No abstract

This approach was utilized for microarray-based gene expression profiling of duodenum mucosa in mice to conduct bioinformatics and network analysis. However, it is also applicable to any differential gene expression analysis, including RNA-seq datasets. Furthermore, the general method structure can be applied to other species, including human. For individuals with limited bioinformatics experience, many of the databases and software in this protocol allow simple inputs for gene list queries, allowing easily understandable analysis. This systems biology protocol can enhance transcriptome data analysis aiding in the generation of hypothesis-driven research and generating testable bioinformatics predictions.

Systems cancer biology analysis of calorie restriction (CR) mechanisms and pathways has not been carried out, leaving therapeutic benefits unclear. Using metadata analysis, we studied gene expression changes in normal mouse duodenum mucosa (DM) response to short-term (2-weeks) 25% CR as a biological model. Our results indicate cancer-associated genes consist of 26% of 467 CR responding differential expressed genes (DEGs). The DEGs were enriched with over-expressed cell cycle, oncogenes, and metabolic reprogramming pathways that determine tissue-specific tumorigenesis, cancer, and stem cell activation; tumor suppressors and apoptosis genes were under-expressed. DEG enrichments suggest telomeric maintenance misbalance and metabolic pathway activation playing dual (anti-cancer and pro-oncogenic) roles. The aberrant DEG profile of DM epithelial cells is found within CR-induced overexpression of Paneth cells and is coordinated significantly across GI tract tissues mucosa. Immune system genes (ISGs) consist of 37% of the total DEGs; the majority of ISGs are suppressed, including cell-autonomous immunity and tumor-immune surveillance. CR induces metabolic reprogramming, suppressing immune mechanics and activating oncogenic pathways. We introduce and argue for our network pro-oncogenic model of the mucosa multicellular tissue response to CR leading to aberrant transcription and pre-malignant states. These findings change the paradigm regarding CR’s anti-cancer role, initiating specific treatment target development. This will aid future work to define critical oncogenic pathways preceding intestinal lesion development and biomarkers for earlier adenoma and colorectal cancer detection.

Systematic analysis of calorie restriction (CR) mechanisms and pathways in cancer biology has not been carried out, leaving therapeutic benefits unclear. Using a systems biology approach and metadata analysis, we studied gene expression changes in the response of normal mouse duodenum mucosa (DM) to short-term (2-weeks) 25% CR as a biological model. We found a high similarity of gene expression profiles in human and mouse DM tissues. Surprisingly, 26% of the 467 CR responding differential expressed genes (DEGs) in mice consist of cancer-associated genes, most never studied in CR contexts. The DEGs were enriched with over-expressed cell cycle, oncogenes, and metabolic reprogramming pathways (MRP) that determine tissue-specific tumorigenesis, cancer, and stem cell activation; tumor suppressors and apoptosis genes were under-expressed. DEG enrichments suggest a misbalance in telomere maintenance and activation of metabolic pathways playing dual (anti-cancer and pro-oncogenic) roles. Immune system genes (ISGs) consist of 37% of the total DEGs; the majority of ISGs are suppressed, including cell-autonomous immunity and tumor immune evasion controls. Thus, CR induces MRP suppressing multiple immune mechanics and activating oncogenic pathways, potentially driving pre-malignant and cancer states. These findings may change the paradigm regarding the anti-cancer role of CR and initiate specific treatment target development.

This approach was utilized for microarray-based gene expression profiling of duodenum mucosa in mice to conduct bioinformatics and network analysis. However, it is also applicable to any differential gene expression analysis, including RNA-seq datasets. Furthermore, the general method structure can be applied to other species, including human. For individuals with limited bioinformatics experience, many of the databases and software in this protocol allow simple inputs for gene list queries, allowing easily understandable analysis. This systems biology protocol can enhance transcriptome data analysis aiding in the generation of hypothesis-driven research and generating testable bioinformatics predictions.