Biology subjects

Ben Izhak, M.

Publications and source records attributed to Ben Izhak, M..

3 recordsLinked to original sources

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↗

bioRxiv2020

Projection of gut microbiome pre and post-bariatric surgery to predict surgery outcome

BackgroundBariatric surgery is often the preferred method to resolve obesity and diabetes, with ~800,000 cases worldwide yearly and high outcome variability. The ability to predict the long-term Body Mass Index (BMI) change following surgery has important implications on individuals and the health care system in general. Given the tight connection between eating habits, sugar consumption, BMI, and the gut microbiome, we tested whether the microbiome before any treatment is associated with different treatment outcomes, as well as other intakes (high-density lipoproteins (HDL), Triglycerides, etc.). ResultsA projection of the gut microbiome composition of obese (sampled before and after bariatric surgery) and slim patients into principal components was performed and the relation between this projection and surgery outcome was studied. The projection reveals 3 different microbiome profiles belonging to slim, obese, and obese who underwent bariatric surgery, with post-surgery more different from the slim than the obese. The same projection allowed for a prediction of BMI loss following bariatric surgery, using only the pre-surgery microbiome. ConclusionsThe gut microbiome can be decomposed into main components depicting the patients development and predicting in advance the outcome. Those may be translated into better clinical management of obese individuals planning to undergo metabolic surgery. ImportanceBMI and diabetes can affect the gut microbiome composition. Bariatric surgery has large variabilities in outcome. The microbiome was previously shown to be a good predictor for multiple diseases. We analyzed here the gut microbiome before and after bariatric surgery and show that: O_LIThe microbiome before surgery can be used to predict surgery outcome. C_LIO_LIPost-surgery microbiome drifts further away from the slim microbiome than pre-surgery obese patients. C_LI These results can lead to a microbiome-based pre-surgery decision whether to perform surgery.

microbiology↗