Dominance of the proximal coordinate frame in determining the locations of hippocampal place cell activity during navigation.

The place-specific activity of hippocampal cells provides downstream structures with information regarding an animal's position within an environment and, perhaps, the location of goals within that environment. In rodents, recent research has suggested that distal cues primarily set the orientation of the spatial representation, whereas the boundaries of the behavioral apparatus determine the locations of place activity. The current study was designed to address possible biases in some previous research that may have minimized the likelihood of observing place activity bound to distal cues. Hippocampal single-unit activity was recorded from six freely moving rats as they were trained to perform a tone-initiated place-preference task on an open-field platform. To investigate whether place activity was bound to the room- or platform-based coordinate frame (or both), the platform was translated within the room at an "early" and at a "late" phase of task acquisition (Shift 1 and Shift 2). At both time points, CA1 and CA3 place cells demonstrated room-associated and/or platform-associated activity, or remapped in response to the platform shift. Shift 1 revealed place activity that reflected an interaction between a dominant platform-based (proximal) coordinate frame and a weaker room-based (distal) frame because many CA1 and CA3 place fields shifted to a location intermediate to the two reference frames. Shift 2 resulted in place activity that became more strongly bound to either the platform- or room-based coordinate frame, suggesting the emergence of two independent spatial frames of reference (with many more cells participating in platform-based than in room-based representations).

Hebbian analysis of the transformation of medial entorhinal grid-cell inputs to hippocampal place fields.

The discovery of grid cells in the medial entorhinal cortex (MEC) permits the characterization of hippocampal computation in much greater detail than previously possible. The present study addresses how an integrate-and-fire unit driven by grid-cell spike trains may transform the multipeaked, spatial firing pattern of grid cells into the single-peaked activity that is typical of hippocampal place cells. Previous studies have shown that in the absence of network interactions, this transformation can succeed only if the place cell receives inputs from grids with overlapping vertices at the location of the place cell's firing field. In our simulations, the selection of these inputs was accomplished by fast Hebbian plasticity alone. The resulting nonlinear process was acutely sensitive to small input variations. Simulations differing only in the exact spike timing of grid cells produced different field locations for the same place cells. Place fields became concentrated in areas that correlated with the initial trajectory of the animal; the introduction of feedback inhibitory cells reduced this bias. These results suggest distinct roles for plasticity of the perforant path synapses and for competition via feedback inhibition in the formation of place fields in a novel environment. Furthermore, they imply that variability in MEC spiking patterns or in the rat's trajectory is sufficient for generating a distinct population code in a novel environment and suggest that recalling this code in a familiar environment involves additional inputs and/or a different mode of operation of the network.

The relationship between the field-shifting phenomenon and representational coherence of place cells in CA1 and CA3 in a cue-altered environment.

Subfields of the hippocampus display differential dynamics in processing a spatial environment, especially when changes are introduced to the environment. Specifically, when familiar cues in the environment are spatially rearranged, place cells in the CA3 subfield tend to rotate with a particular set of cues (e.g., proximal cues), maintaining a coherent spatial representation. Place cells in CA1, in contrast, display discordant behaviors (e.g., rotating with different sets of cues or remapping) in the same condition. In addition, on average, CA3 place cells shift their firing locations (measured by the center of mass, or COM) backward over time when the animal encounters the changed environment for the first time, but not after that first experience. However, CA1 displays an opposite pattern, in which place cells exhibit the backward COM-shift only from the second day of experience, but not on the first day. Here, we examined the relationship between the environment-representing behavior (i.e., rotation vs. remapping) and the COM-shift of place fields in CA1 and CA3. Both in CA1 and CA3, the backward (as well as forward) COM-shift phenomena occurred regardless of the rotating versus remapping of the place cell. The differential, daily time course of the onset/offset of backward COM-shift in the cue-altered environment in CA1 and CA3 (on day 1 in CA1 and from day 2 onward in CA3) stems from different population dynamics between the subfields. The results suggest that heterogeneous, complex plasticity mechanisms underlie the environment-representating behavior (i.e., rotate/remap) and the COM-shifting behavior of the place cell.

A Cell Biological Determination of Integrator Subunit Localization

Uridine-rich small nuclear (U snRNAs), with the exception of the U6 snRNA, are RNA polymerase II (RNAPII) transcripts. The mechanism of 3’ cleavage of snRNAs has been unknown until recently. This area was greatly advanced when 12 of the Integrator complex subunits (IntS) were purified in 2005 through their interaction with the C-terminal domain (CTD) of the large subunit (RpbI) of RNAPII. Subsequently, our lab performed a genome-wide RNAi screen that identified two more members of the complex that we have termed IntS13 and IntS14. We have determined that IntS9 and 11 mediate the 3’ cleavage of snRNAs, but the exact function of the other subunits remains unknown. However, through the use of a U7 snRNA-GFP reporter and RNAi knockdown of the Integrator subunits in Drosophila S2 cells, we have shown that all subunits are required for the proper processing of snRNAs, albeit to differing degrees. Because snRNA transcription takes place in the nucleus of the cell, it is expected that all of the Integrator subunits would exhibit nuclear localization, but the knowledge of discrete subnuclear localization (i.e. to Cajal bodies) of any of the subunits could provide important clues to the function of that subunit. In this study, we used a cell biological approach to determine the localization of the 14 Integrator subunits. We hypothesized that the majority of the subunits would be nuclear, however, a few would display distinct localization to the Cajal bodies, as this is where snRNA genes are localized and transcribed. The specific aims and results are: 1. To determine the subcellular localization of the 14 Integrator subunits. To accomplish this, mCherry and GFP tagged clones were generated for each of the 14 Drosophila and human Integrator subunits. Confocal microscopy studies revealed that the majority of the subunits were diffuse in the nucleus, however, IntS3 formed discrete subnuclear foci. Surprisingly, two of the subunits, IntS2 and 7 were observed in cytoplasmic foci. 2. To further characterize Integrator subunits with unique subcellular localizations. Colocalization studies with endogenous IntS3 and Cajal body marker, coilin, showed that these two proteins overlap, and from this we concluded that IntS3 localized to Cajal bodies. Additionally, colocalization studies with mCherry-tagged IntS2 and 7 and the P body marker, Dcp1, revealed that these proteins colocalize as well. IntS7, however, is more stable in cytoplasmic foci than Dcp1. It was also shown through RNAi knockdown of Integrator subunits, that the cytoplasmic localization of IntS2 and 7 is dependent on the expression of IntS1 and 11 in S2 cells.

Determinants of CD4 cell count decline after treatment interruption

Patients who had started HAART (Highly Active Anti-Retroviral Treatment) under previous aggressive DHHS guidelines (1997) underwent a life-long continuous HAART that was associated with many short term as well as long term complications. Many interventions attempted to reduce those complications including intermittent treatment also called pulse therapy. Many studies were done to study the determinants of rate of fall in CD4 count after interruption as this data would help guide treatment interruptions. The data set used here was a part of a cohort study taking place at the Johns Hopkins AIDS service since January 1984, in which the data were collected both prospectively and retrospectively. The patients in this data set consisted of 47 patients receiving via pulse therapy with the aim of reducing the long-term complications. ^ The aim of this project was to study the impact of virologic and immunologic factors on the rate of CD4 loss after treatment interruption. The exposure variables under investigation included CD4 cell count and viral load at treatment initiation. The rates of change of CD4 cell count after treatment interruption was estimated from observed data using advanced longitudinal data analysis methods (i.e., linear mixed model). Using random effects accounted for repeated measures of CD4 per person after treatment interruption. The regression coefficient estimates from the model was then used to produce subject specific rates of CD4 change accounting for group trends in change. The exposure variables of interest were age, race, and gender, CD4 cell counts and HIV RNA levels at HAART initiation. ^ The rate of fall of CD4 count did not depend on CD4 cell count or viral load at initiation of treatment. Thus these factors may not be used to determine who can have a chance of successful treatment interruption. CD4 and viral load were again studied by t-tests and ANOVA test after grouping based on medians and quartiles to see any difference in means of rate of CD4 fall after interruption. There was no significant difference between the groups suggesting that there was no association between rate of fall of CD4 after treatment interruption and above mentioned exposure variables. ^