Pairwise Coexpression Research Articles

Transcriptome-based identification of key actin-binding proteins associated with high metastatic potential in breast cancer.

Actin-binding proteins (ABPs) are essential for the regulation of morphological plasticity required for tumor cells to metastasize. The aim of this study was to perform an unbiased bioinformatic approach to identify the key ABPs significantly associated with the metastatic potential of breast cancer cells. Microarray data from 181 primary breast cancer samples from our hospital were used, and all genes belonging to the Gene Ontology term actin cytoskeleton organization were obtained from QuickGO. Association with metastasis-free survival probability was tested using Cox proportional hazards regression, and pairwise co-expression was tested by Pearson correlations. Differential expression between different subgroups was analyzed using Wilcoxon tests for dichotomous traits and Kruskal-Wallis tests for categorical traits. Validation was performed using four publicly available breast cancer datasets. ARHGAP25 was significantly associated with a low metastatic potential, and CFL1, TMSB15A, and ACTL8 were significantly associated with a high metastatic potential. A significantly higher expression of CFL1, TMSB15A, and ACTL8 mRNA was found in the more aggressive Her2-positive and triple-negative subtypes as well as in ER-negative samples. Also, these genes were co-expressed in the same tumors. However, only mRNA levels of CFL1 were increased in pN1 compared to pN0 patients. External validation revealed that CFL1 and TMSB15A had significant associations with consistent hazard ratios in two breast cancer cohorts, and among these, CFL1 exhibited the highest hazard ratios. CFL1 showed the strongest correlation with the metastatic potential of breast tumors. Thus, targeted inhibition of CFL1 might be a promising approach to treat malignant breast cancer cells.

On two mathematical representations for “semantic maps”

AbstractWe describe two mathematical representations for what have come to be called “semantic maps”, that is, representations of typological universals of linguistic co-expression with the aim of inferring similarity relations between concepts from those universals. The two mathematical representations are a graph structure and Euclidean space, the latter as inferred through multidimensional scaling. Graph structure representations come in two types. In both types, meanings are represented as vertices (nodes) and relations between meanings as edges (links). One representation is a pairwise co-expression graph, which represents all pairwise co-expression relations as edges in the graph; an example is CLICS. The other is a minimally connected co-expression graph – the “classic semantic map”. This represents only the edges necessary to maintain connectivity, that is, the principle that all the meanings expressed by a single form make up a connected subgraph of the whole graph. The Euclidean space represents meanings as points, and relations as Euclidean distance between points, in a specified number of spatial dimensions. We focus on the proper interpretation of both types of representations, algorithms for constructing the representations, measuring the goodness of fit of the representations to the data, and balancing goodness of fit with informativeness of the representation.

Pan-cancer analysis of non-coding transcripts reveals the prognostic onco-lncRNA HOXA10-AS in gliomas.

Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in cancer and powerful biomarkers; however, most remain uncharacterized. Here, we analyze 5,592 prognostic lncRNAs in 9,446 cancers of 30 types using machine learning. We identify 166 lncRNAs whose expression correlates with survival and improves the accuracy of common clinical variables, molecular features, and cancer subtypes. Prognostic lncRNAs are often characterized by switch-like expression patterns. In low-grade gliomas, HOXA10-AS activation is a robust marker of poor prognosis that complements IDH1/2 mutations, as validated in another retrospective cohort, and correlates with developmental pathways in tumor transcriptomes. Loss- and gain-of-function studies in patient-derived glioma cells, organoids, and xenograft models identify HOXA10-AS as a potent onco-lncRNA that regulates cell proliferation, contact inhibition, invasion, Hippo signaling, and mitotic and neuro-developmental pathways. Our study underscores the pan-cancer potential of the non-coding transcriptome for identifying biomarkers and regulators of cancer progression.

The grapevine (Vitis vinifera L.) floral transcriptome in Pinot noir variety: identification of tissue-related gene networks and whorl-specific markers in pre- and post-anthesis phases

The comprehension of molecular processes underlying the development and progression of flowering in plants is a hot topic, not only because that often the products of interest for human and animal nutrition are linked to the development of fruits or seeds, but also because the processes of gametes formation occurring in sexual organs are at the basis of recombination and genetic variability which constitutes the matter on which evolution acts, whether understood as natural or human driven. In the present study, we used an NGS approach to produce a grapevine flower transcriptome snapshot in different whorls and tissues including calyx, calyptra, filament, anther, stigma, ovary, and embryo in both pre- and post-anthesis phases. Our investigation aimed at identifying hub genes that unequivocally distinguish the different tissues providing insights into the molecular mechanisms that are at the basis of floral whorls and tissue development. To this end we have used different analytical approaches, some now consolidated in transcriptomic studies on plants, such as pairwise comparison and weighted-gene coexpression network analysis, others used mainly in studies on animals or human’s genomics, such as the tau (τ) analysis aimed at isolating highly and absolutely tissue-specific genes. The intersection of data obtained by these analyses allowed us to gradually narrow the field, providing evidence about the molecular mechanisms occurring in those whorls directly involved in reproductive processes, such as anther and stigma, and giving insights into the role of other whorls not directly related to reproduction, such as calyptra and calyx. We believe this work could represent an important genomic resource for functional analyses of grapevine floral organ growth and fruit development shading light on molecular networks underlying grapevine reproductive organ determination.

Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance.

Genome-wide gene expression analysis are routinely used to gain a systems-level understanding of complex processes, including network connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we developed a computational pipeline to assign to every gene its pair-wise genome-wide co-expression distribution to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. We then used a hypergeometric test to determine if specific genes (regulators versus non-regulators) and properties (differentially expressed or not) are associated with a particular distribution shape. We applied our methodology to five publicly available RNA sequencing (RNA-seq) datasets from four organisms in different physiological conditions and tissues. Our results suggest that genes can be assigned consistently to pre-defined distribution shapes, regarding the enrichment of differential expression and regulatory genes, in situations involving contrasting phenotypes, time-series, or physiological baseline data. There is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Our method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.

Combinatorial Approach for Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders.

Early prediction of complex disorders (e.g., autism and other neurodevelopmental disorders) is one of the fundamental goals of precision medicine and personalized genomics. An early prediction of complex disorders can improve the prognosis, increase the effectiveness of interventions and treatments, and enhance the life quality of affected patients. Considering the genetic heritability of neurodevelopmental disorders, we are proposing a novel framework for utilizing rare coding variation for early prediction of these disorders in subset of affected samples. We provide a combinatorial framework for addressing this problem, denoted as Odin (Oracle for DIsorder predictioN), to make a prediction for a small, yet significant, subset of affected cases while having very low false positive rate (FPR) prediction for unaffected samples. Odin also takes advantage of the available functional information (e.g., pairwise coexpression of genes during brain development) to increase the prediction power beyond genes with recurrent variants. Application of our method accurately recovers an additional 8% of autism cases without any severe variant in known recurrent mutated genes with a <1% FPR. Furthermore, Odin predicted a set of 391 genes that severe variants in these genes can cause autism or other developmental delay disorders. Approaches such as the one presented in this paper are needed to translate the biomedical discoveries into actionable items by clinicians. Odin is publicly available at https://github.com/HormozdiariLab/Odin.

Systematic Analysis of Transmitter Coexpression Reveals Organizing Principles of Local Interneuron Heterogeneity.

Broad neuronal classes are surprisingly heterogeneous across many parameters, and subclasses often exhibit partially overlapping traits including transmitter coexpression. However, the extent to which transmitter coexpression occurs in predictable, consistent patterns is unknown. Here, we demonstrate that pairwise coexpression of GABA and multiple neuropeptide families by olfactory local interneurons (LNs) of the moth Manduca sexta is highly heterogeneous, with a single LN capable of expressing neuropeptides from at least four peptide families and few instances in which neuropeptides are consistently coexpressed. Using computational modeling, we demonstrate that observed coexpression patterns cannot be explained by independent probabilities of expression of each neuropeptide. Our analyses point to three organizing principles that, once taken into consideration, allow replication of overall coexpression structure: (1) peptidergic neurons are highly likely to coexpress GABA; (2) expression probability of allatotropin depends on myoinhibitory peptide expression; and (3) the all-or-none coexpression patterns of tachykinin neurons with several other neuropeptides. For other peptide pairs, the presence of one peptide was not predictive of the presence of the other, and coexpression probability could be replicated by independent probabilities. The stochastic nature of these coexpression patterns highlights the heterogeneity of transmitter content among LNs and argues against clear-cut definition of subpopulation types based on the presence of single neuropeptides. Furthermore, the receptors for all neuropeptides and GABA were expressed within each population of principal neuron type in the antennal lobe (AL). Thus, activation of any given LN results in a dynamic cocktail of modulators that have the potential to influence every level of olfactory processing within the AL.

Extensive rewiring of epithelial-stromal co-expression networks in breast cancer

BackgroundEpithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis.ResultsWe develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas.ConclusionsEpithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0675-4) contains supplementary material, which is available to authorized users.

A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize.

Inactivating ion channels augment robustness of subthreshold intrinsic response dynamics to parametric variability in hippocampal model neurons

Voltage-gated ion channels play a critical role in regulating neuronal intrinsic response dynamics (IRD). Here, we computationally analysed the roles of the two inactivating subthreshold conductances (A and T), individually and in various combinations with the non-inactivating h conductance, in regulating several physiological IRD measurements in the theta frequency range. We found that the independent presence of a T conductance, unlike that of an h conductance, was unable to sustain an inductive phase lead in the theta frequency range, despite its ability to mediate theta frequency resonance. The A conductance, on the other hand, when expressed independently, acted in a manner similar to a leak conductance with reference to most IRD measurements. Next, analysing the impact of pair-wise coexpression of these channels, we found that the coexpression of the h and T conductances augmented the range of parameters over which they sustained resonance and inductive phase lead. Additionally, coexpression of the A conductance with the h or the T conductance elicited changes in IRD measurements that were similar to those obtained with the expression of a leak conductance with a resonating conductance. Finally, to understand the global sensitivity of IRD measurements to all parameters associated with models expressing all three channels, we generated 100,000 neuronal models, each built with a unique set of parametric values. We categorized valid models among these by matching their IRD measurements with experimental counterparts, and found that functionally similar models could be achieved even when underlying parameters displayed tremendous variability and exhibited weak pair-wise correlations. Our results suggest that the three prominent subthreshold conductances contribute differently to intrinsic excitability and to phase coding. We postulate that the differential expression and activity-dependent plasticity of these conductances contribute to robustness of subthreshold IRD, whereby response homeostasis is achieved by recruiting several non-unique combinations of these channel parameters.

Co-expression of soybean Dicer-like genes in response to stress and development

Regulation of gene transcription and post-transcriptional processes is critical for proper development, genome integrity, and stress responses in plants. Many genes involved in the key processes of transcriptional and post-transcriptional regulation have been well studied in model diploid organisms. However, gene and genome duplication may alter the function of the genes involved in these processes. To address this question, we assayed the stress-induced transcription patterns of duplicated gene pairs involved in RNAi and DNA methylation processes in the paleopolyploid soybean. Real-time quantitative PCR and Sequenom MassARRAY expression assays were used to profile the relative expression ratios of eight gene pairs across eight different biotic and abiotic stress conditions. The transcriptional responses to stress for genes involved in DNA methylation, RNAi processing, and miRNA processing were compared. The strongest evidence for pairwise co-expression in response to stresses was exhibited by non-paralogous Dicer-like (DCL) genes GmDCL2a-GmDCL3a and GmDCL1b-GmDCL2b, most profoundly in root tissues. Among homoeologous or paralogous DCL genes, the Dicer-like 2 (DCL2) gene pair exhibited the strongest response to stress and most conserved co-expression pattern. This was surprising because the DCL2 duplication event is more ancient than the other DCL duplications. Possible mechanisms that may be driving the DCL2 co-expression are discussed.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview

BackgroundDAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes.ResultsEach significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools.ConclusionsDAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

A Biologically Inspired Measure for Coexpression Analysis

Two genes are said to be coexpressed if their expression levels have a similar spatial or temporal pattern. Ever since the profiling of gene microarrays has been in progress, computational modeling of coexpression has acquired a major focus. As a result, several similarity/distance measures have evolved over time to quantify coexpression similarity/dissimilarity between gene pairs. Of these, correlation coefficient has been established to be a suitable quantifier of pairwise coexpression. In general, correlation coefficient is good for symbolizing linear dependence, but not for nonlinear dependence. In spite of this drawback, it outperforms many other existing measures in modeling the dependency in biological data. In this paper, for the first time, we point out a significant weakness of the existing similarity/distance measures, including the standard correlation coefficient, in modeling pairwise coexpression of genes. A novel measure, called BioSim, which assumes values between -1 and +1 corresponding to negative and positive dependency and 0 for independency, is introduced. The computation of BioSim is based on the aggregation of stepwise relative angular deviation of the expression vectors considered. The proposed measure is analytically suitable for modeling coexpression as it accounts for the features of expression similarity, expression deviation and also the relative dependence. It is demonstrated how the proposed measure is better able to capture the degree of coexpression between a pair of genes as compared to several other existing ones. The efficacy of the measure is statistically analyzed by integrating it with several module-finding algorithms based on coexpression values and then applying it on synthetic and biological data. The annotation results of the coexpressed genes as obtained from gene ontology establish the significance of the introduced measure. By further extending the BioSim measure, it has been shown that one can effectively identify the variability in the expression patterns over multiple phenotypes. We have also extended BioSim to figure out pairwise differential expression pattern and coexpression dynamics. The significance of these studies is shown based on the analysis over several real-life data sets. The computation of the measure by focusing on stepwise time points also makes it effective to identify partially coexpressed genes. On the whole, we put forward a complete framework for coexpression analysis based on the BioSim measure.

Structural Analysis of the Interactions Between Hsp70 Chaperones and the Yeast DNA Replication Protein Orc4p

Hsp70 chaperones, besides their role in assisting protein folding, are key modulators of protein disaggregation, being consistently found as components of most macromolecular assemblies isolated in proteome-wide affinity purifications. A wealth of structural information has been recently acquired on Hsp70s complexed with Hsp40 and NEF co-factors and with small hydrophobic target peptides. However, knowledge of how Hsp70s recognize large protein substrates is still limited. Earlier, we reported that homologue Hsp70 chaperones (DnaK in Escherichia coli and Ssa1-4p/Ssb1-2p in Saccharomyces cerevisiae) bind strongly, both in vitro and in vivo, to the AAA+ domain in the Orc4p subunit of yeast origin recognition complex (ORC). ScORC is the paradigm for eukaryotic DNA replication initiators and consists of six distinct protein subunits (ScOrc1p–ScOrc 6p). Here, we report that a hydrophobic sequence (IL4) in the initiator specific motif (ISM) in Orc4p is the main target for DnaK/Hsp70. The three-dimensional electron microscopy reconstruction of a stable Orc4p2–DnaK complex suggests that the C-terminal substrate-binding domain in the chaperone clamps the AAA+ IL4 motif in one Orc4p molecule, with the substrate-binding domain lid subdomain wedging apart the other Orc4p subunit. Pairwise co-expression in E. coli shows that Orc4p interacts with Orc1/2/5p. Mutation of IL4 selectively disrupts Orc4p interaction with Orc2p. Allelic substitution of ORC4 by mutants in each residue of IL4 results in lethal (I184A) or thermosensitive (L185A and L186A) initiation-defective phenotypes in vivo. The interplay between Hsp70 chaperones and the Orc4p-IL4 motif might have an adaptor role in the sequential, stoichiometric assembly of ScORC subunits.

Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis

Pathway knowledge in public databases enables us to examine how individual metabolites are connected via chemical reactions and what genes are implicated in those processes. For two given (sets of) compounds, the number of possible paths between them in a metabolic network can be intractably large. It would be informative to rank these paths in order to differentiate between them. Focusing on adjacent pairwise coexpression, we developed an algorithm which, for a specified k, efficiently outputs the top k paths based on a probabilistic scoring mechanism, using a given metabolic network and microarray datasets. Our idea of using adjacent pairwise coexpression is supported by recent studies that local coregulation is predominant in metabolism. We first evaluated this idea by examining to what extent highly correlated gene pairs are adjacent and how often they are consecutive in a metabolic network. We then applied our algorithm to two examples of path ranking: the paths from glucose to pyruvate in the entire metabolic network of yeast and the paths from phenylalanine to sinapyl alcohol in monolignols pathways of arabidopsis under several different microarray conditions, to confirm and discuss the performance analysis of our method.

The Functional Effects of Physical Interactions among Toll-like Receptors 7, 8, and 9

Toll-like receptors (TLRs) TLR1, TLR2, TLR4, and TLR6 are evolutionarily conserved, highly homologous, and localized to plasma membranes of host cells and recognize pathogen-associated molecular patterns (PAMPs) derived from bacterial membranes. These receptors cooperate in a pairwise combination to elicit or inhibit the inflammatory signals in response to certain PAMPs. The other TLRs that are evolutionarily closely related and highly homologous are TLR7, TLR8, and TLR9. They are all confined to the membranes of endosomes and recognize similar molecular structures, the oligonucleotide-based PAMPs. However, the cooperative interactions among these receptors that may modulate the inflammatory signaling in response to their cognate agonists are not reported. We report here for the first time the functional effects of one TLR on the other among TLR7, TLR8, and TLR9. The results indicate that TLR8 inhibits TLR7 and TLR9, and TLR9 inhibits TLR7 but not vice versa in HEK293 cells transfected with TLRs in a pairwise combination. This is concluded by selectively activating one TLR over the other by using small molecule TLR agonists. We also show that these inhibitory interactions are the result of direct or indirect physical interactions between the TLRs. The murine TLR8 that does not respond to any known human TLR8 agonists also inhibits both murine and human TLR7. The implications of the inhibitory interactions among these TLRs in host-pathogen recognition and subsequent inflammatory responses are not obvious. However, given the complexity in expression pattern in a particular cell type and the variation in distribution and response to different pathogens and stress signals in different cell types, the inhibitory physical interactions among these TLRs may play a role in balancing the inflammatory outcome from a given cell type to a specific challenge.

High Throughput Screening of Co-Expressed Gene Pairs with Controlled False Discovery Rate (FDR) and Minimum Acceptable Strength (MAS)

Many exploratory microarray data analysis tools such as gene clustering and relevance networks rely on detecting pairwise gene co-expression. Traditional screening of pairwise co-expression either controls biological significance or statistical significance, but not both. The former approach does not provide stochastic error control, and the later approach screens many co-expressions with excessively low correlation. We have designed and implemented a statistically sound two-stage co-expression detection algorithm that controls both statistical significance (false discovery rate, FDR) and biological significance (minimum acceptable strength, MAS) of the discovered co-expressions. Based on estimation of pairwise gene correlation, the algorithm provides an initial co-expression discovery that controls only FDR, which is then followed by a second stage co-expression discovery which controls both FDR and MAS. It also computes and thresholds the set of FDR p-values for each correlation that satisfied the MAS criterion. Using simulated data, we validated asymptotic null distributions of the Pearson and Kendall correlation coefficients and the two-stage error-control procedure; we also compared our two-stage test procedure with another two-stage test procedure using the receiver operating characteristic (ROC) curve. We then used yeast galactose metabolism data to illustrate the advantage of our method for clustering genes and constructing a relevance network. The method has been implemented in an R package "GeneNT" that is freely available from the Comprehensive R Archive Network (CRAN): www.cran.r-project.org/.

Molecular organization of the Ndc80 complex, an essential kinetochore component

The four-protein Ndc80 complex, an essential kinetochore component conserved from yeast to humans, plays an indispensable role in proper chromosome alignment and segregation during mitosis. In higher eukaryotes, the homologous complex probably resides in the middle domain of the trilaminar kinetochore, linking centromeric heterochromatin with microtubule-associated structures. We have prepared recombinant Ndc80 complex by pairwise coexpression of its components (Ndc80p and Nuf2p; Spc24p and Spc25p) and shown that they form independently stable subcomplexes. Rotary shadowing electron microscopy, combined with limited proteolysis and antibody labeling, demonstrates that the heterotetrameric Ndc80 complex is an approximately 570-A-long rod, with globular regions at either end. The shaft contains alpha-helical coiled-coil segments from each of the two subcomplexes, linked end-to-end. When integrated with published observations derived from inactivating the components of Ndc80, the molecular organization we deduce suggests that the Spc24p/Spc25p end of the rod faces the centromere and the Ndc80p/Nuf2p end faces a spindle microtubule.

Characterization of a new gammaTuRC subunit with WD repeats.

The gamma-tubulin ring complex (gammaTuRC), consisting of multiple protein subunits, can nucleate microtubule assembly. Although many subunits of the gammaTuRC have been identified, a complete set remains to be defined in any organism. In addition, how the subunits interact with each other to assemble into gammaTuRC remains largely unknown. Here, we report the characterization of a novel gammaTuRC subunit, Drosophila gamma ring protein with WD repeats (Dgp71WD). With the exception of gamma-tubulin, Dgp71WD is the only gammaTuRC component identified to date that does not contain the grip motifs, which are signature sequences conserved in gammaTuRC components. By performing immunoprecipitations after pair-wise coexpression in Sf9 cells, we show that Dgp71WD directly interacts with the grip motif-containing gammaTuRC subunits, Dgrips84, 91, 128, and 163, suggesting that Dgp71WD may play a scaffolding role in gammaTuRC organization. We also show that Dgrips128 and 163, like Dgrips84 and 91, can interact directly with gamma-tubulin. Coexpression of any of these grip motif-containing proteins with gamma-tubulin promotes gamma-tubulin binding to guanine nucleotide. In contrast, in the same assay Dgp71WD interacts with gamma-tubulin but does not facilitate nucleotide binding.

A polyamine metabolon involving aminopropyl transferase complexes in Arabidopsis.

The conversion of putrescine to spermidine in the biosynthetic pathway of plant polyamines is catalyzed by two closely related spermidine synthases, SPDS1 and SPDS2, in Arabidopsis. In the yeast two-hybrid system, SPDS2 was found to interact with SPDS1 and a novel protein, SPMS (spermine synthase), which is homologous with SPDS2 and SPDS1. SPMS interacts with both SPDS1 and SPDS2 in yeast and in vitro. Unlike SPDS1 and SPDS2, SPMS failed to suppress the speDelta3 deficiency of spermidine synthase in yeast. However, SPMS was able to complement the speDelta4 spermine deficiency in yeast, indicating that SPMS is a novel spermine synthase. The SPDS and SPMS proteins showed no homodimerization but formed heterodimers in vitro. Pairwise coexpression of hemagglutinin- and c-Myc epitope-labeled proteins in Arabidopsis cells confirmed the existence of coimmunoprecipitating SPDS1-SPDS2 and SDPS2-SPMS heterodimers in vivo. The epitope-labeled SPDS and SPMS proteins copurified with protein complexes ranging in size from 650 to 750 kD. Our data demonstrate the existence of a metabolon involving at least the last two steps of polyamine biosynthesis in Arabidopsis.