bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.03.23.003285

Small Molecule Modulation of Microbiome Ecosystem: A Systems Pharmacology Perspective

Abstract

An increasing body of evidence suggests that microbes are not only strongly associated with many human diseases but also responsible for the efficacy, resistance, and toxicity of drugs. Small-molecule drugs which can precisely fine-tune the microbial ecosystem on the basis of individual patients may revolutionize biomedicine. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" process. It is often insufficient and less successful in tackling complex systematic diseases. A systematic pharmacology approach that intervenes multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution. Advances in the Human Microbiome Project have provided numerous genomics data to study microbial interactions in the complex microbiome community. Integrating microbiome data with chemical genomics and other biological information enables us to delineate the landscape for the small molecule modulation of the human microbiome network. In this paper, we construct a disease-centric signed microbe-microbe interaction network using metabolite information of microbes and curated microbe effects on human health from published work. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of pathogenic and commensal species. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We also demonstrate that drugs for diabetes are enriched in the potential inhibitors that target pathogenic microbe without affecting the commensal microbe, thus can be repurposed to modulate the microbiome ecosystem. We further show that periplasmic and cellular outer membrane proteins are overrepresented in the potential drug targets set in pathogenic microbe, but not in the commensal microbe. The systematic studies of polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of microbiome. Author SummaryAs one of the most abundant components in human bodies, the microbiome has an extensive impact on human health. Pathogenic-microbes have become emerging potential therapeutic targets. Small-molecule drugs that only intervene in the growth of a specific pathogenic microbe without considering the interacting dynamics of the microbiome community may disrupt the ecosystem homeostasis, thus can cause drug side effect or prompt drug resistance. To discover novel drugs for safe and effective microbe-targeting therapeutics, a systematic approach is needed to fine-tune the microbiome ecosystem. To this end, we built a disease-centric signed microbe-microbe interaction network which accurately predicts the pathogenic or commensal effect of microbe on human health. Based on annotated and predicted pathogens and commensal species, we performed a systematic survey on therapeutic space and target landscape of existing drugs for modulating the microbiome ecosystem. Enrichment analysis on potential microbe-targeting drugs shows that drugs for diabetes could be repurposed to maintain the healthy state of microbiome. Furthermore, periplasmic and cellular outer membrane proteins are overrepresented in the potential drug targets of pathogenic-microbes, but not in proteins that perturb commensal-microbes. Our study may open a new avenue for the small molecule drug discovery of microbiome.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Liu, Q., Lee, B., Xie, L.. 2020-03-23. Small Molecule Modulation of Microbiome Ecosystem: A Systems Pharmacology Perspective. https://doi.org/10.1101/2020.03.23.003285

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Almond Expression Atlas: A comprehensive multi-tissue transcriptomic resource for almond gene expression

Background: Improving crop adaptation and enhancing agronomic and quality traits requires a deeper understanding of gene expression regulation, for which expression atlases are valuable tools. Almond (Prunus dulcis (Mill.) D.A. Webb) is one of the most economically important tree nut crops worldwide. However, an integrated transcriptomic resource for this species is still lacking. Available data remain fragmented across independent studies, limiting their comparability and reuse. Results: Here, we present the first almond gene expression atlas: a comprehensive multi-tissue transcriptomic resource integrating publicly available RNA sequencing data. A total of 142 high-quality untreated and control samples from 17 independent projects were curated through a metadata standardization process and analyzed using a unified bioinformatics pipeline. Standardized expression profiles were generated for 25,225 annotated genes in the Prunus dulcis Texas v2.0 reference genome. The atlas integrates two complementary expression approaches: a median TPM approach capturing quantitative expression levels and a rank-based strategy implemented using the Bgee expression calling method, designed to improve comparability across heterogeneous datasets. This design enables the identification of tissue-specific candidate genes and the exploration of expression patterns across tissues and developmental stages. Its applicability is demonstrated through two use cases, highlighting its ability to identify biologically meaningful gene sets associated with bud dormancy transitions and to prioritize candidate reference genes for RT-qPCR normalization in almond. Conclusions: The Almond Expression Atlas v1.0 is publicly available through FruitDBase (fruitdbase.csic.es), providing an interactive platform for gene expression exploration and visualization. This resource supports functional genomics studies and facilitates candidate gene discovery in almond, while ensuring data traceability and promoting data reusability.

bioinformatics↗

Leakage-Controlled Benchmarking of Variant Effect Predictors and Development of Gradient-Boosting Consensus Model

Single amino acid mutations are among the most common forms of genetic variation, with effects ranging from negligible to severe disruption of protein function. Accurate quantitative evaluation of protein variants therefore represents a key challenge in medicine and protein engineering. Computational variant effect predictors (VEPs) are widely used to prioritize missense variants, yet their fair comparison is hindered by data circularity, benchmarks restricted to human clinical annotations, and limited availability of code and training data, all of which can inflate apparent performance. Here, we reviewed 18 recent and 3 older, widely adopted variant effect predictors for reproducible benchmarking and assembled a rigorously curated, leakage-controlled Benchmark dataset by integrating clinically annotated human variants from ClinVar and Humsavar as well as cross-species variants with functional annotations. The evaluation on this dataset revealed a narrow overall performance range for the benchmarked tools (AUC 0.631-0.758) and a consistent drop from clinical to functional data. Surprisingly, several tools published two decades ago demonstrated comparable performance to mid-ranked modern predictors. Building on these results, we developed a gradient-boosting consensus model, PredictSNPprot, which achieved an AUC of 0.82 on a held-out test set, outperforming the original PredictSNP meta-predictor and the best individual state-of-the-art tool by 13.6 and 5.7 percentage points, respectively. The Benchmark dataset, including the predictions from the evaluated tools, is available at https://doi.org/10.5281/zenodo.23597130 and PredictSNPprot consensus predictor is available at https://github.com/loschmidt/PredictSNPprot.

bioinformatics↗

MaCADEMIA: a software toolbox for single-molecule fluorescence image quantification

Single-molecule localisation microscopy (SMLM) is a powerful tool in molecular and cellular biology and part of the broad umbrella of super-resolution microscopy. A distinct feature of SMLM, is its capacity for deep quantification, beyond just spatial information, to copy number, stoichiometry, molecular interaction and dynamics in living cells. The hardware required is increasingly straightforward to implement. However, complexity remains in the image analysis where much remains fragmented across specialised software tools. Here, we present MaCADEMIA (Matlab Computational Analysis of Diffusion, Emitter Molecular Intensity and Abundance), a simple yet powerful toolbox of MATLAB functions and associated documentation to enable straightforward extraction of quantitative single-molecule information. MaCADEMIA performs ensemble particle detection achieving localisation accuracies down to 0.5 pixels Root mean squared error (RMSE) across wide signal-to-noise (SNR) ranges on standardised particle tracking benchmarks, consistently matching or exceeding the performance of 14 leading methods. Our integrated workflow enables molecular counting and stoichiometry determination through stepwise photobleaching analysis, mobility measurement via mean squared displacement, colocalisaton analysis and absolute protein copy number quantification using three-dimensional cellular models.

bioinformatics↗