Transcriptomic signatures of chronic active antibody-mediated rejection deciphered by RNA sequencing of human kidney allografts.

Natural killer (NK) cells mediate spontaneous cell-mediated cytotoxicity and antibody-dependent cell-mediated cytotoxicity. This dual functionality could enable their participation in chronic active antibody-mediated rejection (CA-ABMR). Earlier microarray profiling studies have not subcategorized antibody-mediated rejection into CA-ABMR and active-ABMR, and the gene expression pattern of CA-ABMR has not been compared with that of T cell-mediated rejection (TCMR). To fill these gaps, we RNA sequenced human kidney allograft biopsies categorized as CA-ABMR, active-ABMR, TCMR, or No Rejection (NR). Among the 15,910 genes identified in the biopsies, 60, 114, and 231 genes were uniquely overexpressed in CA-ABMR, TCMR, and active-ABMR, respectively; compared to NR, 50 genes were shared between CA-ABMR and active-ABMR, and 164 genes between CA-ABMR and TCMR. The overexpressed genes were annotated to NK cells and T cells in CA-ABMR and TCMR, and to neutrophils and monocytes in active-ABMR. The NK cell cytotoxicity and allograft rejection pathways were enriched in CA-ABMR. Genes encoding perforin, granzymes, and death receptor were overexpressed in CA-ABMR versus active-ABMR but not compared to TCMR. NK cell cytotoxicity pathway gene set variation analysis score was higher in CA-ABMR compared to active-ABMR but not in TCMR. Principal component analysis of the deconvolved immune cellular transcriptomes separated CA-ABMR and TCMR from active-ABMR and NR. Immunohistochemistry of kidney allograft biopsies validated a higher proportion of CD56+ NK cells in CA-ABMR than in active-ABMR. Thus, CA-ABMR was exemplified by the overexpression of the NK cell cytotoxicity pathway gene set and, surprisingly, molecularly more like TCMR than active-ABMR.

AI (Artificial Intelligence) and Hypertension Research.

PURPOSE OF REVIEW: This review a highlights that to use artificial intelligence (AI) tools effectively for hypertension research, a new foundation to further understand the biology of hypertension needs to occur by leveraging genome and RNA sequencing technology and derived tools on a broad scale in hypertension. RECENT FINDINGS: For the last few years, progress in research and management of essential hypertension has been stagnating while at the same time, the sequencing of the human genome has been generating many new research tools and opportunities to investigate the biology of hypertension. Cancer research has applied modern tools derived from DNA and RNA sequencing on a large scale, enabling the improved understanding of cancer biology and leading to many clinical applications. Compared with cancer, studies in hypertension, using whole genome, exome, or RNA sequencing tools, total less than 2% of the number cancer studies. While true, sequencing the genome of cancer tissue has provided cancer research an advantage, DNA and RNA sequencing derived tools can also be used in hypertension to generate new understanding how complex protein network, in non-cancer tissue, adapts and learns to be effective when for example, somatic mutations or environmental inputs change the gene expression profiles at different network nodes. The amount of data and differences in clinical condition classification at the individual sample level might be of such magnitude to overwhelm and stretch comprehension. Here is the opportunity to use AI tools for the analysis of data streams derived from DNA and RNA sequencing tools combined with clinical data to generate new hypotheses leading to the discovery of mechanisms and potential target molecules from which drugs or treatments can be developed and tested. Basic and clinical research taking advantage of new gene sequencing-based tools, to uncover mechanisms how complex protein networks regulate blood pressure in health and disease, will be critical to lift hypertension research and management from its stagnation. The use of AI analytic tools will help leverage such insights. However, applying AI tools to vast amounts of data that certainly exist in hypertension, without taking advantage of new gene sequencing-based research tools, will generate questionable results and will miss many new potential molecular targets and possibly treatments. Without such approaches, the vision of precision medicine for hypertension will be hard to accomplish and most likely not occur in the near future.

Single nucleotide variant counts computed from RNA sequencing and cellular traffic into human kidney allografts.

Advances in bioinformatics allow identification of single nucleotide polymorphisms (variants) from RNA sequence data. In an allograft biopsy, 2 genomes contribute to the RNA pool, 1 from the donor organ and the other from the infiltrating recipient's cells. We hypothesize that imbalances in genetic variants of RNA sequence data of kidney allograft biopsies provide an objective measure of cellular infiltration of the allograft. We performed mRNA sequencing of 40 kidney allograft biopsies, selected to represent a comprehensive range of diagnostic categories. We analyzed the sequencing reads of these biopsies and of 462 lymphoblastoid cell lines from the 1000 Genomes Project, for RNA variants. The ratio of heterozygous to nonreference genome homozygous variants (Het/Hom ratio) on all autosomes was determined for each sample, and the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) score was computed as a complementary estimate of the degree of cellular infiltration into biopsies. The Het/Hom ratios (P = .02) and the ESTIMATE scores (P < .001) were associated with the biopsy diagnosis. Both measures correlated significantly (r = .67, P < .0001), even though the Het/Hom ratio is based on mRNA sequence variation, while the ESTIMATE score uses mRNA expression. Het/Hom ratio and the ESTIMATE score may offer unbiased and quantitative parameters for characterizing cellular traffic into human kidney allografts.

Urinary cell mRNA profiles and differential diagnosis of acute kidney graft dysfunction.

Noninvasive tests to differentiate the basis for acute dysfunction of the kidney allograft are preferable to invasive allograft biopsies. We measured absolute levels of 26 prespecified mRNAs in urine samples collected from kidney graft recipients at the time of for-cause biopsy for acute allograft dysfunction and investigated whether differential diagnosis of acute graft dysfunction is feasible using urinary cell mRNA profiles. We profiled 52 urine samples from 52 patients with biopsy specimens indicating acute rejection (26 acute T cell-mediated rejection and 26 acute antibody-mediated rejection) and 32 urine samples from 32 patients with acute tubular injury without acute rejection. A stepwise quadratic discriminant analysis of mRNA measures identified a linear combination of mRNAs for CD3ε, CD105, TLR4, CD14, complement factor B, and vimentin that distinguishes acute rejection from acute tubular injury; 10-fold cross-validation of the six-gene signature yielded an estimate of the area under the curve of 0.92 (95% confidence interval, 0.86 to 0.98). In a decision analysis, the six-gene signature yielded the highest net benefit across a range of reasonable threshold probabilities for biopsy. Next, among patients diagnosed with acute rejection, a similar statistical approach identified a linear combination of mRNAs for CD3ε, CD105, CD14, CD46, and 18S rRNA that distinguishes T cell-mediated rejection from antibody-mediated rejection, with a cross-validated estimate of the area under the curve of 0.81 (95% confidence interval, 0.68 to 0.93). Incorporation of these urinary cell mRNA signatures in clinical decisions may reduce the number of biopsies in patients with acute dysfunction of the kidney allograft.

RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell-mediated Rejection.

BACKGROUND: Delineation of T-cell genes, gene sets, pathways, and T-cell subtypes associated with acute T cell-mediated rejection (TCMR) may improve its management. METHODS: We performed bulk RNA-sequencing of 34 kidney allograft biopsies (16 Banff TCMR and 18 no rejection [NR] biopsies) from 34 adult recipients of human kidneys. Computational analysis was performed to determine the differential intragraft expression of T-cell genes at the level of single-gene, gene set, and pathways. RESULTS: T-cell signaling pathway gene sets for plenary T-cell activation were overrepresented in TCMR biopsies compared with NR biopsies. Heightened expression of T-cell signaling genes was validated using external TCMR biopsies. Pro- and anti-inflammatory immune gene sets were enriched, and metabolism gene sets were depleted in TCMR biopsies compared with NR biopsies. Gene signatures of regulatory T cells, Th1 cells, Th2 cells, Th17 cells, T follicular helper cells, CD4 tissue-resident memory T cells, and CD8 tissue-resident memory T cells were enriched in TCMR biopsies compared with NR biopsies. T-cell exhaustion and anergy were also molecular attributes of TCMR. Gene sets associated with antigen processing and presentation, and leukocyte transendothelial migration were overexpressed in TCMR biopsies compared with NR biopsies. Cellular deconvolution of graft infiltrating cells by gene expression patterns identified CD8 T cell to be the most abundant T-cell subtype infiltrating the allograft during TCMR. CONCLUSIONS: Our delineation of intragraft T-cell gene expression patterns, in addition to yielding new biological insights, may help prioritize T-cell genes and T-cell subtypes for therapeutic targeting.

Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts.

We tested the hypothesis that single-cell RNA-sequencing (scRNA-seq) analysis of human kidney allograft biopsies will reveal distinct cell types and states and yield insights to decipher the complex heterogeneity of alloimmune injury. We selected 3 biopsies of kidney cortex from 3 individuals for scRNA-seq and processed them fresh using an identical protocol on the 10x Chromium platform; (i) HK: native kidney biopsy from a living donor, (ii) AK1: allograft kidney with transplant glomerulopathy, tubulointerstitial fibrosis, and worsening graft function, and (iii) AK2: allograft kidney after successful treatment of active antibody-mediated rejection. We did not study T-cell-mediated rejections. We generated 7217 high-quality single cell transcriptomes. Taking advantage of the recipient-donor sex mismatches revealed by X and Y chromosome autosomal gene expression, we determined that in AK1 with fibrosis, 42 months after transplantation, more than half of the kidney allograft fibroblasts were recipient-derived and therefore likely migratory and graft infiltrative, whereas in AK2 without fibrosis, 84 months after transplantation, most fibroblasts were donor-organ-derived. Furthermore, AK1 was enriched for tubular progenitor cells overexpressing profibrotic extracellular matrix genes. AK2, eight months after successful treatment of rejection, contained plasmablast cells with high expression of immunoglobulins, endothelial cell elaboration of T cell chemoattractant cytokines, and persistent presence of cytotoxic T cells. In addition to these key findings, our analysis revealed unique cell types and states in the kidney. Altogether, single-cell transcriptomics yielded novel mechanistic insights, which could pave the way for individualizing the care of transplant recipients.

FOXP3 mRNA Profile Prognostic of Acute T Cell-mediated Rejection and Human Kidney Allograft Survival.

BACKGROUND: T cell-mediated rejection (TCMR) is the most frequent type of acute rejection and is associated with kidney allograft failure. Almost 40% of TCMR episodes are nonresponsive to therapy, and molecular mechanisms for the nonresponsiveness are unknown. Our single-center study identified that urinary cell FOXP3 mRNA abundance predicts TCMR reversibility and allograft survival. METHODS: We developed PCR assays and measured absolute copy numbers of transcripts for FOXP3, CD25, CD3E, perforin, and 18S rRNA in 3559 urines from 480 kidney allograft recipients prospectively enrolled in the multicenter Clinical Trials in Organ Transplantation-04. In this replication study, we investigated the association between mRNA profile and TCMR diagnosis, TCMR reversibility, and allograft survival. RESULTS: 18S rRNA normalized levels of mRNA for FOXP3 (P = 0.01, Kruskal-Wallis test), CD25 (P = 0.01), CD3E (P < 0.0001), and perforin (P < 0.0001) were diagnostic of TCMR, but only FOXP3 mRNA level predicted TCMR reversibility (ROC AUC = 0.764; 95% confidence interval, 0.611-0.917; P = 0.008). Multivariable logistic regression analyses showed that urinary cell FOXP3 mRNA level predicted reversal, independent of clinical variables. A composite model of clinical variables and FOXP3 mRNA (AUC = 0.889; 95% CI, 0.781-0.997; P < 0.001) outperformed FOXP3 mRNA or clinical variables in predicting TCMR reversibility (P = 0.01, likelihood ratio test). Multivariable Cox proportional hazards regression analyses showed that FOXP3 mRNA level predicts kidney allograft survival (P = 0.047) but not after controlling for TCMR reversal (P = 0.477). CONCLUSIONS: Urinary cell level of FOXP3 mRNA is diagnostic of TCMR, predicts TCMR reversibility, and is prognostic of kidney allograft survival via a mechanism involving TCMR reversal.

Urinary cell transcriptomics and acute rejection in human kidney allografts.

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.

Landscape of innate immune system transcriptome and acute T cell-mediated rejection of human kidney allografts.

Acute rejection of human allografts has been viewed mostly through the lens of adaptive immunity, and the intragraft landscape of innate immunity genes has not been characterized in an unbiased fashion. We performed RNA sequencing of 34 kidney allograft biopsy specimens from 34 adult recipients; 16 were categorized as Banff acute T cell-mediated rejection (TCMR) and 18 as normal. Computational analysis of intragraft mRNA transcriptome identified significantly higher abundance of mRNA for pattern recognition receptors in TCMR compared with normal biopsies, as well as increased expression of mRNAs for cytokines, chemokines, interferons, and caspases. Intragraft levels of calcineurin mRNA were higher in TCMR biopsies, suggesting underimmunosuppression compared with normal biopsies. Cell-type-enrichment analysis revealed higher abundance of dendritic cells and macrophages in TCMR biopsies. Damage-associated molecular patterns, the endogenous ligands for pattern recognition receptors, as well markers of DNA damage were higher in TCMR. mRNA expression patterns supported increased calcium flux and indices of endoplasmic, cellular oxidative, and mitochondrial stress were higher in TCMR. Expression of mRNAs in major metabolic pathways was decreased in TCMR. Our global and unbiased transcriptome profiling identified heightened expression of innate immune system genes during an episode of TCMR in human kidney allografts.

MicroRNA sequence profiles of human kidney allografts with or without tubulointerstitial fibrosis.

BACKGROUND: MicroRNA (miRNA) alterations accompanying interstitial fibrosis and tubular atrophy (IFTA) in kidney allografts may point toward pathologic mechanisms. Small-RNA sequencing provides information on total miRNA abundance and specific miRNA expression and allows analysis of differential expression based on read counts. METHODS: MiRNA expression profiles of 8 human kidney allograft biopsies (4 IFTA and 4 normal biopsies, discovery set) were characterized using barcoded deep-sequencing of a cDNA library prepared from multiplexed RNA. Statistical analysis of the sequence data guided selection of miRNAs for validation and the levels of selected miRNAs were quantified in 18 biopsies (10 IFTA and 8 normal) using real-time quantitative PCR assays (RT-QPCR). RESULTS: Total miRNA content was 50% lower in RNA from IFTA compared with normal biopsies. Global miRNA profiles clustered in partial agreement with diagnosis. Several miRNAs, including miR-21, 142-3p, and 5p and the cluster comprising miR-506 on chromosome X had twofold to sevenfold higher expression in IFTA compared with normal biopsies, whereas miRNA miR-30b and 30c were lower in IFTA biopsies (miR-30a, -30d, -30e, and all respective star sequences showed similar trends). IFTA and normal biopsy distinction was also noted in the pattern of miRNA nucleotide sequence variations. Differentially expressed miRNAs were confirmed on the larger set of allograft biopsies using RT-QPCR, and the levels of miRNAs were found to be associated with allograft function and survival. CONCLUSION: Differentially expressed miRNAs and their predicted targets identified by deep sequencing are candidates for further investigation to decipher the mechanism and management of kidney allograft fibrosis.