The embryonic trunk neural crest microenvironment regulates the plasticity and invasion of human neuroblastoma via TrkB signaling

Case report: Molecular characterisation of adipose-tissue derived cells from a patient with ROHHAD syndrome

There have been over 100 cases of Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome reported, but there is currently no curative treatment for children with this condition. We aimed to better characterise adipose cells from a child with ROHHAD syndrome. We isolated pre-adipocytes from a 4 year-old female patient with ROHHAD syndrome and assessed proliferation rate of these cells. We evaluated levels of DLP-Pref-1(pre-adipocyte marker) using western blotting, and concentrations of interleukin-6(IL-6) using ELISA. We performed next-generation sequencing (NGS) and bioinformatic analyses on these cells compared to tissue from an age/sex-matched control. The two most up-/down-regulated genes were validated using QPCR. We successfully isolated pre-adipocytes from a fat biopsy, by confirming the presence of Pref-1 and differentiated them to mature adipocytes. Interleukin 6, (Il-6) levels were 5.6-fold higher in ROHHAD cells compared to a control age/sex-matched biopsy. NGS revealed 25,703 differentially expressed genes (DEGs) from ROHHAD cells vs. control of which 2,237 genes were significantly altered. The 20 most significantly up/down-regulated genes were selected for discussion. This paper describes the first transcriptomic analysis of adipose cells from a child with ROHHAD vs. normal control adipose tissue as a first step in identifying targetable pathways/mechanisms underlying this condition with novel therapeutic interventions.

Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects

Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis. Materials and Methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbour (KNN) with 5-fold cross-validation. Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN), and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post-traumatic brain repair, neuroinflammation, and inflammation-linked neuronal injuries. These key genes represent a promising tool for further studies in the diagnosis and early detection of open neural tube defects. Conclusion: These key biomarkers help in the diagnosis and early detection of open neural tube defects, thus evaluating the progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD's. Thus, among other prenatal treatment options present until now, these biomarkers help in the early detection of open neural tube defects, which provides success in both treatment and prevention of these defects in the advanced stage.