Blood gene expression profiling of an early acetaminophen response

Acetaminophen can adversely affect the liver especially when overdosed. We used whole blood as a surrogate to identify genes as potential early indicators of an acetaminophen-induced response. In a clinical study, healthy human subjects were dosed daily with 4g of either acetaminophen or placebo pills for 7 days and evaluated over the course of 14 days. Alanine aminotransferase (ALT) levels for responders to acetaminophen increased between days 4 and 9 after dosing and 12 genes were detected with expression profiles significantly altered within 24 hrs. The early responsive genes separated the subjects by class and dose period. In addition, the genes clustered patients who overdosed on acetaminophen apart from controls and also predicted the exposure classifications with 100% accuracy. The responsive genes serve as early indicators of an acetaminophen exposure and their gene expression profiles can potentially be evaluated as molecular indicators for further consideration

Discernment of possible mechanisms of hepatotoxicity via biological processes over-represented by co-expressed genes

<p>Abstract</p> <p>Background</p> <p>Hepatotoxicity is a form of liver injury caused by exposure to stressors. Genomic-based approaches have been used to detect changes in transcription in response to hepatotoxicants. However, there are no straightforward ways of using co-expressed genes anchored to a phenotype or constrained by the experimental design for discerning mechanisms of a biological response.</p> <p>Results</p> <p>Through the analysis of a gene expression dataset containing 318 liver samples from rats exposed to hepatotoxicants and leveraging alanine aminotransferase (ALT), a serum enzyme indicative of liver injury as the phenotypic marker, we identified biological processes and molecular pathways that may be associated with mechanisms of hepatotoxicity. Our analysis used an approach called Coherent Co-expression Biclustering (cc-Biclustering) for clustering a subset of genes through a coherent (consistency) measure within each group of samples representing a subset of experimental conditions. Supervised biclustering identified 87 genes co-expressed and correlated with ALT in all the samples exposed to the chemicals. None of the over-represented pathways related to liver injury. However, biclusters with subsets of samples exposed to one of the 7 hepatotoxicants, but not to a non-toxic isomer, contained co-expressed genes that represented pathways related to a stress response. Unsupervised biclustering of the data resulted in 1) four to five times more genes within the bicluster containing all the samples exposed to the chemicals, 2) biclusters with co-expression of genes that discerned 1,4 dichlorobenzene (a non-toxic isomer at low and mid doses) from the other chemicals, pathways and biological processes that underlie liver injury and 3) a bicluster with genes up-regulated in an early response to toxic exposure.</p> <p>Conclusion</p> <p>We obtained clusters of co-expressed genes that over-represented biological processes and molecular pathways related to hepatotoxicity in the rat. The mechanisms involved in the response of the liver to the exposure to 1,4-dichlorobenzene suggest non-genotoxicity whereas the exposure to the hepatotoxicants could be DNA damaging leading to overall genomic instability and activation of cell cycle check point signaling. In addition, key pathways and biological processes representative of an inflammatory response, energy production and apoptosis were impacted by the hepatotoxicant exposures that manifested liver injury in the rat.</p

The Impact of Classification of Interest on Predictive Toxicogenomics

The era of toxicogenomics has introduced a new way of monitoring the effect of environmental stressors and toxicants on biological systems via quantification of changes in gene expression. Because the liver is one of the major organs for synthesis and secretion of substances which metabolize endogenous and exogenous materials, there has been a great deal of interest in elucidating predictive and mechanistic genomic markers of hepatotoxicity. This mini-review will bring context to a limited number of toxicogenomics studies which used genomics to evaluate the transcriptional changes in blood and liver in response to acetaminophen (APAP) or other liver toxicants, but differed according to the classification of interest (COI), i.e., the partitioning of the samples a priori according to a common toxicological characteristic. The toxicogenomics studies highlighted are characterized by a classification of either no/low vs. high APAP dose exposure, none vs. observed necrosis, and severity of necrosis. The overlap or lack thereof between the gene classifiers and the modulated biological processes that are elucidated will be discussed to enhance the understanding of the effect of the particular COI model and experimental design used for prediction

Proceedings of the First International Conference on Toxicogenomics Integrated with Environmental Sciences (TIES-2007)

The First International Conference on Toxicogenomics Integrated with Environmental Sciences (TIES-2007) was held at the North Carolina State University McKimmon Center in Raleigh, North Carolina on October 25th and 26th, 2007. Based on the presentations at the conference and the commitment or interest of the presenters to contribute a manuscript of their research, we compiled this collection of articles as proceedings of the conference and an in-depth topical review of the utility of bioinformatics in the fields of toxicogenomics and environmental genomics

23rd Australian and New Zealand Academy of Management Conference (ANZAM 2009)

Despite the POLC definitions of management being sustained over a long period of time, it is surprising to find that there is a lack of empirical research to support them. This paper begins to address this issue by reporting on the initial development of a brief management scale derived from the work of Fayol- the POLC-BMS. Data were collected from a government organization with 912 observations obtained from 304 participants. Exploratory and Confirmatory factor analysis and multi-group invariant analysis was conducted and provided initial support for the POLC-BMS and the functional definitions of management: planning organising leading and controlling. Limitations of the study and suggestions for future research are detailed

Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes

BACKGROUND: Commonly employed clustering methods for analysis of gene expression data do not directly incorporate phenotypic data about the samples. Furthermore, clustering of samples with known phenotypes is typically performed in an informal fashion. The inability of clustering algorithms to incorporate biological data in the grouping process can limit proper interpretation of the data and its underlying biology. RESULTS: We present a more formal approach, the modk-prototypes algorithm, for clustering biological samples based on simultaneously considering microarray gene expression data and classes of known phenotypic variables such as clinical chemistry evaluations and histopathologic observations. The strategy involves constructing an objective function with the sum of the squared Euclidean distances for numeric microarray and clinical chemistry data and simple matching for histopathology categorical values in order to measure dissimilarity of the samples. Separate weighting terms are used for microarray, clinical chemistry and histopathology measurements to control the influence of each data domain on the clustering of the samples. The dynamic validity index for numeric data was modified with a category utility measure for determining the number of clusters in the data sets. A cluster's prototype, formed from the mean of the values for numeric features and the mode of the categorical values of all the samples in the group, is representative of the phenotype of the cluster members. The approach is shown to work well with a simulated mixed data set and two real data examples containing numeric and categorical data types. One from a heart disease study and another from acetaminophen (an analgesic) exposure in rat liver that causes centrilobular necrosis. CONCLUSION: The modk-prototypes algorithm partitioned the simulated data into clusters with samples in their respective class group and the heart disease samples into two groups (sick and buff denoting samples having pain type representative of angina and non-angina respectively) with an accuracy of 79%. This is on par with, or better than, the assignment accuracy of the heart disease samples by several well-known and successful clustering algorithms. Following modk-prototypes clustering of the acetaminophen-exposed samples, informative genes from the cluster prototypes were identified that are descriptive of, and phenotypically anchored to, levels of necrosis of the centrilobular region of the rat liver. The biological processes cell growth and/or maintenance, amine metabolism, and stress response were shown to discern between no and moderate levels of acetaminophen-induced centrilobular necrosis. The use of well-known and traditional measurements directly in the clustering provides some guarantee that the resulting clusters will be meaningfully interpretable

EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data

BACKGROUND: RNA sequencing (RNA-Seq) measures genome-wide gene expression. RNA-Seq data is count-based rendering normal distribution models for analysis inappropriate. Normalization of RNA-Seq data to transform the data has limitations which can adversely impact the analysis. Furthermore, there are a few count-based methods for analysis of RNA-Seq data but they are essentially for pairwise analysis of treatment groups or multiclasses but not pattern-based to identify co-expressed genes. RESULTS: We adapted our extracting patterns and identifying genes methodology for RNA-Seq (EPIG-Seq) count data. The software uses count-based correlation to measure similarity between genes, quasi-Poisson modelling to estimate dispersion in the data and a location parameter to indicate magnitude of differential expression. EPIG-Seq is different than any other software currently available for pattern analysis of RNA-Seq data in that EPIG-Seq 1) uses count level data and supports cases of inflated zeros, 2) identifies statistically significant clusters of genes that are co-expressed across experimental conditions, 3) takes into account dispersion in the replicate data and 4) provides reliable results even with small sample sizes. EPIG-Seq operates in two steps: 1) extract the pattern profiles from data as seeds for clustering co-expressed genes and 2) cluster the genes to the pattern seeds and compute statistical significance of the pattern of co-expressed genes. EPIG-Seq provides a table of the genes with bootstrapped p-values and profile plots of the patterns of co-expressed genes. In addition, EPIG-Seq provides a heat map and principal component dimension reduction plot of the clustered genes as visual aids. We demonstrate the utility of EPIG-Seq through the analysis of toxicogenomics and cancer data sets to identify biologically relevant co-expressed genes. EPIG-Seq is available at: sourceforge.net/projects/epig-seq. CONCLUSIONS: EPIG-Seq is unlike any other software currently available for pattern analysis of RNA-Seq count level data across experimental groups. Using the EPIG-Seq software to analyze RNA-Seq count data across biological conditions permits the ability to extract biologically meaningful co-expressed genes associated with coordinated regulation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2584-7) contains supplementary material, which is available to authorized users