Vorinostat Renders the Replication-Competent Latent Reservoir of Human Immunodeficiency Virus (HIV) Vulnerable to Clearance by CD8 T Cells

Latently human immunodeficiency virus (HIV)-infected cells are transcriptionally quiescent and invisible to clearance by the immune system. To demonstrate that the latency reversing agent vorinostat (VOR) induces a window of vulnerability in the latent HIV reservoir, defined as the triggering of viral antigen production sufficient in quantity and duration to allow for recognition and clearance of persisting infection, we developed a latency clearance assay (LCA). The LCA is a quantitative viral outgrowth assay (QVOA) that includes the addition of immune effectors capable of clearing cells expressing viral antigen. Here we show a reduction in the recovery of replication-competent virus from VOR exposed resting CD4 T cells following addition of immune effectors for a discrete period. TAKE HOME MESSAGE: VOR exposure leads to sufficient production of viral protein on the cell surface, creating a window of vulnerability within this latent reservoir in antiretroviral therapy (ART)-suppressed HIV-infected individuals that allows the clearance of latently infected cells by an array of effector mechanisms

Interval dosing with the HDAC inhibitor vorinostat effectively reverses HIV latency

BACKGROUND. The histone deacetylase (HDAC) inhibitor vorinostat (VOR) can increase HIV RNA expression in vivo within resting CD4+ T cells of aviremic HIV+ individuals. However, while studies of VOR or other HDAC inhibitors have reported reversal of latency, none has demonstrated clearance of latent infection. We sought to identify the optimal dosing of VOR for effective serial reversal of HIV latency

Integrating Diverse Datasets Improves Developmental Enhancer Prediction

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology. © 2014 Erwin et al

Field Flight Dynamics of Hummingbirds during Territory Encroachment and Defense

<div><p>Hummingbirds are known to defend food resources such as nectar sources from encroachment by competitors (including conspecifics). These competitive intraspecific interactions provide an opportunity to quantify the biomechanics of hummingbird flight performance during ecologically relevant natural behavior. We recorded the three-dimensional flight trajectories of Ruby-throated Hummingbirds defending, being chased from and freely departing from a feeder. These trajectories allowed us to compare natural flight performance to earlier laboratory measurements of maximum flight speed, aerodynamic force generation and power estimates. During field observation, hummingbirds rarely approached the maximal flight speeds previously reported from wind tunnel tests and never did so during level flight. However, the accelerations and rates of change in kinetic and potential energy we recorded indicate that these hummingbirds likely operated near the maximum of their flight force and metabolic power capabilities during these competitive interactions. Furthermore, although birds departing from the feeder while chased did so faster than freely-departing birds, these speed gains were accomplished by modulating kinetic and potential energy gains (or losses) rather than increasing overall power output, essentially trading altitude for speed during their evasive maneuver. Finally, the trajectories of defending birds were directed toward the position of the encroaching bird rather than the feeder.</p></div

Video recording setup and example flight trajectories.

<p>Panels A-C show images from each of the three cameras in a recording setup; panel D shows a reconstruction of the 3D scene including the cameras, their positioning, their individual 2D images of the scene, the trajectory of the two hummingbirds in the 2D images as well as the 3D scene and triangulation rays identifying the 3D location of one bird at one instant from the 2D image information. The defending bird information is magenta while the chased bird is cyan; photographs (not to scale) of a defending and chased bird are included to show typical flight posture at the start of the interaction. Photo credit: Ellis Driver.</p

Acceleration in defending trajectories.

<p>The green cross markers show the mean acceleration of defending bird trajectories that approached the feeder from a specific perch (n = 16 trajectories at <i>t</i>_<i>d</i> = 0 and n = 15 at <i>t</i>_<i>d</i> = 0.11 seconds) while pink diamond markers show mean acceleration of all other defending bird trajectories (n = 17 at <i>t</i>_<i>d</i> = 0, n = 15 at 0.09 seconds, and n = 11 at 0.20 seconds). The black asterisk markers are the mean acceleration of all defending trajectories (n = 32 at <i>t</i>_<i>d</i> = 0, n = 30 at 0.09 seconds, and n = 26 at 0.20 seconds). Error bars show the standard error about the mean for the perch and nonperch data only.</p

Acceleration in departing trajectories.

<p>The red triangle markers show the mean acceleration of the chased bird after departing the feeder (n = 31 trajectories at <i>t</i>_<i>c</i> = 0 and n = 30 at 0.29 seconds). The blue circle markers represent the mean acceleration after departure for freely-departing bird trajectories (n = 15). Error bars show the standard error at each time instant. Measurements of the flight speeds of both classes of birds were taken at different times after initial departure and a common timebase <i>t</i>_<i>c</i> created with 0 as the instant where the bird first reaches a speed of 0.4 m s^-1.</p

Kinetic and potential energy in defending trajectories.

<p>Here we show the (A) mass-specific energy, (B) potential energy, and (C) kinetic energy of trajectories from hummingbirds defending the feeder. The green cross markers show data from defending birds that approached the feeder from a specific perch (n = 16 trajectories at <i>t</i>_<i>d</i> = 0 and n = 15 at ≤ -0.11 seconds). The pink diamond markers show all other defending trajectories (n = 17 at <i>t</i>_<i>d</i> = 0, n = 15 at -0.09 to -0.20 seconds, and n = 11 at -0.20 seconds). The black asterisk marker represents all defending trajectories (n = 31 at time zero, n = 30 at -0.09 to -0.20 seconds, and n = 26 at -0.20 seconds). Error bars show the standard error about the mean for perch and nonperch trajectories. Unlike our treatment of departing bird potential energy, in (B) the starting positions of the birds were not set to a common identical initial value but are nevertheless quite similar.</p

Flight speed in departure trajectories.

<p>The red triangle markers represent the mean flight speed of the chased bird after departing from the feeder. Error bars show the standard error (n = 31 trajectories at <i>t</i>_<i>c</i> = 0 and n = 30 at <i>t</i>_<i>c</i> = 0.29 seconds). The blue circle markers show the mean departure flight speed of freely-departing hummingbirds (n = 18 trajectories). Measurements of the flight speeds of both classes of birds were taken at different times after initial departure and a common timebase <i>t</i>_<i>c</i> created with 0 as the instant where the bird first reaches a speed of 0.4 m s^-1.</p

Defending bird trajectories and their classification.

<p>Circles indicate the starting position of the bird trajectory and diamonds its end position. Green lines with solid markers represent defenders using the perch, which was not within the 3D reconstruction volume. The pink lines with open markers represent other defender trajectories. A) A top-down view of the flight trajectories of the defending birds. B) A 3D view of the defending birds. The transparent blue plane at Z = 1.2m represents the approximate end of the uniform descent period of defender trajectories.</p