Integrative omics reveals subtle molecular perturbations following ischemic conditioning in a porcine kidney transplant model

BACKGROUND: Remote Ischemic Conditioning (RIC) has been proposed as a therapeutic intervention to circumvent the ischemia/reperfusion injury (IRI) that is inherent to organ transplantation. Using a porcine kidney transplant model, we aimed to decipher the subclinical molecular effects of a RIC regime, compared to non-RIC controls. METHODS: Kidney pairs (n = 8 + 8) were extracted from brain dead donor pigs and transplanted in juvenile recipient pigs following a period of cold ischemia. One of the two kidney recipients in each pair was subjected to RIC prior to kidney graft reperfusion, while the other served as non-RIC control. We designed an integrative Omics strategy combining transcriptomics, proteomics, and phosphoproteomics to deduce molecular signatures in kidney tissue that could be attributed to RIC. RESULTS: In kidney grafts taken out 10 h after transplantation we detected minimal molecular perturbations following RIC compared to non-RIC at the transcriptome level, which was mirrored at the proteome level. In particular, we noted that RIC resulted in suppression of tissue inflammatory profiles. Furthermore, an accumulation of muscle extracellular matrix assembly proteins in kidney tissues was detected at the protein level, which may be in response to muscle tissue damage and/or fibrosis. However, the majority of these protein changes did not reach significance (p < 0.05). CONCLUSIONS: Our data identifies subtle molecular phenotypes in porcine kidneys following RIC, and this knowledge could potentially aid optimization of remote ischemic conditioning protocols in renal transplantation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-022-09343-3

Mathematically arterialised venous blood is a stable representation of patient acid-base status at steady state following acute transient changes in ventilation

Hyper- or hypoventilation are commonly occurring stress responses to arterial puncture around the time of blood sampling and have been shown to rapidly alter arterial blood acid–base parameters. This study aimed to evaluate a physiology-based mathematical method to transform peripheral venous blood acid–base values into mathematically arterialised equivalents following acute, transient changes in ventilation. Data from thirty patients scheduled for elective surgery were analysed using the physiology-based method. These data described ventilator changes simulating ‘hyper-’ or ‘hypoventilation’ at arterial puncture and included acid–base status from simultaneously drawn blood samples from arterial and peripheral venous catheters at baseline and following ventilatory change. Venous blood was used to calculate mathematically arterialised equivalents using the physiology-based method; baseline values were analysed using Bland–Altman plots. When compared to baseline, measured arterial and calculated arterialised values at each time point within limits of pH: ± 0.03 and PCO(2): ± 0.5 kPa, were considered ‘not different from baseline’. Percentage of values considered not different from baseline were calculated at each sampling timepoint following hyper- and hypoventilation. For the physiological method, bias and limits of agreement for pH and PCO(2) were -0.001 (-0.022 to 0.020) and -0.02 (-0.37 to 0.33) kPa at baseline, respectively. 60 s following a change in ventilation, 100% of the mathematically arterialised values of pH and PCO(2) were not different from baseline, compared to less than 40% of the measured arterial values at the same timepoint. In clinical situations where transient breath-holding or hyperventilation may compromise the accuracy of arterial blood samples, arterialised venous blood is a stable representative of steady state arterial blood

Additional file 2 of Integrative omics reveals subtle molecular perturbations following ischemic conditioning in a porcine kidney transplant model

Additional file 2: Table S1. Transcriptome sequence alignments. An average sequence alignment of 82% was achieved across all samples. Table S2. List of transcripts identified by transcriptomics. A total of 19,220 transcripts were successfully identified across groups. Table S3. Transcripts differentially expressed between RIC and Non-RIC groups. Table S4. qPCR validation on a panel of targets from discovery transcriptomics. IL1B, LTB4R, PDZD3, SLC16A3, and RASL10A were quantified by qPCR. We observed a slight increase in RIC induced SLC16A3 transcripts, but overall, none of the genes quantified reached statistically significance between RIC and non-RIC, with all having a p-value of &gt; 0.2. Table S5. List of proteins identified by proteomics. A total of 7,546 proteins were successfully identified across groups. Table S6. Proteins differentially expressed between RIC and Non-RIC groups. Table S7. List of proteins and phosphosites identified by phosphoproteomics. A total of 3,524 phosphosites were successfully identified across groups

Additional file 1 of Integrative omics reveals subtle molecular perturbations following ischemic conditioning in a porcine kidney transplant model

Additional file 1: Figure S1. Transcriptomic pathway enrichment analysis reveals subtle alteration of tissue inflammation by RIC. All transcripts (n = 33) which were found to be dysregulated in RIC versus non-RIC controls were searched against the Sus scrofa database in STRING. Only interactions of the highest confidence (scores &gt; 0.90) were included in the analysis. Genes associated with immune regulation are highlighted, with those linked to interleukin biology coloured red, while cytokines are coloured purple. Figure S2. Proteomic pathway enrichment analysis uncovers RIC induced tissue leakage and altered inflammation. Proteins (n = 252) which were found to have the greatest dysregulation in RIC versus non-RIC controls were searched against the Sus scrofa database in STRING. Only interactions of the highest confidence (scores &gt; 0.90) were included in the analysis. Proteins/genes of interest are highlighted, with proteins associated with muscle and ECM coloured in blue, blood coagulation in green, and factors associated with innate immunity coloured red. Figure S3. Kidney tissue phosphoproteomics unaffected by RIC. A) Abundance plots indicate no significant difference in the phosphoproteome between RIC and control groups. B) A Christmas tree plot, with Significance B thresholds colour coded. Minor changes were observed between groups

Additional file 1 of Integrative omics reveals subtle molecular perturbations following ischemic conditioning in a porcine kidney transplant model

Additional file 1: Figure S1. Transcriptomic pathway enrichment analysis reveals subtle alteration of tissue inflammation by RIC. All transcripts (n = 33) which were found to be dysregulated in RIC versus non-RIC controls were searched against the Sus scrofa database in STRING. Only interactions of the highest confidence (scores &gt; 0.90) were included in the analysis. Genes associated with immune regulation are highlighted, with those linked to interleukin biology coloured red, while cytokines are coloured purple. Figure S2. Proteomic pathway enrichment analysis uncovers RIC induced tissue leakage and altered inflammation. Proteins (n = 252) which were found to have the greatest dysregulation in RIC versus non-RIC controls were searched against the Sus scrofa database in STRING. Only interactions of the highest confidence (scores &gt; 0.90) were included in the analysis. Proteins/genes of interest are highlighted, with proteins associated with muscle and ECM coloured in blue, blood coagulation in green, and factors associated with innate immunity coloured red. Figure S3. Kidney tissue phosphoproteomics unaffected by RIC. A) Abundance plots indicate no significant difference in the phosphoproteome between RIC and control groups. B) A Christmas tree plot, with Significance B thresholds colour coded. Minor changes were observed between groups

Integrative omics reveals subtle molecular perturbations following ischemic conditioning in a porcine kidney transplant model

Abstract Background Remote Ischemic Conditioning (RIC) has been proposed as a therapeutic intervention to circumvent the ischemia/reperfusion injury (IRI) that is inherent to organ transplantation. Using a porcine kidney transplant model, we aimed to decipher the subclinical molecular effects of a RIC regime, compared to non-RIC controls. Methods Kidney pairs (n = 8 + 8) were extracted from brain dead donor pigs and transplanted in juvenile recipient pigs following a period of cold ischemia. One of the two kidney recipients in each pair was subjected to RIC prior to kidney graft reperfusion, while the other served as non-RIC control. We designed an integrative Omics strategy combining transcriptomics, proteomics, and phosphoproteomics to deduce molecular signatures in kidney tissue that could be attributed to RIC. Results In kidney grafts taken out 10 h after transplantation we detected minimal molecular perturbations following RIC compared to non-RIC at the transcriptome level, which was mirrored at the proteome level. In particular, we noted that RIC resulted in suppression of tissue inflammatory profiles. Furthermore, an accumulation of muscle extracellular matrix assembly proteins in kidney tissues was detected at the protein level, which may be in response to muscle tissue damage and/or fibrosis. However, the majority of these protein changes did not reach significance (

Additional file 2 of Integrative omics reveals subtle molecular perturbations following ischemic conditioning in a porcine kidney transplant model

Additional file 2: Table S1. Transcriptome sequence alignments. An average sequence alignment of 82% was achieved across all samples. Table S2. List of transcripts identified by transcriptomics. A total of 19,220 transcripts were successfully identified across groups. Table S3. Transcripts differentially expressed between RIC and Non-RIC groups. Table S4. qPCR validation on a panel of targets from discovery transcriptomics. IL1B, LTB4R, PDZD3, SLC16A3, and RASL10A were quantified by qPCR. We observed a slight increase in RIC induced SLC16A3 transcripts, but overall, none of the genes quantified reached statistically significance between RIC and non-RIC, with all having a p-value of &gt; 0.2. Table S5. List of proteins identified by proteomics. A total of 7,546 proteins were successfully identified across groups. Table S6. Proteins differentially expressed between RIC and Non-RIC groups. Table S7. List of proteins and phosphosites identified by phosphoproteomics. A total of 3,524 phosphosites were successfully identified across groups