AgeFactDB (http://agefactdb.jenage.de) is a database aimed at the collection and integration of ageing phenotype data including lifespan information. Ageing factors are considered to be genes, chemical compounds or other factors such as dietary restriction, whose action results in a changed lifespan or another ageing phenotype. Any information related to the effects of ageing factors is called an observation and is presented on observation pages. To provide concise access to the complete information for a particular ageing factor, corresponding observations are also summarized on ageing factor pages. In a first step, ageing-related data were primarily taken from existing databases such as the Ageing Gene Database—GenAge, the Lifespan Observations Database and the Dietary Restriction Gene Database—GenDR. In addition, we have started to include new ageing-related information. Based on homology data taken from the HomoloGene Database, AgeFactDB also provides observation and ageing factor pages of genes that are homologous to known ageing-related genes. These homologues are considered as candidate or putative ageing-related genes. AgeFactDB offers a variety of search and browse options, and also allows the download of ageing factor or observation lists in TSV, CSV and XML formats.

Organoids retain the morphological and molecular patterns of their tissue of origin, are self-organizing, relatively simple to handle and accessible to genetic engineering. Thus, they represent an optimal tool for studying the mechanisms of tissue maintenance and aging. Long-term expansion under standard growth conditions, however, is accompanied by changes in the growth pattern and kinetics. As a potential explanation of these alterations, epigenetic drifts in organoid culture have been suggested. Here, we studied histone tri-methylation at lysine 4 (H3K4me3) and 27 (H3K27me3) and transcriptome profiles of intestinal organoids derived from mismatch repair (MMR)-deficient and control mice and cultured for 3 and 20 weeks and compared them with data on their tissue of origin. We found that, besides the expected changes in short-term culture, the organoids showed profound changes in their epigenomes also during the long-term culture. The most prominent were epigenetic gene activation by H3K4me3 recruitment to previously unmodified genes and by H3K27me3 loss from originally bivalent genes. We showed that a long-term culture is linked to broad transcriptional changes that indicate an ongoing maturation and metabolic adaptation process. This process was disturbed in MMR-deficient mice, resulting in endoplasmic reticulum (ER) stress and Wnt activation. Our results can be explained in terms of a mathematical model assuming that epigenetic changes during a long-term culture involve DNA demethylation that ceases if the metabolic adaptation is disturbed.

It is generally accepted that epigenetic modifications, such as DNA and histone methylations, affect transcription and that a gene’s transcription feeds back on its epigenetic profile. Depending on the epigenetic modification, positive and negative feedback loops have been described. Here, we study whether such interrelation are mandatory and how transcription factor networks affect it. We apply self-organizing map machine learning to a published data set on the specification and differentiation of murine intestinal stem cells in order to provide an integrative view of gene transcription and DNA, as well as histone methylation during this process. We show that, although gain/loss of H3K4me3 at a gene promoter is generally considered to be associated with its increased/decreased transcriptional activity, such an interrelation is not mandatory, i.e., changes of the modification level do not necessarily affect transcription. Similar considerations hold for H3K27me3. In addition, even strong changes in the transcription of a gene do not necessarily affect its H3K4me3 and H3K27me3 modification profile. We provide a mechanistic explanation of these phenomena that is based on a model of epigenetic regulation of transcription. Thereby, the analyzed data suggest a broad variance in gene specific regulation of histone methylation and support the assumption of an independent regulation of transcription by histone methylation and transcription factor networks. The results provide insights into basic principles of the specification of tissue stem cells and highlight open questions about a mechanistic modeling of this process.