Biology subjects

Cravedi, P.

Publications and source records attributed to Cravedi, P..

7 recordsLinked to original sources

bioRxiv2026

Targeted Analysis of >1500 Plasma Proteoforms via Individual Ion Mass Spectrometry

Plasma proteomics has long sought to accessibly sense human biology, precisely detect disease states, and advance diagnostics through clinical translation. In recent years, companies with defined panels such as Olink and Somascan have entered the field to complement bottom-up mass spectrometry (BUP). This study leverages a novel mass spectrometry platform to capture targeted proteoform information lost by mainline antibody-, aptamer-, and BUP-driven workflows. The Plasma Proteoform Assay (PPA) uses Individual Ion Mass Spectrometry (I{superscript 2}MS) to resolve mixtures of intact proteins presented by direct injection. Two proteoform panels, PPA 526 and PPA 1514, were defined from human plasma samples obtained from 81 individuals. The panels quantify 526 proteoforms derived from 59 genes and 1,514 proteoforms from 155 genes, respectively. Reproducibility for both panels showed coefficients of variation below 20% for most proteoforms (59-80%). PPA was benchmarked in studies including subjects with hepatic cirrhosis (N=30) and resilient agers carrying a SERPINE1 (PAI 1) mutation (N=27). PPA signatures distinguished SERPINE1 mutation carriers from affected individuals and provided sufficient resolution to discriminate among cirrhosis disease stages. In summary, we present PPA 526 and PPA 1514, the first scalable plasma proteoform panels capable of tracking hundreds to thousands of targets in a few minutes per sample.

biochemistry↗

bioRxiv2025

A T-cell Intrinsic Role for APOL1 Risk Alleles in Allograft Rejection

African Americans have an increased risk of kidney disease due to exonic variants in Apolipoprotein-L1 (G1 and G2). These prevalent variants have also been linked with kidney rejection, but outside of association with African ancestry, underpinning causal mechanisms are unknown. We investigated T-cell function using transgenic mice with physiologic expression of wild type (G0-), G1-, or G2-APOL1. Mice with variant APOL1 showed greater CD8+T-cell activation with expansion of a central memory (TCM) subset. Stimulated G1-CD8+T-cells showed enhanced proliferation and cytokine production, which reversed with APOL1 inhibition. In MHC-mismatched cardiac transplants, G1-mice demonstrated greater CD8+T-cell infiltration and reduced survival. Bulk transcriptome of G1-CD8+T-cells, and single-cell transcriptome of graft infiltrating TCMs, showed enrichment of canonical T-cell receptor (TCR) pathways including Ca2+-signaling. G1-CD8+T-cells demonstrated baseline ER-Ca2+ depletion followed by sustained increases in cytosolic-Ca2+ upon TCR stimulation. G1-CD8+T-cells were more sensitive to Ca2+ chelation, or store-operated Ca2+ entry inhibition, and relatively resistant to calcineurin antagonism vs. G0-CD8+T-cells. Analogously, in a kidney transplant cohort, APOL1-variant recipients developed rejection when they had elevated peripheral TCMs before transplantation and despite significantly higher tacrolimus levels vs G0/G0-AAs with rejection. In summary, we unravel an excitatory T-cell intrinsic mechanism for APOL1 exonic variants, causally linking them with kidney rejection.

immunology↗

bioRxiv2025

Not all alloantibodies are created equal: IgG glycosylation and severity of antibody-mediated rejection in kidney transplantation

IntroductionAntibody-mediated rejection (AMR) is a leading cause of kidney transplant (KT) failure, driven by donor-specific anti-HLA antibodies (DSA). However, not all patients with DSA experience accelerated graft loss, suggesting that factors beyond antibody presence influence AMR severity. Post-translational modifications, particularly glycosylation of Immunoglobulin-G (IgG), play a critical role in modulating antibody function. This study investigates the association between IgG glycosylation profiles and the risk and severity of AMR in KT recipients. MethodsWe prospectively analyzed 65 KT patients, including 26 with acute AMR (aAMR), 27 with chronic-active AMR (caAMR), and 12 controls without rejection. IgG glycosylation was quantified using lectin-based ELISA, focusing on mannose, fucose, sialic acid, and bisecting N-acetylglucosamine (GlcNAc) levels. ResultsResults showed that bisecting GlcNAc levels of total IgG were significantly higher in caAMR patients than controls (p=0.019) and aAMR patients (p=0.045). Multivariable analysis revealed that higher bisecting GlcNAc levels of IgG were independently associated with glomerulitis [g-score, OR: 2.7 (95%CI: 1.2-6.7), p=0.019] and chronic glomerulopathy [cg-score, OR: 2.8 (95%CI: 1.3-7.5), p=0.021], independent of DSA presence. ConclusionsThese findings indicate an association between IgG glycosylation, particularly bisecting GlcNAc, and AMR severity. IgG glycosylation profiles could serve as biomarkers for AMR risk and severity, offering new insights into the mechanisms of AMR and potential therapeutic targets.

immunology↗

bioRxiv2024

Genetic polymorphisms of Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3) gene in African American kidney transplant recipients are associated with post-transplant graft failure

BackgroundAfrican American (AA) kidney transplant recipients exhibit a higher rate of graft loss compared to other racial and ethnic populations, highlighting the need to identify causative factors underlying this disparity. MethodWe analyzed RNA sequences of pretransplant whole blood from subjects followed in three kidney transplant cohorts to identify single nucleotide polymorphisms (SNPs) associated with death censored graft loss (DCGL). We employed a meta-analysis to uncover key transcriptional signatures and pathways associated with the identified SNPs and used single cell RNA to define cellular specificity. We characterized SNP functions using in vitro immunological and survival assays and tested for associations between the identified SNPs and other immune-related diseases using a [~]30,100 subject, electronic health record (EHR)-linked database. ResultsWe uncovered a cluster of four consecutive missense SNPs in the Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3, a negative immune response regulator) gene that strongly associated with DCGL. This LILRB3-4SNPs cluster encodes missense mutations at amino acids 617-618 proximal to a SHP-1/2 phosphatase-binding ITIM motif. LILRB3-4SNPs is specifically enriched within subjects of AA ancestry (8.6% prevalence vs 2.3% in Hispanic and 0.1% in European populations), is not linked to APOL1 G1/G2 alleles, and exhibited a strong association with DCGL. Analysis of PBMC and transplant biopsies from recipients with LILRB3-4SNPs showed evidence of enhanced adaptive immune responsiveness and ferroptosis-associated death in monocytes. Overexpression of the variant allele in THP-1 cells (macrophage line) induced augmented inflammation and ferroptosis, which were attenuated by a ferroptosis inhibitor, verifying a causal link. The LILRB3-4SNPs also associated with multiple systemic and organ-specific immune-related diseases in AAs, consistent with conferring a broadly relevant immune function. Conclusionthe LILRB3-4SNPs represent a functionally important, distinct genetic risk factor for kidney transplant outcome and development/severity of other immune-related diseases in patients of AA ancestry. Pharmacological targeting of ferroptosis should be tested to prevent or treat these disease processes in AA recipients carrying LILRB3-4SNPs.

genetics↗

bioRxiv2023

Understanding the heterogeneity of alloreactive natural killer cell function inkidney transplantation

Human Natural Killer (NK) cells are heterogeneous lymphocytes regulated by variegated arrays of germline-encoded activating and inhibitory receptors. They acquire the ability to detect polymorphic self-antigen via NKG2A/HLA-E or KIR/HLA-I ligand interactions through an education process. Correlations among HLA/KIR genes, kidney transplantation pathology and outcomes suggest that NK cells participate in allograft injury, but mechanisms linking NK HLA/KIR education to antibody-independent pathological functions remain unclear. We used CyTOF to characterize pre- and post-transplant peripheral blood NK cell phenotypes/functions before and after stimulation with allogeneic donor cells. Unsupervised clustering identified unique NK cell subpopulations present in varying proportions across patients, each of which responded heterogeneously to donor cells based on donor ligand expression patterns. Analyses of pre-transplant blood showed that educated, NKG2A/KIR-expressing NK cells responded greater than non-educated subsets to donor stimulators, and this heightened alloreactivity persisted > 6 months post-transplant despite immunosuppression. In distinct test and validation sets of patients participating in two clinical trials, pre-transplant donor-induced release of NK cell Ksp37, a cytotoxicity mediator, correlated with 2-year and 5-year eGFR. The findings explain previously reported associations between NK cell genotypes and transplant outcomes and suggest that pre-transplant NK cell analysis could function as a risk-assessment biomarker for transplant outcomes.

immunology↗

bioRxiv2023

Deep-learning Based Pathological Assessment of Frozen Procurement Kidney Biopsies Predicts Graft Loss and Guides Organ Utilization: A Large-scale Retrospective Study

BackgroundLesion scores on procurement donor biopsies are commonly used to guide organ utilization. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropriate discard. MethodsWe constructed deep-learning based models to recognize kidney tissue compartments in H&E stained sections from procurement biopsies performed at 583 hospitals nationwide in year 2011-2020. The models were trained and tested respectively on 11473 and 3986 images sliced from 100 slides. We then extracted whole-slide abnormality features from 2431 kidneys, and correlated with pathologists scores and transplant outcomes. Finally, a Kidney Donor Quality Score (KDQS) incorporating digital features and the Kidney Donor Profile Index (KDPI) was derived and used in combination with recipient demographic and peri-transplant characteristics to predict graft loss or assist organ utilization. ResultsOur model accurately identified 96% and 91% of normal/sclerotic glomeruli respectively; 94% of arteries/arterial intimal fibrosis regions; 90% of tubules. Three whole-slide features (Sclerotic Glomeruli%, Arterial Intimal Fibrosis%, and Interstitial Fibrosis%) demonstrated strong correlations with corresponding pathologists scores (n=2431), but had superior associations with post-transplant eGFR (n=2033) and graft loss (n=1560). The combination of KDQS and other factors predicted 1- and 4-year graft loss (discovery: n=520, validation: n=1040). Finally, by matching 398 discarded kidneys due to "biopsy findings" to transplanted population, the matched transplants from discarded KDQS<4 group (110/398, 27.6%) showed similar graft survival rate to unmatched transplanted kidneys (2-, 5-year survival rate: 97%, 86%). KDQS [≥] 7 (37/398, 9.3%) and 1-year survival model score [≥] 0.55 were determined to identify possible discards (PPV=0.92). ConclusionThis deep-learning based approach provides automatic and reliable pathological assessment of procurement kidney biopsies, which could facilitate graft loss risk stratification and organ utilization. Translational StatementThis deep-learning based approach provides rapid but more objective, sensitive and reliable assessment of deceased-donor kidneys before transplantation, and improves the prognostic value of procurement biopsies, thus could potentially reduce inappropriate discard and stratify patients needing monitoring or preventative measures after transplantation. The pipeline can be integrated into various types of scanners and conveniently generates report after slide scanning. Such report can be used in conjunction with pathologists report or independently for centers lacking renal pathologists.

bioinformatics↗

bioRxiv2021

Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies

BackgroundInterstitial fibrosis, tubular atrophy, and inflammation are major contributors to renal allograft failure. Here we seek an objective, quantitative pathological assessment of these lesions to improve predictive utility. MethodsWe constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte (MNL) infiltrates from Periodic acid-Schiff (PAS) stained slides of transplant biopsies (training: n=60, testing: n=33) that quantified pathological lesions specific for interstitium, tubules and MNL infiltration. The pipeline was applied to 789 whole slide images (WSI) from baseline (n=478, pre-implantation) and 12-month post-transplant (n=311) protocol biopsies in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. ResultsOur model accurately recognized kidney tissue compartments and MNLs. The digital features significantly correlated with Banff scores, but were more sensitive to subtle pathological changes below the thresholds in Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of 1-year graft loss (p=2.8e-05), while a Composite Damage Score (CDS) in 12-month post-transplant protocol biopsies predicted later graft loss (p=7.3e-05). ITAS and CDS outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITAS or CDS also demonstrated significantly higher incidence of eGFR decline and subsequent graft damage. ConclusionsThis deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies, and demonstrated superior ability for prediction of posttransplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

pathology↗