Biology subjects

Maier, D.

Publications and source records attributed to Maier, D..

5 recordsLinked to original sources

bioRxiv2026

Coculture of Collinsella aerofaciens and Bacteroides thetaiotaomicron under bile acid stress reveals vitamin B6 exchange

Although bile acid-mediated microbiome-host interactions are known to shape gut microbial composition and function, the mechanisms by which bile acid stress influences microbial metabolic interactions remain poorly understood. Here, we investigated the interaction between two key gut microbes, Bacteroides thetaiotaomicron and Collinsella aerofaciens, under deoxycholic acid (DCA) stress. In anaerobic coculture, C. aerofaciens mitigated the inhibitory effects of DCA on B. thetaiotaomicron, primarily through DCA uptake from the medium, as confirmed by DCA quantification. Proteomic analysis showed that DCA broadly disrupted amino acid and vitamin metabolism, particularly in B. thetaiotaomicron. In contrast, coculture promoted widespread metabolic activation in C. aerofaciens, including enhanced vitamin B6 metabolism and increased production of citrulline and ornithine. These findings suggest that metabolic cooperation enhances resistance to bile acid stress and may contribute to gut microbiome resilience, with potential relevance to liver- and bile acid-related disorders.

microbiology↗

bioRxiv2022

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2020

Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case

The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualisation. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case (S4). In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data (S5). A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research. Availabilitywww.zikaknowledgebase.eu FundingEuropean Commissions Seventh Framework Research Programme project PREPARE (FP7-Health n{degrees}602525) and ZIKALLIANCE (MK, H2020; No 734548). Author summaryDuring the recent ZIKV outbreak there was little information about the interactions between Zika virus and the host, however, the massive research response lead to a steep increase in the number of relevant publications within a very short period of time. At the time, there was no structured and comprehensive database available for integrated molecular and physiological data and knowledge about ZIKV infection. Researchers had to manually review the literature (amounting to over 5000 articles on ZIKV during our last update of the ZIKA KB in September 2018) to extract information about host-pathogen interaction and affected molecular, cellular and organ pathways. We explored the use of automated literature analysis and a defined cooperative effort between experts from various scientific, biomedical and information-technology domains to rapidly compile existing pathophysiological knowledge as a potential tool to support investigations during an emergency. This tool is contrasted with conventional approaches that would take months to comb through the massive amount of existing literature. In addition to providing background information for research, scientific publications can be processed to transform textual information into complex networks, which can be integrated with existing knowledge resources to suggest novel hypotheses that potentially contribute to innovative infectious disease research approaches. This study shows that the knowledge extraction and mapping process required to inform clinical and research responses to an emerging epidemic can be efficiently and effectively executed with a dedicated and trained group of experts, a validated process and the necessary tools. Our results further provide an overview of ZIKV biology, allow prediction of drug efficacy and indentify specific host factors and signalling pathways affected by ZIKV.

pathology↗

bioRxiv2019

Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines

Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides accurate predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a powerful basis for the assessment of gastric cancer signaling and for the development and discovery of predictive biomarkers.\n\nAuthor SummaryUnraveling the causal differences between drug responders and non-responders is an important challenge. The information can help to understand molecular mechanisms and to guide the selection and design of targeted therapies. Here, we approach this problem for cetuximab treatment for gastric cancer using mechanistic mathematical modeling. The proposed model describes multiple gastric cancer cell lines and can accurately predict the response in several validation experiments. Our analysis provides a differentiated view on mutations and explains, for instance, the relevance of MET mutations and the insignificance of PIK3CA mutation in the considered cell lines. The model might provide the basis for understanding the recent failure of several clinical studies.

systems biology↗

bioRxiv2019

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

bioinformatics↗