Proteome-wide prediction of bacterial carbohydrate-binding proteins as a tool for understanding commensal and pathogen colonisation of the vaginal microbiome

Abstract

Lectins, such as adhesins and toxins, are carbohydrate-binding proteins that recognise glycans of cells and their secretions. While mediation of microbe-microbe and microbe-host interactions by lectins has long been recognised in the lung and gut, little is known about those in the vagina, where such interactions are implicated in health and various disease states. These include sexually transmitted infections, cervical cancer and poor pregnancy outcomes such as preterm birth. In this study, the curated UniLectin3D database was used to establish a lectin classification based primarily on taxonomy and protein 3D structure. The resulting 109 lectin classes were characterised by specific Hidden Markov Model (HMM) profiles. Screening of microbial genomes in the UniProt and NCBI NR sequence databases resulted in identification of >100 000 predicted bacterial lectins available at unilectin.eu/bacteria. Screening of the complete genomes of 90 isolates from 21 vaginal bacterial species showed that the predicted lectomes (ensemble of predicted lectins) of Lactobacilli associated with vaginal health are substantially less diverse than those of pathogens and pathobionts. Both the number of predicted bacterial lectins, and their specificities for carbohydrates correlated with pathogenicity. This study provides new insights into potential mechanisms of commensal and pathogen colonisation of the reproductive tract that underpin health and disease states.

LectomeXplore, an update of UniLectin for the discovery of carbohydrate-binding proteins based on a new lectin classification

“…All lectins present in one species can be searched in whole or a part of a kingdom. Furthermore, lectins/adhesins can be searched in pathogenic bacteria to support an scenario of the strategy used by some pathogenic microbes to attach to the host glycome ( 22 ). The prediction of the sugar-binding specificity is still the Holy Grail of glycobioinformatics.…”

“…The LectomeXplore normalized prediction/similarity score reflects the similarity between a prediction and the matched profiles. It is generated to rank the results and select the top one, as previously described in ( 22 ).…”

“…The LectomeXplore module is based on a new lectin classification that was built from a collection of conserved motifs. This classification is composed of 109 lectin classes derived from 35 distinct folds detailed in ( 22 ). Each class is defined with a Hidden Markov Model (HMM) sequence profile generated from a manually curated protein alignment with HMMER-hmmbuild ( 23 ).…”

“…Each class is defined with a Hidden Markov Model (HMM) sequence profile generated from a manually curated protein alignment with HMMER-hmmbuild ( 23 ). Screening was performed with the HMMER-hmmsearch followed by post processing described further in this article and in ( 22 ). The new LectomeXplore module is introduced here in detail and illustrated with examples.…”

UniLectin, A One‐Stop‐Shop to Explore and Study Carbohydrate‐Binding Proteins

“…We recently demonstrated the efficiency of LectomeXplore for exploring the genomes of organisms in relation to biological questions. The analysis of the vaginal microbiome and its variations that impact women's health demonstrated that vaginal bacterial species associated with infection and inflammation produce a larger variety of lectins (Bonnardel et al., 2021). The size of the predicted lectome and the variety of putative specificities correlated with pathogenicity.…”