A non-toxic equinatoxin-II reveals the dynamics and distribution of sphingomyelin in the cytosolic leaflet of the plasma membrane.

Sphingomyelin (SM) is a major sphingolipid in mammalian cells. SM is enriched in the extracellular leaflet of the plasma membrane (PM). Besides this localization, recent electron microscopic and biochemical studies suggest the presence of SM in the cytosolic leaflet of the PM. In the present study, we generated a non-toxic SM-binding variant (NT-EqtII) based on equinatoxin-II (EqtII) from the sea anemone Actinia equina, and examined the dynamics of SM in the cytosolic leaflet of living cell PMs. NT-EqtII with two point mutations (Leu26Ala and Pro81Ala) had essentially the same specificity and affinity to SM as wild-type EqtII. NT-EqtII expressed in the cytosol was recruited to the PM in various cell lines. Super-resolution microscopic observation revealed that NT-EqtII formed tiny domains that were significantly colocalized with cholesterol and N-terminal Lyn. Meanwhile, single molecule observation at high resolutions down to 1 ms revealed that all the examined lipid probes including NT-EqtII underwent apparent fast simple Brownian diffusion, exhibiting that SM and other lipids in the cytosolic leaflet rapidly moved in and out of domains. Thus, the novel SM-binding probe demonstrated the presence of the raft-like domain in the cytosolic leaflet of living cell PMs.

Single-molecule localization microscopy reveals STING clustering at the trans-Golgi network through palmitoylation-dependent accumulation of cholesterol.

Stimulator of interferon genes (STING) is critical for the type I interferon response to pathogen- or self-derived DNA in the cytosol. STING may function as a scaffold to activate TANK-binding kinase 1 (TBK1), but direct cellular evidence remains lacking. Here we show, using single-molecule imaging of STING with enhanced time resolutions down to 5 ms, that STING becomes clustered at the trans-Golgi network (about 20 STING molecules per cluster). The clustering requires STING palmitoylation and the Golgi lipid order defined by cholesterol. Single-molecule imaging of TBK1 reveals that STING clustering enhances the association with TBK1. We thus provide quantitative proof-of-principle for the signaling STING scaffold, reveal the mechanistic role of STING palmitoylation in the STING activation, and resolve the long-standing question of the requirement of STING translocation for triggering the innate immune signaling.

Eye movement inhibits the facilitation of perceptual filling-in.

When a small figure is presented in human peripheral vision, it becomes invisible and invaded by surrounding texture, within a few seconds. This visual illusion is called perceptual filling-in. Time to filling-in (filling-in time) is varied by the properties of small figure, surround texture and some experimental conditions. In our preliminary study (Yokota, IEEE/IC-EMBS 2005), we found that incomplete fixation distributes filling-in time. Furthermore, that we can see nothing by restraining eye movement artificially is well known. Therefore, we can consider that filling-in time is influenced by eye movement. Although it has been recently reported that eye movement influences the filling-in occurrence (Martinez-Conde, Neuron 2006), the relation between eye movement and the filling-in time has rarely been reported. For this study, we measured the filling-in time for three subjects, for four surrounding textures, with simultaneous recording of eye movement. The results show that the filling-in time correlates to the standard deviation of the power of the eye distance from the fixation point. Furthermore, we found relatively strong correlation between the filling-in time and the power of high frequency component 50-200 (Hz) in the eye movement, though the correlation of the power of low frequency component 10-50 (Hz) is not so high. Thus we suppose that filling-in is inhibited by small involuntary eye movement.

Influence of small eye movement on perceptual filling-in time.

When a small area that has a different texture from its surroundings is presented to a subject's peripheral vision, that person perceives that the area is filled by its surrounding texture. It disappears within a few seconds under certain circumstances. This illusion is called filling-in. The filling-in time depends on textural properties, the area's size, the eccentricity with which the small area is projected, and so on. Filling-in characteristics must be elucidated to understand the mode of information processing in human vision because filling-in has been considered to contribute greatly to capturing external visual information. Facilitation of filling-in is generally evaluated using the filling-in time. Furthermore, it is well-known that we can see nothing by restraining eye movement artificially. Eye movement is important to acquire visual information. Therefore, we can suppose that facilitation of filling-in is influenced by eye movement. Although it has been recently indicated that eye movement influences the filling-in time while measuring time to filling-in, the relationship between eye movement and the filling-in time has rarely been reported. In this study, we measured the filling-in time, with simultaneous recording of eye movement. Results showed that the filling-in time correlates moderately or weakly with eye movement, under the condition that complete fixation is achieved.

An approximate method for Bayesian entropy estimation for a discrete random variable.

This article proposes an approximated Bayesian entropy estimator for a discrete random variable. An entropy estimator that achieves least square error is obtained through Bayesian estimation of the occurrence probabilities of each value taken by the discrete random variable. This Bayesian entropy estimator requires large amount of calculation cost if the random variable takes numerous sorts of values. Therefore, the present article proposes a practical method for calculating an Bayesian entropy estimate; the proposed method utilizes approximation of the entropy function by a truncated Taylor series. Numerical experiments demonstrate that the proposed entropy estimation method improves estimation precision of entropy remarkably in comparison to the conventional entropy estimation method.