Structure-related clustering of gene expression fingerprints of thp-1 cells exposed to smaller polycyclic aromatic hydrocarbons

This study was undertaken to test the hypothesis that structurally similar PAHs induce similar gene expression profiles. THP-1 cells were exposed to a series of 12 selected PAHs at 50 µM for 24 hours and gene expressions profiles were analyzed using both unsupervised and supervised methods. Clustering analysis of gene expression profiles revealed that the 12 tested chemicals were grouped into five clusters. Within each cluster, the gene expression profiles are more similar to each other than to the ones outside the cluster. One-methylanthracene and 1-methylfluorene were found to have the most similar profiles; dibenzothiophene and dibenzofuran were found to share common profiles with fluorine. As expression pattern comparisons were expanded, similarity in genomic fingerprint dropped off dramatically. Prediction analysis of microarrays (PAM) based on the clustering pattern generated 49 predictor genes that can be used for sample discrimination. Moreover, a significant analysis of Microarrays (SAM) identified 598 genes being modulated by tested chemicals with a variety of biological processes, such as cell cycle, metabolism, and protein binding and KEGG pathways being significantly (p < 0.05) affected. It is feasible to distinguish structurally different PAHs based on their genomic fingerprints, which are mechanism based.     

Evaluation of biological monitoring markers using genomic and proteomic analysis for automobile emission inspectors and waste incinerating workers exposed to polycyclic aromatic hydrocarbons or 2,3,7,8,-tetracholrodedibenzo-p-dioxins

In this study, we investigated the effects of PAHs and dioxin on mRNA and plasma protein expression using genomic and proteomic analysis for automobile emission inspectors and waste incineration workers. About 54 workers from automobile emission inspection offices, 31 workers from waste incinerating company and 84 unexposed healthy subjects were enrolled in this study. Urine and air samples were collected and analyzed by HPLC and GC/MS. Comet assays were carried out to evaluate any DNA damage in mononuclear and polynuclear cells. A significant difference in Olive tail moments in mononuclear cells was observed between exposed and control subjects (P < 0.0001). To examine the differences of the gene expression profile in automobile emission inspectors and waste incineration workers, radioactive complementary DNA microarrays were used to evaluate changes in the expression of 1,152 total genes. The gene expression profiles showed that 11 genes were up-regulated and 4 genes were down-regulated in waste incinerating workers as compared with controls. Plasma proteins were analyzed by 2-dimentional electrophoresis with pH 3-10 NL IPG Dry strip. The protein expression profiles showed that 8 proteins were up- regulated and 1 protein, haptoglobin, was down- regulated in automobile emission inspectors and waste incineration workers. Serum paraoxonase/ arylesterase was found only in the plasma of waste incineration workers. The expression of genes and proteins involved in oxidative stress were up-regulated in both automobile emission inspectors and waste incineration workers. Several proteins, such as transthyrethin, sarcolectin and haptoglobin, that were highly up- or down-regulated, could serve as biological monitoring markers for future study.     

Clustering of time-course gene expression data using functional data analysis

Clustering of gene expression data collected across time is receiving growing attention in the biological literature since time-course experiments allow one to understand dynamic biological processes and identify genes governed by the same processes. It is believed that genes demonstrating similar expression profiles over time might give an informative insight into how underlying biological mechanisms work. In this paper, we propose a method based on functional data analysis (FNDA) to cluster time-dependent gene expression profiles. Consideration of clustering problems using the FNDA setting provides ways to take time dependency into account by using basis function expansion to describe the partially observed curves. We also discuss how to choose the number of bases in the basis function expansion in FNDA. A synthetic cycle data and a real data are used to demonstrate the proposed method and some comparisons between the proposed and existing approaches using the adjusted Rand indices are made.     

Rival penalized competitive learning (RPCL): a topology-determining algorithm for analyzing gene expression data

DNA arrays have become the immediate choice in the analysis of large-scale expression measurements. Understanding the expression pattern of genes provide functional information on newly identified genes by computational approaches. Gene expression pattern is an indicator of the state of the cell, and abnormal cellular states can be inferred by comparing expression profiles. Since co-regulated genes, and genes involved in a particular pathway, tend to show similar expression patterns, clustering expression patterns has become the natural method of choice to differentiate groups. However, most methods based on cluster analysis suffer from the usual problems (i) dead units, and (ii) the problem of determining the correct number of clusters (k) needed to classify the data. Selecting the k has been an open problem of pattern recognition and statistics for decades. Since clustering reveals similar patterns present in the data, fixing this number strongly influences the quality of the result. While there is no theoretical solution to this problem, the number of clusters can be decided by a heuristic clustering algorithm called rival penalized competitive learning (RPCL). We present a novel implementation of RPCL that transforms the correct number of clusters problem to the tractable problem of clustering based on the degree of similarity. This is biologically significant since our implementation clusters functionally co-regulated genes and genes that present similar patterns of expression. This new approach reveals potential genes that are co-involved in a biological process. This implementation of the RPCL algorithm is useful in differentiating groups involved in concerted functional regulation and helps to progressively home into patterns, which are closely similar.     

QSARs for Photoinduced Toxicity of Aromatic Compounds

Abstract

This paper reviews the results of a series of efforts to develop QSAR models for aromatic chemicals whose toxicity is enhanced by the ultraviolet radiation present in sunlight. Photoinduced toxicity of polycyclic aromatic hydrocarbons (PAHs) was found to be a result of competing factors: structural (such as molecular stability and light absorbance) and external (irradiation energy and intensity). These two factors interact, producing a complex, multilinear relationship between toxicity and electronic structure. The HOMO-LUMO gap provided a useful ground-state index to explain the persistence, light absorption, and eventually, the photoinduced toxicity of PAHs. The derived QSAR clearly distinguished phototoxic differences between pairs of structurally similar PAHs, such as phenanthrene and anthracene, benzo [a] anthracene and tetracene, et cetera. Those PAHs exhibiting photoinduced toxicity were consistently within a specific range of the electronic parameter. Further modeling revealed a significant correlation between molecular electronic structure of excited-state PAHs and toxicity. The effect of substituents on photoinduced acute toxicity of PAHs also was investigated. Some substituents such as alkyl and hydroxy moieties do not significantly reduce the HOMO-LUMO gap of parent PAHs. Nitro- and chloro- moieties cause more significant variations of the HOMO-LUMO gap. It is concluded that photoinduced toxicity of PAHs is mainly dictated by the electronic structure of the parent chemicals. Evaluation of the phototoxicity of flexible aromatic molecules (α-terthienyls), generally supported the PAH models.     

Characterization of the azinomycin B biosynthetic gene cluster revealing a different iterative type I polyketide synthase for naphthoate biosynthesis

Azinomycin B is a complex natural product containing densely assembled functionalities with potent antitumor activity. Cloning and sequence analysis of the azi gene cluster revealed an iterative type I polyketide synthase (PKS) gene, five nonribosomal peptide synthetases (NRPSs) genes and numerous genes encoding the biosynthesis of unusual building blocks and tailoring steps for azinomycin B production. Characterization of AziB as a 5-methyl-naphthoic acid (NPA) synthase showed a distinct selective reduction pattern in aromatic polyketide biosynthesis governed by bacterial iterative type I PKSs. Heterologous expression established the PKS-post modification route from 5-methyl-NPA to reach the first building block 3-methoxy-5-methyl-NPA. This proposed azinomycin B biosynthetic pathway sets the stage to investigate the enzymatic mechanisms for building structurally unique and pharmaceutically important groups, including the unprecedented azabicyclic ring system and highly active epoxide moiety.     

Operon prediction based on SVM

The operon is a specific functional organization of genes found in bacterial genomes. Most genes within operons share common features. The support vector machine (SVM) approach is here used to predict operons at the genomic level. Four features were chosen as SVM input vectors: the intergenic distances, the number of common pathways, the number of conserved gene pairs and the mutual information of phylogenetic profiles. The analysis reveals that these common properties are indeed characteristic of the genes within operons and are different from that of non-operonic genes. Jackknife testing indicates that these input feature vectors, employed with RBF kernel SVM, achieve high accuracy. To validate the method, Escherichia coli K12 and Bacillus subtilis were taken as benchmark genomes of known operon structure, and the prediction results in both show that the SVM can detect operon genes in target genomes efficiently and offers a satisfactory balance between sensitivity and specificity.     

Expression patterns of photoperiod and temperature regulated heading date genes in Oryza sativa

In plants, flowering is a major biological phenomenon, which is regulated by an array of interactions occurring between biotic and abiotic factors. In our study, we have compared the expression profiles of flowering genes involved in the flowering pathway, which are influenced by conditions like photoperiod and temperature from seedling to heading developmental stages in two Oryza sativa indica varieties, viz., Zhenshan 97 and Minghui 63 using a expression network approach. Using the network expression approach, we found 17 co-expressed genes having the same expression profile pattern as three key photoperiod flowering genes Hd1, Ehd1 and Hd3a. We also demonstrated that these three co-expressed genes have a similar simulation pattern as temperature flowering genes. Based on our observations, we hypothesize that photoperiod and temperature regulate flowering pathways independently. The present study provides a basis for understanding the network of co-expressed genes involved in flowering pathway and presents a way to demonstrate the behavior of specific gene sets in specific cultivars.     

Identification of Phototoxic Polycyclic Aromatic Hydrocarbons in Sediments Through Sample Fractionation and QSAR Analysis

Abstract

The toxicity of certain polycyclic aromatic hydrocarbons (PAHs) can be greatly increased by simultaneous exposure of test organisms to ultraviolet (UV) wavelengths present in sunlight. This phenomenon, commonly termed photoinduced toxicity, had been evaluated extensively in laboratory settings where only one chemical of concern was present. However, more recent studies have demonstrated that complex mixtures of PAHs present, for example in sediments, also can cause phototoxicity to a variety of aquatic species when the samples are tested in simulated sunlight. Unfortunately, because these types of samples can contain thousands of substituted and unsubstituted PAHs it is difficult, if not impossible, to use conventional analytical techniques to identify those responsible for photoinduced toxicity. The objective of the present study was to link two powerful ecotoxicology tools, toxicity-based fractionation techniques and QSAR models, to identify phototoxic chemicals in a sediment contaminated with PAHs emanating from an oil refinery. Extensive chromatographic fractionation of pore water from the sediment, in conjunction with toxicity testing, yielded a simplified set of sample fractions containing 12 PAHs that were identified via mass spectroscopy. Evaluation of these compounds using a recently developed QSAR model revealed that, based upon their HOMO-LUMO gap energies, about half were capable of producing photoinduced toxicity. We further evaluated the phototoxic potential of the reduced set of PAHs by determining their propensity to bioaccumulate in test organisms, through calculation of octanol-water partition coefficients for the chemicals. These studies represent a novel linkage of sample fractionation methods with QSAR models for conducting an ecological risk assessment.     

Molecular Mechanisms of Anticancer Activity of N-Glycosides of Indolocarbazoles LCS-1208 and LCS-1269

Novel indolocarbazole derivatives named LCS were synthesized by our research group. Two of them were selected as the most active anticancer agents in vivo. We studied the mechanisms of anticancer activity in accordance with the previously described effects of indolocarbazoles. Cytotoxicity was estimated by MTT assay. We analyzed LCS-DNA interactions by circular dichroism in cholesteric liquid crystals and fluorescent indicator displacement assay. The effect on the activity of topoisomerases I and II was studied by DNA relaxation assay. Expression of interferon signaling target genes was estimated by RT-PCR. Chromatin remodeling was analyzed–the effect on histone H1 localization and reactivation of epigenetically silenced genes. LCS-induced change in the expression of a wide gene set was counted by means of PCR array. Our study revealed the cytotoxic activity of the compounds against 11 cancer cell lines and it was higher than in immortalized cells. Both compounds bind DNA; binding constants were estimated—LCS-1208 demonstrated higher affinity than LCS-1269; it was shown that LCS-1208 intercalates into DNA that is typical for rebeccamycin derivatives. LCS-1208 also inhibits topoisomerases I and IIα. Being a strong intercalator and topoisomerase inhibitor, LCS-1208 upregulates the expression of interferon-induced genes. In view of LCSs binding to DNA we analyzed their influence on chromatin stability and revealed that LCS-1269 displaces histone H1. Our analysis of chromatin remodeling also included a wide set of epigenetic experiments in which LCS-1269 demonstrated complex epigenetic activity. Finally, we revealed that the antitumor effect of the compounds is based not only on binding to DNA and chromatin remodeling but also on alternative mechanisms. Both compounds induce expression changes in genes involved in neoplastic transformation and target genes of the signaling pathways in cancer cells. Despite of being structurally similar, each compound has unique biological activities. The effects of LCS-1208 are associated with intercalation. The mechanisms of LCS-1269 include influence on higher levels such as chromatin remodeling and epigenetic effects.