Proteomics and autoimmune kidney disease

Effects of receiving renal biopsy on the prognosis of chronic kidney disease patients with impaired renal function

BACKGROUND

Impaired renal function was not a recognized indication for renal biopsy. The effects of receiving renal biopsy on the renal functional prognosis for chronic kidney disease (CKD) patients with impaired renal function need to be explored.

METHODS

This study retrospectively enrolled 300 renal function impaired CKD patients in Renji Hospital from January 2015 to December 2017, 150 of them received percutaneous renal biopsy while the others did not. The endpoint was ≥ 50% estimated glomerular filtration rate (eGFR) decline from baseline or development of end-stage renal disease (ESRD). Kaplan-Meier analysis with log-rank test was performed to compare the renal survival probability between patients receiving renal biopsy or not. Univariate and multivariate analysis with Cox regression were conducted with predictors of poor renal outcomes in the study cohort.

RESULTS

The median follow-up period was 37.6 months. During the follow-up period, the eGFR of the biopsy group increased from 52.2 ± 14.4 to 67.4 ± 37.8 ml/min/1.73 m², but decreased from 55.3 ± 17.1 to 29.8 ± 19.1 ml/min/1.73 m² in the non-biopsy group. Patients who received renal biopsy had significantly higher renal survival probability (P < 0.001). Cox regression analysis revealed that 24-hour urine protein excretion (24 h UPE) more than 1 g/d was an independent predictor for poor renal outcomes in the non-biopsy group but not in the renal biopsy group (HR = 1.719, P = 0.040).

CONCLUSION

CKD patients with impaired renal function are recommended to receive renal biopsy to make pathological diagnoses, especially for those with the 24-hour urine protein excretion more than 1 g/d.

In-Depth Analysis of the Extracorporeal Proteome Adsorbed to Dialysis Membranes during Hemodialysis

Used hemodialysis membranes (HD-M) are a valuable reservoir of biological information. Proteins bind to HD-M, but whether this process depends on the type of membrane or patient factors or selectively affects specific protein classes has not been adequately elucidated. State-of-the-art proteomics techniques are capable of identifying and quantifying this therapy-specific subproteome to enable the analysis of disease- or membrane-induced pathophysiologies. We demonstrate the feasibility of the deep proteomic characterization of the extracorporeal proteome adsorbed to HD-M. A shotgun proteomics approach using nano-flow liquid chromatography coupled to mass-spectrometry identified 1648 unique proteins eluted by a chaotropic buffer from the HD-M of eight patients. In total, 995 proteins were present in all eluates; a more stringent approach showed that a core proteome of 310 proteins could be identified independently in all samples. Stability of the dialyzer proteome was demonstrated by a >90% re-identification rate on longitudinal samples of a single patient. The core proteome showed an overrepresentation of pathways of hemostasis and the immune system, and showed differences in membrane materials (polysulfone vs. helixone). This study demonstrates that optimized conditions combined with high-performance proteomics enable the in-depth exploration of the subproteome bound to HD-M, yielding a stable core proteome that can be exploited to study patient-specific factors and improve hemodialysis therapy.

Discovery of NEU1 as a candidatedone. renal biomarker for proliferative lupus nephritis chronicity

OBJECTIVE

Proteomic approach was applied to identify candidate biomarkers of chronicity in patients with proliferative lupus nephritis (LN), and their clinicopathological significance and prognostic values were investigated.

METHODS

This study recruited 10 patients with proliferative LN and 6 normal controls (NCs) with proteomic data to compare protein expression profiles, 58 patients with proliferative LN and 10 NCs to verify proteomic data by immunohistochemistry, and 14 patients with proliferative LN with urine samples to evaluate the urinary expression of the biomarker by western blot assay. The composite endpoints included end-stage renal disease and ≥50% reduction from baseline estimated glomerular filtration rate (eGFR).

RESULTS

Proteomics detected 48 proteins upregulated in the group with chronicity index (CI) ≥1 compared with the CI=0 and NC groups. Further pathway analysis was enriched in 'other glycan degradation'. Neuraminidase 1 (NEU1), the most predominant protein in the pathway of other glycan degradation, was highly expressed in the kidney of patients with proliferative LN and could co-localise with podocyte, mesangial cells, endothelial cells and tubule cells. NEU1 expression in the tubulointerstitium area was significantly higher in the CI ≥1 group compared with the CI=0 and NC groups. Moreover, NEU1 expression was significantly correlated with serum creatinine value, eGFR and CI scores, respectively. Urinary NEU1 excretion in the CI ≥1 group was higher than in the CI=0 group and was also positively correlated with CI scores. Furthermore, the high expression of renal NEU1 was identified as an independent risk factor for renal prognosis by multivariate Cox regression analysis (HR, 6.462 (95% CI 1.025 to 40.732), p=0.047).

CONCLUSIONS

Renal NEU1 expression was associated with pathological CI scores and renal outcomes in patients with proliferative LN.

Targeted urine proteomics in lupus nephritis - a meta-analysis

BACKGROUND

Proteomic approaches are central in biomarker discovery. While mass-spectrometry-based techniques are widely used, novel targeted proteomic platforms have enabled the high-throughput detection of low-abundance proteins in an affinity-based manner. Urine has gained growing attention as an ideal biofluid for monitoring renal disease including lupus nephritis (LN).

METHODS

Pubmed was screened for targeted proteomic studies of LN urine interrogating ≥1000 proteins. Data from the primary studies were combined and a meta-analysis was performed. Shared proteins elevated in active LN across studies were identified, and relevant pathways were elucidated using ingenuity pathway and gene ontology analysis. Urine proteomic data was cross-referenced against renal single-cell RNAseq data from LN kidneys.

RESULTS

Two high-throughput targeted proteomic platforms with capacity to interrogate ≥1000 proteins have been used to investigate LN urine. Twenty-three urine proteins were significantly elevated in both studies, including 10 chemokines, and proteins implicated in angiogenesis, and extracellular matrix turnover. Of these, Cathepsin S, CXCL10, FasL, ferritin, macrophage migration inhibitory factor (MIF), and resistin were also significantly elevated within LN kidneys.

CONCLUSION

Targeted urinary proteomics have uncovered multiple novel biomarkers for LN. Further validation in prospective cohorts and mechanistic studies are warranted to establish their clinical utility.

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics

INTRODUCTION

Nephrotic syndrome (NS) is a characterized by massive proteinuria, edema, hypoalbuminemia, and dyslipidemia. Glucocorticoids (GCs), the primary therapy for >60 years, are ineffective in approximately 50% of adults and approximately 20% of children. Unfortunately, there are no validated biomarkers able to predict steroid-resistant NS (SRNS) or to define the pathways regulating SRNS.

METHODS

We performed proteomic analyses on paired pediatric NS patient plasma samples obtained both at disease presentation before glucocorticoid initiation and after approximately 7 weeks of GC therapy to identify candidate biomarkers able to either predict steroid resistance before treatment or define critical molecular pathways/targets regulating steroid resistance.

RESULTS

Proteomic analyses of 15 paired NS patient samples identified 215 prevalent proteins, including 13 candidate biomarkers that predicted SRNS before GC treatment, and 66 candidate biomarkers that mechanistically differentiated steroid-sensitive NS (SSNS) from SRNS. Ingenuity Pathway Analyses and protein networking pathways approaches further identified proteins and pathways associated with SRNS. Validation using 37 NS patient samples (24 SSNS/13 SRNS) confirmed vitamin D binding protein (VDB) and APOL1 as strong predictive candidate biomarkers for SRNS, and VDB, hemopexin (HPX), adiponectin (ADIPOQ), sex hormone-binding globulin (SHBG), and APOL1 as strong candidate biomarkers to mechanistically distinguish SRNS from SSNS. Logistic regression analysis identified a candidate biomarker panel (VDB, ADIPOQ, and matrix metalloproteinase 2 [MMP-2]) with significant ability to predict SRNS at disease presentation (P = 0.003; area under the receiver operating characteristic curve = 0.78).

CONCLUSION

Plasma proteomic analyses and immunoblotting of serial samples in childhood NS identified a candidate biomarker panel able to predict SRNS at disease presentation, as well as candidate molecular targets/pathways associated with clinical steroid resistance.

Preparation of novel anionic polymeric ionic liquid materials and their potential application to protein adsorption

A series of novel anionic polymeric ionic liquid materials were prepared and used to adsorb bovine hemoglobin from bovine blood.

Defining Glomerular Disease in Mechanistic Terms: Implementing an Integrative Biology Approach in Nephrology

Advances in biomedical research allow for the capture of an unprecedented level of genetic, molecular, and clinical information from large patient cohorts, where the quest for precision medicine can be pursued. An overarching goal of precision medicine is to integrate the large-scale genetic and molecular data with deep phenotypic information to identify a new mechanistic disease classification. This classification can ideally be used to meet the clinical goal of the right medication for the right patient at the right time. Glomerular disease presents a formidable challenge for precision medicine. Patients present with similar signs and symptoms, which cross the current disease categories. The diseases are grouped by shared histopathologic features, but individual patients have dramatic variability in presentation, progression, and response to therapy, reflecting the underlying biologic heterogeneity within each glomerular disease category. Despite the clinical challenge, glomerular disease has several unique advantages to building multilayered datasets connecting genetic, molecular, and structural information needed to address the goals of precision medicine in this population. Kidney biopsy tissue, obtained during routine clinical care, provides a direct window into the molecular mechanisms active in the affected organ. In addition, urine is a biofluid ideally suited for repeated measurement from the diseased organ as a liquid biopsy with potential to reflect the dynamic state of renal tissue. In our review, current approaches for large-scale data generation and integration along the genotype-phenotype continuum in glomerular disease will be summarized. Several successful examples of this integrative biology approach within glomerular disease will be highlighted along with an outlook on how achieving a mechanistic disease classification could help to shape glomerular disease research and care in the future.

The Kidney Biopsy in Lupus Nephritis: Past, Present, and Future

Since its incorporation into clinical practice in the 1950s, the percutaneous kidney biopsy has played an important role in advancing our understanding of lupus nephritis (LN). The biopsy findings have been used to classify and subgroup LN in order to obtain an accurate diagnosis and also to inform treatment decisions and predict prognosis. Several classifications schemes have been applied clinically however despite this evolution in histopathologic classification, our ability to predict treatment response and determine prognosis remains limited. In this review we will examine the evolving role of the kidney biopsy in the management of LN, including the potentially larger role the biopsy could play in the future.

A systems approach to renal inflammation in SLE

Lupus disease and its complications including lupus nephritis (LN) are very disabling and significantly impact the quality of life and longevity of patients. Broadly immunosuppressive treatments do not always provide the expected clinical benefits and have significant side effects that contribute to patient morbidity. In the era of systems biology, new strategies are being deployed integrating diverse sources of information (molecular and clinical) so as to identify individual disease specificities and select less aggressive treatments. In this review, we summarize integrative approaches linking molecular disease profiles (mainly tissue transcriptomics) and clinical phenotypes. The main goals are to better understand the pathogenesis of lupus nephritis, to identify the risk factors for renal flare and to find the predictors of both short and long-term clinical outcome. Identification of common key drivers and additional patient-specific key drivers can open the door to improved and individualized therapy to prevent and treat LN.

What is damaging the kidney in lupus nephritis?

Despite marked improvements in the survival of patients with severe lupus nephritis over the past 50 years, the rate of complete clinical remission after immune suppression therapy is <50% and renal impairment still occurs in 40% of affected patients. An appreciation of the factors that lead to the development of chronic kidney disease following acute or subacute renal injury in patients with systemic lupus erythematosus is beginning to emerge. Processes that contribute to end-stage renal injury include continuing inflammation, activation of intrinsic renal cells, cell stress and hypoxia, metabolic abnormalities, aberrant tissue repair and tissue fibrosis. A deeper understanding of these processes is leading to the development of novel or adjunctive therapies that could protect the kidney from the secondary non-immune consequences of acute injury. Approaches based on a molecular-proteomic-lipidomic classification of disease should yield new information about the functional basis of disease heterogeneity so that the most effective and least toxic treatment regimens can be formulated for individual patients.