AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities

Heinken, Almut; Acharya, Geeta; Da, Ravcheev; Hertel, Johannes; Nyga, Malgorzata; Oe, Okpala; Hogan, Marcus; Magnúsdóttir, Stefanía; Martinelli, Filippo; Preciat, German; Jn, Edirisinghe; Cs, Henry; Rm, Fleming; Thiele, Ines

doi:10.1101/2020.11.09.375451

Cited by 27 publications

(48 citation statements)

References 98 publications

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“…were obtained from the AGORA authors 117 . To increase the representativeness of our models, we selected genus-level representatives for abundant vaginal species without a corresponding AGORA model that were present with >5% relative abundance in more than 20 samples: As a common Prevotella species in our cohort did not have a species assignment, we grouped Prevotella abundances to the genus level, and To support and improve the accuracy of our tyramine predictions, we validated the presence of the TDC gene, coding for tyrosine decarboxylase.…”

Section: Creating and Interrogating Vaginal Microbiome Modelsmentioning

confidence: 99%

Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

Kindschuh

Baldini

Liu

et al. 2021

Preprint

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Spontaneous preterm birth (sPTB) is a leading cause of maternal and neonatal morbidity and mortality, yet both its prevention and early risk stratification are limited. The vaginal microbiome has been associated with PTB risk, possibly via metabolic or other interactions with its host. Here, we performed untargeted metabolomics on 232 vaginal samples, in which we have previously profiled the microbiota using 16S rRNA gene sequencing. Samples were collected at 20-24 weeks of gestation from women with singleton pregnancies, of which 80 delivered spontaneously before 37 weeks of gestation. We find that the vaginal metabolome correlates with the microbiome and separates into six clusters, three of which are associated with spontaneous preterm birth (sPTB) in Black women. Furthermore, while we identify five metabolites that associate with sPTB, another five associate with sPTB only when stratifying by race. We identify multiple microbial correlations with metabolites associated with sPTB, including intriguing correlations between vaginal bacteria that are considered sub-optimal and metabolites that were enriched in women who delivered at term. We propose that several sPTB-associated metabolites may be exogenous, and investigate another using metabolic models. Notably, we use machine learning models to predict sPTB risk using metabolite levels, weeks to months in advance, with high accuracy. We show that these predictions are more accurate than microbiome-based and maternal covariates-based models. Altogether, our results demonstrate the potential of vaginal metabolites as early biomarkers of sPTB and highlight exogenous exposures as potential risk factors for prematurity.

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Section: Creating and Interrogating Vaginal Microbiome Modelsmentioning

confidence: 99%

Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

Kindschuh

Baldini

Liu

et al. 2021

Preprint

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“…These considerations lead to a methodological paradigm, called ‘ in silico in vivo association pattern analyses’, which integrates population statistics with constraint-based modelling. This paradigm requires metagenomic data quantifying the microbiome at a body site, corresponding metabolomic measurements from the host, a collection of microbial genome-scale metabolic reconstructions, such as provided by AGORA [4, 26], for generating the microbial community models, and adequate metadata for controlling for important covariates, e.g., age, sex, and body mass index (BMI). Conceptually, in silico in vivo association pattern analyses can be defined by three steps (Fig 2).…”

Section: Resultsmentioning

confidence: 99%

“…We used an average Japanese, as described in [26]. Briefly, the in silico Japanese diet was formulated based on the average daily food consumption of 106 Japanese individuals extracted from food frequency questionnaires and 28 days weighed diet records [45] (Table S6a).…”

Section: Methodsmentioning

confidence: 99%

Causal inference on microbiome-metabolome relations viain silico in vivoassociation pattern analyses

Hertel

Heinken²,

Thiele³

2021

Preprint

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The effects of the microbiome on the host's metabolism are core to understanding the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, entailing a methodology to combine microbiome metabolome association studies with in silico constraint-based microbial community modelling. By dissecting confounding and causal paths, we show that in silico in vivo association pattern analyses allows for causal inference on microbiome-metabolome relations in observational data. Then, we demonstrate the feasibility and validity of our approach on a published multi-omics dataset (n=346), demonstrating causal microbiome-metabolite relations for 43 out of 53 metabolites from faeces. Finally, we utilise the identified in silico in vivo association pattern to estimate the microbial component of the faecal metabolome, revealing that the retrieved metabolite prediction scores correlate with the measured metabolite concentrations, and they also reflect the multivariate structure of the faecal metabolome. Concluding, we integrate with hypothesis-free screening association studies and knowledge-based in silico modelling two major paradigms of systems biology, generating a promising new paradigm for causal inference in metabolic host-microbe interactions.

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“…Thus, various studies have in turn used the genome scale models from the AGORA resource to construct context-specific models ( Bauer et al, 2017 ; Bunesova et al, 2018 ; Tramontano et al, 2018 ; Pryor et al, 2019 ; Yilmaz et al, 2019 ). Recently, the authors of AGORA and their collaborators extended the framework to 7206 strains by incorporating information on the drug-metabolizing potential of the bacterial strains ( Heinken et al, 2020 ).…”

Section: Classification Of Computational Methods In Microbiome-host Interactionsmentioning

confidence: 99%

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

et al. 2021

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The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

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AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities

Cited by 27 publications

References 98 publications

Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome

Causal inference on microbiome-metabolome relations viain silico in vivoassociation pattern analyses

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

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