Mass-spectrometric exploration of proteome structure and function

Plasma proteomic profiling reveals that SERPINE1 is a potential biomarker associated with coronary artery lesions in Kawasaki disease

BACKGROUND

Kawasaki disease (KD) is the most common cause of acquired heart disease in childhood. Coronary artery lesions (CALs) are serious complications of KD that can result in stenosis and thrombosis, but the specific underlying pathogenic mechanisms have not been elucidated. Therefore, exploring biomarkers to help predict early CALs is urgently needed for clinical treatment.

METHODS

Patients were recruited from three independent cohorts. In the discovery cohort, Data-Independent Acquisition Mass Spectrometry (DIA-MS) was performed to screen plasma proteins from healthy controls (HCs), KD patients prior to intravenous immunoglobulin (IVIG) treatment, and KD patients post-IVIG treatment. KD patients were further divided into KD patients without CALs (nCAL) and with CALs (CALs) groups. Bioinformatic analysis was carried out for the differentially expressed proteins (DEPs) and hub proteins. Candidate proteins were quantified by enzyme-linked immunosorbent assay (ELISA) in the validation cohort 1 and 2. Furthermore, candida albicans cell wall extract (CAWS)-induced KD vasculitis mice and cell models were established to investigate the expression of biomarkers identified in the aforementioned clinical cohort.

RESULTS

According to the quantitative proteomics analysis, SERPINE1 was significantly increased in KD patients with CALs. Receiver operating characteristic curves (ROC) revealed that plasma SERPINE1 exhibited greater ability in predicting CALs (AUC = 0.824, P < 0.0001). After IVIG treatment, the concentrations of SERPINE1 in the nCALs group significantly decreased. However, the concentration of SERPINE1 remained persistently elevated in the CALs group. Moreover, the expression of SERPINE1 was significantly upregulated in the heart tissue of KD mice, KD plasma, or tumor necrosis factor-α (TNF-α)-stimulated human coronary artery endothelial cells (HCAECs).

CONCLUSIONS

Overall, our results suggest that the plasma concentration of SERPINE1 might serve as a new potential predictive biomarker for CALs in KD patients.

An Optimized Miniaturized Filter-Aided Sample Preparation Method for Sensitive Cross-Linking Mass Spectrometry Analysis of Microscale Samples

Cross-linking mass spectrometry (XL-MS) is a powerful tool for elucidating protein structures and protein-protein interactions (PPIs) at the global scale. However, sensitive XL-MS analysis of mass-limited samples remains challenging, due to serious sample loss during sample preparation of the low-abundance cross-linked peptides. Herein, an optimized miniaturized filter-aided sample preparation (O-MICROFASP) method was presented for sensitive XL-MS analysis of microscale samples. By systematically investigating and optimizing crucial experimental factors, this approach dramatically improves the XL identification of low and submicrogram samples. Compared with the conventional FASP method, more than 7.4 times cross-linked peptides were identified from single-shot analysis of 1 μg DSS cross-linked HeLa cell lysates (440 vs 59). The number of cross-linked peptides identified from 0.5 μg HeLa cell lysates was increased by 58% when further reducing the surface area of the filter to 0.058 mm2 in the microreactor. To deepen the identification coverage of XL-proteome, five different types of cross-linkers were used and each μg of cross-linked HeLa cell lysates was processed by O-MICROFASP integrated with tip-based strong cation exchange (SCX) fractionation. Up to 2741 unique cross-linked peptides were identified from the 5 μg HeLa cell lysates, representing 2579 unique K-K linkages on 1092 proteins. About 96% of intraprotein cross-links were within the maximal distance restraints of 26 Å, and 75% of the identified PPIs reported by the STRING database were with high confidence (scores ≥0.9), confirming the high validity of the identified cross-links for protein structural mapping and PPI analysis. This study demonstrates that O-MICROFASP is a universal and efficient method for proteome-wide XL-MS analysis of microscale samples with high sensitivity and reliability.

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

Efficient and rapid digestion of proteins with a dual-enzyme microreactor featuring 3-D pores formed by dopamine/polyethyleneimine/acrylamide-coated KIT-6 molecular sieve

The microreactor with two types of immobilized enzymes, exhibiting excellent orthogonal performance, represents an effective approach to counteract the reduced digestion efficiency resulting from the absence of a single enzyme cleavage site, thereby impacting protein identification. In this study, we developed a hydrophilic dual-enzyme microreactor characterized by rapid mass transfer and superior enzymatic activity. Initially, we selected KIT-6 molecular sieve as the carrier for the dual-IMER due to its three-dimensional network pore structure. Modification involved co-deposition of polyethyleneimine (PEI) and acrylamide (AM) as amine donors, along with dopamine to enhance material hydrophilicity. Remaining amino and double bond functional groups facilitated stepwise immobilization of trypsin and Glu-C. Digestion times for bovine serum albumin (BSA) and bovine hemoglobin (BHb) on the dual-IMER were significantly reduced compared to solution-based digestion (1 min vs. 36 h), resulting in improved sequence coverage (91.30% vs. 82.7% for BSA; 90.24% vs. 89.20% for BHb). Additionally, the dual-IMER demonstrated excellent durability, retaining 96.08% relative activity after 29 reuse cycles. Enhanced protein digestion efficiency can be attributed to several factors: (1) KIT-6's large specific surface area, enabling higher enzyme loading capacity; (2) Its three-dimensional network pore structure, facilitating faster mass transfer and substance diffusion; (3) Orthogonality of trypsin and Glu-C enzyme cleavage sites; (4) The spatial effect introduced by the chain structure of PEI and glutaraldehyde's spacing arm, reducing spatial hindrance and enhancing enzyme-substrate interactions; (5) Mild and stable enzyme immobilization. The KIT-6-based dual-IMER offers a promising technical tool for protein digestion, while the PDA/PEI/AM-KIT-6 platform holds potential for immobilizing other proteins or active substances.

Proteomic investigations of dengue virus infection: key discoveries over the last 10 years

INTRODUCTION

Dengue virus (DENV) infection remains one of the most significant infectious diseases in humans. Several efforts have been made to address its molecular mechanisms. Over the last 10 years, proteomics has been widely applied to investigate various aspects of DENV infection.

AREAS COVERED

In this review, we briefly introduce common proteomics approaches using various mass spectrometric modalities followed by summarizing all the discoveries obtained from proteomic investigations of DENV infection over the last 10 years. These include the data on DENV-vector interactions and host responses to address the DENV biology and disease mechanisms. Moreover, applications of proteomics to disease prevention, diagnosis, vaccine design, development of anti-DENV agents and other new treatment strategies are discussed.

EXPERT OPINION

Despite efforts on disease prevention, DENV infection is still a significant global healthcare burden that affects the general population. As summarized herein, proteomic technologies with high-throughput capabilities have provided more in-depth details of protein dynamics during DENV infection. More extensive applications of proteomics and other powerful research tools would provide a promise to better cope and prevent this mosquito-borne infectious disease.

Chemical proteomics approaches for protein post-translational modification studies

The diversity and dynamics of proteins play essential roles in maintaining the basic constructions and functions of cells. The abundance of functional proteins is regulated by the transcription and translation processes, while the alternative splicing enables the same gene to generate distinct protein isoforms of different lengths. Beyond the transcriptional and translational regulations, post-translational modifications (PTMs) are able to further expand the diversity and functional scope of proteins. PTMs have been shown to make significant changes in the surface charges, structures, activation states, and interactome of proteins. Due to the functional complexity, highly dynamic nature, and low presence percentage, the study of protein PTMs remains challenging. Here we summarize and discuss the major chemical biology tools and chemical proteomics approaches to enrich and investigate the protein PTM of interest.

Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas With EWSR1 and CIC::DUX4 Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers

Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.

On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method

Advances in high-throughput high-resolution mass spectrometry and the development of thermal proteome profiling approach (TPP) have made it possible to accelerate a drug target search. Since its introduction in 2014, TPP quickly became a method of choice in chemical proteomics for identifying drug-to-protein interactions on a proteome-wide scale and mapping the pathways of these interactions, thus further elucidating the unknown mechanisms of action of a drug under study. However, the current TPP implementations based on tandem mass spectrometry (MS/MS), associated with employing lengthy peptide separation protocols and expensive labeling techniques for sample multiplexing, limit the scaling of this approach for the ever growing variety of drug-to-proteomes. A variety of ultrafast proteomics methods have been developed in the last couple of years. Among them, DirectMS1 provides MS/MS-free quantitative proteome-wide analysis in 5-min time scale, thus opening the way for sample-hungry applications, such as TPP. In this work, we demonstrate the first implementation of the TPP approach using the ultrafast proteome-wide analysis based on DirectMS1. Using a drug topotecan, which is a known topoisomerase I (TOP1) inhibitor, the feasibility of the method for identifying drug targets at the whole proteome level was demonstrated for an ovarian cancer cell line.

Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n  =  21) compared to the age- and sex-matched controls (n  =  20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.

Amplifiable protein identification via residue-resolved barcoding and composition code counting

Ultrasensitive protein identification is of paramount importance in basic research and clinical diagnostics but remains extremely challenging. A key bottleneck in preventing single-molecule protein sequencing is that, unlike the revolutionary nucleic acid sequencing methods that rely on the polymerase chain reaction (PCR) to amplify DNA and RNA molecules, protein molecules cannot be directly amplified. Decoding the proteins via amplification of certain fingerprints rather than the intact protein sequence thus represents an appealing alternative choice to address this formidable challenge. Herein, we report a proof-of-concept method that relies on residue-resolved DNA barcoding and composition code counting for amplifiable protein fingerprinting (AmproCode). In AmproCode, selective types of residues on peptides or proteins are chemically labeled with a DNA barcode, which can be amplified and quantified via quantitative PCR. The operation generates a relative ratio as the residue-resolved 'composition code' for each target protein that can be utilized as the fingerprint to determine its identity from the proteome database. We developed a database searching algorithm and applied it to assess the coverage of the whole proteome and secretome via computational simulations, proving the theoretical feasibility of AmproCode. We then designed the residue-specific DNA barcoding and amplification workflow, and identified different synthetic model peptides found in the secretome at as low as the fmol/L level for demonstration. These results build the foundation for an unprecedented amplifiable protein fingerprinting method. We believe that, in the future, AmproCode could ultimately realize single-molecule amplifiable identification of trace complex samples without further purification, and it may open a new avenue in the development of next-generation protein sequencing techniques.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Analysis of the aging-related biomarker in a nonhuman primate model using multilayer omics

BACKGROUND

Aging is a prominent risk factor for diverse diseases; therefore, an in-depth understanding of its physiological mechanisms is required. Nonhuman primates, which share the closest genetic relationship with humans, serve as an ideal model for exploring the complex aging process. However, the potential of the nonhuman primate animal model in the screening of human aging markers is still not fully exploited. Multiomics analysis of nonhuman primate peripheral blood offers a promising approach to evaluate new therapies and biomarkers. This study explores aging-related biomarker through multilayer omics, including transcriptomics (mRNA, lncRNA, and circRNA) and proteomics (serum and serum-derived exosomes) in rhesus monkeys (Macaca mulatta).

RESULTS

Our findings reveal that, unlike mRNAs and circRNAs, highly expressed lncRNAs are abundant during the key aging period and are associated with cancer pathways. Comparative analysis highlighted exosomal proteins contain more types of proteins than serum proteins, indicating that serum-derived exosomes primarily regulate aging through metabolic pathways. Finally, eight candidate aging biomarkers were identified, which may serve as blood-based indicators for detecting age-related brain changes.

CONCLUSIONS

Our results provide a comprehensive understanding of nonhuman primate blood transcriptomes and proteomes, offering novel insights into the aging mechanisms for preventing or treating age-related diseases.

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

Assessment of circulating proteins in thyroid cancer: Proteome-wide Mendelian randomization and colocalization analysis

The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (nCase = 3,418, nControl = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections (P BH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7's differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs.

A Look into Ocular Diseases: The Pivotal Role of Omics Sciences in Ophthalmology Research

Precision medicine is a new medical approach which considers both population characteristics and individual variability to provide customized healthcare. The transition from traditional reactive medicine to personalized medicine is based on a biomarker-driven process and a deep knowledge of biological mechanisms according to which the development of diseases occurs. In this context, the advancements in high-throughput omics technologies represent a unique opportunity to discover novel biomarkers and to provide an unbiased picture of the biological system. One of the medical fields in which omics science has started to be recently applied is that of ophthalmology. Ocular diseases are very common, and some of them could be highly disabling, thus leading to vision loss and blindness. The pathogenic mechanism of most ocular diseases may be dependent on various genetic and environmental factors, whose effect has not been yet completely understood. In this context, large-scale omics approaches are fundamental to have a comprehensive evaluation of the whole system and represent an essential tool for the development of novel therapies. This Review summarizes the recent advancements in omics science applied to ophthalmology in the last ten years, in particular by focusing on proteomics, metabolomics and lipidomics applications from an analytical perspective. The role of high-efficiency separation techniques coupled to (high-resolution) mass spectrometry ((HR)MS) is also discussed, as well as the impact of sampling, sample preparation and data analysis as integrating parts of the analytical workflow.

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

Integrating Physiological Features and Proteomic Analyses Provides New Insights in Blue/Red Light-Treated Moso Bamboo (Phyllostachys edulis)

Bamboo is one of the most important nontimber forestry products in the world. Light is not only the most critical source of energy for plant photosynthesis but also involved in regulating the biological processes of plants. However, there are few reports on how blue/red light affects Moso bamboo. This study investigated the growth status and physiological responses of Moso bamboo (Phyllostachys edulis) to blue/red light treatments. The growth status of the bamboo plants was evaluated, revealing that both blue- and red-light treatments promoted plant height and overall growth. Gas exchange parameters, chlorophyll fluorescence, and enzyme activity were measured to assess the photosystem response of Moso bamboo to light treatments. Additionally, the blue light treatment led to a higher chlorophyll content and enzyme activities compared to the red light treatment. A tandem mass tag quantitative proteomics approach identified significant changes in protein abundance under different light conditions with specific response proteins associated with distinct pathways, such as photosynthesis and starch metabolism. Overall, this study provides valuable insights into the physiological and proteomic responses of Moso bamboo to blue/red light treatments, highlighting their potential impact on growth and development.

Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.

Glycoproteomic and proteomic analysis of Burkholderia cenocepacia reveals glycosylation events within FliF and MotB are dispensable for motility

Across the Burkholderia genus O-linked protein glycosylation is highly conserved. While the inhibition of glycosylation has been shown to be detrimental for virulence in Burkholderia cepacia complex species, such as Burkholderia cenocepacia, little is known about how specific glycosylation sites impact protein functionality. Within this study, we sought to improve our understanding of the breadth, dynamics, and requirement for glycosylation across the B. cenocepacia O-glycoproteome. Assessing the B. cenocepacia glycoproteome across different culture media using complementary glycoproteomic approaches, we increase the known glycoproteome to 141 glycoproteins. Leveraging this repertoire of glycoproteins, we quantitively assessed the glycoproteome of B. cenocepacia using Data-Independent Acquisition (DIA) revealing the B. cenocepacia glycoproteome is largely stable across conditions with most glycoproteins constitutively expressed. Examination of how the absence of glycosylation impacts the glycoproteome reveals that the protein abundance of only five glycoproteins (BCAL1086, BCAL2974, BCAL0525, BCAM0505, and BCAL0127) are altered by the loss of glycosylation. Assessing ΔfliF (ΔBCAL0525), ΔmotB (ΔBCAL0127), and ΔBCAM0505 strains, we demonstrate the loss of FliF, and to a lesser extent MotB, mirror the proteomic effects observed in the absence of glycosylation in ΔpglL. While both MotB and FliF are essential for motility, we find loss of glycosylation sites in MotB or FliF does not impact motility supporting these sites are dispensable for function. Combined this work broadens our understanding of the B. cenocepacia glycoproteome supporting that the loss of glycoproteins in the absence of glycosylation is not an indicator of the requirement for glycosylation for protein function.

IMPORTANCE

Burkholderia cenocepacia is an opportunistic pathogen of concern within the Cystic Fibrosis community. Despite a greater appreciation of the unique physiology of B. cenocepacia gained over the last 20 years a complete understanding of the proteome and especially the O-glycoproteome, is lacking. In this study, we utilize systems biology approaches to expand the known B. cenocepacia glycoproteome as well as track the dynamics of glycoproteins across growth phases, culturing media and in response to the loss of glycosylation. We show that the glycoproteome of B. cenocepacia is largely stable across conditions and that the loss of glycosylation only impacts five glycoproteins including the motility associated proteins FliF and MotB. Examination of MotB and FliF shows, while these proteins are essential for motility, glycosylation is dispensable. Combined this work supports that B. cenocepacia glycosylation can be dispensable for protein function and may influence protein properties beyond stability.

CLIPPER 2.0: Peptide-Level Annotation and Data Analysis for Positional Proteomics

Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in both sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable the generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, concomitant progress has not been observed to the same extent in data analysis and post-processing steps, arguably constituting the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide-level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate the applicability of our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0.

Exploring affinity chromatography in proteomics: A comprehensive review

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Exercise training and changes in skeletal muscle mitochondrial proteins: from blots to "omics"

Mitochondria are essential, membrane-enclosed organelles that consist of ∼1100 different proteins, which allow for many diverse functions critical to maintaining metabolism. Highly metabolic tissues, such as skeletal muscle, have a high mitochondrial content that increases with exercise training. The classic western blot technique has revealed training-induced increases in the relatively small number of individual mitochondrial proteins studied (∼5% of the >1100 proteins in MitoCarta), with some of these changes dependent on the training stimulus. Proteomic approaches have identified hundreds of additional mitochondrial proteins that respond to exercise training. There is, however, surprisingly little crossover in the mitochondrial proteins identified in the published human training studies. This suggests that to better understand the link between training-induced changes in mitochondrial proteins and metabolism, future studies need to move beyond maximizing protein detection to adopting methods that will increase the reliability of the changes in protein abundance observed.

Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia

This study aimed to identify key proteomic analytes correlated with response to splenectomy in primary immune thrombocytopenia (ITP). Thirty-four patients were retrospectively collected in the training cohort and 26 were prospectively enrolled as validation cohort. Bone marrow biopsy samples of all participants were collected prior to the splenectomy. A total of 12 modules of proteins were identified by weighted gene co-expression network analysis (WGCNA) method in the developed cohort. The tan module positively correlated with megakaryocyte counts before splenectomy (r = 0.38, p = 0.027), and time to peak platelet level after splenectomy (r = 0.47, p = 0.005). The blue module significantly correlated with response to splenectomy (r = 0.37, p = 0.0031). KEGG pathways analysis found that the PI3K-Akt signalling pathway was predominantly enriched in the tan module, while ribosomal and spliceosome pathways were enriched in the blue module. Machine learning algorithm identified the optimal combination of biomarkers from the blue module in the training cohort, and importantly, cofilin-1 (CFL1) was independently confirmed in the validation cohort. The C-index of CFL1 was >0.7 in both cohorts. Our results highlight the use of bone marrow proteomics analysis for deriving key analytes that predict the response to splenectomy, warranting further exploration of plasma proteomics in this patient population.

An integrated proteomics and metabolomics approach to assess graft quality and predict early allograft dysfunction after liver transplantation: a retrospective cohort study

BACKGROUND

Early allograft dysfunction (EAD) is a common complication after liver transplantation (LT) and is associated with poor prognosis. Graft itself plays a major role in the development of EAD. We aimed to reveal the EAD-specific molecular profiles to assess graft quality and establish EAD predictive models.

METHODS

A total of 223 patients who underwent LT were enrolled and divided into training ( n =73) and validation ( n =150) sets. In the training set, proteomics was performed on graft biopsies, together with metabolomics on paired perfusates. Differential expression, enrichment analysis, and protein-protein interaction network were used to identify the key molecules and pathways involved. EAD predictive models were constructed using machine learning and verified in the validation set.

RESULTS

A total of 335 proteins were differentially expressed between the EAD and non-EAD groups. These proteins were significantly enriched in triglyceride and glycerophospholipid metabolism, neutrophil degranulation, and the MET-related signaling pathway. The top 12 graft proteins involved in the aforementioned processes were identified, including GPAT1, LPIN3, TGFB1, CD59, and SOS1. Moreover, downstream metabolic products, such as lactate dehydrogenase, interleukin-8, triglycerides, and the phosphatidylcholine/phosphorylethanolamine ratio in the paired perfusate displayed a close relationship with the graft proteins. To predict the occurrence of EAD, an integrated model using perfusate metabolic products and clinical parameters showed areas under the curve of 0.915 and 0.833 for the training and validation sets, respectively. It displayed superior predictive efficacy than that of currently existing models, including donor risk index and D-MELD scores.

CONCLUSIONS

We identified novel biomarkers in both grafts and perfusates that could be used to assess graft quality and provide new insights into the etiology of EAD. Herein, we also offer a valid tool for the early prediction of EAD.

Proteomic reference map for sarcopenia research: mass spectrometric identification of key muscle proteins of organelles, cellular signaling, bioenergetic metabolism and molecular chaperoning

During the natural aging process, frailty is often associated with abnormal muscular performance. Although inter-individual differences exit, in most elderly the tissue mass and physiological functionality of voluntary muscles drastically decreases. In order to study age-related contractile decline, animal model research is of central importance in the field of biogerontology. Here we have analyzed wild type mouse muscle to establish a proteomic map of crude tissue extracts. Proteomics is an advanced and large-scale biochemical method that attempts to identify all accessible proteins in a given biological sample. It is a technology-driven approach that uses mass spectrometry for the characterization of individual protein species. Total protein extracts were used in this study in order to minimize the potential introduction of artefacts due to excess subcellular fractionation procedures. In this report, the proteomic survey of aged muscles has focused on organellar marker proteins, as well as proteins that are involved in cellular signaling, the regulation of ion homeostasis, bioenergetic metabolism and molecular chaperoning. Hence, this study has establish a proteomic reference map of a highly suitable model system for future aging research.

Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO

The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.

Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis

The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.

The Landscape and Perspectives of the Human Gut Metaproteomics

The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.

Liquid-phase separations coupled with ion mobility-mass spectrometry for next-generation biopharmaceutical analysis

INTRODUCTION

The pharmaceutical industry continues to expand its search for innovative biotherapeutics. The comprehensive characterization of such therapeutics requires many analytical techniques to fully evaluate critical quality attributes, making analysis a bottleneck in discovery and development timelines. While thorough characterization is crucial for ensuring the safety and efficacy of biotherapeutics, there is a need to further streamline analytical characterization and expedite the overall timeline from discovery to market.

AREAS COVERED

This review focuses on recent developments in liquid-phase separations coupled with ion mobility-mass spectrometry (IM-MS) for the development and characterization of biotherapeutics. We cover uses of IM-MS to improve the characterization of monoclonal antibodies, antibody-drug conjugates, host cell proteins, glycans, and nucleic acids. This discussion is based on an extensive literature search using Web of Science, Google Scholar, and SciFinder.

EXPERT OPINION

IM-MS has the potential to enhance the depth and efficiency of biotherapeutic characterization by providing additional insights into conformational changes, post-translational modifications, and impurity profiles. The rapid timescale of IM-MS positions it well to enhance the information content of existing assays through its facile integration with standard liquid-phase separation techniques that are commonly used for biopharmaceutical analysis.

Characterization of lymphocyte-rich hepatocellular carcinoma and the prognostic role of tertiary lymphoid structures

BACKGROUND & AIMS

Lymphocyte-rich hepatocellular carcinoma (LR-HCC) is largely unknown and a rare subtype of HCC with immune-rich stroma. Tertiary lymphoid structures (TLS), frequently observed in LR-HCC, are known to be prognostically significant in various malignancies; however, their significance in HCC remains unevaluated.

METHODS

Clinicopathologic data of 191 cases of surgically resected conventional HCC (C-HCC, n = 160) and LR-HCC (n = 31) were retrieved. Immunohistochemistry, multiplex immunofluorescence staining, RNA sequencing and proteomic analysis were conducted. Differences between the subtypes were statistically evaluated.

RESULTS

LR-HCC was significantly correlated to larger tumour size, higher Edmondson-Steiner grade, presence of TLS and higher CD3-, CD8- and FOXP3-positive T cell, high PD-1 and PD-L1 expression (p < .001 for all) compared to C-HCC. Patients with LR-HCC exhibited significantly better overall survival (OS) (p = .044) and recurrence-free survival (RFS) (p = .025) than C-HCC. LR-HCC demonstrated TLS signatures with significantly higher proteomic-based immune scores in 14 of 17 types of tumour-infiltrating immune cells. Furthermore, C-HCC with secondary follicles, the most mature form of TLS, exhibited significantly better OS (p = .031) and RFS (p = .033) than those without. Across the global proteome, LR-HCC was well-differentiated from C-HCC and a map of protein-protein interactions between tumour-infiltrating lymphocytes and HCC in tumour microenvironment was completed.

CONCLUSION

LR-HCC is clinicopathologically and molecularly distinct and shows better prognosis compared to C-HCC. Also, the presence of secondary follicle can be an important prognostic marker for better prognosis in both LR-HCC and C-HCC.

Poly-pharmacology of existing drugs: How to crack the code?

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.

Synchronized assembly of the oxidative phosphorylation system controls mitochondrial respiration in yeast

Control of protein stoichiometry is essential for cell function. Mitochondrial oxidative phosphorylation (OXPHOS) presents a complex stoichiometric challenge as the ratio of the electron transport chain (ETC) and ATP synthase must be tightly controlled, and assembly requires coordinated integration of proteins encoded in the nuclear and mitochondrial genome. How correct OXPHOS stoichiometry is achieved is unknown. We identify the Mitochondrial Regulatory hub for respiratory Assembly (MiRA) platform, which synchronizes ETC and ATP synthase biogenesis in yeast. Molecularly, this is achieved by a stop-and-go mechanism: the uncharacterized protein Mra1 stalls complex IV assembly. Two "Go" signals are required for assembly progression: binding of the complex IV assembly factor Rcf2 and Mra1 interaction with an Atp9-translating mitoribosome induce Mra1 degradation, allowing synchronized maturation of complex IV and the ATP synthase. Failure of the stop-and-go mechanism results in cell death. MiRA controls OXPHOS assembly, ensuring correct stoichiometry of protein machineries encoded by two different genomes.

Cyclic Ion Mobility-Mass Spectrometry and Tandem Collision Induced Unfolding for Quantification of Elusive Protein Biomarkers

Sensitive analytical techniques that are capable of detecting and quantifying disease-associated biomolecules are indispensable in our efforts to understand disease mechanisms and guide therapeutic intervention through early detection, accurate diagnosis, and effective monitoring of disease. Parkinson's Disease (PD), for example, is one of the most prominent neurodegenerative disorders in the world, but the diagnosis of PD has primarily been based on the observation of clinical symptoms. The protein α-synuclein (α-syn) has emerged as a promising biomarker candidate for PD, but a lack of analytical methods to measure complex disease-associated variants of α-syn has prevented its widespread use as a biomarker. Antibody-based methods such as immunoassays and mass spectrometry-based approaches have been used to measure a limited number of α-syn forms; however, these methods fail to differentiate variants of α-syn that display subtle differences in only the sequence and structure. In this work, we developed a cyclic ion mobility-mass spectrometry method that combines multiple stages of activation and timed ion selection to quantify α-syn variants using both mass- and structure-based measurements. This method can allow for the quantification of several α-syn variants present at physiological levels in biological fluid. Taken together, this approach can be used to galvanize future efforts aimed at understanding the underlying mechanisms of PD and serves as a starting point for the development of future protein-structure-based diagnostics and therapeutic interventions.

FlexStat: combinatory differentially expressed protein extraction

Motivation

Mass spectrometry-based system proteomics allows identification of dysregulated protein hubs and associated disease-related features. Obtaining differentially expressed proteins (DEPs) is the most important step of downstream bioinformatics analysis. However, the extraction of statistically significant DEPs from datasets with multiple experimental conditions or disease types through currently available tools remains a laborious task. More often such an analysis requires considerable bioinformatics expertise, making it inaccessible to researchers with limited computational analytics experience.

Results

To uncover the differences among the many conditions within the data in a user-friendly manner, here we introduce FlexStat, a web-based interface that extracts DEPs through combinatory analysis. This tool accepts a protein expression matrix as input and systematically generates DEP results for every conceivable combination of various experimental conditions or disease types. FlexStat includes a suite of robust statistical tools for data preprocessing, in addition to DEP extraction, and publication-ready visualization, which are built on established R scientific libraries in an automated manner. This analytics suite was validated in diverse public proteomic datasets to showcase its high performance of rapid and simultaneous pairwise comparisons of comprehensive datasets.

Availability and implementation

FlexStat is implemented in R and is freely available at https://jglab.shinyapps.io/flexstatv1-pipeline-only/. The source code is accessible at https://github.com/kts-desilva/FlexStat/tree/main.

Current genetic diagnostics in inborn errors of immunity

New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.

The Contribution of Proteomics in Understanding Endometrial Protein Expression in Women with Recurrent Implantation Failure

Recurrent implantation failure (RIF) poses a significant challenge in assisted reproductive technology (ART) outcomes. The endometrium plays a crucial role in embryo implantation, and its protein expression profile is integral in determining receptivity. Proteomics has emerged as a valuable tool in unraveling the molecular intricacies underlying endometrial receptivity and RIF. The aim of the present review is to analyze the contribution of proteomics to the understanding of endometrial protein expression in women with RIF, based on the results of significant proteomic studies. Medline/Pubmed databases were searched using keywords pertaining to proteomics combined with terms related to RIF. 15 studies were included in the present review. Several proteins have been found to exbibit differential expression in endometrial biopsies and fluid samples between fertile women and women with RIF during the receptive endometrial phase. The profile of endometrial proteins varied significantly among the studies. Nevertheless, similar changes in the expression levels of annexin-6, progesterone receptor, MMP-2, and MMP-9 in the endometrium of women with RIF, were found in more than one study indicating that certain proteins could potentially be effective biomarkers of endometrial receptivity. Proteomics contributes significantly to the understanding of protein expression in the endometrium of women with RIF and the analysis of proteins in endometrial fluid are promising for improving the clinical management of RIF.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Characterization of the plasma proteome from healthy adult dogs

Introduction

Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine.

Methods

In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose.

Results and discussion

Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

From the outer space to the inner cell: deconvoluting the complexity of Bacillus subtilis disulfide stress responses by redox state and absolute abundance quantification of extracellular, membrane, and cytosolic proteins

Understanding cellular mechanisms of stress management relies on omics data as a valuable resource. However, the lack of absolute quantitative data on protein abundances remains a significant limitation, particularly when comparing protein abundances across different cell compartments. In this study, we aimed to gain deeper insights into the proteomic responses of the Gram-positive model bacterium Bacillus subtilis to disulfide stress. We determined proteome-wide absolute abundances, focusing on different sub-cellular locations (cytosol and membrane) as well as the extracellular medium, and combined these data with redox state determination. To quantify secreted proteins in the culture medium, we developed a simple and straightforward protocol for the absolute quantification of extracellular proteins in bacteria. We concentrated extracellular proteins, which are highly diluted in the medium, using StrataClean beads along with a set of standard proteins to determine the extent of the concentration step. The resulting data set provides new insights into protein abundances in different sub-cellular compartments and the extracellular medium, along with a comprehensive proteome-wide redox state determination. Our study offers a quantitative understanding of disulfide stress management, protein production, and secretion in B. subtilis.

IMPORTANCE

Stress responses play a crucial role in bacterial survival and adaptation. The ability to quantitatively measure protein abundances and redox states in different cellular compartments and the extracellular environment is essential for understanding stress management mechanisms. In this study, we addressed the knowledge gap regarding absolute quantification of extracellular proteins and compared protein concentrations in various sub-cellular locations and in the extracellular medium under disulfide stress conditions. Our findings provide valuable insights into the protein production and secretion dynamics of B. subtilis, shedding light on its stress response strategies. Furthermore, the developed protocol for absolute quantification of extracellular proteins in bacteria presents a practical and efficient approach for future studies in the field. Overall, this research contributes to the quantitative understanding of stress management mechanisms and protein dynamics in B. subtilis, which can be used to enhance bacterial stress tolerance and protein-based biotechnological applications.

The omics era: a nexus of untapped potential for Mendelian chromatinopathies

The OMICs cascade describes the hierarchical flow of information through biological systems. The epigenome sits at the apex of the cascade, thereby regulating the RNA and protein expression of the human genome and governs cellular identity and function. Genes that regulate the epigenome, termed epigenes, orchestrate complex biological signaling programs that drive human development. The broad expression patterns of epigenes during human development mean that pathogenic germline mutations in epigenes can lead to clinically significant multi-system malformations, developmental delay, intellectual disabilities, and stem cell dysfunction. In this review, we refer to germline developmental disorders caused by epigene mutation as "chromatinopathies". We curated the largest number of human chromatinopathies to date and our expanded approach more than doubled the number of established chromatinopathies to 179 disorders caused by 148 epigenes. Our study revealed that 20.6% (148/720) of epigenes cause at least one chromatinopathy. In this review, we highlight key examples in which OMICs approaches have been applied to chromatinopathy patient biospecimens to identify underlying disease pathogenesis. The rapidly evolving OMICs technologies that couple molecular biology with high-throughput sequencing or proteomics allow us to dissect out the causal mechanisms driving temporal-, cellular-, and tissue-specific expression. Using the full repertoire of data generated by the OMICs cascade to study chromatinopathies will provide invaluable insight into the developmental impact of these epigenes and point toward future precision targets for these rare disorders.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

Evolution of techniques and tools for replication fork proteome and protein interaction studies

Understanding the complex network of protein-protein interactions (PPI) that govern cellular functions is essential for unraveling the molecular basis of biological processes and diseases. Mass spectrometry (MS) has emerged as a powerful tool for studying protein dynamics, enabling comprehensive analysis of protein function, structure, post-translational modifications, interactions, and localization. This article provides an overview of MS techniques and their applications in proteomics studies, with a focus on the replication fork proteome. The replication fork is a multi-protein assembly involved in DNA replication, and its proper functioning is crucial for maintaining genomic integrity. By combining quantitative MS labeling techniques with various data acquisition methods, researchers have made significant strides in elucidating the complex processes and molecular mechanisms at the replication fork. Overall, MS has revolutionized our understanding of protein dynamics, offering valuable insights into cellular processes and potential targets for therapeutic interventions.

Interrogating epigenetic mechanisms with chemically customized chromatin

Genetic and genomic techniques have proven incredibly powerful for identifying and studying molecular players implicated in the epigenetic regulation of DNA-templated processes such as transcription. However, achieving a mechanistic understanding of how these molecules interact with chromatin to elicit a functional output is non-trivial, owing to the tremendous complexity of the biochemical networks involved. Advances in protein engineering have enabled the reconstitution of 'designer' chromatin containing customized post-translational modification patterns, which, when used in conjunction with sophisticated biochemical and biophysical methods, allow many mechanistic questions to be addressed. In this Review, we discuss how such tools complement established 'omics' techniques to answer fundamental questions on chromatin regulation, focusing on chromatin mark establishment and protein-chromatin interactions.

An Emerging Approach of Age-Related Hearing Loss Research: Application of Integrated Multi-Omics Analysis

As one of the most common otologic diseases in the elderly, age-related hearing loss (ARHL) usually characterized by hearing loss and cognitive disorders, which have a significant impact on the elderly's physical and mental health and quality of life. However, as a typical disease of aging, it is unclear why aging causes widespread hearing impairment in the elderly. As molecular biological experiments have been conducted for research recently, ARHL is gradually established at various levels with the application and development of integrated multi-omics analysis in the studies of ARHL. Here, the recent progress in the application of multi-omics analysis in the molecular mechanisms of ARHL development and therapeutic regimens, including the combined analysis of different omics, such as transcriptome, proteome, and metabolome, to screen for risk sites, risk genes, and differences in lipid metabolism, etc., is outlined and the integrated histological data further promote the profound understanding of the disease process as well as physiological mechanisms of ARHL. The advantages and disadvantages of multi-omics analysis in disease research are also discussed and the authors speculate on the future prospects and applications of this part-to-whole approach, which may provide more comprehensive guidance for ARHL and aging disease prevention and treatment.

DESP demixes cell-state profiles from dynamic bulk molecular measurements

Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.

Prediction of glycopeptide fragment mass spectra by deep learning

Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.

Uncovering PROTAC Sensitivity and Efficacy by Multidimensional Proteome Profiling: A Case for STAT3

Proteolysis-targeting chimera (PROTAC) is a powerful technology that can effectively trigger the degradation of target proteins. The intricate interplay among various factors leads to a heterogeneous drug response, bringing about significant challenges in comprehending drug mechanisms. Our study applied data-independent acquisition-based mass spectrometry to multidimensional proteome profiling of PROTAC (DIA-MPP) to uncover the efficacy and sensitivity of the PROTAC compound. We profiled the signal transducer and activator of transcription 3 (STAT3) PROTAC degrader in six leukemia and lymphoma cell lines under multiple conditions, demonstrating the pharmacodynamic properties and downstream biological responses. Through comparison between sensitive and insensitive cell lines, we revealed that STAT1 can be regarded as a biomarker for STAT3 PROTAC degrader, which was validated in cells, patient-derived organoids, and mouse models. These results set an example for a comprehensive description of the multidimensional PROTAC pharmacodynamic response and PROTAC drug sensitivity biomarker exploration.

Discovery of plasma proteins associated with ventricular fibrillation during first ST-elevation myocardial infarction via proteomics

AIMS

The underlying biological mechanisms of ventricular fibrillation (VF) during acute myocardial infarction are largely unknown. To our knowledge, this is the first proteomic study for this trait, with the aim to identify and characterize proteins that are associated with VF during first ST-elevation myocardial infarction (STEMI).

METHODS AND RESULTS

We included 230 participants from a Danish ongoing case-control study on patients with first STEMI with VF (case, n = 110) and without VF (control, n = 120) before guided catheter insertion for primary percutaneous coronary intervention. The plasma proteome was investigated using mass spectrometry-based proteomics on plasma samples collected within 24 h of symptom onset, and one patient was excluded in quality control. In 229 STEMI patients {72% men, median age 62 years [interquartile range (IQR): 54-70]}, a median of 257 proteins (IQR: 244-281) were quantified per patient. A total of 26 proteins were associated with VF; these proteins were involved in several biological processes including blood coagulation, haemostasis, and immunity. After correcting for multiple testing, two up-regulated proteins remained significantly associated with VF, actin beta-like 2 [ACTBL2, fold change (FC) 2.25, P < 0.001, q = 0.023], and coagulation factor XIII-A (F13A1, FC 1.48, P < 0.001, q = 0.023). None of the proteins were correlated with anterior infarct location.

CONCLUSION

Ventricular fibrillation due to first STEMI was significantly associated with two up-regulated proteins (ACTBL2 and F13A1), suggesting that they may represent novel underlying molecular VF mechanisms. Further research is needed to determine whether these proteins are predictive biomarkers or acute phase response proteins to VF during acute ischaemia.