Biology subjects

Grabmann, G.

Publications and source records attributed to Grabmann, G..

4 recordsLinked to original sources

bioRxiv2026

A DIA-based quantitative crosslinking mass spectrometryframework for dynamic structural proteomics

Proteins undergo dynamic conformational rearrangements and interactions that are central to their biological functions. Quantitative crosslinking mass spectrometry enables the analysis of those dynamics and molecular interactions, but rigorous confidence assessment and empirical validation strategies for quantitative measurements remain underdeveloped, and integrated analysis of complementary structural features, including monolinks and protein-RNA adducts, remains limited. Here we present a data-independent acquisition (DIA)-based framework for quantitative crosslinking mass spectrometry (DIA-QCLMS) that combines optimized acquisition strategies, crosslink-aware spectral libraries and empirical false-discovery-rate (FDR) validation. The workflow supports crosslinks, monolinks and protein-RNA adducts and integrates spectral-library generation from two crosslinking search engines (xiSEARCH and MS Annika). To enable robust confidence assessment in DIA data, we developed a four-state target-decoy spectral library strategy that explicitly models target-target, target-decoy, decoy-target and decoy-decoy crosslink spectra. Experimental entrapment datasets enabled empirical validation of confidence estimation, whereas benchmarking with PhoX-crosslinked Cas9 demonstrated improved quantitative completeness and reproducibility compared with data-dependent acquisition. Application of the workflow to the ATP-dependent RNA helicase UAP56 (DDX39B) resolved ligand-dependent changes in intramolecular restraints, residue accessibility and candidate RNA-contact sites associated with the transition from an open to a clamped conformation. These results establish DIA-QCLMS as a scalable framework for quantitative structural proteomics and provide practical strategies for confidence-controlled analysis of dynamic protein interactions and conformational states.

biochemistry↗

bioRxiv2024

Urinary multi-omics reveal non-invasive diagnostic biomarkers in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the kidney malignancy with the highest incidence and mortality rates. Despite the high patient burden, there are no biomarkers for rapid diagnosis and public health surveillance. Urine would be an ideal source of ccRCC biomarkers due to the low invasiveness, easy accessibility, and the kidneys intrinsic role in filtering urine. In the present work, by combining proteomics, lipidomics and metabolomics, we detected urogenital metabolic dysregulation in ccRCC patients with increased lipid metabolism, altered mitochondrial respiration signatures and increased urinary lipid content. Importantly, we identify three early-stage diagnostic biomarkers for ccRCC in urine samples: Serum amyloid A1 (SAA1), Haptoglobin (HP) and Lipocalin 15 (LCN15). We further implemented a parallel reaction monitoring mass spectrometry protocol for rapid and sensitive detection of SAA1, HP and LCN15 and combined all three proteins into a diagnostic UrineScore. In our discovery cohort, this score had a performance accuracy of 96% in receiver operating characteristic curve (ROC) analysis for classification of ccRCC versus control cases. Our data identifies tractable and highly efficacious urinary biomarkers for ccRCC diagnosis and serve as a first step towards the development of more rapid and accessible urinary diagnostic platforms.

cancer biology↗

bioRxiv2024

Proteomic and Metabolomic Profiling of Archaeal Extracellular Vesicles from the Human Gut

One potential mechanism for microbiome-host, and microbiome constituents interaction and communication involves extracellular vesicles (EVs). Here, for the first time, we report the capability of two M. smithii strains (ALI and GRAZ-2), Candidatus M. intestini, and Methanosphaera stadtmanae, as underrepresented components of the gut microbiome, to produce EVs. Interesting, size, morphology, and composition of AEVs were comparable to bacterial EVs, as indicated by ultrastructure, composition, proteomic and metabolomic analyses; however, EVs were substantially less prevalent in the studied Archaea. When looking at the proteomics more precisely, although AEVs from M. smithii ALI and M. intestini were found to be carrying unique proteins (n=135 and n=30, respectively), the shared proteins in AEVs within this genus (n=229), were mostly adhesins(/like) proteins, or proteins with IG-like domains. One remarkable observation was the uptake of AEVs obtained from Methanosphaera stadtmanae and the studied Methanobrevibacter species by human monocytes and the subsequent IL-8 secretion.

microbiology↗

bioRxiv2020

Benchmarking non-targeted metabolomics using yeast derived libraries

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is the essential discovery tool in metabolomics. Up to date, standardization and validation remain a challenge. Community wide accepted, cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts, derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of > 200 metabolites, reproducibly recovered in ethanolic extracts by orthogonal LCHRMS methods, different fermentations (over three years) and different laboratories. More specifically, compound identification was based on accurate mass, matching retention times, and MS/MS spectra as compared to authentic standards and internal databases. The library includes metabolites from the classes of 1) organic acids and derivatives (2) nucleosides, nucleotides and analogues, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds and (7) benzoids at expected concentrations ranges of sub-nM to {micro}M. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the Human metabolome data base (HMDB). A large fraction of metabolites was found to be stable for several years when stored at -80{degrees}C. Thus, the yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments, but enabled in-house routines for instrumental performance tests. Finally, the benchmark material opens new avenues for batch to batch corrections in large scale non-targeted metabolomics studies.

biochemistry↗