Variation of butyrate production in the gut microbiome in type 2 diabetes patients.

BACKGROUND: Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student's t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive. RESULTS: By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection. CONCLUSIONS: With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease.

Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci.

BACKGROUND: The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data. RESULTS: For the identification of genes underlying known heterotic QTL, we made use of tight associations between gene expression pattern and the trait of interest, identified by correlation analyses. Using this approach genes strongly associated with heterosis for GY were discovered to be clustered in pericentromeric regions of the complex maize genome. This suggests that expression differences of sequences in recombination-suppressed regions are important in the establishment of heterosis for GY in F1 hybrids and also in the conservation of heterosis for GY across genotypes. Importantly functional analysis of heterosis-associated genes from these genomic regions revealed over-representation of a number of functional classes, identifying key processes contributing to heterosis for GY. Based on the finding that the majority of the analyzed heterosis-associated genes were addtitively expressed, we propose a model referring to the influence of cis-regulatory variation on heterosis for GY by the compensation of fixed detrimental expression levels in parents. CONCLUSIONS: The study highlights the utility of a meta-analysis approach that integrates phenotypic and multi-level molecular data to unravel complex traits in plants. It provides prospects for the identification of genes relevant for QTL, and also suggests a model for the potential role of additive expression in the formation and conservation of heterosis for GY via dominant, multigenic quantitative trait loci. Our findings contribute to a deeper understanding of the multifactorial phenomenon of heterosis, and thus to the breeding of new high yielding varieties.

Alternative splicing and genome complexity.

Alternative splicing of mRNA allows many gene products with different functions to be produced from a single coding sequence. It has recently been proposed as a mechanism by which higher-order diversity is generated. Here we show, using large-scale expressed sequence tag (EST) analysis, that among seven different eukaryotes the amount of alternative splicing is comparable, with no large differences between humans and other animals.

SNP microarray analyses reveal copy number alterations and progressive genome reorganization during tumor development in SVT/t driven mice breast cancer.

BACKGROUND: Tumor development is known to be a stepwise process involving dynamic changes that affect cellular integrity and cellular behavior. This complex interaction between genomic organization and gene, as well as protein expression is not yet fully understood. Tumor characterization by gene expression analyses is not sufficient, since expression levels are only available as a snapshot of the cell status. So far, research has mainly focused on gene expression profiling or alterations in oncogenes, even though DNA microarray platforms would allow for high-throughput analyses of copy number alterations (CNAs). METHODS: We analyzed DNA from mouse mammary gland epithelial cells using the Affymetrix Mouse Diversity Genotyping array (MOUSEDIVm520650) and calculated the CNAs. Segmental copy number alterations were computed based on the probeset CNAs using the circular binary segmentation algorithm. Motif search was performed in breakpoint regions (inter-segment regions) with the MEME suite to identify common motif sequences. RESULTS: Here we present a four stage mouse model addressing copy number alterations in tumorigenesis. No considerable changes in CNA were identified for non-transgenic mice, but a stepwise increase in CNA was found during tumor development. The segmental copy number alteration revealed informative chromosomal fragmentation patterns. In inter-segment regions (hypothetical breakpoint sides) unique motifs were found. CONCLUSIONS: Our analyses suggest genome reorganization as a stepwise process that involves amplifications and deletions of chromosomal regions. We conclude from distinctive fragmentation patterns that conserved as well as individual breakpoints exist which promote tumorigenesis.

Selective regain of egfr gene copies in CD44+/CD24-/low breast cancer cellular model MDA-MB-468.

BACKGROUND: Increased transcription of oncogenes like the epidermal growth factor receptor (EGFR) is frequently caused by amplification of the whole gene or at least of regulatory sequences. Aim of this study was to pinpoint mechanistic parameters occurring during egfr copy number gains leading to a stable EGFR overexpression and high sensitivity to extracellular signalling. A deeper understanding of those marker events might improve early diagnosis of cancer in suspect lesions, early detection of cancer progression and the prediction of egfr targeted therapies. METHODS: The basal-like/stemness type breast cancer cell line subpopulation MDA-MB-468 CD44high/CD24-/low, carrying high egfr amplifications, was chosen as a model system in this study. Subclones of the heterogeneous cell line expressing low and high EGF receptor densities were isolated by cell sorting. Genomic profiling was carried out for these by means of SNP array profiling, qPCR and FISH. Cell cycle analysis was performed using the BrdU quenching technique. RESULTS: Low and high EGFR expressing MDA-MB-468 CD44+/CD24-/low subpopulations separated by cell sorting showed intermediate and high copy numbers of egfr, respectively. However, during cell culture an increase solely for egfr gene copy numbers in the intermediate subpopulation occurred. This shift was based on the formation of new cells which regained egfr gene copies. By two parametric cell cycle analysis clonal effects mediated through growth advantage of cells bearing higher egfr gene copy numbers could most likely be excluded for being the driving force. Subsequently, the detection of a fragile site distal to the egfr gene, sustaining uncapped telomere-less chromosomal ends, the ladder-like structure of the intrachromosomal egfr amplification and a broader range of egfr copy numbers support the assumption that dynamic chromosomal rearrangements, like breakage-fusion-bridge-cycles other than proliferation drive the gain of egfr copies. CONCLUSION: Progressive genome modulation in the CD44+/CD24-/low subpopulation of the breast cancer cell line MDA-MB-468 leads to different coexisting subclones. In isolated low-copy cells asymmetric chromosomal segregation leads to new cells with regained solely egfr gene copies. Furthermore, egfr regain resulted in enhanced signal transduction of the MAP-kinase and PI3-kinase pathway. We show here for the first time a dynamic copy number regain in basal-like/stemness cell type breast cancer subpopulations which might explain genetic heterogeneity. Moreover, this process might also be involved in adaptive growth factor receptor intracellular signaling which support survival and migration during cancer development and progression.

ASTD: The Alternative Splicing and Transcript Diversity database.

The Alternative Splicing and Transcript Diversity database (ASTD) gives access to a vast collection of alternative transcripts that integrate transcription initiation, polyadenylation and splicing variant data. Alternative transcripts are derived from the mapping of transcribed sequences to the complete human, mouse and rat genomes using an extension of the computational pipeline developed for the ASD (Alternative Splicing Database) and ATD (Alternative Transcript Diversity) databases, which are now superseded by ASTD. For the human genome, ASTD identifies splicing variants, transcription initiation variants and polyadenylation variants in 68%, 68% and 62% of the gene set, respectively, consistent with current estimates for transcription variation. Users can access ASTD through a variety of browsing and query tools, including expression state-based queries for the identification of tissue-specific isoforms. Participating laboratories have experimentally validated a subset of ASTD-predicted alternative splice forms and alternative polyadenylation forms that were not previously reported. The ASTD database can be accessed at http://www.ebi.ac.uk/astd.

Liver TAG transiently decreases while PL n-3 and n-6 fatty acids are persistently elevated in insulin resistant mice.

Changes in fatty acid metabolism associated with insulin resistance have been described in rats and humans but have not been well characterized in the frequently used mouse model of diet-induced obesity. To analyse the early phase as well as established insulin resistance, C57BL/6 mice were placed for 1 or 16 weeks on a high fat diet (1w-HFD, 16w-HFD). Endocrine and metabolic parameters indicated that 1w-HFD mice showed a moderate but significant induction of insulin resistance while 16w-HFD mice exhibited profound obesity-associated insulin resistance and dyslipidemias. Significant alterations in fatty acid composition were observed in plasma and liver in both groups. Liver phospholipid-associated arachidonate and docosahexaenoate were increased in both 1w-HFD and 16w-HFD mice, possibly due to increased expression of the desaturases Fads1 and Fads2. Unexpectedly, SCD1 activity and gene expression in liver were decreased in the 1w-HFD group accompanied by diminished total hepatic lipid levels, while they were increased in chronically fed mice. Our data indicate that the early phase of HFD-induced insulin resistance is not associated with elevated liver lipid concentration. Furthermore, the early and persistent rise of arachidonate and docosahexaenoate indicates that insulin resistance is not due to insufficient availability (or concentrations) of polyunsaturated fatty acids as postulated previously.

Variation of butyrate production in the gut microbiome in type 2 diabetes patients / Variación de la producción de butirato en el microbioma intestinal en pacientes con diabetes tipo 2

Background: Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student’s t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive. Results: By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection. Conclusions: With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease.(AU)

Verification of predicted alternatively spliced Wnt genes reveals two new splice variants (CTNNB1 and LRP5) and altered Axin-1 expression during tumour progression.

BACKGROUND: Splicing processes might play a major role in carcinogenesis and tumour progression. The Wnt pathway is of crucial relevance for cancer progression. Therefore we focussed on the Wnt/beta-catenin signalling pathway in order to validate the expression of sequences predicted as alternatively spliced by bioinformatic methods. Splice variants of its key molecules were selected, which may be critical components for the understanding of colorectal tumour progression and may have the potential to act as biological markers. For some of the Wnt pathway genes the existence of splice variants was either proposed (e.g. beta-Catenin and CTNNB1) or described only in non-colon tissues (e.g. GSK3beta) or hitherto not published (e.g. LRP5). RESULTS: Both splice variants--normal and alternative form--of all selected Wnt pathway components were found to be expressed in cell lines as well as in samples derived from tumour, normal and healthy tissues. All splice positions corresponded totally with the bioinformatical prediction as shown by sequencing. Two hitherto not described alternative splice forms (CTNNB1 and LRP5) were detected. Although the underlying EST data used for the bioinformatic analysis suggested a tumour-specific expression neither a qualitative nor a significant quantitative difference between the expression in tumour and healthy tissues was detected. Axin-1 expression was reduced in later stages and in samples from carcinomas forming distant metastases. CONCLUSION: We were first to describe that splice forms of crucial genes of the Wnt-pathway are expressed in human colorectal tissue. Newly described splicefoms were found for beta-Catenin, LRP5, GSK3beta, Axin-1 and CtBP1. However, the predicted cancer specificity suggested by the origin of the underlying ESTs was neither qualitatively nor significant quantitatively confirmed. That let us to conclude that EST sequence data can give adequate hints for the existence of alternative splicing in tumour tissues. That no difference in the expression of these splice forms between cancerous tissues and normal mucosa was found, may indicate that the existence of different splice forms is of less significance for cancer formation as suggested by the available EST data. The currently available EST source is still insufficient to clearly deduce colon cancer specificity. More EST data from colon (tumour and healthy) is required to make reliable predictions.

EASED: Extended Alternatively Spliced EST Database.

We established a database of alternative splice forms (ASforms) for nine eukaryotic organisms. ASforms are defined by comparing high-scoring ESTs with mRNA sequences using BLAST, taking known exon-intron information (from the Ensembl database). Filtering programs compare the ends of each aligned sequence pair for deletions or insertions in the EST sequence, which indicate the existence of alternative splice forms with respect to the exon-intron boundaries. Moreover, we defined the alternative splice profile of each human sequence. It indicates the number of alternatively spliced ESTs (NAE), the number of constitutively spliced ESTs (NCE) as well as the number of alternative splice sites (NSS) per mRNA. NAE and NCE correspond to the EST coverage and can be used as a quality indicator for the predicted alternative splice variants. The NSS value specifies the splice propensity of a gene. Additionally, the tissue type information of all ESTs was included. This allows (i) restriction of the search to certain tissues and (ii) calculation of the tissue-NAEs, tissue-NCEs and tissue-NSS. These scores are suitable for the estimation of tissue specificity of certain ASforms. Furthermore, the developmental stage and disease information of the ESTs is available. EASED is accessible at http://eased.bioinf.mdc-berlin.de/.

Comprehensive Analysis of Genome Rearrangements in Eight Human Malignant Tumor Tissues.

Carcinogenesis is a complex multifactorial, multistage process, but the precise mechanisms are not well understood. In this study, we performed a genome-wide analysis of the copy number variation (CNV), breakpoint region (BPR) and fragile sites in 2,737 tumor samples from eight tumor entities and in 432 normal samples. CNV detection and BPR identification revealed that BPRs tended to accumulate in specific genomic regions in tumor samples whereas being dispersed genome-wide in the normal samples. Hotspots were observed, at which segments with similar alteration in copy number were overlapped along with BPRs adjacently clustered. Evaluation of BPR occurrence frequency showed that at least one was detected in about and more than 15% of samples for each tumor entity while BPRs were maximal in 12% of the normal samples. 127 of 2,716 tumor-relevant BPRs (termed 'common BPRs') exhibited also a noticeable occurrence frequency in the normal samples. Colocalization assessment identified 20,077 CNV-affecting genes and 169 of these being known tumor-related genes. The most noteworthy genes are KIAA0513 important for immunologic, synaptic and apoptotic signal pathways, intergenic non-coding RNA RP11-115C21.2 possibly acting as oncogene or tumor suppressor by changing the structure of chromatin, and ADAM32 likely importance in cancer cell proliferation and progression by ectodomain-shedding of diverse growth factors, and the well-known tumor suppressor gene p53. The BPR distributions indicate that CNV mutations are likely non-random in tumor genomes. The marked recurrence of BPRs at specific regions supports common progression mechanisms in tumors. The presence of hotspots together with common BPRs, despite its small group size, imply a relation between fragile sites and cancer-gene alteration. Our data further suggest that both protein-coding and non-coding genes possessing a range of biological functions might play a causative or functional role in tumor biology. This research enhances our understanding of the mechanisms for tumorigenesis and progression.

Prostate Cancer Associated Lipid Signatures in Serum Studied by ESI-Tandem Mass Spectrometryas Potential New Biomarkers.

Prostate cancer (PCa) is one amongst the most common cancersin western men. Incidence rate ofPCa is on the rise worldwide. The present study deals with theserum lipidome profiling of patients diagnosed with PCa to identify potential new biomarkers. We employed ESI-MS/MS and GC-MS for identification of significantly altered lipids in cancer patient's serum compared to controls. Lipidomic data revealed 24 lipids are significantly altered in cancer patinet's serum (n = 18) compared to normal (n = 18) with no history of PCa. By using hierarchical clustering and principal component analysis (PCA) we could clearly separate cancer patients from control group. Correlation and partition analysis along with Formal Concept Analysis (FCA) have identified that PC (39:6) and FA (22:3) could classify samples with higher certainty. Both the lipids, PC (39:6) and FA (22:3) could influence the cataloging of patients with 100% sensitivity (all 18 control samples are classified correctly) and 77.7% specificity (of 18 tumor samples 4 samples are misclassified) with p-value of 1.612×10-6 in Fischer's exact test. Further, we performed GC-MS to denote fatty acids altered in PCa patients and found that alpha-linolenic acid (ALA) levels are altered in PCa. We also performed an in vitro proliferation assay to determine the effect of ALA in survival of classical human PCa cell lines LNCaP and PC3. We hereby report that the altered lipids PC (39:6) and FA (22:3) offer a new set of biomarkers in addition to the existing diagnostic tests that could significantly improve sensitivity and specificity in PCa diagnosis.

RefPrimeCouch--a reference gene primer CouchApp.

To support a quantitative real-time polymerase chain reaction standardization project, a new reference gene database application was required. The new database application was built with the explicit goal of simplifying not only the development process but also making the user interface more responsive and intuitive. To this end, CouchDB was used as the backend with a lightweight dynamic user interface implemented client-side as a one-page web application. Data entry and curation processes were streamlined using an OpenRefine-based workflow. The new RefPrimeCouch database application provides its data online under an Open Database License. Database URL: http://hpclife.th-wildau.de:5984/rpc/_design/rpc/view.html.

Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors.

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.

An efficient method for modeling kinetic behavior of channel proteins in cardiomyocytes.

Characterization of the kinetic and conformational properties of channel proteins is a crucial element in the integrative study of congenital cardiac diseases. The proteins of the ion channels of cardiomyocytes represent an important family of biological components determining the physiology of the heart. Some computational studies aiming to understand the mechanisms of the ion channels of cardiomyocytes have concentrated on Markovian stochastic approaches. Mathematically, these approaches employ Chapman-Kolmogorov equations coupled with partial differential equations. As the scale and complexity of such subcellular and cellular models increases, the balance between efficiency and accuracy of algorithms becomes critical. We have developed a novel two-stage splitting algorithm to address efficiency and accuracy issues arising in such modeling and simulation scenarios. Numerical experiments were performed based on the incorporation of our newly developed conformational kinetic model for the rapid delayed rectifier potassium channel into the dynamic models of human ventricular myocytes. Our results show that the new algorithm significantly outperforms commonly adopted adaptive Runge-Kutta methods. Furthermore, our parallel simulations with coupled algorithms for multicellular cardiac tissue demonstrate a high linearity in the speedup of large-scale cardiac simulations.

Quantitative high-resolution genomic analysis of single cancer cells.

During cancer progression, specific genomic aberrations arise that can determine the scope of the disease and can be used as predictive or prognostic markers. The detection of specific gene amplifications or deletions in single blood-borne or disseminated tumour cells that may give rise to the development of metastases is of great clinical interest but technically challenging. In this study, we present a method for quantitative high-resolution genomic analysis of single cells. Cells were isolated under permanent microscopic control followed by high-fidelity whole genome amplification and subsequent analyses by fine tiling array-CGH and qPCR. The assay was applied to single breast cancer cells to analyze the chromosomal region centred by the therapeutical relevant EGFR gene. This method allows precise quantitative analysis of copy number variations in single cell diagnostics.

Identification of clinically relevant protein targets in prostate cancer with 2D-DIGE coupled mass spectrometry and systems biology network platform.

Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE) coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.

TOB1 is regulated by EGF-dependent HER2 and EGFR signaling, is highly phosphorylated, and indicates poor prognosis in node-negative breast cancer.

Clinical and animal studies have shown that coexpression of the receptor tyrosine kinases HER2 and epidermal growth factor (EGF) receptor (EGFR) indicates a highly metastatic phenotype of breast cancer. In a cellular model of this phenotype using differential gene expression analysis, we identified TOB1 to be up-regulated depending on EGF stimulation and transduction through phosphorylation of HER2 tyrosine 1248. mRNA expression analysis of breast cancers from a cohort of node-negative patients showed significantly shortened distant metastasis-free survival for patients with high TOB1 expression. In subsequent tissue microarray studies of 725 clinical samples, high HER2 and EGF protein levels were significantly correlated with TOB1 expression in breast cancer, whereas EGFR and EGF levels correlated with TOB1 phosphorylation. We did not observe a correlation between TOB1 expression and cyclin D1, which was previously suggested to mediate the antiproliferative effect of unphosphorylated TOB1. A positive correlation of TOB1 phosphorylation status with proliferation marker Ki67 suggests that elevated TOB1 phosphorylation might abrogate the antiproliferative effect of TOB1 in breast cancer. This suggests a new regulatory role for TOB1 in cancer progression with particular significance in HER2- and/or EGFR-positive breast cancers.

Acute-phase serum amyloid A as a marker of insulin resistance in mice.

Acute-phase serum amyloid A (A-SAA) was shown recently to correlate with obesity and insulin resistance in humans. However, the mechanisms linking obesity-associated inflammation and elevated plasma A-SAA to insulin resistance are poorly understood. Using high-fat diet- (HFD-) fed mice, we found that plasma A-SAA was increased early upon HFD feeding and was tightly associated with systemic insulin resistance. Plasma A-SAA elevation was due to induction of Saa1 and Saa2 expression in liver but not in adipose tissue. In adipose tissue Saa3 was the predominant isoform and the earliest inflammatory marker induced, suggesting it is important for initiation of adipose tissue inflammation. To assess the potential impact of A-SAA on adipose tissue insulin resistance, we treated 3T3-L1 adipocytes with recombinant A-SAA. Intriguingly, physiological levels of A-SAA caused alterations in gene expression closely resembling those observed in HFD-fed mice. Proinflammatory genes (Ccl2, Saa3) were induced while genes critical for insulin sensitivity (Irs1, Adipoq, Glut4) were down-regulated. Our data identify HFD-fed mice as a suitable model to study A-SAA as a biomarker and a novel possible mediator of insulin resistance.

Correlations of mRNA expression and in vitro chemosensitivity to enzastaurin in freshly explanted human tumor cells.

PURPOSE: Enzastaurin (LY317615) is a novel serine/threonine kinase inhibitor, targeting Protein Kinase C-beta (PKC-beta), and PI3K/AKT pathways to inhibit angiogenesis and tumor cell proliferation. The aims of this study were to determine whether Enzastaurin has direct antitumor activity against freshly explanted tumor cells and to correlate mRNA expression of genes related to the proposed mechanism of action of enzastaurin with in vitro chemosensitivity. EXPERIMENTAL DESIGN: Freshly biopsied tumor cells were studied using soft-agar cell cloning experiments (SACCE) to determine the in vitro chemosensitivity to enzastaurin. An aliquot of the same tumor specimens was shock-frozen and total RNA was isolated for standardized multiplex rt-PCR experiments for gene expression of PKC-beta1, PKC-beta2, IL-8, IL-8RA, IL-8RB, Glycogen Synthase Kinase 3 beta (GSK-3beta) and TGF-beta1. Correlations, threshold optimization, sensitivity, specificity, and efficiency were analyzed using the appropriate statistical methodologies. RESULTS: Seventy-two tumor samples were collected and 63 were fully evaluable. Low levels of mRNA expression of GSK-3beta and high levels of mRNA expression of IL-8 were highly significantly correlated with chemosensitivity to enzastaurin. Optimization analyses demonstrated threshold values of 4,000 copies for IL-8 and three copies for GSK-3beta relative to 10(4) copies of beta-actin. However, no correlation between mRNA expression of PKC-beta1, PKC-beta2, IL-8RA, IL-8RB and chemosensitivity to enzastaurin was observed. Expression of TGF-beta1 mRNA was not detectable in the specimens investigated. CONCLUSIONS: mRNA expression levels of IL-8 and GSK-3beta correlate with antitumor activity of enzastaurin. These results form a rational basis for clinical trials to evaluate the expression of these genes as potential predictors for treatment outcome after enzastaurin chemotherapy.