Complex Proteomes Research Articles

Conserved Central Intraviral Protein Interactome of the Herpesviridae Family.

Protein interactions are major driving forces behind the functional phenotypes of biological processes. As such, evolutionary footprints are reflected in system-level collections of protein-protein interactions (PPIs), i.e., protein interactomes. We conducted a comparative analysis of intraviral protein interactomes for representative species of each of the three subfamilies of herpesviruses (herpes simplex virus 1, human cytomegalovirus, and Epstein-Barr virus), which are highly prevalent etiologic agents of important human diseases. The intraviral interactomes were reconstructed by combining experimentally supported and computationally predicted protein-protein interactions. Using cross-species network comparison, we then identified family-wise conserved interactions and protein complexes, which we defined as a herpesviral "central" intraviral protein interactome. A large number of widely accepted conserved herpesviral protein complexes are present in this central intraviral interactome, encouragingly supporting the biological coherence of our results. Importantly, these protein complexes represent most, if not all, of the essential steps required during a productive life cycle. Hence the central intraviral protein interactome could plausibly represent a minimal infectious interactome of the herpesvirus family across a variety of hosts. Our data, which have been integrated into our herpesvirus interactomics database, HVint2.0, could assist in creating comprehensive system-level computational models of this viral lineage.IMPORTANCE Herpesviruses are an important socioeconomic burden for both humans and livestock. Throughout their long evolutionary history, individual herpesvirus species have developed remarkable host specificity, while collectively the Herpesviridae family has evolved to infect a large variety of eukaryotic hosts. The development of approaches to fight herpesvirus infections has been hampered by the complexity of herpesviruses' genomes, proteomes, and structural features. The data and insights generated by our study add to the understanding of the functional organization of herpesvirus-encoded proteins, specifically of family-wise conserved features defining essential components required for a productive infectious cycle across different hosts, which can contribute toward the conceptualization of antiherpetic infection strategies with an effect on a broader range of target species. All of the generated data have been made freely available through our HVint2.0 database, a dedicated resource of curated herpesvirus interactomics purposely created to promote and assist future studies in the field.

PseudoQC: A Regression-Based Simulation Software for Correction and Normalization of Complex Metabolomics and Proteomics Datasets.

Various types of unwanted and uncontrollable signal variations in MS-based metabolomics and proteomics datasets severely disturb the accuracies of metabolite and protein profiling. Therefore, pooled quality control (QC) samples are often employed in quality management processes, which are indispensable to the success of metabolomics and proteomics experiments, especially in high-throughput cases and long-term projects. However, data consistency and QC sample stability are still difficult to guarantee because of the experimental operation complexity and differences between experimenters. To make things worse, numerous proteomics projects do not take QC samples into consideration at the beginning of experimental design. Herein, a powerful and interactive web-based software, named pseudoQC, is presented to simulate QC sample data for actual metabolomics and proteomics datasets using four different machine learning-based regression methods. The simulated data are used for correction and normalization of the two published datasets, and the obtained results suggest that nonlinear regression methods perform better than linear ones. Additionally, the above software is available as a web-based graphical user interface and can be utilized by scientists without a bioinformatics background. pseudoQC is open-source software and freely available at https://www.omicsolution.org/wukong/pseudoQC/.

Direct Proteomic Mapping of Cysteine Persulfidation.

Aims: Cysteine persulfidation (also called sulfhydration or sulfuration) has emerged as a potential redox mechanism to regulate protein functions and diverse biological processes in hydrogen sulfide (H2S) signaling. Due to its intrinsically unstable nature, working with this modification has proven to be challenging. Although methodological progress has expanded the inventory of persulfidated proteins, there is a continued need to develop methods that can directly and unequivocally identify persulfidated cysteine residues in complex proteomes. Results: A quantitative chemoproteomic method termed as low-pH quantitative thiol reactivity profiling (QTRP) was developed to enable direct site-specific mapping and reactivity profiling of proteomic persulfides and thiols in parallel. The method was first applied to cell lysates treated with NaHS, resulting in the identification of overall 1547 persulfidated sites on 994 proteins. Structural analysis uncovered unique consensus motifs that might define this distinct type of modification. Moreover, the method was extended to profile endogenous protein persulfides in cells expressing H2S-generating enzyme, mouse tissues, and human serum, which led to additional insights into mechanistic, structural, and functional features of persulfidation events, particularly on human serum albumin. Innovation and Conclusion: Low-pH QTRP represents the first method that enables direct and unbiased proteomic mapping of cysteine persulfidation. Our method allows to generate the most comprehensive inventory of persulfidated targets of NaHS so far and to perform the first analysis of in vivo persulfidation events, providing a valuable tool to dissect the biological functions of this important modification.

Straightforward and Highly Efficient Strategy for Hepatocellular Carcinoma Glycoprotein Biomarker Discovery Using a Nonglycopeptide-Based Mass Spectrometry Pipeline.

Efficient detection of aberrant glycoproteins in serum is particularly important for biomarker discovery. However, direct quantitation of glycoproteins in serum remains technically challenging because of the extraordinary complexity of the serum proteome. In the current work, we proposed a straightforward and highly efficient strategy by using the nonglycopeptides releasing from the specifically enriched glycoproteins for targeted glycoprotein quantification. With this so-called nonglycopeptide-based mass spectrometry (NGP-MS) strategy, a powerful and nondiscriminatory pipeline for hepatocellular carcinoma (HCC) glycoprotein biomarker discovery, verification, and validation has been developed. First, a data set of 234 NGPs was strictly established for multiple-reaction monitoring (MRM) quantification in serum. Second, the NGPs enriched from 20 HCC serum mixtures and 20 normal serum mixtures were labeled with mTRAQ reagents (Δ0 and Δ8, respectively) to find the differentially expressed glycoproteins in HCC. A total of 97 glycoprotein candidates were preliminarily screened and submitted for absolute quantitation with NGP-based stable-isotope-labeled (SID)-MRM in the individual samples of 38 HCC serum and 24 normal controls. Finally, 21 glycoproteins were absolutely quantified with high quality. The diagnostic sensitivity results showed that three glycoproteins, β-2-glycoprotein 1 (APOH), α-1-acid glycoprotein 2 (ORM2), and complement C3 (C3), could be used for the discrimination between HCC patients and healthy people. A novel glycoprotein biomarker panel [APOH, ORM2, C3, and α-fetoprotein (AFP)] has proven to outperform AFP, the known HCC serum biomarker, alone, in this study. We believe that this strategy and the panel of glycoproteins might hold great clinical value for HCC detection in the future.

Activity-Based Kinome Profiling Using Chemical Proteomics and ATP Acyl Phosphates.

Human kinases are a large family of proteins (500+) that catalyze ATP-dependent phosphorylation of protein and metabolite substrates to regulate diverse facets of cell biology. Dysregulation and mutations of protein kinases are linked to human disease, providing opportunities for developing pharmacological agents as potential therapy. Assessing the selectivity of pharmacological compounds targeting this enzyme class is critical given that off-target activity of kinase inhibitor drugs may result in toxicity. This set of protocols outlines use of ATP acyl phosphate activity-based probes to evaluate the potency and selectivity of kinase inhibitors via fluorescent gel- and mass spectrometry-based detection methods. These competitive chemical proteomic assays can evaluate engagement of >200 native kinase targets directly in complex proteomes. © 2019 by John Wiley & Sons, Inc.

Systematic evaluation of species-independent serum pre-fractionation strategies revealed cost-effective methods to reduce proteome complexity

In this study, the efficiency of one commercial (ProteoMiner™ -PM) and five simple and cost-effective laboratory chemicals (Acetone, TCA/acetone, DTT, ACN and DTT-ACN) based serum protein pre-fractionation strategies was compared in pig model by label-free quantitation based mass spectrometric approach to find out the most suitable strategy for reducing the complexity of serum proteome for subsequent proteomic studies. The highest serum protein depletion percentage and highest depletion of albumin, the most abundant serum protein, was observed in DTT-ACN method. The maximum number of serum proteins was identified in ACN followed by DTT-ACN method and importantly, detection of more number of low-abundant proteins (LAPs) could also be achieved by these two methods. Although PM method resulted into lowest dynamic range of protein abundance, quite a less number of proteins were identified by this method. Overall, sequential depletion using DTT-ACN and ACN methods provided advantage of simultaneous detection of more number of proteins along with LAPs with a reasonably high dynamic range of protein abundances over other methods and thus emerged as cheaper and effective alternatives to the commercial methods. Further, these methods are species-independent and hence can be applied in human and in any livestock species to simplify the serum proteome.

A glycal-based photoaffinity probe that enriches sialic acid binding proteins.

To identify sialic acid binding proteins from complex proteomes, three photocrosslinking affinity-based probes were constructed using Neu5Ac (5 and 6) and Neu5Ac2en (7) scaffolds. Kinetic inhibition assays and Western blotting revealed the Neu5Ac2en-based 7 to be an effective probe for the labeling of a purified gut microbial sialidase (BDI_2946) and a purified human sialic acid binding protein (hCD33). Additionally, LC-MS/MS affinity-based protein profiling verified the ability of 7 to enrich a low-abundance sialic acid binding protein (complement factor H) from human serum thus validating the utility of this probe in a complex context.

Treasure hunt for peptides with undefined chemical modifications: Proteomics identification of differential albumin adducts of 2-nitroimidazole-indocyanine green in hypoxic tumor.

2-Nitroimidazole is a well-known chemical probe targeting hypoxic environments of solid tumors, and its derivatives are widely used as imaging agents to investigate tissue and tumor hypoxia. However, the underlying chemistry for the hypoxia-detection capability of 2-nitroimidazole is still unclear. In this study, we deployed a biotin conjugate of 2-nitroimidazole-indocyanine green (2-nitro-ICG) for the investigation of in vivo hypoxia-probing mechanism of 2-nitro-ICG compounds. By implementing mass spectrometry-based proteomics and exhaustive data mining, we report that 2-nitro-ICG and its fragments modify mouse serum albumin as the primary protein target but at two structurally distinct sites and possibly via two different mechanisms. The identification of probe-modified peptides not only contributes to the understanding of the in vivo metabolism of 2-nitroimidazole compounds but also demonstrates a competent analytical workflow that enables the search for peptides with undefined modifications in complex proteome digests.

The Generation of a Comprehensive Spectral Library for the Analysis of the Guinea Pig Proteome by SWATH-MS.

Advances in liquid chromatography‐mass spectrometry have facilitated the incorporation of proteomic studies to many biology experimental workflows. Data‐independent acquisition platforms, such as sequential window acquisition of all theoretical mass spectra (SWATH‐MS), offer several advantages for label‐free quantitative assessment of complex proteomes over data‐dependent acquisition (DDA) approaches. However, SWATH data interpretation requires spectral libraries as a detailed reference resource. The guinea pig (Cavia porcellus) is an excellent experimental model for translation to many aspects of human physiology and disease, yet there is limited experimental information regarding its proteome. To overcome this knowledge gap, a comprehensive spectral library of the guinea pig proteome is generated. Homogenates and tryptic digests are prepared from 16 tissues and subjected to >200 DDA runs. Analysis of >250 000 peptide‐spectrum matches resulted in a library of 73 594 peptides from 7666 proteins. Library validation is provided by i) analyzing externally derived SWATH files (https://doi.org/10.1016/j.jprot.2018.03.023) and comparing peptide intensity quantifications; ii) merging of externally derived data to the base library. This furnishes the research community with a comprehensive proteomic resource that will facilitate future molecular‐phenotypic studies using (re‐engaging) the guinea pig as an experimental model of relevance to human biology. The spectral library and raw data are freely accessible in the MassIVE repository (MSV000083199).

Abstract 2750: Physical and functional landscape of deubiquitinating enzymes (DUBs) in KRAS mutant lung cancer

Abstract DUBs are involved in tumorigenesis and are of high interest as potential targets for cancer therapy. However, target discovery is problematic due to the absence of clear DUB activity within subsets of human cancers. Our goal was to profile DUBs and their activity in KRAS-mutated lung cancer cell lines using chemical biology strategies. Approximately 25% of patients with Lung adenocarcinoma have tumor associated KRAS mutations in non-small lung cancers (NSCLC), yet specific RAS inhibitors against KRAS-mutated lung cancer have not yet been successfully developed. We used activity-based protein profiling (ABPP) to profile DUBs in 25 distinct human lung cancer cell lines harboring KRAS mutations. ABPP uses chemical probes that are active-site directed and covalently bind to a class of enzymes in complex proteome. Whole cell lysates were incubated with HA-UB-VME and HA-UB-PA probes and ABPP pull-down was performed. Enriched proteins were trypsin digested and peptides were analyzed using liquid chromatography and tandem mass spectrometry (LC-MS/MS). MAXQUANT software was used to quantify DUBs. We related activity of observed DUBs to effects on cell viability by examining publicly available RNAi (Project DRIVE) and CRISPR (PICKLES) databases. A total of 50 DUBs were identified in our ABPP screen. Each cell line expressed at least 32 different DUBs and 22 DUBs were represented in all cell lines. The identified DUBs include 48% USP, 36% OTU, 8% JAMM and 8% UCH & MJD family. USP5, USP7, USP14, USP15, UCHL5, UCHL3, OTUB1, PRP8, PSMD7 and PSMD14 are the highly expressed DUBs. Functional enrichment approach and protein-protein interaction network reveal that 70% of the active DUBs are involved in cell cycle regulation. DDR, RNA splicing, TGF-beta receptor, NF-kB and Wnt signaling are some other enriched pathways. Of the 35 cell cycle DUBs, CRISPR screens identify 14 DUBs and DRIVE data identified 11 DUBS as having effects on cell viability. Combining the ABPP data, shRNA and CRISPR results, OTUD5, BRCC3, UCHL5, UFD1L, USP5, YY1, USP3, USP39, USP37, OTUB1, USP9X and COPS5 have activity in KRAS mutant lung cancer cell lines and demonstrate reduced cell viability with loss of function through CRISPR or RNAi. We have identified and prioritized DUB targets in KRAS mutant lung cancer. Future studies will further validate DUB as targets and identify mechanisms of activity in KRAS mutant lung cancers, as well as examine DUB activity using ABPP in human lung cancer tumor tissues, including adenocarcinoma (KRAS mutant and wild-type) and squamous cell lung cancer. Citation Format: Emma Adhikari, Shikha Mahajan, Harshani Lawrence, Yan Yang, Bin Fang, Eric Haura. Physical and functional landscape of deubiquitinating enzymes (DUBs) in KRAS mutant lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2750.

Proteome Changes Paralleling the Olfactory Conditioning in the Forager Honey Bee and Provision of a Brain Proteomics Dataset.

The olfactory conditioning of the bee proboscis extension reflex (PER) is extensively used as a paradigm in associative learning of invertebrates but with limited molecular investigations. To investigate which protein changes are linked to olfactory conditioning, a non-sophisticated conditioning model is applied using the PER in the honeybee (Apis mellifera). Foraging honeybees are assigned into three groups based on the reflex behavior and training: conditioned using 2-octanone (PER-conditioned), and sucrose and water controls. Thereafter, the brain synaptosomal proteins are isolated and analyzed by quantitative proteomics using stable isotope labeling (TMT). Additionally, the complex proteome dataset of the bee brain is generated with a total number of 5411 protein groups, including key players in neurotransmitter signaling. The most significant categories affected during olfactory conditioning are associated with "SNARE interactions in vesicular transport" (BET1 and VAMP7), ABC transporters, and fatty acid degradation pathways.

Quantification of In Vivo Target Engagement Using Microfluidic Activity-Based Protein Profiling.

Accurate measurement of drug-target interactions in vivo is critical for both preclinical development and translation to clinical studies, yet many assays rely on indirect measures such as biomarkers associated with target activity. Activity-based protein profiling (ABPP) is a direct method of quantifying enzyme activity using active site-targeted small-molecule covalent probes that selectively label active but not inhibitor-bound enzymes. Probe-labeled enzymes in complex proteomes are separated by polyacrylamide gel electrophoresis and quantified by fluorescence imaging. To accelerate workflows and avoid imaging artifacts that make conventional gels challenging to quantify, we adapted protocols for a commercial LabChip GXII microfluidic instrument to permit electrophoretic separation of probe-labeled proteins in tissue lysates and plasma, and quantification of fluorescence (probe/protein labeling ratio of 1:1). Electrophoretic separation on chips occurred in 40 s per sample, and instrument software automatically identified and quantified peaks, resulting in an overall time savings of 3-5 h per 96-well sample plate. Calculated percent inhibition was not significantly different between the two formats. Chip performance was consistent between chips and sample replicates. Conventional gel imaging was more sensitive but required five times higher sample volume than microfluidic chips. Microfluidic chips produced results comparable to those of gels but with much lower sample consumption, facilitating assay miniaturization for scarce biological samples. The time savings afforded by microfluidic electrophoresis and automatic quantification has allowed us to incorporate microfluidic ABPP early in the drug discovery workflow, enabling routine assessments of tissue distribution and engagement of targets and off-targets in vivo.

Development of a Robust Ultrasonic-Based Sample Treatment To Unravel the Proteome of OCT-Embedded Solid Tumor Biopsies.

An effective three-step proteomics workflow is proposed to overcome the pitfalls caused by polymers present in optimum cutting temperature (OCT)-embedded tissue during its preparation for mass spectrometry analysis. First, the OCT-embedded tissue biopsies are cleaned using ethanol and water in a sequential series of ultrasonic washes in an ultrasound bath (35 kHz ultrasonic frequency, 100% ultrasonic amplitude, 2 min of ultrasonic duty time). Second, a fast ultrasonic-assisted extraction of proteins is done using an ultrasonic probe (30 kHz ultrasonic frequency, 50% ultrasonic amplitude, 2 min of ultrasonic duty time, 1 mm diameter tip). Third, a rapid ultrasonic digestion of complex proteomes is performed using a microplate horn assembly device (20 kHz ultrasonic frequency, 25% ultrasonic amplitude, 4 min of ultrasonic duty time). As a proof of concept, the new workflow was applied to human normal and tumor kidney biopsies including chromophobe renal cell carcinomas (chRCCs) and renal oncocytomas (ROs). A successful cluster of proteomics profiles was obtained comprising 511 and 172 unique proteins found in chRCC and RO samples, respectively. The new method provides high sample throughput and comprehensive protein recovery from OCT samples.

Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism.

Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.

Proteome Analysis Using Gel-LC-MS/MS.

This article describes processing of protein samples using 1D SDS gels prior to protease digestion for proteomics workflows that subsequently utilize reversed-phase nanocapillary ultra-high-pressure liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). The resulting LC-MS/MS data are used to identify peptides and thereby infer proteins present in samples ranging from simple mixtures to very complex proteomes. Bottom-up proteome studies usually involve quantitative comparisons across several or many samples. For either situation, 1D SDS gels represent a simple, widely available technique that can be used to either fractionate complex proteomes or rapidly clean up low microgram samples with minimal losses. After gel separation and staining/destaining, appropriate gel slices are excised, and in-gel reduction, alkylation, and protease digestion are performed. Digests are then processed for LC-MS/MS analysis. Protocols are described for either sample fractionation with high-throughput processing of many samples or simple cleanup without fractionation. An optional strategy is to conduct in-solution reduction and alkylation prior to running gels, which is advantageous when a large number of samples will be separated into large numbers of fractions. Optimization of trypsin digestion parameters and comparison to in-solution protease digestion are also described. © 2019 by John Wiley & Sons, Inc.

Standard-flow LC and thermal focusing ESI elucidates altered liver proteins in late stage Niemann-Pick, type C1 disease.

Aim: Mass spectrometry (MS)-based proteomics, particularly with the development of nano-ESI, have been invaluable to our understanding of altered proteins related to human disease. Niemann-Pick, type C1 (NPC1)disease is a fatal, autosomal recessive,neurodegenerative disorder. The resulting defects include unesterified cholesterol and sphingolipids accumulation in the late endosomal/lysosomal system resulting in organ dysfunction including liver disease. Materials & methods: First, we performed MS analysis of a complex mammalian proteome using both nano- and standard-flow ESI with the intent of developing a differential proteomics platform using standard-flow ESI. Next, we measured the differential liver proteome in the NPC1 mouse model via label-free quantitative MS using standard-flow ESI. Results: Using the standard-flow ESI approach, we found altered protein levels including, increased Limp2 and Rab7a in liver tissue ofNpc1-/- compared to control mice. Conclusion: Standard-flow ESI can be a tool for quantitative proteomic studies when sample amount is not limited. Using this method, we have identified new protein markers of NPC1.

Multifaceted Stoichiometry Control of Bacterial Operons Revealed by Deep Proteome Quantification.

More than half of the protein-coding genes in bacteria are organized in polycistronic operons composed of two or more genes. It remains under debate whether the operon organization maintains the stoichiometric expression of the genes within an operon. In this study, we performed a label-free data-independent acquisition hyper reaction monitoring mass-spectrometry (HRM-MS) experiment to quantify the Escherichia coli proteome in exponential phase and quantified 93.6% of the cytosolic proteins, covering 67.9% and 56.0% of the translating polycistronic operons in BW25113 and MG1655 strains, respectively. We found that the translational regulation contributes largely to the proteome complexity: the shorter operons tend to be more tightly controlled for stoichiometry than longer operons; the operons which mainly code for complexes is more tightly controlled for stoichiometry than the operons which mainly code for metabolic pathways. The gene interval (distance between adjacent genes in one operon) may serve as a regulatory factor for stoichiometry. The catalytic efficiency might be a driving force for differential expression of enzymes encoded in one operon. These results illustrated the multifaceted nature of the operon regulation: the operon unified transcriptional level and gene-specific translational level. This multi-level regulation benefits the host by optimizing the efficiency of the productivity of metabolic pathways and maintenance of different types of protein complexes.

Enhancing Sensitivity of Microflow-Based Bottom-Up Proteomics through Postcolumn Solvent Addition.

The introduction of more sensitive mass spectrometers allows researchers to adapt front-end liquid chromatography (LC) to individual needs for the analysis of complex proteomes. Where absolute sensitivity is not paramount, it is advantageous to switch from a highly sensitive nanoflow-LC setup, the de facto standard platform in mass-spectrometry (MS)-based discovery proteomics, to a more robust, high-throughput-compatible microflow or conventional-flow setup. To enhance the microflow-LC-MS electrospray process of complex proteomic samples, we tested the effects of different solvents, including 2-propanol, methanol, and acetonitrile, pure or as mixture with dimethyl sulfoxide, which were added postcolumn to the eluting sample. Postcolumn addition of organic solvents strongly enhanced the electrospray efficiency in microflow-LC-MS experiments and improved the sensitivity across the entire gradient and for early eluting peptides by up to 10-fold. Postcolumn solvent addition did not negatively affect chromatographic performance and resulted in an overall 28-36% increase in identifications at both the protein and peptide levels. The presented microflow-LC-MS workflow, including postcolumn solvent addition, can be easily adopted on any LC-MS/MS platform.

Acutely elevated O-GlcNAcylation suppresses hippocampal activity by modulating both intrinsic and synaptic excitability factors

Post-translational modification (PTM) plays a critical role in increasing proteome complexity and diversifying protein functions. O-GlcNAc modification is a reversible, dynamic and highly abundant PTM catalyzed by a single pair of enzymes, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), regardless of substrates. The two enzymes are particularly enriched in the brain, and recent proteomic studies identified that a large number of neuron-specific proteins undergo O-GlcNAc modification. In addition, pathological conditions with aberrant O-GlcNAcylation such as diabetes and obesity are associated with the higher risk of cognitive decline and memory impairment. However, despite its prevalence in the brain, functional significance of O-GlcNAcylation in regulating neuronal properties remains unclear at the molecular level. Here, we report that an acute increase in O-GlcNAcylation induced by pharmacological inhibition of OGA significantly reduces the intrinsic excitability of hippocampal CA1 neurons through the cooperative modulation of multiple voltage-gated ion channels. Moreover, elevated O-GlcNAcylation also suppresses excitatory synaptic transmission at Schaffer collateral-CA1 synapses through the removal of GluA2-containing AMPA receptors from postsynaptic densities. Collectively, our findings demonstrate that a change in O-GlcNAcylation levels dynamically regulates hippocampal activity at both intrinsic and synaptic levels, providing a mechanistic link between dysregulated O-GlcNAcylation and hippocampal dysfunction.

Exploiting the Dynamic Relationship between Peptide Separation Quality and Peptide Coisolation in a Multiple-Peptide Matches-per-Spectrum Approach Offers a Strategy To Optimize Bottom-Up Proteomics Throughput and Depth.

Peptide cofragmentation leads to chimeric MS/MS spectra that negatively impact traditional single-peptide match-per-spectrum (sPSM) search strategies in proteomics. The collection of chimeric spectra is influenced by peptide coelution and the width of precursor isolation windows. Although peptide cofragmentation can be reduced by advanced chromatography, such as UHPLC and 2D-HPLC separation schemes, and narrower isolation windows, chimeric spectra can still be as high as 30-50% of the total MS/MS spectra collected. Alternatively, cofragmented peptides in chimeric spectra and the use of wider isolation windows benefit multiple-peptide matches-per-spectrum (mPSM) algorithms, such as CharmeRT, which facilitate the identification of several cofragmented peptides. Considering recent advancements in LC and mPSM methodologies, we present a comprehensive examination of the levels of chimeric spectra collected in the analysis of a HeLa digest measured using different LC modes of separation and isolation windows and compare the depth of identifications obtained when these data are annotated using a sPSM or a mPSM approach. Our results demonstrate that MS/MS data derived from 1D-HPLC strategies under different gradient schemes and searched with CharmeRT yielded higher average numbers of PSMs (11%-49%), peptide analytes (10%-16%), and peptide sequences (3%-10%) compared to data derived from 1D-UHPLC runs but searched with a sPSM strategy. Interestingly, data from a 2D-HPLC separation strategy benefits more from the application of CharmeRT results when compared to a 50 cm 1D-UHPLC column employing a 500 min gradient. Overall, these results provide new insights into how to better configure LC-MS/MS measurements for improved throughput and peptide identification in complex proteomes.