predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases.

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions.

rG4detector, a novel RNA G-quadruplex predictor, uncovers their impact on stress granule formation.

RNA G-quadruplexes (rG4s) are RNA secondary structures, which are formed by guanine-rich sequences and have important cellular functions. Existing computational tools for rG4 prediction rely on specific sequence features and/or were trained on small datasets, without considering rG4 stability information, and are therefore sub-optimal. Here, we developed rG4detector, a convolutional neural network to identify potential rG4s in transcriptomics data.

Muscle microRNAs in the cerebrospinal fluid predict clinical response to nusinersen therapy in type II and type III spinal muscular atrophy patients.

Background And Purpose: The antisense oligonucleotide nusinersen (Spinraza) regulates splicing of the survival motor neuron 2 (SMN2) messenger RNA to increase SMN protein expression. Nusinersen has improved ventilator-free survival and motor function outcomes in infantile onset forms of spinal muscular atrophy (SMA), treated early in the course of the disease. However, the response in later onset forms of SMA is highly variable and dependent on symptom severity and disease duration at treatment initiation.

Physically interacting beta-delta pairs in the regenerating pancreas revealed by single-cell sequencing.

Objectives: Until recently, communication between neighboring cells in islets of Langerhans was overlooked by genomic technologies, which require rigorous tissue dissociation into single cells.

Methods: We utilize sorting of physically interacting cells (PICs) with single-cell RNA-sequencing to systematically map cellular interactions in the endocrine pancreas after pancreatectomy.

Results: The pancreas cellular landscape features pancreatectomy associated heterogeneity of beta-cells, including an interaction-specific program between paired beta and delta-cells.

Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a relentless neurodegenerative disease of the human motor neuron system, where variability in progression rate limits clinical trial efficacy. Therefore, better prognostication will facilitate therapeutic progress. In this study, we investigated the potential of plasma cell-free microRNAs (miRNAs) as ALS prognostication biomarkers in 252 patients with detailed clinical phenotyping.

microRNAs trip down memory lane.

A new study by Islam et al, in this issue of EMBO Molecular Medicine, reports three microRNAs in the blood that are linked to inter-individual differences in cognition, prior to any sign of cognitive decline. The novel miRNA biomarkers may assist in predicting the risk of cognitive decline and later of developing dementia and can contribute to decision strategies about early therapeutic interventions.

Rare Variant Burden Analysis within Enhancers Identifies CAV1 as an ALS Risk Gene.

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease. CAV1 and CAV2 organize membrane lipid rafts (MLRs) important for cell signaling and neuronal survival, and overexpression of CAV1 ameliorates ALS phenotypes in vivo. Genome-wide association studies localize a large proportion of ALS risk variants within the non-coding genome, but further characterization has been limited by lack of appropriate tools.