Comparative Analyses of Signature Genes in Acute Rejection and Operational Tolerance

Transcriptome Analysis of Myocardial Ischemic-Hypoxic Injury in Rats and Hypoxic H9C2 Cells

AIMS

This study aimed to address inconsistencies in results between the H9C2 myocardial hypoxia (MH) cell line and myocardial infarction (MI) rat models used in MI research. We identified differentially expressed genes (DEGs) and underlying molecular mechanisms using RNA sequencing technology.

METHODS

RNA sequencing was used to analyse DEGs in MI rat tissues and H9C2 cells exposed to hypoxia for 24 h. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to identify key biological processes and pathways. Weighted correlation network analysis [weighted gene co-expression network analysis (WGCNA)] was used to construct gene co-expression networks, and hub genes were compared with published MI datasets [Gene Expression Omnibus (GEO)] for target identification.

RESULTS

GO analysis revealed enrichment of immune inflammation and mitochondrial respiration processes among 5139 DEGs in MI tissues and 2531 in H9C2 cells. KEGG analysis identified 537 overlapping genes associated with metabolism and oxidative stress pathways. Cross-analyses using the published GSE35088 and GSE47495 datasets identified 40 and 16 overlapping genes, respectively, with nine genes overlapping across all datasets and our models. WGCNA identified a key module in the MI model enriched for mRNA processing and protein binding. GO analysis revealed enrichment of mRNA processing, protein binding and mitochondrial respiratory chain complex I assembly in MI and H9C2 MH models. Five relevant hub genes were identified via a cross-analysis between the 92 hub genes that showed a common expression trend in both models.

CONCLUSIONS

This study reveals both shared and distinct transcriptomic responses in the MI and H9C2 models, highlighting the importance of model selection for studying myocardial ischaemia and hypoxia.

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

Novel transcriptomic signatures associated with premature kidney allograft failure

BACKGROUND

The power to predict kidney allograft outcomes based on non-invasive assays is limited. Assessment of operational tolerance (OT) patients allows us to identify transcriptomic signatures of true non-responders for construction of predictive models.

METHODS

In this observational retrospective study, RNA sequencing of peripheral blood was used in a derivation cohort to identify a protective set of transcripts by comparing 15 OT patients (40% females), from the TOMOGRAM Study (NCT05124444), 14 chronic active antibody-mediated rejection (CABMR) and 23 stable graft function patients ≥15 years (STA). The selected differentially expressed transcripts between OT and CABMR were used in a validation cohort (n = 396) to predict 3-year kidney allograft loss at 3 time-points using RT-qPCR.

FINDINGS

Archetypal analysis and classifier performance of RNA sequencing data showed that OT is clearly distinguishable from CABMR, but similar to STA. Based on significant transcripts from the validation cohort in univariable analysis, 2 multivariable Cox models were created. A 3-transcript (ADGRG3, ATG2A, and GNLY) model from POD 7 predicted graft loss with C-statistics (C) 0.727 (95% CI, 0.638-0.820). Another 3-transcript (IGHM, CD5, GNLY) model from M3 predicted graft loss with C 0.786 (95% CI, 0.785-0.865). Combining 3-transcripts models with eGFR at POD 7 and M3 improved C-statistics to 0.860 (95% CI, 0.778-0.944) and 0.868 (95% CI, 0.790-0.944), respectively.

INTERPRETATION

Identification of transcripts distinguishing OT from CABMR allowed us to construct models predicting premature graft loss. Identified transcripts reflect mechanisms of injury/repair and alloimmune response when assessed at day 7 or with a loss of protective phenotype when assessed at month 3.

FUNDING

Supported by the Ministry of Health of the Czech Republic under grant NV19-06-00031.

A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation

In this study, we aimed to identify transplantation tolerance (TOL)-related gene signature and use it to predict the different types of renal allograft rejection performances in kidney transplantation. Gene expression data were obtained from the Gene Expression Omnibus (GEO) database, differently expressed genes (DEGs) were performed, and the gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted. The machine learning methods were combined to analyze the feature TOL-related genes and verify their predictive performance. Afterward, the gene expression levels and predictive performances of TOL-related genes were conducted in the context of acute rejection (AR), chronic rejection (CR), and graft loss through heatmap plots and the receiver operating characteristic (ROC) curves, and their respective immune infiltration results were also performed. Furthermore, the TOL-related gene signature for graft survival was conducted to discover gene immune cell enrichment. A total of 25 TOL-related DEGs were founded, and the GO and KEGG results indicated that DEGs mainly enriched in B cell-related functions and pathways. 7 TOL-related gene signature was constructed and performed delightedly in TOL groups and different types of allograft rejection. The immune infiltration analysis suggested that gene signature was correlated with different types of immune cells. The Kaplan-Meier (KM) survival analysis demonstrated that BLNK and MZB1 were the prognostic TOL-related genes. Our study proposed a novel gene signature that may influence TOL in kidney transplantation, providing possible guidance for immunosuppressive therapy in kidney transplant patients.

Single-Cell RNA Sequencing Identifies Intra-Graft Population Heterogeneity in Acute Heart Allograft Rejection in Mouse

Transplant rejection remains a major barrier to graft survival and involves a diversity of cell types. However, the heterogeneity of each cell type in the allograft remains poorly defined. In the present study, we used single-cell RNA sequencing technology to analyze graft-infiltrating cells to describe cell types and states associated with acute rejection in a mouse heart transplant model. Unsupervised clustering analysis revealed 21 distinct cell populations. Macrophages formed five cell clusters: two resident macrophage groups, two infiltrating macrophage groups and one dendritic cell-like monocyte group. Infiltrating macrophages were predominantly from allogeneic grafts. Nevertheless, only one infiltrating macrophage cluster was in an active state with the upregulation of CD40, Fam26f and Pira2, while the other was metabolically silent. Re-clustering of endothelial cells identified five subclusters. Interestingly, one of the endothelial cell populations was almost exclusively from allogeneic grafts. Further analysis of this population showed activation of antigen processing and presentation pathway and upregulation of MHC class II molecules. In addition, Ubiquitin D was specifically expressed in such endothelial cell population. The upregulation of Ubiquitin D in rejection was validated by staining of mouse heart grafts and human kidney biopsy specimens. Our findings present a comprehensive analysis of intra-graft cell heterogeneity, describe specific macrophage and endothelial cell populations which mediate rejection, and provide a potential predictive biomarker for rejection in the clinic.

Restoration of Immune Privilege in Human Dermal Papillae Controlling Epithelial-Mesenchymal Interactions in Hair Formation

BACKGROUND

Hair follicles are among a handful of organs that exhibit immune privilege. Dysfunction of the hair follicle immune system underlies the development of inflammatory diseases, such as alopecia areata.

METHODS

Quantitative reverse transcription PCR and immunostaining was used to confirm the expression of major histocompatibility complex class I in human dermal papilla cells. Through transcriptomic analyses of human keratinocyte stem cells, major histocompatibility complex class I was identified as differentially expressed genes. Organ culture and patch assay were performed to assess the ability of WNT3a conditioned media to rescue immune privilege. Lastly, CD8+ T cells were detected near the hair bulb in alopecia areata patients through immunohistochemistry.

RESULTS

Inflammatory factors such as tumor necrosis factor alpha and interferon gamma were verified to induce the expression of major histocompatibility complex class I proteins in dermal papilla cells. Additionally, loss of immune privilege of hair follicles was rescued following treatment with conditioned media from outer root sheath cells. Transcriptomic analyses found 58 up-regulated genes and 183 down-regulated genes related in MHC class I+ cells. Using newborn hair patch assay, we demonstrated that WNT3a conditioned media with epidermal growth factor can restore hair growth. In alopecia areata patients, CD8+ T cells were increased during the transition from mid-anagen to late catagen.

CONCLUSION

Identification of mechanisms governing epithelial and mesenchymal interactions of the hair follicle facilitates an improved understanding of the regulation of hair follicle immune privilege.

An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation

Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.

PD-1 and PD-L1 inhibitors foster the progression of adult T-cell Leukemia/Lymphoma

Immunotherapy through immune checkpoints blockade and its subsequent clinical application has revolutionized the treatment of a spectrum of solid tumors. Blockade of Programmed cell death protein-1 and its ligand has shown promising results in clinical studies. The clinical trials that enrolled patients with different hematopoietic malignancies including non-Hodgkin lymphoma, Hodgkin lymphoma, and acute myeloid leukemia (AML) showed that anti-PD-1 agents could have potential therapeutic effects in the patients. Adult T-cell leukemia/lymphoma (ATLL) is a non-Hodgkin T-cell Lymphoma that is developed in a minority of HTLV-1-infected individuals after a long latency period. The inhibition of PD-1 as a treatment option is currently being investigated in ATLL patients. In this review, we present a summary of the biology of the PD-1/PD-L1 pathway, the evidence in the literature to support anti-PD-1/PDL-1 application in the treatment of different lymphoid, myeloid, and virus-related hematological malignancies, and controversies related to PD-1/PD-L1 blocking in the management of ATLL patients.

Molecular Diversity of Clinically Stable Human Kidney Allografts

IMPORTANCE

Clinical decision and immunosuppression dosing in kidney transplantation rely on transplant biopsy tissue histology even though histology has low specificity, sensitivity, and reproducibility for rejection diagnosis. The inclusion of stable allografts in mechanistic and clinical studies is vital to provide a normal, noninjured comparative group for all interrogative studies on understanding allograft injury.

OBJECTIVE

To refine the definition of a stable allograft as one that is clinically, histologically, and molecularly quiescent using publicly available transcriptomics data.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, the National Center for Biotechnology Information Gene Expression Omnibus was used to search for microarray gene expression data from kidney transplant tissues, resulting in 38 studies from January 1, 2017, to December 31, 2018. The diagnostic annotations included 510 acute rejection (AR) samples, 1154 histologically stable (hSTA) samples, and 609 normal samples. Raw fluorescence intensity data were downloaded and preprocessed followed by data set merging and batch correction.

MAIN OUTCOMES AND MEASURES

The primary measure was area under the receiver operating characteristics curve from a set of feature selected genes and cell types for distinguishing AR from normal kidney tissue.

RESULTS

Within the 28 data sets, the feature selection procedure identified a set of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3) (area under the curve [AUC], 0.98) and 5 immune cell types (CD4+ T-cell central memory [Tcm], CD4+ T-cell effector memory [Tem], CD8+ Tem, natural killer [NK] cells, and Type 1 T helper [TH1] cells) (AUC, 0.92) that were combined into 1 composite Instability Score (InstaScore) (AUC, 0.99). The InstaScore was applied to the hSTA samples: 626 of 1154 (54%) were found to be immune quiescent and redefined as histologically and molecularly stable (hSTA/mSTA); 528 of 1154 (46%) were found to have molecular evidence of rejection (hSTA/mAR) and should not have been classified as stable allografts. The validation on an independent cohort of 6 months of protocol biopsy samples in December 2019 showed that hSTA/mAR samples had a significant change in graft function (r = 0.52, P < .001) and graft loss at 5-year follow-up (r = 0.17). A drop by 10 mL/min/1.73m2 in estimated glomerular filtration rate was estimated as a threshold in allograft transitioning from hSTA/mSTA to hSTA/mAR.

CONCLUSIONS AND RELEVANCE

The results of this prognostic study suggest that the InstaScore could provide an important adjunct for comprehensive and highly quantitative phenotyping of protocol kidney transplant biopsy samples and could be integrated into clinical care for accurate estimation of subsequent patient clinical outcomes.

Knockdown of FOXA2 Impairs Hair-Inductive Activity of Cultured Human Follicular Keratinocytes

Reciprocal interactions between hair-inductive dermal cells and epidermal cells are essential for de novo genesis of hair follicles. Recent studies have shown that outer root sheath (ORS) follicular keratinocytes can be expanded in vitro, but the cultured cells often lose receptivity to hair-inducing dermal signals. In this study, we first investigated whether the hair-inductive activity (trichogenicity) of cultured human ORS follicular keratinocytes was correlated with the cultivation period. ORS follicular keratinocytes from the scalp were cultured for 3, 4, 5, or 6 weeks and were then implanted into nude mice along with freshly isolated neonatal mouse dermal cells. We observed that the trichogenicity of the implanted ORS cells was inversely correlated with their cultivation period. These initial findings prompted us to investigate the differentially expressed genes between the short-term (20 days) and long-term (42 days) cultured ORS cells, trichogenic and non-trichogenic, respectively, by microarray analysis. We found that forkhead box protein A2 (FOXA2) was the most up-regulated transcription factor in the trichogenic ORS cells. Thus, we investigated whether the trichogenicity of the cells was affected by FOXA2 expression. We found a significant decrease in the number of induced hair follicles when the ORS cells were transfected with a FOXA2 small interfering RNA versus control small interfering RNA. Taken together, our data strongly suggest that FOXA2 significantly influences the trichogenicity of human ORS cells.

Rapid progression of adult T-cell leukemia/lymphoma as tumor-infiltrating Tregs after PD-1 blockade

Immune checkpoint inhibitors are a powerful new tool in the treatment of cancer, with prolonged responses in multiple diseases, including hematologic malignancies, such as Hodgkin lymphoma. However, in a recent report, we demonstrated that the PD-1 inhibitor nivolumab led to rapid progression in patients with adult T-cell leukemia/lymphoma (ATLL) (NCT02631746). We obtained primary cells from these patients to determine the cause of this hyperprogression. Analyses of clonality, somatic mutations, and gene expression in the malignant cells confirmed the report of rapid clonal expansion after PD-1 blockade in these patients, revealed a previously unappreciated origin of these malignant cells, identified a novel connection between ATLL cells and tumor-resident regulatory T cells (Tregs), and exposed a tumor-suppressive role for PD-1 in ATLL. Identifying the mechanisms driving this alarming outcome in nivolumab-treated ATLL may be broadly informative for the growing problem of rapid progression with immune checkpoint therapies.

Mechanisms and biomarkers of immune quiescence in kidney transplantation

This review discusses the current understanding of biomarkers of immune quiescence based on reviews of published literature in kidney transplant operational tolerance and mechanistic studies based on a better characterization of the stable, well-functioning renal allograft.