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Bioinformatics, Computational and Systems Biology

Omics methods, modeling, and analysis

Characterizing the Anti-Cancer Potential of Vaginal Microbes and Metabolites

Friday, October 13, 2023
2:00 PM – 2:15 PM PDT
Location: 331 (Center, Level 3)
Introduction::
Several studies have shown interactions between the human microbiome and health, but the functions of the microbiome and its products are not fully elucidated, in part because systematic methods to functionally characterize microbes and their functions are lacking. Here, we propose a systems biology concept to characterize microbes and their products termed the Pharmacobiome. The Pharmacobiome is the characterization of a microbe and its products in terms of known drugs and pathways by studying associations between the microbiome and disease states, phenotypes, pathways, networks, known drug functions, and how the human clinical demographic characteristics impact the above associations (Figure 1A). One application of this concept could be for the precision treatment of cancers using microbial products by characterizing the Anti-cancer Pharmacobiome. Here, we examine this concept by integrating vaginal microbiome multi-omics data from patients with and without bacterial vaginosis with cell line drug screening transcriptomics data to identify vaginal microbes and microbial products with therapeutic potential against female reproductive cancers.




Materials and Methods::
Vaginal swabs and biopsies were collected from 405 HIV-negative women enrolled in the Partners PrEP study (pre-exposure prophylaxis study for HIV) and divided into 2 groups. Group 1 women (315 women) were between ages 18-51 with exposure to a diverse set of HIV-risk factors, vaginal swabs were collected and processed for proteomics and metabolomics data. Group 2 women (90 women) were between ages 25-51 who consented to multiple genital sample collections, vaginal biopsies were collected and processed for transcriptomics data and vaginal swabs were processed for proteomics and metabolomics data. Vaginal microbiome composition data was generated from the vaginal swab elute samples from both groups. Microbiome composition-to-protein correlations, microbiome composition-to-RNA correlations, metabolite-to-protein correlations, and metabolite-to-RNA correlations were calculated via Spearman correlation analysis. The up and down-regulated genes of Cisplatin and Paclitaxel (known chemotherapeutic drugs) were obtained for breast cancer cell line (MCF7) at 10 uM concentration at 24h time point from the Library of Integrated Network-Based Cellular Signatures (LINCS) database. The microbes and metabolites signatures obtained from Spearman correlations were compared to Cisplatin and Paclitaxel signatures for similarities using a Fisher’s Exact test. We hypothesize that microbes and metabolites that have similar cellular signatures as a drug will be able to achieve the partial or total therapeutic function of the drug.




Results, Conclusions, and Discussions::
Results & Discussion: The correlations calculated from the human data (Microbiome composition-protein; microbiome composition-RNA; metabolite-protein; metabolite-RNA), generated gene and protein lists upregulated or downregulated by microbes and metabolites. The microbes' and metabolites' gene signatures were further compared to the gene signatures of Cisplatin and Paclitaxel to assess which microbes and metabolites have similar host fingerprints to the drugs. The microbes and metabolites with p ≤ 0.05 were considered significantly similar to the drug and were classified into 2 tiers. Tier 1 are microbes and metabolites similar to the drug in both up and down-regulated gene signatures while Tier 2 are microbes and metabolites similar to the drug in either up or down-regulated gene signatures. Citrulline and Taurine which are associated with possible anticancer capacities were identified as Tier 1 for Cisplatin and Paclitaxel respectively (Figure 1C). Symbiotic microbes like Lactobacillus iners and Lactobacillus crispatus were identified in either Tier 1 or 2 for both Cisplatin and Paclitaxel (Figure 1B). These results corroborate with bacterial metabolomics data generated by our lab that shows that Lactobacillus crispatus produces Taurine and might be found in similar metabolic pathways.


Conclusions: Although the drug perturbations were on breast cancer cells and the omics data were from the vaginal tract, the literature suggests that both origins share some similar gene expression patterns due to hormonal regulations, making them comparable but also capable of exhibiting type-specific behavior. Our results suggest that some microbes and metabolites might be capable of inducing mucosa gene expression patterns similar to some known chemotherapeutic drugs.






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