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1.  Identification of Chiari Type I Malformation subtypes using whole genome expression profiles and cranial base morphometrics 
BMC Medical Genomics  2014;7:39.
Background
Chiari Type I Malformation (CMI) is characterized by herniation of the cerebellar tonsils through the foramen magnum at the base of the skull, resulting in significant neurologic morbidity. As CMI patients display a high degree of clinical variability and multiple mechanisms have been proposed for tonsillar herniation, it is hypothesized that this heterogeneous disorder is due to multiple genetic and environmental factors. The purpose of the present study was to gain a better understanding of what factors contribute to this heterogeneity by using an unsupervised statistical approach to define disease subtypes within a case-only pediatric population.
Methods
A collection of forty-four pediatric CMI patients were ascertained to identify disease subtypes using whole genome expression profiles generated from patient blood and dura mater tissue samples, and radiological data consisting of posterior fossa (PF) morphometrics. Sparse k-means clustering and an extension to accommodate multiple data sources were used to cluster patients into more homogeneous groups using biological and radiological data both individually and collectively.
Results
All clustering analyses resulted in the significant identification of patient classes, with the pure biological classes derived from patient blood and dura mater samples demonstrating the strongest evidence. Those patient classes were further characterized by identifying enriched biological pathways, as well as correlated cranial base morphological and clinical traits.
Conclusions
Our results implicate several strong biological candidates warranting further investigation from the dura expression analysis and also identified a blood gene expression profile corresponding to a global down-regulation in protein synthesis.
doi:10.1186/1755-8794-7-39
PMCID: PMC4082616  PMID: 24962150
Chiari Type I Malformation; Posterior fossa; Disease subtypes; Whole genome expression; Cranial base morphometrics; Clustering
2.  Selection of competent blastocysts for transfer by combining time-lapse monitoring and array CGH testing for patients undergoing preimplantation genetic screening: a prospective study with sibling oocytes 
BMC Medical Genomics  2014;7:38.
Background
Recent advances in time-lapse monitoring in IVF treatment have provided new morphokinetic markers for embryonic competence. However, there is still very limited information about the relationship between morphokinetic parameters, chromosomal compositions and implantation potential. Accordingly, this study aimed at investigating the effects of selecting competent blastocysts for transfer by combining time-lapse monitoring and array CGH testing on pregnancy and implantation outcomes for patients undergoing preimplantation genetic screening (PGS).
Methods
A total of 1163 metaphase II (MII) oocytes were retrieved from 138 PGS patients at a mean age of 36.6 ± 2.4 years. These sibling MII oocytes were then randomized into two groups after ICSI: 1) Group A, oocytes (n = 582) were cultured in the time-lapse system and 2) Group B, oocytes (n = 581) were cultured in the conventional incubator. For both groups, whole genomic amplification and array CGH testing were performed after trophectoderm biopsy on day 5. One to two euploid blastocysts within the most predictive morphokinetic parameters (Group A) or with the best morphological grade available (Group B) were selected for transfer to individual patients on day 6. Ongoing pregnancy and implantation rates were compared between the two groups.
Results
There were significant differences in clinical pregnancy rates between Group A and Group B (71.1% vs. 45.9%, respectively, p = 0.037). The observed implantation rate per embryo transfer significantly increased in Group A compared to Group B (66.2% vs. 42.4%, respectively, p = 0.011). Moreover, a significant increase in ongoing pregnancy rates was also observed in Group A compared to Group B (68.9% vs. 40.5%. respectively, p = 0.019). However, there was no significant difference in miscarriage rate between the time-lapse system and the conventional incubator (3.1% vs. 11.8%, respectively, p = 0.273).
Conclusions
This is the first prospective investigation using sibling oocytes to evaluate the efficiency of selecting competent blastocysts for transfer by combining time-lapse monitoring and array CGH testing for PGS patients. Our data clearly demonstrate that the combination of these two advanced technologies to select competent blastocysts for transfer results in improved implantation and ongoing pregnancy rates for PGS patients.
doi:10.1186/1755-8794-7-38
PMCID: PMC4077552  PMID: 24954518
Time-lapse monitoring; Array CGH; PGS; Ploidy; Implantation; Miscarriage
3.  Human rhinovirus infection causes different DNA methylation changes in nasal epithelial cells from healthy and asthmatic subjects 
BMC Medical Genomics  2014;7:37.
Background
Mechanisms underlying the development of virus-induced asthma exacerbations remain unclear. To investigate if epigenetic mechanisms could be involved in virus-induced asthma exacerbations, we undertook DNA methylation profiling in asthmatic and healthy nasal epithelial cells (NECs) during Human Rhinovirus (HRV) infection in vitro.
Methods
Global and loci-specific methylation profiles were determined via Alu element and Infinium Human Methylation 450 K microarray, respectively. Principal components analysis identified the genomic loci influenced the most by disease-status and infection. Real-time PCR and pyrosequencing were used to confirm gene expression and DNA methylation, respectively.
Results
HRV infection significantly increased global DNA methylation in cells from asthmatic subjects only (43.6% to 44.1%, p = 0.04). Microarray analysis revealed 389 differentially methylated loci either based on disease status, or caused by virus infection. There were disease-associated DNA methylation patterns that were not affected by HRV infection as well as HRV-induced DNA methylation changes that were unique to each group. A common methylation locus stood out in response to HRV infection in both groups, where the small nucleolar RNA, H/ACA box 12 (SNORA12) is located. Further analysis indicated that a relationship existed between SNORA12 DNA methylation and gene expression in response to HRV infection.
Conclusions
We describe for the first time that Human rhinovirus infection causes DNA methylation changes in airway epithelial cells that differ between asthmatic and healthy subjects. These epigenetic differences may possibly explain the mechanism by which respiratory viruses cause asthma exacerbations.
doi:10.1186/1755-8794-7-37
PMCID: PMC4080608  PMID: 24947756
4.  Whole genome sequencing reveals potential targets for therapy in patients with refractory KRAS mutated metastatic colorectal cancer 
BMC Medical Genomics  2014;7:36.
Background
The outcome of patients with metastatic colorectal carcinoma (mCRC) following first line therapy is poor, with median survival of less than one year. The purpose of this study was to identify candidate therapeutically targetable somatic events in mCRC patient samples by whole genome sequencing (WGS), so as to obtain targeted treatment strategies for individual patients.
Methods
Four patients were recruited, all of whom had received > 2 prior therapy regimens. Percutaneous needle biopsies of metastases were performed with whole blood collection for the extraction of constitutional DNA. One tumor was not included in this study as the quality of tumor tissue was not sufficient for further analysis. WGS was performed using Illumina paired end chemistry on HiSeq2000 sequencing systems, which yielded coverage of greater than 30X for all samples. NGS data were processed and analyzed to detect somatic genomic alterations including point mutations, indels, copy number alterations, translocations and rearrangements.
Results
All 3 tumor samples had KRAS mutations, while 2 tumors contained mutations in the APC gene and the PIK3CA gene. Although we did not identify a TCF7L2-VTI1A translocation, we did detect a TCF7L2 mutation in one tumor. Among the other interesting mutated genes was INPPL1, an important gene involved in PI3 kinase signaling. Functional studies demonstrated that inhibition of INPPL1 reduced growth of CRC cells, suggesting that INPPL1 may promote growth in CRC.
Conclusions
Our study further supports potential molecularly defined therapeutic contexts that might provide insights into treatment strategies for refractory mCRC. New insights into the role of INPPL1 in colon tumor cell growth have also been identified. Continued development of appropriate targeted agents towards specific events may be warranted to help improve outcomes in CRC.
doi:10.1186/1755-8794-7-36
PMCID: PMC4074842  PMID: 24943349
Metastatic colorectal cancer; Whole genome sequencing; KRAS mutations
5.  A systems biology approach to understand the pathophysiological mechanisms of cardiac pathological hypertrophy associated with rosiglitazone 
BMC Medical Genomics  2014;7:35.
Background
Cardiac pathological hypertrophy is associated with a significantly increased risk of coronary heart disease and has been observed in diabetic patients treated with rosiglitazone whereas most published studies do not suggest a similar increase in risk of cardiovascular events in pioglitazone-treated diabetic subjects. This study sought to understand the pathophysiological and molecular mechanisms underlying the disparate cardiovascular effects of rosiglitazone and pioglitazone and yield knowledge as to the causative nature of rosiglitazone-associated cardiac hypertrophy.
Methods
We used a high-fat diet-induced pre-diabetic mouse model to allow bioinformatics analysis of the transcriptome of the heart of mice treated with rosiglitazone or pioglitazone.
Results
Our data show that rosiglitazone and pioglitazone both markedly improved systemic markers for glucose homeostasis, fasting plasma glucose and insulin, and the urinary excretion of albumin. Only rosiglitazone, but not pioglitazone, tended to increase atherosclerosis and induced pathological cardiac hypertrophy, based on a significant increase in heart weight and increased expression of the validated markers, ANP and BNP. Functional enrichment analysis of the rosiglitazone-specific cardiac gene expression suggests that a shift in cardiac energy metabolism, in particular decreased fatty acid oxidation toward increased glucose utilization as indicated by down regulation of relevant PPARα and PGC1α target genes. This underlies the rosiglitazone-associated pathological hypertrophic cardiac phenotype in the current study.
Conclusion
Application of a systems biology approach uncovered a shift in energy metabolism by rosiglitazone that may impact cardiac pathological hypertrophy.
doi:10.1186/1755-8794-7-35
PMCID: PMC4072889  PMID: 24938300
6.  On the identification of potential regulatory variants within genome wide association candidate SNP sets 
BMC Medical Genomics  2014;7:34.
Background
Genome wide association studies (GWAS) are a population-scale approach to the identification of segments of the genome in which genetic variations may contribute to disease risk. Current methods focus on the discovery of single nucleotide polymorphisms (SNPs) associated with disease traits. As there are many SNPs within identified risk loci, and the majority of these are situated within non-coding regions, a key challenge is to identify and prioritize variants affecting regulatory sequences that are likely to contribute to the phenotype assessed.
Methods
We focused investigation on SNPs within lung and breast cancer GWAS loci that reached genome-wide significance for potential roles in gene regulation with a specific focus on SNPs likely to disrupt transcription factor binding sites. Within risk loci, the regulatory potential of sub-regions was classified using relevant open chromatin and epigenetic high throughput sequencing data sets from the ENCODE project in available cancer and normal cell lines. Furthermore, transcription factor affinity altering variants were predicted by comparison of position weight matrix scores between disease and reference alleles. Lastly, ChIP-seq data of transcription associated factors and topological domains were included as binding evidence and potential gene target inference.
Results
The sets of SNPs, including both the disease-associated markers and those in high linkage disequilibrium with them, were significantly over-represented in regulatory sequences of cancer and/or normal cells; however, over-representation was generally not restricted to disease-relevant tissue specific regions. The calculated regulatory potential, allelic binding affinity scores and ChIP-seq binding evidence were the three criteria used to prioritize candidates. Fitting all three criteria, we highlighted breast cancer susceptibility SNPs and a borderline lung cancer relevant SNP located in cancer-specific enhancers overlapping multiple distinct transcription associated factor ChIP-seq binding sites.
Conclusion
Incorporating high throughput sequencing epigenetic and transcription factor data sets from both cancer and normal cells into cancer genetic studies reveals potential functional SNPs and informs subsequent characterization efforts.
doi:10.1186/1755-8794-7-34
PMCID: PMC4066296  PMID: 24920305
GWAS; Lung cancer; Regulatory regions; Gene regulation; Transcription factor binding site alteration; Enhancer; Topological domains
7.  Conserved recurrent gene mutations correlate with pathway deregulation and clinical outcomes of lung adenocarcinoma in never-smokers 
BMC Medical Genomics  2014;7:32.
Background
Novel and targetable mutations are needed for improved understanding and treatment of lung cancer in never-smokers.
Methods
Twenty-seven lung adenocarcinomas from never-smokers were sequenced by both exome and mRNA-seq with respective normal tissues. Somatic mutations were detected and compared with pathway deregulation, tumor phenotypes and clinical outcomes.
Results
Although somatic mutations in DNA or mRNA ranged from hundreds to thousands in each tumor, the overlap mutations between the two were only a few to a couple of hundreds. The number of somatic mutations from either DNA or mRNA was not significantly associated with clinical variables; however, the number of overlap mutations was associated with cancer subtype. These overlap mutants were preferentially expressed in mRNA with consistently higher allele frequency in mRNA than in DNA. Ten genes (EGFR, TP53, KRAS, RPS6KB2, ATXN2, DHX9, PTPN13, SP1, SPTAN1 and MYOF) had recurrent mutations and these mutations were highly correlated with pathway deregulation and patient survival.
Conclusions
The recurrent mutations present in both DNA and RNA are likely the driver for tumor biology, pathway deregulation and clinical outcomes. The information may be used for patient stratification and therapeutic target development.
doi:10.1186/1755-8794-7-32
PMCID: PMC4060138  PMID: 24894543
Lung adenocarcinoma of never smoker; Somatic mutations; Pathway deregulation; Patient survival
8.  Overlap of expression Quantitative Trait Loci (eQTL) in human brain and blood 
BMC Medical Genomics  2014;7:31.
Background
Expression quantitative trait loci (eQTL) are genomic regions regulating RNA transcript expression levels. Genome-wide Association Studies (GWAS) have identified many variants, often in non-coding regions, with unknown functions and eQTL provide a possible mechanism by which these variants may influence observable phenotypes. Limited access and availability of tissues such as brain has led to the use of blood as a substitute for eQTL analyses.
Methods
Here, we evaluate the overlap of eQTL reported in published studies conducted in blood and brain tissues to assess the utility of blood as an alternative to brain tissue in the study of neurological and psychiatric conditions. Expression QTL results from eight published brain studies were compared to blood eQTL identified in from a meta-analysis involving 5,311 individuals. We accounted for differences in SNP platforms and study design by using SNP proxies in high linkage disequilibrium with reported eQTL. The degree of overlap between studies was calculated by ascertaining if an eQTL identified in one study was also identified in the other study.
Results
The percentage of eQTL overlapping for brain and blood expression after adjusting for differences in sample size ranged from 13 - 23% (mean 19.2%). Amongst pairs of brain studies eQTL overlap ranged from 0 - 35%, with higher degrees of overlap found for studies using expression data collected from the same brain region.
Conclusion
Our results suggest that whenever possible tissue specific to the pathophysiology of the disease being studied should be used for transcription analysis.
doi:10.1186/1755-8794-7-31
PMCID: PMC4066287  PMID: 24894490
9.  Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project 
BMC Medical Genomics  2014;7:30.
Background
Invasion of tumor cells into adjacent brain parenchyma is a major cause of treatment failure in glioblastoma. Furthermore, invasive tumors are shown to have a different genomic composition and metabolic abnormalities that allow for a more aggressive GBM phenotype and resistance to therapy. We thus seek to identify those genomic abnormalities associated with a highly aggressive and invasive GBM imaging-phenotype.
Methods
We retrospectively identified 104 treatment-naïve glioblastoma patients from The Cancer Genome Atlas (TCGA) whom had gene expression profiles and corresponding MR imaging available in The Cancer Imaging Archive (TCIA). The standardized VASARI feature-set criteria were used for the qualitative visual assessments of invasion. Patients were assigned to classes based on the presence (Class A) or absence (Class B) of statistically significant invasion parameters to create an invasive imaging signature; imaging genomic analysis was subsequently performed using GenePattern Comparative Marker Selection module (Broad Institute).
Results
Our results show that patients with a combination of deep white matter tracts and ependymal invasion (Class A) on imaging had a significant decrease in overall survival as compared to patients with absence of such invasive imaging features (Class B) (8.7 versus 18.6 months, p < 0.001). Mitochondrial dysfunction was the top canonical pathway associated with Class A gene expression signature. The MYC oncogene was predicted to be the top activation regulator in Class A.
Conclusion
We demonstrate that MRI biomarker signatures can identify distinct GBM phenotypes associated with highly significant survival differences and specific molecular pathways. This study identifies mitochondrial dysfunction as the top canonical pathway in a very aggressive GBM phenotype. Thus, imaging-genomic analyses may prove invaluable in detecting novel targetable genomic pathways.
doi:10.1186/1755-8794-7-30
PMCID: PMC4057583  PMID: 24889866
Radiogenomics; MRI segmentation; Glioblastoma; Imaging genomics; Invasion; Biomarker
10.  Differential expression and role of hyperglycemia induced oxidative stress in epigenetic regulation of β1, β2 and β3-adrenergic receptors in retinal endothelial cells 
BMC Medical Genomics  2014;7:29.
Background
Aberrant epigenetic profiles are concomitant with a spectrum of developmental defects and diseases. Role of methylation is an increasingly accepted factor in the pathophysiology of diabetes and its associated complications. This study aims to examine the correlation between oxidative stress and methylation of β1, β2 and β3-adrenergic receptors and to analyze the differential variability in the expression of these genes under hyperglycemic conditions.
Methods
Human retinal endothelial cells were cultured in CSC complete medium in normal (5 mM) or high (25 mM) glucose to mimic a diabetic condition. Reverse transcription PCR and Western Blotting were performed to examine the expression of β1, β2 and β3-adrenergic receptors. For detections, immunocytochemistry was used. Bisulfite sequencing method was used for promoter methylation analysis. Apoptosis was determined by the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay. Dichlorodihydrofluorescein diacetate (DCFH-DA) assay was used to measure reactive oxygen species (ROS) production in the cells.
Results
β1 and β3-adrenergic receptors were expressed in retinal endothelial cells while β2-adrenergic receptor was not detectable both at protein and mRNA levels. Hyperglycemia had no significant effect on β1 and β2-adrenergic receptors methylation and expression however β3-adrenergic receptors showed a significantly higher expression (p < 0.05) and methylation (p < 0.01) in high and low glucose concentration respectively. Apoptosis and oxidative stress were inversely correlated with β3-adrenergic receptors methylation with no significant effect on β1 and β2-adrenergic receptors. β2-adrenergic receptor was hypermethylated with halted expression.
Conclusion
Our study demonstrates that β1 and β3-adrenergic receptors expressed in human retinal endothelial cells. Oxidative stress and apoptosis are inversely proportional to the extent of promoter methylation, suggesting that methylation loss might be due to oxidative stress-induced DNA damage.
doi:10.1186/1755-8794-7-29
PMCID: PMC4050418  PMID: 24885710
Expression; Methylation; ROS; Adrenergic receptors; Retinal endothelial cells
11.  Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation 
BMC Medical Genomics  2014;7:28.
Background
Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures.
Methods
Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.
Results
The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin.
Conclusion
This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.
doi:10.1186/1755-8794-7-28
PMCID: PMC4066312  PMID: 24885568
Type 1 diabetes; Type 2 diabetes; Gestational diabetes; Module map; Immune cell signatures; Transcriptome analysis
12.  Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era 
BMC Medical Genomics  2014;7:27.
Background
Genome-scale studies of psoriasis have been used to identify genes of potential relevance to disease mechanisms. For many identified genes, however, the cell type mediating disease activity is uncertain, which has limited our ability to design gene functional studies based on genomic findings.
Methods
We identified differentially expressed genes (DEGs) with altered expression in psoriasis lesions (n = 216 patients), as well as candidate genes near susceptibility loci from psoriasis GWAS studies. These gene sets were characterized based upon their expression across 10 cell types present in psoriasis lesions. Susceptibility-associated variation at intergenic (non-coding) loci was evaluated to identify sites of allele-specific transcription factor binding.
Results
Half of DEGs showed highest expression in skin cells, although the dominant cell type differed between psoriasis-increased DEGs (keratinocytes, 35%) and psoriasis-decreased DEGs (fibroblasts, 33%). In contrast, psoriasis GWAS candidates tended to have highest expression in immune cells (71%), with a significant fraction showing maximal expression in neutrophils (24%, P < 0.001). By identifying candidate cell types for genes near susceptibility loci, we could identify and prioritize SNPs at which susceptibility variants are predicted to influence transcription factor binding. This led to the identification of potentially causal (non-coding) SNPs for which susceptibility variants influence binding of AP-1, NF-κB, IRF1, STAT3 and STAT4.
Conclusions
These findings underscore the role of innate immunity in psoriasis and highlight neutrophils as a cell type linked with pathogenetic mechanisms. Assignment of candidate cell types to genes emerging from GWAS studies provides a first step towards functional analysis, and we have proposed an approach for generating hypotheses to explain GWAS hits at intergenic loci.
doi:10.1186/1755-8794-7-27
PMCID: PMC4060870  PMID: 24885462
AP-1; Fibroblast; GWAS; Keratinocyte; Microarray; Neutrophil; TNFRSF9; Transcription factor
13.  Translating a gene expression signature for multiple myeloma prognosis into a robust high-throughput assay for clinical use 
BMC Medical Genomics  2014;7:25.
Background
Widespread adoption of genomic technologies in the management of heterogeneous indications, including Multiple Myeloma, has been hindered by concern over variation between published gene expression signatures, difficulty in physician interpretation and the challenge of obtaining sufficient genetic material from limited patient specimens.
Methods
Since 2006, the 70-gene prognostic signature, developed by the University of Arkansas for Medical Sciences (UAMS) has been applied to over 4,700 patients in studies performed in 4 countries and described in 17 peer-reviewed publications. Analysis of control sample and quality control data compiled over a 12-month period was performed.
Results
Over a 12 month period, the 70-gene prognosis score (range 0–100) of our multiple myeloma cell-line control sample had a standard deviation of 2.72 and a coefficient of variance of 0.03. The whole-genome microarray profile used to calculate a patient’s GEP70 score can be generated with as little as 15 ng of total RNA; approximately 30,000 CD-138+ plasma cells. Results from each GEP70 analysis are presented as either low (70-gene score <45.2) or high (≥45.2) risk for relapse (newly diagnosed setting) or shorter overall survival (relapse setting). A personalized and outcome-annotated gene expression heat map is provided to assist in the clinical interpretation of the result.
Conclusions
The 70-gene assay, commercialized under the name ‘MyPRS®’ (Myeloma Prognostic Risk Score) and performed in Signal Genetics’ CLIA-certified high throughput flow-cytometry and molecular profiling laboratory is a reproducible and standardized method of multiple myeloma prognostication.
doi:10.1186/1755-8794-7-25
PMCID: PMC4032347  PMID: 24885236
14.  Small non-coding RNA signature in multiple sclerosis patients after treatment with interferon-β 
BMC Medical Genomics  2014;7:26.
Background
Non-coding small RNA molecules play pivotal roles in cellular and developmental processes by regulating gene expression at the post-transcriptional level. In human diseases, the roles of the non-coding small RNAs in specific degradation or translational suppression of the targeted mRNAs suggest a potential therapeutic approach of post-transcriptional gene silencing that targets the underlying disease etiology. The involvement of non-coding small RNAs in the pathogenesis of neurodegenerative diseases such as Alzheimer’s , Parkinson’s disease and Multiple Sclerosis has been demonstrated. Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, characterized by chronic inflammation, demyelination and scarring as well as a broad spectrum of signs and symptoms. The current standard treatment for SM is interferon ß (IFNß) that is less than ideal due to side effects. In this study we administered the standard IFN-ß treatment to Relapsing-Remitting MS patients, all responder to the therapy; then examined their sncRNA expression profiles in order to identify the ncRNAs that were associated with MS patients’ response to IFNß.
Methods
40 IFNß treated Relapsing-Remitting MS patients were enrolled. We analyzed the composition of the entire small transcriptome by a small RNA cloning method, using peripheral blood from Relapsing-Remitting MS patients at baseline and 3 and 6 months after the start of IFNß therapy. Real-time qPCR from the same patients group and from 20 additional patients was performed to profile miRNAs expression.
Results
Beside the altered expression of several miRNAs, our analyses revealed the differential expression of small nucleolar RNAs and misc-RNAs.For the first time, we found that the expression level of miR-26a-5p changed related to INF-β response. MiR-26a-5p expression was significantly higher in IFN-β treated RRMS patients at 3 months treatment, keeping quite stable at 6 months treatments.
Conclusions
Our results might provide insights into the mechanisms of action of IFN-β treatment in MS and provide fundamentals for the development of new biomarkers and/or therapeutic tools.
doi:10.1186/1755-8794-7-26
PMCID: PMC4060096  PMID: 24885345
Multiple sclerosis; IFN-β; sncRNA; microRNA; snorRNAs
15.  Whole exome sequencing reveals concomitant mutations of multiple FA genes in individual Fanconi anemia patients 
BMC Medical Genomics  2014;7:24.
Background
Fanconi anemia (FA) is a rare inherited genetic syndrome with highly variable clinical manifestations. Fifteen genetic subtypes of FA have been identified. Traditional complementation tests for grouping studies have been used generally in FA patients and in stepwise methods to identify the FA type, which can result in incomplete genetic information from FA patients.
Methods
We diagnosed five pediatric patients with FA based on clinical manifestations, and we performed exome sequencing of peripheral blood specimens from these patients and their family members. The related sequencing data were then analyzed by bioinformatics, and the FANC gene mutations identified by exome sequencing were confirmed by PCR re-sequencing.
Results
Homozygous and compound heterozygous mutations of FANC genes were identified in all of the patients. The FA subtypes of the patients included FANCA, FANCM and FANCD2. Interestingly, four FA patients harbored multiple mutations in at least two FA genes, and some of these mutations have not been previously reported. These patients’ clinical manifestations were vastly different from each other, as were their treatment responses to androstanazol and prednisone. This finding suggests that heterozygous mutation(s) in FA genes could also have diverse biological and/or pathophysiological effects on FA patients or FA gene carriers. Interestingly, we were not able to identify de novo mutations in the genes implicated in DNA repair pathways when the sequencing data of patients were compared with those of their parents.
Conclusions
Our results indicate that Chinese FA patients and carriers might have higher and more complex mutation rates in FANC genes than have been conventionally recognized. Testing of the fifteen FANC genes in FA patients and their family members should be a regular clinical practice to determine the optimal care for the individual patient, to counsel the family and to obtain a better understanding of FA pathophysiology.
doi:10.1186/1755-8794-7-24
PMCID: PMC4038598  PMID: 24885126
Fanconi anemia; Exome sequencing; DNA repair; Concomitant mutation
16.  Sequence artefacts in a prospective series of formalin-fixed tumours tested for mutations in hotspot regions by massively parallel sequencing 
BMC Medical Genomics  2014;7:23.
Background
Clinical specimens undergoing diagnostic molecular pathology testing are fixed in formalin due to the necessity for detailed morphological assessment. However, formalin fixation can cause major issues with molecular testing, as it causes DNA damage such as fragmentation and non-reproducible sequencing artefacts after PCR amplification. In the context of massively parallel sequencing (MPS), distinguishing true low frequency variants from sequencing artefacts remains challenging. The prevalence of formalin-induced DNA damage and its impact on molecular testing and clinical genomics remains poorly understood.
Methods
The Cancer 2015 study is a population-based cancer cohort used to assess the feasibility of mutational screening using MPS in cancer patients from Victoria, Australia. While blocks were formalin-fixed and paraffin-embedded in different anatomical pathology laboratories, they were centrally extracted for DNA utilising the same protocol, and run through the same MPS platform (Illumina TruSeq Amplicon Cancer Panel). The sequencing artefacts in the 1-10% and the 10-25% allele frequency ranges were assessed in 488 formalin-fixed tumours from the pilot phase of the Cancer 2015 cohort. All blocks were less than 2.5 years of age (mean 93 days).
Results
Consistent with the signature of DNA damage due to formalin fixation, many formalin-fixed samples displayed disproportionate levels of C>T/G>A changes in the 1-10% allele frequency range. Artefacts were less apparent in the 10-25% allele frequency range. Significantly, changes were inversely correlated with coverage indicating high levels of sequencing artefacts were associated with samples with low amounts of available amplifiable template due to fragmentation. The degree of fragmentation and sequencing artefacts differed between blocks sourced from different anatomical pathology laboratories. In a limited validation of potentially actionable low frequency mutations, a NRAS G12D mutation in a melanoma was shown to be a false positive.
Conclusions
These findings indicate that DNA damage following formalin fixation remains a major challenge in laboratories working with MPS. Methodologies that assess, minimise or remove formalin-induced DNA damaged templates as part of MPS protocols will aid in the interpretation of genomic results leading to better patient outcomes.
doi:10.1186/1755-8794-7-23
PMCID: PMC4032349  PMID: 24885028
17.  PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes 
BMC Medical Genomics  2014;7:22.
Background
We propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test.
Twenty test-patients with varied syndromes were selected from the literature. For each patient, the mutation, phenotypic data, and genetic diagnosis were available. Next, control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient’s phenotype and all variations present in the corresponding exome. The physician did not have access to the individual mutations. PhenoVar filtered the search using a cut-off phenotypic match threshold to prevent undesired discovery of incidental findings and ranked the OMIM entries according to diagnostic score.
Results
When assigning the same weight to all variants in the exome, PhenoVar predicted the correct diagnosis in 10/20 patients, while in 15/20 the correct diagnosis was among the 4 highest ranked diagnoses. When assigning a higher weight to variants known, or bioinformatically predicted, to cause disease, PhenoVar’s yield increased to 14/20 (18/20 in top 4). No incidental findings were identified using our cut-off phenotypic threshold.
Conclusion
The phenotype-driven approach described could render widespread use of ES more practical, ethical and clinically useful. The implications about novel disease identification, advancement of complex diseases and personalized medicine are discussed.
doi:10.1186/1755-8794-7-22
PMCID: PMC4030287  PMID: 24884844
Genome; Exome; Encrypted; Sequencing; Clinic; PhenoVar; I-MPOS; I-MPOSE
18.  New horizons in translational bioinformatics: TBC 2013 
BMC Medical Genomics  2014;7(Suppl 1):I1.
doi:10.1186/1755-8794-7-S1-I1
PMCID: PMC4101285
19.  A coupling approach of a predictor and a descriptor for breast cancer prognosis 
BMC Medical Genomics  2014;7(Suppl 1):S4.
Background
In cancer prognosis research, diverse machine learning models have applied to the problems of cancer susceptibility (risk assessment), cancer recurrence (redevelopment of cancer after resolution), and cancer survivability, regarding an accuracy (or an AUC--the area under the ROC curve) as a primary measurement for the performance evaluation of the models. However, in order to help medical specialists to establish a treatment plan by using the predicted output of a model, it is more pragmatic to elucidate which variables (markers) have most significantly influenced to the resulting outcome of cancer or which patients show similar patterns.
Methods
In this study, a coupling approach of two sub-modules--a predictor and a descriptor--is proposed. The predictor module generates the predicted output for the cancer outcome. Semi-supervised learning co-training algorithm is employed as a predictor. On the other hand, the descriptor module post-processes the results of the predictor module, mainly focusing on which variables are more highly or less significantly ranked when describing the results of the prediction, and how patients are segmented into several groups according to the trait of common patterns among them. Decision trees are used as a descriptor.
Results
The proposed approach, 'predictor-descriptor,' was tested on the breast cancer survivability problem based on the surveillance, epidemiology, and end results database for breast cancer (SEER). The results present the performance comparison among the established machine leaning algorithms, the ranks of the prognosis elements for breast cancer, and patient segmentation. In the performance comparison among the predictor candidates, Semi-supervised learning co-training algorithm showed best performance, producing an average AUC of 0.81. Later, the descriptor module found the top-tier prognosis markers which significantly affect to the classification results on survived/dead patients: 'lymph node involvement', 'stage', 'site-specific surgery', 'number of positive node examined', and 'tumor size', etc. Also, a typical example of patient-segmentation was provided: the patients classified as dead were grouped into two segments depending on difference in prognostic profiles, ones with serious results with respect to the pathologic exams and the others with the feebleness of age.
doi:10.1186/1755-8794-7-S1-S4
PMCID: PMC4101306
20.  Comparison of warfarin therapy clinical outcomes following implementation of an automated mobile phone-based critical laboratory value text alert system 
BMC Medical Genomics  2014;7(Suppl 1):S13.
Background
Computerized alert and reminder systems have been widely accepted and applied to various patient care settings, with increasing numbers of clinical laboratories communicating critical laboratory test values to professionals via either manual notification or automated alerting systems/computerized reminders. Warfarin, an oral anticoagulant, exhibits narrow therapeutic range between treatment response and adverse events. It requires close monitoring of prothrombin time (PT)/international normalized ratio (INR) to ensure patient safety. This study was aimed to evaluate clinical outcomes of patients on warfarin therapy following implementation of a Personal Handy-phone System-based (PHS) alert system capable of generating and delivering text messages to communicate critical PT/INR laboratory results to practitioners' mobile phones in a large tertiary teaching hospital.
Methods
A retrospective analysis was performed comparing patient clinical outcomes and physician prescribing behavior following conversion from a manual laboratory result alert system to an automated system. Clinical outcomes and practitioner responses to both alert systems were compared. Complications to warfarin therapy, warfarin utilization, and PT/INR results were evaluated for both systems, as well as clinician time to read alert messages, time to warfarin therapy modification, and monitoring frequency.
Results
No significant differences were detected in major hemorrhage and thromboembolism, warfarin prescribing patterns, PT/INR results, warfarin therapy modification, or monitoring frequency following implementation of the PHS text alert system. In both study periods, approximately 80% of critical results led to warfarin discontinuation or dose reduction. Senior physicians' follow-up response time to critical results was significantly decreased in the PHS alert study period (46.3% responded within 1 day) compared to the manual notification study period (24.7%; P = 0.015). No difference in follow-up response time was detected for junior physicians.
Conclusions
Implementation of an automated PHS-based text alert system did not adversely impact clinical or safety outcomes of patients on warfarin therapy. Approximately 80% immediate recognition of text alerts was achieved. The potential benefits of an automated PHS alert for senior physicians were demonstrated.
doi:10.1186/1755-8794-7-S1-S13
PMCID: PMC4101312
21.  In Silico cancer cell versus stroma cellularity index computed from species-specific human and mouse transcriptome of xenograft models: towards accurate stroma targeting therapy assessment 
BMC Medical Genomics  2014;7(Suppl 1):S2.
Background
The current state of the art for measuring stromal response to targeted therapy requires burdensome and rate limiting quantitative histology. Transcriptome measures are increasingly affordable and provide an opportunity for developing a stromal versus cancer ratio in xenograft models. In these models, human cancer cells are transplanted into mouse host tissues (stroma) and together coevolve into a tumour microenvironment. However, profiling the mouse or human component separately remains problematic. Indeed, laser capture microdissection is labour intensive. Moreover, gene expression using commercial microarrays introduces significant and underreported cross-species hybridization errors that are commonly overlooked by biologists.
Method
We developed a customized dual-species array, H&M array, and performed cross-species and species-specific hybridization measurements. We validated a new methodology for establishing the stroma vs cancer ratio using transcriptomic data.
Results
In the biological validation of the H&M array, cross-species hybridization of human and mouse probes was significantly reduced (4.5 and 9.4 fold reduction, respectively; p < 2x10-16 for both, Mann-Whitney test). We confirmed the capability of the H&M array to determine the stromal to cancer cells ratio based on the estimation of cellularity index of mouse/human mRNA content in vitro. This new metrics enable to investigate more efficiently the stroma-cancer cell interactions (e.g. cellularity) bypassing labour intensive requirement and biases of laser capture microdissection.
Conclusion
These results provide the initial evidence of improved and cost-efficient analytics for the investigation of cancer cell microenvironment, using species-specificity arrays specifically designed for xenografts models.
doi:10.1186/1755-8794-7-S1-S2
PMCID: PMC4101338
22.  IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis 
BMC Medical Genomics  2014;7(Suppl 1):S6.
Background
With the development of high-throughput genotyping and sequencing technology, there are growing evidences of association with genetic variants and complex traits. In spite of thousands of genetic variants discovered, such genetic markers have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. Gene-gene interaction (GGI) analysis is expected to unveil a large portion of unexplained heritability of complex traits.
Methods
In this work, we propose IGENT, Information theory-based GEnome-wide gene-gene iNTeraction method. IGENT is an efficient algorithm for identifying genome-wide gene-gene interactions (GGI) and gene-environment interaction (GEI). For detecting significant GGIs in genome-wide scale, it is important to reduce computational burden significantly. Our method uses information gain (IG) and evaluates its significance without resampling.
Results
Through our simulation studies, the power of the IGENT is shown to be better than or equivalent to that of that of BOOST. The proposed method successfully detected GGI for bipolar disorder in the Wellcome Trust Case Control Consortium (WTCCC) and age-related macular degeneration (AMD).
Conclusions
The proposed method is implemented by C++ and available on Windows, Linux and MacOSX.
doi:10.1186/1755-8794-7-S1-S6
PMCID: PMC4101351
23.  Identification of novel therapeutics for complex diseases from genome-wide association data 
BMC Medical Genomics  2014;7(Suppl 1):S8.
Background
Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.
Methods
We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level.
Results
Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets.
Conclusions
By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments.
doi:10.1186/1755-8794-7-S1-S8
PMCID: PMC4101352
Complex disease; Candidate gene; Drug database; Drug target; Drug repositioning; Genome-wide association study
24.  Integrated analysis of microRNA-target interactions with clinical outcomes for cancers 
BMC Medical Genomics  2014;7(Suppl 1):S10.
Background
Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.
Methods
Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.
Results
We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.
Conclusions
This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.
doi:10.1186/1755-8794-7-S1-S10
PMCID: PMC4101396
Survival Analysis; miRNA-mRNA interaction; miRNA Expression; Gene Expression Profile; Cancer Genes
25.  Automatic detection and resolution of measurement-unit conflicts in aggregated data 
BMC Medical Genomics  2014;7(Suppl 1):S12.
Background
Measurement-unit conflicts are a perennial problem in integrative research domains such as clinical meta-analysis. As multi-national collaborations grow, as new measurement instruments appear, and as Linked Open Data infrastructures become increasingly pervasive, the number of such conflicts will similarly increase.
Methods
We propose a generic approach to the problem of (a) encoding measurement units in datasets in a machine-readable manner, (b) detecting when a dataset contained mixtures of measurement units, and (c) automatically converting any conflicting units into a desired unit, as defined for a given study.
Results
We utilized existing ontologies and standards for scientific data representation, measurement unit definition, and data manipulation to build a simple and flexible Semantic Web Service-based approach to measurement-unit harmonization. A cardiovascular patient cohort in which clinical measurements were recorded in a number of different units (e.g., mmHg and cmHg for blood pressure) was automatically classified into a number of clinical phenotypes, semantically defined using different measurement units.
Conclusions
We demonstrate that through a combination of semantic standards and frameworks, unit integration problems can be automatically detected and resolved.
doi:10.1186/1755-8794-7-S1-S12
PMCID: PMC4101427

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