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1.  Breakthroughs in genomics data integration for predicting clinical outcome 
Journal of biomedical informatics  2012;45(6):1199-1201.
PMCID: PMC3632294  PMID: 23117078
2.  The rise of translational bioinformatics 
Genome Biology  2012;13(8):319.
A report on the 20th International Conference on Intelligent Systems for Molecular Biology (ISMB), held at Long Beach, California, USA, July 15-17, 2012.
PMCID: PMC3491366  PMID: 22943369
Biomarkers; complex diseases; computational medicine; drug repositioning; mechanism classifiers; next-generation sequencing; off-target mechanisms; translational bioinformatics
4.  Curation-free biomodules mechanisms in prostate cancer predict recurrent disease 
BMC Medical Genomics  2013;6(Suppl 2):S4.
Gene expression-based prostate cancer gene signatures of poor prognosis are hampered by lack of gene feature reproducibility and a lack of understandability of their function. Molecular pathway-level mechanisms are intrinsically more stable and more robust than an individual gene. The Functional Analysis of Individual Microarray Expression (FAIME) we developed allows distinctive sample-level pathway measurements with utility for correlation with continuous phenotypes (e.g. survival). Further, we and others have previously demonstrated that pathway-level classifiers can be as accurate as gene-level classifiers using curated genesets that may implicitly comprise ascertainment biases (e.g. KEGG, GO). Here, we hypothesized that transformation of individual prostate cancer patient gene expression to pathway-level mechanisms derived from automated high throughput analyses of genomic datasets may also permit personalized pathway analysis and improve prognosis of recurrent disease.
Via FAIME, three independent prostate gene expression arrays with both normal and tumor samples were transformed into two distinct types of molecular pathway mechanisms: (i) the curated Gene Ontology (GO) and (ii) dynamic expression activity networks of cancer (Cancer Modules). FAIME-derived mechanisms for tumorigenesis were then identified and compared. Curated GO and computationally generated "Cancer Module" mechanisms overlap significantly and are enriched for known oncogenic deregulations and highlight potential areas of investigation. We further show in two independent datasets that these pathway-level tumorigenesis mechanisms can identify men who are more likely to develop recurrent prostate cancer (log-rank_p = 0.019).
Curation-free biomodules classification derived from congruent gene expression activation breaks from the paradigm of recapitulating the known curated pathway mechanism universe.
PMCID: PMC3654873  PMID: 23819917
5.  The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools 
Applying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties.
Materials and Methods
Datasets were the Center for Medicaid and Medicare Services ICD-9-CM to ICD-10-CM mapping files, general equivalence mappings, and statewide Medicaid emergency department billing. Diagnoses were represented as nodes and their mappings as directional relationships. The complex network was synthesized as an aggregate of simpler motifs and tabulation per clinical specialty.
We identified five mapping motif categories: identity, class-to-subclass, subclass-to-class, convoluted, and no mapping. Convoluted mappings indicate that multiple ICD-9-CM and ICD-10-CM codes share complex, entangled, and non-reciprocal mappings. The proportions of convoluted diagnoses mappings (36% overall) range from 5% (hematology) to 60% (obstetrics and injuries). In a case study of 24 008 patient visits in 217 emergency departments, 27% of the costs are associated with convoluted diagnoses, with ‘abdominal pain’ and ‘gastroenteritis’ accounting for approximately 3.5%.
Previous qualitative studies report that administrators and clinicians are likely to be challenged in understanding and managing their practice because of the ICD-10-CM transition. We substantiate the complexity of this transition with a thorough quantitative summary per clinical specialty, a case study, and the tools to apply this methodology easily to any clinical practice in the form of a web portal and analytic tables.
Post-transition, successful management of frequent diseases with convoluted mapping network patterns is critical. The web portal provides insight in linking onerous diseases to the ICD-10 transition.
PMCID: PMC3721160  PMID: 23645552
ICD-9-CM; ICD-10-CM; billing complexity; transition to ICD-10-CM; networks; motifs
6.  Chronic intestinal inflammation induces stress response genes in commensal Escherichia coli 
Gastroenterology  2011;141(5):1842-51.e1-10.
Intestinal microbes induce homeostatic mucosal immune responses, but can also cause inappropriate immune activation in genetically susceptible hosts. While immune responses to bacterial products have been studied extensively, little is known about how intestinal inflammation affects the function of commensal luminal microbes.
Microarrays and real-time PCR were used to profile transcriptional changes in luminal bacteria from wild-type (WT) and IL-10−/− (KO) mice monoassociated with a non-pathogenic murine Escherichia coli isolate (NC101), which causes colitis in gnotobiotic KO mice. Colonic inflammation, innate and adaptive immune responses were measured in WT and KO mice monoassociated with mutant NC101 lacking selected upregulated genes and in KO mice co-colonized with mutant and parental NC101. Intracellular survival of bacteria within primary mouse macrophages and resultant TNF production was measured.
Significant upregulation of the stress response regulon, including the small heat shock proteins IbpA and IbpB that protect E. coli from oxidative stress, was observed in bacteria from KO mice with colitis compared to healthy WT controls. In KO mice, ibpAB expression resulted in reduced colonic histologic inflammation, secretion of IL-12/23p40 by colonic explant cultures, serologic reactivity to NC101 antigens, and IFNγ secretion by stimulated mesenteric lymph node cells. Infection of primary macrophages by bacteria expressing ibpAB was associated with decreased intracellular survival and attenuated TNF secretion.
Chronic intestinal inflammation causes functional alterations in gene expression of a commensal gut bacterium. Further studies of this component of the host-microbial dialogue may identify potential novel therapeutic targets to treat inflammatory bowel diseases.
PMCID: PMC3624969  PMID: 21726510
Inflammation; Bacteria; Gene Expression
7.  Genomic assessment of a multikinase inhibitor, sorafenib, in a rodent model of pulmonary hypertension 
Physiological genomics  2008;33(2):278-291.
Pulmonary hypertension (PH) and cancer pathology share growth factor- and MAPK stress-mediated signaling pathways resulting in endothelial and smooth muscle cell dysfunction and angioproliferative vasculopathy. In this study, we assessed sorafenib, an antineoplastic agent and inhibitor of multiple kinases important in angiogenesis [VEGF receptor (VEGFR)-1–3, PDGF receptor (PDGFR)-β, Raf-1 kinase] as a potential PH therapy. Two PH rat models were used: a conventional hypoxia-induced PH model and an augmented PH model combining dual VEGFR-1 and -2 inhibition (SU-5416, single 20 mg/kg injection) with hypoxia. In addition to normoxia-exposed control animals, four groups were maintained at 10% inspired O2 fraction for 3.5 wk (hypoxia/vehicle, hypoxia/SU-5416, hypoxia/sorafenib, and hypoxia/SU-5416/sorafenib). Compared with normoxic control animals, rats exposed to hypoxia/SU-5416 developed hemodynamic and histological evidence of severe PH while rats exposed to hypoxia alone displayed only mild elevations in hemodynamic values (pulmonary vascular and right ventricular pressures). Sorafenib treatment (daily gavage, 2.5 mg/kg) prevented hemodynamic changes and demonstrated dramatic attenuation of PH-associated vascular remodeling. Compared with normoxic control rats, expression profiling (Affymetrix platform) of lung RNA obtained from hypoxia [false discovery rate (FDR) 6.5%]- and hypoxia/SU-5416 (FDR 1.6%)-challenged rats yielded 1,019 and 465 differentially regulated genes (fold change >1.4), respectively. A novel molecular signature consisting of 38 differentially expressed genes between hypoxia/SU-5416 and hypoxia/SU-5416/sorafenib (FDR 6.7%) was validated by either real-time RT-PCR or immunoblotting. Finally, immunoblotting studies confirmed the upregulation of the MAPK cascade in both PH models, which was abolished by sorafenib. In summary, sorafenib represents a novel potential treatment for severe PH with the MAPK cascade a potential canonical target.
PMCID: PMC3616402  PMID: 18303084
microarrays; SU-5416; bioinformatics
8.  Use of consomic rats for genomic insights into ventilator-associated lung injury 
Increasing evidence supports the contribution of genetic influences on susceptibility/severity in acute lung injury (ALI), a devastating syndrome requiring mechanical ventilation with subsequent risk for ventilator-associated lung injury (VALI). To identify VALI candidate genes, we determined that Brown Norway (BN) and Dahl salt-sensitive (SS) rat strains were differentially sensitive to VALI (tidal volume of 20 ml/kg, 85 breaths/min, 2 h) defined by bronchoalveolar lavage (BAL) protein and leukocytes. We next exploited differential sensitivities and phenotyped both the VALI-sensitive BN and the VALI-resistant SS rat strains by expression profiling coupled to a bioinformatic-intense candidate gene approach (Significance Analysis of Microarrays, i.e., SAM). We identified 106 differentially expressed VALI genes representing gene ontologies such as “transcription” and “chemotaxis/cell motility.” We mapped the chromosomal location of the differentially expressed probe sets and selected consomic SS rats with single BN introgressions of chromosomes 2, 13, and 16 (based on the highest density of probe sets) while also choosing chromosome 20 (low probe sets density). VALI exposure of consomic rats with introgressions of BN chromosomes 13 and 16 resulted in significant increases in both BAL cells and protein (compared to parental SS strain), whereas introgression of BN chromosome 2 displayed a large increase only in BAL protein. Introgression of BN chromosome 20 had a minimal effect. These results suggest that genes residing on BN chromosomes 2, 13, and 16 confer increased sensitivity to high tidal volume ventilation. We speculate that the consomic-microarray-SAM approach is a time- and resource-efficient tool for the genetic dissection of complex diseases including VALI.
PMCID: PMC3616407  PMID: 17468131
rodent mechanical ventilation; consomics; bioinformatics; microarrays; candidate gene approach
9.  Integrating Governance of Research Informatics and Health Care IT Across an Enterprise: Experiences from the Trenches  
Advances in health information technology and biomedical informatics have laid the groundwork for significant improvements in healthcare and biomedical research. For instance, Electronic Health Records can help improve the delivery of evidence-based care, enhance quality, and contribute to discoveries and evidence generation. Despite this promise, there are many challenges to achieving the vision and missions of our healthcare and research enterprises. Given the challenges inherent in doing so, institutions are increasingly moving to establish dedicated leadership and governance models charged with designing, deploying and leveraging various information resources to advance research and advanced care activities at AHCs. Some institutions have even created a new leadership position to oversee such activities, such as the Chief Research Information Officer. This panel will include research informatics leaders discussing their experiences from the proverbial trenches as they work to operationalize such cross-mission governance models. Panelists will start by providing an overview their respective positions and environments, discuss their experiences, and share lessons learned through their work at the intersection of clinical and translational research informatics and Health IT.
PMCID: PMC3845750  PMID: 24303236
Despite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-seq data. In this study, we hypothesize that the “Functional Analysis of Individual Microarray Expression” (FAIME) method we developed could be smoothly extended to RNA-seq data and unveil intrinsic underlying mechanism signatures across different scales of biological data for the same complex disease. Using publicly available RNA-seq data for gastric cancer, we confirmed the effectiveness of this method (i) to translate each sample transcriptome to pathway-scale scores, (ii) to predict deregulated pathways in gastric cancer against gold standards (FDR<5%, Precision=75%, Recall =92%), and (iii) to predict phenotypes in an independent dataset and expression platform (RNA-seq vs microarrays, Fisher Exact Test p<10−6). Measuring at a single-sample level, FAIME could differentiate cancer samples from normal ones; furthermore, it achieved comparative performance in identifying differentially expressed pathways as compared to state-of-the-art cross-sample methods. These results motivate future work on mechanism-level biomarker discovery predictive of diagnoses, treatment, and therapy.
PMCID: PMC3595401  PMID: 23424121
11.  People, organizational, and leadership factors impacting informatics support for clinical and translational research 
In recent years, there have been numerous initiatives undertaken to describe critical information needs related to the collection, management, analysis, and dissemination of data in support of biomedical research (J Investig Med 54:327-333, 2006); (J Am Med Inform Assoc 16:316–327, 2009); (Physiol Genomics 39:131-140, 2009); (J Am Med Inform Assoc 18:354–357, 2011). A common theme spanning such reports has been the importance of understanding and optimizing people, organizational, and leadership factors in order to achieve the promise of efficient and timely research (J Am Med Inform Assoc 15:283–289, 2008). With the emergence of clinical and translational science (CTS) as a national priority in the United States, and the corresponding growth in the scale and scope of CTS research programs, the acuity of such information needs continues to increase (JAMA 289:1278–1287, 2003); (N Engl J Med 353:1621–1623, 2005); (Sci Transl Med 3:90, 2011). At the same time, systematic evaluations of optimal people, organizational, and leadership factors that influence the provision of data, information, and knowledge management technologies and methods are notably lacking.
In response to the preceding gap in knowledge, we have conducted both: 1) a structured survey of domain experts at Academic Health Centers (AHCs); and 2) a subsequent thematic analysis of public-domain documentation provided by those same organizations. The results of these approaches were then used to identify critical factors that may influence access to informatics expertise and resources relevant to the CTS domain.
A total of 31 domain experts, spanning the Biomedical Informatics (BMI), Computer Science (CS), Information Science (IS), and Information Technology (IT) disciplines participated in a structured surveyprocess. At a high level, respondents identified notable differences in theaccess to BMI, CS, and IT expertise and services depending on the establishment of a formal BMI academic unit and the perceived relationship between BMI, CS, IS, and IT leaders. Subsequent thematic analysis of the aforementioned public domain documents demonstrated a discordance between perceived and reported integration across and between BMI, CS, IS, and IT programs and leaders with relevance to the CTS domain.
Differences in people, organization, and leadership factors do influence the effectiveness of CTS programs, particularly with regard to the ability to access and leverage BMI, CS, IS, and IT expertise and resources. Based on this finding, we believe that the development of a better understanding of how optimal BMI, CS, IS, and IT organizational structures and leadership models are designed and implemented is critical to both the advancement of CTS and ultimately, to improvements in the quality, safety, and effectiveness of healthcare.
PMCID: PMC3577661  PMID: 23388243
12.  Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases 
While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits.
We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance.
Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p<2.24×10−12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland–USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes).
Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.
PMCID: PMC3721168  PMID: 23355459
Protein Interactions; SNPs; Complex Disease Inheritance; Adult Onset Diabetes; Crohn's Disease
13.  Oligo- and Polymetastatic Progression in Lung Metastasis(es) Patients Is Associated with Specific MicroRNAs 
PLoS ONE  2012;7(12):e50141.
Strategies to stage and treat cancer rely on a presumption of either localized or widespread metastatic disease. An intermediate state of metastasis termed oligometastasis(es) characterized by limited progression has been proposed. Oligometastases are amenable to treatment by surgical resection or radiotherapy.
We analyzed microRNA expression patterns from lung metastasis samples of patients with ≤5 initial metastases resected with curative intent.
Patients were stratified into subgroups based on their rate of metastatic progression. We prioritized microRNAs between patients with the highest and lowest rates of recurrence. We designated these as high rate of progression (HRP) and low rate of progression (LRP); the latter group included patients with no recurrences. The prioritized microRNAs distinguished HRP from LRP and were associated with rate of metastatic progression and survival in an independent validation dataset.
Oligo- and poly- metastasis are distinct entities at the clinical and molecular level.
PMCID: PMC3518475  PMID: 23251360
14.  Towards Mechanism Classifiers: Expression-anchored Gene Ontology Signature Predicts Clinical Outcome in Lung Adenocarcinoma Patients 
AMIA Annual Symposium Proceedings  2012;2012:1040-1049.
We aim to provide clinically applicable, reproducible, mechanistic interpretations of gene expression changes that lack in gene overlap among predictive gene-signatures. Using a method we recently developed, Functional Analysis of Individual Microarray Expression (FAIME), we provide evidence that Gene Ontology-anchored signatures (GO-signatures) show reliable prognosis in lung cancer. In order to demonstrate the biological congruence and reproducibility of FAIME-derived mechanism classifiers, we chose a disease where gene expression classifiers signatures alone had failed to significantly stratify a larger collection of samples and that exhibited poor or no genetic overlap. For each patient in the two lung adenocarcinoma studies, personalized FAIME-profiles of GO biological processes are generated from genome-wide expression profiles. For both training studies, GO-signatures significantly associated to patient mortality were identified (Prediction Analysis for Microarrays; three-fold cross-validation). These two GO-signatures could effectively stratify patients from an independent validation cohort into sub-groups that show significant differences in disease-free survival (log-rank test P=0.019; P=0.001). Importantly, significant mechanism overlaps assessed by information-theory similarity were detected between the two GO-signatures (Fischer Exact Test p=0.001). Hence, together with machine learning technologies, FAIME could be utilized to develop an ontology-driven and expression-anchored prognostic signature that is personalized for an individual patient.
PMCID: PMC3540430  PMID: 23304380
15.  A Sphingosine 1–Phosphate 1 Receptor Agonist Modulates Brain Death–Induced Neurogenic Pulmonary Injury 
Lung transplantation remains the only viable therapy for patients with end-stage lung disease. However, the full utilization of this strategy is severely compromised by a lack of donor lung availability. The vast majority of donor lungs available for transplantation are from individuals after brain death (BD). Unfortunately, the early autonomic storm that accompanies BD often results in neurogenic pulmonary edema (NPE), producing varying degrees of lung injury or leading to primary graft dysfunction after transplantation. We demonstrated that sphingosine 1–phosphate (S1P)/analogues, which are major barrier-enhancing agents, reduce vascular permeability via the S1P1 receptor, S1PR1. Because primary lung graft dysfunction is induced by lung vascular endothelial cell barrier dysfunction, we hypothesized that the S1PR1 agonist, SEW-2871, may attenuate NPE when administered to the donor shortly after BD. Significant lung injury was observed after BD, with increases of approximately 60% in bronchoalveolar lavage (BAL) total protein, cell counts, and lung tissue wet/dry (W/D) weight ratios. In contrast, rats receiving SEW-2871 (0.1 mg/kg) 15 minutes after BD and assessed after 4 hours exhibited significant lung protection (∼ 50% reduction, P = 0.01), as reflected by reduced BAL protein/albumin, cytokines, cellularity, and lung tissue wet/dry weight ratio. Microarray analysis at 4 hours revealed a global impact of both BD and SEW on lung gene expression, with a differential gene expression of enriched immune-response/inflammation pathways across all groups. Overall, SEW served to attenuate the BD-mediated up-regulation of gene expression. Two potential biomarkers, TNF and chemokine CC motif receptor-like 2, exhibited gene array dysregulation. We conclude that SEW-2871 significantly attenuates BD-induced lung injury, and may serve as a potential candidate to improve human donor availability.
PMCID: PMC3262681  PMID: 21617203
neurogenic pulmonary edema; lung injury; sphingosine 1–phosphate; sphingolipids; lung transplant donors
16.  Variants Affecting Exon Skipping Contribute to Complex Traits 
PLoS Genetics  2012;8(10):e1002998.
DNA variants that affect alternative splicing and the relative quantities of different gene transcripts have been shown to be risk alleles for some Mendelian diseases. However, for complex traits characterized by a low odds ratio for any single contributing variant, very few studies have investigated the contribution of splicing variants. The overarching goal of this study is to discover and characterize the role that variants affecting alternative splicing may play in the genetic etiology of complex traits, which include a significant number of the common human diseases. Specifically, we hypothesize that single nucleotide polymorphisms (SNPs) in splicing regulatory elements can be characterized in silico to identify variants affecting splicing, and that these variants may contribute to the etiology of complex diseases as well as the inter-individual variability in the ratios of alternative transcripts. We leverage high-throughput expression profiling to 1) experimentally validate our in silico predictions of skipped exons and 2) characterize the molecular role of intronic genetic variations in alternative splicing events in the context of complex human traits and diseases. We propose that intronic SNPs play a role as genetic regulators within splicing regulatory elements and show that their associated exon skipping events can affect protein domains and structure. We find that SNPs we would predict to affect exon skipping are enriched among the set of SNPs reported to be associated with complex human traits.
Author Summary
Alternative splicing is a common eukaryotic cellular mechanism that allows for the production of multiple proteins from one gene and occurs in 40%–90% of all human genes. Alternative splicing has been shown to be important for many critical biological processes, including development, evolution, and even psychological behavior. Additionally, alternative splicing has been associated with 15%–50% of human genetic diseases, including breast cancer; however, the precise mechanism by which genetic variations regulate this process remains to be fully elucidated. In this study, we develop an integrative approach that utilizes sequence-based analysis and genome-wide expression profiling to identify genetic variations that may affect alternative splicing. We also evaluate their enrichment among established disease-associated variations. Our study provides insights into the functionality of these variations and emphasizes their importance for complex human traits and diseases.
PMCID: PMC3486879  PMID: 23133393
17.  Deregulation of a Hox Protein Regulatory Network Spanning Prostate Cancer Initiation and Progression 
The aberrant activity of developmental pathways in prostate cancer may provide significant insight into predicting tumor initiation and progression, as well as identifying novel therapeutic targets. To this end, despite shared androgen-dependence and functional similarities to the prostate gland, seminal vesicle cancer is exceptionally rare.
Experimental Design
We conducted genomic pathway analyses comparing patient-matched normal prostate and seminal vesicle epithelial cells to identify novel pathways for tumor initiation and progression. Derived gene expression profiles were grouped into cancer biomodules using a protein–protein network algorithm to analyze their relationship to known oncogenes. Each resultant biomodule was assayed for its prognostic ability against publically available prostate cancer patient gene array datasets.
Analyses show that the embryonic developmental biomodule containing four homeobox gene family members (Meis1, Meis2, Pbx1, and HoxA9) detects a survival difference in a set of watchful-waiting patients (n = 172, P = 0.05), identify men who are more likely to recur biochemically postprostatectomy (n = 78, P = 0.02), correlate with Gleason score (r = 0.98, P = 0.02), and distinguish between normal prostate, primary tumor, and metastatic disease. In contrast to other cancer types, Meis1, Meis2, and Pbx1 expression is decreased in poor-prognosis tumors, implying that they function as tumor suppressor genes for prostate cancer. Immunohistochemical staining documents nuclear basal-epithelial and stromal Meis2 staining, with loss of Meis2 expression in prostate tumors.
These data implicate deregulation of the Hox protein cofactors Meis1, Meis2, and Pbx1 as serving a critical function to suppress prostate cancer initiation and progression.
PMCID: PMC3479663  PMID: 22723371
18.  Replication Analysis for Severe Diabetic Retinopathy 
The purpose of this study is to attempt to replicate the top single nucleotide polymorphism (SNP) associations from a previous genome-wide association study (GWAS) for the sight-threatening complications of diabetic retinopathy in an independent cohort of diabetic subjects from the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR).
This study included 469 type 1 diabetic, Caucasian subjects from WESDR. Cases (n = 208) were defined by prior laser treatment for either proliferative diabetic retinopathy or diabetic macular edema. Controls (n = 261) were all other subjects in the cohort. Three hundred eighty-nine SNPs were tested for association using the Illumina GoldenGate custom array. A retinopathy-only subanalysis was conducted in 437 subjects by removing those with end-stage renal disease. Evaluation for association between cases and controls was conducted by using chi-square tests. A combined analysis incorporated the results from WESDR with the prior GWAS.
No associations were significant at a genome-wide level. The analysis did identify SNPs that can be pursued in future replication studies. The top association was at rs4865047, an intronic SNP, in the gene CEP135 (P value 2.06 × 10−5). The top association from the subanalysis was at rs1902491 (P value 2.81 × 10−5), a SNP that sits upstream of the gene NPY2R.
This study nominates several novel genetic loci that may be associated with severe diabetic retinopathy. In order to confirm these findings, replication and extension in additional cohorts will be necessary as susceptibility alleles for diabetic retinopathy appear to be of modest effect.
In an attempt to replicate the top single nucleotide polymorphism associations from a previous genome-wide association, this study nominates genetic loci that may be associated with severe diabetic retinopathy.
PMCID: PMC3777289  PMID: 22427569
19.  GO-Module: functional synthesis and improved interpretation of Gene Ontology patterns 
Bioinformatics  2011;27(10):1444-1446.
Summary: GO-Module is a web-accessible synthesis and visualization tool developed for end-user biologists to greatly simplify the interpretation of prioritized Gene Ontology (GO) terms. GO-Module radically reduces the complexity of raw GO results into compact biomodules in two distinct ways, by (i) constructing biomodules from significant GO terms based on hierarchical knowledge, and (ii) refining the GO terms in each biomodule to contain only true positive results. Altogether, the features (biomodules) of GO-Module outputs are better organized and on average four times smaller than the input GO terms list (P = 0.0005, n = 16).
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3087953  PMID: 21421553
20.  Simvastatin Attenuates Radiation-Induced Murine Lung Injury and Dysregulated Lung Gene Expression 
Novel therapies are desperately needed for radiation-induced lung injury (RILI), which, despite aggressive corticosteroid therapy, remains a potentially fatal and dose-limiting complication of thoracic radiotherapy. We assessed the utility of simvastatin, an anti-inflammatory and lung barrier–protective agent, in a dose- and time-dependent murine model of RILI (18–(25 Gy). Simvastatin reduced multiple RILI indices, including vascular leak, leukocyte infiltration, and histological evidence of oxidative stress, while reversing RILI-associated dysregulated gene expression, including p53, nuclear factor–erythroid-2–related factor, and sphingolipid metabolic pathway genes. To identify key regulators of simvastatin-mediated RILI protection, we integrated whole-lung gene expression data obtained from radiated and simvastatin-treated mice with protein–protein interaction network analysis (single-network analysis of proteins). Topological analysis of the gene product interaction network identified eight top-prioritized genes (Ccna2a, Cdc2, fcer1 g, Syk, Vav3, Mmp9, Itgam, Cd44) as regulatory nodes within an activated RILI network. These studies identify the involvement of specific genes and gene networks in RILI pathobiology, and confirm that statins represent a novel strategy to limit RILI.
PMCID: PMC3095940  PMID: 20508068
radiation pneumonitis; lung vascular permeability; simvastatin; gene dysregulation; protein–protein interaction
21.  Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants 
Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.
Materials and methods
An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism.
Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001).
The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP).
These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics.
PMCID: PMC3277633  PMID: 22319180
Personal genomics; protein interaction networks; medicine; translational bioinformatics; complex disease; ontology; protein–protein interactions; bioinformatcis; alternative splicing; genetics; network; SNP; protein networks; text-mining; bioinformatics; knowledge representations; uncertain reasoning and decision theory; languages; computational methods
22.  Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer 
PLoS Computational Biology  2012;8(1):e1002350.
Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).
Author Summary
Clinical utilization of multi-gene expression signatures that are predictive of therapeutic response has been steadily increasing, however, interpretation of such results remains challenging because multi-gene signatures, generated from analyzing different patient cohorts, tend to be equally predictive but contain minimal overlap. Whereas pathway-level analyses of expression arrays show promise for generating clinically meaningful mechanistic signatures, current approaches do not permit single-patient based analyses that are independent of cross-group calculations. To bridge the gap between deterministic biological mechanisms of single-gene biomarkers and the statistical predictive power of multi-gene signatures that are disconnected from mechanisms, we developed FAIME, a novel method that transforms microarray gene expression data into individualized patient profiles of molecular mechanisms. We have validated its capability for predicting clinical outcomes, including cancer patient samples derived from six different clinical trial cohorts of head and neck cancers. This method provides opportunities to harness an untapped resource for personal genomics: clinical evaluation and testing of individually interpretable mechanistic profiles derived from gene expression arrays.
PMCID: PMC3266878  PMID: 22291585
23.  Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory 
Thousands of complex-disease single-nucleotide polymorphisms (SNPs) have been discovered in genome-wide association studies (GWAS). However, these intragenic SNPs have not been collectively mined to unveil the genetic architecture between complex clinical traits. The authors hypothesize that biological annotations of host genes of trait-associated SNPs may reveal the biomolecular modularity across complex-disease traits and offer insights for drug repositioning.
Trait-to-polymorphism (SNPs) associations confirmed in GWAS were used. A novel method to quantify trait–trait similarity anchored in Gene Ontology annotations of human proteins and information theory was developed. The results were then validated with the shortest paths of physical protein interactions between biologically similar traits.
A network was constructed consisting of 280 significant intertrait similarities among 177 disease traits, which covered 1438 well-validated disease-associated SNPs. Thirty-nine percent of intertrait connections were confirmed by curators, and the following additional studies demonstrated the validity of a proportion of the remainder. On a phenotypic trait level, higher Gene Ontology similarity between proteins correlated with smaller ‘shortest distance’ in protein interaction networks of complexly inherited diseases (Spearman p<2.2×10−16). Further, ‘cancer traits’ were similar to one another, as were ‘metabolic syndrome traits’ (Fisher's exact test p=0.001 and 3.5×10−7, respectively).
An imputed disease network by information-anchored functional similarity from GWAS trait-associated SNPs is reported. It is also demonstrated that small shortest paths of protein interactions correlate with complex-disease function. Taken together, these findings provide the framework for investigating drug targets with unbiased functional biomolecular networks rather than worn-out single-gene and subjective canonical pathway approaches.
PMCID: PMC3277620  PMID: 22278381
Complex disease; SNP; gene ontology; protein-interaction networks; information theory; translational bioinformatics; complex disease; ontology; bioinformatcis; genetics; network; prostate cancer; protein networks; pathway analysis; network modeling; knowledge representations; uncertain reasoning and decision theory; languages and computational methods
24.  Genetic Interactions between Chromosomes 11 and 18 Contribute to Airway Hyperresponsiveness in Mice 
PLoS ONE  2012;7(1):e29579.
We used two-dimensional quantitative trait locus analysis to identify interacting genetic loci that contribute to the native airway constrictor hyperresponsiveness to methacholine that characterizes A/J mice, relative to C57BL/6J mice. We quantified airway responsiveness to intravenous methacholine boluses in eighty-eight (C57BL/6J X A/J) F2 and twenty-seven (A/J X C57BL/6J) F2 mice as well as ten A/J mice and six C57BL/6J mice; all studies were performed in male mice. Mice were genotyped at 384 SNP markers, and from these data two-QTL analyses disclosed one pair of interacting loci on chromosomes 11 and 18; the homozygous A/J genotype at each locus constituted the genetic interaction linked to the hyperresponsive A/J phenotype. Bioinformatic network analysis of potential interactions among proteins encoded by genes in the linked regions disclosed two high priority subnetworks - Myl7, Rock1, Limk2; and Npc1, Npc1l1. Evidence in the literature supports the possibility that either or both networks could contribute to the regulation of airway constrictor responsiveness. Together, these results should stimulate evaluation of the genetic contribution of these networks in the regulation of airway responsiveness in humans.
PMCID: PMC3254621  PMID: 22253740
25.  Non–Muscle Myosin Light Chain Kinase Isoform Is a Viable Molecular Target in Acute Inflammatory Lung Injury 
Acute lung injury (ALI) and mechanical ventilator-induced lung injury (VILI), major causes of acute respiratory failure with elevated morbidity and mortality, are characterized by significant pulmonary inflammation and alveolar/vascular barrier dysfunction. Previous studies highlighted the role of the non–muscle myosin light chain kinase isoform (nmMLCK) as an essential element of the inflammatory response, with variants in the MYLK gene that contribute to ALI susceptibility. To define nmMLCK involvement further in acute inflammatory syndromes, we used two murine models of inflammatory lung injury, induced by either an intratracheal administration of lipopolysaccharide (LPS model) or mechanical ventilation with increased tidal volumes (the VILI model). Intravenous delivery of the membrane-permeant MLC kinase peptide inhibitor, PIK, produced a dose-dependent attenuation of both LPS-induced lung inflammation and VILI (∼50% reductions in alveolar/vascular permeability and leukocyte influx). Intravenous injections of nmMLCK silencing RNA, either directly or as cargo within angiotensin-converting enzyme (ACE) antibody–conjugated liposomes (to target the pulmonary vasculature selectively), decreased nmMLCK lung expression (∼70% reduction) and significantly attenuated LPS-induced and VILI-induced lung inflammation (∼40% reduction in bronchoalveolar lavage protein). Compared with wild-type mice, nmMLCK knockout mice were significantly protected from VILI, with significant reductions in VILI-induced gene expression in biological pathways such as nrf2-mediated oxidative stress, coagulation, p53-signaling, leukocyte extravasation, and IL-6–signaling. These studies validate nmMLCK as an attractive target for ameliorating the adverse effects of dysregulated lung inflammation.
PMCID: PMC3028257  PMID: 20139351
endotoxin/lipopolysaccharide; nmMLCK; mice; lung injury; endothelial barrier

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