GADD45 promotes locus-specific DNA demethylation and 2C cycling in embryonic stem cells.

Mouse embryonic stem cell (ESC) cultures contain a rare cell population of "2C-like" cells resembling two-cell embryos, the key stage of zygotic genome activation (ZGA). Little is known about positive regulators of the 2C-like state and two-cell stage embryos. Here we show that GADD45 (growth arrest and DNA damage 45) proteins, regulators of TET (TET methylcytosine dioxygenase)-mediated DNA demethylation, promote both states. Methylome analysis of Gadd45a,b,g triple-knockout (TKO) ESCs reveal locus-specific DNA hypermethylation of ∼7000 sites, which are enriched for enhancers and loci undergoing TET-TDG (thymine DNA glycosylase)-mediated demethylation. Gene expression is misregulated in TKOs, notably upon differentiation, and displays signatures of DNMT (DNA methyltransferase) and TET targets. TKOs manifest impaired transition into the 2C-like state and exhibit DNA hypermethylation and down-regulation of 2C-like state-specific genes. Gadd45a,b double-mutant mouse embryos display embryonic sublethality, deregulated ZGA gene expression, and developmental arrest. Our study reveals an unexpected role of GADD45 proteins in embryonic two-cell stage regulation.

Impaired DNA demethylation of C/EBP sites causes premature aging.

Changes in DNA methylation are among the best-documented epigenetic alterations accompanying organismal aging. However, whether and how altered DNA methylation is causally involved in aging have remained elusive. GADD45α (growth arrest and DNA damage protein 45A) and ING1 (inhibitor of growth family member 1) are adapter proteins for site-specific demethylation by TET (ten-eleven translocation) methylcytosine dioxygenases. Here we show that Gadd45a/Ing1 double-knockout mice display segmental progeria and phenocopy impaired energy homeostasis and lipodystrophy characteristic of Cebp (CCAAT/enhancer-binding protein) mutants. Correspondingly, GADD45α occupies C/EBPß/δ-dependent superenhancers and, cooperatively with ING1, promotes local DNA demethylation via long-range chromatin loops to permit C/EBPß recruitment. The results indicate that enhancer methylation can affect aging and imply that C/EBP proteins play an unexpected role in this process. Our study suggests a causal nexus between DNA demethylation, metabolism, and organismal aging.

Integration of single-cell multiomic measurements across disease states with genetics identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Altered function and gene regulation of pancreatic islet beta cells is a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of mechanisms driving T2D is still missing. Here we integrate information from measurements of chromatin activity, gene expression and function in single beta cells with genetic association data to identify disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 non-diabetic, pre-T2D and T2D donors, we robustly identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift in T2D. Subtype-defining active chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is likely induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for identifying mechanisms of complex diseases.

Nutrient regulation of the islet epigenome controls adaptive insulin secretion.

Adaptation of the islet ß cell insulin-secretory response to changing insulin demand is critical for blood glucose homeostasis, yet the mechanisms underlying this adaptation are unknown. Here, we have shown that nutrient-stimulated histone acetylation plays a key role in adapting insulin secretion through regulation of genes involved in ß cell nutrient sensing and metabolism. Nutrient regulation of the epigenome occurred at sites occupied by the chromatin-modifying enzyme lysine-specific demethylase 1 (Lsd1) in islets. ß Cell-specific deletion of Lsd1 led to insulin hypersecretion, aberrant expression of nutrient-response genes, and histone hyperacetylation. Islets from mice adapted to chronically increased insulin demand exhibited shared epigenetic and transcriptional changes. Moreover, we found that genetic variants associated with type 2 diabetes were enriched at LSD1-bound sites in human islets, suggesting that interpretation of nutrient signals is genetically determined and clinically relevant. Overall, these studies revealed that adaptive insulin secretion involves Lsd1-mediated coupling of nutrient state to regulation of the islet epigenome.

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

NEIL1 and NEIL2 DNA glycosylases protect neural crest development against mitochondrial oxidative stress.

Base excision repair (BER) functions not only in the maintenance of genomic integrity but also in active DNA demethylation and epigenetic gene regulation. This dual role raises the question if phenotypic abnormalities resulting from deficiency of BER factors are due to DNA damage or impaired DNA demethylation. Here we investigate the bifunctional DNA glycosylases/lyases NEIL1 and NEIL2, which act in repair of oxidative lesions and in epigenetic demethylation. Neil-deficiency in Xenopus embryos and differentiating mouse embryonic stem cells (mESCs) leads to a surprisingly restricted defect in cranial neural crest cell (cNCC) development. Neil-deficiency elicits an oxidative stress-induced TP53-dependent DNA damage response, which impairs early cNCC specification. Epistasis experiments with Tdg-deficient mESCs show no involvement of epigenetic DNA demethylation. Instead, Neil-deficiency results in oxidative damage specific to mitochondrial DNA, which triggers a TP53-mediated intrinsic apoptosis. Thus, NEIL1 and NEIL2 DNA glycosylases protect mitochondrial DNA against oxidative damage during neural crest differentiation.

Meropenem: a potent drug against superbug as unveiled through bioinformatics approaches.

Global spread of multi-drug resistant bacteria like Klebsiella pneumoniae and Escherichia coli have raised the alarm for researchers and doctors throughout the world. This new mechanism of resistance and the ability of ndm-1 gene to be transferred between the species may end the era of antibiotics treatment. Carbapenems are reliable drugs against many multi-resistant gram-negative pathogens. A 3-D homology model of NDM-1 was built and analysed for elucidation of functional site and binding interactions. This study revealed that meropenem has good interaction with the active sites of the receptor that could retard the spread of antibiotic resistant bacteria.

Java-based diabetes type 2 prediction tool for better diagnosis.

BACKGROUND: The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence. METHODS: In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm. RESULTS: The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format. CONCLUSIONS: This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2.