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Grossmann, J.

Publications and source records attributed to Grossmann, J..

11 recordsLinked to original sources

A combination of phenotypic responses and genetic adaptations enables Staphylococcus aureus to withstand inhibitory molecules secreted by Pseudomonas aeruginosa

Staphylococcus aureus and Pseudomonas aeruginosa frequently co-occur in infections, and there is evidence that their interactions can negatively affect disease outcomes. P. aeruginosa is known to be dominant, often compromising S. aureus through the secretion of inhibitory compounds. We previously demonstrated that S. aureus can become resistant to growth-inhibitory compounds during experimental evolution. While resistance arose rapidly, the underlying mechanisms were not obvious as only a few genetic mutations were associated with resistance, while ample phenotypic changes occurred. We thus hypothesize that resistance may result from a combination of phenotypic responses and genetic adaptation. Here, we tested this hypothesis using proteomics. We first focused on an evolved strain that acquired a single mutation in tcyA (encoding a transmembrane transporter unit) upon exposure to P. aeruginosa supernatant. We show that this mutation leads to a complete abolishment of transporter synthesis, which confers moderate protection against PQS and selenocystine, two toxic compounds produced by P. aeruginosa. However, this genetic effect was minor compared to the fundamental phenotypic changes observed at the proteome level when both ancestral and evolved S. aureus strains were exposed to P. aeruginosa supernatant. Major changes involved the downregulation of virulence factors, metabolic pathways, membrane transporters, and the upregulation of ROS scavengers and an efflux pump. Our results suggest that the observed multi-variate phenotypic response is a powerful adaptive strategy, offering instant protection against competitors in fluctuating environments and reducing the need for hard-wired genetic adaptations. ImportanceDifferent bacterial pathogens can co-occur in infections, where they interact with one another and influence disease severity. Previous research showed that pathogens can evolve and adapt to co-infecting species. Here, we show that evolution through genetic mutations and selection are not necessarily required to change pathogen behavior. Instead, we found that the human pathogen Staphylococcus aureus is able to plastically respond to the presence of Pseudomonas aeruginosa, a competing pathogen. Through proteomics and metabolomics, we demonstrate that S. aureus undergoes substantial proteomic alterations in response to P. aeruginosa by down-regulating virulence factor expression, changing metabolism, and mounting protective measures against toxic compounds. Our work highlights that pathogens possess sophisticated mechanisms to respond to competitors to secure growth and survival in polymicrobial infections. We predict such plastic responses to have significant impacts on infection outcomes.

microbiology↗

Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits significant intra-tumoral heterogeneity (ITH) at both morphological and genetic levels, complicating treatment and contributing to disease progression. Among these, ccRCCs with focal rhabdoid differentiation stand out as highly aggressive tumors distinguished by cells with unique morphological features. However, the correlation between distinct morphological phenotypes, specific molecular alterations, and their influence on tumor behavior remains poorly understood. In this study, we integrated advanced AI-based image analysis with single-cell isolation and multi-omics profiling to dissect the link between clinically relevant morphological and molecular features of ccRCC cells. Using a novel digital pathology workflow, we quantified low-grade, high-grade, and rhabdoid morphologies in ccRCC diagnostic images with unprecedented precision. Subsequently, isolation of two sets of 1,000 morphologically distinct cells for detailed mRNA and protein expression analyses, revealed significant increasing dysregulation associating with higher histopathological grades. Rhabdoid ccRCC cells (grade 4) demonstrated unique molecular profiles, including upregulated FOXM1-driven proliferation, disrupted cell-matrix interactions, and enhanced immune evasion pathways. Despite high T-cell infiltration in rhabdoid areas, we identified a rhabdoid-specific immunosuppressive network driven by cytokines, IFN-beta, and integrin signaling, likely contributing to T-cell exhaustion. Rhabdoid ccRCC cells develop a distinct immunosuppressive signaling network, involving PD-L1 and novel immunomodulatory factors such as CD38 and ITGB2. These findings provide a basis for novel therapeutic strategies targeting these pathways in combination with immunotherapy to improve outcomes for patients with aggressive rhabdoid ccRCC. Key PointsO_LIccRCC is characterized by well-established morphological heterogeneity but the correlation with the underlying molecular aberrations remained elusive. C_LIO_LIBy integrating AI-based image analysis with single cell isolation and deep multi-omics profiling, we dissect the molecular intricacies of ccRCC, from targeted collection of 1,000 morphologically distinct cells. C_LIO_LIOur results demonstrate significant dysregulation of gene and protein expression correlating with higher histopathological grades in ccRCC. C_LIO_LIAggressive ccRCC cells with rhabdoid differentiation (grade 4) display distinct molecular profiles, as they upregulate FOXM1-mediated proliferation, ECM remodeling and the immune evasion responses, suggesting new therapeutic avenues enhancing ICI efficacy in these patients. C_LI

cancer biology↗

Quantitative proteome of bacterial periplasmic predation reveals a prey damaging protease

The rise of antimicrobial resistant pathogens calls for novel ways to kill and damage bacteria. A rich source for bacterial cell-damaging proteins is periplasmic predatory bacterium Bdellovibrio bacteriovorus, which invades, kills and subsequently exits the Gram-negative prey cell. An increased understanding of predatory protein function can be achieved by analyzing their relative abundance at key stages of predation. Here, we present the first quantitative proteome covering the complete predatory life cycle of the bacterial predator B. bacteriovorus killing Escherichia coli, quantifying 2195 predator proteins. From these proteins, nine protein clusters sharing similar expression patterns were identified. Towards the end of the life cycle when the predator exits prey remnants, we identified significant amounts of protease Bd2269. Gene knockout and heterologous expression experiments revealed that Bd2269 is involved in the prey exit process and damages E. coli from within. This quantitative predator proteome is a valuable resource to unravel bacterial predator-prey interactions and advances the search for novel antimicrobial enzymes.

microbiology↗

prolfquapp - A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics

AbstractMass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, traditional interactive data analysis methods become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to non-programmers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements and multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software. By leveraging advanced statistical models from the prolfqua R package, prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics studies, combining efficient data processing with insightful, publication-ready outputs. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/617391v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@10930a2org.highwire.dtl.DTLVardef@57f5dborg.highwire.dtl.DTLVardef@ce0d73org.highwire.dtl.DTLVardef@1d531da_HPS_FORMAT_FIGEXP M_FIG C_FIG This visual table of contents illustrates the workflow and key features of the prolfquapp tool for differential expression analysis in proteomics. On the left are the inputs, like the CSV for annotation and quantification results, YAML for parameters, and FASTA files for protein information. In the center are the prolfquapp and prolfqua R packages and supporting tools like crosstalk and knitr, representing the core processing components. On the right side, the figure highlights the various outputs generated by prolfquapp O_LIXLSX files containing protein abundances, group summaries, and differential expression results. C_LIO_LIHTML reports with text, graphs, interactive volcano plots, and dynamic tables for data exploration. C_LIO_LIPDF documents with detailed protein boxplots and peptide-level matrix plots. C_LIO_LIIntegration with exploreDE for interactive data visualization. C_LI This diagram concisely summarizes the flow from data input to the creation of analysis-ready outputs, offering a clear overview of the prolfquapp toolset.

bioinformatics↗

The CUL4-DDB1-DCAF1 E3 ubiquitin ligase complex regulates PLK4 protein levels to prevent premature centriole duplication

Centrioles play important roles in the assembly of centrosomes and cilia. Centriole duplication occurs once per cell cycle and is dependent on polo-like kinase 4 (PLK4). To prevent centriole amplification, which is a hallmark of cancer, PLK4 protein levels need to be tightly regulated. Here, we show that the Cullin(CUL)4A/B-DDB1-DCAF1, CRL4DCAF1, E3 ligase targets PLK4 for degradation in human cells. DCAF1 binds and ubiquitylates PLK4 in G2 phase to prevent premature centriole duplication in mitosis. In contrast to the regulation of PLK4 by SCF{beta}-TrCP, the interaction between PLK4 and DCAF1 is independent of PLK4 kinase activity and mediated by polo-boxes 1 and 2 of PLK4, suggesting that DCAF1 promotes PLK4 ubiquitylation independently of {beta}-TrCP. Thus, the SCFSlimb/{beta}-TrCP pathway, targeting PLK4 for ubiquitylation based on its phosphorylation state and CRL4DCAF1, which ubiquitylates PLK4 by binding to the conserved PB1-PB2 domain, appear to be complementary ways to control PLK4 abundance to prevent centriole overduplication.

cell biology↗

Haptoglobin buffers lipopolysaccharides to safeguard against aberrant NFκB activation

It has remained yet unclear which soluble factors regulate the anti-inflammatory macrophage phenotype observed in both homeostasis and tumourigenesis. We show here that haptoglobin, a major serum pro-tein with elusive immunoregulatory properties, binds and buffers serum-borne bacterial lipopolysaccharides to attenuate activation of NF{kappa}B in macrophages. Haptoglobin binds different lipopolysaccharides with low micromolar affinities. Given its abundance, haptoglobin constitutes a buffer for lipopolysaccharides, shielding them to safeguard against aberrant inflammatory reactions caused by small fluctuations. Concordantly, NF{kappa}B activation by haptoglobin-associated lipopolysaccharides was delayed relative to stimulation with pure lipopolysaccharide. Our findings warrant evaluation of therapeutic benefits of haptoglobin for inflammatory conditions and re-evaluation of purification strategies. Finally, they allow to elucidate mechanisms of enhanced immunosuppression by oncofetal haptoglobin.

immunology↗

prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. Nevertheless, there is a need for a flexible and easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures. The prolfqua package can model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. It integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The application programmer interface strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis easy and exciting. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard dataset. Finally, we show that the implemented methods allow sensitive and specific differential expression analysis. The prolfqua R package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT licence, and runs on all platforms supported by the R free software environment for statistical computing and graphics.

bioinformatics↗

Rapid and strain-specific resistance evolution of Staphylococcus aureus against inhibitory molecules secreted by Pseudomonas aeruginosa

Pseudomonas aeruginosa and Staphylococcus aureus frequently occur together in polymicrobial infections, and there is evidence that their interactions negatively affect disease outcome in patients. At the molecular level, interactions between the two bacterial species are well-described, with P. aeruginosa usually being the dominant species suppressing S. aureus through a variety of inhibitory molecules. However, in chronic infections the two species interact over prolonged periods of time, and S. aureus might be able to evolve resistance against inhibitory molecules deployed by P. aeruginosa. Here, we used experimental evolution to test this hypothesis by exposing three different S. aureus strains (Cowan I, 6850, JE2) to the growth-inhibitory supernatant of P. aeruginosa PAO1 over 30 days. Prior to evolution, we found that S. aureus strains were inhibited by secreted compounds regulatorily controlled by the PQS quorum-sensing system of P. aeruginosa. Following evolution, S. aureus strains were no longer inhibited: we observed that phenotypic adaptations were strain-specific and involved the up-regulation of virulence traits, such as staphyloxanthin production and the formation of small colony variants. At the genetic level, mutations in membrane transporters were the most frequent evolutionary targets. Our work indicates that adaptations of S. aureus to co-infecting pathogens occurs rapidly and involves both virulence traits and membrane transporters involved in drug resistance. Thus, pathogen evolution could promote species co-existence, complicate treatment options and therefore worsen disease outcome.

microbiology↗

Diurnal Dynamics of the Arabidopsis Rosette Proteome and Phosphoproteome

Plant growth depends on the diurnal regulation of cellular processes, but it is not well understood if and how transcriptional regulation controls diurnal fluctuations at the protein-level. Here we report a high-resolution Arabidopsis thaliana (Arabidopsis) leaf rosette proteome acquired over a 12 h light : 12 h dark diurnal cycle and the phosphoproteome immediately before and after the light-to-dark and dark-to-light transitions. We quantified nearly 5000 proteins and 800 phosphoproteins, of which 288 fluctuated in their abundance and 226 fluctuated in their phosphorylation status. Of the phosphoproteins, 60% were quantified for changes in protein abundance. This revealed six proteins involved in nitrogen and hormone metabolism that had concurrent changes in both protein abundance and phosphorylation status. The diurnal proteome and phosphoproteome changes involve proteins in key cellular processes, including protein translation, light perception, photosynthesis, metabolism and transport. The phosphoproteome at the light-dark transitions revealed the dynamics at phosphorylation sites in either anticipation of or response to a change in light regime. Phosphorylation site motif analyses implicate casein kinase II and calcium/calmodulin dependent kinases among the primary light-dark transition kinases. The comparative analysis of the diurnal proteome and diurnal and circadian transcriptome established how mRNA and protein accumulation intersect in leaves during the diurnal cycle of the plant. Summary StatementThe manuscript provides quantitative information of diurnal changes in the accumulation and phosphorylation of proteins in Arabidopsis thaliana rosettes grown in a 12 h photoperiod. The highly resolved time-scale of the datasets offer new proteome-level insights for future targeted studies.

plant biology↗

A candidate culture-dependent form in apes, nut-cracking, can be individually learnt by naive orangutans (Pongo abelii)

Nut-cracking using hammer tools has been argued to be one of the most complex tool-use behaviours observed in non-human animals (henceforth: animals). Recently, even the United Nations Convention on the Conservation of Migratory Species (CMS) recognised the unique nature of chimpanzee nut-cracking by making it the first animal behaviour to be awarded UN-protected status (Picheta, 2020). So far, only chimpanzees, capuchins and macaques have been observed using tools to crack nuts in the wild (Boesch & Boesch, 1990; Gumert, Kluck, & Malaivijitnond, 2009; Ottoni & Mannu, 2001). However, the learning mechanisms behind this behaviour, and the extent of nut-cracking in other primate species are still unknown. The aim of this study was two-fold. First, we aimed to examine whether other great ape species would develop nut-cracking when provided with all the tools and motivation to do so. Second, we wanted to examine the mechanisms behind the emergence of nut-cracking in a naive sample. Orangutans (Pongo abelii; pygmaeus) have not been observed cracking nuts in the wild, despite having the second most extensive tool-use repertoire of the great apes (after chimpanzees), having the materials for the behaviour in the wild (albeit rarely) and possessing flexible problem-solving capacities. Therefore, orangutans are a valid candidate species for the investigation of the development of nut-cracking. Four nut-cracking-naive orangutans at Leipzig zoo (Pongo abelii; Mage=16; age range=10-19; 4F; at time of testing) were provided with nuts and hammers but were not demonstrated the nut-cracking behavioural form, in order to control for the role of copying social learning in the acquisition of this behaviour. Additionally, we report data from a previously unpublished study by one of the authors (MF) with eight orangutans housed at Zurich zoo (10 Pongo abelii and two Pongo pygmaeus; Mage=14; age range =2-30; 5F; at time of testing) that followed a similar testing paradigm. Out of the twelve orangutans across both testing institutions, at least four individuals, one from Leipzig (Pongo abelii) and three from Zurich (Pongo abelii; pygmaeus), spontaneously expressed nut-cracking with a wooden hammer. These results suggest that the behavioural form of nut-cracking using hammer tools can emerge in orangutans when required through individual learning combined, in some cases, with non-copying social learning mechanisms.

animal behavior and cognition↗

rawDiag - an R package supporting rational LC-MS method optimization for bottom-up proteomics

Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a non-trivial task. Here we present rawDiag, a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan level metadata. rawDiag is implemented as R package and can be executed on the command line, or through a graphical user interface (GUI) for less experienced users. The code runs platform independent and can process a hundred raw files in less than three minutes on current consumer hardware as we show by our benchmark. In order to demonstrate the functionality of our package, we included a real-world example taken from our daily core facility business.

bioinformatics↗