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Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, i.e., the ease vs. difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English, German) read isolated words, we encode and decode word vectors taken from the family of prediction-based word2vec algorithms. We find that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
The outstanding speed of language comprehension necessitates a highly efficient implementation of cognitive-linguistic processes. The domain-general theory of Predictive Coding suggests that our brain solves this problem by continuously forming linguistic predictions about expected upcoming input. The neurophysiological implementation of these predictive linguistic processes, however, is not yet understood. Here, we use EEG (human participants, both sexes) to investigate the existence and nature of online-generated, category-level semantic representations during sentence processing. We conducted two experiments in which some nouns – embedded in a predictive spoken sentence context – were unexpectedly delayed by 1 second. Target nouns were either abstract/concrete (Experiment 1) or animate/inanimate (Experiment 2). We hypothesized that if neural prediction error signals following (temporary) omissions carry specific information about the stimulus, the semantic category of the upcoming target word is encoded in brain activity prior to its presentation. Using time-generalized multivariate pattern analysis, we demonstrate significant decoding of word category from silent periods directly preceding the target word, in both experiments. This provides direct evidence for predictive coding during sentence processing, i.e., that information about a word can be encoded in brain activity before it is perceived. While the same semantic contrast could also be decoded from EEG activity elicited by isolated words (Experiment 1), the identified neural patterns did not generalize to pre-stimulus delay period activity in sentences. Our results not only indicate that the brain processes language predictively, but also demonstrate the nature and sentence-specificity of category-level semantic predictions preactivated during sentence comprehension.
The ability to extract regularities from the environment is arguably an adaptive characteristic of intelligent systems. In the context of speech, statistical learning is thought to be an important mechanism for language acquisition. By considering individual differences in speech auditory-motor synchronization, an independent component analysis of fMRI data revealed that the neural substrates of statistical word form learning are not fully shared across individuals. While a network of auditory and superior pre/motor regions is universally activated in the process of learning, a fronto-parietal network is instead additionally and selectively engaged by some individuals, boosting their performance. Furthermore, interfering with the use of this network via articulatory suppression (producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on language-related statistical learning and reconciles previous contrasting findings, while highlighting the need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.
Across languages, the speech signal is characterized by a predominant modulation of the amplitude spectrum between about 4.3-5.5Hz, reflecting the production and processing of linguistic information chunks (syllables, words) every ∼200ms. Interestingly, ∼200ms is also the typical duration of eye fixations during reading. Prompted by this observation, we demonstrate that German readers sample written text at ∼5Hz. A subsequent meta-analysis with 142 studies from 14 languages replicates this result, but also shows that sampling frequencies vary across languages between 3.9Hz and 5.2Hz, and that this variation systematically depends on the complexity of the writing systems (character-based vs. alphabetic systems, orthographic transparency). Finally, we demonstrate empirically a positive correlation between speech spectrum and eye-movement sampling in low-skilled readers. Based on this convergent evidence, we propose that during reading, our brain’s linguistic processing systems imprint a preferred processing rate, i.e., the rate of spoken language production and perception, onto the oculomotor system.
Precisely estimating event timing is essential for survival, yet temporal distortions are ubiquitous in our daily sensory experience. Here, we tested whether the relative position, relative duration and relative distance in time of two sequentially-organized events —standard S, with constant duration, and comparison C, varying trial-by-trial— are causal factors in generating temporal distortions. We found that temporal distortions emerge when the first event is shorter than the second event. Importantly, a significant interaction suggests that a longer ISI helps counteracting such serial distortion effect only the constant S is in first position, but not if the unpredictable C is in first position. These results suggest the existence of a perceptual bias in perceiving ordered event durations, mechanistically contributing to distortion in time perception. We simulated our behavioral results with a Bayesian model and replicated the finding that participants disproportionately expand first-position dynamic (unpredictable) short events. Our results clarify the mechanics generating time distortions by identifying a hitherto unknown duration-dependent encoding inefficiency in human serial temporal perception, akin to a strong prior that can be overridden for highly predictable sensory events but unfolds for unpredictable ones.
Research points to neurofunctional differences underlying fluent speech production in stutterers and non-stutterers. There has been considerably less work focusing on the processes that underlie stuttered speech, primarily due to the difficulty of reliably eliciting stuttering in the unnatural contexts associated with neuroimaging experiments. We used magnetoencephalography (MEG) to test the hypothesis that stuttering events result from global motor inhibition–a “freeze” response typically characterized by increased beta power in nodes of the action-stopping network. We leveraged a novel clinical interview to develop participant-specific stimuli in order to elicit a comparable amount of stuttered and fluent trials. Twenty-nine adult stutterers participated. The paradigm included a cue prior to a go signal, which allowed us to isolate processes associated with stuttered and fluent trials prior to speech initiation. During this pre-speech time window, stuttered trials were associated with greater beta power in the right pre-supplementary motor area, a key node in the action-stopping network, compared to fluent trials. Beta power in the right pre-supplementary area was related to a clinical measure of stuttering severity. We also found that anticipated words identified independently by participants were stuttered more often than those generated by the researchers, which were based on the participants’ reported anticipated sounds. This suggests that global motor inhibition results from stuttering anticipation. This study represents the largest comparison of stuttered and fluent speech to date. The findings provide a foundation for clinical trials that test the efficacy of neuromodulation on stuttering. Moreover, our study demonstrates the feasibility of using our approach for eliciting stuttering during MEG and functional magnetic resonance imaging experiments so that the neurobiological bases of stuttered speech can be further elucidated.
When speech is too fast, the tracking of the acoustic signal along the auditory pathway deteriorates, leading to suboptimal speech segmentation and decoding of speech information. Thus, speech comprehension is limited by the temporal constraints of the auditory system. Here we ask whether individual differences in auditory-motor coupling strength in part shape these temporal constraints. In two behavioral experiments, we characterize individual differences in the comprehension of naturalistic speech as function of the individual synchronization between the auditory and motor systems and the preferred frequencies of the systems. Obviously, speech comprehension declined at higher speech rates. Importantly, however, both higher auditory-motor synchronization and higher spontaneous speech motor production rates were predictive of better speech-comprehension performance. Furthermore, performance increased with higher working memory capacity (Digit Span) and higher linguistic, model-based sentence predictability – particularly so at higher speech rates and for individuals with high auditory-motor synchronization. These findings support the notion of an individual preferred auditory– motor regime that allows for optimal speech processing. The data provide evidence for a model that assigns a central role to motor-system-dependent individual flexibility in continuous speech comprehension.
Speech imagery (the ability to generate internally quasi-perceptual experiences of speech) is a fundamental ability linked to cognitive functions such as inner speech, phonological working memory, and predictive processing. Speech imagery is also considered an ideal tool to test theories of overt speech. The study of speech imagery is challenging, primarily because of the absence of overt behavioral output as well as the difficulty in temporally aligning imagery events across trials and individuals. We used magnetoencephalography (MEG) paired with temporal-generalization-based neural decoding and a simple behavioral protocol to determine the processing stages underlying speech imagery. We monitored participants’ lip and jaw micromovements during mental imagery of syllable production using electromyography. Decoding participants’ imagined syllables revealed a sequence of task-elicited representations. Importantly, participants’ micromovements did not discriminate between syllables. The decoded sequence of neuronal patterns maps well onto the predictions of current computational models of overt speech motor control and provides evidence for hypothesized internal and external feedback loops for speech planning and production, respectively. Additionally, the results expose the compressed nature of representations during planning which contrasts with the natural rate at which internal productions unfold. We conjecture that the same sequence underlies the motor-based generation of sensory predictions that modulate speech perception as well as the hypothesized articulatory loop of phonological working memory. The results underscore the potential of speech imagery, based on new experimental approaches and analytical methods, and further pave the way for successful non-invasive brain-computer interfaces.
Music, like language, is characterized by hierarchically organized structure that unfolds over time. Music listening therefore requires not only the tracking of notes and beats but also internally constructing high-level musical structures or phrases and anticipating incoming contents. Unlike for language, mechanistic evidence for online musical segmentation and prediction at a structural level is sparse. We recorded neurophysiological data from participants listening to music in its original forms as well as in manipulated versions with locally or globally reversed harmonic structures. We discovered a low-frequency neural component that modulated the neural rhythms of beat tracking and reliably parsed musical phrases. We next identified phrasal phase precession, suggesting that listeners established structural predictions from ongoing listening experience to track phrasal boundaries. The data point to brain mechanisms that listeners use to segment continuous music at the phrasal level and to predict abstract structural features of music.
The selective autophagy of mitochondria is linked to mitochondrial quality control and is critical to a healthy organism. Ubiquitylation is sometimes needed for marking damaged mitochondria for disposal but also for governing the expression and turnover of critical regulatory proteins. We have conducted a CRISPR/Cas9 screen of human E3 ubiquitin ligases for influence on mitophagy under both basal cell culture conditions and following acute mitochondrial depolarisation. We identify two Cullin RING ligases, VHL and FBXL4 as the most profound negative regulators of basal mitophagy. Here we show that these converge through control of the mitophagy adaptors BNIP3 and BNIP3L/NIX, but that this is achieved through different mechanisms. FBXL4 suppression of BNIP3 and NIX levels is mediated via direct interaction and protein destabilisation rather than suppression of HIF1α-mediated transcription. Depletion of NIX but not BNIP3 is sufficient to restore mitophagy levels. Our study enables a full understanding of the aetiology of early onset mitochondrial encephalomyopathy that is supported by analysis of a disease associated mutation. We further show that the compound MLN4924, which globally interferes with Cullin RING ligase activity, is a strong inducer of mitophagy which can provide a research tool in this context as well as a candidate therapeutic agent for conditions linked to mitochondrial quality control.
Compared to sequence analyses, phylogenetic reconstruction from transposable elements (TEs) offers an additional perspective to study evolutionary processes. However, detecting phylogenetically informative TE insertions requires tedious experimental work, limiting the power of phylogenetic inference. Here, we analyzed the genomes of seven bear species using high throughput sequencing data to detect thousands of TE insertions. The newly developed pipeline for TE detection called TeddyPi (TE detection and discovery for Phylogenetic Inference) obtained 150,513 high-quality TE insertions in the genomes of ursine and tremarctine bears. By integrating different TE insertion callers and using a stringent filtering approach, the TeddyPi pipeline produced highly reliable TE insertion calls, which were confirmed by extensive in vitro validation experiments. Screening for single nucleotide substitutions in the flanking regions of the TEs show that these substitutions correlate with the phylogenetic signal from the TE insertions. Our phylogenomic analyses show that TEs are a major driver of genomic variation in bears and enabled phylogenetic reconstruction of a well-resolved species tree, even with strong signals for incomplete lineage sorting and introgression. The analyses show that the Asiatic black, sun and sloth bear form a monophyletic clade. TeddyPi is open source and can be adapted to various TE and structural variation callers. The pipeline makes it easy to confidently extract thousands of TE insertions even from low coverage genomes of non-model organisms, opening new possibilities for biologists to study phylogenies, evolutionary processes as well as rates and patterns of (retro-)transposition and structural variation.
Mental imagery provides an essential simulation tool for remembering the past and planning the future, with its strength affecting both cognition and mental health. Research suggests that neural activity spanning prefrontal, parietal, temporal, and visual areas supports the generation of mental images. Exactly how this network controls the strength of visual imagery remains unknown. Here, brain imaging and transcranial magnetic phosphene data show that lower resting activity and excitability levels in early visual cortex (V1-V3) predict stronger sensory imagery. Electrically decreasing visual cortex excitability using tDCS increases imagery strength, demonstrating a causative role of visual cortex excitability in controlling visual imagery. These data suggest a neurophysiological mechanism of cortical excitability involved in controlling the strength of mental images.
During animal development, it is crucial that cells can sense and adapt to mechanical forces from their environment. Ultimately, these forces are transduced through the actomyosin cortex. How the cortex can simultaneously respond to and create forces during cytokinesis is not well understood. Here we show that under mechanical stress, cortical actomyosin flow switches its polarization during cytokinesis in the C. elegans embryo. In unstressed embryos, longitudinal cortical flows contribute to contractile ring formation, while rotational cortical flow is additionally induced in uniaxially loaded embryos. Rotational cortical flow is required for the redistribution of the actomyosin cortex in loaded embryos. Rupture of longitudinally aligned cortical fibers during cortex rotation releases tension, initiates orthogonal longitudinal flow and thereby contributes to furrowing in loaded embryos. A targeted screen for factors required for rotational flow revealed that actomyosin regulators involved in RhoA regulation, cortical polarity and chirality are all required for rotational flow and become essential for cytokinesis under mechanical stress. In sum, our findings extend the current framework of mechanical stress response during cell division and show scaling of orthogonal cortical flows to the amount of mechanical stress.
Formation of the anteroposterior and dorsoventral body axis in the Caenorhabditis elegans embryo depends on cortical actomyosin flows and advection of polarity determinants. The role of this patterning mechanism in tissue polarization immediately after formation of cell-cell contacts is not fully understood. Here, we demonstrate that planar cell polarity (PCP) is established in the C. elegans embryo at the time of left-right (l/r) symmetry breaking. At this stage, centripetal cortical flows asymmetrically and differentially advect anterior polarity determinants (aPARs) PAR-3, PAR-6 and PKC-3 from cell-cell contacts to the medial cortex, which results in their unmixing from apical myosin. Advection generally requires GSK-3 and CDC-42, while advection of PAR-6 specifically depends on the RhoGAP PAC-1. Concurrent asymmetric retention of PAR-3, E-cadherin/HMR-1, PAC-1 and opposing retention of the antagonistic Wnt pathway components APC/APR-1 and Frizzled/MOM-5 at apical cell-cell contacts leads to planar asymmetries. The most obvious mark of PCP, asymmetric retention of PAR-3 at posterior cell-cell contacts on the left side of the embryo, is required for proper cytokinetic cell intercalation. Hence, our data uncover how PCP can be established through Wnt signaling as well as dissociation and planar asymmetric retention of aPARs mediated by distinct Rho GTPases and their regulators.
Background: Nations are imposing unprecedented measures at large-scale to contain the spread of COVID-19 pandemic. Recent studies indicate that measures such as lockdowns may have slowed down the growth of COVID-19. However, in addition to substantial economic and social costs, these measures also limit the exposure to Ultraviolet-B radiation (UVB). Emerging observational evidence indicate the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. In this observational study, we empirically outline the independent protective roles of lockdown and UVB exposure as measured by ultraviolet index (UVI), whilst also examining whether the severity of lockdown is associated with a reduction in the protective role.
Methods: We apply a log-linear fixed-effects model to a panel dataset of 162 countries over a period of 108 days (n=6049). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth-rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors.
Findings: After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with 17% [-1.8 percentage points] and 77% [-7.9 percentage points] decline in COVID-19 deaths growth rate, indicating their respective protective roles. However, the widely utilized and least severe lockdown (recommendation to not leave the house) already fully mitigates the protective role of UVI by 95% [1.8 percentage points] indicating its downside.
Interpretation: We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find consistent evidence that increase in lockdown severity is associated with a significant reduction in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have provided even more substantial health benefits, than lockdowns alone. For example, we estimate that there would be 21% fewer deaths on average with sufficient UVB exposure while people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns and may support policy decision making in countries imposing such measures.
Competing Interest Statement: RKM is a PhD researcher at Goethe University, Frankfurt. He also is an employee of a multinational chemical company involved in vitamin D business and holds the shares of the company. This study is intended to contribute to the ongoing COVID-19 crisis and is not sponsored by his company. All other authors declare no competing interests. The views expressed in the paper are those of the authors and do not represent that of any organization. No other relationships or activities that could appear to have influenced the submitted work.
In an earlier paper we proposed a recursive model for epidemics; in the present paper we generalize this model to include the asymptomatic or unrecorded symptomatic people, which we call dark people (dark sector). We call this the SEPARd-model. A delay differential equation version of the model is added; it allows a better comparison to other models. We carry this out by a comparison with the classical SIR model and indicate why we believe that the SEPARd model may work better for Covid-19 than other approaches.
In the second part of the paper we explain how to deal with the data provided by the JHU, in particular we explain how to derive central model parameters from the data. Other parameters, like the size of the dark sector, are less accessible and have to be estimated more roughly, at best by results of representative serological studies which are accessible, however, only for a few countries. We start our country studies with Switzerland where such data are available. Then we apply the model to a collection of other countries, three European ones (Germany, France, Sweden), the three most stricken countries from three other continents (USA, Brazil, India). Finally we show that even the aggregated world data can be well represented by our approach.
At the end of the paper we discuss the use of the model. Perhaps the most striking application is that it allows a quantitative analysis of the influence of the time until people are sent to quarantine or hospital. This suggests that imposing means to shorten this time is a powerful tool to flatten the curves.
The clinical and economic value of a successful shutdown during the SARS-CoV-2 pandemic in Germany
(2020)
Background and aim A shutdown of businesses enacted during the SARS-CoV-2 pandemic can serve different goals, e.g., preventing the intensive care unit (ICU) capacity from being overwhelmed (‘flattening the curve’) or keeping the reproduction number substantially below one (‘squashing the curve’). The aim of this study was to determine the clinical and economic value of a shutdown that is successful in ‘flattening’ or ‘squashing the curve’ in Germany.
Methods In the base case, the study compared a successful shutdown to a worst-case scenario with no ICU capacity left to treat COVID-19 patients. To this end, a decision model was developed using, e.g., information on age-specific fatality rates, ICU outcomes, and the herd protection threshold. The value of an additional life year was borrowed from new, innovative oncological drugs, as cancer reflects a condition with a similar morbidity and mortality burden in the general population in the short term as COVID-19.
Results A shutdown that is successful in ‘flattening the curve’ is projected to yield an average health gain between 0.02 and 0.08 life years (0.2 to 0.9 months) per capita in the German population. The corresponding economic value ranges between €1543 and €8027 per capita or, extrapolated to the total population, 4% to 19% of the gross domestic product (GDP) in 2019. A shutdown that is successful in ‘squashing the curve’ is expected to yield a minimum health gain of 0.10 life years (1.2 months) per capita, corresponding to 24% of the GDP in 2019. Results are particularly sensitive to mortality data and the prevalence of undetected cases.
Conclusion A successful shutdown is forecasted to yield a considerable gain in life years in the German population. Nevertheless, questions around the affordability and underfunding of other parts of the healthcare system emerge.
Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.
Orthologs document the evolution of genes and metabolic capacities encoded in extant and ancient genomes. Orthologous genes that are detected across the full diversity of contemporary life allow reconstructing the gene set of LUCA, the last universal common ancestor. These genes presumably represent the functional repertoire common to – and necessary for – all living organisms. Design of artificial life has the potential to test this. Recently, a minimal gene (MG) set for a self-replicating cell was determined experimentally, and a surprisingly high number of genes have unknown functions and are not represented in LUCA. However, as similarity between orthologs decays with time, it becomes insufficient to infer common ancestry, leaving ancient gene set reconstructions incomplete and distorted to an unknown extent. Here we introduce the evolutionary traceability, together with the software protTrace, that quantifies, for each protein, the evolutionary distance beyond which the sensitivity of the ortholog search becomes limiting. We show that the LUCA set comprises only high-traceable proteins most of which have catalytic functions. We further show that proteins in the MG set lacking orthologs outside bacteria mostly have low traceability, leaving open whether their eukaryotic orthologs have just been overlooked. On the example of REC8, a protein essential for chromosome cohesion, we demonstrate how a traceability-informed adjustment of the search sensitivity identifies hitherto missed orthologs in the fast-evolving microsporidia. Taken together, the evolutionary traceability helps to differentiate between true absence and non-detection of orthologs, and thus improves our understanding about the evolutionary conservation of functional protein networks.
Bacteria of the genera Photorhabdus and Xenorhabdus produce a plethora of natural products to support their similar symbiotic lifecycles. For many of these compounds, the specific bioactivities are unknown. One common challenge in natural product research when trying to prioritize research efforts is the rediscovery of identical (or highly similar) compounds from different strains. Linking genome sequence to metabolite production can help in overcoming this problem. However, sequences are typically not available for entire collections of organisms. Here we perform a comprehensive metabolic screening using HPLC-MS data associated with a 114-strain collection (58 Photorhabdus and 56 Xenorhabdus) from across Thailand and explore the metabolic variation among the strains, matched with several abiotic factors. We utilize machine learning in order to rank the importance of individual metabolites in determining all given metadata. With this approach, we were able to prioritize metabolites in the context of natural product investigations, leading to the identification of previously unknown compounds. The top three highest-ranking features were associated with Xenorhabdus and attributed to the same chemical entity, cyclo(tetrahydroxybutyrate). This work addresses the need for prioritization in high-throughput metabolomic studies and demonstrates the viability of such an approach in future research.
Antimicrobial resistance is a major threat to global health and food security today. Scheduling cycling therapies by targeting phenotypic states associated to specific mutations can help us to eradicate pathogenic variants in chronic infections. In this paper, we introduce a logistic switching model in order to abstract mutation networks of collateral resistance. We found particular conditions for which unstable zero-equilibrium of the logistic maps can be stabilized through a switching signal. That is, persistent populations can be eradicated through tailored switching regimens.
Starting from an optimal-control formulation, the switching policies show their potential in the stabilization of the zero-equilibrium for dynamics governed by logistic maps. However, employing such switching strategies, deserve a specific characterization in terms of limit behaviour. Ultimately, we use evolutionary and control algorithms to find either optimal and sub-optimal switching policies. Simulations results show the applicability of Parrondo’s Paradox to design cycling therapies against drug resistance.
We propose a generalized modeling framework for the kinetic mechanisms of transcriptional riboswitches. The formalism accommodates time-dependent transcription rates and changes of metabolite concentration and permits incorporation of variations in transcription rate depending on transcript length. We derive explicit analytical expressions for the fraction of transcripts that determine repression or activation of gene expression, pause site location and its slowing down of transcription for the case of the (2’dG)-sensing riboswitch from Mesoplasma florum. Our modeling challenges the current view on the exclusive importance of metabolite binding to transcripts containing only the aptamer domain. Numerical simulations of transcription proceeding in a continuous manner under time-dependent changes of metabolite concentration further suggest that rapid modulations in concentration result in a reduced dynamic range for riboswitch function regardless of transcription rate, while a combination of slow modulations and small transcription rates ensures a wide range of finely tuneable regulatory outcomes.
Stockpiling neuraminidase inhibitors (NAIs) such as oseltamivir and zanamivir is part of a global effort to be prepared for an influenza pandemic. However, the contribution of NAIs for treatment and prevention of influenza and its complications is largely debatable. Here, we developed a transparent mathematical modelling setting to analyse the impact of NAIs on influenza disease at within-host and population level. Analytical and simulation results indicate that even assuming unrealistically high efficacies for NAIs, drug intake starting on the onset of symptoms has a negligible effect on an individual's viral load and symptoms score. Increasing NAIs doses does not provide a better outcome as is generally believed. Considering Tamiflu's pandemic regimen for prophylaxis, different multiscale simulation scenarios reveal modest reductions in epidemic size despite high investments in stockpiling. Our results question the use of NAIs in general to treat influenza as well as the respective stockpiling by regulatory authorities.
The successful elimination of bacteria such as Streptococcus pneumoniae from a host involves the coordination between different parts of the immune system. Previous studies have explored the effects of the initial pneumococcal load (bacterial dose) on different representations of innate immunity, finding that pathogenic outcomes can vary with the size of the bacterial dose. However, others yield support to the notion of dose-independent factors contributing to bacterial clearance. In this paper, we seek to provide a deeper understanding of the immune responses associated to the pneumococcus. To this end, we formulate a model that realizes an abstraction of the innate-regulatory immune host response. Stability and bifurcation analyses of the model reveal the following trichotomy of pneumococcal outcomes determined by the bifurcation parameters: (i) dose-independent clearance; (ii) dose-independent persistence; and (iii) dose-limited clearance. Bistability, where the bacteria-free equilibrium co-stabilizes with the most substantial steady-state bacterial load is the specific result behind dose-limited clearance. The trichotomy of pneumococcal outcomes here described integrates all previously observed bacterial fates into a unified framework.
COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat for human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. In this work, several mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Considering different starting times of infection, parameters sets that represent infectivity of SARS-CoV-2 are computed and compared with other viral infections that can also cause pandemics.
Based on the target cell model, SARS-CoV-2 infecting time between susceptible cells (mean of 30 days approximately) is much slower than those reported for Ebola (about 3 times slower) and influenza (60 times slower). The within-host reproductive number for SARS-CoV-2 is consistent to the values of influenza infection (1.7-5.35). The best model to fit the data was including immune responses, which suggest a slow cell response peaking between 5 to 10 days post onset of symptoms. The model with eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, both, the target cell model and the model with immune responses, predict that virus may replicate very slowly in the first days after infection, and it could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Transport of lipids across membranes is fundamental for diverse biological pathways in cells. Multiple ion-coupled transporters participate in lipid translocation, but their mechanisms remain largely unknown. Major facilitator superfamily (MFS) lipid transporters play central roles in cell wall synthesis, brain development and function, lipids recycling, and cell signaling. Recent structures of MFS lipid transporters revealed overlapping architectural features pointing towards a common mechanism. Here we used cysteine disulfide trapping, molecular dynamics simulations, mutagenesis analysis, and transport assays in vitro and in vivo, to investigate the mechanism of LtaA, a proton-dependent MFS lipid transporter essential for lipoteichoic acids synthesis in the pathogen Staphylococcus aureus. We reveal that LtaA displays asymmetric lateral openings with distinct functional relevance and that cycling through outward- and inward-facing conformations is essential for transport activity. We demonstrate that while the entire amphipathic central cavity of LtaA contributes to lipid binding, its hydrophilic pocket dictates substrate specificity. We propose that LtaA catalyzes lipid translocation by a ‘trap-and-flip’ mechanism that might be shared among MFS lipid transporters.
The severity of the COVID-19 pandemic, caused by the SARS-CoV-2 coronavirus, calls for the urgent development of a vaccine. The primary immunological target is the SARS-CoV-2 spike (S) protein. S is exposed on the viral surface to mediate viral entry into the host cell. To identify possible antibody binding sites not shielded by glycans, we performed multi-microsecond molecular dynamics simulations of a 4.1 million atom system containing a patch of viral membrane with four full-length, fully glycosylated and palmitoylated S proteins. By mapping steric accessibility, structural rigidity, sequence conservation and generic antibody binding signatures, we recover known epitopes on S and reveal promising epitope candidates for vaccine development. We find that the extensive and inherently flexible glycan coat shields a surface area larger than expected from static structures, highlighting the importance of structural dynamics in epitope mapping.
Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure and arise from structured internal interactions. Yet, most theories of visual perception focus exclusively on the mean responses of individual neurons. Here, we argue that feedback interactions in primary visual cortex (V1) establish the context in which individual neurons process complex stimuli and that changes in visual context give rise to stimulus-dependent SCCs. Measuring V1 population responses to natural scenes in behaving macaques, we show that the fine structure of SCCs is stimulus-specific and variations in response correlations across-stimuli are independent of variations in response means. Moreover, we demonstrate that stimulus-specificity of SCCs in V1 can be directly manipulated by controlling the high-order structure of synthetic stimuli. We propose that stimulus-specificity of SCCs is a natural consequence of hierarchical inference where inferences on the presence of high-level image features modulate inferences on the presence of low-level features.
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. We hypothesize that attentional and contextual top-down signals interact in V1, in a manner that primarily benefits the representation of natural visual stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we show that attention enhances the decodability of stimulus identity from population responses evoked by natural scenes but, critically, not by synthetic stimuli in which higher-order statistical regularities were eliminated. Attentional enhancement of stimulus decodability from population responses occurs in low dimensional spaces, as revealed by principal component analysis, suggesting an alignment between the attentional and the natural stimulus variance. Moreover, natural scenes produce stimulus-specific oscillatory responses in V1, whose power undergoes a global shift from low to high frequencies with attention. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.
Summary We introduce fsbrain, an R package for the visualization of neuroimaging data. The package can be used to visualize vertex-wise and region-wise morphometry data, parcellations, labels and statistical results on brain surfaces in three dimensions (3D). Voxel data can be displayed in lightbox mode. The fsbrain package offers various customization options and produces publication quality plots which can be displayed interactively, saved as bitmap images, or integrated into R notebooks.
Availability and Implementation The software, source code and documentation are available under the MIT license at https://github.com/dfsp-spirit/fsbrain. Releases can be installed directly from the Comprehensive R Archive Network (CRAN).
Grasping the meaning of everyday visual events is a fundamental feat of human intelligence that hinges on diverse neural processes ranging from vision to higher-level cognition. Deciphering the neural basis of visual event understanding requires rich, extensive, and appropriately designed experimental data. However, this type of data is hitherto missing. To fill this gap, we introduce the BOLD Moments Dataset (BMD), a large dataset of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips and accompanying metadata. We show visual events interface with an array of processes, extending even to memory, and we reveal a match in hierarchical processing between brains and video-computable deep neural networks. Furthermore, we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. BMD thus establishes a critical groundwork for investigations of the neural basis of visual event understanding.
Visual scene perception is mediated by a set of cortical regions that respond preferentially to images of scenes, including the occipital place area (OPA) and parahippocampal place area (PPA). However, the differential contribution of OPA and PPA to scene perception remains an open research question. In this study, we take a deep neural network (DNN)-based computational approach to investigate the differences in OPA and PPA function. In a first step we search for a computational model that predicts fMRI responses to scenes in OPA and PPA well. We find that DNNs trained to predict scene components (e.g., wall, ceiling, floor) explain higher variance uniquely in OPA and PPA than a DNN trained to predict scene category (e.g., bathroom, kitchen, office). This result is robust across several DNN architectures. On this basis, we then determine whether particular scene components predicted by DNNs differentially account for unique variance in OPA and PPA. We find that variance in OPA responses uniquely explained by the navigation-related floor component is higher compared to the variance explained by the wall and ceiling components. In contrast, PPA responses are better explained by the combination of wall and floor, that is scene components that together contain the structure and texture of the scene. This differential sensitivity to scene components suggests differential functions of OPA and PPA in scene processing. Moreover, our results further highlight the potential of the proposed computational approach as a general tool in the investigation of the neural basis of human scene perception.
The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.
In meditation practices that involve focused attention to a specific object, novice practitioners often experience moments of distraction (i.e., mind wandering). Previous studies have investigated the neural correlates of mind wandering during meditation practice through Electroencephalography (EEG) using linear metrics (e.g., oscillatory power). However, their results are not fully consistent. Since the brain is known to be a chaotic/nonlinear system, it is possible that linear metrics cannot fully capture complex dynamics present in the EEG signal. In this study, we assess whether nonlinear EEG signatures can be used to characterize mind wandering during breath focus meditation in novice practitioners. For that purpose, we adopted an experience sampling paradigm in which 25 participants were iteratively interrupted during meditation practice to report whether they were focusing on the breath or thinking about something else. We compared the complexity of EEG signals during mind wandering and breath focus states using three different algorithms: Higuchi’s fractal dimension (HFD), Lempel-Ziv complexity (LZC), and Sample entropy (SampEn). Our results showed that EEG complexity was generally reduced during mind wandering relative to breath focus states. We conclude that EEG complexity metrics are appropriate to disentangle mind wandering from breath focus states in novice meditation practitioners, and therefore, they could be used in future EEG neurofeedback protocols to facilitate meditation practice.
Living cells constantly remodel the shape of their lipid membranes. In the endo-plasmic reticulum (ER), the reticulon homology domain (RHD) of the reticulophagy regulator 1 (RETR1/FAM134B) forms dense autophagic puncta that are associated with membrane removal by ER-phagy. In molecular dynamics (MD) simulations, we find that FAM134B-RHD spontaneously forms clusters, driven in part by curvature-mediated attraction. At a critical size, the FAM134B-RHD clusters induce the formation of membrane buds. The kinetics of budding depends sensitively on protein concentration and bilayer asymmetry. Our MD simulations shed light on the role of FAM134B-RHD in ER-phagy and show that membrane asymmetry can be used to modulate the kinetics barrier for membrane remodeling.
Gasdermin-D (GSDMD) is the ultimate effector of pyroptosis, a form of programmed cell death associated with pathogen invasion and inflammation. After proteolytic cleavage by caspases activated by the inflammasome, the GSDMD N-terminal domain (GSDMDNT) assembles on the inner leaflet of the plasma membrane and induces the formation of large membrane pores. We use atomistic molecular dynamics simulations to study GSDMDNT monomers, oligomers, and rings in an asymmetric plasma membrane mimetic. We identify distinct interaction motifs of GSDMDNT with phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2) and phosphatidylserine (PS) head-groups and describe differential lipid binding between the pore and prepore conformations. Oligomers are stabilized by shared lipid binding sites between neighboring monomers acting akin to double-sided tape. We show that already small GSDMDNT oligomers form stable, water-filled and ion-conducting membrane pores bounded by curled beta-sheets. In large-scale simulations, we resolve the process of pore formation by lipid detachment from GSDMDNT arcs and lipid efflux from partial rings. We find that that high-order GSDMDNT oligomers can crack under the line tension of 86 pN created by an open membrane edge to form the slit pores or closed GSDMDNT rings seen in experiment. Our simulations provide a detailed view of key steps in GSDMDNT-induced plasma membrane pore formation, including sublytic pores that explain nonselective ion flux during early pyroptosis.
Nuclear pore complexes (NPCs) mediate nucleocytoplasmic transport. Their intricate 120 MDa architecture remains incompletely understood. Here, we report a near-complete structural model of the human NPC scaffold with explicit membrane and in multiple conformational states. We combined AI-based structure prediction with in situ and in cellulo cryo-electron tomography and integrative modeling. We show that linker Nups spatially organize the scaffold within and across subcomplexes to establish the higher-order structure. Microsecond-long molecular dynamics simulations suggest that the scaffold is not required to stabilize the inner and outer nuclear membrane fusion, but rather widens the central pore. Our work exemplifies how AI-based modeling can be integrated with in situ structural biology to understand subcellular architecture across spatial organization levels.
Ribosomes catalyze protein synthesis by cycling through various functional states. These states have been extensively characterized in vitro, yet their distribution in actively translating human cells remains elusive. Here, we optimized a cryo-electron tomography-based approach and resolved ribosome structures inside human cells with a local resolution of up to 2.5 angstroms. These structures revealed the distribution of functional states of the elongation cycle, a Z tRNA binding site and the dynamics of ribosome expansion segments. In addition, we visualized structures of Homoharringtonine, a drug for chronic myeloid leukemia treatment, within the active site of the ribosome and found that its binding reshaped the landscape of translation. Overall, our work demonstrates that structural dynamics and drug effects can be assessed at near-atomic detail within human cells.
Precise estimates of genome sizes are important parameters for both theoretical and practical biodiversity genomics. We present here a fast, easy-to-implement and precise method to estimate genome size from the number of bases sequenced and the mean sequence coverage. To estimate the latter, we take advantage of the fact that a precise estimation of the Poisson distribution parameter lambda is possible from truncated data, restricted to the part of the coverage distribution representing the true underlying distribution. With simulations we could show that reasonable genome size estimates can be gained even from low-coverage (10X), highly discontinuous genome drafts. Comparison of estimates from a wide range of taxa and sequencing strategies with flow-cytometry estimates of the same individuals showed a very good fit and suggested that both methods yield comparable, interchangeable results.
De novo fatty acid biosynthesis in humans is accomplished by a multidomain protein, the type I fatty acid synthase (FAS). Although ubiquitously expressed in all tissues, fatty acid synthesis is not essential in normal healthy cells due to sufficient supply with fatty acids by the diet. However, FAS is overexpressed in cancer cells and correlates with tumor malignancy, which makes FAS an attractive selective therapeutic target in tumorigenesis. Herein, we present a crystal structure of the condensing part of murine FAS, highly homologous to human FAS, with octanoyl moieties covalently bound to the transferase (MAT) and the condensation (KS) domain. The MAT domain binds the octanoyl moiety in a novel (unique) conformation, which reflects the pronounced conformational dynamics of the substrate binding site responsible for the MAT substrate promiscuity. In contrast, the KS binding pocket just subtly adapts to the octanoyl moiety upon substrate binding. Besides the rigid domain structure, we found a positive cooperative effect in the substrate binding of the KS domain by a comprehensive enzyme kinetic study. These structural and mechanistic findings contribute significantly to our understanding of the mode of action of FAS and may guide future rational inhibitor designs.
Cyclic di-AMP is the only known essential second messenger in bacteria and archaea, regulating different proteins indispensable for numerous physiological processes. In particular, it controls various potassium and osmolyte transporters involved in osmoregulation. In Bacillus subtilis, the K+/H+ symporter KimA of the KUP family is inactivated by c-di-AMP. KimA sustains survival at potassium limitation at low external pH by mediating K+ ions uptake. However, at elevated intracellular K+ concentrations, further K+ accumulation would be toxic. In this study, we reveal the molecular basis of how c-di-AMP binding inhibits KimA. We report cryo-EM structures of KimA with bound c-di-AMP in detergent solution and reconstituted in amphipols. By combining structural data with functional assays and molecular dynamics simulations we reveal how c-di-AMP modulates transport. We show that an intracellular loop in the transmembrane domain interacts with c-di-AMP bound to the adjacent cytosolic domain. This reduces the mobility of transmembrane helices at the cytosolic side of the K+ binding site and therefore traps KimA in an inward-occluded conformation.
Modular polyketide synthases (PKSs) produce complex, bioactive secondary metabolites in assembly line-like multistep reactions. Longstanding efforts to produce novel, biologically active compounds by recombining intact modules to new modular PKSs have mostly resulted in poorly active chimeras and decreased product yields. Recent findings demonstrate that the low efficiencies of modular chimeric PKSs also result from rate limitations in the transfer of the growing polyketide chain across the non-cognate module:module interface and further processing of the non-native polyketide substrate by the ketosynthase (KS) domain. In this study, we aim at disclosing and understanding the low efficiency of chimeric modular PKSs and at establishing guidelines for modular PKSs engineering. To do so, we work with a bimodular PKS testbed and systematically vary substrate specificity, substrate identity, and domain:domain interfaces of the KS involved reactions. We observe that KS domains employed in our chimeric bimodular PKSs are bottlenecks with regards to both substrate specificity as well as interaction with the ACP. Overall, our systematic study can explain in quantitative terms why early oversimplified engineering strategies based on the plain shuffling of modules mostly failed and why more recent approaches show improved success rates. We moreover identify two mutations of the KS domain that significantly increased turnover rates in chimeric systems and interpret this finding in mechanistic detail.
Inspired by the physiology of neuronal systems in the brain, artificial neural networks have become an invaluable tool for machine learning applications. However, their biological realism and theoretical tractability are limited, resulting in poorly understood parameters. We have recently shown that biological neuronal firing rates in response to distributed inputs are largely independent of size, meaning that neurons are typically responsive to the proportion, not the absolute number, of their inputs that are active. Here we introduce such a normalisation, where the strength of a neuron’s afferents is divided by their number, to various sparsely-connected artificial networks. The learning performance is dramatically increased, providing an improvement over other widely-used normalisations in sparse networks. The resulting machine learning tools are universally applicable and biologically inspired, rendering them better understood and more stable in our tests.
Orientation hypercolumns in the visual cortex are delimited by the repeating pinwheel patterns of orientation selective neurons. We design a generative model for visual cortex maps that reproduces such orientation hypercolumns as well as ocular dominance maps while preserving retinotopy. The model uses a neural placement method based on t–distributed stochastic neighbour embedding (t–SNE) to create maps that order common features in the connectivity matrix of the circuit. We find that, in our model, hypercolumns generally appear with fixed cell numbers independently of the overall network size. These results would suggest that existing differences in absolute pinwheel densities are a consequence of variations in neuronal density. Indeed, available measurements in the visual cortex indicate that pinwheels consist of a constant number of ∼30, 000 neurons. Our model is able to reproduce a large number of characteristic properties known for visual cortex maps. We provide the corresponding software in our MAPStoolbox for Matlab.
There is increasing evidence that rapid phenotypic adaptation of quantitative traits is not uncommon in nature. However, the circumstances under which rapid adaptation of polygenic traits occurs are not yet understood. Building on previous concepts of soft selection, i.e. frequency and density dependent selection, I developed and tested the hypothesis that adaptation speed of a polygenic trait depends on the number of offspring per breeding pair in a randomly mating diploid population.
Using individual based modelling on a range of offspring per parent (2–200) in populations of various size (100–10000 individuals), I could show that the by far largest proportion of variance (42%) was explained by the offspring number, regardless of genetic trait architecture (10–50 loci, different locus contribution distributions). In addition, it was possible to identify the majority of the responsible loci and account for even more of the observed phenotypic change with a moderate population size.
The simulation results suggest that offspring numbers may a crucial factor for the adaptation speed of quantitative loci. Moreover, as large offspring numbers translates to a large phenotypic variance in the offspring of each parental pair, this genetic bet hedging strategy increases the chance to contribute to the next generation in unpredictable environments.
Mutations are the ultimate basis of evolution, yet their occurrence rate is known only for few species. We directly estimated the spontaneous mutation rate and the mutational spectrum in the non-biting midge C. riparius with a new approach. Individuals from ten mutation accumulation lines over five generations were deep genome sequenced to count de novo mutations (DNMs) that were not present in a pool of F1 individuals, representing parental genotypes. We identified 51 new single site mutations of which 25 were insertions or deletions and 26 single point mutations. This shift in the mutational spectrum compared to other organisms was explained by the high A/T content of the species. We estimated a haploid mutation rate of 2.1 x 10−9 (95% confidence interval: 1.4 x 10−9 – 3.1 x 10−9) which is in the range of recent estimates for other insects and supports the drift barrier hypothesis. We show that accurate mutation rate estimation from a high number of observed mutations is feasible with moderate effort even for non-model species.
Dendritic spines are crucial for excitatory synaptic transmission as the size of a spine head correlates with the strength of its synapse. The distribution of spine head sizes follows a lognormal-like distribution with more small spines than large ones. We analysed the impact of synaptic activity and plasticity on the spine size distribution in adult-born hippocampal granule cells from rats with induced homo- and heterosynaptic long-term plasticity in vivo and CA1 pyramidal cells from Munc-13-1-Munc13-2 knockout mice with completely blocked synaptic transmission. Neither induction of extrinsic synaptic plasticity nor the blockage of presynaptic activity degrades the lognormal-like distribution but changes its mean, variance and skewness. The skewed distribution develops early in the life of the neuron. Our findings and their computational modelling support the idea that intrinsic synaptic plasticity is sufficient for the generation, while a combination of intrinsic and extrinsic synaptic plasticity maintains lognormal like distribution of spines.
Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction
(2020)
Class I ventral posterior dendritic arborisation (c1vpda) proprioceptive sensory neurons respond to contractions in the Drosophila larval body wall during crawling. Their dendritic branches run along the direction of contraction, possibly a functional requirement to maximise membrane curvature during crawling contractions. Although the molecular machinery of dendritic patterning in c1vpda has been extensively studied, the process leading to the precise elaboration of their comb-like shapes remains elusive. Here, to link dendrite shape with its proprioceptive role, we performed long-term, non-invasive, in vivo time-lapse imaging of c1vpda embryonic and larval morphogenesis to reveal a sequence of differentiation stages. We combined computer models and dendritic branch dynamics tracking to propose that distinct sequential phases of targeted growth and stochastic retraction achieve efficient dendritic trees both in terms of wire and function. Our study shows how dendrite growth balances structure–function requirements, shedding new light on general principles of self-organisation in functionally specialised dendrites.