Cecum lymph node dendritic cells harbor slow-growing bacteria phenotypically tolerant to antibiotic treatment.

In vivo, antibiotics are often much less efficient than ex vivo and relapses can occur. The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103⁺CX₃CR1⁻CD11c⁺ dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

Active dendrites: Adaptation to spike-based communication

Computational analyses of dendritic computations often assume stationary inputs to neurons, ignoring the pulsatile nature of spike-based communication between neurons and the moment-to-moment fluctuations caused by such spiking inputs. Conversely, circuit computations with spiking neurons are usually formalized without regard to the rich nonlinear nature of dendritic processing. Here we address the computational challenge faced by neurons that compute and represent analogue quantities but communicate with digital spikes, and show that reliable computation of even purely linear functions of inputs can require the interplay of strongly nonlinear subunits within the postsynaptic dendritic tree.Our theory predicts a matching of dendritic nonlinearities and synaptic weight distributions to the joint statistics of presynaptic inputs. This approach suggests normative roles for some puzzling forms of nonlinear dendritic dynamics and plasticity.

Lymph Node Colonization Dynamics after Oral Salmonella Typhimurium Infection in Mice

An understanding of how pathogens colonize their hosts is crucial for the rational design of vaccines or therapy. While the molecular factors facilitating the invasion and systemic infection by pathogens are a central focus of research in microbiology, the population biological aspects of colonization are still poorly understood. Here, we investigated the early colonization dynamics of Salmonella enterica subspecies 1 serovar Typhimurium (S. Tm) in the streptomycin mouse model for diarrhea. We focused on the first step on the way to systemic infection - the colonization of the cecal lymph node (cLN) from the gut - and studied roles of inflammation, dendritic cells and innate immune effectors in the colonization process. To this end, we inoculated mice with mixtures of seven wild type isogenic tagged strains (WITS) of S. Tm. The experimental data were analyzed with a newly developed mathematical model describing the stochastic immigration, replication and clearance of bacteria in the cLN. We estimated that in the beginning of infection only 300 bacterial cells arrive in the cLN per day. We further found that inflammation decreases the net replication rate in the cLN by 23%. In ccr7-/- mice, in which dendritic cell movement is impaired, the bacterial migration rate was reduced 10-fold. In contrast, cybb-/- mice that cannot generate toxic reactive oxygen species displayed a 4-fold higher migration rate from gut to cLN than wild type mice. Thus, combining infections with mixed inocula of barcoded strains and mathematical analysis represents a powerful method for disentangling immigration into the cLN from replication in this compartment. The estimated parameters provide an important baseline to assess and predict the efficacy of interventions. © 2013 Kaiser et al.

Hierarchically structured nanocarbon electrodes for flexible solid lithium batteries

The ever increasing demand for storage of electrical energy in portable electronic devices and electric vehicles is driving technological improvements in rechargeable batteries. Lithium (Li) batteries have many advantages over other rechargeable battery technologies, including high specific energy and energy density, operation over a wide range of temperatures (-40 to 70. °C) and a low self-discharge rate, which translates into a long shelf-life (~10 years) [1]. However, upon release of the first generation of rechargeable Li batteries, explosions related to the shorting of the circuit through Li dendrites bridging the anode and cathode were observed. As a result, Li metal batteries today are generally relegated to non-rechargeable primary battery applications, because the dendritic growth of Li is associated with the charging and discharging process. However, there still remain significant advantages in realizing rechargeable secondary batteries based on Li metal anodes because they possess superior electrical conductivity, higher specific energy and lower heat generation due to lower internal resistance. One of the most practical solutions is to use a solid polymer electrolyte to act as a physical barrier against dendrite growth. This may enable the use of Li metal once again in rechargeable secondary batteries [2]. Here we report a flexible and solid Li battery using a polymer electrolyte with a hierarchical and highly porous nanocarbon electrode comprising aligned multiwalled carbon nanotubes (CNTs) and carbon nanohorns (CNHs). Electrodes with high specific surface area are realized through the combination of CNHs with CNTs and provide a significant performance enhancement to the solid Li battery performance. © 2013 Elsevier Ltd.

Correlations strike back (again): The case of associative memory retrieval

It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population. Less studied, though equally pernicious, is the need to take account of dependencies between synaptic weights when decoding patterns previously encoded in an auto-associative memory. We show that activity-dependent learning generically produces such correlations, and failing to take them into account in the dynamics of memory retrieval leads to catastrophically poor recall. We derive optimal network dynamics for recall in the face of synaptic correlations caused by a range of synaptic plasticity rules. These dynamics involve well-studied circuit motifs, such as forms of feedback inhibition and experimentally observed dendritic nonlinearities. We therefore show how addressing the problem of synaptic correlations leads to a novel functional account of key biophysical features of the neural substrate.