Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction

Genetic variants affecting mitochondrial function provide further insights for kidney disease

BACKGROUND

Chronic kidney disease (CKD) is a complex disorder that has become a high prevalence global health problem, with diabetes being its predominant pathophysiologic driver. Autosomal genetic variation only explains some of the predisposition to kidney disease. Variations in the mitochondrial genome (mtDNA) and nuclear-encoded mitochondrial genes (NEMG) are implicated in susceptibility to kidney disease and CKD progression, but they have not been thoroughly explored. Our aim was to investigate the association of variation in both mtDNA and NEMG with CKD (and related traits), with a particular focus on diabetes.

METHODS

We used the UK Biobank (UKB) and UK-ROI, an independent collection of individuals with type 1 diabetes mellitus (T1DM) patients.

RESULTS

Fourteen mitochondrial variants were associated with estimated glomerular filtration rate (eGFR) in UKB. Mitochondrial variants and haplogroups U, H and J were associated with eGFR and serum variables. Mitochondrial haplogroup H was associated with all the serum variables regardless of the presence of diabetes. Mitochondrial haplogroup X was associated with end-stage kidney disease (ESKD) in UKB. We confirmed the influence of several known NEMG on kidney disease and function and found novel associations for SLC39A13, CFL1, ACP2 or ATP5G1 with serum variables and kidney damage, and for SLC4A1, NUP210 and MYH14 with ESKD. The G allele of TBC1D32-rs113987180 was associated with higher risk of ESKD in patients with diabetes (OR:9.879; CI95%:4.440-21.980; P = 2.0E-08). In UK-ROI, AGXT2-rs71615838 and SURF1-rs183853102 were associated with diabetic nephropathies, and TFB1M-rs869120 with eGFR.

CONCLUSIONS

We identified novel variants both in mtDNA and NEMG which may explain some of the missing heritability for CKD and kidney phenotypes. We confirmed the role of MT-ND5 and mitochondrial haplogroup H on renal disease (serum variables), and identified the MT-ND5-rs41535848G variant, along with mitochondrial haplogroup X, associated with higher risk of ESKD. Despite most of the associations were independent of diabetes, we also showed potential roles for NEMG in T1DM.

Laminin-Augmented Decellularized Extracellular Matrix Ameliorating Neural Differentiation and Neuroinflammation in Human Mini-Brains

Non-neural extracellular matrix (ECM) has limited application in humanized physiological neural modeling due to insufficient brain-specificity and safety concerns. Although brain-derived ECM contains enriched neural components, certain essential components are partially lost during the decellularization process, necessitating augmentation. Here, it is demonstrated that the laminin-augmented porcine brain-decellularized ECM (P-BdECM) is xenogeneic factor-depleted as well as favorable for the regulation of human neurons, astrocytes, and microglia. P-BdECM composition is comparable to human BdECM regarding brain-specificity through the matrisome and gene ontology-biological process analysis. As augmenting strategy, laminin 111 supplement promotes neural function by synergic effect with laminin 521 in P-BdECM. Annexin A1(ANXA1) and Peroxiredoxin(PRDX) in P-BdECM stabilized microglial and astrocytic behavior under normal while promoting active neuroinflammation in response to neuropathological factors. Further, supplementation of the brain-specific molecule to non-neural matrix also ameliorated glial cell inflammation as in P-BdECM. In conclusion, P-BdECM-augmentation strategy can be used to recapitulate humanized pathophysiological cerebral environments for neurological study.

Complement Cascade Proteins Correlate with Fibrosis and Inflammation in Early-Stage Type 1 Diabetic Kidney Disease in the Ins2Akita Mouse Model

Diabetic kidney disease (DKD) is characterized by histological changes including fibrosis and inflammation. Evidence supports that DKD is mediated by the innate immune system and more specifically by the complement system. Using Ins2Akita T1D diabetic mice, we studied the connection between the complement cascade, inflammation, and fibrosis in early DKD. Data were extracted from a previously published quantitative-mass-spectrometry-based proteomics analysis of kidney glomeruli of 2 (early DKD) and 4 months (moderately advanced DKD)-old Ins2Akita mice and their controls A Spearman rho correlation analysis of complement- versus inflammation- and fibrosis-related protein expression was performed. A cross-omics validation of the correlation analyses' results was performed using public-domain transcriptomics datasets (Nephroseq). Tissue sections from 43 patients with DKD were analyzed using immunofluorescence. Among the differentially expressed proteins, the complement cascade proteins C3, C4B, and IGHM were significantly increased in both early and later stages of DKD. Inflammation-related proteins were mainly upregulated in early DKD, and fibrotic proteins were induced in moderately advanced stages of DKD. The abundance of complement proteins with fibrosis- and inflammation-related proteins was mostly positively correlated in early stages of DKD. This was confirmed in seven additional human and mouse transcriptomics DKD datasets. Moreover, C3 and IGHM mRNA levels were found to be negatively correlated with the estimated glomerular filtration rate (range for C3 rs = -0.58 to -0.842 and range for IGHM rs = -0.6 to -0.74) in these datasets. Immunohistology of human kidney biopsies revealed that C3, C1q, and IGM proteins were induced in patients with DKD and were correlated with fibrosis and inflammation. Our study shows for the first time the potential activation of the complement cascade associated with inflammation-mediated kidney fibrosis in the Ins2Akita T1D mouse model. Our findings could provide new perspectives for the treatment of early DKD as well as support the use of Ins2Akita T1D in pre-clinical studies.

Kidney Disease and Proteomics: A Recent Overview of a Useful Tool for Improving Early Diagnosis

Kidney disease is a critical and potentially life-threatening degenerative condition that poses a significant global public health challenge due to its elevated rates of morbidity and mortality. It manifests primarily in two distinct clinical forms: acute kidney injury (AKI) and chronic kidney disease (CKD). The development of these conditions hinges on a multitude of factors, including the etiological agents and the presence of coexisting medical conditions. Despite disparities in their underlying pathogenic mechanisms, both AKI and CKD can progress to end-stage kidney disease (ESKD). This advanced stage is characterized by organ failure and its associated complications, greatly increasing the risk of mortality. There is an urgent need to delve into the pathogenic mechanisms underlying these diseases and to identify novel biomarkers that can facilitate earlier diagnosis. Such early detection is crucial for enhancing the efficacy of therapy and impeding disease progression. In this context, proteomic approaches have emerged as invaluable tools for uncovering potential new markers of different pathological conditions, including kidney diseases. In this chapter, we overview the recent discoveries achieved through diverse proteomic techniques aimed at identifying novel molecules that may play a pivotal role in kidney diseases such as diabetic kidney disease (DKD), IgA nephropathy (IgAN), CKD of unknown origin (CKDu), autosomal dominant polycystic kidney disease (ADPKD), lupus nephritis (LN), hypertensive nephropathy (HN), and COVID-19-associated acute kidney injury (COVID-AKI).

Multiple-microarray analysis for identification of key genes involved in diabetic nephropathy

The purpose of our study was to discover genes with significantly aberrant expression in diabetic nephropathy (DN) and to determine their potential mechanism. We acquired renal tubules, glomerulus and blood samples data from DN patients and controls from the GEO database. The differentially expressed genes (DEGs) in renal tubules, glomerulus and blood samples between DN patients and controls were studied. Based on these DEGs, we carried out the functional annotation and constructed protein-protein interaction (PPI) network. By comparing DN patients and controls of DEGs, we acquired the shared DGEs in renal tubules, glomerulus and blood samples of DN patients and controls. DN patients compared to controls, we obtained 3000 DEGs, 3064 DEGs, and 2296 DEGs in renal tubules, glomerulus and blood samples, respectively. The PPI networks of top 40 DEGs in renal tubules, glomerulus and blood samples was consisted of 229 nodes and 229 edges, 540 nodes and 606 edges, and 132 nodes and 124 edges, respectively. In total, 21 shared genes were finally found, including CASP3, DHCR24, CXCL1, GYPC, INHBA, LTF, MT1G, MUC1, NINJ1, PFKFB3, PPP1R3C, CCL5, SRSF7, PHLDA2, RBM39, WTAP, BASP1, PLK2, PDK2, PNPLA4, and SNED1. These genes may be associated with the DN process. Our study provides a basis to explore the potential mechanism and identify novel therapeutic targets for DN.

Biochemical Correction of GM2 Ganglioside Accumulation in AB-Variant GM2 Gangliosidosis

GM2 gangliosidosis is a group of genetic disorders that result in the accumulation of GM2 ganglioside (GM2) in brain cells, leading to progressive central nervous system (CNS) atrophy and premature death in patients. AB-variant GM2 gangliosidosis (ABGM2) arises from loss-of-function mutations in the GM2 activator protein (GM2AP), which is essential for the breakdown of GM2 in a key catabolic pathway required for CNS lipid homeostasis. In this study, we show that intrathecal delivery of self-complementary adeno-associated virus serotype-9 (scAAV9) harbouring a functional human GM2A transgene (scAAV9.hGM2A) can prevent GM2 accumulation in in GM2AP-deficient mice (Gm2a^-/- mice). Additionally, scAAV9.hGM2A efficiently distributes to all tested regions of the CNS within 14 weeks post-injection and remains detectable for the lifespan of these animals (up to 104 weeks). Remarkably, GM2AP expression from the transgene scales with increasing doses of scAAV9.hGM2A (0.5, 1.0 and 2.0 × 10¹¹ vector genomes (vg) per mouse), and this correlates with dose-dependent correction of GM2 accumulation in the brain. No severe adverse events were observed, and comorbidities in treated mice were comparable to those in disease-free cohorts. Lastly, all doses yielded corrective outcomes. These data indicate that scAAV9.hGM2A treatment is relatively non-toxic and tolerable, and biochemically corrects GM2 accumulation in the CNS-the main cause of morbidity and mortality in patients with ABGM2. Importantly, these results constitute proof-of-principle for treating ABGM2 with scAAV9.hGM2A by means of a single intrathecal administration and establish a foundation for future preclinical research.

Recent Advances in Urinary Peptide and Proteomic Biomarkers in Chronic Kidney Disease: A Systematic Review

Biomarker development, improvement, and clinical implementation in the context of kidney disease have been a central focus of biomedical research for decades. To this point, only serum creatinine and urinary albumin excretion are well-accepted biomarkers in kidney disease. With their known blind spot in the early stages of kidney impairment and their diagnostic limitations, there is a need for better and more specific biomarkers. With the rise in large-scale analyses of the thousands of peptides in serum or urine samples using mass spectrometry techniques, hopes for biomarker development are high. Advances in proteomic research have led to the discovery of an increasing amount of potential proteomic biomarkers and the identification of candidate biomarkers for clinical implementation in the context of kidney disease management. In this review that strictly follows the PRISMA guidelines, we focus on urinary peptide and especially peptidomic biomarkers emerging from recent research and underline the role of those with the highest potential for clinical implementation. The Web of Science database (all databases) was searched on 17 October 2022, using the search terms "marker *" OR biomarker * AND "renal disease" OR "kidney disease" AND "proteome *" OR "peptid *" AND "urin *". English, full-text, original articles on humans published within the last 5 years were included, which had been cited at least five times per year. Studies based on animal models, renal transplant studies, metabolite studies, studies on miRNA, and studies on exosomal vesicles were excluded, focusing on urinary peptide biomarkers. The described search led to the identification of 3668 articles and the application of inclusion and exclusion criteria, as well as abstract and consecutive full-text analyses of three independent authors to reach a final number of 62 studies for this manuscript. The 62 manuscripts encompassed eight established single peptide biomarkers and several proteomic classifiers, including CKD273 and IgAN237. This review provides a summary of the recent evidence on single peptide urinary biomarkers in CKD, while emphasizing the increasing role of proteomic biomarker research with new research on established and new proteomic biomarkers. Lessons learned from the last 5 years in this review might encourage future studies, hopefully resulting in the routine clinical applicability of new biomarkers.

Urinary N-acetyl-β-d-glucosaminidase-creatine ratio is a valuable predictor for advanced diabetic kidney disease

BACKGROUND

Many biomarkers show high diagnostic values for diabetic kidney disease (DKD), but fewer studies focus on the predictive assessment of DKD progression by blood and urinary biomarkers.

AIM

This study aims to find powerful risk predictors and identifying biomarkers in blood and urine for DKD progression.

METHODS

A total of 117 patients with type 2 DKD including early and advanced stages and their laboratory parameters were statistically assessed. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the significance of discriminating between early and advanced DKD, and the predictive power for advanced DKD was analyzed by regression analysis and trisector grouping.

RESULTS

N-acetyl-β-d-glucosaminidase-creatine (NAG/CR) level in advanced DKD was statistically higher than that in early DKD (p < 0.05), and there was a higher incidence of advanced DKD (72% vs. 56%) and high odds ratio (OR: 3.917, 95% CI: 1.579-10.011) of NAG/CR with ≥2.79 U/mmol compared with <2.79 U/mmol (p < 0.05). NAG/CR ratio also showed a higher area under the ROC curve of 0.727 (95% CI: 0.616-0.828, p = 0.010) with a high sensitivity (0.75) and a moderate specificity (0.66) when 1.93 U/mmol was set as the optimal cutoff value. The adjusted-multivariable analysis revealed that NAG/CR had an OR of 1.021 (95% CI: 1.024-1.038) and 2.223 (95% CI: 1.231-4.463) based on a continuous and categorical variable, respectively, for risk of advanced DKD. Moreover, the prevalence of advanced DKD exhibited an increasing tendency by an increment of the trisector of NAG/CR.

CONCLUSIONS

This study suggests that NAG/CR ratio is an independent predictor for advanced DKD, and it also can be used as a powerful identifying marker between early and advanced DKD.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

Recurrent 17q12 microduplications contribute to renal disease but not diabetes

BACKGROUND

17q12 microdeletion and microduplication syndromes present as overlapping, multisystem disorders. We assessed the disease phenotypes of individuals with 17q12 CNV in a population-based cohort.

METHODS

We investigated 17q12 CNV using microarray data from 450 993 individuals in the UK Biobank and calculated disease status associations for diabetes, liver and renal function, neurological and psychiatric traits.

RESULTS

We identified 11 17q12 microdeletions and 106 microduplications. Microdeletions were strongly associated with diabetes (p=2×10^-7) but microduplications were not. Estimated glomerular filtration rate (eGFR mL/min/1.73 m²) was consistently lower in individuals with microdeletions (p=3×10^-12) and microduplications (p=6×10^-25). Similarly, eGFR <60, including end-stage renal disease, was associated with microdeletions (p=2×10^-9, p<0.003) and microduplications (p=1×10^-9, p=0.009), respectively, highlighting sometimes substantially reduced renal function in each. Microduplications were associated with decreased fluid intelligence (p=3×10^-4). SNP association analysis in the 17q12 region implicated changes to HNF1B as causing decreased eGFR (NC_000017.11:g.37741642T>G, rs12601991, p=4×10^-21) and diabetes (NC_000017.11:g.37741165C>T, rs7501939, p=6×10^-17). A second locus within the region was also associated with fluid intelligence (NC_000017.11:g.36593168T>C, rs1005552, p=6×10^-9) and decreased eGFR (NC_000017.11:g.36558947T>C, rs12150665, p=4×10^-15).

CONCLUSION

We demonstrate 17q12 microdeletions but not microduplications are associated with diabetes in a population-based cohort, likely caused by HNF1B haploinsufficiency. We show that both 17q12 microdeletions and microduplications are associated with renal disease, and multiple genes within the region likely contribute to renal and neurocognitive phenotypes.

Impaired Amino Acid Metabolism and Its Correlation with Diabetic Kidney Disease Progression in Type 2 Diabetes Mellitus

Background: Metabolomics is useful in elucidating the progression of diabetes; however, the follow-up changes in metabolomics among health, diabetes mellitus, and diabetic kidney disease (DKD) have not been reported. This study was aimed to reveal metabolomic signatures in diabetes development and progression. Methods: In this cross-sectional study, we compared healthy (n = 30), type 2 diabetes mellitus (T2DM) (n = 30), and DKD (n = 30) subjects with the goal of identifying gradual altering metabolites. Then, a prospective study was performed in T2DM patients to evaluate these altered metabolites in the onset of DKD. Logistic regression was conducted to predict rapid eGFR decline in T2DM subjects using altered metabolites. The prospective association of metabolites with the risk of developing DKD was examined using logistic regression and restricted cubic spline regression models. Results: In this cross-sectional study, impaired amino acid metabolism was the main metabolic signature in the onset and development of diabetes, which was characterized by increased N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine levels in both the T2DM and DKD groups. These candidate metabolites could distinguish the DKD group from the T2DM group. In the follow-up study, higher baseline levels of L-valine and isoleucine were significantly associated with an increased risk of rapid eGFR decline in T2DM patients. Of these, L-valine and isoleucine were independent risk factors for the development of DKD. Notably, nonlinear associations were also observed for higher baseline levels of L-valine and isoleucine, with an increased risk of DKD among patients with T2DM. Conclusion: Amino acid metabolism was disturbed in diabetes, and N-acetylaspartic acid, L-valine, isoleucine, asparagine, betaine, and L-methionine could be biomarkers for the onset and progression of diabetes. Furthermore, high levels of L-valine and isoleucine may be risk factors for DKD development.

Predictive significance of joint plasma fibrinogen and urinary alpha-1 microglobulin-creatinine ratio in patients with diabetic kidney disease

Background

Although many biomarkers have high diagnostic and predictive power for diabetic kidney disease (DKD), less studies were performed for the predictive assessment in DKD and its progression with combined blood and urinary biomarkers. This study aims to explore the predictive significance of joint plasma fibrinogen (FIB) concentration and urinary alpha-1 microglobulin-creatinine (α1-MG/CR) ratio in DKD.

Methods

A total of 234 patients with type 2 diabetes were enrolled, and their clinical and laboratory data were retrospectively assessed. A ROC curve analysis was performed to evaluate the power of plasma FIB and urinary α1-MG/CR ratio for identifying DKD and advanced DKD, respectively. The predictive power for DKD and advanced DKD was analyzed by regression analysis.

Results

Plasma FIB and urinary α1-MG/CR levels were higher in patients with DKD than with pure T2D (p<0.001). The multivariate-adjusted odds ratios (ORs) were 5.047 (95%CI: 2.276–10.720) and 2.192 (95%CI: 1.539–3.122) (p<0.001) for FIB and α1-MG/CR as continuous variables for DKD prediction, respectively. The optimal cut-off values were 3.21 g/L and 2.11mg/mmol for identifying DKD, and 5.58 g/L and 11.07 mg/mmol for advanced DKD from ROC curves. At these cut-off values, the sensitivity and specificity of joint FIB and α1-MG/CR were 0.95 and 0.92 for identifying DKD, and 0.62 and 0.67 for identifying advanced DKD, respectively. The area under curve was 0.972 (95%CI: 0.948–0.995) (p<0.001) and 0.611, 95%CI: 0.488–0.734) (p>0.05). The multivariate-adjusted ORs for joint FIB and α1-MG/CR at the cut-off values were 214.500 (95%CI: 58.054–792.536) and 3.252 (95%CI: 1.040–10.175) (p<0.05), respectively.

Conclusion

The present study suggests that joint plasma FIB concentration and urinary α1-MG/CR ratio can be used as a powerful predictor for general DKD, but it is less predictive for advanced DKD.

Prediction of the Short-Term Risk of New-Onset Renal Dysfunction in Patients with Type 2 Diabetes: A Longitudinal Observational Study

Background

Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D.

Methods

This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated.

Results

During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction (P < 0.05). Several logistic models incorporating clinical characteristics and these renal markers were constructed for predicting the short-term risk of new-onset renal dysfunction. Comparatively, the model including age, glycated hemoglobin (HbA1c), hypertension, ACR, A1MCR, UTRF/Cr, and URBP/Cr levels at baseline had the highest potential (C − index = 0.785, P < 0.001). This model was validated using the K-fold cross-validation method; the accuracy was 0.815 ± 0.013 in training sets and 0.784 ± 0.019 in validation sets, indicating a good consistency for predicting the new-onset renal dysfunction risk. Finally, a nomogram based on this model was constructed to provide a quantitative tool to assess the individualized risk of short-term new-onset renal dysfunction.

Conclusion

The model incorporating these markers and clinical features may have a high potential to predict the short-term risk of new-onset renal dysfunction.

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.

Plasma Proteome Signature to Predict the Outcome of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic-peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Pleiotropic Effects of Functional MUC1 Variants on Cardiometabolic, Renal, and Hematological Traits in the Taiwanese Population

MUC1 is a transmembrane mucin involved in carcinogenesis and cell signaling. Functional MUC1 variants are associated with multiple metabolic and biochemical traits. This study investigated the association of functional MUC1 variants with MUC1 DNA methylation and various metabolic, biochemical, and hematological parameters. In total, 80,728 participants from the Taiwan Biobank were enrolled for association analysis using functional MUC1 variants and a nearby gene regional plot association study. A subgroup of 1686 participants was recruited for MUC1 DNA methylation analysis. After Bonferroni correction, we found that two MUC1 variants, rs4072037 and rs12411216, were significantly associated with waist circumference, systolic blood pressure, hemoglobin A1C, renal functional parameters (blood urea nitrogen, serum creatinine levels, and estimated glomerular filtration rate), albuminuria, hematocrit, hemoglobin, red blood cell count, serum uric acid level, and gout risk, with both favorable and unfavorable effects. Causal inference analysis revealed that the association between the variants and gout was partially dependent on the serum uric acid level. Both gene variants showed genome-wide significant associations with MUC1 gene-body methylation. Regional plot association analysis further revealed lead single-nucleotide polymorphisms situated at the nearby TRIM46-MUC1-THBS3-MTX1 gene region for the studied phenotypes. In conclusion, our data demonstrated the pleiotropic effects of MUC1 variants with novel associations for gout, red blood cell parameters, and MUC1 DNA methylation. These results provide further evidence in understanding the critical role of TRIM46-MUC1-THBS3-MTX1 gene region variants in the pathogenesis of cardiometabolic, renal, and hematological disorders.

CRP immunodeposition and proteomic analysis in abdominal aortic aneurysm

OBJECTIVE

The molecular mechanisms of the degeneration of the aortic wall in abdominal aortic aneurysm (AAA) are poorly understood. The monomeric form of C-reactive protein (mCRP) is deposited in damaged cardiovascular organs and aggravates the prognosis; however, it is unknown whether mCRP is deposited in the degenerated aorta of abdominal aortic aneurysm (AAA). We investigated whether mCRP is deposited in AAA and examined the associated pathogenic signaling pathways.

METHODS

Twenty-four cases of AAA were analyzed and their histological features were compared according to the level of serum CRP and the degree of mCRP deposition. Proteomic analysis was performed in AAA cases with strong and diffuse CRP immunopositivity (n = 7) and those with weak, focal, and junctional CRP immunopositivity (n = 3).

RESULTS

mCRP was deposited in the aortic specimens of AAA in a characteristic pattern that coincided with the lesion of the diminished elastic layer of the aortic wall. High serum CRP level was associated with stronger mCRP immunopositivity and a larger maximal diameter of aortic aneurysm. Proteomic analysis in AAA showed that multiple proteins were differentially expressed according to mCRP immunopositivity. Also, ingenuity pathway analysis showed that pathways associated with atherosclerosis, acute phase response, complement system, immune system, and coagulation were enriched in AAA cases with high mCRP immunopositivity.

CONCLUSIONS

AAA showed a characteristic deposition of mCRP, and multiple potentially pathologic signaling pathways were upregulated in AAA cases with strong CRP immunopositivity. mCRP and the aforementioned pathological pathways may serve as targets for managing the progression of AAA.

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.

Plasma Protein Biomarkers Associated with Higher Ovarian Cancer Risk in BRCA1/2 Carriers

Simple Summary

Most hereditary ovarian cancer is associated with BRCA1/2 variants, and risk-reducing salpingo-oophorectomy during the follow-up monitoring of ovarian cancer development in heathy women with the BRCA1/2 variant reduces ovarian cancer incidence. The aim of this study was to identify plasma protein biomarkers that can indicate an increased risk of developing ovarian cancer using a proteomic approach based on a population of genetic variants. Two identified biomarkers among differentially expressed proteins, SPARC and THBS1, had lower plasma concentrations in healthy BRCA1/2 variant carriers than in ovarian cancer patients with the BRCA1/2 variant; concentration of two proteins increased at the onset of ovarian cancer. These protein markers from non-invasive liquid biopsy sampling could be used to help women with the BRCA1/2 variant determine whether to undergo an oophorectomy that could potentially affect the quality of life.

Abstract

Ovarian cancer (OC) is the most lethal gynecologic malignancy and in-time diagnosis is limited because of the absence of effective biomarkers. Germline BRCA1/2 genetic alterations are risk factors for hereditary OC; risk-reducing salpingo-oophorectomy (RRSO) is pursued for disease prevention. However, not all healthy carriers develop the disease. Therefore, identifying predictive markers in the BRCA1/2 carrier population could help improve the identification of candidates for preventive RRSO. In this study, plasma samples from 20 OC patients (10 patients with BRCA1/2 wild type (_wt) and 10 with the BRCA1/2 variant (_var)) and 20 normal subjects (10 subjects with BRCA1/2_wt and 10 with BRCA1/2_var) were analyzed for potential biomarkers of hereditary OC. We applied a bottom-up proteomics approach, using nano-flow LC-MS to analyze depleted plasma proteome quantitatively, and potential plasma protein markers specific to the BRCA1/2 variant were identified from a comparative statistical analysis of the four groups. We obtained 1505 protein candidates from the 40 subjects, and SPARC and THBS1 were verified by enzyme-linked immunosorbent assay. Plasma SPARC and THBS1 concentrations in healthy BRCA1/2 carriers were found to be lower than in OC patients with BRCA1/2_var. If plasma SPARC concentrations increase over 337.35 ng/mL or plasma THBS1 concentrations increase over 65.28 μg/mL in a healthy BRCA1/2 carrier, oophorectomy may be suggested.

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis

PURPOSE

Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail.

EXPERIMENTAL DESIGN

We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples.

RESULTS

BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins. Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information.

CONCLUSIONS AND CLINICAL RELEVANCE

The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.

Urinary Proteomic Characteristics of Hyperuricemia and Their Possible Links with the Occurrence of Its Concomitant Diseases

Hyperuricemia (HUA), a chronic disease caused by metabolic disorders of purine, is often accompanied by other diseases such as gout, type 2 diabetes mellitus (T2DM), and hyperlipidemia. However, little is known about the relationship between HUA and these diseases on the protein level. We performed label-free liquid chromatography MS/MS spectrometry analysis of urine samples from 26 HUA patients and 25 healthy controls, attempting to establish the possible protein links between HUA and these diseases by profiling urine proteome. A total of 2119 proteins were characterized in sample proteomes. Among them, 11 were found decreased and 2 were found increased in HUA samples. Plausible pathways found by enrichment analysis of these differentially expressed proteins (DEPs) include the processes for insulin receptor recycling and lipid metabolism, suggesting potential links between HUA and T2DM and hyperlipidemia. The abundance changes of three key proteins (VATB1, CFAD, and APOC3) involved in these processes were validated by enzyme-linked immunosorbent assay (ELISA). In conclusion, our result provides proteomic evidence, for the first time, that the aberrant pathways enriched by described key DEPs are closely related to the incidence of HUA and its concomitant diseases.

Understanding glomerular diseases through proteomics

INTRODUCTION

Chronic kidney disease is avery common and complex chronic disease. Uncovering the pathological patterns of CKD on the molecular level of bio-fluids and tissue appears to be both vital and promising for a more favorable outcome. We reviewed recently discovered proteomics biomarkers for CKD to provide new insight into disease pathology.

AREAS COVERED

We review the application of proteome analysis in the context of CKD with various etiologies within the last 5 years. Proteins and peptides associated with CKD as derived from multiple sources (urine, blood and tissue) are reported along with their various biological pathways.

EXPERT OPINION

A systematic and theoretical comprehension of the CKD pathology is essential for its successful management. The underlying complexity of the disease further requires specific conditions for reliable and interpretable results. In this context, clinical proteomics has resulted in first encouraging findings in CKD. A more complete understanding of the biological pathways related to the disease, based on the scope of a holistic proteomic approach, could improve substantially the management of CKD, especially when in conjunction with the current trend of personalized medicine.

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC−MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit.