Biology subjects

Ludwig, D.

Publications and source records attributed to Ludwig, D..

5 recordsLinked to original sources

bioRxiv2026

Bro1-Mediated Trafficking Couples TOR Signalling to Cellular Metabolism and Longevity

Adaptation to nutrient availability requires coordination between growth control, metabolism, and intracellular trafficking. In eukaryotes, inhibition of Target of Rapamycin (TOR) signalling robustly promotes stress resistance and longevity, yet how reduced growth signalling is coupled to organelle dynamics and proteome remodelling remains unclear. Here, we identify the conserved ESCRT-associated protein Bro1 as a central integrator of TOR signalling, vacuolar trafficking, and metabolic adaptation. Using fission yeast, we show that Bro1 is required for normal lifespan and for the global proteomic reprogramming that accompanies TOR inhibition. In Bro1 mutant cells, repression of ribosome biogenesis is uncoupled from activation of catabolic, vacuolar, and metabolic pathways, resulting in an altered metabolic state characterised by elevated lipid metabolism and increased abundance of nutrient transporters. Mechanistically, Bro1 promotes TOR-dependent cargo deubiquitination, vacuolar trafficking, and turnover of plasma membrane hexose transporters and enables appropriate nuclear relocalisation of the transcriptional repressor Scr1. In the absence of Bro1, nutrient transporters persist at the cell surface despite TOR inhibition, conferring resistance to TOR inhibitors while impairing stress responses and reducing lifespan. Together, our findings establish Bro1 as a key coordinator linking ESCRT-mediated endosomal-vacuolar trafficking to TOR-dependent metabolic control. By coupling growth suppression to enhanced recycling and cellular maintenance, Bro1 enables the transition from growth to longevity-promoting states, revealing a mechanism connecting intracellular trafficking, metabolism, and ageing.

genetics↗

bioRxiv2025

Skin lipid chemistry influences host-microbiome-pathogen interactions in snake fungal disease (ophidiomycosis)

Within host-microbiome-pathogen systems, the host chemical microenvironment is often overlooked despite its inherent role in host physiology. We used a multifaceted experimental approach encompassing culture-dependent and independent methods, metagenomic and genomic data, and deep neural network modeling to assess the impact of host skin lipid chemistry and the bacterial microbiome on the growth of Ophidiomyces ophidiicola (ophidiomycosis, snake fungal disease). Results suggest that host skin lipid chemistry (e.g., oleic acid, squalene) and bacteria isolated from wild snake skins (e.g., Chryseobacterium sp. and Stenotrophomonas maltophilia) suppress O. ophidiicola growth. Notably, the O. ophidiicola genome contains biosynthetic gene clusters (BGCs) that encode metabolites that may suppress host lipid production, facilitating fungal pathogenicity. The contrastive deep neural network produced a near-perfect alignment of snake skin lipid and microbiome profiles for both individual snakes and disease states. BGCs from bacterial genomes isolated from snake skin overlap with metagenome profiles from wild snakes and correlate with disease state. We highlight antifungal activity found in the diverse lipid milieu of snake skin and bacterial-fungal interactions (BFIs) that structure the skin microbiome. Our results illustrate a strong relationship among a fungal pathogen, the microbiome, and host skin lipid chemistry.

microbiology↗

bioRxiv2025

Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput

Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition methods (Scanning SWATH, or more generally scanning DIA) but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a prototype Zeno trap Q-TOF instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30-40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows. Significance StatementThe advent of faster DIA proteomics methods paved the way for investigating increasingly large sample series, including patient cohorts and strain collections containing thousands of samples. Yet, recent improvements in DIA methods still entailed compromises between analytical sensitivity and selectivity. The presented combination of a scanning quadrupole with fast ion trapping in a Zeno Trap, coined ZT Scan DIA increases quantitative precision and accuracy in fast proteomics experiments. These features of ZT Scan DIA may benefit applications that deal with low input samples and high-throughput proteomic workflows in biomedical cohort studies and systems biology.

systems biology↗

bioRxiv2024

An INS-1 beta-cell proteome highlights the role of fatty acid biosynthesis in glucose-stimulated insulin secretion

Pancreatic beta cells secrete insulin as a response to rising glucose level, a process known as glucose-stimulated insulin secretion (GSIS). In this study, we used liquid chromatography tandem mass spectrometry and data-independent acquisition to acquire proteomes of rat pancreatic INS-1 832/13 beta cells that were short-term stimulated with glucose concentrations ranging from 0 to 20 mM, quantifying the behavior of 3703 proteins across 11 concentrations. Ensemble clustering of proteome profiles revealed unique response patterns of proteins expressed by INS-1 cells. 237 proteins, amongst them proteins associated with vesicular SNARE interactions, protein export, and pancreatic secretion showed an increase in abundance upon glucose stimulation, whilst the majority of proteins, including those associated with metabolic pathways such as glycolysis, the TCA cycle and the respiratory chain, did not respond to rising glucose concentrations. Interestingly, we observe that enzymes participating in fatty acid metabolism, responded distinctly, showing a "switch-on" response upon release of glucose starvation with no further changes in abundance upon increasing glucose levels. We speculate that increased activity of fatty acid metabolic activity might either be part of GSIS by replenishing membrane lipids required for vesicle-mediated exocytosis and/or by providing an electron sink to compensate for the increase in glucose catabolism. Significance of the StudyWe used high-throughput proteomics to capture comprehensive proteome changes 30 minutes post stimulation in the INS-1 832/13 beta cell line. Our study provides insights into the metabolic regulation of glucose-stimulated insulin secretion in pancreatic beta cells, specifically highlighting the early role of fatty acid biosynthesis. These findings suggest a necessary shift in focus from electrochemical to metabolic mechanisms in understanding GSIS, paving the way for future research. As the first to document proteome alterations in the initial phase of GSIS, our study furthermore documents the extent of protein abundance variability when obtaining data after short stimulation times, and therefore highlights the necessity of well-controlled study design and biological replicates. The recorded data set complements existing metabolomic and transcriptomic studies, providing a valuable resource for subsequent investigations.

cell biology↗

bioRxiv2023

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship. HighlightsO_LIAt the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ({rho} = 0.165). C_LIO_LIWhile the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscape C_LIO_LIWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populations C_LIO_LILoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover C_LI

systems biology↗