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Proteomics and Its Applications in Disease 3.0

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Biochemistry".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2673

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Prof. Dr. Lei Zhou

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1. School of Optometry, The Hong Kong Polytechnic University, Hong Kong
2. Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong
3. Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Hong Kong
4. Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong
Interests: proteomics; metabolomics; mass spectrometry; disease biomarker
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Dr. Qingsong Lin

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Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543, Singapore
Interests: proteomics; mass spectrometry; disease biomarker; drug target identification; aquaporin biomimetic membrane
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Dear Colleagues,

This Special Issue is a continuation of our previous successful Special Issue “Proteomics and Its Applications in Disease”.

Recent advances in mass spectrometry-based technologies, e.g., data-independent acquisition (DIA), ion mobility spectrometry (IMS), and multiple reaction monitoring (MRM), have provided superior sensitivity, reproducibility, and throughput in proteomics analysis. This allows researchers to explore diseases by assessing a deeper proteome in a relatively short time with high reproducibility and fewer missing data. No doubt, the applications of proteomics research in diseases not only provide new insights into disease mechanisms, but also novel disease biomarkers and therapeutic targets.

In this Special Issue, we invite you to contribute original research and review articles which focus on (but are not limited to) the following topics related to the applications of proteomics in diseases: disease biomarker (discovery and validation), molecular mechanisms (signaling pathway) of disease, new drug targets, the role of post-translational modifications in disease, targeted proteomics, multi-omics studies, proteomic studies on in vitro cell disease models, animal disease models, or patient cohort studies.

Prof. Dr. Lei Zhou
Dr. Qingsong Lin
Guest Editors

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Keywords

proteomics
quantitative proteomics
biomarkers
signaling pathways
post-translational modifications
disease mechanism
novel therapeutic targets

Related Special Issues

Proteomics and Its Applications in Disease in International Journal of Molecular Sciences (16 articles)
Proteomics and Its Applications in Disease 2.0 in International Journal of Molecular Sciences (10 articles)

Published Papers (3 papers)

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Research

17 pages, 3189 KiB

Open AccessArticle

Functional Insights into the Sphingolipids C1P, S1P, and SPC in Human Fibroblast-like Synoviocytes by Proteomic Analysis

by Thomas Timm, Christiane Hild, Gerhard Liebisch, Markus Rickert, Guenter Lochnit and Juergen Steinmeyer

Int. J. Mol. Sci. 2024, 25(15), 8363; https://doi.org/10.3390/ijms25158363 - 31 Jul 2024

Viewed by 316

Abstract

The (patho)physiological function of the sphingolipids ceramide-1-phosphate (C1P), sphingosine-1-phosphate (S1P), and sphingosylphosphorylcholine (SPC) in articular joints during osteoarthritis (OA) is largely unknown. Therefore, we investigated the influence of these lipids on protein expression by fibroblast-like synoviocytes (FLSs) from OA knees. Cultured human FLSs (n = 7) were treated with 1 of 3 lipid species—C1P, S1P, or SPC—IL-1β, or with vehicle. The expression of individual proteins was determined by tandem mass tag peptide labeling followed by high-resolution electrospray ionization (ESI) mass spectrometry after liquid chromatographic separation (LC-MS/MS/MS). The mRNA levels of selected proteins were analyzed using RT-PCR. The 3sphingolipids were quantified in the SF of 18 OA patients using LC-MS/MS. A total of 4930 proteins were determined using multiplex MS, of which 136, 9, 1, and 0 were regulated both reproducibly and significantly by IL-1β, C1P, S1P, and SPC, respectively. In the presence of IL-1ß, all 3 sphingolipids exerted ancillary effects. Only low SF levels of C1P and SPC were found. In conclusion, the 3 lipid species regulated proteins that have not been described in OA. Our results indicate that charged multivesicular body protein 1b, metal cation symporter ZIP14, glutamine-fructose-6-P transaminase, metallothionein-1F and -2A, ferritin, and prosaposin are particularly interesting proteins due to their potential to affect inflammatory, anabolic, catabolic, and apoptotic mechanisms. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Disease 3.0)

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Figure 1

Figure 1
Venn diagram of proteins that are reproducibly regulated by (A) C1P (red), C1P in the presence of IL-1ß (yellow), and IL-1ß alone (green); (B) S1P (blue), S1P in the presence of IL-1ß (orange), and IL-1ß alone (green); (C) SPC in the presence of IL-1ß (violet) and IL-1ß alone (green); and (D) IL-1ß in the presence of C1P (yellow), S1P (orange), or SPC (violet). The number of proteins that can be reproducibly regulated is shown. In <a href="#app1-ijms-25-08363" class="html-app">Tables S1 and S2</a>, the AR of each protein is listed together with the results of the statistical analysis. Full article ">Figure 2
C1P and S1P reproducibly and significantly regulate the level of proteins in FLSs. Protein levels of (A) stromelysin, charged multivesicular body protein 1b, metal cation symporter ZIP14; (B) cPLA2, cytochrome c oxidase subunit 1, SOD; and (C) long-chain-fatty-acid–CoA ligase 4, ICAM-1, and glutamine fructose-6-Ptransaminase were quantified by MS in duplicate in the 7 biological replicates. The dot plots represent the data obtained from the resulting 14 replicates and illustrate the x-fold abundance of the proteins in the treated FLS cells in comparison to that of only vehicle-treated controls (which are normalized to 1 and shown as a dotted line). The mean value ± SD is represented by lines within each figure (n = 7). * 0.05 ≥ p > 0.01; ** 0.01 ≥ p > 0.001; *** p ≥ 0.001. Full article ">Figure 3
The biological processes of FLS being altered by C1P. The 9 proteins were reproducibly upregulated by more than 1.2-fold by C1P in FLS during 48 h of treatment, and <a href="#app1-ijms-25-08363" class="html-app">Table S1</a> provides further data on these proteins. The Go Slim categories for proteins were generated by Proteome Discoverer 2.5 software using the Gene Ontology (GO) database. Full article ">Figure 4
The molecular functions of FLS being altered by C1P. The 9 proteins were reproducibly upregulated by more than 1.2-fold by C1P in FLS during 48 h of treatment, and <a href="#app1-ijms-25-08363" class="html-app">Table S1</a> provides further data on these proteins. The Go Slim categories for proteins were generated by Proteome Discoverer 2.5 software using the Gene Ontology (GO) database. Full article ">Figure 5
The cellular localization of 9 proteins being regulated by C1P. The 9 proteins were reproducibly upregulated by more than 1.2-fold by C1P in FLS during 48 h of treatment, and <a href="#app1-ijms-25-08363" class="html-app">Table S1</a> provides further data on these proteins. The Go Slim categories for proteins were generated by Proteome Discoverer 2.5 software using the Gene Ontology (GO) database. Full article ">Figure 6
Levels of C1P 16:0 ad SPC 18:1;O2 in knee SF of patients with early-stage or late-stage knee OA. Sphingolipids were quantified by LC-MS/MS in extracts of SF obtained from 8 patients with eOA (black circle) and 10 patients with lOA (blue open circle). Data presented indicate mean ± SD of lipid concentration in SF (N = 8 or 10). Full article ">

20 pages, 6426 KiB

Open AccessArticle

ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction

by Guoqiang He, Qingzu He, Jinyan Cheng, Rongwen Yu, Jianwei Shuai and Yi Cao

Int. J. Mol. Sci. 2024, 25(13), 7237; https://doi.org/10.3390/ijms25137237 - 30 Jun 2024

Viewed by 725

Abstract

In the realm of quantitative proteomics, data-independent acquisition (DIA) has emerged as a promising approach, offering enhanced reproducibility and quantitative accuracy compared to traditional data-dependent acquisition (DDA) methods. However, the analysis of DIA data is currently hindered by its reliance on project-specific spectral libraries derived from DDA analyses, which not only limits proteome coverage but also proves to be a time-intensive process. To overcome these challenges, we propose ProPept-MT, a novel deep learning-based multi-task prediction model designed to accurately forecast key features such as retention time (RT), ion intensity, and ion mobility (IM). Leveraging advanced techniques such as multi-head attention and BiLSTM for feature extraction, coupled with Nash-MTL for gradient coordination, ProPept-MT demonstrates superior prediction performance. Integrating ion mobility alongside RT, mass-to-charge ratio (m/z), and ion intensity forms 4D proteomics. Then, we outline a comprehensive workflow tailored for 4D DIA proteomics research, integrating the use of 4D in silico libraries predicted by ProPept-MT. Evaluation on a benchmark dataset showcases ProPept-MT’s exceptional predictive capabilities, with impressive results including a 99.9% Pearson correlation coefficient (PCC) for RT prediction, a median dot product (DP) of 96.0% for fragment ion intensity prediction, and a 99.3% PCC for IM prediction on the test set. Notably, ProPept-MT manifests efficacy in predicting both unmodified and phosphorylated peptides, underscoring its potential as a valuable tool for constructing high-quality 4D DIA in silico libraries. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Disease 3.0)

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Figure 1

Figure 1
ProPept-ST evaluates the prediction performance of unmodified peptide retention time. (A,B) The prediction accuracy of different models for peptide RT (A) and iRT (B) is compared based on the mean absolute error on various datasets. (C) Scatter plot comparing ProPept-ST predicted RT values with experimentally observed RT values for the SWATH library dataset. (D) Distribution of absolute errors in peptide RT prediction by the ProPept-ST model, both fine-tuned and retrained. Full article ">Figure 2
ProPept-ST assesses the performance of predicting phosphorylated peptide RT and the ablation experiment on the ProPept-ST model. (A) Comparison of the prediction accuracy of various models for phosphorylated peptide RT based on the median absolute error on different datasets. (B) Scatter plot comparing ProPept-ST-predicted iRT values with experimentally observed iRT values for the U2OS_DDA dataset. (C) Median absolute error of RT prediction by ProPept-ST and six other models on the benchmark dataset H4 DDAp. (D) Parameter count comparison between ProPept-ST and six other models. Full article ">Figure 3
ProPept-MT’s performance in predicting RT and IM, as well as the loss curves for training three tasks on specific datasets. (A) Distribution of absolute errors for predicting RT on benchmark datasets for each model. (B,D) Scatter plots showing ProPept-MT’s predictions of RT (B) and IM (D) on the H2 DIA test set. (C) Distribution of absolute errors for predicting IM on benchmark datasets for ProPept-MT and ProPept-ST. (E) Loss curves for training and validation of the three tasks on dataset H5 DDAp for ProPept-MT. (F) On the H7 DDAp training set, the loss curves of ProPept-ST retrained on three tasks and the fine-tuned loss curves of ProPept-MT. Full article ">Figure 4
Performance of ProPept-MT in predicting fragment ion intensity. (A) Histogram distribution of PCC for each peptide on the H1 DDA and H5 DDAp test sets. (B) Mirror plot showing the experimental and predicted values of fragment ion intensities for two specific peptides (unmodified peptide and phosphopeptide). (C) Box plots showing the distribution of PCC, DP, and SA for ProPept-MT on the H1 DDA and H6 DDAp test sets. Full article ">Figure 5
Comparing the performance of ProPept-MT and DeepDIA in predicting RT, IM, and fragment ion intensity. (A) Distribution of absolute errors for predicting RT on different datasets for each model. (B) Distribution of absolute errors for predicting IM on different datasets for ProPept-MT and ProPept-ST. (C) Assessing ProPept-MT’s performance in predicting fragment ion intensity for different precursor charges on benchmark datasets. (D) Distribution of dot product (DP) for predicting fragment ion intensity of 2+ and 3+ precursor charges on the H1 DDA test set for each model. Full article ">Figure 6
The workflow and model architecture of ProPept-MT. (A) ProPept-MT employs a multi-task deep learning model to generate in silico prediction libraries from protein or peptide sequence databases. (B) ProPept-MT is used for predicting RT, IM, and fragment ion intensity for any given unmodified peptide or phosphopeptide. Given the peptide sequence and precursor charge as input, our model uses Transformer encoder modules and a BiLSTM network to calculate context representations for all amino acids, which it finally outputs through separately designed output layers for each task. Full article ">

21 pages, 2726 KiB

Open AccessArticle

Altered Serum Proteins Suggest Inflammation, Fibrogenesis and Angiogenesis in Adult Patients with a Fontan Circulation

by Miriam Michel, David Renaud, Ronny Schmidt, Matthias Einkemmer, Lea Valesca Laser, Erik Michel, Karl Otto Dubowy, Daniela Karall, Kai Thorsten Laser and Sabine Scholl-Bürgi

Int. J. Mol. Sci. 2024, 25(10), 5416; https://doi.org/10.3390/ijms25105416 - 16 May 2024

Viewed by 1110

Abstract

Previous omics research in patients with complex congenital heart disease and single-ventricle circulation (irrespective of the stage of palliative repair) revealed alterations in cardiac and systemic metabolism, inter alia abnormalities in energy metabolism, and inflammation, oxidative stress or endothelial dysfunction. We employed an affinity-proteomics approach focused on cell surface markers, cytokines, and chemokines in the serum of 20 adult Fontan patients with a good functioning systemic left ventricle, and we 20 matched controls to reveal any specific processes on a cellular level. Analysis of 349 proteins revealed 4 altered protein levels related to chronic inflammation, with elevated levels of syndecan-1 and glycophorin-A, as well as decreased levels of leukemia inhibitory factor and nerve growth factor-ß in Fontan patients compared to controls. All in all, this means that Fontan circulation carries specific physiological and metabolic instabilities, including chronic inflammation, oxidative stress imbalance, and consequently, possible damage to cell structure and alterations in translational pathways. A combination of proteomics-based biomarkers and the traditional biomarkers (uric acid, γGT, and cholesterol) performed best in classification (patient vs. control). A metabolism- and signaling-based approach may be helpful for a better understanding of Fontan (patho-)physiology. Syndecan-1, glycophorin-A, leukemia inhibitory factor, and nerve growth factor-ß, especially in combination with uric acid, γGT, and cholesterol, might be interesting candidate parameters to complement traditional diagnostic imaging tools and the determination of traditional biomarkers, yielding a better understanding of the development of comorbidities in Fontan patients, and they may play a future role in the identification of targets to mitigate inflammation and comorbidities in Fontan patients. Full article

(This article belongs to the Special Issue Proteomics and Its Applications in Disease 3.0)

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Figure 1

Figure 1
Distinct abundance variations of proteins in patient and control serum samples (volcano plot). The plot visualizes the adjusted p-values and corresponding log-fold changes (|logFC|). p < 0.05 was considered statistically significant (horizontal red line). The |logFC| cutoffs are indicated as vertical lines. Proteins with a positive |logFC| had a higher abundance in patient samples; proteins with a negative value were in control samples. Proteins with |logFC| < 0.5 and a significant adjusted p-value are defined as differential and are displayed in blue. Proteins indicated in green either feature a |logFC| > 0.5, while not reaching the significance threshold, or feature a significant difference, while not reaching the |logFC| threshold. Full article ">Figure 2
Individual array values for the four proteins with differential abundance in patient and control samples (see <a href="#ijms-25-05416-t002" class="html-table">Table 2</a>). Each sample is measured by four replicate spots per array. Rhombs indicate sample group means. Whiskers indicate one standard deviation. Full article ">Figure 3
Heatmap displaying the relative expression of proteins identified as differential. Values were centered and scaled by proteins. Full article ">Figure 4
Principal component analysis for differential proteins. The scatter plot displays the first two principal components of the samples’ protein signal data based exclusively on the four differentially abundant proteins GLPA, LIF, SDC1, and NGF-β. In the plot, the location of the samples is defined by their first two principal components, i.e., linear combinations of protein features with the largest variance across the samples. Samples with a similar profile are located in close proximity. The percentages given in the axis labels describe the ratio of total variance explained by the respective principal component. Note that in the principal component analysis of the four differential proteins, the distribution of the probands suggests a clustering of patients or controls. Green, controls; red, patients. Full article ">Figure 5
ROC curves for selected parameters. Receiver operating characteristic curves and area under curves for selected traditional and proteomics serum parameters and for combinations thereof with regard to group assignment (patient vs. control). Note the exceeding performance of combinations including γGT, uric acid, or triglyceride serum concentration in combination with one or two of the proteomics variables. The dashed line represents an area under the curve of 0.5. Full article ">Figure 6
Normalized cumulated MDM (proteomics). Cumulative impact of the 526 proteomics-derived analytes examined on classification competence into diseased vs. non-diseased proband serum (dotted curve, red). The linear line (blue) represents the analytes’ rank standardized to 100%. Open circle: the 27/526 topmost ranked proteins (5% of analytes) account for 80% of classification optimum. MDM, mean decrease in the margin (classification impact score). Full article ">

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