Search

Beside its unique nutritional content breast milk also contains live cells from the mother. Fate of these cells in the offspring has not been adequately described. In this study, we aimed to detect and identify maternal cells in the suckling’s blood and the brain. Green fluorescent protein expressing transgenic female mice (GFP+) were used as foster mothers to breastfeed wildtype newborn pups. One week and two months after the birth, blood samples and brains of the sucklings were analyzed to detect presence of GFP+ cells by fluorescence activated cell sorting, polymerase chain reaction and immunohistochemistry on the brain sections and optically cleared brains. The tests confirmed that maternal cells were detectable in the blood and the brain of the pups and that they differentiated into both neuronal and glial cell types in the brain. This phenomenon represents breastfeeding - induced microchimerism in the brain with functional implications remain to be understood. [ABSTRACT FROM AUTHOR]

Objective: Myelin is essential for a healthy nervous system. Myelin formed by oligodendrocytes, accelerates impulse propagation and supports neuronal survival. Demyelination leads to neurodegeneration. In multiple sclerosis (MS) immune attack results in demyelination. Our goal is to dissect interactions among oligodendrocytes, neurons, and immune cells to improve our understanding of myelination and the demyelinating diseases. We aim to discover new targets for remyelination therapies. Methods: We are building tools for analyzing protein-protein and cell-to-cell interactions. To identify genes involved in myelination and MS, we developed a bioinformatics-based strategy, interactome analysis, which combines proteome and gene expression methodologies. Identified genes are evaluated in peripheral blood of MS patients and in mouse models. To accelerate drug discovery, 23 machine learning-based methodologies were assessed for myelin identification in fluorescent microscopy images. Results: Interactome analysis identified hundreds of proteinprotein interactions between pairs of oligodendrocytes, neurons, macrophages, microglia, and T cells. Most significant interactions are further analyzed in vivo and in vitro. Our customizedconvolutional neural network and Boosted Tree methods segmented myelin at over 98% accuracy. Identified myelin can be quantified and visualized in 3D. Conclusion: The interactome analysis yielded novel genes that are likely to be linked to MS. Machine learning-based methodologies are very effective in accelerating myelin quantification and thus, drug screens against demyelinating diseases such as MS. Overall, our innovative analysis strategies employing computer assistance produced novel avenues of exploration for myelination and demyelinating diseases. This study was supported TUBITAK (218S495,316S026), Istanbul Medipol University (BAP2018/06), and Turkish Academy of Sciences (GEBIP). [ABSTRACT FROM AUTHOR]