Biology subjects

Sturm, G.

Publications and source records attributed to Sturm, G..

30 records · Page 2Linked to original sources

bioRxiv2022

Accelerating the clock: Interconnected speedup of energetic and molecular dynamics during aging in cultured human cells

To understand how organisms age, we need reliable multimodal molecular data collected at high temporal resolution, in specific cell types, across the lifespan. We also need interpretative theory that connects aging with basic mechanisms and physiological tradeoffs. Here we leverage a simple cellular replicative aging system combined with mathematical theory to address organismal aging. We used cultured primary human fibroblasts from multiple donors to molecularly and energetically profile entire effective lifespans of up to nine months. We generated high-density trajectories of division rates, telomere shortening, DNA methylation, RNAseq, secreted proteins/cytokines and cell-free DNA, in parallel with bioenergetic trajectories of ATP synthesis rates derived from both mitochondrial oxidative phosphorylation and glycolysis, reflecting total cellular mass-specific metabolic rate (MR). By comparing our cell culture data to data from cells in the body we uncover three fundamental speedups, or rescalings, of MR and molecular aging markers. To explain these rescalings we deploy the allometric theory of metabolism which predicts that the rate of biological aging is related to an organisms size, MR, and the partitioning of energetic resources between growth and maintenance processes. Extending this theory we report three main findings: 1) human cells isolated from the body with faster rates of growth allocate a substantially smaller fraction of their energy budget to maintenance, and correspondingly age 50-300x faster based on multiple molecular markers. 2) Over the course of the cellular lifespan, primary human fibroblasts acquire a >100-fold hypermetabolic phenotype characterized by increased maintenance costs, and associated with increased mtDNA genome density, upregulation of senescence-associated extracellular secretion, and induction of maintenance-related transcriptional programs. 3) Finally, manipulating MR with mitochondria-targeted metabolic, genetic, and pharmacological perturbations predictably altered the molecular rate of aging, providing experimental evidence for the interplay of MR and aging in a human system. These data highlight the key role that the partitioning of energetic resources between growth and maintenance/repair processes plays in cellular aging, and converge with predictions of cross-species metabolic theory indicating that energy metabolism governs how human cells age. Significance StatementHow cells age is of fundamental importance to understanding the diversity of mammalian lifespans and the wide variation in human aging trajectories. By aging primary human fibroblasts over several months in parallel with multi-omics and energetic profiling, we find that as human cells age and progressively divide more slowly, surprisingly, they progressively consume energy faster. By manipulating cellular metabolic rates, we confirm that the higher the cellular metabolic rate, the faster cells experience telomere shortening and epigenetic aging - a speedup phenotype consistent with allometric scaling theory. By modeling robust energetic and molecular aging trajectories across donors and experimental conditions, we find that independent of cell division rates, molecular aging trajectories are predicted by the partitioning of the energy budget between growth and maintenance processes. These results integrate molecular and energetic drivers of aging and therefore have important long-term implications to understand biological aging phenomena ranging from cellular senescence to human longevity.

cell biology↗

bioRxiv2022

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune cell infiltration patterns, which has been linked to both, therapy sensitivity and resistance. However, full understanding of how immune cell phenotypes vary across different patient and tumor subgroups is lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution by integrating 1,212,463 single-cells from 538 samples and 309 patients across 29 datasets, including our own dataset capturing cells with low mRNA content. Based on the cellular composition we stratified patients into immune deserted, B cell, T cell, and myeloid cell subtypes. Using bulk samples with genomic and clinical information, we identified specific cellular components associated with tumor histology and genotypes. Analysis of cells with low mRNA content uncovered distinct subpopulations of tissue-resident neutrophils (TRNs) that acquire new functional properties in the tissue microenvironment, providing evidence for the plasticity of TRNs. TRN-derived gene signature was associated with anti-PD-L1 treatment failure in a large NSCLC cohort. In briefSalcher, Sturm, Horvath et al. integrate single-cell datasets to generate the largest transcriptome atlas in NSCLC, refining patient stratification based on tumor immune phenotypes, and revealing associations of histological subtypes and genotypes with specific cellular composition patterns. Coverage of cells with low mRNA content by single-cell sequencing identifies distinct tissue-resident neutrophil subpopulations, which acquire new properties within the tumor microenvironment. Gene signature from tissue-resident neutrophils is associated with immune checkpoint inhibitor treatment failure. The integrated atlas is publicly available online (https://luca.icbi.at), allowing the dissection of tumor-immune cell interactions in NSCLC. HighlightsO_LIHigh-resolution single-cell atlas of the tumor microenvironment (TME) in NSCLC. C_LIO_LIHistological tumor subtypes and driver genes imprint specific cellular TME patterns. C_LIO_LIscRNA-seq of cells with low transcript count identifies distinct tissue-resident neutrophil (TRN) subpopulations and non-canonical functional properties in the TME niche. C_LIO_LITRN gene signature identifies patients who are refractory to treatment with PD-L1 inhibitors. C_LI O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/491204v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1050b6aorg.highwire.dtl.DTLVardef@3088c8org.highwire.dtl.DTLVardef@6408b8org.highwire.dtl.DTLVardef@1788c22_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

SimBu: Bias-aware simulation of bulk RNA-seq data with variable cell type composition

MotivationAs complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of pseudo-bulk data, generated by aggregating single-cell RNA-seq (scRNA-seq) expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. ResultsWe developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modelling of cell-type-specific mRNA bias using experimentally-derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. ConclusionSimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. AvailabilitySimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. Contactalex.dietrich@tum.de and markus.list@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Chronic Glucocorticoid Stress Reveals Increased Energy Expenditure and Accelerated Aging as Cellular Features of Allostatic Load

Stress triggers anticipatory physiological responses that promote survival, a phenomenon termed allostasis. However, the chronic activation of energy-dependent allostatic responses results in allostatic load, a dysregulated state that predicts functional decline, accelerates aging, and increases mortality in humans. The energetic cost and cellular basis for the damaging effects of allostatic load have not been defined. Here, by longitudinally profiling three unrelated primary human fibroblast lines across their lifespan, we find that chronic glucocorticoid exposure increases cellular energy expenditure by [~]60%, along with a metabolic shift from glycolysis to mitochondrial oxidative phosphorylation (OxPhos). This state of stress-induced hypermetabolism is linked to mtDNA instability, non-linearly affects age-related cytokines secretion, and accelerates cellular aging based on DNA methylation clocks, telomere shortening rate, and reduced lifespan. Pharmacologically normalizing OxPhos activity while further increasing energy expenditure exacerbates the accelerated aging phenotype, pointing to total energy expenditure as a potential driver of aging dynamics. Together, our findings define bioenergetic and multi-omic recalibrations of stress adaptation, underscoring increased energy expenditure and accelerated cellular aging as interrelated features of cellular allostatic load.

cell biology↗

bioRxiv2021

OxPhos Dysfunction Causes Hypermetabolism and Reduces Lifespan in Cells and in Patients with Mitochondrial Diseases

Patients with primary mitochondrial diseases present with fatigue and multi-system disease, are often lean, and die prematurely, but the mechanistic basis for this clinical picture remains unclear. Integrating data from 17 cohorts of patients with mitochondrial diseases (n=690), we find that clinical mitochondrial disorders increase resting energy expenditure, a state termed hypermetabolism. In a longitudinal cellular model of primary patient-derived fibroblasts from multiple donors, we show that genetic and pharmacological disruptions of oxidative phosphorylation (OxPhos) similarly trigger increased energy consumption in a cell-autonomous manner, despite near-normal OxPhos coupling efficiency. Hypermetabolism is associated with mtDNA instability, activation of the integrated stress response, increased extracellular secretion of age-related cytokines and metabokines including GDF15, as well as an accelerated rate of telomere erosion and epigenetic aging, and a reduced Hayflick limit. Together with these dynamic measures, we have generated a longitudinal RNASeq and DNA methylation resource dataset, which reveals conserved, energetically demanding, genome-wide recalibrations in response to OxPhos dysfunction. The increased energetic cost of living, or hypermetabolism, in cells and organisms with OxPhos defects has important biological and clinical implications.

cell biology↗

bioRxiv2021

A Multi-Omics and Bioenergetics Longitudinal Aging Dataset in Primary Human Fibroblasts with Mitochondrial Perturbations

Aging is a process of progressive change. In order to develop biological models of aging, longitudinal datasets with high temporal resolution are needed. Here we report a multi-omic longitudinal dataset for cultured primary human fibroblasts measured across their replicative lifespans. Fibroblasts were sourced from both healthy donors (n=6) and individuals with lifespan-shortening mitochondrial disease (n=3). The dataset includes cytological, bioenergetic, DNA methylation, gene expression, secreted proteins, mitochondrial DNA copy number and mutations, cell-free DNA, telomere length, and whole-genome sequencing data. This dataset enables the bridging of mechanistic processes of aging as outlined by the "hallmarks of aging", with the descriptive characterization of aging such as epigenetic age clocks. Here we focus on bridging the gap for the hallmark mitochondrial metabolism. Our dataset includes measurement of healthy cells, and cells subjected to over a dozen experimental manipulations targeting oxidative phosphorylation (OxPhos), glycolysis, and glucocorticoid signaling, among others. These experiments provide opportunities to test how cellular energetics affect the biology of cellular aging. All data are publicly available at our webtool: https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study/

cell biology↗

bioRxiv2021

Extracellular riboflavin induces anaerobic biofilm formation in Shewanella oneidensis

Some microorganisms can respire with extracellular electron acceptors using an extended electron transport chain to the cell surface. These organisms apply flavin molecules as cofactors to facilitate one-electron transfer catalysed by the terminal reductases and as endogenous electron shuttles. In the model organism Shewanella oneidensis, riboflavin production and excretion triggers a specific biofilm formation response that is initiated at a specific threshold concentration, similar to canonical quorum sensing molecules. Riboflavin-mediated messaging is based on the overexpression of the gene encoding the putrescin decarboxylase speC which leads to posttranscriptional overproduction of proteins involved in biofilm formation. Using a model of growth-dependent riboflavin production under batch and biofilm growth conditions, the number of cells necessary to produce the threshold concentration per time was deduced. Furthermore, our results indicate that specific retention of riboflavin in the biofilm matrix leads to localized concentrations which by far exceed the necessary threshold value. ImportanceFerric iron is the fourth most abundant element of the earth crust. It occurs at neutral pH in the form insoluble iron minerals. The dissimilatory reduction of these minerals is an import part of global geological cycles and is catalyzed by microorganisms through extended respiratory chains to the cell surface. Shewanella oneidensis is one of the best understood model organisms for this kind of extracellular respiration. Flavins are important for the reduction of extracellular electron acceptors by S. oneidensis. since they have a function as (I) cofactors of the terminal reductases and (II) electron shuttles. In this study we reveal that flavin molecules are further employed as quorum sensing molecules. They are excreted by the organisms in a growth dependent manner and lead to anaerobic biofilm formation as a specific response at a certain threshold concentration. Although we know multiple examples of quorum sensing mechanisms, the use of riboflavin was so far not described and at least in S. oneidensis proceeds via a new regulatory routine that proceeds on the trancriptomic and posttranscriptomic level.

microbiology↗

bioRxiv2020

Tissue heterogeneity is prevalent in gene expression studies

BackgroundLack of reproducibility in gene expression studies has recently attracted much attention in and beyond the biomedical research community. Previous efforts have identified many underlying factors, such as batch effects and incorrect sample annotations. Recently, tissue heterogeneity, a consequence of unintended profiling of cells of other origins than the tissue of interest, was proposed as a source of variance that exacerbates irreproducibility and is commonly ignored. ResultsHere, we systematically analyzed 2,692 publicly available gene expression datasets including 78,332 samples for tissue heterogeneity. We found a prevalence of tissue heterogeneity in gene expression data that affects on average 5-15% of the samples, depending on the tissue type. We distinguish cases of severe heterogeneity, which may be caused by mistakes in annotation or sample handling, from cases of moderate heterogeneity, which are more likely caused by tissue infiltration or sample contamination. ConclusionsTissue heterogeneity is a widespread issue in publicly available gene expression datasets and thus an important source of variance that should not be ignored. We advocate the application of quality control methods such as BioQC to detect tissue heterogeneity prior to mining or analysing gene expression data.

bioinformatics↗

bioRxiv2020

Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

SummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementationScirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

bioinformatics↗

bioRxiv2019

Human Aging DNA Methylation Signatures are Conserved but Accelerated in Cultured Fibroblasts

Aging is associated with progressive and site-specific changes in DNA methylation (DNAm). These global changes are captured by DNAm clocks that accurately predict chronological age in humans but relatively little is known about how clocks perform in vitro. Here we culture primary human fibroblasts across the cellular lifespan ([~]6 months) and use four different DNAm clocks to show that age-related DNAm signatures are conserved and accelerated in vitro. The Skin & Blood clock shows the best linear correlation with chronological time (r=0.90), including during replicative senescence. Although similar in nature, the rate of epigenetic aging is approximately 62x times faster in cultured cells than in the human body. Consistent with in vivo data, cells aged under hyperglycemic conditions exhibit an approximately three years elevation in baseline DNAm age. Moreover, candidate gene-based analyses further corroborate the conserved but accelerated biological aging process in cultured fibroblasts. Fibroblasts mirror the established DNAm topology of the age-related ELOVL2 gene in human blood and the rapid hypermethylation of its promoter cg16867657, which correlates with a linear decrease in ELOVL2 mRNA levels across the lifespan. Using generalized additive modeling on twelve timepoints across the lifespan, we also show how single CpGs exhibit loci-specific, linear and nonlinear trajectories that reach rates up to -47% (hypomethylation) to +23% (hypermethylation) per month. Together, these high temporal resolution global, gene-specific, and single CpG data highlight the conserved and accelerated nature of epigenetic aging in cultured fibroblasts, which may constitute a system to evaluate age-modifying interventions across the lifespan.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/605295_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (60K):\norg.highwire.dtl.DTLVardef@101e4f2org.highwire.dtl.DTLVardef@15df049org.highwire.dtl.DTLVardef@4a704dorg.highwire.dtl.DTLVardef@112f970_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG

cell biology↗

bioRxiv2018

Comprehensive evaluation of cell-type quantification methods for immuno-oncology

MotivationThe composition and density of immune cells in the tumor microenvironment profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining, or single-cell sequencing is often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. ResultsWe developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of [~]11,000 cells from the tumor microenvironment to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and [~]1,800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. AvailabilityA snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Contactg.sturm@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Acute Psychological Stress Triggers Circulating Cell-Free Mitochondrial DNA

Intrinsic biological mechanisms transduce psychological stress into physiological adaptation, but the role of mitochondria and mitochondrial DNA (mtDNA) in this process has not been defined in humans. Here, we show that similar to physical injury, psychological stress triggers elevation in circulating cell- free mtDNA (ccf-mtDNA). Healthy midlife adults exposed on two separate occasions to a brief psychological challenge exhibit a 2-3-fold increase in ccf-mtDNA, with no change in nuclear DNA levels, establishing the magnitude and specificity to ccf-mtDNA. In cell-based studies, we show that glucocorticoid signaling - a consequence of psychological stress in humans - is sufficient to induce mtDNA extrusion in a time frame consistent with human psychophysiology. Collectively, these findings provide the first evidence that psychological stress induces ccf-mtDNA and implicate glucocorticoid signaling as a trigger for ccf-mtDNA release. Further work is needed to examine the functional significance of psychological stress-induced ccf-mtDNA as a mitokine in humans.

molecular biology↗