Effective extraction of polyribosomes exposes gene expression strategies in primary astrocytes.

Regulation of mRNA translation in astrocytes gains a growing interest. However, until now, successful ribosome profiling of primary astrocytes has not been reported. Here, we optimized the standard 'polysome profiling' method and generated an effective protocol for polyribosome extraction, which enabled genome-wide assessment of mRNA translation dynamics along the process of astrocyte activation. Transcriptome (RNAseq) and translatome (Riboseq) data generated at 0, 24 and 48 h after cytokines treatment, revealed dynamic genome-wide changes in the expression level of ∼12 000 genes. The data clarify whether a change in protein synthesis rate results from a change in mRNA level or translation efficiency per se. It exhibit different expression strategies, based on changes in mRNA abundance and/or translation efficiency, which are specifically assigned to gene subsets depending on their function. Moreover, the study raises an important take-home message related to the possible presence of 'difficult to extract' polyribosome sub-groups, in all cell types, thus illuminating the impact of ribosomes extraction methodology on experiments addressing translation regulation.

Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias.

Data normalization is a critical step in RNA sequencing (RNA-seq) analysis, aiming to remove systematic effects from the data to ensure that technical biases have minimal impact on the results. Analyzing numerous RNA-seq datasets, we detected a prevalent sample-specific length effect that leads to a strong association between gene length and fold-change estimates between samples. This stochastic sample-specific effect is not corrected by common normalization methods, including reads per kilobase of transcript length per million reads (RPKM), Trimmed Mean of M values (TMM), relative log expression (RLE), and quantile and upper-quartile normalization. Importantly, we demonstrate that this bias causes recurrent false positive calls by gene-set enrichment analysis (GSEA) methods, thereby leading to frequent functional misinterpretation of the data. Gene sets characterized by markedly short genes (e.g., ribosomal protein genes) or long genes (e.g., extracellular matrix genes) are particularly prone to such false calls. This sample-specific length bias is effectively removed by the conditional quantile normalization (cqn) and EDASeq methods, which allow the integration of gene length as a sample-specific covariate. Consequently, using these normalization methods led to substantial reduction in GSEA false results while retaining true ones. In addition, we found that application of gene-set tests that take into account gene-gene correlations attenuates false positive rates caused by the length bias, but statistical power is reduced as well. Our results advocate the inspection and correction of sample-specific length biases as default steps in RNA-seq analysis pipelines and reiterate the need to account for intergene correlations when performing gene-set enrichment tests to lessen false interpretation of transcriptomic data.

eIF2B Mutations Cause Mitochondrial Malfunction in Oligodendrocytes.

Vanishing white matter (VWM) disease (OMIM#306896) is an autosomal recessive neurodegenerative leukodystrophy caused by hypomorphic mutations in any of the five genes encoding the subunits of eukaryotic translation initiation factor 2B (eIF2B). The disease is manifested by loss of cerebral white matter and progressive deterioration upon exposure to environmental and physiological stressors. "Foamy" oligodendrocytes (OLG), increased numbers of oligodendrocytes precursor cells (OPC), and immature defective astrocytes are major neuropathological denominators. Our recent work using Eif2b5R132H/R132H mice uncovered a fundamental link between eIF2B and mitochondrial function. A decrease in oxidative phosphorylation capacity was observed in mutant astrocytes and fibroblasts. While an adaptive increase in mitochondria abundance corrects the phenotype of mutant fibroblasts, it is not sufficient to compensate for the high-energy demand of astrocytes, explaining their involvement in the disease. To date, astrocytes are marked as central for the disease while eIF2B-mutant OLG are currently assumed to lack a cellular phenotype on their own. Here we show a reduced capacity of eIF2B-mutant OPC isolated from Eif2b5R132H/R132H mice to conduct oxidative respiration despite the adaptive increase in their mitochondrial abundance. We also show their impaired ability to efficiently complete critical differentiation steps towards mature OLG. The concept that defective differentiation of eIF2B-mutant OPC could be a consequence of mitochondrial malfunction is in agreement with numerous studies indicating high dependency of differentiating OLG on accurate mitochondrial performance and ATP availability.