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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.
Under natural conditions, the visual system often sees a given input repeatedly. This provides an opportunity to optimize processing of the repeated stimuli. Stimulus repetition has been shown to strongly modulate neuronal-gamma band synchronization, yet crucial questions remained open. Here we used magnetoencephalography in 30 human subjects and find that gamma decreases across ~10 repetitions and then increases across further repetitions, revealing plastic changes of the activated neuronal circuits. Crucially, changes induced by one stimulus did not affect responses to other stimuli, demonstrating stimulus specificity. Changes partially persisted when the inducing stimulus was repeated after 25 minutes of intervening stimuli. They were strongest in early visual cortex and increased interareal feedforward influences. Our results suggest that early visual cortex gamma synchronization enables adaptive neuronal processing of recurring stimuli. These and previously reported changes might be due to an interaction of oscillatory dynamics with established synaptic plasticity mechanisms.
The firing pattern of ventral midbrain dopamine neurons is controlled by afferent and intrinsic activity to generate prediction error signals that are essential for reward-based learning. Given the absence of intracellular in vivo recordings in the last three decades, the subthreshold membrane potential events that cause changes in dopamine neuron firing patterns remain unknown. By establishing stable in vivo whole-cell recordings of >100 spontaneously active midbrain dopamine neurons in anaesthetized mice, we identified the repertoire of subthreshold membrane potential signatures associated with distinct in vivo firing patterns. We demonstrate that dopamine neuron in vivo activity deviates from a single spike pacemaker pattern by eliciting transient increases in firing rate generated by at least two diametrically opposing biophysical mechanisms: a transient depolarization resulting in high frequency plateau bursts associated with a reactive, depolarizing shift in action potential threshold; and a prolonged hyperpolarization preceding slower rebound bursts characterized by a predictive, hyperpolarizing shift in action potential threshold. Our findings therefore illustrate a framework for the biophysical implementation of prediction error and sensory cue coding in dopamine neurons by tuning action potential threshold dynamics.
Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our approach on the analysis of a genome-wide CRISPR screen in hTERT-RPE-1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our approach is general and can be applied on any cell type and with different CRISPR enzymes.
Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulations of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participant’s internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at ≈17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a “miss” showed an action-related modulation at ≈17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at ≈17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the Discrete Fourier Transform (DFT) and the Least Square Spectrum (LSS). DFT and LSS can be applied both on the averaged accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effect) or on the population (random-effect) of simulated participants. Multiple comparisons across frequencies were corrected using False-Discovery-Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated Sensitivity, Specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the averaged-accuracy-time-course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved Sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest Specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effect approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
The human brain achieves visual object recognition through multiple stages of nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models’ prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output M/EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.
In the course of global climate change, central Europe is experiencing more frequent and prolonged periods of drought. The drought years 2018 and 2019 affected European beeches (Fagus sylvatica L.) differently: even in the same stand, drought damaged trees neighboured healthy trees, suggesting that the genotype rather than the environment was responsible for this conspicuous pattern. We used this natural experiment to study the genomic basis of drought resistance with Pool-GWAS. Contrasting the extreme phenotypes identified 106 significantly associated SNPs throughout the genome. Most annotated genes with associated SNPs (>70%) were previously implicated in the drought reaction of plants. Non-synonymous substitutions led either to a functional amino acid exchange or premature termination. A SNP-assay with 70 loci allowed predicting drought phenotype in 98.6% of a validation sample of 92 trees. Drought resistance in European beech is a moderately polygenic trait that should respond well to natural selection, selective management, and breeding.
The gradual heterogeneity of climatic factors pose varying selection pressures across geographic distances that leave signatures of clinal variation in the genome. Separating signatures of clinal adaptation from signatures of other evolutionary forces, such as demographic processes, genetic drift, and adaptation to non-clinal conditions of the immediate local environment is a major challenge. Here, we examine climate adaptation in five natural populations of the harlequin fly Chironomus riparius sampled along a climatic gradient across Europe. Our study integrates experimental data, individual genome resequencing, Pool-Seq data, and population genetic modelling. Common-garden experiments revealed a positive correlation of population growth rates corresponding to the population origin along the climate gradient, suggesting thermal adaptation on the phenotypic level. Based on a population genomic analysis, we derived empirical estimates of historical demography and migration. We used an FST outlier approach to infer positive selection across the climate gradient, in combination with an environmental association analysis. In total we identified 162 candidate genes as genomic basis of climate adaptation. Enriched functions among these candidate genes involved the apoptotic process and molecular response to heat, as well as functions identified in other studies of climate adaptation in other insects. Our results show that local climate conditions impose strong selection pressures and lead to genomic adaptation despite strong gene flow. Moreover, these results imply that selection to different climatic conditions seems to converge on a functional level, at least between different insect species.
One of the major problems in evolutionary biology is to elucidate the relationships between historical events and the tempo and mode of lineage divergence. The development of relaxed molecular clock models and the increasing availability of DNA sequences resulted in more accurate estimations of taxa divergence times. However, finding the link between competing historical events and divergence is still challenging. Here we investigate assigning constrained-age priors to nodes of interest in a time-calibrated phylogeny as a means of hypothesis comparison. These priors are equivalent to historic scenarios for lineage origin. The hypothesis that best explains the data can be selected by comparing the likelihood values of the competing hypotheses, modelled with different priors. A simulation approach was taken to evaluate the performance of the prior-based method and to compare it with an unconstrained approach. We explored the effect of DNA sequence length and the temporal placement and span of competing hypotheses (i.e. historic scenarios) on selection of the correct hypothesis and the strength of the inference. Competing hypotheses were compared applying a posterior simulation analogue of the Akaike Information Criterion and Bayes factors (obtained after calculation of the marginal likelihood with three estimators: Harmonic Mean, Stepping Stone and Path Sampling). We illustrate the potential application of the prior-based method on an empirical data set to compare competing geological hypotheses explaining the biogeographic patterns in Pleurodeles newts. The correct hypothesis was selected on average 89% times. The best performance was observed with DNA sequence length of 3500-10000 bp. The prior-based method is most reliable when the hypotheses compared are not temporally too close. The strongest inferences were obtained when using the Stepping Stone and Path Sampling estimators. The prior-based approach proved effective in discriminating between competing hypotheses when used on empirical data. The unconstrained analyses performed well but it probably requires additional computational effort. Researchers applying this approach should rely only on inferences with moderate to strong support. The prior-based approach could be applied on biogeographical and phylogeographical studies where robust methods for historical inferences are still lacking.
Active transposable elements (TEs) may result in divergent genomic insertion and abundance patterns among conspecific populations. Upon secondary contact, such divergent genetic backgrounds can theoretically give rise to classical Dobzhansky-Muller incompatibilities (DMI), a way how TEs can contribute to the evolution of endogenous genetic barriers and eventually population divergence. We investigated whether differential TE activity created endogenous selection pressures among conspecific populations of the non-biting midge Chironomus riparius, focussing on a Chironomus-specific TE, the minisatellite-like Cla-element, whose activity is associated with speciation in the genus. Using an improved and annotated draft genome for a genomic study with five natural C. riparius populations, we found highly population-specific TE insertion patterns with many private insertions. A highly significant correlation of pairwise population FST from genome-wide SNPs with the FST estimated from TEs suggests drift as the major force driving TE population differentiation. However, the significantly higher Cla-element FST level due to a high proportion of differentially fixed Cla-element insertions indicates that segregating, i.e. heterozygous insertions are selected against. With reciprocal crossing experiments and fluorescent in-situ hybridisation of Cla-elements to polytene chromosomes, we documented phenotypic effects on female fertility and chromosomal mispairings that might be linked to DMI in hybrids. We propose that the inferred negative selection on heterozygous Cla-element insertions causes endogenous genetic barriers and therefore acts as DMI among C. riparius populations. The intrinsic genomic turnover exerted by TEs, thus, may have a direct impact on population divergence that is operationally different from drift and local adaptation.
Bears are iconic mammals with a complex evolutionary history. Natural bear hybrids and studies of few nuclear genes indicate that gene flow among bears may be more common than expected and not limited to the closely related polar and brown bears. Here we present a genome analysis of the bear family with representatives of all living species. Phylogenomic analyses of 869 mega base pairs divided into 18,621 genome fragments yielded a well-resolved coalescent species tree despite signals for extensive gene flow across species. However, genome analyses using three different statistical methods show that gene flow is not limited to closely related species pairs. Strong ancestral gene flow between the Asiatic black bear and the ancestor to polar, brown and American black bear explains numerous uncertainties in reconstructing the bear phylogeny. Gene flow across the bear clade may be mediated by intermediate species such as the geographically wide-spread brown bears leading to massive amounts of phylogenetic conflict. Genome-scale analyses lead to a more complete understanding of complex evolutionary processes. The increasing evidence for extensive inter-specific gene flow, found also in other animal species, necessitates shifting the attention from speciation processes achieving genome-wide reproductive isolation to the selective processes that maintain species divergence in the face of gene flow.
All giraffe (Giraffa) were previously assigned to a single species (G. Camelopardalis) and nine subspecies. However, multi-locus analyses of all subspecies have shown that there are four genetically distinct clades and suggest four giraffe species. This conclusion might not be fully accepted due to limited data and lack of explicit gene flow analyses. Here we present an extended study based on 21 independent nuclear loci from 137 individuals. Explicit gene flow analyses identify less than one migrant per generation, including between the closely related northern and reticulated giraffe. Thus, gene flow analyses and population genetics of the extended dataset confirm four genetically distinct giraffe clades and support four independent giraffe species. The new findings call for a revision of the IUCN classification of giraffe taxonomy. Three of the four species are threatened with extinction, mostly occurring in politically unstable regions, and as such, require the highest conservation support possible.
Welfare is the largest expenditure category in all advanced democracies. Consequently, much literature has studied partisan effects on total and policy-specific welfare expenditure. Yet, these results cannot be trusted: The methodological standard is to apply time-series cross-section-regressions to annual observation data. But governments hardly change annually. Thus, the number of observations is artificially inflated, leading to incorrect estimates. While this problem has recently been acknowledged, it has not been convincingly resolved. We propose Mixed-Effects Models as a solution, which allow decomposing variance into different levels and permit complex cross-classification data structures. We argue that Mixed-Effects models combine the strengths of existing methodological approaches while alleviating their weaknesses. Empirically, we study partisan effects on total and on disaggregated expenditure in 23 OECD-countries, 1960-2012, using several measures of party preferences.
The debate on effects of globalization on welfare states is extensive. Often couched in terms of a battle between the compensation and the efficiency thesis, the scholarly literature has provided contradictory arguments and findings. This article contributes to the scholarly debate by exploring in greater detail the micro-level foundations of compensation theory. More specifically, we distinguish between individual policy preferences for compensatory social policies (unemployment insurance) and human capital-focused social investment policies (education) and expect globalization to mainly affect demand for educational investment. A multi-level analysis of ISSP survey data provides empirical support for this hypothesis. This finding provides an important revision and extension of the classical analytical perspective of compensation theory, because it shows that citizens value the social investment function of the welfare state above and beyond simple compensation via social transfers. This might be particularly relevant in today's skill-centered knowledge economies.
We cannot imagine a political system without opposition. Despite this crucial position in politics, political science has largely neglected to study oppositions. Attempting to fill this gap, this article analyses the institutional opportunities of parliamentary oppositions. It offers a parsimonious framework by distinguishing two dimensions of opposition influence: Some institutions enable oppositions to control governments, while others offer opportunities to present alternatives. A comparison of oppositions’ opportunities in 21 democracies shows that countries fall into four groups along these dimensions: In majoritarian democracies, weak control mechanisms are countered by excellent opportunities to publicize alternatives. Consociational democracies are characterized by strong control mechanisms, but provide only weak opportunities to present alternatives. Moreover, in Southern Europe, control mechanisms and opportunities to present alternatives are weak, while both are pronounced in Nordic Europe. The results are summarized in three indices that can easily be applied in future research examining oppositions and their power.
Phylogenetic analyses of nuclear and mitochondrial genomes have shown that polar bears captured the mitochondrial genome of brown bears some 160,00 years ago. This hybridization event likely led to an extinction of the original polar bear mitochondrial genome. However, parts of the mitochondrial DNA occasionally integrates into the nuclear genome, forming pseudogenes called numts (nuclear mitochondrial integrations). Screening the polar bear genome for numts, we identified only 13 such integrations. Analyses of whole-genome sequences from additional polar bears, brown and American black bears as well as the giant panda indicates that the discovered numts entered the bear lineage before the initial ursid radiation some 14 million years ago. Our findings suggests a low integration rate of numts in the bear lineage and a complete loss of the original polar bear mitochondrial genome.
Background: Ever decreasing costs along with advances in sequencing and library preparation technologies enable even small research groups to generate chromosome-level assemblies today. Here we report the generation of an improved chromosome-level assembly for the Siamese fighting fish (Betta splendens) that was carried out during a practical university Master’s course. The Siamese fighting fish is a popular aquarium fish and an emerging model species for research on aggressive behaviour. We updated the current genome assembly by generating a new long-read nanopore-based assembly with subsequent scaffolding to chromosome-level using previously published HiC data.
Findings: The use of nanopore-based long-read data sequenced on a MinION platform (Oxford Nanopore Technologies) allowed us to generate a baseline assembly of only 1,276 contigs with a contig N50 of 2.1 Mbp, and a total length of 441 Mbp. Scaffolding using previously published HiC data resulted in 109 scaffolds with a scaffold N50 of 20.7 Mbp. More than 99% of the assembly is comprised in 21 scaffolds. The assembly showed the presence of 95.8% complete BUSCO genes from the Actinopterygii dataset indicating a high quality of the assembly.
Conclusion: We present an improved full chromosome-level assembly of the Siamese fighting fish generated during a university Master’s course. The use of ~35× long-read nanopore data drastically improved the baseline assembly in terms of continuity. We show that relatively in-expensive high-throughput sequencing technologies such as the long-read MinION sequencing platform can be used in educational settings allowing the students to gain practical skills in modern genomics and generate high quality results that benefit downstream research projects.
Borders and edges are salient and behaviourally relevant features for navigating the environment. The brain forms dedicated neural representations of environmental boundaries, which are assumed to serve as a reference for spatial coding. Here we expand this border coding network to include the retrosplenial cortex (RSC) in which we identified neurons that increase their firing near all boundaries of an arena. RSC border cells specifically encode walls, but not objects, and maintain their tuning in the absence of direct sensory detection. Unlike border cells in the medial entorhinal cortex (MEC), RSC border cells are sensitive to the animal’s direction to nearby walls located contralateral to the recorded hemisphere. Pharmacogenetic inactivation of MEC led to a disruption of RSC border coding, but not vice versa, indicating network directionality. Together these data shed light on how information about distance and direction of boundaries is generated in the brain for guiding navigation behaviour.