Comparative analysis of the human hepatic and adipose tissue transcriptomes during LPS-induced inflammation leads to the identification of differential biological pathways and candidate biomarkers

<p>Abstract</p> <p>Background</p> <p>Insulin resistance (IR) is accompanied by chronic low grade systemic inflammation, obesity, and deregulation of total body energy homeostasis. We induced inflammation in adipose and liver tissues <it>in vitro </it>in order to mimic inflammation <it>in vivo </it>with the aim to identify tissue-specific processes implicated in IR and to find biomarkers indicative for tissue-specific IR.</p> <p>Methods</p> <p>Human adipose and liver tissues were cultured in the absence or presence of LPS and DNA Microarray Technology was applied for their transcriptome analysis. Gene Ontology (GO), gene functional analysis, and prediction of genes encoding for secretome were performed using publicly available bioinformatics tools (DAVID, STRING, SecretomeP). The transcriptome data were validated by proteomics analysis of the inflamed adipose tissue secretome.</p> <p>Results</p> <p>LPS treatment significantly affected 667 and 483 genes in adipose and liver tissues respectively. The GO analysis revealed that during inflammation adipose tissue, compared to liver tissue, had more significantly upregulated genes, GO terms, and functional clusters related to inflammation and angiogenesis. The secretome prediction led to identification of 399 and 236 genes in adipose and liver tissue respectively. The secretomes of both tissues shared 66 genes and the remaining genes were the differential candidate biomarkers indicative for inflamed adipose or liver tissue. The transcriptome data of the inflamed adipose tissue secretome showed excellent correlation with the proteomics data.</p> <p>Conclusions</p> <p>The higher number of altered proinflammatory genes, GO processes, and genes encoding for secretome during inflammation in adipose tissue compared to liver tissue, suggests that adipose tissue is the major organ contributing to the development of systemic inflammation observed in IR. The identified tissue-specific functional clusters and biomarkers might be used in a strategy for the development of tissue-targeted treatment of insulin resistance in patients.</p

An adipocentric view of the development of insulin resistance

The adipose tissue (AT) is known for energy storage in the form of triglycerides but it is also accepted as an endocrine organ secreting numerous adipokines. These adipokines are involved in processes such as inflammation, metabolism and fertility. In obesity associated with low grade inflammation, the adipokine secretory profile may change leading to deregulation of the AT metabolism and systemic insulin resistance (IR). However, the exact role of the AT in the development of IR is not known. Therefore, our aims were ( 1) to study the role of AT in the development of systemic IR in relation to (A) nutritional overstimulation (glucose dependent insulinotropic polypeptide (GIP) signaling ), (B) clinical parameters related to obesity, (C) LPS induced inflammation and (2) to identify biomarkers specific for inflammation/IR in AT. (A) We found that excess of GIP observed in overnutrition does not serve as a link between obesity and IR. (B) Moreover, we observed that in AT of patients with decreased insulin sensitivity, metabolic genes had decreased expression, while expression of proinflamamtory genes was not altered. This suggests that metabolic alternations in AT might precede inflammation during the development of IR. (C) Additionally, we found that during inflammation both AT and liver display a unique pattern of gene/protein expression, suggesting that the tissue specific proteins could be used as biomarkers to detect tissue specific IR. AT specific biomarker related to inflammation were TNFα, MMP1, and PTX3. The liver specific biomarkers were CXCL9, CXCL3, and follistatin-like 3. Further investigations should explore the possibilities for the development of tissue specific diagnosis of IR and thereby more targeted strategies for its detection.

Fxr-deficiency in mouse liver slices aggravates cyclosporin A toxicity by upregulation of pro-inflammatory genes and downregulation of genes involved in mitochondrial functions

The transcription factor farnesoid X receptor (FXR) governs bile acid and energy homeostasis, is involved in inflammation, and has protective functions in the liver. In the present study we investigated the effect of Fxr deficiency in mouse precision cut liver slices (PCLS) exposed to a model hepatotoxicant cyclosporin A (CsA). It was anticipated that Fxr deficiency could aggravate toxicity of CsA in PCLS and pinpoint to novel genes/processes regulated by FXR

Additional file 1: of Cyclosporin A induced toxicity in mouse liver slices is only slightly aggravated by Fxr-deficiency and co-occurs with upregulation of pro-inflammatory genes and downregulation of genes involved in mitochondrial functions

Validation of known and putative Fxr target genes by q-PCR. Information about primers and functions of genes used in q-PCR to test expression of known and novel putative FXR target genes in WT-PCLS and FXRKO-PCLS exposed to endogenous FXR ligand. Numbers of Taqman assays are given in column “Taqman assay”. Information about functions of the tested genes is according to Gene Cards http://www.genecards.org/ . (PPTX 103 kb

Model steatogenic compounds (amiodarone, valproic acid, and tetracycline) alter lipid metabolism by different mechanisms in mouse liver slices [Necrotic compounds exposures]

Although drug induced steatosis represents a mild type of hepatotoxicity, it can progress into more severe non-alcoholic steatohepatitis. Current models used for safety assessment in drug development and chemical risk assessment do not accurately predict steatosis in humans. Therefore, new models need to be developed to screen compounds for steatogenic properties. We have studied the usefulness of mouse precision-cut liver slices (PCLS) as an alternative to animal testing to gain more insight into the mechanisms involved in the steatogenesis. To this end, PCLS were incubated 24h with the model steatogenic compounds, amiodarone (AMI), valproic acid (VA), and tetracycline (TET). Transcriptome analysis using DNA microarrays was used to identify genes and processes affected by these compounds. AMI and VA upregulated lipid metabolism, whereas processes associated with extracellular matrix remodelling and inflammation were downregulated. TET downregulated mitochondrial functions, lipid metabolism, and fibrosis. From the transcriptomics data it was hypothesized that all 3 compounds affect peroxisome proliferator activated-receptor (PPAR) signalling. Application of PPAR reporter assays classified AMI and VA as PPARγ and triple PPARα/ (β/δ)/γ agonist, respectively, whereas TET had no effect on any of the PPARs. Some of the differentially expressed genes were considered as potential candidate biomarkers to identify PPAR agonists (i.e. AMI and VA) or compounds impairing mitochondrial functions (i.e. TET). Finally, comparison of our findings with publicly available transcriptomics data showed that a number of processes altered in the mouse PCLS was also affected in mouse livers and human primary hepatocytes exposed to known PPAR agonists. Thus mouse PCLS are a valuable model to identify mechanisms of action of compounds altering lipid metabolism. Two sets of candidate biomarkers could be used to screen compounds interfering with lipid metabolism by different mechanisms

Model steatogenic compounds (amiodarone, valproic acid, and tetracycline) alter lipid metabolism by different mechanisms in mouse liver slices [Cholestatic compounds exposures]

Although drug induced steatosis represents a mild type of hepatotoxicity, it can progress into more severe non-alcoholic steatohepatitis. Current models used for safety assessment in drug development and chemical risk assessment do not accurately predict steatosis in humans. Therefore, new models need to be developed to screen compounds for steatogenic properties. We have studied the usefulness of mouse precision-cut liver slices (PCLS) as an alternative to animal testing to gain more insight into the mechanisms involved in the steatogenesis. To this end, PCLS were incubated 24h with the model steatogenic compounds, amiodarone (AMI), valproic acid (VA), and tetracycline (TET). Transcriptome analysis using DNA microarrays was used to identify genes and processes affected by these compounds. AMI and VA upregulated lipid metabolism, whereas processes associated with extracellular matrix remodelling and inflammation were downregulated. TET downregulated mitochondrial functions, lipid metabolism, and fibrosis. From the transcriptomics data it was hypothesized that all 3 compounds affect peroxisome proliferator activated-receptor (PPAR) signalling. Application of PPAR reporter assays classified AMI and VA as PPARγ and triple PPARα/ (β/δ)/γ agonist, respectively, whereas TET had no effect on any of the PPARs. Some of the differentially expressed genes were considered as potential candidate biomarkers to identify PPAR agonists (i.e. AMI and VA) or compounds impairing mitochondrial functions (i.e. TET). Finally, comparison of our findings with publicly available transcriptomics data showed that a number of processes altered in the mouse PCLS was also affected in mouse livers and human primary hepatocytes exposed to known PPAR agonists. Thus mouse PCLS are a valuable model to identify mechanisms of action of compounds altering lipid metabolism. Two sets of candidate biomarkers could be used to screen compounds interfering with lipid metabolism by different mechanisms