Novel plasma peptide markers involved in the pathology of CKD identified using mass spectrometric approach

Prediction Modeling With Many Correlated and Zero-Inflated Predictors: Assessing the Nonnegative Garrote Approach

Building prediction models from mass-spectrometry data is challenging due to the abundance of correlated features with varying degrees of zero-inflation, leading to a common interest in reducing the features to a concise predictor set with good predictive performance given the experiments' resource-intensive nature. In this study, we established and examined regularized regression approaches designed to address zero-inflated and correlated predictors. In particular, we describe a novel two-stage regularized regression approach (ridge-garrote) explicitly modeling zero-inflated predictors using two component variables, comprising a ridge estimator in the first stage and subsequently applying a nonnegative garrotte estimator in the second stage. We contrasted ridge-garrote with one-stage methods (ridge, lasso) and other two-stage regularized regression approaches (lasso-ridge, ridge-lasso) for zero-inflated predictors. We assessed the predictive performance and predictor selection properties of these methods in a comparative simulation study and a real-data case study with the aim to predict kidney function using peptidomic features derived from mass-spectrometry. In the simulation study, the predictive performance of all assessed approaches was comparable, yet the ridge-garrote approach consistently selected more parsimonious models compared to its competitors in most scenarios. While lasso-ridge achieved higher predictive accuracy than its competitors, it exhibited high variability in the number of selected predictors. Ridge-lasso exhibited slightly superior predictive accuracy than ridge-garrote but at the expense of selecting more noise predictors. Overall, ridge emerged as a favorable option when variable selection is not a primary concern, while ridge-garrote demonstrated notable practical utility in selecting a parsimonious set of predictors, with only minimal compromise in predictive accuracy.

Salivary proteomics profiling reveals potential biomarkers for chronic kidney disease: a pilot study

Introduction

Chronic kidney disease (CKD) is a global public health problem, and the absence of reliable and accurate diagnostic and monitoring tools contributes to delayed treatment, impacting patients' quality of life and increasing treatment costs in public health. Proteomics using saliva is a key strategy for identifying potential disease biomarkers.

Methods

We analyzed the untargeted proteomic profiles of saliva samples from 20 individuals with end-stage kidney disease (ESKD) (n = 10) and healthy individuals (n = 10) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify potential biomarkers for CKD. A volcano plot was generated using a p-value of ≤0.05 and a fold change (FC) ≥ 2.0. Multivariate analysis was performed to generate the orthogonal partial least squares discriminant analysis (OPLS-DA) model and the variable importance in projection (VIP) scores. The accuracy of candidate biomarker proteins was evaluated using receiver operating characteristic (ROC) curves.

Results

In total, 431 proteins were identified in the salivary proteomic profile, and 3 proteins were significantly different between the groups: apoptosis inhibitor 5 (API5), phosphoinositide phospholipase C (PI-PLC), and small G protein signaling modulator 2 (Sgsm2). These proteins showed good accuracy based on the ROC curve and a VIP score of >2.0. During pathway enrichment, PI-PLC participates in the synthesis of IP3 and IP4 in the cytosol. Gene ontology (GO) analysis revealed data on molecular functions, biological processes, cellular components, and protein classes.

Conclusion

We can conclude that the salivary API5, PI-PLC, and Sgsm2 can be potential biomarker candidates for CKD detection. These proteins may participate in pathways related to renal fibrosis and other associated diseases, such as mineral and bone disorders.

Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study

Albuminuria may precede decreases in the glomerular filtration rate (GFR) and both tests are insensitive predictors of early stages of kidney disease. Our aim was to characterise the urinary proteome in black African individuals with albuminuria and well-preserved GFR from South Africa. This case-controlled study compared the urinary proteomes of 52 normoalbuminuric (urine albumin: creatinine ratio (uACR) < 3 mg/mmol) and 56 albuminuric (uACR ≥ 3 mg/mmol) adults of black African ethnicity. Urine proteins were precipitated, reduced, alkylated, digested, and analysed using an Evosep One LC (Evosep Biosystems, Odense, Denmark) coupled to a Sciex 5600 Triple-TOF (Sciex, Framingham, MA, USA) in data-independent acquisition mode. The data were searched on SpectronautTM 15. Differentially abundant proteins (DAPs) were filtered to include those with a ≥2.25-fold change and a false discovery rate ≤ 1%. Receiver-operating characteristic curves were used to assess the discriminating abilities of proteins of interest. Pathway analysis was performed using Enrichr software. As expected, the albuminuric group had higher uACR (7.9 vs. 0.55 mg/mmol, p < 0.001). The median eGFR (mL/min/1.73 m2) showed no difference between the groups (111 vs. 114, p = 0.707). We identified 80 DAPs in the albuminuria group compared to the normoalbuminuria group, of which 59 proteins were increased while 21 proteins were decreased in abundance. We found 12 urinary proteins with an AUC > 0.8 and a p < 0.001 in the multivariate analysis. Furthermore, an 80-protein model was developed that showed a high AUC ˃ 0.907 and a predictive accuracy of 91.3% between the two groups. Pathway analysis found that the DAPs were involved in insulin growth factor (IGF) functions, innate immunity, platelet degranulation, and extracellular matrix organization. In albuminuric individuals with a well-preserved eGFR, pathways involved in preventing the release and uptake of IGF by insulin growth factor binding protein were significantly enriched. These proteins are indicative of a homeostatic imbalance in a variety of cellular processes underlying renal dysfunction and are implicated in chronic kidney disease.

Strikingly conserved gene expression changes of polyamine regulating enzymes among various forms of acute and chronic kidney injury

The polyamines spermidine and spermine and their common precursor molecule putrescine are involved in tissue injury and repair. Here, we test the hypothesis that impaired polyamine homeostasis contributes to various kidney pathologies in mice during experimental models of ischemia-reperfusion, transplantation, rhabdomyolysis, cyclosporine treatment, arterial hypertension, diabetes, unilateral ureteral obstruction, high oxalate feeding, and adenine-induced injuries. We found a remarkably similar pattern in most kidney pathologies with reduced expression of enzymes involved in polyamine synthesis together with increased expression of polyamine degrading enzymes. Transcript levels of amine oxidase copper-containing 1 (Aoc1), an enzyme which catalyzes the breakdown of putrescine, were barely detectable by in situ mRNA hybridization in healthy kidneys. Aoc1 was highly expressed upon various experimental kidney injuries resulting in a significant reduction of kidney putrescine content. Kidney levels of spermine were also significantly reduced, whereas spermidine was increased in response to ischemia-reperfusion injury. Increased Aoc1 expression in injured kidneys was mainly accounted for by an Aoc1 isoform that harbors 22 additional amino acids at its N-terminus and shows increased secretion. Mice with germline deletion of Aoc1 and injured kidneys showed no decrease of kidney putrescine content; although they displayed no overt phenotype, they had fewer tubular casts upon ischemia-reperfusion injury. Hyperosmotic stress stimulated AOC1 expression at the transcriptional and post-transcription levels in metanephric explants and kidney cell lines. AOC1 expression was also significantly enhanced after kidney transplantation in humans. These data demonstrate that the kidneys respond to various forms of injury with down-regulation of polyamine synthesis and activation of the polyamine breakdown pathway. Thus, an imbalance in kidney polyamines may contribute to various etiologies of kidney injury.

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

A simple, time- and cost-effective, high-throughput depletion strategy for deep plasma proteomics

We introduce a cost-effective, robust high-throughput-compatible plasma depletion method enabling in-depth profiling of plasma that detects >1300 proteins per run with a throughput of 60 samples per day. The method has been fully validated by processing >3000 samples with no apparent batch effect at a cost for the depletion step of ~$2.5 per sample.

Peptidomics: A Review of Clinical Applications and Methodologies

Improvements in both liquid chromatography (LC) and mass spectrometry (MS) instrumentation have greatly enhanced proteomic and small molecule metabolomic analysis in recent years. Less focus has been on the improved capability to detect and quantify small bioactive peptides, even though the exact sequences of the peptide species produced can have important biological consequences. Endogenous bioactive peptide hormones, for example, are generated by the targeted and regulated cleavage of peptides from their prohormone sequence. This process may include organ specific variants, as proglucagon is converted to glucagon in the pancreas but glucagon-like peptide-1 (GLP-1) in the small intestine, with glucagon raising, whereas GLP-1, as an incretin, lowering blood glucose. Therefore, peptidomics workflows must preserve the structure of the processed peptide products to prevent the misidentification of ambiguous peptide species. The poor in vivo and in vitro stability of peptides in biological matrices is a major factor that needs to be considered when developing methods to study them. The bioinformatic analysis of peptidomics data sets requires the inclusion of specific post-translational modifications, which are critical for the function of many bioactive peptides. This review aims to discuss and contrast the various extraction, analytical, and bioinformatics approaches used for human peptidomics studies in a multitude of matrices.

Applying Proteomics and Integrative "Omics" Strategies to Decipher the Chronic Kidney Disease-Related Atherosclerosis

Patients with chronic kidney disease (CKD) are at increased risk of atherosclerosis and premature mortality, mainly due to cardiovascular events. However, well-known risk factors, which promote "classical" atherosclerosis are alone insufficient to explain the high prevalence of atherosclerosis-related to CKD (CKD-A). The complexity of the molecular mechanisms underlying the acceleration of CKD-A is still to be defied. To obtain a holistic picture of these changes, comprehensive proteomic approaches have been developed including global protein profiling followed by functional bioinformatics analyses of dysregulated pathways. Furthermore, proteomics surveys in combination with other "omics" techniques, i.e., transcriptomics and metabolomics as well as physiological assays provide a solid ground for interpretation of observed phenomena in the context of disease pathology. This review discusses the comprehensive application of various "omics" approaches, with emphasis on proteomics, to tackle the molecular mechanisms underlying CKD-A progression. We summarize here the recent findings derived from global proteomic approaches and underline the potential of utilizing integrative systems biology, to gain a deeper insight into the pathogenesis of CKD-A and other disorders.