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Endogenous clocks enable organisms to adapt their physiology and behavior to daily variation in environmental conditions. Metabolic processes in cyanobacteria to humans are effected by the circadian clock, and its dysregulation causes metabolic disorders. In mouse and Drosophila were shown that the circadian clock directs translation of factors involved in ribosome biogenesis and synchronizes protein synthesis. However, the role of clocks in Drosophila neurogenesis and the potential impact of clock impairment on neural circuit formation and function is less understood. Here we demonstrate that light stimuli or circadian clock causes a defect in neural stem cell growth and proliferation accompanied by reduced nucleolar size. Further, we define that light and clock independently affect the InR/TOR growth regulatory pathway due to the effect on regulators of protein biosynthesis. Altogether, these data suggest that alterations in growth regulatory pathways induced by light and clock are associated with impaired neural development.
Summary statement When echolocating under demanding conditions e.g. noisy, narrow space, or cluttered environments, frugivorous bats adapt their call pattern by increasing the call rate within biosonar groups.
Abstract For orientation, echolocating bats emit biosonar calls and use echoes arising from call reflections. They often pattern their calls into groups which increases the rate of sensory feedback over time. Insectivorous bats emit call groups at a higher rate when orienting in cluttered compared to uncluttered environments. Frugivorous bats increase the rate of call group emission when they echolocate in noisy environments. Here, calls emitted by conspecifics potentially interfere with the bat’s biosonar signals and complicate the echolocation behavior. To minimize the information loss followed by signal interference, bats may profit from a temporally increased sensory acquisition rate, as it is the case for the call groups. In frugivorous bats, it remains unclear if call group emission represents an exclusive adaptation to avoid interference by signals from other bats or if it represents an adaptation that allows to orient under demanding environmental conditions. Here, we compared the emission pattern of the frugivorous bat Carollia perspicillata when the bats were flying in noisy versus silent, narrow versus wide or cluttered versus non-cluttered corridors. According to our results, the bats emitted larger call groups and they increased the call rate within the call groups when navigating in narrow, cluttered, or noisy environments. Thus, call group emission represents an adaptive behavior when the bats orient in complex environments.
Most mammals rely on the extraction of acoustic information from the environment in order to survive. However, the mechanisms that support sound representation in auditory neural networks involving sensory and association brain areas remain underexplored. In this study, we address the functional connectivity between an auditory region in frontal cortex (the frontal auditory field, FAF) and the auditory cortex (AC) in the bat Carollia perspicillata. The AC is a classic sensory area central for the processing of acoustic information. On the other hand, the FAF belongs to the frontal lobe, a brain region involved in the integration of sensory inputs, modulation of cognitive states, and in the coordination of behavioural outputs. The FAF-AC network was examined in terms of oscillatory coherence (local-field potentials, LFPs), and within an information theoretical framework linking FAF and AC spiking activity. We show that in the absence of acoustic stimulation, simultaneously recorded LFPs from FAF and AC are coherent in low frequencies (1-12 Hz). This “default” coupling was strongest in deep AC layers and was unaltered by acoustic stimulation. However, presenting auditory stimuli did trigger the emergence of coherent auditory-evoked gamma-band activity (>25 Hz) between the FAF and AC. In terms of spiking, our results suggest that FAF and AC engage in distinct coding strategies for representing artificial and natural sounds. Taken together, our findings shed light onto the neuronal coding strategies and functional coupling mechanisms that enable sound representation at the network level in the mammalian brain.
The mammalian frontal and auditory cortices are important for vocal behaviour. Here, using local field potential recordings, we demonstrate for the first time that the timing and spatial pattern of oscillations in the fronto-auditory cortical network of vocalizing bats (Carollia perspicillata) predict the purpose of vocalization: echolocation or communication. Transfer entropy analyses revealed predominantly top-down (frontal-to-auditory cortex) information flow during spontaneous activity and pre-vocal periods. The dynamics of information flow depended on the behavioural role of the vocalization and on the timing relative to vocal onset. Remarkably, we observed the emergence of predominantly bottom-up (auditory-to-frontal cortex) information transfer patterns specific echolocation production, leading to self-directed acoustic feedback. Electrical stimulation of frontal areas selectively enhanced responses to echolocation sounds in auditory cortex. These results reveal unique changes in information flow across sensory and frontal cortices, potentially driven by the purpose of the vocalization in a highly vocal mammalian model.
The brains of black 6 mice (Mus musculus) and Seba’s short-tailed bats (Carollia perspicillata) weigh roughly the same and share the mammalian neocortical laminar architecture. Bats have highly developed sonar calls and social communication and are an excellent neuroethological animal model for auditory research. Mice are olfactory and somatosensory specialists and are used frequently in auditory neuroscience, particularly for their advantage of standardization and genetic tools. Investigating their potentially different general auditory processing principles would advance our understanding of how the ecological needs of a species shape the development and function of the mammalian nervous system. We compared two existing datasets, recorded with linear multichannel electrodes down the depth of the primary auditory cortex (A1) while awake, across both species while presenting repetitive stimulus trains with different frequencies (∼5 and ∼40 Hz). We found that while there are similarities between cortical response profiles in bats and mice, there was a better signal to noise ratio in bats under these conditions, which allowed for a clearer following response to stimuli trains. This was most evident at higher frequency trains, where bats had stronger response amplitude suppression to consecutive stimuli. Phase coherence was far stronger in bats during stimulus response, indicating less phase variability in bats across individual trials. These results show that although both species share cortical laminar organization, there are structural differences in relative depth of layers. Better signal to noise ratio in bats could represent specialization for faster temporal processing shaped by their individual ecological niches.
The ability to vocalize is ubiquitous in vertebrates, but neural networks leading to vocalization production remain poorly understood. Here we performed simultaneous, large scale, neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus) during the production of echolocation and non-echolocation calls in bats. This approach allows to assess the general aspects underlying vocalization production in mammals and the unique evolutionary adaptations of bat echolocation. Our findings show that distinct intra-areal brain rhythms in the beta (12-30 Hz) and gamma (30-80 Hz) bands of the local field potential can be used to predict the bats’ vocal output and that phase locking between spikes and field potentials occurs prior vocalization production. Moreover, the fronto-striatal network is differentially coupled in the theta-band during the production of echolocation and non-echolocation calls. Overall, our results present evidence for fronto-striatal network oscillations in motor action prediction in mammals.
Tracking influenza a virus infection in the lung from hematological data with machine learning
(2022)
The tracking of pathogen burden and host responses with minimal-invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory Influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e. cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing purposes of the models. We show here that lung viral load, neutrophil counts, cytokines like IFN-γ and IL-6, and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models shows that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path towards improved tracking and monitoring of influenza infections and possibly other respiratory infections based on minimal-invasively obtained hematological parameters.
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.
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.