Lerman, Lilach O.

Endothelial progenitor cells (EPCs) constitute a promising focus for research in regenerative medicine. These cells originate from the bone marrow and other circulating hematopoietic cells and regulate tissue regeneration and vascular integrity. EPC mobilization serves for neovascularization and re-endothelialization after injury in multiple organ systems, including the renal and cardiovascular systems. Both their number and function may vary in disease states such as CKD, depending on severity, comorbid conditions, and other factors. Therefore, circulating EPC count and function have been proposed as markers for vascular health, and their mobilization or replenishment may offer a potential therapy. Delivery of EPCs has shown success in repairing injured kidneys in animal models of both chronic and AKIs. This approach may be limited by EPC heterogeneity and incomplete characterization that may be addressed by standardization, engineering, or combination with other therapies. The aim of this article was to review the current state and recent advances in our understanding of the role of EPCs in homeostasis and conditions that may lead to their dysregulation in kidney diseases.

Abstract Renal artery stenosis (RAS) is the leading cause of secondary hypertension worldwide. However, current medical and surgical treatment modalities provide minimal benefits for kidney injury. Recent preclinical RAS models have demonstrated promising potential of human mesenchymal stem cells (MSC) and their daughter extracellular vesicles (EV) in improving murine renal function and attenuating inflammation. However, the extent and mechanisms underlying immune rejection of xenogeneic MSCs or EVs are yet undetermined. Therefore, adipose tissue was harvested from adult healthy patients. Adipose-derived MSCs were extracted and cultured, and EVs were isolated from their supernatants via ultra-centrifugation. Then, mice randomly assigned to RAS or sham surgery were divided into 6 groups: sham surgery, RAS, sham + MSC, RAS + MSC, sham + EV, and RAS + EV. Two weeks after intra-aortic injection of MSCs (5 × 105) or EVs (20 µg protein), we compared the intrarenal T-cell and macrophage accumulation, splenic B-cell numbers, circulating cytokines and anti-human antibodies levels among the groups. MSCs and EVs did not influence intrarenal immune cell infiltrations. However, MSCs significantly increased circulating anti-human antibodies. In the spleen, RAS + EV mice showed higher memory IgM+ B-cells but reduced CD19+ B-cells compared to RAS + MSC. In vitro T-cell recall assay showed that both MSCs and EVs exhibited reduced IFN-γ release upon re-stimulation, indicating an immunosuppressive effect. Therefore, xenogeneic MSCs induced a greater humoral response in mice, while EVs triggered a splenic cellular response, but neither elicits discernible kidney rejection. Our results provide key insights into the immunomodulatory mechanisms of MSCs and EVs and immune mechanisms underlying xenograft rejection.

The intrinsic reparative capacity of the adult mammalian kidney is restricted to the ability of scattered tubular-like cells (STCs) to repair damaged kidney cells. Our study provides evidence that chronic renal ischemia alters the mRNA/miRNA profile of angiogenic/vascular development genes of swine STCs, limiting their potential to repair injured tubular cells. Our observations may assist in developing new therapies to improve renal repair in individuals with chronic renal ischemia.

Key Points Activin A is implicated in profibrotic and prosenescent kidney injury and correlates with kidney injury markers in animals and humans. Follistatin, through activin A antagonism, reduces senescence burden, macrophage infiltration, and proinflammatory pathway activation in murine diabetic kidney disease. Follistatin and other antagonists of activin A signaling pathways may be promising, novel therapeutics for diabetic kidney disease. Background Circulating activin A, an inflammatory mediator implicated in profibrotic kidney injury and cellular senescence-induced adipose tissue dysfunction, is increased in human diabetic kidney disease (DKD) and directly correlates with kidney dysfunction. We tested the hypothesis that activin A increases kidney injury, senescent cell abundance, and macrophage infiltration in DKD and antagonism through follistatin (FS) therapy diminishes these effects. Methods An accelerated nephropathy type 2 diabetes (db/db) mouse model was generated by implantation of angiotensin II-loaded osmotic minipumps resulting in increased albuminuria and glomerular and tubular injury. Kidney repair effects of FS (5 µ g intraperitoneal; two doses) were assessed through markers of kidney injury, fibrosis, inflammation, cellular senescence, and macrophage infiltration. In vitro studies examined antiactivin effects of FS on high glucose-exposed human monocytes, renal fibroblasts, and renal tubule epithelial cells. Results Activin A antagonism with FS reduced senescence (p19), proinflammatory (including senescence-associated secretory phenotype), and profibrotic markers including activin A. FS improved kidney morphology, restored podocyte markers (nephrin and Wilms tumor-1), and reduced kidney injury biomarkers, albuminuria and kidney fibrosis. FS decreased kidney macrophage and leukocyte infiltration and absent in melanoma 2 inflammasome activation. FS seemed to suppress inflammation through the toll-like receptor-4/NF kappa-light-chain-enhancer of activated B cells pathway in vivo further supported in human macrophages in vitro . In addition, FS reduced hyperglycemia-induced renal fibroblast activation and renal tubule epithelial cell senescence in vitro . Conclusions Activin A is a mediator of kidney injury through macrophage-associated inflammation in murine DKD. FS acts through senomorphic activities which inhibit profibrotic, proinflammatory, and prosenescence signaling by activin A. Hence, antiactivin targeting may aid in the development of a promising, novel therapeutic for DKD.

Mesenchymal stromal cells (MSCs) possess therapeutic properties, which can be blunted by obesity. Autophagy, a cellular recycling process, is essential for MSC function. We investigated the mechanisms by which obesity affects the properties of MSCs, with a focus on autophagy. Adipose tissue was obtained from kidney donors [body mass index (BMI) <30 kg/m 2 , non-obese] or individuals undergoing weight loss surgery (BMI ≥30 kg/m 2 , obese) for MSC harvesting ( n = 11 each); samples were randomized to sequencing (seq; n = 5 each) or functional studies ( n = 6 each). MSCs were sequenced to determine their epigenetic (5-hydroxymethylcytosine) and transcriptomic profiles across autophagy-related genes using hydroxymethylated DNA immunoprecipitation sequencing and mRNA-seq, respectively. Genes with shared trends in both datasets underwent Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) validation. During functional studies, 2-h starvation was used to induce autophagy in vitro , enabling detection of changes in the protein expression of microtubule-associated protein 1A/1B-light chain-3 and in autophagic flux. Obesity amplified a starvation-induced reduction in autophagic flux in MSCs while promoting earlier generation of new autophagosomes during autophagy initiation. Integrated analysis of the two sequencing datasets revealed 124 differentially hydroxymethylated genes and 30 differentially expressed mRNAs. Among six overlapping autophagy-related genes, three exhibited same-direction trends. Of these, STX12 and SLC25A4 may be implicated in the impact of obesity on autophagic changes in MSCs. Therefore, human obesity may alter autophagy in adipose tissue–derived MSC, and thereby their metabolism and function.

Background: Atherosclerotic carotid artery disease is associated with a high risk of major adverse cardiac events (MACE) even with optimal risk factor management and surgical interventions such as carotid endarterectomy (CEA). Aortic stenosis (AS) is an age-related valve disease that is associated with a high rate of MACE. An artificial intelligence-based AS estimation algorithm (AI-AS) can identify AS with high accuracy using electrocardiogram. This study evaluates the prognostic value of AI-AS in patients with carotid artery disease undergoing CEA. Methods: We included patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 if they had >30 days of follow-up and an electrocardiogram within one year before CEA. The electrocardiogram closest to the CEA date was used to calculate AI-AS (AS probability ranging from 0-1). MACE was defined as the composite of cardiac events, ischemic cerebrovascular accidents, and all-cause mortality during follow-up after CEA. A receiver operating characteristic curve and the Youden index were used to identify the optimal AI-AS cut-off value for predicting MACE. Kaplan Meier plot was used to depict MACE-free survival. Regression models were used to evaluate the predictive value of AI-AS for future MACE adjusting for clinical covariates. Results: A total of 665 patients with a median age of 72 [IQR:66, 78] years and 438 (65.9%) males were included. Over a median follow-up time of 7.1 [IQR:2.7, 11.5] years, MACE occurred in 419 (63%) of the patients, including 187 (44.6%) mortality, 161 (38.4%) cardiac events, and 71 (16.9%) cerebrovascular accidents. Median AI-AS was 0.32 [IQR:0.09, 0.55] in the total cohort, 0.34 [IQR:0.12, 0.56] in patients with MACE, and 0.30 [IQR:0.07, 0.53] in patients without MACE (P=0.038). AI-AS was a significant predictor of MACE (HR:2.38, 95%CI:1.64-3.46, P<0.001) in univariate Cox regression analysis. The optimal cut-off value of AI-AS for MACE was 0.419 (area under the curve: 0.55). Patients with high AI-AS (≥0.419) had a significantly lower MACE-free survival in the Log-rank test ( Figure 1 ) and in the multivariable Cox regression model adjusted for covariates compared to low AI-AS (<0.419) patients ( Table 1 ). Conclusion: AI-AS is an independent predictor of MACE in patients with carotid artery disease after CEA and can be used as a risk-stratifying tool in this patient population. Additional studies are needed to elucidate the mechanistic link between AS and MACE in these patients.

Introduction: Patients with atherosclerotic carotid artery disease are at high risk of mortality in the long-term follow-up after carotid endarterectomy (CEA), partly due to dysrhythmia. Atrial fibrillation (AF) is a common cardiac dysrhythmia linked to stroke and cardiovascular events. An artificial intelligence-based electrocardiogram (ECG) AF algorithm (AI-AF) can effectively identify silent AF. We aimed to assess the effectiveness of AI-AF in predicting mortality in patients with carotid artery disease undergoing CEA. Methods: Patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 were included if they had >30 days follow-up and an ECG within one year before CEA. The ECG closest to the CEA date was used to calculate AI-AF (probability of AF ranging from 0 to 1). All-cause mortality rates after CEA were obtained from clinical records. The optimal AI-AF cut-off value for predicting mortality was calculated using a receiver operating characteristic curve and the Youden index. Long-term survival after CEA was depicted with a Kaplan Meier plot and compared with the Log-rank test. Univariate and multivariate Cox Regression models were used to assess the predictive value of AI-AF for mortality, adjusting for age, sex, body mass index, diabetes mellitus, hypertension, and hyperlipidemia. Results: A total of 636 patients with a median age of 72 [IQR: 66, 78] years and 414 (65.1%) males were included. The median AI-AF was 0.056 [IQR: <0, 0.171]. Over a median follow-up time of 7.1 [IQR: 2.7, 11.5] years, mortality occurred in 326 (51.3%) patients. AI-AF significantly predicted mortality in univariate and multivariate Cox regression analysis (HR: 3.47, 95%CI: 2.34-5.16, P<0.001; HR: 2.30, 95%CI: 1.44-3.68, P<0.001, respectively). The best cut-off value for AI-AF for mortality prediction was 0.025 (area under the curve: 0.608). High and low AI-AF were defined as AI-AF≥ 0.025 and <0.025, respectively. Mortality was significantly higher in high AI-AF compared to low AI-AF patients (P<0.001, Figure 1A,1B), and high AI-AF was a significant predictor of mortality (univariate: Figure 2; multivariate Cox regression: HR: 2.11, 95%CI: 1.56-2.84, P<0.001). Conclusion: AI-AF can identify patients with a higher risk of mortality after CEA. This finding supports a role for AI algorithms in risk stratification of patients with carotid artery disease following CEA. Future studies are needed to elucidate the contribution of AF to all-cause mortality.

Metabolic syndrome (MetS) is associated with low-grade inflammation, which can be exacerbated by renal artery stenosis (RAS) and renovascular hypertension, potentially worsening outcomes through pro-inflammatory cytokines. This study investigated whether mesenchymal stem/stromal cells (MSCs) could reduce fat inflammation in pigs with MetS and RAS. Twenty-four pigs were divided into Lean (control), MetS, MetS + RAS, and MetS + RAS + MSCs. In the MSC-treated group, autologous adipose-derived MSCs (107 cells) were injected into the renal artery six weeks after RAS induction. After four weeks, fat volumes and inflammatory markers were assessed. MSC treatment reduced levels of pro-inflammatory cytokines (MCP-1, TNF-a, IL-6) in the renal vein blood and in perirenal fat. The MSCs also decreased fat fibrosis, restored adipocyte size, and altered adipogenesis-related gene expression, particularly in the perirenal fat. These effects were less pronounced in subcutaneous fat. The MSC therapy attenuated fat inflammation and improved metabolic outcomes in pigs with MetS + RAS, suggesting that adipose-derived MSCs may offer a promising therapeutic approach for metabolic disorders.

Background: Recent studies have reported associations between alterations in gut microbiome composition (GMC) and cardiovascular disease risk factors (CVR). However, data regarding the use of GMC as a biomarker of CVR is scarce. Aims: The current study was designed to assess the distinct patterns in GMC among patients with varying degrees of CVR and/or atherosclerotic cardiovascular disease (ASCVD). Methods: Patients with a range of CVR including hypertension (HTN), hyperlipidemia (HLD), diabetes (DM), and/or ASCVD referring to Mayo Clinic from 2013 to 2018 were prospectively enrolled. DNA extracted from stool samples was analyzed using the V3-V5 region of the 16s data. Microbial α-diversity was assessed by the observed taxonomic units, Shannon, and Chao1 indices. β-diversity was assessed using Bray-Curtis dissimilarity and plotted using principal coordinates analysis. Hierarchical clustering was used to identify patterns in the GMC samples. Random Forest analysis was used to identify the most important clinical factors differentiating the clusters. Results: A total of 211 patients with a median age of 60 [IQR: 50,70] years and with 90 (42.7%) males were included. Two clusters of GMC were identified (Figure 1A) . Cluster 1 and 2 had 104 (49.3%) and 107 (50.7%) patients, respectively. Among CVRs, age and body mass index were the most prominent factors contributing to the difference in GMC among clusters ( Figure 1B ). Cluster 2 had a better α diversity profile than Cluster 1 (Figure 1C-E) . There was no significant difference in TMAO between clusters (P=0.6). Cluster 2 patients were younger (P<0.001), leaner (P=0.007), more physically active (P<0.001), less male (P=0.009), and had a lower prevalence of ASCVD (P=0.003), HTN (P=0.010), and HLD (P=0.005). There was no significant difference in the prevalence of DM (P=0.063), smoking (P=0.446), and alcohol intake (P=0.134) between the clusters. Conclusion: This study suggests the potential of GMC profiling as a valuable biomarker for assessing CVRs, with age and BMI as the most prominent factors associated with GMC clustering.