Biology subjects

Prokisch, H.

Publications and source records attributed to Prokisch, H..

4 recordsLinked to original sources

bioRxiv2018

Mutations in ELAC2 associated with hypertrophic cardiomyopathy impair mitochondrial tRNA 3’-end processing

Dysfunction of mitochondrial gene expression, caused by mutations in either the mitochondrial or nuclear genomes, is associated with a diverse group of human disorders characterized by impaired mitochondrial respiration. Within this group, an increasing number of mutations have been identified in nuclear genes involved in mitochondrial RNA metabolism. For instance, pathogenic mutations have been identified in the genes encoding enzymes involved in the precursor transcript processing, including ELAC2. The ELAC2 gene codes for the mitochondrial RNase Z, which is responsible for endonucleolytic cleavage of the 3 ends of mitochondrial pre-tRNAs. Here, we report the identification of sixteen novel ELAC2 variants in individuals presenting with mitochondrial respiratory chain deficiency, hypertrophic cardiomyopathy and lactic acidosis. We provided further evidence for the pathogenicity of the three previously reported variants by studying the RNase Z activity in an in vitro system and applied this recombinant system to investigate all novel missense variants, confirming the pathogenic role of these new ELAC2 mutations. We also modelled the residues affected by missense mutation in solved RNase Z structures, providing insight into enzyme structure and function. Finally, we show that primary fibroblasts from the individuals with novel ELAC2 variants have elevated levels of unprocessed mitochondrial RNA precursors. Our study thus broadly confirms the correlation of ELAC2 variants with severe infantile-onset forms of hypertrophic cardiomyopathy and mitochondrial respiratory chain dysfunction. One rare missense variant associated with the occurrence of prostate cancer (p.Arg781His) impairs the mitochondrial RNase Z activity of ELAC2, possibly indicating a functional link between tumorigenesis and mitochondrial RNA metabolism.

genetics↗

bioRxiv2018

Quantification and discovery of sequence determinants of protein per mRNA amount in 29 human tissues

Despite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects is still lacking. Here we quantified PTR ratios for 11,575 proteins across 29 human tissues using matched transcriptomes and proteomes. We analyzed the contribution of known sequence determinants of protein synthesis and degradation and 15 novel mRNA and protein sequence motifs that we found by association testing. While the dynamic range of PTR ratios spans more than 2 orders of magnitude, our integrative model predicts PTR ratios at a median precision of 3.2-fold. A reporter assay provided significant functional support for two novel UTR motifs and a proteome-wide competition-binding assay identified motif-specific bound proteins for one motif. Moreover, our direct comparison of protein to RNA levels led to a new metrics of codon optimality. Altogether, this study shows that a large fraction of PTR ratio variance across genes can be predicted from sequence and identified many new candidate post-transcriptional regulatory elements in the human genome.

systems biology↗

bioRxiv2018

OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (OUTlier in RNA-seq fInDER), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read count expectations according to the co-variation among genes resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best correction of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of combining correction for co-variation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a data set, for identifying outlier samples with too many aberrantly expressed genes, and for the P-value-based detection of aberrant gene expression, with false discovery rate adjustment. Overall, OUTRIDER provides a computationally fast and scalable end-to-end solution for identifying aberrantly expressed genes, suitable for use by rare disease diagnostic platforms.

bioinformatics↗

bioRxiv2017

OCR-Stats: Robust estimation and statistical testing of mitochondrial respiration activities using Seahorse XF Analyzer

Accurate quantification of cellular and mitochondrial bioenergetic activity is of great interest in medicine and biology. Mitochondrial stress tests performed with Seahorse Bioscience XF Analyzers allow estimating different bioenergetic measures by monitoring oxygen consumption rates (OCR) of living cells in multi-well plates. However, studies of statistical best practices for determining OCR measurements and comparisons have been lacking so far. Therefore, we performed mitochondrial stress tests in 126 96-well plates involving 203 fibroblast cell lines to understand how OCR behaves across different biosamples, wells, and plates. We show that the noise of OCR is multiplicative, that outlier data points can concern individual measurements or all measurements of a well, and that the inter-plate variation is greater than intra-plate variation. Based on these insights, we developed a novel statistical method, OCR-Stats, that: i) robustly estimates OCR levels modeling multiplicative noise and automatically identifying outlier data points and outlier wells; and ii) performs statistical testing between samples, taking into account the different magnitudes of the between- and within-plates variations. This led to a significant reduction of the coefficient of variation across plates of basal respiration by 36% and of maximal respiration by 32%. Moreover, using positive and negative controls, we show that our statistical test outperforms existing methods, which either suffer from an excess of false positives (within-plates methods), or of false negatives (between-plates methods). Altogether, the aim of this study is to propose statistical good practices to support experimentalists in designing, analyzing, testing and reporting results of mitochondrial stress tests using this high throughput platform.

bioinformatics↗