Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Spatial proteomics identifies a CRTC-dependent viral signaling pathway that stimulates production of interleukin-11

Appropriate cellular recognition of viruses is essential for the generation of an effective innate and adaptive immune response. Viral sensors and their downstream signaling components thus provide a crucial first line of host defense. Many of them exhibit subcellular relocalization upon activation, resulting in the expression of interferon and antiviral genes. To comprehensively identify signaling factors, we analyzed protein relocalization on a global scale during viral infection. cAMP-responsive element-binding protein (CREB)-regulated transcription coactivators 2 and 3 (CRTC2/3) exhibited early cytoplasmic-to-nuclear translocation upon infection with multiple viruses in diverse cell types. This movement was dependent on mitochondrial antiviral signaling protein (MAVS), cyclo-oxygenase proteins, and protein kinase A. A key effect of CRTC2/3 translocation is transcription of the fibro-inflammatory cytokine interleukin (IL)-11. This may be important clinically in viral infections associated with fibrosis, including SARS-CoV-2. Nuclear translocation of CRTC2/3 is, therefore, identified as an important pathway in the context of viral infection.

The role of MKI67 in the regulation of 60S pre-ribosome nucleolar export, transcripts, energy supply, and apoptosis

MKI67 (Ki67) is expressed exclusively in proliferating cells in human tissues, rendering it as a valuable diagnostic marker for cancer. However, the function of this protein in cells remains unclear. In this study, we present the findings on the regulatory functions of MKI67 in conjunction with its partner proteins GNL2 and MDN1, which are involved in pre-ribosome processing, as well as the regulatory functions in its absence. In proliferating HEK293T cells, MKI67 binds contiguously to the chromatin in conjunction with GNL2 and MDN1, localizing most densely to the nucleolar periphery to regulate 60S pre-ribosome export. On the other hand, RNA-seq analysis reveals that these three proteins can independently regulate many target transcripts, but they often share their target transcripts, yet often regulate them at different expression levels. MDN1 depletion strongly downregulates RNA gene transcripts involved in ribosome biogenesis and splicing. In contrast, MKI67 depletion strongly upregulates transcripts of protein-coding genes, including synapse-specific proteins and the mitosis-related protein NEK7. Furthermore, MKI67 depletion coordinately up- or down-regulates the levels of transcripts of several pathways, thereby enabling MKI67-depleted cells to adapt to less active metabolic states. The underlying mechanism by which MKI67 depletion upregulates transcripts appears to involve attenuation of transcript levels in cooperation with mRNA degradation systems, as evidenced by analysis of NEK7 and UNC13A translations. In conclusion, the present results indicate that MKI67 contributes to proliferation via nucleolar export of 60S pre-ribosome particles and high energy supply. Conversely, its absence leads the cells to adapt to the senescent and differentiated conditions.

Potent and selective SETDB1 covalent negative allosteric modulator reduces methyltransferase activity in cells

A promising drug target, SETDB1, is a dual methyl-lysine (Kme) reader and methyltransferase implicated in cancer and neurodegenerative disease progression. To help understand the role of the triple Tudor domain (3TD) of SETDB1, its Kme reader, we first identify a low micromolar potency small molecule ligand, UNC6535, which occupies simultaneously both the TD2 and TD3 reader binding sites. Further optimization leads to the discovery of UNC10013, a covalent 3TD ligand targeting Cys385 of SETDB1. UNC10013 is potent with a kinact/KI of 1.0 × 106 M-1s-1 and demonstrates proteome-wide selectivity. In cells, negative allosteric modulation of SETDB1-mediated Akt methylation occurs after treatment with UNC10013. Therefore, UNC10013 is a potent, selective, and cell-active covalent ligand for the 3TD of SETDB1, demonstrating negative allosteric modulator properties and making it a promising tool to study the biological role of SETDB1 in disease progression.

A functional map of phosphoprotein phosphatase regulation identifies an evolutionary conserved reductase for the catalytic metal ions

Serine/Threonine phosphoprotein phosphatases (PPPs, PP1-PP7) are conserved metalloenzymes and central to intracellular signaling in eukaryotes, but the details of their regulation is poorly understood. To address this, we performed genome-wide CRISPR knockout and focused base editor screens in PPP perturbed conditions to establish a high-resolution functional map of PPP regulation that pinpoints novel regulatory mechanisms. Through this, we identify the orphan reductase CYB5R4 as an evolutionarily conserved activator of PP4 and PP6, but not the closely related PP2A. Heme binding is essential for CYB5R4 function and mechanistically involves the reduction of the metal ions in the active site. Importantly, CYB5R4-mediated activation of PP4 is critical for cell viability when cells are treated with DNA damage-inducing agents known to cause oxidative stress. The discovery of a dedicated PPP reductase points to shared regulatory principles with protein tyrosine phosphatases, where specific enzymes dictate activity by regulating the active site redox state. In sum, our work provides a resource for understanding PPP function and the regulation of intracellular signaling.

Distinct gene signatures define the epithelial cell features of mucinous appendiceal neoplasms and pseudomyxoma metastases

Introduction

Appendiceal mucinous neoplasms (AMN) are rare tumors of the gastrointestinal tract. They metastasize with widespread abdominal dissemination leading to pseudomyxoma peritonei (PMP), a disease with poor prognosis. There are many unknowns about the cellular features of origin, differentiation and progression of AMN and PMP.

Methods

We characterized AMNs, PMPs and matched normal tissues using single-cell RNA-sequencing. We validated our findings with immunohistochemistry, mass spectrometry on malignant ascites from PMP patients and gene expression data from an independent set of PMP tumors.

Results

We identified previously undescribed cellular features and heterogeneity in AMN and PMP tumors. There were gene expression signatures specific to the tumor epithelial cells among AMN and PMP. These signatures included genes indicative of goblet cell differentiation and elevated mucin gene expression. Metastatic PMP cells had a distinct gene expression signature with increased lipid metabolism, inflammatory, JAK-STAT and RAS signaling pathway among others. We observed clonal heterogeneity in a single PMP tumor as well as PMP metastases from the same patient.

Discussion

Our study defined tumor cell gene signatures of AMN and PMP, successfully overcoming challenges of low cellularity and mucinous composition of these tumors. These gene expression signatures provide insights on tumor origin and differentiation, together with the identification of novel treatment targets. The heterogeneity observed within an individual tumor and between different tumors from the same patient, represents a potential source of treatment resistance.

PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 ± 1.6% compared to MS-GF+ data on samples containing mixed HEK and Escherichia coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

JUMPlib: Integrative Search Tool Combining Fragment Ion Indexing with Comprehensive TMT Spectral Libraries

The identification of peptides is a cornerstone of mass spectrometry-based proteomics. Spectral library-based algorithms are well-established methods to enhance the identification efficiency of peptides during database searches in proteomics. However, these algorithms are not specifically tailored for tandem mass tag (TMT)-based proteomics due to the lack of high-quality TMT spectral libraries. Here, we introduce JUMPlib, a computational tool for a TMT-based spectral library search. JUMPlib comprises components for generating spectral libraries, conducting library searches, filtering peptide identifications, and quantifying peptides and proteins. Fragment ion indexing in the libraries increases the search speed and utilizing the experimental retention time of precursor ions improves peptide identification. We found that methionine oxidation is a major factor contributing to large shifts in peptide retention time. To test the JUMPlib program, we curated two comprehensive human libraries for the labeling of TMT6/10/11 and TMT16/18 reagents, with ∼286,000 precursor ions and ∼304,000 precursor ions, respectively. Our analyses demonstrate that JUMPlib, employing the fragment ion index strategy, enhances search speed and exhibits high sensitivity and specificity, achieving approximately a 25% increase in peptide-spectrum matches compared to other search tools. Overall, JUMPlib serves as a streamlined computational platform designed to enhance peptide identification in TMT-based proteomics. Both the JUMPlib source code and libraries are publicly available.

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.

Detecting Human Contaminant Genetically Variant Peptides in Nonhuman Samples

During proteomics data analysis, experimental spectra are searched against a user-defined protein database consisting of proteins that are reasonably expected to be present in the sample. Typically, this database contains the proteome of the organism under study concatenated with expected contaminants, such as trypsin and human keratins. However, there are additional contaminants that are not commonly added to the database. In this study, we describe a new set of protein contaminants and provide evidence that they can be detected in mass spectrometry-based proteomics data. Specifically, we provide evidence that human genetically variant peptides (GVPs) can be detected in nonhuman samples. GVPs are peptides that contain single amino acid polymorphisms that result from nonsynonymous single nucleotide polymorphisms in protein-coding regions of DNA. We reanalyzed previously collected nonhuman data-dependent acquisition (DDA) and data-independent acquisition (DIA) data sets and detected between 0 and 135 GVPs per data set. In addition, we show that GVPs are unlikely to originate from nonhuman sources and that a subset of eight GVPs are commonly detected across data sets.

NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning

De novo peptide sequencing is a valuable technique in mass-spectrometry-based proteomics, as it deduces peptide sequences directly from tandem mass spectra without relying on sequence databases. This database-independent method, however, relies solely on imperfect scoring functions that often lead to erroneous peptide identifications. To boost correct identification, we present NovoRank, a postprocessing tool that employs spectral clustering and machine learning to assign more plausible peptide sequences to spectra. Prior to de novo peptide sequencing, spectral clustering is applied to group similar spectra under the assumption that they originated from the same peptide species. NovoRank then employs a deep learning model, incorporating both cluster-derived proteomic features and individual spectrum characteristics, to rerank the candidate peptides produced by de novo peptide sequencing. Our results show that NovoRank significantly enhances the performance of various de novo peptide sequencing tools, increasing both recall and precision by 0.020 to 0.080 at the peptide-spectrum match (PSM) level. Notably, NovoRank achieves a recall as high as 0.830 for Casanovo at the PSM level. The source code of NovoRank is freely available at https://github.com/HanyangBISLab/NovoRank and is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.

The human zinc-binding cysteine proteome

Zinc is an essential micronutrient that regulates a wide range of physiological processes, most often through zinc binding to protein cysteine residues. Despite being critical for modulation of protein function, the cysteine sites in the majority of the human proteome that are subject to zinc binding remain undefined. Here, we develop ZnCPT, a deep and quantitative mapping of the zinc-binding cysteine proteome. We define 6,173 zinc-binding cysteines, uncovering protein families across major domains of biology that are subject to constitutive or inducible zinc binding. ZnCPT enables systematic discovery of zinc-regulated structural, enzymatic, and allosteric functional domains. On this basis, we identify 52 cancer genetic dependencies subject to zinc binding and nominate malignancies sensitive to zinc-induced cytotoxicity. We discover a mechanism of zinc regulation over glutathione reductase (GSR), which drives cell death in GSR-dependent lung cancers. We provide ZnCPT as a resource for understanding mechanisms of zinc regulation of protein function.

FAST MS: Software for the Automated Analysis of Top-Down Mass Spectra of Polymeric Molecules Including RNA, DNA, and Proteins

Top-down mass spectrometry (MS) enables comprehensive characterization of modified proteins and nucleic acids and, when native electrospray ionization (ESI) is used, binding site mapping of their complexes with native or therapeutic ligands. However, the high complexity of top-down MS spectra poses a serious challenge to both manual and automated data interpretation, even when the protein, RNA, or DNA sequence and the type of modification or the ligand are known. Here, we introduce FAST MS, a user-friendly software that identifies, assigns and relatively quantifies signals of molecular and fragment ions in MS and MS/MS spectra of biopolymers with known sequence and provides a toolbox for statistical analysis. FAST MS searches mass spectra for ion signals by comparing all signals in the spectrum with isotopic profiles calculated from known sequences, resulting in superior sensitivity and an increased number of assigned fragment ions compared to algorithms that rely on artificial monomer units while maintaining the false positive rate on a moderate level (<5%). FAST MS is an open-source, cross-platform software for the accurate identification, localization and relative quantification of modifications, even in complex mixtures of positional isomers of proteins, oligonucleotides, or any other user-defined linear polymer.

Platform-dependent effects of genetic variants on plasma APOL1 and their implications for kidney disease

Mutations in apolipoprotein L1 (APOL1) are strongly associated with an increased risk of kidney disease in individuals of African ancestry, yet the underlying mechanisms remain largely unknown. Plasma proteomics provides opportunities to elucidate mechanisms of disease by studying the effects of disease-associated variants on circulating protein levels. Here, we examine the genetic drivers of circulating APOL1 in individuals of African and European ancestry from four independent cohorts (UK Biobank, AASK, deCODE and Health ABC) employing three proteomic technologies (Olink, SomaLogic and mass spectrometry). We find that disease-associated APOL1 G1 and G2 variants are strong pQTLs for plasma APOL1 in Olink and SomaLogic, but the direction of their effects depends on the proteomic platform. We identify an additional APOL1 missense variant (rs2239785), common in Europeans, exhibiting the same platform-dependent directional discrepancy. Similarly, variants in the kallikrein-kinin pathway ( KLKB1 , F12 , KNG1 ) and their genetic interactions exhibit strong trans -pQTL effects for APOL1 measured by Olink, but not SomaLogic. To explain these discrepancies, we propose a model in which APOL1 mutations and the kallikrein-kinin pathway influence the relative abundance of two distinct APOL1 forms, corresponding to APOL1 bound to trypanolytic factors 1 and 2, which are differentially recognized by different proteomic platforms. We hypothesize that this shift in relative abundance of APOL1 forms may contribute to the development of kidney disease.

Sensitive and Accurate Proteome Profiling of Embryogenesis Using Real-Time Search and TMTproC Quantification

Multiplexed proteomics has become a powerful tool for investigating biological systems. Using balancer-peptide conjugates (e.g., TMTproC complementary ions) in the MS2 spectra for quantification circumvents the ratio distortion problem inherent in multiplexed proteomics. However, TMTproC quantification scans require long Orbitrap transients and extended ion injection times to achieve sufficient ion statistics and spectral resolution. Real-time search (RTS) algorithms have demonstrated increased speed and sensitivity by selectively informing precursor peak quantification. Here, we combine complementary ion quantification with RTS (TMTproC-RTS) to enhance sensitivity while maintaining accuracy and precision in quantitative proteomics at the MS2 level. We demonstrate the utility of this method by quantifying protein dynamics during the embryonic development of Drosophila melanogaster (fly), Ciona robusta (sea squirt), and Xenopus laevis (frog). We quantify 7.8k, 8.6k, and 12.7k proteins in each organism, which is an improvement of 12%, 13%, and 14%, respectively, compared with naive TMTproC analysis. For all three organisms, the newly acquired data outperform previously published datasets and provide a diverse, deep, and accurate database of protein dynamics during embryogenesis, which will advance the study of evolutionary comparison in early embryogenesis.

Function of CSNK2/CK2 selectively affects the endoplasmic reticulum and the Golgi apparatus in mtor-mediated autophagy induction

Selective macroautophagy/autophagy of the endoplasmic reticulum, known as reticulophagy/ER-phagy, is essential to maintain ER homeostasis. We recently showed that members of the autophagy receptor family RETREG/FAM134 are regulated by phosphorylation-dependent ubiquitination. In an unbiased screen we had identified several kinases downstream of MTOR with profound impact on reticulophagy flux, including ATR and CSNK2/CK2. Inhibition of CSNK2 by SGC-CK2-1 prevented regulatory ubiquitination of RETREG1/FAM134B and RETREG3/FAM134C upon autophagy activation as well as the formation of high-density RETREG1- and RETREG3-clusters. Here we report on additional resource data of global proteomics upon CSNK2 and ATR inhibition, respectively. Our data suggests that the function of CSNK2 is mainly limited to the ER/reticulophagy and Golgi/Golgiphagy, while ATR inhibition by VE-822 affects the vast majority of organelles/selective autophagy pathways.Abbreviation: ATRi: ATR inhibitor VE-822; CSNK2i: CSNK2 inhibitor SGC-CK2-1; ER: endoplasmic reticulum.

The IL-4-IL-4Rα axis modulates olfactory neuroimmune signaling to induce loss of smell

IL-4 and IL-13 have non-redundant effects in olfaction, with loss of smell in mice evoked only by intranasal administration of IL-4, but not IL-13. IL-4-evoked pathophysiological effects on olfaction is independent of compromised structural integrity of the olfactory neuroepithelium. IL-4-IL-4Rα signaling modulates neuronal crosstalk with immune cells, suggesting a functional link between olfactory impairment and neuroinflammation. Abbreviations: IL, interleukin; KO, knock-out; wk, week; WT, wild-type.

Myristoylated Neuronal Calcium Sensor-1 captures the preciliary vesicle at distal appendages

The primary cilium is a microtubule-based organelle that cycles through assembly and disassembly. In many cell types, formation of the cilium is initiated by recruitment of preciliary vesicles to the distal appendage of the mother centriole. However, the distal appendage mechanism that directly captures preciliary vesicles is yet to be identified. In an accompanying paper, we show that the distal appendage protein, CEP89, is important for the preciliary vesicle recruitment, but not for other steps of cilium formation (Kanie et al., 2025). The lack of a membrane-binding motif in CEP89 suggests that it may indirectly recruit preciliary vesicles via another binding partner. Here, we identify Neuronal Calcium Sensor-1 (NCS1) as a stoichiometric interactor of CEP89. NCS1 localizes to the position between CEP89 and the centriole-associated vesicle marker, RAB34, at the distal appendage. This localization was completely abolished in CEP89 knockouts, suggesting that CEP89 recruits NCS1 to the distal appendage. Similar to CEP89 knockouts, preciliary vesicle recruitment as well as subsequent cilium formation was perturbed in NCS1 knockout cells. The ability of NCS1 to recruit the preciliary vesicle is dependent on its myristoylation motif and NCS1 knockout cells expressing a myristoylation defective mutant failed to rescue the vesicle recruitment defect despite localizing properly to the centriole. In sum, our analysis reveals the first known mechanism for how the distal appendage recruits the preciliary vesicles.

mTORC1 regulates the pyrimidine salvage pathway by controlling UCK2 turnover via the CTLH-WDR26 E3 ligase

One critical aspect of cell proliferation is increased nucleotide synthesis, including pyrimidines. Pyrimidines are synthesized through de novo and salvage pathways. Prior studies established that the mammalian target of rapamycin complex 1 (mTORC1) promotes pyrimidine synthesis by activating the de novo pathway for cell proliferation. However, the involvement of mTORC1 in regulating the salvage pathway remains unclear. Here, we report that mTORC1 controls the half-life of uridine cytidine kinase 2 (UCK2), the rate-limiting enzyme in the salvage pathway. Specifically, UCK2 is degraded via the CTLH-WDR26 E3 complex during mTORC1 inhibition, which is prevented when mTORC1 is active. We also find that UCK1, an isoform of UCK2, affects the turnover of UCK2 by influencing its cellular localization. Importantly, altered UCK2 levels through the mTORC1-CTLH E3 pathway affect pyrimidine salvage and the efficacy of pyrimidine analog prodrugs. Therefore, mTORC1-CTLH E3-mediated degradation of UCK2 adds another layer of complexity to mTORC1's role in regulating pyrimidine metabolism.

Global cellular proteo-lipidomic profiling of diverse lysosomal storage disease mutants using nMOST

Lysosomal storage diseases (LSDs) comprise ~50 monogenic disorders marked by the buildup of cellular material in lysosomes, yet systematic global molecular phenotyping of proteins and lipids is lacking. We present a nanoflow-based multiomic single-shot technology (nMOST) workflow that quantifies HeLa cell proteomes and lipidomes from over two dozen LSD mutants. Global cross-correlation analysis between lipids and proteins identified autophagy defects, notably the accumulation of ferritinophagy substrates and receptors, especially in NPC1-/- and NPC2-/- mutants, where lysosomes accumulate cholesterol. Autophagic and endocytic cargo delivery failures correlated with elevated lysophosphatidylcholine species and multilamellar structures visualized by cryo-electron tomography. Loss of mitochondrial cristae, MICOS complex components, and OXPHOS components rich in iron-sulfur cluster proteins in NPC2-/- cells was largely alleviated when iron was provided through the transferrin system. This study reveals how lysosomal dysfunction affects mitochondrial homeostasis and underscores nMOST as a valuable discovery tool for identifying molecular phenotypes across LSDs.

Diverse microtubule-destabilizing drugs induce equivalent molecular pathway responses in endothelial cells

Drugs that modulate microtubule (MT) dynamics are well-characterized at the molecular level, yet the mechanisms linking these molecular effects to their distinct clinical outcomes remain unclear. Several MT-destabilizing drugs, including vinblastine, combretastatin A4, and plinabulin, are widely used, or are under evaluation for cancer treatment. Although all three depolymerize MTs, they do so through distinct biochemical mechanisms. Furthermore, their clinical profiles and therapeutic uses differ considerably. To investigate whether differential modulation of molecular pathways might account for clinical differences, we compared gene expression and signaling pathway responses in human pulmonary microvascular endothelial cells (HPMECs), alongside the MT-stabilizing drug docetaxel and the pro-inflammatory cytokine TNF-α. RNA-sequencing and phosphoproteomics revealed that all three MT destabilizers triggered equivalent molecular responses. The substantial changes in gene expression caused by MT destabilization were completely dependent on Rho family GTPase activation. These findings suggest that the distinct clinical profiles of the destabilizing drugs depend on differences in pharmacokinetics (PK) and tissue distribution rather than molecular actions. The washout rate of the three drugs differed, which likely translates to PK differences. Our data provide insights into how MT destabilization triggers signaling changes, potentially explaining how these drugs induce cell cycle re-entry in quiescent cells and how plinabulin ameliorates chemotherapy-induced neutropenia.

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a theoretical formulation of entrapment experiments that allows us to rigorously characterize the behavior of the various entrapment methods. We also propose a more powerful method for evaluating FDR control, and we employ that method, along with other existing techniques, to characterize a variety of popular search tools. We empirically validate our entrapment analysis in the fairly well-understood DDA setup before applying it in the DIA setup. We find that none of the DIA search tools consistently controls the FDR at the peptide level, and the tools struggle particularly with analysis of single cell datasets.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics

Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the past decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.

Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies

Advances in high-throughput omics technologies have enabled system-wide characterization of biological samples across multiple molecular levels, such as the genome, transcriptome, and proteome. However, as sample sizes rapidly increase in large-scale multi-omics studies, sample mix-ups have become a prevalent issue, compromising data integrity and leading to erroneous conclusions. The interconnected nature of multi-omics data presents an opportunity to identify and correct these errors. This review examines the potential sources of sample mix-ups and evaluates the methodologies and tools developed for detecting and correcting these errors, with an emphasis on approaches applicable to proteomics data. We categorize existing tools into three main groups: expression/protein quantitative trait loci-based, genotype concordance-based, and gene/protein expression correlation-based approaches. Notably, only a handful of tools currently utilize the proteogenomics approach for correcting sample mix-ups at the proteomics level. Integrating the strengths of current tools across diverse data types could enable the development of more versatile and comprehensive solutions. In conclusion, verifying sample identity is a critical first step to reduce bias and increase precision in subsequent analyses for large-scale multi-omics studies. By leveraging these tools for identifying and correcting sample mix-ups, researchers can significantly improve the reliability and reproducibility of biomedical research.

Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies

Cross-linking mass spectrometry (XL-MS) allows characterizing protein-protein interactions (PPIs) in native biological systems by capturing cross-links between different proteins (inter-links). However, inter-link identification remains challenging, requiring dedicated data filtering schemes and thorough error control. Here, we benchmark existing data filtering schemes combined with error rate estimation strategies utilizing concatenated target-decoy protein sequence databases. These workflows show shortcomings either in sensitivity (many false negatives) or specificity (many false positives). To ameliorate the limited sensitivity without compromising specificity, we develop an alternative target-decoy search strategy using fused target-decoy databases. Furthermore, we devise a different data filtering scheme that takes the inter-link context of the XL-MS dataset into account. Combining both approaches maintains low error rates and minimizes false negatives, as we show by mathematical simulations, analysis of experimental ground-truth data, and application to various biological datasets. In human cells, inter-link identifications increase by 75% and we confirm their structural accuracy through proteome-wide comparisons to AlphaFold2-derived models. Taken together, target-decoy fusion and context-sensitive data filtering deepen and fine-tune XL-MS-based interactomics.

A Review of Protein Inference

Protein inference is an often neglected though crucial step in most proteomic experiments. In the bottom-up proteomic approach, the actual molecules of interest, the proteins, are digested into peptides before measurement on a mass spectrometer. This approach introduces a loss of information: The actual proteins must be inferred based on the identified peptides. While this might seem trivial, there are certain problems, one of the biggest being the presence of peptides that are shared among proteins. These amino acid sequences can, based on the database used for identification, belong to more than one protein. If such peptides are identified in a sample, it cannot be said which proteins actually were in the sample, but only an estimate on the most probable proteins or protein groups can be given based on a predefined inference strategy.Here we describe the effect of the chosen database for peptide identification on the number of shared peptides. Afterward, the mainly used protein inference methods will be sketched, and the necessity of stringent false discovery rate on peptide and protein level is discussed. Finally, we explain how the tool "PIA or protein inference algorithms" can be used together with the workflow environment KNIME and OpenMS to perform protein inference in a common proteomic experiment.

A widespread family of viral sponge proteins reveals specific inhibition of nucleotide signals in anti-phage defense

Cyclic oligonucleotide-based antiviral signaling systems (CBASS) are bacterial anti-phage defense operons that use nucleotide signals to control immune activation. Here we biochemically screen 57 diverse E. coli and Bacillus phages for the ability to disrupt CBASS immunity and discover anti-CBASS 4 (Acb4) from the Bacillus phage SPO1 as the founding member of a large family of >1,300 immune evasion proteins. A 2.1 Å crystal structure of Acb4 in complex with 3'3'-cGAMP reveals a tetrameric assembly that functions as a sponge to sequester CBASS signals and inhibit immune activation. We demonstrate Acb4 alone is sufficient to disrupt CBASS activation in vitro and enable immune evasion in vivo. Analyzing phages that infect diverse bacteria, we explain how Acb4 selectively targets nucleotide signals in host defense and avoids disruption of cellular homeostasis. Together, our results reveal principles of immune evasion protein evolution and explain a major mechanism phages use to inhibit host immunity.

Analysis of mouse lens morphological and proteomic abnormalities following depletion of βB3-crystallin

Crystallin proteins serve as both essential structural and as well as protective components of the ocular lens and are required for the transparency and light refraction properties of the organ. The mouse lens crystallin proteome is represented by αA-, αB-, βA1-, βA2-, βA3-, βA4-, βB1-, βB2-, βB3-, γA-, γB-, γC-, γD-, γE, γF-, γN-, and γS-crystallin proteins encoded by 16 genes. Their mutations are responsible for lens opacification and early onset cataract formation. While many cataract-causing missense and nonsense mutations are known for these proteins, including the human CRYBB3 gene, the mammalian loss-of function model of the Crybb3 gene remains to be established. Herein, we generated the first mouse model via deletion of the Crybb3 promoter that abolished expression of the βB3-crystallin. Histological analysis of lens morphology using newborn βB3-crystallin-deficient lenses revealed disrupted lens morphology with early-onset phenotypic variability. In-depth lens proteomics at four time points (newborn, 3-weeks, 6-weeks, and 3-months) showed both down- and up-regulation of various proteins, with the highest divergence from control mice observed in 3-months lenses. Apart from the βB3-crystallin, another protein Smarcc1/Baf155 was down-regulated in all four samples. In addition, downregulation of Hspe1, Pdlim1, Ast/Got, Lsm7, Ddx23, and Acad11 was found in three time points. Finally, we show that the βB3-crystallin promoter region, which contains multiple binding sites for the transcription factors AP-2α, c-Jun, c-Maf, Etv5, and Pax6 is activated by FGF2 in primary lens cell culture experiments. Together, these studies establish the mouse Crybb3 loss-of-function model and its disrupted crystallin and non-crystallin proteomes.

CEBPA repression by MECOM blocks differentiation to drive aggressive leukemias

Acute myeloid leukemias (AMLs) have an overall poor prognosis with many high-risk cases co-opting stem cell gene regulatory programs, yet the mechanisms through which this occurs remain poorly understood. Increased expression of the stem cell transcription factor, MECOM, underlies one key driver mechanism in largely incurable AMLs. How MECOM results in such aggressive AML phenotypes remains unknown. To address existing experimental limitations, we engineered and applied targeted protein degradation with functional genomic readouts to demonstrate that MECOM promotes malignant stem cell-like states by directly repressing pro-differentiation gene regulatory programs. Remarkably and unexpectedly, a single node in this network, a MECOM-bound cis-regulatory element located 42 kb downstream of the myeloid differentiation regulator CEBPA, is both necessary and sufficient for maintaining MECOM-driven leukemias. Importantly, targeted activation of this regulatory element promotes differentiation of these aggressive AMLs and reduces leukemia burden in vivo, suggesting a broadly applicable differentiation-based approach for improving therapy.

Improved detection of differentially abundant proteins through FDR-control of peptide-identity-propagation

The goal of proteomics is to identify and quantify peptides and proteins within a biological sample. Almost all algorithms for the identification of peptides in LC-MS/MS data employ two steps: peptide/spectrum matching and peptide-identity-propagation (PIP), also known as match-between-runs. PIP was originally envisioned as a backup method to overcome measurement stochasticity. However, current PIP implementations can routinely account for up to 40% of all results, with that proportion rising as high as 75% in single-cell proteomics. Unlike peptide identities derived through peptide/spectrum matches, for which error estimation has been strictly enforced for decades, peptide identities derived through PIP have not historically been subject to statistical evaluation. As an indispensable component of label free quantification, PIP needs a simple and statistically rigorous method for estimating its error rates. Although several tools claim to control the false discovery rate (FDR) of PIP, these claims cannot be validated as there is currently no accepted method to assess the accuracy of the stated FDR. We present a method for FDR control of PIP, called PIP-ECHO, and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different benchmark datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that only PIP-ECHO can accurately control the FDR of PIP at 1% across all datasets, including single cell. When analyzing spike-in datasets where different known amounts of yeast or E. coli peptides are added to a constant background of human peptides, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis, yielding 53% more differentially abundant proteins than MaxQuant and 146% more than IonQuant.

Impairment of lipid homeostasis causes lysosomal accumulation of endogenous protein aggregates through ESCRT disruption

Protein aggregation increases during aging and is a pathological hallmark of many age-related diseases. Protein homeostasis (proteostasis) depends on a core network of factors directly influencing protein production, folding, trafficking, and degradation. Cellular proteostasis also depends on the overall composition of the proteome and numerous environmental variables. Modulating this cellular proteostasis state can influence the stability of multiple endogenous proteins, yet the factors contributing to this state remain incompletely characterized. Here, we performed genome-wide CRISPRi screens to elucidate the modulators of proteostasis state in mammalian cells, using a fluorescent dye to monitor endogenous protein aggregation. These screens identified known components of the proteostasis network and uncovered a novel link between protein and lipid homeostasis. Increasing lipid uptake and/or disrupting lipid metabolism promotes the accumulation of sphingomyelins and cholesterol esters and drives the formation of detergent-insoluble protein aggregates at the lysosome. Proteome profiling of lysosomes revealed ESCRT accumulation, suggesting disruption of ESCRT disassembly, lysosomal membrane repair, and microautophagy. Lipid dysregulation leads to lysosomal membrane permeabilization but does not otherwise impact fundamental aspects of lysosomal and proteasomal functions. Together, these results demonstrate that lipid dysregulation disrupts ESCRT function and impairs proteostasis.

Genome concentration limits cell growth and modulates proteome composition in Escherichia coli

Defining the cellular factors that drive growth rate and proteome composition is essential for understanding and manipulating cellular systems. In bacteria, ribosome concentration is known to be a constraining factor of cell growth rate, while gene concentration is usually assumed not to be limiting. Here, using single-molecule tracking, quantitative single-cell microscopy, and modeling, we show that genome dilution in Escherichia coli cells arrested for DNA replication limits total RNA polymerase activity within physiological cell sizes across tested nutrient conditions. This rapid-onset limitation on bulk transcription results in sub-linear scaling of total active ribosomes with cell size and sub-exponential growth. Such downstream effects on bulk translation and cell growth are near-immediately detectable in a nutrient-rich medium, but delayed in nutrient-poor conditions, presumably due to cellular buffering activities. RNA sequencing and tandem-mass-tag mass spectrometry experiments further reveal that genome dilution remodels the relative abundance of mRNAs and proteins with cell size at a global level. Altogether, our findings indicate that chromosome concentration is a limiting factor of transcription and a global modulator of the transcriptome and proteome composition in E. coli. Experiments in Caulobacter crescentus and comparison with eukaryotic cell studies identify broadly conserved DNA concentration-dependent scaling principles of gene expression.

Discovery of RNA-Protein Molecular Clamps Using Proteome-Wide Stability Assays

Uncompetitive inhibition is an effective strategy for suppressing dysregulated enzymes and their substrates, but discovery of suitable ligands depends on often-unavailable structural knowledge and serendipity. Hence, despite surging interest in mass spectrometry-based target identification, proteomic studies of substrate-dependent target engagement remain sparse. Herein, we describe a strategy for the discovery of substrate-dependent ligand binding. Using proteome integral solubility alteration (PISA) assays, we show that simple biochemical additives can enable detection of RNA-protein-small molecule complexes in native cell lysates. We apply our approach to rocaglates, molecules that specifically clamp RNA to eukaryotic translation initiation factor 4A (eIF4A), DEAD-box helicase 3X (DDX3X), and potentially other members of the DEAD-box (DDX) helicase family. To identify unexpected interactions, we used a target class-specific thermal window and compared ATP analog and RNA base dependencies for key rocaglate-DDX interactions. We report and validate novel DDX targets of high-profile rocaglates - including the clinical candidate Zotatifin - using limited proteolysis-mass spectrometry and fluorescence polarization (FP) experiments. We also provide structural insight into divergent DDX3X affinities between synthetic rocaglates. Taken together, our study provides a model for screening uncompetitive inhibitors using a chemical proteomics approach and uncovers actionable DDX clamping targets, clearing a path towards characterization of novel molecular clamps and associated RNA helicases.

Pleiotropic tumor suppressive functions of PTEN missense mutations during gliomagenesis

PTEN plays a crucial role in preventing the development of glioblastoma (GBM), a severe and untreatable brain cancer. In GBM, most PTEN deficiencies are missense mutations that have not been thoroughly examined. Here, we leveraged genetically modified mice and isogenic astrocyte cell cultures to investigate the role of clinically relevant mutations (G36E, L42R, C105F, and R173H) in the development of EGFR-driven GBM. We report that the loss of tumor suppression from these mutants is unrelated to their lipid phosphatase activity and rather relate to elevated localization at the cell membrane. Moreover, expression of these PTEN mutations heightened EGFR activity by sequestering EGFR within endomembranes longer and affected its signaling behavior. Through comprehensive studies on global protein phosphorylation and kinase library analyses in cells with the G36E and L42R PTEN mutations, we identified distinct cancer-promoting pathways activated by EGFR, offering targets for treating GBM with these PTEN alterations.

DNAJC13 localization to endosomes is opposed by its J domain and its disordered C-terminal tail

Endosomes are a central sorting hub for membrane cargos. DNAJC13/RME-8 plays a critical role in endosomal trafficking by regulating the endosomal recycling or degradative pathways. DNAJC13 localizes to endosomes through its N-terminal Plekstrin Homology (PH)-like domain, which directly binds endosomal phosphoinositol-3-phosphate (PI(3)P). However, little is known about how DNAJC13 localization is regulated. Here, we show that two regions within DNAJC13, its J domain and disordered C-terminal tail, act as negative regulators of its PH-like domain. Using a structure-function approach combined with quantitative proteomics, we mapped these control points to a conserved YLT motif in the C-terminal tail as well as the catalytic HPD triad in its J domain. Mutation of either motif enhanced DNAJC13 endosomal localization in cells and increased binding to PI(3)P in vitro. Further, these effects required the N-terminal PH-like domain. We show that, similar to other PI(3)P binding domains, the N-terminal PH-like domain binds PI(3)P weakly in isolation and requires oligomerization for efficient PI(3)P binding and endosomal localization. Together, these results demonstrate that interaction between DNAJC13 and PI(3)P serves as a molecular control point for regulating DNAJC13 localization to endosomes.

Isobaric Tagging and Data Independent Acquisition as Complementary Strategies for Proteome Profiling on an Orbitrap Astral Mass Spectrometer

Comprehensive global proteome profiling that is amenable to high throughput processing will broaden our understanding of complex biological systems. Here, we evaluated two leading mass spectrometry techniques, Data Independent Acquisition (DIA) and Tandem Mass Tagging (TMT), for extensive protein abundance profiling. DIA provides label-free quantification with a broad dynamic range, while TMT enables multiplexed analysis using isobaric tags for efficient cross-sample comparisons. We analyzed 18 samples, including four cell lines (IHCF, HCT116, HeLa, MCF7) under standard growth conditions, in addition to IHCF treated with two H2O2 concentrations, all in triplicate. Experiments were conducted on an Orbitrap Astral mass spectrometer, employing Field Asymmetric Ion Mobility Spectrometry (FAIMS). Despite utilizing different acquisition strategies, both the DIA and TMT approaches achieved comparable proteome depth and quantitative consistency, with each method quantifying over 10,000 proteins across all samples, with slightly more protein-level precision for the TMT strategy. Relative abundance correlation analysis showed strong agreement at both peptide and protein levels. Our findings highlight the complementary strengths of DIA and TMT for high-coverage proteomic studies, providing flexibility in method selection based on specific experimental needs.

The PNUTS phosphatase complex controls transcription pause release

Gene expression is regulated by controlling distinct steps of the transcriptional cycle, including initiation, pausing, elongation, and termination. Kinases phosphorylate RNA polymerase II (RNA Pol II) and associated factors to control transitions between these steps and to act as central gene regulatory nodes. Similarly, phosphatases that dephosphorylate these components are emerging as important regulators of transcription, although their roles remain less well understood. Here, we discover that the mouse PNUTS-PP1 phosphatase complex plays an essential role in controlling transcription pause release in addition to its previously described function in transcription termination. Transcription pause release by the PNUTS complex is essential for almost all RNA Pol II-dependent gene transcription, relies on its PP1 phosphatase subunit, and controls the phosphorylation of factors required for pause release and elongation. Together, these observations reveal an essential new role for a phosphatase complex in transcription pause release and show that the PNUTS complex is essential for RNA Pol II-dependent transcription.

TurboCas: A method for locus-specific labeling of genomic regions and isolating their associated protein interactome

Regulation of gene expression during development and stress response requires the concerted action of transcription factors and chromatin-binding proteins. Because this process is cell-type specific and varies with cellular conditions, mapping of chromatin factors at individual regulatory loci is crucial for understanding cis-regulatory control. Previous methods only characterize static protein binding. We present "TurboCas," a method combining a proximity-labeling (PL) enzyme, miniTurbo, with CRISPR-dCas9 that allows for efficient and site-specific labeling of chromatin factors in mammalian cells. Validating TurboCas at the FOS promoter, we identify proteins recruited upon heat shock, cross-validated via RNA polymerase II and P-TEFb immunoprecipitation. These methodologies reveal canonical and uncharacterized factors that function to activate expression of heat-shock-responsive genes. Applying TurboCas to the MYC promoter, we identify two P-TEFb coactivators, the super elongation complex (SEC) and BRD4, as MYC co-regulators. TurboCas provides a genome-specific targeting PL, with the potential to deepen our molecular understanding of transcriptional regulatory pathways in development and stress response.

Impact of noradrenergic inhibition on neuroinflammation and pathophysiology in mouse models of Alzheimer's disease

Norepinephrine (NE) modulates cognitive function, arousal, attention, and responses to novelty and stress, and it also regulates neuroinflammation. We previously demonstrated behavioral and immunomodulatory effects of beta-adrenergic pharmacology in mouse models of Alzheimer's disease (AD). The current studies were designed to block noradrenergic signaling in 5XFAD mice through (1) chemogenetic inhibition of the locus coeruleus (LC), (2) pharmacologic blocking of β-adrenergic receptors, and (3) conditional deletion of β1- or β2-adrenergic receptors (adrb1 or adrb2) in microglia.First, brain-wide AD pathology was mapped in 3D by imaging immunolabeled, cleared 5XFAD brains to assess the overlap between amyloid beta (Aβ) pathology, reactive microglia, and the loss of tyrosine hydroxylase (TH) expression in the catecholaminergic system. To examine the effects of inhibiting the LC NE system in the 5XFAD model, inhibitory (Gi) DREADD receptors were expressed specifically in LC NE neurons. LC NE neurons were chronically inhibited through the subcutaneous pump administration of the DREADD agonist clozapine-N-oxide (CNO). Plasma and brains were collected for assessment of neuroinflammation and pathology. A separate cohort of 5XFAD mice was chronically dosed with the beta-adrenergic antagonist propranolol or vehicle and evaluated for behavior, as well as post-mortem neuroinflammation and pathology. Finally, we used 5XFAD mice with conditional deletion of either adrb1 or adrb2 in microglia to assess neuroinflammation and pathology mediated by β-adrenergic signaling.Using iDISCO+, light sheet fluorescence microscopy, and novel analyses, we detected widespread microgliosis and Aβ pathology, along with modest TH downregulation in fibers across multiple brain regions, in contrast to the spatially limited TH downregulation observed in neurons. Both chemogenetic inhibition of LC adrenergic signaling and pharmacological inhibition of beta-adrenergic receptors potentiated neuroinflammation without altering Aβ pathology. Conditional deletion of adrb1 in microglia did not affect neuroinflammation. Conditional deletion of adrb2 in microglia attenuated inflammation and pathology in females but had no effect in males. Overall, these data support previous observations demonstrating the immunomodulatory effects of beta-adrenergic signaling in the pathophysiology of brain disorders and suggest that adrenergic receptors on cell types other than microglia, such as astrocytes, may mediate some of the disease-modifying effects of β-adrenergic agonists in the brain.

Expanding the Landscape of Aging via Orbitrap Astral Mass Spectrometry and Tandem Mass Tag (TMT) Integration

Aging results in a progressive decline in physiological function due to the deterioration of essential biological processes, such as transcription and RNA splicing, ultimately increasing mortality risk. Although proteomics is emerging as a powerful tool for elucidating the molecular mechanisms of aging, existing studies are constrained by limited proteome coverage and only observe a narrow range of lifespan. To overcome these limitations, we integrated the Orbitrap Astral Mass Spectrometer with the multiplex tandem mass tag (TMT) technology to profile the proteomes of three brain tissues (cortex, hippocampus, striatum) and kidney in the C57BL/6JN mouse model, achieving quantification of 8,954 to 9,376 proteins per tissue (cumulatively 12,749 across all tissues). Our sample population represents balanced sampling across both sexes and three age groups (3, 12, and 20 months), comprising young adulthood to early late life (approximately 20-60 years of age for human lifespan). To enhance quantitative accuracy, we developed a peptide filtering strategy based on resolution and signal-to-noise thresholds. Our analysis uncovered distinct tissue-specific patterns of protein abundance, with age and sex differences in the kidney, while brain tissues exhibit notable age changes and limited sex differences. In addition, we identified both proteomic changes that are linear with age (i.e., continuous) and that have a non-linear pattern (i.e., non-continuous), revealing complex protein dynamics over the adult lifespan. Integrating our findings with early developmental proteomic data from brain tissues highlighted further divergent age-related trajectories, particularly in synaptic proteins. This study not only provides a robust data analysis workflow for TMT datasets generated using the Orbitrap Astral mass spectrometer but also expands the proteomic landscape of aging, capturing proteins with age and sex effects with unprecedented depth.

Identification of two transcription factors that work coordinately to regulate early development in Entamoeba

The protozoan parasite Entamoeba has a life cycle that switches between infective cysts and invasive trophozoites. Encystation, a crucial process in parasite biology, is controlled by different mechanisms including transcriptional control. We identified two nuclear proteins in Entamoeba invadens, EIN_066100 and EIN_085620, that regulate parasite development by binding to a DNA motif (TCACTTTC) in the promoter regions of genes upregulated in the first 8 h of stage conversion. Overexpression of EIN_066100, a homolog of MAK16 protein, resulted in reduced amoebic proliferation without affecting encystation efficiency. Overexpression of EIN_085620, a protein with an RNA-recognition motif (RRM), led to increased encystation efficiency. Glutathione S-transferase (GST) pull down assays revealed that EIN_066100 interacts with EIN_085620 both in vivo and in vitro, and this interaction is mediated by the EIN_085620 RRM domain. By evaluating truncated proteins with deletions at either the N-terminal or C-terminal regions of EIN_066100, we elucidated the importance of its N-terminal region in proper protein localization, proliferation, encystation, and interaction with EIN_085620. Taken together, these results indicate a coordinated role of EIN_066100 and EIN_085620 in regulating Entamoeba development. This work sheds light on the molecular mechanisms in the earliest stages of Entamoeba encystation.IMPORTANCEAn important biological process in the biology of Entamoeba is stage conversion, which plays a crucial role in disease propagation, facilitating parasite survival outside the host and spreading to new hosts. Multiple mechanisms contribute to controlling the expression of amebic stage-specific genes such as epigenetic and transcriptional control. Identification of early transcriptional control regulators is crucial to understanding the initiation of the encystation cascade. We identified two nuclear proteins, EIN_066100 and EIN_085620, involved in the proliferation and developmental regulation of E. invadens. These proteins work by direct binding to each other and mediating encystation efficiency. Study of new regulators involved in Entamoeba development represents an important advance in a critical aspect of parasite biology.

Deciphering fucosylated protein-linked O-glycans in oral Tannerella serpentiformis: Insights from NMR spectroscopy and glycoproteomics

Tannerella serpentiformis is a health-associated Gram-negative oral anaerobe, while its closest phylogenetic relative is the periodontal pathogen Tannerella forsythia. The pathogen employs glycan mimicry through protein O-glycosylation, displaying a terminal nonulosonic acid aiding in evasion of host immune recognition. Like T. forsythia, T. serpentiformis cells are covered with a 2D-crystalline S-layer composed of two abundant S-layer glycoproteins-TssA and TssB. In this study, we elucidated the structure of the O-linked glycans of T. serpentiformis using 1D and 2D NMR spectroscopy analyzing S-layer glycopeptides and β-eliminated glycans. We found that T. serpentiformis produces two highly fucosylated, branched glycoforms carrying non-carbohydrate modifications, with the structure [2-OMe-Fuc-(α1,2)]-4-OMe-Glc-(β1,3)-[Fuc-(α1,4)]-2-NAc-GlcA-(β1,4)-[3-NH2, 2,4-OMe-Fuc-(α1,3)]-Fuc-(α1,4)-Xyl-(β1,4)-[3-OMe-Fuc-(α1,3)]-GlcA-(α1,2)-[Rha-(α1,4]-Gal, where the 3OMe-Fuc is variable; each glycoform contains a rare 2,4-methoxy, 3-amino-modified fucose. These glycoforms support the hypothesis that nonulosonic acid is a hallmark of pathogenic Tannerella species. A combined glycoproteomics and bioinformatics approach identified multiple sites within TssA (14 sites) and TssB (21 sites) to be O-glycosylated. LC-MS/MS confirmed the presence of the Bacteroidetes O-glycosylation motif (D)(S/T) (L/V/T/A/I) in Tannerella species, including the newly identified candidate "N" for the third position. Alphfold2 models of the S-layer glycoproteins were created revealing an almost uniform spatial distribution of the two glycoforms at the N-terminal two thirds of the proteins supported by glycoproteomics, with glycans facing outward. Glycoproteomics identified 921 unique glycopeptide sequences corresponding to 303 unique UniProt IDs. GO-term enrichment analysis versus the entire T. serpentiformis proteome classified these proteins as mainly membrane and cell periphery-associated glycoproteins, supporting a general protein O-glycosylation system in T. serpentiformis.

The use of 4D data-independent acquisition-based proteomic analysis and machine learning to reveal potential biomarkers for stress levels

Research suggests that individuals who experience prolonged exposure to stress may be at higher risk for developing psychological stress disorders. Currently, psychological stress is primarily evaluated by professional physicians using rating scales, which may be prone to subjective biases and limitations of the scales. Therefore, it is imperative to explore more objective, accurate, and efficient biomarkers for evaluating the level of psychological stress in an individual. In this study, we utilized 4D data-independent acquisition (4D-DIA) proteomics for quantitative protein analysis, and then employed support vector machine (SVM) combined with SHAP interpretation algorithm to identify potential biomarkers for psychological stress levels. Biomarkers validation was subsequently achieved through machine learning classification and a substantial amount of a priori knowledge derived from the knowledge graph. We performed cross-validation of the biomarkers using two batches of data, and the results showed that the combination of Glyceraldehyde-3-phosphate dehydrogenase and Fibronectin yielded an average area under the curve (AUC) of 92%, an average accuracy of 86%, an average F1 score of 79%, and an average sensitivity of 83%. Therefore, this combination may represent a potential approach for detecting stress levels to prevent psychological stress disorders.

Deep quantification of substrate turnover defines protease subsite cooperativity

Substrate specificity determines protease functions in physiology and in clinical and biotechnological applications, yet quantitative cleavage information is often unavailable, biased, or limited to a small number of events. Here, we develop qPISA (quantitative Protease specificity Inference from Substrate Analysis) to study Dipeptidyl Peptidase Four (DPP4), a key regulator of blood glucose levels. We use mass spectrometry to quantify >40,000 peptides from a complex, commercially available peptide mixture. By analyzing changes in substrate levels quantitatively instead of focusing on qualitative product identification through a binary classifier, we can reveal cooperative interactions within DPP4's active pocket and derive a sequence motif that predicts activity quantitatively. qPISA distinguishes DPP4 from the related C. elegans DPF-3 (a DPP8/9-orthologue), and we relate the differences to the structural features of the two enzymes. We demonstrate that qPISA can direct protein engineering efforts like the stabilization of GLP-1, a key DPP4 substrate used in the treatment of diabetes and obesity. Thus, qPISA offers a versatile approach for profiling protease and especially exopeptidase specificity, facilitating insight into enzyme mechanisms and biotechnological and clinical applications.

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-Treated Pancreatic Beta Cells

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse posttranslational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated β-cells. We observed a swift response in the global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-κB signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple colocalized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

Enhancing prediction of short linear protein motifs with Wregex 3.0

Short linear motifs (SLiMs) play an important role in protein-protein interactions. However, SLiM patterns are intrinsically permissive and result into many matches that occur just by chance, specially when targeting large datasets. To prioritize these matches as candidates for functional testing, we developed Wregex (Weighted regular expression), which uses a position-specific scoring matrix (PSSM) to order a list of regular expression matches according to a PSSM-derived score. Here we present Wregex 3.0, an improved version with new functionalities such as the support for a second auxiliary motif to help refining prediction of a primary SLiM, and post-translational modifications (PTMs) enrichment taking into account that many regulatory SLiM-mediated interactions are modulated by one or more PTMs. This version also incorporates a number of new features such as a convenient use of subproteomes, showing UniProt annotations such as disordered regions, searching for all known motifs and generating decoy databases for enrichment analysis. We provide case studies to illustrate how these new Wregex functionalities enhance prediction of short linear protein motifs. The Wregex 3.0 server is freely accessible at https://ehubio.ehu.eus/wregex3/.

PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

Electrical synapse molecular diversity revealed by proximity-based proteomic discovery

Neuronal circuits are composed of synapses that are either chemical, where signals are transmitted via neurotransmitter release and reception, or electrical, where signals pass directly through interneuronal gap junction channels. While the molecular complexity that controls chemical synapse structure and function is well appreciated, the proteins of electrical synapses beyond the gap-junction-forming Connexins are not well defined. Yet, electrical synapses are expected to be molecularly complex beyond the gap junctions. Connexins are integral membrane proteins requiring vesicular transport and membrane insertion/retrieval to achieve function, homeostasis, and plasticity. Additionally, electron microscopy of neuronal gap junctions reveals neighboring electron dense regions termed the electrical synapse density (ESD). To reveal the molecular complexity of the electrical synapse proteome, we used proximity-dependent biotinylation (TurboID) linked to neural Connexins in zebrafish. Proteomic analysis of developing and mature nervous systems identifies hundreds of Connexin-associated proteins, with overlapping and distinct representation during development and adulthood. The identified protein classes span cell adhesion molecules, cytoplasmic scaffolds, vesicular trafficking, and proteins usually associated with the post synaptic density (PSD) of chemical synapses. Using circuits with stereotyped electrical and chemical synapses, we define molecular sub-synaptic compartments of ESD localizing proteins, we find molecular heterogeneity amongst electrical synapse populations, and we examine the synaptic intermingling of electrical and chemical synapse proteins. Taken together, these results reveal a new complexity of electrical synapse molecular diversity and highlight a novel overlap between chemical and electrical synapse proteomes. Moreover, human homologs of the electrical synapse proteins are associated with autism, epilepsy, and other neurological disorders, providing a novel framework towards understanding neuro-atypical states.