belogolovski, A. | Search bioRxiv

bioRxiv2021

Progesterone supplementation in mice leads to microbiome alterations and weight gain in a sex-specific manner

BackgroundProgesterone is a steroid hormone produced by the ovaries, involved in pregnancy progression and necessary for successful gestation. We have previously shown that progesterone affects gut microbiota composition and leads to increased relative abundance of Bifidobacterium. ResultsIn non-pregnant female GF mice, levels of progesterone were significantly higher than in SPF mice of the same status. However, no significant differences were observed between GF and SPF males. Females treated with progesterone gained more weight than females treated with a placebo. In contrast to female mice, males treated with progesterone did not gain significantly more weight than males treated with a placebo. Progesterone supplementation led to microbial changes in females but not in males (16S rRNA sequencing). Accordingly, the weight gain observed in female mice treated with progesterone was fully transferable to both male and female germ-free mice via fecal transplantation. ConclusionsWe demonstrate that bacteria play a role in regulating progesterone levels in a female-specific manner. Furthermore, weight gain and metabolic changes associated with progesterone may be mediated by the gut microbiota.

microbiology↗

bioRxiv2020

MIPMLP - Microbiome Pre-processing Machine Learning Pipeline.

16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML. We checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification. We show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand alone version at https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac.il/Home ImportanceMicrobiome composition has been proposed as a biomarker (mic-marker) for multiple diseases. However, a clear analysis of the optimal way to represent the gene sequence counts is still lacking. We propose a simple and straight forward method that significantly improves the accuracy of mic-marker studies. This method can be of use to merge two of the most important advances in biology in the last decade: Microbiome analysis, and the introduction of machine learning methods to biological studies.

microbiology↗