Human Nek7-interactor RGS2 is required for mitotic spindle organization

<p>The mitotic spindle apparatus is composed of microtubule (MT) networks attached to kinetochores organized from 2 centrosomes (a.k.a. spindle poles). In addition to this central spindle apparatus, astral MTs assemble at the mitotic spindle pole and attach to the cell cortex to ensure appropriate spindle orientation. We propose that cell cycle-related kinase, Nek7, and its novel interacting protein RGS2, are involved in mitosis regulation and spindle formation. We found that RGS2 localizes to the mitotic spindle in a Nek7-dependent manner, and along with Nek7 contributes to spindle morphology and mitotic spindle pole integrity. RGS2-depletion leads to a mitotic-delay and severe defects in the chromosomes alignment and congression. Importantly, RGS2 or Nek7 depletion or even overexpression of wild-type or kinase-dead Nek7, reduced γ-tubulin from the mitotic spindle poles. In addition to causing a mitotic delay, RGS2 depletion induced mitotic spindle misorientation coinciding with astral MT-reduction. We propose that these phenotypes directly contribute to a failure in mitotic spindle alignment to the substratum. In conclusion, we suggest a molecular mechanism whereupon Nek7 and RGS2 may act cooperatively to ensure proper mitotic spindle organization.</p

IIS – Integrated Interactome System: A Web-Based Platform for the Annotation, Analysis and Visualization of Protein-Metabolite-Gene-Drug Interactions by Integrating a Variety of Data Sources and Tools

<div><p>Background</p><p>High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted.</p><p>Results</p><p>We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web.</p><p>Conclusions</p><p>We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: <a href="http://www.lge.ibi.unicamp.br/lnbio/IIS/" target="_blank">http://www.lge.ibi.unicamp.br/lnbio/IIS/</a>.</p></div

Interactome of <i>S. cerevisiae</i> encapsulated in liquid core alginate-chitosan capsules vs. cells grown freely in suspension, built from proteome data.

<p>(A) The enriched GO biological processes (p≤0.05) among the up-regulated proteins (red), the down-regulated proteins (green) and the background intermediary proteins (grey) from GPMGDID are depicted in the network by clustering the proteins involved in each of the biological processes with a circle layout. Clusters were assigned only to biological processes containing more than three proteins with at least one from the proteome data; proteins belonging to more than one biological process were assigned to clusters with the best enrichment p-values. More specific biological processes are shown only for proteins with more specific annotation in GO database. The nodes sizes of up- and down-regulated proteins are depicted proportional to their fold change (FC ≥1.3, FDR p≤0.05, as described by Westman et al.) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100385#pone.0100385-Westman1" target="_blank">[29]</a>. (B) Network zoom showing the glycolysis (GO enrichment p-value of 1.7e-02), NADH oxidation (2.1e-04) and ergosterol biosynthetic process (4.3e-15) clusters. (C) Network zoom showing the glycogen biosynthetic process (2.5e-06) cluster. The network was built using first neighbors expansion, deletion of nodes with degree 0 and 1, addition of different colors and sizes to proteins according to their fold change, and was filtered by Class scores A to C. The network was visualized using Cytoscape v2.8.3 and the proteins were distributed according to selected enriched biological processes (GO) from the “Top Enriched BP” node attribute field by using the group attributes layout. The following enriched biological processes clusters are shown in the network: 1. transcription, DNA-dependent (3.8e-25), 2. chromatin silencing at telomere (6.1e-15), 3. positive regulation of RNA elongation from RNA polymerase II promoter (5.0e-10), 4.positive regulation of transcription from RNA polymerase II promoter (4.2e-19), 5. negative regulation of transcription, DNA-dependent (7.1e-03), 6. positive regulation of transcriptional preinitiation complex assembly (2.5e-05), 7. vacuolar acidification (1.1e-10), 8. replicative cell aging (3.9e-12), 9. pseudohyphal growth (3.6e-08), 10. rRNA processing (4.8e-16), 11. maturation of SSU-rRNA from tricistronic rRNA transcript (1.2e-15), 12. regulation of translation (3.2e-10), 13. regulation of translational fidelity (2.7e-05), 14. mitochondrial translation (9.9e-04), 15. mature ribosome assembly (5.2e-04), 16. ribosomal small subunit assembly and maintenance (1.1e-05), 17. ribosomal large subunit biogenesis and assembly (3.9e-12), 18. protein refolding (1.8e-11), 19. protein folding (7.7e-09), 20. mRNA transport (9.7e-08), 21. poly(A)+ mRNA export from nucleus (5.2e-09), 22. protein transport (1.9e-11), 23. ribosomal small subunit export from nucleus (1.3e-08), 24. protein localization (4.9e-07), 25. protein import into nucleus (6.9e-11), 26. protein targeting to ER (5.2e-04), 27. ER to Golgi vesicle-mediated transport (1.1e-07), 28. endocytosis (9.6e-19), 29. lysine biosynthetic process via aminoadipic acid (7.1e-03), 30. pantothenate biosynthetic process (4.5e-04), 31. heme biosynthetic process (1.3e-03), 32. glycolysis (1.7e-02), 33. NADH oxidation (2.1e-04), 34. phospholipid biosynthetic process (1.1e-02), 35. fatty acid metabolic process (8.9e-04), 36. fatty acid biosynthetic process (2.7e-05), 37. protein amino acid N-linked glycosylation (2.1e-03), 38. ergosterol biosynthetic process (4.3e-15), 39. branched chain family amino acid catabolic process (2.4e-04), 40. pentose-phosphate shunt (2.7e-02), 41. 2-oxoglutarate metabolic process (2.4e-03), 42. one-carbon compound metabolic process (1.2e-02), 43. DNA recombination (5.8e-03), 44. metabolic process (3.0e-03), 45. deoxyribonucleotide biosynthetic process (6.6e-06), 46. protein deubiquitination (1.8e-09), 47. aerobic respiration (3.3e-04), 48. glycogen biosynthetic process (2.5e-06), 49. actin cytoskeleton organization and biogenesis (3.0e-05), 50. actin filament organization (5.1e-12), 51. chitin- and beta-glucan-containing cell wall organization and biogenesis (1.0e-12), 52. cell division (2.8e-21), 53. mitosis (1.5e-17), 54. establishment of cell polarity (2.8e-13), 55. TOR signaling pathway (8.3e-09), 56. Ras protein signal transduction (1.2e-07), 57. response to osmotic stress (1.1e-10), 58. response to stress (3.3e-06).</p

Human Nek6 interactome built from yeast two-hybrid data.

<p>(A) hNek6 first neighbors network, showing the bait hNek6 in red, the Y2H first neighbors in blue, the first neighbors described in the GPMGDID database in green, and the metabolites/drugs interactors described in the GPMGDID database in yellow and in different shapes: squares for metabolites and triangles for drugs. The proteins were localized according to their cellular components (GO) described in the “Selected CC” node attribute field by using the Cerebral Cytoscape plugin. (B) hNek6 second neighbors network, showing the second neighbors in orange. The proteins were distributed according to the organic layout. The insertion is depicting the different edge widths, according to our confidence Class scores. (C) hNek6 second neighbors network showing the following protein clusters: 1. top enriched NF-kappaB cascade, 2. first neighbors of cluster 1, 3. enriched NF-kappaB cascade subset of cluster 2, and 4. hNek6 yeast two-hybrid interactors. The proteins were distributed according to the organic and degree-sorted circle layouts, and proteins with degree 0 and 1 were deleted from the network. (D) hNek6 third neighbors network, showing the expansion from the first to the third level of interaction with the third neighbors in purple. The proteins were distributed according to the organic layout. The networks were visualized using Cytoscape v2.8.3.</p

Workflow used in IIS, showing the integration of the (1) SUBMISSION, (2) ANNOTATION, (3) SEARCH and (4) INTERACTOME MODULES for data analysis.

<p>All steps are indicated by arrows alongside a term, out or in parentheses (both in black and bold font) that correspond to a sequence of actions (the term in parentheses meaning the tool/database used in that step).</p

Comparison between the interactomes of (A) primary human epithelial ovarian cancer and (B) metastatic ovarian cancer vs. normal human ovary, built from metabolome data.

<p>The enriched KEGG pathways (p≤0.05) among the up-regulated metabolites (red squares), the down-regulated metabolites (green squares) and the background intermediary proteins (light blue circles for first neighbors and dark blue circles for second neighbors) from GPMGDID are depicted in the networks by clustering the proteins involved in each of the pathways with a circle layout. Enriched KEGG pathways specifically for each network (A) or (B) are depicted in purple and the ones in common are depicted in black. Clusters were assigned only to pathways containing more than three proteins (disease pathways or pathways specific for defined cell types were not considered), and metabolites were assigned only to metabolic pathway clusters containing interacting proteins with the best enrichment p-values. The nodes sizes of up- and down-regulated metabolites are depicted proportional to their fold change (FC≥1.2, p≤0.05, as described by Fong et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100385#pone.0100385-Fong1" target="_blank">[30]</a>) and the nodes sizes of the background intermediary proteins are depicted proportional to their connectivity degree. The networks were built using second neighbors expansion, deletion of nodes with degree 0 and 1 and addition of different colors and sizes to proteins according to their fold change. The networks were visualized using Cytoscape v2.8.3 and the proteins were distributed according to selected enriched pathways (KEGG) from the “Top Enriched KEGG” node attribute field by using the group attributes layout.</p

Comparison between FSW score, degree and Class score.

<p>(A) Degree distribution of hNek6 third neighbors network (γ = −1.59). (B) FSW score distribution of hNek6 third neighbors network (γ = −1.72). (C) Percentage of PPIs characterized by the best FSW score and Class score in hNek6 third neighbors network. (D) Correlation between the average degree and the FSW score of hNek6 third neighbors network from FSW score 0 to 10. Both the degree distribution and the FSW score distribution approximate a power-law and are scale-free in topology. The slopes (γ) were determined by linear fitting where <i>P</i>(<i>k</i>) approximates a power-law: <i>P</i>(<i>k</i>)≈<i>k</i>^−<i>γ</i> (<i>k</i>: total number of links; K: average degree; γ: slope of the distribution on the log-log plot; fsw: functional similarity weight; PPI: protein-protein interaction).</p

Global Protein-Metabolite-Gene-Drug Interaction Database (GPMGDID) construction.

<p>The UniProt Accession was chosen as the reference ID for the unification of the nine different databases used to construct GPMGDID: IntAct, DIP, MINT, BioGRID, HPRD, HMDB, YMDB, ECMDB and DrugBank databases. The interaction redundancies were eliminated by concatenating pairs of interactions with the source (PubMed IDs), generating an interaction pair ID given by UniProtID1_UniProtID2_PubMedID. The resultant database integrates several protein-metabolite-gene-drug interactions classified by source, methodology and organism.</p

Interactions confidence measured by Class scores used to represent different edge widths in the networks.

1<p>Parameters used to calculate the Class scores: interaction described as experimental (not predicted) (+4); interaction described in more than one paper (PubMed ID >1) and at least one paper not describing high-throughput (HT) experiments (+4); interaction described in more than one paper (PubMed ID >1) (+3); interaction described in only one paper (PubMed ID  = 1) (+1); interacting nodes described in the same cellular component (CC) (+1). For novel interactions not described in any paper (PubMed ID  = 0), even if the interacting nodes are described in the same CC, it will be assigned Class score E.</p

Identification of FBXO25-interacting proteins using an integrated proteomics approach

FBXO25 is one of 68 human F-box proteins that serve as specificity factors for a family of ubiquitin ligases composed of Skp1, Rbx1, Cullin1 and F-box protein (SCF1) that are involved in targeting proteins for destruction across the ubiquitin proteasome system. We recently reported that the FBXO25 protein accumulates in novel subnuclear structures named FBXO25-associated nuclear domains (FANDs). Combining two-step affinity purification followed by mass spectrometry with a classical two-hybrid screen, we identified 132 novel potential FBXO25 interacting partners. One of the identified proteins, -actin, physically interacts through its N-terminus with FBXO25 and is enriched in the FBXO25 nuclear compartments. Inhibitors of actin polymerization promote a significant disruption of FANDs, indicating that they are compartments influenced by the organizational state of actin in the nucleus. Furthermore, FBXO25 antibodies interfered with RNA polymerase II transcription in vitro. Our results open new perspectives for the understanding of this novel compartment and its nuclear functions