Urinary proteomics links keratan sulfate degradation and lysosomal enzymes to early type 1 diabetes

Proteomic research on new urinary biomarkers of renal disease in canine leishmaniosis: Survival and monitoring response to treatment

The objective of our study was to search for survival biomarkers (SB) and treatment response monitoring biomarkers (TRMB) in the urinary proteome of dogs with renal disease secondary to canine leishmaniosis (CanL), using UHPLC-MS/MS. The proteomic data are available via ProteomeXchange with identifier PXD042578. Initially, a group of 12 dogs was evaluated and divided into survivors (SG; n = 6) and nonsurvivors (NSG; n = 6). A total of 972 proteins were obtained from the evaluated samples. Then, bioinformatic analysis reduced them to 6 proteins like potential SB increased in the NSG, specifically, Haemoglobin subunit Alpha 1, Complement Factor I, Complement C5, Fibrinogen beta chain (fragment), Peptidase S1 domain-containing protein, and Fibrinogen gamma chain. Afterwards, SG was used to search for TRMB, studying their urine at 0, 30, and 90 days, and 9 proteins that decreased after treatment were obtained: Apolipoprotein E, Cathepsin B, Cystatin B, Cystatin-C-like, Lysozyme, Monocyte differentiation CD14, Pancreatitis-associated precursor protein, Profilin, and Protein FAM3C. Finally, enrichment analysis provided information about the biological mechanisms in which these proteins are involved. In conclusion, this study provides 15 new candidate urinary biomarkers and an improved understanding of the pathogenesis of kidney disease in CanL.

Urinary peptidomics reveals proteases involved in idiopathic membranous nephropathy

BACKGROUND

Idiopathic membranous nephropathy (IMN) is a cause of nephrotic syndrome that is increasing in incidence but has unclear pathogenesis. Urinary peptidomics is a promising technology for elucidating molecular mechanisms underlying diseases. Dysregulation of the proteolytic system is implicated in various diseases. Here, we aimed to conduct urinary peptidomics to identify IMN-related proteases.

RESULTS

Peptide fingerprints indicated differences in naturally produced urinary peptide components among 20 healthy individuals, 22 patients with IMN, and 15 patients with other kidney diseases. In total, 1,080 peptide-matched proteins were identified, 279 proteins differentially expressed in the urine of IMN patients were screened, and 32 proteases were predicted; 55 of the matched proteins were also differentially expressed in the kidney tissues of IMN patients, and these were mainly involved in the regulation of proteasome-, lysosome-, and actin cytoskeleton-related signaling pathways. The 32 predicted proteases showed abnormal expression in the glomeruli of IMN patients based on Gene Expression Omnibus databases. Western blot revealed abnormal expression of calpain, matrix metalloproteinase 14, and cathepsin S in kidney tissues of patients with IMN.

CONCLUSIONS

This work shown the calpain/matrix metalloproteinase/cathepsin axis might be dysregulated in IMN. Our study is the first to systematically explore the role of proteases in IMN by urinary peptidomics, which are expected to facilitate discovery of better biomarkers for IMN.

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

Recent Advances in Mass Spectrometry-Based Glycomic and Glycoproteomic Studies of Pancreatic Diseases

Modification of proteins by glycans plays a crucial role in mediating biological functions in both healthy and diseased states. Mass spectrometry (MS) has emerged as the most powerful tool for glycomic and glycoproteomic analyses advancing knowledge of many diseases. Such diseases include those of the pancreas which affect millions of people each year. In this review, recent advances in pancreatic disease research facilitated by MS-based glycomic and glycoproteomic studies will be examined with a focus on diabetes and pancreatic cancer. The last decade, and especially the last five years, has witnessed developments in both discovering new glycan or glycoprotein biomarkers and analyzing the links between glycans and disease pathology through MS-based studies. The strength of MS lies in the specificity and sensitivity of liquid chromatography-electrospray ionization MS for measuring a wide range of biomolecules from limited sample amounts from many sample types, greatly enhancing and accelerating the biomarker discovery process. Furthermore, imaging MS of glycans enabled by matrix-assisted laser desorption/ionization has proven useful in complementing histology and immunohistochemistry to monitor pancreatic disease progression. Advances in biological understanding and analytical techniques, as well as challenges and future directions for the field, will be discussed.

Urinary proteomics for kidney dysfunction: insights and trends

Introduction: Kidney dysfunction poses a high burden on patients and health care systems. Early detection and accurate prediction of kidney disease progression remains a major challenge. Compared to existing clinical parameters, urinary proteomics has the potential to reveal molecular alterations within the kidney that may alter its function before the onset of clinical symptoms. Thus, urinary proteomics has greater prognostic potential for assessment of kidney dysfunction progression.Areas covered: Advances in urinary proteomics for major causes of kidney dysfunction are discussed. The application of urinary extracellular vesicles for studying kidney dysfunction are discussed. Technological advances in urinary proteomics are discussed. The literature was identified using a database search for titles containing 'proteom*' and 'urin*' and published within the past 5 years. Retrieved literature was manually filtered to retain kidney dysfunctions-related studies.Expert opinion: Despite major advances, diagnosis by urinary proteomics has not been fully applied in any clinical settings. This could be attributed to the complex nature of kidney diseases, in addition to the constraints on study power and feasibility of incorporating mass spectrometry techniques in daily routine analysis. Nevertheless, we are confident that advances in urinary proteomics will soon provide superior insights into kidney disease beyond existing clinical parameters.

A functional outside-in signaling network of proteoglycans and matrix molecules regulating autophagy

Proteoglycans and selected extracellular matrix constituents are emerging as intrinsic and critical regulators of evolutionarily conversed, intracellular catabolic pathways. Often, these secreted molecules evoke sustained autophagy in a variety of cell types, tissues, and model systems. The unique properties of proteoglycans have ushered in a paradigmatic shift to broaden our understanding of matrix-mediated signaling cascades. The dynamic cellular pathway controlling autophagy is now linked to an equally dynamic and fluid signaling network embedded in a complex meshwork of matrix molecules. A rapidly emerging field of research encompasses multiple matrix-derived candidates, representing a menagerie of soluble matrix constituents including decorin, biglycan, endorepellin, endostatin, collagen VI and plasminogen kringle 5. These matrix constituents are pro-autophagic and simultaneously anti-angiogenic. In contrast, perlecan, laminin α2 chain, and lumican have anti-autophagic functions. Mechanistically, each matrix constituent linked to intracellular catabolic events engages a specific cell surface receptor that often converges on a common core of the autophagic machinery including AMPK, Peg3 and Beclin 1. We consider this matrix-evoked autophagy as non-canonical given that it occurs in an allosteric manner and is independent of nutrient availability or prevailing bioenergetics control. We propose that matrix-regulated autophagy is an important outside-in signaling mechanism for proper tissue homeostasis that could be therapeutically leveraged to combat a variety of diseases.