| 1. |
- Bianchi, Matteo, et al.
(författare)
-
Whole-genome genotyping and resequencing reveal the association of a deletion in the complex interferon alpha gene cluster with hypothyroidism in dogs
- 2020
-
Ingår i: BMC Genomics. - : BMC. - 1471-2164. ; 21
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Hypothyroidism is a common complex endocrinopathy that typically has an autoimmune etiology, and it affects both humans and dogs. Genetic and environmental factors are both known to play important roles in the disease development. In this study, we sought to identify the genetic risk factors potentially involved in the susceptibility to the disease in the high-risk Giant Schnauzer dog breed.Results: By employing genome-wide association followed by fine-mapping (top variant p-value=5.7x10(-6)), integrated with whole-genome resequencing and copy number variation analysis, we detected a similar to 8.9 kbp deletion strongly associated (p-value=0.0001) with protection against development of hypothyroidism. The deletion is located between two predicted Interferon alpha (IFNA) genes and it may eliminate functional elements potentially involved in the transcriptional regulation of these genes. Remarkably, type I IFNs have been extensively associated to human autoimmune hypothyroidism and general autoimmunity. Nonetheless, the extreme genomic complexity of the associated region on CFA11 warrants further long-read sequencing and annotation efforts in order to ascribe functions to the identified deletion and to characterize the canine IFNA gene cluster in more detail.Conclusions: Our results expand the current knowledge on genetic determinants of canine hypothyroidism by revealing a significant link with the human counterpart disease, potentially translating into better diagnostic tools across species, and may contribute to improved canine breeding strategies.
|
|
| 2. |
- Gezelius, Emelie, et al.
(författare)
-
Circulating Levels of the Cardiovascular Biomarkers ST2 and Adrenomedullin Predict Outcome within a Randomized Phase III Lung Cancer Trial (RASTEN)
- 2022
-
Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 14:5
-
Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Cardiovascular disease is common in patients with small cell lung cancer, partly reflecting its high correlation with smoking. Cardiovascular comorbidities may limit patient tolerance to cytotoxic drugs, thereby influencing the choice and intensity of treatment and, ultimately, patient survival. In light of the challenges relating to assessing cardiovascular status clinically in newly diagnosed lung cancer, objective biomarkers of cardiovascular vulnerability are warranted. Here, we show that circulating levels of ST2, an established biomarker in heart failure, and adrenomedullin, a vasodilator peptide known to reflect several aspects of cardiovascular status, strongly correlate with survival in small cell lung cancer. Our data, which are based on a large, randomized trial cohort, suggest the potential use of cardiovascular biomarkers in guiding clinicians in making individualized treatment decisions. Cardiovascular comorbidity is common in small cell lung cancer (SCLC) and may significantly affect treatment tolerability and patient outcome. Still, there are no established biomarkers for objective and dynamic assessment as a tool for improved treatment decisions. We have investigated circulating levels of midregional-pro-adrenomedullin (MR-proADM), midregional-pro-atrial-natriuretic peptide (MR-proANP), copeptin (surrogate for vasopressin) and suppression-of-tumorigenicity-2 (ST2), all known to correlate with various aspects of cardiovascular function, in a SCLC cohort (N = 252) from a randomized, controlled trial (RASTEN). For all measured biomarkers, protein levels were inversely associated with survival, particularly with ST2 and MR-proADM, where the top versus bottom quartile was associated with an adjusted hazard ratio of 2.40 (95% CI 1.44-3.98; p = 0.001) and 2.18 (95% CI 1.35-3.51; p = 0.001), respectively, in the entire cohort, and 3.43 (95% CI 1.73-6.79; p < 0.001) and 3.49 (95% CI 1.84-6.60; p < 0.001), respectively, in extensive disease patients. A high combined score of MR-proADM and ST2 was associated with a significantly reduced median OS of 7.0 months vs. 14.9 months for patients with a low combined score. We conclude that the cardiovascular biomarkers MR-proADM and ST2 strongly correlate with survival in SCLC, warranting prospective studies on the clinical utility of MR-proADM and ST2 for improved, individualized treatment decisions.
|
|
| 3. |
- Neiman, Daniel, et al.
(författare)
-
Multiplexing DNA methylation markers to detect circulating cell-free DNA derived from human pancreatic β cells
- 2020
-
Ingår i: JCI Insight. - : American Society for Clinical Investigation. - 2379-3708. ; 5:14
-
Tidskriftsartikel (refereegranskat)abstract
- It has been proposed that unmethylated insulin promoter fragments in plasma derive exclusively from β cells, reflect their recent demise, and can be used to assess β cell damage in type 1 diabetes. Herein we describe an ultrasensitive assay for detection of a β cell–specific DNA methylation signature, by simultaneous assessment of 6 DNA methylation markers, that identifies β cell DNA in mixtures containing as little as 0.03% β cell DNA (less than 1 β cell genome equivalent). Based on this assay, plasma from nondiabetic individuals (N = 218, aged 4–78 years) contained on average only 1 β cell genome equivalent/mL. As expected, cell-free DNA (cfDNA) from β cells was significantly elevated in islet transplant recipients shortly after transplantation. We also detected β cell cfDNA in a patient with KATP congenital hyperinsulinism, in which substantial β cell turnover is thought to occur. Strikingly, in contrast to previous reports, we observed no elevation of β cell–derived cfDNA in autoantibody-positive subjects at risk for type 1 diabetes (N = 32), individuals with recent-onset type 1 diabetes (<4 months, N = 92), or those with long-standing disease (>4 months, N = 38). We discuss the utility of sensitive β cell cfDNA analysis and potential explanations for the lack of a β cell cfDNA signal in type 1 diabetes.
|
|
| 4. |
- Teixeira, Pedro F., et al.
(författare)
-
Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data
- 2024
-
Ingår i: DIABETOLOGIA. - : SPRINGER. - 0012-186X .- 1432-0428.
-
Tidskriftsartikel (refereegranskat)abstract
- The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.
|
|
