Biology subjects

Doll, S.

Publications and source records attributed to Doll, S..

13 recordsLinked to original sources

bioRxiv2025

Droplet-assisted folding of long regulatory RNAs

Long regulatory RNA regions orchestrate complex cellular processes, including gene expression and epigenetic modifications. How these RNAs dynamically fold and refold in response to cellular signals remains poorly understood. Given that RNAs interact with ubiquitous RNA-binding proteins (RBPs) prone to form biomolecular condensates, we explore how protein droplets interacting along an RNA impact its folding process. Attached droplets prevent premature folding by competing with RNA:RNA interactions. When droplets dissolve due to cellular signals, capillary effects cause the RNA to collapse while refolding. We test this process of condensate-guided RNA folding by adapting established RNA secondary structure predictors to mimic various folding pathways and supplement this with coarse-grained simulations. We find that interactions with transient droplets robustly leads to the formation of long-range RNA contacts, which are otherwise hard to achieve. Our results compare favorably with available experimental data. We propose that this strategy, which we call droplet-assisted RNA folding, represents a previously unexplored mechanism for shaping RNA structures. Given the widespread propensity of RBPs to form condensates, this process could play a fundamental role in the structural organization, conditional reshaping, and functional regulation of long regulatory RNAs. SignificanceComplex non-coding RNA regions (e.g. lncRNAs, 3UTRs, etc.) perform several important functions in higher organisms. However, the longer an RNA, the more likely it is to misfold. How can such regulatory regions of more than 1000 nts be reliably folded, considering that premature co-transcriptional folding favors local interactions? We present a mechanism by which biomolecular condensates of RNA-binding proteins (RBPs) assist and control the folding of complex RNAs. Here, droplets of RBPs nucleate around RNA binding motifs, prevent premature misfolding through their RNA chaperone function, and allow robust folding of initially distant regions through coordinated droplet dissolution and detachment. This mechanism provides an alternative perspective on how long RNAs acquire their structures in a context that is functionally dynamic.

molecular biology↗

bioRxiv2024

PRDX6 dictates ferroptosis sensitivity by directing cellular selenium mobilization

Selenium-dependent glutathione peroxidase 4 (GPX4) is the guardian of ferroptosis and prevents unrestrained (phospho)lipid peroxidation by directly reducing phospholipid hydroperoxides (PLOOH) to their corresponding alcohols. However, it remains unclear whether other phospholipid peroxidases can also contribute to ferroptosis prevention, albeit to a varying degree. Here we show that cells lacking GPX4 still exhibit substantial PLOOH reduction capacity, arguing for the presence of alternative PLOOH peroxidases. By scrutinizing potential candidates, we showed that while overexpression of peroxiredoxin 6 (PRDX6), a thiol-specific antioxidant enzyme with reported PLOOH-reducing activity, failed to prevent ferroptosis, its genetic loss markedly sensitizes cancer cells to ferroptosis. Mechanistically, we uncover that PRDX6 facilitates intracellular selenium handling, which is crucial for selenium incorporation into selenoproteins, including GPX4. Consequently, PRDX6 modulates GPX4 expression, thereby dictating the sensitivity of cells to undergo ferroptosis. Our study highlights PRDX6 as a critical factor in ferroptosis prevention by directing cellular selenium mobilization.

cell biology↗

bioRxiv2022

Dynamic human liver proteome atlas reveals functional insights into disease pathways

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell-type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence-unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time-course of primary cell cultures and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serves as spectral library to characterize a human cohort of non-alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web-based dashboard application for the interactively exploration of our resource. HighlightsO_LICell-type resolved liver proteome with copy numbers for 10,500 proteins C_LIO_LITime-course of human liver primary cells uncovers functional proteome shifts C_LIO_LIA human cohort study reveals liver proteome changes in NASH and cirrhosis C_LIO_LIAn interactive web portal integrates the results for easy exploration C_LI

systems biology↗

bioRxiv2021

Integrated phospho-proteogenomic and single-cell transcriptomic analysis of meningiomas establishes robust subtyping and reveals subtype-specific immune invasion

Meningiomas are the most frequent primary intracranial tumors. They can follow a wide clinical spectrum from benign to highly aggressive clinical course. No specific therapy exists for refractory cases or cases not amenable to resection and radiotherapy. Identification of risk of recurrence and malignant transformation for the individual patients is challenging. However, promising molecular markers and prognostic subgrouping by DNA methylation are emerging. Still, the biological underpinnings of these diagnostic subgroups are elusive, and, consequently, no novel therapeutic options arise thereof. Here we establish robust subgroups across the full landscape of meningiomas, consistent through DNA methylation, mutations, the transcriptomic, proteomic and phospho-proteomic level. Pronounced proliferative stress and DNA damage repair signals in malignant cells and in clusters exclusive to recurrent tumors are in line with their higher mitotic activity, but also provide an explanation for the accumulation of genomic instability in anaplastic meningiomas. Although homozygous deletion of CDKN2A/B is a diagnostic marker of high-grade meningioma, the expression of its gene product increased from low to non-deleted high-grade cases. Differences between subgroups in lymphocyte and myeloid cell infiltration, representing a majority of tumor mass in low-grade NF2 tumors, could be assigned to cluster-specific interaction with tumor cells. Activation to a more proinflammatory phenotype and decreased infiltration of myeloid cells in high-grade cases correlated with lower expression of CSF1, located on chromosome arm 1p, whose deletion is known as prognostic marker, with no proposed mechanism before. Our results demonstrate a robust molecular subclassification of a tumor type across multiple layers, provide insight into heterogeneous growth dynamics despite shared pathognomonic mutations, and highlight immune infiltration modulation as a novel target for meningioma therapy.

genomics↗

bioRxiv2021

Sequential defects in cardiac lineage commitment and maturation cause hypoplastic left heart syndrome

BackgroundComplex molecular programs in specific cell lineages govern human heart development. Hypoplastic left heart syndrome (HLHS) is the most common and severe manifestation within the spectrum of left ventricular outflow tract obstruction defects occurring in association with ventricular hypoplasia. The pathogenesis of HLHS is unknown, but hemodynamic disturbances are assumed to play a prominent role. MethodsTo identify perturbations in gene programs controlling ventricular muscle lineage development in HLHS, we performed: i) whole-exome sequencing of 87 HLHS parent-offspring trios, ii) nuclear transcriptomics of cardiomyocytes from ventricles of 4 patients with HLHS and 15 controls at different stages of heart development, iii) single cell RNA sequencing and iv) 3D modeling in iPSCs from 3 patients with HLHS and 3 controls. ResultsGene set enrichment and protein network analyses of damaging de-novo mutations and dysregulated genes from ventricles of patients with HLHS suggested alterations in specific gene programs and cellular processes critical during fetal ventricular cardiogenesis, including cell-cycle and cardiomyocyte maturation. Single-cell and 3D modeling with iPSCs demonstrated intrinsic defects in the cell-cycle/UPR/autophagy hub resulting in disrupted differentiation of early cardiac progenitor lineages leading to defective cardiomyocyte-subtype differentiation/maturation in HLHS. Additionally, premature cell-cycle exit of ventricular cardiomyocytes from HLHS patients prevented normal tissue responses to developmental signals for growth leading to multinucleation/polyploidy, accumulation of DNA damage, and exacerbated apoptosis, all potential drivers of left ventricular hypoplasia in absence of hemodynamic cues. ConclusionsOur results highlight that despite genetic heterogeneity in HLHS, many mutations converge on sequential cellular processes primarily driving cardiac myogenesis, suggesting novel therapeutic approaches.

developmental biology↗

bioRxiv2021

Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed OmicLearn (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researchers results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/434053v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@7b95corg.highwire.dtl.DTLVardef@11a5357org.highwire.dtl.DTLVardef@15586c2org.highwire.dtl.DTLVardef@2357f_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discovery C_LIO_LIThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledge C_LIO_LIOmicLearns web-based interface provides an easy-to-follow platform for classification and gaining insights into the dataset C_LIO_LISeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integrated C_LIO_LIAll results, settings and method text can be exported in publication-ready formats C_LI

biochemistry↗

bioRxiv2020

Spatially and cell-type resolved quantitative proteomic atlas of healthy human skin

Human skin provides both physical integrity and immunological protection from the external environment using functionally distinct layers, cell types and extracellular matrix. Despite its central role in human health and disease, the constituent proteins of skin have not been systematically characterized. Here, we combined advanced tissue dissection methods, flow cytometry and state-of-the-art proteomics to describe a spatially-resolved quantitative proteomic atlas of human skin. We quantified 10,701 proteins as a function of their spatial location and cellular origin. The resulting protein atlas and our initial data analyses demonstrate the value of proteomics for understanding cell-type diversity within the skin. We describe the quantitative distribution of structural proteins, known and novel proteins specific to cellular subsets and those with specialized immunological functions such as cytokines and chemokines. We anticipate that this proteomic atlas of human skin will become an essential community resource for basic and translational research (www.skin.science).

cell biology↗

bioRxiv2020

Limited Environmental Serine Confers Sensitivity to PHGDH Inhibition in Brain Metastasis

A hallmark of metastasis is the adaptation of tumor cells to new environments. Although it is well established that the metabolic milieu of the brain is severely deprived of nutrients, particularly the amino acids serine and its catabolite glycine, how brain metastases rewire their metabolism to survive in the nutrient-limited environment of the brain is poorly understood. Here we demonstrate that cell-intrinsic de novo serine synthesis is a major determinant of brain metastasis. Whole proteome comparison of triple-negative breast cancer (TNBC) cells that differ in their capacity to colonize the brain reveals that 3-phosphoglycerate dehydrogenase (PHGDH), which catalyzes the rate-limiting step of glucose-derived serine synthesis, is the most significantly upregulated protein in cells that efficiently metastasize to the brain. Genetic silencing or pharmacological inhibition of PHGDH attenuated brain metastasis and improved overall survival in mice, whereas expression of catalytically active PHGDH in a non-brain trophic cell line promoted brain metastasis. Collectively, these findings indicate that nutrient availability determines serine synthesis pathway dependence in brain metastasis, and suggest that PHGDH inhibitors may be useful in the treatment of patients with cancers that have spread to the brain. Statement of SignificanceOur study highlights how limited serine and glycine availability within the brain microenvironment potentiates tumor cell sensitivity to serine synthesis inhibition. This finding underscores the importance of studying cancer metabolism in physiologically-relevant contexts, and provides a rationale for using PHGDH inhibitors to treat brain metastasis.

cancer biology↗

bioRxiv2019

A streamlined mass spectrometry-based proteomics workflow forlarge scale FFPE tissue analysis

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Here, we describe an MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from pathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including less than 10,000 cancer cells isolated by laser-capture microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total work up took less than a day. We observed a moderate trend towards lower protein identifications in long-term stored samples (>15 years) but clustering into distinct proteomic subtypes was independent of archival time. Our results underline the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and prove the feasibility of analyzing large tissue cohorts in a robust, timely and streamlined manner.

systems biology↗

bioRxiv2019

Conjugated linolenic fatty acids trigger ferroptosis in triple-negative breast cancer

Ferroptosis is a non-apoptotic form of cell death linked to the accumulation of reactive hydroperoxides generated by oxidation of polyunsaturated fatty acids (PUFAs) in membrane phospholipids. The therapeutic potential of promoting ferroptosis by enriching PUFAs in cancer cells is unknown. We found an association between elevated PUFA levels and vulnerability to ferroptosis in triple-negative breast cancer (TNBC) cells. A screen of PUFAs identified conjugated linolenic acids, including -eleostearate, as ferroptosis inducers. Three conjugated double bonds were required for ferroptotic activity although their positioning and stereochemistry were less significant. Mechanistically, -eleostearate differed from canonical ferroptosis inducers by a distinct dependence on acyl-CoA synthetase long-chain isoforms and by not altering glutathione or glutathione peroxidase 4 activity. Orally administered tung oil, naturally rich in -eleostearate, limited tumor growth and metastasis in an aggressive TNBC xenograft model. These results expand our understanding of ferroptotic cell death and highlight the anti-cancer potential of conjugated PUFAs.

cancer biology↗

bioRxiv2018

Plasma proteome profiling to detect and avoid sample-related biases in biomarker studies

Plasma and serum are rich sources of information regarding an individuals health state and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative read-out of the plasma proteome. Here we employ Plasma Proteome Profiling to define contamination marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma and whole blood of 20 individuals (>6000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments we determine contamination-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource (www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.

epidemiology↗

bioRxiv2018

Single fiber proteomics of respiratory chain defects in mitochondrial disorders

Mitochondrial DNA mutations progressively compromise the respiratory chain of skeletal muscle, resulting in a mosaic of metabolically healthy and defective fibers. The single fiber investigation of this important diagnostic feature has been beyond the capability of large-scale technologies so far. We used laser capture microdissection (LCM) to excise thin sections of individual muscle fibers from frozen biopsies of patients suffering from chronic progressive external ophthalmoplegia. We then applied a highly sensitive mass spectrometry (MS)-based proteomics workflow to analyze healthy and defective muscle fibers within the same biopsy. We quantified more than 4000 proteins in each patient, covering 75% of all respiratory chain subunits, and compared their expression in metabolically healthy and defective muscle fibers. Our findings show that mitochondrial disease causes extensive proteomic rearrangements, affecting the OPA1-dependent cristae remodeling pathway and mitochondrial translation. We provide fiber type-specific information showing that increased expression of fatty acid oxidation enzymes occurs in defective slow but not fast muscle fibers. Our findings shed light on compensatory mechanisms in muscle fibers that struggle with energy shortage and metabolic stress.

pathology↗

bioRxiv2018

A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics

To further integrate mass spectrometry (MS)-based proteomics into biomedical research and especially into clinical settings, high throughput and robustness are essential requirements. They are largely met in high-flow rate chromatographic systems for small molecules but these are not sufficiently sensitive for proteomics applications. Here we describe a new concept that delivers on these requirements while maintaining the sensitivity of current nano-flow LC systems. Low-pressure pumps elute the sample from a disposable trap column, simultaneously forming a chromatographic gradient that is stored in a long storage loop. An auxiliary gradient creates an offset, ensuring the re-focusing of the peptides before the separation on the analytical column by a single high-pressure pump. This simplified design enables robust operation over thousands of sample injections. Furthermore, the steps between injections are performed in parallel, reducing overhead time to a few minutes and allowing analysis of more than 200 samples per day. From fractionated HeLa cell lysates, deep proteomes covering more than 130,000 sequence unique peptides and close to 10,000 proteins were rapidly acquired. Using this data as a library, we demonstrate quantitation of 5200 proteins in only 21 min. Thus, the new system-termed Evosep One-analyzes samples in an extremely robust and high throughput manner, without sacrificing in depth proteomics coverage.

biochemistry↗