The inverse association between DNA gaps and HbA1c levels in type 2 diabetes mellitus

Abstract Naturally occurring DNA gaps have been observed in eukaryotic DNA, including DNA in nondividing cells. These DNA gaps are found less frequently in chronologically aging yeast, chemically induced senescence cells, naturally aged rats, d-galactose-induced aging model rats, and older people. These gaps function to protect DNA from damage, so we named them youth-associated genomic stabilization DNA gaps (youth-DNA-gaps). Type 2 diabetes mellitus (type 2 DM) is characterized by an early aging phenotype. Here, we explored the correlation between youth-DNA-gaps and the severity of type 2 DM. Here, we investigated youth-DNA-gaps in white blood cells from normal controls, pre-DM, and type 2 DM patients. We found significantly decreased youth-DNA-gap numbers in the type 2 DM patients compared to normal controls (P = 0.0377, P = 0.0018 adjusted age). In the type 2 DM group, youth-DNA-gaps correlate directly with HbA1c levels. (r = − 0.3027, P = 0.0023). Decreased youth-DNA-gap numbers were observed in patients with type 2 DM and associated with increased HbA1c levels. Therefore, the decrease in youth-DNA-gaps is associated with the molecular pathogenesis of high blood glucose levels. Furthermore, youth-DNA-gap number is another marker that could be used to determine the severity of type 2 DM

The association between Alu hypomethylation and severity of type 2 diabetes mellitus

Abstract Background Cellular senescence due to genomic instability is believed to be one of the mechanisms causing health problems in diabetes mellitus (DM). Low methylation levels of Alu elements or Alu hypomethylation, an epigenomic event causing genomic instability, were commonly found in aging people and patients with aging phenotypes, such as osteoporosis. Results We investigate Alu methylation levels of white blood cells of type 2 DM, pre-DM, and control. The DM group possess the lowest Alu methylation (P < 0.001, P < 0.0001 adjusted age). In the DM group, Alu hypomethylation is directly correlated with high fasting blood sugar, HbA1C, and blood pressure. Conclusion Genome-wide hypomethylation may be one of the underlining mechanisms causing genomic instability in type 2 DM. Moreover, Alu methylation levels may be a useful biomarker for monitoring cellular senescence in type 2 DM patients

Additional file 1: Figure S1. of The association between Alu hypomethylation and severity of type 2 diabetes mellitus

Alu methylation patterns of COBRA-Alu assay. (A) The Alu amplicons are 133 bp and contain 2 CpG-dinucleotides. (B) A schematic representation of the COBRA-Alu assay shows the methylation patterns of Alu amplicons, including fully methylated loci (mCmC), unmethylated loci (uCuC) and two partially methylated forms (mCuC and uCmC). (C) For bisulfate treatment, methylated cytosine bases are not changed to uracil bases, whereas the unmethylated cytosine bases are converted to uracil bases. After the PCR products are digested with Taq1 restriction enzyme, the digested products are mCmC (43, 32 and 58 bp), uCuC ( 133 bp), mCuC ( 43 and 90 bp) and uCmC ( 75 and 58 bp). (TIFF 98 kb

RIND-EDSBs and chromatin regulators.

<p>(A) The levels of RIND-EDSBs were measured in G0 cells of yeast strains lacking histone deacetylase genes, <i>SIR2, RPD3</i>, and <i>HDA1</i>. The level was decreased in the <i>sir2</i>Δ strain, while it was increased in <i>rpd3</i>Δ strain. (B) No significant change in the level of RIND-EDSBs was observed in yeast strains lacking the silent information regulator genes, <i>SIR1</i>, <i>SIR3</i>, or <i>SIR4</i>. (C) In contrast, deletions of <i>HTZ1</i> and <i>SWR1</i>, genes required for the prevention of heterochromatin spreading, led to significantly increased levels of RIND EDSBs. The values from 9 independent experiments are shown as box plots, with the boxes representing the interquartile ranges (25^th to 75^th percentile) and the median lines representing the 50^th percentile. The whiskers represent the minimum and the maximum values. <i>**P</i><0.001 (Mann-Whitney test).</p

EDSBs were detected in different DNA preparations including HMW DNA (cell→gel), liquid DNA (cell→liquid), and liquid DNA extracted from in-gel HMW DNA (cell→gel→liquid).

<p>(A) The levels of EDSBs from different DNA preparation methods. (B) Subtracted DSBs levels between liquid DNA and other methods. When comparing cell→gel→liquid with cell→liquid, adding in gel preparation step did not increase the number of DSBs significantly. The average levels of EDSBs from 9 independent experiments are shown as histograms with error bars representing SEM.</p

Yeast strains used in this study.

<p>Yeast strains used in this study.</p

EDSBs in various phases of the cell cycle.

<p>(A) EDSBs were measured in asynchronous culture and in yeast cells arrested in G0, G1, S, and M phases. The levels of EDSBs from 9 independent experiments are shown as box plots, with the boxes representing the interquartile ranges (25^th to 75^th percentile) and the median lines representing the 50^th percentile. The whiskers represent the minimum and the maximum values. There was a significant decrease in EDSBs in G0 cells compared to asynchronous culture, such that <i>**P</i><0.001 (Mann-Whitney test). (B) HMW DNA was isolated from apoptotic yeast cells, mixed with control DNA at varying percentages, and analyzed by Ty1-EDSB-LMPCR. The graph represents the mean levels of EDSBs with error bars representing standard deviations. The Ty1-EDSB-LMPCR assay could not detect DNA fragments from apoptotic cells (at 100% apoptotic DNA). Furthermore, apoptotic DNA fragments did not interfere with quantitative measurement of EDSBs.</p

RIND-EDSBs levels using HMW DNA preparation and intranuclear ligation protocols.

<p>(A, B) The levels of RIND-EDSBs were measured in G0 cells of WT, <i>mec1</i>Δ, <i>yku70</i>Δ, <i>nhp6a</i>Δ strains using HMW DNA (A) and intranuclear ligation (B) protocols. (C, D) The levels of RIND-EDSBs in controls and TSA-treated WT and <i>mec1</i>Δ strains using HMW DNA (C) and intranuclear ligation (D) protocols. Bar graphs represent average values and error bars represent standard deviation of triplicate experiments.</p

Streaming Spectral Processing with Consumer-level Graphics Processing Units

This paper describes the implementation of a streaming spectral processing system for realtime audio in a consumer-level onboard GPU (Graphics Processing Unit) attached to an off-the-shelf laptop computer. It explores the implementation of four processes: standard phase vocoder analysis and synthesis, additive synthesis and the sliding phase vocoder. These were developed under the CUDA development environment as plugins for the Csound 6 audio programming language. Following a detailed exposition of the GPU code, results of performance tests are discussed for each algorithm. They demonstrate that such a system is capable of realtime audio, even under the restrictions imposed by a limited GPU capability