Circular RNA Expression and Interaction Patterns Are Perturbed in Amyotrophic Lateral Sclerosis.

Circular RNAs (circRNAs) are a type of long noncoding RNA that are highly abundant and highly conserved throughout evolution and exhibit differential expression patterns in various tissue types in multiple diseases, including amyotrophic lateral sclerosis (ALS). The most well-known function of circRNAs is their ability to act as microRNA (miRNA) sponges. This entails circRNA binding to miRNA, which would otherwise target and degrade messenger RNA, thus affecting gene expression. This study analyzed ALS patient samples from three spinal cord regions to investigate circular transcriptome perturbation and circular RNA-microRNA-mRNA interactions. Using stringent statistical parameters, we identified 92 differentially expressed circRNAs across the spinal cord tissues with the aim of identifying specific circRNAs with biomarker potential. We also found evidence for differential expression of 37 linear RNAs possibly due to miRNA sequestration by circRNAs, thus revealing their potential as novel biomarkers and therapeutic candidates for ALS.

Distinct temporal expression of 5-HT(1A) and 5-HT(2A) receptors on cerebellar granule cells in mice.

Serotonin plays an important role of controlling the physiology of the cerebellum. However, serotonin receptor expression has not been fully studied in the developing cerebellum. We have recently shown that cerebellar granule cells transiently express 5-HT3 receptors. In the present study, we investigate expression of 5-HT1 and 5-HT2 receptors in the mouse cerebellum both during postnatal development and in juvenile mice. Here, we show for the first time that 5-HT1A and 5-HT2A receptors are present on cerebellar granule cells with a distinct temporal expression pattern: 5-HT1A receptors are expressed only during the first 2 weeks, while 5-HT2A receptor expression persists until at least 8 weeks after birth. Because of its prolonged expression pattern, we investigated the electrophysiological properties of the 5-HT2A receptor. 5-HT2A receptors expressed by cerebellar granule cells promote stability by reducing variability of the synaptic response, and they modulate the paired-pulse ratio of the parallel fibre-Purkinje cell synapse. Furthermore, pharmacological block of 5-HT2A receptors enhances short-term synaptic plasticity at the parallel fibre-Purkinje cell synapse. We thus show a novel role for serotonin in controlling function of the cerebellum via 5-HT2A receptors expressed by cerebellar granule cells.

Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values.

Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainability when used directly with RNA expression features for ALS molecular classification. In this paper, we propose a deep-learning-based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then, we employed Shapley additive explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilizing Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.

Clinically Responsive Genomic Analysis Pipelines: Elements to Improve Detection Rate and Efficiency.

Massively parallel sequencing has markedly improved mendelian diagnostic rates. This study assessed the effects of custom alterations to a diagnostic genomic bioinformatic pipeline in response to clinical need and derived practice recommendations relative to diagnostic rates and efficiency. The Genomic Annotation and Interpretation Application (GAIA) bioinformatics pipeline was designed to detect panel, exome, and genome sample integrity and prioritize gene variants in mendelian disorders. Reanalysis of selected negative cases was performed after improvements to the pipeline. GAIA improvements and their effect on sensitivity are described, including addition of a PubMed search for gene-disease associations not in the Online Mendelian Inheritance of Man database, inclusion of a process for calling low-quality variants (known as QPatch), and gene symbol nomenclature consistency checking. The new pipeline increased the diagnostic rate and reduced staff costs, resulting in a saving of US$844.34 per additional diagnosis. Recommendations for genomic analysis pipeline requirements are summarized. Clinically responsive bioinformatics pipeline improvements increase diagnostic sensitivity and increase cost-effectiveness.

Sequences of Endophytic Fungal and Bacterial Communities from Araucaria araucana [(Molina) K. Koch, 1869] in the Coastal and Andes Mountain Ranges, Chile.

Here, we report the results from PCR and sequencing of bacterial 16S rRNA and fungal internal transcribed spacer 1 (ITS1) genes from needle, branch, trunk, and root samples of Araucaria araucana, plus soil and associated insects, collected along the entirety of its geographic distribution in Chile (January 2017 and 2018).