Biology subjects

Prokisch, H.

Publications and source records attributed to Prokisch, H..

14 recordsLinked to original sources

bioRxiv2025

MCUR1-CCDC90B complex is a conserved regulator of metabolic homeostasis

The mitochondrial calcium uniporter regulator 1 (MCUR1) is an evolutionarily conserved protein of the inner mitochondrial membrane1, yet its physiological role has remained elusive. Although initially proposed to function as a subunit of the mitochondrial calcium uniporter complex (MCUC)2-4, emerging evidence suggests that MCUR1 has a broader functional spectrum5-7. Here, we identify a biallelic loss-of-function MCUR1 variant (c.802C>T; p.R268X) in a patient with a progressive neurological phenotype. This mutation leads to loss of MCUR1 protein and exerts a dominant-negative effect on its paralog, CCDC90B. We show that MCUR1 and CCDC90B form a hetero-oligomeric complex whose stability depends on MCUR1. Deletion of MCUR1 and CCDC90B in the fission yeast Schizosaccharomyces pombe, which lacks the MCUC, impairs lipid and amino acid metabolism and causes nitrogen source-dependent growth defects that are rescued by expression of human MCUR1. Patient serum metabolomics confirms an imbalance in the amino acid pool, while MCUR1 deficiency in patient-derived skin fibroblasts upregulates autophagy, perturbs non-essential amino acid metabolism, and limits biosynthetic capacity, resulting in delayed proliferation and migration. These findings redefine the MCUR1-CCDC90B coiled-coil complex as a transmembrane scaffold critical for the integrity of mitochondrial protein complexes and the maintenance of metabolic homeostasis, suggesting a potential link between MCUR1 deficiency and human neurometabolic disease.

cell biology↗

bioRxiv2025

Single cell spatial transcriptomics integration deciphers the morphological heterogeneity of atherosclerotic carotid arteries

The process of arterial atherosclerosis is characterised by accumulation of lipids and fibrous material with accompanying inflammation. As plaques progress, they restrict blood flow and cause rupture, which results in life threatening organ ischemia and dysfunction. Although extensively studied, a clear understanding of plaque heterogeneity and mechanisms that trigger their destabilization remains elusive. Our study reveals the molecular microarchitecture of human carotid artery plaques, using bulk and single-cell RNA sequencing combined with single cell spatial transcriptomics, for which we present optimized cell segmentation algorithms. We identified distinct plaque morphologies linked to different cell type compositions, impacting early and advanced lesion formation, as well as destabilization. Spatial transcriptomics enabled us further to determine an inflammatory smooth muscle cell subtype, localize regions of neovascularization, and assign hotspots for macrophage activity within distinct cellular neighbourhoods across lesions. For different macrophage substates, we propose gradual and locally contained transdifferentiation of subluminal inflammatory HMOX1+ macrophages into a lipid-handling TREM2+ phenotype within border zones of the fibrous cap and necrotic core. Our findings provide insight into the complex heterogeneity of human atherosclerosis by unravelling location and proximity of different mural and immune cell substates involved in plaque progression and vulnerability.

cell biology↗

bioRxiv2025

PROTRIDER: Protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder

Structured abstractO_ST_ABSMotivationC_ST_ABSDetection of gene regulatory aberrations enhances our ability to interpret the impact of inherited and acquired genetic variation for rare disease diagnostics and tumor characterization. While numerous methods for calling RNA expression outliers from RNA-sequencing data have been proposed, the establishment of protein expression outliers from mass spectrometry data is lacking. ResultsHere, we propose and assess various modeling approaches to call protein expression outliers across three datasets from rare disease diagnostics and oncology. We use as independent evidence the enrichment for outlier calls in matched RNA-seq samples and the enrichment for rare variants likely disrupting protein expression. We show that controlling for hidden confounders and technical covariates, while simultaneously modeling the occurrence of missing values, is largely beneficial and can be achieved using conditional autoencoders. Moreover, we find that the differences between experimental and fitted log-transformed intensities by such models exhibit heavy tails that are poorly captured with the Gaussian distribution and report stronger statistical calibration when instead using the Students t-distribution. Our resulting method, PROTRIDER, outperformed baseline approaches based on raw log-intensities Z-scores or on differential expression analysis with limma. The application of PROTRIDER reveals significant enrichments of AlphaMissense pathogenic variants in protein expression outliers. Overall, PROTRIDER provides a method to confidently identify aberrantly expressed proteins applicable to rare disease diagnostics and cancer proteomics. Availability and ImplementationPROTRIDER is freely available at github.com/gagneurlab/PROTRIDER and also available on Zenodo under the DOI zenodo.15569781. ContactJulien Gagneur: gagneur at in.tum.de

bioinformatics↗

bioRxiv2024

Tissue-specific apparent mtDNA heteroplasmy and its relationship with ageing and mtDNA gene expression

Heteroplasmy in the mitochondrial DNA (mtDNA) accumulates with age, but how much it occurs in tissues and how it affects function and physiology is not well-studied. Here we present a comprehensive tissue-specific map of mtDNA heteroplasmy and mtRNA modifications, and their relationships with age and mitochondrial gene expression. We propose a robust variant calling pipeline for mtDNA heteroplasmy and mtRNA modifications using bulk RNAseq data from 49 tissues in GTEx, and a calibrated phenotype association framework for both types of variants. We identify 109 associations between them and donor age, and 784 associations with tissue-specific mtDNA gene expression. Of these, 7 and 18 show cell-type specificity within tissue. In addition, we find 9 instances where these variations mediate donor age effects on mtDNA gene expression. Finally, we confirm previously identified relationships between mt-tRNA modifications and gene expression on their 5, but read- and gene-level investigations reveal previously unmodeled complexities.

genomics↗

bioRxiv2024

The Interpretable Multimodal Machine Learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy

Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet. Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data. Exploiting IMMLs interpretability further empowered biomarker identification. We leveraged the population-based KORA F4/FF4 cohort including 1,091 participants and their deep multimodal characterisation, i.e. clinical data, genomics, methylomics, transcriptomics, proteomics, inflammatory proteins and metabolomics. Clinical data alone is sufficient to stratify individuals with and without DSPN (AUROC = 0.752), whilst predicting DSPN incidence 6.5{+/-}0.2 years later strongly benefits from clinical data complemented with two or more molecular modalities (improved {Delta}AUROC >0.1, achieved AUROC of 0.714). Important and interpretable features of incident DSPN prediction include up-regulation of proinflammatory cytokines, down-regulation of SUMOylation pathway and essential fatty acids, thus yielding novel insights in the disease pathophysiology. These may become biomarkers for incident DSPN, guide prevention strategies and serve as proof of concept for the utility of IMML in studying complex diseases.

bioinformatics↗

bioRxiv2022

Aberrant splicing prediction across human tissues

Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models allow prioritizing rare variants for affecting splicing, their performance on predicting tissue-specific aberrant splicing remains unassessed. Here, we generated the first aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues. At 20% recall, state-of-the-art DNA-based models cap at 10% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by three-fold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to non-coding loss-of-function variant identification and to genetic diagnostics design and analytics.

bioinformatics↗

bioRxiv2021

Protonation-Dependent Sequencing of 5-Formylcytidine in RNA

Chemical modification of cytidine in non-coding RNAs plays a key role in regulating translation and disease. However, the distribution and dynamics of many of these modifications remains unknown due to a lack of sensitive site-specific sequencing technologies. Here we report a protonation-dependent sequencing reaction for detection of 5-formylcytidine (5fC) and 5-carboxycytidine (5caC) in RNA. First, we evaluate how protonation combined with electron-withdrawing substituents alters the molecular orbital energies and reduction of modified cytidine nucleosides, highlighting 5fC and 5caC as reactive species. Next, we apply this reaction to detect these modifications in synthetic oligonucleotides as well as endogenous human tRNA. Finally, we demonstrate the utility of our method to characterize a patient-derived model of 5fC-deficiency, where it enables facile monitoring of both pathogenic loss and exogenous rescue of NSUN3-dependent 5fC within the wobble base of human mitochondrial tRNAMet. These studies showcase the ability of protonation to enhance the reactivity and sensitive detection of 5fC in RNA, and provide a molecular foundation for applying optimized sequencing reactions to better understand the role of oxidized RNA cytidine nucleobases in disease.

biochemistry↗

bioRxiv2020

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BackgroundMolecular multi-omics data provide an in-depth view on biological systems, and their integration is crucial to gain insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans -QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information has been proposed to alleviate network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans -QTL hotspots using human cohort data and data-driven prior information. ResultsWe devised a strategy to integrate QTL with human population scale multi-omics data and comprehensively curated prior information from large-scale biological databases. State-of-the art network inference methods applied to these data and priors were used to recover the regulatory networks underlying trans -QTL hotspots. We benchmarked inference methods and showed, that Bayesian strategies using biologically-informed priors outperform methods without prior data in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. ConclusionWe demonstrate, that existing biological knowledge can be leveraged for the integrative analysis of networks underlying trans associations to deduce novel hypotheses on cell regulatory mechanisms.

bioinformatics↗

bioRxiv2020

Adaptive mitochondrial regulation of the proteasome

The proteasome is the main proteolytic system for targeted protein degradation in the cell. Its function is fine-tuned according to cellular needs. Regulation of proteasome function by mitochondrial metabolism, however, is unknown. Here, we demonstrate that mitochondrial dysfunction reduces the assembly and activity of the 26S proteasome in the absence of oxidative stress. Impaired respiratory complex I function leads to metabolic reprogramming of the Krebs cycle and deficiency in aspartate. Aspartate supplementation activates assembly and activity of 26S proteasomes via transcriptional activation of the proteasome assembly factors p28 and Rpn6. This metabolic adaptation of 26S proteasome function involves sensing of aspartate via the mTORC1 pathway. Metformin treatment of primary human cells similarly reduced assembly and activity of 26S proteasome complexes, which was fully reversible and rescued by supplementation of aspartate or pyruvate. Of note, respiratory dysfunction conferred resistance towards the proteasome inhibitor Bortezomib. Our study uncovers a fundamental novel mechanism of how mitochondrial metabolism adaptively adjusts protein degradation by the proteasome. It thus unravels unexpected consequences of defective mitochondrial metabolism in disease or drug-targeted mitochondrial reprogramming for proteasomal protein degradation in the cell. As metabolic inhibition of proteasome function can be alleviated by treatment with aspartate or pyruvate, our results also have therapeutic implications.

cell biology↗

bioRxiv2019

Detection of aberrant splicing events in RNA-seq data with FRASER

Aberrant splicing is a major cause of rare diseases, yet its prediction from genome sequence remains in most cases inconclusive. Recently, RNA sequencing has proven to be an effective complementary avenue to detect aberrant splicing. Here, we developed FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike existing methods, FRASER captures not only alternative splicing but also intron retention events. This typically doubles the number of detected aberrant events and identified a pathogenic intron retention in MCOLN1. FRASER automatically controls for latent confounders, which are widespread and substantially affect sensitivity. Moreover, FRASER is based on a count distribution and multiple testing correction, reducing the number of calls by two orders of magnitude over commonly applied z score cutoffs, with a minor sensitivity loss. The application to rare disease diagnostics is demonstrated by reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. FRASER is easy to use and freely available.

bioinformatics↗

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↗