Francisco Zorrilla · MRC Toxicology Unit, University of Cambridge · Darwin College · June 2024
🧬 A PhD thesis on microbial community metabolism. I built metaGEM, an open-source workflow for reconstructing genome-scale metabolic models directly from metagenomes, used it to assemble 14,000+ metagenome-assembled genomes (MAGs) across five ecosystems (lab cultures, human gut, plants, soil, oceans), and applied it to four case studies: amino acid auxotrophies in microbial communities, 🧀 starter-culture interactions in cheese fermentation (industrial collaboration), 🏥 Clostridioides difficile suppression by a defined gut community, and 💊 xenobiotic-metabolism profiling via the gutDBX database. Currently transitioning from academia to industry; full profile and case studies at franciscozorrilla.github.io.
- 🧬 metaGEM: open-source Snakemake workflow for metagenome-to-metabolic-model reconstruction (Nucleic Acids Research, 2021).
- 🦠 14,000+ MAGs and matching genome-scale metabolic models across lab cultures, human gut, plant-associated, soil, and ocean metagenomes.
- 🧀 Industrial collaboration on starter-culture metabolic interactions in cheese fermentation (Nature Communications, 2023).
- 🏥 Translational microbiome work: metabolic basis of C. difficile suppression by a 14-member synthetic community (under review).
- 💊 gutDBX: 141,556-sequence database of microbial xenobiotic and pharmacologically relevant metabolism, validated against the MetaCardis cohort.
- 📚 7 publications and manuscripts bundled into the thesis (peer-reviewed and in-prep).
- Highlights
- Chapters
- Associated publications
- Abstract
- Building the thesis from LaTeX source
- Cite this thesis
- About the author
- Acknowledgements
- License
| # | Title | What I built / what I found | Source |
|---|---|---|---|
| 1 🧭 | Introduction | Scope, work objectives, and the two methodological pillars the thesis draws on. | Chapter1/ |
| 2 🧬 | Metagenomics-driven metabolic modelling | Built metaGEM: an end-to-end Snakemake pipeline that reconstructed 14,000+ MAGs and matching GEMs across five ecosystems. | Chapter2/ |
| 3 🔬 | Amino acid auxotrophies | Cross-community statistical analysis across 12,000+ Earth Microbiome Project samples; auxotrophs enriched in host-associated communities and linked to higher antimicrobial drug tolerance. | Chapter3/ |
| 4 🧀 | Cheese flavour formation | Industrial collaboration; identified valine cross-feeding from Streptococcus thermophilus to Lactococcus starter strains shaping cheese flavour (Nat. Commun. 2023). | Chapter4/ |
| 5 🏥 | C. difficile suppression | GEMs plus metabolomics across a 14-member synthetic gut community; identified Stickland metabolism, vitamin B, polyamines, and FOS as candidate suppression mechanisms. | Chapter5/ |
| 6 💊 | gutDBX | Built a 141,556-sequence database for microbial xenobiotic and drug-related metabolism (UHGG + CMMG, KEGG-module filtered), validated against the MetaCardis cohort. | Chapter6/ |
| 7 🔮 | Conclusions | Future directions: ♻️ global plastic-degrading potential, 🌱 paleometagenomics, and long-read sequencing of wild Amazonian cat gut metagenomes. | Chapter7/ |
Appendices A–D are supplementary material for chapters 2, 3, 5, and 6 → Appendix1/ · Appendix2/ · Appendix3/ · Appendix4/.
- 2021 · Nucleic Acids Research: Zorrilla F, Buric F, Patil KR, Zelezniak A. metaGEM: reconstruction of genome-scale metabolic models directly from metagenomes. doi:10.1093/nar/gkab815. → Chapter 2.
- 2021 · mBio: Zrimec J, Kokina M, Jonasson S, Zorrilla F, Zelezniak A. Plastic-degrading potential across the global microbiome correlates with recent pollution trends. doi:10.1128/mBio.02155-21. → Chapter 7.
- 2022 · Nature Microbiology: Yu JSL, Correia-Melo C, Zorrilla F, et al. Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance. doi:10.1038/s41564-022-01072-5. → Chapter 3.
- 2023 · Nature Communications: Melkonian C, Zorrilla F, et al. Microbial interactions shape cheese flavour formation. doi:10.1038/s41467-023-41059-2. → Chapter 4.
- 2024 · Manuscript in preparation: Yousif G, Zorrilla F, et al. Pervasive obligate metabolite cross-feeding in soil bacteria. → Chapter 3.
- 2024 · bioRxiv (under review): Ambat A, van den Berg NI, Zorrilla F, et al. Emergent metabolic interactions in resistance to Clostridioides difficile invasion. doi:10.1101/2024.08.29.610284. → Chapter 5.
- 2024 · Manuscript in preparation: Zorrilla F, Davis T, Patil KR. gutDBX: a resource for profiling the distribution of xenometabolic and pharmacologically relevant metabolism in the gut microbiome. → Chapter 6.
Full abstract (click to expand)
As microbial ecology research has benefited from advancements in omics technologies, the increasing complexity and volume of generated data has in turn stimulated developments in computational biology approaches for analysis and interpretation. This is where my research lies: at the intersection of microbial ecology, metagenomics, and metabolic modelling. More specifically, this thesis outlines the development and applications of omics-driven metabolic modelling approaches to understand microbial community metabolism in diverse ecological contexts. I begin by providing background on systems biology applications of microbial ecology, metagenomic methods, and genome-scale metabolic modelling approaches. Next, I outline the development of metaGEM, a workflow designed to reconstruct context-specific genome-scale metabolic models (GEMs) via metagenome-assembled genomes (MAGs) and predict nutritional dependencies within communities directly from shotgun metagenomic samples. I applied this workflow to five datasets: synthetic lab cultures, human gut, plant associated, bulk soil, and ocean metagenomes, reconstructing over 14,000 MAGs and corresponding GEMs. Having developed this omics-driven metabolic modelling approach, I applied these methods to three case studies including: i) the distribution of auxotrophies across microbial communities, ii) the role of microbial interactions in cheese flavour formation, and iii) the pathogen-suppressing ability of a defined gut sub-community. In the first case study, I discuss two collaborations aiming to understand the prevalence, role, and origin of auxotrophies across microbes. In the first collaboration, I observed a high frequency of predicted amino acid auxotrophs across microbial communities in a large dataset, with a higher percentage of total auxotroph relative abundance in host-associated compared to free-living samples, as well as evidence for higher drug tolerance in auxotrophs. In the second collaboration, I analysed data from sequenced soil isolates and metagenomes which were also screened in the lab for individual amino acid auxotrophies by colleagues. A metagenomics-driven metabolic modelling analysis found that models largely under-estimated auxotrophies, while genomic annotations alone overestimated them. I also found evidence for the enrichment of insertion sequences in auxotrophic genomes, suggesting a possible mechanism for regulation or gene loss. In the second case study, I teamed up with industrial collaborators to analyse microbial interactions that shape cheese flavour formation. Starting with genomes assembled from fermentation cultures, I reconstructed metabolic models and simulated them under varying media conditions, revealing evidence for amino acid auxotrophies across three community members which could be rescued by a fourth microbe, as well as strain-specific metabolisms contributing to flavour formation. In the third case study, I discuss my collaboration with a team of researchers interested in the metabolic interactions that affect a defined 14-member community's ability to suppress Clostridioides difficile. For this study, I reconstructed and simulated GEMs from context-specific genomes, carried out a metabolomics data analysis between suppressive versus non-suppressive samples, and identified genomic signatures that may explain observed differences in the metabolomic landscape. Moving beyond applications of omics-driven metabolic modelling, I outline the development and application of gutDBX, a metagenomic-driven database containing 141,556 non-dereplicated microbial sequences associated with xenobiotic or pharmacologically relevant metabolism from both human and mouse gut. In short, I used a manually curated subset of relevant KEGG modules to filter sequences based on the eggNOG annotations of two catalogues, while validating the resulting database by mapping against the MetaCardis cohort. Interestingly, I found varying correlations between the number of gutDBX hits and serum cholesterol, Shannon-diversity index, and total number of prescribed drugs. Finally, I conclude by highlighting further applications of metaGEM and detailing possible future directions for omics-driven metabolic modelling, including studying the global microbiome plastic-degrading potential, long read sequencing of understudied environments, and paleometagenomics.
This thesis is built on Krishna Kumar's Cambridge PhD thesis template (PhDThesisPSnPDF.cls, GPLv2). The compiled PDF (thesis.pdf) is tracked here for offline access; the Cambridge Apollo deposit is the canonical version of record.
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Plain text:
Zorrilla, F. (2024). Omics-driven and constraint-based modelling of microbial community metabolism. PhD thesis, University of Cambridge. https://doi.org/10.17863/CAM.114621
BibTeX:
@phdthesis{zorrilla2024omics,
author = {Zorrilla, Francisco},
title = {Omics-driven and constraint-based modelling of microbial community metabolism},
school = {University of Cambridge},
year = {2024},
month = jun,
address = {Cambridge, UK},
doi = {10.17863/CAM.114621},
url = {https://www.repository.cam.ac.uk/items/e8039120-7f95-444f-95af-78fe5ce49db6},
note = {Darwin College, MRC Toxicology Unit},
}GitHub also exposes a structured "Cite this repository" widget in the sidebar, driven by CITATION.cff.
Computational biologist working at the intersection of microbial ecology, metagenomics, and metabolic modelling. Currently co-leading WP5 of NCCR Microbiomes at ETH Zürich, and transitioning from academia to industry, available October 2026, targeting agtech, microbial biotech, and AI-for-biology roles. Full profile, project case studies, talks, and contact details live at franciscozorrilla.github.io.
Full acknowledgements live in Acknowledgement/acknowledgement.tex. Special thanks to my supervisor Kiran R. Patil (MRC Toxicology Unit, University of Cambridge / EMBL Heidelberg) and to the colleagues and collaborators who made the work in chapters 2–6 possible.
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