Overdose of acetaminophen (APAP) causes severe centrilobular hepatic necrosis in humans and experimental animals. Here, to explore its mechanism, we administered APAP at subtoxic (150 mg/kg ip) and toxic (500 mg/kg ip) doses to overnight fasted mice. Animals were sacrificed at different time points from 15 min to 4 h postinjection. We assessed liver toxicity by plasma ALT activity and by electron microscopy. Using nylon filter arrays and RTQPCR, we performed genomics analysis in liver. We ran proteomics on liver mitochondrial subfractions using the newly developed quantitative fluorescent 2D-DIGE method (Amersham Pharmacia Biotech UK Limited). As soon as 15 min postinjection, centrilobular hepatocyte mitochondria were already slightly enlarged and GSH total content dropped by a third at top dose. GM-CSF mRNA, which is a granulocyte specific gene likely coming from resident Kupffer cells, was also induced to its maximum of 3-fold at both doses. Chaperone proteins Hsp10 and Hsp60 were readily decreased by half in mitochondria at both doses, most likely by leaking into cytoplasm. Although APAP is known as an apoptotic trigger, no apoptosis was observed at any time point. Most of the protein changes in mitochondria were present at 15 min postinjection, thus preceding most of the gene regulations. The decrease of ATP synthase subunits and beta-oxidation pathway proteins indicated a loss of energy production. As the morphology of mitochondria was also affected very early at top dose, we concluded that APAP toxicity was a direct action of its known reactive metabolite NAPQI, rather than a consequence of gene regulation. However, the latter will either worsen the toxicity or lead toward cell recovery depending on the cellular damage level.

African trypanosomes are not passively transmitted, but they undergo several rounds of differentiation and proliferation within their intermediate host, the tsetse fly. At each stage, the survival and successful replication of the parasites improve their chances of continuing the life cycle, but little is known about specific molecules that contribute to these processes. Procyclins are the major surface glycoproteins of the insect forms of Trypanosoma brucei. Six genes encode proteins with extensive glutamic acid–proline dipeptide repeats (EP in the single-letter amino acid code), and two genes encode proteins with an internal pentapeptide repeat (GPEET). To study the function of procyclins, we have generated mutants that have no EP genes and only one copy of GPEET. This last gene could not be replaced by EP procyclins, and could only be deleted once a second GPEET copy was introduced into another locus. The EP knockouts are morphologically indistinguishable from the parental strain, but their ability to establish a heavy infection in the insect midgut is severely compromised; this phenotype can be reversed by the reintroduction of a single, highly expressed EP gene. These results suggest that the two types of procyclin have different roles, and that the EP form, while not required in culture, is important for survival in the fly.

The surface of Trypanosoma brucei brucei insect forms is covered by an invariant protein coat consisting of procyclins. There are six or seven procyclin genes that encode unusual proteins with extensive tandem repeat units of glutamic acid (E) and proline (P) (referred to as EP repeats), and two genes that encode proteins with internal pentapeptide (GPEET) repeats. Although the EP forms of procyclins have been isolated and characterized by several laboratories, evidence for GPEET procyclin has largely been confined to the expression of its mRNA. To characterize GPEET procyclin further, we isolated the protein from T. b.

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Male rats were treated with various model compounds or the appropriate vehicle controls. Most substances were either well-known hepatotoxicants or showed hepatotoxicity during preclinical testing. The aim of the present study was to determine if biological samples from rats treated with various compounds can be classified based on gene expression profiles. In addition to gene expression analysis using microarrays, a complete serum chemistry profile and liver and kidney histopathology were performed. We analyzed hepatic gene expression profiles using a supervised learning method (support vector machines; SVMs) to generate classification rules and combined this with recursive feature elimination to improve classification performance and to identify a compact subset of probe sets with potential use as biomarkers. Two different SVM algorithms were tested, and the models obtained were validated with a compound-based external cross-validation approach. Our predictive models were able to discriminate between hepatotoxic and nonhepatotoxic compounds. Furthermore, they predicted the correct class of hepatotoxicant in most cases. We provide an example showing that a predictive model built on transcript profiles from one rat strain can successfully classify profiles from another rat strain. In addition, we demonstrate that the predictive models identify nonresponders and are able to discriminate between gene changes related to pharmacology and toxicity. This work confirms the hypothesis that compound classification based on gene expression data is feasible.