The Nma1 protein promotes long distance transport mediated by early endosomes in Ustilago maydis.

Early endosomes (EEs) are part of the endocytic transport pathway and resemble the earliest class of transport vesicles between the internalization of extracellular material, their cellular distribution or vacuolar degradation. In filamentous fungi, EEs fulfill important functions in long distance transport of cargoes as mRNAs, ribosomes, and peroxisomes. Formation and maturation of early endosomes is controlled by the specific membrane-bound Rab-GTPase Rab5 and tethering complexes as CORVET (class C core vacuole/endosome tethering). In the basidiomycete Ustilago maydis, Rab5a is the prominent GTPase to recruit CORVET to EEs; in rab5a deletion strains, this function is maintained by the second EE-associated GTPase Rab5b. The tethering- and core-subunits of CORVET are essential, buttressing a central role for EE transport in U. maydis. The function of EEs in long distance transport is supported by the Nma1 protein that interacts with the Vps3 subunit of CORVET. The interaction stabilizes the binding of Vps3 to the CORVET core complex that is recruited to Rab5a via Vps8. Deletion of nma1 leads to a significantly reduced number of EEs, and an increased conversion rate of EEs to late endosomes. Thus, Nma1 modulates the lifespan of EEs to ensure their availability for the various long distance transport processes.

MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for Everyone.

Quantitative mass spectrometry-based proteomics has become a high-throughput technology for the identification and quantification of thousands of proteins in complex biological samples. Two frequently used tools, MaxQuant and MSstats, allow for the analysis of raw data and finding proteins with differential abundance between conditions of interest. To enable accessible and reproducible quantitative proteomics analyses in a cloud environment, we have integrated MaxQuant (including TMTpro 16/18plex), Proteomics Quality Control (PTXQC), MSstats, and MSstatsTMT into the open-source Galaxy framework. This enables the web-based analysis of label-free and isobaric labeling proteomics experiments via Galaxy's graphical user interface on public clouds. MaxQuant and MSstats in Galaxy can be applied in conjunction with thousands of existing Galaxy tools and integrated into standardized, sharable workflows. Galaxy tracks all metadata and intermediate results in analysis histories, which can be shared privately for collaborations or publicly, allowing full reproducibility and transparency of published analysis. To further increase accessibility, we provide detailed hands-on training materials. The integration of MaxQuant and MSstats into the Galaxy framework enables their usage in a reproducible way on accessible large computational infrastructures, hence realizing the foundation for high-throughput proteomics data science for everyone.

Signal peptide peptidase activity connects the unfolded protein response to plant defense suppression by Ustilago maydis.

The corn smut fungus Ustilago maydis requires the unfolded protein response (UPR) to maintain homeostasis of the endoplasmic reticulum (ER) during the biotrophic interaction with its host plant Zea mays (maize). Crosstalk between the UPR and pathways controlling pathogenic development is mediated by protein-protein interactions between the UPR regulator Cib1 and the developmental regulator Clp1. Cib1/Clp1 complex formation results in mutual modification of the connected regulatory networks thereby aligning fungal proliferation in planta, efficient effector secretion with increased ER stress tolerance and long-term UPR activation in planta. Here we address UPR-dependent gene expression and its modulation by Clp1 using combinatorial RNAseq/ChIPseq analyses. We show that increased ER stress resistance is connected to Clp1-dependent alterations of Cib1 phosphorylation, protein stability and UPR gene expression. Importantly, we identify by deletion screening of UPR core genes the signal peptide peptidase Spp1 as a novel key factor that is required for establishing a compatible biotrophic interaction between U. maydis and its host plant maize. Spp1 is dispensable for ER stress resistance and vegetative growth but requires catalytic activity to interfere with the plant defense, revealing a novel virulence specific function for signal peptide peptidases in a biotrophic fungal/plant interaction.

Proteometabolomics of initial and recurrent glioblastoma highlights an increased immune cell signature with altered lipid metabolism.

BACKGROUND: There is an urgent need to better understand the mechanisms associated with the development, progression, and onset of recurrence after initial surgery in glioblastoma (GBM). The use of integrative phenotype-focused -omics technologies such as proteomics and lipidomics provides an unbiased approach to explore the molecular evolution of the tumor and its associated environment. METHODS: We assembled a cohort of patient-matched initial (iGBM) and recurrent (rGBM) specimens of resected GBM. Proteome and metabolome composition were determined by mass spectrometry-based techniques. We performed neutrophil-GBM cell coculture experiments to evaluate the behavior of rGBM-enriched proteins in the tumor microenvironment. ELISA-based quantitation of candidate proteins was performed to test the association of their plasma concentrations in iGBM with the onset of recurrence. RESULTS: Proteomic profiles reflect increased immune cell infiltration and extracellular matrix reorganization in rGBM. ASAH1, SYMN, and GPNMB were highly enriched proteins in rGBM. Lipidomics indicates the downregulation of ceramides in rGBM. Cell analyses suggest a role for ASAH1 in neutrophils and its localization in extracellular traps. Plasma concentrations of ASAH1 and SYNM show an association with time to recurrence. CONCLUSIONS: We describe the potential importance of ASAH1 in tumor progression and development of rGBM via metabolic rearrangement and showcase the feedback from the tumor microenvironment to plasma proteome profiles. We report the potential of ASAH1 and SYNM as plasma markers of rGBM progression. The published datasets can be considered as a resource for further functional and biomarker studies involving additional -omics technologies.

Targeted and explorative profiling of kallikrein proteases and global proteome biology of pancreatic ductal adenocarcinoma, chronic pancreatitis, and normal pancreas highlights disease-specific proteome remodelling.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the most aggressive and lethal malignancies worldwide with an urgent need for new diagnostic and therapeutic strategies. One major risk factor for PDAC is the pre-indication of chronic pancreatitis (CP), which represents highly inflammatory pancreatic tissue. Kallikreins (KLKs) are secreted serine proteases that play an important role in various cancers as components of the tumor microenvironment. Previous studies of KLKs in solid tumors largely relied on either transcriptomics or immunodetection. We present one of the first targeted mass spectrometry profiling of kallikrein proteases in PDAC, CP, and normal pancreas. We show that KLK6 and KLK10 are significantly upregulated in PDAC (n=14) but not in CP (n=7) when compared to normal pancreas (n=16), highlighting their specific intertwining with malignancy. Additional explorative proteome profiling identified 5936 proteins in our pancreatic cohort and observed disease-specific proteome rearrangements in PDAC and CP. As such, PDAC features an enriched proteome motif for extracellular matrix (ECM) and cell adhesion while there is depletion of mitochondrial energy metabolism proteins, reminiscent of the Warburg effect. Although often regarded as a PDAC hallmark, the ECM fingerprint was also observed in CP, alongside with a prototypical inflammatory proteome motif as well as with an increased wound healing process and proteolytic activity, thereby possibly illustrating tissue autolysis. Proteogenomic analysis based on publicly accessible data sources identified 112 PDAC-specific and 32 CP-specific single amino acid variants, which among others affect KRAS and ANKHD1. Our study emphasizes the diagnostic potential of kallikreins and provides novel insights into proteomic characteristics of PDAC and CP.

Systemic toxicity of snake venom metalloproteinases: Multi-omics analyses of kidney and blood plasma disturbances in a mouse model.

Snakebite envenomation is classified as a Neglected Tropical Disease. Bothrops jararaca venom induces kidney injury and coagulopathy. HF3, a hemorrhagic metalloproteinase of B. jararaca venom, participates in the envenomation pathogenesis. We evaluated the effects of HF3 in mouse kidney and blood plasma after injection in the thigh muscle, mimicking a snakebite. Transcriptomic analysis showed differential expression of 31 and 137 genes related to kidney pathology after 2 h and 6 h, respectively. However, only subtle changes were observed in kidney proteome, with differential abundance of 15 proteins after 6 h, including kidney injury markers. N-terminomic analysis of kidney proteins showed 420 proteinase-generated peptides compatible with meprin specificity, indicating activation of host proteinases. Plasma analysis revealed differential abundance of 90 and 219 proteins, respectively, after 2 h and 6 h, including coagulation-cascade and complement-system components, and creatine-kinase, whereas a semi-specific search of N-terminal peptides indicated activation of endogenous proteinases. HF3 promoted host reactions, altering the gene expression and the proteolytic profile of kidney tissue, and inducing plasma proteome imbalance driven by changes in abundance and proteolysis. The overall response of the mouse underscores the systemic action of a hemorrhagic toxin that transcends local tissue damage and is related to known venom-induced systemic effects.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity.

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.