eLife

Journal: eLife

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eLife Sciences Publications

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2050-084X

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Publications 1 - 10 of 240

Serotonin's many meanings elude simple theories
Dayan, Peter; Huys, Quentin J.M. (2015)
eLife
Neurons that produce serotonin respond in a number of different and complex ways in anticipation and receipt of rewards or punishments.
Stimulation of VTA dopamine inputs to LH upregulates orexin neuronal activity in a DRD2-dependent manner
Harada, Masaya; Serratosa Capdevila, Laia; Wilhelm, Maria; et al. (2024)
eLife
Dopamine and orexins (hypocretins) play important roles in regulating reward-seeking behaviors. It is known that hypothalamic orexinergic neurons project to dopamine neurons in the ventral tegmental area (VTA), where they can stimulate dopaminergic neuronal activity. Although there are reciprocal connections between dopaminergic and orexinergic systems, whether and how dopamine regulates the activity of orexin neurons is currently not known. Here we implemented an opto-Pavlovian task in which mice learn to associate a sensory cue with optogenetic dopamine neuron stimulation to investigate the relationship between dopamine release and orexin neuron activity in the lateral hypothalamus (LH). We found that dopamine release can be evoked in LH upon optogenetic stimulation of VTA dopamine neurons and is also naturally evoked by cue presentation after opto-Pavlovian learning. Furthermore, orexin neuron activity could also be upregulated by local stimulation of dopaminergic terminals in the LH in a way that is partially dependent on dopamine D2 receptors (DRD2). Our results reveal previously unknown orexinergic coding of reward expectation and unveil an orexin-regulatory axis mediated by local dopamine inputs in the LH.
Tandem hnRNP A1 RNA recognition motifs act in concert to repress the splicing of survival motor neuron exon 7
Beusch, Irene; Barraud, Pierre; Moursy, Ahmed; et al. (2017)
eLife
HnRNP A1 regulates many alternative splicing events by the recognition of splicing silencer elements. Here, we provide the solution structures of its two RNA recognition motifs (RRMs) in complex with short RNA. In addition, we show by NMR that both RRMs of hnRNP A1 can bind simultaneously to a single bipartite motif of the human intronic splicing silencer ISS-N1, which controls survival of motor neuron exon 7 splicing. RRM2 binds to the upstream motif and RRM1 to the downstream motif. Combining the insights from the structure with in cell splicing assays we show that the architecture and organization of the two RRMs is essential to hnRNP A1 function. The disruption of the inter-RRM interaction or the loss of RNA binding capacity of either RRM impairs splicing repression by hnRNP A1. Furthermore, both binding sites within the ISS-N1 are important for splicing repression and their contributions are cumulative rather than synergistic.
On the pH-dependence of α-synuclein amyloid polymorphism and the role of secondary nucleation in seed-based amyloid propagation
Frey, Lukas; Ghosh, Dhiman; Qureshi, Bilal M.; et al. (2024)
eLife
The aggregation of the protein alpha-synuclein is closely associated with several neurodegenerative disorders and as such the structures of the amyloid fibril aggregates have high scientific and medical significance. However, there are dozens of unique atomic-resolution structures of these aggregates, and such a highly polymorphic nature of the alpha-synuclein fibrils hampers efforts in disease-relevant in vitro studies on alpha-synuclein amyloid aggregation. In order to better understand the factors that affect polymorph selection, we studied the structures of alpha-synuclein fibrils in vitro as a function of pH and buffer using cryo-EM helical reconstruction. We find that in the physiological range of pH 5.8-7.4, a pH-dependent selection between Type 1, 2, and 3 polymorphs occurs. Our results indicate that even in the presence of seeds, the polymorph selection during aggregation is highly dependent on the buffer conditions, attributed to the non-polymorph-specific nature of secondary nucleation. We also uncovered two new polymorphs that occur at pH 7.0 in phosphate-buffered saline. The first is a monofilament Type 1 fibril that highly resembles the structure of the juvenile-onset synucleinopathy polymorph found in patient-derived material. The second is a new Type 5 polymorph that resembles a polymorph that has been recently reported in a study that used diseased tissues to seed aggregation. Taken together, our results highlight the shallow amyloid energy hypersurface that can be altered by subtle changes in the environment, including the pH which is shown to play a major role in polymorph selection and in many cases appears to be the determining factor in seeded aggregation. The results also suggest the possibility of producing disease-relevant structure in vitro.
Catecholaminergic modulation of meta-learning
Cook, Jennifer L.; Swart, Jennifer C.; Froböse, Monja I.; et al. (2019)
eLife
Niche partitioning facilitates coexistence of closely related honey bee gut bacteria
Brochet, Silvia; Quinn, Andrew; Mars, Ruben A.T.; et al. (2021)
eLife
Ecological processes underlying bacterial coexistence in the gut are not well understood. Here, we disentangled the effect of the host and the diet on the coexistence of four closely related Lactobacillus species colonizing the honey bee gut. We serially passaged the four species through gnotobiotic bees and in liquid cultures in the presence of either pollen (bee diet) or simple sugars. Although the four species engaged in negative interactions, they were able to stably coexist, both in vivo and in vitro. However, coexistence was only possible in the presence of pollen, and not in simple sugars, independent of the environment. Using metatranscriptomics and metabolomics, we found that the four species utilize different pollen-derived carbohydrate substrates indicating resource partitioning as the basis of coexistence. Our results show that despite longstanding host association, gut bacterial interactions can be recapitulated in vitro providing insights about bacterial coexistence when combined with in vivo experiments.
The fibronectin synergy site re-enforces cell adhesion and mediates a crosstalk between integrin classes
Benito-Jardón, Maria; Klapproth, Sarah; Gimeno-Lluch, Irene; et al. (2017)
eLife
Fibronectin (FN), a major extracellular matrix component, enables integrin-mediated cell adhesion via binding of α5β1, αIIbβ3 and αv-class integrins to an RGD-motif. An additional linkage for α5 and αIIb is the synergy site located in close proximity to the RGD motif. We report that mice with a dysfunctional FN-synergy motif (Fn1syn/syn) suffer from surprisingly mild platelet adhesion and bleeding defects due to delayed thrombus formation after vessel injury. Additional loss of β3 integrins dramatically aggravates the bleedings and severely compromises smooth muscle cell coverage of the vasculature leading to embryonic lethality. Cell-based studies revealed that the synergy site is dispensable for the initial contact of α5β1 with the RGD, but essential to re-enforce the binding of α5β1/αIIbβ3 to FN. Our findings demonstrate a critical role for the FN synergy site when external forces exceed a certain threshold or when αvβ3 integrin levels decrease below a critical level.
Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
Sherratt, Katharine; Gruson, Hugo; Grah, Rok; et al. (2023)
eLife
Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts’ predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022. Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models’ predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance. Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models. Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks. Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Agència de Qualitat i Avaluació Sanitàries de Catalunya; Netzwerk Universitätsmedizin; Health Protection Research Unit; Wellcome Trust; European Centre for Disease Prevention and Control; Ministry of Science and Higher Education of Poland; Federal Ministry of Education and Research; Los Alamos National Laboratory; German Free State of Saxony; NCBiR; FISR 2020 Covid-19 I Fase; Spanish Ministry of Health / REACT-UE (FEDER); National Institutes of General Medical Sciences; Ministerio de Sanidad/ISCIII; PERISCOPE European H2020; PERISCOPE European H2021; InPresa; National Institutes of Health, NSF, US Centers for Disease Control and Prevention, Google, University of Virginia, Defense Threat Reduction Agency Background Epidemiological forecasts make quantitative statements about a disease outcome in the near future. Forecasting targets can include measures of prevalent or incident disease and its severity, for some population over a specified time horizon. Researchers, policy makers, and the general public have used such forecasts to understand and respond to the global outbreaks of COVID-19 [1]–[3]. At the same time, forecasters use a variety of methods and models for creating and publishing forecasts, varying in both defining the forecast outcome and in reporting the probability distribution of outcomes [4], [5]. Within Europe, comparing forecasts across both models and countries can support a range of national policy needs simultaneously. European public health professionals operate across national, regional, and continental scales, with strong existing policy networks in addition to rich patterns of cross-border migration influencing epidemic dynamics. A majority of European countries also cooperate in setting policy with inter-governmental European bodies such as the European Centre for Disease Prevention and Control (ECDC). In this case, a consistent approach to forecasting across the continent as a whole can support accurately informing cross-European monitoring, analysis, and guidance [3]. At a regional level, multi-country forecasts can support a better understanding of the impact of regional migration networks. Meanwhile, where there is limited capacity for infectious disease forecasting at a national level, forecasters generating multi-country results can provide an otherwise-unavailable opportunity for forecasts to inform national situational awareness. Some independent forecasting models have sought to address this by producing multi-country results [6]–[9]. Variation in forecast methods and presentation makes it difficult to compare predictive performance between forecast models, and from there to derive objective arguments for using one forecast over another. This confounds the selection of a single representative forecast and reduces the reliability of the evidence base for decisions based on forecasts. A “forecast hub” is a centralised effort to improve the transparency and usefulness of forecasts, by standardising and collating the work of many independent teams producing forecasts [10]. A hub sets a commonly agreed-upon structure for forecast targets, such as type of disease event, spatio-temporal units, or the set of quantiles of the probability distribution to include from probabilistic forecasts. For instance, a hub may collect predictions of the total number of cases reported in a given country for each day in the next two weeks. Forecasters can adopt this format and contribute forecasts for centralised storage in the public domain. This shared infrastructure allows forecasts produced from diverse teams and methods to be visualised and quantitatively compared on a like-for-like basis, which can strengthen public and policy use of disease forecasts. The underlying approach to creating a forecast hub was pioneered in climate modelling and adapted for collaborative epidemiological forecasts of dengue [11] and influenza in the USA [10], [12]. This infrastructure was adapted for forecasts of short-term COVID-19 cases and deaths in the US [13], [14], prompting similar efforts in some European countries [15]–[17]. Standardising forecasts allows for combining multiple forecasts into a single ensemble with the potential for an improved predictive performance. Evidence from previous efforts in multi-model infectious disease forecasting suggests that forecasts from an ensemble of models can be consistently high performing compared to any one of the component models [11], [12], [18]. Elsewhere, weather forecasting has a long-standing use of building ensembles of models using diverse methods with standardised data and formatting in order to improve performance [19], [20]. The European COVID-19 Forecast Hub [21] is a project to collate short term forecasts of COVID-19 across 32 countries in the European region. The Hub is funded and supported by the ECDC, with the primary aim to provide reliable information about the near-term epidemiology of the COVID-19 pandemic to the research and policy communities and the general public [3]. Second, the Hub aims to create infrastructure for storing and analysing epidemiological forecasts made in real time by diverse research teams and methods across Europe. Third, the Hub aims to maintain a community of infectious disease modellers underpinned by open science principles. We started formally collating and combining contributions to the European Forecast Hub in March 2021. Here, we investigate the predictive performance of an ensemble of all forecasts contributed to the Hub in real time each week, as well as the performance of variations of ensemble methods created retrospectively. Methods We developed infrastructure to host and analyse prospective forecasts of COVID-19 cases and deaths. The infrastructure is compatible with equivalent research software from the US [22], [23] and German and Polish COVID-19 [24] Forecast Hubs, and easy to replicate for new forecasting collaborations. All data and code for this analysis are publicly available on Github [25].
A neuronal least-action principle for real-time learning in cortical circuits
Senn, Walter; Dold, Dominik; Kungl, Akos F.; et al. (2024)
eLife
One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal least-action principle for cortical processing of sensory streams to produce appropriate behavioral outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimizes the local somato-dendritic mismatch error within individual neurons. For output neurons, the principle implies minimizing an instantaneous behavioral error. For deep network neurons, it implies the prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory input and the motor feedback during the ongoing sensory-motor transform. Online synaptic plasticity reduces the somatodendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic laws for global real-time computation and learning in the brain.
Function and firing of the Streptomyces coelicolor contractile injection system requires the membrane protein CisA
Casu, Bastien; Sallmen, Joseph W.; Haas, Peter E.; et al. (2025)
eLife
Bacterial contractile injection systems (CIS) are phage tail-like macromolecular complexes that mediate cell-cell interactions by injecting effector proteins into target cells. CIS from (CIS) are localized in the cytoplasm. Under stress, they induce cell death and impact the life cycle. It remains unknown, however, whether CIS require accessory proteins to directly interact with the cytoplasmic membrane to function. Here, we characterize the putative membrane adaptor CisA, a conserved factor in CIS gene clusters across species. We show by cryo-electron tomography imaging and in vivo assays that CIS contraction and function depend on CisA. Using single-particle cryo-electron microscopy, we provide an atomic model of the extended CIS apparatus; however, CisA is not part of the complex. Instead, our findings show that CisA is a membrane protein with a cytoplasmic N-terminus predicted to interact with CIS components, thereby providing a possible mechanism for mediating CIS recruitment to the membrane and subsequent firing. Our work shows that CIS function in multicellular bacteria is distinct from type VI secretion systems and extracellular CIS, and possibly evolved due to the role CIS play in regulated cell death.

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