Ecological interactions in breast cancer: Cell facilitation promotes growth and survival under drug pressure The interplay of positive and negative interactions between drug sensitive and resistant cells determines the efficacy of treatment in heterogeneous cancer cell populations. We developed isogenic estrogen receptor positive (ER+) breast cancer cell lineages sensitive and resistant to ribociclib-induced CDK4/6 inhibition to investigate cell interactions under selective pressure. During treatment with ribociclib alone or in combination with endocrine therapies, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays detected estrone, a less active metabolite, in medium taken from each lineage but detected highly active estradiol only from resistant cells. Because sensitive cells are more dependent on estrogen signaling than resistant cells, they benefit from the increase in local estradiol from resistant cells, leading to facilitated growth when grown under treatment in coculture. We developed a suite of mathematical models to quantify the strength of competition and facilitation between cell lines and evaluate the effects of treatments that modify these interactions. These analyses support the conclusion that resistant cell lineages facilitate growth of sensitive lineages during therapy through the increased production and sharing of local estradiol and themselves suffer from increased competition and suppression as a result. These models show that blocking facilitation has the potential to control both resistant and sensitive cell populations and inhibit the emergence of a refractory population. This combination of experiments and models shows that interactions within heterogeneous cancer cell populations can combine positive and negative interactions to shape the dynamics of resistance.
Curation, inference, and assessment of a globally reconstructed gene regulatory network for Streptomyces coelicolor Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. Even though S. coelicolor has an outstanding variety of regulators among bacteria, little effort to globally study its transcription has been made. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions ([~]41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. Only [~]6% (534/9707) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called "strong" evidence. While for the remaining interactions there is no confirmation of direct binding. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif identification in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the reliability of the predictions, finding the inference from DNA sequence data to be the most trustworthy approach. Finally, we show two applications of the inferred and the curated networks. The inference allowed us to propose novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated network allowed us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.
Dipeptides are minimalistic but sufficient for liquid-liquid phase separation Liquid-liquid phase separation (LLPS) of proteins mediates the assembly of biomolecular condensates involved in physiological and pathological processes. Identifying the minimalistic building blocks and the sequence determinant of protein phase separation is of urgent importance but remains challenging due to the enormous sequence space and difficulties of existing methodologies in characterizing the phase behavior of ultrashort peptides. Here we demonstrate computational tools to efficiently quantify the microscopic fluidity and density of liquid-condensates/solid-aggregates and the temperature-dependent phase diagram of peptides. Utilizing our approaches, we comprehensively predict the LLPS abilities of all 400 dipeptide combinations of coded amino acids based on 492 micro-second molecular dynamics simulations, and observe the occurrences of spontaneous LLPS. We identify 54 dipeptides that form solid-like aggregates and three categories of dipeptides with high LLPS propensity. Our predictions are validated by turbidity assays and differential interference contrast (DIC) microscopy on four representative dipeptides (WW, QW, GF, and VI). Phase coexistence diagrams are constructed to explore the temperature dependence of LLPS. Our results reveal that aromatic moieties are crucial for a dipeptide to undergo LLPS, and hydrophobic and polar components are indispensable. We demonstrate for the first time that dipeptides, minimal but complete, possess multivalent interactions sufficient for LLPS, suggesting that LLPS is a general property of peptides/proteins, independent of their sequence length. This study provides a computational and experimental approach to the prediction and characterization of the phase behavior of minimalistic peptides, and will be helpful for understanding the sequence-dependence and molecular mechanism of protein phase separation.

SignificanceProtein liquid-liquid phase separation (LLPS) is associated with human health and diseases. Identifying the minimalistic building blocks and sequence determinants of LLPS is of urgent importance but remains computationally challenging partially due to the lack of methodologies characterizing the liquid condensates. Herein we provide approaches to evaluate LLPS ability of dipeptides, and screen all 400 dipeptides by MD simulations combined with multi-bead-per-residue models which capture key interactions driving LLPS that are missing in one-bead-per-residue models. Three categories of LLPS dipeptides are identified and the experimentally-verified QW dipeptide is by far the smallest LLPS system. Our results suggest that dipeptides, minimal but complete, possess multivalent interactions sufficient for LLPS, and LLPS is a general property of peptides/proteins, independent of their length. 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. Frequency modulated timer regulates mammalian hibernation Mammalian hibernators decrease basal metabolism and body temperature (Tb) to minimize energy expenditure in harsh seasons. During hibernation, Tb drops to low temperature (<10 {degrees}C) and remains constant for days, known as deep torpor in small mammalian hibernators. Spontaneous interbout arousals interrupt torpor bouts, when Tb recovers to euthermic state ~37 {degrees}C. Torpor-interbout arousal event repeats during several months of hibernation. However, little is known about mechanisms governing Tb fluctuation across torpor-interbout arousal cycles during hibernation. Recent improvement in data-logging techniques enables us to monitor Tb for more than hundred days with high precision, opening up new avenues for quantitative analysis to address the principle governing Tb fluctuation. Here, we analyzed Tb fluctuation across torpor-interbout arousal cycle of Syrian hamster, which can hibernate in response to chronic cold and short photoperiod under a laboratory condition, using generalized harmonic analysis and discovered a model with frequency modulation quantitatively reproducing Tb fluctuation. This analysis also identified an unexpectedly longer period of 120-430 days as the period that modulates another period of several days, generating Tb fluctuation for Syrian hamster. We propose that concerted action of two endogenous periods governs torpor-interbout arousal cycles during hibernation. Universal protein misfolding intermediates can bypass the proteostasis network and remain soluble and less-functional Misfolded protein conformations with decreased functionality can bypass the proteostasis machinery and remain soluble in vivo. This is an unexpected phenomenon as several cellular quality control mechanisms have evolved to rid cells of misfolded proteins. Three questions, then, are: how is it structurally possible for long-lived, soluble, misfolded proteins to bypass the proteostasis machinery and processes? How widespread are these soluble, misfolded states across the proteome? And how long do they persist for? Here, we address these questions using coarse-grain molecular dynamics simulations of the synthesis, termination, and post-translational dynamics of a representative set of cytosolic E. coli proteins. We predict that half of all proteins exhibit subpopulations of misfolded conformations that are likely to bypass molecular chaperones, avoid aggregation, and not be rapidly degraded. These misfolded states may persist for months or longer for some proteins. Structurally characterizing these misfolded states, we observe they have a large amount of native structure, but also contain localized misfolded regions from non-native changes in entanglement, in which a protein segment threads through a loop formed by another portion of the protein that is not found in the native state. The surface properties of these misfolded states are native like, suggesting they may bypass the proteostasis machinery and its regulatory processes to remain soluble, while their entanglements make these states long-lived kinetic traps, as disentanglement requires unfolding of already folded portions of the protein. In terms of function, we predict that one-third of proteins have subpopulations that misfold into less-functional states that have structurally perturbed functional sites yet remain soluble. Data from limited-proteolysis mass spectrometry experiments, which interrogate the misfolded conformations populated by proteins upon unfolding and refolding, are consistent with the structural changes seen in the entangled states of glycerol-3-phosphate dehydrogenase upon misfolding. These results provide an explanation for how proteins can misfold into soluble conformations with reduced functionality that can bypass cellular quality controls, and indicate, unexpectedly, this may be a wide-spread phenomenon in proteomes. Such entanglements are observed in many native structures, suggesting the non-native entanglements we observe are plausible. More broadly, these near-native entangled structures suggest a hypothesis for how synonymous mutations can modulate downstream protein structure and function, with these mutations partitioning nascent proteins between these kinetically trapped states. 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. Diversity of SARS-CoV-2 genome among various strains identified in Lucknow, Uttar Pradesh Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a significant challenge worldwide. Rapid genome sequencing of SARS-CoV-2 is going on across the globe to detect mutations and genomic modifications in SARS-CoV-2. In this study, we have sequenced twenty-three SARS-CoV-2 positive samples collected during the first pandemic from the state of Uttar Pradesh, India. We observed a range of already reported mutations (2-22), including; D614G, L452R, Q613H, Q677H, T1027I in the S gene; S194L in the N gene; Q57H, L106F, T175I in the ORF3. Few unreported mutations such as P309S in the ORF1ab gene; T379I in the N gene; and L52F, V77I in the ORF3a gene were also detected. Phylogenetic genome analysis showed similarity with other SARS-CoV-2 viruses reported from Uttar Pradesh. The observed mutations may be associated with SARS-CoV-2 virus pathogenicity or disease severity. Preprints in motion: tracking changes between preprint posting and journal publication during a pandemic Amidst the COVID-19 pandemic, preprints in the biomedical sciences are being posted and accessed at unprecedented rates, drawing widespread attention from the general public, press and policymakers for the first time. This phenomenon has sharpened longstanding questions about the reliability of information shared prior to journal peer review. Does the information shared in preprints typically withstand the scrutiny of peer review, or are conclusions likely to change in the version of record? We assessed preprints from bioRxiv and medRxiv that had been posted and subsequently published in a journal through 30th April 2020, representing the initial phase of the pandemic response. We utilised a combination of automatic and manual annotations to quantify how an article changed between the preprinted and published version. We found that the total number of figure panels and tables changed little between preprint and published articles. Moreover, the conclusions of 7.2% of non-COVID-19-related and 17.2% of COVID-19-related abstracts undergo a discrete change by the time of publication, but the majority of these changes do not qualitatively change the conclusions of the paper. 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. Simpati: patient classifier identifies signature pathways based on similarity networks for the disease prediction BACKGROUNDPathway-based patient classification is a supervised learning task which implies a model learning pathways as features to predict the classes of patients. The counterpart of enrichment tools for the pathway analysis are fundamental methods for clinicians and biomedical scientists. They allow to find signature cellular functions which help to define and annotate a disease phenotype. They provide results which lead human experts to manually classify patients. It is a paradox that pathwaybased classifiers which natively resolve this objective are not strongly developed. They could simulate the human way of thinking, decipher hidden multivariate relationships between the deregulated pathways and the disease phenotype, and provide more information than a probability value. Instead, there are currently only two classifiers of such kind, they require a nontrivial hyperparameter tuning, are difficult to interpret and lack in providing new insights. There is the need of new classifiers which can provide novel perspectives about pathways, be easy to apply with different biological omics and produce new data enabling a further analysis of the patients.

RESULTSWe propose Simpati, an innovative and interpretable patient classifier based on pathway-specific patient similarity networks. The first classifier to adopt ad-hoc novel algorithms for such graph type. It standardizes the biological high-throughput dataset of patients profiles with a propagation algorithm that considers the interconnected nature of the cells molecules for inferring a new activity score. This allows Simpati to classify with dense, sparse, and non-homogenous omic data. Simpati organizes patients molecules in pathways represented by patient similarity networks for being interpretable, handling missing data and preserving the patient privacy. A network represents patients as nodes and a novel similarity measure determines how much every pair act co-ordinately in a pathway. Simpati detects signature biological processes based on how much the topological properties of the related networks separate the patient classes. In this step, it includes a new cohesive subgroup detection algorithm to handle patients not showing the same pathway activity as the other class members. An unknown patient is then classified by a unique recommender system which considers how much is similar to known patients and distant from being an outlier. Simpati outperforms previously published classifiers on five cancer datasets described with two biological omics, classifies well with sparse data, identifies more relevant pathways associated to the patients disease than the competitors and has the lowest computational requirements.

CONCLUSIONSimpati can serve as generic-purpose pathway-based classifier of patient classes. It provides signature pathways to unveil the altered biological mechanisms of a disease phenotype and to classify patients according to the learnt pathway-specific similarities. The signature condition and patient prediction can be deciphered considering the patient similarity networks which must reveal the members of a patient class more cohesive and similar than the non-members. Simpati divides the pathways in up and downinvolved. Upinvolved when the signaling cascades generated by the altered molecules of the disease patients impact stronger the pathway than the ones of the control class. We provide an R implementation, a graphical user interface and a visualization function for the patient similarity networks. The software is available at: https://github.com/LucaGiudice/Simpati Interacting phenotypes and the coevolutionary process: Interspecific indirect genetic effects alter coevolutionary dynamics Coevolution occurs when species interact to influence one anothers fitness, resulting in reciprocal evolutionary change. In many coevolving lineages, trait expression in one species is modified by the genotypes and phenotypes of the other, forming feedback loops reminiscent of models of intraspecific social evolution. Here, we adapt the theory of within-species social evolution, characterized by indirect genetic effects and social selection imposed by interacting individuals, to the case of interspecific interactions. In a trait-based model, we derive general expressions for multivariate evolutionary change in two species and the expected between-species covariance in evolutionary change when selection varies across space. We show that reciprocal interspecific indirect genetic effects can dominate the coevolutionary process and drive patterns of correlated evolution beyond what is expected from direct selection alone. In extreme cases, interspecific indirect genetic effects can lead to coevolution when selection does not covary between species or even when one species lacks genetic variance. Moreover, our model indicates that interspecific indirect genetic effects may interact in complex ways with cross-species selection to determine the course of coevolution. Importantly, our model makes empirically testable predictions for how different forms of reciprocal interactions contribute to the coevolutionary process. Cellular feedback to organotelluranes displays modulation of antioxidant proteins gene expression Organotelluranes RT3 and RT4 are thiol reagents that induce mitochondrial transition pore (MTP) opening in a sensitive and insensitive manner to cyclosporin A. Although RT3 and RT4 promote glutathione depletion, paradoxically, they are also an efficient antioxidant for membrane lipids. These compounds antagonistic effects elicited the challenging question of how the gene expression of antioxidant enzymes would respond to treatment with these compounds. The influence of RT3 and RT4 on antioxidant enzyme expression was investigated in cultured aortic smooth muscle cells (ASMC). RT3 and RT4 promoted disruption of ionic calcium homeostasis, mitochondrial transmembrane potential ({Delta}{Psi}), and cell death in a dose-dependent manner. The cell death mechanisms responded qualitatively to the increase of the organotellurane concentration and changed from apoptosis to necrosis. RT3 and RT4 increased the expression of thioredoxin significantly. RT3 also increased the expression of glutaredoxin and glutathione peroxidase, slightly the catalase expression without significant effects on SOD expression. The results are consistent with GSH and protein thiol depletion and discussed based on the cell toxicity mechanism exhibited by these compounds.



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org.highwire.dtl.DTLVardef@52bb82org.highwire.dtl.DTLVardef@19476d9org.highwire.dtl.DTLVardef@6aa99org.highwire.dtl.DTLVardef@1aa605e_HPS_FORMAT_FIGEXP  M_FIG C_FIG Distinct SARS-CoV-2 antibody reactivity patterns elicited by natural infection and mRNA Vaccination We analyzed data from two ongoing COVID-19 longitudinal serological surveys in Orange County, CA., between April 2020 and March 2021. A total of 8,476 finger stick blood specimens were collected before and after an aggressive mRNA vaccination campaign. IgG levels were determined using a multiplex antigen microarray containing 10 SARS-CoV-2 antigens, 4 SARS, 3 MERS, 12 Common CoV, and 8 Influenza antigens. Twenty-six percent of 3,347 specimens from unvaccinated Orange County residents in December 2020 were SARS-CoV-2 seropositive. The Ab response was predominantly against nucleocapsid (NP), full length spike and the spike S2 domain. Anti-receptor binding domain (RBD) reactivity was low and there was no cross-reactivity against SARS S1 or SARS RBD. An aggressive mRNA vaccination campaign at the UCI Medical Center started on December 16, 2020 and 6,724 healthcare workers were vaccinated within 3 weeks. Seroprevalence increased from 13% in December to 79% in January, 93% in February and 99% in March. mRNA vaccination induced much higher Ab levels especially against the RBD domain and significant cross-reactivity against SARS RBD and S1 was also observed. Nucleocapsid protein Abs can be used to distinguish individuals in a population of vaccinees to classify those who have been previously infected and those who have not, because nucleocapsid is not in the vaccine. Previously infected individuals developed higher Ab titers to the vaccine than those who have not been previously exposed. These results indicate that mRNA vaccination rapidly induces a much stronger and broader Ab response than SARS-CoV-2 infection. Genetic diversity and connectivity of the Ostreid herpesvirus 1 populations in France: a first attempt to phylogeographic inference for a marine mollusc disease The genetic diversity of viral populations is a key driver of the spatial and temporal diffusion of viruses; yet, studying the diversity of whole genomes from natural populations still remains a challenge. Phylodynamic approaches are commonly used for RNA viruses harboring small genomes, but have only rarely been applied to DNA viruses with larger genomes. Here, we used the Pacific Oyster Mortality Syndrome (POMS, a disease that affects oyster farms around the world) as a model to study the genetic diversity of its causative agent, the Ostreid herpesvirus 1 (OsHV-1) in the three main French oyster-farming areas. Using ultra-deep sequencing on individual moribund oysters and an innovative combination of bioinformatics tools, we de novo assembled 21 OsHV-1 new genomes. Combining quantification of major and minor genetic variations, phylogenetic analysis and ancestral state reconstruction of discrete traits approaches; we assessed the connectivity of OsHV-1 viral populations between the three oyster-farming areas. Our results suggest that the Marennes- Oleron Bay represents the main source of OsHV-1 diversity, from where the virus has dispersed to other farming areas, a scenario consistent with current practices of oyster transfers in France. We demonstrate that phylodynamic approaches can be applied to aquatic DNA viruses to determine how epidemiological, immunological, and evolutionary processes act and potentially interact to shape their diversity patterns.

ImportancePhylogeography is a field of research that attempts to reconstruct the relationships between individual genotypes within a species and then correlate these genealogical relationships with their geographic and temporal origin. This field of research has become an essential step in the understanding of pandemics, in particular to determine the origin, spread and evolution of a pathogen as currently illustrated in studies on viral pandemics. However, because phylogeographic analyses are based on genome variation, stable genomes yield less information than labile genomes. Accordingly, viruses with double-stranded DNA (dsDNA) genomes generally have lower nucleotide diversity than RNA viruses. In this study, by combining the use of both major and minor genetic variations with phylogeographic analyses of the oyster herpesvirus OsHV-1, we highlight genealogical relationships that are not depicted in phylogenetic trees based on consensus viral genomes only. These data offer a plausible scenario reflecting the origin and spread of OsHV-1 populations between oyster- farming sites. Increased susceptibility to Chrysanthemum Yellows phytoplasma infection in Atcals7 ko plants is accompanied by enhanced expression of carbohydrate transporters. Callose deposition around sieve pores, under control of callose synthase 7 (AtCALS7), has been interpreted as a mechanical response to limit pathogen spread in phytoplasma-infected plants. Wild-type and Atcals7ko mutants were therefore employed to unveil the mode of involvement of CALS7 in the plants response to phytoplasma infection. The fresh weights of healthy and CY-(Chrysanthemum Yellows) phytoplasma-infected Arabidopsis wild-type and mutant plants indicated two superimposed effects of the absence of CALS7: a partial impairment of photo-assimilate transport and a stimulated phytoplasma proliferation as illustrated by a significantly increased phytoplasma titre in Atcal7ko mutants. Further studies solely dealt with the effects of CALS7 absence on phytoplasma growth. Phytoplasma infection affected sieve-element substructure to a larger extent in mutants than in wild-type plants, which was also true for the levels of some free carbohydrates. Moreover, infection induced a similar upregulation of gene expression of enzymes involved in sucrose cleavage (AtSUS5, AtSUS6) and transmembrane transport (AtSWEET11) in mutants and wild-type plants, but an increased gene expression of carbohydrate transmembrane transporters (AtSWEET12, AtSTP13, AtSUC3) in infected mutants only. It remains still unclear how the absence of AtCALS7 leads to gene upregulation and how an increased intercellular mobility of carbohydrates and possibly effectors contributes to a higher susceptibility. It is also unclear if modified sieve-pore structures in mutants allow a better spread of phytoplasmas giving rise to higher titre.

Author SummaryPhytoplasma infections are one of the most limiting factors for production of important crops all over the world. Phytoplasma disease epidemics can be handled mainly by insect-vector control using insecticides. Basic information about plant-phytoplasma interactions are still limited, nevertheless it is necessary to design new management and breeding strategies aimed to obtain more tolerant or resistant cultivars. Phytoplasmas are obligate intracellular parasites restricted to the phloem sieve tubes. Callose deposition at the sieve plates has been described since the  70s as a mechanical defence process to limit pathogen spread by occluding sieve pores. Studies reported 40 years later demonstrated that callose at the sieve pores is also involved in sieve-pore development and function and, hence, in mass-flow regulation, carbohydrate metabolism and distribution, and plant growth. Here, we reported on the role(s) of sieve-element callose in phytoplasma-infected Arabidopsis, using a mutant lacking AtCALS7, the enzyme responsible for callose synthesis in the sieve elements. The results indicate that loss of AtCAL7 appears to confer increased susceptibility to phytoplasma infection, due to alterations in expression of genes involved in sugar metabolism and membrane transport. In the long run, the identification of plant resistance or susceptibility traits against phytoplasmas will allow a complete re-organization of chemical control strategies, with obvious opportunities of reducing insecticide burden. RNF213 loss of function reshapes vascular transcriptome and spliceosome leading to disrupted angiogenesis and aggravated vascular inflammatory responses. RationaleMoyamoya disease (MMD) is a rare cerebrovascular occlusive disease that affects Asian population more often. The pathogenesis of MMD is related to mutation in RNF213 gene. However, why, and how RNF213 mutation leads to MMD is still not fully understood.

ObjectiveAnalyze the impact of RNF213 loss of function on vascular cells and the observed changes correlate with MMD.

Methods and resultsRNF213 KD was conducted in HUVEC and vascular smooth muscle cells (vSMCs). First, HUVEC cells showed alteration of angiogenesis, migration under LPS stimulation, Leukocyte endothelial transmigration, and endothelial-to-vSMCs communication.

Transcriptome analysis revealed downregulation of genes regulating cell division and mitosis. Interestingly, Alternative splicing (AS) analysis revealed hundreds of AS events to occur after RNF213 KD in various types of AS. The alternatively spliced genes showed minimal overlap with transcriptome profiling. Pathway analysis revealed many processes and pathways to be regulated by AS events observed. Transcriptome profiling was also performed after LPS treatment and revealed the basis for increased sensitivity to LPS observed in our analysis. AS changes were also observed after LPS treatment in RNF213 KD HUVEC. Different types of AS showed different patterns of convergence or divergence in terms of the regulated pathways after LPS treatment. Finally, transcriptome and AS analysis was performed in vSMCs and showed various processes that impact vSMCs function and phenotype in RNF213 loss of function.

ConclusionOur data provide a wealth of information on RNF213 gene function in vascular tissues and shed important light on different processes that contribute to MMD pathogenesis. Our results signify an immune-driven and hemodynamically linked process of MMD initiation and progression. Hotspots of pest-induced US urban tree death, 2020-2050 O_LIUrban trees are important nature-based solutions for future wellbeing and livability but are at high risk of mortality from insect pests. In the United States (US), 82% of the population live in urban settings and this number is growing, making urban tree mortality a matter of concern for most of its population. Until now, the magnitudes and spatial distributions of risks were unknown.
C_LIO_LIHere, we combine new models of street tree populations in [~]30,000 US communities, species-specific spread predictions for 57 invasive insect species, and estimates of tree death due to insect exposure for 48 host tree genera.
C_LIO_LIWe estimate that 1.4 million street trees will be killed by invasive insects from 2020 through 2050, costing an annualized average of US$ 30M. However, these estimates hide substantial variation: 23% of urban centers will experience 95% of all insect-induced mortality. Further, 90% of all mortality will be due to emerald ash borer (Agrilus planipennis, EAB), which is expected to kill virtually all ash trees (Fraxinus spp.) in >6000 communities.
C_LIO_LIWe define an EAB high-impact zone spanning 902,500km2, largely within the southern and central US, within which we predict the death of 98.8% of all ash trees. "Mortality hotspot cities" include Milwaukee, WI; Chicago, IL; and New York, NY.
C_LIO_LIWe identify Asian wood borers of maple and oak trees as the highest risk future invaders, where a new establishment could cost US$ 4.9B over 30 years.
C_LIO_LIPolicy implications: To plan effective mitigation, managers must know which tree species in which communities will be at the greatest risk, as well as the highest-risk insects. We provide the first country-wide, spatial forecast of urban tree mortality due to invasive insect pests. This framework identifies dominant pest insects and spatial impact hotspots, which can provide the basis for spatial prioritization of spread control efforts such as quarantines and biological control release sites. Our results highlight the need for EAB early-detection efforts as far from current infestations as Seattle, WA. Further, these findings produce a list of biotic and spatiotemporal risk factors for future high-impact US urban forest insect pests.
C_LI GCN5L1 promotes diastolic dysfunction by inhibiting cardiac pyruvate oxidation Left ventricular diastolic dysfunction is a structural and functional condition that precedes the development of heart failure with preserved ejection fraction (HFpEF). The etiology of diastolic dysfunction includes alterations in fuel substrate metabolism that negatively impact cardiac bioenergetics, and may precipitate the eventual transition to heart failure. To date, the molecular mechanisms that regulate early changes in fuel metabolism leading to diastolic dysfunction remain unclear. In this report, we use a diet-induced obesity model and quantitative acetylproteomics in aged mice to show that inhibitory lysine acetylation of the pyruvate dehydrogenase (PDH) complex promotes energetic deficits and diastolic dysfunction in mouse hearts. Cardiomyocyte-specific deletion of the mitochondrial lysine acetylation regulatory protein GCN5L1 prevented hyperacetylation of the PDH complex subunit PDHA1, allowing aged obese mice to continue using pyruvate as a bioenergetic substrate in the heart. Our findings suggest that changes in mitochondrial protein lysine acetylation represent a key metabolic component of diastolic dysfunction that precedes the development of heart failure. A rationalized definition of tumor suppressor microRNAsexcludes miR-34a While several microRNAs (miRNAs) have been proposed to act as tumor suppressors, a consensual definition of tumor suppressing miRNAs is still missing. Similarly to coding genes, we propose that tumor suppressor miRNAs must show evidence of genetic or epigenetic inactivation in cancers, and exhibit an anti-tumorigenic (e.g., anti-proliferative) activity under endogenous expression levels. Here we observe that this definition excludes the most extensively studied tumor suppressor candidate miRNA, miR-34a. In analyzable cancer types, miR-34a does not appear to be down-regulated in primary tumors relatively to normal adjacent tissues. Deletion of miR-34a is occasionally found in human cancers, but it does not seem to be driven by an anti-tumorigenic activity of the miRNA, since it is not observed upon smaller, miR-34a-specific alterations. Its anti-proliferative action was observed upon large, supra-physiological transfection of synthetic miR-34a in cultured cells, and our data indicates that endogenous miR-34a levels do not have such an effect. Our results therefore argue against a general tumor suppressive function for miR-34a, providing an explanation to the lack of efficiency of synthetic miR-34a administration against solid tumors. Robust retrieval of data stored in DNA by de Bruijn graph-based de novo strand assembly DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. The major technical challenges include various errors, such as the strand breaks, rearrangements, and indels that frequently arise during DNA synthesis, amplification, sequencing, and preservation. Through a de novo assembly strategy, we developed an algorithm based on the de Bruijn graph and greedy path search (DBGPS) to address these issues. DBGPS shows distinct advantages in handling DNA breaks, rearrangements, and indels. The robustness of DBGPS is demonstrated by accelerated aging, multiple independent data retrievals, deep error-prone PCR, and large data scale simulations. Remarkably, 6.8 MB of data can be retrieved accurately from a seriously corrupted sample that has been treated at 70 {degrees}C for 70 days. With DBGPS, we were able to achieve a logical density of 1.30 bits/cycle and a physical density of 295 PB/g.

One-Sentence SummaryA de Bruijn graph-based de novo assembly algorithm for DNA data storage enables fast and robust data readouts even with DNA samples that have been severely corrupted. SIX1 Reprograms Myogenic Transcription Factors to Maintain the Rhabdomyosarcoma undifferentiated state Rhabdomyosarcoma (RMS) is a pediatric skeletal muscle sarcoma characterized by the expression of the myogenic-lineage transcription factors (TF) MYOD1 and MYOG. Despite high expression of these TFs, RMS cells fail to terminally differentiate, suggesting the presence of factors that alter their function. Here, we demonstrate that the developmental TF, SIX1, is highly expressed in RMS and is critical to maintain a muscle progenitor-like state. SIX1 loss induces terminal differentiation of RMS cells into myotube-like cells and dramatically impedes tumor growth in vivo. We show that SIX1 maintains the RMS undifferentiated state by controlling enhancer activity and MYOD1 occupancy at loci more permissive to tumor growth over terminal muscle differentiation. Finally, we demonstrate that a gene signature derived from SIX1 loss correlates with differentiation status in RMS and predicts RMS progression in human disease. Our findings demonstrate a master regulatory role for SIX1 in the repression of RMS differentiation via genome-wide alterations in MYOD1-mediated transcription.

HighlightsO_LISIX1 prevents differentiation in RMS while it promotes differentiation during normal development
C_LIO_LIFN-RMS are highly dependent on SIX1 for growth in both zebrafish and mouse xenograft models
C_LIO_LILoss of SIX1 alters the transcriptional landscape of RMS cells, inducing a growth to differentiation switch
C_LIO_LISIX1 knockdown in FN-RMS causes reduced super enhancer-based activity at stem-related genes and enhanced MYOD1 binding to differentiation loci, resulting in the activation of a myogenic differentiation program
C_LIO_LIA gene signature derived from SIX1 loss strongly correlates with myogenic differentiation status and is predictive of advanced RMS.
C_LI Glucose regulates expression of pro-inflammatory genes IL-1β and IL-12 through a mechanism involving hexosamine biosynthesis pathway dependent regulation of α-E catenin in the RAW 264.7 macrophage cell line High glucose levels are associated with changes in macrophage polarization and evidence indicates that the sustained or even short-term high glucose levels modulate inflammatory responses in macrophages. However, the mechanism by which macrophages can sense the changes in glucose levels are not clearly understood. We find that high glucose levels rapidly increase the -E catenin protein level in RAW264.7 macrophages. We also find an attenuation of glucose induced increase of -E catenin when hexosamine biosynthesis pathway is inhibited either with glutamine depletion or with the drugs azaserine and tunicamycin. This indicates the involvement of hexosamine biosynthesis pathway in this process. Then, we investigated the potential role of -E catenin in glucose induced macrophage polarization. We find that the reduction of -E catenin level using siRNA attenuates the glucose induced change of IL-1{beta} mRNA level under LPS stimulated condition. Further, we identified that the depletion of -E catenin also decreases the IL-12 gene expression in basal glucose conditions leading to a reduction of glucose induced changes in IL-12. Together this indicates that -E catenin can sense the changes in glucose levels in macrophages via hexosamine biosynthesis pathway and also can modulate the glucose induced gene expression of inflammatory markers such as IL-1-{beta} and IL-12. This identifies a new part of the mechanism by which macrophages are able to respond to changes in glucose levels. 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. Top-down generation of low-precision representations improves the perception and imagination of fine-scale visual information Predictive-coding perception or imagination requires top-down generated neural representations in the primary visual cortex (V1) to reconstruct or simulate the V1 representations bottom-up stimulated by the seen images. However, top-down generated representations have low coding precision in both perception and imagination tasks. Why and how does the brain use top-down generated low-precision representations to reconstruct or simulate bottom-up stimulated high-precision representations? How can information of fine spatial scale be perceived or imagined using the top-down generated low-precision representations? By modeling the visual system using variational auto-encoder, we reveal that training to generate low-precision representations facilitates high-order cortex to form representations smooth to shape morphing of stimuli. Fine-scale visual information, if sparse, can be more accurately and robustly perceived or creatively imagined by inferring from generated representations with low (instead of high) coding precision. Therefore, generating low-precision representations is not a shortcoming, but instead a strategy that the brain uses to improve the perception and imagination of fine-scale information.