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  • DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation
    BMC Med. Genomics (IF 2.568) Pub Date : 2020-01-08
    Ieva Rauluseviciute; Finn Drabløs; Morten Beck Rye

    Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

    更新日期:2020-01-08
  • MiR-182-5p and its target HOXA9 in non-small cell lung cancer: a clinical and in-silico exploration with the combination of RT-qPCR, miRNA-seq and miRNA-chip
    BMC Med. Genomics (IF 2.568) Pub Date : 2020-01-06
    Li Gao; Shi-bai Yan; Jie Yang; Jin-liang Kong; Ke Shi; Fu-chao Ma; Lin-zhen Huang; Jie Luo; Shu-ya Yin; Rong-quan He; Xiao-hua Hu; Gang Chen

    MiR-182-5p, a cancer-related microRNA (miRNA), modulates tumorigenesis and patient outcomes in various human malignances. This study interroted the clinicopathological significance and molecular mechanisms of miR-182-5p in non-small cell lung cancer (NSCLC). The clinical significance of miR-182-5p in NSCLC subtypes was determined based on an analysis of 124 samples (lung adenocarcinomas [LUADs], n = 101; lung squamous cell carcinomas [LUSCs], n = 23) obtained from NSCLC patients and paired noncancer tissues and an analysis of data obtained from public miRNA-seq database, miRNA-chip database, and the scientific literature. The NSCLC samples (n = 124) were analyzed using the real-time quantitative polymerase chain reaction (RT-qPCR). Potential targets of miR-182-5p were identified using lists generated by miRWalk v.2.0, a comprehensive atlas of predicted and validated targets of miRNA-target interactions. Molecular events of miR-182-5p in NSCLC were unveiled based on a functional analysis of candidate targets. The association of miR-182-5p with one of the candidate target genes, homeobox A9 (HOXA9), was validated using in-house RT-qPCR and dual-luciferase reporter assays. The results of the in-house RT-qPCR assays analysis of data obtained from public miRNA-seq databases, miRNA-chip databases, and the scientific literature all supported upregulation of the expression level of miR-182-5p level in NSCLC. Moreover, the in-house RT-qPCR data supported the influence of upregulated miR-182-5p on malignant progression of NSCLC. In total, 774 prospective targets of miR-182-5p were identified. These targets were mainly clustered in pathways associated with biological processes, such as axonogenesis, axonal development, and Ras protein signal transduction, as well as pathways involved in axonal guidance, melanogenesis, and longevity regulation, in multiple species. Correlation analysis of the in-house RT-qPCR data and dual-luciferase reporter assays confirmed that HOXA9 was a direct target of miR-182-5p in NSCLC. The miR-182-5p expression level was upregulated in NSCLC tissues. MiR-182-5p may exert oncogenic influence on NSCLC through regulating target genes such as HOXA9.

    更新日期:2020-01-07
  • Genome-wide analysis reveals the association between alternative splicing and DNA methylation across human solid tumors
    BMC Med. Genomics (IF 2.568) Pub Date : 2020-01-06
    Xiaohui Sun; Yiping Tian; Jianming Wang; Zeyuan Sun; Yimin Zhu

    Dysregulation of alternative splicing (AS) is a critical signature of cancer. However, the regulatory mechanisms of cancer-specific AS events, especially the impact of DNA methylation, are poorly understood. By using The Cancer Genome Atlas (TCGA) SpliceSeq and TCGA data for ten solid tumor types, association analysis was performed to characterize the potential link between cancer-specific AS and DNA methylation. Functional and pathway enrichment analyses were performed, and the protein-protein interaction (PPI) network was constructed with the String website. The prognostic analysis was carried out with multivariate Cox regressions models. 15,818 AS events in 3955 annotated genes were identified across ten solid tumor types. The different DNA methylation patterns between tumor and normal tissues at the corresponding alternative spliced exon boundaries were shown, and 51.3% of CpG sites (CpGs) revealed hypomethylated in tumors. Notably, 607 CpGs were found to be highly correlated with 369 cancer-specific AS events after permutation tests. Among them, the hypomethylated CpGs account for 52.7%, and the number of down-regulated exons was 173. Furthermore, we found 38 AS events in 35 genes could serve as new molecular biomarkers to predict patient survival. Our study described the relationship between DNA methylation and AS events across ten human solid tumor types and provided new insights into intragenic DNA methylation and exon usage during the AS process.

    更新日期:2020-01-07
  • Identification of biomarkers and drug repurposing candidates based on an immune-, inflammation- and membranous glomerulonephritis-associated triplets network for membranous glomerulonephritis
    BMC Med. Genomics (IF 2.568) Pub Date : 2020-01-07
    Chengwei Zhang; Lei Leng; Zhaozheng Li; Yao Zhao; Jundong Jiao

    Membranous glomerulonephritis (MGN) is a common kidney disease. Despite many evidences support that many immune and inflammation-related genes could serve as effective biomarkers and treatment targets for MGN patients, the potential associations among MGN-, immune- and inflammation-related genes have not been sufficiently understood. Here, a global immune-, inflammation- and MGN-associated triplets (IIMATs) network is constructed and analyzed. An integrated and computational approach is developed to identify dysregulated IIMATs for MGN patients based on expression and interaction data. 45 dysregulated IIMATs are identified in MGN by above method. Dysregulated patterns of these dysregulated IIMATs are complex and various. We identify four core clusters from dysregulated IIMATs network and some of these clusters could distinguish MGN and normal samples. Specially, some anti-cancer drugs including Tamoxifen, Bosutinib, Ponatinib and Nintedanib could become candidate drugs for MGN based on drug repurposing strategy follow IIMATs. Functional analysis shows these dysregulated IIMATs are associated with some key functions and chemokine signaling pathway. The present study explored the associations among immune, inflammation and MGN. Some effective candidate drugs for MGN were identified based on immune and inflammation. Overall, these comprehensive results provide novel insights into the mechanisms and treatment of MGN.

    更新日期:2020-01-07
  • Genome-wide analysis of aberrant methylation of enhancer DNA in human osteoarthritis
    BMC Med. Genomics (IF 2.568) Pub Date : 2020-01-03
    Xiaozong Lin; Li Li; Xiaojuan Liu; Jun Tian; Weizhuo Zheng; Jin Li; Limei Wang

    Osteoarthritis is a chronic musculoskeletal disease characterized by age-related gradual thinning and a high risk in females. Recent studies have shown that DNA methylation plays important roles in osteoarthritis. However, the genome-wide pattern of methylation in enhancers in osteoarthritis remains unclear. To explore the function of enhancers in osteoarthritis, we quantified CpG methylation in human enhancers based on a public dataset that included methylation profiles of 470,870 CpG probes in 108 samples from patients with hip and knee osteoarthritis and hip tissues from healthy individuals. Combining various bioinformatics analysis tools, we systematically analyzed aberrant DNA methylation of the enhancers throughout the genome in knee osteoarthritis and hip osteoarthritis. We identified 16,816 differentially methylated CpGs, and nearly half (8111) of them were from enhancers, suggesting major DNA methylation changes in both types of osteoarthritis in the enhancer regions. A detailed analysis of hip osteoarthritis identified 2426 differentially methylated CpGs in enhancers between male and female patients, and 84.5% of them were hypomethylated in female patients and enriched in phenotypes related to hip osteoarthritis in females. Next, we explored the enhancer methylation dynamics among patients with knee osteoarthritis and identified 280 differentially methylated enhancer CpGs that were enriched in the human phenotypes and disease ontologies related to osteoarthritis. Finally, a comparison of enhancer methylation between knee osteoarthritis and hip osteoarthritis revealed organ source-dependent differences in enhancer methylation. Our findings indicate that aberrant methylation of enhancers is related to osteoarthritis phenotypes, and a comprehensive atlas of enhancer methylation is useful for further analysis of the epigenetic regulation of osteoarthritis and the development of clinical drugs for treatment of osteoarthritis.

    更新日期:2020-01-04
  • 12q14 microduplication: a new clinical entity reciprocal to the microdeletion syndrome?
    BMC Med. Genomics (IF 2.568) Pub Date : 2020-01-03
    Sofia Dória; Daniela Alves; Maria João Pinho; Joel Pinto; Miguel Leão

    12q14 microdeletion syndrome is characterized by low birth weight and failure to thrive, proportionate short stature and developmental delay. The opposite syndrome (microduplication) has not yet been characterized. Our main objective is the recognition of a new clinical entity - 12q14 microduplication syndrome. - as well as confirming the role of HMGA2 gene in growth regulation. Array Comparative Genomic Hybridization (CGH), Karyotype, Fluorescence in situ Hybridization, Quantitative-PCR analysis and Whole exome sequencing (WES) were performed in a girl presenting overgrowth and obesity. Array CGH identified a 1.5 Mb 12q14.3 microduplication involving HMGA2, GRIP1, IRAK3, MSRB3 and TMBIM4 genes. Karyotype and FISH showed that duplication was a de novo insertion of 12q14.3 region on chromosome 9p resulting in an interstitial microduplication. Q-PCR confirmed the duplication only in the proband. WES revealed no pathogenic variants. Phenotypic comparison with patients with 12q14 microdeletion syndrome showed a reciprocal presentation, suggesting a phenotypically recognizable 12q14 microduplication syndrome as well as confirming the role of HMGA2 gene in growth regulation. It is also indicative that other genes, such as IRAK3 and MSRB3 might have of role in weight gain and obesity.

    更新日期:2020-01-04
  • MEG3 promotes proliferation and inhibits apoptosis in osteoarthritis chondrocytes by miR-361-5p/FOXO1 axis
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Anying Wang; Naixia Hu; Yefeng Zhang; Yuanzhen Chen; Changhui Su; Yao Lv; Yong Shen

    This study aimed to investigate the role of long non-coding RNA (lncRNA) maternally expressed 3 (MEG3) and related molecular mechanisms, in osteoarthritis (OA). Cartilage tissues of OA patients and healthy volunteers were isolated and cultured. After transfection with the appropriate constructs, chondrocytes were classified into Blank, pcDNA3.1-NC, pcDNA3.1-MEG3, si-NC, si-MEG3, pcDNA3.1-NC + mimics NC, pcDNA3.1-MEG3 + mimics NC, pcDNA3.1-NC + miR-361-5p mimics and pcDNA3.1-MEG3 + miR-361-5p mimics groups. qRT-PCR was used to detect the expression of MEG3, miR-361-5p and FOXO1. Western blot, luciferase reporter assay, RIP, CCK-8, and flow cytometry analysis were performed to reveal the morphology, proliferation, and apoptotic status of cartilage cells. Histological analysis and immunostaining were conducted in the OA rat model. Expression of MEG3 and FOXO1 was significantly decreased in OA compared with the normal group, while the expression of miR-361-5p was increased. MEG3 might serve as a ceRNA of miR-361-5p in OA chondrocytes. Moreover, using western blot analyses and the CCK-8 assay, MEG3 was shown to target miR-361-5p/FOXO1, elevate cell proliferation, and impair cell apoptosis. Functional analysis in vivo showed that MEG3 suppressed degradation of the cartilage matrix. MEG3 can contribute to cell proliferation and inhibit cell apoptosis and degradation of extracellular matrix (ECM) via the miR-361-5p/FOXO1 axis in OA chondrocytes.

    更新日期:2019-12-31
  • Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-27
    Elham Karimizadeh; Ali Sharifi-Zarchi; Hassan Nikaein; Seyedehsaba Salehi; Bahar Salamatian; Naser Elmi; Farhad Gharibdoost; Mahdi Mahmoudi

    Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients. An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein–protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters. A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues. Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes.

    更新日期:2019-12-30
  • Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Hua Zhong; Mingzhou Song

    Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene’s directional effect on human cancer. We first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia. This study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers.

    更新日期:2019-12-30
  • Region-based interaction detection in genome-wide case-control studies
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Sen Zhang; Wei Jiang; Ronald CW Ma; Weichuan Yu

    In genome-wide association study (GWAS), conventional interaction detection methods such as BOOST are mostly based on SNP-SNP interactions. Although single nucleotides are the building blocks of human genome, single nucleotide polymorphisms (SNPs) are not necessarily the smallest functional unit for complex phenotypes. Region-based strategies have been proved to be successful in studies aiming at marginal effects. We propose a novel region-region interaction detection method named RRIntCC (region-region interaction detection for case-control studies). RRIntCC uses the correlations between individual SNP-SNP interactions based on linkage disequilibrium (LD) contrast test. Simulation experiments showed that our method can achieve a higher power than conventional SNP-based methods with similar type-I-error rates. When applied to two real datasets, RRIntCC was able to find several significant regions, while BOOST failed to identify any significant results. The source code and the sample data of RRIntCC are available at http://bioinformatics.ust.hk/RRIntCC.html. In this paper, a new region-based interaction detection method with better performance than SNP-based interaction detection methods has been proposed.

    更新日期:2019-12-30
  • HiSSI: high-order SNP-SNP interactions detection based on efficient significant pattern and differential evolution
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Xia Cao; Jie Liu; Maozu Guo; Jun Wang

    Detecting single nucleotide polymorphism (SNP) interactions is an important and challenging task in genome-wide association studies (GWAS). Various efforts have been devoted to detect SNP interactions. However, the large volume of SNP datasets results in such a big number of high-order SNP combinations that restrict the power of detecting interactions. In this paper, to combat with this challenge, we propose a two-stage approach (called HiSSI) to detect high-order SNP-SNP interactions. In the screening stage, HiSSI employs a statistically significant pattern that takes into account family wise error rate, to control false positives and to effectively screen two-locus combinations candidate set. In the searching stage, HiSSI applies two different search strategies (exhaustive search and heuristic search based on differential evolution along with χ2-test) on candidate pairwise SNP combinations to detect high-order SNP interactions. Extensive experiments on simulated datasets are conducted to evaluate HiSSI and recently proposed and related approaches on both two-locus and three-locus disease models. A real genome-wide dataset: breast cancer dataset collected from the Wellcome Trust Case Control Consortium (WTCCC) is also used to test HiSSI. Simulated experiments on both two-locus and three-locus disease models show that HiSSI is more powerful than other related approaches. Real experiment on breast cancer dataset, in which HiSSI detects some significantly two-locus and three-locus interactions associated with breast cancer, again corroborate the effectiveness of HiSSI in high-order SNP-SNP interaction identification.

    更新日期:2019-12-30
  • MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Ying Hui; Pi-Jing Wei; Junfeng Xia; Yu-Tian Wang; Chun-Hou Zheng

    Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.

    更新日期:2019-12-30
  • A network clustering based feature selection strategy for classifying autism spectrum disorder
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Lingkai Tang; Sakib Mostafa; Bo Liao; Fang-Xiang Wu

    Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

    更新日期:2019-12-30
  • A new method for mining information of co-expression network based on multi-cancers integrated data
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Mi-Xiao Hou; Ying-Lian Gao; Jin-Xing Liu; Junliang Shang; Rong Zhu; Sha-Sha Yuan

    Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes. We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation. The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

    更新日期:2019-12-30
  • A network view of microRNA and gene interactions in different pathological stages of colon cancer
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Jia Wen; Benika Hall; Xinghua Shi

    Colon cancer is one of the common cancers in human. Although the number of annual cases has decreased drastically, prognostic screening and translational methods can be improved. Hence, it is critical to understand the molecular mechanisms of disease progression and prognosis. In this study, we develop a new strategy for integrating microRNA and gene expression profiles together with clinical information toward understanding the regulation of colon cancer. Particularly, we use this approach to identify microRNA and gene expression networks that are specific to certain pathological stages. To demonstrate the application of our method, we apply this approach to identify microRNA and gene interactions that are specific to pathological stages of colon cancer in The Cancer Genome Atlas (TCGA) datasets. Our results show that there are significant differences in network connections between miRNAs and genes in different pathological stages of colon cancer. These findings point to a hypothesis that these networks signify different roles of microRNA and gene regulation in the pathogenesis and tumorigenesis of colon cancer.

    更新日期:2019-12-30
  • Prediction of comorbid diseases using weighted geometric embedding of human interactome
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Pakeeza Akram; Li Liao

    Comorbidity is the phenomenon of two or more diseases occurring simultaneously not by random chance and presents great challenges to accurate diagnosis and treatment. As an effort toward better understanding the genetic causes of comorbidity, in this work, we have developed a computational method to predict comorbid diseases. Two diseases sharing common genes tend to increase their comorbidity. Previous work shows that after mapping the associated genes onto the human interactome the distance between the two disease modules (subgraphs) is correlated with comorbidity. To fully incorporate structural characteristics of interactome as features into prediction of comorbidity, our method embeds the human interactome into a high dimensional geometric space with weights assigned to the network edges and uses the projection onto different dimension to “fingerprint” disease modules. A supervised machine learning classifier is then trained to discriminate comorbid diseases versus non-comorbid diseases. In cross-validation using a benchmark dataset of more than 10,000 disease pairs, we report that our model achieves remarkable performance of ROC score = 0.90 for comorbidity threshold at relative risk RR = 0 and 0.76 for comorbidity threshold at RR = 1, and significantly outperforms the previous method and the interactome generated by annotated data. To further incorporate prior knowledge pathways association with diseases, we weight the protein-protein interaction network edges according to their frequency of occurring in those pathways in such a way that edges with higher frequency will more likely be selected in the minimum spanning tree for geometric embedding. Such weighted embedding is shown to lead to further improvement of comorbid disease prediction. The work demonstrates that embedding the two-dimension planar graph of human interactome into a high dimensional geometric space allows for characterizing and capturing disease modules (subgraphs formed by the disease associated genes) from multiple perspectives, and hence provides enriched features for a supervised classifier to discriminate comorbid disease pairs from non-comorbid disease pairs more accurately than based on simply the module separation.

    更新日期:2019-12-30
  • Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Junrong Song; Wei Peng; Feng Wang; Jianxin Wang

    Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance. Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN). This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes. The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.

    更新日期:2019-12-30
  • Genome analysis and knowledge-driven variant interpretation with TGex
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-30
    Dvir Dahary; Yaron Golan; Yaron Mazor; Ofer Zelig; Ruth Barshir; Michal Twik; Tsippi Iny Stein; Guy Rosner; Revital Kariv; Fei Chen; Qiang Zhang; Yiping Shen; Marilyn Safran; Doron Lancet; Simon Fishilevich

    The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient’s phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants. We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex’s main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex’s broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards’ recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. Examining use-cases from a variety of TGex users world-wide, we demonstrate its high diagnostic yields (42% for single exome and 50% for trios in 1500 rare genetic disease cases) and critical actionable genetic findings. The platform’s support for integration with EHR and LIMS through dedicated APIs facilitates automated retrieval of patient data for TGex’s customizable reporting engine, establishing a rapid and cost-effective workflow for an entire range of clinical genetic testing, including rare disorders, cancer predisposition, tumor biopsies and health screening. TGex is an innovative tool for the annotation, analysis and prioritization of coding and non-coding genomic variants. It provides access to an extensive knowledgebase of genomic annotations, with intuitive and flexible configuration options, allows quick adaptation, and addresses various workflow requirements. It thus simplifies and accelerates variant interpretation in clinical genetics workflows, with remarkable diagnostic yield, as exemplified in the described use cases. TGex is available at http://tgex.genecards.org/

    更新日期:2019-12-30
  • CDK1 and CCNB1 as potential diagnostic markers of rhabdomyosarcoma: validation following bioinformatics analysis
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-23
    Qianru Li; Liang Zhang; Jinfang Jiang; Yangyang Zhang; Xiaomeng Wang; Qiaochu Zhang; Yang Wang; Chunxia Liu; Feng Li

    Rhabdomyosarcoma (RMS), a common soft-tissue malignancy in pediatrics, presents high invasiveness and mortality. However, besides known changes in the PAX3/7-FOXO1 fusion gene in alveolar RMS, the molecular mechanisms of the disease remain incompletely understood. The purpose of the study is to recognize potential biomarkers related with RMS and analyse their molecular mechanism, diagnosis and prognostic significance. The Gene Expression Omnibus was used to search the RMS and normal striated muscle data sets. Differentially expressed genes (DEGs) were filtered using R software. The DAVID has become accustomed to performing functional annotations and pathway analysis on DEGs. The protein interaction was constructed and further processed by the STRING tool and Cytoscape software. Kaplan–Meier was used to estimate the effect of hub genes on the ending of sarcoma sufferers, and the expression of these genes in RMS was proved by real-time polymerase chain reaction (RT-PCR). Finally, the expression of CDK1 and CCNB1 in RMS was validated by immunohistochemistry (IHC). A total of 1932 DEGs were obtained, amongst which 1505 were up-regulated and 427were down-regulated. Up-regulated genes were largely enriched in the cell cycle, ECM-receptor interaction, PI3K/Akt and p53 pathways, whilst down-regulated genes were primarily enriched in the muscle contraction process. CDK1, CCNB1, CDC20, CCNB2, AURKB, MAD2L1, HIST2H2BE, CENPE, KIF2C and PCNA were identified as hub genes by Cytoscape analyses. Survival analysis showed that, except for HIST2H2BE, the other hub genes were highly expressed and related to poor prognosis in sarcoma. RT-PCR validation showed that CDK1, CCNB1, CDC20, CENPE and HIST2H2BE were significantly differential expression in RMS compared to the normal control. IHC revealed that the expression of CDK1 (28/32, 87.5%) and CCNB1 (26/32, 81.25%) were notably higher in RMS than normal controls (1/9, 11.1%; 0/9, 0%). Moreover, the CCNB1 was associated with the age and location of the patient’s onset. These results show that these hub genes, especially CDK1 and CCNB1, may be potential diagnostic biomarkers for RMS and provide a new perspective for the pathogenesis of RMS.

    更新日期:2019-12-25
  • wtest: an integrated R package for genetic epistasis testing
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-24
    Rui Sun; Xiaoxuan Xia; Ka Chun Chong; Benny Chung-Ying Zee; William Ka Kei Wu; Maggie Haitian Wang

    With the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis. The identification of SNP-SNP, SNP-CpG, and higher order interactions helps explain the genetic etiology of human diseases, yet genome-wide analysis for interactions has been very challenging, due to the computational burden and a lack of statistical power in most datasets. The wtest R package performs association testing for main effects, pairwise and high order interactions in genome-wide association study data, and cis-regulation of SNP and CpG sites in genome-wide and epigenome-wide data. The software includes a number of post-test diagnostic and analysis functions and offers an integrated toolset for genetic epistasis testing. The wtest is an efficient and powerful statistical tool for integrated genetic epistasis testing. The package is available in CRAN: https://CRAN.R-project.org/package=wtest.

    更新日期:2019-12-25
  • Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-24
    Qing Wang; Vassiliki Kotoula; Pei-Chen Hsu; Kyriaki Papadopoulou; Joshua W. K. Ho; George Fountzilas; Eleni Giannoulatou

    The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed. We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed. We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error. Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.

    更新日期:2019-12-25
  • Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-24
    Yin Guo; Huiran Li; Menglan Cai; Limin Li

    Recent high throughput technologies have been applied for collecting heterogeneous biomedical omics datasets. Computational analysis of the multi-omics datasets could potentially reveal deep insights for a given disease. Most existing clustering methods by multi-omics data assume strong consistency among different sources of datasets, and thus may lose efficacy when the consistency is relatively weak. Furthermore, they could not identify the conflicting parts for each view, which might be important in applications such as cancer subtype identification. In this work, we propose an integrative subspace clustering method (ISC) by common and specific decomposition to identify clustering structures with multi-omics datasets. The main idea of our ISC method is that the original representations for the samples in each view could be reconstructed by the concatenation of a common part and a view-specific part in orthogonal subspaces. The problem can be formulated as a matrix decomposition problem and solved efficiently by our proposed algorithm. The experiments on simulation and text datasets show that our method outperforms other state-of-art methods. Our method is further evaluated by identifying cancer types using a colorectal dataset. We finally apply our method to cancer subtype identification for five cancers using TCGA datasets, and the survival analysis shows that the subtypes we found are significantly better than other compared methods. We conclude that our ISC model could not only discover the weak common information across views but also identify the view-specific information.

    更新日期:2019-12-25
  • Parameter, noise, and tree topology effects in tumor phylogeny inference
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-23
    Kiran Tomlinson; Layla Oesper

    Accurate inference of the evolutionary history of a tumor has important implications for understanding and potentially treating the disease. While a number of methods have been proposed to reconstruct the evolutionary history of a tumor from DNA sequencing data, it is not clear how aspects of the sequencing data and tumor itself affect these reconstructions. We investigate when and how well these histories can be reconstructed from multi-sample bulk sequencing data when considering only single nucleotide variants (SNVs). Specifically, we examine the space of all possible tumor phylogenies under the infinite sites assumption (ISA) using several approaches for enumerating phylogenies consistent with the sequencing data. On noisy simulated data, we find that the ISA is often violated and that low coverage and high noise make it more difficult to identify phylogenies. Additionally, we find that evolutionary trees with branching topologies are easier to reconstruct accurately. We also apply our reconstruction methods to both chronic lymphocytic leukemia and clear cell renal cell carcinoma datasets and confirm that ISA violations are common in practice, especially in lower-coverage sequencing data. Nonetheless, we show that an ISA-based approach can be relaxed to produce high-quality phylogenies. Consideration of practical aspects of sequencing data such as coverage or the model of tumor evolution (branching, linear, etc.) is essential to effectively using the output of tumor phylogeny inference methods. Additionally, these factors should be considered in the development of new inference methods.

    更新日期:2019-12-23
  • MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-23
    Yingjun Ma; Tingting He; Leixin Ge; Chenhao Zhang; Xingpeng Jiang

    Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods. In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions. Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.

    更新日期:2019-12-23
  • Heterogeneous network embedding enabling accurate disease association predictions
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-23
    Yun Xiong; Mengjie Guo; Lu Ruan; Xiangnan Kong; Chunlei Tang; Yangyong Zhu; Wei Wang

    It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation. We incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset. We propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation.

    更新日期:2019-12-23
  • HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-23
    Junning Gao; Lizhi Liu; Shuwei Yao; Xiaodi Huang; Hiroshi Mamitsuka; Shanfeng Zhu

    As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and the hierarchical structure of HPO. Specifically, we use a dual graph to regularize Non-negative Matrix Factorization (NMF) in a way that the information from different sources can be seamlessly integrated. In essence, HPOAnnotator solves the sparsity problem of a protein-phenotype association matrix by using a low-rank approximation. By combining the hierarchical structure of HPO and co-annotations of proteins, our model can well capture the HPO semantic similarities. Moreover, graph Laplacian regularizations are imposed in the latent space so as to utilize multiple PPI networks. The performance of HPOAnnotator has been validated under cross-validation and independent test. Experimental results have shown that HPOAnnotator outperforms the competing methods significantly. Through extensive comparisons with the state-of-the-art methods, we conclude that the proposed HPOAnnotator is able to achieve the superior performance as a result of using a low-rank approximation with a graph regularization. It is promising in that our approach can be considered as a starting point to study more efficient matrix factorization-based algorithms.

    更新日期:2019-12-23
  • A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-23
    Bo Xu; Yu Liu; Shuo Yu; Lei Wang; Jie Dong; Hongfei Lin; Zhihao Yang; Jian Wang; Feng Xia

    Prediction of pathogenic genes is crucial for disease prevention, diagnosis, and treatment. But traditional genetic localization methods are often technique-difficulty and time-consuming. With the development of computer science, computational biology has gradually become one of the main methods for finding candidate pathogenic genes. We propose a pathogenic genes prediction method based on network embedding which is called Multipath2vec. Firstly, we construct an heterogeneous network which is called GP−network. It is constructed based on three kinds of relationships between genes and phenotypes, including correlations between phenotypes, interactions between genes and known gene-phenotype pairs. Then in order to embedding the network better, we design the multi-path to guide random walk in GP−network. The multi-path includes multiple paths between genes and phenotypes which can capture complex structural information of heterogeneous network. Finally, we use the learned vector representation of each phenotype and protein to calculate the similarities and rank according to the similarities between candidate genes and the target phenotype. We implemented Multipath2vec and four baseline approaches (i.e., CATAPULT, PRINCE, Deepwalk and Metapath2vec) on many-genes gene-phenotype data, single-gene gene-phenotype data and whole gene-phenotype data. Experimental results show that Multipath2vec outperformed the state-of-the-art baselines in pathogenic genes prediction task. We propose Multipath2vec that can be utilized to predict pathogenic genes and experimental results show the higher accuracy of pathogenic genes prediction.

    更新日期:2019-12-23
  • Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-23
    Jie Hao; Youngsoon Kim; Tejaswini Mallavarapu; Jung Hun Oh; Mingon Kang

    Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified. Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.

    更新日期:2019-12-23
  • Karyotyping and prenatal diagnosis of 47,XX,+ 8[67]/46,XX [13] Mosaicism: case report and literature review
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-21
    Shaohua Sun; Fang Zhan; Jiusheng Jiang; Xuerui Zhang; Lei Yan; Weiyi Cai; Hailiang Liu; Donghua Cao

    Trisomy 8 mosaicism has a wide phenotypic variability, ranging from mild dysmorphic features to severe malformations. This report concluded a female pregnant woman with trisomy 8 mosaicism, and carefully cytogenetic diagnoses were performed to give her prenatal diagnostic information. This report also provides more knowledge about trisomy 8 mosaicism and the prenatal diagnostic for clinicians. In this present study, we reported one case of pregnancy woman with trisomy 8 mosaicism. Noninvasive prenatal testing prompted an abnormal Z-score, but further three dimension color ultrasound result suggested a single live fetus with no abnormality. The phenotypic of the pregnant woman was normal. Based on our results, there were no abnormal initial myeloid cells (< 10− 4), which suggested that the patient had no blood diseases. The peripheral blood karyotype of the patient was 47,XX,+ 8[67]/46,XX [13], and karyotype of amniotic fluid was 46, XX. The next generation sequencing (NGS) result suggested that the proportions of trisomy 8 in different tissues were obviously different; and 0% in amniotic fluid. Last, the chromosomes of the patient and her baby were confirmed using chromosome microarray analysis (CMA), and the results were arr[GRCh37](8) × 3,11p15.5p13(230750–33,455,733) × 2 hmz and normal. This pregnancy woman was trisomy 8 mosaicism, but the phenotypic was normal, and also the fetus was normal. Carefully cytogenetic diagnoses should be performed for prenatal diagnose.

    更新日期:2019-12-21
  • Genome-wide association study in Chinese cohort identifies one novel hypospadias risk associated locus at 12q13.13
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-19
    Zhongzhong Chen; Xiaoling Lin; Yunping Lei; Haitao Chen; Richard H. Finnell; Yaping Wang; Jianfeng Xu; Daru Lu; Hua Xie; Fang Chen

    Hypospadias risk–associated gene variants have been reported in populations of European descent using genome-wide association studies (GWASs). There is little known at present about any possible hypospadias risk associations in Han Chinese populations. To systematically investigate hypospadias risk–associated gene variants in Chinese patients, we performed the first GWAS in a Han Chinese cohort consisting of 197 moderate-severe hypospadias cases and 933 unaffected controls. Suggestive loci (p < 1 × 10− 4) were replicated in 118 cases and 383 controls, as well as in a second independent validation population of 137 cases and 190 controls. Regulatory and protein-protein interactions (PPIs) were then conducted for the functional analyses of candidate variants. We identified rs11170516 with the risk allele G within the SP1/SP7 region that was independently associated with moderate-severe hypospadias [SP1/SP7, rs11170516, Pcombine = 3.5 × 10− 9, odds ratio (OR) = 1.96 (1.59–2.44)]. Results also suggested that rs11170516 is associated with the expression of SP1 as a cis-expression quantitative trait locus (cis-eQTL). Protein SP1 could affect the risk of hypospadias via PPIs. We performed the first GWAS of moderate-severe hypospadias in a Han Chinese cohort, and identified one novel susceptibility cis-acting regulatory locus at 12q13.13, which may regulate a variety of hypospadias-related pathways by affecting proximal SP1 gene expression and subsequent PPIs. This study complements known common hypospadias risk-associated variants and provides the possible role of cis-acting regulatory variant in causing hypospadias.

    更新日期:2019-12-20
  • Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-20
    Yen-Jung Chiu; Yi-Hsuan Hsieh; Yen-Hua Huang

    To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. Our deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

    更新日期:2019-12-20
  • Differential alternative splicing regulation among hepatocellular carcinoma with different risk factors
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-20
    Young-Joo Jin; Seyoun Byun; Seonggyun Han; John Chamberlin; Dongwook Kim; Min Jung Kim; Younghee Lee

    Hepatitis B virus (HBV), hepatitis C virus (HCV), and alcohol consumption are predominant causes of hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying how differently these causes are implicated in HCC development are not fully understood. Therefore, we investigated differential alternative splicing (AS) regulation among HCC patients with these risk factors. We conducted a genome-wide survey of AS events associated with HCCs among HBV (n = 95), HCV (n = 47), or alcohol (n = 76) using RNA-sequencing data obtained from The Cancer Genome Atlas. In three group comparisons of HBV vs. HCV, HBV vs. alcohol, and HCV vs. alcohol for RNA seq (ΔPSI> 0.05, FDR < 0.05), 133, 93, and 29 differential AS events (143 genes) were identified, respectively. Of 143 AS genes, eight and one gene were alternatively spliced specific to HBV and HCV, respectively. Through functional analysis over the canonical pathways and gene ontologies, we identified significantly enriched pathways in 143 AS genes including immune system, mRNA splicing-major pathway, and nonsense-mediated decay, which may be important to carcinogenesis in HCC risk factors. Among eight genes with HBV-specific splicing events, HLA-A, HLA-C, and IP6K2 exhibited more differential expression of AS events (ΔPSI> 0.1). Intron retention of HLA-A was observed more frequently in HBV-associated HCC than HCV- or alcohol-associated HCC, and intron retention of HLA-C showed vice versa. Exon 3 (based on ENST00000432678) of IP6K2 was less skipped in HBV-associated in HCC compared to HCV- or alcohol-associated HCC. AS may play an important role in regulating transcription differences implicated in HBV-, HCV-, and alcohol-related HCC development.

    更新日期:2019-12-20
  • A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-20
    Léon-Charles Tranchevent; Francisco Azuaje; Jagath C. Rajapakse

    The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

    更新日期:2019-12-20
  • Finding prognostic gene pairs for cancer from patient-specific gene networks
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-20
    Byungkyu Park; Wook Lee; Inhee Park; Kyungsook Han

    Molecular characterization of individual cancer patients is important because cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. Many studies have been conducted to identify diagnostic or prognostic gene signatures for cancer from gene expression profiles. However, some gene signatures may fail to serve as diagnostic or prognostic biomarkers and gene signatures may not be found in gene expression profiles. In this study, we developed a general method for constructing patient-specific gene correlation networks and for identifying prognostic gene pairs from the networks. A patient-specific gene correlation network was constructed by comparing a reference gene correlation network from normal samples to a network perturbed by a single patient sample. The main difference of our method from previous ones includes (1) it is focused on finding prognostic gene pairs rather than prognostic genes and (2) it can identify prognostic gene pairs from gene expression profiles even when no significant prognostic genes exist. Evaluation of our method with extensive data sets of three cancer types (breast invasive carcinoma, colon adenocarcinoma, and lung adenocarcinoma) showed that our approach is general and that gene pairs can serve as more reliable prognostic signatures for cancer than genes. Our study revealed that prognosis of individual cancer patients is associated with the existence of prognostic gene pairs in the patient-specific network and the size of a subnetwork of the prognostic gene pairs in the patient-specific network. Although preliminary, our approach will be useful for finding gene pairs to predict survival time of patients and to tailor treatments to individual characteristics. The program for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is available at http://bclab.inha.ac.kr/pancancer.

    更新日期:2019-12-20
  • Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-20
    Zhixun Zhao; Hui Peng; Xiaocai Zhang; Yi Zheng; Fang Chen; Liang Fang; Jinyan Li

    The early diagnosis of lung cancer has been a critical problem in clinical practice for a long time and identifying differentially expressed gene as disease marker is a promising solution. However, the most existing gene differential expression analysis (DEA) methods have two main drawbacks: First, these methods are based on fixed statistical hypotheses and not always effective; Second, these methods can not identify a certain expression level boundary when there is no obvious expression level gap between control and experiment groups. This paper proposed a novel approach to identify marker genes and gene expression level boundary for lung cancer. By calculating a kernel maximum mean discrepancy, our method can evaluate the expression differences between normal, normal adjacent to tumor (NAT) and tumor samples. For the potential marker genes, the expression level boundaries among different groups are defined with the information entropy method. Compared with two conventional methods t-test and fold change, the top average ranked genes selected by our method can achieve better performance under all metrics in the 10-fold cross-validation. Then GO and KEGG enrichment analysis are conducted to explore the biological function of the top 100 ranked genes. At last, we choose the top 10 average ranked genes as lung cancer markers and their expression boundaries are calculated and reported. The proposed approach is effective to identify gene markers for lung cancer diagnosis. It is not only more accurate than conventional DEA methods but also provides a reliable method to identify the gene expression level boundaries.

    更新日期:2019-12-20
  • GTX.Digest.VCF: an online NGS data interpretation system based on intelligent gene ranking and large-scale text mining
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-20
    Yanhuang Jiang; Chengkun Wu; Yanghui Zhang; Shaowei Zhang; Shuojun Yu; Peng Lei; Qin Lu; Yanwei Xi; Hua Wang; Zhuo Song

    An important task in the interpretation of sequencing data is to highlight pathogenic genes (or detrimental variants) in the field of Mendelian diseases. It is still challenging despite the recent rapid development of genomics and bioinformatics. A typical interpretation workflow includes annotation, filtration, manual inspection and literature review. Those steps are time-consuming and error-prone in the absence of systematic support. Therefore, we developed GTX.Digest.VCF, an online DNA sequencing interpretation system, which prioritizes genes and variants for novel disease-gene relation discovery and integrates text mining results to provide literature evidence for the discovery. Its phenotype-driven ranking and biological data mining approach significantly speed up the whole interpretation process. The GTX.Digest.VCF system is freely available as a web portal at http://vcf.gtxlab.com for academic research. Evaluation on the DDD project dataset demonstrates an accuracy of 77% (235 out of 305 cases) for top-50 genes and an accuracy of 41.6% (127 out of 305 cases) for top-5 genes. GTX.Digest.VCF provides an intelligent web portal for genomics data interpretation via the integration of bioinformatics tools, distributed parallel computing, biomedical text mining. It can facilitate the application of genomic analytics in clinical research and practices.

    更新日期:2019-12-20
  • RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-19
    Nathan D. Pennock; Sonali Jindal; Wesley Horton; Duanchen Sun; Jayasri Narasimhan; Lucia Carbone; Suzanne S. Fei; Robert Searles; Christina A. Harrington; Julja Burchard; Sheila Weinmann; Pepper Schedin; Zheng Xia

    Formalin-fixed, paraffin-embedded (FFPE) tissues for RNA-seq have advantages over fresh frozen tissue including abundance and availability, connection to rich clinical data, and association with patient outcomes. However, FFPE-derived RNA is highly degraded and chemically modified, which impacts its utility as a faithful source for biological inquiry. True archival FFPE breast cancer cases (n = 58), stored at room temperature for 2–23 years, were utilized to identify key steps in tissue selection, RNA isolation, and library choice. Gene expression fidelity was evaluated by comparing FFPE data to public data obtained from fresh tissues, and by employing single-gene, gene set and transcription network-based regulon analyses. We report a single 10 μm section of breast tissue yields sufficient RNA for RNA-seq, and a relationship between RNA quality and block age that was not linear. We find single-gene analysis is limiting with FFPE tissues, while targeted gene set approaches effectively distinguish ER+ from ER- breast cancers. Novel utilization of regulon analysis identified the transcription factor KDM4B to associate with ER+ disease, with KDM4B regulon activity and gene expression having prognostic significance in an independent cohort of ER+ cases. Our results, which outline a robust FFPE-RNA-seq pipeline for broad use, support utilizing FFPE tissues to address key questions in the breast cancer field, including the delineation between indolent and life-threatening disease, biological stratification and molecular mechanisms of treatment resistance.

    更新日期:2019-12-19
  • Competitive endogenous RNA (ceRNA) regulation network of lncRNAs, miRNAs, and mRNAs in Wilms tumour
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-16
    Fucai Tang; Zechao Lu; Jiamin Wang; Zhibiao Li; Weijia Wu; Haifeng Duan; Zhaohui He

    Competitive endogenous RNAs (ceRNAs) have revealed a new mechanism of interaction between RNAs. However, an understanding of the ceRNA regulatory network in Wilms tumour (WT) remains limited. The expression profiles of mRNAs, miRNAs and lncRNAs in Wilms tumour samples and normal samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. The EdgeR package was employed to identify differentially expressed lncRNAs, miRNAs and mRNAs. Functional enrichment analyses via the ClusterProfile R package were performed, and the lncRNA–miRNA–mRNA interaction ceRNA network was established in Cytoscape. Subsequently, the correlation between the ceRNA network and overall survival was analysed. A total of 2037 lncRNAs, 154 miRNAs and 3609 mRNAs were identified as differentially expressed RNAs in Wilms tumour. Of those, 205 lncRNAs, 26 miRNAs and 143 mRNAs were included in the ceRNA regulatory network. The results of Gene Ontology (GO) analysis revealed that the differentially expressed genes (DEGs) were mainly enriched in terms related to response to mechanical stimuli, transcription factor complexes, and transcription factor activity (related to RNA polymerase II proximal promoter sequence-specific DNA binding). The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were mainly enriched in pathways related to the cell cycle. The survival analysis results showed that 16 out of the 205 lncRNAs, 1 out of 26 miRNAs and 5 out of 143 mRNAs were associated with overall survival in Wilms tumour patients (P < 0.05). CeRNA networks play an important role in Wilms tumour. This finding might provide effective, novel insights for further understanding the mechanisms underlying Wilms tumour.

    更新日期:2019-12-17
  • Analysis of disease comorbidity patterns in a large-scale China population
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-12
    Mengfei Guo; Yanan Yu; Tiancai Wen; Xiaoping Zhang; Baoyan Liu; Jin Zhang; Runshun Zhang; Yanning Zhang; Xuezhong Zhou

    Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients. We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease). Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.

    更新日期:2019-12-13
  • Systematic computational identification of prognostic cytogenetic markers in neuroblastoma
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-12
    Chao Qin; Xiaoyan He; Yanding Zhao; Chun-Yip Tong; Kenneth Y. Zhu; Yongqi Sun; Chao Cheng

    Neuroblastoma (NB) is the most common extracranial solid tumor found in children. The frequent gain/loss of many chromosome bands in tumor cells and absence of mutations found at diagnosis suggests that NB is a copy number-driven cancer. Despite the previous work, a systematic analysis that investigates the relationship between such frequent gain/loss of chromosome bands and patient prognosis has yet to be implemented. First, we analyzed two NB CNV datasets to select chromosomal bands with a high frequency of gain or loss. Second, we applied a computational approach to infer sample-specific CNVs for each chromosomal band selected in step 1 based on gene expression data. Third, we applied univariate Cox proportional hazards models to examine the association between the resulting inferred copy number values (iCNVs) and patient survival. Finally, we applied multivariate Cox proportional hazards models to select chromosomal bands that remained significantly associated with prognosis after adjusting for critical clinical variables, including age, stage, gender, and MYCN amplification status. Here, we used a computational method to infer the copy number variations (CNVs) of sample-specific chromosome bands from NB patient gene expression profiles. The resulting inferred CNVs (iCNVs) were highly correlated with the experimentally determined CNVs, demonstrating CNVs can be accurately inferred from gene expression profiles. Using this iCNV metric, we identified 58 frequent gain/loss chromosome bands that were significantly associated with patient survival. Furthermore, we found that 7 chromosome bands were still significantly associated with patient survival even when clinical factors, such as MYCN status, were considered. Particularly, we found that the chromosome band chr11p14 has high potential as a novel candidate cytogenetic biomarker for clinical use. Our analysis resulted in a comprehensive list of prognostic chromosome bands supported by strong statistical evidence. In particular, the chr11p14 gain event provided additional prognostic value in addition to well-established clinical factors, including MYCN status, and thereby represents a novel candidate cytogenetic biomarker with high clinical potential. Additionally, this computational framework could be readily extended to other cancer types, such as leukemia.

    更新日期:2019-12-13
  • Genepanel.iobio - an easy to use web tool for generating disease- and phenotype-associated gene lists
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-11
    Aditya Ekawade; Matt Velinder; Alistair Ward; Tonya DiSera; Chase Miller; Yi Qiao; Gabor Marth

    When ordering genetic testing or triaging candidate variants in exome and genome sequencing studies, it is critical to generate and test a comprehensive list of candidate genes that succinctly describe the complete and objective phenotypic features of disease. Significant efforts have been made to curate gene:disease associations both in academic research and commercial genetic testing laboratory settings. However, many of these valuable resources exist as islands and must be used independently, generating static, single-resource gene:disease association lists. Here we describe genepanel.iobio (https://genepanel.iobio.io) an easy to use, free and open-source web tool for generating disease- and phenotype-associated gene lists from multiple gene:disease association resources, including the NCBI Genetic Testing Registry (GTR), Phenolyzer, and the Human Phenotype Ontology (HPO). We demonstrate the utility of genepanel.iobio by applying it to complex, rare and undiagnosed disease cases that had reached a diagnostic conclusion. We find that genepanel.iobio is able to correctly prioritize the gene containing the diagnostic variant in roughly half of these challenging cases. Importantly, each component resource contributed diagnostic value, showing the benefits of this aggregate approach. We expect genepanel.iobio will improve the ease and diagnostic value of generating gene:disease association lists for genetic test ordering and whole genome or exome sequencing variant prioritization.

    更新日期:2019-12-11
  • A female patient with retinoblastoma and severe intellectual disability carrying an X;13 balanced translocation without rearrangement in the RB1 gene: a case report
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-05
    Makiko Tsutsumi; Hiroyoshi Hattori; Nobuhiro Akita; Naoko Maeda; Toshinobu Kubota; Keizo Horibe; Naoko Fujita; Miki Kawai; Yasuko Shinkai; Maki Kato; Takema Kato; Rie Kawamura; Fumihiko Suzuki; Hiroki Kurahashi

    Female carriers of a balanced X; autosome translocation generally undergo selective inactivation of the normal X chromosome. This is because inactivation of critical genes within the autosomal region of the derivative translocation chromosome would compromise cellular function. We here report a female patient with bilateral retinoblastoma and a severe intellectual disability who carries a reciprocal X-autosomal translocation. Cytogenetic and molecular analyses, a HUMARA (Human androgen receptor) assay, and methylation specific PCR (MSP) and bisulfite sequencing were performed using peripheral blood samples from the patient. The patient’s karyotype was 46,X,t(X;13)(q28;q14.1) by G-banding analysis. Further cytogenetic analysis located the entire RB1 gene and its regulatory region on der(X) with no translocation disruption. The X-inactivation pattern in the peripheral blood was highly skewed but not completely selected. MSP and deep sequencing of bisulfite-treated DNA revealed that an extensive 13q region, including the RB1 promoter, was unusually methylated in a subset of cells. The der(X) region harboring the RB1 gene was inactivated in a subset of somatic cells, including the retinal cells, in the patient subject which acted as the first hit in the development of her retinoblastoma. In addition, the patient’s intellectual disability may be attributable to the inactivation of the der(X), leading to a 13q deletion syndrome-like phenotype, or to an active X-linked gene on der (13) leading to Xq28 functional disomy.

    更新日期:2019-12-06
  • Correction to: A functional polymorphism in the promoter of miR-17-92 cluster is associated with decreased risk of ischemic stroke
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-12-02
    Huatuo Huang; Guijiang Wei; Chunfang Wang; Yulan Lu; Chunhong Liu; Rong Wang; Xiang Shi; Jun Yang; Yesheng Wei

    Following publication of the original article [1], it was reported that during the production process, Fig. 3b was omitted from the final article.

    更新日期:2019-12-02
  • Reinterpretation, reclassification, and its downstream effects: challenges for clinical laboratory geneticists
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-11-29
    Julia El Mecky; Lennart Johansson; Mirjam Plantinga; Angela Fenwick; Anneke Lucassen; Trijnie Dijkhuizen; Annemieke van der Hout; Kate Lyle; Irene van Langen

    In recent years, the amount of genomic data produced in clinical genetics services has increased significantly due to the advent of next-generation sequencing. This influx of genomic information leads to continuous changes in knowledge on how genetic variants relate to hereditary disease. These changes can have important consequences for patients who have had genetic testing in the past, as new information may affect their clinical management. When and how patients should be recontacted after new genetic information becomes available has been investigated extensively. However, the issue of how to handle the changing nature of genetic information remains underexplored in a laboratory setting, despite it being the first stage at which changes in genetic data are identified and managed. The authors organized a 7-day online focus group discussion. Fifteen clinical laboratory geneticists took part. All (nine) Dutch clinical molecular genetics diagnostic laboratories were represented. Laboratories in our study reinterpret genetic variants reactively, e.g. at the request of a clinician or following identification of a previously classified variant in a new patient. Participants currently deemed active, periodic reinterpretation to be unfeasible and opinions differed on whether it is desirable, particularly regarding patient autonomy and the main responsibilities of the laboratory. The efficacy of reinterpretation was questioned in the presence of other strategies, such as reanalysis and resequencing of DNA. Despite absence of formal policy regarding when to issue a new report for clinicians due to reclassified genetic data, participants indicated similar practice across all laboratories. However, practice differed significantly between laboratory geneticists regarding the reporting of VUS reclassifications. Based on the results, the authors formulated five challenges needing to be addressed in future laboratory guidelines: 1. Should active reinterpretation of variants be conducted by the laboratory as a routine practice? 2. How does reinterpretation initiated by the laboratory relate to patient expectations and consent? 3. When should reinterpreted data be considered clinically significant and communicated from laboratory to clinician? 4. Should reinterpretation, reanalysis or a new test be conducted? 5. How are reclassifications perceived and how might this affect laboratory practice?

    更新日期:2019-11-29
  • Analysis of genetic networks regulating refractive eye development in collaborative cross progenitor strain mice reveals new genes and pathways underlying human myopia
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-07-30
    Tatiana V. Tkatchenko; Rupal L. Shah; Takayuki Nagasaki; Andrei V. Tkatchenko

    Population studies suggest that genetic factors play an important role in refractive error development; however, the precise role of genetic background and the composition of the signaling pathways underlying refractive eye development remain poorly understood. Here, we analyzed normal refractive development and susceptibility to form-deprivation myopia in the eight progenitor mouse strains of the Collaborative Cross (CC). We used RNA-seq to analyze gene expression in the retinae of these mice and reconstruct genetic networks and signaling pathways underlying refractive eye development. We also utilized genome-wide gene-based association analysis to identify mouse genes and pathways associated with myopia in humans. Genetic background strongly influenced both baseline refractive development and susceptibility to environmentally-induced myopia. Baseline refractive errors ranged from − 21.2 diopters (D) in 129S1/svlmj mice to + 22.0 D in CAST/EiJ mice and represented a continuous distribution typical of a quantitative genetic trait. The extent of induced form-deprivation myopia ranged from − 5.6 D in NZO/HILtJ mice to − 20.0 D in CAST/EiJ mice and also followed a continuous distribution. Whole-genome (RNA-seq) gene expression profiling in retinae from CC progenitor strains identified genes whose expression level correlated with either baseline refractive error or susceptibility to myopia. Expression levels of 2,302 genes correlated with the baseline refractive state of the eye, whereas 1,917 genes correlated with susceptibility to induced myopia. Genome-wide gene-based association analysis in the CREAM and UK Biobank human cohorts revealed that 985 of the above genes were associated with myopia in humans, including 847 genes which were implicated in the development of human myopia for the first time. Although the gene sets controlling baseline refractive development and those regulating susceptibility to myopia overlapped, these two processes appeared to be controlled by largely distinct sets of genes. Comparison with data for other animal models of myopia revealed that the genes identified in this study comprise a well-defined set of retinal signaling pathways, which are highly conserved across different vertebrate species. These results identify major signaling pathways involved in refractive eye development and provide attractive targets for the development of anti-myopia drugs.

    更新日期:2019-11-28
  • Correction to: Sequencing and curation strategies for identifying candidate glioblastoma treatments
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-02
    Mayu O. Frank; Takahiko Koyama; Kahn Rhrissorrakrai; Nicolas Robine; Filippo Utro; Anne-Katrin Emde; Bo-Juen Chen; Kanika Arora; Minita Shah; Heather Geiger; Vanessa Felice; Esra Dikoglu; Sadia Rahman; Xiaolan Fang; Vladimir Vacic; Ewa A. Bergmann; Julia L. Moore Vogel; Catherine Reeves; Depinder Khaira; Anthony Calabro; Duyang Kim; Michelle F. Lamendola-Essel; Cecilia Esteves; Phaedra Agius; Christian Stolte; John Boockvar; Alexis Demopoulos; Dimitris G. Placantonakis; John G. Golfinos; Cameron Brennan; Jeffrey Bruce; Andrew B. Lassman; Peter Canoll; Christian Grommes; Mariza Daras; Eli Diamond; Antonio Omuro; Elena Pentsova; Dana E. Orange; Stephen J. Harvey; Jerome B. Posner; Vanessa V. Michelini; Vaidehi Jobanputra; Michael C. Zody; John Kelly; Laxmi Parida; Kazimierz O. Wrzeszczynski; Ajay K. Royyuru; Robert B. Darnell

    Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.

    更新日期:2019-11-28
  • Identification of single nucleotide variants using position-specific error estimation in deep sequencing data
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-02
    Dimitrios Kleftogiannis; Marco Punta; Anuradha Jayaram; Shahneen Sandhu; Stephen Q. Wong; Delila Gasi Tandefelt; Vincenza Conteduca; Daniel Wetterskog; Gerhardt Attard; Stefano Lise

    Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .

    更新日期:2019-11-28
  • Disruption of chromatin organisation causes MEF2C gene overexpression in intellectual disability: a case report
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-02
    Kevin Yauy; Anouck Schneider; Bee Ling Ng; Jean-Baptiste Gaillard; Satish Sati; Christine Coubes; Constance Wells; Magali Tournaire; Thomas Guignard; Pauline Bouret; David Geneviève; Jacques Puechberty; Franck Pellestor; Vincent Gatinois

    Balanced structural variants are mostly described in disease with gene disruption or subtle rearrangement at breakpoints. Here we report a patient with mild intellectual deficiency who carries a de novo balanced translocation t(3;5). Breakpoints were fully explored by microarray, Array Painting and Sanger sequencing. No gene disruption was found but the chromosome 5 breakpoint was localized 228-kb upstream of the MEF2C gene. The predicted Topologically Associated Domains analysis shows that it contains only the MEF2C gene and a long non-coding RNA LINC01226. RNA studies looking for MEF2C gene expression revealed an overexpression of MEF2C in the lymphoblastoid cell line of the patient. Pathogenicity of MEF2C overexpression is still unclear as only four patients with mild intellectual deficiency carrying 5q14.3 microduplications containing MEF2C are described in the literature. The microduplications in these individuals also contain other genes expressed in the brain. The patient presented the same phenotype as 5q14.3 microduplication patients. We report the first case of a balanced translocation leading to an overexpression of MEF2C similar to a functional duplication.

    更新日期:2019-11-28
  • Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-05
    Lizhong Ding; Zheyun Feng; Yongsheng Bai

    microRNA (miRNA) is a short RNA (~ 22 nt) that regulates gene expression at the posttranscriptional level. Aberration of miRNA expressions could affect their targeting mRNAs involved in cancer-related signaling pathways. We conduct clustering analysis of miRNA and mRNA using expression data from the Cancer Genome Atlas (TCGA). We combine the Hungarian algorithm and blossom algorithm in graph theory. Data analysis is done using programming language R and Python. We first quantify edge-weights of the miRNA-mRNA pairs by combining their expression correlation coefficient in tumor (T_CC) and correlation coefficient in normal (N_CC). We thereby introduce a bipartite graph partition procedure to identify cluster candidates. Specifically, we propose six weight formulas to quantify the change of miRNA-mRNA expression T_CC relative to N_CC, and apply the traditional hierarchical clustering to subjectively evaluate the different weight formulas of miRNA-mRNA pairs. Among these six different weight formulas, we choose the optimal one, which we define as the integrated mean value weights, to represent the connections between miRNA and mRNAs. Then the Hungarian algorithm and the blossom algorithm are employed on the miRNA-mRNA bipartite graph to passively determine the clusters. The combination of Hungarian and the blossom algorithms is dubbed maximum weighted merger method (MWMM). MWMM identifies clusters of different sizes that meet the mathematical criterion that internal connections inside a cluster are relatively denser than external connections outside the cluster and biological criterion that the intra-cluster Gene Ontology (GO) term similarities are larger than the inter-cluster GO term similarities. MWMM is developed using breast invasive carcinoma (BRCA) as training data set, but can also applies to other cancer type data sets. MWMM shows advantage in GO term similarity in most cancer types, when compared to other algorithms. miRNAs and mRNAs that are likely to be affected by common underlying causal factors in cancer can be clustered by MWMM approach and potentially be used as candidate biomarkers for different cancer types and provide clues for targets of precision medicine in cancer treatment.

    更新日期:2019-11-28
  • Discovery of stroke-related blood biomarkers from gene expression network models
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-07
    Konstantinos Theofilatos; Aigli Korfiati; Seferina Mavroudi; Matthew C. Cowperthwaite; Max Shpak

    Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body’s response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke. Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene’s significance in the network was assessed according to the differences in their network’s pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm – support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model. A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes (ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies: < 71% for any single gene, and even models with larger (10–25) numbers of gene transcript biomarkers gave lower predictive accuracies (≤ 82%) than the 6 network-based gene signature classification. miRNA:mRNA target prediction computational analysis revealed 8 differentially expressed micro-RNAs (miRNAs) that are significantly associated with at least 2 of the 6 network-central genes. Network-based models have the potential to identify a more statistically robust pattern of gene expression typical of acute ischemic stroke and to generate hypotheses about possible interactions among functionally relevant genes, leading to the identification of more informative biomarkers.

    更新日期:2019-11-28
  • Correction to: Predicting drug response of tumors from integrated genomic profiles by deep neural networks
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-12
    Yu-Chiao Chiu; Hung-I Harry Chen; Tinghe Zhang; Songyao Zhang; Aparna Gorthi; Li-Ju Wang; Yufei Huang; Yidong Chen

    Following publication of the original article [1], the authors provided an updated funding statement to the article. The updated statement is as follows:

    更新日期:2019-11-28
  • Genome-wide association studies of severe P. falciparum malaria susceptibility: progress, pitfalls and prospects
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-14
    Delesa Damena; Awany Denis; Lemu Golassa; Emile R. Chimusa

    P. falciparum malaria has been recognized as one of the prominent evolutionary selective forces of human genome that led to the emergence of multiple host protective alleles. A comprehensive understanding of the genetic bases of severe malaria susceptibility and resistance can potentially pave ways to the development of new therapeutics and vaccines. Genome-wide association studies (GWASs) have recently been implemented in malaria endemic areas and identified a number of novel association genetic variants. However, there are several open questions around heritability, epistatic interactions, genetic correlations and associated molecular pathways among others. Here, we assess the progress and pitfalls of severe malaria susceptibility GWASs and discuss the biology of the novel variants. We obtained all severe malaria susceptibility GWASs published thus far and accessed GWAS dataset of Gambian populations from European Phenome Genome Archive (EGA) through the MalariaGen consortium standard data access protocols. We noticed that, while some of the well-known variants including HbS and ABO blood group were replicated across endemic populations, only few novel variants were convincingly identified and their biological functions remain to be understood. We estimated SNP-heritability of severe malaria at 20.1% in Gambian populations and showed how advanced statistical genetic analytic methods can potentially be implemented in malaria susceptibility studies to provide useful functional insights. The ultimate goal of malaria susceptibility study is to discover a novel causal biological pathway that provide protections against severe malaria; a fundamental step towards translational medicine such as development of vaccine and new therapeutics. Beyond singe locus analysis, the future direction of malaria susceptibility requires a paradigm shift from single -omics to multi-stage and multi-dimensional integrative functional studies that combines multiple data types from the human host, the parasite, the mosquitoes and the environment. The current biotechnological and statistical advances may eventually lead to the feasibility of systems biology studies and revolutionize malaria research.

    更新日期:2019-11-28
  • Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-17
    Zandra C. Félix Garza; Michael Lenz; Joerg Liebmann; Gökhan Ertaylan; Matthias Born; Ilja C. W. Arts; Peter A. J. Hilbers; Natal A. W. van Riel

    Psoriasis and atopic dermatitis are two inflammatory skin diseases with a high prevalence and a significant burden on the patients. Underlying molecular mechanisms include chronic inflammation and abnormal proliferation. However, the cell types contributing to these molecular mechanisms are much less understood. Recently, deconvolution methodologies have allowed the digital quantification of cell types in bulk tissue based on mRNA expression data from biopsies. Using these methods to study the cellular composition of the skin enables the rapid enumeration of multiple cell types, providing insight into the numerical changes of cell types associated with chronic inflammatory skin conditions. Here, we use deconvolution to enumerate the cellular composition of the skin and estimate changes related to onset, progress, and treatment of these skin diseases. A novel signature matrix, i.e. DerM22, containing expression data from 22 reference cell types, is used, in combination with the CIBERSORT algorithm, to identify and quantify the cellular subsets within whole skin biopsy samples. We apply the approach to public microarray mRNA expression data from the skin layers and 648 samples from healthy subjects and patients with psoriasis or atopic dermatitis. The methodology is validated by comparison to experimental results from flow cytometry and immunohistochemistry studies, and the deconvolution of independent data from isolated cell types. We derived the relative abundance of cell types from healthy, lesional, and non-lesional skin and observed a marked increase in the abundance of keratinocytes and leukocytes in the lesions of both inflammatory dermatological conditions. The relative fraction of these cells varied from healthy to diseased skin and from non-lesional to lesional skin. We show that changes in the relative abundance of skin-related cell types can be used to distinguish between mild and severe cases of psoriasis and atopic dermatitis, and trace the effect of treatment. Our analysis demonstrates the value of this new resource in interpreting skin-derived transcriptomics data by enabling the direct quantification of cell types in a skin sample and the characterization of pathological changes in tissue composition.

    更新日期:2019-11-28
  • Genome-wide association study identifies new susceptible loci of IgA nephropathy in Koreans
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-19
    Kyung Hwan Jeong; Jin Sug Kim; Yu Ho Lee; Yang Gyun Kim; Ju-Young Moon; Su Kang Kim; Sun Woo Kang; Tae Hee Kim; Sang Ho Lee; Yeong Hoon Kim

    Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. Recent evidence suggests that genetic factors are related to the pathogenesis of IgAN. We conducted a genome-wide association study (GWAS) to identify novel genetic susceptibility loci for IgAN in a Korean population. We enrolled 188 biopsy-confirmed IgAN cases and 455 healthy controls for the discovery stage and explored associations between IgAN and single nucleotide polymorphisms (SNPs) using a customized DNA chip. The significant SNPs from the discovery samples were then selected for replication in an independent cohort with 310 biopsy-confirmed IgAN cases and 438 healthy controls. In the first stage, two SNPs (rs10172700 in LOC105373592 and rs2296136 in ANKRD16) were selected for further association analysis in the next stage. In the replication cohort, rs2296136 in ANKRD16 was significantly associated with IgAN (odds ratio [OR] = 1.40, 95% confidence interval [CI] 0.99–1.98, p = 0.05 in log-additive model, OR = 1.55, 95% CI = 1.06–−2.27, p = 0.02 in dominant model, and OR = 0.70, 95% CI = 0.17–−2.84, p = 0.62 in recessive model). rs2296136 in ANKRD16 also showed a significant association with IgAN in the entire study population combining GWAS and replication study (p = 0.0045 in log-additive model, p = 0.0027 in dominant model, and p = 0.76 in recessive model). The SNPs identified in the present study could be good candidate markers for predicting IgAN in Koreans, although further experimental validation is needed.

    更新日期:2019-11-28
  • Criteria for reporting incidental findings in clinical exome sequencing – a focus group study on professional practices and perspectives in Belgian genetic centres
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-20
    Marlies Saelaert; Heidi Mertes; Tania Moerenhout; Elfride De Baere; Ignaas Devisch

    Incidental and secondary findings (IFs and SFs) are subject to ongoing discussion as potential consequences of clinical exome sequencing (ES). International policy documents vary on the reporting of these findings. Discussion points include the practice of unintentionally identified IFs versus deliberately pursued SFs, patient opt-out possibilities and the spectrum of reportable findings. The heterogeneity of advice permits a non-standardised disclosure but research is lacking on actual reporting practices. Therefore, this study assessed national reporting practices for IFs and SFs in clinical ES and the underlying professional perspectives. A qualitative focus group study has been undertaken, including professionals from Belgian centres for medical genetics (CMGs). Data were analysed thematically. All Belgian CMGs participated in this study. Data analysis resulted in six main themes, including one regarding the reporting criteria used for IFs. All CMGs currently use ES-based panel testing. They have limited experience with IFs in clinical ES and are cautious about the pursuit of SFs. Two main reporting criteria for IFs were referred to by all CMGs: the clinical significance of the IF (including pathogenicity and medical actionability) and patient-related factors (including the patient’s preference to know and patient characteristics). The consensus over the importance of these criteria contrasted with their challenging interpretation and application. Points of concern included IFs’ pathogenicity in non-symptomatic persons, IFs concerning variants of uncertain significance, the requirement and definition of medical actionability and patient opt-out possibilities. Finally, reporting decisions were guided by the interaction between the clinical significance of the IF and patient characteristics. This interaction questions the possible disclosure of findings with context-dependent and personal utility, such as IFs concerning a carrier status. To evaluate the IF’s final relevance, a professional and case-by-case deliberation was considered essential. The challenging application of reporting criteria for IFs results in diversified practices and policy perspectives within Belgian CMGs. This echoes international concerns and may have consequences for effective policy recommendations.

    更新日期:2019-11-28
  • Transcriptome sequencing of lncRNA, miRNA, mRNA and interaction network constructing in coronary heart disease
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-23
    Jiangquan Liao; Jie Wang; Yongmei Liu; Jun Li; Lian Duan

    Non-coding RNA has been shown to participate in numerous biological and pathological processes and has attracted increasing attention in recent years. Recent studies have demonstrated that long non-coding RNA and micro RNA can interact through various mechanisms to regulate mRNA. Yet the gene-gene interaction has not been investigated in coronary heart disease (CHD). High throughput sequencing were used to identify differentially expressed (DE) lncRNA, miRNA, and mRNA profiles between CHD and healthy control. Gene Oncology (GO), KEGG enrichment analysis were performed. Gene-gene interaction network were constructed and pivotal genes were screened out. Lentivirus-induced shRNA infection and qRT-PCR were performed to validated the gene-gene interactions. A total of 62 lncRNAs, 332 miRNAs and 366 mRNAs were differentially expressed between CHD and healthy control. GO and KEGG analysis show that immune related molecular mechanisms and biological processes play a role in CHD. The gene-gene interaction network were constructed and visualized based on Pearson correlation coefficients and starBase database. 6 miRNAs in the network were significantly correlated to left ventricular ejection fraction, total choleterol and homocysteine. 2 lncRNAs (CTA-384D8.35 and CTB-114C7.4 (refseq entry LOC100128059)), 1 miRNA (miR-4497), and 1 mRNA (NR4A1) were the pivotal genes. Lentivirus-induced shRNA infection and qRT-PCR had validated the pivotal gene-gene interactions. These results have shown the potential of lncRNA, miRNA, and mRNA as clinical biomarkers and in elucidating pathological mechanisms of CHD from a transcriptomic perspective.

    更新日期:2019-11-28
  • Stoichioproteomics reveal oxygen usage bias, key proteins and pathways in glioma
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-08-29
    Yongqin Yin; Bo Li; Kejie Mou; Muhammad T. Khan; Aman C. Kaushik; Dongqing Wei; Yu-Juan Zhang

    The five-year survival rate and therapeutic effect of malignant glioma is low. Identification of key/associated proteins and pathways in glioma is necessary for developing effective diagnosis and targeted therapy of glioma. In addition, Glioma involves hypoxia-specific microenvironment, whether hypoxia restriction influences the stoichioproteomic characteristics of expressed proteins is unknown. In this study, we analyzed the most comprehensive immunohistochemical data from 12 human glioma samples and 4 normal cell types of cerebral cortex, identified differentially expressed proteins (DEPs), and researched the oxygen contents of DEPs, highly and lowly expressed proteins. Further we located key genes on human genome to determine their locations and enriched them for key functional pathways. Our results showed that although no difference was detected on whole proteome, the average oxygen content of highly expressed proteins is 6.65% higher than that of lowly expressed proteins in glioma. A total of 1480 differentially expressed proteins were identified in glioma, including 226 up regulated proteins and 1254 down regulated proteins. The average oxygen content of up regulated proteins is 2.56% higher than that of down regulated proteins in glioma. The localization of differentially expressed genes on human genome showed that most genes were on chromosome 1 and least on Y. The up regulated proteins were significantly enriched in pathways including cell cycle, pathways in cancer, oocyte meiosis, DNA replication etc. Functional dissection of the up regulated proteins with high oxygen contents showed that 51.28% of the proteins were involved in cell cycle and cyclins. Element signature of oxygen limitation could not be detected in glioma, just as what happened in plants and microbes. Unsaved use of oxygen by the highly expressed proteins and DEPs were adapted to the fast division of glioma cells. This study can help to reveal the molecular mechanism of glioma, and provide a new approach for studies of cancer-related biomacromolecules. In addition, this study lays a foundation for application of stoichioproteomics in precision medicine.

    更新日期:2019-11-28
  • Population structure and transmission modes of indigenous typhoid in Taiwan
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-09-03
    Kai-Yu Wang; De-Jen Lee; Shian-Sen Shie; Chih-Jung Chen

    Indigenous typhoid fever was continuing to be identified in Taiwan which has not been endemic for the enteric fever for more than 20 years. The source and transmission by which the local patients acquired typhoid and the population structure of the indigenous typhoid strains remain not well characterized. During 2001 and 2014, non-duplicated clinical Salmonella enterica serovar Typhi isolates in a hospital were analyzed by whole-genome sequencing (WGS) and determined for pulsotypes. Maximum likelihood phylogeny was constructed by nucleotide alterations in core genomes and compared to the framework of global typhoid strains. Potential source and transmission were traced by correlating the phylogeny and the temporal relationship between isolates. A total of 43 S. Typhi isolates from indigenous cases were analyzed and a majority (39, 90.7%) of them were belonged to six WGS-defined genotypes prevailing mainly in Southeast Asia. Genotype 3.4.0 and a multidrug-resistant type 4.3.1 (also known as pandemic H58 haplotype) were associated respectively with two solitary small-scale outbreaks, implying a transmission mode of importation followed by outbreak. Twelve isolates with nearly identical core genomes were belonged to genotype 3.2.1 but were categorized into three different pulsotypes. The 3.2.1 isolates were identified across 13 years and involved in three clusters and a sporadic case, indicating sustained local transmission of the same strain. The remaining indigenous isolates belonging to three genotypes (2.1, 3.1.2, and 3.0.0) were of substantial genetic diversity and isolated at different time points, indicating independent event of each case. Indigenous typhoid in Taiwan occurred mainly with the forms of small-scale outbreaks or sporadic events likely by contracting imported strains which prevailed in Southeast Asia. Sustained local transmission of certain strain was also evident by WGS analysis, but not by conventional pulsotyping, highlighting the importance of continuing molecular surveillance of typhoid fever with adequate tools in the non-endemic region.

    更新日期:2019-11-28
  • Transcriptome analysis of human monocytic cells infected with Burkholderia species and exploration of pentraxin-3 as part of the innate immune response against the organisms
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-09-06
    Sophie A. Aschenbroich; Eric R. Lafontaine; Maria Cecilia Lopez; Henry V. Baker; Robert J. Hogan

    Burkholderia mallei (Bm) is a facultative intracellular bacterial pathogen causing highly-fatal glanders in solipeds and humans. The ability of Bm to thrive intracellularly is thought to be related to exploitation of host immune response-related genes and pathways. Relatively little is known of the molecular strategies employed by this pathogen to modulate these pathways and evade intracellular killing. This manuscript seeks to fill gaps in the understanding of the interface between Bm and innate immunity by examining gene expression changes during infection of host monocytes. The transcriptome of Bm-infected human Mono Mac-6 (MM6) monocytes was profiled on Affymetrix Human Transcriptome GeneChips 2.0. Gene expression changes in Bm-infected monocytes were compared to those of Burkholderia thailandensis (Bt)-infected monocytes and to uninfected monocytes. The resulting dataset was normalized using Robust Multichip Average and subjected to statistical analyses employing a univariate F test with a random variance model. Differentially expressed genes significant at p < 0.001 were subjected to leave-one-out cross-validation studies and 1st and 3rd nearest neighbor prediction model. Significant probe sets were used to populate human pathways in Ingenuity Pathway Analysis, with statistical significance determined by Fisher’s exact test or z-score. The Pattern Recognition Receptor (PRR) pathway was represented among significantly enriched immune response-related human canonical pathways, with evidence of upregulation across both infections. Among members of this pathway, pentraxin-3 was significantly upregulated by Bm- or Bt-infected monocytes. Pentraxin-3 (PTX3) was demonstrated to bind to both Bt and Burkholderia pseudomallei (Bp), but not Bm. Subsequent assays did not identify a role for PTX3 in potentiating complement-mediated lysis of Bt or in enhancing phagocytosis or replication of Bt in human monocytes. We report on the novel binding of PTX3 to Bt and Bp, with lack of interaction with Bm, suggesting that a possible evasive mechanism by Bm warrants further exploration. We determined that (1) PTX3 may not play a role in activating the lytic pathway of complement in different bacterial species and that (2) the opsonophagocytic properties of PTX3 should be investigated in different primary or immortalized cell lines representing host phagocytes, given lack of binding of PTX3 to MM6 monocytes.

    更新日期:2019-11-28
  • Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping
    BMC Med. Genomics (IF 2.568) Pub Date : 2019-09-09
    Masataka Kikuchi; Norikazu Hara; Mai Hasegawa; Akinori Miyashita; Ryozo Kuwano; Takeshi Ikeuchi; Akihiro Nakaya

    Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10− 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

    更新日期:2019-11-28
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