Correction to: Modeling the temporal dynamics of cervicovaginal microbiota identifies targets that may promote reproductive health

Abstract

Background: Cervicovaginal bacterial communities composed of diverse anaerobes with low Lactobacillus abundance are associated with poor reproductive outcomes such as preterm birth, infertility, cervicitis, and risk of sexually transmitted infections (STIs), including human immunodeficiency virus (HIV). Women in sub-Saharan Africa have a higher prevalence of these high-risk bacterial communities when compared to Western populations. However, the transition of cervicovaginal communities between highand low-risk community states over time is not well described in African populations. Results: We profiled the bacterial composition of 316 cervicovaginal swabs collected at 3-month intervals from 88 healthy young Black South African women with a median follow-up of 9 months per participant and developed a Markov-based model of transition dynamics that accurately predicted bacterial composition within a broader crosssectional cohort. We found that Lactobacillus iners-dominant, but not Lactobacillus crispatus-dominant, communities have a high probability of transitioning to high-risk states. Simulating clinical interventions by manipulating the underlying transition probabilities, our model predicts that the population prevalence of low-risk microbial communities could most effectively be increased by manipulating the movement between L. inersand L. crispatusdominant communities. Conclusions: The Markov model we present here indicates that L. iners-dominant communities have a high probability of transitioning to higher-risk states. We additionally identify transitions to target to increase the prevalence of L. crispatus-dominant communities. These findings may help guide future intervention strategies targeted at reducing bacteria-associated adverse reproductive outcomes among women living in sub-Saharan Africa. © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. * Correspondence: [email protected] Alexander Munoz, Matthew R. Hayward and Seth M. Bloom contributed equally to this work. Ragon Institute of MGH, MIT, and Harvard, 400 Technology Square, Cambridge, MA 02139, USA Harvard Medical School, Boston, MA 02115, USA Full list of author information is available at the end of the article Munoz et al. Microbiome (2021) 9:163 https://doi.org/10.1186/s40168-021-01096-9