women).

Upon comparing the relative abundances of Black women with White women, we discover the key differences between the two ethnicities (Fig. 3C). To begin with, Lactobacillus was found to be less abundant in Black women in comparison with White women, which has been associated with their increased risk of vaginal infections during pregnancy [11]. Pathogenic microbes like Megasphaera, Dialister, Staphylococcus, and Aerococcus, which have been associated with Pregnancyrelated complications [24−27] have been found to have higher relative abundances in Black women as compared to White women. On the other hand, microbes like Eubacterium, Pseudomonas, Clostridium, and Bifidobacterium were found to be more abundant in White women. While Eubacterium [28], Pseudomonas [29], and Clostridium [30] have all been associated with PTB, Bifidobacterium has been associated with a stable healthy vaginal microbiota [31]. Also, 12 microbes (Table S1) were found to be unique in White women, mainly from the Bacillota and Pseudomonadota phyla. Similarly, 8 microbes (Table S1) were found to be unique in Black women, mainly from the Bacillota and Actinomycetota phyla. Thus, taxonomic classification using Parallel- META 3 is instrumental in the identification of microbes other than the already well-studied Lactobacillus, Prevotella, etc., implying the need to study their roles and mechanisms in contributing toward PTB. Fig. 4 shows the unique and common microbes between Term Birth Delivery and PTB Delivery conditions for both ethnicities.

Figure 4.Unique and common microbes between Term Birth Delivery and PTB Delivery conditions. (A) Common microbes (yellow) and Unique microbes (red) in Black women. (B) Common microbes (yellow) and Unique microbes (red) in White women.

Functional profiling of the vaginal microbiota

Functional profiling using the Parallel-META 3 tool yielded the list of genes found in the samples for both Term Birth Delivery and PTB Delivery vaginal microbiota samples in both ethnicities. Functional profiles were generated for each sample and compiled to remove any redundant or duplicate genes. A comprehensive list of various pathways found in vaginal microbiota samples stratified by birth timing (Term/Preterm) and race (black/white), along with their abundances, detected for both conditions and both ethnicities can be found in Table 2 of the Supplementary File.

More than 6000 KO annotated genes were found in Term Birth Delivery samples (Nblack = 6324; Nwhite = 6518), along with gene descriptions, relative abundance in the samples, and pathways in which the genes are present. The genes unique to Term Birth Delivery were identified (Nblack = 1120; Nwhite = 60). This indicates that in the PTB Delivery condition, several genes that might play essential functions are absent.

Meanwhile, more than 5000 KO annotated genes were found in Preterm Birth Delivery samples (Nblack = 5279; Nwhite = 6864), along with gene descriptions, relative abundance in the samples, and pathways in which the genes are present. The genes distinctive to Preterm Birth Delivery were identified (Nblack = 75; Nwhite = 406). Similarly, common genes between Term Birth Delivery and PTB Delivery conditions were also identified (Nblack = 5203; Nwhite = 6458). When comparing PTB Delivery samples to Term Birth Delivery samples, there is a considerable difference in the number of genes discovered, suggesting that many pathways and functions are suppressed or overexpressed in the PTB Delivery condition.

Co-occurrence abundance network building in cytoscape for PTB delivery samples

Significant differences were observed between the ethnicities during the co-occurrence abundance network building for PTB Delivery samples. The correlational analysis of microbes present in the vaginal microbiota in PTB Delivery conditions reveals the strength of the cooccurrence amongst microbes. We visualized the cooccurrence network in Cytoscape 3.9.0, wherein the nodes represent the microbes, and the edges represent the co-interactions between them. The size of the node is directly proportional to the relative abundance of the microbe as found in the samples. The negative cooccurrences have been depicted with red dashed edges, while positive interactions have been depicted with blue edges. The colour gradient depicts the strength of the interaction.

The microbial co-occurrence network for Black women in PTB Delivery condition can be seen in Fig. 5. The network has 50 nodes, and 560 edges (network diameter = 4, the average number of neighbours each node has = 12). Bilophila, an opportunistic pathogen linked to pregnancy complications [32], has a high number of strong interactions (all positive) with other microbes, implying that the presence of Bilophila has a significant impact on the overall composition of the vaginal microbiota. Interestingly, Lactobacillus has the least number of strong interactions with other microbes even with the highest abundance, which suggests that Lactobacillus acts solitarily to maintain a healthy vaginal microbiota and does not have much impact on even the pathogenic microbes, thus leading to pregnancy-related complications like PTB, despite its significant abundance. Microbes such as Streptococcus, Eubacterium, Staphylococcus, Atopobium, and Veillonella were predominantly seen to have negative interactions, which suggests the tendency of these pathogenic microbes to disrupt the vaginal microbiota, progress towards an infectious state, and be a causative factor towards PTB. Additionally, Bilophila, Eubacterium, Clostridium, Aerococcus, Lachnoclostridium, Shuttleworthia, Pseudobutyrivibrio, Streptococcaceae, Fusicatenibacter, Bacteroides, Paenibacillus, Veillonella, Roseburia, Faecalibacterium, Ruminococcus, Blautia have been identified as part of the most interconnected cluster through ClusterViz (Score = 14.533), implying the importance of these microbes in the vaginal microbiota of women who faced a PTB Delivery. Alteration in the presence/absence, and maybe even abundances of these microbes could result in a substantial physiological change in the vaginal microbiota and pregnancy outcome.

Figure 5.(A) Co-occurrence network of microbes present in the vaginal microbiota of Black women in PTB Delivery samples, as visualized by Cytoscape. (B) Top hub microbes found in the PTB microbial co-occurrence network using ClusterViz.

The microbial co-occurrence network for White women in PTB Delivery conditions can be seen in Fig. 6. The microbial co-occurrence network in White women is seen to be much denser with a higher number of edges, than the network for Black women. The network has 50 nodes, and 1100 edges (network diameter = 6, average number of neighbours each node has = 21). Fusicatenibacter, Blautia, Ruminococcus, and Pseudobutyrivibrio, which are pathogenic microbes linked to pregnancy complications [31], were observed with the highest number of strong interactions (all positive) with other microbes, which suggests that their presence or absence has a great influence on the overall composition of the vaginal microbiota. Atopobium has the least number of strong interactions with other microbes. Atopobium is seen to interact only with other pathogenic microbes like Paenibillus, Pseudobutyrivibrio, Fusicatenibacter, Ruminruminococcaceae, Steptococcus, Staphylococcus, and Megasphaera, which have been vaguely associated with vaginal microbiota infections [33−36], but their role in pregnancy-related complications has not been fully explored. Since Atopobium is associated with PTB Delivery conditions [8] and has been shown to demonstrate antibiotic resistance [37], diving into its interactions with other species could prove to be worthwhile in the understanding its role in PTB deliveries, and its possible treatment. Interestingly, Lactobacillus can be seen to have strong negative co-interaction with Prevotella, a microbe often considered as a marker in women who delivered preterm [11, 24]. Prevotella is also the second most abundant microbe present in the network, after Lactobacillus as the most abundant one. The interaction could imply that as the abundance of Prevotella increases, it obstructs the presence of Lactobacillus, disrupting the vaginal microbiota environment and being a causative factor towards PTB Delivery. Additionally, pathogenic microbes like Streptococcaceae, Clostridium, Ruminococcus, Faecalibacterium, Roseburia, Blautia, Pseudobutyrivibrio, Bilophila, Fusicatenibacter, Lachnoclostridium, Eubacterium, Veillonella, Bacteroides, Paenibacillus, Aerococcus, Shuttleworthia have been identified as part of the most interconnected cluster through ClusterViz (Score = 14.533), implying the importance of these microbes in the vaginal microbiota of women who faced a PTB Delivery. The presence and abundance of these hub microorganisms have the potential to induce a dramatic physiological shift in the vaginal microbiota, thereby affecting the likelihood of pregnancy complications.

Figure 6.(A) Co-occurrence network of microbes present in the vaginal microbiota of White women in PTB Delivery samples, as visualized by Cytoscape. (B) Top hub microbes found in the PTB microbial co-occurrence network using ClusterViz.

Exploring the microbial genetic variation of the vaginal microbiota

The unique, as well as the common genes that are present in both the Term Birth Delivery and PTB Delivery conditions, were identified to comprehend the functional implications of the genes present, based on the gene count found in the conditions. Amongst the unique genes found in Term Birth Delivery samples, the top 40 genes (considering their gene counts) were observed. The observed 40 genes were associated with signaling and cellular pathways (Nblack = 15; Nwhite = 8), polyketide biosynthesis (Nblack = 0; Nwhite = 9), carbohydrate metabolism (Nblack = 5; Nwhite = 4), quorum sensing (Nblack = 4; Nwhite = 2), amino acid metabolism (Nblack = 4; Nwhite = 3), disease and infection (Nblack = 5; Nwhite = 3), and more. Similarly, amongst the unique genes found in PTB Delivery samples, the top 40 genes (considering their gene counts) were observed. These genes belonged to pathways associated with lipid metabolism (Nblack = 9; Nwhite = 4), signaling and cellular pathways (Nblack = 6; Nwhite = 11), and energy metabolism (Nblack = 1; Nwhite = 6).

Several disparities were observed even between the ethnicities. Firstly, in Black PTB Delivery samples, we found several bacteria-specific genes like TETX, SFMH, ACFD, LPQH, and MUC2 associated with antimicrobial resistance, bacterial motility, bacterial biofilm formation, tuberculosis infectious condition, parasitic infectious disease condition respectively (as described by the KEGG annotations), unique and significantly expressed in the PTB Delivery condition. We also found two genes unique to Black PTB Delivery samples associated with the MAPK signaling pathway and RAS signaling pathway, namely: FLRT (highest gene count) and PLD1. Another pair of genes, namely: the CYP125A and CHOD gene, which are both associated with steroid/ cholesterol degradation according to the KEGG annotations, were detected only in Black PTB Delivery samples. Additionally, on considering the gene counts for the common genes found in both Term Birth Delivery and Preterm Delivery conditions, around 2920 genes were downregulated in PTB Delivery samples including GPX (glutathione peroxidase), GSR (glutathione reductase), PLC (phospholipase C), NPTA (sodium-dependent phosphate cotransporter), DUSP (atypical dual-specificity phosphatase), and more, while only 236 genes were found to be upregulated. Parallelly, in White PTB Delivery samples, we found the genes PTXA (pertussis toxin subunit 1), PTXB (pertussis toxin subunit 2), and SPHB1 (autotransporter serine protease), which have all been identified as the major toxins of Pertussis (or whooping cough) to be unique to the PTB Delivery samples. Two genes, namely: FANCM and ERCC4 that are associated with replication and repair processes and the Fanconi anaemia pathway, were also identified in the White PTB Delivery samples. Additionally, on considering the gene counts for the common genes found in both Term and Preterm Delivery conditions, around 1457 genes were downregulated in PTB Delivery samples including GPX, GSR, PLC, NPTA, DUSP, and more, while 5001 genes, like MED12 (mediator complex subunit 12) gene, hemolysin III, and more were found to be upregulated. The unique (n = 40N + 40 PTB genes with highest gene count) and common upregulated/ downregulated genes (n = 40 genes with highest gene count) between Term Birth Delivery and PTB Delivery conditions for both ethnicities can be seen in Fig. 7.

Figure 7.Unique and common genes between Term Birth Delivery and PTB Delivery conditions. (A) 40 common genes (yellow) and 40N + 40 PTB unique genes (Red) in White women along with their KEGG IDs. (B) 40 common genes (yellow) and 40N + 40 PTB unique genes (Red) in Black women along with their KEGG IDs.

Pathway abundance comparison between term birth delivery and PTB delivery samples

Genes and their relative abundances eventually contribute toward pathways and bring about physiological impacts on the hosts. We evaluated and compared the pathway abundances between Term Birth Delivery and PTB Delivery conditions to understand the pathways that might be getting inhibited or overstimulated due to differences in gene expression counts/levels (Table S2). As expected, significant differences were exhibited between the ethnicities.

It was observed that in Black women, all the major pathways like signaling and cellular processes, carbohydrate metabolism, DNA repair and recombination, biosynthesis of secondary metabolites, nucleotide metabolism, and amino acid metabolism are getting severely inhibited in PTB conditions suggesting a general decline in their overall health, leading to a more diseased condition and being more prone to having a PTB Delivery. Key pathways that involve carbohydrate metabolism, nucleotide metabolism, signal transduction, replication and repair, and more, have been seriously impacted in the PTB Delivery condition when compared to the Term Birth Delivery condition. Because metabolic pathways have a significant impact on a pregnant woman's overall reproductive health, their inhibition is not a good indication and may be a contributing factor to PTB. This helps us in focusing on specific pathways that are contributing toward a PTB Delivery condition in Black women. On the contrary, in White women, several major pathways like biosynthesis of secondary metabolites, endocrine system, carbohydrate metabolism, cell growth and death, lipid metabolism, metabolism of cofactors and vitamins, signal transduction, and more were shown to be repressed in the PTB Delivery condition, while other pathways involving the excretory system, infectious disease, neurodegenerative disease, replication and repair, circulatory system, and more were found to be overexpressed, resulting in a heterogeneous pattern of pathway expression in White women. This shows that the genes engaged in these pathways are present in higher abundances as compared to Term Birth Delivery conditions and that they have a significant influence in shaping the pregnancy's final outcome. It can also be inferred that PTB may be induced by environmental causes other than vaginal infections. Inhibition of the important pathways in pregnant women suggests a general deterioration of a woman's reproductive health, which increases her risk of PTB. Fig. 8 depicts the main pathways and their expression levels in Term Birth Delivery and PTB Delivery conditions for both ethnicities by considering the average pathway abundance through all the samples.

Figure 8.The main pathways and their expression levels in Term Birth Delivery and PTB Delivery conditions for both ethnicities. (A) Pathway Abundance Comparison in Black Women wherein all the pathways are getting inhibited in PTB Delivery samples when compared to Term Birth Delivery samples. (B) Pathway Abundance Comparison in White Women wherein several pathways are getting inhibited in Term Birth Delivery samples, while others are getting overexpressed when compared to PTB Delivery samples.

Discussion

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Present work compared the composition of the vaginal microbiota between women who had a Term Birth Delivery and women who had a Preterm Birth Delivery (before 37 weeks) amongst both black and white women. Akila Subramaniam et al. (2016) [38] examined and focused on microbial composition variations between women with and without bacterial vaginosis and no individual taxa-level analysis was performed on the basis of birth timing (Term/PTB) and ethnicity (Black/White). However, we found higher species diversity and important microorganisms that may indicate PTB delivery in both ethnicities based on taxonomic classification. Furthermore, we extend the study by creating a co-occurrence abundance network to reveal vaginal microbiota interactions under PTB delivery conditions and carried out functional profiling to explore the genes and pathways involved.

Pathogenic microbes such as Gardnerella, Prevotella, Atopobium, Aerococcus, Clostridium, and more were found to be present in higher relative abundances as compared to Term Birth Delivery samples, signifying their role in disrupting the vaginal microbial environment, and thus being a causative factor for PTB [8, 9, 11, 24]. We found numerous microbes essentially from the Bacillota, Pseudomonadota, and Actinomycetota phyla (most abundant to least) to be unique in PTB samples from black women. Similarly, microbes mainly from Pseudomonadota, Bacillota, and Firmicutes phyla (most abundant to least) were found to be unique in PTB samples of white women. Focused research on these specific microbes could prove to be fruitful for understanding the role of the vaginal microbiota in causing PTB and thereby, preventing it. Our functional analysis revealed the biological significance of the microbes present in the vaginal microbiota in the context of genes present in the samples along with their relative abundances. Using the taxonomic and functional information, the co-occurrence network built showed us interesting connections between the members of the vaginal microbiota. In black women, it was observed that Lactobacillus did not play much role in maintaining the stability of the vaginal microbiota of women who faced PTB Delivery since Lactobacillus did not have any significant or strong co-interactions with the other members of the vaginal microbiota. Microbes from the Bacillota phylum were not only observed to be more abundant in PTB samples but also demonstrated more negative co-interactions with the other microbes and were identified as the hub microbes in the co-occurrence network. Targeting microbes from the Bacillota phylum could show significant improvements in the stability of the vaginal microbiota.

Even in white women, we observed that microbes from the Bacillota phylum were present in significantly high relative abundance, were identified as the hub microbes in the co-occurrence network and were seen to strongly interact with Atopobium which is notoriously known as a marker microbe in PTB Delivery conditions [8], and for its antimicrobial resistance. Therefore, we show that understanding and altering the presence or abundance of Atopobium’s co-interactors could play a major role in reducing the risk of PTB. Subsequently, the identification of unique genes in PTB Delivery conditions along with their KEGG IDs would help to drive focussed research in the future towards preventing PTB since specific pathways can be targeted. All key pathways in Black women are substantially inhibited in PTB, indicating a general deterioration in their overall reproductive health, leading to a more diseased condition and a higher risk of delivering preterm. In White women, pathways associated with infectious diseases and repair processes are more abundant in the PTB Delivery condition, whereas pathways connected to the immune system are more enriched in the Term Birth Delivery state. This allows us to concentrate on particular pathways that contribute to a PTB problem.

The genes PTXA, PTXB, and SPHB1 have all been identified as the primary toxins of Pertussis (or whooping cough), a highly infectious illness that may be fatal in infants [39]. In fact, women who got pertussis vaccination were 32% less likely to give birth prematurely [40], suggesting that these genes may have a role in the development of PTB. Other unique genes, such as FANCM and ERCC4, were linked to Fanconi anaemia, a rare genetic illness marked by congenital defects and often associated with increased premature birth [41]. Furthermore, numerous genes and proteins involved in thyroid hormone synthesis were downregulated in PTB Delivery samples, including GPX, GSR, PLC, and NPTA. The depletion of glutathione has been associated with a significant decrease in serum T3 (triiodothyronine) levels [42]. Additionally, glutathione peroxidase (GPX) is a critical selenoprotein antioxidant that protects thyroid cells from oxidative stress [43]. As a consequence, hypothyroidism and oxidative stress are highly likely to be prevalent in PTB Delivery samples. High levels of oxidative stress have physiological consequences for the ovaries and hypothyroidism, as well as the potential to cause ovarian malfunction [30]. Indeed, premature delivery has been associated with hypothyroidism in euthyroid mothers [44], while preterm new-borns have higher levels of oxidative stress than fullterm infants [45]. Hypothyroidism is frequently associated with “secondary dyslipidemia,” or an imbalance of lipids such as cholesterol [46]. Hypothyroidism is associated with an increase in oxidative stress and cholesterol levels, as well as a decrease in the rate of lipid metabolism [47], as determined by pathway abundance analysis. An increased risk of maternal-fetal issues is connected with higher lipid levels in predisposed women or those with recognized kinds of dyslipidemia [48]. Numerous data also suggest an overstimulated MAP kinase signaling pathway. Hypothyroidism, according to a study, may result in oxidative stress, which activates the MAPK signaling pathway [49]. Additionally, the DUSP gene was revealed to be downregulated, disrupting the MAPK pathway's feedback loop and resulting in overactivation [50]. The FLRT (leucine-rich repeat transmembrane protein) gene, which was identified as being distinctive to PTB Delivery samples, also interacts with FGFR (Fibroblast growth factor receptor), hence activating the MAPK signaling cascade [51]. Additionally, the CYP125A and CHOD genes are involved in cholesterol/steroid breakdown and have been proven to be unique to PTB samples. 17-estradiol is one of the products of the steroid breakdown pathway [52]. 17-estradiol (E2) is also known to promote rapid MAPK activation in mammalian cells [53]. MAPK overexpression may result in senescence and premature aging of fetal tissues, as well as sterile inflammation, which may result in PTB [54]. Additionally, MAP kinases and their signaling pathways have been related to lipid accumulation and dyslipidemia [55]. Preeclampsia, preterm birth, and gestational diabetes are all associated with dyslipidemia during pregnancy, which as our study shows, is impacted by both hypothyroidism and MAPK pathway overactivation [48]. MED12 gene, which was found upregulated in PTB samples, is known to be significantly mutated in hysteromyoma or uterine fibroids [56]. Pregnant women with uterine fibroids are more likely to have PTB and other pregnancy-related complications [57]. Similarly, the upregulated hemolysin III has also been linked to premature delivery [58].

Therefore, we investigated the variations in the composition of the vaginal microbiota between term and PTB Delivery samples, while discovering key microbes other than those that have previously been well studied. We also discovered a slew of distinct but critical genes in PTB Delivery samples, each of which contributes to a disturbed vaginal microenvironment and perhaps PTB in its way.

In conclusion, our work showcased a novel computational approach that gave crucial new insights into the modification of the vaginal microbiota in Term Birth and PTB Delivery for both white and black women. Our findings would provide a solid framework for future research to create management techniques that will aid in achieving a microbial community structure that favours safe delivery without pregnancy-related problems. In this work, we have demonstrated the necessity of investigating the microbial interactions that exist in the vaginal microbiota to better understand its involvement in pregnancy- related complications such as PTB. The keystone hub microbial species discovered have the potential to open new paths for developing microbiota modification techniques to mitigate pregnancy-related complications. Furthermore, the genes we discovered that were specific and unique to the PTB condition, as well as the genes and pathways that were over or under-expressed in the PTB condition, could be targeted for drug development and PTB prevention strategies. We also highlighted the key differences between the vaginal microbiotas of white and black women, since the vaginal microbiota is known to vary in women of different ethnicities [59].

Acknowledgments

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We would like to acknowledge Dr. Ashok K. Chauhan, Founder, and President, Amity University, Uttar Pradesh for providing us the opportunity to conduct research. We would also like to thank Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University for providing us with necessary resources.

Authors Contributions

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Designed the research and method: SK, PN, AS; Performed the research: SK, PN, AS; Supervised and validated the results: PN, AS, DM, BS; Conducted data compilation: SK and PN; Supervised the work and edited manuscript: AS, PN, DM, BS; Wrote the paper: PN, SK. All the authors have read and approved the final manuscript.

Conflict of Interest

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The authors have no financial conflicts of interest to declare.

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