Proteome Profiling of Diabetic Mellitus Patient Urine for Discovery of Biomarkers by Comprehensive MS-Based Proteomics

Hirao, Yoshitoshi; Saito, Suguru; Fujinaka, Hidehiko; Miyazaki, Shigeru; Xu, Bin; Quadery, Ali F.; Elguoshy, Amr; Yamamoto, Keiko; Yamamoto, Tadashi

doi:10.3390/proteomes6010009

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…The average number of identified proteins in individual urine samples was 613 and 589 in the AD and control groups, respectively. This is comparable to previous reports [13, 33, 34]. Recent studies have found that systemic changes, such as insulin resistance, atherosclerosis, and increased inflammation underlie the development of AD [7, 8, 9].…”

Section: Discussionsupporting

confidence: 90%

Molecular Network Analysis of the Urinary Proteome of Alzheimer’s Disease Patients

Watanabe

Hirao

Kasuga

et al. 2019

Dement Geriatr Cogn Disord Extra

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Background/Aims: The identification of predictive biomarkers for Alzheimer’s disease (AD) from urine would aid in screening for the disease, but information about biological and pathophysiological changes in the urine of AD patients is limited. This study aimed to explore the comprehensive profile and molecular network relations of urinary proteins in AD patients. Methods: Urine samples collected from 18 AD patients and 18 age- and sex-matched cognitively normal controls were analyzed by mass spectrometry and semiquantified with the normalized spectral index method. Bioinformatics analyses were performed on proteins which significantly increased by more than 2-fold or decreased by less than 0.5-fold compared to the control (p < 0.05) using DAVID bioinformatics resources and KeyMolnet software. Results: The levels of 109 proteins significantly differed between AD patients and controls. Among these, annotation clusters related to lysosomes, complement activation, and gluconeogenesis were significantly enriched. The molecular relation networks derived from these proteins were mainly associated with pathways of lipoprotein metabolism, heat shock protein 90 signaling, matrix metalloproteinase signaling, and redox regulation by thioredoxin. Conclusion: Our findings suggest that changes in the urinary proteome of AD patients reflect systemic changes related to AD pathophysiology.

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Section: Discussionsupporting

confidence: 90%

Molecular Network Analysis of the Urinary Proteome of Alzheimer’s Disease Patients

Watanabe

Hirao

Kasuga

et al. 2019

Dement Geriatr Cogn Disord Extra

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“…Bjornstad et al., recently reported the relationships between markers of tubular injury and intrarenal hemodynamic function in adults with and without type 1 diabetes ( 44 ). Hirao et al., reported the discovery of a useful biomarker for EDN discovery by proteomics analysis of urine proteomes of DM patients without microalbuminuria and healthy volunteers ( 45 ). Cheung et al.…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis

Gao

Aishwarya²,

et al. 2021

Front. Endocrinol.

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Globally, nearly 40 percent of all diabetic patients develop serious diabetic kidney disease (DKD). The identification of the potential early-stage biomarkers and elucidation of their underlying molecular mechanisms in DKD are required. In this study, we performed integrated bioinformatics analysis on the expression profiles GSE111154, GSE30528 and GSE30529 associated with early diabetic nephropathy (EDN), glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. A total of 1,241, 318 and 280 differentially expressed genes (DEGs) were identified for GSE30258, GSE30529, and GSE111154 respectively. Subsequently, 280 upregulated and 27 downregulated DEGs shared between the three GSE datasets were identified. Further analysis of the gene expression levels conducted on the hub genes revealed SPARC (Secreted Protein Acidic And Cysteine Rich), POSTN (periostin), LUM (Lumican), KNG1 (Kininogen 1), FN1 (Fibronectin 1), VCAN (Versican) and PTPRO (Protein Tyrosine Phosphatase Receptor Type O) having potential roles in DKD progression. FN1, LUM and VCAN were identified as upregulated genes for GDKD whereas the downregulation of PTPRO was associated with all three diseases. Both POSTN and SPARC were identified as the overexpressed putative biomarkers whereas KNG1 was found as downregulated in TDKD. Additionally, we also identified two drugs, namely pidorubicine, a topoisomerase inhibitor (LINCS ID- BRD-K04548931) and Polo-like kinase inhibitor (LINCS ID- BRD-K41652870) having the validated role in reversing the differential gene expression patterns observed in the three GSE datasets used. Collectively, this study aids in the understanding of the molecular drivers, critical genes and pathways that underlie DKD initiation and progression.

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“…Improvements to LC-MS instrumentation have led to a series of studies reporting an increasing number of proteins that constitute the healthy human urine proteome, with Zhao et al [176] reporting more than 6000 proteins. This has caught the attention of organizations seeking non-invasive biomarkers, including HUPO which has dedicated the Human Kidney and Urine Proteome Project (HKUPP) specifically to the analysis of urinary biomarkers [177]. The challenges associated with urinary proteomics studies include inter-patient variability since urine protein concentrations depend on kidney filtration and reabsorption performance which greatly fluctuates within a population.…”

Section: Urine-based Proteomicsmentioning

confidence: 99%

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

2020

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Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including posttranslational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

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Proteome Profiling of Diabetic Mellitus Patient Urine for Discovery of Biomarkers by Comprehensive MS-Based Proteomics

Cited by 11 publications

References 16 publications

Molecular Network Analysis of the Urinary Proteome of Alzheimer’s Disease Patients

Molecular Network Analysis of the Urinary Proteome of Alzheimer’s Disease Patients

Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

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