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1.  Identification of a Functional TPH1 Polymorphism Associated with Irritable Bowel Syndrome Bowel Habit Subtypes 
Background & Aims
Alterations in 5-hydroxytryptamine (5-HT) signaling have been implicated as a factor contributing to the altered bowel habit of irritable bowel syndrome (IBS) patients. Tryptophan hydroxylase 1 (TPH1) is the rate–limiting enzyme in enterochromaffin cell 5-HT biosynthesis. We hypothesized that genetic variants affecting TPH1 gene expression might alter intestinal 5-HT bioavailability and subsequently the propensity for distinct bowel habit subtypes in IBS. In this study, we assessed the only common TPH1 proximal promoter variant (-347C/A; rs7130929) and its association with bowel habit predominance in IBS.
Methods
Electrophoretic mobility shift assays were performed to assess whether the -347C/A allele variant affects the DNA-binding of nuclear factors. Genotype distribution was determined for 422 IBS patients subtyped using Rome III criteria and for 495 healthy controls recruited from two university medical centers. Association with bowel habit was tested using a multinomial logistic regression model controlling for race, anxiety, depression, and study site.
Results
Early growth response factor 1 (EGR-1) bound with higher affinity to a site comprising the minor A-allele of SNP -347C/A. TPH1 genotype frequencies did not differ between IBS patients and controls overall. The CC genotype was more prevalent in the IBS-D subtype (47%) than in the IBS-C (25%) and IBS-M (37%) subtypes (P=0.039) after adjusting for race and other covariates. Colonic biopsies from a small cohort of IBS patients from one center were tested for higher TPH1 mRNA expression in samples with CC compared to CA genotype, but the results did not reach statistical significance.
Conclusions
The TPH1 promoter SNP -347C/A differentially binds EGR1, correlates with IBS bowel habit subtypes and possibly colonic TPH1 expression consistent with its role in modulating intestinal 5-HT signaling.
doi:10.1038/ajg.2013.304
PMCID: PMC4067697  PMID: 24060757
Egr1; Sp1; ZBP-89; 5HT; serotonin; SNP
2.  Pathway Analysis Software: Annotation Errors and Solutions 
Molecular genetics and metabolism  2010;101(2-3):134-140.
Genetic databases contain a variety of annotation errors that often go unnoticed due to the large size of modern genetic data sets. Interpretation of these data sets requires bioinformatics tools that may contribute to this problem. While providing gene symbol annotations for identifiers (IDs) such as microarray probeset, RefSeq, GenBank and Entrez Gene is seemingly trivial, the accuracy is fundamental to any subsequent conclusions. We examine gene symbol annotations and results from three commercial pathway analysis software (PAS) packages: Ingenuity Pathways Analysis, GeneGO and Pathway Studio. We compare gene symbol annotations and canonical pathway results over time and among different input ID types. We find that PAS results can be affected by variation in gene symbol annotations across software releases and the input ID type analyzed. As a result, we offer suggestions for using commercial PAS and reporting microarray results to improve research quality. We propose a wiki type website to facilitate communication of bioinformatics software problems within the scientific community.
doi:10.1016/j.ymgme.2010.06.005
PMCID: PMC2950253  PMID: 20663702
annotation; gene identifiers; microarray; bioinformatics; systems biology
3.  Association between Early Adverse Life Events and Irritable Bowel Syndrome 
Background & Aims
Although childhood and adult abuse are more prevalent among patients with irritable bowel syndrome (IBS) than healthy individuals (controls), other types of early adverse life events (EALs) have not been well characterized. We investigated whether different types of EALs, before an age of 18 years, are more prevalent among patients with IBS, and the effects of gender and non-gastrointestinal symptoms on the relationship between EALs and IBS.
Methods
EALs were evaluated in 294 IBS patients (79% women) and 435 controls (77% women) using the early trauma inventory self report form, which delineates sub-categories of general trauma and physical, emotional, and sexual abuse. Validated questionnaires assessed gastrointestinal, psychological, and somatic symptoms.
Results
Compared to controls, IBS patients reported a higher prevalence of general trauma (78.5% vs 62.3%), physical punishment (60.6% vs 49.2%), emotional abuse (54.9% vs 27.0%), and sexual events (31.2% vs 17.9%) (all P’s <.001). These significant differences were mainly observed in women. Of the EAL domains, emotional abuse was the strongest predictor of IBS (P<.001). Eight of the 27 EAL items were significant (P<.001) and increased the odds of having IBS by 108%–305%. Although EAL and psychological variables were related, EALs had an independent association with IBS (P=.04).
Conclusion
Various types of EALs are associated with development of IBS—particularly among women. Psychological distress and somatic symptoms might contribute to this relationship. When appropriate, EALs and non-gastrointestinal symptoms should be assessed in IBS patients.
doi:10.1016/j.cgh.2011.12.018
PMCID: PMC3311761  PMID: 22178460
ETI-SF; psychology; somatization; nervous system
4.  Serum and Colonic Mucosal Immune Markers in Irritable Bowel Syndrome 
OBJECTIVES
Low-grade colonic mucosal inflammation has been postulated to have an important role in the pathophysiology of irritable bowel syndrome (IBS). The objectives of this study were (i) to identify serum and tissue-based immunological and neuroendocrine markers associated with mucosal inflammation in male (M) and female (F) patients with non-post-infectious IBS (non-PI-IBS) compared with healthy controls and (ii) to assess possible correlations of such markers with IBS symptoms.
METHODS
Sigmoid mucosal biopsies were obtained from 45 Rome II positive IBS patients without a history of PI-IBS (26 F, 35.5% IBS-C, 33.3% IBS-D, 31.1% IBS-A/M) and 41 healthy controls (22 F) in order to measure immunological markers (serum cytokine levels, colonic mucosal mRNA levels of cytokines, mucosal immune cell counts) and neuroendocrine markers associated with mucosal inflammation (corticotropin releasing factor- and neurokinin (NK)-related ligands and receptors, enterochromaffin cells). Symptoms were measured using validated questionnaires.
RESULTS
Of all the serum and mucosal cytokines measured, only interleukin-10 (IL-10) mRNA expression showed a group difference, with female, but not male, patients showing lower levels compared with female controls (18.0 ± 2.9 vs. 29.5 ± 4.0, P = 0.006). Mucosal mRNA expression of NK-1 receptor was significantly lower (1.15 ± 0.19 vs. 2.66 ± 0.56, P = 0.008) in female, but not male, patients compared with healthy controls. No other significant differences were observed.
CONCLUSIONS
Immune cell counts and levels of cytokines and neuropeptides that are associated with inflammation were not significantly elevated in the colonic mucosa of non-PI-IBS patients, and did not correlate with symptoms. Thus, these findings do not support that colonic mucosal inflammation consistently has a primary role in these patients. However, the finding of decreased IL-10 mRNA expression may be a possible biomarker of IBS and warrants further investigation.
doi:10.1038/ajg.2011.423
PMCID: PMC3297737  PMID: 22158028
5.  Methodology and software to detect viral integration site hot-spots 
BMC Bioinformatics  2011;12:367.
Background
Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.
Results
We develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.
Conclusions
The z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.
doi:10.1186/1471-2105-12-367
PMCID: PMC3203353  PMID: 21914224
6.  Protein expression based multimarker analysis of breast cancer samples 
BMC Cancer  2011;11:230.
Background
Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.
Methods
We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.
Results
We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.
Conclusions
We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.
doi:10.1186/1471-2407-11-230
PMCID: PMC3142534  PMID: 21651811
Tissue microarray; breast cancer; tumor marker; prognostic marker; WGCNA
7.  High-Throughput, Sensitive Quantification of Repopulating Hematopoietic Stem Cell Clones ▿ †  
Journal of Virology  2010;84(22):11771-11780.
Retroviral vector-mediated gene therapy has been successfully used to correct genetic diseases. However, a number of studies have shown a subsequent risk of cancer development or aberrant clonal growths due to vector insertion near or within proto-oncogenes. Recent advances in the sequencing technology enable high-throughput clonality analysis via vector integration site (VIS) sequencing, which is particularly useful for studying complex polyclonal hematopoietic progenitor/stem cell (HPSC) repopulation. However, clonal repopulation analysis using the current methods is typically semiquantitative. Here, we present a novel system and standards for accurate clonality analysis using 454 pyrosequencing. We developed a bidirectional VIS PCR method to improve VIS detection by concurrently analyzing both the 5′ and the 3′ vector-host junctions and optimized the conditions for the quantitative VIS sequencing. The assay was validated by quantifying the relative frequencies of hundreds of repopulating HPSC clones in a nonhuman primate. The reliability and sensitivity of the assay were assessed using clone-specific real-time PCR. The majority of tested clones showed a strong correlation between the two methods. This assay permits high-throughput and sensitive assessment of clonal populations and hence will be useful for a broad range of gene therapy, stem cell, and cancer research applications.
doi:10.1128/JVI.01355-10
PMCID: PMC2977897  PMID: 20844053
8.  Quantification of Cytokeratin 5 mRNA Expression in the Circulation of Healthy Human Subjects and after Lung Transplantation 
PLoS ONE  2009;4(6):e5925.
Background
Circulating epithelial progenitor cells are important for repair of the airway epithelium in a mouse model of tracheal transplantation. We therefore hypothesized that circulating epithelial progenitor cells would also be present in normal human subjects and could be important for repair of the airway after lung injury. As lung transplantation is associated with lung injury, which is severe early on and exacerbated during episodes of infection and rejection, we hypothesized that circulating epithelial progenitor cell levels could predict clinical outcome following lung transplantation.
Methodology/Principal Findings
Quantitative Real Time PCR was performed to determine peripheral blood mRNA levels of cytokeratin 5, a previously characterized marker of circulating epithelial progenitor cells. Cytokeratin 5 levels were evaluated in healthy human subjects, in lung transplant recipients immediately post-transplant and serially thereafter, and in heart transplant recipients. All normal human subjects examined expressed cytokeratin 5 in their buffy coat in amounts that were not significantly influenced by age or gender. There was a profound, statistically significant decrease in cytokeratin 5 mRNA expression levels in lung transplant patients compared to healthy human subjects (p = 3.1×10−13) and to heart transplant recipients. There was a moderate negative correlation between improved circulating cytokeratin 5 mRNA levels in lung transplant recipients with recovering lung function, as measured by improved FEV1 values (rho = −0.39).
Conclusions/Significance
Levels of cytokeratin 5 mRNA, a proxy marker for circulating epithelial progenitor cells, inversely correlated with disease status in lung transplant recipients. It may therefore serve as a biomarker of the clinical outcome of lung transplant patients and potentially other patients with airway injury.
doi:10.1371/journal.pone.0005925
PMCID: PMC2691992  PMID: 19529775
9.  Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome 
BMC Systems Biology  2008;2:95.
Background
Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.
Results
We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways.
Conclusion
We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.
doi:10.1186/1752-0509-2-95
PMCID: PMC2625353  PMID: 18986552
10.  Merging microsatellite data: enhanced methodology and software to combine genotype data for linkage and association analysis 
BMC Bioinformatics  2008;9:317.
Background
Correctly merged data sets that have been independently genotyped can increase statistical power in linkage and association studies. However, alleles from microsatellite data sets genotyped with different experimental protocols or platforms cannot be accurately matched using base-pair size information alone. In a previous publication we introduced a statistical model for merging microsatellite data by matching allele frequencies between data sets. These methods are implemented in our software MicroMerge version 1 (v1). While MicroMerge v1 output can be analyzed by some genetic analysis programs, many programs can not analyze alignments that do not match alleles one-to-one between data sets. A consequence of such alignments is that codominant genotypes must often be analyzed as phenotypes. In this paper we describe several extensions that are implemented in MicroMerge version 2 (v2).
Results
Notably, MicroMerge v2 includes a new one-to-one alignment option that creates merged pedigree and locus files that can be handled by most genetic analysis software. Other features in MicroMerge v2 enhance the following aspects of control: 1) optimizing the algorithm for different merging scenarios, such as data sets with very different sample sizes or multiple data sets, 2) merging small data sets when a reliable set of allele frequencies are available, and 3) improving the quantity and 4) quality of merged data. We present results from simulated and real microsatellite genotype data sets, and conclude with an association analysis of three familial dyslipidemia (FD) study samples genotyped at different laboratories. Independent analysis of each FD data set did not yield consistent results, but analysis of the merged data sets identified strong association at locus D11S2002.
Conclusion
The MicroMerge v2 features will enable merging for a variety of genotype data sets, which in turn will facilitate meta-analyses for powering association analysis.
doi:10.1186/1471-2105-9-317
PMCID: PMC2515855  PMID: 18644149

Results 1-10 (10)