GPI-anchored FGF directs cytoneme-mediated bidirectional signaling to self-regulate tissue-specific dispersion How signaling proteins generate a multitude of information to organize tissue patterns is critical to understanding morphogenesis. In Drosophila, FGF produced in wing-disc cells regulates the development of the disc-associated air-sac-primordium (ASP). Here, we show that FGF is Glycosylphosphatidylinositol-anchored to the producing cell surface and that this modification both inhibits free FGF secretion and activates target-specific bidirectional FGF-FGFR signaling through cytonemes. FGF-source and recipient ASP cells extend cytonemes that present FGF and FGFR on their surfaces and reciprocally recognize each other over distance by contacting through CAM-like FGF-FGFR binding. Contact-mediated FGF-FGFR binding induces bidirectional signaling, which, in turn, promotes ASP and source cells to polarize cytonemes toward each other and reinforce signaling contacts. Subsequent un-anchoring of FGFR-bound-FGF from the source cell membrane dissociates cytoneme contacts and delivers FGF target-specifically to ASP cytonemes for paracrine functions. Thus, GPI-anchored FGF organizes both source and recipient cells and self-regulates its cytoneme-mediated tissue-specific dispersion and signaling.
Mouse CHD4-NURD is required for spermatogonia survival and normal meiotic progression Testis development and sustained germ cell production in adults rely on the establishment and maintenance of spermatogonia stem cells and their proper differentiation into spermatocytes. Chromatin remodeling complexes regulate critical processes during gamete development by restricting or promoting accessibility of DNA repair and gene expression machineries to the chromatin. Here, we investigated the role of CHD4 and CHD3 catalytic subunits of the NURD complex during spermatogenesis. Germ cell-specific deletion of Chd4 early in gametogenesis, but not Chd3, resulted in arrested early gamete development due to failed cell survival of neonate undifferentiated spermatogonia stem cell population. Candidate assessment revealed that CHD4 controls expression of Dmrt1 and its downstream target Plzf, both described as prominent regulators of spermatogonia stem cell maintenance. Our results show the requirement of CHD4 in mammalian gametogenesis pointing to functions in gene expression early in the process.
Multifunctional RNA-binding proteins influence mRNA abundance and translational efficiency of distinct sets of target genes RNA-binding proteins (RBPs) can regulate more than a single aspect of RNA metabolism. We searched for such previously undiscovered multifunctionality within a set of 143 RBPs, by defining the predictive value of RBP abundance for the transcription and translation levels of known RBP target genes across 80 human hearts. This led us to newly associate 27 RBPs with cardiac translational regulation in vivo. Of these, 21 impacted both RNA expression and translation, albeit for virtually independent sets of target genes. We highlight a subset of these, including G3BP1, PUM1, UCHL5, and DDX3X, where dual regulation is achieved through differential affinity for target length, by which separate biological processes are controlled. Similar to the RNA helicase DDX3X, the known splicing factors EFTUD2 and PRPF8 - all identified as multifunctional RBPs by our analysis - selectively influence target translation rates depending on 5 UTR structure. Our analyses identify dozens of RBPs as being multifunctional and pinpoint potential novel regulators of translation, postulating unanticipated complexity of protein-RNA interactions at consecutive stages of gene expression. A novel two-generation approach to the population dynamics of gregarious parasitoids Parasitoids, as important natural enemies, help maintain balance in natural ecosystems. Their population dynamics is generally predicted from the number of individuals. Here, using gregarious parasitoids as models, we show that this traditional approach omits one important parameter: the mothers manipulation of offspring fertility due to the clutch size-body size- fertility correlation. As a result of this correlation, when females deliberately adjust the number of offspring laid in a host, they determine not only the number but also the body sizes and reproductive potentials of those offspring. For the first time in the model species Anaphes flavipes, we determined the parasitoids offspring fertility from clutch size. Using this, we experimentally clarified the advantage of specific clutch size combinations and we show that identical fertility in the F1 generation can lead to distinctly different fertility values in the F2 generation. Even with the same number of hosts, lower fertility in the F1 generation can cause higher fertility in the F2 generation. Based on these results, we propose a novel two-generation approach which includes the clutch size-body size-fertility correlation. Our novel approach provides a new perspective for determining the individual fitness levels of gregarious parasitoids with new options for the modelling of parasitoid population dynamics. Microbial loop of a Proterozoic ocean analogue Meromictic Lake Cadagno, an ancient ocean analogue, is known for its permanent stratification and persistent anoxygenic microbial bloom within the chemocline. Although the anaerobic microbial ecology of the lake has been extensively studied for at least 25 years, a comprehensive picture of the microbial food web linking the bacterial layer to phytoplankton and viruses, with explicit measures of primary and secondary production, is still missing. This study sought to better understand the abundances and productivity of microbes in the context of nutrient biogeochemical cycling across the stratified zones of Lake Cadagno. Photosynthetic pigments and chloroplast 16S rRNA gene phylogenies suggested the presence of eukaryotic phytoplankton through the water column. Evidence supported high abundances of Ankyra judayi, a high-alpine adapted chlorophyte, in the oxic mixolimnion where oxygenic-primary production peaked. Through the no- and low-oxygen chemocline and monimolimnion, chlorophytes related to Closteriopsis acicularis, a known genus of meromictic lakes, and Parachlorella kessleri were observed. Chromatium, anoxygenic phototrophic sulfur bacteria, dominated the chemocline along with Lentimicrobium, a genus of known fermenters whose abundance was newly reported in Lake Cadagno. Secondary production peaked in the chemocline suggesting primary producers depend on hetrotrophs for nutrient remineralization. As previously observed, sulfur reducing bacteria (SRBs), especially Desulfocapsa and Desulfobulbus, were present in the chemocline and anoxic monimolimnion. Virus-to-microbe ratios (VMR) peaked in the zone of phytoplankton, yet were at a minimum at the peak of Chromatium. These dynamic trends suggest viruses may play a role in the modulation of oxygenic and anoxygenic photo- and chemosynthesis in Lake Cadagno and other permanently stratified systems.

ImportanceAs a window to the past, the study offers insights into the role of microbial guilds of Proterozoic ocean chemoclines in the production and recycling of organic matter of sulfur- and ammonia-containing ancient oceans. The new observations described here suggest that eukaryotic algae were persistent in the low oxygen upper-chemocline in association with purple and green sulfur bacteria in the lower half of the chemocline. Further, this study provides the first insights into Lake Cadagno viral ecology. High viral abundances suggested viruses may be essential components of the chemocline where their activity may result in the release and recycling of organic matter. The framework developed in this study through the integration of diverse geochemical and biological data types lays the foundation for future studies to quantitatively resolve the processes performed by discrete populations comprising the microbial loop in this early anoxic ocean analogue. Bulk-assembly of monodisperse coacervates and giant unilamellar vesicles with programmable hierarchical complexity Compartmentalization is an essential step for the emergence of protocellular life on the early earth and the endeavor to bottom-up construct synthetic cells in the lab. Among manifold natural strategies for compartmentalization, assembly of lipid vesicles or coacervate droplets with resemblance to cell envelope or cytoplasm have emerged as two dominant paradigms. However, the spontaneous assembly of lipid vesicles or coacervates generally results in polydisperse microcompartments and lacks the ability to efficiently integrate functional building blocks towards defined complexity, which give rise to the difficulty to achieve the order and complexity towards the scope of that of life via spontaneous assembly. Herein, we show that the interplay of coacervates and colloidal particles provides a bulk-assembly approach to form monodisperse coacervates and giant unilamellar vesicles (GUVs) with programmable hierarchical complexity. The hierarchical complexity of the synthetic cell microcompartments can be delicately engineered from the control of the spatial or temporal organization of components in entity unit to manifold forms of interconnected synthetic cell consortia, achieving a compartment hierarchy analogous to the organelle-cell-tissue structure. The increase in complexity gives rise to emergent properties, for instance, collective morphology evolution of coacervate assemblages, gated permeability of GUVs without sophisticated protein machinery, and remarkable structural and functional stability of GUVs at extreme conditions. This work paves an unprecedented step to form monodisperse, hierarchical coacervates and GUVs via bulk-assembly, which provides a prebiotically plausible approach for the proper integration of inanimate matter towards the order and complexity of life and monodisperse, hierarchical microdroplets for follow-up scientific and technological applications. Mutualism-enhancing mutations dominate early adaptation in a microbial community Species interactions drive species evolution while evolution shapes these interactions. The resulting eco-evolutionary dynamics, their outcomes and their repeatability depend on how adaptive mutations available to community members affect fitness and ecologically relevant traits. However, the diversity of adaptive mutations available to community members is not well characterized, and we do not know how this diversity is affected by the ecological milieu. Here we use barcode lineage tracking to address this gap in a competitive mutualism between the yeast Saccharomyces cerevisiae and the alga Chlamydomonas reinhardtii. We find that yeast has access to many adaptive mutations with diverse ecological consequences, in particular, those that increase and reduce the yields of both species. The presence of alga does not change which mutations are adaptive in yeast (i.e., there is no fitness trade-off for yeast between growing alone or with alga), but rather shifts selection to favor yeast mutants that increase the yields of both species and make the mutualism stronger. Thus, in the presence of alga, we find that yeast repeatably evolves to become more cooperative, even though cooperativity is not directly favored by natural selection in our system. Our results demonstrate that ecological interactions not only alter the trajectory of evolution but also dictate its repeatability; in particular, weak mutualisms can repeatably evolve to become stronger. Using Deep Learning to Predict Human Decisions, and Cognitive Models to Explain Deep Learning Models Deep neural networks (DNN) models have the potential to provide new insights in the study of human decision making, due to their high capacity and data-driven design. While these models may be able to go beyond theory-driven models in predicting human behaviour, their opaque nature limits their ability to explain how an operation is carried out. This explainability problem remains unresolved. Here we demonstrate the use of a DNN model as an exploratory tool to identify predictable and consistent human behaviour in value-based decision making beyond the scope of theory-driven models. We then propose using theory-driven models to characterise the operation of the DNN model. We trained a DNN model to predict human decisions in a four-armed bandit task. We found that this model was more accurate than a reinforcement-learning reward-oriented model geared towards choosing the most rewarding option. This disparity in accuracy was more pronounced during times when the expected reward from all options was similar, i.e., no unambiguous good option. To investigate this disparity, we introduced a reward-oblivious model, which was trained to predict human decisions without information about the rewards obtained from each option. This model captured decision-sequence patterns made by participants (e.g., a-b-c-d). In a series of experimental offline simulations of all models we found that the general model was in line with a reward-oriented models predictions when one option was clearly better than the others.

However, when options expected rewards were similar to each other, it was in-line with the reward-oblivious models pattern completion predictions. These results indicate the contribution of predictable but task-irrelevant decision patterns to human decisions, especially when task-relevant choices are not immediately apparent. Importantly, we demonstrate how theory-driven cognitive models can be used to characterise the operation of DNNs, making them a useful explanatory tool in scientific investigation.

Author SummaryDeep neural networks (DNN) models are an extremely useful tool across multiple domains, and specifically for performing tasks that mimic and predict human behaviour. However, due to their opaque nature and high level of complexity, their ability to explain human behaviour is limited. Here we used DNN models to uncover hitherto overlooked aspects of human decision making, i.e., their reliance on predictable patterns for exploration. For this purpose, we trained a DNN model to predict human choices in a decision-making task. We then characterised this data-driven model using explicit, theory-driven cognitive models, in a set of offline experimental simulations. This relationship between explicit and data-driven approaches, where high-capacity models are used to explore beyond the scope of established models and theory-driven models are used to explain and characterise these new grounds, make DNN models a powerful scientific tool. Changes to the mtDNA copy number during yeast culture growth We show that the mtDNA copy number in growing cultures of the yeast Saccharomyces cerevisiae increases by a factor of up to 4, being lowest ([~]10 per haploid genome) and stable during rapid fermentative growth, and highest at the end of the respiratory phase. When yeast are grown on glucose, the onset of the mtDNA copy number increase coincides with the early stages of the diauxic shift, and the increase continues through respiration. A lesser yet still substantial copy number increase occurs when yeast are grown on a nonfermentable carbon source, i.e. when there is no diauxic shift. The mtDNA copy number increase during and for some time after the diauxic shift is not driven by an increase in cell size. The copy number increase occurs in both haploid and diploid strains, but is markedly attenuated in a diploid wild isolate that is a ready sporulator. Strain-to-strain differences in mtDNA copy number are least apparent in fermentation and most apparent in late respiration or stationary phase. While changes in mitochondrial morphology and function were previously known to accompany changes in physiological state, it had not been previously shown that the mtDNA copy number changes substantially over time in a clonal growing culture. The mtDNA copy number in yeast is therefore a highly dynamic phenotype. Scaling effects of temperature on parasitism from individuals to host-parasite systems Parasitism is expected to change in a warmer future, but whether warming leads to substantial increases in parasitism remains unclear. Understanding how warming effects on parasitism in individual hosts (e.g., parasite load) translate to effects on population-level parasitism (e.g., prevalence, R0) remains a major knowledge gap. We analyzed the temperature dependence of parasitism at both host and population levels in thirteen empirical vector-borne host-parasite systems and found a strong, significant positive correlation between the thermal optima of individual- and population-level parasitism. We also found a significant, positive correlation in eleven environmentally-transmitted parasite systems, though several of these systems exhibited thermal optima >5{o}C apart between individual and population levels. Similarly, parasitism thermal optima were close to host performance thermal optima in vector-borne systems but not in environmentally-transmitted systems. We then adapted and simulated simple models for both transmission modes and found a similar pattern to the empirical systems: thermal optima in vector-borne systems were more strongly correlated across scales compared to environmentally-transmitted systems. Generally, our results suggest that information on the temperature-dependence, and specifically the thermal optimum, at either the individual- or population-level should provide a useful--though not quantitatively exact--baseline for predicting temperature dependence at the other level, especially in vector-borne parasite systems. Environmentally-transmitted parasitism may operate by a different set of rules, in which temperature-dependence is decoupled in some systems, requiring the need for trait-based studies of temperature-dependence at individual and population levels. An integrative process-based model for biomass and yield estimation of hardneck garlic (Allium sativum) We introduce an integrative process-driven crop model for garlic (Allium sativum). Building on our previous model that simulated key phenological, morphological, and physiological features of a garlic plant, the new garlic model provides comprehensive and integrative estimations of biomass accumulation and yield formation under diverse environmental conditions. This model also showcases an application of Cropbox to develop a comprehensive crop model. Cropbox is a crop modeling framework featuring declarative modeling language and unified simulation interface for building and improving crop models. Using Cropbox, we first evaluated the model performance against three datasets with an emphasis on biomass and yield measured under different environmental conditions and growing seasons. We then applied the model to simulate optimal planting dates under future climate conditions for assessing climate adaptation strategies between two contrasting locations in South Korea: the current growing region (Gosan) and an unfavorable cold winter region (Chuncheon, Gangwon-do). The model simulated the growth and development of a southern-type cultivar (Namdo, Jeju-do) reasonably well. Under RCP (Representative Concentration Pathway) scenarios, an overall delay in optimal planting date from a week to a month and a slight increase in potential yield were expected in Gosan. Expansion of growing region to northern area including Chuncheon was expected due to mild winter temperatures in the future and may allow Namdo cultivar production in more regions. The predicted optimal planting date in the new region was similar to the current growing region that favors early fall planting. Our new integrative garlic model provides mechanistic, process-driven crop responses to environmental cues and can be useful for assessing climate impacts and identifying crop specific climate adaptation strategies for the future. An mRNA SARS-CoV-2 vaccine employing Charge-Altering Releasable Transporters with a TLR-9 agonist induces neutralizing antibodies and T cell memory The SARS-CoV-2 pandemic has necessitated the rapid development of prophylactic vaccines. Two mRNA vaccines have been approved for emergency use by the FDA and have demonstrated extraordinary effectiveness. The success of these mRNA vaccines establishes the speed of development and therapeutic potential of mRNA. These authorized vaccines encode full-length versions of the SARS-CoV-2 spike protein. They are formulated with Lipid Nanoparticle (LNP) delivery vehicles that have inherent immunostimulatory properties. Different vaccination strategies and alternative mRNA delivery vehicles would be desirable to ensure flexibility of future generations of SARS-CoV-2 vaccines and the development of mRNA vaccines in general.

Here, we report on the development of an alternative mRNA vaccine approach using a delivery vehicle called Charge-Altering Releasable Transporters (CARTs). Using these inherently nonimmunogenic vehicles we can tailor the vaccine immunogenicity by inclusion of co-formulated adjuvants such as oligodeoxynucleotides with CpG motifs (CpG-ODN). Mice vaccinated with the mRNA-CART vaccine developed therapeutically relevant levels of RBD-specific neutralizing antibodies in both the circulation and in the lung bronchial fluids. In addition, vaccination elicited strong and long lasting RBD-specific TH1 T cell responses including CD4+ and CD8+ T cell memory. Integrative transcriptomics reveals ectopic lipid homeostasis mechanisms in non-endocrine cells of the teleost pituitary Directing both organismal homeostasis and physiological adaptation, the pituitary is a key endocrine gland in all vertebrates. It communicates the needs of the organism to different organs by secreting hormones into the bloodstream. Here, we have used the model fish medaka to investigate the developmental dynamics in the pituitary using a comprehensive RNA-seq time series. By linking developmental expression trends to single-cell RNA-seq profiles, we show how the transcriptional activities of cell types change during sexual maturation. One of the most prominent changes is the decline of the non-endocrine folliculo-stellate cell populations, and especially of rare cells expressing genes encoding secreted lipid transport proteins. As these genes are typically associated with the liver, this reveals the existence of unexpected connections between endocrine communication, lipid homeostasis, and sexual maturation. Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact,  dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop  cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease. JACKIE: Fast enumeration of genomic single- and multi-copy target sites and their off-targets for CRISPR and other engineered nuclease systems ZFP-, TALE-, and CRISPR-based methods for genome, epigenome editing and imaging have provided powerful tools to interrogate functions of genomes. Targeting sequence design is vital to the success of these experiments. While existing design software mainly focus on designing target sequence for specific elements, we report here the implementation of JACKIE (Jackie and Alberts Comprehensive K-mer Instances Enumerator), a suite of software for enumerating all single- and multi-copy sites in the genome that can be incorporated for genome-scale designs as well as loaded onto genome browsers alongside other tracks for convenient web-based graphic-user-interface (GUI)-enabled design. We also implement fast algorithms to identify sequence neighborhoods or off-target counts of targeting sequences so that designs with low probability of off-target can be identified among millions of design sequences in reasonable time. We demonstrate the application of JACKIE-designed CRISPR site clusters for genome imaging. High school science fair: Experiences of two groups of undergraduate bioscience students Science fairs offer potential opportunities for students to learn first-hand about the practices of science. Over the past six years we have been carrying out voluntary and anonymous surveys with regional and national groups of high school and post high school students to learn about their high school science fair experiences regarding help received, obstacles encountered, and opinions about the value and impact of science fair. Understanding what students think about science fairs will help educators make science fairs more effective learning opportunities. In this paper, we focus on the findings with two national groups of post high school students - undergraduate research fellows (SURF students) who did research at UT Southwestern Medical Center during 2014-2019 and undergraduates biology students attending the 2019 Howard Hughes Medical Institute Science Education Alliance (SEA) summer symposium. About 25% of the students who completed surveys indicated that they had participated in high school science fair, but more than half attended high schools where science fairs were unavailable. Effectively, 6 out of every 10 students participated in science fair if available. Students who could have participated in high school science fair but chose not to do so identified not enough time and coming up with their project idea as major reasons why not. About half the SURF students favored requiring non-competitive science fair regardless whether they themselves had participated in science fair. On the other hand, less than 1 in 5 thought that competitive science fair should be required. Introduction to the scientific process and general learning were mentioned most frequently as the reasons to require non-competitive science fair; these reasons were mentioned rarely in connection with competitive science fair. Unlike the national cohort of high school students we surveyed previously, who mostly did science fair in 9th and 10th grades, SURF students participated in science fair throughout high school and were twice as likely as high school students to have carried out science fair more than once. In conclusion, our findings suggest that participation of the undergraduate bioscience majors in high school science fairs occurs far more frequently than recognized previously. The findings emphasize further the importance of incentivizing rather than requiring science fair participation, especially in 9th and 10th grades, and the potential value of developing non-competitive science fairs. Environment dependent benefits of inter-individual variation in honey bee recruitment Inter-individual differences in behaviour within the members of a social group can affect the groups productivity. In eusocial insects, individual differences amongst workers in a colony play a central role division of labour and task allocation. Extensive empirical and theoretical work has highlighted variation in response thresholds as a proximate mechanism underlying individual behavioural differences and hence division of labour. However, other response parameters, like response probability and intensity, can affect these differences. In this study, we first extended a previously published agent-based model on honey bee foraging to understand the relative importance of response (dance) probability and response (dance) intensity in the task of recruitment. Comparing variation obtained from the simulations with previously published empirical data, we found that response intensity plays a more important role than probability in producing consistent inter-individual differences in recruitment behaviour. We then explored the benefits provided by this individual variation in recruitment behaviour to the colonys collective foraging effort under different environmental conditions. Our results revealed that individual variation leads to a greater energetic yield per forager, but only when food is abundant. Our study highlights the need to consider all response parameters while studying division of labour and adds to the growing body of evidence linking individual variation in behavioural responses to the success of social groups. Bacterial H-NS contacts DNA at the same irregularly spaced sites in both bridged and hemi-sequestered linear filaments Gene silencing in bacteria is mediated by chromatin proteins, of which Escherichia coli H-NS is a paradigmatic example. H-NS forms nucleoprotein filaments with either one or two DNA duplexes. However, the structures, arrangements of DNA-binding domains (DBDs), and positions of DBD-DNA contacts in linear and bridged filaments are uncertain. To characterize the contacts that silence transcription by RNA polymerase, we combined {middle dot}OH footprinting, molecular dynamics, statistical modeling, and DBD mapping using a chemical nuclease (Fe2+-EDTA) tethered to the DBDs (TEN-map). We find that H-NS DBDs contact DNA at indistinguishable locations in bridged or linear filaments and that the DBDs vary in orientation and position with ~10-bp average spacing. Our results support a hemi-sequestration model of linear-to-bridged H-NS switching in which linear filaments able to inhibit only transcription initiation switch to bridged filaments able to inhibit both initiation and elongation using the same irregularly spaced DNA contact sites.

HighlightsO_LITethered-nuclease mapping (TEN-map) of H-NS DNA-binding domains detects DNA contacts
C_LIO_LIBridged and linear H-NS filaments use the same DNA contact sites
C_LIO_LIH-NS-DNA contacts are unevenly spaced with ~10 bp average separation
C_LIO_LIAT-steps, minor groove width, and electrostatic potential best predict contact sites
C_LI Convergent evolution of multiple mutations improves the viral fitness of SARS-CoV-2 variants by balancing positive and negative selection Multiple mutations have been seen to undergo convergent evolution in SARS-CoV-2 variants of concern. One such evolution occurs in Beta, Gamma, and Omicron variants at three amino acid positions K417, E484, and N501 in the receptor binding domain of the spike protein. We examined the physical mechanisms underlying the convergent evolution of three mutations K417T/E484K/N501Y by delineating the individual and collective effects of mutations on binding to angiotensin converting enzyme 2 receptor, immune escape from neutralizing antibodies, protein stability and expression. Our results show that each mutation serves a distinct function that improves virus fitness supporting its positive selection, even though individual mutations have deleterious effects that make them prone to negative selection. Compared to the wild-type, K417T escapes Class 1 antibodies, has increased stability and expression; however, it has decreased receptor binding. E484K escapes Class 2 antibodies; however, it has decreased receptor binding, stability and expression. N501Y increases receptor binding; however, has decreased stability and expression. When these mutations come together, the deleterious effects are mitigated due to the presence of compensatory effects. Triple mutant K417T/E484K/N501Y has increased receptor binding, escapes both Class 1 and Class 2 antibodies, and has similar stability and expression as that of the wild-type. These results show the implications of presence of multiple mutations on virus evolution that enhance viral fitness on different fronts by balancing both positive and negative selection and improves the chances of selection of mutations together. 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. Testing for phylogenetic signal in single-cell RNA-seq data AO_SCPLOWBSTRACTC_SCPLOWPhylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing (WGS) or single-cell DNA sequencing (scDNA-seq) and are based on calling copy number alterations and single nucleotide variants (SNVs). Here we explore the potential of single-cell RNA sequencing (scRNA-seq) to reconstruct cancer evolutionary dynamics. scRNA-seq is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this paper, we demonstrate for the first time that scRNA-seq data contains sufficient evolutionary signal and can be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer. IRGM/Irgm1 Aggravates Progression of Atherosclerosis by Inducing Macrophage Apoptosis through the MAPK Signaling Pathway AimsAtherosclerosis underlies most cardiovascular diseases, among which acute coronary syndrome (ACS) caused by plaque rupture (PR) often leads to death. Immune-related GTPases (IRGM/Irgm1) have been extensively studied in inflammatory diseases, but their role in atherosclerosis is unclear. Determining how IRGM/Irgm1 promotes atherosclerotic plaque vulnerability will provide information for new biomarkers and/or therapeutic targets.

Methods and resultsWe identified ruptured and unruptured plaques by optical coherence tomography, and found that serum IRGM was highly expressed in patients with ST-segment elevation myocardial infarction in PR. We used ApoE-/-Irgm1+/+, ApoE-/-Irgm1+/- mice and chimeric mice to establish a model of advanced atherosclerosis. The results of pathological experiments showed that Irgm1 caused plaque necrosis. The ratio of neutral lipids and cholesterol crystals increases, while the content of collagen fibers decreases, aggravating the destabilization of atherosclerotic plaques. In vitro, we used multiple approaches to confirm that Irgm1 promotes macrophage apoptosis by promoting the production of reactive oxygen species and activating the MAPK signaling pathway.

ConclusionsIRGM may be a potential risk factor for PR. Mechanistic studies have shown that IRGM/Irgm1 contributes to the formation and rupture of fragile plaques. This is partly mediated by the induction of macrophage apoptosis via the MAPK signaling pathway. IRGM may offer new strategies for early treatment of ACS.

Translation viewOur findings indicate that IRGM/Irgm1 contributes to formation and rupture of vulnerable plaques. It suggests that IRGM may provide a new target for the early treatment of ACS. Impedance scaling for gold and platinum microelectrodes ObjectiveElectrical measurement of the activity of individual neurons is a primary goal for many invasive neural electrodes. Making these "single unit" measurements requires that we fabricate electrodes small enough so that only a few neurons contribute to the signal, but not so small that the impedance of the electrode creates overwhelming noise or signal attenuation. Thus, neuroelectrode design often must strike a balance between electrode size and electrode impedance, where the impedance is often assumed to scale linearly with electrode area.

Approach and main resultsHere we study how impedance scales with neural electrode area and find that the 1 kHz impedance of Pt electrodes (but not Au electrodes) transitions from scaling with area (r-2) to scaling with perimeter (r-1) when the electrode radius falls below 10 microns. This effect can be explained by the transition from planar to spherical diffusion behavior previously reported for electrochemical microelectrodes. Significance: These results provide important intuition for designing small, single unit recording electrodes. Specifically, for materials where the impedance is dominated by a pseudo-capacitance that is associated with a diffusion limited process, the total impedance will scale with perimeter rather than area when the electrode size becomes comparable with the diffusion layer thickness. For Pt electrodes this transition occurs around 10 um radius electrodes. At even lower frequencies (1 Hz) impedance approaches a constant. This transition to r-1 scaling implies that electrodes with a pseudo-capacitance can be made smaller than one might expect before thermal noise or voltage division limits the ability to acquire high-quality single-unit recordings. Hidden dynamics of proteasome autoregulation discovered by cryo-EM data-driven deep learning The cellular functions are executed by macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving conformational continuum of important complexes at atomic level remains a key challenge in the methodology of computational biophysics. Here we introduce a deep-learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy reconstructions of nonequilibrium conformational continuum. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. Applications of this methodology to six experimental datasets demonstrated its generality in breaking resolution barrier of visualizing dynamic components of functional complexes, in choreographing continuous inter-subunit motions and in exploring their  hidden conformational space. Importantly, AlphaCryo4D identified 64 conformers of the substrate-bound human 26S proteasome and reconstituted single-nucleotide-exchange dynamics of proteasomal AAA-ATPase motor during translocation initiation, which together illuminates a grand hierarchical allostery for proteasome autoregulation. G protein-coupled receptor signaling regulates ER-mitochondria contacts Interactions between the endoplasmic reticulum (ER) and mitochondria (Mito) are crucial for many cellular functions, and their interaction levels change dynamically depending on the cellular environment. Little is known about how the interactions between these organelles are regulated within the cell. Here we screened a compound library to identify chemical modulators for ER-Mito contacts. Multiple agonists of G-protein coupled receptors (GPCRs), beta-adrenergic receptors ({beta}-ARs) in particular, scored in this screen. Analyses in multiple orthogonal assays validated that GPCR activation promotes physical and functional interactions between the two organelles. Furthermore, we have elucidated potential downstream effectors mediating GPCR-induced ER-Mito contacts. Given the role of GPCRs in sensing various extracellular signals and subsequently eliciting appropriate cellular responses, our data provide significant insights into the roles of ER-Mito contacts in maintaining cellular homeostasis and in responding to physiological demands or stresses. Super-enhancer signature reveals key mechanisms associated with resistance to non-alcoholicsteatohepatitis in humans with obesity The molecular underpinnings of non-alcoholic steatohepatitis (NASH) development in patients are poorly understood. Active enhancer landscapes are known to determine cell states and behaviors. Super-enhancers, in particular, have helped reveal key disease drivers in several cancer types; however, they remain unexplored in human NASH. To define the enhancer signature of NASH-prone (NP) and NASH-resistant (NR) phenotypes in humans with obesity, we performed chromatin run-on sequencing (ChRO-seq) analysis on liver biopsies of individuals with obesity who were stratified into either NP or NR. We first demonstrated that NP and NR groups exhibit distinct active enhancer signatures. The subsequent identification of NP- and NR-specific super-enhancers revealed the specific genes that are likely the most critical for each of the phenotypes, including HES1 for NP and GATM for NR. Integrative analysis with results from genome-wide association studies of NAFLD and related traits identified disease/trait-loci specific to NP or NR enhancers. Further analysis of the ChRO-seq data pointed to critical roles for serine/glycine metabolism in NASH resistance, which was corroborate by profiling of circulating amino acids in the same patients. Overall, the distinct enhancer signatures of human NP and NR phenotypes revealed key genes, pathways, and transcription factor networks that promote NASH development.