Gene expression signature predicts human islet integrity and transplant functionality in diabetic mice

Biological hypoxia in pre-transplant human pancreatic islets induces transplant failure in diabetic mice

Evaluating the quality of isolated human islets before transplantation is crucial for predicting the success in treating Type 1 diabetes. The current gold standard involves time-intensive in vivo transplantation into diabetic immunodeficient mice. Given the susceptibility of isolated islets to hypoxia, we hypothesized that hypoxia present in islets before transplantation could indicate compromised islet quality, potentially leading to unfavorable outcomes. To test this hypothesis, we analyzed expression of 39 hypoxia-related genes in human islets from 85 deceased donors. We correlated gene expression profiles with transplantation outcomes in 327 diabetic mice, each receiving 1200 islet equivalents grafted into the kidney capsule. Transplantation outcome was post-transplant glycemic control based on area under the curve of blood glucose over 4 weeks. In linear regression analysis, DDIT4 (R = 0.4971, P < 0.0001), SLC2A8 (R = 0.3531, P = 0.0009) and HK1 (R = 0.3444, P = 0.0012) had the highest correlation with transplantation outcome. A multiple regression model of 11 genes increased the correlation (R = 0.6117, P < 0.0001). We conclude that assessing pre-transplant hypoxia in human islets via gene expression analysis is a rapid, viable alternative to conventional in vivo assessments. This approach also underscores the importance of mitigating pre-transplant hypoxia in isolated islets to improve the success rate of islet transplantation.

Validation of an Integrated Genetic-Epigenetic Test for the Assessment of Coronary Heart Disease

BACKGROUND

Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic-epigenetic tools may provide a new avenue for assessing CHD.

METHODS AND RESULTS

In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence-guided, clinically implementable method that uses input from 6 methylation-sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine-phospho-guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches.

CONCLUSIONS

We conclude that this scalable integrated genetic-epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation-sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response.

The Reversion of the Epigenetic Signature of Coronary Heart Disease in Response to Smoking Cessation

Coronary heart disease (CHD) is the leading cause of death worldwide. However, current diagnostic tools for CHD, such as coronary computed tomography angiography (CCTA), are poorly suited for monitoring treatment response. Recently, we have introduced an artificial-intelligence-guided integrated genetic-epigenetic test for CHD whose core consists of six assays that determine methylation in pathways known to moderate the pathogenesis of CHD. However, whether methylation at these six loci is sufficiently dynamic to guide CHD treatment response is unknown. To test that hypothesis, we examined the relationship of changes in these six loci to changes in cg05575921, a generally accepted marker of smoking intensity, using DNA from a cohort of 39 subjects undergoing a 90-day smoking cessation intervention and methylation-sensitive digital PCR (MSdPCR). We found that changes in epigenetic smoking intensity were significantly associated with reversion of the CHD-associated methylation signature at five of the six MSdPCR predictor sites: cg03725309, cg12586707, cg04988978, cg17901584, and cg21161138. We conclude that methylation-based approaches could be a scalable method for assessing the clinical effectiveness of CHD interventions, and that further studies to understand the responsiveness of these epigenetic measures to other forms of CHD treatment are in order.

Predicting endocrine function after total pancreatectomy and islet cell autotransplantation: A novel approach utilizing computed tomography texture analysis

BACKGROUND

Islet cell autotransplantation is an effective method to prevent morbidity associated with type IIIc diabetes after total pancreatectomy. However, there is no valid method to predict long-term endocrine function. Our aim was to assess computed tomography texture analysis as a strategy to predict long-term endocrine function after total pancreatectomy and islet cell autotransplantation.

METHODS

All patients undergoing total pancreatectomy and islet cell autotransplantation from 2007 to 2020 who had high-quality preoperative computed tomography imaging available for texture analysis were included. The primary outcome was optimal long-term endocrine function, defined as stable glycemic control with <10 units of insulin/day.

RESULTS

Sixty-three patients met inclusion criteria. Median yield was 6,111 islet equivalent/kg body weight. At a median follow-up of 64.2 months, 12.7% (n = 8) of patients were insulin independent and 39.7% (n = 25) demonstrated optimal endocrine function. Neither total islet equivalent nor islet equivalent/kg body weight alone were associated with optimal endocrine function. To improve endocrine function prediction, computed tomography texture analysis parameters were analyzed, identifying an association between kurtosis (odds ratio, 2.32; 95% confidence interval, 1.08-4.80; P = .02) and optimal endocrine function. Sensitivity analysis discovered a cutoff for kurtosis = 0.60, with optimal endocrine function seen in 66.7% with kurtosis ≥0.60, compared with only 26.2% with kurtosis <0.60 (P < .01). On multivariate logistic regression including islet equivalent yield, only kurtosis ≥0.60 (odds ratio, 5.61; 95% confidence interval, 1.56-20.19; P = .01) and fewer small islet equivalent (odds ratio, 1.00; 95% confidence interval, 1.00-1.00; P = .02) were associated with optimal endocrine function, with the whole model demonstrating excellent prediction of long-term endocrine function (area under the curve, 0.775).

CONCLUSION

Computed tomography texture analysis can provide qualitative data, that when used in combination with quantitative islet equivalent yield, can accurately predict long-term endocrine function after total pancreatectomy and islet cell autotransplantation.

Building Biomimetic Potency Tests for Islet Transplantation

Diabetes is a disease of insulin insufficiency, requiring many to rely on exogenous insulin with constant monitoring to avoid a fatal outcome. Islet transplantation is a recent therapy that can provide insulin independence, but the procedure is still limited by both the availability of human islets and reliable tests to assess their function. While stem cell technologies are poised to fill the shortage of transplantable cells, better methods are still needed for predicting transplantation outcome. To ensure islet quality, we propose that the next generation of islet potency tests should be biomimetic systems that match glucose stimulation dynamics and cell microenvironmental preferences and rapidly assess conditional and continuous insulin secretion with minimal manual handing. Here, we review the current approaches for islet potency testing and outline technologies and methods that can be used to arrive at a more predictive potency test that tracks islet secretory capacity in a relevant context. With the development of potency tests that can report on islet secretion dynamics in a context relevant to their intended function, islet transplantation can expand into a more widely accessible and reliable treatment option for individuals with diabetes.

Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes

Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques.

Inflammatory biomarkers in the blood and pancreatic tissue of organ donors that predict human islet isolation success and function

The pancreas of brain-dead donors is the primary source of islets for transplantation. However, brain death mediates systemic inflammation, which may affect the quantity and quality of isolated islets. Our aim was to identify inflammatory biomarkers in donor blood and/or pancreatic tissue capable of predicting islet isolation success. Blood samples were collected from 21 pancreas donors and 14 healthy volunteers. Pancreatic tissue samples were also collected from the corresponding donor during organ procurement. Six serum cytokines were measured by a fluorescent bead-based immunoassay, and the expression of fifteen inflammatory target genes was quantified by quantitative reverse transcription polymerase chain reaction (RT-qPCR). There was no correlation between serum inflammatory cytokines and mRNA expression of the corresponding genes in peripheral blood mononuclear cells (PBMCs) or pancreatic tissue. The IL6 expression in pancreatic tissue correlated negatively with post-isolation islet yield. Islets isolated from donors highly expressing IFNG in PBMCs and MAC1 in pancreatic tissue functioned poorly in vivo when transplanted in diabetic NODscid mice. Furthermore, the increased MAC1 in pancreatic tissue was positively correlated with donor hospitalization time. Brain death duration positively correlated with higher expression of IL1B in PBMCs and TNF in both PBMCs and pancreatic tissue but failed to show a significant correlation with islet yield and in vivo function. The study indicates that the increased inflammatory genes in donor pancreatic tissues may be considered as biomarkers associated with poor islet isolation outcome.