Simultaneous dual recordings from vestibular hair cells and their calyx afferents demonstrate multiple modes of transmission at these specialized endings In the vestibular periphery, transmission via conventional synaptic boutons is supplemented by postsynaptic calyceal endings surrounding Type I hair cells. This review focusses on the multiple modes of communication between these receptors and their enveloping calyces as revealed by simultaneous dual-electrode recordings. Classic orthodromic transmission is accompanied by two forms of bidirectional communication enabled by the extensive cleft between the Type I hair cell and its calyx. The slowest cellular communication low-pass filters the transduction current with a time constant 10 -- 100 milliseconds: potassium ions accumulate in the synaptic cleft, depolarizing both the hair cell and afferent to potentials greater than necessary for rapid vesicle fusion in the receptor and potentially triggering action potentials in the afferent. On the millisecond timescale, conventional glutamatergic quantal transmission occurs when hair cells are depolarized to potentials sufficient for calcium influx and vesicle fusion. Depolarization also permits a third form of transmission which occurs over tens of microseconds, resulting from the large voltage- and ion-sensitive cleft-facing conductances in both the hair cell and the calyx that are open at their resting potentials. Current flowing out of either the hair cell or the afferent divides into the fraction flowing across the cleft into its cellular partner, and the remainder flowing out of the cleft and into the surrounding fluid compartment. These findings suggest multiple biophysical bases for the extensive repertoire of response dynamics seen in the population of primary vestibular afferent fibers. The results further suggest that evolutionary pressures drive selection for the calyx afferent.
The brain integrates proprioceptive information to ensure robust locomotion Robust locomotion relies on information from proprioceptors: sensory organs that communicate the position of body parts to the spinal cord and brain. Proprioceptive circuits in the spinal cord are known to regulate locomotion in challenging environments. Yet, the regulatory importance of the brain remains less clear. Here, through mouse genetic studies and in vivo electrophysiology, we examined the role of the brain in integrating proprioceptive information during locomotion. The systemic removal of proprioceptors left the animals in a constantly perturbed state, similar to that observed during mechanically perturbed locomotion in wild type and characterised by longer and less accurate activation patterns. In contrast, after surgically interrupting the ascending proprioceptive projection to the brain through the dorsal column pathway, wild-type mice showed normal walking behaviour, but lost the ability to respond to external perturbations. Our findings provide direct evidence of a pivotal role for ascending proprioceptive information in achieving safe locomotion.
Regulation of the PKD2 channel function by TACAN Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in membrane receptor PKD1 or cation channel PKD2. TACAN (also named TMEM120A), recently reported as an ion channel in neuron cells for mechano and pain sensing, is also distributed in diverse non-neuronal tissues such as kidney, heart and intestine, suggesting its involvement in other functions. In this study, we found that TACAN is in complex with PKD2 in native renal cell lines. Using the two-electrode voltage clamp in Xenopus oocytes we found that TACAN inhibited the channel activity of PKD2 gain-of-function mutant F604P. The first and last transmembrane domains of TACAN were found to interact with the PKD2 C-and N-terminal portions, respectively. We showed that the TACAN N-terminus acted as a blocking peptide and that TACAN inhibits the PKD2 function through the PKD2/TACAN binding. By patch clamping in mammalian cells, we found that TACAN inhibits both the single channel conductance and open probability of PKD2 and mutant F604P. PKD2 co-expressed with TACAN, but not PKD2 alone, exhibited pressure sensitivity. Furthermore, we also found that TACAN aggravates PKD2-dependent tail curvature and pronephric cysts in larval zebrafish, in support of the in vitro inhibitory effects of TACAN. In summary, this study revealed that TACAN acts as a PKD2 inhibitor and mediates mechano sensitivity of the PKD2/TACAN channel complex. Multi-omic analysis of the Arabidopsis clock activator mutant rve 4 6 8 reveals connections to carbohydrate metabolism and proteasome regulation Plants are able to sense changes in their light environments, such as the onset of day and night, as well as anticipate these changes in order to adapt and survive. Central to this ability is the plant circadian clock, a molecular circuit that precisely orchestrates plant cell processes over the course of a day. REVEILLE proteins (RVEs) are recently discovered members of the plant circadian circuitry that activate the evening complex and PRR genes to maintain regular circadian oscillation. The RVE 8 protein and its two homologs, RVE 4 and 6, have been shown to limit the length of the circadian period, with rve 4 6 8 triple-knockout plants possessing an elongated period along with increased leaf surface area, biomass, cell size and delayed flowering relative to wild-type Col-0 plants. Here, using a multi-omics approach consisting of phenomics, transcriptomics, proteomics, and metabolomics we draw novel connections between RVE8-like proteins and a number of core plant cell processes. In particular, we reveal that loss of RVE8-like proteins results in altered carbohydrate, organic acid and lipid metabolism, including a starch excess phenotype at dawn. We further demonstrate that rve 4 6 8 plants have lower levels of 20S proteasome subunits and possess significantly reduced proteasome activity, potentially explaining the increase in cell-size observed in RVE8-like mutants. Overall, this robust, multi-omic dataset, provides substantial new insights into the far reaching impact RVE8-like proteins have on the diel plant cell environment. 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. CDK9 and PP2A regulate RNA polymerase II transcription termination and coupled RNA maturation. CDK9 is a critical kinase required for the productive transcription of protein-coding genes by RNA polymerase II (pol II). As part of P-TEFb, CDK9 phosphorylates the carboxyl-terminal domain (CTD) of pol II and elongation factors, including SPT5, which allows pol II to elongate past the early elongation checkpoint (EEC) encountered soon after initiation. We show that, in addition to halting pol II at the EEC, loss of CDK9 activity causes premature termination of transcription across the last exon, loss of polyadenylation factors from chromatin, and loss of polyadenylation of nascent transcripts. Inhibition of the phosphatase PP2A abrogates the premature termination and loss of polyadenylation caused by CDK9 inhibition, indicating that this kinase/phosphatase pair regulates transcription elongation and RNA processing at the end of protein-coding genes. Our phosphoproteomic analyses after CDK9 inhibition, using either DRB or an analog-sensitive CDK9 cell line, confirm the splicing factor SF3B1 as an additional key target of this kinase. These results emphasize the important roles that CDK9 plays in coupling transcription elongation and termination to RNA maturation downstream of the EEC. Cargo surface fluidity reduces inter-motor mechanical interference, promotes load-sharing and enhances run-lengths in an ATP dependent manner In cells, multiple molecular motors work together as teams to carry cargoes such as vesicles and organelles over long distances to their destinations by stepping along a network of cytoskeletal filaments. How motors that typically mechanically interfere with each other, work as teams is unclear. Here we explored the possibility that purely physical mechanisms may potentially enhance teamwork, both at the single motor and cargo level. To explore these mechanisms, we developed a 3D dynamical simulation of cargo transport along microtubules by teams of canonically non-cooperative kinesin-1 motors. We accounted for cargo membrane fluidity by explicitly simulating the Brownian dynamics of motors on the cargo surface and considered both the load and ATP dependence of single motor functioning. We showed explicitly, for the first time, that surface fluidity leads to the reduction of negative mechanical interference between kinesins, enhancing load sharing thereby decreasing single motor off-rates and increasing processivity. Remarkably, we also showed that, independent of fluidity, increasing numbers of bound motors pull the cargo closer to the microtubule, increasing the on-rates of individual unbound motors, resulting in a cooperative increase in bound motor numbers that depends on 3D cargo geometry. At the cargo level, surface fluidity makes more motors available for binding, though this effect is significant only at low ATP or high motor density. Interestingly, we find that the fluidity induced reduction in mechanical interference dominates over the increased availability of motors for typical physiological and in vitro conditions, this allowing us to reconcile different experimental results in different regimes. Finally, we show that these effects can altogether result in enhanced mechanical efficiency and tunable cargo run-lengths for teams of molecular motors over physiological ranges of fluidity with implications for new experimental validation efforts, transport in vivo, artificial cargo design and multi-motor-driven mechanical processes in general.

Author summaryIn cells, multiple molecular motors work together as teams to carry cargoes such as vesicles and organelles over long distances to their destinations by stepping along a network of protein filaments. How do intrinsically non-cooperative motors function as teams within cells? In this paper, we show, using computer simulations, that the fluid surfaces of cellular cargo reduce the mechanical interference between motors allowing better load sharing and decreasing their detachment rates, thereby increasing the distance over which they can carry cargo. We additionally found that motors pull cargo closer to the filament increasing the attachment rates of other unbound motors. These effects act synergistically with an increased availability of motors due to fluidity to further increase travel distances. Our simulation model accounted for the dynamics of the motors on the cargo surface and their load and ATP dependent kinetics, allowing us to connect single motor properties to overall transport. Our work on understanding how teamwork arises in mechanically coupled motors sheds new light on cellular processes, reconciles existing observations, encourages new experimental validation efforts and can also suggest new ways of improving mechanical efficiency and transport of artificial cargo powered by motor teams. ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes MotivationSingle-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret the biological implications of computational results. Hence, a theory for efficiently annotating individual cells is necessary.

ResultsWe present ASURAT, a computational pipeline for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process, and signaling pathway activity for single-cell transcriptomic data, using correlation graph-based decomposition of genes based on database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for small cell lung cancer and pancreatic ductal adenocarcinoma, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data.

AvailabilityA GPLv3-licensed implementation of ASURAT is on GitHub (https://github.com/keita-iida/ASURAT). A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion Differential adhesion within cell populations enables the emergence of unique patterns in heterogeneous multicellular systems. This process has been previously explored using synthetically engineered heterogenous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models may be leveraged to more systematically explore the key parameters that drive the emergence of different patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. The model was used to predict how varying initial cell seedings, cadherin induction probabilities, or homotypic adhesion strengths between cells impact spheroid patterning, and unsupervised machine learning techniques were used to map system parameters to unique spheroid patterns. The model was then deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern. The Alzheimer's gene SORL1 is a key regulator of endosomal traffic and recycling in human neurons BackgroundLoss of the Sortilin-related receptor 1 (SORL1) gene seems to act as a causal event for Alzheimers disease (AD). Recent studies have established that loss of SORL1, as well as mutations in autosomal dominant AD genes APP and PSEN1/2, pathogenically converge by swelling early endosomes, ADs cytopathological hallmark. Acting together with the retromer trafficking complex, SORL1 has been shown to regulate the recycling of the amyloid precursor protein (APP) out of the endosome, contributing to endosomal swelling and to APP misprocessing. We hypothesized that SORL1 plays a broader role in neuronal endosomal recycling and used human induced pluripotent stem cell derived neurons (hiPSC-Ns) to test this hypothesis. We examined endosomal recycling of three transmembrane proteins linked to AD pathophysiology: APP, the BDNF receptor Tropomyosin-related kinase B (TRKB), and the glutamate receptor subunit AMPA1 (GLUA1).

MethodsWe used isogenic hiPSCs engineered to have SORL1 depleted or to have enhanced SORL1 expression. We differentiated neurons from these cell lines and mapped the trafficking of APP, TRKB and GLUA1 within the endosomal network using confocal microscopy. We also performed cell surface recycling and lysosomal degradation assays to assess the functionality of the endosomal network in both SORL1 depleted and overexpressing neurons. Finally, we analyzed alterations in gene expression in SORL1 depleted neurons using RNA-sequencing.

ResultsWe find that as with APP, endosomal trafficking of GLUA1 and TRKB is impaired by loss of SORL1. We show that trafficking of all three cargo to late endosomes and lysosomes is affected by manipulating SORL1 expression. We also show that depletion of SORL1 significantly impacts the endosomal recycling pathway for APP and GLUA1 at the level of the recycling endosome and trafficking to the cell surface. This has a functional effect on neuronal activity as shown by multi-electrode array (MEA). Conversely, increased SORL1 expression enhances endosomal recycling for APP and GLUA1. Our unbiased transcriptomic data further support SORL1s role in endosomal recycling. We observe altered expression networks that regulate cell surface trafficking and neurotrophic signaling in SORL1 depleted neurons.

ConclusionCollectively, and together with other recent observations, these findings suggest that SORL1 is a broad regulator of retromer-dependent endosomal recycling in neurons, a conclusion that has both pathogenic and therapeutic implications for Alzheimers disease. Fixed or random? On the reliability of mixed-effects models for a small number of levels in grouping variables O_LIBiological data are often intrinsically hierarchical. Due to their ability to account for such dependencies, mixed-effects models have become a common analysis technique in ecology and evolution. While many questions around their theoretical foundations and practical applications are solved, one fundamental question is still highly debated: When facing a low number of levels should we model a grouping (blocking, clustering) variable as a random or fixed effect? In such situation, the variance of the random effect is imprecise, but whether this affects the statistical properties of the population effect is unclear.
C_LIO_LIHere, we analyzed the consequences of including a grouping variable as fixed or random effect in the correctly specified and other possible miss-specified models (too simple or too complex models) for data with small number of levels (2 - 8). For all these options, we calculated type I error rates and power. Moreover, we show how these statistical properties change with the study design.
C_LIO_LIWe found that the model choice does not influence the statistical properties of the population effect when the effect is the same at all levels However, if an ecological effect differs among levels, using a random slope and intercept model, and switching to a fixed-effect model only in case of a singular fit, avoids overconfidence in the results. Additionally, power and type I error are strongly influenced by the number of and difference between levels.
C_LIO_LIWe conclude that inferring the correct random effect structure is of high importance to get correct statistical properties. When in doubt, we recommend starting with the simpler model and using model diagnostics to identify missing components. When having identified the correct structure, we encourage to start with a mixed-effects model independent of the number of levels and switch to a fixed-effect model only in case of a singular fit. With these recommendations, we allow for more informative choices about study design and data analysis and thus make ecological inference with mixed-effects models more robust for small number of levels.
C_LI A platform of patient-derived microtumors identifies therapeutic vulnerabilities in ovarian cancer. BackgroundIn light of the frequent development of therapeutic resistance in cancer treatment, there is a strong need for personalized model systems representing patient tumor heterogeneity, while enabling parallel drug testing and identification of appropriate treatment responses in individual patients. Using ovarian cancer as a prime example of a heterogeneous tumor disease with complex microenvironment and high recurrence rates, we developed a 3D preclinical ovarian cancer model comprised of patient-derived microtumors (PDM) and autologous tumor-infiltrating lymphocytes (TILs) for identification of treatment vulnerabilities and validation of anti-cancer drug efficacy using immunohistochemistry, immune cell phenotyping, functional assays and protein profiling analyses.

MethodsPDM and TILs were isolated from fresh primary ovarian cancer tissue specimen using mechanical disruption and limited enzymatic digestion and were subsequently cultured in suspension in defined media in the absence of serum. The heterogeneous cellular composition of isolated PDM as well as autologous TILs was analyzed by FFPE immunohistochemistry and multi-color flow cytometry, respectively. For in-depth protein profiling of PDM we established Reverse Phase Protein Array (RPPA) analyses of >110 total and phospho-proteins. Treatment efficacy in response to chemotherapeutics as well as immunotherapeutic compounds was assessed in PDM and PDM-TIL co-cultures using a functional viability assay in microplate format.

ResultsThe enzymatic digestion of primary ovarian cancer tissue and suspension culture in defined serum-free media allowed fast and efficient recovery of patient-derived microtumors (PDM). Immunohistochemical analyses demonstrated histopathological comparability of ovarian cancer PDM with corresponding patient tumor tissue. Reverse Phase Protein Array (RPPA)-based analyses of >110 total and phospho-proteins enabled the identification of patient-specific sensitivities to standard, platinum-based therapy and thereby the prediction of potential treatment-responders. Finally, combining PDM and autologous TILs for individual efficacy testing of immune checkpoint inhibitor treatment demonstrated the potential for patient-specific enhancement of cytotoxic TIL activity by this therapeutic approach.

Conclusion3D patient-derived ovarian cancer microtumors represent a preclinical, ex vivo tumor model that reflects intertumoral heterogeneity and represent the cellular complexity of individual patient tumors. Combining protein pathway analysis and anti-cancer drug efficacy testing of PDM enables drug mode-of-action analyses and therapeutic sensitivity prediction within a clinically relevant time frame after surgery. Follow-up studies in larger cohorts are currently under way to further evaluate the applicability of this platform to support clinical decision-making and personalizing cancer treatment. Identifying and correcting multiple sources of misspecification in GWAS summary statistics for 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. Electrocommunication signals indicate motivation to compete during dyadic interactions of an electric fish Animals across species compete for limited resources. While in some species competition behavior is solely based on own abilities, others assess their opponents to facilitate these interactions. Using cues and communication signals, contestants gather information about their opponent, adjust their behavior accordingly, and can thereby avoid high costs of escalating fights. We tracked electrocommunication signals, in particular "rises", and agonistic behaviors of the gymnotiform electric fish Apteronotus leptorhynchus in staged competition experiments. A larger body size relative to the opponent was the sole significant predictor for winners. Sex and the frequency of the continuously emitted electric field were only mildly influencing competition outcome. In males, correlations of body-size and winning were stronger than in females and, especially when losing against females, communication and agonistic interactions were enhanced, hinting towards males being more motivated to compete. Fish that lost competitions emitted the majority of rises in dependence on the competitors relative size and sex. The emission of rises was costly since it provoked ritualized biting or chasing behaviors by the other fish. Despite winners being accurately predictable based on rise numbers already after the initial 25 minutes, losers continued to emit rises. The number of rises emitted by losers and the duration of chasing behaviors depended in similar ways on physical attributes of contestants. The detailed evaluation of these correlations hint towards A. leptorhynchus adjusting their competition behavior according to mutual assessment, where rises could signal a losers motivation to continue assessment through ritualized fighting. Allopurinol Blocks The Formation And Progression Of Aortic Aneurysm In A Mouse Model Of Marfan Syndrome BackgroundThe pathogenesis and progression of aortic aneurysm in Marfan syndrome (MFS) involves dysregulated TGF-{beta} and nitric oxide signaling, altered hemodynamics, and biomechanical forces. Increasing evidence indicates that redox stress participates in MFS aortopathy, though its contribution is not well established. We reported elevated reactive oxygen species (ROS) formation and NADPH oxidase NOX4 upregulation in MFS patients and mice aortic samples. Here we address the contribution of xanthine oxidoreductase (XOR), which is transcribed as dehydrogenase (XDH) and then post-translationally modified to oxidase (XO), catabolizing mainly purines into uric acid and ROS.

Methods and ResultsWe report that in MFS patients, XOR protein levels increased in aortic samples. In MFS mice (Fbn1C1041G/+), XOR mRNA transcripts augmented in the aorta of young but not older MFS mice. Moreover, the enzymatic activity of XO form increased with respect to XDH. The administration of the XOR inhibitor allopurinol (ALO) blocked the progression of aortic root aneurysm in MFS mice. ALO was also protective when administrated before the appearance of aneurysm. MFS-induced elastic fiber fragmentation, fibrotic remodeling, nuclear translocation of pNRF2, and increased 3-nitrotyrosine levels in the aortic tunica media as well as endothelial dysfunction were all prevented by allopurinol. The large overproduction of H2O2 in MFS aorta did not occur in allopurinol-treated mice whose uric acid plasma levels remained unaltered.

ConclusionsThis study strengthens the concept that redox stress is an important determinant of aortic aneurysm formation and progression in MFS and supports a clinical trial for ALO in the pharmacological treatment of MFS aortopathy.

GRAPHICAL ABSTRACT

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org.highwire.dtl.DTLVardef@eab02aorg.highwire.dtl.DTLVardef@1d3b39eorg.highwire.dtl.DTLVardef@1b54d0forg.highwire.dtl.DTLVardef@1d61ad0_HPS_FORMAT_FIGEXP  M_FIG C_FIG Common coupling map advances GPCR-G protein selectivity Two-thirds of human hormones and one-third of clinical drugs act on membrane receptors that couple to G proteins to achieve appropriate functional responses. While G protein transducers from literature are annotated in the Guide to Pharmacology database, two recent large-scale datasets now expand the receptor-G protein  couplome. However, these three datasets differ in scope and reported G protein couplings giving different coverage and conclusions on GPCR-G protein signaling. Here, we report a common coupling map uncovering novel couplings supported by both large-scale studies, the selectivity/promiscuity of GPCRs and G proteins, and how the co-coupling and co-expression of G proteins compare to the families from phylogenetic relationships. The coupling map and insights on GPCR-G protein selectivity will catalyze advances in receptor research and cellular signaling towards the exploitation of G protein signaling bias in design of safer drugs. Mechanisms of splicing substrate definition by modulation of pre-mRNA 3D structural scaffolds Coordination of different serine-arginine-rich (SR) proteins - a class of critical splicing activators - facilitates recognition of the highly degenerate mammalian splice signal sequences against the background sequences. Yet, the mechanistic details of their actions remain unclear. Here we show that cooperative binding of SR proteins to exonic and intronic motifs remodels the pre-mRNA 3D structural scaffold. The scaffold generated by the pre-mRNA-specific combinations of different SR proteins in an appropriate stoichiometry is recognized by U1 snRNP - the 5' splice site recognizing factor. A large excess of U1 snRNP particles displaces majority of the bound SR protein molecules from the remodeled pre-mRNA. A higher than optimal stoichiometry of SR proteins occludes the binding sites on the pre-mRNA, raising the U1 snRNP levels required for SR protein displacement and potentially impeding spliceosome assembly. This novel splicing-related function of U1 snRNP is important for distinguishing the substrate and the non-substrate by U2AF65 - the primary 3' splice site-recognizing factor. Overall, this work elucidates early regulatory steps of mammalian splicing substrate definition by SR proteins. Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals due to various confounding challenges. Here, we demonstrate that enrichment analyses which aggregate SNP-level association statistics at multiple genomic scales--from genes to genomic regions and pathways--have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan, as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African-American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits, as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts. Unheeded SARS-CoV-2 proteins? A deep look into negative-sense RNA SARS-CoV-2 is a novel positive-sense single-stranded RNA virus from the Coronaviridae family (genus Betacoronavirus), which has been established as causing the COVID-19 pandemic. The genome of SARS-CoV-2 is one of the largest among known RNA viruses, comprising of at least 26 known protein-coding loci. Studies thus far have outlined the coding capacity of the positive-sense strand of the SARS-CoV-2 genome, which can be used directly for protein translation. However, it has been recently shown that transcribed negative-sense viral RNA intermediates that arise during viral genome replication from positive-sense viruses can also code for proteins. No studies have yet explored the potential for negative-sense SARS-CoV-2 RNA intermediates to contain protein coding-loci. Thus, using sequence and structure-based bioinformatics methodologies, we have investigated the presence and validity of putative negative-sense ORFs (nsORFs) in the SARS-CoV-2 genome. Nine nsORFs were discovered to contain strong eukaryotic translation initiation signals and high codon adaptability scores, and several of the nsORFs were predicted to interact with RNA-binding proteins. Evolutionary conservation analyses indicated that some of the nsORFs are deeply conserved among related coronaviruses. Three-dimensional protein modelling revealed the presence of higher order folding among all putative SARS-CoV-2 nsORFs, and subsequent structural mimicry analyses suggest similarity of the nsORFs to DNA/RNA-binding proteins and proteins involved in immune signaling pathways. Altogether, these results suggest the potential existence of still undescribed SARS-CoV-2 proteins, which may play an important role in the viral lifecycle and COVID-19 pathogenesis.

Contactpetr.pecinka@osu.cz; tlb20@cam.ac.uk The important choice of reference environment in microevolutionary climate response predictions It is well documented that individuals of wild populations can adjust to climate change by means of phenotypic plasticity, but few reports on adaptation by means of genetically based microevolution caused by selection. Disentanglement of these separate effects requires that the reference environment (the environmental zero-point) is defined, and this should not be done arbitrarily. The problem is that an error in the reference environment may lead to large errors in predicted microevolution. Together with parameter values and initial mean trait values, the reference environment can be estimated from environmental, phenotypic and fitness data. A prediction error method for this purpose is described, with the feasibility shown by simulations. As shown in a toy example, an estimated reference environment may have large errors, especially for small populations. This may still be a better choice than use of an initial environmental value in a recorded time series, or the mean value, which is often used. Another alternative may be to use the mean value of a past and stationary stochastic environment, which the population is judged to have been fully adapted to, in the sense that the expected geometric mean fitness was at a global maximum. Exceptions are cases with constant phenotypic plasticity, where the microevolutionary changes per generation follow directly from phenotypic and environmental data, independent of the chosen reference environment. Balancing sensitivity and specificity in preclinical research The success of scientific discovery in preclinical drug development is based on the different roles of exploration and confirmation. Exploration requires sensitivity while confirmation needs to be specific. Based on published empirical data, we simulated two preclinical research trajectories to examine their effectiveness to detect true effects while keeping animal numbers low. We compare a conventional p-value based approach with a method based on an a priori determined smallest effect size of interest (SESOI). Using a SESOI increases transition rates from exploration to confirmation and results in higher detection rates across the trajectory. Specificity in the SESOI trajectory increases if the prior probability of true effects is low. We conclude that employing a SESOI is superior to a p-value based approach. Based on our findings, we propose a reconsideration of planning and conducting preclinical experiments, especially when the prior probability of true hypotheses is low. Metabolic regulation of ILC2 differentiation into ILC1-like cells during Mycobacterium tuberculosis infection Tissue-resident innate lymphoid cells (ILCs) regulate tissue homeostasis, protect against pathogens at mucosal surfaces and are key players at the interface of innate and adaptive immunity. How ILCs adapt their phenotype and function to environmental cues within tissues remains to be fully understood. Here, we show that Mycobacterium tuberculosis infection alters the phenotype and function of immature lung ILC2 toward a protective interferon-{gamma}-producing ILC1-like population. This differentiation is controlled by type 1 cytokines and is associated with a glycolytic program involving the transcription factor HIF1. Collectively, our data reveal how tissue-resident ILCs adapt to type 1 inflammation toward a pathogen tailored immune response. Population differences in the length and early-life dynamics of telomeres among European pied flycatchers Telomere length and shortening rate are increasingly used as biomarkers for long-term costs in ecological and evolutionary studies because of their relationships with survival and fitness. Both early-life conditions and growth, and later-life stressors can create variation in telomere shortening rate. Studies on between- population telomere length and dynamics are scarce, despite the expectation that populations exposed to varying environmental constraints would present divergent telomere length patterns. The pied flycatcher (Ficedula hypoleuca) is a passerine bird breeding across Eurasia (from Spain to western Siberia) and migrating through the Iberian Peninsula to spend the non-breeding period in sub-Saharan Africa. Thus, different populations show marked differences in migration distance. We studied the large-scale variation of telomere length and early-life dynamics in the pied flycatcher by comparing six European populations across a north- south gradient (Finland, Estonia, England, and Spain) predicting negative effect of migration distance on adult telomere length, and of nestling growth on nestling telomere dynamics. There were clear population differences in telomere length, with English birds from mid-latitudes having the longest telomeres. Telomere length did not thus show consistent latitudinal variation and was not linearly linked to differences in migration distance. Early-life telomere shortening rate tended to vary between populations. Fast growth was associated with shorter telomeres in the early life, but faster nestling growth affected telomeres more negatively in northern than southern populations. While the sources of between-population differences in telomere-related biology remain to be more intensively studied, our study illustrates the need to expand telomere studies at the between-population level. Design of a radial multi-offset detection pattern for in vivo phase contrast imaging of the retinal ganglion cells in humans Previous work has shown that multi-offset detection in adaptive optics scanning laser ophthalmoscopy (AOSLO) can be used to image retinal ganglion cells (RGCs) in monkeys and humans. However, though images of RGCs in anesthetized monkeys with high light levels produced high contrast images of RGCs, images from humans failed to reach the same contrast due to several drawbacks in the previous dual-wavelength multi-offset approach. Our aim here was to design and build a multi-offset detection pattern for humans at safe light levels that could reveal the retinal ganglion cell layer neurons with a contrast, robustness and acquisition time approaching results only previously obtained in monkeys. Here, we present a new imaging system using only one light source, compared to the previous dual-wavelength used on monkeys. Our single-wavelength solution allows for increased light power and eliminates problematic chromatic aberrations. Then, we demonstrate that a radial multi-offset detection pattern with an offset distance of 8-10 Airy Disk Diameter (ADD) is optimal to detect photons multiply scattered in all directions from RGCs thereby enhancing their contrast. This new setup and image processing pipeline led to improved imaging of retinal ganglion cells using multi-offset imaging in AOSLO. A chemical screen based on an interruption of zebrafish gastrulation identifies the HTR2C inhibitor Pizotifen as a suppressor of EMT-mediated metastasis Metastasis is responsible for approximately 90% of cancer-associated mortality but few models exist that allow for rapid and effective screening of anti-metastasis drugs. Current mouse models of metastasis are too expensive and time consuming to use for rapid and high-throughput screening. Therefore, we created a unique screening concept utilizing conserved mechanisms between zebrafish gastrulation and cancer metastasis for identification of potential anti-metastatic drugs. We hypothesized that small chemicals that interrupt zebrafish gastrulation might also suppress metastatic progression of cancer cells and developed a phenotype-based chemical screen to test the hypothesis. The screen used epiboly, the first morphogenetic movement in gastrulation, as a marker and enabled 100 chemicals to be tested in five hours. The screen tested 1280 FDA-approved drugs and identified Pizotifen, an antagonist for serotonin receptor 2C (HTR2C) as an epiboly-interrupting drug. Pharmacologic and genetic inhibition of HTR2C suppressed metastatic progression in a mouse model. Blocking HTR2C with Pizotifen restored epithelial properties to metastatic cells through inhibition of Wnt-signaling. In contrast, HTR2C induced epithelial to mesenchymal transition (EMT) through activation of Wnt-signaling and promoted metastatic dissemination of human cancer cells in a zebrafish xenotransplantation model. Taken together, our concept offers a novel platform for discovery of anti-metastasis drugs. Robust retrieval of data stored in DNA by de Bruijn graph-based de novo strand assembly DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. The major technical challenges include various errors, such as the strand breaks, rearrangements, and indels that frequently arise during DNA synthesis, amplification, sequencing, and preservation. Through a de novo assembly strategy, we developed an algorithm based on the de Bruijn graph and greedy path search (DBGPS) to address these issues. DBGPS shows distinct advantages in handling DNA breaks, rearrangements, and indels. The robustness of DBGPS is demonstrated by accelerated aging, multiple independent data retrievals, deep error-prone PCR, and large data scale simulations. Remarkably, 6.8 MB of data can be retrieved accurately from a seriously corrupted sample that has been treated at 70 {degrees}C for 70 days. With DBGPS, we were able to achieve a logical density of 1.30 bits/cycle and a physical density of 295 PB/g.

One-Sentence SummaryA de Bruijn graph-based de novo assembly algorithm for DNA data storage enables fast and robust data readouts even with DNA samples that have been severely corrupted.