Gene regulatory network (GRN) construction is a central task of systems biology. Integration of different data sources to infer and construct GRNs is an important consideration for the success of this effort. In this paper, we will discuss distinctive strategies of data integration for GRN construction. Basically, the process of integration of different data sources is divided into two phases: the first phase is collection of the required data and the second phase is data processing with advanced algorithms to infer the GRNs. In this paper these two phases are called “structural integration” and “analytic integration,” respectively. Compared with the nonintegration strategies, the integration strategies perform quite well and have better agreement with the experimental evidence. Drosophila has led to some of the greatest successes in studying developmental GRNs to explain complex developmental processes [Biological functions comprise numerous reactions at all levels of biological organization, including cells, tissues, organs, and body, and interchange with the environment. Overall, every life phenomenon found in this multilevel system is supported through many reactions interconnecting with each other to compose the orchestra of life. It is, therefore, crucial to have a systematic perspective in biomedical research. To gain an overview of such a complex system, we can visualize it in the form of a network. For instance, protein-protein interactions, metabolic reactions, and genetic regulations correspond respectively to a protein-protein interaction network (PPI), metabolic network, and gene regulatory network (GRN), which are subnetworks of the complex multi-level system. In the representation of a network, nodes typically correspond to molecules, while edges represent the relationships between nodes. In the study of biological networks, GRNs are one of the most popular models, especially in the field of development. Developmental GRNs provide important clues to elucidate the temporal and spatial dynamics of gene expression during development. The use of sea urchin and rocesses , 2. Tradrocesses . Amazingrocesses . As a cerocesses . In a serocesses . Rescue rocesses , 7.Drosophila embryos provides potent examples of the type of complexities revealed by the study of GRNs. In the sea urchin embryo, blimp1 is autorepressive when its product accumulates to high levels. At the same time, it provides a required input for Wnt8 expression, which produces a positive feedback effect for blimp1 via inducing Tcf to activate blimp1 expression. Wnt8 can infect the adjacent cells/territories with this circular bioinformation flow via diffusion. This flow is terminated due to blimp1 autorepression [Drosophila embryo, Snail repressor activates the synthesis of Delta ligand in the ventral mesoderm via repressing the transcription of Tom, an inhibitor of the Delta, which is called a double-negative gate. Delta triggers Notch signaling in the adjacent cells via diffusion. However, transcription of the Notch signaling target genes is repressed by the intraterritorial Snail repression in the ventral mesoderm itself. An exactly parallel mechanism causing transcriptional alternation inter-territorially is also found in the sea urchin skeletogenic mesoderm [Elucidation of gene regulation in the endomesoderm specification in the sea urchin and in the development of mesoderm . DespiteDuring this blooming period of biomedical research, high-content experimental data is fuelling systems biology research, such as GRN construction at the genome-wide scope. For example, expression microarrays that can detect the relative abundance of gene transcripts by comparing two or more biological samples are commonly used for GRN construction. The new approaches provide a perspective on the global molecular interactions that bridge the gap between the external signal and internal response. There are several popular algorithms being used to construct GRNs from expression data (reviewed in ).G is defined by a set of nodes corresponding to genes V = {x1,…, xn} and a list of Boolean functions F =  describes how genes in the network change their state (on or off) from one time point to the next. The future state of an element is completely determined by the states of other elements (regulators) by means of the underlying logical Boolean functions. In the graphical representation of GRNs, nodes typically represent genes corresponding to the transcription factor proteins or target genes, while edges represent the regulations between the transcription factors and their targets. Boolean networks describe each element as a variable with the value 0 or 1 to represent the state of the element as “off” or “on,” respectively. A Boolean network directed acyclic graph. “Directed” means that there are arrows to indicate causal influences, and “acyclic” means that causal loops are prohibited. For each element, a conditional distribution P(Xv ∣ parents(Xv)) is defined through the application of the conditional probability table (CPT), where parents(Xv) denotes the variables corresponding to the regulators of this element. Thereafter, an optimization approach is applied, with the Bayesian information Criteria (BIC) optimized to infer the best fitting network model among a finite set of models. Second, Bayesian networks model the biomedical network with a xn) of other elements involving a regulation function. In a third alternative, differential equations extract the network from high-throughput experimental data through taking the instantaneous concentration of each element into consideration. The instantaneous concentration of each element is completely determined by the concentration  offers another approach to modeling GRNs based on the probability theory. The mutual dependence of any two elements in the network is measured using MI. It is reported that MI outperforms the correlation in some studies , 11. UsiThe most popular algorithms contributing to the construction of GRNs from genomic expression data were described above. However, each of them has certain drawbacks. The Boolean algorithm assigns each variable a binary value, which could omit important information of continuous variables. Bayesian network construction is promising for representing and inferring causal relationships, but this strategy is only effective for the construction of small GRNs, due to the superexponential increase in the algorithm running time for large networks. The differential equation algorithm requires knowledge of the equation of dynamics and parameter estimation to optimize the GRN model against real data. However, deriving an appropriate equation of dynamics remains a challenge. Furthermore, solving a differential equation system of any realistic complexity is difficult. As to the correlation and mutual information algorithms, manually setting appropriate thresholds without a principled reference poses difficulties. Strategies applying algorithms with these drawbacks are not satisfying; therefore, it motivates us to improve the computational strategies. New strategies continue to be developed against those difficulties. It is a great challenge to refurbish algorithms to improve GRN construction using genomic expression data. Improvements are difficult to obtain algorithmically; however, the integration of multiple types of genome-wide datasets with literature-based information of regulation as prior knowledge is a straightforward alternative to offer improvement. Generally, in the computational GRN construction methods mentioned above, only genomic expression data like microarray data is used to produce the desired network applying one of the algorithms described , 11. BasThe past few decades were an age of rapid progress in the development of biomedical science. Numerous advanced technologies along with well-founded theories lead the way for new findings in industrial and academic biomedical research. For example, biomedical investigators have developed genomic expression by microarray, rapid genome and microbiome sequencing, proteome definition by mass spectrometry, genome-wide protein-DNA binding site definition by ChIP-seq, genomic SNP identification by SNP array, and high-content knowledge by literature mining. Overwhelmed with such impressive quantity of genome-wide achievements, we are encouraged to apply strategies to make good use of them intuitively, such as integrating them properly for GRN construction. First we need to take stock of the status of the biomedical sources that are available to us. http://nar.oxfordjournals.org/). Here is a table  summarizes the biomedical database status each year   exist for many genes, proteins, and keywords. Alias mapping through lookups is critical for retrieving desired data from multiple heterogeneous sources. Warehouse integration arranges desired datasets from multiple sources into a local warehouse  before querying, through loading the required data from distinct sources and converting them into standard formats before being stored locally. Relying less on the Internet connectivity to access data limits the impact of various problems such as access restrictions, network bottlenecks, low response times, and the occasional unavailability of sources. Moreover, using local warehouses allows for improved accuracy, efficiency, and flexibility for the subsequent query as it is performed locally. However, this integration has an important drawback of the overall system maintenance. It is expensive to have the warehouse updated regularly to reflect those modifications of heterogeneous external sources , 16. Furhttp://www.ebi.ac.uk/) are three representative data warehouses. Given the appeal of these resources, efforts are increasingly made to improve the warehouse strategy against its drawbacks. The GeNS platform is one of the efforts to improve the efficiency of database maintenance. GeNS is a biological data integration platform for warehouse integration [NCBI, the UCSC Genome Browser , and EMBegration . Represeegration . TherefoMediator-based integration retrieves desired datasets from multiple heterogeneous sources at the time of querying through query translation, as opposed to the data translation that is manifested at the time of database creation in warehouse integration , 16. TheThere are two main approaches for establishing the mediator, global-as-view (GAV) and local-as-view (LAV) , 16. Thehttp://www.redbooks.ibm.com/abstracts/sg246290.html/), the source-specific wrapper symbolizes its data sources for further integration. The mediator approach is a very popular approach of data integration. Platforms like K2, TAMBIS, Discovery-Link, and BACIIS are all designed based on this approach. In the Discovery-Link platform  in yeast; thus, the similarities of GyrA and GyrB to some segment of topoisomerase II might be used to predict their functional interaction in E. coli. STRING was developed as a multi-dimensional integration interface by combining its three original components  together with genomic expression and genome-wide dataset of protein-protein interaction discovered via text mining from PubMed abstract, and so forth. Putative protein-protein interaction of the PPI can be evaluated with the confidence score of functional association between two proteins across those genome-wide datasets. Different datasets are weighted differently for their respective contribution to the confidence score. In the STRING project, a weight was assigned to each dataset by benchmarking the performance of the prediction in this dataset against a common reference set of trusted knowledge. The developers chose the functional grouping of proteins maintained at KEGG (Kyoto Encyclopedia of Genes and Genomes) as the common reference set. The benchmark weight of each dataset in STRING corresponded to the probability of finding the linked proteins that were predicted in this dataset within the same KEGG pathway. In the equation of the confidence score, the confidence score is taken as S, the weight of each dataset is taken as Si, and i is the number of qualfied datasets with incidence of the pairwise connection. Therefore, the confidence score of the putative protein-protein interaction is evaluated through qualifying the naive Bayesian probability of the incidence of the corresponding protein connection across those multiple datasets under the assumption of independence of the various datasets. Larger confidence scores indicate higher confidence in a functional protein-protein association:Si is the weight assigned to each dataset over the common reference set.The STRING web application was designed to infer the PPI via integrating multiple types of genome-wide datasets. It was primarily constructed from the integration of three genome-wide datasets, including phylogenetic profiles, a database of transcription units, and a database of gene-fusion events –24. Phylhttp://STRING-db.org/) of the protein-protein interactions seeded by Gata4, a well-known transcription factor in cardiac development.The confidence score for each putative protein-protein interaction is, therefore, a Bayesian-probability-like score supported by several types of genome-wide datasets. Putative PPI thus follows the evaluation in the genome-wide scope to gain confidence. in silico genome-wide interaction datasets into a network  into the kernel matrix by diffusion kernels. Diffusion kernels are functions for processing the network structure to mine the underlying relationships between nodes in the kernel matrix. However, this resulted in a regional kernel matrix of pairwise protein connections given a genome-wide scope because only the pairwise kernel values (the intensity of pairwise protein associations) of the proteins that were in the known part of PPI could be reconstructed. The regional kernel matrix could approximately recover the known PPI when the appropriate threshold  A genome-wide dataset  with the same objective can be used to establish a new kernel matrix. Kato et al. took multiple types of genome-wide datasets into consideration for the PPI construction. They combined these new generated kernel matrices, each of which was calculated from a particular genome-wide dataset, such as genomic expression and genome-wide phylogenetic profiles, into a combined kernel matrix of pairwise protein associations in yeast. δ.  They integrated the combined kernel matrix with the regional kernel matrix of the known part of yeast PPI to infer the integrated kernel matrix of pairwise protein connections that offered the pairwise kernel values in the genome-wide scope to be able to qualify the PPI edges via comparing the kernel values against the threshold Accuracy of edge prediction was measured by a 10-fold cross-validation. With setting the parameter of the degree of kernel diffusion to 3.0 when translating the known PPI into the kernel matrix by diffusion kernels, the ROC score was 0.929 for the inferred yeast PPI. The supervised learning improves the PPI construction via integrating the experimentally-proven evidence of the study objective as the supervisor into the analysis of the other relevant genome-wide datasets. Thus the GRNs construction can also apply the schema of the supervised learning via having the known transcription factor-target gene regulations as the prior knowledge to be integrated with the other expression-relevant genome-wide datasets. One study compared supervised methods with unsupervised methods for GRN construction and found that the supervised methods are more reliable than the unsupervised ones .In recent decades, a large amount of experimental evidence about biological networks has been collected, and this was coupled with progress in elucidating the network topological features. Approaches that have contributed to these strides in network biology include scale-free networks, small world networks, adaptive motifs, feed-back motifs, “AND” and “OR” logic motifs, and modular networks. Therefore, a systematic effort utilizing the network topological features will be required and will benefit the effectiveness of network construction. Modularity is one of the most accepted network topological features of GRNs. The modularity of GRNs can be represented by gene module members that are co-regulated via shared transcription factors combinatorially binding their promoters. Therefore, members in such gene modules manifest coexpression. Genomic expression and the genome-wide transcription factor-DNA binding sites are thus, integrated into GRN construction by identifying coexpressed genes with conserved TF binding sites in their promoters , 30. TwoGRAM is an algorithm for discovering GRNs by incorporating information from transcription factor (TF) binding motifs, genome sequence, and genomic expression . RegulatSaccharomyces cerevisiae. The GRN was reconstructed via identification of modules. Gene modules were also identified as groups of genes annotated with similar pathways. Identified gene modules were controlled by more than one TF, which was the evidence for inferring the TFs' interactions (protein-protein interactions). GRAM can assign different regulators to genes with similar expression patterns, which cannot be accomplished using the expression clustering methods alone. Moreover, by applying the enrichment test of specific DNA binding motifs, genes in the discovered modules are more likely to be coregulated when compared with the set of genes obtained using genomic location analysis alone. In the GRAM project, this schema was applied to the TF binding motif data of 106 TFs and over 500 microarray expression experiments in Saccharomyces cerevisiae genomic expression dataset with the genome-wide TF binding sites that were inferred via searching the corresponding TF binding motifs in the genome-wide scope. In their framework, a regulatory module was a set of genes that were regulated in concert by a shared regulation program. A regulation program specified the expression of the genes in the module as a function of the expression of a small set of regulators . However, there is an important caveat with this approach: it is rational to apply the naive Bayesian theorem only when the situation satisfies the basic assumption that each type of source dataset is independent of any other. Therefore, under this assumption, there is no dependency between any two types. However, in reality some datasets have known dependencies. For example, in the case of STRING, the datasets of experiments, databases, and text mining are not completely independent of each other. The method of evaluating individual weight is also a controversial part of this schema. In the case of STRING, KEGG is used as the standard for calculating the weights. However, KEGG is an incomplete database in the genome-wide scope, and it is actually constructed from various experiments, databases, and text-mining resources, so it is necessarily dependent on those resources. It is, therefore, not a good standard, as it is biased—giving high weights to its own resources while giving low weights to the others. This may promote its accuracy but limit its predictive power. Hence, naïve Bayesian applications in GRN construction may be affected by those limitations. Supervised learning integrates prior knowledge of the study objective with the other relevant genome-wide datasets to learn the networks . However, the quality of its prediction varies with the quantity of the prior knowledge. Also, when multiple datasets are involved, weighting each dataset properly is still problematic. If we employ the nonweighting integration approach to make the primary prediction of the unknown part before it is trained by the prior knowledge, we may have better quality on the overall prediction even when the quantity of the prior knowledge is relatively small. The schema of network topology is a compelling strategy of GRN construction via integrating genomic expression with genome-wide TF binding sites. It associates the two sources through the modularity feature to connect the gene co-expression with the conserved TF binding sites on their promoters. However, as mentioned in the two examples, the TF binding sites are inferred from the corresponding TF binding motifs via a genome-wide blast. It will be improved when the CHIP-seq datasets regarding different TFs are employed instead to generate the genome-wide TF binding sites. It is a developing schema that keeps step with the development of our knowledge of network topological features. The schemas of supervised learning and network topology application may be described as advanced forms of the schema of Bayesian application, progressing from the naive to evidence-based logic. These approaches use principled and logical integration of datasets rather than integration only. Along with the increased experimentally proven knowledge about regulatory relationships, the schema of network topology application can be combined with supervised learning to gain increased confidence in the inferred GRNs. Overall, a positive-feedback effect that contributes to better GRNs helps to develop our knowledge of additional GRN topological features, while the more topological features provide more or better clues for GRNs' construction. The PPI could be embedded into the GRN to assess the TFs' combinatory regulations. structural integrations for extracting and recombining the required datasets and analytic integrations for processing the queried datasets to infer GRNs. There are three main types of structural integration: warehouse integration naively aggregates the required datasets into local storage before data querying, mediator-based integration establishes a mediator application to retrieve the required datasets via reformatting the user's query into the formats of queries in local data sources at the time of data processing, and the navigational integration follows the chain of data querying at the time of data processing via using the query outputs of one step in the process as query inputs in a next step. In a subsequent analytic integration, the schema of Bayesian applications use the naive Bayesian probability to integrate multiple types of genome-wide datasets into a scoring system to compute a confidence score for inferred GRNs. Supervised learning integrates the prior knowledge of the study objective with the other relevant genome-wide datasets to learn the GRNs. And the schema of network topology applications integrate genomic expression with genome-wide TF binding sites through the modularity feature to connect gene co-expression with conserved TF binding sites in their promoters to foster the GRNs construction. Overall, the integration strategies perform well and reliably as compared to the non-integration strategies. Structural integration and analytic integration take central roles in the overall integration strategy of GRN construction. GRN construction via integration of multiple types of genome-wide datasets or via literature-based information about regulation as the prior knowledge partly avoids or overcomes the drawbacks of the nonintegration strategies. Along with the continuous increase in the availability of new data sources, new opportunities emerge for us to use integration strategies to construct GRNs. There are two main categories of integration strategies: Recently, cooperation of traditional experimental approaches with computational approaches has energized biomedical research. These new approaches offer the ability to computationally infer novel hypotheses from prior knowledge and relevant datasets to guide experimentation by setting research priorities. A salient example of this successful cooperation defines how to rationally extend the ribosome biogenesis pathway in yeast . After r


The DAF-12 target genes near these binding sites include an extensive network of interconnected heterochronic and microRNA genes. We also identify the genes encoding components of the miRISC, which is required for the control of target genes by microRNA, as a target of DAF-12 regulation. During reproductive development, many of these target genes are misregulated in daf-12(0) mutants, but this only infrequently results in developmental phenotypes. In contrast, we and others have found that null daf-12 mutations enhance the phenotypes of many miRISC and heterochronic target genes. We also find that environmental fluctuations significantly strengthen the weak heterochronic phenotypes of null daf-12 alleles. During diapause, DAF-12 represses the expression of many heterochronic and miRISC target genes, and prior work has demonstrated that dauer formation can suppress the heterochronic phenotypes of many of these target genes in post-dauer development. Together these data are consistent with daf-12 acting to ensure developmental robustness by committing the animal to adult or dauer developmental programs despite variable internal or external conditions.The nuclear receptor DAF-12 has roles in normal development, the decision to pursue dauer development in unfavorable conditions, and the modulation of adult aging. Despite the biologic importance of DAF-12, target genes for this receptor are largely unknown. To identify DAF-12 targets, we performed chromatin immunoprecipitation followed by hybridization to whole-genome tiling arrays. We identified 1,175 genomic regions to be bound  Caenorhabditis elegans many life history decisions, such as interrupting reproductive development to form a dauer and the rate of aging, are under the control of the DAF-12 nuclear receptor. Recent work has started to elucidate the endocrine regulation of DAF-12, but few target genes for this receptor have been identified. In this study, we use chromatin immunoprecipitation to identify in vivo target genes for DAF-12. Among the targets are multiple genes involved in developmental progression, which are termed heterochronic genes, and subunits of the miRISC, which is required for the control of target genes by microRNA. In daf-12 null worms, both heterochronic and miRISC genes are mis-regulated, but development is usually normal. This led us to examine whether DAF-12 normally acts to reinforce commitment to dauer or adult development and prevent disruption by environmental or genetic fluctuations. Consistent with this, we find that daf-12 mutants are hypersensitive to environmental fluctuations and mutations in individual target genes. These results suggest a novel role for daf-12 as a phenotypic capacitor that ensures commitment to reproductive or dauer development despite internal and external variations.In  C. elegans, the nuclear receptor DAF-12 alters life history in response to the environment. During development, when conditions are unfavorable, due to starvation, crowding or high temperature, daf-12 promotes diapause and formation of dauer larvae daf-36 Rieske-like oxygenase and the daf-9 cytochrome P450 enzyme From bacteria to humans, organisms sense environmental cues and reprogram life history by altering gene expression patterns. In the nematode C. elegans determines specific cell fate programs for each larval stage daf-12 alleles, especially those in phenotypic class 1, have heterochronic phenotypes din-1, as daf-12(rh61);din-1 mutants develop normally let-7 family miRNAs hbl-1 and lin-41 respectively DAF-12 also alters life history by regulation of the heterochronic circuit. The heterochronic pathway in et al identified DAF-12 binding sites in vitro. However, a small number of genes were described and the in vivo relevance of these target genes was not always clear. To identify novel genes regulated by DAF-12, we developed transgenic animals expressing an epitope-tagged daf-12-transgene. These worms were used to perform chromatin immunoprecipitation followed by hybridization of the precipitated DNA to whole genome tiling arrays. From the arrays, we identified 1175 genomic regions bound to DAF-12 and these regions are within 5 Kb of 3179 genes. Importantly, we detected binding sites in multiple heterochronic genes, miRISC (miRNA-induced silencing complex) genes, and a total of 40 miRNAs, suggesting that DAF-12 controls developmental progression through the direct regulation of these targets.Previously, Shostak daf-12(0) null allele daf-12(rh61rh411), but did not result in abnormal development daf-12(0) worms can enhance multiple heterochronic phenotypes, indicating that daf-12 null worms are sensitized to genetic mutations. Further, we found that daf-12(0) worms are sensitized to environmental variability and show stronger heterochronic phenotypes in variable instead of constant conditions. In addition, multiple DAF-12 target genes were repressed in dauer, suggesting that in diapause DAF-12 can ‘reset’ the heterochronic circuit to ensure a stereotyped developmental program. These results illustrate a role for DAF-12 in the commitment to reproductive versus dauer development and suggest a novel function for this nuclear receptor to ensure a stereotyped phenotype despite internal and external variability.Biological systems are subject to mutations and environmental variation during development, but still are able to ensure a stereotyped developmental process. This is possible because organisms can buffer many perturbations and produce an invariant output. This capacity is called ‘robustness’ daf-12(rh61rh411) (daf-12(0)) background (C. elegansdaf-12 exon 1 via homologous recombination daf-12(rh61rh411); unc-119(ed3)), we obtained two integrated lines, ALF4 and ALF9. In each of these strains, DAF-12 is produced solely from the transgene. During the generation of our transgenic lines, a new commercially available DAF-12 antibody was released and this antibody was able to detect the fusion protein  null allele, as starved transgenic worms form SDS resistant dauers  and high affinity binding (F35G12.2 or the DAF-12 co-repressor din-1) were quantified in both ALF9 and ALF4. Both the TAP and DAF-12 antibody precipitate the same regions in two separate transgenic strains . To identify additional target genes, we then optimized the assay by comparing ALF9 to ALF4 using IgG sepharose that recognized the TAP tag or the anti-DAF-12 antibody. Regions of no binding ( strains . Importadaf-12(0). We chose L2 and adult stages because daf-12 is known to perform important and separate functions at these stages. Its activity at L2 is important for the dauer decision and developmental progression, whereas in the adult it is known to be required for influencing longevity din-1 and F35G12.2 is highly reproducible, as each of the replicas shows similar binding regions and intensity (ChIP enrichment ratio of 16 and 21 respectively).To identify binding regions, arrays from three independent worm collections were analyzed and compared with http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28350) daf-12(0) negative control. This resulted in 1175 genomic regions bound to DAF-12. 1155 were within 5 Kb of 3179 genes and just 20 genomic regions (1.7%) were not associated with any known gene (daf-12(0) was also positive is found in C. elegans, via chromatin immunoprecipitation followed by sequencing (ChIP-seq) et allit-1 gene, which was extensively validated in their study.From the resulting tiling array data, peaks representing DAF-12 binding sites were identified using TAS software (Affymetrix) , all identified components of the miRISC complex (RF = 9.4 p<1.46×10−6), including regulatory factors such as nhl-2 and xrn-2p<3.014e−04) and a total of 51 ribosome subunits (rpl and rps) (RF = 1.7 p<2.268e−04). Genes involved in development, ribosomal function, mitochondrial function, and aging were found to be particularly over-represented , a previously identified DAF-12 binding site  within the DAF-12 AF-2 domain, which for other nuclear receptors is involved in recruiting co-activators after ligand binding daf-12(rh284) allele and causes a weak Daf-c phenotype at 25°C, perhaps due to impaired DAF-12 transcriptional activity Another putative target is the promoter region of the CEOP4404 operon, which contains the daf-3, din-1 and daf-16 expression, but also by regulation of daf-16 activity through controlling CBP-1 expression. Furthermore, DAF-12 could also regulate daf-16 activity by controlling the expression of akt-1, akt-2 and pdk-1in vivo to DAF-12.We then analyzed DAF-12 target genes previously reported to be involved in dauer formation . 2 Kb prProper regulation of the heterochronic circuit is essential for normal development dre-1lin-41lin-28lin-42A and kin-20  and daf-12(0) worms were collected at different developmental stages, and heterochronic gene expression was quantified by RT-qPCR. As seen in daf-12(0) worms show significant differences in expression when compared to N2. Importantly, DAF-12 can either repress or activate the same gene depending on the developmental stage (i.e. lin-41 at L4 or adult stage). Interestingly, the circadian clock gene lin-42, which has been suggested to be important for the molting process We then examined the daf-12(rh284), a DAF-12 mutant unable to promote transcriptional activity  worms (data not shown). Our findings point to a role for DAF-12 in regulation of heterochronic and miRISC gene expression during post-embryonic development.As many heterochronic genes were mis-regulated in L3, we focused on this stage. We compared mRNA levels of different heterochronic and miRISC genes from N2 or activity , using NL3 stage . Consistdaf-12(rh284) still retains some aspect of DAF-12 activity, or because the mis-regulation of these genes is tissue-specific, we created transgenic animals carrying alg-2 and ain-1 promoter regions upstream of GFP in WT or daf-12(0) mutants. As seen in daf-12(0) worms at L2 stage show impaired alg-2 expression most notably in the pharynx when compared to WT. Further, expression of ain-1 at L3 stage was also impaired in daf-12(0) worms  worms . Taken tdaf-12(0) mutants development usually occurs normally rrf-3 mutant, which is hypersensitive to RNAi rrf-3 and rrf-3;daf-12 were exposed to miRISC gene RNAi, to see whether loss of daf-12 could unmask developmental defects. When rrf-3 worms were exposed to ain-1 RNAi, only 3–4% developed protruding (Pvl) or burst vulva (Bvl) phenotypes. In contrast, approximately 30% of rrf-3;daf-12 animals showed a penetrant vulva phenotype .Although many genes involved in development were found in our array, in henotype . No vulvrrf-3 worms with ain-1, ain-2, alg-2 or nhl-2 RNAi produced an increase in body length (daf-12(0) worms daf-12(0) worms were fed nhl-2 or ain-2 RNAi, a synergistic response was observed  animals nhl-2 knock-down induced a modest increase in seam cell number, a synergistic phenotype was revealed in daf-12(0), as visualized with a nuclear localized SCM::GFP transgene. Furthermore, this synergistic response was also seen when other miRISC genes were down-regulated  worms exhibited a synergistic phenotype upon treatment with ain-1 RNAi (n-1 RNAi  or with n-1 RNAi .daf-12(+) worms can compensate for the loss of miRISC function to ensure normal development. However, daf-12(0) worms are unable to buffer these mutations. Further, previous observations have shown that daf-12(0) worms demonstrate enhanced developmental defects in response to mutations to multiple heterochronic genes that are also DAF-12 targets (daf-12(0) worms to buffer genetic mutations during development. These findings point to a role for DAF-12 in ensuring developmental robustness during reproductive development, which we further test below.These results show that  targets . Perhapsdaf-2(e1371) or daf-2(e1371);daf-12(rh61rh411) worms were grown at 25°C. At this temperature, daf-2 form dauers while daf-2;daf-12 double mutants continue reproductive development. Worms at L3 stage or dauers were collected for mRNA quantification using NanoString analysis. As seen in The results presented above show that DAF-12 plays a key role in post-embryonic development and directly regulates the heterochronic circuit, miRISC genes and miRNA expression. We reasoned that these target genes might be repressed in diapause, as DAF-12 binds to the co-repressor DIN-1 to promote dauer formation lin-42A upstream of a GFP reporter. The same promoter region was subjected to mutagenesis to eliminate DAF-12 binding sites, such as DR and M-2 domains ::GFP were compared, the GFP transcript retained 25-fold more expression than the endogenous lin-42 transcript  worms are not able to buffer genetic mutations among key developmental genes such as miRISC genes, we hypothesized that this nuclear receptor could have a role as a phenotypic capacitor. Phenotypic capacitors act to minimize the effects of either environmental or internal variations of a stereotyped developmental process. To test whether daf-12 behaves this manner, we exposed WT or daf-12(0) worms carrying the SCM::GFP transgene to environmental fluctuations during development. Developing worms were grown for 72 hours at 20°C (control) or in a variable environment consisting of shifts between 15°C and 25°C every 4 hours. As seen in daf-12(0) worms. Interestingly, the number of seam cells in both sides is different, suggesting that environmental fluctuation leads to stochastic errors in seam cell division. When compared daf-12(0), stress conditions almost double the number of worms with strong heterochronic phenotypes (daf-12(0) worms are sensitized to environmental conditions and confirm that DAF-12 has phenotypic capacitor properties.As enotypes . Togethein vivo target genes for DAF-12 in the L2 and adult stages. Several lines of evidence support the specificity and relevance of the binding sites found in our array. First, the highest density of binding sites is found within a 1.5 Kb region upstream of target genes, consistent with DAF-12 often acting by binding to proximal promoter regions. We also found binding sites within genes corresponding to promoter regions of alternative isoforms of the same transcript, such as din-1S and lin-42A. Second, DAF-12 binding sites overlap with RNA Pol II binding sites annotated in an independent ChIP-seq analysis, suggesting that DAF-12 binding sites correspond to regions of active transcription. Third, two independent strains (ALF4 and ALF9) carrying a TAP-tagged daf-12, precipitated either with IgG sepharose or with an anti-DAF-12 antibody, show the same binding regions  shows heterochronic phenotypes alg-2 and ain-1 promoters, suggesting that DAF-12 could regulate both miRNA expression and activity.Our finding that multiple heterochronic genes contain DAF-12 binding sites, suggests that this receptor directly regulates the heterochronic circuit and is consistent with previous observations that the gain of function allele let-7 processing from pri and pre-let-7 lin-28 is repressed by DAF-12 from the L2 to L4 stages. Assuming a conserved function in C. elegans, lin-28 repression by DAF-12 could be required for let-7 activity to promote execution of L3 programs. We also found the F box gene dre-1 to be regulated by DAF-12. While no target proteins have been found so far for this ubiquitin ligase, its down-regulation produces heterochronic defects dre-1 by DAF-12 could result in effects on protein stability or degradation. DAF-12 can also regulate its own expression, as well as genes that modulate its activity, such as the co-repressor DIN-1. Our results suggest that DAF-12 regulates gene expression by mechanisms including direct regulation of target promoters, miRNA biogenesis, miRNA activity via miRISC expression and perhaps control of miRNA maturation via lin-28 , unc-119(ed3)) was generated by crossing DP38 (unc-119(ed3)) and AA86 (daf-12(rh61rh411)). The unc-119 phenotype was verified by behavior and daf-12 null allele was verified by PCR. AA86, AA82 (daf-12(rh284)), AA34 (daf-12(rh61)), JR667 (unc-119(e2498::Tc1)III; wIs51) and DR1568 (daf-2(e1371)) were obtained from the CGC which is supported by NIH funding. MM5438  was kindly provided by Dr Morris F. Maduro (UC Riverside).ALF3 strain  by homologous recombination in the SW106 E. coli strain as previously described galK gene was inserted into the fosmid between amino acids 26 and 27 in exon 1 using a PCR product generated from pMOD4 galK-GT using oligos containing 50 bp regions of fosmid homology , bafIs62 (lin-42p::GFP)) was generated by integration of an extra chromosomal array by TMP/UV irradiation unc-119 (ed3), bafIs63 (lin-42pmut::GFP)) was directly obtained from bombardment. ALF62 and ALF63 were then crossed with HT1593 (unc-119(ed3)) to generate daf-12(+) strains. ALF70 (unc-119 (ed3), bafIs70 ) and ALF72 (unc-119 (ed3), bafIs70 (ain-1p::GFP)) were generated by biolistic bombardment of the DP38 strain (unc-119(ed3)). These were crossed with ALF3 to place the reporters in a daf-12(0) background (ALF73 and ALF74 respectively).ALF4 and ALF9 strains were obtained by biolistic bombardment of ALF3 with the TAP-tagged C. elegans were simultaneously broken up and fixed by brief sonication in PBS containing 1% formaldehyde. Total fixation time was 30 minutes at room temperature, after which the reaction was quenched by the addition of glycine to a final concentration of 0.125 M. Preparation of chromatin and ChIP was carried out essentially as described Synchronized worms at mid-L2 stage and young adults were collected by floatation, rinsed with milliQ water (DEPC treated) and frozen at −80°C. Whole Equal amounts of L2 and adult chromatin (20 µg each) were mixed together and samples were pre-cleared with protein A agarose beads (Invitrogen). Genomic DNA regions of interest were isolated using an antibody against DAF-12 . After incubation at 4°C overnight, protein A agarose beads were used to isolate the immune complexes. Complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65°C, and ChIP DNA was purified by phenol-chloroform extraction and ethanol precipitation.Quantitative PCR (QPCR) reactions were carried out in triplicate on specific genomic regions using SYBR Green Supermix (Bio-Rad), site-specific primer pairs, and an aliquot of ChIP DNA. The resulting signals were normalized for primer efficiency by carrying out QPCR for each primer pair using Input DNA.C. elegans Tiling 1.0R arrays at 45°C overnight. Arrays were washed and scanned, and the resulting CEL files were analyzed using Affymetrix TAS software that computed for each probe estimates of fold enrichment  over hybridization with input DNA. At the same time, TAS calculated for each probe a p-value by applying a Wilcoxon signed rank test. A threshold of 2.5 was selected, which corresponds to probe intensities approximately 2.5× stronger on the ChIP array than on the Input array. Additional TAS threshold parameters were MinRun = 180 bp, MaxGap = 300 bp. TAS analysis showed that the selected threshold of 2.5 corresponds approximately to a p-value of 0.01. In addition, the false discovery rate (FDR) was estimated as previously described http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28350).ChIP and Input DNAs were amplified by whole-genome amplification (WGA) using the GenomePlex WGA Kit (Sigma). The resulting amplified DNAs were purified, quantified, and tested by QPCR at the same specific genomic regions as the original ChIP DNA to assess quality of the amplification reactions. Amplified DNAs were fragmented and labeled using the DNA Terminal Labeling Kit from Affymetrix, and then hybridized to Affymetrix GeneChip Protein was extracted from N2, ALF3, AA86 or transgenic worms by boiling in LDS loading buffer (Invitrogen). After sonication, samples were run on a 10% polyacrylamide gel and transferred to PVDF. Membranes were probed with 1∶750 α-DAF-12 , and 1∶3000 actin  followed by IRDye 680 goat anti-rabbit (1∶10000) (Li-Cor Biosciences). For detection of DAF-12 expressed in HEK cells, α-myc  was used at a dilution of 1∶1000.http://www.r-project.org/). To find de novo DAF-12 binding sites, we identified all sequence intervals associated with genes that are active in experimental samples, but not in the control. We submitted 600 unique sequences of 100 nt each, centered on the strongest DAF-12 binding signals to MEME (http://meme.mbcr.net) after masking repeat sequences using RepeatMasker (http://www.repeatmasker.org). As a negative control, we also likewise analyzed 600 unique sequences of 100 nt each from intervals with the weakest DAF-12 binding signals associated with genes that did not score positive in the control or experimental samples 1 and 2.Using the Affymetrix TAS software (Affymetrix), we identified all genes associated with probes that were positive in transgenic Chromatin IPs, but negative in the control and plotted the distance between the bound probe and the ATG proximal to each gene. We next downloaded available GO annotations for all genes associated with bound probes  and classified them using the GO.db library (Version 2.2.0) in R . The DAF-12 rh284 mutant was made with the Quick-Change mutagenesis kit (Stratagene). For luciferase reporter genes, fragments between 2 Kb and 3.5 Kb upstream the ATG were PCR amplified and subcloned into pGL3 Luc Promoter (Promega). For GFP reporter strains, promoter regions were subcloned in pPD95.75 vector (A gift of Dr. Andy Fire). Oligos sequences are available in unc-119 rescue fragment was introduced by recombination as previously described lin-42::GFP promoter mutant was synthesized and subcloned into pPD95.75 vector (Celtek Bioscience).The DAF-12 A1 cDNA was PCR amplified from HEK 293T cells were cultured and transfected  in 24well plates, with DMEM and 10% charcoal FBS treated (Hyclone). Briefly, 225 ng of either pCMV myc DAF-12 A1 or pCMV myc vector were co-transfected with 12.5 ng of pRL-TK (Renilla luciferase) and 25 ng pGL3 Luc promoter with the promoter of interest (Promega). The following day, cells were stimulated with 0.5 µM (25R)-Δ7-DA Transgenic worms were mounted for digital photography using an Olympus BX51 upright microscope. All photos within a panel were taken on the same day with identical camera settings to allow direct comparison. Quantification of the GFP reporters was performed with image J software and analyzed with an unpaired t-test.L3 worms were transferred to RNAi plates at 16°C for approximately 72 hr. 10 adults were transferred to new RNAi plates and left for 3 hr to lay eggs before being removed. Analysis was performed with the adult F1 progeny.daf-12(0) worms carrying the SCM::GFP transgene were obtained by hypochlorite treatment and incubated for 72 hours at 20°C or 15°C–25°C cycles of 4 hours each with a programmable incubator .Eggs from WT or Dataset S1(PDF)Click here for additional data file.Figure S1Enriched DAF-12 binding sites were analyzed by (A) DREME or (B) MEME.(PDF)Click here for additional data file.Figure S2Comparison between PHA-4 and DAF-12 target genes.(EPS)Click here for additional data file.Figure S3lin-42 locus.DAF-12 binding sites at the (EPS)Click here for additional data file.Figure S4daf-12(0).Heterochronic phenotypes enhanced by (PDF)Click here for additional data file.Figure S5lin-42A promoter. Mutations are shown above the WT sequence.Mutation of M-2 and DR domains (highlighted) within (PDF)Click here for additional data file.Figure S6Heterochronic phenotypes suppressed by dauer formation.(PDF)Click here for additional data file.Figure S7Table of DAF-12 target miRNAs.(PDF)Click here for additional data file.Figure S8Oligos used to subclone promoter regions for pGL3 Luc and pPD95.75 vectors. Restriction sites added for subcloning are indicated in red.(PDF)Click here for additional data file.Table S1Genes within 1 Kb, 2 Kb and 5 Kb of identified DAF-12 binding sites.(XLS)Click here for additional data file.Table S2daf-12(0)) was positive.Genes identified as DAF-12 binding sites when control ((XLS)Click here for additional data file.Table S3Gene ontology analysis of DAF-12 targets with DAVID program.(XLS)Click here for additional data file.


TaRab7, was identified and characterized from a cDNA library of wheat leaves infected with Puccinia striiformis f. sp. tritici (Pst) the wheat stripe rust pathogen. The gene was predicted to encode a protein of 206 amino acids, with a molecular mass of 23.13 KDa and an isoeletric point (pI) of 5.13. Further analysis revealed the presence of a conserved signature that is characteristic of Rab7, and phylogenetic analysis demonstrated that TaRab7 has the highest similarity to a small GTP binding protein gene (BdRab7-like) from Brachypodium distachyon. Quantitative real-time PCR assays revealed that the expression of TaRab7 was higher in the early stage of the incompatible interactions between wheat and Pst than in the compatible interaction, and the transcription level of TaRab7 was also highly induced by environmental stress stimuli. Furthermore, knocking down TaRab7 expression by virus induced gene silencing enhanced the susceptibility of wheat cv. Suwon 11 to an avirulent race CYR23. These results imply that TaRab7 plays an important role in the early stage of wheat-stripe rust fungus interaction and in stress tolerance.Small GTP-binding proteins function as regulators of specific intercellular fundamental biological processes. In this study, a small GTP-binding protein Rab7 gene, designated as  Small GTP-binding proteins are monomeric G proteins with molecular masses of 20–40 kDa. Small G proteins in eukaryotes from yeast to human constitute a superfamily with at least five families  including more than 100 members The Rab proteins belong to the small guanosine triphosphatases (GTPases) superfamily. Rabs are thought to act as molecular switches, which play an essential role in both endocytic and exocytic traffic in eukaryotic cells, being active in their GTP-bound state and inactive in their GDP-bound state Arabidopsis, Kwon et al. RabG3b, transgenic plants overexpressing wild-type and constitutively active RabG3b displayed expanded programmed cell death (PCD) – hypersensitive response (HR) upon infection and accelerated leaf senescence. Their results suggest that RabG3b may regulate PCD associated with pathogen response and senescence. PCD-HR is one of numerous complicated defense responses that allow plants to survive invasion by various infectious pathogens.Approximately 70 members have been identified in mammals, and Rab7 is one of the Rab proteins that have been investigated extensively. Rab7 is regarded as a key regulator in endo-lysosomal trafficking based on the extensive investigations in the past decades Pucinia striiformis f. sp. tritici (Pst), is one of the most devastating pathogens of wheat, causing significant yield loss in wheat production worldwide. One of the most effective methods to control stripe rust is the use of resistant wheat cultivars. Comprehending molecular mechanisms of interactions between wheat and the stripe rust pathogen is important to the rational use of resistance genes in the improvement of cultivars. Although the structure and function of Rab7 in trafficking has been studied, little is known about its function in the interaction between wheat and the stripe rust fungus. In this study, we isolated and characterized a wheat Rab7 homologue, designated TaRab7. The expression profile of TaRab7 was determined in wheat seedlings inoculated with Pst and plants subjected to environmental stimuli. Subcellular localization of TaRab7 was also determined to reveal its function in the interaction, and expression of TaRab7 in an incompatible wheat-Pst interaction was decreased by virus induced gene silencing (VIGS), further indicating its important role in wheat defense against the rust pathogen.Wheat stripe rust fungus, TaRab7, was cloned by the reverse-transcription technique. Sequence analysis indicated that TaRab7 includes a complete 618 bp open-reading frame (ORF) which encodes a putative protein composed of 206 amino acids. The predicted protein of TaRab7 had a theoretical molecular weight of 23.13 kD with an isoeletric point (pI) of 5.13. The analysis by InterProScan indicated that TaRab7 contains domains for guanine nucleotide binding that are conserved among members of the Rab subfamily. In addition, it is highly homologous at the protein level to Rab7 in Oryza sativa, Hordeum vulgare, Arabidopsis thaliana and other organisms  until 24 hpi and was more than 4-fold higher than the control group before 24 hpi. The expression level peaked at 12 hpi. On the other hand, the relative expression of TaRab7 in the compatible interaction was much lower than that in the incompatible interaction at 12 hpi and 24 hpi and was even down-regulated to some extent after 12 hpi. The results indicated that the higher expression of TaRab7 was associated with defense to the stripe rust fungus.Quantitative RT-PCR analysis showed that the gene was strongly up-regulated in the incompatible interaction . The expTaRab7 was induced by environmental stresses  and peaked at 12 hpt with 13-fold higher than that of the control. After high salinity treatment, the expression level of TaRab7 was overall 5-fold higher than the control group at 2 hpt and peaked at 12 hpt. When the leaves were exposed in low temperature (4°C), the expression level of TaRab7 was also up-regulated as early as 2 hpt. Together, these results suggest a general role of TaRab7 in stress responses.The expression pattern of stresses . In the TaRab7, we constructed the pCaMV35S:TaRab7-GFP fusion vector and control plasmid pCaMV35S: GFP, and introduced them into onion epidermal cells by particle bombardment. The TaRab7 protein was predominantly localized in the cytoplasm, perinuclear area and nucleus, whereas the protein in the control was uniformly distributed throughout the cell  system. It is an effective reverse genetics tool in barley and wheat PDS) in cv. Suwon 11 by inoculating the second leaf of the wheat seedlings with BSMV: TaPDS. At 9 days post-inoculation (dpi), the mild chlorotic mosaic symptoms became visible on the third leaf, and at 15 dpi systematic photo-bleaching was observed. Wheat seedlings inoculated with sterile buffer developed normal leaves under the same conditions, indicating that silencing of TaPDS occurred specifically in BSMV:TaPDS infected plants. Two-leaf stage wheat seedlings were infected with recombinant BSMV: TaRab7 virus, which contains a 154-bp fragment amplified with primers TaRab7-VIGS-F and TaRab7-VIGS-R  and CYR31 (compatible). Conspicuous HR was elicited by CYR23 on leaves previously infected with BSMV:γ and BSMV:TaRab7  and CYR31 (compatible). The results showed that, in the incompatible interaction, TaRab7 was up-regulated at 6 hpi and the expression remained at a high level, however, in the compatible interaction, TaRab7 was also about 4-fold higher than that in the control group at 6 hpi but it began to down-regulate since then. During the interaction between the host and pathogen, once the defense mechanism was triggered, both in the compatible and incompatible interactions TaRab7 was up-regulated, however, at 12 hpi in the incompatible interaction the gene was about 9-fold higher over the control while in the compatible group the gene was down-regulated compared with 6 hpi. It is supposed that there is a required minimum of expression level to initiate or contribute to the expression of wheat response to Pst while it did not reach in the compatible reaction. In this sense, when TaRab7 was silenced, the expression level of TaRab7 was not high enough to inhibit the spread of the pathogen in the incompatible interaction. This observation suggests that TaRab7 might play a positive role in wheat defense to Pst after the initial penetration and recognition between plant and stripe rust pathogen in the incompatible interaction.Plant defense responses against microbial infection are often triggered during the initial stages of plant-pathogen interactions Arabidopsis was proposed as a link between resistance to pathogens and acclimation to low-humidity and low-temperature conditions TaRab7 is highly induced by environmental stresses such as high salt, low temperature and dehydration. Thus, those results demonstrate that TaRab7 might play an important role in regulating some downstream genes during the plant's tolerance to different environmental stimuli. In this way, TaRab7 could participate in the basal resistance of wheat to these stresses.Several plant defense response genes are reported to express substantially under abiotic stresses and their transcript accumulation can be activated by different environmental stresses and pathogens. These findings suggest that different stress signaling pathways share certain components and that plant responses to biotic and abiotic stresses overlap. For instance, the CPN1 gene in TaRab7 was knocked down by BSMV-VIGS, the hyphal length and hyphal branching increased, indicating that the susceptibility to Pst was enhanced after inoculation with avirulent Pst race CYR23. On TaRab7 knocked-down plants, few small uredia were observed on seedling leaves. In this sense, TaRab7 is important for wheat resistance to the stripe rust fungus. Hypersensitive cell death was further examined for discontinuity of cytoplasmic strands and from auto fluorescence of dead cells. There were no necrotic cells by 24 hpi on leaves inoculated with avirulent race CYR23  and wheat (Triticum aestivum) cultivar Suwon 11 were used in the study. Suwon 11 that possesses stripe rust resistance gene YrSuTwo Total RNA was isolated with the Trizol reagent  and digested with DNase I  as described TaRab7  and transformed into Escherichia coli (JM109) by the CaCl2 procedure Special primers of http://www.dnastar.com), BLAST (http://www.ncbi.nlm.nih.gov/blast/), and ORF Finder (http://www.ncbi.nlm.nih.gov/gorf/ gorf.html) programs. ClustalW 1.83 TaRab7 gene sequence was deposited in GenBank (GenBank accession number JQ364960).DNA sequences were analyzed with the DNASTAR (TaRab7 open reading frame (ORF) were amplified by PCR with specific primers containing PstI and BamHI sites  were transformed into onion epidermal cells by the particle bombardment at a helium pressure of 7.6 MPa (1100 psi) using the PDS-100/He system . Transformed materials were incubated in darkness at 24°C for 18–24 h in a growth chamber. GFP signals were detected with a Zeiss LSM 510 confocal laser microscope using a 488 nm filter . The experiment was performed twice and with three replicates each time.The cDNA fragments containing the TaRab7 was cloned in its 3′ untranslated region and then replaced the TaPDS coding sequence in BSMV:TaPDS to construct the plasmid designed for gene silencing as Holzberg in vitro transcripts were produced from linearized plasmids containing the tripartite BSMV genome in vitro transcription kit . Two-leaf-stage wheat seedlings were infected with BSMV as described A 216-bp cDNA fragment of Pst. The samples were stained and the leaf segments were observed with an Olympus BX-51 microscope  for infection sites and lengths and branches of infection hyphae The leaves inoculated with BSMV were sampled at 0, 24, 48, 120 hpi with


Drosophila melanogaster fecundity. Genetic variation in female fecundity in flies derived from a wild orchard population is heritable and largely independent of other measured life history traits. We map a portion of this variation to a single QTL and then use deficiency mapping to further refine this QTL to 5 candidate genes. Ubiquitous expression of RNAi against only one of these genes, an aquaporin encoded by Drip, reduces fecundity. Within our mapping population Drip mRNA level in the head, but not other tissues, is positively correlated with fecundity. We localize Drip expression to a small population of corazonin producing neurons located in the dorsolateral posterior compartments of the protocerebrum. Expression of Drip–RNAi using both the pan-neuronal ELAV-Gal4 and the Crz-Gal4 drivers reduces fecundity. Low-fecundity RILs have decreased Crz expression and increased expression of pale, the enzyme encoding the rate-limiting step in the production of dopamine, a modulator of insect life histories. Taken together these data suggest that natural variation in Drip expression in the corazonin producing neurons contributes to standing variation in fitness by altering the concentration of two neurohormones.To gain insight into the molecular genetic basis of standing variation in fitness related traits, we identify a novel factor that regulates the molecular and physiological basis of natural variation in female  Drosophila melanogaster. Using a variety of mapping techniques, we show that differential expression of the aquaporin Drip in nervous tissue affects natural variation in female fecundity. We further go on to describe a novel domain of expression of Drip in neurons that produce the insect stress hormone corazonin and demonstrate that differential expression of Drip in these neurons affects female fecundity putatively through modulating the concentration of corazonin and dopamine. This surprising and novel observation highlights the benefit of exploiting natural genetic variation to identify the molecular processes underlying phenotypic traits.A major goal of modern evolutionary biology is to elucidate the genetic basis of standing genetic variation underlying fitness traits. This goal is important for a comprehensive picture of the evolutionary process, because it allows us to understand the mode of natural selection on fitness traits and identify the molecular and physiological processes that affect fitness traits. Here, we describe our work to identify the molecular genetic and physiological basis for natural variation in a core life history trait, fecundity, of  The life history of an organism—its reproductive schedule and lifespan—are fundamental characteristics intrinsically related to its evolutionary fitness. Due to the close relationship of life history with fitness, theory predicts life history traits should be subject to strong natural selection Arabidopsis thaliana genetic variation in reproductive timing in response to vernalization has been mapped to polymorphisms in FLOWERING LOCUS C Melitaea cinxia, is associated with polymorphism in phosphoglucose isomerase and succinate dehydrogenaseDrosophila melanogaster, genetic variation in reproductive diapause has been mapped to components of the insulin signaling D. melanogaster in particular, extensive work has examined natural variation in age specific survival, a core life history component. Using traditional quantitative trait locus (QTL) mapping techniques, dozens of positional loci affecting lifespan have been identified Dox-A2, tup, ms(2)35Ci, stc, Lim3, Ddc, and catsup; methuselahThese efforts have mapped natural variation in life history traits to specific loci in a variety of organisms mth and InR; D. melanogaster. We document segregating genetic variation in fecundity and identify a QTL underlying this variation. We localize the polymorphism in this QTL to a region containing 5 genes including the aquaporin, Drip. Drip expression in head tissue is variable in our mapping population as is two genes putatively downstream of Drip, Crz and pale. Drip has been previously described to function in malphigian tubules where it serves to regulate water balance Drip is expressed in a small population of corazonin producing neurons in the fly brain and Drip expression in these neurons modulates fecundity. This work localizes a new polymorphic locus that effects a core life history trait and identifies a novel function and domain of expression for Drip.While considerable progress has been made uncovering the genetic basis for natural variation in lifespan, relatively little is known about natural variation in reproductive output. Natural genetic variation in only two genes has been associated with variation in fecundity  derived from an orchard population in Winters, CA. These RILs were chosen to maximize genetic variance in ovariole number and thorax length, two morphological determinants of fecundity D. melanogaster chromosomes Given the genetic contribution to phenotypic variance in these traits within the population, we sought to identify chromosomal regions associated with these traits and their response to the environment. Multiple imputation QTL mapping a priori contrast that fecundity among genotypes satisfied  =  = (A/deficiency)>(B/deficiency), where A is a second chromosome from high fecundity RIL allele, B is a second chromosome from the low fecundity RIL allele, balancer is the ‘wild-type’ balancer chromosome, and deficiency is particular aberration on the second chromosome. This contrast was only satisfied for two deficiencies: Df(2R)enA and Df(2R)ED2230 ED2230 . Importaand Drip .tubulin-Gal4 driver and measured fecundity. This experiment was not performed for CG34054 because the appropriate RNAi line did not exist. Each of these RNAi transgenes reduced mRNA of their targeted genes by 2.5 to 3.25 log2-fold  and carcasses  from 3–5 day old mated females. Drip mRNA abundance did not differ among alleles in samples from the ovary-LRT or the carcass . Natural variation in fecundity caused by variation at the Drip locus is not likely to be caused by protein variation at Drip. As alternative explanations, nucleotide polymorphisms responsible for these expression differences might be caused by the synonymous SNPs if they affect mRNA stability or processing, or by polymorphisms in 3′ or 5′ UTRs or in intronic or 5′ enhancer regions. Based on the deficiency mapping, if the causative polymorphism resides in the 5′ enhancer region, it must occur within 6.5 Kb of the transcriptional start site.To understand how variation in Drip might contribute to differences in fecundity we sequenced the Drip has been reported as expressed in the malphigian tubules Drip expression from the adult head. We used immunohistochemistry to determine which head tissues are responsible for this expression. Drip protein is stained in about 12 neurons located in the dorsolateral posterior compartments of the protocerebrum (Drip-IR (Drip-RNAi) expressed with the pan neuronal driver ELAV-Gal4 effectively reduced Drip mRNA measured from adult heads by ∼1.75 log2-fold (p<0.001) and reduced fecundity by ∼20% . These lines are a subset of a larger population of RILs For phenotypic assays, RILs were reared at controlled density of 50 eggs/vial in media containing either 0.2% and 0.6% yeast by volume (YBV). Sugar, cornmeal, agar, and tegosept concentrations  were kept constant. Rearing vials were kept at 25°C, 12L:12D at 40% relative humidity (RH). Each RIL was reared in four to five replicate vials for each of two replicate blocks. We also reared subsets of these RILs in 0.6% YBV under controlled densities for mRNA extraction. Extractions were made from three to five day old, mated females.Df(2R)BSC328, Df(2R)enA, Df(2R)ED2155, Df(2R)ED2230 . Progeny of all crosses were reared in media containing 0.6% YBV as above.RNAi over expression was performed using UAS-IR lines  dyed green with food coloring  and sprinkled with live yeast granules. Flies were kept at 25°C, 12L:12D and 40% RH during fecundity assays. Vials were changed daily for 14 days and stored at 4°C until the eggs were counted.See See xeggs is the fecundity of an individual female at age x (xage), R is the set of random effects  for a particular cross and ε is the normally distributed error. To calculate estimates of total fecundity (age 1–14) for a particular genotype we fit this function using the mixed effect model package lme4For QTL mapping, quantitative complementation and RNAi screens fecundity was analyzed as a function valued trait e.g. . We usedy = Mi+E+Mi:E+ε, and the reduced model, y = E+ε, where y is estimated fecundity, Mi is the effect of marker or pseudomarker i, E the effect of larval environment and Mi:E their interaction. The difference in LOD score between the full and reduced model represents the strength of association between a particular genomic region and variation in fecundity segregating amongst the RILs. Statistical significance of QTL was determined by permutation testing QTL mapping was performed with estimates of total fecundity for each RIL  and dissected in RNA Later (Qiagen). Tissue was stored overnight at 4°C in RNA Later prior to RNA extraction. RNA from the RNAi over expression experiments was extracted from whole flies for tub-Gal4 crosses and from heads in ELAV-Gal4 and Crz-Gal4 crosses in three biological replicates, each containing tissue from 10–15 female flies using TRIzol reagent (Invitrogen). Purity and quantity of RNA was determined spectrophotometrically  and treated with DNase to remove residual DNA contamination (Ambion). Reverse transcription and quantification were performed using iScript One-Step RT-PCR kit with SYBR Green (Bio Rad) or SensiFAST SYBR One-Step Kit (Bioline) and measured on either a ABI prism 7300 Sequence Detection System (Applied Biosystems) or Eco (Illumina) qPCR machine. Gene expression for each biological replicate was measured three times . mRNA levels of Rpl32 were used to normalize mRNA levels of target genes. qPCR data were analyzed using the qpcR package p values and confidence intervals were calculated from permutation tests. For more information on primers see mRNA transcript levels were measured with reverse-transcription qPCR. Flies were reared as above and frozen at −80°C until RNA extraction. To measure 3–5 day-old virgin females were dissected under PBS (pH 7.4). The brain tissues were fixed for 30 minutes at room temperature in 4% paraformaldehyde in PBS, and then were incubated in primary antibody for 48 hours at 4°C, and in secondary antibody for 24 hours at 4°C. Antibodies used were: rabbit anti-Drip , 1∶500, Figure S1Phenotypic and genetic correlations between fecundity and ovariole number, thorax length or development time. In (A–C), points represent total fecundity and either ovariole number (A), thorax length (B) or development time (C) of individual flies. In (D–F) points represent genetic estimates of fecundity and genetic means of ovariole number (D), thorax length (E) or development time (F). In (C), points are jittered for visual clarity. Black circles represent flies reared in media containing 0.2% yeast by volume (YBV) and white circles represent flies reared in media containing 0.6% YBV.(PDF)Click here for additional data file.Figure S2x-axis represents the tester, “wild-type” chromosome or the deficiency chromosome. The y-axis represents estimated fecundity. Black and white circles represent the high and low fecundity RIL alleles, respectively. Error bars represent 95% CI based on non-parametric bootstrap resampling (5000 replicates), conditional on fly.Results of complementation tests with deficiencies that complement the two RIL alleles. In each panel, the (PDF)Click here for additional data file.Figure S32 fold change in gene expression of target genes due to over expression of RNAi constructs with tub-Gal4 relative to the control cross, normalized to Rpl32. (B–C) Log2 fold change in Drip mRNA relative to control cross, normalized to Rpl32, caused by overexpression of RNAi construct with (B) Elav-Gal4 and (C) crz-Gal4 Error bars represent 95% CI based on permutations; see text for details.Efficient knockdown of target genes with RNAi. (A) Log(PDF)Click here for additional data file.Figure S4Crz neurons expressing Drip-RNAi. The brain of Crz-Gal4/+ (A) and Crz-Gal4/UAS-Drip-IR  stained with anti-Drip antibody. (B′) Higher contrast view of (B) reveals weakly stained neurons (arrows). Crz-Gal4 carries UAS-Dcr-2 transgene. Scale bar, 20 µm.Reduction of Drip immunoreactivity in (TIF)Click here for additional data file.Table S1Mixed effect model results for ovariole number.(DOC)Click here for additional data file.Table S2Mixed effect model results for thorax length.(DOC)Click here for additional data file.Table S3Mixed effect model results for development time.(DOC)Click here for additional data file.Table S4Mixed effect model results for fecundity.(DOC)Click here for additional data file.Table S5Phenotypic and genetic correlation matrices.(DOC)Click here for additional data file.Table S6Quantitative complementation results.(DOC)Click here for additional data file.Table S7RNAi over expression results.(DOC)Click here for additional data file.Text S1Supplemental materials and methods.(DOC)Click here for additional data file.


The process of gene transcription requires the participation of a large number of factors that collectively promote the accurate and efficient expression of an organism's genetic information. In eukaryotic cells, a subset of these factors can control the chromatin environments across the regulatory and transcribed units of genes to modulate the transcription process and to ensure that the underlying genetic information is utilized properly. This article focuses on two such factors—the highly conserved histone chaperones Spt6 and FACT—that play critical roles in managing chromatin during the gene transcription process. These factors have related but distinct functions during transcription and several recent studies have provided exciting new insights into their mechanisms of action at transcribed genes. A discussion of their respective roles in regulating gene transcription, including their shared and unique contributions to this process, is presented. In eukaryotic cells, gene transcription takes place in the context of chromatin, a protein-DNA structure that includes the nucleosome—a particle composed of DNA and core histone proteins—as its fundamental unit . The preSpt6 was originally identified in the suppressor of Ty (Spt) screens and selection experiments in Saccharomyces cerevisiae, which were designed to identify genes that when mutated or expressed at abnormal levels could suppress the deleterious effects of insertions of Ty and solo δ elements at certain biosynthetic genes [δ elements [ic genes . Soon afelements –13. Subselements –16. ThesS. cerevisiae model system. The gene encoding the first component of the yeast FACT (yFACT) complex, Spt16 , was isolated as an SPT gene based on its ability to suppress the transcriptional defects of δ element insertions at the LYS2 and HIS4 genes when expressed from a high copy number plasmid and was shown to encode an essential protein involved in transcription regulation of several genes [α in biochemical experiments [in vitro reconstitution experiments [Similarly to Spt6, the components of the FACT (FAcilitates Chromatin Transcription) complex were first isolated through genetic and biochemical approaches in the al genes –21. The eriments  and lateeriments . Subsequeriments , 25. In eriments , 27. Theeriments .in vitro [spt6 mutant, Spt6 function can still be required to prevent cryptic intragenic transcription, thus pointing to functions for Spt6 in preventing intragenic transcription that are, at least in some cases, independent from its classical role as a histone chaperone during the transcription elongation process [The initial discovery of the ability of Spt6 to interact with histones and to assemble nucleosomes in vitro  foreshadin vitro , 30. Thein vitro , 32. Thein vitro . These fin vitro –36 wherein vitro –37. Thusin vitro , which w process .spt6 mutation [spt6 mutant background argues that Spt6-driven histone chaperoning activity during transcription elongation does not strongly impact the gene expression process [SER3 gene is accompanied by Spt6- and yFACT-dependent reassembly of nucleosomes, which, in turn, are thought to prevent binding of transcription activators required for SER3 expression, ultimately resulting in transcription repression of SER3 [The chaperoning activity of Spt6 is also required for proper control of transcription initiation. Recent studies in yeast have indicated that nucleosome reassembly activity driven by Spt6 occurs over certain gene promoters and that this activity is required for proper transcriptional repression , 38. Themutation  but that process . Therefo of SER3 .in vitro experiments that showed that (i) FACT can interact with nucleosomes and its activity is required for efficient transcription elongation on nucleosomal templates, (ii) the two subunits of FACT can interact with H2A-H2B dimers and (H3-H4)2 tetramers, and (iii) FACT activity can promote loss of H2A-H2B dimers from nucleosomes [2 tetramers can suppress defects in yFACT function both in vivo and in vitro [—thus, histone mutations that favor nucleosome disassembly decrease the dependency on yFACT activity, a finding consistent with a role for FACT in promoting histone eviction. Interestingly, additional studies have shown that yFACT activity is required principally at genes that contain stable nucleosomes positioned over the 5′ end of their coding units, suggesting that FACT-mediated histone eviction at the early stages of transcription elongation is a particularly important event for ensuring proper Pol II progression throughout the length of a transcribed gene [—therefore, facilitation of histone removal during the transcription elongation process may represent a major difference in the activities of FACT and Spt6.The histone-chaperoning activity of the FACT complex during transcription elongation is required for two distinct, but potentially mechanistically related, processes: facilitation of histone removal in front of elongating Pol II and nucleosome reassembly in the wake of Pol II passage. Evidence for the former process has come principally from leosomes , 27, 40.in vitro —thus, hibed gene . Whereasspt16 mutants and mutations in factors involved in deposition of histones onto DNA [in vitro [in vitro, suggesting that part of FACT's in vivo function may include participation in nucleosome reassembly during transcription elongation through interactions with all core histones [Similarly to Spt6, the FACT complex has critical roles in the reassembly of nucleosomes following passage of Pol II over transcribed units. Initial evidence for a role for FACT in transcription-dependent nucleosome reassembly came from experiments in yeast showing synthetic lethal genetic interactions between onto DNA . Whereasin vitro , subsequhistones . Recent histones , 44–46. histones , 48, in histones . CollectWhat are the consequences of defective FACT-mediated nucleosome reassembly during transcription elongation? Given the shared functions in transcription-dependent chromatin reassembly with Spt6, it is not surprising that mutations in FACT can also result in cryptic transcription initiation defects , 49, 50.SPT6 was also shown to cause phenotypes consistent with excess accumulation of free histones in cells [An additional consequence of loss of FACT chaperoning activity has been recently described by Chávez and colleagues. In these studies, a failure of yFACT to properly deposit histones during transcription elongation was shown to lead to abnormally high intracellular levels of free histones, which, in turn, led to a delay in cell cycle progression at the G1 phase by repressing expression of a G1-cyclin gene . Therefoin cells , thus raSER3 gene as described earlier, yFACT and Spt6 can both repress transcription initiation through their transcription elongation-dependent nucleosome reassembly activities [As is the case for Spt6, FACT activity is also required for proper regulation of transcription initiation. Whereas, as described earlier, Spt6 is required for repression of transcription initiation through its ability to directly promote nucleosome reassembly over gene promoter regions , 38, FACtivities .in vitro [in vivo Spn1/Iws1 may assist Spt6 in releasing itself from nucleosomes following nucleosome reassembly. Together, the interactions observed between Spt6 and histones, nucleosomes and naked DNA are likely to represent snapshots of a series of events that normally occur during the Spt6-mediated nucleosome reassembly process in the context of gene transcription.The mechanistic details for the interactions that occur between Spt6 and nucleosomes during the chaperoning process are still under investigation, but early and more recent studies have shown that Spt6 can bind double-stranded DNA in vitro  as well in vitro , 61. Thein vitro . Interesin vitro [Similarly to Spt6, the yFACT complex requires the assistance of Nhp6 in order to bind to nucleosomes in vitro . A seriein vitro , 65. A min vitro . Whereasin vitro , 67, a min vitro , 68, 69.in vivo and future research will undoubtedly shed more light on the mechanistic details of these processes.Regardless of the exact mechanism, efficient FACT-facilitated eviction of histones during transcription elongation likely requires specific posttranslational histone modifications. In particular, monoubiquitination of histone H2B  has been shown to prime nucleosomes for FACT-mediated H2A-H2B dimer loss . In addispt6 mutant appears to be due to an indirect effect resulting from decreased levels of the Set2 protein in the spt6 mutant background [+ mRNA in the nucleus [Both Spt6 and FACT have the ability to influence the chromatin environment across transcribed genes by affecting histone posttranslational modifications. Spt6 activity has recently been linked to methylation of lysine 36 of histone H3 (H3K36me), a modification catalyzed by the Set2 histone methyltransferase associated with the reestablishment of proper nucleosome structure in the wake of Pol II passage through the recruitment of the Rpd3S histone deacetylase complex and subsequent histone deacetylation –76. In tckground , 78. Int nucleus . Thus, Sin vitro and in vivo experiments have also demonstrated a requirement for FACT activity in promoting H2B monoubiquitination, a role for H2B monoubiquitination in maintenance of FACT at transcribed regions and a cooperative relationship between H2B monoubiquitination and FACT activity in reassembling nucleosomes during transcription elongation [As indicated earlier, monoubiquitination of histone H2B facilitates FACT-mediated histone eviction during transcription elongation. Interestingly, ongation , 80. Thuongation . These fIn addition to being key contributors to the initiation and elongation phases of transcription, Spt6 and FACT are also involved in functions related to mRNA processing and nuclear export. In mammalian cells, the Pol II-associated complex discussed earlier composed of Spt6, Iws1, and HYPB/Set2 plays important roles in ensuring proper mRNA splicing and efficient mRNA export from the nucleus , 82. TheS. cerevisiae and D. melanogaster provided compelling evidence that, as had been anticipated based on its characteristics as an elongation factor [Pioneering immunofluorescence and biochemical experiments performed in n factor , Spt6 phn factor –90. MoreHsp70 loci in flies upon heat-shock treatment a few seconds after the recruitment of Pol II to the promoter [Chromatin immunoprecipitation (ChIP) assays in yeast have shown that Spt6 interacts with several constitutively expressed genes in a manner similar to that seen for Pol II . Similarpromoter . CollectWhat are the mechanisms that control Spt6 association with transcribed genes? A critical function involved in directing Spt6 interaction with the elongating complex is carried out by a tandem SH2 (tSH2) domain located at the C-terminus of the protein. Analyses of recently solved crystal structures of the Spt6 tSH2 domain derived from different organisms have shown that the overall structure of this domain is evolutionarily conserved and a series of biochemical experiments has shown that this domain mediates interactions between Spt6 and the C-terminal domain (CTD) heptad repeats of Pol II , 94, 95.in vitro or in vivo assays [in vitro [Drosophila Hsp70 genes, arrival of the Ser2 Pol II CTD kinase P-TEFb precedes Spt6 recruitment [Whereas the direct interaction between the tSH2 domain of Spt6 and Pol II clearly contributes to the association of Spt6 with transcribed genes, it is not the sole mechanism involved in recruitment of Spt6 to active genes. In support of this notion, a mutation within the SH2 domain of murine Spt6 that lowers the affinity of Spt6 to Ser2-phoshporylated Pol II CTD did not affect transcription output levels in either o assays . Furthero assays . The facin vitro ) or may ruitment . In addiruitment , 82.Drosophila and in S. cerevisiae provide some insights into the nature of these mechanisms. In Drosophila, impairment of the Paf1 complex—a multifunctional complex associated with Pol II that coordinates a variety of transcription-related processes, including recruitment of several transcription factors to genes and various posttranslational modifications of histones [ Hsp70 gene [Drosophila Hsp70 gene does not appear to be an indirect effect due to lower CTD phosphorylation since at this locus the levels of Ser2-phosphorylation of the Pol II CTD are not affected by the depletion of the Paf1 complex [CYC1 gene. In this case, Spt6 recruitment is dependent on Spn1/Iws1 [CYC1 gene) remains to be more fully elucidated  sites—may simply dissociate from chromatin in conjunction with the Pol II complex. On the other hand, those factors shown to disengage from transcribed genes at or upstream from the pA sites, such as the Paf1 complex , 92, musPMA1 and ADH1 genes, Spt6 and yFACT disengage at similar locations past the pA sites [PMA1 and to only modestly affect the dissociation of Spt6 from the PMA1 and ADH1 genes [Whereas, as indicated earlier, global studies in yeast have indicated that at an “average” gene Spt6 and yFACT disengage from chromatin at different sites , suggestpA sites , 105 raipA sites , 105. ThpA sites . InteresH1 genes , 105. ThH1 genes , 103. AdWhereas our understanding of the roles and the mechanisms of action of both Spt6 and FACT in the transcription process has increased dramatically since their original identification more than two decades ago, many questions remain to be addressed and future research will undoubtedly provide a more complete picture of the mechanistic details of the transcription process in general, and, more specifically, of the contributions of these two key histone chaperones to this process. In particular, it will be of interest to determine how the functions of Spt6 and FACT are coordinated with those of other histone chaperones known to operate during transcription, such as Asf1 and Nap1 , 5. Trad


Because the expression patterns in NSPCs are related to the symptoms of intellectual disability and microcephaly in PQBP1 gene-mutated patients, we investigated the transcriptional regulation of PQBP1 by NSPC-specific transcription factors. We selected 132 genome sequences that matched the consensus sequence for the binding of Sox2 and POU transcription factors upstream and downstream of the mouse PQBP1 gene. We then screened the binding affinity of these sequences to Sox2-Pax6 or Sox2-Brn2 with gel mobility shift assays and found 18 genome sequences that interacted with the NSPC-specific transcription factors. Some of these sequences had cis-regulatory activities in Luciferase assays and in utero electroporation into NSPCs. Furthermore we found decreased levels of expression of PQBP1 protein in NSPCs of heterozygous Sox2-knockout mice in vivo by immunohistochemistry and western blot analysis. Collectively, these results indicated that Sox2 regulated the transcription of PQBP1 in NSPCs.PQBP1 is a nuclear-cytoplasmic shuttling protein that is engaged in RNA metabolism and transcription. In mouse embryonic brain, our previous  PQBP1 functionally connects transcription and splicing when it is located in the nucleus PQBP1 gene are linked to intellectual disabilities (ID) that are characterized by mental retardation, microcephaly, and short stature PQBP1 gene-mutated patients and of stem cell-related pathology in neurodegeneration, we investigated the transcriptional regulation of PQBP1 by NSPC-specific transcription factors in this study.PQBP1 has been implicated in both neurodegenerative diseases and developmental disorders.PQBP1 has been shown to interact with the two polyQ disease proteins, ataxin-1 and huntingtin Especially, we focused on the role of Sox2 in the transcriptional regulation of PQBP1 in NSPCs. Sox2 is one of the most critical transcription factors for NSPCs. The stemness or pluripotency of NSPCs is mainly attributed to Sox2, which is clearly demonstrated in studies of induced pluripotent stem (iPS) cells Sox2, which is a HMG-box-containing transcription factor that binds to the minor groove of DNA, forms a heterodimer with a POU transcription factor Pax6 is expressed in apical and basal progenitor cells. Brn2 is highly expressed in NSPCs. Therefore, they could be the partner of Sox2 PQBP1 gene that were homologous to the Sox2-Pax6 or Sox2-Brn2-binding consensus sequence for their binding activity and transcriptional activity. Consequently we identified 18 genome sequences that interacted with the NSPC-specific transcription factors. In utero electroporation into NSPCs revealed that some of them had cis-regulatory activities in vivo. Furthermore, we found decreased expression of PQBP1 protein in NSPCs of heterozygous Sox2-knockout mice. All these results in this study supported that Sox2 regulates the transcription of PQBP1 in NSPCs.Hence, we employed the first 2 types of heterodimers , and screened for the genome sequence surrounding the in situ hybridization that mouse PQBP1 mRNA is expressed in mature neurons, but not in glia, in adult brains PQBP1 is dominantly expressed in the periventricular zone in embryonic brain PQBP1was highly expressed in NSPCs, but it was also weakly expressed in differentiating neurons . In addition, as already mentioned we previously confirmed that PQBP1 is not highly expressed in glia compared to neurons PQBP1. Thus, we did not use the complex in the following experiments.In addition, we performed a gel shift assay with Sox2-Oct6  and ChIP assay are overlapped.PQBP1 gene (Supplementary cis-activities were tested in a luciferase assay. We cotransfected reporter and effector plasmids (Sox2+Pax6 or Sox2+Brn2) into P19 cells  mice in which the Sox2 expression was almost 50% of control mice , full-length Brn2 , the DNA-binding domain of Brn2, full-length Pax6 , the DNA-binding domain of Pax6 , and the DNA-binding domain of Oct6  were amplified by polymerase chain reaction from B6 wild-type mouse embryonic brain RNA and subcloned into pGEX-6P-1  between BamHI-EcoRI  or BamHI-XhoI (for Pax6 full-length and DNA-binding domain). The sequences were confirmed after subcloning.For construction of the GST-fusion vectors, cDNA fragments of full-length Sox2  and again subcloned into pCI-neo  with single SalI site (Sox2 and Brn2) or SalI and NotI sites (Pax6).Escherichia coli cells (BL21) by 0.1 mM IPTG for 6 h at 25°C. The cells were collected, washed twice with PBS, resuspended in lysis buffer , and sonicated. GST-fusion proteins were purified by glutathione-sepharose 4B according to the commercial protocol . Sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis was performed in order to confirm the sizes of the GST-fusion proteins. Western blot analysis was performed in order to test the validity of the proteins with anti-Sox2 , anti-pax6 , anti-Brn2, and anti-GST  antibodies.GST-fusion proteins of full-length and the DNA-binding domain of transcription factors were induced in Sense and antisense oligonucleotides at 5′ terminals containing 5 dGs at the 5′-end were annealed and radiolabeled with a Random Primer DNA Labeling Kit  with [α-32P]dCTP . Twenty nanogram of each GST-fusion protein was incubated with 40,000–50,000 cpm of labeled probe in binding buffer  with poly-dIdC  at room temperature for 30 min and then separated in a 6% polyacrylamide gel with 0.25×TBE (25 min prerun and then run at 4°Cat 120 V for 2.5 h). For heterodimerization, two proteins (each 20 ng) were preincubated in binding buffer at 4°C for 100 min before the addition of the probe. The reaction was extended for another h at 4°C and separated by electrophoresis. Radioactivity of the band was quantified with BAS4000 .E18 mouse cortex was fixed with 4% paraformaldehyde overnight at 4°C and embedded in paraffin. The paraffin blocks were cut into 5 µm sagittal sections. After rehydration, the sections were stained with anti-PQBP1 that was produced in rabbit  and anti-Sox2 that was produced in goat . The stains were visualized with AlexaFluor 488 in rabbit and Cy3 goat-conjugated secondary antibodies . DAPI solution  was added to the secondary antibody solutionsin order tovisualize the nuclei. Digital images were captured by an Olympus IX71 microscope  with a 10X/0.30objective lens , 7 or a Neurospheres prepared from E14 mouse telencephalon were dissolved in SDS sample buffer . Protein concentration was quantified with the BCA method . Twenty-five µg of proteins were applied for each lane. Anti-Sox2 produced in rabbit  and anti-PQBP1produced in rabbit  primary antibodies were used at 1∶500, and anti-GAPDH produced in mouse  was used at 1∶10,000. Horseradish peroxidase-conjugated anti-Rabbit IgG  and horseradish peroxidase-conjugated anti-Mouse IgG  were used at 1∶3,000. Antibodies were diluted in TBS with Tween 20 (TBST) in 5% skim milk. ECL prime  was used to detect the bands according to the commercial protocol.cis-elements were subcloned into pGreenFire-1-mCMV reporter vector. The pGreenFire-1-mCMV reporter vector (200 ng), the pCI-neo effecter , and pGL4.73 [hRluc/SV40] (Promega Corporation) (5 ng), which was used as an internal control, were cotransfected into P19 cells (4×104/96-well plate) by lipofectamine 2000  according to the commercial protocol, when the cells became 80% confluent. Transfection was done at least in triplicate. Luciferase values were tested with a dual-glo luciferase assay system (Promega Corporation), and the average luciferase expression is presented with standard errors.pGreenFire1-PC(Sox2+Brn2)-mCMV, pGreenFire1-NC(Sox2+Brn2)-mCMV, pGreenFire1-PC(Sox2+Pax6)-mCMV, or pGreenFire1-NC(Sox2+Pax6)-mCMV possessing active in utero electroporation, plasmids were prepared withan Endo-Free plasmid purification kit . One µg of tetramerized pGreenFire1-PC (Sox2+Pax6)-mCMV or pGreenFire1-NC (Sox2+pax6)-mCMV For This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of the Tokyo Medical and Dental University (Permit Number: 0130225A). All surgery was performed under ether of isoflurane anesthesia, and every effort was made to minimize suffering.Figure S1Confirmation of the gel mobility shift assay conditions. Positive control probes (l probes  bound to(TIFF)Click here for additional data file.Figure S2Actual data of gel mobility shift assay in screening of cis-elements. (A) Screening of cis-elements by a gel mobility shift assay with Sox2-Brn2 or Sox2-Pax6 full-length protein heterodimer. (B) Screening of cis-elements by a gel mobility shift assay with the Brn2 or Pax6 DNA-binding domain (DBD).(TIFF)Click here for additional data file.Figure S3Screening of cis-elements by gel mobility shift assay with the Oct6 DNA-binding domain (DBD). The left panel shows a representative gel mobility shift of the Sox2-Oct6 consensus probe by Oct6-DBD. A NF-κB consensus probe was used as a negative control. The graphs on the right show the radioactivity in the expected area of the gel shift of Oct6-DBD (surrounded by red line).(TIFF)Click here for additional data file.Figure S4Locations of the double-positive sequences both upstream and downstream of the PQBP1 gene. The double-screening positive cis-elements were distributed both upstream and downstream of the PQBP1 gene as clusters.(TIFF)Click here for additional data file.Figure S5Larger magnification of in utero electroporated NSPCs. (A) NSPCs transfected by pGreenFire1-16A-mCMV and pLVSIN-CAG-pur-DsRed were visualized by confocal microscopy with 40X water emersion lens. (B) A negative control plasmid, pGreenFire1-NC (Sox2+Pax6)-mCMV was also transfected into E14 embryonic brains by in utero electroporation. No EGFP signal was detected on E15.(TIFF)Click here for additional data file.