Progress in understanding primary glomerular disease: insights from urinary proteomics and in-depth analyses of potential biomarkers based on bioinformatics.

Chronic kidney disease (CKD) has become a global public health challenge. While primary glomerular disease (PGD) is one of the leading causes of CKD, the specific pathogenesis of PGD is still unclear. Accurate diagnosis relies largely on invasive renal biopsy, which carries risks of bleeding, pain, infection and kidney vein thrombosis. Problems with the biopsy procedure include lack of glomeruli in the tissue obtained, and the sampling site not being reflective of the overall lesion in the kidney. Repeated renal biopsies to monitor disease progression cannot be performed because of the significant risks of bleeding and kidney vein thrombosis. On the other hand, urine collection, a noninvasive method, can be performed repeatedly, and urinary proteins can reflect pathological changes in the urinary system. Advancements in proteomics technologies, especially mass spectrometry, have facilitated the identification of candidate biomarkers in different pathological types of PGD. Such biomarkers not only provide insights into the pathogenesis of PGD but also are important for diagnosis, monitoring treatment, and prognosis. In this review, we summarize the findings from studies that have used urinary proteomics, among other omics screens, to identify potential biomarkers for different types of PGD. Moreover, we performed an in-depth bioinformatic analysis to gain a deeper understanding of the biological processes and protein-protein interaction networks in which these candidate biomarkers may participate. This review, including a description of an integrated analysis method, is intended to provide insights into the pathogenesis, noninvasive diagnosis, and personalized treatment efforts of PGD and other associated diseases.

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.

Progress in understanding COVID-19: insights from the omics approach.

Sequencing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome is a crucial task for controlling the ongoing coronavirus disease (COVID-19) pandemic. However, elucidating the pathological mechanisms of SARS-CoV-2 in humans has been challenging. A comprehensive analysis of the molecular characteristics of SARS-CoV-2 and molecular changes in COVID-19 patients may have practical significance in developing assays for the detection of SARS-CoV-2 and formulating clinical treatment strategies against COVID-19. The omics approach for studying biochemical mechanisms can be used to elucidate the molecular characteristics and pathophysiology of SARS-CoV-2. The omics-scale research on COVID-19 has been carried out rapidly, bringing hope for developing a robust diagnostic assay, discovering reliable biomarkers to assess disease progression, and developing therapeutic drugs and vaccines. In this review, we summarize, from an omics perspective, the strategies for the detection of SARS-CoV-2 antigens and antibodies against the virus, the metabolomic and proteomic changes in COVID-19 patients, and the progress of research on anti-SARS-CoV-2 drugs with their potential clinical applications.

Comprehensive Analyses of the Immunoglobulin Proteome for the Classification of Glomerular Diseases.

Glomerular diseases, which are currently diagnosed using an invasive renal biopsy, encompass numerous disease subtypes that often display similar clinical manifestations even though they have different therapeutic regimes. Therefore, a noninvasive assay is needed to classify and guide the treatment of glomerular diseases. Here, we develop and apply a high-throughput and quantitative microarray platform to characterize the immunoglobulin proteome in the serum from 419 healthy and diseased patients. The immunoglobulin proteome-clinical variable correlation network revealed novel pathological mechanisms of glomerular diseases. Furthermore, an immunoglobulin proteome-multivariate normal distribution (IP-MiND) mathematical model based on the correlation network classified healthy volunteers and patients with idiopathic membranous nephropathy with an average recall of 48% (23-80%) in the discovery cohort and 64% (63-65%) in an independent validation cohort. Our results demonstrate the translational utility of our microarray platform to glomerular diseases as well as its clinical potential in characterizing other human diseases.

A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines.

BACKGROUND: Novel coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations of immune response and vaccine efficacy are deemed inadequate. OBJECTIVES: The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough infections. It aimed to elucidate the synergistic effects of epidemiological factors, humoral immunity, and cellular immunity. Additionally, regression curves were used to determine the correlation between the protective efficacy of the vaccine and the stimulated immune response. METHODS: Employing LASSO for high-dimensional data analysis, the study utilised four machine learning algorithms-logistical regression, random forest, LGBM classifier, and AdaBoost classifier-to comprehensively assess the immune response following booster vaccination. RESULTS: Neutralising antibody levels exhibited a rapid surge post-booster, escalating to 102.38 AU/mL at one week and peaking at 298.02 AU/mL at two weeks. Influential factors such as sex, age, disease history, and smoking status significantly impacted post-booster antibody levels. The study further constructed regression curves for neutralising antibodies, non-switched memory B cells, CD4+T cells, and CD8+T cells using LASSO combined with the random forest algorithm. CONCLUSION: The establishment of an artificial intelligence evaluation system emerges as pivotal for predicting breakthrough infection prognosis after the COVID-19 booster vaccination. This research underscores the intricate interplay between various components of immunity and external factors, elucidating key insights to enhance vaccine effectiveness. 3D modelling discerned distinctive interactions between humoral and cellular immunity within prognostic groups (Class 0-2). This underscores the critical role of the synergistic effect of humoral immunity, cellular immunity, and epidemiological factors in determining the protective efficacy of COVID-19 vaccines post-booster administration.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort.

BACKGROUND: Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection. METHODS: Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections. RESULTS: Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms. CONCLUSION: SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Immunology of SARS-CoV-2 infection and vaccination.

Comprehensive elucidation of humoral immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination is critical for understanding coronavirus disease 2019 (COVID-19) pathogenesis in general and developing antibody-based diagnostic and therapeutic strategies specifically. Following the emergence of SARS-CoV-2, significant scientific research has been conducted worldwide using omics, sequencing and immunologic approaches. These studies have been critical to the successful development of vaccines. Here, the current understanding of SARS-CoV-2 immunogenic epitopes, humoral immunity to SARS-CoV-2 structural proteins and non-structural proteins, SARS-CoV-2-specific antibodies, and T-cell responses in convalescents and vaccinated individuals are reviewed. Additionally, we explore the integrated analysis of proteomic and metabolomic data to examine mechanisms of organ injury and identify potential biomarkers. Insight into the immunologic diagnosis of COVID-19 and improvements of laboratory methods are highlighted.

Metformin combined with rapamycin ameliorates podocyte injury in idiopathic membranous nephropathy through the AMPK/mTOR signaling pathway.

Autophagy activation protects against podocyte injury in idiopathic membranous nephropathy (IMN). The AMPK/mTOR signaling pathway is a vital autophagy regulatory pathway. Metformin promotes autophagy, whereas rapamycin is an autophagy agonist. However, the therapeutic mechanisms of metformin and rapamycin in IMN remain unclear. Thus, we examined the mechanisms of action of metformin and rapamycin in IMN by regulating the AMPK/mTOR autophagy signaling pathway. Female Sprague-Dawley (SD) rats were treated with cationic bovine serum albumin (C-BSA) to establish an IMN model and were randomly divided into IMN model, metformin, rapamycin, and metformin + rapamycin groups. A control group was also established. Metformin and rapamycin were used as treatments. Renal histological changes, urinary protein excretion, the protein expression levels of key AMPK/mTOR signaling pathway proteins, renal tissue cell apoptosis, and autophagy-associated proteins (Beclin 1 and LC3) were examined. In addition, a C5b-9 sublysis model using the MPC-5 mouse podocyte cell line was established to verify the effect of metformin combined with rapamycin on podocytes. Metformin combined with rapamycin improved urinary protein excretion in IMN rats. Metformin combined with rapamycin attenuated the inflammatory response, renal fibrosis, and podocyte foot process fusion. In addition, it improved autophagy in podocytes as demonstrated by the enhanced expression of Beclin-1, p-AMPK/AMPK, LC3-II/I, and autophagosomes in podocytes and decreased p-mTOR/mTOR expression. In conclusion, metformin combined with rapamycin decreased proteinuria, improved renal fibrosis and podocyte autophagy via AMPK/mTOR pathway in IMN rats. The metformin and rapamycin decreased proteinuria and inproved renal fibrosis in IMN model rats.

Novel Biomarkers in Membranous Nephropathy.

Membranous nephropathy (MN) is the main cause of adult nephrotic syndrome (NS). The pathogenesis of MN is complex and involves subepithelial immune complex deposition. Approximately one-third of patients with MN develop end-stage renal disease (ESRD). Timely diagnosis and reasonable intervention are the keys to improving prognosis. In recent years, with the development of high-throughput technologies, such as mass spectrometry (MS), microarray, and sequencing technologies, the discovery of biomarkers for MN has become an important area of research. In this review, we summarize the significant progress in biomarker identification. For example, a variety of podocyte target antigens and their autoantibodies have been reported. Phospholipase A2 receptor (PLA2R) is the most well-established target antigen in MN. PLA2R and its autoantibodies have clinical significance, with both diagnostic and therapeutic value for MN. In addition, a variety of new biomarkers, including proteins, metabolites, noncoding RNAs (ncRNAs), and immune cells, have recently been found. These MN-related biomarkers have great significance in the diagnosis, progression, prognosis, and treatment response of MN.