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. 2023 Oct 4;27(1):234–248. doi: 10.1080/19768354.2023.2265150

Comparative transcriptome analysis between long- and short-term survival after pig-to-monkey cardiac xenotransplantation reveals differential heart failure development

Byeonghwi Lim a, Min-Jae Jang a, Seung-Mi Oh a, Jin Gu No b, Jungjae Lee a, Sang Eun Kim b, Sun A Ock b, Ik Jin Yun c, Junseok Kim d, Hyun Keun Chee d, Wan Seop Kim e, Hee Jung Kang f, Kahee Cho g, Keon Bong Oh b,CONTACT, Jun-Mo Kim a,✉
PMCID: PMC10552608  PMID: 37808548

ABSTRACT

Cardiac xenotransplantation is the potential treatment for end-stage heart failure, but the allogenic organ supply needs to catch up to clinical demand. Therefore, genetically-modified porcine heart xenotransplantation could be a potential alternative. So far, pig-to-monkey heart xenografts have been studied using multi-transgenic pigs, indicating various survival periods. However, functional mechanisms based on survival period-related gene expression are unclear. This study aimed to identify the differential mechanisms between pig-to-monkey post-xenotransplantation long- and short-term survivals. Heterotopic abdominal transplantation was performed using a donor CD46-expressing GTKO pig and a recipient cynomolgus monkey. RNA-seq was performed using samples from POD60 XH from monkey and NH from age-matched pigs, D35 and D95. Gene-annotated DEGs for POD60 XH were compared with those for POD9 XH (Park et al. 2021). DEGs were identified by comparing gene expression levels in POD60 XH versus either D35 or D95 NH. 1,804 and 1,655 DEGs were identified in POD60 XH versus D35 NH and POD60 XH versus D95 NH, respectively. Overlapped 1,148 DEGs were annotated and compared with 1,348 DEGs for POD9 XH. Transcriptomic features for heart failure and inhibition of T cell activation were observed in both long (POD60)- and short (POD9)-term survived monkeys. Only short-term survived monkey showed heart remodeling and regeneration features, while long-term survived monkey indicated multi-organ failure by neural and hormonal signaling as well as suppression of B cell activation. Our results reveal differential heart failure development and survival at the transcriptome level and suggest candidate genes for specific signals to control adverse cardiac xenotransplantation effects.

KEYWORDS: Cardiac xenotransplantation, survival period, heart failure, transcriptome, network analysis

Introduction

Heart transplantation is the only treatment for end-stage heart failure, but the clinical supply of allogenic organ is greatly scarce (Lund et al. 2017; Rossano et al. 2017; Längin et al. 2018). Cardiac xenotransplantation can be a potential alternative (Cooper and Pierson 2022; Kobashigawa 2022). Currently, pigs are considered the most appropriate donors, because they are similar to humans in size and physiology, and have a large litter size and a short maturation period (Cooper et al. 2002). To date, pig-to-monkey heart xenografts have been widely studied using various wild-type and transgenic pigs and monkeys, including baboon, cynomolgus, and rhesus (Cooper et al. 2014; Lee et al. 2018), but solving immunological rejections and dysregulations remain a challenge (Lu et al. 2020).

Heart xenograft usually causes three main rejections: hyperacute rejection (HAR), acute humoral xenograft rejection (AHXR), and acute cellular rejection (Cascalho and Platt 2001; Lu et al. 2020). HAR and AHXR were nearly removed by using α-1,3-galactosyltransferase gene-knockout (GTKO) pigs expressing human complement regulatory proteins such as CD46, CD55, and CD59, in addition to exogenous immunosuppressive therapies (Mohiuddin et al. 2014, 2016). However, coagulation dysregulation is another barrier (Cooper et al. 2016), resulting in thrombotic microangiopathy (TM), systemic consumptive coagulopathy (CC), and systemic inflammatory response (Bühler et al. 2000; Cooper and Bottino 2015; Long et al. 2016). To prevent coagulation problems, various combinatorial expressions of human coagulation regulation proteins such as thrombomodulin and CD39, and anti-inflammatory protein, CD73, in pigs have been utilized (Fischer et al. 2016; Mohiuddin et al. 2016; Lee et al. 2018). Although these techniques were used to tackle coagulation dysregulation, survival periods were still irregular even with the same immunosuppressive therapies, especially in cynomolgus monkeys (Lee et al. 2018). Therefore, comparing gene expression patterns between long- and short-term survival periods in xenotransplanted hearts may help understand what determines the survival of cynomolgus monkeys transplanted with hearts from transgenic pigs. RNA-seq is one of useful methods for understanding biological mechanisms (Ahn et al. 2022; Kim et al. 2022).

In this study, we detected changes in transcriptomes based on RNA-seq data between the xenotransplanted heart (XH) of postoperative day 60 (POD60) monkey and normal heart (NH) of age-matched pigs, and performed comparative analyses between XHs of POD60 and POD9 monkeys to understand survival period-related differences.

Materials and methods

Ethics statement

The experimental procedures were approved by the Orient Bio Institutional Animal Care and Use Committee (IACUC No. ORIENT-IACUC-16141 and ORIENT-IACUC-19015). All procedures conformed to the guidelines from the NIH Guide for the Care and Use of Laboratory Animals.

Animals and surgery

A 35-day-old (D35) male homozygous GTKO pig expressing human CD46 was used as a donor. Pre-transplantation porcine heart weight was 35.00 g. The recipient, a five-year-old male cynomolgus monkey (Macaca fascicularis), was housed in a clean cage. The body weight of the monkey on the day of transplantation was 6.27 kg. Surgery was performed using heterotopic abdominal method (Yang et al. 2017), and immunosuppression was also induced in the monkey. Details are described in Supplementary materials.

Euthanasia

Before euthanasia, anesthesia was induced with an intravenous bolus of ketamine 30 mg/kg and xylazine 3 mg/kg. The monkey at terminal was sacrificed by inducing exsanguination from vena cava and abdominal artery under anesthesia.

Xenotransplanted porcine cardiac tissue collection

Small fragments collected randomly with ventricle samples were plunged into liquid nitrogen for RNA-seq or 4% paraformaldehyde for histological analyses. An age-matched litter of the donor (D35 GTKO) and age-matched litter of the POD60 (D95 GTKO) were sacrificed, and their NH samples were collected. Figure 1(A) shows a schematic overview of this study.

Figure 1.

Figure 1.

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Schematic of experimental design and histological observations for pig-to-monkey cardiac xenotransplantation. (A) Summary of experiments and comparative analyses with time points. Xenotransplantation was performed using the heterotopic abdominal method. Heart samples from a xenotransplanted cynomolgus monkey (POD60 XH) and two age-matched pigs (D35 and D95 NHs) were used in this experiment. Comparison analyses using the previous POD9 XH results were conducted to detect differential gene expression patterns depending on the survival period (Park et al. 2021). (B) XH interior views. The porcine heart weighed 80.62 g at POD60. Cardiac cross-section showed that the atrium space narrowed as the graft thickened. (C) XH photomicrographs showing no evidence of acute cellular and antibody-mediated rejections. Patchy interstitial fibrosis in the myocardium fatty metaplasia in the epicardium and organizing thrombi in the endocardium was observed.

Hematological and biochemical analysis of a cynomolgus monkey

The recipient blood parameters were analyzed on POD −7, 0, 3, 8, 14, 21, 28, 35, 42, 49, 57 and 60. Detailed methods are described in a previous study (Park et al. 2021). Comparative analysis was performed using hematological parameters of short-term survived (POD9 XH) monkey.

Histological analysis

The specimens were immobilized in 10% neutral buffered formalin and embedded in paraffin. The paraffin blocks were sliced to a thickness of 3 μm. The sections were deparaffinized, rehydrated, and stained with hematoxylin and eosin.

RNA-Seq

Total RNA was isolated from NH (D35 and D95 pigs) and XH POD60 tissues using TRIzol method. To check the quality of extracted RNA, RNA-integrity number (RIN) was calculated using 2100 Bioanalyzer (Table S1). Total cDNA was synthesized for library construction. Constructed libraries were analyzed with Illumina HiSeq 4000 (Illumina, Inc., San Diego, CA), and paired-end (2 × 100 base pair) sequencing was performed.

Differentially expressed gene (DEG) and functional analyses

After quality check, trimming, mapping, and counting, raw counts were obtained. DEG analyses were done for POD60 XH as a case versus (vs) D35 and D95 NH as controls. Multidimensional scaling (MDS) was carried out to demonstrate sample similarities. More detailed methods are described in the Supplementary materials and a previous study (Lim et al. 2020). The overlapping DEGs for POD60 XH based on D35 and D95 NH were classified as up- and down-regulated DEGs, and used for functional annotations. Enrichment analyses were performed with biological processes (BPs) of gene ontology (GO) and pathways of Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Comparative analyses

Gene-annotated DEGs for POD60 XH were compared with annotated DEGs for POD9 XH based on D31 NH (D9 survived monkey) in our previous study (Park et al. 2021). The DEGs were classified into three types: POD9-specific DEGs, overlapping DEGs, and POD60-specific DEGs, and functional annotations to KEGG pathways were performed. Network analysis was performed to cluster the enriched KEGG pathways, and we focused on the clustered KEGG pathways having similar molecular functions for functional profiling between POD9 and POD60 monkeys.

Quantitative real-time PCR (qPCR) analyses

To technically validate gene expression in POD60 and POD9, immune-related genes were selected based on FC values, and qPCR was performed using NH and XH samples. Total RNA was extracted from the tissues, and then cDNA was synthesized. The qPCR analyses were performed using StepOne Real-Time PCR system (ABI, Foster City, CA) with SYBR Green. Pig primers used for qPCR are summarized in Table S2.

Results

Recipient blood analyses

Table 1 presents values obtained from the hematological and biochemical analyses. High-sensitivity C-reactive protein (hs-CRP) levels increased immediately after cardiac xenotransplantation in a long-term survived monkey. As hemoglobin (Hb) and hematocrit (Hct) increased rapidly in POD57, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) increased, and antithrombin III (AT III) and protein C decreased. Platelets gradually increased till POD8 and decreased, only to rapidly increase at POD57 and decrease at POD60. Segmented neutrophils were maintained higher than normal, and lymphocytes were lower than normal post-xenotransplantation. Eosinophils gradually decreased from POD28. Prothrombin time (PT) remained constant and rapidly extended at POD60. Troponin I, a myocardial damage indicator, increased at POD3, but briefly decreased after that, and then rapidly increased towards POD60. Lactate dehydrogenase (LDH) decreased slightly from POD8, increased at POD35, then showed a decreasing trend before rapidly increasing to POD60. Hematological analysis of short-term survived monkey (POD9 XH) is shown in a previous study (Park et al. 2021). Upon comparing with POD60 XH, hs-CRP showed a significant difference between POD9 XH and POD60 XH. In the case of POD9 XH, hs-CRP rapidly increased at POD1, but decreased immediately, and then rapidly increased at POD7 just before death (Figure S1).

Table 1.

Hematological and biochemical blood parameters of recipient cynomolgus monkey.

Type Post-Operative Day (POD)
POD-7 POD0 POD3 POD8 POD14 POD21 POD28 POD35 POD42 POD49 POD57 POD60
Calcium 10.30 11.00 9.20 9.80 9.50 8.60 8.50 9.00 8.70 8.90 8.40 8.20
Phosphorus 3.60 3.40 5.00 4.20 4.60 4.70 4.40 5.20 4.70 4.40 9.40 3.90
BUN 27.00 26.00 22.00 18.00 20.00 21.00 21.00 21.00 26.00 28.00 63.00 79.00
Uric acid 0.40 0.80 0.10 0.80 0.20 0.40 0.10 3.80 0.20 0.50 0.50 0.30
Creatinine 0.97 0.94 0.73 0.70 0.74 0.86 0.68 0.72 0.56 0.42 0.76 0.52
IDMS MDRD GFR 121.80 126.20 169.00 177.30 166.20 139.70 183.00 171.20 228.70 318.60 160.70 249.00
Na 142.00 145.00 143.00 141.00 146.00 146.00 146.00 119.00 145.00 149.00 151.00 168.00
K 5.20 6.50 5.20 6.80 5.10 4.90 4.70 29.10 5.80 5.50 5.30 5.70
Cl 103.00 105.00 104.00 103.00 106.00 107.00 109.00 124.00 102.00 104.00 106.00 131.00
Total CO2 (TCO2) 20.00 25.00 25.00 25.00 16.00 22.00 21.00 28.00 23.00 25.00 23.00 26.00
High-sensitivity C-reactive protein (hs-CRP) 0.16 1.42 7.54 0.25 0.34 2.92 3.61 1.84 1.66 2.53 1.35 0.79
Aspartate aminotransferase (AST) 53.00 132.00 462.00 98.00 42.00 58.00 54.00 382.00 60.00 63.00 413.00 1345.00
Alanine aminotransferase (ALT) 37.00 60.00 309.00 103.00 45.00 27.00 26.00 36.00 53.00 79.00 166.00 279.00
Total protein 7.10 8.40 5.60 7.10 6.50 5.80 4.90 14.10 4.90 5.20 4.80 3.10
Albumin 4.40 4.80 3.10 3.90 4.00 3.40 2.80 6.60 2.80 3.00 3.00 1.80
Total bilirubin (TBIL) 0.30 0.10 0.30 <0.1 0.20 0.30 0.20 0.40 0.30 0.40 2.00 0.70
Alkaline phosphatase (Alk. Phos) 249.00 247.00 251.00 276.00 265.00 222.00 220.00 220.00 226.00 215.00 325.00 508.00
Cholesterol (Chol) 105.00 91.00 104.00 81.00 95.00 73.00 75.00 81.00 92.00 94.00 25.00 20.00
Gamma-glutamyl transferase (GGT) 48.00 46.00 27.00 42.00 56.00 41.00 39.00 38.00 38.00 34.00 47.00 39.00
Tacrolimus – – 14.40 9.30 12.60 7.20 7.80 8.80 8.20 9.30 30.40 30.40
White blood cell (WBC) 16.89 6.22 10.62 14.67 18.36 12.35 13.49 12.49 13.18 15.19 3.30 6.76
Red blood cell (RBC) 6.13 5.93 3.93 4.47 5.00 4.70 4.72 5.07 5.41 5.69 7.36 5.34
Hemoglobin (Hb) 14.40 14.10 9.30 10.50 11.80 11.10 10.90 11.50 11.90 12.10 15.70 11.30
Hematocrit (Hct) 46.50 45.50 29.70 35.30 39.50 37.40 37.40 44.10 41.60 41.60 51.70 38.00
Mean corpuscular volume (MCV) 75.90 76.70 75.60 79.00 79.00 79.60 79.20 87.00 76.90 73.10 70.20 71.20
Mean corpuscular hemoglobin (MCH) 23.50 23.80 23.70 23.50 23.60 23.60 23.10 22.70 22.00 21.30 21.30 21.20
Mean corpuscular hemoglobin concentration (MCHC) 31.00 31.00 31.30 29.70 29.90 29.70 29.10 26.10 28.60 29.10 30.40 29.70
Red cell distribution width (RDW) 14.10 14.80 14.70 17.10 16.20 16.30 15.50 24.60 14.60 14.50 17.10 15.20
Platelet 400.00 448.00 545.00 726.00 412.00 331.00 382.00 560.00 492.00 345.00 708.00 198.00
Plateletcrit (PCT) 0.45 0.47 0.62 0.76 0.45 0.40 0.44 – 0.53 0.36 0.75 0.20
Mean platelet volume (MPV) 11.30 10.50 11.30 10.50 10.90 12.00 11.60 – 10.90 10.40 10.70 10.30
Platelet distribution width (PDW) 12.90 11.70 13.00 11.90 12.60 14.20 13.60 – 13.30 13.70 14.40 12.80
Segmented neutrophils (Seg.neut.) 44.90 89.00 71.70 85.90 89.30 89.20 89.30 78.80 90.60 94.10 77.90 89.90
Lymphocyte 48.40 1.60 1.60 4.20 1.80 2.30 3.30 14.70 3.90 1.40 2.10 2.10
Monocyte 4.60 5.50 25.00 7.90 6.90 6.20 6.80 5.80 5.20 4.30 20.00 7.70
Eosinophil 2.00 3.90 1.60 1.90 1.90 2.20 0.50 0.60 0.20 0.10 0.00 0.00
Basophil 0.10 0.00 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.00 0.30
Absolute neutrophil count (ANC) 7584.00 5536.00 7615.00 12602.00 16395.00 11016.00 12047.00 9842.00 11941.00 14294.00 2571.00 6077.00
Prothrombin time and international normalized ratio (PT INR) 0.78 0.77 0.81 0.71 0.75 0.83 0.82 0.80 0.79 0.82 – 1.95
Prothrombin time % (PT %) 150.00 153.00 142.00 179.00 160.00 134.00 136.00 144.00 147.00 136.00 – 41.00
Prothrombin time sec (PT sec) 8.90 8.80 9.20 8.10 8.60 9.50 9.40 9.10 9.00 9.40 – 22.10
Activated partial thromboplastine time (aPTT) 21.00 19.40 20.60 21.10 19.50 21.40 31.70 23.30 23.00 22.40 – 30.00
Fibrinogen 307.00 418.00 615.00 284.00 327.00 349.00 353.00 323.00 310.00 327.00 – 155.00
Antithrombin Ⅲ (AT Ⅲ) 93.00 103.00 74.00 99.00 124.00 101.00 85.00 81.00 75.00 74.00 – 33.00
Protein C 299.00 298.00 269.00 188.00 332.00 281.00 230.00 261.00 239.00 238.00 – 50.00
D-dimer 0.01 0.01 0.03 0.05 0.00 0.02 0.06 0.00 0.02 0.04 – 0.10
Lactate dehydrogenase (LDH) 1076.00 2138.00 3935.00 2172.00 937.00 868.00 1410.00 5376.00 1327.00 1127.00 – 5624.00
Troponin I 0.01 <0.01 16.85 0.85 0.35 3.67 6.33 3.04 3.67 7.44 – 249.63
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Histological assessment

Histological changes following XH of a dead monkey were observed at POD60 (Figure 1(B,C)). The transplanted porcine heart grew from 35.00 g to 80.62 g at POD60, taking up most of the monkey’s abdominal cavity. The heart transplant suture was maintained without any bleeding. Histological analysis of cardiac cross-sections of the POD60 revealed that the atrium space narrowed as the graft thickened. We observed patchy interstitial fibrosis in the myocardium fatty metaplasia in the epicardium. Organizing thrombi in the endocardium was observed with evidence of neither acute cellular nor antibody-mediated rejection.

Transcriptomic data processing and significant DEGs in XH

406 million paired-end sequence reads were generated from D35 NH (3 replicates), D95 NH (4 replicates), and POD60 XH (3 replicates), showing 40.7 million average number of reads (Table S1). 40.1 million reads per sample passed the trimming process and were mapped to pig reference genome 11.1.100 with 88.86% unique (ranging from 83.30 to 90.86%) and 95.06% overall (ranging from 88.52 to 97.05%) mapping rates.

Cardiac transcriptome data showed clear clustering for each group (D35 NH, D95 NH, and POD60 XH) in MDS (Figure 2(A)). DEGs were identified by comparing the gene expression levels in POD60 XH (D95 porcine heart) with those in D35 and D95 NH. The comparison of POD60 XH vs. D35 NH was designed to identify pre- and post-transplantation survival cardiac differences, and POD60 XH vs. D95 NH was to identify NH and XH cardiac differences at the same-age. 1,804 and 1,655 DEGs were identified in POD60 XH vs. D35 NH and POD60 XH vs. D95 NH, respectively (Figure 2(B)). The overlapping 1,148 DEGs (605 up- and 543 down-regulated DEGs) are shown in a Venn diagram (Figure 2(C)), with their gene information in Table S3. We observed that many DEGs between POD60 XH vs. D35 NH (64%) and POD60 XH vs. D95 NH (69%) were overlapped with the same expression tendency.

Figure 2.

Figure 2.

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Entire XH transcriptome analyses. (A) Multidimensional scaling showed separate groups among porcine NH and XH transcriptome samples. (B) Volcano plots indicating significant DEGs in XH compared to a normal. (C) Venn diagram described the overlapping number of DEGs between POD60 XH vs. D35 NH and POD60 XH vs. D95 NH. (D) GO treemaps for biological processes were created by overlapping up- and down-regulated DEGs based on the P-values. The most significant representative terms were colored green. (E) Pyramid plot indicated KEGG-enriched pathways generated by overlapping up- and down-regulated DEGs with −log10 P-values > 1.0.

Functional annotations of DEGs

Functional enrichment based on the GO (Figure 2(D)) and KEGG (Figure 2(E)) databases was conducted using 1148 overlapping DEGs. For the overlapping 605 up-regulated DEGs, the BPs of GO were enriched to 100 terms indicating representative as the ‘chemokine-mediated signaling pathway’. For the overlapping 543 down-regulated DEGs, the BPs of GO were enriched to 34 terms indicating representative as the ‘immune response’. The most significant representative terms in overlapping up- and down-regulated DEGs were the ‘chemokine-mediated signaling pathway’ (−log10 P-value = 7.02) and the ‘immune response’ (−log10 P-value = 2.13). Table S4 shows detailed GO enrichment information.

The KEGG enrichment based on the overlapping 605 up-regulated DEGs showed immune signaling-related pathways and cell signaling-related pathways. The KEGG enrichment based on the overlapping 543 down-regulated DEGs showed heart failure-related pathways and fat metabolism-related pathways. Three pathways were identified in both analyses based on up- and down-regulated DEGs. Table S5 shows detailed KEGG enrichment information.

Comparing XHs of long- and short-term survived monkeys

The overlapping 1,148 DEGs for long-term survived monkey (POD60) were gene-annotated to compare with the 1,348 DEGs for short-term survived monkey (POD9) in the previous study (Park et al. 2021), resulting in 824 gene symbols (DEGs for POD60 monkey). Venn diagram showed POD9-specific 988 DEGs, overlapping 360 DEGs, and POD60-specific 464 DEGs (Figure 3(A)).

Figure 3.

Figure 3.

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Comparative analyses between POD9 and POD60 cynomolgus monkeys with XH. (A) Venn diagram showing the overlapping DEGs of POD9 and POD60 monkeys with XH. The number of DEGs were indicated after gene annotation. (B) KEGG pathways were enriched using POD9-specific DEGs, overlapping DEGs, and POD60-specific DEGs. Pathways were squared based on functions with colors (cytokine signaling (blue), neural and cell signaling (yellow), heart failure (red), immune signaling and fat metabolism (green)). (C) Functional network based on significantly enriched KEGG pathways. Diamond size indicates significance (−log10 P-value) and line width shows fold enrichment in each pathway. Diamond size for common pathways were calculated as average.

Functional enrichment based on KEGG pathways is shown in Figure 3(B), and their gene-sharing-based pathway network indicates clusters (Figure 3(C)). The cytokine signaling cluster was significantly common in POD9-specific, overlapping, and POD60-specific DEGs. The cell signaling cluster and neural & hormonal signaling significantly showed in POD9-specific DEGs. The heart failure cluster significantly showed in the overlapping DEGs. The immune signaling cluster and fat metabolism significantly showed in POD60-specific DEGs. Interestingly, two neural & hormonal signaling were identified in both POD9 and POD60.

Gene modulations associated with xenograft survival

We focused on the highly significant pathways for xenograft survival-associated gene modulation. The ‘cytokine-cytokine receptor interaction’ gene modulation in cytokine signaling showed 22 POD9-specific, 13 overlapping, and 15 POD60-specific DEGs (proteins) (Figure 4). The ‘neuroactive ligand–receptor interaction’ gene modulation in neural & hormonal signaling indicated 25 POD9-specific and 18 POD60-specific DEGs (proteins) (Figure S2). The ‘cell cycle’ gene modulation in cell signaling showed 15 POD9-specific DEGs (proteins) (Figure 5). The ‘hypertrophic cardiomyopathy’ gene modulation in heart failure showed 7 overlapping DEGs (proteins) (Figure 6).

Figure 4.

Figure 4.

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‘Cytokine-cytokine receptor interaction’ gene modulation in cytokine signaling associated with xenograft survival. Left and right of proteins show log2 FC of DEGs for POD9 and POD60 monkeys, respectively. Proteins corresponding to DEGs are indicated with a thick border: POD9-specific DEGs (yellow), overlapping DEGs (red), and POD60-specific DEGs (green). The maximum changes (absolute log2 FC) among genes were applied in each protein as the representative values. Heatmap shows gene names corresponding to each protein with log2 FC values of POD9 and POD60 monkeys.

Figure 5.

Figure 5.

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‘Cell cycle’ gene modulation in cell signaling associated with xenograft survival. Left and right of proteins show log2 FC of DEGs for POD9 and POD60 monkeys, respectively. Proteins corresponding to DEGs are indicated with a thick border: POD9-specific DEGs (yellow). The maximum changes (absolute log2 FC) among genes were applied in each protein as the representative values. Heatmap shows gene names corresponding to each protein with log2 FC values of POD9 and POD60 monkeys.

Figure 6.

Figure 6.

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‘Hypertrophic cardiomyopathy’ gene modulation in xenograft survival-associated heart failure. Left and right of proteins show log2 FC of DEGs for POD9 and POD60 monkeys, respectively. Proteins corresponding to DEGs are indicated with a thick border: overlapping DEGs (red). The maximum changes (absolute log2 FC) among genes were applied in each protein as the representative values. Heatmap shows gene names corresponding to each protein with log2 FC values of POD9 and POD60 monkeys.

qPCR validations

We performed qPCR experiments using 8 immune-related genes (IL1A, LY96, IRF7, CCL21, FOS, CXCR6, EGF, and IL1RAP), and identified consistent results with RNA-seq except for IL1A in POD60 and CCL21 in POD9 showing different expression tendency (Figure 7). Gene expression was generally very low in the monkey tissues, but CXCR6 and EGF genes were expressed at a relatively high level. This result may suggest high similarity between monkeys and pigs.

Figure 7.

Figure 7.

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qPCR validation of gene expression based on RNA-seq results. Pig primers were utilized, and eight genes (IL1A, LY96, IRF7, CCL21, FOS, CXCR6, EGF, and IL1RAP) were tested. Different lowercase letters above dots show significant differences (P-value < 0.0001) among samples as determined by one-way ANOVA with Tukey’s multiple comparison test.

Discussion

Gradual heart failure in POD60 cynomolgus monkey

The incidence of HAR and AHXR was greatly reduced in XH of cynomolgus monkeys, therefore their survival periods were extended. Here, we investigated hematological and biochemical features (Table 1) in XH of the longest-survived monkey (POD60) without HAR and AHXR, using GTKO/CD46 pig as a donor (Lee et al. 2018). As a major change during survival, troponin I level increased post-operation at POD3, and then decreased as a recovery. However, it increased from POD21, indicating continuous heart damage till POD60. The monkey endured relatively well, but it rapidly deteriorated from POD49 with heart failure. In addition, we observed increased Hb and Hct levels at POD57, considerably by edema and hemoconcentration due to poor heart function, and increased AST and ALT levels indicating liver damage. Moreover, albumin and blood coagulation factors (fibrinogen, AT III, and protein C) were decreased, and PT sec was increased at POD60, indicating liver failure. Additionally, dehydration coupled with terminal hemolysis (high BUN, Hb up and falling, Hct up and falling, rising K, rising LDH, falling platelet at late POD, and normal creatinine level) and malnutrition (low albumin at late POD) were observed. Finally, high LDH levels indicated the possibility of multi-organ failure including heart and lung. A similar tendency of phenotypes for heart and lung failure was identified in cardiac xenograft using baboons (Längin et al. 2018). The gene expression associated with phenotypes were shown in Table S6. LDHA (LDH) was highly up-regulated (3.39 and 2.80) and TNNI1 (Troponin I) was down-regulated (−7.28 and −5.47) in both comparisons. Among them, highly regulated LDHA expression was consistent with blood phenotype LDH (Table 1).

Transcriptomes and its molecular functions of POD60 XH compared to NHs

We identified functional features based on transcriptomes by comparing POD60 XH of the longest survived monkey and two age-matched NHs. MDS showed three distinctly divided clusters for each group using whole transcriptomes (Figure 2(A)). 1,804 and 1,655 DEGs were identified in POD60 XH vs. D35 NH and POD60 XH vs. D95 NH, indicating major transcriptomic changes by xenotransplantation even with strict criteria (FDR < 0.05 and log2 FC ≥ 2) (Figure 2(B)). To reduce age-dependent bias, overlapping 1,148 DEGs (605 up-regulated and 543 down-regulated) were used for functional enrichment analyses (Figure 2(C)), showing significant terms related to immune signaling, neural & hormonal signaling, and heart failure (Figure 2(E)). In xenotransplantation research, pro-inflammatory cytokines are important rejection and coagulopathy features (Ezzelarab et al. 2015; Zhao et al. 2019).

Inflammatory and coagulation-related cytokines (IFNG, TNF-α, IL12, MCP-1, IL8, and IL6) were measured post-transplantation in the serum of pig-to-baboon XH recipients, reporting an increase of MCP-1 and IL6 with a high CRP (Ezzelarab et al. 2015). However, we identified that the expression of CCL2 (CCL2; MCP-1) and CXCL8 (CXCL8; IL8) among them was significantly up-regulated in POD60 XH (Table S3). IL6 expression was not significantly changed post-transplantation in our study: relatively low hs-CRP level was postulated (Table 1), but CC and presumably TM were observed (Figure 1(B)). Pro-inflammatory chemokine CXCL8 recruits and activates neutrophils during inflammation (French et al. 2018). It is believed that the CXCL8 was up-regulated in XH, not serum, due to CXCL8-mediated chemotaxis. We also confirmed that immune-related genes (CD2, CD4, CD6, CD8A, CD22, CD226, and SLA-DOA) were significantly down-regulated in POD60 XH (Table S3). CD4 and CD8, coreceptors for T cell receptors, assist in communicating with antigen-presenting cells (APCs) having major histocompatibility complex (MHC) class II and I, respectively. Reportedly, CD2, CD6, and CD226 were expressed in T cells (Sayre and Reinherz 1988; Bowen et al. 1995; Bottino et al. 2003), and CD22 in B cells (Crocker et al. 1998). These results suggest that XH T and B cells were mainly suppressed and rejection-protected by immunosuppressive therapies.

We also identified that ACTC1, ACE, and CACNG6 were down-regulated, while IGF1 was up-regulated in POD60 XH (Table S3). ACTC1 in skeletal muscle affects muscle contraction related to pump function and causes heart failure with reduced expression (Jiang et al. 2010). In addition, ACE inhibition dilates the blood vessels and lowers pressure, resulting in reduced heart failure (Flather et al. 2000). Moreover, abnormal Ca2+ handling is a key yet unclear pathophysiological mechanism in heart failure (Lou et al. 2012), which might consider the down-regulated calcium channel stabilizer CACNG6. Furthermore, high circulating IGF1 levels are reportedly associated with heart failure, including hypertrophy (Al-Obaidi et al. 2001). Therefore, cardiac xenotransplantation indicates comparable transcriptomic features of heart failure except for the ACE gene.

Changes in the nervous system and hormone signaling changes have been reported to be involved in heart failure (Lymperopoulos et al. 2013; Oakley et al. 2013). In this study, adrenergic receptor-related ADRA2C was significantly down-regulated in POD60 XH, but cholinergic receptor genes were not changed (Table S3). In addition, thyroid stimulating and calcitonin hormone receptor genes, TSHR and CALCR were significantly down-regulated (Table S3). TSHR was reported to induce B-type natriuretic peptide, suggesting a role in heart failure-associated hypothyroidism (Huang et al. 2014), while CALCR plays a critical role in calcium homeostasis, thereby affecting heart function (Maïmoun and Sultan 2009). These results indicate that heart failure occurred due to abnormal neural activities and multi-organ failure influenced by dysregulated hormonal signaling. Taken all together, we show that long-term survived cynomolgus XH exhibits specific transcriptomic differences in immune, neural, and hormonal signaling pathways, which may play crucial roles in heart failure.

Transcriptomic differences of heart failure between long- and short-term survived monkeys

The 1,348 annotated genes of POD60 XH in this study were compared with the 824 ones of POD9 XH in a previous study (Figure 3(A)) (Park et al. 2021). Among them, 360 overlapping genes were significantly enriched (Figure 3(B)) and clustered (Figure 3(C)); we focused on ‘hypertrophic cardiomyopathy’ to identify gene modulations in heart failure (Figure 6). ITGA2, ITGA8, and IGF1 were commonly up-regulated in POD60 and POD9 XHs, while ACTC1, ACE, CACNG6, and ITGB6 were commonly down-regulated. Transcriptomic changes of down-regulated ACTC1, ACE, CACNG6, and up-regulated IGF1, indicate similar heart failure signatures between POD60 and POD9 (Flather et al. 2000; Al-Obaidi et al. 2001; Jiang et al. 2010; Lou et al. 2012). However, only POD9 XH showed significantly up-regulated IL6, which participates in the innate immune response. IL6 induces acute-phase proteins, CRP, complement system proteins, and a coagulation cascade (Villar-Fincheira et al. 2021), which can result in coagulopathy. In fact, POD60 XH showed a relatively low hs-CRP level (Figure 7), but CC and presumably TM were present (Figure 1(B)) similar to POD9 XH (Park et al. 2021). Therefore, both long- and short-term survived monkeys have comparable transcriptomic features for heart failure except cytokine signaling.

Up-regulated cell signaling in short-term survived monkey

Heart remodeling occurs post-transplantation, and heart failure ensues if the remodeling lasts (Raichlin et al. 2009). A previous study also suggests that residual cardiomyocytes in impaired hearts re-enter the cell cycle to obtain proliferative capacity by cell cycle-related gene alterations or growth-related signal regulation (Azevedo et al. 2015). In our study, significant cell signaling-related pathways were identified by enrichment analysis using POD9-specific 988 DEGs (Figure 3). We focused on the ‘cell cycle’ term to identify gene modulations in cell signaling (Figure 5). Fifteen genes (MYC, CDKN1A, CDC6, ORC1, MCM2, MCM3, MCM4, MCM5, BUB1, CCNB1, CCNB2, CCNB3, PKMYT1, PLK1, and ESPL1) were up-regulated in POD9 XH, but not in POD60 XH. MYC plays an important role in regulating cell growth, apoptosis, and differentiation (Dang 1999). CDKN1A encoded p21, which induces p53-dependent cell cycle arrest in response to DNA damage (Engeland 2018). CDC6 plays an important role in cell cycle coordination of the S phase (Borlado and Méndez 2008). ORC1 encodes highly conserved six subunits protein complex essential for DNA replication initiation (Hossain et al. 2021). In addition, MCM2, MCM3, MCM4, and MCM5 also encode highly conserved mini-chromosome maintenance proteins for genome replication initiation (Maiorano et al. 2006). Moreover, BUB1 was reported to affect spindle assembly checkpoint and chromosome alignment during metaphase (Meraldi and Sorger 2005), and CCNB1, CCNB2, and CCNB3 were known to encode mitosis regulatory proteins for G2/M transition phase control (Wu et al. 2010; Farshadi et al. 2019). PKMYT1 encoded Myt1, which inactivates cell cycle Cdks through phosphorylation (Booher et al. 1997). PLK1 encoded plk1, which supports centrosome maturation in late G2/early prophase and the bipolar spindle establishment (Alfaro et al. 2021). ESPL1 regulates the sister chromatid separation (Guo et al. 2013). In summary, we found several critical genes up-regulated in POD9 XH and hypothesize that heart remodeling and regeneration are induced. Consequently, it is believed that heart remodeling and regeneration can further adversely affect XH and its survival period.

Differences in neural and hormonal signaling

Unbalanced neural and hormonal signaling can lead to heart failure (Lymperopoulos et al. 2013; Oakley et al. 2013). We identified significant pathways related to neural and hormonal signaling (Figure 3) and focused on the ‘neuroactive ligand–receptor interaction’ term to find out specific gene modulations (Figure S2). Remarkably, adrenergic-related ADRA2C was down-regulated only in POD60 XH for this study, while cholinergic-related CHRM4, CHRNB2, and CHRNB4 were up-regulated only in POD9 XH. ADRA2C was reported to have a critical role in regulating neurotransmitter release from sympathetic nerves and adrenergic neurons (Bücheler et al. 2002). CHRM4, CHRNB2, and CHRNB4 were known to regulate cellular responses, including ion channel mediation by binding acetylcholine (Resende and Adhikari 2009). In addition, TSHR and CALCR encoding hormone receptors were down-regulated only in POD60 XH, indicating an unbalancing expression of receptors that can lead to multi-organ failure, especially thyroid (Klein and Ojamaa 2001). In the POD60 XH, high LDH indicated multi-organ failure risk (Table 1). To sum up, we confirm and suggest that multi-organ failure by unbalanced expression of receptors in neural and hormonal signaling is associated with long-term XH survival.

Immunological differences based on transcriptomes

Immunosuppression is important to avoid immune rejection in xenotransplantation. Recently, HAR and AHXR in the early xenograft period were dramatically reduced by using GTKO pigs (Mohiuddin et al. 2014, 2016). Cellular xenograft rejection may occur days to weeks post-transplantation and is mediated by innate and adaptive immune responses (Cadili and Kneteman 2008). In this study, we identified significant immune signaling-related pathways (Figure 3) and focused on the ‘cell adhesion molecules’ term to identify gene modulations (Figure S3). CD4, CD22, and SLA-DOA were significantly down-regulated only in POD60 XH. CD4 assists in communicating with APCs such as B cells using MHC class II, inducing maturation of helper T cells for both innate and adaptive immune responses. In addition, CD22 plays a key role in B cell responses and innate immunity (Clark and Giltiay 2018). CD2, CD6, CD8A, and CD226 were commonly down-regulated in both POD60 and POD9. CD2, CD6, and CD226 play a critical role in T cell activation (Carrasco et al. 2017; Binder et al. 2020; Huang et al. 2020). CD8 recognizes APCs expressing MHC class I, and the interaction induces maturation of cytotoxic T cells (O'Rourke and Mescher 1993). Collectively, T cell activation was commonly suppressed in both long- and short-term survived monkeys, but B cell activation was more suppressed in long-term survived monkey. Besides immunosuppression, cytokine signaling changes were also identified (Figure 3), and we focused on highly regulated genes. CX3CR1 was commonly down-regulated in POD60 and POD9 XHs (Figure 4). CX3CR1 is involved in leukocyte adhesion, and it is reported that inhibiting CX3CR1 significantly prolonged mouse cardiac allograft survival (Robinson et al. 2000). We confirmed that CX3CR1 was more down-regulated in POD60 than in POD9, resulting in relatively long-term survival in POD60. CCL20, CCL5, and CXCL11 play an important role in the attraction of leukocytes. CCL20 was up-regulated in POD9, while CCL5 and CXCL11 were up-regulated in POD60. CCL5-encoded RANTES is a pivotal mediator of the cellular infiltrate in graft atherosclerosis (Pattison et al. 1996) and is important for macrophage recruitment in cardiac allograft rejection (Azzawi et al. 1998). Additionally, POD9 showed significantly up-regulated proinflammatory cytokines IL1a and IL6, which can mediate toxic effects by promoting macrophages (El-Ouaghlidi et al. 1999). Therefore, both long- and short-term survived monkey XHs indicate transcriptome-regulated chemotaxis features of leukocytes including macrophages, and proinflammatory cytokines may play a major role in determining the survival period.

Conclusions

We performed pig-to-cynomolgus monkey cardiac xenotransplantation using GTKO/CD46 pigs. Hematological, biochemical, and histological analyses were conducted on the xenotransplanted monkey. In addition to phenotypic measurement, RNA-seq-based transcriptomes were produced in POD60 XH and age-matched NHs. DEG profiling in POD60 XH compared to NHs was performed, and its functions were identified. We compared POD60 with POD9 using annotated genes to identify long- and short-term survival differences. In the functional network, 6 clusters (cytokine signaling, cell signaling, neural & hormonal signaling, heart failure, immune signaling, and fat metabolism) were identified. Heart failures were shown in both long- and short-term survived XHs. However, heart remodeling and regeneration signaling was up-regulated only in short-term survived XH. Moreover, heart failure and multi-organ failure features by neural and hormonal signaling in long-term survived XH were confirmed. In terms of immune signaling, T cell activation was inhibited in both, but only long-term survived XH showed B cell suppression. In addition, rejection-inducing chemotaxis features were identified and proinflammatory cytokines were confirmed only in short-term survived XH. These results provide the first discovery of differential heart failure development by survival periods at the transcriptome level and suggest candidate genes in each signaling to control side effects in heart xenotransplantation.

Supplementary Material

Supplemental Material
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Funding Statement

This work was carried out with the support of the Cooperative Research Program for Agriculture Science and Technology Development of the Rural Development Administration, Republic of Korea (PJ01136301) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C1005830). This research was supported by the Chung-Ang University Graduated Research Scholarship in 2022.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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