Nonalcoholic fatty liver disease (NAFLD) is a heterogeneous disease driven by genetic and environmental factors. MicroRNAs (miRNAs) serve as pleiotropic post‐transcriptional regulators of cellular pathways. Although several miRNAs have been associated with NAFLD and fibrosis, there are limited studies in humans examining their differential association with pathogenic factors or histological features of NAFLD. We examined the differential relationships of five of the best‐described circulating microRNAs (miR‐34a, miR‐122, miR‐191, miR‐192, and miR‐200a) with histological features and pathogenic factors of NAFLD. A cross‐sectional study was conducted to examine the relationship between relative levels of circulating microRNAs standardized by z‐scores and histological features of NAFLD, common NAFLD genetic polymorphisms, and insulin resistance measured by the enhanced lipoprotein insulin resistance index in 132 subjects with biopsy‐proven NAFLD. We found that miR‐34a, miR‐122, miR‐192, miR‐200a, but not miR‐191, strongly correlate with fibrosis in NAFLD by increases of 0.20 to 0.40 SD (P < 0.005) with each stage of fibrosis. In multivariate analysis, miR‐34a, miR‐122, and miR‐192 levels are independently associated with hepatic steatosis and fibrosis, but not lobular inflammation or ballooning degeneration, whereas miR‐200a is only associated with fibrosis. Among the four miRNAs, miR‐34a, miR‐122, and miR‐192 are associated with pathogenic factors of NAFLD, including insulin resistance measured by eLP‐IR, patatin‐like phospholipase domain containing 3 I148M, and transmembrane 6 superfamily 2 (TM6SF2) E167K polymorphisms. In contrast, miR‐200a is only associated with the TM6SF2 E167K variant. Finally, miR‐34a has the strongest predictive value for various stages of fibrosis, with C‐statistic approximates–combined predictive score for miRNAs. Conclusion: miR‐34a, miR‐122, miR‐192, and miR‐200a demonstrate strong associations with NAFLD severity by histology, but differential associations with pathogenic factors.

Disclosures: L.I. Silverman: 3A; Discgenics, Inc.. 4; Discgenics, Inc. G. Dulatova: 3A; Discgenics, Inc.. 4; Discgenics, Inc. T. Tandeski: 3A; Discgenics, Inc. J. Ward: 3A; Discgenics, Inc.. S. Chintalacharuvu: None. T. Wolff: None. J. Nunes: None. A. Howard: None. K. Howard: None. M. Parrish: None. K. Foley: 1; Medtronic. 3B; Medtronic, ArthroCare, Nuvasive, Discgenics,. 4; Discgenics, Truevision.

al., 2009). Low complexity, no known post-processing modifications, simple detection and amplification methods, tissue-specific expression profiles, and sequence conservation between humans and model organisms make extracellular miRNAs ideal candidates for genomic biomarkers to reflect and study various physiopathological conditions of the body.Ideally, the most clinically powerful information would come directly from the tissue of interest.To understand cancer, one must look at malignant cells, much as one must analyze brain tissue to understand the complexities of neuroscience.However, many of these tissues are difficult to access or impossible to reach without potential injury to the patient.Alternative, or "surrogate", tissues can provide a means of assessing the genomic changes in the tissue of interest, without fear of harming the donor.For example, surrogate tissues may contact the tissue of interest and retain sloughed cells, secreted molecules or the contents of dying cells.While these molecular signals may not exactly mirror the tissue of origin, in many cases they are reproducible and can clearly point to underlying biology.Clinical material suitable for biomarker testing can be divided into 2 different types.The first are those that require minimally invasive procedures to obtain.This type includes blood, cerebrospinal fluid, tissue biopsies and so on.Type 2 tissues are those that can be obtained without any invasive means: hair, saliva, tears, epidermal cells, urine, etc.In some cases, acquisition of the material may not be passive.Examples of Type 1 and Type 2 samples are listed in Table 1.

Copyright information: Taken from Optimization of oligonucleotide arrays and RNA amplification protocols for analysis of transcript structure and alternative splicingGenome Biology 2003;4(10):R66-R66.Published online 19 Sep 2003PMCID:PMC328455.Copyright © 2003 Castle et al., licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. 3' splice sites (5' exon edge); 5' splice sites (3' exon edge). These conditions allow accurate prediction of splice sites. The dashed line marks actual splice sites, by which the exons were aligned, and the small circles mark the predicted splice-site locations for each exon after convolution with the Haar wavelet (see Materials and methods for details). Although the short exon-10 sequence contains several AT-rich regions within the exon which hybridize poorly and create a dip in the intensity profile (lower magenta curve), the splice-site predictions for exon 10 are consistent with the others.

BACKGROUND:Genome-wide gene expression profiling of whole blood is an attractive method for discovery of biomarkers due to its non-invasiveness, simple clinical site processing and rich biological content. Except for a few successes, this technology has not yet matured enough to reach its full potential of identifying biomarkers useful for clinical prognostic and diagnostic applications or in monitoring patient response to therapeutic intervention. A variety of technical problems have hampered efforts to utilize this technology for identification of biomarkers. One significant hurdle has been the high and variable concentrations of globin transcripts in whole blood total RNA potentially resulting in non-specific probe binding and high background. In this study, we investigated and quantified the power of three whole blood profiling approaches to detect meaningful biological expression patterns.METHODS:To compare and quantify the impact of different mitigation technologies, we used a globin transcript spike-in strategy to synthetically generate a globin-induced signature and then mitigate it with the three different technologies. Biological differences, in globin transcript spiked samples, were modeled by supplementing with either 1% of liver or 1% brain total RNA. In order to demonstrate the biological utility of a robust globin artifact mitigation strategy in biomarker discovery, we treated whole blood ex vivo with suberoylanilide hydroxamic acid (SAHA) and compared the overlap between the obtained signatures and signatures of a known biomarker derived from SAHA-treated cell lines and PBMCs of SAHA-treated patients.RESULTS:We found cDNA hybridization targets detect at least 20 times more specific differentially expressed signatures (2597) between 1% liver and 1% brain in globin-supplemented samples than the PNA (117) or no treatment (97) method at FDR = 10% and p-value < 3x10-3. In addition, we found that the ex vivo derived gene expression profile was highly concordant with that of the previously identified SAHA pharmacodynamic biomarkers.CONCLUSIONS:We conclude that an amplification method for gene expression profiling employing cDNA targets effectively mitigates the negative impact on data of abundant globin transcripts and greatly improves the ability to identify relevant gene expression based pharmacodynamic biomarkers from whole blood.

BACKGROUND:Obesity is a particularly complex disease that at least partially involves genetic and environmental perturbations to gene-networks connecting the hypothalamus and several metabolic tissues, resulting in an energy imbalance at the systems level.RESULTS:To provide an inter-tissue view of obesity with respect to molecular states that are associated with physiological states, we developed a framework for constructing tissue-to-tissue coexpression networks between genes in the hypothalamus, liver or adipose tissue. These networks have a scale-free architecture and are strikingly independent of gene-gene coexpression networks that are constructed from more standard analyses of single tissues. This is the first systematic effort to study inter-tissue relationships and highlights genes in the hypothalamus that act as information relays in the control of peripheral tissues in obese mice. The subnetworks identified as specific to tissue-to-tissue interactions are enriched in genes that have obesity-relevant biological functions such as circadian rhythm, energy balance, stress response, or immune response.CONCLUSIONS:Tissue-to-tissue networks enable the identification of disease-specific genes that respond to changes induced by different tissues and they also provide unique details regarding candidate genes for obesity that are identified in genome-wide association studies. Identifying such genes from single tissue analyses would be difficult or impossible.

ABSTRACT Endoplasmic reticulum-associated degradation (ERAD) mediates the turnover of short-lived and misfolded proteins in the ER membrane or lumen. In spite of its important role, only subtle growth phenotypes have been associated with defects in ERAD. We have discovered that the ERAD proteins Ubc7 (Qri8), Cue1, and Doa10 (Ssm4) are required for growth of yeast that express high levels of the sterol biosynthetic enzyme, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR). Interestingly, the observed growth defect was exacerbated at low temperatures, producing an HMGR-dependent cold sensitivity. Yeast strains lacking UBC7 , CUE1 , or DOA10 also assembled aberrant karmellae (ordered arrays of membranes surrounding the nucleus that assemble when HMGR is expressed at high levels). However, rather than reflecting the accumulation of abnormal karmellae, the cold sensitivity of these ERAD mutants was due to increased HMGR catalytic activity. Mutations that compromise proteasomal function also resulted in cold-sensitive growth of yeast with elevated HMGR, suggesting that improper degradation of ERAD targets might be responsible for the observed cold-sensitive phenotype. However, the essential ERAD targets were not the yeast HMGR enzymes themselves. The sterol metabolite profile of ubc7Δ cells was altered relative to that of wild-type cells. Since sterol levels are known to regulate membrane fluidity, the viability of ERAD mutants expressing normal levels of HMGR was examined at low temperatures. Cells lacking UBC7 , CUE1 , or DOA10 were cold sensitive, suggesting that these ERAD proteins have a role in cold adaptation, perhaps through effects on sterol biosynthesis.

Lithium inhibits inositol monophosphatase at therapeutically effective concentrations, and it has been hypothesized that depletion of brain inositol levels is an important chemical alteration for lithium's therapeutic efficacy in bipolar disorder. We have employed adult rat cortical slices as a model to investigate the gene regulatory consequences of inositol depletion effected by lithium using cytidine diphosphoryl-diacylglycerol as a functionally relevant biochemical marker to define treatment conditions. Genes coding for the neuropeptide hormone pituitary adenylate cyclase activating polypeptide (PACAP) and the enzyme that processes PACAP's precursor to the mature form, peptidylglycine alpha-amidating monooxygenase, were upregulated by inositol depletion. Previous work has shown that PACAP can increase tyrosine hydroxylase (TH) activity and dopamine release, and we found that the gene for GTP cyclohydrolase, which effectively regulates TH through synthesis of tetrahydrobiopterin, was also upregulated by inositol depletion. We propose that modulation of brain PACAP signaling might represent a new opportunity in the treatment of bipolar disorder.

Microarrays offer a high-resolution means for monitoring pre-mRNA splicing on a genomic scale. We have developed a novel, unbiased amplification protocol that permits labeling of entire transcripts. Also, hybridization conditions, probe characteristics, and analysis algorithms were optimized for detection of exons, exon-intron edges, and exon junctions. These optimized protocols can be used to detect small variations and isoform mixtures, map the tissue specificity of known human alternative isoforms, and provide a robust, scalable platform for high-throughput discovery of alternative splicing.

Increased levels of HMG-CoA reductase induce cell type- and isozyme-specific proliferation of the endoplasmic reticulum. In yeast, the ER proliferations induced by Hmg1p consist of nuclear-associated stacks of smooth ER membranes known as karmellae. To identify genes required for karmellae assembly, we compared the composition of populations of homozygous diploid S. cerevisiae deletion mutants following 20 generations of growth with and without karmellae. Using an initial population of 1,557 deletion mutants, 120 potential mutants were identified as a result of three independent experiments. Each experiment produced a largely non-overlapping set of potential mutants, suggesting that differences in specific growth conditions could be used to maximize the comprehensiveness of similar parallel analysis screens. Only two genes, UBC7 and YAL011W, were identified in all three experiments. Subsequent analysis of individual mutant strains confirmed that each experiment was identifying valid mutations, based on the mutant's sensitivity to elevated HMG-CoA reductase and inability to assemble normal karmellae. The largest class of HMG-CoA reductase-sensitive mutations was a subset of genes that are involved in chromatin structure and transcriptional regulation, suggesting that karmellae assembly requires changes in transcription or that the presence of karmellae may interfere with normal transcriptional regulation.

BACKGROUNDA more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy.METHODSUsing microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses.RESULTSAmong the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome.CONCLUSIONSThe gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

In yeast, increased levels of the sterol biosynthetic enzyme, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase isozyme, Hmg1p, induce assembly of nuclear-associated ER membranes called karmellae. To identify additional genes involved in karmellae assembly, we screened temperature-sensitive mutants for karmellae assembly defects. Two independently isolated, temperature-sensitive strains that were also defective for karmellae biogenesis carried mutations in VPS16, a gene involved in vacuolar protein sorting. Karmellae biogenesis was defective in all 13 other vacuole biogenesis mutants tested, although the severity of the karmellae assembly defect varied depending on the particular mutation. The hypersensitivity of 14 vacuole biogenesis mutants to tunicamycin was well correlated with pronounced defects in karmellae assembly, suggesting that the karmellae assembly defect reflected alteration of ER structure or function. Consistent with this hypothesis, seven of eight mutations causing defects in secretion also affected karmellae assembly. However, the vacuole biogenesis mutants were able to proliferate their ER in response to Hmg2p, indicating that the mutants did not have a global defect in the process of ER biogenesis.

Increased expression of certain ER membrane proteins leads to biogenesis of novel ER membrane arrays. These structures provide models in which to explore the mechanisms by which cells control the size and organization of organelles in response to changing physiological demands. In yeast, elevated levels of HMG-CoA reductase induce ER arrays known as karmellae. Cox and co-workers (1997) discovered that karmellae assembly is toxic to ire1 mutants. These mutants are unable to initiate the unfolded protein response, which enables cells to adjust levels of ER chaperones in response to stresses. We sought to determine whether the karmellae-dependent death of ire1 mutants was due to karmellae assembly or to increased levels of HMG-CoA reductase activity. Unexpectedly, we found that ire1 cells could assemble normal levels of karmellae that were structurally identical to those of wild-type cells. In addition, karmellae assembly did not itself induce the unfolded protein response. Certain ire1 strains produced significant numbers of transformants that were unable to utilize galactose as sole carbon source. These results suggest that the karmellae-dependent death of certain ire1 strains may simply reflect their inability to grow on galactose.

In all eukaryotic cells that have been examined, specific membrane arrays are induced in response to increased levels of the ER membrane protein, HMG-CoA reductase. Analysis of these inducible membranes has the potential to reveal basic insights into general membrane assembly. Yeast express two HMG-CoA reductase isozymes, and each isozyme induces a morphologically distinct proliferation of the endoplasmic reticulum. The isozyme encoded by HMG1 induces karmellae, which are long stacks of membranes that partially enclose the nucleus. In contrast, the isozyme encoded by HMG2 induces short stacks of membrane that may be associated with the nucleus, but are frequently present at the cell periphery. To understand the molecular nature of the different cellular responses to Hmg1p and Hmg2p, we mapped the region of Hmg1p that is needed for karmellae assembly. For this analysis, a series of exchange alleles was examined in which a portion of the Hmg2p membrane domain was replaced with the corresponding Hmg1p sequences. Results of this analysis indicated that the ER lumenal loop between predicted transmembrane domains 6 and 7 was both necessary and sufficient for karmellae assembly, when present in the context of an HMG-CoA reductase membrane domain. Immunoblotting experiments ruled out the simple possibility that differences in the amounts of the various chimeric HMG-CoA reductase proteins was responsible for the altered cellular responses. Our results are consistent with the hypothesis that each yeast isozyme induces or organizes a qualitatively different organization of ER membrane.