Inverse gene expression patterns for macrophage activating hepatotoxicants and peroxisome proliferators in rat liver

Abstract Macrophage activation contributes to adverse effects produced by a number of hepatotoxic compounds. Transcriptional profiles elicited by two macrophage activators, LPS and zymosan A, were compared to those produced by 100 paradigm compounds (mostly hepatotoxicants) using cDNA microarrays. Several hepatotoxicants previously reported to activate liver macrophages produced transcriptional responses similar to LPS and zymosan, and these were used to construct a gene signature profile for macrophage activators in the liver. Measurement of cytokine mRNAs in the same liver samples by RT-PCR independently confirmed that these compounds are associated with macrophage activation. In addition to expected effects on acute phase proteins and metabolic pathways that are regulated by LPS and inflammation, a strong induction was observed for many endoplasmic reticulum-associated stress/chaperone proteins. Additionally, many genes in our macrophage activator signature profile were well-characterized PPARa-induced genes which were repressed by macrophage activators. A shared gene signature profile for peroxisome proliferators was determined using a training set of clofibrate, WY 14643, diethylhexylphthalate, diisononylphthalate, perfluorodecanoic acid, perfluoroheptanoic acid, and perfluorooctanoic acid. The signature profile included macrophage activator-induced genes that were repressed by peroxisome proliferators. NSAIDs comprised an interesting pharmacological class in that some compounds, notably diflunisal, co-clustered with peroxisome proliferators whereas several others co-clustered with macrophage activators, possibly due to endotoxin exposure secondary to their adverse effects on the gastrointestinal system. While much of these data confirmed findings from the literature, the transcriptional patterns detected using this toxicogenomics approach showed relationships between genes and biological pathways requiring complex analysis to be discerned.

GPR139, an Orphan Receptor Highly Enriched in the Habenula and Septum, Is Activated by the Essential Amino Acids L-Tryptophan and L-Phenylalanine s

ABSTRACT GPR139 is an orphan G-protein-coupled receptor expressed in the central nervous system. To identify its physiologic ligand, we measured GPR139 receptor activity from recombinant cells after treatment with amino acids, orphan ligands, serum, and tissue extracts. GPR139 activity was measured using guanosine 59-O-(3-[ Sequence alignment revealed that GPR139 is highly conserved across species, and RNA sequencing studies of rat and human tissues indicated its exclusive expression in the brain and pituitary gland. Immunohistochemical analysis showed specific expression of the receptor in circumventricular regions of the habenula and septum in mice. Together, these findings suggest that L-Trp and L-Phe are candidate physiologic ligands for GPR139, and we hypothesize that this receptor may act as a sensor to detect dynamic changes of L-Trp and L-Phe in the brain

Mutational Analysis of the Transcription Factor IIIB-DNA Target of Ty3 Retroelement Integration*

The Ty3 retrovirus-like element inserts preferentially at the transcription initiation sites of genes transcribed by RNA polymerase III. The requirements for transcription factor (TF) IIIC and TFIIIB in Ty3 integration into the two initiation sites of the U6 gene carried on pU6LboxB were previously examined. Ty3 integrates at low but detectable frequencies in the presence of TFIIIB subunits Brf1 and TATA-binding protein. Integration increases in the presence of the third subunit, Bdp1. TFIIIC is not essential, but the presence of TFIIIC specifies an orientation of TFIIIB for transcriptional initiation and directs integration to the U6 gene-proximal initiation site. In the current study, recombinant wild type TATA-binding protein, wild type and mutant Brf1, and Bdp1 proteins and highly purified TFIIIC were used to investigate the roles of specific protein domains in Ty3 integration. The amino-terminal half of Brf1, which contains a TFIIB-like repeat, contributed more strongly than the carboxyl-terminal half of Brf1 to Ty3 targeting. Each half of Bdp1 split at amino acid 352 enhanced integration. In the presence of TFIIIB and TFIIIC, the pattern of integration extended downstream by several base pairs compared with the pattern observed in vitro in the absence of TFIIIC and in vivo, suggesting that TFIIIC may not be present on genes targeted by Ty3 in vivo. Mutations in Bdp1 that affect its interaction with TFIIIC resulted in TFIIIC-independent patterns of Ty3 integration. Brf1 zinc ribbon and Bdp1 internal deletion mutants that are competent for polymerase III recruitment but defective in promoter opening were competent for Ty3 integration irrespective of the state of DNA supercoiling. These results extend the similarities between the TFIIIB domains required for transcription and Ty3 integration and also reveal requirements that are specific to transcription

Mutational analysis of the transcription factor IIIB-DNA target of Ty3 retroelement integration.

The Ty3 retrovirus-like element inserts preferentially at the transcription initiation sites of genes transcribed by RNA polymerase III. The requirements for transcription factor (TF) IIIC and TFIIIB in Ty3 integration into the two initiation sites of the U6 gene carried on pU6LboxB were previously examined. Ty3 integrates at low but detectable frequencies in the presence of TFIIIB subunits Brf1 and TATA-binding protein. Integration increases in the presence of the third subunit, Bdp1. TFIIIC is not essential, but the presence of TFIIIC specifies an orientation of TFIIIB for transcriptional initiation and directs integration to the U6 gene-proximal initiation site. In the current study, recombinant wild type TATA-binding protein, wild type and mutant Brf1, and Bdp1 proteins and highly purified TFIIIC were used to investigate the roles of specific protein domains in Ty3 integration. The amino-terminal half of Brf1, which contains a TFIIB-like repeat, contributed more strongly than the carboxyl-terminal half of Brf1 to Ty3 targeting. Each half of Bdp1 split at amino acid 352 enhanced integration. In the presence of TFIIIB and TFIIIC, the pattern of integration extended downstream by several base pairs compared with the pattern observed in vitro in the absence of TFIIIC and in vivo, suggesting that TFIIIC may not be present on genes targeted by Ty3 in vivo. Mutations in Bdp1 that affect its interaction with TFIIIC resulted in TFIIIC-independent patterns of Ty3 integration. Brf1 zinc ribbon and Bdp1 internal deletion mutants that are competent for polymerase III recruitment but defective in promoter opening were competent for Ty3 integration irrespective of the state of DNA supercoiling. These results extend the similarities between the TFIIIB domains required for transcription and Ty3 integration and also reveal requirements that are specific to transcription

Additional file 1: Figure S1. of Metabolomic biosignature differentiates melancholic depressive patients from healthy controls

Effect of storage time correction. Figure S2. The distributions of original and imputed metabolite features: Glyoxylate ratio, Caffeine ratio, Elaidicacid ratio and Indole 3 propionic acid ratio. Figure S3. QQ plots of the p-values of the two-sample t-tests on raw features, k-means and hierarchical clustering representatives. Table S1. Classification performance obtained by Random Forest on metabolite data using the standard undersampling technique. Table S2. Classification performance obtained by Support Vector Machines on metabolite data using the standard undersampling technique. Table S3. Top 30 individual metabolic features selected by different feature selection methods. Table S4. Top 30 individual metabolic features selected by different feature selection methods. Table S5. Top cluster-representatives (K-means) selected by different feature selection methods. Table S6. Top cluster-representatives (hierarchical clustering) selected by different feature selection methods. Table S7. Top cluster-representatives (hierarchical clustering) selected by different feature selection methods. (DOCX 812 kb