An analysis of differential gene expression in peripheral nerve and muscle utilizing RNA sequencing after polyethylene glycol nerve fusion in a rat sciatic nerve injury model.

Application of polyethylene glycol (PEG) to a peripheral nerve injury at the time of primary neurorrhaphy is thought to prevent Wallerian degeneration via direct axolemma fusion. The molecular mechanisms of nerve fusion and recovery are unclear. Our study tested the hypothesis that PEG alters gene expression in neural and muscular environments as part of its restorative properties. Lewis rats underwent unilateral sciatic nerve transection with immediate primary repair. Subjects were randomly assigned to receive either PEG treatment or standard repair at the time of neurorrhaphy. Samples of sciatic nerve distal to the injury and tibialis muscle at the site of innervation were harvested at 24 hours and 4 weeks postoperatively. Total RNA sequencing and subsequent bioinformatics analyses were used to identify significant differences in differentially expressed genes (DEGs) and their related biological pathways (p<0.05) in PEG-treated subjects compared to non-PEG controls. No significant DEGs were identified in PEG-treated sciatic nerve compared to controls after 24 hours, but 1,480 DEGs were identified in PEG-treated tibialis compared to controls. At 4 weeks, 918 DEGs were identified in PEG-treated sciatic nerve, whereas only 3 DEGs remained in PEG-treated tibialis compared to controls. DEGs in sciatic were mostly upregulated (79%) and enriched in pathways present during nervous system development and growth, whereas DEGs in muscle were mostly downregulated (77%) and related to inflammation and tissue repair. Our findings indicate that PEG application during primary neurorrhaphy leads to significant differential gene regulation in the neural and muscular environment that is associated with improved functional recovery in animals treated with PEG compared to sham non-PEG controls. A detailed understanding of key molecules underlying PEG function in recovery after peripheral nerve repair may facilitate amplification of PEG effects through systemic or focal treatments at the time of neurotmesis.

Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning.

Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

Identification of Novel Loci Shared by Juvenile Idiopathic Arthritis Subtypes Through Integrative Genetic Analysis.

OBJECTIVE: Juvenile idiopathic arthritis (JIA) is the most common chronic immune-mediated joint disease among children and encompasses a heterogeneous group of immune-mediated joint disorders classified into 7 subtypes according to clinical presentation. However, phenotype overlap and biologic evidence suggest a shared mechanistic basis between subtypes. This study was undertaken to systematically investigate shared genetic underpinnings of JIA subtypes. METHODS: We performed a heterogeneity-sensitive genome-wide association study encompassing a total of 1,245 JIA cases (classified into 7 subtypes) and 9,250 controls, followed by fine-mapping of candidate causal variants at each genome-wide significant locus, functional annotation, and pathway and network analysis. We further identified candidate drug targets and drug repurposing opportunities by in silico analyses. RESULTS: In addition to the major histocompatibility complex locus, we identified 15 genome-wide significant loci shared between at least 2 JIA subtypes, including 10 novel loci. Functional annotation indicated that candidate genes at these loci were expressed in diverse immune cell types. CONCLUSION: This study identified novel genetic loci shared by JIA subtypes. Our findings identified candidate mechanisms underlying JIA subtypes and candidate targets with drug repurposing opportunities for JIA treatment.

Evaluating sequence data quality from the Swift Accel-Amplicon CFTR Panel.

Cystic fibrosis (CF) is one of the most common genetic diseases worldwide with high carrier frequencies across different ethnicities. Next generation sequencing of the cystic fibrosis transmembrane conductance regulator (CFTR) gene has proven to be an effective screening tool to determine carrier status with high detection rates. Here, we evaluate the performance of the Swift Biosciences Accel-Amplicon CFTR Capture Panel using CFTR-positive DNA samples. This assay is a one-day protocol that allows for one-tube reaction of 87 amplicons that span all coding regions, 5' and 3'UTR, as well as four intronic regions. In this study, we provide the FASTQ, BAM, and VCF files on seven unique CFTR-positive samples and one normal control sample (14 samples processed including repeated samples). This method generated sequencing data with high coverage and near 100% on-target reads. We found that coverage depth was correlated with the GC content of each exon. This dataset is instrumental for clinical laboratories that are evaluating this technology as part of their carrier screening program.