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1.  Segmental neurofibromatosis type 2: discriminating two hit from four hit in a patient presenting multiple schwannomas confined to one limb 
A clinical overlap exists between mosaic Neurofibromatosis Type 2 and sporadic Schwannomatosis conditions. In these cases a molecular analysis of tumors is recommended for a proper genetic diagnostics. This analysis is challenged by the fact that schwannomas in both conditions bear a somatic double inactivation of the NF2 gene. However, SMARCB1-associated schwannomas follow a four-hit, three-step model, in which both alleles of SMARCB1 and NF2 genes are inactivated in the tumor, with one of the steps being always the loss of a big part of chromosome 22 involving both loci.
Case presentation
Here we report a 36-year-old woman who only presented multiple subcutaneous schwannomas on her right leg. To help discriminate between both possible diagnoses, an exhaustive molecular genetic and genomic analysis was performed on two schwannomas of the patient, consisting in cDNA and DNA sequencing, MLPA, microsatellite multiplex PCR and SNP-array analyses. The loss of a big part of chromosome 22 (22q12.1q13.33) was identified in both tumors. However, this loss involved the NF2 but not the SMARCB1 locus. SNP-array analysis revealed the presence of the same deletion breakpoint in both schwannomas, indicating that this alteration was actually the first NF2 inactivating hit. In addition, a distinct NF2 point mutation in each tumor was identified, representing independent second hits. In accordance with these results, no deletions or point mutations in the SMARCB1 gene were identified. None of the mutations were present in the blood. Two of the patient’s children inherited chromosome 22 deleted in schwannomas of the mother, but in its wild type form.
These results conclusively confirm the segmental mosaic NF2 nature of the clinical phenotype presented.
PMCID: PMC4310195
Neurofibromatosis type 2; Schwannomatosis; Multiple schwannomas; Segmental mosaicism; Genetic diagnostics
3.  Gene-environment interactions and obesity: recent developments and future directions 
BMC Medical Genomics  2015;8(Suppl 1):S2.
Obesity, a major public health concern, is a multifactorial disease caused by both environmental and genetic factors. Although recent genome-wide association studies have identified many loci related to obesity or body mass index, the identified variants explain only a small proportion of the heritability of obesity. Better understanding of the interplay between genetic and environmental factors is the basis for developing effective personalized obesity prevention and management strategies. This article reviews recent advances in identifying gene-environment interactions related to obesity and describes epidemiological designs and newly developed statistical approaches to characterizing and discovering gene-environment interactions on obesity risk.
PMCID: PMC4315311
4.  Individualized medicine enabled by genomics in Saudi Arabia 
BMC Medical Genomics  2015;8(Suppl 1):S3.
The biomedical research sector in Saudi Arabia has recently received special attention from the government, which is currently supporting research aimed at improving the understanding and treatment of common diseases afflicting Saudi Arabian society. To build capacity for research and training, a number of centres of excellence were established in different areas of the country. Among these, is the Centre of Excellence in Genomic Medicine Research (CEGMR) at King Abdulaziz University, Jeddah, with its internationally ranked and highly productive team performing translational research in the area of individualized medicine. Here, we present a panorama of the recent trends in different areas of biomedical research in Saudi Arabia drawing from our vision of where genomics will have maximal impact in the Kingdom of Saudi Arabia. We describe advances in a number of research areas including; congenital malformations, infertility, consanguinity and pre-implantation genetic diagnosis, cancer and genomic classifications in Saudi Arabia, epigenetic explanations of idiopathic disease, and pharmacogenomics and personalized medicine. We conclude that CEGMR will continue to play a pivotal role in advances in the field of genomics and research in this area is facing a number of challenges including generating high quality control data from Saudi population and policies for using these data need to comply with the international set up.
PMCID: PMC4315314
5.  Molecular genetics of human primary microcephaly: an overview 
BMC Medical Genomics  2015;8(Suppl 1):S4.
Autosomal recessive primary microcephaly (MCPH) is a neurodevelopmental disorder that is characterised by microcephaly present at birth and non-progressive mental retardation. Microcephaly is the outcome of a smaller but architecturally normal brain; the cerebral cortex exhibits a significant decrease in size. MCPH is a neurogenic mitotic disorder, though affected patients demonstrate normal neuronal migration, neuronal apoptosis and neural function. Twelve MCPH loci (MCPH1-MCPH12) have been mapped to date from various populations around the world and contain the following genes: Microcephalin, WDR62, CDK5RAP2, CASC5, ASPM, CENPJ, STIL, CEP135, CEP152, ZNF335, PHC1 and CDK6. It is predicted that MCPH gene mutations may lead to the disease phenotype due to a disturbed mitotic spindle orientation, premature chromosomal condensation, signalling response as a result of damaged DNA, microtubule dynamics, transcriptional control or a few other hidden centrosomal mechanisms that can regulate the number of neurons produced by neuronal precursor cells. Additional findings have further elucidated the microcephaly aetiology and pathophysiology, which has informed the clinical management of families suffering from MCPH. The provision of molecular diagnosis and genetic counselling may help to decrease the frequency of this disorder.
PMCID: PMC4315316
6.  The role of epigenetics in personalized medicine: challenges and opportunities 
BMC Medical Genomics  2015;8(Suppl 1):S5.
Epigenetic alterations are considered to be very influential in both the normal and disease states of an organism. These alterations include methylation, acetylation, phosphorylation, and ubiquitylation of DNA and histone proteins (nucleosomes) as well as chromatin remodeling. Many diseases, such as cancers and neurodegenerative disorders, are often associated with epigenetic alterations. DNA methylation is one important modification that leads to disease. Standard therapies are given to patients; however, few patients respond to these drugs, because of various molecular alterations in their cells, which may be partially due to genetic heterogeneity and epigenetic alterations. To realize the promise of personalized medicine, both genetic and epigenetic diagnostic testing will be required. This review will discuss the advances that have been made as well as the challenges for the future.
PMCID: PMC4315318
7.  Evolutionary Diagnosis of non-synonymous variants involved in differential drug response 
BMC Medical Genomics  2015;8(Suppl 1):S6.
Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response.
We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly.
The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.
PMCID: PMC4315320
8.  Performance of case-control rare copy number variation annotation in classification of autism 
BMC Medical Genomics  2015;8(Suppl 1):S7.
A substantial proportion of Autism Spectrum Disorder (ASD) risk resides in de novo germline and rare inherited genetic variation. In particular, rare copy number variation (CNV) contributes to ASD risk in up to 10% of ASD subjects. Despite the striking degree of genetic heterogeneity, case-control studies have detected specific burden of rare disruptive CNV for neuronal and neurodevelopmental pathways. Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations. We investigated performance of different methods and estimated the percentage of ASD subjects that could be reliably classified based on presumed etiologic CNV they carry.
We analyzed 1,892 Caucasian ASD subjects and 2,342 matched controls. Rare CNVs (frequency 1% or less) were detected using Illumina 1M and 1M-Duo BeadChips. Conditional Inference Forest (CF) typically performed as well as or better than other classification methods. We found a maximum AUC (area under the ROC curve) of 0.533 when considering all ASD subjects with rare genic CNVs, corresponding to 7.9% correctly classified ASD subjects and less than 3% incorrectly classified controls; performance was significantly higher when considering only subjects harboring de novo or pathogenic CNVs. We also found rare losses to be more predictive than gains and that curated neurally-relevant annotations (brain expression, synaptic components and neurodevelopmental phenotypes) outperform Gene Ontology and pathway-based annotations.
CF is an optimal classification approach for case-control rare CNV data and it can be used to prioritize subjects with variants potentially contributing to ASD risk not yet recognized. The neurally-relevant annotations used in this study could be successfully applied to rare CNV case-control data-sets for other neuropsychiatric disorders.
PMCID: PMC4315323
Copy number variation (CNV); Autism Spectrum Disorders (ASD); rare genetic variants; machine learning classification; Random Forest (RF)
9.  Sprouty2 mediated tuning of signalling is essential for somite myogenesis 
BMC Medical Genomics  2015;8(Suppl 1):S8.
Negative regulators of signal transduction cascades play critical roles in controlling different aspects of normal embryonic development. Sprouty2 (Spry2) negatively regulates receptor tyrosine kinases (RTK) and FGF signalling and is important in differentiation, cell migration and proliferation. In vertebrate embryos, Spry2 is expressed in paraxial mesoderm and in forming somites. Expression is maintained in the myotome until late stages of somite differentiation. However, its role and mode of action during somite myogenesis is still unclear.
Here, we analysed chick Spry2 expression and showed that it overlaps with that of myogenic regulatory factors MyoD and Mgn. Targeted mis-expression of Spry2 led to inhibition of myogenesis, whilst its C-terminal domain led to an increased number of myogenic cells by stimulating cell proliferation.
Spry2 is expressed in somite myotomes and its expression overlaps with myogenic regulatory factors. Overexpression and dominant-negative interference showed that Spry2 plays a crucial role in regulating chick myogenesis by fine tuning of FGF signaling through a negative feedback loop. We also propose that mir-23, mir-27 and mir-128 could be part of the negative feedback loop mechanism. Our analysis is the first to shed some light on in vivo Spry2 function during chick somite myogenesis.
PMCID: PMC4315326
Somite myogenesis; FDF/Spry2 signalling; proliferation; negative regulation
10.  Identifying a gene expression signature of frequent COPD exacerbations in peripheral blood using network methods 
Exacerbations of chronic obstructive pulmonary disease (COPD), characterized by acute deterioration in symptoms, may be due to bacterial or viral infections, environmental exposures, or unknown factors. Exacerbation frequency may be a stable trait in COPD patients, which could imply genetic susceptibility. Observing the genes, networks, and pathways that are up- and down-regulated in COPD patients with differing susceptibility to exacerbations will help to elucidate the molecular signature and pathogenesis of COPD exacerbations.
Gene expression array and plasma biomarker data were obtained using whole-blood samples from subjects enrolled in the Treatment of Emphysema With a Gamma-Selective Retinoid Agonist (TESRA) study. Linear regression, weighted gene co-expression network analysis (WGCNA), and pathway analysis were used to identify signatures and network sub-modules associated with the number of exacerbations within the previous year; other COPD-related phenotypes were also investigated.
Individual genes were not found to be significantly associated with the number of exacerbations. However using network methods, a statistically significant gene module was identified, along with other modules showing moderate association. A diverse signature was observed across these modules using pathway analysis, marked by differences in B cell and NK cell activity, as well as cellular markers of viral infection. Within two modules, gene set enrichment analysis recapitulated the molecular signatures of two gene expression experiments; one involving sputum from asthma exacerbations and another involving viral lung infections. The plasma biomarker myeloperoxidase (MPO) was associated with the number of recent exacerbations.
A distinct signature of COPD exacerbations may be observed in peripheral blood months following the acute illness. While not predictive in this cross-sectional analysis, these results will be useful in uncovering the molecular pathogenesis of COPD exacerbations.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0072-y) contains supplementary material, which is available to authorized users.
PMCID: PMC4302028  PMID: 25582225
Network analysis; Chronic obstructive pulmonary disease; Gene expression profiling; Biomarker
11.  PD_NGSAtlas: a reference database combining next-generation sequencing epigenomic and transcriptomic data for psychiatric disorders 
BMC Medical Genomics  2014;7(1):71.
Psychiatric disorders such as schizophrenia (SZ) and bipolar disorder (BP) are projected to lead the global disease burden within the next decade. Several lines of evidence suggest that epigenetic- or genetic-mediated dysfunction is frequently present in these disorders. To date, the inheritance patterns have been complicated by the problem of integrating epigenomic and transcriptomic factors that have yet to be elucidated. Therefore, there is a need to build a comprehensive database for storing epigenomic and transcriptomic data relating to psychiatric disorders.
We have developed the PD_NGSAtlas, which focuses on the efficient storage of epigenomic and transcriptomic data based on next-generation sequencing and on the quantitative analyses of epigenetic and transcriptional alterations involved in psychiatric disorders. The current release of the PD_NGSAtlas contains 43 DNA methylation profiles and 37 transcription profiles detected by MeDIP-Seq and RNA-Seq, respectively, in two distinct brain regions and peripheral blood of SZ, BP and non-psychiatric controls. In addition to these data that were generated in-house, we have included, and will continue to include, published DNA methylation and gene expression data from other research groups, with a focus on psychiatric disorders. A flexible query engine has been developed for the acquisition of methylation profiles and transcription profiles for special genes or genomic regions of interest of the selected samples. Furthermore, the PD_NGSAtlas offers online tools for identifying aberrantly methylated and expressed events involved in psychiatric disorders. A genome browser has been developed to provide integrative and detailed views of multidimensional data in a given genomic context, which can help researchers understand molecular mechanisms from epigenetic and transcriptional perspectives. Moreover, users can download the methylation and transcription data for further analyses.
The PD_NGSAtlas aims to provide storage of epigenomic and transcriptomic data as well as quantitative analyses of epigenetic and transcriptional alterations involved in psychiatric disorders. The PD_NGSAtlas will be a valuable data resource and will enable researchers to investigate the pathophysiology and aetiology of disease in detail. The database is available at
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0071-z) contains supplementary material, which is available to authorized users.
PMCID: PMC4308070  PMID: 25551368
Schizophrenia; Bipolar disorder; Next-generation sequencing; Epigenomic and transcriptomic data; Brain; Blood
12.  SNP arrays: comparing diagnostic yields for four platforms in children with developmental delay 
BMC Medical Genomics  2014;7(1):70.
Molecular karyotyping is now the first-tier genetic test for patients affected with unexplained intellectual disability (ID) and/or multiple congenital anomalies (MCA), since it identifies a pathogenic copy number variation (CNV) in 10-14% of them. High-resolution microarrays combining molecular karyotyping and single nucleotide polymorphism (SNP) genotyping were recently introduced to the market. In addition to identifying CNVs, these platforms detect loss of heterozygosity (LOH), which can indicate the presence of a homozygous mutation or uniparental disomy. Since these abnormalities can be associated with ID and/or MCA, their detection is of particular interest for patients whose phenotype remains unexplained. However, the diagnostic yield obtained with these platforms is not confirmed, and the real clinical value of LOH detection has not been established.
We selected 21 children affected with ID, with or without congenital malformations, for whom standard genetic analyses failed to provide a diagnosis. We performed high-resolution SNP array analysis with four platforms (Affymetrix Genome-Wide Human SNP Array 6.0, Affymetrix Cytogenetics Whole-Genome 2.7 M array, Illumina HumanOmni1-Quad BeadChip, and Illumina HumanCytoSNP-12 DNA Analysis BeadChip) on whole-blood samples obtained from children and their parents to detect pathogenic CNVs and LOHs, and compared the results with those obtained on a moderate resolution array-based comparative genomic hybridization platform (NimbleGen CGX-12 Cytogenetics Array), already used in the clinical setting.
We identified a total of four pathogenic CNVs in three patients, and all arrays successfully detected them. With the SNP arrays, we also identified a LOH containing a gene associated with a recessive disorder consistent with the patient’s phenotype (i.e., an informative LOH) in four children (including two siblings). A homozygous mutation within the informative LOH was found in three of these patients. Therefore, we were able to increase the diagnostic yield from 14.3% to 28.6% as a result of the information provided by LOHs.
This study shows the clinical usefulness of SNP arrays in children with ID, since they successfully detect pathogenic CNVs, identify informative LOHs that can lead to the diagnosis of a recessive disorder. It also highlights some challenges associated with the use of SNP arrays in a clinical laboratory.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0070-0) contains supplementary material, which is available to authorized users.
PMCID: PMC4299176  PMID: 25539807
Comparative genomic hybridization (CGH); Congenital abnormalities; Consanguinity; DNA copy number variation (CNV); Intellectual disability; Loss of heterozygosity (LOH); Microarray analysis; Single nucleotide polymorphism (SNP); Uniparental disomy (UPD)
13.  A 3-biomarker-panel predicts renal outcome in patients with proteinuric renal diseases 
BMC Medical Genomics  2014;7(1):75.
Clinical and histological parameters are valid prognostic markers in renal disease, although they may show considerable interindividual variability and sometimes limited prognostic value. Novel molecular markers and pathways have the potential to increase the predictive prognostic value of the so called “traditional markers”.
Transcriptomics profiles from laser-capture microdissected proximal tubular epithelial cells from routine kidney biopsies were correlated with a chronic renal damage index score (CREDI), an inflammation score (INSCO), and clinical parameters. We used data from 20 renal biopsies with various proteinuric renal diseases with a median follow-up of 49 months (discovery cohort). For validation we performed microarrays from whole kidney biopsies from a second cohort consisting of 16 patients with a median follow-up time of 28 months (validation cohort).
562 genes correlated with the CREDI score and 285 genes correlated with the INSCO panel, respectively. 39 CREDI and 90 INSCO genes also correlated with serum creatinine at follow-up. After hierarchical clustering we identified 5 genes from the CREDI panel, and 10 genes from the INSCO panel, respectively, which showed kidney specific gene expression. After exclusion of genes, which correlated to each other by > 50% we identified VEGF-C from the CREDI panel and BMP7, THBS1, and TRIB1 from the INSCO panel. Traditional markers for chronic kidney disease progression and inflammation score predicted 44% of the serum creatinine variation at follow-up. VEGF-C did not further enhance the predictive value, but BMP7, THBS1 and TRIB1 together predicted 94% of the serum creatinine at follow up (p < 0.0001). The model was validated in a second cohort of patients yielding also a significant prediction of follow up creatinine (48%, p = 0.0115).
We identified and validated a panel of three genes in kidney biopsies which predicted serum creatinine at follow-up and therefore might serve as biomarkers for kidney disease progression.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0075-8) contains supplementary material, which is available to authorized users.
PMCID: PMC4301948  PMID: 25540021
Histogenomics; Transcriptomics; Microarray; Biomarker; Chronic kidney disease; Prognosis; Bioinformatics
14.  Heterogeneity analysis of the proteomes in clinically nonfunctional pituitary adenomas 
BMC Medical Genomics  2014;7(1):69.
Clinically nonfunctional pituitary adenomas (NFPAs) without any clinical elevation of hormone and with a difficulty in its early-stage diagnosis are highly heterogeneous with different hormone expressions in NFPA tissues, including luteinizing hormone (LH)-positive, follicle-stimulating hormone (FSH)-positive, LH/FSH-positive, and negative (NF). Elucidation of molecular mechanisms and discovery of biomarkers common and specific to those different subtypes of NFPAs will benefit NFPA patients in early-stage diagnosis and individualized treatment.
Two-dimensional gel electrophoresis (2DGE) and PDQuest image analyses were used to compare proteomes of different NFPA subtypes (NF-, LH-, FSH-, and LH/FSH-positive) relative to control pituitaries (Con). Differentially expressed proteins (DEPs) were characterized with mass spectrometry (MS). Each set of DEPs in four NFPA subtypes was evaluated with overlap analysis and signaling pathway network analysis with comparison to determine any DEP and pathway network that are common and specific to each NFPA subtype.
A total of 93 differential protein-spots were determined with comparison of each NFPA type (NF-, LH-, FSH-, and LH/FSH-positive) versus control pituitaries. A total of 76 protein-spots were MS-identified (59 DEPs in NF vs. Con; 65 DEPs in LH vs. Con; 63 DEPs in FSH vs. Con; and 55 DEPs in LH/FSH vs. Con). A set of DEPs and pathway network data were common and specific to each NFPA subtype. Four important common pathway systems included MAPK-signaling abnormality, oxidative stress, mitochondrial dysfunction, and cell-cycle dysregulation. However, these pathway systems were, in fact, different among four NFPA subtypes with different protein-expression levels of most of nodes, different protein profiles, and different pathway network profiles.
These result data demonstrate that common and specific DEPs and pathway networks exist in four NFPA subtypes, and clarify proteome heterogeneity of four NFPA subtypes. Those findings will help to elucidate molecular mechanisms of NFPAs, and discover protein biomarkers to effectively manage NFPA patients towards personalized medicine.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0069-6) contains supplementary material, which is available to authorized users.
PMCID: PMC4302698  PMID: 25539738
Nonfunctional pituitary adenoma; Proteome; Heterogeneity; Two-dimensional gel electrophoresis; Mass spectrometry; Differentially expressed protein; Pathway network
15.  Systematic analysis of the clinical and biochemical characteristics of maternally inherited hypertension in Chinese Han families associated with mitochondrial 
BMC Medical Genomics  2014;7:73.
Mitochondrial DNA mutations may be associated with cardiovascular disease, including the common cardiac vascular disease, hypertension.
In this study we performed segregation analysis and systematically evaluated the entire mitochondrial genome in nine maternally inherited hypertension probands from Chinese Han families. We also performed clinical, genetic and molecular characterization of 74 maternally inherited members from these families and 216 healthy controls.
In the maternally inherited members, 12 had coronary heart disease (CHD), six had cerebrovascular disease, five had diabetes, nine had hyperlipidemia and three had renal disease. Laboratory tests showed that the sodium and potassium levels in blood of the maternally inherited members were higher than those of the control group (P < 0.01), while no differences were observed in fasting blood glucose (FBG), total cholesterol (TC), triglyceride, low density lipoprotein cholesterol (LDL-c) and creatinine levels (P > 0.05). The high density lipoprotein cholesterol (HDL-c) level of the maternally inherited members was lower than that of the control group (P = 0.04). The whole mitochondrial DNA sequence analysis revealed a total of 172 base changes, including 17 in ribosomal RNA (rRNA) genes, four in transfer RNA (tRNA) genes, and 22 amino acid substitutions. The remainder were synonymous changes or were located in non-coding regions. We identified seven amino acid changes in the nine maternally inherited hypertension families, including four mutations in ATPase6 and three in Cytb. More interestingly, tRNASer(UCN) 7492 T > C was absent in controls and was present in <1% of 2704 mtDNAs, indicating potential functional significance.
This study showed that mutations in mtDNA may contribute to the pathogenesis of hypertension in these Chinese Han families. In the near future, identification of additional mtDNA mutations may indicate further candidate genes for hypertension.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0073-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4331388  PMID: 25539907
mitochondrial DNA; Hypertension; Chinese; Mutations; Maternal
16.  Transcriptome profiling and pathway analysis of genes expressed differentially in participants with or without a positive response to topiramate treatment for methamphetamine addiction 
BMC Medical Genomics  2014;7(1):65.
Developing efficacious medications to treat methamphetamine dependence is a global challenge in public health. Topiramate (TPM) is undergoing evaluation for this indication. The molecular mechanisms underlying its effects are largely unknown. Examining the effects of TPM on genome-wide gene expression in methamphetamine addicts is a clinically and scientifically important component of understanding its therapeutic profile.
In this double-blind, placebo-controlled clinical trial, 140 individuals who met the DSM-IV criteria for methamphetamine dependence were randomized to receive either TPM or placebo, of whom 99 consented to participate in our genome-wide expression study. The RNA samples were collected from whole blood for 50 TPM- and 49 placebo-treated participants at three time points: baseline and the ends of weeks 8 and 12. Genome-wide expression profiles and pathways of the two groups were compared for the responders and non-responders at Weeks 8 and 12. To minimize individual variations, expression of all examined genes at Weeks 8 and 12 were normalized to the values at baseline prior to identification of differentially expressed genes and pathways.
At the single-gene level, we identified 1054, 502, 204, and 404 genes at nominal P values < 0.01 in the responders vs. non-responders at Weeks 8 and 12 for the TPM and placebo groups, respectively. Among them, expression of 159, 38, 2, and 21 genes was still significantly different after Bonferroni corrections for multiple testing. Many of these genes, such as GRINA, PRKACA, PRKCI, SNAP23, and TRAK2, which are involved in glutamate receptor and GABA receptor signaling, are direct targets for TPM. In contrast, no TPM drug targets were identified in the 38 significant genes for the Week 8 placebo group. Pathway analyses based on nominally significant genes revealed 27 enriched pathways shared by the Weeks 8 and 12 TPM groups. These pathways are involved in relevant physiological functions such as neuronal function/synaptic plasticity, signal transduction, cardiovascular function, and inflammation/immune function.
Topiramate treatment of methamphetamine addicts significantly modulates the expression of genes involved in multiple biological processes underlying addiction behavior and other physiological functions.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0065-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4279796  PMID: 25495887
Topiramate; Pharmacogenetics; Genes; Pathways; Transcriptome; Addiction treatment; Methamphetamine addiction
17.  High throughput exome coverage of clinically relevant cardiac genes 
BMC Medical Genomics  2014;7(1):67.
Given the growing use of whole-exome sequencing (WES) for clinical diagnostics of complex human disorders, we evaluated coverage of clinically relevant cardiac genes on WES and factors influencing uniformity and depth of coverage of exonic regions.
Two hundred and thirteen human DNA samples were exome sequenced via Illumina HiSeq using different versions of the Agilent SureSelect capture kit. 50 cardiac genes were further analyzed including 31 genes from the American College of Medical Genetics (ACMG) list for reporting of incidental findings and 19 genes associated with congenital heart disease for which clinical testing is available. Gene coordinates were obtained from two databases, CCDS and Known Gene and compared. Read depth for each region was extracted from the exomes and used to assess capture variability between kits for individual genes, and for overall coverage. GC content, gene size, and inter-sample variability were also tested as potential contributors to variability in gene coverage.
All versions of capture kits (designed based on Consensus coding sequence) included only 55% of known genomic regions for the cardiac genes. Although newer versions of each Agilent kit showed improvement in capture of CCDS regions to 99%, only 64% of Known Gene regions were captured even with newer capture kits. There was considerable variability in coverage of the cardiac genes. 10 of the 50 genes including 6 on the ACMG list had less than the optimal coverage of 30X. Within each gene, only 32 of the 50 genes had the majority of their bases covered at an interquartile range ≥30X. Heterogeneity in gene coverage was modestly associated with gene size and significantly associated with GC content.
Despite improvement in overall coverage across the exome with newer capture kit versions and higher sequencing depths, only 50% of known genomic regions of clinical cardiac genes are targeted and individual gene coverage is non-uniform. This may contribute to a bias with greater attribution of disease causation to mutations in well-represented and well-covered genes. Improvements in WES technology are needed before widespread clinical application.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0067-8) contains supplementary material, which is available to authorized users.
PMCID: PMC4272796  PMID: 25496018
Exome sequencing; Coverage; Congenital heart disease; Cardiac; Genomics
18.  Overexpression of miR-21-5p as a predictive marker for complete tumor regression to neoadjuvant chemoradiotherapy in rectal cancer patients 
BMC Medical Genomics  2014;7(1):68.
Neoadjuvant chemoradiotherapy (nCRT) followed by radical surgery is the preferred treatment strategy for locally advanced rectal cancer. However, complete tumor regression is observed in a significant proportion of patients after nCRT, making them ideal candidates for alternative treatment strategies to this considerably morbid procedure. Identification of such patients based on clinical findings (complete clinical response - cCR) is difficult mainly because it relies on subjective clinical and imaging studies. Our goal was to identify biomarkers capable of predicting complete response to nCRT.
We analyzed miRNA expression profile using deep sequencing in rectal tumor biopsies prior to nCRT. Differential expression was investigated by EdgeR for a training (n = 27) and a validation (n = 16) set of patients to identify miRNAs associated with treatment response (complete vs. incomplete). In vitro experiments with two cancer cell lines were also performed in order to evaluate the possible role of miRNAs on response to nCRT.
We found 4 miRNAs differentially expressed between complete and incomplete responders to nCRT. In addition, validation was performed using an independent group of patients and miR-21-5p was confirmed as being overexpressed in complete responders. Overall sensitivity and specificity of miR-21-5p expression in predicting complete response to nCRT was 78% and 86% respectively. Interestingly, in a subset of patients with cCR followed by early local recurrence, the expression level of miR-21-5p was considerably low, similarly to incomplete responders. We also found SATB1, a miR-21-5p target gene and known multidrug resistance gene, whose expression was inversely correlated with miR-21-5p expression. Finally, we performed functional experiments and showed that miR-21-5p and SATB1 may be directly involved with poor response to nCRT in rectal cancer patients.
This study suggests miR-21-5p as a promising predictive biomarker, which should aid in the selection of patients with cCR to nCRT that potentially could be spared from radical surgery.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0068-7) contains supplementary material, which is available to authorized users.
PMCID: PMC4279677  PMID: 25496125
miRNA; Predictive biomarker; Rectal cancer; miR-21-5p; SATB1; Chemoradiotherapy
19.  Integrated differential transcriptome maps of Acute Megakaryoblastic Leukemia (AMKL) in children with or without Down Syndrome (DS) 
BMC Medical Genomics  2014;7(1):63.
The incidence of Acute Megakaryoblastic Leukemia (AMKL) is 500-fold higher in children with Down Syndrome (DS) compared with non-DS children, but the relevance of trisomy 21 as a specific background of AMKL in DS is still an open issue. Several Authors have determined gene expression profiles by microarray analysis in DS and/or non-DS AMKL. Due to the rarity of AMKL, these studies were typically limited to a small group of samples.
We generated integrated quantitative transcriptome maps by systematic meta-analysis from any available gene expression profile dataset related to AMKL in pediatric age. This task has been accomplished using a tool recently described by us for the generation and the analysis of quantitative transcriptome maps, TRAM (Transcriptome Mapper), which allows effective integration of data obtained from different experimenters, experimental platforms and data sources. This allowed us to explore gene expression changes involved in transition from normal megakaryocytes (MK, n=19) to DS (n=43) or non-DS (n=45) AMKL blasts, including the analysis of Transient Myeloproliferative Disorder (TMD, n=20), a pre-leukemia condition.
We propose a biological model of the transcriptome depicting progressive changes from MK to TMD and then to DS AMKL. The data indicate the repression of genes involved in MK differentiation, in particular the cluster on chromosome 4 including PF4 (platelet factor 4) and PPBP (pro-platelet basic protein); the gene for the mitogen-activated protein kinase MAP3K10 and the thrombopoietin receptor gene MPL. Moreover, comparing both DS and non-DS AMKL with MK, we identified three potential clinical markers of progression to AMKL: TMEM241 (transmembrane protein 241) was the most over-expressed single gene, while APOC2 (apolipoprotein C-II) and ZNF587B (zinc finger protein 587B) appear to be the most discriminant markers of progression, specifically to DS AMKL. Finally, the chromosome 21 (chr21) genes resulted to be the most over-expressed in DS and non-DS AMKL, as well as in TMD, pointing out a key role of chr21 genes in differentiating AMKL from MK.
Our study presents an integrated original model of the DS AMLK transcriptome, providing the identification of genes relevant for its pathophysiology which can potentially be new clinical markers.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0063-z) contains supplementary material, which is available to authorized users.
PMCID: PMC4304173  PMID: 25476127
Down Syndrome (Trisomy 21); Acute Megakaryoblastic Leukemia (AMKL); Transient Myeloproliferative Disorder (TMD); Megakaryocyte (MK); Gene expression profile; Integrated transcriptome map
20.  FLAGS, frequently mutated genes in public exomes 
BMC Medical Genomics  2014;7(1):64.
Dramatic improvements in DNA-sequencing technologies and computational analyses have led to wide use of whole exome sequencing (WES) to identify the genetic basis of Mendelian disorders. More than 180 novel rare-disease-causing genes with Mendelian inheritance patterns have been discovered through sequencing the exomes of just a few unrelated individuals or family members. As rare/novel genetic variants continue to be uncovered, there is a major challenge in distinguishing true pathogenic variants from rare benign mutations.
We used publicly available exome cohorts, together with the dbSNP database, to derive a list of genes (n = 100) that most frequently exhibit rare (<1%) non-synonymous/splice-site variants in general populations. We termed these genes FLAGS for FrequentLy mutAted GeneS and analyzed their properties.
Analysis of FLAGS revealed that these genes have significantly longer protein coding sequences, a greater number of paralogs and display less evolutionarily selective pressure than expected. FLAGS are more frequently reported in PubMed clinical literature and more frequently associated with diseased phenotypes compared to the set of human protein-coding genes. We demonstrated an overlap between FLAGS and the rare-disease causing genes recently discovered through WES studies (n = 10) and the need for replication studies and rigorous statistical and biological analyses when associating FLAGS to rare disease. Finally, we showed how FLAGS are applied in disease-causing variant prioritization approach on exome data from a family affected by an unknown rare genetic disorder.
We showed that some genes are frequently affected by rare, likely functional variants in general population, and are frequently observed in WES studies analyzing diverse rare phenotypes. We found that the rate at which genes accumulate rare mutations is beneficial information for prioritizing candidates. We provided a ranking system based on the mutation accumulation rates for prioritizing exome-captured human genes, and propose that clinical reports associating any disease/phenotype to FLAGS be evaluated with extra caution.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0064-y) contains supplementary material, which is available to authorized users.
PMCID: PMC4267152  PMID: 25466818
21.  “Genotype-first” approaches on a curious case of idiopathic progressive cognitive decline 
BMC Medical Genomics  2014;7(1):66.
In developing countries, many cases with rare neurological diseases remain undiagnosed due to limited diagnostic experience. We encountered a case in China where two siblings both began to develop idiopathic progressive cognitive decline starting from age six, and were suspected to have an undiagnosed neurological disease.
Initial clinical assessments included review of medical history, comprehensive physical examination, genetic testing for metabolic diseases, blood tests and brain imaging. We performed exome sequencing with Agilent SureSelect exon capture and Illumina HiSeq2000 platform, followed by variant annotation and selection of rare, shared mutations that fit a recessive model of inheritance. To assess functional impacts of candidate variants, we performed extensive biochemical tests in blood and urine, and examined their possible roles by protein structure modeling.
Exome sequencing identified NAGLU as the most likely candidate gene with compound heterozygous mutations (chr17:40695717C > T and chr17:40693129A > G in hg19 coordinate), which were documented to be pathogenic. Sanger sequencing confirmed the recessive patterns of inheritance, leading to a genetic diagnosis of Sanfilippo syndrome (mucopolysaccharidosis IIIB). Biochemical tests confirmed the complete loss of activity of alpha-N-acetylglucosaminidase (encoded by NAGLU) in blood, as well as significantly elevated dermatan sulfate and heparan sulfate in urine. Structure modeling revealed the mechanism on how the two variants affect protein structural stability.
Successful diagnosis of a rare genetic disorder with an atypical phenotypic presentation confirmed that such “genotype-first” approaches can particularly succeed in areas of the world with insufficient medical genetics expertise and with cost-prohibitive in-depth phenotyping.
PMCID: PMC4267425  PMID: 25466957
22.  Evaluation of an integrated clinical workflow for targeted next-generation sequencing of low-quality tumor DNA using a 51-gene enrichment panel 
BMC Medical Genomics  2014;7(1):62.
Improvements in both performance and cost for next-generation sequencing (NGS) have spurred its rapid adoption for clinical applications. We designed and optimized a pan-cancer target-enrichment panel for 51 well-established oncogenes and tumor suppressors, in conjunction with a bioinformatic pipeline informed by in-process controls and pre- and post-analytical quality control measures.
The evaluation of this workflow consisted of sequencing mixtures of intact DNA to establish analytical sensitivity and precision, utilization of heuristics to identify systematic artifacts, titration studies of intact and FFPE samples for input optimization, and incorporation of orthogonal sequencing strategies to increase both positive predictive value and variant detection. We also used 128 FFPE samples to assess clinical accuracy and incorporated the previously described quantitative functional index (QFI) for sample qualification as part of detailing complete system performance.
We observed a concordance correlation coefficient of 0.99 between the observed versus expected percent variant at 250 ng input across 4 independent sequencing runs. A subset of the systematic variants were confirmed to be barely detectable on an independent sequencing platform (Wilcox signed-rank test p-value <10−16), and the incorporation of orthogonal sequencing strategies increased the harmonic mean of sensitivity and positive predictive value of mutation detection by 41%. In one cohort of FFPE tumor samples, coverage and inter-platform concordance were positively correlated with the QFI, emphasizing the need for pre-analytical sample quality control to reduce the risk of false positives and negatives. In a separate cohort of FFPE samples, the 51-gene panel achieved 78% sensitivity (95% CI = 56.3, 92.5) with 100% PPV (95% CI = 81.5, 100.0) based on known mutations at 7.9% median abundance. By sequencing specimens using an orthogonal NGS technology, sensitivity was improved to 87.0% (95% CI = 66.4,97.2) while maintaining PPV.
The results highlight the value of process integration in a comprehensive targeted NGS system, enabling both discovery and diagnostic applications, particularly when sequencing low-quality cancer specimens.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0062-0) contains supplementary material, which is available to authorized users.
PMCID: PMC4241214  PMID: 25395014
23.  Sleep quality, BDNF genotype and gene expression in individuals with chronic abdominal pain 
BMC Medical Genomics  2014;7(1):61.
Sleep quality and genetics may contribute to the etiology of gastrointestinal (GI) symptoms. Individuals with impaired sleep often have a number of associated symptoms including chronic abdominal pain (CAP). The current study examined the interactions of brain-derived neurotrophic factor (BDNF) genotype with sleep quality in persons with CAP and healthy controls. In addition, associations among sleep quality, BDNF genotype, and gene expression were explored in the participants.
Data were collected on 59 participants (46% male, 61% White, 26.9 ± 6.6 years; CAP (n=19) and healthy controls (n=40)). Participants with CAP reported poorer sleep quality compared to healthy controls. BDNF genotype, categorized as Val/Val homozygotes versus the Met carriers.
Microarray analysis found twenty-four differentially expressed genes by a two-fold magnitude in participants with poor sleep quality compared to good sleep quality, and seven differentially expressed genes comparing CAP to healthy control. Three specific genes in the pain group overlap with sleep quality, including insulin-like growth factor 1 (IGF1), spermatogenesis associated serine-rich 2-like (SPATS2L), and immunoglobulin heavy constant gamma 1 or mu (IGHG1/// IGHM). BDNF was shown to have an interaction effect with GI and sleep symptoms.
Participants with CAP reported poor sleep quality compared to healthy controls. The role of the BDNF Met allele on differential gene expression was not distinct as main factor, but impacted interactions with sleep quality and CAP. Down-regulation of IGF1, SPATS2L, and IGHG1 expression may be related to the etiology of poor sleep quality and CAP.
Trial registration # NCT00824941
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0061-1) contains supplementary material, which is available to authorized users.
PMCID: PMC4226913  PMID: 25358868
Sleep; Gene expression; BDNF; Chronic abdominal pain
24.  A common gene expression signature in Huntington’s disease patient brain regions 
BMC Medical Genomics  2014;7(1):60.
Gene expression data provide invaluable insights into disease mechanisms. In Huntington’s disease (HD), a neurodegenerative disease caused by a tri-nucleotide repeat expansion in the huntingtin gene, extensive transcriptional dysregulation has been reported. Conventional dysregulation analysis has shown that e.g. in the caudate nucleus of the post mortem HD brain the gene expression level of about a third of all genes was altered. Owing to this large number of dysregulated genes, the underlying relevance of expression changes is often lost in huge gene lists that are difficult to comprehend.
To alleviate this problem, we employed weighted correlation network analysis to archival gene expression datasets of HD post mortem brain regions.
We were able to uncover previously unidentified transcription dysregulation in the HD cerebellum that contained a gene expression signature in common with the caudate nucleus and the BA4 region of the frontal cortex. Furthermore, we found that yet unassociated pathways, e.g. global mRNA processing, were dysregulated in HD. We provide evidence to show that, contrary to previous findings, mutant huntingtin is sufficient to induce a subset of stress response genes in the cerebellum and frontal cortex BA4 region. The comparison of HD with other neurodegenerative disorders showed that the immune system, in particular the complement system, is generally activated. We also demonstrate that HD mouse models mimic some aspects of the disease very well, while others, e.g. the activation of the immune system are inadequately reflected.
Our analysis provides novel insights into the molecular pathogenesis in HD and identifies genes and pathways as potential therapeutic targets.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-014-0060-2) contains supplementary material, which is available to authorized users.
PMCID: PMC4219025  PMID: 25358814
Neurodegenerative diseases; Huntington’s disease; Transcriptional dysregulation; Network analysis; Therapeutic targets
25.  Identifying disease genes by integrating multiple data sources 
BMC Medical Genomics  2014;7(Suppl 2):S2.
Now multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data is an effective method to identify disease genes.
In this paper, we propose a multiple data integration method based on the theory of Markov random field (MRF) and the method of Bayesian analysis for identifying human disease genes. The proposed method is not only flexible in easily incorporating different kinds of data, but also reliable in predicting candidate disease genes.
Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. Predictions are evaluated by the leave-one-out method. The proposed method achieves an AUC score of 0.743 when integrating all those biological data in our experiments.
PMCID: PMC4243092  PMID: 25350511
disease gene; data integration; Markov random field; Bayesian analysis

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