Biomarkers Of Kidney Disease Research Articles

Apolipoprotein L1 High-Risk Genotypes and Albuminuria in Sub-Saharan African Populations.

Recessive inheritance of African-specific APOL1 kidney risk variants is associated with higher risk of nondiabetic kidney disease, progression to kidney failure, and early-onset albuminuria that precedes eGFR decline. The effect of APOL1 risk variants on kidney disease in continental Africans is understudied. Objectives of this study were to determine APOL1 risk allele prevalence and associations between APOL1 genotypes and kidney disease in West, East, and South Africa. This cross-sectional population-based study in four African countries included 10,769 participants largely aged 40-60 years with sociodemographic and health information, anthropometry data, and blood and urine tests for biomarkers of kidney disease. APOL1 risk alleles were imputed from the H3Africa genotyping array, APOL1 risk allele and genotype frequencies were determined, and genetic associations were assessed for kidney disease. Kidney disease was defined as the presence of eGFR <60 ml/min per 1.73 m2, albuminuria, or a composite end point including eGFR <60 ml/min per 1.73 m2 and/or albuminuria. High G1 allele frequencies occurred in South and West Africa (approximately 7%-13%). G2 allele frequencies were highest in South Africa (15%-24%), followed by West Africa (9%-12%). Associations between APOL1 risk variants and albuminuria were significant for recessive (odds ratio, 1.63; 95% confidence interval, 1.25 to 2.12) and additive (odds ratio, 1.39; 95% confidence interval, 1.09 to 1.76) models. Associations were stronger for APOL1 G1/G1 genotypes versus G0/G0 (odds ratio, 3.87; 95% confidence interval, 2.16 to 6.93) compared with either G2/G2 (odds ratio, 1.65; 95% confidence interval, 1.09 to 2.51) or G1/G2 (odds ratio, 1.24; 95% confidence interval, 0.83 to 1.87). No association between APOL1 risk variants and eGFR <60 ml/min per 1.73 m2 was observed. APOL1 G1 and G2 alleles and high-risk genotype frequencies differed between and within West and South Africa and were almost absent from East Africa. APOL1 risk variants were associated with albuminuria but not eGFR <60 ml/min per 1.73 m2. There may be differential effects of homozygous G1 and G2 genotypes on albuminuria that require further investigation. This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_05_16_CJN14321121.mp3.

Novel Biomarkers of Kidney Disease in Advanced Heart Failure: Beyond GFR and Proteinuria.

Kidney disease is a common finding in patients with heart failure and can significantly impact treatment decisions and outcomes. Abnormal kidney function is currently determined in clinical practice using filtration markers in the blood to estimate glomerular filtration rate, but the manifestations of kidney disease in the setting of heart failure are much more complex than this. In this manuscript, we review novel biomarkers that may provide a more well-rounded assessment of kidney disease in patients with heart failure. Galectin-3, ST2, FGF-23, suPAR, miRNA, GDF-15, and NAG may be prognostic of kidney disease progression. L-FABP and suPAR may help predict acute kidney injury (AKI). ST2 and NAG may be helpful in diuretic resistance. Several biomarkers may be useful in determining prognosis of long-term kidney disease progression, prediction of AKI, and development of diuretic resistance. Further research into the mechanisms of kidney disease in heart failure utilizing many of these biomarkers may lead to the identification of therapeutic targets.

Exploration of a panel of urine biomarkers of kidney disease in two paediatric cohorts with Type 1 diabetes mellitus of differing duration

BackgroundThe pathogenesis of diabetic kidney disease (DKD) is complex and involves both glomerular and tubular dysfunction. A global assessment of kidney function is necessary to stage DKD, a progressive kidney disease that is likely to begin in childhood. The present study evaluated whether kidney injury biomarkers identified as early DKD biomarkers in adults have any prognostic value in the very early stages of childhood diabetes.MethodsWe measured urine free Retinol-binding protein 4 (UfRBP4), albumin (UAlb), Kidney injury molecule-1 (KIM-1) and the microRNAs miR-155, miR-126 and miR-29b in two cohorts of paediatric T1DM patients without evidence of DKD, but with diabetes of short-duration, ≤ 2.5 years (SD, n = 25) or of long-duration, ≥ 10 years (LD, n = 29); non-diabetic siblings (H, n = 26) were recruited as controls. A p value < 0.05 was considered significant for all results.ResultsUfRBP4 and UAlb were not significantly different across the three groups. No differences were found in KIM-1 excretion between any of the three groups. UfRBP4 was correlated with UAlb in all three groups (r 0.49; p < 0.001), whereas KIM-1 showed no correlation with albumin excretion. Among microRNAs, miR-29b was higher in all diabetic children compared with the H control group (p = 0.03), whereas miR-155 and miR-126 were not significantly different. No differences were found between the SD and LD groups for all three microRNAs. No associations were identified between these biomarkers with sex, age, BMI, eGFR, T1DM duration or glycaemic control.ConclusionsUfRBP4, KIM-1, miR-155, and miR-126 were unaffected by the presence and duration of diabetes, whereas miR-29b showed a modest elevation in diabetics, regardless of duration. These data support the specificity of a panel of urine biomarkers as DKD biomarkers, rather than any relationship to diabetes per se or its duration, and not as early DKD biomarkers in a paediatric setting.

MO619: Landscape RNA Profiling of Urinary Extracellular Vesicles in Patients with Diabetic Nephropathy

Abstract BACKGROUND AND AIMS Recent studies have revealed that urinary extracellular vesicles (uEV) are secreted with rich biological information useful for understanding the pathophysiological conditions and identifying potential biomarkers for kidney disease. Diabetic nephropathy (DN) has become the major cause of CKD worldwide, while the features of uEV in DN were poorly understood. The present study aims to provide the landscape and in-depth analysis of the transcriptome characteristics of uEV in DN. METHOD Urine was collected from a total of 40 subjects, including healthy controls (HC, n = 14), type 2 DM (T2DM, n = 16), and renal biopsy-confirmed DN (n = 10). uEV was isolated by differential centrifugation and total RNA was extracted for long RNA sequencing. A model based on CIBERSORT was calculated to trace the genetic source of uEV toward resident cells of urinary system. Differentially expressed genes (DEG) between DN and HC and DN and T2DM were applied to gene ontology (GO), Reactome pathway enrichment, as well as gene set enrichment analysis (GSEA). Correlation of the transcriptome profiling between uEV and kidney tissue in DN was also explored. RESULTS About 67%∼68% of transcripts in RNA-seq were mRNA, 11%∼12% were LncRNA and 7%∼10% were CircRNA, and the abundance of mRNA was greater than other RNA types. By CIBERSORT model applying marker genes of podocytes, epithelial cells of tubule of different segments, loop of Henle, and collecting duct, and bladder transitional epithelial cells. The uEV was shown to originate mainly from the collecting duct epithelial cells of the kidney and bladder transitional epithelial cells (Fig. 1a). Confocal laser microscopy showed the co-localization of CD63 and AQP2 (Fig. 1b) in kidney tissues. GO and Reactome analysis were further performed in upregulated mRNA between DN and T2DM and DN and HC to explore the mRNA profiling in terms of the biological significance. Impressively, ‘extracellular exosome’ as well as other terms associated with constitution of EV, ‘plasma membrane’ and ‘extracellular space’ were enriched by GO cellular component (CC) analysis in DN. Besides, Reactome analysis identified pathways of ‘immune system’, ‘extracellular matrix organization’ and ‘degradation of extracellular matrix’ reflecting the pathogenesis mechanism of DN (Fig. 1c). Additionally, microarray data of DN glomeruli and tubule from the GEO database were analyzed in comparison to uEV RNA-seq data. GSEA showed that great similarities were found in the top 10 terms of Reactome pathways (Fig. 2). CONCLUSION In conclusion, this study provides a landscape transcriptome profile, the genetic trace and the biological function terms of uEV in DN. uEV holds potentials in reflecting the transcriptome characteristics of renal tissue, which is of significance for understanding the pathogenesis of DN and biomarker discovery.

MO384: Identifying Long Non-Coding RNA Biomarkers From Urinary Exosomes for Diabetic Nephropathy

Abstract BACKGROUND AND AIMS Diabetic nephropathy (DN) is the common cause of end-stage renal disease. However, the early and specific diagnostic biomarkers are still lacking. Urinary exosomes are a potential source of biomarkers for kidney disease due to their renal cell origination, stability, and noninvasive collection. Long non-coding RNAs (lncRNAs) are increasingly recognized to be involved in diverse pathophysiological processes including kidney disease. Here we set to characterize the lncRNAs profiles in urinary exosomes of DN with aims to identify novel non-invasive biomarkers of DN. METHOD First-morning urine was collected from 11 healthy people, 9 patients with diabetes mellitus (DM), and 8 patients with biopsy-proven DN. Urinary exosomes were pelleted by ultracentrifugation and total RNA was extracted for next generation sequencing (NGS). The lncRNA profile of urinary exosomes was first characterized in terms of the detectable RNA numbers, expression levels in different groups. Then differential gene expression analysis was performed to facilitate the discovery of potential biomarkers. RESULTS Similar tremendous number of lncRNAs was detected with a total number of 6439 transcripts present in urinary exosomes in each group. In terms of the expression levels, the percentage of transcripts with TPM above 1 was 31.4%, 32.8%, 33.2% in the healthy group, DM and DN patients respectively, suggesting that no significant alteration of lncRNAs numbers and distribution in DN and diabetes conditions. Next, the differential expression analysis was performed to identify the distinct differential lncRNAs. Compared with healthy people, 18 lncRNAs were upregulated and 67 lncRNAs were downregulated in DM group, while 37 unregulated and 80 downregulated lncRNAs were identified in DN group. Moreover, the profile of lncRNAs in urinary exosomes was significantly different in DN patients compared to DM group with 59 lncRNAs upregulated and 36 lncRNAs downregulated. To explore the candidate biomarkers, 12 upregulated lncRNAs and 4 downregulated lncRNAs from the two comparison sets (DN versus Healthy, DN versus Healthy) were identified, among which FLJ16779 was present with the most remarkable difference. Further analysis showed that the sensitivity and specificity of FLJ16779 for distinguishing DN from healthy people are both 87.5%, and the area under the curve (AUC) was 0.9531. Additionally, the sensitivity and specificity of FLJ16779 for distinguishing DN from DM were 75% and 87.5% respectively, and AUC was 0.9063. CONCLUSION Urinary exosomes contain rich lncRNAs information with differential expression profiles in DN patients compared to DM and healthy controls, suggesting the possible involvement of lncRNAs in the pathogenesis of DN. lncRNA FLJ16779 might be a promising non-invasive biomarker of DN which requires further validation.

Chronic Pain State Mediates Development of Hippocampal and Renal Inflammatory Responses

Chronic pain and related stress have been previously linked to the development of mood disorders and dysfunction of peripheral organs such as the kidney. While the underlying neurophysiological mechanisms remain elusive, here we examined the effects of chronic pain on activation of immune‐inflammatory responses in the brain and kidney. Male rats were exposed to chronic inflammatory pain for up to 42 days [multiple injections of Freund’s adjuvant (CFA) into the hind paw], which produced a state of chronic allodynia and enhanced behavioral emotionality. Biochemical analysis of the hippocampus, a limbic region that regulates mood and stress responses, showed that CFA evoked increases in expression of ionized calcium binding adaptor molecule 1 (IBA1) and NLRP3 inflammasome proteins, known markers of microglial activation and neuroinflammatory responses, respectively. An increase in the neutrophil gelatinase‐associated lipocalin (NGAL) and IL‐18 are recognized as early diagnostic injury and inflammatory biomarkers of kidney disease. NLRP3 is implicated in the pathogenesis of kidney diseases by regulation of renal inflammation. Analysis via immunocytochemistry demonstrated that CFA also evoked significant increases in NGAL, IL‐18 and NLRP3 protein levels in the renal glomeruli and tubules, suggesting that chronic pain and related stress effects induce renal inflammation and possibly a reduction in kidney function. Together, these findings provide new evidence to support a mechanistical understanding of a bidirectional relationship between chronic pain‐related stress and kidney dysfunction. Further understanding of this relationship could contribute to the identification of novel treatment strategies to diminish both mental health and renal physiological consequences of chronic pain.

Association of plasma neutrophil gelatinase-associated lipocalin and thoracic aorta calcification in maintenance hemodialysis patients with and without diabetes

BackgroundNeutrophil gelatinase-associated lipocalin (NGAL) is not only a bone-derived factor involved in metabolism, but also a biomarker of kidney disease and cardiovascular pathophysiology. We conducted this cross-sectional observational study to explore relationships between plasma NGAL and thoracic aorta calcification (TAC) in maintenance hemodialysis (MHD) patients with and without diabetes.MethodsPlasma NGAL was measured by ELISA, TAC was evaluated via computed tomography scan using a 3D quantification method or chest radiography aortic arch calcification score. Spearman correlation, Logistic regression and Partial correlation analysis were used to describe the correlations between NGAL and TAC.ResultsPlasma NGAL levels were lower in MHD patients with diabetes compared to those without diabetes (49.33(42.37, 55.48) vs 56.78(44.37, 674.13) ng/mL, P = 0.026). In MHD patients without diabetes, lg (NGAL) was positively correlated with ARC value(R = 0.612, P = 0.003) analyzed by Spearman correlation; for partial correlation analysis, lg (NGAL) was positively correlated with ARC value, after adjusting for age and sex (R = 0.550, P = 0.015), adjusting for age, sex and CHD (R = 0.565, P = 0.015), adjusting for age, sex, CHD and Alb (R = 0.536, P = 0.027), or adjusting for age, sex, CHD, Alb, and dialyzer membrane (polysulfone) (R = 0.590, P = 0.016); however, when adjusting for age, sex, CHD, Alb and Ca, the correlation between lg (NGAL) and ARC value disappeared. Positive correlation were found between NGAL and Ca (R = 0.644, P < 0.001), Ca and ACR (R = 0.534, P = 0.013) in Spearman coefficient analysis.ConclusionThere were positive correlations among plasma NGAL, serum Ca and ARC in MHD patients without diabetes; which suggests that NGAL is possibly a participant in cardiovascular calcification, in non-diabetic MHD.

Asymptomatic Pyuria as a Prognostic Biomarker in Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) has phenotypic variability only partially explained by established biomarkers that do not readily assess pathologically important factors of inflammation and kidney fibrosis. We evaluated asymptomatic pyuria (AP), a surrogate marker of inflammation, as a biomarker for disease progression. We performed a retrospective cohort study of adult patients with ADPKD. Patients were divided into AP and no pyuria (NP) groups. We evaluated the effect of pyuria on kidney function and kidney volume. Longitudinal models evaluating kidney function and kidney volume rate of change with respect to incidences of AP were created. There were 687 included patients (347 AP, 340 NP). The AP group had more women (65% versus 49%). Median ages at kidney failure were 86 and 80 years in the NP and AP groups (log rank, P=0.49), respectively, for patients in Mayo Imaging Class (MIC) 1A-1B as compared with 59 and 55 years for patients in MIC 1C-1D-1E (log rank, P=0.02), respectively. Compared with the NP group, the rate of kidney function (ml/min per 1.73 m2 per year) decline shifted significantly after detection of AP in the models, including all patients (-1.48; P<0.001), patients in MIC 1A-1B (-1.79; P<0.001), patients in MIC 1C-1D-1E (-1.18; P<0.001), and patients with PKD1 (-1.04; P<0.001). Models evaluating kidney volume rate of growth showed no change after incidence of AP as compared with the NP group. AP is associated with kidney failure and faster kidney function decline irrespective of the ADPKD gene, cystic burden, and cystic growth. These results support AP as an enriching prognostic biomarker for the rate of disease progression.

Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease.

Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.

Renal and Inflammatory Proteins as Biomarkers of Diabetic Kidney Disease and Lupus Nephritis.

Current methods for differentiation of kidney disease types are unspecific and may be invasive. Thus, there is a need for development of new biomarkers of kidney disorders that are specific and less invasive. In this study, we analyzed serum samples of diabetic kidney disease (DKD) and lupus nephritis (LN) patients to identify biomarkers of these two disorders. Serum samples were analyzed by Simple Plex assays. We calculated the area under the curve (AUC) as well as receiver operating characteristics (ROC) to obtain the sensitivity and specificity and other biomarker-related variables of apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), interleukin- (IL-) 18, Lipocalin-2/NGAL, epidermal growth factor (EGF), u-Plasminogen Activator (uPA), and C-reactive protein (CRP) as potential biomarkers. Protein levels of ASC, IL-18, EGF, and Lipocalin-2/NGAL were higher in DKD and LN patients when compared to controls, whereas only uPA was elevated in DKD patients and CRP in LN patients. As determined by the AUC, of the six analytes studied, EGF (AUC = 0.9935), Lipocalin-2/NGAL (0.9554), ASC (0.7666), and uPA (0.7522) are reliable biomarkers of DKD, whereas EGF (1.000), Lipocalin-2/NGAL (0.9412), uPA (0.7443), and IL-18 (0.7384) are more reliable for LN. The biomarkers analyzed can differentiate between healthy and affected individuals. However, there was no difference between the levels of these biomarkers in DKD vs LN. Thus, although these biomarkers cannot be used to categorize patients between DKD and LN, they are useful as biomarkers of renal pathology.

Structural and Electrochemical Analysis of Copper-Creatinine Complexes: Application in Creatinine Detection

The formation of complexes between copper ions and biomolecules plays important roles in biological systems. In this work, the structures and electrochemical properties of copper-creatinine complexes were investigated by both experimental and computational approaches. DFT calculation revealed the possible structures of copper-creatinine complexes and provided the data of formation energies, bond lengths, and charge distribution. The properties of the complexes were further investigated by cyclic voltammetry, UV-visible spectrophotometry, X-ray absorption spectroscopy, and scanning electron microscopy. The combination of experimental and computational findings revealed that CuII binds with creatinine via the endocyclic nitrogen. In aqueous environment, the [Cu(creatinine)2(H2O)2]2+ complex is formed. The reduction of [Cu(creatinine)2(H2O)2]2+ formed a stable 1:4 complex between CuI and creatinine. Importantly, the understanding of the electrochemical behaviors of copper-creatinine complexes leads to the development of a novel sensor for the detection of creatinine, a biomarker for kidney diseases. Although creatinine itself is not electroactive, the complex formation with copper allows the species to be detected electrochemically with the sensitivity of 6.09 ± 0.13 μA mM−1 and the limit of detection (3sB/m) of 35 μM.

POS-196 Metabolomic analysis with machine learning identified biomarkers to predict rapid decliner in diabetic kidney disease

Diabetic kidney disease still remains an important health care problem. Early identification of rapid decliners of diabetic kidney disease helps early interventions on the target population as well as efficient execution of clinical trials. Recent advancement in a comprehensive measurement of metabolites and machine learning analysis enables exploratory research, although the method of interpreting the results has not been determined. In this research, we performed comprehensive mass spectrometry of metabolites and explainable machine learning analysis to explore potential biomarkers of diabetic kidney disease.

Serum Cystatin C as a Biomarker for Early Diabetic Kidney Disease and Dyslipidemia in Young Type 1 Diabetes Patients.

Background and objectives: This study aimed to assess the clinical significance of serum cystatin C in the early diagnosis of renal injury and its association with dyslipidemia in young T1D patients. Materials and Methods: A total of 779 subjects were evaluated for kidney function by estimating glomerular filtration rate (eGFR) based on serum creatinine (eGFRcreat) and cystatin C (eGFRcys). Results: The median age of study subjects was 16.2 years (2.1;26.4), diabetes duration—5.3 years (0.51;24.0). The median of HbA1c was 8% (5.2;19.9) (64 mmol/mol (33.3;194)); 24.2% of participants had HbA1c < 7% (53 mmol/mol). Elevated albumin excretion rate was found in 13.5% of subjects. The median of cystatin C was 0.8 mg/L (0.33;1.71), the median of creatinine—63 µmol/L (6;126). The median of eGFRcys was lower than eGFRcreat (92 mL/min/1.73 m2 vs. 101 mL/min/1.73 m2, p < 0.001). A total of 30.2% of all patients were classified as having worse kidney function when using cystatin C vs. creatinine for eGFR calculation. Linear correlations were found between cystatin C and HbA1c, r = −0.088, p < 0.05, as well as cystatin C and HDL, r = −0.097, p < 0.01. Conclusions: This study showed that cystatin C might be used as an additional biomarker of early kidney injury in young patients with T1D.

Integrated Analysis of Single-Cell RNA-seq and Bulk RNA-seq in the Identification of a Novel ceRNA Network and Key Biomarkers in Diabetic Kidney Disease

Integrated Analysis of Single-Cell RNA-seq and Bulk RNA-seq in the Identification of a Novel ceRNA Network and Key Biomarkers in Diabetic Kidney Disease

Biomarkers for early diagnosis of diabetic kidney disease: still a long way to go.

Biomarkers for early diagnosis of diabetic kidney disease: still a long way to go.

Circulating Plasma Biomarkers in Biopsy-Confirmed Kidney Disease.

Biomarkers for noninvasive assessment of histopathology and prognosis are needed in patients with kidney disease. Using a proteomics assay, we measured a multimarker panel of 225 circulating plasma proteins in a prospective cohort study of 549 individuals with biopsy-confirmed kidney diseases and semiquantitative assessment of histopathology. We tested the associations of each biomarker with histopathologic lesions and the risks of kidney disease progression (defined as ≥40% decline in eGFR or initiation of KRT) and death. After multivariable adjustment and correction for multiple testing, 46 different proteins were associated with histopathologic lesions. The top-performing markers positively associated with acute tubular injury and interstitial fibrosis/tubular atrophy were kidney injury molecule-1 (KIM-1) and V-set and Ig domain-containing protein 2 (VSIG2), respectively. Thirty proteins were significantly associated with kidney disease progression, and 35 were significantly associated with death. The top-performing markers for kidney disease progression were placental growth factor (hazard ratio per doubling, 5.4; 95% confidence interval, 3.4 to 8.7) and BMP and activin membrane-bound inhibitor (hazard ratio, 3.0; 95% confidence interval, 2.1 to 4.2); the top-performing markers for death were TNF-related apoptosis-inducing ligand receptor-2 (hazard ratio, 2.9; 95% confidence interval, 2.0 to 4.0) and CUB domain-containing protein-1 (hazard ratio, 2.4; 95% confidence interval, 1.8 to 3.3). We identified several plasma protein biomarkers associated with kidney disease histopathology and adverse clinical outcomes in individuals with a diverse set of kidney diseases. This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_12_28_CJN09380721.mp3.

Nephron mass determines the excretion rate of urinary extracellular vesicles.

Urinary extracellular vesicles (uEVs) are emerging as non‐invasive biomarkers for various kidney diseases, but it is unknown how differences in nephron mass impact uEV excretion. To address this, uEV excretion was measured before and after human kidney donor nephrectomy and rat nephrectomy. In male and female donors, uEVs were quantified in cell‐free spot and 24‐h urine samples using nanoparticle tracking analysis (NTA), EVQuant, and CD9‐time‐resolved fluorescence immunoassay. Female donors had significantly lower total kidney volume (TKV) and excreted 49% fewer uEVs than male donors. uEV excretion correlated positively with estimated glomerular filtration rate (eGFR), creatinine clearance, and TKV (R's between 0.6 and 0.7). uEV excretion rate could also be predicted from spot urines after multiplying spot uEV/creatinine by 24‐h urine creatinine. Donor nephrectomy reduced eGFR by 36% ± 10%, but the excretion of uEVs by only 16% (CD9+ uEVs ‐37%, CD9‐ uEVs no decrease). Donor nephrectomy increased the podocyte marker WT‐1 and the proximal tubule markers NHE3, NaPi‐IIa, and cubilin in uEVs two‐ to four‐fold when correcting for the nephrectomy. In rats, the changes in GFR and kidney weight correlated with the changes in uEV excretion rate (R = 0.46 and 0.60, P < 0.01). Furthermore, the estimated degree of hypertrophy matched the change in uEV excretion rate (1.4‐ to 1.5‐fold after uninephrectomy and four‐fold after 5/6th nephrectomy). Taken together, our data show that uEV excretion depends on nephron mass, and that nephrectomy reduces uEV excretion less than expected based on nephron loss due to compensatory hypertrophy. The major implication of our findings is that a measure for nephron mass or uEV excretion rate should be included when comparing uEV biomarkers between individuals.

Polyclonal aptamer libraries as binding entities on a graphene FET based biosensor for the discrimination of apo- and holo-retinol binding protein 4.

Oligonucleotide DNA aptamers represent an emergently important class of binding entities towards as different analytes as small molecules or even whole cells. Without requiring the canonical isolation of individual aptamers following the SELEX process, the focused polyclonal libraries prepared by this in vitro evolution and selection can directly be used to label their dedicated targets and to serve as binding molecules on surfaces. Here we report the first instance of a sensor able to discriminate between loaded and unloaded retinol-binding protein 4 (RBP4), an important biomarker for the prediction of diabetes and kidney disease. The sensor relies on two aptamer libraries tuned such that they discriminate between the protein isoforms, requiring no further sample labelling to detect RBP4 in both states. The evolution, binding properties of the libraries and the functionalization of graphene FET sensor chips are presented as well as the functionality of the resulting biosensor.

Design and Fabrication of Morphology Assorted Copper Oxide Nanostructure for Electrochemical Sensing of Kidney Disease Biomarker Creatinine

Design and Fabrication of Morphology Assorted Copper Oxide Nanostructure for Electrochemical Sensing of Kidney Disease Biomarker Creatinine

Exosomal MicroRNAs as Potential Biomarkers of Hepatic Injury and Kidney Disease in Glycogen Storage Disease Type Ia Patients.

Glycogen storage disease type Ia (GSDIa) is an inherited metabolic disorder caused by mutations in the enzyme glucose-6-phosphatase-α (G6Pase-α). Affected individuals develop renal and liver complications, including the development of hepatocellular adenoma/carcinoma and kidney failure. The purpose of this study was to identify potential biomarkers of the evolution of the disease in GSDIa patients. To this end, we analyzed the expression of exosomal microRNAs (Exo-miRs) in the plasma exosomes of 45 patients aged 6 to 63 years. Plasma from age-matched normal individuals were used as controls. We found that the altered expression of several Exo-miRs correlates with the pathologic state of the patients and might help to monitor the progression of the disease and the development of late GSDIa-associated complications.