Integrated multi-omics approaches to improve classification of chronic kidney disease

Role of traditional and new biomarkers in the assessment of chronic kidney diseases: a comprehensive analysis of the biochemical, molecular and clinical dimensions

BACKGROUND

Kidney function is necessary for the diagnosis and treatment of renal diseases. Traditional biomarkers like creatinine have limitations due to their susceptibility to interference and fluctuation. This study's objective is to test and compare the efficacy of conventional and innovative biomarkers in evaluating kidney function and disease.

METHODS

We looked at creatinine, cystatin C, parathyroid hormone (PTH), electrolytes, interleukin-6 (IL-6), C-reactive protein (CRP), the APA I gene, and kidney injury molecule-1 (KIM-1). The present study focused on the stability, sensitivity, and specificity of biomarkers using a combination of traditional and innovative analytical techniques.

RESULTS

Present results showed that creatinine, although commonly used as a measure, frequently overestimates renal function as a result of chromogenic interference. On the other hand, cystatin C showed better sensitivity and was less reliant on influences outside the kidneys. Kidney biomarkers, such as KIM-1, exhibit the potential for identifying acute kidney injury at an early stage. Furthermore, there was a positive correlation between increased levels of CRP and PTH and the progression of kidney disease to more advanced stages.

CONCLUSION

This study emphasizes the importance of combining traditional and new biomarkers to improve the accuracy of diagnosing and managing kidney illness. The more effective use of biomarkers will result in improved patient outcomes.

Multi-omics analysis of host-microbiome interactions in a mouse model of congenital hepatic fibrosis

BACKGROUND

Congenital hepatic fibrosis (CHF) caused by mutations in the polycystic kidney and hepatic disease 1 (PKHD1) gene is a rare genetic disorder with poorly understood pathogenesis. We hypothesized that integrating gut microbiome and metabolomic analyses could uncover distinct host-microbiome interactions in CHF mice compared to wild-type controls.

METHODS

Pkhd1del3-4/del3-4 mice were generated using CRISPR/Cas9 technology. Fecal samples were collected from 11 Pkhd1del3-4/del3-4 mice and 10 littermate wild-type controls. We conducted a combined study using 16 S rDNA sequencing for microbiome analysis and untargeted metabolomics. The gut microbiome and metabolome data were integrated using Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), which helped identify key microbial and metabolic features associated with CHF.

RESULTS

CHF mouse model was successfully established. Our analysis revealed that the genera Mucispirillum, Eisenbergiella, and Oscillibacter were core microbiota in CHF, exhibiting significantly higher abundance in Pkhd1del3-4/del3-4 mice and strong positive correlations among them. Network analysis demonstrated robust associations between the gut microbiome and metabolome. Multi-omics dimension reduction analysis demonstrated that both the microbiome and metabolome could effectively distinguish CHF mice from controls, with area under the curve of 0.883 and 0.982, respectively. A significant positive correlation was observed between the gut microbiome and metabolome, highlighting the intricate relationship between these two components.

CONCLUSION

This study identifies distinct metabolic and microbiome profiles in Pkhd1del3-4/del3-4 mice. Multi-omics analysis effectively differentiates CHF mice from controls and identified potential biomarkers. These findings indicate that gut microbiota and metabolites are integral to the pathogenesis of CHF, offering novel insights into the disease mechanism.

AI-Assisted Plasmonic Diagnostics Platform for Osteoarthritis and Rheumatoid Arthritis With Biomarker Quantification Using Mathematical Models

Osteoarthritis (OA) and rheumatoid arthritis (RA) are major causes of functional impairment, disability, and chronic pain, leading to a substantial rise in healthcare costs. Despite differences in pathophysiology, these diseases share overlapping features that complicate diagnosis, necessitating early, more accurate, and cost-effective diagnostic tools. This study introduces an innovative plasmonic diagnostics platform for rapid and accurate label-free diagnosis of OA and RA. The sensing platform utilizes a highly dense urchin-like gold nanoarchitecture (UGN), which enhances the surface plasmonic area to significantly amplify the Raman signal. The feasibility of the developed platform for arthritis diagnosis is demonstrated by analyzing the synovial fluid (SVF) of patients. Assisted by a machine learning model, Raman signals of OA and RA groups are successfully classified with high clinical sensitivity and specificity. Metabolic biomarkers are further investigated using mathematical models of combined Pearson correlation coefficient (PCC) and non-negative matrix factorization (NMF), suggesting valuable insights for arthritis biomarker quantification. In addition, RA severity is studied using the sensing platform by classifying results from the hematology test, achieving successful stage discrimination. This platform offers a versatile, affordable, and scalable in-clinic arthritis diagnostic solution with potential applications in diagnosing and monitoring other diseases through biofluid analysis.

Imaging and spatially resolved mass spectrometry applications in nephrology

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. The complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis

Background

Machine learning (ML) models are being increasingly employed to predict the risk of developing and progressing diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). However, the performance of these models still varies, which limits their widespread adoption and practical application. Therefore, we conducted a systematic review and meta-analysis to summarize and evaluate the performance and clinical applicability of these risk predictive models and to identify key research gaps.

Methods

We conducted a systematic review and meta-analysis to compare the performance of ML predictive models. We searched PubMed, Embase, the Cochrane Library, and Web of Science for English-language studies using ML algorithms to predict the risk of DKD in patients with T2DM, covering the period from database inception to April 18, 2024. The primary performance metric for the models was the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.

Results

26 studies that met the eligibility criteria were included into the meta-analysis. 25 studies performed internal validation, but only 8 studies conducted external validation. A total of 94 ML models were developed, with 81 models evaluated in the internal validation sets and 13 in the external validation sets. The pooled AUC was 0.839 (95% CI 0.787-0.890) in the internal validation and 0.830 (95% CI 0.784-0.877) in the external validation sets. Subgroup analysis based on the type of ML showed that the pooled AUC for traditional regression ML was 0.797 (95% CI 0.777-0.816), for ML was 0.811 (95% CI 0.785-0.836), and for deep learning was 0.863 (95% CI 0.825-0.900). A total of 26 ML models were included, and the AUCs of models that were used three or more times were pooled. Among them, the random forest (RF) models demonstrated the best performance with a pooled AUC of 0.848 (95% CI 0.785-0.911).

Conclusion

This meta-analysis demonstrates that ML exhibit high performance in predicting DKD risk in T2DM patients. However, challenges related to data bias during model development and validation still need to be addressed. Future research should focus on enhancing data transparency and standardization, as well as validating these models' generalizability through multicenter studies.

Systematic Review Registration

https://inplasy.com/inplasy-2024-9-0038/, identifier INPLASY202490038.

Mass spectrometry-based analysis of eccrine sweat supports predictive, preventive and personalised medicine in a cohort of breast cancer patients in Austria

Objective

Metabolomics measurements of eccrine sweat may provide novel and relevant biomedical information to support predictive, preventive and personalised medicine (3PM). However, only limited data is available regarding metabolic alterations accompanying chemotherapy of breast cancer patients related to residual cancer burden (RCB) or therapy response. Here, we have applied Metabo-Tip, a non-invasive metabolomics assay based on the analysis of eccrine sweat from the fingertips, to investigate the feasibility of such an approach, especially with respect to drug monitoring, assessing lifestyle parameters and stratification of breast cancer patients.

Methods

Eccrine sweat samples were collected from breast cancer patients (n = 9) during the first cycle of neoadjuvant chemotherapy at four time points in this proof-of-concept study at a Tertiary University Hospital. Metabolites in eccrine sweat were analysed using mass spectrometry. Blood plasma samples from the same timepoints were also collected and analysed using a validated targeted metabolomics kit, in addition to proteomics and fatty acids/oxylipin analysis.

Results

A total of 247 exogenous small molecules and endogenous metabolites were identified in eccrine sweat of the breast cancer patients. Cyclophosphamide and ondansetron were successfully detected and monitored in eccrine sweat of individual patients and accurately reflected the administration schedule. The non-essential amino acids asparagine, serine and proline, as well as ornithine were significantly regulated in eccrine sweat and blood plasma over the therapy cycle. However, their distinct time-dependent profiles indicated compartment-specific distributions. Indeed, the metabolite composition of eccrine sweat seems to largely resemble the composition of the interstitial fluid. Plasma proteins and fatty acids/oxylipins were not affected by the first treatment cycle. Individual smoking habit was revealed by the simultaneous detection of nicotine and its primary metabolite cotinine in eccrine sweat. Stratification according to RCB revealed pronounced differences in the metabolic composition of eccrine sweat in these patients at baseline, e.g., essential amino acids, possibly due to the systemic contribution of breast cancer and its impact on metabolic turnover.

Conclusion

Mass spectrometry-based analysis of metabolites from eccrine sweat of breast cancer patients successfully qualified lifestyle parameters for risk assessment and allowed us to monitor drug treatment and systemic response to therapy. Moreover, eccrine sweat revealed a potentially predictive metabolic pattern stratifying patients by the extent of the metabolic activity of breast cancer tissue at baseline. Eccrine sweat is derived from the otherwise hardly accessible interstitial fluid and, thus, opens up a new dimension for biomonitoring of breast cancer in secondary and tertiary care. The simple sample collection without the need for trained personnel could also enable decentralised long-term biomonitoring to assess stable disease or disease progression. Eccrine sweat analysis may indeed significantly advance 3PM for the benefit of breast cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-025-00396-6.

Precision Medicine Proof-of-Concept Study of a TNF Inhibitor in FSGS and Treatment-Resistant Minimal Change Disease

Key Points

Precision medicine trials are feasible in patients with primary glomerular diseases. Patients with FSGS and the best-preserved kidney parenchyma demonstrated the most favorable biomarker response to short-term adalimumab treatment. Targeted therapies for FSGS are more likely to succeed during the course of disease when the injury pathway is activated and can be modified.

Background

FSGS and treatment-resistant minimal change disease (TR-MCD) are heterogeneous disorders with subgroups defined by distinct underlying mechanisms of glomerular and tubulointerstitial injury. A noninvasive urinary biomarker profile has been generated to identify patients with intrakidney TNF activation and to predict response to anti-TNF treatment. We conducted this proof-of-concept, multicenter, open-label clinical trial to test the hypothesis that in patients with FSGS or TR-MCD and evidence of intrarenal TNF activation based on their biomarker profile, short-term treatment with adalimumab would reverse the elevated urinary excretion of monocyte chemoattractant protein-1 (MCP-1) and tissue inhibitor of metalloproteinases 1.

Methods

Patients with FSGS or TR-MCD, eGFR >30 ml/min per 1.73 m2, urine protein:creatinine ratio ≥1.5 g/g, and age 6–80 years were eligible for this trial. Adalimumab, 20–40 mg, was administered through subcutaneous injection every 2 weeks for five doses. Participants were evaluated at weeks 0 (baseline), 2, 8, and 10. Excretion of urinary monocyte chemoattractant protein-1, urinary tissue inhibitor of metalloproteinases 1, urinary excretion of EGF, and plasma monocyte chemoattractant protein-1 were measured at each visit.

Results

Seven participants were enrolled, with median baseline urine protein:creatinine ratio 12.1 mg/mg (interquartile range [IQR], 2.2–18.6), serum albumin 2.4 g/dl (IQR, 2.0–2.8), and eGFR 57 ml/min per 1.73 m2 (IQR, 44–96). On the basis of self-report, they received all prescribed doses of adalimumab. The patients with the most favorable response on the basis of changes in urinary biomarkers had the best preserved kidney parenchyma based on urinary excretion of EGF.

Conclusions

Precision medicine trials are feasible in rare glomerular disorders. In this pilot study, adalimumab resulted in a heterogenous response of the candidate mechanistic-predictive biomarkers of TNF-mediated inflammation in patients with FSGS or TR-MCD. A reduction was seen in a subgroup of patients with preserved kidney parenchyma. The findings may reflect the challenge to reverse chronic injury at advanced stages of kidney disease or insufficient intrarenal target engagement with the intervention drug dose.

Clinical Trial registry name and registration number:

NCT04009668 .

Development of a digital platform for nursing monitoring of patients with chronic kidney failure

PURPOSE

This study aims to develop and propose the key criteria and elements necessary to be included in digital platforms for achieving high-quality monitoring of patients with chronic kidney failure.

METHODS

The research was conducted from 2021 to 2023 in Moscow, Russia. A total of 75 patients comprised the experimental group (digital monitoring), while an equal number constituted the control group (standard nursing care).

RESULTS

Patients in the experimental group highly rated the convenience (4.6 ± 0.3) and accessibility (4.7 ± 0.4) levels of the monitoring system compared to those in the control group (convenience: 3.8 ± 0.4, accessibility: 3.9 ± 0.3). Furthermore, it was found that the level of patient satisfaction in the experimental group (4.4 ± 0.3) noticeably exceeded that in the control group (3.9 ± 0.4). The effectiveness of digital platforms is supported by data on the timeliness of detecting changes in patient's health status. In the experimental group, the response time to deteriorating health conditions decreased by 30% compared to the control group.

CONCLUSION

The conclusions of our study underscore the necessity of integrating digital monitoring platforms into medical practice. Monitoring utilizing digital technologies has the potential to significantly enhance patient satisfaction levels as well as promptness in responding to changes in their health status.

From Adipose to Ailing Kidneys: The Role of Lipid Metabolism in Obesity-Related Chronic Kidney Disease

Obesity has emerged as a significant public health crisis, closely linked to the pathogenesis and progression of chronic kidney disease (CKD). This review explores the intricate relationship between obesity-induced lipid metabolism disorders and renal health. We discuss how excessive free fatty acids (FFAs) lead to lipid accumulation in renal tissues, resulting in cellular lipotoxicity, oxidative stress, and inflammation, ultimately contributing to renal injury. Key molecular mechanisms, including the roles of transcriptional regulators like PPARs and SREBP-1, are examined for their implications in lipid metabolism dysregulation. The review also highlights the impact of glomerular and tubular lipid overload on kidney pathology, emphasizing the roles of podocytes and tubular cells in maintaining kidney function. Various therapeutic strategies targeting lipid metabolism, including pharmacological agents such as statins and SGLT2 inhibitors, as well as lifestyle modifications, are discussed for their potential to mitigate CKD progression in obese individuals. Future research directions are suggested to better understand the mechanisms linking lipid metabolism to kidney disease and to develop personalized therapeutic approaches. Ultimately, addressing obesity-related lipid metabolism disorders may enhance kidney health and improve outcomes for individuals suffering from CKD.

Multi-omics analysis sandbox toolkit for swift derivations of clinically relevant genesets and biomarkers

The utilization of multi-omics research has gained popularity in clinical investigations. However, effectively managing and merging extensive and diverse datasets presents a challenge due to its intricacy. This research introduces a Multi-Omics Analysis Sandbox Toolkit, an online platform designed to facilitate the exploration, integration, and visualization of datasets ranging from single-omics to multi-omics. This platform establishes connections between clinical data and omics information, allowing for versatile analysis and storage of both single and multi-omics data. Additionally, users can repeatedly utilize and exchange their findings within the platform. This toolkit offers diverse alternatives for data selection and gene set analysis. It also presents visualization outputs, potential candidates, and annotations. Furthermore, this platform empowers users to collaborate by sharing their datasets, analyses, and conclusions with others, thus enhancing its utility as a collaborative research tool. This Multi-Omics Analysis Sandbox Toolkit stands as a valuable asset in comprehensively grasping the influence of diverse factors in diseases and pinpointing potential biomarkers. [BMB Reports 2024; 57(12): 521-526].

Urinary sediment mRNA as a potent biomarker of IgA nephropathy

BACKGROUND

The quantification of mRNA expression in urinary sediments is a reliable biomarker for various diseases. However, few studies have investigated the clinical relevance of urinary mRNA levels in IgA nephropathy (IgAN). Thus, we investigated the expression of urinary mRNAs and their clinical significance in IgAN.

METHODS

Overall, 200 patients with biopsy-proven IgAN, 48 disease controls, and 76 healthy controls were enrolled. We identified the differential expression of mRNAs in renal tissue between patients with IgAN and normal subjects using the Gene Expression Omnibus dataset and selected candidate mRNAs. mRNA expression in the urinary sediment was measured using quantitative real-time polymerase chain reaction. Associations between urinary mRNA levels and clinicopathological parameters were analyzed and the predictive value of mRNAs for disease progression was evaluated.

RESULTS

The urinary expression of CCL2, CD14, DNMT1, FKBP5, Nephrin, and IL-6 was significantly upregulated in patients with IgAN compared with healthy controls. C3, FLOT1, and Podocin levels were significantly correlated with renal function, where C3, FLOT1, and TfR levels were significantly correlated with urinary protein excretion. During follow-up, 26 (13.0%) patients with IgAN experienced disease progression, defined as a greater than 50% reduction in the estimated glomerular filtration rate or progression to end-stage renal disease. Urinary mRNA levels of FLOT1 (HR 3.706, 95% CI 1.373-10.005, P = 0.010) were independently associated with an increased risk of disease progression.

CONCLUSIONS

Our results suggest that urinary sediment mRNAs are a useful biomarker in IgAN patients. Further studies with larger sample sizes and longer follow-up durations are required.

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

Clinical glycoproteomics: methods and diseases

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities

Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.

A Single-Cell Transcriptome Profiling of Triptolide-Induced Nephrotoxicity in Mice

Triptolide (TP), an active component isolated from the traditional Chinese herb Tripterygium wilfordii Hook F (TWHF), shows great promise for treating inflammation-related diseases. However, its potential nephrotoxic effects remain concerning. The mechanism underlying TP-induced nephrotoxicity is inadequately elucidated, particularly at single-cell resolution. Hence, single-cell RNA sequencing (scRNA-seq) of kidney tissues from control and TP-treated mice is performed to generate a thorough description of the renal cell atlas upon TP treatment. Heterogeneous responses of nephron epithelial cells are observed after TP exposure, attributing differential susceptibility of cell subtypes to excessive reactive oxygen species and increased inflammatory responses. Moreover, TP disrupts vascular function by activating endothelial cell immunity and damaging fibroblasts. Severe immune cell damage and the activation of pro-inflammatory Macro_C1 cells are also observed with TP treatment. Additionally, ligand-receptor crosstalk analysis reveals that the SPP1 (osteopontin) signaling pathway targeting Macro_C1 cells is triggered by TP treatment, which may promote the infiltration of Macro_C1 cells to exacerbate renal toxicity. Overall, this study provides comprehensive information on the transcriptomic profiles and cellular composition of TP-associated nephrotoxicity at single-cell resolution, which can strengthen the understanding of the pathogenesis of TP-induced nephrotoxicity and provide valuable clues for the discovery of new therapeutic targets to ameliorate TP-associated nephrotoxicity.

Pediatric contributions and lessons learned from the NEPTUNE cohort study

Primary glomerular diseases are rare entities. This has hampered efforts to better understand the underlying pathobiology and to develop novel safe and effective therapies. NEPTUNE is a rare disease network that is focused on patients of all ages with minimal change disease, focal segmental glomerulosclerosis, and membranous nephropathy. It is a longitudinal cohort study that collects detailed demographic, clinical, histopathologic, genomic, transcriptomic, and metabolomic data. The goal is to develop a molecular classification for these disorders that supersedes the traditional pathological features-based schema. Pediatric patients are important contributors to this ongoing project. In this review, we provide a snapshot of the children and adolescents enrolled in NEPTUNE and summarize some key observations that have been made based on the data accumulated during the study. In addition, we describe the development of NEPTUNE Match, a program that aims to leverage the multi-scalar information gathered for each individual patient to provide guidance about potential clinical trial participation based on the molecular characterization and non-invasive biomarker profile. This represents the first organized effort to apply principles of precision medicine to the treatment of patients with primary glomerular disease. NEPTUNE has proven to be an invaluable asset in the study of glomerular diseases in patients of all ages including children and adolescents.

Targeting Lactobacillus johnsonii to reverse chronic kidney disease

Accumulated evidence suggested that gut microbial dysbiosis interplayed with progressive chronic kidney disease (CKD). However, no available therapy is effective in suppressing progressive CKD. Here, using microbiomics in 480 participants including healthy controls and patients with stage 1-5 CKD, we identified an elongation taxonomic chain Bacilli-Lactobacillales-Lactobacillaceae-Lactobacillus-Lactobacillus johnsonii correlated with patients with CKD progression, whose abundance strongly correlated with clinical kidney markers. L. johnsonii abundance reduced with progressive CKD in rats with adenine-induced CKD. L. johnsonii supplementation ameliorated kidney lesion. Serum indole-3-aldehyde (IAld), whose level strongly negatively correlated with creatinine level in CKD rats, decreased in serum of rats induced using unilateral ureteral obstruction (UUO) and 5/6 nephrectomy (NX) as well as late CKD patients. Treatment with IAld dampened kidney lesion through suppressing aryl hydrocarbon receptor (AHR) signal in rats with CKD or UUO, and in cultured 1-hydroxypyrene-induced HK-2 cells. Renoprotective effect of IAld was partially diminished in AHR deficiency mice and HK-2 cells. Our further data showed that treatment with L. johnsonii attenuated kidney lesion by suppressing AHR signal via increasing serum IAld level. Taken together, targeting L. johnsonii might reverse patients with CKD. This study provides a deeper understanding of how microbial-produced tryptophan metabolism affects host disease and discovers potential pathways for prophylactic and therapeutic treatments for CKD patients.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

The current use of proteomics and metabolomics in glomerulonephritis: a systematic literature review

BACKGROUND

Glomerulonephritis inherently leads to the development of chronic kidney disease. It is the second most common diagnosis in patients requiring renal replacement therapy in the United Kingdom. Metabolomics and proteomics can characterise, identify and quantify an individual's protein and metabolite make-up. These techniques have been optimised and can be performed on samples including kidney tissue, blood and urine. Utilising omic techniques in nephrology can uncover disease pathophysiology and transform the diagnostics and treatment options for glomerulonephritis.

OBJECTIVES

To evaluate the utility of metabolomics and proteomics using mass spectrometry and nuclear magnetic resonance in glomerulonephritis.

METHODS

The systematic review was registered on PROSPERO (CRD42023442092). Standard and extensive Cochrane search methods were used. The latest search date was March 2023. Participants were of any age with a histological diagnosis of glomerulonephritis. Descriptive analysis was performed, and data presented in tabular form. An area under the curve or p-value was presented for potential biomarkers discovered.

RESULTS

Twenty-seven studies were included (metabolomics (n = 9)), and (proteomics (n = 18)) with 1818 participants. The samples analysed were urine (n = 19) blood (n = 4) and biopsy (n = 6). The typical outcome themes were potential biomarkers, disease phenotype, risk of progression and treatment response.

CONCLUSION

This review shows the potential of metabolomic and proteomic analysis to discover new disease biomarkers that may influence diagnostics and disease management. Further larger-scale research is required to establish the validity of the study outcomes, including the several proposed biomarkers.

Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics

Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.

Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.

Genetic association and transferability for urinary albumin-creatinine ratio as a marker of kidney disease in four Sub-Saharan African populations and non-continental individuals of African ancestry

Background

Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts.

Methods

Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations.

Results

Two genome-wide significant (P < 5 × 10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained.

Conclusion

This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.

Mannose and glycine: Metabolites with potentially causal implications in chronic kidney disease pathogenesis

BACKGROUND

Chronic Kidney Disease (CKD) represents a global health challenge, with its etiology and underlying mechanisms yet to be fully elucidated. Integrating genomics with metabolomics can offer insights into the putatively causal relationships between serum metabolites and CKD.

METHODS

Utilizing bidirectional Mendelian Randomization (MR), we assessed the putatively causal associations between 486 serum metabolites and CKD. Genetic data for these metabolites were sourced from comprehensive genome-wide association studies, and CKD data were obtained from the CKDGen Consortium.

RESULTS

Our analysis identified four metabolites with a robust association with CKD risk, of which mannose and glycine showed the most reliable causal relationships. Pathway analysis spotlighted five significant metabolic pathways, notably including "Methionine Metabolism" and "Arginine and Proline Metabolism", as key contributors to CKD pathogenesis.

CONCLUSION

This study underscores the potential of certain serum metabolites as biomarkers for CKD and illuminates pivotal metabolic pathways in CKD's pathogenesis. Our findings lay the groundwork for potential therapeutic interventions and warrant further research for validation.

Identification and analysis of diverse cell death patterns in diabetic kidney disease using microarray-based transcriptome profiling and single-nucleus RNA sequencing

BACKGROUND

Diabetic kidney disease (DKD) is the most lethal complication of diabetes. Diverse programmed cell death (PCD) has emerged as a crucial disease phenotype that has the potential to serve as an indicator of renal function decline and can be used as a target for researching drugs for DKD.

METHODS

Microarray-based transcriptome profiling and single-nucleus transcriptome sequencing (snRNA-seq) related to DKD were retrieved from the Gene Expression Omnibus (GEO) database. 13 PCD-related genes (including alkaliptosis, apoptosis, autophagy-dependent cell death, cuproptosis, disulfidptosis, entotic cell death, ferroptosis, lysosome-dependent cell death, necroptosis, netotic cell death, oxeiptosis, parthanatos, and pyroptosis) were obtained from various public databases and reviews. The gene set variation analysis (GSVA) analysis was used to explore the pathway activity of these 13 PCDs in DKD, and the pathway activity of these PCDs in different renal cells was studied based on DKD-related snRNA-seq data. To identify the core PCDs that play a significant role in DKD, we analyzed the relationships between different types of PCD and immune infiltration, fibrosis-related gene expression levels, glomerular filtration rate (GFR), and diagnostic efficiency in DKD. Using the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we screened for core death genes among the core PCDs and constructed a cell death-related signature (CDS) risk score based on the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, we validated the predictive performance of the CDS risk score in an independent validation set.

RESULTS

We identified 4 core PCD pathways, namely entotic cell death, apoptosis, necroptosis, and pyroptosis in DKD, and further applied the WGCNA algorithm to screen 4 core death genes (CASP1, CYBB, PLA2G4A, and CTSS) and constructed a CDS risk score based on these genes. The CDS risk score demonstrated high diagnostic efficiency for DKD patients, and those with higher scores had higher levels of immune cell infiltration and poorer GFR.

CONCLUSION

Our study sheds light on the fact that multiple PCDs contribute to the progression of DKD, highlighting potential therapeutic targets for treating this disease.

Identification of Senescence-Associated Biomarkers in Diabetic Glomerulopathy Using Integrated Bioinformatics Analysis

Background

Cellular senescence is thought to play a significant role in the onset and development of diabetic nephropathy. The goal of this study was to explore potential biomarkers associated with diabetic glomerulopathy from the perspective of senescence.

Methods

Datasets about human glomerular biopsy samples related to diabetic nephropathy were systematically obtained from the Gene Expression Omnibus database. Hub senescence-associated genes were investigated by differential gene analysis and Least Absolute Shrinkage and Selection Operator analysis. Cluster analysis was employed to identify senescence molecular subtypes. A single-cell dataset was used to validate the above findings and further evaluate the senescence environment. The relationship between these genes and the glomerular filtration rate was explored based on the Nephroseq database. These gene expressions have also been explored in various kidney diseases.

Results

Twelve representative senescence-associated genes (VEGFA, IQGAP2, JUN, PLAT, ETS2, ANG, MMP14, VEGFC, SERPINE2, CXCR2, PTGES, and EGF) were finally identified. Biological changes in immune inflammatory response, cell cycle regulation, metabolic regulation, and immune microenvironment have been observed across different molecular subtypes. The above results were also validated based on single-cell analysis. Additionally, we also identified several significantly altered cell communication pathways, including COLLAGEN, PTN, LAMININ, SPP1, and VEGF. Finally, almost all these genes could well predict the occurrence of diabetic glomerulopathy based on receiver operating characteristic analysis and are associated with the glomerular filtration rate. These genes are differently expressed in various kidney diseases.

Conclusion

The present study identified potential senescence-associated biomarkers and further explored the heterogeneity of diabetic glomerulopathy that might provide new insights into the diagnosis, assessment, management, and personalized treatment of DN.

Prediction of biomarkers associated with membranous nephropathy: Bioinformatic analysis and experimental validation

Membranous nephropathy (MN), the most prevalent form of nephrotic syndrome in non-diabetic adults globally, is currently the second most prevalent and fastest-increasing primary glomerular disease in China. Numerous renal disorders are developed partly due to ferroptosis. However, its relationship to the pathogenesis of MN has rarely been investigated in previous studies; actually, ferroptosis is closely linked to the immune microenvironment and inflammatory response, which might affect the entire process of MN development. In this study, we aimed to identify ferroptosis-related genes that are potentially related to immune cell infiltration, which can further contribute to MN pathogenesis. The microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Ferroptosis-related differentially expressed genes (FDEGs) were identified, which were further used for functional enrichment analysis. The common genes identified using the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithm and the support vector machine recursive feature elimination (SVM-RFE) algorithm were used to identify the characteristic genes related to ferroptosis. The feasibility of the 7 genes as a distinguishing factor was assessed using the receiver operating characteristic (ROC) curve, with the area under the curve (AUC) score serving as the evaluation metric. Gene set enrichment analysis (GSEA) and correlation analysis of these genes were further performed. The correlation between the expression of these genes and immune cell infiltration inferred by single sample gene set enrichment analysis (ssGSEA) algorithm was explored. As a result, 7 genes, including NR1D1, YTHDC2, EGR1, ZFP36, RRM2, RELA and PDK4, which were most relevant to immune cell infiltration, were identified to be potential diagnostic genes in MN patients. Next, the signature genes were validated with other GEO datasets. In the subsequent steps, we conducted quantitative real-time fluorescence PCR (qRT-PCR) analysis and immunohistochemistry (IHC) method on the cationic bovine serum albumin (C-BSA) induced membranous nephropathy (MN) rat model and the passive Heymann nephritis (pHN) rat model to examine characteristic genes. Finally, we analysed the mRNA expression patterns of hub genes in MN patients and normal controls using the Nephroseq V5 online platform. In concise terms, our study successfully identified biomarkers specific to MN patients and delved into the potential interplay between these markers and immune cell infiltration. This knowledge bears significance for the diagnosis and prospective treatment strategies for individuals affected by MN.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes

Large-scale high-dimensional multiomics studies are essential to unravel molecular complexity in health and disease. We developed an integrated system for tissue sampling (CryoGrid), analytes preparation (PIXUL), and downstream multiomic analysis in a 96-well plate format (Matrix), MultiomicsTracks96, which we used to interrogate matched frozen and formalin-fixed paraffin-embedded (FFPE) mouse organs. Using this system, we generated 8-dimensional omics data sets encompassing 4 molecular layers of intracellular organization: epigenome (H3K27Ac, H3K4m3, RNA polymerase II, and 5mC levels), transcriptome (messenger RNA levels), epitranscriptome (m6A levels), and proteome (protein levels) in brain, heart, kidney, and liver. There was a high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles confirmed known organ-specific superenhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic profiles, known to be poorly correlated with transcriptomic data, can be more accurately predicted by the full suite of multiomics data, compared with using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.

Proximal tubule cells in blood and urine as potential biomarkers for kidney disease biopsy

Early diagnosis and treatment are crucial for managing kidney disease, yet there remains a need to further explore pathological mechanisms and develop minimally invasive diagnostic methods. In this study, we employed single-cell RNA sequencing (scRNA-seq) to assess the cellular heterogeneity of kidney diseases. We analyzed gene expression profiles from renal tissue, peripheral blood mononuclear cells (PBMCs), and urine of four patients with nephritis. Our findings identified 12 distinct cell subsets in renal tissues and leukocytes. These subsets encompassed fibroblast cells, mesangial cells, epithelial cells, proximal tubule cells (PTCs), and six immune cell types: CD8+ T cells, macrophages, natural killer cells, dendritic cells, B cells, and neutrophils. Interestingly, PTCs were present in both PBMCs and urine samples but absent in healthy blood samples. Furthermore, several populations of fibroblast cells, mesangial cells, and PTCs exhibited pro-inflammatory or pro-apoptotic behaviors. Our gene expression analysis highlighted the critical role of inflammatory PTCs and fibroblasts in nephritis development and progression. These cells showed high expression of pro-inflammatory genes, which could have chemotactic and activating effect on neutrophils. This was substantiated by the widespread in these cells. Notably, the gene expression profiles of inflammatory PTCs in PBMCs, urine, and kidney tissues had high similarity. This suggests that PTCs in urine and PBMCs hold significant potential as alternative markers to invasive kidney biopsies.

Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults

Background

Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).

Methods

We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).

Results

ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies.

Conclusions

Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites.

Funding

This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice

BACKGROUND

Diagnosis and staging of diabetic kidney disease (DKD) via the serial assessment of routine laboratory indices lacks the granularity required to resolve the heterogeneous disease mechanisms driving progression in the individual patient. A systems nephrology approach may help resolve mechanisms underlying this clinically apparent heterogeneity, paving a way for targeted treatment of DKD.

SUMMARY

Given the limited access to kidney tissue in routine clinical care of patients with DKD, data derived from renal tissue in preclinical model systems, including animal and in vitro models, can play a central role in the development of a targeted systems-based approach to DKD. Multi-centre prospective cohort studies, including the Kidney Precision Medicine Project (KPMP) and the European Nephrectomy Biobank (ENBiBA) project, will improve access to human diabetic kidney tissue for research purposes. Integration of diverse data domains from such initiatives including clinical phenotypic data, renal and retinal imaging biomarkers, histopathological and ultrastructural data, and an array of molecular omics (transcriptomics, proteomics, etc.) alongside multi-dimensional data from preclinical modelling offers exciting opportunities to unravel individual-level mechanisms underlying progressive DKD. The application of machine and deep learning approaches may particularly enhance insights derived from imaging and histopathological/ultrastructural data domains.

KEY MESSAGES

Integration of data from multiple model systems (in vitro, animal models, and patients) and from diverse domains (clinical phenotypic, imaging, histopathological/ultrastructural, and molecular omics) offers potential to create a precision medicine approach to DKD care wherein the right treatments are offered to the right patients at the right time.

New discoveries in the field of metabolism by applying single-cell and spatial omics

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.

Identification and analysis of cellular senescence-associated signatures in diabetic kidney disease by integrated bioinformatics analysis and machine learning

Background

Diabetic kidney disease (DKD) is a common complication of diabetes that is clinically characterized by progressive albuminuria due to glomerular destruction. The etiology of DKD is multifactorial, and numerous studies have demonstrated that cellular senescence plays a significant role in its pathogenesis, but the specific mechanism requires further investigation.

Methods

This study utilized 5 datasets comprising 144 renal samples from the Gene Expression Omnibus (GEO) database. We obtained cellular senescence-related pathways from the Molecular Signatures Database and evaluated the activity of senescence pathways in DKD patients using the Gene Set Enrichment Analysis (GSEA) algorithm. Furthermore, we identified module genes related to cellular senescence pathways through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm and used machine learning algorithms to screen for hub genes related to senescence. Subsequently, we constructed a cellular senescence-related signature (SRS) risk score based on hub genes using the Least Absolute Shrinkage and Selection Operator (LASSO), and verified mRNA levels of hub genes by RT-PCR in vivo. Finally, we validated the relationship between the SRS risk score and kidney function, as well as their association with mitochondrial function and immune infiltration.

Results

The activity of cellular senescence-related pathways was found to be elevated among DKD patients. Based on 5 hub genes (LIMA1, ZFP36, FOS, IGFBP6, CKB), a cellular senescence-related signature (SRS) was constructed and validated as a risk factor for renal function decline in DKD patients. Notably, patients with high SRS risk scores exhibited extensive inhibition of mitochondrial pathways and upregulation of immune cell infiltration.

Conclusion

Collectively, our findings demonstrated that cellular senescence is involved in the process of DKD, providing a novel strategy for treating DKD.

Metabolic rewiring and epigenetic reprogramming in leptin receptor-deficient db/db diabetic nephropathy mice

BACKGROUND

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease in the United States. Emerging evidence suggests that mitochondrial metabolism and epigenetics play an important role in the development and progression of DN and its complications. For the first time, we investigated the regulation of cellular metabolism, DNA methylation, and transcriptome status by high glucose (HG) in the kidney of leptin receptor-deficient db/db mice using multi-omics approaches.

METHODS

The metabolomics was performed by liquid-chromatography-mass spectrometry (LC-MS), while epigenomic CpG methylation coupled with transcriptomic gene expression was analyzed by next-generation sequencing.

RESULTS

LC-MS analysis of glomerular and cortex tissue samples of db/db mice showed that HG regulated several cellular metabolites and metabolism-related signaling pathways, including S-adenosylmethionine, S-adenosylhomocysteine, methionine, glutamine, and glutamate. Gene expression study by RNA-seq analysis suggests transforming growth factor beta 1 (TGFβ1) and pro-inflammatory pathways play important roles in early DN. Epigenomic CpG methyl-seq showed HG revoked a list of differentially methylated regions in the promoter region of the genes. Integrated analysis of DNA methylation in the promoter regions of genes and gene expression changes across time points identified several genes persistently altered in DNA methylation and gene expression. Cyp2d22, Slc1a4, and Ddah1 are some identified genes that could reflect dysregulated genes involved in renal function and DN.

CONCLUSION

Our results suggest that leptin receptor deficiency leading to HG regulates metabolic rewiring, including SAM potentially driving DNA methylation and transcriptomic signaling that could be involved in the progression of DN.

Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli

The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease.

Genome-wide mRNA profiling in urinary extracellular vesicles reveals stress gene signature for diabetic kidney disease

Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome. T1D DKD groups showed 13 upregulated genes prevalently expressed in proximal tubules, correlated with hyperglycemia and involved in cellular/oxidative stress homeostasis. We used six of them (GPX3, NOX4, MSRB, MSRA, HRSP12, and CRYAB) to construct a transcriptional "stress score" that reflected long-term decline of kidney function and could even identify normoalbuminuric individuals showing early decline. We thus provide workflow and web resource for studying uEV transcriptomes in clinical urine samples and stress-linked DKD markers as potential early non-invasive biomarkers or drug targets.

Multiplexed Gene Engineering Based on dCas9 and gRNA-tRNA Array Encoded on Single Transcript

Simultaneously, multiplexed genome engineering and targeting multiple genomic loci are valuable to elucidating gene interactions and characterizing genetic networks that affect phenotypes. Here, we developed a general CRISPR-based platform to perform four functions and target multiple genome loci encoded in a single transcript. To establish multiple functions for multiple loci targets, we fused four RNA hairpins, MS2, PP7, com and boxB, to stem-loops of gRNA (guide RNA) scaffolds, separately. The RNA-hairpin-binding domains MCP, PCP, Com and λN22 were fused with different functional effectors. These paired combinations of cognate-RNA hairpins and RNA-binding proteins generated the simultaneous, independent regulation of multiple target genes. To ensure that all proteins and RNAs are expressed in one transcript, multiple gRNAs were constructed in a tandemly arrayed tRNA (transfer RNA)-gRNA architecture, and the triplex sequence was cloned between the protein-coding sequences and the tRNA-gRNA array. By leveraging this system, we illustrate the transcriptional activation, transcriptional repression, DNA methylation and DNA demethylation of endogenous targets using up to 16 individual CRISPR gRNAs delivered on a single transcript. This system provides a powerful platform to investigate synthetic biology questions and engineer complex-phenotype medical applications.

The Geroprotective Drug Candidate CMS121 Alleviates Diabetes, Liver Inflammation, and Renal Damage in db/db Leptin Receptor Deficient Mice

db/db mice, which lack leptin receptors and exhibit hyperphagia, show disturbances in energy metabolism and are a model of obesity and type 2 diabetes. The geroneuroprotector drug candidate CMS121 has been shown to be effective in animal models of Alzheimer's disease and aging through the modulation of metabolism. Thus, the hypothesis was that CMS121 could protect db/db mice from metabolic defects and thereby reduce liver inflammation and kidney damage. The mice were treated with CMS121 in their diet for 6 months. No changes were observed in food and oxygen consumption, body mass, or locomotor activity compared to control db/db mice, but a 5% reduction in body weight was noted. Improved glucose tolerance and reduced HbA1c and insulin levels were also seen. Blood and liver triglycerides and free fatty acids decreased. Improved metabolism was supported by lower levels of fatty acid metabolites in the urine. Markers of liver inflammation, including NF-κB, IL-18, caspase 3, and C reactive protein, were lowered by the CMS121 treatment. Urine markers of kidney damage were improved, as evidenced by lower urinary levels of NGAL, clusterin, and albumin. Urine metabolomics studies provided further evidence for kidney protection. Mitochondrial protein markers were elevated in db/db mice, but CMS121 restored the renal levels of NDUFB8, UQCRC2, and VDAC. Overall, long-term CMS121 treatment alleviated metabolic imbalances, liver inflammation, and reduced markers of kidney damage. Thus, this study provides promising evidence for the potential therapeutic use of CMS121 in treating metabolic disorders.

Revisiting the Role of NAG across the Continuum of Kidney Disease

Acute and chronic kidney diseases are an evolving continuum for which reliable biomarkers of early disease are lacking. The potential use of glycosidases, enzymes involved in carbohydrate metabolism, in kidney disease detection has been under investigation since the 1960s. N-acetyl-beta-D-glucosaminidase (NAG) is a glycosidase commonly found in proximal tubule epithelial cells (PTECs). Due to its large molecular weight, plasma-soluble NAG cannot pass the glomerular filtration barrier; thus, increased urinary concentration of NAG (uNAG) may suggest injury to the proximal tubule. As the PTECs are the workhorses of the kidney that perform much of the filtration and reabsorption, they are a common starting point in acute and chronic kidney disease. NAG has previously been researched, and it is widely used as a valuable biomarker in both acute and chronic kidney disease, as well as in patients suffering from diabetes mellitus, heart failure, and other chronic diseases leading to kidney failure. Here, we present an overview of the research pertaining to uNAG’s biomarker potential across the spectrum of kidney disease, with an additional emphasis on environmental nephrotoxic substance exposure. In spite of a large body of evidence strongly suggesting connections between uNAG levels and multiple kidney pathologies, focused clinical validation tests and knowledge on underlining molecular mechanisms are largely lacking.

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

Background

The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies.

Methods

Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression.

Results

MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Conclusions

The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

[Staging of kidney disease today and tomorrow]

Since September 2022, there is a new, German-language glossary for the nomenclature of renal function and renal disease, aligned with international technical terms and KDIGO guidelines for a more precise and uniform description of the facts. Terms such as "renal disease," "renal insufficiency," or "acute renal failure" should be avoided and replaced with "disease" or "functional impairment."The KDIGO guideline recommends in patients with CKD stage G3a, in addition to the determination of serum creatinine, the additional determination of cystatin to confirm the CKD stage. A combination of serum creatinine and cystatin C to estimate GFR without taking into account the so-called race coefficient seems to be more accurate in African Americans than the previous eGFR formulas. However, there is no recommendation on this in international guidelines yet. For Caucasians, the formula does not change.Renal function impairment lasting more than 7 days but less than 3 months is called acute kidney disease (AKD). The AKD stage is the critical time window for therapeutic interventions to reduce the risk of progression in kidney disease.A future, expanded AKI definition incorporating biomarkers will allow patients to be divided into subclasses according to functional and structural limitations, thus mapping the two-dimensionality of AKI. By using artificial intelligence, large amounts of data from clinical parameters, blood and urine samples, histopathological and molecular markers (including proteomics and metabolomics data) can be used integratively for the graduation of CKD and thus contribute significantly to individualized therapy.

Title: Bioinformatic Identification of Genes Involved in Diabetic Nephropathy Fibrosis and their Clinical Relevance

Tubulointerstitial fibrosis is an important pathological feature of diabetic nephropathy that is associated with impaired renal function. However, the mechanism by which fibrosis occurs in diabetic nephropathy is unclear. Differentially expressed genes were identified from transcriptome profiles of renal tissue from diabetic patients and unilateral ureteral obstruction mice and intersected to obtain genes that may be involved in diabetic fibrosis. Biological function analysis and protein-protein interaction network analysis were performed. ROC curve and Pearson correlation analysis between hub genes were performed and glomerular filtration rate estimated. Finally, the RNA levels of hub genes were measured using real-time PCR. A total of 283 genes were identified as potentially involved in diabetic nephropathy fibrosis. TYROBP, CTSS, LCP2, LUM and TLR7 were identified as aberrantly expressed hub genes. Immune cell infiltration analysis demonstrated higher numbers of cytotoxic lymphocytes, B lineage cells, monocyte lineage cells, myeloid dendritic cells, neutrophils, and fibroblasts in the diabetic nephropathy group. The areas under ROC curves for TYROBP, CTSS, LCP2, LUM and TLR7 were 0.9167, 0.9583, 0.9917, 0.93333, and 0.9583, respectively (P < 0.001), and their correlation coefficients with estimated glomerular filtration rate were - 0.8332, - 0.752, - 0.7875, - 0.7567, and - 0.7136, respectively (P < 0.001). The RNA levels of TYROBP, CTSS, LUM and TLR7 were upregulated in high-glucose-treated human renal tubular epithelial cells (P < 0.005). Our study identified TYROBP, CTSS, LCP2, LUM and TLR7 as potentially involved in diabetic nephropathy fibrosis. Furthermore, TYROBP, CTSS, LUM and TLR7 may be associated with epithelial-mesenchymal transition of tubular epithelial cells.