Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, and some of its forms are progressive. This study describes the profiling of hepatic gene expression and serum protein content in patients with different subtypes of NAFLD. Liver biopsy specimens from 98 bariatric surgery patients were classified as normal, steatosis alone, steatosis with nonspecific inflammation, and nonalcoholic steatohepatitis (NASH). Microarray hybridizations were performed in triplicate and the microarray expression levels of a selected group of genes were confirmed using real-time quantitative reverse-transcriptase polymerase chain reaction. Serum protein profiles of the same patients were determined by SELDI-TOF mass spectrometry. Of 98 obese patients, 91 were diagnosed with NAFLD (12 steatosis alone, 52 steatosis with nonspecific inflammation, and 27 NASH), and 7 patients without NAFLD served as obese controls. Each group of NAFLD patients was compared with the obese controls, and 22 genes with more than twofold differences in expression levels were revealed. Proteomics analyses were performed for the same group comparisons and revealed twelve significantly different protein peaks. In conclusion, this genomic/proteomic analysis suggests differential expression of several genes and protein peaks in patients within and across the forms of NAFLD. These findings may help clarify the pathogenesis of NAFLD and identify potential targets for therapeutic intervention. (HEPATOLOGY 2005;42:665-674.)

Several differentially expressed genes in patients with NASH are related to lipid metabolism and extracellular matrix remodeling. Additionally, genes related to liver regeneration, apoptosis, and the detoxification process were differentially expressed. These findings may help clarify the molecular pathogenesis of NASH and identify potential targets for therapeutic intervention.

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Thioredoxin reductase 1 (TrxR1) is a cytosolic enzyme that plays a central role in controlling cellular redox homeostasis. TrxR1 can transduce regulatory redox signals through NADPH-dependent reduction of thioredoxin (Trx), which is able to reduce a broad spectrum of target enzymes and regulate the activity of several transcription factors (e.g., p53 and NF-κB). The TrxR1/Trx system is involved in every step of cancer biology, ranging from transformation and progression to invasion, metastasis and resistance to therapy. TrxR1 was also recently identified as one key enzyme involved in cell death induced by interferon-β (IFN-β)/all-trans retinoic acid (ATRA) anti-cancer treatment.Our study employed small interference RNA (siRNA) and microarray techniques to investigate the effect of TrxR1 silencing on gene expression in HepG2 cells. We also investigated TrxR1-mediated cell response to IFN-β/ATRA treatment.We identified TrxR1-dependent genes with functions related to several cellular processes such as apoptosis (SOX4), ubiquitination (Ubiquitin D, F-box protein 25), organization of cytoskeletal/extracellular matrix (Keratin 19, Fibronectin 1) and transport (Cystine/ Glutamate transporter).We also investigated the effect of TrxR1 siRNA on the protein profile using surface enhanced laser desorption ionization time-of-flight (SELDI-TOF) technology. Profiles confirmed significant involvement of TrxR1 in cell response to IFN-β/ATRA.

Often microarray studies require a reference to indirectly compare the samples under observation. References based on pooled RNA from different cell lines have already been described (here referred to as RNA-R), but they usually do not exhaustively represent the set of genes printed on a chip, thus requiring many adjustments during the analyses. A reference could also be generated in vitro transcribing the collection of cDNA clones printed on the microarray in use (here referred to as T3-R). Here we describe an alternative and simpler PCR-based methodology to construct a similar reference (Chip-R), and we extensively test and compare it to both RNA-R and T3-R. The use of both Chip-R and T3-R dramatically increases the number of signals on the slides and gives more reproducible results than RNA-R. Each reference preparation is also evaluated in a simple microarray experiment comparing two different RNA populations. Our results show that the introduction of a reference always interferes with the analysis. Indeed, the direct comparison is able to identify more up- or down-regulated genes than any reference-mediated analysis. However, if a reference has to be used, Chip-R and T3-R are able to guarantee more reliable results than RNA-R.

The introduction of microarray technology, which is a multiplexed hybridization-based process, allows simultaneous analysis of a large number of nucleic acid transcripts. This massively parallel analysis of a cellular genome will become essential for guiding disease diagnosis and molecular profiling of an individual patient's tumor. Nucleic acid based microarrays can be used for: gene expression profiling, single-nucleotide polymorphisms (SNPs) detection, array-comparative genomic hybridizations, comparisons of DNA-methylation status, and microRNA evaluation.A multitude of commercial platforms are available to construct and analyze the microarrays. Typical workflow for a microarray experiment is: preparation of cDNA or gDNA, array construction, hybridization, fluorescent detection, and analysis. Since many sources of variability can affect the outcome of one experiment and there is a multitide of microarray platforms available, microarray standards have been developed to provide industry-wide quality control and information related to each microarray. In this chapter, we review array construction, methodologies, and applications relevant to molecular profiling.

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