Clinical proteomics最新文献

Background: Alternative N-glycosylation of serum proteins has been observed in colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and gastric cancer (GC), while comparative study among those three cancers has not been reported before. We aimed to identify serum N-glycans signatures and introduce a discriminative model across the gastrointestinal cancers.

Methods: The study population was initially screened according to the exclusion criteria process. Serum N-glycans profiling was characterized by a high-throughput assay based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Diagnostic model was built by random forest, and unsupervised machine learning was performed to illustrate the differentiation between the three major gastrointestinal (GI) cancers.

Results: We have found that three major gastrointestinal cancers strongly associated with significantly decreased mannosylation and mono-galactosylation, as well as increased sialylation of serum glycoproteins. A highly accurate discriminative power (> 0.90) for those gastrointestinal cancers was obtained with serum N-glycome based predictive model. Additionally, serum N-glycome profile exhibited distinct distributions across GI cancers, and several altered N-glycans were hyper-regulated in each specific disease.

Conclusions: Serum N-glycome profile was differentially expressed in three major gastrointestinal cancers, providing a new clinical tool for cancer diagnosis and throwing a light upon the disease-specific molecular signatures.

Background: COVID19 is a pandemic that has affected millions around the world since March 2020. While many patients recovered completely with mild illness, many patients succumbed to various organ morbidities. This heterogeneity in the clinical presentation of COVID19 infection has posed a challenge to clinicians around the world. It is therefore crucial to identify specific organ-related morbidity for effective treatment and better patient outcomes. We have carried out serum-based proteomic experiments to identify protein biomarkers that can flag organ dysfunctions in COVID19 patients.

Methods: COVID19 patients were screened and tested at various hospitals across New Delhi, India. 114 serum samples from these patients, with and without organ morbidities were collected and annotated based on clinical presentation and treatment history. Of these, 29 samples comprising of heart, lung, kidney, gastrointestinal, liver, and neurological morbidities were considered for the discovery phase of the experiment. Proteins were isolated, quantified, trypsin digested, and the peptides were subjected to liquid chromatography assisted tandem mass spectrometry analysis. Data analysis was carried out using Proteome Discoverer software. Fold change analysis was carried out on MetaboAnalyst. KEGG, Reactome, and Wiki Pathway analysis of differentially expressed proteins were carried out using the STRING database. Potential biomarker candidates for various organ morbidities were validated using ELISA.

Results: 254 unique proteins were identified from all the samples with a subset of 12-31 differentially expressed proteins in each of the clinical phenotypes. These proteins establish complement and coagulation cascade pathways in the pathogenesis of the organ morbidities. Validation experiments along with their diagnostic parameters confirm Secreted Protein Acidic and Rich in Cysteine, Cystatin C, and Catalase as potential biomarker candidates that can flag cardiovascular disease, renal disease, and respiratory disease, respectively.

Conclusions: Label free serum proteomics shows differential protein expression in COVID19 patients with morbidity as compared to those without morbidity. Identified biomarker candidates hold promise to flag organ morbidities in COVID19 for efficient patient care.

Background: Spatial proteomics seeks to understand the spatial organization of proteins in tissues or at different subcellular localization in their native environment. However, capturing the spatial organization of proteins is challenging. Here, we present an innovative approach termed Spatial Proteomics through On-site Tissue-protein-labeling (SPOT), which combines the direct labeling of tissue proteins in situ on a slide and quantitative mass spectrometry for the profiling of spatially-resolved proteomics.

Materials and methods: Efficacy of direct TMT labeling was investigated using seven types of sagittal mouse brain slides, including frozen tissues without staining, formalin-fixed paraffin-embedded (FFPE) tissues without staining, deparaffinized FFPE tissues, deparaffinized and decrosslinked FFPE tissues, and tissues with hematoxylin & eosin (H&E) staining, hematoxylin (H) staining, eosin (E) staining. The ability of SPOT to profile proteomes at a spatial resolution was further evaluated on a horizontal mouse brain slide with direct TMT labeling at eight different mouse brain regions. Finally, SPOT was applied to human prostate cancer tissues as well as a tissue microarray (TMA), where TMT tags were meticulously applied to confined regions based on the pathological annotations. After on-site direct tissue-protein-labeling, tissues were scraped off the slides and subject to standard TMT-based quantitative proteomics analysis.

Results: Tissue proteins on different types of mouse brain slides could be directly labeled with TMT tags. Moreover, the versatility of our direct-labeling approach extended to discerning specific mouse brain regions based on quantitative outcomes. The SPOT was further applied on both frozen tissues on slides and FFPE tissues on TMAs from prostate cancer tissues, where a distinct proteomic profile was observed among the regions with different Gleason scores.

Conclusions: SPOT is a robust and versatile technique that allows comprehensive profiling of spatially-resolved proteomics across diverse types of tissue slides to advance our understanding of intricate molecular landscapes.

Background: The gut-brain axis has garnered increasing attention, with observational studies suggesting its involvement in the disease activity and progression of inflammatory bowel disease (IBD), but the precise mechanisms remain unclear.

Materials and methods: In this study, we aimed to investigate "novel proteins" underlying IBD in the brain using a comprehensive multi-omics analysis approach. We performed integrated analyses of proteomics and transcriptomics in the human prefrontal cortex (PFC) tissue, coupled with genome-wide association studies (GWAS) of IBD, crohn's disease (CD), and ulcerative colitis (UC). This included performing protein-wide association studies (PWAS), transcriptome-wide association studies (TWAS), Mendelian randomization (MR), and colocalization analysis to identify brain proteins associated with IBD and its subtypes.

Results: PWAS analyses identified and confirmation 9, 9, and 6 brain proteins strongly associated with IBD, CD, and UC, respectively. Subsequent MR analyses revealed that increased abundance of GPSM1, AUH, TYK2, SULT1A1, and FDPS, along with corresponding gene expression, led to decreased risk of IBD. For CD, increased abundance of FDPS, SULT1A1, and PDLIM4, along with corresponding gene expression, also decreased CD risk. Regarding UC, only increased abundance of AUH, along with corresponding gene expression, was significantly associated with decreased UC risk. Further TWAS and colocalization analyses at the transcriptome level supported strong associations of SULT1A1 and FDPS proteins with reduced risk of IBD and CD.

Conclusion: The two "novel proteins," SULT1A1 and FDPS, are strongly associated with IBD and CD, elucidating their causal relationship in reducing the risk of IBD and CD. This provides new clues for identifying the pathogenesis and potential therapeutic targets for IBD and CD.

Background: The diagnosis and treatment of colorectal cancer (CRC), especially metastatic colorectal cancer (mCRC), is a major priority and research challenge. We screened for expression differences in the plasma exosomal proteomes of patients with mCRC, those with CRC, and healthy controls (HCs) to discover potential biomarkers for mCRC.

Methods: Plasma samples from five patients with mCRC, five patients with CRC, and five HCs were collected and processed to isolate exosomes by ultracentrifugation. Exosomal protein concentrations were determined using the BCA kit, and liquid chromatography-mass spectrometry was utilized to identify and analyze the proteins.

Results: From the exosomes isolated from plasma samples, a total of 994 quantifiable proteins were detected, including 287 differentially expressed proteins identified by quantitative proteomics analyses. Totals of 965, 963 and 968 proteins were identified in mCRC patients, CRC patients, and HCs, respectively. The study identified 83 proteins with differential expression in the plasma exosomes of mCRC patients. The top 10 upregulated proteins in the mCRC group and CRC groups were ITGA4, GNAI1, SFTPA2, UGGT1, GRN, LBP, SMIM1, BMP1, HMGN5, and MFAP4, while the top 10 downregulated proteins were PSMB8, LCK, RAB35, PSMB4, CD81, CD63, GLIPR2, RAP1B, RAB30, and CES1. Western Blot validation data confirmed that ITGA4 and GNAI1 were unequivocally enriched in plasma-derived exosomes from mCRC patients.

Conclusions: These differential proteins offer potential new candidate molecules for further research on the pathogenesis of mCRC and the identification of therapeutic targets. This study sheds light on the potential significance of plasma exosome proteomics studies in our understanding and treatment of mCRC.

Background: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, and the prevalence of CVDs increases markedly with age. Due to the high energetic demand, the heart is highly sensitive to mitochondrial dysfunction. The complexity of the cardiac mitochondrial proteome hinders the development of effective strategies that target mitochondrial dysfunction in CVDs. Mammalian mitochondria are composed of over 1000 proteins, most of which can undergo post-translational modifications (PTMs). Top-down proteomics is a powerful technique for characterizing and quantifying proteoform sequence variations and PTMs. However, there are still knowledge gaps in the study of age-related mitochondrial proteoform changes using this technique. In this study, we used top-down proteomics to identify intact mitochondrial proteoforms in young and old hearts and determined changes in protein abundance and PTMs in cardiac aging.

Methods: Intact mitochondria were isolated from the hearts of young (4-month-old) and old (24-25-month-old) mice. The mitochondria were lysed, and mitochondrial lysates were subjected to denaturation, reduction, and alkylation. For quantitative top-down analysis, there were 12 runs in total arising from 3 biological replicates in two conditions, with technical duplicates for each sample. The collected top-down datasets were deconvoluted and quantified, and then the proteoforms were identified.

Results: From a total of 12 LC-MS/MS runs, we identified 134 unique mitochondrial proteins in the different sub-mitochondrial compartments (OMM, IMS, IMM, matrix). 823 unique proteoforms in different mass ranges were identified. Compared to cardiac mitochondria of young mice, 7 proteoforms exhibited increased abundance and 13 proteoforms exhibited decreased abundance in cardiac mitochondria of old mice. Our analysis also detected PTMs of mitochondrial proteoforms, including N-terminal acetylation, lysine succinylation, lysine acetylation, oxidation, and phosphorylation. Data are available via ProteomeXchange with the identifier PXD051505.

Conclusion: By combining mitochondrial protein enrichment using mitochondrial fractionation with quantitative top-down analysis using ultrahigh-pressure liquid chromatography (UPLC)-MS and label-free quantitation, we successfully identified and quantified intact proteoforms in the complex mitochondrial proteome. Using this approach, we detected age-related changes in abundance and PTMs of mitochondrial proteoforms in the heart.

Background: Progressive supranuclear palsy (PSP) is a neurodegenerative disorder often misdiagnosed as Parkinson's Disease (PD) due to shared symptoms. PSP is characterized by the accumulation of tau protein in specific brain regions, leading to loss of balance, gaze impairment, and dementia. Diagnosing PSP is challenging, and there is a significant demand for reliable biomarkers. Existing biomarkers, including tau protein and neurofilament light chain (NfL) levels in cerebrospinal fluid (CSF), show inconsistencies in distinguishing PSP from other neurodegenerative disorders. Therefore, the development of new biomarkers for PSP is imperative.

Methods: We conducted an extensive proteome analysis of CSF samples from 40 PSP patients, 40 PD patients, and 40 healthy controls (HC) using tandem mass tag-based quantification. Mass spectrometry analysis of 120 CSF samples was performed across 13 batches of 11-plex TMT experiments, with data normalization to reduce batch effects. Pathway, interactome, cell-type-specific enrichment, and bootstrap receiver operating characteristic analyses were performed to identify key candidate biomarkers.

Results: We identified a total of 3,653 unique proteins. Our analysis revealed 190, 152, and 247 differentially expressed proteins in comparisons of PSP vs. HC, PSP vs. PD, and PSP vs. both PD and HC, respectively. Gene set enrichment and interactome analysis of the differentially expressed proteins in PSP CSF showed their involvement in cell adhesion, cholesterol metabolism, and glycan biosynthesis. Cell-type enrichment analysis indicated a predominance of neuronally-derived proteins among the differentially expressed proteins. The potential biomarker classification performance demonstrated that ATP6AP2 (reduced in PSP) had the highest AUC (0.922), followed by NEFM, EFEMP2, LAMP2, CHST12, FAT2, B4GALT1, LCAT, CBLN3, FSTL5, ATP6AP1, and GGH.

Conclusion: Biomarker candidate proteins ATP6AP2, NEFM, and CHI3L1 were identified as key differentiators of PSP from the other groups. This study represents the first large-scale use of mass spectrometry-based proteome analysis to identify cerebrospinal fluid (CSF) biomarkers specific to progressive supranuclear palsy (PSP) that can differentiate it from Parkinson's disease (PD) and healthy controls. Our findings lay a crucial foundation for the development and validation of reliable biomarkers, which will enhance diagnostic accuracy and facilitate early detection of PSP.

Background: Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze-thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics.

Methods: A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank.

Results: Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways.

Conclusions: This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events.