Biology subjects

Zimmer, R.

Publications and source records attributed to Zimmer, R..

12 recordsLinked to original sources

bioRxiv2026

Fuzzifier*: Robust and Sensitive Multi-omics Data Analysis

MotivationCategorization is an important means for interpreting data and drawing conclusions. Often, the derived categories provide evidence for diagnostic or even therapeutic approaches. The standard pipelines for differential analysis of multi-omic high-throughput, and in particular single-cell data, yield (ranked) lists of possibly differential features after applying appropriate effect sizes or significance thresholds of computed p-value and/or foldchange. ResultsWe propose the Fuzzifier* pipeline for the differential analysis of any type of high-throughput data, either raw input data or fold-change data of groups of a (small or large) number of replicates. In Fuzzifier*, categorization can be applied to any step of the analysis pipeline according to custom-designed fuzzy concepts (Fuzzifier). Thus, any (fuzzified) analysis option corresponds to a path in a commutative diagram specifying the Fuzzifier* pipeline. Fuzzifier* computes a user-defined set of paths and presents an overview of the results, thereby identifying both highly reliable (consensus) and sensitive (path-specific) features. Fuzzifier* is a method that can be applied to any analysis pipeline to obtain different views on the data and yield more reliable results. This is demonstrated by the identification of context-specific miRNAs for individual cancer types from TCGA data. Fuzzifier* could both validate known cancer-specific miRNAs and identify novel candidates. In comparison to statistical tests, Fuzzifier* focuses on value distributions of tumor and normal samples as well as paired foldchange distributions and, thus, identifies condition-specific features from a relatively small number of replicates. Availability and Implementationhttps://github.com/zimmerlab/fuzzifier Contactoffensperger@bio.ifi.lmu.de and zimmer@ifi.lmu.de

bioinformatics↗

bioRxiv2025

Text Mining miRNA-gene Interactions for the mx-plore Platform

miRNAs are post-transcriptional regulators which bind to the specific mRNA of expressed genes and induce increased mRNA degradation, thereby reducing the gene expression of their target genes. miRNAs perform their regulatory functions in a context-specific manner. Several databases contain collections of miRNA-target interactions, which are either experimentally validated, derived from high-throughput experiments, or computationally predicted or captured from existing literature. For most databases, the specific context of the miRNA-target interaction is unknown or missing in these databases. mx-plore is not only a database of text-dervied miRNA-gene interactions, but it enhances the found interactions including detailed context information from scientific publications. To derive the entries in the mx-plore database, a newly developed text mining strategy combining dependency graph analysis and rule-based systems has been used. The mx-plore platform makes miRNA-gene interactions accessible and searchable on various levels, such as by cell type, disease or involved processes. The platform is available at https://rest.bio.ifi.lmu.de/mxplore and corresponding source code is deposited on GitHub. Author summaryO_LIThe mx-plore platform is a comprehensive database of miRNA:target interactions as described in the scientific literature. C_LIO_LImx-plore is a new text-mining approach to extract context-dependent miRNA interactions from Pubmed abstracts and PMC full texts. C_LIO_LImx-plore allows to query the comprehensive database in a context-specific manner, both on- and off-line. C_LI

bioinformatics↗

bioRxiv2024

Immunothrombolytic monocyte-neutrophil axes dominate the single-cell landscape of human thrombosis

AbstractThrombotic diseases remain the major cause of death and disability worldwide with insufficient preventive and therapeutic strategies available. In the last decades a prominent inflammatory component has been identified as a key driver in the initiation and propagation of thrombosis - named thromboinflammation. However, a comprehensive investigation of the human immune system in thromboinflammation, beyond histological quantification, is lacking, which is essential for the development of novel therapeutic approaches. We therefore mapped the trajectories, functional states, and intercommunication of immune cells in stroke thrombi, retrieved by thrombectomy, at single-cell resolution. We reveal distinct leukocyte subpopulations with prothrombotic and, surprisingly, prominent fibrinolytic properties characterized by aberrant activation of intracellular host defense as well as hypoxia induced pathways. A prominent thrombolytic PLAUhigh, PLAURhigh, THBDhigh thrombus neutrophil subset, also expressing high levels of pro-recanalizing VEGFA and VEGFB, dominated the thrombus neutrophil environment. On the other hand CD16high NR4A1high non-classical monocytes with strong CXCL8, CXCL2, CXCL1 and CXCL16 mediated neutrophil- attracting and PLAU, PLAUR, THBD and TFPI mediated thrombolytic properties defined the thrombus monocyte environment. These thrombus monocyte subsets were characterized by high expression of TIMP1 and TREM1. These novel innate immune- cell subsets provide insights into the thrombogenic and pro-resolving properties of innate immune-cells. To provide mechanistic insight into these multi-omic findings, we utilized reverse translation approaches. In vitro as well as murine in vivo thrombosis models underlined the causal relevance of these immune-cell axes for thrombolysis: NR4A1high thrombus monocytes acquired a neutrophil-chemoattractive transcriptomic phenotype, neutrophils continuously infiltrated established murine thrombi in vivo and acquired a HIF1-mediated thrombolytic phenotype in vitro. A depletion of NR4A1high thrombus monocytes reduced thrombus neutrophil influx and exacerbated thrombosis in vivo. Together, this unravels cross-communicating monocyte and neutrophil subsets with thrombus-resolving properties and provide a publicly accessible immune-landscape of thrombosis. This provides a valuable resource for future research on thrombo- inflammation and might pave the way for novel immune-modulatory approaches for prevention or resolution of thrombosis.

immunology↗

bioRxiv2023

Detecting Differential Alternative Splicing in Mass Spectrometry-based Proteomics Data

Alternative splicing can substantially diversify biological cell states and influence cellular function. The functional impact of splicing has to be estimated at protein level, typically by mass spectrometry (MS) -based proteomics. Although this technology measures increasingly large peptides sets, distinguishing isoform-specific peptides are rare, limiting detection and quantification of splicing. We introduce MS-EmpiReS, a quantification-based computational approach for differential alternative splicing detection in proteomics data. Its core principle is to differentially quantify peptides mapping to different regions of genes. This approach increased the number of testable peptides hundred-fold in a clinical cancer cohort, resulting in a large number of cancer-relevant splicing candidates. Splicing events detected by both MS-EmpiReS and deep RNA sequencing correlated well but also provided complementary information. The proteomics data allowed us to define a per-sample splicing score to separate cancer conditions. Finally, deep brain proteomes from different mice separated strongly by the lower abundance protein splicing isoform.

bioinformatics↗

bioRxiv2023

Resident and recruited macrophages differentially contribute to cardiac healing after myocardial ischemia

Cardiac macrophages are heterogenous in phenotype and functions, which has been associated with differences in their ontogeny. Despite extensive research, our understanding of the precise role of different subsets of macrophages in ischemia/reperfusion injury remains incomplete. We here investigated macrophage lineages and ablated tissue macrophages in homeostasis and after I/R injury in a CSF1R-dependent manner. Genomic deletion of a fms-intronic regulatory element (FIRE) in the Csf1r locus resulted in specific absence of resident homeostatic and antigen-presenting macrophages, without affecting the recruitment of monocyte-derived macrophages to the infarcted heart. Specific absence of homeostatic, monocyte-independent macrophages altered the immune cell crosstalk in response to injury and induced proinflammatory neutrophil polarization, resulting in impaired cardiac remodelling without influencing infarct size. In contrast, continuous CSF1R inhibition led to depletion of both resident and recruited macrophage populations. This augmented adverse remodelling after I/R and led to an increased infarct size and deterioration of cardiac function. In summary, resident macrophages orchestrate inflammatory responses improving cardiac remodelling, while recruited macrophages determine infarct size after I/R injury. These findings attribute distinct beneficial effects to different macrophage populations in the context of myocardial infarction. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/539799v4_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@14fa4bcorg.highwire.dtl.DTLVardef@1c86bbborg.highwire.dtl.DTLVardef@118edfcorg.highwire.dtl.DTLVardef@1b06916_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Adaptation of bacterial proteome reveals a key role of the cell envelope in starvation survival

Bacteria reorganize their physiology upon entry to stationary phase. What part of this reorganization improves starvation survival is a difficult question, because the change in physiology includes a global reorganization of the proteome, envelope and metabolism of the cell. In this work, we used several trade-offs between fast growth and long survival to statistically score over 2000 E. coli proteins for their global correlation with death rate. The combined ranking allowed us to narrow down the set of proteins that positively correlate with survival and validate the causal role of a subset of proteins. Remarkably, we found that important survival genes are related to the cell envelope, i.e., periplasm and outer membrane, because maintenance of envelope integrity of E. coli plays a crucial role during starvation. Our results uncover a new protective feature of the outer membrane that adds to the growing evidence that the outer membrane is not only a barrier that prevents abiotic substances from reaching the cytoplasm, but essential for bacterial proliferation and survival. Standfirst textA trade-off between the two major modes of bacterial lifestyle, growth and starvation can be explained by bacteria investing resources into the cell envelope to make it impermeable to ions, which improves their lifespan but comes at the expense of slowing down growth. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/492425v2_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@7534d7org.highwire.dtl.DTLVardef@e02e43org.highwire.dtl.DTLVardef@1fd4acdorg.highwire.dtl.DTLVardef@39e716_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA trade-off between growth rate and death rate confines fitness of bacteria across environments. C_LIO_LIAnalysis of proteome signatures in 126 conditions across five independent perturbations reveals the cell envelope as a key determinant of death rate. C_LIO_LIThe trade-off can be abolished by changing environment to a low-salt, but osmo-balanced medium where cell envelope integrity is not limiting. C_LI

systems biology↗

bioRxiv2021

Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection

The immune system of most SARS-CoV-2 infected individuals limits viral spread to the upper airways without pulmonary involvement. This prevents the development of pneumonic COVID-19. However, the protective immunological responses causative of successful viral containment in the upper airways remain unclear. Here, we combine longitudinal single-cell RNA sequencing, proteomic profiling, multidimensional flow cytometry, RNA-Seq of FACS-sorted leukocyte subsets and multiplex plasma interferon profiling to uncover temporally resolved protective immune signatures in non-pneumonic and ambulatory SARS-CoV-2 infected patients. We compare host responses in a high-risk patient population infected with SARS-CoV-2 but without pulmonary involvement to patients with COVID-19 pneumonia. Our data reveal a distinct immunological signature of successful viral containment, characterized by an early prominent interferon stimulated gene (ISG) upregulation across immune cell subsets. In addition, reduced cytotoxic potential of Natural Killer (NK) and T cells, as well as a monocyte phenotype with immune-modulatory potential are hallmarks of protective immunity. Temporal resolution across disease trajectories highlights ISG upregulation as particularly prominent early in the disease and confirms increased expression also in comparison to healthy controls. We validate this distinct temporal ISG signature by in-depth RNA-seq of FACS-sorted leukocyte subsets in a large prospective ambulatory SARS-CoV-2 infected cohort confirming early and robust ISG upregulation particularly in monocytes and T cells. In conclusion, our data demonstrate a protective ISG phenotype in patients with successful containment of SARS-CoV-2 infection without progression to COVID-19. This early protective interferon response might be exploited as a therapeutic approach and for disease course prediction.

immunology↗

bioRxiv2020

EmpiReS: Differential Analysis of Gene Expression and Alternative Splicing

While absolute quantification is challenging in high-throughput measurements, changes of features between conditions can often be determined with high precision. Therefore, analysis of fold changes is the standard method, but often, a doubly differential analysis of changes of changes is required. Differential alternative splicing is an example of a doubly differential analysis, i.e. fold changes between conditions for different isoforms of a gene. EmpiRe is a quantitative approach for various kinds of omics data based on fold changes for appropriate features of biological objects. Empirical error distributions for these fold changes are estimated from Replicate measurements and used to quantify feature fold changes and their directions. We assess the performance of EmpiRe to detect differentially expressed genes applied to RNA-Seq using simulated data. It achieved higher precision than established tools at nearly the same recall level. Furthermore, we assess the detection of alternatively Spliced genes via changes of isoform fold changes (EmpiReS) on distribution-free simulations and experimentally validated splicing events. EmpiReS achieves the best precision-recall values for simulations based on different biological datasets. We propose EmpiRe(S) as a general, quantitative and fast approach with high reliability and an excellent trade-off between sensitivity and precision in (doubly) differential analyses.

bioinformatics↗

bioRxiv2019

Towards a gold standard for benchmarking gene set enrichment analysis

BackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.\n\nResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.\n\nConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.\n\nAvailabilityhttp://bioconductor.org/packages/GSEABenchmarkeR

bioinformatics↗

bioRxiv2019

Integrative functional genomics decodes herpes simplex virus 1

Since the genome of herpes simplex virus 1 (HSV-1) was first sequenced more than 30 years ago, its predicted 80 genes have been intensively studied. Here, we unravel the complete viral transcriptome and translatome during lytic infection with base-pair resolution by computational integration of multi-omics data. We identified a total of 201 viral transcripts and 284 open reading frames (ORFs) including all known and 46 novel large ORFs. Multiple transcript isoforms expressed from individual gene loci explain translation of the vast majority of novel viral ORFs as well as N-terminal extensions (NTEs) and truncations thereof. We show that key viral regulators and structural proteins possess NTEs, which initiate from non-canonical start codons and govern subcellular protein localization and packaging. We validated a novel non-canonical large spliced ORF in the ICP0 locus and identified a 93 aa ORF overlapping ICP34.5 that is thus also deleted in the FDA-approved oncolytic virus Imlygic. Finally, we extend the current nomenclature to include all novel viral gene products. Taken together, this work provides a valuable resource for future functional studies, vaccine design and oncolytic therapies.

microbiology↗

bioRxiv2019

MS-EmpiRe utilizes peptide-level noise distributions for ultra sensitive detection of differentially abundant proteins

1Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of proteins in a wide range of biological and biomedical applications. Protein changes need to be reliably derived from a large number of measured peptide intensities and their corresponding fold changes. These fold changes vary considerably for a given protein.\n\nNumerous instrumental setups aim to reduce this variability, while current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe (github.com/zimmerlab/MS-EmpiRe), which explicitly accounts for the noise underlying peptide fold changes. We derive dataset-specific, intensity-dependent empirical error distributions, which are used for individual weighing of peptide fold changes to detect differentially abundant proteins. The method requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. In a recently published proteome-wide benchmarking dataset, MS-EmpiRe doubles the number of correctly identified changing proteins at a correctly estimated FDR cutoff in comparison to state-of-the-art tools. We confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe provides rapid processing (< 2min) and is an easy to use, general-purpose tool.

bioinformatics↗

bioRxiv2017

Multi-reference spectral library yields almost complete coverage of heterogeneous LC-MS/MS data sets

Spectral libraries play a central role in the analysis of data-independent-acquisition (DIA) proteomics experiments. A main assumption in current spectral library tools is that a single characteristic intensity pattern (CIP) suffices to describe the fragmentation of a peptide in a particular charge state (peptide charge pair). However, we find that this is often not the case. We carry out a systematic evaluation of spectral variability over public repositories and in-house datasets. We show that spectral variability is widespread and partly occurs under fixed experimental conditions. Using clustering of preprocessed spectra, we derive a limited number of Multiple Characteristic Intensity Patterns (MCIPs) for each peptide charge pair, which allow almost complete coverage of our heterogeneous dataset without affecting the false discovery rate. We show that a MCIP library derived from public repositories performs in most cases similar to a \"custom-made\" spectral library, which has been acquired under identical experimental conditions as the query spectra. We apply the MCIP approach to a DIA data set and observe a significant increase in peptide recognition. We propose the MCIP approach as an easy-to-implement addition to current spectral library search engines and as a new way to utilize the data stored in spectral repositories.

bioinformatics↗