Discovering Rare Cell Types through Information-based Dimensionality Reduction Dimensionality reduction is crucial to summarizing the complex transcriptomic landscape of single cell datasets for downstream analyses. However, current dimensionality reduction approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly-defined populations. Here, we present surprisal component analysis (SCA), a technique that leverages the information-theoretic notion of surprisal for dimensionality reduction, and demonstrate its ability to improve the representation of clinically important populations that are indistinguishable using existing pipelines. For example, in cytotoxic T-cell data, SCA cleanly separates the gamma-delta and MAIT cell subpopulations, which are not detectable via PCA, ICA, scVI, or a wide array of specialized rare cell recovery tools. We also show that, when used instead of PCA, SCA improves downstream imputation to more accurately restore mRNA dropouts and recover important gene-gene relationships. SCAs information-theoretic paradigm opens the door to more meaningful signal extraction, with broad applications to the study of complex biological tissues in health and disease.
pkgndep: a tool for heaviness analysis on R package dependencies SummaryNumerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of unique dependencies that a parent brings to a package, and we proposed possible solutions for reducing the complexity of dependencies by optimizing the use of heavy parents. We implemented the metric in a new R package pkgndep which provides an intuitive way for dependency heaviness analysis. Based on pkgndep, we additionally performed a global analysis of dependency heaviness on CRAN and Bioconductor ecosystems and we revealed top packages that have significant contributions of high dependency heaviness to their child packages.

Availability and implementationThe package pkgndep and documentations are freely available from the Comprehensive R Archive Network (CRAN) https://cran.r-project.org/package=pkgndep. The dependency heaviness analysis for all 21,741 CRAN and Bioconductor packages retrieved on 2021-10-28 are available at https://pkgndep.github.io/.
Where there's smoke, there's fuel: dynamic vegetation data improve predictions of wildfire hazard in the Great Basin Wildfires are a growing management concern in western US rangelands, where invasive annual grasses have altered fire regimes and contributed to an increased incidence of catastrophic large wildfires. Fire activity in arid, non-forested ecosystems is thought to be largely controlled by interannual variation in fuel amount, which in turn is controlled by antecedent weather. Thus, long-range forecasting of fire activity in rangelands should be feasible given annual estimates of fuel quantity. Using a 32 yr time series of spatial data, we employed machine learning algorithms to predict the relative probability of large (>405 ha) wildfire in the Great Basin based on fine-scale annual and 16-day estimates of cover and production of vegetation functional groups, weather, and multitemporal scale drought indices. We evaluated the predictive utility of these models with a leave-one-year-out cross-validation, building spatial hindcasts of fire probability for each year that we compared against actual footprints of large wildfires. Herbaceous aboveground biomass production, bare ground cover, and long-term drought indices were the most important predictors of burning. Across 32 fire seasons, 88% of the area burned in large wildfires coincided with the upper 3 deciles of predicted fire probabilities. At the scale of the Great Basin, several metrics of fire activity were moderately to strongly correlated with average fire probability, including total area burned in large wildfires, number of large wildfires, and maximum fire size. Our findings show that recent years of exceptional fire activity in the Great Basin were predictable based on antecedent weather-driven growth of fine fuels and reveal a significant increasing trend in fire probability over the last three decades driven by widespread changes in fine fuel characteristics. Trade-offs in biosensor optimization for dynamic pathway engineering Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design and calibration of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature, and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy and pharmaceutical sectors. Contribution of arm movements to balance recovery after tripping in older adults. Falls are common in daily life, often caused by trips and slips and, particularly in older adults, with serious consequences. Although arm movements play an important role in balance control, there is limited research into the role of arm movements during balance recovery after tripping in older adults. We investigated how older adults use their arms to recover from a trip and the difference in the effects of arm movements between fallers (n=5) and non-fallers (n=11).

Sixteen older males and females (69.7{+/-}2.3 years) walked along a walkway and were occasionally tripped over suddenly appearing obstacles. We analysed the first trip using a biomechanical model based on full-body kinematics and force-plate data to calculate whole body orientation during the trip and recovery phase. With this model, we simulated the effects of arm movements at foot-obstacle impact and during trip recovery on body orientation.

Apart from an increase in sagittal plane forward body rotation at touchdown in fallers, we found no significant differences between fallers and non-fallers in the effects of arm movements on trip recovery. Like earlier studies in young adults, we found that arm movements during the recovery phase had most favourable effects in the transverse plane: by delaying the transfer of angular momentum of the arms to the body, older adults rotated the tripped side more forward thereby allowing for a larger recovery step. Older adults that are prone to falling might improve their balance recovery after tripping by learning to prolong ongoing arm movements. Brain endothelial STING1 activation by Plasmodium-sequestered heme promotes cerebral malaria via type I IFN response. Cerebral malaria (CM) is a life-threatening form of Plasmodium falciparum infection caused by brain inflammation. Brain endothelium dysfunction is a hallmark of CM pathology, which is also associated with the activation of the type I interferon (IFN) inflammatory pathway. The molecular triggers and sensors eliciting brain type I IFN cellular responses during CM remain largely unknown. We herein identified the stimulator of interferon response cGAMP interactor 1 (STING1) as the key innate immune sensor that induces Ifn{beta}1 transcription in the brain of mice infected with Plasmodium berghei (Pba). This STING1/IFN{beta}-mediated response increases brain CXCL10 governing the extent of brain leucocyte infiltration and blood-brain barrier (BBB) breakdown, and determining CM lethality. The critical role of brain endothelial cells (BECs) in fueling type I IFN-driven brain inflammation was demonstrated in brain endothelial-specific IFN{beta}-reporter and STING1-deficient Pba-infected mice, which are significantly protected from CM lethality. Moreover, extracellular particles (EPs) released from Pba-infected erythrocytes activated STING1-dependent type I IFN response in BECs, a response requiring intracellular acidification. Fractionation of the EPs enabled us to identify a defined fraction carrying hemoglobin degradation remnants that activates STING1/IFN{beta} in the brain endothelium, a process correlated with heme content. Notably, stimulation of STING1-deficient BECs with heme, docking experiments and in vitro binding assays unveiled that heme is a putative STING1 ligand. This work shows that heme resultant from the parasite heterotrophic activity operates as an alarmin triggering brain endothelial inflammatory responses via STING1/IFN{beta}/CXCL10 axis crucial to CM pathogenesis and lethality.

SignificanceCM results from loss of blood-brain endothelial barrier function caused by unrestrained inflammatory response in the natural course of infection by Plasmodium parasites. However, the role of brain endothelium in triggering inflammatory mechanisms is still undetermined. We found that the innate immune sensor STING1 is crucial for production of IFN{beta} in brain endothelial cells in Plasmodium-infected mice. This in turn stimulates CXCL10-mediated recruitment of leukocytes and subsequent brain inflammation and tissue damage. We identified within extracellular particles released from Plasmodium-infected erythrocytes, a fraction containing products of hemoglobin degradation, namely heme, which we show can bind STING1. Our results unravel a new angle of CM pathogenesis: heme contained in particles triggers the STING/IFN{beta}/CXCL10 axis in brain endothelial cells. Interplay between receptor binding, immune escape and protein stability determines the natural selection of SARS-CoV-2 variants Emergence of new SARS-CoV-2 variants has raised concerns at the effectiveness of vaccines and antibody therapeutics developed against the unmutated wild-type virus. We examined the effect of 12 most commonly occurring mutations in the receptor binding domain on its expression, stability, activity, and antibody escape potential-some of the factors that may influence the natural selection of mutants. Recombinant proteins were expressed in human cells. Stability was measured using thermal denaturation melts. Activity and antibody escape potential were measured using isothermal titration calorimetry in terms of binding to ACE2 and to a neutralizing human antibody CC12.1, respectively. Our results show that variants differ in their expression levels with the two least stable variants showing lesser expression. Out of the 8 well-expressed mutants, only 2 (N501Y and K417T/E484K/N501Y) showed stronger affinity to ACE2, 4 (Y453F, S477N, T478I and S494P) have similar affinity, whereas the other 2 (K417N and E484K) have weaker affinity when compared to the wild-type. In terms of CC12.1 binding, when compared to the wild-type, 4 variants (K417N, Y453F, N501Y and K417T/E484K/N501Y) have weaker affinity, 2 (S477N and S494P) have similar affinity, and 2 (T478I and E484K) have stronger affinity. Taken together, these results indicate that multiple factors contribute towards the natural selection of variants, and all these factors need be considered to understand the evolution of the virus. In addition, since not all variants can escape a given neutralizing antibody, antibodies to treat new variants can be chosen based on the specific mutations in that variant. A CNN model with feature integration for MI EEG subject classification in BMI ObjectiveElectroencephalogram (EEG) based motor imagery (MI) classification is an important aspect in brain-machine interfaces (BMIs) which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, the MI classification task is challenging due to inherent complex properties, inter-subject variability, and low signal-to-noise ratio (SNR) of EEG signals. To overcome the above-mentioned issues, the current work proposes an efficient multi-scale convolutional neural network (MS-CNN).

ApproachIn the framework, discriminant user-specific features have been extracted and integrated to improve the accuracy and performance of the CNN classifier. Additionally, different data augmentation methods have been implemented to further improve the accuracy and robustness of the model.

Main resultsThe model achieves an average classification accuracy of 93.74% and Cohens kappa-coefficient of 0.92 on the BCI competition IV2b dataset outperforming several baseline and current state-of-the-art EEG-based MI classification models.

SignificanceThe proposed algorithm effectively addresses the shortcoming of existing CNN-based EEG-MI classification models and significantly improves the classification accuracy. A liquid +TIP-network regulates microtubule dynamics through tubulin condensation Regulation of microtubule dynamics is essential for diverse cellular functions, and proteins that bind to dynamic microtubule ends can regulate network dynamics. Here we show, that two conserved microtubule end-binding proteins, CLIP-170 and EB3, undergo phase separation and form dense liquid-networks. When CLIP-170 and EB3 act together the multivalency of the network increases, which synergistically increases the amount of protein in the dense phase. In vitro and in cells these liquid networks can condense tubulin. In vitro in the presence of microtubules, EB3/CLIP-170 phase separation can co-condense tubulin all along the microtubule. At this condition microtubule growth speed increases up to two-fold and depolymerization events are strongly reduced, compared to conditions with phase separation deficient networks. Our data show that phase separated EB3/CLIP-170 networks impact microtubule growth dynamics beyond direct protein-microtubule interactions. Selective Ablation of 3' RNA ends and Processive RTs Facilitate Direct cDNA Sequencing of Full-length Host Cell and Viral Transcripts Alternative splicing (AS) is necessary for viral proliferation in host cells and a critical regulatory component of viral gene expression. Conventional RNA-seq approaches provide incomplete coverage of AS due to their short read-lengths and are susceptible to biases and artifacts introduced in prevailing library preparation methodologies. Moreover, viral splicing studies are often conducted separately from host cell transcriptome analysis, precluding an assessment of the viral manipulation of host splicing machinery. To address current limitations, we developed a quantitative full-length direct cDNA sequencing strategy to simultaneously profile viral and host cell transcripts. This nanopore-based approach couples processive reverse transcriptases with a novel one-step chemical ablation of 3 RNA ends (termed CASPR) which decreases ribosomal RNA reads and enriches for poly-adenylated coding sequences. We extensively validate our approach using synthetic reference transcripts and show CASPR doubles the breadth of coverage per transcript and increases detection of long transcripts (>4kb), while being functionally equivalent to PolyA+ selection for transcript quantification. We used our approach to interrogate host cell and HIV-1 transcript dynamics during viral reactivation and identified novel putative HIV-1 host factors containing exon skipping or novel intron retentions and delineated the HIV-1 transcriptional state associated with these differentially regulated host factors. 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. Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores Publicly available genome-wide association studies (GWAS) summary statistics exhibit uneven quality, which can impact the validity of follow-up analyses. First, we present an overview of possible misspecifications that come with GWAS summary statistics. Then, in both simulations and real data analyses, we show that additional information such as imputation INFO scores, allele frequencies, and per-variant sample sizes in GWAS summary statistics can be used to detect possible issues and correct for misspecifications in the GWAS summary statistics. One important motivation for us is to improve the predictive performance of polygenic scores built from these summary statistics. Unfortunately, due to the lack of reporting standards for GWAS summary statistics, this additional information is not systematically reported. We also show that using well-matched LD references can improve model fit and translate into more accurate prediction. Finally, we discuss how to make polygenic score methods such as lassosum and LDpred2 more robust to these misspecifications to improve their predictive power. Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, LK-DFBA, that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this frameworks success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that the optimal constraint approach was generally robust to local perturbations of the system, indicating that just a single wild-type dataset could allow identification of the ideal constraint for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems. High-pressure sprayed siRNAs trigger influence the efficiency but not the profile of transitive silencing In plants, small interfering RNAs (siRNAs) are a quintessential class of RNA interference (RNAi)-inducing molecules produced by the endonucleolytic cleavage of double stranded RNAs (dsRNAs). In order to ensure robust RNAi, siRNAs are amplified through a positive feedback mechanism called transitivity. Transitivity relies on RNA-DIRECTED-RNA POLYMERASE 6 (RDR6)-mediated dsRNA synthesis using siRNA-targeted RNA. The newly synthesized dsRNA is subsequently cleaved into secondary siRNAs by DICER-LIKE (DCL) endonucleases. Just like primary siRNAs, secondary siRNAs are also loaded into ARGONAUTE proteins (AGOs) to form an RNA-induced silencing complex (RISC) reinforcing the cleavage of the target RNA. Although the molecular players underlying transitivity are well established, the mode of action of transitivity remains elusive. In this study, we investigated the influence of primary target sites on transgene silencing and transitivity using the GFP-expressing Nicotiana benthamiana 16C line, high pressure spraying protocol (HPSP), and synthetic 22-nucleotide (nt) long siRNAs. We found that the 22-nt siRNA targeting the 3 of the GFP transgene was less efficient in inducing silencing when compared to the siRNAs targeting the 5 and middle region of the GFP. Moreover, sRNA sequencing of locally silenced leaves showed that the amount but not the profile of secondary RNAs is shaped by the occupancy of the primary siRNA triggers on the target RNA. Our findings suggest that RDR6-mediated dsRNA synthesis is not primed by primary siRNAs and that dsRNA synthesis appears to be generally initiated at the 3 end of the target RNA.

SIGNIFICANCE STATEMENTThis work answers a long-standing question about the role of siRNA triggers in initiating transitive silencing. By using high-pressure spraying-mediated delivery of synthetic siRNAs, we provided experimental evidence that target position of 22-nt-long primary siRNAs influences the efficiency of RDR6-driven double stranded RNA (dsRNA) synthesis, but it does not change the profile of accumulating secondary siRNAs originating from processing of RDR6-produced dsRNA. Herbivore life histories are altered by drought stress in their hosts plants Drought associated with climate change can stress plants, altering their interactions with phytophagous arthropods. Dryness not only impacts cultivated plants but also their parasites, which in some cases are favoured by drought. Herbivorous arthropods feeding on drought-stressed plants typically produce bigger offspring and develop faster. However, how much responses to abiotic factors differ among populations of a species remains poorly documented. Here, we evaluate variability among populations of a major agricultural pest, the two spotted spider mite, Tetranychus urticae, in response to drought-stress. We compare key life-history parameters of twelve populations that originate from climates ranging from wet and cool Atlantic locations to medium to dry hot Mediterranean locations. We evaluated how plant drought stress affects four life history traits: development time, fecundity, sex-ratio and emigration rate in an experiment comparing well-watered and drought-stressed bean plants. Mites feeding on drought-stressed plants developed faster and attempted to leave leaves less often, and young females were more fecund. The mites from wet temperate climates exhibited greater plasticity between the two water regimes than mites originating from dryer and hot climates. Thus, climate in the area of origin influences mite response to drought. Primary brain cell infection by Toxoplasma gondii reveals the extent and dynamics of parasite differentiation and its impact on neuron biology. Toxoplasma gondii is a eukaryotic parasite that forms latent cysts in the brain of immunocompetent individuals. The latent parasite infection of the immune-privileged central nervous system is linked to most complications. With no drug currently available to eliminate the latent cysts in the brain of infected hosts, the consequences of neurons long-term infection are unknown. It has long been known that T. gondii specifically differentiates into a latent form (bradyzoite) in neurons, but how the infected neuron responds to the infection remains to be elucidated. We have established a new in vitro model resulting in the production of mature bradyzoite cysts in brain cells. Using dual, host and parasite RNA-seq, we characterized the dynamics of differentiation of the parasite, revealing the involvement of key pathways in this process. Moreover, we identified how the infected brain cells responded to the parasite infection revealing the drastic changes that take place. We showed that neuronal-specific pathways are strongly affected, with synapse signaling being particularly affected, especially glutamatergic synapse signaling. The establishment of this new in vitro model allows investigating both the dynamics of parasite differentiation and the specific response of neurons to the long-term infection by this parasite. Second Heart Field-derived Cells Contribute to Angiotensin II-mediated Ascending Aortopathies BackgroundThe ascending aorta is a common location for aneurysm and dissection. This aortic region is populated by a mosaic of medial and adventitial cells that are embryonically derived from either the second heart field (SHF) or the cardiac neural crest. SHF-derived cells populate areas that coincide with the spatial specificity of thoracic aortopathies. The purpose of this study was to determine whether and how SHF-derived cells contribute to ascending aortopathies.

MethodsAscending aortic pathologies were examined in patients with sporadic thoracic aortopathies and angiotensin II (AngII)-infused mice. Ascending aortas without overt pathology from AngII-infused mice were subjected to mass spectrometry assisted proteomics, and molecular features of SHF-derived cells were determined by single cell transcriptomic analyses. Genetic deletion of either low-density lipoprotein receptor-related protein 1 (Lrp1) or transforming growth factor-{beta} receptor 2 (Tgfbr2) in SHF- derived cells was conducted to examine the impact of SHF-derived cells on vascular integrity.

ResultsPathologies in human ascending aortic aneurysmal tissues were predominant in outer medial layers and adventitia. This gradient was mimicked in mouse aortas following AngII infusion that was coincident with the distribution of SHF-derived cells. Proteomics indicated that brief AngII infusion, prior to overt pathology, evoked downregulation of SMC proteins and differential expression of extracellular matrix proteins, including several LRP1 ligands. LRP1 deletion in SHF-derived cells augmented AngII-induced ascending aortic aneurysm and rupture. Single cell transcriptomic analysis revealed that brief AngII infusion decreased Lrp1 and Tgfbr2 mRNA abundance in SHF-derived cells and induced a unique fibroblast population with low abundance of Tgfbr2 mRNA. SHF-specific Tgfbr2 deletion led to embryonic lethality at E12.5 with dilatation of the outflow tract and retroperitoneal hemorrhage. Integration of proteomic and single cell transcriptomics results identified plasminogen activator inhibitor 1 (PAI1) as the most increased protein in SHF-derived SMCs and fibroblasts during AngII infusion. Immunostaining revealed a transmural gradient of PAI1 in both ascending aortas of AngII-infused mice and human ascending aneurysmal aortas that mimicked the gradient of medial and adventitial pathologies.

ConclusionSHF-derived cells exert a critical role in maintaining vascular integrity through LRP1 and TGF-{beta} signaling associated with increases of aortic PAI1.



O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/930917v6_ufig1.gif" ALT="Figure 1">
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org.highwire.dtl.DTLVardef@185e3d9org.highwire.dtl.DTLVardef@131d702org.highwire.dtl.DTLVardef@11afa42org.highwire.dtl.DTLVardef@242786_HPS_FORMAT_FIGEXP  M_FIG C_FIG Clinical PerspectiveO_ST_ABSWhat is new?C_ST_ABS- SHF-derived SMCs and fibroblasts associate with AngII-induced aortic pathologies.
- AngII induces a distinct fibroblast sub-cluster that is less abundant for mRNAs related to major extracellular components and TGF{beta} ligands and receptors, but more abundant for proliferative genes.
- TGFBR2 deletion in SHF-derived cells are embryonic lethal with significant dilatation of the outflow tract in mice.
- SHF-specific deletion of LRP1 leads to aortic pathologies in mice, supporting the importance of SHF-derived cells in maintaining ascending aortic wall integrity.


What are the clinical implications?- Heterogeneity of the embryonic origins of SMCs and fibroblasts contributes to complex mechanisms of vasculopathy formation, which should be considered when investigating the pathogenesis of thoracic aortopathies. Nanocrown electrodes for reliable and robust intracellular recording of cardiomyocytes and cardiotoxicity screening Drug-induced cardiotoxicity arises primarily when a compound alters the electrophysiological properties of cardiomyocytes. Features of intracellular action potentials (iAPs) are powerful biomarkers that predict proarrhythmic risks. However, the conventional patch clamp techniques for measuring iAPs are either laborious and low throughput or not suitable for measuring electrically connected cardiomyocytes. In the last decade, a number of vertical nanoelectrodes have been demonstrated to achieve parallel and minimally-invasive iAP recordings. Nanoelectrodes show great promise, but the large variability in success rate, signal strength, and the low throughput of device fabrication have hindered them from being broadly adopted for proarrhythmia drug assessment. In this work, we developed vertically-aligned and semi-hollow nanocrown electrodes that are mechanically robust and made through a scalable fabrication process. Nanocrown electrodes achieve >99% success rates in obtaining intracellular access through electroporation, allowing reliable and simultaneous iAP recordings from up to 57 human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs). The accuracy of nanocrown electrode recordings is validated by simultaneous patch clamp recording from the same cell. Nanocrown electrodes enable prolonged iAP recording for continual monitoring of the same cells upon the sequential addition of four to five incremental drug doses. In this way, the dose-response data is self-referencing, which avoids the cell-to-cell variations inherent to hPSC-CMs. We are hopeful that this technology development is a step towards establishing an iAP screening assay for preclinical evaluation of drug-induced arrhythmogenicity. An automated workflow for multiplexed single-cell proteomics sample preparation at unprecedented sensitivity. The analysis of single-cell proteomes has recently become a viable complement to transcriptomics and genomics studies. Proteins are the primary driver of cellular functionality and mRNA levels are often an unreliable proxy of such. Therefore, the global analysis of the proteome is essential to study cellular identities. Multiplexed and label-free mass spectrometry-based approaches with single-cell resolution have lately attributed surprising heterogeneity to presumed homogenous cell populations. Even though specialized experimental designs and instrumentation have demonstrated remarkable advances, the efficient sample preparation of single cells still lag. Here, we introduce the proteoCHIP, a universal option for single-cell proteomics sample preparation at surprising sensitivity and throughput. The automated processing using a commercial system combining single-cell isolation and picoliter dispensing, the cellenONE(R), reduces final sample volumes to low nanoliters submerged in a hexadecane layer simultaneously eliminating error-prone manual sample handling and overcoming evaporation. The specialized proteoCHIP design allows direct injection of single cells via a standard autosampler resulting in around 1,500 protein groups per analytical run at remarkable reporter ion signal to noise while reducing or eliminating the carrier proteome. We identified close to 2,600 proteins across 170 multiplexed single cells from two highly similar human cell types. This dedicated loss-less workflow allows distinguishing in vitro co-differentiated cell types of self-organizing cardiac organoids based on indicative markers across 150 single cells. In-depth characterization revealed enhanced cellular motility of cardiac endothelial cells and sarcomere organization in cardiomyocytes. Our versatile and automated sample preparation has not only proven to be easily adaptable but is also sufficiently sensitive to drive biological applications of single-cell proteomics. Comparison of hybrid learning and remote education in the implementation of the 'Adopt a Microorganism' methodology The Internet has changed the way teachers and students access information and build knowledge. The recent COVID-19 pandemic has created challenges for both teachers and students and a demand for new methodologies of remote learning. In the life sciences, mixing online content with practical activities represents an even greater challenge. In microbiology, the implementation of an active teaching methodology, the #Adopt project, based on the social network Facebook(R), represents an excellent option for connecting remote education with classroom activities. In 2020, the version applied in high school, "Adopt a Microorganism", was adapted to meet the demands of emergency remote education owing to the suppression of face-to-face activities caused by the pandemic. In the present study, we assessed how the change in methodology impacted the discourse richness of students from high school integrated with technical education in the Business Administration program of the Federal Institute of Sao Paulo, Sorocaba Campus. Three questionnaires related to the groups of microorganisms (Archaea, Bacteria, Virus, Fungi, and Protozoan) were applied. The students responses in the 2019 and 2020 classes were compared concerning content richness and multiplicity of concepts through the application of the Shannon diversity index, an approach that is generally used to assess biodiversity in different environments. The observed results suggest that remote learning provided students with a conceptual basis and richness of content equivalent to that achieved by students subjected to the hybrid teaching model. In conclusion, this study suggests that the #Adopt project methodology increases students discourse richness in microbiology even without face-to-face traditional classes. "Neighbourhood watch" model: embryonic epiblast cells assess positional information in relation to their neighbours In many developing and regenerating systems, tissue pattern is established through gradients of informative morphogens, but we know little about how cells interpret these. Using experimental manipulation of early chick embryos including misexpression of an inducer (VG1 or ACTIVIN) and an inhibitor (BMP4), we test two alternative models for their ability to explain how the site of primitive streak formation is positioned relative to the rest of the embryo. In one model, cells read morphogen concentrations cell-autonomously. In the other, cells sense changes in morphogen status relative to their neighbourhood. We find that only the latter model can account for the experimental results, including some counter-intuitive predictions. This mechanism (which we name "neighbourhood watch" model) illuminates the classic "French Flag Problem" and how positional information is interpreted by a sheet of cells in a large developing system.

Summary statementIn a large developing system, the chick embryo before gastrulation, cells interpret gradients of positional signals relative to their neighbours to position the primitive streak, establishing bilateral symmetry. Recurrent exon-deleting activating mutations in AHR act as drivers of urinary tract cancer Bladder cancer has a high recurrence rate and low survival of advanced stage patients. Few genetic drivers of bladder cancer have thus far been identified. We performed in-depth structural variant analysis on whole-genome sequencing data of 206 metastasized urinary tract cancers. In ~10% of the patients, we identified recurrent in-frame deletions of exons 8 and 9 in the aryl hydrocarbon receptor gene (AHR{Delta}e8-9), which codes for a ligand-activated transcription factor. Pan-cancer analyses show that AHR{Delta}e8-9 is highly specific to urinary tract cancer and mutually exclusive with other bladder cancer drivers. In AHR{Delta}e8-9 the ligand-binding domain is disrupted and we show that this results in ligand-independent AHR-pathway activation. In bladder organoids, AHR{Delta}e8-9 induces a transformed phenotype that is characterized by upregulation of AHR target genes, downregulation of differentiation markers and upregulation of genes associated with stemness and urothelial cancer. Furthermore, AHR{Delta}e8-9 expression results in anchorage independent growth of bladder organoids, indicating tumorigenic potential. DNA-binding deficient AHR{Delta}e8-9 fails to induce transformation, suggesting a role for AHR target genes in the acquisition of the oncogenic phenotype. In conclusion, we show that AHR{Delta}e8-9 is a novel driver of urinary tract cancer and that the AHR pathway could be an interesting therapeutic target. Dramatic differences in male and female mortality trends for selected European cohorts over ~20 years. Longevity is of considerable interest. Collation of recent data after World War II by the Human Mortality Database allowed analyses, previously unattainable, of modal death-ages for sufficient numbers of selected European cohorts. The aim was to track modes and medians/means ([&ge;]60 years old (y)) of all-cause mortality for both sexes. The only highest-quality, large-number Lexis data available were analyzed: from nine countries: Denmark, Finland, France, Iceland, Italy, Netherlands, Norway, Sweden and Switzerland; raw-data modes (and medians/means [&ge;]60y, plus thin-plate-spline averages), were analyzed, plus pooled data. Here we show that for cohorts 1880-[~]1900 dramatic sex differences existed between death-age changes with all countries except Iceland showing male modal negative trends lasting [~]10-20 years and medians in all countries near-constant or negative lasting [~]10-20 years; whereas females from most countries showed fairly constant positive trends (except Finnish modes and Norwegian medians). For cohorts [~]1900-1919 male and female modal trends were positive (except Dutch and Icelandic cohorts and Finnish females). The net results were that male mortality modes for Danish, Icelandic, Italian, Dutch, Swedish and Norwegian 1919 cohorts were roughly the same as for 1880 cohorts, whereas female death-age modes increased. Results clarify previously knowledge concerning sex differences during this period. Despite improved environment in late adulthood over this period, this did not translate into increased male longevity and earlier events might have sealed their fate, especially in Denmark, Italy, Netherlands, Norway, aand Sweden (and, later, Iceland). An integrative approach leads to the discovery of a novel anti-leukemic peptide from human milk Chemotherapy in childhood leukemia is associated with late morbidity in leukemic survivors, while certain patient subsets are relatively resistant to standard chemotherapy. It is therefore important to identify new agents with sensitivity and selectivity towards leukemic cells, while having less systemic toxicity. Peptide-based therapeutics has gained much attention during the last few years. Here, we used an integrative workflow combining mass spectrometric peptide library construction, in silico anticancer peptide screening, and in vitro leukemic cell studies to discover a novel anti-leukemic peptide having 3+charges and alpha-helical structure, namely HMP-S7, from human breast milk. HMP-S7 showed cytotoxic activity against four distinct leukemic cell lines in a dose-dependent manner but had no effect on solid malignancies or representative normal cells. HMP-S7 induced leukemic cell death by penetrating the plasma membrane to enter the cytoplasm and cause leakage of lactate dehydrogenase, thus acting in a membranolytic manner. Importantly, HMP-S7 exhibited anti-leukemic effect against patient-derived leukemic cells ex vivo. In conclusion, HMP-S7 is a selective anti-leukemic peptide with promise which requires further validation in preclinical and clinical studies.

TeaserIn silico screening of naturally occurring human milk peptides discovers a new anticancer peptide that kills leukemic cells in vitro and ex vivo. Mutation rate dynamics reflect ecological change in an emerging zoonotic pathogen While mutation is often deleterious, it can also be adaptive. Mutation rates vary both within and between bacterial species, and understanding what drives this variation is essential for understanding the evolutionary dynamics of bacterial populations. In this study we investigate two factors that are predicted to influence the trade-off between the costs and benefits of mutation: ecology and genome size. To investigate the relationship between these factors and mutation rate we conducted mutation accumulation experiments on eight strains of the emerging zoonotic pathogen Streptococcus suis. Natural variation within this species allows us to compare tonsil carriage and invasive disease isolates, from both more and less pathogenic populations, with a wide range of genome sizes. We find that invasive disease isolates have repeatedly evolved mutation rates that are higher than those of closely related carriage isolates, regardless of variation in genome size. Independent of this variation in overall rate, we also observe a stronger bias towards G/C to A/T mutations in isolates from more pathogenic populations, whose genomes tend to be smaller and more AT-rich. Our results suggest that ecology is a stronger correlate of mutation rate than genome size over these timescales, and that transitions to invasive disease are consistently accompanied by rapid increases in mutation rate. These results shed light on the impact ecology can have on the adaptive potential of bacterial pathogens. The Consequences of Differential Origin Licensing Dynamics in Distinct Chromatin Environments MCM complexes are loaded onto chromosomes to license DNA replication origins in G1 phase of the cell cycle, but it is not yet known how mammalian MCM complexes are adequately distributed to both euchromatin and heterochromatin. To address this question, we combined time-lapse live-cell imaging with fixed cell immunofluorescence imaging of single human cells to quantify the relative rates of MCM loading in heterochromatin and euchromatin at different times within G1. We report here that MCM loading in euchromatin is faster than in heterochromatin in very early G1, but surprisingly, heterochromatin loading accelerates relative to euchromatin loading in middle and late G1. These different loading dynamics require ORCA-dependent differences in ORC distribution during G1. A consequence of heterochromatin origin licensing dynamics is that cells experiencing a truncated G1 phase from premature cyclin E expression enter S phase with under-licensed heterochromatin, and DNA damage accumulates preferentially in heterochromatin in the subsequent S/G2 phase. Thus G1 length is critical for sufficient MCM loading, particularly in heterochromatin, to ensure complete genome duplication and to maintain genome stability.