Biology subjects

Menon, M. C.

Publications and source records attributed to Menon, M. C..

6 recordsLinked to original sources

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↗

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↗

bioRxiv2022

Multiscale genetic architecture of donor-recipient differences reveals intronic LIMS1 locus mismatches associated with long-term renal transplant survival

BackgroundLong-term kidney allograft survival remains suboptimal. Emerging evidence indicates donor-recipient (D-R) mismatches outside of human leukocyte antigens (HLA) contribute to graft survival but mechanisms remain unclear, specifically for those mismatches within intronic regions. MethodsWe analyzed genome-wide SNP data of D-R pairs from two well-phenotyped kidney transplant cohorts (median follow-up ~1800 days), Genomics of Chronic Allograft Rejection (GoCAR; n=385) and Clinical Trials in Organ Transplantation 1/17 (CTOT1/17; n=146), quantifying genetic mismatches outside of HLA for every D-R pair at variant, gene, and genome-wide scales. ResultsUnbiased genome-wide screen of GoCAR D-R pairs uncovered the LIMS1 locus as the topranked candidate where D-R mismatches associated with death censored graft loss (DCGL). Independent of HLA, a previously unreported relationship between mismatches at a highly linked, intronic haplotype of 30 SNPs was seen as associated with DCGL, with confirmatory association in intra-ancestry D-Rs. Validation testing within the CTOT-01/17 showed similar associations with DCGL. Haplotype D-R mismatches showed a dosage effect, and the introduction of minor alleles in the donor to major allele-carrying recipients showed a greater risk of DCGL. Both the new LIMS1 haplotype and the previously reported LIMS1 SNP rs893403 are expression quantitative trait loci (eQTL) for the gene GCC2 in recipient immune cells, without alterations in GCC2 or LIMS1 coding sequences. Transcriptome enrichment analyses performed on whole blood and within multiple T cell subsets demonstrated significant associations of GCC2 gene, and of either allelic locus, with regulation of TGF-beta-SMAD signaling, implying a role in Treg function and association with rejection. ConclusionsOur analysis unravels intronic non-HLA SNP mismatches within LIMS1 that do not induce protein sequence variation but associate with DCGL. By acting as cis-eQTLs for GCC2 expression, these SNPs modulates TGF-beta signaling and T cell function, associating with immune events and graft outcomes. The findings have clinical implications for risk assessment and individualized therapy in kidney transplant recipients.

genetics↗

bioRxiv2022

Comparative evaluation of glomerular morphometric techniques reveals differential technical artefacts between FSGS and normal glomeruli

Morphometric estimates of mean glomerular volume (MGV) have clinical implications, over and above histologic data. However, MGV estimation is time-consuming, could waste tissue sections and requires expertise limiting its utility in retrospective clinical studies. MethodsWe evaluated MGV using both plastic and paraffin-embedded tissue from control and FSGS mice (n=10 each) using the gold-standard Disector/Cavalieri technique (Vglom-Cav) and other reported techniques [2- or 3-profile technique, Weibel-Gomez method (W-G)]. Within Vglom-Cav we examined the precision of MGV estimation while using MGVs obtained from 5- or 10-individual glomeruli measurements vs the true mean (20 glomeruli). ResultsIn both FSGS and controls, we identified an acceptable precision of 10-glomerular sampling vs true MGV within Vglom-Cav technique [88 (79-94) % of MGV obtained were within 10% of the true MGV]. The 5-glomerular sampling was less precise [70 (56, 81) % of MGV obtained were within 10% of true MGV]. In plastic based techniques, 2- or 3-profile MGVs showed greater concordance with Vglom-Cav, than W-G MGV. The new 3-profile technique offered incremental benefit to the existing 2-profile method (improved Lins concordance in control and FSGS animals). We observed a consistent reduction of Vglom values within control animals (52+/-0.06%) in paraffin-embedded tissue (vs corresponding methods in plastic) demonstrating a clear shrinkage artefact due to tissue processing. FSGS glomeruli showed significantly less and more variable shrinkage artefact likely due to glomerular fibrosis. ConclusionWe report the precision of 5- or 10-glomerular sampling for MGV estimation using controls and FSGS animals. We demonstrate and quantify the shrinkage bias in MGV during tissue processing for paraffin-embedding that also differentiated control animals and FSGS. Our findings have implications for experimental studies using glomerular morphometry.

pathology↗

bioRxiv2021

AMP-Kinase mediates regulation of glomerular volume and podocyte survival

We reported that Shroom3 knockdown, via Fyn inhibition, induced albuminuria with foot process effacement (FPE) without glomerulosclerosis (FSGS) or podocytopenia. Interestingly, knockdown mice had reduced podocyte volumes. Human minimal change disease, where podocyte Fyn inactivation was reported, also showed lower glomerular volumes than FSGS. We hypothesized that lower glomerular volume prevented the progression to podocytopenia. To test this hypothesis, we utilized unilateral- and 5/6th nephrectomy models in Shroom3 knockdown mice. Knockdown mice exhibited lower glomerular volume, and less glomerular and podocyte hypertrophy after nephrectomy. FYN-knockdown podocytes had similar reductions in podocyte volume, implying Fyn was downstream of Shroom3. Using SHROOM3- or FYN-knockdown, we confirmed reduced podocyte protein content, along with significantly increased phosphorylated AMP-kinase, a negative regulator of anabolism. AMP-Kinase activation resulted from increased cytoplasmic redistribution of LKB1 in podocytes. Inhibition of AMP-Kinase abolished the reduction in glomerular volume and induced podocytopenia in mice with FPE, suggesting a protective role for AMP-Kinase activation. In agreement with this, treatment of glomerular injury models with AMP-Kinase activators restricted glomerular volume, podocytopenia and progression to FSGS. In summary, we demonstrate the important role of AMP-Kinase in glomerular volume regulation and podocyte survival. Our data suggest that AMP-Kinase activation adaptively regulates glomerular volume to prevent podocytopenia in the context of podocyte injury.

systems biology↗

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↗