Browsing by Author "Lammons, John W."

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    Association of Chlamydia trachomatis burden with the vaginal microbiota, bacterial vaginosis, and metronidazole treatment
    (Frontiers Media, 2023-12-12) Ardizzone, Caleb M.; Taylor, Christopher M.; Toh, Evelyn; Lillis, Rebecca A.; Elnaggar, Jacob H.; Lammons, John W.; Dehon Mott, Patricia; Duffy, Emily L.; Shen, Li; Quayle, Alison J.; Microbiology and Immunology, School of Medicine
    Bacterial vaginosis (BV), a dysbiosis of the vaginal microbiota, is a common coinfection with Chlamydia trachomatis (Ct), and BV-associated bacteria (BVAB) and their products have been implicated in aiding Ct evade natural immunity. Here, we determined if a non-optimal vaginal microbiota was associated with a higher genital Ct burden and if metronidazole, a standard treatment for BV, would reduce Ct burden or aid in natural clearance of Ct infection. Cervicovaginal samples were collected from women at enrollment and, if testing positive for Ct infection, at a follow-up visit approximately one week later. Cervical Ct burden was assessed by inclusion forming units (IFU) and Ct genome copy number (GCN), and 16S rRNA gene sequencing was used to determine the composition of the vaginal microbiota. We observed a six-log spectrum of IFU and an eight-log spectrum of GCN in our study participants at their enrollment visit, but BV, as indicated by Amsel’s criteria, Nugent scoring, or VALENCIA community state typing, did not predict infectious and total Ct burden, although IFU : GCN increased with Amsel and Nugent scores and in BV-like community state types. Ct burden was, however, associated with the abundance of bacterial species in the vaginal microbiota, negatively with Lactobacillus crispatus and positively with Prevotella bivia. Women diagnosed with BV were treated with metronidazole, and Ct burden was significantly reduced in those who resolved BV with treatment. A subset of women naturally cleared Ct infection in the interim, typified by low Ct burden at enrollment and resolution of BV. Abundance of many BVAB decreased, and Lactobacillus increased, in response to metronidazole treatment, but no changes in abundances of specific vaginal bacteria were unique to women who spontaneously cleared Ct infection.
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    Characterization of Vaginal Microbial Community Dynamics in the Pathogenesis of Incident Bacterial Vaginosis, a Pilot Study
    (Wolters Kluwer, 2023) Elnaggar, Jacob H.; Lammons, John W.; Taylor, Christopher M.; Toh, Evelyn; Ardizzone, Caleb M.; Dong, Amy; Aaron, Kristal J.; Luo, Meng; Tamhane, Ashutosh; Lefkowitz, Elliot J.; Quayle, Alison J.; Nelson, David E.; Muzny, Christina A.; Microbiology and Immunology, School of Medicine
    Background: Despite more than 60 years of research, the etiology of bacterial vaginosis (BV) remains controversial. In this pilot study, we used shotgun metagenomic sequencing to characterize vaginal microbial community changes before the development of incident BV (iBV). Methods: A cohort of African American women with a baseline healthy vaginal microbiome (no Amsel criteria, Nugent score 0-3 with no Gardnerella vaginalis morphotypes) were followed for 90 days with daily self-collected vaginal specimens for iBV (≥2 consecutive days of a Nugent score of 7-10). Shotgun metagenomic sequencing was performed on select vaginal specimens from 4 women, every other day for 12 days before iBV diagnosis. Sequencing data were analyzed through Kraken2 and bioBakery 3 workflows, and specimens were classified into community state types. Quantitative polymerase chain reaction was performed to compare the correlation of read counts with bacterial abundance. Results: Common BV-associated bacteria such as G. vaginalis , Prevotella bivia , and Fannyhessea vaginae were increasingly identified in the participants before iBV. Linear modeling indicated significant increases in G. vaginalis and F . vaginae relative abundance before iBV, whereas the relative abundance of Lactobacillus species declined over time. The Lactobacillus species decline correlated with the presence of Lactobacillus phages. We observed enrichment in bacterial adhesion factor genes on days before iBV. There were also significant correlations between bacterial read counts and abundances measured by quantitative polymerase chain reaction. Conclusions: This pilot study characterizes vaginal community dynamics before iBV and identifies key bacterial taxa and mechanisms potentially involved in the pathogenesis of iBV.
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    Predicting Bacterial Vaginosis Development using Artificial Neural Networks
    (medRxiv, 2025-05-05) Elnaggar, Jacob H.; Lammons, John W.; Ardizzone, Caleb M.; Aaron, Kristal J.; Jacobs, Clayton; Graves, Keonte J.; George, Sheridan D.; Luo, Meng; Tamhane, Ashutosh; Łaniewski, Paweł; Quayle, Alison J.; Herbst-Kralovetz, Melissa M.; Cerca, Nuno; Muzny, Christina A.; Taylor, Christopher M.; Microbiology and Immunology, School of Medicine
    Bacterial vaginosis (BV) is a dysbiosis of the vaginal microbiome, characterized by the depletion of protective Lactobacillus spp. and overgrowth of anaerobes. Artificial neural network (ANN) modeling of vaginal microbial communities offers an opportunity for early detection of incident BV (iBV). 16S rRNA gene sequencing and quantitative PCR was performed on longitudinal vaginal specimens collected from participants within 14 days of iBV or from healthy participants to calculate the inferred absolute abundance (IAA) of vaginal bacterial taxa. ANNs were trained using the IAA of vaginal taxa from 420 vaginal specimens to classify individual vaginal specimens as either pre-iBV (collected before iBV onset) or Healthy. Feature importance was assessed to understand how specific vaginal micro-organisms contributed to model predictions. ANN modeling accurately classified >97% of specimens as either pre-iBV or Healthy (sensitivity >96%, specificity >98%) using IAA of 20 vaginal taxa. Model prediction accuracy was maintained when training models using only a few key vaginal taxa. Models trained using only the top five most important features achieved an accuracy of >97%, sensitivity >92%, and specificity >99%. Model predictive accuracy was further improved by training models on specimens from white and black participants separately; using only three feature models achieved an accuracy >96%, sensitivity >91%, and specificity >91%. Feature analysis found that Lactobacillus species L. gasseri and L. jensenii differed in how they contributed to model predictions in models trained with data stratified by race. A total of 420 vaginal specimens were analyzed, providing a robust dataset for model training and validation.