Biology subjects

Szyrwiel, L.

Publications and source records attributed to Szyrwiel, L..

11 recordsLinked to original sources

bioRxiv2026

SERBP1 is a master regulator of ribosome interactions

Ribosome function depends on interactions with diverse proteins whose activities determine translational efficiency, impose quality control and activate signaling pathways. These many competing activities must in turn be regulated. SERBP1 is an abundant cellular factor that interacts with ribosomes at multiple functionally critical sites on both dormant and active ribosomes. Here, we perform mass spectrometry across sucrose gradients in untreated and stressed cells and find that SERBP1 modulates ribosome interactions with many factors involved in processes including mRNA degradation, translational control, ribosome quality control and ribosome degradation. We then define the role of SERBP1 in protection of ribosomes against selective 40S degradation in the context of mTOR inhibition through its competition with the E3 ligase RNF10 and the atypical kinase RIOK3. Our work reveals SERBP1 as a key player in maintaining ribosome subunit balance and more broadly in the regulation of diverse ribosome activities.

molecular biology↗

bioRxiv2026

SILAC-Site: A streamlined workflow for determining phosphopeptide stoichiometries

Mass spectrometry-based proteomics enables in-depth investigation of protein phosphorylation, quantifying tens of thousands of phosphosites per sample following phosphopeptide enrichment. However, critical information on phosphosite occupancy (stoichiometry) is typically lost during the phospho-enrichment process. Here, we introduce SILAC-Site, a SILAC-based fractionation-free and chemical labelling-free workflow for direct phosphosite stoichiometry evaluation using stable isotope labeling and phosphatase treatment. By acquiring treated or untreated peptides together with their heavy-labelled dephosphorylated counterparts within the same LC-MS runs, this approach provides internally controlled stoichiometry estimates compatible with high-throughput data-independent acquisition proteomics. Applying SILAC-Site to S. cerevisiae, we show that the majority of phosphopeptides identified only after enrichment possess low stoichiometries, and that inferred stoichiometry strongly correlates with the direct detection of phosphopeptides in samples without enrichment. Based on these findings, we propose the analysis of samples without enrichment as a simple complementary addition to a typical phosphoproteomics workflow, facilitating recovery of phosphorylation stoichiometry information.

biochemistry↗

bioRxiv2025

Large-scale quantitative benchmark reveals accuracy limits of proteomics

Quantitative proteomics has become a streamlined method for high-throughput interrogation of biological samples, with a wide range of applications in both basic and clinical research. We present a benchmark data set and methodology for evaluating the accuracy of proteomics in a high-throughput setting. We build upon previous works by incorporating the variation of sample amounts and the background proteome into the experiment design. This approach allows us to comprehensively map the factors that determine the accuracy of proteomic measurements, revealing significant systematic errors missed by previous works. We envision these data to assist in future method optimisation as well as provide a comprehensive benchmark for data analysis software algorithms.

biochemistry↗

bioRxiv2025

Pho-Tip: one-pot dephosphorylation for rapid and sensitive analysis of DIA phosphoproteomics data

Recent advances in instrumentation and data processing have transformed data-independent acquisition (DIA) proteomics into a reliable technology for quantitative profiling of post-translational modifications. However, analysis of DIA phosphoproteomics data is challenging due to the large search space, wherein all combinations of phosphosites on a peptide need to be considered. Current approaches therefore face significant hurdles in detecting low-abundant phosphorylated peptides, in particular when working with low sample amounts. Here we introduce Pho-Tip, a lossless one-pot dephosphorylation strategy. We show that Pho-Tip enables comprehensive mapping of phosphorylated peptide sequences, facilitating streamlined creation of experiment-focused in silico predicted spectral libraries and thus rapid and sensitive analysis of DIA phosphoproteomics experiments.

biochemistry↗

bioRxiv2024

The Molecular Landscape of Cellular Metal Ion Biology

Metal ions play crucial roles in cells, yet the broader impact of metal availability on biological networks remains underexplored. We generated genome-wide resources, systematically quantifying yeast cell growth, metallomic, proteomic, and genetic responses upon varying each of its essential metal ions (Ca, Cu, Fe, K, Mg, Mn, Mo, Na, Zn), over several orders of magnitude. We find that metal ions deeply impact cellular networks, with 57.6% of the proteome, including most signalling pathways, responding. While the biological response to each metal is distinct, our data reveals common properties of metal responsiveness, such as concentration interdependencies and metal homeostasis. We describe a compendium of metal-dependent cellular processes and reveal that several understudied genes can be functionally annotated based on their metal responses. Furthermore, we report that metalloenzymes occupy central nodes in the metabolic network and are more likely to be encoded by isozymes, resulting in system-wide responsiveness to metal availability.

systems biology↗

bioRxiv2023

The amino acid sequence determines protein abundance through its conformational stability and reduced synthesis cost.

Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, the amino acid sequence is tailored to meet the required abundance of proteomes, underscoring the intricate relationship between sequence and functional demand. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here, we demonstrate that the amino acid sequence predicts abundance by shaping a proteins conformational stability. We show that increasing the abundance provides metabolic cost benefits, underscoring the evolutionary advantage of maintaining a highly abundant and stable proteome. Specifically, using a deep learning model (BERT), we predict 56% of protein abundance variation in Saccharomyces cerevisiae solely based on amino acid sequence. The model reveals latent factors linking sequence features to protein stability. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations increasing abundance significantly alter protein polarity and hydrophobicity, underscoring a connection between protein stability and abundance. Through molecular dynamics simulations and in vivo experiments in yeast, we confirm that abundance-enhancing mutations result in longer-lasting and more stable protein expression. Importantly, these sequence changes also reduce metabolic costs of protein synthesis, elucidating the evolutionary advantage of cost-effective, high-abundance, stable proteomes. Our findings support the role of amino acid sequence as a pivotal determinant of protein abundance and stability, revealing an evolutionary optimization for metabolic efficiency.

evolutionary biology↗

bioRxiv2023

Speedy-PASEF: Analytical flow rate chromatography and trapped ion mobility for deep high-throughput proteomics

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition and data analysis with the DIA-NN software suite, for conducting fast, high-quality proteomic experiments that require only moderate sample amounts. For instance, using a 500-l/min flow rate and a 3-minute chromatographic gradient, Speedy-PASEF quantified 5,211 proteins from 2 g of a mammalian cell-line standard at high quantitative accuracy and precision. We further used Speedy-PASEF to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-minute chromatographic gradient and alternating column regeneration on a dual pump system, for processing 398 samples per day. Speedy-PASEF delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates. Speedy-PASEF thus facilitates acquisition of high-quality proteomes in large numbers.

biochemistry↗

bioRxiv2022

Slice-PASEF: fragmenting all ions for maximum sensitivity in proteomics

Quantitative mass spectrometry (MS)-based proteomics has become a streamlined technology with a wide range of usage. Many emerging applications, such as single-cell proteomics, spatial proteomics of tissue sections and the profiling of low-abundant posttranslational modifications, require the analysis of minimal sample amounts and are thus constrained by the sensitivity of the workflow. Here, we present Slice-PASEF, a mass spectrometry technology that leverages trapped ion mobility separation of ions to attain the theoretical maximum of tandem MS sensitivity. We implement Slice-PASEF using a new module in our DIA-NN software and show that Slice-PASEF uniquely enables precise quantitative proteomics of low sample amounts. We further demonstrate its utility towards a range of applications, including single cell proteomics and degrader drug screens via ubiquitinomics.

biochemistry↗

bioRxiv2022

Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan

Metabolism is fundamentally intertwined with the ageing process. We here report that a key determinant of cellular lifespan is not only nutrient supply and intracellular metabolism, but also metabolite exchange interactions that occur between cells. Studying chronological ageing in yeast, we observed that metabolites exported by young, exponentially growing, cells are re- imported during the stationary phase when cells age chronologically, indicating the existence of cross-generational metabolic interactions. We then used self-establishing metabolically cooperating communities (SeMeCos) to boost cell-cell metabolic interactions and observed a significant lifespan extension. A search for the underlying mechanisms, coupling SeMeCos, metabolic profiling, proteomics and genome-scale metabolic modelling, attributed a specific role to methionine consumer cells. These cells were enriched over time, adopted glycolytic metabolism and increased export of protective metabolites. Glycerol, in particular, accumulated in the communal metabolic environment and extended the lifespan of all cells in the community in a paracrine fashion. Our results hence establish metabolite exchange interactions as a determinant of the ageing process and show that metabolically cooperating cells shape their metabolic environment to achieve lifespan extension.

biochemistry↗

bioRxiv2021

High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe

The dia-PASEF technology exploits ion mobility separation for high-sensitivity analysis of complex proteomes. Here, we demonstrate neural network-based processing of the ion mobility data, which we implement in the DIA-NN software suite. Using spectral libraries generated with the MSFragger-based FragPipe computational platform, the DIA-NN analysis of dia-PASEF raw data increases the proteomic depth by up to 69% compared to the originally published dia-PASEF workflow. For example, we quantify over 5200 proteins from 10ng of HeLa peptides separated with a 95-minute nanoflow gradient, and over 5000 proteins from 200ng using a 4.8-minute separation with an Evosep One system. In complex samples, featuring a mix of human and yeast lysates, the workflow detects over 11700 proteins in single runs acquired with a 100-minute nanoflow gradient, while demonstrating quantitative precision. Hence, the combination of FragPipe and DIA-NN provides a simple-to-use software platform for dia-PASEF data analysis, yielding significant gains in high-sensitivity proteomics.

bioinformatics↗

bioRxiv2020

Single amino acid-promoted reactions link a non-enzymatic chemical network to the early evolution of enzymatic pentose phosphate pathway

How metabolic pathways emerged in early evolution remains largely unknown. Recently discovered chemical networks driven by iron and sulfur resemble reaction sequences found within glycolysis, gluconeogenesis, the oxidative and reductive Krebs cycle, the Wood Ljungdahl as well as the S-adenosylmethionine pathways, components of the core cellular metabolic network. These findings suggest that the evolution of central metabolism was primed by environmental chemical reactions, implying that non-enzymatic reaction networks served as a "template" in the evolution of enzymatic activities. We speculated that the turning point for this transition would depend on the catalytic properties of the simplest structural components of proteins, single amino acids. Here, we systematically combine constituents of Fe(II)-driven non-enzymatic reactions resembling glycolysis and pentose phosphate pathway (PPP), with single proteinogenic amino acids. Multiple reaction rates are enhanced by amino acids. In particular, cysteine is able to replace (and/or complement) the metal ion Fe(II) in driving the non-enzymatic formation of the RNA-backbone metabolite ribose 5-phosphate from 6-phosphogluconate, a rate-limiting reaction of the oxidative PPP. In the presence of both Fe(II) and cysteine, a complex is formed, enabling the non-enzymatic reaction to proceed at a wide range of temperatures. At mundane temperatures, this minimal enzyme-like complex achieves a much higher specificity in the formation of ribose 5-phosphate than the Fe(II)-driven reaction at high temperatures. Hence, simple amino acids can accelerate key steps within metal-promoted metabolism-like chemical networks. Our results imply a stepwise scenario, in which environmental chemical networks served as primers in the early evolution of the metabolic network structure. Significance StatementThe evolutionary roots of metabolic pathways are barely understood. Here we show results consistent with a stepwise scenario during the evolution of (enzymatic) metabolism, starting from non-enzymatic chemical networks. By systematic screening of metabolic-like reactivities in vitro, and using high-throughput analytical techniques, we identify an iron/cysteine complex to act as a minimal enzymelike complex, which consists of a metal ion, an amino acid, and a sugar phosphate ligand. Integrated in a metal-driven, non-enzymatic pentose phosphate pathway, it promotes the formation of the RNA-backbone precursor ribose 5-phosphate at ambient temperature.

biochemistry↗