Philosopher: a versatile toolkit for shotgun proteomics data analysis

Protein Binder Toolbox for Studies of Solute Carrier Transporters

Transporters of the solute carrier superfamily (SLCs) are responsible for the transmembrane traffic of the majority of chemical substances in cells and tissues and are therefore of fundamental biological importance. As is often the case with membrane proteins that can be heavily glycosylated, a lack of reliable high-affinity binders hinders their functional analysis. Purifying and reconstituting transmembrane proteins in their lipidic environments remains challenging and standard approaches to generate binders for multi-transmembrane proteins, such as SLCs, channels or G protein-coupled receptors (GPCRs) are lacking. While generating protein binders to 27 SLCs, we produced full length protein or cell lines as input material for binder generation by selected binder generation platforms. As a result, we obtained 525 binders for 22 SLCs. We validated the binders with a cell-based validation workflow using immunofluorescent and immunoprecipitation methods to process all obtained binders. Finally, we demonstrated the potential applications of the binders that passed our validation pipeline in structural, biochemical, and biological applications using the exemplary protein SLC12A6, an ion transporter relevant in human disease. With this work, we were able to generate easily renewable and highly specific binders against SLCs, which will greatly facilitate the study of this neglected protein family. We hope that the process will serve as blueprint for the generation of binders against the entire superfamily of SLC transporters.

Central role of SUMOylation in the regulation of chromatin interactions and transcriptional outputs of the androgen receptor in prostate cancer cells

The androgen receptor (AR) is pivotal in prostate cancer (PCa) progression and represents a critical therapeutic target. AR-mediated gene regulation involves intricate interactions with nuclear proteins, with many mediating and undergoing post-translational modifications that present alternative therapeutic avenues. Through chromatin proteomics in PCa cells, we identified SUMO ligases together with nuclear receptor coregulators and pioneer transcription factors within the AR's protein network. Intriguingly, this network displayed a significant association with SUMO2/3. To elucidate the influence of SUMOylation on AR chromatin interactions and subsequent gene regulation, we inhibited SUMOylation using ML-792 (SUMOi). While androgens generally facilitated the co-occupancy of SUMO2/3 and AR on chromatin, SUMOi induced divergent effects dependent on the type of AR-binding site (ARB). SUMOi augmented AR's pioneer-like binding on inaccessible chromatin regions abundant in androgen response elements (AREs) and diminished its interaction with accessible chromatin regions sparse in AREs yet rich in pioneer transcription factor motifs. The SUMOi-impacted ARBs divergently influenced AR-regulated genes; those associated with AR-mediated activation played roles in negative regulation of cell proliferation, while those with AR-mediated repression were involved in pattern formation. In conclusion, our findings underscore the pervasive influence of SUMOylation in shaping AR's role in PCa cells, potentially unveiling new therapeutic strategies.

Quantitative Comparison of Deparaffinization, Rehydration, and Extraction Methods for FFPE Tissue Proteomics and Phosphoproteomics

Formalin-fixed paraffin-embedded (FFPE) tissues are suitable for proteomic and phosphoproteomic biomarker studies by data-independent acquisition mass spectrometry. The choice of the sample preparation method influences the number, intensity, and reproducibility of identifications. By comparing four deparaffinization and rehydration methods, including heptane, histolene, SubX, and xylene, we found that heptane and methanol produced the lowest coefficients of variation (CVs). Using this, five extraction methods from the literature were modified and evaluated for their performance using kidney, leg muscle, lung, and testicular rat organs. All methods performed well, except for SP3 due to insufficient tissue lysis. Heat n' Beat was the fastest and most reproducible method with the highest digestion efficiency and lowest CVs. S-Trap produced the highest peptide yield, while TFE produced the best phosphopeptide enrichment efficiency. The quantitation of FFPE-derived peptides remains an ongoing challenge with bias in UV and fluorescence assays across methods, most notably in SPEED. Functional enrichment analysis demonstrated that each method favored extracting some gene ontology cellular components over others including chromosome, cytoplasmic, cytoskeleton, endoplasmic reticulum, membrane, mitochondrion, and nucleoplasm protein groups. The outcome is a set of recommendations for choosing the most appropriate method for different settings.

Tissue Usage Preference and Intrinsically Disordered Region Remodeling of Alternative Splicing Derived Proteoforms in the Heart

A computational analysis of mass spectrometry data was performed to uncover alternative splicing derived protein variants across chambers of the human heart. Evidence for 216 non-canonical isoforms was apparent in the atrium and the ventricle, including 52 isoforms not documented on SwissProt and recovered using an RNA sequencing derived database. Among non-canonical isoforms, 29 show signs of regulation based on statistically significant preferences in tissue usage, including a ventricular enriched protein isoform of tensin-1 (TNS1) and an atrium-enriched PDZ and LIM Domain 3 (PDLIM3) isoform 2 (PDLIM3-2/ALP-H). Examined variant regions that differ between alternative and canonical isoforms are highly enriched with intrinsically disordered regions. Moreover, over two-thirds of such regions are predicted to function in protein binding and RNA binding. The analysis here lends further credence to the notion that alternative splicing diversifies the proteome by rewiring intrinsically disordered regions, which are increasingly recognized to play important roles in the generation of biological function from protein sequences.

Non-immune factors cause prolonged myofibroblast phenotype in implanted synthetic heart valve scaffolds

The clinical application of heart valve scaffolds is hindered by complications associated with the activation of valvular interstitial cell-like (VIC-like) cells and their transdifferentiation into myofibroblasts. This study aimed to examine several molecular pathway(s) that may trigger the overactive myofibroblast phenotypes in the implanted scaffolds. So, we investigated the influence of three molecular pathways - macrophage-induced inflammation, the TGF-β1-SMAD2, and WNT/β-catenin β on VIC-like cells during tissue engineering of heart valve scaffolds. We implanted electrospun heart valve scaffolds in adult sheep for up to 6 months in the right ventricular outflow tract (RVOT) and analyzed biomolecular (gene and protein) expression associated with the above three pathways by the scaffold infiltrating cells. The results showed a gradual increase in gene and protein expression of markers related to the activation of VIC-like cells and the myofibroblast phenotypes over 6 months of scaffold implantation. Conversely, there was a gradual increase in macrophage activity for the first three months after scaffold implantation. However, a decrease in macrophage activity from three to six months of scaffold tissue engineering suggested that immunological signal factors were not the primary cause of myofibroblast phenotype. Similarly, the gene and protein expression of factors associated with the TGF-β1-SMAD2 pathway in the cells increased in the first three months but declined in the next three months. Contrastingly, the gene and protein expression of factors associated with the WNT/β-catenin pathway increased significantly over the six-month study. Thus, the WNT/β-catenin pathway could be the predominant mechanism in activating VIC-like cells and subsequent myofibroblast phenotype.

Elevated levels of iodide promote peroxidase-mediated protein iodination and inhibit protein chlorination

At inflammatory sites, immune cells generate oxidants including H₂O₂. Myeloperoxidase (MPO), released by activated leukocytes employs H₂O₂ and halide/pseudohalides to form hypohalous acids that mediate pathogen killing. Hypochlorous acid (HOCl) is a major species formed. Excessive or misplaced HOCl formation damages host tissues with this linked to multiple inflammatory diseases. Previously (Redox Biology, 2020, 28, 101331) we reported that iodide (I⁻) modulates MPO-mediated protein damage by decreasing HOCl generation with concomitant hypoiodous acid (HOI) formation. HOI may however impact on protein structure, so in this study we examined whether and how HOI, from peroxidase/H₂O₂/I⁻ systems ± Cl⁻, modifies proteins. Experiments employed MPO and lactoperoxidase (LPO) and multiple proteins (serum albumins, anastellin), with both chemical (intact protein and peptide mass mapping, LC-MS) and structural (SDS-PAGE) changes assessed. LC-MS analyses revealed dose-dependent iodination of anastellin and albumins by LPO/H2O2 with increasing I⁻. Incubation of BSA with MPO/H2O2/Cl⁻ revealed modest chlorination (Tyr286, Tyr475, ∼4 %) and Met modification. Lower levels of these species, and extensive iodination at specific Tyr and His residues (>20 % modification with ≥10 μM I⁻) were detected with increasing I⁻. Anastellin dimerization was inhibited by increasing I⁻, but less marked changes were observed with albumins. These data confirm that I⁻ competes with Cl⁻ for MPO and is an efficient HOCl scavenger. These processes decrease protein chlorination and oxidation, but result in extensive iodination. This is consistent with published data on the presence of iodinated Tyr on neutrophil proteins. The biological implications of protein iodination relative to chlorination require further clarification.

Protein surface chemistry encodes an adaptive resistance to desiccation

Cellular desiccation - the loss of nearly all water from the cell - is a recurring stress in an increasing number of ecosystems that can drive proteome-wide protein unfolding and aggregation. For cells to survive this stress, at least some of the proteome must disaggregate and resume function upon rehydration. The molecular determinants that underlie the ability of proteins to do this remain largely unknown. Here, we apply quantitative and structural proteomic mass spectrometry to desiccated and rehydrated yeast extracts to show that some proteins possess an innate capacity to survive extreme water loss. Structural analysis correlates the ability of proteins to resist desiccation with their surface chemistry. Remarkably, highly resistant proteins are responsible for the production of the cell's building blocks - amino acids, metabolites, and sugars. Conversely, those proteins that are most desiccation-sensitive are involved in ribosome biogenesis and other energy consuming processes. As a result, the rehydrated proteome is preferentially enriched with metabolite and small molecule producers and depleted of some of the cell's heaviest consumers. We propose this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration.

Unraveling the mechanism of small molecule induced activation of Staphylococcus aureus signal peptidase IB

Staphylococcus aureus signal peptidase IB (SpsB) is an essential enzyme for protein secretion. While inhibition of its activity by small molecules is a well-precedented mechanism to kill bacteria, the mode of activation is however less understood. We here investigate the activation mechanism of a recently introduced activator, the antibiotic compound PK150, and demonstrate by combined experimental and Molecular Dynamics (MD) simulation studies a unique principle of enzyme stimulation. Mass spectrometric studies with an affinity-based probe of PK150 unravel the binding site of PK150 in SpsB which is used as a starting point for MD simulations. Our model shows the localization of the molecule in an allosteric pocket next to the active site which shields the catalytic dyad from excess water that destabilizes the catalytic geometry. This mechanism is validated by the placement of mutations aligning the binding pocket of PK150. While the mutants retain turnover of the SpsB substrate, no stimulation of activity is observed upon PK150 addition. Overall, our study elucidates a previously little investigated mechanism of enzyme activation and serves as a starting point for the development of future enzyme activators.

An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using ProteographTM and Trapped Ion Mobility Mass Spectrometry

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

The mycobacterial glycoside hydrolase LamH enables capsular arabinomannan release and stimulates growth

Mycobacterial glycolipids are important cell envelope structures that drive host-pathogen interactions. Arguably, the most important are lipoarabinomannan (LAM) and its precursor, lipomannan (LM), which are trafficked from the bacterium to the host via unknown mechanisms. Arabinomannan is thought to be a capsular derivative of these molecules, lacking a lipid anchor. However, the mechanism by which this material is generated has yet to be elucidated. Here, we describe the identification of a glycoside hydrolase family 76 enzyme that we term LamH (Rv0365c in Mycobacterium tuberculosis) which specifically cleaves α-1,6-mannoside linkages within LM and LAM, driving its export to the capsule releasing its phosphatidyl-myo-inositol mannoside lipid anchor. Unexpectedly, we found that the catalytic activity of this enzyme is important for efficient exit from stationary phase cultures, potentially implicating arabinomannan as a signal for growth phase transition. Finally, we demonstrate that LamH is important for M. tuberculosis survival in macrophages.

Glycan-Tailored Glycoproteomic Analysis Reveals Serine is the Sole Residue Subjected to O-Linked Glycosylation in Acinetobacter baumannii

Protein glycosylation is a ubiquitous process observed across all domains of life. Within the human pathogen Acinetobacter baumannii, O-linked glycosylation is required for virulence; however, the targets and conservation of glycosylation events remain poorly defined. In this work, we expand our understanding of the breadth and site specificity of glycosylation within A. baumannii by demonstrating the value of strain specific glycan electron-transfer/higher-energy collision dissociation (EThcD) triggering for bacterial glycoproteomics. By coupling tailored EThcD-triggering regimes to complementary glycopeptide enrichment approaches, we assessed the observable glycoproteome of three A. baumannii strains (ATCC19606, BAL062, and D1279779). Combining glycopeptide enrichment techniques including ion mobility (FAIMS), metal oxide affinity chromatography (titanium dioxide), and hydrophilic interaction liquid chromatography (ZIC-HILIC), as well as the use of multiple proteases (trypsin, GluC, pepsin, and thermolysis), we expand the known A. baumannii glycoproteome to 33 unique glycoproteins containing 42 glycosylation sites. We demonstrate that serine is the sole residue subjected to glycosylation with the substitution of serine for threonine abolishing glycosylation in model glycoproteins. An A. baumannii pan-genome built from 576 reference genomes identified that serine glycosylation sites are highly conserved. Combined this work expands our knowledge of the conservation and site specificity of A. baumannii O-linked glycosylation.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease

Population-level variation and mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized. We defined prototypical insulin secretion responses to three macronutrients in islets from 140 cadaveric donors, including those with type 2 diabetes. The majority of donors' islets exhibited the highest insulin response to glucose, moderate response to amino acid, and minimal response to fatty acid. However, 9% of donors' islets had amino acid responses, and 8% had fatty acid responses that were larger than their glucose-stimulated insulin responses. We leveraged this heterogeneity and used multi-omics to identify molecular correlates of nutrient responsiveness, as well as proteins and mRNAs altered in type 2 diabetes. We also examined nutrient-stimulated insulin release from stem cell-derived islets and observed responsiveness to fat but not carbohydrate or protein-potentially a hallmark of immaturity. Understanding the diversity of insulin responses to carbohydrate, protein, and fat lays the groundwork for personalized nutrition.

Combining FAIMS based glycoproteomics and DIA proteomics reveals widespread proteome alterations in response to glycosylation occupancy changes in Neisseria gonorrhoeae

Protein glycosylation is increasingly recognized as a common protein modification across bacterial species. Within the Neisseria genus O-linked protein glycosylation is conserved yet closely related Neisseria species express O-oligosaccharyltransferases (PglOs) with distinct targeting activities. Within this work, we explore the targeting capacity of different PglOs using Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) fractionation and Data-Independent Acquisition (DIA) to allow the characterization of the impact of changes in glycosylation on the proteome of Neisseria gonorrhoeae. We demonstrate FAIMS expands the known glycoproteome of wild type N. gonorrhoeae MS11 and enables differences in glycosylation to be assessed across strains expressing different pglO allelic chimeras with unique substrate targeting activities. Combining glycoproteomic insights with DIA proteomics, we demonstrate that alterations within pglO alleles have widespread impacts on the proteome of N. gonorrhoeae. Examination of peptides known to be targeted by glycosylation using DIA analysis supports alterations in glycosylation occupancy occurs independently of changes in protein levels and that the occupancy of glycosylation is generally low on most glycoproteins. This work thus expands our understanding of the N. gonorrhoeae glycoproteome and the roles that pglO allelic variation may play in governing genus-level protein glycosylation.

In vitro reconstitution of epigenetic reprogramming in the human germ line

Epigenetic reprogramming resets parental epigenetic memories and differentiates primordial germ cells (PGCs) into mitotic pro-spermatogonia or oogonia. This process ensures sexually dimorphic germ cell development for totipotency1. In vitro reconstitution of epigenetic reprogramming in humans remains a fundamental challenge. Here we establish a strategy for inducing epigenetic reprogramming and differentiation of pluripotent stem-cell-derived human PGC-like cells (hPGCLCs) into mitotic pro-spermatogonia or oogonia, coupled with their extensive amplification (about >1010-fold). Bone morphogenetic protein (BMP) signalling is a key driver of these processes. BMP-driven hPGCLC differentiation involves attenuation of the MAPK (ERK) pathway and both de novo and maintenance DNA methyltransferase activities, which probably promote replication-coupled, passive DNA demethylation. hPGCLCs deficient in TET1, an active DNA demethylase abundant in human germ cells2,3, differentiate into extraembryonic cells, including amnion, with de-repression of key genes that bear bivalent promoters. These cells fail to fully activate genes vital for spermatogenesis and oogenesis, and their promoters remain methylated. Our study provides a framework for epigenetic reprogramming in humans and an important advance in human biology. Through the generation of abundant mitotic pro-spermatogonia and oogonia-like cells, our results also represent a milestone for human in vitro gametogenesis research and its potential translation into reproductive medicine.

Beta-Hydroxybutyrate Promotes Basal Insulin Secretion While Decreasing Glucagon Secretion in Mouse and Human Islets

Dietary carbohydrates raise blood glucose levels, and limiting carbohydrate intake improves glycemia in patients with type 2 diabetes. Low carbohydrate intake (< 25 g) allows the body to utilize fat as its primary fuel. As a consequence of increased fatty acid oxidation, the liver produces ketones to serve as an alternative energy source. β-Hydroxybutyrate (βHB) is the most abundant ketone. While βHB has a wide range of functions outside of the pancreas, its direct effects on islet cell function remain understudied. We examined human islet secretory response to acute racemic βHB treatment and observed increased insulin secretion at a low glucose concentration of 3 mM. Because βHB is a chiral molecule, existing as both R and S forms, we further studied insulin and glucagon secretion following acute treatment with individual βHB enantiomers in human and C57BL/6J mouse islets. We found that acute treatment with R-βHB increased insulin secretion and decreased glucagon secretion at physiological glucose concentrations in both human and mouse islets. Proteomic analysis of human islets treated with R-βHB over 72 hours showed altered abundance of proteins that may promote islet cell health and survival. Collectively, our data show that physiological concentrations of βHB influence hormone secretion and signaling within pancreatic islets.

deTELpy: Python package for high-throughput detection of amino acid substitutions in mass spectrometry datasets

MOTIVATION

Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry.

RESULTS

Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions.

AVAILABILITY AND IMPLEMENTATION

deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

Identifying Ciliary Proteins in Mammalian Retinas using a Gentle Extraction Method

Mutations in retinal primary cilia are responsible for human blindness but the mechanisms are not fully understood (Wheway et al., 2014). Characterizing the proteome of an organelle such as cilia, is a fruitful way to understand its function but methods often require considerable sample quantities. Here we develop a method to isolate the primary cilia of photoreceptor cells from bovine retinas. Through LC/MS/MS proteomics analysis we identify proteins enriched for cilia function including ciliopathy disease. This study shows our method can be used to isolate retinal primary cilia to obtain sufficient quantities of native protein samples.

Exploring the Ocular Surface Microbiome and Tear Proteome in Glaucoma

Although glaucoma is a leading cause of irreversible blindness worldwide, its pathogenesis is incompletely understood, and intraocular pressure (IOP) is the only modifiable risk factor to target the disease. Several associations between the gut microbiome and glaucoma, including the IOP, have been suggested. There is growing evidence that interactions between microbes on the ocular surface, termed the ocular surface microbiome (OSM), and tear proteins, collectively called the tear proteome, may also play a role in ocular diseases such as glaucoma. This study aimed to find characteristic features of the OSM and tear proteins in patients with glaucoma. The whole-metagenome shotgun sequencing of 32 conjunctival swabs identified Actinobacteria, Firmicutes, and Proteobacteria as the dominant phyla in the cohort. The species Corynebacterium mastitidis was only found in healthy controls, and their conjunctival microbiomes may be enriched in genes of the phospholipase pathway compared to glaucoma patients. Despite these minor differences in the OSM, patients showed an enrichment of many tear proteins associated with the immune system compared to controls. In contrast to the OSM, this emphasizes the role of the proteome, with a potential involvement of immunological processes in glaucoma. These findings may contribute to the design of new therapeutic approaches targeting glaucoma and other associated diseases.

High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection

The synthesis and degradation rates of proteins form an essential component of gene expression control. Heavy water labeling has been used in conjunction with mass spectrometry to measure protein turnover rates, but the optimal analytical approaches to derive turnover rates from the isotopomer patterns of deuterium labeled peptides continue to be a subject of research. Here we describe a method, which comprises a reverse lookup of numerically approximated peptide isotope envelopes, coupled to the selection of optimal isotopomer pairs based on peptide sequence, to calculate the molar fraction of new peptide synthesis in heavy water labeling mass spectrometry experiments. We validated this approach using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes. We then re-analyzed 17 proteome-wide turnover experiments from four mouse organs, and showed that the method increases the coverage of well-fitted peptides in protein turnover experiments by 25-82%. The method is implemented in the Riana software tool for protein turnover analysis, and may avail ongoing efforts to study the synthesis and degradation kinetics of proteins in animals on a proteome-wide scale.

What’s new

We describe a reverse lookup method to calculate the molar fraction of new synthesis from numerically approximated peptide isotopomer profiles in heavy water labeling mass spectrometry experiments. Using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes at various proportions, we show that this method provides a straightforward way to calculate the proportion of new proteins in a protein pool from arbitrarily chosen isotopomer ratios. We next analyzed which of the isotopomer pairs within the peptide isotope envelope yielded isotopomer time courses that fit most closely to kinetic models, and found that the identity of the isotopomer pair depends partially on the number of deuterium accessible labeling sites of the peptide. We next derived a strategy to automatically select the isotopomer pairs to calculate turnover rates based on peptide sequence, and showed that this increases the coverage of existing proteome-wide turnover experiments in multiple data sets of the mouse heart, liver, kidney, and skeletal muscle by up to 25-82%.

Novel Approach to Exploring Protease Activity and Targets in HIV-associated Obstructive Lung Disease using Combined Proteomic-Peptidomic Analysis

Background

Obstructive lung disease (OLD) is increasingly prevalent among persons living with HIV (PLWH). However, the role of proteases in HIV-associated OLD remains unclear.

Methods

We combined proteomics and peptidomics to comprehensively characterize protease activities. We combined mass spectrometry (MS) analysis on bronchoalveolar lavage fluid (BALF) peptides and proteins from PLWH with OLD (n=25) and without OLD (n=26) with a targeted Somascan aptamer-based proteomic approach to quantify individual proteases and assess their correlation with lung function. Endogenous peptidomics mapped peptides to native proteins to identify substrates of protease activity. Using the MEROPS database, we identified candidate proteases linked to peptide generation based on binding site affinities which were assessed via z-scores. We used t-tests to compare average forced expiratory volume in 1 second per predicted value (FEV1pp) between samples with and without detection of each cleaved protein and adjusted for multiple comparisons by controlling the false discovery rate (FDR).

Findings

We identified 101 proteases, of which 95 had functional network associations and 22 correlated with FEV1pp. These included cathepsins, metalloproteinases (MMP), caspases and neutrophil elastase. We discovered 31 proteins subject to proteolytic cleavage that associate with FEV1pp, with the top pathways involved in small ubiquitin-like modifier mediated modification (SUMOylation). Proteases linked to protein cleavage included neutrophil elastase, granzyme, and cathepsin D.

Interpretations

In HIV-associated OLD, a significant number of proteases are up-regulated, many of which are involved in protein degradation. These proteases degrade proteins involved in cell cycle and protein stability, thereby disrupting critical biological functions.

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

Generation of a Deep Mouse Brain Spectral Library for Transmembrane Proteome Profiling in Mental Disease Models

Transmembrane (TM) proteins constitute over 30% of the mammalian proteome and play essential roles in mediating cell-cell communication, synaptic transmission, and plasticity in the central nervous system. Many of these proteins, especially the G protein-coupled receptors (GPCRs), are validated or candidate drug targets for therapeutic development for mental diseases, yet their expression profiles are underrepresented in most global proteomic studies. Herein, we establish a brain TM protein-enriched spectral library based on 136 data-dependent acquisition runs acquired from various brain regions of both naïve mice and mental disease models. This spectral library comprises 3043 TM proteins including 171 GPCRs, 231 ion channels, and 598 transporters. Leveraging this library, we analyzed the data-independent acquisition data from different brain regions of two mouse models exhibiting depression- or anxiety-like behaviors. By integrating multiple informatics workflows and library sources, our study significantly expanded the mental stress-perturbed TM proteome landscape, from which a new GPCR regulator of depression was verified by in vivo pharmacological testing. In summary, we provide a high-quality mouse brain TM protein spectral library to largely increase the TM proteome coverage in specific brain regions, which would catalyze the discovery of new potential drug targets for the treatment of mental disorders.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Mapping medically relevant RNA isoform diversity in the aged human frontal cortex with deep long-read RNA-seq

Determining whether the RNA isoforms from medically relevant genes have distinct functions could facilitate direct targeting of RNA isoforms for disease treatment. Here, as a step toward this goal for neurological diseases, we sequenced 12 postmortem, aged human frontal cortices (6 Alzheimer disease cases and 6 controls; 50% female) using one Oxford Nanopore PromethION flow cell per sample. We identified 1,917 medically relevant genes expressing multiple isoforms in the frontal cortex where 1,018 had multiple isoforms with different protein-coding sequences. Of these 1,018 genes, 57 are implicated in brain-related diseases including major depression, schizophrenia, Parkinson's disease and Alzheimer disease. Our study also uncovered 53 new RNA isoforms in medically relevant genes, including several where the new isoform was one of the most highly expressed for that gene. We also reported on five mitochondrially encoded, spliced RNA isoforms. We found 99 differentially expressed RNA isoforms between cases with Alzheimer disease and controls.

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Arginine deprivation enriches lung cancer proteomes with cysteine by inducing arginine-to-cysteine substitutants

Many types of human cancers suppress the expression of argininosuccinate synthase 1 (ASS1), a rate-limiting enzyme for arginine production. Although dependency on exogenous arginine can be harnessed by arginine-deprivation therapies, the impact of ASS1 suppression on the quality of the tumor proteome is unknown. We therefore interrogated proteomes of cancer patients for arginine codon reassignments (substitutants) and surprisingly identified a strong enrichment for cysteine (R>C) in lung tumors specifically. Most R>C events did not coincide with genetically encoded R>C mutations but were likely products of tRNA misalignments. The expression of R>C substitutants was highly associated with oncogenic kelch-like epichlorohydrin (ECH)-associated protein 1 (KEAP1)-pathway mutations and suppressed by intact-KEAP1 in KEAP1-mutated cancer cells. Finally, functional interrogation indicated a key role for R>C substitutants in cell survival to cisplatin, suggesting that regulatory codon reassignments endow cancer cells with more resilience to stress. Thus, we present a mechanism for enriching lung cancer proteomes with cysteines that may affect therapeutic decisions.

Cryo-EM structures of the human Elongator complex at work

tRNA modifications affect ribosomal elongation speed and co-translational folding dynamics. The Elongator complex is responsible for introducing 5-carboxymethyl at wobble uridine bases (cm5U34) in eukaryotic tRNAs. However, the structure and function of human Elongator remain poorly understood. In this study, we present a series of cryo-EM structures of human ELP123 in complex with tRNA and cofactors at four different stages of the reaction. The structures at resolutions of up to 2.9 Å together with complementary functional analyses reveal the molecular mechanism of the modification reaction. Our results show that tRNA binding exposes a universally conserved uridine at position 33 (U33), which triggers acetyl-CoA hydrolysis. We identify a series of conserved residues that are crucial for the radical-based acetylation of U34 and profile the molecular effects of patient-derived mutations. Together, we provide the high-resolution view of human Elongator and reveal its detailed mechanism of action.

Unexpected Noncovalent Off-Target Activity of Clinical BTK Inhibitors Leads to Discovery of a Dual NUDT5/14 Antagonist

Cofactor mimicry represents an attractive strategy for the development of enzyme inhibitors but can lead to off-target effects due to the evolutionary conservation of binding sites across the proteome. Here, we uncover the ADP-ribose (ADPr) hydrolase NUDT5 as an unexpected, noncovalent, off-target of clinical BTK inhibitors. Using a combination of biochemical, biophysical, and intact cell NanoBRET assays as well as X-ray crystallography, we confirm catalytic inhibition and cellular target engagement of NUDT5 and reveal an unusual binding mode that is independent of the reactive acrylamide warhead. Further investigation of the prototypical BTK inhibitor ibrutinib also revealed potent inhibition of the largely unstudied NUDIX hydrolase family member NUDT14. By exploring structure-activity relationships (SARs) around the core scaffold, we identify a potent, noncovalent, and cell-active dual NUDT5/14 inhibitor. Cocrystallization experiments yielded new insights into the NUDT14 hydrolase active site architecture and inhibitor binding, thus providing a basis for future chemical probe design.

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

Plasma proteome profiling reveals dynamic of cholesterol marker after dual blocker therapy

Dual blocker therapy (DBT) has the enhanced antitumor benefits than the monotherapy. Yet, few effective biomarkers are developed to monitor the therapy response. Herein, we investigate the DBT longitudinal plasma proteome profiling including 113 longitudinal samples from 22 patients who received anti-PD1 and anti-CTLA4 DBT therapy. The results show the immune response and cholesterol metabolism are upregulated after the first DBT cycle. Notably, the cholesterol metabolism is activated in the disease non-progressive group (DNP) during the therapy. Correspondingly, the clinical indicator prealbumin (PA), free triiodothyronine (FT3) and triiodothyronine (T3) show significantly positive association with the cholesterol metabolism. Furthermore, by integrating proteome and radiology approach, we observe the high-density lipoprotein partial remodeling are activated in DNP group and identify a candidate biomarker APOC3 that can reflect DBT response. Above, we establish a machine learning model to predict the DBT response and the model performance is validated by an independent cohort with balanced accuracy is 0.96. Thus, the plasma proteome profiling strategy evaluates the alteration of cholesterol metabolism and identifies a panel of biomarkers in DBT.

In-Depth Proteome Coverage of In Vitro-Cultured Treponema pallidum and Quantitative Comparison Analyses with In Vivo-Grown Treponemes

Previous mass spectrometry (MS)-based global proteomics studies have detected a combined total of 86% of all Treponema pallidum proteins under infection conditions (in vivo-grown T. pallidum). Recently, a method was developed for the long-term culture of T. pallidum under in vitro conditions (in vitro-cultured T. pallidum). Herein, we used our previously reported optimized MS-based proteomics approach to characterize the T. pallidum global protein expression profile under in vitro culture conditions. These analyses provided a proteome coverage of 94%, which extends the combined T. pallidum proteome coverage from the previously reported 86% to a new combined total of 95%. This study provides a more complete understanding of the protein repertoire of T. pallidum. Further, comparison of the in vitro-expressed proteome with the previously determined in vivo-expressed proteome identifies only a few proteomic changes between the two growth conditions, reinforcing the suitability of in vitro-cultured T. pallidum as an alternative to rabbit-based treponemal growth. The MS proteomics data have been deposited in the MassIVE repository with the data set identifier MSV000093603 (ProteomeXchange identifier PXD047625).

Mass Spectrometry-Based Proteomics of Poly(methylmethacrylate)-Embedded Bone

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely employed technique in proteomics research for studying the proteome biology of various clinical samples. Hard tissues, such as bone and teeth, are routinely preserved using synthetic poly(methyl methacrylate) (PMMA) embedding resins that enable histological, immunohistochemical, and morphological examination. However, the suitability of PMMA-embedded hard tissues for large-scale proteomic analysis remained unexplored. This study is the first to report on the feasibility of PMMA-embedded bone samples for LC-MS/MS analysis. Conventional workflows yielded merely limited coverage of the bone proteome. Using advanced strategies of prefractionation by high-pH reversed-phase liquid chromatography in combination with isobaric tandem mass tag labeling resulted in proteome coverage exceeding 1000 protein identifications. The quantitative comparison with cryopreserved samples revealed that each sample preparation workflow had a distinct impact on the proteomic profile. However, workflow replicates exhibited a high reproducibility for PMMA-embedded samples. Our findings further demonstrate that decalcification prior to protein extraction, along with the analysis of solubilization fractions, is not preferred for PMMA-embedded bone. The biological applicability of the proposed workflow was demonstrated using samples of human PMMA-embedded alveolar bone and the iliac crest, which revealed anatomical site-specific proteomic profiles. Overall, these results establish a crucial foundation for large-scale proteomics studies contributing to our knowledge of bone biology.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

An RNA-dependent and phase-separated active subnuclear compartment safeguards repressive chromatin domains

The nucleus is composed of functionally distinct membraneless compartments that undergo phase separation (PS). However, whether different subnuclear compartments are connected remains elusive. We identified a type of nuclear body with PS features composed of BAZ2A that associates with active chromatin. BAZ2A bodies depend on RNA transcription and BAZ2A non-disordered RNA-binding TAM domain. Although BAZ2A and H3K27me3 occupancies anticorrelate in the linear genome, in the nuclear space, BAZ2A bodies contact H3K27me3 bodies. BAZ2A-body disruption promotes BAZ2A invasion into H3K27me3 domains, causing H3K27me3-body loss and gene upregulation. Weak BAZ2A-RNA interactions, such as with nascent transcripts, promote BAZ2A bodies, whereas the strong binder long non-coding RNA (lncRNA) Malat1 impairs them while mediating BAZ2A association to chromatin at nuclear speckles. In addition to unraveling a direct connection between nuclear active and repressive compartments through PS mechanisms, the results also showed that the strength of RNA-protein interactions regulates this process, contributing to nuclear organization and the regulation of chromatin and gene expression.

O-linked glycosylations in human milk casein and major whey proteins during lactation

As glycosylations are difficult to analyze, their roles and effects are poorly understood. Glycosylations in human milk (HM) differ across lactation. Glycosylations can be involved in antimicrobial activities and may serve as food for beneficial microorganisms. This study aimed to identify and analyze O-linked glycans in HM by high-throughput mass spectrometry. 184 longitudinal HM samples from 66 donors from day 3 and months 1, 2, and 3 postpartum were subjected to a post-translational modification specific enrichment-based strategy using TiO2 and ZrO2 beads for O-linked glycopeptide enrichment. β-CN was found to be a major O-linked glycoprotein, additionally, αS1-CN, κ-CN, lactotransferrin, and albumin also contained O-linked glycans. As glycosyltransferases and glycosidases are involved in assembling the glycans including O-linked glycosylations, these were further investigated. Some glycosyltransferases and glycosidases were found to be significantly decreasing through lactation, including two O-linked glycan initiator enzymes (GLNT1 and GLNT2). Despite their decrease, the overall level of O-linked glycans remained stable in HM over lactation. Three different motifs for O-linked glycosylation were enriched in HM proteins: Gly-Xxx-Xxx-Gly-Ser/Thr, Arg-Ser/Thr and Lys-Ser/Thr. Further O-linked glycan motifs on β-CN were observed to differ between intact proteins and endogenous peptides in HM.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

Droplet-based proteomics reveals CD36 as a marker for progenitors in mammary basal epithelium

Deep proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present DROPPS (droplet-based one-pot preparation for proteomic samples), an accessible low-input platform that generates high-fidelity proteomic profiles of 100-2,500 cells. By applying DROPPS within the mammary epithelium, we elucidated the connection between mitochondrial activity and clonogenicity, identifying CD36 as a marker of progenitor capacity in the basal cell compartment. We anticipate that DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations.

DNA-PK and ATM drive phosphorylation signatures that antagonistically regulate cytokine responses to herpesvirus infection or DNA damage

The DNA-dependent protein kinase, DNA-PK, is an essential regulator of DNA damage repair. DNA-PK-driven phosphorylation events and the activated DNA damage response (DDR) pathways are also components of antiviral intrinsic and innate immune responses. Yet, it is not clear whether and how the DNA-PK response differs between these two forms of nucleic acid stress-DNA damage and DNA virus infection. Here, we define DNA-PK substrates and the signature cellular phosphoproteome response to DNA damage or infection with the nuclear-replicating DNA herpesvirus, HSV-1. We establish that DNA-PK negatively regulates the ataxia-telangiectasia-mutated (ATM) DDR kinase during viral infection. In turn, ATM blocks the binding of DNA-PK and the nuclear DNA sensor IFI16 to viral DNA, thereby inhibiting cytokine responses. However, following DNA damage, DNA-PK enhances ATM activity, which is required for IFN-β expression. These findings demonstrate that the DDR autoregulates cytokine expression through the opposing modulation of DDR kinases.

Functionalizing tandem mass tags for streamlining click-based quantitative chemoproteomics

Mapping the ligandability or potential druggability of all proteins in the human proteome is a central goal of mass spectrometry-based covalent chemoproteomics. Achieving this ambitious objective requires high throughput and high coverage sample preparation and liquid chromatography-tandem mass spectrometry analysis for hundreds to thousands of reactive compounds and chemical probes. Conducting chemoproteomic screens at this scale benefits from technical innovations that achieve increased sample throughput. Here we realize this vision by establishing the silane-based cleavable linkers for isotopically-labeled proteomics-tandem mass tag (sCIP-TMT) proteomic platform, which is distinguished by early sample pooling that increases sample preparation throughput. sCIP-TMT pairs a custom click-compatible sCIP capture reagent that is readily functionalized in high yield with commercially available TMT reagents. Synthesis and benchmarking of a 10-plex set of sCIP-TMT reveal a substantial decrease in sample preparation time together with high coverage and high accuracy quantification. By screening a focused set of four cysteine-reactive electrophiles, we demonstrate the utility of sCIP-TMT for chemoproteomic target hunting, identifying 789 total liganded cysteines. Distinguished by its compatibility with established enrichment and quantification protocols, we expect sCIP-TMT will readily translate to a wide range of covalent chemoproteomic applications.

Multiomics Approach Reveals Serum Biomarker Candidates for Congenital Zika Syndrome

The Zika virus (ZIKV) can be vertically transmitted, causing congenital Zika syndrome (CZS) in fetuses. ZIKV infection in early gestational trimesters increases the chances of developing CZS. This syndrome involves several pathologies with a complex diagnosis. In this work, we aim to identify biological processes and molecular pathways related to CZS and propose a series of putative protein and metabolite biomarkers for CZS prognosis in early pregnancy trimesters. We analyzed serum samples of healthy pregnant women and ZIKV-infected pregnant women bearing nonmicrocephalic and microcephalic fetuses. A total of 1090 proteins and 512 metabolites were identified by bottom-up proteomics and untargeted metabolomics, respectively. Univariate and multivariate statistical approaches were applied to find CZS differentially abundant proteins (DAP) and metabolites (DAM). Enrichment analysis (i.e., biological processes and molecular pathways) of the DAP and the DAM allowed us to identify the ECM organization and proteoglycans, amino acid metabolism, and arachidonic acid metabolism as CZS signatures. Five proteins and four metabolites were selected as CZS biomarker candidates. Serum multiomics analysis led us to propose nine putative biomarkers for CZS prognosis with high sensitivity and specificity.

Target-decoy false discovery rate estimation using Crema

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

Integrated stress response plasticity governs normal cell adaptation to chronic stress via the PP2A-TFE3-ATF4 pathway

The integrated stress response (ISR) regulates cell fate during conditions of stress by leveraging the cell's capacity to endure sustainable and efficient adaptive stress responses. Protein phosphatase 2A (PP2A) activity modulation has been shown to be successful in achieving both therapeutic efficacy and safety across various cancer models; however, the molecular mechanisms driving its selective antitumor effects remain unclear. Here, we show for the first time that ISR plasticity relies on PP2A activation to regulate drug response and dictate cellular fate under conditions of chronic stress. We demonstrate that genetic and chemical modulation of the PP2A leads to chronic proteolytic stress and triggers an ISR to dictate cell fate. More specifically, we uncovered that the PP2A-TFE3-ATF4 pathway governs ISR cell plasticity during endoplasmic reticular and cellular stress independent of the unfolded protein response. We further show that normal cells reprogram their genetic signatures to undergo ISR-mediated adaptation and homeostatic recovery thereby successfully avoiding toxicity following PP2A-mediated stress. Conversely, oncogenic specific cytotoxicity induced by chemical modulation of PP2A is achieved by activating chronic and irreversible ISR in cancer cells. Our findings propose that a differential response to chemical modulation of PP2A is determined by intrinsic ISR plasticity, providing a novel biological vulnerability to selectively induce cancer cell death and improve targeted therapeutic efficacy.

Cathepsin D inhibition during neuronal differentiation selectively affects individual proteins instead of overall protein turnover

Cathepsin D (CTSD) is a lysosomal aspartic protease and its inherited deficiency causes a severe pediatric neurodegenerative disease called neuronal ceroid lipofuscinosis (NCL) type 10. The lysosomal dysfunction in the affected patients leads to accumulation of undigested lysosomal cargo especially in none-dividing cells, such as neurons, resulting in death shortly after birth. To explore which proteins are mainly affected by the lysosomal dysfunction due to CTSD deficiency, Lund human mesencephalic (LUHMES) cells, capable of inducible dopaminergic neuronal differentiation, were treated with Pepstatin A. This inhibitor of "acidic" aspartic proteases caused accumulation of acidic intracellular vesicles in differentiating LUHMES cells. Pulse-chase experiments involving stable isotope labelling with amino acids in cell culture (SILAC) with subsequent mass-spectrometric protein identification and quantification were performed. By this approach, we studied the degradation and synthesis rates of 695 and 680 proteins during early and late neuronal LUHMES differentiation, respectively. Interestingly, lysosomal bulk proteolysis was not altered upon Pepstatin A treatment. Instead, the protease inhibitor selectively changed the turnover of individual proteins. Especially proteins belonging to the mitochondrial energy supply system were differentially degraded during early and late neuronal differentiation indicating a high energy demand as well as stress level in LUHMES cells treated with Pepstatin A.