As part of the Food and Drug Administration led MicroArray Quality Control Phase-II (MAQC-II) effort to develop and validate predictive signatures, we used gene expression data acquired from the blood of rats chemically stressed to identify gene- and pathway-based indicators of liver necrosis. Although others have used blood to either predict the exposure of a single drug 1
or to survey a compendium of hepatotoxicants 2, 6
, we took a more formal and comprehensive approach to evaluate the genomic indicators in blood for prediction of liver necrosis across a variety of chemical compounds that target the liver. Our work is the first demonstration of the usefulness of blood as a surrogate tissue to extract genomic indicators for predicting the manifestation of necrosis in the liver based on hepatocellular stress from a drug, therapeutic or across a wide variety of hepatotoxicants. Importantly, the findings are verified by an independent data set comprised of gene expression data from samples stressed by compounds with different characteristics. Acetaminophen is a therapeutic agent while carbon tetrachloride and allyl alcohol are compounds with no pharmacologic benefit. Furthermore, while as hepatotoxicants acetaminophen and carbon tetrachloride require more P450 isoenzyme for bioactivation, allyl alcohol differs in that it requires higher oxygen levels for oxygen-dependent bioactivation 18
. Despite these salient differences, from our analysis, the results demonstrate that using the genomic indicators in blood to predict liver necrosis is somewhat of a general phenomenon and is presumably independent of the choice of hepatotoxicant, the extent of chemical stress or the use as a therapeutic.
Our findings are consistent with the role of organ-to-organ communication that has been previously reported for acetaminophen-induced toxicity 19
and the role of transmigration of leukocytes into the liver vasculature by inflammatory mediators at the onset of hepatotoxicity contributing to acute liver injury 20, 21
. In contrast to the blood-to-liver prediction, the gene and pathway signatures from the liver to predict the blood were not as highly predictive as those acquired from the blood to predict the liver ( and ). A possible reason for this phenomenon may be the fact that the dynamic range and overall changes in gene expression that are statistically significant in the liver are quite different and much greater than what is detected in the blood (Supplementary materials Figure 3
). Another possibility could be that many of the animals in this study had lesions other than just necrosis or that the phenotypic response that the classifier captured was for a general necrotic lesion whereas the end-point for the validation data set samples was for a specific form of necrosis. An area for further investigation is the determination of a more complex classification based on the histopathology data to predict a composite representation of liver injury which encompasses many end-points.
The genes and pathways acquired from the blood expression data that comprised the classifiers for prediction of necrosis of the liver represent biological mechanisms related to a severe immune response, induction of apoptosis, targeting of the mitochondria and angiogenesis. These mechanisms agree with the current literature on drug-related hepatotoxicity 22–24
but the latter may be related to the formation of new blood vessels during the regeneration of the liver to compensate for the loss of hepatocytes. Interestingly, one of our top ranking pathway-based classifiers for predicting necrosis in the liver points to the Toll-like receptor (TLR) signaling pathway leading to a cell proinflammatory response. TLRs are a class of single membrane-spanning non-catalytic receptors that recognize structurally conserved molecules derived from microbes and activate immune cell responses. Recently, Yohe et al
reported the role of TLR4
in acetaminophen-mediated hepatotoxicity in endotoxin-responsive mice.
We found that the genes for interleukin 1 receptor-type II (Il1r2
), chemokine (c-c motif) ligand 2 (Ccl2
) and chemokine (c-x-c motif) ligand 10 (Cxcl10
) were most frequently selected for prediction among all the classifiers built and six of the nine most frequent genes have blood gene expression profiles that separated the liver samples fairly well based on the presence or absence of necrosis (). Two pathways with high predictability, the regulation of apoptosis by mitochondrial proteins and the anti-apoptotic TNFs
, have three genes that overlap: B-cell CLL/Lymphoma 2 (Bcl2
), TNF receptor superfamily member 1a (Tnfrsf1a
) and Bcl2
-related protein A1 (Bcl2a1
). The latter encodes a member of the Bcl2
protein family. The proteins of this family form hetero- or homodimers and act as anti- and pro-apoptotic regulators. Coincidently, the biological processes that these predictor genes represent match several of the enriched Gene Ontology (GO) categories and KEGG pathways from the biclusters of up- and down-regulated (co-expressed) genes from the Compendium data set liver samples (Supplementary materials Figure 4
, Supplementary materials Table 3
and Supplementary file C).
In order to assess the possible mechanisms that the predictor genes contribute to the liver injury phenotype, we built a direct interaction (DI) network using signature genes as seed nodes and the MetaCore collection of over 300,000 curated protein interactions as the source of edges and connected genes (). The network revealed that nine signature genes are commonly regulated by 10 transcription factors (TFs) with Ccl2 regulated by seven TFs and S100A9 by six. The downstream targets of the signature genes belong to many of the biological processes ranked highly significant in enrichment and are involved in liver injury: inflammation, extracellular matrix remodeling and apoptosis.
Network analysis of the upstream and downstream regulation. The nine genes (marked with solid circles) are direct targets of 10 transcription factors. The downstream genes belong to three processes implicated in liver injury.
Although blood serum levels of alanine aminotransferase (ALT) have been historically used as a gold standard clinical chemistry marker of liver injury, the enzyme measurements do not always correlate well with histopathologic data 26
(i.e., the true nature and extent of the liver damage is not always proportional to the elevation in the serum enzyme activity 27
). Recently, a study was performed that measured the level of gene expression of haptoglobin (Hp
) in blood and compared it to serum ALT as a marker of liver damage 28
. The group found that Hp
gene expression was more sensitive as an indicator of liver damage. Other genes in our predictor list play a role in inflammation. For instance, the chemokine Cxcl10
is a marker of inflammation found in many models of inflammatory liver diseases 29, 30
and is thought to be mainly expressed by hepatocytes but also by macrophages and stellate cells 31
make up a complex found in leukocytes that appears to be an anti-inflammatory protein 32
. Finally, matrix metallopeptidase 8 (Mmp8
), a neutrophil collagenease, is involved in the control of the polymorphonuclear cell feed-forward mechanism in an inflammatory process 33
and others have correlated peripheral blood expression of Mmp8
as a marker of idiopathic pulmonary fibrosis 34
Genome-wide expression profiling using microarray technologies provides a practical way of surveying the global transcriptional response of a stressor on biological systems35
. Using this system to assay peripheral blood for the identification of novel biomarkers of drug-induced liver injury (DILI) is intriguing and may be a useful diagnostic test in the near future 36
. Other assay systems have been proposed or used as a model for identifying serum biomarkers as candidates for liver injury 3, 37–42
. Our results strongly support the claim that genomic indicators in the blood can serve as biomarkers of necrosis as a form of a chemically-stressed adverse effect on the rat liver and give credence to the acquisition of gene expression signatures from minimal invasive biomaterial sources potentially for diagnostic testing of DILI in humans.