ABSTRACT
BACKGROUND: End-stage renal disease patients on hemodialysis (ESRD) patients are at high risk for contracting COVID-19. In this propensity matched cohort study, we examined the prevalence of COVID-19 in emergency room (ER) patients and examined whether clinical outcomes varied by ESRD status. METHODS: Patients who visited George Washington University Hospital ER from April 2020 to April 2021 were reviewed for COVID-19 and ESRD status. Among COVID-positive ER patients, the propensity for ESRD was calculated using a logistic regression model to create a propensity-matched sample of ESRD vs non-ESRD COVID-19 patients. A multivariable model examined whether ESRD was an independent predictor of death and other outcomes in COVID-19 patients. RESULTS: Among the 27,106 ER patients, 2689 of whom were COVID-positive (9.9%). The odds of testing positive for COVID-19 were 0.97 ([95% CI: 0.78-1.20], p = 0.76) in ESRD vs non-ESRD patients after adjusting for age, sex, and race. There were 2414 COVID-positive individuals with non-missing data, of which 98 were ESRD patients. In this COVID-positive sample, ESRD patients experienced a higher incidence of stroke, sepsis, and pneumonia than non-ESRD individuals. Significant independent predictors of death included age, race, sex, insurance status, and diabetes mellitus. Those with no insurance had odds of death that was 212% higher than those with private insurance (3.124 [1.695-5.759], p < 0.001). ESRD status was not an independent predictor of death (1.215 [0.623-2.370], p = 0.57). After propensity-matching in the COVID-positive patients, there were 95 ESRD patients matched with 283 non-ESRD individuals. In this sample, insurance status continued to be an independent predictor of mortality, while ESRD status was not. ESRD patients were more likely to have lactic acidosis (36% vs 15%) and length of hospital stay ≥ 7 days (48% vs 31%), but no increase in odds for any studied adverse outcomes. CONCLUSIONS: In ER patients, ESRD status was not associated with higher odds for testing positive for COVID-19. Among ER patients who were COVID positive, ESRD was not associated with mortality. However, insurance status had a strong and independent association with death among ER patients with COVID-19.
Subject(s)
COVID-19 , Kidney Failure, Chronic , Humans , Cohort Studies , Prevalence , COVID-19/epidemiology , COVID-19/complications , Kidney Failure, Chronic/epidemiology , Kidney Failure, Chronic/therapy , Kidney Failure, Chronic/complications , Renal Dialysis , Retrospective Studies , Risk FactorsABSTRACT
Calcium is a critical signaling molecule in many cell types including immune cells. The calcium-release activated calcium channels (CRAC) responsible for store-operated calcium entry (SOCE) in immune cells are gated by STIM family members functioning as sensors of Ca2+ store content in the endoplasmic reticulum. We investigated the effect of SOCE blocker BTP2 on human peripheral blood mononuclear cells (PBMC) stimulated with the mitogen phytohemagglutinin (PHA). We performed RNA sequencing (RNA-seq) to query gene expression at the whole transcriptome level and identified genes differentially expressed between PBMC activated with PHA and PBMC activated with PHA in the presence of BTP2. Among the differentially expressed genes, we prioritized genes encoding immunoregulatory proteins for validation using preamplification enhanced real time quantitative PCR assays. We performed multiparameter flow cytometry and validated by single cell analysis that BTP2 inhibits cell surface expression CD25 at the protein level. BTP2 reduced significantly PHA-induced increase in the abundance of mRNAs encoding proinflammatory proteins. Surprisingly, BTP2 did not reduce significantly PHA-induced increase in the abundance of mRNAs encoding anti-inflammatory proteins. Collectively, the molecular signature elicited by BTP2 in activated normal human PBMC appears to be tipped towards tolerance and away from inflammation.
ABSTRACT
BACKGROUND: We developed urinary cell mRNA profiling for noninvasive diagnosis of acute T cell mediated rejection (TCMR) and BK virus nephropathy (BKVN), two significant post-transplant complications. Our profiling protocol for the multicenter Clinical Trial of Transplantation-04 (CTOT-04) study consisted of centrifugation of urine to prepare cell pellets, washes, addition of an RNA preservative, storage at 800C and shipment in cold containers to our Gene Expression Monitoring (GEM) Core for RNA isolation and quantification of mRNA in RT-qPCR assays. To simplify profiling, we developed a filter-based protocol (ZFBP) that eliminated the need for centrifugation, RNA preservative, storage at 800C, and shipment in cold containers for mRNA profiling. Furthermore, we trained kidney allograft recipients to perform the filtration of urine at home using the filter and post the urinary cell lysate containing the RNA at ambient temperature to our GEM Core for profiling. Here, we report our refinement of ZFBP and investigation of its diagnostic performance characteristics. METHODS: Total RNA was isolated from kidney allograft biopsy-matched urines using a filter-based protocol complemented by a silica-membrane-based cartridge for mRNA enrichment, the Weill Cornell Hybrid Protocol (WCHP). Absolute copy numbers of CD3ε mRNA, CXCL10 mRNA, and 18S rRNA, components of the CTOT-04 three-gene TCMR diagnostic signature, and urinary cell BKV VP 1 mRNA copy number were measured using RT-qPCR assays. Mann-Whitney test, Fischer exact test, and receiver operating characteristic (ROC) curve analysis were used for data analyses. RESULTS: Urinary cell three-gene TCMR diagnostic signature scores in urines processed using the WCHP discriminated kidney allograft recipients with TCMR (12 TCMR biopsies from 11 patients) from those without TCMR or BKVN (29 No TCMR/No BKVN biopsies from 29 patients). The median (25th and 75th percentiles) score of the CTOT-04 three-gene TCMR diagnostic signature was -0.448 (-1.664, 0.204) in the TCMR group and - 2.542 (-3.267, -1.365) in the No TCMR/ No BKVN group (P = 0.0005, Mann-Whitney test). ROC curve analysis discriminated the TCMR group from the No TCMR/ No BKVN group; the area under the ROC curve (AUROC) was 0.84 (95% Confidence Intervals [CI], 0.69 to 0.98) (P < 0.001), and TCMR was diagnosed with a sensitivity of 67% (95% CI, 35 to 89) at a specificity of 86% (95% CI, 67 to 95) using the CTOT-04 validated cutpoint of -1.213 (P = 0.0016, Fisher exact test). BKV VP1 mRNA copy number in urines processed using the WCHP discriminated patients with BKVN (n = 7) from patients without TCMR or BKVN (n = 29) and the AUROC was 1.0 (95% CI, 1.00 to 1.00) (P < 0.0001) and BKVN was diagnosed with a sensitivity of 86% (95% CI, 42 to 99) at a specificity of 100% (95% CI, 85 to 100) with the previously validated cutpoint of 6.5 × 108 BKV-VP1 mRNA copies per microgram of RNA (P < 0.0001, Fisher exact test). CONCLUSION: Urine processed using the WCHP predicted TCMR and BKVN in kidney allograft recipients. WCHP represents not only a significant advance toward the portability of urinary cell mRNA profiling but also improved patient management by minimizing their visits for urine collection.
Subject(s)
BK Virus , Kidney Transplantation , Polyomavirus Infections , Humans , Kidney Transplantation/adverse effects , BK Virus/genetics , RNA, Messenger/genetics , T-Lymphocytes , Kidney , Polyomavirus Infections/diagnosis , RNA , Allografts , Graft Rejection/diagnosis , Graft Rejection/urine , Multicenter Studies as TopicABSTRACT
BACKGROUND: Patients with chronic kidney disease (CKD) have dysbiosis, dysmetabolism, and immune dysregulation. Gut microbiome plays an important role shaping the immune system which is an important modulator of CKD progression. METHODS: We compared the effect of a diet low in protein and high in fiber (LP-HF; n = 7) to that of diet rich in protein, but low in fiber (HP-LF; n = 7) on gut microbiome and T-cell commitment in male CKD (Alb/TGF-ß1) mice. The gut microbiomes of these mice were subjected to 16S rRNA taxonomic profiling at baseline, 6 weeks and 12 weeks of the study. RESULTS: The LP-HF diet was associated with an increase in Butyricicoccus pullicaecorum BT, a taxon whose functions include those closely related to butyric acid synthesis (Kendall's W statistic = 180 in analysis of microbiome composition). HP-LF diet was associated with increased abundance of two predominantly proteolytic bacterial strains related to Parabacteroides distasonis (W statistic = 173), Mucispirillum schaedleri, and Bacteroides dorei (W statistic = 192). Pathway analysis suggested that the LP-HF diet induced carbohydrate, lipid, and butyrate metabolism. As compared with HP-LF mice, LP-HF mice had 1.7-fold increase in CD4+Foxp3+Treg cells in spleen and 2.4-fold increase of these cells in peripheral blood. There was an 87% decrease in percentage of CD4+ Th17 + cells in spleen and an 85% decrease in peripheral blood, respectively, in LP-HF mice compared to the HP-LF mice. CONCLUSION: The LP-HF diet promotes the proliferation of saccharolytic bacteria and favors T-cell commitment toward Treg cells in a CKD mouse of model. Clinical significance of the finding needs to be further investigated.
Subject(s)
Gastrointestinal Microbiome , Renal Insufficiency, Chronic , Mice , Male , Animals , T-Lymphocytes, Regulatory , RNA, Ribosomal, 16S/genetics , Dietary Fiber/pharmacology , Dietary Proteins/pharmacology , Mice, Inbred C57BLABSTRACT
Chronic kidney disease is a major public health concern that affects millions of people globally. Alterations in gut microbiota composition have been observed in patients with chronic kidney disease. Nevertheless, the correlation between the gut microbiota and disease severity has not been investigated. In this study, we performed shot-gun metagenomics sequencing and identified several taxonomic and functional signatures associated with disease severity in patients with chronic kidney disease. We noted that 19 microbial genera were significantly associated with the severity of chronic kidney disease. The butyrate-producing bacteria were reduced in patients with advanced stages of chronic kidney diseases. In addition, functional metagenomics showed that two-component systems, metabolic activity and regulation of co-factor were significantly associated with the disease severity. Our study provides valuable information for the development of microbiota-oriented therapeutic strategies for chronic kidney disease.
Subject(s)
Butyrates/metabolism , Gastrointestinal Microbiome/physiology , Renal Insufficiency, Chronic/microbiology , Adolescent , Bacteria/genetics , Bacteria/metabolism , Feces/microbiology , Female , Gastrointestinal Microbiome/genetics , Humans , Male , Metagenomics/methods , Severity of Illness IndexSubject(s)
Acute Kidney Injury/epidemiology , COVID-19/epidemiology , Academic Medical Centers , Acute Kidney Injury/diagnosis , Acute Kidney Injury/mortality , Acute Kidney Injury/therapy , Aged , COVID-19/diagnosis , COVID-19/mortality , COVID-19/therapy , Female , Hospital Mortality , Hospitalization , Hospitals, Urban , Humans , Incidence , Kidney Function Tests , Male , Middle Aged , New York City/epidemiology , Recovery of Function , Retrospective Studies , Risk Factors , Time Factors , Treatment OutcomeSubject(s)
Acute Kidney Injury/therapy , Anticoagulants/therapeutic use , COVID-19/complications , Continuous Renal Replacement Therapy/instrumentation , Equipment Failure/statistics & numerical data , Acute Kidney Injury/virology , Aged , Arginine/analogs & derivatives , Arginine/therapeutic use , Citric Acid/therapeutic use , Disposable Equipment , Female , Heparin, Low-Molecular-Weight/therapeutic use , Humans , Male , Middle Aged , Pipecolic Acids/therapeutic use , SARS-CoV-2 , Sulfonamides/therapeutic use , Time FactorsABSTRACT
Identification of a shared gene expression pattern between T cell-mediated rejection (TCMR) and antibody-mediated rejection (AMR) in human kidney allografts may help prioritize targets for the treatment of both types of acute rejection. METHODS: We performed RNA sequencing and bioinformatics of genome-wide transcriptome profiles of urinary cells to identify novel mRNAs shared between TCMR and AMR and of mechanistic relevance. Customized RT-QPCR assays were then used to validate their abundance in urinary cells. Urinary cell transcriptome profiles and mRNA abundance were assessed in 22 urine samples matched to 22 TCMR biopsies, 7 samples matched to 7 AMR biopsies, and 24 samples matched to 24 No Rejection (NR) biopsies and correlated with biopsy diagnosis. RESULTS: RNA sequencing data and bioinformatics identified 127 genes in urine to be shared between TCMR and AMR. We selected 3 novel mRNAs-ITM2A, SLAMF6, and IKZF3-for absolute quantification and validation by customized RT-QPCR assays. The abundance of all 3 mRNAs was significantly higher in urine matched to TCMR or AMR than in urine matched to NR biopsies. Receiver-operating-characteristic curve analysis showed that all 3 mRNAs distinguished TCMR or AMR from NR. Their abundance was similar in patients with TCMR and those with AMR. CONCLUSIONS: State-of-the-art antirejection therapies are mostly effective to treat TCMR but not AMR. Our identification of mRNAs shared between TCMR and AMR and contributing to T cell-B cell interactions may help prioritize therapeutic targets for the simultaneous treatment of TCMR and AMR.
ABSTRACT
BACKGROUNDRNA sequencing (RNA-Seq) is a molecular tool to analyze global transcriptional changes, deduce pathogenic mechanisms, and discover biomarkers. We performed RNA-Seq to investigate gene expression and biological pathways in urinary cells and kidney allograft biopsies during an acute rejection episode and to determine whether urinary cell gene expression patterns are enriched for biopsy transcriptional profiles.METHODSWe performed RNA-Seq of 57 urine samples collected from 53 kidney allograft recipients (patients) with biopsies classified as acute T cell-mediated rejection (TCMR; n = 22), antibody-mediated rejection (AMR; n = 8), or normal/nonspecific changes (No Rejection; n = 27). We also performed RNA-Seq of 49 kidney allograft biopsies from 49 recipients with biopsies classified as TCMR (n = 12), AMR (n = 17), or No Rejection (n = 20). We analyzed RNA-Seq data for differential gene expression, biological pathways, and gene set enrichment across diagnoses and across biospecimens.RESULTSWe identified unique and shared gene signatures associated with biological pathways during an episode of TCMR or AMR compared with No Rejection. Gene Set Enrichment Analysis demonstrated enrichment for TCMR biopsy signature and AMR biopsy signature in TCMR urine and AMR urine, irrespective of whether the biopsy and urine were from the same or different patients. Cell type enrichment analysis revealed a diverse cellular landscape with an enrichment of immune cell types in urinary cells compared with biopsies.CONCLUSIONSRNA-Seq of urinary cells and biopsies, in addition to identifying enriched gene signatures and pathways associated with TCMR or AMR, revealed genomic changes between TCMR and AMR, as well as between allograft biopsies and urinary cells.