Random Photon Absorption Model Elucidates How Early Gain Control in Fly Photoreceptors Arises from Quantal Sampling

Many diurnal photoreceptors encode vast real-world light changes effectively, but how this performance originates from photon sampling is unclear. A 4-module biophysically-realistic fly photoreceptor model, in which information capture is limited by the number of its sampling units (microvilli) and their photon-hit recovery time (refractoriness), can accurately simulate real recordings and their information content. However, sublinear summation in quantum bump production (quantum-gain-nonlinearity) may also cause adaptation by reducing the bump/photon gain when multiple photons hit the same microvillus simultaneously. Here, we use a Random Photon Absorption Model (RandPAM), which is the 1st module of the 4-module fly photoreceptor model, to quantify the contribution of quantum-gain-nonlinearity in light adaptation. We show how quantum-gain-nonlinearity already results from photon sampling alone. In the extreme case, when two or more simultaneous photon-hits reduce to a single sublinear value, quantum-gain-nonlinearity is preset before the phototransduction reactions adapt the quantum bump waveform. However, the contribution of quantum-gain-nonlinearity in light adaptation depends upon the likelihood of multi-photon-hits, which is strictly determined by the number of microvilli and light intensity. Specifically, its contribution to light-adaptation is marginal (≤1%) in fly photoreceptors with many thousands of microvilli, because the probability of simultaneous multi-photon-hits on any one microvillus is low even during daylight conditions. However, in cells with fewer sampling units, the impact of quantum-gain-nonlinearity increases with brightening light

MyD88 Primes Macrophages for Full-Scale Activation by Interferon-γ yet Mediates Few Responses to Mycobacterium tuberculosis

Macrophages are activated from a resting state by a combination of cytokines and microbial products. Microbes are often sensed through Toll-like receptors signaling through MyD88. We used large-scale microarrays in multiple replicate experiments followed by stringent statistical analysis to compare gene expression in wild-type (WT) and MyD88−/− macrophages. We confirmed key results by quantitative reverse transcription polymerase chain reaction, Western blot, and enzyme-linked immunosorbent assay. Surprisingly, many genes, such as inducible nitric oxide synthase, IRG-1, IP-10, MIG, RANTES, and interleukin 6 were induced by interferon (IFN)-γ from 5- to 100-fold less extensively in MyD88−/− macrophages than in WT macrophages. Thus, widespread, full-scale activation of macrophages by IFN-γ requires MyD88. Analysis of the mechanism revealed that MyD88 mediates a process of self-priming by which resting macrophages produce a low level of tumor necrosis factor. This and other factors lead to basal activation of nuclear factor κB, which synergizes with IFN-γ for gene induction. In contrast, infection by live, virulent Mycobacterium tuberculosis (Mtb) activated macrophages largely through MyD88-independent pathways, and macrophages did not need MyD88 to kill Mtb in vitro. Thus, MyD88 plays a dynamic role in resting macrophages that supports IFN-γ–dependent activation, whereas macrophages can respond to a complex microbial stimulus, the tubercle bacillus, chiefly by other routes