Bulky DNA adduct levels in normal-appearing colon mucosa,and the prevalence of colorectal adenomas

Abstract

Purpose: Examine the association between bulky DNA adduct levels in colon mucosa and colorectal adenoma prevalence, and explore the correlation between adduct levels in leukocytes and colon tissue.

Methods: Bulky DNA adduct levels were measured using ³²P-postlabelling in biopsies of normal-appearing colon tissue and blood donated by 202 patients. Multivariable logistic regression was used to examine associations between DNA adducts, and interactions of DNA adduct-DNA repair polymorphisms, with the prevalence of colorectal adenomas. Correlation between blood and tissue levels of DNA adducts was evaluated using Spearman’s correlation coefficient.

Results: An interaction between bulky DNA adduct levels and XPA rs1800975 on prevalence of colorectal adenoma was observed. Among individuals with lower DNA repair activity, increased DNA adduct levels were associated with increased colorectal adenoma prevalence (OR?=?1.41 per SD increase, 95%CI: 0.92–2.18). Conversely, among individuals with normal DNA activity, an inverse association was observed (OR?=?0.60 per SD increase, 95%CI: 0.34–1.07). Blood and colon DNA adduct levels were inversely correlated (ρ?=??0.20).

Conclusions: Among genetically susceptible individuals, higher bulky DNA adducts in the colon was associated with the prevalence of colorectal adenomas. The inverse correlation between blood and colon tissue measures demonstrates the importance of quantifying biomarkers in target tissues.     

Simplified method for the collection, storage, and comet assay analysis of DNA damage in whole blood

Single-cell gel electrophoresis (comet assay) is one of the most common methods used to measure oxidatively damaged DNA in peripheral blood mononuclear cells (PBMC), as a biomarker of oxidative stress in vivo. However, storage, extraction, and assay workup of blood samples are associated with a risk of artifactual formation of damage. Previous reports using this approach to study DNA damage in PBMC have, for the most part, required the isolation of PBMC before immediate analysis or freezing in cryopreservative. This is very time-consuming and a significant drain on human resources. Here, we report the successful storage of whole blood in ~ 250 μl volumes, at − 80 °C, without cryopreservative, for up to 1 month without artifactual formation of DNA damage. Furthermore, this blood is amenable for direct use in both the alkaline and the enzyme-modified comet assay, without the need for prior isolation of PBMC. In contrast, storage of larger volumes (e.g., 5 ml) of whole blood leads to an increase in damage with longer term storage even at − 80 °C, unless a cryopreservative is present. Our “small volume” approach may be suitable for archived blood samples, facilitating analysis of biobanks when prior isolation of PBMC has not been performed.     

Acid-sensing ion channel 3 mediates peripheral anti-hyperalgesia effects of acupuncture in mice inflammatory pain

Background  

Peripheral tissue inflammation initiates hyperalgesia accompanied by tissue acidosis, nociceptor activation, and inflammation mediators. Recent studies have suggested a significantly increased expression of acid-sensing ion channel 3 (ASIC3) in both carrageenan- and complete Freund's adjuvant (CFA)-induced inflammation. This study tested the hypothesis that acupuncture is curative for mechanical hyperalgesia induced by peripheral inflammation.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.     

Conditional random pattern model for copy number aberration detection

Background  

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.     

Identification of DNA sequence variation in Campylobacter jejuni strains associated with the Guillain-Barré syndrome by high-throughput AFLP analysis

Background  

Campylobacter jejuni is the predominant cause of antecedent infection in post-infectious neuropathies such as the Guillain-Barré (GBS) and Miller Fisher syndromes (MFS). GBS and MFS are probably induced by molecular mimicry between human gangliosides and bacterial lipo-oligosaccharides (LOS). This study describes a new C. jejuni-specific high-throughput AFLP (htAFLP) approach for detection and identification of DNA polymorphism, in general, and of putative GBS/MFS-markers, in particular.     

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

     

A critical evaluation of DNA adducts as biological markers for human exposure to polycyclic aromatic compounds

The causative role of polycyclic aromatic hydrocarbons (PAH) in human carcinogenesis is undisputed. Measurements of PAH-DNA adduct levels in easily accessible white blood cells therefore represent useful early endpoints in exposure intervention or chemoprevention studies. The successful applicability of DNA adducts as early endpoints depends on several criteria: i. adduct levels in easily accessible surrogate tissues should reflect adduct levels in target-tissues, ii. toxicokinetics and the temporal relevance should be properly defined. iii. sources of interand intra-individual variability must be known and controllable, and finally iv. adduct analyses must have advantages as compared to other markers of PAHexposure. In general, higher DNA adduct levels or a higher proportion of subjects with detectable DNA adduct levels were found in exposed individuals as compared with nonexposed subjects, but saturation may occur at high exposures. Furthermore, DNA adduct levels varied according to changes in exposure, for example smoking cessation resulted in lower DNA adduct levels and adduct levels paralleled seasonal variations of air-pollution. Intraindividual variation during continuous exposure was low over a short period of time (weeks), but varied significantly when longer time periods (months) were investigated. Inter-individual variation is currently only partly explained by genetic polymorphisms in genes involved in PAH-metabolism and deserves further investigation. DNA adduct measurements may have three advantages over traditional exposure assessment: i. they can smooth the extreme variability in exposure which is typical for environmental toxicants and may integrate exposure over a longer period of time. Therefore, DNA adduct assessment may reduce the monitoring effort. ii. biological monitoring of DNA adducts accounts for all exposure routes. iii. DNA adducts may account for inter-individual differences in uptake, elimination, distribution, metabolism and repair amongst exposed individuals. In conclusion, there is now a sufficiently large scientific basis to justify the application of DNA adduct measurements as biomarkers in exposure assessment and intervention studies. Their use in risk-assessment, however, requires further investigation.