Multiscale Embedded Gene Co-expression Network Analysis

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|³), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.     

Expression and Replication Studies to Identify New Candidate Genes Involved in Normal Hearing Function

Considerable progress has been made in identifying deafness genes, but still little is known about the genetic basis of normal variation in hearing function. We recently carried out a Genome Wide Association Study (GWAS) of quantitative hearing traits in southern European populations and found several SNPs with suggestive but none with significant association. In the current study, we followed up these SNPs to investigate which of them might show a genuine association with auditory function using alternative approaches. Firstly, we generated a shortlist of 19 genes from the published GWAS results. Secondly, we carried out immunocytochemistry to examine expression of these 19 genes in the mouse inner ear. Twelve of them showed distinctive cochlear expression patterns. Four showed expression restricted to sensory hair cells (Csmd1, Arsg, Slc16a6 and Gabrg3), one only in marginal cells of the stria vascularis (Dclk1) while the others (Ptprd, Grm8, GlyBP, Evi5, Rimbp2, Ank2, Cdh13) in multiple cochlear cell types. In the third step, we tested these 12 genes for replication of association in an independent set of samples from the Caucasus and Central Asia. Nine out of them showed nominally significant association (p<0.05). In particular, 4 were replicated at the same SNP and with the same effect direction while the remaining 5 showed a significant association in a gene-based test. Finally, to look for genotype-phenotype relationship, the audiometric profiles of the three genotypes of the most strongly associated gene variants were analyzed. Seven out of the 9 replicated genes (CDH13, GRM8, ANK2, SLC16A6, ARSG, RIMBP2 and DCLK1) showed an audiometric pattern with differences between different genotypes further supporting their role in hearing function. These data demonstrate the usefulness of this multistep approach in providing new insights into the molecular basis of hearing and may suggest new targets for treatment and prevention of hearing impairment.     

An Efficient Weighted Graph Strategy to Identify Differentiation Associated Genes in Embryonic Stem Cells

In the past few decades, embryonic stem cells (ESCs) were of great interest as a model system for studying early developmental processes and because of their potential therapeutic applications in regenerative medicine. However, the underlying mechanisms of ESC differentiation remain unclear, which limits our exploration of the therapeutic potential of stem cells. Fortunately, the increasing quantity and diversity of biological datasets can provide us with opportunities to explore the biological secrets. However, taking advantage of diverse biological information to facilitate the advancement of ESC research still remains a challenge. Here, we propose a scalable, efficient and flexible function prediction framework that integrates diverse biological information using a simple weighted strategy, for uncovering the genetic determinants of mouse ESC differentiation. The advantage of this approach is that it can make predictions based on dynamic information fusion, owing to the simple weighted strategy. With this approach, we identified 30 genes that had been reported to be associated with differentiation of stem cells, which we regard to be associated with differentiation or pluripotency in embryonic stem cells. We also predicted 70 genes as candidates for contributing to differentiation, which requires further confirmation. As a whole, our results showed that this strategy could be applied as a useful tool for ESC research.     

A Genome-wide Approach to Identify the Genes Involved in Biofilm Formation in E. coli

Biofilm forming cells are distinctive from the well-investigatedplanktonic cells and exhibit a different type of gene expression.Several new Escherichia coli genes related to biofilm formationhave recently been identified through genomic approaches suchas DNA microarray analysis. However, many others involved inthis process might have escaped detection due to poor expression,regulatory mechanism, or genetic backgrounds. Here, we screeneda collection of single-gene deletion mutants of E. coli named‘Keio collection’ to identify genes required forbiofilm formation. Of the 3985 mutants of non-essential genesin the collection thus examined, 110 showed a reduction in biofilmformation nine of which have not been well characterized yet.Systematic and quantitative analysis revealed the involvementof genes of various functions and reinforced the importancein biofilm formation of the genes for cell surface structuresand cell membrane. Characterization of the nine mutants of function-unknowngenes indicated that some of them, such as yfgA that geneticallyinteracts with a periplasmic chaperone gene surA together withyciB and yciM, might be required for the integrity of outermembrane.     

基于转录组和WGCNA的甘薯花青素合成相关基因共表达网络的构建及核心基因的挖掘*

加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)可通过聚类鉴定共表达的基因模块来研究生物学数据与相应性状之间的关系。甘薯[Ipomoea batatas (L.)Lam.]是世界上营养丰富的块根作物之一,紫薯是甘薯的一种特殊品种,因含有大量的花青素而具有较高的营养价值。因此,培育花青素含量高的优质紫薯一直以来都是甘薯育种家所追求的目标。利用传统的育种方法已经培育了一些紫薯品种,但其周期长、工作量大、见效慢,所以急需通过分子设计育种手段来培育高产优质的紫薯新品种。花青素合成相关关键基因的挖掘对紫薯的分子育种具有重要意义。为了挖掘甘薯花青素合成相关基因,以紫薯品种‘徐紫薯3号'和白薯品种‘徐薯18号'的块根为材料进行了转录组测序(RNA-seq),并结合公共数据库中已公布的甘薯基因组信息以及43份紫薯和45份非紫薯块根的RNA-seq数据,通过分析在不同样本间表达量差异大的前50%的基因中选择了26 760个基因进行WGCNA分析。结果表明,利用WGCNA鉴定出28个共表达模块,其中4个为紫薯特异性模块(Grey60模块和Black模块与紫薯显著正相关,Brown模块和Blue模块与紫薯显著负相关)。利用GO功能富集分析发现紫薯特异性模块Grey60可以显著富集到类黄酮和花青素代谢过程。通过计算模块内基因的连通性,分析挖掘到Grey60模块中有47个核心基因,其中包括已报道的8个花青素合成相关基因MYB113、CHS、3个CHI、F3H、GST和LDOX。利用qRT-PCR验证了其中7个核心基因的表达模式。通过构建核心基因的互作网络发现:MYB不仅与已知的花青素合成相关基因bHLH、CHI、GST、F3'H、CHS等存在互作,同时也与DUF914、ABCC4等转运蛋白基因互作;WRKY3与多个核心基因存在互作,如LDOX、GST、CHS等。为高花青素含量紫薯新品种的培育和紫薯花青素生物合成机制的解析提供了理论基础和新思路。     

ICan: An Integrated Co-Alteration Network to Identify Ovarian Cancer-Related Genes

Background

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer.

Results

We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183).

Conclusion

In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.     

Sleeping Beauty Mouse Models Identify Candidate Genes Involved in Gliomagenesis

Genomic studies of human high-grade gliomas have discovered known and candidate tumor drivers. Studies in both cell culture and mouse models have complemented these approaches and have identified additional genes and processes important for gliomagenesis. Previously, we found that mobilization of Sleeping Beauty transposons in mice ubiquitously throughout the body from the Rosa26 locus led to gliomagenesis with low penetrance. Here we report the characterization of mice in which transposons are mobilized in the Glial Fibrillary Acidic Protein (GFAP) compartment. Glioma formation in these mice did not occur on an otherwise wild-type genetic background, but rare gliomas were observed when mobilization occurred in a p19Arf heterozygous background. Through cloning insertions from additional gliomas generated by transposon mobilization in the Rosa26 compartment, several candidate glioma genes were identified. Comparisons to genetic, epigenetic and mRNA expression data from human gliomas implicates several of these genes as tumor suppressor genes and oncogenes in human glioblastoma.     

基于加权共变化网络分析水稻根系微生物群落间的差异

为了解水稻根系微生物群落间的差异,比较水稻根内、根表以及根际3个生态位微生物群落组成差异;探究水稻根系生态位之间、变化种群间的相互关系;以期为今后水稻根系微生物研究提供具有参考价值的依据.本研究利用WGCNA算法对水稻根系3个生态位的微生物群落数据分别构建共表达网络,找出根内、根表以及根际微生物群落间的差异网络,以网络为单位比较分析不同生态位间微生物群落的差异,并基于共变化网络分析进一步探究差异种群间的相互关系.通过WGCNA算法对水稻根系3个生态位的微生物群落进行共表达网络分析,结果发现:在水稻根内-根表-根际3个生态位间,微生物群落构成的共表达互作网络存在差异.进一步分析3个生态位间差异网络,发现根际-根表差异网络中的OTUs分布于6个门18个属中,优势菌门为变形菌门(Proteobacteria,72.97％);在根际-根内差异网络中的OTUs分布于9个门35个属,优势菌门为变形菌门(Proteobacteria,66.36％)、放线菌门(Actinobacteria,9.09％)、拟杆菌门(Bacteroidetes,10.9％);根表-根内差异网络中的OTUs分布于12个门36个属中,其中优势菌门为变形菌门(Proteobacteria,41.41％)、拟杆菌门(Bacteroidetes,10.10％)、厚壁菌门(Firmicutes,12.12％)、疣微菌门(Verrucomicrobia,10.10％).Rhodobacter、No-vosphingobium等3个核心菌属、Blvii28、Dechloromonas等6个核心菌属和Cellvibrio、Geobacter等5个核心菌属,分别在根际-根表差异种群微生物共变化网络、根际-根内差异种群微生物共变化网络和根表-根内差异种群微生物共变化网络中起重要的调控作用.     

Screening of a Leptospira biflexa Mutant Library To Identify Genes Involved in Ethidium Bromide Tolerance

Leptospira spp. are spirochete bacteria comprising both pathogenic and free-living species. The saprophyte L. biflexa is a model bacterium for studying leptospiral biology due to relative ease of culturing and genetic manipulation. In this study, we constructed a library of 4,996 random transposon mutants in L. biflexa. We screened the library for increased susceptibility to the DNA intercalating agent, ethidium bromide (EtBr), in order to identify genetic determinants that reduce L. biflexa susceptibility to antimicrobial agents. By phenotypic screening, using subinhibitory EtBr concentrations, we identified 29 genes that, when disrupted via transposon insertion, led to increased sensitivity of the bacteria to EtBr. At the functional level, these genes could be categorized by function as follows: regulation and signaling (n = 11), transport (n = 6), membrane structure (n = 5), stress response (n = 2), DNA damage repair (n = 1), and other processes (n = 3), while 1 gene had no predicted function. Genes involved in transport (including efflux pumps) and regulation (two-component systems, anti-sigma factor antagonists, etc.) were overrepresented, demonstrating that these genes are major contributors to EtBr tolerance. This finding suggests that transport genes which would prevent EtBr to enter the cell cytoplasm are critical for EtBr resistance. We identified genes required for the growth of L. biflexa in the presence of sublethal EtBr concentration and characterized their potential as antibiotic resistance determinants. This study will help to delineate mechanisms of adaptation to toxic compounds, as well as potential mechanisms of antibiotic resistance development in pathogenic L. interrogans.     

Rat Hepatocytes Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Liver Regeneration after Partial Hepatectomy

The recovery of liver mass is mainly mediated by proliferation of hepatocytes after 2/3 partial hepatectomy (PH) in rats. Studying the gene expression profiles of hepatocytes after 2/3 PH will be helpful to investigate the molecular mechanisms of liver regeneration (LR). We report here the first application of weighted gene co-expression network analysis (WGCNA) to analyze the biological implications of gene expression changes associated with LR. WGCNA identifies 12 specific gene modules and some hub genes from hepatocytes genome-scale microarray data in rat LR. The results suggest that upregulated MCM5 may promote hepatocytes proliferation during LR; BCL3 may play an important role by activating or inhibiting NF-kB pathway; MAPK9 may play a permissible role in DNA replication by p38 MAPK inactivation in hepatocytes proliferation stage. Thus, WGCNA can provide novel insight into understanding the molecular mechanisms of LR.     

Using DNA Barcodes to Identify Bird Species Involved in Birdstrikes

Abstract: We determined effectiveness of using mitochondrial DNA barcodes (cytochrome c oxidase subunit 1 [CO1]) to identify bird-aircraft collision (birdstrike) cases that lacked sufficient feather evidence for morphological diagnosis. From September through December 2006, 821 samples from birdstrike events occurring in the United States were submitted for DNA analysis. We successfully amplified a CO1 DNA barcode product from 554 (67.5%) of the samples; 267 (32.5%) did not contain viable DNA and depended on morphological methods (microscopy) for Order or Family level identification. We deemed 19 cases inconclusive either because the DNA barcode recovered from the sample did not meet our 98% match criteria when compared to the Barcode of Life Database (BoLD) or because the DNA barcode matched to a set of ≥ 2 closely related species with overlapping barcodes, preventing complete species identification. Age of the sample (≤6 months) did not affect DNA viability, but initial condition of the sample and the collection method was critical to DNA identification success. The DNA barcoding approach has great potential in aiding in identification of birds (and wildlife) for airfield management practices, particularly in regions of the world that lack the vast research collections and individual expertise for morphologic identifications.