Research group of microbiome

Alumni

Madis Jaagura, PhD

• Group-related publication(s): “Comorbidities confound metabolomics studies of human disease”

Seungbaek Lee, PhD (visiting PhD student)

• Supervisors: Andres Salumets, Elin Org, Terhi Piltonen, Riikka K. Arffman
• Thesis title: “Mining biomarkers for infertility-associated conditions. Studies on polycystic ovary syndrome and recurrent implantation failure through microbiome and AI-based approaches”

Lisann Adamson, MSc

• Supervisors: Elin Org, Annabel Klemets
• Thesis title: "Differences in gut microbiota in patients with inflammatory bowel disease"

Linda Sootak, BSc

• Supervisors: Kertu Liis Krigul, Elin Org
• Thesis title: "Associations between the oral microbiome and colorectal cancer"

Claudia Maria Ruhno, BSc

• Supervisors: Kertu Liis Krigul, Elin Org
• Research interests: Using mouse model to study the human gut microbiome
• Email: [email protected]

Johanna Toodu, BSc

• Supervisor: Kertu Liis Krigul
• Thesis title: “Possibilities for microbiome analysis from fecal occult blood tests used in the screening of colorectal cancer in Estonia”

Valida Kazimova, BSc

• Supervisors: Kertu Liis Krigul, Ingrid Reppo, Elin Org
• Research interests: Microbiome-drug interactions in Type II Diabetes
• Email: [email protected]

Jennifer Neumaier, MSc

• Supervisors: Oliver Aasmets, Elin Org
• Thesis title: Benchmarking Machine Learning performances with Microbiome Data
• Email: [email protected]

Cheung Wai (Alex) Chan, MSc

• Supervisors: Oliver Aasmets
• Thesis title: Gut microbiome in adjustment disorders
• Email: [email protected]

Kristina Muhu, MSc

• Supervisors: Oliver Aasmets
• Thesis title: Prediction models based on log-ratio transformations for diagnosing colorectal cancer

Triin Bulõgina, BSc

• Supervisors: Oliver Aasmets
• Thesis title: Colorectal cancer prediction models using gut microbiome as a predictor

Johana Värv (Koppel), MSc

• Supervisor: Elin Org
• Thesis title: “Soolestiku mikroobide vahendusel moodustuv metaboliit trimetülamiin N-oksiid (TMAO) ja selle seosed tervisenäitajatega Soome populatsioonis”

In 2017, we started collecting oral and stool microbiome samples from the Estonian population in order to investigate the role of the host microbiome in health and disease. The study participants were selected from the Estonian Biobank.

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Sample types:

• 2509 Stool samples; 328 samples with a second measurement ~ 4.5 years later
• 2509 Oral samples
• 2509 Plasma samples
  

Data for analysis:

• Stool metagenomics data (avg. ~20M reads per person + deeper sequencing ~60M reads per person)
• Metagenome assembled genomes, i.e. MAGs (Estonian reference including >80 000 assembled genomes)
• Continuous linkings to electronic health records (EHRs) - diseases, medication usage, medical procedures
• Self-reported questionnaires (health, environmental/lifestyle data, mental health)
• Nightingale NMR metabolomics
• Host genotypes
  

Research interest:

• Prospective disease risk prediction
• Metagenome-assembled genomes, i.e. MAGs (Bacteria, Archaea) and functional analysis
• Developing statistical methodology - improving cross-cohort replication, causal inference

In collaboration with partners from the Tartu University Faculty of Medicine, Institute of Clinical Medicine (Prof. Margus Lember, Prof. Kalle Kisand), the Gastroenterology Department of Internal Medicine Clinic of Tartu University Hospital (Assoc. Prof. Riina Salupere, Dr Hendrik Laja, Dr Karin Kull), and the Department of Surgical and Gynecological Oncology of the Surgery Clinic of Tartu University Hospital (Dr Jaan Soplepmann, Dr Heigo Reima, Dr Taavi Põdramägi, Dr Andri Jääger), we investigate the role of the gut microbiome in the development of colorectal cancer (CRC). We currently collect samples from individuals who participate in the national CRC screening program in Estonia and from individuals who have been diagnosed with colorectal cancer and its pre-cancerous stages. The overall aim of the project is to find new microbiome-based biomarkers that would allow earlier and more accurate detection of colon cancer and contribute to the development of new diagnostic methods for CRC.

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Sample types:

• Oral samples
• Different stool samples (FIT tubes, stabilisation buffer, Fresh-frozen),
• Blood
• Biopsy samples (polyp tissue, healthy epithelial tissue, tumour tissue)
  

Data and samples for analysis:

• metagenomic and 16S rRNA gene sequencing,
• DNA, RNA
• histology data,
• Self-reported health questionnaire
  

Research interest:

• Identification of cancer-related signals in the microbiome of various sample sites
• Identification of microbiome signatures in pre-cancerous stages

Gut microbiome alterations have been clearly linked with inflammatory human diseases. Multiple reports have shown that patients with ulcerative colitis have impaired intestinal barrier (so-called “leaky gut”), and bacteria may translocate into circulation via the gut-blood-liver axis. Moreover, recent studies have shown that blood has unique circulating microbial signatures in certain diseases. In collaboration with The Internal Medicine Clinic of Tartu University Hospital and The Gastroenterology department (Assoc. Prof. Riina Salupere, Dr Hendrik Laja), we would like to determine circulating blood and stool microbiome signatures in Ulcerative colitis, Crohn’s disease and Primary Sclerosing Cholangitis and evaluate the diagnostic and prognostic value of these biomarkers.

Sample types:

• >200 stool samples (>100 cases + >100 controls from EstMB)
  

Data for analysis:

• Stool metagenomics data
• Stool 16S rRNA gene sequencing data
• Self-reported questionnaires (health, environmental/lifestyle data)
  

Research interest:

Identification of IBD-related signals in the microbiome

Rheumatic diseases - ENDOTARGET study (Populational cohort). Funded by H2020 (2023-2027)

Rheumatic diseases (RDs) affect more than 40% of Europe’s population and cause significant disability, pain, reduced lifespan and a very high economic burden (~240B€/year due to rheumatic and musculoskeletal disease in Europe). The study, including numerous international partners, explores the relationship between gut microbiota, gut permeability, and systemic endotoxemia with a special focus on the three most abundant rheumatic diseases (RDs): osteoarthritis, rheumatoid arthritis and spondylarthritis.

Statin treatment (Interventional trial) TeamPerMed project

Research evidence has shown that the bacterial community in our gut can significantly influence the effectiveness of statin therapy, including both response to treatment and side effects. This study aims to determine the microbial community in the gut and evaluate the interactions between microbes and statins. Analyzing intestinal microbes allows us to investigate two main aspects: firstly, how the microbial community of the gut affects the effectiveness of statin treatment (the suitability of the drug and the occurrence of side effects); and secondly, how the use of statins can, in turn, affect the microbial community of the gut. During the study, we collect samples of the microbiome from different time points, which will allow us to monitor changes in your microbiome community over a longer period and, more precisely, investigate the relationship between microbes and other health parameters collected in the study.

Parkinson’s disease (Clinical cohort, collaboration with Dr Toomas Toomosoo)

Parkinson's is a common neurological disease which directly affects around 1.2 million people in Europe. New studies strongly suggest a link between the gut microbiome and Parkinson's disease. The study aims to identify Parkinson-related microbiome signatures in the stool and evaluate the diagnostic and prognostic value of these biomarkers in relation to environmental factors.

• A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument. (2025). Taba et al. Gut Microbes.
• Faecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations. (2025). Nishijima et al. Cell.
• Effects of data transformation and model selection on feature importance in microbiome classification data. (2025). Karwowska & Aasmets et al. Microbiome.
• The reproductive tract microbiome in women with polycystic ovary syndrome and across different menstrual cycle phases. (2025). Lee & Aasmets et al. Human Reproduction.
• A history of repeated antibiotic usage leads to microbiota-dependent mucus defects (2024). Krigul & Feeney et al. Gut microbes.
• Association of Accelerometer‐Determined Physical Activity and Sedentary Behavior With the Gut Microbiome in Middle‐Aged Women: A Compositional Data Approach. (2024). Pérez-Prieto et al. Scandinavian Journal of Medicine & Science in Sports.
• Gut microbiome in endometriosis: a cohort study on 1000 individuals. (2024). Pérez-Prieto et al. BMC medicine.
• Comorbidities confound metabolomics studies of human disease. (2024). Jaagura et al. Scientific Reports.
• Gut bacteriome and mood disorders in women with PCOS. (2024). Lee et al. Human Reproduction.
• Cell-Free Microbial DNA Analysis: Effects of Blood Plasma and Serum Quantity, Biobanking Protocols, and Isolation Kits. (2024). Nikitina et al. Biopreservation and Biobanking.
• A toolbox of machine learning software to support microbiome analysis (2023). Marcos-Zambrano et al. Frontiers in microbiology.
• Uterine Microbiome: Does the Sampling Technique Matter?. (2023). Lüll & Org Seminars in Reproductive Medicine.
• Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort. (2022). Aasmets & Krigul et al. Nature Communications.
• Evaluating the clinical relevance of the enterotypes in the Estonian microbiome cohort. (2022). Aasmets et al. Frontiers in Genetics.
• Differences in microbial profile of endometrial fluid and tissue samples in women with in vitro fertilization failure are driven by Lactobacillus abundance (2022). Lüll et al. Acta Obstetricia et Gynecologica Scandinavica
• Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation. (2021). Aasmets et al. mSystems.
• Using fecal immunochemical tubes for the analysis of the gut microbiome has the potential to improve colorectal cancer screening. (2021). Krigul et al. Scientific Reports.
• Large-scale association analyses identify host factors influencing human gut microbiome composition. (2021). Kurilshikov et al. Nature Genetics.
• Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. (2021). Marcos-Zambrano et al. Frontiers in Microbiology
• The gut microbiome in polycystic ovary syndrome and its association with metabolic traits. (2021). Lüll et al. The Journal of Clinical Endocrinology & Metabolism.

• Antibiotic usage can damage the protective mucus layer in the gut (Popular science article)
• Hidden bacteria may shape your health! (Popular science article)
• Gut metagenome associations with extensive digital health data (Microbiome Virtual International Forum Conference presentation about Estonian Microbiome Project)
• TIMESPAN Keynote: The impact of microbiota on host diseases and drugs by Prof. Elin Org (TIMESPAN's General Assembly meeting in 2023)
• Small country with a great potential for microbiome studies (Popular science article)
• "International researchers see the opportunity we have here": Meet Elin Org, Associate Professor of Genomics and Microbiomics, University of Tartu, and EMBO Installation Grantee (Article by EMBO Communications)
• The gut microbiome can predict changes in glucose regulation (Popular science article)
• Fecal microbiome predicts treatment response after the initiation of semaglutide or empagliflozin uptake (Microbiome Virtual International Forum Conference presentation)