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Project description:Checkpoint inhibitors (CPIs) targeting PD-1/PD-L1 and CTLA-4 have revolutionized cancer treatment but can trigger autoimmune complications including CPI-induced diabetes (CPI-DM), which occurs preferentially with PD-1 blockade. We found evidence of pancreatic inflammation in patients with CPI-DM with shrinkage of pancreases, increased pancreatic enzymes, and in a case from a patient who died with CPI-DM, peri-islet lymphocytic infiltration. In the NOD mouse model, anti-PD-L1 but not anti-CTLA-4 induces DM rapidly. RNA sequencing revealed that cytolytic IFNγ+ CD8+ T cells infiltrated islets with anti-PD-L1. Changes in β cells were predominantly driven by IFNγ and TNFα and included induction of a novel β cell population with transcriptional changes suggesting dedifferentiation. IFNγ increased checkpoint ligand expression and activated apoptosis pathways in human β cells in vitro. Treatment with anti-IFNγ and anti-TNFα prevented CPI-DM in anti-PD-L1 treated NOD mice. CPIs targeting the PD-1/PD-L1 pathway result in transcriptional changes in β cells and immune infiltrates that may lead to the development of diabetes. Inhibition of inflammatory cytokines can prevent CPI-DM, suggesting a strategy for clinical application to prevent this complication.
2022-10-27 | GSE208644 | GEO
Project description:Checkpoint inhibitors (CPIs) targeting PD-1/PD-L1 and CTLA-4 have revolutionized cancer treatment but can trigger autoimmune complications including CPI-induced diabetes (CPI-DM), which occurs preferentially with PD-1 blockade. We found evidence of pancreatic inflammation in patients with CPI-DM with shrinkage of pancreases, increased pancreatic enzymes, and in a case from a patient who died with CPI-DM, peri-islet lymphocytic infiltration. In the NOD mouse model, anti-PD-L1 but not anti-CTLA-4 induces DM rapidly. RNA sequencing revealed that cytolytic IFNγ+ CD8+ T cells infiltrated islets with anti-PD-L1. Changes in β cells were predominantly driven by IFNγ and TNFα and included induction of a novel β cell population with transcriptional changes suggesting dedifferentiation. IFNγ increased checkpoint ligand expression and activated apoptosis pathways in human β cells in vitro. Treatment with anti-IFNγ and anti-TNFα prevented CPI-DM in anti-PD-L1 treated NOD mice. CPIs targeting the PD-1/PD-L1 pathway result in transcriptional changes in β cells and immune infiltrates that may lead to the development of diabetes. Inhibition of inflammatory cytokines can prevent CPI-DM, suggesting a strategy for clinical application to prevent this complication.
2022-10-27 | GSE209587 | GEO
Project description:Diabetes mellitus (DM) is a disorder that disrupts the body from shifting glucose into the cells resulting in hyperglycaemia (1). Insulin dependence or DM that resembles type I diabetes in humans is commonly observed in dogs (2). Canine DM diagnosis is based on fasting hyperglycaemia and glucosuria with clinical presentation of polyuria, polydipsia, polyphagia and weight loss (2). Its treatment goal is blood glucose control, which can be accomplished through insulin therapy, dietary modification and control of concurrent disorders (3). The major complications of DM include diabetic nephropathy, diabetic neuropathy, diabetic retinopathy, diabetic cardiomyopathy and atherosclerosis induced by chronic hyperglycaemia via several pathways (4). The proposed unifying mechanism that mediates the tissue-damaging effects of hyperglycaemia is superoxide overproduction (5). Effective monitoring is required for DM treatment to reduce the risk of progression and complication. Hence, the identification of novel biomarkers is being researched (6, 7). Proteomics has been recognised as an important tool for establishing a diagnosis of disease aetiology and monitoring therapy outcomes (8, 9). Proteomic patterns were applied to detect diabetes and complications, as well as to evaluate treatment effectiveness in humans (10-12). There is limited information on proteomic data in DM dogs (13-15). In a proteomic analysis of serum samples from DM dogs, most upregulated proteins are involved in oxidative state, defence and inflammation (13). Medicinal plants are utilised in DM dogs as an adjunct medicine in combination with standard treatment to prevent the development of long-term diabetes complications and improve overall well-being. Curcumin, the most phytochemically active curcuminoid extracted from Curcuma longa, has gained attention in human and laboratory animals. Curcumin is known to have antioxidant, anti-inflammatory and anticancer properties (16-18). In humans and experimental animals with DM, curcumin has an antioxidant potential of enhanced reduced glutathione (GSH) and reduced malondialdehyde (MDA) levels (19, 20). The anti-inflammatory effects of curcumin in DM were reported via decreased interleukin-1β (IL‐1β), interleukin-6 (IL‐6), interleukin-8 (IL‐8) and tumour necrosis factor-α levels and also diminished monocyte chemoattractant protein-1 and C-reactive protein levels (19-21). There has been no published evidence of the impact, safety and proteomic profiles of curcuminoids, particularly curcumin, in client-own DM dogs. Accordingly, the aims of the present study were (1) to evaluate the effects of curcuminoid supplementation on canine DM-associated oxidative stress and inflammation, (2) to determine the safety of curcuminoid supplementation in canine diabetes and (3) to determine whether curcuminoid has an impact on proteins implicated in DM-associated complications by a proteomic analysis.
2022-12-15 | PXD037667 | Pride
Project description:Compared with plasma sEV (Con-sEV) from control rats, plasma sEV (DM-sEV) from 8-week diabetic rats significantly induced cardiomyocyte apoptosis as evidenced by increased percentage of apoptotic cells and activity of pro-apoptotic protein caspase 3. The proapoptotic effect of DM-sEV was blunted by RNase but not proteinase K, suggesting that DM-sEV exerted the cardiotoxic effects mainly through their contained RNAs. Increasing evidence suggests that miRNAs are the most important molecules by which sEV regulate recipient cell function. To identify the sEV-containing specific miRNAs responsible for the effects, Con-sEV and DM-sEV were subjected to miRNA sequencing.
2023-09-24 | GSE243560 | GEO
Project description:Epithelial and stromal/mesenchymal limbal stem cells contribute to corneal homeostasis and cell renewal. Extracellular vesicles (EVs), including exosomes (Exos), can be paracrine mediators of intercellular communication. Previously, we described cargos and regulatory roles of limbal stromal cell (LSC)-derived Exos in non-diabetic (N) and diabetic (DM) limbal epithelial cells (LEC). Presently, we quantify the miRNA and proteome profiles of human LEC-derived Exos and their regulatory roles in N- and DM-LSC. We revealed some miRNA and protein differences in DM vs. N-LEC-derived Exos' cargos including proteins involved in Exo biogenesis and packaging that may affect Exo production and ultimately cellular crosstalk and corneal function. Treatment by N-Exos, but not by DM-Exos enhanced wound healing in cultured N-LSC and increased proliferation rate in N and DM LSCs vs. corresponding untreated (control) cells. N-Exos treated LSC reduced keratocyte markers ALDH3A1 and lumican, and increased MSC markers CD73, CD90 and CD105 vs. control LSC. These being opposite to the changes quantified in wounded LSCs. Overall, N-LEC Exos have a more pronounced effect on LSC wound healing, proliferation, and stem cell marker expression than DM-LEC Exos. This suggests that regulatory miRNA and protein cargo differences in DM- vs. N-LEC-derived Exos could contribute to the disease state.
2023-09-29 | GSE243345 | GEO
Project description:Background: Diabetes mellitus (DM) is a recognized risk factor for dementias, including AD, increasing its odds by two-fold. Because DM is potentially modifiable risk factor, a greater understanding of the mechanisms linking DM to the clinical expression of AD may provide insights into much needed dementia therapeutics. Epigenetics offers a novel approach, and previously under-investigated 5-hydroxymethylcytosines (5hmC) is now emerging as a promising measure to investigate in diabetes related conditions. Methods: Using 5hmC-Seal, a highly sensitive chemical labeling technique developed by our team, we performed genome-wide profiling of 5hmC in circulating cell-free DNA (cfDNA) and prefrontal cortex tissue from 80 individuals across four groups: AD, DM, DM and AD (AD+DM), and non-AD/non-DM controls. We used differential analysis and machine-learning to explore whether 5hmC and biological pathways might be implicated in DM-associated AD under a balanced design. Results: We uncovered distinct 5hmC genome-wide signatures and biological pathways associated with AD or AD+DM compared to non-AD/non-DM controls or DM alone. We further demonstrated potential diagnostic value of 5hmC profiling in circulating cfDNA through feature selection based on machine learning. Conclusions: Genome-wide 5hmC profiling uncovered genomic features and biological pathways linking DM to DM-associated AD. Our findings also demonstrate the potential of utilizing 5hmC in circulating cfDNA as diagnostic biomarkers or disease monitoring tools, with the ultimate goal of preventing or ameliorating DM-associated AD dementia and improving clinical outcomes.
2023-05-16 | GSE203337 | GEO
Project description:Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features give an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features give an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.
2022-10-12 | GSE215221 | GEO
Project description:Background: Trisomy 21 causes Down syndrome (DS), but the mechanisms by which the extra chromosome leads to deficient intellectual and immune function are not well understood. Results: Here, we profile CpG methylation in DS and control cerebral and cerebellar cortex of adults and cerebrum of fetuses. We purify neuronal and non-neuronal nuclei and T-lymphocytes and find biologically relevant genes with DS-specific methylation (DS-DM) in brain cells. Some genes show brain-specific DS-DM, while others show stronger DS-DM in T cells. Both 5-methyl-cytosine and 5-hydroxy-methyl-cytosine contribute to the DS-DM. Thirty percent of genes with DS-DM in adult brain cells also show DS-DM in fetal brains, indicating early onset of these epigenetic changes, and we find early maturation of methylation patterns in DS brain and lymphocytes. Some, but not all, of the DS-DM genes show differential expression. DS-DM preferentially affected CpGs in or near specific transcription factor binding sites, implicating a mechanism involving altered transcription factor binding. Methyl-seq of brain DNA from mouse models with sub-chromosomal duplications mimicking DS reveals partial but significant overlaps with human DS-DM and shows that multiple chromosome 21 genes contribute to the downstream epigenetic effects. Conclusions: These data point to novel biological mechanisms in DS and have general implications for trans effects of chromosomal duplications and aneuploidies on epigenetic patterning. Examination of methylation changes in two mouse models of Down syndrome with sub-chromosomal duplications, Dp(10)1Yey and Dp(16)1Yey, compared to one littermate wild type mouse using whole genome bisulfite sequencing.
2015-10-30 | E-GEOD-74505 | biostudies-arrayexpress
Project description:In traditional Chinese medicine (TCM), blood stasis syndrome (BSS) is mainly manifested by the increase of blood viscosity, platelet adhesion rate and aggregation, and the change of microcirculation, resulting in vascular endothelial injury. It is an important factor in the development of diabetes mellitus (DM). The aim of this study was to screen out the potential candidate microRNAs (miRNAs) in DM patients with BSS by high-throughput sequencing (HTS) and bioinformatics analysis. CRL-1730 human umbilical vein endothelial cells (HUVECs) were incubated with 10% human serum to establish models of DM with BSS, DM without BSS (NBS) and normal control (NC). Total RNA of each sample was extracted and sequenced by the Hiseq2000 platform. Differentially expressed miRNAs (DE-miRNAs) and mRNAs (DE-mRNAs) were screened between samples. Target genes of miRNAs were predicted by softwares. Gene Ontology (GO) and pathway enrichment analysis of the target genes were conducted. According to the significantly enriched GO annotations and pathways (P value ≤0.001), we selected the key miRNAs of DM with BSS.
2018-04-03 | GSE109266 | GEO