An image-based RNAi screen identifies SH3BP1 as a key effector of Semaphorin 3E–PlexinD1 signaling

Extracellular signals have to be precisely interpreted intracellularly and translated into diverse cellular behaviors often mediated by cytoskeletal changes. Semaphorins are one of the largest families of guidance cues and play a critical role in many systems. However, how different cell types translate extracellular semaphorin binding into intracellular signaling remains unclear. Here we developed and performed a novel image-based genome-wide functional RNAi screen for downstream signaling molecules that convert the interaction between Semaphorin 3E (Sema3E) and PlexinD1 into cellular behaviors. One of the genes identified in this screen is a RhoGAP protein, SH3-domain binding protein 1 (SH3BP1). We demonstrate that SH3BP1 mediates Sema3E-induced cell collapse through interaction with PlexinD1 and regulation of Ras-related C3 botulinum toxin substrate 1 (Rac1) activity. The identification and characterization of SH3BP1 as a novel downstream effector of Sema3E-PlexinD1 provides an explanation for how extracellular signals are translated into cytoskeletal changes and unique cell behavior, but also lays the foundation for characterizing other genes identified from our screen to obtain a more complete picture of plexin signaling

An Intermittent Live Cell Imaging Screen for siRNA Enhancers and Suppressors of a Kinesin-5 Inhibitor

Kinesin-5 (also known as Eg5, KSP and Kif11) is required for assembly of a bipolar mitotic spindle. Small molecule inhibitors of Kinesin-5, developed as potential anti-cancer drugs, arrest cell in mitosis and promote apoptosis of cancer cells. We performed a genome-wide siRNA screen for enhancers and suppressors of a Kinesin-5 inhibitor in human cells to elucidate cellular responses, and thus identify factors that might predict drug sensitivity in cancers. Because the drug's actions play out over several days, we developed an intermittent imaging screen. Live HeLa cells expressing GFP-tagged histone H2B were imaged at 0, 24 and 48 hours after drug addition, and images were analyzed using open-source software that incorporates machine learning. This screen effectively identified siRNAs that caused increased mitotic arrest at low drug concentrations (enhancers), and vice versa (suppressors), and we report siRNAs that caused both effects. We then classified the effect of siRNAs for 15 genes where 3 or 4 out of 4 siRNA oligos tested were suppressors as assessed by time lapse imaging, and by testing for suppression of mitotic arrest in taxol and nocodazole. This identified 4 phenotypic classes of drug suppressors, which included known and novel genes. Our methodology should be applicable to other screens, and the suppressor and enhancer genes we identified may open new lines of research into mitosis and checkpoint biology

Identification of Genes That Promote or Antagonize Somatic Homolog Pairing Using a High-Throughput FISH–Based Screen

The pairing of homologous chromosomes is a fundamental feature of the meiotic cell. In addition, a number of species exhibit homolog pairing in nonmeiotic, somatic cells as well, with evidence for its impact on both gene regulation and double-strand break (DSB) repair. An extreme example of somatic pairing can be observed in Drosophila melanogaster, where homologous chromosomes remain aligned throughout most of development. However, our understanding of the mechanism of somatic homolog pairing remains unclear, as only a few genes have been implicated in this process. In this study, we introduce a novel high-throughput fluorescent in situ hybridization (FISH) technology that enabled us to conduct a genome-wide RNAi screen for factors involved in the robust somatic pairing observed in Drosophila. We identified both candidate “pairing promoting genes” and candidate “anti-pairing genes,” providing evidence that pairing is a dynamic process that can be both enhanced and antagonized. Many of the genes found to be important for promoting pairing are highly enriched for functions associated with mitotic cell division, suggesting a genetic framework for a long-standing link between chromosome dynamics during mitosis and nuclear organization during interphase. In contrast, several of the candidate anti-pairing genes have known interphase functions associated with S-phase progression, DNA replication, and chromatin compaction, including several components of the condensin II complex. In combination with a variety of secondary assays, these results provide insights into the mechanism and dynamics of somatic pairing

On Stability Switches and Bifurcation of the Modified Autonomous Van der Pol–Duffing Equations via Delayed State Feedback Control

This paper considers the Modified Autonomous Van der Pol–Duffing equation subjected to dynamic state feedback, which can well characterize the dynamic behaviors of the nonlinear dynamical systems. Both the issues of local stability switches and the Hopf bifurcation versus time delay are investigated. Associating with the τ decomposition strategy and the center manifold theory, the delay stable intervals and the direction and stability of the Hopf bifurcation are all determined. Specifically, the computation of purely imaginary roots (symmetry to the real axis), the positive real root formula for cubic equation and the sophisticated bilinear form of adjoint operators are proposed, which make the calculations mentioned in our discussion unified and simple. Finally, the typical numerical examples are shown to illustrate the correctness and effectiveness of the practical technique

Relationship between nuclear volume and pairing.

<p>a, Rank-order plot of the percentage ± SD of large nuclei. A nuclei was considered large if its volume was at or greater than the 95^th percentile volume of control cells. X-axis denotes the RNAi target. <i>P</i> values were determined by an unpaired <i>t</i> test. Inset, the frequency of single-signal nuclei was plotted against the frequency of large nuclei. The coefficient of determination <i>R²</i> is a measure of how well the data fit a linear regression, with values close to or exactly one representing a perfect fit. As <i>R²</i> = 0.004, there is no significant correlation between the percentages of paired nuclei and large nuclei. A minimum number of 250 nuclei were scored for each dsRNA. b, The number of FISH signals was plotted against the volume of each nucleus following <i>borr</i> and <i>scra</i> RNAi. No correlation was found between the degree of unpairing (number of FISH signals) and the size of the nuclei.</p

RNAi of candidate anti-pairers enhances heterochromatic pairing frequencies.

<p>a, Representative FISH images are shown for RNAi depletion of anti-pairers (<i>cdk8</i>, <i>cap-H2</i>, and <i>orc1</i>), where the number of FISH signals per nucleus is decreased as compared to that of control. Each resulted in a significant increase in the percentage of single-signal nuclei (noted) for both 359 and dodeca (<i>P</i><0.05). n denotes number of nuclei scored. Scale bars equal 5 µm. b, FACS plot (upper) of Kc₁₆₇ cells sorted into G1, early S (S1), late S (S2), and G2/M subpopulations and the percentage of nuclei producing a single FISH signal ± SD when targeting 359, AACAC, and dodeca in each. <i>P</i> values were determined by an unpaired <i>t</i> test. A minimum number of 100 nuclei were scored for each subpopulation. c, Example of a nucleus in which inter-signal distances were measured. Dot-plot displays the average inter-signal distances per nucleus ± the standard error of the mean (SEM). <i>Cap-H2</i>, <i>ORC1</i> and <i>lacZ</i> RNAi results are noted for reference and red box denotes hits that exhibited a significant shift in the distances per nucleus within the population (<i>P</i><0.01) based on an unpaired <i>t</i> test with unequal variance. Insets, relative frequencies of inter-signal distances following <i>Cap-H2</i> and <i>ORC1</i> RNAi compared to a <i>lacZ</i> RNAi control. d, Representative FISH images of a nucleus that produced a single signal for each probe (paired) and a nucleus with partially or fully overlapped 359 and dodeca signals (clustered). No significant difference in clustering levels was observed by this assay following depletion of any anti-pairer as compared to control. Graph displays results for the 16 candidate anti-pairers found to produce a significant reduction in inter-signal distances following RNAi in c (red box). A minimum number of 300 nuclei were scored for each dsRNA.</p

Pairing models involving both candidate pairing promoting and anti-pairing factors.

<p>a, Summary of candidate pairing factors identified in the screen. Green boxes denote candidate pairing promoters (hatched green were those identified only in the primary screen) and red boxes denote candidate anti-pairers. A representative sampling of pairing promoters were tested (italicized) and found (asterisk) to be important for euchromatic pairing. Proteins are grouped based on either a known function or localization pattern. Candidate pairing promoters found to elicit RNAi phenotypes dependent on Cap-H2 are presented as potential condensin II regulators (question marks). Note one dsRNA targets both CG42550 and CG14463 (separated by comma). b, Model for how compaction and intrachromosomal interactions compete with homolog pairing. Although all chromosomal regions may transiently unpair prior to or during S-phase, homolog pairing (red circles) of heterochromatic centromeric regions (grey lines) may be in competition with intrachromosomal interactions (black circles), causing pairing to occur less often, more slowly, or with less stability than homolog pairing of less compacted euchromatic regions (blue lines), where the paucity of repeated sequences reduces the likelihood of intrachromosomal interactions. This figure is not meant to imply a causal or dependent relationship between heterochromatic and euchromatic pairing, although such a relationship may exist. c, Model for pairing through the cell cycle. Proper spindle formation and chromosome segregation during anaphase/telophase of mitosis may bundle centromeric heterochromatic regions to spindle poles and directly facilitate or accelerate homolog recognition. Such interactions would then be maintained through G1. During S-phase, however, the pairing of regions is perhaps more dynamic, becoming antagonized and then re-paired subsequently. In this case, not all pairing interactions would be reestablished until the following mitosis. Euchromatic pairing is not depicted.</p

RNAi of a subset of pairing promoters causes Cap-H2–dependent pairing disruption.

<p>a, Representative FISH images are shown for RNAi knockdown of candidate pairing promoters <i>slmb</i> and <i>pav</i>, where the number of single-signal dodeca FISH signals per nucleus (noted) is decreased as compared to <i>lacZ</i> RNAi control (<i>P</i><0.05). Co-depletion of Cap-H2 increases the number of single-signal dodeca FISH signals per nucleus (<i>P</i><0.05 compared to <i>slmb</i> and <i>pav</i> RNAi alone). <i>pav</i> RNAi also produces multi-nucleated cells and large nuclei (hashed circles), characteristic of cytokinesis defects that lead to aneuploidy, which are also observed following <i>pav cap-H2</i> double RNAi treatment. n denotes number of nuclei scored. Scale bars equal 5 µm. Also see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002667#pgen-1002667-t001" target="_blank">Table 1</a>. b, Relative frequencies of interhomolog distances (unpaired = two signals >1.0 µm apart) based on dodeca FISH ± SD for three tests. Cap-H2 co-depletion also reduces the distances between signals following <i>slmb</i> and <i>pav</i> RNAi (<i>P</i><0.05), another indication that pairing is restored. c, The percentage of large nuclei ± SD following <i>pav</i> and <i>polo</i> RNAi in the presence and absence of <i>cap-H2</i> RNAi. Although the frequency of large nuclei in <i>pav cap-H2</i> is significantly reduced as compared to that of <i>pav</i> (<i>P</i> = 0.0072), both were significantly increased compared to controls (<i>P</i><0.0001). The frequency of large nuclei in <i>polo cap-H2</i> was not significantly different as compared to that of <i>polo</i> (<i>P</i> = 0.3791). A minimum number of 500 nuclei were scored for each experiment.</p