Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., scRNA-seq, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEXs advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build-upon previously hard-won scientific insights.
DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression Background and ObjectiveNot everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible.

MethodTo examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV.

ResultsThe results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu.

ConclusionsDeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.
Linking individual differences in human V1 to perception around the visual field A central question in neuroscience is how the organization of cortical maps relates to perception, for which human primary visual cortex (V1) is an ideal model system. V1 nonuniformly samples the retinal image, with greater cortical magnification (surface area per degree of visual field) at the fovea than periphery and at the horizontal than vertical meridian. Moreover, the size and cortical magnification of V1 varies greatly across individuals. Here, we used fMRI and psychophysics in the same observers to quantify individual differences in V1 cortical magnification and contrast sensitivity at the four polar angle meridians. Across observers, the overall size of V1 and localized cortical magnification positively correlated with contrast sensitivity. Moreover, greater cortical magnification and higher contrast sensitivity at the horizontal than the vertical meridian were strongly correlated. These data reveal a link between cortical anatomy and visual perception at the level of individual observer and stimulus location. Structure-function analysis of the nsp14 N7-guanine methyltransferase reveals an essential role in Betacoronavirus replication As coronaviruses (CoVs) replicate in the host cell cytoplasm, they rely on their own capping machinery to ensure the efficient translation of their mRNAs, protect them from degradation by cellular 5 exoribonucleases, and escape innate immune sensing. The CoV nonstructural protein 14 (nsp14) is a bi-functional replicase subunit harboring an N-terminal 3'-to-5' exoribonuclease (ExoN) domain and a C-terminal (N7-guanine)-methyltransferase (N7-MTase) domain that is presumably involved in viral mRNA capping. Here, we aimed to integrate structural, biochemical, and virological data to assess the importance of conserved N7-MTase residues for nsp14s enzymatic activities and virus viability. We revisited the crystal structure of severe acute respiratory syndrome (SARS)-CoV nsp14 to perform an in silico comparative analysis between betacoronaviruses. We identified several residues likely involved in the formation of the N7-MTase catalytic pocket, which presents a fold distinct from the Rossmann fold observed in most known MTases. Next, for SARS-CoV and Middle East respiratory syndrome-CoV, site-directed mutagenesis of selected residues was used to assess their importance for in vitro enzymatic activity. Most of the engineered mutations abolished N7-MTase activity, while not affecting nsp14-ExoN activity. Upon reverse engineering of these mutations into different betacoronavirus genomes, we identified two substitutions (R310A and F426A in SARS-CoV nsp14) abrogating virus viability and one mutation (H424A) yielding a crippled phenotype across all viruses tested. Our results identify the N7-MTase as a critical enzyme for betacoronavirus replication and define key residues of its catalytic pocket that can be targeted to design inhibitors with a potential pan-coronaviral activity spectrum.

Significance StatementThe ongoing SARS-CoV-2 pandemic emphasizes the urgent need to develop efficient broad-spectrum anti-CoV drugs. The structure-function characterization of conserved CoV replicative enzymes is key to identifying the most suitable drug targets. Using a multidisciplinary comparative approach and different betacoronaviruses, we characterized the key conserved residues of the nsp14 (N7-guanine)-methyltransferase, a poorly defined subunit of the CoV mRNA-synthesizing machinery. Our study highlights the unique structural features of this enzyme and establishes its essential role in betacoronavirus replication, while identifying two residues that are critical for the replication of the four betacoronaviruses tested, including SARS-CoV-2. Morphology of immatures of the thelytokous ant, Monomorium triviale Wheeler (Formicidae: Myrmicinae: Solenopsidini) with descriptions of the extraordinary last-instar queen larvae The ant genus Monomorium is one of the most species-rich but taxonomically problematic groups in the hyperdiverse subfamily Myrmicinae. An East Asian species, M. triviale Wheeler, produces both reproductive queens and sterile workers via obligate thelytokous parthenogenesis. Here, we describe the immature forms of M. triviale based on light and scanning electron microscopy observations, with a note on the striking caste dimorphism in the last larval instar. The last-instar queen larvae were easily recognized by their large size, "aphaenogastroid" body shape, and rows of doorknob-like tubercles on the lateral and dorsal body surface. This type of queen-specific structure has not been found in ants in general, let alone congeneric species found in Japan. In stark contrast to the queen larvae, worker larvae showed a "pheidoloid" body shape and a body surface similar to other ants. The worker larvae were estimated to have three instars, consistent with previously described congeners. The pupae of both castes had no cocoon, a characteristic commonly described in other Myrmicinae species. In total, the developmental period from egg to adult worker averaged 59 days under 25{degrees}C. We discuss possible functions of the tubercles of queen larvae based on previous studies. A constraints-based theory of the primary cause of senescence: imbalance of epigenetic and non-epigenetic information in histone crosstalk Cellular aging has been progressively elucidated by science. However, the fundamental cause of senescence--i.e., why organisms age at the multicellular-individual level--remains unclear. A recent theory of individuated multicellularity describes the emergence and growth of crucial information content for cell differentiation. This information is mostly conveyed in the non-epigenetic (i.e., transcription uncorrelated) histone crosstalk near transcription start sites. According to this theory, the non-epigenetic content emerges and grows at the expense of the information capacity for epigenetic content. If this "reassignment" of information capacity continues after adulthood, it may explain the senescence phenomenon. Here, I present a novel, falsifiable theory describing an uninterrupted growth of capacity for non-epigenetic information at the expense of that for epigenetic information not only during ontogeny but also throughout adulthood. As a byproduct, this continuous "reassignment" of capacity effectively creates an information imbalance in histone crosstalk, which in turn overregulates transcriptional levels. This overregulation is to be understood as transcriptional levels becoming more and more accurate but also less and less precise with respect to the needs of the multicellular individual--up to the point of dysfunctionality. This epigenetic/non-epigenetic information imbalance is proposed to be the primary reason why individuated multicellular organisms senesce. A Hierarchy of Biomolecular Proportional-Integral-Derivative Feedback Controllers for Robust Perfect Adaptation and Dynamic Performance Proportional-Integral-Derivative (PID) feedback controllers have been the most widely used controllers in industry for almost a century due to their good performance, simplicity, and ease of tuning. Motivated by their success in various engineering disciplines, PID controllers recently found their way into synthetic biology, where the design of feedback molecular control systems has been identified as an important goal. In this paper, we consider the mathematical realization of PID controllers via biomolecular interactions. We propose an array of topologies that offer a compromise between simplicity and high performance. We first demonstrate that different Proportional-Integral (PI) controllers exhibit different capabilities for enhancing the dynamics and reducing variance (cell-to-cell variability). Next, we introduce several derivative controllers that are realized based on incoherent feedforward loops acting in a feedback configuration. Alternatively, we show that differentiators can be realized by placing molecular integrators in a negative feedback loop--an arrangement that can then be augmented by PI components to yield PID feedback controllers. We demonstrate that the derivative component can be exploited for enhancing system stability, dramatically increasing the molecular control systems dynamic performance, and for reducing the noise effect on the output of interest. The PID controller features are established through various deterministic and stochastic analyses as well as numerical simulations. Finally, we provide an experimental demonstration using a recently developed hybrid setup, the cyberloop, where the controller is implemented in silico to control a biological genetic circuit in vivo. The large array of novel biomolecular PID controllers introduced here forms a basis for the design and construction of advanced high-performance biomolecular control systems that robustly regulate the dynamics of living systems. Source-sink connectivity: A novel interictal EEG marker for seizure localization Over 15 million epilepsy patients worldwide have drug-resistant epilepsy (DRE). Successful surgery is a standard of care treatment for DRE but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Surgical success rates vary between 20-80% because no clinically validated biological markers of the EZ exist. Localizing the EZ is a costly and time-consuming process beginning with non-invasive neuroimaging and often followed by days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity (e.g., low-voltage high frequency activity) on individual channels occurring immediately before seizures or spikes that occur on interictal iEEG (i.e., between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients.

Intracranial EEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aim to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the EZ. We hypothesize that when a patient is not clinically seizing, it is because the EZ is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, patient-specific dynamical network models (DNMs) were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics (SSMs). We validated the SSMs in a retrospective analysis of 65 patients by using the SSMs of the annotated EZ to predict surgical outcomes. The SSMs predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians predictions (surgical success rate of this dataset). In failed outcomes, we identified regions of the brain with high SSMs that were untreated. When compared to high frequency oscillations, the most commonly proposed interictal iEEG feature for EZ localization, SSMs outperformed in predictive power (by a factor of 1.2) suggesting SSMs may be an interictal iEEG fingerprint of the EZ. Human 14-3-3 proteins site-selectively bind the mutational hotspot region of SARS-CoV-2 nucleoprotein modulating its phosphoregulation Phosphorylated within its Ser/Arg-rich region, the SARS-CoV-2 nucleoprotein (N) recruits the phosphopeptide-binding human 14-3-3 proteins that play a well-recognized role in replication of many viruses. Here we use genetic code expansion to demonstrate that phosphorylation of SARS-CoV-2 N at either of two pseudo-repeats centered at Ser197 and Thr205 is sufficient for 14-3-3 binding. According to fluorescence anisotropy, the pT205-motif, present in SARS-CoV-2 but not in SARS-CoV, is preferred over the pS197-motif by all seven human 14-3-3 isoforms, which display unforeseen pT205/pS197 binding selectivity hierarchy. Crystal structures demonstrate that pS197 and pT205 are mutually exclusive 14-3-3-binding sites, whereas SAXS and biochemical data indicate 14-3-3 binding occludes the Ser/Arg-rich region, inhibiting its dephosphorylation. This Ser/Arg-rich region of N is highly prone to mutations, as exemplified by the Omicron and Delta variants, with our data suggesting how the strength of its 14-3-3 binding can be linked with the replicative fitness of the virus. Impacts of an urban sanitation intervention on fecal indicators and the prevalence of human fecal contamination in Mozambique Fecal source tracking (FST) may be useful to assess pathways of fecal contamination in domestic environments and to estimate the impacts of water, sanitation, and hygiene (WASH) interventions in low-income settings. We measured two non-specific and two human-associated fecal indicators in water, soil, and surfaces before and after a shared latrine intervention from low-income households in Maputo, Mozambique participating in the Maputo Sanitation (MapSan) trial. Up to a quarter of households were impacted by human fecal contamination, but trends were unaffected by improvements to shared sanitation facilities. The intervention reduced E. coli gene concentrations in soil but did not impact culturable E. coli or the prevalence of human FST markers in a difference-in-differences analysis. Using a novel Bayesian hierarchical modeling approach to account for human marker diagnostic sensitivity and specificity, we revealed a high amount of uncertainty associated with human FST measurements and intervention effect estimates. The field of microbial source tracking would benefit from adding measures of diagnostic accuracy to better interpret findings, particularly when FST analyses convey insufficient information for robust inference. With improved measures, FST could help identify dominant pathways of human and animal fecal contamination in communities and guide implementation of effective interventions to safeguard health.

SYNOPSISAn urban sanitation intervention had minimal and highly uncertain effects on human fecal contamination after accounting for fecal indicator sensitivity and specificity.

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org.highwire.dtl.DTLVardef@312d2dorg.highwire.dtl.DTLVardef@8cf2caorg.highwire.dtl.DTLVardef@197b549org.highwire.dtl.DTLVardef@fefeb3_HPS_FORMAT_FIGEXP  M_FIG C_FIG Genome archaeology of two laboratory Salmonella enterica enterica sv Typhimurium The Salmonella research community has used strains and bacteriophages over decades, exchanging useful new isolates among laboratories for study of cell surface antigens, metabolic pathways and restriction-modification studies. Here we present the sequences of two laboratory Salmonella strains (STK005, an isolate of LB5000; and its descendant ER3625). In the ancestry of LB5000, segments of ~15 and ~42 kb were introduced from Salmonella enterica sv Abony 103 into Salmonella enterica sv Typhimurium LT2, forming strain SD14; this strain is thus a hybrid of S. enterica isolates. Strains in the SD14 lineage were used to define flagellar antigens from the 1950s to the 1970s, and to define three restriction-modification systems from the 1960s to the 1980s. LB5000 was also used as host in phage typing systems used by epidemiologists. In the age of cheaper and easier sequencing, this resource will provide access to the sequence that underlies the extensive literature. Fractal polymer with loops recapitulates key features of chromosome organization Chromosomes are exceedingly long polymers compacted in a cell nucleus. While it has long been suggested that mammalian chromosomes during interphase are folded into loops, experimental detection of such loops has remained a daunting task. The most comprehensive experimental information about chromosome spatial organization is provided by Hi-C experiments that measure the frequency of contacts between all chromosomal positions. The lack of a tractable physical model of a polymer folded into loops limits our ability to interpret experimental data. It thus remains unknown how to obtain accurate and quantitative information about the nature of chromosomal looping from Hi-C. Here, we introduce a model of a polymer with random loops, solve it analytically and extend it by simulations for real chains. Remarkably, our model faithfully reproduces complex shapes of experimental contact probability curves universal among mammalian cells. Furthermore, our model allows one to estimate sizes of randomly positioned chromosomal loops from experimental data. We also show that excluded volume in real chains can induce osmotic and topological repulsion between loops, stiffening interphase chromosomes. Thus, our new framework allows interpretation of experimental data and suggests that interphase chromosomes are crumpled polymers further folded into a sequence of randomly positioned loops. Clonal Dynamics of Cancer-Initiating Cells in a Mouse Genetic Mosaic Model of Serous Ovarian Cancer High-grade serous ovarian cancer (HGSOC) is the most lethal gynecologic malignancy. Early detection remains the most promising strategy to decrease mortality and requires a deep understanding of the cancer initiation process. Recent breakthroughs identified the fallopian tube (FT) Pax8+ cells as a cell-of-origin for HGSOC. However, the progressive trajectory from an initiated Pax8+ cell toward malignancy remains elusive. Here, we use a mouse genetic mosaic system called MADM (Mosaic Analysis with Double Markers), which generates sporadic mutant cells labeled with GFP, to trace the clonal dynamics of HGSOC development. Surprisingly, we observed dichotomous clonal expansion kinetics of individual cancer-initiating Pax8+ cells; while the vast majority stalled immediately, a few significantly expanded. Clonal analysis of wildtype cells showed concordant dichotomy, suggesting that a small fraction of Pax8+ cells intrinsically carry higher expansion potential. Furthermore, although all wildtype clones stalled shortly after development, some mutant clones continued to progress, indicating that oncogenic mutations prolong clonal expansion beyond the cell-intrinsic potential. Finally, intra-clonal fate mapping revealed that progressing mutant clones have a higher propensity to produce Pax8+ cells, linking imbalanced cell differentiation to disease progression. In summary, our studies reveal that the earliest clonal expansion during HGSOC initiation emerges when oncogenic mutations exaggerate the intrinsic expansion potential of a rare Pax8+ subset and bias its differentiation potential, warranting future investigation as a readout for early detection and cancer prevention.

SIGNIFICANCE STATEMENTEffective early detection is the most promising approach to manage cancer, but it requires a thorough understanding of cancer initiation and early progression. Serous ovarian cancer has been found to originate from Pax8+ cells in the fallopian tube. To assess the clonal dynamics of cancer-initiating cells, we established a mouse genetic mosaic model that enables clonal analysis of mutant Pax8+ cells in the fallopian tube. We uncovered that the earliest clonal event in ovarian cancer development emerges from a rare subset of stem/progenitor-like cells with intrinsic expansion potential, and that oncogenic mutations exaggerate and prolong this potential to promote cancer initiation. Our findings pinpoint a precise target for future early-detection and cancer prevention studies.

HIGHLIGHTSO_LIOvarian cancer initiation revealed by a mouse genetic mosaic model.
C_LIO_LIDichotomous expansion capacity of initiated cells at the clonal level.
C_LIO_LISynergies between cell-intrinsic properties and onco-mutations fuels clonal expansion.
C_LIO_LIImbalanced differentiation within highly expansive clones leads to Pax8 clusters.
C_LI High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0 MotivationGene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

ResultsIn this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data.

AvailabilityThe inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/). Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. Flexible control of Pavlovian-instrumental transfer based on expected reward value The Pavlovian-instrumental transfer (PIT) paradigm is widely used to assay the motivational influence of reward-predictive cues, reflected by their ability to invigorate instrumental behavior. Leading theories of incentive learning assume that cues are assigned motivational properties based on the value of the rewards they predict. We outline an alternative account of PIT which recognizes that reward-predictive cues may suppress rather than motivate instrumental behavior under certain conditions. We posit that cues capable of triggering a strong reward expectancy will tend to inhibit instrumental behavior, which is exploratory by nature, in order to facilitate retrieval of the expected reward before it is lost or stolen. According to this view, the motivation to engage in instrumental behavior during a cue should be inversely related to the value of the predicted reward, since there is more to lose by failing to secure a high-value reward than a low-value reward. We tested this hypothesis in rats using a high-conflict PIT protocol which elicits competing instrumental and food-port entry responses. In Experiment 1, cues signaling different reward magnitudes (1, 3, or 9 pellets) differentially influenced these behaviors. Whereas the 1-pellet cue increased instrumental behavior, cues signaling 3 or 9 pellets suppressed instrumental behavior and elicited high levels of food-port activity. Experiment 2 found that reward-predictive cues suppressed instrumental behavior and increased food-port activity in a flexible manner that was disrupted by post-training reward devaluation. We discuss how the PIT task provides a useful tool for studying cognitive control over cue-motivated behavior in rodents. Implications of the (co)evolution of agriculture and resource foraging for the maintenance of species diversity and community structure Agriculture is found in numerous taxa such as humans, ants, beetles, fishes and even bacteria. This type of niche construction has evolved independently from hunting, though many species remain primarily predators. When a consumer has a positive effect on its resource, we can expect an allocative cost of agriculture, as the agricultural care diverts time and energy from other activities. Defending the resource against predators may divert time from its consumption (exploitation cost). The cost may also occur on the foraging of alternative resources, for instance if the consumer spends more time nearby the farmed resource and underexploiting resources elsewhere (opportunity cost). We here investigate transitions from predation to agriculture in a simple three-species model of a farmer that consumes two resources and has a positive effect on one. We study the conditions for the (co)evolution of the investment into agriculture and specialization on the two resources, and its consequences on the ecological dynamics of the community. Eco-evolutionary dynamics generate a feedback between the evolution of agriculture and specialization on the helped resource, that can lead to varying selected intensity of agriculture, from generalist strategies with no agriculture, to specialist farmers, with possible coexistence between these two extreme strategies. Systematic monitoring of photosynthetically-derived oxidative signals by a peroxiredoxin-based biosensor Transmission of reductive cues from the photosynthetic electron transport chain to redox-regulated proteins plays a crucial role in activating chloroplast metabolism. However, deciphering the role of their counterbalanced oxidative signals is challenging due to monitoring difficulties. Here, we demonstrate the light-depended redox modification of chloroplast-targeted 2-Cys peroxiredoxins and introduce peroxiredoxin-based biosensors to monitor photosynthetically-derived oxidative signals. By employing a set of genetically encoded biosensors, we show the induction of oxidative signals under habitual light intensities and their inverse relationship with NADPH levels, unraveling the combined activity of reducing and oxidizing signals in fine-tuning chloroplast metabolism. A faster increase in carbon assimilation rates during photosynthesis induction phase was measured in plants deficient in 2-Cys peroxiredoxins compared to wild-type, suggesting the involvement of oxidative signals in attenuating photosynthesis under variable light environments. We suggest that oxidative signals measured by peroxiredoxin-based biosensors reflect the limitation to photosynthesis imposed by the redox regulatory system.

One-Sentence SummaryA genetically encoded biosensor unmasked the dominant role of photosynthetically-derived oxidative signals under habitual conditions. Sensing their plasma membrane curvature allows migrating cells to circumvent obstacles Cell migration is a hallmark out-of-equilibrium process in biology. In addition to persistent self-propelled motion, many cells display remarkable adaptive behaviors when they navigate complex environments within the body. Combining theory and experiments, we identify a curvature-sensing mechanism underlying obstacle avoidance in immune-like cells. The genetic perturbation of this machinery leads to a reduced capacity to evade obstructions combined with faster and more persistent cell migration in obstacle-free environments. We propose that the active polymerization of the actin cytoskeleton at the advancing edge of migrating cells is locally inhibited by the curvature-sensitive BAR protein Snx33 in regions with inward plasma membrane curvature. This coupling between actin and membrane dynamics leads to a mechanochemical instability that generates complex protrusive patterns at the cellular front. Adaptive motility thus arises from two simultaneous curvature-dependent effects, i) the specific reduction of propulsion in regions where external objects deform the plasma membrane and ii) the intrinsic patterning capacity due to the membrane-actin coupling that promotes spontaneous changes in the cells protrusions. Our results show how cells utilize actin- and plasma membrane biophysics to sense their environment, allowing them to adaptively decide if they should move ahead or turn away. On the basis of our findings, we propose that the natural diversity of BAR proteins may allow cells to tune their curvature sensing machinery to match the shape characteristics in their environment. Novel Integrative Modeling of Molecules and Morphology across Evolutionary Timescales Evolutionary models account for either population- or species-level processes, but usually not both. We introduce a new model, the FBD-MSC, which makes it possible for the first time to integrate both the genealogical and fossilization phenomena, by means of the multispecies coalescent (MSC) and the fossilized birth-death (FBD) processes. Using this model, we reconstruct the phylogeny representing all extant and many fossil Caninae, recovering both the relative and absolute time of speciation events. We quantify known inaccuracy issues with divergence time estimates using the popular strategy of concatenating molecular alignments, and show that the FBD-MSC solves them. Our new integrative method and empirical results advance the paradigm and practice of probabilistic total evidence analyses in evolutionary biology. Nuclear alpha-synuclein is present in the human brain and is modified in dementia with Lewy bodies Dementia with Lewy bodies is pathologically defined by the cytoplasmic accumulation of alpha-synuclein within neuronal cells in the brain. Alpha-synuclein is predominately pre-synaptic, but has been reported present in various subcellular compartments in cell and animal models. In particular, nuclear alpha-synuclein is evident in-vitro and in disease models and has been associated with altered DNA integrity, gene transcription, nuclear homeostasis. However, owing to various factors, the presence of alpha-synuclein in the nuclei of human brain cells remains controversial, as does its role in synucleinopathies.

Here, we close this gap and provide a unique demonstration confirming the presence of nuclear alpha-synuclein in post-mortem brain tissue obtained from cases of dementia with Lewy bodies as well as from controls via immunohistochemistry, immunoblot, and label-free mass-spectrometry.

Discrete intra-nuclear alpha-synuclein puncta reactive against phosphorylated serine 129-alpha-synuclein and pan-alpha-synuclein antibodies were observed in cortical neurons and non-neuronal cells in fixed brain sections and in isolated nuclear preparations from Dementia with Lewy bodies cases and matched controls. Subsequent biochemical analysis of subcellular fractionated tissue confirmed alpha-synuclein as present in a nuclear fraction at levels ~ 10-fold lower than in the cytoplasm. Critically, however, an increase in monomeric nuclear alpha-synuclein phosphorylated as serine 129 was observed in cases of dementia with Lewy bodies alongside higher molecular weight pan- and phosphorylation reactive alpha-synuclein species, consistent with the formation of intranuclear phosphorylated alpha-synuclein oligomers. Furthermore, the presence of nuclear alpha-synuclein was confirmed via label free mass spectrometry, as 6 unique alpha-synuclein derived peptide sequences were identified in nuclear fractions (71.4% sequence coverage).

Collectively, our data confirm the presence of nuclear alpha-synuclein in human brain tissue and describe nuclear pathology associated with dementia with Lewy bodies. These findings address a major controversy in the synucleinopathy field by confirming the presence of nuclear alpha-synuclein in autoptic human brain tissue and, for the first time, identify that alpha-synuclein is aggregated into novel and potentially pathological assemblies in the nucleus as part of the disease process associated with dementia with Lewy bodies and thus may contribute to the disease phenotype. Tuft cell-produced cysteinyl leukotrienes and IL-25 synergistically initiate lung type 2 inflammation Aeroallergen sensing by airway epithelial cells can trigger pathogenic immune responses leading to chronic type 2 inflammation, the hallmark of airway diseases such as asthma. Airway tuft cells are specialized chemosensory epithelial cells and the dominant source of the epithelial cytokine IL-25 in the trachea and of cysteinyl leukotrienes (CysLTs) in the naive murine nasal mucosa. The interaction of IL-25 and CysLTs and the contribution of tuft cell-derived CysLTs to the development of allergen-triggered inflammation in the airways has not been clarified. Here we show that inhalation of LTC4 in combination with a subthreshold dose of IL-25 leads to dramatic synergistic induction of type 2 inflammation throughout the lungs, causing rapid eosinophilia, dendritic cell (DC) and inflammatory type 2 innate lymphoid cell (ILC2) expansion, and goblet cell metaplasia. While lung eosinophilia is dominantly mediated through the classical CysLT receptor CysLT1R, type 2 cytokines and activation of innate immune cells require signaling through both CysLT1R and CysLT2R. Tuft cell-specific deletion of the terminal enzyme requisite for CysLT production, Ltc4s, was sufficient to reduce both the innate immune response in the lung - eosinophilia, KLRG1+ ILC2 activation and DC recruitment - and the systemic immune response in the draining lymph nodes after inhalation of the mold aeroallergen Alternaria. Our findings identify surprisingly potent synergy of CysLTs and IL-25 downstream of aeroallergen-trigged activation of airway tuft cells leading to a highly polarized type 2 immune response and further implicate airway tuft cells as powerful modulators of type 2 immunity in the lungs.

One Sentence SummaryTuft cells produce two highly synergistic mediators: LTC4 and IL-25 to cooperatively induce allergen-driven airway inflammation. If Research is Not the Evidence, What is it? The Lack of Research Citations in Health Vlogs Doctor-influencers have the knowledge needed to understand scientific research, and they have the social media popularity to share it with large audiences. In this article, I explore the treatment of evidence in vlogs by Egyptian doctor-influencers: how they present it and what they use instead of it. I answer the following questions: 1) How do doctor-influencers present evidence on social media? 2) If evidence is not the research, then what is it? The data was collected through in-depth interviews with 12 Egyptian doctor-influencers from different specializations; two focus groups; and a critical discourse analysis of 48 of the influencers most popular and most engaging videos. I found that the doctor-influencers cited academic research only in the videos about medical controversies, new treatments, misinformation, or common medical mistakes. In most of their videos, the Egyptian doctor-influencers scarcely referred to medical research. This is because they believe that their audiences would not understand evidence due to their low educational attainment, weak research skills, and the language barrier (as most medical research is published in English rather than Arabic). These findings highlight the challenges of communicating science in societies with a low level of education. In these settings, communicators may need to move beyond mediating research studies on social media and instead focus on simplifying, translating, and connecting evidence-based information to the cultural context. RESTORING GLUTAMATE RECEPTOR SIGNALING IN PANCREATIC ALPHA CELLS RESCUES GLUCAGON RESPONSES IN TYPE 1 DIABETES Glucagon secretion from pancreatic alpha cells is crucial to prevent hypoglycemia. For reasons still unknown, people with type 1 diabetes lose this glucoregulatory mechanism and are susceptible to dangerous hypoglycemia. Here we show that alpha cells in living pancreas slices from donors with type 1 diabetes failed to secrete glucagon in response to decreases in glucose concentration, thus mirroring the in vivo unresponsiveness to hypoglycemia. Glucagon content and responses to KCl depolarization were not affected, suggesting that alpha cells retained their secretory potential. By contrast, alpha cells had severely impaired signaling via glutamate receptors of the AMPA/kainate type. Under healthy conditions, activating these receptors was required to elicit full glucagon responses to decreases in glucose levels. In type 1 diabetes, reactivating residual glutamate receptor function with the positive allosteric modulators cyclothiazide and aniracetam restored glucagon secretion in response to hypoglycemia. These positive allosteric modulators are already approved to treat other conditions and could be repurposed to prevent hypoglycemia and improve management of diabetes. Embryonic exposure to cannabidiol disrupts active neural circuits, an effect increased by Δ-9-tetrahydrocannabinol and involving CB1R and CB2R in zebrafish In the light of legislative changes and the widespread use of cannabis as a recreational and medicinal drug, delayed effects of cannabis upon brief exposure during embryonic development are of high interest as early pregnancies often go undetected. Here, zebrafish embryos were exposed to cannabidiol (CBD) and {Delta}-9-tetrahydrocannabinol l (THC) until the end of gastrulation (1-10 hours post-fertilization) and analyzed later in development (4-5 days post-fertilization). In order to measure neural activity, we implemented CaMPARI (Calcium-Modulated Photoactivatable Ratiometric Integrator) and optimized the protocol for a 96-well format complemented by locomotor analysis. Our results revealed that neural activity was decreased by CBD more than THC. At higher doses, both cannabinoids could dramatically reduce neural activity and locomotor activity. Interestingly, the decrease was more pronounced when CBD and THC were combined. At the receptor level, CBD-mediated reduction of locomotor activity was partially prevented using cannabinoid type 1 and 2 receptor inhibitors. Overall, we report that CBD toxicity occurs via two cannabinoid receptors and is synergistically enhanced by THC exposure to negatively impact neural activity late in larval development. Future studies are warranted to reveal other cannabinoids and receptors involved in this pathway to understand the subsequent health implications of cannabis consumption on fetal development. The maize gene maternal derepression of r1 (mdr1) encodes a DNA glycosylase with maternal and paternal fertility functions Demethylation of transposons can activate expression of nearby genes and cause imprinted gene expression in endosperm, and it is hypothesized to lead to expression of transposon siRNAs that reinforce silencing in the next generation through transfer either into egg or embryo. Here we describe maternal derepression of r1 (mdr1), which encodes a DNA glycosylase with homology to Arabidopsis DEMETER and which is partially responsible for demethylation of thousands of regions in endosperm. Instead of promoting siRNA expression in endosperm, MDR1 activity inhibits it. Methylation of most repetitive DNA elements in endosperm is not significantly affected by MDR1, with an exception of Helitrons. While maternally-expressed imprinted genes preferentially overlap with MDR1 demethylated regions, the majority of genes that overlap demethylated regions are not imprinted. Double mutant megagametophytes lacking both MDR1 and its close homolog DNG102 result in early seed failure, and double mutant microgametophytes fail pre-fertilization. These data establish DNA demethylation by glycosylases as essential in maize endosperm and pollen and suggest that neither transposon repression nor genomic imprinting are its main function in endosperm.