In-Depth Serum Proteomics by DIA-MS with In Silico Spectral Libraries Reveals Dynamics during the Active Phase of Systemic Juvenile Idiopathic Arthritis

Host-derived protein profiles of human neonatal meconium across gestational ages

Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted an extensive proteomic analysis of first meconium from 259 newborns across varied gestational ages to delineate protein composition and elucidate its relevance to neonatal diseases. The first meconium samples were collected, with the majority obtained before feeding, and the mean time for the first meconium passage from the anus was 11.9 ± 9.47 h. Our analysis revealed 5370 host-derived meconium proteins, which varied depending on sex and gestational age. Specifically, meconium from preterm infants exhibited elevated concentrations of proteins associated with the extracellular matrix. Additionally, the protein profiles of meconium also exhibited unique variations depending on both specific diseases, including gastrointestinal diseases, congenital heart diseases, and maternal conditions. Furthermore, we developed a machine learning model to predict gestational ages using meconium proteins. Our model suggests that newborns with gastrointestinal diseases and congenital heart diseases may have immature gastrointestinal systems. These findings highlight the intricate relationship between clinical parameters and meconium protein composition, offering potential for a novel approach to assess neonatal gastrointestinal health.

Site-Specific Analysis of Core and Antenna Fucosylation on Serum Glycoproteins

Fucosylation is an important structural feature of glycans and plays an essential role in the regulation of glycoprotein functions. Fucosylation can be classified into core- (CF) and antenna-fucosylation (AF, also known as (sialyl-) Lewis) based on the location on N-glycans, and they perform distinct biological functions. In this study, core- and antenna-fucosylated N-glycans on human serum glycoproteins that hold great clinical application values were systematically characterized at the site-specific level using StrucGP combined with the recently developed fucosylation assignment method. The results showed that fucosylation was widely distributed on serum glycoproteins, with 50% of fucosylated glycopeptides modified by AF N-glycans, 37% by CF N-glycans, and 13% by dual-fucosylated N-glycans. Interestingly, CF and AF N-glycans preferred to modify different groups of serum glycoproteins with different tissue origins and were involved in distinctive biological processes. Specifically, AF N-glycoproteins are mainly from the liver and participated in complement activation, blood coagulation, and endopeptidase activities, while CF N-glycoproteins originate from diverse tissues and are mainly involved in cell adhesion and signaling transduction. These data further enhanced our understanding of fucosylation on circulation glycoproteins.

Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins

Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.

In-depth proteomic analysis of juvenile dermatomyositis serum reveals protein expression associated with muscle-specific autoantibodies

OBJECTIVES

The clinical symptoms and complications of JDM differ depending on the type of muscle-specific autoantibodies (MSAs) present. We aimed to identify protein expression profiles specific for MSAs that characterize various clinical features by comprehensively analyzing the proteins present in the serum of patients with JDM.

METHODS

We analysed sera from patients with JDM that were positive for anti-melanoma differentiation-associated protein 5 (MDA5) antibodies (n = 5), anti-nuclear matrix protein 2 (NXP2) antibodies (n = 5) and anti-transcriptional intermediary factor 1 alpha or gamma subunit (TIF1-γ) antibodies (n = 5), and evaluated healthy controls (n = 5) via single-shot liquid chromatography-tandem mass spectrometry (MS) in data-independent acquisition mode, which is superior for comparative quantitative analysis. We identified different protein groups based on MSAs and performed pathway analysis to understand their characteristics.

RESULTS

We detected 2413 proteins from serum MS analysis; 508 proteins were commonly altered in MSAs, including many myogenic enzymes and IFN-regulated proteins. Pathway analysis using the top 50 proteins that were upregulated in each MSA group revealed that the type I IFN and proteasome pathways were significantly upregulated in the anti-MDA5 antibody group alone.

CONCLUSION

Although JDM serum contains many proteins commonly altered in MSAs, the pathways associated with clinical features of MSAs differ based on protein accumulation. In-depth serum protein profiles associated with MSAs may be useful for developing therapeutic target molecules and biomarkers.

An explorative study on proteomic analyses related to inflammation and pain in children with juvenile idiopathic arthritis

BACKGROUND

Our aim was attempting to find proteins involved in the pain process and correlating with pain but not degree of inflammation in children with juvenile idiopathic arthritis (JIA), using a proteomics panel.

METHODS

A total of 87 plasma samples were collected from 51 children with JIA (51 at diagnosis in a higher disease activity state, 18 at follow-up in a lower disease activity state) and 18 healthy controls. Relative levels of 92 proteins related to a wide range of biological processes in inflammation were obtained using a proximity extension assay panel. Comparisons between children with and without JIA, in different disease categories, by juvenile disease activity score (JADAS27) and degree of pain on a visual analogue scale (VAS), were performed using parametric and non-parametric statistical methods.

RESULTS

Nineteen proteins involved in arthritic inflammation, such as interleukin 6 (IL-6) and S100 protein A12, were higher in patients with JIA than controls, seven decreased significantly during treatment, and 18 correlated significantly with JADAS27. Three proteins correlated with pain VAS scores in unadjusted analyses: the glial cell line-derived neurotrophic factor (GDNF), transforming growth factor beta, and IL-18R1. Levels of GDNF correlated significantly with pain VAS scores but not with JADAS27.

CONCLUSIONS

Plasma levels of 18 of 92 tested proteins correlated with degree of disease activity. Levels of three proteins correlated with pain, and levels of one, GDNF, originating from neural cells, correlated with pain without correlating with inflammatory degree, suggesting that it may play a role in pain in JIA. Further studies in larger cohorts are warranted.

An Optimized Data-Independent Acquisition Strategy for Comprehensive Analysis of Human Plasma Proteome

Cartography of the plasma proteome remains technically challenging, primarily due to the abundance and dynamic range of plasma proteins and their concentrations, exceeding ten orders of magnitude, including low-abundant tissue-derived proteins in the pg/mL range. Data-independent acquisition mass spectrometry (DIA-MS) has seen advances in unbiased mass spectrometry-based proteomic analysis of the plasma proteome. Here, we describe a comprehensive proteomic workflow of human plasma from clinically relevant sample (10 μL) that includes anti-protein immunodepletion and highly sensitive sample preparation workflow, with optimized scheduled isolation DIA-MS and deep learning analysis. This approach results in over 960 proteins quantified from a single-shot analysis of broad dynamic range, across 8 orders of magnitude (8.2 ng/L to 0.67 g/L). We further compare data-dependent acquisition (DDA) MS to highlight the advantage in protein quantification and inter-sample variation. These developments have provided streamlined identification of the human plasma proteome, including low-abundant tissue-enriched proteins, and applications toward understanding the plasma proteome.

Early Cancer Biomarker Discovery Using DIA-MS Proteomic Analysis of EVs from Peripheral Blood

One of the cornerstones of effective cancer treatment is early diagnosis. In this context, the identification of proteins that can serve as cancer biomarkers in bodily fluids ("liquid biopsies") has gained attention over the last decade. Plasma and serum fractions of blood are the most commonly investigated sources of potential cancer liquid biopsy biomarkers. However, the high complexity and dynamic range typical of these fluids hinders the sensitivity of protein detection by the most commonly used mass spectrometry technology (data-dependent acquisition mass spectrometry (DDA-MS)). Recently, data-independent acquisition mass spectrometry (DIA-MS) techniques have overcome the limitations of DDA-MS, increasing sensitivity and proteome coverage. In addition to DIA-MS, isolating extracellular vesicles (EVs) can help to increase the depth of serum/plasma proteome coverage by improving the identification of low-abundance proteins which are a potential treasure trove of diagnostic molecules. EVs, the nano-sized membrane-enclosed vesicles present in most bodily fluids, contain proteins which may serve as potential biomarkers for various cancers. Here, we describe a detailed protocol that combines DIA-MS and EV methodologies for discovering and validating early cancer biomarkers using blood serum. The pipeline includes size exclusion chromatography methods to isolate serum-derived extracellular vesicles and subsequent EV sample preparation for liquid chromatography and mass spectrometry analysis. Procedures for spectral library generation by DDA-MS incorporate methods for off-line peptide separation by microflow HPLC with automated fraction concatenation. Analysis of the samples by DIA-MS includes recommended protocols for data processing and statistical methods. This pipeline will provide a guide to discovering and validating EV-associated proteins that can serve as sensitive and specific biomarkers for early cancer detection and other diseases.

Effects of short-term waterfall forest aerosol air exposure on rat lung proteomics

Background

Chronic exposure to airborne microparticles has been shown to increase the incidence of several chronic diseases. Previous studies have found that waterfall forest aerosols contribute to a diminished immune stress response in patients with asthma. However, the specific effects of short-term waterfall forest aerosol exposure on lung proteins have not been fully elucidated.

Methods

This study used liquid chromatography-tandem mass spectrometry (LC-MS) to analyze changes in protein expression in the lungs of rats exposed to short-term waterfall forest aerosol environments. Specific protein markers were identified using bioconductivity analysis screening and validated using immunohistochemistry.

Results

Waterfall forest aerosol environment exposure on day 5 downregulated the expression of the classical inflammatory pathway nuclear factor κB (NF-κB) signaling pathway. As the waterfall forest aerosol environment increased due to the duration of exposure, it was involved in oxidative phosphorylation and then hormone signaling in lung cells from the very beginning. In contrast, at day 15 of exposure, there is an effect on the regulation of the immune-related high-affinity IgE receptor pathway. In addition, iron-sulfur Rieske protein (Uqcrfs1), mitochondrial Tu translation elongation factor (Tufm) and ribosomal protein L4 (Rpl4) were identified as possible bioindicators for the evaluation of air quality.

Conclusions

These results provide a comprehensive proteomic analysis that supports the positive contribution of a good air quality environment to lung health.

Evaluation of the Suitability of Dried Saliva Spots for In-Depth Proteome Analyses for Clinical Applications

Previously, we performed nontargeted proteome analysis using dried blood spots (DBSs) that are widely used in newborn screening for the clinical diagnosis of congenital genetic diseases and immunodeficiency. We have developed an efficient and simple pretreatment method for DBSs that can detect more than 1000 proteins. To complement proteins that are difficult to detect via DBS analysis with less invasive alternative body fluids, we conducted this study to investigate the proteins detected from dried saliva spots (DSSs) using single-shot LC-MS/MS, which is practical in clinical settings. We also clarified whether DSSs have the same advantages as DBSs, and we investigated the influence of saliva collection conditions and the storage environment on their protein profile. As a result, we detected approximately 5000 proteins in DSSs and whole saliva, and we concluded that they were sufficient to complement the proteins lacking in the blood analysis. DSSs could be used as an alternative tool to DBSs for detecting the presence of causative proteins.