Master of Science, University of Heidelberg (2017)
Bachelor of Science, University of Heidelberg (2014)
Doctor of Philosophy, Eberhard Karls Universitat Tubingen (2022)
Dr. rer. nat., European Molecular Biology Laboratory (EMBL) & Eberhard-Karls-University Tübingen, Bioinformatics (2022)
Master of Science, Ruprecht-Karls-University Heidelberg, Molecular Biotechnology (2017)
Bachelor of Science, Ruprecht-Karls-University Heidelberg, Molecular Biotechnology (2014)
Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.
2021; 22 (1): 93
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .
View details for DOI 10.1186/s13059-021-02306-1
View details for PubMedID 33785070
View details for PubMedCentralID PMC8008609
Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer.
2019; 25 (4): 679-689
Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10-5). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.
View details for DOI 10.1038/s41591-019-0406-6
View details for PubMedID 30936547
View details for PubMedCentralID PMC7984229
A faecal microbiota signature with high specificity for pancreatic cancer.
2022; 71 (7): 1359-1372
Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.To explore the faecal and salivary microbiota as potential diagnostic biomarkers.We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase.Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.
View details for DOI 10.1136/gutjnl-2021-324755
View details for PubMedID 35260444
View details for PubMedCentralID PMC9185815
Microbiota-dependent activation of the myeloid calcineurin-NFAT pathway inhibits B7H3- and B7H4-dependent anti-tumor immunity in colorectal cancer.
2022; 55 (4): 701-717.e7
Bacterial sensing by intestinal tumor cells contributes to tumor growth through cell-intrinsic activation of the calcineurin-NFAT axis, but the role of this pathway in other intestinal cells remains unclear. Here, we found that myeloid-specific deletion of calcineurin in mice activated protective CD8+ T cell responses and inhibited colorectal cancer (CRC) growth. Microbial sensing by myeloid cells promoted calcineurin- and NFAT-dependent interleukin 6 (IL-6) release, expression of the co-inhibitory molecules B7H3 and B7H4 by tumor cells, and inhibition of CD8+ T cell-dependent anti-tumor immunity. Accordingly, targeting members of this pathway activated protective CD8+ T cell responses and inhibited primary and metastatic CRC growth. B7H3 and B7H4 were expressed by the majority of human primary CRCs and metastases, which was associated with low numbers of tumor-infiltrating CD8+ T cells and poor survival. Therefore, a microbiota-, calcineurin-, and B7H3/B7H4-dependent pathway controls anti-tumor immunity, revealing additional targets for immune checkpoint inhibition in microsatellite-stable CRC.
View details for DOI 10.1016/j.immuni.2022.03.008
View details for PubMedID 35364006
Calorie restriction improves metabolic state independently of gut microbiome composition: a randomized dietary intervention trial.
2022; 14 (1): 30
The gut microbiota has been suggested to play a significant role in the development of overweight and obesity. However, the effects of calorie restriction on gut microbiota of overweight and obese adults, especially over longer durations, are largely unexplored.Here, we longitudinally analyzed the effects of intermittent calorie restriction (ICR) operationalized as the 5:2 diet versus continuous calorie restriction (CCR) on fecal microbiota of 147 overweight or obese adults in a 50-week parallel-arm randomized controlled trial, the HELENA Trial. The primary outcome of the trial was the differential effects of ICR versus CCR on gene expression in subcutaneous adipose tissue. Changes in the gut microbiome, which are the focus of this publication, were defined as exploratory endpoint of the trial. The trial comprised a 12-week intervention period, a 12-week maintenance period, and a final follow-up period of 26 weeks.Both diets resulted in ~5% weight loss. However, except for Lactobacillales being enriched after ICR, post-intervention microbiome composition did not significantly differ between groups. Overall weight loss was associated with significant metabolic improvements, but not with changes in the gut microbiome. Nonetheless, the abundance of the Dorea genus at baseline was moderately predictive of subsequent weight loss (AUROC of 0.74 for distinguishing the highest versus lowest weight loss quartiles). Despite the lack of consistent intervention effects on microbiome composition, significant study group-independent co-variation between gut bacterial families and metabolic biomarkers, anthropometric measures, and dietary composition was detectable. Our analysis in particular revealed associations between insulin sensitivity (HOMA-IR) and Akkermansiaceae, Christensenellaceae, and Tanerellaceae. It also suggests the possibility of a beneficial modulation of the latter two intestinal taxa by a diet high in vegetables and fiber, and low in processed meat.Overall, our results suggest that the gut microbiome remains stable and highly individual-specific under dietary calorie restriction.The trial, including the present microbiome component, was prospectively registered at ClinicalTrials.gov NCT02449148 on May 20, 2015.
View details for DOI 10.1186/s13073-022-01030-0
View details for PubMedID 35287713
View details for PubMedCentralID PMC8919571
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.
2022; 19 (2): 179-186
Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics.
View details for DOI 10.1038/s41592-021-01343-9
View details for PubMedID 35027765
View details for PubMedCentralID PMC8828471
Unravelling the collateral damage of antibiotics on gut bacteria.
2021; 599 (7883): 120-124
Antibiotics are used to fight pathogens but also target commensal bacteria, disturbing the composition of gut microbiota and causing dysbiosis and disease1. Despite this well-known collateral damage, the activity spectrum of different antibiotic classes on gut bacteria remains poorly characterized. Here we characterize further 144 antibiotics from a previous screen of more than 1,000 drugs on 38 representative human gut microbiome species2. Antibiotic classes exhibited distinct inhibition spectra, including generation dependence for quinolones and phylogeny independence for β-lactams. Macrolides and tetracyclines, both prototypic bacteriostatic protein synthesis inhibitors, inhibited nearly all commensals tested but also killed several species. Killed bacteria were more readily eliminated from in vitro communities than those inhibited. This species-specific killing activity challenges the long-standing distinction between bactericidal and bacteriostatic antibiotic classes and provides a possible explanation for the strong effect of macrolides on animal3-5 and human6,7 gut microbiomes. To mitigate this collateral damage of macrolides and tetracyclines, we screened for drugs that specifically antagonized the antibiotic activity against abundant Bacteroides species but not against relevant pathogens. Such antidotes selectively protected Bacteroides species from erythromycin treatment in human-stool-derived communities and gnotobiotic mice. These findings illluminate the activity spectra of antibiotics in commensal bacteria and suggest strategies to circumvent their adverse effects on the gut microbiota.
View details for DOI 10.1038/s41586-021-03986-2
View details for PubMedID 34646011
View details for PubMedCentralID PMC7612847
Commensal Clostridiales strains mediate effective anti-cancer immune response against solid tumors.
Cell host & microbe
2021; 29 (10): 1573-1588.e7
Despite overall success, T cell checkpoint inhibitors for cancer treatment are still only efficient in a minority of patients. Recently, intestinal microbiota was found to critically modulate anti-cancer immunity and therapy response. Here, we identify Clostridiales members of the gut microbiota associated with a lower tumor burden in mouse models of colorectal cancer (CRC). Interestingly, these commensal species are also significantly reduced in CRC patients compared with healthy controls. Oral application of a mix of four Clostridiales strains (CC4) in mice prevented and even successfully treated CRC as stand-alone therapy. This effect depended on intratumoral infiltration and activation of CD8+ T cells. Single application of Roseburia intestinalis or Anaerostipes caccae was even more effective than CC4. In a direct comparison, the CC4 mix supplementation outperformed anti-PD-1 therapy in mouse models of CRC and melanoma. Our findings provide a strong preclinical foundation for exploring gut bacteria as novel stand-alone therapy against solid tumors.
View details for DOI 10.1016/j.chom.2021.08.001
View details for PubMedID 34453895
Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis.
2021; 13 (1): 117
Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a "healthy-like" status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies.Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS.Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.
View details for DOI 10.1186/s13073-021-00925-8
View details for PubMedID 34271980
View details for PubMedCentralID PMC8284018
Metabolic models predict bacterial passengers in colorectal cancer.
Cancer & metabolism
2020; 8: 3
Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment.We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers.We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome.Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.
View details for DOI 10.1186/s40170-020-0208-9
View details for PubMedID 32055399
View details for PubMedCentralID PMC7008539
Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation.
2019; 25 (4): 667-678
Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.
View details for DOI 10.1038/s41591-019-0405-7
View details for PubMedID 30936548
Extensive transmission of microbes along the gastrointestinal tract.
The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 individuals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy individuals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease.
View details for DOI 10.7554/eLife.42693
View details for PubMedID 30747106
View details for PubMedCentralID PMC6424576
Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells.
Methods in molecular biology (Clifton, N.J.)
2018; 1711: 103-132
Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .
View details for DOI 10.1007/978-1-4939-7493-1_6
View details for PubMedID 29344887
View details for PubMedCentralID PMC6126574
Red Blood Cells Preconditioned with Hemin Are Less Permissive to Plasmodium Invasion In Vivo and In Vitro.
2015; 10 (10): e0140805
Malaria is a parasitic disease that causes severe hemolytic anemia in Plasmodium-infected hosts, which results in the release and accumulation of oxidized heme (hemin). Although hemin impairs the establishment of Plasmodium immunity in vitro and in vivo, mice preconditioned with hemin develop lower parasitemia when challenged with Plasmodium chabaudi adami blood stage parasites. In order to understand the mechanism accounting for this resistance as well as the impact of hemin on eryptosis and plasma levels of scavenging hemopexin, red blood cells were labeled with biotin prior to hemin treatment and P. c. adami infection. This strategy allowed discriminating hemin-treated from de novo generated red blood cells and to follow the infection within these two populations of cells. Fluorescence microscopy analysis of biotinylated-red blood cells revealed increased P. c. adami red blood cells selectivity and a decreased permissibility of hemin-conditioned red blood cells for parasite invasion. These effects were also apparent in in vitro P. falciparum cultures using hemin-preconditioned human red blood cells. Interestingly, hemin did not alter the turnover of red blood cells nor their replenishment during in vivo infection. Our results assign a function for hemin as a protective agent against high parasitemia, and suggest that the hemolytic nature of blood stage human malaria may be beneficial for the infected host.
View details for DOI 10.1371/journal.pone.0140805
View details for PubMedID 26465787
View details for PubMedCentralID PMC4605744