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To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Proceedings of the National Academy of Sciences of the United States of America 119(12), e2109717119 (2022). doi:10.1073/pnas.2109717119, Neural mechanisms behind stereopsis, which requires simultaneous disparity inputs from two eyes, have remained mysterious. Here we show how ultrafast mirror-symmetric photomechanical contractions in the frontal forward-facing left and right eye photoreceptors give Drosophila superresolution three-dimensional (3D) vision. By interlinking multiscale in vivo assays with multiscale simulations, we reveal how these photoreceptor microsaccades—by verging, diverging, and narrowing the eyes’ overlapping receptive fields—channel depth information, as phasic binocular image motion disparity signals in time. We further show how peripherally, outside stereopsis, microsaccadic sampling tracks a flying fly’s optic flow field to better resolve the world in motion. These results change our understanding of how insect compound eyes work and suggest a general dynamic stereo-information sampling strategy for animals, robots, and sensors., Published by National Acad. of Sciences, Washington, DC

SignificanceTo move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes' optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes' whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enable

Ca2+-activated K+ channels (BK and SK) are ubiquitous in synaptic circuits, but their role in network adaptation and sensory perception remains largely unknown. Using electrophysiological and behavioral assays and biophysical modeling, we discover how visual information transfer in mutants lacking the BK channel (dSlo- ), SK channel (dSK- ), or both (dSK- ;; dSlo- ) is shaped in the female fruit fly (Drosophila melanogaster) R1-R6 photoreceptor-LMC circuits (R-LMC-R system) through synaptic feedforward-feedback interactions and reduced R1-R6 Shaker and Shab K+ conductances. This homeostatic compensation is specific for each mutant, leading to distinctive adaptive dynamics. We show how these dynamics inescapably increase the energy cost of information and promote the mutants' distorted motion perception, determining the true price and limits of chronic homeostatic compensation in an in vivo genetic animal model. These results reveal why Ca2+-activated K+ channels reduce network excitability (energetics), improving neural adaptability for transmitting and perceiving sensory information.SIGNIFICANCE STATEMENT In this study, we directly link in vivo and ex vivo experiments with detailed stochastically operating biophysical models to extract new mechanistic knowledge of how Drosophila photoreceptor-interneuron-photoreceptor (R-LMC-R) circuitry homeostatically retains its information sampling and transmission capacity against chronic perturbations in its ion-channel composition, and what is the cost of this compensation and its impact on optomotor behavior. We anticipate that this novel approach will provide a useful template to other model organisms and computational neuroscience, in general, in dissecting fundamental mechanisms of homeostatic compensation and deepening our understanding of how biological neural networks work.

Understanding a neuron's input-output relationship is a longstanding challenge. Arguably, these signalling dynamics can be better understood if studied at three levels of analysis: computational, algorithmic and implementational (Marr, 1982). But it is difficult to integrate such analyses into a single platform that can realistically simulate neural information processing. Multiscale dynamical "whole-cell" modelling, a recent systems biology approach, makes this possible. Dynamical "whole-cell" models are computational models that aim to account for the integrated function of numerous genes or molecules to behave like virtual cells in silico. However, because constructing such models is laborious, only a couple of examples have emerged since the first one, built for Mycoplasma genitalium bacterium, was reported in 2012. Here, we review dynamic "whole-cell" neuron models for fly photoreceptors and how these have been used to study neural information processing. Specifically, we review how the models have helped uncover the mechanisms and evolutionary rules of quantal light information sampling and integration, which underlie light adaptation and further improve our understanding of insect vision. [Abstract copyright: Copyright © 2021. Published by Elsevier B.V.]

Neural mechanisms behind stereopsis, which requires simultaneous disparity inputs from two eyes, have remained mysterious. Here we show how ultrafast mirror-symmetric photomechanical contractions in the frontal forward-facing left and right eye photoreceptors giveDrosophilasuper-resolution 3D-vision. By interlinking multiscalein vivoassays with multiscale simulations, we reveal how these photoreceptor microsaccades - by verging, diverging and narrowing the eyes’ overlapping receptive fields - channel depth information, as phasic binocular image motion disparity signals in time. We further show how peripherally, outside stereopsis, microsaccadic sampling tracks a flying fly’s optic flow field to better resolve the world in motion. These results change our understanding of how insect compound eyes work and suggest a general dynamic stereo-information sampling strategy for animals, robots and sensors.Significance statementTo move efficiently, animals must continuously work out their x,y,z-positions in respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigatedin vivoover the eyes’ whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a new morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enableDrosophilasuper-resolution 3D-vision and proposes neural computations for accurately predicting these flies’ depth-perception dynamics, limits, and visual behaviors.

SummaryLung cancer is the leading cause of cancer death worldwide. Genome-wide association studies have revealed genetic risk factors, highlighting the role of smoking, family history, telomere regulation, and DNA damage-repair in lung cancer etiology. Many studies have focused on a single ethnic group to avoid confounding from variability in allele frequencies across populations; however, comprehensive multi-ethnic analyses may identify variants that are more likely to be causal. This large-scale, multi- ethnic meta-analyses identified 28 novel risk loci achieving genome-wide significance. Leading candidates were further studied using single-cell methods for evaluating DNA-damage. DNA-damage promoting activities were confirmed for selected genes by knockdown genes and overexpression studies.

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of this study has been to integrate the complete GWAS results with a large-scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n=1,038) to identify candidate causal genes for lung cancer. We performed transcriptome-wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma, small cell lung cancer) and smoking subgroups (never- and ever-smokers). We performed replication analysis using lung data from the Genotype-Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever-smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (PTWAS =1.09E-99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (PTWAS =3.72E-6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3-adenocarcinoma on 9p13.3 was replicated using GTEx (PTWAS =6.55E-5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influence lung cancer risk. This article is protected by copyright. All rights reserved.

Lung cancer is a multifactorial disease caused by both environmental exposures and genetic factors. Genome and transcriptome-wide association studies (GWAS and TWAS) successfully identified many genetic variants and candidate causal genes associated with lung cancer. However, the lack of functional studies of these genes remains a major bottleneck to clinical translation. Recently, we discovered that large networks of evolutionarily conserved DNA damageome proteins (DDPs) promote DNA damage and genome instability. Here, we discovered a lung cancer-associated DNA damageome network. We further discovered that one of the lung cancer DDPs, Aquaporin 3 (AQP3), can promote ROS dependent DNA damage upon overproduction. Furthermore, it potentiates DNA damage by a low dose of arsenic exposure, creating unique double strand break hotspots which help predict mutational signatures. We screened for high endogenous DNA damage levels for more than 40 TWAS and GWAS-nominated candidates using high-throughput single cell-based flow cytometric assays. We optimized and performed Mre11-Cut&Tag (Cleavage Under Targets and Tagmentation) to map double strand breaks, which are the most deleterious form of DNA damage. In addition, we used END-Seq to provide a much higher resolution of the DSB map and uncovered hotspots in arsenic-detoxifying genes. We are implementing duplex sequencing to map the mutational signatures caused by arsenic and the arsenic-Aquaporin 3 interaction. Our study brings function to endogenous DNA damage and DNA damageome proteins that interact with environmental toxicants. Mechanistic insights into how low-dose arsenic interacts with risk genes yield critical knowledge for the prevention, diagnosis, and treatment of arsenic-associated diseases. We also uncovered several early biomarkers to potentially predict the long-term health impacts of arsenic. Citation Format: Jun Xia, Mu Lin, Zhuoyi Song, Gail Fernandes, Susan Rosenberg, Chris Amos. Lung cancer associated DNA Damageome reveals a genome destabilizing role of Aquaporin 3 [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A018.

We recently finished the largest transcriptome-wide association study (TWAS) analysis of the lung cancer genome-wide association studies (GWAS)-nominated loci, which revealed 18 new candidate causal genes for lung cancer. The recent discovery of the DNA damageome proteins (DDPs) predicts that many GWAS/TWAS genes are driving cancer by DNA damage-promoting mechanisms. Here, we have screened all 18 candidates and validated the DDP activities for at least five of these genes. We also developed a series of single-cell assays to profile cancer-relevant properties and assign their cancer-driving roles to genome instability. Last, we have demonstrated a reactive oxygen species (ROS)-dependent role of AQP3 overproduction-induced DNA damage, a pan-cancer mechanism. Methods: We measured endogenous DNA damage levels for all TWAS-nominated candidates using high-throughput single cell-based flow cytometric assays. Additional high-throughput single-cell assays were developed to quantify reactive oxygen species, replication fork stalling, and other DNA-related cancer properties, which allowed us to rapidly harvest functional information. Results: We screened 18 lung cancer TWAS-nominated genes for DNA damage in a lung cell line, and five showed increased DNA damage, including recently confirmed AQP3 overproduction, IREB2 knockdown, newly identified RNASET2 overproduction, TRIM38 knockdown, and JAML knockdown cells. None of these genes has been previously reported to be associated with DNA damage, prioritizing these lung cancer genes for further genome-destabilizing mechanistic studies. AQP3 overproduction promotes DNA damage in a variety of cell lines with different tissues of origin, including lung, kidney, and colon, supporting the hypothesis that DNA damage induced by AQP3 overproduction is universal. Moreover, we have shown that AQP3 overproduction causes high ROS at a level higher than 2mM H2O2 treatment, and ROS quencher N-acetyl-l-cysteine (NAC) can reduce AQP3 overproduction-induced DNA damage, supporting the conclusion that AQP3 overproduction induces ROS-dependent DNA damage. Furthermore, additional functional dissections revealed a high replication fork-stalling phenotype for a previously uncharacterized lung cancer risk protein, KIAA0930, providing the first-ever functional annotation of this uncharacterized protein. Conclusions: We established a high-throughput functional validation platform that measures endogenous DNA damage and other cancer genome-related properties for lung cancer susceptibility genes. More importantly, the strategy for predicting DNA damageome genes and the functional validation platform are transferable to other cancers. Citation Format: Jun Xia, Zhuoyi Song, Chris Amos. Post-GWAS in lung cancer: From transcriptome-wide association to the DNA damageome by Integrative functional screens [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3532.

Ca(2+)-activated K(+) channels (BK and SK) are ubiquitous in synaptic circuits, but their role in network adaptation and sensory perception remains largely unknown. Using electrophysiological and behavioral assays and biophysical modeling, we discover how visual information transfer in mutants lacking the BK channel (dSlo(−)), SK channel (dSK(−)), or both (dSK(−);; dSlo(−)) is shaped in the female fruit fly (Drosophila melanogaster) R1–R6 photoreceptor-LMC circuits (R-LMC-R system) through synaptic feedforward-feedback interactions and reduced R1–R6 Shaker and Shab K(+) conductances. This homeostatic compensation is specific for each mutant, leading to distinctive adaptive dynamics. We show how these dynamics inescapably increase the energy cost of information and promote the mutants' distorted motion perception, determining the true price and limits of chronic homeostatic compensation in an in vivo genetic animal model. These results reveal why Ca(2+)-activated K(+) channels reduce network excitability (energetics), improving neural adaptability for transmitting and perceiving sensory information. SIGNIFICANCE STATEMENT In this study, we directly link in vivo and ex vivo experiments with detailed stochastically operating biophysical models to extract new mechanistic knowledge of how Drosophila photoreceptor-interneuron-photoreceptor (R-LMC-R) circuitry homeostatically retains its information sampling and transmission capacity against chronic perturbations in its ion-channel composition, and what is the cost of this compensation and its impact on optomotor behavior. We anticipate that this novel approach will provide a useful template to other model organisms and computational neuroscience, in general, in dissecting fundamental mechanisms of homeostatic compensation and deepening our understanding of how biological neural networks work.

Formaldehyde exposure is toxic to the brains of mammals, but the mechanism remains unclear. We investigated the effects of inhaled formaldehyde on anxiety, depression, cognitive capacity and central levels of glucocorticoid receptor and tyrosine hydroxylase in mice. After exposure to 0, 1 or 2 ppm gaseous formaldehyde for one week, we measured anxiety-like behavior using open field and elevated plus-maze tests, depression-like behavior using a forced swimming test, learning and memory using novel object recognition tests, levels of glucocorticoid receptors in the hippocampus and tyrosine hydroxylase in the Arc, MPOA, ZI and VTA using immuhistochemistry. We found that inhalation of 1 ppm formaldehyde reduced levels of anxiety-like behavior. Inhalation of 2 ppm formaldehyde reduced body weight, but increased levels of depression-like behavior, impaired novel object recognition, and lowered the numbers of glucocorticoid receptor immonureactive neurons in the hippocampus and tyrosine hydroxylase immonureactive neurons in the ventral tegmental area and the zona incerta, medial preoptic area. Different concentrations of gaseous formaldehyde result in different effects on anxiety, depression-like behavior and cognition ability which may be associated with alterations in hippocampal glucocorticoid receptors and brain tyrosine hydroxylase levels.

Several fly species have distinctly red‐coloured eyes, meaning that the screening pigments that provide a restricted angular sensitivity of the photoreceptors may perform poorly in the longer wavelength range. The functional reasons for the red transparency and possible negative visual effects of the spectral properties of the eye‐colouring screening pigments are discussed within the context of the photochemistry, arrestin binding and turnover of the visual pigments located in the various photoreceptor types. A phylogenetic survey of the spectral properties of the main photoreceptors of the Diptera indicates that the transition of the brown eye colour of the Nematocera and lower Brachycera to a much redder eye colour of the higher Brachycera occurred around the emergence of the Tabanidae family.

Refractory period (RP) plays a central role in neural signaling. Because it limits an excitable membrane's recovery time from a previous excitation, it can restrict information transmission. Classically, RP means the recovery time from an action potential (spike), and its impact to encoding has been mostly studied in spiking neurons. However, many sensory neurons do not communicate with spikes but convey information by graded potential changes. In these systems, RP can arise as an intrinsic property of their quantal micro/nanodomain sampling events, as recently revealed for quantum bumps (single photon responses) in microvillar photoreceptors. Whilst RP is directly unobservable and hard to measure, masked by the graded macroscopic response that integrates numerous quantal events, modeling can uncover its role in encoding. Here, we investigate computationally how RP can affect encoding of graded neural responses. Simulations in a simple stochastic process model for a fly photoreceptor elucidate how RP can profoundly contribute to nonlinear gain control to achieve a large dynamic range. [Abstract copyright: © 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.]

Genome and transcriptome-wide association studies (GWAS and TWAS) have been successful in identifying genetic variants and candidate causal genes associated with lung cancer. However, systematic identification and functional studies of these genes remains a major bottleneck to clinical translation. Endogenous DNA damage has been implicated in genome instability and carcinogenesis, and we have recently discovered large networks of DNA damage-up proteins that are mostly non-DNA repair proteins [Xia J et al., Cell 176 (2019): 127–143], with many more predicted. Here, we validated several lung cancer-associated genes from GWAS and TWAS as DNA damage-up genes, which can potentially explain the cause of gene susceptibility to lung cancer. We performed a comprehensive exome-wide association study and TWAS analysis of the GWAS-nominated loci using expression quantitative trait loci (eQTL) analysis, which revealed many new candidate causal genes for lung cancer. We further tested endogenous DNA damage levels for many of these candidates using high-throughput cell-based assays and discovered that an alteration of the expression levels of multiple genes elevates the endogenous DNA damage, which is an early biomarker for genome instability and cancer. In our recent comprehensive TWAS, we mapped seventeen candidate-causal genes among previously described lung cancer GWAS loci. These include a new susceptibility locus for lung adenocarcinoma with AQP3 as the underlying causal gene and a new susceptibility gene, NEXN, for lung cancer in never-smokers. Additionally, we discovered that IREB2 is the gene most strongly associated with lung cancer on chromosome 15q25. In a separate study, we found that KIAA0930 was overproduced across multiple cancer types, including lung cancer. We used high-throughput cell-based assays and showed that overproduction of AQP3 or KIAA0930 elevated endogenous DNA damage in the lung fibroblast cell line and in kidney epithelial cells, which suggests a pan-cancer phenotype. Additionally, knocking down IREB2 increases spontaneous DNA damage in the lung fibroblast cell line, but this was not observed by knocking down NEXN. We are also implementing unbiased pooled screenings for other functional readouts. We established a functional validation platform that measures endogenous DNA damage for lung cancer susceptibility genes, and more importantly, this functional pipeline can be generalized to other cancers or diseases. Our discovery will potentially annotate many GWAS and TWAS genes as DNA damage-up genes and assign their cancer-driving roles to genome instability. Citation Format: Jun Xia, Zhuoyi Song, Xuemei Ji, James Mckay, Yohan Bossé, Christopher I Amos. A high-throughput functional screen discovered that endogenous DNA damage underlies mechanisms of selected gene susceptibility factors predisposing to lung cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr LB-A06. doi:10.1158/1535-7163.TARG-19-LB-A06

Light intensities (photons s–1 μm–2) in a natural scene vary over several orders of magnitude from shady woods to direct sunlight. A major challenge facing the visual system is how to map such a large dynamic input range into its limited output range, so that a signal is neither buried in noise in darkness nor saturated in brightness. A fly photoreceptor has achieved such a large dynamic range; it can encode intensity changes from single to billions of photons, outperforming man‐made light sensors. This performance requires powerful light adaptation, the neural implementation of which has only become clear recently. A computational fly photoreceptor model, which mimics the real phototransduction processes, has elucidated how light adaptation happens dynamically through stochastic adaptive quantal information sampling. A Drosophila R1–R6 photoreceptor's light sensor, the rhabdomere, has 30,000 microvilli, each of which stochastically samples incoming photons. Each microvillus employs a full G‐protein‐coupled receptor signalling pathway to adaptively transduce photons into quantum bumps (QBs, or samples). QBs then sum the macroscopic photoreceptor responses, governed by four quantal sampling factors (limitations): (i) the number of photon sampling units in the cell structure (microvilli), (ii) sample size (QB waveform), (iii) latency distribution (time delay between photon arrival and emergence of a QB), and (iv) refractory period distribution (time for a microvillus to recover after a QB). Here, we review how these factors jointly orchestrate light adaptation over a large dynamic range.

A photoreceptor's information capture is constrained by the structure and function of its light‐sensitive parts. Specifically, in a fly photoreceptor, this limit is set by the number of its photon sampling units (microvilli), constituting its light sensor (the rhabdomere), and the speed and recoverability of their phototransduction reactions. In this review, using an insightful constructionist viewpoint of a fly photoreceptor being an ‘imperfect’ photon counting machine, we explain how these constraints give rise to adaptive quantal information sampling in time, which maximises information in responses to salient light changes while antialiasing visual signals. Interestingly, such sampling innately determines also why photoreceptors extract more information, and more economically, from naturalistic light contrast changes than Gaussian white‐noise stimuli, and we explicate why this is so. Our main message is that stochasticity in quantal information sampling is less noise and more processing, representing an ‘evolutionary adaptation’ to generate a reliable neural estimate of the variable world.

All sensory receptors adapt, i.e., they constantly adjust their sensitivity to external stimuli to match the current natural environment [1]. Electrophysiological responses of sensory receptors from widely different modalities seem to exhibit common features related to adaptation, and these features can be used to examine the underlying sensory transduction mechanisms [1,2]. Among the principal senses, mechanosensation remains the least understood at the cellular level [3]. To gain greater insights into mechanosensory signalling, we investigated if mechanosensation displayed adaptive dynamics that could be explained by similar biophysical mechanisms in other sensory modalities. To do this, we adapted a fly photoreceptor model [4] to describe the primary transduction process for a stretch-sensitive mechanoreceptor, taking into account the viscoelastic properties of the accessory muscle fibres [5] and the biophysical properties of known mechanosensitive channels (MSCs). The model's output is in remarkable agreement with the electrical properties of a primary ending of an isolated decapsulated spindle; ramp-and-hold stretch evokes a characteristic pattern of potential change, consisting of a large dynamic depolarization during the ramp phase and a smaller static depolarization during the hold phase [6]. The initial dynamic component is likely to be caused by both the mechanical properties of the muscle fibres and a refractory state of MSCs. Consistent with literature, the current model predicts that the dynamical component is due to a rapid stress increase during the ramp [7]. More novel predictions from the model are the mechanisms to explain the initial peak in the dynamical component. At the onset of the ramp, all MSCs are sensitive to external stimuli, but as they become refractory (clipped inactivated state), fewer MSCs are able to respond to the continuous stretch, causing a sharp decrease after the peak response. The same mechanism could contribute a faster component in 'sensory habituation' of a mechanoreceptor, in which a receptor responds more strongly to the first stimulus episode during repetitive stimulation [8].

Light intensities change enormously in terrestrial environments, from murky starlit night to scorching daylight [1]. Photoreceptors of land animals have evolved with specialized photon absorption structures and adaptive mechanisms to cope with this large input range. They can effectively encode the vastly varying light intensities to macroscopic voltage responses, capturing the temporal structure of natural contrast changes within their limited output range. However, if photoreceptors were purely photon counters, counting every single photon that hits them, their outputs would readily saturate at bright daylight. To lessen this problem, it has been suggested that sublinear summation in quantum bump production (quantum-gain nonlinearity) may reduce the bump/photon gain at the instances when multiple photons hit the same photon-sampling-unit (multi-photon-hits) [2]. Here, we quantify the contribution of this nonlinearity to light adaptation, using a Random Photon Absorption Model for microvillar photoreceptors. We show that the quantum-gain nonlinearity affects only marginally (≤ 1%) photoreceptors with many microvilli, such as those of flies. This is because, with tens of thousands of photon-sampling-units, the probability of multiple photons hitting on any one of them simultaneously is very low. However, this nonlinear mechanism is predicted to affect a photoreceptor's encoding more, if the cell has fewer microvilli, especially when it faces a bright daylight environment.

Sensory neurons integrate information about the world, adapting their sampling to its changes. However, little is understood mechanistically how this primary encoding process, which ultimately limits perception, depends upon stimulus statistics. Here, we analyze this open question systematically by using intracellular recordings from fly (Drosophila melanogasterandCoenosia attenuata) photoreceptors and corresponding stochastic simulations from biophysically realistic photoreceptor models. Recordings show that photoreceptors can sample more information from naturalistic light intensity time series (NS) than from Gaussian white-noise (GWN), shuffled-NS or Gaussian-1/f stimuli; integrating larger responses with higher signal-to-noise ratio and encoding efficiency to large bursty contrast changes. Simulations reveal how a photoreceptor's information capture depends critically upon the stochastic refractoriness of its 30,000 sampling units (microvilli). In daylight, refractoriness sacrifices sensitivity to enhance intensity changes in neural image representations, with more and faster microvilli improving encoding. But for GWN and other stimuli, which lack longer dark contrasts of real-world intensity changes that reduce microvilli refractoriness, these performance gains are submaximal and energetically costly. These results provide mechanistic reasons why information sampling is more efficient for natural/naturalistic stimulation and novel insight into the operation, design, and evolution of signaling and code in sensory neurons.

BACKGROUND: In fly photoreceptors, light is focused onto a photosensitive waveguide, the rhabdomere, consisting of tens of thousands of microvilli. Each microvillus is capable of generating elementary responses, quantum bumps, in response to single photons using a stochastically operating phototransduction cascade. Whereas much is known about the cascade reactions, less is known about how the concerted action of the microvilli population encodes light changes into neural information and how the ultrastructure and biochemical machinery of photoreceptors of flies and other insects evolved in relation to the information sampling and processing they perform. RESULTS: We generated biophysically realistic fly photoreceptor models, which accurately simulate the encoding of visual information. By comparing stochastic simulations with single cell recordings from Drosophila photoreceptors, we show how adaptive sampling by 30,000 microvilli captures the temporal structure of natural contrast changes. Following each bump, individual microvilli are rendered briefly ( approximately 100-200 ms) refractory, thereby reducing quantum efficiency with increasing intensity. The refractory period opposes saturation, dynamically and stochastically adjusting availability of microvilli (bump production rate: sample rate), whereas intracellular calcium and voltage adapt bump amplitude and waveform (sample size). These adapting sampling principles result in robust encoding of natural light changes, which both approximates perceptual contrast constancy and enhances novel events under different light conditions, and predict information processing across a range of species with different visual ecologies. CONCLUSIONS: These results clarify why fly photoreceptors are structured the way they are and function as they do, linking sensory information to sensory evolution and revealing benefits of stochasticity for neural information processing.

Drosophila photoreceptors transduce light signals to voltage responses. In natural environment, light intensity can change over 10 log range, whereas the coding range of photoreceptors is only about 50 mV. Thus, to reliably represent natural light changes photoreceptors rely upon powerful adaptation. How do different molecular mechanisms of phototransduction enable light adaptation? Insect photoreceptors are highly polarized, containing light-sensitive and light-insensitive parts of separate functions. The light-sensitive rhabdomere contains a matrix of 30,000 independent phototransduction units (microvilli), which capture and covert light energy into bursts of currents, fluxing through TRP/TRPL channels inside the microvilli. The light insensitive cell-body, which contains voltage-gated channels, then shapes up the resulting voltage responses. Recently, we produced a two-compartmental biophysical model of a Drosophila photoreceptor [1]. Using this model we showed that the dynamic ratio of the used and unused microvilli (ultrastucture), their stochastic reactions and calcium feedbacks cause fast adaptation of the responses, while a global feedback though the membrane voltage compresses the responses to their limited size. However, this model fails to capture slow adaptive changes in the data, which could be important for adjusting the photoreceptor dynamics to changing image statistics in a more Bayesian way. As a first step, we wish to test whether calcium could diffuse from the responding microvillus to its neighbours, affecting the speed and sensitivity of their separate reaction cascades. We call this hypothetical cross-talk between microvilli global-diffusional-calcium feedback, and ask if it could induce the slow adaptation. To investigate this question, we extend our biophysical model of Drosophila photoreceptor to a 3D-reaction-diffusion model; taking into account the 3D diffusion of calcium. Here, the signalling molecules are loaded in a reconstructed macro-cell structure, described by a 3D tetrahedral mesh. The reaction pathways are simulated using STEPS (STochastic Engine for Pathway Simulation), based on Gillespie’s Stochastic Simulation Algorithm, extended for diffusion.