Biology subjects

Santos, H. M.

Publications and source records attributed to Santos, H. M..

4 recordsLinked to original sources

bioRxiv2026

To Recover or Not to Recover: A Urinary Proteomic Perspective on Severe Acute Kidney Injury

Rationale & Objective Early prediction of renal non-recovery after acute kidney injury (AKI) could support risk-adapted follow-up and clinical decision-making. We evaluated whether a urinary fatty acid-binding protein 3 (FABP3)/apolipoprotein A-I (APOA1) ratio based on Total Protein Approach (TPA) quantification could discriminate renal recovery from non-recovery after severe AKI. Study Design Exploratory biomarker discovery study with outcome-blinded independent validation. Setting & Participants Critically ill adults with KDIGO stage 2 or 3 AKI. Urine was collected 48 hours after AKI diagnosis. The discovery cohort included 20 patients (10 recovered and 10 non-recovered), and the outcome-blinded validation cohort included 10 patients. New Predictor Urinary TPA-derived FABP3/APOA1 ratio (AKI^TPA index). Outcome Renal recovery at day 14, defined as survival with serum creatinine returning to within 0.3 mg/dL or 150% of baseline; patients not meeting these criteria were classified as having non-recovery. Analytical Approach Urinary proteins were analyzed by LC-MS/MS and quantified using TPA. Differential analysis identified FABP3 and APOA1 as oppositely associated with renal outcome. A predefined FABP3/APOA1 threshold of 1 classified patients as non-recovery-like (>1) or recovery-like (<1) and was subsequently applied to the outcome-blinded validation cohort. Results In the discovery cohort, the AKI^TPA index correctly classified 17 of 20 patients (accuracy, 85.0%; sensitivity, 80.0%; specificity, 90.0%; ROC AUC, 0.930). In the validation cohort, 9 patients were evaluable, of whom 7 were correctly classified (accuracy, 77.8%; sensitivity, 75.0%; specificity, 80.0%; ROC AUC, 0.950). Limitations The exploratory design and limited cohort size warrant further validation in larger, independent populations. Conclusions The urinary FABP3/APOA1 TPA ratio showed encouraging preliminary discrimination of renal recovery after severe AKI and warrants prospective clinical validation in larger, independent multicenter cohorts.

biochemistry↗

bioRxiv2026

Absolute quantitative proteomics guides patient-stratified drug repurposing in clear cell and papillary renal cell carcinoma.

BackgroundRenal cell carcinoma (RCC) is a highly heterogeneous disease in which distinct molecular subtypes exhibit characteristic genomic, metabolic, and microenvironmental features that influence therapeutic response. Substantial inter-patient variability exists within each subtype, resulting in markedly different clinical outcomes even among tumours of the same histological category. Proteomics provides a direct readout of tumour biology and pathway activity, complementing genomic information and enabling the identification of patient-specific actionable vulnerabilities. We applied a Total Protein Approach (TPA)-based prescriptomics framework that integrates absolute quantitative proteomics with curated drug-target knowledge to nominate patient-specific drug-repurposing options, positioned as coadjuvants to the prevailing standard of care. MethodsSeventeen human kidney tissue specimens, seven clear cell RCC (ccRCC), five papillary RCC (pRCC), and five normal adjacent tissues (NAT), were retrieved from the publicly available PRIDE repository (PXD023296) and reanalysed by TPA-based absolute quantification applied to previously acquired label-free LC-MS/MS data. Differential expression analysis between each tumour subtype and NAT identified subtype-specific upregulated proteins, wich were intersected with Therapeutic Target Database (TTD) to nominate FDA-approved drugs targeting dysregulated proteins as candidate repurposing strategies. ResultsccRCC and pRCC produced distinct proteome-wide upregulation profiles consistent with their known biological drivers. TPA index stratification nominated bempedoic acid (ACLY inhibitor) and tipiracil hydrochloride (TYMP inhibitor) as patient-stratified candidates for ccRCC, and auranofin (TXNRD1 inhibitor), bempedoic acid, and mipomersen (APOB-directed antisense oligonucleotide) for pRCC. ACLY was the only top-priority target shared across both subtypes, pointing to a candidate cross-subtype metabolic vulnerability. Secondary candidates emerged from protein-protein interaction network analysis in both subtypes.. ConclusionsThis study presents a quantitative proteomics framework for translating individual-patient proteomic dysregulation into coadjuvant drug-repurposing hypotheses across the principal RCC subtypes. By combining the TPA for absolute protein quantification with prescriptomics-guided drug-target mapping, we show that ccRCC and pRCC harbour distinct, individually stratifiable therapeutic vulnerabilities. These findings provide a proof-of-concept for proteomics-based treatment stratification in RCC and establish a scalable framework that, pending functional validation, could inform personalised therapeutic decision-making across RCC subtypes.

biochemistry↗

bioRxiv2024

AI-driven Classification of Heart Failure Preserved and Reduced Ejection Fraction Patients Using the Total Protein Approach

Heart failure (HF) presents two major subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF), each one with distinct metabolic characteristics. This study utilized artificial intelligence, high-resolution mass spectrometry and the Total Protein Approach (TPA) to identify key features differentiating these subtypes. Aldolase A (ALDOA), a glycolytic enzyme, was found upregulated in HFrEF patients, reflecting an increased glycolysis pathway, while Arginase 1 (ARG1), a key enzyme in the urea cycle, was also elevated, indicating an increased urea pathway. In contrast, HFpEF patients showed TPA ALDOA and ARG1 levels similar to healthy controls. The combined use of ALDOA and ARG1 TPA values successfully classified 82% of patients (14 out of 17). Additionally, most HFpEF patients were over 80 years old, suggesting an age-related metabolic shift. The combination of ALDOA and ARG1 are promising biomarkers for distinguishing HFpEF and HFrEF using the TPA approach, with potential implications for targeted therapies.

biochemistry↗

bioRxiv2021

Pathway centered analysis to guide clinical decision-making in precision medicine

Changes in the human proteome caused by disease before, during and after medical care is phenotype-dependent, so the proteome of each individual at any time point is a snapshot of the bodys response to disease and to disease treatment. Here, we introduce a new concept named differential Personal Pathway index (dPPi). This tool extracts and summates comprehensive disease-specific information contained within an individuals proteome as a holistic way to follow the response to disease and medical care over time. We demonstrate the principle of the dPPi algorithm on proteins found in urine from patients suffering from neoplasia of the bladder. The relevance of the dPPi results to the individual clinical cases is described. The dPPi concept can be extended to other malignant and non-malignant diseases, and to other types of biopsies, such as plasma, serum or saliva. We envision the dPPi as a tool for clinical decision-making in precision medicine.

biochemistry↗