Timescales of multineuronal activity patterns reflect temporal structure of visual stimuli

The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5-20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales.     

Separable models for age-structured population genetics

This paper is concerned with the applications of nonlinear age-dependent dynamics to population genetics. Age-structured models are formulated for a single autosomal locus with an arbitrary number of alleles. The following cases are considered: a) haploid populations with selection and mutation; b) monoecious diploid populations with or without mutation reproducing by self-fertilization or by two types of random mating. The diploid models do not deal with selection. For these cases the genic and genotypic frequencies evolve towards time-persistent forms, whether the total population size tends towards exponential growth or not.     

The twin-arginine signal peptide of PhoD and the TatAd/Cd proteins of Bacillus subtilis form an autonomous Tat translocation system.

The bacterial twin-arginine translocation (Tat) pathway has been recently described for PhoD of Bacillus subtilis, a phosphodiesterase containing a twin-arginine signal peptide. The expression of phoD is co-regulated with the expression of tatA(d) and tatC(d) genes localized downstream of phoD. To characterize the specificity of PhoD transport further, translocation of PhoD was investigated in Escherichia coli. By using gene fusions, we analyzed the particular role of the signal peptide and the mature region of PhoD in canalizing the transport route. A hybrid protein consisting of the signal peptide of beta-lactamase and mature PhoD was transported in a Sec-dependent manner indicating that the mature part of PhoD does not contain information canalizing the selected translocation route. Pre-PhoD, as well as a fusion protein consisting of the signal peptide of PhoD (SP(PhoD)) and beta-galactosidase (LacZ), remained cytosolic in the E. coli. Thus, SP(PhoD) is not recognized by E. coli transport systems. Co-expression of B. subtilis tatA(d)/C(d) genes resulted in the processing of SP(PhoD)-LacZ and periplasmic localization of LacZ illustrating a close substrate specificity of the TatA(d)/C(d) transport system. While blockage of the Sec-dependent transport did not affect the localization of SP(PhoD)-LacZ, translocation and processing was dependent on the pH gradient of the cytosolic membrane. Thus, the minimal requirement of a functional Tat-dependent protein translocation system consists of a twin-arginine signal peptide-containing Tat substrate, its specific TatA/C proteins, and the pH gradient across the cytosolic membrane.     

A concentration-dependent analysis method for high density protein microarrays

Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.     

microRNAs Expression as Novel Genetic Biomarker for Early Prediction and Continuous Monitoring in Pulmonary Cancer

One of the main causes of death in the world is lung cancer. According to the World Health Organization, the annual incidence of lung cancer increases significantly. Moreover, lung cancer accounts for one of the highest mortality rates, mainly due to late detection. Numerous studies have been conducted in order to identify new biomarkers for early diagnosis and for monitoring and evaluation of lung cancer stages. An ideal biomarker candidate is represented by the analysis of microRNAs expression. In this paper, we want to summarize microRNAs expressions in lung cancer. We also want to present the expression of microRNAs depending on the evolution of lung cancer. For this study, we analyzed the studies available in scientific databases, such as PubMed and Scopus. The studies were selected using the search keywords “microRNAs expression,” “lung cancer,” and “genetic biomarkers.” The most significant articles were selected for the study, following rigorous analysis. To evaluate and monitor lung cancer, the expression of microRNAs may be used successfully due to increased specificity and selectivity. However, further studies are needed on the assignment and validation of microRNAs for each type of lung cancer, respectively, for each stage of evolution.     

Toponomics analysis of functional interactions of the ubiquitin ligase PAM (Protein Associated with Myc) during spinal nociceptive processing

Protein associated with Myc (PAM) is a giant E3 ubiquitin ligase of 510 kDa. Although the role of PAM during neuronal development is well established, very little is known about its function in the regulation of synaptic strength. Here we used multiepitope ligand cartography (MELC) to study protein network profiles associated with PAM during the modulation of synaptic strength. MELC is a novel imaging technology that utilizes biomathematical tools to describe protein networks after consecutive immunohistochemical visualization of up to 100 proteins on the same sample. As an in vivo model to modulate synaptic strength we used the formalin test, a common model for acute and inflammatory pain. MELC analysis was performed with 37 different antibodies or fluorescence tags on spinal cord slices and led to the identification of 1390 PAM-related motifs that distinguish untreated and formalin-treated spinal cords. The majority of these motifs related to ubiquitin-dependent processes and/or the actin cytoskeleton. We detected an intermittent colocalization of PAM and ubiquitin with TSC2, a known substrate of PAM, and the glutamate receptors mGluR5 and GLUR1. Importantly these complexes were detected exclusively in the presence of F-actin. A direct PAM/F-actin interaction was confirmed by colocalization and cosedimentation. The binding of PAM toward F-actin varied strongly between the PAM splice forms found in rat spinal cords. PAM did not ubiquitylate actin or alter actin polymerization and depolymerization. However, F-actin decreased the ubiquitin ligase activity of purified PAM. Because PAM activation is known to involve its translocation, the binding of PAM to F-actin may serve to control its subcellular localization as well as its activity. Taken together we show that defining protein network profiles by topological proteomics analysis is a useful tool to identify previously unknown protein/protein interactions that underlie synaptic processes.     

Daytime Sleep Enhances Consolidation of the Spatial but Not Motoric Representation of Motor Sequence Memory

Motor sequence learning is known to rely on more than a single process. As the skill develops with practice, two different representations of the sequence are formed: a goal representation built under spatial allocentric coordinates and a movement representation mediated through egocentric motor coordinates. This study aimed to explore the influence of daytime sleep (nap) on consolidation of these two representations. Through the manipulation of an explicit finger sequence learning task and a transfer protocol, we show that both allocentric (spatial) and egocentric (motor) representations of the sequence can be isolated after initial training. Our results also demonstrate that nap favors the emergence of offline gains in performance for the allocentric, but not the egocentric representation, even after accounting for fatigue effects. Furthermore, sleep-dependent gains in performance observed for the allocentric representation are correlated with spindle density during non-rapid eye movement (NREM) sleep of the post-training nap. In contrast, performance on the egocentric representation is only maintained, but not improved, regardless of the sleep/wake condition. These results suggest that motor sequence memory acquisition and consolidation involve distinct mechanisms that rely on sleep (and specifically, spindle) or simple passage of time, depending respectively on whether the sequence is performed under allocentric or egocentric coordinates.     

Striatal and Hippocampal Involvement in Motor Sequence Chunking Depends on the Learning Strategy

Motor sequences can be learned using an incremental approach by starting with a few elements and then adding more as training evolves (e.g., learning a piano piece); conversely, one can use a global approach and practice the whole sequence in every training session (e.g., shifting gears in an automobile). Yet, the neural correlates associated with such learning strategies in motor sequence learning remain largely unexplored to date. Here we used functional magnetic resonance imaging to measure the cerebral activity of individuals executing the same 8-element sequence after they completed a 4-days training regimen (2 sessions each day) following either a global or incremental strategy. A network comprised of striatal and fronto-parietal regions was engaged significantly regardless of the learning strategy, whereas the global training regimen led to additional cerebellar and temporal lobe recruitment. Analysis of chunking/grouping of sequence elements revealed a common prefrontal network in both conditions during the chunk initiation phase, whereas execution of chunk cores led to higher mediotemporal activity (involving the hippocampus) after global than incremental training. The novelty of our results relate to the recruitment of mediotemporal regions conditional of the learning strategy. Thus, the present findings may have clinical implications suggesting that the ability of patients with lesions to the medial temporal lobe to learn and consolidate new motor sequences may benefit from using an incremental strategy.