Lipoteichoic acid (LTA) derived from Staphylococcus aureus is reported to be a ligand of TLR2. However, we previously demonstrated that LTA fraction prepared from bacterial cells contains lipoproteins, which activate cells via TLR2. In this study, we investigated the immunobiological activity of LTA fraction obtained from S. aureus wild-type strain, lipoprotein diacylglycerol transferase deletion (Δlgt) mutant, which lacks palmitate-labeled lipoproteins, and its complemented strain and evaluated the activity of LTA molecule. LTA fraction was prepared by butanol extraction of the bacteria followed by hydrophobic interaction chromatography. Although all LTA fractions activated cells through TLR2, the LTA from Δlgt mutant was 100-fold less potent than those of wild-type and complemented strains. However, no significant structural difference in LTA was observed in NMR spectra. Further, alanylation of LTA molecule showed no effect in immunobiological activity. These results showed that not LTA molecule but lipoproteins are dominant immunobiologically active TLR2 ligand in S. aureus.

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We demonstrate intracellular manipulation of synaptic vesicles in living neurons by optical trapping. When an infrared trapping laser is focused on synapses of a neuronal cell labeled with a fluorescent endocytic marker, fluorescence is observed at the focal spot. The fluorescence spectrum is attributed to fluorescent dye in the synaptic vesicles, indicating excitation by two-photon absorption of the trapping laser. The fluorescence intensity increases gradually within ∼100 s of laser irradiation, suggesting that trapping force causes vesicles assembly at the focus. Our method can be applied to manipulate synaptic transmission of a particular neuron in a neuronal network.

Rat hippocampal neurons reorganized into complex networks in a culture dish with 64 planar microelectrodes and the electrical activity of neurons were recorded from individual sites. Multi-site recording system for extracellular action potentials was used for recording the activity of living neuronal networks and for applying input from the outer world to the network. The living neuronal network was able to distinguish among patterns of evoked action potentials based on different input, suggesting that the living neuronal network can express several pattern independently, meaning that it has fundamental mechanisms for intelligent information processing. We are developing a “biomodeling system,” in which a living neuronal network is connected to a moving robot with premised control rules corresponding to a genetically provided interface of neuronal networks to peripheral systems. Premised rules are described in fuzzy logic and the robot can generate instinctive behavior, avoiding collision. Sensor input from the robot body was sent to a neuronal network, and the robot moved based on commands from the living neuronal network. This is a good modeling system to analyze interaction between biological information processing and electrical devices.