LazySampling and LinearSampling: Fast Stochastic Sampling of RNA Secondary Structure with Applications to SARS-CoV-2 Many RNAs fold into multiple structures at equilibrium. The classical stochastic sampling algorithm can sample secondary structures according to their probabilities in the Boltzmann ensemble, and is widely used. However, this algorithm, consisting of a bottom-up partition function phase followed by a top-down sampling phase, suffers from three limitations: (a) the formulation and implementation of the sampling phase are unnecessarily complicated; (b) the sampling phase repeatedly recalculates many redundant recursions already done during the partition function phase; (c) the partition function runtime scales cubically with the sequence length. These issues prevent stochastic sampling from being used for very long RNAs such as the full genomes of SARS-CoV-2. To address these problems, we first adopt a hypergraph framework under which the sampling algorithm can be greatly simplified. We then present three sampling algorithms under this framework, among which the LazySampling algorithm is the fastest by eliminating redundant work in the sampling phase via on-demand caching. Based on LazySampling, we further replace the cubic-time partition function by a linear-time approximate one, and derive LinearSampling, an end-to-end linear-time sampling algorithm that is orders of magnitude faster than the standard one. For instance, LinearSampling is 176x faster (38.9s vs. 1.9h) than Vienna RNAsubopt on the full genome of Ebola virus (18,959 nt). More importantly, LinearSampling is the first RNA structure sampling algorithm to scale up to the full-genome of SARS-CoV-2 without local window constraints, taking only 69.2 seconds on its reference sequence (29,903 nt). The resulting sample correlates well with the experimentally-guided structures. On the SARS-CoV-2 genome, LinearSampling finds 23 regions of 15 nt with high accessibilities, which are potential targets for COVID-19 diagnostics and drug design.

See code: https://github.com/LinearFold/LinearSampling
JACKIE: Fast enumeration of genomic single- and multi-copy target sites and their off-targets for CRISPR and other engineered nuclease systems ZFP-, TALE-, and CRISPR-based methods for genome, epigenome editing and imaging have provided powerful tools to interrogate functions of genomes. Targeting sequence design is vital to the success of these experiments. While existing design software mainly focus on designing target sequence for specific elements, we report here the implementation of JACKIE (Jackie and Alberts Comprehensive K-mer Instances Enumerator), a suite of software for enumerating all single- and multi-copy sites in the genome that can be incorporated for genome-scale designs as well as loaded onto genome browsers alongside other tracks for convenient web-based graphic-user-interface (GUI)-enabled design. We also implement fast algorithms to identify sequence neighborhoods or off-target counts of targeting sequences so that designs with low probability of off-target can be identified among millions of design sequences in reasonable time. We demonstrate the application of JACKIE-designed CRISPR site clusters for genome imaging.
Probing the effect of glycosaminoglycan depletion on integrin interactions with collagen I fibrils in the native ECM environment. Fibrillar collagen-integrin interactions in the extracellular matrix (ECM) regulate a multitude of cellular processes and cell signalling. Collagen I fibrils serve as the molecular scaffolding for connective tissues throughout the human body and are the most abundant protein building blocks in the ECM. The ECM environment is diverse, made up of several ECM proteins, enzymes, and proteoglycans. The contents of the ECM environment are modulated by disease and aging and may influence these critical collagen-integrin interactions. In particular, glycosaminoglycans (GAGs), anionic polysaccharides that decorate proteoglycans, become depleted in the ECM with natural aging and their mis-regulation has been linked to cancers and other diseases. The impact of GAG concentration in the ECM environment on collagen interactions is not well understood. Here, we integrate protein adhesion assays with liquid high resolution atomic force microscopy (AFM) to assess the affects of GAG depletion on the interaction of collagen I fibrils with the integrin 2I domain. Adhesion assays demonstrate that 2I preferentially binds to GAG-depleted collagen I fibrils. By amplitude modulated AFM in air and in solution, we find that GAG-depleted collagen I fibrils retain structural features of the native fibrils, including their characteristic D-banding pattern, a key structural motif. AFM fast force mapping in solution shows that GAG depletion reduces the stiffness of individual fibrils, lowering the indentation modulus by half compared to native fibrils. Together these results shed new light on how GAGs influence collagen I fibril- integrin interactions and may aid in strategies to treat diseases that result from GAG misregulation.

Statement for broader audienceAging and disease result in mis-regulation of glycosaminoglycan (GAG) levels in the extracellular matrix (ECM), which may affect fibrillar collagen interactions that are vital for cellular processes. Here, we characterize the impact of GAG depletion on collagen-integrin 2I domain interactions and collagen fibril topography and stiffness. We show that GAG depletion increases collagen-2I binding and reduces stiffness in comparison to native fibrils. These results may inform on strategies for treating GAG mis-regulation. Cell type-specific inference of differential expression in spatial transcriptomics Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed (DE) within specific cell types across tissue context. We show that current approaches cannot learn cell type-specific DE due to changes in cell type composition across space and the fact that measurement units often detect transcripts from more than one cell type. Here, we introduce a statistical method, Cell type-Specific Inference of Differential Expression (C-SIDE), that identifies cell type-specific patterns of differential gene expression while accounting for localization of other cell types. We model spatial transcriptomics gene expression as an additive mixture across cell types of general log-linear cell type-specific expression functions. This approach provides a unified framework for defining and identifying gene expression changes in a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Furthermore, our approach enables statistical inference across multiple samples and replicates when such data is available. We demonstrate, through simulations and validation experiments on Slide-seq and MER-FISH datasets, that our approach accurately identifies cell type-specific differential gene expression and provides valid uncertainty quantification. Lastly, we apply our method to characterize spatially-localized tissue changes in the context of disease. In an Alzheimers mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. We make our C-SIDE method publicly available as part of the open source R package https://github.com/dmcable/spacexr. The evolution of mechanisms to produce phenotypic heterogeneity in microorganisms In bacteria and other microorganisms, the cells within a population often show extreme phenotypic variation. Different species use different mechanisms to determine how distinct phenotypes are allocated between individuals, including coordinated, random, and genetic determination. However, it is not clear if this diversity in mechanisms is adaptive--arising because different mechanisms are favoured in different environments--or is merely the result of non-adaptive artifacts of evolution. We use theoretical models to analyse the relative advantages of the two dominant mechanisms to divide labour between reproductives and helpers in microorganisms. We show that coordinated specialisation is more likely to evolve over random specialisation in well-mixed groups when: (i) social groups are small; (ii) helping is more "essential"; and (iii) there is a low metabolic cost to coordination. We find analogous results when we allow for spatial structure with a more detailed model of cellular filaments. More generally, this work shows how diversity in the mechanisms to produce phenotypic heterogeneity could have arisen as adaptations to different environments. α-catenin links integrin adhesions to F-actin to regulate ECM mechanosensing and rigidity-dependence Both cell-cell and cell-matrix adhesions are regulated by mechanical signals, but the mechanobiological processes that mediate the crosstalk between these structures are poorly understood. Here we show that - catenin, a mechanosensitive protein that is classically associated with cadherin-based adhesions, directly interacts with and regulates integrin adhesions. -catenin is recruited to the edges of mesenchymal cells, where it interacts with F-actin. This is followed by mutual retrograde flow of -catenin and F-actin from the cell edge, during which -catenin interacts with vinculin within integrin adhesions. This interaction affects adhesion maturation, stress-fiber assembly, and force transmission to the matrix. In epithelial cells, -catenin is present in cell-cell adhesions and absent from cell-matrix adhesions. However, when these cells undergo epithelial-to-mesenchymal transition, -catenin transitions to cell-matrix adhesions, where it facilitates proper mechanosensing. This is highlighted by the ability of -catenin-depleted cells to grow on soft matrices. These results suggest a dual role of -catenin in mechanosensing, through both cell-cell and cell-matrix adhesions. A nontoxic tetrodotoxin analog, 5,6,11-trideoxytetrodotoxin, as an olfactory chemoattractant for the grass puffer Toxic puffers contain the potent neurotoxin, tetrodotoxin (TTX). Although TTX is considered to serve as a defense substance, previous behavioral studies have demonstrated that TTX acts as an attractive pheromone for some toxic puffers. To elucidate the physiological mechanism of putative pheromonal action of TTX, we examined whether grass puffers Takifugu alboplumbeus can detect TTX. Electroolfactogram (EOG) results suggest that the olfactory epithelium (OE) of grass puffers responded to a type of TTX analog (5,6,11-trideoxyTTX), although it did not respond to TTX. We also examined the attractive action of 5,6,11-trideoxyTTX on grass puffers by recording their swimming behavior under dark conditions. Grass puffers preferred to stay on the side of the aquarium where 5,6,11-trideoxyTTX was administered, and their swimming speed decreased. Additionally, odorant-induced labeling of olfactory sensory neurons by immunohistochemistry against neural activity marker (phosphorylated extracellular signal regulated kinase; pERK) revealed that labeled olfactory sensory neurons were localized in the region surrounding "islets" where there was considered as nonsensory epithelium. 5,6,11-trideoxyTTX has been known to accumulate in grass puffers, but its toxicity is much lower (almost nontoxic) than TTX. Our results suggest that toxic puffers may positively use this unknown function TTX analog as an olfactory chemoattractant. Data-Driven Network Models for Genetic Circuits From Time-Series Data with Incomplete Measurements Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements. We introduce a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a historical genetic circuit and a new E. coli based transcriptional event detector. Metacommunity dynamics and the detection of species associations in co-occurrence analyses: why patch disturbance matters O_LI1: Many statistical methods attempt to detect species associations - and so infer inter-specific interactions - from species co-occurrence patterns. Habitat heterogeneity and out-of-equilibrium colonization histories are well recognized as potentially causing species associations, even when interactions are absent. The potential for patch disturbance, a classical component of metacommunity dynamics, to also drive spurious species associations has however been over-looked.
C_LIO_LI2: Using a new general metacommunity model, we derive mathematical predictions regarding how patch disturbance would affect the patterns of species associations detected in "null" co-occurrence matrices. We also conduct numerical simulations to test our predictions and to compare the performance of several widespread statistical methods, including direct tests of pairwise independence, matrix permutation approaches and joint species distribution modelling.
C_LIO_LI3: We show how classical metacommunity dynamics can produce statistical associations, both positive and negative, even when species do not interact, when there is no habitat heterogeneity, and at equilibrium. This occurs as soon as there is some rate of patch disturbance (i.e. simultaneous extinction of several species in a patch) and/or a finite life-span of patches, a common feature of a broad range of plant, animal or microbial systems.
C_LIO_LI4: Patch disturbance can compromise species co-occurrence analyses and cause the artefactual detection of species associations if not taken into account. Including patch age (i.e. the time since the last patch disturbance event) as a covariate in a joint species distribution model can resolve the artefact. However, this requires additional data that often are not available in practice. We argue that the consequences of patch disturbance should not be underestimated when analysing species distribution patterns in metacommunity-like systems.
C_LI Diverse wMel variants of Wolbachia pipientis differentially rescue fertility and cytological defects of the bag of marbles partial loss of function mutation in Drosophila melanogaster In Drosophila melanogaster, the maternally inherited endosymbiont Wolbachia pipientis interacts with germline stem cell genes during oogenesis. One such gene, bag of marbles (bam) is the key switch for differentiation and also shows signals of adaptive evolution for protein diversification. These observations have led us to hypothesize that W. pipientis could be driving the adaptive evolution of bam for control of oogenesis. To test this hypothesis, we must understand the specificity of the genetic interaction between bam and W. pipientis. Previously, we documented that the W. pipientis variant, wMel, rescued the fertility of the bamBW hypomorphic mutant as a transheterozygote over a bam null. However, bamBW was generated more than 20 years ago in an uncontrolled genetic background and maintained over a balancer chromosome. Consequently, the chromosome carrying bamBW accumulated mutations that have prevented controlled experiments to further assess the interaction. Here, we used CRISPR/Cas9 to engineer the same single amino acid bam hypomorphic mutation (bamL255F) and a new bam null disruption mutation into the w1118 isogenic background. We assess the fertility of wildtype bam, bamL255F/bamnull hypomorphic, and bamL255F/ bamL255F mutant females, each infected individually with ten W. pipientis wMel variants representing three phylogenetic clades. Overall, we find that all of the W. pipientis variants tested here rescue bam hypomorphic fertility defects with wMelCS-like variants exhibiting the strongest rescue effects. Additionally, these variants did not increase wildtype bam female fertility. Therefore, both bam and W. pipientis interact in genotype-specific ways to modulate female fertility, a critical fitness phenotype. Inhibitors of ERp44, PDIA1, and AGR2 induce disulfide-mediated oligomerization of Death Receptors 4 and 5 and cancer cell death Breast cancer mortality remains unacceptably high, indicating a need for safer and more effective therapeutic agents. Disulfide bond Disrupting Agents (DDAs) were previously identified as a novel class of anticancer compounds that selectively kill cancers that overexpress the Epidermal Growth Factor Receptor (EGFR) or its family member HER2. DDAs kill EGFR+ and HER2+ cancer cells via the parallel downregulation of EGFR, HER2, and HER3 and activation/oligomerization of Death Receptors 4 and 5 (DR4/5). However, the mechanisms by which DDAs mediate these effects are unknown. Affinity purification analyses employing biotinylated-DDAs reveal that the Protein Disulfide Isomerase (PDI) family members AGR2, PDIA1, and ERp44 are DDA target proteins. Further analyses demonstrate that shRNA-mediated knockdown of AGR2 and ERp44, or expression of ERp44 mutants, enhance basal and DDA-induced DR5 oligomerization. DDA treatment of breast cancer cells disrupts PDIA1 and ERp44 mixed disulfide bonds with their client proteins. Together, the results herein reveal DDAs as the first small molecule, active site inhibitors of AGR2 and ERp44, and demonstrate roles for AGR2 and ERp44 in regulating the activity, stability, and localization of DR4 and DR5, and activation of Caspase 8. Plucking a string or playing a G? Choice type impacts human reinforcement learning In reinforcement learning (RL) experiments, participants learn to make rewarding choices in response to different stimuli; RL models use outcomes to estimate stimulus-response values which change incrementally. RL models consider any response type indiscriminately, ranging from more concretely defined motor choices (e.g. pressing a key with the index finger), to more general choices that can be executed in a number of ways (e.g. getting dinner at the restaurant). But does the learning process vary as a function of the choice type? In Experiment 1, we show that it does: participants were slower and less accurate in learning correct choices of a general format compared to learning more concrete, motor actions. Using computational modeling, we show that two mechanisms contribute to this. First, there was evidence of irrelevant credit assignment: the values of motor actions interfered with the values of other choice dimensions, resulting in more incorrect choices when the correct response is not defined by a single motor action; second, information integration for relevant general choices was slower. In Experiment 2, we replicated and further extended the findings from Experiment 1, by showing that slowed learning was attributable to weaker working memory use, rather than slowed RL learning. In both experiments we ruled out the explanation that the difference in performance between two condition types was driven by difficulty/different levels of complexity. We conclude that defining a more abstract choice space used by multiple learning systems for credit assignment recruits executive resources, limiting how much such processes then contribute to fast learning. Exploring functional protein covariation across single cells using nPOP Many biological processes, such as the cell division cycle, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell proteomics with sufficiently high throughput and accuracy. Toward this goal, we developed the nano-ProteOmic sample Preparation (nPOP) method for single-cell proteomics. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 picoliter volumes and performs all subsequent preparation steps in small droplets on a fluorocarbon-coated slide. This design enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes below 20 nl. We used nPOP to prepare 1,888 single cells and 128 negative controls in a single batch. Their analysis enabled quantifying the covariation between thousands of proteins and cell-cycle protein markers. Many protein sets covaried with the cell cycle similarly across all cell types and states, reflecting cell-type independent cell cycle functions. However, the cell cycle covariation of other protein sets differed markedly between cell types, even within subpopulation of melanoma cells expressing markers for drug-resistance priming. The cells expressing these markers accumulated in the G1 phase of the cell cycle and exhibited different covariation of enzymes catabolizing glucose. These results demonstrate that protein covariation across single cells may reveal functionally concerted biological differences between closely related cell states.

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org.highwire.dtl.DTLVardef@1e23b1org.highwire.dtl.DTLVardef@b93faeorg.highwire.dtl.DTLVardef@3c00forg.highwire.dtl.DTLVardef@362b41_HPS_FORMAT_FIGEXP  M_FIG C_FIG 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). Single-cell Transcriptomic Profiling Unveils Cardiac Cell-type Specific Response to Maternal Hyperglycemia Underlying the Risk of Congenital Heart Defects Congenital heart disease (CHD) is the most prevalent structural malformations of the heart affecting [~]1% of live births. To date, both damaging genetic variations and adverse environmental exposure such as maternal diabetes have been found to cause CHD. Clinical studies show [~]fivefold higher risk of CHD in the offspring of mothers with pregestational diabetes. Maternal pregestational diabetes affects the gene regulatory networks key to proper cardiac development in the fetus. However, the cell-type specificity of these gene regulatory responses to maternal diabetes and their association with the observed cardiac defects in the fetuses remains unknown. To uncover the transcriptional responses to maternal diabetes in the early embryonic heart, we used an established murine model of pregestational diabetes. In this model, we have previously demonstrated an increased incidence of CHD. Here, we show maternal hyperglycemia (matHG) elicits diverse cellular responses during heart development by single-cell RNA-sequencing in embryonic hearts exposed to control and matHG environment. Through differential gene-expression and pseudotime trajectory analyses of this data, we identified changes in lineage specifying transcription factors, predominantly affecting Isl1+ second heart field progenitors and Tnnt2+ cardiomyocytes with matHG. Using in vivo cell-lineage tracing studies, we confirmed that matHG exposure leads to impaired second heart field-derived cardiomyocyte differentiation. Finally, this work identifies matHG-mediated transcriptional determinants in cardiac cell lineages elevate CHD risk and show perturbations in Isl1-dependent gene-regulatory network (Isl1-GRN) affect cardiomyocyte differentiation. Functional analysis of this GRN in cardiac progenitor cells will provide further mechanistic insights into matHG-induced severity of CHD associated with diabetic pregnancies. From fitting the average to fitting the individual: A cautionary tale for mathematicalmodelers An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.

Author summaryAs we enter the era of healthcare where personalized medicine becomes a more common approach to treating cancer patients, harnessing the power of mathematical models will only become more essential. Using a preclinical dataset on cancer immunotherapy, we explore the challenges and limitations that arise when trying to move from fitting and making predictions for the population-level average, to fitting and making predictions for an individual. We find that the standard of approach of picking a single fitting methodology and a single cost function may end up having limited predictive value when applied to individual data. We also show how having a small amount of the right additional experimental data, and establishing the robustness of average treatment response, can help improve confidence in personalized model predictions. 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. Sensing Local Field Potentials with a Directional and Scalable Depth Array: the DISC array A variety of electrophysiology tools are available to the neurosurgeon for diagnosis, functional therapy, and neural prosthetics. However, no tool can currently address these three critical needs: (i) access to all cortical regions in a minimally invasive manner; (ii) recordings with microscale, mesoscale, and macroscale resolutions simultaneously; and (iii) access to spatially distant multiple brain regions that constitute distributed cognitive networks. We present a novel device for recording local field potentials (LFPs) with the form factor of a stereo-electroencephalographic electrode but combined with radially positioned microelectrodes and using the lead body to shield LFP sources, enabling directional sensitivity and scalability, referred to as the DISC array. As predicted by our electro-quasistatic models, DISC demonstrated significantly improved signal-to-noise ratio, directional sensitivity, and decoding accuracy from rat barrel cortex recordings during whisker stimulation. Critically, DISC demonstrated equivalent fidelity to conventional electrodes at the macroscale and uniquely, revealed stereoscopic information about current source density. Directional sensitivity of LFPs may significantly improve brain-computer interfaces and many diagnostic procedures, including epilepsy foci detection and deep brain targeting. Analysis of science journalism reveals gender and regional disparities in coverage Science journalism is a critical way in which the public can remain informed and benefit from new scientific findings. Such journalism also shapes the publics view of the current state of scientific findings and legitimizes experts. Those covering science can only cite and quote a limited number of sources. Sources may be identified by the journalists research or by recommendations by other scientists. In both cases, biases may influence who is identified and ultimately included as an expert. To analyze possible biases in science journalism, we analyzed 22,001 non-research articles published by Nature. We chose to analyze Nature non-research articles since its research articles provide a natural comparator. Our analysis considered two possible sources of disparity: gender and name origin. To explore these sources of disparity, we extracted cited authors names as well as extracted names of quoted speakers. While citations and quotations within a piece do not reflect the entire information-gathering process, they can provide insight into the demographics of visible sources. We then used the extracted names to predict gender and name origin of the cited authors and speakers.

In order to appropriately quantify the level of difference, we must identify a suitable reference set for comparison. We chose first and last authors within primary research articles in Nature and a subset of Springer Nature articles in the same time period as our comparator. In our analysis, we found a skew towards male quotation in Nature science journalism-related articles. However, quotation is trending toward equal representation at a faster rate than first and last authorship in academic publishing. Interestingly, we found that the gender disparity in Nature quotes was column-dependent, with the "Career Features" column reaching gender parity. Our name origin analysis found a significant over-representation of names with predicted Celtic/English origin and under-representation of names with a predicted East Asian origin. This finding was observed both in extracted quotes and journal citations, but dampened in citations. A bacterial effector counteracts host autophagy by promoting degradation of an autophagy component Beyond its role in cellular homeostasis, autophagy plays anti- and pro-microbial roles in host-microbe interactions, both in animals and plants. One prominent role of anti-microbial autophagy is to degrade intracellular pathogens or microbial molecules, in a process termed xenophagy. Consequently, microbes evolved mechanisms to hijack or modulate autophagy to escape elimination. Although well-described in animals, the extent to which xenophagy contributes to plant-bacteria interactions remains unknown. Here, we provide evidence that Xanthomonas campestris pv. vesicatoria (Xcv) suppresses host autophagy by utilizing type-III effector XopL. XopL interacts with and degrades the autophagy component SH3P2 via its E3 ligase activity to promote infection. Intriguingly, XopL is targeted for degradation by defense-related selective autophagy mediated by NBR1/Joka2, revealing a complex antagonistic interplay between XopL and the host autophagy machinery. Our results implicate plant antimicrobial autophagy in depletion of a bacterial virulence factor and unravels an unprecedented pathogen strategy to counteract defense-related autophagy. Bacterial community dynamics during embryonic development of the little skate (Leucoraja erinacea) BackgroundMicrobial transmission from parent to offspring is hypothesized to be widespread in vertebrates. However, evidence for this is limited as many evolutionarily important clades remain unexamined. There is currently no data on the microbiota associated with any Chondrichthyan species during embryonic development, despite the global distribution, ecological importance, and phylogenetic position of this clade. In this study, we take the first steps towards filling this gap by investigating the microbiota associated with embryonic development in the little skate, Leucoraja erinacea, a common North Atlantic species and popular system for chondrichthyan biology.

MethodsTo assess the potential for bacterial transmission in an oviparous chondrichthyan, we used 16S rRNA amplicon sequencing to characterize the microbial communities associated with the skin, gill, and egg capsule of the little skate, at six points during ontogeny. Community composition was analyzed using the QIIME2 pipeline and microbial continuity between stages was tracked using FEAST.

ResultsWe identify site-specific and stage-specific microbiota dominated by the bacterial phyla Proteobacteria and Bacteroidetes. This composition is similar to, but distinct from, that of previously published data on the adult microbiota of other chondrichthyan species. Our data reveal that the skate egg capsule harbors a highly diverse bacterial community-particularly on the internal surface of the capsule-and facilitates intergenerational microbial transfer to the offspring. Embryonic skin and external gill tissues host similar bacterial communities; the skin and gill communities later diverge as the internal gills and skin denticles develop.

ConclusionsOur study is the first exploration of the chondrichthyan microbiota throughout ontogeny and provides the first evidence of vertical transmission in this group. Autoantibodies against Progranulin and IL-1 receptor antagonist due to immunogenic posttranslational isoforms contribute to hyperinflammation in critically ill COVID-19 STRUCTURED ABSTRACTHyperinflammation is frequently observed in patients with severe COVID-19. Inadequate and defective IFN type I responses against SARS-CoV-2, associated with autoantibodies in a proportion of patients, lead to severe courses of disease. In addition, hyperactive responses of the humoral immune system have been described.

In the current study we investigated a possible role of neutralizing autoantibodies against antiinflammatory mediators. Plasma from adult patients with severe and critical COVID-19 was screened by ELISA for antibodies against PGRN, IL-1-Ra, IL-10, IL-18BP, IL-22BP, IL-36-Ra, CD40, IFN-2, IFN-{gamma}, IFN-{omega} and serpinB1. Autoantibodies were characterized and the antigens were analyzed for immunogenic alterations.

In a discovery cohort with severe to critical COVID-19 high titers of PGRN-autoantibodies were detected in 11 of 30 (36.7%), and of IL-1-Ra-autoantibodies in 14 of 30 (46.7%) patients. In a validation cohort of 64 patients with critical COVID-19 high-titer PGRN-Abs were detected in 25 (39%) and IL-1-Ra-Abs in 32 of 64 patients (50%). PGRN-Abs and IL-1-Ra-Abs belonged to IgM and several IgG subclasses. In separate cohorts with non-critical COVID-19, PGRN-Abs and IL-1-Ra-Abs were detected in low frequency (i.e. in < 5% of patients) and at low titers. Neither PGRN-nor IL-1-Ra-Abs were found in 40 healthy controls vaccinated against SARS-CoV-2 or 188 unvaccinated healthy controls. PGRN-Abs were not cross-reactive against SARS-CoV-2 structural proteins nor against IL-1-Ra. Plasma levels of both free PGRN and free IL-1-Ra were significantly decreased in autoantibody-positive patients compared to Ab-negative and non-COVID-19 controls. In vitro PGRN-Abs from patients functionally reduced PGRN-dependent inhibition of TNF- signaling, and IL-1-Ra-Abs from patients reduced IL-1-Ra- or anakinra-dependent inhibition of IL-1{beta} signaling. The pSer81 hyperphosphorylated PGRN isoform was exclusively detected in patients with high-titer PGRN-Abs; likewise, a hyperphosphorylated IL-1-Ra isoform was only found in patients with high-titer IL-1-Ra-Abs. Thr111 was identified as the hyperphophorylated amino acid of IL-1-Ra. In longitudinally collected samples hyperphosphorylated isoforms of both PGRN and IL-1-Ra emerged transiently, and preceded the appearance of autoantibodies. In hospitalized patients, the presence of IL-1-Ra-Abs or IL-1-Ra-Abs in combination with PGRN-Abs was associated with a higher morbidity and mortality.

To conclude, neutralizing autoantibodies to IL-1-Ra and PGRN occur in a significant portion of patients with critical COVID-19, with a concomitant decrease in circulating free PGRN and IL-1-Ra, indicative of a misdirected, proinflammatory autoimmune response. The break of self-tolerance is likely caused by atypical hyperphosphorylated isoforms of both antigens, whose appearances precede autoantibody induction. Our data suggest that these immunogenic secondary modifications are induced by the SARS-CoV-2-infection itself or the inflammatory environment evoked by the infection and predispose for a critical course of COVID-19. Fatal COVID-19 and non-COVID-19 Acute Respiratory Distress Syndrome is Associated with Incomplete Alveolar Type 1 Epithelial Cell Differentiation from the Transitional State Without Fibrosis ARDS due to COVID-19 and other etiologies results from injury to the alveolar epithelial cell (AEC) barrier resulting in noncardiogenic pulmonary edema, which causes acute respiratory failure; clinical recovery requires epithelial regeneration. During physiologic regeneration in mice, AEC2s proliferate, exit the cell cycle, and transiently assume a transitional state before differentiating into AEC1s; persistence of the transitional state is associated with pulmonary fibrosis in humans. It is unknown whether transitional cells emerge and differentiate into AEC1s without fibrosis in human ARDS and why transitional cells differentiate into AEC1s during physiologic regeneration but persist in fibrosis. We hypothesized that incomplete but ongoing AEC1 differentiation from transitional cells without fibrosis may underlie persistent barrier permeability and fatal acute respiratory failure in ARDS. Immunostaining of postmortem ARDS lungs revealed abundant transitional cells in organized monolayers on alveolar septa without fibrosis. They were typically cuboidal or partially spread, sometimes flat, and occasionally expressed AEC1 markers. Immunostaining and/or interrogation of scRNAseq datasets revealed that transitional cells in mouse models of physiologic regeneration, ARDS, and fibrosis express markers of cell cycle exit but only in fibrosis express a specific senescence marker. Thus, in severe, fatal early ARDS, AEC1 differentiation from transitional cells is incomplete, underlying persistent barrier permeability and respiratory failure, but ongoing without fibrosis; senescence of transitional cells may be associated with pulmonary fibrosis. Effect of a low concentration in chloride, sodium and potassium on oocyte maturation, oocyte hydration, ovulation and egg quality in rainbow trout. Water salinity is an important environmental factor known to have detrimental effects on salmonid reproduction, mostly when migrating female broodfish are held in sea water. In contrast, data obtained in freshwater are scarce and the impact of low water salinity, and more specifically of low water concentrations in sodium, chloride and potassium, during reproduction in freshwater is currently unknown. For this reason, and because ion and water fluxes are critical for the final steps of the female gamete formation, including oocyte hydration and ovulation, the aim of the present study was to investigate the impact of low salinity water on final oocyte maturation, ovulation and, ultimately, on egg quality, using rainbow trout as a physiological model and relevant aquaculture species.

Fish from the same commercial strain were raised either in a site characterized by low concentrations of Na+, K+, and Cl- ions in the water or in a closely located control site exhibiting higher concentration in these elements. Egg quality and duration of final oocyte maturation were investigated using innovative phenotyping tools such as automatic assessment of egg viability using the VisEgg system and non-invasive echograph-based monitoring of final oocyte maturation duration, respectively. Oocyte hydration during final oocyte maturation and after ovulation was also investigated. Finally, molecular phenotyping was performed using real-time PCR-based monitoring of several key players of final oocyte maturation and ovulation associated with ion and water transport, inflammation, proteolytic activity, and coagulation. Oocyte hydration and gene expression data were analyzed in the light of the duration of final oocyte maturation.

Here we show that low water salinity (i.e., low water concentration in chloride, sodium and potassium) negatively influences final oocyte maturation, ovulation and, ultimately, egg quality. Low water salinity triggered delayed ovulation and lower oocyte viability. When investigating the impact of low water salinity on final oocyte maturation duration, individuals presenting the most severe phenotypes exhibited impaired oocyte hydration and abnormally reduced gene expression levels of several key players of the ovulatory process. While the under expression of water (i.e., aquaporins) and ion (i.e., solute carriers) transporters is consistent with impaired oocyte hydration, our observations also indicate that the overall ovulatory gene expression program is disrupted. Our results raise the question of the mechanisms underlying the negative influence of low salinity water on the dynamics of the preovulatory phase, on the control of the oocyte homeostasis, including hydration, and on the overall success of the maturation-ovulation process.

HighlightsO_LILow water salinity impairs final oocyte maturation and egg quality in rainbow trout
C_LIO_LILow water salinity induces delayed ovulation and impaired oocyte hydration
C_LIO_LILow water salinity induces a dysregulation of several key ovulatory genes
C_LIO_LIMonitoring of final oocyte maturation can be performed using ultrasound staging
C_LI Brain simulation augments machine-learning-based classification of dementia INTRODUCTIONComputational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimers disease.

METHODSWe enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid {beta} PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimers Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification.

RESULTSThe combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the Alzheimers-typical spatial distribution.

DISCUSSIONThe cause-and-effect implementation of local hyperexcitation caused by Amyloid {beta} can improve the machine-learning-driven classification of Alzheimers and demonstrates TVBs ability to decode information in empirical data employing connectivity-based brain simulation.

RESEARCH IN CONTEXTO_LISYSTEMATIC REVIEW. Machine-learning has been proven to augment diagnostics of dementia in several ways. Imaging-based approaches enable early diagnostic predictions. However, individual projections of long-term outcome as well as differential diagnosis remain difficult, as the mechanisms behind the used classifying features often remain unclear. Mechanistic whole-brain models in synergy with powerful machine learning aim to close this gap.
C_LIO_LIINTERPRETATION. Our work demonstrates that multi-scale brain simulations considering Amyloid {beta} distributions and cause-and-effect regulatory cascades reveal hidden electrophysiological processes that are not readily accessible through measurements in humans. We demonstrate that these simulation-inferred features hold the potential to improve diagnostic classification of Alzheimers disease.
C_LIO_LIFUTURE DIRECTIONS. The simulation-based classification model needs to be tested for clinical usability in a larger cohort with an independent test set, either with another imaging database or a prospective study to assess its capability for long-term disease trajectories.
C_LI The coiled coil of FtsLB is a structurally detuned element critical for modulating activation in bacterial cell division The FtsLB complex is a key regulator of bacterial cell division, existing in either an off or on state which supports the activation of septal peptidoglycan synthesis. In Escherichia coli, residues known to be critical for this activation are located in a region near the C-terminal end of the periplasmic coiled-coil domain of FtsLB, raising questions about the precise role of this conserved domain in the activation mechanism. Here, we investigate an unusual cluster of polar amino acids found within the core of the FtsLB coiled coil. We hypothesized that these amino acids likely reduce the structural stability of the domain and thus may be important for governing conformational changes. We found that mutating these positions to hydrophobic residues increased the thermal stability of FtsLB but caused cell division defects, suggesting that the coiled-coil domain is a "detuned" structural element. In addition, we identified suppressor mutations within the polar cluster, indicating that the precise identity of the polar amino acids is important for fine-tuning the structural balance between the off and on states. We propose a revised structural model of the tetrameric FtsLB (named the "Y-model") in which the periplasmic domain splits into a pair of coiled-coil branches. In this configuration, the hydrophilic terminal moieties of the polar amino acids remain more favorably exposed to water than in the original four-helix bundle model ("I-model"). We propose that a shift in this architecture, dependent on its marginal stability, is involved in activating the FtsLB complex and triggering septal cell wall reconstruction.