Biology subjects

Doellinger, J.

Publications and source records attributed to Doellinger, J..

10 recordsLinked to original sources

bioRxiv2024

Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics

Antimicrobial resistance (AMR) is an increasing challenge for therapy of bacterial infections. Currently, patient treatment is guided by antimicrobial susceptibility testing (AST) using phenotypic assays and species identification by MALDI-ToF biotyping. Bacterial phenotype prediction using omics technologies could offer several advantages over current diagnostic methods. It would allow species identification and AST to be combined in a single measurement, it would eliminate the need for secondary cultivation and could enable the prediction of phenotypes beyond AMR, such as virulence. In this study, the potential of proteomics for clinical microbiology was evaluated in an analysis of 126 clinical isolates covering 16 species including all ESKAPE genera and 29 of the most common AMR determinants. For this purpose, a flexible workflow was developed, which enables to report the AMR phenotype and the species of primary cultures within 2h. Proteomics provided high specificity (99.9%) and sensitivity (94.4 %) for AMR detection, while allowing species identification from very large sequence databases with high accuracy. The results show, that proteomics is well suited for phenotyping clinical bacterial isolates and has the potential to become a valuable diagnostic tool for clinical microbiology in the future.

microbiology↗

bioRxiv2024

Cytosolic factors controlling PASTA kinase-dependent ReoM phosphorylation

Bacteria adapt the biosynthesis of their envelopes to specific growth conditions and prevailing stress factors. Peptidoglycan (PG) is the major component of the cell wall in Gram-positive bacteria, where PASTA kinases play a central role in PG biosynthesis regulation. Despite their importance for growth, cell division and antibiotic resistance, the mechanisms of PASTA kinase activation are not fully understood. ReoM, a recently discovered cytosolic phosphoprotein, is one of the main substrates of the PASTA kinase PrkA in the Gram-positive human pathogen Listeria monocytogenes. Depending on its phosphorylation, ReoM controls proteolytic stability of MurA, the first enzyme in the PG biosynthesis pathway. The late cell division protein GpsB has been implicated in PASTA kinase signalling. Consistently, we show that L. monocytogenes prkA and gpsB mutants phenocopied each other. Analysis of in vivo ReoM phosphorylation confirmed GpsB as an activator of PrkA leading to the description of structural features in GpsB that are important for kinase activation. We further show that ReoM phosphorylation is growth-phase dependent and that this kinetic is reliant on the protein phosphatase PrpC. ReoM phosphorylation was inhibited in mutants with defects in MurA degradation, leading to the discovery that artificial MurA overexpression prevented ReoM phosphorylation. Overexpressed MurA must adopt its substrate-bound closed conformation and interact with ReoM to exert this effect, but the extracellular PASTA domains of PrkA or MurJ flippases where not required. Our results indicate that intracellular signals control ReoM phosphorylation and extend current models describing the mechanisms of PASTA kinase activation.

microbiology↗

bioRxiv2022

Optimizing data points per protein increases protein identifications while maintaining quantitative precision in short gradient data-independent acquisition proteomics

The combination of short liquid chromatography (LC) gradients and data independent acquisition (DIA) by mass spectrometry (MS) has proven its huge potential for high-throughput proteomics. This methodology benefits from the speed of the latest generation of mass spectrometers, which enable short MS cycle times needed to provide sufficient sampling of sharp LC peaks. However, the optimization of isolation window schemes resulting in a certain number of data points per peak (DPPP) is understudied, although it is one of the most important parameters for the outcome of this methodology. In this study, we show that substantially reducing the number of DPPP for short gradient DIA massively increases protein identifications while maintaining quantitative precision. A deeper analysis of the underlying effects shows that a reduction of DPPP increases the selectivity of the data analysis, allowing fragment ions to be tracked over a longer period of the LC gradient. This in turn increases the number of precursors identified per protein, keeping the number of data points per protein nearly constant even at long cycle times. When proteins are inferred from its precursors, quantitative precision is maintained at low DPPP while greatly increasing proteomic depth. This strategy enabled us quantifying 6018 HeLa proteins (> 80,000 precursor identifications) with coefficients of variation below 20% in 30 min using a Q Exactive Orbitrap mass spectrometer, which corresponds to a throughput of 29 samples per day. This indicates that the potential of high-throughput DIA-MS has not been fully exploited yet.

biochemistry↗

bioRxiv2022

Perchlorate-Specific Proteomic Stress Responses of Debaryomyces hansenii Could Enable Microbial Survival in Martian Brines

If life exists on Mars, it would face several challenges including the presence of perchlorates, which destabilize biomacromolecules by inducing chaotropic stress. However, little is known about perchlorate toxicity for microorganism on the cellular level. Here we present the first proteomic investigation on the perchlorate-specific stress responses of the halotolerant yeast Debaryomyces hansenii and compare these to generally known salt stress adaptations. We found that the responses to NaCl and NaClO4-induced stresses share many common metabolic features, e.g., signaling pathways, elevated energy metabolism, or osmolyte biosynthesis. However, several new perchlorate-specific stress responses could be identified, such as protein glycosylation and cell wall remodulations, presumably in order to stabilize protein structures and the cell envelope. These stress responses would also be relevant for life on Mars, which - given the environmental conditions - likely developed chaotropic defense strategies such as stabilized confirmations of biomacromolecules and the formation of cell clusters.

cell biology↗

bioRxiv2021

Deep time course proteomics of SARS-CoV and SARS-CoV-2-infected human lung epithelial cells (Calu-3) reveals strong induction of interferon-stimulated gene (ISG) expression by SARS-CoV-2 in contrast to SARS-CoV

SARS-CoV and SARS-CoV-2 infections are characterized by remarkable differences, including contagiosity and case fatality rate. The underlying mechanisms are not well understood, illustrating major knowledge gaps of coronavirus biology. In this study, protein expression of SARS-CoV- and SARS-CoV-2-infected human lung epithelial cell line Calu-3 was analysed using data-independent acquisition mass spectrometry (DIA-MS). This resulted in the so far most comprehensive map of infection-related proteome-wide expression changes in human cells covering the quantification of 7478 proteins across 4 time points. Most notably, the activation of interferon type-I response was observed, which surprisingly is absent in other recent proteome studies, but is known to occur in SARS-CoV-2-infected patients. The data reveal that SARS-CoV-2 triggers interferon-stimulated gene (ISG) expression much stronger than SARS-CoV, which reflects the already described differences in interferon sensitivity. Potentially, this may be caused by the enhanced expression of viral M protein of SARS-CoV in comparison to SARS-CoV-2, which is a known inhibitor of type I interferon expression. This study expands the knowledge on the host response to SARS-CoV-2 infections on a global scale using an infection model, which seems to be well suited to analyse innate immunity.

systems biology↗

bioRxiv2020

Optimization of data-independent acquisition using predicted libraries for deep and accurate proteome profiling

In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micro-pillar array columns as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4 % were obtained even at only 1.5 data points per peak (full width at half maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically-corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology and molecular biology.

systems biology↗

bioRxiv2019

Evaluation of MALDI-ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures

1.Bacillus cereus plays an often unrecognized role in food borne diseases. Food poisoning caused by this pathogen is manifested by either diarrhea or emesis. While different enterotoxins have been linked to the diarrheal type of B. cereus infections, the emetic toxin cereulide is responsible for the second type. Due to the relatively high prevalence of cereulide associated food poisoning, methods for simple and reliable detection of cereulide producing strains are of utmost importance. Currently, liquid-chromatography coupled to mass spectrometry (LC-MS) is used for sensitive, specific and quantitative cereulide detection, but this technique requires specialized LC-MS equipment, which is often not available in microbiology routine diagnostic laboratories. The last decade has witnessed the advent of matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-ToF MS) as a simple, rapid and cost-efficient technique for identification of microbial pathogens in routine diagnostics. Just recently, two different studies reported on the application of MALDI-ToF MS for either the differentiation of emetic and non-emetic strains of B. cereus or for direct detection of cereulide from bacterial colony smears. However, no method evaluation and optimization was performed in frame of these studies. Thus, additional investigations on the selectivity and sensitivity of MALDI-TOF MS for cereulide detection are needed before implementation of this method in routine diagnostics can be considered. These aspects prompted us to investigate open or controversial issues and to systematically test sample preparation methods, commonly used for microbial identification for their suitability to detect the emetic toxin directly from bacteria. Based on our experimental findings we propose a MALDI-ToF MS workflow that allows identification of B. cereus and sensitive detection of cereulide in parallel, using standard, linear-mode MALDI-ToF MS equipment. The experimental protocol is based on the well-established ethanol/formic acid extraction method and offers, if required, possibilities for further characterization by more sophisticated LC-MS-based methods. In summary, the ease of use and the achieved level of analytical sensitivity as well as the wide-spread availability of standard MALDI-ToF MS equipment in clinical microbiological laboratories provides a promising tool to improve and to facilitate routine diagnostics of B. cereus associated food intoxications.

microbiology↗

bioRxiv2019

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data

1.Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient - less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.

microbiology↗

bioRxiv2019

TaxIt: An iterative and automated computational pipeline for untargeted strain-level identification using MS/MS spectra from pathogenic samples

Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes.\n\nWe present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to improve candidate confidence.\n\nWe apply our iterative workflow on several samples of bacterial and viral origin. In comparison to non-iterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted and continuously growing sequence resources such as the NCBI databases and is available under open-source license at https://gitlab.com/rki_bioinformatics.

bioinformatics↗

bioRxiv2018

Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics based on Acid Extraction

Mass spectrometry is the method of choice for deep and comprehensive analysis of proteomes and has become a key technology to support the progress in life science and biomedicine. However, sample preparation in proteomics is not standardized and contributes to a lack of reproducibility. The main challenge is to extract all proteins in a manner that enables efficient digestion into peptides and is compatible with subsequent mass spectrometric analysis. Current methods are based on the idea of removing detergents or chaotropic agents during sample processing, which are essential for protein extraction but interfere with digestion and LC-MS. These multi-step preparations are prone to losses, biases and contaminations, while being time-consuming and labor-intensive.\n\nWe report a universal detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which is based on a simple three-step procedure, acidification, neutralization and digestion. SPEED is a one-pot method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction. SPEED-based sample processing is highly reproducible, provides exceptional peptide yields and enables preparation even of tissue samples with less than 15 min hands-on time and without any special equipment. Evaluation of SPEED performance revealed, that the number of quantified proteins and the quantitative reproducibility are superior compared to the well-established sample processing protocols FASP, ISD-Urea and SP3 for various sample types, including human cells, bacteria and tissue, even at low protein starting amounts.

systems biology↗