Clinical proteomics最新文献

Background: Tuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis.

Methods: In the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) patients and 40 healthy controls (HC) was collected. Biomarker panels were selected based on random forest (RF) analysis.

Results: A total of 3,868 proteins and 1,272 annotated metabolic features were detected using pairwise comparisons. Using AUC ≥ 0.80 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC for PTB/HC, PTB/CAP and PTB/LCA of 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570.

Conclusion: Our data demonstrate that metabolomic and proteomic analysis can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity. The receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of PTB through these urine biomarkers.

Background: The effect of varying brain ventricular volume on the cerebrospinal fluid (CSF) proteome has been discussed as possible confounding factors in comparative protein level analyses. However, the relationship between CSF volume and protein levels remains largely unexplored. Moreover, the few existing studies provide conflicting findings, indicating the need for further research.

Methods: Here, we explored the association between levels of 88 pre-selected CSF proteins and ventricular volume derived from magnetic resonance imaging (MRI) measurements in 157 cognitively healthy 70-year-olds from the H70 Gothenburg Birth Cohort Studies, including individuals with and without pathological levels of Alzheimer's disease (AD) CSF markers (n = 123 and 34, respectively). Both left and right lateral, the inferior horn as well as the third and the fourth ventricular volumes were measured. Different antibody-based methods were employed for the protein measurements, with most being analyzed using a multiplex bead-based microarray technology. Furthermore, the associations between the protein levels and cortical thickness, fractional anisotropy, and mean diffusivity were assessed.

Results: CSF levels of many brain-derived proteins correlated with ventricular volumes in A-T- individuals, with lower levels in individuals with larger ventricles. The strongest negative correlations with total ventricular volume were observed for neurocan (NCAN) and neurosecretory protein VGF (rho = -0.34 for both). Significant negative correlations were observed also for amyloid beta (Ab) 38, Ab40, total tau (t-tau), and phosphorylated tau (p-tau), with correlation ranging between - 0.34 and - 0.28, while no association was observed between ventricular volumes and Ab42 or neurofilament light chain (NfL). Proteins with negative correlations to ventricular volumes further demonstrated negative correlations to mean diffusivity and positive correlation to fractional anisotropy. However, only weak or no correlations were observed between the CSF protein levels and cortical thickness. A + T + individuals demonstrated higher CSF protein levels compared to A-T- individuals with the most significant differences observed for neurogranin (NRGN) and synuclein beta (SNCB).

Conclusions: Our findings suggest that the levels of many brain-derived proteins in CSF may be subjected to dilution effects depending on the size of the brain ventricles in healthy individuals without AD pathology. This phenomenon could potentially contribute to the inter-individual variations observed in CSF proteomic studies.

Background: The vitreous humor serves as a window into the physiological and pathological processes of the eye, particularly the retina. Diabetic retinopathy (DR), a leading cause of blindness, involves hyperglycemia-induced damage to retinal cells, leading to ischemia and elevated nitric oxide levels, culminating in vascular proliferation. Rhegmatogenous retinal detachment (RD) results from a break in the neuroretina, triggering ischemia, photoreceptor death, and cellular proliferation. Proliferative vitreoretinopathy (PVR) further complicates these conditions through fibrous proliferation. Despite their prevalence and potential for blindness, our understanding of the molecular mechanisms underlying these vitreoretinal diseases is incomplete.

Methods and results: To elucidate disease mechanisms and identify potential therapeutic targets, we conducted a comparative proteomic analysis of vitreous samples from DR, RD, and macular pucker (P) patients, which were chosen as controls. LC-MS analysis identified 988 quantifiable proteins, with distinct clustering observed among disease groups. Differential expression analysis revealed 202 proteins in RD vs. P and 167 in DR vs. P, highlighting distinct proteomic signatures. Enrichment analysis identified glucose metabolism as an altered process in both diseases, suggesting common pathways despite differing etiologies. Notably, aldo-keto reductase family 1 member B1 (AKR1B1) has emerged as a potential key player in both DR and RD, indicating its role in glucose metabolism and inflammation. In silico drug screening identified diclofenac, an approved ophthalmic non-steroidal anti-inflammatory drug (NSAID), as a potential therapeutic agent targeting AKR1B1.

Conclusion: Our study revealed distinct proteomic signatures and common pathways in vitreoretinal diseases, highlighting AKR1B1 as a potential therapeutic target. Using diclofenac during diagnosis and postoperative care for diabetic retinopathy or rhegmatogenous retinal detachment may reduce complications, lower costs, and improve quality of life. Future research will focus on confirming AKR1B1's role in vitreoretinal diseases and understanding diclofenac's mechanism of action.

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: Mucosal healing is the therapeutic target for ulcerative colitis (UC). While amino acids (AAs) and the gut microbiota are known to be involved in the pathogenesis of UC, their specific roles in mucosal healing have not been fully defined.

Objectives: To longitudinally assess the changes in AA concentrations and the gut microbiota composition in the context of mucosal healing in UC patients, with the aim of identifying new biomarkers with predictive value for mucosal healing in UC patients and providing a new theoretical basis for dietary therapy.

Methods: A total of 15 UC patients with infliximab-induced mucosal healing were enrolled. Serum and fecal AA concentrations before and after mucosal healing were determined via targeted metabolomics. A receiver operating characteristic (ROC) curve was plotted to evaluate the value of different AAs in predicting mucosal healing in UC patients. The changes in the composition of the fecal gut microbiota were analyzed via metagenomics, and bioinformatics was used to analyze the functional genes and metabolic pathways associated with different bacterial species. Spearman correlation analyses of fecal AAs with significantly different concentrations and the differentially abundant bacterial species before and after mucosal healing were performed.

Results: 1. The fecal concentrations of alanine, aspartic acid, glutamic acid, glutamine, glycine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine were significantly decreased after mucosal healing. The serum concentrations of alanine, cysteine and valine significantly increased, whereas that of aspartic acid significantly decreased. Glutamic acid, leucine, lysine, methionine and threonine could accurately predict mucosal healing in UC patients, and the area under the curve (AUC) was > 0.9. After combining the 5 amino acids, the AUC reached 0.96. 2. There were significant differences in the gut microbiota composition before and after mucosal healing in UC, characterized by an increase in the abundance of beneficial microbiota (Faecalibacterium prausnitzii and Bacteroides fragilis) and a decrease in the abundance of harmful microbiota (Enterococcus faecalis). LEfSe analysis identified 57 species that could predict mucosal healing, and the AUC was 0.7846. 3. Amino acid metabolic pathways were enriched in samples after mucosal healing, was associated with the abundance of multiple species, such as Faecalibacterium prausnitzi, Bacteroides fragilis, Bacteroides vulgatus and Alistipes putredinis. 4. The fecal concentrations of several AAs were negatively correlated with the abundance of a variety of beneficial strains, such as Bacteroides fragilis, uncultured Clostridium sp., Firmicutes bacterium CAG:103, Adlercreutzia equolifaciens, Coprococcus comes and positively correlated with the abundance of several ha

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.