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Ramoz, N.

Publications and source records attributed to Ramoz, N..

2 recordsLinked to original sources

Contributions of the gut microbiota and intestinal gluconeogenesis to metabolic adaptations in a mouse model of anorexia nervosa.

Anorexia nervosa is a metabolic-psychiatric disorder characterized by severe food restriction, often accompanied by hyperactivity, associated with high mortality and the lack of specific pharmacological treatment. Despite a marked energy deficit, patients paradoxically maintain euglycemia, suggesting adaptations in energy homeostasis. This study aims to characterize the interactions between food restriction, the gut microbiota and peripheral organs in a mouse model mimicking anorexia nervosa. Our results show that food-restricted mice exhibit improved glucose tolerance and increased expression of the gluconeogenic genes G6pc and Pck1 in the intestine, suggesting an adaptation in endogenous glucose production. Gut microbiota analysis reveals a marked shift in composition in food-restricted mice, with an increase in the Lachnospiraceae and Marinifilaceae families and a decrease in Lactobacillaceae. These changes are associated with metabolic parameters such as glycemia, body weight, and GLP-1 levels. Transfer of microbiota from food-restricted mice into control mice improves glucose tolerance and increases the gluconeogenic gene expression. Furthermore, the improvement in glucose tolerance is abolished in mice lacking intestinal gluconeogenesis, suggesting that intestinal gluconeogenesis is required for the effects of the FR microbiota on glucose homeostasis. Overall, these findings highlight a complex metabolic adaptation in a mouse model of anorexia nervosa, involving interactions between the gut microbiota, intestinal gluconeogenesis and maintenance of energy homeostasis.

physiology↗

Auto-qPCR: A Python based web app for automated and reproducible analysis of qPCR data

Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.

molecular biology↗