[Show abstract][Hide abstract] ABSTRACT: Our current understanding of how natural genetic variation affects gene expression beyond
well-annotated coding genes is still limited. The use of deep sequencing technologies for the study
of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we
generated the first recombinant strain library for fission yeast and conducted an RNA-seq-based QTL
study of the coding, non-coding, and antisense transcriptomes. We show that the frequency of distal
effects (trans-eQTLs) greatly exceeds the number of local effects
(cis-eQTLs) and that non-coding RNAs are as likely to be affected by eQTLs as
protein-coding RNAs. We identified a genetic variation of swc5 that modifies the
levels of 871 RNAs, with effects on both sense and antisense transcription, and show that this
effect most likely goes through a compromised deposition of the histone variant H2A.Z. The strains,
methods, and datasets generated here provide a rich resource for future studies.
Molecular Systems Biology 11/2014; 10(11). DOI:10.15252/msb.20145123 · 10.87 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background
Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.
We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance.
We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users.
[Show abstract][Hide abstract] ABSTRACT: The shortage of molecular information on cell cycle changes along embryonic stem cell (ESC) differentiation prompts an in silico approach, which may provide a novel way to identify candidate genes or mechanisms acting in coordinating the two programs. We analyzed germ layer specific gene expression changes during the cell cycle and ESC differentiation by combining four human cell cycle transcriptome profiles with thirteen in vitro human ESC differentiation studies. To detect cross-talk mechanisms we then integrated the transcriptome data that displayed differential regulation with protein interaction data. A new class of non-transcriptionally regulated genes was identified, encoding proteins which interact systematically with proteins corresponding to genes regulated during the cell cycle or cell differentiation, and which therefore can be seen as interface proteins coordinating the two programs. Functional analysis gathered insights in fate-specific candidates of interface functionalities. The non-transcriptionally regulated interface proteins were found to be highly regulated by post-translational ubiquitylation modification, which may synchronize the transition between cell proliferation and differentiation in ESCs.
Stem Cell Research 09/2014; 13(2). DOI:10.1016/j.scr.2014.07.008 · 3.69 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Pseudomonas putida is a versatile bacterial species adapted to soil and its fluctuations. Like many other species living in soil, P. putida often faces water limitation. Alginate, an exopolysaccharide (EPS) produced by P. putida, is known to create hydrated environments and alleviate the effect of water limitation. In addition to alginate, P. putida is capable of producing cellulose (bcs), putida exopolysaccharide a (pea), and putida exopolysaccharide b (peb). However, unlike alginate, not much is known about their roles under water limitation. Hence, in this study we examined the role of different EPS components under mild water limitation. To create environmentally realistic water limited conditions as observed in soil, we used the Pressurized Porous Surface Model. Our main hypothesis was that under water limitation and in the absence of alginate other exopolysaccharides would be more active to maintain homeostasis. To test our hypothesis, we investigated colony morphologies and whole genome transcriptomes of P. putida KT2440 wild type and its mutants deficient in synthesis of either alginate or all known EPS. Overall our results support that alginate is an important exopolysaccharide under water limitation and in the absence of alginate other tolerance mechanisms are activated.
[Show abstract][Hide abstract] ABSTRACT: The objective of this study was the application of the synthetic promoter library (SPL) technology for modulation of actinorhodin production in Streptomyces coelicolor A3(2). The SPL technology was used to optimize the expression of a pathway specific positive transcriptional regulator ActII orf4, which activates the transcription of the S. coelicolor actinorhodin biosynthetic gene cluster. The native actII orf4 promoter was replaced with synthetic promoters, generating a S. coelicolor library with a broad range of expression levels of actII orf4. The resulting library was screened based on the yield of actinorhodin. Selected strains were further physiologically characterized. One of the strains from the library, ScoSPL20, showed considerably higher yield of actinorhodin and final actinorhodin titer, compared to S. coelicolor wild type and S. coelicolor with actII orf4 expressed from a strong constitutive promoter. ScoSPL20 demonstrated exceptional productivity despite having a comparatively weak expression from the promoter. Interestingly, the ScoSPL20 promoter was activated at a much earlier stage of growth compared to the wild type, demonstrating the advantage of fine-tuning and temporal tuning of gene expression in metabolic engineering. Transcriptome studies were performed in exponential and actinorhodin-producing phase of growth to compare gene expression between ScoSPL20 and the wild type. To our knowledge, this is the first successful application of the SPL technology for secondary metabolite production in filamentous bacteria.
PLoS ONE 06/2014; 9(6):e99701. DOI:10.1371/journal.pone.0099701 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Target gene identification for transcription factors is a prerequisite for the systems wide understanding of organismal behaviour.
NAM-ATAF1/2-CUC2 (NAC) transcription factors are amongst the largest transcription factor families in plants, yet limited
data exist from unbiased approaches to resolve the DNA-binding preferences of individual members. Here, we present a TF-target
gene identification workflow based on the integration of novel protein binding microarray data with gene expression and multi-species
promoter sequence conservation to identify the DNA-binding specificities and the gene regulatory networks of 12 NAC transcription
factors. Our data offer specific single-base resolution fingerprints for most TFs studied and indicate that NAC DNA-binding
specificities might be predicted from their DNA-binding domain's sequence. The developed methodology, including the application
of complementary functional genomics filters, makes it possible to translate, for each TF, protein binding microarray data
into a set of high-quality target genes. With this approach, we confirm NAC target genes reported from independent in vivo analyses. We emphasize that candidate target gene sets together with the workflow associated with functional modules offer
a strong resource to unravel the regulatory potential of NAC genes and that this workflow could be used to study other families
of transcription factors.
Nucleic Acids Research 06/2014; 42(12). DOI:10.1093/nar/gku502 · 9.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: RNA binding proteins (RBPs) are key players in the regulation of gene expression. In this chapter we discuss the main protein-RNA recognition modes used by RBPs in order to regulate multiple steps of RNA processing. We discuss traditional and state-of-the-art technologies that can be used to study RNAs bound by individual RBPs, or vice versa, for both in vitro and in vivo methodologies. To help highlight the biological significance of RBP mediated regulation, online resources on experimentally verified protein-RNA interactions are briefly presented. Finally, we present the major tools to computationally infer RNA binding sites according to the modeling features and to the unsupervised or supervised frameworks that are adopted. Since some RNA binding site search algorithms are derived from DNA binding site search algorithms, we discuss the commonalities and novelties introduced to handle both sequence and structural features uniquely characterizing protein-RNA interactions.
[Show abstract][Hide abstract] ABSTRACT: Abstract Schizosaccharomyces pombe is a popular model eukaryotic organism to study diverse aspects of mammalian biology, including responses to cellular stress triggered by redox imbalances within its compartments. The review considers the current knowledge on the signaling pathways that govern the transcriptional response of fission yeast cells to elevated levels of hydrogen peroxide. Particular attention is paid to the mechanisms that yeast cells employ to promote cell survival in conditions of intermediate and acute oxidative stress. The role of the Sty1/Spc1/Phh1 mitogen-activated protein kinase in regulating gene expression at multiple levels is discussed in detail.
Critical Reviews in Microbiology 02/2014; DOI:10.3109/1040841X.2013.870968 · 6.02 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Type 1 diabetes is due to destruction of pancreatic β-cells. Lysine deacetylase inhibitors (KDACi) protect β-cells from inflammatory destruction in vitro and are promising immunomodulators. Here we demonstrate that the clinically well-tolerated KDACi vorinostat and givinostat revert diabetes in the nonobese diabetic (NOD) mouse model of type 1 diabetes and counteract inflammatory target cell damage by a mechanism of action consistent with transcription factor-rather than global chromatin-hyperacetylation. Weaning NOD mice received low doses of vorinostat and givinostat in their drinking water until 100-120 d of age. Diabetes incidence was reduced by 38% and 45%, respectively, there was a 15% increase in the percentage of islets without infiltration, and pancreatic insulin content increased by 200%. Vorinostat treatment increased the frequency of functional regulatory T-cell subsets and their transcription factors Gata3 and FoxP3 in parallel to a decrease in inflammatory dendritic cell subsets and their cytokines IL-6, IL-12, and TNF-α. KDACi also inhibited LPS-induced Cox-2 expression in peritoneal macrophages from C57BL/6 and NOD mice. In insulin-producing β-cells, givinostat did not upregulate expression of the anti-inflammatory genes Socs1-3 or sirtuin-1 but reduced levels of IL-1β + IFN-γ-induced proinflammatory Il1a, Il1b, Tnfα, Fas, Cxcl2, and reduced cytokine-induced ERK phosphorylation. Further, NF-κB genomic iNos promoter binding was reduced by 50%, and NF-κB-dependent mRNA expression was blocked. These effects were associated with NF-κB subunit p65 hyperacetylation. Taken together, these data provide a rationale for clinical trials of safety and efficacy of KDACi in patients with autoimmune disease such as type 1 diabetes.
Proceedings of the National Academy of Sciences 01/2014; 111(3). DOI:10.1073/pnas.1320850111 · 9.67 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a 3-factor full-factorial design. Correlation analysis across 8 environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.
[Show abstract][Hide abstract] ABSTRACT: Ocular adnexal lymphoma (i.e. lymphoma with involvement of the orbit, eyelids, conjunctiva, lacrimal gland and lacrimal sac), although rare, is common among malignant tumors involving the ocular adnexal region. The main subtypes are low-grade extranodal marginal zone lymphoma (EMZL) and aggressive diffuse large B-cell lymphoma (DLBCL). In rare cases low-grade EMZL are reported to transform to DLBCL. It is unclear, however, which molecular events distinguish low-grade disease from aggressive, potentially fatal, disease, and here, we aimed at investigating whether miRNA profiles could distinguish these subtypes.
Using LNA-based arrays from Exiqon we performed global miRNA expression profiling of 18 EMZLs and 25 DLBCLs involving ocular adnexal sites in order to investigate changes in the miRNA expression in low- versus high-grade disease. Findings were confirmed by RTq-PCR.
Our analysis revealed 43 miRNAs with altered expression profiles in DLBCL compared to EMZL. Seven of the miRNAs down-regulated in DLBCL relative to EMZL showed enrichment for a direct transcriptional repression by the oncoprotein MYC. We also report a possible loss-of-regulation of NFKB1 and its downstream miRNAs. In addition, our analysis identified a group of DLBCLs whose expression profiles resembled that of EMZL. Although transformation of EMZL to DLBCL in the ocular adnexal region is rare, we hypothesize that the intermediate group may potentially derive from transformation of EMZL that was not recognized by histology.
We conclude that fundamental differences in miRNA expression exist between ocular adnexal EMZL and DLBCL, mainly due to differences in MYC and NF-κB regulatory pathways.
[Show abstract][Hide abstract] ABSTRACT: Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a high-density peptide chip technology that allows for probing of the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique, we have experimentally identified thousands of putative SH2-peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2-mediated probabilistic interaction network, which we make available as a community resource in the PepspotDB database. A predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells.
[Show abstract][Hide abstract] ABSTRACT: Purpose To investigate changes in microRNA (miR) expression in ocular adnexal extranodal marginal zone lymphoma (EMZL) and diffuse large B-cell lymphoma (DLBCL), and to perform a pathway analysis of differentially expressed miRs. Methods Global miR expression profiling was performed using miRCURYTM LNA miR-arrays, v. 11.0, (Exiqon) on formalin-fixed paraffin-embedded tissue from 18 ocular adnexal EMZLs and 25 DLBCLs. The result of microarray was confirmed using quantitative real time (RT)-PCR. Predicted and validated targets of the significantly differentially expressed miRs were obtained from MiR-walk. Transcription factor (TF)-miR interactions were obtained from the TransmiR database. Results The microarray results revealed two up-regulated and 41 down-regulated miRs in the DLBCLs compared to the EMZLs. Further, the differentially expressed miRs separated EMZLs and DLBCLs into two distinct entities using a supervised clustering analysis. These findings were confirmed by quantitative RT-PCR. Five of the down-regulated miRs (let-7g, miR-26a, miR-29a, miR-29c and miR-221) were identified to be regulated negatively by MYC. A small set of miRs (let-7g, miR-199a-5, miR-26a, miR-27a* and miR-342-3p) that regulate NFKB1 were all down-regulated in the DLBCLs compared to the EMZL samples. Further, the NFKB1 regulated miRs (miR-29a/b/c and miR-125b) were down-regulated significantly in the DLBCLs compared with the EMZLs (log2 fold-changes: miR-29a/b/c < -1.9 and -1.1 for miR-125b). Conclusion We demonstrated that fundamental differences in miR expression exist between ocular adnexal EMZLs and DLBCLs, mainly due to differences in MYC and NF-kB regulatory pathways.
[Show abstract][Hide abstract] ABSTRACT: Identifying causal genes that underlie complex traits such as susceptibility to disease is a primary aim of genetic and biomedical studies. Genetic mapping of quantitative trait loci (QTL) and gene expression profiling based on high-throughput technologies are common first approaches toward identifying associations between genes and traits; however, it is often difficult to assess whether the biological function of a putative candidate gene is consistent with a particular phenotype. Here, we have implemented a network-based disease gene prioritization approach for ranking genes associated with quantitative traits and diseases in livestock species. The approach uses ortholog mapping and integrates information on disease or trait phenotypes, gene-associated phenotypes, and protein-protein interactions. It was used for ranking all known genes present in the cattle genome for their potential roles in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to Escherichia coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis. Our study provides a general framework for prioritizing genes associated with various complex traits in different species. To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions, and biomedical text data have been integrated systematically for ranking candidate genes in any livestock species.
[Show abstract][Hide abstract] ABSTRACT: Water deprivation can be a major stressor to microbial life in surface and subsurface soil. In unsaturated soils, the matric potential (Ψ(m)) is often the main component of the water potential, which measures the thermodynamic availability of water. A low matric potential usually translates into water forming thin liquid films in the soil pores. Little is known of how bacteria respond to such conditions, where, in addition to facing water deprivation that might impair their metabolism, they have to adapt their dispersal strategy as swimming motility may be compromised. Using the pressurized porous surface model (PPSM), which allows creation of thin liquid films by controlling Ψ(m), we examined the transcriptome dynamics of Pseudomonas putida KT2440. We identified the differentially expressed genes in cells exposed to a mild matric stress (-0.4 MPa) for 4, 24, or 72 h. The major response was detected at 4 h before gradually disappearing. Upregulation of alginate genes was notable in this early response. Flagellar genes were not downregulated, and the microarray data even suggested increasing expression as the stress prolonged. Moreover, we tested the effect of polyethylene glycol 8000 (PEG 8000), a nonpermeating solute often used to simulate Ψ(m), on the gene expression profile and detected a different profile than that observed by directly imposing Ψ(m). This study is the first transcriptome profiling of KT2440 under directly controlled Ψ(m) and also the first to show the difference in gene expression profiles between a PEG 8000-simulated and a directly controlled Ψ(m).
[Show abstract][Hide abstract] ABSTRACT: Tolerogenic dendritic cells (DC) that are maturation-resistant and locked in a semimature state are promising tools in clinical applications for tolerance induction. Different immunomodulatory agents have been shown to induce a tolerogenic DC phenotype, such as the biologically active form of vitamin D (1,25(OH)(2)D(3)), glucocorticoids, and a synergistic combination of both. In this study, we aimed to characterize the protein profile, function and phenotype of DCs obtained in vitro in the presence of 1,25(OH)(2)D(3), dexamethasone (DEX), and a combination of both compounds (combi). Human CD14(+) monocytes were differentiated toward mature DCs, in the presence or absence of 1,25(OH)(2)D(3) and/or DEX. Cells were prefractionated into cytoplasmic and microsomal fractions and protein samples were separated in two different pH ranges (pH 3-7NL and 6-9), analyzed by 2D-DIGE and differentially expressed spots (p < 0.05) were identified after MALDI-TOF/TOF analysis. In parallel, morphological and phenotypical analyses were performed, revealing that 1,25(OH)(2)D(3)- and combi-mDCs are closer related to each other than DEX-mDCs. This was translated in their protein profile, indicating that 1,25(OH)(2)D(3) is more potent than DEX in inducing a tolerogenic profile on human DCs. Moreover, we demonstrate that combining 1,25(OH)(2)D(3) with DEX induces a unique protein expression pattern with major imprinting of the 1,25(OH)(2)D(3) effect. Finally, protein interaction networks and pathway analysis suggest that 1,25(OH)(2)D(3), rather than DEX treatment, has a severe impact on metabolic pathways involving lipids, glucose, and oxidative phosphorylation, which may affect the production of or the response to ROS generation. These findings provide new insights on the molecular basis of DC tolerogenicity induced by 1,25(OH)(2)D(3) and/or DEX, which may lead to the discovery of new pathways involved in DC immunomodulation.
Journal of Proteome Research 11/2011; 11(2):941-71. DOI:10.1021/pr200724e · 4.25 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.
[Show abstract][Hide abstract] ABSTRACT: Laboratory evolution experiments have led to important findings relating organism adaptation and genomic evolution. However, continuous monitoring of long-term evolution has been lacking for natural systems, limiting our understanding of these processes in situ. Here we characterize the evolutionary dynamics of a lineage of a clinically important opportunistic bacterial pathogen, Pseudomonas aeruginosa, as it adapts to the airways of several individual cystic fibrosis patients over 200,000 bacterial generations, and provide estimates of mutation rates of bacteria in a natural environment. In contrast to predictions based on in vitro evolution experiments, we document limited diversification of the evolving lineage despite a highly structured and complex host environment. Notably, the lineage went through an initial period of rapid adaptation caused by a small number of mutations with pleiotropic effects, followed by a period of genetic drift with limited phenotypic change and a genomic signature of negative selection, suggesting that the evolving lineage has reached a major adaptive peak in the fitness landscape. This contrasts with previous findings of continued positive selection from long-term in vitro evolution experiments. The evolved phenotype of the infecting bacteria further suggests that the opportunistic pathogen has transitioned to become a primary pathogen for cystic fibrosis patients.
Proceedings of the National Academy of Sciences 05/2011; 108(18):7481-6. DOI:10.1073/pnas.1018249108 · 9.67 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Ayurveda, the traditional Indian medicine is one of the most ancient, yet living medicinal traditions. In the present work, we developed an in silico library of natural products from Ayurveda medicine, coupled with structural information, plant origin and traditional therapeutic use. Following this, we compared their structures with those of drugs from DrugBank and we constructed a structural similarity network. Information on the traditional therapeutic use of the plants was integrated in the network in order to provide further evidence for the predicted biologically active natural compounds. We hereby present a number of examples where the traditional medicinal use of the plant matches with the medicinal use of the drug that is structurally similar to a plant component. With this approach, we have brought to light a number of obscure compounds of natural origin (e.g. kanugin, norruffscine, isoazadirolide) that could provide the basis and inspiration for further lead development. Apart from the identification of novel natural leads in drug discovery, we envisage that this integrated in silico ethnopharmacology approach could find applications in the elucidation of the molecular basis of Ayurveda medicine and in drug repurposing.