GEF-H1 Modulates Localized RhoA Activation during Cytokinesis under the Control of Mitotic Kinases

Formation of the mitotic cleavage furrow is dependent upon both microtubules and activity of the small GTPase RhoA. GEF-H1 is a microtubule-regulated exchange factor that couples microtubule dynamics to RhoA activation. GEF-H1 localized to the mitotic apparatus in HeLa cells, particularly at the tips of cortical microtubules and the midbody, and perturbation of GEF-H1 function induced mitotic aberrations, including asymmetric furrowing, membrane blebbing, and impaired cytokinesis. The mitotic kinases Aurora A/B and Cdk1/Cyclin B phosphorylate GEF-H1, thereby inhibiting GEF-H1 catalytic activity. Dephosphorylation of GEF-H1 occurs just prior to cytokinesis, accompanied by GEF-H1-dependent GTP-loading on RhoA. Using a live cell biosensor, we demonstrate distinct roles for GEF-H1 and Ect2 in regulating Rho activity in the cleavage furrow, with GEF-H1 catalyzing Rho activation in response to Ect2-dependent localization and initiation of cell cleavage. Our results identify a GEF-H1-dependent mechanism to modulate localized RhoA activation during cytokinesis under the control of mitotic kinases

Identification of cofilin and LIM-domain-containing protein kinase 1 as novel interaction partners of 14-3-3 zeta.

Proteins of the 14-3-3 family have been implicated in various physiological processes, and are thought to function as adaptors in various signal transduction pathways. In addition, 14-3-3 proteins may contribute to the reorganization of the actin cytoskeleton by interacting with as yet unidentified actin-binding proteins. Here we show that the 14-3-3 zeta isoform interacts with both the actin-depolymerizing factor cofilin and its regulatory kinase, LIM (Lin-11/Isl-1/Mec-3)-domain-containing protein kinase 1 (LIMK1). In both yeast two-hybrid assays and glutathione S-transferase pull-down experiments, these proteins bound efficiently to 14-3-3 zeta. Deletion analysis revealed consensus 14-3-3 binding sites on both cofilin and LIMK1. Furthermore, the C-terminal region of 14-3-3 zeta inhibited the binding of cofilin to actin in co-sedimentation experiments. Upon co-transfection into COS-7 cells, 14-3-3 zeta-specific immunoreactivity was redistributed into characteristic LIMK1-induced actin aggregations. Our data are consistent with 14-3-3-protein-induced changes to the actin cytoskeleton resulting from interactions with cofilin and/or LIMK1

Modular Protein Expression Toolbox (MoPET), a standardized assembly system for defined expression constructs and expression optimization libraries.

The design and generation of an optimal expression construct is the first and essential step in in the characterization of a protein of interest. Besides evaluation and optimization of process parameters (e.g. selection of the best expression host or cell line and optimal induction conditions and time points), the design of the expression construct itself has a major impact. However, the path to this final expression construct is often not straight forward and includes multiple learning cycles accompanied by design variations and retesting of construct variants, since multiple, functional DNA sequences of the expression vector backbone, either coding or non-coding, can have a major impact on expression yields. To streamline the generation of defined expression constructs of otherwise difficult to express proteins, the Modular Protein Expression Toolbox (MoPET) has been developed. This cloning platform allows highly efficient DNA assembly of pre-defined, standardized functional DNA modules with a minimal cloning burden. Combining these features with a standardized cloning strategy facilitates the identification of optimized DNA expression constructs in shorter time. The MoPET system currently consists of 53 defined DNA modules divided into eight functional classes and can be flexibly expanded. However, already with the initial set of modules, 792,000 different constructs can be rationally designed and assembled. Furthermore, this starting set was used to generate small and mid-sized combinatorial expression optimization libraries. Applying this screening approach, variants with up to 60-fold expression improvement have been identified by MoPET variant library screening

Solute Carrier Transporters as Potential Targets for the Treatment of Metabolic Disease

The solute carrier (SLC) superfamily comprises more than 400 transport proteins mediating the influx and efflux of substances such as ions, nucleotides, and sugars across biological membranes. Over 80 SLC transporters have been linked to human diseases, including obesity and type 2 diabetes (T2D). This observation highlights the importance of SLCs for human (patho)physiology. Yet, only a small number of SLC proteins are validated drug targets. The most recent drug class approved for the treatment of T2D targets sodium-glucose cotransporter 2, product of the SLC5A2 gene. There is great interest in identifying other SLC transporters as potential targets for the treatment of metabolic diseases. Finding better treatments will prove essential in future years, given the enormous personal and socioeconomic burden posed by more than 500 million patients with T2D by 2040 worldwide. In this review, we summarize the evidence for SLC transporters as target structures in metabolic disease. To this end, we identified SLC13A5/sodium-coupled citrate transporter, and recent proof-of-concept studies confirm its therapeutic potential in T2D and nonalcoholic fatty liver disease. Further SLC transporters were linked in multiple genome-wide association studies to T2D or related metabolic disorders. In addition to presenting better-characterized potential therapeutic targets, we discuss the likely unnoticed link between other SLC transporters and metabolic disease. Recognition of their potential may promote research on these proteins for future medical management of human metabolic diseases such as obesity, fatty liver disease, and T2D. SIGNIFICANCE STATEMENT: Given the fact that the prevalence of human metabolic diseases such as obesity and type 2 diabetes has dramatically risen, pharmacological intervention will be a key future approach to managing their burden and reducing mortality. In this review, we present the evidence for solute carrier (SLC) genes associated with human metabolic diseases and discuss the potential of SLC transporters as therapeutic target structures

General overview of the MoPET design and implemented functional parts.

<p>(A) Modular structure of the MoPET system consisting of the eight basic module types Promoter, Signal Peptide, N-TAG (N-terminal tag), N-Linker (N-terminal linker), Core Protein, C-Linker (C-terminal linker), C-TAG (C-terminal tag) and the vector. Boxes show the fusion sites separating the modules and indicating the reading frame. (B) Layout of level 0 storage plasmids for the respective module positions and the destination backbones. The level 0 storage plasmids confer resistance to kanamycin and allow blue white selection for cloning purposes. Cloning into this plasmid set can be performed via BpiI, with a final BsaI based assembly in the ampicillin resistant level 1 backbones. (C) Compilation of the functional modules compatible with the MoPET system. DKTH-hFc-His (human IgG1 Fc sequence starting with DKTH), PKSC-hFc-His (human IgG1 Fc sequence starting with PKSC), HSA (human serum albumin), mFc (murine Fc sequence), Avi-tag [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176314#pone.0176314.ref024" target="_blank">24</a>]. (D) Overview of the functional features of the three basic backbones. P: OriP [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176314#pone.0176314.ref025" target="_blank">25</a>], pA: poly adenylation site, RBG: rabbit beta-globin [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176314#pone.0176314.ref026" target="_blank">26</a>], SV40 Simian virus 40, bGH: bovine growth hormone [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176314#pone.0176314.ref027" target="_blank">27</a>], Neo^R: neomycin resistance, Ap^R: ampicillin resistance, Km^R: kanamycin resistance.</p

Expression optimization library of an artificial two domain cytokine.

<p>(A) Cytokine expression optimization library design. Listed functional modules were included in two separate reactions. In one reaction domain A is used at the N-TAG position and domain B at the core protein position. In the second reaction domain B is used at the N-TAG position and domain A at the core protein position. The resulting final library has a theoretical complexity of 600. (B) Distribution of the implemented modules in accordance to their functional class of a test set of 88 randomly selected clones. (C) Expression titer of the 88 randomly selected clones as determined by ELISA in two independent experiments (Pearson’s Correlation r = 0.8642, R2 = 0.7468, P (two-tailed) <0.0001). Red diamond represents the starting expression level. All clones are correctly assembled and include all functions essential for expression like promoter and signal peptide. (D) Construct design of the Top 4 expression constructs.</p

Expression test library of hPTK7-ECD1-7.

<p>(A) hPTK7-ECD1-7 expression optimization library design. All listed functional modules were included in a single reaction resulting in a theoretical complexity for the final library of 72. (B) Expression test of 20 unique expression clones in deep well expression (DWP) system (y-axis) or in a tube spin expression system (x-axis). Red diamond represents starting expression level of the original construct. Yellow and green diamonds show the expression level of duplicates of the same construct (yet derived from independent E. coli clones) for 2 cases (Pearson’s Correlation r = 0.7380, R2 = 0.5447, P (two-tailed) <0.0001). (C) Construct design of the Top 5 expression constructs in DWP and Tube spin expression.</p