@article{SiptrothMoskalenkoKrumbiegeletal.2023, author = {Siptroth, Julienne and Moskalenko, Olga and Krumbiegel, Carsten and Ackermann, J{\"o}rg and Koch, Ina and Pospisil, Heike}, title = {Variation of butyrate production in the gut microbiome in type 2 diabetes patients}, series = {International Microbiology}, volume = {26}, journal = {International Microbiology}, publisher = {Springer Nature}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17802}, pages = {601 -- 610}, year = {2023}, abstract = {Background Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student's t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive. Results By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection. Conclusions With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease.}, language = {en} } @article{SiptrothMoskalenkoKrumbiegeletal.2023, author = {Siptroth, Julienne and Moskalenko, Olga and Krumbiegel, Carsten and Ackermann, J{\"o}rg and Koch, Ina and Pospisil, Heike}, title = {Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks}, series = {Discover Artificial Intelligence}, volume = {3}, journal = {Discover Artificial Intelligence}, number = {1}, publisher = {Springer Nature}, issn = {2731-0809}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17497}, year = {2023}, abstract = {Background Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients. Methods Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways. Results It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674). Conclusion With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis.}, language = {en} } @article{PfeilSiptrothPospisiletal.2023, author = {Pfeil, Juliane and Siptroth, Julienne and Pospisil, Heike and Frohme, Marcus and Hufert, Frank T. and Moskalenko, Olga and Yateem, Murad and Nechyporenko, Alina}, title = {Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition}, series = {Big Data and Cognitive Computing}, volume = {7}, journal = {Big Data and Cognitive Computing}, number = {1}, publisher = {MDPI}, issn = {2504-2289}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17184}, year = {2023}, abstract = {Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96\% accuracy. Samples from healthy persons were detected with a specificity of 97\% and those from T2D individuals with a sensitivity of 92\%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.}, language = {en} }