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Kaizeler, A.

Publications and source records attributed to Kaizeler, A..

2 recordsLinked to original sources

Revisiting Stress Granule Transcriptomes Suggests Mitochondrial RNA Enrichment Despite Methodological Bias

Stress granules (SGs) are dynamic, membraneless cytoplasmic condensates that form in response to diverse cellular stressors. Although proposed to modulate stress responses by selectively sequestering proteins and RNAs, their precise molecular composition and function remain unclear. Reported SG transcriptomes differ substantially due to methodological discrepancies, notably between differential centrifugation (DC) and proximity labeling (PL). Here, we reanalyse publicly available human SG transcriptomes across multiple stressors, cell types, and isolation strategies. DC-based profiles were strongly shaped by RNA length, consistent with a physical bias inherent to the sedimentation-based separation. Correcting for this effect reveals limited concordance between studies. Mitochondrially encoded RNAs nonetheless consistently stand out as a distinctively regulated transcript class, without a uniform direction of enrichment/depletion across datasets, and with a modest but significant enrichment in our consensus SG signature. Immunofluorescence experiments further support, without definitively demonstrating, sequestration of mitochondrial dsRNA by SGs upon stress. Since leakage of mitochondrial nucleic acids is a well-characterised damage-associated molecular pattern linked to inflammation, their sequestration in SGs suggests a potential role in modulating immune-related stress responses. These findings refine our understanding of SG composition, underscore the need to control for technical artifacts in SG isolation, and provide a framework to distinguish genuine biological signals from methodological noise in SG research.

cell biology↗

Exploring molecular signatures of senescence with markeR, an R toolkit for evaluating gene sets as phenotypic markers

Many biological processes, including cellular senescence, manifest as diverse phenotypes across cell types and conditions. Lacking definitive markers, researchers often rely on the expression of sets of genes to identify these complex states. However, multiple approaches exist to summarise gene set expression into quantitative metrics (i.e., signatures), each with distinct strengths and limitations, and we know of no consensual framework to systematically evaluate their performance across datasets. We therefore developed markeR, an open-source, modular R package that evaluates gene sets as phenotypic markers using scoring and enrichment-based approaches. markeR generates interpretable metrics and intuitive visualisations for benchmarking gene signatures and exploring their associations with study variables. As a case study, we applied markeR to 9 published senescence-related gene sets across 25 RNA-seq datasets, 6 human cell types and 12 senescence-inducing conditions. Gene set performance varied widely: some signatures (e.g., SenMayo) were robust senescence markers across contexts, while others (e.g., MSigDB sets) performed poorly. We further applied markeR to 49 GTEx tissues, revealing tissue- and age-related differences in senescence-associated signals. Together, these findings emphasise the difficulty of characterising molecular phenotypes and demonstrate markeRs potential for the systematic evaluation of gene sets in various biological contexts. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/692517v3_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@65bbb4org.highwire.dtl.DTLVardef@1062cc7org.highwire.dtl.DTLVardef@65f3b5org.highwire.dtl.DTLVardef@163228b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗