SARS-CoV-2 RdRp uses NDPs as a substrate and is able to incorporate NHC into RNA from diphosphate form molnupiravir The coronavirus disease 2019 (COVID-19) has been ravaging throughout the world for more than two years and has severely impaired both human health and the economy. The causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) employs the viral RNA-dependent RNA polymerase (RdRp) complex for genome replication and transcription, making RdRp an appealing target for antiviral drug development. Here, we reveal that RdRp can recognize and utilize nucleoside diphosphates (NDPs) as a substrate to synthesize RNAs with an efficiency of about two thirds of using nucleoside triphosphates (NTPs) as a substrate. NDPs incorporation is also template-specific and has high fidelity. Moreover, RdRp can incorporate {beta}-d-N4-hydroxycytidine (NHC) into RNA while using diphosphate form molnupiravir (MDP) as a substrate. We also observed that MDP is a better substrate for RdRp than the triphosphate form molnupiravir (MTP).
Brain deletion of Serf2 shifts amyloid conformation in a mouse model Loss of protein homeostasis accelerates ageing and contributes to age-related brain diseases like Parkinsons and Alzheimers diseases. Disease-specific proteins become aggregation-prone and form amyloid deposits that characterize disease, but the biological mechanisms driving their transition in cells and tissues are poorly understood. MOAG-4/SERF accelerates aggregation and toxicity of disease-related proteins in worm- and cell models. Whether MOAG-4/SERF affects amyloid pathology in brain, however, has remained unknown. Here we show that brain-specific knockout of SERF2 is viable and shifts aggregation in a mouse model for amyloid-beta aggregation. Without affecting amyloid-beta levels, knockout of SERF2 redirects its assembly into structurally and morphologically distinct amyloid fibrils and deposits. Our results show that SERF modifies the structural outcome of the aggregation process in mammalian brain. With structural polymorphisms recently being associated with disease severity and progression, our results may help to understand the molecular basis for structural diversity and find possibilities for structure-based interventions.

HighlightsO_LILoss of SERF2 slows embryonic development and causes perinatal lethality
C_LIO_LISERF2 affects proliferation in a cell-autonomous fashion
C_LIO_LIBrain-specific Serf2 knockout does not affect viability or A{beta} production
C_LIO_LIBrain deletion of Serf2 shifts the amyloid conformation of A{beta}
C_LI
RNA-binding protein Mub1 and the nuclear RNA exosome act to fine-tune environmental stress response The nuclear RNA exosome plays a key role in mediating degradation and processing of multiple cellular RNAs. Recognition of the specific RNA substrates by the exosome is mediated by the RNA-binding co-factors. Transient binding of co-factors either to the exosome or the substrate RNAs as well as their rapid decay make identification of the co-factors challenging. Here, we employ a comparative poly(A)+ RNA interactome capture approach in the fission yeast exosome mutants to identify proteins that interact with poly(A)+ RNA in an exosome-dependent manner. Our analyses identified multiple proteins whose occupancy on RNA is altered in the exosome mutants including zinc-finger protein Mub1. Mub1 is required to maintain the levels of a subset of the exosome RNA substrates including mRNAs encoding for stress-responsive proteins. Removal of the zinc finger domain leads to loss of RNA suppression under non-stressed conditions, altered expression of heat shock genes in response to stress, and reduced growth at elevated temperature. These findings highlight importance of exosome-dependent mRNA degradation in buffering gene expression networks to mediate cellular adaptation to stress. 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. Using Obviously-Related Instrumental Variables to Increase the Predictive Power of Polygenic Scores The conventional way of boosting the predictive power of polygenic scores is to increase the GWAS sample size by meta-analyzing GWAS results of multiple cohorts. In this paper, we challenge this convention. Through simulations, we show that Instrumental Variable (IV) regression using two polygenic scores constructed from independent GWAS summary statistics outperforms the typical Ordinary Least Squares (OLS) model employing a single meta-analysis based polygenic score in terms of bias, root mean squared error, and statistical power. We verify the empirical validity of the simulations by predicting educational attainment (EA) and height in a sample of siblings from the UK Biobank. We show that IV regression between-families approaches the SNP-based heritability, and improves the predictive power of polygenic scores by 12% (height) to 22% (EA). Furthermore, IV regression within-families provides the tightest lower bound for the direct genetic effect, increasing the lower bound for EA from 2.0% to 3.4%, and for height from 28.9% to 37.7%. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions AO_SCPLOWBSTRACTC_SCPLOWDrug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (AUC = 0.83 for synergy, AUC = 0.98 for antagonism) in predicting drug interactions for E. coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population-FBA identified two sub-populations of E. coli cells defined by regulation of cis- to trans-isomerization of unsaturated fatty acids with distinct effects on drug interactions. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

SO_SCPLOWIGNIFICANCEC_SCPLOWWorldwide, 700,000 people die each year from drug-resistant infections. Drug combinations have great potential to reduce the spread of drug-resistant bacteria. However, their potency is impacted by both the pathogen growth environment and the heterogeneity in pathogen metabolism. The metabolic heterogeneity in a pathogen population allows them to survive antibiotic treatment. Here we present a flexible machine-learning framework that utilizes diverse data types to effectively search through the large design space of both sequential and simultaneous combination therapies across hundreds of simulated growth conditions and pathogen metabolic states. Our approach can serve as a useful guide for the selection of robustly synergistic drug combinations. Electrical Stimulated GLUT4 Signaling Attenuates Critical Illness-Associated Muscle Wasting BackgroundCritical illness myopathy (CIM) is a debilitating condition characterized by the preferential loss of the motor protein myosin. CIM is a byproduct of critical care, attributed to impaired recovery, long-term complications, and mortality. CIM pathophysiology is complex, heterogeneous and remains incompletely understood, however loss of mechanical stimuli contributes to critical illness associated muscle atrophy and weakness. Passive mechanical loading (ML) and electrical stimulation (ES) therapies augment muscle mass and function. While having beneficial outcomes, the mechanistic underpinning of these therapies is less known. Therefore, here we aimed to assess the mechanism by which chronic supramaximal ES ameliorates CIM in a unique experimental rat model of critical care.

MethodsRats were subjected to 8 days critical care conditions entailing deep sedation, controlled mechanical ventilation, and immobilization with and without direct soleus ES. Muscle size and function were assessed at the single cell level. RNAseq and Western blotting were employed to understand the mechanisms driving ES muscle outcomes in CIM.

ResultsFollowing 8 days of controlled mechanical ventilation and immobilization, soleus muscle mass, Myosin:Actin ratio and single muscle fiber maximum force normalized to cross-sectional area (specific force) were reduced by 40-50% (p< 0.0001). ES significantly reduced the loss of soleus muscle fiber cross-sectional area (CSA) and Myosin:Actin ratio by approximately 30% (p< 0.05) yet failed to effect specific force. RNAseq pathway analysis revealed downregulation of insulin signaling in the soleus muscle following critical care and GLUT4 trafficking was reduced by 55% leading to an 85% reduction of muscle glycogen content (p< 0.01). ES promoted phosphofructokinase and insulin signaling pathways to control levels (p< 0.05), consistent with the maintenance of GLUT4 translocation and glycogen levels. AMPK, but not AKT, signaling pathway was stimulated following ES, where the downstream target TBC1D4 increased 3 logFC (p= 0.029) and AMPK-specific P-TBC1D4 levels were increased approximately 2-fold (p= 0.06). Reduction of muscle protein degradation rather than protein synthesis promoted soleus CSA, as ES reduced E3 ubiquitin proteins, Atrogin-1 (p= 0.006) and MuRF1 (p= 0.08) by approximately 50%, downstream of AMPK-FoxO3.

ConclusionsES maintained GLUT4 translocation through increased AMPK-TBC1D4 signaling leading to improved muscle glucose homeostasis. Soleus CSA and myosin content was promoted through reduced protein degradation via AMPK-FoxO3 E3 ligases, Atrogin-1 and MuRF1. These results demonstrate chronic supramaximal ES reduces critical care associated muscle wasting, preserved glucose signaling and reduced muscle protein degradation in CIM. Novel Feline Papillomavirus Isolate P20 Assembled from Metagenomic Data Isolated from the Human Skin of a House Cat Owner A feline papillomavirus was assembled from metagenomic sequencing data collected from the human skin of a house cat owner. This circular papillomavirus strain P20 is 8069 bp in length, has a GC content of 54.38%, and displays genome organization typical of feline papillomaviruses with six annotated protein coding regions. The genome exhibits approximately 75% sequence similarity to other feline papillomavirus genomes. Highly scaled measurements of C. elegans development suggest that physical constraints guide growth trajectories and animal shape Growth control establishes organism size, requiring mechanisms to sense and adjust growth during development. Studies of single cells revealed that size homeostasis uses distinct control methods. In multicellular organisms, mechanisms that regulate single cell growth must integrate control across organs and tissues during development to generate adult size and shape. We leveraged the roundworm Caenorhabditis elegans as a scalable and tractable model to collect precise growth measurements of thousands of individuals, measure feeding behavior, and quantify changes in animal size and shape during a densely sampled developmental time course. As animals transitioned from one developmental stage to the next, we observed changes in body aspect ratio while body volume remained constant. Then, we modeled a physical mechanism by which constraints on cuticle stretch could cause changes in C. elegans body shape. The model-predicted shape changes are consistent with those observed in the data. Theoretically, cuticle stretch could be sensed by the animal to initiate larval-stage transitions, providing a means for physical constraints to influence developmental timing and growth rate in C. elegans.

HighlightsO_LIBody size measurements of thousands of animals in a dense developmental time course
C_LIO_LIGrowth rate exhibits nonlinear dynamics in both length and width
C_LIO_LIChanges in body shape but not volume occur during periods of increased quiescence
C_LIO_LIDynamics of animal shape consistent with a length-based threshold in cuticle stretch
C_LIO_LIModeling of cuticle stretch dynamics suggests a novel mode for growth control
C_LI Synergistic balance of local segregation and global integration mechanisms in large-scale brain networks underlies perceptual experience Perceptual processes unfold along spatial and temporal hierarchies in brain dynamics. In daily lives, speech perception requires binding of spatiotemporally disjoint auditory and visual cues by distributed brain areas. The network-level information processing involving these brain areas can be characterized by two complementary mechanisms, functional segregation which refers to localization of processing within few areas, and integration which pertains to the cooperation among relevant functional modules. Here, we demonstrate using fMRI recordings that subjective perceptual experience of multisensory speech stimuli--real and illusory--are represented in differential states of segregation-integration balance. We were able to parametrically control the inter-subject variability of illusory/cross-modal perception by introducing temporal lags in the incongruent auditory-visual articulations of speech sounds using the McGurk paradigm. The functional segregation-integration balance is captured using two alternative computational approaches. First, the segregation of brain areas as modules responsible for illusory speech perception is captured using standardized parametric statistical approaches. The functional integration was quantified from exploring the temporal correlation of the relevant functional modules with the dynamics of all other brain areas. We observed that the regions with higher activation during illusory perception tended to have lower levels of cooperation with the rest of the brain indicating high levels of local segregation, but reduced global integration. Second, using graph theoretic measures of local clustering, modularity, and participation coefficient, which capture the contribution of segregation and integration among functional modules, we further validated the observation of enhanced local segregation and diminished global integration during the illusory/cross-modal perception. Further, the perceptual binding network that exhibited this observation was anticorrelated with other intrinsic functional brain networks, such as dorsal attention and default mode networks, and most importantly, inter-subject variability of perceptual categorization could be explained from the altered balance of local segregation-global integration. Evaluation of US state pollinator plans using 3 evidence-based policymaking frameworks After the US federal government created a national pollinator protection plan in 2015, many states followed with their own. Since their goal is to promote pollinating insect conservation, we wanted to know whether the state plans are using best practices for evidence-based science policy. In early 2019 we found and downloaded every existing, publicly available US state pollinator protection plan. We then used content analysis to assess the goals, scope, and implementation of state-level pollinator protection plans across the US. This analysis was conducted using three distinct frameworks for evidence-based policymaking: US Department of Interior Adaptive Resources Management (ARM), US Environmental Protection Agency management pollinator protection plan (MP3) guidance, and Pew Trusts Pew-MacAthur Results First Project elements of evidence-based state policymaking (PEW) framework. Then we scored them using the framework criteria, to assess whether the plans were using known best practices for evidence based policymaking. Of the 31 states with a state pollinator plan, Connecticut was the state with the lowest total score across the three evaluation frameworks. The state with the highest overall scores, across the three frameworks, was Missouri. Most states did not score highly on the majority of the frameworks. Overall, many state plans were lacking policy elements that address monitoring, evaluation, and adjustment. These missing elements impact states ability to achieve their conservation goals. Our results indicate that states can improve their pollinator conservation policies to better match evidence-based science policy guidance, regardless of which framework is used. First detection of herpesvirus and frequency of mycoplasma infection in free-ranging Hermann s tortoises (Testudo hermanni), and in potential pet vectors AO_SCPLOWBSTRACTC_SCPLOWTwo types of pathogens cause highly contagious upper respiratory tract diseases (URTD) in Chelonians: testudinid herpesviruses (TeHV) and a mycoplasma (Mycoplasma agassizii). In captivity, these infections are frequent and can provoke outbreaks. Pet trade generates international flow of tortoises, often without sanitary checking; individuals intentionally or accidentally released in the wild may spread pathogens. A better understanding of the transmission of infectious agents from captivity to wild tortoises is needed. Many exotic individuals have been introduced in populations of the endangered western Hermanns tortoise (Testudo hermanni hermanni), notably spur-thighed tortoises (Testudo graeca). We assessed the presence of TeHV and mycoplasma in native western Hermanns tortoises and in potential pet vectors in south-eastern France. Using a large sample (N=572 tortoises), this study revealed, by PCR, the worrying presence of herpesvirus in 7 free-ranging individuals (3 sub-populations). Additionally, Mycoplasma agassizii was detected, by PCR, in 15 of the 18 populations sampled with a frequency ranging from 3.4% (1 of 29 tortoises) to 25% (3 of 12 tortoises). Exotic spur-thighed tortoises showed high frequency of mycoplasma infection in captivity (18.2%) and in individuals (50%) found in native Hermanns tortoise sub-populations, suggesting that this species could be a significant vector. The paucity of information of TeHV on European tortoise URTD in natural settings, especially in combination with mycoplasma, prompts for further studies. Indeed, sick tortoises remain concealed and may not be easily detected in the field. Our results indicate a good health for most infected tortoise but it should be screened in the field as well as in captivity. 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. Membrane insertion mechanism of the caveolae coat protein Cavin1 Caveolae are small plasma membrane invaginations, important for control of membrane tension, signaling cascades and lipid sorting. The caveolae coat protein Cavin1 is essential for shaping such high curvature membrane structures. Yet, a mechanistic understanding of how Cavin1 assembles at the membrane interface is lacking. Here, we used model membranes combined with biophysical dissection and computational modelling to show that Cavin1 inserts into membranes. We establish that initial PI(4,5)P2-dependent membrane adsorption of the trimeric helical region 1 (HR1) of Cavin1 mediates the subsequent partial separation and membrane insertion of the individual helices. Insertion kinetics of the HR1 is further enhanced by the presence of flanking negatively charged disordered regions, which was found important for the co-assembly of Cavin1 with Caveolin1 in living cells. We propose that this intricate mechanism potentiates membrane curvature generation and facilitates dynamic rounds of assembly and disassembly of Cavin1 at the membrane.

Significance statementCaveolae are cholesterol enriched membrane invaginations coupled to severe muscle and lipid disorders. Their formation is dependent on assembly of the protein Cavin1 at the lipid membrane interface driving membrane curvature. In this work, we dissect the mechanism for how Cavin1 binds and inserts into membranes using a combination of biochemical and biophysical characterization as well as computational modelling. The proposed model for membrane assembly potentiates dynamic switching between shielded and exposed hydrophobic helices used for membrane insertion and clarifies how Cavin1 can drive membrane curvature and the formation of caveolae. Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3 in size with as good as 5 m resolution at a rate of up to one scan every 30 minutes. The system tracked the movement of Bacillus subtilis populations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point "first contact", before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions. Anaplerotic flux into the Calvin-Benson cycle. Hydrogen isotope evidence for in vivo occurrence in C3 metabolism - As the central carbon uptake pathway in photosynthetic cells, the Calvin-Benson cycle is among the most important biochemical cycles for life on Earth. A carbon flux of anaplerotic origin (i.e., through the chloroplast-localised oxidative branch of the pentose phosphate pathway) into the Calvin-Benson cycle was proposed recently.
- Here, we measured intramolecular deuterium abundances in leaf starch of Helianthus annuus grown at varying ambient CO2 concentrations, Ca. Additionally, we modelled deuterium fractionations expected for the anaplerotic pathway and compared modelled with measured fractionations.
- We report deuterium fractionation signals at H1 and H2 of starch glucose. Below a Ca change point, these signals increase with decreasing Ca consistent with modelled fractionations by anaplerotic flux. Under standard conditions (Ca=450 ppm corresponding to intercellular CO2 concentrations, Ci, of 328 ppm), we estimate negligible anaplerotic flux. At Ca=180 ppm (Ci=140 ppm), more than 10% of the glucose 6-phosphate entering the starch biosynthesis pathway is diverted into the anaplerotic pathway.
- In conclusion, we report evidence consistent with anaplerotic carbon flux into the Calvin-Benson cycle in vivo. We propose the flux may help to (i) maintain high levels of ribulose 1,5-bisphosphate under source-limited growth conditions to facilitate photorespiratory nitrogen assimilation required to build-up source strength and (ii) counteract oxidative stress. Fatal neuroinvasion and SARS-CoV-2 tropism in K18-hACE2 mice is partially independent on hACE2 expression Animal models recapitulating distinctive features of severe COVID-19 are critical to enhance our understanding of SARS-CoV-2 pathogenesis. Transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) under the cytokeratin 18 promoter (K18-hACE2) represent a lethal model of SARS-CoV-2 infection. The precise mechanisms of lethality in this mouse model remain unclear. Here, we evaluated the spatiotemporal dynamics of SARS-CoV-2 infection for up to 14 days post-infection. Despite infection and moderate pneumonia, rapid clinical decline or death of mice was invariably associated with viral neuroinvasion and direct neuronal injury (including brain and spinal neurons). Neuroinvasion was observed as early as 4 dpi, with virus initially restricted to the olfactory bulb supporting axonal transport via the olfactory neuroepithelium as the earliest portal of entry. No evidence of viremia was detected suggesting neuroinvasion occurs independently of entry across the blood brain barrier. SARS-CoV-2 tropism was not restricted to ACE2-expressing cells (e.g., AT1 pneumocytes), and some ACE2-positive lineages were not associated with the presence of viral antigen (e.g., bronchiolar epithelium and brain capillaries). Detectable ACE2 expression was not observed in neurons, supporting overexpression of ACE2 in the nasal passages and neuroepithelium as more likely determinants of neuroinvasion in the K18-hACE2 model. Although our work incites caution in the utility of the K18-hACE2 model to study global aspects of SARS-CoV-2 pathogenesis, it underscores this model as a unique platform for exploring the mechanisms of SARS-CoV-2 neuropathogenesis that may have clinical relevance acknowledging the growing body of evidence that suggests COVID-19 may result in long-standing neurologic consequences.

IMPORTANCECOVID-19 is predominantly a respiratory disease caused by SARS-CoV-2 that has infected more than 191 million people with over 4 million fatalities (2021-07-20). The development of animal models recapitulating distinctive features of severe COVID-19 is critical to enhancing our understanding of SARS-CoV-2 pathogenesis and in the evaluation of vaccine and therapeutic efficacy. Transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) under the cytokeratin 18 promoter (K18-hACE2) represent a lethal model of SARS-CoV-2 infection. Here, we show lethality of this model is invariably associated with viral neuroinvasion linked with viral replication and assembly. Importantly, pneumonia albeit invariably present was generally moderate with the absence of culturable infectious virus at peak neuroinvasion. The dynamics of viral neuroinvasion and pneumonia were only partially dependent on hACE2. Overall, this study provides an in-depth sequential characterization of the K18-hACE2 model following SARS-CoV-2 infection, highlighting its significance to further study the mechanisms of SARS-CoV-2 neuropathogenesis. A Novel Paradigm for Deep Reinforcement Learning of Biomimetic Systems Mechanisms behind neural control of movement have been an active area of research. Goal-directed movement is a common experimental setup used to understand these mechanisms and relevant neural pathways. On the one hand, optimal feedback control theory is used to model and make quantitative predictions of the coordinated activations of the effectors, such as muscles, joints or limbs. While on the other hand, evidence shows that higher centres such as Basal Ganglia and Cerebellum are involved in activities such as reinforcement learning and error correction. In this paper, we provide a framework to build a digital twin of relevant sections of the human spinal cord using our NEUROiD platform. The digital twin is anatomically and physiologically realistic model of the spinal cord at cellular, spinal networks and system level. We then build a framework to learn the supraspinal activations necessary to perform a simple goal directed movement of the upper limb. The NEUROiD model is interfaced to an Opensim model for all the musculoskeletal simulations. We use Deep Reinforcement Learning to obtain the supraspinal activations necessary to perform the goal directed movement. As per our knowledge, this is the first time an attempt is made to learn the stimulation pattern at the spinal cord level, especially by limiting the observation space to only the afferent feedback received on the Ia, II and Ib fibers. Such a setup results in a biologically realistic constrained environment for learning. Our results show that (1) Reinforcement Learning algorithm converges naturally to the triphasic response observed during goal directed movement (2) Increasing the complexity of the goal gradually helped to accelerate learning (3) Modulation of the afferent inputs were sufficient to execute tasks which were not explicitly learned, but were closely related to the learnt task. Long term analysis of social structure: evidence of age-based consistent associations in male Alpine ibex Despite its recognized importance for understanding the evolution of animal sociality as well as for conservation, long term analysis of social networks of animal populations is still relatively uncommon. We investigated social network dynamics in males of a gregarious mountain ungulate (Alpine ibex, Capra ibex) over ten years focusing on groups, sub-groups and individuals, exploring the dynamics of sociality over different scales. Despite the social structure changing between seasons, the Alpine ibex population was highly cohesive: fission-fusion dynamics lead almost every male in the population to associate with each other male at least once. Nevertheless, we found that male Alpine ibex showed preferential associations that were maintained across seasons and years. Age seemed to be the most important factor driving preferential associations while other characteristics, such as social status, appeared less crucial. We also found that centrality measures were influenced by age and were also related to individual physical condition. The multi-scale and long-term frame of our study helped us show that ecological constrains, such as resource availability, may play a role in shaping associations in a gregarious species, but they cannot solely explain sociality and preferential association that are likely also to be driven by life-history linked physiological and social needs. Our results highlight the importance of long-term studies based on individually recognizable subjects to help us build on our understanding of the evolution of animal sociality. A flexible method for estimating tip diversification rates across a range of speciation and extinction scenarios Estimates of diversification rates at the tips of a phylogeny provide a flexible approach for correlation analyses with multiple traits and to map diversification rates in space, while also avoiding the uncertainty of deep time rate reconstructions. Available methods for tip rate estimation make different assumptions, and thus their accuracy usually depends on characteristics of the underlying model generating the tree. Here we introduce MiSSE, a trait-free, state-dependent speciation and extinction approach that can be used to estimate varying speciation, extinction, net-diversification, turnover rates, and extinction fraction at the tips of the tree. We compare the accuracy of tip rates inferred by MiSSE against similar methods and demonstrate that, due to certain characteristics of the model, the error is generally low across a broad range of speciation and extinction scenarios. MiSSE can be used alongside regular phylogenetic comparative methods in trait related diversification hypotheses, and we also describe a simple correction to avoid pseudoreplication from sister tips in analyses of independent contrasts. Finally, we demonstrate the capabilities of MiSSE, with a renewed focus on classic comparative methods, to examine correlation between plant height and turnover rates in eucalypts, a species-rich lineage of flowering plants. Citation needed? Wikipedia and the COVID-19 pandemic BackgroundWith the COVID-19 pandemics outbreak, millions flocked to Wikipedia for updated information. Amid growing concerns regarding an "infodemic", ensuring the quality of information is a crucial vector of public health. Investigating if and how Wikipedia remained up to date and in line with science is key to formulating strategies to counter misinformation. Using citation analyses, we asked: which sources informed Wikipedias COVID-19-related articles before and during the pandemics first wave (January-May 2020).

ResultsWe found that coronavirus-related articles referenced trusted media sources and high-quality academic research. Moreover, despite a surge in COVID-19 preprints, Wikipedia had a clear preference for open-access studies published in respected journals and made little use of preprints. Building a timeline of English COVID-19 articles from 2001-2020 revealed a nuanced trade-off between quality and timeliness. It further showed how preexisting articles on key topics related to the virus created a framework for integrating new knowledge. Supported by a rigid sourcing policy, this "scientific infrastructure" facilitated contextualization and regulated the influx of new information. Lastly, we constructed a network of DOI-Wikipedia articles, which showed the shifting landscape of pandemic-related knowledge on Wikipedia and how academic citations create a web of shared knowledge supporting topics like COVID-19 vaccine development.

ConclusionsUnderstanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also reveals how, aided by what we term its "citizen encyclopedists", it successfully fended off COVID-19 disinformation and how this unique model may be deployed in other contexts. Protein contact map prediction using multiple sequence alignment dropout and consistency learning for sequences with fewer homologs The prediction of protein contact map needs enough normalized number of effective sequence (Nf) in multiple sequence alignment (MSA). When Nf is small, the predicted contact maps are often not satisfactory. To solve this problem, we randomly selected a small part of sequence homologs for proteins with large Nf to generate MSAs with small Nf. From these MSAs, input features were generated and were passed through a consistency learning network, aiming to get the same results when using the features generated from the MSA with large Nf. The results showed that this method effectively improves the prediction accuracy of protein contact maps with small Nf. Coronavirus-associated molecular mimicry through homology to a SARS-CoV-2 peptide could be leading to susceptibility in patients with HLA-A*02:01 and HLA-A*24:02 serotypes This study aims to predict autoimmunity-related pathological mechanisms that possess risk for individuals with specific human leukocyte antigen (HLA) serotypes and shared by certain coronaviruses including SARS-CoV-2, based on homology to a SARS-CoV-2 peptide. With the given aim, 1-) coronavirus-associated sequences, which are homologous to the 15mer SARS-CoV-2 peptide CFLGYFCTCYFGLFC, are obtained. 2-) Human peptides that have at least 7 residue matches with those coronavirus sequences, and the SARS-CoV-2 15mer, are found. 3-) Epitope pairs, which are sourced by those aligned coronavirus and human sequences are identified. 4-) Epitope pairs that are predicted to bind strongly not only to the same HLA allele with each other but also to the same HLA allele as those of the respective alignment of the SARS-CoV-2 peptide are selected. Following are the identified proteins or peptides (with HLA-A*02:01 or HLA-A*24:02 epitopes), as described in 1-to-4: Immunoglobulin heavy chain junction regions, CRB1 isoform I precursor, slit homolog 2 protein, hCG1995581, hCG2028737, phospholipid phosphatase-related protein type 2. Among those, CRB1 isoform I precursor sequence with the predicted HLA-A*24:02 epitope aligns with the highest number of different sequences. Results imply autoimmunity risk in COVID-19 patients with HLA-A*02:01 and HLA-A*24:02 serotypes, through molecular mimicry, as a shared pathogenicity risk that can be prevalent upon getting infected with certain coronaviruses. These can pave way to improved risk groups assessment and autoimmunity treatment options, for COVID-19 and its associated diseases. Also, the approach in this study can be used to predict prospective pathologies of the transmissible variants in susceptible humans. Synthetic Cooling Agents in US-marketed E-cigarette Refill Liquids and Popular Disposable E-cigarettes: Chemical Analysis and Risk Assessment. BackgroundMenthol, through its cooling and pleasant sensory effects, facilitates smoking and tobacco product initiation, resulting in the high popularity of mint/menthol-flavored E-cigarettes. More recently, E-cigarette vendors started marketing synthetic cooling agents as additives that impart a cooling effect but lack a characteristic minty odor. Knowledge about content of synthetic coolants in US-marketed E-cigarette products and associated health risks is limited.

MethodsE-liquid vendor sites were searched with the terms "koolada", "kool/cool", "ice", or WS-3/WS-23, denoting individual cooling agents, and relevant refill E-liquids were purchased. "Ice" flavor varieties of Puffbar, the most popular disposable E-cigarette brand, were compared with non-"Ice" varieties. E-liquids were characterized, and synthetic coolants quantified using GC/MS. Margin of exposure (MOE), a risk assessment parameter, was calculated to assess the risk associated with synthetic coolant exposure from E-cigarette use.

ResultsWS-3 was detected in 24/25 refill E-liquids analyzed. All Puffbar flavor varieties contained either WS-23 (13/14) or WS-3 (5/14), in both "Ice"- and non-"Ice" flavors. Modeling consumption of WS-3 from vaped E-liquids, resulted in MOEs below the safe margin of 100 for most daily use scenarios. MOEs for WS-23 were <100 for 10/13 Puffbar flavors in all use scenarios. Puffbar power specifications are identical to Juul devices.

ConclusionsSynthetic cooling agents (WS-3/WS-23) were present in US-marketed E-cigarettes, at levels that may result in consumer exposures exceeding safety thresholds set by regulatory agencies. Synthetic coolants are not only found in mint-or menthol-flavored products, but also in fruit- and candy-flavored products, including popular disposable E-cigarette products such as Puffbar.

ImplicationsSynthetic cooling agents are widely used in "kool/cool"- and "ice"-flavored E-liquids and in E-liquids without these labels, both as a potential replacement for menthol or to add cooling  notes to non-menthol flavors. These agents may be used to bypass current and future regulatory limits on menthol content in tobacco products, and not just E-cigarettes. Since synthetic cooling agents are odorless, they may not fall under the category of "characterizing flavor", potentially circumventing regulatory measures based on this concept. Regulators need to consider the additional health risks associated with exposure to synthetic cooling agents. Survival Genie, a web platform for survival analysis across pediatric and adult cancers. The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-Seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-Seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile.

The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/. Recycling of cell surface membrane proteins from yeast endosomes is regulated by ubiquitinated Ist1 Trafficking of cell surface membrane proteins to and from the plasma membrane impinges on myriad biological processes and ensures correct cellular function. Upon internalisation, most surface proteins are either sent to the lysosome for degradation or recycled back to the plasma membrane. Although these endosomal trafficking pathways control surface protein activity, the precise regulatory features and division of labour between interconnected pathways is poorly defined. Furthermore, how well endosomal trafficking mechanisms are conserved across eukaryotes is unclear. In yeast, we show cargo recycling back to the plasma membrane occurs through distinct pathways. In addition to the established retrograde recycling pathways via the late Golgi, which are used by synaptobrevins and driven by cargo ubiquitination, we find nutrient transporter recycling bypasses the Golgi in a pathway driven by cargo deubiquitination. Nutrient transporters rapidly internalise to, and recycle from, endosomes marked by Vps4 and the ESCRT-III associated factor Ist1. This endosome population serve as both  early and  recycling endosomes, implying these features of endosomal organisation are evolutionarily conserved. Ist1 has previously been implicated in recycling in yeast and other eukaryotes. We reveal Ist1 ubiquitination regulates its endosomal recruitment and is required for cargo recycling. Additionally, the ubiquitin-binding adaptor Npl4 and the essential ATPase Cdc48 regulate endosomal Ist1 and are required for cargo recycling, suggesting mechanistic features of recycling from endosomes to the plasma membrane are also conserved.