Biomarkers of human gastrointestinal tract regions

Dysregulation of intestinal epithelial cell performance is associated with an array of pathologies whose onset mechanisms are incompletely understood. While whole-genomics approaches have been valuable for studying the molecular basis of several intestinal diseases, a thorough analysis of gene expression along the healthy gastrointestinal tract is still lacking. The aim of this study was to map gene expression in gastrointestinal regions of healthy human adults and to implement a procedure for microarray data analysis that would allow its use as a reference when screening for pathological deviations. We analyzed the gene expression signature of antrum, duodenum, jejunum, ileum, and transverse colon biopsies using a biostatistical method based on a multivariate and univariate approach to identify region-selective genes. One hundred sixty-six genes were found responsible for distinguishing the five regions considered. Nineteen had never been described in the GI tract, including a semaphorin probably implicated in pathogen invasion and six novel genes. Moreover, by crossing these genes with those retrieved from an existing data set of gene expression in the intestine of ulcerative colitis and Crohn’s disease patients, we identified genes that might be biomarkers of Crohn’s and/or ulcerative colitis in ileum and/or colon. These include CLCA4 and SLC26A2, both implicated in ion transport. This study furnishes the first map of gene expression along the healthy human gastrointestinal tract. Furthermore, the approach implemented here, and validated by retrieving known gene profiles, allowed the identification of promising new leads in both healthy and disease states.     

Cross-Study Projections of Genomic Biomarkers: An Evaluation in Cancer Genomics

Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a “common currency” that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies – in cancer and other diseases – have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.     

Six Serum miRNAs Fail to Validate as Myotonic Dystrophy Type 1 Biomarkers

Myotonic dystrophy type 1 (DM1) is an autosomal dominant genetic disease caused by expansion of a CTG microsatellite in the 3’ untranslated region of the DMPK gene. Despite characteristic muscular, cardiac, and neuropsychological symptoms, CTG trinucleotide repeats are unstable both in the somatic and germinal lines, making the age of onset, clinical presentation, and disease severity very variable. A molecular biomarker to stratify patients and to follow disease progression is, thus, an unmet medical need. Looking for a novel biomarker, and given that specific miRNAs have been found to be misregulated in DM1 heart and muscle tissues, we profiled the expression of 175 known serum miRNAs in DM1 samples. The differences detected between patients and controls were less than 2.6 fold for all of them and a selection of six candidate miRNAs, miR-103, miR-107, miR-21, miR-29a, miR-30c, and miR-652 all failed to show consistent differences in serum expression in subsequent validation experiments.     

Gemcitabine and Arabinosylcytosin Pharmacogenomics: Genome-Wide Association and Drug Response Biomarkers

Cancer patients show large individual variation in their response to chemotherapeutic agents. Gemcitabine (dFdC) and AraC, two cytidine analogues, have shown significant activity against a variety of tumors. We previously used expression data from a lymphoblastoid cell line-based model system to identify genes that might be important for the two drug cytotoxicity. In the present study, we used that same model system to perform a genome-wide association (GWA) study to test the hypothesis that common genetic variation might influence both gene expression and response to the two drugs. Specifically, genome-wide single nucleotide polymorphisms (SNPs) and mRNA expression data were obtained using the Illumina 550K® HumanHap550 SNP Chip and Affymetrix U133 Plus 2.0 GeneChip, respectively, for 174 ethnically-defined “Human Variation Panel” lymphoblastoid cell lines. Gemcitabine and AraC cytotoxicity assays were performed to obtain IC₅₀ values for the cell lines. We then performed GWA studies with SNPs, gene expression and IC₅₀ of these two drugs. This approach identified SNPs that were associated with gemcitabine or AraC IC₅₀ values and with the expression regulation for 29 genes or 30 genes, respectively. One SNP in IQGAP2 (rs3797418) was significantly associated with variation in both the expression of multiple genes and gemcitabine and AraC IC₅₀. A second SNP in TGM3 (rs6082527) was also significantly associated with multiple gene expression and gemcitabine IC50. To confirm the association results, we performed siRNA knock down of selected genes with expression that was associated with rs3797418 and rs6082527 in tumor cell and the knock down altered gemcitabine or AraC sensitivity, confirming our association study results. These results suggest that the application of GWA approaches using cell-based model systems, when combined with complementary functional validation, can provide insights into mechanisms responsible for variation in cytidine analogue response.     

Expression of Adipocyte Biomarkers in a Primary Cell Culture Models Reflects Preweaning Adipobiology

A cohort of genes was selected to characterize the adipogenic phenotype in primary cell cultures from three tissue sources. We compared the quantitative expression of biomarkers in culture relative to their expression in vivo because the mere presence or absence of expression is minimally informative. Although all biomarkers analyzed have biochemical functions in adipocytes, the expression of some of the biomarkers varied enormously in culture relative to their expression in the adult fat tissues in vivo, i.e. inguinal fat for white adipocytes and brite cells, interscapular brown adipose tissue for brown adipocytes, and ear mesenchymal stem cells for white adipocytes from adult mice. We propose that the pattern of expression in vitro does not reflect gene expression in the adult mouse; rather it is predominantly the expression pattern of adipose tissue of the developing mouse between birth and weaning. The variation in gene expression among fat depots in both human and rodent has been an extensively studied phenomenon, and as recently reviewed, it is related to subphenotypes associated with immune function, the inflammatory response, fat depot blood flow, and insulin sensitivity. We suggest that adipose tissue biology in the period from birth to weaning is not just a staging platform for the emergence of adult white fat but that it has properties to serve the unique needs of energy metabolism in the newborn. A case in point is the differentiation of brite cells that occurs during this period followed by their involution immediately following weaning.     

KIAA1462, A Coronary Artery Disease Associated Gene,Is a Candidate Gene for Late Onset Alzheimer Disease in APOE Carriers

Alzheimer disease (AD) is a devastating neurodegenerative disease affecting more than five million Americans. In this study, we have used updated genetic linkage data from chromosome 10 in combination with expression data from serial analysis of gene expression to choose a new set of thirteen candidate genes for genetic analysis in late onset Alzheimer disease (LOAD). Results in this study identify the KIAA1462 locus as a candidate locus for LOAD in APOE4 carriers. Two genes exist at this locus, KIAA1462, a gene associated with coronary artery disease, and “rokimi”, encoding an untranslated spliced RNA The genetic architecture at this locus suggests that the gene product important in this association is either “rokimi”, or a different isoform of KIAA1462 than the isoform that is important in cardiovascular disease. Expression data suggests that isoform f of KIAA1462 is a more attractive candidate for association with LOAD in APOE4 carriers than “rokimi” which had no detectable expression in brain.     

Identification of Epigenetic Biomarkers of Lung Adenocarcinoma through Multi-Omics Data Analysis

Epigenetic mechanisms such as DNA methylation or histone modifications are essential for the regulation of gene expression and development of tissues. Alteration of epigenetic modifications can be used as an epigenetic biomarker for diagnosis and as promising targets for epigenetic therapy. A recent study explored cancer-cell specific epigenetic biomarkers by examining different types of epigenetic modifications simultaneously. However, it was based on microarrays and reported biomarkers that were also present in normal cells at a low frequency. Here, we first analyzed multi-omics data (including ChIP-Seq data of six types of histone modifications: H3K27ac, H3K4me1, H3K9me3, H3K36me3, H3K27me3, and H3K4me3) obtained from 26 lung adenocarcinoma cell lines and a normal cell line. We identified six genes with both H3K27ac and H3K4me3 histone modifications in their promoter regions, which were not present in the normal cell line, but present in ≥85% (22 out of 26) and ≤96% (25 out of 26) of the lung adenocarcinoma cell lines. Of these genes, NUP210 (encoding a main component of the nuclear pore complex) was the only gene in which the two modifications were not detected in another normal cell line. RNA-Seq analysis revealed that NUP210 was aberrantly overexpressed among the 26 lung adenocarcinoma cell lines, although the frequency of NUP210 overexpression was lower (19.3%) in 57 lung adenocarcinoma tissue samples studied and stored in another database. This study provides a basis to discover epigenetic biomarkers highly specific to a certain cancer, based on multi-omics data at the cell population level.     

Temporal Expression of Peripheral Blood Leukocyte Biomarkers in a Macaca fascicularis Infection Model of Tuberculosis; Comparison with Human Datasets and Analysis with Parametric/Non-parametric Tools for Improved Diagnostic Biomarker Identification

A temporal study of gene expression in peripheral blood leukocytes (PBLs) from a Mycobacterium tuberculosis primary, pulmonary challenge model Macaca fascicularis has been conducted. PBL samples were taken prior to challenge and at one, two, four and six weeks post-challenge and labelled, purified RNAs hybridised to Operon Human Genome AROS V4.0 slides. Data analyses revealed a large number of differentially regulated gene entities, which exhibited temporal profiles of expression across the time course study. Further data refinements identified groups of key markers showing group-specific expression patterns, with a substantial reprogramming event evident at the four to six week interval. Selected statistically-significant gene entities from this study and other immune and apoptotic markers were validated using qPCR, which confirmed many of the results obtained using microarray hybridisation. These showed evidence of a step-change in gene expression from an ‘early’ FOS-associated response, to a ‘late’ predominantly type I interferon-driven response, with coincident reduction of expression of other markers. Loss of T-cell-associate marker expression was observed in responsive animals, with concordant elevation of markers which may be associated with a myeloid suppressor cell phenotype e.g. CD163. The animals in the study were of different lineages and these Chinese and Mauritian cynomolgous macaque lines showed clear evidence of differing susceptibilities to Tuberculosis challenge. We determined a number of key differences in response profiles between the groups, particularly in expression of T-cell and apoptotic makers, amongst others. These have provided interesting insights into innate susceptibility related to different host `phenotypes. Using a combination of parametric and non-parametric artificial neural network analyses we have identified key genes and regulatory pathways which may be important in early and adaptive responses to TB. Using comparisons between data outputs of each analytical pipeline and comparisons with previously published Human TB datasets, we have delineated a subset of gene entities which may be of use for biomarker diagnostic test development.     

Learning Biomarkers of Pluripotent Stem Cells in Mouse

Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.     

mRNA sequence analysis and quantitative expression of the ADAMTS4 gene in the thoroughbred horse

Cartilage increases flexibility of motion and helps protect the body from physical shock. Strong physical shock or some biological factor could cause joint disease. ADAMTS4 (a disintegrin and metalloproteinase with thrombospondin motifs 4) has been related to degradation of aggrecans in cartilage. It has been associated with joint disease, which could influence the ability of horses to exercise. Here, we performed sequence analysis and expression profiling of the ADAMTS4 gene in thoroughbred horses. Quantitative real-time RT-PCR data indicated that higher expression of the ADAMTS4 gene appeared in the cartilage tissues compared to those of pancreas, stomach, lung and colon. The expression pattern was also higher in the muscle tissues after exercise than before exercise. These data could be of great use for further studies in relation to both horse racing and joint disease.     

Evaluation of mRNA Biomarkers to Identify Risk of Hospital Acquired Infections in Children Admitted to Paediatric Intensive Care Unit

Objectives

Hospital-acquired infections (HAI) are associated with significant mortality and morbidity and prolongation of hospital stay, adding strain on limited hospital resources. Despite stringent infection control practices some children remain at high risk of developing HAI. The development of biomarkers which could identify these patients would be useful. In this study our objective was to evaluate mRNA candidate biomarkers for HAI prediction in a pediatric intensive care unit.

Design

Serial blood samples were collected from patients admitted to pediatric intensive care unit between March and June 2012. Candidate gene expression (IL1B, TNF, IL10, CD3D, BCL2, BID) was quantified using RT-qPCR. Comparisons of relative gene expression between those that did not develop HAI versus those that did were performed using Mann Whitney U-test.

Patients

Exclusion criteria were: age <28 days or ≥16 years, expected length of stay < 24 hours, expected survival < 28 days, end-stage renal disease and end-stage liver disease. Finally, 45 children were included in this study.

Main Results

The overall HAI rate was 30% of which 62% were respiratory infections. Children who developed HAI had a three-fold increase in hospital stay compared to those who did not (27 days versus 9 days, p<0.001). An increased expression of cytokine genes (IL1B and IL10) was observed in patients who developed HAI, as well as a pro-apoptosis pattern (higher expression of BID and lower expression of BCL2). CD3D, a key TCR co-factor was also significantly down-modulated in patients who developed HAI.

Conclusions

To our knowledge, this is the first study of mRNA biomarkers of HAI in the paediatric population. Increased mRNA expressions of anti-inflammatory cytokine and modulation of apoptotic genes suggest the development of immunosuppression in critically ill children. Immune monitoring using a panel of genes may offer a novel stratification tool to identify HAI risk.     

Notch3 and Hey-1 as Prognostic Biomarkers in Pancreatic Adenocarcinoma

In order to achieve a better outcome for pancreatic cancer patients, reliable biomarkers are required which allow for improved diagnosis. These may emanate from a more detailed molecular understanding of the aggressive nature of this disease. Having previously reported that Notch3 activation appeared to be associated with more aggressive disease, we have now examined components of this pathway (Notch1, Notch3, Notch4, HES-1, HEY-1) in more detail in resectable (n = 42) and non-resectable (n = 50) tumours compared to uninvolved pancreas. All three Notch family members were significantly elevated in tumour tissue, compared to uninvolved pancreas, with expression maintained within matched lymph node metastases. Furthermore, significantly higher nuclear expression of Notch1, -3 and -4, HES-1, and HEY-1 (all p≤0.001) was noted in locally advanced and metastatic tumours compared to resectable cancers. In survival analyses, nuclear Notch3 and HEY-1 expression were significantly associated with reduced overall and disease-free survival following tumour resection with curative intent, with nuclear HEY-1 maintaining independent prognostic significance for both outcomes on multivariate analysis. These data further support a central role for Notch signalling in pancreatic cancer and suggest that nuclear expression of Notch3 and its target gene, HEY-1, merit validation in biomarker panels for diagnosis, prognosis and treatment efficacy. A peptide fragment of Notch3 was detected in plasma from patients with inoperable pancreatic cancer, but due to wide inter-individual variation, mean levels were not significantly different compared to age-matched controls.     

Specific Biomarkers Are Associated with Docetaxeland Gemcitabine-Resistant NSCLC Cell Lines

Five-year survival rate for lung cancer is limited to 10% to 15%. Therefore, the identification of novel therapeutic prognostic factors is an urgent requirement. The aim of this study is thus to highlight specific biomarkers in chemoresistant non-small cell lung cancer cell lines. Therefore, we checked—in the control condition as well as after short-term pharmacological treatment with either docetaxel or gemcitabine—the expression of genes such as tumor suppressor genes (CDKN2A, DAPK, FHIT, GSTP1, MGMT, RARβ2, RASSF1A, and TIMP3), genes associated with drug resistance (BRCA1, COX2, ERCC1, IGFBP3, RRM1, and TUBB3), and stemness-related genes (CD133, OCT4, and SLUG) in two cellular models of squamous carcinoma (CAEP) and adenocarcinoma (RAL) of the lung originally established. Their promoter methylation profile was also evaluated. Drug-related genes were upregulated. Cisplatin resistance matched with high levels of BRCA1 and ERCC1 in both cell lines; docetaxel sensitivity of CAEP cells was associated to levels of TUBB3 lower than RAL cells. Although CAEP cells were more sensitive to gemcitabine, both cell lines showed high levels of RRM1. Stemness-related genes were downregulated in the control condition but became upregulated in docetaxel-resistant cells, indicating the selection of a population with stemness features. We did not find an unequivocal correspondence between gene expression and respective DNA promoter methylation status, suggesting the involvement of additional mechanisms of gene expression regulation. These results highlight specific biomarkers consistent with the different responses of the two cell lines to standard pharmacological treatments and indicate specific molecular traits for their chemoresistance.     

The Effect of Statins on Blood Gene Expression in COPD

BackgroundCOPD is currently the fourth leading cause of death worldwide. Statins are lipid lowering agents with documented cardiovascular benefits. Observational studies have shown that statins may have a beneficial role in COPD. The impact of statins on blood gene expression from COPD patients is largely unknown.ObjectiveIdentify blood gene signature associated with statin use in COPD patients, and the pathways underpinning this signature that could explain any potential benefits in COPD.MethodsWhole blood gene expression was measured on 168 statin users and 451 non-users from the ECLIPSE study using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and surrogate variables. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser.Results25 genes were differentially expressed between statin users and non-users at an FDR of 10%, including LDLR, CXCR2, SC4MOL, FAM108A1, IFI35, FRYL, ABCG1, MYLIP, and DHCR24. The 25 genes were significantly enriched in cholesterol homeostasis and metabolism pathways. The resulting gene signature showed correlation with Huntington’s disease, Parkinson’s disease and acute myeloid leukemia gene signatures.ConclusionThe blood gene signature of statins’ use in COPD patients was enriched in cholesterol homeostasis pathways. Further studies are needed to delineate the role of these pathways in lung biology.     

DANCER: a program for digital anatomical reconstruction of gene expression data

A digital anatomy construction (DANCER) program was developed for gene expression data. DANCER can be used to reconstruct anatomical images from in situ hybridization images, microarray or other gene expression data. The program fills regions of a drawn figure with the corresponding values from a gene expression data set. The output of the program presents the expression levels of a particular gene in a particular region relative to other regions. The program was tested with values from experimental in situ hybridization autoradiographs and from a microarray experiment. Reconstruction of in situ hybridization data from adult rat brain made by DANCER corresponded well with the original autoradiograph. Reconstruction of microarray data from adult mouse brains provided images that reflect actual expression levels. This program should help to provide visualization and interpretation of data derived from gene expression experiments. DANCER may be freely downloaded.     

SNP-based large-scale identification of allele-specific gene expression in human B cells

Polymorphism and variations in gene expression provide the genetic basis for human variation. Allelic variation of gene expression, in particular, may play a crucial role in phenotypic variation and disease susceptibility. To identify genes with allelic expression in human cells, we genotyped genomic DNA and cDNA isolated from 31 immortalized B cell lines from three Centre d'Etude du Polymorphisme Humain (CEPH) families using high-density single-nucleotide polymorphism (SNP) chips containing 13,900 exonic SNPs. We identified seven SNPs in five genes with monoallelic expression, 146 SNPs in 125 genes with allelic imbalance in expression with preferentially higher expression of one allele in a heterozygous individual. The monoallelically expressed genes (ERAP2, MDGA1, LOC644422, SDCCAG3P1 and CLTCL1) were regulated by cis-acting, non-imprinted differential allelic control. In addition, all monoallelic gene expression patterns and allelic imbalances in gene expression in B cells were transmitted from parents to offspring in the pedigree, indicating genetic transmission of allelic gene expression. Furthermore, frequent allele substitution, probably due to RNA editing, was also observed in 21 genes in 23 SNPs as well as in 48 SNPs located in regions containing no known genes. In this study, we demonstrated that allelic gene expression is frequently observed in human B cells, and SNP chips are very useful tools for detecting allelic gene expression. Overall, our data provide a valuable framework for better understanding allelic gene expression in human B cells.     

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

Objectives

To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.

Methods

We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.

Results

Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.

Conclusion

Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications.     

Systematic Analysis of the Gene Expression in the Livers of Nonalcoholic Steatohepatitis: Implications on Potential Biomarkers and Molecular Pathological Mechanism

Non-alcoholic steatohepatitis (NASH) is a severe form of non-alcoholic fatty liver disease (NAFLD). The molecular pathological mechanism of NASH is poorly understood. Recently, high throughput data such as microarray data together with bioinformatics methods have become a powerful way to identify biomarkers and to investigate pathogenesis of diseases. Taking advantage of well characterized microarray datasets of NASH livers, we performed a systematic analysis of potential biomarkers and possible pathological mechanism of NASH from a bioinformatics perspective.CodeLink Human Whole Genome Bioarrays were analyzed to find differentially expressed genes (DEGs) between controls and NASH patients. Four methods were used to identify DEGs and the intersection of DEGs identified by these methods was subsequently used for both biomarker prediction and molecular pathological mechanism analysis. For biomarker prediction, rank aggregation was used to rank DEGs identified by all these methods according to their significance of different expression. Alcohol dehydrogenase 4 (ADH4) exhibited the highest rank suggesting the most significant differential expression between normal and disease condition. Together with the previous report demonstrating the association between ADH4 and the pathogenesis of NASH, our data suggest that ADH4 could be a potential biomarker for NASH. For molecular pathological mechanism analysis, two clusters of highly correlated annotation terms and genes in these terms were identified based on the intersection of DEGs. Then, pathways enriched with these genes were identified to construct the network. Using this network, both for the first time, amino acid catabolism is implicated to play a pivotal role and urea cycle is implicated to be involved in the development of NASH.The results of our study identified potential biomarkers and suggested possible molecular pathological mechanism of NASH. These findings provide a comprehensive and systematic understanding of the pathogenesis of NASH and may facilitate the diagnosis, prevention and treatment of NASH.