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Akhavan, S. R.

Publications and source records attributed to Akhavan, S. R..

2 recordsLinked to original sources

PyBootNet: A Python Package for Bootstrapping and Network Construction

PyBootNet is a user-friendly Python package that integrates bootstrapping analysis and correlation network construction. The package offers functions for generating bootstrapped network metrics, statistically comparing network metrics among datasets, and visualizing bootstrapped networks. PyBootNet is designed to be accessible and efficient, with minimal dependencies and straightforward input requirements. To demonstrate its functionality, we applied PyBootNet functions to compare networks within two disparate microbial community datasets: a mouse gut microbiome study and a microbiome study of a built environment. The PyBootNet functions applied include data preprocessing, bootstrapping, correlation matrix calculation, network statistics computation, and network visualization. In both datasets, we show that PyBootNet can generate robust bootstrapped network metrics and identify significant differences in one or more network metrics between pairs of networks. We also show that PyBootNet can create bootstrapped network graphs and identify clusters of nodes that are highly interconnected. We also confirmed its computational efficiency and scalability, which allows it to handle large and complex datasets. PyBootNet provides a powerful and extendible Python bioinformatics solution for bootstrapping analysis and network construction that can be applied to microbial, gene, metabolite and other biological data appropriate for network correlation comparison and analysis.

bioinformatics↗

Genetic hypogonadal (Gnrh1 hpg) mouse model uncovers influence of reproductive axis on maturation of the gut microbiome during puberty

The gut microbiome plays a key role in human health and gut dysbiosis is linked to many sex-specific diseases including autoimmune, metabolic, and neurological disorders. Activation of the hypothalamic-pituitary-gonadal (HPG) axis during puberty leads to sexual maturation and development of sex differences through the action of gonadal sex steroids. While the gut microbiome also undergoes sex differentiation, the mechanisms involved remain poorly understood. Using a genetic hypogonadal (hpg) mouse model, we sampled the fecal microbiome of male and female wild-type and hpg mutant mice before and after puberty to determine how microbial taxonomy and function are influenced by age, sex, and the HPG axis. We showed that HPG axis activation during puberty is required for sexual maturation of the gut microbiota composition, community structure, and metabolic functions. We also demonstrated that some sex differences in taxonomic composition and amine metabolism developed independently of the HPG axis, indicating that sex chromosomes are sufficient for certain sex differences in the gut microbiome. In addition, we showed that age, independent of HPG axis activation, led to some aspects of pubertal maturation of the gut microbiota community composition and putative functions. These results have implications for microbiome-based treatments, indicating that sex, hormonal status, and age should be considered when designing microbiome-based therapeutics.

microbiology↗