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‎projects/_posts/2023-11-01-gut-dysbiosis-disease-association.md‎

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---
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layout: project
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title: "Gut dysbiosis / disease associations"
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contributors: [nkarcher, ihmgonnet, spekel]
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contributors: [nkarcher, ihmgonnet, spekel, essex]
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handle: dysbiosis
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status: ongoing
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type: dataset
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grant:
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grant_url:
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image: /assets/images/projects/dysbiosis.png
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tagline: Can we obtain a quantitative understanding of gut dysbiosis to rationalise microbiome modulation?
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tags:
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tagline: Can we obtain a quantitative understanding of gut dysbiosis to rationalise microbiome modulation?
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tags:
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# Data and code
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github:
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github:
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neurovault:
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openneuro:
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figshare:

‎projects/_posts/2023-11-01-statistical-modeling-meta-analysis.md‎

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layout: project
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title: "Statistical modelling and meta-analyses"
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contributors: [nkarcher, fspringer, ihmgonnet, sromano, spekel]
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contributors: [nkarcher, fspringer, ihmgonnet, sromano, spekel, essex]
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handle: stats
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status: ongoing
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type: dataset
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grant:
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grant_url:
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image: /assets/images/projects/meta-analysis.png
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tagline: How to effectively utilise statistical modelling and machine learning to delineate microbiome-disease signatures and identify robust biomarkers for disease diagnosis and prognosis?
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tags:
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tagline: How to effectively utilise statistical modelling and machine learning to delineate microbiome-disease signatures and identify robust biomarkers for disease diagnosis and prognosis?
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tags:
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# Data and code
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github:
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github:
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neurovault:
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openneuro:
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figshare:
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---
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{% include JB/setup %}
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The human gut microbiome (the complex ecosystem of microbes in the human intestines) is recognized as an important factor influencing human health. In the last decade, a plethora of studies have described associations between the gut microbiome and human diseases, manifesting in the gut, such as colorectal cancer, or in other organ systems, such as cardiovascular or neurodegenerative diseases (see [dysbiosis](/projects/gut-dysbiosis-disease-association) project). These studies have sparked interest in the use of the human microbiome for diagnostic and prognostic purposes in the form of biomarkers identified through statistical modelling and machine learning. However, differences in methodologies and lack of thorough statistical assessments have often generated discrepant results across studies. To address these issues, we develop software frameworks for the identification of associations and machine-learning based microbiome disease signatures and biomarkers. Importantly, we engineer them to handle (observed) confounders and make them applicable in meta-analyses allowing researchers to compare findings across cohorts to obtain more robust findings. We have validated our software pipelines across many microbiome-disease association data sets and implemented easy-to-use interfaces.
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The human gut microbiome (the complex ecosystem of microbes in the human intestines) is recognized as an important factor influencing human health. In the last decade, a plethora of studies have described associations between the gut microbiome and human diseases, manifesting in the gut, such as colorectal cancer, or in other organ systems, such as cardiovascular or neurodegenerative diseases (see [dysbiosis](/projects/gut-dysbiosis-disease-association) project). These studies have sparked interest in the use of the human microbiome for diagnostic and prognostic purposes in the form of biomarkers identified through statistical modelling and machine learning. However, differences in methodologies and lack of thorough statistical assessments have often generated discrepant results across studies. To address these issues, we develop software frameworks for the identification of associations and machine-learning based microbiome disease signatures and biomarkers. Importantly, we engineer them to handle (observed) confounders and make them applicable in meta-analyses allowing researchers to compare findings across cohorts to obtain more robust findings. We have validated our software pipelines across many microbiome-disease association data sets and implemented easy-to-use interfaces.

‎team/_posts/2018-12-31-essex-morgan.md‎

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title: Morgan Essex
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position: Intern / MSc Student
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current_position: PhD Student at MDC Berlin
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position: Project Manager and Postdoctoral Researcher
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handle: essex
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science_names: [Essex M]
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image: essex-morgan.png
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alumni: true
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linkedin:
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alumni: false
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country: [us]
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# social
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email:
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email: m.e.essex@lumc.nl
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linkedin: messex
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orcid: 0000-0001-8758-7497
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researchgate: Morgan-Essex
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scholar: bZuOHS8AAAAJ
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github:
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github: sxmorgan
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---
2018

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Morgan Essex was a Intern / MSc Student in the lab from 2017-12-01 to 2018-12-31. Afterwards she did her PhD at MDC/Charite Berlin with Sofia Forslund.
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Morgan joined in 2025 as a project leader, having been an MSc student and PhD collaborator of the group since 2018. She completed her doctoral research with Sofia Forslund at the Charité Hospital Berlin in 2024, focusing on hypothesis generation, robust statistical methods for (confounded) clinical microbiome studies, and immune-mediated disease. Before that, she studied engineering and pharmaceutical sciences at Purdue University and systems biology at Heidelberg University. Her scientific interest in studying the microbial world within began with a personal health and nutrition journey.
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Morgan’s primary role in the group is research and administrative support. To help the group run smoothly, she will help supervise and coordinate PhD projects, pitch in with statistical analyses and publications, and organize group knowledge and resources. Morgan’s PhD research dove into the statistics of disease association studies and confounders in microbiome data, interests that began in the Zeller group and remain part of its core expertise. She is passionate about scientific communication and policy, open science, and the power of interdisciplinary collaboration to translate knowledge into impact.

‎team/_posts/2019-10-01-baghai-arassi-maral.md‎

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title: Maral Baghai Arassi
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position: Clinician Scientist
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position: Clinician Scientist [shared]
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handle: mbaghai
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science_names: [Baghai Arassi M]
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image: baghai-arassi-maral.jpg

‎team/_posts/2021-02-01-karcher-nicolai.md‎

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title: Nicolai Karcher
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position: Postdoctoral Researcher
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current_position: Senior Bioinformatician at EMBL Heidelberg, Typas Group
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handle: nkarcher
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nickname: Nic
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image: karcher-nicolai.jpg
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science_names: [Karcher N]
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alumni: false
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alumni: true
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country: [de]
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# social
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linkedin: nicolai-karcher-a0700a201
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Nic has been a postdoc in the Zeller lab since May 2022. Before that, he did his PhD with Nicola Segata (University of Trento, Italy) in collaboration with the Zeller lab. For his PhD work he used shotgun metagenomics data to study strain-level variation and strain transmission characteristics of human gut bacteria.
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Nic was a postdoc in the Zeller lab from May 2022 to December 2024. Before that, he did his PhD with Nicola Segata (University of Trento, Italy) in collaboration with the Zeller lab. For his PhD work he used shotgun metagenomics data to study strain-level variation and strain transmission characteristics of human gut bacteria.
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His current research interests are disease signatures of the human gut microbiome (with a particular interest in colorectal cancer), how microbial metabolism of drugs is connected to solid organ transplantation outcomes as well as image analysis of intra-tumoral microbial FISH data.

‎team/_posts/2021-05-19-springer-fabian.md‎

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title: Fabian Springer
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position: PhD Student
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position: PhD Student [shared]
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handle: fspringer
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science_names: [Springer F]
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image: springer-fabian.jpg
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Fabian joined the group as a PhD student in May 2021 at EMBL Heidelberg. He obtained his Bachelor's degree in Biochemistry from the University of Tübingen and his Master's in Systems Biology from Heidelberg University, specialising in multi-omics analysis.
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In his current PhD research, Fabian is exploring tumour tissue microbiomes, broadening the scope beyond colorectal cancer to various other cancer types. He focuses on studying microbiomes in low-bacterial biomass samples and establishing connections between microbial composition and molecular tumour characteristics. His research aims at deciphering the complex interactions between host cells and tissue-resident microbes, potentially illuminating new facets of cancer biology.
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‎team/alumni/2024-02-21-morgan-essex.md‎

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